Accelerometer and GPS Data to Analyze Built Environments and Physical Activity.

Purpose: Most built environment studies have quantified characteristics of the areas around participants' homes. However, the environmental exposures for physical activity (PA) are spatially dynamic rather than static. Thus, merged accelerometer and global positioning system (GPS) data were utilized to estimate associations between the built environment and PA among adults. Methods: Participants (N = 142) were recruited on trails in Massachusetts and wore an accelerometer and GPS unit for 1-4 days. Two binary outcomes were created: moderate-to-vigorous PA (MVPA vs. light PA-to-sedentary); and light-to-vigorous PA (LVPA vs. sedentary). Five built environment variables were created within 50-meter buffers around GPS points: population density, street density, land use mix (LUM), greenness, and walkability index. Generalized linear mixed models were fit to examine associations between environmental variables and both outcomes, adjusting for demographic covariates. Results: Overall, in the fully adjusted models, greenness was positively associated with MVPA and LVPA (odds ratios [ORs] = 1.15, 95% confidence interval [CI] = 1.03, 1.30 and 1.25, 95% CI = 1.12, 1.41, respectively). In contrast, street density and LUM were negatively associated with MVPA (ORs = 0.69, 95% CI = 0.67, 0.71 and 0.87, 95% CI = 0.78, 0.97, respectively) and LVPA (ORs = 0.79, 95% CI = 0.77, 0.81 and 0.81, 95% CI = 0.74, 0.90, respectively). Negative associations of population density and walkability with both outcomes reached statistical significance, yet the effect sizes were small. Conclusions: Concurrent monitoring of activity with accelerometers and GPS units allowed us to investigate relationships between objectively measured built environment around GPS points and minute-by-minute PA. Negative relationships between street density and LUM and PA contrast evidence from most built environment studies in adults. However, direct comparisons should be made with caution since most previous studies have focused on spatially fixed buffers around home locations, rather than the precise locations where PA occurs.

Accelerometer and GPS Analysis of Trail Use and Associations With Physical Activity.

BACKGROUND: Concurrent use of accelerometers and global positioning system (GPS) data can be used to quantify physical activity (PA) occurring on trails. This study examined associations of trail use with PA and sedentary behavior (SB) and quantified on trail PA using a combination of accelerometer and GPS data. METHODS: Adults (N = 142) wore accelerometer and GPS units for 1-4 days. Trail use was defined as a minimum of 2 consecutive minutes occurring on a trail, based on GPS data. We examined associations between trail use and PA and SB. On trail minutes of light-intensity, moderate-intensity, and vigorous-intensity PA, and SB were quantified in 2 ways, using accelerometer counts only and with a combination of GPS speed and accelerometer data. RESULTS: Trail use was positively associated with total PA, moderate-intensity PA, and light-intensity PA (P < .05). On trail vigorous-intensity PA minutes were 346% higher when classified with the combination versus accelerometer only. Light-intensity PA, moderate-intensity PA, and SB minutes were 15%, 91%, and 85% lower with the combination, respectively. CONCLUSIONS: Adult trail users accumulated more PA on trail use days than on nontrail use days, indicating the importance of these facilities for supporting regular PA. The combination of GPS and accelerometer data for quantifying on trail activity may be more accurate than accelerometer data alone and is useful for classifying intensity of activities such as bicycling.