# Exploring Acceptability & Potential Reach of Game-Based & Social Network Strategies for Improving PrEP & HIV Self-Testing Uptake among Latinx Sexual Minority Men Living in an EHE Priority Jurisdiction

> **NIH NIH F31** · UNIVERSITY OF MIAMI SCHOOL OF MEDICINE · 2024 · $42,749

## Abstract

PROJECT SUMMARY/ABSTRACT
The goal of this F31-Diversity application, submitted by a predoctoral investigator from a disadvantaged
background, is to examine the configurations of the online gaming and offline friendship networks of Latinx men
who have sex with men (LMSM) and the extent to which these network structures and characteristics can
influence LMSM access to pre-exposure prophylaxis (PrEP) information and the distribution of HIV self-testing
(HIVST) kits. Despite the field’s infancy, online game-based interventions have been found to be acceptable
among sexual and gender minority adolescents and adults and have positive effects for increasing knowledge,
and improving attitudes and behaviors related to HIV prevention. Few studies have investigated the combined
use of online game-based and offline friendship network approaches to increase awareness and uptake of
PrEP/HIVST in the LMSM community. This mixed-methods social network study will analyze and integrate two
data sources collected specifically for this F31-Diversity project as part of Dr. Mariano Kanamori’s (Main Sponsor)
R01 entitled, “PrEParados: A Social Network Study of Latino MSM for Facilitating Progress in the PrEP Cascade”
from April 2022 to August 2023 (R01MH12572). First data source: egocentric and two-mode network data (N=73
egos and 153 alters). Second data source: qualitative data including 90-minute semi-structured individual
interviews with LMSM online gamers (N=40). Both data sources include information from LMSM living in Miami-
Dade County, Florida, (MDC), the metropolitan statistical area with the highest HIV incidence (42.4 per 100,000)
and second highest HIV prevalence (979.9 per 100,000) rates in the nation. Among incident HIV cases in MDC,
64.4% are attributed to Latinx individuals, and 81.5% are attributed to MSM. Design. The Network Flow Model
and Social Contagion Theory will guide the following research aims: Aim 1: Characterize social networks of
LMSM online gamers. It will include the description of network size, type of games played, frequency of gameplay
with others, format of gameplay, and the sociodemographic characteristics of their online gaming partners. Aim
2: Understand the potential reach of LMSM online gamers to promote PrEP messaging and disseminate HIVST
kits in their offline friendship networks. Egocentric and two-mode network analyses will be used to: (A) determine
the structures of offline friendship and affiliation networks of LMSM online gamers, and (B) identify specific
characteristics of these structures (e.g., type of games played, homogeneity, homophily) associated with the
dissemination of PrEP information and distribution of HIVST kits. Aim 3: Explore the acceptability of a combined
online game and offline friendship networks-based approach to promote PrEP information and HIVST kit
dissemination. Qualitative findings will inform the design of an intervention that bundles social network and online
game-based approaches to increase Pr...

## Key facts

- **NIH application ID:** 11009143
- **Project number:** 1F31MH138212-01
- **Recipient organization:** UNIVERSITY OF MIAMI SCHOOL OF MEDICINE
- **Principal Investigator:** Lacey Despres
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $42,749
- **Award type:** 1
- **Project period:** 2024-07-01 → 2027-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11009143

## Citation

> US National Institutes of Health, RePORTER application 11009143, Exploring Acceptability & Potential Reach of Game-Based & Social Network Strategies for Improving PrEP & HIV Self-Testing Uptake among Latinx Sexual Minority Men Living in an EHE Priority Jurisdiction (1F31MH138212-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/11009143. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*