Education, Faculty of
Kinesiology, School of
DSpace
UBCV
Pascoal, Alexis
Master of Science - MSc
University of British Columbia
Physical literacy (PL) is a multidimensional construct that encompasses fundamental movement skills, confidence, motivation, and knowledge and understanding, and may be an important determinant of children’s participation in physical activity (PA) and mental health. To assess these relationships, a longitudinal cohort study design was utilized in elementary schools in West Vancouver. The purpose of this study was to assess whether PL predicted mental health outcomes in children at three time intervals over two years (2022-2024), independent of physical activity. Data were collected initially from 358 children (Grades 2–3) across 14 elementary schools. PL was measured using a composite score, which was assessed using the Sport for Life PLAYfun and PLAYself tools. MVPA was captured with 7-day accelerometry, and parent-reported mental health was assessed with the Strengths and Difficulties Questionnaire (SDQ). The primary research question examined the direct relationship between the PL Composite score and mental health, independent of PA. A second exploratory question investigated the relationship between mental health and the PLAYfun and PLAYself tools independently. Missing data were addressed through multiple imputation. Blocked multiple regression models were used to predict SDQ outcomes across all time intervals. PL Composite scores significantly predicted lower internalizing scores (B = -0.54, p = 0.05), and total difficulties (B = -0.84, p = 0.05) between T2–T3. PLAYfun scores significantly predicted lower with internalizing symptoms at both T1–T2 (B = -0.061, p = 0.015) and T2–T3 (B = -0.11, p = 0.009), externalizing behaviour from T2–T3 (B = -0.098, p = 0.043), and total difficulties from T1–T2 (B = -0.087, p = 0.037), and T2–T3 (B = -0.21, p = 0.017). Gender was only a significant predictor of internalizing behaviour at T1-T2 (B = -0.52, p = 0.02). Overall, there was a weak but significant relationship between PL and mental health outcomes, independent of MVPA. The exploratory findings suggest a stronger relationship between fundamental movement skills and mental health. These findings may suggest that fostering physical competence and skill development in schools may support children’s mental health.
PHYSICAL LITERACY AS A PREDICTOR OF CHILD MENTAL HEALTH: A LONGITUDINAL ANALYSIS by Alexis Pascoal B.Sc., Queen’s University, 2022 B.Ed., Queen’s University, 2023 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) October 2025 © Alexis Pascoal, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Physical Literacy as a Predictor of Child Mental Health: A Longitudinal Analysis submitted by Alexis Pascoal in partial fulfilment of the requirements for the degree of Master of Science in Kinesiology Examining Committee: Dr. Guy Faulkner, Professor, Kinesiology, UBC Supervisor Dr. Mark Beauchamp, Professor, Kinesiology, UBC Supervisory Committee Member Dr. Louise Mâsse, Professor, Population and Public Health, UBC Supervisory Committee Member iii Abstract Physical literacy (PL) is a multidimensional construct that encompasses fundamental movement skills, confidence, motivation, and knowledge and understanding, and may be an important determinant of children’s participation in physical activity (PA) and mental health. To assess these relationships, a longitudinal cohort study design was utilized in elementary schools in West Vancouver. The purpose of this study was to assess whether PL predicted mental health outcomes in children at three time intervals over two years (2022-2024), independent of physical activity. Data were collected initially from 358 children (Grades 2–3) across 14 elementary schools. PL was measured using a composite score, which was assessed using the Sport for Life PLAYfun and PLAYself tools. MVPA was captured with 7-day accelerometry, and parent-reported mental health was assessed with the Strengths and Difficulties Questionnaire (SDQ). The primary research question examined the direct relationship between the PL Composite score and mental health, independent of PA. A second exploratory question investigated the relationship between mental health and the PLAYfun and PLAYself tools independently. Missing data were addressed through multiple imputation. Blocked multiple regression models were used to predict SDQ outcomes across all time intervals. PL Composite scores significantly predicted lower internalizing scores (B = -0.54, p = 0.05), and total difficulties (B = -0.84, p = 0.05) between T2–T3. PLAYfun scores significantly predicted lower with internalizing symptoms at both T1–T2 (B = -0.061, p = 0.015) and T2–T3 (B = -0.11, p = 0.009), externalizing behaviour from T2–T3 (B = -0.098, p = 0.043), and total difficulties from T1–T2 (B = -0.087, p = 0.037), and T2–T3 (B = -0.21, p = 0.017). Gender was only a significant predictor of internalizing behaviour at T1-T2 (B = -0.52, p = 0.02). iv Overall, there was a weak but significant relationship between PL and mental health outcomes, independent of MVPA. The exploratory findings suggest a stronger relationship between fundamental movement skills and mental health. These findings may suggest that fostering physical competence and skill development in schools may support children’s mental health. v Lay Summary Physical literacy (PL) is defined as the motivation, confidence, physical competence, and knowledge and understanding to value and take responsibility for physical activity (PA) engagement for life. Amid declining PA levels in children, PL has emerged to promote lifelong PA participation and positive health outcomes. This study examined whether PL could predict children’s mental health over two years, independent of PA. Children’s fundamental movement skills and self-perceptions of PA and ability were measured using the Sport for Life PLAYtools, and their daily PA was measured using accelerometers. Parents reported on children’s mental health through the Strengths and Difficulties Questionnaire. PL was found to predict certain aspects of mental health across one-year intervals, though effects were small. Fundamental movement skills were more strongly related to mental health than children’s physical self-perceptions. These findings suggest that developing fundamental movement skills may play a small but important role in supporting children’s mental health. vi Preface This thesis is original, unpublished work by the author Alexis Pascoal, and builds upon baseline findings presented by Tang et al. (2023). Alexis was responsible for all major areas of this project, including conducting data collection, analysis, and preparation of the document. As well, Alexis led the third and final wave of data collection, alongside the Population and Physical Activity Lab. Dr. Guy Faulkner led the design of this study protocol, provided guidance throughout data analysis, and approved the final written document of this thesis. Committee members Dr. Mark Beauchamp and Dr. Louise Mâsse provided guidance and feedback on the statistical methodology and written document. Graduate Research Assistants and trained students in the Population and Physical Activity Lab conducted the first two waves of the present study. This project was part of a larger initiative called Physical Literacy for Communities (PL4C), a 36-month project funded by the Public Health Agency of Canada (PHAC), delivered by Sport for Life. The project was designed to support physical literacy in 150,000 children and youth aged 2-18 across Canada. As a part of the larger PL4C initiative, this focused evaluation, known as ‘WAVES PL’, took place in collaboration with the West Vancouver School District (WVSD) and two Physical Literacy mentors, Amber Pascual and Erin Crawford. The study was approved by the University of British Columbia Behavioural Research Ethics Board (H21-03586). The use of Generative AI in this thesis was limited to the use of ChatGPT5, which was utilized to determine appropriate steps to complete statistical data analysis in SPSS ver. 28 (IBM SPSS, Chicago, IL), and to check for writing clarity and grammar. While Generative AI is a useful supplementary resource, all interpretations, ideas, and conclusions remain my own, ensuring that this work abides by academic integrity. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ................................................................................................................................. x List of Figures ............................................................................................................................... xi List of Abbreviations .................................................................................................................. xii Acknowledgements .................................................................................................................... xiii Dedication .................................................................................................................................... xv Chapter 1. Introduction................................................................................................................ 1 1.1 The Benefits of Physical Activity ......................................................................................... 1 1.2 Mental Health in Children and Youth ................................................................................... 2 1.3 A Turn to Physical Literacy .................................................................................................. 3 1.4 Literature Review: The Relationship between Physical Literacy and Mental Health .......... 4 1.5 Physical Literacy for Communities (PL4C) ......................................................................... 9 1.6 Research Question and Hypotheses .................................................................................... 10 Chapter 2. Methodology ............................................................................................................. 11 2.1 Study Design ....................................................................................................................... 11 2.2 Study Protocol Timeline ..................................................................................................... 12 2.3 Data Security ....................................................................................................................... 12 2.4 Measures and Data Collection ............................................................................................ 12 2.4.1 Child-level Measures ................................................................................................... 12 2.4.2 Parent Reported Measures ........................................................................................... 16 2.5 Data Treatment.................................................................................................................... 17 2.5.1 Age, Gender, and Consent ........................................................................................... 17 2.5.2 Accelerometry .............................................................................................................. 18 viii 2.5.3 Parent Questionnaire .................................................................................................... 18 2.5.4 Physical Literacy Composite Score ............................................................................. 19 2.6 Statistical Analysis .............................................................................................................. 19 2.6.1 Preliminary Analysis .................................................................................................... 19 2.6.2 Model Justification: Calculating the Intra-class Correlation Coefficient .................... 20 2.6.3 Missing Data ................................................................................................................ 22 2.6.4 Regression Analyses .................................................................................................... 23 Chapter 3. Results ....................................................................................................................... 25 3.1 Overview ............................................................................................................................. 25 3.1.1 Participant Characteristics ........................................................................................... 25 3.1.2 Physical Activity .......................................................................................................... 27 3.1.3 Physical Literacy .......................................................................................................... 28 3.1.4 Mental Health............................................................................................................... 29 3.1.5 Descriptive Analysis of Bivariate Correlations at Each Timepoint ............................. 30 3.2 Main Results: Physical Literacy Predicting Mental Health Over Time ............................. 33 3.2.1 Internalizing Behaviour ............................................................................................... 33 3.2.2 Externalizing Behaviour .............................................................................................. 38 3.2.3 Prosocial Behaviour ..................................................................................................... 43 3.2.4 Total Difficulties .......................................................................................................... 48 Chapter 4. Discussion ................................................................................................................. 53 4.1 Overview ............................................................................................................................. 53 4.2 Potential mechanisms explaining the Physical Literacy-Mental Health Relationship ....... 56 4.3 Differential Associations with Mental Health: Comparing Fundamental Movement Skills and Physical Self-Perceptions ................................................................................................... 57 4.4 Mental Health Over Time ................................................................................................... 62 4.5 Consideration of External Factors ...................................................................................... 62 4.6 Strengths and Limitations ................................................................................................... 64 4.7 Implications......................................................................................................................... 67 4.8 Future Directions ................................................................................................................ 69 Chapter 5. Conclusion ................................................................................................................ 71 Bibliography ................................................................................................................................ 72 Appendices ................................................................................................................................... 92 ix Appendix A. Missing Data and Participant Attrition................................................................ 92 Participant Attrition .............................................................................................................. 92 Missing data .......................................................................................................................... 92 Appendix B. Strength and Difficulties Questionnaire .............................................................. 94 Appendix C. Overview of PLAYtools (Physical Literacy Assessment for Youth) Instruments................................................................................................................................................... 95 x List of Tables Table 1. Intraclass Correlation Coefficients (ICCs) and Between-School Variance Estimates for all Outcome Variables Across Timepoints ................................................................................... 22 Table 2. Descriptive Characteristics of Participants at Each Timepoint (T1–T3) ....................... 26 Table 3. Year 1-3 Participants Physical Activity Behaviours ...................................................... 28 Table 4. Physical Literacy Scores at each Timepoint (T1-T3) .................................................... 29 Table 5. Strength and Difficulties Questionnaire (SDQ) Scores at each Timepoint (T1-T3) ...... 29 Table 6. Complete Case Spearman Correlation Matrix for all variables at T1-T3 ...................... 31 Table 7A. Regression Coefficients for Multiple Imputation Block Entry Model Using PL Composite Scores Predicting Internalizing Behaviour Over all Timepoints ................................ 34 Table 7B. Regression Coefficients for Multiple Imputation Block Entry Model Using PLAYfun and PLAYself Predicting Internalizing Behaviour Over all Timepoints ....................................... 36 Table 8A. Regression Coefficients for Multiple Imputation Block Entry Model Using PL Composite Scores Predicting Externalizing Behaviour Over all Timepoints ............................... 39 Table 8B. Regression Coefficients for Multiple Imputation Block Entry Model Using PLAYfun and PLAYself Predicting Externalizing Behaviour Over all Timepoints ...................................... 41 Table 9A. Regression Coefficients for Multiple Imputation Block Entry Model Using PL Composite Scores Predicting Prosocial Behaviour Over all Timepoints ..................................... 44 Table 9B. Regression Coefficients for Multiple Imputation Block Entry Model Using PLAYfun and PLAYself Predicting Prosocial Behaviour Over all Timepoints ............................................. 46 Table 10A. Regression Coefficients for Multiple Imputation Block Entry Model Using PL Composite Scores Predicting Total Difficulties Over all Timepoints ........................................... 49 Table 10B. Regression Coefficients for Multiple Imputation Block Entry Model Using PLAYfun and PLAYself Predicting Total Difficulties Over all Timepoints .................................................. 51 Table 11. Baseline Demographic Comparisons Between Participants Lost to Follow-up and Those Retained.............................................................................................................................. 92 Table 12. Summary of Missing Data and Descriptive Statistics for All Study Variables ........... 93 xi List of Figures Figure 1. Conceptual model linking physical literacy, physical activity, and health. CVD cardiovascular disease, OA osteoarthritis. Adapted from Cairney et al., 2019. Page 13. ........... 5 Figure 2. 25-Item Strengths and Difficulties Questionnaire with three-factor model color coding (INT= Internalizing Problems, EXT = Externalizing Problems, and PRO = Prosocial Behaviour) ....................................................................................................................................................... 94 Figure 3. PLAYfun assessment tool scoring sheet from the PLAYtools (Physical Literacy Assessment for Youth) instrument. Retrieved from the PLAYTools website (Canadian Sport for Life, n.d)........................................................................................................................................ 95 Figure 4. PLAYfun scoring summary sheet for calculating PLAYfun Score across physical domains. Retrieved from the PLAYTools website (Canadian Sport for Life, n.d)........................ 96 Figure 5. PLAYself questionnaire for assessing children’s self-perceptions of physical literacy. Retrieved from the PLAYTools website (Canadian Sport for Life, n.d). ...................................... 96 Figure 6. PLAYself questionnaire for assessing children’s self-perceptions of physical literacy. Retrieved from the PLAYTools website (Canadian Sport for Life, n.d). ...................................... 97 Figure 7. PLAYself scoring summary sheet for calculating PLAYself Score across each subsection. Retrieved from the PLAYTools website (Canadian Sport for Life, n.d). ................... 97 xii List of Abbreviations CI Confidence Interval CAPL Canadian Assessment of Physical Literacy EXSEM Exercise and Self-Esteem Model FIML Full Information Maximum Likelihood HRQOL Health-Related Quality of Life ICC Intra-class coefficient LPA Light Physical Activity MAR Missing at Random MCAR Missing Completely at Random MH Mental Health MI Multiple Imputation MPA Moderate Physical Activity MVPA Moderate-to-Vigorous Physical Activity PA Physical Activity PL Physical Literacy PL4C Physical Literacy for Communities SD Standard Deviation SDQ Strengths and Difficulties Questionnaire SE Standard Error VPA Vigorous Physical Activity WHO World Health Organization xiii Acknowledgements I would firstly like to express gratitude to my supervisor, Dr. Guy Faulkner. Thank you for the countless opportunities you’ve provided me for learning and growth—academically, professionally, and personally. From the thought-provoking conversations to the many laughs, you’ve fostered a truly special lab environment. I feel incredibly grateful to have had your guidance and support throughout this journey. To my committee members, Dr. Mark Beauchamp and Dr. Louise Mâsse, thank you for your invaluable expertise and thoughtful feedback throughout this process. Our discussions challenged me to think more critically and helped deepen my understanding of both conceptual frameworks and statistical methods. I have learned immensely from your insight and support. To my friends in the Population Physical Activity Lab: I’m so lucky to have gone through this alongside each of you. Thank you for always being a sounding board and for making the lab such a collaborative and enjoyable environment. I’m incredibly grateful for the friendships we built in and outside the lab. To all my Vancouver friends, thank you for being my village. Thank you for showing up in so many ways: attending guest lectures, celebrating milestones, and being there during the difficult seasons. Your presence has made a lasting impact, and I’m forever grateful for each of you. To my family: your support through every chapter of my life has shaped who I am. To my sisters, Sam and Erica, thank you for being role models in every sense. I’ve always felt I had big shoes to fill, but you have let me do it my own way. I couldn’t imagine navigating milestones like this without your love and encouragement. xiv And finally, to my partner Dan—thank you for your constant encouragement and support through the ups and downs of graduate school. You’ve always believed in me, encouraged my wild ideas, and helped me dream big. I’m so glad we grew through this chapter together. xv Dedication This work is dedicated to all who need the reminder that you are enough. Believe in yourself, and trust that you are much more capable than you give yourself credit for. 1 Chapter 1. Introduction 1.1 The Benefits of Physical Activity The benefits of physical activity for children and youth are well-known (Landry & Driscoll, 2012). Evidence suggests that engaging in regular physical activity and limiting sedentary behaviour helps build muscular strength, improves cardiovascular fitness, and enhances bone health, while also playing a vital role in preventing chronic diseases such as obesity, type II diabetes, and certain cancers (Centers for Disease Control and Prevention, 2024). Importantly, the benefit of physical activity extends beyond just physical health. Physical activity is thought to improve mental health through neurobiological, psychosocial, and behavioural mechanisms, improving mood, reducing symptoms of depression and anxiety, and boosting self-esteem and self-concept (Lubans et al., 2016). As with adult populations, physical activity in children and youth is associated with improved self-esteem, self-concept, and decreased levels of depression and anxiety (Calfas & Taylor, 1994; Dale et al., 2019; Landry & Driscoll, 2012). Physical activity is also shown to improve academic performance, cognition (Donnelly et al., 2016; Singh et al., 2019), and social interaction (Bailey, 2006). The Canadian 24-hour movement Guidelines for Children and Youth recommend children accumulate at least 60 minutes of moderate-to-vigorous physical activity (MVPA) every day (Tremblay et al., 2016). These guidelines also suggest that children aged 5-13 years should get 9-11 hours of good quality sleep and limit their screen-based sedentary behaviour. These three components: move more, sleep well, and sit less, form the foundation of the guidelines. Importantly, in a recent systematic review and meta-analysis, only between 4.8% and 10.8% of children (5-11 years), and 1.6% and 9.7% of adolescents (12-17 years) met all 3 components of 2 the Canadian 24-Hour Movement Guidelines (Rollo et al., 2020). Globally, the World Health Organization (WHO) reported that approximately 80% of adolescents worldwide are not meeting the recommended 150 minutes of MVPA per week (World Health Organization, 2022). Meeting all three 24-Hour Movement Guidelines has been associated in preschoolers, children, and adolescents with physical (e.g., adiposity, physical fitness), psychosocial (e.g., well-being, health-related quality of life), and cognitive benefits (e.g., academic performance, global cognition; Roman-Viñas et al., 2016). Therefore, exploring ways to increase physical activity engagement among Canadian children and youth and limit sedentary behaviour should be a top priority. 1.2 Mental Health in Children and Youth The World Health Organization defines mental health as a state that enables people to cope with the stresses of life, realize their abilities, learn and work well, and contribute to their community (World Health Organization, 2022). This includes emotional well-being, resilience, social functioning, and the capacity to work or study effectively. Child and adolescent mental health has been a rising concern over recent years. An estimated 13.4% of children (ages 6-18 years) have mental disorders (Polanczyk et al., 2015), which significantly contributes to the global burden of diseases. According to the most recent Canadian Health Survey on Children and Youth (2023), approximately one in five youth who reported their mental health as “good” in 2019 no longer felt that way four years later. There is extensive evidence highlighting the importance of promoting mental health early in childhood. This is because it is crucial to children’s cognitive, emotional, and social development, and can decrease the risk of non-communicable diseases and mortality (Batelaan et al., 2016; Correll et al., 2017). Considering that childhood represents a period of rapid growth and development, neuronal plasticity, and 3 self-concept formulation, this period may also be critical for establishing physical activity behaviours that impact mental health throughout the life course (Lubans et al., 2016). The connection between physical activity and mental health is well-documented, with regular physical activity being an effective intervention for reducing the risk of mental health issues (Czosnek et al., 2019). Active children also tend to have better mood regulation, improved self-esteem and lower levels of anxiety and depression (Lubans et al., 2016). Moreover, these benefits contribute to a higher quality of life and enhanced emotional well-being, extending into adulthood (Dale et al., 2019). Therefore, promoting physical activity in childhood may help prevent poor mental health, improve lifelong health benefits, and reduce the burden of mental disorders (Wakschlag et al., 2019). 1.3 A Turn to Physical Literacy Alongside the decline in mental health among children and youth, there is growing concern about rising levels of physical inactivity (Ortlieb et al., 2013; Tapia-Serrano et al., 2022). According to the ParticipACTION 2024 Child & Youth Report Card, only 39% of children and youth (ages 5-17 years) in Canada met the recommendation of 60 minutes of moderate-to-vigorous intensity physical activity per day. Only 4% of this population met the combined 24-Hour Movement Guidelines, as measured through self-reports of physical activity, sedentary behaviours, and sleep (ParticipACTION, 2024). Importantly, only 53% of students met physical education benchmarks of “receiving 150 minutes of physical education per week”, or taking Physical Education in high school (ParticipACTION, 2024). Given these challenges, a turn to physical literacy as a solution to the physical inactivity ‘crisis’ has become a primary interest in many educational and public health institutions (Mavilidi et al., 2019; Cairney et al., 2019; Edwards et al., 2017). Physical literacy encompasses the motivation, confidence, physical 4 competence, and knowledge and understanding necessary to value and take responsibility for engaging in physical activities throughout life (Whitehead, 2019). It is commonly referred to as a multidimensional, experiential convergence of motor, affect, social, and cognitive components (Whitehead, 2019; Cairney et al., 2019). These include physical competence, motivation, confidence, and understanding and knowledge to value and take responsibility for physical activity for life (Canadian Sport for Life, n.d). In a metaphorical sense, physical literacy has been referred to as developing literacy within a physical setting (Edwards et al., 2017). PL advocates suggest that it is the foundation which must be developed to support children and youth to be physically active across the lifespan (Whitehead, 2010, p. 5; Cairney et al., 2019) 1.4 Literature Review: The Relationship between Physical Literacy and Mental Health While much of the existing literature explores the relationship between physical literacy and physical activity behaviour, less is known about whether physical literacy is directly associated with positive mental health in children and youth (Christodoulides et al., 2023; Melby et al., 2022). Such a direct association is plausible. In a review of the mechanisms explaining the mental health benefits of physical activity for children and youth, Lubans et al. (2016) found the most consistent evidence for changes in perceived competence and self-perceptions, which lead to improved global self-concept and self-esteem. This finding is notable considering perceived competence and confidence are important components of physical literacy. The relationship between these components and mental health benefits is highlighted in a conceptual model of physical literacy proposed by Cairney et al. (Figure 1; 2019). This model illustrates how perceived competence can enhance confidence in engaging in physical activity, fostering positive feelings of enjoyment, a sense of fun, and belonging, ultimately improving self-esteem. In 5 understanding how these domains interact, one can speculate that promoting physical literacy may directly enhance mental health, independent of physical activity. This suggests that greater physical literacy, as distinct from the accumulation of health-enhancing physical activity, may uniquely contribute to mental health by fostering qualities such as self-esteem, confidence, and motivation. Figure 1. Conceptual model linking physical literacy, physical activity, and health. CVD: cardiovascular disease, OA: osteoarthritis. Adapted from Cairney et al., 2019. There is emerging evidence to support this speculation. Over the past decade, several studies have investigated the relationship between physical literacy and both physical and mental health. A recent bibliometric analysis of Web of Science identified 141 documents published between 2014 and 2022 that explored these relationships (Urbano-Mairena et al., 2023). This highlights a significant growth in physical literacy research over the past eight years, reflecting increasing global recognition of its role in promoting various aspects of health. 6 To explore this further, I conducted a scoping review of recent literature to investigate the direct relationship between physical literacy and mental health (Pascoal et al., 2025). The search was performed using PubMed (1996), Medline (1966), Embase (1947), PsycINFO (1806), and Sportdiscus (1930), and identified five cross-sectional studies with the variables of interest. Overall, my review found consistent evidence for a direct association between physical literacy and mental health. This relationship was initially identified in a study conducted by Melby, Nielsen, et al. (2022). They found that physical literacy (calculated as the aggregate of three domains: physical competence, motivation and confidence, and knowledge and understanding) was independently, significantly and inversely associated with three aspects of psychosocial well-being: internalizing problems, externalizing problems, and total difficulties. In this study, the authors utilized a validated Danish version of the Canadian Assessment of Physical Literacy (CAPL; Longmuir et al., 2018) to measure PL, a parent-reported Strengths and Difficulties Questionnaire to measure psychosocial well-being and mental health (Goodman, 1997), and a thigh-worn Axivity AX3 accelerometry to estimate the daily amount of moderate-to-vigorous physical activity. The strongest associations were found between PL and both the total difficulties score (β = −0.32, p < 0.001) and the externalizing score (β = −0.31, p < 0.001). Overall, physical literacy, MVPA, and the cofounding variables (including age, sex, and socioeconomic status) accounted for 12% of the variance in the externalizing score (p < 0.001), 8% of the variance in the internalizing score (p=0.011), 10% of the variance in the total difficulties score, and 5% (p=0.001) of the variance in the prosocial score (p < 0.001) (Melby, Nielsen, et al., 2022). Similar results were found in the WAVES baseline study (Time 1 in this thesis) conducted by Tang and colleagues (2023) who utilized the PLAYtools (Canadian Sport for Life, 7 n.d.) to measure children’s physical literacy, the parent-reported Strength and Difficulties Questionnaire as the mental health measure (Goodman, 1997), and daily average minutes of MVPA from GT3X+BT wrist-worn triaxial accelerometers. Overall, Tang and colleagues found that PL was significantly and negatively associated with total difficulties (r = -0.19, p < 0.001) and internalizing problems (r = -0.28, p < 0.001), but not with prosocial problems (r = 0.10, p = 0.08) or externalizing problems (r = -0.07, p = 0.19). Importantly, no significant mediation was found between PL and total difficulties (B = -0.04, β = -0.01), suggesting a direct relationship between PL and mental health (Tang et al., 2023). Building on the findings of the WAVES baseline study, this current research aims to extend these results by examining how physical literacy predicts mental health over two years (2022-2024). A significant association between physical literacy and mental health was also found in a sample of 187 early adolescents (Blain et al., 2021). In this study, the authors measured physical literacy using the CAPL and assessed mental health through a 10-item Positive and Negative Affect Schedule for children, alongside vitality measured with the Subjective Vitality Scale (Bostic et al., 2000; Ryan & Frederick, 1997). They found that physical literacy was significantly and positively associated with vitality (β=0.53, p<.001), and positive affect (β = 0.39, p <.001), and significantly and negatively related to negative affect (β = −0.25, p <.05). Overall, physical literacy explained 28% of variance in vitality, 15% of variance in positive affect, and 6% of variance in negative affect. While these results suggest that other variables or influences outside of physical literacy contribute to the overall levels of vitality, positive, and negative affect, a portion of the variance is directly attributable to physical literacy. Several studies have also investigated the relationship between physical literacy and resilience. For example, Jefferies and colleagues (2019) conducted a study on schoolchildren 8 aged 9-12 and found that resilience scores were positively correlated with confidence (r = 0.25, p = 0.001) and overall physical literacy (r = 0.21, p = 0.004). Resilience was also positively correlated with self-reported physical literacy (r = 0.30, p < 0.001) and peer relations (r = 0.31, p < 0.001), which were positively associated with overall physical literacy (r = 0.16, p = 0.033; Jefferies et al., 2019). However, since physical activity was not included in this study, it is unclear how much of the variance in the association between physical literacy and mental health could be attributed to physical activity. While many of these studies focus on child populations, significant associations between PL and well-being have also been found in adolescent and young adult samples (Ma et al., 2021; Melby, Elsborg, et al., 2022; She et al., 2023). For example, in adolescents (aged 13-15), Melby, Elsborg and colleagues (2022) found a significant and positive association between PL and all well-being outcomes (well-being composite score (β = 0.24, p < 0.001), self-esteem (β = 0.24, p < 0.001), life satisfaction (β = 0.19, p < 0.001), loneliness (β = 0.23, p < 0.001), and body satisfaction (β = 0.30, p < 0.001)). They also found that exercise participation was associated with all aspects of well-being except for self-esteem. Their measures included a context-specific questionnaire to measure PL, various subscales to measure mental health, and an indirect measure of exercise participation to investigate these relationships. It is important to note that the authors developed the PL questionnaire, and the exercise participation was measured indirectly through a single question. Evidence supporting the validity and reliability of these measures is limited. Overall, the existing literature provides evidence of direct associations between physical literacy and mental health, independent of physical activity. However, it is essential to acknowledge that all studies examining this relationship to date have been cross-sectional in 9 design. This limits the ability to determine the directionality of the relationship between physical literacy and mental health. This underscores the need for longitudinal research to clarify these associations and to examine whether fostering physical literacy, physical activity, or both is most effective in promoting mental health in children. 1.5 Physical Literacy for Communities (PL4C) An opportunity to explore these relationships exists in the context of the Physical Literacy for Communities (PL4C) initiative in Canada. The PL4C initiative, an effort by the Public Health Agency of Canada (PHAC) delivered by Sport for Life, is a 36-month project designed to enhance physical literacy in 150,000 children and youth aged 2-18 across Canada. This long-term approach aims to support individuals and foster healthy communities by promoting physical literacy. The project's evaluation is guided by the RE-AIM framework, which measures reach, adoption, implementation, and partner engagement. The overarching goals of PL4C are to raise awareness about physical literacy, support its development in children and youth, and increase physical activity levels in Canadian communities. The initiative focuses on building capacity through partnerships and collaboration among leaders in sport, education, recreation, and health sectors (Canadian Sport for Life, n.d.). In British Columbia, Canada, the West Vancouver School District (WVSD) represents an established community to participate in this initiative, with a population of over 7,200 students across 14 public elementary schools and three secondary schools. For approximately five years, this district has delivered a school-based physical literacy program led by two mentors who provide ongoing support to elementary school teachers. This encompasses offering in-classroom tools and resources for teachers, providing individualized support for educational assistants, and conducting assessments of children's fundamental motor skills, including year-end follow-ups. Using a longitudinal dose-response cohort study design, 10 the physical literacy of grade two students across all 14 West Vancouver elementary schools was assessed at three different points between 2022 and 2024. As part of the larger national PL4C initiative, this focused evaluation—known as ‘WAVES PL’—provides an opportunity to examine the interrelationships between physical literacy and mental health within the sample over two years. Importantly, at baseline, physical literacy was shown to have an independent association with children’s internalizing problems and total difficulties (Tang et al. 2023), suggesting a plausible direct relationship between physical literacy and mental health. Whether such associations persist over time is the focus of my research questions. 1.6 Research Question and Hypotheses The objective of this study was to investigate the relationship between physical literacy, physical activity, and mental health over two years. I hypothesized that physical literacy, as assessed among school children initially in Grades 2-3, would predict mental health outcomes (as assessed by the Strengths and Difficulties Questionnaire) one and two years later, independent of physical activity. Considering the growing emphasis on promoting positive mental health in schools, results from this research may strengthen the case for prioritizing physical literacy as a foundation for holistic well-being, with implications for education policy, curriculum design, and broader child well-being initiatives. 11 Chapter 2. Methodology 2.1 Study Design Fourteen elementary schools from the West Vancouver School District were invited and consented to participate in the WAVES project. At baseline, physical literacy was found to have an independent association with children’s internalizing problems and total difficulties (Tang et al., 2023). This secondary data analysis extends this initial cross-sectional study through a longitudinal cohort design, tracking the same group of students over three distinct time points to assess trends in the relationship between physical literacy and mental health. This type of study design is valuable for examining how one variable predicts another over time, allowing researchers to infer temporal relationships while minimizing variability by following the same participants across multiple time points. Time points were spread out over two years, with data collection in April-June of 2022, 2023, and 2024. At baseline (T1), all students in grade 2 and some in grade 2/3 split classes were invited to participate (n= 473). A total of 342 children consented and participated at T1. Approximately one (T2) and two (T3) years later, follow-up assessments were conducted with the same children who participated in the initial assessment. At all three time points, parents and children received written consent and assent forms and had the opportunity to withdraw consent at any time. School visits were scheduled at the convenience of teachers to minimize disruption to classroom routines. Students who completed in-school physical literacy assessments and provided valid accelerometer data were compensated with a $20 gift card. Additionally, each school received a donation for physical activity resources. The study was approved by the University of British Columbia Behavioural Research Ethics Board (H21-03586). 12 2.2 Study Protocol Timeline In April and May 2022, the research team began baseline assessments of participants. At this stage, the research coordinator scheduled school visits with both principals and participating year group teachers to identify suitable times for working with participating classes and for using a larger space in the school, such as a gym or an alternative. Dates and times were then scheduled with all schools throughout the two-month data collection period. For each school visit, 4-5 trained researchers conducted the assessments. The same four to five researchers were involved in all three years of the study, with some additions at T2 and T3. In April and May 2023, and May and June 2024, the same protocol was followed. 2.3 Data Security Throughout the study, all completed questionnaires and movement skill assessment forms were kept in a secure storage cabinet in the Population Physical Activity Lab (LMRS337) on the UBC Vancouver campus. All computer files were password-protected and stored on encrypted, UBC-owned devices held on campus. All participants’ responses were de-identified and assigned participant IDs. Participant ID information was stored on a separate, encrypted file stored in a separate location. Following the completion of the project, the data was contained within an encrypted, password-protected file, which was stored on an encrypted and password-protected computer in Dr. Guy Faulkner’s lab in the School of Kinesiology at the University of British Columbia, for a minimum of 5 years, as per the university's requirements. 2.4 Measures and Data Collection 2.4.1 Child-level Measures 2.4.1.1 Physical Literacy 13 Physical literacy was assessed using the Physical Literacy Assessment for Youth (PLAY) tools specifically developed for children aged 7 or older (Canadian Sport for Life, n.d.). PLAY consists of six different tools (PLAYfun, PLAYbasic, PLAYself, PLAYparent, PLAYcoach, and PLAYinventory), comprising workbooks, forms, and tracking sheets, designed to assess physical literacy. For the present longitudinal project, only PLAYself and PLAYfun were applied to ensure a comprehensive assessment of physical literacy by combining objective performance measures with subjective self-perceptions. This dual approach aimed to provide a holistic understanding of physical literacy, and data collected with both have been studied and shown to exhibit robust psychometric properties (Barnett et al., 2023). The PLAYfun assessment is a physical evaluation of fundamental movement skills, consisting of 18 skills divided into five domains: running (3 tasks), locomotion (5 tasks), object control for both upper and lower body (6 tasks: 4 for the upper body and 2 for the lower body), and balance/stability (4 tasks). The quality of performance for each task was evaluated using continuous criterion-referenced visual analogue scales, divided into four categories: initial (<25), emerging (25—<50), competent (50—<75), and proficient (75—100; Appendix C, Figure 3). A score above 50 generally signifies an entry-level ("acquired") motor competence, while a lower score reflects a "developing" level of motor competence. For each of the five skill domains, an average score is calculated based on the specific tasks within that domain, along with a total PLAYfun score, which represents the average score across all five domains (Appendix C, Figure 4). Validity and reliability have been found in test scores using the PLAYfun measurement tool for measuring physical literacy in children and youth aged 6-14 (Caldwell et al., 2021; Cairney et al., 2018; Stearns et al., 2019). Inter-rater reliability has also demonstrated very good to excellent reliability (ICC = 0.87–0.94) in various samples (Caldwell et al., 2021; Cairney et al., 2018; 14 Stearns et al., 2019). Additionally, it has shown acceptable internal consistency with two raters in one sample (Stearns et al., 2019). In the present study, all research assistants underwent training sessions before using this tool. Each task was evaluated independently by two research assistants, and the final score for each task was determined by averaging the scores from both assessors. Five out of the 18 tasks (one from each domain) that constitute the short form of PLAYfun (known as PLAYbasic; Appendix C, Figure 5) were provided through the semi-annual assessments by two school-based physical literacy specialists. At T1, the inter-rater reliability for each skill between these physical literacy mentors was found to be very good (ICC=0.81–0.89), and acceptable to very good (ICC=0.72–0.87) for the two research assistants in the baseline study (Tang et al., 2023), as examined by two-way random effects ICC. The PLAYself questionnaire was used to assess children’s perceptions of their physical literacy, reflecting their motivation and self-efficacy regarding participation in physical activity (Canadian Sport for Life, 2024). This tool aims to assess the components of physical literacy, including confidence, motivation, knowledge, and understanding. The PLAYself questionnaire consists of 22 questions organized into four subsections: (1) Environment, where children rate their ability to perform sports and activities in different physical settings using a 5-point Likert scale ranging from “never tried” (1) to “excellent” (5); (2) Statements about participating in sports and activities based on cognitive and affective factors, where children rate their agreement on a 4-point Likert scale from “not true at all” (1) to “very true” (4); (3) Children’s ranking of the importance of different literacies (e.g., literacy, numeracy, and physical literacy) in school, at home with family, and with friends, on a 4-point Likert scale ranging from “strongly disagree” (1) to “strongly agree” (4); and (4) Children’s perceived fitness level, where they respond to a single item with “disagree (1)/agree (2)” options, indicating if it limits their participation in 15 chosen activities (Appendix C, Figure 6). A score is calculated for each of the first three subsections, along with a total PLAYself score. The overall PLAYself score is the average of the scores from each subsection, excluding the fitness question (Appendix C, Figure 7). A higher score (ranging from 0 to 100) indicates a greater self-perceived physical literacy. Data collected using the PLAYself tool has demonstrated excellent test-retest reliability (ICC = 0.81–0.84), good to excellent internal consistency (Person Separation Index = 0.70–0.82), and good convergent validity through comparisons to existing measures of related concepts (motivation, self-concept/self-confidence; correlation range: 0.42–0.78) among 300 Canadian children aged 8–14 years (Jefferies et al., 2021). In this study, researchers also provided rephrasing and clarification of the PLAYself tools to guide assessment completion. 2.4.1.2 Physical Activity While the primary focus of this study was the direct relationship between physical literacy and mental health, I also examined physical activity via wrist-worn accelerometry to determine the extent to which it contributed to this relationship. MVPA was measured for seven days using an Actigraph GT3X+BT (ActiGraph, Pensacola, FL) triaxial accelerometer, programmed to record data every minute. Actigraph GT3X+BT accelerometers are a reliable measure of physical activity in children and have been validated in several studies (Scott et al., 2017; Stearns et al., 2019) with children. In the present study, accelerometers were worn on the participant’s non-dominant wrist to improve compliance (Fairclough et al., 2016; McLellan et al., 2018; Scott et al., 2017). Participants were instructed to wear the accelerometer for a consecutive seven days except during water-based activities (e.g., swimming, showering, and bathing). The GT3X+BT accelerometer was set up to capture unprocessed acceleration data at a sampling rate of 30 Hz. Specific to wrist-worn accelerometers for children aged 8-12, Chandler’s 16 cut points were utilized to classify different physical activity intensities and sedentary behaviour (Chandler et al., 2016). Physical activity was categorized into: ≤305 (sedentary), 306 – 817 (LPA; light physical activity), 818 – 1968 (MPA; moderate physical activity), ≥1969 (VPA; vigorous physical activity). A 5-second epoch length was used to align with the development of these cut points (Migueles et al., 2017). 2.4.2 Parent Reported Measures 2.4.2.1 Parent Questionnaire Participants’ parents were asked to complete a questionnaire regarding demographic information (e.g., parents’ and their partner’s (if applicable) age, work conditions, education, and cultural background). The questionnaire also asked for the quantity of time their child spent in sports and outdoors, screen time during weekends and school days, usual mode of travel to and from school, and sleep duration. This information was utilized to describe the sample better. 2.4.2.2 Strengths and Difficulties Questionnaire Child mental health was parent-reported using the Strengths and Difficulties Questionnaire (SDQ; Goodman, 1997; Goodman et al., 1998). The SDQ is widely used across various global contexts to assess children’s psychosocial attributes, including both positive and negative behaviours (Goodman, 1997; Goodman et al., 1998). The questionnaire consists of 25 items rated on a 3-point Likert scale, with options ranging from “Not true” (0) to “Certainly true” (2). Five items are reverse-coded to ensure consistency in scoring (Appendix B, Figure 2). The SDQ was originally designed to measure five subscales: Hyperactivity/inattention, Conduct problems, Emotional symptoms, Peer relationship problems, and Prosocial behaviour. However, recent research suggests that a three-factor model may be more suitable for studies involving low-risk young children (Goodman et al., 2010). This alternative structure groups 17 items into three dimensions: internalizing behaviours (combining the emotional symptoms and peer problems scales), externalizing behaviours (combining the conduct problems and hyperactivity scales), and prosocial behaviours, which are assessed independently. Scores for internalizing and externalizing behaviours range from 0–20, while the prosocial scale ranges from 0–10. The three-factor model was chosen for this study due to its demonstrated strong convergent and discriminant validity, particularly in low-risk populations, and its alignment with broader constructs of child mental health (Goodman et al., 2010). Additionally, the SDQ provides a Total Difficulties score (0–40) by summing the internalizing and externalizing subscales. This score has been validated for use in assessing overall mental health in children aged 5–16 globally (Achenbach et al., 2008; Goodman et al., 2010; Goodman & Goodman, 2009). Higher scores on the Total Difficulties and subscales indicate more significant problems, whereas higher scores on the Prosocial scale reflect better social behaviour. 2.5 Data Treatment 2.5.1 Age, Gender, and Consent Alongside the PLAYself assessments, participants’ age and gender were collected. Participants’ gender was categorized into three groups: boys, girls, and non-binary. Due to the small sample size of non-binary participants, they were excluded from the final analysis to ensure robustness of statistical comparisons. For gender at T2 and T3, the data from T1 was used to populate any missing values, under the assumption that gender remained the same over time. For individuals whose age was missing at T2 and T3, the age from T1 was carried forward, adding one or two years as appropriate for each subsequent time point. 18 2.5.2 Accelerometry In some instances, despite participants and their parents consenting, children may have been absent during the school visit and therefore did not receive an accelerometer. At T1, 14 participants were absent and therefore did not receive one. At T2 and T3, 30 and 14 participants, respectively, did not receive an accelerometer due to absence. Additionally, at T1 and T2, one participant lost their accelerometer, while at T3, two participants lost the device. Accelerometer data were downloaded and processed using ActiLife v. 6.13.4 (ActiGraph, Pensacola, FL). In line with most previous studies (Skender et al., 2016), valid data were defined as ≥4 days of wear (≥10 h/day), including at least three weekdays and one weekend day, with waking hours identified via the Cole–Kripke sleep algorithm (Quante et al., 2018). Periods of ≥30 consecutive minutes of zero counts were classified as non-wear time (Vanhelst et al., 2019). After applying these criteria, valid data were available for 258 participants at T1, 199 at T2, and 187 at T3. From the processed files, wear time, steps, and minutes in each intensity category were extracted. Analyses concentrated on MVPA, as it is more consistently associated with psychological and social health outcomes compared to lower-intensity activity (Poitras et al., 2016). This is also in alignment with the Canadian 24-Hour Movement Guidelines, which recommend at least 60 minutes of daily MVPA for children and youth (Poitras et al., 2016). Wear time was also examined in relation to MVPA to assess whether it should be included as a covariate in regression models (see Table 6). 2.5.3 Parent Questionnaire For the parent-reported measurements, answers about all parents’ and their partners’ work status were categorized into three groups: full-time work (at least one parent works full-time), part-time work (at least one parent works part-time, but no parent works full-time), and 19 out of work (no parent works full-time nor part-time). Parents’ highest education represents the highest education level of either responding parent or their partner. The responding parent reported on their cultural background. 2.5.4 Physical Literacy Composite Score Given the multidimensional nature of physical literacy, it has been recommended to combine different PLAYtools for a more comprehensive assessment of a child’s overall physical literacy (Edwards et al., 2017). Therefore, as adapted from Caldwell et al. (2020), a composite physical literacy score was calculated by summing the z-scores of the PLAYfun and PLAYself assessments. Importantly, z-scores were derived from the full sample across all timepoints, ensuring comparability over time. Research supports using a composite score to better reflect the multiple domains of physical literacy, including movement competence, motivation, confidence, and knowledge (Caldwell et al., 2020; Edwards et al., 2017), as measurement of these concepts individually are not specifically designed to measure the components of physical literacy (Cairney et al., 2019; Caldwell et al., 2020). Therefore, the composite score was used in the primary analysis, and the individual scores (PLAYfun and PLAYself) were used as an exploratory question to determine their individual relationships to mental health outcomes. 2.6 Statistical Analysis 2.6.1 Preliminary Analysis All statistical analyses were conducted using SPSS ver. 28 (IBM SPSS, Chicago, IL). Descriptive statistics were used to summarize demographic characteristics at each time point. For continuous variables (child age), means and standard deviations were reported. For categorical variables (gender, disability status, parental education, number of children, parent work conditions, and cultural background), frequencies and percentages were provided at Time 1, 20 Time 2, and Time 3 to describe the sample across waves. All continuous variables were assessed for normality using the Shapiro-Wilk test, skewness, kurtosis, and visual inspection of histograms. For continuous variables that met assumptions of normality, including PL Composite Scores, PLAYfun, PLAYself, and MVPA, repeated-measures ANOVAs were conducted to examine differences across the three time points. Mauchly’s test of sphericity was applied to determine if sphericity assumptions were violated (Mauchly, 1940). In these cases, Greenhouse-Geisser was utilized to assess within-subject effects. For continuous variables that significantly violated normality assumptions, including all SDQ subscale scores, the Friedman test was used as an alternative to compare scores over time. Where the Friedman test was significant (p < 0.05), post-hoc pairwise comparisons were conducted using Wilcoxon signed-rank tests with Bonferroni corrections. Wilcoxon signed-rank tests reporting a corrected p-value less than 0.05 were deemed significant. To assess potential attrition bias, baseline characteristics were compared between participants who completed all three waves of data collection (T1–T3) and those who dropped out at subsequent timepoints (Appendix A). Chi-square tests were conducted for the following categorical variables: disability status, parents’ highest education, parents’ highest work conditions, parent age category, partner age category, cultural background, and gender, to determine whether those lost to follow-up differed from retained participants. No significant differences in baseline demographics were found between participants who remained in the study and those lost to follow-up, suggesting minimal attrition bias (Table 11, Appendix A). 2.6.2 Model Justification: Calculating the Intra-class Correlation Coefficient To determine the extent to which SDQ outcome scores were clustered at the school level, intraclass correlation coefficients (ICCs) were calculated for all outcome variables across each 21 time point (T1, T2, and T3), including internalizing, externalizing, prosocial behaviour, and total difficulties scores, using the following formula: 𝐼𝐶𝐶 = 𝜎𝑏𝑒𝑡𝑤𝑒𝑒𝑛2𝜎𝑏𝑒𝑡𝑤𝑒𝑒𝑛2 + 𝜎𝑤𝑖𝑡ℎ𝑖𝑛2 The ICC represents the proportion of variance in the outcome that is attributable to differences between schools, providing insight into whether multilevel modelling is statistically justified. In addition to ICCs, design effects were calculated using the formula: 𝐷𝑒𝑠𝑖𝑔𝑛 𝐸𝑓𝑓𝑒𝑐𝑡𝑠 = 1 + (?̅? − 1) × 𝐼𝐶𝐶 Where ?̅? represents the average number of children per school (25.57). Design effects provide a complementary perspective by quantifying the extent to which clustering may inflate standard errors and are often used to determine whether clustering should be accounted for in statistical models (Hox, 2010). Monte Carlo simulations by Muthén and Satorra (1995) demonstrated that when design effects are less than 2, clustering does not meaningfully impact the estimation of standard errors for regression parameters. However, when design effects exceed 2, clustering must be accounted for to ensure accurate estimation, typically through multi-level modelling. Across all outcomes and timepoints, ICCs ranged from 0 to 0.035, with most values being very small or near-zero. The largest ICC observed was for prosocial behaviour at T1 (ICC = 0.035; Design Effect = 1.86), indicating minimal clustering. All other ICCs were below 0.02, and none of the calculated design effects exceeded the threshold of 2 (Table 1). This suggests that the effect of clustering at the school level is limited and unlikely to substantially bias standard errors. Therefore, multi-level modelling was deemed unnecessary for the main analyses, and all models were analyzed at the individual level. However, a key limitation of this analysis is that while 22 between-school clustering was examined, within-school clustering (i.e., at the classroom level) was not assessed. This was due to the variability in class sizes and the absence of consistent classroom identifiers across timepoints. As a result, the analysis may underestimate the true degree of nesting, particularly if classroom-level factors influenced mental health outcomes. Table 1. Intraclass Correlation Coefficients (ICCs) and Between-School Variance Estimates for all Outcome Variables Across Timepoints Timepoint Measure Between-School Variance Estimate Residual Variance Estimate ICC Design Effect T1 Internalizing 0.12 6.86 0.018 1.43 Externalizing 0 10.34 0 1 Prosocial 0.11 3.02 0.035 1.86 Total Difficulties 0.06 23.25 0.003 1.07 T2 Internalizing 0 7.01 0 1 Externalizing 0.05 10.59 0.005 1.13 Prosocial 0.004 3.19 0.001 1.03 Total Difficulties 0 25.50 0 1.0 T3 Internalizing 0.12 8.90 0.014 1.34 Externalizing 0 14.99 0 1.0 Prosocial 0.041 3.41 0.01 1.29 Total Difficulties 0 35.58 0 1.0 2.6.3 Missing Data The missingness of variables included in the analysis was assessed using Little’s MCAR (Missing Completely at Random) test. Results indicated that the data were missing completely at random, χ² (1721) = 83.41, p = 1.00. Because the MCAR assumption was satisfied, complete case analysis would have been an acceptable approach to handle missing data without introducing bias. However, a complete case analysis would have substantially reduced the 23 sample size, excluding roughly one-third of participants at T3. Both Full Information Maximum Likelihood (FIML) and Multiple Imputation (MI) were considered to handle the missing data, as the analysis results using FIML and MI data are thought to be asymptotically equivalent (Baraldi & Enders, 2010). MI was chosen over FIML due to the structure of the dataset, which includes a large number of variables. As highlighted by Moore et al. (2025), multiple imputation can leverage all available variables during the imputation process, drawing on the broader data structure to inform the handling of missingness. Additionally, MI is particularly advantageous when addressing multiple research questions (Moore et al., 2025). In contrast, FIML relies solely on the variables explicitly included in the analysis model, potentially overlooking relevant information unless auxiliary variables are explicitly added (Graham, 2003; Newman, 2014). While auxiliary variables can be added to enhance FIML estimation, including too many variables may cause estimation issues. Therefore, given the complexity of the dataset, multiple imputation offered a more comprehensive way to handle the missing data. This ultimately allowed preservation of statistical power in using data across all time points, creating more generalizable parameter estimates across models. 2.6.4 Regression Analyses Regression analyses were conducted utilizing the imputed datasets using a block-entry method to examine predictors of each SDQ subscale over three time intervals: T1-T2, T2-T3, and T1-T3. Two separate block entry models were utilized. The primary independent variable of interest was the PL Composite score. To assess domain-specific effects, a second model was utilized with PLAYfun and PLAYself as predictors. These components were examined separately from the PL composite score to avoid multicollinearity, as the PL Composite is derived from these two measures. Unlike the PL composite score, no a priori hypotheses were 24 made regarding the individual components; therefore, analyses including these separate variables were conducted for exploratory purposes only. In both models, block one included gender and the previous year’s mental health as covariates. Entering these covariates first accounted for the variance they explained in the outcome and assessed whether the main predictor added incremental value above these known factors. This allows the isolation of each predictor’s independent association with the outcome. In block 2, predictors of interest were introduced: PL composite scores and PLAYself/PLAYfun to determine whether each was independently associated with the outcome of interest. Finally, in block 3, average daily MVPA was added to assess whether the observed effects of physical literacy were independent of physical activity. This provides an opportunity to evaluate the unique contributions of physical literacy beyond that explained by MVPA alone. To evaluate how much additional variance was explained with each block of predictors, R² change values were reported for each of the imputed datasets. As SPSS does not pool R² change statistics across the pooled dataset (the average of all 5 imputed datasets) manually, the reported values represent the average R² change and p-value range across all five imputed datasets. Following recommendations for MI, the significance of overall fit and incremental fit was pooled using Rubin’s rules. R² values are therefore reported as the means and standard deviations across the five imputations for descriptive context (van Ginkel, 2019). 25 Chapter 3. Results 3.1 Overview 3.1.1 Participant Characteristics Table 2 presents participant demographics. At T1, 358 children and their parents completed consent and assent forms, including 51.1% boys, 47.2% girls, and 1.7% non-binary participants (Table 2). This represents a response rate of 75% among eligible families. At T2, 280 students participated, including 50.4% boys, 48.2% girls, and 1.4% non-binary participants (see Table 2), reflecting a retention rate of approximately 78% from T1 to T2. At T3, 263 students participated, including 53.6% boys, 46% girls, and 0.4% non-binary participants (see Table 2), resulting in a retention rate of 73% from T1. At T1, the mean age was 7.51 years (SD = 0.6). At T2 and T3, mean age increased to 8.45 (SD = 0.5) and 9.42 (SD = 0.5). At T1, most children had at least one sibling (n=199; 58.7%), which changed to 136 (55.7%) at T3, accounting for participant attrition. At T1, ten children (4.1%) reported experiencing long-lasting disabilities, including: attention deficit hyperactivity disorder (n=2), asthma (n=2), both autism and epilepsy (n=1), and both autism and attention deficit hyperactivity disorder (n=3). Three participants also reported having a disability but did not specify the nature of the disability. These reports remained consistent over the two-year study period. Overall, the demographic characteristics of the sample at T3 were relatively consistent with those of the broader West Vancouver population (Statistics Canada, 2021). West Vancouver is characterized by a higher proportion of adults aged 40 or older, high levels of post-secondary educational attainment (92%), and substantial ethnic diversity, with over half identifying as 26 visible minorities. In our study, most parents at T3 were aged either 30 to 44 years (n = 143; 58.6%) or 45 years and over (n = 98; 40.2%), with only a small proportion under 30 years old (n = 2; 0.8%; Appendix A, Figure 3). The majority of their partners were also aged 30 to 44 years (n = 102; 45.7%) or 45 and over (n = 120; 53.8%), with very few under 30 (n = 1; 1.0%). In terms of education, families most often reported the highest level at home as a university degree (n = 69; 53.3%) or graduate-level education (n = 51; 39.5%), with fewer reporting college (n = 7; 5.4%) or secondary school education (n = 1; 0.8%; Table 2). Employment varied, with 45.1% of parents working full-time, 31.1% part-time, and 23.4% not currently employed. With respect to cultural background, the majority of parents self-identified as Asian (39.3%) or European (31.1%), followed by another cultural background not listed (21.7%), Hispanic (2.9%), African (1.6%), and Indigenous (0.8%). An additional 2.5% preferred not to disclose their cultural background (Table 2). Table 2. Descriptive Characteristics of Participants at Each Timepoint (T1–T3) Characteristic Time 1 Time 2 Time 3 Age (year)a n = 335 n = 280 n = 263 Mean (SD) 7.5 (0.552) 8.45 (0.512) 9.42 (0.519) Genderab n = 335 n = 280 n = 263 Boy 176 (52.5%) 141 (50.4%) 141 (53.6%) Girl 153 (45.7) 135 (48.2%) 121 (46%) Non-Binary 6 (1.8%) 4 (1.4%) 1 (0.4%) Disability n = 343 n = 266 n = 244 Yes 6 (1.7%) 11 (4.1%) 10 (4.1%) No 337 (98.3%) 255 (95.9%) 234 (95.9%) Siblings n = 339 n = 256 n = 244 0 64 (18.9%) 58 (21.9%) 56 (23.0%) 1 199 (58.7%) 153 (57.5%) 136 (55.7%) 2 60 (17.7%) 46 (17.4%) 42 (17.2%) 3 16 (4.7%) 8 (3.0%) 10 (4.1%) Parent's age (year) n = 342 n = 256 n = 244 Under 30 2 (0.6%) 0 2 (0.8%) 27 30 to 44 253 (74.0%) 176 (66.4%) 143 (58.6%) 45 or over 87 (25.4%) 89 (33.6%) 98 (40.2%) Partner's age (year) n = 324 n = 249 n = 223 Under 30 4 (1.2%) 0 1 (1.0%) 30 to 44 191 (59.0%) 127 (51.0%) 102 (45.7%) 45 or over 129 (39.8%) 122 (49%) 120 (53.8%) Parent's highest education n = 342 n = 265 n = 244 Elementary 0 (0.0%) 0 (0.0%) 0 (0.0%) Secondary School 7 (2%) 2 (0.8%) 5 (2%) College 14 (4.1%) 10 (3.8%) 10 (4.1%) University 164 (48%) 140 (52.8%) 127 (52%) Graduate School 154 (45%) 112 (42.3%) 101 (41.4%) Not applicable 3 (0.9%) 0 (0%) 1 (0.4%) Parents’s highest work conditions n = 339 n = 265 n = 244 Full-time 129 (38.1%) 110 (41.5%) 110 (45.1%) Part-time 100 (29.5%) 80 (30.2%) 76 (31.1%) Out of work 110 (32.4%) 75 (28.3%) 57 (23.4%) Parent's culture background n = 335 n = 261 n = 244 Indigenous 2 (0.6%) 3 (1.1%) 2 (0.8%) Hispanic 10 (3.0%) 8 (3.1%) 7 (2.9%) Asian 154 (46.0%) 115 (44.1%) 96 (39.3%) European 97 (29.0%) 81 (31.0%) 76 (31.1%) African 3 (0.9%) 2 (0.8%) 4 (1.6%) Other 69 (20.6%) 48 (18.4%) 53 (21.7%) Prefer not to say 0 (0%) 4 (1.5%) 6 (2.5%) an gender and age were filled in from previous years based on participants who had valid PLAYfun and PLAYself scores 3.1.2 Physical Activity At T1, 258 participants met wear time criteria (183 boys, 166 girls, and 4 non-binaries). On average, participants engaged in an average of 111.1 minutes (SD = 30.3) of MVPA per day (Table 3). At T2, 199 participants met wear time criteria, averaging 113.3 minutes (SD = 31.2) of MVPA per day, and at T3, 187 participants averaged 109.2 minutes (SD = 30.2). At T1 and T2, a total of 97% of participants accumulated over 60 minutes of MVPA per day and could be classified as meeting the physical activity guidelines. At T3, this number slightly decreased to 95% of participants accumulating over 60 minutes of MVPA every day. However, there were no statistically significant differences in MVPA across time points. Using Spearman’s Rank 28 correlations (p < 0.05), there was a weak but significant correlation between MVPA and wear time only at T1 (ρ = 0.39, p < 0.001) and T3 (ρ = 0.19, p = 0.009), but not at T2. Accelerometer wear time also significantly differed across the three time points (Table 3). Importantly, the standard deviation for wear time at each time point was less than 60 minutes per day, demonstrating little variability in wear time across participants. Given this may reduce the likelihood of it having a strong influence on MVPA, average wear time was not included as a control variable in the multiple regression analyses. Table 3. Year 1-3 Participants Daily MVPA Time 1 Time 2 Time 3 Statistical testa MVPA 111.1 (30.28) 113.30 (31.18) 109.18 (30.22) F (2, 208) = 2.37, p = 0.10, η² = 0.02 Accelerometer Wear Time (min/d) 800.04 (57.50) 825.53 (51.19) 831.17 (50.0) F (2, 208) = 12.18, p < 0.001, η² = 0.11 aRepeated Measures ANOVA 3.1.3 Physical Literacy Table 4 presents descriptive statistics for the PLAYself, PLAYfun, and composite physical literacy variables across all time points. At Year 1, on average, children's motor competence levels across the five PLAYfun domains were in the 'late emerging' phase (M = 45.3, SD = 5.7). At Year 2, scores increased to the 'early competent' phase (M = 51.3, SD = 5.6), and by Year 3, children were, on average, in the 'mid-competent' phase (M = 57.1, SD = 5.9). These scores significantly increased over time (F(1.88, 364.86) = 602.69, p < 0.001, η² = 0.76). Additionally, at year 1, children self-reported an average PLAYself score of 68.9 (SD = 12.3). Scores increased at year 2 (M = 71.6, SD = 11.9), followed by a slight decrease at year 3 (M = 70.8, SD = 12.1). A repeated measured ANOVA revealed that these changes were also statistically significant (F(1.92, 404.58) = 8.18, p <.001, η² = 0.037). Finally, at year 1, children had an average PL composite score of –0.45 (SD = 0.65). Scores increased at year 2 (M = 0.13, 29 SD = 0.61) and again at year 3 (M = 0.49, SD = 0.75). These changes over time were statistically significant (F(1.88, 354.55) = 244.21, p < 0.001, η² = 0.56), indicating that more than half of the variance in PL composite scores was explained by time. Comparisons showed that scores at each time point differed significantly from one another, with the largest increase observed from year 1 to year 3. Table 4. Physical Literacy Scores at each Timepoint (T1-T3) Year 1 Year 2 Year 3 Statistical testa PLAYfun 45.3 (5.7) 51.3 (5.6) 57.1 (5.9) F (1.88, 364.86) = 602.69, p < 0.001, η² =0.76 PLAYself 68.9 (12.3) 71.6 (11.9) 70.8 (12.1) F (1.92, 404.58) = 8.18, p < 0.001, η² = 0.04 PL Composite Score –0.45 (0.65) +0.13 (0.61) +0.49 (0.75) F (1.88, 354.55) = 244.21, p < 0.001, η² = 0.56 aRepeated Measures ANOVA 3.1.4 Mental Health Table 5 presents mean scores and standard deviations for SDQ subscales across the three time points. Freidman tests indicated no statistically significant differences in internalizing, externalizing, prosocial behaviour, or total difficulties scores across years (all p > 0.05). Although minor fluctuations were observed, mental health measures remained relatively stable over time. Table 5. Strengths and Difficulties Questionnaire (SDQ) Scores at each Timepoint (T1-T3) Year 1 Year 2 Year 3 Statistical testa Internalizing Behaviour 2.83 (2.5) 2.70 (2.6) 3.00 (2.9) χ²(2) = 2.19, p = 0.33 Externalizing Behaviour 4.53 (3.2) 4.29 (3.3) 4.82 (3.7) χ²(2) = 3.72, p = 0.16 Prosocial Behaviour 7.36 (4.6) 6.98 (5.1) 7.82 (5.7) χ²(2) = 0.47, p = 0.79 Total Difficulties 8.26 (1.8) 8.27 (1.9) 8.21 (1.9) χ²(2) = 4.15, p = 0.13 aFreidman test 30 3.1.5 Descriptive Analysis of Bivariate Correlations at Each Timepoint A non-parametric test was utilized due to the non-normal distributions of several variables, as determined visually and by Shapiro-Wilk tests. Associations between variables were assessed using Spearman’s rank-order correlations (Table 6). Correlations were examined between physical literacy components (PLAYfun, PLAYself, PL Composite), average daily MVPA, and mental health outcomes (SDQ internalizing, externalizing, and prosocial behaviour, and SDQ Total) at all timepoints. All correlations were calculated using complete case data. 31 Table 6. Complete Case Spearman Correlation Matrix for all variables at T1-T3 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1. SDQ Int. 2022 1.00 2. SDQ Ext. 2022 0.29** 1.00 3. Prosocial 2022 -0.22** -0.36** 1.00 4. Total Diff. 2022 0.72** 0.85** -0.37** 1.00 5. PLAYself 2022 -0.11 0.09 0.10 0.00 1.00 6. PLAYfun 2022 -0.29** -0.08 0.11 -0.19** 0.17** 1.00 7. PL Comp. 2022 -0.26** 0.01 0.13* -0.13* 0.76** 0.74** 1.00 8. MVPA 2022 -0.22** 0.09 0.03 -0.06 0.11 0.25** 0.21** 1.00 9. SDQ Int. 2023 0.60** 0.27** -0.16* 0.50** -0.07 -0.21** -0.17** -0.10 1.00 10. SDQ Ext. 2023 0.16** 0.59** -0.31** 0.48** 0.05 -0.13 -0.04 0.04 0.31** 1.00 11. Prosocial 2023 -0.07 -0.26** 0.51** -0.21** 0.10 0.05 0.11 0.01 -0.09 -0.37** 1.00 12. Total Diff. 2023 0.45** 0.54** -0.29** 0.61** 0.00 -0.21** -0.13* -0.06 0.74** 0.84** -0.29** 1.00 13. PLAYself 2023 -0.14* -0.05 0.17** -0.11 0.43** 0.20** 0.40** 0.16* -0.04 -0.04 0.12 -0.03 1.00 14. PLAYfun 2023 -0.24** -0.17** 0.09 -0.23** 0.08 0.54** 0.36** 0.25** -0.21** -0.21** 0.18** -0.24** 0.14* 1.00 15. PL Comp. 2023 -0.26** -0.13* 0.13* -0.21** 0.36** 0.47** 0.52** 0.28** -0.17** -0.16* 0.21** -0.19** 0.78** .688** 1.00 16. MVPA 2023 -0.05 0.03 0.17* -0.03 0.13 0.22** 0.20** 0.65** -0.10 0.08 0.15* -0.01 0.24** .324** .352** 1.00 17. SDQ Int. 2024 0.44** 0.28** -0.17** 0.41** -0.03 -0.21** -0.17* -0.06 0.56** 0.30** -0.15* 0.49** -0.11 -0.25** -0.24** 0.02 1.00 18. SDQ Ext. 2024 0.28** 0.65** -0.31** 0.59** 0.02 -0.18** -0.10 0.06 0.29** 0.68** -0.33** 0.60** -0.11 -0.29** -0.24** -0.02 .462** 1.00 19. Prosocial 2024 -0.11 -0.22** 0.47** -0.21** 0.05 0.02 0.05 -0.06 -0.09 -0.19** 0.58** -0.16* 0.19** 0.13 0.15* 0.07 -0.19** -0.32** 1.00 20. Total Diff. 2024 0.40** 0.58** -0.30** 0.60** 0.00 -0.22** -0.15* 0.00 0.46** 0.61** -0.29** 0.65** -0.13 -0.32** -0.28** -0.02 0.77** 0.91** -0.30** 1.00 21. PLAYself 2024 -0.21** -0.12 0.22** -0.17** 0.46** 0.33** 0.50** 0.17* -0.10 -0.02 0.16* -0.07 0.57** 0.28** 0.58** 0.21** -0.21** -0.17* 0.25** -0.22** 1.00 22. PLAYfun 2024 -0.30** -0.24** 0.09 -0.31** 0.14* 0.55** 0.46** 0.37** -0.27** -0.20** 0.02 -0.28** 0.31** 0.58** 0.57** 0.35** -0.22** -0.24** 0.04 -0.27** 0.35** 1.00 23. PL Comp. 2024 -0.33** -0.23** 0.17* -0.31** 0.33** 0.49** 0.54** 0.31** -0.24** -0.14* 0.09 -0.22** 0.50** 0.49** 0.67** 0.31** -0.28** -0.24** 0.15* -0.30** 0.80** 0.82** 1.00 24. MVPA 2024 -0.04 0.01 0.04 -0.02 0.12 0.31** 0.26** 0.73** -0.02 0.11 0.04 0.05 0.06 0.36** 0.24** 0.66** -0.01 0.04 -0.05 0.01 0.13 0.42** 0.34** 1.00 * - p < 0.05, ** - p < 0.01 32 As expected, significant strong positive correlations were found between all SDQ subscales and total difficulties scores across time points, reflecting their shared measurement components (e.g., SDQ internalizing and total difficulties at T1: ρ = 0.78). Moderate associations were observed between PLAYfun and average daily MVPA, with correlations increasing across time (T1: ρ = 0.30; T2: ρ = 0.32; T3: ρ = 0.42). This suggests a moderate relationship between the fundamental movement skill domain of physical literacy with moderate-to-vigorous physical activity levels. Similar trends were found between PL composite scores and MVPA, which remained relatively stable across time (T1: ρ = 0.24; T2: ρ = 0.35; T3: ρ = 0.34). Weak to moderate associations were observed between PLAYself and MVPA, with significant correlations at T1 (ρ = 0.15) and T2 (ρ = 0.24), suggesting that self-perceptions of physical literacy are related to physical activity, though not strongly. In contrast, associations between PLAYself and SDQ outcomes at T1 and T2 were weak or non-significant, but became more pronounced at T3, where PLAYself demonstrated significant negative associations with all SDQ outcomes (e.g., Total Difficulties: ρ = –0.22). MVPA was moderately negatively associated with SDQ internalizing at T1 (ρ = –0.25), and weakly positively related to prosocial scores at T2 (ρ = 0.15) but showed no notable associations with other SDQ subscales over time. 33 3.2 Main Results: Physical Literacy Predicting Mental Health Over Time 3.2.1 Internalizing Behaviour After imputation, baseline internalizing scores emerged as the strongest predictor of future internalizing symptoms across all models (all p < 0.001; Table 7). In the block entry model using the PL composite as the main predictor, the PL composite significantly predicted SDQ internalizing scores only in the final block, when MVPA was included in the model, between T2–T3 (B = -0.54, p = 0.05), accounting for only 1% of the variance in internalizing behaviour one year later (mean R² = 0.01, p range = 0.002-0.77; Table 7A). When physical literacy components were analyzed separately, PLAYfun scores were significantly associated with internalizing symptoms at both T1–T2 (B = -0.06, p = 0.015) and T2–T3 (B = -0.11, p = 0.009), suggesting that higher fundamental movement skills predicted lower internalizing problems one year later (Table 7B). Notably, these results strengthened after the addition of MVPA in block 3, at both T1-T2 (B= -0.06, p = 0.012) and T2-T3 (B= -0.12, p = 0.008). In contrast, PLAYself scores were not significantly associated with internalizing symptoms at any time point. At T1–T2, the addition of PLAYfun and PLAYself accounted for 2.2% of the variance in internalizing behaviour (mean R² = 0.022, p = 0.002–0.03), and at T2–T3, accounted for 3.5% (mean R² = 0.035, p < 0.004). The inclusion of MVPA did not result in a significant increase in explained variance, except for in T1-T3. At this time point, adding MVPA in Block 3 increased the explained variance from R² = 0.002 to R² = 0.01 (ΔR² = 0.008), although MVPA did not independently predict internalizing symptoms (p = 0.13). Additionally, gender was only a significant predictor at T1–T2 (B = -0.52, p = 0.02), suggesting that girls exhibited a stronger negative relationship between physical literacy and internalizing symptoms during this period. 34 Table 7A. Regression Coefficients for Multiple Imputation Block Entry Model Using PL Composite Scores Predicting Internalizing Behaviour Over all Timepoints Unstandardized Coefficients 95% Confidence Interval for B Time Period Model Predictor B Std. Error t-value p-value Lower Bound Upper Bound R Squared Change Range R Squared Change Mean R Squared SD P-value Range T1-T2 Block 1 Constant 2.46 0.42 5.90 <.001 1.63 3.29 0.16 – 0.22 0.19 0.03 <0.001 Gender -0.52 0.23 -2.31 0.02 -0.97 -0.08 Baseline SDQ Int. 0.38 0.06 6.96 <.001 0.27 0.50 Block 2 Constant 2.41 0.42 5.76 <.001 1.58 3.24 0.001 – 0.007 0.004 0.002 0.09 – 0.49 Gender -0.53 0.23 -2.32 0.02 -0.98 -0.08 Baseline SDQ Int. 0.38 0.06 6.63 <.001 0.26 0.49 PL Comp -0.19 0.19 -1.00 0.32 -0.56 0.19 Block 3 Constant 1.89 0.95 1.98 0.07 -0.21 3.98 0.001 – 0.007 0.004 0.002 0.09 – 0.49 Gender -0.53 0.23 -2.28 0.02 -0.98 -0.07 Baseline SDQ Int. 0.39 0.07 5.82 <.001 0.25 0.53 PL Comp -0.23 0.19 -1.18 0.24 -0.61 0.16 MVPA 0.00 0.01 0.69 0.50 -0.01 0.02 T2-T3 Block 1 Constant 2.23 0.45 4.98 <.001 1.35 3.11 0.13-0.17 0.16 0.02 <0.001 Gender -0.21 0.25 -0.83 0.41 -0.70 0.29 Baseline SDQ Int. 0.43 0.06 7.01 <.001 0.31 0.55 Block 2 Constant 2.27 0.45 5.03 <.001 1.38 3.16 0.00-0.02 0.01 0.01 0.003-0.33 Gender -0.19 0.25 -0.75 0.45 -0.69 0.31 Baseline SDQ Int. 0.42 0.06 6.87 <.001 0.30 0.54 PL Comp -0.42 0.27 -1.54 0.14 -1.00 0.16 Block 3 Constant 1.20 0.85 1.41 0.17 -0.58 2.98 0.00-0.02 0.01 0.01 0.002-0.77 Gender -0.20 0.25 -0.79 0.43 -0.70 0.30 Baseline SDQ Int. 0.42 0.06 7.18 <.001 0.30 0.53 PL Comp -0.54 0.26 -2.04 0.05 -1.08 0.01 MVPA 0.01 0.01 1.42 0.19 -0.01 0.02 T1-T3 Block 1 Constant 2.57 0.46 5.55 <.001 1.66 3.48 0.08-0.09 0.08 0.004 <0.001 Gender -0.20 0.28 -0.71 0.48 -0.74 0.35 35 Baseline SDQ Int. 0.29 0.05 5.46 <.001 0.18 0.39 Block 2 Constant 2.54 0.47 5.42 <.001 1.62 3.46 0.001-0.004 0.002 0.002 0.21-0.72 Gender -0.21 0.28 -0.74 0.46 -0.75 0.34 Baseline SDQ Int. 0.28 0.05 5.25 <.001 0.18 0.38 PL Comp -0.16 0.21 -0.74 0.46 -0.58 0.26 Block 3 Constant 1.37 0.89 1.53 0.14 -0.45 3.18 0.002-0.02 0.01 0.007 0.006-0.355 Gender -0.18 0.28 -0.66 0.51 -0.74 0.37 Baseline SDQ Int. 0.30 0.06 5.50 <.001 0.19 0.41 PL Comp -0.24 0.22 -1.10 0.27 -0.69 0.20 MVPA 0.01 0.01 1.58 0.13 0.00 0.02 36 Table 7B. Regression Coefficients for Multiple Imputation Block Entry Model Using PLAYfun and PLAYself Predicting Internalizing Behaviour Over all Timepoints Unstandardized Coefficients 95% Confidence Interval for B R Squared Change Range R Squared Change Mean R Squared SD P-value Range Time Period Model Predictor B Std. Error t-value p-value Lower Bound Upper Bound T1-T2 Block 1 Constant 2.37 0.43 5.51 <.001 1.52 3.23 0.15-0.21 0.18 0.02 <0.001 Gender -0.39 0.25 -1.56 0.12 -0.87 0.10 Baseline SDQ Int. 0.40 0.05 7.51 <.001 0.29 0.50 Block 2 Constant 4.48 1.65 2.72 0.01 1.08 7.89 0.02-0.03 0.02 0.00 0.002-0.03 Gender -0.43 0.24 -1.75 0.08 -0.91 0.05 Baseline SDQ Int. 0.37 0.06 6.40 <.001 0.25 0.48 Playself 0.01 0.01 1.04 0.30 -0.01 0.04 PLAYfun -0.06 0.03 -2.49 0.02 -0.11 -0.01 Block 3 Constant 4.37 1.70 2.57 0.02 0.84 7.91 0-0.004 0.002 0.00 0.20-0.88 Gender -0.43 0.24 -1.75 0.08 -0.91 0.05 Baseline SDQ Int. 0.37 0.06 6.30 <.001 0.25 0.49 Playself 0.01 0.01 1.02 0.31 -0.01 0.03 PLAYfun -0.06 0.03 -2.55 0.01 -0.11 -0.01 MVPA 0.00 0.01 0.40 0.69 -0.01 0.01 T2-T3 Block 1 Constant 2.41 0.57 4.26 <.001 1.26 3.55 0.14-0.19 0.17 0.02 <0.001 Gender -0.25 0.32 -0.78 0.44 -0.90 0.40 Baseline SDQ Int. 0.45 0.06 7.41 <.001 0.33 0.57 Block 2 Constant 9.48 2.05 4.63 <.001 5.44 13.52 0.02-0.04 0.04 0.00 <0.001-0.004 Gender -0.25 0.30 -0.85 0.40 -0.85 0.34 Baseline SDQ Int. 0.41 0.06 6.72 <.001 0.29 0.53 Playself -0.02 0.01 -1.52 0.13 -0.04 0.01 PLAYfun -0.11 0.04 -2.75 0.01 -0.19 -0.03 Block 3 Constant 9.60 2.03 4.73 <.001 5.60 13.60 0.001-0.009 0.006 0.00 0.04-0.25 Gender -0.25 0.30 -0.84 0.40 -0.86 0.35 Baseline SDQ Int. 0.40 0.06 6.56 <.001 0.28 0.52 Playself -0.02 0.01 -1.87 0.06 -0.05 0.00 37 PLAYfun -0.12 0.04 -2.87 0.01 -0.21 -0.04 MVPA 0.01 0.01 1.30 0.20 0.00 0.02 T1-T3 Block 1 Constant 2.64 0.56 4.72 <.001 1.52 3.75 0.07-0.09 0.08 0.00 <0.001 Gender -0.11 0.34 -0.32 0.75 -0.78 0.57 Baseline SDQ Int. 0.30 0.06 5.14 <.001 0.19 0.42 Block 2 Constant 5.06 2.44 2.08 0.06 -0.31 10.44 0.01-0.04 0.02 0.01 <0.001-0.15 Gender -0.18 0.34 -0.54 0.60 -0.85 0.49 Baseline SDQ Int. 0.27 0.06 4.48 <.001 0.15 0.39 Playself 0.01 0.02 0.59 0.56 -0.03 0.05 PLAYfun -0.06 0.04 -1.86 0.08 -0.14 0.01 Block 3 Constant 4.66 2.40 1.94 0.08 -0.57 9.89 0.001-0.01 0.006 0.00 0.04-0.47 Gender -0.16 0.34 -0.47 0.64 -0.84 0.52 Baseline SDQ Int. 0.28 0.06 4.65 <.001 0.16 0.40 Playself 0.01 0.02 0.46 0.65 -0.03 0.05 PLAYfun -0.07 0.04 -1.95 0.07 -0.15 0.01 MVPA 0.01 0.01 1.30 0.20 0.00 0.02 38 3.2.2 Externalizing Behaviour After imputation, baseline externalizing scores consistently emerged as the strongest predictor of future externalizing symptoms across all models (all p < 0.001; Table 8). In the block entry model using the PL composite as the main predictor, the PL composite was not a significant predictor of SDQ externalizing scores at any time point. Across all time periods, the inclusion of PL composite in block 2, or MVPA in block 3, did not significantly explain any of the variance in externalizing outcomes (Table 8A). When physical literacy components were analyzed separately (Table 8B), higher PLAYfun scores significantly predicted lower externalizing behaviour from T2–T3 (B = -0.098, p = 0.043), accounting for 1.4% of the total variance explained (mean R² = 0.014). This relationship increased slightly (B = -0.105, p = 0.042) after the addition of MVPA to the model in block 3. Higher PLAYself scores did not significantly predict externalizing behaviour at any time points. Additionally, neither PLAYfun nor PLAYself were significant predictors across the full T1–T3 interval. The inclusion of PLAYfun and PLAYself did not significantly contribute to any changes in explained variance across any timepoints (Table 8B). As with internalizing symptoms, gender was not a significant predictor in any of the models, indicating that externalizing symptoms did not differ by gender. 39 Table 8A. Regression Coefficients for Multiple Imputation Block Entry Model Using PL Composite Scores Predicting Externalizing Behaviour Over all Timepoints Unstandardized Coefficients 95% Confidence Interval for B R Squared Change Range R Squared Change Mean R Squared SD P-value Range Time Period Model Predictor B Std. Error t-value p-value Lower Bound Upper Bound T1-T2 Block 1 Constant 1.92 0.49 3.95 <.001 0.97 2.87 0.28- 0.31 0.29 0.01 <0.001 Gender -0.01 0.26 -0.06 0.96 -0.53 0.50 Baseline SDQ Ext. 0.50 0.04 11.46 <.001 0.41 0.58 Block 2 Constant 1.86 0.50 3.74 <.001 0.88 2.84 0.00 0.00 0.00 0.17-0.92 Gender -0.02 0.26 -0.06 0.95 -0.53 0.50 Baseline SDQ Ext. 0.50 0.04 11.45 <.001 0.41 0.58 PL Comp -0.14 0.23 -0.60 0.55 -0.59 0.32 Block 3 Constant 1.76 0.77 2.30 0.02 0.24 3.29 0.00 0.00 0.00 0.36-0.89 Gender -0.02 0.26 -0.07 0.95 -0.53 0.50 Baseline SDQ Ext. 0.50 0.04 11.23 <.001 0.41 0.58 PL Comp -0.15 0.23 -0.63 0.53 -0.62 0.32 MVPA 0.00 0.01 0.17 0.87 -0.01 0.01 T2-T3 Block 1 Constant 2.75 0.62 4.41 <.001 1.52 3.98 0.22-0.26 0.24 0.02 <0.001 Gender -0.14 0.33 -0.43 0.67 -0.78 0.50 Baseline SDQ Ext. 0.59 0.06 9.33 <.001 0.47 0.72 Block 2 Constant 2.79 0.62 4.48 <.001 1.56 4.03 0.00-0.01 0.01 0.00 0.01- 0.24 Gender -0.12 0.33 -0.36 0.72 -0.77 0.53 Baseline SDQ Ext. 0.58 0.06 9.27 <.001 0.46 0.71 PL Comp -0.39 0.29 -1.35 0.18 -0.97 0.19 Block 3 Constant 3.03 1.07 2.84 0.01 0.81 5.24 0.00 0.00 0.00 0.18-0.95 Gender -0.12 0.34 -0.36 0.72 -0.78 0.54 Baseline SDQ Ext. 0.59 0.06 9.49 <.001 0.46 0.71 PL Comp -0.37 0.31 -1.18 0.25 -0.99 0.26 MVPA 0.00 0.01 -0.31 0.76 -0.02 0.01 T1-T3 Block 1 Constant 2.27 0.63 3.61 <.001 1.03 3.50 0.25- 0.29 0.27 0.02 <0.001 Gender 0.02 0.35 0.06 0.96 -0.67 0.71 Baseline SDQ Ext. 0.59 0.05 10.96 <.001 0.48 0.70 40 Block 2 Constant 2.19 0.66 3.34 0.00 0.90 3.49 0.00- 0.01 0.00 0.00 0.06-0.60 Gender 0.01 0.35 0.03 0.98 -0.67 0.69 Baseline SDQ Ext. 0.59 0.05 10.97 <.001 0.48 0.69 PL Comp -0.22 0.33 -0.65 0.52 -0.92 0.49 Block 3 Constant 2.10 1.33 1.59 0.14 -0.79 5.00 0.00 0.00 0.00 0.13- 0.99 Gender 0.00 0.35 0.01 1.00 -0.68 0.69 Baseline SDQ Ext. 0.59 0.05 10.80 <.001 0.48 0.69 PL Comp -0.23 0.37 -0.61 0.55 -1.04 0.58 MVPA 0.00 0.01 0.10 0.92 -0.02 0.02 41 Table 8B. Regression Coefficients for Multiple Imputation Block Entry Model Using PLAYfun and PLAYself Predicting Externalizing Behaviour Over all Timepoints Unstandardized Coefficients 95% Confidence Interval for B R Squared Change Range R Squared Change Mean R Squared SD P-value Range Time Period Model Predictor B Std. Error t-value p-value Lower Bound Upper Bound T1-T2 Block 1 Constant 2.27 0.68 3.34 0.00 0.86 3.68 0.23-0.30 0.30 0.03 <0.001 Gender -0.09 0.38 -0.25 0.81 -0.90 0.71 Baseline SDQ Ext. 0.50 0.06 8.88 <.001 0.39 0.62 Block 2 Constant 3.53 1.74 2.03 0.05 0.03 7.03 0.003-0.007 0.005 0.00 0.18-0.58 Gender -0.12 0.39 -0.30 0.77 -0.95 0.71 Baseline SDQ Ext. 0.49 0.06 8.66 <.001 0.38 0.61 Playself 0.00 0.02 0.26 0.80 -0.03 0.04 PLAYfun -0.03 0.03 -1.21 0.23 -0.09 0.02 Block 3 Constant 3.41 1.70 2.00 0.05 0.01 6.80 0-0.008 0.002 0.00 0.05-0.91 Gender -0.12 0.40 -0.29 0.78 -0.97 0.74 Baseline SDQ Ext. 0.49 0.06 8.42 <.001 0.37 0.61 Playself 0.00 0.02 0.18 0.86 -0.03 0.04 PLAYfun -0.04 0.03 -1.27 0.21 -0.10 0.02 MVPA 0.00 0.01 0.57 0.58 -0.01 0.02 T2-T3 Block 1 Constant 2.86 0.66 4.36 <.001 1.57 4.15 0.16-0.25 0.21 0.04 <0.001 Gender -0.04 0.37 -0.12 0.91 -0.78 0.69 Baseline SDQ Ext. 0.55 0.08 7.26 <.001 0.39 0.71 Block 2 Constant 8.44 2.87 2.94 0.01 2.67 14.21 0.007-0.02 0.01 0.00 0.01-0.20 Gender -0.06 0.37 -0.15 0.88 -0.78 0.67 Baseline SDQ Ext. 0.53 0.08 6.80 <.001 0.37 0.70 Playself -0.01 0.02 -0.35 0.73 -0.04 0.03 PLAYfun -0.10 0.05 -2.06 0.04 -0.19 0.00 Block 3 Constant 8.50 2.87 2.96 0.01 2.74 14.26 0.001-0.008 0.004 0.00 0.06-0.91 Gender -0.05 0.37 -0.13 0.90 -0.77 0.68 Baseline SDQ Ext. 0.53 0.08 6.91 <.001 0.37 0.68 Playself -0.01 0.02 -0.46 0.65 -0.04 0.03 PLAYfun -0.11 0.05 -2.08 0.04 -0.21 0.00 42 MVPA 0.00 0.01 0.41 0.69 -0.02 0.03 T1-T3 Block 1 Constant 2.41 0.67 3.57 <.001 1.09 3.73 0.19-0.29 0.23 0.04 <0.001 Gender 0.12 0.39 0.31 0.76 -0.64 0.88 Baseline SDQ Ext. 0.57 0.07 8.16 <.001 0.43 0.72 Block 2 Constant 4.07 2.11 1.93 0.06 -0.15 8.29 0.003-0.009 0.006 0.00 0.11-0.51 Gender 0.06 0.39 0.16 0.88 -0.70 0.82 Baseline SDQ Ext. 0.56 0.07 7.85 <.001 0.41 0.71 Playself 0.01 0.02 0.50 0.62 -0.03 0.04 PLAYfun -0.05 0.03 -1.37 0.17 -0.11 0.02 Block 3 Constant 3.79 2.18 1.74 0.09 -0.60 8.19 0-0.01 0.006 0.00 0.01-0.70 Gender 0.07 0.39 0.19 0.85 -0.70 0.84 Baseline SDQ Ext. 0.55 0.07 8.02 <.001 0.41 0.69 Playself 0.01 0.02 0.37 0.72 -0.03 0.04 PLAYfun -0.06 0.03 -1.72 0.09 -0.12 0.01 MVPA 0.01 0.01 0.98 0.34 -0.01 0.03 43 3.2.3 Prosocial Behaviour After imputation, baseline prosocial scores were the only consistent and significant predictor of future prosocial behaviour across all models (all p < 0.001; Table 9). Neither the PL composite score nor its individual components (PLAYfun and PLAYself) were significantly associated with prosocial scores at any time point. The inclusion of these physical literacy variables resulted in minimal significant changes in explained variance across all models (Table 9). Similarly, MVPA did not significantly predict prosocial behaviour nor contribute meaningful increases in explained variance when added in Block 3. These findings suggest that physical literacy and MVPA were not related to changes in prosocial outcomes over time. Gender was also not a significant predictor in any model. 44 Table 9A. Regression Coefficients for Multiple Imputation Block Entry Model Using PL Composite Scores Predicting Prosocial Behaviour Over all Timepoints Unstandardized Coefficients 95% Confidence Interval for B R Squared Change Range R Squared Change Mean R Squared SD P-value Range Time Period Model Predictor B Std. Error t-value p-value Lower Bound Upper Bound T1-T2 Block 1 Constant 3.94 0.62 6.38 <.001 2.64 5.25 0.20- 0.29 0.24 0.03 <0.001 Gender 0.23 0.20 1.13 0.27 -0.19 0.64 Baseline SDQ Int. 0.49 0.06 8.74 <.001 0.38 0.60 Block 2 Constant 4.02 0.63 6.38 <.001 2.69 5.34 0.00 0.00 0.00 0.13-0.69 Gender 0.23 0.20 1.14 0.26 -0.19 0.65 Baseline Prosocial 0.48 0.06 8.61 <.001 0.37 0.60 PL Comp 0.10 0.14 0.74 0.46 -0.17 0.37 Block 3 Constant 4.02 0.76 5.26 <.001 2.40 5.63 0.00 0.00 0.00 0.35- 0.93 Gender 0.23 0.20 1.14 0.26 -0.18 0.65 Baseline Prosocial 0.48 0.06 8.68 <.001 0.37 0.59 PL Comp 0.10 0.14 0.75 0.45 -0.16 0.37 MVPA 0.00 0.00 0.00 1.00 -0.01 0.01 T2-T3 Block 1 Constant 2.81 0.51 5.50 <.001 1.78 3.84 0.30- 0.38 0.34 0.03 <0.001 Gender 0.32 0.18 1.80 0.08 -0.04 0.68 Baseline Prosocial 0.59 0.07 8.92 <.001 0.45 0.74 Block 2 Constant 2.84 0.56 5.07 <.001 1.69 4.00 0.00 0.00 0.00 0.10-0.98 Gender 0.32 0.18 1.78 0.08 -0.04 0.68 Baseline Prosocial 0.59 0.07 8.12 <.001 0.43 0.75 PL Comp 0.05 0.19 0.27 0.79 -0.36 0.46 Block 3 Constant 3.16 0.57 5.51 <.001 2.02 4.30 0.00-0.01 0.00 0.00 0.07- 0.56 Gender 0.32 0.18 1.81 0.08 -0.03 0.68 Baseline Prosocial 0.60 0.08 7.98 <.001 0.43 0.76 PL Comp 0.09 0.18 0.48 0.64 -0.30 0.47 MVPA 0.00 0.00 -1.08 0.28 -0.01 0.00 T1-T3 Block 1 Constant 3.38 0.50 6.73 <.001 2.39 4.37 0.24-0.29 0.26 0.02 <.001 Gender 0.32 0.19 1.66 0.10 -0.06 0.70 Baseline Prosocial 0.53 0.06 9.46 <.001 0.42 0.64 Block 2 Constant 3.39 0.52 6.57 <.001 2.37 4.41 0.00 0.00 0.00 0.47-0.81 45 Gender 0.32 0.19 1.66 0.10 -0.06 0.71 Baseline Prosocial 0.53 0.06 9.22 <.001 0.41 0.64 PL Comp 0.02 0.14 0.16 0.87 -0.26 0.31 Block 3 Constant 3.90 0.77 5.08 <.001 2.29 5.51 0.00-0.02 0.01 0.01 0.003-0.97 Gender 0.31 0.20 1.54 0.13 -0.09 0.71 Baseline Prosocial 0.53 0.06 9.46 <.001 0.42 0.65 PL Comp 0.07 0.15 0.46 0.65 -0.23 0.37 MVPA -0.01 0.01 -1.04 0.32 -0.02 0.01 46 Table 9B. Regression Coefficients for Multiple Imputation Block Entry Model Using PLAYfun and PLAYself Predicting Prosocial Behaviour Over all Timepoints Unstandardized Coefficients 95% Confidence Interval for B R Squared Change Range R Squared Change Mean R Squared SD P-value Range Time Period Model Predictor B Std. Error t-value p-value Lower Bound Upper Bound T1-T2 Block 1 Constant 4.62 0.46 10.06 <.001 3.72 5.52 0.16-0.20 0.18 0.02 <0.001 Gender 0.20 0.19 1.05 0.30 -0.18 0.57 Baseline SDQ Ext. 0.39 0.05 7.62 <.001 0.29 0.50 Block 2 Constant 3.80 0.88 4.33 <.001 2.08 5.52 0.002-0.005 0.004 0.00 0.36-0.66 Gender 0.20 0.18 1.09 0.28 -0.17 0.57 Baseline Prosocial 0.39 0.05 7.48 <.001 0.29 0.49 Playself 0.01 0.01 0.88 0.38 -0.01 0.02 PLAYfun 0.01 0.02 0.63 0.53 -0.02 0.04 Block 3 Constant 3.77 0.88 4.27 <.001 2.03 5.50 0-0.001 0.0006 0.00 0.56-0.91 Gender 0.20 0.18 1.09 0.28 -0.17 0.57 Baseline Prosocial 0.39 0.05 7.47 <.001 0.28 0.49 Playself 0.01 0.01 0.82 0.42 -0.01 0.02 PLAYfun 0.01 0.02 0.54 0.59 -0.03 0.04 MVPA 0.00 0.00 0.38 0.71 -0.01 0.01 T2-T3 Block 1 Constant 3.82 0.58 6.60 <.001 2.64 4.99 0.17-0.23 0.20 0.02 <0.001 Gender 0.33 0.21 1.55 0.14 -0.11 0.77 Baseline Prosocial 0.46 0.07 6.78 <.001 0.32 0.59 Block 2 Constant 1.80 1.18 1.53 0.13 -0.51 4.11 0.005-0.02 0.01 0.00 0.04-0.31 Gender 0.33 0.21 1.55 0.14 -0.11 0.78 Baseline Prosocial 0.43 0.07 6.31 <.001 0.29 0.58 Playself 0.01 0.01 0.95 0.35 -0.01 0.02 PLAYfun 0.03 0.02 1.38 0.17 -0.01 0.08 Block 3 Constant 1.76 1.18 1.50 0.14 -0.55 4.08 0-0.006 0.003 0.00 0.08-0.75 Gender 0.33 0.22 1.54 0.14 -0.12 0.78 Baseline Prosocial 0.43 0.07 6.30 <.001 0.29 0.58 Playself 0.01 0.01 1.13 0.26 -0.01 0.03 PLAYfun 0.04 0.03 1.47 0.15 -0.01 0.09 47 MVPA 0.00 0.00 -0.72 0.48 -0.01 0.01 T1-T3 Block 1 Constant 4.59 0.49 9.33 <.001 3.62 5.55 0.13-0.15 0.14 0.00 <.001 Gender 0.26 0.21 1.22 0.23 -0.17 0.69 Baseline Prosocial 0.37 0.05 7.19 <.001 0.27 0.47 Block 2 Constant 4.08 1.08 3.77 <.001 1.92 6.24 0-0.007 0.003 0.00 0.22-0.99 Gender 0.26 0.21 1.25 0.22 -0.16 0.69 Baseline Prosocial 0.37 0.05 7.09 <.001 0.27 0.47 Playself 0.01 0.01 0.56 0.58 -0.01 0.02 PLAYfun 0.00 0.02 0.25 0.80 -0.03 0.04 Block 3 Constant 4.25 1.10 3.85 <.001 2.04 6.47 0.003-0.01 0.008 0.00 0.02-0.26 Gender 0.25 0.21 1.19 0.24 -0.17 0.68 Baseline Prosocial 0.37 0.05 7.23 <.001 0.27 0.47 Playself 0.01 0.01 0.69 0.50 -0.01 0.03 PLAYfun 0.01 0.02 0.61 0.54 -0.02 0.04 MVPA -0.01 0.00 -1.53 0.13 -0.01 0.00 48 3.2.4 Total Difficulties After imputation, baseline total difficulties scores were the strongest and most consistent predictors of future total difficulties across all time points (all p < 0.001; Table 10). In the block entry model using the PL composite as the main predictor (Table 10A), the PL composite was a significant negative predictor of total difficulties from T2–T3. Specifically, higher PL composite scores at T2 significantly predicted lower total difficulties at T3 (B = -0.84, p = 0.05 in Block 2; B = -1.04, p = 0.01 in Block 3), accounting for a mean increase in explained variance of 1% (mean R² = 0.01). Only at T2-T3, the inclusion of MVPA in block 3 strengthened the relationship between the PL composite and total difficulties, but very minimally. However, MVPA consistently did not predict total difficulties across all models. No significant effects of the PL composite were observed from T1–T2. When PLAYfun and PLAYself were analyzed separately (Table 10B), PLAYfun emerged as a consistent negative predictor of total difficulties from T1–T2 (B = -0.087, p = 0.037) and T2–T3 (B = -0.212, p = 0.017). PLAYself was not a significant predictor at any time point. Including PLAYself and PLAYfun significantly accounted for 2.7% of the variance in total difficulties at T2 and T3 (mean R² = 0.027, p = 0.001-0.02). As in previous models, gender was not significantly associated with total difficulties, indicating similar patterns for boys and girls. 49 Table 10A. Regression Coefficients for Multiple Imputation Block Entry Model Using PL Composite Scores Predicting Total Difficulties Over all Timepoints Unstandardized Coefficients 95% Confidence Interval for B R Squared Change Range R Squared Change Mean R Squared SD P-value Range Time Period Model Predictor B Std. Error t-value p-value Lower Bound Upper Bound T1-T2 Block 1 Constant 4.15 0.77 5.38 <.001 2.63 5.68 0.26-0.29 0.27 0.01 <0.001 Gender -0.54 0.40 -1.36 0.17 -1.32 0.24 Baseline Total Diff. 0.49 0.05 10.66 <.001 0.40 0.58 Block 2 Constant 4.08 0.78 5.25 <.001 2.55 5.62 0.00 0.00 0.00 0.13-0.80 Gender -0.55 0.40 -1.38 0.17 -1.33 0.23 Baseline Total Diff. 0.48 0.05 10.39 <.001 0.39 0.57 PL Comp -0.26 0.35 -0.73 0.47 -0.96 0.44 Block 3 Constant 3.25 1.41 2.30 0.03 0.32 6.17 0.00-0.01 0.00 0.00 0.04-0.79 Gender -0.55 0.40 -1.36 0.17 -1.33 0.24 Baseline Total Diff. 0.49 0.05 9.96 <.001 0.39 0.58 PL Comp -0.33 0.37 -0.90 0.38 -1.08 0.42 MVPA 0.01 0.01 0.82 0.42 -0.01 0.02 T2-T3 Block 1 Constant 14.35 2.00 7.16 <.001 10.07 18.63 0.02-0.09 0.06 0.03 <0.001 Gender -0.57 0.53 -1.08 0.28 -1.62 0.48 Baseline Total Diff. -0.64 0.24 -2.67 0.02 -1.18 -0.11 Block 2 Constant 13.80 2.01 6.85 <.001 9.50 18.09 0.01-0.02 0.01 0.00 0.02-0.09 Gender -0.55 0.53 -1.03 0.30 -1.60 0.50 Baseline Total Diff. -0.57 0.24 -2.37 0.04 -1.11 -0.04 PL Comp -0.84 0.42 -1.99 0.05 -1.67 -0.01 Block 3 Constant 12.03 1.95 6.16 <.001 8.09 15.98 0.00-0.03 0.02 0.01 0.001-0.86 Gender -0.56 0.52 -1.08 0.28 -1.57 0.46 Baseline Total Diff. -0.61 0.26 -2.39 0.04 -1.18 -0.04 PL Comp -1.04 0.42 -2.48 0.01 -1.86 -0.22 MVPA 0.02 0.02 1.25 0.25 -0.02 0.05 T1-T3 Block 1 Constant 4.51 0.89 5.06 <.001 2.76 6.26 0.23-0.26 0.24 0.01 <.001 Gender -0.20 0.48 -0.43 0.67 -1.14 0.74 50 Baseline Total Diff. 0.52 0.05 10.01 <.001 0.42 0.63 Block 2 Constant 4.46 0.90 4.98 <.001 2.70 6.22 0.00 0.00 0.00 0.24-0.92 Gender -0.22 0.48 -0.45 0.65 -1.16 0.73 Baseline Total Diff. 0.52 0.05 9.90 <.001 0.42 0.62 PL Comp -0.22 0.39 -0.56 0.58 -1.00 0.56 Block 3 Constant 2.68 1.48 1.81 0.08 -0.27 5.63 0.00-0.01 0.01 0.00 0.01- 0.31 Gender -0.19 0.48 -0.40 0.69 -1.14 0.76 Baseline Total Diff. 0.52 0.05 9.95 <.001 0.42 0.63 PL Comp -0.38 0.43 -0.87 0.39 -1.26 0.50 MVPA 0.02 0.01 1.52 0.14 -0.01 0.04 51 Table 10B. Regression Coefficients for Multiple Imputation Block Entry Model Using PLAYfun and PLAYself Predicting Total Difficulties Over all Timepoints Unstandardized Coefficients 95% Confidence Interval for B R Squared Change Range R Squared Change Mean R Squared SD P-value Range Time Period Model Predictor B Std. Error t-value p-value Lower Bound Upper Bound T1-T2 Block 1 Constant 4.41 1.00 4.40 <.001 2.37 6.44 0.22-0.26 0.24 0.02 <0.001 Gender -0.54 0.54 -1.00 0.33 -1.63 0.56 Baseline Total Diff. 0.49 0.05 9.63 <.001 0.39 0.60 Block 2 Constant 7.33 2.54 2.88 0.00 2.32 12.34 0.008-0.009 0.009 0.00 0.12-0.16 Gender -0.60 0.54 -1.12 0.27 -1.70 0.50 Baseline Total Diff. 0.47 0.05 9.13 <.001 0.37 0.58 Playself 0.01 0.02 0.66 0.51 -0.02 0.05 PLAYfun -0.08 0.04 -1.92 0.06 -0.16 0.00 Block 3 Constant 7.02 2.56 2.74 0.01 1.98 12.06 0.001-0.003 0.002 0.00 0.23-0.42 Gender -0.59 0.53 -1.11 0.27 -1.67 0.49 Baseline Total Diff. 0.47 0.05 9.22 <.001 0.37 0.58 Playself 0.01 0.02 0.53 0.60 -0.03 0.05 PLAYfun -0.09 0.04 -2.09 0.04 -0.17 -0.01 MVPA 0.01 0.01 0.98 0.33 -0.01 0.02 T2-T3 Block 1 Constant 5.68 1.12 5.09 <.001 3.45 7.91 0.16-0.23 0.19 0.03 <0.001 Gender -0.60 0.60 -1.00 0.32 -1.79 0.60 Baseline Total Diff. 0.48 0.06 7.55 <.001 0.36 0.61 Block 2 Constant 17.44 4.96 3.52 0.00 7.13 27.75 0.02-0.04 0.03 0.00 <0.001-0.02 Gender -0.64 0.60 -1.06 0.30 -1.85 0.58 Baseline Total Diff. 0.45 0.07 6.79 <.001 0.31 0.58 Playself -0.01 0.02 -0.30 0.77 -0.06 0.04 PLAYfun -0.21 0.08 -2.56 0.02 -0.38 -0.04 Block 3 Constant 17.66 5.04 3.50 0.00 7.12 28.20 0.001-0.01 0.006 0.00 0.01-0.21 Gender -0.64 0.59 -1.08 0.29 -1.82 0.55 Baseline Total Diff. 0.44 0.07 6.29 <.001 0.30 0.58 Playself -0.01 0.02 -0.56 0.58 -0.06 0.03 PLAYfun -0.23 0.10 -2.42 0.03 -0.44 -0.03 52 MVPA 0.01 0.01 0.81 0.44 -0.02 0.04 T1-T3 Block 1 Constant 5.07 1.27 4.00 <.001 2.47 7.66 0.17-0.24 0.20 0.03 <0.001 Gender -0.44 0.73 -0.60 0.56 -1.97 1.09 Baseline Total Diff. 0.52 0.07 7.83 <.001 0.39 0.65 Block 2 Constant 8.03 3.29 2.44 0.02 1.45 14.60 0.003-0.02 0.01 0.00 0.01-0.52 Gender -0.56 0.73 -0.76 0.45 -2.09 0.97 Baseline Total Diff. 0.49 0.06 7.77 <.001 0.37 0.62 Playself 0.02 0.03 0.57 0.58 -0.05 0.09 PLAYfun -0.09 0.06 -1.52 0.13 -0.20 0.03 Block 3 Constant 7.35 3.39 2.17 0.04 0.53 14.16 0-0.02 0.01 0.00 0.002-0.80 Gender -0.53 0.71 -0.74 0.47 -2.00 0.95 Baseline Total Diff. 0.50 0.06 7.95 <.001 0.37 0.62 Playself 0.01 0.03 0.43 0.68 -0.05 0.08 PLAYfun -0.11 0.06 -1.69 0.10 -0.24 0.02 MVPA 0.02 0.01 1.22 0.25 -0.01 0.05 53 Chapter 4. Discussion 4.1 Overview The objective of this study was to explore if physical literacy (operationalized by the PL Composite score) predicted mental health after one- and two-year time intervals among elementary school children. Overall, the findings highlight a small, yet significant relationship between physical literacy and mental health, specifically in the second year of measurement (T2-T3). Internalizing behaviours and total difficulties showed the most consistent relationships. This suggests that physical literacy is more strongly linked to reduced emotional symptoms and peer problems (domains of the SDQ associated with inward-facing challenges such as anxiety, withdrawal, or low self-esteem), compared to externalizing behaviours (like hyperactivity and conduct problems) and prosocial behaviours. Importantly, an exploratory question addressed which physical literacy components showed the strongest associations: fundamental movement skills as measured with the PLAYfun tool or perceived competence as measured with the PLAYself tool. Overall, associations between PLAYfun scores and mental health were stronger than those between PLAYself scores and mental health. This suggests that fundamental movement skills may play a more important role in children’s mental health at this developmental stage. Several cross-sectional studies have explored the relationship between physical literacy, physical activity, and mental health or well-being in children and adolescents under the age of 15 (Blain et al., 2021; Jefferies et al., 2019; Melby, Elsborg, et al., 2022; Melby, Nielsen, et al., 2022; Tang et al., 2023). Other work has linked physical literacy with health-related quality of life (Caldwell et al., 2020), and several more recent cross-sectional studies have investigated physical literacy and mental health in adolescent and young adult populations (Dong et al., 2023; 54 Gao et al., 2024; Kan et al., 2024; Leung et al., 2025; Ma et al., 2021; She et al., 2023; Wang et al., 2024; Yang et al., 2024; Zhu et al., 2025). The cross-sectional baseline findings reported by Tang et al. (2023) can now be extended. At baseline, PLAYself scores were not associated with mental health outcomes, whereas PLAYfun scores showed significant negative associations with internalizing problems (r(305) = –0.28, p <.001) and total difficulties (r(305) = –0.19, p <.001). These are consistent with the longitudinal findings, where PLAYfun was associated with total difficulties and internalizing behaviour, though Tang et al. (2023) also observed associations between the PL composite and prosocial scores. While PL may demonstrate cross-sectional relationships with prosocial behaviour, these effects do not persist over time, likely because prosocial behaviour is more influenced by multiple developmental and contextual factors. Additionally, several prosocial items overlap conceptually with peer and social-emotional domains captured in the internalizing and externalizing scores (Figure 2, Appendix B), suggesting their unique predictive contribution may be diminished once these broader domains are modelled over time. Using a composite PL measure, Melby and Nielsen et al. (2022) also found associations with internalizing scores (β = –0.24, p < .001), which were very similar to the current study. However, they further reported associations with total difficulties (β = –0.32, p < .001) and externalizing scores (β = –0.31, p < .001). This discrepancy may reflect differences in measurement tools, as their study used a Danish version of the CAPL, which may capture slightly different dimensions of PL compared with the PLAYtools employed in the current study. Finally, research investigating PL in relation to broader constructs of well-being also supports the findings. For example, the positive correlations between resilience and confidence (rs = 0.25, p = 0.001), overall PL (rs = 0.21, p =0.004), and self-reported PL (rs = 0.30, p <.001) found by 55 Jefferies et al. (2019) support PL’s overall relationship with positive psychological outcomes. This trend is similarly found through the positive relationships between PL, vitality (β = 0.53, p < 0.001), and positive affect (β = 0.39, p < 0.001), as well as a negative relationship with negative affect (β = –0.25, p < 0.05) in the study by Blain et al. (2021). These results are similar to those reported by Melby, Elsborg, et al. (2022), who linked physical literacy to multiple aspects of well-being. Other studies utilizing the PL composite scores have shown a direct relationship to health-related quality of life (HRQOL) and life satisfaction. For example, Caldwell et al. (2020) found PL was directly, significantly associated with health-related quality of life in a sample of 222 school-aged children (β = 1.73, p < 0.001; Caldwell et al., 2020). Similar patterns have emerged in college populations, where higher PL has been linked to greater life satisfaction (β = 0.21, t = 8.19, p < 0.01; Kan et al., 2024) and quality of life (Gao et al., 2024). Research in adult populations further reinforces these findings, with PL emerging as a significant predictor of both well-being and mental health. For example, Yang et al. (2024) found PL positively predicted subjective well-being (β = 1.230, t = 15.208, p < 0.001) among a sample of 1968 Chinese university students, as measured through online questionnaires. Consistent findings have also been observed in similar adult research (Ma et al., 2021; Zhu et al., 2025). Overall, these findings support growing evidence that physical literacy is linked to children’s emotional and behavioural functioning (Cairney et al., 2019; Fortnum et al., 2018). However, to my knowledge, the current study is the first to provide longitudinal evidence that physical literacy predicts future mental health among children. This addresses a key limitation of existing research, which relies on cross-sectional research designs. Overall, results on this topic 56 also emphasize the importance of considering developmental stage, context, and measurement comparisons when interpreting these relationships over time. 4.2 Potential mechanisms explaining the Physical Literacy-Mental Health Relationship Cairney’s conceptual model for Physical Literacy across the lifespan provides a foundation for speculating a direct relationship between physical literacy and mental health (Cairney et al., 2019). Unlike other PL theoretical foundations, this model integrates psychological and experiential components that are essential to understanding how physical literacy may influence health outcomes. One speculative theoretical explanation for PL predicting better mental health might be through the lens of the Basic Psychological Needs sub-theory of Self-Determination Theory (SDT), which emphasizes the role of competence, autonomy, and relatedness in fostering psychological well-being. Physical literacy, particularly through its affective components of motivation and confidence, has the potential to satisfy these needs in physical education or exercise settings (Ryan & Deci, 2000; Vallerand, 2000). When these needs are satisfied, individuals can experience well-being in an activity, which is necessary for sustained intrinsic motivation for the activity. Therefore, fostering physical environments that promote autonomy, relatedness, and competence is critical for the development of psychological well-being in children. Importantly, these effects can extend beyond physical activity contexts. For example, if the affective domains of confidence and motivation developed through physical literacy go beyond just motor action, they may also provide or help young people acquire the skills and abilities to better negotiate for, and navigate to, resources that sustain their well-being in different contexts (Jefferies et al., 2019). Therefore, the emotional and psychological benefits experienced during movement or motor skill acquisition may generalize to broader domains of 57 life and further influence global well-being (Melby & Nielsen, 2022; Standage et al., 2012). Moreover, frustration of these needs, such as when physical literacy is low, may contribute to decreased well-being and disengagement. This theoretical framing provides a plausible explanation as to why physical literacy, even when independent of physical activity levels, contributes to mental health outcomes. 4.3 Differential Associations with Mental Health: Comparing Fundamental Movement Skills and Physical Self-Perceptions Although the PL composite score was significantly associated with internalizing behaviours and total difficulties, examination of the contribution of its two measured components was revealing. PLAYself scores were not associated with any mental health outcomes. However, existing conceptual models, such as the Exercise and Self-Esteem Model (EXSEM), suggest that changes in physical self-perceptions may influence mental health outcomes (e.g., physical self-worth; Sonstroem & Morgan, 1989). This contrast with theoretical foundations may be explained by the fact that self-perceptions are less stable or accurate in children aged 7–10, who are still developing the cognitive capacity to realistically assess their abilities. Young children tend to report higher or overly optimistic levels of self-competence (Bouffard et al., 2003; Harter & Pike, 1984). This misalignment may help explain the lack of significant associations between PLAYself scores and all four SDQ mental health measures. Conversely, high movement competence, as measured by the PLAYfun tool, may have a more direct effect on children’s experience of success, self-efficacy, peer validation, and motivation, all of which contribute to mental health. Given that the PL composite significantly predicted internalizing problems and total difficulties, this is likely explained by the stronger predictive value of the PLAYfun score. 58 Specific associations between PLAYfun scores and internalizing behaviour may also be explained by how motor skills foster positive psychosocial behaviours. The Environmental Stress Hypothesis suggests that poor motor competence contributes to internalizing problems through a cascade of negative psychosocial experiences (Cairney et al., 2013; Mancini et al., 2019). Research consistently shows that children with poor motor skills often face a range of psychosocial challenges (Skinner & Piek, 2001). Compared to their peers, they tend to be more introverted, report lower physical and social self-competence, and experience higher levels of anxiety (Lingam et al., 2012; Schoemaker & Kalverboer, 1994). Much of this research concerns Developmental Coordination Disorder—a condition characterized by problems with fine and/or gross motor coordination that result in impairment in everyday functioning, play, and academic achievement (Gibbs et al., 2007). Specifically, Skinner and Piek (2001) found that children and adolescents with Developmental Coordination Disorder (DCD) reported lower self-worth, reduced social support, and elevated anxiety compared to those without motor difficulties. In contrast, children with higher motor competence are more likely to experience success, social inclusion, and positive reinforcement in physical environments (Mancini et al., 2016; Mancini, Rigoli, Cairney, et al., 2016). These positive interactions help foster a stronger sense of self-worth, confidence, and emotional well-being. Additionally, a recent systematic review found that sport competence and skill mastery, two important components of physical literacy, mediated the relationship between physical activity and mental health (White et al., 2024). Therefore, it is arguable that even in highly active cohorts, it is the quality of motor experiences and development of physical competence that matters most. This may help explain the stronger associations between PLAYfun and internalizing behaviour, as children with higher competence are likely to have more positive, socially reinforcing experiences. Additionally, children with 59 poor motor proficiency often experience challenges in behavioural self-regulation (Miller et al., 2023) and performing everyday tasks, which has been recognized as a primary predictor of both interpersonal and intrapersonal difficulties (Caçola, 2016; Mancini et al., 2016). These children are more likely to be excluded by peers, experience conflict in the classroom, and develop negative self-perceptions, factors that contribute to frustration and stress (Bornstein et al., 2010). As a result, they may exhibit more externalizing behaviours such as aggression or hyperactivity as a coping mechanism in response to these challenges (Medeiros et al., 2022). Children with developmental coordination disorder have also been found to have higher rates of inattention (Missiuna et al., 2014). Together, these mechanisms offer a plausible explanation as to why fundamental movement skills were more strongly associated with both internalizing and externalizing behaviours, and total difficulties. Importantly, internalizing and externalizing behaviours rarely occur in isolation. Many children experience overlapping symptoms, and one domain often exacerbates the other over time (Rommelse et al., 2009). For example, work by Mederios et al. (2022) showed that externalizing behaviours may act as a pathway to internalizing problems such as anxiety or depression, serving as a critical mediator in the relationship between poor motor skills and children's emotional well-being. As such, it is not surprising that significant associations were observed between PLAYfun scores and total difficulties. Findings related to prosocial behaviour and physical literacy (or components thereof) remain mixed in the literature. Consistent with our results, Hirata et al. (2021) found no significant associations between prosocial behaviour and movement competence (utilizing the Movement Assessment Battery for Children–Second edition (MABC2)) in a longitudinal study in young children (3-6 years old). However, Bart et al. (2007) found conflicting results, reporting 60 that the motor abilities of 88 children aged 5 years were positively related to social and emotional adjustment in elementary school. These conflicting results are difficult to explain and highlight the possibility that while higher motor skills may support peer inclusion, they do not necessarily consistently drive prosocial intent. Even if a child has high motor competence, Wentzel et al. (2007) suggests that prosocial behaviour aligns more with their personal values and goals, social expectations, and emotional factors such as empathy. These inconsistencies may also be explained by differences in developmental stage. The relationship between physical literacy and prosocial behaviour may become more evident later in adolescence, when cognitive maturity increases, peer relationships take on greater social importance, and prosocial behaviour becomes more strongly aligned with personal values and social norms. This reiterates a need to investigate these relationships over longer periods, such as from childhood to adolescence (Hirata et al., 2021; Piek et al., 2008; Mancini, Rigoli, Heritage, et al., 2016). In all models, the relationship between physical literacy and mental health was tested to see if it varied by gender. A significant relationship was only found with internalizing behaviour between T1-T2 (B = -0.52, p = 0.02), suggesting that girls exhibited a stronger negative relationship between physical literacy and internalizing behaviour from T1–T2. Gender was not significant in any other models, suggesting that minimal gender differences exist in the longitudinal relationship between physical literacy and mental health. Although limited, existing research on gender differences in this relationship suggests only modest differences. For example, in a college population, Ma et al. (2021) reported that physical literacy was associated with mental health in both males and females, explaining 21% of the variance in males and 28% in females. Importantly, when examining only physical literacy differences between boys and girls, studies using a composite score and studies isolating PLAYfun and PLAYself have 61 reported minimal differences (Caldwell et al., 2020; Caldwell et al., 2021). Several other studies, however, have reported that boys tend to score higher than girls in the self-description domain of the PLAYself, particularly when a broader age range is considered (Ahn & Fedewa, 2011; Bailey, 2006; Dale et al., 2019; Donnelly et al., 2016; Gilic et al., 2022; Singh et al., 2019; Rush & Simmons, 2014). Previous studies also suggest that maturation affects children’s physical self-perceptions differently by gender (Fairclough & Ridgers, 2010). Therefore, it is reasonable to assume that perceived physical competence might be impacted by the gender maturation process each child goes through. In terms of fundamental movement skills, boys typically are found to outperform girls on the PLAYfun total score, as well as on the individual domains of running, upper body, and lower body object control (Caldwell et al., 2021). However, other research suggests no gender differences in locomotor skill performance in either childhood or adolescence (Barnett et al., 2010). Beyond physical literacy, gender differences in mental health outcomes have been consistently reported in the literature. Several studies have shown differences in prevalence and clinical presentation, with internalizing problems such as anxiety and depression more common among girls (Van Droogenbroeck et al., 2018), and externalizing problems such as conduct problems and ADHD more common among boys (Herrmann et al., 2024; Verhulst et al., 2003). Therefore, our small yet significant finding of higher PL being more strongly related to lower internalizing symptoms in girls aligns with previous literature and highlights potential importance for PL implementation. These established gender differences in both physical literacy and mental health highlight the potential value of stratifying future research by gender to better understand whether physical literacy contributes differently to mental health outcomes. 62 4.4 Mental Health Over Time Importantly, mental health (as measured by the SDQ) remained relatively stable over time in this study. While some fluctuation and short-term changes were observed, they were not significant. These results align with previous longitudinal research showing that mental health in childhood and adolescence tends to follow consistent patterns over short time intervals, when measured by the SDQ (Christensen et al., 2017), and even during significant events such as the COVID-19 pandemic (Guzman Holst et al., 2023). Understanding that mental health tends to remain stable over time is important for interpreting the findings of this study. It may provide context as to why significant relationships between physical literacy components and mental health outcomes were not observed across timepoints. Rather than reflecting a lack of effect, this stability suggests that mental health trajectories in childhood and adolescence are relatively entrenched short term, meaning that shifts are likely gradual and influenced by sustained, long-term changes in behaviour or environment. This highlights the need for longer-term longitudinal research to more accurately capture gradual changes in mental health and the influence of predictors over time. 4.5 Consideration of External Factors The small proportion of variance in mental health outcomes explained by physical literacy may reflect the influence of other protective factors. As Marsh and Hattie (1996) highlight in their structural models of self-concept, well-being is shaped by contributions from multiple domains, such as academic, social, and physical. This suggests that while physical literacy may strengthen the physical domain of self-concept, other areas, such as academic success or social relationships, may contribute more meaningfully to an individual’s mental health. In addition, research underscores the role of individual-level factors like self-regulation, 63 perceived competence, self-esteem, and a sense of mastery, all of which support mental well-being (Moffitt, 2011; Gala, 2015; Lee, 2016; Buchanan, 2023). Supportive environments also matter. Positive home experiences and strong social connections with parents, siblings, and peers are consistently shown to protect against mental health challenges (Buchanan, 2023; Sanji, 2024). Both the quality and quantity of these relationships appear especially important. Broader factors like access to quality education and higher socioeconomic status further contribute to resilience by offering structure, stability, and opportunity (Maurer, 2023; Buchanan, 2023). Recent research suggests that youth with low socioeconomic status, such as those from economically disadvantaged or racialized backgrounds, may be at risk of low physical literacy or FMS (Barnett et al., 2019). Several studies have also found that cultural background can influence fitness levels and motor competence between different countries (D’Hondt et al., 2019; Li et al., 2020). A plausible reason for this may be how beliefs, practices, and possible language barriers impact physical activity behaviour (Caperchione et al., 2009). For example, in some cultures, educational attainment may be prioritized over physical activity, and levels of parent support and encouragement for physical activity may be perceived to be lower (Wilson & Dollman, 2007). This is important, as children who engage in regular structured sport or grow up in households that actively support physical development tend to have more quality opportunities to practice and refine movement skills. Considering these factors in the context of this study is particularly important given the demographic makeup of the sample, which was relatively affluent and highly educated. Altogether, these layers of protection may help explain why the relationship between physical literacy and mental health appears relatively modest in the present results. 64 4.6 Strengths and Limitations Strengths of this study included its longitudinal design, which allowed for the analysis of relationships over time. This directly addresses an important gap in the existing research, as highlighted in my recent scoping review (Pascoal et al., under review). The use of multiple imputation also strengthened the results by reducing bias due to systematic attrition, a common problem in multi-wave longitudinal studies (Asendorpf et al., 2014; Erber, 1979; Little & Rubin, 2002). In addition, the use of device-based measures for physical activity reduced the risk of self-report bias and improving the precision of MVPA estimates (Adamo et al., 2009; Skender et al., 2016). Another key strength of this study was the utilization of the physical literacy composite score, as adapted by Caldwell et al. (2020). Previous work suggests that a composite score, rather than a single physical literacy assessment tool, may better reflect the multiple domains of physical literacy (Cairney et al., 2018; Caldwell et al., 2020; Edwards et al., 2017). Therefore, inclusion of this score as a primary independent variable aimed to better represent the holistic nature of physical literacy. Additionally, the validity and reliability of the PLAYtools are important to discuss. The PLAYfun and PLAYself scores have shown evidence of good internal structure (Barnett et al., 2023). Tests within a sample of 300 Canadian children aged 8-14 years confirmed test-retest reliability (ICC = 0.81-0.84), and convergent and construct validity, aligning with current definitions of physical literacy (Jefferies et al., 2021). The PLAYfun tool has also demonstrated good internal consistency scores when averaged across raters (Jefferies et al., 2021). The PLAYfun has also been found to have very good inter-rater agreement in recent studies (ICC = 0.81-0.89; Cairney et al., 2018; Tang et al., 2023). Finally, when compared to the other tools (such as the CAMSA obstacle course), the moderate to large correlations demonstrate 65 good convergent validity for the PLAYfun score, as both these tools were designed to measure the motor competence component of physical literacy (Stearns et al., 2019). However, in a recent review of the validity, reliability, and feasibility of physical literacy assessments designed for children, the PLAYtools were not considered the best assessment approach in terms of validity evidence (Barnett et al., 2023). Importantly, while there is evidence supporting the construct validity of the PLAYself tool, it primarily assesses perceived competence, knowledge and understanding. Other important aspects of physical literacy, such as motivation and affective experiences, are not assessed. These measurement considerations reflect a more pragmatic approach to the understanding of the concept of physical literacy (see Edwards et al., 2018). This more practical perspective seeks to identify and use measures that are compatible with evidence-based practice. In contrast, an idealist approach reflects thinking that physical literacy is inherently subjective and holistic in nature (Whitehead, 2001). As physical literacy is a holistic concept, the domains of physical literacy (e.g., physical, affective, and cognitive) cannot be separated. As a result, measuring those domains of physical literacy separately would contradict the holistic philosophical underpinnings of the concept. This tension between pragmatic and idealist approaches highlights the challenge of assessing PL in research. In this context, the PL composite score used in the current study should be viewed as an imperfect but practical approximation of a child’s physical literacy. It is also important to consider the validity and reliability of test scores derived from the Strengths and Difficulties Questionnaire. Although the SDQ has demonstrated sufficient psychometric properties in older children within both community (Koskelainen et al., 2000) and clinical samples (Becker et al., 2004), evidence for its reliability and validity in lower primary 66 school-aged children is mixed (Stone et al., 2010). Consistent with these findings, other longitudinal research reports minimal variability in SDQ scores over time (Christensen et al., 2017), suggesting the tool may have limited sensitivity for detecting changes in child mental health within this age group. This raises questions about its appropriateness as the sole measure in such contexts. Considering scores in the study were based solely on parent-reported child mental health, it may have restricted the ability to capture the full scope of children’s emotional, cognitive, and social experiences. Best practice recommends using multiple informants when completing the SDQ, as psychosocial problems are highly situational (Achenbach et al., 1987; Goodman et al., 2000) and ratings may be influenced by the rater’s perceptions and the context. Including children’s self-reports could also provide a more comprehensive perspective. Another approach is utilizing the KIDSCREEN-52 questionnaire, which has been used in similar research (Melby, Nielsen, et al., 2022). This questionnaire captures multiple domains, including physical and psychological well-being, moods and emotions, self-perceptions, and autonomy, and may be more sensitive to changes in mental health (The KIDSCREEN Group Europe, n.d.). However, its use is limited to children aged 8–18 years, which should be a consideration if employed in the future. Another alternative is the Child Behaviour Checklist (CBCL), which is typically parent-reported, but can combine teacher and youth components to assess internalizing and externalizing problems (Canadian Agency for Drugs and Technologies in Health, n.d.). However, while comprehensive, the CBCL does not address physical well-being and may be lengthy and burdensome for younger participants. Thus, careful selection of measurement tools remains essential to ensure accurate, feasible, and developmentally appropriate assessment of children’s mental health in future research. 67 Other limitations in this study are important to note. Firstly, the generalizability of the findings is limited by the homogeneity of the sample. For example, the sample was drawn from a single, relatively affluent school district in West Vancouver, where families were generally of high socioeconomic status, highly educated, and where most participants were meeting physical activity guidelines. As such, these sample characteristics may limit the generalizability of the findings. The impact of contextual behaviours (e.g., behaviours outside of school) was also not considered (e.g., participation in organized sport, family co-participation in physical activity, or active transport). This is important, as the quality and nature of physical activity experiences (e.g., team sports versus individual activities) may be more influential for mental health than volume of physical activity as reflected by daily minutes of MVPA as measured in the current study. Lastly, the study was underpowered to account for classroom-level nesting. Specifically, at later timepoints, some classes had <5 participants, which is considered too small for comparison between groups (Bell et al., 2010). Limitations of the SPSS software also restricted the ability to retrieve R² estimates across imputed pooled datasets, hence why averages were descriptively presented. Therefore, the significance of these results should be interpreted with caution. 4.7 Implications Overall, this study suggests that physical literacy, but particularly the development of FMS, is linked in some respects to short-term mental health outcomes in children, independent of physical activity. While the associations were modest, the findings of this study indicate that children with stronger FMS may experience fewer internalizing and externalizing problems compared to children with delayed skill development. This supports previous intervention work 68 showing that targeted FMS programs can positively influence psychosocial well-being and behavioural outcomes (Tompsett et al., 2017). In addressing the question, “Is it important to be physically literate, physically active, or both?”, the evidence here points to an interdependent relationship. Strong movement skills form a foundation for physical literacy and are often developed and reinforced through regular physical activity. Competence and confidence in movement can encourage greater participation in physical activity, while ongoing participation further refines skills. This creates a reinforcing cycle that may be particularly important for supporting mental health (Cairney et al., 2019). Practically, this underscores the importance of ensuring that children are given opportunities to develop a wide range of movement skills in supportive and inclusive environments. Early interventions, particularly those embedded within school curricula, should prioritize structured yet enjoyable movement experiences that promote competence, confidence, and autonomy. This speaks to a need for shifting the focus from the amount of intensity of physical activity to the quality of movement experiences and environments. Existing literature supports this, highlighting the importance of providing children with ample opportunities to engage in activities that build motor skills, fitness, knowledge, understanding, and autonomous motivation, which can enable them to be active across different contexts (Appleton & Duda, 2016; Melby, Nielsen, et al., 2022). These contexts should be varied and appropriately challenging to foster a sense of mastery and confidence. 69 4.8 Future Directions While this study provides preliminary evidence linking some aspects of physical literacy to short-term mental health outcomes in children, several areas warrant further exploration. Firstly, a more comprehensive measurement of physical literacy is needed. This study found that self-reported physical competence, knowledge, and understanding of physical activity were not predictive of mental health outcomes in this sample; however, these findings may be influenced by the common overestimation of ability in younger populations (Bouffard et al., 2003; Harter & Pike, 1984). The PLAYself tool used in this study also did not cover the affective dimension of physical literacy, which limits the ability to capture the entire concept of physical literacy. Future research may benefit from pairing PLAYself with items from CAPL-2 (Longmuir et al., 2018) or other tools that more comprehensively capture these domains. Similarly, future research should consider employing alternative or complementary mental health measurement tools to improve sensitivity in assessing mental health. Multi-informant approaches that integrate child self-reports alongside parent and/or teacher perspectives could address potential biases and enhance the accuracy of detecting meaningful changes over time. An important next step is to move beyond observational designs towards experimental research, such as a randomized controlled trial, to help determine causal relationships. Including multiple time points across key developmental stages would also allow for a more nuanced understanding of how these relationships evolve over time. Finally, given that physical literacy accounted for only a small proportion of the variance in mental health outcomes in this study, future research should examine additional factors that may influence these relationships. These potential variables may include demographic characteristics, socioeconomic status, school and community environments, and broader psychosocial influences. These factors may also act as 70 mediators or moderators that interact with physical literacy to shape mental health. Controlling for or examining interactions with these variables could provide a more complete understanding of the pathways involved and help inform the design of targeted, evidence-based interventions aimed at improving both physical literacy and mental health in children. 71 Chapter 5. Conclusion This study offers new evidence that physical literacy predicts better mental health among young children. While existing research has acknowledged a relationship between physical literacy and mental health, much of it has relied on cross-sectional designs or emphasized physical activity as the primary pathway. By examining these relationships longitudinally and directly, it was found that physical literacy predicted internalizing problems and total difficulties across 1-year intervals. 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Missing Data and Participant Attrition Participant Attrition Analyses indicated no significant baseline demographic differences between retained participants and those lost to follow-up, suggesting that attrition did not systematically bias the results (Table 2B). Table 11. Baseline Demographic Comparisons Between Participants Lost to Follow-up and Those Retained Characteristic T1 only T1, T2 Statistical testa T1, T2, T3 Statistical testa Gender (2DF) n = 358 n = 306 χ²= 1.14, p = 0.57 n = 195 χ² = 0.50, p = 0.78 Disability (2 DF) n = 343 n = 266 χ² = 1.90, p = 0.17 n = 244 χ²= 1.73, p = 0.19 Siblings (4DF) n = 339 n = 256 χ² = 6.12, p = 0.11 n = 244 χ² = 4.11, p = 0.25 Parent's age (year) (3DF) n = 342 n = 256 χ² = 0.75, p = 0.69 n = 244 χ² = 0.10, p = 0.95 Partner's age (year) (3DF) n = 324 n = 249 χ² = 5.06, p = 0.08 n = 223 χ² = 2.77, p = 0.25 Parent's highest education (4DF) n = 342 n = 254 χ² = 5.14, p = 0.27 n = 186 χ² = 4.70, p = 0.32 Parents’s highest work conditions (2DF) n = 339 n = 290 χ² = 5.35, p = 0.07 n = 187 χ² = 11.51, p = 0.003** Parent's culture background (5DF) n = 335 n = 288 χ² = 6.16, p = 0.29 n = 138 χ²= 5.59, p = 0.35 aChi-squared test Missing data Across all 24 study variables, a total of 2125 data points were missing out of 8592 possible entries (see Table 6). This resulted in an overall missingness rate of approximately 24.7%. This degree of missingness is not uncommon in longitudinal study designs involving multiple timepoints, considering that data collected relies on valid accelerometry measures and repeated follow-ups. Measures with greater than 20% missing data included PLAYself (22.6% at T1 and T2, 27.9% at T3), PLAYfun (10.6% at T1, 24.3% at T2, and 31.0% at T3), PL composite 93 scores (10.9% at T1, 25.1% at T2, and 32.4% at T3), and average MVPA (27.9% at T1, 44.4% at T2, and 47.8% at T3). SDQ subscales (internalizing problems, externalizing problems, total difficulties, and prosocial behaviour) also had missingness exceeding 20% at T2 (all 26.3%) and exceeding 30% at T3 (31.8–32.1%). Table 12. Summary of Missing Data and Descriptive Statistics for All Study Variables Variable Missing Valid N Mean Std. Deviation N Percent SDQ Int. 2022 16 4.5% 342 2.88 2.645 SDQ Ext. 2022 16 4.5% 342 4.60 3.220 SDQ Total Difficulties 2022 16 4.5% 342 7.48 4.836 SDQ Prosocial 2022 16 4.5% 342 8.31 1.774 PLAYself 2022 81 22.6% 277 71.59 11.92 PLAYfun 2022 38 10.6% 320 45.34 5.74 PL Composite 2022 39 10.9% 319 0.017 1.51 Avg MVPA 2022 100 27.9% 258 111.08 30.28 SDQ Int. 2023 94 26.3% 264 2.75 2.65 SDQ Ext. 2023 94 26.3% 264 4.23 3.27 SDQ Total Difficulties 2023 94 26.3% 264 6.98 5.06 SDQ Prosocial 2023 94 26.3% 264 8.31 1.792 PLAYself 2023 81 22.6% 277 71.59 11.92 PLAYfun 2023 87 24.3% 271 51.29 4.61 PL Composite 2023 90 25.1% 268 0.01 1.54 Avg MVPA 2023 159 44.4% 199 113.30 31.18 SDQ Int. 2024 115 32.1% 243 3.10 3.015 SDQ Ext. 2024 114 31.8% 244 5.00 3.884 SDQ Total Difficulties 2024 115 32.1% 243 8.11 5.996 SDQ Prosocial 2024 114 31.8% 244 8.19 1.898 PLAYself 2024 100 27.9% 258 70.84 12.12 PLAYfun 2024 111 31.0% 247 57.14 5.87 PL Composite 2024 116 32.4% 242 0.004 1.64 Avg MVPA 2024 171 47.8% 187 109.18 30.22 94 Appendix B. Strength and Difficulties Questionnaire Figure 2. 25-Item Strengths and Difficulties Questionnaire with three-factor model color coding (INT= Internalizing Problems, EXT = Externalizing Problems, and PRO = Prosocial Behaviour) 95 Appendix C. Overview of PLAYtools (Physical Literacy Assessment for Youth) Instruments Figure 3. PLAYfun assessment tool scoring sheet from the PLAYtools (Physical Literacy Assessment for Youth) instrument. Retrieved from the PLAYTools website (Canadian Sport for Life, n.d). 96 Figure 4. PLAYfun scoring summary sheet for calculating PLAYfun Score across physical domains. Retrieved from the PLAYTools website (Canadian Sport for Life, n.d). Figure 5. PLAYbasic Physical Literacy Tracking Sheet used for observing and recording fundamental movement tasks among children. Retrieved from the PLAYTools website (Canadian Sport for Life, n.d). 97 Figure 6. PLAYself questionnaire for assessing children’s self-perceptions of physical literacy. Retrieved from the PLAYTools website (Canadian Sport for Life, n.d). Figure 7. PLAYself scoring summary sheet for calculating PLAYself Score across each subsection. Retrieved from the PLAYTools website (Canadian Sport for Life, n.d).
Thesis/Dissertation
eng
Kinesiology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Faulkner, Guy E. J., 1970-
Physical literacy as a predictor of child mental health : a longitudinal analysis
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