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arXiv · 2610.10822

Guided Reflection for Personal Sleep Insight in Everyday Sleep Tracking

Bokyung Kim·Amama Mahmood·Honghao Zhao·Molly E. Atwood·Luis F. Buenaver·Ziang Xiao·Chien-Ming Huang
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Abstract

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.

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Bokyung Kim, Amama Mahmood, Honghao Zhao, Molly E. Atwood, Luis F. Buenaver, Ziang Xiao, Chien-Ming Huang. 2026-10-07. Guided Reflection for Personal Sleep Insight in Everyday Sleep Tracking. https://arxiv.org/abs/2610.10822

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