Design and Validation of a Minimum Dataset for a Self‐Care Mobile Application for Patients With Diabetic Retinopathy: Descriptive‐Validation Study

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.

Abbreviations

DR

diabetic retinopathy

MDS

minimum dataset

1. Introduction

Diabetic retinopathy (DR) is one of the most common microvascular complications of diabetes and the leading cause of vision loss among the elderly, with over 100 million people worldwide suffering from DR [1, 2]. DR is the fifth most common cause of preventable blindness and the fifth most common cause of moderate to severe visual impairment in individuals aged 50 and above [3]. This disease occurs when high blood sugar damages the retina's blood vessels, and if not effectively managed, it can potentially lead to vision loss [4]. Behavioral interventions such as monitoring blood sugar, adherence to a healthy diet, physical activity, and pharmacological treatments reduce and maintain normal blood sugar levels [5].

Self‐care is a necessary and inseparable component of treatment in chronic disorders, including DR [6]. Self‐care is the capacity of people and societies to preserve and advance health, prevent disease, and control disease and disability with or without the help of medical practitioners [7]. Self‐care strategies cover many spheres, including nutrition, exercise, medicine, emotions, sleep, and medical attention [8]. Improving long‐term problems in diabetic patients depends much on these self‐care strategies [9].

The healthcare industry has been profoundly affected by the fast advancement of mobile health technologies, which have now become a necessary instrument for encouraging self‐care by allowing patient involvement in illness management [10, 11, 12].

According to a report by the International Telecommunication Union (ITU), more than 74.3% of Iran is covered by mobile phones. This high penetration and acceptance of mobile phones, coupled with the increase in digital literacy in the community, have provided a suitable platform for the development and effectiveness of mHealth interventions [13].

Mobile health technologies more successfully provide self‐care strategies by raising self‐efficacy, boosting adherence, and offering supportive services, including communication, education, and reminders to encourage healthy behavior [14, 15].

Using patient‐centered care and enhanced patient involvement, mobile health apps present a means to raise patient independence without direct physician involvement [16, 17, 18]. Several studies have proven that mobile health apps have affected blood sugar control, prevention of vision loss, and eye care in patients with retinopathy [19, 20, 21].

The most important stage in designing and implementing any information system is determining the minimum dataset (MDS) required for that information system [22]. The MDS refers to a common set of elements necessary for managing, evaluating, and planning an information system [23]. Identifying the essential data elements and defining them coherently improves the quality of information [24]. For the optimal design and development of a mobile application, it is required to determine and identify accurately all the MDS required and requirements [25].

Despite the proliferation of mHealth applications in the healthcare system, to date, no study has been conducted to identify the data elements and requirements for developing a self‐care mobile application for patients with DR.

The lack of a MDS structured and validated leads to information fragmentation, bias, confusion, and the spread of misinformation among researchers and developers of mobile health applications. The lack of a MDS structure also leads to a decrease in the quality of healthcare, a lack of comparability of the results of the effectiveness of the applications, and a failure to meet the educational and information needs of patients.

This study fills an obvious gap that was previously unexplored in studies by identifying and specifying a structured MDS for DR mobile applications. Therefore, this study aims to identify and determine the MDS for designing a mobile self‐care application for patients with DR.

2. Literature Review

In a study conducted in 2025 by Paydar et al. [26] with the aim of determining a comprehensive national MDS for the epidermolysis bullosa (EB) information management system in Iran, this study used a combined approach based on systematic search, focus group, and Delphi technique. This study identified 103 data elements in two clinical and management groups based on expert validation. Unlike the present study, this study based the validation approach of data elements solely on expert opinion.

Designing a MDS for a septic shock registry system in children aged 1 month to 18 years can help policymakers, researchers, and healthcare providers in data management and collection. In a similar study, 183 data elements were included in two stages based on expert opinion as required data items for a MDS for a pediatric septic shock registry [27].

A descriptive‐analytical study was conducted to identify the MDS for a mobile self‐care application for managing overweight and obesity in children and adolescents from the perspective of experts. In this study, 15 nutritionists validated 81 data elements through a questionnaire consisting of four main sections: demographic data, assessment data, treatment recommendations, and usability [22].

In all the above studies, as in many existing studies in this field, the identification and validation of the MDS was carried out only with the opinions of experts. However, the present study, with a patient‐centered approach, in addition to validation through experts, also measured the opinions and perspectives of patients in determining the MDS.

3. Materials and Methods

This descriptive‐analytical study was conducted between 2024 and 2025. This study aims to design and validate the MDS for designing a mobile self‐care application for patients with DR. This study was approved in 2024 by the National Ethics Committee in Biomedical Research under the ethical code IR.ABADANUMS.REC.1403.034, by the Abadan University of Medical Sciences.

3.1. Design Phase

A comprehensive review of the research literature from academic sources was conducted to extract the MDS for designing a mobile self‐care application for patients with DR. Accordingly, electronic databases such as PubMed, Web of Science, Scopus, and Google Scholar, as well as library resources, were searched up to October 2024. The search for references was conducted using the keywords MDS, mobile health, and self‐care in combination with keywords related to DR through the AND operator. All keywords used were selected based on Medical Subject Headings (MeSH) terms. All articles not in English were excluded. Articles and sources containing insufficient details relevant to the research objectives, conference papers, review articles or letters to the editor were also excluded. Based on the established criteria, the retrieved studies were entered into EndNote software, version 20, and duplicates were removed. The remaining studies were independently reviewed and screened by two researchers (A.P. & Z.D). Disagreements between the identified studies were resolved through consultation with the third author (H.V.L).

In order to identify and include the needs and perspectives of patients with DR, three focus group sessions were held. Five women and five men with DR participated in the focus group sessions based on purposive sampling. In order to create purposeful discussions in the sessions, a facilitator led the sessions. The data elements identified from the literature review along with the qualitative data obtained from the sessions, were compiled into a questionnaire. This questionnaire was considered the same for both groups of experts and patients. Three specialists in endocrinology, three ophthalmologists, three health information management specialists, and three medical informatics experts were selected through convenient sampling. They reviewed, edited, and validated the extracted data elements.

3.2. Validation Phase

The validation phase was conducted with the participation of experts as scientific experts and patients as empirical experts.

3.3. Specialists Participation

In order to reach a comprehensive scientific consensus on the application data element set, the Delphi technique was used. A specialized and interdisciplinary panel was formed based on simple random sampling, including specialists from medical informatics, health information management, ophthalmology, and endocrinology. All specialists were university faculty members with relevant work experience. In this phase, we used the questionnaire designed by the researchers in the previous stage and validated by the specialists. This researcher‐made questionnaire contained 55 closed‐ended questions and one open‐ended question to gather feedback from the specialists. The questionnaire was divided into three sections: administrative, clinical, and functional. The scores for the questionnaire items were based on a 5‐point Likert scale, which included the following options: 5 = very important, 4 = important, 3 = no idea, 2 = slightly important, and 1 = not important.

To assess the consistency of expert opinions and minimize inter‐observer variability, the Delphi technique was employed using multiple rounds. Each expert independently reviewed and scored the data elements without influence from other panel members. After the first round, summary statistics of responses (means, percentages) were shared with the panel to inform reconsideration of items during the subsequent round(s). Additionally, inclusion criteria for data elements (e.g., acceptance thresholds for agreement of 75%) ensured that only items with satisfactory consensus were retained in the final MDS. Any major discrepancies between experts were discussed and resolved by involving the research team or by conducting focused rounds. This iterative process aimed to reduce disagreement and achieve consensus.

To maintain the integrity of the Delphi consensus by reducing the possibility of collaboration or consultation among panel members, questionnaires were distributed and collected individually to maintain anonymity and avoid face‐to‐face discussions. Second, participants were instructed to complete the questionnaires independently and to refrain from sharing their responses with other experts during the study period. They were assured that their responses would remain confidential. All specialists completed and responded to the questionnaire. The Delphi technique was used to achieve expert consensus on essential data elements for designing a self‐care mobile application for patients with DR. A 75% agreement threshold was applied to determine consensus for inclusion of each item in the MDS. This criterion is commonly used in Delphi studies as a balanced threshold that ensures sufficient expert agreement while maintaining the inclusion of relevant items. The criteria used to determine the inclusion of data elements in the final MDS are shown in Figure 1.

Figure 1.

Schematic of the process for determining the MDS for diabetic retinopathy.

3.4. Patient Participation

In order to include the views and needs of patients with DR, at this stage, a questionnaire that had been previously designed and validated by experts was distributed to 20 patients with DR (10 women and 10 men) aged 20–60 years. In addition to closed questions, the questionnaire also included an open question that collected qualitative feedback from patients about their needs and priorities. These individuals entered the study after being informed of the purpose of the study and obtaining informed consent. This was done to ensure that the app could meet the real needs of the patients. The feedback collected from the patients will be considered in the final design. Data analysis in this study was performed using descriptive statistics and SPSS software version 26.

4. Results

4.1. Results of Specialists’ Participation

Twenty experts from four disciplines, health information management, endocrinology, ophthalmology, and medical informatics, contributed to validating the MDS for DR. Seventy‐five percent of the participants were female, while the remainder were male. Thirty‐five percent of the participants had 5–10 years of relevant experience (Table 1).

Table 1.

Frequency and demographic information of experts participating in data element validation.

Index	Variables	Number	Percentage
	Sex
1	Male	5	25
	Female	15	75
	Specialization
2	Endocrinology and metabolism	5	25
	Health information management	5	25
	Ophthalmology	5	25
	Medical informatics	5	25
	Age (years)
3	30–40	5	25
	40–50	8	4
	50–60	5	25
	> 60	2	10
	Work experience (years)
4	5–10	7	35
	10–15	6	30
	15–20	5	25
	> 20	2	10
5	Total	20	100

In the first round of the Delphi technique, 15 data elements in the administrative group, 20 in the clinical group, and 20 in the functional group were reviewed. After the Delphi technique was conducted, the elements that received more than 75% approval from the experts were selected as the final MDS. Some data elements that received less than 50% approval were eliminated. In comparison, elements that had between 50% and 75% agreement from the experts were moved to the second round of the Delphi technique for further review (Table 2).

Table 2.

The main data element for the MDS for diabetic retinopathy is the Delphi technique.

Main data elements	The number of data elements	The first round of Delphi			Second round of Delphi			Final number of data elements
		< 50%	50%–75%	> 75%	< 50%	50%–75%	> 75%
Administrative elements	15	2	3	10	0	0	3	13
Clinical elements	20	1	4	15	0	0	4	19
Functional elements	20	1	4	15	0	0	4	19

The administrative category (three domains), the clinical category (four domains), and the functional category (three domains) were identified and categorized as major domains of the MDS in the design of a DR self‐care mobile application (Figure 2).

Figure 2.

Categorizing the minimum dataset for designing a diabetic retinopathy self‐care mobile application.

Table 3 shows the results of the first round of the Delphi technique and the accepted or rejected data elements. In the first round, the data elements “religion” and “income” from the administrative data section, “Consumption of drugs, cigarettes, and alcohol” from the clinical data section, and the data element “Calculate BMI” from the functional data section received less than 50% agreement from the experts and were therefore removed.

Table 3.

Main data elements for the MDS for diabetic retinopathy in the first round of Delphi.

Section	Main data elements	Data elements	Mean	Percentage (%)	Result
Administrative elements	Demographic	Name	4/8	96	Accept
		Gender	4/6	92	Accept
		Age	4/8	96	Accept
		National identity number (NID)	4/3	86	Accept
		Father name	3.2	64	Second round
		Date of birth	4.1	82	Accept
		Place of Birth	4.1	82	Accept
		Religion	2.2	44	Reject
		Race	3.1	62	Second round
	Address of residence	Email	3.5	70	Second round
		Phone number	4.3	86	Accept
		Place of residence (city, village)	4.2	84	Accept
	Social‐economic	Marital status	4.3	86	Accept
		Occupation	3.80	76	Accept
		Income	2.1	42	Reject
Clinical elements	A bout DR	Definition of the disease	4.3	86	Accept
		Risk factors	4.4	88	Accept
		Types of diabetes	4.4	88	Accept
		Complications	4.6	92	Accept
		Signs and symptoms	4.8	96	Accept
		Therapeutic procedures	4.5	90	Accept
		Diagnostic procedures	4.6	92	Accept
		Medication	4.9	98	Accept
		Common issues for patients	4.6	92	Accept
	lifestyle	Consumption of drugs, cigarettes and alcohol	2.3	46	Reject
		Dietary recommendations	4.2	84	Accept
		Physical activity	3.60	72	Second round
	Medical history	Diabetes history	3.70	74	Second round
		Eye treatment history	3.70	74	Second round
	Educational information	Self‐care strategies	4.8	96	Accept
		Stress management	4.6	92	Accept
		Managing activities of daily living	3.3	66	Second round
		Ophthalmologist care	5	100	Accept
		Nutrition management	4.2	84	Accept
		Frequently Asked Questions (FAQ)	4.6	92	Accept
Functional elements	Capabilities	Patient notebook	4.1	82	Accept
		Calculate BMI	2.4	48	Reject
		Motivational message	3.6	72	Second round
		List of cancer treatment centers	3.5	70	Second round
		Collecting data	3.8	76	Accept
		Password protection	5	100	Accept
		Reports	3.4	68	Second round
		Eye exercises	4.7	94	Accept
		Useful links	3.6	72	Second round
		Transfer data or reports to healthcare providers	4.4	88	Accept
	Reminders	Medication reminder	5	100	Accept
		Specialist reminder	5	100	Accept
		Daily blood sugar monitoring reminder	4.9	98	Accept
	Monitoring	Eye symptoms tracking	4.9	98	Accept
		Diet tracking	4.6	92	Accept
		Blood glucose tracking	5	100	Accept
		Blood pressure tracking	4.8	96	Accept
		Medication tracking	4.9	98	Accept
		Physical activity tracking	4.4	88	Accept
		Insulin tracking	4.9	98	Accept

The result of the second round of the Delphi technique led to the confirmation of all the data elements. Table 4 shows the results of the second round of the Delphi technique.

Table 4.

Main data elements for the MDS for diabetic retinopathy in the second round of Delphi.

Main data elements	Data elements	Mean	Percentage (%)	Result
Administrative elements	Race	4	80	Accept
	Email	4.2	84	Accept
	Father name	3.9	78	Accept
Clinical elements	Managing activities of daily living	46	92	Accept
	Eye treatment history	4.8	96	Accept
	Diabetes history	4.3	86	Accept
	Physical activity	4.2	84	Accept
Functional elements	Reports	4.4	84	Accept
	Useful links	4.3	86	Accept
	Motivational message	4.7	94	Accept
	List of cancer treatment centers	4.5	90	Accept

4.2. Results of Patient Participation

Demographic information of the patients participating in the study is presented in Table 5. Of the 20 patients with DR participating in this study, 50% were male and the remaining 50% were female. A total of 40% of the participants were single and had a bachelor's degree. Also, the age range of 20–30 years had the highest number of participants in this study.

Table 5.

Frequency and demographic information of patients participating in the study.

Index	Variables	Number	Percentage
	Sex
1	Male	10	50
	Female	10	50
	Education status
2	Diploma	3	15
	Higher diploma/University	5	25
	BS	8	40
	MSc	4	20
	Age (years)
3	20–30	8	40
	30–40	5	25
	40–50	5	25
	50–60	2	10
	Marital status
4	Single	8	40
	Married	12	60
5	Total	20	100

After distributing the questionnaire among 20 patients with DR, it was determined that functional elements were more important than other elements from the patients’ perspective, and race also received the lowest importance among the data elements, two out of five. Table 6 shows the frequency distribution of responses from patients with DR regarding the MDS.

Table 6.

Distribution of patients’ responses regarding the minimum dataset required for a diabetic retinopathy self‐care app.

Section	Main data elements	Data elements	Mean	Percentage (%)	Result
Administrative elements	Demographic	Name	5	100	Accept
		Gender	4.2	84	Accept
		Age	4.9	96	Accept
		National identity number (NID)	4	80	Accept
		Father name	3.9	78	Accept
		Date of birth	4	80	Accept
		Place of Birth	4.1	82	Accept
		Religion	3.8	76	Accept
		Race	3.8	76	Accept
	Address of residence	Email	4.1	82	Accept
		Phone number	4.7	94	Accept
		Place of residence (city, village)	4.5	90	Accept
	Social‐economic	Marital status	4.4	88	Accept
		Occupation	4	80	Accept
		Income	3.9	78	Accept
Clinical elements	A bout DR	Definition of the disease	5	100	Accept
		Risk factors	4.9	98	Accept
		Types of Diabetes	4.4	88	Accept
		Complications	5	100	Accept
		Signs and symptoms	4.7	94	Accept
		Therapeutic procedures	4.5	90	Accept
		Diagnostic procedures	4.6	92	Accept
		Medication	4.8	96	Accept
		Common issues for patients	4.9	98	Accept
	lifestyle	Consumption of drugs, cigarettes and alcohol	4.3	86	Accept
		Dietary recommendations	4.7	94	Accept
		Physical activity	4.2	84	Accept
	Medical history	Diabetes history	3.80	76	Accept
		Eye treatment history	3.80	76	Accept
	Educational Information	Self‐care strategies	4.8	96	Accept
		Stress management	5	100	Accept
		Managing activities of daily living	4.2	84	Accept
		Ophthalmologist care	5	100	Accept
		Nutrition management	5	100	Accept
		Frequently Asked Questions (FAQ)	4.2	84	Accept
Functional elements	Capabilities	Patient notebook	4.4	88	Accept
		Calculate BMI	4.1	82	Accept
		Motivational message	4.2	84	Accept
		List of cancer treatment centers	4.5	90	Accept
		Collecting data	4.5	90	Accept
		Password protection	5	100	Accept
		Reports	4.6	92	Accept
		Eye exercises	4.9	98	Accept
		Useful links	4.2	84	Accept
		Transfer data or reports to healthcare providers	5	100	Accept
	Reminders	Medication reminder	5	100	Accept
		Specialist reminder	5	100	Accept
		Daily blood sugar monitoring reminder	5	100	Accept
	Monitoring	Eye symptoms tracking	5	100	Accept
		Diet tracking	5	100	Accept
		Blood glucose tracking	5	100	Accept
		Blood pressure tracking	4.9	98	Accept
		Medication tracking	5	100	Accept
		Physical activity tracking	4.4	88	Accept
		Insulin tracking	4.9	98	Accept

5. Discussion

This study successfully identified and validated the MDS essential to design a mobile app self‐care tailored to patients with DR. Using the Delphi technique, expert consensus was obtained on data elements including administrative, clinical, and functional domains. A focus group was also formed to engage patients and consider their needs and attitudes. The results of this study, by considering the opinions of experts and patients in identifying the MDS for designing a mobile self‐care program, will increase patient engagement, reduce complications, and ultimately improve the quality of care for people with DR.

In the present study, 55 data elements were extracted and identified across three sections: administrative, clinical, and functional. Like many other studies [24, 28, 29], the Delphi technique was used for data element validation. The Delphi technique is a method for reaching consensus among experts in a specific field, which reduces bias in individual decision‐making [30].

Given the small size of the expert panel, subgroup analyses were not performed to assess gender and age. However, the present study's approach of anonymous and independent validation resulted in minimizing any potential bias related to demographic characteristics. Therefore, it is recommended that larger and more diverse expert panels be used for subgroup analysis in future studies.

After two rounds of the Delphi technique, 51 elements were identified as the final MDS. Four data elements were rejected. The data elements “religion” and “income” from the administrative data section, “Consumption of drugs, cigarettes, and alcohol” from the clinical data section, and “Calculate BMI” from the functional data section were unable to gain consensus from the experts as part of the final MDS for DR after validation.

In the administrative data group, 15 data elements were validated in two rounds of the Delphi technique, resulting in 13 data elements being recognized as essential for designing a mobile self‐care application for patients with DR. The minimum demographic dataset is necessary for identifying, contacting, and tracking patients [31]. This MDS plays a role in personalizing care, receiving healthcare tailored to each patient's specific needs, and identifying groups of patients at higher risk.

The data element “father's name” did not score in the first round of the Delphi technique, and in the second round, its score was 3.9 out of 5. This is the lowest score among the data elements accepted in the second round of the Delphi technique. However, this element was included after the consensus of the expert panel during the Delphi validation process. In some countries, such as Iran, the father's name is used as an important supplementary identifier along with the patient's first and last name to prevent misidentification of individuals with similar names. This helps to increase the accuracy and reliability of the data and improve the process of tracking, following up, and reporting patients in the self‐care program.

Determining the MDS and identifying the needs of patients with DR is a strong initial step in mobile application development [32]. This can ensure the success of mobile applications for DR, but this alone is not enough. Therefore, it is emphasized that future research should consider user‐centered design and pilot studies in addition to identifying needs.

Many mHealth studies are plagued by multiple flaws. Conflicting evidence and common methodological flaws in this area include small sample sizes and cultural biases that limit the generalizability of study results. Another methodological flaw in this area is the inadequate involvement of end‐users or the failure to address their views and needs in both the design and evaluation stages. Most mHealth applications are still developed primarily based on expert opinions, while the actual views and needs of patients are less considered. On the other hand, the prevalence of usability evaluation methods and application development cycles lacking scientific support leads to the production of conflicting and unfounded results and evidence.

An applied study by Ahmadi et al. [33] in 2019 aimed at designing a MDS for creating a registry of patients with gestational diabetes. In this study, obstetricians, pharmacists, nurses, and midwives identified three main data element groups, including administrative, clinical, and pharmaceutical data element groups, and validated them using the Delphi technique [33].

A study to determine the minimum essential dataset for the electronic medical record of diabetic foot ulcers was conducted by Ghazi Saeedi et al. [34] in 2021. In this descriptive‐analytical study, 23 informational elements from the demographic data group and 86 informational elements from the clinical data group were identified and determined [34].

In the clinical data group, of the 20 data elements that were validated, 19 data elements were identified as the minimum essential dataset after two rounds of the Delphi technique. In the functional data group, except for the “Calculate BMI” data element, the rest were recognized as essential after two rounds of the Delphi technique in designing a mobile self‐care application for patients with DR. In the clinical data group, the highest score and necessity were in the “educational information” area.

While mobile health apps are recognized as useful tools for providing educational information, reminders, and guidance in various fields that can increase patients’ self‐care knowledge, they also face significant challenges [35, 36]. Inequality in access to mobile phones, specific causes of mobile health apps, as well as declining user engagement and engagement over time in using mobile apps, are some of the important challenges that mobile health apps may face.

In the focus group sessions, it was clear from talking to patients that patients with DR place more importance on the functional elements of the apps than on the management and clinical elements. This need identified from the sessions could be due to the lack or lack of access to comprehensive DR self‐care apps. Inequalities in access to healthcare services could also lead to a lack or lack of access to self‐care apps that are in line with the cultural needs of that community.

Results of a study conducted by Umaefulam et al. [19] show that mobile health interventions increased awareness and behavior change in DR patients regarding diabetes‐related eye care. These interventions also helped patients become more aware and empowered about the importance of eating a healthy diet, taking their medications as prescribed, and having regular medical and eye exams.

In the present study, 20 data elements in the functional data group were identified and validated across three modules: reminders, monitoring, and application capabilities. In this section, the “blood glucose tracking” data element received the highest score (five out of five). Similar to the present study, in the survey by Salari et al. [37], which aimed to determine the minimum features for mobile diabetes applications, the data element “blood glucose tracking” received a high score, demonstrating its importance in diabetic patients.

Litgo et al. [38], in a 2017 study, state that one of the most commonly used features in mobile applications for diabetes management by patients is blood glucose tracking, indicating that blood glucose tracking is essential to patients and healthcare professionals. According to Khajooei et al. [39], who assessed Persian‐language diabetes applications, all mobile apps could monitor blood glucose levels. Thus, “blood glucose tracking” is a necessary data element in mobile applications for DR since blood glucose control is crucial in DR patients.

Another essential data element in this section is “reminders.” Reminders can significantly impact medication adherence and patient self‐efficacy [40, 41]. Medication adherence is crucial for blood glucose control. DR patients can benefit from reminders for daily insulin use, blood glucose measurement, diet adherence, and physical activity [42, 43].

In the present study, two data elements, “Specialist reminder” and “medication reminder,” received high scores of 5. In 2024, a study by Laktarashi et al. [44] aimed at designing and evaluating a mobile application for the self‐care of patients with retinopathy. In this study, the two reminders, “doctor appointment” and “medication,” were also introduced as reminders in the designed application.

Forming a specialized panel in the form of a multidisciplinary team of specialists in endocrinology, health information management, ophthalmology, and medical informatics, as well as utilizing the opinions and suggestions of these specialists, is one of the strengths of the present research. One limitation of the current study is the use of resources, articles, and guidelines for self‐care in DR that are only available in English. In contrast, other resources in languages other than English were not considered.

Some patients with DR diabetes become severely blinded or blind. One of the remarkable limitations of this study is the inability to use effective and efficient use of self ‐care apps in this range of patients despite the usability. The present study emphasizes that in future studies, complementary tools such as the use of voice assistants should be considered.

Another limitation of the present study is the limited range of experts participating in the expert panel, which may have introduced cultural biases. Therefore, in the next phase of the study, namely the development and evaluation of the application, it is necessary to expand the number of participating experts and recruit behavioral and cultural science experts to improve the quality of the panel and reduce bias.

The present study, with the help of focus group and Delphi methods, attempts to collect and incorporate the needs, preferences, and perspectives of professionals and patients. Therefore, it is emphasized that future studies should include the co‐design of the DR self‐care mobile application with the participation of patients and relevant professionals to increase the usability and effectiveness of the application.

6. Conclusion

The present study aimed to identify and determine the minimum essential dataset for developing a mobile self‐care application for patients with DR. The Ministry of Health, and Medical Education, healthcare specialists, software designers, and developers can use the findings of this research to develop a high‐quality application that addresses the educational and informational needs of patients with retinopathy, which may improve the quality of life for patients with DR and enhance their quality of care.

Author Contributions

Atefeh Paghe: conceptualization, investigation, writing – review and editing, writing – original draft, methodology. Hossein Valizadeh Laktarashi: writing – original draft, investigation, data curation. Zahra Daeechini: data curation, investigation, writing – original draft. Amir Hossein Daeechini: writing – review and editing, writing – original draft, conceptualization, methodology, supervision.

Funding

The authors received no specific funding for this work.

Disclosure

All authors have read and approved the final version of the manuscript Amir Hossein Daeechini had full access to all of the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.

Ethics Statement

This study was approved in 2024 by the National Ethics Committee in Biomedical Research under the ethical code IR.ABADANUMS.REC.1403.034, by the Abadan University of Medical Sciences.

Consent

The authors have nothing to report.

Conflicts of Interest

The authors declare no conflicts of interest.

Transparency Statement

The lead author, Amir Hossein Daeechini, affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

Data Availability Statement

The data that support the findings of this study are available from the corresponding authors upon reasonable request.