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uTECH: Machine Learning for HIV Prevention Among Substance Using GBMSM — uTECH  

HIV Infections Clinical Trial

Official title:
uTECH: Machine Learning for HIV Prevention Among Substance Using GBMSM

Clinical Trial Summary

This project seeks to develop and test the acceptability, appropriateness and feasibility of uTECH, a novel social media "big data" machine learning intervention for HIV-negative substance-using sexual and gender minority people who have sex with men that aims to reduce HIV transmission risk by integrating biomedical and behavioral risk reduction strategies, including pre-exposure prophylaxis (PrEP) for HIV prevention and medication assisted treatment (MAT) for substance use harm reduction

Clinical Trial Description

The project will occur in two phases. In Phase 1, we will conduct qualitative interviews with gay and bisexual men who have sex with men (GBMSM) using an iterative user-centered design process, which will result in a refined version of the uTECH intervention. In Phase 2, we will conduct a comparative acceptability, appropriateness and feasibility trial with 330 individuals, who will be randomized to (1) receive the uTECH intervention and an existing, evidence-based motivational enhancement intervention for HIV risk and substance use prevention (YMHP) or (2) receive YMHP alone. uTECH is innovative in that it includes both core intervention modules and highly personalized intervention content based on participants' social media use. The tailored intervention content can be delivered via text message or Facebook messenger. This content relies on our previously developed machine learning algorithm, which helps participants understand their technology-use behavior in relation to HIV-risk and substance use. ;

Study Design

Related Conditions & MeSH terms

Clinical Trial Details
NCT number NCT04710901
Study type Interventional
Source University of California, Los Angeles
Contact
Status Active, not recruiting
Phase N/A
Start date November 24, 2020
Completion date May 31, 2024
View Details
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