Improving Context-Aware Personalized Nudging: Using Wearable Sensors to Reduce Sedentary Behavior.

Abstract

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.