Education, Faculty of
Kinesiology, School of
DSpace
UBCV
Tang, Yiling
Doctor of Philosophy - PhD
University of British Columbia
Depression has remained a global mental health concern for decades. Physical activity (PA) interventions have been shown to alleviate depressive symptoms; however, access to PA programs tailored specifically for individuals with depression remains limited. This dissertation focused on the development of an evidence-based, app-based PA behaviour change intervention (MoodMover) for individuals with depression, following a systematic and phased approach. The development process integrated the Obesity-Related Behavioural Intervention Trials (ORBIT) model and the Integrate, Design, Assess, and Share (IDEAS) framework, and was grounded in the Multi-Process Action Control (M-PAC) framework. Study 1 involved a systematic review of internet-based self-help (IGSH) interventions that incorporated PA promotion for depression. The review highlighted the need for well-designed, theory-driven PA apps targeting depression and contributed to six recommendations for the future development and evaluation of IGSH PA interventions. Study 2 was a secondary data analysis examining the moderating role of mental health status within an adapted M-PAC framework. Findings suggested that mental health status only partially moderated the relationships in the model, supporting the appropriateness of the M-PAC framework for understanding PA behaviour among adults with poor mental health. Nevertheless, further prospective research is needed to test the full model. Study 3 described the formative development of MoodMover, including needs assessment, intervention planning, development, and usability testing, presented following the IDEAS framework. Both qualitative and quantitative usability testing results indicated good usability and strong interest in using an enhanced version of the app. These findings informed Study 4, a single-arm, pre-post proof-of-concept and feasibility study that fits within Phases IIa and IIb of the ORBIT model. Proof-of-concept results from Study 4 were generally encouraging and demonstrated the potential of MoodMover to promote PA among individuals with depression. However, usability and feasibility outcomes indicated that further refinements are needed before progressing to a larger-scale efficacy trial. Collectively, the studies in this dissertation contributed to the development of the first app-based, PA-focused behaviour change intervention specifically designed for individuals with depression. While further refinement and evaluation are necessary, MoodMover shows promise as a scalable and accessible adjunctive treatment for depression through the promotion of PA.
MOODMOVER: THE DEVELOPMENT OF AN APP-BASED BEHAVIOURAL INTERVENTION FOR INCREASING PHYSICAL ACTIVITY AMONG PEOPLE WITH DEPRESSION by YILING TANG MSc., The University of Edinburgh, 2019 BSc., Chengdu Medical College, 2014 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Kinesiology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) September 2025 © Yiling Tang, 2025ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: MOODMOVER: THE DEVELOPMENT OF AN APP-BASED BEHAVIOURAL INTERVENTION FOR INCREASING PHYSICAL ACTIVITY AMONG PEOPLE WITH DEPRESSION submitted by Yiling Tang in partial fulfilment of the requirements for the degree of Doctor of Philosophy In Kinesiology Examining Committee: Guy Faulkner, Professor, School of Kinesiology, UBC Supervisor Eli Puterman, Professor, School of Kinesiology, UBC Supervisory Committee Member Sam Liu, Associate Professor, School of Exercise Science, Physical & Health Education, University of Victoria Victoria Victoria (UVic) Supervisory Committee Member Supe visory Committee Member Robinder P. Bedi, Professor, Educational and Counselling Psychology, and Special Education, UBC Universit Exa iner Linda Li, Professor, Department of Physical Therapy, UBC University Examiner Ralph Maddison, Professor, School of Exercise and Nutrition Sciences, Deakin University External Examiner Additional Supervisory Committee Members: Raymond Lam, Professor, Department of Psychiatry, UBC Supervisory Committee Member iii Abstract Depression has remained a global mental health concern for decades. Physical activity (PA) interventions have been shown to alleviate depressive symptoms; however, access to PA programs tailored specifically for individuals with depression remains limited. This dissertation focused on the development of an evidence-based, app-based PA behaviour change intervention (MoodMover) for individuals with depression, following a systematic and phased approach. The development process integrated the Obesity-Related Behavioural Intervention Trials (ORBIT) model and the Integrate, Design, Assess, and Share (IDEAS) framework, and was grounded in the Multi-Process Action Control (M-PAC) framework. Study 1 involved a systematic review of internet-based self-help (IGSH) interventions that incorporated PA promotion for depression. The review highlighted the need for well-designed, theory-driven PA apps targeting depression and contributed to six recommendations for the future development and evaluation of IGSH PA interventions. Study 2 was a secondary data analysis examining the moderating role of mental health status within an adapted M-PAC framework. Findings suggested that mental health status only partially moderated the relationships in the model, supporting the appropriateness of the M-PAC framework for understanding PA behaviour among adults with poor mental health. Nevertheless, further prospective research is needed to test the full model. Study 3 described the formative development of MoodMover, including needs assessment, intervention planning, development, and usability testing, presented following the IDEAS framework. Both qualitative and quantitative usability iv testing results indicated good usability and strong interest in using an enhanced version of the app. These findings informed Study 4, a single-arm, pre-post proof-of-concept and feasibility study that fits within Phases IIa and IIb of the ORBIT model. Proof-of-concept results from Study 4 were generally encouraging and demonstrated the potential of MoodMover to promote PA among individuals with depression. However, usability and feasibility outcomes indicated that further refinements are needed before progressing to a larger-scale efficacy trial. Collectively, the studies in this dissertation contributed to the development of the first app-based, PA-focused behaviour change intervention specifically designed for individuals with depression. While further refinement and evaluation are necessary, MoodMover shows promise as a scalable and accessible adjunctive treatment for depression through the promotion of PA. v Lay Summary Exercise is a recommended first-line treatment for depression in Canada. However, access to exercise programs remains limited due to the small number of programs specifically designed for depression, as well as barriers such as cost and location. Mobile app-based physical activity (PA) promotion interventions may offer an accessible alternative or complementary treatment option. This dissertation aimed to develop an evidence-based app, MoodMover, to help individuals with depression increase their PA levels. The development process involved identifying needs, planning the intervention, developing the app, conducting usability testing, and evaluating its preliminary effectiveness and feasibility. MoodMover was co-developed with input from experts in depression and PA, along with individuals with depression. While the current version of MoodMover is not yet ready for widespread use, early findings are promising. The app shows potential as a complementary treatment for depression and a foundation for integrating exercise-based digital tools into mental health care in Canada. vi Preface This dissertation is comprised of four original studies and one protocol paper with details below. Three of the four studies are published manuscripts, and the fourth has been submitted for peer review. Study 1 (Chapter 3): This is a published manuscript. *Tang, Y., Gierc, M., Lam, R. W., Liu, S., & Faulkner, G. (2022). The Effectiveness of internet-guided self-help interventions to promote physical activity among individuals with depression: Systematic review. JMIR Mental Health, 9(12), e38049. YTâs contribution: â¢ Co-created study design with supervisor GF â¢ Lead the development of search strategies and literature search â¢ Lead investigator for data analysis and interpretation â¢ Primary author of manuscript Co-authorsâ contribution: â¢ GF revised and approved the study design, was the fifth contributing author of the manuscript, attained/provided funding, helped with the screening process and interpretation of the data, as well as revised and approved the final version of the manuscript â¢ MG assisted with study design, analysis, and interpretation of the data and writing of the manuscript â¢ RWL revised the article and approved the final version of the manuscript for submission â¢ SL revised the article and approved the final version of the manuscript for submission Study 2 (Chapter 4): This is a published manuscript. *Tang, Y., Gierc, M., Whiteford, V., Rhodes, R. E., & Faulkner, G. (2023). Exploring correlates of physical activity using the multi-process action control framework: is there a moderating role for mental health? International Journal of Sport and Exercise Psychology, 1-19. YTâs contributions: â¢ Co-created study design with supervisor GF and second author MG â¢ Lead investigator for analysis and interpretation â¢ Primary author of manuscript vii Co-authorsâ contribution: â¢ GF revised and approved the study design, was the fifth contributing author of the manuscript, attained/provided funding, helped with interpretation of the data and revised and approved the final version of the manuscript â¢ MG assisted with interpretation of the data and revised the article and approved the final version of the manuscript for submission â¢ WV revised the article and approved the final version of the manuscript for submission â¢ RER assisted with the interpretation of the data and revised the article and approved the final version of the manuscript for submission Study 3 (Chapter 5): This is a published manuscript. *Tang, Y., Gierc, M., La, H., Kim, J., Liu, S., Lam, R. W., Puterman, E., & Faulkner, G. (2025). MoodMover: Development and usability testing of an mHealth physical activity intervention for depression. Digital Health, 11. YTâs contributions: â¢ Author of ethics application (UBC, Office of Research Services, Behavioural Research Ethics Board, âAssessing the usability of an app-based behavioural intervention for increasing physical activity among people with depressionâ #H24-00047; Ethical approval was obtained from the UBC Behavioural Research Ethics Board.) â¢ Lead the adaptation of intervention content and prototype design â¢ Co-created study design with supervisor GF â¢ Lead investigator for data collection, analysis and interpretation â¢ Primary author of the manuscript Co-authorsâ contributions: â¢ GF revised and approved the ethics application and study design, attained/provided funding, assisted with intervention content development, helped with interpretation of the data and revised and approved the final version of the manuscript â¢ MG assisted with intervention content adaption, revised and approved the final version of the manuscript â¢ HL provided software technical support and approved the final version of the manuscript â¢ KJ assisted with data collection, reviewed all coding done by YT and helped interpret data through discussion and approved the final version of the manuscript â¢ All other co-authors (SL, RWL, EP) revised and approved the final version of the manuscript viii Study 4 (Chapter 6): This is a published manuscript. *Tang, Y., Gierc, M., La, H., Liu, S., Lam, R. W., Puterman, E., & Faulkner, G. (2025). Feasibility and preliminary effects of an app-based physical activity intervention for individuals with depression (MoodMover): A protocol for a single-arm, pre-post intervention study YTâs contributions: â¢ Author of ethics application (UBC, Office of Research Services, Behavioural Research Ethics Board, âFeasibility and preliminary efficacy of an app-based physical activity intervention for individuals with depression (MoodMover)â #H24-01820; Ethical approval was obtained from the UBC Behavioural Research Ethics Board.) â¢ Registration at ClinicalTrials.gov (NCT06573125) â¢ Co-created study design with supervisor GF and committee members (SL, EP, RWL) â¢ Primary author of the manuscript Co-authorsâ contributions: â¢ GF revised and approved the ethics application and study design, attained/provided funding, and revised and approved the final version of the manuscript â¢ HL provided software technical consultations â¢ All other co-authors (MG, SL, RWL, EP, HL) revised and approved the final version of the manuscript Study 4 (Chapter 7): This is a manuscript under review *Tang, Y., Gierc, M., Liu, S., Lam, R. W., Puterman, E., & Faulkner, G. (under review). Feasibility and proof-of-concept of an app-based physical activity intervention for individuals with depression (MoodMover): A single-arm, pre-post study. YTâs contributions: â¢ Lead investigator for data collection, analysis and interpretation â¢ Primary author of the manuscript Co-authorsâ contributions: â¢ GF helped with the interpretation of the data and revised and approved the final version of the manuscript â¢ All other co-authors (MG, SL, RWL, EP) revised and approved the final version of the manuscript ix The Use of GenAI in This Dissertation: ChatGPT was used to generate two figures (Figures 7.2 and 7.3) in Study 4 (Chapter 7). Research data were carefully reviewed to ensure they were not altered through the use of ChatGPT. ChatGPT transcripts are provided in Appendix AB. x Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents .......................................................................................................................... x List of Tables ............................................................................................................................... xiv List of Figures ............................................................................................................................. xvi List of Abbreviations ................................................................................................................ xviii Acknowledgements ................................................................................................................... xxii Dedication ................................................................................................................................. xxvi Chapter 1: Introduction ............................................................................................................... 1 1.1 Prevalence of Depression in Canada ..................................................................................... 2 1.2 Aetiology of Depression ....................................................................................................... 3 1.3 Treatments of Depression in Canada .................................................................................... 8 1.4 Mechanisms of Physical Activity on Depression................................................................ 13 1.5 Clinical Guidelines of Physical Activity for Depression .................................................... 17 1.6 Translational Gap Between Guidelines and Clinical Practice ............................................ 19 1.7 App-based Physical Activity Interventions as an Option .................................................... 24 1.8 Thesis Objectives ................................................................................................................ 27 Chapter 2: General Structure .................................................................................................... 29 Chapter 3: The Effectiveness of Internet-Guided Self-Help Interventions to Promote Physical Activity Among Individuals with Depression: Systematic Review .......................... 34 3.1 Background ......................................................................................................................... 34 3.2 Methods............................................................................................................................... 38 3.3 Results ................................................................................................................................. 44 xi 3.4 Discussion ........................................................................................................................... 57 3.5 Conclusions ......................................................................................................................... 65 Chapter 4: Exploring Correlates of Physical Activity Using the Multi-Process Action Control Framework: Is There a Moderating Role for Mental Health? ................................. 66 4.1 Background ......................................................................................................................... 66 4.2 Methods............................................................................................................................... 70 4.3 Results ................................................................................................................................. 74 4.4 Discussion ........................................................................................................................... 82 4.5 Conclusions ......................................................................................................................... 87 Chapter 5: MoodMover: Development and Usability Testing of an MHealth Physical Activity Intervention for Depression ......................................................................................... 88 5.1 Background ......................................................................................................................... 88 5.2 Methods and Results ........................................................................................................... 90 5.3 Discussion ..........................................................................................................................112 5.4 Conclusions ........................................................................................................................117 Chapter 6: Feasibility and Preliminary Effects of an App-Based Physical Activity Intervention for Individuals with Depression (MoodMover): A Protocol for a Single-Arm, Pre-Post Intervention Study ...................................................................................................... 118 6.1 Background ........................................................................................................................118 6.2 Materials and methods ...................................................................................................... 120 6.3 Discussion ......................................................................................................................... 140 Chapter 7: Feasibility and Proof-of-Concept of an App-Based Physical Activity Intervention for Individuals with Depression (MoodMover): A Single-Arm, Pre-Post Study..................................................................................................................................................... 143 7.1 Background ....................................................................................................................... 143 7.2 Materials and Methods ...................................................................................................... 145 xii 7.3 Results ............................................................................................................................... 153 7.4 Discussion ......................................................................................................................... 163 7.5 Conclusions ....................................................................................................................... 171 Chapter 8: Discussion ............................................................................................................... 172 8.1 Overview ........................................................................................................................... 172 8.2 Summary of Findings and Discussion .............................................................................. 174 8.3 Contributions..................................................................................................................... 181 8.4 General Limitations .......................................................................................................... 194 8.5 Future Directions .............................................................................................................. 196 8.6 Conclusions ....................................................................................................................... 205 References .................................................................................................................................. 207 Appendices ................................................................................................................................. 232 Appendix A. Chapter 3: PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 checklist. .............................................................................................. 232 Appendix B. Chapter 3: Search strategies for the first round of searches .............................. 235 Appendix C. Chapter 3: Search strategies for the second round of searches ......................... 243 Appendix D. Chapter 5: Outline of the content of MoodMover and the corresponding M-PAC constructs and behaviour change techniques .......................................................................... 250 Appendix E. Chapter 5: Consolidated criteria for reporting qualitative studies (COREQ): 32-item checklist .......................................................................................................................... 252 Appendix F. Chapter 5: Screening questionnaire.................................................................... 255 Appendix G: Chapter 5: Demographic survey. ....................................................................... 256 Appendix H. Chapter 5: Semi-structured interview questions. .............................................. 258 Appendix I. Chapters 5-7: Adapted MAUQ â Patient version for standalone apps ............... 259 Appendix J. Chapter 5: Participant characteristics (N=9). ..................................................... 260 Appendix K. Chapter 6: The SPIRIT 2013 Checklist. ............................................................ 261 xiii Appendix L. Chapters 6 and 7: Screening questionnaire ........................................................ 266 Appendix M. Chapter 6: Topics of major lessons ................................................................... 269 Appendix N. Chapters 6 and 7: Demographic survey and clinical information. .................... 270 Appendix O. Chapters 6 and 7: Satisfaction survey ............................................................... 275 Appendix P. Chapters 6 and 7: Physical Activity Adult Questionnaire (PAAQ) .................... 276 Appendix Q. Chapters 6 and 7: GAD-7 Anxiety .................................................................... 278 Appendix R. Chapters 6 and 7: The Pittsburgh Sleep Quality Index (PSQI) ......................... 279 Appendix S. Chapters 6 and 7: WHODAS 2.0 12-item version, self-administered ............... 281 Appendix T. Chapters 6 and 7: M-PAC constructs ................................................................. 283 Appendix U. Chapter 7: STROBE Statement. ........................................................................ 286 Appendix V. Chapter 7: A list of unique BCTs used in this version of MoodMover ............. 290 Appendix W. Chapter 7: Baseline characteristics. .................................................................. 291 Appendix X. Chapter 7: App engagement, usability, and satisfaction. ................................... 294 Appendix Y. Chapter 7: Smartphone-based step counts and app use over time ..................... 295 Appendix Z. Chapter 7: Simple linear regression results for participant characteristics of feasibility results ..................................................................................................................... 296 Appendix AA. Chapter 7: Correlations between changes in PAa and PHQ-9b ....................... 302 Appendix AB. Chapter 7: ChatGPT transcripts ...................................................................... 303 Appendix AC. Additional manuscripts completed during the degree program, but are not included in the dissertation ..................................................................................................... 304 xiv List of Tables Table 3.1. Participant characteristics. ........................................................................................... 47 Table 3.2. Characteristics of the included trials. ........................................................................... 48 Table 3.3. Intervention behaviour change techniques for physical activity promotion. ............... 54 Table 3.4. Quality of evidence of outcomes of interest. ............................................................... 57 Table 4.1. Participant characteristics. ........................................................................................... 76 Table 4.2. Descriptive statistics and bivariate correlations of all variables (n=13,881). .............. 76 Table 4.3. Standardized direct effects and bias-corrected 95% confidence intervals. .................. 80 Table 4.4. Unstandardized direct effects and bias-corrected 95% confidence intervals (CIs) and moderation effects. ........................................................................................................................ 80 Table 4.5. Standardized total effects, indirect effects, and direct effects on MVPA and bias-corrected 95% confidence intervals (CIs) ..................................................................................... 81 Table 5.1. Outline of the content of MoodMoverâs major lessons and the corresponding M-PAC construct(s) and behaviour change technique(s). .......................................................................... 95 Table 5.2. Usability of MoodMover prototypes as assessed by the adapted MAUQ for standalone apps. ............................................................................................................................................ 105 Table 5.3. Interview question responses. .................................................................................... 106 Table 5.4. A list of additional ideas for improvement with sample quotations. ...........................112 Table 6.1. Pre-determined progression criteria. .......................................................................... 129 xv Table 7.1. Predetermined criteria for progression. ...................................................................... 149 Table 7.2. Changes in smartphone-based step counts and other self-reported measures from baseline to post-intervention. ...................................................................................................... 162 xvi List of Figures Figure 1.1. A proposed framework to integrate the major physiological mechanisms of depressive disorders, with each other and with social and psychological determinants. ................................. 7 Figure 1.2. A unified theory of depression. ..................................................................................... 8 Figure 2.1. The general structure and timeline of this thesis. ....................................................... 31 Figure 2.2. The revised ORBIT model. ........................................................................................ 32 Figure 2.3. The IDEAS framework. .............................................................................................. 33 Figure 3.1. PRISMA flow diagram of first round search. ............................................................. 45 Figure 3.2. PRISMA flow diagram of second round search. ........................................................ 46 Figure 3.3. Risk of bias assessments of PA and depression of included studies. .......................... 56 Figure 4.1. Model with all possible paths ..................................................................................... 77 Figure 4.2. Final model for better mental health group. ............................................................... 78 Figure 4.3. Final model for poor mental health group. ................................................................. 78 Figure 5.1. The IDEAS framework. .............................................................................................. 90 Figure 5.2. Graded goal recommendations of MoodMover. ......................................................... 93 Figure 5.3. A workflow of the refined prototype of MoodMover. ................................................ 99 Figure 5.4. Screenshots of a major lesson................................................................................... 100 Figure 5.5. Point levels and incentives. .......................................................................................110 Figure 5.6. Screenshots of action planning. .................................................................................110 xvii Figure 6.1. The schedule of enrollment, interventions, and assessments. .................................. 125 Figure 6.2. Screenshots of home screen, step tracker, exercise logging, action planning, and a card of a major lesson (for illustrative purposes only). ...................................................................... 127 Figure 6.3. Screenshots of the peer-to-peer communication forum. ........................................... 128 Figure 7.1. Participant flow diagram. ......................................................................................... 155 Figure 7.2. Weekly app use duration during the intervention phase. .......................................... 157 Figure 7.3. Heatmap of smartphone-based step counts over time. ............................................. 160 Figure 8.1. Flow of the PhD........................................................................................................ 174 Figure 8.2. The hypothesized pathway of MoodMover. ............................................................. 188 Figure 8.3. Locating the translational intervention development frameworks within the NIH stage model to establish a common language. ..................................................................................... 201 xviii List of Abbreviations 5HT: 5-hydroxytryptamine ACT: Acceptance and commitment therapy ANOVA: One-way analysis of variance BA: Behavioural activation BCT: Behaviour change technique BCTTV1: Behaviour change technique taxonomy, version 1 BDI-II: Beck Depression Inventory, second version BDNF: Brain-derived neurotrophic factor CBT: Cognitive behavioural therapy CANMAT: Canadian Network for Mood and Anxiety Treatments CCHS: Canadian Community Health Study CCHS-MH: Canadian Community Health Study â Mental Health CES-D: Centre for Epidemiological Studies Depression CFI: Comparative fit index CG: Control group CONSORT: Consolidated Standards of Reporting Trials COREQ: COnsolidated criteria for REporting Qualitative DSM-V-TR: Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision xix HAPA: Health Action Process Approach GAD-7: Generalized Anxiety Disorder 7 GPAQ: Global Physical Activity Questionnaire GRADE: Grading of Recommendation, Assessment, Development and Evaluation GP: General practitioner HPA: Hypothalamus-Pituitary-Adrenal IDEAS: Integrate, Design, Assess, and Share IFI: Incremental fit index IG: Intervention group IGSH: Internet-guided self-help IL: Interleukin IPAQ: International Physical Activity Questionnaire IPAQ-SF: International Physical Activity Questionnaire-Short Form MADRS-S: Montgomery-Ã sberg Depression Rating Scale: Self-rated Version MAR: Missing at Random MAUQ: mHealth App Usability Questionnaire MCAR: Missing Completely at Random MDD: Major depressive disorder METs: Metabolic equivalents xx MNAR: Missing Not at Random M-PAC: Multi-Process Action Control MVPA: Moderate-to-vigorous physical activity MOST: Multiphase Optimization Strategy NICE: National Institute for Health and Care Excellence ORBIT: Obesity-Related Behavioural Intervention Trials PA: Physical activity PHQ-8: Patient Health Questionnaire, 8-item PHQ-9: Personal Health Questionnaire, 9-item PRISMA: Preferred Reporting Items for Systematic Review and Meta-Analysis PSQI: Pittsburgh Sleep Quality Index QIDS-C: Quick Inventory of Depressive Symptomatology - clinician rating QIDS-SR: Quick Inventory of Depressive Symptomatology - self-report RCT: Randomized controlled trial RMSEA: Root-mean squared error of approximation ROB 2: Risk of bias tool, version 2 SDT: Self-Determination Theory SEP: Socioeconomic position SMD: Standard mean difference xxi SMARTs: Sequential, multiple assignment, randomized trials SPIRIT: Standard Protocol Items: Recommendations for Interventional Trials SRMR: Standardized Root Mean Squared Residual STROBE: Strengthening the Reporting of Observational Studies in Epidemiology TLI: Tucker-Lewis index VIF: Variance inflation factor WHO: World Health Organization WHODAS 2.0: World Health Organization Disability Assessment Schedule 2.0 xxii Acknowledgements I would like to begin by acknowledging that the land on which this work was conducted is the unceded territory of the Coast Salish Peoples, including the territories of the xwmÉÎ¸kwÉyÌÉm (Musqueam), Skwxwú7mesh (Squamish), Stó:lÅ and SÉlÌílwÉtaÊ/Selilwitulh (Tsleil- Waututh) Nations. I would also like to take this opportunity to acknowledge the support and guidance I have received over the past five years from my supervisory committee, the Pop-PA Lab, the Pathverse team, collaborators, volunteers, community partners, UBC staff, participants, friends, and last but not least, my family. To my supervisor, Dr. Guy Faulkner: I want to express my deepest gratitude to you. Thank you for taking a chance on me, a student with no publications before starting my PhD. I submitted my application just before the outbreak of COVID-19, received the offer, and began my PhD during the first year of the pandemic. I credit your unwavering support for helping me feel calm and reassured during that challenging time. I still remember the day I completed my 14-day self-quarantine and you welcomed me by walking with me around campus, introducing me to UBC, all while we were both wearing masks. That moment meant a lot. Iâve often told others how brilliant you are as a world-leading researcher in the field of physical activity and mental health, how supportive you are as a supervisor, and how kind and fun you are as a friend. You offered me the flexibility I needed, along with timely and thoughtful guidance, and you patiently tolerated my careless mistakes. Iâll always remember the many âfirstsâ I experienced during my PhD: my first xxiii published paper, my first time watching baseball games, playing curling and disc golf, and trying âStorm the Wallâ and âDay of the Longboat.â Iâm also grateful for all the celebrations you organized for any of the lab members, whether for small accomplishments or major milestones. None of these experiences would have been possible without you being such an open-minded and thoughtful lab leader. You are THE BEST supervisor in the world. Thank you again for believing in me. To my committee, Dr. Sam Liu, Dr. Raymond Lam, and Dr. Eli Puterman: I am deeply grateful for your continued support and guidance over the years. Your mentorship has played a vital role in shaping my growth as a researcher. Thank you for your encouragement, insightful feedback, and direct, constructive advice. Your dedication, energy, and commitment to impactful research have been truly inspiring. You consistently challenged me to think critically and expand my understanding of what research can and should be. Thank you for actively contributing to every stage of my PhD. This dissertation would not be the same without your comprehensive guidance and the unique expertise each of you brought to this journey. To Pop-PA lab members: Kelly Wunderlich, Madelaine Gierc, Jack Zhan, Caroline Wu, Madison Ong, Victoria Whiteford, Derek Paterson, Cassandra Kell-Cattrysse, Julia McKenna, Dasha Sosna, Yawei Wei, Alexis Pascoal, Negin Riazi, Matthew Fagan, Mahabhir Kandola, Katie Weatherson, Jackie Lee, and to all the visiting scholars and students I had the pleasure of meeting during my time in the lab: Beatrix Alguren, Aitana Garcia Estela, Evelyn Martín Moraleda, Henry xxiv Blake, Daniel Camiletti, and Sakura Koriyama. It has been such an honour to work alongside some of the most brilliant and kind people I know. I truly appreciate how welcoming and supportive you have all been, not only in my research but also throughout my life. Iâve loved the atmosphere in our lab, where conversations flowed freely, and we could talk about everything from research to life. Thank you for making my PhD experience so much richer. To the Pathverse team, Dr. Sam Liu, Henry La, and Amanda Willms: Thank you all for your ongoing support throughout the development of the intervention. A special thanks to Henry. Your support has been invaluable. I truly canât imagine how my research would have progressed without your timely and generous help, especially during weekends. Your responsiveness and commitment made a real difference. To my collaborators, Jiali (Stacy) He, Beatrix Algurén, Chelsea Pelletier, Patti-Jean Naylor, Ryan E. Rhodes, Geralyn R. Ruissen, Victoria Whiteford, Juehee Kim, and Sitong Chen: Thank you for the opportunity to work alongside you. Iâm truly grateful for your collaboration, insights, and support throughout our work together. To participants: Thank you for generously sharing your time and insights. Your contributions have been invaluable, and I have learned so much from each of you. To all volunteers, research assistants, UBC librarians, faculty staff, especially those in the School of Kinesiology, and community mental health group facilitators: Thank you for your support throughout various stages of my research and campus life, whether through assisting with xxv recruitment, data collection, or developing search strategies. Your contributions have been invaluable, and I am sincerely grateful for your help. To my friends, Jiali (Stacy), Olivia, Eric, Yuheng, Jason and James. Thank you for bringing so much colour and joy into my life throughout my PhD journey. Because of you, my experience was not only about research but also filled with laughter, friendship, and unforgettable memories. Stacy, youâve been more than just my best friend; youâve also been a valued collaborator whose insights and support have deeply enriched my work. Eric, Yuheng, Jason and James, I will always cherish our friendship. From snowboarding and surfing to pool, ping pong, and everything in between, we travelled, learned, and grew together, and Iâm so grateful for those shared moments. Olivia, Iâm thankful to have lived with you during the final phase of my PhD. Your dedication to your own work and your constant encouragement inspired me to stay focused and keep going. To my family: Your constant support and understanding have been the foundation of my journey. Thanks to you, I was able to pursue my PhD with peace of mind, knowing I could always count on your unconditional love. Even though you may not fully grasp the details of my research, you never questioned my choices. Instead, you gave me the freedom and confidence to follow my passions, and for that, I am deeply thankful. xxvi Dedication This work is dedicated to my Mom and Dad. Your unconditional love and support made this journey possible. Mom, I love you more!1 Chapter 1: Introduction Depression is a common mental disorder characterized by persistent sadness and a lack of interest in activities that one used to enjoy (World Health Organization, 2021). According to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-V-TR), an individual with major depressive disorder (MDD) must experience a total of five or more symptoms, with at least one of the symptoms being either a depressed mood or a loss of interest or pleasure, accompanied by other somatic or non-somatic symptoms (e.g., significant changes in weight or appetite, insomnia or hypersomnia, lack of concentration, or suicidal ideation) for a minimum of two weeks (DSM-V-TR, 2022). Depression has been associated with increased risks and severity of multiple chronic diseases, such as cardiovascular disease, type II diabetes, and obesity (World Health Organization, 2019). People with depression experience a substantially reduced quality of life (Hohls et al., 2021). At its most severe, depression can lead to suicidal ideation and behaviour, causing substantial premature mortality (Machado et al., 2018; Patel et al., 2016). A very recent population-based cohort study in Hongâ¯Kong evaluated the extent of premature mortality among 126,573 individuals with depression from 2002 to 2021 (Lo et al., 2025). It found that those with depression exhibited significantly higher all-cause, natural-cause, and unnatural-cause mortality rates compared to the general population. Depressed patients lost an excess of 4-6 years of life compared to the general population. The Global Burden of Disease 2010 survey estimated that depression was responsible for at least 2.2 million excess deaths worldwide (Whiteford et al., 2013). 2 To set the stage for this research, I will first provide an overview of studies examining the prevalence of depression in Canada, followed by a brief summary of the aetiology of depression. I will then describe the most commonly used treatments for depression and outline the rationale for exploring alternative treatment options. Subsequently, I will introduce physical activity (PA) as a modifiable lifestyle factor with well-documented antidepressant effects and discuss its potential mechanisms of action, while noting its underutilization in depression management. Finally, I will describe the potential role of app-based PA interventions for depression and highlight the need for the development of a new app tailored to the needs of this population. 1.1 Prevalence of Depression in Canada Depression has a global prevalence of over 300 million people (World Health Organization, 2017). According to the Canadian Network for Mood and Anxiety Treatments (CANMAT) (Lam et al., 2016), the annual prevalence of MDD in 2012 was 4.7%, meaning that over 1.5 million Canadians aged over 15 years were affected by depression, as reported in the Canadian Community Health Study - Mental Health (CCHS-MH), and the lifetime prevalence was 11.3% (Patten et al., 2015). In addition, the same survey revealed that only half of Canadians aged 15 years and over with a mood disorder have been professionally diagnosed (Pelletier et al., 2017). Subsequently, 7% of Canadian adults aged 18 and over screened positive for MDD as indicated by CCHS 2015-2019 data (Shields et al., 2021). This prevalence surged to 15.2% during the COVID-19 pandemic based on a representative sample from eight Canadian provinces (Shields et al., 2021). In the most recent 2022 Mental Health and Access to Care Survey, the annual prevalence of MDD increased to 7.6%, 3 and the lifetime prevalence rose to 14.0% in 2022 (Stephenson, 2023). The annual economic costs of depression among employed Canadians were estimated at $32.3 billion (CAD) in 2016, as reported by the Conference Board of Canada (Stonebridge & Sutherland, 2016). 1.2 Aetiology of Depression The aetiology of depression remains inconclusive after decades of research. A number of hypotheses of depression at different levels, such as biochemical, cognitive, and behavioural, have been proposed, but no single hypothesis explains the pathophysiologic mechanisms that cause depression due to the complexity of depression (Cui et al., 2024). The following section provides a brief overview of some of these hypothesized mechanisms. 1.2.1 Pathophysiological mechanisms of depression 1.2.1.1 Neuroanatomy of depression.The neuroanatomy of depression is complex and involves several critical brain regions, many of which exhibit altered structures and activity in depressed patients (Ajagbe et al., 2024). One key observation is the volume decrease in the hippocampus (Brosch et al., 2022; Nolan et al., 2020; Oakes et al., 2017) that plays a critical role in cognitive function (Sweatt, 2004), mood regulation and memory formation (Becker & Wojtowicz, 2007). 1.2.1.2 Neurochemical hypotheses of depression. The neurochemical mechanisms involve neurotransmitters and their receptors (Ghallab & Elassal, 2024). The amine hypothesis has served as the major hypothesis of depression for decades, postulating the dysregulation of three major monoamine systems: serotonin (5-hydroxytryptamine, 5HT), norepinephrine, and dopamine in 4 depressed patients (Krishnan & Nestler, 2008; Perez-Caballero et al., 2019). The monoamine neurotransmitter system plays a critical role in regulating feelings of sadness and loss of interest (Ghallab & Elassal, 2024). However, this classical monoamine hypothesis has been questioned in light of the limited efficacy of antidepressants, which demonstrate effectiveness in less than 50% of patients, and a delayed effect in depressive symptoms despite acute changes in amine levels (Lee et al., 2010; Perez-Caballero et al., 2019; Posternak & Zimmerman, 2005). Such skepticism has been further supported by a recent overview of reviews by Moncrieff et al. (2022) that found no consistent evidence of the relationship between serotonin and depression, which challenges the empirical substantiation of the serotonergic theory of depression. The receptor hypothesis of depression suggests that numerous receptors (e.g., 5-HT receptor) play a pivotal role in explaining the pathogenesis of depression (Wang et al., 2021). Dysregulated levels of receptors contribute to the onset of depression. 1.2.1.3. Hormonal-endocrine dysregulation of depression. The neuroendocrine hypothesis of depression suggests that abnormal endocrine system activity is a potential contributor to depression, including alterations in the levels of growth hormone, thyroid hormones abnormalities, and dysregulated Hypothalamus-Pituitary-Adrenal (HPA) axis function. Considerable research has also implicated the role of the HPA axis in depression, especially stress-induced depression (Duval et al., 2022). 1.2.1.4. Neuroinflammation hypothesis of depression. Representing the cytokine/inflammation hypothesis of depression, inflammatory cytokines have also been 5 increasingly suggested to contribute in depression pathophysiology (Haase & Brown, 2015). Common inflammatory cytokines can be categorized into two groups: pro-inflammatory (e.g., interleukin-1 (IL-1) and IL-6) and anti-inflammatory (e.g., IL-4) (Dinarello, 1997). It is suggested that an imbalance between pro- and anti-inflammatory cytokines may contribute to the pathogenesis of depression (Dunn, 2006). 1.2.1.5. Neuroplasticity and neurogenesis of depression. The neurotrophic factor hypothesis was first proposed by Duman et al. (1997), postulating that depression may be attributed to abnormal levels of neurotrophic factors, particularly brain-derived neurotrophic factor (BDNF), leading to neuronal atrophy, decreased neurogenesis in key brain areas, particularly in the hippocampus, and loss of glial cells (Duman & Monteggia, 2006). Neurotrophic factors (neurotrophins) are a family of proteins that support the growth, survival, and differentiation of neurons (Duman & Monteggia, 2006). 1.2.2 Psychosocial factors of depression A range of psychosocial factors have been proposed to contribute to the aetiology of depression. Social determinants, including poverty, low socioeconomic position (SEP), low level of social support, life stressors, and family violence, emerge as potential contributors to the onset of depression (Bowen & Muhajarine, 2006). In addition, rumination has long been acknowledged as one of the most important contributors to the onset of depression (Nolen-Hoeksema, 1991). A systematic review of 30 studies showed that perseverative negative thinking, such as worry and rumination, predicts subsequent depression (Trick et al., 2016). Other psychosocial factors (e.g., 6 self-efficacy or self-esteem) can also influence negative cognitive processing by causing negative self-evaluation and rumination (Bandura, 1991). Low self-efficacy has been linked with higher severity of depression (Kangas et al., 2015). Self-esteem is a concept that represents a comprehensive expression of cognitive, behavioural, and affective elements (Agarwala & Sharma, 2016). Low levels of self-esteem can cause many psychological problems, including depression, and this connection is considered a vicious cycle (Agarwala & Sharma, 2016). Low self-esteem has also been related to other psychosocial risk factors, such as the quality of close relationships and early adverse experience. 1.2.3 Unified theory or framework of depression Roy and Campbell (2013) proposed a unifying framework that integrates major biological hypotheses of depression with psychosocial stress (Figure 1.1). Roy and colleagues present that psychosocial stress, such as that caused by significant life events, serves as a potent trigger for depressive episodes. Stress activates the HPA axis, and when stress becomes chronic, it often results in persistent HPA hyperactivity. HPA-axis hyperactivity acts as a central integrator, bridging psychosocial stress with three key biological mechanisms implicated in depression: monoamine imbalance, neuroanatomical changes, and inflammatory processes (Roy & Campbell, 2013). 7 Figure 1.1. A proposed framework to integrate the major physiological mechanisms of depressive disorders, with each other and with social and psychological determinants. Source: Roy and Campbell (2013) Dean and Keshavan (2017) also proposed a unified theory of depression (Figure 1.2), suggesting that the interactions between numerous genetic and environmental factors trigger one or more pathophysiological mechanisms, which in turn cause depressive symptoms in different domains. This unified theory suggests that all of the proposed biological and neurochemical pathophysiological mechanisms of depression are reciprocally connected with each other (Dean & Keshavan, 2017). These examples of unified theories highlight the potentially complex interaction of biological and psychosocial factors that may cause depressive symptoms. Similarly, 8 successfully treating depression is unlikely to be explained by one definitive mechanism. Figure 1.2. A unified theory of depression. Source: Dean and Keshavan (2017) 1.3 Treatments of Depression in Canada In Canada, the management of depression follows a 2-phase treatment model (acute and maintenance phases) recommended by the CANMAT guidelines (Lam et al., 2024; Lam et al., 2016). The main objective during the acute treatment phase is to achieve remission, typically defined as the resolution of symptoms as measured by standardized clinical scales. The maintenance phase focuses on sustaining remission, recovering physical functioning and quality of life, and preventing future depressive episodes. A wide range of treatment options is available 9 for MDD, including psychological treatments, pharmacotherapy, neurostimulation (e.g., repetitive transcranial magnetic stimulation), lifestyle interventions (e.g., exercise), and complementary and alternative medicine treatments (e.g., acupuncture) (Lam et al., 2024). Medication and psychological therapies remain the most commonly prescribed treatments for depression in Canada (Siobhan et al., 2017; Wong et al., 2014). 1.3.1 Medications The treatment of depression through pharmaceutical methods mainly lies in the monoamine hypothesis of depression (discussed above) that compasses three major monoamine systems: 5HT, NE, and DA (Perez-Caballero et al., 2019). A large-scale study using electronic medical record data from the Canadian Primary Care Sentinel Surveillance Network demonstrated that 85% of Canadians with a diagnosis of depression who seek care in a primary care setting were receiving a prescription of some form of antidepressants, and selective serotonin reuptake inhibitors were the most prescribed antidepressants (Wong et al., 2014). The CANMAT 2016 guidelines recommended the second-generation antidepressants as first-line treatments only for patients with a moderate and greater severity of MDD and patients with mild MDD under certain conditions, such as lack of response to other non-pharmacological interventions (Kennedy et al., 2016). According to the updated 2023 guidelines, 17 antidepressants are recommended as first-line treatments for MDD regardless of severity (Lam et al., 2024). However, research has reported that the majority of individuals who remitted or recovered from clinical depression after successful treatment are likely to experience relapse or recurrence 10 in their lifetimes (Kessing et al., 2004; Monroe & Harkness, 2011; Mueller et al., 1999). Relapse and recurrence are both considered repeat episodes of depression. Generally, relapse is defined as the return of the remitted episode, while recurrence refers to the onset of another depressive episode that occurs after recovery (Monroe & Harkness, 2011; Rush et al., 2006). A wide range of risk factors has been associated with the high rate of relapse and recurrence following treatment termination (Buckman et al., 2018), particularly the number of previous episodes (Keller et al., 1983; Maj et al., 1992), childhood maltreatment, and residual symptoms. To prevent relapse or recurrence, long-term usage of antidepressants has been widely adopted (Nierenberg et al., 2003). For example, Reimherr et al. (1998) suggested maintaining antidepressant use for up to a year after remission. Moreover, Thase et al., 2006, as cited in Buckman et al. (2018), suggested to delay or prevent recurrence by indefinite medication usage for patients with a history of recurrent or chronic depression, although there is evidence suggesting the use of antidepressants is associated with increased risks of relapse or recurrence of depression (Andrews et al., 2011; Andrews et al., 2012). 1.3.2 Psychological treatments With the recognition of drug-related side effects with long-term usage (e.g., weight gain, sexual dysfunction, and gastrointestinal issues like nausea and vomiting) (Kennedy & Giacobbe, 2007; McLachlan, 2018; Wang et al., 2018), as well as the limited efficacy of antidepressants, especially in cases of mild depression (Fournier et al., 2010), psychotherapies, in particular cognitive behavioural therapy (CBT), are widely recommended as initial treatments for mild to moderate depression in Canada (Parikh et al., 2016). This aligns with the 2023 CANMAT 11 guidelines, which recommend psychotherapy as the preferred first-line treatment for mild depression, when accessible, considering the relative benefits and risks compared to antidepressants (Lam et al., 2024). The treatment of depression through psychotherapies is a complex, multi-dimensional process that involves interactions among multiple mechanisms on different levels, including physiological, affective, behavioural, and cognitive aspects (Marwood et al., 2018). Various other types of psychotherapies are available, such as behavioural activation (BA) and interpersonal psychotherapy. Cognitive therapy is the best-studied psychological treatment for depression, grounded in the premise that the presence of unhealthy beliefs and maladaptive information processing contributes to the etiology of depression (Cuijpers, Berking, et al., 2013; Dozois & Beck, 2011). The focus of cognitive therapy lies in helping patients understand the influence of cognition on behaviour and affect. These therapies monitor, examine, and restructure patientsâ automatic thoughts, and ultimately alter core beliefs (Dozois & Beck, 2011). A meta-analysis by Marwood et al. (2018) found that psychotherapies have robust effects on brain function, consistently decreasing emotional reactivity in limbic regions (i.e., insula and anterior cingulate cortex) post-therapy. This was consistent with findings of a previous systematic review of CBT for depression (Franklin et al., 2016). Psychological treatments, particularly CBT and BA, appear to have an enduring effect on reducing the risk of relapse and preventing recurrence (Cuijpers, Hollon, et al., 2013; Dobson et al., 2008). The meta-analysis by Cuijpers, Hollon, et al. (2013) indicated that there is no difference in reducing the risk of relapse between acute CBT treatment and continued medication treatment 12 after remission. However, there remain several significant challenges associated with psychotherapies. First, psychotherapies generally include a dozen or more sessions over 3-4 months, demanding considerable effort from patients and potentially imposing a financial burden (Craighead et al., 2015; Liu et al., 2017). Second, individuals with depression may avoid seeking care due to perceived stigma and discrimination (Cooney et al., 2013). Third, many people suffering from depression encounter difficulties in accessing psychotherapy due to geographical barriers and a global shortage of qualified psychotherapists (Patel et al., 2016; Thornicroft et al., 2017; World Health Organization, 2017). In 2018, 43.8% of Canadians aged 12 and older who reported a need for mental health care felt that their needs were either unmet or only partially met (Statistics Canada, 2019). Accordingly, counselling or therapy needs were the most likely to go unmet, compared to needs for medication (Statistics Canada, 2019). In a more recent 2022 report, among Canadian adults who met diagnostic criteria for mood, anxiety, or substance use disorders, only 43.8% received some form of counselling or psychotherapy (Stephenson, 2023). Of those who accessed care, 64.3% felt their needs were met. Moreover, the estimated wait time from initial referral to receiving the first counselling session was 25 days in 2023â2024, with one in ten individuals waiting more than 20 weeks (Canadian Institute for Health Information, 2024). Such delays likely discourage patients from seeking mental health care. Therefore, alternative or complementary treatments that are effective and widely accessible are urgently needed for large-scale depression management. 13 1.3.3 Physical activity as a treatment option for depression PA has been recognized as a beneficial lifestyle behaviour in preventing and reducing depression (Teychenne et al., 2020). It is defined as âany bodily movement produced by skeletal muscles that result in energy expenditureâ (Caspersen et al., 1985). Regular engagement in moderate-to-vigorous PA (MVPA) can yield numerous other intermediate and long-term health benefits, both physically and mentally, such as improved sleep quality, prevention of multiple cancers, and reduced anxiety (Anokye et al., 2012; Centers for Disease Control and Prevention, n.d.; Kraus et al., 2019). An overview of systematic reviews on the effectiveness of PA interventions demonstrated a medium effect size (median standard mean difference [SMD]: -0.66) in reducing depressive symptoms based on 14 systematic reviews comprising 273 randomized controlled trials (RCTs) among patients diagnosed with depression (Singh et al., 2023). Singh and colleagues also concluded that individuals with depression are those most likely to benefit from PA interventions, when compared to individuals with other mental health conditions. More recently, a systematic review by Noetel et al. (2024) on the effects of exercise for depression further supports exercise as an effective treatment, with walking or jogging being more effective than other forms of exercise, particularly when performed at higher intensity. 1.4 Mechanisms of Physical Activity on Depression The mechanisms by which PA, mainly exercise (known as a form of planned and structured PA), reduces depression have also been extensively studied, though the findings remain less 14 conclusive. Generally, the mechanisms fall into two categories: neurobiological and psychosocial. 1.4.1 Neurobiological mechanisms of physical activity Multiple potential neurobiological mechanisms have been suggested to explain the beneficial role of PA on depression (Gujral et al., 2017; Hossain et al., 2024; Ross et al., 2022; Wegner et al., 2014). These mechanisms align with several major hypotheses of depression (discussed in Section 1.2), including the neurotrophin and neurogenesis hypothesis, the neuroendocrine hypothesis, the cytokine/inflammation hypothesis, and the monoamine hypothesis. Exercise has been associated with regulated neurogenesis dysfunction. Exercise is known to increase the expression of neurotrophic growth factors (e.g., BDNF), which could improve the ability to cope with depression (Garza et al., 2004; Hossain et al., 2024). The increase of β-endorphins and vascular endothelial growth factor has also been linked to exercise in depression management (Ernst et al., 2006; Fabel et al., 2003). In addition, research demonstrated that exercise is able to reduce depression by regulating the HPA-axis activity (Lopresti et al., 2013), inhibiting pro-inflammatory factors (Mathur & Pedersen, 2008), and regulating the neuroimmune system (e.g., reducing IL-6) (Eyre & Baune, 2012). These findings align with the neuroendocrine hypothesis and the cytokine/inflammation hypothesis of depression, respectively. Exercise could also increase the reduced serotonin, dopamine, and norepinephrine levels (Lin & Kuo, 2013; Mandolesi et al., 2018) that were attributed to depression, aligning with the monoamine hypothesis of depression. More interestingly, while the overview of reviews by Moncrieff et al. (2022) revealed inconsistent evidence regarding the relationship between serotonin and depression, this 15 study suggests that exercise may serve as a viable approach to regulating the serotonin system, which may be disrupted by antidepressant use. 1.4.2 Psychosocial mechanisms of physical activity In contrast to the effort devoted to understanding biological mechanisms, less research focuses on the psychosocial benefits of PA on depression. A non-systematic review by Kandola et al. (2019) identified three popular psychosocial mechanisms of PA in treating depression, including the enhancement of self-esteem, self-efficacy, and social support. More recently, a systematic review by Hossain et al. (2024) emphasized additional pathways, including improvements in sleep quality, reductions in stress and anxiety, and further support for enhanced self-esteem and social support. Exercise at various intensity levels has been linked to increased overall self-esteem, especially physical self-perception and body image, regardless of actual improvements in body composition (Alleva et al., 2015; Feuerhahn et al., 2014; Moore et al., 2011). Legrand (2014) found that the mediating role of self-esteem remains among people with depression. Self-efficacy, a specific domain of self-evaluation, has also been proposed as a possible explanation for the antidepressant effect of exercise (Brosse et al., 2002). Self-efficacy is defined as the perceived confidence in the ability to perform a particular behaviour (Bandura, 1997). It is a key construct of several theories applied to understanding PA behaviour change, e.g., Social Cognitive Theory (Bandura, 1997), the Theory of Planned Behaviour (Ajzen, 1991), and the Transtheoretical Model (Prochaska & DiClemente, 1983). Research consistently reports that low self-efficacy represents 16 a common barrier to exercise. This appears to be especially true for people with depression, as low self-efficacy has been linked with higher severity of depression (Kangas et al., 2015). In contrast, research also suggests that engaging in PA could potentially enhance feelings of self-efficacy (Higgins et al., 2014; Rodgers et al., 2014). In this regard, the initiation of exercise may elicit a virtuous circle of increased self-efficacy that leads to further increased exercise engagement, although more research involving people with depression is necessary (Kandola et al., 2019). Increased self-efficacy and mastery can also lead to higher self-esteem (Hossain et al., 2024). Moreover, social support can foster a sense of belonging and alleviate feelings of isolation commonly experienced by individuals with depression (Hossain et al., 2024). PA, particularly group-based exercise, could offer opportunities for supportive social interactions (Kandola et al., 2019). A cross-sectional study of 40,401 Norwegian adults found that social support and engagement partially mediated the inverse association between leisure-time PA and depressive symptoms (Harvey et al., 2010). Similarly, among 1,200 college students, social support mediated the exercise and depression relationship, although the results were at a cross-sectional level (Zhang et al., 2022). 1.4.2.1 Other potential mechanisms. PA and exercise may also improve depression through other mechanisms, such as sleep regulation (Hossain et al., 2024), distraction and behavioural activation (Brosse et al., 2002; Morgan, 1985; Nasstasia et al., 2019). Sleep disturbances are among the most prevalent symptoms of depression and can further worsen symptoms (Hossain et al., 2024). Exercise contributes to better sleep hygiene by regulating sleep 17 routines and enhancing overall sleep quality (Khazaie et al., 2023; Lederman et al., 2019). In contrast, mixed findings exist regarding the potential mediating role of distracting people from negative thoughts and rumination. Broderick (2005) postulated that engaging in low-intensity PA, such as walking, may offer benefits in interrupting rumination, thereby supporting the potential role of distraction in managing depression. However, Craft (2005) indicated a short-term relationship between distraction and depression at week 3 but not at week 9, providing limited support for distraction as a potential pathway. Similarly, a meta-analytic review of Rood et al. (2009) yielded only modest evidence regarding distraction's role in depression. Interestingly, increasing PA may be considered as a way to reduce sedentary behaviour, which has been recognized as an independent behavioural risk factor for depression (Kandola et al., 2021). In summary, a range of plausible mechanisms have been identified to potentially explain the antidepressant effects associated with physical activity. Identifying a single causal mechanism to explain these antidepressant effects is unlikely given the varied and complex causes of depression (see Section 1.2.3). 1.5 Clinical Guidelines of Physical Activity for Depression Evidence has been well-established to support PA, especially exercise, as a viable treatment for depression, with efficacy at least comparable to that of antidepressants and psychotherapies (Ekkekakis, 2020). Several countries have released clinical guidelines recommending exercise and PA for depression treatment. The 2009 National Institute for Health and Clinical Excellence (NICE) 18 guidelines in Britain were the most widely used guidance that built upon evidence from Rethorst et al. (2009) to recommend a dosage of three 45-60 min supervised exercise sessions per week for over 10â14 weeks for mild to moderate depression (National Institute for Health and Clinician Excellence, 2009). In 2022, NICE updated their guidelines for subclinical to severe depression with only recommendations within group settings provided. It suggests at least one moderate aerobic exercise session for ten weeks with a group of 8 people (National Institute for Health and Clinician Excellence, 2022). The European Psychiatric Association recommended a similar dosage: two to three 45-60 min sessions of supervised aerobic and/or aerobic and resistance training exercise at a moderate intensity per week for MDD (Stubbs et al., 2018). In Canada, exercise was recommended by the CANMAT as an additional first-line standalone treatment option for mild-to-moderate depression, and a second-line adjunctive treatment option for moderate-to-severe depression (Ravindran et al., 2016). CANMAT recommended a lower dosage of two or three 30-minute sessions at a moderate intensity for at least 9 weeks for mild-to-moderate depression (Ravindran et al., 2016). Supervised exercise was also recommended for adherence (Ravindran et al., 2016; Stanton & Reaburn, 2014). The updated 2023 guidelines recommend engaging in three to four sessions of 30- to 40-minute supervised exercise at low to moderate intensity per week for a minimum of nine weeks for mild depression (Lam et al., 2024). Overall, there is a significant overlap among exercise recommendations for depression, which are comparable to the PA guidelines for the general population to engage in at least 150 minutes of MVPA per week (World Health Organization, 2020). 19 1.6 Translational Gap Between Guidelines and Clinical Practice A major challenge remains regarding the translation of the clinical PA guidelines into practice (Ekkekakis, 2020). After the release of the 2009 NICE guidelines, a British study of referral data of over 7,000 patients with depression or anxiety still reported no referrals of patients with depression to exercise programs (Richards et al., 2012). Likewise, an analysis of daily depression care delivered by six general practitioners (GPs) and 22 mental health care providers in the Netherlands found that advice on exercise was only provided as one of general lifestyle advice for the self-management of mild depression, while referrals to running therapy were rare (Hermens et al., 2014). In cases of more severe depression, psychotherapies and medications are generally offered or referred (Hermens et al., 2014). In the United States, healthcare providers were reported to offer advice on increasing PA (exercise) to only 26.9% of patients with mild depressive symptoms and 29.5% of those with moderate-severe symptoms (Grabovac et al., 2020). Moreover, the authors hypothesized that physicians were more motivated to provide advice on exercise based on patientsâ weight status, rather than the severity of depression. Grabovac et al. (2020) found that only 13% and 23% of normal-weight patients with mild depressive symptoms and those with moderate to severe symptoms, respectively, received exercise advice. This is not unexpected since exercise is not a recommended frontline treatment for depression in the United States; instead, it is one of many complementary treatments that can be considered when pharmacotherapies and psychotherapies are ineffective or unacceptable (American Psychosocial 20 Association, 2019; Ekkekakis, 2020). In Canada, limited research exists regarding exercise prescription and referral schemes for mental health (Glowacki, Weatherson, et al., 2019). The majority of Canadian physicians (85%) seldom provide written exercise prescriptions, accounting for less than 10% of medical visits (OâBrien et al., 2017). Exercise continues to be an underutilized treatment option for depression, even in the presence of established CANMAT guidelines (Glowacki, 2020). 1.6.1 Translational barriers It is widely acknowledged that healthcare providers (e.g., GPs) play an important role in the implementation of research evidence into clinical practice (Glowacki, Weatherson, et al., 2019). One of the common translational barriers is their lack of awareness of the existing evidence and guidelines. Searle et al. (2012) indicated that many GPs personally endorse incorporating PA advice, despite their limited awareness of the evidence base. GPs are offering PA advice anecdotally, with existing guidelines (what to do) being sparsely followed, let alone providing guidance on 'how to do' it (Searle et al., 2012). Various other challenges (e.g., a lack of training in PA assessment and promotion, lack of time, lack of organizational support) have been reported, collectively leading to the underutilization of exercise and PA as a treatment option for depression in many countries, including Canada (Faulkner, 2016; Glowacki, Weatherson, et al., 2019). An âExercise and Depression Toolkitâ has been developed to assist mental health care providers who have little knowledge and training in exercise or awareness of guidelines (Glowacki, 2020). This toolkit aims to encourage collaboration between mental health care providers and their patients in 21 considering exercise as a viable treatment option for depression in Canada (Glowacki, Arbour-Nicitopoulos, et al., 2019). A follow-up evaluation study of this toolkit yielded promising results regarding its acceptability and perceived effectiveness among six Canadian health care providers (Glowacki et al., 2021). Notably, the authors emphasized that the toolkit is solely the first step in bridging the translational gap in Canada (Glowacki, 2020). In addition, Glowacki, Weatherson, et al. (2019) indicated that the belief of GPs that their patients with mental illness would not adhere to PA promotion recommendations is also one of the predominant barriers for healthcare practitioners in encouraging PA participation. Globally, a high prevalence of physical inactivity among people with depression is of concern. People with MDD reported lower overall PA levels (SMD=-0.25, 95% CI -0.03 to 0.15), lower MVPA levels (SMD=-0.30, 95% CI â0.40 to 0.21), and higher sedentary behaviour levels (SMD=0.09, 95% CI 0.01 to 0.18) (Schuch et al., 2017). A meta-analysis of 28 studies with 29,523 individuals with severe mental illness reported that 60.2% (95% CI 49.5-69.9%) of people with MDD did not meet the recommended 150 minutes of MVPA per week (Vancampfort et al., 2017). In contrast, a meta-analysis and meta-regression by Stubbs, Vancampfort, et al. (2016) investigated the dropout rates across 52 exercise interventions for people with depression and indicated that exercise is well-accepted by this population, with lower dropout rates in the exercise conditions compared to control conditions. Furthermore, it has been argued that lower dosages of PA may be needed for the prevention and treatment of depression (Fortier et al., 2020). The existing PA guidelines for depression may be limited by heterogeneity between studies and a small range of dosages tested, 22 and thus, do not represent the optimal dosage. An 11-year longitudinal study indicated that even one hour of PA/week can contribute to the prevention of depression (Harvey et al., 2018). Moreover, the findings suggest that low levels of regular leisure-time exercise at any intensity could protect against subsequent depression. These findings were in line with evidence found in an umbrella review of Singh et al. (2023), indicating that interventions with a weekly duration of less than 150 min showed greater effects on the improvement of depression, compared with those with longer weekly durations. One possible explanation is that interventions with shorter weekly durations may be easier to adhere to, suggesting that a high weekly dosage of PA may not be necessary for managing depression (Singh et al., 2023). More promisingly, a multi-study analysis demonstrated that 5 minutes of exercising in green places, in particular with the presence of water, may contribute to the treatment of depression through improving self-esteem and mood (Barton & Pretty, 2010). Overall, Fortier et al. (2020) proposed that Canadian practitioners and policymakers should adopt the perspective that âevery bit of PA helps, and some is better than noneâ. In addition, Singh et al. (2023) indicated that higher intensities of PA had larger beneficial effects on depressive symptoms compared to low intensity of PA, based on five reviews of 63 component RCTs. The âloss of efficacyâ of low-intensity PA may be attributed to failing to stimulate neurobiological mechanisms. However, the authors highlighted that PA at low intensity still had positive effects on improving depression (Singh et al., 2023). As for types of PA, there was evidence suggesting leisure time PA and active transport (e.g., walking and biking) (White et al., 2017), and domestic PA (e.g., gardening) (Richards et al., 2015), are preferred when it comes 23 to alleviating depression (Fortier et al., 2020), though Singh et al. (2023) revealed that all PA modes can be beneficial for depression through different mechanisms. The existing recommendations for people with depression often refer to structured, supervised, and/or group-based aerobic interventions in community or gym settings. These exercise programs may share similar barriers (e.g., accessibility) as psychotherapies (Cooney et al., 2013). In line with this, healthcare providers using the âExercise and Depression Toolkitâ failed to refer most patients with depression to structured exercise programs due to barriers like cost (Glowacki et al., 2021). Moreover, there is evidence suggesting that exercising at home is preferred by people with mental illness rather than exercising in gyms (Ussher et al., 2007). Alternative referral mechanisms are required if structured and supervised exercise programs are not available or if such programs are not of interest to potential participants for any reason. Fortier et al. (2020) and Lam et al. (2024) emphasized the importance of aligning with patients' personal preferences and maximizing available choices. Thus, lifestyle PA interventions (Dunn et al., 1998), promoting the daily incorporation of self-selected activities at any intensities into oneâs everyday life, may offer a promising alternative to structured exercise programs. Lifestyle behaviour interventions, also known as behaviour change interventions, have gained significant interest in the prevention and mitigation of chronic diseases, including depression (Sarris et al., 2014; Young et al., 2018). These interventions can potentially contribute to chronic disease management by reducing harmful behaviours, increasing beneficial behaviours, or promoting the usage of interventions for chronic diseases (Michie et al., 2018). The smallest, 24 replicable, active intervention components are known as behaviour change techniques (BCTs; Michie et al., 2013). However, there has been a paucity of interventions specifically aimed at promoting lifestyle PA behaviour changes to treat depression (Glowacki et al., 2017), representing another translational barrier. Therefore, lifestyle PA behaviour interventions, if effective, could be recommended as an alternative or complementary antidepressant treatment option for clients interested in PA promotion but who are not interested or able to access supervised exercise programs. This approach is likely to assist in bridging the translational gap to facilitate greater integration of PA and exercise into clinical practice for depression in Canada. 1.7 App-based Physical Activity Interventions as an Option With the advent and widespread availability of smartphone technology, mobile applications (apps) have been increasingly used to deliver behaviour change interventions (Fitzgerald & McClelland, 2017; McKay et al., 2018). Globally, 59% of people owned a smartphone in 2017, with significantly higher rates observed in developed countries and among younger generations (Pew Research Center, 2021). In Canada, smartphone ownership is widespread, where an estimated 84.4% of Canadians aged 15 and over owned a smartphone as of 2020 (Statistics Canada, 2020), and ownership rates continue to rise annually. The potential of mobile apps is reflected by their versatility, availability, affordability, and scalability (Elias et al., 2014; Fitzgerald & McClelland, 2017; Luxton et al., 2011). Compared to conventional treatments and other digital formats (e.g., computer-based), mobile-based apps allow 25 participants to access the intervention based on their needs whenever and wherever they want. Most people with mental illness express their willingness to use apps for health management (Proudfoot et al., 2010; Torous et al., 2014). In particular, a cross-sectional survey involving 149 veterans with depression or anxiety revealed that 73.1% of them expressed an interest in using an app for mental health purposes, particularly for increasing PA (75.8%, 113/149) (Lipschitz et al., 2019). A survey of 176 individuals with depressive or anxiety symptoms found that 77.8% of them had health apps downloaded on their Android devices, with exercise or fitness-related apps and mental health/wellness apps being the first and third most common, respectively (Rubanovich et al., 2017). Thus, people with depression may find mobile app-based interventions for increasing lifestyle PA to improve depression attractive. 1.7.1 Do we need another app? There are numerous commercial apps available for increasing PA among the general population (Lewis et al., 2017). By 2013, there were over 5000 PA promotion apps available for download in iTunes and Google Play (Middelweerd et al., 2014). The content analysis study of Middelweerd et al. (2014) reviewed 64 apps designed for increasing PA among adults through individually tailored feedback and identified an average of a combination of five BCTs, with the most common ones being providing feedback and self-monitoring. However, the authors pointed out that the adoption of BCTs across the majority of these apps fell short of an optimal level. In addition, these apps were generally criticized for an atheoretical foundation and for reaching end-users with a lack of efficacy evidence (Arigo et al., 2019; Cowan et al., 2013; Lam et al., 2024; 26 Simões et al., 2018; West et al., 2012). Accordingly, there does remain the need for an app that is theoretically informed and evidence-based. Behaviour change interventions can be more effective with a theoretical foundation (Glanz & Bishop, 2010). In addition, employing theories could assist developers in identifying the most influential factors to address and potential explanations for the failure of interventions (Byrne, 2020; Czajkowski et al., 2015; Glanz & Bishop, 2010). In particular, given the illness-specific barriers people with depression often experience to becoming more active (Cody et al., 2022; Glowacki et al., 2017), apps that are tailored for depression make intuitive sense. A final rationale concerns assisting patients in identifying resources that are credible and evidence-based (Lam et al., 2024). Given the vast number of available apps, patients may inadvertently select PA apps that are inappropriate or potentially unsafe (Lam et al., 2024). Developing a new PA promotion app for depression, grounded in an evidence-based framework, rigorously evaluated through a series of trials, and disseminated through referral pathways, may help address this concern. In particular, informed discussions between healthcare providers and patients could support more effective dissemination. Partnering with CANMAT could facilitate the development of these referral pathways for depression in Canada. For instance, CANMAT-endorsed materials could be used by clinicians to introduce the app during routine consultations. This approach may also enhance engagement and retention of the proposed app. User engagement with digital health interventions typically declines over time, with fewer than one-third of 27 individuals with depression continuing to use them beyond six weeks (Lam et al., 2024). A pilot study investigating the feasibility and preliminary efficacy of a self-help BA mobile app found that participants recruited in person demonstrated higher levels of engagement (Dahne et al., 2019). Pratap et al. (2020) analyzed user engagement data from eight digital health studies involving 109,914 individuals with and without various diseases (e.g., asthma, heart disease, and depression) and found that clinician referral is one of the significant factors associated with longer retention time. Together, these points highlight a strong need for developing a well-designed, app-based intervention to promote PA for individuals with depression. Endorsement by CANMAT may be an optimal strategy to support its integration into clinical practice, enhancing credibility, engagement, retention, efficacy, and broader dissemination. 1.8 Thesis Objectives In summary, research is needed to address the implementation gap of clinical guidelines for the treatment of depression in Canada. Exercise has been recommended as a frontline treatment for depression, but it is not clear how, or to whom, mental health practitioners could refer patients to exercise programming. With the possibility of such programming not being available or attractive to some, complementary, self-guided mHealth interventions might have a role to play. This is the overarching objective of my dissertation: to take a careful, sequential approach in developing and testing an app-based intervention specifically designed to increase lifestyle PA among people with depression. If successful, my research will lay the foundations for a scalable 28 intervention that could be integrated into mental health care across Canada. 29 Chapter 2: General Structure The general structure of this dissertation is presented in Figure 2.1. The first study (Chapter 3; Tang et al., 2022) was a systematic review of the effectiveness of internet-guided self-help (IGSH) interventions in increasing physical activity (PA) among people with depression. The review aimed to identify if there was a need for new app-based PA interventions for this population, synthesize existing evidence in this field, and potentially inform the development of such an app. This review yielded four relevant IGSH interventions (three web-based and one app-based) with a PA component. While findings suggested the potential of IGSH interventions in depression management, no significant improvements in PA were observed, indicating that symptom improvements may be driven by other components. Six recommendations were proposed to guide future development and evaluation of IGSH PA interventions. One recommendation from Study 1 was the a priori consideration of theory, including using theory to guide the selection of behaviour change techniques (BCTs) in mhealth interventions. In alignment with this recommendation, the second study (Chapter 4; Tang et al., 2023) was a secondary data analysis, which aimed to investigate the potential adoption of the Multi-Process Action Control (M-PAC) framework to guide the development of an app designed to increase PA among individuals with depression. The M-PAC framework offers a pragmatic approach to behaviour change aimed at bridging the intention-behaviour gap (see more details in Chapter 4; Rhodes, 2021). I tested the moderating role of mental health status within an adapted M-PAC model. Results found that mental health status only partially moderated this adapted M-PAC 30 framework, indicating the potential of the M-PAC framework in guiding the development of PA behaviour change interventions for people with poor mental health. Taken together, the first two studies helped to identify the necessity for new, well-designed apps to promote PA and the potential to use the M-PAC framework to address the typical intention-behaviour gap more effectively among individuals with depression. Study 1 was also able to propose a few preliminary guidelines to inform future relevant interventions, and Study 2 provided ideas regarding more influential theoretical constructs for these interventions to emphasize. Collectively, these findings informed the development and testing of an app-based PA behaviour change intervention (MoodMover) for people with depression in Studies 3 & 4. 31 Figure 2.1. The general structure and timeline of this thesis. One approach to enhance the efficacy and sustainability of behavioural interventions is to develop the intervention following a rigorous intervention development process (OâCathain et al., 2019). The Obesity-Related Behavioural Intervention Trials (ORBIT; Czajkowski et al., 2015; Powell et al., 2020) model is a multi-phased framework that aims to establish translational pathways from behavioural and social science theory or findings to chronic disease treatments Study 4: Feasibility and preliminary efficacy result studyFeasibility and proof-of-concept of an app-based physical activity intervention for individuals with depression (MoodMover): A single-arm, pre-post studyStudy 4: Feasibility and preliminary efficacy proposal (Published in May 2025)Feasibility and Preliminary Effects of an App-based Physical Activity Intervention for Individuals With Depression (MoodMover): A Protocol for a Single-arm, Pre-post Intervention StudyStudy 3: App development and usability testing (Published in Feb 2025)MoodMover: Development and Usability Testing of an MHealth Physical Activity Intervention for DepressionStudy 2: Evaluate the suitability of a behavioural theory: M-PAC (Published in Jun 2023)Exploring Correlates of Physical Activity Using the Multi-Process Action Control Framework: Is There a Moderating Role for Mental Health?Study 1: Identify the need for a new app (Published in Dec 2022)The Effectiveness of Internet-guided Self-help Interventions to Promote Physical Activity Among Individuals with Depression: Systematic Review32 (Czajkowski et al., 2015) (See Figure 2.2). The ORBIT model begins by identifying a significant clinical problem that may be addressed through a behavioural intervention, followed by two main pre-efficacy phases: Phase I (Design) and Phase II (Preliminary testing). As mobile-based PA interventions for individuals with depression have been rarely developed or evaluated, and given the ORBIT modelâs emphasis on early-stage intervention development, it was selected to guide the development of MoodMover. Figure 2.2. The revised ORBIT model. Source: Powell et al. (2020) In addition, to foster app-related creativity, the Integrate, Design, Assess, and Share (IDEAS; Mummah et al., 2016) framework, developed for guiding the development of digital behavioral interventions, has been integrated. This framework encompasses 10 steps that can be categorized into 4 overarching categories: Integrate, Design, Assess, and Share (See Figure 2.3). Utilizing design thinking and user-centered design principles, these steps underscore an iterative approach and engage a multidisciplinary team to incorporate diverse perspectives. 33 Figure 2.3. The IDEAS framework. Source: Mummah et al. (2016) In summary, Study 3 describes the development process of MoodMover, an app-based intervention for promoting PA among people with depression, following the integration of the two selected systematic behavioural intervention development frameworks. In this study, the development of MoodMover specifically incorporates four steps (i.e., Ideate, Prototype, Gather, Build) of the Design stage with Phase I of the ORBIT model. In addition, a usability testing study was conducted to gather user feedback (Gather) and further refine the app (Build). Chapter 6 describes the protocol for a feasibility study evaluating the proof-of-concept, feasibility, and acceptability of MoodMover among a sample of adults with depressive symptoms. Results of the study are presented in Chapter 7. A final discussion chapter provides a summary of the key findings from each study, followed by a discussion of the theoretical, methodological, and practical implications of this research. It then reflects on common limitations across all studies and concludes with a comprehensive discussion of potential future directions, including opportunities for intervention refinement and optimization, evaluation, personalization and adaptation, and implementation. 34 Chapter 3: The Effectiveness of Internet-Guided Self-Help Interventions to Promote Physical Activity Among Individuals with Depression: Systematic Review 3.1 Background Depression is a chronic mental health condition characterized by sadness; anhedonia; and secondary physical, cognitive, and emotional symptoms. People with depression often experience lower quality of life (Angermeyer et al., 2002; Hansson, 2002; Sivertsen et al., 2015), increased risk of suicide (Cavanagh et al., 2003; Ferrari et al., 2013), and increased risk of having co-morbid chronic diseases (e.g., diabetes, asthma, chronic lung disease, coronary heart disease) which can result in premature death (Lotfaliany et al., 2018; World Health Organization, 2019). Historically, it was estimated that > 300 million people experienced depression worldwide (World Health Organization, 2017), with most people never diagnosed (Arokiasamy et al., 2017; Lecrubier, 2007; Lotfaliany et al., 2018). Surveillance studies have observed a marked increase in the prevalence and severity of depressive symptoms since the start of the COVID-19 pandemic. For instance, Bueno-Notivol et al. (2021) observed that the prevalence of self-reported depression was 7 times higher in 2020 (25%) than in 2017 (3.44%) throughout the world. Similarly, a meta-analysis by Nochaiwong et al. (2021) estimated that over a quarter of people globally (28%) self-reported depressive symptoms during the COVID-19 outbreak in 2020. Physical activity (PA) â âany bodily movement produced by skeletal muscles that results in energy expenditure (Caspersen et al., 1985)â - has long been recognized as an important health-35 promoting behaviour. In recent years, it has also been identified as being beneficial in the prevention and treatment of depressive disorders (Kandola et al., 2019; Teychenne et al., 2020). Exercise, a subset of PA that is typically planned and structured with the goal of increasing or maintaining fitness (Caspersen et al., 1985), is now recommended as a monotherapy for mild-to-moderate depression in Canada (Ravindran et al., 2016). PA presents several advantages over conventional treatments for depression (i.e., psychotherapy and antidepressant medication). Amongst them: PA has minimal negative side effects, is affordable, and is potentially more accessible (Blumenthal et al., 2012). Rebar and Taylor (2017) have suggested that PA could be a cost-effective method of treating depression worldwide. As an additional benefit, a large body of literature confirms the positive side-effects of engaging in regular PA, including heightened health-related quality of life (i.e., physical and mental well-being) (Anokye et al., 2012), chronic disease prevention (including obesity, type II diabetes, coronary heart diseases, and several cancers), and reduced risk of premature mortality (Carlson et al., 2018; Kraus et al., 2019). Globally, a large minority (31.1%) of adults do not meet minimum recommended levels of PA (Hallal et al., 2012). Data from the 2016-2017 Canadian Health Measures Survey (CHMS), suggests that only 16% of Canadian adults meet the current PA recommendation of 150 minutes of moderate to vigorous physical activity (MVPA) per week (Center for Surveillance and Applied Research, 2021). People with depression are more likely to experience lower PA levels than those without depression (Song et al., 2012; Stubbs, Koyanagi, et al., 2016), in part due to symptoms like pain and discomfort, insomnia, cognitive difficulties, fatigue, and anhedonia (Blumenthal et 36 al., 2012; Stubbs, Koyanagi, et al., 2016; Vancampfort et al., 2017). Tailored approaches to help people with depression initiate and maintain PA are needed (Gerber et al., 2016). 3.1.1 Internet-Guided Self-help interventions Internet-Guided Self-help (IGSH) interventions, a form of eHealth intervention, could be 1 mechanism for supporting PA behaviour change among people with depression. EHealth interventions are defined as those that use information and communication technologies to enable health care, including supporting health behaviour engagement (Ritterband et al., 2006; Strecher, 2007). As such, eHealth interventions are quite broad, and include interventions such as just-in-time adaptive interventions, wearable technology, telehealth, and social media. Research has demonstrated that most people with mental disorders have an interest in trying eHealth interventions (such as smartphone apps) to monitor and manage their health concerns (Proudfoot et al., 2010; Torous et al., 2014). IGSH interventions are characterized by web-based or app-based programs that are primarily self-guided. Some will offer limited support from a professional or paraprofessional (Borgueta et al., 2018). Compared with synchronous eHealth interventions that likely require costlier direct consultation (e.g., telehealth, live internet-based therapy, and live-streamed exercise sessions) (Naslund et al., 2015), IGSH programs delivered via web or mobile devices have the potential for broad reach and scalability at a relatively low cost. They also allow participants to access the intervention content at their own pace (Barak et al., 2009; McCrabb et al., 2019; vanât Hof et al., 2009). These features may be particularly beneficial for people with depression who 37 experience symptoms, such as fatigue and disrupted sleep patterns. Additionally, many people with depression experience stigma that negatively affects treatment-seeking (Clement et al., 2015), and have preferences to manage symptoms on their own (Jennings et al., 2015). Offering self-help interventions could be potentially useful to increase help-seeking rates by mitigating stigma (Levin et al., 2018). Systematic reviews and meta-analyses have found eHealth interventions effective at increasing short-term PA participation in non-clinical populations, including young people (Lau et al., 2011; McIntosh et al., 2017), adults (Davies et al., 2012), and older adults (Muellmann et al., 2018; Núñez de ArenasâArroyo et al., 2021). Other research on eHealth interventions suggests that interventions that adopt theory and/or incorporate evidence-based behaviour change techniques (BCTs) are generally associated with greater effects and adherence (Murray, 2012; Webb et al., 2010). A systematic review of meta-analyses on the effectiveness of self-help and internet-guided interventions has suggested that these programs are effective in treating depression (Ma et al., 2021; vanât Hof et al., 2009). In fact, computerized cognitive behavioural therapy (CBT) has been recommended as a treatment for subthreshold or mild-to-moderate depression in the United Kingdom (National Institute for Health and Clinician Excellence, 2009). Similarly, a systematic review by Andersson and Cuijpers (2009) suggested that guided internet-based psychological interventions were more effective than those unguided for adult depression. Less is known about whether IGSH PA interventions for people with depression are effective at 1) increasing PA engagement and 2) reducing depressive symptoms. To date, two 38 reviews have investigated eHealth PA interventions for individuals with mental illness (Moran et al., 2018; Rosenbaum et al., 2015), and one review (Carneiro et al., 2021) specifically examined web-based interventions. However, these reviews investigated eHealth interventions for mental illness generally, rather than IGSH programs for depression specifically. These are noteworthy distinctions, as the strongest research evidence of the benefits of PA-based treatments is for depression (Ravindran et al., 2016); to our knowledge, no clinical guidelines exist for PA-based treatment for anxiety, schizophrenia, or other mental health conditions. In addition, the reviews included both experimental and observational studies (Moran et al., 2018). Given the rapid pace of technological development and the growing concerns of physical inactivity among people with depression, it is likely this research field has expanded in recent years. There is a need for an updated systematic review of high-quality studies specific to depression. Thus, the primary objective of this systematic review was to assess the effectiveness of IGSH interventions in promoting PA and alleviating depressive symptoms among people with depression. The secondary objective of this study was to understand study characteristics, such as attrition rates and intervention design, to explore factors associated with successful interventions and areas for future growth. 3.2 Methods This systematic review was guided by the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) approach (Page, McKenzie, et al., 2021; Page, Moher, et 39 al., 2021). The protocol was pre-registered on PROSPERO (CRD42020221713). The PRISMA checklist is provided in Appendix A. 3.2.1 Searches Two rounds of searches were conducted in December 2020 and November 2021. The search strategy was developed in consultation with a university rehabilitation sciences librarian. Seven electronic databases were searched: MEDLINE (via Ovid), PsycINFO (EBSCOhost), CINAHL (EBSCOhost), Web of Science, the Cochrane Central Register of Controlled Trials (CENTRAL) (via Ovid), EMBASE (via Ovid), and SportDiscus (EBSCOhost). In addition, OpenGrey and ProQuest Dissertations were searched to identify grey literature that matched the inclusion criteria. Lastly, the reference lists of included studies were searched to identify additional eligible studies. The December 2020 search used a combination of controlled vocabulary (e.g., Medical Subject Headings [MeSH]) and keywords related to âphysical activityâ, âdepressionâ, and âeHealthâ. For comprehensiveness, the search included keywords for all types of eHealth interventions. The search strategies for all databases are presented in Appendix B. The selected keywords were informed by previous systematic reviews and protocols in relevant areas. A revised search was conducted in November 2021, with two changes: (1) results limited to the period of December 1, 2020 to November 5, 2021, and (2) the search strategy was updated to better reflect the inclusion criteria and reduce irrelevant records informed by the experience of 40 the December 2020 search. For example, the keywords âtext messagingâ and âvideo conferencingâ were deleted. The updated search strategies for all included databases are in Appendix C. 3.2.2 Eligibility criteria Study selection criteria were based on the Population, Intervention, Comparison, Outcome, Study designs (PICOS) framework (Liberati et al., 2009; Moher et al., 2009). Studies that met the following criteria were included in this review: Participants. Individuals with a clinical diagnosis of depression (e.g., by Diagnostic and Statistical Manual of Mental Disorders criteria) and/or individuals with clinically significant depressive symptoms based on a validated self-report tool (e.g., Patient Health Questionnaire 9-Item (PHQ-9) or Beck Depression Inventory-II (BDI-II) scores) were considered eligible for inclusion. Studies that used non-validated items (e.g., âAre you depressed? Yes/Noâ) or did not use recognized diagnostic criteria were excluded. There were no restrictions based on age, gender, nationality, and/or ethnicity. Interventions. Interventions were considered eligible if they were delivered via web-based platforms or mobile apps (e.g., via smartphone, tablet) and included content designed to increase PA (e.g., asynchronous PA programming, education, PA goal setting). Studies were included even if promoting PA was the secondary objective, so long as change in PA was measured and reported. By definition, interventions were primarily self-guided/automatic; however, interventions that offered degrees of human support (e.g., providing personalized feedback via telephone or e-mail) were permitted. In contrast, interventions primarily based on in-person support (e.g., telephone 41 counselling, text messaging) were excluded. Likewise, live-streamed interventions (e.g., live Zoom yoga classes) were excluded, as these are synchronous events organized in a live virtual space. There were no restrictions based on the type of PA, PA intensity, bout length, program frequency or duration, or follow-up period. Comparison. Any comparators were considered for inclusion. For instance, a PA intervention delivered in-person, an alternative eHealth intervention, or a wait-list control group. No restrictions were placed on the nature of the comparison group. Outcomes. The primary outcome was a change in PA levels. Both device-based (e.g., accelerometer, pedometer) and self-report (e.g., PA diary, questionnaires) measures were considered. Secondary outcomes included: changes in the severity of depression, assessed by validated measures; and the acceptability of treatment, assessed by (1) reported indicators of intervention engagement (Christensen et al., 2009); and (2) dropout rates at postintervention. Engagement was defined as the extent to which participants undertook the content of the intervention. Dropout rate, also called attrition rate, was defined as the percentage of participants who were randomized to a group but failed to complete it. Study designs. Randomized controlled trials (RCTs) and quasi-experimental studies with control groups and pre- and posttest measurements were considered. Studies were eligible if they were a published original manuscript or thesis/dissertation. Papers were excluded if they did not contain original findings (e.g., editorials, reviews), if they were research protocols/proposals, or if they were conference abstracts. 42 Other. Papers were considered eligible for inclusion if they were published in English. There were no limitations placed on the date of publication. 3.2.3 Study selection A total of 2 authors (first round: YT and JL; second round: YT and MG) independently screened articles for inclusion using the agreed-upon study eligibility or ineligibility criteria. Screening occurred in 3 phases, facilitated by Covidence systematic review software (Veritas Health Innovation). First, duplicate records were removed automatically using Covidence. Second, titles and abstracts were independently screened for relevance by two reviewers. In the case of disagreement, the reviewers met to discuss and reach a consensus. Finally, full-text copies of the selected studies were screened (YT, JL, DD, and MG) to confirm eligibility. Disagreements between the 2 researchers were resolved through discussion. A third reviewer (GF) was consulted if consensus could not be reached. Following selection, the reference list for each included study was searched to identify additional eligible studies. The PRISMA flow diagrams are presented in Figure 1 and Figure 2. 3.2.3 Data extraction The following items were extracted by one author (YT) and reviewed for accuracy by another reviewer (first round: DD; second round: MG): 1) author and year, 2) study design, 3) country, 4) sample size, 5) participant characteristics, such as mean age and gender, 6) inclusion and exclusion criteria, 7) intervention details, including the level of contact, 8) comparator, 9) main outcomes (measurement tools), 10) additional outcomes (measurement tools), 11) main findings 43 with regard to PA and depression symptomatology, 12) follow-up period, 13) BCTs, and 14) funding sources. A third author (GF) was consulted to resolve discrepancies. Level of contact was categorized as high, moderate, and low, following procedures described by Ma et al. (2021). If the support was directly provided by a qualified therapist (e.g., exercise trainers providing personalized feedback), the level of contact was high. If the support was provided by members of the research team or trained students, it was considered moderate. If there was no direct contact with an interventionist (e.g., only automated reminder emails were sent out), the contact was considered low. 3.2.4 Risk of bias The Cochrane risk of bias tool, version 2 (ROB 2) (Higgins et al., 2019; Sterne et al., 2019) was used to assess the risk of bias for both PA and depression outcomes. This tool includes 5 domains of risk of bias: randomization process, deviations from intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result. The risk of bias for each outcome in each study was judged as high, some concerns, or low. Risk of bias was assessed by two independent reviewers (YT and MG). In the case of disagreement, the reviewers met to discuss and reach a consensus. The remaining disagreements were resolved by consulting a third author (GF). 3.2.5 Data synthesis Meta-analysis was not feasible because the included studies were too heterogeneous in their design and reporting of results. Study interventions and outcome characteristics were 44 summarized using narrative synthesis and descriptive statistics. When the effect size was reported as Cohenâs d (standardized mean difference), it was interpreted as small (Cohen d = 0.2), moderate (Cohen d = 0.5), or large (Cohen d = 0.8) (Nakagawa & Cuthill, 2007). 3.2.6 Confidence in the cumulative evidence The Grading of Recommendation, Assessment, Development and Evaluation (GRADE; Balshem et al., 2011) criteria were used to rate the certainty of the cumulative evidence based on the risk of bias, imprecision, inconsistency, indirectness, and publication bias. The quality of evidence was categorized as high, moderate, low, or very low. Table 3.4 presents a summary of findings. 3.3 Results A total of 45,461 records were identified: 42,052 in December 2020, and an additional 3409 in November 2021. After removing duplicates, 28,668 articles underwent title or abstract screening and 97 underwent full-text review. The first round of searches (December 2020) produced 4 articles. The second round of searches (November 2021) produced 1 additional article, which was subsequently deleted because it was a duplicate. A total of 4 eligible studies were included in this systematic review. The PRISMA flow diagrams are presented in Figure 3.1 and Figure 3.2. 45 Figure 3.1. PRISMA flow diagram of first round search. 46 Figure 3.2. PRISMA flow diagram of second round search. 3.3.1 Characteristics of included trials Study design. Tables 3.1 and 3.2 summarize the characteristics of the 4 included studies. All 4 included studies were dual-arm RCTs. None of the studies utilized an active control group (e.g., sham intervention) or treatment comparison group (e.g., synchronous in-person therapy). Rather, the studies used either a waitlist or treatment-as-usual group. 47 Table 3.1. Participant characteristics. Author, year Target population Sample size Age mean (SD) Sex (F/M) Pre-existing psychiatric treatment Therapy Medication Combined Guo et al. (2020) HIV and major depressive disorder 300 IGa: 150 CGb: 150 IG: 28.0 (5.8) CG: 28.6 (5.9) IG: 8/142 CG: 15/135 0c 0c 0c Haller et al. (2018) Adults (20-65 years) with major depression 20 IG: 14 CG: 6 IG: 43 (14) CG: 51 (12) IG: 10/4 CG: 3/3 IG: 2 CG: 0 IG: 7 CG: 3 IG: 3 CG: 3 Lambert et al. (2018) Adults with at least moderate depressive symptoms 62 IG: 32 CG: 30 IG: 39.3 (12.0) CG: 36.9 (12.6) IG: 26/6 CG: 26/4 IG: 1 CG: 7 IG: 18 CG: 18 IG: N/Ad; CG: N/A Ström et al. (2013) Mild to moderate major depression diagnosis and sedentary lifestyle 48 IG: 24 CG: 24 IG: 48.8 (12.7) CG: 49.6 (8.7) IG: 20/4 CG: 20/4 IG: 0 CG: 0 IG: 3 CG: 4 IG: N/A; CG: N/A aIG: Intervention group; bCG: Control group; cPotential participants excluded if they were currently on psychiatric treatment. dN/A: not applicable. 48 Table 3.2. Characteristics of the included trials. Author, year Intervention Duration of Intervention Control Attrition rate Outcome measurements Effect Size Guo et al. (2020) Cognitive behavioural stress management course + PAa promotion via Wechat APP 12 weeks Waitlist control + usual care of HIV 8.7% IGb: 11 cases CGc: 15 cases PA: METsd calculated from Chinese version of the GPAQe Depression: CES-Df (Main) PHQ-9g (Secondary) PA outcome: non-significant between-group differences (3 months MET: â1898 (â4285 to 489), P = .12); Depressive outcome: Between-group mean difference: â5.77 (95%CI: â7.82, â3.71), Cohenâs d = 0.66, P < .001 Haller et al. (2018) Online platform with weekly exercise schedules and motivational feedback, as well as an additional biweekly group training session 8 weeks Treatment as usual 15% IG: 3 cases CG: 0 cases PA: Baecke questionnaire Depression: QIDS-SRh QIDS-Ci Total habitual PA outcome: Eta2 = 0.36, P =.007; Depressive outcome: after 6-12 days: non-significant between-group differences (QIDS-SR: P = .06; Eta² = 0.2) post-treatment: non-significant between-group differences Lambert et al. (2018) Web-based modular-fashioned course with evidence-based 8 weeks Waitlist control + treatment as usual 19% IG: 7 cases CG: 5 cases Device-based PA: Min per week of objective MVPAj in 10-min bouts Self-reported PA: PA outcome: non-significant between-group differences (Between-group adjusted mean difference: Device-based PA: 16.4 (â43.7 to 76.5); Self-49 Author, year Intervention Duration of Intervention Control Attrition rate Outcome measurements Effect Size treatment based on behavioural activation and PA promotion IPAQ-SFk Depression: PHQ-8l reported PA: 0.2 (â8.7 to 9.2)); Depressive outcome: Between-group adjusted mean difference: â3.6 (95% CI: -6.1, -1.1) Cohenâs d = 0.93 Ström et al. (2013) Web-based guided self-help PA program with nine text modules, written feedback, and home assignments, from therapists 9 weeks Waitlist control 0% IG: 0 cases CG: 0 cases PA: IPAQm Depression: BDI-IIn (Main) MADRS-So (Secondary) PA outcome: non-significant between-group differences (Cohenâs d = 0.20); Depressive outcome: Between-group: BDI-II: Cohenâs d = 0.67 MADRS-S: Cohenâs d = 0.62 aPA: Physical Activity; bIG: Intervention group; cCG: Control group; dMETs: Metabolic equivalents; eGPAQ: Global Physical Activity Questionnaire; fCES-D: Centre for Epidemiological Studies Depression; gPHQ-9: Patient Health Questionnaire 9-item; hQIDS-SR: Quick Inventory of Depressive Symptomatology - self-report; iQIDS-C: Quick Inventory of Depressive Symptomatology - clinician rating; jMVPA: Moderate-to-vigorous physical activity; kIPAQ-SF: International Physical Activity Questionnaire-Short Form; lPHQ-8: Patient Health Questionnaire 8-item; mInternational Physical Activity Questionnaire; nBDI-â ¡: Beck Depression Inventory - second version; oMADRS-S: Montgomery-Ã sberg Depression Rating Scale: Self-rated Version. 50 Sample characteristics. The studies were conducted in 4 different countries: China (Guo et al., 2020), Germany (Haller et al., 2018), the United Kingdom (Lambert et al., 2018), and Sweden (Ström et al., 2013). Together, the 4 studies included 430 people with depression. The sample size varied from 20 (Haller et al., 2018) to 300 (Guo et al., 2020). Guo et al. (2020) included adults with a clinical diagnosis of HIV, with the majority (92.3%) of participants being men. The remaining 3 (75%) studies included higher rates of female participation (65.0%) (Haller et al., 2018) to 86.7% (Lambert et al., 2018)). The mean age ranged from 28.3 (SD = 5.8) (Guo et al., 2020) to 49.2 (SD = 10.07) years (Ström et al., 2013). The 3 studies (Haller et al., 2018; Lambert et al., 2018; Ström et al., 2013) included participants who were receiving psychiatric treatment including psychotherapy or medication. Although the studies varied in their reporting of sociodemographic characteristics, it was generally observed that samples were well-educated (i.e., the majority of participants having completed high school or more) and currently employed. In addition, the 3 studies (Guo et al., 2020; Lambert et al., 2018; Ström et al., 2013) reported participantsâ baseline PA levels, with different proportions of them being physically active (13% (Lambert et al., 2018) to 60.5% (Ström et al., 2013)). Measures. Of the 4 studies, 3 (75%) used self-report methods to measure PA: metabolic equivalents calculated from the Global Physical Activity Questionnaire (Guo et al., 2020), habitual PA scores calculated from the Baecke Physical Activity Questionnaire (Haller et al., 2018), and the International Physical Activity Questionnaire (Ström et al., 2013). In contrast, Lambert et al. (2018) used a combination of self-report (International Physical Activity QuestionnaireâShort Form) and device-based measures (accelerometry, with MVPA reported as minutes per week in 10-minute bouts). Intervention characteristics. Interventions typically provided discrete âmodulesâ of 51 information with distinct learning objectives. One study by Guo et al. (2020) delivered the intervention via a preexisting commercial app (WeChat). The other 3 (75%) trials used web-based PA interventions. The intervention length ranged from 8 weeks (Haller et al., 2018; Lambert et al., 2018) to 3 months (Guo et al., 2020). In addition, 2 (50%) studies (Haller et al., 2018; Lambert et al., 2018) reported short-term post-intervention effects, while Guo et al. (2020) and Ström et al. (2013) assessed longer-term maintenance (6- and 9-months and 6-month maintenance, respectively). All 4 studies incorporated weekly contact. Moreover, 2 (50%) studies (Haller et al., 2018; Lambert et al., 2018) reported a moderate level of contact. Haller et al. (2018) and Ström et al. (2013) included a high level of contact with participants receiving feedback from a therapist via phone or an encrypted web-based platform, respectively. In addition, Haller et al. (2018) incorporated an optional biweekly face-to-face training session led by a sports therapist. 3.3.2 Attrition and engagement Dropout rates were relatively low, ranging from 0% (Ström et al., 2013) to 19% (Lambert et al., 2018) across the 4 studies (see Table 2). In terms of intervention engagement, participants in the study by Guo et al. (2020) completed 55% of the program (i.e., 9 content modules and 3 review modules). In the study by Haller et al. (2018), participants were recommended to complete a maximum of 3 endurance sessions and 2 strength training sessions per week for 8 weeks. Participants completed 84% (16 [IQR: 9 - 19] of 19 [IQR: 15 - 21] of recommended endurance sessions and 90% (9 [IQR: 4 - 12] of 10 [IQR 8 - 13]) of recommended strength training sessions. In the study by Lambert et al. (2018), only 1 participant used all 13 modules: the introduction, 8 weekly modules, 1 generic problem-solving module, and 3 unlockable modules. A total of 53% (17/32) of the participants completed at least the introduction module and the first 2 weekly modules, and 8 participants completed at least four weekly modules. In the study by Ström et al. 52 (2013), 58% (14/24) of intervention participants completed all 9 modules. 3.3.3 Effectiveness of interventions All 4 studies (Guo et al., 2020; Haller et al., 2018; Lambert et al., 2018; Ström et al., 2013) reported a change in depressive symptoms as the main outcome and change in PA as a secondary outcome. All studies observed a positive effect of the intervention on depression severity between baseline and posttreatment, 3 (75%) of which (Guo et al., 2020; Lambert et al., 2018; Ström et al., 2013) reported significant and moderate or large between-group differences. The app-based intervention by Guo et al. (2020) found a moderate to large effect on depressive symptoms in the intervention group versus the waitlist control group (mean difference = -5.77; 95% CI: -7.8 to -3.71; Cohen d = 0.66, P < .001), which was sustained at 6 months (Cohen d = 0.63, P < .001) and 9 months (Cohen d = 0.51, P < .001) after intervention. However, there were no significant changes in PA levels (metabolic equivalents) from baseline to follow-up in either group. Haller et al. (2018), Lambert et al. (2018), and Ström et al. (2013) all examined web-based PA interventions. Similar to Guo et al. (2020), Haller et al reported significantly reduced depressive symptoms on the Quick Inventory of Depressive Symptomatology Clinician Rating (P = .02) and Self-Report Quick Inventory of Depressive Symptomatology (P = .001) from before to after the intervention. However, there were no significant differences between the intervention and control groups in reducing depressive symptoms after 6 to 12 days (η2 = 0.2, P = .06) and after treatment. In terms of PA improvement, Haller et al. reported significantly increased total habitual PA (η2 = 0.36, P = .007) in the intervention group after the 8-week intervention. In the study by Ström et al, the web-based intervention was effective in reducing depressive symptoms, reflected both in Montgomery-Ã sberg Depression Rating Scale: Self-rated Version and BDI-II. For example, the 53 BDI-II results showed a moderate between-group effect size (Cohen d = 0.67; 95% CI: .09 to 1.25). Ström et al. also reported increased PA levels in both the intervention group and the control group. However, this change did not significantly differ between groups. In Lambert et al., the results showed greater changes in depressive symptoms (Patient Health Questionnaire 8-item scores: adjusted mean difference = â3.6, 95% CI: -6.1 - -1.1) at 2 months in the intervention group. The intervention group also reported a higher median of minutes of device-measured MVPA in 10-minute bouts (97.6 [IQR: 49.7 â 166.3]) than the control group (13.0 [IQR: 0.0 â 131.4]), although this difference was not statistically significant. 3.3.4 Theory and BCTs A large body of evidence illustrates that interventions are more effective when they are informed by theory and integrated with evidence-based BCTs. The studies by Lambert et al. (2017; 2018) were the only study that explicitly describes the adoption of a behavior change or knowledge translation framework, the Centre for eHealth Research and Disease Management roadmap, to guide intervention design and evaluation. Three (75%) studies were informed by a therapeutic approach: Guo et al. (2020) adopted cognitive behavioral and stress management principles; Lambert et al. (2018) was informed by behaviour activation; and Ström et al. (2013) incorporated aspects of acceptance and commitment therapy (ACT) and motivational interviewing. With regard to behaviour change theories, Lambert et al. (2018) applied Self-Determination Theory (SDT) to behavioural activation as the underlying theory of PA behaviour change, and Ström et al. (2013) was informed by the SDT and the Transtheoretical (Stages of Change) Model. The remaining studies (Guo et al., 2020; Haller et al., 2018) described an atheoretical or eclectic approach that integrated a few common BCTs for PA promotion. 54 BCTs for increasing PA were coded according to Michie et al. (2013) BCT Taxonomy. In the intervention groups, âgoal setting (behaviour)â (n = 4), âproblem solvingâ (n = 3), âreview behaviour goal(s)â (n = 3), âfeedback on behaviourâ (n = 3), âself-monitoring of behaviourâ (n=3), and âinformation about health consequencesâ (n = 3) were the 6 most commonly used BCTs. The highest number of BCTs were identified in both Ström et al. (2013) (n=15) and Lambert et al. (2018) (n = 15). Table 3.3 provides an overview of BCT use across studies. Table 3.3. Intervention behaviour change techniques for physical activity promotion. BCTa Guo et al. (2020) Haller et al. (2018) Lambert et al. (2018) Ström et al. (2013) Total (n/N) Number of BCTs, n 7 8 15 15 -b 1.1 Goal setting (behaviour) üc ü ü ü 4/4 1.2 Problem solving ü - ü ü 3/4 1.4 Action planning - - ü ü 2/4 1.5 Review behaviour goal(s) - ü ü ü 3/4 1.7 Review outcome goal(s) - - - ü 1/4 1.8 Behavioural contract - ü - - 1/4 2.2 Feedback on behaviour ü ü - ü 3/4 2.3 Self-monitoring of behaviour ü - ü ü 3/4 2.6 biofeedback - ü - - 1/4 3.3 Social support (emotional) - ü - - 1/4 4.1 Instructions on how to perform the behaviour ü ü ü - 3/4 4.4 Behavioural experiments - - ü - 1/4 5.1 Information about health consequences ü - ü ü 3/4 5.4 Monitoring of emotional consequences - - ü - 1/4 55 BCTa Guo et al. (2020) Haller et al. (2018) Lambert et al. (2018) Ström et al. (2013) Total (n/N) 5.6 Information about emotional consequences - - ü ü 2/4 6.1 Demonstration of the behaviour - ü ü - 2/4 7.1 Prompts/cues - - ü - 1/4 8.3 Habit formation - - - ü 1/4 8.7 Graded tasks - - ü - 1/4 9.1 Credible source - - ü - 1/4 9.2 Pros and cons - - - ü 1/4 9.3 Comparative imagining of future outcomes - - - ü 1/4 10.4 Social reward - - ü - 1/4 10.9 Self-reward - - - ü 1/4 11.2 Reduce negative emotions ü - - - 1/4 15.3 Focus on past success - - - ü 1/4 16.2 Imaginary reward - - - ü 1/4 aBCT: Behaviour change technique; bNot available. cIndicates that a behavior change technique was used. 3.3.4 Risk of bias within studies The ROB 2 (Higgins et al., 2019; Sterne et al., 2019) tool was used to assess the risk of bias of each outcome (PA and depression) of each included RCT (Figure 3). Overall, the ârandomization processâ was properly used in all studies. In addition, there were no concerns related to âdeviations from the intended interventionsâ and âselection of the reported resultâ. However, in terms of PA, 2 (50%) studies (Haller et al., 2018; Lambert et al., 2018) were judged 56 as having a high risk of overall bias owing to âmissing outcome dataâ. Three (75%) studies (Guo et al., 2020; Haller et al., 2018; Ström et al., 2013) showed some concerns owing to the subjective measurement of PA outcomes. In terms of depression, 3 (75%) studies (Guo et al., 2020; Haller et al., 2018; Lambert et al., 2018) presented concerns owing to missing outcome data or outcome measurements. Figure 3.3. Risk of bias assessments of PA and depression of included studies. 3.3.5 Quality of cumulative evidence The GRADE assessments are presented in Table 3.4. The 4 selected RCTs started as high-quality evidence but were subsequently rated down owing to inconsistency and imprecision. 57 Table 3.4. Quality of evidence of outcomes of interest. Outcomes Risk of bias Publication bias Inconsistency Indirectness Imprecision Quality of evidence Physical Activity Some concerns to higha Not suspected Serious Not serious Serious ââ¯â¯â¯ Very low Depression Low to some concernsa Not suspected Not serious Not serious Serious ââââ¯ Moderate a: detailed information was presented in Figure 3.3. 3.4 Discussion To our knowledge, this is the first systematic review of IGSH interventions to increase PA in people with depression. A total of 4 studies met our inclusion criteria: 3 (75%) web-based RCTs and 1 (25%) app-based RCT. The 3 (75%) studies (Guo et al., 2020; Haller et al., 2018; Ström et al., 2013) reported that PA levels increased at postintervention in both treatment and control groups. Overall, between-group differences in PA were small and generally not significant. In contrast, 3 (75%) studies found significant medium to large reductions in depressive symptoms; the final study reported a significant reduction in both the intervention and control groups. Collectively, these results suggested that IGSH interventions may be helpful for managing clinical depression. Why did these interventions fail to increase PA levels? Although explanations may vary across studies, several common factors were noted. In 2 studies (Guo et al., 2020; Lambert et al., 2018), PA was presented as part of a larger depression management strategy, in conjunction with other components. For example, the Run4Love intervention by Guo et al. (2020) featured sessions on cognitive behavioral and stress management strategies. As PA was not the primary focus of these interventions, the nonsignificant results were not altogether surprising. More-intensive interventions, that is, those with a central focus on PA behavior change, may be required to produce clinically meaningful changes in PA behavior. From a methodological perspective, the 3 (75%) 58 studies had small sample sizes, which is associated with an increased risk of type 2 error (Banerjee et al., 2009). An additional explanation for the nonsignificant differences in PA is low engagement. Low engagement has previously been identified as a major challenge to eHealth interventions and is likely to negatively impact the effectiveness of interventions (Andrews et al., 2018; Schubart et al., 2011; Schure et al., 2019). In our review, 3 of the 4 (75%) studies were marked by low participant engagement despite different tactics to enhance adherence (e.g., reminders via phone calls or emails and tailored feedback). In addition, Lambert et al. (2018) included some level of tailored content by incorporating graded tasks, where participants could select their preferred physical activities (easier to harder) for the following week from week 3 onward. However, 47% (15/32) of the participants did not reach week 3. The only study with a positive effect on PA change (Haller et al., 2018) reported a remarkably higher adherence rate, with participants completing 84% of the recommended endurance sessions and 90% of recommended strength sessions. This study not only tailored the exercise duration or intensity for participants from the beginning of the intervention but also provided weekly personalized feedback. This indicated that personalized IGSH interventions could potentially be more effective than standardized interventions in increasing PAâsomething that is well-established in the broader literature (Ghanvatkar et al., 2019). Similarly, the importance of personalized components in engagement with technology-based interventions has been emphasized by both people with depression and practitioners (Doherty et al., 2021; Patoz et al., 2021; Stiles-Shields et al., 2017). Additional supportive strategies, such as providing weekly individualized feedback to enhance adherence, should be incorporated throughout the intervention. Future IGSH interventions should consider combining tailored content based on participantsâ preferences and conditions (physical or mental) and weekly 59 personalized feedback for potentially improving engagement with the intervention content. In addition, the types of measurements used may have affected the results. Only Lambert et al. (2018) incorporated both subjective and device-based PA measurements. Even then, less than half of the participants in both the intervention and control groups provided valid device-based PA data, which may have significantly affected PA outcomes. The other 3 (75%) studies (Guo et al., 2020; Haller et al., 2018; Ström et al., 2013) relied solely on self-reported PA measures, which were subject to recall bias. It is worth noting that none of these self-reported PA measures were developed specifically for people with mental health disorders and may fail to accurately assess PA in the selected studies (Rosenbaum et al., 2020). Moreover, 3 (75%) studies (Guo et al., 2020; Lambert et al., 2018; Ström et al., 2013) incorporated a âself-monitoring of behaviourâ BCT component. For example, Ström et al. (2013) provided the intervention group with a pedometer. Consequently, we would expect the treatment and control groups to differ in their awareness and ability to recall PA. Finally, approximately half of the participants were physically active before enrolling in the study (Guo et al., 2020; Lambert et al., 2018). In short, PA promotion effects may be influenced by ceiling effects. 3.4.1 Depressive symptoms In contrast to PA outcomes, more consistent evidence was found for depressive symptom reduction. Three of the included studies (Guo et al., 2020; Lambert et al., 2018; Ström et al., 2013) reported a medium to large effect on decreasing depression symptoms relative to controls. The final study (Haller et al., 2018) reported a significant reduction in both the intervention and control groups. These results were consistent with a previous meta-analysis that found IGSH interventions to be effective in reducing depression among college students (Ma et al., 2021). As PA is generally unaffected by these interventions, the improvements observed across studies are likely attributable 60 to the various therapeutic components used. Within the field of clinical psychology, evidence-based therapies are regularly informed by theory; for example, CBT, behavioral activation, ACT, and interpersonal therapy all possess distinct theoretical foundations that uniquely guide case conceptualization and treatment activities. The 3 studies incorporated â¥1 therapeutic modalities: cognitive behavioral and stress management approaches (Guo et al., 2020), behavioural activation (Lambert et al., 2018), and ACT and motivational interviewing (Ström et al., 2013). This is in line with an earlier study suggesting that therapist-guided web-based CBT has a large effect on depression outcomes (Ruwaard et al., 2009), and other studies suggesting behavioral activation strategies, delivered either in person or over the internet, can be as effective as CBT for depression management (Carlbring et al., 2013; Dimidjian et al., 2011; Richards et al., 2016). One of the 4 studies (Haller et al., 2018) did not observe a significant between-group decrease in depressive symptoms, although early antidepressive findings (6-12 days) were noted on the threshold of significance (P=.06). There were 2 possible explanations. First, the sample size was small (n=20) and the groups were unequal (intervention group=14; control group=6). Therefore, it is likely that this study was underpowered (Sullivan & Feinn, 2012). Second, the authors observed that 1 of the 6 participants in the control group reported a full remission from severe depression after treatment. Although not impossible, this is an unusual occurrence and likely resulted in an inflated index of depressive symptom reduction in the control group. Therefore, the between-group difference was considered less reliable. In contrast, the significant and meaningful reduction in depression in the IG group was believed to be attributed to PA participation. In short, all studies reported that IGSH interventions are feasible and effective in treating depression. 3.4.2 Intervention characteristics & BCTs In general, research suggests that theory-based interventions are more effective than 61 interventions without theory (Bohlen et al., 2020; Murray, 2012; Webb et al., 2010). In addition to providing a framework for selecting evidence-based BCTs, theories also aid in understanding factors that mediate behavior changes and the reasons for intervention success or failure (Brawley, 1993; Stacey et al., 2015). Only 1 study in this review explicitly identified the empirical or theoretical basis for BCT selection for PA promotion. Specifically, Lambert et al. (2017; 2018) reported that SDT was adopted to inform their eMotion intervention. Similarly, Ström et al. (2013) were inspired by an earlier study that used SDT as the theoretical framework. Apart from theories, the importance of intervention development frameworks for guiding the development process of behavior change interventions has also been highlighted to reduce the risk of research waste and increase the effectiveness and sustainability of interventions (Czajkowski & Hunter, 2021; OâCathain et al., 2019). Only Lambert et al. (2017; 2018) identified a framework: in their case, the Center for eHealth Research and Disease Management road map. In contrast to theory, all 4 interventions used multiple BCTs. The most common BCTs identified were goal setting, problem solving, feedback, reviewing behavioral goals, providing information about health consequences, and instructions on how to perform the behavior. As noted by Bohlen et al. (2020), these BCTs are frequently used in interventions for the general population. Within the literature, there is growing interest in how to optimally combine BCTs to support behavior change, with several studies suggesting that BCT combinations might differ across populations and behavioral targets (French et al., 2014; McEwan et al., 2016; Olander et al., 2013; Van Genugten et al., 2016). A previous factorial trial of an internet-based intervention (Schroé et al., 2020) found that combining action planning, coping planning, and self-monitoring induced and amplified the effect on increasing MVPA in healthy adults. Interestingly, both Lambert et al. (2018) and Ström et al. (2013), who included this combination of BCTs, reported no change in PA 62 behavior compared with the control groups. Although methodological limitations must be emphasized, this observed inconsistency raised the question of tailoring BCTs to the population. We cannot assume that strategies that are effective for the general population are a good fit for people with depression. Rather, different or additional BCTs may be required to support PA promotion. A scoping review of the barriers to and facilitators of exercise in people with depression suggested that interventions should incorporate BCTs that target the emotion domain (e.g., to address low mood) (Glowacki et al., 2017). Cane et al. (2015) recommended 4 BCTs for the emotion domain: âsocial support (emotional)â, âinformation about emotional consequencesâ, âself-assessment of affective consequencesâ, and âreduce negative emotionsâ. In our review, all 4 studies (Guo et al., 2020; Haller et al., 2018; Lambert et al., 2018; Ström et al., 2013) addressed aspects of emotion (e.g., both Lambert et al. (2018) and Ström et al. (2013) touched upon the emotional consequences of PA), although none of the studies used all 4 BCTs to target this domain. The lack of population-tailored BCTs might partly explain the inconsistent results regarding PA outcomes. Future IGSH interventions for PA behavior change among people with depression should test different combinations of BCTs and consider integrating more BCTs that focus on the emotion domain. 3.4.3 Attrition and engagement The included IGSH interventions showed relatively low dropout rates (0%-19%). In contrast, Meyerowitz-Katz et al. (2020) reported a dropout rate of 40% (95% CI: 16-63%) among RCTs on mobile health interventions. Josephine et al. (2017) also found a mean intervention dropout rate of 37% within internet-based and mobile-based interventions for people with depression. There have been some studies suggesting that higher levels of therapy contact have a 63 positive impact on acceptance of the intervention (Bennett et al., 2019; O'brien & Daley, 2011). All the included studies incorporated at least a moderate level of participant contact, including weekly contact with study-affiliated personnel. Three studies (Guo et al., 2020; Haller et al., 2018; Ström et al., 2013) provided individualized support; these reported lower attrition and higher study engagement compared with the study by Lambert et al. (2018), who provided weekly automated reminder emails. This is consistent with previous studies that suggested regular motivational feedback may enhance the adherence to internet-based intervention (Andersson et al., 2005; Ströhle, 2009). Similarly, our findings suggest that regular individualized feedback may contribute to reduced dropout rates. Further research is needed to explore the effects of different levels of interventionist contact on IGSH interventions. 3.4.4 Strengths and limitations This review provides an updated summary of IGSH interventions for PA promotion in people with depression. Two rounds of searches were conducted to ensure inclusion of all available evidence. A notable strength of this review was the application of the BCT taxonomy to identify potentially useful ingredients for informing future intervention development. We addressed the gaps observed in previous reviews by including only RCTs and specifically focusing on people with depression. Similar to all studies, this review was not without limitations. First, both the limited number of studies in this review and their heterogeneity in measurement precluded the meta-analysis and limited our ability to draw clear conclusions. Encouragingly, this is a rapidly evolving field, and several promising protocols were identified during article screening (e.g., study by Sylvia et al. (2021)). We are hopeful that more evidence will become available in the next several years. Second, none of the studies had a primary outcome of PA, and because of the measures used, there is a very 64 low level of certainty of PA evidence included. Third, only studies published in English were searched and screened. Fourth, this study used an a priori definition of IGSH interventions, which conceptualized these interventions as technology-facilitated and primarily self-guided, with the option of in-person support. Although having a clear definition is a strength, we did not explicitly designate the degree of in-person support that would render the studies ineligible. Study selection was determined by researchers through a process of discussion. As such, some studies were excluded from this study, which other research groups may have considered eligible or ineligible. Finally, we coded the BCTs using a dichotomous system (i.e., yes or no). This review was unable to speak to the quality and rigor of BCT administration within the included studies. 3.4.5 Future directions Despite increasing interest in eHealth and IGSH interventions, this review identified only 4 studies that (1) included a PA intervention component, (2) assessed changes in PA, and (3) specifically targeted people with depression. However, there were notable gaps in the research design. None of the 4 identified studies identified PA behavior change as their primary objective; interventions contained relatively little content focused on PA promotion; studies generally featured small samples; and half of the included studies did not incorporate behavior change theory for BCT selection or follow a systematic framework of behavioral intervention development. In contrast, not only is PA now recommended as a first-line monotherapy for mild to moderate depression but also there is a large and robust body of evidence regarding the different theories, BCT, and characteristics that can support successful eHealth or IGSH intervention. In summary, there appears to be a large gap between general research and its specific application in IGSH interventions to promote PA in people with depression. Therefore, there is a great need for high-quality and thoughtful research on IGSH 65 interventions for people with depression. On the basis of the results of this review, future research should be characterized by the following: (1) a specific focus on PA promotion within people with depression, including diverse populations of people with depression (e.g., older adults, racialized communities, and individuals in rural or remote communities); (2) a priori consideration of theory, including using theory to guide BCT selection; (3) a rigorous development process following a systematic intervention development framework; (4) defining specific intervention targets (e.g., meeting Canadian Network for Mood and Anxiety Treatments Guidelines (Ravindran et al., 2016) of 3 x 30-minute bouts of MVPA per week); (5) exploration of the mediators and moderators of behavior change, as defined by theory; and (6) using validated tools to assess before and after changes in depressive symptoms and PA, with inclusion of objective measures when possible. Further questions for examination included exploring the optimum amount and modality of guided support, personalization and tailoring considerations, PA programing (e.g., frequency, intensity, type, and time), the moderating effect of patient characteristics (e.g., baseline fitness and symptom severity), and knowledge translation and intervention scaling. 3.5 Conclusions An emerging body of evidence suggests that IGSH PA interventions are feasible and have the potential to reduce depressive symptoms in people with depression. More well-designed and tailored interventions are needed to assess the overall efficacy and feasibility of using IGSH interventions to help people with depression increase PA. Future research on such interventions should be theoretically informed in its development and implementation and test different combinations of BCTs, particularly those targeting the emotion domain, to verify their efficacy in increasing PA among people with depression. 66 Chapter 4: Exploring Correlates of Physical Activity Using the Multi-Process Action Control Framework: Is There a Moderating Role for Mental Health? 4.1 Background The occurrence of mental health conditions, including mental disorders, psychosocial disabilities, and other mental states associated with distress, impairment, or self-harm (World Health Organization, 2022a), have been a health concern for decades. The 2019 Global Burden of Disease study established that mental disorders remain amongst the leading causes for global burden since 1990 (GBD 2019 Mental Disorders Collaborators, 2022). The two most common mental disorders, depression and anxiety, have been estimated at a global cost of $1 trillion US dollars annually (Chisholm et al., 2016). Seeking innovative care and treatments strategies for people with mental health conditions has been identified as a global health priority (World Health Organization, 2022b). The positive associations between physical activity (PA) and mental health are well established. There is a growing body of evidence linking PA with the physical and mental well-being of people with psychosocial disabilities, for instance, by reducing social isolation and perceived stigma (Giummarra et al., 2022; Sancassiani et al., 2018). Prospective population-based studies have found that being physically active may prevent the onset of mental illnesses like depression and anxiety (Schuch et al., 2019), and that PA may be an effective treatment for some mental disorders (Ashdown-Franks et al., 2020; Ravindran et al., 2016). Despite the mental and physical health benefits of PA, individuals with mental illness are less physically active than the general population (Vancampfort et al., 2017). For example, individuals with depression spend less time in total PA and moderate to vigorous PA (MVPA), and engage in higher levels of sedentary behaviour (Schuch et al., 2017) compared to individuals 67 without depression. Research on the barriers to PA has demonstrated that symptoms like perceived stress, low mood, low energy, and lack of motivation serve as primary barriers among people with mental illness (Glowacki et al., 2017). Simply put, the symptomatology of common mental illnesses may present specific challenges to initiating and maintaining PA (Pomp et al., 2010). Moreover, people with mental health issues are likely to experience barriers like physical co-morbidity (e.g., obesity), a lack of self-confidence, financial stress, and low perceived social support (Firth et al., 2016; World Health Organization, 2022b). Given these unique factors, research specific to this population is warranted, particularly with the goal of exploring strategies to increase PA. Health behaviour change theories have been developed and used to inform PA interventions in the general population with some success (Gourlan et al., 2016). Theory provides a âroad-mapâ for intervention design and evaluation, while identifying how behaviour change is mediated (Brawley, 1993; Stacey et al., 2015). Employing theories assists interventionists in pinpointing the most significant factors to address (Byrne, 2020). However, theory-based interventions that adopt common theories often exhibit limited efficacy in changing behaviours when implemented among individuals with poor mental health (e.g., Chalder et al., 2012). One explanation is that the effect of antecedents identified by a particular theory is either mitigated or negated by the presence of mental health symptoms. For example, Self-Determination Theory (Deci & Ryan, 2012) speaks to the importance of intrinsic motivation; however, the anhedonic symptoms of depression affect individualsâ capacity to experience pleasure. As a result, this may alter the affective attitudes of individuals with poor mental health towards engaging in physical activity, resulting a negative effect on their intentions and actual behaviour change. Similarly, Social Cognitive Theory (Bandura, 1997) highlights self-efficacy 68 and outcome expectancies; both of which may be negatively impacted by anxiety symptoms (e.g., low perceived capacity to cope with stressors, anticipating danger/risk). This raises concerns about the utility of traditional social psychology theories for predicting behaviour and exploring the determinants of exercise among individuals with poor mental health in order to develop more effective interventions. Social cognitive theories are especially common within exercise psychology. These theories are focused on the cognitive antecedents and outcomes of health behaviour. A particularly common construct is intention: whether a person plans to engage in a particular behaviour at a particular time and place (Ajzen, 1991). Though theories frequently identify intention as a proximal determinant of behaviour (Sheeran & Webb, 2016), research has illustrated that simply having a PA intention does not inherently result in behaviour (Rhodes & Dickau, 2013). In fact, 46% of people with an exercise intention will not exercise (Rhodes & de Bruijn, 2013). Theories in which intention is placed as the proximal antecedent of behaviour have been criticized because they fail to explicitly consider intention-behaviour discordance (Sheeran & Webb, 2016). The Multi-Process Action Control (M-PAC) framework is a pragmatic approach to behaviour change that aims to reduce the intention-behaviour gap (Rhodes, 2021; https://onlineacademiccommunity.uvic.ca/mpac/). It expands upon previous social cognitive theories, highlighting both intention formation and action-control processes (i.e., initiation and maintenance). M-PAC consists of three layered factors: reflective processes, regulatory processes, and reflexive processes (Rhodes, 2017). Reflective motivational constructs primarily inform intention formation, and include: affective judgments, instrumental attitudes, perceived opportunity, and perceived capability. Regulatory processes are the regulatory âtacticsâ (e.g., 69 action planning) that help translate intention into PA behaviour, particularly during early adoption of behaviour through moderating or mediating pathways. Lastly, reflexive constructs develop through repeated behavioural engagement, and help maintain behaviour over the long-term. For example, habit and exercise identity. While all three factors contribute to behaviour engagement, the relative contribution of each one is hypothesized to differ as a person moves from intention formation to initiated action control to sustained action control (Rhodes, 2017). The M-PAC framework has been used to understand and promote PA behaviours in various populations (Kwan et al., 2021; Liu et al., 2019; Rhodes, Beauchamp, et al., 2021; Rhodes, Berry, et al., 2019; Vallerand et al., 2019). However, to our knowledge, no previous study has applied the M-PAC framework among people with poor mental health. Researchers have found inconsistent evidence on whether people with mental disorders have a lower intention to exercise compared to healthy controls (Krämer et al., 2014; Leas & McCabe, 2007; Pomp et al., 2010; Rouse et al., 2011). However, people with depression appear to have challenges with converting high PA intention into PA behaviour (Krämer et al., 2014). That is, people with depression experience a greater intention-behaviour gap. Given the M-PACâs interest in addressing the intention-behaviour gap, it may be a particularly suitable theoretical approach for individuals with mental health difficulties like depression. The primary objective of the current study was to test an adapted M-PAC model in people with good vs. poor mental health, and evaluate if mental health status moderates the intention formation and intention-behaviour transition, where regulatory processes were considered as a mediator. A deeper understanding of potential moderating effect of mental health status would undoubtedly prove valuable in adapting and modifying existing interventions to suit the needs of individuals with subclinical mental symptoms or mental disorders. By tailoring interventions to the unique 70 circumstances of these individuals and employing theory-based approaches, practitioners can potentially maximize the effectiveness of physical activity interventions for clinical populations. As a preliminary exploratory study, two general hypotheses were proposed: (1) The adapted M-PAC model is appropriate for understanding PA behaviour among people with either good or poor mental health, as suggested by a good model fit; and, (2) Mental health status only moderates some pathways within the model, both positively and negatively. For example, given anhedonic symptoms common to people with poor mental health, the relationships between affective attitudes and intentions, and behaviour may be moderated by mental health status. 4.2 Methods 4.2.1 Participants and Study Design This study received approval from the University of British Columbia Office of Research Ethics (#H21-00859) prior to initiating data analysis. The current study is a secondary analysis of cross-sectional data collected through the June 2020 ParticipACTION Community Better Challenge (see Truelove et al., 2020). ParticipACTION is a Canadian non-profit PA promotion organization. The Community Better Challenge is a national initiative that encourages Canadians to increase their PA engagement. The challenge was open to all Canadians, regardless of baseline PA level, health status, location, or sociodemographic characteristics. Participants were invited to complete an online survey when registering for the challenge; responses to this survey were used in the analysis. Participants were considered eligible for inclusion in this study if they were: (i) between the ages of 18 and 65, and (ii) had complete data for the purposes of our planned analyses, defined as >80% of results data. The total sample consisted of 17,305 participants. A total of 96 participants were excluded for not reporting their sex; 2,064 were excluded for being 71 younger than 18 or older than 65; and 1,264 were removed due to missing data. The final sample consists of 13,881 adults. 4.2.2 Measures Demographics. Demographic variables, including age and sex, were collected for descriptive purposes. For analysis purposes, sex was dummy coded as female (1), male (2), or gender diverse (3). Mental health. Self-reported mental health status was measured using one item: âIn general how would you rate your mental health?â derived from the Canadian Community Health Survey â Mental Health (questionnaire available at https://datalib.usask.ca/dli/health/cchs/2012-mh/doc/cchs_mh_que.pdf). Responses were made on a 1 (Poor) to 5 (Excellent) scale. Participants who reported their health as âpoorâ or âfairâ were grouped together (âpoor mental healthâ, 0). Likewise, participants who rated their mental health as âgoodâ, âvery goodâ, or âexcellentâ were grouped together (âgood mental healthâ; 2). This single-item of self-rated mental health (SRMH) has been widely used in national population health surveys in Canada (Ahmad et al., 2014). Fair or poor self-rated mental health among Canadians has been found to be associated with a wide variety of mental health morbidity measures, such as having a self-reported mental disorder diagnosed by a health professional in a nationally representative study of Canadians (Mawani & Gilmour, 2010). Moderate to vigorous physical activity. MVPA was measured with a single item adapted from the International Physical Activity Questionnaire (IPAQ): âModerate to vigorous physical activities refer to activities that take at least moderate physical effort and make you breathe somewhat harder than normal. Throughout the last week, how much time did you spend engaging in moderate-to-vigorous physical activity? (e.g., if you have walked 20 minutes on 4 days, please 72 report 80 minutes)â (Craig et al., 2003). According to the IPAQ data truncation rules, participants (n = 164) who reported more than 3 hours MVPA per day were truncated to 1260 mins/week (3 h/day) (IPAQ Research Committee, 2005). Participants who reported engaging in MVPA for at least 150 minutes/week were classified as meeting current PA guidelines (i.e., sufficiently active for health benefits). Reflective processes. Instrumental attitudes and affective attitudes were each measured using three items with a 7-point scale derived from Rhodes et al. (2010). Each item shared the same stem, âFor me, participating in regular physical activity over the next month would be ...â. Responses were made on a 7-point scale, with higher responses indicating more positive attitudes. Instrumental attitudes included not/very beneficial; not/very useful; and very unwise/wise. Affective attitudes included very unenjoyable/enjoyable; very unpleasant/pleasant; and very boring/exciting. As per Lance et al.âs recommendations (Lance et al., 2006), the items exhibited very good (affective attitudes, α = .87) to excellent (instrumental attitudes, α = .91) inter-item reliability. Perceived opportunity and perceived capability were each assessed using three items, as recommended by Rhodes et al. (2006). For example, âI have the opportunity to be physically active every week if I really had toâ (opportunity) and âI have the skills I need to be physically activeâ (capability). Responses were made on a 5-point scale ranging from strongly disagree to strongly agree. The items had acceptable (opportunity, Î±â¯= .70; capability, Î±â¯= .67) inter-item reliability (Ursachi et al., 2015). Intentions. Intentions were measured using three items adapted from Rhodes et al. (2010). For example, âI am committed to engage in physical activity over the next month.â 73 Participants were asked to rate each item on a 5-point scale ranging from strongly disagree to strongly agree. The scale demonstrated very good inter-item reliability (α = .89). Regulatory processes. Regulations were measured using three items related to self-monitoring (âI often monitor how physically active I amâ), goal-setting (âI often set physical activity goals for myselfâ), and action planning (âI often plan when, what, where and how I am going to be physically activeâ), which were derived from Rovniak et al. (2002). Responses were made on a 5-point scale ranging from strongly disagree to strongly agree. The inter-item reliability for regulations was very good (α = .81). 4.2.3 Data analysis Multigroup path analyses (AMOS Ver.27) assessed the moderation effect of mental health status among intention formation and intention translation relationships. Missing data were handled in two ways. Cases (n = 1264) were removed if participants did not respond to any items of the scales. Missing values of age were replaced with the mean of whole sample. Before testing the model, descriptive statistics and zero-order correlations of the study variables were computed using SPSS (Ver. 27). All scale data were summed and averaged across the number of items per measure. Independent t-tests were used to evaluate the differences between groups with continuous data. The relations of MVPA and age with sex and mental health status were detected using one-way analysis of variance (ANOVA). The effect size was presented as partial eta squared (ηp2). Variance inflation factor (VIF) statistics were also calculated to examine for multicollinearity and found to be below 2, which is within the designated parameter limits (VIF < 10) and tolerance values are above 0.5 for which the threshold value is 0.20 (Field, 2013). Preliminary checks for univariate normality were done and we observed fairly normal distributions for all of our variables in terms of skewness (± 3) (Kline, 2015). The kurtosis value 74 of instrumental attitudes and MVPA was 5.71 and 6.21 respectively. The remaining kurtosis values ranged from benign to 3.8. While this does violate strict rules of normality, it is within the rules suggested by West et al. (1995) that a kurtosis value larger than 7 indicates a substantial departure from normality for samples greater than 300. Spearmanâs correlation was used to assess and present the zero-order correlations. Model fit was assessed using chi-squared (Ï2) test, comparative fit index (CFI), and the incremental fit index (IFI), Tucker-Lewis index (TLI), the Standardized Root Mean Squared Residual (SRMR), and root-mean squared error of approximation (RMSEA). Non-significant Ï2 (p > .05) and Ï2/df with values between 1 to 3 indicating good fit (Carmines & McIver, 1981). CFI, IFI, and TLI with values greater than .95, an SRMR close to .08, and the RMSEA with value lower than .06 and an upper 90% confidence interval (HI90) less than .08 and PCLOSE >.5 indicate good overall fit (Hu & Bentler, 1999). Multivariate normality was also checked, and multivariate non-normality was observed (Kurtosis: 54.159, c.r. = 196.154). Therefore, Bollen-Stine bootstrapping (Bollen & Stine, 1992) was used to indicate model fit. In this case, the significance of the direct effects was evaluated using 95% bias-corrected bootstrap confidence intervals (CIs) based on 6000 bootstrapping samples. Bollen-Stine bootstrap results with a p value greater than .05 indicate that the proposed model is consistent with the sample data (Walker & Smith, 2017). Multigroup analysis estimand, MyGroupDifferences.AmosEstimandVB, developed by Gaskin (2016) was used to assess the moderation effects of mental health status. All path coefficients were reported in both standardized and unstandardized formats. 4.3 Results Table 4.1 shows the characteristics of the sample. The mean age of the sample was 46.7 (SD = 10.7) years, and 83.8% were female. Most participants (n = 10,389) reported good to 75 excellent mental health, whereas 3,492 participants reported poor (n = 681) or fair (n = 2,811) mental health. On average, individuals in the better mental health group had a score of 3.6 (SD = 0.7), which was twice as high as the score of individuals in the poor mental health group (M = 1.8, SD = 0.4). Participants were younger in the poor mental health group (M = 44.1, SD = 10.7) compared to the good mental health group (M = 47.6, SD = 10.5), F(1, 12997) = 296.62, p < .001, ηp2 = 0.022. On average, participants spent 232.7 mins/week in MVPA, and 57.9% reported meeting MVPA guidelines. Self-reported MVPA was significantly higher for people with good mental health (M = 246.9, SD = 220.2) compared to those with poor mental health (M = 190.3, SD = 194.2), F(1, 13879) = 183.00, p < .001, ηp2 = 0.013. Table 4.2 presents other descriptive statistics and Spearman correlation coefficients of the study variables. Most of the variables were correlated with each other; however, data distributions for instrumental attitudes, intentions, and MVPA were moderately to seriously leptokurtic. ANOVA results suggested that MVPA was affected by sex, with higher MVPA reported by men compared with women and people identifying as gender diverse, F(2, 13878) = 27.50, p < .001. As such, sex and age were included as control variables in subsequent analyses. Figure 4.1 presents the model with all possible paths between variables and is a just-identified model with zero degree of freedom. The model presented one non-significant path between opportunity and MVPA in both good mental health (B = -2.10, 95% bias-corrected CI [-10.80, 6.40], p = .630) and poor mental health groups (B = -10.04, 95% bias-corrected CI [-22.22, 2.48], p = .115). 76 Table 4.1. Participant characteristics. Characteristics Total (n=13,881) Good Mental Health (n=10,389) Poor Mental Health (n=3,492) M SD M SD M SD Age 46.7 10.7 47.6 10.5 44.1 10.7 Mental health 3.14 1.0 3.6 0.7 1.8 0.4 Sex (n/%) Female 11626/83.8 8633/83.1 2993/85.7 Male 2216/16.0 1740/16.7 476/13.6 Gender diverse 39/0.3 16/0.2 23/0.7 MVPA (mins/week) 232.7 215.3 246.9 220.2 190.3 194.2 Female 227.1 209.9 241.1 213.3 187.0 194.3 Male 262.9 240.1 276.6 249.6 212.7 193.6 Gender diverse 167.9 173.9 188.2 203.7 153.7 153.1 Meeting the PA guidelines (n/%) 8032/57.9 6383/61.4 1649/47.2 Female 6649/57.2 5265/62.9 1384/46.2 Male 1367/61.7 1111/63.9 256/53.8 Gender diverse 16/41.0 7/43.8 9/39.1 Table 4.2. Descriptive statistics and bivariate correlations of all variables (n=13,881). Correlations (Spearmanâs rho) 1 2 3 4 5 6 7 8 Mean SD 1 Age -- 46.7 10.7 2 MVPA .11** -- 232.7 215.3 3 Affective .01 .28** -- 5.5 1.4 4 Instrumental .01 .17** .60** -- 6.2 1.3 5 Capability -.01 .31** .40** .29** -- 3.8 0.6 6 Opportunity -.03** .18** .30** .32** .42** -- 4.2 0.6 7 Intentions .02** .33** .44** .35** .55** .49** -- 4.3 0.7 8 Regulations .01 .34** .41** .28** .48** .38** .57** -- 3.7 0.8 77 Correlations (Spearmanâs rho) 1 2 3 4 5 6 7 8 Mean SD Skewness -.37 2.19 -.93 -2.35 -1.03 -.81 -1.24 -.74 Kurtosis -.65 6.21 .55 5.71 2.53 2.84 3.84 .41 **p < .01, two-tailed Figure 4.1. Model with all possible paths 78 The final model with only significant paths as depicted in Figure 4.2 and Figure 4.3 had a non-significant Bollen-Stine bootstrap value (p = .256) indicating that the model fitted the data well. The chi-square per degree of freedom (CMIN/DF = 1.477, p = .228) indicated model fit as well. In addition, other model fit indices, CFI = 1.000, TLI = .999, IFI = 1.000, SRMR = .001, RMSEA = .006, HI 90 = .019, PCLOSE = 1.000) also suggested good model fit. Figure 4.2. Final model for better mental health group. Note. Age and sex were included as control variables in the final model. Figure 4.3. Final model for poor mental health group. Note. Age and sex were included as control variables in the final model. -.07 .12 .07 -.07 .12 .10 79 4.3.1 Direct effects The standardized direct effects are depicted in Table 4.3. The direct effects within the model were all significant for people with good mental health. Negative direct effects were only found within the relationships of instrumental attitudes on intentions (Î²â¯= -.06, 95% bias-corrected CI [-.08, -.03], p < .001), regulations (β = -.07, 95% bias-corrected CI [-.10, -.05], p < .001) and MVPA (β = -.04, 95% bias-corrected CI [-.07, -.02], p < .001). For the poor mental health group, most of the direct effects (12/14) within this model were significant. Significantly negative direct effects were also observed between instrumental attitudes on regulations (β = -.07, 95% bias-corrected CI [-.10, -.03], p = .001) and MVPA (β = -.05, 95% bias-corrected CI [-.09, -.01], p = .007). However, the direct effect of instrumental attitudes on intentions was not significant in this group (β = -.03, 95% bias-corrected CI [-.07, .01], p = .184). In addition, no significant effect was found between perceived opportunity and regulations (β = .02, 95% bias-corrected CI [-.01, .06], p = .194). 4.3.2 Moderating effects of mental health status On testing the moderation effect of mental health status (see Table 4.4), only unstandardized values were provided. Mental health significantly moderated the relationship between capability and intentions (B = 0.20, 95% bias-corrected CI [0.14, 0.26], p < .001): capability is a stronger prediction of intention for people with good mental health compared to people with poor mental health. Similar patterns were found in the relationship between opportunity and regulations (B = 0.11, 95% bias-corrected CI [0.05, 0.17], p = .001), affective attitudes and MVPA (B = 13.59, 95% bias-corrected CI [6.88, 20.24], p < .001), and regulations and MVPA (B = 11.42, 95% bias-corrected CI [1.15, 21.96], p < .001). Mental health als80 Table 4.3. Standardized direct effects and bias-corrected 95% confidence intervals. Good Mental Health Poor Mental Health Parameter Estimateâ¯ Lower Upper p Estimate Lower Upper p Intentions â Instrumental -.06 -.08 -.03 .000 -.03 -.07 .01 .184 Intentions â Capability .38 .35 .41 .000 .21 .17 .25 .000 Intentions â Affective .18 .15 .21 .000 .28 .23 .32 .000 Intentions â Opportunity .30 .27 .33 .000 .29 .25 .33 .000 Regulations â Instrumental -.07 -.10 -.05 .000 -.07 -.10 -.03 .001 Regulations â Capability .21 .18 .24 .000 .17 .13 .21 .000 Regulations â Affective .21 .19 .24 .000 .19 .15 .23 .000 Regulations â Opportunity .10 .08 .13 .000 .02 -.01 .06 .194 Regulations â Intentions .31 .28 .33 .000 .36 .32 .40 .000 MVPA â Instrumental -.04 -.07 -.02 .000 -.05 -.09 -.01 .007 MVPA â Capability .08 .06 .11 .000 .12 .08 .15 .000 MVPA â Affective .15 .12 .17 .000 .07 .04 .11 .000 MVPA â Intentions .07 .04 .09 .000 .10 .06 .13 .000 MVPA â Regulations .14 .12 .16 .000 .12 .08 .15 .000 Table 4.4. Unstandardized direct effects and bias-corrected 95% confidence intervals (CIs) and moderation effects. Good Mental Health Poor Mental Health Moderation Effects Parameter Estimate Lower Upper p Estimate Lower Upper p Estimate Lower Upper p SE Intentions â Instrumental -0.03 -0.04 -0.02 .000 -0.02 -0.04 0.01 .184 -0.02 -0.04 0.01 .186 0.01 Intentions â Capability 0.42 0.39 0.46 .000 0.23 0.18 0.28 .000 0.20 0.14 0.26 .000 0.03 Intentions â Affective 0.09 0.08 0.10 .000 0.14 0.11 0.16 .000 -0.05 -0.07 -0.02 .000 0.01 Intentions â Opportunity 0.34 0.31 0.37 .000 0.33 0.29 0.37 .000 0.01 -0.04 0.06 .699 0.03 Regulations â Instrumental -0.05 -0.06 -0.03 .000 -0.05 -0.07 -0.02 .001 0.00 -0.03 0.03 1.000 0.02 Regulations â Capability 0.29 0.25 0.33 .000 0.24 0.18 0.29 .000 0.05 -0.02 0.11 .145 0.03 Regulations â Affective 0.13 0.12 0.15 .000 0.12 0.09 0.14 .000 0.01 -0.02 0.05 .389 0.02 Regulations â Opportunity 0.15 0.11 0.18 .000 0.04 -0.02 0.09 .195 0.11 0.05 0.17 .001 0.03 Regulations â Intentions 0.37 0.34 0.41 .000 0.47 0.41 0.52 .000 -0.09 -0.16 -0.02 .007 0.03 MVPA â Instrumental -7.42 -11.13 -3.70 .000 -7.97 -14.15 -2.09 .007 0.55 -6.34 7.70 .872 3.57 MVPA â Capability 30.83 21.64 39.43 .000 37.11 26.20 48.34 .000 -6.28 -21.16 7.53 .371 7.30 MVPA â Affective 24.12 20.25 28.11 .000 10.53 5.01 16.14 .000 13.59 6.88 20.24 .000 3.43 MVPA â Intentions 21.94 13.25 30.44 .000 28.63 17.11 39.40 .000 -6.70 -20.98 7.32 .350 7.25 MVPA â Regulations 37.66 31.53 43.75 .000 26.24 17.88 34.58 .000 11.42 1.15 21.96 .032 5.34 81 Table 4.5. Standardized total effects, indirect effects, and direct effects on MVPA and bias-corrected 95% confidence intervals (CIs) Good Mental Health Poor Mental Health Bias-corrected 95% CIs Bias-corrected 95% CIs Estimate Lower Upper p Estimate Lower Upper p Standardized total effects on MVPA Instrumental -.06 -.08 -.04 .000 -.06 -.10 -.03 .001 Affective .19 .17 .22 .000 .13 .10 .17 .000 Capability .15 .13 .17 .000 .17 .13 .20 .000 Opportunity .05 .04 .06 .000 .04 .03 .06 .000 Intentions .11 .08 .13 .000 .14 .10 .17 .000 Regulations .14 .12 .16 .000 .18 .08 .15 .000 Standardized indirect effects on MVPA Instrumental -.02 -.02 -.01 .000 -.01 -.02 -.01 .001 Affective .05 .04 .06 .000 .06 .05 .08 .000 Capability .07 .06 .08 .000 .05 .04 .06 .000 Opportunity .05 .04 .06 .000 .04 .03 .06 .000 Intentions .04 .04 .05 .000 .04 .03 .06 .000 Standardized direct effects on MVPA Instrumental -.04 -.07 -.02 .000 -.05 -.09 -.01 .007 Affective .15 .12 .17 .000 .07 .04 .11 .000 Capability .08 .06 .11 .000 .12 .08 .15 .000 Intentions .07 .04 .09 .000 .10 .06 .13 .000 Regulations .14 .12 .16 .000 .12 .08 .15 .000 82 significantly moderated the relationships between affective attitudes and intentions (B = -0.05, 95% bias-corrected CI [-0.07, -0.02], p = .032), and intentions and regulations (B = -.09, 95% bias-corrected CI [-0.16, -0.02], p = .007), however, in a reverse way. Affective attitudes more strongly predict intentions, and intentions more strongly predicts regulations directly among people with poor mental health. The standardized total effects, indirect effects, and direct effects on MVPA behaviour are presented in Table 4.5. With the exception of instrumental attitudes, all predictors were positively associated with MVPA in both groups. Specially, affective attitudes were the strongest predictor of MVPA in good mental health (total effect: β = .19, bias-corrected 95% CI [.17, .22], p < .001). For people with poor mental health, perceived capability was the strongest correlate of MVPA (total effect: β = .17, bias-corrected 95% CI [.13, .20], p < .001), followed by intentions (total effect: β = .14, bias-corrected 95% CI [.10, .17], p < .001) and affective attitudes (total effect: β = .13, bias-corrected 95% CI [.10, .17], p < .001). 4.4 Discussion To our knowledge, this is the first study applying an adapted M-PAC framework to MVPA behaviour among people with poor mental health. Encouragingly, results indicate that this adapted M-PAC framework is appropriate for understanding MVPA in people with poor mental health. Consistent with our first hypothesis, the proposed model provided a very good fit to the data. Perceived capability, affective attitudes, intentions, and regulatory tactics were found to be important predictors of MVPA for people with good or poor mental health. Perceived opportunity and instrumental attitudes were less influential predictors. Consistent with our second hypothesis, results indicated that mental health status partially moderated the model. 83 Specifically, findings indicate that the MVPA level of people with poor mental health was positively associated with affective attitudes, perceived capability, intentions, and regulatory skills directly; and that two of these pathways varied in their strength between the two groups. Previous studies have suggested significant relationships between self-efficacy (i.e., perceived confidence to perform a given behaviour), intentions, and PA behaviour among adults (Koring et al., 2012; Rovniak et al., 2002). Indeed, self-efficacy is one of the most consistent predictors of PA engagement (McAuley & Blissmer, 2000). Mirroring these findings from other populations, perceived capability was identified as the strongest correlate of PA behaviour among people with poor mental health (β = .17, p < .001). Our results resonate with a previous study that found that PA levels are positively related to perceived competence among people with severe mental illness (Farholm et al., 2017). Consistent with theory (Bandura, 1997; Rhodes, 2017), people with poor mental health who have higher confidence in their capability to perform MVPA are more likely to report engaging in MVPA. The robust evidence on the relationship between MVPA and self-efficacy/perceived competency â combined with well-established strategies for promoting self-efficacy, such as graduated mastery experiences â would support the value of prioritizing this construct in future research among individuals with poor mental health. Like perceived capability, we found that affective attitudes also had a positive direct effect on MVPA behaviour in both the good and poor mental health groups. Our results were in line with a previous study applying M-PAC to predict PA among adolescents which found both affective attitudes and perceived capability to be significantly associated with behavioural enactment (Kwan et al., 2021). Similarly, previous studies suggest that the relationship between affective attitudes and PA is not fully mediated by intentions (Conner, 2020; Conroy & Berry, 84 2017; Stevens et al., 2020). Though the relationship between affective attitudes and MVPA was significantly stronger among people with good mental health vs. poor mental health, affective attitudes were nonetheless the second-strongest reflective correlate of MVPA for people with poor mental health. Therefore, individuals with poor mental health may be more likely to be active when they have positive affective evaluations of MVPA, which is in agreement with Suterwala et al. (2016). Moreover, affective attitudes were positively associated with intention formation in both groups, which is consistent with previous findings regarding the differential role of affective and instrumental attitudes on intention in general (Kraft et al., 2005; Lowe et al., 2002). Our findings suggest that mental health status could moderate the direct effect of affective attitudes on intentions, with a stronger effect found in the poor mental health group. This adds evidence to previous studies that addressing the emotion domain might be critical when designing PA interventions for people with mental disorders such as depression (Glowacki et al., 2017). Future PA intervention research for people with poor mental health should explicitly incorporate and test behaviour change techniques that target emotions, such as âreducing negative emotionsâ or providing âinformation about emotional consequencesâ (see Cane et al. (2012)). It is noteworthy that our study has revealed that instrumental attitudes might function as a negative suppressor (Akinwande et al., 2015; Conger, 1974) in the proposed model. The zero-order correlation analyses revealed positive correlations between instrumental attitudes and intentions, regulations, and MVPA, while the model demonstrated negative path coefficients. Additionally, when instrumental attitudes were removed from the model, the path coefficients of affective attitudes and other variables displayed slightly weaker associations (data not shown). However, since our study was exploratory in nature and was not intended to identify suppressor variables, the 85 observed suppression effect was unexpected and lacked interpretability, as suggested by Ludlow and Klein (2014). Future researchers should investigate whether âinstrumental attitudesâ is truly a suppressor, or the suppression effect was observed by chance. Intentions and regulations were found to have direct effects on PA behaviour in both groups. Mental health status moderated the direct effect of regulations on MVPA, with a stronger effect found among those with good mental health. Previous research on regulatory processes demonstrated their importance in mediating the intention-PA relationship (Carraro & Gaudreau, 2013; Rhodes & Dickau, 2013). For example, action control factors like action planning and coping planning were found to be consistent mediators between intentions and behaviours (Zhang et al., 2019). Previous research suggests that action planning has a main effect on PA only if participants had high intentions, which suggests that people with higher intentions could make more use of action planning strategies than others (Lippke et al., 2005; Scholz et al., 2008). Future studies should assess the mediating role of regulations via longitudinal study designs. Interestingly, no significant between-group difference was observed between intentions and MVPA. This finding contrasts with Krämer et al. (2014), who found that depressive symptoms moderated the intention-PA relationship. Inconsistency in the findings might be explained by sample differences: relative to Krämer, our sample was quite active and self-selected to participate in a physical activity intervention. Our cross-sectional design may also have impacted findings. 4.4.1 Strengths and Limitations This is the first exploratory study to obtain evidence for understanding PA behaviour among people with poor mental health using a comprehensive theoretical framework that consists of both intention formation and intention-behaviour transition. Our study identified the 86 potential to develop PA behavioural intervention for people with poor mental health based on the M-PAC framework. In addition, this study provides preliminary guidance for the adapting existing M-PAC informed physical activity interventions for people with poor mental health. Several limitations need to be acknowledged in this study. First, as a cross-sectional study we are unable to infer causal relationships between variables as the M-PAC framework hypothesizes. Future studies should test this model in longitudinal studies, with a minimum of three timepoints recommended. Second, a single-item self-rated mental health scale was administered. Though our measure is well-established and commonly used (Ahmad et al., 2014), a single item is unable to capture the variability of mental health presentations: for example, the presence/absence of a diagnosis, symptom type, or differentiating between low well-being and mental illness. A similar issue was observed in PA measurement: MVPA was assessed using a single self-report question. Third, our sample self-reported generally high levels of MVPA, which are not representative of the general population or individuals with mental health problems. Finally, reflexive processes, the third layer of the M-PAC framework, were not assessed in this study. This may partially explain the low explained variance of MVPA (11% in the better mental health group; 9% in the poor mental health group) in this model, as reflexive processes are suggested to explain the most variance in PA (Rhodes, La, et al., 2021). This may be particularly the case as our sample reported high MVPA suggesting the majority of them were in a state of behavioural maintenance and thus likely highly influenced by reflexive processes. Future experimental studies should include measures of habit and exercise identity to better understand not only the initiation of PA, but also the maintenance of PA among people with poor mental health. 87 4.5 Conclusions Overall, our study suggests that the M-PAC framework is an appropriate framework for understanding PA behaviour among adults with poor mental health. In particular, results highlight the potential role of perceived capability and affective attitude. Future research using the complete M-PAC framework and employing a longitudinal study design is needed to better understand PA promotion in this population. 88 Chapter 5: MoodMover: Development and Usability Testing of an MHealth Physical Activity Intervention for Depression 5.1 Background Physical activity (PA) is recognized as a modifiable lifestyle behaviour with a positive impact on the prevention and management of depression (Mammen & Faulkner, 2013; McKeon et al., 2022). A recent overview of systematic reviews on the effectiveness of PA interventions demonstrated a medium effect size (median standard mean difference [SMD]: -0.66) in reducing depressive symptoms based on 14 systematic reviews comprising 273 randomized controlled trials (RCTs) among patients diagnosed with depression (Singh et al., 2023). In Canada (Ravindran et al., 2016) and many other nations, such as the United Kingdom (National Institute for Health and Clinician Excellence, 2022), exerciseâidentified as planned and purposeful PAâis advocated as a standalone treatment for mild-to-moderate depressive episodes. However, the global prevalence of physical inactivity (i.e., not meeting PA guidelines) among people with depression remains a significant concern (Vancampfort et al., 2017). A previous meta-analysis reported that individuals with severe mental illness were 50% less likely to be physically active than healthy participants (Vancampfort et al., 2017). Behavioural change interventions that promote habitual PA hold promise particularly for those who may not be interested in, or able to access, structured exercise interventions. However, very few behavioural interventions with a focus on PA promotion have been available for people with depression (Glowacki et al., 2017). Machaczek et al. (2018) found that three out of five behavioural interventions led to an increase in PA uptake among people with depression, showing improvements in direct measures of PA (Mailey et al., 2010; Piette et al., 2011) and indirect measures of PA through muscular endurance and aerobic fitness (Forsyth et al., 2015). Wong et al. 89 (2021) identified five RCTs of multi-component lifestyle interventions that combined a PA component and other lifestyle factors among people with major depressive disorder or depressive symptoms, noting significant positive effects on reducing depressive symptoms. Although these interventions have shown the potential to increase PA and reduce depressive symptoms, both Machaczek et al. (2018) and Wong et al. (2021) emphasized the need for interventions with greater accessibility. Mobile applications (apps) are increasingly being utilized to implement interventions aimed at behaviour change because of their wide reach and ubiquity (Fitzgerald & McClelland, 2017; McKay et al., 2018). In Canada, national data from 2020 showed that 84.4% of Canadians aged 15 and over owned a smartphone, and this ownership rate continues to rise annually (Statistics Canada, 2020). Moreover, over three-quarters of individuals with depressive or anxiety symptoms reported having health apps on their devices, with exercise and mental health apps ranking among the most common categories (Rubanovich et al., 2017). Collectively, these findings point to the potential feasibility of app-based lifestyle modifications in PA as an option for treating depression on a large scale. However, our previous systematic review highlighted the deficit of PA behavioural apps for depression (Tang et al., 2022). As of November 2021, only one study by Guo et al evaluated such an intervention reporting effectiveness in reducing depressive symptoms, but not in promoting PA when compared to a waitlist/usual care control group. Their PA component, lacking both a theoretical basis and systematic development, was less amenable to rigorous evaluation and improvement. Additionally, low user engagement, a prevalent issue among apps for this population (Michie et al., 2017; Molloy & Anderson, 2021; Schubart et al., 2011), may contribute to its limited impact. This review underscored the need for well-designed apps that improve user engagement 90 to effectively increase PA in people with depression. One approach to enhance the efficacy and sustainability of behavioural interventions is to develop such interventions following a rigorous intervention development process (OâCathain et al., 2019). This study selected the Integrate, DEsign, Assess, and Share (IDEAS) framework (Mummah et al., 2016) that was specifically proposed for facilitating the development process of digital behavioural interventions and fostering app creativity. Overall, this paper describes the first two stages, Integrate and Design, of the development process for the app-based intervention, MoodMover, which was designed to increase PA among individuals with depression, following the IDEAS framework (Figure 5.1). 5.2 Methods and Results The development process consisted of three main phases: (1) identifying intervention needs and planning (Steps 1-2 of IDEAS); (2) intervention development (Steps 3-5 of IDEAS); and (3) usability testing and refinement (Steps 6-7 of IDEAS). Figure 5.1. The IDEAS framework. Source: Mummah et al. (2016) 5.2.1 Phase I: Identifying intervention needs and planning 5.2.1.1 Intervention needs. During this phase, we first conducted a systematic review of internet-based self-guided interventions aimed at increasing PA for depression to determine the intervention needs and potential intervention components and strategies (Tang et al., 2022). This review identified that few mobile apps designed to increase PA specifically target individuals with 91 depression, highlighting the need for well-designed apps with enhanced engagement and efficacy. In response, we followed the IDEAS framework to design MoodMover. A multi-disciplinary team that consists of professionals in the field of depression and PA was involved in the entire development processes. M-PAC Framework. Like other behavioural development frameworks, the IDEAS framework also emphasizes the importance of incorporating behaviour change theories into the development processes for identifying influential factors and potential explanations for the failure of interventions (Byrne, 2020; Glanz & Bishop, 2010; Mummah et al., 2016; Oldenburg et al., 2010). Instead of traditional social cognitive theories, such as the Theory of Planned Behaviour and the Social Cognitive Theory, the multi-disciplinary team selected the Multi-Process Action Control (M-PAC) framework (Rhodes, 2017, 2021) with an acknowledgement of the intention-behaviour gap, where merely having an intention to engage in PA does not always lead to behaviour change (Rhodes & Dickau, 2013). In the M-PAC framework, intention is viewed as a decisional construct, with core determinants that include reflective processes (i.e., instrumental attitude, affective attitude, perceived capability, and perceived opportunity). Among these, two ongoing reflective constructs (perceived opportunity and affective attitude) along with regulatory processes (e.g., action/coping planning) collectively facilitate the intention-behaviour transition (action control). Further, the M-PAC framework emphasizes the importance of two reflexive processes, namely habit and identity, in sustaining effective action control. The M-PAC has been applied to understand and promote PA in various populations (Kwan et al., 2021; Liu et al., 2019; Rhodes, Beauchamp, et al., 2021; Rhodes, Berry, et al., 2019; Vallerand et al., 2019), and has been deemed suitable for informing PA promotion interventions among people with poor mental health (Tang et al., 2023). Moreover, 92 the M-PAC framework includes a set of theory-driven behaviour change techniques (BCTs) (Michie et al., 2013), which guide intervention content development. 5.2.1.2 Target behaviour. We conducted an exploratory literature scan to determine the intervention target behaviour, i.e., what âdoseâ of PA is needed for a clinically meaningful reduction in depressive symptoms. Various PA guidelines encourage the general population to engage in at least 150 minutes (30 min/day*5 days) of moderate PA per week for substantial health benefits (e.g., Ross et al. (2020) and Bull et al. (2020)). Walking is the primary form of PA behaviour targeted by MoodMover due to its accessibility, countability, and ability to be incorporated into the daily lives of all people without severe physical disabilities. A meta-analysis by Robertson et al. (2012) suggested that walking exhibits a similar effect size for treating depression or depressive symptoms compared to other types of exercise. A 30-minute session of moderate-intensity walking is estimated to accumulate around 3000-4000 steps (Marshall et al., 2009; Tudor-Locke & Bassett, 2004; Tudor-Locke et al., 2011). As such, an increase of 3000 daily steps was deemed a clinically relevant behavioural goal for MoodMover. In addition, a commentary by Otto et al on exercise for depression suggested adding steps to individualsâ baseline PA levels gradually. Similarly, previous web-based PA interventions proposed and applied ramped goals of 1000 extra steps above baseline every two weeks until achieving a total increment of 3000 steps (Gilson et al., 2013; Trinh et al., 2018). Despite less than half of the participants achieving the ultimate goal, both studies indicated significant increases in step counts at post-intervention. Given these, MoodMover adopted a similar graded approach by recommending participants to set their goals as increasing 1000 steps/day above baseline at week 2, followed by adding an additional 1000 daily steps every two weeks until meeting a total increase of 3000 93 steps/day (See Figure 5.2). This aligns with the findings of Sporrel et al. (2021), suggesting that assigning goals to users and using adaptively tailored goals may be more effective than user-set or generic goals. Moreover, MoodMover encourages end-users to gradually increase their daily goal achievement to 5 days/week, aligning with the potential importance of frequency in cultivating PA habits (Otto et al., 2007). This goal is likely to be both realistic and effective for individuals with depression. Figure 5.2. Graded goal recommendations of MoodMover. 5.2.2 Phase II: Intervention development During this phase, the multi-disciplinary team discussed content modifications, brainstormed potential app features and modes of delivery, and developed prototypes. This development process was highly iterative, being repeated until the research team reached consensus that the prototype was ready for usability testing. 5.2.2.1 Intervention content. The content of MoodMover was adapted from an existing web-based, 10-lesson M-PAC intervention designed to promote PA among young adults with low mood and/or depression engaged in community primary clinical care (Ross, 2023). This program 94 was selected for adaption due to its good initial acceptability among a similar population of individuals with poor mental health, and its alignment with the M-PAC framework. The adaption of the content was guided by previous literature with consultations with the professionals in our multi-disciplinary team. A recent study by Tang et al. (2023) underscored the potential significance of certain M-PAC constructs, namely capability, affective attitudes, intentions, and regulatory processes, in promoting PA among individuals with poor mental health. Conversely, perceived opportunity and instrumental attitudes were less pronounced in this population. In response, while the modified content retains all the constructs of the M-PAC framework, it places more emphasis on perceived capability, affective attitudes, regulatory processes, and reflexive processes but less emphasis on perceived opportunity, and instrumental attitudes. This focus on affective attitudes is consistent with evidence from other behaviour change frameworks, suggesting that emotion domains may be a crucial aspect that has been overlooked in previous interventions targeting changes in PA behaviour among individuals with depression (Glowacki et al., 2017). All theoretical constructs have been mapped to BCTs. Table 5.1 presents an overview of MoodMover's content of major lessons, targeted M-PAC constructs, and corresponding BCTs; a full version with complementary lessons is shown in Appendix D. Despite the original content of the web-based intervention being evaluated in collaboration with end-users and researchers, and incorporating various BCTs (Ross, 2023), we reassessed the alignment of targeted theoretical constructs with the taxonomy of BCTs (Michie et al., 2013) to identify additional intervention components that may be effective and preferred by end-users. 95 Table 5.1. Outline of the content of MoodMoverâs major lessons and the corresponding M-PAC construct(s) and behaviour change technique(s). Modules Major topics covered M-PAC construct(s) targeted Behaviour change technique(s) used 1) Feeling better through daily activity â¢ Exercise as a treatment option for depression â¢ Mood and physical activity interaction â¢ Physical benefits of exercise â¢ Goal-setting Affective attitudes, Instrumental attitudes; Behavioural regulation Information about health consequences (5.1)* Information about emotional consequences (5.6)* Behavioural practice/rehearsal (8.1)* Goal setting (behaviour) (1.1)* Self-monitoring of behaviour (2.3)* Credible source (9.1)* 2) Making physical activity enjoyable â¢ Introducing affect â¢ The importance of enjoying physical activity â¢ Strategies and activities to increase the enjoyment of physical activity Affective attitudes; Behavioural regulation Information about emotional consequences (5.6)* Behavioural practice/rehearsal (8.1)* Self-monitoring of behaviour (2.3)* 3) Building your self-confidence â¢ Self-efficacy â¢ How to increase self-efficacy â¢ Exercise experiences shared by peers Perceived capability Behavioural regulation Goal setting (behaviour) (1.1)* Self-monitoring of behaviour (2.3)* Information about othersâ approval (6.3)* Behavioural practice/rehearsal (8.1)* 4) Building your physical activity opportunity â¢ The influence of environment on behaviour â¢ How to build environment for physical activity â¢ Brainstorm physical activity opportunities â¢ Grab and go activities Perceived opportunity Goal setting (behaviour) (1.1)* Self-monitoring of behaviour (2.3)* Prompts/cues (7.1)* Behavioural practice/rehearsal (8.1)* Restructuring the physical environment (12.1)* Adding objects to the environment (12.5)* 5) Developing self-regulatory skills â¢ Action planning â¢ Coping planning Behavioural regulation Goal setting of behaviour (1.1)* Problem solving (1.2)* Action planning (1.4)* Self-monitoring of behaviour (2.3)* Behavioural practice/rehearsal (8.1)* 6) Drawing on social support â¢ Introducing social support â¢ Building your social support Perceived opportunity Behavioural regulation Goal setting (behaviour) (1.1)* Self-monitoring of behaviour (2.3)* 96 Modules Major topics covered M-PAC construct(s) targeted Behaviour change technique(s) used â¢ Thinking strategies for exercise â¢ Positive self-talk Social support (practical) (3.2)* Social support (emotional) (3.3)* Behavioural practice/rehearsal (8.1)* Self-talk (15.4)* 7) Forming an exercise habit â¢ Introducing habit â¢ Relating habit to physical activity â¢ How to form a habit Habit Goal setting (behaviour) (1.1)* Self-monitoring of behaviour (2.3)* Behavioural practice/rehearsal (8.1)* Habit formation (8.3)* 8) Building your exercise identity â¢ Introducing WHO guidelines â¢ Introducing exercise identity â¢ Ways to increase exercise identity Identity Goal setting (behaviour) (1.1)* Self-monitoring of behaviour (2.3)* Behavioural practice/rehearsal (8.1)* Incompatible beliefs (13.3)* Valued self-identity (13.4)* Note. M-PAC: Multi-Process Action Control; WHO: World Health Organization. *The numbers in the brackets refers to the behaviour change techniques in the âBCT taxonomy v1â by Michie et al (Michie et al., 2013)97 Overall, the revised program consists of eight major lessons accompanied by eight weekly complementary lessons. With an additional run-in period of one week (Week 1) for introducing the program and obtaining baseline step data, MoodMover results in a nine-week intervention. The major lessons generally follow the order of the three processes in the M-PAC framework, beginning with intention-formation (Lessons 1-4; 6), followed by action control adoption (Lessons 1-2; 4-6), and concluding with action control maintenance (Lessons 7-8). It is worth noting that constructs for intention-behaviour transition (i.e., perceived opportunity, affective attitudes, and regulatory processes) were emphasized early and integrated throughout the program, with the assumption that most individuals who volunteered to participate in a PA behavioural intervention already had intentions to be physically active (Rhodes & Rebar, 2017). Several behavioural strategies, such as positive self-talk after exercising and increasing pleasant activities recommended by Otto and Smits (Otto & Smits, 2009), were incorporated. To match the temporal dynamics of symptom severity, especially for energy and motivation (Kornfield et al., 2020; Simblett et al., 2019), MoodMover added information about the fluctuating nature of depression and implemented strategies to emphasise engaging in PA when feeling more energetic and less fatigued. 5.2.2.2 Prototype development. The research team iteratively developed multiple prototypes on Pathverse, continuously refining the appâs content, features, navigation, layout, and aesthetic design. Pathverse is a "no code" mHealth intervention development platform (Liu et al., 2022) (see https://pathverse.ca/en/), which aligns with the M-PAC framework and enables the creation of tailored behavioural interventions. It supports rapid prototyping, allowing the customization of app content and features through a web portal. Three independent researchers (GF, MF, and MG) assessed the content and the design of the app, and provided feedback for 98 making necessary modifications. Each modification of the app content and design was reviewed and approved by at least three team members. Any discrepancies were addressed through discussions within the research team to reach a consensus. A computer programmer (HL) from Pathverse was engaged in this iterative process to provide technical support. During this process, a few major modifications were implemented. For example, to further enhance engagement and increase PA, financial incentives (BCT: â10.1 Material incentive (behaviour)â) were incorporated into the gamification feature that Pathverse has enabled. Gamification features are associated with increased engagement in PA apps and increased PA levels (Maher et al., 2022). MoodMover incentivizes lesson completion, allowing participants to earn 20 points for each major lesson and 10 points for each complementary lesson. Every 60 points earned are equal to a CAD $5 e-gift card. In addition, inspired by Webb et al. (2010) to enhance practical applications, two modes of delivery, âautomated tailored feedbackâ and âenriched information environmentâ, associated with higher effects on behaviour change were considered. Unfortunately, due to limited resources and time constraints, âautomated tailored feedbackâ was not incorporated. Instead, the prototype of MoodMover included an enriched information environment (i.e., images, videos, and short podcasts), along with automated follow-up messages (e.g., reminders/notifications), and peer-to-peer access (i.e., community forum). Furthermore, the prototype incorporated a mood tracking feature by adapting the existing exercise logging feature, potentially reinforcing the sense of connection between PA and mood enhancement. Overall, the refined prototype contains various features (e.g., step tracker and community forum) and lessons with content framed within the M-PAC framework (one major/week and one complementary/week). Figure 5.3 illustrates a workflow of the refined prototype of MoodMover. 99 Each lesson, except for the Week 1 introduction module, includes educational content presented on lesson cards in a variety of formats (e.g., texts, images, GIFs, and podcasts), along with surveys and/or quizzes. Figure 5.4 displays screenshots of a major lesson as an example. Figure 5.3. A workflow of the refined prototype of MoodMover. 100 Figure 5.4. Screenshots of a major lesson. 5.2.3 Phase III: Usability test After the research team was satisfied with the modified intervention package, a usability test of the MoodMover prototype was conducted to receive user feedback. This phase aims to explore users' general interest in the app and their willingness to integrate it into their regular routines. It also sought to identify any potential barriers to consistent usage, gather suggestions for improvement, and capture unexpected innovative ideas or opportunities that might arise. To comprehensively analyze MoodMover from end-usersâ perspectives, we replicated a prior usability study of a Pathverse app (Willms et al., 2023) in employing a formative mixed methods approach. Ethics approval to conduct this study was received by the Research Ethics Board at the University of British Columbia (#H24-00047). Appendix E presents the COREQ (COnsolidated criteria for REporting Qualitative research) checklist. Participants were recruited through REACH BC (https://reachbc.ca/), an initiative in 101 British Columbia, Canada, that facilitates participant recruitment for health research. Potential participants who expressed interest were contacted by one researcher (YT) via email and invited to complete a screening questionnaire (Appendix F) online to confirm their eligibility. All eligible patients received further information about the study via email, and completed a demographic survey (Appendix G; e.g., gender) after confirming their participation prior to the meeting. All participants provided verbal consent at the beginning of the Zoom meeting on the recording. Written informed consent was waived by the Research Ethics Board. Inclusion criteria for participants include: (1) outpatients aged 18â64 years who self-report a current diagnosis of major depressive disorder, irrespective of its severity, and/or at least mild depressive symptoms, as indicated by a score of 5 and above (mild: 5-9, moderate: 10-19, and severe: 20-27) on the Patient Health Questionnaire, 9-item (PHQ-9) (Kroenke et al., 2001); (2) have a valid email address; (3) possess an iPhone or Android smartphone with internet access to download and use the app; (4) able to read and write in English. Patients with the following self-reported conditions were excluded: physical disability preventing exercise, active psychosis or mania, active suicidal ideation, severe cognitive impairment, and current pregnancy. Participants who reported 90 minutes or more of at least moderate-intensity PA per week were also considered ineligible. Participants were compensated with a CAD $20 Amazon e-gift card for their time. 5.2.3.1 Procedures and measurements. Participants were asked to sign up on Pathverse to access MoodMover using the same email through which they had been contacted by the researcher. The virtual interviews lasted approximately 60 minutes and were conducted over participantsâ smartphones by the first author who has received graduate training in qualitative research. During the meeting, participants first partook in a series of goal-oriented tasks while sharing their phone screens, aimed at obtaining user feedback and identifying potential navigation issues within 102 specific areas of the prototype. The tasks included: 1) logging onto the app; 2) completing the introduction module; 3) syncing steps from either Google Fit (Android) or Health (iOS); 4) completing the first major lesson; 5) setting a personal step goal; 6) logging one exercise session; 7) completing one complementary module; 8) scanning the fifth lesson (Self-regulatory skills) and creating an action plan after watching the tutorial embedded; and 9) scanning the sixth lesson (Social support) and sharing their experience in the community forum. Participants were encouraged to "think-aloud", sharing their ongoing thought processes and any challenges they encountered while using the program throughout these tasks. A separate research assistant (JK) served as an observer, responsible for documenting any issues encountered by participants during the tasks. Both the transcriptions of the "think-aloud" sessions and the field notes were integral to the data analysis process. After completing the goal-oriented tasks, a semi-structured interview followed aiming at gathering open-ended feedback and obtaining deeper insights. Interview questions (e.g., âwhat did you like best about the MoodMover app?â; see details in Appendix H) were mainly derived from the previous usability testing study of a Pathverse app (Willms et al., 2023). Last, participants completed a usability evaluation tool (Appendix I) revised from the patient version of the mHealth App Usability Questionnaire (MAUQ) (Zhou et al., 2019) for standalone apps. MAUQ has been considered the gold-standard reference for usability analysis of mHealth apps (Patel & Thind, 2020). The original MAUQ is a new, 18-item validated usability questionnaire, assessing three domains of usability guided by the International Organization for Standardization definition of usability (International Organization for Standardization, 2018): ease of use (MAUQ_E; 5 items), interface and satisfaction (MAUQ_I; 7 items), and usefulness (MAUQ_U; 6 items). Given that MoodMover does not provide healthcare services, its usefulness 103 was assessed with a single item: âThe app would be useful for my mental health and well-being.â Participants were asked to rate each item on a 7-point Likert scale ranging from 1 (strongly disagree) to 7 (strongly agree). A higher total score and averages of each domain indicate better app usability. In its validation study, the MAUQ demonstrated strong criterion validity, as indicated by high positive correlation coefficients with both the Post-Study System Usability Questionnaire and its subscales, as well as with the System Usability Scale, with correlations ranging from r = .72 to .86 (Zhou et al., 2019). Additionally, construct validity was supported by a high positive correlation coefficient between MAUQ_I and MAUQ_E (r = .75)(Zhou et al., 2019). The internal consistency of the MAUQ_E (Cronbach alpha=0.847) and MAUQ_I subscales (Cronbach alpha=0.908) is high (Zhou et al., 2019). Since no specific threshold of the MAUQ has been established to indicate good usability, we set a cut-off score of â¥ 5 for each domain to indicate acceptable usability, based on previous mHealth app usability studies (Tong et al., 2022; Xie et al., 2023). 5.2.3.2 Data analysis. Quantitative data were analyzed using Microsoft Excel to describe participant demographics and summarize the MAUQ results descriptively. The interviews and think-aloud recordings were independently transcribed verbatim using Otter (www.otter.ai) and analyzed in Microsoft Excel by two researchers (YL and JK). Any discrepancies were resolved through discussions to ensure the consistency of analyses. Qualitative content analysis approach (Hsieh & Shannon, 2005) was employed for its ability to provide consistent content categories of interest and flexibility in identifying emerging patterns. The data analysis began after the first usability session was conducted to ensure that issues identified in early sessions can inform later sessions using constant comparative analyses. Additionally, some of the issues identified (e.g., functional bugs) and suggestions raised by previous participants were used to inform ongoing 104 modifications, which were then evaluated by the subsequent participants. Using deductive content analysis, the collected data were coded to correspond to three broad categories: app design (e.g., layout, navigation and aesthetics), content (e.g., educational material) and features (e.g., step tracker), and ideas for improvement. These data were cross-referenced with notes taken by the researcher (JK) during the think-aloud process. Subsequently, inductive content analysis was also used to further explore sub-categories. 5.2.3.3 Results of usability test. A total of 49 individuals who expressed interest in participating through REACH BC were contacted. Of the 19 individuals who met the eligibility criteria, 12 (63.2%) were enrolled, while the remaining individuals did not respond to the invitation emails. Of these enrolled participants, two did not attend the scheduled meeting without providing a reason. One participant was excluded due to technical issues not related to Pathverse/MoodMover. The remaining nine participants (five women, three men, one non-binary) had a mean age of 38.4 years, ranging from 24 to 53 years. Six participants self-reported a clinical diagnosis of major depressive disorder. All participants self-reported a mean of 14.1 points on the PHQ-9. Of these, three scored within the mild threshold, while four were categorized as moderate, and two as severe. Further demographics are displayed in Appendix J. Due to time constraints, only the first seven goal-oriented tasks were assigned to all participants. Task 8, scanning the fifth lesson (Self-regulatory skills) and creating an action plan, was assigned to three participants. Task 9, which involved scanning the âLesson 6: Social Supportâ and sharing their experience in the community forum, was not assigned; instead, participants were introduced to the âSocial - Community Forumâ feature and asked to provide feedback. 105 Table 5.2. Usability of MoodMover prototypes as assessed by the adapted MAUQ for standalone apps. Domains and statements Mean (SD) Median (min, max) Ease of use 5.84 (0.88) 6.20 (3.80, 6.60) The app was easy to use. 5.67 (1.00) 6 (4, 7) It was easy for me to learn to use the app. 6.00 (0.87) 6 (5, 7) The navigation was consistent when moving between screens. 5.56 (1.13) 6 (3, 7) The interface of the app allowed me to use all the functions (such as setting a step goal, logging a physical activity session, receiving notifications) offered by the app. 5.67 (1.87) 6 (1, 7) Whenever I made a mistake using the app, I could recover easily and quickly. 6.33 (1.00) 7 (4, 7) Interface and satisfaction 5.67 (1.25) 5.71 (2.71, 6.71) I like the interface of the app. 5.56 (1.94) 6 (1, 7) The information in the app was well organized, so I could easily find the information I needed. 5.44 (1.13) 6 (4, 7) The app adequately acknowledged and provided information to let me know the progress of my action. 5.56 (0.73) 6 (4, 6) I feel comfortable using this app in social settings. 5.44 (1.94) 6 (1, 7) The amount of time involved in using this app has been fitting for me. 5.67 (1.66) 6 (2, 7) I would use this app again. 6.22 (1.20) 7 (4, 7) Overall, I am satisfied with this app. 5.78 (1.56) 6 (2, 7) Usefulness The app would be useful for my mental health and well-being. 6.33 (1.00) 7 (4, 7) Overall MAUQ 5.79 (1.04) 6 (3.38, 6.62) Note. MAUQ: mHealth App Usability Questionnaire106 Table 5.3. Interview question responses. Interview questions Summary Sample quotations Q1: Do you use, or have you used, any mental health/PA apps/wearable devices? Why/why not? Most participants reported having used PA or mental health apps before at least for short periods of time. Some participants expressed a clear need for a PA app tailored for people with depression. It's actually surprising that no one has done this [develop an app specifically designed for depression] yet... especially since depression is such a common mental health issue. [Mood23] Q2: What did you like best about the MoodMover app? Gamification with incentives, exercise logging with mood tracking, and resources with YouTube workout videos were the top three mentioned features. Many participants also favored the flexible attitude conveyed by MoodMover. I like that the more activities you do, you accumulate points. [Mood04] Q3: What did you like least about the app? No major issues were reported. Some participants mentioned that the initial navigation took some time. There's nothing that really strikes me. [Mood09] Q4: How easy was it to navigate or find your way around the app? Most participants explicitly commented that the app is intuitive, simple, or easy to navigate, although the initial navigation may take them some time. I feel it was pretty easyâ¦ Like even without the instructions. [Mood42] Q5: What did you think about the overall look of the app? Most participants favored the overall look of the app, except for two of three men. Pretty nice overall look of the app. [Mood49] Q6: What did you think about the information provided on the app? Most participants explicitly stated that the content is very helpful, and the scripting is basic and easy to understand. Very helpfulâ¦explained in a very simple, easy to understand way. [Mood04] Q7: Is there anything you think the app might be missing? Strategies. A participant mentioned providing daily suggestions for accumulating steps. Maybe a daily suggestion of how to get more steps or something like that. [Mood05] Q8: Would you be interested in using the upgraded MoodMover in the future to increase your PA and reduce depressive symptoms? Why/why not? Nearly all participants showed a strong interest in using the upgraded MoodMover in the future, even for Mood02 who recorded a relatively low score on MAUQ. Definitely. Why not? Because at the end of the day, it's all resources at one place. And againâ¦I know that there are researchers, there are experts behind designing the app that shows you tangible benefits. [Mood02] Note. MAUQ: mHealth App Usability Questionnaire; PA: Physical Activity107 Quantitative results: MAUQ Table 5.2 presents the results of the adapted MAUQ. The prototypes of MoodMover received an average MAUQ score of 5.79 (SD = 1.04), ranging from 5.08 to 6.62 across most (8/9) participants, indicating good to high usability. When looking at the individual domains, MoodMover received an average score of 5.84 (SD = 0.88) for âEase of useâ, with 91.1% (41/45) of the responses scoring 5 (âsomewhat agreeâ) or higher. In the âInterface and satisfactionâ domain, the average score was 5.67 (1.25), with 81.0% (51/63) of responses being at least âsomewhat agreeâ. The average score for âUsefulnessâ was 6.33 (SD =1.00), with eight (88.9%) participants scoring 6 or 7. Qualitative feedback Table 5.3 displays a summary of participants' responses to semi-structured interview questions with sample quotations. More detailed usability results from the think-aloud processes and semi-structured interviews are categorized into three predefined broad areas: app design, content and features, and ideas for improvement. App design No participants encountered major difficulties in navigating through the app. All participants logged onto the app (Task 1) without additional instructions. Most participants experienced no issues completing the lessons (Tasks 2, 4, 7-8). An issue encountered exclusively by Android users was that the keyboard hid part of the textbox when entering survey responses, which has been reported to the Pathverse team. Additionally, four participants did not notice that some lesson cards were longer and required scrolling down for more information. To address this, relevant instructions were added and the content of many longer cards was divided into two or more cards as suggested. In terms of layout, three participants preferred the âMy Pointsâ tab, which 108 shows participantsâ accumulated points, to be displayed on the main page rather than under the âMenuâ. Comments relating to aesthetics were positive. In particular, participants appreciated the selection of pictures and the colors. I love your pictures. Good selection of pictures really representing lots of different peopleâ¦ good colors, bright colors, stimulating colorsâ¦those were all visually soothing, but inspiring. Like they're motivating colors. [Mood04] Content and Features Content Participants mentioned their favorable perceptions of the gradually increasing step goal recommendations and the nonjudgmental manner consistently conveyed by the app, highlighted by the flexibility it encourages in meeting step goals. Most participants deemed the recommended step goals achievable, manageable, or attainable. Some users got confused by the examples of step goals (i.e., a baseline of 2000 daily steps) provided on the goal recommendations card, which was then removed. Mood07 suggested adding the time equivalence of steps when introducing the recommended step goals. This information was added as a tip card, which many subsequent participants appreciated. In terms of content design, all participants favored the incorporation of mixed media (e.g., images, GIFs, and podcasts) within the lessons. Some participants explicitly preferred fewer plain texts and more infographics. Features Key features were evaluated through tasks including syncing steps (Task 3), setting step goals (Task 5), logging an exercise session (Task 6), and creating an action plan (Task 8). With the intention of developing a self-help app requiring minimal human support, participants were expected to complete the tasks after reading the instructions provided in the corresponding lessons. However, many participants needed some verbal guidance, even though these were generally 109 minimal. Step tracker and exercise logging Both tools were embedded within the âTrackersâ tab. Most participants agreed that the instructions were straightforward and easy to understand, although some participants had questions about whether MoodMover can sync step data from their fitness trackers. This issue was resolved by adding a Q&A card that explains the app only retrieves step data from fitness trackers linked to Google Fit or Apple Health. However, many participants forgot the instructions after completing the module and needed additional verbal instructions to locate the tab. There are instructions in the moduleâ¦And then once I completed the module and want to go do what I had to do. I forgot a little bit and I had to find my way to things. [Mood05] In response, the instructional infographics were transformed into short videos. The videos were favored by subsequent participants over the initial infographics and were further refined based on their feedback, including a change from landscape to vertical. Regarding exercise logging, most participants particularly appreciated this feature, especially for the encouragement of logging short sessions (e.g., 5 or 10 minutes) and the ability to record their mood after exercising using emojis. That's cuteâ¦It is very motivating to have the option to put the mood. [Mood23] Gamification: My Points Figure 5.5 illustrates different levels of earned points and their corresponding incentives. Most participants were surprised by and excited about this feature. When participants were asked whether the amount of money corresponding to the points (i.e., every 60 points = $5) was motivating, most said they would be motivated, particularly because not much commitment was required. It doesn't matter. It could be $1â¦and I'd still be like, âI'm getting something.â [Mood49] 110 Figure 5.5. Point levels and incentives. Goals: Action planning Action planning was implemented using the âGoalsâ feature (Figure 5.6). Mood42 was excited about the ability to review a list of completed action plans, and stated, âI'd have a list of all of themâ¦that's cool.â However, two issues were identified. First, the tab was labelled âGoalsâ, which caused confusion with the step âgoalâ within the step tracker. Second, the action plan example listed below the textbox was obscured when the keyboard appeared. Figure 5.6. Screenshots of action planning. 111 Social - Community Forum Only two participants showed interest in and favored the social feature, while most participants expressed a lack of interest. After being told that the forum was completely anonymous, some participants showed increased interest. If it's anonymous, then I might be inclined to use it, especially on a day where I'm struggling, I might just check in and see âis anyone else having a hard time getting out walking today?â I know I am. It might be a way to provide some support without feeling vulnerable or exposed. So, I think that can be a really helpful tool. [Mood07] Resources Most participants particularly appreciated the curated selection of workout YouTube videos with different durations, intensity, and types. Three of them mentioned this feature as one of the best parts of the app. In particular, many participants favored the selected walking workout videos designed to help accumulate a certain number of steps, which can align with the recommended step goals of MoodMover. More relevant YouTube videos were selected by the researcher and grouped into a separate subcategory titled âVideos for Accumulating Steps.â Ideas for improvement Table 5.4 shows a list of other ideas for improvement along with sample quotations. Minor modifications were made immediately following the interviews. Further adjustments were discussed with the Pathverse development team to enhance the usability of MoodMover. 112 Table 5.4. A list of additional ideas for improvement with sample quotations. Ideas for improvement Modifications or actions Layout âI think just having a date up here [lesson previews] that says coming on this day...would be super helpful.â [Mood47] Consulted with the Pathverse team to add scheduled release dates on the lesson previews. Navigation âI would expect this to be the main page because there's only one program here.â [Mood49] Consulted with the Pathverse team to set the study page as the main page for participants registered in only one study Content âAdd a disclaimer on the top to remind participants even if there are many cards in one lesson. Each of them includes very limited content.â [Mood02] A tip card was added Exercise logging and mood tracking âPeople with depression is almost never happy, and the neutral is just blah. We are usually in between.â [Mood23] Consulted with Pathverse team to add additional mood choices between neutral and happy. Goals âI think it would be nice to have a box for each thing...instead of an action plan box. If there was a "What" box, "When" box, "Where" box.â [Mood42] Consulted with Pathverse team to break down for each domain Social â Community Forum âThere should be something where you can connect to technicians for any technical problems.â [Mood02] Added a topic âIf you need technical supportâ¦â to allow users to report technical issues using the social feature 5.3 Discussion This study describes the development of the first app-based intervention focused on increasing PA among individuals with depression, grounded in the M-PAC framework. The study adhered to the first five steps of the IDEAS framework to iteratively develop prototypes and 113 proceeded to the sixth and seventh steps for evaluating the usability of the prototype among end-users using a mixed-methods approach and refining the prototype. The involvement of a multidisciplinary team facilitated the app's development, bringing more divergent and creative ideas while enabling the convergence of solutions to efficiently refine the prototype (Mummah et al., 2016). The highly iterative, user-centered development processes enhanced the usability and acceptability of the tested MoodMover prototypes by aligning with end-usersâ needs. The development of behavioural interventions has frequently been criticized for lacking the guidance of a systematic and well-delineated framework, leading to an increased chance of having flaws that cause research waste (Hawe, 2015; Macleod et al., 2014). Following the IDEAS framework, the development of MoodMover was primarily informed by the results of a comprehensive, systematic review for identifying intervention needs (Tang et al., 2022) and a theoretical paper evaluating the use of the M-PAC framework among individuals with poor mental health (Tang et al., 2023). The need for an app specifically designed to increase PA among people with depression was further highlighted by our participants during the interviews. The content of MoodMover was adapted from a web-based M-PAC intervention for PA (Ross, 2023) and enhanced through a curated selection of behavioural strategies based on prior literature, as well as consultations with experts in the fields of depression and PA. In addition, the research team improved the content design (e.g., assure confidentiality and add institutional logo to images to improve credibility) by considering the barriers and facilitators that individuals with depression face when engaging with mHealth technologies to improve the usability, acceptability, and adoption of MoodMover (Kornfield et al., 2020; Simblett et al., 2019). The collection of both qualitative and quantitative data from end-users provided a comprehensive understanding of the usability of MoodMover. Overall, the MAUQ results 114 indicated good usability. Qualitative feedback received from think-aloud processes and semi-structured interviews revealed deeper insights into all aspects of the program. Most identified usability issues were minor and considerable positive feedback was received. Especially, all participants expressed their interests in using an updated version of MoodMover in the future, which adds support to the usability and acceptability of the MoodMover program. The simplicity of the Pathverse platform and the involvement of a Pathverse programmer facilitated rapid prototyping throughout the design phases. This enabled ongoing modifications to MoodMover, allowing for highly iterative development with continuous evaluation by subsequent participants. One potential concern that remained was that some users did not readily recall early instructions after navigating through the different lessons. This issue could be addressed by upgrading the app to allow participants to resume unfinished lessons, thereby enabling them to use the tools while reading the instructions, or by conducting a short orientation session to review the app and its features. Considering time and resources, a 15-minute virtual orientation session will be integrated into the program begins in the planned feasibility study. Features such as gamification with financial incentives, exercise logging with mood tracking, and resources including YouTube workout videos selected by researchers were particularly favoured by participants. In particular, financial incentives have consistently been shown to increase PA participation in the short term, though the effects on sustained PA in adults have yielded mixed results (Barte & Wendel-Vos, 2017; Luong et al., 2021; Mitchell et al., 2020). There is no consensus on whether the use of incentives or rewards inhibits intrinsic motivation, which may impact the maintenance of PA behaviour after the removal of incentives (Mitchell et al., 2020; Ryan & Deci, 2000). To our knowledge, no review has evaluated the use of financial incentives in PA behaviour change programs among individuals with depression. Given these, 115 while directly incentivizing the desired outcomes, such as step counts or meeting step goals, were more likely to increase step counts post-intervention (Barte & Wendel-Vos, 2017), MoodMover instead incentivizes lesson completion. This approach aims to enhance participant engagement with the content, which can potentially lead to increased PA participation and mitigate the impact of removing incentives (Omran et al., 2018). Additionally, the integration of points-based gamification feature may potentially enhance this effect (Xu et al., 2022). Exercise logging along with a mood self-tracking component holds promise in increasing PA by enhancing awareness of the positive correlation between PA and improved mood (Caldeira et al., 2017). The favourable view of this feature in our study is consistent with previous evidence; mood tracking tools are generally well-accepted among individuals with mental illnesses (Caldeira et al., 2017). In addition, (Torous et al., 2020) found an average attrition rate of 26.2%, rising to 47.8% when adjusted for publication bias, among mental health apps for depression, with lower rates observed in those with mood tracking (18.4%). The incorporation of mood tracking is likely to increase adherence and engagement of MoodMover. 5.3.1 Limitations Although the sample size in our study was deemed sufficient for usability testing (Lewis, 1994; Nielsen & Landauer, 1993; Schmettow, 2012; Virzi, 1992) and comprised of diverse physically inactive participants across different genders, a wide range of age groups, and varying severities of depressive symptoms, ongoing modifications to MoodMover may warrant a larger sample size. However, we ensured that any modifications were reviewed by at least three subsequent participants. In addition, recruiting participants from a single research website and limiting participation to individuals who owned a smartphone potentially introduced selection bias, reducing the representativeness of the sample. This approach may have overlooked the needs of 116 marginalized groups, potentially contributing to existing mental health inequities from the outset of the research. This limitation should be carefully considered in future studies, especially in large-scale trials assessing the effectiveness and implementation of MoodMover. For instance, future studies could consider providing mobile devices on loan to participants who do not own a smartphone. Moreover, the limited sample size hindered the ability to examine the determinants of usability (e.g., gender, age, and severity of depression), which should be further investigated in a larger-scale trial. Furthermore, the interviews were conducted over Zoom to eliminate the geographic barriers facing potential participants and mimic adoption in the real world. While the entire meeting was recorded as a video file, participants had the option to turn off their cameras, making the interpretation of facial expressions impossible. Moreover, not all tasks were assigned to all participants as planned to reduce respondent burden, which may have prevented the identification of additional usability issues. In particular, this led to a less comprehensive understanding of the perceived usability of the âGoals (action planning)â and âSocial â Community Forumâ features. In addition, the tested prototypes did not contain the actual podcasts. Participants were informed that podcasts would be used to deliver certain content; therefore, we were unable to obtain feedback on usability issues related to the podcasts. Moreover, most participants had the opportunity to scan through only two or three lessons during the meeting, which limited their ability to provide in-depth feedback on the content within MoodMover. However, since the content was adapted from a previous web-based intervention that had been reviewed by end-users, and the refined content was reviewed by professionals in our multidisciplinary team, we believe this limitation will not unduly impact the usability, acceptability, and potential efficacy of MoodMover. 117 5.4 Conclusions This study describes the rigorous development of the first app-based, M-PAC theory-guided intervention aimed at PA promotion designed for people with depression following the IDEAS framework. The comprehensive usability testing indicated good usability of the tested MoodMover prototypes and enabled a user-centered approach to refine the app to better align with end-usersâ preferences and needs. At this point, the research team reached a consensus to proceed to the next stage of the IDEAS framework, Assess, to evaluate the feasibility and preliminary efficacy of the refined intervention package of MoodMover. 118 Chapter 6: Feasibility and Preliminary Effects of an App-Based Physical Activity Intervention for Individuals with Depression (MoodMover): A Protocol for a Single-Arm, Pre-Post Intervention Study 6.1 Background Depression is the leading cause of disability, affecting approximately 280 million people worldwide (World Health Organization, 2023). Despite a wide range of evidence-based treatments, such as cognitive behavioral therapy and medication, access to adequate care may be limited due to concerns about side effects, resource constraints, a shortage of healthcare professionals, and the stigma associated with seeking mental health services (World Health Organization, 2021). In Canada, more than half of people seeking mental health care reported unmet needs, particularly for counseling or therapy (Statistics Canada, 2019). Given these barriers, effective alternatives and mental health services with enhanced accessibility are considered a global priority (Araya et al., 2018). The positive impact of physical activity (PA) on both preventing and managing depression is well-established (Anokye et al., 2012; Centers for Disease Control and Prevention, n.d.; Kraus et al., 2019). Many structured exercise programs have demonstrated moderate antidepressant effects (Heissel et al., 2023), and in Canada, exercise is a recommended first-line treatment for mild-to-moderate depression (Ravindran et al., 2016). However, like psychotherapy, structured exercise interventions often face concerns related to cost and accessibility (Cooney et al., 2013). Behaviour change interventions that promote PA may serve as viable alternatives or adjuncts to conventional treatments, extending their reach to those unable or unwilling to engage in structured exercise programming. Mobile applications (apps) are increasingly used to deliver behaviour change interventions 119 due to their ability to significantly enhance accessibility (Fitzgerald & McClelland, 2017; McKay et al., 2018). As of 2013, iTunes and Google Play provided access to a vast selection of over 5,000 apps designed to promote PA (Middelweerd et al., 2014). However, these apps have often faced criticism due to the absence of rigorous efficacy testing (Arigo et al., 2019; Middelweerd et al., 2014), variable use of behaviour change techniques (Bondaronek et al., 2018; Direito et al., 2014), and were not tailored to people with depression. In particular, our systematic review identified only one randomized controlled trial (RCT) investigating an app-based intervention designed for individuals with depression by November 2021, which demonstrated limited user engagement and efficacy in changing PA (Tang et al., 2022). This study will investigate the feasibility and preliminary effects of MoodMover, a 9-week app-based intervention designed to promote PA among people with depression. The development and usability testing of the prototype are described by Tang, Gierc, La, Kim, et al. (2025). This intervention, theoretically grounded in the Multi-Process Action Control (M-PAC) framework (Rhodes, 2017) for better intention-behavior transition, was developed following the Integrate, DEsign, Assess, and Share (IDEAS) framework (Mummah et al., 2016) for the enhancement of digital intervention development. The intervention was developed and designed using a no-code app development platform called Pathverse (Liu et al., 2022) and can be accessed through the Pathverse app (available in both the iOS and Android). The usability of MoodMover was deemed good in our mixed-methods formative study and has been further refined based on feedback from end-users. The revised Obesity-Related Behavioral Intervention Trials (ORBIT) (Powell et al., 2020) provides a clear path for early intervention development before large efficacy trials. The current study is a part of Phase IIa: Proof-of-concept and Phase IIb: Pilot and Feasibility Testing of the ORBIT model, and we will administer the refined MoodMover in a single-arm, pre-post 120 experimental study. The primary focus of this trial is to assess the feasibility and acceptability of MoodMover, which includes evaluating the recruitment plan, resources (e.g., time and cost), core assessments, intervention fidelity, acceptability of intervention, participant retention, and user engagement. Additionally, we will investigate the preliminary effects of MoodMover in terms of changes in daily step counts as the primary behavioral target. We will also examine changes in depressive symptoms and explore the relationship between these changes. Five main hypotheses were formulated for the study: 1. The recruitment process will be feasible. 2. PA, as assessed by both smartphone-generated and self-reported data, will show a clinically significant increase (Tang, Gierc, La, Kim, et al., 2025) after completing the intervention. 3. Self-reported depressive symptoms will show improvements after completing the intervention. 4. Improvements in the intervention target (i.e., PA) will be associated with positive changes in the clinical outcome (i.e., depressive symptoms). 5. Improvements will be observed in constructs of the M-PAC framework (e.g., affective attitudes). 6.2 Materials and methods 6.2.1 Study design This study is an open-label, single-arm pre-post experimental trial with an optional 9-week follow-up. The study design, procedures, and data analyses will largely follow those of previous non-randomized feasibility and efficacy studies (e.g., Inch et al., 2019; Ross, 2023; Takahashi et al., 2019). We adhered to the definition of feasibility trials as outlined in the 2010 Consolidated Standards of Reporting Trials (CONSORT) guidelines (Eldridge et al., 2016; Powell et al., 2020). 121 This non-randomized pre-post study with the primary aim of evaluating feasibility will inform future RCTs (Bond et al., 2023). In addition, we will evaluate preliminary effects of the intervention and report estimations, regarding the behavioural outcome. The reporting of this protocol follows a guide proposed by Thabane and Lancaster (Thabane & Lancaster, 2019), recommending the use of the SPIRIT (Standard Protocol Items: Recommendations for Interventional Trials) checklist (Chan et al., 2013), supplemented by items from the CONSORT 2010 statement (see Appendix K). The final study will follow a guide proposed by Lancaster and Thabane (Lancaster & Thabane, 2019), recommending the use of the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement alongside with the CONSORT extension for pilot and feasibility trials. Ethical approval is obtained from the University of British Columbia Behavioural Research Ethics Board (H24-01820). Any substantive protocol modifications (e.g., amendments that can affect study validity) (Chan et al., 2013) will be formally reviewed and approved by the Research Ethics Boards. A list of amendments will be transparently reported in the final study. This trial is registered at ClinicalTrials.gov (NCT06573125). 6.2.2 Participation eligibility This study will include outpatients aged 18â64 years who self-report a diagnosis of major depressive disorder or self-report at least mild depressive symptoms, as indicated by scoring at least 5 on the Patient Health Questionnaire, 9-item (PHQ-9) (Kroenke et al., 2001). Participants will be required to demonstrate literacy in English, possess an active email address, and own either an iPhone or an Android smartphone with internet capability to download the Pathverse app to access the MoodMover program. This study will include individuals who self-report engaging in less than 90 minutes of moderate-vigorous PA per week. Concomitant care and interventions will be allowed during the trial; participants may continue using medications provided the dosage 122 remains unchanged. All participants must give fully informed consent on REDCap (Harris et al., 2009). Exclusion criteria include self-reporting of a physical disability and/or health condition that prevents exercise (e.g., unstable angina, uncontrolled diabetes, acute heart failure), active psychosis or mania, active suicidal thoughts, severe cognitive impairment (e.g., major neurocognitive disorder), and/or being currently pregnant. In addition, individuals will not be eligible if they anticipate a major absence (e.g., vacation, surgery) in the next three months. 6.2.3 Recruitment Patients will be recruited using multiple strategies. First, a brief introduction to this research study will be posted on REACH BC (https://reachbc.ca/) and other research institute websites in Canada (e.g., Vancouver Coastal Health Research Institute ). Second, UBC mental health counselling services and other healthcare providers across Canada, as well as free depression advocacy groups (potential groups can be found at https://mharesource.rnao.ca/advocacy-groups-mental-health-canada; https://mdabc.net/resources/mdabc-support-groups/) will be approached for potential dissemination of study information. Third, posters will be placed in different hospitals and clinics in British Columbia, Canada. Notably, remote recruitment will be employed to mimic real-world conditions, and not all recruitment strategies will be implemented simultaneously. The selection and modification of recruitment strategies will consider available resources, time constraints, and the speed of recruitment. Potential participants will complete a screening questionnaire on REDCap (see Appendix L). Individuals who are found ineligible due to active suicidal thoughts will be informed immediately via email, and they will be provided with information about other community resources available for mental health support. Those who meet the eligibility criteria will be invited to participate in the study. Interested and eligible individuals will receive detailed 123 study information via email and will complete the informed consent process electronically and all baseline measures of interest on REDCap. Due to remote recruitment, only electronic signatures will be obtained, as approved by the ethics committee. Potential participants will be given one week to consider the information provided, and they will receive a second invitation to confirm their participation through email. Participants can withdraw from the treatment or the study at any time. 6.2.4 Procedures After an electronical consent form and baseline measures are completed, a researcher will register participants to the MoodMover program on the Pathverse admin web portal using their provided email address. Participants will receive an email with a MoodMover user guide, an instructional letter including information about downloading the Pathverse app from either the Apple Store or Google Play, and how to share their smartphone screens over Zoom. In particular, they will be instructed to sign up and log into Pathverse using the email address they provided. Participants will be required to take a 15-minute one-on-one orientation session via Zoom over their smartphones. Before the orientation, participants will be asked to read through the MoodMover user guide. During the orientation, participants will share their smartphone screens and complete the first introduction module themselves while the researcher observes and provides explanations as needed, particularly regarding the ramped step goal recommendations. Following the completion of the first module, the researcher will hold a brief Q&A session to address any remaining questions or concerns the participant may have. Detailed scripts will be used to ensure consistent delivery of information across participants. Participants will be asked if they consent to having their session recorded at the start of their orientation and will be asked to verbally confirm their consent at the beginning of the recording. Only five orientation sessions will be recorded and 124 assessed by a research assistant using a fidelity checklist after each session, which will allow immediate modifications to improve treatment fidelity (Teresi et al., 2022). The start date of the program will be set as the day of the orientation session. Participants can postpone the start date if needed by informing the researcher via email. After completing the intervention, participants will undergo a re-assessment on REDCap. Participants will be encouraged to keep the app on their smartphones and continue using the app after completing the program. Choosing not to uninstall the app will be considered as providing consent, allowing the researcher to access and track their user engagement for a follow-up at 9 weeks. These participants will be approached on the last day of the follow-up period to sync their steps with MoodMover again, allowing the researcher to collect their PA data during the entire follow-up period. In addition, participants will be asked to complete a satisfaction questionnaire on REDCap. All study participants will be compensated $10 or gifts of equivalent value for completing each pre- and post-intervention assessment. The enrollment, intervention, and assessment schedule, following the recommended format by the SPIRIT guidelines, aligning with relevant items from the CONSORT 2010 guidelines (Eldridge et al., 2016), is presented in Figure 6.1. 125 Figure 6.1. The schedule of enrollment, interventions, and assessments. Note. GAD-7: Generalized Anxiety Disorder 7; MAUQ: mHealth App Usability Questionnaire; M-PAC: Multi-Process Action Control; PAAQ: Physical Activity Adult Questionnaire; PHQ-9: Patient Health Questionnaire â 9 items; PSQI: Pittsburgh Sleep Quality Index; WHODAS 2.0: World Health Organization Disability Assessment Schedule 2.0 aIncludes: sex, gender, race/ethnicity, age, height, weight, the highest level of education, employment status, income, disease duration, medication use, other ongoing treatments relevant to depression management, substance use, and current and past usage of physical activity apps or devices. bFor early discontinuation. 126 6.2.5 Intervention MoodMover is a 9-week app-based intervention designed to increase PA among people with depression delivered through Pathverse (Tang, Gierc, La, Kim, et al., 2025). The content was contextualized for people with depression based on an existing 10-week, web-based PA intervention for young adults guided by the M-PAC framework (Ross, 2023). The current version of MoodMover consists of one run-in module for introduction and baseline self-monitoring, eight major modules, and eight complementary (optional) modules. After completing the first week, participants will be encouraged to set daily step goals by increasing an additional 1000 steps above their baseline PA level (week 1) every two weeks until reaching an increment of 3000 steps per day (Gilson et al., 2013; Tang, Gierc, La, Kim, et al., 2025; Trinh et al., 2018). Appendix M depicts each major lesson's topic. A combination of different formats has been employed for delivering educational content, including videos, podcasts, and illustrated articles. The app incorporated a wide range of behavioral change techniques (BCTs; e.g., self-monitoring and graded tasks) following the BCT taxonomy V1 (Michie et al., 2013) to implement the behavioral constructs of the M-PAC model. Apart from structured psychoeducational content, the app also offers diverse behavioral features, allowing participants to set step goals and action plans, monitor their progress, and engage in anonymous communication with other participants on pre-determined topics related to exercising. To improve engagement and promote PA, MoodMover also incorporated brief notifications and a gamification element where participants can earn 20 points for completing each major lesson and 10 points for completing each complementary lesson. Every 60 points earned is equivalent to a $5 CAD e-gift card. Screenshots in Fig 2 illustrate the home screen, step tracker, exercise log (with mood monitoring after exercising), and a card of a major lesson of the refined prototype of MoodMover. Screenshots in Fig 3 illustrate the community 127 forum of MoodMover. The program details are described elsewhere (Tang, Gierc, La, Kim, et al., 2025). Participants will be instructed to read the lessons and complete all activities within each lesson over a 9-week period, with one lesson (week 1) and two lessons (one major and one complementary lesson; week 2-9) released at pre-scheduled times per week. Figure 6.2. Screenshots of home screen, step tracker, exercise logging, action planning, and a card of a major lesson (for illustrative purposes only). 128 Figure 6.3. Screenshots of the peer-to-peer communication forum. 6.2.6 Measures 6.2.6.1 Demographic and clinical information All participants will be asked to provide demographic and clinical information (see Appendix N), such as sex and gender, race/ethnicity, age, height, weight, highest level of education achieved, employment status, income, duration of depression, medication use, other ongoing treatments relevant to depression management, substance use (e.g., cannabis), and current and past usage of PA apps or devices. 129 6.2.6.2 Primary feasibility and acceptability outcomes The traffic light system will be used to determine whether proceeding to a RCT based on the worst-performing feasibility criterion (Avery et al., 2017). The 3-tiered progression criteria require setting cut-offs for green (proceed without changes), amber (proceed with amendments), and red (stop proceeding) zones for each of the key feasibility criterion. Table 1 presents the pre-determined thresholds for recruitment, adherence, usability, and retention based on previous literature, our usability testing study of MoodMover (Tang, Gierc, La, Kim, et al., 2025), pragmatic considerations, and expert insight within the research team. In addition, compliance to the graded goal setting and user satisfaction will be assessed to support the interpretation of feasibility and acceptability. Table 6.1. Pre-determined progression criteria. Feasibility outcomes Green zone (go) Amber zone (amend) Red zone (stop) Recruitment â¥65% of eligible participants 40%-<65% of eligible participants <40% of eligible participants Adherence â¥70% of participants complete the minimum requirements, that is, completing 5 major lessons 40%-<70% of participants complete the minimum requirements, that is, completing 5 major lessons <40% of participants complete the minimum requirements, that is, completing 5 major lessons Retention â¥70% participants complete the post-intervention assessment 40%-<70% participants complete the post-intervention assessment <40% participants complete the post-intervention assessment Usability â¥70% of participants scored 5 and over on MAUQ 40%-<70% of participants scored 5 and over on MAUQ <40% of participants scored 5 and over on MAUQ Note. MAUQ: mHealth App Usability Questionnaire. 130 Recruitment. Recruitment rate will be calculated by dividing the number of people with depression who enrolled in the trial by the number of interested individuals meeting the eligibility criteria. Successful recruitment will be defined as achieving a rate of 65%. This progression criteria was based on studies with similar feasibility-based designs and those sampling from adults with mental illness (e.g., Firth et al., 2016). The time taken, source of recruitment, and the number of recruitment sites (if placed posters) to recruit the targeted sample size will also be recorded. Adherence. Intervention adherence will be based on the proportion of participants who complete the majority (five out of eight) of major lessons. Previous systematic reviews identified a wide range of adherence rates for mHealth apps among people with depression. For example, 2/3 of studies in Serrano-Ripoll et al. (2022) demonstrated an adherence rate of less than 50% (if reported). In contrast, Kerst et al. (2020) observed adherence rates ranging from 70% to 94% in the majority (5/7) of the reviewed studies; however, remarkably lower adherence rates (22% and 35%) in two studies. In this study, an adherence rate of 70% will be deemed as falling within the green zone. Other user engagement metric data (i.e., time spent on each module) will also be downloaded from the Pathverse admin web portal. Various available objective engagement metrics, as summarized in Molloy and Anderson (Molloy & Anderson, 2021), will be calculated and reported, including use of specific program features (e.g., number of times participants changed their step goals, number of action plans completed, and number of exercise sessions logged), program use by the number of active days (i.e. number of days the program is used at least once), total duration of use, and average duration for completing the major and complementary modules. Participants who complete all 8 major modules will be categorized as âcomplete usersâ; those who complete 2-8 modules will be categorized as âincomplete usersâ; and those who do not use the app after the orientation session will be categorized as ânonusersâ, respectively. Ongoing monitoring 131 of adherence will be employed. Participants who have not opened the app for two weeks will be contacted for a check-in via email. Reasons for non-adherence (if provided) will be recorded. Usability. The adapted, patient version of the mHealth app usability questionnaire (MAUQ) (Zhou et al., 2019) designed for standalone apps will be employed. The MUAQ contains 13 items (see Appendix I, and Tang, Gierc, La, Kim, et al. (2025)). It evaluates three domains of usability as defined by the International Organization for Standardization definition of usability (International Organization for Standardization, 2018): ease of use (MAUQ_E; 5 items, e.g., âThe app was easy to useâ), usefulness (MAUQ_U; 1 item, e.g., âThe app would be useful for my mental health and well-beingâ), and interface and satisfaction (MAUQ_I; 7 items, e.g., âThe information in the app was well organized, so I could easily find the information I needed.â). Participants will be asked to rate each item on a 7-point Likert scale (1 = strongly disagree to 7 = strongly agree). An average score of 5 and over will be deemed as acceptable consistent with Tang, Gierc, La, Kim, et al. (2025). Retention rates. Retention will be determined by the proportion of participants completing both pre- and post-intervention questionnaires. Torous et al. (2020) found an average attrition rate of 26.2%, rising to 47.8% when adjusted for publication bias, among mental health apps for depression, with lower rates observed in those with mood tracking (18.4%) and human feedback (11.7%). An attrition rate of 30% will be defined as acceptable (green zone) for MoodMover. Additionally, reasons for discontinuation and any adverse events will be recorded. Compliance. The compliance rate to the graded goal setting will be reported. Specifically, the proportion of participants who successfully set their step goal in week 2, and those who modified their step goals in week 4 and week 6, will be reported separately. Participants who followed all the instructions across these time points will be deemed as having achieved âfull 132 complianceâ, while those who followed instructions at one or two time points will be deemed as having achieved âpartial complianceâ. Participants who did not follow instructions at any time point will be deemed as having achieved âno complianceâ. Satisfaction. Usersâ satisfaction levels will be evaluated using Melin et al.âs mHealth Satisfaction Questionnaire (Melin et al., 2020) at both post-intervention and the 9-week follow-up (optional). This 14-item questionnaire is recommended by Hajesmaeel-Gohari et al. (2022) as it was specifically designed for mHealth applications (see Appendix O). Participants will be asked to rate on a 5-point Likert scale (1 = strongly disagree to 5 = strongly agree). Among 14 items, four items are negatively stated and will be reversed in the analyses. The total score ranges from 14 to 70, with a higher score indicating a higher satisfaction level. In addition, participants will be asked to list three things they liked and disliked about the intervention. 6.2.6.3 Secondary outcomes While this study is not powered to determine the intervention's efficacy definitively, we will still collect data on each outcome to investigate interval estimates of the changes. The response rate and completion rate of each measure will be reported to inform the feasibility of administering the selected measures in a future trial. Additionally, we aim to assess the potential of MoodMover to produce clinically significant PA and depression changes in a larger evaluation. Physical activity (behavioral outcome). Change in daily step counts will be collected using the smartphoneâs built-in step counting function, which will be synced with MoodMover and can be downloaded from the Pathverse admin web portal. A significant increase of 3000 daily steps for 5 days per week will be deemed as reaching the clinically significant behavioral goal of MoodMover (Tang, Gierc, La, Kim, et al., 2025). Moreover, to evaluate self-reported PA, we will utilize the Canadian Physical Activity Adult Questionnaire (PAAQ) (Colley et al., 2018). This 133 questionnaire is designed in accordance with the Canadian Physical Activity Guidelines, which recommend a minimum of 150 minutes of MVPA per week for adults (see Appendix P). It has been widely employed for monitoring PA in Canadian adults and has demonstrated a stronger association with accelerometer-based data than the International Physical Activity Questionnaire (Colley et al., 2018; Garriguet et al., 2015). Along with the PAAQ questionnaire, participants will be asked at post-intervention whether they have linked a smartwatch or fitness device to Health (iOS) or Google Fit (Android) throughout the program. Additionally, they will be asked how often (almost always, sometimes, seldom) they carried their phone (or wore their smartwatch, if used) during their non-sedentary waking hours during workdays and weekend (âHow frequently do you carry your phone/wear your smartwatch with you during your non-sedentary waking hours?â). Depressive symptoms (clinical outcome). Depression severity will be evaluated utilizing the PHQ-9 (Kroenke et al., 2001), a widely used self-report scale that demonstrates sensitivity in detecting changes after treatment in psychiatric patients (Beard et al., 2016). Participants will be asked to rate 9 items, using 0-to-3 Likert scales, resulting in a total score ranging from 0 to 27 (see S3 File). Higher scores reflect more severe depressive symptoms. The classification of depression severity will be as follows: a score of 5-9 will be considered indicative of mild depressive symptoms, 10-14 as moderate, 15-19 as moderately severe, and 20-27 as severe depressive symptoms (Kroenke, 2021). To define a clinically meaningful change in depression, McMillan et al. (2010) proposed a reduction of 5 points on the PHQ-9, along with a PHQ-9 score shift from â¥ 10 at baseline to â¤ 9 at post-intervention to be considered clinically relevant. Given that individuals with mild depressive symptoms will be eligible for the current study, this classification will be exclusively applied to those who scored 10 or higher at baseline. Anxiety. Anxiety will be measured by the Generalized Anxiety Disorder 7 scale (GAD-7) 134 (Spitzer et al., 2006) (see Appendix Q). The GAD-7 is a seven-item validated (Löwe et al., 2008) instrument for measuring the severity of anxiety on four-point Likert scales (0 = ânot at allâ to 3 = ânearly every dayâ). The total score on the scale ranges from 0 to 21, with higher scores indicating severe anxiety. The classification of anxiety severity will be as follows: 5 for mild, 10 for moderate and 15 for severe (Löwe et al., 2008). Sleep quality. The Pittsburgh Sleep Quality Index (PSQI) (Buysse et al., 1989) is a self-rated, 19-item questionnaire with strong reliability and validity (Mollayeva et al., 2016) that assesses various sleep-related problems over the past month (see Appendix R). The 19 questions are categorized into seven components, including sleep quality, sleep duration, sleep latency, habitual sleep efficiency, sleep disorders, use of sleep medication, and daytime dysfunction. Participants will be asked to rate on a 4-point Likert scale ranging from 0 to 3. A total score ranging from 0 to 21 can be summed for these seven components, with a higher score reflecting poorer sleep quality. A total score of >5 indicates a poor quality of sleep. Functional disability. World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) (Ãstün, 2010) will be utilized to evaluate participants' health and ability to perform activities in six areas of functionality: cognition, mobility, self-care, getting along, life activities, and participation (see Appendix S). The WHODAS 2.0 comprises 12 items. For each item, participants will be asked to estimate the extent of their disability over the past 30 days, using a 5-point Likert scale ranging from 1 (none) to 5 (extreme/cannot do). The total score on the scale ranges from 0 to 100, with higher scores indicating a greater degree of disability. WHODAS 2.0 has good internal consistency across all domains (α = .77 - .98) (Garin et al., 2010). 6.2.6.4 M-PAC constructs The measures of the reflective and regulatory processes within the M-PAC framework will 135 be primarily derived from Tang et al. (2023). The M-PAC survey is presented in Appendix T. Reflective processes. Affective attitudes and instrumental attitudes will be measured on three 7-point bipolar scales, respectively. All items share the same stem: âFor me, participating in regular PA over the next month would be ...â Higher scores indicate more positive attitudes. The items had demonstrated very good to excellent inter-item reliability in Tang et al. (2023) (affective attitudes, α = .87; instrumental attitudes, α = .91). Perceived opportunity and perceived capability will be assessed using three 5-point Likert scales, ranging from 1 (strongly disagree) to 5 (strongly agree), respectively. The items have shown acceptable inter-item reliability (opportunity, Î±â¯= .70; capability, Î±â¯= .67) (Ursachi et al., 2015). Intention strength. Intention strength will be measured using three items (e.g., I am committed to engage in physical activity over the next month). Participants will rate these items on 5-point Likert scales ranging from 1 (strongly disagree) to 5 (strongly agree). The items have exhibited very good inter-item reliability (α = .89) (Tang et al., 2023). In addition, decisional intention will be evaluated using one item regarding the intention to perform more PA and achieve the behavioral outcome goal of increasing 3000 daily steps above their baseline PA levels for most days per week. Participants will be asked to provide binary responses (Yes/No). Regulatory processes. Regulations will be measured using 5-point Likert scales with three items pertaining to self-monitoring, goal-setting, and action planning. The inter-item reliability for the regulatory process measures has been found to be very good (α = .81) (Tang et al., 2023). Reflexive processes. Habit will be assessed using three items (e.g., "I engage in regular physical activity without having to consciously remember it"), which have been modified from Rhodes and Lim (Rhodes & Lim, 2016) and were originally derived from the subscale of the Self-Report Habit Index (Gardner et al., 2012). Identity will be measured using three items (e.g., "When 136 I describe myself to others, I usually include my involvement in physical activity"), which have been adapted from previous studies (Rhodes & Lim, 2016; Wilson & Muon, 2008). Both measures will utilize a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) for participant responses. The inter-item reliability of the habit measure has demonstrated excellent internal consistency (α = .93), while the identity measure has shown very good internal consistency (α = .87) (Rhodes & Lim, 2016). 6.2.7 Sample size justification As a single-arm feasibility trial, the ideal sample size was informed by the recommendations for the intervention arm of pilot RCTs, based on the "traffic light" progression criteria (Lewis et al., 2021). The traffic light system proposed by Lewis et al. (2021) is regarded as a recent and robust approach for sample size justification in feasibility and pilot studies and has been recommended for application in the PA domain (Hollman et al., 2024). Four feasibility criteria (i.e., recruitment, adherence, usability, and retention) were considered individually (see Table 1). To achieve over 90% statistical power for each criterion, as aligned with the quick look-up grid for sample size based on the normal approximation approach in Lewis et al. (2021), the study requires: (1) 36 participants for recruitment; (2) 25 participants in the intervention group to assess adherence; (3) 25 participants in the intervention group to assess usability; and (4) 25 participants for retention. The overall sample size was determined by the criterion with the highest requirement: adherence and usability, requiring 25 participants per arm. Considering a 30% attrition rate, the sample size for adherence increases to 36 participants. With an expected 65% recruitment success as our previous usability testing study indicated, 55 eligible patients need to be invited to achieve the target sample size (55 = 1/0.65 Ã 36). Considering the available resources and time, a pragmatic sample size range of 20-36 will be adopted, incorporating commonly accepted sample size rule of 137 thumbs. Julious (2005) suggests a minimum of 12 participants per arm, whereas Kieser and Wassmer (1996) recommended a range of 10-20 per arm for pilot studies. 6.2.8 Data management During data collection, all questionnaire data will be collected and collated on the UBC-hosted data management platform, REDCap. App-related data will be collected by Pathverse and stored in Canada on Amazon Web Services (AWS) (Amazon Web Services). Five orientation sessions will be transcribed using Otter.ai (Otter.ai, 2024), which stores data in the AWS West region, United States. After the intervention, all data will be downloaded and securely stored on a UBC-secured, password-protected computer, and then transferred to UBC's secure OneDrive. All files will be password-protected and encrypted. Only authorized researchers involved in the study will have access to the data. After data collection is completed, any personal identifying information will be deleted. As is common practice in scientific research, other researchers may access the de-identified data after publication. Participants will be fully informed of these procedures before consenting to this trial. 6.2.9 Statistical analysis All analyses will be conducted using either SPSS ver. 29 or R (version 4.2.2) via RStudio (version 2024.12.0+467). Descriptive statistics will be presented for all measures. Missing data in feasibility variables and smartphone-based step counts will be reported and interpreted as part of the feasibility results. In a specific case of missing smartphone-based step data in the final week (week 9), the last observation carried forward method will be used to substitute missing data with values from week 8 for preliminary effects related analyses, as both weeks fall within the PA maintenance phase. The strategy for addressing missing data in other secondary outcomes will be determined by evaluating the degree and pattern of missingness, i.e., Missing Completely at 138 Random (MCAR), Missing at Random (MAR), Missing Not at Random (MNAR) (Rubin, 1976). For example, Littleâs MCAR tests (Little, 1988) will be conducted using SPSS. If the data are determined to be MCAR or MAR, mean substitution will be applied when the amount of missing data is minimal (<5-10%). Pairwise deletion will be employed when there is moderate missing data (10-20%). In cases of a substantial amount of missing data (>20%) or if the missing data are determined to be MNAR, no further analyses will be conducted to avoid biased estimates. The impact of potentially influential factors (e.g., depression severity and age) on feasibility results (e.g., lesson completion), will be explored using regression-based analyses. A separate regression model will be run for each factor. Regression analyses will be conducted only if at least 10 participants per variable provide valid data, adhering to established rules of thumb for regression analyses (Harrell, 2001). Additionally, we will explore the changes in secondary outcomes (e.g., PA, depression, and M-PAC constructs) over time. Given the small sample size and two time points (pre- and post-intervention), paired t-tests will be used for continuous outcomes, while equivalent non-parametric tests (i.e., Wilcoxon signed-rank tests) will be applied for non-normal distributions. For binary outcomes, McNemarâs test will be employed. The association between changes in PA and changes in depressive symptoms will be assessed using Pearsonâs or Spearmanâs correlation analysis. As an underpowered single-arm feasibility study, the reporting will focus on estimation. Results will be reported with 75%, 85%, and 95% confidence intervals, while considering clinically meaningful changes as suggested by Lee et al. (2014) and Walters (2009). A binary variable will be generated to indicate whether a participant met the behavioral step goal (1) or not (0). Specifically, the pre-determined clinically meaningful decrease in depression will be incorporated into the interpretation of the results for those who scored 10 or above on the PHQ-9 at baseline. 139 6.2.10 Harms and data monitoring The research protocol described here is categorized as low research risk and low to moderate participant vulnerability. The intervention is not anticipated to cause physical harm. The app focuses on promoting lifestyle physical activities, which carry no greater risk than participants would experience in their daily lives. Although using an app for data collection introduces moderate privacy and confidentiality risks, the Pathverse platform employs strong technical standards and encryption. Access to the Pathverse web portal is password-protected, and all collected data is deidentified and encrypted during transit and storage. Therefore, privacy and confidentiality risks are considered low. Participants, who may have varying levels of depression or other mental health issues, represent a vulnerable group. They might experience a change in their mental health status inherently or emotional distress if unable to meet daily step goals. As a remote study, researchers will not be able to provide in-person and/or immediate support for participants, placing this risk in the moderate range. However, concomitant care is permitted, including stable medication use, contributing to a low to moderate level of participant vulnerability. Additionally, participants will be provided with information on 24/7 mental health supports (e.g., national crisis lines) in case of urgent need. As this trial has a short duration (9 weeks) and generally low-risk considerations, a data monitoring committee is not incorporated. 6.2.11 Timeline and dissemination plans Recruitment is anticipated to begin on November 1, 2024. While efforts will be made to achieve the ideal sample size, recruitment will conclude at the 6-month mark from the start of data collection (estimated to be May 1, 2025) at the latest if the minimum sample size is reached. Trial results will be disseminated via publications in peer-reviewed journals and via presentations at academic conferences. 140 6.3 Discussion To provide greater evidence-based treatment options for patients with depression who are not interested in, or have no access to conventional depression treatments, well-designed app-based intervention that can increase PA and sustain those increases over time may be beneficial. This trial will explore the feasibility, usability, and acceptability of a 9-week, theory-based (M-PAC) app designed for people with depression following a combination of two systematic and rigorous behavioral intervention development frameworks (IDEAS and ORBIT). This approach is crucial for refining the intervention, improving study procedures, and informing the design of future definitive studies (Eldridge et al., 2016). In addition, we will explore the potential effect of MoodMover on participantsâ behavioral (PA) and clinical outcomes (e.g., depression). We hypothesize that this intervention will demonstrate potential in increasing multiple M-PAC constructs, such as affective attitudes, regulatory skills, habit, and identity. Both habit and identity are powerful theoretical constructs that we hope can produce sustained, long-term change in exercise behavior patterns. One strength of this study is that the current version of MoodMover has passed usability testing among the target population and has been refined based on participant feedback (Tang, Gierc, La, Kim, et al., 2025). In addition, MoodMover was designed based on the M-PAC framework, a behaviour change framework that has been deemed suitable for people with poor mental health (Tang et al., 2023), and incorporated a series of BCTs corresponding to the M-PAC constructs, along with additional BCTs to enhance engagement. Moreover, this study will include a more robust analysis of user engagement data powered by the Pathverse platform, whereas most previous studies with mHealth apps for depression reported only one or two metrics (Molloy & Anderson, 2021). Researchers can closely track PA behavior (i.e., daily step counts) and the timing 141 and reasons for app discontinuation, shedding light on the app usage lifecycle. In addition, an extensive range of potential influential factors will be assessed, aiding in the understanding of the associations between engagement and other variables. Understanding how app use changes over extended periods of time, and assessing various mediating variables and process measures, will provide insights into the mechanisms behind the interventions' success or failure and inform either moving to a larger efficacy trial, moving backwards to the previous phases of the IDEAS model for refinement, or abandoning further development of MoodMover. A potential limitation is the common high dropout rates and low user engagement often faced by mHealth apps for depression (Torous et al., 2020) which could impact the evaluation of MoodMover's preliminary effects. Torous and colleagues (2020) also found that apps incorporating mood monitoring showed a considerably lower dropout rate of 18.4%. Given this, we have incorporated a mood monitoring element by asking participants to rate their mood after exercising when they log their exercise behaviors. Moreover, to enhance the likelihood of sustained app usage, we have introduced several evidence-based elements: a gamification feature with incentives, a peer-to-peer support forum, reminders/notifications, an easy-to-use interface, and a combination of multiple formats to deliver educational content (see Tang, Gierc, La, Kim, et al. (2025)). We anticipate that these strategies will increase adherence and engagement while potentially improving MoodMover's preliminary effects in promoting PA. The exit satisfaction survey will provide feedback on the features of MoodMover perceived as being most and least helpful. In addition, the inclusion criteria for this feasibility study - including a broad age range, a spectrum of depression severity (from mild to clinically significant), and the allowance for concurrent clinical care and medication use - were intentionally designed to reflect real-world conditions. However, due to the small sample size, this heterogeneity may limit the interpretability of the 142 feasibility outcomes. Large-scale future studies may be warranted to examine subgroup differences through stratified analyses. Overall, this study fits within Phase IIa and Phase IIb of the ORBIT model. The present study will provide insights into the potential of MoodMover serving as an alternative strategy or adjunct treatment for managing mild-to-moderate depression, which can be implemented into clinical mental health care in Canada. Despite the possibility of null effects, this study will yield valuable data related to recruitment plans, trial processes and procedures (e.g., core assessments and app features), resources, trial management, and other feasibility variables (e.g., user engagement and satisfaction). In contrast, promising results will allow us to move to the next step, Phase IIc: Phase II Efficacy Trial, of the ORBIT model for testing the efficacy of MoodMover on behavioural outcome in an appropriately powered RCT. Other study designs, such as sequential, multiple assignment, randomized trials (SMARTs) (Lei et al., 2012), micro-randomized trials (Klasnja et al., 2015), single-case designs (Dallery & Raiff, 2014), dose-finding methods, may also be considered for assessing MoodMover in the future. Acknowledgements The authors would like to thank all the research assistants for their willingness to help. 143 Chapter 7: Feasibility and Proof-of-Concept of an App-Based Physical Activity Intervention for Individuals with Depression (MoodMover): A Single-Arm, Pre-Post Study 7.1 Background Depression is the largest contributor to disability, affecting approximately 280 million people worldwide (World Health Organization, 2023). It has been linked with a higher incidence of various non-communicable diseases such as cardiovascular diseases (Rajan et al., 2020), type II diabetes (Knol et al., 2006), pulmonary disorders (Xu et al., 2008), and 10-14 years of premature mortality (Laursen et al., 2016). Approximately 60% of those with major depressive disorder (MDD) are diagnosed with comorbidities, contributing to an increased disease burden worldwide (Armbrecht et al., 2020). The annual economic burden of depression in Canada is estimated to be over $12 billion for the health-care system (Tanner et al., 2020). Despite the increased availability of many evidence-based treatments (e.g., psychotherapy, antidepressants) over recent decades (Ormel et al., 2022), access to these first-line treatments remains low globally due to various barriers. These include concerns about the side effects of antidepressants, limited resources, a critical shortage of healthcare professionals, and the stigma associated with seeking mental health care (World Health Organization, 2021). In Canada, more than half of the people seeking mental health care reported unmet needs, particularly for counselling or therapy (Statistics Canada, 2019). More discouragingly, when Ormel et al. (2022) sought to explain the "more treatment but no less depression paradox", they proposed three primary explanations: (i) the published literature tends to overestimate the short- and long-term efficacy of treatments, (ii) the effectiveness of treatments is notably reduced when applied in real-world settings, and (iii) the impact of treatment varies significantly between chronic-recurrent and non-recurrent cases. Taken together, these findings underscore the urgent need for effective 144 complementary mental health interventions that are both more accessible and effective in real-world contexts (Araya et al., 2018). Physical activity (PA) is widely recognized as a modifiable health behaviour that can help prevent and manage depression (Heissel et al., 2023; Noetel et al., 2024; Singh et al., 2023). It has also been consistently associated with various other physical and mental health benefits, such as improved sleep quality, prevention of multiple cancers, and reduced anxiety (Anokye et al., 2012; Kraus et al., 2019). Based on the existing evidence, exercise has been recommended as a treatment for depression in Canada by both the 2016 and 2023 Canadian Network for Mood and Anxiety Treatments (CANMAT) guidelines (Lam et al., 2024; Ravindran et al., 2016), and in the United Kingdom (National Institute for Health and Clinician Excellence, 2022). However, exercise remains an underutilized depression treatment option (Glowacki, 2020). There is now a critical need to translate guidelines into practice by developing accessible, evidence-based options to support individuals with depression in increasing their PA levels. To enhance the accessibility of such interventions, mobile applications (apps) have emerged as a widely adopted mode of delivery (Fitzgerald & McClelland, 2017; McKay et al., 2018). MoodMover, a 9-week smartphone app-based intervention, was developed to promote PA behaviour change and potentially alleviate depressive symptoms. The intervention is theoretically grounded in the Multi-Process Action Control (M-PAC) framework (Rhodes, 2017) and integrates two systematic behavioural intervention development frameworks to achieve synergistic effects: (i) the Obesity-Related Behavioural Intervention Trials (ORBIT) (Powell et al., 2020) for an ultimate goal of managing chronic disease, and (ii) the Integrate, Design, Assess, and Share (IDEAS) (Mummah et al., 2016) model for the enhancement of digital intervention development. 145 The detailed, step-by-step development process and usability testing of the MoodMover prototype are described elsewhere (Tang, Gierc, La, Kim, et al., 2025). The current study aligns with Phase IIa: Proof-of-concept and Phase IIb: Pilot and Feasibility Testing of the ORBIT model. This study aimed to determine the feasibility and acceptability of the current version of MoodMover in people with depression, along with its preliminary effects on behavioural (i.e., daily step counts) and clinical outcomes (i.e., depressive symptoms). The findings will inform whether the intervention is ready for evaluation in a more rigorous study: that is, ORBIT Phase IIc. 7.2 Materials and Methods 7.2.1 Study Design This study was a single-arm pre-post trial. The full protocol for the current trial has been previously published and contains a detailed description of the methods used, study outcomes and progression criteria (Tang, Gierc, La, Liu, et al., 2025). The reporting of this study followed a guide proposed by Lancaster and Thabane (Lancaster & Thabane, 2019), recommending the use of the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) statement alongside the CONSORT extension for pilot and feasibility trials (Appendix U). Ethical approval was obtained from the University of British Columbia Behavioural Research Ethics Board (H24-01820). This trial is registered at ClinicalTrials.gov (NCT06573125). 7.2.2 Intervention The MoodMover intervention is a 9-week, self-guided, program accessible via the Pathverse app (Liu et al., 2022), available on both iPhone and Android devices. The program begins with a 1-week run-in period to collect baseline step data, followed by an 8-week intervention phase, and concludes with an optional 9-week follow-up period. During the intervention phase, participants receive two psychoeducational lessons per week: one major and 146 one optional complementary lesson, released at pre-scheduled times. Various formats are used to deliver the educational content, including podcasts, GIFs, and illustrated texts. In addition, MoodMover incorporates a gamification component based on lesson completion, awarding 20 and 10 points for completing major and complementary lessons, respectively. For every 60 points accumulated, participants receive a $5 CAD Amazon e-gift card. The MoodMover app employs a progressive step goal-setting strategy, encouraging participants to increase their daily step count by 1,000 steps above their baseline (established during the run-in week), with additional 1,000-step increments every two weeks, ultimately targeting a total increase of 3,000 steps per day from baseline (Gilson et al., 2013; Tang, Gierc, La, Kim, et al., 2025; Trinh et al., 2018). The program is embedded with diverse features, operationalizing a range of behaviour change techniques (BCTs) (Michie et al., 2013) to apply the theoretical constructs defined by the M-PAC model. A list of unique BCTs used in this version of MoodMover is provided in Appendix V. Coding was conducted independently by two coders (YT and VW) using the BCT Taxonomy version 1 (BCTTv1) (Michie et al., 2013), with discrepancies resolved through discussion. 7.2.3 Participants Participants were adults aged 18â64 years who either self-reported a diagnosis of MDD or scored â¥ 5 on the Patient Health Questionnaire, 9-Item (PHQ-9), indicating at least mild depressive symptoms (Kroenke et al., 2001). All participants were smartphone owners (either iPhone or Android), demonstrated literacy in English, had an active email address, and were physically inactive (self-reported less than 90 minutes of moderate-to-vigorous PA (MVPA) per week). Concomitant care and interventions were permitted, provided that their dosage remained stable throughout the program. Individuals who met any of the following criteria were excluded: self-147 reported physical disability or health condition that prevents exercise, self-reported active psychosis or mania, active suicidal thoughts, severe cognitive impairment, being currently pregnant, or expected a major absence during the intervention. All participants provided informed consent prior to initiating the study (Harris et al., 2009). 7.2.4 Recruitment Participants were recruited from November 15, 2024 to March 26, 2025. Recruitment methods included an electronic posting on a local research studies database (https://reachbc.ca/); paper posters at three community spaces; and contacting a total of 28 local non-profit mental health associations and free public depression advocacy groups to request dissemination of study information. Interested volunteers were directed to an eligibility screening questionnaire on REDCap (see Appendices I; L; N-T for all questionnaires used in the current study). 7.2.5 Procedures After providing informed consent, the researcher (YT) registered participants who completed the pre-intervention assessments on Pathverse. Participants were then provided with a MoodMover user guide and an instructional letter outlining preparatory tasks for the orientation session. On the first day, participants attended a 15-minute one-on-one orientation via Zoom on their smartphones. Five orientation sessions were recorded. According to the study protocol, orientation session recordings were to be transcribed immediately and assessed by a research assistant using a fidelity checklist to enable timely modifications for improving treatment fidelity after each session (Teresi et al., 2022). However, this protocol was not feasible due to the short intervals (e.g., less than one hour) between sessions. As an alternative, the researcher (YT) took notes after each orientation session, cross-checked them with the fidelity checklist, and implemented improvements (e.g., revised the script to ensure conciseness and clarity) in 148 subsequent sessions. The recordings were later reviewed to verify the accuracy and completeness of the fidelity assessments based on the session notes. After completing the intervention, participants were invited to complete post-intervention assessments on REDCap. Along with the invitation, participants were reminded of the optional follow-up, which included continued tracking of their app engagement and step data for an additional 9 weeks. Participants who chose not to continue could either delete the app or inform the researcher (YT) of their decision to withdraw. Those who opted in were approached on the final day of the follow-up period to complete an intervention satisfaction questionnaire and sync their step data with MoodMover, allowing for PA data collection throughout the entire follow-up period. Participants were compensated with a $10 CAD Amazon e-gift card for completing assessments at each time point. All procedures were conducted entirely remotely. 7.2.6 Measures Further details are available in the published protocol paper (Tang, Gierc, La, Liu, et al., 2025). 7.2.6.1 Demographic and clinical information Demographic data and clinical information, such as sex and gender, race/ethnicity, age, other ongoing treatments relevant to depression management, substance use (e.g., cannabis), and current and past usage of PA apps or devices, were collected. 7.2.6.2 Primary feasibility and acceptability outcomes The traffic light system (Avery et al., 2017) was used to determine progression to a randomized controlled trial (RCT) for efficacy testing. The decision was based on the lowest-performing criterion among the following metrics: (i) recruitment, (ii) adherence, (iii) usability, and (iv) retention (Table 7.1). 149 Table 7.1. Predetermined criteria for progression. Definition Green zone Yellow zone Red zone Action Proceed with no changes Proceed with amendments Stop intervention development Metric Recruitment Proportion of eligible participants who provided consent > 65% 40-64% < 39% Adherence Proportion of participants who completed â¥ 5 major lessons > 70% 40-69% < 39% Usability Proportion of participants reporting a score > 5 on the MAUQ > 70% 40-69% < 39% Retention Proportion of participants who completed the baseline questionnaire > 70% 40-69% < 39% Note. MAUQ: mHealth app usability questionnaire Recruitment. The recruitment rate was defined as the proportion of eligible individuals who provided consent in the program. Additionally, the recruitment duration, sources, and the number of sites where posters were displayed were documented. Adherence. Intervention adherence was defined as the proportion of participants completing at least five of the eight major lessons. A few additional engagement metrics, such as average time spent on major and complementary modules and total app use duration (excluding time spent on features under the menu tab), were extracted from the Pathverse admin portal. Participants were classified as âcomplete usersâ (all eight major modules completed), âincomplete usersâ (2-7 modules completed), or ânonusersâ (no app use after orientation/introduction module). Participants inactive for two weeks were contacted via email for a check-in, and any reported reasons for non-adherence were documented. Usability. The adapted patient version of the mHealth app usability questionnaire (MAUQ) (Tang, Gierc, La, Kim, et al., 2025; Zhou et al., 2019) for standalone apps was utilized, consisting 150 of thirteen items. Participants rated each item on a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree). An average score of 5 or higher was considered indicative of acceptable usability (Tang, Gierc, La, Kim, et al., 2025). Retention. Retention was defined as the proportion of participants who completed both pre- and post-intervention assessments. Reasons for early discontinuation and any reported adverse events were documented. Compliance and satisfaction. Compliance with the graded goal-setting protocol and user satisfaction were evaluated as indicators of feasibility and acceptability. Participants who followed the goal-setting instructions at all designated time points (weeks 2, 4, and 6) were classified as having âfull complianceâ. Those who followed instructions at one or two time points were considered to have âpartial complianceâ, while those who did not follow instructions at any time were categorized as having âno complianceâ. User satisfaction was measured using the mHealth Satisfaction Questionnaire (Melin et al., 2020), with scores ranging from 14 to 70, where higher scores indicate greater satisfaction. Participants were also asked to list three things they liked and three things they disliked about the intervention. Acceptability of outcome measures. The response rate and the amount of complete data for self-reported outcomes were reported. 7.2.6.3 Secondary outcomes Physical activity (behavioural outcome). Daily step counts were obtained from the smartphoneâs built-in step-tracking feature and downloaded via the Pathverse admin web portal. Self-reported PA was assessed using the Canadian Physical Activity Adult Questionnaire (PAAQ) (Colley et al., 2018) at both baseline and post-intervention. In addition, participants indicated whether they had connected a smartwatch or fitness tracker to Health (iOS) or Google Fit (Android) 151 throughout the program. They also reported the frequency (almost always, sometimes, seldom) with which they carried their phone or wore their smartwatch during non-sedentary waking hours on both weekdays and weekends. Depressive symptoms (clinical outcome). The PHQ-9 was employed to measure self-reported depressive symptoms (Kroenke et al., 2001). Participants rated nine items on a 0-to-3 Likert scale, yielding a total score between 0 and 27, with higher scores indicating greater symptom severity. Depression severity was classified as follows: 5-9 (mild), 10-14 (moderate), 15-19 (moderately severe), and 20-27 (severe) (Kroenke, 2021). A clinically meaningful improvement was defined by McMillan et al. (2010) as a 5-point reduction on the PHQ-9 and a shift from a baseline score of â¥ 10 to â¤ 9 post-intervention. As individuals with mild depressive symptoms were eligible for this study, this criterion was applied only to participants with baseline scores of 10 or higher. Anxiety. Anxiety symptoms were assessed using the Generalized Anxiety Disorder, 7-Item (GAD-7) (Spitzer et al., 2006). The GAD-7 consists of seven items rated on a 0-to-3 Likert scale (Löwe et al., 2008), with a maximum score of 21. Higher scores indicate more severe anxiety. Anxiety severity was classified as follows: 5-9 (mild), 10-14 (moderate), 15-21 (severe) (Löwe et al., 2008). Sleep quality. The 19-item Pittsburgh Sleep Quality Index (PSQI) (Buysse et al., 1989) was used to assess sleep quality. Participants rated items on a 0-to-3 Likert scale, with scores summed across seven components (e.g., sleep duration and sleep latency) to yield a total score ranging from 0 to 21. Higher scores indicate poorer sleep quality, with a total score greater than 5 signifying poor sleep quality. 152 Functional disability. World Health Organization Disability Assessment Schedule 2.0 (WHODAS 2.0) (Ãstün, 2010) was used to evaluate participants' health and ability to perform activities across six functional areas (e.g., cognition and mobility). The WHODAS 2.0 consists of 12 items, where participants estimate the extent of their disability over the past 30 days using a 5-point Likert scale. The total score ranges from 0 to 100, with higher scores indicating greater disability. M-PAC constructs. Items to assess M-PAC constructs were primarily derived from Tang et al. (2023), Rhodes and Lim (2016), and Wilson and Muon (2008). Affective and instrumental attitudes were assessed using three 7-point bipolar scales. Perceived opportunity and capability were evaluated with three 5-point Likert scales, ranging from 1 (strongly disagree) to 5 (strongly agree). Intention strength was measured with three items (e.g., "I am committed to engaging in physical activity over the next month"), rated on a 5-point Likert scale from 1 (strongly disagree) to 5 (strongly agree). Additionally, decisional intention was assessed with one item regarding the goal to increase daily steps by 3000 above baseline for most days per week. Participants responded with a binary (Yes/No) answer. Regulations were measured using 5-point Likert scales with three items related to self-monitoring, goal-setting, and action planning. Both habit and identity were measured with three items on a 5-point Likert scale, respectively. Further details are described in the protocol (Tang, Gierc, La, Liu, et al., 2025). 7.2.7 Sample size justification Overall, a pragmatic sample size range of 20-36 participants was estimated and adopted, following commonly accepted sample size rule of thumbs for feasibility trials (Julious, 2005; Kieser & Wassmer, 1996) and recommendations for pilot RCT intervention arms using the "traffic light" progression criteria (Lewis et al., 2021). 153 7.2.8 Statistical analysis All analyses were performed using SPSS ver. 29 (IBM Corporation) or R (version 4.2.2) via RStudio (version 2024.12.0+467) (Boston, MA, USA). Descriptive statistics were presented for all measures. The strategies for addressing missing data were determined by evaluating the degree and pattern of missingness. When reporting the results, participants were grouped into: (i) Study completers, defined as those who remained enrolled through week 9, regardless of their level of weekly engagement; and (ii) Non-completers, which included non-starters (those who enrolled but did not attend the orientation session) and early dropouts (those who used the app briefly before discontinuing). Regression-based analyses were used to explore the influence of potential participant-level factors (e.g., sex, age) on feasibility results, with separate models for each factor. Changes in secondary outcomes (e.g., PA, depression, and M-PAC constructs) over time were assessed using paired t-tests for continuous data, Wilcoxon signed-rank tests for non-normal distributions, and McNemarâs test for binary outcomes. Corrections for multiple comparisons were not applied, as the analyses were exploratory in nature. Pearsonâs and Spearmanâs correlation analyses were conducted to examine the associations between changes in PA and changes in depressive symptoms. The results of secondary outcomes were reported with 75%, 85%, and 95% confidence intervals (CIs) for estimation purposes (Lee et al., 2014; Walters, 2009). The predetermined clinically meaningful decrease in depression was also incorporated into the interpretation for those with baseline PHQ-9 scores of 10 or higher. 7.3 Results 7.3.1 Recruitment Of the 215 adults who self-referred to MoodMover (Figure 7.1), 197 (91.6%) were recruited through REACH BC. During the first month, recruitment progressed at a promising pace: 154 122 individuals expressed interest, 75 completed the screening survey, 27 were deemed eligible, and 16 enrolled. However, outreach to advocacy groups was less effective, with 0 of the 15 contacted groups responding. Recruitment slowed during the holiday period (December 15, 2024, to January 5, 2025) and declined further after two months. In response, additional advocacy groups were contacted in January and February, with 4 out of 13 responding, resulting in 15 referrals. An additional three participants were recruited through posters. Nine potential participants were deemed ineligible due to their prior participation in the previous usability study (Tang, Gierc, La, Kim, et al., 2025). A total of 70 participants did not finish the screening process. Of the remaining 136 individuals screened, 51 physically inactive adults with at least mild depression symptoms were deemed eligible. Thirty-two participants provided informed consent, resulting in a recruitment rate of 62.7% (32/51), which was slightly below the predefined progression criterion threshold (>65%). 7.3.2 Baseline characteristics Three participants did not initiate the pre-intervention assessments, and one participant initiated but completed less than 5% of items. This resulted in a pre-intervention response rate of 90.6% (29/32) and a pre-intervention completion rate of 87.5% (28/32). Appendix W presents baseline characteristics of the 28 participants and compares study completers with non-completers. The participants had a mean age of 39.8 years (SD 13.4). Completers appeared older than non-completers (44.1 vs 34.0 years), although no statistical comparisons were conducted. Twenty participants (71.4%) self-identified as women, four (14.3%) as men, three (10.7%) as non-binary, and 1 (3.6%) preferred not to answer. Most participants were White (18/28, 64.3%), and half had obtained a Bachelorâs degree or higher. A total of 17 participants (60.7%) self-reported a diagnosis of MDD, with most of them (13/17, 76.5%) having 155 had the diagnosis for more than two years. Most participants (24/28, 85.7%) were currently taking antidepressants, with the majority of those (14/24, 58.3%) having been on them for more than two years. A total of 23 participants (82.1%) were currently using or had previously used other PA apps or devices. Figure 7.1. Participant flow diagram. 156 7.3.3 Feasibility and acceptability Feasibility and acceptability results are presented in Appendix X. Adherence. A total of 21 participants attended orientation sessions and received the intervention. Intervention adherence, defined as the proportion of participants completing at least five major lessons, was 66.7% (14/21), falling within the amber zone of the predetermined progression criterion. No participants were classified as ânonusersâ (i.e., no app use following the orientation session). Ten participants (47.6%) were identified as âcomplete usersâ, having completed all major lessons, while the remaining 11 participants (52.4%) were classified as âincomplete usersâ, with a mean of 3.6 major lessons (SD = 2.1) completed. Among those who met the adherence criterion, the average number of completed major lessons was 7.4 (SD = 1.2). On average, participants spent 8.0 minutes (SD = 4.7) and 3.9 minutes (SD = 3.0) on each major and complementary lesson, respectively. All participants, except one, completed the first two major lessons and the first complementary lesson. Throughout the 8-week intervention phase (weeks 2-9), participants engaged with the app for an average of 102.6 minutes (SD = 65.7), with app activity recorded on 55.0% of days (30.8/56 days; SD = 15.2) with at least one use. Figure 7.2 demonstrates how the duration of weekly app use changed over the intervention period (see Appendix Y for details). Participants synced their steps a mean of 28.5 times (SD = 17.1; range = 0-63). Most participants (19/21, 90.5%) synced their steps at least 12 times, except for two Android users who experienced serious technical issues related to the step syncing feature. 157 Figure 7.2. Weekly app use duration during the intervention phase. Note. Time spent on features under the menu tab, including Q&A, resources, survey history, and my points, is not included due to data unavailability. Of the 21 app users, 18 (85.7%) used the goal-setting feature, 11 (52.4%) used the action planning feature, and 17 (81.0%) used the exercise logging and mood tracking feature. Ten participants (47.6%) used the community forum to report technical issues (n=6) or share their exercise experiences (n=5). Participants completed a mean of 6.5 action plans (SD = 17.8, range 0-82) and self-logged an average of 6.7 exercise sessions (SD = 9.9; range 0-36). Each participant recorded an average of 4.5 light PA sessions (SD = 7.6) and 2.2 MVPA sessions (SD = 3.8), accumulating 135.7 minutes of light PA (SD = 176.7) and 109.1 minutes of MVPA (SD = 207.5), respectively. Participants reported a mean mood rating of 2.7 (SD = 0.4) on a 0-to-4 scale after exercising. Compliance. Participants modified their step goals an average of 2.3 times (SD 1.5; range 0-5). Nine out of 21 participants (42.9%) demonstrated full compliance, adhering to all instructions across the designated time points to set new step goals. Another nine participants (42.9%) were classified as partially compliant, adjusting their step goals at one or two time points. Specifically, 158 17 participants (81.0%) set a step goal in week 2, while 15 (71.4%) and 9 (42.9%) modified their goals as instructed in weeks 4 and 6, respectively. Three participants (14.3%) did not modify their step goals at any time and were categorized as non-compliant. Retention rates. A total of 16 participants completed both pre- and post-intervention questionnaires, yielding a retention rate of 57.1% (16/28), which falls within the amber zone of the progression criterion for retention. Reported reasons for discontinuation included being too busy (n=2), weather-related challenges (n=1), technical issues (n=1), mental health issues (n=1), and falling behind in the lessons (n=1). Adverse events. One participant reported an adverse event of feeling pressured to engage in activities that did not feel natural (e.g., self-set action plan reminders were intrusive due to a frequently changing schedule). No other adverse events were reported. Usability. Half (8 of 16) of participants rated MoodMover as 5 or greater on MAUQ, with an average mean score of 5.01 (SD = 1.02) - equivalent to the amber zone based on the predetermined criterion on usability. Participants rated MoodMover as having higher usability in terms of interface design and overall satisfaction (M = 5.00; SD = 1.07) and usefulness (M = 5.44; SD = 1.31), while lower usability scores were reported for ease of use (M = 4.94; SD = 1.03). A total of 10 participants (47.6%) experienced varying levels of technical issues. Satisfaction. Usersâ satisfaction levels ranged from 36 to 66, with a mean of 53.56 (SD = 8.11). 7.3.4 Factors affecting app engagement, usability and satisfaction Age was consistently and positively associated with app engagement metrics (i.e., total app use duration and active app use days during intervention phase, major lesson completion, and average minutes spent on each major lesson), usability (MAUQ), and satisfaction metrics across 159 the 75%, 85%, and 95% CIs. For example, the association between age and total app usage duration (mins) was B=3.45 (75% CI 2.47-4.44; 85% CI 2.21-4.69; 95% CI 1.72-5.18). The association between age and satisfaction was B=0.38 (75% CI 0.22-0.53; 85% CI 0.18-0.58; 95% CI 0.10-0.66). Additionally, participantsâ BMI, baseline depression levels, and baseline self-reported MVPA levels were associated with certain app engagement, usability, and satisfaction metrics. For instance, the association between baseline depression and usability was B=-0.70 (75% CI -0.96 to -0.43; 85% CI -1.03 to -0.36; 95% CI -1.17 to -0.23). Full results are presented in Appendix Z. Results involving categorical variables should be interpreted with caution due to instability and wide variability caused by levels with very small sample sizes (n<5). A total of 16 participants provided open-ended feedback on the features they liked and disliked about the app. The most frequently cited positive aspects included reminders (n=7), ramped goals (n=6), and lessons (n=6). Conversely, the most commonly mentioned areas for improvement were step syncing (n=9; including the need to carry their phone, n=3), navigation (n=3), exercise logging (n=3), and the user interface (n=3). 7.2.5 Secondary outcomes Table 2 presents changes in smartphone-based step counts and other self-reported measures from baseline to post-intervention. Of the 21 participants, 18 successfully synced their step data to Pathverse, with a mean of 4378.4 daily steps (SD = 2114.5; n=18) recorded in week 1. By week 9, the average changed to 5204.0 steps (SD = 2322.5; n=14), with a median change of -131.5 steps. The CIs suggest that this change was not statistically significant. Figure 3 displays a heatmap of each participantâs weekly smartphone-based step counts across the 9-week period. A total of three participants (3/14, 21.4%) achieved an increase of at least 3,000 steps over the nine weeks. All three reported linking their smartwatches with the Health app (iOS) or Google Fit (Android), and 160 two of them wore their smartwatches consistently on both weekdays and weekends. Among the remaining 11 participants who provided valid step data across all nine weeks, five did not use smartwatches, and three reported seldom or only sometimes carrying their phones. Of the six who linked smartwatches, only two reported wearing them consistently. In contrast to the step count data, participants self-reported engaging in a mean of 82.9 minutes of MVPA per week at baseline (SD = 86.4; n=28), which increased to 206.8 minutes (SD = 193.2; n=16) post-intervention, with a median difference of 57.5 minutes. After excluding one extreme post-intervention value (>3 SD from the mean), the mean was 167.3 minutes (SD = 114.8) per week, yielding a mean difference of 71.3 minutes. The corresponding 75%, 85%, and 95% CIs were 28.10 to 114.43 minutes, 16.48 to 126.05 minutes, and -5.88 to 148.41 minutes, respectively. Depression scores decreased from pre-intervention (M = 14.0; SD = 4.4) to post-intervention (M = 7.9; SD = 5.7), with a mean difference of -5.31. The 75%, 85%, and 95% CIs were -7.04 to -3.59, -7.50 to -3.12, and -8.39 to -2.24, respectively, indicating a robust effect. Figure 7.3. Heatmap of smartphone-based step counts over time. Note. Each row represents one participant. Red borders indicate participants with >3000 daily 161 steps/week increase at Week 9 (created by ChatGPT). Among participants with baseline PHQ-9 scores â¥ 10 (n=12), nine (75%) achieved a clinically meaningful reduction of â¥ 5 points, shifting from â¥ 10 at baseline to â¤ 9 post-intervention. Among these 12 participants, the paired t-test revealed a mean difference of -6.50. The 75%, 85%, and 95% CIs were -8.62 to -4.38, -9.20 to -3.80, and -10.34 to -2.66, respectively. All M-PAC constructs showed either significant or non-significant increases at post-intervention, except for intention strength and decisional intention. Intention strength showed a small, non-significant decrease (mean difference = -0.08), with all CIs crossing zero. Decisional intention demonstrated a non-significant decrease in the likelihood of endorsing âYesâ post-intervention (OR = 0.33), with all CIs crossing one. Regulation exhibited the largest and statistically significant improvement, with a mean difference of 0.81. The 75%, 85%, and 95% CIs were 0.54 to 1.08, 0.47 to 1.15, and 0.34 to 1.29, respectively. 7.2.6 Correlations between changes in PA and depression A Pearson correlation indicated a moderate negative association between changes in smartphone-based step count and depression (r=-0.55; 75% CI -0.75 to -0.27; 85% CI -0.78 to -0.18; 95% CI -0.84 to -0.03). In contrast, a Spearman rank-order correlation, bootstrapped with 2000 resamples, indicated a weaker, non-significant association (Ï=-0.29; 75% CI -0.59 to 0.06; 85% CI -0.66 to 0.15; 95% CI -0.76 to 0.31). 162 Table 7.2. Changes in smartphone-based step counts and other self-reported measures from baseline to post-intervention. Baseline (n=28) Post-intervention (n=16) Pre-Post changes mean (SD)/n (%) mean (SD)/n (%) Mean/Median difference/Odds Ratio 75%CI 85%CI 95%CI Smartphone-based steps 4378.4 (2114.5, n=18) 5204.1 (2322.5, n= 14) -131.5a [-760.05, 385.36] [-944.14, 394.14] [-947.50, 859.57] PAAQ 82.9 (86.4) 206.8 (193.2) 57.5a [-17.50, 170.00] [-25.00, 185.00] [-27.44, 210.00] Post PAAQ (n=15) 167.3 (114.8) 71.27 [28.10, 114.43] [16.48, 126.05] [-5.88,148.41] PHQ-9 14.0 (4.4) 7.9 (5.7) -5.31 [-7.04, -3.59] [-7.50, -3.12] [-8.39, -2.24] GAD-7 15.5 (6.3) 10.0 (5.7) -7.00 [-7.00, -7.00] [-10.50, -3.50] [-14.00, 0.00] WHODAS 2.0 26.5 (9.8) 24.4 (7.6) -1.38 [-3.47, 0.72] [-4.04, 1.29] [-5.11, 2.36] PSQI 12.3 (3.4) 11.3 (2.9) -1.00 [-1.00, 0.00] [-1.00, 0.50] [-1.00, 1.00] M-PAC constructs Affective attitudes 4.61 (1.41) 5.77 (0.90) 0.65 [0.42, 0.87] [0.36, 0.93] [0.24, 1.05] Instrumental attitudes 6.20 (0.76) 6.67 (0.49) 0.33a [0.33, 0.50] [0.17, 0.67] [0.00, 0.67] Perceived capability 3.76 (0.68) 4.00 (0.39) 0.17a [-0.33, 0.50] [-0.33, 0.66] [-0.33, 0.66] Perceived opportunity 4.23 (0.59) 3.96 (0.66) 0.00a [-0.50, 0.00] [-0.66, 0.00] [-0.66, 0.00] Intention strength 3.92 (0.54) 4.06 (0.51) -0.08 [-0.26, 0.09] [-0.30, 0.13] [-0.39, 0.22] Decisional intention 0.33b [0.08, 1.06] [0.06, 1.30] [0.03, 1.86] Yes 23 (82.1%) 10 (62.5%) No 5 (17.9%) 6 (37.5%) Regulations 2.64 (0.91) 3.52 (0.47) 0.81 [0.54, 1.08] [0.47, 1.15] [0.34, 1.29] Habit 2.03 (0.93) 3.00 (0.87) 0.33a [0.00, 1.00] [0.00, 1.00] [0.00, 1.50] Identity 2.12 (0.78) 2.92 (0.95) 0.16a [0.00, 0.66] [0.00, 1.00] [-0.16, 1.33] Note. GAD-7: Generalized Anxiety Disorder 7; M-PAC: Multi-Process Action Control; PAAQ: Canadian Physical Activity Adult Questionnaire; PHQ-9: Patient Health Questionnaire â 9 items; PSQI: Pittsburgh Sleep Quality Index; WHODAS 2.0: World Health Organization Disability Assessment Schedule 2.0 aMedian difference; The Wilcoxon signed-rank test was used due to violation of normality. Bootstrap sampling (2,000 iterations) was used to estimate the 75%, 85%, and 95% confidence intervals. bOdds ratio; For binary outcome, McNemar's Test was conducted. 163 Additionally, Pearson correlation revealed a strong negative association between changes in self-reported MVPA and depression (r=-0.77; 75% CI -0.87 to -0.60; 85% CI -0.89 to -0.54; 95% CI -0.91 to -0.44). Similarly, Spearman rank correlation indicated a strong negative relationship (Ï=-0.85; 75% CI -0.91 to -0.73; 85% CI -0.93 to -0.69; 95% CI -0.95 to -0.61), with bootstrapping based on 2000 resamples. After excluding the identified outlier, both Pearson and Spearman correlations indicated similarly strong negative associations, with coefficients of r=-0.80 and Ï=-0.82, respectively (Appendix AA). 7.4 Discussion This proof-of-concept and feasibility study represents an early stage in the development of MoodMover, an app-based intervention designed to increase PA among individuals with depression. All four feasibility criteria fell within the amber zone, indicating suboptimal feasibility and the need for modifications. Despite this, self-reported MVPA increased substantially, and clinically meaningful improvements in depressive symptoms were observed among most study completers who had self-reported at least moderate levels of depressive symptoms at baseline. Although no significant changes were detected in smartphone-based step counts or several secondary outcomes, the study was not powered to detect such effects. Overall, these findings support cautious progression to the next subphase of the ORBIT model, Phase IIc: Efficacy, to evaluate an upgraded version of MoodMover with the necessary amendments in a fully powered RCT. 7.4.1 Feasibility and acceptability Our study demonstrated a recruitment rate of 62.7%, slightly below the predetermined target of 65%. This aligns with a previous digital health intervention with an mHealth app for depression and anxiety, which reported a recruitment rate of 63.2% among eligible participants 164 (Tsirmpas et al., 2022). Although the criterion was not met, recruitment remained promising, particularly given the relatively modest nature of the recruitment strategy, which largely relied on the REACH BC website. This approach limited exposure to individuals not registered on the platform, thereby reducing the broad accessibility typically offered by mHealth interventions. Limited engagement from advocacy groups and mental health associations further constrained access to individuals more likely to be interested in self-help programs. Involving mental health partners early in the design and development process may improve reach and promote the scalability of the intervention. Future studies with a larger sample size could incorporate social media recruitment (e.g., Facebook, X, and Reddit) and establish stronger partnerships with healthcare providers and mental health associations to enhance outreach and engage hard-to-reach populations (Bremer & Sarker, 2023). A retention rate of 57.1% (16/28) was observed in the current study. Although this fell within the amber zone of the predetermined progression criterion, it was either comparable to or better than the attrition rates reported in previous digital interventions for depression. A review by Richards and Richardson (2012) reported a 57% attrition rate in computer-based psychological treatments for depression, with substantially higher dropout rates (74%) observed in unsupported interventions. More recently, Linardon and Fuller-Tyszkiewicz (2020) found a mean attrition rate of 32.1% (95% CI 17.0 to 52.2%) at long-term follow-up (>8 weeks) across smartphone-based interventions for depressive symptoms, although substantial heterogeneity was noted (I²=96%). Torous et al. (2020) found a lower pooled dropout rate of 26.2%, but also reported considerable heterogeneity (I²=93.4%) and significant publication bias (Eggerâs t=3.9, P < 0.001). When adjusted for publication bias, the estimated attrition rate increased to 47.8% (95% CI 35.8 to 60.0%) (Torous et al., 2020). Given that MoodMover provided no ongoing support beyond an initial 165 orientation session and access to technical assistance, the observed retention rate appears promising. In addition, more than half of the attrition (7/12, 58.3%) occurred prior to the orientation session. Two participants cited an overwhelming enrollment process as their reason for withdrawal, suggesting that barriers unrelated to the intervention content may have contributed to early dropout. When excluding participants who did not receive any intervention, the retention rate increased to 76.2% (16/21). This figure aligns with retention rates observed in previous app-based interventions that included mood monitoring features, where dropout rates averaged 18.4% (95% CI 10.9 to 29.4%) (Torous et al., 2020). These findings highlight the importance of refining the MoodMover research protocol, particularly by simplifying the enrollment process to enhance participant retention. For instance, replacing text-based preparation guidance for orientation with a short instructional video could improve accessibility. While a comprehensive user guide can be informative, it may feel overwhelming for participants, especially those with more severe depressive symptoms. Embedding the guide within the app as an optional resource could reduce perceived burden while maintaining usability. More feasibility trials can be conducted to explore optimal enrollment and strategies to enhance retention before moving forward to a large RCT. Similarly, the adherence rate of the current version of MoodMover (66.7%) fell within the amber zone of the pre-set criterion (40-70%), indicating that potential modifications may be needed before proceeding to an RCT. Although it did not reach the green zone, the adherence rate was higher than that of most previous mHealth apps for depression, which have reported low adherence rates (<50%) in prior reviews (Linardon & Fuller-Tyszkiewicz, 2020; Rismawan et al., 2021; Serrano-Ripoll et al., 2022). For trials targeting depression, Linardon and Fuller-Tyszkiewicz (2020) found a mean percentage of ânonusersâ of 41.2% (k = 2; range: 25â58%), a mean percentage of âcomplete adherersâ of 34% (k = 3; range: 14â68%), and mean numbers of 166 logins ranging from 14 to 21 times (k = 3). In the current study, no participants were classified as nonusers after completing the orientation session. Additionally, a relatively high rate of complete users was observed, with 47.6% completing all major modules. This may be attributed to several strategies implemented to enhance adherence, such as gamification points with modest incentives for lesson completion, the involvement of end users in the development and design process prior to the launch of this trial, and the integration of a peer-support community forum (Torous et al., 2018). Nevertheless, despite usability testing conducted prior to the trial, more than half of the participants encountered different levels of unexpected technical problems during the program, which may have adversely affected their adherence and app engagement. This was reflected in the fact that only half of the participants reported an acceptable level of usability, and satisfaction ratings varied considerably. User-friendliness and technical stability have been identified as key factors influencing adherence to mHealth interventions (Jakob et al., 2022). The functionality of MoodMover should therefore be further refined and rigorously tested prior to a future trial. Additionally, future research should further explore and test strategies that may enhance adherence. Promisingly, participants engaged with the app on an average of 30.8 days, suggesting relatively sustained engagement compared to typical patterns in health app usage in real-world settings (Baumel et al., 2019). Weekly app use duration declined over time, with sharper drops from weeks 2 to 3 and weeks 5 to 6, then appearing to stabilize between and after these periods. This declining usage trajectory over time is commonly observed in smartphone-based interventions (Linardon & Fuller-Tyszkiewicz, 2020). The large standard deviations indicated substantial variability in engagement, with some participants using the app considerably more than others. This pattern was consistently reflected in the use of specific app features, which varied in both frequency and number of users. Step tracking and exercise logging and mood tracking were 167 the most commonly utilized features. In contrast, fewer participants engaged with the action planning and community forum components, and usage of these features was limited and inconsistent. MoodMover included daily prompts to sync steps, log exercise sessions, and reflect on mood. These reminders aligned with the features that participants engaged with most frequently. This may suggest that tailoring notifications to specific app features may reinforce user engagement and support continued use of those components. Future studies could explore adaptive notification strategies and personalized messaging as tools for improving engagement and encouraging behaviour change among individuals with depression. Moreover, total app use time and its variability across participants were likely underestimated due to the unavailability of data on time spent in features under the menu tab, particularly the Resources section, which includes exercise videos. The Pathverse platform could be further improved to provide more accurate and comprehensive metadata. Regression analyses revealed that age was the only participant-level factor that consistently influenced app engagement, usability, and satisfaction in the current study. This finding aligns with the observed trend that younger participants were more likely to drop out during the intervention. Due to limited information on reasons for early discontinuation in the current study, we were unable to offer a proper explanation for this pattern. Future research should further investigate factors influencing engagement, implement systematic tracking of dropout reasons, and explore strategies specifically aimed at supporting sustained engagement, particularly among younger users. 7.4.2 Behavioural and clinical outcomes Only three participants achieved the behavioural goal of increasing their daily step count 168 by 3,000 steps per week, as measured by smartphone-generated data. A likely contributing factor was the inconsistent use of tracking devices across the intervention: most participants (7 of 11) who failed to achieve a 3,000-step increase reported they inconsistently carried their smartphone and/or inconsistent use of their smartwatch. Consequently, reported step count data is not an accurate representation of actual daily steps. In addition, 56.3% of participants reported concerns related to the step syncing component, including perceived inaccuracy and the inconvenience of needing to carry their phones. This interpretation is supported by discrepancies with self-reported MVPA: participants reported an average increase of 57.5 minutes of MVPA per week at post-intervention, which increased to 71.3 minutes per week after excluding one extreme outlier. The latter results were statistically significant at the 75% and 85% CIs, though not at the 95% CI. Future evaluation of MoodMover should include device-based PA measurement through accelerometry, with complementary devices (e.g., Fitbits) to facilitate participant self-monitoring. More promisingly, the current study found a mean reduction of 5.31 points on the PHQ-9, with statistically significant results across all CIs. Among participants with moderate to severe depressive symptoms at baseline (PHQ-9 â¥ 10), 75.0% reported a clinically meaningful reduction at post-intervention, with a mean reduction of 6.50 points on the PHQ-9. The CIs excluded zero but included the predetermined threshold of five, suggesting a potentially clinically meaningful effect (Lee et al., 2014). In addition, improvements in PA levels, particularly self-reported MVPA, were moderately to strongly associated with reductions in depressive symptoms. In terms of M-PAC constructs, affective attitudes and regulations showed the largest significant increases, with mean changes of 0.65 and 0.81, respectively. The two reflexive constructs, habit and identity, demonstrated small increases, shifting from a level of disagreement to a more neutral stance. As the regulatory and reflexive constructs are critical for behavioural maintenance (Rhodes, 2017) 169 and given the potential importance of perceived emotional benefits for individuals with depression (Glowacki et al., 2017), the favourable changes in PA and depression may be attributable in part to the intervention. While these findings were not definitive due to the small sample size, they are encouraging and support progression to the next phase of the ORBIT model, where a larger trial with sufficient power could be conducted to demonstrate statistical significance. 7.4.3 Strengths and Limitations A key strength of this study was the systematic development of MoodMover, guided by the M-PAC framework, following two established behaviour change intervention development frameworks. This approach also enabled the integration of theory-driven BCTs. The current study followed the BCTTv1 (Michie et al., 2013) to report the applied BCTs, including intended and unintended, in a detailed and transparent manner. Using standardized BCT definitions enhances the potential for future studies to synthesize evidence on the effectiveness of specific techniques in digital PA change interventions for depression. In addition, this study reported a range of objective app engagement data that provided valuable insights into the feasibility and acceptability of MoodMover. Given the multicomponent nature of most mHealth interventions, such data are essential for guiding future refinement and optimization of the app. Another strength of this study was the use of validated measurement tools, which enhances the reliability and comparability of the findings. In addition, the integration of device-based PA tracking via smartphones or smartwatches allowed for continuous monitoring of changes in activity levels throughout the intervention. However, this approach had an important limitation. Smartphone-based tracking depends on participants consistently carrying their phones, which likely resulted in an underestimation of actual activity levels. Further limitations should also be noted. The generalizability of the findings is limited. All 170 participants were required to own a smartphone, which may have excluded individuals from lower socioeconomic backgrounds. However, smartphone ownership in Canada is relatively high, with an estimated 84.4% of the population aged 15 and over owning a smartphone as of 2020 (Statistics Canada, 2020). In addition, while improvements in self-reported PA and reductions in depressive symptoms were observed, the small sample size limits the ability to draw firm conclusions about the efficacy of MoodMover. Moreover, as noted earlier, there was a deviation from the protocol regarding the fidelity checking process due to short intervals between orientation sessions. While this deviation was unlikely to have affected the feasibility, acceptability, or preliminary effects of MoodMover, a more practical fidelity checking procedure should be developed for future studies. This may involve scheduling orientation sessions with sufficient spacing between participants or incorporating automated, interactive instructions in Pathverse, which could ultimately eliminate the need for live orientations and reduce participant burden. Additionally, technical issues encountered during both the orientation sessions and the intervention phase may have significantly impacted the feasibility and proof-of-concept results. Furthermore, only 25% of participants identified as male, and just two male participants completed the study. This highlights the need to evaluate the feasibility and acceptability of MoodMover in a more diverse and representative population. Importantly, although single-arm trials offer several advantages, such as reduced ethical and implementation challenges, lower resource and sample size requirements, and the ability to collect intervention response data quickly, they also have key limitations (Wang et al., 2024). The absence of a control group meant that we were unable to assess the feasibility and acceptability of randomization and blinding, and the interpretation of results cannot be attributed solely to the intervention, thereby reducing the strength of the conclusions (Wang et al., 2024). Lastly, the long-term maintenance of intervention effects remains unknown. Future trials should 171 incorporate structured follow-up assessments to examine the sustainability of observed outcomes. 7.5 Conclusions Overall, the proof-of-concept findings are encouraging and support the potential of MoodMover in increasing PA among individuals with depression. However, the usability and feasibility of the current version were suboptimal, indicating that a larger-scale RCT should proceed only after necessary amendments have been implemented and evaluated. This suggests moving backward to the previous phase of the ORBIT model (Phase Ib: Refine) to refine MoodMover. One or more feasibility studies assessing the upgraded version of MoodMover are recommended to further examine its usability and feasibility prior to progressing to a full-scale trial. Acknowledgements We are grateful to Henry La for providing technical support for the app. The authors thank Victoria Whiteford for her assistance with BCT coding and Sarah Chang for her help with participant recruitment. The authors also acknowledge mental health care providers and the advocacy group facilitators for sharing study information with their clients. This study was funded by a Michael Smith Health Research BC Research Trainee Award to MG and a Canadian Institutes of Health Research (CIHR) Foundation Award to GF. We used the generative AI tool ChatGPT (OpenAI, 2024) to create Figures 2 and 3. The summary of ChatGPT transcripts is made available as Appendix AB. 172 Chapter 8: Discussion 8.1 Overview Over the past few decades, depressive disorders have been recognized as major contributors to the global burden of disease (Liu et al., 2020; Zhang et al., 2024). Since starting my PhD in 2020, the same year the COVID-19 pandemic emerged, numerous studies have documented a global rise in mental health concerns. A meta-analysis of population studies from 49 countries found that self-reported depression increased by 25.6% during the pandemic compared to pre-pandemic levels (Santomauro et al., 2021). In Canada, findings from the 2022 Mental Health and Access to Care Survey indicated that over 5 million Canadians aged 15 and older reported experiencing a mood, anxiety, or substance use disorder in the previous year (Stephenson, 2023). Among them, nearly 59% reported needing counselling, yet only 44% received it. Of those who accessed care, just 64% felt their needs were met, underscoring ongoing challenges in both access to, and adequacy of, mental health services (Stephenson, 2023). Given the rapid advancement of technology and widespread smartphone ownership, mobile apps may offer a novel and scalable approach to supporting population-level depression management. Many previous systematic reviews and meta-analyses have reported small-to-moderate effects of mobile applications on depressive symptoms, suggesting that these apps hold promise for managing depression (Duarte-Díaz et al., 2023; Khademian et al., 2021; Lecomte et al., 2020; Linardon et al., 2019; Serrano-Ripoll et al., 2022; Weisel et al., 2019). Most recently, Fuster-Casanovas et al. (2025) conducted a meta-analysis examining the efficacy of digital health interventions for depression in primary care and observed similarly small but statistically significant antidepressant effects. The updated 2023 Canadian Network for Mood and Anxiety Treatments (CANMAT) guidelines advocate for the potential of digital health interventions to 173 complement and enhance the accessibility of depression management (Lam et al., 2024). However, the current evidence base and recommended interventions are primarily limited to guided internet-based cognitive behavioural therapy and guided behavioural activation (Lam et al., 2024). Existing self-guided digital health interventions have demonstrated limited efficacy and are therefore recommended as second-line adjunctive tools for individuals with mild to moderate depression only when clinician support is available(Lam et al., 2024). Given the documented antidepressant benefits and broad-ranging advantages of physical activity (PA) (Centers for Disease Control and Prevention, n.d.; Noetel et al., 2024; Singh et al., 2023), along with patient interest in PA apps for health management (Lipschitz et al., 2019; Rubanovich et al., 2017), mobile PA behaviour change interventions may represent an understudied yet promising complement to traditional depression treatments, appealing to individuals seeking accessible support through PA. This dissertation presents the phased development of an app-based, theory-guided (M-PAC; Rhodes (2017)) intervention for promoting PA among people with depression in Canada, following a combination of two rigorous behavioural intervention development frameworks: the Obesity-Related Behavioural Intervention Trials (ORBIT) model (Powell et al., 2020) and the Integrate, DEsign, Assess, and Share (IDEAS) framework (Mummah et al., 2016). This work presented a systematic review to identify intervention needs (Study 1), a secondary analysis evaluating the application of the M-PAC framework to understand PA behaviour among people with poor mental health (Study 2), formative development of the app-based program (MoodMover) and usability testing (Study 3), before finishing with an evaluation of proof-of-concept and feasibility testing (Study 4). Figure 8.1 outlines the workflow of the PhD. This body of work serves as a step-by-step guide for mHealth developers and researchers, providing a rigorous, user-centred approach to designing digital behaviour change interventions for individuals with depression. The approach 174 may also be applicable to other chronic conditions. Additionally, it informs future iterations of MoodMover, ensuring continued refinement and effectiveness to avoid research waste. This final chapter synthesizes the key findings of each study, illustrates how they collectively contributed to the development of the current MoodMover prototype, outlines the methodological, theoretical, and practical contributions of this PhD, and discusses research limitations and future research directions. Figure 8.1. Flow of the PhD. Note. M-PAC: Multi-Process Action Control framework; ORBIT: The Obesity-Related Behavioural Intervention Trials; PA: Physical activity. 8.2 Summary of Findings and Discussion 8.2.1 Study 1: A systematic review of internet-guided self-help interventions Chapter 3 outlined the first phase of this PhD research, which was a systematic review aimed at identifying internet-guided self-help (IGSH) interventions that promote PA among individuals with depression, as well as evaluating their effectiveness (Tang et al., 2022). The Systematic Review(ORBIT Discovery)â¢ Lack of internet-based self-help targeted PA for depressionâ¢ Only one app-based intervention identifiedâ¢ Low engagement and poor effectiveness M-PAC evaluation(ORBIT Phase Ia)â¢ Evaluating the use of M-PAC among people with poor mental health MoodMover Development(ORBIT Phase Ib)â¢ Development of MoodMoverâ¢ Guided by M-PACâ¢ Followed the IDEAS frameworkâ¢ Usability testing Proof-of-concept and Feasibility(ORBIT Phase IIa and IIb)â¢ Feasibilily and acceptability testing among people with depressionâ¢ Preliminary effects (proof-of-concept)175 findings revealed a significant gap in the literature, specifically the absence of IGSH interventions specifically designed to target PA in this population. Four studies were identified, with only one being app-based. While most studies reported significant reductions in depressive symptoms, they showed minimal or non-significant effects on PA levels. Given the small number of studies identified and the heterogeneity of measurement methods used, it was not possible to draw definitive conclusions regarding effectiveness. The limited effectiveness in increasing PA may be attributed to factors such as the lack of focus on PA behaviour change, low participant engagement, reliance on self-reported PA measures, and potential ceiling effects due to participantsâ pre-existing activity levels. Additionally, the PA component of most interventions lacked both a theoretical basis and systematic development, making it less amenable to rigorous evaluation and improvement. These findings informed six preliminary recommendations for future intervention development and evaluation: (i) focus on PA promotion for depression; (ii) use theory to guide behaviour change technique (BCT) selection; (iii) apply systematic, interactive development frameworks; (iv) set clear behaviour targets; (v) examine theory-based mediators or moderators; and (vi) use validated, preferably device-based, measures of PA, and depressive symptoms. Collectively, this review highlights the necessity for a well-designed, app-based, theory-driven behavioural intervention with a central focus on PA among individuals with depression. These findings directly informed the three subsequent studies presented in this dissertation. 8.2.2 From study 1 to study 2: Selection of the behavioural intervention development framework and theory To ensure a systematic and rigorous development of the behavioural intervention, OâCathain et al. (2019) emphasized the importance of following a rigorous behaviour intervention development framework, aligning with the third recommendation of Study 1 (Tang et al., 2022). 176 The ORBIT model (Powell et al., 2020) was selected for its strengths: first, it provides a clear framework for early intervention development, which is essential given the limited research in this area. Second, unlike general health promotion models such as the PRECEDE-PROCEED model (Green, 1974) and Intervention Mapping (IM) (Eldredge et al., 2016), the ORBIT model is specifically designed to translate research into behavioural interventions for chronic diseases, aligning well with my research focus. Third, it prioritizes clinically meaningful outcomes over mere statistical significance, ensuring that interventions are impactful for patients, clinicians, and policymakers (Powell et al., 2020). Fourth, the model fosters interdisciplinary collaboration, promotes transparency through defined milestones, and allows flexibility in adapting phases based on context. Additionally, it emphasizes iterative refinement through small trials to build a strong evidence base, minimizing research waste. The ORBIT model also supports diverse research designs (e.g., the sequential, multiple assignment, randomized trials (SMARTs) (Lei et al., 2012), Just-in-time adaptive interventions (JITAIs) (Nahum-Shani et al., 2018), and micro-randomized trials (MRT) (Klasnja et al., 2015)) and encourages parallel studies to optimize resources. Finally, its use of standardized medical terminology enhances clinical relevance. The ORBIT model, along with many other behavioural intervention development frameworks, emphasizes the importance of integrating behaviour change theories to identify key factors and understand intervention failures (Czajkowski et al., 2015), aligning with the second and fifth recommendations of Study 1 (Tang et al., 2022). Theoretical frameworks are typically viewed as critical in PA research (Rhodes & Nigg, 2011). Early research in exercise psychology often examined a wide range of isolated variables, with limited attention to their interrelationships (Courneya, 2004). Over the past three decades, from the late 1980s and early 1990s to the present, much work has been conducted to develop theoretical models to understand, explain, and guide 177 PA interventions (Rhodes & Nigg, 2011). Rhodes, McEwan, et al. (2019) overviewed four types of frameworks: social cognitive approaches, humanistic/organismic approaches, social ecological approaches, and dual process approaches. They concluded that integrated theoretical approaches may be the best for intervening in PA (Rhodes, McEwan, et al., 2019). Given that most behavioural theories in PA research originate from broader psychological disciplines, particularly social cognitive theories, there is growing support for developing PA-specific models or adapting existing ones to include PA-relevant constructs (Rhodes & Nigg, 2011). The M-PAC framework builds upon well-established behavioural antecedents from these traditional approaches and was specifically developed with PA interventions in mind, which offers a pragmatic structure for interventionists (Rhodes, 2017). It is a pragmatic approach that expands upon intention-focused social cognitive theories, highlighting both intention formation and intervention-behaviour transition (Rhodes, 2017). While M-PAC had been applied to various populations (Kwan et al., 2021; Liu et al., 2019; Rhodes, Beauchamp, et al., 2021; Rhodes, Berry, et al., 2019; Vallerand et al., 2019), it had not yet been evaluated in individuals with poor mental health, which led us to conduct Study 2. 8.2.3 Study 2: The application of M-PAC among people with poor mental health Chapter 4 outlined the second phase of this PhD, which focused on evaluating the use of the M-PAC framework in understanding PA behaviour and behaviour change among individuals with poor mental health (Study 2; Tang et al., 2023). This was achieved through a secondary analysis of cross-sectional data from 13,881 adults who participated in the June 2020 ParticipACTION Community Better Challenge. The findings suggest that an adapted M-PAC model is well-suited for explaining moderate-to-vigorous PA (MVPA) in this population. Additionally, the study identified perceived capability, affective attitudes, intentions, and 178 regulatory tactics as stronger predictors of MVPA than perceived opportunities and instrumental attitudes in both individuals with good and poor mental health. These insights informed the selection of theoretical constructs emphasized in the intended app-based intervention and their corresponding behaviour change techniques (BCTs) (Michie et al., 2013). 8.2.4 Study 3: Formative development of MoodMover Chapter 5 described the formative development process of the intended app-based PA promotion intervention for depression, MoodMover, prior to feasibility testing (Tang, Gierc, La, Kim, et al., 2025). This process encompassed the Discovery, Phase Ia: Define, and Phase Ib: Refine stages of the ORBIT model. To enhance app creativity, the Integrate and DEsign stages of the IDEAS model (Mummah et al., 2016), a digital behaviour change intervention development framework, was incorporated. Notably, Study 3 (Tang, Gierc, La, Kim, et al., 2025) was structured concisely following the IDEAS framework to improve readability. The integration encompassed: (i) a systematic review of the literature (Study 1); (ii) an evaluation of behavioural theory to identify potential theoretical constructs (Study 2); (iii) exploratory literature scans of the targeted behaviour and clinically meaningful changes in depression; (iv) intervention content development; (v) prototype development; (vi) usability testing; and (vii) app refinement. These steps illustrate how findings from prior studies and existing literature informed the development of MoodMover. Study 1 identified intervention needs and potential strategies, while Study 2 determined key theoretical constructs, informed content development, and guided the selection of BCTs for the app. Exploratory literature reviews further established clinically meaningful milestones for both behavioural and clinical outcomes, as suggested by the ORBIT model. The educational content of MoodMover was adapted from an existing web-based, 10-lesson M-PAC intervention designed to promote PA among young adults with low mood and/or depression in community primary care 179 settings (Ross, 2023). Our multidisciplinary team discussed content modifications, brainstormed app features, and determined delivery methods. Using the Pathverse platform (Liu et al., 2022), the team developed a series of prototypes with support from Pathverse programming staff, refining the app iteratively until it was ready for usability testing. Nine participants completed a 60-minute one-on-one Zoom usability session via smartphone, performing goal-oriented tasks such as logging in, completing modules, syncing step data, and setting step goals while verbalizing their thoughts. Afterward, participants took part in a semi-structured interview and completed a usability questionnaire. Both qualitative and quantitative data indicated that the MoodMover app had good usability and was well-received, with most identified issues being minor. Minor modifications (e.g., adjusting the amount of information on each card and revising the format for delivering certain content) were implemented immediately after the interviews, with additional refinements discussed and executed in collaboration with the Pathverse development team. Overall, the usability study provided robust evidence supporting the usability and acceptability of MoodMover among the target population. Based on these findings, and following the implementation of necessary modifications, I advanced to the Assess stage of the IDEAS framework, corresponding to Phase II: Preliminary Testing of the ORBIT model, to evaluate the feasibility and preliminary effects of the refined MoodMover in Study 4. 8.2.5 Study 4: A feasibility and Proof-of-concept testing of MoodMover Chapters 6 and 7 present the protocol and findings from the feasibility and proof-of-concept evaluation of MoodMover, conducted using a single-arm, pre-post study design. This study falls within Phase IIa: Proof-of-concept and Phase IIb: Pilot and Feasibility Testing of the ORBIT model. Participants were asked to use the app as instructed for 9 weeks, with an optional 9-week follow-180 up period. On the first day of their program, a 15-minute one-on-one orientation was conducted over Zoom. Engagement with the app and smartphone-based step counts were tracked with tracking data downloaded from the Pathverse admin web portal. The feasibility of MoodMover was evaluated based on four primary pre-determined criteria following the traffic light system (Avery et al., 2017): recruitment, adherence, usability and retention. Our findings found that all four feasibility criteria fell within the amber zone of the predetermined progression thresholds, suggesting suboptimal feasibility. A total of 32 out of 51 eligible participants with a self-reported clinical diagnosis of major depressive disorder or at least mild depressive symptoms based on the PHQ-9 were enrolled, resulting in a recruitment rate of 62.7%. Among these, 28 completed baseline assessments, 21 attended the orientation session and received the intervention, and 14 completed at least five major lessons. Of the 16 participants who completed the post-intervention assessments, 8 rated the usability of MoodMover as five or higher. These outcomes correspond to adherence, retention, and usability rates of 66.7% (14/21), 57.1% (16/28), and 50.0% (8/16), respectively, based on pre-set definitions. Although these outcomes did not reach the predefined green zone, the recruitment rate appears promising given the modest recruitment strategies employed in the current study. Adherence and retention rates were comparable to, or in some cases exceeded, those reported in previous digital interventions for depression. The usability rate was likely affected by unforeseen technical issues and may be improved through more rigorous beta testing prior to future evaluations of the app. In addition, regression analyses revealed that age was consistently associated with app engagement, usability, and satisfaction. Given that non-completers were generally younger than completers, future studies should explore additional strategies to enhance retention and engagement among younger users. Proof-of-concept was evaluated by assessing changes in PA behaviour and depressive 181 symptoms over the intervention period. Paired t-tests indicated significant pre-post changes in self-reported MVPA and depressive symptoms across the 75%, 85%, and 95% confidence intervals. Among participants with clinically elevated depressive symptoms at baseline (PHQ-9 â¥ 10), 75.0% (9/12) achieved a clinically meaningful reduction in symptom severity. However, only three participants met the behavioural goal of increasing their daily step count by 3,000 steps per week, as measured by the built-in step-tracking function of their smartphones. Additionally, paired t-tests of device-based data revealed a decrease in step counts from baseline to post-intervention, which contradicted the self-reported MVPA results. This discrepancy is likely attributable to inconsistent smartphone use among participants, such as whether they connected their smartwatches to their phones, the duration of smartwatch wear time, or the amount of time they carried their phones. Overall, the findings indicate that MoodMover shows promise in promoting PA among individuals with depression and contributing to depression management on a broader scale. However, the feasibility of the current version remains limited. Further modifications are required and should be reassessed before advancing to a more rigorous efficacy trial. 8.3 Contributions 8.3.1 Theoretical contribution: Examining the moderation effects of mental health status within the M-PAC framework The second study (Tang et al., 2023) presented in Chapter 4 examined whether mental health status moderates key constructs within the M-PAC framework in predicting MVPA. Our findings indicated that while mental health status partially moderated the model, with certain constructs functioning differently, M-PAC remains an appropriate framework for understanding PA behaviour in individuals with poor mental health. These findings offer preliminary insights into the key correlates of PA behaviour among people with poor mental health, providing guidance for 182 interventionists seeking to develop or tailor interventions for this population using the M-PAC framework. Interest in the M-PAC framework has increased alongside growing recognition of the intention-behaviour gap (Feil et al., 2023). The predictive utility of the M-PAC framework for explaining variance in PA behaviour has mainly been examined and supported among adolescents and university students (Brown et al., 2024; Kwan et al., 2021; Porter et al., 2024). Study 2 enhances the application of the M-PAC framework by addressing a gap in previous research, where the framework had been used for developing a web-based intervention for individuals with low mood or depression (Ross, 2023), but had not been evaluated. The moderating effects observed in Study 2 support prior research that emphasizes the need for population-specific adaptations rather than relying solely on general theories with fixed constructs (Glowacki, 2020). For example, affective judgements were positively associated with intention formation and may also have a direct effect on MVPA (Tang et al., 2023). Among individuals with poor mental health, affective judgements emerged as the second-strongest reflective correlate of MVPA (Tang et al., 2023). This finding is consistent with prior literature identifying the emotion domain of the Theoretical Domains Framework (Atkins et al., 2017) as a potential core component of interventions for individuals with depression, which has often been overlooked in intervention work (Glowacki et al., 2017). These findings suggest that PA interventions for depression should place greater emphasis on affective judgements early and throughout the intervention. The development of MoodMover was informed by these insights. In Study 4, which evaluated the feasibility and proof-of-concept of the current version of MoodMover, affective attitudes toward exercise demonstrated the second-largest increase from baseline to post-intervention. Although definitive conclusions cannot be drawn due to the small sample size, affective attitudes remain a 183 key construct that warrants further investigation in future studies. In addition, Study 2 found that instrumental attitudes may have functioned as a negative suppressor in the model (Tang et al., 2023). Although difficult to interpret given the exploratory nature of the study, this finding is consistent with literature showing that instrumental attitudes are generally weaker predictors of health behaviours compared to affective judgements (Rhodes, 2017). Although retaining negative suppressors in theoretical models can help clarify the influence of stronger predictors and contribute to more precise, theory-based strategies, they may not be ideal intervention targets. Further research is needed to determine whether this effect reflects a true suppression mechanism or a statistical anomaly. Generally, our findings support that interventions for promoting PA should not solely focus on instrumental attitudes. It is worth noting that the M-PAC model tested in Study 2 only examined the reflective and regulatory processes. Given that behaviour change interventions typically involve individuals who have already formed at least a decisional intention (as a prerequisite for participation), emphasizing reflexive processes in intervention design may enhance effectiveness (Rhodes, 2024). Thus, when using our findings to inform intervention design, we strongly recommend also emphasizing these two reflexive constructs of habit and identity. In addition, nearly half of the participants in the poor mental health group in Study 2 self-reported being physically active, which limited the representativeness of the sample for individuals with mental health problems. This may have impacted associations between M-PAC constructs and PA behaviour, introduced ceiling effects, and limited generalizability to less physically active individuals with depression. Furthermore, this study was a secondary analysis of a cross-sectional dataset, which precluded causal inference and definitive conclusions. Future research should seek to evaluate the full M-PAC model using a more representative sample and a prospective study design, in order to provide a more comprehensive 184 understanding of physical activity behaviour change for guiding intervention development for individuals with depression. 8.3.2 Methodological contribution: A guide for translating knowledge to practice This dissertation represents a knowledge translation effort aimed at applying behavioural theory and evidence-based findings into real-world action. Czajkowski and Hunter (2021) reviewed 11 major intervention development frameworks to guide the selection of optimal approaches for various research questions and to better support early-phase behavioural translational research. They recommended using one or a combination of these frameworks to enhance the development of behavioural interventions through potential synergies (Czajkowski & Hunter, 2021). To the best of my knowledge, this is the first work to integrate a stepped or phased-based approach, the ORBIT model, with a digital intervention-specific framework, the IDEAS model. This dissertation focuses on the early development phases prior to efficacy testing, embedding the Integrate and DEsign stages of the IDEAS model within the ORBIT framework. This integration aimed to foster creativity in app development while maintaining a focus on clinical outcomes. This work serves as a practical guide for researchers and practitioners seeking to translate healthcare knowledge and evidence-based practices into real-world interventions, particularly in the context of digital health behaviour change. The following sections outline how development frameworks shaped the research process and provided a structured approach with key considerations to inform future intervention development. 8.3.1.1 The Discovery phase of the ORBIT model. The ORBIT model (Figure 2.2) begins with the Discovery phase, which identifies a significant clinical problem that could be addressed through behavioural treatment. Chapter 1 details the importance of addressing depression, reviews current front-line treatments, explores the potential of PA as a treatment option, examines its 185 underlying mechanisms, highlights the gap between clinical PA guidelines and practices, and discusses the potential of app-based PA interventions to bridge this gap. Drawing on these insights, I conducted a systematic review (Study 1; Tang et al., 2022) to explore existing IGSH interventions and synthesize their effectiveness. This Discovery phase closely parallels the Empathize phase (Phase 1) of the IDEAS framework, as both focus on exploring and understanding the needs of the target population to inform intervention development (Mummah et al., 2016; Powell et al., 2020). Broadly, the ORBIT model emphasizes drawing from basic behavioural and social science research (e.g., reviews) and promoting collaboration between clinical practitioners, behavioural scientists, and social scientists to identify intervention needs (Powell et al., 2020). In contrast, the IDEAS framework highlights the importance of design thinking, encouraging researchers to conduct qualitative research at this need-finding stage to gather deeper insights from users (Mummah et al., 2016). The integration of the ORBIT model and IDEAS framework shaped this phase by focusing both on the potential potency of digital PA interventions and synthesizing evidence regarding the target populationâs receptivity to technology-based approaches. Notably, the ORBIT model provides clearer direction by prioritizing a clinical problem from the outset, whereas the IDEAS framework focuses primarily on behaviour change and addresses health impact more briefly in later phases. For any behaviour change intervention, however, it is crucial to consider how behavioural shifts translate into meaningful health outcomes throughout all stages of development and evaluation. 7.3.1.2 Phase Ia: Define of the ORBIT model. Following the identification of depression as the clinical problem and the need for a PA-based app intervention, I advanced to Phase Ia: Define, the first subphase of Phase I: Design in the ORBIT model. This subphase involves drawing on relevant behavioural and social sciences research to identify potential intervention components, 186 establish scientific pathways through which the intervention may impact depression, and determine milestones based on clinically significant changes in behavioural and clinical outcomes. Powell et al. (2020) proposed three guiding questions for this phase: 1) What is the ultimate clinical endpoint and how much improvement on it would be clinically relevant? 2) What is the behavioural risk factor target of treatment and how much improvement in it is needed to move the clinical endpoint? 3) What components, strategies for change, and level of implementation are needed to improve the behavioural risk factor? To address the first two questions, I conducted exploratory literature scans. The PHQ-9 was selected as the depression scale, and the cut points proposed by McMillan et al. (2010) were adopted because they account for baseline depression severity and align well with the characteristics of the target population. However, it is important to note that clinical endpoints may vary depending on whether the intervention is intended as a monotherapy or an adjunct. This distinction is critical: interventions with strong adjunctive potential may be prematurely abandoned if evaluated against monotherapy benchmarks during early trials. Even if self-help behaviour change apps like MoodMover do not meet stringent clinical thresholds on their own, they may still offer substantial value when used in combination with other treatments. In such contexts, clinical guidelines and expert input can inform the selection of more context-appropriate endpoints. In addressing the second question, the ORBIT model emphasizes setting a behavioural target sufficient to produce clinically meaningful outcomes. This question aligns with the Specify phase (Phase 2) of the IDEAS framework, which aims to establish a highly specific target behaviour that could provide significant health benefits. The IDEAS framework offers more 187 detailed guidance for selecting a target behaviour, outlining five key principles: (1) broad acceptability; (2) demonstrated health benefits; (3) specificity and measurability; (4) emphasis on increasing behaviour; and (5) potential for positive spillover effects. As a well-established modified behaviour factor, many clinically significant guidelines exist for PA (Lam et al., 2024; National Institute for Health and Clinician Excellence, 2009; Perraton et al., 2010; Ravindran et al., 2016; World Health Organization, 2020). There is likewise flexibility in selecting behavioural milestones, in line with the flexibility in clinical endpoints discussed above. For novel behavioural risk factors lacking an existing evidence base, the ORBIT model recommends conducting observational studies to examine their relationship with relevant biomedical risk factors for determining clinically meaningful behavioural cut-points. The third question parallels the Ground phase (Phase 3) of the IDEAS framework, which emphasizes the identification of intervention components and behavioural strategies informed by established theoretical models. The M-PAC framework was evaluated in Study 2 for its potential to support the transition from intention to sustained PA engagement. Informed by those findings, aligned BCTs were identified with additional techniques drawn from Michie et al. (2013) taxonomy. I then developed a hypothesized pathway (Figure 8.2) to illustrate how MoodMover may reduce depressive symptoms, supporting progression to ORBIT Phase Ib: Refine. 188 Figure 8.2. The hypothesized pathway of MoodMover. Note. BCT: Behaviour change technique; M-PAC: Multi-Process Action Control framework; PHQ-9: Patient Health Questionnaire, 9-item. However, selecting an appropriate theory for a given context remains a challenge (French et al., 2012). A lack of clear guidance on how to choose and apply theory has likely contributed to its underutilization in intervention development (Michie et al., 2011). This may have also led to a situation in which many interventions are not theory-driven, even when they are described as such (Michie & Prestwich, 2010). The IDEAS framework emphasizes the use of one or more behavioural theories to identify key constructs to target and highlights Social Cognitive Theory (Bandura, 1991), the Health Belief Model (Janz & Becker, 1984), and the Transtheoretical Model (Prochaska & Velicer, 1997) as commonly applied frameworks for guiding the development of mHealth interventions (Mummah et al., 2016). However, this widespread practice of selecting theoretical frameworks based on convention rather than empirical justification has been cited as a factor contributing to the limited effectiveness of many interventions (Moore & Evans, 2017). Moore and Evans further underscore the necessity for researchers to demonstrate a nuanced understanding of the determinants and maintenance of a target behaviour within its specific context 189 (i.e., time and space). This concern is particularly salient for more interactive and time-intensive mobile behavioural interventions, such as JITAIs. As mobile health technologies proliferate, it is unsurprising that mobile app-based behavioural interventions are rapidly increasing, even if they are not specifically designed to target depression. Existing behaviour change theories have been acknowledged as insufficient for enabling intra-individual tailoring (Riley et al., 2011). Scholars have called for the development of more dynamic system models, potentially drawing on control systems engineering, to address this gap (Riley et al., 2011). Thus, for developers and practitioners designing interactive mobile interventions, it is critical to apply a discerning lens when selecting behavioural theories, particularly when guided by the IDEAS framework. 8.3.1.3 Phase Ib: Refine of the ORBIT model. During this subphase, the primary goal is to identify the essential intervention components to avoid being overly complex while maintaining the strength to achieve clinically significant improvements in PA behaviour. To better align with the digital context and highly iterative development of MoodMover, I drew further guidance from the next four phases of the IDEAS framework: Ideate, Prototype, Gather, and Build (Mummah et al., 2016). Phase 4 (Ideate) involved brainstorming creative strategies to implement theory-driven constructs and BCTs identified in Phase Ia: Define. In Phase 5 (Prototype), low-cost prototypes were created using the no-code platform Pathverse (see https://pathverse.ca/en/), allowing real-time content updates and iterative refinements based on team input. Phase 6 (Gather) focused on usability testing with nine adults experiencing varying levels of depressive symptoms, generating both quantitative and qualitative feedback to guide improvements. Finally, Phase 7 (Build) centred on developing a fully functional minimum viable product, integrating user experience, visual 190 design, logic, and analytics. This phase incorporated refinements from prior phases and ensured the app was ready for feasibility testing. The user-friendly Pathverse platform, supported by a dedicated programmer, facilitated a continuous, iterative development process that cycled through ideation, prototyping, and feedback gathering. Importantly, the fact that the founders and developers of Pathverse are behavioural interventionists and that the platform was developed in accordance with the M-PAC framework have significantly strengthened and shortened the intervention development process (Liu et al., 2022). In contrast, many clinical researchers face challenges in collaborating with external software developers due to limited technical expertise and disciplinary gaps, often resulting in inefficient communication (Marcu et al., 2022). Integrating the appropriate expertise within multidisciplinary teams is critical to overcoming these barriers and enhancing the design of digital behaviour change interventions (Mummah et al., 2016). As Study 3 (Tang, Gierc, La, Kim, et al., 2025) offers more detailed information on this decision-making process, the following section highlights key reflections. First, the IDEAS framework (Mummah et al., 2016) emphasizes divergent thinking within the multidisciplinary team to generate a wide range of creative and innovative ideas in Phase 4: Ideate. It encourages open discussions and debate throughout the process, with the understanding that weaker ideas will naturally fall away while stronger ones rise to the top (Mummah et al., 2016). In contrast, the ORBIT model provides more comprehensive guidance for examining treatment dimensions, including optimal dose (e.g., duration, frequency), mode of delivery, essential components, engagement strategies, timing of delivery and assessments, and potential treatment moderators in refinement studies (Czajkowski et al., 2015). The ORBIT model also outlines a range of potential methods (e.g., single-case designs) for addressing key refinement questions (Czajkowski et al., 2015). However, while conducting a series of refinement studies may enhance 191 the effectiveness of an intervention, many of the methods recommended in the development and evaluation phases of the ORBIT model may be too complex to keep pace with the rapid evolution of mobile technology. In the current PhD work, a formal evaluation of essential components was not conducted; instead, practical applications were determined through team discussions guided by the IDEAS framework. Each BCT is operationalized through specific intervention components, such as various app features, with weekly education serving as the primary strategy for implementing most BCTs. It is highly recommended that future studies or behavioural scientists systematically identify essential components and optimal delivery packages using factorial and fractional factorial designs (FFDs) (Czajkowski et al., 2015). These methods, considered gold standards in the development of high-quality multicomponent interventions (Chakraborty et al., 2009), are particularly valuable for screening out inactive or non-essential components. Second, the Pathverse platform supported ongoing updating of content via the administrative portal, with changes immediately reflected in the app. Prototypes were therefore developed sequentially rather than in parallel, despite evidence that parallel prototyping can result in more diverse and effective designs (Dow et al., 2010). Third, I worked with Pathverse staff to assess the feasibility of implementing Phase 4 design ideas that were not yet supported by the platform. Given the resource and time constraints inherent to a PhD project, it was not feasible to incorporate all proposed features or to delay development in anticipation of future enhancements to the technological capabilities of Pathverse. For instance, tailoring based on fluctuating symptom profiles, self-reported barriers to exercise, or just-in-time adaptive elements was not included at this stage. Nonetheless, these remain promising areas for future development as the platform evolves. 8.3.1.4. Phase IIa: Proof-of-concept and Phase IIb: Pilot and Feasibility Testing of the 192 ORBIT model. In Phase IIa, the goal is to assess whether the intervention can achieve the behavioural goal, in our case, helping participants gradually increase their daily step counts by 3,000 on most days per week. This subphase recommended quasi-experimental, within-subject (pre-post) studies with a small, non-representative sample (Powell et al., 2020). In Phase IIb, pilot and other feasibility studies should be conducted to ensure the feasibility and acceptability of behavioural interventions, addressing key aspects such as recruitment plans, trial processes and procedures (e.g., assessment, treatment, retention), resources, trial management, and interventions and comparators (if applicable) (Powell et al., 2020). In Study 4, I conducted a single-arm, pre-post study that fits within both subphases to evaluate the feasibility, acceptability and potential effects of the refined MoodMover, which also corresponded to Phase 8: Pilot in the IDEAS framework. Study 4 found that the testing version of MoodMover demonstrated suboptimal feasibility but showed potential effectiveness in increasing PA and reducing depressive symptoms. As a next step, it is recommended to return to Phase Ib: Refine of the ORBIT model to improve the app design and further optimize its usability, acceptability, and feasibility. In summary, this work was guided by the integrated application of the ORBIT model and IDEAS framework, with the M-PAC framework informing the design of MoodMover. Studies 1 through 4 described the early phases of a systematic, phased approach to intervention development prior to large-scale efficacy trials. Future research seeking to integrate these two frameworks may use this work as a reference for guiding similar development efforts. 8.3.3 Applied contribution: A prototype app with potential real-world application My entire PhD work collectively contributed to the development of MoodMover, an app-based self-help program tailored to promote PA participation in individuals with depression. While numerous commercial apps for mental health or PA are available for download in app stores, they 193 typically focus on one or the other and have been criticized for lacking systematic development and rigorous efficacy testing (Lam et al., 2024; Lewis et al., 2017). MoodMover, however, is the first PA-focused intervention co-designed to a limited extent with individuals with depression, grounded in a particular understanding of PA behaviour among people with poor mental health. Although the current version of MoodMover demonstrated suboptimal feasibility based on predetermined criteria, it shows potential for improvement with necessary modifications. A further refined version may have meaningful public health implications given the promising proof-of-concept results. Typically, even interventions with small average effects can yield significant public health impacts when applied to large populations, as a modest yet meaningful number of individuals may experience physical and mental health benefits resulting from modest improvement in PA levels. The proof-of-concept results in Study 4 showed a significant increase in self-reported MVPA, and 75% of participants with moderate depressive symptoms achieved a clinically meaningful reduction in symptom severity. In addition, although the program offers up to $20 CAD as an incentive for lesson completion per participant, this amount is significantly lower than the annual economic burden per patient associated with depression in Canada (Tanner et al., 2020). The relatively low incremental costs of delivering this app-based program may make it feasible to implement on a large scale, given the widespread ownership of smartphones in Canada (Statistics Canada, 2020). Overall, while MoodMover is not yet a fully developed intervention ready for clinical implementation, preliminary feasibility and proof-of-concept results indicate its potential as a scalable, accessible, and effective complementary option for individuals who may face challenges engaging in traditional exercise programs. The development of MoodMover adhered to two rigorous behavioural intervention frameworks and a behaviour theory, incorporating detailed and 194 transparent reporting of the intervention design and applied BCTs. This systematic approach will guide future refinement and evaluation for continuous improvement. This work may represent the starting point of a promising approach to help bridge the translational gap and facilitate greater integration of PA and exercise into clinical practice for depression in Canada particularly for those who cannot or would prefer not to access supervised and structured exercise programming. 8.4 General Limitations While this PhD work presents valuable methodological, theoretical, and practical implications, it is important to acknowledge its limitations. Each study has discussed its specific limitations in detail; this section highlights the most common and significant ones across the studies. One primary limitation is that no partners from mental health associations or advocacy groups were involved in the development process thus far. While our multidisciplinary team included experts in PA, depression, and app development, the absence of practitioner partnerships may have influenced the feasibility outcomes. For instance, the lack of support from advocacy groups in Study 4 may have impacted recruitment rates and, potentially, engagement and retention rates. Dahne et al. (2019) found that participants recruited through direct contact with a clinician showed a higher engagement with a mental health app. Likewise, although I conducted a systematic review and exploratory reviews to identify intervention needs, and involved end-users by conducting a usability study and receiving feedback from them, individuals with depression as research partners could be involved in the development process from the beginning. Guided by the IDEAS framework and limited design flexibility offered by Pathverse, we adopted this user-centred design approach, which emphasizes understanding users and their needs primarily through feedback (Mummah et al., 2016). User-centred design has been the most widely applied method 195 for involving end-users in digital health (Moore et al., 2019), whereas co-design or participatory design may represent a more promising approach by positioning end-users as partners from the outset rather than as consultants (Bernaerts et al., 2024). Co-design or participatory design highlights active partnership with end-users, engaging them throughout the entire development cycle and promoting equal decision-making involving idea generation (Sanders & Stappers, 2008). In short, these approaches differ in their starting point and the depth of user involvement (Bernaerts et al., 2024). Co-design offers added value through enriched ideas, greater cultural sensitivity, and improved acceptability and usability, even among vulnerable populations such as individuals with mental health conditions (Brotherdale et al., 2024). Nonetheless, implementing co-creation with people experiencing depression may raise concerns such as tensions between researchers and end-users in decision-making and practical considerations regarding feasibility, including the resources, cost, and scalability of the process (Slattery et al., 2020). In addition, although I aimed to follow two rigorous behaviour change intervention development frameworks in the development of MoodMover, the recommended best practices were not always fully implemented due to time and resource constraints associated with a PhD project. Moreover, selection bias may have been introduced due to participant recruitment being limited to a single research website in Study 3. In Study 4, over 90% of potential participants were also identified from the same source, limiting the diversity of the sample. Also, although the PHQ-9 demonstrated excellent psychometric properties and has been widely used globally to screen for the presence and severity of depression (Kroenke, 2021; Kroenke et al., 2010), it has several limitations. First, it is based on the nine diagnostic criteria for major depressive disorder in the Diagnostic and Statistical Manual of Mental Disorders, which excludes other meaningful symptoms such as isolation and loneliness (Malpass et al., 2016). Second, it contains four somatic 196 items (e.g., sleep disturbance and fatigue) that may reflect physical illness rather than depression (Leavens et al., 2012). Third, there is inconsistency in the recommended cut-off scores for identifying clinically significant depression, which complicates interpretation across studies and populations (Hansson et al., 2009; Manea et al., 2012). We adopted the PHQ-9 for its brevity, strong psychometric properties, and alignment with other depression-related projects in our lab. Finally, although collaboration with Pathverse offered several advantages for app development, including reduced costs and shorter development timelines, its nature as a shared platform housing many concurrent studies limited the flexibility and creativity of the app design. Updates unrelated to the current project, requested by other studies, may have also contributed to unnecessary interruptions in the user experience. Moreover, although Pathverse enabled the collection of a broader range of app engagement metadata compared to many previous mHealth apps (Molloy & Anderson, 2021), the current metrics still do not fully capture the depth and breadth of user engagement. While open-ended questions were included in the post-intervention assessments to shed light on participantsâ experiences with the current version of MoodMover, more in-depth semi-structured interviews are needed to obtain a comprehensive understanding of the interventionâs feasibility and acceptability. Pathverse represents a practical solution for projects with limited funding and is particularly appropriate for early-stage intervention development and evaluation. However, for clinical implementation and large-scale dissemination, the development of an independent, standalone application would be more suitable to ensure sustained support, greater design flexibility, and reliable long-term maintenance. This needs to be weighed against the costs of developing a bespoke MoodMover app. 8.5 Future Directions 8.5.1 Intervention refinement and optimization 197 In alignment with the ORBIT model, the feasibility and proof-of-concept results from Study 4 informed several short-term directions for future research. Progression criteria should not be considered as a strict threshold but rather as an indication of whether modifications should be made (Bugge et al., 2013). As all four feasibility criteria fell within the amber zone in Study 4, the findings suggest that revisions are necessary and that all modifications should be re-evaluated in one or more subsequent feasibility trials. In addition, although Study 4 found a significant pre-post increase in self-reported MVPA, contradictory findings from smartphone-based step count data, along with concerns reported by participants regarding the accuracy of the built-in step-tracking system, suggest that modifications to the step-tracking feature and the PA data collection protocol are warranted to ensure more reliable device-based measurements. The initial decision to rely on smartphone-integrated tracking was intended to minimize intervention costs and reduce access barriers in anticipation of broader implementation. However, inaccurate self-monitoring may undermine participant motivation and engagement in PA behaviour change. These findings informed the decision to move back to the prior subphase, Phase Ib: Refine of the ORBIT model, to optimize the app design. Specifically, this may include providing Fitbit watches to participants who do not own a smartwatch to enable more accurate step tracking and self-monitoring, along with offering additional instructional materials on smartwatch use. Although a systematic review of 67 quantitative studies reported that Fitbit is less effective at measuring MVPA, it is acceptably accurate at determining step counts (Feehan et al., 2018). In addition, people with depression have previously reported that using a Fitbit is feasible and acceptable as a tool (Chum et al., 2017; Naslund et al., 2015). Future studies should evaluate the feasibility and acceptability of MoodMover when incorporating devices such as Fitbits as an active component among individuals with depression. For evaluation purposes, ActiGraph accelerometers should also be used at 198 baseline and post-intervention to provide more accurate estimates of PA for proof-of-concept assessment. In addition, further content adjustments may be informed by a deeper understanding of PA behaviour change among individuals with depression. Evaluating the full M-PAC framework in a more representative sample will support ongoing refinement of intervention content. The M-PAC framework is an open, testable model that can be enhanced by incorporating additional mediators and temporal dynamics (Rhodes, 2017). Future research may also explore ways to optimize the M-PAC framework to better support behaviour change in this population. Future research should also include factorial analyses or dismantling trials to help identify the most effective configuration of components within the intervention package, retaining only those essential for effectiveness. This approach is recommended in Phase Ib: Refine of the ORBIT model. However, it was not conducted in the current research due to time and resource constraints. In addition to factorial designs, other alternative designs, such as SMARTs (Lei et al., 2012) and MRT (Klasnja et al., 2015), may also be considered to inform the development and optimization of intervention components in a more resource-efficient way (Hekler et al., 2016; Sucala et al., 2020). Each of these designs has distinct strengths and limitations and differs from RCTs in both purpose and structure. Notably, Hrynyschyn et al. (2022) proposed that these designs should be regarded as complementary rather than alternatives to RCTs. More importantly, to guide the future optimization process for MoodMover, the Multiphase Optimization Strategy (MOST) (Collins & Kugler, 2018; Collins et al., 2007), a systematic framework for optimizing and evaluating behavioural interventions, may be incorporated in future research. While the ORBIT model acknowledges that optimization can occur throughout intervention development, it primarily situates this process in Phase Ib (Refine) and offers limited 199 guidance for optimization in later phases (Guastaferro & Pfammatter, 2023; Powell et al., 2020). That said, ORBIT lacks clear methodological guidance for continuous optimization. In contrast, the MOST framework places optimization at the core of its approach, using complementary experimental designs to support iterative, data-driven refinements (Collins & Kugler, 2018). MOST encompasses three phases: preparation, optimization, and evaluation (Collins & Kugler, 2018). During the preparation phase, researchers develop a conceptual model, identify and pilot candidate intervention components, and define the optimization objective. Moving forward, the optimization phase focuses on empirically identifying which components or combinations of components achieve the optimization goal. In MOST, optimization is clearly defined as achieving intervention EASE by balancing Effectiveness with Affordability (within budget), Scalability (feasible in real-world settings), and Efficiency (including only active components). Finally, the optimized intervention is tested against an appropriate comparator, typically using an RCT in the evaluation phase. MOST views optimization as an ongoing process that accommodates changes in context, funding, or dissemination needs (Collins & Kugler, 2018). In instances where contextual conditions or implementation constraints change, MOST does not prescribe a return to earlier phases of research; rather, it encourages investigators to draw upon empirical findings from prior optimization trials to inform necessary adjustments to the intervention package before advancing to the evaluation phase (Collins & Kugler, 2018). Although both the ORBIT and MOST frameworks endorse the use of factorial designs, their recommended use diverges. ORBIT situates factorial designs within Phase Ib (Refine) as a method for assessing the performance of discrete intervention components (Powell et al., 2020). In contrast, MOST advocates for the use of adequately powered factorial experiments not only to examine individual and combined 200 component effects but also to produce efficacy-related evidence that can guide progression toward subsequent effectiveness trials (Collins & Kugler, 2018). This divergence reflects a fundamental difference between the two frameworks. While MOST employs experimental designs explicitly to facilitate decision-making regarding the inclusion or modification of intervention components, ORBIT, despite offering a degree of methodological flexibility, does not clearly delineate a phase in which designs serving both refinement and efficacy functions may be integrated (Guastaferro & Pfammatter, 2023). Consequently, transitioning from ORBIT to MOST may offer the advantage of clearer methodological guidance for optimization. Early-stage feasibility studies offer greater flexibility for such transitions. The preparation phase of MOST broadly aligns with Phases Ia and Ib, as well as the early segments of Phase II (IIa and IIb), within the ORBIT model (see Figure 8.3; derived from Guastaferro and Pfammatter (2023)), suggesting a degree of conceptual overlap in the early stages of intervention development and refinement. Nevertheless, it is important to acknowledge that shifting between frameworks is not always methodologically feasible or advisable, particularly in the later stages of intervention development, as it may result in missing essential preparatory work required by the new framework (Guastaferro & Pfammatter, 2023). Therefore, if a shift from the ORBIT model to the MOST framework is considered, it is essential to systematically identify and complete any activities specified within MOSTâs preparation phase that were not addressed during the corresponding phases of ORBIT. 201 Figure 8.3. Locating the translational intervention development frameworks within the NIH stage model to establish a common language. Note. MOST: Multiphase Optimization Strategy; MRC, Medical Research Council; ORBIT, Obesity-Related Behavioral Intervention Trials. The activities of the stages for the NIH Stage Model are provided in italics so as to provide a common denominator to understand the activities and objectives of the phases specified by each of the Translational Intervention Development Frameworks. Shaded cells indicate the activities of the phases to be in the spirit of the framework, even though the particular activities are not called out in the framework. We recognize there is no definitive boundary between stages; i.e., some preparation phase work in MOST could be pure efficacy. Source: Guastaferro and Pfammatter (2023) 8.5.2 Intervention evaluation Following the ORBIT model, when the optimized MoodMover is found to be feasible and acceptable, and demonstrates promising proof-of-concept results, the next step would be to advance to Phase IIc of the ORBIT model: the Phase II Efficacy Trial. This subphase generally recommends randomized pilot testing to determine whether the intervention can produce clinically significant improvements in behavioural outcomes and whether it performs better than non-specific factors or existing treatments (Powell et al., 2020). Progression to Phase III: Efficacy is warranted if participants in the MoodMover group exhibit clinically meaningful improvements in PA that are not observed in the control group. Efficacy trials in Phase IIc and Phase III differ as to 202 whether the goal is to assess the efficacy of MoodMover in behavioural change or clinical outcome change (Powell et al., 2020). In Phase III, the ORBIT model recommends conducting rigorous RCTs with maximal control of confounding variables to ensure valid and reliable outcomes. This progressive approach helps optimize intervention effectiveness while reducing research waste (Powell et al., 2020). If the MOST framework is adopted, efficacy evidence from a fully powered optimization trial can guide the progression to an RCT to evaluate effectiveness (discussed above) (Collins & Kugler, 2018). Consistent with ORBIT, the MOST framework and many other studies also emphasize that a sufficiently powered RCT remains a critical component of the research process (Collins & Kugler, 2018; Collins et al., 2007; Hrynyschyn et al., 2022; Klasnja et al., 2015; Spring et al., 2020). A key consideration in adopting RCTs for evaluation is the balance between methodological rigour and the fast-paced evolution of mobile technology. The time required to complete a robust RCT may risk rendering the digital intervention outdated by the time results are available. Although fully powered RCTs remain the gold standard for intervention evaluation, they may limit opportunities for timely updates and improvements (Hrynyschyn et al., 2022; Sucala et al., 2020). In addition, traditional RCTs aim to minimize potential confounding factors and maximize internal validity, which has raised concerns regarding their generalizability beyond controlled settings. Given these considerations, stepped-wedge designs or pragmatic RCTs have been proposed as more suitable approaches for evaluating digital interventions, particularly digital behaviour change interventions, which are typically considered complex (Hrynyschyn et al., 2022). Unlike strictly controlled explanatory RCTs, pragmatic RCTs focus on assessing the effectiveness of interventions under real-world conditions to inform and improve practice (Patrick, 2024). Future evaluations of the refined MoodMover should consider employing stepped-wedge designs or 203 pragmatic RCTs. Additionally, an appropriate follow-up period is necessary to assess the long-term impact of the intervention. 8.5.3 Intervention personalization and adaptation The major challenge faced by digital behaviour change interventions is the complexity of human behaviour change (Sucala et al., 2020). âOne-size-fits-allâ interventions have been criticized for failing to address the diverse nature of health behaviours across different populations (Nahum-Shani et al., 2018). This concern is particularly relevant for mHealth apps, which aim to reach large populations (Sucala et al., 2020). Prior research has shown that tailored digital interventions can lead to improved health behaviour outcomes (Broekhuizen et al., 2012). In the present PhD work, the capacity for tailoring within MoodMover was limited by the technological constraints of the Pathverse platform and a lack of existing evidence regarding how individual and contextual factors influence app engagement and PA behaviour among individuals with depression. Early applications of personalization in digital interventions involved tailoring content and features based on individualsâ baseline characteristics (Patrick et al., 2016). The current version of MoodMover incorporates limited personalization by recommending step goals based on baseline activity levels. Preliminary findings from Study 4 identified age as a relatively consistent factor influencing usersâ engagement with the app, suggesting that future personalization efforts may benefit from age-based tailoring. However, additional high-quality research is needed to support this direction further. Additionally, Study 4 revealed that certain features, such as action planning via the Goals function, were used infrequently and with substantial variability among participants. Similarly, only five participants engaged with the community forum as intended (i.e., for exercise-related discussions). Future studies with larger samples should investigate the associations between feature use and behavioural and clinical outcomes, and explore whether these associations 204 vary by participant-level characteristics. Such insights would support the development of more effectively tailored intervention components. Moving forward, evolving MoodMover from a limited precision-based approach to a JITAI (Nahum-Shani et al., 2018) may represent an important intermediate direction for future research. JITAIs adapt over time to an individualâs changing status and context, aiming to deliver support precisely when it is most needed (Nahum-Shani et al., 2015). This approach may be particularly well suited to the fluctuating nature of depressive symptoms. To build the evidence base for developing appropriate decision rules for tailoring, future studies should attempt to collect real-time data, such as through ecological momentary assessments (EMAs) (Intille, 2007) and advanced passive data capture technologies via the app and wearable devices. Moreover, recent research in the field has been exploring the use of control systems engineering to optimize adaptive interventions (Conroy et al., 2019; Hekler et al., 2018). Control systems engineering offers promising methods for the iterative refinement of adaptive interventions designed to promote real-world health behaviour change. Control engineering allows for defining decision rules using mathematical models that consider multiple tailoring variables, intervention options, and user responses simultaneously (Hekler et al., 2018). However, implementing these methods will inevitably exceed the technical capabilities of the Pathverse platform. More advanced forms of adaptation may require transforming MoodMover into a standalone app and reducing its scalability. Another important aspect of adaptation involves the potential to translate MoodMover into different languages to facilitate implementation across diverse countries. Notably, a research team in China has expressed interest in translating MoodMoverâs educational content into Chinese as part of a multi-component intervention for individuals with depression in that context. 8.5.4 Implementation 205 As noted, a key potential implication of this work is its contribution to the development of a scalable, accessible, and effective complementary treatment option within Canadaâs depression management system. As a long-term research direction, the trade-off between intervention precision and scalability should be carefully evaluated. Moreover, a comprehensive implementation plan for MoodMover will be essential to bridge the gap between research and practice. Digital health interventions have often proven difficult to implement due to challenges such as digital inequity factors (e.g., low digital literacy, cost of devices, and lack of internet access) (Lam et al., 2024), privacy and security concerns (Lam et al., 2024), misalignment with existing systems (Ross et al., 2018), and inadequate implementation planning (Ross et al., 2018). A meta-analysis by Duarte-Díaz et al. (2023) found that mobile interventions can be a viable method for delivering depression care, but are most effective when combined with faceâtoâface sessions. Future research should explore factors that facilitate or hinder the integration of MoodMover into existing depression care systems, assess its cost-effectiveness, determine the necessary resources for effective implementation, and explore potential implementation strategies. This provides another rationale for shifting from the ORBIT model to the MOST framework, as the latter offers guidance across the entire research-to-practice pipeline and emphasizes designing for implementation from the outset (Guastaferro & Pfammatter, 2023). Once MoodMover is deployed in real-world settings, ongoing refinement using advanced analytics will be crucial to optimize the intervention and maximize its impact (Sucala et al., 2020). 8.6 Conclusions This dissertation presents a series of studies that contributed to the development of the first app-based, PA-focused behaviour change intervention specifically designed for individuals with depression. MoodMover may represent an initial step toward addressing the lack of effective, 206 evidence-informed self-guided digital interventions for depression by providing a safe and engaging option for individuals who are interested in this form of support. The development of MoodMover was guided by the integration of two rigorous behaviour change intervention development frameworks, the ORBIT model and the IDEAS framework, with the M-PAC framework informing the intervention content. Key stakeholders, including experts in depression and PA, app developers, and individuals with lived experience of depression, were actively involved in the development process. Initial feasibility findings were suboptimal, whereas proof-of-concept results were relatively promising, indicating that modifications are necessary before proceeding to larger trials. While further refinement and evaluation are warranted, MoodMover shows potential as a scalable and accessible complementary treatment for depression through the promotion of PA. 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Section and Topic Item # Checklist item Location where item is reporteda TITLE Title 1 Identify the report as a systematic review. 1 ABSTRACT Abstract 2 See the PRISMA 2020 for Abstracts checklist. 1-2 INTRODUCTION Rationale 3 Describe the rationale for the review in the context of existing knowledge. 3-6 Objectives 4 Provide an explicit statement of the objective(s) or question(s) the review addresses. 6 METHODS Eligibility criteria 5 Specify the inclusion and exclusion criteria for the review and how studies were grouped for the syntheses. 7-9 Information sources 6 Specify all databases, registers, websites, organisations, reference lists and other sources searched or consulted to identify studies. Specify the date when each source was last searched or consulted. 7 Search strategy 7 Present the full search strategies for all databases, registers and websites, including any filters and limits used. 7, Multimedia Appendix 2,3 Selection process 8 Specify the methods used to decide whether a study met the inclusion criteria of the review, including how many reviewers screened each record and each report retrieved, whether they worked independently, and if applicable, details of automation tools used in the process. 9-10 Data collection process 9 Specify the methods used to collect data from reports, including how many reviewers collected data from each report, whether they worked independently, any processes for obtaining or confirming data from study investigators, and if applicable, details of automation tools used in the process. 9-10 Data items 10a List and define all outcomes for which data were sought. Specify whether all results that were compatible with each outcome domain in each study were sought (e.g. for all measures, time points, analyses), and if not, the methods used to decide which results to collect. 10 10b List and define all other variables for which data were sought (e.g. participant and intervention characteristics, funding sources). Describe any assumptions made about any missing or unclear information. 10 Study risk of bias assessment 11 Specify the methods used to assess risk of bias in the included studies, including details of the tool(s) used, how many reviewers assessed each study and whether they worked independently, and if applicable, details of automation tools used in the process. 11 Effect measures 12 Specify for each outcome the effect measure(s) (e.g. risk ratio, mean difference) used in the synthesis or presentation of results. 11 Synthesis methods 13a Describe the processes used to decide which studies were eligible for each synthesis (e.g. tabulating the study intervention characteristics and comparing against the planned groups for each synthesis (item #5)). 11 13b Describe any methods required to prepare the data for presentation or synthesis, such as handling of missing summary statistics, or data conversions. 11 13c Describe any methods used to tabulate or visually display results of individual studies and syntheses. 11 13d Describe any methods used to synthesize results and provide a rationale for the choice(s). If meta-analysis was performed, describe the model(s), method(s) to identify the presence and extent of statistical heterogeneity, and software package(s) used. N/A 233 Section and Topic Item # Checklist item Location where item is reporteda 13e Describe any methods used to explore possible causes of heterogeneity among study results (e.g. subgroup analysis, meta-regression). N/A 13f Describe any sensitivity analyses conducted to assess robustness of the synthesized results. N/A Reporting bias assessment 14 Describe any methods used to assess risk of bias due to missing results in a synthesis (arising from reporting biases). 11 Certainty assessment 15 Describe any methods used to assess certainty (or confidence) in the body of evidence for an outcome. 11 RESULTS Study selection 16a Describe the results of the search and selection process, from the number of records identified in the search to the number of studies included in the review, ideally using a flow diagram. 12-14, Figure 1, 2 16b Cite studies that might appear to meet the inclusion criteria, but which were excluded, and explain why they were excluded. N/A Study characteristics 17 Cite each included study and present its characteristics. 14-23, Table 1-3 Risk of bias in studies 18 Present assessments of risk of bias for each included study. 23-24, Figure 3 Results of individual studies 19 For all outcomes, present, for each study: (a) summary statistics for each group (where appropriate) and (b) an effect estimate and its precision (e.g. confidence/credible interval), ideally using structured tables or plots. 19-20, Table 2 Results of syntheses 20a For each synthesis, briefly summarise the characteristics and risk of bias among contributing studies. 23 20b Present results of all statistical syntheses conducted. If meta-analysis was done, present for each the summary estimate and its precision (e.g. confidence/credible interval) and measures of statistical heterogeneity. If comparing groups, describe the direction of the effect. N/A 20c Present results of all investigations of possible causes of heterogeneity among study results. N/A 20d Present results of all sensitivity analyses conducted to assess the robustness of the synthesized results. N/A Reporting biases 21 Present assessments of risk of bias due to missing results (arising from reporting biases) for each synthesis assessed. N/A Certainty of evidence 22 Present assessments of certainty (or confidence) in the body of evidence for each outcome assessed. 24 DISCUSSION Discussion 23a Provide a general interpretation of the results in the context of other evidence. 25-30 23b Discuss any limitations of the evidence included in the review. 25-30 23c Discuss any limitations of the review processes used. 31-32 23d Discuss implications of the results for practice, policy, and future research. 32-34 OTHER INFORMATION Registration and protocol 24a Provide registration information for the review, including register name and registration number, or state that the review was not registered. 6-7 24b Indicate where the review protocol can be accessed, or state that a protocol was not prepared. 6-7 24c Describe and explain any amendments to information provided at registration or in the protocol. 7 Support 25 Describe sources of financial or non-financial support for the review, and the role of the funders or sponsors in the review. 34 Competing interests 26 Declare any competing interests of review authors. 34 Availability of data, 27 Report which of the following are publicly available and where they can be found: template data collection forms; data extracted from N/A 234 Section and Topic Item # Checklist item Location where item is reporteda code and other materials included studies; data used for all analyses; analytic code; any other materials used in the review. From: Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021;372:n71. doi: 10.1136/bmj.n71 For more information, visit: http://www.prisma-statement.org/ aThe page numbers shown match those from the corresponding published paper. 235 Appendix B. Chapter 3: Search strategies for the first round of searches Search Strategy for MEDLINE (Ovid) Search number Query 1 depression/ or exp Depressive Disorder/ or Dysthymic Disorder/ or Mood Disorders/ or Affective Symptoms/ or Adjustment Disorders/ 2 (Depress* or melancholia* or Dysthymi* or Mood disorder* or affective disorder* or Affective symptom* or Adjustment disorder* or Reactive disorder*).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 3 1 or 2 4 telemedicine/ or telerehabilitation/ or exp Internet/ or exp Videoconferencing/ or exp Cell Phone/ or wearable electronic devices/ or fitness trackers/ 5 (Mobile health or Telehealth or Telemedicine or Telerehabilitation or E-rehab* or Ehealth or Mhealth or Mobile application* or mobile technolog* or mobile healthcare or smartphone* or e-Health or m-Health or tele-Health or mobile device* or smart-phone or cell-phone or tele-rehabilitation or computer-based intervention* or Internet or Web or Internet-based or Web-based or computer-based or Online intervention* or Web based intervention* or Internet based intervention* or Videoconferenc* or Mobile phone* or Smart phone* or Cell phone* or Telephone* or Tablet* or App-based or App* or Text messag* or Texting* or iCBT or Internet-based cognitive behavio?r or computer-based cognitive behavio?r or (Wearable adj3 technolog*) or (Wearable adj3 device*) or (Electronic adj3 device*) or sports watch* or smartwatch* or activity tracker* or apple watch* or Activity tracker* or Fitness tracker*).mp. 6 4 or 5 7 exercise/ or cool-down exercise/ or gymnastics/ or muscle stretching exercises/ or exp Physical Fitness/ or exp Physical Conditioning, Human/ or Warm-Up Exercise/ or physical endurance/ or exercise tolerance/ or exp Exercise Therapy/ or exp Sports/ or Sports Medicine/ or exp Exercise Movement Techniques/ or exp Walking/ or exp Running/ or Swimming/ or Diving/ or "Physical Education and Training"/ or tai ji/ or yoga/ or Dance Therapy/ or Leisure Activities/ 8 (Exercise* or aerobic or Physical activit* or Physical inactivit* or Calisthenic* or Gymnastic* or Stretching* or Physical fitness or Human physical training or Human physical conditioning or Physical endurance or endurance training or Physical stamina or Athletic* or Sport* or biking or cycling or bicycling or Pilates or Pilates-based or Ambulation or Walk* or Treadmill* or Active transport* or active living or Run or Running* or Jog or Jogging* or Swim* or Diving* or Physical education or Yoga or Tai chi or Tai ji or Tai-chi or Dance* or Dancing or Weight lifting* or weight training or Leisure activit* or gardening or Recreational activit* or recreation activit* or Strength training* or Resistance training* or Weight-bearing or Physical therap*).mp. 9 7 or 8 10 (Intervention* or program* or trial* or rct or quasi-experimental or randomi?ed controlled trial* or randomi?ed trial* or controlled clinical trial* or clinical trial* or randomi?ed clinical trial* or randomi?ed control trial* or wait-list or experiment*).mp. 11 3 and 6 236 12 11 and 9 13 12 and 10 Search strategy for PsycInfo (EBSCOhost) Search number Query S1 (DE "Telemedicine" OR DE "Online Therapy" OR DE "Teleconferencing" OR DE "Teleconsultation" OR DE "Telerehabilitation" OR DE "Computer Assisted Therapy" OR DE "Mobile Applications" OR DE "Electronic Health Services" OR DE "Digital Interventions" OR DE "Mobile Health" OR DE "Precision Medicine" OR DE "Wearable Devices" OR DE "Mobile Devices" OR DE "Mobile Phones" OR DE "Tablet Computers" OR DE "Smartphones" ) S2 âMobile healthâ or Telehealth or Telemedicine or Telerehabilitation or E-rehab* or Ehealth or Mhealth or âMobile application*â or âmobile technolog*â or âmobile healthcareâ or smartphone* or e-Health or m-Health or tele-Health or âmobile device*â or smart-phone or cell-phone or tele-rehabilitation or âcomputer-based intervention*â or Internet or Web or Internet-based or Web-based or computer-based or âOnline intervention*â or âWeb based intervention*â or âInternet based intervention*â or Videoconferenc* or âMobile phone*â or âSmart phone*â or âCell phone*â or âTelephone*â or Tablet* or App-based or App* or âText messag*â or Texting* or iCBT or âInternet-based cognitive behavio#râ or âcomputer-based cognitive behavio#râ or (Wearable N3 technolog*) or (Wearable N3 device*) or (Electronic N3 device*) or âsports watch*â or smartwatch* or âactivity tracker*â or âapple watch*â or âActivity tracker*â or âFitness tracker*â S3 S1 OR S2 S4 (DE "Physical Activity" OR DE "Actigraphy" OR DE "Exercise" OR DE "Physical Fitness" OR DE "Aerobic Exercise" OR DE "Weightlifting" OR DE "Yoga" OR DE "Activity Level" OR DE "Health Behavior") S5 Exercise* or aerobic or âPhysical activit*â or âPhysical inactivit*â or Calisthenic* or Gymnastic* or Stretching* or âPhysical fitnessâ or âHuman physical trainingâ or âHuman physical conditioningâ or âPhysical enduranceâ or âendurance trainingâ or âPhysical staminaâ or Athletic* or Sport* or biking or cycling or bicycling or Pilates or Pilates-based or Ambulation or Walk* or Treadmill* or âActive transport*â or âactive livingâ or Run or Running* or Jog or Jogging* or Swim* or Diving* or âPhysical educationâ or Yoga or âTai chiâ or âTai jiâ or Tai-chi or Dance* or Dancing or âWeight lifting*â or âweight trainingâ or âLeisure activit*â or gardening or âRecreational activit*â or ârecreation activit*â or âStrength training*â or âResistance training*â or Weight-bearing or âPhysical therap*â S6 S4 OR S5 S7 (DE "Major Depression" OR DE "Anaclitic Depression" OR DE "Dysthymic Disorder" OR DE "Endogenous Depression" OR DE "Late Life Depression" OR DE "Postpartum Depression" OR DE "Reactive Depression" OR DE "Recurrent Depression" OR DE "Treatment Resistant Depression" ) S8 Depress* or melancholia* or Dysthymi* or âMood disorder*â or âaffective disorder*â or âAffective symptom*â or âAdjustment disorder*â or âReactive disorder*â S9 S7 OR S8 237 S10 (TI Intervention* OR program* OR trial* OR rct OR "quasi experimental" OR quasi-experimental OR "randomi#ed controlled trial*" OR "randomi#ed trial*" OR âcontrolled clinical trial*â OR âclinical trial*â OR ârandomi#ed clinical trial*â OR ârandomi#ed control trial*â OR wait-list OR experiment*) S11 (AB Intervention* OR program* OR trial* OR rct OR "quasi experimental" OR quasi-experimental OR "randomi#ed controlled trial*" OR "randomi#ed trial*" OR âcontrolled clinical trial*â OR âclinical trial*â OR ârandomi#ed clinical trial*â OR ârandomi#ed control trial*â OR wait-list OR experiment*) S12 S10 OR S11 S13 S3 AND S6 S14 S9 AND S13 S15 S12 AND S14 Search strategy for EMBASE(via Ovid) Search number Query 1 depression/ or Mood Disorder/ or Affective Symptoms/ or Adjustment Disorders/ 2 (Depress* or melancholia* or Dysthymi* or Mood disorder* or affective disorder* or Affective symptom* or Adjustment disorder* or Reactive disorder*).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 3 1 or 2 4 exp telehealth/ or mobile health application/ or web-based intervention/ or videoconferencing/ or wearable computer/ or actigraph/ or exp smart watch/ or exp sports equipment/ 5 (Mobile health or Telehealth or Telemedicine or Telerehabilitation or E-rehab* or Ehealth or Mhealth or Mobile application* or mobile technolog* or mobile healthcare or smartphone* or e-Health or m-Health or tele-Health or mobile device* or smart-phone or cell-phone or tele-rehabilitation or computer-based intervention* or Internet or Web or Internet-based or Web-based or computer-based or Online intervention* or Web based intervention* or Internet based intervention* or Videoconferenc* or Mobile phone* or Smart phone* or Cell phone* or Telephone* or Tablet* or App-based or App* or Text messag* or Texting* or iCBT or Internet-based cognitive behavio?r or computer-based cognitive behavio?r or (Wearable adj3 technolog*) or (Wearable adj3 device*) or (Electronic adj3 device*) or sports watch* or smartwatch* or activity tracker* or apple watch* or Activity tracker* or Fitness tracker*).mp. 6 4 or 5 7 exp physical activity/ or exp kinesiotherapy/ or exp exercise/ or exp sport/ or fitness/ or physical education/ or sports medicine/ or dance therapy/ or endurance/ 238 8 (Exercise* or aerobic or Physical activit* or Physical inactivit* or Calisthenic* or Gymnastic* or Stretching* or Physical fitness or Human physical training or Human physical conditioning or Physical endurance or endurance training or Physical stamina or Athletic* or Sport* or biking or cycling or bicycling or Pilates or Pilates-based or Ambulation or Walk* or Treadmill* or Active transport* or active living or Run or Running* or Jog or Jogging* or Swim* or Diving* or Physical education or Yoga or Tai chi or Tai ji or Tai-chi or Dance* or Dancing or Weight lifting* or weight training or Leisure activit* or gardening or Recreational activit* or recreation activit* or Strength training* or Resistance training* or Weight-bearing or Physical therap*).mp. 9 7 or 8 10 3 and 6 11 9 and 10 12 (Intervention* or program* or trial* or rct or quasi-experimental or randomi?ed controlled trial* or randomi?ed trial* or controlled clinical trial* or clinical trial* or randomi?ed clinical trial* or randomi?ed control trial* or wait-list or experiment*).mp. 13 11 and 12 Search strategy for Web of Science Search number Query #1 TS=(depress* or dysthymi* or distress* or âcommon mental healthâ or âmood disorder*â or âaffective disorder*â or âaffective symptom*â or âadjustment disorder*â) #2 TS=(âMobile application*â or âmobile technolog*â or âmobile healthcareâ or mHealth or eHealth or teleHealth or digital or internet-based or web-based or app-based or apps or âmobile phone*â or smartphone* or computer* or tablet* or virtual or online or video* or e-Health or m-Health or tele-Health or âmobile healthâ or âmobile device*â or smart-phone or âsmart phone*â or âcell phone*â or cell-phone or telerehabilitation or tele-rehabilitation or âcomputer-based intervention*â or âwearable technolog*â or e-rehab* or iCBT or âcomputer-based cognitive behavio$râ or computer-based or âsports watch*â or smartwatch* or âactivity tracker*â or âapple watch*â or âtext messag*â) #3 TS=(âPhysical activit*â or âPhysical inactivit*â or âSedentary behavio*â or aerobic or âphysical fitnessâ or walk* or run or running or yoga or âstrength trainingâ or sports or âactive livingâ or âactive transportâ or âleisure activit*â or âresistance trainingâ or âendurance trainingâ or âweight trainingâ or Tai-chi or swim* or jog or jogging or dance* or dancing or gardening or treadmill* or biking or cycling or pilates or ârecreation activit*â or âphysical therap*â) #4 AB=(Intervention* OR program* OR trial* OR rct OR quasi-experimental OR "randomi$ed controlled trial*" OR "randomi$ed trial*" OR âcontrolled clinical trial*â OR âclinical trial*â OR ârandomi$ed clinical trial*â OR ârandomi$ed control trial*â OR wait-list OR experiment*) #5 AB=(allocat* OR control) #6 #4 OR #5 #7 #1 AND #2 AND #3 AND #6 239 Search strategy for SportDiscus (EBSCOhost) Search number Query Thesaurus S1 TX "Mobile application*" OR "mobile technolog*" OR "mobile healthcare" OR mHealth OR eHealth OR teleHealth OR digital OR internet-based OR web-based OR app-based OR apps OR "mobile phone*" OR smartphone* OR computer* OR tablet* OR virtual OR online OR video* OR e-Health OR m-Health OR tele-Health OR "mobile health" OR "mobile device*" OR smart-phone OR "smart phone*" OR "cell phone*" OR cell-phone OR telerehabilitation OR âtele-rehabilitationâ OR âcomputer-based intervention*â OR âwearable technolog*â OR e-rehab* OR iCBT OR âcomputer-based cognitive behavio#râ OR computer-based OR âsports watch*â OR smartwatch* OR âactivity tracker*â OR âapple watch*â OR âtext messag*â S2 (DE "EXERCISE" ) OR (DE "PHYSICAL activity") Explode âexerciseâ S3 TX "Physical activit*" OR "Physical inactivit*" OR "Sedentary behavio*" OR aerobic OR "physical fitness" OR walk* OR run OR running OR yoga OR "strength training" OR sports OR "active living" OR "active transport" OR "leisure activit*" OR "resistance training" OR "endurance training" OR "weight training" OR Tai-chi OR swim* OR jog OR jogging OR dance* OR dancing OR gardening OR treadmill* OR biking OR cycling OR pilates OR ârecreation activit*â OR âphysical therap*â S4 S2 OR S3 S5 (DE "MENTAL depression" OR DE "DEPRESSION in college students" ) Explode âmental depressionâ S7 TX (depress* OR dysthymi* OR distress* OR âcommon mental healthâ OR âmood disorder*â OR âaffective disorder*â OR âaffective symptom*â OR âadjustment disorder*â) S7 S1 AND S4 AND S5 S8 (AB Intervention* OR program* OR trial* OR rct OR "quasi experimental" OR quasi-experimental OR "randomi#ed controlled trial*" OR "randomi#ed trial*" OR âcontrolled clinical trial*â OR âclinical trial*â OR ârandomi#ed clinical trial*â OR ârandomi#ed control trial*â OR wait-list OR experiment* OR allocat* OR control) S9 (TI Intervention* OR program* OR trial* OR rct OR "quasi experimental" OR quasi-experimental OR "randomi#ed controlled trial*" OR "randomi#ed trial*" OR âcontrolled clinical trial*â OR âclinical trial*â OR ârandomi#ed clinical trial*â OR ârandomi#ed control trial*â OR wait-list OR experiment* OR allocat* OR control) S10 S9 OR S9 S1 S6 AND S9 240 Search strategy in CINAHL (EBSCOhost) Search number Query S1 (MH "Telemedicine+") OR (MH "Telehealth") OR (MH "Videoconferencing") OR (MH "Mobile Applications") OR (MH "Smartphone") OR (MH "Internet-Based Intervention") OR (MH "Accelerometers") S2 TX âMobile healthâ or Telehealth or Telemedicine or Telerehabilitation or E-rehab* or Ehealth or Mhealth or âMobile application*â or âmobile technolog*â or âmobile healthcareâ or smartphone* or e-Health or m-Health or tele-Health or âmobile device*â or smart-phone or cell-phone or tele-rehabilitation or âcomputer-based intervention*â or Internet or Web or Internet-based or Web-based or computer-based or âOnline intervention*â or âWeb based intervention*â or âInternet based intervention*â or Videoconferenc* or âMobile phone*â or âSmart phone*â or âCell phone*â or âTelephone*â or Tablet* or App-based or App* or âText messag*â or Texting* or iCBT or âInternet-based cognitive behavio#râ or âcomputer-based cognitive behavio#râ or (Wearable N3 technolog*) or (Wearable N3 device*) or (Electronic N3 device*) or âsports watch*â or smartwatch* or âactivity tracker*â or âapple watch*â or âActivity tracker*â or âFitness tracker*â S3 S1 OR S2 S4 (MH "Exercise+") OR (MH "Activities of Daily Living") OR (MH "Aerobic Exercises+") OR (MH "Muscle Strengthening+") OR (MH "Walking+") OR (MH "Upper Extremity Exercises+") OR (MH "Physical Activity") OR (MH "Physical Fitness+") OR (MH "Leisure Activities") OR (MH "Sports+") OR (MH "Resistance Training") OR (MH "Dance Therapy") OR (MH "Physical Therapy+") OR (MH "Telerehabilitation") OR (MH "Home Physical Therapy") OR (MH "Rehabilitation, Athletic") OR (MH "Weight Lifting") S5 TX Exercise* or aerobic or âPhysical activit*â or âPhysical inactivit*â or Calisthenic* or Gymnastic* or Stretching* or âPhysical fitnessâ or âHuman physical trainingâ or âHuman physical conditioningâ or âPhysical enduranceâ or âendurance trainingâ or âPhysical staminaâ or Athletic* or Sport* or biking or cycling or bicycling or Pilates or Pilates-based or Ambulation or Walk* or Treadmill* or âActive transport*â or âactive livingâ or Run or Running* or Jog or Jogging* or Swim* or Diving* or âPhysical educationâ or Yoga or âTai chiâ or âTai jiâ or Tai-chi or Dance* or Dancing or âWeight lifting*â or âweight trainingâ or âLeisure activit*â or gardening or âRecreational activit*â or ârecreation activit*â or âStrength training*â or âResistance training*â or Weight-bearing or âPhysical therap*â S6 S5 OR S6 S7 (MH "Depression+") OR (MH "Adjustment Disorders+") OR (MH "Affective Disorders, Psychotic") S8 TX (Depress* or melancholia* or Dysthymi* or âMood disorder*â or âaffective disorder*â or âAffective symptom*â or âAdjustment disorder*â or âReactive disorder*â) S9 S7 OR S8 S10 S3 AND S6 AND S9 S11 (AB Intervention* OR program* OR trial* OR rct OR "quasi experimental" OR quasi-experimental OR "randomi#ed controlled trial*" OR "randomi#ed trial*" OR âcontrolled clinical trial*â OR âclinical trial*â OR ârandomi#ed clinical trial*â OR ârandomi#ed control trial*â OR wait-list OR experiment*) S12 (TI Intervention* OR program* OR trial* OR rct OR "quasi experimental" OR quasi-experimental OR "randomi#ed controlled trial*" OR "randomi#ed trial*" OR âcontrolled clinical trial*â OR âclinical trial*â OR ârandomi#ed clinical trial*â OR ârandomi#ed control trial*â OR wait-241 list OR experiment*) S13 S11 OR S12 S14 S10 AND S13 Search strategy for the Cochrane Central Register of Controlled Trials (CENTRAL)(via Ovid) Search number Query 1 exp depression/ or exp depressive disorder/ 2 (depress* or dysthymi* or distress* or common mental health or mood disorder* or affective disorder* or affective symptom* or adjustment disorder*).mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 3 1 or 2 4 exp telemedicine/ 5 (Mobile application* or mobile technolog* or mobile healthcare or mHealth or eHealth or teleHealth or digital or internet-based or web-based or app-based or apps or mobile phone* or smartphone* or computer* or tablet* or virtual or online or video* or e-Health or m-Health or tele-Health or mobile health or mobile device* or smart-phone or smart phone* or cell phone* or cell-phone or telerehabilitation or tele-rehabilitation or computer-based intervention* or wearable technolog* or e-rehab* or iCBT or computer-based cognitive behavio?r or computer-based or sports watch* or smartwatch* or activity tracker* or apple watch* or text messag*).mp. 6 4 or 5 7 exp exercise/ 8 (Physical activit* or Physical inactivit* or Sedentary behavio* or aerobic or physical fitness or walk* or run or running or yoga or strength training or sports or active living or active transport or leisure activit* or resistance training or endurance training or weight training or Tai-chi or swim* or jog or jogging or dance* or dancing or gardening or treadmill* or biking or cycling or pilates or recreation activit* or physical therap*).mp. 9 7 or 8 10 3 and 6 and 9 Search strategy for OpenGrey (depress* OR dysthymi* OR distress* OR âcommon mental healthâ OR âmood disorder*â OR âaffective disorder*â OR âaffective symptom*â OR âadjustment disorder*â) AND ("physical activity" OR âphysical inactivityâ OR âsedentary behavio*â OR "exercise" OR "aerobic" OR "run" OR "running" OR "yoga" OR "jog" 242 OR "jogging" OR "swim" OR "swimming" OR "physical fitness" OR "active transport" OR "leisure activit*" OR âendurance trainingâ OR âresistance trainingâ OR "weight training" OR "Tai Chi" OR "sports" OR "walk" OR "walking") AND ("eHealth" OR "mHealth" OR "telemedicine" OR "app" OR "mobile" OR "online" OR "mobile application" OR "telehealth") Search strategy for ProQuest Search number Query Filters S1 noft(Intervention* or program* or trial* or rct or quasi-experimental or randomized controlled trial* or randomized trial* or controlled clinical trial* or clinical trial* or randomized clinical trial* or randomized control trial* or wait-list or experiment* or allocat* or control) Limit to full text S2 noft(depress* or dysthymi* or distress* or common mental health or mood disorder* or affective disorder* or affective symptom* or adjustment disorder*) Limit to full text S3 noft(Mobile application* or mobile technolog* or mobile healthcare or mHealth or eHealth or teleHealth or digital or internet-based or web-based or app-based or apps or mobile phone* or smartphone* or computer* or tablet* or virtual or online or video* or e-Health or m-Health or tele-Health or mobile health or mobile device* or smart-phone or smart phone* or cell phone* or cell-phone or telerehabilitation or tele-rehabilitation or computer-based intervention* or wearable technolog* or e-rehab* or iCBT or computer-based cognitive behavio?r or computer-based or sports watch* or smartwatch* or activity tracker* or apple watch* or text messag*) Limit to full text S4 noft(Physical activit* or Physical inactivit* or Sedentary behavio* or aerobic or physical fitness or walk* or run or running or yoga or strength training or sports or active living or active transport or leisure activit* or resistance training or endurance training or weight training or Tai-chi or swim* or jog or jogging or dance* or dancing or gardening or treadmill* or biking or cycling or pilates or recreation activit* or physical therap*) Limit to full text S5 1 AND 2 AND 3 AND 4 243 Appendix C. Chapter 3: Search strategies for the second round of searches Search Strategy for MEDLINE (Ovid) Search number Query 1 depression/ or exp Depressive Disorder/ or Dysthymic Disorder/ or Mood Disorders/ or Affective Symptoms/ or Adjustment Disorders/ 2 Depress*.mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 3 1 or 2 4 exp Internet/ or exp Cell Phone/ 5 (Mobile health or E-rehab* or Ehealth or Mhealth or Mobile application* or mobile technolog* or mobile healthcare or smartphone* or e-Health or m-Health or mobile device* or smart-phone or cell-phone or computer-based intervention* or Internet or Web or computer or Internet-based or Web-based or computer-based or Online intervention* or Web based intervention* or Internet based intervention* or Mobile phone* or Smart phone* or Cell phone* or App-based or App*).mp. 6 4 or 5 7 exercise/ or cool-down exercise/ or gymnastics/ or muscle stretching exercises/ or exp Physical Fitness/ or exp Physical Conditioning, Human/ or Warm-Up Exercise/ or physical endurance/ or exercise tolerance/ or exp Exercise Therapy/ or exp Sports/ or Sports Medicine/ or exp Exercise Movement Techniques/ or exp Walking/ or exp Running/ or Swimming/ or Diving/ or "Physical Education and Training"/ or tai ji/ or yoga/ or Dance Therapy/ or Leisure Activities/ 8 (Exercise* or aerobic or Physical activit* or Physical inactivit* or Stretching* or Physical fitness or Physical endurance or endurance training or Physical stamina or Athletic* or Sport* or biking or cycling or bicycling or Pilates or Pilates-based or Ambulation or Walk* or Treadmill* or Active transport* or active living or Run or Running* or Jog or Jogging* or Swim* or Diving* or Physical education or Yoga or Tai chi or Tai ji or Tai-chi or Dance* or Dancing or Weight lifting* or weight training or Leisure activit* or gardening or Recreational activit* or recreation activit* or Strength training* or Resistance training* or Weight-bearing or Physical therap*).mp. 9 7 or 8 10 (rct or quasi-experimental or randomi?ed controlled trial* or randomi?ed trial* or randomi?ed clinical trial* or randomi?ed control trial* or wait-list or experiment* or randomly).mp. 11 3 and 6 12 11 and 9 13 12 and 10 244 Search strategy for EMBASE(via Ovid) Search number Query 1 depression/ or Mood Disorder/ or Affective Symptoms/ or Adjustment Disorders/ 2 Depress*.mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, keyword, floating subheading word, candidate term word] 3 1 or 2 4 mobile health application/ or web-based intervention/ 5 (Mobile health or E-rehab* or Ehealth or Mhealth or Mobile application* or mobile technolog* or mobile healthcare or smartphone* or e-Health or m-Health or mobile device* or smart-phone or cell-phone or computer-based intervention* or Internet or Web or computer or Internet-based or Web-based or computer-based or Online intervention* or Web based intervention* or Internet based intervention* or Mobile phone* or Smart phone* or Cell phone* or App-based or App*).mp. 6 4 or 5 7 exp physical activity/ or exp kinesiotherapy/ or exp exercise/ or exp sport/ or fitness/ or physical education/ or sports medicine/ or dance therapy/ or endurance/ 8 (Exercise* or aerobic or Physical activit* or Physical inactivit* or Stretching* or Physical fitness or Physical endurance or endurance training or Physical stamina or Athletic* or Sport* or biking or cycling or bicycling or Pilates or Pilates-based or Ambulation or Walk* or Treadmill* or Active transport* or active living or Run or Running* or Jog or Jogging* or Swim* or Diving* or Physical education or Yoga or Tai chi or Tai ji or Tai-chi or Dance* or Dancing or Weight lifting* or weight training or Leisure activit* or gardening or Recreational activit* or recreation activit* or Strength training* or Resistance training* or Weight-bearing or Physical therap*).mp. 9 7 or 8 10 3 and 6 11 9 and 10 12 (rct or quasi-experimental or randomi?ed controlled trial* or randomi?ed trial* or randomi?ed clinical trial* or randomi?ed control trial* or wait-list or experiment* or randomly).mp. 13 11 and 12 Search strategy for the Cochrane Central Register of Controlled Trials (CENTRAL)(via Ovid) Search number Query 1 Depress*.mp. [mp=title, abstract, heading word, drug trade name, original title, device manufacturer, drug manufacturer, device trade name, 245 keyword, floating subheading word, candidate term word] 2 (Mobile health or E-rehab* or Ehealth or Mhealth or Mobile application* or mobile technolog* or mobile healthcare or smartphone* or e-Health or m-Health or mobile device* or smart-phone or cell-phone or computer-based intervention* or Internet or Web or computer or Internet-based or Web-based or computer-based or Online intervention* or Web based intervention* or Internet based intervention* or Mobile phone* or Smart phone* or Cell phone* or App-based or App*).mp. 3 exp exercise/ or exp physical endurance/ or physical fitness/ or exp sports/ or exp exercise therapy/ or Dance Therapy/ or Sports Medicine/ 4 (Exercise* or aerobic or Physical activit* or Physical inactivit* or Stretching* or Physical fitness or Physical endurance or endurance training or Physical stamina or Athletic* or Sport* or biking or cycling or bicycling or Pilates or Pilates-based or Ambulation or Walk* or Treadmill* or Active transport* or active living or Run or Running* or Jog or Jogging* or Swim* or Diving* or Physical education or Yoga or Tai chi or Tai ji or Tai-chi or Dance* or Dancing or Weight lifting* or weight training or Leisure activit* or gardening or Recreational activit* or recreation activit* or Strength training* or Resistance training* or Weight-bearing or Physical therap*).mp. 5 3 or 4 6 1 and 2 7 6 and 5 Search strategy for PsycInfo (EBSCOhost) Search number Query S1 (DE "Online Therapy" OR DE "Computer Assisted Therapy" OR DE "Mobile Applications" OR DE "Electronic Health Services" OR DE "Digital Interventions" OR DE "Mobile Health" OR DE "Mobile Devices" OR DE "Mobile Phones" OR DE "Tablet Computers" OR DE "Smartphones" ) S2 âMobile healthâ or E-rehab* or Ehealth or Mhealth or âMobile application*â or âmobile technolog*â or âmobile healthcareâ or smartphone* or e-Health or m-Health or âmobile device*â or smart-phone or cell-phone or âcomputer-based intervention*â or Internet or Web or computer or Internet-based or Web-based or computer-based or âOnline intervention*â or âWeb based intervention*â or âInternet based intervention*â or âMobile phone*â or âSmart phone*â or âCell phone*â or App-based or App* S3 S1 OR S2 S4 (DE "Physical Activity" OR DE "Exercise" OR DE "Physical Fitness" OR DE "Aerobic Exercise" OR DE "Weightlifting" OR DE "Yoga") S5 Exercise* or aerobic or âPhysical activit*â or âPhysical inactivit*â or Stretching* or âPhysical fitnessâ or âHuman physical trainingâ or âHuman physical conditioningâ or âPhysical enduranceâ or âendurance trainingâ or âPhysical staminaâ or Athletic* or Sport* or biking or cycling or bicycling or Pilates or Pilates-based or Ambulation or Walk* or Treadmill* or âActive transport*â or âactive livingâ or Run or Running* or Jog or Jogging* or Swim* or Diving* or âPhysical educationâ or Yoga or âTai chiâ or âTai jiâ or Tai-chi or Dance* or Dancing or âWeight lifting*â or âweight trainingâ or âLeisure activit*â or gardening or âRecreational activit*â or ârecreation activit*â or âStrength training*â or âResistance training*â or Weight-bearing or âPhysical therap*â 246 S6 S4 OR S5 S7 (DE "Major Depression" OR DE "Anaclitic Depression" OR DE "Dysthymic Disorder" OR DE "Endogenous Depression" OR DE "Late Life Depression" OR DE "Postpartum Depression" OR DE "Reactive Depression" OR DE "Recurrent Depression" OR DE "Treatment Resistant Depression" ) S8 Depress* S9 S7 OR S8 S10 (TI rct OR "quasi experimental" OR quasi-experimental OR "randomi#ed controlled trial*" OR "randomi#ed trial*" OR ârandomi#ed clinical trial*â OR ârandomi#ed control trial*â OR wait-list OR experiment*) S11 (AB rct OR "quasi experimental" OR quasi-experimental OR "randomi#ed controlled trial*" OR "randomi#ed trial*" OR ârandomi#ed clinical trial*â OR ârandomi#ed control trial*â OR wait-list OR experiment* OR randomly) S12 S10 OR S11 S13 S3 AND S6 S14 S9 AND S13 S15 S12 AND S14 Search strategy for Web of Science Search number Query #1 TS=Depress* #2 TS=(âMobile healthâ or E-rehab* or Ehealth or Mhealth or âMobile application*â or âmobile technolog*â or âmobile healthcareâ or smartphone* or e-Health or m-Health or âmobile device*â or smart-phone or cell-phone or âcomputer-based intervention*â or Internet or Web or computer or Internet-based or Web-based or computer-based or âOnline intervention*â or âWeb based intervention*â or âInternet based intervention*â or âMobile phone*â or âSmart phone*â or âCell phone*â or Tablet* or App-based or App*) #3 TS=(Exercise* or aerobic or âPhysical activit*â or âPhysical inactivit*â or Stretching* or âPhysical fitnessâ or âHuman physical trainingâ or âHuman physical conditioningâ or âPhysical enduranceâ or âendurance trainingâ or âPhysical staminaâ or Athletic* or Sport* or biking or cycling or bicycling or Pilates or Pilates-based or Ambulation or Walk* or Treadmill* or âActive transport*â or âactive livingâ or Run or Running* or Jog or Jogging* or Swim* or Diving* or âPhysical educationâ or Yoga or âTai chiâ or âTai jiâ or Tai-chi or Dance* or Dancing or âWeight lifting*â or âweight trainingâ or âLeisure activit*â or gardening or âRecreational activit*â or ârecreation activit*â or âStrength training*â or âResistance training*â or Weight-bearing or âPhysical therap*â) #4 TS=(rct OR quasi-experimental OR "randomi$ed controlled trial*" OR "randomi$ed trial*" OR ârandomi$ed clinical trial*â OR ârandomi$ed control trial*â OR wait-list OR experiment* OR randomly) 247 #5 #1 AND #2 AND #3AND #4 Search strategy for SportDiscus (EBSCOhost) Search number Query Thesaurus S1 âMobile healthâ or E-rehab* or Ehealth or Mhealth or âMobile application*â or âmobile technolog*â or âmobile healthcareâ or smartphone* or e-Health or m-Health or tele-Health or âmobile device*â or smart-phone or cell-phone or âcomputer-based intervention*â or Internet or Web or computer or Internet-based or Web-based or computer-based or âOnline intervention*â or âWeb based intervention*â or âInternet based intervention*â or âMobile phone*â or âSmart phone*â or âCell phone*â or Tablet* or App-based or App* S2 DE "EXERCISE" OR DE "EXERCISE videos" OR DE "PHYSICAL activity" OR DE "PHYSICAL education" OR DE "PHYSICAL fitness" OR DE "PHYSICAL training & conditioning" OR DE "SPORTS" OR DE "WARMUP" Explode âexerciseâ S3 Exercise* or aerobic or âPhysical activit*â or âPhysical inactivit*â or Stretching* or âPhysical fitnessâ or âHuman physical trainingâ or âHuman physical conditioningâ or âPhysical enduranceâ or âendurance trainingâ or âPhysical staminaâ or Athletic* or Sport* or biking or cycling or bicycling or Pilates or Pilates-based or Ambulation or Walk* or Treadmill* or âActive transport*â or âactive livingâ or Run or Running* or Jog or Jogging* or Swim* or Diving* or âPhysical educationâ or Yoga or âTai chiâ or âTai jiâ or Tai-chi or Dance* or Dancing or âWeight lifting*â or âweight trainingâ or âLeisure activit*â or gardening or âRecreational activit*â or ârecreation activit*â or âStrength training*â or âResistance training*â or Weight-bearing or âPhysical therap*â S4 S2 OR S3 S5 (DE "MENTAL depression" OR DE "DEPRESSION in college students" OR DE "MOOD (Psychology)" OR DE "AFFECTIVE disorders") Explode âmental depressionâ S6 Depress* S7 S5 OR S6 S8 S1 AND S4 AND S7 S9 (AB rct OR "quasi experimental" OR quasi-experimental OR "randomi#ed controlled trial*" OR "randomi#ed trial*" OR ârandomi#ed clinical trial*â OR ârandomi#ed control trial*â OR wait-list OR experiment* or randomly) S10 (TI rct OR "quasi experimental" OR quasi-experimental OR "randomi#ed controlled trial*" OR "randomi#ed trial*" OR ârandomi#ed clinical trial*â OR ârandomi#ed control trial*â OR wait-list OR experiment*) S11 S9 OR S10 S12 S8 AND S11 248 Search strategy in CINAHL (EBSCOhost) Search number Query S1 (MH "Mobile Applications") OR (MH "Smartphone") OR (MH "Internet-Based Intervention") S2 AB âMobile healthâ or E-rehab* or Ehealth or Mhealth or âMobile application*â or âmobile technolog*â or âmobile healthcareâ or smartphone* or e-Health or m-Health or âmobile device*â or smart-phone or cell-phone or âcomputer-based intervention*â or Internet or Web or Internet-based or Web-based or computer-based or âOnline intervention*â or âWeb based intervention*â or âInternet based intervention*â or âMobile phone*â or âSmart phone*â or âCell phone*â or Tablet* or App-based or App* S3 S1 OR S2 S4 (MH "Exercise+") OR (MH "Activities of Daily Living") OR (MH "Aerobic Exercises+") OR (MH "Muscle Strengthening+") OR (MH "Walking+") OR (MH "Physical Activity") OR (MH "Physical Fitness+") OR (MH "Leisure Activities") OR (MH "Sports+") OR (MH "Resistance Training") OR (MH "Dance Therapy") OR (MH "Physical Therapy+") OR (MH "Home Physical Therapy") OR (MH "Rehabilitation, Athletic") OR (MH "Weight Lifting") S5 AB Exercise* or aerobic or âPhysical activit*â or âPhysical inactivit*â or Stretching* or âPhysical fitnessâ or âHuman physical trainingâ or âHuman physical conditioningâ or âPhysical enduranceâ or âendurance trainingâ or âPhysical staminaâ or Athletic* or Sport* or biking or cycling or bicycling or Pilates or Pilates-based or Ambulation or Walk* or Treadmill* or âActive transport*â or âactive livingâ or Run or Running* or Jog or Jogging* or Swim* or Diving* or âPhysical educationâ or Yoga or âTai chiâ or âTai jiâ or Tai-chi or Dance* or Dancing or âWeight lifting*â or âweight trainingâ or âLeisure activit*â or gardening or âRecreational activit*â or ârecreation activit*â or âStrength training*â or âResistance training*â or Weight-bearing or âPhysical therap*â S6 S5 OR S6 S7 (MH "Depression+") OR (MH "Adjustment Disorders+") OR (MH "Affective Disorders, Psychotic") S8 AB Depress* S9 S7 OR S8 S10 S3 AND S6 AND S9 S11 (AB rct OR "quasi experimental" OR quasi-experimental OR "randomi#ed controlled trial*" OR "randomi#ed trial*" OR ârandomi#ed clinical trial*â OR ârandomi#ed control trial*â OR wait-list OR experiment* OR randomly) S12 (TI rct OR "quasi experimental" OR quasi-experimental OR "randomi#ed controlled trial*" OR "randomi#ed trial*" OR ârandomi#ed clinical trial*â OR ârandomi#ed control trial*â OR wait-list OR experiment*) S13 S11 OR S12 S14 S10 AND S13 249 Search strategy for OpenGrey ((((Depress*) AND (Exercise* OR aerobic OR âPhysical activit*â OR âPhysical inactivit*âOR âPhysical fitnessâ OR âHuman physical trainingâ OR âHuman physical conditioningâ OR âPhysical enduranceâ OR âendurance trainingâ OR âPhysical staminaâ OR Athletic* OR Sport* OR biking OR cycling OR bicycling OR Pilates OR Pilates-based OR Ambulation OR Walk* OR Treadmill* OR âActive transport*â OR âactive livingâ OR Run OR Running* OR Jog OR Jogging* OR Swim* OR Diving* OR âPhysical educationâ OR Yoga OR âTai chiâ OR âTai jiâ OR Tai-chi OR Dance* OR Dancing OR âWeight lifting*â OR âweight trainingâ OR âLeisure activit*â OR gardening OR âRecreational activit*â OR ârecreation activit*â OR âStrength training*â OR âResistance training*â OR Weight-bearing OR âPhysical therap*â)) AND (eHealth OR mHealth OR app* OR mobile OR online OR âmobile applicationâ OR telehealth OR âmobile technolog*â OR âmobile healthcareâ OR smartphone* OR e-Health OR m-Health OR Internet OR Web OR computer OR Internet-based OR Web-based OR computer-based OR âOnline intervention*â OR âWeb based intervention*â)) AND (rct OR âquasi experimentalâ OR quasi-experimental OR ârandomized controlled trial*â OR ârandomized trial*âOR ârandomized clinical trial*â OR ârandomized control trial*â OR wait-list OR experiment* OR randomly)) lang:"en" Search strategy for ProQuest Search number Query Filters S1 noft(rct or quasi-experimental or randomized controlled trial* or randomized trial* or randomized clinical trial* or randomized control trial* or wait-list or experiment* or randomly) Limit to full text S2 noft(Depress* or melancholia* or Dysthymi* or Mood disorder* or affective disorder* or Affective symptom* or Adjustment disorder* or Reactive disorder*) Limit to full text S3 noft(Mobile health or Telehealth or Telemedicine or Telerehabilitation or E-rehab* or Ehealth or Mhealth or Mobile application* or mobile technolog* or mobile healthcare or smartphone* or e-Health or m-Health or tele-Health or mobile device* or smart-phone or cell-phone or tele-rehabilitation or computer-based intervention* or Internet or Web or Internet-based or Web-based or computer-based or Online intervention* or Web based intervention* or Internet based intervention* or Videoconferenc* or Mobile phone* or Smart phone* or Cell phone* or Telephone* or Tablet* or App-based or App* or Text messag* or Texting* or iCBT or Internet-based cognitive behavior or computer-based cognitive behavior or (Wearable N/3 technolog*) or (Wearable N/3 device*) or (Electronic N/3 device*) or sports watch* or smartwatch* or activity tracker* or apple watch* or Activity tracker* or Fitness tracker*) Limit to full text S4 noft(Exercise* or aerobic or Physical activit* or Physical inactivit* or Calisthenic* or Gymnastic* or Stretching* or Physical fitness or Human physical training or Human physical conditioning or Physical endurance or endurance training or Physical stamina or Athletic* or Sport* or biking or cycling or bicycling or Pilates or Pilates-based or Ambulation or Walk* or Treadmill* or Active transport* or active living or Run or Running* or Jog or Jogging* or Swim* or Diving* or Physical education or Yoga or Tai chi or Tai ji or Tai-chi or Dance* or Dancing or Weight lifting* or weight training or Leisure activit* or gardening or Recreational activit* or recreation activit* or Strength training* or Resistance training* or Weight-bearing or Physical therap*) Limit to full text S5 1 AND 2 AND 3 AND 4 250 Appendix D. Chapter 5: Outline of the content of MoodMover and the corresponding M-PAC constructs and behaviour change techniques Modules Topics covered 1) Feeling better through daily activity â¢ Exercise as a treatment option for depression â¢ Mood and physical activity interaction â¢ Physical benefits of exercise â¢ Goal-setting Complementary module 1 â¢ Reward progress â¢ Overcome low mood 2) Making physical activity enjoyable â¢ Introducing affect â¢ The importance of enjoying physical activity â¢ Strategies and activities to increase the enjoyment of physical activity Complementary module 2 â¢ Motivation to exercise â¢ Overcome bad weather â¢ Workout videos 3) Building your self-confidence â¢ Self-efficacy â¢ How to increase self-efficacy â¢ Exercise experiences shared by peers Complementary module 3 â¢ More exercise experiences 4) Building your physical activity opportunity â¢ The influence of environment on behaviour â¢ How to build environment for physical activity â¢ Brainstorm physical activity opportunities â¢ Grab and go activities Complementary module 4 â¢ Overcome fatigue â¢ Except physical sensations â¢ Mindfulness 5) Developing self-regulatory skills â¢ Action planning â¢ Coping planning Complementary module 5 â¢ Protected time 6) Drawing on social support â¢ Introducing social support â¢ Build your social support â¢ Thinking strategies for exercise â¢ Positive self-talk Complementary module 6 â¢ Positive reflective and self-talk 7) Forming an exercise habit â¢ Introducing habit â¢ Relating habit to physical activity â¢ How to form a habit (repetition, scripts, environmental cues) Complementary module 7 â¢ Exercise can be difficult 8) Building your exercise identity â¢ Introducing WHO guidelines â¢ Introducing exercise identity â¢ Ways to increase exercise identity (commitment, enjoyment, social comparison, passion) Complementary module 8 â¢ A surprise, bonus lesson with encouraging messages Note. M-PAC = Multi-Process Action Control; WHO = World Health Organization. 251 *The numbers in the brackets refers to the behaviour change techniques in the âBCT taxonomy v1â by Michie et al (Michie et al., 2013) 252 Appendix E. Chapter 5: Consolidated criteria for reporting qualitative studies (COREQ): 32-item checklist Developed from: Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. International Journal for Quality in Health Care. 2007. Volume 19, Number 6: pp. 349 â 357 Item No Guide Questions/Description Reported on Page # a Domain 1: Research team and reflexivity Personal Characteristics 1. Interviewer/ facilitator Which author/s conducted the interview or focus group? Pg 15-16 2. Credentials What were the researcherâs credentials? E.g., PhD, MD Corresponding author information 3. Occupation What was their occupation at the time of the study? Corresponding author information 4. Gender Was the researcher male or female? N/A 5. Experience and training What experience or training did the researcher have? Pg 15 Relationship with participants 6. Relationship established Was a relationship established prior to study commencement? N/A 7. Participant knowledge of the interviewer What did the participants know about the researcher? e.g. personal goals, reasons for doing the research? N/A 8. Interviewer characteristics What characteristics were reported about the interviewer/facilitator? e.g. Bias, assumptions, reasons and interests in the research topic N/A Domain 2: study design Theoretical framework 9. Methodological orientation and Theory What methodological orientation was stated to underpin the study? e.g. grounded theory, discourse analysis, ethnography, phenomenology, content analysis Pg 17 Participant selection 253 Item No Guide Questions/Description Reported on Page # a 10. Sampling How were participants selected? e.g., purposive, convenience, consecutive, snowball Pg 14 11. Method of approach How were participants approached? e.g., face-to-face, telephone, mail, email Pg 14-15 12. Sample size How many participants were in the study? Pg 18 13. Non-participation Setting How many people refused to participate or dropped out? Reasons? Pg 18 14. Setting of data collection Where was the data collected? e.g., home, clinic, workplace Pg 15 15. Presence of nonparticipants Was anyone else present besides the participants and researchers? Pg 16 16. Description of sample What are the important characteristics of the sample? e.g. demographic data, date Pg 18 Data collection 17. Interview guide Were questions, prompts, and guides provided by the authors? Was it pilot tested? Pg 14-15 18. Repeat interviews Were repeat interviews carried out? If yes, how many? N/A 19. Audio/visual recording Did the research use audio or visual recording to collect the data? Pg 16 20. Field notes Were field notes made during and/or after the interview or focus group? Pg 16 21. Duration What was the duration of the interviews or focus group? Pg 15 22. Data saturation Was data saturation discussed? N/A 23. Transcripts returned Were transcripts returned to participants for comment and/or correction? N/A Domain 3: analysis and findings Data analysis 24. Number of data coders How many data coders coded the data? Pg 17 25. Description of the coding tree Did the authors provide a description of the coding tree? Pg 17-18 26. Derivation of themes Were themes identified in advance or derived from the data? N/A 27. Software What software, if applicable, was used to manage the data? Pg 17 28. Participant checking Did participants provide feedback on the findings? N/A Reporting 29. Quotations presented Were participant quotations presented to illustrate the themes/findings? Was each quotation identified? e.g., participant number Pg 22-27; Tables 3 and 4 254 Item No Guide Questions/Description Reported on Page # a 30. Data and findings consistent Was there consistency between the data presented and the findings? Pg 22-27 31. Clarity of major themes Were major themes clearly presented in the findings? Pg 22-27 32. Clarity of minor themes Is there a description of diverse cases or a discussion of minor themes? Pg 22-27 aThe page and table numbers shown match those from the corresponding published paper. 255 Appendix F. Chapter 5: Screening questionnaire. 1. What is your age? _____________ 2. Do you have a smartphone with internet access that can download and use an app? ( ) Yes ( ) No If No, end of the survey. 3. Have you been diagnosed with depression? ( ) Yes ( ) No If No, please complete the BDI-II questionnaire. 4. Please indicate how many minutes of moderate-to-vigorous physical activity you engaged in the past 7 days. Moderate-to-vigorous physical activity refers to any physical movement that gets your heart pumping faster and makes you breathe a bit harder. This includes activities like brisk walking, cycling, or any exercise that makes you feel warmer and slightly out of breath. ___________ If achieving 150 mins of moderate-to-vigorous physical activity per week, end of the survey. 5. Do you currently have any physical disabilities that prevent you from engaging in regular exercise or physical activity? ( ) Yes ( ) No If Yes, end of the survey. 6. Have you experienced any of the following conditions in the past month: active psychosis or mania, active suicidal thoughts, or severe difficulties with thinking and memory? ( ) Yes ( ) No If Yes, end of the survey. 7. If you are female, are you currently pregnant? ( ) Yes ( ) No If Yes, end of the survey. 8. Have you been diagnosed with another major primary psychiatric disorder, such as psychosis or bipolar disorder? ( ) Yes ( ) No If Yes, end of the survey. 256 Appendix G: Chapter 5: Demographic survey. 1. What is your age? 2. Please indicate your gender. o Woman o Man o Non-binary person o Prefer not to Answer 3. The Canadian Census identifies the following categories in its Census of the Population. Please indicate how you self-identify. This self-identification is not intended as an indication of oneâs place of origin, citizenship, language or culture and recognizes that there are differences both between and among subgroups of persons of colour. If you are of mixed-descent, please indicate this by selecting all that apply, rather than using the âotherâ line unless parts of your self-identification do not appear in this list. âª Indigenous peoples of Canada âª Indigenous (outside of Canada) âª Arab âª Black âª Chinese (including Mainland China, Hong Kong, Macau and Taiwan) âª Filipino âª Japanese âª Korean âª Latin, Central, or South American (e.g., Brazilian, Chilean, Colombian, Mexican) âª South Asian (e.g., Indian, Pakistani, Sri Lankan, etc.) âª Southeast Asian (e.g., Cambodian, Indonesian, Laotian Vietnamese, etc.) âª West Asian (e.g., Afghan, Iranian, Syrian, etc.) âª White âª Other âª Prefer not to answer 4. Please indicate your ancestry here (if âOtherâ is selected to Q3). 5. What is the highest degree or level of education you have completed? o Some high school o High school o Certificate or diploma o Bachelorâs degree o Masterâs degree o PhD o Professional degree o Prefer not to answer 6. What is your current employment status? o Employed â part time (including self-employed) o Employed â full time (including self-employed o Stay-at-home parent o Homemaker o On maternity/parental leave o On medical or disability 257 o Retired o Unemployed o Other o Prefer not to answer 7. Please indicate your current employment status here (if âOtherâ is selected to Q6). 8. What is your before-tax household income? o $0 - $19,999 o $20,000 - $39,999 o $40,000 - $59,999 o $60,000 - $79,999 o $80,000 - $99,999 o $100,000 - $119,999 o $120,000 - $139,999 o $140,000 or greater o Prefer not to answer 258 Appendix H. Chapter 5: Semi-structured interview questions. Q1: Do you use, or have you used, any mental health/physical activity apps/wearable devices? Why/why not? Q2: What did you like best about the MoodMover app? Q3: What did you like least about the app? Q4: How easy was it to navigate or find your way around the app? Q5: What did you think about the overall look of the app? Q6: What did you think about the information provided on the app? Q7: Is there anything you think the app might be missing? Q8: Would you be interested in using the upgraded MoodMover in the future to increase your physical activity and reduce depressive symptoms? Why/why not? 259 Appendix I. Chapters 5-7: Adapted MAUQ â Patient version for standalone apps (Responses on a 1 to 7, Strongly Disagree to Strongly Agree, scale) Ease of use S1. The app was easy to use. S2. It was easy for me to learn to use the app. S3. The navigation was consistent when moving between screens. S4. The interface of the app allowed me to use all the functions (such as setting a step goal, logging a physical activity session, receiving notifications) offered by the app. Interface and satisfaction S5. Whenever I made a mistake using the app, I could recover easily and quickly. S6. I like the interface of the app. S7. The information in the app was well organized, so I could easily find the information I needed. S8. The app adequately acknowledged and provided information to let me know the progress of my action. S9. I feel comfortable using this app in social settings. S10. The amount of time involved in using this app has been fitting for me. S11. I would use this app again. S12. Overall, I am satisfied with this app. Usefulness S13. The app would be useful for my mental health and well-being. 260 Appendix J. Chapter 5: Participant characteristics (N=9). Characteristics n Gender Woman 5 Man 3 Non-binary 1 Age, mean (SD) 38.4 (10.5) Age group (years) 18-30 2 30-40 4 40-50 1 50-60 2 Ethnicity White 5 White and Indigenous 1 Chinese 1 South Asian 1 Latin, Central, or South American 1 Education Level High school 1 Certificate or diploma 1 Bachelor 4 Master 3 Employment statue Employed (full-time) 3 Employed (part-time) 3 On medical or disability 3 Income level $20,000 - $39,999 1 $40,000 - $59,999 1 $60,000 - $79,999 1 $80,000 - $99,999 2 $100,000 - $119,999 1 $120,00 - $139,999 2 $140,000 or greater 1 PHQ-9, mean (SD) 14.1 (6.47) Mild: 5-9 3 Moderate: 10-20 4 Severe: 20-27 2 Physical activity, n of days/week with at least 30 mins of MVPA 0 3 1 4 2 2 Note. MVPA: Moderate-to-vigorous physical activity; PHQ-9: Patient Health Questionnaire - 9 items.261 Appendix K. Chapter 6: The SPIRIT 2013 Checklist. Recommended items to address in a clinical trial protocol, supplemented by items from CONSORT 2020 statement extension to pilot and feasibility trials (modified), as recommended by Thabane and Lancaster. Section/Item Item No Description Addressed on page numbera Administrative information Title 1 Descriptive title identifying the study design, population, interventions, and, if applicable, trial acronym 1 Trial registration 2a Trial identifier and registry name. If not yet registered, name of intended registry 2 2b All items from the World Health Organization Trial Registration Data Set NA Protocol version 3 Date and version identifier NA Funding 4 Sources and types of financial, material, and other support Funding statement Roles and responsibilities 5a Names, affiliations, and roles of protocol contributors 1 5b Name and contact information for the trial sponsor NA 5c Role of study sponsor and funders, if any, in study design; collection, management, analysis, and interpretation of data; writing of the report; and the decision to submit the report for publication, including whether they will have ultimate authority over any of these activities NA 5d Composition, roles, and responsibilities of the coordinating centre, steering committee, endpoint adjudication committee, data management team, and other individuals or groups overseeing the trial, if applicable NA Introduction Background and rationale 6a Description of research question and justification for undertaking the trial, including summary of relevant studies (published and unpublished) examining benefits and harms for each intervention 3-5 6b Explanation for choice of comparators NA Objectives 7 Specific objectives or hypotheses Replaced by CONSORT extension (items 2a and 2b) Trial design 8 Description of trial design including type of trial (eg, parallel group, crossover, factorial, single group), 5 262 Section/Item Item No Description Addressed on page numbera allocation ratio, and framework (eg, superiority, equivalence, noninferiority, exploratory) Methods: Participants, interventions, and outcomes Study setting 9 Description of study settings (eg, community clinic, academic hospital) and list of countries where data will be collected. Reference to where list of study sites can be obtained 5 Eligibility criteria 10 Inclusion and exclusion criteria for participants. If applicable, eligibility criteria for study centres and individuals who will perform the interventions 6 Interventions 11a Interventions for each group with sufficient detail to allow replication, including how and when they will be administered 6-10 11b Criteria for discontinuing or modifying allocated interventions for a given trial participant (eg, drug dose change in response to harms, participant request, or improving/worsening disease) 7 11c Strategies to improve adherence to intervention protocols, and any procedures for monitoring adherence (eg, drug tablet return, laboratory tests) 8-9 11d Relevant concomitant care and interventions that are permitted or prohibited during the trial 6 Outcomes 12 Primary, secondary, and other outcomes, including the specific measurement variable (eg, systolic blood pressure), analysis metric (eg, change from baseline, final value, time to event), method of aggregation (eg, median, proportion), and time point for each outcome Replaced by CONSORT extension (item 6a) Participant timeline 13 Time schedule of enrolment, interventions (including any run-ins and washouts), assessments, and visits for participants. A schematic diagram is highly recommended Fig 1 Sample size 14 Estimated number of participants needed to achieve study objectives and how it was determined, including clinical and statistical assumptions supporting any sample size calculations Replaced by CONSORT extension (item 7a) Recruitment 15 Strategies for achieving adequate participant enrolment to reach target sample size 7 Methods: Assignment of interventions (for controlled trials) Allocation: Sequence generation 16a Method of generating the allocation sequence (eg, computer-generated random numbers), and list of any factors for stratification NA Allocation concealment 16b Mechanism of implementing the allocation sequence (eg, central telephone; sequentially numbered, opaque, NA 263 Section/Item Item No Description Addressed on page numbera mechanism sealed envelopes), describing any steps to conceal the sequence until interventions are assigned Implementation 16c Who will generate the allocation sequence, who will enrol participants, and who will assign participants to interventions NA Blinding (masking) 17a Who will be blinded after assignment to interventions (eg, trial participants, care providers, outcome assessors, data analysts), and how NA 17b If blinded, circumstances under which unblinding is permissible, and procedure for revealing a participantâs allocated intervention during the trial NA Methods: Data collection, management, and analysis Data collection methods 18a Plans for assessment and collection of outcome, baseline, and other trial data, including any related processes to promote data quality (eg, duplicate measurements, training of assessors) and a description of study instruments (e.g., questionnaires, laboratory tests) along with their reliability and validity, if known. Reference to where data collection forms can be found, if not in the protocol Fig 1; 11-18 18b Plans to promote participant retention and complete follow-up, including list of any outcome data to be collected for participants who discontinue or deviate from intervention protocols Fig 1; 13 Data management 19 Plans for data entry, coding, security, and storage, including any related processes to promote data quality 19-20 Statistical methods 20a Statistical methods for analysing primary and secondary outcomes. Reference to where other details of the statistical analysis plan can be found Replaced by CONSORT extension (item 12a) 20b Methods for any additional analyses (eg, subgroup and adjusted analyses) 20-21 20c Definition of analysis population relating to protocol non-adherence (eg, as randomised analysis), and any statistical methods to handle missing data 20 Methods: Monitoring Data monitoring 21a Composition of data monitoring committee (DMC); summary of its role and reporting structure; statement of whether it is independent from the sponsor and competing interests 21-22 21b Description of any interim analyses and stopping guidelines, including who will have access to these interim results and make the final decision to terminate the trial 22 Harms 22 Plans for collecting, assessing, reporting, and Fig 1; 14; 21-264 Section/Item Item No Description Addressed on page numbera managing solicited and spontaneously reported adverse events and other unintended effects of trial interventions or trial conduct 22 Auditing 23 Frequency and procedures for auditing trial conduct, if any, and whether the process will be independent from investigators and the sponsor NA Ethics and dissemination Research ethics approval 24 Plans for seeking research ethics committee/institutional review board (REC/IRB) approval 5-6 Protocol amendments 25 Plans for communicating important protocol modifications to relevant parties (eg, investigators, REC/IRBs, trial participants, trial registries, journals, regulators) 6 Consent or assent 26a Who will obtain informed consent or assent from potential trial participants or authorized surrogates, and how 7 26b Additional consent provisions for collection and use of participant data and biological specimens in ancillary studies NA Confidentiality 27 How personal information about potential and enrolled participants will be collected, shared, and maintained in order to protect confidentiality Consent form Declaration of interests 28 Financial and other competing interests for principal investigators for the overall trial and each study site Competing interests statement Access to data 29 Statement of who will have access to the final trial dataset, and disclosure of contractual agreements that limit such access for investigators 19-20; 22; Consent form Ancillary and post-trial care 30 Provisions for ancillary and post-trial care, and for compensation to those who suffer harm from trial participation NA Dissemination policy 31a Plans for investigators and sponsor to communicate trial results to participants, healthcare professionals, the public, and other relevant groups (eg, via publication, reporting in results databases) 22 31b Authorship eligibility guidelines and any intended use of professional writers NA 31c Plans, if any, for granting public access to the full protocol, participant-level dataset, and statistical code NA Appendices Informed consent materials 32 Model consent form and other related documentation given to participants and authorized surrogates NA Biological specimens 33 Plans for collection, laboratory evaluation, and storage of biological specimens for genetic or molecular analysis in the current trial and for future use in ancillary studies NA 265 *It is strongly recommended that this checklist be read in conjunction with the SPIRIT 2013 Explanation & Elaboration for important clarification on the items. Amendments to the protocol should be tracked and dated. The SPIRIT checklist is copyrighted by the SPIRIT Group under the Creative Commons âAttribution-NonCommercial-NoDerivs 3.0 Unportedâ license. aThe page and table numbers shown match those from the corresponding published paper. 2. CONSORT 2020 statement extension to pilot and feasibility trials (modified for single-arm trial). Section/topic and item no. Extension for single-arm pilot trials item descriptor Page no.a Introduction Background and objectives 2a Scientific background and explanation of rationale for future definitive trial, and reasons for a single-arm pilot trial 3-4 2b Specific objectives or research questions for single-arm pilot trial 4-5 Methods Outcomes 6a Completely defined prespecified assessments or measurements to address each pilot trial objective specified in 2b, including how and when they were assessed Fig 1; 11-18 Sample size 7a Rationale for numbers in the single-arm pilot trial 18-19 Analytical methods 12a Methods used to address each pilot trial objective whether qualitative or quantitative 20-21 aThe page and table numbers shown match those from the corresponding published paper.266 Appendix L. Chapters 6 and 7: Screening questionnaire Part 1. 1. Are you aged between 18-64 years? o Yes o No (if selected, proceed to End of survey) 2. Do you have a smartphone, either iPhone or Android, with internet access that can download and use an app? o Yes o No (if selected, proceed to End of survey) 3. Are you able to read English? Are you able to respond to questions in spoken English? o Yes o No (if selected, proceed to End of survey) 4. Do you currently have a clinical diagnosis of a major depressive disorder? o Yes o No 5. If any, do you anticipate any major changes to your current antidepressant treatment regimen (e.g., changes in medication type or dosage, adjustments to psychotherapy) within the next 3 months? o Yes (if selected, proceed to End of survey) o No o Iâm not taking any antidepressant treatments 6. Do you currently have any physical disabilities that prevent you from engaging in regular exercise or physical activity? o Yes (if selected, proceed to End of survey) o No 7. Have you experienced any of the following conditions in the past month: active psychosis or mania, active suicidal thoughts, or severe difficulties with thinking and memory? o Yes (if selected, proceed to End of survey) o No 8. Do you have an active alcohol or substance use disorder? o Yes (if selected, proceed to End of survey) o No 9. If you are female, are you currently pregnant? o Yes (if selected, proceed to End of survey) o No o Not applicable 10. Have you been diagnosed with another major primary psychiatric disorder, such as psychosis? o Yes (if selected, proceed to End of survey) o No 10. In the past week, on how many days have you done a total of 30 minutes or more of physical activity, which was enough to raise your breathing rate? This may include sport, traditional games, exercise, and brisk walking or cycling for recreation or to get to and from places, but should not include housework or physical activity that may be part of your job. o 0 o 1 o 2 o 3 o 4 267 o 5 o 6 o 7 If >= 3 days/per week (achieving 90 mins of moderate-to-vigorous physical activity per week), procced to End of survey. 11. Do you anticipate a major absence (e.g., monthly long vacation, surgery) in the next 3 months? o Yes (if selected, proceed to End of survey) o No 12. Do you have a valid and active email address that you prefer to use to communicate with us and engage in this study? o Yes (if selected, proceed to Q13) o No (if selected, proceed to End of survey) 13. Please provide your email address: Part 2: Patient Health Questionnaire, 9-item (PHQ-9) 10. How difficult have these problems made it for you to do your work, take care of things at home, or get along with other people? â¢ Not difficult at all â¢ Somewhat difficult Over the last 2 weeks, how often have you been bothered by any of the following problems? Not at all Several days More than half the days Nearly every day 1. Little interest or pleasure in doing things 0 1 2 3 2. Feeling down, depressed, or hopeless 0 1 2 3 3. Trouble falling or staying asleep, or sleeping too much 0 1 2 3 4. Feeling tired or having little energy 0 1 2 3 5. Poor appetite or overeating 0 1 2 3 6. Feeling bad about yourself â or that you are a failure or have let yourself or your family down 0 1 2 3 7. Trouble concentrating on things, such as reading the newspaper or watching television 0 1 2 3 8. Moving or speaking so slowly that other people could have noticed? Or the opposite â being so fidgety or restless that you have been moving around a lot more than usual 0 1 2 3 9. Thoughts that you would be better off dead or of hurting yourself in some way 0 1 2 3 268 â¢ Very difficult â¢ Extremely difficult 269 Appendix M. Chapter 6: Topics of major lessons Modules Topics covered 1) Feeling better through daily activity â¢ Exercise as a treatment option for depression â¢ Mood and physical activity interaction â¢ Physical benefits of exercise â¢ Goal-setting 2) Making physical activity enjoyable â¢ Introducing affect â¢ The importance of enjoying physical activity â¢ Strategies and activities to increase the enjoyment of physical activity 3) Building your self-confidence â¢ Self-efficacy â¢ How to increase self-efficacy â¢ Exercise experiences shared by peers 4) Building your physical activity opportunity â¢ The influence of environment on behaviour â¢ How to build environment for physical activity â¢ Brainstorm physical activity opportunities â¢ Grab and go activities 5) Developing self-regulatory skills â¢ Action planning â¢ Coping planning 6) Drawing on social support â¢ Introducing social support â¢ Build your social support â¢ Thinking strategies for exercise â¢ Positive self-talk 7) Forming an exercise habit â¢ Introducing habit â¢ Relating habit to physical activity â¢ How to form a habit (repetition, scripts, environmental cues) 8) Building your exercise identity â¢ Introducing WHO guidelines â¢ Introducing exercise identity â¢ Ways to increase exercise identity (commitment, enjoyment, social comparison, passion) Note. WHO: World Health Organization. 270 Appendix N. Chapters 6 and 7: Demographic survey and clinical information. 1. What is your age? 2. What is your height (m)? 3. What is your weight (kg)? 4. Please indicate your sex at birth. o Female o Male o Prefer not to Answer 5. Please indicate your gender. o Woman o Man o Non-binary person o Prefer not to Answer 6. The Canadian Census identifies the following categories in its Census of the Population. Please indicate how you self-identify. This self-identification is not intended as an indication of oneâs place of origin, citizenship, language or culture and recognizes that there are differences both between and among subgroups of persons of colour. If you are of mixed-descent, please indicate this by selecting all that apply, rather than using the âotherâ line unless parts of your self-identification do not appear in this list. âª Indigenous peoples of Canada âª Indigenous (outside of Canada) âª Arab âª Black âª Chinese (including Mainland China, Hong Kong, Macau and Taiwan) âª Filipino âª Japanese âª Korean âª Latin, Central, or South American (e.g., Brazilian, Chilean, Colombian, Mexican) âª South Asian (e.g., Indian, Pakistani, Sri Lankan, etc.) âª Southeast Asian (e.g., Cambodian, Indonesian, Laotian Vietnamese, etc.) âª West Asian (e.g., Afghan, Iranian, Syrian, etc.) âª White âª Other âª Prefer not to answer 7. Please indicate your ancestry here (if âOtherâ is selected to Q3). 8. What is the highest degree or level of education you have completed? o Some high school o High school o Certificate or diploma o Bachelorâs degree o Masterâs degree o PhD o Professional degree o Prefer not to answer 9. What is your current employment status? o Employed â part time (including self-employed) o Employed â full time (including self-employed o Stay-at-home parent 271 o Homemaker o On maternity/parental leave o On medical or disability o Retired o Unemployed o Other o Prefer not to answer 10. Please indicate your current employment status here (if âOtherâ is selected to Q6). 11. What is your before-tax household income? o $0 - $19,999 o $20,000 - $39,999 o $40,000 - $59,999 o $60,000 - $79,999 o $80,000 - $99,999 o $100,000 - $119,999 o $120,000 - $139,999 o $140,000 or greater o Prefer not to answer 12. Do you currently have a clinical diagnosis of major depressive disorder? o Yes Ã 13 o No Ã 14 13. How long have you been diagnosed with depression? o Less than 1 month o 1-6 months o 6-12 months o 1-2 years o More than 2 years 14. How long have you been experiencing symptoms of depression? o Less than 1 month o 1-6 months o 6-12 months o 1-2 years o More than 2 years 15. Are you currently taking any medication for depression? o Yes Ã 16 o No Ã 18 16. Please specify the name(s) and dosage(s) of the medication(s) you are currently taking: 17. How long have you been taking this/these medication(s)? o Less than 1 month o 1-6 months o 6-12 months o 1-2 years o More than 2 years 18. Are you currently undergoing any other treatments for depression (e.g., psychotherapy, counseling, etc.)? o Yes Ã 19 o No Ã 26 272 19. If yes, please specify the type(s) of treatment(s): 20. How long have you been receiving this/these treatment(s)? o Less than 1 month o 1-6 months o 6-12 months o 1-2 years o More than 2 years 21. On a scale of 1 to 10, how effective do you find your current treatment for managing your depression symptoms? Scale from 1 (Not effective) to 10 (Very effective) 22. Have you noticed any side effects from your current treatment? o Yes o No 23. If yes, please specify the side effects: 24. Do you have any other ongoing medical conditions that are being treated along with your depression? o Yes o No 25. If yes, please specify the condition(s) and treatment(s): 26. Have you previously been treated for depression? o Yes Ã 25 o No Ã 26 27. If yes, please specify the type(s) of previous treatment(s) and duration: Alcohol Use (From CCWS) For the purpose of this survey, a drink means: â¢ 341 ml or 12 oz. of beer or cooler (bottle, can, or draft) â¢ 142 ml or 5 oz. of wine â¢ 43 ml or 1.5 oz. of liquor or spirit (straight or mixed) Include light beer. Exclude de-alcoholised beer or coolers (0.5% alcohol) or cocktails such as Virgin Mary or Shirley Temple. The physiological effects of alcohol vary by biological sex. For this question, refer to the number of drinks that corresponds with your biological sex at birth. 28. During the past 30 days, how often have you had 4 or more drinks (female sex) OR 5 or more drinks (male sex) on one occasion? âOn one occasionâ means at the same time or within a couple of hours of each other. o Daily or almost daily o 2 to 5 times a week o Once a week o 2 to 3 times in the past 30 days o Once in the past 30 days o Not in the past 30 days 273 o I do not drink alcohol o I don't know o I prefer not to answer Cannabis Use (From CCWS) The next questions are about cannabis. In this survey when we use the term cannabis, this includes marijuana (e.g., weed, pot), hashish, hash oil or any other products made from the cannabis plant, but not synthetic cannabinoids. When we ask about use, this includes using cannabis in its dry form or when mixed or processed into another product such as an edible, an extract, a concentrate, including hashish, a liquid, or other product. Cannabis use may include use for medical and/or non-medical purposes. 29. In the past 12 months, have you used cannabis? o Yes Ã 30 o No o I donât know o I prefer not to answer 30. If yes, in the past 30 days, how often did you use cannabis? o Not in the past 30 days o 1 day in the past 30 days o 2 or 3 days in the past 30 days o 1 or 2 day(s) per week o 3 or 4 days per week o 5 or 6 days per week o Daily o I don't know o I prefer not to answer 31. Are you currently using any other physical activity apps or devices (e.g., Fitbit, Garmin, Apple Watch)? o Yes Ã 32 o No Ã 33 32. How frequently do you use these physical activity apps or devices? o Not in the past 30 days o 1 day in the past 30 days o 2 or 3 days in the past 30 days o 1 or 2 day(s) per week o 3 or 4 days per week o 5 or 6 days per week o Daily o I don't know o I prefer not to answer 33. Have you ever used any physical activity apps or devices in the past? o Yes Ã 34 o No 34. For how long did you use these physical activity apps or devices? o Less than 1 week o 2-4 weeks o 1-2 months o 2-3 months o 3-4 months 274 o 4-6 months o Over 6 months 275 Appendix O. Chapters 6 and 7: Satisfaction survey Part 1. The mHealth Satisfaction Questionnaire, 14-item Strongly Disagree 1 2 3 4 Strongly Agree 5 What did you think about using the health app? It was easy to use It was good to use The time spent using it has been acceptable It has been difficult to remember to use it The introduction of how to use it was sufficient It was too time consuming It interrupted me in my daily activities It was boring to use It was a disturbance I can recommend it to others How did you experience the health app? It has motivated me to change my lifestyle habits It has helped me to understand the benefits of improving my lifestyle habits It has helped me to understand how I need to change my lifestyle habits It has helped me set personal goals for my lifestyle habits in a way that I could not have done on my own Part 2. Please list three aspects of the program/MoodMover that you particularly liked and three aspects that you disliked. You may refer to things such as the content, delivery, or support. 276 Appendix P. Chapters 6 and 7: Physical Activity Adult Questionnaire (PAAQ) The following questions are about various types of physical activities done in the last 7 days. 1. In the last 7 days, that is from last Sunday to yesterday, did you use active ways like walking or cycling to get to places such as work, school, the bus stop, the shopping centre or to visit friends (active transportation)? (Yes or No) 2. In the last 7 days, on which days did you do these activities? (Monday through Sunday) 3. How much time in total, in the last 7 days, did you spend doing these activities? (Type in answer) 4. In the last 7 days, did you do sports, fitness or recreational physical activities, organized or non-organized? Examples are walking, home or gym exercise, swimming, cycling, running, skiing, dancing and all team sports. (Yes or No) 5. Did any of these recreational physical activities make you sweat at least a little and breathe harder? (Yes or No) 6. In the last 7 days, on which days did you do these recreational activities that made you sweat at least a little and breathe harder? (Monday through Sunday) 7. In the last 7 days, how much time in total did you spend doing these activities that made you sweat at least a little and breathe harder? (Type in answer) 8. In the last 7 days, did you do any other physical activities while at work, in or around your home or while volunteering? Examples are carrying heavy loads, shoveling, and household chores such as vacuuming or washing windows. (Yes or No) 9. Did any of these other physical activities make you sweat at least a little and breathe harder? (Yes or No) 10. In the last 7 days, on which days did you do these other activities that made you sweat at least a little and breathe harder? (Monday through Sunday) 11. In the last 7 days, how much time in total did you spend doing these activities that made you sweat at least a little and breathe harder? (Type in answer) 12. If you have reported any minutes of physical activity that made you sweat at least a little and breathe harder. Of these activities, were there any of vigorous intensity, meaning they caused you to be out of breath? (Yes or No) 13. In the last 7 days, how much time in total did you spend doing vigorous activities that caused you to be out of breath? (Type in answer) 14. During the program, did you link a smartwatch or fitness device to the Health app (iOS) or Google Fit (Android)? â[post-intervention only] (Yes or No) 15. How often did you carry your phone or wear your smartwatch during your non-sedentary waking hours on workdays? â[post-intervention only] (Almost always, Sometimes, Seldom) 277 16. How often did you carry your phone or wear your smartwatch during your non-sedentary waking hours on weekends? â[post-intervention only] (Almost always, Sometimes, Seldom) 278 Appendix Q. Chapters 6 and 7: GAD-7 Anxiety Over the last two weeks, how often have you been bothered by the following problems? Not at all Several days More than half the days Nearly every day 1. Feeling nervous, anxious, or on edge 0 1 2 3 2. Not being able to stop or control worrying 0 1 2 3 3. Worrying too much about different things 0 1 2 3 4. Trouble relaxing 0 1 2 3 5. Being so restless that it is hard to sit still 0 1 2 3 6. Becoming easily annoyed or irritable 0 1 2 3 7. Feeling afraid, as if something awful might happen 0 1 2 3 Column totals _____ + _____ + _____ + _____ = Total score _______ If you checked any problems, how difficult have they made it for you to do your work, take care of things at home, or get along with other people? Not difficult at all Somewhat difficult Very difficult Extremely difficult â¡ â¡ â¡ â¡ Source: Primary Care Evaluation of Mental Disorders Patient Health Questionnaire (PRIME-MD-PHQ). The PHQ was developed by Drs. Robert L. Spitzer, Janet B.W. Williams, Kurt Kroenke, and colleagues. For research information, contact Dr. Spitzer at ris8@columbia.edu. PRIME-MD® is a trademark of Pfizer Inc. Copyright© 1999 Pfizer Inc. All rights reserved. Reproduced with permission Scoring GAD-7 Anxiety Severity This is calculated by assigning scores of 0, 1, 2, and 3 to the response categories, respectively, of ânot at all,â âseveral days,â âmore than half the days,â and ânearly every day.â GAD-7 total score for the seven items ranges from 0 to 21. 0â4: minimal anxiety 5â9: mild anxiety 10â14: moderate anxiety 15â21: severe anxiety 279 Appendix R. Chapters 6 and 7: The Pittsburgh Sleep Quality Index (PSQI) Instructions: The following questions relate to your usual sleep habits during the past month only. Your answers indicate the most accurate reply for the majority of days and nights in the past month. Please answer all the questions. During the past month, 1. When have you usually gone to bed? _________________ 2. How long (in minutes) has it taken you to fall asleep each night? ________________ 3. When have you usually gotten up in the morning? _________________ 4. How many hours of actual sleep do you get at night? (This may be different than the numbers of hours you spend in bed) _________________ 5. During the past month, how often have you had trouble sleeping because youâ¦ Not during the past month (0) Less than once a week (1) Once or twice a week (2) Three or more times a week (3) a. Cannot get to sleep within 30 minutes b. Wake up in the middle of the night or early morning c. Have to get up to use the bathroom d. Cannot breathe comfortably e. Cough or snore loudly f. Feel too cold g. Feel too hot h. Have bad dreams i. Have pain j. Other reason(s), please describe, including how often you have had trouble sleeping because of this reason(s): 6. During the past month, how often have you taken medicine (prescribed or âover the counterâ) to help you sleep? 7. During the past month, how often have you had trouble staying awake while driving, eating meals, or engaging in social activity? 8. During the past month, how much of a problem has it been for you to keep up enthusiasm to get things done? Very good (0) Fairly good (1) Fairly bad (2) Very bad (3) 9. During the past month, how would you rate your sleep quality overall? Scoring the PSQI 280 In scoring the PSQI, seven component scores are derived, each scored 0 (no difficulty) to 3 (severe difficulty). The component scores are summed to produce a global score (range 0 to 21). Higher scores indicate worse sleep quality. Component 1: Subjective sleep qualityâquestion 9 #9 Score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C1______ Component 2: Sleep latencyâquestions 2 and 5a #2 Score (â¤15min=0; 16-30 min=1; 31-60 min=2; >60 min=3) +#5a Score (if sum is equal 0=0; 1-2=1; 3-4=2; 5-6=3) . . . . . . . . . . . . . . . . . . . . . . . . . C2______ Component 3: Sleep durationâquestion 4 #4 Score (>7=0; 6-7=1; 5-6=2; <5=3) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C3______ Component 4: Sleep efficiencyâquestions 1, 3, and 4 (total # of hours asleep)/(total # of hours in bed) x 100 >85%=0, 75%-84%=1, 65%-74%=2, <65%=3 . . . . . . . . . . . . . . . . . . . . . . C4______ Component 5: Sleep disturbanceâquestions 5b-5j Sum of scores #5b to #5j (0=0; 1-9=1; 10-18=2; 19-27=3) . . . . . . . . . . . . . C5______ Component 6: Use of sleep medicationâquestion 6 #6 Score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . C6______ Component 7: Daytime dysfunction #7 Score + #8 Score (0=0; 1-2=1; 3-4=2; 5-6=3) . . . . . . . . . . . . . . . . . . . . . C7______ Add the seven component scores together______ Global PSQI Score_______ Citation: Buysse, DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ: The Pittsburgh Sleep Quality Index (PSQI): A new instrument for psychiatric research and practice. Psychiatry Research 28:193-213, 1989 281 Appendix S. Chapters 6 and 7: WHODAS 2.0 12-item version, self-administered This questionnaire asks about difficulties due to health conditions. Health conditions include diseases or illnesses, other health problems that may be short or long lasting, injuries, mental or emotional problems, and problems with alcohol or drugs. Think back over the past 30 days and answer these questions, thinking about how much difficulty you had doing the following activities. For each question, please circle only one response. In the past 30 days, how much difficulty did you have in: S1 Standing for long periods such as 30 minutes? None Mild Moderate Severe Extreme or cannot do S2 Taking care of your household responsibilities? None Mild Moderate Severe Extreme or cannot do S3 Learning a new task, for example, learning how to get to a new place? None Mild Moderate Severe Extreme or cannot do S4 How much of a problem did you have joining in community activities (for example, festivities, religious or other activities) in the same way as anyone else can? None Mild Moderate Severe Extreme or cannot do S5 How much have you been emotionally affected by your health problems? None Mild Moderate Severe Extreme or cannot do Please continue to next page... 282 In the past 30 days, how much difficulty did you have in: S6 Concentrating on doing something for ten minutes? None Mild Moderate Severe Extreme or cannot do S7 Walking a long distance such as a kilometre [or equivalent]? None Mild Moderate Severe Extreme or cannot do S8 Washing your whole body? None Mild Moderate Severe Extreme or cannot do S9 Getting dressed? None Mild Moderate Severe Extreme or cannot do S10 Dealing with people you do not know? None Mild Moderate Severe Extreme or cannot do S11 Maintaining a friendship? None Mild Moderate Severe Extreme or cannot do S12 Your day-to-day work? None Mild Moderate Severe Extreme or cannot do H1 Overall, in the past 30 days, how many days were these difficulties present? Record number of days H2 In the past 30 days, for how many days were you totally unable to carry out your usual activities or work because of any health condition? Record number of days H3 In the past 30 days, not counting the days that you were totally unable, for how many days did you cut back or reduce your usual activities or work because of any health condition? Record number of days 283 Appendix T. Chapters 6 and 7: M-PAC constructs Physical Activity The next questions ask about your thoughts and feelings about physical activity. Before we begin, please take an extra minute to read this section. It is very important that you understand what we mean when we say physical activity. Physical activity is movement that takes effort and makes your heart beat faster. You might start to breathe harder, feel warm or flushed, or sweat. Examples of physical activity include brisk walking, swimming, tennis, cycling, basketball, hiking, or paddle boarding ATTITUDES TOWARD PHYSICAL ACTIVITY Affective Attitudes (Rhodes et al., 2010) For me, participating in regular physical activity over the next month would be: (Responses on a 1 to 7 scale) 1. Unenjoyable to enjoyable 2. Boring to very exciting 3. Unpleasant to very pleasant Instrumental Attitudes (Rhodes et al., 2010) For me, participating in regular physical activity over the next month would be: (Responses on a 1 to 7 scale) 1. Unwise to very wise 2. Not beneficial to very beneficial 3. Useless to very useful PERCEIVED CAPABILITY (Rhodes et al., 2006) These questions ask you about your confidence and/or control over engaging in regular physical activity. Please select a number for each question using the scale provided. Consider what you would do for each question, assuming you wanted to do the task. Please indicate your level of agreement with the following statements: (Responses on a 1 to 5, Strongly Disagree to Strongly Agree, scale) 1. I have the skills I need to be physically active 2. I am physically capable of doing moderate to vigorous physical activity for 150 min across a week if I really had to 284 3. I am confident in my ability to engage in physical activity. PERCEIVED OPPORTUNITY FOR PHYSICAL ACTIVITY (Rhodes et al, 2006) These questions ask you about your opportunity for engaging in regular exercise. Please circle a number for each question using the scale provided. (Responses on a 1 to 5, Strongly Disagree to Strongly Agree, scale) 1. I have the opportunity to be physically active every week if I really had to 2. I will have opportunities to increase my physical activity over the next month 3. I will have opportunities to track my physical activities via the app over the next month. INTENTION STRENGTH TO BE PHYSICALLY ACTIVE (Rhodes et al., 2010) These questions ask you about your intention for engaging in regular exercise. Please circle a number for each question using the scale provided. (Responses on a 1 to 5, Strongly Disagree to Strongly Agree, scale) 1. I am committed to engage in physical activity over the next month 2. I am motivated to engage in PA over the next month 3. In the next 6 months, I have intentions to be physically active regularly DECISIONAL INTENTIONS TO BE PHYSICALLY ACTIVE The following will ask you about your intention to be regularly physically active. I intend to increase 3000 steps above my baseline daily steps for most days per week. (Yes/No) BEHAVIOURAL REGULATION FOR PHYSICAL ACTIVITY (Rovniak et al. 2002) Sometimes we use strategies to help us to be physically active. Please use the following scale to answer the questions below. (Responses on a 1 to 5, Strongly Disagree to Strongly Agree, scale) 1. I often monitor how physically active I am 2. I often set physical activity goals 3. I often plan, when, what, where, and how I am going to be physically active HABIT OF PHYSICAL ACTIVITY (Gardner, Abraham, Lally, & de Bruijn, 2012; Rhodes and Lim, 2016) The following questions ask about your habits regarding physical activity. Habits are behaviors that are so practiced they are often done without much thought or motivation. For example, for many people driving a car becomes a habit when one can think about all sorts of things, concentrate 285 on the road, and give little thought to operating the controls of the car. Sometimes travelling to and from work becomes such a habit that we do not even think about our route anymore. Please select the answer that best represents you. (Responses on a 1 to 5, Strongly Disagree to Strongly Agree, scale) 1. I engage in regular physical activity without having to consciously remember it 2. I engage in regular physical activity automatically 3. I engage in regular physical activity without consciously thinking about it PHYSICAL ACTIVITY IDENTITY (Wilson & Muon, 2008; Rhodes and Lim, 2016) The following questions concern your personal beliefs about exercise. Please indicate the degree to which you agree or disagree with each statement when thinking about your exercise participation. (Responses on a 1 to 5, Strongly Disagree to Strongly Agree, scale) 1. I consider myself someone who is physically active 2. When I describe myself to others, I usually include my involvement in physical activity 3. Others see me as someone who does physical activity regularly 286 Appendix U. Chapter 7: STROBE Statement. Part 1. Checklist of items that should be included in reports of cohort studies Item no. Recommendation Page no.a Title and abstract 1 (a) Indicate the studyâs design with a commonly used term in the title or the abstract (b) Provide in the abstract an informative and balanced summary of what was done and what was found 1-2 Introduction Background/rationale 2 Explain the scientific background and rationale for the investigation being reported 3-4 Objectives 3 State specific objectives, including any prespecified hypotheses 4-5 Methods Study design 4 Present key elements of study design early in the paper 5 Setting 5 Describe the setting, locations, and relevant dates, including periods of recruitment, exposure, follow-up, and data collection 6-7 Participants 6 (a) Give the eligibility criteria, and the sources and methods of selection of participants. Describe methods of follow-up 6 (b) For matched studies, give matching criteria and number of exposed and unexposed Variables 7 Clearly define all outcomes, exposures, predictors, potential confounders, and effect modifiers. Give diagnostic criteria, if applicable 8-12 Data sources/ measurement 8* For each variable of interest, give sources of data and details of methods of assessment (measurement). Describe comparability of assessment methods if there is more than one group 8-12 Bias 9 Describe any efforts to address potential sources of bias 7 Study size 10 Explain how the study size was arrived at 12 Quantitative variables 11 Explain how quantitative variables were handled in the analyses. If applicable, describe which groupings were chosen and why 12-13 Statistical methods 12 (a) Describe all statistical methods, including those used to control for confounding 12-13 (b) Describe any methods used to examine subgroups and interactions (c) Explain how missing data were addressed (d) If applicable, explain how loss to follow-up was addressed (e) Describe any sensitivity analyses 287 Item no. Recommendation Page no.a Results Participants 13* (a) Report numbers of individuals at each stage of studyâeg numbers potentially eligible, examined for eligibility, confirmed eligible, included in the study, completing follow-up, and analysed 13-14; Figure 1 (b) Give reasons for non-participation at each stage (c) Consider use of a flow diagram Descriptive data 14* (a) Give characteristics of study participants (eg demographic, clinical, social) and information on exposures and potential confounders 15; Multimedia Appendix 4 (b) Indicate number of participants with missing data for each variable of interest (c) Summarise follow-up time (eg, average and total amount) Outcome data 15* Report numbers of outcome events or summary measures over time 15-22 Other analyses 17 Report other analyses doneâeg analyses of subgroups and interactions, and sensitivity analyses 22; Multimedia Appendix 5,8,9 Discussion Key results 18 Summarise key results with reference to study objectives 23-28 Limitations 19 Discuss limitations of the study, taking into account sources of potential bias or imprecision. Discuss both direction and magnitude of any potential bias 28-30 Interpretation 20 Give a cautious overall interpretation of results considering objectives, limitations, multiplicity of analyses, results from similar studies, and other relevant evidence 30 Generalisability 21 Discuss the generalisability (external validity) of the study results NA Other information Funding 22 Give the source of funding and the role of the funders for the present study and, if applicable, for the original study on which the present article is based 30 *Give information separately for exposed and unexposed groups. 288 Note: An Explanation and Elaboration article discusses each checklist item and gives methodological background and published examples of transparent reporting. The STROBE checklist is best used in conjunction with this article (freely available on the Web sites of PLoS Medicine at http://www.plosmedicine.org/, Annals of Internal Medicine at http://www.annals.org/, and Epidemiology at http://www.epidem.com/). Information on the STROBE Initiative is available at http://www.strobe-statement.org. aThe page and table numbers shown match those from the corresponding published paper. Part 2. CONSORT 2020 statement extension to pilot and feasibility trials (modified for single-arm trial). Section/topic and item no. Extension for single-arm pilot trials item descriptor Page no. Introduction Background and objectives 2a Scientific background and explanation of rationale for future definitive trial, and reasons for a single-arm pilot trial 3-4 2b Specific objectives or research questions for single-arm pilot trial 4-5 Methods Outcomes 6a Completely defined prespecified assessments or measurements to address each pilot trial objective specified in 2b, including how and when they were assessed 8-12 Sample size 7a Rationale for numbers in the single-arm pilot trial 12 Analytical methods 12a Methods used to address each pilot trial objective whether qualitative or quantitative 12-13 Results Numbers analysed 16 For each objective, number of participants (denominator) included in each analysis. If relevant, these numbers should be by randomised group 15-19 Outcomes and estimation 17 For each objective, results including expressions of uncertainty (such as 95% confidence interval) for any estimates. If relevant, these results should be by randomised group 19-22; Table 1, Multimedia Appendix 5, 6, 8, 9 Discussion Interpretation 22 Interpretation consistent with feasibility trial objectives and findings, balancing potential benefits and harms, and considering other relevant evidence 22-30 22a Implications for progression from feasibility to future definitive trial, including any proposed amendments 22-30 Other information Registration Registration number for feasibility trial and name of trial registry 2 289 23 Protocol 24 Where the feasibility trial protocol can be accessed, if available 5 aThe page and table numbers shown match those from the corresponding published paper.290 Appendix V. Chapter 7: A list of unique BCTs used in this version of MoodMover No. Behaviour Change Techniquesa Certainty 1 Goal setting (behaviour) (1.1) ++ 2 Problem solving (1.2) ++ 3 Action planning (1.4) ++ 4 Review behaviour goal(s) (1.5) ++ 5 Discrepancy between current behaviour and goal (1.6) ++ 6 Self-monitoring of behaviour (2.3) ++ 7 Self-monitoring of outcome(s) of behaviour (2.4) ++ 8 Social support (emotional) (3.3) + 9 Instruction on how to perform a behaviour (4.1) + 10 Information about antecedents (4.2) ++ 11 Information about health consequences (5.1) ++ 12 Monitoring of emotional consequences (5.4) ++ 13 Information about emotional consequences (5.6) ++ 14 Information about others' approval (6.3) ++ 15 Prompts/cues (7.1) ++ 16 Behaviour substitution (8.2) ++ 17 Habit formation (8.3) ++ 18 Graded tasks (8.7) ++ 19 Credible source (9.1) ++ 20 Non-specific incentive (10.6) ++ 21 Self-incentive (10.7) ++ 22 Reduce negative emotions (11.2) + 23 Restructuring the physical environment (12.1) + 24 Incompatible beliefs (13.3) + 25 Self-talk (15.4) ++ aThe numbers in the brackets refer to the behaviour change techniques in the âBCT taxonomy v1â by Michie et al. 2013. 291 Appendix W. Chapter 7: Baseline characteristics. Variables Total (N=28), n (%) / Mean ± SD Completers (n=16), n (%) / Mean ± SD Non-completers (n=12), n (%) / Mean ± SD Age (years) 39.8 ± 13.4 44.1 ± 13.2 34.0 ± 12.0 BMIa (kg/m²) 27.8 ± 5.1 26.9 ± 4.1 29.1 ± 6.2 Sex Male 7 (25.0%) 2 (12.5%) 5 (41.7%) Female 21 (75.0%) 14 (87.5%) 7 (58.3%) Gender Men 4 (14.3%) 1 (6.2%) 3 (25%) Women 20 (71.4%) 13 (81.2%) 7 (58.3%) Non-binary 3 (10.7%) 2 (12.5%) 1 (8.3%) Prefer not to answer 1 (3.6%) 0 1 (8.3%) Race/Ethnicity White 18 (64.3%) 10 (62.5%) 8 (66.7%) Non-White 10 (35.7%) 6 (37.5%) 4 (33.3%) Education Level High School or Less 7 (25.0%) 3 (18.8%) 4 (33.3%) Certificate or diploma 7 (25.0%) 4 (25.0%) 3 (25.0%) Bachelorâs Degree 9 (32.1%) 4 (25.0%) 5 (41.7%) Masterâs Degree 4 (14.3%) 4 (25.0%) 0 Professional Degree 1 (3.6%) 1 (6.2%) 0 Employment Status Employed Full-Time 9 (32.1%) 6 (37.5%) 3 (25.0%) Employed Part-Time 3 (10.7%) 2 (12.5%) 1 (8.3%) Student 5 (17.9%) 3 (18.8%) 2 (16.7%) On medical or disability 7 (25.0%) 4 (25%) 3 (25%) Unemployed/Other 4 (14.3%) 1 (6.2%) 3 (25.0%) Before-Tax Household Income $ 0 - $ 19,999 9 (32.1%) 4 (25.0%) 5 (41.7%) $20,000 - $39,999 5 (17.9%) 1 (6.2%) 4 (33.3%) $40,000 - $59,999 1 (3.6%) 1 (6.2%) 0 $60,000 - $79,999 2 (7.1%) 2 (12.5%) 0 $80,000 - $99,999 1 (3.6%) 0 1 (8.3%) $100,000 - $119,999 1 (3.6%) 0 1 (8.3%) $120,000 - $139,999 2 (7.1%) 2 (12.5%) 0 $140,000 or greater 4 (14.3%) 4 (25.0%) 0 Prefer not to answer 3 (10.7%) 2 (12.5%) 1 (8.3%) PHQ-9b 14.0 ± 4.4 13.2 ± 3.9 15.1 ± 4.9 MDDc Diagnosis Yes 17 (60.7%) 9 (56.2%) 8 (66.7%) No 11 (39.3%) 7 (43.8%) 4 (33.3%) Duration of MDD Diagnosis (n=17) 292 Variables Total (N=28), n (%) / Mean ± SD Completers (n=16), n (%) / Mean ± SD Non-completers (n=12), n (%) / Mean ± SD 1-6 months 1 (5.9%) 1 (6.2%) 0 6-12 months 1 (5.9%) 1 (6.2%) 0 1-2 years 2 (11.8%) 1 (6.2%) 1 (8.3%) More than 2 years 13 (76.5%) 6 (37.5%) 7 (58.3%) Duration of Depressive Symptoms, no MDD diagnosis (n=11) 1-2 years 1 (9.1%) 1 (8.3%) More than 2 years 10 (90.9%) 7 (43.8%) 3 (25%) Currently on Antidepressants 24 (85.7%) 14 (87.5%) 10 (83.3%) Duration of Medication Use Less than 1 month 1 (4.2%) 1 (6.2%) 0 1-6 months 3 (12.5%) 2 (12.5%) 1 (8.3%) 6-12 months 4 (16.7%) 1 (6.2%) 3 (25%) 1-2 years 1 (4.2%) 1 (6.2%) 0 More than 2 years 14 (58.3%) 9 (56.2%) 5 (41.7%) Prefer not to answer 1 (4.2%) 0 1 (4.2%) Currently in Other Treatments for Depression (n=13) 13 (56.5%) 7 (43.8%) 6 (50%) Counselling 10 (76.9%) 5 (71.4%) 5 (83.3%) Group CBTd 1 (7.7%) 1 (14.3%) 0 Other 2 (15.4%) 1 (14.3%) 1 (16.7%) Duration of Such Treatments (n=13) 1-6 months 2 (15.4%) 1 (14.3%) 1 (16.7%) 6-12 months 4 (30.8%) 2 (28.6%) 2 (33.3%) 1-2 years 2 (15.4%) 1 (14.3%) 1 (16.7%) More than 2 years 5 (38.5%) 3 (42.9%) 2 (33.3%) Effectiveness of Current Treatments (1-10 scale) 5.8 ± 1.7 (n = 23) 5.7 ± 1.3 (n = 13) 5.9 ± 2.1 (n = 10) Side Effects of Current Treatments 11 (37.8%) 7 (63.6%) 4 (36.4%) Currently in Other Treatments Not for Depression 13 (56.5%) 10 (62.5%) 3 (25.0%) Alcohol Use (past month) 2 to 5 times a week 3 (10.7%) 2 (12.5%) 1 (8.3%) 2 to 3 times in the past 30 days 2 (7.1%) 1 (6.2%) 1 (8.3%) Once in the past 30 days 3 (10.7%) 2 (12.5%) 1 (8.3%) Not in the past 30 days 11 (39.3%) 6 (37.5%) 5 (41.7%) I do not drink alcohol 9 (32.1%) 5 (31.2%) 4 (33.3%) Cannabis Use (past 12 months) Yes 10 (35.7%) 6 (37.5%) 4 (33.3%) No 18 (64.3%) 10 (62.5%) 8 (66.7%) Currently Using Other PAe Apps or Devices 13 (46.4%) 6 (37.5%) 7 (58.3%) Daily 7 (53.8%) 3 (50.0%) 4 (57.1%) Others 6 (46.2%) 3 (50.0%) 3 (42.9%) History of Using Other PA Apps or Devices 293 Variables Total (N=28), n (%) / Mean ± SD Completers (n=16), n (%) / Mean ± SD Non-completers (n=12), n (%) / Mean ± SD Yes 10 (66.7%) 8 (50%) 2 (16.7%) No 5 (33.3%) 2 (12.5%) 3 (25%) Self-reported MVPAf (PAAQg, mins/week) 82.9 ± 86.4 91.6 ± 84.7 71.3 ± 91.1 aBMI: Body Mass Index. bPHQ-9: Patient Health Questionnaire â 9 item. cMDD: Major depressive disorder. dCBT: Cognitive behavioural therapy. ePA: Physical activity. fMVPA: Moderate-to-vigorous physical activity. gPAAQ: Physical Activity Adults Questionnaire. 294 Appendix X. Chapter 7: App engagement, usability, and satisfaction. Total (N=21) Study completers (n=16) Early dropouts (n=5) App engagement Mean ± SD Min Max Mean ± SD Min Max Mean ± SD Min Max Total active app use (days) Run-in period (week 1) 4.0 ± 2.0 1 7 3.9 ± 2.2 1 7 4.2 ± 1.5 2 6 Intervention period (weeks 2-9) 30.8 ± 15.2 3 56 37.1 ± 10.8 22 56 10.6 ± 7.6 3 23 Total active app use duration (mins) 119.6 ± 66.9 16.6 245.2 139.6 ± 59.9 26.0 245.2 55.6 ± 47.3 16.6 137.4 Run-in period (week 1) 17.0 ± 5.7 7.0 31.3 16.9 ± 6.2 7.0 31.3 17.1 ± 4.5 13.6 24.8 Intervention period (weeks 2-9) 102.6 ± 65.7 1.4 222.9 122.6 ± 58.0 17.7 222.9 38.5 ± 46.9 1.4 120.5 Number of major lessons completed 5.7 ± 2.7 0 8 6.9 ± 1.6 4 8 1.8 ± 1.1 0 3 Number of complementary lessons completed 5.5 ± 2.8 0 8 6.8 ± 1.7 4 8 1.6 ± 1.1 0 3 Time spent on each major lesson (mins) 8.0 ± 4.7 0 17.4 8.4 ± 4.0 2.6 15.8 6.6 ± 6.6 0 17.4 Time spent on each complementary lesson (mins) 3.9 ± 3.0 0 13.4 3.4 ± 2.1 0.3 7.1 5.5 ± 5.1 0 13.4 Number of times of step syncing 28.5 ± 17.1 0 63 34.1 ± 15.3 0 63 10.6 ± 7.5 0 20 Number of surveys completed 19.7 ± 10.2 0 28 24.6 ± 5.6 13 28 4.2 ± 2.4 0 6 Number of action plans completed 6.5 ± 17.8 0 82 8.5 ± 20.2 0 82 0.2 ± 0.4 0 1 Number of times of step goal adjustments 2.3 ± 1.5 0 5 2.8 ± 1.3 0 5 0.8 ± 0.8 0 2 Number of exercise sessions logged 6.7 ± 9.9 0 36 8.6 ± 10.7 0 36 0.6 ± 0.9 0 2 Total logged LPAa sessions 4.5 ± 7.6 0 29 5.8 ± 8.4 0 29 0.4 ± 0.9 0 2 Total logged MVPAb sessions 2.2 ± 3.8 0 16 2.9 ± 4.2 0 16 0.2 ± 0.4 0 1 Total logged LPA minutes 135.7 ± 176.7 0 588 172.2 ± 187.6 0 588 19.0 ± 42.5 0 95 Total logged MVPA minutes 109.1 ± 207.5 0 884 141.9 ± 229.2 0 884 4.0 ± 8.9 0 20 Mood after LPA sessions 2.7 ± 0.3 (n=13) 2.3 3.1 2.7 ± 0.3 (n=12) 2.3 3.1 2.5 (n=1) Mood after MVPA sessions 2.8 ± 0.4 (n=11) 2.0 3.5 2.9 ± 0.3 (n=10) 2.5 3.5 2.0 (n=1) Overall Mood 2.7 ± 0.4 (n=16) 2.0 3.5 2.8 ± 0.3 (n=14) 2.4 3.5 2.3 ± 0.4 (n=2) 2.0 2.5 MAUQc 5.01 ± 1.02 3.23 6.92 Satisfaction 53.56 ± 8.11 36 66 aLPA: Light physical activity. bMVPA: Moderate-to-vigorous physical activity. cMAUQ: mHealth App Usability Questionnaire. 295 Appendix Y. Chapter 7: Smartphone-based step counts and app use over time Baseline Week 2 Week 3 Week 4 Week 5 mean (SD, n) mean (SD, n) mean (SD, n) mean (SD, n) mean (SD, n) Smartphone-based steps 4378.3 (2114.5, n=18) 6409.5 (2574.6, n=18) 5263.2 (1782.3, n=18) 4996.6 (1551.4, n=18) 5610.2 (2613.2, n=17) Number of days met the recommended step goals 4.1 (1.7, n=18) 3.4 (1.9, n=18) 2.6 (2.1, n=18) 2.7 (2.0, n=17) Weekly app use duration (mins) 24.7 (16.1, n=21) 14.8 (14.6, n=20) 13.3 (11.3, n=20) 14.3 (6.2, n=17) Week 6 Week 7 Week 8 Week 9 mean (SD, n) mean (SD, n) mean (SD, n) mean (SD, n) Smartphone-based steps 5446.6 (2402.4, n=16) 5528.1 (2272.6, n=15) 5474.0 (2626.3, n=15) 5204.0 (2322.7, n=14) Number of days met the recommended step goals 2.0 (1.8, n=16) 1.9 (2.0, n=15) 1.7 (1.9, n=15) 1.6 (2.3, n=14) Weekly app use duration (mins) 18.5 (14.1, n=16) 10.8 (11.9, n=16) 9.2 (11.0, n=16) 13.5 (17.3) 296 Appendix Z. Chapter 7: Simple linear regression results for participant characteristics of feasibility results Total app usage duration (mins)a Total active app use (days)a Predictors B SE 75%CI 85%CI 95%CI B SE 75%CI 85%CI 95%CI Age (year) 3.45 0.83 [2.47, 4.44] [2.21, 4.69] [1.72, 5.18] 0.42 0.25 [0.12, 0.71] [0.04, 0.79] [-0.10, 0.94] BMIc -0.06 3.32 [-4.00, 3.88] [-5.04, 4.92] [-7.01, 6.89] -0.81 0.75 [-1.69, 0.08] [-1.93, 0.32] [-2.37, 0.76] Baseline PHQ-9d -5.06 3.94 [-9.73, -0.38] [-10.96, 0.85] [-13.30, 3.19] 0.68 0.94 [-0.44, 1.79] [-0.73, 2.09] [-1.29, 2.64] Baseline MVPAe (PAAQf) -0.14 0.19 [-0.37, 0.08] [-0.43, 0.14] [-0.54, 0.26] -0.01 0.05 [-0.07, 0.04] [-0.08, 0.06] [-0.11, 0.08] MDDg diagnosis Intercept 83.93 19.34 [60.99, 106.88] [54.92, 112.94] [43.46, 124.41] 26.00 4.44 [20.73, 31.27] [19.34, 32.66] [16.71, 35.29] Yes 39.19 28.02 [5.93, 72.44] [-2.86, 81.23] [-19.47, 97.84] 10.00 6.43 [2.37, 17.63] [0.35, 19.65] [-3.47, 23.47] Genderh (Intercept) 62.02 31.36 [24.67, 99.37] [14.75, 109.30] [-4.14, 128.18] -2.19 12.43 [-17.06, 12.68] [-21.05, 16.66] [-28.68, 24.30] Women 69.22 34.56 [28.07, 110.38] [17.13, 121.32] [-3.68, 142.13] 0.81 12.43 [-14.06, 15.68] [-18.05, 19.66] [-25.68, 27.30] Non-binary -26.44 44.35 [-79.26, 26.38] [-93.30, 40.42] [-120.00, 67.13] -10.69 12.43 [-25.56, 4.18] [-29.55, 8.16] [-37.18, 15.80] Sexh (Intercept) 56.11 24.43 [27.12, 85.10] [19.46, 92.76] [4.97, 107.25] 17.17 5.21 [10.99, 23.35] [9.35, 24.98] [6.27, 28.07] Female 65.08 28.91 [30.77, 99.38] [21.71, 108.45] [4.57, 125.58] 19.03 6.16 [11.72, 26.34] [9.79, 28.28] [6.14, 31.93] Race/Ethnicityh (Intercept) 107.32 18.04 [85.73, 128.90] [79.95, 134.68] [68.87, 145.76] 30.69 4.54 [25.26, 36.12] [23.81, 37.58] [21.02, 40.36] Chinese -46.82 49.4 [-105.94, 12.29] [-121.77, 28.12] [-152.11, 58.46] -2.19 12.43 [-17.06, 12.68] [-21.05, 16.66] [-28.68, 24.30] Latin, Central, or South American -9.71 49.4 [-68.82, 49.41] [-84.65, 65.24] [-114.99, 95.58] 0.81 12.43 [-14.06, 15.68] [-18.05, 19.66] [-25.68, 27.30] South Asian -51.42 49.4 [-110.53, 7.69] [-126.36, 23.53] [-156.70, 53.87] -10.69 12.43 [-25.56, 4.18] [-29.55, 8.16] [-37.18, 15.80] Education levelh (Intercept) 87.13 21.73 [61.12, 113.14] [54.16, 120.10] [40.81, 133.45] 20.86 5.18 [14.65, 27.06] [12.99, 28.72] [9.81, 31.91] Certificate or diploma 19.38 33.67 [-20.91, 59.68] [-31.70, 70.47] [-52.38, 91.15] 11.94 8.03 [2.33, 21.55] [-0.24, 24.13] [-5.17, 29.06] High school -34.45 46.1 [-89.63, 20.72] [-104.40, 35.49] [-132.72, 63.81] 9.14 11.00 [-4.02, 22.30] [-7.54, 25.83] [-14.29, 32.58] Master's degree 93.86 36.04 [50.73, 136.99] [39.18, 148.54] [17.04, 170.68] 25.64 8.60 [15.36, 35.93] [12.60, 38.68] [7.32, 43.96] Some high school -29.70 46.10 [-84.87, 25.47] [-99.65, 40.25] [-127.97, 68.56] 4.64 11.00 [-8.52, 17.80] [-12.04, 21.33] [-18.79, 28.08] Employment Statush (Intercept) 110.44 23.06 [82.84, 138.04] [75.45, 145.43] [61.29, 159.59] 30.75 4.71 [25.11, 36.39] [23.60, 37.90] [20.71, 40.79] Employed - part time 4.71 44.16 [-48.13, 57.55] [-62.29, 71.71] [-89.41, 98.83] 0.58 9.02 [-10.21, 11.38] [-13.10, 14.27] [-18.65, 19.81] On medical or disability 33.11 39.94 [-14.69, 80.91] [-27.49, 93.71] [-52.03, 118.24] 13.50 8.16 [3.73, 23.27] [1.12, 25.88] [-3.89, 30.89] Student -59.86 44.16 [-112.70, -7.02] [-126.86, 7.14] [-153.98, 34.26] 1.58 9.02 [-9.21, 12.38] [-12.10, 15.27] [-17.65, 20.81] Unemployed -22.74 51.57 [-84.45, 38.97] [-100.98, 55.50] [-132.65, 87.17] -17.75 10.53 [-30.36, -5.14] [-33.73, -1.77] [-40.20, 4.70] Before-Tax Household Incomeh (Intercept) 90.06 26.12 [58.61, 121.51] [50.10, 130.02] [33.63, 146.49] 27.17 6.35 [19.52, 34.82] [17.45, 36.89] [13.44, 40.89] $20k-$39,999 15.61 52.24 [-47.30, 78.52] [-64.31, 95.54] [-97.25, 128.47] -2.17 12.71 [-17.47, 13.13] [-21.60, 17.27] [-29.62, 25.28] $60k-$79,999 22.23 52.24 [-40.68, 85.13] [-57.70, 102.15] [-90.63, 135.09] 14.33 12.71 [-0.97, 29.63] [-5.10, 33.77] [-13.12, 41.78] $120k-$139,999 61.03 52.24 [-1.88, 123.94] [-18.89, 140.96] [-51.83, 173.89] 2.33 12.71 [-12.97, 17.63] [-17.10, 21.77] [-25.12, 29.78] 297 Total app usage duration (mins)a Total active app use (days)a Predictors B SE 75%CI 85%CI 95%CI B SE 75%CI 85%CI 95%CI $140k or greater 55.76 41.3 [6.03, 105.49] [-7.43, 118.95] [-33.46, 144.98] 10.83 10.05 [-1.26, 22.93] [-4.53, 26.20] [-10.87, 32.53] Prefer not to answer -47.53 45.24 [-102.01, 6.95] [-116.75, 21.69] [-145.27, 50.21] -1.17 11.00 [-14.42, 12.08] [-18.00, 15.67] [-24.94, 22.60] Currently on Antidepressantsh (Intercept) 96.02 38.85 [49.91, 142.12] [37.73, 154.31] [14.70, 177.34] 23.67 8.85 [13.16, 34.17] [10.39, 36.95] [5.14, 42.20] Yes 7.67 41.97 [-42.13, 57.47] [-55.29, 70.63] [-80.17, 95.51] 8.28 9.56 [-3.07, 19.62] [-6.07, 22.62] [-11.74, 28.29] Duration of Medication Useh (Intercept) 113.63 19.88 [89.77, 137.49] [83.35, 143.92] [70.99, 156.28] 31.82 4.94 [25.89, 37.75] [24.30, 39.34] [21.22, 42.41] 1-6 months 34.86 42.95 [-16.68, 86.41] [-30.55, 100.28] [-57.25, 126.98] 7.52 10.67 [-5.29, 20.32] [-8.74, 23.77] [-15.37, 30.40] 6-12 months -67.25 68.87 [-149.90, 15.40] [-172.15, 37.65] [-214.96, 80.46] 5.18 17.11 [-15.35, 25.72] [-20.88, 31.24] [-31.52, 41.88] Currently in Other Treatments for Depression (Intercept) 103.78 18.68 [81.61, 125.94] [75.76, 131.79] [64.69, 142.87] 25.77 3.92 [21.12, 30.42] [19.89, 31.65] [17.57, 33.97] Yes -3.10 30.26 [-39.01, 32.80] [-48.50, 42.29] [-66.44, 60.23] 13.11 6.35 [5.57, 20.64] [3.58, 22.63] [-0.18, 26.39] Currently in Other Treatments Not for Depression (Intercept) 58.72 16.13 [39.57, 77.86] [34.52, 82.92] [24.95, 92.49] 26.90 4.79 [21.22, 32.58] [19.71, 34.09] [16.87, 36.93] Yes 83.76 22.29 [57.31, 110.21] [50.32, 117.20] [37.11, 130.42] 7.37 6.62 [-0.48, 15.23] [-2.56, 17.30] [-6.48, 21.22] Alcohol useh (Intercept) 95.6 26.06 [64.49, 126.71] [56.19, 135.01] [40.35, 150.85] 28.00 5.87 [20.99, 35.01] [19.12, 36.88] [15.56, 40.44] 2-3 times 78.39 73.72 [-9.60, 166.38] [-33.08, 189.86] [-77.88, 234.66] -5.00 16.60 [-24.82, 14.82] [-30.10, 20.10] [-40.19, 30.19] 2-5 times/week 18.6 47.58 [-38.20, 75.40] [-53.35, 90.55] [-82.27, 119.47] -7.67 10.72 [-20.46, 5.12] [-23.87, 8.54] [-30.38, 15.05] Not past 30 days 11.22 35.69 [-31.38, 53.82] [-42.75, 65.18] [-64.44, 86.87] 8.25 8.04 [-1.34, 17.84] [-3.90, 20.40] [-8.79, 25.29] Once past 30 days -38.5 55.29 [-104.49, 27.50] [-122.10, 45.10] [-155.70, 78.71] 10.00 12.45 [-4.86, 24.86] [-8.83, 28.83] [-16.39, 36.39] Cannabis use (Intercept) 106.11 17.95 [84.81, 127.41] [79.18, 133.03] [68.54, 143.67] 27.43 3.96 [22.73, 32.13] [21.48, 33.37] [19.13, 35.72] Yes -10.55 31.09 [-47.43, 26.34] [-57.18, 36.09] [-75.61, 54.52] 10.00 6.86 [1.86, 18.14] [-0.30, 20.30] [-4.37, 24.37] Currently Using PAi Apps or Devices (Intercept) 111.55 20.09 [87.71, 135.39] [81.41, 141.69] [69.50, 153.60] 34.18 4.58 [28.75, 39.61] [27.32, 41.04] [24.61, 43.76] Yes -18.81 29.11 [-53.36, 15.73] [-62.49, 24.86] [-79.74, 42.12] -7.18 6.63 [-15.05, 0.68] [-17.13, 2.76] [-21.06, 6.69] History of Using PA Apps or Devicesh (Intercept) 120.38 39.45 [73.49, 167.27] [61.07, 179.70] [37.51, 203.26] 29.00 8.89 [18.44, 39.56] [15.64, 42.36] [10.33, 47.67] Yes -12.14 46.26 [-67.13, 42.84] [-81.70, 57.41] [-109.32, 85.04] -2.00 10.13 [-14.04, 10.04] [-17.23, 13.23] [-23.28, 19.28] aDuring the intervention phase (weeks 2-9) bMAUQ: mHealth App Usability Questionnaire. cBMI: Body Mass Index; dPHQ-9: Patient Health Questionnaire - 9 item eMVPA: Moderate-to-vigorous physical activity; fPAAQ: Physical Activity Adults Questionnaire. gMDD: Major depressive disorder; hIncludes groups with very small sample sizes (n<5). Confidence intervals should be interpreted with caution due to instability and wide variability. iPA: Physical activity. 298 Major lesson completion Average minutes spent on each major lesson Predictors B SE 75%CI 85%CI 95%CI B SE 75%CI 85%CI 95%CI Age (year) 0.07 0.04 [0.02, 0.12] [0.01, 0.14] [-0.02, 0.16] 0.21 0.06 [0.14, 0.29] [0.12, 0.31] [0.08, 0.35] BMIc -0.14 0.13 [-0.29, 0.02] [-0.33, 0.06] [-0.41, 0.14] 0.31 0.23 [0.04, 0.58] [-0.03, 0.65] [-0.16, 0.78] Baseline PHQ-9d 0.18 0.16 [-0.01, 0.37] [-0.06, 0.42] [-0.16, 0.52] -0.63 0.25 [-0.93, -0.33] [-1.01, -0.25] [-1.16, -0.10] Baseline MVPAe (PAAQf) 0.00 0.01 [-0.01, 0.01] [-0.01, 0.01] [-0.02, 0.02] -0.01 0.01 [-0.02, 0.01] [-0.03, 0.01] [-0.04, 0.02] MDDg diagnosis Intercept 4.73 0.76 [3.83, 5.63] [3.59, 5.86] [3.14, 6.31] 7.72 1.44 [5.56, 9.88] [5.56, 9.88] [4.70, 10.73] Yes 2.07 1.10 [0.77, 3.37] [0.43, 3.72] [-0.22, 4.37] 0.59 2.09 [-2.54, 3.72] [-2.54, 3.72] [-3.78, 4.95] Genderh (Intercept) 4.00 1.38 [2.36, 5.64] [1.92, 6.08] [1.09, 6.91] 8.06 2.53 [5.05, 11.07] [4.25, 11.87] [2.73, 13.39] Women 2.71 1.52 [0.91, 4.52] [0.42, 5.00] [-0.49, 5.92] 1.22 2.79 [-2.09, 4.54] [-2.98, 5.42] [-4.65, 7.1] Non-binary 0.00 1.95 [-2.32, 2.32] [-2.94, 2.94] [-4.11, 4.11] -5.13 3.57 [-9.39, -0.88] [-10.52, 0.25] [-12.67, 2.41] Sexh (Intercept) 3.67 0.97 [2.52, 4.82] [2.21, 5.12] [1.64, 5.70] 7.72 2.01 [5.33, 10.11] [4.69, 10.75] [3.49, 11.95] Female 2.87 1.15 [1.51, 4.23] [1.15, 4.59] [0.47, 5.27] 2.19 2.26 [-0.49, 4.87] [-1.19, 5.57] [-2.53, 6.91] Race/Ethnicityh (Intercept) 6.08 0.76 [5.17, 6.98] [4.93, 7.22] [4.47, 7.69] 8.56 1.39 [6.89, 10.22] [6.45, 10.67] [5.59, 11.52] Chinese -1.58 2.07 [-4.05, 0.90] [-4.72, 1.56] [-5.99, 2.84] -3.90 3.81 [-8.45, 0.66] [-9.68, 1.88] [-12.02, 4.22] Latin, Central, or South American -3.08 2.07 [-5.55, -0.60] [-6.22, 0.06] [-7.49, 1.34] 0.82 3.81 [-3.74, 5.38] [-4.96, 6.60] [-7.30, 8.94] South Asian -1.08 2.07 [-3.55, 1.40] [-4.22, 2.06] [-5.49, 3.34] -2.96 3.81 [-7.52, 1.60] [-8.74, 2.82] [-11.08, 5.16] Education levelh (Intercept) 4.71 1.1 [3.40, 6.03] [3.05, 6.38] [2.37, 7.06] 7.20 1.80 [5.05, 9.35] [4.47, 11.87] [3.37, 11.02] Certificate or diploma 1.09 1.7 [-0.95, 3.12] [-1.50, 3.67] [-2.54, 4.71] 1.52 2.78 [-1.81, 4.85] [-2.70, 5.75] [-4.41, 7.46] High school 1.29 2.33 [-1.50, 4.07] [-2.25, 4.82] [-3.68, 6.25] -2.81 3.81 [-7.37, 1.75] [-8.59, 2.97] [-10.93, 5.31] Master's degree 2.29 1.82 [0.11, 4.47] [-0.48, 5.05] [-1.60, 6.17] 4.45 2.98 [0.88, 8.01] [-0.07, 8.96] [-1.90, 10.79] Some high school 0.79 2.33 [-2.00, 3.57] [-2.75, 4.32] [-4.18, 5.75] -1.05 3.81 [-5.61, 3.51] [-6.83, 4.74] [-9.17, 7.08] Employment Statush (Intercept) 6 0.89 [4.94, 7.06] [4.65, 7.35] [4.11, 7.89] 8.72 1.75 [6.63, 10.81] [6.07, 11.37] [5.00, 12.44] Employed - part time -0.67 1.7 [-2.70, 1.37] [-3.25, 1.91] [-4.29, 2.96] 1.01 3.34 [-2.99, 5.01] [-4.06, 6.08] [-6.11, 8.13] On medical or disability 1.25 1.54 [-0.59, 3.09] [-1.08, 3.58] [-2.03, 4.53] 0.26 3.02 [-3.36, 3.88] [-4.32, 4.85] [-6.18, 6.71] Student 0.67 1.7 [-1.37, 2.70] [-1.91, 3.25] [-2.96, 4.29] -4.52 3.34 [-8.52, -0.52] [-9.59, 0.55] [-11.64, 2.60] Unemployed -3.5 1.99 [-5.88, -1.12] [-6.51, -0.49] [-7.73, 0.73] -1.01 3.9 [-5.68, 3.66] [-6.93, 4.91] [-9.33, 7.31] Before-Tax Household Incomeh (Intercept) 5.50 1.14 [4.13, 6.87] [3.76, 7.24] [3.04, 7.96] 8.77 1.99 [6.37, 11.18] [5.72, 11.83] [4.44, 13.10] $20k-$39,999 0.00 2.28 [-2.74, 2.74] [-3.49, 3.49] [-4.92, 4.92] 0.31 3.97 [-4.50, 5.11] [-5.80, 6.42] [-8.35, 8.97] $60k-$79,999 2.50 2.28 [-0.24, 5.24] [-0.99, 5.99] [-2.42, 7.42] -0.91 3.97 [-5.71, 3.90] [-7.02, 5.21] [-9.56, 7.75] 299 Major lesson completion Average minutes spent on each major lesson Predictors B SE 75%CI 85%CI 95%CI B SE 75%CI 85%CI 95%CI $120k-$139,999 1.00 2.28 [-1.74, 3.74] [-2.49, 4.49] [-3.92, 5.92] 1.53 3.97 [-3.27, 6.34] [-4.58, 7.64] [-7.13, 10.19] $140k or greater 0.75 1.80 [-1.42, 2.92] [-2.01, 3.51] [-3.14, 4.64] 1.06 3.14 [-2.74, 4.86] [-3.77, 5.89] [-5.78, 7.91] Prefer not to answer -1.50 1.97 [-3.88, 0.88] [-4.52, 1.52] [-5.76, 2.76] -4.47 3.97 [-9.27, 0.34] [-10.58, 1.64] [-13.13, 4.19] Currently on Antidepressantsh (Intercept) 5.33 1.58 [3.46, 7.20] [2.97, 7.70] [2.03, 8.63] 6.24 2.73 [2.15, 10.33] [2.15, 10.33] [0.53, 11.94] Yes 0.44 1.70 [-1.58, 2.47] [-2.11, 3.00] [-3.12, 4.01] 2.05 2.95 [-2.37, 6.47] [-2.37, 6.47] [-4.11, 8.22] Duration of Medication Useh (Intercept) 5.82 0.86 [4.78, 6.85] [4.51, 7.13] [3.97, 7.67] 8.81 1.37 [6.73, 10.90] [6.73, 10.90] [5.87, 11.75] 1-6 months 0.18 1.86 [-2.05, 2.42] [-2.65, 3.02] [-3.81, 4.17] 3.44 2.96 [-1.07, 7.94] [-1.07, 7.94] [-2.90, 9.78] 6-12 months 2.18 2.98 [-1.40, 5.76] [-2.36, 6.73] [-4.22, 8.58] -4.99 4.74 [-12.21, 2.24] [-12.21, 2.24] [-15.16, 5.19] Currently in Other Treatments for Depression (Intercept) 4.85 0.69 [4.03, 5.66] [3.82, 5.88] [3.41, 6.28] 8.57 1.31 [7.01, 10.12] [6.60, 10.53] [5.83, 11.31] Yes 2.28 1.11 [0.96, 3.60] [0.61, 3.95] [-0.05, 4.61] -1.50 2.12 [-4.01, 1.02] [-4.68, 1.69] [-5.94, 2.94] Currently in Other Treatments Not for Depression (Intercept) 5.00 0.84 [4.01, 5.99] [3.75, 6.25] [3.25, 6.75] 5.13 1.21 [3.69, 6.56] [3.31, 6.94] [2.59, 7.66] Yes 1.36 1.15 [-0.01, 2.73] [-0.37, 3.09] [-1.05, 3.78] 5.48 1.67 [3.50, 7.46] [2.98, 7.98] [1.99, 8.97] Alcohol useh (Intercept) 5.57 1.10 [4.26, 6.88] [3.91, 7.23] [3.25, 7.90] 6.41 1.73 [4.34, 8.48] [3.79, 9.03] [2.74, 10.09] 2-3 times -0.57 3.10 [-4.28, 3.13] [-5.26, 4.12] [-7.15, 6.01] 9.01 4.9 [3.16, 14.86] [1.59, 16.42] [-1.39, 19.40] 2-5 times/week 0.10 2.00 [-2.30, 2.49] [-2.93, 3.12] [-4.15, 4.34] 2.66 3.17 [-1.12, 6.44] [-2.13, 7.44] [-4.05, 9.37] Not past 30 days -0.07 1.50 [-1.87, 1.72] [-2.34, 2.20] [-3.26, 3.11] 2.37 2.37 [-0.46, 5.20] [-1.22, 5.96] [-2.66, 7.40] Once past 30 days 1.93 2.33 [-0.85, 4.71] [-1.59, 5.45] [-3.01, 6.86] -1.34 3.68 [-5.72, 3.05] [-6.90, 4.22] [-9.13, 6.46] Cannabis use (Intercept) 5.43 0.72 [4.57, 6.29] [4.34, 6.51] [3.92, 6.94] 8.18 1.28 [6.66, 9.69] [6.26, 10.09] [5.51, 10.85] Yes 0.86 1.25 [-0.63, 2.34] [-1.02, 2.73] [-1.76, 3.48] -0.54 2.21 [-3.16, 2.08] [-3.86, 2.78] [-5.17, 4.09] Currently Using PAi Apps or Devices (Intercept) 6.55 0.78 [5.62, 7.47] [5.38, 7.71] [4.92, 8.17] 8.16 1.44 [6.45, 9.87] [6.00, 10.32] [5.14, 11.17] Yes -1.75 1.13 [-3.08, -0.41] [-3.44, -0.06] [-4.10, 0.61] -0.34 2.09 [-2.81, 2.14] [-3.47, 2.80] [-4.71, 4.03] History of Using PA Apps or Devicesh (Intercept) 4.67 1.44 [2.96, 6.38] [2.50, 6.83] [1.64, 7.69] 10.48 2.76 [7.20, 13.76] [6.33, 14.63] [4.68, 16.28] Yes 2.58 1.69 [0.58, 4.59] [0.05, 5.12] [-0.96, 6.13] -3.19 3.24 [-7.04, 0.66] [-8.06, 1.68] [-10.00, 3.61] 300 MAUQb Satisfaction Predictors B SE 75%CI 85%CI 95%CI B SE 75%CI 85%CI 95%CI Age (year) 0.04 0.02 [0.02, 0.06] [0.02, 0.07] [0.01, 0.08] 0.38 0.13 [0.22, 0.53] [0.18, 0.58] [0.10, 0.66] BMIc 0.10 0.06 [0.03, 0.18] [0.01, 0.20] [-0.03, 0.23] 0.17 0.53 [-0.46, 0.80] [-0.63, 0.97] [-0.96, 1.30] Baseline PHQ-9d -0.01 0.07 [-0.09, 0.08] [-0.12, 0.10] [-0.16, 0.14] -0.19 0.56 [-0.86, 0.48] [-1.04, 0.66] [-1.39, 1.01] Baseline MVPAe (PAAQf) -0.01 0.00 [-0.01, -0.005] [-0.01, -0.004] [-0.01, -0.003] -0.07 0.02 [-0.09, -0.05] [-0.10, -0.04] [-0.11, -0.03] MDDg diagnosis Intercept 4.64 0.38 [4.18, 5.09] [4.06, 5.21] [3.83, 5.45] 50.29 2.95 [46.75, 53.83] [45.79, 54.78] [43.96, 56.61] Yes 0.67 0.50 [0.07, 1.27] [-0.1, 1.44] [-0.41, 1.75] 5.83 3.93 [1.11, 10.54] [-0.16, 11.81] [-2.61, 14.26] Genderh (Intercept) 4.62 0.94 [3.48, 5.75] [3.17, 6.06] [2.58, 6.65] 50.00 7.36 [41.14, 58.86] [38.75, 61.25] [34.11, 65.89] Women 0.63 0.98 [-0.55, 1.81] [-0.87, 2.12] [-1.49, 2.74] 5.46 7.63 [-3.73, 14.65] [-6.22, 17.14] [-11.03, 21.95] Non-binary -0.88 1.16 [-2.28, 0.51] [-2.65, 0.88] [-3.38, 1.61] -7.00 9.01 [-17.85, 3.85] [-20.78, 6.78] [-26.46, 12.46] Sexh (Intercept) 4.69 0.74 [3.80, 5.58] [3.56, 5.82] [3.10, 6.29] 49.50 5.82 [42.52, 56.48] [40.64, 58.36] [37.02, 61.98] Female 0.37 0.79 [-0.59, 1.32] [-0.84, 1.58] [-1.34, 2.07] 4.64 6.22 [-2.82, 12.11] [-4.83, 14.12] [-8.70, 17.99] Race/Ethnicityh (Intercept) 5.27 0.24 [4.98, 5.56] [4.90, 5.64] [4.74, 5.80] 55.40 1.88 [53.10, 57.70] [52.46, 58.34] [51.21, 59.59] Chinese -1.77 0.58 [-2.48, -1.06] [-2.68, -0.86] [-3.07, -0.47] -16.40 4.61 [-22.03, -10.77] [-23.59, -9.21] [-26.67, -6.13] Latin, Central, or South American -0.96 0.79 [-1.93, 0.01] [-2.20, 0.27] [-2.73, 0.80] -2.40 6.24 [-10.03, 5.23] [-12.14, 7.34] [-16.31, 11.51] South Asian -1.04 0.79 [-2.01, -0.07] [-2.27, 0.20] [-2.80, 0.73] -5.40 6.24 [-13.03, 2.23] [-15.14, 4.34] [-19.31, 8.51] Education levelh (Intercept) 5.17 0.44 [4.63, 5.71] [4.48, 5.86] [4.19, 6.16] 55.75 3.10 [51.96, 59.54] [50.92, 60.58] [48.84, 62.66] Certificate or diploma -0.33 0.63 [-1.09, 0.44] [-1.30, 0.65] [-1.72, 1.07] -6.25 4.39 [-11.60, -0.90] [-13.09, 0.59] [-16.02, 3.52] High school -1.44 0.77 [-2.38, -0.51] [-2.64, -0.25] [-3.15, 0.27] -12.75 5.37 [-19.31, -6.19] [-21.12, -4.38] [-24.72, -0.78] Master's degree 0.15 0.63 [-0.61, 0.92] [-0.82, 1.13] [-1.24, 1.55] 4.50 4.39 [-0.85, 9.85] [-2.34, 11.34] [-5.27, 14.27] Some high school 1.75 0.99 [0.54, 2.96] [0.21, 3.29] [-0.46, 3.96] 6.25 6.93 [-2.22, 14.72] [-4.56, 17.06] [-9.20, 21.70] Employment Statush (Intercept) 5.44 0.41 [4.93, 5.94] [4.80, 6.07] [4.53, 6.34] 56.17 3.28 [52.19, 60.15] [51.10, 61.24] [48.95, 63.38] Employed - part time -0.28 0.83 [-1.28, 0.72] [-1.56, 1.00] [-2.10, 1.54] -3.67 6.55 [-11.63, 4.29] [-13.81, 6.48] [-18.09, 10.76] On medical or disability -0.53 0.65 [-1.32, 0.26] [-1.54, 0.48] [-1.97, 0.90] -0.92 5.18 [-7.21, 5.38] [-8.94, 7.10] [-12.32, 10.49] Student -1.41 0.72 [-2.28, -0.54] [-2.52, -0.30] [-2.98, 0.16] -10.83 5.68 [-17.73, -3.94] [-19.62, -2.05] [-23.33, 1.66] Unemployed 0.18 1.09 [-1.15, 1.51] [-1.51, 1.87] [-2.22, 2.58] 1.83 8.67 [-8.70, 12.36] [-11.58, 15.25] [-17.25, 20.92] Before-Tax Household Incomeh (Intercept) 5.35 0.56 [4.66, 6.03] [4.47, 6.22] [4.09, 6.6] 55.00 4.50 [49.47, 60.53] [47.92, 62.08] [44.82, 65.18] $20k-$39,999 -1.04 1.24 [-2.57, 0.49] [-2.99, 0.92] [-3.85, 1.77] 9.00 10.06 [-3.38, 21.38] [-6.84, 24.84] [-13.77, 31.77] $60k-$79,999 0.12 0.96 [-1.07, 1.3] [-1.4, 1.63] [-2.06, 2.29] -1.50 7.80 [-11.09, 8.09] [-13.77, 10.77] [-19.13, 16.13] $120k-$139,999 -1.04 0.96 [-2.22, 0.15] [-2.55, 0.48] [-3.22, 1.14] -4.00 7.80 [-13.59, 5.59] [-16.27, 8.27] [-21.63, 13.63] 301 MAUQb Satisfaction Predictors B SE 75%CI 85%CI 95%CI B SE 75%CI 85%CI 95%CI $140k or greater -0.12 0.79 [-1.08, 0.85] [-1.35, 1.12] [-1.89, 1.66] -1.25 6.37 [-9.08, 6.58] [-11.27, 8.77] [-15.65, 13.15] Prefer not to answer -1.19 0.96 [-2.38, -0.01] [-2.71, 0.32] [-3.37, 0.99] -9.00 7.80 [-18.59, 0.59] [-21.27, 3.27] [-26.63, 8.63] Currently on Antidepressantsh (Intercept) 5.88 0.71 [5.04, 6.73] [4.81, 6.96] [4.37, 7.4] 61.00 5.54 [54.35, 67.65] [52.56, 69.44] [49.12, 72.88] Yes -0.99 0.76 [-1.9, -0.09] [-2.14, 0.16] [-2.61, 0.62] -8.50 5.92 [-15.61, -1.39] [-17.52, 0.52] [-21.20, 4.20] Duration of Medication Useh (Intercept) 5.15 0.33 [4.74, 5.55] [4.62, 5.67] [4.4, 5.89] 53.00 2.18 [50.34, 55.66] [49.60, 56.40] [48.14, 57.86] 1-6 months -0.11 0.78 [-1.06, 0.85] [-1.33, 1.12] [-1.85, 1.64] 8.00 5.11 [1.75, 14.25] [0.03, 15.97] [-3.40, 19.40] 6-12 months -0.53 1.06 [-1.82, 0.76] [-2.18, 1.12] [-2.89, 1.83] -3.00 6.90 [-11.42, 5.42] [-13.75, 7.75] [-18.37, 12.37] Currently in Other Treatments for Depression (Intercept) 5.11 0.35 [4.69, 5.53] [4.58, 5.65] [4.36, 5.86] 52.78 2.78 [49.44, 56.11] [48.54, 57.01] [46.82, 58.74] Yes -0.22 0.53 [-0.86, 0.42] [-1.03, 0.59] [-1.36, 0.92] 1.79 4.20 [-3.25, 6.84] [-4.61, 8.19] [-7.22, 10.81] Currently in Other Treatments Not for Depression (Intercept) 4.95 0.43 [4.43, 5.47] [4.29, 5.61] [4.02, 5.87] 51.33 3.34 [47.32, 55.34] [46.24, 56.42] [44.17, 58.50] Yes 0.11 0.55 [-0.55, 0.76] [-0.73, 0.94] [-1.07, 1.28] 3.57 4.23 [-1.51, 8.64] [-2.87, 10.01] [-5.50, 12.63] Alcohol useh (Intercept) 5.71 0.44 [5.17, 6.25] [5.02, 6.39] [4.73, 6.68] 55.20 4.04 [50.30, 60.10] [48.95, 61.45] [46.31, 64.09] 2-3 times -0.09 1.09 [-1.41, 1.23] [-1.77, 1.59] [-2.48, 2.3] 2.80 9.89 [-9.21, 14.81] [-12.51, 18.11] [-18.97, 24.57] 2-5 times/week -0.67 0.83 [-1.68, 0.34] [-1.95, 0.61] [-2.49, 1.16] -0.70 7.55 [-9.87, 8.47] [-12.39, 10.99] [-17.33, 15.93] Not past 30 days -1.22 0.6 [-1.95, -0.49] [-2.15, -0.29] [-2.54, 0.1] -2.37 5.47 [-9.01, 4.27] [-10.83, 6.09] [-14.40, 9.67] Once past 30 days -1.17 0.83 [-2.18, -0.16] [-2.45, 0.11] [-2.99, 0.66] -6.70 7.55 [-15.87, 2.47] [-18.39, 4.99] [-23.33, 9.93] Cannabis use (Intercept) 5.06 0.33 [4.66, 5.46] [4.55, 5.57] [4.34, 5.78] 54.20 2.64 [51.03, 57.37] [50.18, 58.22] [48.54, 59.86] Yes -0.13 0.55 [-0.78, 0.53] [-0.96, 0.71] [-1.3, 1.04] -1.70 4.31 [-6.87, 3.47] [-8.26, 4.86] [-10.94, 7.54] Currently Using PAi Apps or Devices (Intercept) 4.91 0.33 [4.51, 5.31] [4.4, 5.41] [4.2, 5.62] 51.30 2.46 [48.34, 54.26] [47.55, 55.05] [46.02, 56.58] Yes 0.28 0.54 [-0.37, 0.93] [-0.54, 1.11] [-0.88, 1.45] 6.03 4.02 [1.21, 10.86] [-0.09, 12.16] [-2.59, 14.66] History of Using PA Apps or Devicesh (Intercept) 5.08 0.77 [4.15, 6.0] [3.9, 6.25] [3.42, 6.74] 51.00 5.72 [44.12, 57.88] [42.26, 59.74] [38.65, 63.35] Yes -0.21 0.86 [-1.24, 0.82] [-1.52, 1.1] [-2.07, 1.64] 0.38 6.39 [-7.32, 8.07] [-9.40, 10.15] [-13.43, 14.18] 302 Appendix AA. Chapter 7: Correlations between changes in PAa and PHQ-9b Correlations 75% CI 85% CI 95% CI Smartphone-based step counts Pearson's r = -0.55 [-0.75, -0.27] [-0.78, -0.18] [-0.84, -0.03] Spearman's Ï = -0.29 [-0.59, 0.06] [-0.66, 0.15] [-0.76, 0.31] Self-reported MVPAc Pearson's r = -0.77 [-0.87, -0.60] [-0.89, -0.54] [-0.91, -0.44] Spearman's Ï = -0.85 [-0.91, -0.73] [-0.93, -0.69] [-0.95, -0.61] Self-reported MVPA (no outlier) Pearson's r = -0.80 [-0.89, -0.65] [-0.91, -0.60] [-0.93, -0.49] Spearman's Ï = -0.82 [-0.89, -0.69] [-0.91, -0.65] [-0.94, -0.55] aPA: Physical activity. bPHQ-9: Patient Health Questionnaire â 9 item. cMVPA: Moderate-to-vigorous physical activity. 303 Appendix AB. Chapter 7: ChatGPT transcripts Authors: Please create a line chart showing the following data of weekly app use duration (in minutes) and their standard deviations over the 8-week intervention phase. Please label the x-axis as "Week" and the y-axis as "Mean app use duration (mins)." ChatGPT: Figure 7.2. Authors: Please create a heatmap of the following data of weekly step count data over 9 weeks. Label the x-axis as "Week" and the y-axis as "Participant." Include a scale on the right side of the figure to illustrate the range of step counts. Highlight the participant who increased their daily steps by 3,000 at week 9 compared to baseline with a red rectangle. ChatGPT: Figure 7.3. 304 Appendix AC. Additional manuscripts completed during the degree program, but are not included in the dissertation Tang, Y., He, J., Gierc, M., Ruissen, G. R., & Faulkner, G. (2024). Application of Ecological momentary assessment (EMA) in assessing the relationship between affect and movement behaviors among people with mood disorders: a scoping review. International Review of Sport and Exercise Psychology, 1-35. Algurén, B., Tang, Y., Pelletier, C., Naylor, P. J., & Faulkner, G. (2024). Biopsychosocial and Environmental Correlates of Childrenâs Motor Competence: An Exploratory Study. Sports Medicine-Open, 10(1), 90. Tang, Y., Algurén, B., Pelletier, C., Naylor, P. J., & Faulkner, G. (2023). Physical Literacy for Communities (PL4C): physical literacy, physical activity and associations with wellbeing. BMC Public Health, 23(1), 1266. He, J., Tang, Y., Lin, J., Faulkner, G., Tsang, H. W., & Chan, S. H. (2022). Non-invasive brain stimulation combined with psychosocial intervention for depression: a systematic review and meta-analysis. BMC Psychiatry, 22(1), 273. Chen, S., Ma, J., Hong, J., Chen, C., Yang, Y., Yang, Z., ... & Tang, Y. (2022). A public health milestone: China publishes new Physical Activity and Sedentary Behaviour Guidelines. Journal of Physical Activity, Sedentary and Sleep Behaviors, 1(1), 9.
Thesis/Dissertation
eng
Kinesiology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Faulkner, Guy E. J., 1970-
MoodMover : the development of an app-based behavioural intervention for increasing physical activity among people with depression
Text