{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:44:44Z","timestamp":1785545084498,"version":"3.56.0"},"reference-count":45,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Mhealth Uhealth"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Self-monitoring of blood glucose (SMBG) is recommended in clinical practice guidelines, including those in China, as part of patient education, self-management, and empowerment. With technological advancements, telecommunication technologies are now used for telemonitoring in health care. Mobile apps have become a practical tool for SMBG among patients with type 2 diabetes mellitus (T2DM). However, the long-term effectiveness of this approach in real-world practice requires further exploration.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n The study aims to determine the effectiveness of mobile app\u2013assisted SMBG in improving glycemic control in patients with T2DM at 12 months, in addition to standard care, in Ningbo, China.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n In this retrospective cohort study, adults with T2DM who registered at the National Metabolic Management Center, Ningbo, for the first time between September 1, 2019, and June 30, 2022, and received standardized diabetes management were included. The study compared 2 groups: those who opted for mobile app\u2013assisted SMBG and those who did not. Propensity score matching matched the mobile app\u2013assisted SMBG group with the control group based on similar baseline characteristics. Glycemic control\u2013related outcomes were compared at 12-month follow-up. Linear and logistic regression models were used to estimate mean differences and odds ratios (ORs) along with 95% CIs, respectively, and adjustments were made for baseline characteristics.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n A total of 160 patients (80 in each group) were included in the study. In the mobile app\u2013assisted SMBG group, the median (IQR) frequency of blood glucose monitoring was 0 (0-2) times per week, with 28% (22\/80) monitoring their blood glucose at least twice per week, and the app usage frequency was 1 (0-3) time per week, with 40% (32\/80) logging in at least twice per week. There were no statistically significant differences observed between the mobile app\u2013assisted SMBG group and the control group in glycemic control outcomes at 12 months. Specifically, the results showed no significant difference in (1) fasting blood glucose and glycosylated hemoglobin\n \n levels (mean difference \u22120.17 mmol\/L, 95% CI \u22120.85 to 0.51 mmol\/L;\n P<\/jats:italic>\n =.62 and \u22120.12%, 95% CI \u22120.58% to 0.33%;\n P<\/jats:italic>\n =.59, respectively) and (2) the proportion of patients achieving or maintaining fasting blood glucose at <7 mmol\/L and\n \n glycosylated hemoglobin at <7% (OR 0.89, 95% CI 0.46-1.73;\n P<\/jats:italic>\n =.74 and OR 0.91, 95% CI 0.44-1.88;\n P<\/jats:italic>\n =.79, respectively).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n In a real-world cohort of patients with T2DM in Ningbo, China, mobile app\u2013assisted SMBG did not lead to statistically significant improvements in glycemic control at 12 months. This suggests that in a well-resourced setting, standard care alone may be relatively effective. However, opportunities for further improvement remain. The lack of observed benefit may be due to process-related issues, such as suboptimal engagement with the intervention. Addressing these challenges should be a focus of future research.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/65919","type":"journal-article","created":{"date-parts":[[2025,9,2]],"date-time":"2025-09-02T21:22:34Z","timestamp":1756848154000},"page":"e65919-e65919","source":"Crossref","is-referenced-by-count":0,"title":["Mobile App\u2013Assisted Self-Monitoring of Blood Glucose in Type 2 Diabetes in Ningbo, China: 12-Month Retrospective Cohort Study"],"prefix":"10.2196","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-6540-9759","authenticated-orcid":false,"given":"Xujia","family":"Ma","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3235-8168","authenticated-orcid":false,"given":"Kaushik","family":"Chattopadhyay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2411-9295","authenticated-orcid":false,"given":"Miao","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6301-3544","authenticated-orcid":false,"given":"Li","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6930-9530","authenticated-orcid":false,"given":"Jialin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,9,2]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m997","article-title":"Prevalence of diabetes recorded in mainland China using 2018 diagnostic criteria from the American Diabetes Association: national cross sectional study","volume":"369","author":"Li","journal-title":"BMJ"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2023.1062903","article-title":"Trends in economic burden of type 2 diabetes in China: based on longitudinal claim data","volume":"11","author":"Liu","journal-title":"Front Public Health"},{"key":"R3","unstructured":"Magliano DJ Boyko EJ IDF Diabetes Atlas 10th Edition Scientific Committee. 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