Continual Optimization and Personalization of Just-in-Time Adaptive Interventions for SUD Prevention, Treatment, and Recovery

项目概要：项目3 在后大流行时代的现代世界，数字技术正日益成为 提供药物使用障碍和艾滋病毒预防、治疗和康复服务。长期 拟议项目的目标是使数字健康技术能够提供干预服务， 前所未有的效率和可持续性。我们建议整合行为科学的思想， 人工智能开发方法，用于（1）持续优化即时自适应数字健康 针对社会变化和不断变化的人群治疗需求的干预措施，以及（2）个性化 及时适应每个人不断变化的治疗需求的数字健康干预措施。这将使 第二代即时适应性数字健康干预措施，具有增强和高度可持续性 有效性为达致这个长远目标，我们会：（目标1）促进可持续的干预成效 通过整合从人工智能（即强化学习）到 开发算法，随着时间的推移不断优化移动的健康干预措施;（目标2）满足差异 通过推广Aim 1算法来构建和不断优化个人特定的移动的 （目标3）测试、评估和完善目标1和2中开发的算法， （目标4）传播所开发的算法，以便它们可以很容易地应用于SUD/HIV 预防、治疗和康复。我们将为SUD/HIV科学家举办研讨会，并出版 教程和新的研究在南，艾滋病毒，和方法的场地。我们将与传播和 培训中心开发免费的、用户友好的软件，使SUD/HIV科学家能够开发、优化、 并评估他们自己的即时适应性数字健康干预措施。

PROJECT SUMMARY: PROJECT 3 In the modern, post-pandemic world, digital technology is becoming an increasingly important vehicle for the delivery of substance use disorder (SUD) and HIV prevention, treatment, and recovery services. The long-term goal of the proposed project is to enable digital health technology to deliver intervention services with unprecedented effectiveness and sustainability. We propose to integrate ideas from behavioral science and artificial intelligence to develop methodology for (1) continual optimization of just-in-time adaptive digital health interventions in response to societal changes and evolving population treatment needs and (2) personalized just-in-time adaptive digital health interventions to each individual's evolving treatment needs. This will enable a second generation of just-in-time adaptive digital health interventions with enhanced and highly sustainable effectiveness. To achieve this long-term goal, we will: (Aim 1) Promote sustainable intervention effectiveness and engagement by integrating approaches from artificial intelligence — namely reinforcement learning — to develop algorithms that continually optimize mobile health interventions over time; (Aim 2) Meet differential individual needs by generalizing Aim 1 algorithms to construct and continually optimize person-specific mobile health interventions; (Aim 3) Test, evaluate, and refine the algorithms developed in Aims 1 and 2 in extensive simulations; and (Aim 4) Disseminate the developed algorithms so that they can be readily applied in SUD/HIV prevention, treatment, and recovery. We will conduct workshops for SUD/HIV scientists and publish both tutorials and new research in SUD, HIV, and methodology venues. We will work with the Dissemination and Training Core to develop free, user-friendly software that will enable SUD/HIV scientists to develop, optimize, and evaluate their own just-in-time adaptive digital health interventions.