Studying the Digital Intervention Engagement–Mediated Relationship Between Intrapersonal Measures and Pre-Exposure Prophylaxis Adherence in Sexual and Gender Minority Youth: Secondary Analysis of a Randomized Controlled Trial (Preprint)
Bibliographic record
Abstract
BACKGROUND Improving adherence to pre-exposure prophylaxis (PrEP) via digital health interventions (DHIs) for young sexual and gender minority men who have sex with men (YSGMMSM) is promising for reducing the HIV burden. Measuring and achieving effective engagement (sufficient to solicit PrEP adherence) in YSGMMSM is challenging. OBJECTIVE This study is a secondary analysis of the primary efficacy randomized controlled trial (RCT) of Prepared, Protected, Empowered (P3), a digital PrEP adherence intervention that used causal mediation to quantify whether and to what extent intrapersonal behavioral, mental health, and sociodemographic measures were related to effective engagement for PrEP adherence in YSGMMSM. METHODS In May 2019, 264 YSGMMSM were recruited for the primary RCT via social media, community sites, and clinics from 9 study sites across the United States. For this secondary analysis, 140 participants were eligible (retained at follow-up, received DHI condition in primary RCT, and completed trial data). Participants earned US currency for daily use of P3 and lost US currency for nonuse. Dollars accrued at the 3-month follow-up were used to measure engagement. PrEP nonadherence was defined as blood serum concentrations of tenofovir-diphosphate and emtricitabine-triphosphate that correlated with ≤4 doses weekly at the 3-month follow-up. Logistic regression was used to estimate the total effect of baseline intrapersonal measures on PrEP nonadherence, represented as odds ratios (ORs) with a null value of 1. The total OR for each intrapersonal measure was decomposed into direct and indirect effects. RESULTS For every US $1 earned above the mean (US $96, SD US $35.1), participants had 2% (OR 0.98, 95% CI 0.97-0.99) lower odds of PrEP nonadherence. Frequently using phone apps to track health information was associated with a 71% (OR 0.29, 95% CI 0.06-0.96) lower odds of PrEP nonadherence. This was overwhelmingly a direct effect, not mediated by engagement, with a percentage mediated (PM) of 1%. Non-Hispanic White participants had 83% lower odds of PrEP nonadherence (OR 0.17, 95% CI 0.05-0.48) and had a direct effect (PM=4%). Participants with depressive symptoms and anxiety symptoms had 3.4 (OR 3.42, 95% CI 0.95-12) and 3.5 (OR 3.51, 95% CI 1.06-11.55) times higher odds of PrEP nonadherence, respectively. Anxious symptoms largely operated through P3 engagement (PM=51%). CONCLUSIONS P3 engagement (dollars accrued) was strongly related to lower odds of PrEP nonadherence. Intrapersonal measures operating through P3 engagement (indirect effect, eg, anxious symptoms) suggest possible pathways to improve PrEP adherence DHI efficacy in YSGMMSM via effective engagement. Conversely, the direct effects observed in this study may reflect existing structural disparity (eg, race and ethnicity) or behavioral dispositions toward technology (eg, tracking health via phone apps). Evaluating effective engagement in DHIs with causal mediation approaches provides a clarifying and mechanistic view of how DHIs impact health behavior. CLINICALTRIAL ClinicalTrials.gov; NCT03320512; https://clinicaltrials.gov/study/NCT03320512
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.014 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".