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Abstract 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,Mage = 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.

Abstract Background Safer conception counseling (SCC) to promote the use of safer conception methods (SCM) is not yet part of routine family planning or HIV care. Guidelines for the use of SCM have been published, but to date there are no published controlled evaluations of SCC. Furthermore, it is unknown whether standard methods commonly used in resource constrained settings to integrate new services would be sufficient, or if enhanced training and supervision would result in a more efficacious approach to implementing SCC. Methods In a hybrid, cluster randomized controlled trial, six HIV clinics were randomly assigned to implement the SCC intervention Our Choice using either a high (SCC1) or low intensity (SCC2) approach (differentiated by amount of training and supervision), or existing family planning services (usual care). Three hundred eighty-nine HIV clients considering childbearing with an HIV-negative partner enrolled. The primary outcome was self-reported use of appropriate reproductive method (SCM if trying to conceive; modern contraceptives if not) over 12 months or until pregnancy. Results The combined intervention groups used appropriate reproductive methods more than usual care [20.8% vs. 6.9%; adjusted OR (95% CI)=10.63 (2.79, 40.49)], and SCC1 reported a higher rate than SCC2 [27.1% vs. 14.6%; OR (95% CI)=4.50 (1.44, 14.01)]. Among those trying to conceive, the intervention arms reported greater accurate use of SCM compared to usual care [24.1% vs. 0%; OR (95% CI)=91.84 (4.94, 1709.0)], and SCC1 performed better than SCC2 [34.6% vs. 11.5%; OR (95% CI)=6.43 (1.90, 21.73)]. The arms did not vary on modern contraception use among those not trying to conceive. A cost of $631 per person was estimated to obtain accurate use of SCM in SCC1, compared to $1014 in SCC2. Conclusions More intensive provider training and more frequent supervision leads to greater adoption of complex SCM behaviors and is more cost-effective than the standard low intensity implementation approach. Trial registration Clinicaltrials.gov, NCT03167879 ; date registered May 23, 2017.

Abstract 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.

Abstract Background Youth are active in multiple locations, but it is unknown whether more physical activity in one location is associated with less in other locations. This cross-sectional study examines whether on days with more physical activity in a given location, relative to their typical activity in that location, youth had less activity in other locations (i.e., within-person associations/compensation). Methods Participants were 528 adolescents, ages 12 to 16 (M = 14.12, SD = 1.44, 50% boys, 70% White non-Hispanic). Accelerometer and Global Positioning System devices were used to measure the proportion of time spent in moderate-to-vigorous physical activity (MVPA) in five locations: home, home neighborhood, school, school neighborhood, and other locations. Mixed-effects regression was used to examine within-person associations of MVPA across locations and moderators of these associations. Results Two of ten within-participant associations tested indicated small amounts of compensation, and one association indicated generalization across locations. Higher at-school MVPA (relative to the participant’s average) was related to less at-home MVPA and other-location MVPA (Bs = −0.06 min/day). Higher home-neighborhood MVPA (relative to the participant’s average) was related to more at-home MVPA (B = 0.07 min/day). Some models showed that compensation was more likely (or generalization less likely) in boys and non-whites or Hispanic youth. Conclusions Consistent evidence of compensation across locations was not observed. A small amount of compensation was observed for school physical activity, suggesting that adolescents partially compensated for high amounts of school activity by being less active in other locations. Conversely, home-neighborhood physical activity appeared to carry over into the home, indicating a generalization effect. Overall these findings suggest that increasing physical activity in one location is unlikely to result in meaningful decreases in other locations. Supporting physical activity across multiple locations is critical to increasing overall physical activity in youth.