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

arXiv: https://arxiv.org/abs/2502.13920 · HTML: https://mlsanity.com/paper/2502.13920/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Builds on (most cited first, 3 of 3)

- [Explainable Automated Fact-Checking for Public Health Claims](https://mlsanity.com/paper/2010.09926/) (2020)
- [Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity](https://mlsanity.com/paper/1909.03539/) (2019)
- [From Classification to Clinical Insights: Towards Analyzing and Reasoning About Mobile and Behavioral Health Data With Large Language Models](https://mlsanity.com/paper/2311.13063/) (2023)

## Built on by (most cited first, 7 of 7)

- [Tailored Behavior-Change Messaging for Physical Activity: Integrating Contextual Bandits and Large Language Models](https://mlsanity.com/paper/2506.07275/) (2025)
- [Designing KRIYA: An AI Companion for Wellbeing Self-Reflection](https://mlsanity.com/paper/2601.14589/) (2026)
- [Structured Exploration vs. Generative Flexibility: A Field Study Comparing Bandit and LLM Architectures for Personalised Health Behaviour Interventions](https://mlsanity.com/paper/2603.06330/) (2026)
- [SAGE: Sensor-Augmented Grounding Engine for LLM-Powered Sleep Care Agent](https://mlsanity.com/paper/2604.16342/) (2026)
- [Talking to Your Data: Exploring Embodied Conversation as an Interface for Personal Health Reflection](https://mlsanity.com/paper/2606.17767/) (2026)
- [Toward Personalized Sleep Guidance from Wearable Data Using Language Models](https://mlsanity.com/paper/2609.22463/) (2026)
- [A Task-Oriented Multi-Agent Framework for Complex Wearable Health Analysis](https://mlsanity.com/paper/2609.24107/) (2026)

## Abstract (arXiv)

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.