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

针对 SUD 预防、治疗和恢复的及时适应性干预措施的持续优化和个性化

• 批准号: 10640293
• 负责人: SUSAN A MURPHY
• 金额: $ 48.07万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10640293
• 关键词: AIDS prevention; Adult; Affect; Algorithms; Artificial Intelligence; Bayesian Method; Behavioral Medicine; Behavioral Sciences; COVID-19; Cellular Phone; Collaborations; Computer software; Data; Devices; Drug usage; Educational workshop; Effectiveness; Effectiveness of Interventions; Explosion; Generations; Goals; HIV; Health Technology; Individual; Intervention; Journals; Learning; Location; Methodology; Modernization; Motivation; Participant; Peer Review; Persons; Play; Population; Prevention strategy; Psychological reinforcement; Publishing; Recovery; Research; Role; Scientist; Services; Societies; Stress; Substance Use Disorder; Testing; Time; Training; Variant; Work; Writing; Youth; adaptive intervention; craving; delivery vehicle; design; digital; digital delivery; digital health; digital intervention; digital technology; disorder prevention; effective intervention; individualized prevention; intervention cost; intervention delivery; mHealth; opioid epidemic; overpopulation; person centered; population based; post-pandemic; prevention service; preventive intervention; recovery services; response; service intervention; simulation; smart watch; substance use; therapy design; treatment response; treatment services; trend; unnecessary treatment; user friendly software; user-friendly; wearable device; young adult

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.

项目总结：项目3