UNDERSTANDING HOW GUYS USE TECH TO FIND SUBSTANCE USE & SEXUAL PARTNERS
This study seeks to better understand how cisgender men, transgender men, transgender women, and nonbinary individuals people use technology to find substance use and sexual partners. Addressing substance use among this population is critical to preventing future and reducing current HIV and STI infections.
Public Health Issues
- HIV incidence among gay, bisexual and other men who have sex with men (hereafter MSM) continues to rise, driven in part by substance use. MSM are increasingly using social networking sites (SNS) to find substance use and sexual partners.
- However, no studies currently exist that use automated, real-time data collection, analysis procedures, and machine learning to monitor and interact with men who seek substances and sexual partners. This project will conduct research using SNS data collection to aid in understanding patterns of substance use and HIV risk behavior among cisgender men, transgender men, transgender women, and nonbinary individuals who have sex with men in order to develop an agile program that we are calling uTECH.
- In a previous study, we studied how men seeking substance use and sexual partners use apps and utilized qualitative data from focus group interviews to create and tailor a culturally congruent data collection and mining module (DCMM), which can be used to gather and sort large quantities of SNS data related to MSM substance use and sexual partner seeking. For this study, we will conduct interviews at 5 timepoints (baseline, 3-month, 6-month, 9-month, 12-month) with 330 participants to test the acceptability and feasibility of a machine-learning enhanced HIV and substance use harm reduction evidence-based intervention, YMHP. All participants will participate in automated smartphone data collection using a Data Collection Mining Module installed on their phones (eWellness app), and approximately half of the participants will be randomized in receiving tailored uTECH intervention text messages that are generated by an algorithm that assesses risk based on their mined data.
To develop and assess the utility of a culturally congruent machine learning platform (uTECH), an HIV prevention intervention that studies and predicts HIV risk and substance use behavior by building a predictive model (algorithm) using SNS use patterns and other inputs, and engages the user in promoting health and harm reduction.
Publications and Products From This Research
The study is currently enrolling. Analysis and findings from this research is coming soon.
National Institutes of Health (NIH)
ABOUT THE PROJECT
Researchers: Ian W. Holloway
Location: United States
Sample: This nationwide online/remote study will recruit 18-29 year old LGBTQIA+ people who have sex with men, use substances and/or alcohol, and use online apps to find sexual partners for participation in the uTECH intervention.
Timeline: 2020 to now