Six Digital Health Technologies That Will Transform Diabetes
Open accessAbstract
The digital health revolution is transforming the landscape of medicine through innovations in sensor data, software, and wireless communication tools. As one of the most prevalent chronic diseases in the United States, diabetes is particularly impactful as a model disease for which to apply innovation. As with any other newly developed technologies, there are three key questions to consider: 1) How can the technology benefit people with diabetes?, 2) What barriers must be overcome to further advance the technology?, and 3) How will the technology be applied in the future?. In this article, we highlight six areas of innovation that have the potential to reduce the burden of diabetes for individuals living with the condition and their families as well as provide measurable benefits for all stakeholders involved in diabetes care. The six technologies which have the potential to transform diabetes care are (i) telehealth, (ii) incorporation of diabetes digital data into the electronic health record, (iii) qualitative hypoglycemia alarms, (iv) artificial intelligence, (v) cybersecurity of diabetes devices, and (vi) diabetes registries. To be successful, a new digital health technology must be accessible and affordable. Furthermore, the people and communities that would most likely benefit from the technology must be willing to use the innovation in their management of diabetes.
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The application of ML in diabetes care and research has been widely explored in basic biomedical research, translational research, and clinical practice.
“The application of ML in diabetes care and research has been widely explored in basic biomedical research,16 translational research,14 and clinical practice17 (Figure 1).”
From Artificial intelligence in diabetes management: Advancements, opportunities, and challenges (2023) · cites this paper as [17]
Cites
Economic Costs of Diabetes in the U.S. in 2017
American Diabetes Association (2018) · Diabetes Care · doi:10.2337/dci18-0007 · Cited by 2,528 · see citing works
Dorsey, E. R., & Topol, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmra1601705 · Cited by 1,211 · see citing works
Use of Telehealth During the COVID-19 Pandemic: Scoping Review
Doraiswamy, S., Abraham, A., Mamtani, R., & Cheema, S. (2020) · Journal of Medical Internet Research · doi:10.2196/24087 · Cited by 698 · see citing works
The T1D exchange clinic registry
Beck, R. W., Tamborlane, W. V., Bergenstal, R. M., Miller, K. M., DuBose, S. N., Hall, C. A., & for the T1D Exchange Clinic Network (2012) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2012-1561 · Cited by 456 · see citing works
Machine Learning Methods to Predict Diabetes Complications
Dagliati, A., Marini, S., Sacchi, L., Cogni, G., Teliti, M., Tibollo, V., De Cata, P., Chiovato, L., & Bellazzi, R. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817706375 · Cited by 407 · see citing works
Artificial Intelligence: The Future for Diabetes Care
Ellahham, S. H. (2020) · The American Journal of Medicine · doi:10.1016/j.amjmed.2020.03.033 · Cited by 379 · see citing works
Roberts, E. T., & Mehrotra, A. (2020) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2020.2666 · Cited by 354 · see citing works
Impact of clinical registries on quality of patient care and clinical outcomes: A systematic review
Hoque, D. M. E., Kumari, V., Hoque, M., Ruseckaite, R., Romero, L., & Evans, S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0183667 · Cited by 301 · see citing works
Faruque, L. I., Wiebe, N., Ehteshami-Afshar, A., Liu, Y., Dianati-Maleki, N., Hemmelgarn, B. R., Manns, B. J., & Tonelli, M. (2017) · Canadian Medical Association Journal · doi:10.1503/cmaj.150885 · Cited by 281 · see citing works
Direct-To-Consumer Telehealth May Increase Access To Care But Does Not Decrease Spending
Ashwood, J. S., Mehrotra, A., Cowling, D., & Uscher‐Pines, L. (2017) · Health Affairs · doi:10.1377/hlthaff.2016.1130 · Cited by 278 · see citing works
The Clinical Endocrinology Workforce: Current Status and Future Projections of Supply and Demand
Vigersky, R. A., Fish, L., Hogan, P., Stewart, A., Kutler, S., Ladenson, P. W., McDermott, M., & Hupart, K. H. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2014-2257 · Cited by 238 · see citing works
Digital diabetes: Perspectives for diabetes prevention, management and research
Fagherazzi, G., & Ravaud, P. (2018) · Diabetes & Metabolism · doi:10.1016/j.diabet.2018.08.012 · Cited by 191 · see citing works
Paying for Telemedicine After the Pandemic
Mehrotra, A., Bhatia, R. S., & Snoswell, C. L. (2021) · JAMA · doi:10.1001/jama.2020.25706 · Cited by 168 · see citing works
Digital Medicine: A Primer on Measurement
Coravos, A., Goldsack, J. C., Karlin, D. R., Nebeker, C., Perakslis, E. D., Zimmerman, N., & Erb, M. K. (2019) · Digital Biomarkers · doi:10.1159/000500413 · Cited by 161 · see citing works
Nano, J., Carinci, F., Okunade, O., Whittaker, S., Walbaum, M., Barnard-Kelly, K. D., Barthelmes, D., Benson, T., Calderon‐Margalit, R., Dennaoui, J., et al. (2020) · Diabetic Medicine · doi:10.1111/dme.14286 · Cited by 141 · see citing works
Smartphone-Based Technology in Diabetes Management
Doupis, J., Festas, G., Tsilivigos, C., Efthymiou, V., & Kokkinos, A. D. (2020) · Diabetes Therapy · doi:10.1007/s13300-020-00768-3 · Cited by 140 · see citing works
Artificial intelligence for diabetic retinopathy screening, prediction and management
Gunasekeran, D. V., Ting, D. S. W., Tan, G. S. W., & Wong, T. Y. (2020) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000693 · Cited by 133 · see citing works
Artificial Intelligence Methodologies and Their Application to Diabetes
Rigla, M., García-Sáez, G., Pons, B., & Hernando, M. E. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817710475 · Cited by 131 · see citing works
Digital health technology and diabetes management: 数字化医疗技术与糖尿病管理
Cahn, A., Akirov, A., & Raz, I. (2018) · Journal of Diabetes · doi:10.1111/1753-0407.12606 · Cited by 102 · see citing works
Heintzman, N. D. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815622453 · Cited by 98 · see citing works
Wu, C., Wu, Z., Yang, L., Zhu, W., Zhang, M., Zhu, Q., Chen, X., & Pan, Y. (2018) · Medicine · doi:10.1097/md.0000000000012962 · Cited by 86 · see citing works
A virtual type 2 diabetes clinic using continuous glucose monitoring and endocrinology visits
Dixon, R. F., Zisser, H., Layne, J. E., Barleen, N. A., Miller, D. P., Moloney, D. P., Majithia, A. R., Gabbay, R. A., & Riff, J. (2020) · Journal of Diabetes Science and Technology · doi:10.1177/1932296819888662 · Cited by 80 · see citing works
Cybersecurity for Connected Diabetes Devices
Klonoff, D. C. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815583334 · Cited by 79 · see citing works
Telehealth delivery of the Diabetes Prevention Program to rural communities
Vadheim, L. M., Patch, K., Brokaw, S. M., Carpenedo, D., Butcher, M. K., Helgerson, S. D., & Harwell, T. S. (2017) · Translational Behavioral Medicine · doi:10.1007/s13142-017-0496-y · Cited by 72 · see citing works
National diabetes registries: do they make a difference?
Bak, J. C. G., Serné, E. H., Kramer, M. H. H., Nieuwdorp, M., & Verheugt, C. L. (2020) · Acta Diabetologica · doi:10.1007/s00592-020-01576-8 · Cited by 71 · see citing works
Direct-to-Consumer Telemedicine
Elliott, T., & Yopes, M. C. (2019) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2019.06.027 · Cited by 59 · see citing works
Top 10 Tips for Successfully Implementing a Diabetes Telehealth Program
Crossen, S. S., Raymond, J. K., & Neinstein, A. B. (2020) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2020.0042 · Cited by 57 · see citing works
Siegel, A. P., Daneshkhah, A., Hardin, D. S., Shrestha, S., Varahramyan, K., & Agarwal, M. (2017) · Journal of Breath Research · doi:10.1088/1752-7163/aa6ac6 · Cited by 51 · see citing works
Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments
Diouri, O., Cigler, M., Vettoretti, M., Mader, J., Choudhary, P., & Renard, E. (2021) · Diabetes/Metabolism Research Reviews · doi:10.1002/dmrr.3449 · Cited by 51 · see citing works
Everson, J., Patel, V., & Adler‐Milstein, J. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa323 · Cited by 49 · see citing works
On Definition of Artificial Intelligence
Radchenko, A. N., & Yurevich, E. I. (1972) · Springer eBooks · doi:10.1007/978-3-662-40393-8_7 · Cited by 44 · see citing works
Klonoff, D. C., Gutierrez, A., Fleming, A., & Kerr, D. (2019) · Journal of Diabetes Science and Technology · doi:10.1177/1932296819839996 · Cited by 41 · see citing works
Self-citationDigital health technology and diabetes management
(2018) · BDJ · doi:10.1038/sj.bdj.2018.249 · Cited by 40 · see citing works
Uscher‐Pines, L., Thompson, J., Taylor, P., Dean, K., Yuan, T., Tong, I., & Mehrotra, A. (2020) · Journal of Medical Internet Research · doi:10.2196/22727 · Cited by 34 · see citing works
Cahn, A., Akirov, A., & Raz, I. (2017) · Journal of Diabetes · Cited by 32 · see citing works
Hypo- and Hyperglycemic Alarms
Howsmon, D. P., & Bequette, B. W. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815583507 · Cited by 31 · see citing works
Kompala, T., & Neinstein, A. B. (2020) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0000000000000600 · Cited by 29 · see citing works
The 21st Century Cures Act: A Competitive Apps Market and the Risk of Innovation Blocking
Gordon, W. J., & Mandl, K. D. (2020) · Journal of Medical Internet Research · doi:10.2196/24824 · Cited by 28 · see citing works
Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management
Broome, D. T., Hilton, C. B., & Mehta, N. (2020) · Current Diabetes Reports · doi:10.1007/s11892-020-1287-2 · Cited by 27 · see citing works
Diabetes, like COVID-19, is a wicked problem
Kerr, D., & GLANTZ, N. M. (2020) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(20)30312-0 · Cited by 27 · see citing works
Self-citationStandards for Medical Device Cybersecurity in 2018
Yuan, S., Fernando, A., & Klonoff, D. C. (2018) · Journal of Diabetes Science and Technology · doi:10.1177/1932296818763634 · Cited by 25 · see citing works
Remvig, L. S., Madsen, R. E., Sejling, A., Sørensen, J. A., Snogdal, L. S., Folkestad, L., & Juhl, C. B. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600612 · Cited by 24 · see citing works
A portable system for monitoring physiological responses to hypoglycaemia
Harris, N. D., Baykouchev, S. B., Marques, J. L. B., Cochrane, T., George, E., Heller, S. R., & Ward, J. D. (1996) · Journal of Medical Engineering & Technology · doi:10.3109/03091909609008998 · Cited by 23 · see citing works
Klonoff, D. C. (2020) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820932958 · Cited by 15 · see citing works
Impaired Awareness of Hypoglycaemia
Frier, B. M. (2013) · Wiley eBooks · doi:10.1002/9781118695432.ch6 · Cited by 10 · see citing works
TINSLEY, L. J., Wong, N. D., Reusch, J. E. B., Arnold, S. V., Kosiborod, M., Tang, Y., Laffel, L. M., & Mehta, S. N. (2020) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2020.107591 · Cited by 4 · see citing works
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Artificial intelligence in diabetes management: Advancements, opportunities, and challenges
Guan, Z., Li, H., Liu, R., Cai, C., Liu, Y., Li, J., Wang, X., Huang, S., Wu, L., Liu, D., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101213 · Cited by 287 · see citing works
“15 The application of ML in diabetes care and research has been widely explored in basic biomedical research, 16 translational research, 14 and clinical practice 17 (Figure 1).”
BackgroundAmed, S., Pinkney, S., Abdulhussein, F. S., Virani, A., Zachariuk, C., Tamana, S. K., Muralidharan, S., Görges, M., Barrett, B., van Rooij, T., et al. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000861 · Cited by 1 · see citing works
“Digital health is, therefore, a rapidly emerging area in T1D [28], but knowledge gaps in implementation and impact are hindering its adoption.”
- Authors
- Klonoff, A. N., Lee, W.-A. (., Xu, N. Y., Nguyen, K. T., DuBord, A., & Kerr, D.
- Affiliations
- University of Southern California (US)
- LAC+USC Medical Center (US)
- Diabetes Technology Society (US)
- University of California, San Francisco (US)
- Sansum Diabetes Research Institute (US)
- Corresponding author
- Klonoff, Alexander N.
- Venue
- Journal of Diabetes Science and Technology
- Publisher
- SAGE Publications
- Published
- 2021-09-24
- Volume
- 17
- Issue
- 1
- Pages
- 239-249
- ISSN
- 1932-2968 (ISSN-L)
- PMC
- PMC9846384
- OpenAlex
- W3200367091
- Cited by
- 44
- References
- 46
- Citations by year
- 2026: 9 · 2025: 21 · 2024: 7 · 2023: 4 · 2022: 2 · 2021: 1
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Diabetes Science and Technology (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Keywords
- digital health technologies, diabetes management, telemedicine, electronic health records, artificial intelligence, diabetes registry, chronic disease management, technology adoption barriers, accessibility and affordability
- MeSH terms
- Artificial Intelligence, Diabetes Mellitus, Humans, Software, Telemedicine, Biomedical Technology
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