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A Scoping Review of Large Language Models in Personal Sleep Wellness.

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Large language models offer personalized sleep guidance through digital tools. This review of 21 papers shows their potential in nonclinical sleep health applications, though evidence is still preliminary.

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Area of Science:

  • Digital Health
  • Artificial Intelligence in Healthcare
  • Sleep Science

Background:

  • Growing emphasis on sleep health for well-being.
  • Increasing use of digital tools for sleep management.
  • Emergence of large language models (LLMs) for personalized digital health applications.

Purpose of the Study:

  • To synthesize research on LLMs for nonclinical, user-focused sleep health applications.
  • To identify core use cases, data sources, and methodologies in this emerging field.
  • To examine current capabilities, challenges, and future opportunities for AI-driven sleep support.

Main Methods:

  • Scoping survey of 21 academic papers.
  • Categorization of LLM applications into four use cases: education, condition-specific support, personalized coaching, and CBT-based self-help.
  • Analysis of data sources, model architectures, fine-tuning, personalization strategies, and evaluation frameworks.

Main Results:

  • LLMs are explored for educational Q&A, sleep apnea support, personalized coaching, and CBT self-help.
  • Diverse data sources (wearables, self-reports, synthetic data) and personalization strategies are employed.
  • Evaluation methods vary from expert reviews to pilot user studies, with preliminary findings.

Conclusions:

  • LLMs show promise for personalized, conversational sleep guidance in nonclinical settings.
  • Methodological challenges include data diversity, personalization techniques, and robust evaluation.
  • Further research is needed to establish external validity and generalizability, as current evidence is preliminary.