Education, Faculty of
Kinesiology, School of
DSpace
UBCV
Haight, Brook
Master of Science - MSc
University of British Columbia
Background. Current research on employee well-being largely entails focus on physical and mental health and illness, as well as functioning specific to the workplace. Yet, more recent conceptualizations of well-being extend beyond the presence or absence of health or illness to concepts of thriving or languishing, measured using an outcome-wide measurement approach. Accordingly, healthcare worker (HCW) multidimensional well-being as an overarching concept should be inclusive of a broad range of ill-being and well-being indictors that span multiple dimensions, including physical, psychological, and psychosocial realms of life. A recent 12- week mHealth exercise intervention for HCWs in Vancouver, BC demonstrated significant treatment effects for depressive symptoms, burnout symptoms, and absenteeism among those randomized to an exercise versus waitlist control condition. Secondary analyses were conducted to examine whether positive treatment effects could extend to a broader range of multidimensional ill-being and well-being outcomes. Methods. Data was from the COVID-19 Pandemic and Exercise for Healthcare Workers (COPE HCW) trial, a two-arm parallel randomized controlled trial for low-active HCWs working at Providence Health Care centers across Vancouver, BC. The effects of engagement with a suite of exercise applications for 80 minutes per week for 12 weeks (vs. waitlist control) on indicators of multidimensional ill-being and well-being were examined. Treatment effects for physical health symptoms, recent global stress, work-to-personal life interference, overall physical health, overall mental/emotional health, life satisfaction, psychological flourishing, resilience, and work- to-personal life enhancement were analyzed using intent-to-treat analyses with a structural equation modelling (SEM) growth model approach. Results. By the end of the trial, significant treatment effects were observed for all outcomes, such that those in the exercise condition (vs. waitlist control) reported significant reductions in ill-being and improvements in well-being measures. Adherence to the intervention decreased over time, and treatment effects were most apparent for those who engaged with the exercise apps the most. Conclusions. mHealth exercise is one individual-level behavioral intervention which was successful in improving multidimensional ill-being and well-being among HCWs. Findings will help inform future employee wellness programming by providing evidence that mHealth exercise can be used as a modality to support well-being across different life domains.
COVID-19 PANDEMIC AND EXERCISE FOR HEALTHCARE WORKERS (COPE HCW) TRIAL: SECONDARY ANALYSES EXAMINING THE EFFECTS OF AT-HOME EXERCISE ON HEALTHCARE WORKERS’ MULTIDIMENSIONAL WELL-BEING by Brook Haight A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Kinesiology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) February 2024 © Brook Haight, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: COVID-19 Pandemic and Exercise for Healthcare Workers (COPE HCW) trial: Secondary analyses examining the effects of at-home exercise on healthcare workers’ multidimensional well-being submitted by Brook Haight in partial fulfilment of the requirements for the degree of Master of Science in Kinesiology Examining Committee: Dr. Eli Puterman, Associate Professor, School of Kinesiology, UBC Supervisor Dr. Mark Beauchamp, Professor, School of Kinesiology, UBC Supervisory Committee Member Dr. Guy Faulkner, Professor, School of Kinesiology, UBC Supervisory Committee Member Dr. Nicola Hodges, Professor, School of Kinesiology, UBC Additional Examiner iii Abstract Background. Current research on employee well-being largely entails focus on physical and mental health and illness, as well as functioning specific to the workplace. Yet, more recent conceptualizations of well-being extend beyond the presence or absence of health or illness to concepts of thriving or languishing, measured using an outcome-wide measurement approach. Accordingly, healthcare worker (HCW) multidimensional well-being as an overarching concept should be inclusive of a broad range of ill-being and well-being indictors that span multiple dimensions, including physical, psychological, and psychosocial realms of life. A recent 12-week mHealth exercise intervention for HCWs in Vancouver, BC demonstrated significant treatment effects for depressive symptoms, burnout symptoms, and absenteeism among those randomized to an exercise versus waitlist control condition. Secondary analyses were conducted to examine whether positive treatment effects could extend to a broader range of multidimensional ill-being and well-being outcomes. Methods. Data was from the COVID-19 Pandemic and Exercise for Healthcare Workers (COPE HCW) trial, a two-arm parallel randomized controlled trial for low-active HCWs working at Providence Health Care centers across Vancouver, BC. The effects of engagement with a suite of exercise applications for 80 minutes per week for 12 weeks (vs. waitlist control) on indicators of multidimensional ill-being and well-being were examined. Treatment effects for physical health symptoms, recent global stress, work-to-personal life interference, overall physical health, overall mental/emotional health, life satisfaction, psychological flourishing, resilience, and work-to-personal life enhancement were analyzed using intent-to-treat analyses with a structural equation modelling (SEM) growth model approach. iv Results. By the end of the trial, significant treatment effects were observed for all outcomes, such that those in the exercise condition (vs. waitlist control) reported significant reductions in ill-being and improvements in well-being measures. Adherence to the intervention decreased over time, and treatment effects were most apparent for those who engaged with the exercise apps the most. Conclusions. mHealth exercise is one individual-level behavioral intervention which was successful in improving multidimensional ill-being and well-being among HCWs. Findings will help inform future employee wellness programming by providing evidence that mHealth exercise can be used as a modality to support well-being across different life domains. v Lay Summary The present work examined the effect of 12-weeks of at-home exercise using a suite of apps on British Columbian healthcare workers’ ill-being and well-being, as measured by a wide array of physical, psychological, and psychosocial outcomes. As compared to those who were waitlisted, healthcare workers who received access to the apps reported significantly lower ill-being and higher well-being across outcomes. Results demonstrated the efficacy of app-based exercise for healthcare worker ill-being and well-being, providing evidence to support the use of this exercise modality in future employee wellness programming. vi Preface The COPE HWC Trial was conducted by the Fitness, Aging, and Stress Lab at the University of British Columbia (Vancouver) in collaboration with Providence Health Care (PHC). The trial was approved by the Behavioural Research Ethics Board at the University of British Columbia (#H21-02612) and the Institutional Ethics Board at Providence Health Care. I contributed to research identification and design by collaborating with community partners and the research team to confirm trial details and methods. As trial Coordinator, I contributed at all stages of the project, including creation of trial materials, training and management of research assistants, facilitation of trial, and data collection. Primary results of the COPE HCW Trial have been published elsewhere (Boucher et al., 2023). My contributions to the primary manuscript are outlined above and also include revisions. This thesis presents novel results based on secondary analyses of trial data. The statistical analyses were completed with assistance from Ben Hives. A first draft of this thesis was written independently by myself. Dr. Eli Puterman and Dr. Vincent Gosselin Boucher were co-principal investigators of the COPE HCW trial, leading all stages of the research process including creation of primary manuscript. Agnes Black was lead research partner from PHC, and collaborated on research design, recruitment, facilitation, manuscript creation, and dissemination. As coordinator for health promotion and psychological safety at PHC, Suzanne Nguyen assisted with design, recruitment, and dissemination. Ben Hives, Dr. Bruno Zumbo, and Dr. Yan Liu assisted with power analysis, data analysis, and creation of results. Dr. Mark Beauchamp contributed to study concept and vii design. Stacey Hutton and Aaliya Merali-Dewji acted as study exercise physiologists. All authors contributed to revisions of manuscript. A team of 20 research assistants helped with participant screening and data collection. The following is a citation for the primary results of the COPE HCW trial, which have been published online: Boucher, V. G., Haight, B. L., Hives, B. A., Zumbo, B. D., Merali-Dewji, A., Hutton, S., Liu, Y., Nguyen, S., Beauchamp, M. R., Black, A. T., & Puterman, E. (2023). Effects of 12 weeks of at-home, application-based exercise on healthcare workers’ depressive symptoms, burnout, and absenteeism: A randomized clinical trial. JAMA Psychiatry, 80(11), 1101-1109. viii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ....................................................................................................................... viii List of Tables .............................................................................................................................. xiii List of Figures ............................................................................................................................. xiv List of Symbols and Abbreviations ............................................................................................xv Acknowledgements .................................................................................................................... xvi Dedication ................................................................................................................................. xviii Chapter 1: Introduction ................................................................................................................1 1.1 Multidimensional Well-Being ........................................................................................ 2 1.1.1 Measuring Multidimensional Well-Being .............................................................. 3 1.2 Positive and Negative Indicators of Well-Being and Pathology .................................... 5 1.3 Employee Ill-Being and Well-Being .............................................................................. 7 1.3.1 Healthcare Worker Ill-Being and Well-Being ........................................................ 8 1.4 Indicators of HCW Ill-Being ........................................................................................ 10 1.4.1 Physical Health Symptoms ................................................................................... 10 1.4.2 Recent Global Stress ............................................................................................. 11 1.4.3 Work-to-Personal Life Interference ...................................................................... 12 1.5 Indicators of HCW Well-Being .................................................................................... 13 1.5.1 Overall Physical Health ........................................................................................ 13 ix 1.5.2 Overall Mental/Emotional Health ......................................................................... 14 1.5.3 Life Satisfaction .................................................................................................... 15 1.5.4 Psychological Flourishing ..................................................................................... 16 1.5.5 Resilience .............................................................................................................. 17 1.5.6 Work-to-Personal Life Enhancement ................................................................... 18 1.6 Exercise and HCW Ill-Being and Well-Being .............................................................. 19 1.6.1 Exercise Interventions for Ill-Being and Well-Being ........................................... 21 1.7 Exercise Intervention in the Context of the COVID-19 Pandemic ............................... 23 1.7.1 mHealth Exercise Interventions ............................................................................ 25 1.8 The Present Research .................................................................................................... 27 1.8.1 Objectives and Hypotheses ................................................................................... 27 Chapter 2: Methods .....................................................................................................................29 2.1 Pre-registration and Trial Design .................................................................................. 29 2.2 Participants .................................................................................................................... 29 2.2.1 Sample Size Calculation ....................................................................................... 30 2.3 Procedure ...................................................................................................................... 30 2.3.1 Recruitment ........................................................................................................... 30 2.3.2 Randomization ...................................................................................................... 31 2.3.3 Intervention ........................................................................................................... 31 2.4 Measures ....................................................................................................................... 32 2.4.1 Sociodemographic Measures ................................................................................ 32 2.4.2 Adherence to Intervention ..................................................................................... 32 2.4.3 Ill-Being Measures ................................................................................................ 33 x 2.4.3.1 Physical Health Symptoms ............................................................................... 33 2.4.3.2 Recent Global Stress ......................................................................................... 33 2.4.3.3 Work-to-Personal Life Interference .................................................................. 33 2.4.4 Well-Being Measures ............................................................................................ 34 2.4.4.1 Overall Physical Health .................................................................................... 34 2.4.4.2 Overall Mental/Emotional Health ..................................................................... 34 2.4.4.3 Life Satisfaction ................................................................................................ 35 2.4.4.4 Psychological Flourishing ................................................................................. 35 2.4.4.5 Resilience .......................................................................................................... 35 2.4.4.6 Work-to-Personal Life Enhancement ............................................................... 35 2.5 Changes to the Trial ...................................................................................................... 36 2.6 Analysis Plan ................................................................................................................ 36 2.6.1 Intention-to-Treat Analyses .................................................................................. 37 2.6.2 Per-Protocol Analyses ........................................................................................... 37 2.6.3 Follow-up Analyses .............................................................................................. 38 2.7 Missing Data ................................................................................................................. 38 Chapter 3: Results ........................................................................................................................39 3.1 Participants .................................................................................................................... 39 3.2 Missing Data ................................................................................................................. 39 3.3 Adherence to Intervention ............................................................................................. 40 3.4 Intention-to-Treat Analyses .......................................................................................... 40 3.4.1 Indicators of Ill-Being ........................................................................................... 41 3.4.1.1 Physical Health Symptoms ............................................................................... 41 xi 3.4.1.2 Recent Global Stress ......................................................................................... 41 3.4.1.3 Work-to-Personal Life Interference .................................................................. 41 3.4.2 Indicators of Well-Being ....................................................................................... 42 3.4.2.1 Overall Physical Health .................................................................................... 42 3.4.2.2 Overall Mental/Emotional Health ..................................................................... 42 3.4.2.3 Life Satisfaction ................................................................................................ 42 3.4.2.4 Psychological Flourishing ................................................................................. 42 3.4.2.5 Resilience .......................................................................................................... 43 3.4.2.6 Work-to-Personal Life Enhancement ............................................................... 43 3.5 Per-Protocol Analyses ................................................................................................... 43 3.5.1 Indicators of Ill-Being ........................................................................................... 43 3.5.1.1 Physical Health Symptoms ............................................................................... 44 3.5.1.2 Recent Global Stress ......................................................................................... 44 3.5.1.3 Work-to-Personal Life Interference .................................................................. 44 3.5.2 Indicators of Well-Being ....................................................................................... 44 3.5.2.1 Overall Physical Health .................................................................................... 45 3.5.2.2 Life Satisfaction ................................................................................................ 45 3.5.2.3 Psychological Flourishing ................................................................................. 45 3.5.2.4 Resilience .......................................................................................................... 45 3.5.2.5 Work-to-Personal Life Enhancement ............................................................... 45 3.6 Follow-up Analyses ...................................................................................................... 46 Chapter 4: Discussion ..................................................................................................................47 4.1 Exercise and HCW Ill-Being ........................................................................................ 47 xii 4.2 Exercise and HCW Well-Being .................................................................................... 49 4.3 Adherence to Intervention ............................................................................................. 52 4.4 Follow-up Analyses ...................................................................................................... 53 4.5 Limitations .................................................................................................................... 54 4.6 Future Directions .......................................................................................................... 57 4.7 Conclusion .................................................................................................................... 60 Tables ............................................................................................................................................61 Figures ...........................................................................................................................................70 Footnotes .......................................................................................................................................80 References .....................................................................................................................................81 Appendices ..................................................................................................................................103 Appendix A Dependent Variable Scales ................................................................................. 103 Appendix B Supplemental Tables .......................................................................................... 111 xiii List of Tables Table 1 Descriptive Statistics for Outcome Measures by Condition ............................................ 61 Table 2 Biweekly Missingness of Outcomes Measures ............................................................... 62 Table 3 Biweekly Complete Missingness of Outcome Measures by Condition .......................... 63 Table 4 Summary of Model Fit ..................................................................................................... 64 Table 5 Intention-to-Treat Biweekly Effect Sizes ........................................................................ 66 Table 6 Per-Protocol Biweekly Effect Sizes ................................................................................. 67 Table S1 Intention-to-Treat Biweekly Effect Sizes, Covarying the Effects of Age ................... 111 Table S2 Intention-to-Treat Biweekly Effect Sizes, Covarying the Effects of Income .............. 112 Table S3 Intention-to-Treat Effects and Within Condition Changes Over Time ....................... 113 Table S4 Intention-to-Treat Biweekly Effect Sizes, Conservative p-value ................................ 116 Table S5 Per-Protocol Biweekly Effect Sizes, Conservative p-value ........................................ 117 xiv List of Figures Figure 1 Flexible Conceptual Map Concerning Human Flourishing ............................................ 70 Figure 2 Intention-to-Treat Biweekly Effect Sizes and Trajectories ............................................ 71 Figure 3 Per-Protocol Biweekly Effect Sizes and Trajectories .................................................... 76 xv List of Symbols and Abbreviations α = Cronbach’s Alpha CFI = Comparative fit index CI = Confidence interval COPE = COVID-19 Pandemic and Exercise COPE HCW = COVID-19 Pandemic and Exercise for Healthcare Workers ePARmed-X+ = Physical Activity Readiness Medical Examination ES = Effect size HCW = Healthcare worker N = Sample size p = p-value PAR-Q+ = Physical Activity Readiness Questionnaire PHC = Providence Health Care RMSEA = Root mean square error of approximation SD = Standard deviation SEM = Structural equation modelling SRMR = Standardized root mean square residual TLI = Tucker–Lewis index xvi Acknowledgements I would like to acknowledge that this research was conducted on the traditional, ancestral, and unceded territory of the xwməθkwəy̓əm (Musqueam) People. I would like to thank my thesis supervisor, Dr. Eli Puterman, for his unwavering support and investment in me as a student and individual. Your sincerity and care for others has been brilliantly apparent since the first time I met with you over Zoom. I have learned so much from you throughout this degree, and I look up to you as a great example of what a leader should be. I hope to continue working with and learning from you for years to come! Additionally, I would like to thank Dr. Vincent Gosselin Boucher for his continuous mentorship and for guiding me through the ins and outs of facilitating a trial. Your enthusiasm for the project was contagious and I not only learned a ton from working with you, but also had a lot of fun while doing so. I am very excited to see where this work takes you! Further, I would like to thank Ben Hives for his mentorship and assistance with data analysis. You are a stats wizard and your love for stats shined through in your mentorship. Thanks for being a great lab mate and for your patience with these analyses. This project could not have been completed without the wonderful team we had in place. I would like to acknowledge and thank the research assistants who consistently dedicated their care, time, and energy to the trial. Thank you to my committee members for their guidance throughout the xvii project. Many thanks go to PHC and all contributing authors for their guidance and support. In particular, I extend a big thank you to Agnes Black and Suzanne Nguyen for their ongoing collaboration and partnership. I appreciate the opportunity to further my learning of knowledge translation and community partnership and I hope to continue connecting with you in the future. Lastly, I would like to thank my family and friends for their ongoing encouragement throughout this degree. I greatly appreciate the countless phone calls from home, hugs, and dog snuggles. I am also very grateful to have met friends in Vancouver who have consistently kept a smile on my face and made this a chapter of my life I will forever cherish. Funding Acknowledgement: The COPE HCW Trial was financially supported by an Equipment and Research Accelerator Fund from the University of British Columbia. I received funding from the Canadian Institutes of Health Research Canada Graduate Scholarship. xviii Dedication I would like to dedicate this thesis to healthcare workers. Thank you for your selflessness, dedication, and perseverance. 1 Chapter 1: Introduction Occupational wellness efforts have traditionally centered around health and safety procedures in the workplace, as well as reduction of employee illness and absenteeism. However, perspectives on employee wellness have more recently expanded to also encourage a broader approach to both alleviating ill-being and promoting well-being of employees (Bank et al., 2013; Schulte et al., 2015). Researchers have recommended the measurement of a broader notion of well-being (i.e., multidimensional well-being) across multiple dimensions of life (e.g., psychological, social, physical, spiritual) using an outcome-wide measurement approach (Ryff et al., 2020; VanderWeele & Lomas, 2022). When examining employee multidimensional well-being, it is important to consider both negative indicators of ill-being and positive indicators of well-being, as ill-being and well-being do not always lie on a continuum (Vanderweele et al., 2020a). Measuring indicators of ill-being and well-being separately, yet simultaneously, may provide additional insight to the human condition (Vanderweele et al., 2020a). Accordingly, research examining employee health and well-being should be inclusive of a wide array of ill-being and well-being outcomes that span multiple realms of life, including physical, psychological, and psychosocial dimensions. Healthcare worker (HCW) health and well-being worsened throughout the COVID-19 pandemic (e.g., Canadian Medical Association, 2021; Lai et al., 2020), highlighting the need for initiatives aimed at improving multidimensional ill-being and well-being amongst this population. Exercise interventions have been successful in improving physical and psychological health of highly stressed populations (e.g., caregivers; Puterman et al., 2018), as well as during the pandemic (Puterman et al., 2022). Thus, exercise may be one individual-level behavioural intervention that can be used to support the health and well-being of HCWs. The Covid-19 2 Pandemic and Exercise for Healthcare Workers (COPE HCW) trial was a 12-week randomized controlled trial which examined the use of at-home mHealth exercise to improve the health and well-being of Providence Health Care employees. HCWs randomized to the exercise condition demonstrated significantly lower levels of depressive symptoms, symptoms of burnout, and absenteeism, as compared to those waitlisted (Boucher et al., 2023a). To expand such findings, in the present work secondary analyses were conducted to examine the effect of the COPE HCW trial on a wider array of physical, psychological, and psychosocial ill-being and well-being outcomes. 1.1 Multidimensional Well-Being What constitutes well-being, as well as well-being conceptual frameworks, have been a contention among scholars (VanderWeele et al., 2020b). A lack of well-defined boundaries surrounding well-being constructs, disagreement regarding terminology, and imprecise definitions fuels this controversy (VanderWeele et al., 2020b). Commentary stemming from an interdisciplinary workshop on the measurement of well-being (VanderWeele & Lomas, 2022) concluded that definitions of overall well-being have traditionally been too narrow in scope. Such definitions have focused on the measurement of single constructs (e.g., life satisfaction, physical disease) within specific well-being dimensions (e.g., psychological, health, social) rather than considering well-being across multiple realms of life more broadly (Vanderweele, 2017; Vanderweele et al., 2020a). Such narrow definitions of well-being conflict with the World Health Organization’s definition of health, “a state of complete physical, mental, and social well-being” (World Health Organization, 1946, p.1), which remains in place today and encourages a broader multidimensional perspective on health and well-being. Although positive psychology 3 has moved away from this tradition, within occupational health research especially, health and well-being are often seen as merely the absence of injury or disease (Fox et al., 2022). In effort to position a broader notion of well-being into a comprehensive framework, VanderWeele and Lomas (2022) proposed the flexible conceptual map concerning human flourishing. This conceptual map (see Figure 1) includes the term multidimensional well-being to describe “the relative attainment of a state in which all aspects of a person’s physical, mental, social, and spiritual life are good” (VanderWeele & Lomas, 2022, p.37). Nested within multidimensional well-being are more specific dimensions of well-being including mental well-being, physical well-being, social well-being, and spiritual well-being. Each dimension of well-being can further be broken down into dimension-specific constructs and measures. For example, psychological flourishing is a measure of mental well-being, which is a dimension of multidimensional well-being (VanderWeele & Lomas, 2022). This conceptual map offers a clear framework to define and measure a broad notion of well-being -- multidimensional well-being. 1.1.1 Measuring Multidimensional Well-Being Although the flexible conceptual map concerning human flourishing (VanderWeele & Lomas, 2022) provides a framework to organize notions of well-being, there remains inconsistency across studies regarding which dimensions and specific measures of well-being to include in research (Schulte et al., 2015; VanderWeele et al., 2020b). It has been suggested that measures of well-being should be selected based on the study-specific research goals and context (VanderWeele et al., 2020b). Multidimensional well-being may also best be captured by employing an ‘outcome-wide’ measurement approach, wherein applicable measures across multiple well-being dimensions are simultaneously measured to gain a more complete picture of one’s state (Ryff et al., 2020; Vanderweele et al., 2020a). 4 Similarly, research on well-being, although traditionally more psychological in nature, should also strive to include measures of physical health. Physical health is often examined as a downstream outcome in well-being research (i.e., the predictive effects of well-being on subsequent health), however, physical health is a core component of one’s experience of overall well-being (Vanderweele, 2017). Although mental and physical well-being are argued as central components of multidimensional well-being, they themselves do not capture the breadth of well-being as proposed by VanderWeele and Lomas (2022), and measures of social and spiritual dimensions of well-being should also be included when possible. As different research goals and contexts will create variation in the measurement of multidimensional well-being, it is also important that researchers express clearly what measures they are including in their research, as well as the domain(s) in which they are measuring well-being (Fox et al., 2022). For instance, a global domain (previously referred to as a context-free domain; Fox et al., 2022) includes measures of well-being that are not tied to one particular context (e.g., overall life satisfaction). Measurement of multidimensional well-being in a work-specific domain includes measures that tease apart one’s experiences as they relate to their career (e.g., job satisfaction). Measuring a work-family domain may include measures of work-to-personal life spillover (i.e., measures of how much one’s work life impedes or enhances their personal life; Fox et al., 2022). Stating the domain in which one plans to measure well-being helps ensure that findings are not overgeneralized to different contexts. In the present work, I refer to multidimensional well-being (hereafter referred to as ‘well-being’) as an overarching concept encompassing work-family and global, physical, mental, and psychosocial dimensions. 5 1.2 Positive and Negative Indicators of Well-Being and Pathology Exponents of multidimensional well-being also suggest that a lack of ill-being is different than positive health, and novel information about the human condition can be gained from measuring both positive and negative indicators of well-being and pathology (Schulte et al., 2015; Vanderweele et al., 2020a). This is supported by the Dual Continua Model of Well-Being and Psychopathology, introduced by Keith Tudor (1996) and further augmented by Corey Keyes (2002). In this model, it is proposed that well-being and pathology are distinct, yet related continua, and that each includes a broad range of positive and negative outcomes. The pathogenic continuum lies on the horizontal axis and ranges from illness (e.g., high depressive symptoms) to health (e.g., low depressive symptoms), measuring diagnosable disorders such as clinical psychological disorders as classified by the DSM-5. The vertical, salutogenic continuum measures a broader notion of well-being, ranging from languishing (e.g., stress) to flourishing or thriving (e.g., life satisfaction), and is used to measure non-clinical health and functioning (Keyes, 2002; Tudor, 1996). Measuring a variety of pathogenic and salutogenic outcomes across populations of different sociodemographic makeups and heterogenous settings, researchers have repeatedly found a two oblique factor model consistent with this model (Franken et al., 2018; Keyes et al., 2008; Petrillo et al., 2015). This model provides strong rationale for measuring both positive and negative indicators to capture well-being more holistically. Consistent with this model, VanderWeele and colleagues (2020a) call for the inclusion of a “positive epidemiology” (i.e., a broader focus not only on the reduction of disease but also on the promotion of positive states) in health and well-being research (p. 189). They argue that ill-being and well-being, or illness and health, do not always lie on continua, and that there is value in considering positive and negative indicators separately, yet simultaneously. For example, 6 measuring both positive and negative outcomes may help explain why some individuals appear to flourish in adversity while others do not. Positive epidemiology in the broadest sense should be inclusive of physical, psychological, social, and spiritual dimensions, reflecting the idea that health and well-being are best captured by considering multiple realms of one’s life (Vanderweele et al., 2020a). Consistent with this broader notion of well-being, in addition to mitigating a problem, well-being interventions should then be created to also increase positive well-being, and to include outcome measures that span well-being dimensions (Vanderweele et al., 2020a). In the present work I focused on a wide array of psychological, psychosocial, and physical ill-being and well-being measures, as our previously published work on this trial reported on the more specific primary outcome of depressive symptoms, and secondary healthcare-related outcomes of burnout symptoms and absenteeism (Boucher et al., 2023a). Examining a broader range of both ill-being and well-being outcomes in addition to pathogenic outcomes encourages an empowering approach that aims to reduce stigma associated with illness, and shifts attention to how we can better support functioning regardless of health state (Yeung & Johnston, 2020). Indicators of well-being also provide distinct and novel information about the human condition that cannot be gleaned from solely measuring ill-being and illness (Vanderweele et al., 2020a). For example, one can be simultaneously functioning well while experiencing disease, and both experiences are important to understand and are valuable targets for intervention. High well-being has also been shown to have downstream effects on pathogenic outcomes, including both reducing the risk of being diagnosed with an illness (e.g., depression) and reducing the severity of symptoms experienced (Keyes et al., 2010). Further, high levels of well-being have been positively associated with many adaptive outcomes, including engagement in healthier behaviours (e.g., healthier dietary intake, less consumption of 7 alcohol), more positive social relationships, and greater productiveness at work (for review see Great Britain Department of Health, 2014). Measurement of well-being should therefore not only take an outcome-wide approach but should be inclusive of a range of both positive (e.g., psychological flourishing) and negative (e.g., physical health symptoms) outcomes that span multiple dimensions (e.g., mental, psychosocial, and physical well-being; Vanderweele et al., 2020a). 1.3 Employee Ill-Being and Well-Being In an occupational setting, employee well-being traditionally centres around the alleviation of deficits in the workplace, including the reduction of illness and management of health and safety (Schulte et al., 2015). More recently, legislation has shifted to support both the prioritization and promotion of employee physical and mental well-being. For example, in 2013 the Mental Health Commission of Canada released the National Standard for Psychological Health and Safety in the Workforce (i.e., the Standard). The Standard is a comprehensive framework which not only sets guidelines for mental health risk prevention and management, but also encourages organizations to promote and foster psychologically healthy workplaces (Bank et al., 2013). Supported by the Standard (Bank et al., 2013), broader notions of employee well-being and workplace quality of life have been given increasing priority by organizations (Tomo & de Simone, 2017), a movement which has only been fueled by the COVID-19 pandemic (Peters et al., 2022). Organizations such as governments, schools, and healthcare systems have acknowledged the importance of promoting well-being in the workplace and seek initiatives that can help to support the psychological health and well-being of their employees (Plough & Ahchraya, 2020). However, by only considering work-specific experiences of ill-being and well- 8 being, we lack consideration for the ‘whole person’. Such measurement only captures experiences in one domain of life and may signal a lack of care for the employee outside of the workplace. It is well evidenced that the conditions of (e.g., shift work) and experiences within (e.g., low co-worker support) one’s occupation are related to indicators of global (e.g., low self-reported physical health; Shields & Wilkins, 2006); and work-family ill-being (e.g., work-to-personal life interference; Carlson et al., 2011). It is argued then that employee ill-being and well-being are multidimensional concepts and need to be examined by exploring a broad range of outcomes, extending past employee health and functioning specific to the workplace (i.e., in global domains; Fox et al., 2022; Schulte et al., 2015). 1.3.1 Healthcare Worker Ill-Being and Well-Being HCWs, individuals employed in diverse professions within the healthcare system (e.g., nurses, physicians, occupational therapists, technicians, administrative staff; Mohanty et al., 2019) frequently report long shifts, overtime, and conflict at work (Shields & Wilkins, 2005). HCWs are frequently exposed to physical (e.g., heavy patient handling), chemical (e.g., strong cleaning chemicals), biological (e.g., infectious diseases), mental (e.g., high stress procedures), and emotional hazards (e.g., emotionally intense cases; for reviews see Joseph & Joseph, 2016; Mohanty et al., 2019; Tomo & de Simone, 2017). It is not surprising then, that HCWs experience higher rates of work stress as compared to other working professionals (i.e., 45% of Canadian HCWs reported high work stress vs. 31% of all other employees; Wilkins, 2007). This is concerning as cross-sectional and prospective studies have consistently demonstrated that both acute and cumulative stress associate positively with poorer indicators of physical and mental ill-being and well-being (e.g., Albert et al., 2017; Haight et al., 2023; Lockwood et al., 2022). 9 Since the COVID-19 pandemic, HCWs have reported high rates of physical and mental ill-being and pathology. Compared to pre-pandemic, HCWs reported less physical activity (Boucher, et al., 2023b) and reported more frequent somatic health symptoms (Barello et al., 2020). In addition, an increase in depression, anxiety, distress, and insomnia (Lai et al., 2020), and poorer overall self-reported mental health (Statistics Canada, 2021a) were reported by HCWs who had direct contact with COVID-19 patients. Decreases in well-being have also been observed during (vs. prior to) the pandemic among HCWs. As measured by the National Physician Health Survey (Canadian Medical Association, 2021), the percent of physicians that could be classified as flourishing (measured across psychological, emotional, and social dimensions; Lamers et al., 2011) decreased from 63% in 2017 to 47% in 2021. Such consequences to ill-being and well-being may have been aggravated by the increased workload (e.g., higher volume of patients), increased working hours, uncertainty regarding the virus and changing protocols, isolation from quarantine, and increased risk of exposure to the virus experienced by HCWs during the pandemic (Mehta et al., 2021; Peters et al., 2022). In the present secondary analysis of a trial addressing health and well-being of HCWs during the pandemic, I examined trial effects on a wide range of physical, psychological, and psychosocial indicators of ill-being (i.e., physical symptoms, life stress, work-to-personal life interference) and well-being (i.e., overall physical health, overall mental health, life satisfaction, psychological flourishing, resilience, work-to-personal life enhancement). Such outcomes were selected by merit of their relevance to the high stress HCW population, as detailed in the subsequent subsections. 10 1.4 Indicators of HCW Ill-Being 1.4.1 Physical Health Symptoms Physical health symptoms, such as headache, backache, fatigue, joint pain, and muscle weakness, are important indicators of both one’s current and future well-being. For example, physical symptoms may coincide with high daily stress, as research suggests that experiencing daily stressors is associated with increased odds of, and more severe, daily physical symptoms (Haight et al., 2023). Individuals who report more daily physical health symptoms also tend to report higher levels of negative affect (Charles & Almeida, 2006). This is concerning as higher negative affect is associated with increased mortality risk (Wilson et al., 2003). Further, individuals who report more daily physical symptoms also tend to report greater odds of reporting functional disability and chronic health conditions in the future (Leger et al., 2015). Due to the nature of their jobs (e.g., patient handling, heavy lifting), many HCWs are highly susceptible to physical health symptoms such as musculoskeletal pain (Mohanty et al., 2019), with musculoskeletal disorders being one of the leading causes of absenteeism in nurses (Menzel, 2008). Increased contact with chemicals often leads to high reports of headaches, irritation, and cold-like symptoms amongst HCWs (Mohanty et al., 2019). A meta-analysis on psychosocial work conditions and musculoskeletal disorders in HCWs (Bernal et al., 2015) concluded that low job control, low social support, and high effort-reward imbalance were positively associated with greater odds of experiencing musculoskeletal pain. A recent review and meta-analysis suggested that HCWs experienced high rates of somatic health symptoms during the pandemic (Theocharis, 2023). For instance, one cross-sectional study of hospital workers reported that 49.7% of respondents experienced somatic symptoms, with higher levels of somatic symptoms reported by workers with higher levels of job 11 stress (Lixia et al., 2022). Similar percentages of HCWs who reported somatic symptoms during the pandemic were observed across other studies included in Theocharis’ (2023) review (e.g., 42.7% of nurses across 42 health centres reporting low energy, pain, and breathing difficulty; Hong et al., 2021). Therefore, evidence points to the need for well-being interventions which target somatic symptoms among Canadian HCWs. 1.4.2 Recent Global Stress Recent global stress encompasses both one’s perception of how stressful their environment tends to be and how well they perceive they can manage such demands (Cohen et al., 1983; Lazarus & Folkman, 1984). It is important to examine global stress that extends past experiences within the workplace, as experiences of stress at work may spill-over and interact with experiences of stress outside of the workplace (Bolger et al., 1989; Wethington, 2000), and a global measure may capture a more complete indication of one’s recent stress across life domains. The negative consequences of global stress for physical and mental health outcomes are well documented. For example, reviews and meta-analyses suggest that higher levels of psychological stress have been positively prospectively associated with a higher risk (risk ratio = 1.27) of incident coronary heart disease (Richardson et al., 2012), an unhealthy diet (Khaled et al., 2020), and onset of depression (Maddock & Pariante, 2001). As compared to other professions, Canadian HCWs tend to report higher levels of stress (Statistics Canada, 2017b; Wilkins, 2007). Many characteristics of healthcare professions lend themselves to stress-inducing circumstances. For example, shiftwork, higher number of weekly work hours, and working for others (vs. self-employed) have all been cross-sectionally related to increased stress among HCWs (Wilkins, 2007). Internationally, evidence suggests that the pandemic only amplified the prevalence of HCWs reporting high perceived stress, with one 12 Wuhan cross-sectional study reporting 59% of their HCW sample as having experienced moderate to severe stress (e.g., Du et al., 2020). Within Canada, there is mixed evidence regarding levels of stress experienced by HCWs during the pandemic (Ménard et al., 2022). One cross-sectional study of 650 Ontario HCWs during the pandemic concluded that a majority of the HCWs reported mild stress, suggesting overall resilience in the workforce (notably, 13.5% of the sample reported severe stress; Ménard et al., 2022). In contrast, a cross-sectional study of 1,414 HCWs from Alberta found that 81.7% of the HCWs reported moderate or high stress (Mrklas et al., 2020). Differences in reports of stress among Canadian HCWs may be a result of differences in timing of studies (i.e., beginning versus later stages of pandemic) and differences in sample characteristics. Nevertheless, such work highlights that HCW stress is an important factor to attempt to reduce. 1.4.3 Work-to-Personal Life Interference Work-to-personal life interference refers to how much one’s work responsibilities impede functioning within their personal life (Grzywacz & Marks, 2000). For instance, when a HCW experiences an emotionally straining work shift, it is possible that such distress spills over into their relationships at home, and negatively influences their personal life interactions (e.g., less patience, less energy for social interactions). As reviewed by Mihelic and Tekavcic (2014), work-to-personal life interference is associated with many adverse outcomes, such as poor physical health (e.g., hypertension, obesity), unhealthy behaviours (e.g., overeating, less exercise), and increased likelihood of having a psychological disorder (e.g., depression, post-traumatic stress disorder). Among nurses specifically, work-to-personal life interference has been cross-sectionally related to greater turnover intention, lower job satisfaction, and poorer perceived quality of care (Labrague et al., 2021). 13 Cross-sectional surveys suggest that HCWs report higher levels of work-to-personal life interference than the general population (e.g., Fuß et al., 2008). A review of the antecedents of work-to-personal life interference suggests that shiftwork, working overtime hours, having less job support, and experiencing high pressure related to workload have been linked to higher work-to-personal life interference for HCWs (Mihelic & Tekavcic, 2014). During the pandemic, cross-sectional evidence suggested that work-to-personal life interference was higher in 2020 compared to 2019 for Turkish HCWs (Şahin et al., 2021). Further, among HCWs in China, high work-to-personal life interference was positively associated with high anxiety symptoms during the pandemic (Liang et al., 2023). These authors suggested increased working hours and workload resulting from the pandemic as potential factors that led to high work-to-personal life interference among HCWs (Liang et al., 2023). 1.5 Indicators of HCW Well-Being 1.5.1 Overall Physical Health Self-reported health, commonly measured in large population surveys by a single item assessing one’s general perceptions of their physical health (e.g., Canadian Community Health Survey; Statistics Canada, 2003) provides a simple indicator of individual-level health. Prospectively, general self-reported health has been linked with use of outpatient health services, hospitalization, and mortality, in that those who reported higher self-reported health had lower odds of all outcomes (DeSalvo et al., 2005). Additionally, in a review of 27 longitudinal studies, more favourable global perceptions of health were consistently predictive of lower odd ratios of mortality, even after controlling for objective health status (Idler & Benyamini, 1997). Pre-pandemic, Canadian nurses reported similar physical health as compared to employees across other professions (e.g., in 2005 93% of Canadian employees reported good to 14 excellent health, a statistic that did not change when considering nurses only; Shields & Wilkins, 2006). Findings from the same Statistics Canada survey suggested that one third of nurses reported that their physical health negatively affected their ability to handle their workload (Shields & Wilkins, 2006). Self-reported health among HCWs may vary according to a variety of organizational and psychosocial factors. Among nurses, poor self-reported health has been associated with working evening shifts, high job strain, low co-worker support, high physical demands at work, poor working relationships, and limited decision-making power at work Shields & Wilkins, 2006). Research examining changes in HCWs’ self-reported physical health during the pandemic is lacking. However, cross-sectional research on the general Canadian population suggests that 16.4% of Canadians reported fair or poor physical health during the third wave of the pandemic (Spring 2021; Michaud et al., 2022). 1.5.2 Overall Mental/Emotional Health Self-reported mental health is often measured in large population surveys using a one-item scale assessing global perceptions of mental or emotional health status (e.g., Canadian Community Health Survey; Statistics Canada, 2003). The one-item measure is believed to reflect one’s beliefs regarding their emotional regulation, depressive symptoms, and overall distress, and is an indication of perceived psychological well-being rather than clinical psychological health (Mawani & Gilmour, 2010). Using large nationally representative data, ratings of overall mental health have been cross-sectionally associated with higher physical role functioning (i.e., physical health interfering less with the completion of work or other activities), and higher emotional role functioning (i.e., emotional problems interfering less with completion of work or other activities; Fleishman & Zuvekas, 2007). 15 As reported in findings from the 2005 National Survey of the Work and Health of Nurses (Shields & Wilkins, 2006), 6% of Canadian nurses reported fair or poor global mental health, a higher percentage than reported by employees across other professions (3-4%). During the early stages of the pandemic (i.e., Winter of 2020), using a crowdsourcing survey, 33% of Canadian HCWs reported poor mental health (Statistics Canada, 2021a). To further elucidate the change in self-reported mental health of HCWs during the pandemic, during the Winter of 2020, 70% of Canadian HCWs reported that their overall mental health had declined as compared to March 2020. Having direct contact with COVID-19 infected patients and lacking access to personal protective equipment was associated with worse global self-reported mental health among these HCWs (Statistics Canada, 2021a). 1.5.3 Life Satisfaction Measures of global life satisfaction -- subjective evaluations of one’s life overall -- are purposefully broad in that respondents can use information about their lives that they believe to be relevant when cognitively forming judgements about their satisfaction (Diener, 1984). Although it is common for occupational well-being research to include measures of job satisfaction, the importance of integrating evaluations of overall life satisfaction in this research is underappreciated and underscores the notion that employee well-being promotion should extend past the workplace (Erdogan et al., 2012). Using data from a large nationally representative sample of adults from the United States, higher levels of current life satisfaction have been linked prospectively with more positive self-rated health, reduced risk of physical limitations and mortality, higher life purpose and financial mastery, and lower depression (Kim et al., 2021). 16 The bottom-up approach to life satisfaction proposes that evaluations of life satisfaction are informed by evaluations of satisfaction within specific life domains (e.g., work; Heller et al., 2004). In line with this approach, many work-related conditions have been shown to be predictive of overall life satisfaction. For example, a review of 193 studies has found positive associations between higher life satisfaction and work factors such as a supportive work environment, satisfaction with professional growth, and more autonomy (Erdogan et al., 2012). Compared to pre-pandemic, there was a drastic decrease in life satisfaction reported by Canadians, with reports being at an all-time low early in the pandemic (Helliwell et al., 2020). Less is known regarding Canadian HCWs’ change in life satisfaction during the pandemic, however based on worse job satisfaction reported among HCWs who cared for COVID-19 patients (Lavoie-Tremblay et al., 2022) I would expect patterns of life satisfaction among HCWs to have been worse than, or reflect that of, the general population. 1.5.4 Psychological Flourishing Psychological flourishing, as a positive measure of well-being, captures one’s psychosocial and emotional prosperity including levels of optimism, purpose and meaning, engagement with personally meaningful activities, and rewarding relationships (Diener et al., 2010). Prospectively, using national surveys, high levels of flourishing have been associated with decreased odds of mental illness including major depressive disorder, generalized anxiety disorder, and panic disorder (Keyes et al., 2010). Further, over a 10-year period, individuals who were flourishing were found to have a 62% decrease in risk of all-cause mortality compared to those who were not flourishing (Keyes & Simoes, 2012). Due to the high prevalence of, and focus on, mental illness (e.g., depression, burnout) in research on HCWs, there remains a lack of research on HCW psychological flourishing and its 17 antecedents (Mascaro et al., 2022). Among Canadian workers more broadly, higher job control, support, and security, as well as lower occupational physical strain, have been cross-sectionally associated with higher levels of flourishing (Fan et al., 2019). Albeit a small sample size, Jarden and colleagues (2023) found that Australian nurses’ flourishing did not change significantly between 2019 and 2020, and remained fairly high (i.e., mean score of ~47, range of 8-56). A cross-sectional study of medical residents in Atlanta, Georgia conducted between 2018-2020 concluded that 55.2% of HCW trainees could be categorized as flourishing, a prevalence which decreased when examining primary caregivers only (Mascaro et al., 2022). 1.5.5 Resilience Resilience describes one’s ability to positively respond and adapt to unfavourable circumstances (Luthar & Cicchetti, 2000). High resilience is required among HCWs to safeguard them from frequent exposure to physical (e.g., heavy patient handling), mental (e.g., high stress procedures), and emotional (e.g., emotionally intense cases) hazards and high work stress (Hart et al., 2014). Higher resilience captures one’s ability to cope better and problem-solve more effectively when faced with high-stress situations (Rushton et al., 2015). An integrative review found that among nurses, reporting higher resilience is positively associated with improved work-related outcomes such as higher work engagement and social support, as well as lower mental illness including burnout, post-traumatic stress disorder, depression, and anxiety (Baskin & Bartlett, 2021). During the COVID-19 pandemic, levels of resilience among the general population were lower than previously reported (Killgore et al., 2020). An integrative review of 32 studies examining HCW resilience during the pandemic concluded that HCWs reported moderate levels of resilience, indicating that HCWs were able to adapt to stressful situations sometimes but not 18 always (i.e., scores ranging from 35.54 – 92.77 out of a possible 100). Differences were found by type of profession, wherein nurses (vs. physicians and support workers) as well as frontline (vs. non-frontline) workers reported lower levels of resilience (Baskin & Bartlett, 2021). Accordingly, interventions which promote maintenance of resilience among HCWs following the pandemic must be considered. 1.5.6 Work-to-Personal Life Enhancement Work-to-personal life enhancement refers to the degree to which work responsibilities facilitate positive functioning in personal life (Grzywacz & Marks, 2000). For instance, when a HCW experiences a high degree of support from their employer, it is possible that this experience may spillover into their relationships at home, and positively influence their personal life interactions (e.g., more patience, more energy for social interactions). An integrative review of the outcomes of work-to-personal life enhancement suggests that high enhancement is associated with many adaptive work factors (e.g., high job satisfaction, low turnover intention), relationship factors (e.g., high marital satisfaction, high relationship quality), psychological factors (e.g., low burnout, low depression), and health factors (e.g., low cholesterol levels, low body mass index; Agrawal & Mahajan, 2021). A systematic review of the prevalence and predictors of work-to-personal life enhancement among HCWs is missing; however, cross-sectional studies suggest that correlates of high work-to-personal life enhancement among HCWs include high organizational support (Ghislieri et al., 2017), support from family (Zhang et al., 2021), prosocial motivation (Zhang et al., 2021), and work engagement (Ikeda et al., 2022). Overall, there is a lack of research examining how work-to-personal life enhancement of HCWs changed during the pandemic. Nonetheless, cross-sectional research suggests that known predictors of low work-to-personal 19 life enhancement (e.g., unsupportive family attitudes towards participation in pandemic relief efforts) may have had an adverse influence on the work-to-personal life enhancement of HCWs during the pandemic (i.e., enhancement was lower for those whose family reported unsupportive attitudes; Zhang et al., 2021). 1.6 Exercise and HCW Ill-Being and Well-Being As detailed above, there is a lack of research concerning how many ill-being and well-being outcomes have changed for HCWs since pre-pandemic. Nonetheless, when evidence is available, authors suggest that indicators of HCW ill-being and well-being appear to have worsened during the pandemic. HCWs will continue to carry the burden brought on by the pandemic, resulting in higher staff turnover and increased workload and work stress for those remaining in the profession (Grady et al., 2022; Mehta et al., 2021). Accordingly, interventions aimed at improving ill-being and supporting well-being of HCWs are needed. The Mental Health Commission of Canada suggests that initiatives targeting HCW well-being should ideally be implemented at the organizational, team, and individual level (Grady et al., 2022). For example, at the organizational level, focus from the government can be placed on financially supporting healthcare organizations to invest in company-wide psychological support (e.g., counsellors), and hiring new HCWs to support short-staffed centres. At the team level, interventions targeting social support in the workplace and improved communication between team members could be viable. At the individual level, healthcare organizations can provide their employees with mental health crisis phone lines, educational well-being resources, and opportunities for stress-management support (e.g., mindfulness apps, opportunity for exercise; Grady et al., 2022). Although high burden on HCWs must be addressed through intervention at all three intersecting levels, it remains critical for healthcare organizations to support the well- 20 being of their employees by providing increased opportunities for individual-level modifications (e.g., behavioural interventions) aimed at improving ill-being and well-being (Grady et al., 2022; Kinman et al., 2020). One avenue of improving ill-being and well-being in high-stress populations at the individual level is through increasing levels of exercise. It is well established that exercise has positive physical and psychological effects (e.g., Ashdown-Franks et al., 2019; Reiner et al., 2013). A systematic review of 15 longitudinal studies demonstrated that among previously healthy adults from the general population (N = 288,724 across studies), higher levels of intentional physical activity (i.e., physical activity that does not include activities of daily living) was related to lower obesity, weight gain, risk of coronary heart disease, risk of type 2 diabetes mellitus, and lower incidence of Alzheimer’s disease and dementia (Reiner et al., 2013). With respect to the psychological benefits of exercise, in a meta-review of 27 systematic reviews, Ashdown-Franks and colleagues (2019) concluded that exercise (as compared to control conditions) reduced symptoms of depression, anxiety and stress related disorders, and schizophrenia. Multiple mechanisms have been proposed to explain the links between exercise and multidimensional well-being outcomes. For instance, both physiological (e.g., release of endorphins, reduced inflammation, improved cardiorespiratory fitness) and psychological (e.g., distraction, increased self-efficacy and mastery) mechanisms have been argued to play a role in the relationship between exercise engagement and indices of ill-being and well-being (for review see Mikkelsen et al., 2017). Albeit consistent evidence that exercise is beneficial for a range of physical and psychological outcomes, the review papers mentioned above have a larger focus on 21 pathological outcomes, and reviews on the relationships between exercise and multidimensional ill-being and well-being are mostly missing. 1.6.1 Exercise Interventions for Ill-Being and Well-Being The positive treatment effect of exercise for health and well-being of high stress populations more specifically has also been evidenced through intervention research. For example, Puterman and colleagues (2018) conducted a 24-week randomized controlled trial among highly stressed and physically inactive caregivers of family members with dementia-related disorders. Those randomized to the exercise (vs. waitlist control) condition were asked to progressively increase the length, intensity, and frequency of their aerobic exercise for nine weeks until they reached 150 minutes of moderate-to-vigorous aerobic activity, and then maintain this amount throughout weeks 9-24. Free gym memberships, access to online fitness videos, accelerometers, and personalized fitness coaches were provided to support continued engagement. Throughout the intervention, 73% of caregivers adhered to the assigned exercise. Increased engagement in aerobic exercise resulted in a wide range of treatment effects compared to waitlisted participants, including apparent lengthened telomeres, increased cardiorespiratory fitness, decreased waist circumference, and decreased perceived psychological stress (Puterman et al., 2018). Further, treatment effects were observed for mental health and psychological ill-being/well-being outcomes among the exercise (vs. waitlist) group, including decreased depressive symptomatology and perceived caregiver burden, as well as increases in mastery (i.e., perceived control over one’s life; Hives et al., 2021). Throughout the duration of the intervention, caregivers in the exercise group also reported decreases in rumination, anger, and loneliness as well as increases in social support and positive feelings towards their caregiver role, though treatment effects were not demonstrated when compared to the waitlist control (Hives et 22 al., 2021). At the daily level, exercisers (vs. those waitlisted) reported more positive affect and mastery, and less negative affect and rumination from pre- to post-intervention (Caplin et al., 2021). Not only were the highly stressed caregivers able to overcome barriers to physical inactivity throughout the 24-week trial, but increased exercise improved physical and psychological health and well-being over time. Despite its evidenced benefit, exercise is rarely used as a behavioural intervention by healthcare organizations to improve HCW ill-being and well-being (Buselli et al., 2021). Rather, individual-level interventions designed to improve HCW ill-being and well-being during the pandemic were exclusively psychoeducational and psychosocial in nature. These included access to online resources for stress reduction and access to talk therapy as examples (Buselli et al., 2021). The value of behavioural intervention is further reinforced by the fact that exercise interventions are traditionally less financially burdensome, less stigmatizing, more accessible, and more inclusive to those experiencing sub-clinical psychological symptoms (i.e., experiencing ill-being rather than illness) as compared to prescription medications or psychiatric treatments (Busch et al., 2016; Cooney et al., 2013). Additionally, exercise is recommended as a frontline, standalone treatment for mild to moderate depression (Ravindran et al., 2016). Exercise interventions supported by healthcare organizations and staff leadership may be particularly beneficial for HCWs, as populations experiencing high stress, such as HCWs, face increased barriers to physical activity (Stults-Kolehmainen & Sinha, 2014). For instance, HCWs often work shift work and have limited time, making it more difficult to independently increase exercise levels (Shields & Wilkins, 2005). 23 1.7 Exercise Intervention in the Context of the COVID-19 Pandemic While HCWs already faced increased barriers to physical activity, the pandemic created additional barriers to physical activity and exercise for the general population as well as HCWs more specifically (e.g., fitness centre closures, increased working hours for HCWs; Ammar et al., 2020; Mehta et al., 2021). Juxtaposed against the increases in ill-being and declines in well-being experienced by HCWs during the pandemic (as reviewed in detail above), levels of physical activity also decreased throughout the pandemic. This decrease in physical activity during the pandemic among the general population is evidenced through several national and international studies (e.g., Ammar et al., 2020; Boucher, et al., 2023b; Chen et al., 2021). Further, changes in activity levels of Canadians throughout the pandemic were found to be unequally distributed at the start of the pandemic, whereby 40.5% of individuals who were already inactive (vs. 22.4% of those active) became even less active (Lesser & Nienhuis, 2020). Although causality cannot be implied due to the cross-sectional design, changes in activity reported by Lesser and Nienhuis corresponded with differences in well-being within their sample. More specifically, inactive (vs. active) individuals reported significantly lower well-being, and inactive individuals who maintained their activity levels or became more active during the pandemic reported higher psychological, social, and emotional well-being than those who became even less active (Lesser & Nienhuis, 2020). Within the context that only 18% of Canadians met recommended levels of physical activity (i.e., 150 minutes of moderate-to-vigorous physical activity per week) prior to the pandemic (Statistics Canada, 2017a), Lesser and Nienhuis’ findings become even more concerning. Less is known regarding changes in physical activity levels during the pandemic of HCWs more specifically. However, recently, using data from the International COVID-19 24 Awareness and Responses Evaluation (iCARE) Study, Boucher and colleagues (2023b) provided insight into physical activity behaviours of Canadian HCWs between April 2020 and February 2022. At nine different time points, distinct samples (Ntotal = 1,615) of HCWs cross-sectionally reported the extent to which the pandemic impacted their physical activity (“not at all” to a “great extent”) as well as the extent their physical activity changed during the pandemic (“I don’t do this” to “I do this a lot more”). They found that 39% of HCWs reported that their physical activity levels had decreased as compared to before the pandemic. Further, females (vs. males), younger (vs. older) HCWs, and those with less (vs. more) education, reported less physical activity engagement since the beginning of the pandemic (Boucher, et al., 2023b). These results provide preliminary evidence that Canadian HCWs have also experienced decreased engagement in physical activity throughout the pandemic. One reason for the observed wide-spread decrease in physical activity at the beginning of the pandemic may be the early introduction of restrictions imposed on the public through governments, such as stay at home mandates and fitness centre closures that limited opportunity for physical activity and exercise engagement primarily to one’s home (Ammar et al., 2020; Chen et al., 2021). Although such mandates limited opportunity for exercise (which may have had downstream effects on ill-being and well-being), the restrictions also created opportunity for exercise intervention delivery innovations, which have the potential to be useful during and following the pandemic. More specifically, the pandemic highlighted a need for approaches to increasing exercise engagement that can be implemented across large populations within the confinements of restricted space and time (e.g., at-home) at low cost (Marcus et al., 1998). 25 1.7.1 mHealth Exercise Interventions As most Canadians have access to both home internet and/or a smartphone, mobile health (mHealth) is one mode of exercise programming delivery that allows for low cost and wide implementation (Direito et al., 2017; Statistics Canada, 2021b). mHealth, as defined by the World Health Organization (2011), involves the use of mobile technologies to support public health initiatives and practices. Health-related mobile applications (e.g., exercise applications aiming at improving physical fitness) have become increasingly popular over recent years. For example, in 2017, over 318,000 health-related mobile applications were available for download (Aitken et al, 2017). The popularity and scalability of such applications make them desirable as tools for mHealth exercise interventions aiming to improve physical activity behaviours. In favour of the sustained use of mHealth, a recent systematic review and meta-analysis of 117 mHealth interventions for physical activity behaviour change concluded that mHealth was successful (i.e., small to moderate effect sizes) in increasing moderate-to-vigorous physical activity, and such effects extended past six months post intervention. Further, by analyzing data across randomized controlled trials, the meta-analysis concluded that use of mHealth resulted in 26 additional minutes of weekly moderate-to-vigorous physical activity (Mönninghoff et al., 2021). Such reviews help substantiate claims that mHealth interventions can be a successful modality for fostering physical activity behaviour change. This is particularly encouraging as mHealth interventions are a promising approach to increasing exercise engagement during pandemics as they allow for flexibility in the timing and location of a workout and provide increased opportunity for exercise during repeated fitness center closures. At the start of the pandemic, Puterman and colleagues (2022) completed the COVID-19 Pandemic and Exercise (COPE) trial, a six-week mHealth exercise intervention aimed at 26 improving the psychological health of physically distanced and inactive adults from the general population. Participants (N = 334) were randomized to one of three active conditions: body weight interval training, yoga, or a combination of body weight interval training and yoga. Participants were asked to complete a minimum of four 20-minute workouts per week for six weeks using Down DogTM exercise apps that could easily be used from the comfort of one’s home, and individually customized according to one’s capability and time restrictions. Results demonstrated a significant reduction in depressive symptoms for those randomized to the active conditions when compared to those in the waitlist control condition, highlighting the usefulness of this behavioural intervention modality for physically inactive adults (Puterman et al., 2022). Building on the COPE trial (Puterman et al., 2022), Boucher and colleagues (2023a) completed the COVID-19 Pandemic and Exercise for Healthcare Workers (COPE HCW) trial from April to September 2022. The COPE HCW trial was a 12-week randomized controlled trial examining the use of at-home mHealth exercise to improve health and well-being among HCWs in Vancouver. HCWs (N = 288) were randomized to either an active or waitlist control condition, and active participants were asked to complete a minimum of 4 20-minute workouts per week using the suite of Down Dog exercise apps. Methodological details of the COPE HCW trial are elaborated on in greater detail below (see Methods section). Treatment effects were shown in favour of the exercise condition, in that those who received access to Down Dog showed lower depressive symptomology (primary outcome), cynicism (i.e., disinterest, negativity, or detachment from work; Maslach et al., 2018), exhaustion (i.e., lack of physical or psychological energy; Maslach et al., 2018), and absenteeism (i.e., self-reported sick days) as compared to those waitlisted (Boucher et al., 2023a). This trial extended findings from the first COPE trial to demonstrate the usefulness of mHealth for mental and physical health outcomes among a 27 population of HCWs during the pandemic. However, in addition to testing effects of the COPE HCW trial on depressive symptoms, burnout symptoms, and absenteeism, it remains critical to assess treatment effects across an even broader range of ill-being and well-being outcomes. Doing so may ultimately help to better support the overall well-being of HCWs following the pandemic. 1.8 The Present Research In summary, HCW well-being is a multidimensional construct which should be measured using an outcome-wide approach inclusive of a wide variety of positive and negative outcomes across physical, psychological, and psychosocial dimensions (Schulte et al., 2015; Vanderweele et al., 2020a). HCWs experienced worsened ill-being and well-being throughout the pandemic (e.g., Baskin & Bartlett, 2021; Statistics Canada, 2021a; Theocharis, 2023). Exercise is an individual-level behavioural intervention that may be used by healthcare organizations to support the multidimensional ill-being and well-being of HCWs (Ashdown-Franks et al., 2019; Reiner et al., 2013). mHealth offers a flexible approach to exercise intervention, allowing for low cost and wide implementation, as well as increased opportunity for at-home exercise during fitness centre closures (i.e., during pandemics; Direito et al., 2017). A recent mHealth exercise intervention was successful in improving psychological health of HCWs during the pandemic (COPE HCW trial; Boucher et al., 2023a). However, secondary analyses of this trial are needed to determine whether exercise effects extend to a wider array of physical, psychological, and psychological ill-being and well-being outcomes among HCWs. 1.8.1 Objectives and Hypotheses The purpose of these secondary analyses of the COPE HCW trial was to examine whether engagement in 12 weeks of at-home exercise using a suite of apps improved physical, 28 psychological, and psychosocial indicators of ill-being and well-being of low-active HCWs compared to those waitlisted. It was hypothesized that there would be significant treatment effects for a broad range of ill-being and well-being outcomes, including physical health symptoms, recent global stress, work-to-personal life interference, overall physical health, overall mental/emotional health, life satisfaction, psychological flourishing, resilience, and work-to-personal life enhancement. Specifically, improvements were expected among these outcomes in the participants randomized to the exercise condition compared to the waitlist control condition. 29 Chapter 2: Methods 2.1 Pre-registration and Trial Design These secondary analyses used data collected from April to September 2022, as part of the COPE HCW trial. The COPE HCW Trial was a 12-week, at-home, two-arm (i.e., exercise vs. waitlist control) parallel randomized controlled trial conducted in collaboration with Providence Health Care (PHC), a network of 10 Vancouver acute and long-term care health centres (St. Paul’s Hospital, Mount Saint Joseph Hospital, Holy Family Hospital, Youville Residence, St. Vincent’s: Langara, St. Vincent’s: Honoria Conway-Heather, St. Vincent’s: Brock Fahrni, St. John Hospice, Providence Crosstown Clinic, and Community Dialysis Units) affiliated with the Vancouver Coastal Health authority. A representative advisory board and PHC leadership assisted during the design and recruitment phases. The trial was pre-registered on February 22nd, 2022 and March 7th, 2022 at ClinicalTrials.gov (NCT05271006) and the Open Science Framework (https://osf.io/8qfxh/), respectively. Methodological details specific to these secondary analyses were also preregistered on the Open Science Framework on October 4th, 2023. 2.2 Participants HCWs were recruited from ten PHC centres across Vancouver, British Columbia by means of targeted emails, advertisements posted online and in PHC buildings, and in-hospital recruitment booths. PHC advisory board members and leadership assisted in the creation of recruitment plans and materials, with the goal of inclusive recruitment of varied genders, body sizes, ages, and visible ethnicity and race differences. To be eligible, HCWs had to be older than 18, have access to the internet, working at PHC at the time of recruitment in either full- or part-time positions, be low-active, and be physically able to start exercise. The level of physical 30 activity was assessed by research assistants during the screening process (outlined further below) using The Stanford Leisure-Time Activity Categorical Item (Kiernan et al., 2013). HCWs were considered low-active if they scored 1-3 on the item. Physical readiness was assessed using the Physical Activity Readiness Questionnaire (PAR-Q+) as well as the Physical Activity Readiness Medical Examination (ePARmed-X+; Warburton et al., 2014). Further assessment from a certified exercise physiologist or family physician was required when not cleared by the ePARmed-X+ (Bredin et al., 2013). 2.2.1 Sample Size Calculation As determined by Optimal Design Software (Raudenbush et al., 2011), using a two-level non-linear growth model accounting for repeated measures design (six time points, level-1 residual variability = 8.798, level-1 coefficient variability = 22.548), our target sample size was 560 participants across arms for the primary outcome of depressive symptomatology. This calculation was based on an a priori goal of achieving a power of 0.80 or greater to detect as statistically significant (α = 0.05, two-tailed), a small effect size in the primary outcome of depressive symptomatology (δ = 0.30), accounting for covarying across ten PHC locations, and a 20% attrition rate. This sample size calculation was preregistered on the Open Science Framework on March 18th, 2022 (https://osf.io/8qfxh/). The study was not powered specifically to the secondary outcomes presented here. 2.3 Procedure 2.3.1 Recruitment Interested HCWs were directed to our study website (www.copehcwtrial.com) to schedule an initial phone appointment, during which they were screened against inclusion criteria by a research assistant following a scripted Qualtrics survey. While on the phone, 31 interested HCWs were then asked to complete an electronic version of the PAR-Q+ and were directed to further complete the ePARmed-X+ if needed (Bredin et al., 2013). Those not cleared by these questionnaires were asked to consult our team of Clinical Exercise Physiologists (certified by the Canadian Society for Exercise Physiology) or their family physician for clearance to participate. Following this, those eligible were asked to review and sign an electronic consent form if they consented to participating in the outlined research. 2.3.2 Randomization After providing informed consent and independently completing the baseline survey, eligible participants were randomly assigned to an exercise or waitlist control condition. A randomized sequence was generated using randomized blocks of four and the research team was blinded to the randomization until assignment following completion of the baseline survey. Participants were notified by phone of their condition and were provided with detailed next steps, including how to download and use the intervention apps if randomized to the exercise condition. All participants received a participant booklet by email outlining all procedures when they obtained access to the intervention apps. 2.3.3 Intervention Those randomized to the exercise condition were immediately provided a one-year free subscription to the mobile platform ‘Down DogTM’ and were asked to exercise at minimum four days a week for 20 minutes a day for 12 weeks using the suite of apps (i.e., their choice of body weight interval training, yoga, running, and/or barre). Down DogTM accounts were created using study email addresses linked with participant IDs to ensure that we could track participants’ adherence to the apps over time. The amount of exercise required of participants was selected to remain consistent with Puterman and colleagues’ (2022) COPE Trial. However, it is noted that 32 80 minutes a week is below the recommended physical activity guidelines for physical (World Health Organization, 2022) and mental health (Ravindran et al., 2016). Those waitlisted were asked to maintain their pre-study activity levels and did not receive the one-year free access to Down DogTM until the conclusion of the 12-week intervention. To assess changes in the dependent variables over time, in addition to the baseline survey, all participants were asked to complete a survey every two weeks, from baseline to week 12, for a total of 7 surveys. Every time a participant completed a survey their name was entered into an end-of-study draw to win one of 22 prizes. All procedures were approved by the Behavioural Research Ethics Board at the University of British Columbia (#H21-02612) and the Institutional Ethics Board at Providence Health Care. 2.4 Measures All self-report measures (unless specified below) were administered at baseline and biweekly until week 12 using QualtricsTM. Scales were adapted so that HCWs were asked to reflect on the past two weeks. Full measures are presented in Appendix A. 2.4.1 Sociodemographic Measures At baseline only, participants indicated their age, sex assigned at birth, gender identity, ethnicity, Indigenous status, lived experience as a trans-person, disability status, visual minority identification, marital status, education, income, employment status, job title, and typical work shift. The categorization of sociodemographic measures has been published elsewhere (see Table 1 in Boucher et al., 2023). 2.4.2 Adherence to Intervention Participants’ use of the exercise apps was tracked by Down DogTM and recorded on a weekly basis by research assistants. To describe intervention adherence, the number of times the 33 applications were used per week and weekly minutes of exercise were recorded. Mean minutes of weekly app use each week and across the 12-week intervention were calculated and used to conduct per-protocol analyses, as described in further detail below. 2.4.3 Ill-Being Measures 2.4.3.1 Physical Health Symptoms Using a modified checklist from the Midlife in the United States Daily Stress project, participants indicated whether they had experienced (0 = no, 1 = yes) any of 13 physical health symptoms during the last two weeks (Larsen & Kasimatis, 1991). Symptoms included diarrhea/ constipation/ other stomach problems, headache, backache, muscle soreness, fatigue, joint pain, muscle weakness, cough, sore throat, fever, chills, other cold and flu symptoms, nausea, or none. Responses were summed such that a higher score indicated a higher number of physical health symptoms. 2.4.3.2 Recent Global Stress Participants rated the amount of stress in their lives during the past two weeks using a 5-point scale ranging from 1 (not at all stressful) to 5 (extremely stressful). Higher scores indicated a higher level of life stress. This measure was adapted from a global scale that has been frequently used in Statistic Canada surveys (e.g., Statistics Canada, 2022) and scores have shown a weighted kappa of 0.66 for a 3-month test-retest study (Littman et al., 2006). 2.4.3.3 Work-to-Personal Life Interference Using three items drawn from the Health and Retirement Study (e.g., “Job worries or problems distract me when I am not at work”; Smith et al., 2013), participants indicated the degree to which their work spilled over and interfered with their personal life during the past two weeks on a 4-point scale ranging from 1 (rarely) to 4 (most of the time). Items were summed 34 (max = 12), such that higher scores indicated greater interference (Cronbach’s arange = 0.73 – 0.80). 2.4.4 Well-Being Measures 2.4.4.1 Overall Physical Health Participants rated their overall physical health during the past two weeks on a 5-point scale ranging from 1 (poor) to 5 (excellent) using the item “For the past two weeks, would you say your PHYSICAL HEALTH is excellent, very good, good, fair, or poor?”. Higher scores indicated more positive physical health. This measure was adapted from a global scale that has been frequently used in Statistic Canada surveys including the Canadian Community Health Survey – Annual Component (e.g., Statistics Canada, 2022). Scores from general self-rated health single-item measures have shown to have strong validity and reliability (Desalvo et al., 2006). 2.4.4.2 Overall Mental/Emotional Health Participants rated their overall mental or emotional health during the past two weeks on a 5-point scale ranging from 1 (poor) to 5 (excellent) using the item “For the past two weeks, would you say your MENTAL OR EMOTIONAL HEALTH is excellent, very good, good, fair, or poor?”. Higher scores indicated more positive mental or emotional health. This measure was adapted from a global scale that has been frequently used in Statistic Canada surveys (e.g., Statistics Canada, 2022). There are a lack of studies validating and testing the reliability of scores from this measure. However, scores from this scale have shown moderate associations with other mental health scales and health outcomes (e.g., health service utilization), albeit some variability observed across ethnic groups (Ahmad et al., 2014). 35 2.4.4.3 Life Satisfaction Using the Satisfaction with Life Scale (Diener et al., 1985), participants rated their agreement with five items (e.g., “The conditions of my life are excellent”) during the past two weeks on a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). Ratings were summed such that higher scores (max = 35) indicated greater satisfaction with one’s life (Cronbach’s arange = 0.88 – 0.93). 2.4.4.4 Psychological Flourishing Using the eight-item Flourishing Scale (Diener et al., 2010), participants indicated their agreement with items describing success within different domains of functioning (e.g., relationships, life purpose) during the past two weeks on a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). Ratings for each item (e.g., “I am engaged and interested in my daily activities”) were summed such that higher scores (max = 56) represented more positive functioning (Cronbach’s arange = 0.86 – 0.94). 2.4.4.5 Resilience Using the six-item Brief Resilience Scale (Smith et al., 2008), participants rated their ability to bounce back from stressful events during the past two weeks on a 5-point scale ranging from 1 (strongly disagree) to 5 (strongly agree). Items (e.g., “I have a hard time making it through stressful events”) were reverse scored where appropriate and summed such that higher scores (max = 30) indicated greater resilience (Cronbach’s arange = 0.86 – 0.94). 2.4.4.6 Work-to-Personal Life Enhancement Using three-items drawn from the Health and Retirement Study (e.g., “Because of my job, I am in a better mood at home”; Smith et al., 2013), participants indicated the degree to which their work spilled over and enhanced their personal life during the past two weeks on a 4- 36 point scale ranging from 1 (rarely) to 4 (most of the time). Items were summed (max = 12), such that higher scores indicated greater enhancement (Cronbach’s arange = 0.66 – 0.81). 2.5 Changes to the Trial From mid-May to July 2022, new interest in the COPE HCW trial significantly lessened despite active recruitment efforts (e.g., in-person recruitment booths, online and in-person advertisements). At that time, it was also discovered that a majority (79%) of the 288 randomized participants were employed at one of the ten PHC centres (St. Paul’s Hospital). Accordingly, we felt that exploring differences in trial outcomes between PHC locations was no longer feasible and thus decided to cease recruitment. In support of this decision, meta-analyses on physical activity for mental health (Teychenne et al., 2020) and the COPE Trial (Puterman et al., 2022) suggested that small-to-medium (δ = 0.40) or medium (δ = 0.50) effect sizes for the primary outcome of depressive symptoms could be expected, of which the trial was powered to detect without accounting for nesting across PHC locations. Further details regarding these changes can be viewed on Open Science Framework (https://osf.io/kvunh), posted on July 4th, 2022. 2.6 Analysis Plan Data were analyzed using R statistical Software (version 4.1.0). Descriptive statistics (mean and standard deviation) were calculated for all outcome variables. For participants within the exercise condition, mean frequency and minutes of weekly app use was calculated per week and across the 12-week intervention. To test for reverse causality (i.e., lower initial ill-being and higher well-being resulting in greater use of the apps or completion of survey rates), I regressed mean weekly minutes of app use and participants’ rates of completion of biweekly surveys onto baseline outcome measures. 37 2.6.1 Intention-to-Treat Analyses To assess intervention effects, intent-to-treat analyses were conducted using a structural equation modelling (SEM) growth model approach. Such models estimate latent variables for the slope, intercept, and curve. SEM models were used to allow for the partition of variance across weeks, so that week-by-week effect sizes could be calculated. Both linear and quadradic latent growth models were tested to determine best model fit. In cases when both linear and quadradic models were a good fit, the simpler model (i.e., linear) was selected. The Feingold approach (a Cohen’s d equivalent; Feingold, 2019) was used to compute effect sizes to examine biweekly effect sizes between the exercise and waitlist control groups. The Feingold approach to calculating standardized effect sizes operates by dividing the treatment effect by the sum of the variance of the intercept and the variance of the current and previous timepoints. As stated by Feingold (2019), the magnitude of the standardized effect sizes can be interpreted like a Cohen’s d, in which < 0.2 is classified as a very small effect, 0.2 is a small effect, 0.5 is medium effect, and 0.8 is large effect. Biweekly intervention effects were considered significant when the 95% confidence interval did not cross 0.00. As multiple indicators of both ill-being and well-being were examined, follow-up analyses were conducted using a more conservative threshold for significance wherein biweekly intervention effects were considered significant when the 99% confidence interval did not cross 0.00. 2.6.2 Per-Protocol Analyses Per-protocol analyses were conducted to determine intervention effects for profiles of different overall engagement with the Down DogTM applications. Three different engagement profiles were determined; (1) per-protocol -- those who on average used the apps as much or 38 more than they were asked to on a weekly basis (i.e., 80+ minutes/week, n = 30), (2) low -- those who on average used the apps 20-79 minutes a week (n = 73), or (3) very low – those who on average used the apps less than 20 minutes a week on average (n = 37). These per-protocol analyses were also conducted using a SEM growth model approach to determine weekly effect sizes for each adherence group compared to waitlist control. 2.6.3 Follow-up Analyses Follow-up analyses were conducted to address the multiple testing and increased risk of Type 1 error (i.e., false positives) that stemmed from nine effect sizes being calculated per biweekly assessment. Intention-to-treat and per-protocol analyses examining biweekly intervention effects for each outcome were re-run using a SEM growth model approach with a more conservative confidence interval set at 99% rather than 95%. In this case, biweekly effects were considered significant if the 99% confidence interval did not cross 0.00. 2.7 Missing Data The final sample consisted of all randomized participants. Partially missing scale data within each week were accounted for using random forest-based multiple imputation (Mayer, 2019). Scales that were entirely incomplete were treated as missing and robust maximum likelihood estimation was used for all models to allow for this missingness. To assess whether missingness was completely at random, the number of missing surveys per participant was calculated and regressed on baseline sociodemographic variables and outcomes measures in separate models. 39 Chapter 3: Results 3.1 Participants Participant flow through recruitment and descriptive statistics of sociodemographic characteristics have been published elsewhere (see Figure 1 and Table 1, respectively, in Boucher et al., 2023a). The final sample consisted of 288 participants, 142 (49.3%) randomized to the exercise condition and 146 (50.7%) randomized to the waitlist control condition. Most participants identified as women (85.4%) and the mean (SD) age was 41.0 (10.8) years. Descriptive statistics of secondary outcome measures at baseline are presented in Table 1. 3.2 Missing Data See Table 2 for the number of partially (i.e., responses to at least one item -- but not all --in an instrument were missing) and completely (i.e., participant did not answer any items within the instrument) missing surveys biweekly, as well as the percent of total surveys missing per outcome across the 12-week intervention. Across the 12 weeks and all outcomes, the percentages of total surveys that were partially missing item responses were no higher than 0.35%, and the percentages of total surveys that were completely missing were no higher than 15.48%. The number of completely missing surveys per outcome separated by condition is presented in Table 3. Number of missing surveys per participant was regressed onto sociodemographic variables (i.e., age, gender, education, income, direct vs. indirect care role) and outcome measures at baseline to analyze whether biweekly complete missingness was at random. Age, F(1, 284) = 8.21, p = .004 and income, F(25, 260) = 1.79, p = .013 were the only significant predictors of biweekly complete scale missingness. Results suggest that data were missing at random, supporting the integrity of the estimates provided by the SEM models (Little & Rubin, 40 2020). Covarying effects of age and income did not change the significance of biweekly intervention effects, (see Tables S1-S2 in Appendix B). The two exceptions to this were the Week 6 (ES = -0.14, 95% CI [-0.28, 0.00]) and Week 8 (ES = -0.19, 95% CI [-0.37, 0.00]) effect sizes for physical health symptoms, which were no longer significant after covarying for income. 3.3 Adherence to Intervention The average number of sessions and minutes of Down DogTM use per week for all participants in the exercise condition, as well as separately for each of the three app engagement profiles (i.e., per-protocol, low, very low) has been published elsewhere (see eTable 5; Boucher et al., 2023a). Throughout the intervention, the average number of Down DogTM sessions per week (target = four) tended to decrease, with an average of 3.92 (2.19) sessions during the first week, and an average of 1.56 (1.94) sessions in the final week. A similar pattern was observed for average minutes of app use per week (target = 80 minutes), with an average of 73.27 (41.49) minutes during week one, and 34.71 (45.98) minutes during week 12. To examine whether baseline characteristics and outcomes predicted greater use of the apps or completion of surveys, mean weekly minutes of app use and participants’ rates of completion of biweekly surveys were regressed onto baseline outcome measures. None of the ill-being or well-being scores at baseline significantly predicted greater use of the apps (prange = .089 - .887) or rate of biweekly survey completion (prange =.130 - .961), confirming a lack of reverse causality. 3.4 Intention-to-Treat Analyses A summary of SEM model fit and which model (i.e., linear versus quadradic) was chosen for each outcome are included in Table 4. Fit for either linear or quadradic models were judged for each outcome on four indices of fit, including the Comparative Fit Index, Tucker–Lewis Index, Standardized Root Mean Square Residual, and Root Mean Square Error of 41 Approximation. Scaled chi-squared tests were conducted to determine if linear and quadradic model fit were significantly different from each other for each outcome. If statistically different, the model with best fit (as judged using the above indices) was selected. When no statistical difference was observed between models, the simpler model (i.e., linear model) was selected. Overall intention-to-treat effects and within condition changes over time for each outcome are presented in Table S3 and trajectories are illustrated in Figure 2. Intention-to-treat biweekly effect sizes and 95% confidence intervals are presented in Table 5 and illustrated in Figure 2. 3.4.1 Indicators of Ill-Being 3.4.1.1 Physical Health Symptoms Biweekly intervention effects were significant at each assessment period, such that those in the exercise condition reported significantly fewer physical health symptoms than those waitlisted. At week 2, a very small effect was observed (ES = -0.15, 95% CI [-0.28, -0.01]), whereas at week 12 a small effect was observed (ES = -0.29, 95% CI [-0.56, -0.02]). 3.4.1.2 Recent Global Stress Compared to those waitlisted, participants in the exercise condition reported significant reductions in recent global stress at each biweekly assessment period. Intervention effects were very small at Week 2 (ES = -0.09, 95% CI [-0.14, -0.04]), and were small-to-medium at week 12 (ES = -0.49, 95% CI [-0.76, -0.21]). 3.4.1.3 Work-to-Personal Life Interference Significant intervention effects began by week 6, such that those in the exercise condition reported small reductions in work-to-personal life interference versus those waitlisted (week 6 42 ES = -0.21, 95% CI [-0.39, -0.02]). By the end of the trial, intervention effects were small-to-medium (week 12 ES = -0.32, 95% CI [-0.54, -0.10]). 3.4.2 Indicators of Well-Being 3.4.2.1 Overall Physical Health Significant improvements in overall physical health were observed for the exercise condition, as compared to those waitlisted, at each biweekly assessment period. Very small intervention effects were observed at week 2 (ES = 0.08, 95% CI [0.04, 0.13]), whereas medium effects were observed by week 12 (ES = 0.50, 95% CI [0.22, 0.77]). 3.4.2.2 Overall Mental/Emotional Health Small improvements in overall mental/emotional health were observed for the exercise condition as compared to the waitlist control condition at week 2 (ES = 0.22, 95% CI [0.10, 0.34]). Effects were significant throughout the trial, with a medium sized intervention effect observed by week 12 (ES = 0.59, 95% CI [0.30, 0.88]). 3.4.2.3 Life Satisfaction Compared to the waitlist control condition, those in the exercise arm showed significant improvements in life satisfaction at each biweekly assessment period. Very small intervention effects were observed at week 2 (ES = 0.12, 95% CI [0.03, 0.21]), whereas small-to-medium effects were observed at week 12 (ES = 0.40, 95% CI [0.20, 0.61]). 3.4.2.4 Psychological Flourishing Biweekly intervention effects were significant throughout the trial, such that significant improvements in flourishing were observed for the exercise condition compared to those waitlisted. Intervention effects were very small at week 2 (ES = 0.11, 95% CI [0.01, 0.21]), and were small by week 12 (ES = 0.29, 95% CI [0.04, 0.54]). 43 3.4.2.5 Resilience Intervention effects became significant by week 8 (ES = 0.26, 95% CI [0.05, 0.48]), such that those in the exercise condition showed small significant improvements in resilience compared to those waitlisted. Intervention effects were small-to-medium in magnitude by the end of the trial (week 12 ES = 0.42, 95% CI [0.18, 0.67]). 3.4.2.6 Work-to-Personal Life Enhancement Compared to those waitlisted, participants in the exercise condition showed significant improvements in work-to-personal life enhancement at each biweekly assessment period. Intervention effects were very small at week 2 (ES = 0.06, 95% CI [0.02, 0.12]), and were in the small-to-medium range at week 12 (ES = 0.36, 95% CI [0.09, 0.63]). 3.5 Per-Protocol Analyses Biweekly intervention effects for changes in each outcome for per-protocol engagement profile (80+ average weekly minutes of app use; n = 30), low engagement profile (20-79 minutes; n = 73), and very low engagement profile (<20 minutes; n = 37) compared to the waitlist control condition (n = 146) are presented in Table 6 and illustrated in Figure 3. Figure 3 also illustrates the trajectory of each group over time, based on the within-condition growth model coefficients (i.e., intercept, slope, and curve). 3.5.1 Indicators of Ill-Being Across ill-being outcomes, there was a higher number of significant biweekly intervention effects for those who completed a minimum of 80 minutes per week of app use (i.e., per-protocol group) as compared to those who completed between 20-79 minutes per week of app use (i.e., low use group). Across outcomes, no significant intervention effects were observed for those who completed less than 20 minutes per week of app use (i.e., very low use group). 44 3.5.1.1 Physical Health Symptoms Biweekly intervention effects ranged from -0.22 [-0.43, -0.01] to -0.45 [-0.88, -0.01] for the per-protocol group (four out of six were significant), -0.13 [-0.28, 0.01] to -0.33 [-0.61, -0.04] for the low use group (three out of six were significant), and -0.04 [-0.31, 0.24] to -0.39 [-0.91, 0.13] for the very low use group (no effects were significant). 3.5.1.2 Recent Global Stress Biweekly intervention effects ranged from -0.15 [-0.23, -0.06] to -0.83 [-1.30, -0.35] for the per-protocol group (all effects were significant), -0.07 [-0.13, -0.02] to -0.42 [-0.73, -0.12] for the low use group (all effects were significant), and -0.01 [-0.11, 0.09] to -0.06 [-0.63, 0.51] for the very low use group (no effects were significant). 3.5.1.3 Work-to-Personal Life Interference Biweekly intervention effects ranged from -0.19 [-0.33, -0.04] to -0.58 [-0.92, -0.25] for the per-protocol group (all effects were significant), -0.08 [-0.18, 0.02] to -0.25 [-0.50, -0.00] for the low use group (three out of six were significant), and 0.20 [-0.18, 0.59] to -0.13 [-0.69, 0.43] for the very low use group (no effects were significant). 3.5.2 Indicators of Well-Being Across well-being outcomes, there was a higher number of significant biweekly effects for the per-protocol group as compared to the low use group and the very low use group. Further, there was a higher number of significant intervention effects for the low use group as compared to the very low use group, as no biweekly effects for the very low use group (vs. waitlist control) were significant. The per-protocol model did not converge for overall mental / emotional health and therefore is not reported here. 45 3.5.2.1 Overall Physical Health Biweekly intervention effects ranged from 0.12 [0.06, 0.18] to 0.70 [0.33, 1.06] for the per-protocol group (all effects were significant), 0.07 [0.01, 0.12] to 0.39 [0.05, 0.73] for the low use group (all effects were significant), and 0.06 [-0.06, 0.18] to 0.34 [-0.36, 1.04] for the very low use group (no effects were significant). 3.5.2.2 Life Satisfaction Biweekly intervention effects ranged from 0.18 [0.05, 0.30] to 0.64 [0.29, 0.99] for the per-protocol group (all effects were significant), 0.12 [0.01, 0.23] to 0.29 [0.05, 0.53] for the low use group (all effects were significant), and 0.05 [-0.15, 0.25] to 0.44 [-0.02, 0.89] for the very low use group (no effects were significant). 3.5.2.3 Psychological Flourishing Biweekly intervention effects ranged from 0.23 [0.08, 0.37] to 0.50 [0.09, 0.91] for the per-protocol group (all effects were significant), 0.07 [-0.05, 0.19] to 0.19 [-0.10, 0.48] for the low use group (no effects were significant), and 0.07 [-0.13, 0.28] to 0.28 [-0.26, 0.83] for the very low use group (no effects were significant). 3.5.2.4 Resilience Biweekly intervention effects ranged from 0.17 [-0.00, 0.34] to 0.61 [0.13, 1.08] for the per-protocol group (five out of six were significant), 0.05 [-0.06, 0.17] to 0.37 [0.09, 0.64] for the low use group (two out of six were significant), and -0.08 [-0.25, 0.09] to 0.41 [-0.13, 0.94] for the very low use group (no effects were significant). 3.5.2.5 Work-to-Personal Life Enhancement Biweekly intervention effects ranged from 0.10 [0.02, 0.17] to 0.57 [0.12, 1.02] for the per-protocol group (all effects were significant), 0.05 [-0.00, 0.10] to 0.28 [-0.02, 0.58] for the 46 low use group (no effects were significant), and 0.04 [-0.07, 0.15] to 0.25 [-0.44, 0.92] for the very low use group (no effects were significant). 3.6 Follow-up Analyses To account for the increased chance of Type 1 error (i.e., false positives) introduced by the multiple testing of 6 effects for the 6 timepoints (e.g., weeks 2, 4, etc.) across 9 outcomes, intention-to-treat and per-protocol analyses were also conducted with more conservative 99% confidence intervals. Intention-to-treat biweekly effect sizes with 99% confidence intervals in lieu of 95% confidence intervals are presented in Table S4 in Appendix B. When applying this more conservative approach, some confidence intervals crossed 0.00, yet, by and large, most intention-to-treat biweekly effects remained significant. More specifically, biweekly effects were no longer significant for physical symptoms at weeks 2, 4, 6, and 12; work-to-personal life interference at weeks 6 and 8; psychological flourishing at weeks 2, 4, 6, and 12; and resilience at week 8 (i.e., 11 out of 49 or 22.45% of effects were no longer significant). Per-protocol biweekly effect sizes and 99% confidence intervals are presented in Table S5 in Appendix B. After applying a more conservative confidence interval, within the per-protocol group, 16 out of 45 (35.56%) biweekly effects were no longer significant. Within the low app use group, 18 out of 26 (69.23%) biweekly effects were no longer significant. No changes were observed within the very low use group, as all effects remained insignificant. 47 Chapter 4: Discussion These secondary analyses aimed to examine the effect of a 12-week at-home mHealth exercise trial on HCWs’ multidimensional well-being. Significant intervention effects were observed across all ill-being and well-being outcomes, such that those in the exercise condition reported lower ill-being and higher well-being over 12 weeks compared to those waitlisted. Most (77.55%) biweekly treatment effects remained significant even after applying a more conservative 99% confidence interval. Baseline ill-being and well-being outcomes did not significantly predict mean weekly minutes of app use or rate of completion of biweekly surveys, supporting intervention effects over a reverse causality hypothesis. Adherence to the intervention declined throughout the trial, and per-protocol analyses suggested that a higher number of biweekly intervention effects were significant for those who adhered to the requested weekly exercise (80+ minutes per week vs. lower app use groups). Results suggest that when adhered to, at-home mHealth exercise can be a successful individual-level behavioural intervention for a wide array of ill-being and well-being multidimensional outcomes. 4.1 Exercise and HCW Ill-Being The effect of 12 weeks of mHealth exercise (vs. waitlist control) on physical health symptoms, recent global stress, and work-to-personal life interference was analyzed. By week 12, small-to-medium exercise effects were observed for all three indicators of HCW ill-being. In line with the flexible conceptual map concerning human flourishing (see Figure 1; VanderWeele & Lomas, 2022), the intervention effects on ill-being outcomes spanned physical, psychological, and psychosocial dimensions, indicating that exercise may be used to target HCW ill-being more globally across different realms of functioning. 48 Such findings have important implications for HCWs’ daily experiences and their future health and well-being. HCWs who received access to the exercise apps (vs. waitlisted) reported experiencing fewer physical health symptoms, such as headaches, joint pain, and stomach problems in their daily lives. This is important, as experiencing more physical health symptoms has been positively associated with increased odds of chronic health conditions and functional disability in the future (Leger et al., 2015). Those in the exercise condition also perceived their lives to be less stressful. Lower levels of stress have prospectively been associated with a lower risk of coronary heart disease and depression (Maddock & Pariante, 2001; Richardson et al., 2012). Stress is also associated with increased burnout among HCWs (Yates, 2020), and therefore, the observed stress reduction may help to protect the workforce from future emotional exhaustion, cynicism, and lack of professional efficacy. Imperatively, lower burnout among HCW is associated with fewer medication errors, higher patient satisfaction, and increased productivity at work (for review see Yates et al., 2020). Lastly, these HCWs had less trouble fulfilling personal responsibilities, more energy to devote to social interactions, and less distraction from work-related issues outside of work. Lower work-to-personal life interference has been associated with positive organizational outcomes such as lower levels of employee turnover (Labrague et al., 2021). Results of the effect of mHealth exercise on recent global stress align with the extant literature pointing to the benefits of exercise for reducing psychological stress (for review see Mikkelsen et al., 2017), including among highly stressed adults (Puterman et al., 2018). However, apart from the benefit of mHealth exercise for reducing psychological stress, stress is also a physiological state wherein cardiovascular and endocrine changes are observed (Epel et al., 2018). In the future, it would be insightful to measure the effect of mHealth exercise on 49 HCWs’ physiological responses to stress both at work and outside of the workplace. Wearable technologies, such as HexoskinTM health sensors that are able to continuously measure respiratory, cardiac, and physical activity outcomes throughout the day, are one method by which some of these physiological measures can be included in future work. Intervention effects were observed as early as week 2 for physical health symptoms and recent global stress, yet were observed slightly later (i.e., starting at week 6) for work-to-personal life interference. In line with the notion that there is a general pattern in perceiving stress that transcends into multiple life domains (Epel et al., 2018), it may be that stress takes longer to trickle down into more specific life domains, such as the work-family domain as compared to global domains (Luo et al., 2022). This may be especially true for psychosocial dimensions of ill-being measured in the work-family domain, as effects captured are dependent on social interactions with others and may take a longer period to transpire. Whether the delayed timing of intervention effects on work-to-personal life interference (as compared to the other ill-being outcomes) is typical is unknown as there are a lack of exercise interventions targeting this outcome, let alone among HCWs. Our results, however, provide preliminary evidence that decreases in work distress impeding one’s personal life can be observed after having access to exercise apps (vs. maintaining current activity levels) for as little as six weeks among this population. 4.2 Exercise and HCW Well-Being In line with inclusion of a “positive epidemiology” in health and well-being research (Vanderweele et al., 2020a, p. 189), I also analyzed intervention effects on six positive indicators of well-being to gain a more holistic view of the effect of the COPE HCW trial on HCW multidimensional well-being. Significant intervention effects were observed for all indicators of 50 HCW well-being, extending previous literature highlighting positive exercise effects for physical and mental health (e.g., Ashdown-Franks et al., 2019; Reiner et al., 2013) to a wider array of multidimensional well-being outcomes. More specifically, by week 12, medium effects were observed for overall physical health, overall mental/emotional health, and life satisfaction, such that those in the exercise condition had significantly higher scores than those waitlisted. In other words, having access to mHealth exercise resulted in HCWs experiencing more positive perceptions of their physical, mental, and emotional health, as well as feeling more satisfied with the conditions of their life in general. Importantly, feeling more positive about physical and mental health, as well as being more satisfied with life overall, is associated with a reduced risk of mental illness, physical limitations, and mortality in the future (Fleishman & Zuvekas, 2007; Idler & Benyamini, 1997; Kim et al., 2021). By week 12, small-to-medium positive intervention effects were observed for resilience and work-to-personal life enhancement, and a small positive intervention effect was observed for psychological flourishing. HCWs with access to mHealth exercise were able to respond and adapt to stressful events and unfavorable circumstances more positively, which is encouraging as HCWs (e.g., nurses) are faced with a high number of stressors at work (for reviews see Joseph & Joseph, 2016; Mohanty et al., 2019; Tomo & de Simone, 2017). Outside of their work experiences, access to mHealth resulted in a more positive mood, as well as energy and time for personal responsibilities and social interactions. Finally, HCWs with access to mHealth exercise felt more optimistic, respected, socially supported, competent, capable, as well as engaged in daily life and the happiness of others. 51 Interestingly, intervention effects were not observed for resilience until week 8 of the 12-week intervention. Some researchers suggest that resilience is more of a stable (vs. malleable) psychological trait (Jacelon, 1997). In contrast, other literature is consistent with our finding that levels of resilience can be shifted (i.e., Jackson et al., 2007; Leys et al., 2020). Our results support the idea that HCW resilience is malleable, however, as shown in the present work, intervention effects may take longer to transpire for this outcome. Future exercise trials should be conducted to gain additional insight as to how long HCWs (or others) need to engage in mHealth exercise before experiencing improvements in resilience. Nonetheless, our findings point to the need for sustainable long-term mHealth exercise interventions to best support this highly stressed population in responding and adapting to adverse circumstances more positively. The benefit of expanding organizational wellness efforts to not only reduce illness and injury but also to support the positive functioning of employees is supported by our finding that all six indicators of multidimensional well-being improved among HCWs who received access to the exercise apps compared to those waitlisted. Supporting the positive functioning of employees with workplace wellness initiatives creates an empowering approach that signals care for employees regardless of their health status (Yeung & Johnston, 2020). Further, implementing employee well-being initiatives may translate into preventative action at the organizational level. That is, supporting positive functioning of employees may have downstream effects on reducing risk of future employee illness and absenteeism, thus reducing associated financial costs and lost labour for the organization (Keyes et al., 2010). Our findings provide initial evidence that behavioural interventions should target both ill-being and well-being, separately yet simultaneously, as improvement of each distinct outcome can help to improve overall multidimensional well-being and create a generally healthier workforce. 52 4.3 Adherence to Intervention For those in the exercise condition, mean minutes of weekly Down DogTM use declined from 73.27 (SD = 41.49) minutes during week one to 34.71 (SD = 45.98) during week 12. Nonetheless, significant intervention effects were observed for all ill-being and well-being outcomes by week 12. To further tease apart this discrepancy between lack of adherence and significant intervention effects, per protocol analyses were run, comparing three separate app engagement profiles (i.e., per-protocol [80+ minutes per week of app use], low [20-79 minutes/ week], or very low [< 20 minutes/ week]) to the waitlist control condition for each outcome. A consistent pattern was found, wherein a higher number of biweekly intervention effects were significant for the per-protocol group than the low use group, and effects were nonsignificant for the very low engagement group. Such findings suggest that intervention effects on multidimensional well-being outcomes were driven by those who adhered to or surpassed the exercise protocol. Why might have adherence declined throughout the trial? To gain further insight into the barriers to exercise participants’ may have faced during the trial, a subsample of those in the exercise condition (selected from each of the app engagement groups) completed post-intervention qualitative interviews discussing their experiences of the trial. Although outside the scope of the present work, in brief, results suggest that the main barriers to exercise among these participants (regardless of how much they used the apps) were a lack of opportunity (e.g., due to perceived time commitment of family commitments and home tasks), other priorities (e.g., prioritizing rest and conservation of energy between long shifts), and a lack of motivation (e.g., low intention to exercise, low intrinsic motivation, low energy levels especially among shift workers; McKenna, 2023). Further, when comparing interviews from HCWs who engaged less 53 versus more with the apps, a main reason for disengagement among low users was a lack of intrinsic motivation (i.e., motivation stemming from inherent satisfaction or interest; Deci & Ryan, 1985; McKenna, 2023). In light of these barriers, future mHealth exercise interventions for HCWs should complement HCWs’ unique schedules and priorities, as well as consider innovative ways to increase participants’ intrinsic motivation to exercise. Intrinsic motivation may be increased in future trials by creating environments which foster HCWs’ competence and autonomy in engaging in mHealth exercise, as well as increase opportunity for social relatedness (i.e., supporting one’s innate basic psychological needs to foster intrinsic motivation; Deci & Ryan, 2000). For example, educational sessions (including topics such as using the apps, importance of exercise, setting and perusing goals, etc.) could be integrated throughout future trials to increase HCWs’ competence and autonomy in completing app-based exercise, therefore, increasing intrinsic motivation to increase exercise. In addition, the COPE HCW trial was not intentionally designed to encourage social interaction1. Future trials may benefit from inclusion of a social component, as social relatedness is a known mediator of behaviour change and may increase motivation to exercise (Deci & Ryan, 2000). For example, separating participants into age-similar cohorts (a known predictor of exercise intervention adherence; Dunlop & Beauchamp, 2012) and incorporating virtual or in-person group check-ins alongside the apps may be feasible. 4.4 Follow-up Analyses To account for the multiple testing that derived 9 effect sizes per biweekly assessment, I also determined if the significance of biweekly effects changed when the confidence interval was set to 99% rather than 95% for each effect size. In large, even when applying a more conservative confidence interval, 77.55% of biweekly intention-to-treat effects remained 54 significant, highlighting the strength of our findings. This suggests that the outcomes measured are distinct indicators of ill-being and well-being that can be independently shifted using mHealth exercise. When applying a more conservative confidence interval for the per-protocol analyses, 35.56% of biweekly effects within the per-protocol group and 69.23% of biweekly effects within the low use group were no longer significant. No changes were observed for the very low use group as all effects were nonsignificant initially. Although a high percentage of effects (particularly in the low use group) were no longer significant when applying a 99% confidence interval, it is important to highlight that effect sizes were as large as -0.83 for the per-protocol group and -0.42 for the low use group. This suggests that the variance across the exercise participants was large enough that despite medium-to-large effect sizes, the range of estimates crossed 0.00 and effects did not reach significance. Especially within each app engagement group, this may have been a result of smaller sample sizes and the trial not being specifically powered to these secondary outcomes. In future trials, if these indicators of multidimensional well-being are of primary interest, researchers should take the effect sizes found in the present work into consideration to ensure that their trials are adequately powered to detect biweekly effects more accurately. Nonetheless, many of the biweekly effects (especially within the per-protocol group) remained significant even when considering a conservative 99% confidence interval. 4.5 Limitations Several limitations to the present study should be considered. First, as mentioned above, the present set of secondary analyses was not powered for the outcomes reported. Rather, the COPE HCW trial was powered during design stages of the trial for the primary outcome of 55 depressive symptoms (Boucher et al., 2023a). This increased the risk of both Type 1 (i.e., false positive) and Type 2 (i.e., false negative) errors in the present work. However, past literature suggests associations between some of the outcomes included here (e.g., resilience, flourishing) and depressive symptoms (e.g., Mcdermott et al., 2020). Therefore, although not powered directly for the included outcomes, it may be reasonable to expect that the sample size was large enough to detect meaningful effects for these non-clinical outcomes. Effect sizes reported in the present work will then help to ensure that future trials addressing HCW multidimensional well-being are powered to detect the smallest effects. Second, this work favored the inclusion of psychological indicators of well-being over indicators of ill-being and outcomes that spanned other dimensions (except for a small number of physical and psychosocial outcomes). Future work should aim to include a more even spread of positive and negative measures that span dimensions (VanderWeele & Lomas, 2022).). Although simultaneous measurement of indicators of ill-being and well-being across multiple dimensions would best capture multidimensional well-being, researchers should also balance inclusion of many measures with participant burden. One method which may be considered in future work is to create a questionnaire combining short forms of selected measures of which scores have been shown to be reliable and valid. Outcomes should also be evidenced to be changed by exercise and relevant to this population. Third, several single-item measures (i.e., overall physical health, overall mental/emotional health, and recent global stress) were used in the present study. Such measures are useful as they are face valid (i.e., appears to measure what it is intended to; Holden, 2010) and minimize participant burden. This is particularly useful for HCWs who lack time and energy to complete lengthy surveys. Although frequently used in large national surveys (e.g., the 56 Canadian Community Health Survey – Annual Component; Statistics Canada, 2022), scores from the measure recent global stress have yet to be validated. Using global single-item measures of ill-being and well-being across multiple dimensions may be useful for future measurement of multidimensional well-being. As such, research is needed to assess their psychometrics among HCWs and the general population to confidently use these single-item measures in future projects. Fourth, another limitation of this study is that a majority of the sample identified as cis-women and reported working as nurses primarily at St. Paul’s Hospital. This sample was representative of the PHC population, as St. Paul’s Hospital houses the most employees and a majority of HCWs across PHC locations are nurses. However, several groups remained underrepresented in the present work, including those who identified as men, non-binary, those who have lived experience as a trans person, physicians, surgeons, pharmacists, and those who work at other Providence Health Care locations (e.g., clinics and hospices), as examples. In the future, greater emphasis should be placed on creating larger advisory boards that are inclusive of members of these underrepresented groups so that insights can be shared on how to best recruit and include these individuals in research. Future partnerships with other health authorities in British Columbia may also help to widen the reach of such mHealth exercise trials to a wider array of individuals across healthcare professions. Although exercise effects were observed for all outcomes by the end of the trial, it is important to note that the selected amount of weekly exercise (80+ minutes per week) was below national recommendations. This amount of exercise was selected to remain consistent with the first COPE trial (Puterman et al., 2022) and was based on the recommendation from the World Health Organization to engage in 75 minutes of vigorous or 150 minutes of moderate physical 57 activity per week (Bull et al., 2020). Therefore, if participants completed 80 minutes of vigorous intensity using the apps, they would meet global recommendations. Since the original COPE trial (Puterman et al., 2022), however, the Canadian 24-hour movement guidelines were developed and recommend 150 minutes of moderate-to-vigorous intensity exercise per week, without differentiating lengths of time for different exercise intensities (Canadian Society for Exercise Physiology, 2021). Consequently, the amount of weekly exercise requested in the COPE HCW trial was less than national recommendations. Nonetheless, research suggests increasing levels of physical activity to any degree has benefit for physical and mental health (Pearce et al., 2022; Warburton & Bredin, 2016), which was also reflected in our results. After a review of the literature on physical activity and health, Warburton and Bredin (2016) emphasize that even as little as half of the recommended weekly physical activity (i.e., 75 minutes) is beneficial for a wide array of health outcomes (e.g., reduced risk of chronic diseases). Further, researchers have found that 75 minutes of weekly physical activity reduced future risk of depressive symptoms by 18%, which suggests that engaging in physical activity at levels lower than those recommended each week is more beneficial than inactivity (Pearce et al., 2022). The results from these secondary analyses provide additional evidence that less than 150 minutes of moderate-to-vigorous physical activity (i.e., as little as 80 minutes) a week is advantageous for physical, psychological, and psychosocial indicators of ill-being and well-being. 4.6 Future Directions Future iterations of this trial should take the aforementioned concerns into consideration and work to tailor the trial to this population and their needs for sustaining mHealth exercise. There are a variety of approaches that could be taken to increase sustained engagement with the exercise applications. First, additional attention can be given to the affective correlates of 58 behaviour change. Feelings of pleasure (vs. displeasure) during physical activity are predictive of increases in future physical activity (Rhodes & Kates, 2015; Stevens et al., 2020). In the future, trials should use ecological momentary assessment to measure HCWs’ affect tied to the event of exercising. This can provide feedback as to what exercises are most pleasurable, and therefore best to assign for future iterations (Stevens et al., 2020). Second, future trials could integrate personal goal setting with mental contrasting. A systematic review and meta-analysis concluded that setting physical activity goals had a medium effect (Cohen’s d = 0.55, 95% CI [ 0.43 - 0.67]) on physical activity behaviour, in that goal setting (vs. no goal setting) led to increased activity (McEwan et al., 2016). Mental contrasting occurs after a physical activity goal is set and fosters realistic and positive expectations regarding behaviour change. HCWs can be asked to envision the most favourable outcome of their physical activity goal, and to contrast this with the most prominent obstacle hindering their ability to achieve that goal (Oettingen, 2012; Ruissen et al., 2018). Mental contrasting has been shown to encourage individuals to surmount their perceived obstacles and has led to increased physical activity (Ruissen et al., 2018). Third, the trial could be lengthened in the future so that extra weeks could be spent at the beginning of the intervention to progressively increase the length, frequency, and intensity of mHealth exercise over time, to reach a target dose of exercise that can then be maintained for 12 weeks. A similar design (in combination with motivational interviewing) was successful among highly stressed informal caregivers in the past (Puterman et al., 2018), wherein caregivers were asked to progressively increase the length, frequency, and intensity of their aerobic exercise for nine weeks, and then maintained 150 minutes of moderate-to-vigorous activity for 15 weeks. Overall, 73% of caregivers adhered to the assigned exercise throughout the trial. 59 Fourth, as HCWs’ use of the Down DogTM apps can be tracked on a weekly basis by research assistants, this information can be used to create progress reports and personalized feedback that can be sent to participants throughout future trials. HCWs can then compare their feedback to their personalized behaviour change goals which may guide and focus their attention on continual progress towards their goal (McEwan et al., 2016). In addition, for those who are not meeting their personal goals or engaging in the requested amount of mHealth exercise, one-on-one fitness coaches trained in motivational interviewing can assist with increasing motivation, as done in Puterman and colleagues’ trial with family caregivers (2018). This may help to ensure that feedback is used in a positive and motivating manner rather than as a discouraging comparative standard (Miller & Rollnick, 2013). Finally, time-tailored motivational emails or text messages could be sent to participants throughout future trials to remind them of the requested mHealth exercise. Using technology to send motivational reminders in an inexpensive way for researchers to reach participants at times during exercise trials when they may need extra support (Heron & Smyth, 2010). This approach has been successful for promoting health behaviour change in the past, including increasing exercise (Patrick et al., 2009; Puterman et al., 2018). Overall, integrating additional trial components that support adherence to exercise may result in further health and well-being benefits for HCWs. It is important to reiterate that to increase program sustainability and associated benefits for HCW multidimensional well-being, intervention should occur at three intersecting levels – the individual, team, and organization (Grady et al., 2022). Results of the present work suggest a benefit of individual-level behavioral interventions for HCWs. However, this is just part of the solution and needs to be supplemented with support from coworkers, organizational leaders, and 60 government organizations. To sustain the benefits observed in the present trial, partnerships with health authorities to help fund and support long-term integrations of such programs across British Columbia are needed. Future incorporation of health economic evaluations into exercise trials will be valuable to quantify the downstream effects of employee well-being (e.g., financial savings associated with less absenteeism and greater employee retention) for health authorities so that they are more inclined to support the integration of long-term employee well-being programs into their organizations. 4.7 Conclusion Extending results from the COPE HCW trial (Boucher et al., 2023a), these secondary analyses demonstrated the positive impact of a 12-week at-home mHealth exercise intervention on HCW's multidimensional well-being. An outcome-wide measurement approach was taken that included an array of ill-being and well-being outcomes that spanned physical, psychological, and psychosocial dimensions, capturing a more holistic view of HCWs' experiences (Schulte et al., 2015; Vanderweele et al., 2020a). Significant reductions and improvements were seen in all ill-being and well-being outcomes, respectively. Adherence to the intervention decreased over time, and a higher number of biweekly intervention effects were significant for those who engaged with the exercise apps the most. In the future, emphasis should be placed on supporting HCWs through sustained engagement with the exercise apps. When adhered to, app-based exercise may be one individual-level behavioural intervention to reduce ill-being and improve well-being among a highly stressed population during and following the pandemic. Results may be informative for future wellness programming among health authorities in British Columbia. 61 Tables Table 1 Descriptive Statistics for Outcome Measures by Condition Exercise Condition (N = 142) Waitlist Control Condition (N = 146) Physical Heath Symptoms (Mean, SD) 2.99 (1.88) 2.88 (1.95) Recent Global Stress (Mean, SD) 3.52 (0.71) 3.35 (0.71) Work-to-Personal Life Interference (Mean, SD) 7.30 (2.23) 6.87 (2.37) Overall Physical Health (Mean, SD) 2.54 (0.84) 2.53 (0.86) Overall Mental/ Emotional Health (Mean, SD) 2.44 (0.80) 2.68 (0.87) Life Satisfaction (Mean, SD) 21.42 (6.22) 22.73 (6.06) Psychological Flourishing (Mean, SD) 45.05 (7.32) 45.85 (7.41) Resilience (Mean, SD) 19.37 (4.33) 19.95 (4.33) Work-to-Personal Life Enhancement (Mean, SD) 5.71 (1.82) 6.08 (2.11) Note. SD = standard deviation. 62 Table 2 Biweekly Missingness of Outcomes Measures Missingness Week 0 Week 2 Week 4 Week 6 Week 8 Week 10 Week 12 Total % Physical Health Symptoms Complete 2 20 30 48 62 71 72 305 15.13% Partial NA NA NA NA NA NA NA NA NA Recent Global Stress Complete 2 18 30 48 58 71 71 298 14.78% Partial NA NA NA NA NA NA NA NA NA Work-to-Personal Life Spillover Complete 2 20 32 48 62 72 76 312 15.48% Partial 0 1 0 5 0 1 0 7 0.35% Overall Physical Health Complete 2 18 30 48 58 71 71 298 14.78% Partial NA NA NA NA NA NA NA NA NA Overall Mental/ Emotional Health Complete 2 18 30 48 58 71 71 298 14.78% Partial NA NA NA NA NA NA NA NA NA Life Satisfaction Complete 2 20 30 48 62 71 73 306 15.18% Partial 0 0 0 2 0 0 0 2 0.10% Psychological Flourishing Complete 2 20 30 48 60 71 72 303 15.02% Partial 0 0 1 0 0 0 0 1 0.05% Resilience Complete 2 20 30 48 62 71 72 305 15.13% Partial 0 0 0 0 0 0 0 0 0.00% Note. Complete = participant did not answer any items within the instrument and therefore the measure can be considered completely missing. Partial = responses to at least one item (but not all) in an instrument are missing and therefore the measure can be considered partially missing. % = total missing divided by the total number of surveys sent across the 7 timepoints (i.e., 288 x 7 = 2,016). Partial missingness of symptoms is not available as the absence of a symptoms is equivalent to a missing response. 63 Table 3 Biweekly Complete Missingness of Outcome Measures by Condition Condition Week 0 Week 2 Week 4 Week 6 Week 8 Week 10 Week 12 Total % Physical Health Symptoms Exercise 0 12 18 28 36 41 42 177 17.81% Control 2 8 12 20 26 30 30 128 12.52% Recent Global Stress Exercise 0 10 18 28 36 41 42 175 17.61% Control 2 8 12 20 22 30 29 123 12.04% Work-to-Personal Life Spillover Exercise 0 12 20 28 36 42 44 182 18.31% Control 2 8 12 20 26 30 32 130 12.71% Overall Physical Health Exercise 0 10 18 28 36 41 42 175 17.61% Control 2 8 12 20 22 30 29 123 12.04% Overall Mental/ Emotional Health Exercise 0 10 18 28 36 41 42 175 17.61% Control 2 8 12 20 22 30 29 123 12.04% Life Satisfaction Exercise 0 12 18 28 36 41 43 178 17.91% Control 2 8 12 20 26 30 30 128 12.52% Psychological Flourishing Exercise 0 12 18 28 36 41 42 177 17.81% Control 2 8 12 20 24 30 30 126 12.33% Resilience Exercise 0 12 18 28 36 41 42 177 17.81% Control 2 8 12 20 26 30 30 128 12.52% Note. nexercise = 142, ncontrol = 146. Complete missingness = no response to all items in a measure. % = total missing divided by the total number of surveys sent across the 7 timepoints (i.e., 142 x 7 = 994 for exercise condition, 146 x 7 = 1,022 for waitlist control). 64 Table 4 Summary of Model Fit Indices Intention-to-Treat Per-Protocol Threshold Physical Health Symptoms (Quadradic) CFI 0.956 0.946 >.90 or >.95 TLI 0.946 0.927 >.90 or >.95 SRMR 0.047 0.048 <0.05 = good, <0.08 = acceptable RMSEA 0.059 0.056 <0.06 = good Recent Global Stress (Linear) CFI 0.925 0.926 >.90 or >.95 TLI 0.925 0.918 >.90 or >.95 SRMR 0.072 0.064 <0.05 = good, <0.08 = acceptable RMSEA 0.066 0.057 <0.06 = good Work-to-Personal Life Interference (Quadradic) CFI 0.996 0.999 >.90 or >.95 TLI 0.996 0.998 >.90 or >.95 SRMR 0.031 0.028 <0.05 = good, <0.08 = acceptable RMSEA 0.027 0.014 <0.06 = good Overall Physical Health (Linear) CFI 0.949 0.945 >.90 or >.95 TLI 0.949 0.940 >.90 or >.95 SRMR 0.055 0.053 <0.05 = good, <0.08 = acceptable RMSEA 0.061 0.056 <0.06 = good Overall Mental/ Emotional Health (Quadradic) CFI 0.942 NA >.90 or >.95 TLI 0.929 NA >.90 or >.95 SRMR 0.052 NA <0.05 = good, <0.08 = acceptable RMSEA 0.067 NA <0.06 = good Note. Selected model (i.e., linear vs. quadradic) is indicated in the first column. 65 Table 4 [Continued] Summary of Model Fit Indices Intention-to-Treat Per-Protocol Threshold Life Satisfaction (Quadradic) CFI 1.000 1.000 >.90 or >.95 TLI 1.005 1.002 >.90 or >.95 SRMR 0.021 0.023 <0.05 = good, <0.08 = acceptable RMSEA 0.000 0.000 <0.06 = good Psychological Flourishing (Quadradic) CFI 0.983 0.986 >.90 or >.95 TLI 0.980 0.981 >.90 or >.95 SRMR 0.057 0.050 <0.05 = good, <0.08 = acceptable RMSEA 0.053 0.043 <0.06 = good Resilience (Quadradic) CFI 0.995 0.983 >.90 or >.95 TLI 0.994 0.977 >.90 or >.95 SRMR 0.070 0.060 <0.05 = good, <0.08 = acceptable RMSEA 0.033 0.053 <0.06 = good Work-to-Personal Life Enhancement (Linear) CFI 0.997 0.999 >.90 or >.95 TLI 0.997 0.999 >.90 or >.95 SRMR 0.053 0.043 <0.05 = good, <0.08 = acceptable RMSEA 0.021 0.008 <0.06 = good Note. Selected model (i.e., linear vs. quadradic) is indicated in the first column. CFI = comparative fit index. TLI = Tucker–Lewis index. SRMR = Standardized Root Mean Square Residual. RMSEA = root mean square error of approximation. 66 Table 5 Intention-to-Treat Biweekly Effect Sizes Estimate [95% CI] Week 2 Week 4 Week 6 Week 8 Week 10 Week 12 Physical Health Symptoms -0.15* [-0.28, -0.01] -0.25* [-0.47, -0.03] -0.31* [-0.57, -0.06] -0.34* [-0.60, -0.08] -0.33* [-0.58, -0.08] -0.29* [-0.56, -0.02] Recent Global Stress -0.09* [-0.14, -0.04] -0.17* [-0.26, -0.07] -0.24* [-0.38, -0.10] -0.32* [-0.51, -0.14] -0.41* [-0.64, -0.18] -0.49* [-0.76, -0.21] Work-to-Personal Life Interference -0.08 [-0.17, 0.01] -0.15 [-0.30, 0.00] -0.21* [-0.39, -0.02] -0.25* [-0.46, -0.05] -0.29* [-0.50, -0.08] -0.32* [-0.54, -0.10] Overall Physical Health 0.08* [0.04, 0.13] 0.17* [0.08, 0.26] 0.25* [0.11, 0.39] 0.33* [0.15, 0.52] 0.41* [0.19, 0.64] 0.50* [0.22, 0.77] Overall Mental/ Emotional Health 0.22* [0.10, 0.34] 0.39* [0.19, 0.59] 0.51* [0.27, 0.75] 0.59* [0.33, 0.84] 0.61* [0.36, 0.86] 0.59* [0.30, 0.88] Life Satisfaction 0.12* [0.03, 0.21] 0.22* [0.07, 0.37] 0.30* [0.12, 0.48] 0.36* [0.16, 0.55] 0.39* [0.20, 0.59] 0.40* [0.20, 0.61] Psychological Flourishing 0.11* [0.01, 0.21] 0.20* [0.03, 0.36] 0.26* [0.06, 0.47] 0.30* [0.08, 0.51] 0.31* [0.08, 0.53] 0.29* [0.04, 0.54] Resilience 0.06 [-0.03, 0.15] 0.13 [-0.03, 0.28] 0.19 [-0.00, 0.39] 0.26* [0.05, 0.48] 0.34* [0.12, 0.56] 0.42* [0.18, 0.67] Work-to-Personal Life Enhancement 0.06* [0.02, 0.12] 0.12* [0.03, 0.21] 0.18* [0.05, 0.32] 0.24* [0.06, 0.42] 0.30* [0.08, 0.52] 0.36* [0.09, 0.63] Note. Biweekly Feingold’s effects sizes for changes in each outcome for those in the exercise condition as compared to waitlist control. * 95% confidence interval does not cross 0.00. 67 Table 6 Per-Protocol Biweekly Effect Sizes Physical Health Symptoms Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 -0.22* [-0.43, -0.01] -0.13 [-0.28, 0.01] -0.04 [-0.31, 0.24] Week 4 -0.37* [-0.73, -0.02] -0.23 [-0.48, 0.01] -0.08 [-0.54, 0.38] Week 6 -0.44* [-0.86, -0.02] -0.29* [-0.57, -0.01] -0.14 [-0.68, 0.39] Week 8 -0.45* [-0.88, -0.01] -0.33* [-0.61, -0.04] -0.21 [-0.76, 0.33] Week 10 -0.38 [-0.78, 0.03] -0.32* [-0.60, -0.04] -0.29 [-0.80, 0.21] Week 12 -0.25 [-0.64, 0.15] -0.29 [-0.62, 0.03] -0.39 [-0.91, 0.13] Recent Global Stress Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 -0.15* [-0.23, -0.06] -0.07* [-0.13, -0.02] -0.01 [-0.11, 0.09] Week 4 -0.28* [-0.45, -0.12] -0.14* [-0.25, -0.04] -0.02 [-0.22, 0.17] Week 6 -0.42* [-0.65, -0.18] -0.21* [-0.37, -0.06] -0.03 [-0.32, 0.26] Week 8 -0.55* [-0.87, -0.24] -0.28* [-0.48, -0.08] -0.04 [-0.42, 0.34] Week 10 -0.69* [-1.09, -0.30] -0.35* [-0.61, -0.10] -0.05 [-0.53, 0.43] Week 12 -0.83* [-1.30, -0.35] -0.42* [-0.73, -0.12] -0.06 [-0.63, 0.51] Work-to-Personal Life Interference Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 -0.19* [-0.33, -0.04] -0.08 [-0.18, 0.02] 0.12 [-0.05, 0.30] Week 4 -0.34* [-0.59, -0.10] -0.15 [-0.32, 0.03] 0.19 [-0.11, 0.50] Week 6 -0.46* [-0.76, -0.15] -0.20 [-0.41, 0.02] 0.20 [-0.18, 0.59] Week 8 -0.54* [-0.87, -0.21] -0.23* [-0.46, -0.00] 0.15 [-0.28, 0.59] Week 10 -0.58* [-0.91, -0.25] -0.25* [-0.48, -0.02] 0.04 [-0.44, 0.52] Week 12 -0.58* [-0.92, -0.25] -0.25* [-0.50, -0.00] -0.13 [-0.69, 0.43] Note. Biweekly Feingold’s effects sizes for changes in each outcome for per-protocol engagement profile (80+ average weekly minutes of app use; n = 30), low engagement profile (20-79 minutes; n= 73), and very low engagement profile (<20 minutes; n = 37) compared to waitlist control condition (n = 146). SE = standard error. * 95% confidence interval does not cross 0.00. 68 Table 6 [Continued] Per-Protocol Biweekly Effect Sizes Overall Physical Health Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 0.12* [0.06, 0.18] 0.07* [0.01, 0.12] 0.06 [-0.06, 0.18] Week 4 0.24* [0.11, 0.36] 0.13* [0.02, 0.25] 0.12 [-0.12, 0.35] Week 6 0.35* [0.17, 0.54] 0.20* [0.02, 0.37] 0.17 [-0.18, 0.52] Week 8 0.47* [0.22, 0.71] 0.26* [0.03, 0.49] 0.23 [-0.24, 0.69] Week 10 0.58* [0.28, 0.88] 0.32* [0.04, 0.60] 0.28 [-0.30, 0.86] Week 12 0.70* [0.33, 1.06] 0.39* [0.05, 0.73] 0.34 [-0.36, 1.04] Overall Mental/Emotional Health Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 NA NA NA Week 4 NA NA NA Week 6 NA NA NA Week 8 NA NA NA Week 10 NA NA NA Week 12 NA NA NA Life Satisfaction Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 0.18* [0.05, 0.30] 0.12* [0.01, 0.23] 0.05 [-0.15, 0.25] Week 4 0.32* [0.10, 0.55] 0.21* [0.02, 0.40] 0.11 [-0.23, 0.45] Week 6 0.44* [0.16, 0.73] 0.27* [0.05, 0.50] 0.18 [-0.24, 0.59] Week 8 0.54* [0.22, 0.85] 0.31* [0.07, 0.54] 0.26 [-0.19, 0.70] Week 10 0.61* [0.28, 0.94] 0.32* [0.09, 0.55] 0.34 [-0.10, 0.78] Week 12 0.64* [0.29, 0.99] 0.29* [0.05, 0.53] 0.44 [-0.02, 0.89] Note. Biweekly Feingold’s effects sizes for changes in each outcome for per-protocol engagement profile (80+ average weekly minutes of app use; n = 30), low engagement profile (20-79 minutes; n= 73), and very low engagement profile (<20 minutes; n = 37) compared to waitlist control condition (n = 146). SE = standard error. * 95% confidence interval does not cross 0.00. Estimates for overall mental health are not available as the per protocol model did not converge. 69 Table 6 [Continued] Per-Protocol Biweekly Effect Sizes Psychological Flourishing Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 0.23* [0.08, 0.37] 0.07 [-0.05, 0.19] 0.07 [-0.13, 0.28] Week 4 0.39* [0.15, 0.62] 0.12 [-0.07, 0.31] 0.14 [-0.20, 0.47] Week 6 0.50* [0.20, 0.81] 0.16 [-0.07, 0.40] 0.19 [-0.23, 0.61] Week 8 0.56* [0.21, 0.90] 0.19 [-0.06, 0.43] 0.23 [-0.22, 0.68] Week 10 0.55* [0.18, 0.93] 0.20 [-0.05, 0.44] 0.26 [-0.21, 0.73] Week 12 0.50* [0.09, 0.91] 0.19 [-0.10, 0.48] 0.28 [-0.26, 0.83] Resilience Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 0.17 [-0.00, 0.34] 0.05 [-0.06, 0.17] -0.08 [-0.25, 0.09] Week 4 0.31* [0.02, 0.60] 0.11 [-0.08, 0.31] -0.10 [-0.40, 0.20] Week 6 0.42* [0.06, 0.79] 0.17 [-0.07, 0.41] -0.06 [-0.44, 0.32] Week 8 0.51* [0.10, 0.91] 0.23 [-0.02, 0.49] 0.04 [-0.39, 0.46] Week 10 0.57* [0.14, 1.00] 0.30* [0.04, 0.56] 0.19 [-0.27, 0.66] Week 12 0.61* [0.13, 1.08] 0.37* [0.09, 0.64] 0.41 [-0.13, 0.94] Work-to-Personal Life Enhancement Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 0.10* [0.02, 0.17] 0.05 [-0.00, 0.10] 0.04 [-0.07, 0.15] Week 4 0.19* [0.04, 0.34] 0.09 [-0.01, 0.19] 0.08 [-0.15, 0.31] Week 6 0.29* [0.06, 0.51] 0.14 [-0.01, 0.29] 0.12 [-0.22, 0.46] Week 8 0.38* [0.08, 0.69] 0.19 [-0.01, 0.39] 0.16 [-0.29, 0.62] Week 10 0.47* [0.10, 0.85] 0.23 [-0.01, 0.48] 0.20 [-0.36, 0.77] Week 12 0.57* [0.12, 1.02] 0.28 [-0.02, 0.58] 0.25 [-0.44, 0.92] Note. Biweekly Feingold’s effects sizes for changes in each outcome for per-protocol engagement profile (80+ average weekly minutes of app use; n = 30), low engagement profile (20-79 minutes; n= 73), and very low engagement profile (<20 minutes; n = 37) compared to waitlist control condition (n = 146). SE = standard error. * 95% confidence interval does not cross 0.00. 70 Figures Figure 1 Flexible Conceptual Map Concerning Human Flourishing Note. A conceptual map of the multidimensional well-being framework proposed by VanderWeele and Lomas (2022; p. 37). The present study does not measure flourishing in its broadest sense, as we do not measure the contexts in which participants live. For example, a participant could report high well-being while living in a poor environment, and thus would not be flourishing overall.Dimension-Specific Constructs and Measures Ontological dimensions, each encompassing dimension specific constructs and measures“The relative attainment of a state in which all aspects of a person’s physical, mental, social, and spiritual life are good” “The relative attainment of a state in which all aspects of a person’s life are good including the contexts in which that person lives” Overall FlourishingMultidimensional Well-BeingMental Well-BeingE.g., Psychological FlourishingPhysical Well-BeingE.g., Physical Health SymptomsSocial Well-BeingE.g., Relationship Quality Spiritual Well-BeingE.g., Service to Others 71 Figure 2 Intention-to-Treat Biweekly Effect Sizes and Trajectories a) b) c) d) Note. A & B: Biweekly Feingold’s effects sizes (error bars = 95% confidence intervals) for changes in each outcome for those in the exercise as compared to waitlist control condition. C & D: Trajectories of each outcome for exercise and waitlist control groups throughout the trial (shaded area = 95% confidence intervals). 72 Figure 2 [Continued] Intention-to-Treat Biweekly Effect Sizes and Trajectories e) f) g) h) Note. E & F: Biweekly Feingold’s effects sizes (error bars = 95% confidence intervals) for changes in each outcome for those in the exercise as compared to waitlist control condition. G & H: Trajectories of each outcome for exercise and waitlist control groups throughout the trial (shaded area = 95% confidence intervals). 73 Figure 2 [Continued] Intention-to-Treat Biweekly Effect Sizes and Trajectories i) j) k) l) Note. I & J: Biweekly Feingold’s effects sizes (error bars = 95% confidence intervals) for changes in each outcome for those in the exercise as compared to waitlist control condition. K & L: Trajectories of each outcome for exercise and waitlist control groups throughout the trial (shaded area = 95% confidence intervals). Figure 2 [Continued] 74 Intention-to-Treat Biweekly Effect Sizes and Trajectories m) n) o) p) Note. M & N: Biweekly Feingold’s effects sizes (error bars = 95% confidence intervals) for changes in each outcome for those in the exercise as compared to waitlist control condition. O & P: Trajectories of each outcome for exercise and waitlist control groups throughout the trial (shaded area = 95% confidence intervals). 75 Figure 2 [Continued] Intention-to-Treat Biweekly Effect Sizes and Trajectories q) r) Note. Q: Biweekly Feingold’s effects sizes (error bars = 95% confidence intervals) for changes in enhancement for those in the exercise as compared to waitlist control condition. R: Trajectories of enhancement for exercise and waitlist control groups throughout the trial (shaded area = 95% confidence intervals). 76 Figure 3 Per-Protocol Biweekly Effect Sizes and Trajectories a) Physical Symptoms b) Recent Global Stress c) Physical Symptoms d) Recent Global Stress Note. A & B: Biweekly Feingold’s effects sizes (error bars = 95% confidence intervals) for changes in each outcome for per-protocol engagement profile (80+ average weekly minutes of app use; n = 30), low engagement profile (20-79 minutes; n= 73), and very low engagement profile (<20 minutes; n = 37) compared to waitlist control condition (n = 146). C & D: Trajectories of each outcome for participants in each profile of app engagement and the waitlist control condition throughout the trial (shaded area = 95% confidence intervals). 77 Figure 3 [Continued] Per-Protocol Biweekly Effect Sizes and Trajectories e) Interference f) Physical Health g) Interference h) Physical Health Note. E & F: Biweekly Feingold’s effects sizes (error bars = 95% confidence intervals) for changes in each outcome for per-protocol engagement profile (80+ average weekly minutes of app use; n = 30), low engagement profile (20-79 minutes; n= 73), and very low engagement profile (<20 minutes; n = 37) compared to waitlist control condition (n = 146). G & H: Trajectories of each outcome for participants in each profile of app engagement and the waitlist control condition throughout the trial (shaded area = 95% confidence intervals). 78 Figure 3 [Continued] Per-Protocol Biweekly Effect Sizes and Trajectories i) Life Satisfaction j) Flourishing k) Life Satisfaction l) Flourishing Note. I & J: Biweekly Feingold’s effects sizes (error bars = 95% confidence intervals) for changes in each outcome for per-protocol engagement profile (80+ average weekly minutes of app use; n = 30), low engagement profile (20-79 minutes; n= 73), and very low engagement profile (<20 minutes; n = 37) compared to waitlist control condition (n = 146). K & L: Trajectories of each outcome for participants in each profile of app engagement and the waitlist control condition throughout the trial (shaded area = 95% confidence intervals). 79 Figure 3 [Continued] Per-Protocol Biweekly Effect Sizes and Trajectories m) Resilience n) Enhancement o) Resilience p) Enhancement Note. M & N: Biweekly Feingold’s effects sizes (error bars = 95% confidence intervals) for changes in each outcome for per-protocol engagement profile (80+ average weekly minutes of app use; n = 30), low engagement profile (20-79 minutes; n = 73), and very low engagement profile (<20 minutes; n = 37) compared to waitlist control condition (n = 146). O & P: Trajectories of each outcome for participants in each profile of app engagement and the waitlist control condition throughout the trial (shaded area = 95% confidence intervals). 80 Footnotes 1 Biweekly until the end of the 12-week intervention, participants in the exercise condition self-reported whether they completed any of the Down Dog workouts with others in their household (0 = no, 1 = yes). The number and percentage of exercise condition participants (i.e., divided by 142 x 100) who answered yes are included below. These data were not included in these secondary analyses as the trial was not designed to include a social component and analyzing the benefit of exercising with others on adherence was not an aim of the present work. Also, very few participants completed workouts with others as they were not specifically encouraged to do so, limiting the use of this data. Subsequent papers focused on predictors of adherence to the COPE HCW trial may utilize these data. Further, as mentioned in the discussion, future iterations of the trial may benefit from incorporating a social component to mediate behaviour change (Deci & Ryan, 2000). Week 2 Week 4 Week 6 Week 8 Week 10 Week 12 18 (12.68%) 8 (5.63%) 10 (7.04%) 8 (5.63%) 5 (3.52%) 4 (2.82%) 81 References Agrawal, M., & Mahajan, R. (2021). Work–family enrichment: an integrative review. International Journal of Workplace Health Management, 14(2), 217–241. https://doi.org/10.1108/IJWHM-04-2020-0056/FULL/XML Ahmad, F., Jhajj, A. K., Stewart, D. E., Burghardt, M., & Bierman, A. S. (2014). Single item measures of self-rated mental health: A scoping review. BMC Health Services Research, 14(1), 1–11. https://doi.org/10.1186/1472-6963-14-398/FIGURES/2 Aitken, M., Clancy, B., Nass, D. (2017). The growing value of digital health: Evidence and impact on human health and the healthcare system. The IQVIA Institute. https://regresearchnetwork.org/wp-content/uploads/the-growing-value-of-digital-health.pdf Albert, M. A., Durazo, E. M., Slopen, N., Zaslavsky, A. M., Buring, J. E., Silva, T., Chasman, D., & Williams, D. R. (2017). Cumulative psychological stress and cardiovascular disease risk in middle aged and older women: Rationale, design, and baseline characteristics. American Heart Journal, 192, 1–12. https://doi.org/10.1016/J.AHJ.2017.06.012 Ammar, A., Brach, M., Trabelsi, K., Chtourou, H., Boukhris, O., Masmoudi, L., Bouaziz, B., Bentlage, E., How, D., Ahmed, M., Müller, P., Müller, N., Aloui, A., Hammouda, O., Paineiras-Domingos, L. L., Braakman-Jansen, A., Wrede, C., Bastoni, S., Pernambuco, C. S., … Hoekelmann, A. (2020). Effects of COVID-19 home confinement on eating behaviour and physical activity: Results of the ECLB-COVID19 International Online Survey. Nutrients, 12(6), 1583. https://doi.org/10.3390/NU12061583 Ashdown-Franks, G., Firth, J., Carney, R., Carvalho, A. F., Hallgren, M., Koyanagi, A., Rosenbaum, S., Schuch, F. B., Smith, L., Solmi, M., Vancampfort, D., & Stubbs, B. (2019). Exercise as medicine for mental and substance use disorders: A meta-review of the benefits for 82 neuropsychiatric and cognitive outcomes. Sports Medicine, 50(1), 151–170. https://doi.org/10.1007/S40279-019-01187-6 Bank, J., Ferrero, J., Langlais, D., Rankin, E., Baynton, M. A., Bertrand, R., Arnold, I., Bradley, L., Mahajan, S., Thrasher, A., & Nestaiko, M. (2013). The National Standard of Canada: Psychological health and safety in the workplace — Prevention, promotion, and guidance to staged implementation. CSA group and Bureau de normalisation du Québec. Barello, S., Palamenghi, L., & Graffigna, G. (2020). Burnout and somatic symptoms among frontline healthcare professionals at the peak of the Italian COVID-19 pandemic. Psychiatry Research, 290, 113129. https://doi.org/10.1016/J.PSYCHRES.2020.113129 Baskin, R. G., & Bartlett, R. (2021). Healthcare worker resilience during the COVID-19 pandemic: An integrative review. Journal of Nursing Management, 29(8), 2329–2342. https://doi.org/10.1111/JONM.13395 Bernal, D., Campos-Serna, J., Tobias, A., Vargas-Prada, S., Benavides, F. G., & Serra, C. (2015). Work-related psychosocial risk factors and musculoskeletal disorders in hospital nurses and nursing aides: A systematic review and meta-analysis. International Journal of Nursing Studies, 52(2), 635–648. https://doi.org/10.1016/J.IJNURSTU.2014.11.003 Bolger, N., DeLongis, A., Kessler, R. C., & Wethington, E. (1989). The Contagion of Stress across Multiple Roles. Journal of Marriage and the Family, 51(1), 175. https://doi.org/10.2307/352378 Boucher, V. G., Haight, B. L., Hives, B. A., Zumbo, B. D., Merali-Dewji, A., Hutton, S., Liu, Y., Nguyen, S., Beauchamp, M. R., Black, A. T., & Puterman, E. (2023a). Effects of 12 weeks of at-home, application-based exercise on health care workers’ depressive symptoms, burnout, and absenteeism: A randomized clinical trial. JAMA Psychiatry, 80(11), 1101-1109. https://doi.org/10.1001/jamapsychiatry.2023.2706 83 Boucher, V. G., Haight, B. L., Léger, C., Deslauriers, F., Bacon, S. L., Lavoie, K. L., & Puterman, E. M. (2023b). Canadian healthcare workers’ mental health and health behaviours during the COVID-19 pandemic: Results from nine representative samples between April 2020 and February 2022. Canadian Journal of Public Health, 114(5), 823-839. https://doi.org/10.17269/s41997-023-00807-z Bredin, S. S. D., Gledhill, N., Jamnik, V. K., & Warburton, D. E. R. (2013). PAR-Q+ and ePARmed-X+: New risk stratification and physical activity clearance strategy for physicians and patients alike. Canadian Family Physician, 59(3), 273–277. Bull, F. C., Al-Ansari, S. S., Biddle, S., Borodulin, K., Buman, M. P., Cardon, G., Carty, C., Chaput, J. P., Chastin, S., Chou, R., Dempsey, P. C., DiPietro, L., Ekelund, U., Firth, J., Friedenreich, C. M., Garcia, L., Gichu, M., Jago, R., Katzmarzyk, P. T., Lambert, E., … Willumsen, J. F. (2020). World Health Organization 2020 guidelines on physical activity and sedentary behaviour. British Journal of Sports Medicine, 54(24), 1451–1462. https://doi.org/10.1136/bjsports-2020-102955 Busch, A. M., Ciccolo, J. T., Puspitasari, A. J., Nosrat, S., Whitworth, J. W., & Stults-Kolehmainen, M. A. (2016). Preferences for exercise as a treatment for depression. Mental Health and Physical Activity, 10, 68–72. https://doi.org/10.1016/J.MHPA.2015.12.004 Buselli, R., Corsi, M., Veltri, A., Baldanzi, S., Chiumiento, M., Lupo, E. Del, Marino, R., Necciari, G., Caldi, F., Foddis, R., Guglielmi, G., & Cristaudo, A. (2021). Mental health of health care workers (HCWs): A review of organizational interventions put in place by local institutions to cope with new psychosocial challenges resulting from COVID-19. Psychiatry Research, 299, 113847. https://doi.org/10.1016/J.PSYCHRES.2021.113847 Canadian Medical Association. (2021). National physican health survey. https://www.cma.ca/sites/default/files/2022-08/NPHS_final_report_EN.pdf 84 Canadian Society for Exercise Physiology. (2021). Canadian 24-Hour Movement Guidelines: An integration of physical activity, sedentary behaviour, and sleep. 24-Hour Movement Guidelines. https://csepguidelines.ca Caplin, A., Beauchamp, M. R., Weiss, J., Schilf, S., Hives, B., DeLongis, A., Johansen, K. L., Epel, E. S., & Puterman, E. (2021). Effects of aerobic exercise training on daily psychological processes in family caregivers: Secondary analyses of a randomized controlled trial. Affective Science, 2(1), 48. https://doi.org/10.1007/S42761-020-00026-6 Carlson, D. S., Grzywacz, J. G., Ferguson, M., Hunter, E. M., Clinch, C. R., & Arcury, T. A. (2011). Health and turnover of working mothers after childbirth via the work-family interface: An analysis across time. Journal of Applied Psychology, 96(5), 1045–1054. https://doi.org/10.1037/A0023964 Charles, S. T., & Almeida, D. M. (2006). Daily reports of symptoms and negative affect: Not all symptoms are the same. Psychology & Health, 21(1), 1–17. https://doi.org/10.1080/14768320500129239 Chen, L., Li, J., Xia, T., Matthews, T. A., Tseng, T. S., Shi, L., Zhang, D., Chen, Z., Han, X., Li, Y., Li, H., Wen, M., & Su, D. (2021). Changes of exercise, screen time, fast food consumption, alcohol, and cigarette smoking during the COVID-19 pandemic among adults in the United States. Nutrients, 13(10), 3359. https://doi.org/10.3390/NU13103359 Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of health and social behavior, 385-396. Cooney, G. M., Dwan, K., Greig, C. A., Lawlor, D. A., Rimer, J., Waugh, F. R., Mcmurdo, M., & Mead, G. (2013). Exercise for depression (Review). Cochrane Database of Systematic Reviews, 9, 1–157. http://www.thecochranelibrary.com 85 Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. Springer. Deci, E. L., & Ryan, R. M. (2000). The" what" and" why" of goal pursuits: Human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227-268. https://doi.org/10.1207/S15327965PLI1104_01 DeSalvo, K. B., Fan, V. S., McDonell, M. B., & Fihn, S. D. (2005). Predicting mortality and healthcare utilization with a single question. Health Services Research, 40(4), 1234–1246. https://doi.org/10.1111/J.1475-6773.2005.00404.X Desalvo, K. B., Fisher, W. P., Tran, K., Bloser, N., Merrill, W., & Peabody, J. (2006). Assessing measurement properties of two single-item general health measures. Quality of Life Research, 15, 191–201. https://doi.org/10.1007/s11136-005-0887-2 Diener, E. (1984). Subjective well-being. Psychological Bulletin, 95(3), 542–575. https://doi.org/10.1037/0033-2909.95.3.542 Diener, E. D., Emmons, R. A., Sem, R. J. L., & Griffin, S. (1985). The Satisfaction with Life Scale. Journal of Personality Assessment, 49(1), 71–75. https://doi.org/https://doi.org/10.1207/s15327752jpa4901_13 Diener, E., Wirtz, D., Tov, W., Kim-Prieto, C., Choi, D., Oishi, S., & Biswas-Diener, R. (2010). New well-being measures: Short scales to assess flourishing and positive and negative feelings. Social Indicators Research, 97(2), 143–156. https://doi.org/10.1007/s11205-009-9493-y Direito, A., Carraça, E., Rawstorn, J., Whittaker, R., & Maddison, R. (2017). mHealth technologies to influence physical activity and sedentary behaviors: Behavior change techniques, systematic review and meta-analysis of randomized controlled trials. Annals of Behavioral Medicine, 51(2), 226–239. https://doi.org/10.1007/S12160-016-9846-0 86 Du, J., Dong, L., Wang, T., Yuan, C., Fu, R., Zhang, L., Liu, B., Zhang, M., Yin, Y., Qin, J., Bouey, J., Zhao, M., & Li, X. (2020). Psychological symptoms among frontline healthcare workers during COVID-19 outbreak in Wuhan. General Hospital Psychiatry, 67, 144. https://doi.org/10.1016/J.GENHOSPPSYCH.2020.03.011 Dunlop, W. L., & Beauchamp, M. R. (2012). The relationship between intra-group age similarity and exercise adherence. American Journal of Preventive Medicine, 42(1), 53-55. https://doi.org/10.1016/j.amepre.2011.08.018 Epel, E. S., Crosswell, A. D., Mayer, S. E., Prather, A. A., Slavich, G. M., Puterman, E., & Mendes, W. B. (2018). More than a feeling: A unified view of stress measurement for population science. Frontiers in Neuroendocrinology, 49, 146-169. https://doi.org/10.1016/j.yfrne.2018.03.001 Erdogan, B., Bauer, T. N., Truxillo, D. M., & Mansfield, L. R. (2012). Whistle while you work: A review of the life satisfaction literature. Journal of Management, 38(4), 1038–1083. https://doi.org/10.1177/0149206311429379 Fan, J. K., Mustard, C., & Smith, P. M. (2019). Psychosocial work conditions and mental health: Examining differences across mental illness and well-being outcomes. Annals of Work Exposures and Health, 63(5), 546–559. https://doi.org/10.1093/annweh/wxz028 Feingold, A. (2019). New approaches for estimation of effect sizes and their confidence intervals for treatment effects from randomized controlled trials. The Quantitative Methods for Psychology, 15(2), 96. Fleishman, J. A., & Zuvekas, S. H. (2007). Global self-rated mental health: Associations with other mental health measures and with role functioning. Medical Care, 45(7), 602–609. 87 Fox, K. E., Johnson, S. T., Berkman, L. F., Sianoja, M., Soh, Y., Kubzansky, L. D., & Kelly, E. L. (2022). Organisational- and group-level workplace interventions and their effect on multiple domains of worker well-being: A systematic review. Work & Stress, 36(1), 30–59. https://doi.org/10.1080/02678373.2021.1969476 Franken, K., Lamers, S. M. A., Ten Klooster, P. M., Bohlmeijer, E. T., & Westerhof, G. J. (2018). Validation of the Mental Health Continuum-Short Form and the dual continua model of well-being and psychopathology in an adult mental health setting. Journal of Clinical Psychology, 74(12), 2187–2202. https://doi.org/10.1002/JCLP.22659 Fuß, I., Nübling, M., Hasselhorn, H. M., Schwappach, D., & Rieger, M. A. (2008). Working conditions and work-family conflict in German hospital physicians: Psychosocial and organisational predictors and consequences. BMC Public Health, 8(1), 1–17. https://doi.org/10.1186/1471-2458-8-353/FIGURES/1 Ghislieri, C., Gatti, P., Molino, M., & Cortese, C. G. (2017). Work–family conflict and enrichment in nurses: between job demands, perceived organisational support and work–family backlash. Journal of Nursing Management, 25(1), 65–75. https://doi.org/10.1111/JONM.12442 Grady, C., Chênevert, D., & Coderre-Ball, A. (2022). Exploring two psychosocial factors for health-care workers: Support for psychological self-care and protection from moral distress in the workplace: Facilitators and barriers. Mental Health Commission of Canada. https://mentalhealthcommission.ca/wp-content/uploads/2022/09/Psychosocial-factors-for-health-care-workers.pdf Great Britain Department of Health. (2014). Wellbeing across the life course: What works to improve wellbeing? https://www.bl.uk/collection-items/compendium-of-factsheets-wellbeing-across-the-lifecourse-what- works-to-improve-wellbeing 88 Grzywacz, J. G., & Marks, N. F. (2000). Reconceptualizing the work-family interface: an ecological perspective on the correlates of positive and negative spillover between work and family. Journal of Occupational Health Psychology, 5(1), 111–126. https://doi.org/10.1037/1076-8998.5.1.111 Haight, B. L., Peddie, L., Crosswell, A., Hives, B. A., Almeida, D. M., & Puterman, E. (2023). Combined effects of cumulative stress and daily stressors on daily health. Health Psychology, 42(5), 325-334. https://doi.org/10.1037/hea0001281 Hart, P. L., Brannan, J. D., & de Chesnay, M. (2014). Resilience in nurses: An integrative review. Journal of Nursing Management, 22(6), 720–734. https://doi.org/10.1111/J.1365-2834.2012.01485.X Heller, D., Watson, D., & Ilies, R. (2004). The role of person versus situation in life satisfaction: A critical examination. Psychological Bulletin, 130(4), 574–600. https://doi.org/10.1037/0033-2909.130.4.574 Helliwell, J. F., Schellenberg, G., & Fonberg, J. (2020). The COVID-19 pandemic and life satisfaction in Canada. Statistics Canada. www.statcan.gc.ca Heron, K. E., & Smyth, J. M. (2010). Ecological momentary interventions: Incorporating mobile technology into psychosocial and health behaviour treatments. British Journal of Health Psychology, 15(1), 1-39. https://doi.org/10.1348/135910709X466063 Hives, B. A., Buckler, E. J., Weiss, J., Schilf, S., Johansen, K. L., Epel, E. S., & Puterman, E. (2021). The effects of aerobic exercise on psychological functioning in family caregivers: Secondary analyses of a randomized controlled trial. Annals of Behavioral Medicine, 55(1), 65. https://doi.org/10.1093/ABM/KAAA031 89 Holden, R. R. (2010). Face validity. In The corsini encyclopedia of psychology. Wiley Online Library. https://doi.org/10.1002/9780470479216.corpsy0341 Hong, S., Ai, M., Xu, X., Wang, W., Chen, J., Zhang, Q., Wang, L., & Kuang, L. (2021). Immediate psychological impact on nurses working at 42 government-designated hospitals during COVID-19 outbreak in China: A cross-sectional study. Nursing Outlook, 69(1), 6–12. https://doi.org/10.1016/J.OUTLOOK.2020.07.007 Idler, E. L., & Benyamini, Y. (1997). Self-rated health and mortality: A review of twenty-seven community studies. Journal of Health and Social Behavior, 38(1), 21–37. Ikeda, S., Eguchi, H., Hiro, H., Mafune, K., Hino, A., Koga, K., Nishimura, K., & Nakashima, M. (2022). Work engagement mediates the relationship between job resources and work-to-family positive spillover (WFPS) for home-visit nursing staff. Environmental and Occupational Health Practice, 4, 1–8. https://doi.org/10.1539/eohp.2021-0012-OA Jacelon, C. S. (1997). The trait and process of resilience. Journal of advanced nursing, 25(1), 123-129. Jackson, D., Firtko, A., & Edenborough, M. (2007). Personal resilience as a strategy for surviving and thriving in the face of workplace adversity: a literature review. Journal of Advanced Nursing, 60(1), 1-9. https://doi.org/10.1111/j.1365-2648.2007.04412.x Jarden, R. J., Jarden, A. J., Weiland, T. J., Taylor, G., Brockenshire, N., Rutherford, M., Carbery, C., Moroney, K., Joshanloo, M., Gerdtz, M., & Rebecca Jarden, C. J. (2023). Nurses’ well-being during the coronavirus (2019) pandemic: A longitudinal mixed-methods study. Nursing Open, 10, 24–35. https://doi.org/10.1002/nop2.1275 90 Joseph, B., & Joseph, M. (2016). The health of the healthcare workers. Indian Journal of Occupational and Environmental Medicine, 20(2), 71. https://doi.org/10.4103/0019-5278.197518 Keyes, C. L. M. (2002). The mental health continuum: From languishing to flourishing in life. Journal of Health and Social Behavior, 43(2), 207–222. https://doi.org/10.2307/3090197 Keyes, C. L. M., Dhingra, S. S., & Simoes, E. J. (2010). Change in level of positive mental health as a predictor of future risk of mental illness. American Journal of Public Health, 100(2), 2366–2371. https://doi.org/10.2105/AJPH.2010 Keyes, C. L. M., & Simoes, E. J. (2012). To flourish or not: Positive mental health and all-cause mortality. American Journal of Public Health, 102(11), 2164–2172. https://doi.org/10.2105/AJPH Keyes, C. L. M., Wissing, M., Potgieter, J. P., Temane, M., Kruger, A., & van Rooy, S. (2008). Evaluation of the mental health continuum–short form (MHC–SF) in setswana-speaking South Africans. Clinical Psychology & Psychotherapy, 15(3), 181–192. https://doi.org/10.1002/CPP.572 Khaled, K., Tsofliou, F., Hundley, V., Helmreich, R., & Almilaji, O. (2020). Perceived stress and diet quality in women of reproductive age: A systematic review and meta-analysis. Nutrition Journal, 19(1), 1–15. https://doi.org/10.1186/S12937-020-00609-W/FIGURES/3 Kiernan, M., Schoffman, D. E., Lee, K., Brown, S. D., Fair, J. M., Perri, M. G., & Haskell, W. L. (2013). The Stanford Leisure-Time Activity Categorical Item (L-Cat): A single categorical item sensitive to physical activity changes in overweight/obese women. International Journal of Obesity, 37(12), 1597–1602. https://doi.org/10.1038/ijo.2013.36 91 Killgore, W. D. S., Taylor, E. C., Cloonan, S. A., & Dailey, N. S. (2020). Psychological resilience during the COVID-19 lockdown. Psychiatry Research, 291, 113216. https://doi.org/10.1016/J.PSYCHRES.2020.113216 Kim, E. S., Delaney, S. W., Tay, L., Chen, Y., Diener, E., & Vanderweele, T. J. (2021). Life satisfaction and subsequent physical, behavioral, and psychosocial health in older adults. The Milbank Quarterly, 99(1), 209. https://doi.org/10.1111/1468-0009.12497 Kinman, G., Teoh, K., & Harriss, A. (2020). Supporting the well-being of healthcare workers during and after COVID-19. Occupational Medicine, 70(5), 294–296. https://doi.org/10.1093/OCCMED/KQAA096 Labrague, L. J., Ballad, C. A., & Fronda, D. C. (2021). Predictors and outcomes of work–family conflict among nurses. International Nursing Review, 68(3), 349–357. https://doi.org/10.1111/INR.12642 Lai, J., Ma, S., Wang, Y., Cai, Z., Hu, J., Wei, N., Wu, J., Du, H., Chen, T., Li, R., Tan, H., Kang, L., Yao, L., Huang, M., Wang, H., Wang, G., Liu, Z., & Hu, S. (2020). Factors associated with mental health outcomes among health care workers exposed to Coronavirus Disease 2019. JAMA Network Open, 3(3), e203976–e203976. https://doi.org/10.1001/JAMANETWORKOPEN.2020.3976 Lamers, S. M. A., Westerhof, G. J., Bohlmeijer, E. T., ten Klooster, P. M., & Keyes, C. L. M. (2011). Evaluating the psychometric properties of the Mental Health Continuum-Short Form (MHC-SF). Journal of Clinical Psychology, 67(1), 99-110. https://doi.org/10.1002/jclp.20741 Larsen, R. J., & Kasimatis, M. (1991). Day-to-day physical symptoms: Individual differences in the occurrence, duration, and emotional concomitants of minor daily illnesses. Journal of Personality, 59(3), 387–423. https://doi.org/10.1111/J.1467-6494.1991.TB00254.X 92 Lavoie-Tremblay, M., Gélinas, C., Aubé, T., Tchouaket, E., Tremblay, D., Gagnon, M. P., & Côté, J. (2022). Influence of caring for COVID-19 patients on nurse’s turnover, work satisfaction and quality of care. Journal of Nursing Management, 30(1), 33–43. https://doi.org/10.1111/JONM.13462 Lazarus, R. S., & Folkman, S. (1984). Stress, coping and adaption. Springer. Leger, K. A., Charles, S. T., Ayanian, J. Z., & Almeida, D. M. (2015). The association of daily physical symptoms with future health. Social Science & Medicine, 143, 241–248. https://doi.org/10.1016/J.SOCSCIMED.2015.08.050 Lesser, I. A., & Nienhuis, C. P. (2020). The impact of COVID-19 on physical activity behavior and well-being of Canadians. International Journal of Environmental Research and Public Health, 17(11), 3899. https://doi.org/10.3390/IJERPH17113899 Leys, C., Arnal, C., Wollast, R., Rolin, H., Kotsou, I., & Fossion, P. (2020). Perspectives on resilience: personality trait or skill?. European journal of trauma & dissociation, 4(2), 100074. Liang, Z., Wang, Y., Wei, X., Wen, W., Ma, J., Wu, J., Huang, S., & Qin, P. (2023). Prevalence and associated factors of depressive and anxiety symptoms among healthcare workers in the post-pandemic era of COVID-19 at a tertiary hospital in Shenzhen, China: A cross-sectional study. Frontiers in Public Health, 11, 1094776. https://doi.org/10.3389/FPUBH.2023.1094776 Little, R. J., & Rubin, D. B. (2020). Statistical analysis with missing data (3rd ed.). John Wiley & Sons. Littman, A. J., White, E., Satia, J. A., Bowen, D. J., & Kristal, A. R. (2006). Reliability and validity of 2 single-item measures of psychosocial stress. Epidemiology, 17(4), 398–403. https://www.jstor.org/stable/20486241 93 Lixia, W., Xiaoming, X., Lei, S., Su, H., Wo, W., Xin, F., Jianmei, C., Qi, Z., Ming, A., & Li, K. (2022). A cross-sectional study of the psychological status of 33,706 hospital workers at the late stage of the COVID-19 outbreak. Journal of Affective Disorders, 297, 156–168. https://doi.org/10.1016/J.JAD.2021.10.013 Lockwood, K. G., Peddie, L., Crosswell, A. D., Hives, B. A., Slopen, N., Almeida, D. M., & Puterman, E. (2022). Effects of Chronic Burden Across Multiple Domains and Experiences of Daily Stressors on Negative Affect. Annals of Behavioral Medicine, 56(10), 1056–1067. https://doi.org/10.1093/ABM/KAAC001 Luo, J., Zhang, B., Willroth, E. C., Mroczek, D. K., & Roberts, B. W. (2022). The roles of general and domain-specific perceived stress in healthy aging. The Journals of Gerontology: Series B, 77(3), 536-549. https://doi.org/10.1093/geronb/gbab134 Luthar, S. S., & Cicchetti, D. (2000). The construct of resilience: Implications for interventions and social policies. Development and Psychopathology, 12(4), 857–885. https://doi.org/10.1017/S0954579400004156 Maddock, C., & Pariante, C. M. (2001). How does stress affect you? An overview of stress, immunity, depression and disease. Epidemiology and Psychiatric Sciences, 10(3), 153–162. https://doi.org/10.1017/S1121189X00005285 Marcus, B. H., Owen, N., Forsyth, L. H., Cavill, N. A., & Fridinger, F. (1998). Physical activity interventions using mass media, print media, and information technology. American Journal of Preventive Medicine, 15(4), 362–378. https://doi.org/10.1016/S0749-3797(98)00079-8 Mascaro, J. S., Wallace, A., Hyman, B., Haack, C., Hill, C. C., Moore, M. A., Lund, M. B., Nehl, E. J., Bergquist, S. H., & Cole, S. W. (2022). Flourishing in healthcare trainees: Psychological well-being and the conserved transcriptional response to adversity. International Journal of 94 Environmental Research and Public Health, 19(4), 2255. https://doi.org/10.3390/IJERPH19042255 Maslach, C., Jackson, S. E., Leither, M. P., Schaufeli, W. B., & Schwab, R. L. (2018). Maslach Burnout Inventory: Fourth edition. Mind Garden. https://www.mindgarden.com/117-maslach-burnout-inventory-mbi Mawani, F. N., & Gilmour, H. (2010). Validation of self-reported mental health. Health Reports, 21(3), 61–75. www.statcan.gc.ca Mayer, M. (2019). MissRanger: Fast imputation of missing values (R package version). Mcdermott, R. C., Fruh, S. M., Williams, S., Hauff, C., Graves, R. J., Melnyk, B. M., & Hall, H. R. (2020). Nursing students’ resilience, depression, well‐being, and academic distress: Testing a moderated mediation model. Journal of Advanced Nursing, 76(12), 3385-3397. https://doi.org/10.1111/jan.14531 McEwan, D., Harden, S. M., Zumbo, B. D., Sylvester, B. D., Kaulius, M., Ruissen, G. R., Dowd, A. J., & Beauchamp, M. R. (2016). The effectiveness of multi-component goal setting interventions for changing physical activity behaviour: A systematic review and meta-analysis. Health Psychology Review, 10(1), 67-88. https://doi.org/10.1080/17437199.2015.1104258 McKennna, J. (2023). The COPE HCW Trial: evaluating the engagement of healthcare workers in an mhealth exercise app intervention. Unpublished thesis, School of Kinesiology, University of British Columbia, Vancouver, Canada. Mehta, S., Machado, F., Kwizera, A., Papazian, L., Moss, M., Azoulay, É., & Herridge, M. (2021). COVID-19: a heavy toll on health-care workers. The Lancet. Respiratory Medicine, 9(3), 226. https://doi.org/10.1016/S2213-2600(21)00068-0 95 Ménard, A. D., Soucie, K., Freeman, L. A., & Ralph, J. L. (2022). “My problems aren’t severe enough to seek help”: Stress levels and use of mental health supports by Canadian hospital employees during the COVID-19 pandemic. Health Policy, 126(2), 106–111. https://doi.org/10.1016/J.HEALTHPOL.2022.01.002 Menzel, N. (2008). Underreporting of musculoskeletal disorders among health care workers: Research needs. Workplace Health and Safety Journal, 56(12), 487–494. Michaud, D. S., Marro, L., Denning, A., Shackleton, S., Toutant, N., Cameron-Blake, E., & McNamee, J. P. (2022). Implications of the COVID-19 pandemic on self-reported health status and noise annoyance in rural and non-rural Canada. Scientific Reports, 12(1), 1–10. https://doi.org/10.1038/s41598-022-19907-w Mihelic, K. K., & Tekavcic, M. (2014). Work-family conflict: A review of antecedents and outcomes. International Journal of Management & Information Systems (IJMIS), 18(1), 15–26. https://doi.org/10.19030/IJMIS.V18I1.8335 Mikkelsen, K., Stojanovska, L., Polenakovic, M., Bosevski, M., & Apostolopoulos, V. (2017). Exercise and mental health. Maturitas, 106, 48–56. https://doi.org/10.1016/J.MATURITAS.2017.09.003 Miller, W. R., & Rollnick, S. (2013). Motivational interviewing: Helping people change (3rd ed.). Guilford Press. Mohanty, A., Kabi, A., & Mohanty, A. P. (2019). Health problems in healthcare workers: A review. Journal of Family Medicine and Primary Care, 8(8), 2568–2572. https://doi.org/10.4103/JFMPC.JFMPC_431_19 Mönninghoff, A., Kramer, J. N., Hess, A. J., Ismailova, K., Teepe, G. W., Car, L. T., Müller-Riemenschneider, F., & Kowatsch, T. (2021). Long-term effectiveness of mHealth physical 96 activity interventions: Systematic review and meta-analysis of randomized controlled trials. J Med Internet Res, 23(4), e26699. https://doi.org/10.2196/26699 Mrklas, K., Shalaby, R., Hrabok, M., Gusnowski, A., Vuong, W., Surood, S., Urichuk, L., Li, D., Li, X. M., Greenshaw, A. J., & Agyapong, V. I. O. (2020). Prevalence of perceived stress, anxiety, depression, and obsessive-compulsive symptoms in health care workers and other workers in Alberta during the COVID-19 Pandemic: Cross-sectional survey. JMIR Mental Health,7(9), E22408. https://doi.org/10.2196/22408 Oettingen, G. (2012). Future thought and behaviour change. European Review of Social Psychology, 23(1), 1-63. https://doi.org/10.1080/10463283.2011.643698 Patrick, K., Raab, F., Adams, M., Dillon, L., Zabinski, M., Rock, C., Griswold, W., & Norman, G. (2009). A text message-based intervention for weight loss: Randomized controlled trial. Journal of Medical Internet Research, 11(1), e1100. https://doi.org/10.2196/jmir.1100 Pearce, M., Garcia, L., Abbas, A., Strain, T., Schuch, F. B., Golubic, R., Kelly, P., Khan, S., Utukuri, M., Laird, Y., Mok, A., Smith, A., Tainio, M., Brage, S., & Woodcock, J. (2022). Association between physical activity and risk of depression: A systematic review and meta-analysis. JAMA psychiatry, 79(6), 550-559. https://doi.org/10.1001/jamapsychiatry.2022.0609 Peters, S. E., Dennerlein, J. T., Wagner, G. R., & Sorensen, G. (2022). Work and worker health in the post-pandemic world: a public health perspective. The Lancet Public Health, 7(2), e188–e194. https://doi.org/10.1016/S2468-2667(21)00259-0 Petrillo, G., Capone, V., Caso, D., & Keyes, C. L. M. (2015). The Mental Health Continuum–Short Form (MHC–SF) as a measure of well-being in the Italian context. Social Indicators Research, 121(1), 291–312. https://doi.org/10.1007/S11205-014-0629-3/METRICS 97 Plough, A. L., & Ahchraya, K. (2020). The imperative for well-being in an inequitable world. In A. L. Plough (Ed.), Well-being: Expanding the definition of progress: Insights from practitioners, researchers, and innovators from around the globe (Vol. 3, pp. xi–xviii). Oxford University Press. Puterman, E., Hives, B., Mazara, N., Grishin, N., Webster, J., Hutton, S., Koehle, M. S., Liu, Y., & Beauchamp, M. R. (2022). COVID-19 Pandemic and Exercise (COPE) trial: A multigroup pragmatic randomised controlled trial examining effects of app-based at-home exercise programs on depressive symptoms. British Journal of Sports Medicine, 56(10), 546–552. https://doi.org/10.1136/BJSPORTS-2021-104379 Puterman, E., Weiss, J., Lin, J., Schilf, S., Slusher, A. L., Johansen, K. L., & Epel, E. S. (2018). Aerobic exercise lengthens telomeres and reduces stress in family caregivers: A randomized controlled trial - Curt Richter Award Paper 2018. Psychoneuroendocrinology, 98, 245–252. https://doi.org/10.1016/J.PSYNEUEN.2018.08.002 Raudenbush, S.W., Spybrook, J., Bloom, H., Congdon, R., Hill, C., & Martínez, A. (2011). Optimal Design Software for Multi-level and Longitudinal Research (Version 3.01). Ravindran, A. V., Balneaves, L. G., Faulkner, G., Ortiz, A., Mcintosh, D., Morehouse, R. L., Ravindran, L., Yatham, L. N., Kennedy, S. H., Lam, R. W., Macqueen, G. M., Milev, R. v, & Parikh, S. V. (2016). Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 clinical guidelines for the management of adults with Major Depressive Disorder: Section 5. Complementary and alternative medicine treatments. The Canadian Journal of Psychiatry, 61(9), 576–587. https://doi.org/10.1177/0706743716660290 98 Reiner, M., Niermann, C., Jekauc, D., & Woll, A. (2013). Long-term health benefits of physical activity - A systematic review of longitudinal studies. BMC Public Health, 13(1), 1–9. https://doi.org/10.1186/1471-2458-13-813/FIGURES/1 Rhodes, R. E., & Kates, A. (2015). Can the affective response to exercise predict future motives and physical activity behavior? A systematic review of published evidence. Annals of Behavioral Medicine, 49(5), 715-731. https://doi.org/10.1007/s12160-015-9704-5 Richardson, S., Shaffer, J. A., Falzon, L., Krupka, D., Davidson, K. W., & Edmondson, D. (2012). Meta-analysis of perceived stress and its association with incident coronary heart disease. The American Journal of Cardiology, 110(12), 1711–1716. https://doi.org/10.1016/J.AMJCARD.2012.08.004 Ruissen, G. R., Rhodes, R. E., Crocker, P. R. E., & Beauchamp, M. R. (2018). Effects of an affective mental contrasting intervention on physical activity: A randomized controlled trial. Health Psychology, 37(1), 51-60. https://doi.org/10.1037/hea0000551 Rushton, C. H., Batcheller, J., Schroeder, K., & Donohue, P. (2015). Burnout and resilience among nurses practicing in high-intensity settings. American Journal of Critical Care, 24(5), 412–421. https://doi.org/10.4037/ajcc2015291 Ryff, C. D., Boylan, J. M., & Kirsch, J. A. (2020). Disagreement about recommendations for measurement of well-being. Preventive Medicine, 139, 106049. https://doi.org/10.1016/j.ypmed.2020.106049 Şahin, S., Adegbite, W. M., & Tiryaki Şen, H. (2021). How do family supportive supervisors affect nurses’ thriving: A research before and during COVID-19 pandemic. Archives of Psychiatric Nursing, 35(6), 602–609. https://doi.org/10.1016/J.APNU.2021.08.008 99 Schulte, P. A., Guerin, R. J., Schill, A. L., Bhattacharya, A., Cunningham, T. R., Pandalai, S. P., Eggerth, D., & Stephenson, C. M. (2015). Considerations for incorporating “well-being” in public policy for workers and workplaces. American Journal of Public Health, 105(8), e31. https://doi.org/10.2105/AJPH.2015.302616 Shields, M., & Wilkins, K. (2005). National survey of the work and health of nurses. Canadian Institute for Health Information. https://secure.cihi.ca/free_products/NHSRep06_ENG.pdf Shields, M., & Wilkins, M. (2006). Findings from the 2005 National Survey of the Work and Health of Nurses. Health Canada. www.hc-sc.gc.ca Smith, B. W., Dalen, J., Wiggins, K., Tooley, E., Christopher, P., & Bernard, J. (2008). The Brief Resilience Scale: Assessing the ability to bounce back. International Journal of Behavioral Medicine, 15, 194–200. https://doi.org/https://doi.org/10.1080/10705500802222972 Smith, J., Fisher, G., Ryan, L., Clarke, P., House, J., Weir, D., & The HRS Psychosocial Working Group. (2013). Psychosocial and Lifestyle Questionnaire 2006-2010: Documentation report core section LB. Health and Retirement Study. https://hrsonline.isr.umich.edu/sitedocs/userg/HRS2006-2010SAQdoc.pdf Statistics Canada. (2003). Questionnaire(s) and Reporting guide(s) - Canadian Community Health Survey (CCHS) - Questionnaire for Cycle 2.1 - January 2003 - November 2003. https://www23.statcan.gc.ca/imdb/p3Instr.pl?Function=getInstrumentList&Item_Id=33184&UL=1V& Statistics Canada. (2017a). The Daily — Canadian Health Measures Survey: Activity monitor data. https://www150.statcan.gc.ca/n1/daily-quotidien/170419/dq170419e-eng.htm Statistics Canada. (2017b). Work-Related Stress. 100 Statistics Canada. (2021a). Mental health among health care workers in Canada during the COVID-19 pandemic. https://www150.statcan.gc.ca/n1/en/daily-quotidien/210202/dq210202a-eng.pdf?st=ta66qKpY Statistics Canada. (2021b). Smartphone use and smartphone habits by gender and age group, inactive. https://doi.org/https://doi.org/10.25318/2210011501-eng Statistics Canada. (2022). Canadian Community Health Survey (CCHS) - annual component - 2022. https://www.statcan.gc.ca/en/survey/household/3226 Stevens, C. J., Baldwin, A. S., Bryan, A. D., Conner, M., Rhodes, R. E., & Williams, D. M. (2020). Affective determinants of physical activity: A conceptual framework and narrative review. Frontiers in Psychology, 11, 1-19. https://doi.org/10.3389/fpsyg.2020.568331 Stults-Kolehmainen, M. A., & Sinha, R. (2014). The effects of stress on physical activity and exercise. Sports Medicine, 44(1), 81. https://doi.org/10.1007/S40279-013-0090-5 Teychenne, M., White, R. L., Richards, J., Schuch, F. B., Rosenbaum, S., & Bennie, J. A. (2020). Do we need physical activity guidelines for mental health: What does the evidence tell us? Mental Health and Physical Activity, 18, 100315. https://doi.org/10.1016/J.MHPA.2019.100315 Theocharis, A. (2023). Somatic symptoms associated with mental distress during the COVID-19 pandemic: a systematic review. Australasian Psychiatry, 31(2), 147–156. https://doi.org/10.1177/10398562231156380 Tomo, A., & de Simone, S. (2017). Exploring factors that affect the well-being of healthcare workers. International Journal of Business and Management, 12(6). https://doi.org/10.5539/ijbm.v12n6p49 Tudor, K. (1996). Mental Health Promotion: Paradigms and Practice. Routledge. https://books.google.ca/books?hl=en&lr=&id=aAeNF_qx4j8C&oi=fnd&pg=PR7&dq=keith+tud 101 or+1996+dual+continuum&ots=A0EKIb6t3L&sig=2pYFLZ8VJqjVZl2ny-lTo-9nDmU#v=onepage&q=keith%20tudor%201996%20dual%20continuum&f=false Vanderweele, T. J. (2017). On the promotion of human flourishing. PNAS, 114(31), 8148–8156. https://doi.org/10.1073/pnas.1702996114 Vanderweele, T. J., Chen, Y., Long, K., Kim, E. S., Trudel-Fitzgerald, C., & Kubzansky, L. D. (2020a). Positive Epidemiology? Epidemiology, 31(2), 189–193. https://doi.org/10.1097/EDE.0000000000001147 VanderWeele, T. J., & Lomas, T. (2022). Terminology and the Well-being Literature. Affective Science, 4(1), 36–40. https://doi.org/10.1007/S42761-022-00153-2 VanderWeele, T. J., Long, K. N. G., & Balboni, M. J. (2021). Tradition-specific measures of spiritual well-being. In M. T. Lee & L. D. Kubzansky (Eds.), Measuring well-being: Interdisciplinary perspectives from the social sciences and the humanities (pp. 482-398). Oxford University Press. https://doi.org/0.1093/oso/9780197512531.003.0017 VanderWeele, T. J., Trudel-Fitzgerald, C., Allin, P., Farrelly, C., Fletcher, G., Frederick, D. E., Hall, J., Helliwell, J. F., Kim, E. S., Lauinger, W. A., Lee, M. T., Lyubomirsky, S., Margolis, S., McNeely, E., Messer, N., Tay, L., Viswanath, V., Węziak-Białowolska, D., & Kubzansky, L. D. (2020b). Current recommendations on the selection of measures for well-being. Preventive Medicine, 133, 106004. https://doi.org/10.1016/J.YPMED.2020.106004 Warburton, D. E., & Bredin, S. S. (2016). Reflections on physical activity and health: What should we recommend? The Canadian Journal of Cardiology, 32(4), 495–504. https://doi.org/10.1016/j.cjca.2016.01.024 102 Warburton, D. E. R., Jamnik, V. K., Bredin, S. S. D., & Gledhill, N. (2014). The ePARmed-X+ Physician Clearance Follow-up. Health & Fitness Journal of Canada, 7(2), 35-38. https://doi.org/10.14288/hfjc.v7i2.183 Wethington, E. (2000). Contagion of stress. Advances in Group Processes, 17, 229–253. https://doi.org/10.1016/S0882-6145(00)17010-9/FULL/XML Wilkins, K. (2007). Work stress among healthcare providers. Health Reports, 18(4), 33–36. Wilson, R. S., Bienias, J. L., Mendes de Leon, C. F., Evans, D. A., & Bennett, D. A. (2003). Negative affect and mortality in older persons. American Journal of Epidemiology, 158(9), 827-835. World Health Organization. (1946). Constitution of the World Health Organization: Principles. https://www.who.int/about/governance/constitution World Health Organization. (2011). mHealth: New horizons for health through mobile technologies: Second global survey on eHealth. World Health Organization. (2022). Physical activity - Key facts. https://www.who.int/news-room/fact-sheets/detail/physical-activity. Yates, S. W. (2020). Physician stress and burnout. The American journal of medicine, 133(2), 160-164. https://doi.org/10.1016/j.amjmed.2019.08.034 Yeung, O., & Johnston, K. (2020). Defining the mental wellness economy. Global Wellness Institute. https://globalwellnessinstitute.org/industry-research/defining-the-mental-wellness-economy-2020/ Zhang, Y., Yang, M., & Wang, R. (2021). Factors associated with work–family enrichment among Chinese nurses assisting Wuhan’s fight against the 2019 COVID-19 pandemic. Journal of Clinical Nursing, 1–12. 103 Appendices Appendix A Dependent Variable Scales Physical Health Symptoms Item Text: In the past two weeks, have you experienced any of the following symptoms: 1. None 2. Diarrhea/constipation / other stomach problems 3. Headache 4. Backache 5. Muscle soreness 6. Fatigue 7. Joint pain 8. Muscle weakness 9. Cough 10. Sore throat 11. Fever 12. Chills 13. Other cold and flu symptoms 14. Nausea Response Options: A check box was provided for each option in which participants could select it or leave it blank. Scoring: Each symptom was binary coded as an individual variable in which 1 represents the presence of the symptom and 0 represented the absence. 104 Recent Global Stress Item Text: Thinking about the amount of stress in your life for the past two weeks, would you say that most days are: Response Options: Not at all stressful, Not very stressful, A bit stressful, Quite a bit stressful, and Extremely stressful Scoring: 1 = Not at all stressful and 5 = Extremely stressful 105 Overall Physical Health Item Text: For the past two weeks, would you say your PHYSICAL HEALTH is excellent, very good, good, fair, or poor? Response Options: Excellent, Very Good, Good, Fair, and Poor Scoring: 1 = Poor and 5 = Excellent 106 Overall Mental/ Emotional Health Item Text: For the past two weeks, would you say your MENTAL OR EMOTIONAL HEALTH is excellent, very good, good, fair, or poor? Response Options: Excellent, Very Good, Good, Fair, and Poor Scoring: 1 = Poor and 5 = Excellent 107 Life Satisfaction Item Text: Below are five statements that you may agree or disagree with. Using the 1-7 scale below, indicate your agreement for the past two weeks with each item. Please be open and honest in your responding. 1. In most ways my life is close to my ideal 2. The conditions of my life are excellent 3. I am satisfied with my life 4. So far I have gotten the important things I want in life 5. If I could live my life over, I would change almost nothing Response Options: Strongly agree, Agree, Slightly agree, Neither agree nor disagree, Slightly disagree, Disagree, Strongly disagree Scoring: Each item is scored with 1 = Strongly disagree and 7 = Strongly agree. Scale is scored with the sum score of all items. 108 Psychological Flourishing Item Text: Below are 8 statements with which you may agree or disagree about how you have been feeling during your past two weeks. Using the 1–7 scale below, indicate your agreement with each item by indicating that response for each statement. 1. I lead a purposeful and meaningful life. 2. My social relationships are supportive and rewarding 3. I am engaged and interested in my daily activities 4. I actively contribute to the happiness and well-being of others 5. I am competent and capable in the activities that are important to me 6. I am a good person and live a good life 7. I am optimistic about my future 8. People respect me Response Options: Strongly disagree, Somewhat disagree, A little disagree, Neither agree nor disagree, A little agree, Somewhat agree, and Strongly agree Scoring: Each item is scored with 1 = Strongly disagree and 7 = Strongly agree. Scale is scored with the sum score of all items. 109 Resilience Item Text: Please respond to each item by marking one box per row. Please respond for the past two weeks. 1. I tend to bounce back quickly after hard times 2. I have a hard time making it through stressful events* 3. It does not take me long to recover from a stressful event 4. It is hard for me to snap back when something bad happens* 5. I usually come through difficult times with little trouble 6. I tend to take a long time to get over set-backs in my life* Response Options: Strongly Disagree, Disagree, Neutral, Agree, Strongly Agree Scoring: Each item is scored with 1 = Strongly Disagree and 5 = Strongly Agree. Scale is scored with the sum score of all items. Note: items 2, 4, and 6 are reverse-coded. 110 Work-to-Personal Life Spillover Item Text: Please use the scale below to answer the next set of questions, thinking about the past 2 weeks. 1. My work schedule makes it difficult to fulfill personal responsibilities. 2. Because of my job, I don’t have the energy to do things with my family or other important people in my life. 3. Job worries or problems distract me when I am not at work 4. My work leaves me enough time to attend to my personal responsibilities. 5. My work gives me energy to do things with my family and other important people in my life. 6. Because of my job, I am in a better mood at home. Response Options: Rarely, Sometimes, Often, Most of the time Scoring: Each item is scored with 1 = Rarely and 4 = Most of the time. Subscale: Work-to-personal life enhancement: Sum of score for items 4, 5, 6; Work-to-personal life interference: Sum of score for items 1, 2, 3. 111 Appendix B Supplemental Tables Table S1 Intention-to-Treat Biweekly Effect Sizes, Covarying the Effects of Age Estimate [95% CI] Week 2 Week 4 Week 6 Week 8 Week 10 Week 12 Physical Health Symptoms -0.05* [-0.10, -0.00] -0.10* [-0.20, -0.01] -0.15* [-0.29, -0.01] -0.20* [-0.38, -0.01] -0.24* [-0.47, -0.01] -0.29* [-0.57, -0.02] Recent Global Stress -0.09* [-0.14, -0.04] -0.17* [-0.26, -0.07] -0.24* [-0.38, -0.10] -0.32* [-0.51, -0.14] -0.41* [-0.64, -0.17] -0.48* [-0.76, -0.20] Work-to-Personal Life Interference -0.08 [-0.17, 0.01] -0.15 [-0.30, 0.01] -0.20* [-0.40, -0.01] -0.25* [-0.46, -0.05] -0.29* [-0.51, -0.08] -0.32* [-0.55, -0.09] Overall Physical Health 0.08* [0.04, 0.13] 0.17* [0.07, 0.26] 0.25* [0.11, 0.38] 0.32* [0.14, 0.51] 0.40* [0.18, 0.63] 0.49* [0.21, 0.76] Overall Mental/ Emotional Health 0.21* [0.09, 0.33] 0.38* [0.18, 0.58] 0.50* [0.25, 0.74] 0.57* [0.32, 0.82] 0.60* [0.34, 0.85] 0.58* [0.29, 0.88] Life Satisfaction 0.12* [0.03, 0.21] 0.21* [0.06, 0.36] 0.29* [0.10, 0.47] 0.35* [0.15, 0.54] 0.39* [0.19, 0.58] 0.40* [0.20, 0.61] Psychological Flourishing 0.05* [0.01, 0.09] 0.10* [0.02, 0.18] 0.15* [0.03, 0.28] 0.20* [0.03, 0.37] 0.25* [0.04, 0.46] 0.30* [0.05, 0.55] Resilience 0.06 [-0.03, 0.16] 0.13 [-0.06, 0.26] 0.20 [-0.04, 0.36] 0.27* [0.01, 0.44] 0.34* [0.07, 0.52] 0.42* [0.13, 0.62] Work-to-Personal Life Enhancement 0.06* [0.01, 0.10] 0.12* [0.03, 0.21] 0.18* [0.04, 0.31] 0.24* [0.05, 0.42] 0.29* [0.07, 0.52] 0.35* [0.08, 0.62] Note. Biweekly Feingold’s effects sizes for changes in each outcome for those in the exercise as compared to waitlist control. * 95% confidence interval does not cross 0.00. 112 Table S2 Intention-to-Treat Biweekly Effect Sizes, Covarying the Effects of Income Estimate [95% CI] Week 2 Week 4 Week 6 Week 8 Week 10 Week 12 Physical Health Symptoms -0.04* [-0.10, -0.00] -0.09* [-0.19, -0.00] -0.14 [-0.28, 0.00] -0.19 [-0.37, 0.00] -0.23* [-0.46, -0.00] -0.28* [-0.55, -0.00] Recent Global Stress -0.08* [-0.13, -0.03] -0.16* [-0.26, -0.06] -0.23* [-0.37, -0.09] -0.31* [-0.49, -0.13] -0.39* [-0.62, -0.16] -0.46* [-0.74, -0.19] Work-to-Personal Life Interference -0.07 [-0.16, 0.02] -0.13 [-0.28, 0.02] -0.19* [-0.37, -0.00] -0.23* [-0.43, -0.03] -0.27* [-0.47, -0.06] -0.29* [-0.52, -0.07] Overall Physical Health 0.08* [0.03, 0.12] 0.15* [0.06, 0.24] 0.22* [0.09, 0.36] 0.30* [0.12, 0.48] 0.37* [0.14, 0.59] 0.44* [0.17, 0.71] Overall Mental/ Emotional Health 0.09* [0.04, 0.14] 0.18* [0.09, 0.28] 0.27* [0.13, 0.41] 0.36* [0.18, 0.55] 0.46* [0.22, 0.69] 0.55* [0.27, 0.83] Life Satisfaction 0.12*[0.02, 0.21] 0.21* [0.05, 0.36] 0.28* [0.09, 0.47] 0.33* [0.13, 0.53] 0.37* [0.17, 0.56] 0.37* [0.17, 0.58] Psychological Flourishing 0.04* [0.00, 0.09] 0.09* [0.00, 0.17] 0.13* [0.00, 0.26] 0.17* [0.01, 0.34] 0.21* [0.01, 0.42] 0.26* [0.01, 0.50] Resilience 0.04 [-0.05, 0.14] 0.10 [-0.06, 0.26] 0.16 [-0.04, 0.36] 0.22* [0.01, 0.44] 0.30* [0.07, 0.52] 0.38* [0.13, 0.62] Work-to-Personal Life Enhancement 0.05* [0.01, 0.10] 0.10* [0.01, 0.19] 0.16* [0.02, 0.29] 0.21* [0.03, 0.39] 0.26* [0.04, 0.48] 0.31* [0.04, 0.58] Note. Biweekly Feingold’s effects sizes for changes in each outcome for those in the exercise as compared to waitlist control. * 95% confidence interval does not cross 0.00. 113 Table S3 Intention-to-Treat Effects and Within Condition Changes Over Time Physical Health Symptoms Waitlist Control Condition Exercise Condition Est SD CI Est SD CI I 3.02* 0.15 2.72, 3.32 3.01* 0.16 2.70, 3.33 S -0.06 0.06 -0.17, 0.05 0.01 0.06 -0.01, 0.21 Q 0.00 0.00 -0.01, 0.01 -0.01 0.00 -0.02, 0.00 Treatment Effect for Trajectories Over Time Est SD CI I*Condition 0.00 0.22 -0.43, 0.43 S*Condition -0.16* 0.08 -0.31, -0.01 Q*Condition 0.01 0.01 0.00, 0.02 Recent Global Stress Waitlist Control Condition Exercise Condition Est SD CI Est SD CI I 3.43* 0.05 3.33, 3.53 3.34* 0.06 3.24, 3.45 S -0.05* 0.01 -0.07, -0.04 -0.02* 0.01 -0.03, -0.01 Q NA NA NA NA NA NA Treatment Effect for Trajectories Over Time Est SD CI I*Condition 0.09 0.07 -0.06, 0.23 S*Condition -0.03* 0.01 -0.05, -0.01 Q*Condition NA NA NA Work-to-Personal Life Interference Waitlist Control Condition Exercise Condition Est SD CI Est SD CI I 7.25* 0.17 6.91, 7.59 7.01* 0.19 6.63, 7.39 S -0.21* 0.05 -0.30, -0.12 -0.11* 0.04 -0.18, -0.04 Q 0.01* 0.00 0.00, 0.02 0.01* 0.00 0.00, 0.01 Treatment Effect for Trajectories Over Time Est SD CI I*Condition 0.24 0.26 -0.27, 0.76 S*Condition -0.10 0.06 -0.21, 0.02 Q*Condition 0.00 0.00 -0.01, 0.01 Note. Nwaitlist = 146. Nexercise = 142. I = intercept. S = slope. Q = quadratic. Est = estimate. SD = standard deviation. CI = 95% confidence interval. * p ≤ 0.05. 114 Table S3 [Continued] Intention-to-Treat Effects and Within Condition Changes Over Time Overall Physical Health Waitlist Control Condition Exercise Condition Est SD CI Est SD CI I 2.62* 0.06 2.49, 2.75 2.58* 0.06 2.46, 2.70 S 0.05* 0.01 0.03, 0.06 0.01* 0.01 0.00, 0.02 Q NA NA NA NA NA NA Treatment Effect for Trajectories Over Time Est SD CI I*Condition 0.04 0.09 -0.13, 0.21 S*Condition 0.04* 0.01 0.02, 0.06 Q*Condition NA NA NA Overall Mental/Emotional Health Waitlist Control Condition Exercise Condition Est SD CI Est SD CI I 2.47* 0.06 2.35, 2.6 2.62* 0.07 2.48, 2.75 S 0.14* 0.02 0.09, 0.18 0.03 0.02 -0.01, 0.07 Q -0.01* 0.00 -0.01, 0.00 0.00 0.00 0.00, 0.00 Treatment Effect for Trajectories Over Time Est SD CI I*Condition -0.14 0.09 -0.33, 0.04 S*Condition 0.11* 0.03 0.04, 0.17 Q*Condition -0.01* 0.00 -0.01, 0.00 Life Satisfaction Waitlist Control Condition Exercise Condition Est SD CI Est SD CI I 21.4* 0.51 20.4, 22.4 22.6* 0.49 21.6, 23.6 S 0.60* 0.13 0.35, 0.85 0.20* 0.10 0.00, 0.40 Q -0.03* 0.01 -0.05, -0.01 -0.01 0.01 -0.03, 0.00 Treatment Effect for Trajectories Over Time Est SD CI I*Condition -1.25 0.71 -2.64, 0.14 S*Condition 0.40* 0.16 0.08, 0.72 Q*Condition -0.02 0.01 -0.04, 0.01 Note. Nwaitlist = 146. Nexercise = 142. I = intercept. S = slope. Q = quadratic. Est = estimate. SD = standard deviation. CI = 95% confidence interval. * p ≤ 0.05. 115 Table S3 [Continued] Intention-to-Treat Effects and Within Condition Changes Over Time Psychological Flourishing Waitlist Control Condition (N = 146) Exercise Condition (N = 142) Est SD CI Est SD CI I 44.8* 0.59 43.7, 46.0 45.3* 0.63 44.1, 46.6 S 0.08 0.16 -0.24, 0.40 -0.40* 0.16 -0.71, -0.09 Q 0.00 0.01 -0.03, 0.02 0.02 0.01 0.00, 0.04 Treatment Effect for Trajectories Over Time Est SD CI I*Condition -0.50 0.86 -2.18, 1.18 S*Condition 0.47* 0.22 0.04, 0.90 Q*Condition -0.02 0.02 -0.06, 0.01 Resilience Est SD CI Est SD CI I 19.40* 0.34 18.7, 20.0 19.70* 0.35 19.0, 20.4 S 0.29* 0.09 0.11, 0.47 0.17* 0.07 0.03, 0.31 Q -0.01 0.01 -0.03, 0.00 -0.01* 0.01 -0.02, 0.00 Treatment Effect for Trajectories Over Time Est SD CI I*Condition -0.30 0.49 -1.26, 0.67 S*Condition 0.12 0.12 -0.10, 0.35 Q*Condition 0.00 0.01 -0.02, 0.02 Work-to-Personal Life Enhancement Est SD CI Est SD CI I 5.75* 0.14 5.49, 6.02 6.06* 0.15 5.76, 6.36 S 0.07* 0.02 0.03, 0.10 0.01 0.01 -0.02, 0.03 Q NA NA NA NA NA NA Treatment Effect for Trajectories Over Time Est SD CI I*Condition -0.30 0.21 -0.71, 0.10 S*Condition 0.06* 0.02 0.02, 0.10 Q*Condition NA NA NA Note. Nwaitlist = 146. Nexercise = 142. I = intercept. S = slope. Q = quadratic. Est = estimate. SD = standard deviation. CI = 95% confidence interval. * p ≤ 0.05. 116 Table S4 Intention-to-Treat Biweekly Effect Sizes, Conservative p-value Estimate [95% CI] Week 2 Week 4 Week 6 Week 8 Week 10 Week 12 Physical Health Symptoms -0.15 [-0.32, 0.03] -0.25 [-0.54, 0.03] -0.31 [-0.65, 0.02] -0.34* [-0.69, -0.00] -0.33* [-0.66, -0.00] -0.29 [-0.65, 0.07] Recent Global Stress -0.09* [-0.15, -0.02] -0.17* [-0.29, -0.04] -0.24* [-0.43, -0.06] -0.32* [-0.57, -0.08] -0.41* [-0.72, -0.10] -0.49* [-0.85, -0.12] Work-to-Personal Life Interference -0.08 [-0.19, 0.04] -0.15 [-0.34, 0.05] -0.21 [-0.45, 0.04] -0.25 [-0.52, 0.01] -0.29* [-0.57, -0.02] -0.32* [-0.61, -0.02] Overall Physical Health 0.08* [0.02, 0.15] 0.17* [0.05, 0.29] 0.25* [0.07, 0.43] 0.33* [0.09, 0.57] 0.41* [0.12, 0.71] 0.50* [0.14, 0.86] Overall Mental/ Emotional Health 0.22* [0.06, 0.38] 0.39* [0.13, 0.66] 0.51* [0.20, 0.83] 0.59* [0.26, 0.92] 0.61* [0.28, 0.94] 0.59* [0.21, 0.97] Life Satisfaction 0.12* [0.00, 0.24] 0.22* [0.02, 0.42] 0.30* [0.06, 0.54] 0.36* [0.10, 0.61] 0.39* [0.14, 0.65] 0.40* [0.13, 0.67] Psychological Flourishing 0.11 [-0.02, 0.25] 0.20 [-0.02, 0.41] 0.26 [-0.01, 0.53] 0.30* [0.01, 0.58] 0.31* [0.01, 0.60] 0.29 [-0.04, 0.62] Resilience 0.06 [-0.06, 0.18] 0.13 [-0.08, 0.33] 0.19 [-0.06, 0.45] 0.26 [-0.02, 0.54] 0.34* [0.05, 0.63] 0.42* [0.10, 0.74] Work-to-Personal Life Enhancement 0.06* [0.00, 0.12] 0.12* [0.00, 0.24] 0.18* [0.00, 0.36] 0.24* [0.01, 0.48] 0.30* [0.01, 0.59] 0.36* [0.01, 0.71] Note. Biweekly Feingold’s effects sizes for changes in each outcome for those in the exercise as compared to waitlist control. * 99% confidence interval does not cross 0.00. 117 Table S5 Per-Protocol Biweekly Effect Sizes, Conservative p-value Physical Health Symptoms Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 -0.22 [-0.50, 0.06] -0.13 [-0.33, 0.06] -0.04 [-0.40, 0.33] Week 4 -0.37 [-0.84, 0.09] -0.23 [-0.55, 0.08] -0.08 [-0.69, 0.52] Week 6 -0.44 [-0.99, 0.11] -0.29 [-0.66, 0.07] -0.14 [-0.85, 0.56] Week 8 -0.45 [-1.02, 0.13] -0.33 [-0.70, 0.05] -0.21 [-0.93, 0.50] Week 10 -0.38 [-0.91, 0.16] -0.32 [-0.69, 0.04] -0.29 [-0.96, 0.37] Week 12 -0.25 [-0.77, 0.28] -0.29 [-0.72, 0.13] -0.39 [-1.08, 0.30] Recent Global Stress Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 -0.15* [-0.26, -0.04] -0.07* [-0.15, -0.00] -0.01 [-0.14, 0.12] Week 4 -0.28* [-0.50, -0.07] -0.14* [-0.28, -0.01] -0.02 [-0.28, 0.24] Week 6 -0.42* [-0.73, -0.10] -0.21* [-0.41, -0.01] -0.03 [-0.41, 0.34] Week 8 -0.55* [-0.97, -0.14] -0.28* [-0.55, -0.01] -0.04 [-0.54, 0.46] Week 10 -0.69* [-1.22, -0.17] -0.35* [-0.69, -0.02] -0.05 [-0.68, 0.58] Week 12 -0.83* [-1.45, -0.21] -0.42* [-0.82, -0.02] -0.06 [-0.81, 0.69] Work-to-Personal Life Interference Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 -0.19 [-0.38, 0.00] -0.08 [-0.21, 0.05] 0.12 [-0.11, 0.36] Week 4 -0.34* [-0.66, -0.02] -0.15 [-0.37, 0.08] 0.19 [-0.21, 0.59] Week 6 -0.46* [-0.86, -0.06] -0.20 [-0.48, 0.08] 0.20 [-0.30, 0.71] Week 8 -0.54* [-0.97, -0.11] -0.23 [-0.53, 0.07] 0.15 [-0.42, 0.72] Week 10 -0.58* [-1.01, -0.15] -0.25 [-0.56, 0.06] 0.04 [-0.59, 0.67] Week 12 -0.58* [-1.03, -0.14] -0.25 [-0.58, 0.08] -0.13 [-0.87, 0.61] Note. Biweekly Feingold’s effects sizes for changes in each outcome for per-protocol engagement profile (80+ average weekly minutes of app use; n = 30), low engagement profile (20-79 minutes; n= 73), and very low engagement profile (<20 minutes; n = 37) compared to waitlist control condition (n = 146). SE = standard error. * 99% confidence interval does not cross 0.00. 118 Table S5 [Continued] Per-Protocol Biweekly Effect Sizes, Conservative p-value Overall Physical Health Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 0.12* [0.04, 0.20] 0.07 [-0.01, 0.14] 0.06 [-0.10, 0.21] Week 4 0.24* [0.07, 0.40] 0.13 [-0.02, 0.28] 0.12 [-0.20, 0.43] Week 6 0.35* [0.11, 0.59] 0.20 [-0.03, 0.42] 0.17 [-0.29, 0.63] Week 8 0.47* [0.15, 0.79] 0.26 [-0.04, 0.56] 0.23 [-0.39, 0.84] Week 10 0.58* [0.18, 0.97] 0.32 [-0.05, 0.69] 0.28 [-0.48, 1.04] Week 12 0.70* [0.22, 1.18] 0.39 [-0.06, 0.83] 0.34 [-0.58, 1.25] Overall Mental/Emotional Health Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 NA NA NA Week 4 NA NA NA Week 6 NA NA NA Week 8 NA NA NA Week 10 NA NA NA Week 12 NA NA NA Life Satisfaction Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 0.18* [0.01, 0.34] 0.12 [-0.03, 0.27] 0.05 [-0.21, 0.31] Week 4 0.32* [0.03, 0.62] 0.21 [-0.04, 0.46] 0.11 [-0.33, 0.55] Week 6 0.44* [0.07, 0.81] 0.27 [-0.02, 0.57] 0.18 [-0.37, 0.72] Week 8 0.54* [0.13, 0.95] 0.31 [-0.00, 0.62] 0.26 [-0.33, 0.84] Week 10 0.61* [0.17, 1.04] 0.32* [0.01, 0.62] 0.34 [-0.24, 0.92] Week 12 0.64* [0.18, 1.10] 0.29 [-0.02, 0.61] 0.44 [-0.16, 1.03] Note. Biweekly Feingold’s effects sizes for changes in each outcome for per-protocol engagement profile (80+ average weekly minutes of app use; n = 30), low engagement profile (20-79 minutes; n= 73), and very low engagement profile (<20 minutes; n = 37) compared to waitlist control condition (n = 146). SE = standard error. * 99% confidence interval does not cross 0.00. Estimates for overall mental health are not available as the per protocol model did not converge. 119 Table S5 [Continued] Per-Protocol Biweekly Effect Sizes, Conservative p-value Psychological Flourishing Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 0.23* [0.04, 0.41] 0.07 [-0.09, 0.23] 0.07 [-0.20, 0.35] Week 4 0.39* [0.07, 0.70] 0.12 [-0.13, 0.37] 0.14 [-0.31, 0.58] Week 6 0.50* [0.10, 0.91] 0.16 [-0.14, 0.47] 0.19 [-0.36, 0.74] Week 8 0.56* [0.11, 1.01] 0.19 [-0.13, 0.51] 0.23 [-0.36, 0.82] Week 10 0.55* [0.06, 1.04] 0.20 [-0.13, 0.52] 0.26 [-0.36, 0.88] Week 12 0.50 [-0.04, 1.04] 0.19 [-0.19, 0.57] 0.28 [-0.44, 1.00] Resilience Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 0.17 [-0.06, 0.39] 0.05 [-0.09, 0.20] -0.08 [-0.30, 0.15] Week 4 0.31 [-0.07, 0.69] 0.11 [-0.14, 0.37] -0.10 [-0.49, 0.29] Week 6 0.42 [-0.06, 0.91] 0.17 [-0.14, 0.49] -0.06 [-0.55, 0.44] Week 8 0.51 [-0.02, 1.04] 0.23 [-0.10, 0.57] 0.04 [-0.52, 0.59] Week 10 0.57* [0.00, 1.13] 0.30 [-0.04, 064] 0.19 [-0.42, 0.80] Week 12 0.61 [-0.02, 1.23] 0.37* [0.01, 0.73] 0.41 [-0.29, 1.11] Work-to-Personal Life Enhancement Per-Protocol Low Use Very low Use Time Estimate [95% CI] Estimate [95% CI] Estimate [95% CI] Week 2 0.10 [-0.00, 0.19] 0.05 [-0.02, 0.11] 0.04 [-0.11, 0.19] Week 4 0.19 [-0.01, 0.39] 0.09 [-0.04, 0.23] 0.08 [-0.22, 0.38] Week 6 0.29 [-0.01, 0.59] 0.14 [-0.06, 0.34] 0.12 [-0.33, 0.57] Week 8 0.38 [-0.02, 0.78] 0.19 [-0.07, 0.45] 0.16 [-0.43, 0.76] Week 10 0.47 [-0.02, 0.97] 0.23 [-0.09, 0.56] 0.20 [-0.54, 0.94] Week 12 0.57 [-0.02, 1.17] 0.28 [-0.11, 0.68] 0.25 [-0.65, 1.14] Note. Biweekly Feingold’s effects sizes for changes in each outcome for per-protocol engagement profile (80+ average weekly minutes of app use; n = 30), low engagement profile (20-79 minutes; n= 73), and very low engagement profile (<20 minutes; n = 37) compared to waitlist control condition (n = 146). SE = standard error. * 99% confidence interval does not cross 0.00.
Thesis/Dissertation
eng
Kinesiology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Puterman, Eli
Covid-19 pandemic and exercise for healthcare workers (COPE HCW) trial : secondary analyses examining the effects of at-home exercise on healthcare workers' multidimensional well-being
Text