用强化学习动态切换心理诊疗与日常健康支持，提升长期参与度。
Exploring Reinforcement Learning for Fluid Transitions Between Clinical Mental Healthcare and Everyday Wellness Support
- 基于上下文博弈模型，动态选择临床与日常类日记提示。
- 干预结束后仍显现效果，提示需设置渐退期。
- 自适应强度可防倦怠，适合长期心理健康管理人群。
心理健康状态起伏不定，但临床干预与日常健康支持通常割裂，导致转诊环节频繁失效。本文探索强化学习（RL）在构建数字健康系统中的应用，实现临床与日常干预的主动协同，形成连贯的照护路径。我们设计了一个上下文带宽模型，从临床与日常两个资源库中动态选择日记提示，以优化持续日记行为这一总体健康目标，并在一项为期四周的探索性研究中部署（N=38）。研究发现：首先，许多由RL优化的干预序列带来的益处仅在干预结束后显现，引发思考：是否应在临床-健康照护路径中加入逐步退出阶段？若然，何时及如何设置？其次，对RL生成干预最投入的参与者随时间加深参与，而始终接受固定干预者后期多出现倦怠并退出，提示：在融合临床与日常干预的系统中，应何时降低强度以避免倦怠、又何时维持强度以最大化疗效？
原文摘要 · Abstract (English)
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?
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