Background

Investigation of physical activity and dietary behaviors across locations can inform "setting-specific" health behavior interventions and improve understanding of contextual vulnerabilities to poor health. This study examined how physical activity, sedentary time, and dietary behaviors differed across home, school, and other locations in young adolescents.

Methods

Participants were adolescents aged 12-16 years from the Baltimore-Washington, DC and the Seattle areas from a larger cross-sectional study. Participants (n = 472) wore an accelerometer and Global Positioning Systems (GPS) tracker (Mean days = 5.12, SD = 1.62) to collect location-based physical activity and sedentary data. Participants (n = 789) completed 24-h dietary recalls to assess dietary behaviors and eating locations. Spatial analyses were performed to classify daily physical activity, sedentary time patterns, and dietary behaviors by location, categorized as home, school, and "other" locations.

Results

Adolescents were least physically active at home (2.5 min/hour of wear time) and school (2.9 min/hour of wear time) compared to "other" locations (5.9 min/hour of wear time). Participants spent a slightly greater proportion of wear time in sedentary time when at school (41 min/hour of wear time) than at home (39 min/hour of wear time), and time in bouts lasting ≥30 min (10 min/hour of wear time) and mean sedentary bout duration (5 min) were highest at school. About 61% of daily energy intake occurred at home, 25% at school, and 14% at "other" locations. Proportionately to energy intake, daily added sugar intake (5 g/100 kcal), fruits and vegetables (0.16 servings/100 kcal), high calorie beverages (0.09 beverages/100 kcal), whole grains (0.04 servings/100 kcal), grams of fiber (0.65 g/100 kcal), and calories of fat (33 kcal/100 kcal) and saturated fat (12 kcal/100 kcal) consumed were nutritionally least favorable at "other" locations. Daily sweet and savory snacks consumed was highest at school (0.14 snacks/100 kcal).

Conclusions

Adolescents' health behaviors differed based on the location/environment they were in. Although dietary behaviors were generally more favorable in the home and school locations, physical activity was generally low and sedentary time was higher in these locations. Health behavior interventions that address the multiple locations in which adolescents spend time and use location-specific behavior change strategies should be explored to optimize health behaviors in each location.

Background

A better understanding of the extent to which psychosocial and environmental correlates of physical activity are specific to locations would inform intervention optimization.

Purpose

To investigate cross-sectional associations of location-general and location-specific variables with physical activity and sedentary time in three common locations adolescents spend time.

Methods

Adolescents (N = 472,M_age = 14.1,SD = 1.5) wore an accelerometer and global positioning systems (GPS) tracker and self-reported on psychosocial (e.g., self-efficacy) and environmental (e.g., equipment) factors relevant to physical activity and sedentary time. We categorized each survey item based on whether it was specific to a location to generate psychosocial and environmental indices that were location-general or specific to either school, non-school, or home location. Physical activity (MVPA) and sedentary time were based on time/location match to home, school, or all "other" locations. Mixed-effects models investigated the relation of each index with location-specific activity.

Results

The location-general and non-school physical activity psychosocial indices were related to greater MVPA at school and "other" locations. The school physical activity environment index was related to greater MVPA and less sedentary time at school. The home activity environment index was related to greater MVPA at home. The non-school sedentary psychosocial index was related to less sedentary time at home. Interactions among indices revealed adolescents with low support on one index benefited (i.e., exhibited more optimal behavior) from high support on another index (e.g., higher scores on the location-general PA psychosocial index moderated lower scores on the home PA environment index). Concurrent high support on two indices did not provide additional benefit.

Conclusions

No psychosocial or environment indices, including location-general indices, were related to activity in all locations. Most of the location-specific indices were associated with activity in the matching location(s). These findings provide preliminary evidence that psychosocial and environmental correlates of activity are location specific. Future studies should further develop location-specific measures and evaluate these constructs and whether interventions may be optimized by targeting location-specific psychosocial and environmental variables across multiple locations.