{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T22:34:52Z","timestamp":1785537292679,"version":"3.56.0"},"reference-count":50,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Diabetes"],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n Basal rate (BR) adjustment is crucial for managing type 1 diabetes mellitus, accounting for 30% to 50% of total daily insulin needs. All current closed-loop systems revert to the user\u2019s usual pump BR (known as manual mode) in the event of closed loop failure. Furthermore, access to closed-loop systems remains relatively low in low- and middle-income countries and among those without suitable health insurance. Accurately adjusting the BR remains challenging, leading to hypo- or hyperglycemia, and research on optimizing the BR is limited.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study proposed an adaptive algorithm that uses continuous glucose monitoring data to identify BR inaccuracies without requiring meal intake information.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n The OhioT1DM dataset formed the basis for implementing this methodology. Each composite day was generated by excluding bolus insulin profiles lacking meal intake information and by calculating hourly blood glucose (BG) relative levels along with their corresponding reliability measures, enabling assessment of deviations from the recommended BR (ie, a BG relative change of 0 mg\/dL). Both a noninferiority analysis and a classification precision metric were used to assess the practicality of this approach compared to using meal data.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Data from 12 participants showed noninferiority of the no-meal method: using a 20% noninferiority margin on absolute BG relative change, 9 of 12 participants met the criterion (1-sided\n P<\/jats:italic>\n <.05). Classification precision was 73.9% (139\/188) of meals correctly classified on average per participant (SD 11.8%; 95% CI 67.2%-79.7%). The daily cumulative BG average was 200.6 mg\/dL (SD 61.7 mg\/dL; 11.1 mmol\/L, SD 3.4 mmol\/L; 95% CI 161.4\u2013239.8 mg\/dL), with peak values reaching 270.15 mg\/dL (14.99 mmol\/L). Furthermore, 99.3% (286\/288) of the BG relative values (SD 0.5%; 95% CI 97.5%\u201099.8%) that were unaffected by external factors were associated with incorrect BR settings, with deviations ranging from \u221225.5 to 46 mg\/dL (\u22121.58 to 2.59 mmol\/L).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Current strategies to optimize BR settings are inadequate, and our approach of a personalized basal tuner (PBT) helps better analyze BR without relying on meal intake information. Indeed, without an optimally set BR, in the event of the closed loop reverting to manual mode, patients may be exposed to persistent hypo- or hyperglycemia, leading to safety and efficacy issues. Future work will focus on generating BR recommendations through the application of this algorithm in clinical practice to assist clinicians in setting BR in low- and middle-income countries, where closed-loop systems are not prevalent, to help increase time in range.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/72769","type":"journal-article","created":{"date-parts":[[2025,11,28]],"date-time":"2025-11-28T14:01:01Z","timestamp":1764338461000},"page":"e72769-e72769","source":"Crossref","is-referenced-by-count":1,"title":["Toward a Personalized Basal Tuner for Detecting Basal Rate Inaccuracies in Type 1 Diabetes Mellitus Without Meal Data: Algorithm Development and Retrospective Validation Study"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3944-3234","authenticated-orcid":false,"given":"Daniel","family":"Gasca Garcia","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6076-6997","authenticated-orcid":false,"given":"Hood","family":"Thabit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4075-861X","authenticated-orcid":false,"given":"Paul W","family":"Nutter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9301-5049","authenticated-orcid":false,"given":"Simon","family":"Harper","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2025,11,26]]},"reference":[{"issue":"8","key":"R1","doi-asserted-by":"publisher","first-page":"e230132","DOI":"10.1530\/EC-23-0132","article-title":"The changing landscape of automated insulin delivery in the management of type 1 diabetes","volume":"12","author":"Lakshman","journal-title":"Endocr Connect"},{"issue":"7","key":"R2","doi-asserted-by":"publisher","first-page":"e14087","DOI":"10.2196\/14087","article-title":"Real-world use of do-it-yourself artificial pancreas systems in children and adolescents with type 1 diabetes: online survey and analysis of self-reported clinical outcomes","volume":"7","author":"Braune","journal-title":"JMIR Mhealth Uhealth"},{"key":"R3","unstructured":"Important safety information for basal-IQ technology. 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