Better Adherence, Richer Context: A Field Evaluation of LLM-Powered Conversational Voice Diaries for Sleep
arXiv:2606.18596v1 Announce Type: cross Abstract: 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
The proliferation of LLMs and smart speaker technology enables the deployment of conversational AI for health applications in a field setting.
This study demonstrates how LLM-powered interfaces can improve adherence and data richness in behavioral health, potentially transforming passive data collection into active, nuanced interactions.
Traditional static health diaries may be replaced by dynamic, conversational AI interfaces that offer adaptive follow-up and deeper context for health monitoring.
- · AI-powered health tech companies
- · Smart speaker manufacturers
- · Behavioral sleep medicine clinics
- · Patients with chronic conditions
- · Static health diary providers
- · Traditional health survey companies
Increased patient engagement and richer qualitative data collection in mental and behavioral health interventions.
Expansion of conversational AI in home health monitoring beyond sleep, covering other chronic conditions and wellness.
Ethical and privacy concerns around sensitive health data collected by autonomous AI systems in personal environments will intensify.
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Read at arXiv cs.AI