We explored the viability of Large Language Models (LLMs) for triggering and personalizing content for Just-in-Time Adaptive Interventions (JITAIs) in digital health. JITAIs are being explored as a key mechanism for sustainable behavior change, adapting interventions to an individual's current context and needs. However, traditional rule-based and machine learning models for JITAI implementation face scalability and reliability limitations, such as lack of personalization, difficulty in managing multi-parametric systems, and issues with data sparsity. To investigate JITAI implementation via LLMs, we tested the contemporary overall performance-leading model 'GPT-4' with examples grounded in the use case of fostering heart-healthy physical activity in outpatient cardiac rehabilitation. Three personas and five sets of context information per persona were used as a basis of triggering and personalizing JITAIs. Subsequently, we generated a total of 450 proposed JITAI decisions and message content, divided equally into JITAIs generated by 10 iterations with GPT-4, a baseline provided by 10 laypersons (LayPs), and a gold standard set by 10 healthcare professionals (HCPs). Ratings from 27 LayPs indicated that JITAIs generated by GPT-4 were superior to those by HCPs and LayPs over all assessed scales: i.e., appropriateness, engagement, effectiveness, and professionality. This study indicates that LLMs have significant potential for implementing JITAIs as a building block of personalized or "precision" health, offering scalability, effective personalization based on opportunistically sampled information, and good acceptability.

翻译：我们探索了大语言模型（LLMs）在数字健康中用于触发和个性化即时自适应干预（JITAIs）内容的可行性。JITAIs正被研究作为可持续行为改变的关键机制，根据个体当前情境和需求调整干预措施。然而，传统基于规则和机器学习的JITAI实施方法面临可扩展性和可靠性限制，例如缺乏个性化、难以管理多参数系统以及数据稀疏性问题。为研究通过LLMs实施JITAI，我们测试了当前整体性能领先的模型'GPT-4'，并以促进门诊心脏康复中有益心脏健康的体力活动用例为基础。使用了三个角色模型及每个角色的五组情境信息作为触发和个性化JITAIs的依据。随后，我们生成了总共450个提议的JITAI决策和消息内容，平均分为由GPT-4通过10次迭代生成的JITAIs、由10名非专业人员（LayPs）提供的基线，以及由10名医疗专业人员（HCPs）设定的金标准。来自27名非专业人员的评分表明，在所有评估尺度（即适宜性、参与度、有效性和专业性）上，GPT-4生成的JITAIs均优于HCPs和LayPs生成的JITAIs。本研究显示，LLMs在作为个性化或“精准”健康构建模块的JITAI实施中具有显著潜力，提供了可扩展性、基于机会性采样信息的有效个性化能力以及良好的可接受性。