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

> **NIH NIH DP2** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2021 · $13,728

## Abstract

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.

## Key facts

- **NIH application ID:** 10400487
- **Project number:** 3DP2DA049296-01S1
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Ian Walter Holloway
- **Activity code:** DP2 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $13,728
- **Award type:** 3
- **Project period:** 2019-08-15 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10400487

## Citation

> US National Institutes of Health, RePORTER application 10400487, uTECH: Machine Learning for HIV Prevention Among Substance Using GBMSM (3DP2DA049296-01S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10400487. Licensed CC0.

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