Journal of Diabetes Science and Technology最新文献

{"title":"What is the Relationship Between Time in Range, Time in Tight Range, and HbA1c in Youth and Young Adults With Type 1 Diabetes? Results From the German/Austrian/Luxembourgian/Swiss Diabetes Prospective Follow-Up Registry.","authors":"Marie-Anne Burckhardt, Marie Auzanneau, Joachim Rosenbauer, Elisabeth Binder, Jantje Weiskorn, Melanie Hess, Christof Klinkert, Joaquina Mirza, Lara-Sophie Zehnder, Sandra Wenzel, Kerstin Placzek, Reinhard W Holl","doi":"10.1177/19322968241288870","DOIUrl":"10.1177/19322968241288870","url":null,"abstract":"<p><strong>Objectives: </strong>Time in range (TIR, 70-180 mg/dL) is an established marker of glycemic control. More recently, time in tight range (TTR, 70-140 mg/dL) has been proposed as well. The aim of this study was to examine the relationship between TIR, TTR, and HbA1c in youth and young adults with type 1 diabetes (T1D) in the German/Austrian/Luxembourgian/Swiss Diabetes Prospective Follow-up (DPV) registry.</p><p><strong>Methods: </strong>Data of youth and young adults aged ≤25 years with T1D for >3 months, documented in the DPV registry between 2019 and 2022 were analyzed. The most recent available HbA1c and corresponding continuous glucose monitoring (CGM) profiles in the 12 preceding weeks with at least 80% completeness were included. Associations were investigated using correlation and adjusted regression models.</p><p><strong>Results: </strong>1901 individuals (median age 14.0 years [IQR 10.4-16.9]) were included in the analysis. TIR and TTR correlated strongly, r = 0.965 (95% CI [0.962, 0.968]), <i>P</i> < .001. TTR estimates predicted from TIR were significantly higher in the group with high coefficient of variation (CV group ≥ 36%), <i>P</i> < .001. Correlations between TIR or TTR and HbA1c were both strong, r = -0.764 (95% CI [-0.782, -0.745]) and r = -0.777 (95% CI [-0.795, -0.759]), both <i>P</i> < .001, with no significant difference (<i>P</i> = .312) However, adjusted regression models indicated a slightly better fit for the prediction of HbA1c from TIR compared with TTR.</p><p><strong>Conclusions: </strong>Based on large, real-world data from a multinational registry, TIR and TTR correlated strongly, and both showed a good prediction of HbA1c. TTR estimates predicted from TIR were significantly higher in people with high glucose variability (CV).</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"886-893"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571145/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643763","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":"Effects of Personalized DiaBetes TEXT Messaging Combined with Peer Support Education on Patients With Type 2 Diabetes: A Randomized Controlled Trial.","authors":"Debby Syahru Romadlon, Hui-Chuan Huang, Yu-Chi Chen, Sophia H Hu, Rudy Kurniawan, Tri Juli Edi Tarigan, Safiruddin Al Baqi, Faizul Hasan, Hsiao-Yean Chiu","doi":"10.1177/19322968251314501","DOIUrl":"10.1177/19322968251314501","url":null,"abstract":"<p><strong>Background: </strong>To investigate the effects of personalized DiaBetes TEXT messaging combined with Peer Support Education (DB-TEXT+ PSE) on clinical outcomes in patients with type 2 diabetes.</p><p><strong>Methods: </strong>An assessor-blinded, three-arm randomized controlled trial recruited 84 participants between December 2022 and July 2023. Participants were randomly assigned to a DB-TEXT + PSE group, a DB-TEXT group, or a professional education program (PEP) group. Primary outcomes included glycated hemoglobin (HbA_1c), fasting blood glucose (FBG) levels, and clinical remission rates, whereas secondary outcomes measured lipid profiles, fatigue, sleep quality, depression, and quality of life (QoL). Outcomes were assessed at baseline (T0), three months (T1), and six months (T2) postintervention.</p><p><strong>Results: </strong>Relative to PEP, personalized DB-TEXT + PSE and DB-TEXT alone led to significantly reduced HbA_1c levels at T1 and decreased FBG levels at T1 and T2. Type 2 diabetes remission was achieved by 35.7% and 50% of the participants in the DB-TEXT + PSE group, by 17.9% and 25% in the DB-TEXT group, and by 3.7% and 3.7% in the PEP group at T1 and T2, respectively. Relative to the PEP, DB-TEXT + PSE led to significantly reduced TC levels, diastolic blood pressure (DBP), and systolic blood pressure (SBP) and improved sleep quality and QoL over time.</p><p><strong>Conclusions: </strong>Personalized DB-TEXT + PSE is more effective at enhancing clinical outcomes, reducing fatigue, improving sleep quality, and improving quality of life in patients with type 2 diabetes than DB-TEXT alone and PEPs are. Educators can incorporate personalized DB-TEXT + PSE as a component of diabetes education programs and thereby integrate digital interventions and supplementary programs.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"825-835"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11780624/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143066196","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":"A Performance-Based Adaptation Index for Automated Insulin Delivery Systems.","authors":"Jenny L Diaz C, Patricio Colmegna, Elliot Pryor, Marc D Breton","doi":"10.1177/19322968251315499","DOIUrl":"10.1177/19322968251315499","url":null,"abstract":"<p><strong>Background: </strong>Automated insulin delivery (AID) algorithms can benefit from tuning of their aggressiveness to meet individual needs, as insulin requirements vary among and within users. We introduce the Performance-Based Adaptation Index (PAI), a tool designed to enable automatic adjustment of an AID system aggressiveness based on continuous glucose monitoring (CGM) metrics.</p><p><strong>Methods: </strong>PAI integrates two CGM-based metrics-one for hypoglycemia and another for hyperglycemia exposure-over a previous time window into a single index (<math><mrow><msub><mi>α</mi><mi>θ</mi></msub></mrow></math>). We propose two methods to compute <math><mrow><msub><mi>α</mi><mi>θ</mi></msub></mrow></math>: one based on time in range (TIR, 70-180 mg/dL), and the other on glycemic risk indices. Using <math><mrow><msub><mi>α</mi><mi>θ</mi></msub></mrow></math>, we developed a multiplicative strategy to adjust the AID system's aggressiveness, accounting for situations where <math><mrow><msub><mi>α</mi><mi>θ</mi></msub></mrow></math> cannot be reliably calculated. The feasibility of this method was assessed in-silico using the UVA/Padova Type 1 Diabetes Simulator and our full closed-loop algorithm (UVA-model predictive control (MPC)) across five scenarios: optimal tuning (baseline), conservative and aggressive tunings, and temporary and permanent changes in insulin needs. Glycemic outcomes were evaluated from the simulated glucose traces.</p><p><strong>Results: </strong>Negligible performance variations were observed in the baseline scenario. For the conservative scenario, adjusting <math><mrow><msub><mi>α</mi><mi>θ</mi></msub></mrow></math> improved TIR (35.1% vs 71.8%) and increased total daily insulin (32.1 U vs 41.2 U). Conversely, for the aggressive scenario, it reduced hypoglycemia exposure (TBR: 2.6% vs 1.4%) and overall insulin usage (45.6 U vs 43.0 U).</p><p><strong>Conclusion: </strong>In-silico results demonstrated the safety and efficacy of using PAI to automatically tune the UVA-MPC controller, achieving TIR values above 70% under fully closed-loop conditions and across various physiological states. Clinical validation of these results is warranted.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"942-953"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11803600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143254443","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":"Continuous Glucose Monitoring Profiles in Elite-Level Professional European Football Players.","authors":"Kristina Skroce, Andrea Zignoli, Niko Mihic, David J Lipman, Lauren V Turner, Michael C Riddell, Howard C Zisser","doi":"10.1177/19322968251388668","DOIUrl":"10.1177/19322968251388668","url":null,"abstract":"<p><strong>Background: </strong>This descriptive observational study reports on continuous glucose monitoring (CGM) data, using a novel glucose biosensor (Abbott Libre Sense Glucose Sport Biosensor), during professional game play and during daily life in elite European football players.</p><p><strong>Methods: </strong>Eighteen healthy male elite football players (age: 27.5 ± 5.1 years; height 180.1 ± 7.2 cm, weight 74.2 ± 9.1 kg, UEFA Champions League club) participated, with a subset examined for a single game for active (n = 10) and reserve (n = 4) players. Group comparisons used unpaired <i>t</i>-tests or Wilcoxon rank-sum tests; within-group differences used repeated measures one-way analysis of variance or Friedman test. Descriptive statistics were summarized for 24-hour data for daytime (06:00 am-10:59 pm) and nighttime (11:00 pm-05:59 am).</p><p><strong>Results: </strong>Higher mean CGM glucose was observed during-game in active compared with reserve players (159 ± 23 vs 133 ± 25 mg/dL, <i>P</i> = .09), with significantly higher time above range (TAR, 72.8 ± 32.02 vs 29.7 ± 37.9%, <i>P</i> = .04) and lower time in range (TIR, 26.7 ± 31.9 vs 70.3 ± 37.9%, <i>P</i> = .04). In the 90 minute pre- to 180 minute post-game period, TAR (57.3 ± 26.6% vs 16.1 ± 20.2%, <i>P</i> = .02) and mean iG (149 ± 19 vs 123 ± 14 mg/dL, <i>P</i> = .02) remained higher for active players. For all 18 players, TIR was 89.4 ± 11.7 and 91.6 ± 13.7%, TAR was 5.9 ± 6.7 and 2.9 ± 5.7%, and time below range was 4.5 ± 10.5 and 5.3 ± 13.2% for day and night, respectively.</p><p><strong>Conclusions: </strong>This observational study suggests that elite European footballers may have significant increases in glycemia, as measured by CGM, supporting the notion that mild hyperglycemia can occur during and after active competition in healthy and metabolically normal athletes, perhaps because of competition stress.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"620-626"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13129474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145523451","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":"Expert Clinical Interpretation of Continuous Glucose Monitor Reports From Individuals Without Diabetes.","authors":"Nicole L Spartano, Brenton Prescott, Maura E Walker, Eleanor Shi, Guhan Venkatesan, David Fei, Honghuang Lin, Joanne M Murabito, David Ahn, Tadej Battelino, Steven V Edelman, G Alexander Fleming, Guido Freckmann, Rodolfo J Galindo, Michael Joubert, M Cecilia Lansang, Julia K Mader, Boris Mankovsky, Nestoras N Mathioudakis, Viswanathan Mohan, Anne L Peters, Viral N Shah, Elias K Spanakis, Kayo Waki, Eugene E Wright, Mihail Zilbermint, Howard A Wolpert, Devin W Steenkamp","doi":"10.1177/19322968251315171","DOIUrl":"10.1177/19322968251315171","url":null,"abstract":"<p><strong>Background: </strong>Clinical interpretation of continuous glucose monitoring (CGM) data for people without diabetes has not been well established. This study aimed to investigate concordance among CGM experts in recommending clinical follow-up for individuals without diabetes, based upon their independent review of CGM data.</p><p><strong>Methods: </strong>We sent a survey out to expert clinicians (<i>n</i> = 18) and asked them to evaluate 20 potentially challenging Dexcom G6 Pro CGM reports (and hemoglobin A1c [HbA1c] and fasting venous blood glucose levels) from individuals without diabetes. Clinicians reported whether they would recommend follow-up and the reasoning for their decision. We performed Fleiss Kappa interrater reliability to determine agreement among clinicians.</p><p><strong>Results: </strong>More than half of expert clinicians (56-100%, but no clear consensus) recommended follow-up to individuals who spent >2% time above range (>180 mg/dL), even if HbA1c <5.7% and fasting glucose <100 mg/dL. There were no observed trends for recommending follow-up based on mean glucose or glucose management indicator. Overall, we observed poor agreement in recommendations for who should receive follow-up based on their CGM report (Fleiss Kappa = 0.36).</p><p><strong>Conclusions: </strong>High discordance among expert clinicians when interpreting potentially challenging CGM reports for people without diabetes highlights the need for more research in developing normative data for people without diabetes. Future work is required to develop CGM criteria for identifying potentially high-risk individuals who may progress to prediabetes or type 2 diabetes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"727-735"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11822776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143399419","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":"Continuous Glucose Monitoring-Derived Glycemic Profiles and Correlates in Young Adults Ages 18 to 26 Years.","authors":"Nicholas N Arce, My H Vu, Braden Barnett, David S Black","doi":"10.1177/19322968251398876","DOIUrl":"10.1177/19322968251398876","url":null,"abstract":"<p><strong>Objective: </strong>Continuous glucose monitoring (CGM) is increasingly applied in populations without diabetes, yet existing reference ranges are largely derived from middle-aged or older adults. This study characterized CGM metrics in young adults without diabetes and examined variation by sex, age, body mass index (BMI), and physical activity (PA).</p><p><strong>Method: </strong>Participants wore an unmasked Dexcom G7 CGM for up to 10 days under free-living conditions. Glycemic metrics were derived using the <i>iglu</i> R package and summarized as median [IQR]. Associations with sex, age, BMI, and PA were evaluated using Wilcoxon tests, Spearman correlations, and quantile regressions.</p><p><strong>Results: </strong>A total of 105 participants (age = 21 years [range: 18-26], BMI 23 kg/m² [21-25]; 72% female; 72% non-White) provided ≥48hr of CGM data. Compared with females, males had higher mean sensor glucose (110 [103-119] vs 104 [99-108] mg/dL; <i>P</i> < .01), eA1c (5.4[5.2-5.8] vs 5.2[5.1-5.4]; <i>P</i> < .01), area under the curve (110[102-119] vs 103[99-108]; <i>P</i> = .01), and daily episodes >140 mg/dL (1.6[1.0-2.6] vs 1.3[0.7-2.0]; <i>P</i> = .03). Age correlated with CV (<i>r</i> = .20, <i>P</i> = .04). BMI was inversely correlated with CV (<i>r</i> = -.35), MAGE (<i>r</i> = -.35), and MODD (<i>r</i> = -.27), all <i>P</i> < .001. Physical activity was modestly associated with reduced glycemic burden.</p><p><strong>Conclusion: </strong>CGM revealed sex differences in young adults-males exhibited higher mean glucose and excursions-while both sexes maintained normoglycemic patterns. Age, BMI, and PA were linked to variability indices. Findings provide CGM reference data for young adults and highlight the importance of biological and behavioral factors in glycemic regulation.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"627-638"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12711503/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145701027","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":"Twelve-Month Real-World Use of an Advanced Hybrid Closed-Loop System Versus Previous Therapy in a Dutch Center For Specialized Type 1 Diabetes Care.","authors":"Pim Dekker, Tim van den Heuvel, Arcelia Arrieta, Javier Castañeda, Dick Mul, Henk Veeze, Ohad Cohen, Henk-Jan Aanstoot","doi":"10.1177/19322968241290259","DOIUrl":"10.1177/19322968241290259","url":null,"abstract":"<p><strong>Background: </strong>Complexity of glucose regulation in persons with type 1 diabetes (PWDs) necessitates increased automation of insulin delivery (AID). This study aimed to analyze real-world data over 12 months from PWDs who started using the MiniMed 780G (MM780G) advanced hybrid closed-loop (aHCL) AID system at the Diabeter clinic, focusing on glucometrics and clinical outcomes.</p><p><strong>Methods: </strong>Persons with type 1 diabetes switching to the MM780G system were included. Clinical data (e.g. HbA1c, previous modality) was collected from Diabeter's electronic health records and glucometrics (time in range [TIR], time in tight range [TITR], time above range [TAR], time below range [TBR], glucose management indicator [GMI]) from CareLink Personal for a 12-month post-initiation period of the MM780G system. Outcomes were age-stratified, and the MM780G system was compared with previous use of older systems (MM640G and MM670G). Longitudinal changes in glucometrics were also evaluated.</p><p><strong>Results: </strong>A total of 481 PWDs were included, with 219 having prior pump/sensor system data and 334 having monthly longitudinal data. After MM780G initiation, HbA1c decreased from 7.6 to 7.1% (<i>P</i> < .0001) and the percentage of PWDs with HbA1c <7% increased from 30% to 50%. Glucose management indicator and TIR remained stable with mean GMI of 6.9% and TIR >70% over 12 months. Age-stratified analysis showed consistent improvements of glycemic control across all age groups, with older participants achieving better outcomes. Participants using recommended system settings achieved better glycemic outcomes, reaching TIR up to 77% and TTIR up to 55%.</p><p><strong>Conclusions: </strong>Use of MM780G system results in significant and sustained glycemic improvements, consistent across age groups and irrespective of previous treatment modalities.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1003-1014"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571609/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142501267","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 User-Centered Design of a Clinical Dashboard and Patient-Facing App for Gestational Diabetes.","authors":"Jasmine R Kirkwood, Jane Dickson, Marryat Stevens, Areti Manataki, Robert S Lindsay, Deborah J Wake, Rebecca M Reynolds","doi":"10.1177/19322968241301792","DOIUrl":"10.1177/19322968241301792","url":null,"abstract":"<p><strong>Background: </strong>The number of pregnancies affected by gestational diabetes mellitus (GDM) is growing. With the increased use of smartphones and predictive modeling, a mobile health (mHealth) solution could be developed to improve care and management of GDM while streamlining care through risk stratification.</p><p><strong>Methods: </strong>A user-centered mHealth tool was designed from ethnographic observations and 11 semi-structured interviews (six health care professionals [HCPs] and five women with GDM), followed by iterative changes and evaluation from three feedback groups with 31 participants (17 HCPs, 14 researchers) and 13 questionnaires with women with GDM.</p><p><strong>Results: </strong>\"MyGDM\" includes a clinical dashboard that centralizes the clinic's patients, highlighting off-target blood glucose and predicting the need for pharmacological intervention. It is linked with a patient-facing app that includes structured education, culturally inclusive language options, and meal ideas. Through the feedback sessions, iterative changes were made around visualization and patient safety, and participants were positive toward the potential user experience. In the 13 questionnaires with women with GDM, 100% said it would fit into their lifestyle and help them manage GDM. Educational resources and the \"request a call\" functions were well received with 61.5% (8/13) and 69.2% (9/13) saying they were very likely or likely to use these, respectively.</p><p><strong>Conclusion: </strong>A user-centered mHealth tool consisting of a clinical dashboard linked with a patient-facing app for GDM care and management has been designed. Evaluation of the interactive design by end users was positive and showed that it met their needs.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"930-941"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11607713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142750374","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":"Older Adults Benefit From Virtual Support for Continuous Glucose Monitor Use But Require Longer Visits.","authors":"Ruth S Weinstock, Dan Raghinaru, Robin L Gal, Richard M Bergenstal, Amy Bradshaw, Terra Cushman, Craig Kollman, Davida Kruger, Mary L Johnson, Teresa McArthur, Beth A Olson, Sean M Oser, Tamara K Oser, Roy W Beck, Korey Hood, Grazia Aleppo","doi":"10.1177/19322968241294250","DOIUrl":"10.1177/19322968241294250","url":null,"abstract":"<p><strong>Background: </strong>Older adults may be less comfortable with continuous glucose monitoring (CGM) technology or require additional education to support use. The Virtual Diabetes Specialty Clinic study provided the opportunity to understand glycemic outcomes and support needed for older versus younger adults living with diabetes and using CGM.</p><p><strong>Methods: </strong>Prospective, virtual study of adults with type 1 diabetes (T1D, N = 160) or type 2 diabetes (T2D, N = 74) using basal-bolus insulin injections or insulin pump therapy. Remote CGM diabetes education (3 scheduled visits over 1 month) was provided by Certified Diabetes Care and Education Specialists with additional visits as needed. CGM-measured glycemic metrics, HbA1c and visit duration were evaluated by age (<40, 40-64 and ≥65 years).</p><p><strong>Results: </strong>Median CGM use was ≥95% in all age groups. From baseline to 6 months, time 70 to 180 mg/dL improved from 45% ± 22 to 57% ± 16%; 50 ± 25 to 65 ± 18%; and 60 ± 28 to 69% ± 18% in the <40, 40-64, and ≥65-year groups, respectively (<40 vs 40-64 years <i>P</i> = 0.006). Corresponding values for HbA1c were 8.0% ± 1.6 to 7.3% ± 1.0%; 7.9 ± 1.6 to 7.0 ± 1.0%; and 7.4 ± 1.4 to 7.1% ± 0.9% (all <i>P</i> > 0.05). Visit duration was 41 min longer for ages ≥65 versus <40 years (<i>P</i> = 0.001).</p><p><strong>Conclusions: </strong>Adults with diabetes experience glycemic benefit after remote CGM use training, but training time for those >65 years is longer compared with younger adults. Addressing individual training-related needs, including needs that may vary by age, should be considered.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"994-1002"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571625/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142564567","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":"Building a Diabetes Educator Program for Remote Patient Monitoring Billing.","authors":"Franziska K Bishop, Annette Chmielewski, Jeannine Leverenz, Shannon Lin, Barry Conrad, Anjoli Martinez-Singh, Laura Pike, David Scheinker, Priya Prahalad, David M Maahs","doi":"10.1177/19322968241308920","DOIUrl":"10.1177/19322968241308920","url":null,"abstract":"<p><strong>Objective: </strong>Develop workflows and billing processes for a Certified Diabetes Care and Education Specialist (CDCES)-led remote patient monitoring (RPM) program to transition the Teamwork, Targets, Technology, and Tight Control (4T) Study to our clinic's standard of care.</p><p><strong>Methods: </strong>We identified stakeholders within a pediatric endocrinology clinic (hospital compliance, billing specialists, and clinical informatics) to identify, discuss, and approve billing codes and workflow. The group evaluated billing code stipulations, such as the timing of continuous glucose monitor (CGM) interpretation, scope of work, providers' licensing, and electronic health record (EHR) documentation to meet billing compliance standards. We developed a CDCES workflow for asynchronous CGM interpretation and intervention and initiated an RPM billing pilot.</p><p><strong>Results: </strong>We built a workflow for CGM interpretation (billing code: 95251) with the CDCES as the service provider. The workflow includes data review, patient communications, and documentation. Over the first month of the pilot, RPM billing codes were submitted for 52 patients. The average reimbursement rate was $110.33 for commercial insurance (60% of patients) and $46.95 for public insurance (40% of patients) per code occurrence.</p><p><strong>Conclusions: </strong>Continuous involvement of CDCES and hospital stakeholders was essential to operationalize all relevant aspects of clinical care, workflows, compliance, documentation, and billing. CGM interpretation with RPM billing allows CDCES to work at the top of their licensing credential, increase clinical care touch points, and provide a business case for expansion. As evidence of the clinical benefits of RPM increases, the processes developed here may facilitate broader adoption of revenue-generating CDCES-led care to fund RPM.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"868-874"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11683829/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173987","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}