uTECH: Machine Learning for HIV Prevention Among Substance Using GBMSM

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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.