Preferences for mHealth Features to Support Engagement in the HIV Pre-Exposure Prophylaxis Cascade Among Men Who Have Sex With Men in Peru: Cross-Sectional Online Survey (Preprint)
Bibliographic record
Abstract
Background: Despite policy-level progress, implementation of oral HIV preexposure prophylaxis (PrEP) remains limited in Latin America. In Peru, men who have sex with men (MSM) account for most new HIV diagnoses, yet uptake remains low. Widespread smartphone ownership and the use of digital platforms present an opportunity to expand access through mobile health (mHealth) interventions. However, limited data exist on user preferences to guide the design of mHealth tools in Spanish-speaking Latin American settings. Objective: This study aimed to assess preferences for mHealth features to support PrEP engagement among Peruvian MSM and their association with PrEP cascade stages. Methods: We conducted a cross-sectional online survey (June-August 2023) among 600 HIV-negative MSM residing in Peru (median age 29, IQR 24-35 years), recruited via Facebook, Instagram, WhatsApp (Meta Platforms, Inc), and Grindr (Grindr LLC). The survey assessed communication platform use, interest in mHealth features measured on a 4-point Likert scale, and PrEP cascade stages. Exploratory factor analysis (principal axis factoring with Promax rotation) identified domains of mHealth preferences, from which median domain scores were calculated. Bivariate analyses used chi-square tests and Wilcoxon rank sum tests. Multivariable logistic regression models (α=.05), with covariates selected using stepwise procedures from candidate sociodemographic and behavioral variables, estimated associations between each domain score and PrEP cascade stages, each modeled as a separate binary outcome. Results: Nearly all participants (589/600, 98.2%) reported owning a smartphone. WhatsApp was the most frequently used and preferred platform for PrEP support, with 547 (91.2%) reporting frequent use and 302 (50.3%) ranking it first. Exploratory factor analysis identified three mHealth preference domains: informational support (Cronbach α=0.94), self-management tools (Cronbach α=0.94), and interactive communication (Cronbach α=0.91). Among participants, 483 (80.5%) had decided to use PrEP, 190 (31.7%) had sought PrEP, and 109 (18.2%) had initiated PrEP. Higher informational support was associated with the decision to use PrEP (adjusted odds ratio [aOR] 4.54, 95% CI 3.36-6.28; P<.001), seeking PrEP (aOR 1.43, 95% CI 1.10-1.89; P=.001), and PrEP initiation (aOR 1.64, 95% CI 1.16-2.44; P=.009). Self-management tools showed similar associations with the decision to use PrEP (aOR 3.23, 95% CI 2.51-4.22; P<.001), seeking PrEP (aOR 1.34, 95% CI 1.06-1.70; P=.02), and PrEP initiation (aOR 1.49, 95% CI 1.11-2.05; P=.01). Interactive communication was associated with the decision to use PrEP (aOR 2.74, 95% CI 2.15-3.53; P<.001) but not with initiation. Conclusions: Preferences for mHealth features were associated with engagement at multiple stages of the PrEP cascade among MSM in Peru. Informational support features demonstrated the most consistent associations with cascade engagement. These findings provide empirical evidence on user-prioritized digital functions that could support early engagement in HIV prevention services in a Latin American implementation context. Integrating culturally tailored mHealth tools within widely used platforms such as WhatsApp may strengthen early PrEP cascade engagement and support scalable digital strategies for HIV prevention in Peru and similar settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".