# Transforming Wearable Data into Personal Health Insights using Large Language Model Agents

arXiv: https://arxiv.org/abs/2406.06464 · HTML: https://mlsanity.com/paper/2406.06464/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 32 of its 71 references that are in the map.
- First posted on arXiv 10 Jun 2024.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)
- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 30 of 32)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation](https://mlsanity.com/paper/2305.01210/) (2023)
- [Cognitive Architectures for Language Agents](https://mlsanity.com/paper/2309.02427/) (2023)
- [Describe, Explain, Plan and Select: Interactive Planning with Large Language Models Enables Open-World Multi-Task Agents](https://mlsanity.com/paper/2302.01560/) (2023)
- [StructGPT: A General Framework for Large Language Model to Reason over Structured Data](https://mlsanity.com/paper/2305.09645/) (2023)
- [ToolQA: A Dataset for LLM Question Answering with External Tools](https://mlsanity.com/paper/2306.13304/) (2023)
- [Data Interpreter: An LLM Agent For Data Science](https://mlsanity.com/paper/2402.18679/) (2024)
- [Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning](https://mlsanity.com/paper/2301.13808/) (2023)
- [DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoning](https://mlsanity.com/paper/2402.17453/) (2024)
- [Towards Accurate Differential Diagnosis with Large Language Models](https://mlsanity.com/paper/2312.00164/) (2023)
- [Natural Language to Code Generation in Interactive Data Science Notebooks](https://mlsanity.com/paper/2212.09248/) (2022)
- [EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education](https://mlsanity.com/paper/2308.02773/) (2023)
- [EHRAgent: Code Empowers Large Language Models for Few-shot Complex Tabular Reasoning on Electronic Health Records](https://mlsanity.com/paper/2401.07128/) (2024)
- [Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://mlsanity.com/paper/2305.02466/) (2023)
- [Human-AI Collaboration Enables More Empathic Conversations in Text-based Peer-to-Peer Mental Health Support](https://mlsanity.com/paper/2203.15144/) (2022)
- [LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation](https://mlsanity.com/paper/2305.11490/) (2023)
- [Large Language Models to Identify Social Determinants of Health in Electronic Health Records](https://mlsanity.com/paper/2308.06354/) (2023)
- [Facilitating Self-Guided Mental Health Interventions Through Human-Language Model Interaction: A Case Study of Cognitive Restructuring](https://mlsanity.com/paper/2310.15461/) (2023)
- [Large Language Model for Table Processing: A Survey](https://mlsanity.com/paper/2402.05121/) (2024)
- [The Capability of Large Language Models to Measure Psychiatric Functioning](https://mlsanity.com/paper/2308.01834/) (2023)
- [SheetAgent: Towards A Generalist Agent for Spreadsheet Reasoning and Manipulation via Large Language Models](https://mlsanity.com/paper/2403.03636/) (2024)
- [Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics](https://mlsanity.com/paper/2307.02502/) (2023)
- [Sequential Models in the Synthetic Data Vault](https://mlsanity.com/paper/2207.14406/) (2022)
- [IMBUE: Improving Interpersonal Effectiveness through Simulation and Just-in-time Feedback with Human-Language Model Interaction](https://mlsanity.com/paper/2402.12556/) (2024)
- [Mathify: Evaluating Large Language Models on Mathematical Problem Solving Tasks](https://mlsanity.com/paper/2404.13099/) (2024)
- [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, 19 of 19)

- [What Are the Odds? Language Models Are Capable of Probabilistic Reasoning](https://mlsanity.com/paper/2406.12830/) (2024)
- [LLaSA: A Sensor-Aware LLM for Natural Language Reasoning of Human Activity from IMU Data](https://mlsanity.com/paper/2406.14498/) (2024)
- [A Scalable Framework for Evaluating Health Language Models](https://mlsanity.com/paper/2503.23339/) (2025)
- [ComBodied Agents: a New Paradigm of Human-Centric Agentic AI](https://mlsanity.com/paper/2608.10915/) (2026)
- [Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis](https://mlsanity.com/paper/2406.16252/) (2024)
- [Wearable Foundation Models Should Go Beyond Static Encoders](https://mlsanity.com/paper/2603.19564/) (2026)
- [A Framework for Longitudinal Health AI Agents](https://mlsanity.com/paper/2604.12019/) (2026)
- [SleepCoT: A Lightweight Personalized Sleep Health Model via Chain-of-Thought Distillation](https://mlsanity.com/paper/2410.16924/) (2024)
- [Agentic AI in Healthcare & Medicine: A Seven-Dimensional Taxonomy for Empirical Evaluation of LLM-based Agents](https://mlsanity.com/paper/2602.04813/) (2026)
- [Query-Conditioned Graph Retrieval for Contextualized LLM Reasoning in Personalized Wearable Data](https://mlsanity.com/paper/2605.18763/) (2026)
- [A Self-Evolving Agent for Longitudinal Personal Health Management](https://mlsanity.com/paper/2607.13940/) (2026)
- [The Cost of Language: Centroid Erasure Exposes and Exploits Modal Competition in Multimodal Language Models](https://mlsanity.com/paper/2604.14363/) (2026)
- [Think Fast, Talk Smart: Partitioning Deterministic and Neural Computation for Structured Health Text Generation](https://mlsanity.com/paper/2605.29652/) (2026)
- [Personal Care Utility: Health as Everyday Infrastructure](https://mlsanity.com/paper/2606.14145/) (2026)
- [Enhancing Fitness Intelligence through Domain-Specific LLM Post-Training](https://mlsanity.com/paper/2607.02118/) (2026)
- [WearableQA: A Benchmark for Health Reasoning over Real-World Wearable Data](https://mlsanity.com/paper/2609.05405/) (2026)
- [LongAgent: History-Guided Agentic Search for Longitudinal Outcome Prediction](https://mlsanity.com/paper/2609.15859/) (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)

## Abstract (arXiv)

Deriving personalized insights from popular wearable trackers requires complex numerical reasoning that challenges standard LLMs, necessitating tool-based approaches like code generation. Large language model (LLM) agents present a promising yet largely untapped solution for this analysis at scale. We introduce the Personal Health Insights Agent (PHIA), a system leveraging multistep reasoning with code generation and information retrieval to analyze and interpret behavioral health data. To test its capabilities, we create and share two benchmark datasets with over 4000 health insights questions. A 650-hour human expert evaluation shows that PHIA significantly outperforms a strong code generation baseline, achieving 84% accuracy on objective, numerical questions and, for open-ended ones, earning 83% favorable ratings while being twice as likely to achieve the highest quality rating. This work can advance behavioral health by empowering individuals to understand their data, enabling a new era of accessible, personalized, and data-driven wellness for the wider population.