OBJECTIVES: To compare adolescents’ physical activity at home, near home, at school, near school, and at other locations. METHODS: Adolescents (N = 549) were ages 12 to 16 years (49.9% girls, 31.3% nonwhite or Hispanic) from 447 census block groups in 2 US regions. Accelerometers and Global Positioning System devices assessed minutes of and proportion of time spent in moderate to vigorous physical activity (MVPA) in each of the 5 locations. Mixed-effects regression compared MVPA across locations and demographic factors. RESULTS: Forty-two percent of adolescents’ overall MVPA occurred at school, 18.7% at home, 18.3% in other (nonhome, nonschool) locations, and 20.6% near home or school. Youth had 10 more minutes (30% more) of overall MVPA on school days than on nonschool days. However, the percentage of location time spent in MVPA was lowest at school (4.8% on school days) and highest near home and near school (9.5%–10.4%). Girls had 2.6 to 5.5 fewer minutes per day of MVPA than boys in all locations except near school. CONCLUSIONS: Although a majority of adolescents’ physical activity occurred at school, the low proportion of active time relative to the large amount of time spent at school suggests potential for increasing school-based activity. Increasing time spent in the neighborhood appears promising for increasing overall physical activity, because a high proportion of neighborhood time was active. Increasing youth physical activity to support metabolic health requires strategies for increasing use of physical activity–supportive locations (eg, neighborhoods) and environmental and program improvements in unsupportive locations (eg, schools, homes).

Brief structured physical activity in the classroom is effective for increasing student physical activity. The present study investigated the associat

The purpose of this study was to compare estimates of sedentary time on weekdays vs. weekend days in older adults and determine if these patterns vary by measurement method. Older adults (N = 230, M = 83.5, SD = 6.5 years) living in retirement communities completed a questionnaire about sedentary behavior and wore an ActiGraph accelerometer for seven days. Participants engaged in 9.4 (SD = 1.5) hr per day of accelerometer-measured sedentary time, but self-reported engaging in 11.4 (SD = 4.9) hr per day. Men and older participants had more accelerometer-measured sedentary time than their counterparts. The difference between accelerometer-measured weekday and weekend sedentary time was nonsignificant. However, participants self-reported 1.1 hr per day more sedentary time on weekdays compared with weekend days. Findings suggest self-reported but not accelerometer-measured sedentary time should be investigated separately for weekdays and weekend days, and that self-reports may overestimate sedentary time in older adults.

Introduction: Sitting patterns predict several healthy aging outcomes. These patterns can potentially be measured using hip-worn accelerometers, but current methods are limited by an inability to detect postural transitions. To overcome these limitations, we developed the Convolutional Neural Network Hip Accelerometer Posture (CHAP) classification method. Methods: CHAP was developed on 709 older adults who wore an ActiGraph GT3X+ accelerometer on the hip, with ground-truth sit/stand labels derived from concurrently worn thigh-worn activPAL inclinometers for up to 7 d. The CHAP method was compared with traditional cut-point methods of sitting pattern classification as well as a previous machine-learned algorithm (two-level behavior classification). Results: For minute-level sitting versus nonsitting classification, CHAP performed better (93% agreement with activPAL) than did other methods (74%–83% agreement). CHAP also outperformed other methods in its sensitivity to detecting sit-to-stand transitions: cut-point (73%), TLBC (26%), and CHAP (83%). CHAP’s positive predictive value of capturing sit-to-stand transitions was also superior to other methods: cut-point (30%), TLBC (71%), and CHAP (83%). Day-level sitting pattern metrics, such as mean sitting bout duration, derived from CHAP did not differ significantly from activPAL, whereas other methods did: activPAL (15.4 min of mean sitting bout duration), CHAP (15.7 min), cut-point (9.4 min), and TLBC (49.4 min). Conclusion: CHAP was the most accurate method for classifying sit-to-stand transitions and sitting patterns from free-living hip-worn accelerometer data in older adults. This promotes enhanced analysis of older adult movement data, resulting in more accurate measures of sitting patterns and opening the door for large-scale cohort studies into the effects of sitting patterns on healthy aging outcomes.