Abstract
ABSTRACT
Background and Aim
Diabetic retinopathy is one of the most common microvascular complications of diabetes, with over 100 million people affected worldwide. The development of mobile health applications can play an effective role in managing and monitoring diabetic retinopathy. Still, the development of these applications first requires the identification of the minimum dataset. Therefore, the purpose of this study is to identify and determine the minimum dataset as the first step in designing a self‐care mobile application for patients with diabetic retinopathy.
Methods
This Descriptive‐Validation Study was conducted in 2025 in two phases: design and validation of the MDS. In the first phase, a comprehensive review of the research literature was conducted and electronic databases such as PubMed, Web of Science, Scopus, and Google Scholar were searched until October 2024. Then, data elements were extracted and identified. In the second phase, these elements were validated by 20 experts from the fields of endocrinology, ophthalmology, and health information management using the Delphi technique. Then, in order to include patients’ opinions, a researcher‐made questionnaire was administered to 20 patients with diabetic retinopathy.
Results
Fifty‐five MDS elements were validated in three domains: administrative, clinical, and functional data, using two rounds of the Delphi technique. Data elements with over 75% expert approval were included in the final dataset: 13 administrative, 19 clinical, and 19 functional elements.
Conclusion
The Ministry of Health and Medical Education, app designers, and developers can utilize the findings of this study to develop a high‐quality application that addresses the educational and informational needs of patients with diabetic retinopathy.