bims-glumda Biomed News
on CGM data in management of diabetes
Issue of 2026–03–01
sixteen papers selected by
Mott Given
  1. Continuous glucose monitoring (CGM) use in people living with diabetes on maintenance dialysis: a retrospective audit and observational cohort study.
  2. Advances in continuous glucose monitoring and their impact on glycemic control in diabetes.
  3. Defining a glycemic persistence index (GPI) for continuous glucose monitoring.
  4. Trends in use of continuous glucose monitors among individuals with type 2 diabetes enrolled in Medicare Advantage (2021-2023).
  5. "It's part of me": A study of the self-construal of people with diabetes using wearable medical devices.
  6. The Potential Role of Noninvasive Glucose Monitoring in the Management of People with Type 2 Diabetes.
  7. Corrigendum to: "Differences Between Glycemia Estimates from Hemoglobin A1c and Continuous Glucose Monitoring and Their Association with Complete Blood Counts".
  8. Association of daytime circadian-aligned activity with glycemic control in type 2 diabetes: Insights from continuous glucose monitoring and wearable data.
  9. Persistent Burden of Severe Hypoglycemia and Impaired Awareness of Hypoglycemia Among People With Type 1 Diabetes Despite Technology Use: A Follow-up Survey.
  10. Psychological Aspects and Implications of Food Addiction and Glucose Control in Type 2 Diabetes: A Pilot Mixed-Methods Study.
  11. Association of Physical Activity and Socioeconomic Status With Glycaemic Control in Adults With Type 1 Diabetes: A Cross-Sectional Study Using CGM Data.
  12. Associations of time in tight range, time in range, and glycated hemoglobin with albuminuria and diabetic kidney disease in adult type 2 diabetes: a cross-sectional study.
  13. Implementation of a Personalized Medicine Approach in Patients With Type 2 Diabetes Mellitus Receiving Multiple Daily Insulin Injections (POMA Project): Protocol for a Before-and-After Intervention Study.
  14. Reduced-exertion high-intensity interval training (REHIT) provides limited improvement in post-prandial and 24-hour glycemia in healthy, physically active men.
  15. GlucoseGo: a simple tool to predict hypoglycaemia during exercise in type 1 diabetes.
  16. Characterising the impact of shift work on diet and glucose variability in healthcare employees living with type 2 diabetes: The Shift-Diabetes study.
  1. BMC Nephrol. 2026 Feb 27.
    Continuous glucose monitoring (CGM) use in people living with diabetes on maintenance dialysis: a retrospective audit and observational cohort study.
    Chi Peng Chan, Gauri Jagadesh, Srikanth Bellary, Jyoti Baharani.
      
    Keywords:  Continuous glucose monitoring; Diabetes; Dialysis; Hypoglycaemia; Time in range
    DOI:  https://doi.org/10.1186/s12882-026-04816-1
  2. J Diabetes Investig. 2026 Feb 25.
    Advances in continuous glucose monitoring and their impact on glycemic control in diabetes.
    Yi-Hsuan Lai, Bo-Jen Chen, Wei-Hsiang Chuang, Yi-Der Jiang.
      
    Keywords:  continuous glucose monitoring; diabetes mellitus; glycemic control
    DOI:  https://doi.org/10.1111/jdi.70273
  3. Res Sq. 2026 Feb 11. pii: rs.3.rs-8841862. [Epub ahead of print]
    Defining a glycemic persistence index (GPI) for continuous glucose monitoring.
    Ren Zhang.
      Chronic exposure to elevated glucose is a central feature of dysglycemia across the spectrum from prediabetes to diabetes. Continuous glucose monitoring (CGM) provides rich temporal glucose data, but effective summarization that integrates the magnitude and duration of sustained hyperglycemia into a single and parameter-free scalar remains challenging. We introduce the glycemic persistence index (GPI), a simple, threshold-free CGM-derived metric defined as the largest integer k such that at least k minutes are spent at glucose levels ≥ k mg/dL within a day. Geometrically, after ranking glucose values in decreasing order, GPI is given by the intersection at which glucose level and cumulative duration take the same value. Analysis of a public CGM dataset showed strong correlations between GPI and daily mean glucose and glucose variance, while substantial heterogeneity at fixed GPI values indicated that GPI captures complementary information beyond average exposure or overall variability. As a simple, device-independent, and threshold-free scalar, GPI quantifies hyperglycemia by jointly capturing its magnitude and duration, enabling consistent and intuitive glycemic profiling accessible to both specialists and non-specialists.
    DOI:  https://doi.org/10.21203/rs.3.rs-8841862/v1
  4. J Manag Care Spec Pharm. 2026 Mar;32(3): 329-335
    Trends in use of continuous glucose monitors among individuals with type 2 diabetes enrolled in Medicare Advantage (2021-2023).
    Emily Boudreau, Jeremiah Rastegar, Kaylyn Swankoski, Alexandra Hames, Mark Mugavin, Joseph S Ross.
       BACKGROUND: In 2023, the Centers for Medicare and Medicaid Services (CMS) expanded continuous glucose monitoring (CGM) coverage in Medicare among individuals with type 2 diabetes and insulin use; however, little is known about current trends in CGM use among Medicare beneficiaries with type 2 diabetes.
    OBJECTIVE: To assess trends in CGM use among Medicare beneficiaries with type 2 diabetes from 2021 to 2023 and to compare demographic and clinical characteristics of beneficiaries who used CGMs to those who did not in 2023.
    METHODS: This was a retrospective, repeated cross-sectional analysis. Using data from a large national Medicare Advantage (MA) plan, we described CGM use among MA beneficiaries with type 2 diabetes and evidence of insulin use to assess monthly trends in use from 2021 to 2023 and, using only 2023 data, examined characteristics of beneficiaries using CGMs.
    RESULTS: We found that the prevalence of CGM use among MA beneficiaries with type 2 diabetes and insulin use increased from 1.4% in January 2021 to 17.2% in December 2023. Among the 2023 cohort, CGM users compared with nonusers had more primary care physician (PCP) visits and a higher likelihood of having a visit with an endocrinologist. CGM users were more clinically complex (ie, exhibiting higher clinical risk scores using the Deyo-Charlson Comorbidity Index and the Diabetes Complications Severity Index), more likely to have visits with both PCPs and endocrinologists, and more likely to be younger, be White, be with a disability, and live in rural areas.
    CONCLUSIONS: CGM utilization in an MA population increased concurrent with the expanded clinical guideline changes and Medicare coverage, though certain types of beneficiaries were more likely to use CGMs than others in 2023. Awareness of the differences in uptake of CGMs among beneficiaries could aid future education and outreach opportunities.
    DOI:  https://doi.org/10.18553/jmcp.2026.32.3.329
  5. Soc Sci Med. 2026 Feb 23. pii: S0277-9536(26)00191-7. [Epub ahead of print]396 119115
    "It's part of me": A study of the self-construal of people with diabetes using wearable medical devices.
    Yixin Cui, Pengxiang Li.
      This study examines how continuous glucose monitoring (CGM), a wearable medical device, and the lived experience of diabetes collectively reshape the self-construal of people with diabetes. Using "content-context diaries" combined with semi-structured interviews, the research explores how CGM shapes three interrelated dimensions of the self: the datafied individual self, the shared relational self, and the narrative collective self. These constructs capture individuals' evolving relationships with health data, interpersonal interactions, and narrative engagement within online communities, respectively. Collectively, these dynamically interacting dimensions underpin a multifaceted and interdependent process of self-construal, ultimately culminating in what we term an Adaptive Health Identity. The findings also highlight a critical concern: individuals with limited technological or health literacy may face barriers in accessing CGM, emphasizing the need for supportive measures to foster its equitable development and sustainability.
    Keywords:  Continuous glucose monitoring; People with diabetes; Social media; Tripartite self; Wearable medical device
    DOI:  https://doi.org/10.1016/j.socscimed.2026.119115
  6. J Diabetes Sci Technol. 2026 Feb 28. 19322968261427022
    The Potential Role of Noninvasive Glucose Monitoring in the Management of People with Type 2 Diabetes.
    Julia K Mader, Lutz Heinemann.
       BACKGROUND: Continuous glucose monitoring (CGM) has come a long way and is standard for patients with type 1 diabetes and many with type 2 diabetes. Several attempts to establish noninvasive glucose monitoring, that is, measuring glucose without puncturing the skin, have not been successful yet.
    METHOD: A different approach is the monitoring of volatile organic compounds in breath.
    RESULTS: This addresses a number of limitations of current invasive glucose monitoring techniques. This should enhance compliance, adherence, clinical outcomes, and quality of life. It might also reduce costs associated with CGM.
    CONCLUSIONS: A recent publication in this journal indicates the clinical value of this approach by presenting data from a clinical study. The respective pros and cons will be discussed briefly.
    Keywords:  CGM systems; breath analysis; glucose monitoring; noninvasive
    DOI:  https://doi.org/10.1177/19322968261427022
  7. Diabetes Technol Ther. 2026 Feb 26. 15209156261425672
    Corrigendum to: "Differences Between Glycemia Estimates from Hemoglobin A1c and Continuous Glucose Monitoring and Their Association with Complete Blood Counts".
      
    DOI:  https://doi.org/10.1177/15209156261425672
  8. Metabolism. 2026 Feb 19. pii: S0026-0495(26)00080-6. [Epub ahead of print]179 156570
    Association of daytime circadian-aligned activity with glycemic control in type 2 diabetes: Insights from continuous glucose monitoring and wearable data.
    Da Young Lee, Jung-Been Lee, Inha Jung, So Young Park, Ji Hee Yu, Ji A Seo, Heon-Jeong Lee, Nan Hee Kim.
       BACKGROUND: Prior research on circadian rhythms have primarily focused on the risk of diabetes, with limited evidence on their impact in glycemic control among individuals with type 2 diabetes. This study investigated the association between Fitbit-derived circadian rhythm parameters and continuous glucose monitoring (CGM) metrics.
    METHOD: Data were analyzed from 122 insulin-treated patients with type 2 diabetes who concurrently wore real-time CGM devices (Dexcom G6) and activity trackers (Fitbit Inspire 2) for 10 days. Cosinor analyses were used to derive circadian parameters from wearable-based heart rate data. Associations between time-of-day-specific activity metrics and CGM outcomes were evaluated using partial Spearman correlations and multivariable logistic regression.
    RESULTS: Stronger circadian rhythmicity-characterized by greater amplitude and higher goodness-of-fit (R2)-was significantly associated with improved glycemic outcomes and reduced glucose variability. Higher daytime step counts and lower sedentary time were associated with reduced hyperglycemia and variability. Longer sleep duration was inversely associated with hypoglycemia (TBR <70) and glucose variability indices. Notably, circadian robustness (R2) and afternoon step counts emerged as independent predictors of achieving comprehensive CGM-based targets after adjusting for key clinical and behavioral confounders.
    CONCLUSIONS: In this cross-sectional exploratory analysis, greater daytime physical activity and stronger circadian rhythmicity were associated with improved glycemic control and reduced glucose variability. These findings are hypothesis-generating and support the need for prospective trials testing circadian-aligned behavioral interventions.
    Keywords:  Circadian rhythm; Continuous glucose monitoring; Cosinor analysis; Type 2 diabetes
    DOI:  https://doi.org/10.1016/j.metabol.2026.156570
  9. Diabetes Care. 2026 Feb 26. pii: dc252345. [Epub ahead of print]
    Persistent Burden of Severe Hypoglycemia and Impaired Awareness of Hypoglycemia Among People With Type 1 Diabetes Despite Technology Use: A Follow-up Survey.
    Jennifer L Sherr, Rebecca L Molinsky, Tathabbai Pakalapati, Jordan S Sherwood, Megan E Peter, Emilee M Cornelius, Jeremy Pettus.
       OBJECTIVE: To assess longitudinal trends in glycemic metrics, prevalence of severe hypoglycemic events (SHEs), impaired awareness of hypoglycemia (IAH), and technology use (continuous glucose monitoring [CGM], automated insulin delivery [AID]) in a real-world U.S. cohort of adults with type 1 diabetes.
    RESEARCH DESIGN AND METHODS: This was a cross-sectional study of adults with type 1 diabetes conducted ∼2 years after participants enrolled in the original retrospective observational study. Participants self-reported technology use, insulin delivery method, glycated hemoglobin (HbA1c), IAH, and SHEs. Change was assessed among these variables from the initial and follow-up study.
    RESULTS: Approximately 2 years after the original survey, 1,056 adults responded to the follow-up survey and were eligible for analysis (53% response rate; mean [SD] age: 46 [16] years; mean [SD] type 1 diabetes duration: 29 [16] years; 71% female; 97% White). Most reported using CGMs in the original study (91.8%) and at follow-up (94.4%), while the use of AID increased 17.7%. In the original study, 61.7% reported HbA1c <7% vs. 67.4% at follow-up. Proportions of individuals with IAH and SHEs remained high at ∼30% and ∼20%, respectively, in both studies.
    CONCLUSIONS: Although most participants used CGM and the use of AID increased, approximately one-third of respondents did not achieve HbA1c targets, ∼20% continued to have SHEs in the last year, and ∼30% had IAH. This highlights that while CGM and AID systems are a significant advancement, their use alone has not mitigated the risk of severe hypoglycemia, and glucose management still remains suboptimal.
    DOI:  https://doi.org/10.2337/dc25-2345
  10. Healthcare (Basel). 2026 Feb 07. pii: 420. [Epub ahead of print]14(4):
    Psychological Aspects and Implications of Food Addiction and Glucose Control in Type 2 Diabetes: A Pilot Mixed-Methods Study.
    David J Johnson, Laura A Buchanan, Erin M Saner, Matthew W Calkins, Julienne K Kirk.
      Background/Objectives: Type 2 diabetes (T2D) affects more than 38 million Americans and remains a leading public health challenge. Behavioral self-management is central to glycemic control but is often undermined by dysregulated and addictive-like eating behaviors. Continuous glucose monitoring (CGM) offers immediate feedback that may strengthen self-regulation, yet the psychological processes linking CGM use, food addiction (FA), and behavior change are poorly understood. This secondary mixed-methods study examined how CGM-supported group medical visits (GMVs) influence glycemic outcomes and FA symptoms in adults with diabetes. Methods: Adults with T2D participated in a 14-week GMV program integrating CGM review with education on nutrition, physical activity, sleep, stress, and intermittent fasting. Thirteen participants had paired CGM summaries and psychosocial data. Quantitative outcomes included mean glucose, glycemic variability, time-in-range (TIR), and symptoms of food addiction using the modified Yale Food Addiction Scale 2.0 (mYFAS 2.0). Qualitative data came from open-ended surveys analyzed using reflexive thematic analysis. Integration followed a convergent design, merging individual change trajectories with thematic interpretations and case vignettes. Results: Mean glucose decreased by 21 mg/dL and TIR improved by 9 percentage points. Among six participants with baseline FA symptoms, all showed reductions in self-reported mYFAS 2.0 symptom counts. Four moved from mild to no symptoms, one from moderate to no symptoms, and one from severe to no symptoms. Across the full sample, the mean change was a reduction of 1.2 in the mYFAS 2.0 symptom counts per participant. Thematic analysis identified four interrelated psychological mechanisms: enhanced awareness of food-glucose relationships, increased accountability through shared tracking, motivation via gamified self-monitoring, and relief from cognitive burden associated with dietary uncertainty. Conclusions: Integrating CGM feedback into GMVs was associated with improvements in glycemic metrics and reductions in addictive-like eating symptoms in this pilot sample. These findings position CGM as a behavioral intervention tool that complements its traditional monitoring role and highlight the value of combining real-time biofeedback with group-based support in diabetes care.
    Keywords:  continuous glucose monitoring; food addiction; group medical visits; patient activation
    DOI:  https://doi.org/10.3390/healthcare14040420
  11. Diabetes Metab Res Rev. 2026 Mar;42(3): e70146
    Association of Physical Activity and Socioeconomic Status With Glycaemic Control in Adults With Type 1 Diabetes: A Cross-Sectional Study Using CGM Data.
    Fernando Sebastian-Valles, Rafael Simó, Jose Alfonso Arranz Martín, Mercè Abad, Alicia Justel Enriquez, Cristina Pérez-Fernández, Sara Jiménez Blanco, Cristina Hernández, Mónica Marazuela, Olga Simó-Servat.
       AIMS: This study examined the independent and combined effects of physical activity and socioeconomic status (SES) on glycaemic control in adults with T1D using continuous glucose monitoring (CGM).
    METHODS: A cross-sectional study included 423 adults with T1D from a public healthcare setting in whom physical activity was self-assessed via the short-form International Physical Activity Questionnaire (IPAQ). SES was estimated using mean annual net income by census tract. Glycaemic outcomes included time in range (TIR) and time in tight range (TITR), derived from CGM, and HbA1c. Multivariable linear models and four-way mediation analyses were conducted.
    RESULTS: Higher physical activity and income were independently associated with better glycaemic control. The highest activity quartile was associated with +8.0% TIR and -0.47% HbA1c (p < 0.01). Physical activity partially mediated the effect of income on TIR (pure indirect effect β = 2.42, p = 0.013), accounting for 23% of the total effect. No significant SES-activity interaction was observed. Physically active individuals also had a 16% lower insulin requirement and better lipid profile, independent of income. A modest TBR increase occurred without longer hypoglycaemia.
    CONCLUSION: Physical activity is associated with improved glycaemic and lipid control in T1D patients, regardless of income. Promoting physical activity might reduce SES-related glycaemic disparities and improve outcomes.
    Keywords:  IPAQ; SES; physical activity; time in range; type 1 diabetes
    DOI:  https://doi.org/10.1002/dmrr.70146
  12. BMC Endocr Disord. 2026 Feb 26.
    Associations of time in tight range, time in range, and glycated hemoglobin with albuminuria and diabetic kidney disease in adult type 2 diabetes: a cross-sectional study.
    Yanling Yang, Zhigu Liu, Yintong Huang, Hongrong Deng, Xubin Yang, Jinhua Yan, Longyi Zeng, Beisi Lin, Wen Xu.
       AIMS: To evaluate associations of the indicators of glycemic control, HbA1c, continuous glucose monitoring (CGM)-derived time in tight range (TITR; 70-140 mg/dL), and time in range (TIR; 70-180 mg/dL) with albuminuria and diabetic kidney disease (DKD) in type 2 diabetes mellitus (T2DM).
    METHODS: This cross-sectional study included 697 patients with T2DM. Albuminuria (UACR ≥ 30 mg/g) and DKD (eGFR < 60 mL/min/1.73 m² or albuminuria) were assessed. Multivariable logistic regression analyzed associations of TITR, TIR, and HbA1c (continuous/tertiles) with albuminuria and DKD.
    RESULTS: Among the participants, the prevalence of albuminuria and DKD was 34.6% and 37.9%, respectively. After full adjustment, higher TITR and TIR were independently associated with lower odds of albuminuria (TITR: OR 0.98, 95% CI 0.97-0.99; TIR: OR 0.98, 0.96-0.99) and DKD (TITR: OR 0.98, 0.97-0.99; TIR: OR 0.98, 0.96-0.99). The highest (vs. lowest) tertiles of TITR and TIR had significantly reduced risks (for albuminuria; TITR: OR 0.36, TIR: OR 0.42; for DKD, TITR: OR 0.35, TIR: OR 0.40; all p < 0.001). HbA1c showed weaker threshold associations (highest tertile ORs: albuminuria: 2.31, DKD: 2.31; p < 0.05). However, the AUCs for TITR, TIR, and HbA1c showed no significant differences. Restricted cubic spline analysis demonstrated a significant dose-response relationship, wherein higher levels of TITR/TIR were associated with progressively lower risks of albuminuria and DKD. Subgroup analyses revealed that diabetes duration significantly modified the association of TITR (P for interaction ≤ 0.021), with stronger inverse associations observed in patients with diabetes duration ≥ 10 years. In contrast, TIR showed a consistent association across diabetes duration subgroups.
    CONCLUSIONS: Both TITR and TIR are independently associated with albuminuria and diabetic kidney disease. While TIR serves as a consistent marker across different disease stages, TITR may offer refined risk stratification, particularly in individuals with longer diabetes duration. These metrics provide complementary value to HbA1c for assessing renal risk.
    CLINICAL TRIAL NUMBER: Not applicable.
    Keywords:  Albuminuria; Continuous glucose monitoring; Diabetic kidney disease; Time in range; Time in tight range; Type 2 diabetes mellitus
    DOI:  https://doi.org/10.1186/s12902-026-02204-2
  13. JMIR Res Protoc. 2026 Feb 24. 15 e85375
    Implementation of a Personalized Medicine Approach in Patients With Type 2 Diabetes Mellitus Receiving Multiple Daily Insulin Injections (POMA Project): Protocol for a Before-and-After Intervention Study.
    POMA Study Group
       Background: The management of type 2 diabetes mellitus (T2DM) remains a complex clinical challenge, particularly for patients requiring multiple daily insulin injections (MDI). Advances in precision medicine and continuous glucose monitoring (CGM) have created opportunities to personalize treatment and potentially reduce the therapeutic burden on people with T2DM. Assessing β cell function and autoimmunity could help identify patients with T2DM eligible for simplified regimens without compromising glycemic control.
    Objective: The aim of this study is to test a simple personalized medicine protocol in routine clinical practice for people with T2DM treated with MDI. The intervention is based on evaluating C-peptide and glutamic acid decarboxylase autoantibody status with the goal of improving diagnostic accuracy and optimizing treatment.
    Methods: This is a pragmatic before-and-after intervention study involving people with T2DM currently receiving MDI across primary care centers and a referral hospital in the Lleida health care region in Catalonia (Spain). Eligible participants will undergo clinical and laboratory assessment, including C-peptide and glutamic acid decarboxylase autoantibody testing, and wear a CGM device. On the basis of a predefined algorithm, patients may either continue or discontinue prandial insulin. The primary outcome is the proportion of patients in whom prandial insulin is discontinued and remains discontinued over 6 months. Secondary outcomes include changes in hemoglobin A1c, CGM metric variables, quality of life, adherence, and treatment satisfaction.
    Results: Recruitment was completed on March 31, 2025. The follow-up phase is ongoing and expected to conclude by September 30, 2025. Data analysis will begin thereafter.
    Conclusions: This study will evaluate the feasibility and impact of implementing a personalized therapeutic approach for persons with T2DM receiving MDI in real-world clinical settings. If effective, this strategy could contribute to safer, simpler, and more individualized diabetes care.
    Keywords:  C-peptide; anti-GAD65 autoantibody; continuous glucose monitoring; multiple insulin doses; precision medicine; type 2 diabetes mellitus
    DOI:  https://doi.org/10.2196/85375
  14. J Exerc Sci Fit. 2026 Apr;24(2): 200452
    Reduced-exertion high-intensity interval training (REHIT) provides limited improvement in post-prandial and 24-hour glycemia in healthy, physically active men.
    Bilal A Chaudhry, Jesse Stabile Morrell, Summer B Cook, Nathan J Goodwin, Michael S Brian.
       Introduction: Reduced-Exertion High-Intensity Interval Training (REHIT) is a 10-min cycling regimen that has improved glucose tolerance in adults with metabolic dysfunction. However, the ecological relevance of REHIT for application in healthy, middle-aged men as a prevention tool to improve glucose control remains unknown. This study aimed to investigate the effects of a single bout of REHIT following a mixed meal on 3h post-prandial and 24h blood glucose measures in physically active, middle-aged men compared to a non-exercise control condition (Non-EX).
    Methods: Twenty physically active men (Age: 52 ± 8 years; VO2max: 44.5 ± 6.0 mL min-1·kg-1; BMI: 24.3 ± 1.7 kg m-2) completed a randomized crossover study comparing REHIT to Non-EX. All participants completed both interventions 30 min after consuming a standardized breakfast. Each study condition was separated by 48 h. Continuous glucose monitors (CGMs) measured interstitial glucose continuously throughout the study. CGM data were analyzed to assess post-prandial, 24h mean glucose, and 24h glucose variability (standard deviation; mean amplitude of glycemic excursions, MAGE; coefficient of variation, CV).
    Results: REHIT led to a significantly shorter glucose peak-to-nadir (p = 0.014). While a significant condition × time interaction was observed (p < 0.001), there was no significant effect of condition (p = 0.725). REHIT vs. Non-EX did not lower 3h post-breakfast glucose responses (113 ± 22 vs. 114 ± 19 mg dL-1, p = 0.809), 24h mean glucose (119 ± 14 vs. 117 ± 13 mg dL-1, p = 0.453) or measures of glucose variability including 24h standard deviation of blood glucose (17 ± 5 vs. 18 ± 6 mg dL, p = 0.173), 24h MAGE (2.5 ± 0.9 vs. 2.6 ± 0.9 mmol L-1, p = 0.474), and 24h CV (14.6 ± 4.0 vs. 14.9 ± 4.6 %, p = 0.746), respectively.
    Conclusion: While REHIT led to transient reductions in post-prandial glucose in active middle-aged men, there was no clinically meaningful reduction in 3h post-prandial or 24h glucose control.
    Keywords:  Continuous glucose monitoring; Diabetes; Interval training
    DOI:  https://doi.org/10.1016/j.jesf.2026.200452
  15. Diabetologia. 2026 Feb 25.
    GlucoseGo: a simple tool to predict hypoglycaemia during exercise in type 1 diabetes.
    Catherine L Russon, Michael J Allen, Emma Cockcroft, John S Pemberton, Anne-Marie Frohock, Neil Vaughan, Richard M Pulsford, Robert C Andrews.
       AIMS/HYPOTHESIS: This study aimed to develop an accessible tool, derived using machine learning, to predict hypoglycaemia risk at the start of exercise and to provide clear, rapid risk assessment to support safer participation in exercise.
    METHODS: Data from four diverse studies were combined, encompassing 16,430 exercise sessions from 834 participants aged 12-80 years using various insulin delivery methods. The XGBoost algorithm was used to develop two models: a comprehensive model and a simplified model for predicting hypoglycaemia during exercise.
    RESULTS: The comprehensive model (406 variables) achieved a mean ROC AUC of 0.89. The simplified model, using only starting glucose, exercise duration and glucose trend arrows, achieved a comparable ROC AUC of 0.87. The simplified model performed consistently across exercise types and insulin delivery methods. In collaboration with individuals with type 1 diabetes, this model was translated into GlucoseGo, a user-friendly traffic-light heatmap displaying hypoglycaemia risk based on the three variables.
    CONCLUSIONS/INTERPRETATION: The GlucoseGo heatmap provides a practical, accessible tool for predicting hypoglycaemia risk immediately before exercise. It may empower individuals with type 1 diabetes to exercise more safely, reduce hypoglycaemic episodes, and increase engagement in physical activity.
    Keywords:  Continuous glucose monitoring; Decision support; Exercise; Hypoglycaemia; Machine learning; Risk prediction; Self-management; Type 1 diabetes
    DOI:  https://doi.org/10.1007/s00125-026-06692-8
  16. Diabet Med. 2026 Feb 24. e70262
    Characterising the impact of shift work on diet and glucose variability in healthcare employees living with type 2 diabetes: The Shift-Diabetes study.
    Rachel Gibson, Maria D'Annibale, Luigi Palla, Felicia Tejo, Barbara McGowan, Celine Vetter, Fabiana Lorencatto, Nick Oliver, Nicola Guess.
       AIMS: To characterise differences in dietary intake, glucose variability, and activity in free-living healthcare shift workers with type 2 diabetes (T2D) across varying work conditions.
    METHODS: Healthcare shift workers with T2D were monitored over 10 days, covering night shifts, day shifts, and rest days. Data were collected using blinded continuous glucose monitoring, activity trackers, and diet/sleep diaries. Within-person comparisons were made for mean glucose (MG), coefficient of variation (CV), mean absolute glucose change (MAG), mean amplitude of glycaemic excursion (MAGE), continuous overlapping net glycaemic action (CONGA), dietary intake (food choices, nutrient intake), and activity/rest periods.
    RESULTS: The study sample (n = 37; 89.2% women) were mainly employed as nurses or midwives (62.2%). Energy intake was highest (2199 kcal SD 648) on a day when a night shift was worked. Percentage of energy intake from sweet snacks was higher on a night shift compared with a rest day after a night shift (13.4 SD 12.0% vs. 7.8 SD 11.8%, p = 0.013). Night shifts had the highest eating occasions (7.0 SD 2.2) and rest after night (RAN) the lowest (3.4 SD 1.6), p < 0.001. No differences were reported for MG, MAGE, or CV. MAG and CONGA were higher for night shift compared with RAN shift (p = 0.029). Step counts were higher on night shift days (13,775, SD 4270 p = 0.016), and participants were awake longer (22.2 h SD 2.4 h, p < 0.001) compared with other day types.
    CONCLUSIONS: Night shifts are associated with prolonged wakefulness, increased activity, and distinct dietary behaviours. Tailored interventions are needed to support night shift workers with T2D in managing their condition effectively.
    Keywords:  continuous glucose monitoring healthcare employees; diet; shift work; type 2 diabetes; workplace health
    DOI:  https://doi.org/10.1111/dme.70262
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