{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T18:00:11Z","timestamp":1790791211471,"version":"4.1.0"},"reference-count":23,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Form Res"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n \n Suboptimal glycemic control remains a significant public health challenge among adults with type 2 diabetes and prediabetes, with 47.4% of US adults with diagnosed diabetes having hemoglobin A\n 1c<\/jats:sub>\n (HbA\n 1c<\/jats:sub>\n ) \u22657%. Remote glucose monitoring programs have shown promise for supporting self-management, but real-world evidence on the causal impact of varying patient engagement levels on glycemic outcomes remains limited.\n <\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n \n This study aimed to estimate the causal dose-response relationship between patient engagement, operationalized as weekly glucose monitoring frequency, and glycemic control measured by HbA\n 1c<\/jats:sub>\n among adults enrolled in a comprehensive primary care\u2013integrated remote monitoring program.\n <\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n \n We conducted a retrospective cohort study of 1436 adults with type 2 diabetes or prediabetes enrolled in the Unika Health program between 2019 and 2024. The program integrated Bluetooth-connected glucose meters, a mobile app, structured lifestyle coaching, and primary care coordination across 74 physician practices. Engagement was defined as mean weekly glucose monitoring frequency during the first 6 months. The causal effect of monitoring frequency on 6-month HbA\n 1c<\/jats:sub>\n was estimated using marginal structural models (MSMs) with inverse probability weighting to address time-varying confounding. Covariates included age, sex, BMI, baseline HbA\n 1c<\/jats:sub>\n , comorbidities, medication status, and physical activity level.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n The cohort was predominantly older (95.4%, 1370\/1436 aged \u226546 years), with 82.6% (1186\/1436) having hypertension and 45.9% (659\/1436) classified as obese. Overall, HbA\n 1c<\/jats:sub>\n decreased by a mean of 0.54 (SD 1.47; 95% CI 0.47\u20100.62) percentage points (\n P<\/jats:italic>\n <.001) over 6 months. Cluster analysis identified 3 engagement tiers: low (n=835; mean 2.55, SD 1.35 measurements\/week), medium (n=493; mean 6.19, SD 1.46 measurements\/week), and high (n=108; mean 12.59, SD 3.03 measurements\/week). A monotonic dose-response was observed, with mean HbA\n 1c<\/jats:sub>\n reductions of 0.38 (SD 1.40; 95% CI 0.29\u20100.48), 0.71 (SD 1.50; 95% CI 0.58\u20100.85), and 1.01 (SD 1.67; 95% CI 0.69\u20101.32) percentage points for the low, medium, and high tiers, respectively (all\n P<\/jats:italic>\n <.001 by 2-tailed paired\n t<\/jats:italic>\n test). In weighted MSMs, each additional weekly measurement was associated with a 0.05 (95% CI 0.03\u20100.07) percentage point greater HbA\n 1c<\/jats:sub>\n reduction (\n P<\/jats:italic>\n <.001). Among patients with high baseline HbA\n 1c<\/jats:sub>\n (\u22659%), the high-engagement group achieved a mean reduction of 3.12 (SD 1.97; 95% CI 2.29\u20103.94) percentage points. Findings were consistent in sensitivity analyses at 3 months (\u03b2=\u22120.04;\n P<\/jats:italic>\n <.001) and 12 months (\u03b2=\u22120.03;\n P<\/jats:italic>\n =.003) and across alternative weighting specifications.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n \n Higher engagement with a digitally enabled, primary care\u2013integrated remote glucose monitoring program was causally associated with significantly greater HbA\n 1c<\/jats:sub>\n reductions in adults with type 2 diabetes and prediabetes. These findings support scalable remote patient monitoring strategies that actively foster sustained patient engagement as an effective approach to improving glycemic control and reducing the burden of diabetes-related complications at a population level.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/95679","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T17:55:06Z","timestamp":1784829306000},"page":"e95679-e95679","source":"Crossref","is-referenced-by-count":0,"title":["Remote Glucose Monitoring Engagement and Glycemic Control in Type 2 Diabetes and Prediabetes: Retrospective Cohort Study"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3235-3543","authenticated-orcid":false,"given":"Yong","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9398-6598","authenticated-orcid":false,"given":"Nitish","family":"Nagesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3253-1300","authenticated-orcid":false,"given":"Ajan","family":"Subramanian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-7300-0990","authenticated-orcid":false,"given":"Li","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-6093-4708","authenticated-orcid":false,"given":"Zhiyu","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8264-655X","authenticated-orcid":false,"given":"Yichen","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3735-0406","authenticated-orcid":false,"given":"Weiyi","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5003-299X","authenticated-orcid":false,"given":"Iman","family":"Azimi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0725-1155","authenticated-orcid":false,"given":"Amir M","family":"Rahmani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1805-1271","authenticated-orcid":false,"given":"Randall S","family":"Stafford","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2026,9,30]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1016\/j.diabres.2021.109119","article-title":"IDF Diabetes Atlas: global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045","volume":"183","author":"Sun","journal-title":"Diabetes Res Clin Pract"},{"issue":"6","key":"R2","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1016\/S2213-8587(17)30097-9","article-title":"The global economic burden of diabetes in adults aged 20-79 years: a cost-of-illness study","volume":"5","author":"Bommer","journal-title":"Lancet Diabetes Endocrinol"},{"issue":"7","key":"R3","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1001\/jamainternmed.2017.1233","article-title":"Glucose self-monitoring in non-insulin-treated patients with type 2 diabetes in primary care settings: a randomized trial","volume":"177","author":"Young","journal-title":"JAMA Intern Med"},{"issue":"8","key":"R4","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.2337\/dc06-2073","article-title":"Longitudinal study of new and prevalent use of self-monitoring of blood glucose","volume":"29","author":"Karter","journal-title":"Diabetes Care"},{"issue":"7","key":"R5","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1055\/s-2006-924152","article-title":"Is the frequency of self-monitoring of blood glucose related to long-term metabolic control? 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