uTECH: Machine Learning for HIV Prevention Among Substance Using GBMSM

项目摘要 同性恋、双性恋和其他男男性行为者（GBMSM）受到的影响不成比例 物质使用是GBMSM中艾滋病毒风险的重要影响; 寻求性和物质使用的伴侣主要转移到网上和地理社会 为GBMSM设计的联网平台（例如，Grindr）。的技术进步 收集和挖掘“大数据”，为行为健康干预提供信息， 近年来，它还没有直接应用于艾滋病毒的预防和药物。 在GBMSM中使用危害减少。与此同时，尽管生物医学领域取得了重大进展， 艾滋病毒预防（即，暴露前预防[PrEP]）和物质使用危害减少（即， 药物辅助治疗[MAT]），这些策略未被GBMSM充分利用。我的研究 我和我的团队对社交媒体数据挖掘和机器学习进行了形成性研究 通过NIDA A/START（R 03），确定与以下方面相关的技术使用模式： GBMSM中的艾滋病毒风险和物质使用我们建立了计算功能的一个 文化定制的社交媒体数据挖掘程序之间的物质使用GBMSM。我现在 利用这项技术开发艾滋病预防干预措施是一项重大的科学风险 对于GBMSM，暂命名为uTECH，利用机器学习的见解来触发 个性化的干预内容，以增加生物医学艾滋病毒预防和物质 使用伤害减少。具体而言，我建议进行一项分两个阶段的研究。在第一阶段，我将 与GBMSM进行定性访谈，以告知迭代开发和改进 关于UTECH在第二阶段，我将在一个 比较执行科学试验。在这个阶段，我将（a）招募种族多样的艾滋病毒感染者- 阴性，使用GBMSM的物质;（B）将他们随机分配到uTECH干预或a （c）通过6项指标衡量可接受性、适当性和可行性 干预后几个月。我的主要实施科学成果将是可接受性 (i.e.,干预措施的可接受性[AIM]）、适当性（即，干预 适当的措施[IAM]）和可行性（即，干预措施的可行性[编辑]我 我相信，“大数据”和新技术的力量可以被用于有效的艾滋病毒 使用GBMSM物质预防。在艾滋病毒预防疲劳日益加剧的时代 GBMSM，提供快速，方便和高度个性化的干预措施的能力， 这是一个重振艾滋病毒预防工作的机会。

Project Summary Gay, bisexual and other men who have sex with men (GBMSM) are disproportionately impacted by HIV in the U.S. Substance use is an important influence on HIV risk among GBMSM; and partner seeking for both sex and substance use have largely moved online and to geosocial networking platforms designed for GBMSM (e.g., Grindr). Technological advances in the collection and mining of “big data” to inform behavioral health interventions have increased in recent years but have not been applied directly to HIV prevention and substance use harm reduction among GBMSM. At the same time, despite major advances in biomedical HIV prevention (i.e., pre-exposure prophylaxis [PrEP]) and substance use harm-reduction (i.e., medication assisted therapy [MAT]), these strategies are underutilized by GBMSM. My research team and I conducted formative research on social media data mining and machine learning through a NIDA A/START (R03) to identify patterns of technology use that are associated with HIV risk and substance use among GBMSM. We established computational functionality of a culturally tailored social media data mining program among substance using GBMSM. I now take an important scientific risk to use this technology to develop an HIV prevention intervention for GBMSM, tentatively titled uTECH, that leverages insights from machine learning to trigger personalized intervention content in order to increase biomedical HIV prevention and substance use harm reduction. Specifically, I propose to conduct a two-phase study. In Phase 1 I will conduct qualitative interviews with GBMSM to inform the iterative development and refinement of uTECH. In Phase 2, I will test the acceptability, appropriateness and feasibility of uTECH in a comparative implementation science trial. For this phase, I will (a) enroll racially diverse, HIV- negative, substance using GBMSM; (b) randomize them to either the uTECH intervention or a comparison group; and (c) measure acceptability, appropriateness and feasibility through 6 months post-intervention. My primary implementation science outcomes will be acceptability (i.e., Acceptability of Intervention Measure [AIM]), appropriateness (i.e., Intervention Appropriateness Measure [IAM]), and feasibility (i.e., Feasibility of Intervention Measure [FIM]). I believe that the power of “big data” and new technologies can be harnessed for effective HIV prevention with substance using GBMSM. In the era of increasing HIV prevention fatigue among GBMSM, the ability to deliver quick, convenient and highly personalized interventions presents an opportunity to reinvigorate HIV prevention.