# PhysioLLM: Supporting Personalized Health Insights with Wearables and Large Language Models

arXiv: https://arxiv.org/abs/2406.19283 · HTML: https://mlsanity.com/paper/2406.19283/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2024.

## Built on by (most cited first, 16 of 16)

- [Vital Insight: Assisting Experts' Context-Driven Sensemaking of Multi-modal Personal Tracking Data Using Visualization and Human-In-The-Loop LLM](https://mlsanity.com/paper/2410.14879/) (2024)
- [An LLM-Powered Agent for Physiological Data Analysis: A Case Study on PPG-based Heart Rate Estimation](https://mlsanity.com/paper/2502.12836/) (2025)
- [HealthSLM-Bench: Benchmarking Small Language Models for Mobile and Wearable Healthcare Monitoring](https://mlsanity.com/paper/2509.07260/) (2025)
- [Causal Stories from Sensor Traces: Auditing Epistemic Overreach in LLM-Generated Personal Sensing Explanations](https://mlsanity.com/paper/2605.08590/) (2026)
- [SleepCoT: A Lightweight Personalized Sleep Health Model via Chain-of-Thought Distillation](https://mlsanity.com/paper/2410.16924/) (2024)
- [SePA: A Search-enhanced Predictive Agent for Personalized Health Coaching](https://mlsanity.com/paper/2509.04752/) (2025)
- [Personalization of Large Foundation Models for Health Interventions](https://mlsanity.com/paper/2601.03482/) (2026)
- [Personalized Causal Graph Reasoning for LLMs: An Implementation for Dietary Recommendations](https://mlsanity.com/paper/2503.00134/) (2025)
- [FM-FoG: A Real-Time Foundation Model-based Wearable System for Freezing-of-Gait Mitigation](https://mlsanity.com/paper/2509.24176/) (2025)
- [VitalDiagnosis: AI-Driven Ecosystem for 24/7 Vital Monitoring and Chronic Disease Management](https://mlsanity.com/paper/2601.15798/) (2026)
- [From 'What' to 'How' and 'Why': Sharing LLM-Generated Retrospective Summaries of Older Adults' Passive Tracking Data with Remote Family Members](https://mlsanity.com/paper/2606.03876/) (2026)
- [Talking to Your Data: Exploring Embodied Conversation as an Interface for Personal Health Reflection](https://mlsanity.com/paper/2606.17767/) (2026)
- [When Derived Measurements Mislead: Quantifying and Mitigating LLM Over-Trust with Privileged-Modality Reliability Evidence](https://mlsanity.com/paper/2607.28421/) (2026)
- [Large language models in medical time series analysis](https://mlsanity.com/paper/2609.22262/) (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)

We present PhysioLLM, an interactive system that leverages large language models (LLMs) to provide personalized health understanding and exploration by integrating physiological data from wearables with contextual information. Unlike commercial health apps for wearables, our system offers a comprehensive statistical analysis component that discovers correlations and trends in user data, allowing users to ask questions in natural language and receive generated personalized insights, and guides them to develop actionable goals. As a case study, we focus on improving sleep quality, given its measurability through physiological data and its importance to general well-being. Through a user study with 24 Fitbit watch users, we demonstrate that PhysioLLM outperforms both the Fitbit App alone and a generic LLM chatbot in facilitating a deeper, personalized understanding of health data and supporting actionable steps toward personal health goals.