You are here

Article Text

Article menu

Download PDFPDF

XML
Original research
Conformity of Diabetes Mobile apps with the Chronic Care Model
  1. Raheleh Salari1,
  2. Sharareh R Niakan Kalhori1,
  3. Marjan Ghazisaeidi1 and
  4. Farhad Fatehi1,2,3
  1. 1Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Science, Tehran, Iran
  2. 2Australian eHealth Research Centre, CSIRO, Brisbane, Queensland, Australia
  3. 3Centre for Online Health, The University of Queensland, Brisbane, Queensland, Australia
  1. Correspondence to Sharareh R Niakan Kalhori, Department of Health Information Management, School of Allied Medical Sciences, Tehran University of Medical Science, Tehran, Iran, Postcode:1416753955; sh-rniakank{at}sina.tums.ac.ir

Abstract

Background Despite the growing use of mobile applications (apps) for chronic disease management, the evidence on the effectiveness of this technology on clinical and behavioural outcomes of the patients is scant. Many studies highlight the importance of the theoretical foundations of mobile-based interventions. One of the most widely accepted models for the management of chronic diseases, such as diabetes, is the Chronic Care Model (CCM). In this study, we investigated the conformity of the selected diabetes mobile apps with CCM.

Method We searched online journal databases related to diabetes mobile apps to find common features. Then considering the components of the CCM as a reference model, features of some popular and top-ranking apps were compared with CCM.

Results Among 23 studied apps, 34 per cent of them had medium conformity and 66 per cent of these apps were in weak conformity. The self-management support component is covered by 100 per cent of them. Ninety-five per cent of apps have covered the proactive follow-up component.

Conclusions App conformance with CCM is generally weak. App developers are recommended to give greater consideration to established theoretical models in their design and implementation.

  • mobile health
  • mhealth
  • mobile apps
  • mobile applications
  • chronic care model
  • diabetes type 2

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjhci-2019-000017

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Introduction

    Diabetes mellitus (DM) is a major public health concern worldwide.1 The difficulties of living with diabetes, with its multipart regimen and need for behaviour change, are challenging and good self-care is difficult to achieve.2 Moreover, the management of diabetes complications is a multifaceted process that requires frequent interactions and collaboration among patients and physicians to address clinical issues and lifestyle change.3 To overcome these difficulties, mobile health (mhealth) has shown potential to help healthcare providers and support patients for improving diabetes care. There are different reports for patient-centric mobile apps applied for different diseases and condition in routine care4 such as chronic obstruction pulmonary disease (COPD),5 6 diabetes,7–9 cardiac disease10 11 physical activity and nutrition management.12 13

    There has been an extraordinary increase in the number of health mobile apps over the past several years.14 In spite of the abundance of available apps, little has been published about how to design mhealth interventions to achieve a desirable and sustainable effect in the long term.15

    To overcome this situation, researchers suggested that mhealth apps should be developed based on guidelines or theoretical models.16 One of the most popular and comprehensive models for the management of chronic care diseases such as diabetes is the chronic care model (CCM). It is reported that planned care within a managed care system could improve the care delivery system for people with diabetes.3 17 18 This model consists of six major components: delivery system design; self-care strategies; decision support and expert system; information support; community linkages; and health systems' support.19 It is focused on resources, improvement of the quality of care and patient self-care capabilities. Several studies have demonstrated that if the model of diabetes care is appropriate and consistent with the CCM elements, quality of life and healthcare planning would be improved.20–22 This model could be used as a suitable framework for covering the gap between the healthcare provider advice and practical care implementation especially as self-care oriented by patients.23 As a solution, it might be concluded that healthcare providers need to plan new connections between mhealth apps and healthcare professionals in the frame of guidelines such as an integrated and consistent strategy according to CCM components.17 Therefore, the aim of this research was to investigate the extent to which the features of diabetes mobile apps, as a new solution for patients’ self-care improvement, conform to the components of CCM.

    Methods

    Identification of the features of diabetes mobile apps

    First, a review of the literature was performed to identify the most common features of diabetes mobile apps. We searched major biomedical databases including Web of Science, PubMed and Science Direct using the following English keywords: 'diabetes', 'blood sugar', 'mobile health', 'mobile apps', 'smartphone' and 'mobile app', in a period from 2012 to 2017. Also, the references of the included articles were explored for additional relevant articles.

    We did not consider diabetes life-style apps such as those targeting solely diet and physical activity. We considered just diabetes self-care apps. In the next step, we tried to provide a definition for each feature in order to determine the area of diabetes care that they address according to CCM components. Some features were well-defined and we extracted their definitions from the literature.7 24–29 However, the definition of some others was vague, and we tried to provide acceptable definitions based on reviewed apps.

    Currently, the only benchmark for rating apps is user’s rate (UR).30 Inevitably, we considered high-rate apps which are more probably embedded with key functions and more likely to be in line with CCM components. For this purpose, the most popular apps which had at least 4 or more rating score on the app store were selected.

    Also, the most commonly popular apps based on their rates were chosen which might be in both Google Play and Apple Store too. We also collected the related technical information for all categories and sub-categories apps. In order to ensure a representative analysis despite missing selection criteria, we defined one day (20 July 2017) to record all found apps with title and developer. Therefore, the basis for the systematic and comparative app analysis was defined by the list of Android apps (online supplementary file). Next, due to a high number of available apps, we scanned the most popular apps by names in the Android marketplace and then downloaded just free apps because of cost considerations. We worked with popular diabetes mobile apps (eg, MySugr Diabetes Logbook) to get familiar with features practically. Afterwards, we searched considering all related android apps available in the Google Play Store in December 2017 returned by searching the Store using the aforementioned keywords. We downloaded the top ranking apps based on users' scoring rate.

    We applied a number of inclusion and exclusion criteria as follows. The inclusion criteria: free of charge apps; related to diabetes directly; and had at least 4 or more rating score. Exclusion criteria: supporting only a single feature (for example, insulin calculation); not designed for diabetes self-care; provide information source only, such as a journal and magazine; not updated within 12 months prior to the search; and targeting solely fitness, physical activity or diet of diabetic patients only. Using these criteria, we found 23 free apps.

    Qualitative analysis

    In this step, the elements of each CCM component were identified based on the literature.31–34 Each component of CCM has some elements that could be realised by their definitions provided by Pearson et al.33 Next, it was required to investigate the conformity of top-ranking free diabetes mobile apps, which included in this study with the elements of CCM components. Each definition of features was compared with existing capabilities and potentials of the mobile app features. A reference table was developed to compare the conformity level of mobile apps features to elements of CCM components. Finally, the authors used the reference table to compare apps. One of the authors evaluated selected apps based on reference table (online supplementary appendix) and then results of evaluation was discussed with another author. For rating conformity of feature with CCM elements, we considered 0–25 per cent as weak conformity, 26–75 per cent as medium conformity and more than 76 per cent for good conformity. The authors' agreement regarding understudy diabetes apps conformity with CCM was measured by calculating Cohen’s kappa coefficient index using SPSS version 22. Cohen’s kappa of 0.75 or higher was designated as an acceptable agreement between the authors. Cohen’s kappa is a robust statistic for reliability testing and can range from 0 to 1, where closer to 1 represents higher agreement between evaluators and 1 represents perfect agreement.35

    Results

    Definition of diabetes app features and CCM components

    Online supplementary table 1 provides the list of 32 features identified in the diabetes apps based on the literature and mhealth apps. This table consists of features' definitions as previously explained.

    The components of CCM have been defined33 as follows:

    • Delivery system redesign means some changes in the organisation of care delivery. For example: A) increase physicians’ role in care management; B) specify practice team activities; and C) delegate care from physician to non-physician.

    • Self-care strategies mean to make some efforts to increase patient’s involvement in their own care. For example: A) education materials provided; B) provide psychosocial support; and C) assess self-care skills and needs.

    • Decision support means to make some guidelines, education and expertise to inform care decisions. For example: A) guide individualised care planning; and B) facilitate specialty/expert consultation in individual cases.

    • Information support means to make some changes to facilitate the use of information about patients, their care and their outcomes. For example: A) provide data collection tools; and B) provide reminders for care planning and management.

    • Community linkages mean to do some activities in order to increase community involvement. For example: A) seek community resources or collaborate with the community to meet patient/population needs; and provide services or programme for members at the community level.

    • Health system support means to make some leadership, practitioner and financial support. For example: A) coordinate/communicate among subsystems:; and B) acquire leadership support for chronic care improvement.

    Online supplementary table 2 was completed based on conformity between the definition of each element of CCM with the definition of mobile app features. Online supplementary table 2 shows that a limited number of the components related to CCM are considered in the design of the apps. More than 95 per cent of features have covered three components including proactive follow-up, self-care support and information for the management system. The other components of the CCM, in most cases, have been covered by 30 per cent of features. If we compare this situation with the apps that are already available in the Google Play Store, this percentage would be even less, because very few of those available apps have these features.

    Comparison of selected diabetes mobile apps with reference table of CCM

    According to criteria mentioned in the previous section, 23 apps were extracted and tested one by one to find out their content. Results are shown in table 3. For each app, the authors calculated the number of features that have been covered. Among 23 studied apps, eight apps (34%) had medium conformity and 14 apps (66%) had weak conformity. The self-management support component is covered by all of the apps. Twenty-one apps (95%) have covered the proactive follow-up component. These statistics are also obtained for the component of the information support for care management. The least coverage related to patient registry system, self-management assessment and planned visit elements with only one app (4%).

    Discussion

    This study aimed to examine the extent to which diabetes mobile apps have conformity with components of the CCM. From online supplementary table 2 it is clear that the 'proactive follow-up' element in 'delivery system design' component has been covered by different features: it might be due to a mobile app’s ability to provide necessary services to patients in the absence of doctors and in the patient’s private life.36 The other elements in the 'delivery system design' component may suggest providing team-based services, and since one of the goals of mobile apps is patient-centred care, this feature has been mostly covered in a patient-centered manner.

    The second component of CCM includes some strategies that help patients improve their self-care. Applied new technologies empower patients in learning new skills, thus it is supposed that mhealth enhances self-care abilities.34 Some elements of a self-care strategy's component such as 'collaborative decision making with patients' or 'patient activation/psychosocial support' can be satisfied by special feature including colour coding or messaging. Hence, the 'delivery system design' and 'self-care strategies' are two components that have almost been implemented in diabetes mobile apps.

    In the features of studied mobile apps, almost none of the component of 'decision support and expert system' were considered. Fundamentally, the CCM's decision-making is based on guides, evidence-based guidelines, protocols and standards; usages to provide the required care.17 Despite the importance of this issue, however, many mobile apps did not apply even elementary proposition of guidelines in their design.31

    The fourth component, 'information support', has three elements which correspond to the collection, integration and distribution of information. Institutional and personal information would be used to make a proper decision and treatment. As a result, it is better that an app has specific capabilities such as synchronisation with some tools such as social network, electronic registries and electronic health/medical records. The rate of adoption of these registries and records is less than what was expected in mobile apps, this issue partially could be related to usability issues such as national infrastructure, security and privacy considerations, and other obstacles.37 Having a personal health record (PHR) that can be kept by the patient and his family can support the maintenance of more complete and accurate health information. These personal files can also be kept on the mobile device, but there are various challenges. Patients or their family may find it difficult to understand the medical terminologies. Another challenge is to provide support and assistance to patients as real- time support might be very costly. Another challenge is security and privacy of the data and ensuring that the data is entered correctly by users in such systems. On the other hand, the creation and maintenance of these cases is a partnership of patients and care providers, while patients are not specialised in medical matters.38

    However, it should be taken into consideration that the use of electronic tools such as electronic health record (EHR) in gathering, storing and sharing data can be helpful to cover this component and it is better to invest in these facilities as soon as possible.39

    'Community linkage' would provide information about community resources to support patients' needs or provide services or programmes for the community. There is still a research gap to examine the effectiveness of online communication systems to support self-care.40 More studies are needed in this regard.

    Regarding the last component, 'health system support', there is little information about how to design mhealth interventions that integrate with healthcare systems in the field of chronic care disease.17 It seems that the essential features such as personalisation and decision support features in apps have important effects on care improvement and these features are also recommended in the guidelines.31

    Many researchers suggest involving innovative and inclusive technologies and tools for their positive impact on self-care diseases such as diabetes due to their direct effects on lifestyle. Mobile phone interventions provide an inexpensive and effective role in encouraging patients to promote self-care41 42 and better interactions between the patient and the physician through the effective integration of patients' daily monitoring.43–46 This will increase the patients’ awareness about their condition.47 Therefore, these possibilities make an opportunity to record disease-related information and transfer it to healthcare professionals. Another reason for the use of these technologies is the inherent characteristics, such as powerful technical capabilities, availability in all locations, people's dependency on, and the possibility of, customisation. Technical functions of mhealth can provide access to customised intervention based on different parameters such as age, sex, and health status of patients at both individual and social level. Also, it can be used for short message services (SMS), software applications and multimedia, such as image and video. These technologies provide direct interaction between the patient and the healthcare and provide real time and immediate assistance when needed by the patient.48 49

    Health mobile apps can provide cost-effective foundation for caring for patients affected by chronic disease such as diabetes.28 For example, it can be available in low-income areas and even a large number of people to monitoring and healthcare.48 Also it can be useful for adherence to drug prescriptions, encourage them to have healthy lifestyles, and improve their knowledge and self-management abilities.50 Reports of the cost effectiveness of health mobile apps like these, have been proven in some studies, but not in all of them.51

    Nevertheless, in spite of all of these potential advantages regarding mhealth, the role of mhealth and apps in diabetes management has not yet been widespread.52 53 This is not only due to the lack of CCM consideration in apps development, as far as we know, other requirements are also needed to implement the optimal usage of health mobile apps to diabetes care. These requirements which are shown in CCM may include constructive interaction between the informed and active patient, and the experienced and prepared healthcare team. These factors, in addition, to consider CCM's components in the mobile app architecture, are effective and important to achieve the desired results. If we design a diabetes app architecture along with all CCM's components' consideration, the data may not be still forwarded completely and accurately to be monitored by the service provider and the ultimate goal of the CCM, which is improving the patient ability to manage her/his own chronic illness truly, would not be covered.

    The data produced in the process of interaction between the provider and the patient can be collected, analysed and retrieved in a database. Statistical and mathematical algorithms might be used in order to develop intelligent modelling and provide a prediction of diabetes-related outcomes such as blood glucose, weight, calorie intake and even HbA1c levels to achieve the ultimate goal of the CCM. This is possible when data are collected longitudinally and build a rich database. Therefore, to achieve such an ideal output, cost and time are needed, but ultimately, when the system reaches the degree of automatic operation, can improve outcomes and processes of care smartly based on the CCM approach.

    Despite the advantages of many mobile-based systems for improving healthcare, if not properly understood and used, could lead to misdiagnosis and increased risk. The US Food and Drug Administration (FDA) consider the same risk-based approach to mobile phone apps such as other medical devices as well. It even announces a list of apps as an example that has been able to receive FDA approval and describes how to get the confirmation. This file has been edited in its latest version on 8 October 2018.54 In addition, the security and privacy of the information that patients enter is also another challenge. Although many researchers have suggested state-of-the-art technologies such as the use of intelligent lenses for continuous monitoring of patients, it has always been advised that patients themselves are aware of what information is being collected and for what purpose it is used.8 55 So, as one of the most crucial aspects of using these tools, it should be resolved by considering secure transmission and secure and private data storage. Therefore, the relevant laws and regulations should consider all of these and show that the patient's safety is a priority.56 Nevertheless, many medical apps have not passed the necessary regulatory control and might be dangerous for patient safety.57 Legal frameworks developing to prevent the introduction of unsafe and high-risk programmes also need to allow further innovation.58 Surveillance should be based on a patient-centred approach, while keeping up-to-date with relevant laws and protecting the safety of the patient.59

    We considered the date of the latest app update to review and report, but of course, the number of apps and their updates keeps changing. Also, some features are just from published descriptions or articles. There are variances between the explanation in the article and the actual features. We did not have the time and resources to install and test every app. This study was limited to Google Play Store for android apps and can be extended for other app stores in future. Further study may address the trend of apps improvement according to their progress towards CCM conformity.

    Conclusion

    The results of our study showed that conformity of diabetes mobile apps with the CCM is generally weak. Currently available mobile apps address limited components of the model. Many apps just have focused on some essential options to collect blood pressure, blood glucose, weight and activity tracking. In the future, the ideal apps may have all the suggested features entirely to create an integrated, rich and compatible system. The result of this study can help software developers and intervention designers develop more effective mobile apps for diabetes management according to the recommendations of CCM.

    References

    1. 1.↵
      1. International Diabetes Federation
      . Diabetes atlas 6th edition (Internet), 2014. Available: http://www.idf.org/diabetesatlas/update [Accessed 14 May 2016].
    2. 2.↵
      1. Lustria ML,
      2. Brown LL
      . Information and communication technologies for diabetes self-management and education: user-centered perspectives. In: Health informatics: a patient‐centered approach to diabetes, 2010: 229–70.
    3. 3.↵
      1. Smith SA,
      2. Shah ND,
      3. Bryant SC, et al
      . Chronic care model and shared care in diabetes: randomized trial of an electronic decision support system. Mayo Clin Proc2008;83:747–57.doi:10.4065/83.7.747
      OpenUrlCrossRefPubMedWeb of Science
    4. 4.↵
      1. Lustig TA
      . The role of telehealth in an evolving health care environment: workshop summary. National Academies Press, 2012.
    5. 5.↵
      1. Williams V,
      2. Price J,
      3. Hardinge M, et al
      . Using a mobile health application to support self-management in COPD: a qualitative study. Br J Gen Pract2014;64:e392–400.doi:10.3399/bjgp14X680473
      OpenUrlAbstract/FREE Full Text
    6. 6.↵
      1. Ding H,
      2. Moodley Y,
      3. Kanagasingam Y, et al
      . A mobile-health system to manage chronic obstructive pulmonary disease patients at home. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, San Diego, CA, 2012:2178–81.
    7. 7.↵
      1. Garabedian LF,
      2. Ross-Degnan D,
      3. Wharam JF
      . Mobile phone and smartphone technologies for diabetes care and self-management. Curr Diab Rep2015;15:1–9.doi:10.1007/s11892-015-0680-8
      OpenUrlCrossRef
    8. 8.↵
      1. Eng DS,
      2. Lee JM
      . The promise and peril of mobile health applications for diabetes and endocrinology. Pediatr Diabetes2013;14:231–8.doi:10.1111/pedi.12034
      OpenUrlCrossRefPubMedWeb of Science
    9. 9.↵
      1. Baron JS,
      2. Hirani S,
      3. Newman SP
      . A randomised, controlled trial of the effects of a mobile telehealth intervention on clinical and patient-reported outcomes in people with poorly controlled diabetes. J Telemed Telecare2017;23:207–16.doi:10.1177/1357633X16631628
      OpenUrl
    10. 10.↵
      1. Martínez-Pérez B,
      2. de la Torre-Díez I,
      3. López-Coronado M, et al
      . Mobile apps in cardiology: review. JMIR Mhealth Uhealth2013;1:e1-15doi:10.2196/mhealth.2737
    11. 11.↵
      1. Stuckey MI,
      2. Shapiro S,
      3. Gill DP, et al
      . A lifestyle intervention supported by mobile health technologies to improve the cardiometabolic risk profile of individuals at risk for cardiovascular disease and type 2 diabetes: study rationale and protocol. BMC Public Health2013;13:1–10.doi:10.1186/1471-2458-13-1051
      OpenUrlCrossRefPubMed
    12. 12.↵
      1. van der Weegen S,
      2. Verwey R,
      3. Spreeuwenberg M, et al
      . The development of a mobile monitoring and feedback tool to stimulate physical activity of people with a chronic disease in primary care: a user-centered design. JMIR Mhealth Uhealth2013;1:1–13.doi:10.2196/mhealth.2526
      OpenUrl
    13. 13.↵
      1. Turner-McGrievy GM,
      2. Beets MW,
      3. Moore JB, et al
      . Comparison of traditional versus mobile app self-monitoring of physical activity and dietary intake among overweight adults participating in an mHealth weight loss program. J Am Med Inform Assoc2013;20:513–8.doi:10.1136/amiajnl-2012-001510
      OpenUrlCrossRefPubMed
    14. 14.↵
      1. Dolan B
      . An analysis of consumer health applications for Apple’s iPhone 2012, 2012. Mobi health news. Available: http://mobihealthnews. com/17925/just-launched-our-2012-consumer-health-applications-report/ [Accessed 27 May 2018].
    15. 15.↵
      1. Rao A,
      2. Hou P,
      3. Golnik T, et al
      . Evolution of data management tools for managing self-monitoring of blood glucose results: a survey of iPhone applications. J Diabetes Sci Technol2010;4:949–57.doi:10.1177/193229681000400426
      OpenUrlCrossRefPubMed
    16. 16.↵
      1. Ye Q,
      2. Khan U,
      3. Boren SA, et al
      . An analysis of diabetes mobile applications features compared to AADE7™: addressing self-management behaviors in people with diabetes. J Diabetes Sci Technol2018;12:808–16.doi:10.1177/1932296818754907
      OpenUrl
    17. 17.↵
      1. Wagner EH
      . Chronic disease management: what will it take to improve care for chronic illness?Eff Clin Pract1998;1:2–4.
      OpenUrlPubMed
    18. 18.↵
      1. Nutting PA,
      2. Dickinson WP,
      3. Dickinson LM, et al
      . Use of chronic care model elements is associated with higher-quality care for diabetes. Ann Fam Med2007;5:14–20.doi:10.1370/afm.610
      OpenUrlAbstract/FREE Full Text
    19. 19.↵
      1. Glasgow RE,
      2. Orleans CT,
      3. Wagner EH, et al
      . Does the chronic care model serve also as a template for improving prevention?Milbank Q2001;79:579–612.doi:10.1111/1468-0009.00222
      OpenUrlCrossRefPubMedWeb of Science
    20. 20.↵
      1. Ku GMV,
      2. Kegels G
      . Adapting chronic care models for diabetes care delivery in low-and-middle-income countries: a review. World J Diabetes2015;6:566–75.doi:10.4239/wjd.v6.i4.566
      OpenUrl
    21. 21.↵
      1. Stellefson M,
      2. Dipnarine K,
      3. Stopka C
      . The chronic care model and diabetes management in US primary care settings: a systematic review. Prev Chronic Dis2013;10:1–18.doi:10.5888/pcd10.120180
      OpenUrl
    22. 22.↵
      1. Siminerio LM,
      2. Piatt G,
      3. Zgibor JC
      . Implementing the chronic care model for improvements in diabetes care and education in a rural primary care practice. Diabetes Educ2005;31:225–34.doi:10.1177/0145721705275325
      OpenUrlCrossRefPubMedWeb of Science
    23. 23.↵
      1. Strickland PAO,
      2. Hudson SV,
      3. Piasecki A, et al
      . Features of the chronic care model (CCM) associated with behavioral counseling and diabetes care in community primary care. J Am Board Fam Med2010;23:295–305.doi:10.3122/jabfm.2010.03.090141
      OpenUrlAbstract/FREE Full Text
    24. 24.↵
      1. Nundy S,
      2. Dick JJ,
      3. Goddu AP, et al
      . Using mobile health to support the chronic care model: developing an institutional initiative. Int J Telemed Appl2012;2012:1–8.doi:10.1155/2012/871925
      OpenUrl
    25. 25.↵
      1. Issom D-Z,
      2. Woldaregay AZ,
      3. Chomutare T, et al
      . Mobile applications for people with diabetes published between 2010 and 2015. Diabetes Manag2015;5:539–50.doi:10.2217/dmt.15.40
      OpenUrl
    26. 26.↵
      1. El-Gayar O,
      2. Timsina P,
      3. Nawar N, et al
      . Mobile applications for diabetes self-management: status and potential. J Diabetes Sci Technol2013;7:247–62.doi:10.1177/193229681300700130
      OpenUrlCrossRefPubMed
    27. 27.↵
      1. Klonoff DC
      . The current status of mHealth for diabetes: will it be the next big thing?J Diabetes Sci Technol2013;7:749–58.doi:10.1177/193229681300700321
      OpenUrlCrossRefPubMed
    28. 28.↵
      1. Årsand E,
      2. Frøisland DH,
      3. Skrøvseth SO, et al
      . Mobile health applications to assist patients with diabetes: lessons learned and design implications. J Diabetes Sci Technol2012;6:1197–206.doi:10.1177/193229681200600525
      OpenUrlPubMed
    29. 29.↵
      1. Arnhold M,
      2. Quade M,
      3. 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 Res2014;16:e104:1–18.doi:10.2196/jmir.2968
      OpenUrlCrossRef
    30. 30.↵
      1. Demidowich AP,
      2. Lu K,
      3. Tamler R, et al
      . An evaluation of diabetes self-management applications for android smartphones. J Telemed Telecare2012;18:235–8.doi:10.1258/jtt.2012.111002
      OpenUrlCrossRefPubMed
    31. 31.↵
      1. Chomutare T,
      2. Fernandez-Luque L,
      3. Årsand E, et al
      . Features of mobile diabetes applications: review of the literature and analysis of current applications compared against evidence-based guidelines. J Med Internet Res2011;13:e65:1–11.doi:10.2196/jmir.1874
      OpenUrlCrossRef
    32. 32.↵
      1. Tsai AC,
      2. Morton SC,
      3. Mangione CM, et al
      . A meta-analysis of interventions to improve care for chronic illnesses. Am J Manag Care2005;11:478–88.
      OpenUrlPubMedWeb of Science
    33. 33.↵
      1. Pearson ML,
      2. Wu S,
      3. Schaefer J, et al
      . Assessing the implementation of the chronic care model in quality improvement collaboratives. Health Serv Res2005;40:978–96.doi:10.1111/j.1475-6773.2005.00397.x
      OpenUrlCrossRefPubMedWeb of Science
    34. 34.↵
      1. Mallow JA,
      2. Theeke LA,
      3. Barnes ER, et al
      . Using mHealth tools to improve rural diabetes care guided by the chronic care model. Online J Rural Nurs Health Care2014;14:43–65.doi:10.14574/ojrnhc.v14i1.276
      OpenUrlPubMed
    35. 35.↵
      1. McHugh ML
      . Interrater reliability: the kappa statistic. Biochem Med2012;22:276–82.doi:10.11613/BM.2012.031
      OpenUrl
    36. 36.↵
      1. Harris LT,
      2. Tufano J,
      3. Le T, et al
      . Designing mobile support for glycemic control in patients with diabetes. J Biomed Inform2010;43(5 Suppl):S37–S40.doi:10.1016/j.jbi.2010.05.004
      OpenUrlCrossRefPubMed
    37. 37.↵
      1. Wiljer D,
      2. Urowitz S,
      3. Apatu E, et al
      . Patient accessible electronic health records: exploring recommendations for successful implementation strategies. J Med Internet Res2008;10:1–14.doi:10.2196/jmir.1061
      OpenUrlCrossRef
    38. 38.↵
      . Barriers to the adoption and use of personal health record systems. Proceedings of the 2011 iConference, 2011.
    39. 39.↵
      1. Kharrazi H,
      2. Chisholm R,
      3. VanNasdale D, et al
      . Mobile personal health records: an evaluation of features and functionality. Int J Med Inform2012;81:579–93.doi:10.1016/j.ijmedinf.2012.04.007
      OpenUrlCrossRefPubMed
    40. 40.↵
      1. Moreau M,
      2. Gagnon M-P,
      3. Boudreau F
      . Development of a fully automated, web-based, tailored intervention promoting regular physical activity among insufficiently active adults with type 2 diabetes: integrating the I-change model, self-determination theory, and motivational interviewing components. JMIR Res Protoc2015;4:e25.doi:10.2196/resprot.4099
    41. 41.↵
      1. Årsand E,
      2. Tatara N,
      3. Østengen G, et al
      . Mobile phone-based self-management tools for type 2 diabetes: the few touch application. J Diabetes Sci Technol2010;4:328–36.doi:10.1177/193229681000400213
      OpenUrlCrossRefPubMed
    42. 42.↵
      1. Misono AS,
      2. Cutrona SL,
      3. Choudhry NK, et al
      . Healthcare information technology interventions to improve cardiovascular and diabetes medication adherence. Am J Manag Care2010;16(12 Suppl HIT):SP82–92.
      OpenUrlPubMedWeb of Science
    43. 43.↵
      1. Tsang MW,
      2. Mok M,
      3. Kam G, et al
      . Improvement in diabetes control with a monitoring system based on a hand-held, touch-screen electronic diary. J Telemed Telecare2001;7:47–50.doi:10.1258/1357633011936138
      OpenUrlCrossRefPubMed
    44. 44.↵
      1. Forjuoh SN,
      2. Reis MD,
      3. Couchman GR, et al
      . Incorporating PDA use in diabetes self-care: a central Texas primary care research network (CenTexNet) study. J Am Board Fam Med2007;20:375–84.doi:10.3122/jabfm.2007.04.060166
      OpenUrlAbstract/FREE Full Text
    45. 45.↵
      . Technology failure analysis: understanding why a diabetes management tool developed for a personal digital assistant (PDA) didn’t work in a randomized control trial. AMIA Annual Symposium Proceedings, 2003.
    46. 46.↵
      1. Montori VM,
      2. Helgemoe PK,
      3. Guyatt GH, et al
      . Telecare for patients with type 1 diabetes and inadequate glycemic control: a randomized controlled trial and meta-analysis. Diabetes Care2004;27:1088–94.doi:10.2337/diacare.27.5.1088
      OpenUrlAbstract/FREE Full Text
    47. 47.↵
      1. Lyles CR,
      2. Harris LT,
      3. Le T, et al
      . Qualitative evaluation of a mobile phone and Web-based collaborative care intervention for patients with type 2 diabetes. Diabetes Technol Ther2011;13:563–9.doi:10.1089/dia.2010.0200
      OpenUrlCrossRefPubMedWeb of Science
    48. 48.↵
      1. Free C,
      2. Phillips G,
      3. Galli L, et al
      . The effectiveness of mobile-health technology-based health behaviour change or disease management interventions for health care consumers: a systematic review. PLoS Med2013;10:e1001362.doi:10.1371/journal.pmed.1001362
    49. 49.↵
      1. Wilhide Iii CC,
      2. Peeples MM,
      3. Anthony Kouyaté RC
      . Evidence-based mHealth chronic disease mobile app intervention design: development of a framework. JMIR Res Protoc2016;5:1–10.doi:10.2196/resprot.4838
      OpenUrl
    50. 50.↵
      1. Fioravanti A,
      2. Fico G,
      3. Salvi D, et al
      . Automatic messaging for improving patients engagement in diabetes management: an exploratory study. Med Biol Eng Comput2015;53:1285–94.doi:10.1007/s11517-014-1237-8
      OpenUrl
    51. 51.↵
      1. de la Torre-Díez I,
      2. López-Coronado M,
      3. Vaca C, et al
      . Cost-utility and cost-effectiveness studies of telemedicine, electronic, and mobile health systems in the literature: a systematic review. Telemed J E Health2015;21:81–5.doi:10.1089/tmj.2014.0053
      OpenUrlCrossRefPubMed
    52. 52.↵
      1. Nundy S,
      2. Dick JJ,
      3. Solomon MC, et al
      . Developing a behavioral model for mobile phone-based diabetes interventions. Patient Educ Couns2013;90:125–32.doi:10.1016/j.pec.2012.09.008
      OpenUrlCrossRefPubMed
    53. 53.↵
      1. Nelson LA,
      2. Mulvaney SA,
      3. Gebretsadik T, et al
      . Disparities in the use of a mHealth medication adherence promotion intervention for low-income adults with type 2 diabetes. J Am Med Inform Assoc2016;23:12–18.doi:10.1093/jamia/ocv082
      OpenUrlCrossRefPubMed
    54. 54.↵
      Mobile medical applications. Available: https://www.fda.gov/medicaldevices/digitalhealth/mobilemedicalapplications/default.htm [Accessed 26 Feb 2019].
    55. 55.↵
      1. Fatehi F,
      2. Menon A,
      3. Bird D
      . Diabetes care in the digital era: A synoptic overview. Curr Diab Rep2018;18:1–8.doi:10.1007/s11892-018-1013-5
      OpenUrlCrossRefPubMed
    56. 56.↵
      1. Yetisen AK,
      2. Martinez-Hurtado JL,
      3. da Cruz Vasconcellos F, et al
      . The regulation of mobile medical applications. Lab Chip2014;14:833–40.doi:10.1039/c3lc51235e
      OpenUrlCrossRefPubMed
    57. 57.↵
      1. Lewis TL,
      2. Wyatt JC
      . mHealth and mobile medical apps: a framework to assess risk and promote safer use. J Med Internet Res2014;16:1–8.doi:10.2196/jmir.3133
      OpenUrlCrossRef
    58. 58.↵
      1. Cortez NG,
      2. Cohen IG,
      3. Kesselheim AS, et al
      . FDA regulation of mobile health technologies. N Engl J Med2014;371:372–9.doi:10.1056/NEJMhle1403384
      OpenUrlCrossRefPubMedWeb of Science
    59. 59.↵
      1. Barton AJ
      . The regulation of mobile health applications. BMC Med2012;10:1–4.doi:10.1186/1741-7015-10-46
      OpenUrlCrossRefPubMed

    Footnotes

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient consent for publication Not required.

    • Provenance and peer review Not commissioned; externally peer reviewed.