Mobile App-Based Interventions to Support Diabetes Self-Management: A Systematic Review of Randomized Controlled Trials to Identify Functions Associated with Glycemic Efficacy
Open accessAbstract
Background: Mobile health apps for diabetes self-management have different functions. However, the efficacy and safety of each function are not well studied, and no classification is available for these functions. Objective: The aims of this study were to (1) develop and validate a taxonomy of apps for diabetes self-management, (2) investigate the glycemic efficacy of mobile app-based interventions among adults with diabetes in a systematic review of randomized controlled trials (RCTs), and (3) explore the contribution of different function to the effectiveness of entire app-based interventions using the taxonomy. Methods: We developed a 3-axis taxonomy with columns of clinical modules, rows of functional modules and cells of functions with risk assessments. This taxonomy was validated by reviewing and classifying commercially available diabetes apps. We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, the Chinese Biomedical Literature Database, and ClinicalTrials.gov from January 2007 to May 2016. We included RCTs of adult outpatients with diabetes that compared using mobile app-based interventions with usual care alone. The mean differences (MDs) in hemoglobin A1c (HbA1c) concentrations and risk ratios of adverse events were pooled using a random-effects meta-analysis. After taxonomic classification, we performed exploratory subgroup analyses of the presence or absence of each module across the included app-based interventions. Results: Across 12 included trials involving 974 participants, using app-based interventions was associated with a clinically significant reduction of HbA1c (MD 0.48%, 95% CI 0.19%-0.78%) without excess adverse events. Larger HbA1c reductions were noted among patients with type 2 diabetes than those with type 1 diabetes (MD 0.67%, 95% CI 0.30%-1.03% vs MD 0.37%, 95% CI –0.12%-0.86%). Having a complication prevention module in app-based interventions was associated with a greater HbA1c reduction (with complication prevention: MD 1.31%, 95% CI 0.66%-1.96% vs without: MD 0.38%, 95% CI 0.09%-0.67%; intersubgroup P=.01), as was having a structured display (with structured display: MD 0.69%, 95% CI 0.32%-1.06% vs without: MD 0.69%, 95% CI –0.18%-0.53%; intersubgroup P=.03). However, having a clinical decision-making function was not associated with a larger HbA1c reduction (with clinical decision making: MD 0.19%, 95% CI –0.24%-0.63% vs without: MD 0.61%, 95% CI 0.27%-0.95%; intersubgroup P=.14). Conclusions: The use of mobile app-based interventions yields a clinically significant HbA1c reduction among adult outpatients with diabetes, especially among those with type 2 diabetes. Our study suggests that the clinical decision-making function needs further improvement and evaluation before being added to apps.
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?
To utilize these types of BCTs, interventions usually included features such as self-monitoring of blood glucose, exercise, and diet; educational materials; and patient-healthcare provider communication.
“To utilize these types of BCTs, interventions usually included features such as self-monitoring of blood glucose, exercise, and diet; educational materials; and patient–healthcare provider communication.10,13,27”
From Effective behavioral change techniques in m-health app supported interventions for glycemic control among patients with type 2 diabetes: A meta-analysis and meta-regression analysis of randomized controlled trials (2025) · cites this paper as [27]
The Results (Characteristics and Classifications of Included Trials; page unavailable) report monitoring in all 12 apps, lifestyle modification in 11, patient–clinician communication in 9, and general education in 6. The taxonomy also describes logging diet and activity. These passages support the listed features, but the paper does not establish that they were included specifically to implement the cited behavior-change techniques, and education appeared in only half the interventions. (LLM, conf=0.83)
Experimental — accuracy not yet measuredWe chose 2007 as the starting year because most m-health apps were released after 2007.
“We chose 2007 as the starting year because most m-health apps were released after 2007.10,27”
From Effective behavioral change techniques in m-health app supported interventions for glycemic control among patients with type 2 diabetes: A meta-analysis and meta-regression analysis of randomized controlled trials (2025) · cites this paper as [27]
The Methods > Data Sources and Searches passage (page unavailable) says the review searched studies from January 1, 2007, but gives no reason for that start date. The Introduction discusses the number of available apps, not when most m-health apps were released. Our copy therefore does not establish the release-date claim. (LLM, conf=0.72)
Experimental — accuracy not yet measuredBenefits have been seen with the use of behavior change apps to drive improvements in glycemic control in people with diabetes through improved adherence to treatment protocols.
“Benefits have also been seen with the use of behavior change apps to drive improvements in glycemic control in people with diabetes through improved adherence to treatment protocols.”
From What is the clinical value of mHealth for patients? (2020) · cites this paper as [11]
Management-based mHealth interventions have been particularly successful at reducing HbA1c levels among people with T2DM.
“Management based interventions have proven to be particularly successful at reducing haemoglobin A 1c (HbA 1c ) levels amongst people with T2DM [14].”
From Cost and cost-effectiveness of mHealth interventions for the prevention and control of type 2 diabetes mellitus: A systematic review (2020) · cites this paper as [14]
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
Bias in meta-analysis detected by a simple, graphical test
Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997) · BMJ · doi:10.1136/bmj.315.7109.629 · Cited by 56,722 · see citing works
Quantifying heterogeneity in a meta‐analysis
Higgins, J. P. T., & Thompson, S. G. (2002) · Statistics in Medicine · doi:10.1002/sim.1186 · Cited by 36,763 · see citing works
Operating characteristics of a rank correlation test for publication bias
Begg, C. B., & Mazumdar, M. (1994) · Biometrics · doi:10.2307/2533446 · Cited by 16,955 · see citing works
Grading quality of evidence and strength of recommendations
(2004) · BMJ · doi:10.1136/bmj.328.7454.1490 · Cited by 8,477 · see citing works
Prevalence of Diabetes among Men and Women in China
Yang, W., Lu, J., Weng, J., Jia, W., Ji, L., Xiao, J., Shan, Z., Liu, J., Tian, H., Ji, Q., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa0908292 · Cited by 3,031 · see citing works
Self-citationPrevalence and Control of Diabetes in Chinese Adults
Xu, Y. (2013) · JAMA · doi:10.1001/jama.2013.168118 · Cited by 2,710 · see citing works
Rapid health transition in China, 1990–2010: findings from the Global Burden of Disease Study 2010
Yang, G., Wang, Y., Zeng, Y., Gao, G. F., Liang, X., Zhou, M., Wan, X., Yu, S., Jiang, Y., Naghavi, M., et al. (2013) · The Lancet · doi:10.1016/s0140-6736(13)61097-1 · Cited by 1,984 · see citing works
Whitehead, L., & Seaton, P. (2016) · Journal of Medical Internet Research · doi:10.2196/jmir.4883 · Cited by 743 · cited 1× · see citing works
“In contrast, according to a recent systemic review [13], there were only 5 randomized controlled trials (RCTs) assessing the effectiveness of apps in diabetes self-management.”
Liang, X., Wang, Q., Yang, X., Cao, J., Chen, J., Mo, X., Huang, J., Wang, L., & Gu, D. (2011) · Diabetic Medicine · doi:10.1111/j.1464-5491.2010.03180.x · Cited by 515 · see citing works
Association Between Diabetes and Cause-Specific Mortality in Rural and Urban Areas of China
Bragg, F., Holmes, M. V., Iona, A., Yu, C., Du, H., Chen, Y., Bian, Z., Yang, L., Herrington, W. G., Bennett, D., et al. (2017) · JAMA · doi:10.1001/jama.2016.19720 · Cited by 495 · see citing works
Self-citationChomutare, T., Fernandez-Luque, L., Årsand, E., & Hartvigsen, G. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1874 · Cited by 451 · see citing works
Can We Systematically Review Studies That Evaluate Complex Interventions?
Shepperd, S., Lewin, S., Straus, S., Clarke, M., Eccles, M. P., Fitzpatrick, R., Wong, G., & Sheikh, A. (2009) · PLoS Medicine · doi:10.1371/journal.pmed.1000086 · Cited by 408 · 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
Mobile Applications for Diabetes Self-Management: Status and Potential
El-Gayar, O., Timsina, P., Nawar, N., & Eid, W. (2013) · Journal of Diabetes Science and Technology · doi:10.1177/193229681300700130 · Cited by 370 · 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
Diabetes Management via Mobile Phones: A Systematic Review
Holtz, B., & Lauckner, C. (2012) · Telemedicine and e-Health · doi:10.1089/tmj.2011.0119 · Cited by 318 · see citing works
Holmen, H., Torbjørnsen, A., Wahl, A. K., Jenum, A. K., Småstuen, M. C., Årsand, E., & Ribu, L. (2014) · JMIR mHealth and uHealth · doi:10.2196/mhealth.3882 · Cited by 294 · cited 6× · see citing works
“C: 50/41 Norway Holmen, 2014 [41]; Torbjornsen, 2014 [42]”
InfluentialCharpentier, 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
mHealth and Mobile Medical Apps: A Framework to Assess Risk and Promote Safer Use
Lewis, T., & Wyatt, J. C. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3133 · Cited by 281 · see citing works
Powell, A. C., Landman, A., & Bates, D. W. (2014) · JAMA · doi:10.1001/jama.2014.2564 · Cited by 236 · see citing works
FDA Regulation of Mobile Health Technologies
Cortez, N. G., Cohen, I. G., & Kesselheim, A. S. (2014) · New England Journal of Medicine · doi:10.1056/nejmhle1403384 · Cited by 229 · see citing works
Diabetes Self-Management Care via Cell Phone: A Systematic Review
Krishna, S., & Boren, S. A. (2008) · Journal of Diabetes Science and Technology · doi:10.1177/193229680800200324 · Cited by 225 · see citing works
Mobile health applications to assist patients with diabetes: lessons learned and design implications
Årsand, E., Frøisland, D. H., Skrøvseth, S. O., Chomutare, T., Tatara, N., Hartvigsen, G., & Tufano, J. T. (2012) · Journal of Diabetes Science and Technology · doi:10.1177/193229681200600525 · Cited by 214 · see citing works
DialBetics: A Novel Smartphone-based Self-management Support System for Type 2 Diabetes Patients
Waki, K., Fujita, H., Uchimura, Y., Omae, K., Aramaki, E., Kato, S., Lee, H., Kobayashi, H., Kadowaki, T., & Ohe, K. (2014) · Journal of Diabetes Science and Technology · doi:10.1177/1932296814526495 · Cited by 203 · see citing works
Smartphone apps for calculating insulin dose: a systematic assessment
Huckvale, K., Adomaviciute, S., Prieto, J. T., Leow, M. K., & Car, J. (2015) · BMC Medicine · doi:10.1186/s12916-015-0314-7 · Cited by 200 · see citing works
Evaluation of a mobile phone telemonitoring system for glycaemic control in patients with diabetes
Istepanian, R. S., Zitouni, K., Harry, D., Moutosammy, N., Sungoor, A., Tang, B., & Earle, K. A. (2009) · Journal of Telemedicine and Telecare · doi:10.1258/jtt.2009.003006 · Cited by 177 · 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
Self-citationA Ubiquitous Chronic Disease Care system using cellular phones and the internet
Yoo, H. J., Park, M. S., Kim, T. N., Yang, S. J., Cho, G. J., Hwang, T. G., Baik, S. H., Choi, D. S., Park, G. H., & Choi, K. M. (2009) · Diabetic Medicine · doi:10.1111/j.1464-5491.2009.02732.x · Cited by 161 · see citing works
An evaluation of diabetes self-management applications for Android smartphones
Demidowich, A. P., Lu, K., Tamler, R., & Bloomgarden, Z. T. (2012) · Journal of Telemedicine and Telecare · doi:10.1258/jtt.2012.111002 · Cited by 154 · see citing works
A systematic review of IT for diabetes self-management: Are we there yet?
El-Gayar, O., Timsina, P., Nawar, N., & Eid, W. (2013) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2013.05.006 · Cited by 148 · see citing works
Hsu, W. C., Lau, K. H. K., Huang, R., Ghiloni, S., Le, H., Gilroy, S., Abrahamson, M., & Moore, J. (2016) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2015.0160 · Cited by 135 · see citing works
Torbjørnsen, A., Jenum, A. K., Småstuen, M. C., Årsand, E., Holmen, H., Wahl, A. K., & Ribu, L. (2014) · JMIR mHealth and uHealth · doi:10.2196/mhealth.3535 · Cited by 117 · 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
Self-citationMedical Care and Payment for Diabetes in China: Enormous Threat and Great Opportunity
Yang, W., Zhao, W., Xiao, J., Li, R., Zhang, P., Kissimova‐Skarbek, K., Schneider, E., Jia, W., Ji, L., Guo, X., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0039513 · Cited by 91 · see citing works
Self-citationRao, A. V., Hou, P., Golnik, T., Flaherty, J., & Vu, S. (2010) · Journal of Diabetes Science and Technology · doi:10.1177/193229681000400426 · Cited by 80 · 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
Baron, J., Hirani, S. P., & Newman, S. P. (2016) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x16631628 · Cited by 55 · see citing works
Managing diabetes mellitus using information technology: a systematic review
Riazi, H., Larijani, B. A., Langarizadeh, M., & Shahmoradi, L. (2015) · Journal of Diabetes & Metabolic Disorders · doi:10.1186/s40200-015-0174-x · Cited by 51 · see citing works
Popular Glucose Tracking Apps and Use of mHealth by Latinos With Diabetes: Review
Williams, J. P., & Schroeder, D. G. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.3986 · Cited by 49 · see citing works
A systematic review on incentive-driven mobile health technology: As used in diabetes management
de Ridder, M., Kim, J., Jing, Y., Khadra, M., & Nanan, R. (2017) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x15625539 · Cited by 48 · see citing works
Mobile Health: empowering patients and driving change
Bradway, M., Årsand, E., & Grøttland, A. (2015) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2015.01.001 · Cited by 38 · see citing works
Advising patients on selecting trustful apps for diabetes self-care
Basilico, A., Marceglia, S. R. F., Bonacina, S., & Pinciroli, F. (2016) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2016.02.005 · Cited by 26 · see citing works
Mobile applications for diabetes management: efficacy issues and regulatory challenges
Istepanian, R. S. H. (2015) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(15)00414-3 · Cited by 24 · see citing works
Hirsch, I. B., & Parkin, C. G. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296815626457 · Cited by 22 · see citing works
Diabetes management goes digital
Danes, M., & Whinder, F. (2013) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(13)70100-1 · Cited by 6 · see citing works
Weak Educational Components in mHealth Devices for Diabetes Support Available on the Italian Market
Rossi, M. G., & Bigi, S. (2016) · Journal of Diabetes Science and Technology · doi:10.1177/1932296816638671 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Greenwood, D. A., Gee, P. M., Fatkin, K. J., & Peeples, M. (2017) · Journal of Diabetes Science and Technology · doi:10.1177/1932296817713506 · Cited by 526 · see citing works
What is the clinical value of mHealth for patients?
Rowland, S. P., Fitzgerald, J. E., Holme, T., Powell, J., & McGregor, A. (2020) · npj Digital Medicine · doi:10.1038/s41746-019-0206-x · Cited by 454 · see citing works
Timpel, P., Oswald, S., Schwarz, P. E. H., & Harst, L. (2020) · Journal of Medical Internet Research · doi:10.2196/16791 · Cited by 243 · see citing works
Identification of barriers to insulin therapy and approaches to overcoming them
Russell‐Jones, D., Pouwer, F., & Khunti, K. (2018) · Diabetes, Obesity and Metabolism · doi:10.1111/dom.13132 · Cited by 238 · see citing works
Stein, N., & Brooks, K. (2017) · JMIR Diabetes · doi:10.2196/diabetes.8590 · Cited by 191 · see citing works
ISPAD Clinical Practice Consensus Guidelines 2018: diabetes technologies
Sherr, J. L., Tauschmann, M., Battelino, T., de Bock, M., Forlenza, G., Roman, R., Hood, K. K., & Maahs, D. M. (2018) · Pediatric Diabetes · doi:10.1111/pedi.12731 · Cited by 197 · see citing works
Liu, K., Xie, Z., & Or, C. K. (2020) · JMIR mHealth and uHealth · doi:10.2196/15779 · Cited by 181 · see citing works
Rapid evidence review of mobile applications for self-management of diabetes
Veazie, S., Winchell, K., Gilbert, J., Paynter, R., Ivlev, I., Eden, K. B., Nussbaum, K., Weiskopf, N., Guise, J.-M., & Helfand, M. (2018) · Journal of General Internal Medicine · doi:10.1007/s11606-018-4410-1 · Cited by 170 · see citing works
Aminuddin, H. B., Jiao, N., Jiang, Y., Hong, J., & Wang, W. (2021) · International Journal of Nursing Studies · doi:10.1016/j.ijnurstu.2019.02.003 · Cited by 157 · see citing works
Yang, Y., Lee, E. Y., Kim, H.-S., Lee, S.-H., Yoon, K.-H., & Cho, J.-H. (2020) · JMIR mHealth and uHealth · doi:10.2196/16266 · Cited by 148 · see citing works
Salazar, A., de Sola, H., Failde, I., & Moral-Munoz, J. A. (2018) · JMIR mHealth and uHealth · doi:10.2196/10718 · Cited by 154 · see citing works
Effect of mHealth in improving oral hygiene: A systematic review with meta‐analysis
Toniazzo, M. P., Nodari, D., Muniz, F. W. M. G., & Weidlich, P. (2019) · Journal of Clinical Periodontology · doi:10.1111/jcpe.13083 · Cited by 136 · see citing works
Futuristic biosensors for cardiac health care: an artificial intelligence approach
Vashistha, R., Dangi, A. K., Kumar, A., Chhabra, D., & Shukla, P. (2018) · 3 Biotech · doi:10.1007/s13205-018-1368-y · Cited by 113 · see citing works
Zhang, Y., Li, X., Luo, S., Liu, C., Xie, Y., Guo, J., Liu, F., & Zhou, Z. (2019) · JMIR mhealth and uhealth · doi:10.2196/12658 · Cited by 109 · see citing works
Rinaldi, G., Hijazi, A., & Haghparast-Bidgoli, H. (2020) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2020.108084 · Cited by 85 · see citing works
Lee, E. Y., Cha, S.-A., Yun, J.-S., Lim, S.-Y., Lee, J.-H., Ahn, Y.-B., Yoon, K.-H., Hyun, M. K., & Ko, S.-H. (2022) · Journal of Medical Internet Research · doi:10.2196/37430 · Cited by 83 · see citing works
Stevens, S., Gallagher, S., Andrews, T., Ashall-Payne, L., Humphreys, L., & Leigh, S. (2022) · Frontiers in Clinical Diabetes and Healthcare · doi:10.3389/fcdhc.2022.936752 · Cited by 84 · see citing works
Nouri, S. S., Adler-Milstein, J., Thao, C., Acharya, P., Barr-Walker, J., Sarkar, U., & Lyles, C. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa024 · Cited by 70 · see citing works
Leong, C. M., Lee, T.-I., Chien, Y.-M., Kuo, L.-N., Kuo, Y.-F., & Chen, H.-Y. (2022) · Journal of Medical Internet Research · doi:10.2196/31449 · Cited by 79 · see citing works
Castensøe-Seidenfaden, P., Husted, G. R., Jensen, A. K., Hommel, E., Olsen, B., Pedersen-Bjergaard, U., Kensing, F., & Teilmann, G. (2018) · JMIR mHealth and uHealth · doi:10.2196/mhealth.9487 · Cited by 63 · see citing works
Gong, E., Zhang, Z., Jin, X., Liu, Y., Zhong, L., Wu, Y., Zhong, X., Yan, L. L., & Oldenburg, B. (2020) · JMIR mHealth and uHealth · doi:10.2196/14836 · Cited by 55 · see citing works
Self-citationQan'ir, Y., & Song, L. (2019) · Psycho-Oncology · doi:10.1002/pon.5158 · Cited by 45 · see citing works
El-Gayar, O., Ofori, M., & Nawar, N. (2021) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2021.103839 · Cited by 45 · see citing works
Jeon, E., & Park, H.-A. (2019) · JMIR Diabetes · doi:10.2196/11590 · Cited by 38 · see citing works
Li, J., Wei, D., Liu, S., Li, M., Chen, X., Chen, L., Wu, Y., Zhou, W., Ouyang, L., Tan, C., et al. (2021) · JMIR mhealth and uhealth · doi:10.2196/23338 · Cited by 44 · see citing works
Greenwood, D. A., Litchman, M. L., Isaacs, D., Blanchette, J. E., Dickinson, J. K., Hughes, A., Colicchio, V. D., Ye, J., Yehl, K., Todd, A., et al. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968211036430 · Cited by 31 · see citing works
Evaluation of mobile applications for patients with diabetes mellitus: A scoping review
Lee, J. L., & Kim, Y. (2024) · Healthcare · doi:10.3390/healthcare12030368 · Cited by 29 · see citing works
Wu, Y., Zhou, Y., Wang, X., Zhang, Q., Yao, X., Li, X., Li, J., Tian, H., & Li, S. (2019) · JMIR mHealth and uHealth · doi:10.2196/13971 · Cited by 29 · see citing works
Self-citationTarricone, R., Petracca, F., Svae, L., Cucciniello, M., & Ciani, O. (2024) · eBioMedicine · doi:10.1016/j.ebiom.2024.105091 · Cited by 26 · see citing works
Hakami, A. M., Almutairi, B., Alanazi, A. S., & Alzahrani, M. A. (2024) · Cureus · doi:10.7759/cureus.51791 · Cited by 20 · see citing works
Rivière, C., Brureau, L., Parnot, C., Becherirat, S., Duverger, C., Picchi, H., Le Roy, A., Vuagnat, P., Schernberg, A., Vanquaethem, H., et al. (2023) · International Journal of Cancer · doi:10.1002/ijc.34196 · Cited by 12 · see citing works
Rinaldi, G., Hijazi, A., & Haghparast-Bidgoli, H. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-027490 · Cited by 12 · see citing works
Liu, K., & Xia, Y. (2025) · Digital Health · doi:10.1177/20552076251326126 · Cited by 7 · see citing works
“10,13,27 The univariate meta-regression analyses revealed that the number of BCTs was not associated with improved HbA1c.”
Giovanazzi, A., Gios, L., Mastellaro, M., Gentilini, M. A., Valent, F., Condini, S., Bincoletto, G., Bacchiega, A., Zorzi, A., Malfatti, G., et al. (2023) · Trials · doi:10.1186/s13063-023-07515-6 · Cited by 4 · see citing works
- Authors
- Wu, Y., Yao, X., Vespasiani, G., Nicolucci, A., Dong, Y., Kwong, J., Li, L., Sun, X., Tian, H., & Li, S.
- Affiliations
- Sichuan University (CN)
- Center for Outcomes Research and Clinical Epidemiology (IT)
- Corresponding author
- Li, Sheyu
- ORCID
- Venue
- JMIR mHealth and uHealth
- Publisher
- JMIR Publications Inc.
- Published
- 2017-03-14
- Volume
- 5
- Issue
- 3
- Pages
- e35
- ISSN
- 2291-5222 (ISSN-L)
- PMC
- PMC5373677
- OpenAlex
- W3159078634
- Cited by
- 283
- References
- 46
- Citations by year
- 2026: 13 · 2025: 23 · 2024: 24 · 2023: 20 · 2022: 35 · 2021: 27 · 2020: 44 · 2019: 48 · 2018: 33 · 2017: 16
- FWCI
- 6.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- JMIR mhealth and uhealth (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundation of China (2015CB942800, 21534008, 81400811)
- Sichuan University
- National Key Research and Development Program of China (2015CB942800)
- West China Hospital, Sichuan University
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- mobile health apps, diabetes self-management, glycemic control, HbA1c, type 1 diabetes, type 2 diabetes, complication prevention, adverse events, smartphone-based intervention, diabetes education, diabetes self-monitoring, medication reminders, exercise recommendations, sleep monitoring, blood glucose monitoring
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
