用AI聊天机器人引导肥胖人群想象未来，帮其减少即时满足倾向。
AI-Facilitated Episodic Future Thinking For Adults with Obesity
- 基于GPT-4-Turbo构建对话式AI，生成个性化未来情景提示。
- 用户反馈显示该工具能有效激发对未来的想象与目标反思。
- 适合关注行为改变的心理健康干预研究者或从业者使用。
情景化未来思维（EFT）涉及生动地想象个人未来的事件与经历，已被证明可降低延迟折扣（即更倾向于即时满足而非长远收益），并促进多种适应不良健康行为的改变。本文提出EFTeacher，一个由GPT-4-Turbo大语言模型驱动的AI聊天机器人，专为生活方式相关疾病人群设计，用于生成个性化EFT提示。通过混合方法研究，包括可用性评估、基于内容特征的问卷及半结构化访谈，结果显示参与者认为EFTeacher具有良好的沟通性与支持性，能通过互动对话促进想象与未来目标反思。用户赞赏其自适应与个性化能力，但部分反馈指出对话重复和响应冗长的问题。研究证实大语言模型驱动的聊天机器人在针对适应不良健康行为的EFT干预中具备应用潜力。
原文摘要 · Abstract (English)
Episodic Future Thinking (EFT) involves vividly imagining personal future events and experiences in detail. It has shown promise as an intervention to reduce delay discounting-the tendency to devalue delayed rewards in favor of immediate gratification- and to promote behavior change in a range of maladaptive health behaviors. We present EFTeacher, an AI chatbot powered by the GPT-4-Turbo large language model, designed to generate EFT cues for users with lifestyle-related conditions. To evaluate the feasibility and usability of EFTeacher, we conducted a mixed-methods study that included usability assessments, user evaluations based on content characteristics questionnaires, and semi-structured interviews. Qualitative findings indicate that participants perceived EFTeacher as communicative and supportive through an engaging dialogue. The chatbot facilitated imaginative thinking and reflection on future goals. Participants appreciated its adaptability and personalization features, though some noted challenges such as repetitive dialogue and verbose responses. Our findings underscore the potential of large language model-based chatbots in EFT interventions targeting maladaptive health behaviors.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。