用可穿戴设备触发大模型聊天机器人，实现个性化压力干预。
Wearable Meets LLM for Stress Management: A Duoethnographic Study Integrating Wearable-Triggered Stressors and LLM Chatbots for Personalized Interventions
- 通过可穿戴设备检测生理信号，触发大模型生成干预建议。
- 仅五分之一的事件需要干预，简要描述事件更利于有效干预。
- 适合关注实时心理健康与个性化服务的开发者和研究者。
本研究采用双人自传式研究方法，探讨集成可穿戴设备的大型语言模型（LLM）聊天机器人在个性化压力管理中的应用，以应对即时性与定制化干预日益增长的需求。两位研究人员在22天内，根据可穿戴设备检测到的生理提示进行互动，记录压力源语句，并利用这些信息向基于LLM的聊天机器人寻求定制化干预。他们通过自传式日记记录体验，并在每周讨论中分析聊天机器人回应的相关性、清晰度与影响。结果显示，尽管大多数可穿戴设备触发的事件具有意义，但仅有五分之一需要干预；且使用简要事件描述进行定制的干预比通用回复更有效。本研究揭示了可穿戴设备与大模型结合在实时心理支持与行为改变中的潜力，推动更具用户中心性的心理健康工具发展。
原文摘要 · Abstract (English)
We use a duoethnographic approach to study how wearable-integrated LLM chatbots can assist with personalized stress management, addressing the growing need for immediacy and tailored interventions. Two researchers interacted with custom chatbots over 22 days, responding to wearable-detected physiological prompts, recording stressor phrases, and using them to seek tailored interventions from their LLM-powered chatbots. They recorded their experiences in autoethnographic diaries and analyzed them during weekly discussions, focusing on the relevance, clarity, and impact of chatbot-generated interventions. Results showed that even though most events triggered by the wearable were meaningful, only one in five warranted an intervention. It also showed that interventions tailored with brief event descriptions were more effective than generic ones. By examining the intersection of wearables and LLM, this research contributes to developing more effective, user-centric mental health tools for real-time stress relief and behavior change.
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