Despite the prevalence of sleep-tracking devices, many individuals struggle to translate data into actionable improvements in sleep health. Current methods often provide data-driven suggestions but may not be feasible and adaptive to real-life constraints and individual contexts. We present HealthGuru, a novel large language model-powered chatbot to enhance sleep health through data-driven, theory-guided, and adaptive recommendations with conversational behavior change support. HealthGuru's multi-agent framework integrates wearable device data, contextual information, and a contextual multi-armed bandit model to suggest tailored sleep-enhancing activities. The system facilitates natural conversations while incorporating data-driven insights and theoretical behavior change techniques. Our eight-week in-the-wild deployment study with 16 participants compared HealthGuru to a baseline chatbot. Results show improved metrics like sleep duration and activity scores, higher quality responses, and increased user motivation for behavior change with HealthGuru. We also identify challenges and design considerations for personalization and user engagement in health chatbots.
View on arXiv@article{wang2025_2502.13920,
title={ Exploring Personalized Health Support through Data-Driven, Theory-Guided LLMs: A Case Study in Sleep Health },
author={ Xingbo Wang and Janessa Griffith and Daniel A. Adler and Joey Castillo and Tanzeem Choudhury and Fei Wang },
journal={arXiv preprint arXiv:2502.13920},
year={ 2025 }
}