ArticleNature medicine2025
A personal health large language model for sleep and fitness coaching.
Article in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Generative AI in Precision Nutrition: A Review of Current Developments and Future Directions.Nutrients · 2026Pooled it
- Personal Health Large Language Models and the Negotiation of Medical Authority in Clinical Care: Opportunities, Risks, and Governance.Journal of medical Internet research · 2026Article
- Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 2026Article
- Personalised health plan development using agentic AI in Singapore's national preventive care programme: a pilot study.NPJ digital medicine · 2026Article
- Personalized Insights Derived from Wearable Device Data and Large Language Models to Improve Well-Being.medRxiv : the preprint server for health sciences · 2026Article
- Advances in artificial intelligence empowering early-stage human health: Current landscape and future directions.Innovation (Cambridge (Mass.)) · 2026Review
- A scalable framework for evaluating health language models.NPJ digital medicine · 2026Article
- Transforming wearable data into personal health insights using large language model agents.Nature communications · 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
- State of clinical AI in 2026.BMJ digital health & AI · 2026Review
- WMRE2030: integrating wearable devices, multi-omics, and artificial intelligence-driven real-time feedback into a daily-scale closed-loop framework for a new era of precision exercise.Frontiers in physiology · 2026Review
- Extending BEHRT to UK Biobank: assessing transformer model performance in clinical prediction.Frontiers in digital health · 2026Article
- Do we need AI guardians to protect us from health information overload?NPJ digital medicine · 2025Article
- Personalised modelling of routine variability and affective states.NPJ digital medicine · 2025Article
- Article
- Interpreting psychiatric digital phenotyping data with large language models: a preliminary analysis.BMJ mental health · 2025Article
- Ladders to better health? Identifying the cognitive pathways and barriers to value attainment in wearable health device usage.Digital healthArticle
Corrections and comments
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Authors and funding
34 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although large language models (LLMs) show promise for clinical healthcare applications, their utility for personalized health monitoring using wearable device data remains underexplored. Here we introduce the Personal Health Large Language Model (PH-LLM), designed for applications in sleep and fitness. PH-LLM is a version of the Gemini LLM that was finetuned for text understanding and reasoning when applied to aggregated daily-resolution numerical sensor data. We created three benchmark datasets to assess multiple complementary aspects of sleep and fitness: expert domain knowledge, generation of personalized insights and recommendations and prediction of self-reported sleep quality from longitudinal data. PH-LLM achieved scores that exceeded a sample of human experts on multiple-choice examinations in sleep medicine (79% versus 76%) and fitness (88% versus 71%). In a comprehensive evaluation involving 857 real-world case studies, PH-LLM performed similarly to human experts for fitness-related tasks and improved over the base Gemini model in providing personalized sleep insights. Finally, PH-LLM effectively predicted self-reported sleep quality using a multimodal encoding of wearable sensor data, further demonstrating its ability to effectively contextualize wearable modalities. This work highlights the potential of LLMs to revolutionize personal health monitoring via tailored insights and predictions from wearable data and provides datasets, rubrics and benchmark performance to further accelerate personal health-related LLM research.
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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.