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Large language models (LLMs) are capable of many natural language tasks, yetthey are far from perfect. In health applications, grounding and interpretingdomain-specific and non-linguistic data is important. This paper investigatesthe capacity of LLMs to deliver multi-modal health predictions based oncontextual information (e.g. user demographics, health knowledge) andphysiological data (e.g. resting heart rate, sleep minutes). We present acomprehensive evaluation of eight state-of-the-art LLMs with diverse promptingand fine-tuning techniques on six public health datasets (PM-Data, LifeSnaps,GLOBEM, AW_FB, MIT-BIH & MIMIC-III). Our experiments cover thirteen consumerhealth prediction tasks in mental health, activity, metabolic, sleep, andcardiac assessment. Our fine-tuned model, Health-Alpaca exhibits comparableperformance to larger models (GPT-3.5 and GPT-4), achieving the bestperformance in 5 out of 13 tasks. Ablation studies highlight the effectivenessof context enhancement strategies, and generalization capability of thefine-tuned models across training datasets and the size of training samples.Notably, we observe that our context enhancement can yield up to 23.8%improvement in performance. While constructing contextually rich prompts(combining user context, health knowledge and temporal information) exhibitssynergistic improvement, the inclusion of health knowledge context in promptssignificantly enhances overall performance.

Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data