Feasibility and Acceptability of a mHealth Patient Navigation Intervention to Increase Pre-Exposure Prophylaxis Uptake in Racially and Ethnically Diverse Sexual and Gender Minority Youth in Los Angeles (PrEPresent): Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
Background: Pre-exposure prophylaxis (PrEP) is a powerful tool to prevent the transmission of HIV. Interventions promoting PrEP must focus on populations most impacted by systemic barriers to uptake. Historically, young sexual minority men and transgender women have the highest demonstrated rates of new HIV diagnoses, but prevalence within other gender minority populations is now being studied. Few interventions have focused on addressing PrEP uptake with sexual and gender minority (SGM) youth, particularly through mobile health (mHealth) technologies. Built on the successful foundation of the HealthMpowerment Platform, PrEPresent aimed to engage SGM youth across diverse gender, racial, and ethnic identities in the Greater Los Angeles area. Objective: The aim of this study was to evaluate the feasibility and acceptability of a digital peer patient-navigation PrEP uptake app. Methods: PrEPresent incorporated patient activation theory into an mHealth intervention. The study took place over a 6-month period with visits at baseline, 3 months, and 6 months. The intervention period lasted from baseline to 3 months. Control participants received an information-only app. Intervention participants received an enhanced app and access to an interventionist, the PrEPresentative. Intervention participants could meet with the PrEPresentative four times over the 3-month period via phone, Health Insurance Portability and Accountability Act-compliant videoconferencing, or an in-app text messaging. PrEP uptake was measured through survey responses and the UrSure rapid urine test of tenofovir. Results: PrEPresent comprised of 147 sexual and gender-diverse participants-75 participants were randomized into the control arm and 72 into the intervention arm. A total of 48% (71/147) were Latinx and 18% (27/147) were Black or African American. Most (98/147, 67%) were transgender or gender diverse, and the remaining (49/147, 33%) were cisgender men. PrEP was initiated by 25% (14/56) of intervention participants and 19% (11/58) of control participants. In total, 50% (36/72) of intervention participants completed two or more sessions with the interventionist. Intervention participants had an average of 15.93 (SD 15.85) logins compared to 6.31 (SD 9.27) logins for control participants. Average use of the mHealth platform was 9.51 (SD 11.47) minutes for intervention participants and 3.03 (SD 5.70) minutes for control participants. Conclusions: PrEPresent met primary outcome measures of feasibility and acceptability. Despite this, PrEP uptake was low, and use of the platform was low compared to other HealthMpowerment projects. While mHealth offers promising HIV prevention outcomes, fostering active app engagement is crucial in promoting behavior change. Mixed success in initiating PrEP uptake across mHealth interventions involving SGM youth warrants further inquiry into how these platforms can address prevention barriers with this population. Interventions targeting uptake and adherence will need to adapt as the landscape of PrEP delivery evolves with the adoption of on-demand and long-acting injectable modalities.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».