Mobile Health–Based Motivational Interviewing to Promote SARS-CoV-2 Vaccination in Rural Adults: Protocol for a Pilot Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Despite documented effectiveness, the public health impact of vaccinations is severely limited by misperceptions, hesitancy, and poor acceptance. Messaging from health care providers has not yet been optimized to overcome these barriers and has not been tailored to groups that face health disparities, such as rural Americans. Because vaccines have become controversial, as illustrated by the public response to the SARS-CoV-2 vaccines, traditional approaches that use persuasive education or advice to change perspectives are unlikely to have long-term effects and may even be counterproductive. Alternatively, motivational interviewing (MI) is a conversational approach to address modifiable behavior and its empathic nature can be useful when navigating challenging topics. Although MI has been found to be efficacious in improving vaccination rates among children and adolescents, it is unknown whether MI can reduce vaccine hesitancy and health disparities among underserved rural adults. Further, the ideal mode of delivery for MI is unknown, especially "dose," "intensity," and integration with mobile health (mHealth). Therefore, it is essential to investigate the efficacy of MI in promoting vaccine uptake in rural populations to reduce health disparities. OBJECTIVE: This study aims to develop and evaluate the feasibility, acceptability, and preliminary efficacy of our mHealth-based MI intervention to diminish SARS-CoV-2 vaccine hesitancy (MOTIVACC). METHODS: This pilot study uses mixed methods. A 2-phase study will be conducted: convening a community advisory panel to understand barriers and facilitators to vaccination and mHealth uptake among adults (phase 1, n=16-20), and a pilot 3-group single-blind randomized controlled trial (RCT) for 8 weeks (phase 2, N=60). In the RCT, we recruit adults who have received no previous dose of the COVID-19 vaccine and randomize them into one of three arms: standard MI (SMI; n=20), intensive MI (IMI; n=20), or mHealth-based MOTIVACC (n=20). The primary RCT outcomes are positive change in vaccine hesitancy and intention to obtain the vaccines, measured on Likert scales. The secondary RCT outcome is the actual vaccine receipt. RESULTS: Phase 1 of this study was approved by the ethics committees of both the University of Oklahoma and Oklahoma State University in July 2022, and was completed in June 2023. Phase 2 of this study was approved by the ethics committee at the University of Oklahoma in April 2024. CONCLUSIONS: This randomized trial will evaluate the preliminary efficacy of MI for targeting SARS-CoV-2 vaccine hesitancy, as well as compare traditional MI versus mHealth-based MI. This will provide pivotal data on scalable strategies to assist in navigating vaccine hesitancy, including in rural populations. TRIAL REGISTRATION: ClinicalTrials.gov NCT05977192; https://clinicaltrials.gov/study/NCT05977192. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64010.
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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 | 0,031 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,008 | 0,005 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,086 | 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 ».