Contextualization and Individualization for Just-in-Time Adaptive Interventions to Reduce Sedentary Behavior

Matthew Saponaro, Ajith Vemuri, Greg Dominick, and Keith Decker (University of Delaware)

View paper in the ACM journal

Abstract: Wearable technology opens opportunities to reduce sedentary behavior; however, commercially available devices do not provide tailored coaching strategies. Just-In-Time Adaptive Interventions (JITAI) provide such a framework; however most JITAI are conceptual to date. We conduct a study to evaluate just-in-time nudges in free-living conditions in terms of receptiveness and nudge impact. We first quantify baseline behavioral patterns in context using features such as location and step count, and assess differences in individual responses. We show there is a strong inverse relationship between average daily step counts and time spent being sedentary indicating that steps are steadily taken throughout the day, rather than in large bursts. Interestingly, the effect of nudges delivered at the workplace is larger in terms of step count than those delivered at home. We develop Random Forest models to learn nudge receptiveness using both individualized and contextualized data. We show that step count is the least important identifier in nudge receptiveness, while location is the most important. Furthermore, we compare the developed models with a commercially available smart coach using post-hoc analysis. The results show that using the contextualized and individualized information significantly outperforms non-JITAI approaches to determine nudge receptiveness.

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