NCT04710901: uTECH

UTECH: Machine Learning for HIV Prevention Among Substance Using GBMSM

Completed NA Last updated 25 November 2024

What this trial tests

NA trial testing uTECH + YMHP in Sexually Transmitted Diseases in 388 participants. Completed in 31 May 2024.

Timeline

24 November 2020

Primary endpoint
31 May 2024

31 May 2024

Quick facts

Lead sponsor	University of California, Los Angeles
Phase	NA
Status	Completed
Study type	INTERVENTIONAL
Allocation	randomized
Design	parallel
Masking	none
Primary purpose	prevention
Enrollment	388
Start date	24 November 2020
Primary completion	31 May 2024
Estimated completion	31 May 2024
Sites	1 location across United States

Drugs / interventions tested

• uTECH + YMHP
• YMHP
• uTECH

Conditions studied

• Sexually Transmitted Diseases — all drugs for Sexually Transmitted Diseases →
• HIV Infections — all drugs for HIV Infections →
• Implementation Science — all drugs for Implementation Science →
• Substance Use — all drugs for Substance Use →

Sponsor

University of California, Los Angeles

Who can join

18 and older, male only, with Sexually Transmitted Diseases or HIV Infections. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

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

Publications & conference data

3 peer-reviewed publications reference this trial (live from Europe PMC):

1. Novel Machine Learning HIV Intervention for Sexual and Gender Minority Young People Who Have Sex With Men (uTECH): Protocol for a Randomized Comparison Trial.
   Holloway IW, Wu ESC, Boka C, Young N, et al · · 2024 · cited 2× · PMID 39163591 · DOI 10.2196/58448
2. Utilizing Machine Learning for Predicting PrEP Use Status Among Sexual and Gender Minority Young Adults.
   Boka C, Yonko EA, Beikzadeh M, Kärkkäinen K, et al · · 2026 · PMID 41519977 · DOI 10.1007/s11121-025-01872-1
3. Identifying Substance Use and High-Risk Sexual Behavior Among Sexual and Gender Minority Youth by Using Mobile Phone Data: Development and Validation Study.
   Beikzadeh M, Holloway IW, Kärkkäinen K, Hong C, et al · · 2025 · PMID 40795338 · DOI 10.2196/68013