Journal of Diabetes Science and Technology最新文献

{"title":"Beyond the Mean: A Machine Learning-Based Trend Analysis of CGM Metrics for Improved HbA_1C Estimation in Type 2 Diabetes.","authors":"Camilla H N Thomsen, Simon L Cichosz, Thomas Kronborg, Stine Hangaard, Peter Vestergaard, Irl B Hirsch, David C Klonoff, Morten H Jensen","doi":"10.1177/19322968261441312","DOIUrl":"https://doi.org/10.1177/19322968261441312","url":null,"abstract":"<p><strong>Background: </strong>Hemoglobin A_1C (HbA_1C) is the gold standard for assessing long-term glycemic control in people with diabetes. Increasing use of continuous glucose monitoring (CGM) has led to adoption of the glucose management indicator (GMI) as a CGM‑based HbA_1C estimate, but GMI often differs from laboratory HbA_1C, especially in type 2 diabetes. This discordance may be associated with the fact that GMI, as a measure of central tendency, fails to capture temporal glycemic trends and variability that relate to HbA_1C formation.</p><p><strong>Objective: </strong>To evaluate whether combining CGM-derived metrics capturing variability, excursions, and temporal trends improves estimation of laboratory-measured HbA_1C in type 2 diabetes.</p><p><strong>Methods: </strong>A machine learning framework was applied to CGM data from a three-month randomized trial, including 159 participants with type 2 diabetes. Participants had ≥70% CGM data coverage and valid end-of-trial HbA_1C. From a standardized 90-day CGM window, 51 metrics were extracted. Benchmark models (mean glucose and GMI) were compared with models developed using forward and exhaustive feature selection with threefold cross-validated multiple linear regression.</p><p><strong>Results: </strong>Benchmark models yielded <i>R</i>-squared = 0.53. A forward selection model including five metrics (GMI at night, night-to-overall mean glucose ratio, glycemic risk assessment diabetes equation, time in tight range [3.0-7.8 mmol/L], time above range [13.9 mmol/L] at night) improved <i>R</i>-squared to 0.60. The best-performing model (substituting GRADE at night for GMI at night) achieved a similar <i>R</i>-squared (0.61). Nighttime and hyperglycemia‑related metrics were consistently selected.</p><p><strong>Conclusion: </strong>Continuous glucose monitoring‑based HbA_1C estimation improves when variability and temporal patterns are included. Nighttime hyperglycemia adds notable predictive value, though further validation is needed.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968261441312"},"PeriodicalIF":3.7,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetes Technology Across the Lifespan of People With Diabetes (Ages 2-88 Years): Insights From the dt-Report on Usage Trends Across all Age Groups.","authors":"Norbert Hermanns, Dominic Ehrmann, Lutz Heinemann, Timm Roos, Bernhard Kulzer, Michael Resl, Julia K Mader, Derek Brandt, Cordelia Truempy, Lisa Margret Koch, Maria Luisa Balmer, Francesco Javier Ampudia-Blasco, Peter Diem","doi":"10.1177/19322968261444045","DOIUrl":"https://doi.org/10.1177/19322968261444045","url":null,"abstract":"<p><strong>Background: </strong>The number of adults with diabetes and older age is increasing, yet little is known about age-related differences in real-world diabetes technology use. This analysis examines how uptake, clinical outcomes, and user experience vary across age groups in people with type 1 or type 2 diabetes.</p><p><strong>Methods: </strong>Self-reported data from 2056 individuals with diabetes in Germany, Austria, and Switzerland who completed the diabetes technology report 2024/2025 survey were analyzed. Age-related trends in the use of continuous glucose monitoring (CGM), continuous subcutaneous insulin infusion (CSII), and automated insulin delivery (AID) were assessed using generalized additive and segmented logistic regression models. Outcomes included HbA1c, diabetes distress (PAID-5), severe hypoglycemia (SH), and, among AID users, satisfaction.</p><p><strong>Results: </strong>Among people with type 1 diabetes, CGM usage was consistently high across age groups (eg, 94% in 20-29 years; 92% in ≥70 years). Automated insulin delivery usage peaked in adolescents (81% in 10-19 years) and declined to 36% in adults ≥70 years. In type 2 diabetes, CGM use increased with age (48% in 35-44 years; 72% in ≥70 years). The HbA1c remained stable over the age span (±0.25%). Diabetes distress declined with age (Problems Areas in Diabetes Ouestionnaire - 5 Items (PAID-5): 7.8 in <30 vs 4.2 in ≥70 years). The risk of SH did not increase with age; among CSII users, older participants had lower odds of SH (OR 0.03, <i>p</i> = .001). Automated insulin delivery satisfaction was highest in adults aged 60 to 69 years (88.7/100) and lowest in adolescents (79.1/100).</p><p><strong>Conclusions: </strong>Diabetes technologies are widely used and well tolerated across age groups. Older adults benefit comparably, but barriers to AID use remain.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968261444045"},"PeriodicalIF":3.7,"publicationDate":"2026-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Insulin Delivery Systems: Impact on Clinical Practice, Discontinuation, Rejection, and Reasons for Not Managing Well.","authors":"Timm Roos, Norbert Hermanns, Lutz Heinemann, Dominic Ehrmann, Derek Brandt, Julia K Mader, Michael Resl, Peter Diem, Cordelia Trümpy, Jose Fernando Garcia-Tirado, Bernhard Kulzer","doi":"10.1177/19322968261441314","DOIUrl":"https://doi.org/10.1177/19322968261441314","url":null,"abstract":"<p><strong>Background: </strong>Automated insulin delivery (AID) systems can significantly improve glycemic outcomes in people with type 1 diabetes (PWT1D). Despite their clinical efficacy, little is known about their uptake in clinical care, or about the perspectives and experiences of health care professionals (HCPs) and people with diabetes (PWD) with this relatively new technology.Furthermore, research is limited on broader populations, such as people with type 2 diabetes (PWT2D) and cross-country comparisons.</p><p><strong>Methods: </strong>This study analyzes data from the dt-report 2025, a multinational online survey of PWD and HCPs from Germany, Austria, Switzerland, and Spain, which was conducted at the end of 2024. The surveys assessed, among others, AID relevance, indications, barriers, daily use, satisfaction, and support needs.</p><p><strong>Results: </strong>In total, 1294 HCPs and 2535 PWD from Germany, Austria, Switzerland, and Spain took part. Health care professionals identified fully closed-loop systems as the most promising future therapy. While 90% of PWT1D were seen as candidates for AID therapy, 55% of PWT2D on intensified insulin therapy were also considered likely to benefit. Nonetheless, 22% of eligible PWD rejected AID, citing device burden and general concerns regarding its usability. Health care professionals reported a discontinuation rate of 8%. Satisfaction with AID was generally high. Regression analyses identified technical problems, lack of trust, and unrealistic expectations as significant predictors of lower satisfaction and poorer management in PWD.</p><p><strong>Conclusion: </strong>Despite increasing user rates of AID systems, a significant proportion reports problems in managing this technology or aborting its use. Addressing reasons for this may increase the uptake in clinical care. There was also a positive view of HCPs on AID use in PWT2D.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968261441314"},"PeriodicalIF":3.7,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Automatic Correction Boluses in an Advanced Hybrid Closed-Loop System: Algorithm Performance and Clinical Implications.","authors":"Giuseppe Papa, Rossella Cannarella, Concetta Finocchiaro, Massimiliano Anzaldi, Carmelo Gusmano, Roberto Palazzolo, Gianfranco Gruttadauria, Luca Patti, Donatella Lo Presti, Salvatore Scirè Calabrisotto, Andrea Tumminia, Rosita A Condorelli, Sandro La Vignera, Aldo E Calogero","doi":"10.1177/19322968261446967","DOIUrl":"https://doi.org/10.1177/19322968261446967","url":null,"abstract":"<p><strong>Background: </strong>Automatic correction boluses (ABs) are a key feature of advanced hybrid closed-loop systems, yet their clinical relevance remains incompletely characterized.</p><p><strong>Methods: </strong>We retrospectively analyzed insulin delivery and continuous glucose monitoring (CGM) data from 287 individuals with type 1 diabetes using the MiniMed 780G for ≥6 months with ≥80% auto-mode usage, recruited from four diabetes centers in Sicily. We quantified the contribution of ABs to total daily insulin dose (TDD) and total bolus insulin and evaluated associations with demographic factors, pump settings, and dietary habits using multivariate regression and receiver operating characteristic (ROC) analyses.</p><p><strong>Results: </strong>ABs accounted for 18.3% of TDD and 33.1% of total bolus insulin. AB/TDD was higher in pediatric participants and inversely associated with diabetes duration and AID use. Active insulin time and carbohydrate intake independently predicted AB/TDD. Higher AB/TDD was associated with lower time in range, higher glucose variability, time above range, and GMI; in pediatric participants, it was also associated with reduced time below range. An AB/TDD ≤20.3% predicted TIR >70% (AUC 0.785) and ≤18.6% predicted GMI <7% (AUC 0.828).</p><p><strong>Conclusions: </strong>ABs represent a substantial component of insulin delivery in MiniMed 780G users. Higher AB contribution is associated with poorer glycemic outcomes. Identified thresholds may support system optimization in clinical practice.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968261446967"},"PeriodicalIF":3.7,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using Stakeholder Input to Develop a Best Practice Advisory (BPA) to Increase Prescribing of Automated Insulin Delivery (AID) Systems for People With Type 1 Diabetes (PwT1D).","authors":"Emma Ospelt, Trevon Wright, Justin Indyk, Nicole Rioles, Susan Thapa, Asher Beckwitt, Osagie Ebekozien, Rachel Hopkins, Joseph Erardi, Abha Choudhary, Meenal Gupta, Mary Pat Gallagher, Jeniece Ilkowitz, Georgia Davis, Nestoras Mathioudakis, Risa M Wolf","doi":"10.1177/19322968261443333","DOIUrl":"https://doi.org/10.1177/19322968261443333","url":null,"abstract":"<p><strong>Background: </strong>Diabetes technology use is increasing, yet disparities remain in adoption among people with type 1 diabetes (PwT1D). The Best Practice Advisories to Reduce Inequities in Technology Use (\"BPA-TECH\") study is a multi-site quality improvement (QI) initiative that seeks to develop, deploy, and evaluate a BPA aimed at standardizing prescribing of diabetes technologies.</p><p><strong>Methods: </strong>This mixed-methods study sought feedback from PwT1D, their care partners, and diabetes clinicians through the T1D Exchange QI Collaborative (T1DX-QI) using electronic surveys, interviews, and focus groups to develop and refine the BPA.</p><p><strong>Results: </strong>The T1DX annual survey with general BPA questions was completed by 56 participating centers (38 pediatric and 18 adult), and 31 diabetes care clinicians from eight T1DX-QI centers (five pediatric and three adult) participated in focus groups. An online survey was completed by 101 PwT1D and/or their care partners, followed by structured interviews with nine adult PwT1D and ten care partners. In response to the annual survey, 48% of pediatric and 28% of adult centers thought a BPA would be useful for increasing automated insulin delivery (AID) use. During focus groups, clinicians expressed concerns about workflow integration and alert fatigue. On surveys, most PwT1D and care partner stakeholder groups (96%) said a BPA would help remind diabetes clinicians to discuss technology with patients, and 77% agreed that a BPA could help PwT1D use these technologies, recommending a cadence of every three months.</p><p><strong>Conclusions: </strong>Successful BPA development and implementation requires addressing clinician concerns about workflow and alert fatigue, while aligning with PwT1D and care partners' expectations for the cadence of conversations on AID systems.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968261443333"},"PeriodicalIF":3.7,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attitudes and Barriers Toward Automated Insulin Delivery Systems Among Health Care Providers in Germany: A Network Analysis From the dt-Report.","authors":"Dominic Ehrmann, Timm Roos, Jens Kröger, Hansjörg Mühlen, Winfried Keuthage, Andreas Lueg, Nikolaus Scheper, Lutz Heinemann, Derek Brandt, Norbert Hermanns, Maria Luisa Balmer, José Garcia-Tirado, Bernhard Kulzer","doi":"10.1177/19322968261442286","DOIUrl":"https://doi.org/10.1177/19322968261442286","url":null,"abstract":"<p><strong>Background: </strong>Automated insulin delivery (AID) systems improve glycemic control and reduce treatment burden in people with type 1 diabetes, yet their uptake remains suboptimal in many countries. Understanding barriers to the usage of AID systems from the health care professional (HCP) perspective is essential to support wider adoption.</p><p><strong>Methods: </strong>Physicians and diabetes educators/nurses were invited to complete an online survey assessing the current use of diabetes technology as well as attitudes and barriers to AID and insulin pump therapy. The survey was conducted from October to December 2022. A network analysis was conducted to analyze the associations between different barriers to usage of AID systems and insulin pumps.</p><p><strong>Results: </strong>Data from 594 HCPs (220 physicians and 374 diabetes educators/nurses) were analyzed. In 2022, HCPs estimated that 11% of their patients with type 1 diabetes used an AID system. They reported that 20% to 27% of eligible patients refuse to use an AID system; 5% to 7% of the users of an AID system stopped using it. The majority of HCPs (68.4% and 60.7%) reported an increased need for education. The most important barriers to start using an AID system were a lack of training and education materials, body image issues, overload, and insufficient training of the members of the diabetes team.</p><p><strong>Conclusions: </strong>There was a sharp increase in AID use in Germany from 2019 to 2022. The results highlight the need for adequate training materials for people with diabetes and the diabetes team.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968261442286"},"PeriodicalIF":3.7,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of High-Dose Ascorbic Acid on HS 312 Continuous Glucose Monitoring Accuracy: A Two-Phase Pilot Study in Taiwan.","authors":"Mary Wen Reng Ho, Yi Ting Ho, Chia Yin Chen, Mei Yueh Lee","doi":"10.1177/19322968261442924","DOIUrl":"10.1177/19322968261442924","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968261442924"},"PeriodicalIF":3.7,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13149344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Intervention Increasing Sleep Duration Among People With Type 2 Diabetes: Pilot Randomized Controlled Trial.","authors":"Ryohei Nakada, Daniel R Lane, Shuya Iwata, Akihiro Isogawa, Masao Toyoda, Hideaki Sawaki, Takashi Murata, Shinichi Kajino, Hironori Waki, Shunsuke Kato, Tomoya Kawaguchi, Yushi Hirota, Shuichiro Saito, Seiji Nishikage, Kazuki Yokota, Yuya Tsurutani, Atsuhito Tone, Kengo Miyoshi, Toshimasa Yamauchi, Masaomi Nangaku, Kayo Waki","doi":"10.1177/19322968261445108","DOIUrl":"10.1177/19322968261445108","url":null,"abstract":"<p><strong>Introduction and objective: </strong>Short sleep duration is associated with worse hemoglobin A_1c (HbA_1c), and treatment guidelines recommend sufficient sleep duration. However, it is unclear whether extending sleep duration of short-sleeping people with type 2 diabetes causes improved HbA_1c. We aimed to assess the effect on HbA_1c of increasing sleep duration.</p><p><strong>Methods: </strong>We developed a personalized digital intervention based on the theory of planned behavior and conducted a single-blind, two-arm randomized controlled trial with 70 short-sleeping (≤6 hours) people with type 2 diabetes in Japan. Both arms measured sleep duration using an actigraph and a sleep diary. The intervention group received bedtime advancement support, including achievable bedtime goal setting and feedback. The primary outcome was between-arm difference in HbA_1c change after 12 weeks.</p><p><strong>Results: </strong>The baseline average sleep duration and HbA_1c were 5.96 ± 1.26 (hours) and 8.30 ± 0.91 (%) in the intervention group, and 5.94 ± 0.96 (hours) and 8.09 ± 0.51 (%) in the control group, respectively. Sleep duration improved with statistical significance (between-arm difference in change: 32.8 minutes, <i>P</i> = .0042). However, HbA_1c did not reduce significantly (between-arm difference in change: -0.11%, <i>P</i> = .51), and increases in sleep duration were not significantly associated with HbA_1c reduction (<i>P</i> = .69).</p><p><strong>Conclusion: </strong>In this randomized trial, an average sleep extension of approximately 30 minutes over 12 weeks did not result in a significant improvement in HbA_1c among short-sleeping adults with type 2 diabetes. Further studies with larger sample sizes and greater sleep extension are warranted.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968261445108"},"PeriodicalIF":3.7,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13149349/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Relevance of Error Grid Analysis in ISO 15197.","authors":"Stefan Pleus, Annette Baumstark, Nina Jendrike, Jochen Mende, Delia Waldenmaier, Cornelia Haug, Guido Freckmann","doi":"10.1177/19322968261445932","DOIUrl":"10.1177/19322968261445932","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968261445932"},"PeriodicalIF":3.7,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13149337/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of Continuous Glucose Monitoring and Insulin Pump Technologies in People With Type 2 Diabetes: Insights From a Survey of Health Care Professionals and Patients in Germany, Austria, and Switzerland.","authors":"Mirjam Elisabeth Eiswirth, Michael Resl, Bernhard Kulzer, Timm Roos, Cordelia Truempy, Maria Luisa Balmer, Lilian Witthauer, Julia K Mader, Peter Diem, Lutz Heinemann, Dominic Ehrmann, Norbert Hermanns","doi":"10.1177/19322968261444041","DOIUrl":"10.1177/19322968261444041","url":null,"abstract":"<p><strong>Background: </strong>Diabetes technologies, such as continuous glucose monitoring (CGM), insulin pumps, and automated insulin delivery (AID) systems, are increasingly used by people with type 2 diabetes (PWT2D), with growing clinical evidence supporting their therapeutic benefit. To describe the extent of adoption, perceived benefits, and future expectations, both health care professionals (HCPs) and PWT2D data from the dt-report 2025 were analyzed.</p><p><strong>Methods: </strong>From November to December 2025, HCP and PWT2D participated in the dt-report providing their attitudes, expectations, and predictions regarding the use of diabetes technology in type 2 diabetes. Frequencies from specific responses were analyzed.</p><p><strong>Results: </strong>Data from 1078 HCPs and 450 PWT2D from the DACH region were analyzed for questions regarding the use of technology in type 2 diabetes. Continuous glucose monitoring was the most widely endorsed technology across both groups, with 58% of the survey participants using a CGM, and 1% using a pump. Health care professionals estimated 87% of PWT2D on intensive insulin therapy would benefit from CGM and saw indications among non-intensive insulin users (62%) and those on oral therapies (55%). Future use of CGM and AID systems was anticipated by both HCPs and PWT2D, including many currently not using such systems. Smart pens and stand-alone insulin pumps were viewed less favorably. Reported barriers included lack of awareness, reimbursement limitations, digital literacy, and usability concerns.</p><p><strong>Conclusion: </strong>The findings indicate growing openness toward diabetes technologies among PWT2D and broader perceived indications among HCPs. However, uptake remains limited, particularly outside of intensive insulin therapy. These insights are of relevance for future clinical guidance, access strategies, and patient education.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968261444041"},"PeriodicalIF":3.7,"publicationDate":"2026-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13149341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147838693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}