Rapid evidence review of mobile applications for self-management of diabetes
Open accessAbstract
Background Patients with diabetes lack information on which commercially available applications (apps) improve diabetes-related outcomes. We conducted a rapid evidence review to examine features, clinical efficacy, and usability of apps for self-management of type 1 and type 2 diabetes in adults.Methods Ovid/Medline and the Cochrane Database of Systematic Reviews were searched for systematic reviews and technology assessments. Reference lists of relevant systematic reviews were examined for primary studies. Additional searches for primary studies were conducted online, through Ovid/Medline, Embase, CINAHL, and ClinicalTrials.gov . Studies were evaluated for eligibility based on predetermined criteria, data were extracted, study quality was assessed using a risk of bias tool, information on app features was collected, and app usability was assessed. Results are summarized qualitatively.Results Fifteen articles evaluating 11 apps were identified: six apps for type 1 and five apps for type 2 diabetes. Common features of apps included setting reminders and tracking blood glucose and hemoglobin A1c (HbA1c), medication use, physical activity, and weight. Compared with controls, use of eight apps, when paired with support from a healthcare provider or study staff, improved at least one outcome, most often HbA1c. Patients did not experience improvements in quality of life, blood pressure, or weight, regardless of app used or type of diabetes. Study quality was variable. Of the eight apps available for usability testing, two were scored "acceptable," three were "marginal," and three were "not acceptable."Discussion Limited evidence suggests that use of some commercially available apps, when combined with additional support from a healthcare provider or study staff, may improve some short-term diabetes-related outcomes. The impact of these apps on longer-term outcomes is unclear. More rigorous and longer-term studies of apps are needed.Registration This review was funded by the Agency for Healthcare Research and Quality (AHRQ). The protocol is available at: http://www.effectivehealthcare.ahrq.gov/topics/diabetes-mobile-devices/research-protocol .
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Prescription of mHealth programmes from healthcare practice is scarce due to the lack of randomized controlled trials that determine their effectiveness on clinical outcomes.
“However, the prescription of mHealth programmes from healthcare practice is scarce due to the lack of randomized controlled trials that determine their effectiveness on clinical outcomes [16,17].”
From Impact of a primary care-based mobile health intervention to ‘sit less and move more’ on HbA1c, blood pressure, and other clinical outcomes in office employees with type 2 diabetes: A randomized controlled trial (2025) · cites this paper as [16]
The Summary of Evidence and Conclusion and Next Steps (pages unavailable) describe limited evidence for commercially available diabetes apps and call for more rigorous, longer-term studies of clinical outcomes. The paper does not say that prescribing mHealth programmes in healthcare practice is scarce or that a shortage of randomized trials causes scarce prescribing; its Apps for Type 2 Diabetes section reports six randomized controlled trials. (LLM, conf=0.86)
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Cites
SUS: A 'Quick and Dirty' Usability Scale
(1996) · Usability Evaluation In Industry · doi:10.1201/9781498710411-35 · Cited by 8,989 · see citing works
Usability Evaluation In Industry
(1996) · CRC Press eBooks · doi:10.1201/9781498710411 · Cited by 4,400 · see citing works
National Standards for Diabetes Self-Management Education and Support
Haas, L. B., Maryniuk, M. D., Beck, J. K., Cox, C. E., Duker, P. N., Edwards, L. J., Fisher, E. B., Hanson, L., Kent, D., Kolb, L. E., et al. (2012) · Diabetes Care · doi:10.2337/dc12-1707 · Cited by 670 · see citing works
Quinn, C. C., Shardell, M. D., Terrin, M. L., Barr, E. A., Ballew, S. H., & Gruber-Baldini, A. L. (2011) · Diabetes Care · doi:10.2337/dc11-0366 · Cited by 650 · see citing works
Assessing the Risk of Bias of Individual Studies in Systematic Reviews of Health Care Interventions
Viswanathan, M., Patnode, C. D., Berkman, N. D., Bass, E. B., Chang, S., Hartling, L., Murad, M. H., Treadwell, J. R., & Kane, R. L. (2017) · doi:10.23970/ahrqepcmethguide2 · Cited by 448 · see citing works
Arnhold, M., Quade, M., & Kirch, W. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.2968 · Cited by 417 · see citing works
Determining what individual SUS scores mean
(2009) · Journal of Usability Studies archive · doi:10.5555/2835587.2835589 · Cited by 412 · see citing works
Hou, C., Carter, B., Hewitt, J., Francisa, T., & Mayor, S. (2016) · Diabetes Care · doi:10.2337/dc16-0346 · Cited by 411 · see citing works
Quinn, C. C., Clough, S. S., Minor, J. M., Lender, D., Okafor, M. C., & Gruber-Baldini, A. (2008) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2008.0283 · Cited by 406 · see citing works
Effectiveness of mHealth interventions for patients with diabetes: An overview of systematic reviews
Kitsiou, S., Paré, G., Jaana, M., & Gerber, B. S. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173160 · Cited by 390 · see citing works
2017 national standards for diabetes self-management education and support
Beck, J., Greenwood, D. A., Blanton, L., Bollinger, S. T., Butcher, M. K., Condon, J. E., Cypress, M., Faulkner, P., Fischl, A. H., Francis, T., et al. (2017) · Diabetes Care · doi:10.2337/dci17-0025 · Cited by 348 · see citing works
Kirwan, M., Vandelanotte, C., Fenning, A., & Duncan, M. J. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2588 · Cited by 343 · see citing works
A Review of Telehealth Service Implementation Frameworks
Van Dyk, L. (2014) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph110201279 · Cited by 300 · see citing works
Charpentier, G., Benhamou, P.-Y., Dardari, D., Clergeot, A., Franc, S., Schaepelynck-Belicar, P., Catargi, B., Melki, V., Chaillous, L., Farret, A., et al. (2011) · Diabetes Care · doi:10.2337/dc10-1259 · Cited by 292 · see citing works
Wu, Y., Yao, X., Vespasiani, G., Nicolucci, A., Dong, Y., Kwong, J., Li, L., Sun, X., Tian, H., & Li, S. (2017) · JMIR mHealth and uHealth · doi:10.2196/mhealth.6522 · Cited by 283 · see citing works
T2DM Self-Management via Smartphone Applications: A Systematic Review and Meta-Analysis
Cui, M., Wu, X., Mao, J., Wang, X., & Nie, M. (2016) · PLOS ONE · doi:10.1371/journal.pone.0166718 · Cited by 269 · see citing works
Wayne, N., Perez, D. F., Kaplan, D. M., & Ritvo, P. (2015) · Journal of Medical Internet Research · doi:10.2196/jmir.4871 · Cited by 192 · see citing works
Wu, Y., Yao, X., Vespasiani, G., Nicolucci, A., Dong, Y., Kwong, J. S. W., Li, L., Sun, X., Tian, H., & Li, S. (2018) · JMIR mhealth and uhealth · doi:10.2196/mhealth.8789 · Cited by 176 · see citing works
Rossi, M. C. E., Nicolucci, A., Di Bartolo, P., Bruttomesso, D., Girelli, A., Ampudia, F. J., Kerr, D., Ceriello, A., Mayor, C. D. L. Q., Pellegrini, F., et al. (2010) · Diabetes Care · doi:10.2337/dc09-1327 · Cited by 174 · see citing works
Fu, H., McMahon, S. K., Gross, C. R., Adam, T. J., & Wyman, J. F. (2017) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2017.06.016 · Cited by 173 · see citing works
Zhou, W., Chen, M., Yuan, J., & Sun, Y. (2016) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2016.03.018 · Cited by 107 · see citing works
Rossi, M. C., Nicolucci, A., Lucisano, G., Pellegrini, F., Bartolo, P. D., Miselli, V., Anichini, R., & Vespasiani, O. B. O. T. D. S. G. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0021 · Cited by 105 · see citing works
Tailored Communication Within Mobile Apps for Diabetes Self-Management: A Systematic Review
Holmen, H., Wahl, A. K., Småstuen, M. C., & Ribu, L. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7045 · Cited by 82 · see citing works
Drion, I., Pameijer, L. R., van Dijk, P. R., Groenier, K. H., Kleefstra, N., & Bilo, H. J. G. (2015) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815585871 · Cited by 74 · see citing works
Telemedicine and type 1 diabetes: Is technology per se sufficient to improve glycaemic control?
Franc, S., Borot, S., Ronsin, O., Quésada, J.-L., Dardari, D., Fagour, C., Renard, É., Leguerrier, A.-M., Vigeral, C., Moreau, F., et al. (2013) · Diabetes & Metabolism · doi:10.1016/j.diabet.2013.09.001 · Cited by 65 · see citing works
EPC Methods: An Exploration of Methods and Context for the Production of Rapid Reviews
Hartling, L., Guise, J., Kato, E. U., Anderson, J., Aronson, N., Belinson, S., Berliner, E., Dryden, D. M., Featherstone, R. M., Foisy, M., et al. (2015) · Cited by 62 · see citing works
Self-citationMobile Diabetes Intervention for Glycemic Control in 45- to 64-Year-Old Persons With Type 2 Diabetes
Quinn, C. C., Shardell, M. D., Terrin, M. L., Barr, E. A., Park, D., Shaikh, F., Guralnik, J. M., & Gruber‐Baldini, A. L. (2014) · Journal of Applied Gerontology · doi:10.1177/0733464814542611 · Cited by 61 · see citing works
Data-Driven Personalized Feedback to Patients with Type 1 Diabetes: A Randomized Trial
Skrøvseth, S. O., Årsand, E., Godtliebsen, F., & Joakimsen, R. M. (2015) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2014.0276 · Cited by 51 · see citing works
Garg, S. K., Shah, V. N., Aktürk, H. K., Beatson, C. R., & Snell‐Bergeon, J. K. (2017) · Diabetes Therapy · doi:10.1007/s13300-017-0272-5 · Cited by 40 · see citing works
An ICT-Based Diabetes Management System Tested for Health Care Delivery in the African Context
Takenga, C., Berndt, R.-D., Musongya, O., Kitero, J., Katoke, R., Molo, K., Kazingufu, B., Meni, M., Vikandy, M., & Takenga, H. (2014) · International Journal of Telemedicine and Applications · doi:10.1155/2014/437307 · Cited by 38 · see citing works
Kleinman, N. J., Shah, A., Shah, S., Phatak, S., & Viswanathan, V. (2016) · Diabetes Care · doi:10.2337/dc16-0869 · Cited by 21 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Diagnosis of Diabetes Mellitus Using Gradient Boosting Machine (LightGBM)
Rufo, D. D., Debelee, T. G., Ibenthal, A., & Negera, W. G. (2021) · Diagnostics · doi:10.3390/diagnostics11091714 · Cited by 317 · see citing works
Zhang, Y., Liu, C., Luo, S., Xie, Y., Liu, F., Li, X., & Zhou, Z. (2019) · Journal of Medical Internet Research · doi:10.2196/15023 · Cited by 207 · see citing works
Effectiveness of Disease-Specific mHealth Apps in Patients With Diabetes Mellitus: Scoping Review
Eberle, C., Löhnert, M., & Stichling, S. (2021) · JMIR mHealth and uHealth · doi:10.2196/23477 · Cited by 160 · see citing works
“mHealth apps offer novel possibilities, and first steps have been taken in this regard by a small but growing part of the DM community [26-29].”
Varma, N., Cygankiewicz, I., Turakhia, M. P., Heidbuchel, H., Hu, Y.-F., Chen, L. Y., Couderc, J.-P., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · Circulation: Arrhythmia and Electrophysiology · doi:10.1161/circep.120.009204 · Cited by 74 · see citing works
Alòs, F., Colomer, M. À., Bort-Roig, J., Martin-Cantera, C., Minaya, A., Saigí, I., Sitjar-Suñer, M., & Puig-Ribera, A. (2025) · Primary Care Diabetes · doi:10.1016/j.pcd.2025.05.010 · Cited by 11 · see citing works
- Authors
- Veazie, S., Winchell, K., Gilbert, J., Paynter, R., Ivlev, I., Eden, K. B., Nussbaum, K., Weiskopf, N., Guise, J.-M., & Helfand, M.
- Affiliations
- Portland VA Research Foundation (US)
- Oregon Health & Science University (US)
- Corresponding author
- Veazie, Stephanie
- ORCID
- Venue
- Journal of General Internal Medicine
- Published
- 2018-05-08
- Volume
- 33
- Issue
- 7
- Pages
- 1167-1176
- ISSN
- 0884-8734 (ISSN-L)
- 1525-1497
- PMC
- PMC6025680
- OpenAlex
- W2800324513
- Cited by
- 170
- References
- 31
- Citations by year
- 2026: 10 · 2025: 20 · 2024: 11 · 2023: 24 · 2022: 25 · 2021: 31 · 2020: 27 · 2019: 18 · 2018: 4
- FWCI
- 30.55
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of General Internal Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Health and Human Services
- AcademyHealth
- National Institutes of Health
- Agency for Healthcare Research and Quality (HHSA 290 2017 00003C)
- U.S. National Library of Medicine (T15LM007088)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Keywords
- mobile health apps, diabetes self-management, type 1 diabetes, type 2 diabetes, glycemic control, HbA1c, blood glucose monitoring, medication adherence, physical activity monitoring, app usability, healthcare provider support, quality of life
- MeSH terms
- Self-Management, Diabetes Mellitus, Humans, Self Care, Randomized Controlled Trials as Topic, Evidence-Based Medicine, Cell Phone, Mobile Applications
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