Predicting physical activity levels in individuals with schizophrenia through integrated global positioning system and accelerometer data

Abstract

Research has documented that individuals with schizophrenia spend a significant amount of time in sedentary activity and often do not meet physical activity (PA) guidelines (Stubbs et al., 2016). Environmental factors have long been studied as facilitators or barriers to PA. Among individuals with schizophrenia, environmental factors are known to predict walking and moderate to vigorous PA (Vancampfort et al., 2013). Additionally, environmental characteristics explain 16.8% of sitting time of individuals with schizophrenia (Vancampfort et al., 2014), with factors such as neighborhood infrastructure (e.g., sidewalks or parks) or access to fitness equipment in the home reducing time spent in sedentary behavior. Consistent with Barker’s Behavior Settings Theory (Schoggen, 1989) this study is predicated on the expectation that certain locations are associated with behaviors that involve more or less PA. Thus, there is a need to study current PA levels associated with certain locations in the community, which could lead to development of ecological interventions that are personalized to a person’s time and location preference to maximize PA performance. To address this need, we propose a new methodology called Personalized PA level estimation for specific Locations over time (PerPAL) to better predict PA levels by location. The development of the personalized models involves identifying recurring locations (Townley et al., 2018) for an individual and using their baseline PA data, location, and time-window to predict future PA levels for specific locations (Brusilovskiy et al., 2016) and time-windows.