Improving Context-Aware Personalized Nudging

Objectives.To improve nudge outcome classification accuracy in a context-aware personalized nudging framework using wearable sensor data targeted to reduce sedentary behavior using Just-in-Time Adaptive Interventions (JITAIs).Methods.Data were collected using a custom smartwatch application in a free-living observational study conducted at the University of Delaware (Newark, Delaware, USA) between Spring 2021 and Fall 2022.A total of 18 participants were enrolled.The system continuously recorded motion, physiological, and contextual data and delivered adaptive behavioral prompts.A decision-tree model was trained using sitting and walking bouts enriched with contextual features such as time, location, physiological state, and prior intervention outcomes.Behavioral responses were automatically evaluated using sensor-derived outcomes.Results.The proposed model improved classification accuracy for nudge outcomes from 0.42 to 0.78 across 787 sitting bouts.A walking-nudge model achieved an accuracy of 0.70 on 207 walking bouts.Nudged walking bouts were longer in duration, covered greater distances, and exhibited higher average speeds than non-nudged bouts.Conclusions.Context-aware adaptive nudging can improve both the timing and behavioral effectiveness of wearable-based interventions.Incorporating contextual and historical features enables personalized and behaviorally meaningful intervention delivery.Policy Implications.Wearable-based adaptive interventions offer a scalable and cost-effective strategy to reduce sedentary behavior and support population-level health promotion.