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Background: Recent statistics highlight cardiovascular diseases (CVD) as a major global cause of death. This review examines the methodological approaches and the main results of independent, stratified, and joint association of sedentary time (ST) and physical activity (PA) on CVD outcomes. Methods: We searched PubMed, Embase, Web of Science, and Scopus for prospective cohorts that examined the independent, stratified, or joint associations of ST and PA with CVD outcomes. Independent associations were defined as analyses mutually adjusted for PA and ST. Stratified associations were considered when there was a reference group in each stratum of PA or ST, and joint associations were defined by a single reference group for all other combined categories of ST and PA levels. Results: Of 45 articles, 69% explored independent association of ST or PA on CVD outcomes, while 31% using a stratified/joint approach. Most studies used self-reports for ST and PA and focused on CVD mortality. Mutually adjusted analyses identified ST positively and PA inversely associated to CVD outcomes. Stratified studies showed higher ST’s pronounced impact on CVD for lower PA levels. High PA mitigated but did not eliminate ST’s negative impact. Joint analyses revealed highest CVD risk in those with both high ST and low PA, and elevated risk in various intermediate combinations. Conclusions: Employing independent, stratified, and joint association approaches can yield distinct and complementary public health messages aimed at promoting cardiovascular health. Recommendations should aim to not only to encourage boosting PA levels, but also, concurrently decrease ST.

Background: Adult-led organized settings for children (eg, classrooms) provide opportunities for physical activity (PA). The structure of setting time may influence inequalities (ie, unequalness) in the distribution of PA. This study examined differences in PA inequality by setting and time-segment purpose in time-segmented organized group settings for children. Methods: PA and setting meetings were assessed using accelerometer and video observation data from school, before-/after-school, and youth club groups (n = 30) for third- through sixth-grade children (n = 699) in 2 rural US communities. Meetings (n = 130) were time-segmented into smaller units (sessions; n = 835). Each session was assigned a purpose code (eg, PA). Accelerometer data were paired with the meetings and sessions, and the Gini coefficient quantified inequality in activity counts and moderate to vigorous PA minutes for each segment. Beta generalized estimating equations examined differences in PA inequality by setting and session purpose. Results: Activity count inequality was lowest (P < .05) during youth club meetings (Gini = 0.17, 95% CI, 0.14–0.20), and inequality in moderate to vigorous PA minutes was greatest (P < .01) during school (Gini = 0.34, 95% CI, 0.30–0.38). Organized PA sessions (Gini = 0.20, 95% CI, 0.17–0.23) had lower activity count inequality (P < .0001) than academic (Gini = 0.30, 95% CI, 0.27–0.34), enrichment (Gini = 0.31, 95% CI, 0.27–0.36), and nonactive recreation (Gini = 0.30, 95% CI, 0.25–0.34) sessions. Inequality in moderate to vigorous PA minutes was lower (P < .05) in organized PA (Gini = 0.26, 95% CI, 0.20–0.32) and free play (Gini = 0.28, 95% CI, 0.19–0.39) than other sessions. Conclusions: PA inequality differed by setting time structure, with lower inequality during organized PA sessions. The Gini coefficient can illuminate PA inequalities in organized settings and may inform population PA improvement efforts in rural communities.

Background: Physical activity (PA) and nutrition in children have an impact on overall physical and mental well-being, cognitive, and social development. This study aims to report on the best current available evidence on PA, body composition proxies, and nutritional status of South African children and adolescents, based on the published findings between 2018 and 2022, which comprise the 2022 Healthy Active Kids South Africa Report Card. Methods: A comprehensive literature search of online databases, along with hand searching and a gray literature search, was conducted based on PA, body composition proxies, and nutrition indicators defined, in part, by the Active Healthy Kids Global Alliance. Results: Compared with the 2018 report card, there was an improvement in the majority of PA indicators which include overall PA (B–), active transportation (B–), physical fitness (B–), and government policy and programs (C). Body composition proxies and most of the nutrition indicators remained unchanged. The indicators that regressed from 2018 to 2022 included community and environmental influences (D), as well as participation in organized sport (D–). Conclusions: Despite the apparent improvement in overall PA levels in children and adolescents, there is a lack of tangible evidence of actual implementation of policies and programs. There was also a lack of nationally representative data for most indicators. Overall, there is a need to identify intersectoral, equitable approaches for promoting PA and healthy eating in South African children and adolescents and ongoing monitoring and surveillance.

Background: Although the prescription of physical activity in clinical care has been advocated worldwide, in the Netherlands, “Exercise is Medicine” (E = M) is not yet routinely implemented in clinical care. Methods: A set of implementation strategies was pilot implemented to test its feasibility for use in routine care by clinicians in 2 departments of a university medical center. An extensive learning process evaluation was performed, using structured mixed methods methodology, in accordance with the Reach, Effect, Adoption, Implementation, and Maintenance framework. Results: From 5 implementation strategies employed (education, E = M tool embedded in the electronic medical records, lifestyle coach situated within the department, overviews of referral options, and project support), the presence of adequate project support was a strong facilitator of the implementation of E = M. Also, the presence of the lifestyle coach within the department seemed essential for referral rate. Although clinicians appreciated the E = M tool, barriers hampered its use in practice. Conclusions: Specific implementation strategies, tailored to the setting, are effective in facilitating the implementation of E = M with specific regard to education for clinicians on E = M, deployment of a lifestyle coach within a department, and project coordination. Care providers do see a future for lifestyle coaches who are structurally embedded in the hospital, to whom they can easily refer.

Background: This study aimed to compare the overall prevalence, stratified by sex and age group of global physical activity (GPA), active commuting to school (ACS), and sedentary behavior (SB) among adolescents from ten Latin American countries, and to assess the correlation of Development Index with the indicators. Methods: This research is grounded on data from the Global School-Based Student Health Survey (2009–2015) and the 2015 Brazilian National School Health Survey. The prevalence and 95% confidence intervals for GPA (≥5 d/wk), ACS (≥1 d/wk), and SB (>2 h/d) were calculated using the chi-square test to compare the sexes (male; female) and age group (≤13 y; 14 y; 15 y; ≥16 y). We also performed Pearson correlation analysis with the Human Development Index. Results: The prevalence of indicators ranged from 16.1% to 28.2% for GPA, from 56.7% to 71.2% for ACS, and from 20.7% to 62.6% for SB. Boys generally had a higher prevalence of GPA and ACS, and girls had a higher prevalence of SB. The prevalence of the indicators by age group varied between countries, with significant differences observed in some, depending on each indicator. A positive correlation was observed between Human Development Index, GPA, and SB. Conclusion: Health promotion policies must include guidelines that encourage and promote a more active and less sedentary lifestyle among young people in Latin America, considering specific groups, the local socioeconomic context, and differences between countries.

Background: Despite the existence of physical activity policies across many countries, insufficient physical activity remains a major global public health problem. Physical inactivity is an emergent feature of complex systems; it results from a wide range of factors at multiple levels that interact to influence behavior. Traditional approaches to public policy often fail within complex systems, largely due to unpredictability in how the system will respond. Adaptive policies, which are designed to allow for uncertainty about future system behavior and to change over time, may offer a promising solution. In this paper, we introduce the concept of adaptive policies and illustrate how this innovative approach to policy making may be beneficial for reducing physical inactivity. Design: Drawing on existing literature and guiding principles for policy making, we provide 3 examples to illustrate how the concept of adaptive policies can be applied to address physical inactivity. Discussion: The examples illustrate how changes to the way policies and interventions are developed, implemented, and evaluated could help to overcome some of the limitations in existing practices. A key challenge will be engaging policymakers to take a broader perspective of the physical activity system, develop policies that are designed to be adaptable across a range of different future scenarios, and embrace uncertainty and long-term adaptability. Conclusion: Adaptive policies may support decision makers globally to achieve the widespread and sustained changes necessary to increase population levels of physical activity.

Background: Pickleball has grown tremendously in recent years, yet little evidence exists regarding pickleball-related injuries. This scoping review extends current work on pickleball participation by identifying positive and negative health effects associated with the sport. We summarize how pickleball impacts the health and well-being of adult participants. Methods: Searches were conducted on MEDLINE, CINAHL, ProQuest Nursing, ERIC, SPORTDiscus, PsycINFO, Scopus, CBCA Complete, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and ProQuest Dissertations and Theses. Selected studies considered aspects of health and/or well-being of adult pickleball participants. Using the population/concept/context framework, participants were healthy, able-bodied adults 18 years of age or over, who had played pickleball at least once. The positive and negative outcomes of pickleball on participants’ health and well-being (concept) within the context of pickleball participation were examined. Full-text articles written in English since 2013 were included. Extracted data were tabulated, and a descriptive summary with thematic analysis was completed. Results: This scoping review comprised 27 articles that met the inclusion criteria. Pickleball is promising as an exercise intervention for all adults, and there is evidence of positive social and psychological effects, and health and fitness benefits to participating in pickleball by older adults. Conclusions: Although we are still in the early stages of studying pickleball, there have been some documented health benefits of using the sport as a physical exercise intervention for adults. More research is needed on the types, prevalence, and severity of pickleball injuries and the sport’s impact on younger players.

Manual exploratory behaviors during object interaction that form the basis of tool use behavior, are mostly qualitatively characterized in terms of their frequency and duration of occurrence. To fully understand their functional and clinical significance, quantitative movement characterization is needed alongside their qualitative analysis. However, there are two challenges in quantifying them—(a) reliably classifying the type of movement and (b) performing this classification on a time series automatically. Here, we propose a machine learning-based classification method to address these challenges. We measured three common exploratory behaviors (object rotation, fingering, and throwing) in college-aged adults using “sensorized objects” that had wireless Inertial Measurement Units embedded in them. We then calculated several statistical features based on linear acceleration and angular velocity data to train machine learning classifiers to identify these behaviors. All classifiers identified the behaviors with a substantially higher accuracy (average accuracy = 84.95 ± 4.16%) than chance level (33.33%). Of all models tested, Support Vector Machine Quadratic, Support Vector Machine Medium Gaussian, and Narrow Neural Network were the best models in classifying the three behaviors (average accuracy = 89.34 ± 0.12%). This classification method shows potential for automating movement characterization of exploratory behaviors, thereby may aid early assessment of neurodevelopmental disorders.