A New Approach to Identify the Location and Intensity of Physical Activity in Urban Areas: The Use of Accelerometer and Global Positioning Systems Data

Abstract

Recent studies showed that physical inactivity leads to chronic diseases and death. Various practices and policies, which have been discussed to promote physical activity, require information about the barriers of physical activity. Research focused on not only biological, social and psychological characteristics but also physical environment characteristics as barriers of physical activity. Yet, which and how physical environmental characteristics affect individual’s physical activity is still unknown (as research on physical environment characteristics produced contradictory findings). Methodological differences may have led to such contradictory findings. Subjective and objective methods have been used to identify people’s physical activity levels. Subjective methods (such as self-report, face to face interviews and telephone surveys) tend to produce semi-reliable data which is based on personal declaration. Objective observation methods, such as physical activity monitors, have been acknowledged to produce more reliable data. Accelerometer, a kind of physical activity monitor, sorts and stores intensity of physical activity (sedentary, light, moderate and vigorous physical activity parameters) with reference to date, time and activity counts. Although, accelerometers fail to reveal information about the location of the activity, combined data of accelerometer and Global Positioning Systems (GPS) provides this information. The present study aims to introduce a new methodology to map the location where children are active and inactive. 51 children (9-12 years old), who were attending sportive activities in a private Sport School in Izmir, volunteered to participate. Information about the intensity of their physical activity was collected via accelerometer (Actigraph wGT3X-BT) and information about where their activities took place was collected via GPS (Qstarz Travel Recorder XT). Participants were asked to wear the devices for 7 days (5 week days, 2 weekend days). Accelerometer data analyzed with ActiLife software, and the GPS data analyzed with Qstarz Data Viewer software. The data were matched and combined via ArcGIS. Based on the combined data participants’ physical activity intensities were mapped.