Evidence map›Paper›PMID 41519977›Full record

ArticlePrevention science : the official journal of the Society for Prevention Research2026

Utilizing Machine Learning for Predicting PrEP Use Status Among Sexual and Gender Minority Young Adults.

Callisto Boka, Elizabeth A Yonko, Mehrab Beikzadeh, Kimmo Kärkkäinen, Chenglin Hong, Majid Sarrafzadeh, Ian W Holloway

Registry-linked trialAbstract read
In one paragraph

Article in Prevention science : the official journal of the Society for Prevention Research, 2026. 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 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Callisto BokaDepartment of Epidemiology, University of California, Los Angeles, CA, USA. cboka@g.ucla.edu.ORCID http://orcid.org/0009-0003-2149-6244
Elizabeth A YonkoDepartment of Epidemiology, University of California, Los Angeles, CA, USA.
Mehrab BeikzadehDepartment of Computer Science, University of California, Los Angeles, Los Angeles, USA.
Kimmo Kärkkäinen, Optum, Los Angeles, USA.
Chenglin HongSchool of Social Work, University of Connecticut, Hartford, CA, USA.
Majid SarrafzadehDepartment of Computer Science, University of California, Los Angeles, Los Angeles, USA.
Ian W HollowaySchool of Nursing, University of California, Los Angeles, Los Angeles, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pre-exposure prophylaxis (PrEP) is a highly effective biomedical prevention tool for HIV yet remains underutilized among key populations, particularly among young sexual and gender minorities (SGM). Recognizing the popularity of specific dating and social media apps among SGM young adults, we leveraged user data from these platforms to build a machine learning (ML) model that could inform targeted, data-driven interventions aimed at improving PrEP uptake and adherence. We adapted eWellness, an Android mobile app, to passively collect data from research participants capturing mobile app usage, keystroke patterns and logs, and GPS location data between 2021 and 2024. These data were used to train a ML model to predict self-reported PrEP use. Model accuracy was evaluated through F1 scores across different data types and feature combinations. Study protocols were developed in collaboration with community partners and adhered to strict ethical and privacy standards. A total of 82 SGM young adults participated, with 46 (56%) reporting PrEP use at baseline. Our machine learning model demonstrated good predictive accuracy for predicting PrEP use and non-use, achieving an F1 score of 0.84 (PrEP use) and 0.82 (non-use) outcomes when incorporating data from all mobile apps, including messaging, dating, and social media mobile apps. By contrast, predictions based solely on social media mobile app usage, language associated with sexual behavior and substance use risk, or location monitoring demonstrated worse accuracy (F1 scores of 0.79/0.75, 0.70/0.57, and 0.70/0.52, respectively). Additional feature extraction methods, as well as various combinations of these features, were also tested. However, none achieved predictive accuracy as well as the model incorporating all mobile app usage data combined. This study demonstrates the potential of machine learning to accurately predict PrEP use status among SGM young adults. The findings offer a foundation for developing more personalized PrEP promotion strategies, particularly among SGM young adults who use social media and dating apps. Future research should assess the model's adaptability across diverse SGM subgroups to further inform intervention development. Registry: ClinicalTrials.gov, ID: NCT04710901, November 9, 2020.

Indexed as

HIV InfectionsMachine LearningMobile ApplicationsPre-Exposure ProphylaxisSexual and Gender MinoritiesAdolescentFemaleHumansMalePredictive Learning ModelsRandomized Controlled Trials as TopicYoung AdultMachine learning modelMHealthPre-exposure prophylaxis (PrEP)Sexual and gender minoritiesYoung adults

Identifiers

PMID41519977
PMCPMC13550048

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.