Developing a Culturally Adapted Digital Health Application for Older Hispanic Adults With Type 2 Diabetes: Protocol for a Qualitative and Pilot Study
Bibliographic record
Abstract
Background: Over 40% of Hispanic individuals have below basic health literacy levels, significantly impacting their ability to manage chronic conditions such as type 2 diabetes (T2D). Chronic disease self-management, essential for improving adherence, is often hindered by modifiable factors such as health literacy. Ambulatory care clinical (ACC) pharmacists, through comprehensive medication management (CMM), play a critical role in addressing these barriers; however, current methods for delivering CMM vary significantly, limiting effectiveness. Digital technology may offer significant potential for improving medication adherence, particularly among Hispanic individuals who use smartphones to seek health information. Objective: This study aims to revise, develop, and pilot test a culturally adapted, individually tailored digital health app designed to enhance health literacy and medication adherence among older Hispanic adults with T2D by integrating interactive educational modules into pharmacist-delivered CMM. Methods: Our team previously developed a prototype computer-delivered, culturally adapted intervention targeting health literacy and adherence among older Hispanic individuals. The proposed project involves transforming this intervention into a modernized digital health app. We will achieve 2 specific aims. The first aim is to refine and modernize previously developed content into an interactive digital health app suitable for older Hispanic adults with T2D and ACC pharmacists. A total of 20 patients and 10 ACC pharmacists will review and provide feedback on the app modules. Usability and acceptability will be measured using validated tools, including the System Usability Scale, Adjectival Ease of Use Scale, and the technology acceptance model. The second aim is to conduct a pilot test with 40 Hispanic adults (aged ≥50 years) diagnosed with T2D. Participants will be recruited from a local Federally Qualified Health Center where ACC pharmacists manage diabetes care. Patients will complete a baseline CMM session, interact with the app modules, and return for 1 or 2 CMM follow-up visits. We will assess medication adherence, hemoglobin A1c, and fasting glucose levels as primary outcomes. Results: The digital app has been modernized and completed for acceptability and usability testing. We will begin testing the hypothesis that the app will have above-average usability and acceptability (System Usability Scale: mean score ≥71 and Adjectival Ease of Use Scale: mean score ≥5), with an overall goal that the digital app will be acceptable (via the technology acceptance model) by ≥90% of the patient participants and ACC pharmacists. In addition, anticipated health outcomes for aim 2 include improvements in medication adherence (>80%), hemoglobin A1c (≥0.75% reduction), and fasting glucose (≥25% reduction) from baseline. Conclusions: When the study is completed, it is expected that the culturally adapted digital health app will be acceptable and usable among both patients and pharmacists. Successful results will enable wider dissemination of the refined app and establish a framework adaptable to other chronic diseases, diverse languages, cultures, and health care settings.
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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 | 0.038 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.035 | 0.005 |
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".