Accepted for/Published in: JMIR Research Protocols
Date Submitted: Mar 15, 2024
Date Accepted: Jun 24, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Novel Machine-Learning HIV Intervention for Sexual and Gender Minority Young People Who Have Sex with Men (uTECH): Protocol for Randomized Comparison Trial
ABSTRACT
Background:
Sexual and gender minority (SGM) young people are disproportionately affected by HIV in the United States, and substance use is a major driver of new infections. People who use online venues to meet sexual partners are more likely to report substance use, sexual risk behaviors, and sexually transmitted infections (STIs). To our knowledge, no machine learning interventions have been developed that utilize online and digital technologies to inform and personalize HIV and substance use prevention efforts with sexual and gender minorities.
Objective:
This study is a two-arm randomized comparison trial that tests the acceptability, appropriateness, and feasibility of the uTECH intervention, a text message intervention using a machine learning algorithm to promote substance use harm reduction and safer behaviors among 18-29 year old sexual and gender minority people who have sex with men. This intervention will be compared to YMHP (Young Men’s Health Program) alone, an existing CDC Best Evidence intervention for young SGM men, that consists of 4 motivational interviewing-based counseling sessions. The YMHP condition will receive YMHP sessions and will be compared to the uTECH+YMHP condition, which includes YMHP sessions as well as uTECH text messages.
Methods:
In a study funded by the National Institutes of Health, we will recruit and enroll sexual and gender minority participants (aged 18 to 29) in the United States (N=330) to participate in a 12-month study. All participants will receive 4 counseling sessions conducted over Zoom with a Masters-level social worker. Participants in the uTECH+YMHP condition will receive curated text messages informed by a machine learning algorithm that seeks to promote HIV and substance use risk reduction strategies as well as undergoing YMHP counseling. Over the course of the study, participants will receive between $350 to $450 in incentives, depending on condition assignment. We hypothesize the uTECH+YMHP intervention will be considered acceptable, appropriate, and feasible to most participants. We also hypothesize that participants in the combined condition will experience enhanced and more durable reductions in substance use and sexual risk behaviors compared to participants receiving YMHP alone.
Results:
This study will test the acceptability, appropriateness, and feasibility of uTECH for sexual and gender minority young adults in the United States (N=330). uTECH seeks to reduce HIV transmission by sending personalized and curated messages promoting harm reduction and safer sex behaviors based on a machine learning algorithm.
Conclusions:
This study aims develop and test the acceptability, appropriateness, and feasibility of uTECH, a novel approach to reduce HIV risk and substance use among sexual and gender minority young adults. Clinical Trial: Clinical Trials ID: NCT04710901
Citation
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Copyright
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