bims-glumda Biomed News
on CGM data in management of diabetes
Issue of 2026–01–25
fifteen papers selected by
Mott Given
  1. Clinical Impact of Continuous Glucose Monitoring in Noninsulin Treated Type 2 Diabetes: A Review.
  2. Efficacy of an AI-Enabled Low Glucose Prediction: A Pooled Performance Analysis With Capillary Blood Glucose as Ground Truth.
  3. Performance of the Fifth Generation of a 16-Day Continuous Glucose Monitoring System in Adults with Diabetes.
  4. Association of perceived diabetes stigma with time below range <3.0 mmol/L and anxiety in adults with type 1 diabetes using continuous glucose monitoring.
  5. Hybrid Deep Learning Framework for Continuous Blood Glucose Monitoring and Gestational Diabetes Risk Prediction.
  6. Continuous Glucose Monitoring-Based Machine Learning Identification of Diurnal Glycemic Patterns and Diabetes Distress in Type 2 Diabetes.
  7. Barriers and facilitators to continuous glucose monitor use in type 1 diabetes: A biopsychosocial model of the adolescent experience.
  8. Effects of continuous glucose monitoring on physical activity and diet in diabetes: a systematic review and meta-analysis.
  9. Accuracy of Dexcom One+ in Patients with Diabetes or Stress Hyperglycemia Hospitalized in Cardiac Intensive Care Unit.
  10. A Randomized Study of the Glunovo Real-time CGM Effectiveness in Individuals With Poorly Controlled Type 2 Diabetes.
  11. Response to Comment on: "Nocturnal Hypoglycemia: Characterization with Continuous Glucose Monitoring in a Real-World Setting".
  12. Trajectory of glycemic control from late adolescence to young adulthood with type 1 diabetes: A longitudinal study.
  13. Prospective multicenter evaluation of glycemic and patient-reported outcomes following transition to a next-generation continuous glucose monitoring system in users of advanced hybrid closed-loop technology.
  14. CGM-derived efficacy and overall safety of once-weekly insulin efsitora alfa (efsitora) relative to day of administration in adults with type 2 diabetes.
  15. A stretchable continuous glucose monitor for skin-conformable wound management.
  1. Diabetes Technol Ther. 2026 Jan 23. 15209156251414980
    Clinical Impact of Continuous Glucose Monitoring in Noninsulin Treated Type 2 Diabetes: A Review.
    Grazia Aleppo, Anders L Carlson, Janet B McGill, Hamza Alshannaq, Rodolfo Galindo, Davida Kruger, Carol J Levy, Jessica Y Matuoka, Sabrina Ilham, Guillermo Umpierrez, Gregory J Norman.
      Continuous glucose monitoring (CGM) technology is becoming increasingly available to people with diabetes using insulin therapy; however, availability for people with type 2 diabetes (T2D) not on insulin remains limited. For people with T2D, there is strong evidence of glycemic benefit with CGM use for those treated with insulin, and CGM is accepted as standard of care. This review explores the impact of CGM use on glycemic and patient-reported outcomes in noninsulin treated populations with T2D, reporting outcomes from 10 identified randomized controlled trials and 15 nonrandomized studies. We report evidence that supports the use of this technology in people with T2D not using insulin.
    Keywords:  continuous glucose monitoring; glycemic control; hemoglobin A1c; noninsulin therapy; type 2 diabetes
    DOI:  https://doi.org/10.1177/15209156251414980
  2. J Diabetes Sci Technol. 2026 Jan 18. 19322968251412451
    Efficacy of an AI-Enabled Low Glucose Prediction: A Pooled Performance Analysis With Capillary Blood Glucose as Ground Truth.
    Timor Glatzer, Ajandek Peak, Eemeli Leppäaho, Patrick Lustenberger, Pau Herrero, Magí Andorrà, Ellen van Maren.
       BACKGROUND: Hypoglycemia is a critical challenge for insulin-dependent people with diabetes using multiple daily injections (MDI), who rely on reactive responses to continuous glucose monitoring (CGM) alerts. To meet the need for a proactive safety tool, we evaluated the performance of the Low Glucose Predict (LGP) feature in the Accu-Chek SmartGuide Predict App.
    METHODS: This retrospective analysis pooled data from three prospective trials, including 85 subjects over 2709 recording days. The LGP feature uses a XGBoost model to predict low glucose events up to 30 minutes in advance. Performance was assessed rigorously against both capillary blood glucose (BG) and CGM values, including an analysis with "close-call" predictions (+10 mg/dL above the threshold). Metrics included sensitivity, specificity, and ROC-AUC.
    RESULTS: Against the stringent capillary BG reference, LGP showed high performance: sensitivity of 87.13% and specificity of 97.43% (ROC-AUC 0.9787). Including close-call events improved sensitivity to 91.89% and specificity to 98.09%. Referenced against CGM, sensitivity was 94.40% and specificity was 98.25%. The system provided an actionable mean lead time of 14.71 ± 8.30 minutes (CGM reference), with a low average daily true notification rate of 1.31 (2.60 including close-calls).
    CONCLUSION: The LGP feature is an accurate, highly sensitive, and specific tool for timely, proactive low glucose prediction, validated against both capillary BG and CGM. This predictive intelligence is a crucial mechanism for people with diabetes to safely mitigate hypoglycemia risk, addressing a significant clinical gap and potentially reducing fear of hypoglycemia and diabetes distress.
    Keywords:  alarm fatigue; artificial intelligence; continuous glucose monitoring (CGM); hypoglycemia prediction
    DOI:  https://doi.org/10.1177/19322968251412451
  3. Diabetes Technol Ther. 2026 Jan 23. 15209156261416870
    Performance of the Fifth Generation of a 16-Day Continuous Glucose Monitoring System in Adults with Diabetes.
    Shichen Chang, Yibing Lu, Hongwei Ling, Guanqun Zhang, Yanan Zhang, Xiaoying Li, Lin Zhao.
       BACKGROUND: This study evaluated the accuracy and safety of the Anytime 5Pro continuous glucose monitoring (CGM) system, a real-time, factory-calibrated device, over a 16-day period in adults with diabetes.
    METHODS: Adult participants with type 1 or type 2 diabetes were recruited from three clinical sites in China. Each participant was equipped with four sensors (one on each upper arm and two on the abdomen) for a period of up to 16 days. To evaluate sensor performance, participants were randomly assigned to one of three 7-hour clinic sessions on days 1 or 2, days 7, 8 or 9, and day 16. During the sessions, the real-time glucose values measured by Anytime 5Pro CGM system were compared with venous blood glucose values measured by the EKF blood glucose detector. Primary endpoints for assessment were the mean absolute relative difference (MARD), the proportion of CGM values within ± 20%/±20 mg/dL of reference values, and the percentage of paired points within Zones A and B of the consensus error grids.
    RESULTS: In the cohort of 72 participants (287 sensors), the CGM system exhibited an overall MARD of 8.58%, with 96.59% of values within the ± 20%/±20 mg/dL criteria. Comparative analysis revealed similar accuracy between arm (MARD: 8.58%; agreement: 97.03%) and abdomen (MARD: 8.58%; agreement: 96.16%) sensor placements. Throughout the 16-day wear period, the ± 20%/±20 mg/dL agreement rates remained above 95%, with MARDs ranging from 8.30%-8.88%. Consensus error grid analysis showed 99.99% of points in Zones A and B. No serious adverse events were reported.
    CONCLUSIONS: The system demonstrated accurate glucose measurements across the 16-day wear period, irrespective of sensor placement site or glucose concentration.
    Keywords:  Anytime 5Pro; MARD; accuracy; continuous glucose monitoring; diabetes; factory calibration
    DOI:  https://doi.org/10.1177/15209156261416870
  4. Diabet Med. 2026 Jan 18. e70204
    Association of perceived diabetes stigma with time below range <3.0 mmol/L and anxiety in adults with type 1 diabetes using continuous glucose monitoring.
    Seohyun Kim, Soojin Park, Sang-Man Jin, Jae Hyeon Kim, Gyuri Kim.
       AIMS: This study aimed to evaluate the association of stigma related to type 1 diabetes with CGM-derived data and psychological outcomes in adults with type 1 diabetes.
    METHODS: In this cross-sectional study, 104 adults with type 1 diabetes undergoing continuous glucose monitoring (CGM) completed the Type 1 Diabetes Stigma Assessment Scale (DSAS-1), Patient Health Questionnaire-9 (PHQ-9), generalized anxiety disorder-7 (GAD-7), and Diabetes Distress Scale (DDS). Thirty-day standard CGM data with ≥70% sensor wear time of CGM was analysed. Linear regression was used to evaluate the potential relationship between the DSAS-1 scores and CGM-derived hypoglycaemia metrics.
    RESULTS: Higher DSAS-1 total score was independently associated with increased time below range <3.0 mmol/L (adjusted β = 0.011 per point; 95% CI: 0.0018,0.0202; p = 0.019) but not with <3.9 mmol/L. Elevated stigma associated with anxiety (adjusted OR, 1.086; 95% CI:, 1.035,1.152; p = 0.002) with no significant link to depression. Item-level analyses identified DSAS-1 items related to differential treatment (items 15 and 19) and blame/judgement (items 11, 14, and 17) as being significantly associated with clinically significant hypoglycaemia. Associations were consistent across subgroups, especially among participants with a longer diabetes duration and a higher coefficient of variation of CGM glucose levels, calculated as glucose standard deviation divided by mean glucose and expressed as a percentage.
    CONCLUSIONS: In adults with type 1 diabetes using CGM, perceived stigma was significantly correlated with more time spent in hypoglycaemia and greater anxiety. Further studies are needed to identify causal relationships between stigma and clinically significant hypoglycaemia in people with type 1 diabetes.
    Keywords:  anxiety; hypoglycaemia; stigma; time below range; type 1 diabetes
    DOI:  https://doi.org/10.1111/dme.70204
  5. Curr Diabetes Rev. 2026 Jan 09.
    Hybrid Deep Learning Framework for Continuous Blood Glucose Monitoring and Gestational Diabetes Risk Prediction.
    Kiran Kumar Chanumolu, Muni Nagamani G, Muni Hemalatha G.
       BACKGROUND: Gestational diabetes mellitus (GDM) affects almost 10%-12% of pregnancies worldwide, threatening maternal and fetal life. Continuous glucose monitoring (CGM) forms the backbone of managing GDM, and the current methodologies largely disregard physiological and behavioral factors, thereby greatly reducing accuracy and clinical interpretability.
    METHODS: A hybrid deep learning framework was developed by fusing CGM with multi-sensing modality data, including heart rate, activity levels, sleep patterns, and dietary intake. For data preprocessing, Kalman filtering was applied for temporal alignment, adaptive normalization provided outlier handling and imputation, while the CNN-BiLSTM backbone with attention was harnessed for feature extraction. A Multi-Task Attention Fusion Network (MTAFN) was used to predict glucose values and classify GDM risk simultaneously, while SHAP and dynamic smoothing contributed to interpretability sets.
    RESULTS: The framework was validated on an extended OhioT1DM dataset with adaptations for pregnancy. It reached a glucose prediction RMSE of 9.8 mg/dL and a GDM risk classification accuracy of 93%. Compared to competitive approaches, the present solution attained a 25% better accuracy on interpretability and an improvement in sensitivity and specificity of about 4-6% across various physiological conditions.
    DISCUSSION: The use of multi-sensing data increased prediction robustness by capturing complex physiological dependencies. The SHAP-based interpretability justified the predictions through a physiological lens. With an attention mechanism for feature weighting, it was possible to identify crucial variables like meal intake and nighttime variability in the workflow sets.
    CONCLUSION: The hybrid framework proposed here is reliable for clinically interpretable continuous glucose monitoring and GDM risk predictions. Its application with high reliability can lead to integrating it within clinical protocols for real-time maternal care sets.
    Keywords:  Continuous glucose monitoring; deep learning; gestational diabetes prediction; interpretive dynamic smoothing; multi-sensor data integration; scenarios.
    DOI:  https://doi.org/10.2174/0115733998380389251111041618
  6. J Diabetes Sci Technol. 2026 Jan 18. 19322968251412449
    Continuous Glucose Monitoring-Based Machine Learning Identification of Diurnal Glycemic Patterns and Diabetes Distress in Type 2 Diabetes.
    Minjung Lee, Soohyun Nam.
       BACKGROUND: To identify diurnal glycemic patterns in adults with type 2 diabetes (T2D) using continuous glucose monitoring (CGM)-based machine learning and examine their association with diabetes distress, a key psychosocial outcome.
    METHODS: In this observational study, 137 adults with T2D wore blinded CGM (FreeStyle Libre Pro), yielding 1657 days of data. Glycemic patterns were identified using unsupervised machine learning via Gaussian mixture modeling, validated with Bayesian information criterion and silhouette scores. Diabetes distress was assessed with the 17-item Diabetes Distress Scale and analyzed through analysis of covariance (ANCOVA), adjusting for age, sex, body mass index, diabetes duration, and glucose management indicator.
    RESULTS: Clustering identified four distinct glycemic profiles: Cluster 1 (suboptimal control, nocturnal hypoglycemia; 15.8%), Cluster 2 (suboptimal control, nocturnal hyperglycemia; 27.1%), Cluster 3 (poorly controlled, prolonged hyperglycemia; 21.1%), and Cluster 4 (well controlled; 36.1%). Diabetes distress scores varied significantly: participants in Cluster 3 reported the highest distress (mean = 2.37, 95% CI = 1.99-2.76), while Cluster 4 reported the lowest (mean = 1.67, 95% CI = 1.48-1.86; P = .03). Effect sizes indicated differences corresponded to clinically meaningful categories of "little or no distress" vs "moderate distress."
    CONCLUSIONS: CGM-based machine learning identified physiologically distinct glycemic phenotypes that were also associated with psychosocial burden. This work demonstrates the added value of integrating CGM-derived profiles with patient-reported outcomes. These findings highlight the potential of CGM phenotyping to support precision diabetes care by enabling early identification of high-risk subgroups, guiding tailored behavioral and psychosocial interventions, and informing technology-enabled decision tools that connect physiological monitoring with emotional well-being in T2D management.
    Keywords:  CGM; clustering; continuous glucose monitoring; diabetes distress; diurnal glycemic patterns; machine learning; patient-reported outcomes; precision medicine; type 2 diabetes
    DOI:  https://doi.org/10.1177/19322968251412449
  7. Health Psychol Open. 2025 Jan-Dec;12:12 20551029251408544
    Barriers and facilitators to continuous glucose monitor use in type 1 diabetes: A biopsychosocial model of the adolescent experience.
    Kimberly S Hsiung, Karishma A Datye, David G Schlundt.
      Our study aims to understand the barriers and facilitators surrounding continuous glucose monitors (CGMs) in adolescents experiencing diabetes distress from type 1 diabetes through a biopsychosocial lens. We qualitatively analyzed interviews of 21 adolescents and coded their emotional experiences. Findings show that biologically, adolescents noted improved mood with healthier glucose ranges and future health prospects. Psychologically, adolescents preferred a sense of control over when to use, and take a break from, their CGM. Socially, they described mixed feelings surrounding how CGM use impacts relationships with friends, with family, and at school. The biopsychosocial framework captures the complexity and interplay among these factors, highlighting the desire for identity exploration, sense of belonging, and good health as important themes in adolescent diabetes management with a CGM. Clinicians can play a crucial role by bringing a biopsychosocial understanding of the CGM experience into care conversations for adolescents and families to consider.
    Keywords:  adolescents; biopsychosocial model; continuous glucose monitors; emotional experience; type 1 diabetes
    DOI:  https://doi.org/10.1177/20551029251408544
  8. Int J Behav Nutr Phys Act. 2026 Jan 21.
    Effects of continuous glucose monitoring on physical activity and diet in diabetes: a systematic review and meta-analysis.
    Mengsi Peng, Peng Shen, Kwang Joon Kim.
      
    Keywords:  continuous glucose monitoring; diabetes; diet; physical activity
    DOI:  https://doi.org/10.1186/s12966-025-01870-0
  9. Diabetes Technol Ther. 2026 Jan 23. 15209156251407717
    Accuracy of Dexcom One+ in Patients with Diabetes or Stress Hyperglycemia Hospitalized in Cardiac Intensive Care Unit.
    Jerzy Hohendorff, Barbara Zawislak, Michalina Adamczyk-Hohendorff, Maciej Bagienski, Stanislaw Bartus, Tomasz Klupa, Maciej T Malecki.
       BACKGROUND: Continuous glucose monitoring (CGM) systems may assist in glucose management for patients in the cardiac intensive care unit (CICU). We aimed to assess the accuracy, feasibility, and tolerability of Dexcom One+ in comparison with standard blood glucose measurements.
    MATERIALS AND METHODS: From September 2024 to May 2025, we included patients with known diabetes or hyperglycemia on admission >140 mg/dL who were hospitalized in the CICU for acute coronary syndrome and/or heart failure. Sensors were inserted into the upper arms, and glucose readings were obtained using a dedicated receiver. Glucose levels were measured with the Cobas Pulse glucometer (Roche Diagnostics) at the routine frequency in the CICU as part of standard care. Accuracy was evaluated by the mean absolute relative difference (MARD). Clinical performance was assessed through Consensus Error Grid analysis. The feasibility outcome included the number of early sensor detachments and sensor failures. Safety outcomes encompassed skin reactions.
    RESULTS: We obtained 999 CGM-reference glucose paired samples from 48 patients (39 with previously diagnosed type 2 diabetes aged 73.5 ± 9.6 years. The mean HbA1c was 7.1 ± 1.3%. Overall, 725 paired samples were obtained during oxygen therapy, 362 during vasopressor infusion, and 280 during combined oxygen therapy and vasopressor infusion. CGM use duration was 4.0 ± 3.2 days. There were 3 reference readings below 70 mg/dL, 658 within the 70-180 mg/dL range, and 338 above 180 mg/dL. Overall, MARD was 11.6% (95% CI: 10.9-12.2). 93.5% of readings were in Zone A, 6.1% in Zone B, and 0.4% in Zone C. No readings were found in Zone D + E. We observed one mild hematoma at the insertion site, three sensor failures, and three early detachments.
    CONCLUSIONS: In patients in CICU, the Dexcom One+ system showed acceptable accuracy and could support glucose monitoring.
    Keywords:  Dexcom One+; cardiac intensive care unit; consensus error grid; mean absolute relative difference; time in range
    DOI:  https://doi.org/10.1177/15209156251407717
  10. J Endocr Soc. 2026 Feb;10(2): bvaf165
    A Randomized Study of the Glunovo Real-time CGM Effectiveness in Individuals With Poorly Controlled Type 2 Diabetes.
    Elisa Lazzaroni, Vincenzo Cimino, Alessandra Gandolfi, Camilla Tinari, Loredana Bucciarelli, Paola Morpurgo, Moufida Ben Nasr, Roberta Maria Fiorina, Ida Pastore, Laura Baruffaldi, Fabrizio Losurdo, Francesca D'Addio, Gian Vincenzo Zuccotti, Laura Montefusco, Antonio Rossi, Maria Elena Lunati, Paolo Fiorina.
       Objective: We aim to evaluate the effectiveness of the novel real-time continuous glucose monitoring (rtCGM) system "Glunovo" in improving glycemic control and patient outcomes in individuals with poorly controlled type 2 diabetes (T2D).
    Research Design and Methods: This prospective, open-label, randomized controlled trial included 172 patients with T2D from the Fatebenefratelli-Sacco Hospital in Milan. Participants were randomized into 2 groups: 86 patients received the Glunovo rtCGM system (case group), whereas 86 continued standard self-monitoring blood glucose with glucometers (control group). The primary outcome was the change in hemoglobin A1c levels after 6 months. Secondary outcomes included glucose metrics and patient well-being assessed by the World Health Organization-5 Well-Being Scale.
    Results: After 6 months, the Glunovo group showed a significantly higher reduction in hemoglobin A1c levels (Δ = -1.4%) compared to the control group (Δ = -0.6%). Time in range significantly increased in the rtCGM group (Δ = +18.4%). Time above range and glucose management indicator showed a greater reduction in the rtCGM group, with no changes in the time below range. Patient satisfaction increased significantly over the study period with the rtCGM system.
    Conclusion: The use of the Glunovo rtCGM system significantly improved glycemic control and patient satisfaction compared to self-monitoring blood glucose. These findings suggest that the Glunovo rtCGM is an effective tool for managing poorly controlled T2D.
    Clinical trial registration: NCT07089979.
    Keywords:  Hb1Ac; rtCGM; type 2 diabetes
    DOI:  https://doi.org/10.1210/jendso/bvaf165
  11. Diabetes Technol Ther. 2026 Jan 23. 15209156251412814
    Response to Comment on: "Nocturnal Hypoglycemia: Characterization with Continuous Glucose Monitoring in a Real-World Setting".
    Delphine Theodorou, Michael Mitter, Bernhard Ruch, Timor Glatzer, Josip Zivkovic.
      
    DOI:  https://doi.org/10.1177/15209156251412814
  12. Horm Res Paediatr. 2026 Jan 22. 1-16
    Trajectory of glycemic control from late adolescence to young adulthood with type 1 diabetes: A longitudinal study.
    Min Jee Kim, Yun Jeong Lee, Da Hye Lee, Choong Ho Shin, Young Ah Lee.
       INTRODUCTION: Improving outcomes for transition-aged patients with type 1 diabetes (T1D) requires understanding glycemic trajectories and modifiable factors. We evaluated longitudinal glycated hemoglobin (HbA1c) trends, target attainment, and associated factors in Korean youth with T1D.
    METHODS: This retrospective cohort included 354 patients diagnosed before age 14 years with HbA1c data at three or more distinct ages between 15 and 22 years, followed at Seoul National University Children's Hospital 1999-2024. Linear mixed-effects models assessed factors associated with HbA1c trajectory.
    RESULTS: Mean HbA1c declined from 9.0% (75 mmol/mol) at age 15 years to 8.2% (66 mmol/mol) at age 22 years (-0.103% [-1.1 mmol/mol] per year, p < 0.001). Older age, male sex, continuous glucose monitoring (CGM) use, and parental college education were independently associated with lower HbA1c over time (all p < 0.05). At age 22 years, there were no CGM users in the 2006-2015 cohort, whereas 25.2% used CGM in the 2016-2024 cohort. At this age, 19.6% achieved HbA1c < 7% (53 mmol/mol), whereas 24.8% remained at ≥ 9%. Among those with HbA1c ≥ 9% at age 15 years, nearly half remained ≥ 9% at age 22 years, while approximately one-tenth improved to < 7%.
    CONCLUSIONS: Although glycemic control improved with age, a substantial proportion of adolescents and young adults with T1D failed to meet HbA1c targets. Given that CGM use was a key factor associated with better control, increasing CGM uptake alongside tailored support may improve outcomes during the transition to adulthood.
    DOI:  https://doi.org/10.1159/000550458
  13. Ther Adv Endocrinol Metab. 2026 ;17 20420188251407515
    Prospective multicenter evaluation of glycemic and patient-reported outcomes following transition to a next-generation continuous glucose monitoring system in users of advanced hybrid closed-loop technology.
    Lía Nattero-Chávez, Mar Lorenzo, María Soledad Ruiz de Adana, Olga Simó-Servat, Mercè Abad, Carmen Quirós, María Durán Martínez, Alejandra Quintero Tobar, Ane Bayona, Teresa Ruiz Gracia, Esther de la Calle de la Villa, María José Picón.
       Aims: To assess the real-world impact of transitioning from the Dexcom G6® to G7® system in individuals with type 1 diabetes (T1D) using the Tandem t:slim X2™ with Control-IQ™. Primary outcomes included glycemic control changes, while secondary outcomes evaluated patient-reported outcomes (PROMs) and experiences (PREMs).
    Methods: A 3-month prospective, multicenter, observational study was conducted in individuals previously using Dexcom G6 and Control-IQ. Glycemic control was assessed via TIR70-180 and HbA1c. PREMs and PROMs were measured using validated questionnaires (Diabetes Quality of Life (DQoL), Type 1 Diabetes Life (ViDa1), Diabetes Distress Scale (DDS), and Diabetes Treatment Satisfaction Questionnaire). Statistical analyses were stratified by baseline TIR70-180 (>70% vs <70%) and sex.
    Results: The study included 92 participants (mean age 38 ± 13 years), with a baseline TIR70-180 of 76% ± 10%. The overall TIR70-180 remained stable throughout the study. However, among participants with baseline TIR70-180 <70%, a significant increase in TIR70-180 was observed, rising from 61% ± 9% to 65% ± 7% (p = 0.007). In consonance, HbA1c levels showed a significant decline, from 7.3% ± 0.9% to 7.0% ± 0.6% (p = 0.001). In addition, both glucose management indicator (GMI) and mean glucose levels significantly decreased over time, reflecting an overall improvement in glycemic control. These changes (GMI and glucose levels) were consistent across groups, with no significant differences based on baseline TIR70-180 stratification. Quality of life and diabetes distress improved (DQoL, ViDa1, and DDS), especially in participants with lower baseline TIR70-180 and in women. Some reported increased connectivity issues, but none led to treatment discontinuation.
    Conclusion: In Control-IQ users, the transition to G7® did not significantly impact glycemic control overall. However, a subgroup of patients with suboptimal baseline control (TIR70-180 <70%) may benefit from this change, and enhanced quality of life and diabetes distress, especially in women.
    Keywords:  Control-IQ™; Dexcom G7®; closed-loop system; continuous glucose monitoring system; patient-reported outcomes; real-world evidence; time in range; type 1 diabetes
    DOI:  https://doi.org/10.1177/20420188251407515
  14. Diabetes Obes Metab. 2026 Jan 22.
    CGM-derived efficacy and overall safety of once-weekly insulin efsitora alfa (efsitora) relative to day of administration in adults with type 2 diabetes.
    Thomas Martens, Tadej Battelino, Simon Heller, Anuj Bhargava, Linong Ji, Sreenivasa Murthy, Rebecca J Threlkeld, Xiaoqi Li, Ada Leticia Murro, Kristen Syring, Vidhi Patel.
      
    Keywords:  basal insulin; continuous glucose monitoring (CGM); insulin therapy; type 2 diabetes
    DOI:  https://doi.org/10.1111/dom.70419
  15. Biosens Bioelectron. 2025 Dec 27. pii: S0956-5663(25)01222-9. [Epub ahead of print]298 118345
    A stretchable continuous glucose monitor for skin-conformable wound management.
    Binbin Cui, Ivo Pang, Shilei Dai, Xiaonan Chen, Dingyao Liu, Xinyu Tian, Wensheng Lin, Jing Bai, Xinran Zhang, Shiming Zhang.
      Chronic wounds, especially those linked to diabetes, require continuous monitoring and timely interventions to prevent severe complications. The emergence of smart bandages equipped with multifunctional sensors offers a promising avenue to alleviate the burden on healthcare systems while minimizing delays in medical care. However, conventional electrochemical sensors often face limitations, including inadequate detection sensitivity and poor mechanical compatibility with biological tissue, thereby restricting their utility in smart bandages for long-term wound care applications. In this study, we present a stretchable continuous glucose monitor (CGM), based on organic electrochemical transistors (OECTs), for chronic wound care. By leveraging the amplification capability and the mechanical properties of stretchable OECTs, this platform achieves both high sensitivity and tissue compatibility. To demonstrate the potential for practical use, the sensors are integrated into a compact, coin-sized wearable readout system, enabling continuous and comfortable wound monitoring.
    DOI:  https://doi.org/10.1016/j.bios.2025.118345
This page is being maintained by Thomas Krichel. It was last updated on 2026‒01‒25 at 0:07.