最后的JITAI？探索大语言模型在即时自适应干预中的应用：在概念性心脏康复环境中促进身体活动
The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Setting
AI总结：
本研究探索用大语言模型GPT-4生成即时自适应干预，在心脏康复场景中优于人类专家，证明生成式AI可提升个性化健康干预的效能。
摘要：
我们评估了使用大语言模型（LLMs）在数字健康领域的即时自适应干预（JITAIs）中触发和个性化内容的可行性。作为情境感知计算的一种代表性交互模式，JITAIs正被探索其支持可持续行为改变的潜力，根据个体的当前情境和需求调整干预措施。针对传统JITAI实施模型面临的严重可扩展性和灵活性限制，我们在心脏康复中促进心脏健康活动的用例中测试了GPT-4来建议JITAIs。使用三个代表不同程度心血管疾病（CVD）患者的角色，每个角色设置五个情境集，我们生成了450个JITAI决策和消息。这些决策和消息与10名非专业人员（LayPs）和10名医疗保健专业人员（HCPs）所创建的进行了系统评估。GPT-4生成的JITAIs在所有指标（即适当性、参与度、有效性和专业性）上均超越了人类生成的干预建议，优于LayPs和HCPs。这些结果凸显了LLMs在个性化健康干预中增强JITAI实施的潜力，展示了生成式AI如何可能革新情境感知计算。
英文摘要：
We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individual's current context and needs. Challenging traditional JITAI implementation models, which face severe scalability and flexibility limitations, we tested GPT-4 for suggesting JITAIs in the use case of heart-healthy activity in cardiac rehabilitation. Using three personas representing patients affected by CVD with varying severeness and five context sets per persona, we generated 450 JITAI decisions and messages. These were systematically evaluated against those created by 10 laypersons (LayPs) and 10 healthcare professionals (HCPs). GPT-4-generated JITAIs surpassed human-generated intervention suggestions, outperforming both LayPs and HCPs across all metrics (i.e., appropriateness, engagement, effectiveness, and professionalism). These results highlight the potential of LLMs to enhance JITAI implementations in personalized health interventions, demonstrating how generative AI could revolutionize context-aware computing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
