Mental health struggles wax and wane, yet clinical and wellness interventions typically operate separately, causing frequent breakdowns at care transitions. We explore reinforcement learning (RL) as a means to build digital health systems that deliver clinical and wellness interventions proactively, as part of a coherent care journey. We ask: what complexities does designing such a system involve? We built a contextual bandit that dynamically selects journaling prompts from clinical and wellness repertoires to optimize for an overarching health goal (sustained journaling) and deployed it in a four-week exploratory study (N=38). We found that, first, many benefits of RL-optimized intervention sequences appeared only after interventions ended, raising the question: Should systems that offer coherent clinical-wellness care journeys include stepping-back periods? If so, when and how? Second, participants most engaged with RL-generated interventions deepened their engagement over time, while those most engaged with a constant intervention tended to burn out and drop out later. It raises the question: When should a system blending clinical and wellness interventions reduce intensity to prevent burnout in versus sustain it to maximize treatment gains?

翻译：精神健康问题往往呈现波动性特征，而临床干预与健康维护措施通常各自独立运作，导致护理过渡环节频繁出现断裂。本研究探索强化学习作为构建数字健康系统的技术路径，旨在将临床干预与健康维护措施有机整合、主动实施，形成连贯的护理旅程。我们提出核心研究问题：设计此类系统需要应对哪些复杂性？我们构建了情境赌博机模型，能够动态地从临床与健康维护两个干预库中选取日志记录提示，以优化整体健康目标（持续日志记录），并在为期四周的探索性研究（N=38）中进行了部署。研究主要发现：其一，经强化学习优化的干预序列的诸多效益仅在干预结束后显现，由此引发新问题：提供连贯临床-健康护理旅程的系统是否需要设置缓冲期？若需要，应何时实施、如何实施？其二，与强化学习生成干预措施互动最频繁的参与者，其参与度随时间持续深化；而与恒定干预措施互动最多的参与者，则倾向于后期出现倦怠与退出。这引出了关键问题：当系统混合使用临床与健康维护干预时，应在何时降低干预强度以防止参与者倦怠，又应在何时维持强度以最大化治疗效果？