The effectiveness of mobile application for monitoring diabetes mellitus and hypertension in the adult and elderly population: Systematic review and meta-analysis
Open accessAbstract
Context: Arterial Hypertension (AH) and Diabetes Mellitus (DM) are diseases that are getting worse all over the world. Linked to this advance, is the growing digital health market with numerous mobile health applications, which aim to help patients and professionals in the proper management of chronic diseases. The aim of this study was to analyze, through a systematic review and meta-analysis, the effectiveness of using mobile health applications in monitoring AH and/or DM in the adult and elderly population. Methods: The systematic review and meta-analysis was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Metanalyses guidelines and involved searching five databases – Medline/PubMed, Embase, CINAHL, Virtual Library in Health and Cochrane Library. The review included randomized and cohort clinical trials testing the effects of the intervention on changing biochemical parameters and clinical efficacy in people treated for AH and/or DM. The quality of the selected studies was assessed based on the evaluation criteria of the Joanna Briggs Institute tool. The random effects meta-analysis method was used to explain effect distribution between studies, by Stata® software (version 11.0) and publication bias was examined by visual inspection of graphs and Egger test. Results: We included 26 studies in the systematic review and 17 in the meta-analysis. These studies were published between 2014 to 2022 in 14 countries. Were reported improvement in knowledge and self-management of AH and DM, social motivation with treatment and behavioral change, reduction in glycated hemoglobin values, fasting glucose and blood pressure, improvement in adherence to drug treatment, among others. The result of the meta-analysis showed that there is evidence that the use of mobile applications can help reduce glycated hemoglobin by 0.39% compared to the usual care group. Conclusions: Monitoring and self-monitoring of behaviors and health care related to AH and DM in adults and the elderly through mobile applications, has clinically significant effectiveness in reducing glycated hemoglobin levels. Future studies should provide more evidence and recommendations for best practices and development of digital health interventions. Trial registration: PROSPERO. International Prospective Registry of Systematic Reviews. CRD42022361928.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
A meta-analysis by de Souza Ferreira et al., including 17 trials, showed a 0.39% (95% CI 0.24 to 0.54) reduction in HbA1c with mobile apps for monitoring diabetes and hypertension compared to traditional care.
“Among reviews concerned with diabetes and mHealth system, a meta-analysis study by de Souza Ferreira and colleagues, including 17 trials on diabetes highlighted the effectiveness of mobile apps for monitoring diabetes and hypertension, showing a 0.39% (95% CI 0.24 to 0.54) reduction in HbA1c compared to traditional care (32).”
From Impact of digital health on Type 2 diabetes management: a randomised controlled trial of the ‘TreC Diabete’ platform (TELEMECHRON Study) (2025) · cites this paper as [32]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Saeedi, P., Petersohn, I., Salpea, P., Malanda, B., Karuranga, S., Unwin, N., Colagiuri, S., Guariguata, L., Motala, A. A., Ogurtsova, K., et al. (2019) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2019.107843 · Cited by 10,792 · see citing works
The global epidemiology of hypertension
Mills, K. T., Stefanescu, A. R., & He, J. (2020) · Nature Reviews Nephrology · doi:10.1038/s41581-019-0244-2 · Cited by 4,533 · see citing works
Zhou, B., Carrillo-Larco, R. M., Danaei, G., Riley, L. M., Paciorek, C. J., Stevens, G. A., Gregg, E. W., Bennett, J. E., Solomon, B., Singleton, R. K., et al. (2021) · The Lancet · doi:10.1016/s0140-6736(21)01330-1 · Cited by 3,902 · see citing works
McInnes, M. D. F., Moher, D., Thombs, B. D., McGrath, T. A., Bossuyt, P. M., and the PRISMA-DTA Group, Clifford, T., Cohen, J. F., Deeks, J. J., Gatsonis, C., et al. (2018) · JAMA · doi:10.1001/jama.2017.19163 · Cited by 3,210 · see citing works
Improved tests for a random effects meta‐regression with a single covariate
Knapp, G., & Hartung, J. (2003) · Statistics in Medicine · doi:10.1002/sim.1482 · Cited by 2,075 · see citing works
Eysenbach, G., & CONSORT-EHEALTH Group (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1923 · Cited by 1,863 · see citing works
Ming, L. C., Untong, N., Aliudin, N. A., Osili, N., Kifli, N., Tan, C. S., Goh, K. W., Ng, P. W., Al‐Worafi, Y. M., Lee, K. S., et al. (2020) · JMIR mhealth and uhealth · doi:10.2196/19796 · Cited by 570 · see citing works
Content Analysis of Mobile Health Applications on Diabetes Mellitus
Izahar, S., Lean, Q. Y., Hameed, M. A., Murugiah, M. K., Patel, R. P., Al‐Worafi, Y. M., Wong, T. W., & Ming, L. C. (2017) · Frontiers in Endocrinology · doi:10.3389/fendo.2017.00318 · Cited by 371 · see citing works
mHealth 2.0: Experiences, Possibilities, and Perspectives
Becker, S., Miron-Shatz, T., Schumacher, N., Krocza, J., Diamantidis, C., & Albrecht, U.-V. (2014) · JMIR mHealth and uHealth · doi:10.2196/mhealth.3328 · Cited by 354 · see citing works
Morawski, K., Ghazinouri, R., Krumme, A., Lauffenburger, J. C., Lu, Z., Durfee, E., Oley, L., Lee, J., Mohta, N., Haff, N., et al. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.0447 · Cited by 333 · see citing works
Akbar, S., Coiera, E. W., & Magrabi, F. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz175 · Cited by 246 · see citing works
Mobile Apps for Medication Management: Review and Analysis
Tabi, K., Randhawa, A. S., Choi, F., Mithani, Z., Albers, F., Schnieder, M., Nikoo, M., Vigo, D., Jang, K. L., Demlová, R., et al. (2019) · JMIR mhealth and uhealth · doi:10.2196/13608 · Cited by 170 · see citing works
Telemedicine Technologies for Diabetes in Pregnancy: A Systematic Review and Meta-Analysis.
Ming, W.-K., Mackillop, L. H., Farmer, A. J., Loerup, L., Bartlett, K., Levy, J. C., Tarassenko, L., Velardo, C., Kenworthy, Y., & Hirst, J. E. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.6556 · Cited by 167 · see citing works
Sun, C., Sun, L., Xi, S., Zhang, H., Wang, H., Feng, Y., Deng, Y., Wang, H., Xiao, X., Wang, G., et al. (2019) · JMIR mHealth and uHealth · doi:10.2196/10664 · Cited by 124 · see citing works
Connected Health in Hypertension Management
Omboni, S. (2019) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2019.00076 · Cited by 112 · see citing works
Huang, Z., Tan, E. J. H., Lum, E., Sloot, P. M. A., Boehm, B. O., & Car, J. (2019) · JMIR mhealth and uhealth · doi:10.2196/14914 · Cited by 100 · see citing works
Kassavou, A., Wang, M., Mirzaei, V., Shpendi, S., & Hasan, R. (2022) · JMIR mHealth and uHealth · doi:10.2196/34767 · Cited by 91 · see citing works
Chao, D. Y., Lin, T. M., & Ma, W.-Y. (2019) · JMIR Diabetes · doi:10.2196/11017 · Cited by 82 · see citing works
Spaulding, E. M., Marvel, F. A., Piasecki, R. J., Martin, S. S., & Allen, J. K. (2021) · JMIR Cardio · doi:10.2196/18834 · Cited by 70 · see citing works
Lee, L. L., Mulvaney, C. A., Wong, Y. K. Y., Chan, E. S., Watson, M. C., & Lin, H. H. (2021) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd008823.pub2 · Cited by 68 · see citing works
Kumar, S., Moseson, H., Uppal, J., & Juusola, J. L. (2018) · The Diabetes Educator · doi:10.1177/0145721718765650 · Cited by 68 · see citing works
Older Adult Self-Efficacy Study of Mobile Phone Diabetes Management
Quinn, C. C., Khokhar, B., Weed, K., Barr, E. A., & Gruber‐Baldini, A. L. (2015) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2014.0341 · Cited by 56 · see citing works
Zhai, Y., & Yu, W. (2020) · Medical Science Monitor · doi:10.12659/msm.926719 · Cited by 45 · see citing works
Bailey, D. P., Mugridge, L. H., Dong, F., Zhang, X., & Chater, A. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17124414 · Cited by 39 · see citing works
Understanding and Preventing Health Concerns About Emerging Mobile Health Technologies
Materia, F. T., Faasse, K., & Smyth, J. M. (2020) · JMIR mhealth and uhealth · doi:10.2196/14375 · Cited by 38 · see citing works
Identifying features of a mobile-based application for self-care of people living with T2DM
Mehraeen, E., Noori, T., Nazeri, Z., Heydari, M., Mehranfar, A., Moghaddam, H. R., & Aghamohammadi, V. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108544 · Cited by 38 · see citing works
Were, M. C., Sinha, C., & Catalani, C. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz071 · Cited by 36 · see citing works
Design and Development of a Mobile-Based Self-Care Application for Patients with Type 2 Diabetes
Mehraeen, E., Mehrtak, M., Janfaza, N., Karimi, A., Heydari, M., Mirzapour, P., & Mehranfar, A. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211007124 · Cited by 35 · see citing works
Santos, D. S., Batistelli, C. R. S., Lara, M. M. D. S., Ferreira, E. D. S., Moreira, T., & Cotta, R. M. (2022) · Diabetology & Metabolic Syndrome · doi:10.1186/s13098-022-00846-5 · Cited by 21 · see citing works
Self-citationLee, S., Lee, Y., Lee, S., Islam, S. M. S., & Kim, S. (2018) · JMIR mhealth and uhealth · doi:10.2196/12385 · Cited by 20 · see citing works
Patnaik, L., Panigrahi, S. K., Sahoo, A., Mishra, D., Muduli, A. K., & Beura, S. (2022) · International Journal of Preventive Medicine · doi:10.4103/ijpvm.ijpvm_92_20 · Cited by 20 · see citing works
Kim, H., Yang, S. J., Jeong, Y. J., Kim, Y., Hong, S. W., & Cho, J. (2017) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2017.41.3.213 · Cited by 13 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Giovanazzi, A., Gios, L., Gentilini, M. A., Mastellaro, M., Bartolotta, P., Nicolussi Giacomaz, L., Eccher, C., & Inchiostro, S. (2025) · Frontiers in Clinical Diabetes and Healthcare · doi:10.3389/fcdhc.2025.1589548 · Cited by 2 · see citing works
- Authors
- de Souza Ferreira, E., de Aguiar Franco, F., dos Santos Lara, M. M., Levcovitz, A. A., Dias, M. A., Moreira, T. R., de Oliveira, A. H. M., & Cotta, R. M. M.
- Affiliations
- Universidade Federal de Viçosa (BR)
- Universidade Federal do Tocantins (BR)
- Venue
- BMC Health Services Research
- Published
- 2023-08-12
- Volume
- 23
- Issue
- 1
- Pages
- 855-855
- ISSN
- 1472-6963 (ISSN-L)
- PMC
- PMC10423411
- OpenAlex
- W4385774197
- Cited by
- 42
- References
- 32
- Citations by year
- 2026: 16 · 2025: 19 · 2024: 6 · 2023: 1
- FWCI
- 2.12
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Health Services Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (306358/2020)
- Fundação de Amparo à Pesquisa do Estado de Minas Gerais , Brasil (APQ-02708-21)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Physical Sciences › Computer Science › Information Systems › Mobile Learning in Education
- Keywords
- mobile health apps, diabetes mellitus, hypertension, chronic disease self-management, HbA1c, blood pressure control, fasting blood glucose, medication adherence, elderly patients, digital health interventions, behavior change
- MeSH terms
- Adult, Aged, Chronic Disease, Diabetes Mellitus, Glycated Hemoglobin, Humans, Hypertension, Mobile Applications
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review