Toward Personalized Sleep Guidance from Wearable Data Using Language Models

Sleep health notes

Sleep health notes will be added by the Sleepatch editorial team.

中文解读

中文解读待补充：本站会优先为失眠研究、睡眠质量改善、昼夜节律等高价值睡眠研究添加中文说明。

Original abstract

Sleep monitoring using wearable data has shown promise for personal health, yet large language model (LLM)-based summarization and question answering remain insufficient for personalized sleep guidance. Training specialized models, however, often requires costly expert annotation. Moreover, privacy and accessibility concerns motivate lightweight, local deployment for end users. We present a two-stage framework to address these challenges. Specifically, in Stage~1, a multi-agent LLM pipeline reasons structured sleep guidance from unannotated wearable records, enabling scalable dataset construction. Stage~2 distills guidance reasoning trajectories into small language models (SLMs) through supervised fine-tuning and integrates a training-free Best-of-$N$ selection strategy to enhance inference. Experimental results demonstrate our method outperforms commercial general and medical LLMs and open-source models. Human evaluation further supports the quality of the generated guidance and the feasibility of personalized sleep guidance with SLMs.

Links and sources

• Official / arXiv page
• PDF from original source

Comments