Background: It is reported that 81% of adolescents are insufficiently active. Schools play a pivotal role in promoting physical activity (PA) and reducing sedentary behavior (SB). The aim of this systematic review and meta-analysis was to evaluate classroom-based PA and SB interventions in adolescents. Methods: A search strategy was developed using the Population Intervention Comparison Outcome Study (PICOS) design framework. Articles were screened using strict inclusion criteria. Study quality was assessed using the Effective Public Health Practice Project quality assessment tool (http://www.ephpp.ca/tools.html). Outcome data for preintervention and postintervention were extracted, and effect sizes were calculated using Cohen’s d. Results: The strategy yielded 7574 potentially relevant articles. Nine studies were included for review. Study quality was rated as strong for 1 study, moderate for 5 studies, and weak for 3 studies. Five studies were included for meta-analyses, which suggested that the classroom-based interventions had a nonsignificant effect on PA (P = .55, d = 0.05) and a small, nonsignificant effect on SB (P = .16, d = −0.11). Conclusion: Only 9 relevant studies were found, and the effectiveness of the classroom-based PA and SB interventions varied. Based on limited empirical studies, there is not enough evidence to determine the most effective classroom-based methodology to increase PA and SB.

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Introduction: Understanding daily movement patterns (i.e., the relative proportion of time spent on Sedentary behaviour (SB), light physical activity (LPA), moderate-vigorous physical activity (MVPA) and sleep) to establish links with chronic diseases is a contemporary topic. Little research has looked at the effects of 24-hour movement behaviour on bone health (particularly bone mineral density (BMD)) despite investigation on other health parameters. The aim of this rapid narrative review was to build on previous work published on the association between SB and BMD in older adults by investigating the association of the more holistic 24- hour movement behaviours in relation to BMD. Methods: A database search of Web of Science and NIH PubMed was conducted using broad MeSH terms (older adults, sedentary, and bone). Searches were limited from the year 2019 onward. Results/Discussion: Five published articles were reviewed, and there are discrepancies amongst the findings which could be attributed to the different measurement methods (Peripheral Quantitative Computed Tomography (pQCT) versus Dual-energy X-Ray Absorptiometry (DEXA)) and the sites of measured BMD. There are also notable differences between genders, with BMD in men appearing to be most reliant on time spent in MVPA, compared to women who appear to see benefit to BMD with replacement of SB with LPA and not require more time spent in MVPA. This highlights the complexity of movement behaviours and requires further research.

Modern smartphones such as the iPhone contain an integrated accelerometer, which can be used to measure body movement and estimate the volume and intensity of physical activity. Objectives: The primary objective was to assess the validity of the iPhone to measure step count and energy expenditure during laboratory-based physical activities. A further objective was to compare free-living estimates of physical activity between the iPhone and the ActiGraph GT3X+ accelerometer. Methods: Twenty healthy adults wore the iPhone 5S and GT3X+ in a waist-mounted pouch during bouts of treadmill walking, jogging, and other physical activities in the laboratory. Step counts were manually counted, and energy expenditure was measured using indirect calorimetry. During two weeks of free-living, participants (n = 17) continuously wore a GT3X+ attached to their waist and were provided with an iPhone 5S to use as they would their own phone. Results: During treadmill walking, iPhone (703 ± 97 steps) and GT3X+ (675 ± 133 steps) provided accurate measurements of step count compared with the criterion method (700 ± 98 steps). Compared with indirect calorimetry (8 ± 3 kcal·min−1), the iPhone (5 ± 1 kcal·min−1) underestimated energy expenditure with poor agreement. During free-living, the iPhone (7,990 ± 4,673 steps·day−1) recorded a significantly lower (p < .05) daily step count compared with the GT3X+ (9,085 ± 4,647 steps·day−1). Conclusions: The iPhone accurately estimated step count during controlled laboratory walking but recorded a significantly lower volume of physical activity compared with the GT3X+ during free-living.

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