发表机构
University of North Carolina at Chapel Hill; The Ohio State University; Harvard University; University of Michigan（北卡罗来纳大学教堂山分校; 俄亥俄州立大学; 哈佛大学; 密歇根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对移动健康干预算法设计问题，提出基于条件时间序列扩散模型开发“JITAI-Twins”数字孪生的方法，经多步更新，在HeartSteps干预部署预阶段验证，能更好再现目标亚群结构，为算法设计决策提供模拟。
AI 中文摘要
移动健康干预越来越多地使用在线学习和决策算法来个性化何时推动用户采取更健康的行为，但设计不佳的算法可能会给参与者带来负担并使其失去参与度。因此，在每次实际部署之前，新的算法设计决策应针对现实模拟用户进行审查。我们提出了一种开发“JITAI-Twins”的方法，即在即时自适应干预（JITAI）部署之前，用于比较候选在线算法的目标亚群数字孪生。该方法基于条件时间序列扩散模型构建，具有时间一致性（未来行动不影响生成的过去），并支持从三个信息源分三步进行重复更新：在大型观测数据集上进行预训练，在相关人群的小型先前干预部署上进行微调，以及根据领域科学家的专业知识对下一个目标人群进行推理时校准。我们在长期运行的HeartSteps系列（v2至v4）身体活动建议干预部署的每个预部署阶段验证了该数字孪生，将每个连续部署视为即将进行的研究。所提出的方法比简单的模拟器更好地再现了目标亚群的时间和参与者之间的结构。这些结果表明，我们的数字孪生可用于在目标部署运行之前进行模拟，这是测试和为在线算法设计决策提供信息的先决条件。
英文摘要
Mobile-health interventions increasingly use online learning and decision making algorithms to personalize when to nudge users toward healthier behavior, but a poorly designed algorithm can burden and disengage participants. New algorithm design decisions should therefore be vetted against realistic simulated users before each real-life deployment. We propose a method to develop ``JITAI-Twins'': digital twins of a target subpopulation for comparing candidate online algorithms before a just-in-time adaptive intervention (JITAI) deployment. The method builds on a conditional time-series diffusion model that is temporally consistent (future actions do not affect the generated past), and it supports repeated updating from three sources of information, in three steps: pre-training on a large observational dataset, fine-tuning on small prior intervention deployments in related populations, and inference-time calibration to the next target population from domain-scientist expertise. We validate the twin at each pre-deployment stage of the long-running HeartSteps series (v2 through v4) of physical-activity suggestion intervention deployments, treating each successive deployment as an upcoming study. The proposed method reproduces the target subpopulation's temporal and between-participant structure better than simpler simulators. These results suggest that our twin can be used to simulate a target deployment before it runs, the prerequisite for testing and informing online algorithm design decisions.
