Correlates of Engagement Within an Online HIV Prevention Intervention for Single Young Men Who Have Sex With Men: Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Digital HIV interventions (DHI) have been efficacious in reducing sexual risk behaviors among sexual minority populations, yet challenges in promoting and sustaining users' engagement in DHI persist. Understanding the correlates of DHI engagement and their impact on HIV-related outcomes remains a priority. This study used data from a DHI (myDEx) designed to promote HIV prevention behaviors among single young men who have sex with men (YMSM; ages 18-24 years) seeking partners online. OBJECTIVE: The goal of this study is to conduct a secondary analysis of the myDex project data to examine whether YMSM's online behaviors (eg, online partner-seeking behaviors and motivations) are linked to participants' engagement (ie, the number of log-ins and the number of sessions viewed). METHODS: We recruited 180 YMSM who were randomized into either myDEx arm or attention-control arm using a stratified 2:1 block randomization. In the myDEx arm, we had 120 YMSM who had access to the 6-session intervention content over a 3-month period. We used Poisson regressions to assess the association between YMSM's baseline characteristics on their DHI engagement. We then examined the association between the participants' engagement and their self-reported changes in HIV-related outcomes at the 3-month follow-up. RESULTS: The mean number of log-ins was 5.44 (range 2-14), and the number of sessions viewed was 6.93 (range 0-22) across the 3-month trial period. In multivariable models, the number of log-ins was positively associated with high education attainment (estimated Poisson regression coefficient [β]=.22; P=.045). The number of sessions viewed was associated with several baseline characteristics, including the greater number of sessions viewed among non-Hispanic YMSM (β=.27; P=.002), higher education attainment (β=.22; P=.003), higher perceived usefulness of online dating for hookups (β=.13; P=.002) and perceived loneliness (β=.06; P=.004), as well as lower experienced online discrimination (β=-.01; P=.007) and limerence (β=-.02; P=.004). The number of sessions viewed was negatively associated with changes in internalized homophobia (β=-.06; P<.001) and with changes in perceived usefulness of online dating for hookups (β=-.20; P<.001). There were no significant associations between the number of log-ins and changes in the participants' behaviors at the 90-day follow-up. CONCLUSIONS: DHI engagement is linked to participants' sociodemographic and online behaviors. Given the importance of intervention engagement in the intervention's effectiveness, DHIs with personalized intervention components that consider the individuals' differences could increase the overall engagement and efficacy of DHIs. TRIAL REGISTRATION: ClinicalTrials.gov NCT02842060; https://clinicaltrials.gov/ct2/show/NCT02842060.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".