To evaluate secular trends in recreational sedentary behavior among high school students in the United States between 2003 and 2015. A series of cross-sectional assessments over a 12-year period. Data from the 2003 to 2015 Youth Risk Behavior Surveillance System was used. Samples of 10 978 to 14 894 adolescents, drawn every 2 years: 2003 to 2015. The evaluated recreational self-reported sedentary behaviors included TV hours and computer hours that are not schoolwork. For the entire sample, and using polynomial orthogonal coefficients via regression modeling, there was an upward linear trend for total sedentary behavior hours (β = 0.03; p = .001), a downward linear trend in TV watching (β = −0.06; p < .001), and an upward linear trend in computer use (β = 0.08; p < .001) from 2003 to 2015. Similar linear trends (p < .001) were observed across several subpopulations, including the groups by gender, race/ethnicity, and body mass index. However, various subpopulations differed in TV watching, with black or African American, and obese adolescents having the highest TV watching hours, respectively (eg, 3.82 h/d vs 3.13 h/d in 2015; blacks vs whites; p < .05). Various subpopulations also differed in computer use, with obese adolescents (4.26 h/d in 2015) having the highest computer use. There were significant changes from 2003 to 2015 in sedentary behavior patterns in the US adolescent population. Total recreational sedentary behavior increased in this period. Specifically, TV viewing decreased while computer use increased. Continued monitoring of sedentary behavior trends is needed to better understand the changing behaviors of American adolescents and how they relate to changes chronic disease risk. To evaluate secular trends in recreational sedentary behavior among high school students in the United States between 2003 and 2015. A series of cross-sectional assessments over a 12-year period. Data from the 2003 to 2015 Youth Risk Behavior Surveillance System was used. Samples of 10 978 to 14 894 adolescents, drawn every 2 years: 2003 to 2015. The evaluated recreational self-reported sedentary behaviors included TV hours and computer hours that are not schoolwork. For the entire sample, and using polynomial orthogonal coefficients via regression modeling, there was an upward linear trend for total sedentary behavior hours (β = 0.03; p = .001), a downward linear trend in TV watching (β = −0.06; p < .001), and an upward linear trend in computer use (β = 0.08; p < .001) from 2003 to 2015. Similar linear trends (p < .001) were observed across several subpopulations, including the groups by gender, race/ethnicity, and body mass index. However, various subpopulations differed in TV watching, with black or African American, and obese adolescents having the highest TV watching hours, respectively (eg, 3.82 h/d vs 3.13 h/d in 2015; blacks vs whites; p < .05). Various subpopulations also differed in computer use, with obese adolescents (4.26 h/d in 2015) having the highest computer use. There were significant changes from 2003 to 2015 in sedentary behavior patterns in the US adolescent population. Total recreational sedentary behavior increased in this period. Specifically, TV viewing decreased while computer use increased. Continued monitoring of sedentary behavior trends is needed to better understand the changing behaviors of American adolescents and how they relate to changes chronic disease risk.

Objective: To compile and quantify the effectiveness of accumulated short-bout exercise interventions on reducing the obesity indices in adults using meta-analysis. Data Source: PubMed, PsycINFO, CINAHL, Cochrane Library, and SportDiscus. Study Inclusion and Exclusion Criteria: (1) Description of a short-bout exercise trial (<30 minutes); (2) obesity indices must be measured pre- and postintervention; and (3) only adults and published in English. Data Extraction: Two independent reviewers extracted data and assessed the quality of the studies included. Of 3257 articles retrieved, 18 studies met the inclusion criteria. Based on the Downs and Black checklist, the methodological quality of the included studies was fairly robust. Data Synthesis: Pooled effect sizes (ESs) were calculated using a random effects model. Results: Average intervention length was approximately 16 weeks (ranged from 4 to 72 weeks). All weighted mean ES values for each obesity index measure were non-negative, ranging from small to large (ES = 0.33-0.96) in magnitude. Weighted mean ES for body mass (BM; n = 18; ES = 0.51, 95% confidence interval [CI] = 0.22-0.80), body mass index (BMI; n = 13; ES = 0.61, 95% CI = 0.24-0.97), waist circumference (n = 9; ES = 0.44, 95% CI = 0.15-0.73), body fat percentage (BF%; n = 8; ES = 0.33, 95% CI = 0.09-0.58), skinfold (n = 7; ES = 0.96, 95% CI = 0.39 -1.53), and fat mass (FM; n = 6; ES = 0.55, 95% CI = 0.21-0.90) were statistically significant. Moderator effects of intervention length (weeks) were observed for BM (Q _between [Cochran’s Q: a measure of heterogeneity between studies] = 6.83, P < .05); BMI (Q _between = 13.93, P < .05); and FM (Q _between = 10.41, P < .05). Intervention length >10 weeks was more effective than shorter (≤10) intervention period for reducing BM, BMI, and FM. Conclusion: Accumulated short bouts of exercise have a beneficial effect on reducing the obesity indices among adults. The current study can help health researchers and practitioners in designing their intervention programs, which can be applied within schools, clinics, and communities.

The purpose of this study is to establish evidence of validity for wearable activity monitors providing real-time cadence against a criterion measure. Thirty-six healthy adults, aged 18–65 years, participated in the study. Four activity monitors including 2 watch-based monitors and 2 cadence sensors attaching to shoelaces were tested. Each participant completed the study protocol consisting of 2 distinct components: (1) treadmill protocol and (2) overground protocol. Lin’s concordance correlation and mean absolute percentage error (MAPE) were calculated for the comparisons between the criterion and measures of the monitors. Bland–Altman analysis was performed to determine the mean bias and 95% limits of agreement. All activity monitors showed high correlations with the criterion measures (p < .01). Lower correlations were observed at slow walking speeds in the watch-based monitors. In contrast, consistent and strong correlations were found with both cadence sensors regardless of walking speeds (p < .01). Similar patterns were observed in the MAPE scores. Greater than 90% of the participants were able to maintain prescribed walking intensity using real-time cadence. The results suggest that the wearable activity monitors are an acceptable measure of real-time cadence and provide the potential to improve intensity-based prescription of physical activity using the monitors.

The purpose of this study was to assess trends in physical education and physical activity among U.S. high school students from 2011 to 2017, respectively, and to evaluate temporal trends in the relationship between physical education class participation and physical activity levels. Data from a total of 51,616 high school students who participated in the Youth Risk Behavior Surveillance System (YRBSS) from 2011 to 2017 were analyzed for this study. Physical education was measured as follows: In an average week when you are in school, on how many days do you go to physical education classes? Physical activity was measured as follows: During the past 7 days, on how many days were you physically active for a total of at least 60 min per day? Meeting physical activity guideline was defined as engaging in 7 days per week of at least 60 min per day. Across the 2011–2017 YRBSS, there were no linear (p = 0.44)/quadratic trends (p = 0.37) in physical education and linear (p = 0.27)/quadratic trends (p = 0.25) in physical activity, respectively. Regarding the trends in the association between physical education and physical activity, there were no statistically significant linear (unstandardized regression coefficient (b) = 0.013, p = 0.43) or quadratic (p = 0.75) trends; however, at the sample level, there was a slight increase in the odds ratio effect sizes from 2011 to 2017 (ES = 1.80–1.98). The pooled association between physical education participation and physical activity was statistically significant, OR = 1.88, 95% CI = 1.75–2.03, p < 0.001. That is, on average across the four YRBSS cycles, students who participated in physical education had 1.88 times higher odds of meeting physical activity guideline when compared to students who did not participate in physical education. The trends of physical education and physical activity did not change between 2011 and 2017. Overall, we observed a significant relationship between participation in physical education and physical activity. There was a non-significant yet stable trend of association between participation in physical education and physical activity over time.

This study aimed to (1) investigate age-associated trends in depression and cognition, (2) determine whether the association between depression and cognition varies across age, and (3) test whether...