Usability and Cultural Relevance of an mHealth App for Hispanic/Latino Individuals Living With Rheumatoid Arthritis: Protocol for a Mixed Methods Study
Bibliographic record
Abstract
Background: Hispanic and Latino individuals represent 14.6% of rheumatoid arthritis (RA) cases in the United States and experience significant disparities in access to rheumatologic care, disease management, and health outcomes. Mobile health (mHealth) apps are promising tools to improve patient-provider communication and self-management among populations with language and literacy barriers. However, few RA-focused digital health interventions (DHIs) have been culturally adapted for Spanish-speaking Hispanic and Latino individuals. Objective: This study aims to assess the health literacy, eHealth literacy, technology trust, and digital self-efficacy of Hispanic and Latino individuals with RA, and to evaluate the cultural relevance, usability, and patient satisfaction of the Spanish-language RunRA app. Additionally, it will explore health care providers' perceptions of the app's usefulness for clinical decision-making and communication with Hispanic and Latino patients. Methods: A prospective, iterative convergent mixed methods design integrated with the Framework for Reporting Adaptations and Modifications-Expanded (FRAME) will be used. We will recruit 25 Hispanic and Latino patients with RA and 7 Spanish-speaking health care professionals. Quantitative data will include standardized questionnaires (SAHL-S, eHEALS, Human-Computer Trust Scale, Digital Self-Efficacy Scale) and app analytics. Qualitative data will be collected via interviews and focus groups using the Cultural Relevance Questionnaire (CRQ), System Usability Scale (SUS), and Mobile Application Rating Scale (uMARS). Data will be analyzed using an independent intramethod strategy, with integration guided by FRAME to inform culturally relevant app modifications. Results: We anticipate enrolling 32 participants (25 patients and 7 providers). This study will be the first to evaluate the cultural relevance and usability of an mHealth app specifically designed for Spanish-speaking Hispanic and Latino individuals living with RA. Conclusions: Our long-term goal is to assess the potential for the mHealth app to act as a vehicle for the dissemination of accurate, useful, usable, and understandable health information to populations that experience health disparities and their health care providers. Findings will inform iterative refinements to RunRA and contribute to the development of culturally responsive DHIs aimed at improving communication, shared decision-making, and health outcomes in underserved populations.
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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 | 0.067 | 0.053 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.009 |
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".