This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
Research in type 1 diabetes management has increased exponentially since the irruption of mobile health apps for its remote and self-management. Despite this fact, the features affect in the disease management and patient empowerment are adopted by app makers and provided to the general population remain unexplored.
To study the gap between literature and available apps for type 1 diabetes self-management and patient empowerment and to discover the features that an ideal app should provide to people with diabetes.
The methodology comprises systematic reviews in the scientific literature and app marketplaces. We included articles describing interventions that demonstrated an effect on diabetes management with particular clinical endpoints through the use of mobile technologies. The features of these apps were gathered in a taxonomy of what an ideal app should look like to then assess which of these features are available in the market.
The literature search resulted in 231 matches. Of these, 55 met the inclusion criteria. A taxonomy featuring 3 levels of characteristics was designed based on 5 papers which were selected for the synthesis. Level 1 includes 10 general features (Personalization, Family support, Agenda, Data record, Insulin bolus calculator, Data management, Interaction, Tips and support, Reminders, and Rewards) Level 2 and Level 3 included features providing a descriptive detail of Level 1 features. Eighty apps matching the inclusion criteria were analyzed. None of the assessed apps fulfilled the features of the taxonomy of an ideal app. Personalization (70/80, 87.5%) and Data record (64/80, 80.0%) were the 2 top prevalent features, whereas Agenda (5/80, 6.3%) and Rewards (3/80, 3.8%) where the less predominant. The operating system was not associated with the number of features (
There are significant gaps between research and the market in mobile health for type 1 diabetes management. While the literature focuses on aspects related to gamification, rewarding, and social communities, the available apps are focused on disease management aspects such as data record and appointments. Personalized and tailored empowerment features should be included in commercial apps for large-scale assessment of potential in the self-management of the disease.
Diabetes mellitus is a metabolic syndrome, which comprises an impaired insulin production and action [
Despite the promise of mobile health (mHealth) in the specific field of diabetes [
In their review from 2011, Chomutare et al [
Since then, several studies have been conducted to test the effectiveness of apps in reduced samples of patients. In such studies, researchers evaluate apps with different approaches and patient groups, yielding conflicting conclusions due to the different methodology of the interventions [
This study aimed to assess whether app manufacturers adopted the findings from mHealth evidence-based interventions in diabetes. The rationale is to mind the gap between research and the market to identify the features that are not available in commercial apps. The methodology comprises 2 systematic reviews using the PRISMA methodology, one in the scientific literature (without meta-analysis) and the other in app marketplaces. The systematic review of scientific literature is focused on interventions with apps on patients with T1DM into randomized controlled trials (RCTs). In the first stage, we built the schema of what could be an ideal diabetes app (including all the features which have shown a positive effect on diabetes management) and to then analyze the characteristics of available apps and their distance to the ideal app.
The primary objective of this study was to review scientific literature using the PRISMA methodology to enumerate evidence-based features that have demonstrated a positive effect in the management of T1DM in RCTs. Inclusion criteria were defined as (1) a mHealth intervention on T1DM patients using an app for remote and self-management of the disease and (2) the intervention was performed in an RCT and reports on clinical outcomes (HbA1c, in-range time or self-monitoring blood glucose). Exclusion criteria included (1) gray literature, (2) studies not reporting RCTs on T1DM (eg, type 2 diabetes, gestational diabetes), and (3) studies not using an app (eg, text messages, manual notes) or not describing the app’s functionality. The secondary objective was to compare the evidence-based features with the characteristics of the available apps.
The source of the literature review were online journal databases and indexers (PubMed, Medline, Google Scholar, and Cochrane Trials). We searched a combination of keywords including type 1 diabetes mellitus, mobile health, RCTs and self-management. The complete search strategy, combination of keywords in the queries and results for PubMed are described in
Subsequently, the approach for recruiting apps was twofold. First, a web search was conducted by using keywords of 3 different groups: (1) “diabetes mellitus 1 AND apps AND (android OR iPhone) AND self-management,” (2) “diabetes mellitus,” and (3) “diabetes apps AND self-management.” A second search was conducted in Google Play and the App Store to recruit a greater number of apps. In this case, we introduced 2 keywords: “diabetes AND management.” After collecting all the available apps, the screening and selection were done in the same way as with the publications (using a PRISMA flow diagram). Subsequently, the selected apps were downloaded and tested to know which of the characteristics obtained with the systematic literature review were available in the apps.
Data from the literature review and the apps assessment was extracted by 2 of the authors (AMM and EJP) using a structured data form. For the literature review, we extracted data related to the study (year of publication, sample size, the age of participants, methodology, intervention, clinical endpoints, features, usability, and satisfaction). Studies were assessed using the Cochrane Collaboration’s tool to assess the risk of bias of included (selection, reporting, performance, and attrition) [
A descriptive analysis of the features in the apps was done before association analytics. Association of the type of features and the type of operative system was evaluated with a two-way analysis of variance, in which we assessed the
These 6 studies reported on the results evaluating the benefits of apps in the management of T1DM (
Selection of the literature for evidence-based features of mobile apps for type 1 diabetes management and empowerment.
Characteristics of the studies evaluating mobile apps for type 1 diabetes mellitus management and empowerment.
Characteristics | Castensøe-Seidenfaden [ |
Cafazzo [ |
Goyal [ |
Kirwan [ |
Clemens [ |
Ryan [ |
Publication year | 2018 | 2012 | 2017 | 2013 | 2017 | 2017 |
Intervention, n | 76 | 20 | 46 | 25 | 81 | 18 |
Age (years), mean (SD) | 17.6 (2.6) | 14.9 (1.3) | 14.1 (1.7) | 35.9 (10.6) | 14.0 (10.4-15.9)a | 40 (13.9) |
Timeb (years), mean (SD) | 8.0 (4.5) | NSc | 7.1 (3.2) | 19.7 (9.6) | 4.9 (2.7-7.5)a | 27.3 (14.9) |
Duration (months) | 12 | 3 | 12 | 6 | 3.6 | 4 |
Intervention type | Usual care App | App | Usual care App | App Feedback | Retrospective analysis | Usual care App |
App name | Young with Diabetes | Bant | Bant | Glucose Buddy | NS | NS |
HbA1cd outcome | No significant change | No significant change | Decrease by 0.58% ( |
Decrease in mean (SD) from 9.08% (1.18%) to 7.8% (0.75%) | No significant change | Decrease in median (9.1% to 7.8%) |
SMBGe outcome | — | Increased mean daily frequency (2.4 to 3.6, |
— | No significant change | Increased 2.3 times | — |
App perceived usefulnessf | Chat Room (among young people) | Reminders, blood glucose regulation, insulin and food regulation, emergency readiness, exercise | Trending feature, logbook, and home menu (statistics) | NAg; texting extensively used | Data synchronization | Bolus |
User satisfaction | >80% would recommend | 88% would continue to use | 76% “satisfied/very satisfied” |
NA | NA | NA |
aMedian and interquartile range.
bSince diagnosis.
cNS: not specified.
dHbA1c: hemoglobin A1c.
eSMBG: self-monitoring blood glucose.
fEither a 5-point or 10-point Likert scale was used to score.
gNA: not assessed.
Overall, the included trials adequately achieved a low level of risk of bias (
The majority of these studies assessed the features of the app which had a higher perceived usefulness or a good adoption among study subjects. Castensøe-Seidenfaden et al [
Cochrane Collaboration’s tool risk-of-bias assessment for the clinical outcomes (hemoglobin A1c and self-monitoring blood glucose changes) of the included study papers.
Castensøe-Seidenfaden [ |
Cafazzo [ |
Goyal [ |
Kirwan [ |
Clemens [ |
Ryan [ |
|
Random sequence generation | Low | High | Low | Low | High | High |
Allocation concealment | Low | High | Low | Low | High | Low |
Blinding of participants and personnel | N/Aa | N/A | N/A | N/A | N/A | N/A |
Blinding of outcome assessment | Low | High | High | Low | High | Unclear |
Incomplete outcome data | Low | Low | Low | High | Low | Low |
Selective reporting | Low | Low | Low | High | Low | Low |
Other bias | Low | Unclear | Low | Unclear | Unclear | Low |
aN/A: not applicable (both patient and doctors know the group they are allocated).
Clemens and Staggs [
Three of these studies evaluated the user satisfaction in terms of willingness to use the app after the trial and willingness to recommend its use to peers. Each showed a high percentage of subjects willing to continue using the app and willing to recommend the app to peers [
The qualitative study of the apps used in the studies allowed us to extract the characteristics of the apps that users rated as key and researchers considered of value. These features were sorted into functional areas beyond the traditional 4 clinical areas of diabetes management taxonomy (glycemic control, carbohydrate intake, insulin and exercise) [
Family support was a feature in which relatives can perform a follow-up of the data introduced by the patient in which we have identified read access and read-and-write access. This feature also contains literature support for relatives in the management of T1DM through web links and books. The Agenda feature was a common factor in the apps analyzed in the review, and it was mainly devoted for scheduling medical and personal appointments. Another recurrent feature was the storage of measurements and reports related to food intake, insulin intake, and physical activity, which are under the Data record feature. In this feature, we made a distinction between the apps that had manual data entry (eg, forms, pictures, voice recognition, avatar) and the automatic entry of data using wireless sensors, mobile phone sensors (such as an accelerometer for physical activity) and smartwatches and wearables. The last feature (right side in
The Data management feature involved the capability of the app of exporting, storing, and analyzing the data collected in the app. In the review, one of the top-rated features was the graphical representation of measurements and the calculation of statistics based on these measurements. The second feature in the right side of
We evaluated the available apps through an online search, the App Store (Apple) and Google Play (Android) for diabetes management and support. Following the PRISMA methodology approach (
Android and iOS apps were tested during a month since many apps needed control over a longer period to provide data (graphics, statistics) to the user. A mHealth expert analyzed the apps and collected all the features which matched the taxonomy of the ideal diabetes management app. Features were flagged with a Boolean value depending on whether the app provided the analyzed feature. Occasionally, the mHealth expert was provided with clarification to ensure that the analysis was more precise.
Taxonomy of the features of an ideal app according to the evidence-based effectiveness of mobile health in diabetes support and empowerment.
Selection of the available apps for type 1 diabetes management and empowerment.
Apps included in the analysis. From top-down left-right: Balansio, Bant, BeatO, Beyond type 1 diabetes, Blood glucose tracker, BlueLoop, Brook, Carbs & Cals, Center health, Checkmate diabetes, Chron, Contour diabetes, Dario, Diabetes a la carta, Diabetes & Me, Diabetes connect, Diabetes diary, Diabetes diet and management, Diabetes digest, Diabetes evaluation, Diabetes experience day, Diabetes ID, Diabetes insight, Diabetes kit blood, Diabetes metrics, Diabetes PA, Diabetes pal, Diabetes parent, Diabetes passport, Diabetes pilot pro, Diabetes plus, Diabetes treatment, Diabetes vue, Diabetika, Diaguard, Diario de sangre, Diasend, DMI from zero to hero, Dnurse, Dottli, Dr. Diabetes, Easy diabetes, Glooko, Glucool diabetes, Glucosa compañero, Glucose buddy, Glucose wiz, Uright, Glucosio, GluQUO, Health2sync, Helparound, iFora, Inrange, Insulclock, Kids and teens diabetes, Kingfit, La diabetes M, Life in control, MedM diabetes, Meet me, Mi glucemia, Monitor de glucosa, Mumoactive, My diabetic alert, Nagbot, Neptun, One drop, Ontrack diabetes, PredictBGL, Social diabetes, SOS diabetes, Sugar sense, Sugarmate, Track3lite.
None from the 80 analyzed apps fulfilled the criteria of the taxonomy of an ideal app. Only BlueLoop had 9 out of the 10 ideal features in level 1.
Feature frequency in the reviewed diabetes management apps for the 3 hierarchy levels.
Level hierarchy | Apps containing the feature, n (%) | ||
70 (100.0) | |||
User profile and picture | 40 (57.1) | ||
Blood glucose diary | 64 (91.4) | ||
Mood | 14 (20.0) | ||
Goals | 33 (47.1) | ||
Insulin type and dosage | 21 (30.0) | ||
Tailored advice | 8 (11.4) | ||
24 (34.3) | |||
Weight | 24 (34.3) | ||
Height | 14 (20.0) | ||
Hemoglobin A1c | 17 (24.3) | ||
Blood pressure | 15 (21.4) | ||
Alcohol | 1 (0.1) | ||
Age | 6 (8.6) | ||
Gender | 14 (0.2) | ||
Tobacco | 1 (0.1) | ||
Years with type 1 diabetes mellitus | 12 (17.1) | ||
Physical activity | 35 (50.0) | ||
30 (100.0) | |||
30 (100.0) | |||
Read and write | 8 (26.7) | ||
Read | 22 (73.3) | ||
7 (23.3) | |||
Books | 12 (40.0) | ||
Web pages | 7 (23.3) | ||
29 (100.0) | |||
Appointments | 29 (100.0) | ||
64 (100.0) | |||
Manual | 62 (96.9) | ||
30 (46.9) | |||
Glucometer | 27 (42.2) | ||
Mobile phone | 17 (26.6) | ||
Smartwatch/wearable | 1 (1.6) | ||
20 (100.0) | |||
17 (85.0) | |||
Bolus | 9 (45.0) | ||
7 (35.0) | |||
Basal | 9 (45.0) | ||
60 (100.0) | |||
34 (56.7) | |||
Comma-separated values | 16 (26.7) | ||
Excel | 6 (10.0) | ||
10 (16.7) | |||
Other | 20 (33.3) | ||
Cloud storage | 28 (46.7) | ||
Statistics | 18 (30.0) | ||
Charts | 51 (85.0) | ||
23 (100.0) | |||
Endocrinologists | 16 (69.6) | ||
Parents/mentors | 14 (60.9) | ||
Among users | 9 (39.1) | ||
60 (100.0) | |||
User manual of the app | 46 (76.7) | ||
Diet/food information | 28 (46.7) | ||
24/7 medical support | 7 (11.7) | ||
Emergency | 11 (18.3) | ||
33 (100.0) | |||
Blood glucose control and insulin | 33 (100.0) | ||
Insulin/strips purchase | 28 (84.8) | ||
Appointments | 29 (87.9) | ||
3 (100.0) | |||
App Store/Google Play credit | 0 (0.0) | ||
Inside the app | 3 (100.0) | ||
Other (Amazon, eBay, etc.) | 1 (33.3) |
Level 2 features in Personalization were dominated by the Blood glucose diary (64/70, 91.4%), whereas Tailored advice was only present in 11.4% (8/70). If we look into level 3 Measurements in Personalization, only 0.1% (1/70) of the apps included Tobacco and Alcohol, and 17.1% (12/70) included a field for Years of diagnosis. With regards to Family support, 100.0% (30/30) featured Read access, and 26.7% (8/30) included Writing access to relatives.
Fifty-one of 60 apps (85.0%) featuring Data management had the possibility of drawing charts with the stored measurements, and more than a half (34/60, 56.7%), offered the possibility of data exportation in several formats. Nearly a half (28/60, 46.7%) offered the possibility of storing data in the Cloud, and only 30.0% (18/60) had the option of calculating statistics.
Level 2 features of Tips and support showed that not all the apps have a user manual (46/60, 76.7%), less than a half (28/60, 46.7%) provided Diet/food information, only 11.67% (7/60) had 24/7 Medical support specific for diabetes, and 18.3% (11/60) had Emergency support.
All the Reminders (33/33, 100%) were for blood glucose and insulin schedule. A majority were for purchasing fungibles (28/33, 84.8%) and Appointments (29/33, 87.9%). Three of 3 (100%) provided Rewards inside the app, and only 1/3 (33.3%) gave Rewards in online markets.
Apps were classified according to the number of level 1 features they offered.
The relationship among the number of level 1 features and the Feature Factor.
Distribution of level 1 features classified according to the number of level 1 features in the apps divided into quartiles.
We conducted a systematic review to discover the features of the apps that had shown an effect in T1DM management. We found a big gap between research and market in the apps for supporting and empowering T1DM patients. While research is currently testing the effectiveness of mHealth in the improvement of clinical outcomes related to T1DM and therapy adherence, the characteristics of such apps are heterogeneous and not consistently justified. The systematic search discovered 6 studies consisting of mHealth interventions on 266 participants with a study duration ranging from 3 to 12 months. Studies described the app and assessed the user perceived usefulness of the app characteristics. Three of the 6 (50%) studies also reported on user satisfaction. Features of the apps were categorized and merged into a taxonomy of what would be an ideal app for T1DM management and patient empowerment (
The newly proposed taxonomy featured 3 hierarchical levels, the first of which has 10 areas. Subsequent level 2 and level 3 features are embedded into level 1 features, enabling us to detail what type of feature is offered to the app user (eg, Personalization: setting up a user profile). Regarding the interaction of patients and health care professionals, we discovered apps including contact to and support from endocrinologists and diabetes educators (
Moreover, this study explored systematically the features that are present in apps available at zero cost for users on the internet and mobile apps markets. Following the PRISMA approach, we found 80 apps which were analyzed in detail for 30 days. None of the assessed apps fulfilled the features of the taxonomy of an ideal app, but 1 featured 9 characteristics (BlueLoop). Personalization (70/80, 87.5%) and Data record (64/80, 80.0%) were the 2 top prevalent features, whereas Rewards (3/80, 3.8%) was the less predominant. We did not find an association on the number of features (
In a secondary analysis apps were classified according to the number of level 1 features and sorted into quartiles (
Patient empowerment is essential for T1DM management and control [
This research stresses the fact that there is a need to consider the key features to be included in an app for T1DM. This consideration is straightforward related to the ultimate objective of the app. Is it for recording and storing measurements? Is it for managing other aspects as the disposables or the appointments? Is it targeted to empower the user? Is it to build a social community? Goyal and colleagues argued that the design of an app for T1DM young patients had to consider 3 factors: (1) relationship to technology, (2) how this relationship might make a difference to users, and (3) considering when it might not be a suitable mechanism to use [
The taxonomy of features was designed based on previous clinical interventions [
In 2017, Holtz et al [
Gamification and coaching techniques are also a promising feature of mobile health apps for diabetes management. Sannino et al [
The review of apps allowed us to conclude that level 1 features in apps have a balanced distribution (
In this study, we have discovered Data record and Personalization as the most prevalent features in mHealth diabetes apps. This finding should be further explored to know how many of these apps that also offer a dashboard for professional management. A recent study has discovered a decrease of the consultation time in type 2 diabetes management by using artificial intelligence and predictive modeling [
Finally, for the main purpose of disease management apps, rather than investigating the number of app features, we should investigate what kind of features could achieve the goal effectively. In our study, we were not able to distinguish which feature (or combination of features) was helping patients to achieve their goals. Acceptability and usability studies may help to identify the features that have a higher impact on the self-management of the disease, but further research should be conducted to critically identify the sets of features valuable for patients.
A limitation of this study is the set of papers and apps selected for the literature review and the app review. The authors may have omitted significant contributions for both searches due to the incompleteness of the query commands and mismatch in the searches. We have focused our research on apps tested in RCTs with significant clinical end-points. Authors are aware of the relevant research done in the past and conducted in the present in the design of T1DM apps, which do not involve clinical endpoints nor RCTs, which may also contribute to the taxonomy of an ideal app defined in this paper. Another limitation is that the graphical user interface (GUI) of the apps are not evaluated or studied. If the GUI is not appropriate, many features might be less accessible and thus less used.
This study assessed the existing gap between research and market in mobile health apps for the management of T1DM and the empowerment of patients. The mHealth has potential to support the management of T1DM, to catalyze the information exchange between patients, parents, and caregivers, and to empower and educate patients in the management of T1DM. The current landscape of apps for T1DM does not seem to be close to what researchers promote from RCTs and user-centered design. A majority of the apps mainly support the collection of measurements, and only a few of them offer a wide range of features for a personalize self-management. Rewards and social communities are not yet well adopted in market environments.
Search Strategy and results in PubMed (MEDLINE).
American Association of Diabetes Educators 7 Self-Care Behaviors
comma-separated values
Feature Factor
graphical user interface
hemoglobin A1c
mobile health
randomized controlled trial
self-monitoring blood glucose
type 1 diabetes mellitus
The authors wish to acknowledge the ITACA Institute (Universitat Politècnica de València) for making possible the publication of this paper through the Excellence Support program for the publication in high-impact international journals.
None declared.