Does home neighbourhood supportiveness influence the location more than volume of adolescent's physical activity? An observational study using Global Positioning Systems

Background: Environmental characteristics of home neighbourhoods are hypothesised to be associated with residents’ physical activity levels, yet many studies report only weak or equivocal associations. We theorise that this may be because neighbourhood characteristics influence the location of activity more than the volume. Using a sample of UK adolescents, we examine the role of home neighbourhood supportiveness for physical activity, both in terms of volume of activity undertaken and a measure of proximity to home at which activity takes place. Methods: Data were analysed from 967 adolescents living in and around the city of Bristol, UK. Each participant wore an accelerometer and a GPS device for seven days during school term time. These data were integrated into a Geographical Information System containing information on the participants’ home neighbourhoods and measures of environmental supportiveness. We then identified the amount of out-of-school activity of different intensities that adolescents undertook inside their home neighbourhood and examined how this related to home neighbourhood supportiveness. Results: We found that living in a less supportive neighbourhood did not negatively impact the volume of physical activity that adolescents undertook. Indeed these participants recorded similar amounts of activity (e.g. 20.5 mins per day of moderate activity at weekends) as those in more supportive neighbourhoods (18.6 mins per day). However, the amount of activity adolescents undertook inside their home neighbourhood did differ according to supportiveness; those living in less supportive locations had lower odds of recording activity inside their home neighbourhood. This was observed across all intensities of activity including sedentary, light, moderate, and vigorous. Conclusions: Our findings suggest that the supportiveness of the neighbourhood around home may have a greater influence on the location of physical activity than the volume undertaken. This finding is at odds with the premise of the socio-ecological models of physical activity that have driven this research field for the last two decades, and has implications for future research, as by simply measuring volumes of activity we may be underestimating the impact of the environment on physical activity behaviours

Automated time activity classification based on global positioning system (GPS) tracking data

<p>Abstract</p> <p>Background</p> <p>Air pollution epidemiological studies are increasingly using global positioning system (GPS) to collect time-location data because they offer continuous tracking, high temporal resolution, and minimum reporting burden for participants. However, substantial uncertainties in the processing and classifying of raw GPS data create challenges for reliably characterizing time activity patterns. We developed and evaluated models to classify people's major time activity patterns from continuous GPS tracking data.</p> <p>Methods</p> <p>We developed and evaluated two automated models to classify major time activity patterns (i.e., indoor, outdoor static, outdoor walking, and in-vehicle travel) based on GPS time activity data collected under free living conditions for 47 participants (N = 131 person-days) from the Harbor Communities Time Location Study (HCTLS) in 2008 and supplemental GPS data collected from three UC-Irvine research staff (N = 21 person-days) in 2010. Time activity patterns used for model development were manually classified by research staff using information from participant GPS recordings, activity logs, and follow-up interviews. We evaluated two models: (a) a rule-based model that developed user-defined rules based on time, speed, and spatial location, and (b) a random forest decision tree model.</p> <p>Results</p> <p>Indoor, outdoor static, outdoor walking and in-vehicle travel activities accounted for 82.7%, 6.1%, 3.2% and 7.2% of manually-classified time activities in the HCTLS dataset, respectively. The rule-based model classified indoor and in-vehicle travel periods reasonably well (Indoor: sensitivity > 91%, specificity > 80%, and precision > 96%; in-vehicle travel: sensitivity > 71%, specificity > 99%, and precision > 88%), but the performance was moderate for outdoor static and outdoor walking predictions. No striking differences in performance were observed between the rule-based and the random forest models. The random forest model was fast and easy to execute, but was likely less robust than the rule-based model under the condition of biased or poor quality training data.</p> <p>Conclusions</p> <p>Our models can successfully identify indoor and in-vehicle travel points from the raw GPS data, but challenges remain in developing models to distinguish outdoor static points and walking. Accurate training data are essential in developing reliable models in classifying time-activity patterns.</p