ArticleNature communications2026
Transforming wearable data into personal health insights using large language model agents.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Consumer-oriented (wearable) sleep technology: a systematic SWOT analysis and recommendations for athletes and fitness enthusiasts.Frontiers in sports and active living · 2026Pooled it
- A Personal Health App and Wearable Co-Design Framework for Rare and Complex Diseases: User-Centered, Collaborative Co-Design Study.JMIR mHealth and uHealth · 2026Article
- Personal Health Large Language Models and the Negotiation of Medical Authority in Clinical Care: Opportunities, Risks, and Governance.Journal of medical Internet research · 2026Article
- Digital phenotyping with large language models to detect depressive state changes in patients.NPJ digital medicine · 2026Article
- A framework for longitudinal health AI agents.Nature health · 2026Article
- A scalable framework for evaluating health language models.NPJ digital medicine · 2026Article
- Promises and challenges of applying large language models in the healthcare domain.Frontiers in digital health · 2026Review
- Uncertainty-aware federated temporal learning with explainable LLM-based coaching for privacy-preserving wearable health systems.Frontiers in artificial intelligence · 2026Article
- Large language model-driven agents in nursing practice: A scoping review.International journal of nursing sciences · 2025Review
- A personal health large language model for sleep and fitness coaching.Nature medicine · 2025Article
- Engagements with Generative AI and Personal Health Informatics: Opportunities for Planning, Tracking, Reflecting, and Acting around Personal Health Data.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2025Article
- A Multi-Agent LLM Network for Suggesting and Correcting Human Activity and Posture Annotations.Proceedings of the ... ACM International Conference on Ubiquitous Computing . UbiComp (Conference) · 2025Article
- Profile-aided distillation framework for personalized sleep analysis with compact models using LLM-guided synthetic data.Frontiers in physiology · 2025Article
Corrections and comments
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.