Better Adherence, Richer Context: A Field Evaluation of LLM-Powered Conversational Voice Diaries for Sleep
Researchers have developed an LLM-powered conversational voice diary to improve sleep tracking for behavioral sleep medicine and insomnia treatment. In a four-week study with university students, this voice-based system demonstrated higher adherence and collected richer contextual data compared to a traditional text-based mobile diary. While participants found the voice diary easier to integrate into their routines, it also resulted in less complete structured data for some fields, highlighting a trade-off between detailed self-reporting and data precision. AI
IMPACT May improve adherence and data richness in health self-reporting tools, though precision trade-offs need addressing.