Location-specific psychosocial and environmental correlates of physical activity and sedentary time in young adolescents: preliminary evidence for location-specific approaches from a cross-sectional observational study.

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.

Investigating associations between physical activity-related neighborhood built environment features and child weight status to inform local practice.

Despite evidence of the importance of neighborhood built environment features in relation to physical activity and obesity, research has been limited in informing localized practice due to small sample sizes and limited geographic coverage. This demonstration study integrated data from a local pediatric health system with nationally available neighborhood built environment data to inform local decision making around neighborhood environments and childhood obesity. Height/weight from clinic visits and home neighborhood measures from the U.S. Environmental Protections Agency and WalkScore were obtained for 15,989 6-17 year olds. Multilevel models accounted for the nested data structure and were adjusted for neighborhood income and child sociodemographics. In 9-17 year olds, greater street connectivity and walkability were associated with a 0.01-0.04 lower BMIz (Ps = .009-.017) and greater residential density, street connectivity, and walkability were associated 5-7% lower odds of being overweight/obese (Ps = .004-.044) per standard deviation increase in environment variable. 45.9% of children in the lowest walkability tertile were overweight or obese, whereas 43.1% of children in the highest walkability tertile were overweight or obese. Maps revealed areas with low walkability and a high income-adjusted percent of children overweight/obese. In the Kansas City area, data showed that fewer children were overweight/obese in more walkable neighborhoods. Integrating electronic health records with neighborhood environment data is a replicable process that can inform local practice by highlighting the importance of neighborhood environment features locally and pointing to areas most in need of interventions.

Differences in adolescent activity and dietary behaviors across home, school, and other locations warrant location-specific intervention approaches.

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.

Automated High-Frequency Observations of Physical Activity Using Computer Vision.

PURPOSE: To test the validity of the Ecological Video Identification of Physical Activity (EVIP) computer vision algorithms for automated video-based ecological assessment of physical activity in settings such as parks and schoolyards. METHODS: Twenty-seven hours of video were collected from stationary overhead video cameras across 22 visits in nine sites capturing organized activities. Each person in the setting wore an accelerometer, and each second was classified as moderate-to-vigorous physical activity or sedentary/light activity. Data with 57,987 s were used to train and test computer vision algorithms for estimating the total number of people in the video and number of people active (in moderate-to-vigorous physical activity) each second. In the testing data set (38,658 s), video-based System for Observing Play and Recreation in Communities (SOPARC) observations were conducted every 5 min (130 observations). Concordance correlation coefficients (CCC) and mean absolute errors (MAE) assessed agreement between (1) EVIP and ground truth (people counts+accelerometry) and (2) SOPARC observation and ground truth. Site and scene-level correlates of error were investigated. RESULTS: Agreement between EVIP and ground truth was high for number of people in the scene (CCC = 0.88; MAE = 2.70) and moderate for number of people active (CCC = 0.55; MAE = 2.57). The EVIP error was uncorrelated with camera placement, presence of obstructions or shadows, and setting type. For both number in scene and number active, EVIP outperformed SOPARC observations in estimating ground truth values (CCC were larger by 0.11-0.12 and MAE smaller by 41%-48%). CONCLUSIONS: Computer vision algorithms are promising for automated assessment of setting-based physical activity. Such tools would require less manpower than human observation, produce more and potentially more accurate data, and allow for ongoing monitoring and feedback to inform interventions.

Adherence With Multiple National Healthy Lifestyle Recommendations in a Large Pediatric Center Electronic Health Record and Reduced Risk of Obesity.

OBJECTIVE: To evaluate the utility of a routine assessment of lifestyle behaviors incorporated into the electronic health record (EHR) to quantify lifestyle practices and obesity risk at a pediatric primary care center. PATIENTS AND METHODS: Participants included 24,255 patients aged 2 to 18 years whose parent/caregiver completed a self-report lifestyle assessment during a well-child examination (January 1, 2013, through June 30, 2016). Cross-sectional analyses of age, race/ethnicity, body mass index, and lifestyle assessment responses were performed. Outcome measures included prevalence of patients meeting consensus recommendations for physical activity; screen time; and dairy, water, and fruit/vegetable consumption and the odds of obesity based on reported lifestyle behaviors. RESULTS: Prevalence of meeting recommendations for lifestyle behaviors was highest for physical activity (84%), followed by screen time (61%) and consumption of water (51%), dairy (27%), and fruits/vegetables (10%). Insufficient physical activity was the strongest predictor of obesity (odds ratio [OR], 1.65; 95% CI, 1.51-1.79), followed by excess screen time (OR, 1.36; 95% CI, 1.27-1.45). Disparities existed across ages, races/ethnicities, and sexes for multiple lifestyle habits. Youth who met 0 or 1 lifestyle recommendation were 1.45 to 1.71 times more likely to have obesity than those meeting all 5 recommendations. CONCLUSION: Healthy behaviors vary in prevalence, as does their association with obesity. This variation is partially explained by age, sex, and race/ethnicity. Meeting national recommendations for specific behaviors is negatively associated with obesity in a dose-dependent manner. These findings support the assessment of lifestyle behaviors in primary care as one component of multilevel initiatives to prevent childhood obesity.

Automated Ecological Assessment of Physical Activity: Advancing Direct Observation.

Technological advances provide opportunities for automating direct observations of physical activity, which allow for continuous monitoring and feedback. This pilot study evaluated the initial validity of computer vision algorithms for ecological assessment of physical activity. The sample comprised 6630 seconds per camera (three cameras in total) of video capturing up to nine participants engaged in sitting, standing, walking, and jogging in an open outdoor space while wearing accelerometers. Computer vision algorithms were developed to assess the number and proportion of people in sedentary, light, moderate, and vigorous activity, and group-based metabolic equivalents of tasks (MET)-minutes. Means and standard deviations (SD) of bias/difference values, and intraclass correlation coefficients (ICC) assessed the criterion validity compared to accelerometry separately for each camera. The number and proportion of participants sedentary and in moderate-to-vigorous physical activity (MVPA) had small biases (within 20% of the criterion mean) and the ICCs were excellent (0.82-0.98). Total MET-minutes were slightly underestimated by 9.3-17.1% and the ICCs were good (0.68-0.79). The standard deviations of the bias estimates were moderate-to-large relative to the means. The computer vision algorithms appeared to have acceptable sample-level validity (i.e., across a sample of time intervals) and are promising for automated ecological assessment of activity in open outdoor settings, but further development and testing is needed before such tools can be used in a diverse range of settings.

Patient Attendance and Outcomes in a Structured Weight Management Program.

OBJECTIVES: To determine service utilization and identify patient characteristics associated with service utilization in youth with obesity presenting for structured weight management, and to explore weight-related outcomes associated with service utilization. STUDY DESIGN: In this retrospective study conducted between January 2008 and December 2013, we examined variables associated with the care of 2089 patients aged 2-18 years presenting for an initial visit to 2 tertiary care-based, multidisciplinary structured weight management clinics. RESULTS: Only 53% of patients returned for a second visit, 29% returned for a third visit, and virtually none (0.5%) completed the recommended 6 visits within 6 months. Patients who were Hispanic, government-insured, and whose parent/s spoke Spanish were more likely to return to clinic. Of those patients who returned for at least a second visit, 70% demonstrated a reduction in or maintenance of body mass index z-score. CONCLUSIONS: Patient retention remains a significant barrier to effective pediatric weight management. Structured weight management programs should increase their efforts to engage patients and families at the initial visit and identify and address barriers to follow up.