Portable Global Positioning Units to Complement Accelerometry-Based Physical Activity Monitors

PURPOSE:This study examines the usefulness of complementing accelerometry-based physical activity measurement with spatial data from portable global positioning system (GPS) units to determine where physical activity occurs. METHODS:First, using the geographic distribution of data points and Bland–Altman plots, we examined GPS unitsʼ validity and interunit reliability by measuring the distance to a geodetic point. We also assessed interunit reliability by comparing GPS data collected in three built environment contexts. Second, we conducted a pilot study in which 35 participants wore GPS units and accelerometers in free-living conditions for 3 d. Moderate and vigorous physical activity (MVPA) bouts were matched to GPS data. We classified each bout as occurring inside or outside the participantʼs home neighborhood. Using unpaired t-tests and Fisherʼs exact tests, we compared neighborhood attributes for participants having the majority of MVPA bouts within their home neighborhood, relative to those with most bouts away from their home neighborhood. RESULTS:Average distance from each unit to the geodetic point was 3.02 m (SD 2.51). Average bias among units using Bland–Altman plots was 0.90 m, ranging from −0.22 to 1.86 m, within the limits of agreement. For interunit reliability in the built environment contexts, the mean distance difference among units ranged between 10.7 m (SD 11.9) and 20.1 m (SD 21.8). For the pilot study involving participants, GPS data were available for 59.3% of all bouts (67% of MVPA time), of which 46% were in the participantsʼ neighborhood. Participants obtaining most of their MVPA in their neighborhoods tend to live in areas with higher population density, housing unit density, street connectivity, and more public parks. CONCLUSION:Data recorded by portable GPS units is sufficiently precise to track participantsʼ movements. Successful matching of activity monitor and locational data suggests GPS is a promising tool for complementing accelerometry-based physical activity measures. Our pilot analysis shows evidence that the relationship between environment and activity can be clarified by examining where physical activity occurs.