更好的依从性，更丰富的上下文：基于LLM的对话式语音睡眠日记的现场评估
Better Adherence, Richer Context: A Field Evaluation of LLM-Powered Conversational Voice Diaries for Sleep
AI总结：
通过现场实验评估基于LLM的对话式语音睡眠日记，发现相比文本日记，语音日记提高了依从性并收集了更详细的上下文信息，但结构化字段完整性较低。
摘要：
睡眠日记是行为睡眠医学和失眠认知行为疗法的核心，但每日完成难以维持，静态形式通常为解释夜间睡眠变化提供的上下文有限。我们设计了一个基于LLM的对话式语音日记，通过主动智能音箱提示、结构化对话输入和自适应后续对话，提供临床基础的早晚睡眠日记问题。我们在为期四周的受试者间现场研究中评估了该系统，涉及30名大学生，使用匹配的日记项目、报告窗口和提醒间隔，与基于文本的移动日记进行比较。与文本日记相比，对话式语音日记显示出更高的依从性，并引发了关于日常习惯、压力源、环境条件和其他睡眠相关因素的更详细上下文自我报告。参与者还描述语音日记更容易融入日常，尽管感知完成时间更长。然而，基于语音的对话输入导致某些结构化日记字段的完整性较低，揭示了表达丰富性与结构化精度之间的权衡。这些发现展示了使用基于LLM的对话式语音助手进行纵向健康自我报告的前景和挑战。
英文摘要：
Sleep diaries are central to behavioral sleep medicine and cognitive behavioral therapy for insomnia, yet daily completion is difficult to sustain, and static forms often provide limited context for interpreting night-to-night sleep variation. We designed an LLM-powered conversational voice diary that delivers clinically grounded morning and evening sleep diary questions through proactive smart-speaker prompts, structured conversational intake, and adaptive follow-up dialogue. We evaluated the system in a four-week between-subjects field study with 30 university students, comparing it with a text-based mobile diary using matched diary items, reporting windows, and reminder intervals. Compared with the text-based diary, the conversational voice diary showed higher adherence and elicited more detailed contextual self-report about routines, stressors, environmental conditions, and other sleep-related factors. Participants also described the voice diary as easier to integrate into daily routines, despite longer perceived completion time. However, voice-based conversational intake produced lower completeness for some structured diary fields, revealing a trade-off between expressive richness and structured precision. These findings show both the promise and the challenge of using LLM-powered conversational voice assistants for longitudinal health self-report.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
