Addiction Center, Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA; Michigan Innovations in Addiction Care through Research and Education, University of Michigan, Ann Arbor, MI, USA.
Addiction Center, Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
Contemp Clin Trials. 2024 Oct;145:107667. doi: 10.1016/j.cct.2024.107667. Epub 2024 Aug 17.
Emerging adult (EA) cannabis use is associated with increased risk for health consequences. Just-in-time adaptive interventions (JITAIs) provide potential for preventing the escalation and consequences of cannabis use. Powered by mobile devices, JITAIs use decision rules that take the person's state and context as input, and output a recommended intervention (e.g., alternative activities, coping strategies). The mHealth literature on JITAIs is nascent, with additional research needed to identify what intervention content to deliver when and to whom.
Herein we describe the protocol for a pilot study testing the feasibility and acceptability of a micro-randomized trial for optimizing MiWaves mobile intervention app for EAs (ages 18-25; target N = 120) with regular cannabis use (≥3 times per week). Micro-randomizations will be determined by a reinforcement learning algorithm that continually learns and improves the decision rules as participants experience the intervention. MiWaves will prompt participants to complete an in-app twice-daily survey over 30 days and participants will be micro-randomized twice daily to either: no message or a message [1 of 6 types varying in length (short, long) and interaction type (acknowledge message, acknowledge message + click additional resources, acknowledge message + fill in the blank/select an option)]. Participants recruited via social media will download the MiWaves app, and complete screening, baseline, weekly, post-intervention, and 2-month follow-up assessments. Primary outcomes include feasibility and acceptability, with additional exploratory behavioral outcomes.
This study represents a critical first step in developing an effective mHealth intervention for reducing cannabis use and associated harms in EAs.
新兴成人（EA）的大麻使用与增加健康后果的风险有关。即时自适应干预（JITAI）为预防大麻使用的升级和后果提供了潜力。由移动设备提供动力，JITAI 使用决策规则，将人的状态和环境作为输入，并输出建议的干预措施（例如，替代活动，应对策略）。即时自适应干预的移动健康文献尚处于起步阶段，需要进一步研究确定何时以及向谁提供何种干预内容。
本文描述了一项试点研究的方案，该研究旨在测试 MiWaves 移动干预应用程序优化的可行性和可接受性，该应用程序针对经常使用大麻（每周≥3 次）的 EA（18-25 岁；目标 N=120）。微随机化将通过强化学习算法确定，该算法会随着参与者体验干预措施而不断学习和改进决策规则。MiWaves 将提示参与者在 30 天内每天两次完成应用内调查，参与者每天将被微随机化两次，要么：无消息，要么：消息[1/6 种类型，长度不同（短，长）和交互类型（确认消息，确认消息+点击其他资源，确认消息+填空/选择选项）]。通过社交媒体招募的参与者将下载 MiWaves 应用程序，并完成筛选，基线，每周，干预后和 2 个月随访评估。主要结果包括可行性和可接受性，以及其他探索性行为结果。
这项研究代表了为减少 EA 中大麻使用及其相关危害而开发有效的移动健康干预措施的关键第一步。
