Exploring Personalized Health Support through Data-Driven, Theory-Guided LLMs: A Case Study in Sleep Health

TL;DR

This paper introduces HealthGuru, a novel LLM-powered chatbot that provides personalized, theory-guided sleep health recommendations by integrating wearable data and behavioral techniques, demonstrating improved sleep outcomes in real-world use.

Contribution

It presents a new multi-agent framework combining wearable data, contextual modeling, and behavior change techniques within an LLM chatbot for personalized sleep health support.

Findings

Improved sleep duration and activity scores among users.

Higher quality responses and increased motivation for behavior change.

Effective integration of data-driven insights with behavioral theories.

Abstract

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.