Optimizing an App-Based Just-in-Time Adaptive Intervention for Stimulant Use Among Sexual Minority Men Living with HIV: Protocol for a Community-Engaged Research Approach and Hybrid-Experimental Design
Notice bibliographique
Résumé
BACKGROUND: In the United States, sexual minority men (SMM) are disproportionately affected by HIV. For this population, intersecting sexual minority- and HIV-related stressors add to general life stress, increasing health risks. Stress is associated with HIV progression and is linked to transmission risk behaviors, such as medication nonadherence and substance use. Substance use is a particularly important risk factor for HIV transmission; beyond injection drug use, recreational use is associated with sexual transmission risk behaviors and an increased risk of HIV among SMM. Interventions targeting stress responses may be especially useful for HIV risk reduction among substance-using SMM (SUSMM) living with HIV (LWH). Positive affect interventions have shown promise in reducing stress in the context of chronic illness, including HIV. However, few studies have examined these interventions and their potential health benefits for SUSMM-LWH. OBJECTIVE: This protocol describes a project (R34DA053999) that builds on our pilot work exploring the induction of positive affect as a potential intervention to reduce stress and improve health outcomes among SMM-LWH. This protocol aims to employ a community-engaged research approach, using the multiphase optimization strategy, to iteratively tailor our app-based positive affect intervention, TeaTime, for SUSMM-LWH. METHODS: In phase 1, we enrolled 10 SUSMM-LWH into an open-phase pilot using our existing app-based ecological momentary intervention (EMI), which employs a just-in-time adaptive intervention design. We collaborated with a community advisory board to tailor our intervention content and app design using focus group feedback. In phase 2, we conducted a pilot factorial optimization trial to assess the acceptability and feasibility of adding 2 new features that may enhance intervention design and delivery. Specifically, we piloted a 2×2 factorial design, randomizing 80 SUSMM-LWH to receive either (1) random craving prompts or (2) smartwatch integration or both or neither. Analyses will examine group differences on feasibility and acceptability outcomes, as measured by the System Usability Scale and Mobile App Rating Scale, as well as retention and engagement. RESULTS: The project was funded in February 2022, and data collection for phase 1 was completed in December 2023. Phase 2 was launched in July 2024, with data collection completed by August 2025. Results from phase 1 are expected to be submitted for publication by December 2025. CONCLUSIONS: In this protocol, we combine methods from social science, intervention science, and software development into a single innovative approach to enhance and optimize an app-based, just-in-time adaptive, EMI for SUSMM-LWH. Findings from this study will support the development of a fully optimized intervention ready for evaluation and implementation. In a future randomized controlled trial, the ecological momentary assessment design will also enable the collection of day-level data on intervention efficacy. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/76741.
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 | 0,014 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,004 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,004 | 0,004 |
| Intégrité de la recherche | 0,005 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,071 | 0,009 |
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 ».