bims-glumda Biomed News
on CGM data in management of diabetes
Issue of 2026–03–29
twenty papers selected by
Mott Given
  1. Comment on "Systematic Review of Continuous Glucose Monitor Accuracy in the Hypoglycemia Range for Non-Critical Care Ward Hospitalized People Living With Diabetes".
  2. Glycemic Variability Bridges Time in Range and Time in Tight Range: A Unified Equation for Both Type 1 and Type 2 Diabetes Based on Large-Scale Continuous Glucose Monitoring Data.
  3. Cost-Effectiveness of Continuous Glucose Monitoring With Remote Patient Monitoring in Pediatric Patients With Newly Diagnosed Type 1 Diabetes in the U.S.
  4. Impact of Real-Time Continuous Glucose Monitoring on Medication Use and Glycemic Control in Type 2 Diabetes Across Therapy Regimens.
  5. State of the Art and Future Perspectives of Continuous Glucose Monitoring in Intensive and Non-Intensive Care Settings.
  6. Real-time continuous glucose monitoring with subcutaneously inserted sensor correlates better than subcutaneously implantable sensor with glycated hemoglobin, glycated albumin and fructosamine.
  7. A Study Protocol for a Randomized, Controlled Trial: Improving Glucose Time-in-Range in Diabetes in African Youth (DAYTime).
  8. Association Between Hypnotics and Glycemic Variability Assessed by Continuous Glucose Monitoring Under Real-Life Conditions in Patients With Type 2 Diabetes: A Cross-Sectional Study.
  9. Multi-Patient Analysis of Steroid-Induced Hyperglycemia in Diabetic Patients Using Continuous Glucose Monitoring.
  10. Continuous Glucose Monitoring Identifies Youth with Prediabetes at Risk for Glycemic Progression.
  11. Continuous glucose monitoring and risks of acute and chronic diabetes-related complications and mortality in adults with type 1 diabetes: a nationwide cohort study.
  12. When an OS update becomes a hypoglycemia trigger: a "software change safety case" for CGM ecosystems.
  13. A Quantitative Framework for Evaluating the Performance of Algorithm-Directed Whole-Population Remote Patient Monitoring: Tutorial for Type 1 Diabetes Care.
  14. Temporal Discontinuity in Continuous Glucose Monitoring: How Midnight Segmentation Generates Systematic Measurement Artifact.
  15. Addressing barriers to CGM prescriptions in primary care: a quality improvement initiative.
  16. Associations Between Sleep Quality and Continuous Glucose Monitoring-Derived Metrics in Individuals With Type 2 Diabetes.
  17. Improving glycaemic outcomes with subsidised access to diabetes technology in a young adult population with type 1 diabetes.
  18. Associations of continuous glucose monitor derived time in range and glycaemic variability with diet lifestyle and demographics.
  19. Quality-Score (Q-Score) Can Be More Sensitive than %Time in Range and Several Other CGM Metrics in Detecting Responses to Therapeutic Interventions.
  20. Real-World Follow-Up of People With Type 2 Diabetes Using a Mobile Diabetes App With Connected Glucose Meters Finds Improvements in Glycemia Are Durable Over 5 Years.
  1. J Diabetes Sci Technol. 2026 Mar 27. 19322968261433043
    Comment on "Systematic Review of Continuous Glucose Monitor Accuracy in the Hypoglycemia Range for Non-Critical Care Ward Hospitalized People Living With Diabetes".
    Saad Ahmad, Raja Azan Arafat.
      
    Keywords:  alarm fatigue; continuous glucose monitoring (CGM); hypoglycemia; inpatient diabetes management; mean absolute relative difference (MARD); non-critical care
    DOI:  https://doi.org/10.1177/19322968261433043
  2. Diabetes Obes Metab. 2026 Mar 27.
    Glycemic Variability Bridges Time in Range and Time in Tight Range: A Unified Equation for Both Type 1 and Type 2 Diabetes Based on Large-Scale Continuous Glucose Monitoring Data.
    Yuan Yao, Zhigang Hu, Yifei Mo, Mingsong Han, Li Cao, Jinghao Cai, Xiaojing Ma, Jingyi Lu, Xiaobing Wu, Jian Zhou.
       AIMS: This study aimed to establish a regression model for the relationship between time in range (TIR) and time in tight range (TITR) in individuals with type 1 diabetes (T1D) and type 2 diabetes (T2D) based on real-world continuous glucose monitoring (CGM) data.
    MATERIALS AND METHODS: A cross-sectional analysis was conducted on over 200 000 CGM users with diabetes. Participants self-reported basic demographic and clinical details via in-app fields. Exponential regression models were constructed to examine the TIR-TITR association for individuals with T1D and T2D, respectively. After controlling for coefficient of variation (CV), the model was extended to provide more precise glycemic targets for clinical use. Model performance was evaluated using the coefficient of determination (R2), root mean square error (RMSE), and Akaike information criterion (AIC).
    RESULTS: The TIR-TITR relationship exhibited a nonlinear relationship. Exponential models (TITRT1D = 8.54436 × exp[0.02414 × TIR]; TITRT2D = 5.52189 × exp[0.02839 × TIR]) provided the best fit compared to linear and quadratic models. A TIR of 70% corresponded to TITR values of 40.3%-46.3%, whereas achieving TITR of 50% required TIR of 73.2%-77.6%. For TIR below 60%, each 5% TIR increment boosted TITR by less than 5% points; above 60%, gains exceeded 5% points. Additionally, the inclusion of CV in the model was associated with reduced differences between the fitted T1D and T2D curves and improved the model's performance (TITR = 2.18448 × exp[0.03749 × TIR] +0.94018 × CV-14.99420).
    CONCLUSIONS: This study established the exponential model for TIR-TITR relationship in individuals with T1D and T2D, using a real-world CGM dataset. The model may provide new insights into the setting of individualized treatment goals.
    Keywords:  coefficient of variation; diabetes; real‐time continuous glucose monitoring; time in range; time in tight range
    DOI:  https://doi.org/10.1111/dom.70702
  3. Diabetes Care. 2026 Mar 25. pii: dc252825. [Epub ahead of print]
    Cost-Effectiveness of Continuous Glucose Monitoring With Remote Patient Monitoring in Pediatric Patients With Newly Diagnosed Type 1 Diabetes in the U.S.
    4T Research Team*
       OBJECTIVE: The use of continuous glucose monitoring (CGM) with remote patient monitoring (RPM) continues to grow. We evaluated the cost-effectiveness of CGM with RPM compared with self-monitoring of blood glucose (SMBG) and CGM alone.
    RESEARCH DESIGN AND METHODS: We simulated type 1 diabetes progression with a Markov model in 5-year-old patients over a 20-year, 50-year, and lifetime horizon. We tracked diabetic ketoacidosis (DKA), severe hypoglycemia (SH), and seven chronic complications: retinopathy, neuropathy, nephropathy, cardiovascular disease, end-stage renal disease, lower-extremity amputation, and blindness. We compared three interventions: SMBG, CGM, and CGM with RPM. Efficacy estimates were derived from meta-analyses of pediatric CGM studies and the results of the Teamwork, Targets, Technology, and Tight Glycemia Study (4T Study 1). We evaluated quality-adjusted life years (QALYs) and health care costs (2022 U.S. dollars) discounted at 3% annually. We performed extensive sensitivity analyses.
    RESULTS: Compared with SMBG, CGM increased QALYs by 0.09 and costs by $8,900 over 20 years; CGM with RPM increased QALYs by 0.37, and costs by $10,300. CGM with RPM yielded more QALYs at a lower incremental cost-effectiveness ratio compared with CGM ($27,400/QALY vs. $103,700/QALY, respectively). Results were robust across sensitivity analyses and time horizons. CGM with RPM remained cost-effective when achieving at least 30% of 4T's clinical efficacy.
    CONCLUSIONS: CGM with RPM delivers superior health outcomes compared with SMBG and CGM and is likely cost-effective for patients with newly diagnosed type 1 diabetes. Despite higher intervention costs, CGM with RPM can reduce complications costs and generate net health care savings.
    DOI:  https://doi.org/10.2337/dc25-2825
  4. Diabetes Metab Syndr Obes. 2026 ;19 574769
    Impact of Real-Time Continuous Glucose Monitoring on Medication Use and Glycemic Control in Type 2 Diabetes Across Therapy Regimens.
    Blake C L Liu, Katia L Hannah, Poorva M Nemlekar, Courtney R Green, Gregory J Norman.
       Purpose: This study evaluated how real-time continuous glucose monitoring (RT-CGM) use was associated with medication use patterns and A1c changes among people with type 2 diabetes (T2D) using non-insulin therapy (NIT), basal insulin therapy (BIT), or intensive insulin therapy (IIT). We hypothesized that RT-CGM use would be associated with more frequent medication class changes and reduction in polypharmacy, alongside greater improvements in A1c compared to CGM non-use.
    Patients and Methods: This retrospective study analyzed US administrative claims data from the Optum Clinformatics Data Mart database between 09/01/2016 and 06/30/2024. People with T2D were divided into NIT, BIT, or IIT cohorts. Each cohort contained two groups: Dexcom RT-CGM users or CGM non-users and 1:1 propensity score matching was performed to minimize differences between the groups. We compared changes in medication use and A1c levels between RT-CGM users and CGM non-users over 12 months.
    Results: Over 1.6 million people with T2D were identified prior to matching and stratified by therapy regimen as T2D-NIT, -BIT, or -IIT. After propensity score matching, the proportion of RT-CGM users taking ≥4 medications declined significantly and RT-CGM users had more net changes in their medications than CGM non-users. In addition, greater improvements in A1c were observed among RT-CGM users compared to CGM non-users in all cohorts (difference-in-differences of -0.4%, -0.5%, and -0.4% for T2D-NIT, T2D-BIT, and T2D-IIT, respectively, all p<0.0001). More RT-CGM users also achieved a mean A1c <7.0% or <8.0% at follow-up overall and among those not meeting the target at baseline.
    Conclusion: Higher rates of medication changes were associated with RT-CGM use. RT-CGM use was associated with significantly and meaningfully improved A1c levels among all T2D cohorts. The findings indicate that RT-CGM use could help all people with T2D improve their glycemia.
    Keywords:  A1c; administrative claims; anti-diabetes medication; diabetes technology; medication optimization; real-time continuous glucose monitoring
    DOI:  https://doi.org/10.2147/DMSO.S574769
  5. Diabetes Metab Res Rev. 2026 Mar;42(3): e70154
    State of the Art and Future Perspectives of Continuous Glucose Monitoring in Intensive and Non-Intensive Care Settings.
    Lorenzo Lucaccini Paoli, Alessandro Rizzi, Linda Tartaglione, Daniele D'Amore, Laila Vinti, Annarita Barberio, Gianmario Sciaraffia, Luca Viti, Mauro Di Leo, Alfredo Pontecorvi, Dario Pitocco.
      The development of advanced diabetes technology, such as continuous glucose monitoring (CGM) systems, has permitted in outpatient setting to significantly improve metabolic control by reducing hypoglycemia, ameliorating glycated haemoglobin and reducing glycaemic variability. CGM has emerged as a promising tool to improve glycaemic control in hospitalised patients, potentially reducing the incidence of severe hypoglycemia and hyperglycemia. The availability of real-time glucose data enables more informed clinical decision-making and facilitates prompt adjustments to therapeutic regimens, which is particularly valuable in a dynamic hospital environment. Despite these potential advantages, the implementation of CGM in general ward settings faces several barriers, including a lack of familiarity and experience among healthcare providers as well as the relatively high cost of CGM sensors and associated equipment. Further research is warranted to evaluate the feasibility, clinical benefits, cost-effectiveness, and potential limitations of CGM deployment in general hospital wards. Therefore, the purpose of this review is to provide a comprehensive overview of the current implementation and future potential of CGM in non-Intensive Care Unit (non-ICU) and Intensive Care Unit (ICU) settings for adults with diabetes mellitus or hyperglycemia.
    Keywords:  continuous glucose monitoring; diabetes mellitus; glycaemic control; hospital setting; non‐intensive care unit
    DOI:  https://doi.org/10.1002/dmrr.70154
  6. Minerva Endocrinol (Torino). 2026 Mar 25.
    Real-time continuous glucose monitoring with subcutaneously inserted sensor correlates better than subcutaneously implantable sensor with glycated hemoglobin, glycated albumin and fructosamine.
    Sanja Medenica, Roberto Negro, Djordje Popovic, Michele Chicone, Luigi E Greco.
       BACKGROUND: Real-time continuous glucose monitoring (RT-CGM) is widely used in patients with type 1 diabetes (T1D) to improve glycemic control by reducing postprandial glucose peaks and hypoglycemic episodes. In addition, traditional biomarkers such as glycated hemoglobin (HbA1c), glycated albumin, and fructosamine provide retrospective estimates of glucose regulation over varying timeframes. This study aimed to evaluate the correlation between these biomarkers and glycemic metrics obtained from two types of RT-CGM systems: an implantable sensor (Eversense E3) and subcutaneously inserted sensors (Dexcom G6 and Guardian 4).
    METHODS: We analyzed data from 35 patients with T1D: 13 used the Eversense E3 system, and 22 used Dexcom G6 or Guardian 4. Mean blood glucose (MBG) and time in range (TIR) were assessed at multiple time points and correlated with HbA1c, glycated albumin, and fructosamine levels.
    RESULTS: In the Eversense group, no significant correlation was observed between CGM-derived metrics and any of the biomarkers. Conversely, in the Dexcom/Guardian group, MBG and TIR demonstrated significant correlations with all biomarkers, showing large effect sizes for HbA1c and fructosamine, and medium for glycated albumin.
    CONCLUSIONS: These findings suggest that the Dexcom G6 and Guardian 4 systems more reliably reflect established biochemical markers of mid- to long-term glucose control, while Eversense may be less consistent in this regard. This highlights the importance of sensor selection when interpreting CGM data for clinical or research applications in diabetes management.
    DOI:  https://doi.org/10.23736/S2724-6507.26.04486-6
  7. Methods Protoc. 2026 Mar 08. pii: 43. [Epub ahead of print]9(2):
    A Study Protocol for a Randomized, Controlled Trial: Improving Glucose Time-in-Range in Diabetes in African Youth (DAYTime).
    Thereza Piloya-Were, Catherine Nyangabyaki, Elizabeth Pappenfus, Expeditus Ahimbisibwe, Ezrah Trevor Rwakinanga, Lin Zhang, Silver Bahendeka, Antoinette Moran.
      Metabolic control is poor in East Africa for youth with type1 diabetes (T1D). Self-monitoring of blood glucose (SMBG) by fingerstick 2-3 times daily is routine care. This randomized controlled trial (RCT) will test the hypothesis that providing continuous glucose monitoring (CGM) to Ugandan youth with T1D will improve glucose time-in-range (TIR glucose 3.9-10.0 mmol/L) and be cost effective in this setting. Ugandan youth with T1D (n = 180, age 4-26 years) will be divided into four 12-month cohorts (August 2022-August 2027). Half will receive unblinded Freestyle Libre 2 Flash CGM for 12 months. For six months, control subjects received sufficient test strips for SMBG three times daily while wearing blinded Freestyle Libre Pro CGM (for endpoint assessment), and then they switch to unblinded CGM for six months. Everyone receives monthly diabetes education. The primary endpoints are as follows: (1) the six-month change from baseline in glucose TIR, unblinded CGM versus SMBG; (2) a cost analysis of CGM versus SMBG. The TIR hypothesis will be tested by linear mixed effects models. Cost analysis assumptions include direct material and indirect costs like hospitalizations, missed school/work, and diabetes complications. The study will inform T1D management guidelines in a low resource setting using evidence-based recommendations.
    Keywords:  adolescents; children; continuous glucose monitoring; less-resourced nations; type 1 diabetes
    DOI:  https://doi.org/10.3390/mps9020043
  8. J Diabetes Res. 2025 ;2025(1): e1748628
    Association Between Hypnotics and Glycemic Variability Assessed by Continuous Glucose Monitoring Under Real-Life Conditions in Patients With Type 2 Diabetes: A Cross-Sectional Study.
    Taichi Muramatsu, Miku Otsuka, Daisuke Yamamuro, Mikan Yamanouchi, Saaya Fujisaka, Akifumi Kushiyama, Takako Kikuchi.
       BACKGROUND: Insomnia is common in patients with Type 2 diabetes and can negatively affect glycemic control. However, the effect of hypnotic use on glycemic variability remains unclear. Therefore, we investigated the association between hypnotic use and glycemic variability in patients with Type 2 diabetes.
    METHODS: This cross-sectional study enrolled patients with Type 2 diabetes who underwent continuous glucose monitoring (CGM) between June 1, 2017, and February 28, 2022. Patients were classified into six groups based on their insomnia status and hypnotic use: noninsomnia, hypnotic nonusers, benzodiazepine (BZD) users, nonbenzodiazepine (non-BZD) users, orexin receptor antagonist (ORA) users, and melatonin receptor agonist (MRA) users. We used the standard deviation (SD) of glucose, the coefficient of variation (CV) of glucose, and the mean of daily difference (MODD) as indicators of glycemic variability. The independent association between hypnotic use and glycemic variability was assessed using a multiple linear regression model.
    RESULTS: A total of 534 patients were included in the analysis (mean age: 67.7 ± 10.1 years old; mean diabetes duration: 14.5 ± 8.4 years). Thirty-seven patients (6.9%) used hypnotics, including BZD (n = 13), non-BZD (n = 10), ORA (n = 11), and MRA (n = 3). The SD was significantly higher in non-BZD users (53.6 mg/dL, 95% confidence interval [CI]: 42.9-64.3) than in the noninsomnia group (40.5 mg/dL, 95% CI: 39.5-41.5). MODD was also significantly higher in non-BZD users (50.1 mg/dL, 95% CI: 38.0-62.1) than in the noninsomnia group (35.6 mg/dL, 95% CI: 34.5-36.7). In contrast, the CV was not significantly different between non-BZD users and the noninsomnia group. When analyzed separately for different times of the day, the nocturnal CV was significantly higher in non-BZD users than in the noninsomnia group.
    CONCLUSIONS: The use of non-BZDs was associated with within-day and between-day glycemic variability measured by CGM in patients with Type 2 diabetes.
    DOI:  https://doi.org/10.1155/jdr/1748628
  9. J Pain Res. 2026 ;19 569171
    Multi-Patient Analysis of Steroid-Induced Hyperglycemia in Diabetic Patients Using Continuous Glucose Monitoring.
    Pravardhan Birthi, Mahesh Pattabiraman, Akila Ramaswamy, Mritunjay Kumar, Akshay Agrawal, Anterpreet Dua, Srinivas R Nalamachu, Surabhi Chandra.
       Introduction: Steroid-induced hyperglycemia (SIH) is a frequent complication of glucocorticoid therapy, yet most prior studies have relied on sparse glucose measurements that limit understanding of timing and trajectory. Continuous glucose monitoring (CGM) offers high-resolution insights into these fluctuations with continuous temporal resolution that enables identification of transient and delayed glycemic responses.
    Methods: In this prospective, single-arm observational study, CGM data were analyzed from 58 adults with diabetes who received a single standard-of-care steroid injection for interventional pain management. Participants received dexamethasone (n=38), methylprednisolone (n=15), or triamcinolone (n=5). Dexcom G7 CGM recorded glucose every 5 minutes for up to 10 days post-injection. Glucose trajectories were summarized and stratified by steroid type, gender, and age (<64.6 vs ≥64.6 years).
    Results: Across steroid types, glucose increased within ~2 hours after injection. Dexamethasone produced the most consistent excursion, peaking at ~220-225 mg/dL and returning toward baseline by 24-36 hours. Methylprednisolone showed a more moderate delayed increase (~175-185 mg/dL) with sustained elevation for several days. Triamcinolone demonstrated similar peak levels (~220 mg/dL) but marked variability, limiting interpretability. Females generally exhibited higher and earlier peaks than males, most notably with dexamethasone. Younger patients showed more dynamic excursions and less uniform recovery, whereas older patients demonstrated flatter trajectories and more stable return toward baseline. Data completeness was highest through 48-72 hours post-injection.
    Discussion: This study provides one of the most detailed CGM characterizations of SIH, demonstrating steroid-specific and demographic-modified responses. Dexamethasone displayed a reproducible excursion phase, methylprednisolone a delayed pattern, and triamcinolone inconsistent variability. These findings highlight the utility of CGM for identifying temporal SIH features, with the first 48 to 72 hours yielding clinically reliable monitoring window.
    Keywords:  continuous glucose monitors; diabetes; metabolic disorders; pain; steroid-induced hyperglycemia; steroids
    DOI:  https://doi.org/10.2147/JPR.S569171
  10. Diabetes Technol Ther. 2026 Mar 23. 15209156261435244
    Continuous Glucose Monitoring Identifies Youth with Prediabetes at Risk for Glycemic Progression.
    Natalie Segev, Nancy A Crimmins, Roohi Kharofa, Amy S Shah.
       OBJECTIVE: Pediatric prediabetes is common among youth with obesity, yet most do not progress to type 2 diabetes, and many regress to normoglycemia. Youth-onset type 2 diabetes has a severe course not well predicted by degree of adiposity or hemoglobin A1c (HbA1c) alone. This study used continuous glucose monitoring (CGM) to characterize glycemic patterns in youth with prediabetes and to determine whether baseline CGM metrics identify those at highest risk for HbA1c progression over 6 months.
    RESEARCH DESIGN AND METHODS: Youth aged 10-18 years with obesity and HbA1c 5.7%-6.4% were enrolled in a prospective 6-month observational cohort study, with baseline and follow-up assessments including HbA1c and 14 days of CGM wear. CGM metrics were derived using the R iglu package. Changes over time and associations with follow-up HbA1c and HbA1c change were analyzed using paired t-tests and PRESS-based forward-selected linear regression models, adjusting for clinical covariates.
    RESULTS: Among 29 youth with prediabetes, HbA1c and most CGM metrics remained stable over 6 months, with 32% regressing to normoglycemia. One participant progressed to HbA1c 6.5% who demonstrated markedly elevated baseline CGM glycemic variability and hyperglycemia. While multiple baseline CGM metrics were associated with follow-up HbA1c and HbA1c change in univariable analyses, PRESS-based multivariable models identified time spent above 140 mg/dL as the strongest and only consistent independent predictor of both higher follow-up HbA1c and worsening glycemic trajectory.
    CONCLUSIONS: CGM metrics outperformed HbA1c in predicting short-term glycemic progression in youth with prediabetes, supporting CGM as a promising adjunctive tool for early risk stratification.
    Keywords:  continuous glucose monitoring; obesity; pediatric; prediabetes
    DOI:  https://doi.org/10.1177/15209156261435244
  11. Diabetologia. 2026 Mar 21.
    Continuous glucose monitoring and risks of acute and chronic diabetes-related complications and mortality in adults with type 1 diabetes: a nationwide cohort study.
    Ji Yoon Kim, Seohyun Kim, Jae Hyeon Kim.
       AIMS/HYPOTHESIS: We investigated the association between continuous glucose monitoring (CGM) and acute and chronic diabetes-related complications and mortality risk in adults with type 1 diabetes.
    METHODS: This study included adults with type 1 diabetes who received intensive insulin therapy, based on data from the Korean National Health Insurance Service Cohort (2016-2022). The primary outcomes were diabetic ketoacidosis (DKA), severe hypoglycaemia (SH), end-stage kidney disease (ESKD), cardiovascular disease (CVD) and all-cause mortality. Between-group analyses (comparing outcomes between CGM users and non-users) were conducted using Cox proportional hazards regression models, and within-group analyses (comparing outcomes before and after CGM initiation) were performed using paired t tests.
    RESULTS: A total of 17,018 individuals (8509 CGM users and 8509 non-users) were included. CGM users had lower rates of DKA (adjusted hazard ratio [aHR] 0.40; 95% CI 0.33, 0.48), ESKD (aHR 0.43; 95% CI 0.32, 0.56), CVD (aHR 0.28; 95% CI 0.23, 0.33) and all-cause mortality (aHR 0.38; 95% CI 0.32, 0.46) than non-users. The aHR for SH was comparable between the two groups (aHR 0.92; 95% CI 0.77, 1.10 for users vs non-users). However, among CGM users, the mean frequency of SH decreased by 61.5% after CGM initiation (p<0.001). The frequencies of DKA and CVD-related hospitalisation or emergency department visits also decreased by 60.0% and 50.0%, respectively (p<0.001 for both).
    CONCLUSIONS/INTERPRETATION: In this nationwide cohort study of adults with type 1 diabetes, CGM users had lower rates of both acute and chronic diabetes-related complications and all-cause mortality compared with non-users.
    Keywords:  Cardiovascular disease; Continuous glucose monitoring; Diabetes-related complications; Diabetic ketoacidosis; End-stage kidney disease; Mortality; Severe hypoglycaemia; Type 1 diabetes
    DOI:  https://doi.org/10.1007/s00125-026-06709-2
  12. Ther Adv Endocrinol Metab. 2026 ;17 20420188261435029
    When an OS update becomes a hypoglycemia trigger: a "software change safety case" for CGM ecosystems.
    Mulavagili Vijayasimha, Mulavagili Srikanth.
      
    Keywords:  Continuous Glucose Monitoring (CGM); digital health ecosystems; hypoglycemia
    DOI:  https://doi.org/10.1177/20420188261435029
  13. JMIR Diabetes. 2026 Mar 25. 11 e72676
    A Quantitative Framework for Evaluating the Performance of Algorithm-Directed Whole-Population Remote Patient Monitoring: Tutorial for Type 1 Diabetes Care.
    Jamie Kurtzig, Ananta Addala, Franziska K Bishop, Paul Dupenloup, Johannes O Ferstad, Ramesh Johari, David M Maahs, Priya Prahalad, Dessi P Zaharieva, David Scheinker.
       Unlabelled: Clinics continue to adopt care models shaped by the algorithmic analysis of continuous glucose monitoring (CGM) data, such as remote patient monitoring for type 1 diabetes. No clinic-facing quantitative framework currently exists to track the impact of such algorithm-directed care on patient outcomes and clinical workload. We used CGM data from the Teamwork, Targets, Technology, and Tight Control (4T) Study (Pilot n=135 and Study 1 n=133), in which algorithms enable precision, whole-population care by directing clinician attention to patients with deteriorating glucose management. Youth meeting criteria for clinical review are then contacted by Certified Diabetes Care and Education Specialists. Through iterative data analysis and meetings with a variety of stakeholders, we identified metrics for reviewing and revising clinical workloads, glucose management, and timeliness of care. For each metric, we developed an interactive dashboard to provide clinical and administrative leaders with an overview of the program. The metrics to track clinical workload were the total number of youths (1) in the program, (2) in each study, and (3) cared for by each clinician. The metrics to track glucose management were the number of youths meeting each criterion for review, including (4) total, (5) for each clinician, and (6) for each study. The metric to track timeliness of care was (7) the number of days since meeting criteria for clinical review. When presented at regular program leadership meetings, the metrics facilitated data-driven decision-making about clinical and operational components of the program. In this paper, we describe the process of developing and operationalizing this reproducible, clinician-facing key performance indicator tool to monitor an algorithm-enabled remote patient monitoring program. As the role of algorithms grows in directing clinical effort and prioritizing patients for care, this framework may help clinics track clinical workload, patient outcomes, and the timeliness of care.
    Keywords:  chronic disease; continuous glucose monitoring; precision medicine; quality improvement; remote patient monitoring; type 1 diabetes
    DOI:  https://doi.org/10.2196/72676
  14. Diabetes Technol Ther. 2026 Mar 23. 15209156261435242
    Temporal Discontinuity in Continuous Glucose Monitoring: How Midnight Segmentation Generates Systematic Measurement Artifact.
    Mark Paull.
      Continuous glucose monitoring (CGM) generates dense physiological time-series data sampled every 5 min across 24-h periods. Standard analytical approaches impose calendar-based temporal boundaries-treating midnight as a natural segmentation point-despite glucose homeostasis operating through continuous circadian oscillators that recognize no such delimiter. This structural misalignment introduces a systematic measurement artifact: biologically continuous overnight patterns are bisected at 00:00, artificially inflating glycemic variability estimates and obscuring individual circadian phase relationships. We analyzed approximately 60 days of CGM data, comparing three binning strategies: (1) 24 linear hour-bins (conventional), (2) 36 linear 40-min bins (resolution control), and (3) 36 angular 10° bins (circular topology). Shannon entropy with variance-weighted probabilities quantified information content. Bootstrap resampling (1000 iterations) and null topology permutation (random within-day time-permutation, 1000 iterations) distinguished genuine temporal structure from mathematical artifact. Circular representation demonstrated 12.1% higher information entropy compared to linear binning at matched resolution (3.56 vs. 3.18 bits, P < 0.001, bootstrap percentile method), with nonoverlapping confidence intervals (95% CI: 3.41-3.71 vs. 3.06-3.30). Increasing from 24 to 36 bins in linear space produced zero entropy change (3.18→3.18), isolating topological continuity as the information-preserving factor. Midnight boundary created 2.8-fold reduction in continuity correlation (r = 0.31 vs. r = 0.87, P < 0.001) for biologically adjacent timepoints. Analysis showed nonrandom angular variance structure (P < 0.001), with 1.97-fold variance differential between evening (21:20-00:00) and midday (11:20-14:00) zones. Midnight segmentation introduces quantifiable information loss through temporal discontinuity. Circular time representation-mapping 24-h cycles onto angular coordinates using established directional statistics-eliminates this artifact while preserving temporal information. Current glycemic variability metrics (coefficient of variation, time-in-range) calculated within midnight-bounded periods inherit discontinuity artifacts, potentially misclassifying normal circadian oscillations as pathological variability. Adoption of circular frameworks would align CGM analytics with chronobiological principles and enable individual circadian phenotyping without data manipulation. This represents methodological infrastructure requiring prospective validation for clinical utility.
    Keywords:  circadian rhythms; continuous glucose monitoring; information theory; measurement artifact; time-series analysis
    DOI:  https://doi.org/10.1177/15209156261435242
  15. BMJ Open Qual. 2026 Mar 26. pii: e003654. [Epub ahead of print]15(1):
    Addressing barriers to CGM prescriptions in primary care: a quality improvement initiative.
    Ashley Yoo, Nikitha Cherayil, Rose Sanchez Rosado, Anna Rubin.
      The American Diabetes Association has declared continuous glucose monitoring (CGM) use to be the standard of care in patients with diabetes mellitus (DM) on insulin. While primary care providers (PCPs) manage most patients with DM, the adoption of CGMs in the primary care setting remains significantly lower than in endocrinology practices. PCPs have reported education, insurance authorisation and challenges of a PCP environment as significant barriers to CGM use. Our project sought to increase CGM prescriptions at our academic primary care clinic by creating tools to address these barriers.This is a single-centre quality improvement study at our academic primary care practice with the aim to increase CGM prescriptions in our patients with DM prescribed insulin, excluding patients seen by the endocrinology department. Three interventions were introduced over a 10-month period: (1) an educational pamphlet detailing insurance coverage, ordering and documentation requirements, (2) electronic health record tools to aid in ordering and documentation and (3) a didactic session focused on CGM data interpretation. The number of CGM prescriptions beginning August 2024 was reported monthly. Provider comfort with CGM prescription and data interpretation was assessed using a Likert scale of 1-5 (5 being the most comfortable).Following all three interventions, CGM prescriptions increased by 6.6%. On survey, providers reported improvements in correctly prescribing CGMs and comfort in interpreting CGM data. Referrals to endocrinology for type 2 DM also decreased by 25.3%.Despite known benefits of use in DM care, CGMs are underused in the primary care setting. Provider education and tools led to an increase in CGM prescriptions and improved PCP comfort with CGM use. These interventions demonstrate an effective way to address key barriers to CGM use in primary care.
    Keywords:  Diabetes mellitus; General practice; Graduate medical education; Primary care; Quality improvement
    DOI:  https://doi.org/10.1136/bmjoq-2025-003654
  16. J Sleep Res. 2026 Mar 27. e70331
    Associations Between Sleep Quality and Continuous Glucose Monitoring-Derived Metrics in Individuals With Type 2 Diabetes.
    Soohyun Nam, Minjung Lee, Sangchoon Jeon, Garrett Ash, Stuart Weinzimer, Robin Whittemore.
      Sleep is a critical component of cardiometabolic health, yet individuals with Type 2 diabetes (T2D) experience disproportionately poor sleep quality. While extensive research links sleep duration and quality with HbA1c, less is known about the relationship between sleep and continuous glucose monitoring (CGM)-derived metrics, which capture short-term glycemic variability (GV) and time in range (TIR). Growing evidence suggests that CGM-derived metrics, particularly GV and TIR, are strongly associated with diabetes-related complications and all-cause mortality, underscoring their clinical importance. We conducted a study to examine associations between self-reported sleep quality and CGM-derived metrics among 137 adults with T2D. Participants completed the Pittsburgh Sleep Quality Index (PSQI) and wore blinded CGM devices for 14 days. CGM-derived metrics included intraday- and interday-GV (coefficient of variation, J-index, high/low blood glucose indices, mean of daily differences [MODD]), TIR, time above range (TAR) and time below range (TBR). Multivariable linear regression adjusted for age, sex, body mass index, diabetes duration, depressive symptoms and race/ethnicity. Overall, 69% of participants reported poor sleep. Poor sleep quality was independently associated with higher TAR (daytime β = 0.18, p = 0.04; nighttime β = 0.13, p = 0.04), lower TIR (daytime β = -0.09, p = 0.04; nighttime β = -0.05, p = 0.04) and greater day-to-day GV (β = 0.22, p = 0.03) and higher hyperglycemia risk (β = 0.23, p = 0.04). These findings suggest that poor sleep quality in T2D is linked to increased hyperglycemia exposure, reduced TIR and unstable day-to-day GV, independent of clinical factors. Addressing sleep as a modifiable lifestyle factor and integrating sleep assessments with CGM may provide actionable insights to guide personalised diabetes management.
    Keywords:  Type 2 diabetes; continuous glucose monitoring; glucose variability; sleep quality; time above range; time in range
    DOI:  https://doi.org/10.1111/jsr.70331
  17. Diabet Med. 2026 Mar 17. e70261
    Improving glycaemic outcomes with subsidised access to diabetes technology in a young adult population with type 1 diabetes.
    Andrew-Hyun Lee, Gabrielle Stokes, Kaye Farrell, Deborah Jane Holmes-Walker.
       AIMS: The availability of diabetes technologies has increased, although access can be limited for young people with type 1 diabetes (T1D). In Australia, access to subsidised continuous glucose monitors (CGM) has expanded significantly, while insulin pump access remains limited. This study evaluated the impact of changes in availability of diabetes technologies on glycaemic outcomes in young adults with T1D.
    METHODS: 418 with T1D aged 15-25 years attending a young adult diabetes clinic in Sydney were reviewed between July 2019 and June 2024. The primary outcome was change in glycaemia as measured by glycosylated haemoglobin (HbA1c) across 5 years.
    RESULTS: CGM use increased from 29.4% to 76.2% over the study duration, and uptake increased in all sociodemographic groups likely due to universal subsidisation. Insulin pump use remained unchanged (52-57%), as pump access remains limited by cost. An increase in pump therapy was only observed in those of the lowest sociodemographic cohort, attributed to expansions in the insulin pump program targeted to low-income households, although a reduction in usage was observed in middle socio-economic groups. Use of hybrid closed loop (HCL) insulin pumps increased from 5.1% to 35.4% in line with CGM uptake. With increased uptake of technologies, median HbA1c improved from 68 mmol/mol (8.4%) to 64 mmol/mol (8.0%) (p < 0.001). CGM and HCL demonstrated independent benefits to glycaemia. Rates of severe hypoglycaemia and diabetic ketoacidosis were low at 1.08 and 4.90/100 person-years, respectively. Pump therapy was associated with reduced DKA.
    CONCLUSIONS: Overall, improvements in glycaemia in young adults were achieved with greater accessibility to subsidised diabetes technologies.
    Keywords:  continuous glucose monitoring; delivery of health care; insulin infusion systems; type 1 diabetes mellitus; young adult
    DOI:  https://doi.org/10.1111/dme.70261
  18. Nat Commun. 2026 Mar 27.
    Associations of continuous glucose monitor derived time in range and glycaemic variability with diet lifestyle and demographics.
    Kate M Bermingham, Harry A Smith, Emma L Duncan, Javier T Gonzalez, Ana M Valdes, Paul W Franks, Linda Delahanty, Hassan S Dashti, Richard Davies, George Hadjigeorgiou, Jonathan Wolf, Andrew T Chan, Tim D Spector, Sarah E Berry.
      Continuous glucose monitors (CGMs) provide detailed glucose profiles, but their relevance to health outcomes in individuals without diabetes remains unclear. Here we assess time in range (TIR3.9-5.6 and TITR3.9-7.8) and glycaemic variability in individuals (N = 3,634; age 46 ± 12 y; 83% female; BMI 27 ± 6 kg/m²) from PREDICT 1 (NCT03479866), PREDICT 2 (NCT03983733), and PREDICT 3 (NCT04735835) without diabetes or prediabetes, and explore associations with demographic, diet, lifestyle, cardiometabolic markers, and predicted cardiovascular risk. Outcomes are non-pre-defined exploratory analyses. Higher TIR3.9-5.6 is associated with lower HbA1c, OGTT glucose, carbohydrate intake, and higher protein intake. Sleep duration is inversely correlated with mean glucose. TIR3.9-5.6 provided moderate discrimination for predicted ASCVD 10-year risk (AUC = 0.75). While CGM metrics show potential to capture some components of glycaemic physiology, longer-term health outcomes are required to demonstrate whether CGM monitoring has utility for health management in euglycaemic individuals.
    DOI:  https://doi.org/10.1038/s41467-026-70308-3
  19. Diabetes Technol Ther. 2026 Mar 25. 15209156261423504
    Quality-Score (Q-Score) Can Be More Sensitive than %Time in Range and Several Other CGM Metrics in Detecting Responses to Therapeutic Interventions.
    David Rodbard, Petra Augstein, Peter Heinke, Eckhard Salzsieder, Andreas Thomas, Jörg Reindel.
       PURPOSE: To evaluate the relative sensitivity of several available CGM metrics for the detection of the effects of clinical interventions in people with type 1 diabetes (T1D) and type 2 diabetes (T2D).
    METHODS: Real-world data from people with poor glycemic control (hemoglobin A1c 8.2 ± 1.3%) for 120 people with T1D and 92 people with T2D, using Libre 2 CGM. Analysis of CGM data from 3 days prior to admission and 2 days immediately before discharge from ∼8 days of in-hospital care with changes in therapy as prescribed by hospital-based diabetes specialists. CGM metrics included: quality-score (Q-Score), Time in Range (TIR) (3.9-10 mmol/L), Time Above Range (>10 mmol/L), Time Below Range (<3.9 mmol/L), Mean Sensor Glucose, Glucose Management Indicator, Glycemia Risk Index, Glucose Daily Range, and Mean of Absolute Daily Differences (MODD). We evaluated the paired differences in all metrics pre- and postintervention within subjects using classical paired Student's t tests.
    RESULTS: The Q-Score showed the largest effects in terms of Student's t-values for T1D, for T2D, and for all (T1D and T2D) subjects after pooling, indicating better sensitivity for detection of an effect than TIR or seven other metrics. One of the five components of the Q-Score, MODD, a classical measure of stability of glucose patterns from day to day, showed the second-best sensitivity in evaluating changes within subjects specifically for people with T1D.
    CONCLUSION: We observed consistent differences in sensitivity for the detection of the effects of therapeutic interventions, with Q-Score being superior to eight alternatives. This study needs replication using additional patient populations and multiple types of interventions to evaluate its generalizability and applicability to both randomized controlled clinical trials and real-world clinical data.
    Keywords:  CGM; Q-Score; clinical trials; time in ranges (TIR); type 1 diabetes; type 2 diabetes
    DOI:  https://doi.org/10.1177/15209156261423504
  20. J Diabetes Sci Technol. 2026 Mar 23. 19322968261429941
    Real-World Follow-Up of People With Type 2 Diabetes Using a Mobile Diabetes App With Connected Glucose Meters Finds Improvements in Glycemia Are Durable Over 5 Years.
    Mike Grady, Stephen MacKintosh, Matthew Fryett, Stuart Phillips, Elizabeth Holt.
       OBJECTIVE: Evidence from long-term, real-world use of blood glucose (BG) monitoring technologies is sparse. We investigated whether using a diabetes app with connected meters could support durable diabetes management improvements in people with type 2 diabetes (T2D) over 5-years.
    METHODS: Anonymized glucose and app analytics from 501 people with T2D were extracted from our server. The first 14 days using the app were compared with the last 14 days of each consecutive year for 5 years, using paired within-subject differences. Subjects had ≥365 BG readings per year.
    RESULTS: People with T2D improved BG readings in range (RIR, 70-180 mg/dL) by +6.9 percentage points (%pts, 74.6% to 81.5%) and readings in tight range (RITR, 70-140 mg/dL) by +7.8%pts (49.2% to 57.0%) at year 1. Year 1 improvements in RIR and RITR remained evident at year 5 (+7.5%pts and +7.7%pts, respectively). Reductions in hyperglycemic readings (>180 and >250 mg/dL) explained the improvements in RIR and RITR over the 5-years. Mean BG reduced by -9.1 mg/dL at year 1 (150.2 to 141.1 mg/dL) and this was sustained at year 5 (-10.6 mg/dL, 150.2 to 139.6 mg/dL). Subjects performed BG checks at a consistent level, equivalent to 1.8 to 2.1 checks per day, over 5 years. All these glycemic changes were significant (p<0.001). Higher app engagement (>4 app sessions per week) effected better diabetes management.
    CONCLUSION: Real-world follow-up of people with type 2 diabetes using a diabetes app with connected meters found improvements in glycemia were durable over 5-years.
    Keywords:  blood glucose monitoring; diabetes app; real-world evidence
    DOI:  https://doi.org/10.1177/19322968261429941
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