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This paper describes the development of a mobile app for diabetes mellitus (DM) control and self-management and presents the results of long-term usage of this system in the Czech Republic. DM is a chronic disease affecting large numbers of people worldwide, and this number is continuously increasing. There is massive potential to increase adherence to self-management of DM with the use of smartphones and digital therapeutics interventions.
This study aims to describe the process of development of a mobile app, called Mobiab, for DM management and to investigate how individual features are used and how the whole system benefits its long-term users. Using at least 1 year of daily records from users, we analyzed the impact of the app on self-management of DM.
We have developed a mobile app that serves as an alternative form to the classic paper-based protocol or diary. The development was based on cooperation with both clinicians and people with DM. The app consists of independent individual modules. Therefore, the user has the possibility to use only selected features that they find useful. Mobiab was available free of charge on Google Play Store from mid-2014 until 2019. No targeted recruitment was performed to attract users.
More than 500 users from the Czech Republic downloaded and signed up for the mobile app. Approximately 80% of the users used Mobiab for less than 1 week. The rest of the users used it for a longer time and 8 of the users produced data that were suitable for long-term analysis. Additionally, one of the 8 users provided their medical records, which were compared with the gathered data, and the improvements in their glucose levels and overall metabolic stability were consistent with the way in which the mobile app was used.
The results of this study showed that the usability of a DM-centered self-management smartphone mobile app and server-based systems could be satisfactory and promising. Nonetheless, some better ways of motivating people with diabetes toward participation in self-management are needed. Further studies involving a larger number of participants are warranted to assess the effect on long-term diabetes management.
This paper describes the development of a mobile app for diabetes mellitus (DM) self-management and discusses the results of its long-term usage by selected users after 5 years. The design of the app (called Mobiab) consisted of a holistic process involving end-user requirements, expert involvement, incorporation of behavioral change theory, data security, and data privacy considerations.
DM is a chronic disease affecting large numbers of people throughout the world, and this number is continuously increasing. According to the International Diabetes Federation, there are 537 million adults worldwide has diagnosed with DM [
There is massive potential to increase involvement with self-management of DM using smartphones and digital therapeutics interventions. Mobile health (mHealth) applications can also reduce barriers to the availability of the health care system; for example, time constraints or limited access to care providers [
The aims of this study are to (1) explore how long-term usage of such a system may benefit its users, (2) describe the process of development of a mobile app focused on self-management of people with DM, and (3) evaluate the demand for individual features or modules.
We developed the Mobiab system within the context of OLDES (www.oldes.eu), a European Union (EU) multicenter project involving 4 companies, 2 universities, and 2 university hospitals. The OLDES project focused on developing information technology for the purposes of eHealth applications [
The Mobiab system offers an alternative to a paper-based diary—an Android mobile app and a web portal aimed at supporting DM self-management. Compared with a paper-based diary, the main benefit is the immediate feedback for inputted data in the form of graphs and basic statistics showing the user’s compliance with diet or providing self-monitoring of blood glucose levels. The Mobiab system was designed in a client-server architecture with a storage system on the server. Mobiab requires an internet connection on mobile devices. In the beginning—that is, in 2014—this approach was restricted by lower availability of internet connection [
The concept underlying Mobiab consists of a mobile app, data collection from medical devices, and data storage (
Scheme of system architecture. API: application programming interface.
The mobile app consists of individual modules that are independent of each other and need only the basis of the app (
Scheme of the mobile app and individual modules.
Food intake is the most complex module and provides the functionality for recording food that is consumed. This module now contains a food database with more than 9000 Czech food items. The database has gradually been expanded and checked for data accuracy by other users. There are several approaches to food consumption logging:
Search in the whole database
Search in favorite items
Browse all food items and filter by categories
Browse user’s meals or simply take a photo of the food.
The user enters the amount of food after searching for the specific food item. The time stamp for the consumption and the food category is predefined by the current time; however, this can be changed by the user. To enable the user to change their mind, the description of the nutrition, and the size of the portion (in grams), and the carbohydrates (in grams) are displayed before the final dialog is saved. The changes in values are facilitated by an intuitive visualization of all measured medical data (
The physical activities module was designed similarly to the food intake module: the database contains more than 400 activities that can be browsed by categories or searched by name. It is necessary to select one activity and to enter the duration of the activity for logging. The caloric expenditure is computed with the user’s weight and the duration of the activity. Owing to this approach, the computed caloric expenditure may not always match the real expenditure and should be considered solely a guide.
Screenshots of the mobile app: food intake, glycemia monitoring, and insulin doses.
The Glycemic Monitoring module has a simple design for easy usage. It has an input part for entering values; for example, glucose levels, the date and time of measurement, and notes. The second part of the module is an overview of the values for the selected day, or a graph for the selected time range (
Data were collected through Mobiab over a period of 5 years (from January 2016), although Mobiab had been available on Google Play Store from mid-2014 only until 2019. No advertisement was used to recruit users, they found the mobile app in an organic reach. Over this period, over 500 users from the Czech Republic, who used the app for different lengths of time. Approximately 200 users did not report any DM, approximately 150 users reported type 1 DM, and approximately 175 users reported type 2 DM. Approximately 80% of the users used the mobile app for less than one week. The remaining 20% of the users used Mobiab for a longer time with a decreasing usage trend as it was also noted previously [
At least 3600 records of food intakes
At least 360 records of glycaemia measurements
At least 360 records of insulin doses
At least 1080 records of physical activities
At least 360 records of weight measurements
At least 360 records of pressure measurements.
Meeting one of these conditions was considered to provide evidence of long-term usage. Details about users (
Basic users’ statistics.
User ID | Sex | Birth year | Height (cm) | Diabetes mellitus type | Active days, n |
ID 1141 | Male | 1962 | 173 | Type 2 | 1749 |
ID 1196 | Male | 1960 | 178 | Type 2 | 1261 |
ID 1224 | Female | 1976 | 162 | Type 1 | 1623 |
ID 1289 | Female | 1941 | 162 | No diabetes | 1626 |
ID 1412 | Female | 1976 | 162 | Type 2 | 96 |
ID 1432 | Male | 1958 | 175 | Type 1 | 804 |
ID 1545 | Male | 1967 | 188 | Type 2 | 881 |
ID 1558 | Male | 1967 | 170 | Type 2 | 247 |
Number of records and daily averages.
Patient ID | Food entries, n (daily mean) | Glycemic measures, n (daily mean) | Insulin doses, n (daily mean) | Physical activities, n (daily mean) | Weight measures, n (daily mean) | Pressure measures, n (daily mean) |
ID 1141 | 34425 (19.67) | —a | — | 13,515 (7.73) | 1690 (0.97) | 1478 (0.85) |
ID 1196 | — | 1164 (0.92) | — | — | — | — |
ID 1224 | 9470 (5.83) | 1932 (1.19) | 2166 (1.33) | 4562 (2.81) | — | 449 (0.33) |
ID 1289 | 15,729 (9.67) | — | — | — | — | — |
ID 1412 | — | 466 (4.85) | — | — | — | — |
ID 1432 | 3757 (5.86) | 799 (0.99) | 1199 (1.53) | 2982 (4.18) | — | — |
ID 1545 | — | 857 (0.97) | 697 (0.79) | — | — | 859 (0.98) |
ID 1558 | — | 538 (2.18) | — | — | — | — |
a—: not available.
In total, 8 users (5 male, 3 female) fulfilled the inclusion criteria for long-term analysis, 5 of whom stated that they had type 2 DM, 2 had type 1 DM, and 1 was without DM. The average age of all users was approximately 57 years. All 8 users were invited to provide medical records, but only one user (ID 1141) was willing to share them. We were particularly interested in the development of the following clinical parameters during use of the app: hemoglobin A1c (HbA1c), glycemia, triglycerides, and cholesterol (total, low-density lipoprotein, and high-density lipoprotein cholesterol). The summary of records for the whole 7 years of the user who provided medical records are presented in
The analysis of the data had to use two approaches owing to missing user medical records: the first approach is an analysis of usage of the application, including any beneficial trends for DM management, and the second approach is to make a direct comparison between the medical records and the entered values and trends of the user ID 1141.
Selected medical records of user ID 1141.
Date | Hemoglobin A1c levels (mmol/mol) | Glycemia (blood glucose measured in terms of mmol/L) | Total cholesterol (mmol/L) | Low-density lipoprotein cholesterol (mmol/L) | High-density lipoprotein cholesterol (mmol/L) | Triglycerides (mmol/L) |
January 1, 2014 | —a | 4.6 | 4.3 | 2.82 | 1.15 | 1.07 |
April 17, 2016 | — | 18.17 | — | — | — | — |
April 26, 2016 | 90 | 7.9 | 4.17 | 2.62 | 1.4 | 0.85 |
July 21, 2016 | 36 | 4.7 | — | — | — | — |
November 11, 2016 | 29 | 5 | 4.58 | 2.39 | 1.49 | 0.67 |
March 6, 2017 | 32 | 4.8 | 3.81 | 1.98 | 1.66 | 0.53 |
July 17, 2017 | 34 | 4.5 | 4.05 | — | — | 0.81 |
April 23, 2018 | 35 | 5.3 | 3.73 | 2.08 | 1.48 | 0.51 |
September 17, 2018 | 34 | 5 | 4.11 | 2.52 | 1.55 | 0.73 |
February 4, 2019 | — | 4.9 | 3.96 | 2.54 | 1.18 | 0.93 |
June 17, 2019 | 35 | 4.9 | 3.66 | 2.03 | 1.46 | 0.6 |
November 4, 2019 | 35 | 5.1 | 4.26 | 2.57 | 1.39 | 0.99 |
March 19, 2020 | 36 | 4.7 | 3.3 | 1.65 | 1.44 | 0.5 |
July 13, 2020 | 35 | 5.1 | 3.78 | 2.09 | 1.44 | 0.65 |
November 13, 2020 | 36 | 5.2 | 3.77 | 2.01 | 1.62 | 0.64 |
March 22, 2021 | 35 | 5.4 | 3.71 | 1.98 | 1.53 | 0.78 |
a—:not available.
Ethics approval from the ethics committee of our university was not required for this study. All users agreed to use anonymized data for purposes of research and data analysis during sign-up process, which is required for the app usage.
At first, we analyzed the long-term food intake. Users ID 1141 and ID 1289 recorded their food intake regularly. They were strictly taking their diet plan and followed energy and sugar intake limits. User ID 1141 still uses the mobile app, and his performance is described in detail in the following section. Two other users, ID 1224 and ID 1432, enter data irregularly every few days.
Nevertheless, ID 1224 used the app for over 4 years, and ID 1432 used it for 2 years. Interestingly, both of these users has type 1 DM, and they used the app much more regularly for entering glycemic values and insulin dosage than for food intake recording. The glycemic records (
Records of blood sugar in the first year of app use.
User ID 1141 (male, 60 years old, type 2 DM) was selected for the case study because he was willing to share his medical records and other information about his health and lifestyle. This person had been diagnosed as having type 2 DM randomly during an emergency examination on April 17, 2016. Before that, he had already been treated for high blood pressure and for hyperlipidemia. After the diagnosis of DM, he has been treated with medication (Glucophage XR, 500 mg) and he had been looking for some supporting mobile app. He started dieting and the records show that he has followed the diet constantly for the whole time that he has used the app. In total, he has entered over 34,000 food records. Positive results were soon obtained. With regular exercise (stationary exercise bike and walking) he reduced his weight from 127 kg to 84 kg, and his waist circumference decreased from 141 cm to 107 cm within 1 year. In the last 3 years, these values have increased moderately, as of March 2021, his weight was 101 kg because he has not been able to exercise intensely owing to joint pain and he stopped entering new waist circumference values (
Weight and waist circumference records for the entire period of app usage.
Blood pressure records for the entire period of app usage.
Medical records for hemoglobin A1c and glycemia.
Medical records for cholesterol and triglycerides. HDL: high-density lipoprotein, LDL: low-density lipoprotein.
The main goal of the Mobiab system is exploring benefits of long-term usage of technology for DM self-management. The system simplifies manual entering and documenting of measured values associated with treatment monitoring and self-management of DM and provides a user-friendly summary of their self-management efforts. The Mobiab system contributes to the user’s education and a better understanding of the disease by providing continuous recordings of all essential data, including food intake, caloric expenditure, blood glucose levels, insulin dosage, body weight, and blood pressure. In addition, we might argue that the Mobiab system contributes to long-term outcomes of DM management, as demonstrated in several use cases. Several studies have suggested the usefulness of electronic self-management systems in managing DM [
The collection of medical data using Mobiab was beneficial to users with both diabetes types. Previously, it had been necessary for people with DM to record medical values manually in a diabetes diary. Using Mobiab, user ID 1141 has already been able to record his food consumption, exercises, weight changes, and blood pressure continuously for 1749 days. In addition, the user achieved positive changes in blood glucose levels (
Some systems applied training participants ranging from telephone [
Only a few technology-related issues were reported. The main comments stemmed from the use of the app without an internet connection, mainly at the beginning of the app launch. While there was considerable effort to ensure complete app functionality without the internet connection by caching all parameters as in the case of earlier systems [
The Mobiab data set is highly variable in terms of the usage of the modules. Not every user used the same set of modules (
However, there is a concern about placing too much confidence in managing DM using mHealth apps [
However, the long-term usage of apps developed for managing DM using self-management tools remains low [
Most of the studies referenced in this paper were single-center pilots validating short-term results of the examined mobile apps. Undoubtedly, more clinical trials with extended follow-up periods are needed to evaluate the long-term effect of diabetes-related mobile apps on glucose management and quality of life, and sustainability of self-management using the mHealth ecosystem [
A major strength of this study is the involvement of 5 persons with type 2 DM, 2 persons with type 1 DM, and 1 person without DM, each of whom could use the system for a long time and enter a significant amount of data. However, the small number of participants is a limitation of our study. A very small set of users is insufficient to thoroughly test and validate the self-management compliance of the Mobiab system. In addition, even this small number of participants did not use all the modules that the system provides.
Another limitation is the integration of only one glucometer. We implemented seamless glucose data transfer using a specific glucose meter (Fora Diamond MINI) and blood pressure monitor (Fora Active P30 Plus). Technical documentation and cooperation with manufacturers would be needed to connect other devices.
A further limitation is the web-based portal for physicians. A total of 5 clinicians in our expert advisory group indicated that clinicians already use some commercial software (eg, Medtronic CareLink), and that the use of different software is an unnecessary complication. The solution would be to have a communication interface to connect the mobile app to an already established system. Data integration with existing hospital information systems was not implemented as a part of our work, because we had no specification of the communication interface. However, this integration activity remains open for future work, when new versions of the hospital system are incorporated with application programming interface functionality.
The results of this study have shown that the usability of a smartphone app and server-based systems are potentially satisfactory and promising. The collection of long-term data on diabetes and overall metabolic management can be supported by a modular app such as Mobiab. Our system, based on the needs and requirements of its intended users, has attempted to maximize the potential to enhance self-management and increase user adherence. In this study, 8 users evaluated app functionality in long-term monitoring. A case study has presented and analyzed the particularly successful involvement with the system. However, we cannot yet claim that the Mobiab app provides people with diabetes a well-utilized tool for their self-management to help prevent complications. An assessment of the effectiveness of the app in improving self-management over time requires further studies involving a larger number of participants. Some redesign of the mobile app will probably be required owing to continuous changes in the development of mobile apps. However, the principles of the modules and functions work well and will likely be preserved.
diabetes mellitus
European Union
mobile health
The study was supported by the Research Center for Informatics, grant number CZ.02.1.01/0.0/16_019/0000765 and by the grant Biomedical data acquisition, processing and visualization, number SGS19/171/OHK3/3T/13.
None declared.