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Diabetes is a significant public health issue. Saudi Arabia has the highest prevalence of type 2 diabetes mellitus (T2DM) in the Arab world. Currently, it affects 31.6% of the general population, and the prevalence of T2DM is predicted to rise to 45.36% by 2030. Mobile health (mHealth) offers improved and cost-effective care to people with T2DM. However, the efficiency of engagement strategies and features of this technology need to be reviewed and standardized according to stakeholder and expert perspectives.
The main objective of this study was to identify the most agreed-upon features for T2DM self-management mobile apps; the secondary objective was to identify the most agreed-upon strategies that prompt users to use these apps.
In this study, a 4-round modified Delphi method was applied by experts in the domain of diabetes care.
In total, 11 experts with a mean age of 47.09 years (SD 11.70) consented to participate in the study. Overall, 36 app features were generated. The group of experts displayed weak agreement in their ranking of intervention components (Kendall W=0.275;
This is the first study in the region consisting of a local panel of experts from the diabetes field gathering together. We used an iterative process to combine the experts’ opinions into a group consensus. The results of this study could thus be useful for health app developers and HCPs and inform future decision making on the topic.
Type 2 diabetes mellitus (T2DM) is one of the leading causes of disability and mortality worldwide, creating a substantial economic burden on health systems and social well-being [
Diabetes is a chronic, complex syndrome requiring condition-related knowledge and self-management skills to optimize glycemic control and improve health outcomes. Mobile apps could be helpful tools for supporting chronic disease screening, enhancing the ability of people with diabetes to manage the disease, and ensuring their easy accessibility to health care services. These apps boost the opportunity to increase health care access for vulnerable populations. The apposite use of mobile health (mHealth) offers improved and cost-effective care to people with T2DM through improved diabetes self-management [
Globally, the annual growth in the penetration rate of internet and mobile phone use is estimated to be 13%. In Saudi Arabia, which has a total population of 33.25 million, 30.25 million (91%) are internet users, 25 million (75%) are active social media users, and there are 56.80 million (171%) mobile subscriptions [
Several systematic reviews, meta-analyses, and meta-regression analyses found that digital or telemedicine interventions could be more effective in enhancing the outcomes of managing T2DM compared to standard care. The apps seem to increase the perception of self-care by contributing to better knowledge among people with T2DM. People with T2DM also become more confident in dealing with their illness, primarily due to a decrease in fears resulting from a lack of information; for example, they develop more confidence in how they should deal with potential hypoglycemic episodes [
A systematic review that evaluated free mobile apps for control and self-management of type 1 diabetes mellitus (T1DM) available in 2015 found that 56 of these apps did not even meet the minimum requirements or work efficiently. While there was a significant number of available apps, the study results indicated that only 9 of the 65 reviewed mobile apps are adaptable and useful for self-management [
A study of several focus groups concerning patient perspectives and expectations of diabetes self-management and eHealth found that there were significant variations in the health care services experienced by patients with diabetes, and none of the evaluated services met all users' needs [
Delphi techniques were developed in the 1950s. They are a structured and multistage process in which a panel of experts is invited to be a part of a series of rounds to identify and achieve consensus on a specific issue. The consensus is sought through information feedback and iteration in the form of rounds and phases. The process is terminated when a consensus is reached. The anonymity offered by the Delphi method can reduce the inhibition that usually occurs during decision-making; individuals are more open in their answers when their answers are deidentified. The Delphi technique is useful when working with highly subjective elements in which it is difficult to determine their intrinsic value [
The main objective of this study was to identify the most agreed-upon features for T2DM self-management mobile apps. The secondary objective is to identify the most agreed-upon strategies that encourage users to use these apps. Our study aimed to determine an expert panel’s consensus opinion on two questions:
What are the diabetes management components that diabetes experts agree are most likely to be effective when delivered via a mobile app?
What are the engagement strategies that diabetes experts believe are the most likely to be effective for diabetes management when using a mobile app?
A 4-round modified Delphi study was conducted to identify mobile app features and engagement strategies for T2DM self-management. There were three reasons for choosing the Delphi technique: (1) the Delphi technique has been useful for measuring group consensus concerning medical information technology when used in health care; (2) there was no established evidence regarding self-management of T2DM or engagement strategies in the context of developing mobile apps, and (3) the outcomes of Delphi decisions focus on decision making in fields that are strongly susceptible to change. Individual decision makers have more influence than those with underlying rules [
In total, 11 multidisciplinary experts were purposively selected from a range of scientific networks. Their professions included family physician, clinical informatician, diabetologist, clinical pharmacist, endocrinologist, exercise physiologist, nurse diabetes educator, health educator, clinical dietitian, consultant in medical education, and psychologist. Previous literature reports have indicated that the Delphi technique does not require a particular sample size. The minimum number of participants should be at least 7, although 10 to 20 are advisable [
Profiles of the expert panel members (N=11).
Professional title | Latest academic degree | Specialized fields | Years of experience in health care | Years of experience in diabetes care | Number of patients per week | Years of experience in health informatics research | Years of experience in diabetes research | Gender |
Professor, public health researcher, consultant in family and community medicine | FRCGP | Diabetes prevention and management, primary care, public health, evaluation research, health services research, health promotion, health education, patient-centered care design | 33 | 33 | 18 | 0 | 33 | Male |
Professor, consultant in family and community medicine, researcher | Diploma in Medical Education | Primary care research, patient-physician decision making, interprofessional education, public health research, medical education, knowledge translation, communication skills | 32 | 32 | 14 | 2 | 32 | Male |
Assistant professor, researcher, consultant diabetologist | Fellowship in diabetes | Diabetes prevention and management (tertiary care), clinical trials, population health research, patient education, community participation | 10 | 7 | 70 | 0 | 6 | Male |
Endocrinologist consultant, president of Saudi Society of Endocrinology and Metabolism | SBIM | Health services (not for profit), diabetes prevention and management (tertiary care), metabolic diseases | 30 | 30 | 45 | 0 | 30 | Male |
Assistant professor, consultant, Director of King Abdullah Arabic Health Encyclopedia | Diploma in research methodology | Health care information technology, informatics, health care management, medical education, patient safety, clinical research, public health, telehealth | 17 | 17 | 0 | 10 | 1 | Male |
Nurse educator, head of the education department | BSc | Patient education, diabetes prevention and self-management, clinical research | 25 | 25 | 100 | 0 | 15 | Female |
Health education specialist | BSc | Health education, diabetes prevention and self-management, primary and tertiary care | 5 | 3 | 50 | 0 | 1 | Female |
Consultant psychologist | Psychology fellowship | Psychology care, diabetes psychology prevention management care, clinical research, communication skills | 10 | 5 | 10 | 0 | 5 | Male |
Senior clinical dietitian | MSc | Clinical research, diabetes nutrition prevention management care, primary and tertiary care | 6 | 3 | 30 | 0 | 3 | Female |
Assistant professor, clinical pharmacist | PhD | Clinical trials, clinical pharmacist, patient education, patient adherence | 19 | 7 | 8 | 0 | 4 | Female |
Assistant professor, consultant physiology | PhD | Diabetes prevention and management, primary and tertiary care | 21 | 3 | 20 | 0 | 3 | Male |
Experts were selected from hospitals, academic institutes, and associations in Saudi Arabia. Moreover, we also invited those who had participated in recent regional diabetes conferences. The inclusion criteria were comprised of 3 parameters: (1) clinicians or researchers with expertise in diabetes care, (2) who possessed at least 3 years of experience in the diabetes care field, and (3) who were willing to participate.
This study included steering committee that was comprised of a diabetes educator; a health informatics specialist and consultant in family medicine and medical informatics; and a consultant in family medicine and diabetologist. The committee oversaw the design, execution, and analysis of all phases of this study. An agreement was reached by discussion to approve the participant selection, consensus threshold, survey format and questions, and analysis process.
A minimum of 3 survey rounds were prospectively planned; rounds would continue until a consensus agreement was reached (
The modified Delphi process followed in this study.
The primary draft survey was pilot tested by two experts with expertise in diabetes research who not included in the original study. Pilot testing determined the time it took to complete the survey and whether the survey wording was clear. After minor amendments arising from the pilot testing, the first round survey format was considered complete.
By using a survey with open-ended questions, the first round aimed to generate a list of items. Participants were asked to consider which items should be included when creating a mobile app designed to help people with diabetes self-manage their illness. The initial survey consisted of three sections: (1) a brief overview of the Delphi process; (2) demographic information; (3) two open-ended questions asking participants which items should be included when designing the mobile app. The experts were invited to provide at least three responses to each question:
Which features or components of management care delivered by the mobile app would be most effective in helping patients with diabetes improve their health?
What are the best strategies or techniques that should be used to engage people with diabetes to use such a mobile app?
Each question was preceded with the following statement: “Please answer the questions below based on your clinical experience, knowledge of the guidelines, and relevant theories. Please provide at least 3 responses for each question.”
In the second round, a checklist was generated from the first round results submitted by the experts, and they were asked their opinion. Furthermore, they were also asked to accept, delete, or modify each item. Qualitative responses offered insight into the differences in experts’ perspectives. The items had been modified via qualitative feedback.
In the third round of the Delphi process, participants were invited to rate the importance of each item of the alphabetically ordered checklist generated from the first and second rounds. They were asked to rate their agreement with each of these techniques for the 3 different questions on a 5-point Likert-scale. The scale consisted of 5 responses: 1=strongly disagree, 2=disagree, 3=neither agree nor disagree, 4=agree, and 5=strongly agree, as well as the option, “I do not know.”
Experts were provided with the items listed alphabetically, alongside the mean of agreement ratings. Experts were asked to rank their responses from 1 (most likely to be the best) to n=the total number of items (least likely to be the best) for each question. At this stage, participants were only asked to rank responses for which there had been broad agreement in previous rounds. At this point, there was the option to make final comments, if any.
Quantitative data analyses were performed with SPSS (version 20.0; IBM Corp). The descriptive statistics for each item were calculated, including the mean agreement scores, standard deviation, and the Kendall W statistic which employs the chi-square test to test the independence of the component rankings.
Based on Research and Development Corporation (RAND) recommendations regarding participant feedback, the findings from the previous round were compiled for Rounds 2, 3, and 4 [
After the first round, the total number of completed questionnaires was 11. For each question, two individuals from the steering committee assessed, reviewed, and summarized the results of the first round independently. Similar ideas were clustered together into emerging components. Duplicate responses were merged. Responses that were not related to the study objectives, such as “consume high fiber diet through vegetable and fruit,” were deemed not applicable and they were removed. The two reviewers met to discuss any differences arising from these independent analyses. Subsequently, the results were reviewed, and a discussion was held to validate the results and categorize them into domains.
The total number of completed questionnaires was 11. After all responses were received, the steering committee compared the responses and reviewed the proposals to generate the final list.
The total number of completed surveys was 11. Participants were asked to rate their agreement with the 36 mobile app features and 21 engagement strategies mentioned in the first and second rounds on a 5-point Likert scale. Descriptive statistics for each item were calculated, including the mean agreement scores and standard deviation.
In the final round, participants were asked to rank the mobile features from 1 (highest) to 36 (lowest). The value of the 36 features was determined in the first and second rounds. Moreover, participants were asked to rank each engagement strategy from 1 (highest) to 21 (lowest). The value of the 21 engagement strategies was determined in the first and second rounds. Descriptive statistics for each item were computed, including the mean ranking scores and standard deviation (SD). We used the Kendall W coefficient of concordance, which measures the extent to which judges agree on their rankings of items. The Kendall W statistic uses the chi-square test to investigate the independence of the components’ rankings.
Ethical approval to conduct this study was obtained from the King Saud University Institutional Review Board (number E-17-2608). All participants were asked to sign a consent form prior to starting the rounds. Experts were asked to create a list of the best features and engagement strategies, which were subsequently rated and ranked anonymously to avoid biases.
A total of 11 experts consented to participate in the study. They had a mean age of 47.09 years (SD 11.70; range 28-62). In total, 4 rounds of Delphi exercises were completed over 6 months, and all of the expert participants (n=11) completed all 4 rounds.
A total of 77 features were generated in response to the first question, “what are the diabetes management components that diabetes experts agree are most likely to be effective when delivered via an app,” with an average of 7 features mentioned per participant in the first and second rounds (
There were 5 top features: (1) adjusting insulin doses according to carbohydrate counting and blood glucose readings (5.36); (2) alert caregiver (such as by text message) for abnormal or critical readings (6.09); (3) nutrition education (12.45); (4) contacts for guidance if required (12.64); and (5) offering patient-specific education tailored to the user’s goals, needs, and blood glucose readings (12.90).
Responses generated by the expert group concerning the features.
Rank | Responses generateda | Domain | Agreement ratingb | Ranking scorec | ||||
|
|
|
Mean (SD) | Mode |
Agree: disagreed | Mean (SD) | Mode |
|
1 | Reminder for the health care providers’ appointments, screening, routine lab tests, etc | Follow-up care | 4.73 (0.47) | 5 | 11:0 | 13.00 (7.58) | 14 | |
2 | Carbohydrate counter and health diet planning | Nutrition and diet management | 4.72 (0.47) | 4 | 11:0 | 13.73 (7.72) | 12 | |
3 | Nutrition education | Nutrition and diet management | 4.64 (0.50) | 5 | 11:0 | 12.45 (8.51) | 3 | |
4 | Contacts for guidance if needed | Follow-up care | 4.64 (0.50) | 5 | 11:0 | 12.64 (12.22) | 1 | |
5 | Offer patient-specific education tailored to the user’s goals, needs, and blood glucose readings | Education | 4.64 (0.50) | 5 | 11:0 | 12.90 (11.54) | 5 | |
6 | Find the nearest urgent health care services/centers | Follow-up care | 4.64 (0.50) | 5 | 11:0 | 15.18 (10.33) | 12 | |
7 | Database for local restaurants and stores providing diabetes-friendly foods | Nutrition and diet management | 4.64 (0.50) | 5 | 11:0 | 19.45 (9.09) | 25 | |
8 | Providing Arabic and English versions | Mobile design and features | 4.64 (0.67) | 5 | 10:1 | 21.36 (12.47) | 29 | |
9 | Medication reminders by notifications | Medication | 4.54 (0.69) | 5 | 10:1 | 15.09 (7.28) | 7 | |
10 | Syncing with electronic medical record/personal health record | Mobile design and features | 4.54 (0.52) | 5 | 11:0 | 24.82 (8.67) | 32 | |
11 | Syncing with meters, continuous glucose monitors, or insulin pumps | Mobile design and features | 4.54 (0.52) | 5 | 11:0 | 27.18 (6.40) | 26 | |
12 | Alert caregiver (eg, by SMS text message) for abnormal or critical readings | Home monitoring | 4.45 (0.52) | 4 | 11:0 | 6.09 (5.16) | 2 | |
13 | Blood glucose monitoring diary; give results for averages in graphs | Home monitoring | 4.45 (0.69) | 5 | 10:1 | 14.64 (8.32) | 9 | |
14 | Individualized exercise suggestions or prescriptions | Physical activity | 4.45 (0.69) | 5 | 10:1 | 14.82 (6.92) | 13 | |
15 | Assessing medication adherence | Medication | 4.45 (0.52) | 4 | 11:0 | 15.73 (10.46) | 6 | |
16 | Online consultation (communication and patient monitoring) by health care providers | Follow-up care | 4.45 (0.69) | 5 | 10:1 | 17.18 (8.54) | 17 | |
17 | Prescription refill reminders | Follow-up care | 4.45 (0.52) | 4 | 11:0 | 17.73 (8.94) | 16 | |
18 | Special occasion management during fasting, Hajj, sick days, and travel | Education | 4.45 (0.69) | 5 | 10:1 | 21.18 (9.63) | 7 | |
19 | Medication reconciliation (a comprehensive list of medications should include all prescribed medications, herbals, vitamins, etc) | Medication | 4.45 (0.69) | 5 | 10:1 | 21.27 (8.95) | 19 | |
20 | Create, view, and manage alerts for personalized target goals | Education | 4.36 (0.50) | 4 | 11:0 | 17.64 (9.32) | 10 | |
21 | General education (such as foot care, wound care, dental care, psychiatric symptoms associated with diabetes, driving, how to use the glucometer) | Education | 4.36 (0.81) | 5 | 9:2 | 19.09 (10.23) | 11 | |
22 | Database for traditional low-carbohydrate recipes and traditional food carbohydrates and calories | Nutrition and diet management | 4.36 (0.92) | 5 | 10:1 | 20.09 (10.14) | 4 | |
23 | Sharing blood glucose readings with health care providers | Home monitoring | 4.36 (0.50) | 4 | 11:0 | 21.54 (8.64) | 10 | |
24 | Activity sensor trackers (such as step counters) or integration with wearable trackers | Physical activity | 4.27 (0.65) | 4 | 10:1 | 17.18 (9.73) | 6 | |
25 | Reminders to check blood glucose levels and ketones if needed | Home monitoring | 4.27 (0.47) | 4 | 11:0 | 17.18 (9.58) | 3 | |
26 | Food barcode scanner and meal picture detection to log food | Nutrition and diet management | 4.27 (0.65) | 4 | 10:1 | 19.00 (9.89) | 6 | |
27 | Workouts and exercise demonstrations | Physical activity | 4.27 (0.65) | 4 | 10:1 | 24.54 (11.58) | 33 | |
28 | Find the nearest health and fitness activities | Physical activity | 4.18 (0.87) | 4 | 10:1 | 24.36 (10.13) | 34 | |
29 | Pill identifier, drug list name with detailed information | Medication | 4.18 (1.33) | 5 | 8:3 | 25.73 (11.05) | 36 | |
30 | Integrating the app with common social media platforms | Social media and communication | 4.18 (0.75) | 4 | 9:2 | 27.45 (7.05) | 26 | |
31 | A food diary to track meals | Nutrition and diet management | 4.09 (0.70) | 4 | 9:2 | 20.82 (8.33) | 19 | |
32 | Using anxiety and depression scales and providing customized advice to see health care providers | Psychosocial care | 4.09 (0.70) | 4 | 9:2 | 30.00 (7.17) | 29 | |
33 | Adjusting insulin doses according to carbohydrate counting and blood glucose readings | Medication | 4.00 (0.77) | 4 | 8:3 | 5.36 (6.47) | 5 | |
34 | Allowing for chat services for communication between users | Social media and communication | 4.00 (0.77) | 4 | 10:1 | 21.54 (10.00) | 8 | |
35 | Body measurement trackers | Nutrition and diet management | 3.91 (0.94) | 4 | 8:3 | 22.18 (7.92) | 15 | |
36 | Alert caregiver (such as by SMS text message) for missed doses | Home monitoring | 3.82 (0.98) | 4 | 9:2 | 15.81 (11.98) | 8 |
aThe features are ordered in terms of the mean agreement score from round 3.
bAgreement rating, where 1=strongly disagree and 5=strongly agree.
cRanking score, where 1=highest and 36=lowest.
dAgree: disagree is the ratio of “agree” and “strongly agree” to “neither,” “disagree,” and “strongly disagree,” which was used as an inclusion criterion for round 3.
A total of 53 engagement strategies were generated in response to the second question, “what are the engagement strategies that diabetes experts believe are the most likely to be effective in diabetes management when using a mobile app.” There were an average of 3.5 strategies suggested per participant in the first and second rounds (
The top 5 engagement strategies included several parameters: (1) a user-friendly design (such as an educational and age-appropriate design; 2.82); (2) the app should be free (3.73): (3) allowing the user to communicate or send information/data to a health care provider (5.36); (4) health care providers prescribing the mobile app in the clinic and asking about the patients’ app use compliance during clinical visits (6.91), and (5) flexibility and customization (7.91).
Responses generated by the expert group concerning engagement strategies.
Rank | Responses generateda | Domain | Agreement ratingb | Ranking scorec | ||||
|
|
|
Mean (SD) | Mode | Agree: disagreed | Mean (SD) | Mode | |
1 | The app should be free | Cost | 4.64 (0.67) | 5 | 10:1 | 3.73 (3.35) | 2 | |
2 | Allowing the user to communicate or send information/data to health care providers | Communication or support | 4.54 (0.522) | 5 | 11:0 | 5.36 (3.04) | 8 | |
3 | A user-friendly design (such as educational and age-appropriate design) | Easy to use | 4.45 (0.52) | 4 | 11:0 | 2.82 (2.64) | 1 | |
4 | Flexibility and customization | Easy to use | 4.45 (0.52) | 4 | 11:0 | 7.91 (6.92) | 4 | |
5 | Cross-platform device syncing (sync between mobile and other devices) | Features | 4.36 (0.67) | 4 | 10:1 | 13.73 (5.60) | 16 | |
6 | Allowing chat services so users can communicate and support each other | Communication or support | 4.27 (0.47) | 4 | 11:0 | 9.45 (5.28) | 12 | |
7 | Taking feedback and adding new features | Communication or support | 4.27 (0.47) | 4 | 11:0 | 10.00 (4.86) | 8 | |
8 | Having health coaches explain the usefulness of the app through health campaigns | Advertisement | 4.27 (0.65) | 4 | 10:1 | 10.36 (4.94) | 10 | |
9 | Using accessibility features (such as text-to-speech for people with limited vision) | Features | 4.27 (0.65) | 4 | 10:1 | 11.00 (4.19) | 12 | |
10 | Using varying teaching methods (such as audiovisual, illustration, alarm) | Communication or support | 4.18 (0.75) | 4 | 9:2 | 13.18 (5.88) | 13 | |
11 | Allowing the users to share their progress with family, friends, others through integration with common social media platforms | Communication or support | 4.18 (0.60) | 4 | 10:1 | 13.45 (5.94) | 17 | |
12 | Syncing with electronic medical records/personal health record | Features | 4.18 (0.60) | 4 | 10:1 | 13.45 (4.27) | 20 | |
13 | Having the official websites and social media accounts of scientific associations and organizations recommend the use of the app | Advertisement | 4.09 (0.54) | 4 | 10:1 | 14.82 (4.12) | 14 | |
14 | Health care providers prescribing the mobile app in the clinic and asking about the compliance of the patients to use the app during the clinical visit | Advertisement or follow-up | 4.00 (1.26) | 5 | 8:3 | 6.91 (3.64) | 3 | |
15 | Providing educational posters in the patient waiting area with a QR code to download the app | Advertisement | 4.00 (1.18) | 4 | 9:2 | 8.91 (4.87) | 6 | |
16 | Providing services from trustworthy or well-known health care providers | Advertisement | 3.82 (1.40) | 4 | 9:2 | 9.18 (5.15) | 4 | |
17 | Connecting the app with popular activity tracking devices (such as smartwatches, bands) | Features | 3.82 (1.54) | 4 | 9:2 | 12.73 (5.31) | 11 | |
18 | Using colloquial terms in the push notification and information | Features | 3.82 (0.87) | 4 | 8:3 | 17.64 (2.20) | 18 | |
19 | Providing inspirational and motivational quotes | Communication or support | 3.64 (1.36) | 4 | 8:3 | 13.09 (4.70) | 10 | |
20 | Rewarding the users by offering nonfinancial incentives (such as gamification) | Cost | 3.27 (1.74) | 4 | 7:4 | 14.82 (5.09) | 11 | |
21 | Rewarding the users by offering financial incentives | Cost | 3.18 (1.78) | 3 | 5:6 | 18.45 (3.50) | 21 |
aThe strategies are ordered in terms of the mean agreement score from round 3.
bAgreement rating, where 1=strongly disagree and 5=strongly agree.
cRanking score, where 1=highest and 21=lowest.
dAgree: disagree is the ratio of “agree” and “strongly agree” to “neither,” “disagree,” and “strongly disagree,” which was used as an inclusion criterion for round 3.
In this study, participating diabetes experts expected many features to be included in a health app for people with diabetes. However, their level of agreement was slightly low, likely due to the variety of the experts’ backgrounds and the differing amounts of experience [
There is sufficient evidence from the literature supporting the effectiveness of the top features generated in this study, which are listed in descending order according to the level of rating agreement: (1) a reminder for the health care providers’ appointments, screening, and routine lab tests, which has been mentioned in several studies as a common use of health technology [
For the contacts for guidance feature
For the “offering patient-specific education tailored to the user’s goals, their needs, and blood glucose readings” feature, end users appreciate that it saves time and provides instructions tailored to their specific condition [
Regarding the “finding the nearest urgent health care services/centers” feature, the app could detect the location of the user with the help of GPS and the global system for mobile communications (GSM) network, and thereby display information about the nearest medical centers. By clicking on a particular hospital, all information regarding that hospital could be provided; Furthermore, this is particularly advantageous for travelers as they could be connected with nearby health care centers if needed, which is a common concern for travelers with chronic diseases [
For the “provision of Arabic and English versions” feature, a recent Chinese study showed that English- and Chinese-language diabetes self-management apps constitute more than 80% of the 2000 available diabetes apps. Furthermore, the Chinese and English apps have more downloads and are more comprehensive with regard to clinically relevant functions compared to diabetes apps in other languages [
For the “medication reminders by notifications” feature
For the “syncing with electronic medical records/personal health record
Overall, there was a moderate degree of consensus among experts on their rankings of the engagement strategies that they believe are most likely to be effective in diabetes management when using a diabetic self-management health app. Strategies with the highest mean of agreement (ratings above 4.40) have already been addressed in the literature. Regarding the “app being free” feature, 36/48 (75%) of the included apps were free, and the average cost of the paid apps was Aus $4.37 (US $3.13) [
The importance of “allowing the user to communicate or send information/data to a health care provider” has been demonstrated at the patient and health care levels [
study focused on app-based interventions in managing chronic respiratory diseases, diabetes, and hypertension demonstrated the perceived ability of HCP-motivated patients and empowered them to properly self-manage their condition. The use of health technology in two-way communication between HCPs and patients proved to impact the patients’ health outcomes [
Studies found that user-friendly design
Several studies have mentioned “flexibility and customization” as a requirement [
One of the strengths of our study was having two people with diabetes on the study panel of experts. Therefore, besides their professional experience, they provided input into how the app could contribute to their life as patients with diabetes. The number of experts included was within the recommended range. Moreover, all the rounds were summarized and discussed by the steering committee, who are experts in the field.
There were several potential limitations identified in this study. First, we recruited experts with various backgrounds, which could have caused the significant levels of disagreement in the ranking. At the same time, if we had selected other experts, that could have led to a different result. Furthermore, there was considerable overlap among many of the information items volunteered by the Delphi participants. The use of a Delphi approach for selecting intervention components is not guaranteed to result in the best choices, which might affect the results’ reliability; this is a known limitation of the Delphi technique.
Another limitation is that our panel consisted only of experts. We did not include patients with diabetes, although two of the participants coincidentally had diabetes. To determine whether these same features and engagement strategies are desired by the end users, another validation study should be conducted among a population of people with diabetes to bridge the gap between the perspectives of experts and end users.
To our knowledge, this is the first study in the Middle East and North Africa that gathered a local panel of experts from the diabetes field and used an iterative process to combine the experts’ opinions into a group consensus. Consensus agreement does not mean that all of the right answers have been found, but rather indicates that a level of participant agreement has been reached. The information items resulting from this modified Delphi survey represent the opinions of an expert panel.
This study allowed us to reach a consensus on several important questions related to diabetes management components that experts agreed were the most likely to be effective when delivered via a mobile app. Furthermore, it shed light on the engagement strategies that diabetes experts believed would be the most likely to be effective in diabetes management when using a mobile app. The results of this study could thus be useful for health authorities and HCPs for future decision making on this topic.
The 36 features and 21 engagement strategies identified in this study should guide developers considering mobile app development targeting people with diabetes and other similar chronic diseases. For further research, we recommend that the results of this study should be verified and validated by building a prototype app that includes the features and strategies described in this study and recruiting people with diabetes to test it. Moreover, it would be more interesting to know if any of the top 10 app downloads globally or regionally contain these features and which combinations are most commonly found. How easily are these features implemented in an app? Are there cultural contexts that make certain feature more appropriate? Are any of the features not currently found in any app? These and other questions remain to be answered.
Intervention components generated by the experts in the first round.
Engagement strategies generated by the experts in the first round.
International Diabetes Federation
mobile health
type 2 diabetes mellitus
type 1 diabetes mellitus
health care provider
We would like to thank all the experts who participated in this study. The project was supported by King Saud University, Deanship of Scientific Research, Research Chair of Evidence-Based Healthcare and Knowledge Translation.
None declared.