Evidence map›Paper›PMID 39163591›Full record

ArticleJMIR research protocols2024

Novel Machine Learning HIV Intervention for Sexual and Gender Minority Young People Who Have Sex With Men (uTECH): Protocol for a Randomized Comparison Trial.

Ian W Holloway, Elizabeth S C Wu, Callisto Boka, Nina Young, Chenglin Hong, Kimberly Fuentes, Kimmo Kärkkäinen, Mehrab Beikzadeh, Alexandra Avendaño, Juan C Jauregui and 8 more

Registry-linked trialAbstract readClinical Trial Protocol
In one paragraph

Article in JMIR research protocols, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04710901 (UTECH), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04710901 nacompletednot on this map

UTECH: Machine Learning for HIV Prevention Among Substance Using GBMSM

TypeinterventionalSponsorUniversity of California, Los AngelesRan2020 to 2024Enrolled388ConditionsSexually Transmitted Diseases, HIV Infections, Implementation Science, Substance UseArmsuTECH + YMHP, YMHP, uTECH
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Utilizing Machine Learning for Predicting PrEP Use Status Among Sexual and Gender Minority Young Adults.Prevention science : the official journal of the Society for Prevention Research · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Ian W HollowayDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0001-7454-8632
Elizabeth S C WuDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0002-3015-8795
Callisto BokaDepartment of Epidemiology, UCLA Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0009-0003-2149-6244
Nina YoungDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0009-0006-7063-9135
Chenglin HongDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0002-9652-388X
Kimberly FuentesDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0003-4826-4980
Kimmo KärkkäinenDepartment of Computer Science, UCLA Samueli School Of Engineering, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0002-5188-9191
Mehrab BeikzadehDepartment of Computer Science, UCLA Samueli School Of Engineering, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0002-9610-0457
Alexandra AvendañoDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0009-0004-4388-6697
Juan C JaureguiDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0001-6524-7301
Aileen ZhangDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0009-0009-0160-5953
Lalaine SevillanoDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0002-5522-6715
Colin FyfeDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0009-0000-5515-1920
Cal D BrisbinDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0002-7554-9140
Raiza M BeltranDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0001-9380-585X
Luisita CorderoDepartment of Social Welfare, UCLA Luskin School of Public Affairs, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0009-0005-1819-6333
Jeffrey T ParsonsMindful Designs, Teaneck, NJ, United States.ORCID 0000-0002-6875-7566
Majid SarrafzadehDepartment of Computer Science, UCLA Samueli School Of Engineering, University of California, Los Angeles, Los Angeles, CA, United States.ORCID 0000-0001-8407-8689

Funding

uTECH: Machine Learning for HIV Prevention Among Substance Using GBMSMDP2DA049296 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI HOLLOWAY, IAN WALTER · 2019 to 2021
$2.4M
NIDA NIH HHS DP2 DA049296
6 · The paper itself

Abstract

backgroundSexual 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 web-based venues to meet sex partners are more likely to report substance use, sexual risk behaviors, and sexually transmitted infections. To our knowledge, no machine learning (ML) interventions have been developed that use web-based and digital technologies to inform and personalize HIV and substance use prevention efforts for SGM young people.

objectiveThis study aims to test the acceptability, appropriateness, and feasibility of the uTECH intervention, a SMS text messaging intervention using an ML algorithm to promote HIV prevention and substance use harm reduction among SGM people aged 18 to 29 years who have sex with men. This intervention will be compared to the Young Men's Health Project (YMHP) alone, an existing Centers for Disease Control and Prevention best evidence intervention for young SGM people, which 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 SMS text messages.

methodsIn a study funded by the National Institutes of Health, we will recruit and enroll SGM participants (aged 18-29 years) in the United States (N=330) to participate in a 12-month, 2-arm randomized comparison trial. All participants will receive 4 counseling sessions conducted over Zoom (Zoom Video Communications, Inc) with a master's-level social worker. Participants in the uTECH+YMHP condition will receive curated SMS text messages informed by an ML algorithm that seek to promote HIV and substance use risk reduction strategies as well as undergoing YMHP counseling. We hypothesize that 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. Appropriate statistical methods, models, and procedures will be selected to evaluate primary hypotheses and behavioral health outcomes in both intervention conditions using an α<.05 significance level, including comparison tests, tests of fixed effects, and growth curve modeling.

resultsThis study was funded in August 2019. As of June 2024, all participants have been enrolled. Data analysis has commenced, and expected results will be published in the fall of 2025.

conclusionsThis study aims to develop and test the acceptability, appropriateness, and feasibility of uTECH, a novel approach to reduce HIV risk and substance use among SGM young adults.

trial registrationClinicalTrials.gov NCT04710901; https://clinicaltrials.gov/study/NCT04710901. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58448.

Indexed as

HIV InfectionsHomosexuality, MaleMachine LearningSexual and Gender MinoritiesText MessagingAdolescentAdultFemaleHumansMaleRandomized Controlled Trials as TopicUnited StatesYoung Adultharm reductionHIVmachine learningmHealthmobile appmobile healthmobile phonemotivational interviewingsexual and gender minoritysubstance usetext messagingYMHPYoung Men’s Health Project

Identifiers

PMID39163591
PMCPMC11372318

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.