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Design and Validation of a Minimum Dataset for a Self-Care Mobile Application for Patients With Diabetic Retinopathy:
Atefeh Paghe1, Hossein Valizadeh Laktarashi2,3, Zahra Daeechini4
1Student Research Committee, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Tehran Iran.
Health Science Reports
|May 1, 2026
Summary
This study identified a minimum dataset for diabetic retinopathy self-care apps. The dataset includes 51 essential administrative, clinical, and functional data elements for effective patient management.
Area of Science:
- Ophthalmology
- Endocrinology
- Health Informatics
Background:
- Diabetic retinopathy affects over 100 million people globally.
- It is a common microvascular complication of diabetes.
- Mobile health applications are crucial for managing diabetic retinopathy.
Purpose of the Study:
- To identify the minimum dataset for designing diabetic retinopathy self-care mobile applications.
- To establish a foundation for developing effective mHealth tools for patients.
Main Methods:
- A descriptive-validation study conducted in two phases: design and validation.
- Literature review of electronic databases (PubMed, Web of Science, Scopus, Google Scholar) until October 2024.
- Delphi technique with 20 experts and a questionnaire for 20 patients with diabetic retinopathy.
Main Results:
- Fifty-five minimum dataset (MDS) elements were validated across administrative, clinical, and functional domains.
- 13 administrative, 19 clinical, and 19 functional elements achieved over 75% expert approval.
- A final dataset of 51 elements was established.
Conclusions:
- The identified minimum dataset is essential for developing high-quality diabetic retinopathy self-care applications.
- Findings will guide app designers and developers in meeting patient needs.
- This research supports the use of mHealth for diabetic retinopathy management.
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