Evaluation of the Implementation and Effectiveness of a Mobile Health Intervention to Improve Outcomes for People With HIV in the Washington, DC Cohort: Study Protocol for a Cluster Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Gaps remain in achieving retention in care and durable HIV viral load suppression for people with HIV in Washington, DC (hereafter DC). Although people with HIV seeking care in DC have access to a range of supportive services, innovative strategies are needed to enhance patient engagement in this setting. Mobile health (mHealth) interventions have shown promise in reaching previously underengaged groups and improving HIV-related outcomes in various settings. OBJECTIVE: This study will evaluate the implementation and effectiveness of a clinic-deployed, multifeature mHealth intervention called PositiveLinks (PL) among people with HIV enrolled in the DC Cohort, a longitudinal cohort of people with HIV receiving care in DC. A cluster randomized controlled trial will be conducted using a hybrid effectiveness-implementation design and will compare HIV-related outcomes between clinics randomized to PL versus usual care. METHODS: The study aims are threefold: (1) We will perform a formative evaluation of PL in the context of DC Cohort clinics to test the feasibility, acceptability, and usability of PL and tailor the platform for use in this context. (2) We will conduct a cluster randomized controlled trial with 12 DC Cohort clinics randomized to PL or usual care (n=6 [50%] per arm) and measure the effectiveness of PL by the primary outcomes of patient visit constancy, retention in care, and HIV viral load suppression. We aim to enroll a total of 482 participants from DC Cohort clinic sites, specifically including people with HIV who show evidence of inconsistent retention in care or lack of viral suppression. (3) We will use the Consolidated Framework for Implementation Research (CFIR) and the Reach Effectiveness Adoption Implementation Maintenance (RE-AIM) framework to measure implementation success and identify site, patient, provider, and system factors associated with successful implementation. Evaluation activities will occur pre-, mid-, and postimplementation. RESULTS: Formative data collection was completed between April 2021 and January 2022. Preliminary mHealth platform modifications have been performed, and the first round of user testing has been completed. A preimplementation evaluation was performed to identify relevant implementation outcomes and design a suite of instruments to guide data collection for evaluation of PL implementation throughout the trial period. Instruments include those already developed to support DC Cohort Study activities and PL implementation in other cohorts, which required modification for use in the study, as well as novel instruments designed to complete data collection, as guided by the CFIR and RE-AIM frameworks. CONCLUSIONS: Formative and preimplementation evaluations will be completed in spring 2022 when the trial is planned to launch. Specifically, comprehensive formative data analysis will be completed following data collection, coding, preliminary review, and synthesis. Corresponding platform modifications are ready for beta testing within the DC Cohort. Finalization of the platform for use in the trial will follow beta testing. TRIAL REGISTRATION: ClinicalTrials.gov NCT04998019; https://clinicaltrials.gov/ct2/show/NCT04998019. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/37748.
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 | 0,072 | 0,058 |
| Méta-épidémiologie (sens strict) | 0,008 | 0,004 |
| Méta-épidémiologie (sens large) | 0,012 | 0,007 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,005 | 0,003 |
| Intégrité de la recherche | 0,009 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,057 | 0,012 |
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 ».