|Year : 2021 | Volume
| Issue : 3 | Page : 285-292
Systematic review and scientific rating of commercial apps available in India for diabetes prevention
Harish Ranjani1, Sharma Nitika1, Raveendran Hariharan1, Harikrishnan Charumeena1, Nick Oliver2, Rajendra Pradeepa1, John Campbell Chambers3, Ranjit Unnikrishnan1, Viswanathan Mohan1, Parizad E Avari2, Ranjit Mohan Anjana1
1 Dr. Mohan’s Diabetes Specialities Centre and Madras Diabetes Research Foundation, Chennai, Tamil Nadu, India
2 Department of Metabolism, Diabetes and Reproduction, Imperial College London, London, UK
3 Department of Epidemiology and Biostatistics, Imperial College London, London, UK; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
|Date of Submission||02-Jun-2021|
|Date of Decision||21-Jun-2021|
|Date of Acceptance||23-Jun-2021|
|Date of Web Publication||30-Sep-2021|
Dr. Harish Ranjani
Sr. Scientist and Head—Translational Research, Dr. Mohan’s Diabetes Specialities Centre and Madras Diabetes Research Foundation, Conron Smith Rd, Chennai 600086, Tamil Nadu.
Source of Support: None, Conflict of Interest: None
Objectives: We aimed to evaluate the quality of currently available health apps for prevention of type 2 diabetes among Asian Indians using validated rating scales. Materials and Methods: Using the keywords, “diabetes prevention,” “healthy lifestyle,” and “fitness,” a total of 1486 apps available in India via Google Play were assessed for eligibility by two independent reviewers. After initial screening using specific inclusion and exclusion criteria, 50 apps underwent a pre-specified rating based on user reviews, number of downloads, and app size. Sixteen apps that scored ≥ 9 were shortlisted for further review using the Mobile App Rating Scale (MARS). The mean MARS scores (for categories I and II) were used to identify the top ranked apps. Results: The mean score for Category I of MARS rating was highest for “Google Fit: Health and Activity Tracking” (4.55/5). This was followed by “Healthifyme—Diet Plan, Health, and Weight Loss” (4.45/5). For Category II of MARS, “Diabetes M,” “Google Fit: Health and Activity Tracking,” “Calorie Counter—My Fitness Pal,” and “Healthifyme—Diet Plan, Health, and Weight Loss” all scored equally well. On comparing the advantages and disadvantages of each of these applications, “Google Fit: Health and Activity Tracking” and “Healthifyme—Diet Plan, Health, and Weight Loss” again ranked the best. Conclusion: Our review identifies two commercially available apps “Google Fit: Health and Activity Tracking” and “Healthifyme—Diet Plan, Health, and Weight Loss” as being user friendly and good quality. Although encouraging, further research is needed to evaluate the efficacy of these apps for the prevention of diabetes.
Keywords: Apps, diabetes prevention, mHealth, quality, scientific rating, systematic review
|How to cite this article:|
Ranjani H, Nitika S, Hariharan R, Charumeena H, Oliver N, Pradeepa R, Chambers JC, Unnikrishnan R, Mohan V, Avari PE, Anjana RM. Systematic review and scientific rating of commercial apps available in India for diabetes prevention. J Diabetol 2021;12:285-92
|How to cite this URL:|
Ranjani H, Nitika S, Hariharan R, Charumeena H, Oliver N, Pradeepa R, Chambers JC, Unnikrishnan R, Mohan V, Avari PE, Anjana RM. Systematic review and scientific rating of commercial apps available in India for diabetes prevention. J Diabetol [serial online] 2021 [cited 2021 Nov 30];12:285-92. Available from: https://www.journalofdiabetology.org/text.asp?2021/12/3/285/327313
| Introduction|| |
Diabetes is a complex chronic disease, affecting more than 77 million individuals in India. With the increasing prevalence of type 2 diabetes (T2D), the estimated global healthcare expenditure amounts to more than USD 760 billion and is set to increase to USD 845 billion in the next 45 years. With the increasing burden of T2D and a shortage of diabetes health professionals, innovative solutions using digital interventions provide scalable new approaches to T2D prevention and management.
With India accounting for 20% of the global smartphone market and approximately 370 million users, mobile technology has the potential to change many lives. Smartphones provide an ideal combination of user-accessibility with high functionality of customized content. Having achieved high penetration across Asia, it is estimated the number of smartphone users in India will double to reach between 650 and 700 million by 2023, rendering mobile health (mHealth) an indispensable component of health care.
The term mHealth refers to clinical and public health activities, made available through smartphone devices, which offer health-related information and services to people anywhere, anytime. mHealth encourages users to be part of their own health management plan, particularly when it relates to prevention and/or self-management for chronic conditions. The number of mHealth apps available at Google Play in the first quarter of 2020 was 43,285, compared with 23,955 in the first quarter of 2015. These mHealth apps allow people to be connected with their healthcare providers like never before. International guidelines support the use of mHealth apps for the prevention of T2D and cardiovascular disease (CVD) in high-risk individuals., However, little information on the quality of apps intended for diabetes self-management is available, and merely selecting health apps on the basis of popularity does not provide any meaningful information on app quality., Hence, in this study, we aimed to collate data on currently available commercial health apps and evaluate their potential quality as tools for prevention of T2D, with particular reference to Asian Indians.
| Materials and Methods|| |
We conducted a systematic search of mobile apps related to diabetes prevention. The search comprised three stages:
Stage 1: Searching the Android platform
Two independent primary reviewers (S. N., R. H.) searched for health apps commercially available in India on the Android platform (Google Play Store) on June 3, 2020. The Android market share for smartphone usage in India is 96%; hence, we limited the search to apps available on the Android platform using three key phrases: “diabetes prevention,” “healthy lifestyle,” and “fitness.” Other keywords such as “exercise” and “physical activity” were not included, as these searches yielded generic apps focussing on exercise routines and strategies related to athletic training. Using the keyword “diabetes” alone yielded apps for diagnosing and/or managing diabetes.
In total, 1486 apps in the Google Play Store matched with the keywords: 500 apps using the keyword “diabetes prevention,” 498 apps for “healthy lifestyle,” and 488 apps for “fitness” [Figure 1]. These were assessed for eligibility by the primary reviewers, with each reviewer assessing approximately 750 applications.
|Figure 1: Flow chart to depict the systematic search of the apps on an Android platform|
Click here to view
Inclusion criteria were an average user rating ≥ 3/5, more than 10,000 downloads, minimum 50 user reviews, app updated in the last 6 months, and available in the English language. As we were looking for apps which could be used across the country, we preferred to select apps available in English which is the link language across urban India. Exclusion criteria were medical apps providing diagnostic or clinician-led healthcare (for example, “DIABNEXT—Make your diabetes management easy” and “Diabetes Diagnostics”), apps with consultation provided (“Gadge Diabetes Care”), pharmacy apps, non-Asian food charts (apps that focussed on American/western diets and not relevant to Indian cultural preferences or habits), apps for lifestyle quotes (“Best Life Tips”), apps on unconventional diets for weight loss (“Total Keto Diet”), apps promoting a specific organization or products (“Medworks Management of Diabetes,” “Lefum Health,” “Lenovo Life”), and promotional apps (e.g., fitness bands, fitness centers). Premium features requiring payment from the user were also excluded; however, apps providing a basic version without additional cost were included. Based on these exclusion criteria, reviewer 1 and reviewer 2 excluded 680 and 693 apps, respectively. Thus, reviewer 1 shortlisted 50 apps and reviewer 2 shortlisted 47 apps [Figure 1].
Subsequently, detailed reviews posted by users were explored to study the app features and specifications. An internally devised rating scale (Annexure 1) was applied based on the following four parameters that reflect their availability in the Play Store: (a) overall rating of the app, (b) number of downloads, (c) size of the app, and (d) number of reviews. Each primary reviewer rated their list of apps on a scale of 1–4 [1 being the lowest and 4 being the highest, total rating out of 16 (4*4)]. There were 16 apps that received a total rating score ≥9 from both reviewers, and these were shortlisted for further evaluation (Annexure 2).
Stage 2: Call for commercial apps
In conjunction with the systematic search outlined earlier, additional apps were identified by contacting developers (for apps which already have been published in the Google Play Store). This was done simultaneously along with stage 1, in view of potentially relevant apps being missed in the search due to the descriptions provided in the Google Play Store. Hence, an official invite to participate was sent to commercial developers. An advertisement was published on our official website (http://mdrf.in/mdrf-nihr.html) and also on social platforms, including Facebook, Instagram, and LinkedIn. We received 106 responses but only one response lead was relevant to our study (“Fittr”). To increase the number of responses, we did another process of Search Engine Optimization. This process added 20 more apps, out of which 11 had already been shortlisted by the two primary reviewers in their initial search. The remaining nine apps were reviewed and rated (as performed in Stage 1) but none scored above 9.
A secondary reviewer (H. R.) reviewed the apps shortlisted by the two primary reviewers to verify and resolve any disputes regarding the final 16 apps selected from stages 1 and 2.
Stage 3: Application of the Mobile App Rating Scale
The Mobile App Rating Scale (MARS) is a medical app quality rating tool that provides multidimensional assessment of app quality indicators, as well as app subjective quality [Figure 2]., MARS contains two main categories, namely, “Application Quality” (comprising of Sections A to D) and “Application Subjective Quality” (Section E).
For ease of understanding, we have renamed “Application Quality” as Category I and “Application Subjective Quality” as Category II. Category I assesses the objective app quality and is divided into four scales (A–D): engagement, functionality, aesthetics, and information quality. MARS items are scored using a 5-point Likert scale (1—inadequate, 2—poor, 3—acceptable, 4—good, and 5—excellent). The mean is calculated for each section (A–D), and these are averaged to provide an overall mean score (out of 5) as the app quality mean score.
Category II is the Application Subjective Quality (Section E), which evaluates the user’s overall satisfaction. It contains four items with questions on recommendation, frequency of use, willingness to buy the app, and the overall rating given for the app from the user point of view. The apps are scored and presented separately category wise.
The two primary reviewers (S. N., R. H.) independently downloaded the 15 shortlisted apps (one app could not be downloaded due to technical issues) on their Android phones and used them extensively to conduct in-depth evaluations of each app before rating them using MARS. The reviewers were trained to use the MARS instrument by watching an online tutorial, to ensure rating consistency among the reviewers. The mean MARS scores were used to identify the best quality apps. Kappa statistic was used to estimate inter-reviewer (rater) reliability between the two primary reviewers. The reliability estimate was 0.77 (Cohen Kappa calculated using SPSS, version 15). As the domains in this scale are subjective in nature, this reliability estimate was considered to be very good.
| Results|| |
The 15 selected apps have been briefly described in Annexure 2 and their results from the MARS rating tool on app quality (sections A–D) are outlined in [Figure 3]. For section A, Engagement, the “Google Fit: Health and Activity Tracking” app had the highest engagement mean score of 4.50/5 for including the notification features for activity tips, goal progress tips, goals adjustment, and completed goals. Meanwhile, “Beat Diabetes” had the lowest engagement mean score of 2.30/5, due to being less informative and less interactive.
|Figure 3: Mean score of MARS sections. (A) App Engagement, (B) Functionality, (C) Aesthetics, and (D) Information (all 15 apps)|
Click here to view
In section B, Functionality, the “Diabetes Diary: Blood Glucose Tracker” app scored the highest on functionality, with a mean score of 5.00/5. “mySugr: Diabetes App and Blood Sugar Tracking” and “Diabetes Forum” had the lowest Functionality mean score of 3.90/5. The “mySugr: Diabetes App and Blood Sugar Tracking” app required an external glucometer to be connected and “Diabetes Forum” had glitches in the registration and user log in processes.
For section C, Aesthetics, the mean score for all the apps was above 4.00. “Calorie Counter: MyNetDiary, Food Diary Tracker” had the highest score of 5.00/5. The app had a clear and orderly organized layout with high-quality graphics and visual design. The color scheme further enhanced the app features. In contrast, “Beat Diabetes” had the lowest aesthetics mean score of 3.65/5.
In section D, Information, “Healthifyme” and “Google Fit: Health and Activity Tracking” had the highest score of 4.40/5. Both the apps provided a thorough description of the app components and had specific and achievable goals. The quality of information was well written and relevant to the goals of the app. Visual information was clear and logical. “Google Fit: Health and Activity Tracking” app also was evidence-based. The “Diabetes Diary: Blood Glucose Tracker” app had the lowest information mean score of 3.30/5. This app did not appear to have specific, measurable goals.
The mean MARS scores for each of the 15 apps downloaded by the two primary reviewers are shown in [Table 1]. According to the mean scores of both reviewers for Category I, “Google Fit: Health and Activity Tracking” was ranked first with the average app quality mean score of 4.55. This was followed by “Healthifyme—Diet Plan, Health, and Weight Loss” with a mean score of 4.45. Two apps, namely, “Calorie Counter—My Fitness Pal” and “Habits Diabetes Coach” were tied at number 3 (mean score 4.30). These were followed by “Calorie Counter—MyNetDiary, Food Diary Tracker” and “Diabetic Recipes: Healthy Food” (mean score 4.25) at number 4 and “Diabetic Diet Recipes: Control Diabetes and Sugar,” “Beat O SMART Diabetes Management,” and “8fit Workouts and Meal Planner” (mean score 4.20) at number 5.
From Category II of the MARS, “Diabetes M” had the highest App Subjective Quality Mean Score of 4.80/5, scoring highly on all the parameters, including app recommendation, frequency of use, willingness to buy, and the overall rating. Conversely, the two apps “Diabetes Diary: Blood Glucose Tracker” and “Beat Diabetes” had the lowest App Subjective Quality Mean Score of 2.75/5. “Beat Diabetes” scored lowest for willingness to buy. Thus, “Diabetes M,” “Google Fit: Health and Activity Tracking,” and “Calorie Counter—My Fitness Pal” and “Healthifyme” were the top ranked apps as per their app subjective quality mean scores. This was followed by “8fit Workouts and Meal Planner” and “Habits Diabetes Coach” tied at number 4 and “Noom: Health and Weight” at number 5. However, an in-depth evaluation of the apps revealed that “Diabetes M” may not be suitable for the Indian cuisine (needs cultural adaptation) and was difficult to access. “Healthifyme” had more features (tracked weight, activity, food, and water and provides a personal coach) when compared with “Calorie Counter—My Fitness Pal.” Hence, our top ranked apps considering both the “App quality mean score” (Category I) and “App subjective quality mean score” (Category II) were Google fit and Heathifyme.
| Discussion|| |
To our knowledge, this is the first study to conduct a systematic review and compare quality of mHealth apps available in the Play Store for diabetes prevention in Asian Indians, using a standardized rating tool. We found two commercially available apps “Google Fit: Health and Activity Tracking” and “Healthifyme—Diet Plan, Health, and Weight Loss” ranked highly among the apps available in the Google Play Store for the prevention of diabetes in Asian Indians.
Lifestyle factors such as diet, physical inactivity, excess alcohol consumption, cigarette smoking, drug abuse, stress affecting mental health, and lack of sleep are widely recognized as major determinants of metabolic diseases including obesity, T2D, and CVD in most populations., Most of these lifestyle behaviors form the base for developing an mHealth app. After analyzing the 15 shortlisted apps with respect to these health behaviors, we found that the majority dealt with diet and/or physical activity and only alluded very briefly to the topics of mental health, stress, sleep, and other lifestyle behaviors.
The “Google Fit: Health and Activity Tracking” app ranked well because although it is focussed on physical activity, it scored highly on Engagement and Functionality. The app is intuitive to use and records physical fitness activities, such as walking, running, and cycling. It uses this information to estimate calories burned. Other features offered by the app include weight history, duration of exercise, heart rate monitor, and sleep tracker (with the help of a third-party application), as well as enabling personalized goal settings, with customized tips and actionable coaching. Reports on the Play store suggest that there were over five million downloads within 6 months of its release. “Google Fit: Health and Activity Tracking” has previously been reported to be an “effortless and affordable activity tracker with the potential to significantly extend the number of activity tracker users compared to other devices.” The main disadvantage of this app was that it does not track food; however, it can connect to many other apps and devices providing a common platform to bring data from various sources together.
Next were “Healthifyme and Calorie Counter—My fitness pal.” “Healthifyme” ranked better than “Calorie Counter—My Fitness Pal” in its Functionality and Information. “Healthifyme” focusses on weight loss, fitness, and diabetes prevention through behavioral lifestyle changes. The app sends the user reminders to track their weight, activity, food, and water to meet fitness goals. The app also provides personalized reminders for walking and workout and has an artificial intelligence-powered smart nutritionist. The premium version of the app offers personalized coaching by diet, fitness, and yoga experts. The app also has success stories to motivate users, as well as more than 500 recipes and nutritional information. In a recent publication by an economist reviewing information technology for primary healthcare in India, “Healthifyme” has been called a promising behavioral application; however, research-backed evidence of its impact is not yet available.
Other apps that scored highly were “Habits Diabetes Coach,” “8fit Workouts and Meal Planner,” “Noom: Health and Weight,” “Diabetes:M,” and “Calorie Counter—MyNetDiary, Food Diary Tracker.” “Habits Diabetes Coach” can track glucose, activity, diet, and weight, give medication reminders, has videos and a lifestyle coach for better engagement. “8fit Workouts and Meal Planner” has an attractive user interface, good quality video content, and categorized workouts, including yoga, strength, and high intensity exercise. In contrast, “Noom: Health and Weight” has an in-built steps counter, food log, graphs to track weight, and customized course plans. “Diabetes:M” has lot of features with detailed report generation, which include weight, pulse, blood pressure, cholesterol, Hb1Ac, and sites of injection. This app allows the user to maintain multiple profiles, has a smart assistant, advises on insulin bolus dose, can add reminders, give options for data management and data sharing, discussion groups, and track blood glucose levels. “Calorie Counter—MyNetDiary, Food Diary Tracker” has meals and activity log, barcode reader, weight chart, basic weight loss plan, vitamin and pulse rate log, daily calorie budget, and feature to upload recipes. However, there are limited workout plans, complicated graphs, charts and food tracker, lack of Indian food data, and limited features in the basic version of the app affected overall App Quality, Engagement and Information scores of these apps.
All the apps reviewed in this study had a mean MARS score above 3.75 except for one. This suggests that the apps had an overall acceptable level of quality. On evaluating the individual aspects of the app quality mean score, we found the mean score for Engagement to be the lowest (3.6) for all the 15 apps, followed by Information (4.0). The mean score for Aesthetics and Functionality was much higher at 4.4. These findings are consistent with previous reviews showing that most apps perform better in the domains of functionality and aesthetics, compared with engagement and information., Similar outcomes were reported in another systematic review on mindfulness apps by Mani et al., in which mean engagement scores were low, with aesthetics and information scales found to be moderate. A review of commercial apps available for weight loss also showed that Information scored the lowest among the MARS domains, indicating an overall lack of evidence-based content. This suggests that further investment by developers in evidence-based, data-driven content, combined with change techniques known to be effective in changing relevant behavior patterns, is required. This may improve the overall app quality, regardless of the perceived aesthetic qualities of the app.
Our study has some limitations. The search terms used in the study were restricted to mHealth apps commercially available in India. Also, the search conducted in Google Play Store (for Android phones) might have restricted the results of this review as some of the apps are only found in the App Store (for Apple phones). This study evaluated the basic version available of the selected apps, which would be widely accessible to general population. However, we acknowledge the limitation that paid premium versions may be available for download. In addition, considering the dynamic development of apps, popularity and ratings may change very quickly over time.
| Conclusions|| |
While an increasing number of lifestyle and behavior change apps are being developed for diabetes prevention and management, the scientific evidence base underlying their use is still limited. Our review identifies two commercially available apps “Google Fit: Health and Activity Tracking” and “Healthifyme—Diet plan, Health and Weight Loss” as being highly rated for their usability. Further research through randomized controlled trials is required to evaluate the usability of these apps in diabetes prevention in Asian Indians.
At Imperial College London, the infrastructure support was provided by the NIHR Imperial Biomedical Research Centre and the NIHR Imperial Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health and Social Care.
Financial support and sponsorship
The research was supported by the UK National Institute for Health Research (NIHR) Official Development Assistance (ODA, award 16/136/68). The views expressed are those of the author(s) and not necessarily those of the NIHR.
Conflicts of interest
N. O. has received honoraria for speaking and advisory board participation from Abbott Diabetes, Dexcom, Medtronic Diabetes, and Roche Diabetes. V. M. has received research or educational grants or honoraria for speaking engagements or serving on advisory boards from Novo Nordisk, Servier, MSD, Novartis, Eli Lilly, M/s. USV, Lifescan J&J, Sanofi Aventis, Merck, Astra Zeneca, Boehringer Ingelheim, Abbott, and from several Indian pharmaceutical companies.
Conceptualization of paper and writing the initial draft: H. R. and R. M. A.; Methodology: S. N., R. H., H. C., H. R.; Data curation: R. H., S. N., R. M. A., P. A.; Review and Editing: All authors. All authors have read and approved the final version of the manuscript and agree with the order of presentation of the authors.
| References|| |
IDF SEA members [Internet]. Idf.org. Available from: https://idf.org/our-network/regions-members/south-east-asia/members/94-india.html [Last accessed on 2021 Mar 29].
Williams R, Karuranga S, Malanda B, Saeedi P, Basit A, Besançon S, et al
. Global and regional estimates and projections of diabetes-related health expenditure: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res Clin Pract 2020;162:108072.
Digital India Technology to transform a connected nation. Available from: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/Digital%20India%20Technology%20to%20transform%20a%20connected%20nation/MGI-Digital-India-Report-April-2019.pdf [Last accessed on 2021 Mar 25].
Jusoh S. A survey on trend, opportunities and challenges of mHealth apps. Int J Interact Mob Technol 2017;11:73.
Farrington C, Aristidou A, Ruggeri K. mHealth and global mental health: Still waiting for the mH2 wedding? Global Health 2014;10:17.
Klasnja P, Pratt W. Managing health with mobile technology. Interactions 2014;21:66-9.
Sittig S, Wang J, Iyengar S, Myneni S, Franklin A. Incorporating behavioral trigger messages into a mobile health app for chronic disease management: Randomized clinical feasibility trial in diabetes. JMIR MHealth UHealth 2020;8:e15927.
Google Play: Number of available medical apps as of Q4 2020 [Internet]. Statista.com. Available from: https://www.statista.com/statistics/779919/health-apps-available-google-play-worldwide/. [Last accessed on 2021 Mar 25].
Milne-Ives M, Lam C, De Cock C, Van Velthoven MH, Meinert E. Mobile apps for health behavior change in physical activity, diet, drug and alcohol use, and mental health: Systematic review. JMIR MHealth UHealth 2020;8:e17046.
WHO | Global action plan for the prevention and control of NCDs 2013–2020 [Internet]. 2014. Available from: http://www.who.int/nmh/publications/ncd-action-plan/en [Last accessed on 2021 Mar 25].
Cummings E, Borycki EM, Roehrer E. Issues and considerations for healthcare consumers using mobile applications. Stud Health Technol Inform 2013;183:227-31.
Girardello A, Michahelles F. AppAware: Which mobile applications are hot? In: Proceedings of the 12th international conference on Human computer interaction with mobile devices and services—MobileHCI ’10. NY, USA: ACM Press; 2010.
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;3:e27.
Mobile Operating System Market Share India [Internet]. Statcounter.com. Available from: https://gs.statcounter.com/os-market-share/mobile/india [Last accessed on 2021 Mar 29].
Stoyanov SR, Hides L, Kavanagh DJ, Wilson H. Development and validation of the user version of the Mobile Application Rating Scale (uMARS). JMIR MHealth UHealth 2016;4:e72.
The Mobile App Rating Scale (MARS) and its Application to the Development of a Youth [Internet]. C4tbh.org. 2017. Available from: https://www.c4tbh.org/seminar/title-tba-5/ [Last accessed on 2021 Mar 25].
Handbook of Inter-Rater-Reliability. Available from: http://infinity.wecabrio.com/read/970806280-handbook-of-inter-rater-reliability-the-definitive.pdf [Last accessed on 2021 Mar 25].
Menaspà P. Effortless activity tracking with Google Fit. Br J Sports Med 2015;49:1598.
Weber MB, Ranjani H, Staimez LR, Anjana RM, Ali MK, Narayan KM, et al
. The stepwise approach to diabetes prevention: Results from the D-CLIP randomized controlled trial. Diabetes Care 2016;39:1760-7.
Diabetes Prevention Program (DPP) Research Group. The Diabetes Prevention Program (DPP): Description of lifestyle intervention. Diabetes Care 2002;25:2165-71.
Mor N. Information technology for primary healthcare in India [Internet]. Unpublished; 2020. Available from: http://dx.doi.org/10.13140/RG.2.2.25513.13925 [Last accessed on 2021 Mar 25].
Gong E, Zhang Z, Jin X, Liu Y, Zhong L, Wu Y, et al
. Quality, functionality, and features of Chinese mobile apps for diabetes self-management: Systematic search and evaluation of mobile apps. JMIR MHealth UHealth 2020;8:e14836.
Schoeppe S, Alley S, Rebar AL, Hayman M, Bray NA, Van Lippevelde W, et al
. Apps to improve diet, physical activity and sedentary behaviour in children and adolescents: A review of quality, features and behaviour change techniques. Int J Behav Nutr Phys Act 2017;14:83.
Mani M, Kavanagh DJ, Hides L, Stoyanov SR. Review and evaluation of mindfulness-based iPhone apps. JMIR MHealth UHealth 2015;3:e82.
Bardus M, van Beurden SB, Smith JR, Abraham C. A review and content analysis of engagement, functionality, aesthetics, information quality, and change techniques in the most popular commercial apps for weight management. Int J Behav Nutr Phys Act 2016;13:35.
Arnhold M, Quade M, Kirch W. Mobile applications for diabetics: A systematic review and expert-based usability evaluation considering the special requirements of diabetes patients age 50 years or older. J Med Internet Res 2014;16:e104.
Fleming GA, Petrie JR, Bergenstal RM, Holl RW, Peters AL, Heinemann L. Diabetes digital app technology: Benefits, challenges, and recommendations. A consensus report by the European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) Diabetes Technology Working Group. Diabetologia 2020;63:229-41.
Nikolaou CK, Lean ME. Mobile applications for obesity and weight management: Current market characteristics. Int J Obes (Lond) 2017;41:200-2.
Waterlander W, Whittaker R, McRobbie H, Dorey E, Ball K, Maddison R, et al
. Development of an evidence-based mHealth weight management program using a formative research process. JMIR MHealth UHealth 2014;2:e18.
Ranjani H, Nitika S, Anjana RM, Ramalingam S, Mohan V, Saligram N. Impact of noncommunicable disease text messages delivered via an app in preventing and managing lifestyle diseases: Results of the “myArogya” worksite-based effectiveness study from India. J Diabetol 2020;11:90. [Full text]
Muralidharan S, Ranjani H, Anjana RM, Allender S, Mohan V. Mobile health technology in the prevention and management of type 2 diabetes. Indian J Endocrinol Metab 2017;21:334-40.
He S. MHealth app development: Current practices and future perspectives [Internet]. Conductscience.com. 2020. Available from: https://conductscience.com/mhealth-app-development-current-practices-and-future-perspectives. [Last accessed on 2021 Mar 25].
Kazemi A, Salmani H, Shakibafard A, Fatehi F. New and emerging mobile technologies for healthcare (mHealth): A horizon scanning study. Front Health Inform 2019;8:17.
Stephan LS, Dytz Almeida E, Guimaraes RB, Ley AG, Mathias RG, Assis MV, et al
. Processes and recommendations for creating mHealth apps for low-income populations. JMIR MHealth UHealth 2017;5:e41.
Nurgalieva L, O’Callaghan D, Doherty G. Security and privacy of mHealth applications: A scoping review. IEEE Access 2020;8:104247-68.
[Figure 1], [Figure 2], [Figure 3]
[Table 1], [Table 2], [Table 3]