用聊天机器人帮人把睡眠数据变成可执行的改善方案
Exploring Personalized Health Support through Data-Driven, Theory-Guided LLMs: A Case Study in Sleep Health
- 结合可穿戴设备与行为理论，动态生成个性化建议
- 8周实测显示睡眠时长和活动评分提升，用户动力更强
- 适合关注睡眠健康、需要持续支持的普通人
尽管睡眠追踪设备普及，许多人仍难以将数据转化为实际的睡眠改善行动。现有方法多为数据驱动建议，但缺乏对真实生活约束和个体情境的适应性。我们提出 HealthGuru——一款基于大语言模型的聊天机器人，通过数据驱动、理论指导且自适应的推荐，结合对话式行为改变支持来提升睡眠健康。其多智能体框架整合可穿戴设备数据、上下文信息及上下文多臂赌博机模型，生成定制化助眠活动建议。系统支持自然对话，融合数据洞察与行为改变理论。在16名参与者为期八周的真实环境部署研究中，HealthGuru相比基线聊天机器人显著改善了睡眠时长与活动评分，响应质量更高，用户行为改变动机增强。研究还识别出个性化与用户参与度的关键挑战与设计考量。
原文摘要 · Abstract (English)
Despite the prevalence of sleep-tracking devices, many individuals struggle to translate data into actionable improvements in sleep health. Current methods often provide data-driven suggestions but may not be feasible and adaptive to real-life constraints and individual contexts. We present HealthGuru, a novel large language model-powered chatbot to enhance sleep health through data-driven, theory-guided, and adaptive recommendations with conversational behavior change support. HealthGuru's multi-agent framework integrates wearable device data, contextual information, and a contextual multi-armed bandit model to suggest tailored sleep-enhancing activities. The system facilitates natural conversations while incorporating data-driven insights and theoretical behavior change techniques. Our eight-week in-the-wild deployment study with 16 participants compared HealthGuru to a baseline chatbot. Results show improved metrics like sleep duration and activity scores, higher quality responses, and increased user motivation for behavior change with HealthGuru. We also identify challenges and design considerations for personalization and user engagement in health chatbots.
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