Developing a Culturally Adapted Digital Health Application for Older Hispanic Adults With Type 2 Diabetes: Protocol for a Qualitative and Pilot Study
Notice bibliographique
Résumé
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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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 | 0,038 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,003 | 0,003 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,006 | 0,003 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,035 | 0,005 |
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 ».