发表机构
The Johns Hopkins University; Department of Psychiatry and Behavioral Sciences, The Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学; 约翰斯·霍普金斯大学医学院精神病学与行为科学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于LLM的语音助手DREAM，通过引导式反思帮助用户解读睡眠变化，实地研究显示其可提升用户睡眠洞察与使用意愿。
AI 中文摘要
数字睡眠技术让睡眠追踪变得便捷，但用户往往难以理解自身睡眠变化的含义。行为睡眠医学通过引导式发现解决这一问题，帮助患者形成个人解读而非仅接收现成解释。为将该方法应用于日常睡眠追踪，我们提出DREAM——一款基于大语言模型（LLM）的语音助手，它可监控对话式睡眠日记，选择性引导用户解读有意义的睡眠变化，并利用用户的解读定制后续教育内容。我们与睡眠专家迭代协作设计了DREAM，并开展了为期六周的实地研究（样本量N=14），将其与通用教育对照组进行对比。使用DREAM的参与者报告称，他们获得了更强的个人睡眠洞察、动机及系统使用意愿，且能更清晰地说明尝试相关策略的理由。研究结果表明，引导式反思使参与者更主动地解读自身睡眠。此外，我们认为专家参与并非知识的单次传递，而是将隐性判断明确化、可检验化的持续过程。
英文摘要
Digital sleep technologies make tracking accessible, yet users often struggle to interpret what changes in their sleep mean. Behavioral sleep medicine addresses this through guided discovery, helping patients develop personal interpretations rather than simply receiving explanations. To bring this to everyday tracking, we present DREAM, an LLM-powered voice assistant that monitors conversational sleep diaries, selectively invites users to interpret meaningful changes, and uses their interpretation to tailor subsequent education. We co-designed DREAM with sleep specialists iteratively and evaluated it in a six-week field study (N=14) against a generic-education control. DREAM participants reported greater personal sleep insight, motivation, and willingness to use the system, and described a clearer rationale for trying strategies. Our findings suggest that guided reflection made participants more active interpreters of their own sleep. Furthermore, we argue that expert involvement is not a single transfer of knowledge but an ongoing process of making tacit judgment explicit and testable.
