Search

Additional file 1: Supplementary 1. Differences in movement behaviors during school days and weekends by gender and the achievement of Canadian 24-H movement guidelines.

Additional file 1: File S1. Scoping Reviews (PRISMA-ScR) Checklist. File S2. Search strategy. File S3. Detailed summary of topics and key findings.

Additional file 2: Supplementary 2. Comparisons in movement behaviors between school days and weekends stratified by gender and the achievement of Canadian 24-H movement guidelines.

Behavioural therapy and pharmaceutical treatments are typically recommended first treatment approaches for depression. However, more than half of patients do not achieve remission after first-line antidepressant treatment, and about 30% do not reach remission after multiple treatment trials (Rush et al., 2006). Repetitive transcranial magnetic stimulation (rTMS) is a well-tolerated and promising neuromodulatory approach for patients with treatment resistant depression (Milev et al., 2016). It has shown success in treating traditional treatment-resistant individuals with MDD (Sehatzadeh et al., 2019). Furthermore, through depression treatment, habitual physical activity may increase and serve as a behavioral marker of successful treatment (Yun et al., 2020). This possibility has received little research attention. As Cuijper and colleagues (2014) noted, it is remarkable that there is so little evidence on whether treatments for depression among adults have an impact on physical activity and whether changes in physical activity mediate the outcomes of these treatments. Future work should systematically examine the role of PA before, during and after depression treatments as important synergistic mechanisms may be at play in the treatment of MDD (Fagan et al., 2019). One mechanism through which physical activity may confer mental health benefits is by improving affect. Positive activated affect is described as a subjective mental state of positive energy and engagement. In two well-cited meta-analyses, both acute (Reed & Ones, 2006) and regular aerobic physical activity (Reed & Buck, 2009) are associated with a moderate increase in self-reported positive activated affect. Simply, exercise can help people “feel good” in the right circumstances. Demonstrating a bidirectional relationship, evidence suggests that individuals who experience higher levels of positive incidental affect are more likely to be physically active (Liao et al., 2015). We are not aware of any research examining the physical activity-affect relationship among individuals receiving treatment for depression. Accordingly, this study will first examine changes in physical activity during a six-week r-TMS treatment period and identify when and how (minutes of light, moderate-to-vigorous physical activity) changes in physical activity occurs among responders (individuals who have a 50% reduction in depressive symptoms at the end of treatment) compared to non-responders. Second, we will examine the bidirectional relationship between affect and physical activity during the treatment period.

Healthcare workers (HCWs, i.e., nurses, physicians, hospital staffs) report greater distress, depression, and poorer physical health than the average employed global citizen. These disparities have only been amplified in the context of the COVID-19 pandemic. Recent international studies have shown that there is an increase in reported depression, anxiety, insomnia, and distress (34.0-71.4%) in healthcare workers (HCW) who have been exposed to COVID-19 patients. These negative outcomes during the pandemic are supported by a large (N = 105338) international study on which I am co-investigator (iCARE Study). Preliminary analyses with Canadian representative data (Total N = 18018; HCW N=1069 identify that from June 2020 to June 2021, HCW’s quality of life has worsened (26% to 48%; 10% more among women) and HCWs feel anxious (to a great extent: 19% to 24%; and somewhat: 29% to 36%) and depressed (to a great extent: 10% to 14%; and somewhat: 29% to 34%%). These findings, along with several international studies, suggest that the COVID-19 pandemic has more negatively impacted HCWs than in the general population. The temporal effect, access to resources, and sociodemographic index will be considered in this meta-meta-analysis.

There is a high prevalence of anxiety in children and adolescents, however, having a disability may further heighten the risk of developing an anxiety disorder. Physical activity may be one way for children and adolescents with disabilities to alleviate anxiety, yet, little is known about the relationship between anxiety and physical activity among this population. For children and adolescents with disabilities, physical activity may lessen the effect of anxiety, assist in anxiety symptom management, or act as a protective mechanism through decreasing the likelihood of developing anxiety disorders later in life. This review will synthesize the available evidence in order to identify future research priorities concerning the relationship between anxiety and physical activity in children and adolescents with disabilities.

A scoping review protocol to systematically map the emerging evidence on the impact of COVID-19 on the physical activity, sleep and sedentary behaviour of children and youth.

Mood disorders, also known as affective disorders, are the leading causes of disability in Canada and globally. The Diagnostic and Statistical Manual of Mental Disorder (DSM-5-TR; American Psychiatric Association & PsychiatryOnline Premium Package, 2022) distinguishes between two major categories of mood disorders: depressive and bipolar disorders. It is estimated that 4.4% of individuals globally experience depression each year (World Health Organization, 2017), and 1% experience type I or type II bipolar disorder (BD；Merikangas et al., 2011). The lifetime prevalence of depression is estimated to be even higher in Western industrialized nations, such as Canada (9.9%；Patten et al., 2015) and the United States (20.6%；Hasin et al., 2018)). It is generally acknowledged that all types of mood disorders are negatively correlated with quality of life (Cramer et al., 2010). Moreover, Cavanagh et al. (2003) reported that 90% of all suicide-related deaths occur among people who are experiencing mental disorders, especially bipolar disorder and depression. A subsequent meta-analysis concluded that suicide attempts are prevalent both among individuals with BD I (36.3%) and BD II (32.4%) (Dennehy et al., 2011). The heavy burden of mood disorders on individuals, families, and society warrants effective and cost-effective treatments. There is strong evidence for bidirectional relationships between the movement behaviours (physical activity, sedentary behaviour and sleep; Ross et al., 2020) and mental health. On one hand, engagement in movement behaviours has been found to promote mental health; whereas insufficient engagement is associated with poor mental health (Baglioni et al., 2011; Rollo et al., 2020). Regular physical activity has been recommended to treat mild-to-moderate depression in Canada (Ravindran et al., 2016), and has been suggested to be effective in the treatment of BD (Miranda-Pettersen et al., 2020). Moreover, it is well known that physical inactivity, high levels of sedentary behaviour, especially high volumes of screen time, and sleep problems have been linked to increased risk of depression and BDs (Franzen & Buysse, 2022; Kandola et al., 2019; Kandola et al., 2021; Schuch et al., 2018). On the other hand, being ‘mentally healthy’ may also be an important antecedent of engaging in optimal doses of those movement behaviours. For example, symptoms such as fatigue, psychomotor retardation, and anhedonia may actively discourage engagement in physical activity (Glowacki et al., 2017). Canada’s 24-hour movement guidelines for adults and older adults (Ross et al., 2020) recommend that adults (1) accumulate of at least 150 minutes of moderate-vigorous physical activity (MVPA) per week and (2) participate in muscle strengthening activities at least twice a week. Additionally, older adults are encouraged to participate in activities that challenge balance. Children and youth are encouraged to accumulate at least 60 minutes of MVPA per day (Tremblay et al., 2016). Sedentary behaviour, defined as waking activity characterized by a sitting, reclining, or lying posture that requires low energy expenditure (i.e., less than 1.5 metabolic equivalents), should be limited 8 hours per day or less for adults. Screen-based activities are common contributors to sedentary behaviour in many populations (Leask et al., 2015; Rideout et al., 2010; Thorp et al., 2011). Not only might these behaviours independently influence health outcomes, but they have also potential impacts on sleep behaviour (Costigan et al., 2013). Adults should also aim for 7 to 9 hours of good-quality sleep on a regular basis, with consistent bed and wake-up times. Both individually and collectively, the movement behaviours have been shown to provide numerous health benefits. An important aspect of the guidelines is recognition that the four movement behaviours interact to influence health. Movement behaviours are characterized by temporal variability and are influenced by a range of dynamic psychological processes (e.g., affect) and co-occurring events (e.g., social/environmental factors, interpersonal dynamics) (Ruissen et al., 2021). Problematic fluctuations of affect are one of the common features of mood disorders. People with depression (Wichers et al., 2007) and BDs (Curtiss et al., 2019; Havermans et al., 2010) have showed altered patterns in affective responses to stressors. A previous meta-analysis suggested that exercise has been associated with elevated positive affect (Reed & Ones, 2006). Also, insufficient sleep is believed to increase negative emotion (van der Helm & Walker, 2010). However, much research employs cross-sectional designs using one-time assessments of affective phenomena (affect, emotion, and mood) and movement behaviours. Such designs cannot address the dynamic nature of interactions between movement behaviours and affective phenomena, and are not able to reveal the directional associations among these variables (Kanning et al., 2013; Shiyko & Ram, 2011; Wilson et al., 1982). Recent years has seen increased interest in ecological momentary assessment (EMA) as a tool for real-time data capture for examining temporal relationships between adaptive behaviours and mental health (Hamaker & Wichers, 2017). EMA allows researchers to capture data immediately or within a short amount of time after an event occurs, therefore improving the precision of relationship evaluations (Stone & Shiffman, 1994; Wenze & Miller, 2010). In conjunction with advanced statistical modelling techniques (Ruissen et al., 2021), there is great potential in using EMA to tease apart relationships between movement behaviours and affective phenomena among individuals with mood disorders. Moreover, Wenze and Miller (2010) have suggested that it is feasible to use EMA methodology among people with mood disorders. Understanding the temporal relationships between affective phenomena and movement behaviours may inform clinical management and well-being intervention development including just-in-time adaptive interventions (Hardeman et al., 2019). To the best of our knowledge, no previous reviews have examined the temporal relationships between affective phenomena and movement behaviours among people with mood disorders. Thus, the purpose of this scoping review was to both synthesize what have been done and examine how research was conducted on this topic (Munn et al., 2018; Sabiston et al., 2022). Results have the potential to inform whether a systematic review is needed and guide future research about the use of EMA in evaluating relationships between affective phenomena and movement behaviours among people with mood disorders. Methods: This scoping review will follow the six-stage framework for conducting scoping reviews, developed by Arksey and O'Malley (2005), and later revised by Levac et al. (2010). This study will report the results and outcomes following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA-ScR) (Tricco et al., 2018). Stage 1: Identifying the research questions The objective of this review is to summarize current evidence regarding the use of EMA methodology among people with mood disorders in studies exploring the relationship between movement behaviours and affective phenomena. Notably, affective phenomena including mood, emotion, and affect, have been interchangeably used in previous literature, which may cause pitfalls (Ekkekakis, 2013, p. 33). In this review, emotion is a short-term reaction to a personally-significant stimuli, whereas mood refers to a more general and sustained disposition toward a particular emotion, and can occur without a specific stimulus. In short, emotion is a state, whereas mood is a trait. Affect is a less consistently defined concept in the literature. Niven (2013) defined affect as an umbrella concept which includes both mood and emotion. Note that, in clinical settings, it is also sometimes used to refer to a person’s emotional expression. Whereas, Ekkekakis (2013) suggested that affect is a momentary and dimensional (positive or negative) feeling that could constantly exist alone or be part of emotion or mood. Kirkland and Cunningham (2011) also considered affect as a necessary but insufficient component of emotions. Research questions: The purpose of this scoping review is to summarize the application of EMA in examining the association between the affective phenomena (mood, emotion, affect) and movement behaviour (physical activity, sedentary behaviour, sleep). Specifically, this scoping review will examine: 1. What is known about the relationship between movement behaviours and mood/emotion/affect among individuals with mood disorders? 2. How are the constructs of mood/emotion/affect conceptually and operationally defined within included studies? 3. What EMA technologies have been utilized? 4. What methodologies are typically adopted? For example: Duration of EMA monitoring, EMA prompts frequency, mental health screening tools, and use of comparison groups. 5. What is participant retention? 6. What methodological limitations have been identified? 7. What are recommended future research directions? Stage 2: Identifying relevant studies Searches will be conducted in the eight following electronic databases: PubMed, Medline (via Ovid), Embase (via Ovid), PsycINFO (via EBSCO), CINAHL (via EBSCO), SPORTDiscus (via EBSCO), Scopus, and Web of Science from their inception to June 10th, 2022. The databases will be searched using controlled vocabulary (e.g., MeSH terms in Medline) and keywords. Relevant terms included keywords related to EMA (e.g., experience sampling), mood disorders (e.g., depression, bipolar disorders) and movement behaviours (e.g., physical activity, exercise, sleep, screen time, sedentary behaviour). The search strategy was drafted by YL, and was refined based on the discussion with a university librarian and team members (Appendix 1). Reference lists of included studies will also be checked for identifying additional studies. Stage 3: Study selection Covidence, a review management software, will be employed to remove duplicates and conduct the selecting processes. Two authors will independently conduct the title and abstract screening. Full-texts will be independently reviewed by two authors for the remaining articles that passed title and abstract screening. Conflicts will be resolved by discussion. When necessary, a third author will be consulted for reaching a consensus. Inclusion and exclusion criteria: Peer-reviewed publications will be included if they were: 1) written in English, 2) include participants diagnosed with a mood disorder (e.g., major depressive disorder, bipolar I disorder) or using validated clinical scales, 3) use EMA to assess mood/emotion/affect, 4) include a measure of movement behaviours (e.g., minutes of physical activity), and 5) assess the relationships between mood/emotion/affect and movement behaviours. Only quantitative studies will be considered. Grey literature, theses/dissertations, reviews, qualitative studies and case studies will be excluded. Additionally, studies that do not quantify the amount of movement behaviours will be excluded: for example, studies that examine perceived sleep quality rather than amount of sleep. There are no restrictions with regard to participant gender, age, or the measurements of movement behaviours (self-reported or device-based). Stage 4: Data charting A data-charting form will be developed by one author and will be further refined by other authors by discussion. The following items will be extracted by two authors independently: 1) authors and year, 2) country, 3) methodological approach, 4) participant characteristics (e.g., sample size, age, gender, diagnosis), 5) EMA characteristics (e.g., schedule, device, attrition rate), 6) measurement of affect, 7) definitions of mood/emotion/affect, 8) measurements of movement behaviours, 9) main findings, and 10) future research directions. Disagreements will be solved by discussion. The extraction spreadsheet will be managed using Microsoft Excel. Stage 5: Collating, summarizing, and reporting the results We will summarize the evidence in both narrative format and descriptive tables. The manuscript will be submitted to a peer-reviewed journal. Stage 6: Consultation Once the data has been charted, an experienced researcher (GF) will be consulted to advise throughout the process. References: American Psychiatric Association, & PsychiatryOnline Premium Package. (2022). Diagnostic and statistical manual of mental disorders: DSM-5-TR (Fifth, text revision. ed.). American Psychiatric Association Publishing. https://go.exlibris.link/Vk4rmlBb Arksey, H., & O'Malley, L. (2005). Scoping studies: towards a methodological framework. International journal of social research methodology, 8(1), 19-32. Baglioni, C., Battagliese, G., Feige, B., Spiegelhalder, K., Nissen, C., Voderholzer, U., Lombardo, C., & Riemann, D. (2011). Insomnia as a predictor of depression: a meta-analytic evaluation of longitudinal epidemiological studies. Journal of affective disorders, 135(1-3), 10-19. Costigan, S. A., Barnett, L., Plotnikoff, R. C., & Lubans, D. R. (2013). The health indicators associated with screen-based sedentary behavior among adolescent girls: a systematic review. Journal of Adolescent Health, 52(4), 382-392. Cramer, V., Torgersen, S., & Kringlen, E. (2010). Mood disorders and quality of life. A community study. Nordic Journal of Psychiatry, 64(1), 58-62. Curtiss, J., Fulford, D., Hofmann, S. G., & Gershon, A. (2019). Network dynamics of positive and negative affect in bipolar disorder. Journal of affective disorders, 249, 270-277. Dennehy, E. B., Marangell, L. B., Allen, M. H., Chessick, C., Wisniewski, S. R., & Thase, M. E. (2011). Suicide and suicide attempts in the systematic treatment enhancement program for bipolar disorder (STEP-BD). Journal of affective disorders, 133(3), 423-427. Ekkekakis, P. (2013). The measurement of affect, mood, and emotion: A guide for health-behavioral research. Cambridge University Press. Franzen, P. L., & Buysse, D. J. (2022). Sleep disturbances and depression: risk relationships for subsequent depression and therapeutic implications. Dialogues in clinical neuroscience. Glowacki, K., Duncan, M. J., Gainforth, H., & Faulkner, G. (2017). Barriers and facilitators to physical activity and exercise among adults with depression: A scoping review. Mental Health and Physical Activity, 13, 108-119. https://doi.org/10.1016/j.mhpa.2017.10.001 Hamaker, E. L., & Wichers, M. (2017). No time like the present: Discovering the hidden dynamics in intensive longitudinal data. Current Directions in Psychological Science, 26(1), 10-15. Hardeman, W., Houghton, J., Lane, K., Jones, A., & Naughton, F. (2019). A systematic review of just-in-time adaptive interventions (JITAIs) to promote physical activity. International Journal of Behavioral Nutrition and Physical Activity, 16(1), 1-21. Hasin, D. S., Sarvet, A. L., Meyers, J. L., Saha, T. D., Ruan, W. J., Stohl, M., & Grant, B. F. (2018). Epidemiology of adult DSM-5 major depressive disorder and its specifiers in the United States. JAMA psychiatry, 75(4), 336-346. Havermans, R., Nicolson, N. A., Berkhof, J., & deVries, M. W. (2010). Mood reactivity to daily events in patients with remitted bipolar disorder. Psychiatry Research, 179(1), 47-52. Kandola, A., Ashdown-Franks, G., Hendrikse, J., Sabiston, C. M., & Stubbs, B. (2019). Physical activity and depression: Towards understanding the antidepressant mechanisms of physical activity. Neuroscience & Biobehavioral Reviews, 107, 525-539. https://doi.org/https://doi.org/10.1016/j.neubiorev.2019.09.040 Kandola, A., Owen, N., Dunstan, D., & Hallgren, M. (2021). Prospective relationships of adolescents’ screen-based sedentary behaviour with depressive symptoms: the Millennium Cohort Study. Psychological medicine, 1-9. Kanning, M. K., Ebner-Priemer, U. W., & Schlicht, W. M. (2013). How to investigate within-subject associations between physical activity and momentary affective states in everyday life: A position statement based on a literature overview. Frontiers in psychology, 4, 187. Kirkland, T., & Cunningham, W. A. (2011). Neural basis of affect and emotion. Wiley Interdisciplinary Reviews: Cognitive Science, 2(6), 656-665. Leask, C. F., Harvey, J. A., Skelton, D. A., & Chastin, S. F. (2015). Exploring the context of sedentary behaviour in older adults (what, where, why, when and with whom). European Review of Aging and Physical Activity, 12(1), 1-8. Levac, D., Colquhoun, H., & O'Brien, K. K. (2010). Scoping studies: advancing the methodology. Implementation science, 5(1), 1-9. Merikangas, K. R., Jin, R., He, J.-P., Kessler, R. C., Lee, S., Sampson, N. A., Viana, M. C., Andrade, L. H., Hu, C., & Karam, E. G. (2011). Prevalence and correlates of bipolar spectrum disorder in the world mental health survey initiative. Archives of general psychiatry, 68(3), 241-251. Munn, Z., Peters, M. D., Stern, C., Tufanaru, C., McArthur, A., & Aromataris, E. (2018). Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach. BMC medical research methodology, 18(1), 1-7. Niven, K. (2013). Affect. In M. D. Gellman & J. R. Turner (Eds.), Encyclopedia of Behavioral Medicine (pp. 49-50). Springer New York. https://doi.org/10.1007/978-1-4419-1005-9_1088 Patten, S. B., Williams, J. V., Lavorato, D. H., Wang, J. L., McDonald, K., & Bulloch, A. G. (2015). Descriptive epidemiology of major depressive disorder in Canada in 2012. The Canadian Journal of Psychiatry, 60(1), 23-30. Reed, J., & Ones, D. S. (2006). The effect of acute aerobic exercise on positive activated affect: A meta-analysis. Psychology of Sport and Exercise, 7(5), 477-514. Rideout, V. J., Foehr, U. G., & Roberts, D. F. (2010). Generation m 2: Media in the lives of 8-to 18-year-olds. Henry J. Kaiser Family Foundation. Rollo, S., Antsygina, O., & Tremblay, M. S. (2020). The whole day matters: understanding 24-hour movement guideline adherence and relationships with health indicators across the lifespan. Journal of sport and health science, 9(6), 493-510. Ross, R., Chaput, J.-P., Giangregorio, L. M., Janssen, I., Saunders, T. J., Kho, M. E., Poitras, V. J., Tomasone, J. R., El-Kotob, R., & McLaughlin, E. C. (2020). Canadian 24-Hour Movement Guidelines for Adults aged 18–64 years and Adults aged 65 years or older: an integration of physical activity, sedentary behaviour, and sleep. Applied Physiology, Nutrition, and Metabolism, 45(10), S57-S102. Ruissen, G. R., Zumbo, B. D., Rhodes, R. E., Puterman, E., & Beauchamp, M. R. (2021). Analysis of dynamic psychological processes to understand and promote physical activity behaviour using intensive longitudinal methods: A primer. Health psychology review, 1-34. Sabiston, C. M., Vani, M., de Jonge, M., & Nesbitt, A. (2022). Scoping reviews and rapid reviews. International Review of Sport and Exercise Psychology, 15(1), 91-119. Schuch, F. B., Vancampfort, D., Firth, J., Rosenbaum, S., Ward, P. B., Silva, E. S., Hallgren, M., Ponce De Leon, A., Dunn, A. L., & Deslandes, A. C. (2018). Physical activity and incident depression: a meta-analysis of prospective cohort studies. American Journal of Psychiatry, 175(7), 631-648. Shiyko, M. P., & Ram, N. (2011). Conceptualizing and estimating process speed in studies employing ecological momentary assessment designs: A multilevel variance decomposition approach. Multivariate Behavioral Research, 46(6), 875-899. Stone, A. A., & Shiffman, S. (1994). Ecological momentary assessment (EMA) in behavorial medicine. Annals of behavioral medicine. Thorp, A. A., Owen, N., Neuhaus, M., & Dunstan, D. W. (2011). Sedentary behaviors and subsequent health outcomes in adults: a systematic review of longitudinal studies, 1996–2011. American journal of preventive medicine, 41(2), 207-215. Tremblay, M. S., Carson, V., Chaput, J.-P., Connor Gorber, S., Dinh, T., Duggan, M., Faulkner, G., Gray, C. E., Gruber, R., & Janson, K. (2016). Canadian 24-hour movement guidelines for children and youth: an integration of physical activity, sedentary behaviour, and sleep. Applied Physiology, Nutrition, and Metabolism, 41(6), S311-S327. Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D., Horsley, T., & Weeks, L. (2018). PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Annals of internal medicine, 169(7), 467-473. van der Helm, E., & Walker, M. P. (2010). The role of sleep in emotional brain regulation. Wenze, S. J., & Miller, I. W. (2010). Use of ecological momentary assessment in mood disorders research. Clinical psychology review, 30(6), 794-804. Wichers, M., Myin-Germeys, I., Jacobs, N., Peeters, F., Kenis, G., Derom, C., Vlietinck, R., Delespaul, P., & Van Os, J. (2007). Genetic risk of depression and stress-induced negative affect in daily life. The British journal of psychiatry, 191(3), 218-223. Wilson, T. D., Laser, P. S., & Stone, J. I. (1982). Judging the predictors of one's own mood: Accuracy and the use of shared theories. Journal of Experimental Social Psychology, 18(6), 537-556. World Health Organization. (2017). Depression and other common mental disorders: global health estimates. https://apps.who.int/iris/bitstream/handle/10665/254610/WHOMSD?sequence=1

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Supplemental material, sj-docx-2-sjp-10.1177_14034948211031392 for The epidemiology of muscle-strengthening activity among adolescents from 28 European countries by Jason A. Bennie, Guy Faulkner and Jordan J. Smith in Scandinavian Journal of Public Health

Supplemental material, sj-docx-1-sjp-10.1177_14034948211031392 for The epidemiology of muscle-strengthening activity among adolescents from 28 European countries by Jason A. Bennie, Guy Faulkner and Jordan J. Smith in Scandinavian Journal of Public Health

Supplemental material, sj-docx-2-sjp-10.1177_14034948211031392 for The epidemiology of muscle-strengthening activity among adolescents from 28 European countries by Jason A. Bennie, Guy Faulkner and Jordan J. Smith in Scandinavian Journal of Public Health

Additional file 1.

Additional file 1:. Complete children and youth movement and play behaviours survey items.

Additional file 4. Review #2 (docx.). Search Process for Family and Sedentary Behaviour Literature Review (review #2). Themes from the sedentary behaviour literature review (review #2). References for the papers included in the family and sedentary behaviour review (review #2), organized by theme.

Additional file 2. Table S1 (docx). Inclusion and exclusion criteria for systematic literature searches on family and the physical activity (review #1), sedentary (review #2), and sleep (review #3) behaviours of children and youth.

Additional file 6. Table S2 (doc.). Inclusion and exclusion criteria for systematic literature searches on family systems theory (review #4), parental correlates (review #5), and family interventions (review #6) for movement behaviours of children and youth.

Additional file 3. Review #1 (docx.). Search Process for Family and Physical Activity Literature Review (review #1). Themes from the physical activity literature review (review #1). References for the papers included in the family and physical activity review (review #1), organized by theme.

Additional file 1: STROBE Checklist.

Additional file 9:. Figure S1 (doc.). Search process of the Review of Reviews on Interventions Involving the Family to Change Child and Youth Physical Activity, Sedentary Behaviour, and Sleep (review #6).

Additional file 1 Survey (PDF file of on-line survey). Full survey questions used to solicit assessments and comments from stakeholders on the draft Consensus Statement.

Additional file 7. Review #4 (docx.). Search Process for the Family Systems in the Context of Child Health Behaviour Change and Family-Based Interventions Scoping Review (review #4). References extracted from the Family Systems in the Context of Child Health Behaviour Change Scoping Review (review #4).

Additional file 8. Review #5 (docx.). Search Process for the Correlates of Parental Support of Child and Youth Physical Activity, Sedentary Behaviour, and Sleep Systematic Review (review #5). References Extracted from the Correlates of Parental Support of Child and Youth Physical Activity, Sedentary Behaviour, and Sleep Systematic Review (review #5).

Additional file 2: Mobile Application Rating Scale (MARS) and self-score.

Additional file 3: Table 1. Inside and outside hobbies during COVID-19 virus outbreak. Table 2. New or returning family hobbies during the COVID-19 virus outbreak. Table 3. Use of online resources during the COVID-19 virus outbreak.

Additional file 2:. STROBE statement checklist of items for cross-sectional studies.

Additional file 5. Review #3 (docx.). Search Process for Family and Sleep Behaviour Literature Review (review #3). Themes from the sleep literature review (review #3). References for the papers included in the family and sleep review (review #3), organized by theme.

Background: Taking regular exercise, whether cardiovascular‐type exercise or resistance exercise, may help people to give up smoking, particularly by reducing cigarette withdrawal symptoms and cravings, and by helping to manage weight gain. Objectives: To determine the effectiveness of exercise‐based interventions alone, or combined with a smoking cessation programme, for achieving long‐term smoking cessation, compared with a smoking cessation intervention alone or other non‐exercise intervention. Search Methods: We searched the Cochrane Tobacco Addiction Group Specialised Register for studies, using the term 'exercise' or 'physical activity' in the title, abstract or keywords. The date of the most recent search was May 2019. Selection Criteria: We included randomised controlled trials that compared an exercise programme alone, or an exercise programme as an adjunct to a cessation programme, with a cessation programme alone or another non‐exercise control group. Trials were required to recruit smokers wishing to quit or recent quitters, to assess abstinence as an outcome and have follow‐up of at least six months. Data Collection and Analysis: We followed standard Cochrane methods. Smoking cessation was measured after at least six months, using the most rigorous definition available, on an intention‐to‐treat basis. We calculated risk ratios (RRs) and 95% confidence intervals (CIs) for smoking cessation for each study, where possible. We grouped eligible studies according to the type of comparison, as either smoking cessation or relapse prevention. We carried out meta‐analyses where appropriate, using Mantel‐Haenszel random‐effects models. Main Results: We identified 24 eligible trials with a total of 7279 adult participants randomised. Two studies focused on relapse prevention among smokers who had recently stopped smoking, and the remaining 22 studies were concerned with smoking cessation for smokers who wished to quit. Eleven studies were with women only and one with men only. Most studies recruited fairly inactive people. Most of the trials employed supervised, group‐based cardiovascular‐type exercise supplemented by a home‐based exercise programme and combined with a multi‐session cognitive behavioural smoking cessation programme. The comparator in most cases was a multi‐session cognitive behavioural smoking cessation programme alone. Overall, we judged two studies to be at low risk of bias, 11 at high risk of bias, and 11 at unclear risk of bias. Among the 21 studies analysed, we found low‐certainty evidence, limited by potential publication bias and by imprecision, comparing the effect of exercise plus smoking cessation support with smoking cessation support alone on smoking cessation outcomes (RR 1.08, 95% CI 0.96 to 1.22; I2 = 0%; 6607 participants). We excluded one study from this analysis as smoking abstinence rates for the study groups were not reported. There was no evidence of subgroup differences according to the type of exercise promoted; the subgroups considered were: cardiovascular‐type exercise alone (17 studies), resistance training alone (one study), combined cardiovascular‐type and resistance exercise (one study) and type of exercise not specified (two studies). The results were not significantly altered when we excluded trials with high risk of bias, or those with special populations, or those where smoking cessation intervention support was not matched between the intervention and control arms. Among the two relapse prevention studies, we found very low‐certainty evidence, limited by risk of bias and imprecision, that adding exercise to relapse prevention did not improve long‐term abstinence compared with relapse prevention alone (RR 0.98, 95% CI 0.65 to 1.47; I2 = 0%; 453 participants). Authors' Conclusions: There is no evidence that adding exercise to smoking cessation support improves abstinence compared with support alone, but the evidence is insufficient to assess whether there is a modest benefit. Estimates of treatment effect were of low or very low certainty, because of concerns about bias in the trials, imprecision and publication bias. Consequently, future trials may change these conclusions.

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: To determine the effects of pharmacological interventions for treating weight gain in people with schizophrenia.

Results from the complete case analyses. (DOCX 52 kb)

Additional file 4. Post-Summit Stakeholder Engagement Survey (Day 2)

Additional file 1. Pre-Meeting Survey (Day 1)

List of all of the variables included in the imputation model. (DOCX 22 kb)

Additional file 2: Measures of Health Behaviors

Additional file 3. Post-Workshop Stakeholder Engagement Survey (Day 1)

Additional file 2. Â Pre-Meeting Survey (Day 2)

Supplementary Tables S1-S7. (PDF 1166Â kb)

Purpose: To understand the awareness of sedentary behavior, as well as the perceived facilitators and barriers to reducing sedentary behaviors from the perspectives of patients undertaking an exercise-based cardiac rehabilitation program, and from staff involved in supporting patient self-management. Materials and methods: A qualitative study was conducted at a large cardiac rehabilitation program in a metropolitan city in Canada. Guided by an ecological framework, semi-structured interviews were conducted individually with 15 patients, and in two focus groups with six staff. Transcribed interviews were analyzed by thematic analysis. Results: Patients placed little importance on reducing sedentary behavior as they were unconvinced of the health benefits, did not perceive themselves to be sedentary, or associated such behaviors with enjoyment and relaxation. While staff were aware of the risks, they saw them as less critical than other health behaviors. Intrapersonal factors (physical and psychosocial health) and environment factors (the information environment, socio-cultural factors) within leisure time, the home, and work, influenced sedentary behavior. Conclusions: While these findings require further testing, future interventions may be effective if aimed at increasing awareness of the health benefits of reducing sedentary behavior, utilizing existing behavior change strategies, and using a participatory approach to tailor strategies to patients.Implications for rehabilitationCardiac rehabilitation programs effectively use exercise promotion to improve the health of people with established cardiovascular disease.As sedentary lifestyles become more prevalent, recommendations to reduce the health risks of prolonged sedentary behavior that are specific to the characteristics and prognostic profiles of cardiac rehabilitation patients are needed.Cardiac rehabilitation programs must consider extending existing behavior change strategies utilized for exercise promotion towards addressing sedentary behaviors in order to be effective at reducing the sedentary time of patients.A participatory approach involving both patients and health professionals can support patients in reducing their sedentary behavior by providing a supportive environment for behavior change, increasing awareness and understanding of risks, discussing the feasibility of potential strategies, and setting achievable and actionable goals. Cardiac rehabilitation programs effectively use exercise promotion to improve the health of people with established cardiovascular disease. As sedentary lifestyles become more prevalent, recommendations to reduce the health risks of prolonged sedentary behavior that are specific to the characteristics and prognostic profiles of cardiac rehabilitation patients are needed. Cardiac rehabilitation programs must consider extending existing behavior change strategies utilized for exercise promotion towards addressing sedentary behaviors in order to be effective at reducing the sedentary time of patients. A participatory approach involving both patients and health professionals can support patients in reducing their sedentary behavior by providing a supportive environment for behavior change, increasing awareness and understanding of risks, discussing the feasibility of potential strategies, and setting achievable and actionable goals.

Detailed evidence to decision framework explanation for the Canadian 24-Hour Movement Guidelines for the Early Years (0–4 years): An Integration of Physical Activity, Sedentary Behaviour, and Sleep. (DOC 90 kb)

Stakeholder Survey (English followed by French version). (DOC 202Â kb)

Search strategies. (PDF 58Â kb)

Background The Carrot Rewards app was developed as part of an innovative public-private partnership to reward Canadians with loyalty points, exchangeable for retail goods, travel rewards, and groceries for engaging in healthy behaviors such as walking. Objective This study examined whether a multicomponent intervention including goal setting, graded tasks, biofeedback, and very small incentives tied to daily step goal achievement (assessed by built-in smartphone accelerometers) could increase physical activity in two Canadian provinces, British Columbia (BC) and Newfoundland and Labrador (NL). Methods This 12-week, quasi-experimental (single group pre-post) study included 78,882 participants; 44.39% (35,014/78,882) enrolled in the Carrot Rewards “Steps” walking program during the recruitment period (June 13–July 10, 2016). During the 2-week baseline (or “run-in”) period, we calculated participants’ mean steps per day. Thereafter, participants earned incentives in the form of loyalty points (worth Can $0.04 ) every day they reached their personalized daily step goal (ie, baseline mean+1000 steps=first daily step goal level). Participants earned additional points (Can $0.40) for meeting their step goal 10+ nonconsecutive times in a 14-day period (called a “Step Up Challenge”). Participants could earn up to Can $5.00 during the 12-week evaluation period. Upon meeting the 10-day contingency, participants could increase their daily goal by 500 steps, aiming to gradually increase the daily step number by 3000. Only participants with ≥5 valid days (days with step counts: 1000-40,000) during the baseline period were included in the analysis (n=32,229).The primary study outcome was mean steps per day (by week), analyzed using linear mixed-effects models. Results The mean age of 32,229 participants with valid baseline data was 33.7 (SD 11.6) years; 66.11% (21,306/32,229) were female. The mean daily step count at baseline was 6511.22. Over half of users (16,336/32,229, 50.69%) were categorized as “physically inactive,” accumulating Conclusions Providing very small but immediate rewards for personalized daily step goal achievement as part of a multicomponent intervention increased daily step counts on a population scale, especially for physically inactive individuals and individuals who engaged more with the walking program. Positive effects in both BC and NL provide evidence of replicability.

Additional file 1. Instructions and tracking sheet provided to participants in the free-living test.

BackgroundBehavioral economics has stimulated renewed interest in financial health incentives worldwide. The Carrot Rewards app was developed as part of a public-private partnership to reward Canadians with loyalty points (eg, movies and groceries) for downloading the app, referring friends, and completing an average of 1 to 2 educational health quizzes per week (“micro-learning”), with long-term objectives of increasing health knowledge and encouraging healthy behaviors. ObjectiveThe main objective of this study was to evaluate uptake of a loyalty points-based mHealth app during the exclusive 3-month launch period in British Columbia (BC), Canada. The secondary aims were to describe the health and sociodemographic characteristics of users, as well as participation levels (eg, proportion of quizzes completed and friends referred). MethodsThe app was promoted via loyalty program email campaigns (1.64 million emails). Number of downloads and registrations (users enter age, gender, and valid BC postal code to register) were collected. Additional sociodemographics were inferred by linking postal codes with census data at the local health area (LHA) level. Health risk assessments were also deployed. Participation levels were collected over 3 months and descriptive data were presented. ResultsIn 3 months, 67,464 individuals downloaded the app; in its first week, Carrot Rewards was the most downloaded health app in Canada. Among valid users (n=57,885; at least one quiz completed), the majority were female (62.96%; 36,446/57,885) and aged 18 to 34 years (54.34%; 31,459/57,885). More than half of the users (52.40%; 30,332/57,885) resided in LHAs where the median personal income was below the provincial average (Can $28,765). Furthermore, 64.42% (37,291/57,885) of users lived in metropolitan (ie, urban) LHAs, compared with 56.17% of the general BC population. The most prevalent risk factors were “not” meeting physical activity guidelines (72.70%; 31,765/43,692) and “not” getting the flu shot last year (67.69%; 30,286/44,739). Regarding participation, 60.05% (34,761/57,885) of users were classified as “very high” engagers (>75% quiz completion rate). ConclusionsEarly results suggest that loyalty points may promote mHealth app uptake. The app was downloaded by younger females especially, and BC residents from higher and lower income regions were equally represented. Loyalty points appear to have driven participation throughout the inaugural 3-month period (ie, quiz completion).

Appendix 1. Adjusted criteria for the Effective Public Health Practice Project quality assessment tool for quantitative studies. (DOCX 33 kb)

Appendix 1. Adjusted criteria for the Effective Public Health Practice Project quality assessment tool for quantitative studies. (DOCX 33 kb)

Stakeholder Survey (English followed by French version). (DOC 202Â kb)