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Background Independent of physical activity levels, youth sedentary behaviors (SB) have negative health outcomes. SB prevalence estimates during discretionary periods of the day (e.g., after-school), inform the need for targeted period-specific interventions. This systematic review aimed to determine children’s and adolescents’ SB prevalence during the after-school period. Methods A computerized search was conducted in October 2015 (analysed November 2015). Inclusion criteria were: published in a peer-reviewed English journal; participants aged 5-18 years; measured overall after-school sedentary time (ST) objectively, and/or specific after-school SBs (e.g., TV viewing) objectively or subjectively; and provided the percentage of the after-school period spent in ST/SB or duration of behavior and period to calculate this. Where possible, findings were analyzed by location (e.g., after-school care/‘other’ locations). The PRISMA guidelines were followed. Results Twenty-nine studies were included: 24 included children (≤12 years), four assessed adolescents (>12 years) and one included both; 20 assessed ST and nine assessed SB. On average, children spent 41 % and 51 % of the after-school period in ST when at after-school care and other locations respectively. Adolescents spent 57 % of the after-school period in ST. SBs that children and adolescents perform include: TV viewing (20 % of the period), non-screen based SB (including homework; 20 %), screen-based SB (including TV viewing; 18 %), homework/academics (13 %), motorised transport (12 %), social SB (9 %), and screen-based SB (excluding TV viewing; 6 %). Conclusion Children spent up to half of the after-school period in ST and this is higher among adolescents. A variety of screen- and non-screen based SBs are performed after school, providing key targets for interventions. Trial registration PROSPERO registration number CRD42015010437

Background Few efficacious physical activity interventions are successfully translated and sustained in practice. We propose a practical guide for researchers to increase the likelihood of successful implementation and scale up of physical activity interventions in practice contexts. The guide is based on two principles: (i) differences between the research and practice context can be addressed during intervention development and implementation planning by focusing on system, delivery personnel, and intervention characteristics; and (ii) early planning for implementation barriers and facilitators can improve subsequent translation into practice. Methods From the published literature, we identified evidence of strategies to improve research-practice translation, along with narrative descriptions of different approaches to addressing translational challenges. These, along with constructs taken from widely cited implementation outcome, process, and mechanistic models were collated and inform the guide. Results The resultant PRACTIS guide ( PRACT ical planning for I mplementation and S cale-up) comprised the following four iterative steps: Step 1 ) Characterize the parameters of the implementation setting; Step 2 ) Identify and engage key stakeholders across multiple levels within the delivery system(s); Step 3 ) Identify contextual barriers and facilitators to implementation, and; Step 4 ) Address potential barriers to effective implementation. Conclusions A lack of practical guidance for researchers on how to effectively plan implementation and scale up of physical activity interventions prevents us moving quickly from evidence to action. We recommend that intervention development and adaptation for broad and sustained implementation be prioritized early in intervention planning and include active engagement from delivery organizations and stakeholders. The PRACTIS guide is also relevant for clinical and public health researchers in other areas of prevention.

In this study we examined the associations of physical education class participation with physical activity among adolescents. We analysed the Global School-based Student Health Survey data from 65 countries (N = 206,417; 11–17 years; 49% girls) collected between 2007 and 2016. We defined sufficient physical activity as achieving physical activities ≥ 60 min/day, and grouped physical education classes as ‘0 day/week’, ‘1–2 days/week’, and ‘ ≥ 3 days/week’ participation. We used multivariable logistic regression to obtain country-level estimates, and meta-analysis to obtain pooled estimates. Compared to those who did not take any physical education classes, those who took classes ≥ 3 days/week had double the odds of being sufficiently active (OR 2.05, 95% CI 1.84–2.28) with no apparent gender/age group differences. The association estimates decreased with higher levels of country’s income with OR 2.37 (1.51–3.73) for low-income and OR 1.85 (1.52–2.37) for high-income countries. Adolescents who participated in physical education classes 1–2 days/week had 26% higher odds of being sufficiently active with relatively higher odds for boys (30%) than girls (15%). Attending physical education classes was positively associated with physical activity among adolescents regardless of sex or age group. Quality physical education should be encouraged to promote physical activity of children and adolescents.

Government responses to managing the COVID-19 pandemic may have impacted the way individuals were able to engage in physical activity. Digital platforms are a promising way to support physical activity levels and may have provided an alternative for people to maintain their activity while at home. This study aimed to examine associations between the use of digital platforms and adherence to the physical activity guidelines among Australian adults and adolescents during the COVID-19 stay-at-home restrictions in April and May 2020. A national online survey was distributed in May 2020. Participants included 1188 adults (mean age 37.4 years, SD 15.1; 980/1188, 82.5% female) and 963 adolescents (mean age 16.2 years, SD 1.2; 685/963, 71.1% female). Participants reported demographic characteristics, use of digital platforms for physical activity over the previous month, and adherence to moderate- to vigorous-intensity physical activity (MVPA) and muscle-strengthening exercise (MSE) guidelines. Multilevel logistic regression models examined differences in guideline adherence between those who used digital platforms (ie, users) to support their physical activity compared to those who did not (ie, nonusers). Digital platforms include streaming services for exercise (eg, YouTube, Instagram, and Facebook); subscriber fitness programs, via an app or online (eg, Centr and MyFitnessPal); facilitated online live or recorded classes, via platforms such as Zoom (eg, dance, sport training, and fitness class); sport- or activity-specific apps designed by sporting organizations for participants to keep up their skills (eg, TeamBuildr); active electronic games (eg, Xbox Kinect); and/or online or digital training or racing platforms (eg, Zwift, FullGaz, and Rouvy). Overall, 39.5% (469/1188) of adults and 26.5% (255/963) of adolescents reported using digital platforms for physical activity. Among adults, MVPA (odds ratio [OR] 2.0, 95% CI 1.5-2.7), MSE (OR 3.3, 95% CI 2.5-4.5), and combined (OR 2.7, 95% CI 2.0-3.8) guideline adherence were higher among digital platform users relative to nonusers. Adolescents' MVPA (OR 2.4, 95% CI 1.3-4.3), MSE (OR 3.1, 95% CI 2.1-4.4), and combined (OR 4.3, 95% CI 2.1-9.0) guideline adherence were also higher among users of digital platforms relative to nonusers. Digital platform users were more likely than nonusers to meet MVPA and MSE guidelines during the COVID-19 stay-at-home restrictions in April and May 2020. Digital platforms may play a critical role in helping to support physical activity engagement when access to facilities or opportunities for physical activity outside the home are restricted.

Background Globally, many children fail to meet the World Health Organization’s physical activity and sedentary behaviour guidelines. Schools are an ideal setting to intervene, yet despite many interventions in this setting, success when delivered under real-world conditions or at scale is limited. This systematic review aims to i) identify which implementation models are used in school-based physical activity effectiveness, dissemination, and/or implementation trials, and ii) identify factors associated with the adoption, implementation and sustainability of school-based physical activity interventions in real-world settings. Methods The review followed PRISMA guidelines and included a systematic search of seven databases from January 1st, 2000 to July 31st, 2018: MEDLINE, EMBASE, CINAHL, SPORTDiscus, PsycINFO, CENTRAL, and ERIC. A forward citation search of included studies using Google Scholar was performed on the 21st of January 2019 including articles published until the end of 2018. Study inclusion criteria: (i) a primary outcome to increase physical activity and/or decrease sedentary behaviour among school-aged children and/or adolescents; (ii) intervention delivery within school settings, (iii) use of implementation models to plan or interpret study results; and (iv) interventions delivered under real-world conditions. Exclusion criteria: (i) efficacy trials; (ii) studies applying or testing school-based physical activity policies, and; (iii) studies targeting special schools or pre-school and/or kindergarten aged children. Results 27 papers comprising 17 unique interventions were included. Fourteen implementation models (e.g., RE-AIM, Rogers’ Diffusion of Innovations, Precede Proceed model), were applied across 27 papers. Implementation models were mostly used to interpret results ( n  = 9), for planning evaluation and interpreting results ( n  = 8), for planning evaluation ( n  = 6), for intervention design ( n  = 4), or for a combination of designing the intervention and interpreting results ( n  = 3). We identified 269 factors related to barriers ( n  = 93) and facilitators ( n  = 176) for the adoption ( n  = 7 studies), implementation ( n  = 14 studies) and sustainability ( n  = 7 studies) of interventions. Conclusions Implementation model use was predominately centered on the interpretation of results and analyses, with few examples of use across all study phases as a planning tool and to understand results. This lack of implementation models applied may explain the limited success of interventions when delivered under real-world conditions or at scale. Trial registration PROSPERO ( CRD42018099836 ).

OBJECTIVE: Observational studies show breaking up prolonged sitting has beneficial associations with cardiometabolic risk markers, but intervention studies are required to investigate causality. We examined the acute effects on postprandial glucose and insulin levels of uninterrupted sitting compared with sitting interrupted by brief bouts of light- or moderate-intensity walking. RESEARCH DESIGN AND METHODS: Overweight/obese adults (n = 19), aged 45–65 years, were recruited for a randomized three-period, three-treatment acute crossover trial: 1) uninterrupted sitting; 2) seated with 2-min bouts of light-intensity walking every 20 min; and 3) seated with 2-min bouts of moderate-intensity walking every 20 min. A standardized test drink was provided after an initial 2-h period of uninterrupted sitting. The positive incremental area under curves (iAUC) for glucose and insulin (mean [95% CI]) for the 5 h after the test drink (75 g glucose, 50 g fat) were calculated for the respective treatments. RESULTS: The glucose iAUC (mmol/L) ⋅ h after both activity-break conditions was reduced (light: 5.2 [4.1–6.6]; moderate: 4.9 [3.8–6.1]; both P < 0.01) compared with uninterrupted sitting (6.9 [5.5–8.7]). Insulin iAUC (pmol/L) ⋅ h was also reduced with both activity-break conditions (light: 633.6 [552.4–727.1]; moderate: 637.6 [555.5–731.9], P < 0.0001) compared with uninterrupted sitting (828.6 [722.0–950.9]). CONCLUSIONS: Interrupting sitting time with short bouts of light- or moderate-intensity walking lowers postprandial glucose and insulin levels in overweight/obese adults. This may improve glucose metabolism and potentially be an important public health and clinical intervention strategy for reducing cardiovascular risk.

Background Sustainable shifts in population behaviours require system-level implementation and embeddedness of large-scale health interventions. This paper aims to understand how different contexts of scaling up interventions affect mechanisms to produce intended and unintended scale up outcomes. Methods A mixed method study combining a realist perspective and systems analysis (causal loop diagrams) of scaled-up physical activity and/or nutrition interventions implemented at a state/national level in Australia (2010–18). The study involved four distinct phases: Phase 1 expert consultation, database and grey literature searches to identify scaled-up interventions; Phase 2 generating initial Context-Mechanism-Outcome configurations (CMOs) from the WHO ExpandNet framework for scaling up; Phase 3 testing and refining CMOs via online surveys and realist interviews with academics, government and non-government organisations (NGOs) involved in scale up of selected interventions ( Phase 1 ); and Phase 4 generating cross-case mid-range theories represented in systems models of scaling up; validated by member checking. Descriptive statistics were reported for online survey data and realist analysis for interview data. Results Seven interventions were analysed, targeting nutrition ( n  = 1), physical activity (n = 1), or a combination ( n  = 5). Twenty-six participants completed surveys; 19 completed interviews. Sixty-three CMO pathways underpinned successful scale up, reflecting 36 scale up contexts, 8 key outcomes; linked via 53 commonly occurring mechanisms. All five WHO framework domains were represented in the systems models. Most CMO pathways included ‘intervention attributes’ and led to outcomes ‘community sustainability/embeddedness’ and ‘stakeholder buy-in/perceived value’. Irrespective of interventions being scaled in similar contexts (e.g., having political favourability); mechanisms still led to both intended and unintended scale up outcomes (e.g., increased or reduced sustainability). Conclusion This paper provides the first evidence for mechanisms underpinning outcomes required for successful scale up of state or nationally delivered interventions. Our findings challenge current prerequisites for effective scaling suggesting other conditions may be necessary. Future scale up approaches that plan for complexity and encourage iterative adaptation throughout, may enhance scale up outcomes. Current linear, context-to-outcome depictions of scale up oversimplify what is a clearly a complex interaction between perceptions, worldviews and goals of those involved. Mechanisms identified in this study could potentially be leveraged during future scale up efforts, to positively influence intervention scalability and sustainability.

Background: Fundamental movement skill (FMS) competence is positively associated with physical activity (PA). However, levels of both FMS and PA are lower than expected. Current reviews of interventions to improve FMS and PA have shown that many school-based programs have achieved positive outcomes, yet the maintenance of these interventions is variable. Teachers play a central role in the success and longevity of school-based interventions. Despite the importance of teacher engagement, research into the nature and quality of teacher training in school-based PA and FMS interventions has received little attention. Objective: The aim of this systematic review was to investigate the type and quantity of teacher training in school-based physical education PA and/or FMS interventions, and to identify what role teacher training had on the intervention outcome. Methods: A systematic search of eight electronic databases was conducted. Publication date restrictions were not implemented in any database, and the last search was performed on 1 March 2015. School physical education-based interventions facilitated by a school teacher, and that included a quantitative assessment of FMS competence and/or PA levels were included in the review. Results: The search identified 39 articles. Eleven of the studies measured FMS, 25 studies measured PA and three measured both FMS and PA. Nine of the studies did not report on any aspect of the teacher training conducted. Of the 30 studies that reported on teacher training, 25 reported statistically significant intervention results for FMS and/or PA. It appears that teacher training programs: are )= 1 day; provide comprehensive subject and pedagogy content; are framed by a theory or model; provide follow-up or ongoing support; and measure teacher satisfaction of the training, are more effective at improving student outcomes in FMS and/or PA. However, the provision of information regarding the characteristics of the teacher training was largely inadequate. Therefore, it was difficult to ascertain which teacher training characteristics were most important in relation to intervention effectiveness. Conclusion: It is clear that whilst teachers are capable of making substantial improvements in student outcomes in PA and FMS, the findings of this review suggest the teacher training component of school-based PA and/or FMS interventions is not only under-reported but is under-studied, and, perhaps as a result, the value of teacher training is not widely understood. What remains unclear, due to poor reporting, is what role teacher training is having on these outcomes. (Autor).

Children's after-school physical activity (PA) and sedentary behaviours (SB) are not well understood, despite the potential this period holds for intervention. This study aimed to describe children's after-school physical activity and sedentary behaviours; establish the contribution this makes to daily participation and to achieving physical activity and sedentary behaviours guidelines; and to determine the association between after-school moderate- to vigorous-intensity physical activity (MVPA), screen-based sedentary behaviours and achieving the physical activity and sedentary behaviour guidelines. Children (n = 406, mean age 8.1 years, 58% girls) wore an ActiGraph GT3X accelerometer. The percentage of time and minutes spent sedentary (SED), in light- physical activity (LPA) and MVPA between the end-of-school and 6pm (weekdays) was calculated. Parents (n = 318, 40 years, 89% female) proxy-reported their child's after-school participation in screen-based sedentary behaviours. The contribution that after-school SED, LPA, MVPA, and screen-based sedentary behaviours made to daily levels, and that after-school MVPA and screen-based sedentary behaviours made to achieving the physical activity/sedentary behaviour guidelines was calculated. Regression analysis determined the association between after-school MVPA and screen-based sedentary behaviours and achieving the physical activity/sedentary behaviours guidelines. Children spent 54% of the after-school period SED, and this accounted for 21% of children's daily SED levels. Boys spent a greater percentage of time in MVPA than girls (14.9% vs. 13.6%; p<0.05), but this made a smaller contribution to their daily levels (27.6% vs 29.8%; p<0.05). After school, boys and girls respectively performed 18.8 minutes and 16.7 minutes of MVPA, which is 31.4% and 27.8% of the MVPA (p<0.05) required to achieve the physical activity guidelines. Children spent 96 minutes in screen-based sedentary behaviours, contributing to 84% of their daily screen-based sedentary behaviours and 80% of the sedentary behaviour guidelines. After-school MVPA was positively associated with achieving the physical activity guidelines (OR: 1.31, 95%CI 1.18, 1.44, p<0.05), and after-school screen-based sedentary behaviours were negatively associated with achieving the sedentary behaviours guidelines (OR: 0.97, 95%CI: 0.96, 0.97, p<0.05). The after-school period plays a critical role in the accumulation of children's physical activity and sedentary behaviours. Small changes to after-school behaviours can have large impacts on children's daily behaviours levels and likelihood of meeting the recommended levels of physical activity and sedentary behaviour. Therefore interventions should target reducing after-school sedentary behaviours and increasing physical activity.

Background Excessive screen time behaviors performed by children and parents at home is a major public health concern. Identifying whether child and parent screen time behaviors cluster and understanding correlates of these familial clusters can help inform interventions for the whole family. This study characterized familial typologies of screen time behaviors and identified key modifiable correlates of these typologies. Methods Parents participating in the cross-sectional Sitting in the Home (SIT) study reported the duration (mins/day) they and their child (aged 11.2 ± 2.62 years) spent in six screen time behaviors at home (computer/laptop for home/work, computer/laptop for leisure, TV/videos/DVDs, tablet/smart phone for home/work, tablet/smart phone for leisure, and electronic games) and completed items related to 21 potential correlates framed by an adapted Social Cognitive Theory, Family Perspective. Latent Class Analysis was used to identify typologies based on parent and child data for the six behaviors. Multinomial logistic regression analysis assessed the relative risk of typology membership for each potential correlate, adjusting for child and parent age and sex. Results The sample comprised 542 parent-child dyads (parents: 40.7 ± 6.3 yrs., 94% female; children: 11.2 ± 2.6 yrs., 46% female). Three typologies were identified: 1) high computer/moderate TV ( n  = 197); 2) high TV/tablet/smartphone, low computer ( n  = 135); and 3) low-screen users ( n  = 210). ‘Low-screen users’ spent the least amount of time in all screen time behaviors (assigned as reference category). Greater child preference for screen time behaviors, parental support for screen time behaviors and frequency of homework requiring a tablet/laptop were associated with higher odds of being in the ‘high computer/moderate TV’ typology. The odds of being in the ‘high TV/tablet/smartphone, low computer’ typology were greater amongst children with a higher preference for screen time behaviors, and lower among more active parents. Conclusions Three familial typologies of screen time behaviors were identified. The findings highlight that screen time in the home can be influenced by the home environment, parental behaviours and role modelling, child preferences as well as school policies. Findings can inform the development of family screen time interventions, however more research exploring the influence of factors outside of the home is warranted.