This paper is in the following e-collection/theme issue:

Reviews on Diabetes Technologies and Innovations (20) Mobile Health (mhealth) (2777) Diabetes Self-Management (464) Apps, Mobile, Wearables for Diabetes (288) mHealth for Treatment Adherence (386) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1317)

Published on in Vol 7, No 2 (2022): Apr-Jun

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33264, first published .
Smartphone Apps for Diabetes Medication Adherence: Systematic Review

Smartphone Apps for Diabetes Medication Adherence: Systematic Review

Smartphone Apps for Diabetes Medication Adherence: Systematic Review

Authors of this article:

Sheikh Mohammed Shariful Islam1 Author Orcid Image ;   Vinaytosh Mishra2 Author Orcid Image ;   Muhammad Umer Siddiqui3 Author Orcid Image ;   Jeban Chandir Moses4 Author Orcid Image ;   Sasan Adibi4 Author Orcid Image ;   Lemai Nguyen4 Author Orcid Image ;   Nilmini Wickramasinghe5 Author Orcid Image
Article Authors Cited by (11) Tweetations (23) Metrics

    Review

    1Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Faculty of Health, Deakin University, Melbourne, Australia

    2College of Healthcare Management and Economics, Gulf Medical University, Ajman, United Arab Emirates

    3Department of Internal Medicine, Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA, United States

    4School of Information Technology, Deakin University, Burwood, Australia

    5Iverson Health Innovation Research Institute, Swinburne University of Technology, Melbourne, Australia

    Corresponding Author:

    Sheikh Mohammed Shariful Islam, MBBS, MPH, PhD

    Institute for Physical Activity and Nutrition

    School of Exercise and Nutrition Sciences, Faculty of Health

    Deakin University

    221 Burwood Highway

    Burwood

    Melbourne, 3125

    Australia

    Phone: 61 0392468393

    Email: shariful@deakin.edu.au


    Background: Diabetes is one of the leading noncommunicable chronic diseases globally. In people with diabetes, blood glucose levels need to be monitored regularly and managed adequately through healthy lifestyles and medications. However, various factors contribute to poor medication adherence. Smartphone apps can improve medication adherence in people with diabetes, but it is not clear which app features are most beneficial.

    Objective: This study aims to systematically review and evaluate high-quality apps for diabetes medication adherence, which are freely available to the public in Android and Apple app stores and present the technical features of the apps.

    Methods: We systematically searched Apple App Store and Google Play for apps that assist in diabetes medication adherence, using predefined selection criteria. We assessed apps using the Mobile App Rating Scale (MARS) and calculated the mean app-specific score (MASS) by taking the average of app-specific scores on 6 dimensions, namely, awareness, knowledge, attitudes, intention to change, help-seeking, and behavior change rated on a 5-point scale (1=strongly disagree and 5=strongly agree). We used the mean of the app’s performance on these 6 dimensions to calculate the MASS. Apps that achieved a total MASS mean quality score greater than 4 out of 5 were considered to be of high quality in our study. We formulated a task-technology fit matrix to evaluate the apps for diabetes medication adherence.

    Results: We identified 8 high-quality apps (MASS score≥4) and presented the findings under 3 main categories: characteristics of the included apps, app features, and diabetes medication adherence. Our framework to evaluate smartphone apps in promoting diabetes medication adherence considered physiological factors influencing diabetes and app features. On evaluation, we observed that 25% of the apps promoted high adherence and another 25% of the apps promoted moderate adherence. Finally, we found that 50% of the apps provided low adherence to diabetes medication.

    Conclusions: Our findings show that almost half of the high-quality apps publicly available for free did not achieve high to moderate medication adherence. Our framework could have positive implications for the future design and development of apps for patients with diabetes. Additionally, apps need to be evaluated using a standardized framework, and only those promoting higher medication adherence should be prescribed for better health outcomes.

    JMIR Diabetes 2022;7(2):e33264

    doi:10.2196/33264

    Keywords

    smartphones; digital health; diabetes; medication adherence; applications; apps; mHealth; mobile health; task-technology fit 


    Diabetes is one of the leading noncommunicable chronic diseases globally and poses a significant challenge to individuals’ physical and mental health and quality of life [1,2]. According to the International Diabetes Federation, in 2019, approximately 463 million adults (9.3% of the global adult population) aged 20-79 years lived with diabetes [3,4]. By 2045, the number of people with diabetes is projected to surge to 700 million [3,4]. People with diabetes face long-term disease burden and financial costs [5], and an estimated 79% of adults with diabetes live in transitional countries [3]. In addition, more than 1.1 million children and adolescents are living with type 1 diabetes mellitus (T1DM) [3], involving autoimmune destruction of pancreatic β-cells, resulting in insulin deficiency [6]. Aging, urbanization, and lifestyle factors are causative factors of type 2 diabetes mellitus (T2DM) [4], which is a chronic metabolic disorder characterized by insulin insensitivity as a result of insulin resistance, declining insulin production, and eventual pancreatic β-cell failure [7]. Further, deaths due to diabetes have reached an alarming 4.2 million annually [3].

    In patients with diabetes, blood glucose (BG) levels need to be monitored regularly and managed adequately to maintain health and well-being [8]. Nevertheless, almost half of the people with diabetes remain nonadherent to their prescribed medications [9-11], leading to uncontrolled diabetes, poor outcomes, and lower quality of life [10,12-14]. Nonadherence to medical therapy also results in increased absenteeism, hospitalization risk, and need for health care, which have an enormous economic impact on individuals and society [15,16]. Several factors contribute to poor adherence to medication, including complex dosing regimens, clinical inertia, safety concerns, socioeconomic issues, costs of medication, ethnicity, patient education and beliefs, social support, and polypharmacy [13,17-19]. The Prospective Urban Rural Epidemiology (PURE) study showed that 26.9% and 63.0% of households in low-income countries could not afford metformin and insulin, respectively [20]. High adherence to diabetes treatment has a beneficial impact on BMI, lipid and glycemic control, and emotional and physical performance [21]. Younger age, higher numbers of medications, and higher hemoglobin A1c levels have been associated with lower medication adherence among patients with T2DM [22].

    The widespread applications of information and communication technologies (ICTs) in the health sector have resulted in significant improvements in the health care delivery system, such as promoting patient-centered health care, improving quality of care, and educating health professionals and patients [23]. ICTs, including web-based, mobile phone–based, and digital technologies for electronic capture, storage, processing, and information exchange, have been used to prevent and manage chronic disease and improve medication adherence [5,24-27]. Several studies have shown mobile health as an effective and cost-effective approach for improving diabetes care [5,28-30]. The Global Observatory for eHealth, World Health Organization, defines mobile health (mHealth) as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices.” mHealth capitalizes on mobile phones’ core utility of voice and SMS as well as more complex functionalities and applications, including general packet radio service, mobile telecommunications, a global positioning system, and Bluetooth technology [31].

    Smartphone apps on diabetes management and self-management are available for the public to download from major app stores, including Google Play and Apple App Store [32,33]. Further, several apps are available, which can collect health data, providing clinical decision support systems and assisting with medication adherence [34]. mHealth interventions are promising for the treatment and management of diabetes [35]. Further, there is strong evidence for the efficacy of apps for lifestyle modification in patients with T2DM [36] and self-managed tasks in improving health outcomes [37]. Apps have been effective in increasing treatment adherence among patients with various conditions, such as asthma, heart failure, hypertension, and HIV [38]; medication adherence among patients with depression, cardiovascular disease, Parkinson disease, hypertension, and multimorbidity [39]; older adults with coronary heart disease [40]; and essential hypertension [41]. Although a few recent reviews evaluated medication adherence among individuals with diabetes [42-44], to our knowledge, no systematic reviews have been undertaken to identify high-quality apps and evaluate their features for diabetes medication adherence. Hence, this study systematically reviews and evaluates apps available for diabetes medication adherence and presents the technical features of high-quality apps freely available to the public in Google Play and Apple App Store.


    Design

    This study adopted the principles of a systematic review process to identify and select apps, including using an app quality assessment tool, a search strategy, predefined inclusion criteria to screen apps, app rating and selection, and data extraction for qualitative analysis.

    App Selection and Assessment

    Search Strategy

    Globally, the dominating operating systems in the smartphone market are Android and iOS [45]. Hence, we searched Google Play (Android) and Apple App Store (iOS) in May 2020 for apps used for diabetes medication adherence without country-specific restrictions. The key search terms used “Diabetes OR Diabetic OR Diabetics OR blood glucose OR blood sugar” AND “medication OR medicine OR drugs OR medication adherence OR medication support.” The search produced a list of apps for screening. Figure 1 illustrates the process of app selection from the respective app stores.

    ‎
    Figure 1. App selection steps followed for Google Play and Apple App Store. MARS: Mobile App Rating Scale, T2DM: type 2 diabetes mellitus.
    View this figure
    App Screening

    Two researchers (VM and MS) screened the app titles and descriptions from the app stores, using the inclusion and exclusion criteria as shown in Textbox 1. In cases of disagreement, the third reviewer (SMSI) intervened to evaluate the situation and reach a consensus. We considered apps available on both the Apple and Google platforms as single apps. Further, we included apps with more than 100,000 downloads to identify the most common apps used in diabetes medication adherence, following the approach of a similar study to assess the quality of apps and perform a content analysis of apps targeting medical adherence [46]. We evaluated high-quality apps using the Mobile App Rating Scale (MARS) [47]. Apps achieving a total mean quality score greater than 4 out of 5 in our study were considered to be of high quality [48]. MARS is a validated tool that measures app quality across 5 multidimensional layers to assess the quality of apps: (1) engagement, (2) functionality, (3) aesthetics, (4) information, and (5) app subjective quality [47]. The MARS scale gives equal weightage to all 5 dimensions and uses a 5-point rating scale from 1 to 5 with 1=inadequate and 5=excellent. The maximum score for each dimension is 25 for engagement, 20 for functionality, 15 for aesthetics, 35 for information, and 20 for app subjective quality. We calculated the total mean score by determining the mean of the average score for the 5 dimensions [47].

    App selection criteria.

    Inclusion criteria:

    • Apps for adults with type 2 diabetes
    • Apps with functionality supporting medication adherence or self-management features
    • Apps in the English language
    • Apps available for free and not requiring a paid subscription
    • Apps with >500 user ratings

    Exclusion criteria:

    • Apps intended only for use by health care professionals and not general public
    • Apps not updated since January 1, 2018
    • Apps providing only diabetes education or suggestions for medication reminders
    • Apps with country restrictions
    • Apps that had any technical issues such as problems with downloading, logging in, and crashing
    Textbox 1. App selection criteria.
    App Assessment

    To determine which apps to include in this study, the apps obtained after preliminary screening and application of the initial inclusion criteria were downloaded and used by the two authors (VM and MS) independently to test their functionality. After discussion, the quality assessment was reported and in case of disagreements, input from the third author was sought. Further, we calculated the mean app-specific score (MASS) by taking the average of app-specific scores on 6 dimensions, namely, awareness, knowledge, attitudes, intention to change, help-seeking, and behavior change. These are also rated on a 5-point scale with 1=strongly disagree and 5=strongly agree. We used the mean of the app’s performance on these 6 dimensions to calculate the MASS [47]. We further assessed the perceived impact of the app on the users’ knowledge, attitudes, intentions to change, and the likelihood of actual change on the target health behavior (diabetes medication adherence in our case). Multimedia Appendix 1 presents an app-specific evaluation and the MASS for the considered apps.

    App Rating and Selection

    We considered apps with a MASS greater than 4 to identify high-quality apps for diabetes medication adherence.

    Data Extraction

    Two reviewers performed the data extraction. One reviewer evaluated Apple App Store’s apps, and the other reviewer evaluated Google Play apps. Before reviewing the apps, the reviewers conferred and decided to include all the critical features that the apps provide. We specifically focused on medication reminders and the adherence features that the apps offer. The reviewers extracted information on the app features using a predetermined Excel (Microsoft Inc) sheet. The Excel sheet was developed on the basis of a pilot app assessment exercise with 5 apps with extensive features; the Excel sheet was refined for this study. We excluded apps that could not be assessed owing to country-specific or other restrictions. We included additional features of free apps that were available upon subscription only.

    Data Analysis

    We grouped the apps on the basis of the operating system (ie, Android and iOS), presented the mean MARS rating, and summarized the main features. We also presented the MASS and app-specific rating for medication adherence based on awareness, knowledge, attitude, intention to change, help-seeking, and behavior change.


    Overview

    We identified 249 apps in Google Play and 209 apps in the Apple App Store. The initial inclusion criteria resulted in 63 and 39 Apps for Google Play and Apple App Store, respectively. Finally, 8 apps with a MARS greater than 4 were included. The mean MASS for the included apps was 4.2, and the average app rating by users was 4.7. Figure 2 depicts the app selection methodology followed for both Apple App Store and Google Play for selecting the relevant apps that satisfy the selection criteria.

    ‎
    Figure 2. Summary of app selection for review. MASS: mean app-specific score.
    View this figure

    Characteristics of the Included Apps

    All 8 apps were available in the Apple Store [49-56], 7 were available in Google Play [49-55], and one app had the option to be accessed through a web application and Amazon Alexa [56]. Given that all the apps were available in the Apple App Store, we extracted information for the apps from the App Store. Table 1 presents an overview of the apps. In contrast, Multimedia Appendix 2 presents detailed information for each app.

    Table 1. Overview of the apps.
    AppsOperating systemCategoryAvailable languages, nData privacySellerIn-app purchases




    		IdentifiedDeidentified

    Diabetes:M [49]Android and iOSMedical8YesYesChronic disease software development companyYes
    mySugr - Diabetes Tracker Log [52]Android and iOSMedical24YesNoDigital health companyYes

    Health2Sync [51]Android and iOSMedical4YesNoDigital health companyYes

    MyTherapy Pill Reminder [53]Android and iOSMedical30NoYes

    		Health, wellness, and fitnessNo
    One Drop: Transform Your Life [54]Android and iOSHealth and fitness11YesYesSoftware and Technology servicesYes

    Glucose Buddy Diabetes Tracker [50]Android and iOSMedical31YesYesIndividualYes

    OneTouch Reveal [55]Android and iOSMedical14YesYesDiagnostic systems manufacturerNo
    Sugarmate [56]iOSMedical5Required to provide with the next app updateRequired to provide with the next app updateSoftware companyNo

    The Apple App Store apps were categorized as medical [49-53,55,56] and health and fitness [54] apps. Including English language, the apps were available in around 5 [51,56], 10 [49,54,55], 20 [52], and 30 languages [50,53]. Additionally, all the apps had family sharing options (ie, the app could be shared with and used by 6 family members) [49-56]. Apart from one app [56], the apps defined their privacy policy explicitly, defining the identified [49-52,54,55] and deidentified data [49,50,53-55], although it differed between the apps. For example, data such as health and fitness information, contact information, identifiers, diagnostics, location, user content, and usage data were considered “identified” data by one app [52]. In contrast, the same data were considered “deidentified” data by another app [53]. Further, the apps were sold by software companies [54,56], digital health companies [49,51,52,55], health and fitness companies [53], and individuals [50]. Finally, although all the apps were free [49-56], they had an in-app purchase option [49-52,54]; that is, users had the option to pay a prescribed amount to access additional features, such as premium subscription features [49-52] and access to a coach [54].

    The compatible operating system (OS) version for the apps varied drastically between the apps. For the apps to run smoothly, 9.0 [49], 10.3 [56], 11.0 [51], 12.0 [50], 13.0 [53-55], 13.2 [52] or later versions of the OS were required.

    App Features

    Overview

    In this section, we discuss the salient features of the apps evaluated against standard parameters. However, we have considered the free app features for this analysis (and we have not considered the in-app features). Figure 3 illustrates the apps and their corresponding features.

    ‎
    Figure 3. Apps and their corresponding features. CGM: continuous glucose monitoring, DM: diabetes mellitus, T1DM: type 1 diabetes mellitus, T2DM: type 2 diabetes mellitus.
    View this figure
    Device Objective and Target Population

    The primary objective of the apps was to manage diabetes [49-52,55], manage diabetes and heart health [54], assist in medication tracking [53], and function as a companion to a continuous glucose monitoring (CGM) device [56]. Although a few apps have been developed for the general population irrespective of their medical condition [50,53,56], a few other apps aimed to address the needs of patients with T2DM [51,54]. In contrast, other apps had functionalities to address the health and well-being needs of patients with T1DM, T2DM, or gestational diabetes mellitus [49,52,55]. The apps had age ratings of 4+ [50-54,56] and 17+ [49,55].

    BG Reading

    The apps had different methodologies and used varying technologies to capture the BG recordings. For example, some apps could log in to the recordings manually [50,53]. In contrast, a few other apps could capture CGM recordings from the integrated BG monitoring (BGM) devices [55,56]. Nevertheless, some other apps could manually log in the BG recordings and CGM recordings from the integrated BGM devices [49,51,52,54].

    Health Data

    In addition to recording BG readings, the apps had the option to capture other health-related data, including food consumed [49,50,52,54-56], blood pressure [50-54], weight [50-54], daily activity and steps walked [52,54-56], and medication [49,50,52,54]. Further, regarding food consumption, the apps specifically considered the intake of carbohydrates [49,50,52,55,56], protein [49], fats [49], and calories [49].

    Device Integration

    There are various BGM or CGM devices available in the market, and some apps could integrate and function with multiple devices, such as Dexcom, OneTouch, and Accu-Chek [49,51,54]. In contrast, other apps functioned exclusively with a device, such as Accu-Chek [52] and Dexcom [49,50,54]. Moreover, an app developed by the seller of the OneTouch device aimed to capture CGM recordings to perform analysis tasks integrating other health parameters [55]. In contrast, a seller not related to the Dexcom device developed an app to capture the CGM readings in real time and perform other tasks [56].

    Reports

    The apps had features to generate reports on BG levels, taking into consideration other factors including food consumed [49,50,52,54-56], physical activity [49,54,55], medication [49,50,52,54], and other vital recordings [50,51,53,54,56]. From the reports generated, the apps provided an overview of short durations of captured data such as whenever the app is launched [51]; hourly [50], daily [49,52,54,56], weekly [49,52,54], and fortnightly [55]; and longer durations such as monthly [49,52,54] and yearly [49]. Moreover, a few apps could alert individuals when their BG levels dropped below or increased past threshold values through notifications [49,55], SMS text messaging [51,56], or phone calls [56].

    Reminders

    Forgetting to take medication is a significant cause of medication nonadherence among patients [57]; hence, apps with reminders could improve adherence. The apps had reminders to check glucose levels [49], take meals [49,54,55], medications [53,54], exercise [53,54], and address other personalized medical needs [51,55].

    Adherence Motivation

    To continuously support individuals in their journey of diabetes medication adherence, apps have provided options to invite clinicians, nutritionists, family, friends, and loved ones to view their progress and accordingly assist them [51,53,54]; have patient information for further support [49], providing personalized tips [51,53,55]; and deliver an education plan featuring 5-minute lessons [50]. In addition, a few apps had gamification features (ie, elements of game-playing), such as point-scoring, competition with others, and challenges to encourage engagement [52,54-56].

    Diabetes Medication Adherence

    Among people with diabetes, optimum glycemic control is essential, and good adherence is associated with a lower risk of all-cause mortality and hospitalization [58]. Many models have been studied to explore the acceptance and use of technologies. One such model is task-technology fit (TTF). TTF states that a technology should be effective in completing the assigned task, which will lead to an increase in user performance and adoption [59]. TTF rates the task characteristics and technology characteristics of apps, which affects the effectiveness and adoption of the apps by the user. Accordingly, in this section, we used the TTF and quantified the effectiveness of apps in diabetes adherence to evaluate whether the apps promote adherence to diabetes medication [60,61]. A recent study used TTF to evaluate the effectiveness of the mHealth app in delivering health care services [62].

    Primary prevention and mitigating the severity of diabetes revolve around regular BG checks, food consumed, physical activity, other vital readings, and adherence to medication [63]. Hence, they are the primary factors considered in this study. Although apps have been found to improve awareness of medication adherence and reduce self-reported barriers to medication adherence among medication-nonadherent patients with diabetes [64], a study observed that a large proportion of diabetes self-management apps lacked features for enhancing medication adherence and safety [65]. Consequently, among the app features, we considered the overview (report generated and graphically presented for a period), reminders (customized alerts to perform tasks), notifications (alerts when BG shoots up or drops below the threshold), assistance (additional support provided by various stakeholders), and motivation (assistance from loved ones and challenges through gamification) as the imperative app features needed to support an individual in diabetes medication adherence.

    Table 2 presents a TTF matrix for diabetes medication adherence to evaluate the apps against the defined primary factors and app features. In the matrix, we have marked “yes” when factors and features are available. We then totaled the score for primary factors and app features separately. We defined a score of 5 in both primary factors and app features as apps assisting in very high adherence, 4 and above in either primary factors or app features as apps assisting in high adherence, and a score of 3 and above in either primary factors or app features as apps assisting in moderate adherence. We further defined a low adherence app as apps scoring 2 and above in either primary factors or app features and any score below 2 for primary factors or app features as apps resulting in poor adherence.

    Table 2. The task-technology fit matrix for diabetes medication adherence.
    AppPrimary factorsApp features
    Blood glucoseFoodPhysical ActivityMedicationVital signsScoreOverviewReminderNotificationAssistanceMotivationScore
    Diabetes:M [49]YesYesYesYesNo4YesYesYesYesNo4
    mySugr - Diabetes Tracker Log [52]YesYesNoYesNo3YesNoNoNoYes2
    Health2Sync [51]YesNoNoNoYes2YesYesYesYesYes5
    MyTherapy Pill Reminder [53]YesNoNoNoYes2
    		YesNoYesYes3
    One Drop: Transform Your Life [54]YesYesYesYesYes5YesYesNoNoYes3
    Glucose Buddy Diabetes Tracker [50]YesNoNoYesYes3YesNoNoYesNo2
    OneTouch Reveal [55]YesYesYesYesNo4YesYesYesYesYes5
    Sugarmate [56]YesYesNoYesYes4YesNoYesNoYes3

    According to our evaluation, none of the apps assists in very high adherence, and there are no poor-adherence apps. In addition, we found that 3 apps each assisted in high adherence (25%) [49,55] and moderate adherence to diabetes medication (25%) [54,56]. In contrast, 4 other apps were in the low adherence category in assisting with diabetes medication adherence (50%) [50-53].


    Principal Findings

    In this systematic review, we identified 8 high-quality apps publicly available in the Apple App Store and Google Play free of charge. The mean MASS of the apps was 4.2, while the average user ratings was 4.7. Only 2 apps showed high adherence and 2 apps showed moderate adherence, while half of the apps showed low adherence. However, since the global market is poised for rapid growth and could have widespread implications in delivering personalized health care [66], interoperable mHealth solutions available in the Apple App Store and Google Play are generally designed and developed for widespread adaptability. Our systematic review suggests evaluating apps using a standardized framework before prescribing them to patients with diabetes, and using behavioral theories for improving medication adherence.

    All the apps were available in English and more than 5 other languages [49-56]. Moreover, bilingual English-speaking patients expressed the need for language translation to understand and communicate using the apps [67], and the selected apps could address this concern through the multilanguage option provided. However, globally, 79% of adults with diabetes live in transitional countries [3]. An increase in mobile phone subscriptions over the years, including in transitional countries [68], and the constant technological advancements in mobile phones [69] promote mHealth as effective for use in transitional countries having barriers, such as lack of infrastructure and equipment and technology gaps [70]. Hence, if the selected apps have languages that predominate in transitional countries with a higher prevalence of diabetes, they might assist in diabetes management, reducing the burden on the health care system and curbing mortality rates.

    Deidentification is essential for protecting patient privacy and removing identifiers that directly or indirectly point to a person [71]. However, there is variability in the definition of deidentification [72]. For instance, among the considered apps, health and fitness, contact information, identifiers, diagnostics, location, user content, and usage data were considered identified data by one app [52]. In contrast, the same data were deemed deidentified data by another app [53]. This discrepancy could be due to differences in definitions and legal practice followed in countries where the apps have been developed [72]. Hence, health care apps developed in accordance with the guidelines of legislation, such as the US Health Insurance Portability and Accountability Act and the European General Data Protection Regulation, could have uniformity in defining identified and deidentified data [72]. Accordingly, following global regulatory guidelines could bring uniformity in the use of identified and deidentified data.

    In-app purchases permit the user to purchase added services from within an app, and 5 apps considered in this study had this feature [49-52,54]. Remarkably, in-app purchase options work well in promoting health apps wherein the essential functions are offered freely [73]. However, the intention to upgrade to a paid subscription is driven by the subscription features, benefits, and price value [74]. Hence, developing free health apps with in-app purchase options will be necessary to promote the app; however, the content should provide sufficient value to retain subscriptions [74].

    A few apps described in this study could be used by all patients with diabetes [50,53,56]. In contrast, other apps were specifically targeted to patients with T2DM [51,54] or to those with T1DM, T2DM, or gestational DM [49,52,55]. Nevertheless, apps with specific descriptions were sought more by users compared to those with general descriptions [75]. Some apps had an age rating of ≥4 [50-54,56] and ≥17 [49,55]. T1DM affects children [3], and although some apps were indicated as suitable for patients with T1DM, the age rating was ≥17 [49,55]. Another app had an age rating of ≥4 and could be used by patients with T1DM [52]. Hence, developing apps with specific descriptions and appropriate age ratings could assist the appropriate target population.

    We have considered physiological factors influencing diabetes and app features to develop and assess apps using the TTF matrix.

    Limitations of the Research

    This review had certain limitations. First, in this study, we included apps that had greater than 500 ratings. Therefore, we could have eliminated several new apps with fewer than 500 reviews, but that might have had a higher MASS. Second, we included only apps in the English language, possibly missing effective apps in languages other than English. Third, our search date was limited to May 2020. However, the constraints associated with the severity of the COVID-19 pandemic could have enabled the development and usage of apps that satisfied our selection criteria. Finally, we limited our search to the apps available in the major app stores: Apple App Store and Google Play. However, these stores account for 80% (4.41 million) of the global apps as of May 2020 [76].

    Implications and Future Research

    Our findings provide guidelines for app developers to develop an app that assists in diabetes medication adherence. Further, several apps are very likely to be developed and prescribed during the pandemic period [77,78]. Therefore, evaluating the apps using our framework could help the apps to provide high medication adherence for better health and well-being of patients with diabetes. With the advancement in smartphone technology, various health vitals could be captured and transferred in real time for analysis [79-81]. The application of machine learning and big data could provide a wealth of predictive information to the patient and to health care professionals [82-87]. Furthermore, high-quality apps coupled with evidence-based ICT programs using user-centric designs, wearable device, and machine learning approaches could be used to provide personalized interventions for people with diabetes [84,87-93]. Hence, our findings could have practical and research implications for diabetes medication adherence.

    Conclusions

    Our framework to evaluate smartphone apps in promoting diabetes medication adherence has considered physiological factors influencing diabetes and app features. Therefore, our findings could have positive implications for the design and development of apps for patients with diabetes. Additionally, the available apps could be evaluated in accordance with our framework, and those apps promoting higher medication adherence could be prescribed for better health outcomes.

    Acknowledgments

    SMSI is funded by the National Heart Foundation of Australia (102112) and a National Health and Medical Research Council Emerging Leadership Fellowship (APP1195406). We thank Jedha Denning, PhD student, Deakin University, for assisting in this study and Capstone Editing Services for professional English language editing.

    Authors' Contributions

    SMSI, VM, and MUS conceptualized the paper. VM and MUS downloaded the apps and performed screening and data extraction. SMSI, VM, and MUS wrote the first draft. All other authors provided data, developed models, reviewed results, provided guidance on methodology, or reviewed and contributed to the manuscript. All authors approved the final version of the manuscript.

    Conflicts of Interest

    None declared.

    ‎
    Multimedia Appendix 1

    Apps Selected for the Review and MASS.

    DOCX File , 15 KB
    ‎
    Multimedia Appendix 2

    Detailed information on all apps.

    DOCX File , 21 KB
    1. Islam SMS, Purnat TD, Phuong NTA, Mwingira U, Schacht K, Fröschl G. Non-communicable diseases (NCDs) in developing countries: a symposium report. Global Health 2014 Dec 11;10(1):81 [FREE Full text] [CrossRef] [Medline]
    2. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract 2022 Jan;183:109119. [CrossRef] [Medline]
    3. International Diabetes Federation.   URL: https://www.idf.org/aboutdiabetes/what-is-diabetes/facts-figures.html [accessed 2020-12-10]
    4. Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, IDF Diabetes Atlas Committee. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9 edition. Diabetes Res Clin Pract 2019 Nov;157:107843. [CrossRef] [Medline]
    5. Islam SMS, Peiffer R, Chow CK, Maddison R, Lechner A, Holle R, et al. Cost-effectiveness of a mobile-phone text messaging intervention on type 2 diabetes—A randomized-controlled trial. Health Policy and Technology 2020 Mar;9(1):79-85. [CrossRef]
    6. Kahanovitz L, Sluss PM, Russell SJ. Type 1 Diabetes - A Clinical Perspective. Point Care 2017 Mar;16(1):37-40 [FREE Full text] [CrossRef] [Medline]
    7. Olokoba AB, Obateru OA, Olokoba LB. Type 2 diabetes mellitus: a review of current trends. Oman Med J 2012 Jul 16;27(4):269-273 [FREE Full text] [CrossRef] [Medline]
    8. Nguyen L, Adibi S, Wickramasinghe N. Towards a Better Life for Diabetic Patients: Developing and Integrating a Non-invasive Self-Management Support Tool Within a Smart Digital Companion. In: Delivering Superior Health and Wellness Management with IoT and Analytics. Cham: Springer; 2019:207-222.
    9. Aminde LN, Tindong M, Ngwasiri CA, Aminde JA, Njim T, Fondong AA, et al. Adherence to antidiabetic medication and factors associated with non-adherence among patients with type-2 diabetes mellitus in two regional hospitals in Cameroon. BMC Endocr Disord 2019 Apr 03;19(1):35 [FREE Full text] [CrossRef] [Medline]
    10. Islam SMS, Biswas T, Bhuiyan FA, Mustafa K, Islam A. Patients' perspective of disease and medication adherence for type 2 diabetes in an urban area in Bangladesh: a qualitative study. BMC Res Notes 2017 Mar 21;10(1):131 [FREE Full text] [CrossRef] [Medline]
    11. Polonsky W, Henry R. Poor medication adherence in type 2 diabetes: recognizing the scope of the problem and its key contributors. PPA 2016 Jul;Volume 10:1299-1307. [CrossRef]
    12. Islam SMS, Uddin R, Zaman SB, Biswas T, Tansi T, Chegini Z, et al. Healthcare seeking behavior and glycemic control in patients with type 2 diabetes attending a tertiary hospital. Int J Diabetes Dev Ctries 2020 Oct 08;41(2):280-287. [CrossRef]
    13. Safita N, Islam SMS, Chow CK, Niessen L, Lechner A, Holle R, et al. The impact of type 2 diabetes on health related quality of life in Bangladesh: results from a matched study comparing treated cases with non-diabetic controls. Health Qual Life Outcomes 2016 Sep 13;14(1):129 [FREE Full text] [CrossRef] [Medline]
    14. Islam SMS, Rawal LB, Niessen LW. Prevalence of depression and its associated factors in patients with type 2 diabetes: A cross-sectional study in Dhaka, Bangladesh. Asian J Psychiatr 2015 Oct;17:36-41. [CrossRef] [Medline]
    15. Cutler RL, Fernandez-Llimos F, Frommer M, Benrimoj C, Garcia-Cardenas V. Economic impact of medication non-adherence by disease groups: a systematic review. BMJ Open 2018 Jan 21;8(1):e016982 [FREE Full text] [CrossRef] [Medline]
    16. Shariful Islam SM, Lechner A, Ferrari U, Laxy M, Seissler J, Brown J, et al. Healthcare use and expenditure for diabetes in Bangladesh. BMJ Glob Health 2017 Jan 03;2(1):e000033 [FREE Full text] [CrossRef] [Medline]
    17. Bailey C, Kodack M. Patient adherence to medication requirements for therapy of type 2 diabetes. Int J Clin Pract 2011 Mar;65(3):314-322. [CrossRef] [Medline]
    18. Islam SMS, Islam MT, Uddin R, Tansi T, Talukder S, Sarker F, et al. Factors associated with low medication adherence in patients with Type 2 diabetes mellitus attending a tertiary hospital in Bangladesh. Lifestyle Medicine 2021 Sep 07;2(4). [CrossRef]
    19. Kherallah R, Al Rifai M, Kamat I, Krittanawong C, Mahtta D, Lee MT, et al. Prevalence and predictors of cost-related medication nonadherence in individuals with cardiovascular disease: Results from the Behavioral Risk Factor Surveillance System (BRFSS) survey. Prev Med 2021 Dec;153:106715 [FREE Full text] [CrossRef] [Medline]
    20. Chow CK, Ramasundarahettige C, Hu W, AlHabib KF, Avezum A, Cheng X, et al. Availability and affordability of essential medicines for diabetes across high-income, middle-income, and low-income countries: a prospective epidemiological study. Lancet Diabetes Endocrinol 2018 Oct;6(10):798-808. [CrossRef]
    21. Marinho FS, Moram CBM, Rodrigues PC, Leite NC, Salles GF, Cardoso CRL. Treatment Adherence and Its Associated Factors in Patients with Type 2 Diabetes: Results from the Rio de Janeiro Type 2 Diabetes Cohort Study. J Diabetes Res 2018 Nov 27;2018:8970196-8970198 [FREE Full text] [CrossRef] [Medline]
    22. Aloudah NM, Scott NW, Aljadhey HS, Araujo-Soares V, Alrubeaan KA, Watson MC. Medication adherence among patients with Type 2 diabetes: A mixed methods study. PLoS One 2018 Dec 11;13(12):e0207583 [FREE Full text] [CrossRef] [Medline]
    23. Islam SMS, Tabassum R. Implementation of information and communication technologies for health in Bangladesh. Bull World Health Organ 2015 Aug 31;93(11):806-809. [CrossRef]
    24. Gutierrez MA, Moreno RA, Rebelo MS. Information and Communication Technologies and Global Health Challenges. In: Global Health Informatics: How Information Technology Can Change our Lives in a Globalized World. Cambridge, MA: Academic Press; 2017:50-93.
    25. Rouleau G, Gagnon M, Côté J. Impacts of information and communication technologies on nursing care: an overview of systematic reviews (protocol). Syst Rev 2015 May 23;4(1):75 [FREE Full text] [CrossRef] [Medline]
    26. Shariful Islam SM, Niessen LW, Ferrari U, Ali L, Seissler J, Lechner A. Effects of Mobile Phone SMS to Improve Glycemic Control Among Patients With Type 2 Diabetes in Bangladesh: A Prospective, Parallel-Group, Randomized Controlled Trial. Diabetes Care 2015 Aug;38(8):e112-e113. [CrossRef] [Medline]
    27. Dening J, Islam SMS, George E, Maddison R. Web-Based Interventions for Dietary Behavior in Adults With Type 2 Diabetes: Systematic Review of Randomized Controlled Trials. J Med Internet Res 2020 Aug 28;22(8):e16437 [FREE Full text] [CrossRef] [Medline]
    28. Chen YR, Schulz PJ. The Effect of Information Communication Technology Interventions on Reducing Social Isolation in the Elderly: A Systematic Review. J Med Internet Res 2016 Jan 28;18(1):e18 [FREE Full text] [CrossRef] [Medline]
    29. Shariful Islam SM, Farmer AJ, Bobrow K, Maddison R, Whittaker R, Pfaeffli Dale LA, et al. Mobile phone text-messaging interventions aimed to prevent cardiovascular diseases (Text2PreventCVD): systematic review and individual patient data meta-analysis. Open Heart 2019 Oct 09;6(2):e001017 [FREE Full text] [CrossRef] [Medline]
    30. Shariful Islam SM, Lechner A, Ferrari U, Seissler J, Holle R, Niessen LW. Mobile phone use and willingness to pay for SMS for diabetes in Bangladesh. J Public Health (Oxf) 2016 Mar 16;38(1):163-169. [CrossRef] [Medline]
    31. mHealth: new horizons for health through mobile technologies: second global survey on eHealth. World Health Organization. 2011.   URL: https://www.who.int/goe/publications/goe_mhealth_web.pdf [accessed 2022-04-13]
    32. Larbi D, Randine P, Årsand E, Antypas K, Bradway M, Gabarron E. Methods and Evaluation Criteria for Apps and Digital Interventions for Diabetes Self-Management: Systematic Review. J Med Internet Res 2020 Jul 06;22(7):e18480 [FREE Full text] [CrossRef] [Medline]
    33. Veazie S, Winchell K, Gilbert J, Paynter R, Ivlev I, Eden KB, et al. Rapid Evidence Review of Mobile Applications for Self-management of Diabetes. J Gen Intern Med 2018 Jul 8;33(7):1167-1176 [FREE Full text] [CrossRef] [Medline]
    34. Kubben P. Mobile apps, in Fundamentals of Clinical Data Science. In: Kubben P, Dumontier M, Dekker A, editors. Fundamentals of Clinical Data Science. Cham: Springer; 2019:171-179.
    35. Wang Y, Min J, Khuri J, Xue H, Xie B, A Kaminsky L, et al. Effectiveness of Mobile Health Interventions on Diabetes and Obesity Treatment and Management: Systematic Review of Systematic Reviews. JMIR Mhealth Uhealth 2020 Apr 28;8(4):e15400 [FREE Full text] [CrossRef] [Medline]
    36. Wu X, Guo X, Zhang Z. The Efficacy of Mobile Phone Apps for Lifestyle Modification in Diabetes: Systematic Review and Meta-Analysis. JMIR Mhealth Uhealth 2019 Jan 15;7(1):e12297 [FREE Full text] [CrossRef] [Medline]
    37. Jeffrey B, Bagala M, Creighton A, Leavey T, Nicholls S, Wood C, et al. Mobile phone applications and their use in the self-management of Type 2 Diabetes Mellitus: a qualitative study among app users and non-app users. Diabetol Metab Syndr 2019 Oct 16;11(1):84 [FREE Full text] [CrossRef] [Medline]
    38. Pérez-Jover V, Sala-González M, Guilabert M, Mira JJ. Mobile Apps for Increasing Treatment Adherence: Systematic Review. J Med Internet Res 2019 Jun 18;21(6):e12505 [FREE Full text] [CrossRef] [Medline]
    39. Armitage LC, Kassavou A, Sutton S. Do mobile device apps designed to support medication adherence demonstrate efficacy? A systematic review of randomised controlled trials, with meta-analysis. BMJ Open 2020 Jan 30;10(1):e032045 [FREE Full text] [CrossRef] [Medline]
    40. Park LG, Ng F, K Shim J, Elnaggar A, Villero O. Perceptions and experiences of using mobile technology for medication adherence among older adults with coronary heart disease: A qualitative study. Digit Health 2020 May 20;6:2055207620926844 [FREE Full text] [CrossRef] [Medline]
    41. Mohammadi Tork Abad S, Negahban Bonabi T, Heidari S. Effectiveness of smartphone-based medication reminder application on medication adherence of patients with essential hypertension: A clinical trial study. J Nurs Midwifery Sci 2020;7(4):219. [CrossRef]
    42. McGovern A, Tippu Z, Hinton W, Munro N, Whyte M, de Lusignan S. Systematic review of adherence rates by medication class in type 2 diabetes: a study protocol. BMJ Open 2016 Feb 29;6(2):e010469 [FREE Full text] [CrossRef] [Medline]
    43. Capoccia K, Odegard PS, Letassy N. Medication Adherence With Diabetes Medication: A Systematic Review of the Literature. Diabetes Educ 2016 Feb 04;42(1):34-71. [CrossRef] [Medline]
    44. Anderson L, Nuckols TK, Coles C, Le MM, Schnipper JL, Shane R, Members of the PHARM-DC Group. A systematic overview of systematic reviews evaluating medication adherence interventions. Am J Health Syst Pharm 2020 Jan 08;77(2):138-147 [FREE Full text] [CrossRef] [Medline]
    45. Chmielarz W. The Usage of Smartphone and Mobile Applications from the Point of View of Customers in Poland. Information 2020 Apr 17;11(4):220. [CrossRef]
    46. Park JYE, Li J, Howren A, Tsao NW, De Vera M. Mobile Phone Apps Targeting Medication Adherence: Quality Assessment and Content Analysis of User Reviews. JMIR Mhealth Uhealth 2019 Jan 31;7(1):e11919 [FREE Full text] [CrossRef] [Medline]
    47. Stoyanov SR, Hides L, Kavanagh DJ, Zelenko O, Tjondronegoro D, Mani M. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015 Mar 11;3(1):e27 [FREE Full text] [CrossRef] [Medline]
    48. LeBeau K, Huey LG, Hart M. Assessing the Quality of Mobile Apps Used by Occupational Therapists: Evaluation Using the User Version of the Mobile Application Rating Scale. JMIR Mhealth Uhealth 2019 May 01;7(5):e13019 [FREE Full text] [CrossRef] [Medline]
    49. Diabetes:M. Manage Diabetes Like a Pro!. Apple Inc.   URL: https://apps.apple.com/au/app/diabetes-m/id1196733537 [accessed 2022-06-02]
    50. Glucose Buddy Diabetes Tracker (Version 5.328). Apple Inc.   URL: https://apps.apple.com/us/app/glucose-buddy-diabetes-tracker/id294754639 [accessed 2022-06-02]
    51. Health2Sync (Version 2.5.2). Apple Inc.   URL: https://apps.apple.com/au/app/health2sync/id806136243 [accessed 2022-06-02]
    52. mySugr - Diabetes Tracker Log (Version 3.83.4). Apple Inc.   URL: https://apps.apple.com/au/app/mysugr-diabetes-tracker-log/id516509211 [accessed 2022-06-02]
    53. MyTherapy Pill Reminder (Version 3.75.2). Apple Inc.   URL: https://apps.apple.com/au/app/mytherapy-pill-reminder/id662170995 [accessed 2022-06-02]
    54. One Drop: Better Health Today (Version 6.17.0). Apple Inc.   URL: https://apps.apple.com/us/app/one-drop-diabetes-management/id972238816 [accessed 2022-06-02]
    55. OneTouch Reveal (Version 5.3). Apple Inc.   URL: https://apps.apple.com/us/app/onetouch-reveal/id651293599?shortlink=ed2241e6&pid=US%20Site [accessed 2022-06-02]
    56. Sugarmate | Diabetes Tracker (Version 2.6.23). Apple Inc.   URL: https://apps.apple.com/us/app/sugarmate/id1111093108 [accessed 2022-06-02]
    57. Alshehri K, Altuwaylie TM, Alqhtani A, Albawab AA, Almalki AH. Type 2 Diabetic Patients Adherence Towards Their Medications. Cureus 2020 Feb 10;12(2):e6932 [FREE Full text] [CrossRef] [Medline]
    58. Khunti N, Khunti N, Khunti K. Adherence to type 2 diabetes management. Br J Diabetes 2019 Dec 17;19(2):99-104. [CrossRef]
    59. Goodhue DL, Thompson RL. Task-Technology Fit and Individual Performance. MIS Quarterly 1995 Jun;19(2):213. [CrossRef]
    60. Spies R, Grobbelaar S, Botha A. A Scoping Review of the Application of the Task-Technology Fit Theory. 2020 Presented at: 19th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2020; April 6-8, 2020; Skukuza p. 397-408. [CrossRef]
    61. Wickramasinghe N, Schaffer JL, Seitz J, Muhammad I, Vogel D. Fit-Viability Model Examination of e-Health Solutions. In: Wickramasinghe N, Schaffer J, editors. Theories to Inform Superior Health Informatics Research and Practice. Cham: Springer; 2018:251-269.
    62. Zaidi S, Kazi AM, Riaz A, Ali A, Najmi R, Jabeen R, et al. Operability, Usefulness, and Task-Technology Fit of an mHealth App for Delivering Primary Health Care Services by Community Health Workers in Underserved Areas of Pakistan and Afghanistan: Qualitative Study. J Med Internet Res 2020 Sep 17;22(9):e18414 [FREE Full text] [CrossRef] [Medline]
    63. Blaslov K, Naranđa FS, Kruljac I, Renar IP. Treatment approach to type 2 diabetes: Past, present and future. World J Diabetes 2018 Dec 15;9(12):209-219 [FREE Full text] [CrossRef] [Medline]
    64. Huang Z, Tan E, Lum E, Sloot P, Boehm BO, Car J. A Smartphone App to Improve Medication Adherence in Patients With Type 2 Diabetes in Asia: Feasibility Randomized Controlled Trial. JMIR Mhealth Uhealth 2019 Sep 12;7(9):e14914. [CrossRef]
    65. Huang Z, Lum E, Jimenez G, Semwal M, Sloot P, Car J. Medication management support in diabetes: a systematic assessment of diabetes self-management apps. BMC Med 2019 Jul 17;17(1):127 [FREE Full text] [CrossRef] [Medline]
    66. Li D. 5G and intelligence medicine-how the next generation of wireless technology will reconstruct healthcare? Precis Clin Med 2019 Dec;2(4):205-208 [FREE Full text] [CrossRef] [Medline]
    67. Albrecht U, Behrends M, Schmeer R, Matthies HK, von Jan U. Usage of multilingual mobile translation applications in clinical settings. JMIR Mhealth Uhealth 2013 Apr 23;1(1):e4 [FREE Full text] [CrossRef] [Medline]
    68. DataBank: World Development Indicators. The World Bank.   URL: https://databank.worldbank.org/reports.aspx?source=2&series=IT.CEL.SETS.P2 [accessed 2022-09-20]
    69. Harris A, Cooper M. Mobile phones: Impacts, challenges, and predictions. Hum Behav & Emerg Tech 2019 Feb 18;1(1):15-17. [CrossRef]
    70. Kruse C, Betancourt J, Ortiz S, Valdes Luna SM, Bamrah IK, Segovia N. Barriers to the Use of Mobile Health in Improving Health Outcomes in Developing Countries: Systematic Review. J Med Internet Res 2019 Oct 09;21(10):e13263 [FREE Full text] [CrossRef] [Medline]
    71. Ahmed T, Aziz MMA, Mohammed N. De-identification of electronic health record using neural network. Sci Rep 2020 Oct 29;10(1):18600 [FREE Full text] [CrossRef] [Medline]
    72. Chevrier R, Foufi V, Gaudet-Blavignac C, Robert A, Lovis C. Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review. J Med Internet Res 2019 May 31;21(5):e13484 [FREE Full text] [CrossRef] [Medline]
    73. Aydin G, Silahtaroglu G. Insights into mobile health application market via a content analysis of marketplace data with machine learning. PLoS One 2021 Jan 6;16(1):e0244302 [FREE Full text] [CrossRef] [Medline]
    74. Mäntymäki M, Islam AN, Benbasat I. What drives subscribing to premium in freemium services? A consumer value‐based view of differences between upgrading to and staying with premium. Info Systems J 2019 Aug 16;30(2):295-333. [CrossRef]
    75. Paglialonga A, Lugo A, Santoro E. An overview on the emerging area of identification, characterization, and assessment of health apps. J Biomed Inform 2018 Jul;83:97-102 [FREE Full text] [CrossRef] [Medline]
    76. Almalki M, Giannicchi A. Health Apps for Combating COVID-19: Descriptive Review and Taxonomy. JMIR Mhealth Uhealth 2021 Mar 02;9(3):e24322 [FREE Full text] [CrossRef] [Medline]
    77. Kakhi K, Alizadehsani R, Khosravi A, Nahavandi S, Islam SMS. Challenges of Internet of Medical Things for Electronic Healthcare. 2021 Presented at: 2021 IEEE 4th International Conference and Workshop Óbuda on Electrical and Power Engineering (CANDO-EPE); November 17-18, 2021; Budapest. [CrossRef]
    78. Moses JC, Adibi S, Shariful Islam SM, Wickramasinghe N, Nguyen L. Application of Smartphone Technologies in Disease Monitoring: A Systematic Review. Healthcare (Basel) 2021 Jul 14;9(7):889 [FREE Full text] [CrossRef] [Medline]
    79. Fischer F, Kleen S. Possibilities, Problems, and Perspectives of Data Collection by Mobile Apps in Longitudinal Epidemiological Studies: Scoping Review. J Med Internet Res 2021 Jan 22;23(1):e17691 [FREE Full text] [CrossRef] [Medline]
    80. Mishra V, Islam SMS. Framework for the Adoption of Healthcare 4.0 – An ISM Approach. In: Bali V, Bhatnagar V, Sinha S, Johri P, editors. Disruptive Technologies for Society 5.0: Exploration of New Ideas, Techniques, and Tools. Boca Raton, FL: CRC Press; 2021.
    81. Zaman SB, Khan RK, Evans RG, Thrift AG, Maddison R, Islam SMS. Exploring Barriers to and Enablers of the Adoption of Information and Communication Technology for the Care of Older Adults With Chronic Diseases: Scoping Review. JMIR Aging 2022 Jan 07;5(1):e25251 [FREE Full text] [CrossRef] [Medline]
    82. Khan ZF, Alotaibi SR. Applications of Artificial Intelligence and Big Data Analytics in m-Health: A Healthcare System Perspective. J Healthc Eng 2020 Sep 01;2020:8894694 [FREE Full text] [CrossRef] [Medline]
    83. Islam S, Talukder A, Awal MA, Siddiqui MMU, Ahamad MM, Ahammed B, et al. Machine Learning Approaches for Predicting Hypertension and Its Associated Factors Using Population-Level Data From Three South Asian Countries. Front Cardiovasc Med 2022;9:839379 [FREE Full text] [CrossRef] [Medline]
    84. Howlader KC, Satu MS, Awal MA, Islam MR, Islam SMS, Quinn JMW, et al. Machine learning models for classification and identification of significant attributes to detect type 2 diabetes. Health Inf Sci Syst 2022 Dec 09;10(1):2 [FREE Full text] [CrossRef] [Medline]
    85. Chowdhury NK, Kabir MA, Rahman MM, Islam SMS. Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method. Comput Biol Med 2022 Jun;145:105405 [FREE Full text] [CrossRef] [Medline]
    86. Abdalrada AS, Abawajy J, Al-Quraishi T, Islam SMS. Prediction of cardiac autonomic neuropathy using a machine learning model in patients with diabetes. Ther Adv Endocrinol Metab 2022 Mar 22;13:20420188221086693 [FREE Full text] [CrossRef] [Medline]
    87. Abdalrada AS, Abawajy J, Al-Quraishi T, Islam SMS. Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study. J Diabetes Metab Disord 2022 Jan 12. [CrossRef]
    88. Islam SMS, Ahmed S, Uddin R, Siddiqui MU, Malekahmadi M, Al Mamun A, et al. Cardiovascular diseases risk prediction in patients with diabetes: Posthoc analysis from a matched case-control study in Bangladesh. J Diabetes Metab Disord 2021 Jun 15;20(1):417-425 [FREE Full text] [CrossRef] [Medline]
    89. Shariful Islam SM, Chow CK, Redfern J, Kok C, Rådholm K, Stepien S, et al. Effect of text messaging on depression in patients with coronary heart disease: a substudy analysis from the TEXT ME randomised controlled trial. BMJ Open 2019 Feb 20;9(2):e022637 [FREE Full text] [CrossRef] [Medline]
    90. Dening J, George ES, Ball K, Islam SMS. User-centered development of a digitally-delivered dietary intervention for adults with type 2 diabetes: The T2Diet study. Internet Interv 2022 Apr;28:100505 [FREE Full text] [CrossRef] [Medline]
    91. Chow CK, Ariyarathna N, Islam SMS, Thiagalingam A, Redfern J. mHealth in Cardiovascular Health Care. Heart Lung Circ 2016 Aug;25(8):802-807. [CrossRef] [Medline]
    92. Cho Y, Lee S, Islam SMS, Kim SY. Theories Applied to m-Health Interventions for Behavior Change in Low- and Middle-Income Countries: A Systematic Review. Telemed J E Health 2018 Oct;24(10):727-741 [FREE Full text] [CrossRef] [Medline]
    93. Islam SMS, Cartledge S, Karmakar C, Rawstorn JC, Fraser SF, Chow C, et al. Validation and Acceptability of a Cuffless Wrist-Worn Wearable Blood Pressure Monitoring Device Among Users and Health Care Professionals: Mixed Methods Study. JMIR Mhealth Uhealth 2019 Sep 14;7(10):e14706 [FREE Full text] [CrossRef] [Medline]

    ‎
    BG: blood glucose
    BGM: blood glucose monitoring
    CGM: continuous glucose monitoring
    ICT: information and communication technology
    MARS: Mobile App Rating Scale
    MASS: mean app-specific score
    mHealth: mobile health
    OS: operating system
    T1DM: type 1 diabetes mellitus
    T2DM: type 2 diabetes mellitus
    TTF: task-technology fit

    Edited by K Mizokami-Stout; submitted 30.08.21; peer-reviewed by D Frost, M Grady; comments to author 07.11.21; revised version received 24.02.22; accepted 08.04.22; published 21.06.22

    Copyright

    ©Sheikh Mohammed Shariful Islam, Vinaytosh Mishra, Muhammad Umer Siddiqui, Jeban Chandir Moses, Sasan Adibi, Lemai Nguyen, Nilmini Wickramasinghe. Originally published in JMIR Diabetes (https://diabetes.jmir.org), 21.06.2022.

    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 Diabetes, is properly cited. The complete bibliographic information, a link to the original publication on https://diabetes.jmir.org/, as well as this copyright and license information must be included.