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A multitude of mhealth (mobile health) apps have been developed in recent years to support effective self-management of patients with diabetes mellitus type 1 or 2.
We carried out a systematic review of all currently available diabetes apps for the operating systems iOS and Android. We considered the number of newly released diabetes apps, range of functions, target user groups, languages, acquisition costs, user ratings, available interfaces, and the connection between acquisition costs and user ratings. Additionally, we examined whether the available applications serve the special needs of diabetes patients aged 50 or older by performing an expert-based usability evaluation.
We identified relevant keywords, comparative categories, and their specifications. Subsequently, we performed the app review based on the information given in the Google Play Store, the Apple App Store, and the apps themselves. In addition, we carried out an expert-based usability evaluation based on a representative 10% sample of diabetes apps.
In total, we analyzed 656 apps finding that 355 (54.1%) offered just one function and 348 (53.0%) provided a documentation function. The dominating app language was English (85.4%, 560/656), patients represented the main user group (96.0%, 630/656), and the analysis of the costs revealed a trend toward free apps (53.7%, 352/656). The median price of paid apps was €1.90. The average user rating was 3.6 stars (maximum 5). Our analyses indicated no clear differences in the user rating between free and paid apps. Only 30 (4.6%) of the 656 available diabetes apps offered an interface to a measurement device. We evaluated 66 apps within the usability evaluation. On average, apps were rated best regarding the criterion “comprehensibility” (4.0 out of 5.0), while showing a lack of “fault tolerance” (2.8 out of 5.0). Of the 66 apps, 48 (72.7%) offered the ability to read the screen content aloud. The number of functions was significantly negative correlated with usability. The presence of documentation and analysis functions reduced the usability score significantly by 0.36 and 0.21 points.
A vast number of diabetes apps already exist, but the majority offer similar functionalities and combine only one to two functions in one app. Patients and physicians alike should be involved in the app development process to a greater extent. We expect that the data transmission of health parameters to physicians will gain more importance in future applications. The usability of diabetes apps for patients aged 50 or older was moderate to good. But this result applied mainly to apps offering a small range of functions. Multifunctional apps performed considerably worse in terms of usability. Moreover, the presence of a documentation or analysis function resulted in significantly lower usability scores. The operability of accessibility features for diabetes apps was quite limited, except for the feature “screen reader”.
Compared to early mobile phones, today’s smartphones and tablet PCs offer a considerably wider range of functionalities. Mobile applications (apps) are increasingly used in managing various tasks in daily life. Currently, more than 900,000 apps are available in the Apple App Store (operating system: iOS, developer: Apple) and more than 700,000 apps in the Google Play Store (operating system: Android, developer: Google) [
Within the health care sector, apps are supporting the management of illnesses, thereby promoting health awareness and well-being [
We carried out a systematic review of all currently available diabetes apps for the operating systems iOS and Android, between February 2013 and April 2013. Our review aimed to provide an overview of the number of newly released apps, range of functions, target user groups, languages, acquisition costs, popularity/user ratings, the ability to connect to measurement devices, and the connection between acquisition costs and user ratings.
Diabetes prevalence increases with age. Thus, the elderly are a large target group that could benefit from diabetes apps. However, several studies have shown a lack of acceptance and a subpar use of innovative mobile technologies among this age group [
In order to better assess and quantify usability for the elderly, we carried out an expert-based usability evaluation based on a representative 10% sample of diabetes apps available as of April 2013. Therewith, we examined to what extent existing diabetes applications serve the usability requirements of diabetes patients aged 50 or older.
Until now, just a few reviews of diabetes apps had been conducted [
Our review focused on the leading operating systems for mobile devices, iOS and Android. The analysis was carried out using the Apple App Store for iOS apps and the Google Play Store for Android apps. We focused exclusively on diabetes apps available in English and German.
As a first step, we identified keywords to ensure that every relevant diabetes app was detected. Therefore, we chose the following German and English keywords, directly related to diabetes mellitus: Diabetes, Blood Sugar/Blutzucker, Glucose/Glukose. Every hit was reviewed in terms of its relevance and explicit link to diabetes mellitus. This pre-selection was necessary due to the growing number of misleading descriptions (spam techniques) for apps, caused partly by non-existent or low admission requirements for novel apps. In the Google Play Store, no admission requirements currently exist for newly developed apps, whereas iOS apps are first internally reviewed by an app review board. All apps with an explicit link to diabetes mellitus were included in the analysis. The basis for the systematic and comparative market analysis was defined by categories and respective subcategories/specifications outlined in
We considered all the available information given by both the stores and the apps and collected the information for all categories and subcategories/specifications. In some cases, the structure of the app stores and the provided information differed strongly from one another, so we applied different approaches for the analysis of iOS and Android apps.
Categories and respective subcategories/specifications extracted from diabetes apps.
Category | Subcategory/specifications |
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App name |
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App language |
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Date of release/date of latest update (the acquisition of the release date was only possible for iOS apps; for Android apps, only the date of the latest update could be recorded) |
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Availability of a desktop application |
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App exclusively for the iOS operating system |
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App exclusively for the Android operating system |
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App for both operating systems available |
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Name of the developer |
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Freeware |
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Exact price |
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Availability as “lite” version (paid apps sometimes offer free or cheaper lite versions with limited functionality) |
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Number of downloads/installations |
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User rating |
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Number of user ratings |
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Documentation function |
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Information function |
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Data forwarding/communication function |
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Analysis function |
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Recipe suggestions |
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Reminder function/timer |
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Advisory function/therapy support |
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Patients |
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Physicians/qualified health personnel |
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Both user groups |
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Availability of an interface/connectivity to an external sensor(s)/device |
The analysis of iOS apps was conducted using the information available in the Apple App Store. In contrast to the Google Play Store, the Apple App Store offers several options for filtering the search results by choosing thematic subcategories. The results can additionally be sorted by relevance, popularity, user rating, and date of release. During the survey period, a sorting function was only available for the iPad, so the whole iOS app survey was performed via the iPad.
For the analysis, we chose the subcategories “Health and Fitness” and “Medicine”. Subsequently, the displayed apps were sorted by their date of release. The date of release served as an objective characteristic, which was necessary for a reliable and reproducible acquisition of all diabetes apps. The number of hits given by the Apple App Store corresponded exactly to the number of relevant apps.
We checked every app hit with regard to its availability for iPad and iPhone. Additionally, we verified whether the app was offered exclusively for the operating system iOS or also for Android. The market analysis of diabetes apps for iOS resulted in 390 hits.
By using the information available in the Google Play Store, the analysis of Android apps was conducted. To date, this app store offers no option to filter the search results for apps according to individual needs. Furthermore, the given “numbers of hits” is not only the number of apps but also the number of detected search terms in the app title and the app description. Thus, the search term “diabetes” led to more than 1000 hits in the Google Play Store. Keeping the limitations in mind, the number of available apps was a considerable overestimation.
In order to ensure a representative analysis despite missing selection criteria, we defined one day (03/06/2013) to record all found apps with title and developer. This definition will enable future app review processes. Additionally, every app was crosschecked for availability of an iOS version. Altogether, we found 380 diabetes apps available for the operating system Android.
To examine the usability of currently available diabetes applications for the elderly, we performed an expert-based usability evaluation. With this method, usability experts put themselves in the role of potential or current users to examine products in terms of usability. We performed a summative evaluation as we exclusively included apps whose development was already finished [
Due to the high number of apps available for review, the usability evaluation was based on a representative 10% sample of existing diabetes apps as of April 2013. The sample was chosen on a random basis. The evaluation was performed by three independent experts, as suggested by Nielsen [
The basis for the usability evaluation was defined by a specially created set of usability criteria considering interaction processes, interface design, and comprehensibility of content (
To lower barriers for persons with reduced or limited cognitive and physical skills, iOS and Android offer different accessibility features. We tested the operability of three features for each tested app in a separate test run. We have chosen features that are relevant to the elderly and were offered by both operating systems:
Screen reader—Voice over (iOS)/Talk back (Android): dichotomous scale
“Larger Type” as an additional measurement for “possibility to flexibly adapt the size of operating elements and displayed images”: dichotomous scale
“Invert colors” as an additional measurement for “sufficient color contrast”: 5-point Likert scale
According to the methodical approach of Barnum, the evaluators run through typical scenarios of use to conduct their evaluation [
The chosen method offers a high level of validity and comparability due to its guideline-based approach and closed response categories [
Evaluated usability and assessment criteria for diabetes apps for the elderly.
Main criterion/subcriteria | Description of characteristics | Assessment criteria | |
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Avoidance of foreign language and technical terms | 5-point Likert scale (1=does not apply at all; 5=does fully apply) |
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Use of generally intelligible symbols and terms | |
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If necessary, provision of additional explanations [ |
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Self-explanatory images and depictions, understandable without further support and explanations [ |
5-point Likert scale (1=does not apply at all; 5= does fully apply) |
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Easily understandable and internally consistent menu structures | 5-point Likert scale (1=does not apply at all; 5=does fully apply) |
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Avoidance of strong hierarchical menu structures and too many functionalities [ |
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Clear, distinguishable colors for images and depictions or choice of color-neutral depictions | 5-point Likert scale (1=does not apply at all; 5=does fully apply) |
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Avoidance of too glaring colors [ |
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Sufficient size of screen as well as input and output fields [ |
5-point Likert scale (1=does not apply at all; 5=does fully apply) |
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Ability to adapt size of operating elements and displayed images according to individual needs, capabilities, and preferences [ |
Dichotomous scale (applicable, not applicable) |
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Instant response to entered data, including easily understandable error messages in case of erroneous data input [ |
5-point Likert scale (1=does not apply at all; 5=does fully apply) |
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Ability to use the application without prior knowledge | 5-point Likert scale (1=does not apply at all; 5=does fully apply) |
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Ease of learning | |
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Fast achievement of a first feeling of success [ |
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Simple distinction between click-sensitive and non-click-sensitive areas, also without prior knowledge of the features of the touchscreen technology [ |
5-point Likert scale (1=does not apply at all; 5=does fully apply) |
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Reducing probability of erroneous data input by limiting choice to meaningful values | 5-point Likert scale (1=does not apply at all; 5=does fully apply) |
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Efficient proofreading mode and/or helpful user feedback, for example, in case of erroneous data input [ |
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Avoidance of registration at online platforms (but partly contrary to data protection regulations) [ |
Dichotomous scale (applicable, not applicable) |
In total, we examined 656 apps during the review process. As a result, we created three data sets (
The first diabetes app for iOS (according to Apple App Store as of April 2013) was developed and released on July 17, 2008 (name: Glucose-Charter
Annual release figures for diabetes apps.
The majority (85.4%, 560/656) of the examined apps were in English, especially the apps running exclusively on an Android operating system, (90.2%, 240/266). Apps with German as operating language were of relatively low number (14.6%, 96/656) (
Language of available diabetes apps as of April 2013.
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Operating system | |||
Category | Subcategory | iOS (n=276) | Android (n=266) | iOS and Android (n=114) | Total (n=656) |
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English | 229 (83.0) | 240 (90.2) | 91 (79.8) | 560 (85.4) |
German | 47 (17.0) | 26 (9.8) | 23 (20.2) | 96 (14.6) |
The acquisition costs and the ratio of free to paid apps differed strongly between the two operating systems (
The analysis of app price distribution revealed that a greater number of free apps were available across all apps (53.7%, 352/656). This appeared to be driven by Android apps where 63.5% (169/266) were free compared with 36.5% (97/266) paid. The reverse trend was observed for iOS where only 37.7% (104/276) were free compared with 62.3% (172/276) paid (
The price of paid apps differed strongly between the operating systems (
Price distribution of apps and annual proportions of free apps since 2008.
Category | Subcategory | Operating system | |||
iOS |
Android |
iOS and Android |
Total |
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Free | 104 (37.7) | 169 (63.5) | 79 (69.3) | 352 (53.7) |
Paid | 172 (62.3) | 97 (36.5) | 35 (30.7) | 304 (46.3) | |
Paid/Lite version available | 18 (6.5) | 11 (4.1) | 6 (5.3) | 35 (5.3) | |
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2013 (by April) | 6/20 (30.0) | 60/87 (69.0) | 33/42 (78.6) | 99/149 (66.4) |
2012 | 58/104 (55.8) | 79/108 (73.7) | 40/55 (72.7) | 177/267 (66.3) | |
2011 | 23/71 (32.4) | 27/58 (46.6) | 6/16 (37.5) | 56/145 (38.6) | |
2010 | 13/52 (25.0) | 3/12 (25.0) | 0/1 (0.0) | 16/65 (24.6) | |
2009 | 3/23 (13.0) | 0/1 (100.0) | 0/0 (0.0) | 3/24 (12.5) | |
2008 | 1/6 (16.7) | 0/0 (0.0) | 0/0 (0.0) | 1/6 (16.7) |
Price distribution of paid diabetes apps available as of April 2013.
Examining the range of functions of diabetes apps demonstrated that most were limited to one function (54.1%, 355/656). Only 185/656 (28.2%) combined two functions, and three or more functions were offered by 116/656 (17.7%) of the apps available as of April 2013 (
A total of 348/656 (53.0%) apps and thus the majority of diabetes apps available as of April 2013 offered a documentation function (
The recording of the blood glucose values mainly occurred via manual data input. Only a small number of apps offered the option to transfer the data wirelessly and automatically from the measuring device via Bluetooth to the mobile device.
The documentation function may be linked with an analysis function, which opens up the possibility to analyze the recorded data and to graphically display the results (
In total, 226 (34.5%) of the examined diabetes apps offered an information function, including the ability to inform about the illness, its diagnosis, the course of the disease, various treatment options, medication, and secondary diseases (
A data forwarding/communication function was offered by 204/656 (31.1%) apps. With this function, the user has the opportunity to send the recorded data via email to the attending physician, family members, and/or friends (
Surprisingly, only 58/656 (8.8%) of the diabetes apps provided an advisory function or any other kind of therapeutic support (
Besides the previously described functions, 95/656 (14.5%) of the apps included suggestions for recipes suitable for the needs of diabetics (
As an example,
Number of functions, target user groups, and popularity/user ratings of diabetes apps available as of April 2013.
Category | Subcategory | Operating system | |||
iOS |
Android |
iOS and Android |
Total |
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1 function | 134 (48.6) | 156 (58.6) | 65 (57.0) | 355 (54.1) |
2 functions | 87 (31.5) | 71 (26.7) | 27 (23.7) | 185 (28.2) | |
3 functions | 36 (13.0) | 25 (9.4) | 13 (11.4) | 74 (11.3) | |
4 functions | 15 (5.4) | 11 (4.1) | 9 (7.9) | 35 (5.3) | |
> 4 functions | 4 (1.4) | 3 (1.1) | 0 (0.0) | 7 (1.1) | |
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Patients | 263 (95.3) | 260 (97.7) | 107 (93.9) | 630 (96.0) |
Physicians/qualified health personnel | 19 (6.9) | 17 (6.4) | 14 (12.3) | 50 (7.6) | |
Patients and physicians/qualified health personnel | 6 (2.2) | 11 (4.1) | 7 (6.1) | 24 (3.7) | |
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Share of apps with rating, n (%) | 31 (11.2) | 189 (71.0) | 75 (65.8) | 295 (45.0) |
Median number of ratings | 9.0 | 6.0 | 6.0 | 7.0 | |
Median number of stars (max 5) | 3.5 | 4.0 | 4.0 | 3.8 |
Range of functions of diabetes apps available as of April 2013.
The vast majority (96.0%, 630/656) of the examined apps were designed specifically for patients, 24/656 (3.7%) apps addressed both patients and physicians/qualified health personnel, and only 50/656 (7.6%) were specifically designed for the target group physicians/qualified health personnel (
User ratings are a highly valuable and realistic evaluation of the additional benefits of apps. It is thus remarkable that just 31/656 (11.2%) of the apps designed exclusively for iOS were rated by users. In comparison, 189/266 (71.0%) of the Android apps and 75/114 (65.8%) of the apps running on both operating systems were rated (
Not only was the lower number of rated iOS apps conspicuous, the median of awarded stars was also lower than for Android apps (
Except for the ratings, the Google Play Store gave information about the number of downloads (ie, the number of installations) as another indicator of the app popularity. This information was not given by the Apple App Store. Hence, it was not possible to compare this indicator between both operating systems. But it has been shown that the number of downloads tended to correlate with the number of ratings and awarded stars.
During the analysis, the question arose of whether there is a connection between the price of an app and the level of user ratings. The results indicated that there existed a positive correlation between the acquisition costs and the number of given stars, for the price range of €0.01 to €5.00 (
In general, free apps were rated more frequently than paid apps. With a share of 56.5% (204/361), they received the highest number of given ratings compared to just 27.5% (28/102; price range: €0.01-€1.00) up to 41.7% (5/10; price range: €10.00-€100.00) of the paid apps. However, it has to be considered that the number of free apps was considerably higher than the number of price-intensive apps.
Distribution of user rating differentiated by acquisition costs as of April 2013.
Contrary to our initial expectations, only a limited number of diabetes apps possessed an interface to an external sensor or a measuring device (eg, for the measurement of blood glucose). Predominantly, apps developed for both operating systems were able to connect with an external sensor/device (7.9%, 9/114). Rarely, iOS apps (2.5%, 7/276) offered this feature compared to Android apps (5.3%, 14/266).
The majority of apps that were able to connect to an external measuring device transmitted the data via a Bluetooth interface. This interface enabled a wireless data transfer to the mobile device or to a PC. Some of the measuring devices already offered an automated transmission of the measured values in real time. There were two options for data synchronization: (1) wireless transfer of measured values to a mobile device and synchronization with the Internet, mostly to an online patient diary (registration required), and (2) wireless transfer of measured values to a PC, transfer of data to an online platform (registration required), and synchronization with a mobile device in the second step (eg, System Health Vault via Microsoft).
In total, we evaluated 66 out of 656 diabetes apps within the usability evaluation (
For all main and subcriteria, we averaged the evaluations of all three experts. The values of the main criteria represent the mean of the corresponding subcriteria (
Analyzing the results, the majority of evaluations were in the range of 3.0 to 4.0, which corresponded to a moderate to good rating of the apps included in the 10% sample. All tested apps received the best rating for the subcriteria “use of understandable semantics” and “simple comprehensibility and interpretability of displayed images and depictions” with a total average value of 4.1 (
In the second run, we evaluated the three chosen accessibility features. The results show that their operability was rather limited. The highest values were observable for the screen reader features Voice over (iOS) and Talkback (Android); 25 (86.2%) of the 29 iOS apps offered the ability to read the screen content aloud compared to 19 (67.9%) of the 28 Android apps, and just 4 (44.4%) of the 9 apps designed for both operating systems. The feature “invert colors” showed no considerable improvement of color contrast compared to the results of our evaluation without testing this feature. The results for testing the feature “large type” differed widely. While none of the iOS apps offered this feature, 11/27 (40.7%) Android apps offered contents in large font (
While conducting our systematic review, we hypothesized that usability decreases with an increasing number of functions. Hence, we additionally investigated the relationship between the main usability criteria and the number of functions by conducting several correlation analyses. The results shown in
Furthermore, we analyzed the relationship between the usability score and specific functions based on the differences in functionality we found in our systematic review. Therefore, we conducted multiple linear regression analysis to control for potential confounding effects of other functions offered by the same app (
Usability scores from expert-based usability evaluation by operating system, shown as mean values.
Main criterion | Subcriteria | Operating system | |||
iOS |
Android |
iOS and Android |
Total |
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mean (SD) | |||
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4.1 (0.53) | 4.0 (0.43) | 3.7 (0.35) | 4.0 (0.48) |
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Use of understandable semantics | 4.3 (0.58) | 4.0 (0.45) | 3.8 (0.45) | 4.1 (0.54) |
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Simple comprehensibility and interpretability of displayed images and depictions | 4.2 (0.54) | 4.1 (0.53) | 4.0 (0.37) | 4.1 (0.51) |
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Simple, self-explanatory menu structures | 3.7 (0.82) | 3.9 (0.84) | 3.3 (0.66) | 3.7 (0.82) |
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3.4 (0.36) | 3.6 (0.38) | 3.2 (0.36) | 3.5 (0.40) | |
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Sufficient color contrast | 3.5 (0.52) | 3.8 (0.47) | 3.1 (0.89) | 3.6 (0.60) |
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Sufficient color contrast with accessibility feature “invert colors” | 3.2 (0.65) | 3.9 (0.55) | 3.4 (0.56) | 3.5 (0.68) |
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Big size of operating elements | 3.4 (0.69) | 3.2 (0.57) | 3.1 (0.18) | 3.3 (0.59) |
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Ability to adapt the size of operating elements and displayed imagesa, n (%) | 8 (27.6%) | 4 (14.3%) | 2 (22.2%) | 14 (21.2%) |
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Ability to adapt the size of operating elements and displayed images with accessibility feature “large type”a, n (%) | 0 (0.0%) | 11 (40.7%)b | 3 (37.5%)b | 14 (21.2%) |
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3.4 (0.43) | 3.2 (0.44) | 3.2 (0.38) | 3.3 (0.43) |
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Instant and easily understandable feedback | 3.3 (0.66) | 3.3 (0.53) | 3.5 (0.47) | 3.3 (0.58) |
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Intuitive usability | 3.6 (0.68) | 3.5 (0.72) | 3.3 (0.56) | 3.5 (0.68) |
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Simple recognition of click-sensitive areas | 3.1 (0.65) | 2.8 (0.45) | 2.9 (0.48) | 3.0 (0.55) |
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Accessibility Features: Voice over (iOS), Talkback (Android)a, n (%) | 25 (86.2%) | 19 (67.9%) | 4 (44.4%) | 48 (72.7%) |
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2.5 (0.95) | 2.8 (0.87) | 3.5 (0.43) | 2.8 (0.89) | |
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Fault tolerance/Efficient fault management | 2.5 (0.95) | 2.8 (0.87) | 3.5 (0.43) | 2.8 (0.89) |
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Password-protected servicesa, n (%) | 5 (17.2%) | 4 (14.3%) | 3 (33.3%) | 12 (18.2%) |
Number of functions per app |
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1.6 (0.82) | 1.7 (0.85) | 1.6 (1.13) | 1.7 (0.89) |
Total Usability Score |
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3.3 (0.40) | 3.3 (0.38) | 3.4 (0.48) | 3.3 (0.39) |
aThe values of this subcriterion show means of frequencies.
bOne observation was missing for this subcriterion and the corresponding operating system. Accordingly n is reduced by 1.
Spearman’s rank correlation coefficients comparing number of functions with main usability criteria scores.
Number of functions | Main usability criteria scores | |||
Comprehensibility | Presentation | Usability | Fault tolerance | |
1 | −.29* ( |
−.25* ( |
−.25* ( |
.46** ( |
*5% significance level
**1% significance level
Multiple regression analysis: relationship between usability score and functions.a
Variable | Coefficient (b) | 95% CI |
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Information function | −.11 | −0.29 to 0.07 | −1.23 | .22 |
Recipe suggestions | .06 | −0.15 to 0.27 | 0.58 | .56 |
Documentation function | −.36 | −0.57 to −0.15 | −3.43 | .001b |
Analysis function | −.21 | −0.39 to −0.02 | −2.23 | .03c |
Reminder function/timer | −.04 | −0.42 to 0.33 | −0.23 | .82 |
Advisory function/therapeutic support | −.12 | −0.38 to 0.14 | −0.90 | .37 |
Data forwarding/communication function | .04 | −0.20 to 0.27 | 0.31 | .76 |
Intercept | 3.72 | 3.53 to 3.91 | 38.97 | <.001b |
n=66 |
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aOrdinary Least Squares regression with robust standard errors
b1% significance level
c5% significance level
The systematic review showed that a large number of diabetes apps are available. Providers may be entering the market as a result of the rising number of patients suffering from diabetes. For users, especially patients, it becomes increasingly difficult to find an app in this plethora of options that is suitable for one’s own needs. This problem is caused by a lack of effective search criteria and filter functions in the app stores. More frequently, apps are chosen that appear first in the search results for diabetes apps. The sorting criteria in the app stores are not apparent. New apps from relatively unknown developers could have difficulties being listed among the first results.
At the same time, many apps offered similar functionalities, mostly a documentation function, which is consistent with earlier findings of Martínez-Pérez et al [
As an example, one app mainly offered labels like “after breakfast” or “after lunch” for the documentation of measured blood glucose values, which implies postprandial states. But, for most diabetics, the blood glucose values
Taking a look into the future, we expect that the data forwarding function, especially to the attending physician, will gain significantly more importance. A regular transmission of data to their physician linked with frequent feedback can be a valuable therapy support, particularly for people in rural regions that are or will become affected by a shortage of doctors [
Additionally, the automated transmission of measured values in real time from the measuring device to the mobile device will probably spread and is an important driver for the perceived ease of use as El-Gayar et al point out [
Notwithstanding the functions offered by diabetes apps, their effects on patients’ self-management and, accordingly, on important indicators, as for example the HbA1c value, have to be evaluated. A comprehensive, representative, and long-term study investigating these health effects is lacking so far. But different studies focusing on the outcomes of mobile phone interventions, such as SMS, point out a slightly positive influence as shown in the reviews of Holtz et al [
Glucose meters with automated transmission of blood glucose values to mobile devices.
As a supplement to our systematic review, we conducted an expert-based usability evaluation to examine the usability of currently available diabetes apps for patients aged 50 or older. Therefore, we focused on the age group with the highest diabetes prevalence. The results show moderate to good evaluations (range 3.0-4.0) for all reviewed usability criteria, which is in accordance with the results of Demidovich et al [
The evaluation showed moderate results for the main criterion “presentation” (3.5). Our test of three accessibility features indicated a very good operability of the screen readers, especially for Voice over (86.2%) offered by iOS. However, the operability of the features “invert colors” (3.5) and “large type” (21.2%) was rather restricted. Additionally, the minority of diabetes apps (17.8% of the iOS apps) were developed specifically for tablet PCs. However, we assess them as more suitable and user-friendly for elderly diabetes patients due to their larger display and bigger illustrations. With increasing age, cognitive and physical skills are declining, such as eyesight, visual acuity, color vision, contrast detection, and hearing [
The criterion “fault tolerance” rated worst with a score of 2.8 (
Our correlation and regression analyses indicated a strong link between usability and the number and kind of functions. In particular, the number of functions and all main usability criteria were significantly negative correlated. These results cast a different light on the aforementioned outcomes of our usability test. Hence, the moderate to good usability scores applied mainly to apps offering a small range of functions. This relation inverted when we looked upon the considerably lower usability scores for multifunctional apps (
Differed by functions, apps offering a documentation and an analysis function performed worse in terms of usability. This result is surprising as the documentation function is most commonly offered with a share of 53.0%. It can be a valuable support for all diabetes patients measuring and recording blood glucose level regularly. But as interviews with diabetes patients aged 50 or older (conducted in Germany in 2013) have shown, most of them prefer documentation by means of a conventional diary (results not shown). One reason they named was the aforementioned lack of usability and therefore a too complicated and time-consuming handling. Moreover, the use of these two functions is characterized by a higher level of human-technology interaction than, for example, the use of an information function. Of course, this can be accompanied by a wider scope of error sources and usability barriers.
Altogether, the potential of diabetes apps for assisting and supporting diabetes patients aged 50 or older is large. In particular, the target group aged between 50 and 60 years holds great potential as people of this age are already quite familiar with mobile devices and apps [
The systematic review and the expert-based usability test were conducted within the project “InnoMedTec”. In that project, we investigate the question: “How should a mobile application be designed to support an effective self-management for diabetes patients aged 50 or older?” Our market analysis provided the basis for a survey among diabetes patients aged 50 or older and physicians, which we conducted in the second half of 2013. Within guided interviews, we investigated the current use, acceptance promoting/inhibiting factors, potentially needed support, and concrete design features for the development of a diabetes app. Merging the results of the systematic market review and the survey, a user- and needs-oriented prototype app for diabetics aged 50 or older will be developed this year. To guarantee usability and needs orientation, the prospective users and usability experts are involved in the product development process right from the beginning. User- and expert-based usability tests are performed regularly. The results are integrated continuously in the app optimization until its finalization.
The conducted review focused exclusively on apps for the currently leading operating systems, iOS and Android. Currently available diabetes apps for other operating systems, such as Windows Phone, Blackberry OS, or Symbian, were not considered within the analysis. The app publication date was solely available for iOS apps, but not for Android apps. Here, the date of the last update served as reference value. Due to that fact, the results concerning the annually new released diabetes apps were not directly comparable.
The app information was gathered by studying the descriptions in the app stores and within the app itself. More detailed information, such as download statistics, were not available for analysis. Perhaps this information would enable more detailed results concerning the user groups, for example, differentiated by gender, age groups, or type of diabetes.
Within our usability evaluation, we investigated usability criteria exclusively. We evaluated neither the quality of content and functions nor their effectiveness. Furthermore, it has to be mentioned that one usability evaluation cannot claim to cover all possible and critical usage situations that can possibly occur [
We would also stress that we examined a sample of all available diabetes apps, not just a sample of apps developed specifically for the elderly. Hence, many of the apps we evaluated do not claim to be particularly suitable for this age group.
Despite the huge amount of currently available diabetes apps, most of them offer a small number of similar functionalities. Patients and physicians should be directly involved during the app development to tackle the lack of usability and needs-orientation for its main target group diabetics. We think that data forwarding options and automated transmission of measured values to mobile devices will gain more importance in the future.
The usability of diabetes apps for patients aged 50 or older was moderate to good. But this result applied mainly to apps offering a small range of functions. Multifunctional apps performed considerably worse in terms of usability. Differed by functions, the documentation and analysis function indicated significantly lower usability scores. The operability of accessibility features for diabetes apps was quite limited, except for the feature “screen reader”.
Dataset diabetes app review.
Screenshots of apps with documentation and analysis function.
Screenshots of apps with information function.
Screenshots of apps with communication function.
Screenshots of a highly reviewed app with various functionalities.
Usability scores differed by operating systems and apps.
mobile applications
glycated hemoglobin
This study is conducted within the InnoMedTec project (Grant no. 100098212), which is funded by the European Social Fund and the Free State of Saxony. We would like to thank Dr Lars Renner, Robert Gurke, Andrea Mathe, Tiffany Evans, and Maike Bellmann for their feedback in the writing process and careful reading. We also would like to express our thanks to our reviewers for their valuable feedback and helpful suggestions that helped improve our paper. Findings from this study have been previously shown in a poster presentation at the Diabetes Congress 2013, Leipzig (Germany), and as an oral presentation at the Telemed 2013, Berlin (Germany).
None declared.