Mobile Health–Based Motivational Interviewing to Promote SARS-CoV-2 Vaccination in Rural Adults: Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
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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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 | 0.031 | 0.027 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.086 | 0.012 |
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".