Cited by 125Mobile Health and mHealth ApplicationsDiabetes Management and ResearchDiabetes Management and Education

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

Fleming, G. Alexander, Petrie, John R., Bergenstal, Richard M., Holl, Reinhard W., Peters, Anne L., Heinemann, Lutz·2020·Diabetologia

Abstract

Digital health technology, especially digital and health applications ('apps'), have been developing rapidly to help people manage their diabetes. Numerous health-related apps provided on smartphones and other wireless devices are available to support people with diabetes who need to adopt either lifestyle interventions or medication adjustments in response to glucose-monitoring data. However, regulations and guidelines have not caught up with the burgeoning field to standardise how mobile health apps are reviewed and monitored for patient safety and clinical validity. The available evidence on the safety and effectiveness of mobile health apps, especially for diabetes, remains limited. The European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) have therefore conducted a joint review of the current landscape of available diabetes digital health technology (only stand-alone diabetes apps, as opposed to those that are integral to a regulated medical device, such as insulin pumps, continuous glucose monitoring systems, and automated insulin delivery systems) and practices of regulatory authorities and organisations. We found that, across the USA and Europe, mobile apps intended to manage health and wellness are largely unregulated unless they meet the definition of medical devices for therapeutic and/or diagnostic purposes. International organisations, including the International Medical Device Regulators Forum and WHO, have made strides in classifying different types of digital health technology and integrating digital health technology into the field of medical devices. As the diabetes digital health field continues to develop and become more fully integrated into everyday life, we wish to ensure that it is based on the best evidence for safety and efficacy. As a result, we bring to light several issues that the diabetes community, including regulatory authorities, policymakers, professional organisations, researchers, people with diabetes and healthcare professionals, needs to address to ensure that diabetes health technology can meet its full potential. These issues range from inadequate evidence on app accuracy and clinical validity to lack of training provision, poor interoperability and standardisation, and insufficient data security. We conclude with a series of recommended actions to resolve some of these shortcomings.

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  1. [5]5. Ramchandani N, Heptulla RA (2012) New technologies for diabetes: a review of the present and the future. Int J Pediatr Endocrinol 2012(1):28
  2. [8]8. Elenko E, Speier A, Zohar D (2015) A regulatory framework emerges for digital medicine. Nat Biotechnol 33(7):697 -702. https://doi.org/10.1038/nbt.3284 · DOI
  3. [13]13. Kebede MM, Pischke CR (2019) Popular diabetes apps and the impact of diabetes app use on self-care behaviour: a survey among the digital community of persons with diabetes on social media. Front Endocrinol (Lausanne) 10:135. https://doi.org/10.3389/ fendo.2019.00135
  4. [14]14. Bergenstal RM, Johnson M, Powers MA et al (2008) Adjust to target in type 2 diabetes. Diabetes Care 31(7):1305 -1310. https:// doi.org/10.2337/dc07-2137 · DOI
  5. [15]15. Conroy DE, Yang CH, Maher JP (2014) Behavior change techniques in top-ranked mobile apps for physical activity. Am J Prev Med 46(6):649 -652. https://doi.org/10.1016/j.amepre.2014.01.010 · DOI
  6. [26]26. Hood M, Wilson R, Corsica J, Bradley L, Chirinos D, Vivo A (2016) What do we know about mobile applications for diabetes self-management? A review of reviews. J Behav Med 39(6):981 -994. https://doi.org/10.1007/s10865-016-9765-3 · DOI · Paper page
  7. [27]27. Frøisland DH, Årsand E (2015) Integrating visual dietary documentation in mobile-phone-based self-management application for adolescents with type 1 diabetes. J Diabetes Sci Technol 9(3): 541 -548. https://doi.org/10.1177/1932296815576956 · DOI
  8. [30]30. Arnhold M, Quade M, Kirch W (2014) 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 16(4):e104
  9. [31]31. Drincic A, Prahalad P, Greenwood D, Klonoff DC (2016) Evidence-based mobile medical applications in diabetes. Endocrinol Metab Clin N Am 45(4):943 -965. https://doi.org/10. 1016/j.ecl.2016.06.001
  10. [33]33. Kerr D, Gabbay RA, Klonoff DC (2018) Finding real value from digital diabetes health: is digital health dead or in need of resuscitation? J Diabetes Sci Technol 12(5):911 -913. https://doi.org/10. 1177/1932296818771200
  11. [34]34. Schuren J, Califf RM (2016) Need for a national evaluation system for health technology. JAMA 316(11):1153 -1154
  12. [39]39. Kramer DB, Xu S, Kesselheim AS (2012) Regulation of medical devices in the United States and European Union. N Engl J Med 36(9):848 -855
  13. [41]41. Van Norman GA (2016) Drugs and devices: comparison of European and U.S. approval processes. JACC Basic Transl Sci 1(5):399 -412. https://doi.org/10.1016/j.jacbts.2016.06.003 · DOI · Paper page
  14. [50]50. Kvedar JC, Fogel AL, Elenko E, Zohar D (2016) Digital medicine ' s march on chronic disease. Nat Biotechnol 34(3):239 -246
  15. [64]64. Ventola CL (2014) Mobile devices and apps for health care professionals: uses and benefits. P T 39(5):356 -364
  16. [65]65. Baratloo A, Hosseini M, Negida A, el Ashal G (2015) Part 1: simple definition and calculation of accuracy, sensitivity and specificity. Emerg (Tehran) 3(2):48 -49
  17. [66]66. Hogan NM, Kerin MJ (2012) Smart phone apps: smart patients, steer clear. Patient Educ Couns 89(2):360 -361. https://doi.org/10. 1016/j.pec.2012.07.016
  18. [68]68. Bierbrier R, Lo V, Wu RC (2014) Evaluation of the accuracy of smartphone medical calculation apps. J Med Internet Res 16(2): e32. https://doi.org/10.2196/jmir.3062 · DOI
  19. [69]69. Lum E, Jimenez G, Huang Z et al (2019) Decision support and alerts of apps for self-management of blood glucose for type 2 diabetes. JAMA 321(15):1530 -1532. https://doi.org/10.1001/ jama.2019.1644
  20. [71]71. Ristau RA, Yang J, White JR (2013) Evaluation and evolution of diabetes mobile applications: key factors for health care professionals seeking to guide patients. Diabetes Spectrum 26(4):211 -215
  21. [80]80. Sardi L, Idri A, Fernandez-Aleman JL (2017) A systematic review of gamification in e-Health. J Biomed Inform 71:3148
  22. [81]81. Blenner SR, Köllmer M, Rouse AJ, Daneshvar N, Williams C, Andrews LB (2016) Privacy policies of android diabetes apps and sharing of health information. JAMA 315(10):1051 -1052. https:// doi.org/10.1001/jama.2015.19426 · DOI
  23. [82]82. Klonoff DC (2015) Cybersecurity for connected diabetes devices. J Diabetes Sci Technol 9(5):1143 -1147. https://doi.org/10.1177/ 1932296815583334
  24. [84]84. Wyatt JC (2018) How can clinicians, specialty societies and others evaluate and improve the quality of apps for patient use? BMC Med 16:225 Publisher ' s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. <!-- image -->

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Authors
Fleming, G. A., Petrie, J. R., Bergenstal, R. M., Holl, R. W., Peters, A. L., & Heinemann, L.
Affiliations
  • University of Glasgow (GB)
  • Park Nicollet Clinic (US)
  • Universität Ulm (DE)
  • University of Southern California (US)
Corresponding author
Fleming, G. Alexander
Venue
Diabetologia
Published
2019-12-05
Volume
63
Issue
2
Pages
229-241
ISSN
  • 0012-186X (ISSN-L)
  • 1432-0428
OpenAlex
W2992264800
Cited by
125
References
8
Citations by year
2026: 16 · 2025: 21 · 2024: 28 · 2023: 20 · 2022: 15 · 2021: 15 · 2020: 9 · 2019: 1
FWCI
13.70
Type
journal article
Language
en
Open access
closed
Locations
  • Diabetologia (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
Keywords
diabetes apps, mobile health, digital health technologies, diabetes self-management, medical device regulation, clinical validity, interoperability, data security, patient safety, regulatory standards, health technology policy
MeSH terms
Smartphone, Blood Glucose, Diabetes Mellitus, Europe, Humans, Insulin, Insulin Infusion Systems, United States, Blood Glucose Self-Monitoring, Consensus, Mobile Applications

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