Context-Aware Probabilistic Models for Predicting Future Sedentary Behaviors of Smartphone Users.

Abstract

Sedentary behaviors are now prevalent as most modern jobs are done while seated. However, such sedentary behaviors have been found to increase the risk of several ailments including diabetes, cardiovascular disease, and all-cause mortality. Current interventions are mostly reactive and are triggered after the user has already been sedentary. Behavior change theory suggests that preventive sedentary interventions, which are triggered before a person becomes sedentary, are more likely to succeed. In this paper, we characterize user patterns of sedentary behaviors by analyzing smartphone-sensor data in a real-world dataset. Our work reveals location types (where), times of day/week (when), and smartphone contexts in which sedentary behaviors are most likely. Leveraging our findings, we then propose a set of context-aware probabilistic models that can predict sedentary behaviors in advance by analyzing smartphone sensor data. Our Context-Aware Predictive (CAP) models leverage smartphone-sensed contextual variables and the user's history of sedentary behaviors to predict their future sedentary behaviors. We rigorously analyze the performance of our models and discuss the implications of our work.