bims-glumda Biomed News
on CGM data in management of diabetes
Issue of 2026–06–28
twenty papers selected by
Mott Given
  1. Continuous Glucose Monitoring in Type 2 Diabetes: Clinical Outcomes, Disparities in Access, and Cost-effectiveness.
  2. Patterns of One-Year Change in HbA1c and Continuous Glucose Monitoring (CGM) Metrics in Older Adults with Type 2 Diabetes.
  3. Real-time continuous glucose monitoring in adults with type 2 diabetes managed with non-intensive insulin regimens: impact on glycemia and self-management.
  4. Evaluating clinical outcomes of using Undermyfork Mobile app with Dexcom G7: Protocol for CGM-Undermyfork randomized controlled trial.
  5. Perspectives on Continuous Glucose Monitoring Among Adults with Type 2 Diabetes in the United Kingdom: Cross-Sectional Survey.
  6. Association Between Longitudinal Time in Range and Albuminuria in Insulin-Treated Diabetes.
  7. Comparison of HbA1c and Time in Range in the Prediction of Large for Gestational Age in Pregnancies Involving Type 1 Diabetes.
  8. FDA Approves First Over-the-Counter Continuous Glucose Monitor for Children.
  9. Beyond HbA1c: A CGM-centred three-pillar framework for glycaemic variability in pre-diabetes and type 2 diabetes.
  10. Real-Time Continuous Glucose Monitoring Among People With Type 2 Diabetes and End-Stage Kidney Failure Undergoing Maintenance Hemodialysis: A Randomized Clinical Trial.
  11. Glycemic Patterns Revealed by Continuous Glucose Monitoring in Patients with Type 2 Diabetes Undergoing Intermittent Hemodialysis: A Pilot Study.
  12. The Impact of Hypoglycaemia on Quality of Life in Adults With Type 1 Diabetes Using Contemporary Diabetes Technologies: A US National Survey.
  13. Glycemic Risk Across Exercise Modalities in Adults with Type 1 Diabetes Using Continuous Glucose Monitoring and Wearable Sensors: A Prospective Cohort Study.
  14. Association of diet composition and meal intervals with short-term glycemic control and glycemic variability among type 2 diabetes mellitus patients.
  15. Continuous glucose monitoring for prediabetes classification in a large real-world cohort: Comparison with HbA1c and fasting plasma glucose.
  16. Empowering digital health management with on-device large language models for glucose prediction: a model development and validation study.
  17. Percent Time Below Range Is Strongly Associated with Clinically Important Level 2 Hypoglycemia Events in Individuals with Type 1 Diabetes.
  18. Effect of a Personalized Mobile App on Glucose Control in Adults With Prediabetes and Type 2 Diabetes: Exploratory Pilot Randomized Controlled Trial.
  19. Prediction of Nocturnal Hypoglycemia Following Exercise in Type 1 Diabetes Using Temporally Structured CGM-Derived Digital Biomarkers.
  20. Precision Nutrition Using Continuous Glucose Monitors: Hype, Hope, and the Road Ahead.
  1. Curr Diab Rep. 2026 Jun 22. pii: 19. [Epub ahead of print]26(1):
    Continuous Glucose Monitoring in Type 2 Diabetes: Clinical Outcomes, Disparities in Access, and Cost-effectiveness.
    Giuliana Perini Villanueva, Walter M Solorzano, Estelle Everett.
       PURPOSE OF REVIEW: This review summarizes recent evidence on continuous glucose monitoring (CGM) in adults with type 2 diabetes mellitus (T2D), focusing on clinical effectiveness, patient-reported outcomes, disparities in use, and policy and economic considerations.
    RECENT FINDINGS: Studies from 2020 to 2025 show that CGM use in T2D is associated with consistent improvements in glycosylated hemoglobin (HbA1c), time in range, and diabetes self-management across insulin and non-insulin treatment regimens. Emerging observational data suggest reductions in mortality and health care utilization, and cost-effectiveness analyses consistently demonstrate that CGM represents a high-value intervention across payer settings. Despite these benefits, CGM uptake remains variable, with persistent disparities by age, race, and ethnicity, insurance coverage, and care setting. CGM is an effective and cost-effective tool for T2D management, but inequities in access limit its impact. Future research should address implementation in safety-net and primary care settings, evaluate over-the-counter CGM, and assess long-term clinical and health system outcomes.
    Keywords:  Continuous glucose monitoring; Cost-effectiveness; Health disparities; Health policy; Type 2 diabetes
    DOI:  https://doi.org/10.1007/s11892-026-01631-8
  2. Clin Chem. 2026 Jun 25. pii: hvag067. [Epub ahead of print]
    Patterns of One-Year Change in HbA1c and Continuous Glucose Monitoring (CGM) Metrics in Older Adults with Type 2 Diabetes.
    Tianyu Cao, Michael Fang, Amelia S Wallace, Natalie Daya Malek, Arielle Valint, Dan Wang, Justin B Echouffo-Tcheugui, Scott L Zeger, Elizabeth Selvin.
       BACKGROUND: How changes in hemoglobin A1c (HbA1c) and continuous glucose monitoring (CGM) metrics track together over time is poorly understood, particularly in type 2 diabetes. We investigated the patterns of change in HbA1c and CGM metrics among older adults with type 2 diabetes.
    METHODS: We analyzed data from 88 Atherosclerosis Risk in Communities (ARIC) study participants (baseline age, 82 years; 28% Black race and 42% women) who had HbA1c and 14-days of CGM assessed by standardized protocols at visit 9 (2021-22) and visit 10 (2023). HbA1c, CGM mean glucose, and time in range (TIR, 70-180 mg/dL) were compared across visits. Discordance was defined as having a different direction or magnitude of change, based on an absolute HbA1c change of 0.5% and the corresponding changes in CGM metrics derived from linear mixed-effect models.
    RESULTS: Over a median of 1.6 (IQR, 1.3-1.8) years, HbA1c, CGM mean glucose, and TIR showed moderate to strong correlations (r ∼0.5 to 0.7) across visits, and HbA1c had the lowest within-person variability (CVw = 8.4%). Approximately one-third of the participants had discordant changes between HbA1c and CGM metrics, with percentage agreement of 68.2% between HbA1c and CGM mean glucose, and 67.0% between HbA1c and TIR. Similar results were found in subgroups by sex, race, diabetes medication use, and after excluding participants with reduced kidney function.
    CONCLUSIONS: Among older adults with type 2 diabetes, long-term changes in HbA1c and CGM metrics are frequently discordant. This suggests the complementary nature of using HbA1c and CGM together to monitor glucose control.
    DOI:  https://doi.org/10.1093/clinchem/hvag067
  3. J Diabetes Metab Disord. 2026 Dec;25(2): 165
    Real-time continuous glucose monitoring in adults with type 2 diabetes managed with non-intensive insulin regimens: impact on glycemia and self-management.
    Ayman Al Hayek, Wael M Alzahrani, Mo'men Al Ajlouni, Mohamed A Al Dawish.
       Background: Real-time continuous glucose monitoring (RT-CGM) is increasingly used to support glycemic optimization and enhance diabetes self-management. However, evidence regarding its clinical and behavioral effects in adults with type 2 diabetes (T2D) who are not using intensive insulin therapy remains limited.
    Methods: This prospective, single-arm, 12-week study evaluated adults with T2D and suboptimal glycemic control (HbA1c > 7.0%) who were not using intensive insulin therapy and subsequently initiated RT-CGM. Clinical, metabolic, and glycemic parameters, as well as patient-reported outcomes assessed using the Diabetes Self-Management Questionnaire (DSMQ), were recorded. CGM metrics, including time in range (%TIR70-180), time above range (%TAR), time below range (%TBR), glucose management indicator (GMI), and the Glycemia Risk Index (GRI) with its components, were analyzed at 30 and 90 days.
    Results: A total of 118 participants were included in the study. RT-CGM use was associated with improved HbA1c (MD - 0.44%, p < 0.001). Body weight decreased modestly (MD - 0.76 kg), and capillary glucose declined (MD - 18.75 mg/dL). Basal insulin-treated users showed a reduction in daily dose (MD - 4.78 U/day), with a similar decrease in twice-daily insulin users (MD - 5.2 U/day; p < 0.001). RT-CGM use was associated with higher %TIR70-180 (MD + 4.68%), reductions in %TBR and %TAR, and concurrent improvements in %GMI and GRI (all p < 0.001). The DSMQ total raw score improved (MD + 4.09), and the total scale score increased (MD + 0.85).
    Conclusion: Twelve weeks of RT-CGM use were associated with improvements in glycemic control, reductions in insulin requirements, favorable changes in CGM-derived risk metrics, and enhanced diabetes self-management behaviors in adults with T2D managed with non-intensive insulin regimens.
    Supplementary Information: The online version contains supplementary material available at 10.1007/s40200-026-01982-9.
    Keywords:  Continuous glucose monitoring; Diabetes mellitus, type 2; Glycemic Control
    DOI:  https://doi.org/10.1007/s40200-026-01982-9
  4. Contemp Clin Trials. 2026 Jun 23. pii: S1551-7144(26)00172-2. [Epub ahead of print] 108386
    Evaluating clinical outcomes of using Undermyfork Mobile app with Dexcom G7: Protocol for CGM-Undermyfork randomized controlled trial.
    Kallie Brown, Mike Ushakov, Eugene Molodkin, Samantha R Spierling Bagsic, Camille Bell, Athena Philis-Tsimikas.
       BACKGROUND: Mobile health apps have the potential to enhance education for glycemic management in individuals with type 2 diabetes (T2D). The growing desire to use diabetes-related mobile applications indicates the industry's broad effort in leveraging technology to improve health outcomes in diabetes patients. However, many available applications still lack evidence-based features, and applications that go through clinical validation often do not include an active control in their studies. The purpose of this in-progress trial is to evaluate the effectiveness of a novel photo-based food diary smartphone app (Undermyfork) that visually connects meals to glucose excursions to promote behavior change.
    METHODS: This is a randomized, controlled, prospective, parallel-group trial with an active comparator, enrolling N = 90 adults with T2D, elevated HbA1c, and continuous glucose monitoring (CGM) naivety in a real-world community-based health system. Participants complete a run-in/screening period, and if eligible, are randomized into the intervention to use CGM alone (Group 1) or to use the Undermyfork mobile app in addition to CGM (Group 2). The trial will examine comparative effectiveness in improving CGM metrics, HbA1c, and diabetes self-care behaviors over 4 months.
    DISCUSSION: We hypothesize that Group 2 will have more evident improvements in CGM metrics and HbA1c given Undermyfork's focus on utilizing CGM data to understand meal-related decisions that may allow participants to gain insights into the relationship between their dietary choices and glucose fluctuations throughout the day. This trial may have implications for improved glycemic management, reduced risk of diabetes-related complications, and enhanced well-being for individuals with T2D.
    CLINICAL TRIAL REGISTRATION: Clinicaltrials.govNCT06501612https://clinicaltrials.gov/study/NCT06501612.
    Keywords:  Continuous glucose monitoring; Diabetes technology; Food logging; Mobile health applications; Type 2 diabetes
    DOI:  https://doi.org/10.1016/j.cct.2026.108386
  5. JMIR Form Res. 2026 Jun 26. 10 e89898
    Perspectives on Continuous Glucose Monitoring Among Adults with Type 2 Diabetes in the United Kingdom: Cross-Sectional Survey.
    Albandari Alharbi, Elena Lammila-Escalera, Benedict Hayhoe, Reham Aldakhil, Azeem Majeed, Austen El-Osta, Ana Luisa Neves.
       BACKGROUND: Type 2 diabetes (T2D) is one of the most common noncommunicable diseases, requiring ongoing lifestyle changes and continuous glucose management through medication, diet, and physical activity. Traditional self-monitoring of blood glucose can be burdensome, especially with frequent finger pricks. As continuous glucose monitoring (CGM) becomes more affordable and accessible, it offers benefits such as increased glucose awareness, behavioral modifications, and reduced anxiety. However, challenges remain, including cost, discomfort, skin reactions, and privacy concerns. In the United Kingdom, perceptions of CGM among people with T2D, including both users and nonusers, are not well understood, limiting insight into factors influencing adoption and sustained use.
    OBJECTIVE: This study aims to explore how adults with T2D perceive the benefits and challenges of using CGM, including both current users and nonusers.
    METHODS: This study used a cross-sectional, online survey using YouGov's nationally representative panel to explore experiences of CGM among adults with T2D in the United Kingdom. A total of 531 participants were recruited from November to December 2024. Thematic analysis of responses to 2 open-ended questions identified key perceived benefits and challenges associated with CGM use.
    RESULTS: A total of 531 adults with T2D completed the YouGov online survey. Over half were male (297/531, 55.9%) and aged 65 years and older (281/531, 52.9%). Two-thirds (347/531, 65.3%) had lived with T2D for more than 5 years, and 9.6% (51/531) use or had previously used a CGM. Overall, 50.8% (270/531) responded to at least one free-text question, with 49% (260/531) commenting on benefits and 33.1% (176/531) on challenges. Thematic analysis identified five key benefit themes: (1) reduced monitoring burden, described as eliminating frequent finger prick testing and simplifying daily routines; (2) lifestyle feedback, enabling participants to better understand how diet and physical activity influence glucose levels; (3) greater control, by supporting more informed decision-making and increasing confidence in self-management; (4) feeling safer, through alerts for hypo- and hyperglycemia; and (5) sharing data with clinicians, which facilitated communication and more collaborative care. The main challenges were (1) access barriers, including restrictive eligibility criteria and the high cost of self-funding; (2) device issues, such as discomfort, inconvenience, and practical difficulties wearing the sensor; (3) technology reliance, with concerns about depending on devices rather than listening to bodily cues; (4) emotional strain, including anxiety, over-monitoring, and increased preoccupation with glucose levels; and (5) data concerns, particularly regarding accuracy, interpretation, and privacy.
    CONCLUSIONS: Adults with T2D, including both users and nonusers, described CGM as a practical and empowering tool that improves understanding, safety, and collaboration with health care providers. Nevertheless, access barriers, usability issues, and emotional and data-related burdens remain major obstacles to equitable adoption. Addressing these through improved affordability, digital literacy support, and customized clinical guidance may support ongoing and inclusive CGM use in routine care.
    Keywords:  CGM; T2D; continuous glucose monitoring; diabetes self-management; diabetes technology; digital health; type 2 diabetes
    DOI:  https://doi.org/10.2196/89898
  6. Diabetes Technol Ther. 2026 Jun 23. 15209156261462229
    Association Between Longitudinal Time in Range and Albuminuria in Insulin-Treated Diabetes.
    Kai Yoshimura, Yuya Miyazaki, Motoko Kobayashi, Mei Nakatsuji, Shuichiro Saito, Seiji Nishikage, Akane Yamamoto, Tomofumi Takayoshi, Shun-Ichiro Asahara, Wataru Ogawa, Yushi Hirota.
       BACKGROUND: Cross-sectional associations between continuous glucose monitoring (CGM) metrics and diabetic kidney disease (DKD) have been reported, but evidence based on longitudinal CGM metrics collected over extended periods has remained limited.
    OBJECTIVE: To evaluate the relationship between longitudinal CGM metrics and albuminuria in diabetes.
    METHODS: A single-center, retrospective, longitudinal observational study was conducted for 190 individuals with insulin-treated diabetes who had at least 3 years of CGM data. The primary exposure was time in range (TIR) across follow-up, with secondary exposures being time in tight range (TITR) and mean glycated hemoglobin (HbA1c) level. The primary outcome was the urinary albumin-to-creatinine ratio (UACR) measured closest to the end of follow-up. Multivariable linear regression and restricted cubic spline analyses were performed.
    RESULTS: Over a median follow-up of 7.3 years, each 10-percentage-point increase in TIR was associated with an 11.8% decrease in UACR (95% confidence interval [CI], 1.5%-21.0% decrease). TITR showed a similar direction of association, but this association was not statistically significant. Each 1-percentage-point decrease in mean HbA1c level was associated with a 21.0% decrease in UACR (95% CI, 4.4%-34.6% decrease). Restricted cubic spline analysis suggested possible nonlinear associations for TIR and TITR, although these patterns were attenuated in sensitivity analyses.
    CONCLUSIONS: Longitudinal CGM metrics, in particular TIR, were associated with albuminuria in diabetes, providing support for their clinical relevance for DKD.
    Keywords:  albuminuria; continuous glucose monitoring; diabetic kidney disease; longitudinal study
    DOI:  https://doi.org/10.1177/15209156261462229
  7. Diagnostics (Basel). 2026 Jun 18. pii: 1900. [Epub ahead of print]16(12):
    Comparison of HbA1c and Time in Range in the Prediction of Large for Gestational Age in Pregnancies Involving Type 1 Diabetes.
    Katarzyna Rutkowska, Klaudia Czarnik, Katarzyna Cypryk.
      Background/Objectives: While satisfactory glycaemic control is possible with specialist care from a diabetologist and modern therapies, women with type 1 diabetes are still subject to poorer obstetric outcomes, even with optimal management. Methods: The analysis comprised a cohort of 55 pregnant patients with type 1 diabetes who attended the Diabetology Outpatient Clinic between 2018 and 2023; all were recruited no later than the first trimester. Qualified patients underwent medical interviews and physical examinations. Insulin pump, continuous glucose monitoring (CGM) system, and postpartum data were collected. Results: The median glycated haemoglobin (HbA1c) at the beginning of pregnancy was 6.1%, with means of 5.9% and 6.0% in the following trimesters. Only 1/3 of the women achieved the recommended HbA1c value throughout pregnancy. The average/median time in range (TIR) in each trimester was ≤70%. Among the women who achieved the recommended TIR target, the infants tended to have lower birth weights but a higher likelihood of jaundice. Almost half of the newborns were large for gestational age (LGA), and a third were macrosomic. The strongest predictor of LGA was a mean blood glucose level > 124 mg/dL in the third trimester, which increased the risk of LGA by almost 12 times. Conclusions: Good diabetes control does not prevent LGA/macrosomia. TIR appears to be a better predictor of obstetric complications, including LGA. A mean glucose level ≥ 124 mg/dL in the third trimester greatly increases the risk of LGA.
    Keywords:  continuous glucose monitoring; neonatal outcomes; personal insulin pump; pregnancy; type 1 diabetes mellitus
    DOI:  https://doi.org/10.3390/diagnostics16121900
  8. JAMA. 2026 Jun 26.
    FDA Approves First Over-the-Counter Continuous Glucose Monitor for Children.
    Samantha Anderer.
      
    DOI:  https://doi.org/10.1001/jama.2026.8314
  9. Diabet Med. 2026 Jun 25. e70404
    Beyond HbA1c: A CGM-centred three-pillar framework for glycaemic variability in pre-diabetes and type 2 diabetes.
    Taoming Qian, Donghao Guo, Meijun Zhang, Yuhan Liu, Juan Jin.
       BACKGROUND: Traditional management of type 2 diabetes mellitus (T2DM) and pre-diabetes centres on HbA1c reduction, yet this single metric fails to capture glycaemic variability (GV)-an independent driver of β-cell dysfunction, endothelial damage, oxidative stress, inflammation and cardiovascular risk, even in the pre-diabetes stage. Unlike existing reviews that focus predominantly on T2DM or HbA1c-centric approaches, this perspective articulates a unified three-pillar closed-loop framework that integrates continuous glucose monitoring (CGM) across the entire pre-diabetes-to-remission continuum.
    PERSPECTIVE: We synthesize evidence from prospective cohorts, mechanistic studies, randomized controlled trials and real-world data to demonstrate that GV is a modifiable therapeutic target. CGM uniquely reveals dynamic glucose patterns invisible to HbA1c, enabling precise, real-time interventions that improve time in range (TIR), reduce GV and hypoglycaemia and support the achievement of clinical remission (HbA1c <48 mmol/mol (<6.5%) without glucose-lowering medication for ≥3 months) in subsets of patients.
    CONCLUSIONS: CGM-centred management of glycaemic stability offers a clinically actionable paradigm shift from static, average-glucose control to dynamic, precision-guided care. The proposed three-pillar framework-tiered personalized targets, data-driven intelligent decision support and empowered patient-clinician collaboration-provides a structured roadmap grounded in current evidence. Long-term outcomes in pre-diabetes, cost-effectiveness and global accessibility remain important areas for future investigation.
    Keywords:  HbA1c; continuous glucose monitoring; glycaemic variability; pre‐diabetes; type 2 diabetes
    DOI:  https://doi.org/10.1111/dme.70404
  10. Diabetes Care. 2026 Jun 26. pii: dc260404. [Epub ahead of print]
    Real-Time Continuous Glucose Monitoring Among People With Type 2 Diabetes and End-Stage Kidney Failure Undergoing Maintenance Hemodialysis: A Randomized Clinical Trial.
    Rodolfo J Galindo, Bobak Moazzami, Amany Gerges, Ina Flores, Giuliana Arevalo, Limin Peng, Ian H de Boer, Katherine R Tuttle, Guillermo E Umpierrez.
       OBJECTIVE: There is a need for improved glycemia monitoring tools for people with type 2 diabetes (T2D) and end-stage kidney failure (ESKF).
    RESEARCH DESIGN AND METHODS: This prospective, randomized, crossover trial compared the efficacy of real-time continuous glucose monitoring (rtCGM) with capillary blood glucose (CBG) testing in adults with T2D and ESKF undergoing hemodialysis. The primary outcome was percentage of time below range (%TBR) <70 mg/dL.
    RESULTS: The %TBR <70 mg/dL was not significantly different between groups (mean 1.17% ± 1.8 vs. 1.29% ± 2.7; P = 0.28). Compared with CBG testing, percentage time in range (%TIR) was higher (63.4% ± 24 vs. 54.5% ± 23) and mean glucose lower (173.6 ± 37 vs. 187.7 ± 38 mg/dL) after the rtCGM intervention, while percentage time above range (%TAR) >180 mg/dL (35.3% ± 25 vs. 44.3% ± 23) and >250 mg/dL decreased (12.3% ± 15 vs. 18.8% ± 19) (all P ≤ 0.01).
    CONCLUSIONS: In adults with T2D and ESKF undergoing hemodialysis, TBR was minimal and not influenced by rtCGM use. Compared with CBG testing, %TIR and %TAR improved during the rtCGM intervention. Future studies are needed to confirm the benefits of rtCGM in this population.
    DOI:  https://doi.org/10.2337/dc26-0404
  11. Med Sci (Basel). 2026 Jun 16. pii: 324. [Epub ahead of print]14(2):
    Glycemic Patterns Revealed by Continuous Glucose Monitoring in Patients with Type 2 Diabetes Undergoing Intermittent Hemodialysis: A Pilot Study.
    Miguel Angel Cuevas-Budhart, Joel Salvador Becerra-Barrera, Rogelio Iván Silva-Rueda, Daniela Vallejo-Avalos, Maricruz Ponce-Villavicencio, María Begoña Ilabaca Avendaño, Marcela Ávila-Díaz, Ramón Paniagua.
      Introduction: Glycemic control in patients with type 2 diabetes mellitus undergoing intermittent hemodialysis represents a clinical challenge. The pathophysiological alterations inherent to chronic kidney disease (CKD) and the dialysis procedure limit the usefulness of traditional metrics. In this context, continuous glucose monitoring (CGM) enables dynamic assessment of glycemic profiles and can reveal patterns of dysglycemia that go undetected in routine clinical practice. Methods: An observational, cross-sectional, and analytical pilot study involved 10 patients from the hemodialysis (HD) unit. CGM was carried out for 14 days. A paired analysis was performed to compare glycemic parameters on days with and without HD. Statistical evaluation was performed using the Shapiro-Wilk test and Student's t-test; a p-value < 0.05 indicated statistical significance. Results: Time in range (TIR) showed considerable interindividual variability (24-100%), with hyperglycemia being the predominant factor. During HD sessions, glucose levels showed a marked intradialytic decline followed by incomplete post-dialysis recovery, a pattern that differed from non-dialysis days (paired t-test, p < 0.001; n = 10 paired observations). These findings should be interpreted as exploratory. Hypoglycemic episodes were infrequent, whereas persistent hyperglycemia prevailed. Conclusions: CGM reveals metabolic dysregulation frequently overlooked by traditional indicators such as glycated hemoglobin (HbA1c). These exploratory findings suggest that CGM may provide clinically relevant information in this population, although larger studies are needed before therapeutic recommendations can be established.
    Keywords:  continuous glucose monitoring; diabetes mellitus type 2; glycemic variability; hemodialysis; time in range
    DOI:  https://doi.org/10.3390/medsci14020324
  12. Diabetes Obes Metab. 2026 Jun 25.
    The Impact of Hypoglycaemia on Quality of Life in Adults With Type 1 Diabetes Using Contemporary Diabetes Technologies: A US National Survey.
    Yu Kuei Lin, Uffe Søholm, Alexandria Ratzki-Leewing, Wen Ye, Emily Hepworth, Annika Agni, Stephanie A Amiel, Jane Speight.
       BACKGROUND: Use of contemporary diabetes technologies, including continuous glucose monitoring (CGM) and hybrid closed-loop insulin pumps (HCLs), has expanded rapidly among people with Type 1 diabetes. However, limited research has characterised the impact of hypoglycaemia on quality of life in this population.
    METHODS: We analysed cross-sectional survey data from US adults aged ≥ 18 years who were using CGM or HCL and who were recruited through the T1D Exchange. Survey responses included participant characteristics, diabetes technology use, and hypoglycaemia-specific quality of life measured by the 12-item Hypoglycaemia Impact Profile (HIP-12), which assesses hypoglycaemia-related impact across 12 major life domains. HIP-12 composite and domain-specific scores were summarised and compared between CGM-only (without HCL) and HCL users.
    RESULTS: The analytic sample included 796 participants (mean age 47 years; 53% female), of whom 23% used CGM only and 77% used HCL. Overall, 96% reported at least one life domain negatively affected by hypoglycaemia, and 31% reported negative impacts across 10 or more of the 12 assessed domains. The most frequently negatively affected domains were sleep, leisure activities, emotional well-being, spontaneity and physical activity/fitness. Compared with CGM-only users, HCL users reported greater hypoglycaemia-related negative impacts on leisure activities (p < 0.001), physical activity/fitness (p < 0.001), spontaneity (p = 0.011), sex life (p = 0.014) and work or studies (p = 0.021).
    CONCLUSIONS: Hypoglycaemia continues to adversely affect multiple related quality-of-life domains despite using contemporary diabetes technologies. Routine clinical assessment of and clinical support for hypoglycaemia's impact remain important. Further research is needed to clarify the mechanisms underlying these impacts and to guide targeted interventions.
    Keywords:  Type 1 diabetes; continuous glucose monitors; diabetes technology; hybrid closed‐loop insulin pumps; hypoglycaemia; quality of life
    DOI:  https://doi.org/10.1111/dom.71028
  13. J Funct Morphol Kinesiol. 2026 Jun 08. pii: 231. [Epub ahead of print]11(2):
    Glycemic Risk Across Exercise Modalities in Adults with Type 1 Diabetes Using Continuous Glucose Monitoring and Wearable Sensors: A Prospective Cohort Study.
    Dimna Zoila Alfaro Quezada, Paul César Velásquez Porras, Alicia Olinda Neyra Aranda, Henri Emmanuel López Gómez, Roberto Carlos Dávila-Morán, Vilma Luz Aparicio-Salas, Zoraida Loaiza-Ortiz, Lupe Marilu Huanca Rojas, Digmer Pablo Riquez Livia, Lindomira Castro Llaja, Liliana Inés Romero Núñez.
      Background: Exercise provides important health benefits for adults with type 1 diabetes; however, it remains associated with substantial glycemic instability that may vary according to exercise modality, intensity, duration, and clinical context. Continuous glucose monitoring (CGM) and wearable sensors offer an opportunity to characterize exercise-related glycemic responses under real-world conditions, yet prospective free-living data remain limited. Objective: This study aimed to evaluate glycemic risk across exercise modalities in adults with type 1 diabetes using CGM and wearable sensors in a real-world prospective cohort. Methods: This prospective cohort study was conducted under free-living conditions in 120 adults with type 1 diabetes. Participants were followed during habitual exercise using CGM, wearable sensor data, and session-level exercise classification. A total of 1568 valid exercise sessions were analyzed and categorized as aerobic, resistance, interval-based, or mixed exercise. The primary outcomes were immediate glucose change and time below range during exercise and within 6 h post-exercise. Secondary outcomes included severe biochemical hypoglycemia, time in range, time above range, glycemic variability, delayed hypoglycemia, nocturnal hypoglycemia, and rescue carbohydrate intake. Results: Glycemic risk differed across exercise modalities. Aerobic exercise was associated with the greatest immediate glucose decline, the highest time below range, the highest frequency of delayed post-exercise hypoglycemia, and the greatest need for rescue carbohydrate intake. Resistance exercise showed the most favorable acute glycemic profile, whereas interval-based and mixed exercise showed intermediate patterns. The associations between exercise modality and glycemic risk were modified by pre-exercise glucose level, time of day, and insulin delivery modality. Sensitivity analyses were consistent with the primary findings. Conclusions: In adults with type 1 diabetes monitored under real-world conditions, glycemic risk varies meaningfully across exercise modalities and is further shaped by clinically relevant contextual factors. These findings support a more individualized interpretation of exercise-related glycemic responses using CGM and wearable-derived data.
    Keywords:  continuous glucose monitoring; exercise modality; glycemic risk; hypoglycemia; physical activity; real-world cohort; type 1 diabetes; wearable sensors
    DOI:  https://doi.org/10.3390/jfmk11020231
  14. BMC Endocr Disord. 2026 Jun 25.
    Association of diet composition and meal intervals with short-term glycemic control and glycemic variability among type 2 diabetes mellitus patients.
    Mohammed Azhar Hussain, H Anil Kumar, Mahadevamma Lingaiah.
       BACKGROUND: Glycemic variability (GV) is an independent contributor to diabetes related complications beyond glycated hemoglobin. While dietary macronutrient composition influences glycemic responses, the importance of meal timing and nutrient distribution in real world settings remain unclear particularly in Indian diets.
    METHODS: In this observational study, 47 adults with type 2 diabetes mellitus who completed 10-14 days of continuous glucose monitoring (CGM) were included in the final analysis. The median CGM wear duration was 14(14-14) days and the median CGM analyzed days was 11(10-11) days. Associations between dietary variable and CGM derived metrics including Time in Range (TIR), Time above Range (TAR) and percentage coefficient of variation (%CV) were assessed using Spearman correlation and multivariable mixed-effects regression models adjusted for total energy intake, body mass index (BMI), HbA1c, and insulin/insulin-secretagogue use.
    RESULTS: Higher carbohydrate intake was associated with higher %CV (ρ = 0.354, p = 0.017), higher TAR (ρ = 0.343, p = 0.021), higher average glucose (ρ = 0.355, p = 0.017), and lower TIR (ρ = -0.378, p = 0.010) in subject-level analyses. Longer lunch-dinner and breakfast-dinner intervals were associated with higher TIR, whereas the breakfast-lunch interval showed no clear association with glycemic outcomes. In mixed-effects analyses accounting for repeated observations and participant-level covariates, habitual dietary carbohydrate composition remained associated with glycemic variability, whereas meal-timing associations were less consistent after adjustment.
    CONCLUSIONS: Habitual dietary carbohydrate composition remained associated with glycemic variability, whereas meal-timing associations were less consistent in adjusted analyses. Future studies are needed to clarify the role of dietary timing strategies in optimizing CGM-derived glycemic outcomes.
    CLINICAL TRIAL NUMBER: Not applicable.
    Keywords:  Diet composition; Glycemic variability; Time in range; Type 2 diabetes mellitus
    DOI:  https://doi.org/10.1186/s12902-026-02381-0
  15. Diabetes Res Clin Pract. 2026 Jun 25. pii: S0168-8227(26)00314-1. [Epub ahead of print] 113394
    Continuous glucose monitoring for prediabetes classification in a large real-world cohort: Comparison with HbA1c and fasting plasma glucose.
    Gabriella Rákóczi, Dávid Csányi, Roland Molontay, Gergely Agócs, Boglárka Lilla Szentes, Jimin Lee, Anett Rancz, Péter Hegyi, Emese Sipter.
       AIMS: Prediabetes classification based on hemoglobin A1c (HbA1c) and fasting plasma glucose (FPG) shows substantial discordance, potentially limiting early identification of individuals at metabolic risk. We evaluated whether continuous glucose monitoring (CGM) metrics can classify prediabetes with performance approaching that of HbA1c and FPG.
    METHODS: We conducted a cross-sectional analysis of 1,883 participants from the Human Phenotype Project with concurrent CGM, HbA1c, and FPG data. Individual CGM metrics were evaluated across four ADA-based laboratory reference definitions, and a combined model incorporating six clinically selected CGM metrics was additionally assessed. Discrimination was quantified using the area under the receiver operating characteristic curve (AUC).
    RESULTS: HbA1c- and FPG-based classifications showed marked discordance, with only 9.5% of participants meeting both prediabetes criteria. Several CGM metrics demonstrated classification performance approaching that of laboratory markers (AUC range 0.645-0.677). Discrimination improved when prediabetes was defined by both criteria, but no single marker achieved high performance independently.
    CONCLUSIONS: CGM metrics achieve classification performance approaching conventional laboratory markers while capturing complementary aspects of glycemic regulation. These findings support CGM as a complementary rather than standalone tool alongside conventional markers, particularly when laboratory results are discordant. Prospective studies incorporating oral glucose tolerance testing and longitudinal outcomes are warranted.
    Keywords:  Classification; Continuous glucose monitoring; Fasting plasma glucose; HbA1c; Prediabetes
    DOI:  https://doi.org/10.1016/j.diabres.2026.113394
  16. EBioMedicine. 2026 Jun 25. pii: S2352-3964(26)00226-4. [Epub ahead of print]129 106343
    Empowering digital health management with on-device large language models for glucose prediction: a model development and validation study.
    Taiyu Zhu, Joanna Howson, Alejo Nevado-Holgado.
       BACKGROUND: Long-term management of chronic diseases such as diabetes is increasingly based on wearable technologies, particularly continuous glucose monitoring (CGM), integrated with smartphone-based digital health systems. When combined with artificial intelligence, especially deep learning, these systems offer highly personalised decision support, including glucose prediction. Although large language models (LLMs) have demonstrated strong performance across various healthcare tasks, their integration into day-to-day digital health remains limited, primarily due to privacy concerns associated with transmitting sensitive data to remote servers. Recent advances in lightweight LLMs create new opportunities for secure and local deployment.
    METHODS: In this study, we first evaluated the zero-shot glucose prediction performance of eight pretrained lightweight LLMs across multiple model families. None achieved clinically viable outputs, highlighting the need for domain-specific adaptation. To address this, we propose GluLLM, a multimodal adaptor-based framework that enhances pretrained LLMs for on-device glucose forecasting. GluLLM integrates CGM data, daily activity logs, and electronic health records using customised encoder and decoder modules while preserving the foundational capabilities of pretrained LLMs. We trained and evaluated GluLLM on the REPLACE-BG dataset, which includes 226 individuals with type 1 diabetes, and validated it on an external cohort comprising 207 individuals with type 2 diabetes or without diabetes.
    FINDINGS: Compared with 15 state-of-the-art deep learning baselines for time-series prediction, GluLLM (LLaMA 3.2 1B backbone) demonstrated superior performance, with significantly lower 30-min root mean square error than the strongest baseline (Crossformer) on REPLACE-BG and Móstoles (20.6 ± 3.5 and 9.6 ± 2.9 mg/dL; p < 0.001), and improved hypoglycaemia prediction (glucose <70 mg/dL; AUROC: 0.79 and 0.84; AUPRC: 0.55 and 0.60), respectively. Furthermore, deployment of GluLLM on two smartphone platforms demonstrated feasible computational requirements, with acceptable CPU and memory usage and low inference latency.
    INTERPRETATION: GluLLM demonstrates that LLMs can support the next generation of smartphone-based digital health systems, delivering real-time, privacy-preserving clinical decision support.
    FUNDING: Novo Nordisk Postdoctoral Fellowship run in partnership with the University of Oxford.
    Keywords:  Continuous glucose monitoring; Deep learning; Digital health; Glucose prediction; Large language models; On-device inference
    DOI:  https://doi.org/10.1016/j.ebiom.2026.106343
  17. Diabetes Technol Ther. 2026 Jun 23. 15209156261461810
    Percent Time Below Range Is Strongly Associated with Clinically Important Level 2 Hypoglycemia Events in Individuals with Type 1 Diabetes.
    Peter Calhoun, Richard T Liggins, Zoey Li, Michael C Riddell, Richard M Bergenstal, Gregg D Simonson, Viral N Shah, Susan Peers, Jennifer L Sherr, Roy W Beck.
       OBJECTIVE: To evaluate the relationship between continuous glucose monitoring (CGM)-measured % time <54 mg/dL (%T < 54) and level 2 hypoglycemic events (L2 events; ≥15 min <54 mg/dL) in individuals with type 1 diabetes (T1D).
    METHODS: These analyses examined the associations between CGM-measured %T < 54 and L2 events from eight clinical trials over 3-6 months in participants with T1D.
    RESULTS: Data from 1532 participants with T1D were analyzed (mean age 37 ± 21 years; 72% adults): 43% using automated insulin delivery (AID), 43% CGM users not using AID (34% multiple daily injections [MDI]; 66% standard pump), and 14% self-monitoring blood glucose (SMBG) users not using CGM (58% MDI; 42% standard pump). There was a strong correlation between %T < 54 and L2 event rate (r = 0.97), but the relationship differed by the average duration of L2 events. For those with 1% T < 54, the predicted L2 event rate per week was 2.4 events for those with short L2 events (average <30 min), 1.9 events for those with medium duration of L2 events (average 30-60 min), and 1.2 events for those with long L2 events (average >60 min). Those meeting hypoglycemic targets (<1% T < 54) had on average 0.6 L2 events per week, irrespective of technology use. Those not meeting targets (≥1% T < 54) had on average 2.9 L2 events per week, but this differed based on technology use and observed %T < 54.
    CONCLUSIONS: L2 event frequency and %T < 54 are strongly correlated, but the relationship differs by L2 event duration. Therefore, both frequency and duration of L2 events should be reported together. Time-below-range metrics incorporate both aspects and are core CGM endpoints that summarize overall amount of hypoglycemia exposure.
    Keywords:  automated insulin delivery; continuous glucose monitoring; evaluation; hypoglycemia; type 1 diabetes
    DOI:  https://doi.org/10.1177/15209156261461810
  18. JMIR Hum Factors. 2026 Jun 24. 13 e87692
    Effect of a Personalized Mobile App on Glucose Control in Adults With Prediabetes and Type 2 Diabetes: Exploratory Pilot Randomized Controlled Trial.
    Elena Lalama, Marta Csanalosi, Stefan Kabisch, Saskia Wilson-Barnes, Lazaros P Gymnopoulos, Kosmas Dimitropoulos, Konstantinos Rouskas, Argiriou Anagnostis, Ioannis Oikonomidis, Leontios Hadjileontiadis, Veronique Cornelissen, Maria Hassapidou, Ioannis Pagkalos, Sofia Balula Dias, Kathryn Hart, Andreas F H Pfeiffer.
       BACKGROUND: The incidence of type 2 diabetes (T2D) continues to increase, and the lack of individualized therapy strategies hinders patient engagement with and commitment to a healthy lifestyle. The PROTEIN project aimed to facilitate users in choosing healthy living, thereby improving their metabolism and T2D management.
    OBJECTIVE: This study aims to assess the efficacy of a personalized mobile app to achieve a 5% time in range (TIR) improvement over a 12-week intervention in adults with prediabetes or T2D.
    METHODS: We conducted an exploratory pilot randomized controlled trial with 21 individuals with T2D or prediabetes who used a continuous glucose monitoring system and the PROTEIN mobile app for personalized meals and exercise recommendations based on their glucose levels and physical activity.
    RESULTS: The TIR of the participants increased (P<.05; from 71.8%, SD 27.3% to 76%, SD 28.1%) with individual use of the PROTEIN app but did not achieve a 5% improvement overall; however, given the exploratory design and small sample size, this finding should be interpreted with caution. Glycated hemoglobin, fasting blood glucose, and body weight did not fluctuate throughout the 12-week intervention. The dropout rate was high, and the average duration of use of the PROTEIN app was 42 (range 5-84) days.
    CONCLUSIONS: Our results showed a modest increase in TIR with the use of the PROTEIN app; however, considering the exploratory design and small sample size, this finding should be interpreted as preliminary. Integrating wearables and automated personalization for well-being is an innovative approach that must keep pace with the accelerated development of ever-evolving technologies. The COVID-19 pandemic was a major obstacle to recruitment in our clinical trial.
    TRIAL REGISTRATION: ClinicalTrials.gov NCT05951140; https://clinicaltrials.gov/study/NCT05951140.
    Keywords:  CGM; continuous glucose monitoring; glucose management; personalized nutrition; time in range; type 2 diabetes
    DOI:  https://doi.org/10.2196/87692
  19. Sensors (Basel). 2026 Jun 17. pii: 3842. [Epub ahead of print]26(12):
    Prediction of Nocturnal Hypoglycemia Following Exercise in Type 1 Diabetes Using Temporally Structured CGM-Derived Digital Biomarkers.
    Agnese Piersanti, Gaia Maria Manes, Libera Lucia Del Giudice, Laura Burattini, Christian Göbl, Andrea Tura, Micaela Morettini.
      Nocturnal hypoglycemia (NH) following exercise represents a critical challenge in the management of type 1 diabetes (T1D), particularly in pediatric populations, where its occurrence is associated with severe adverse outcomes and increased caregiver burden. This study aimed to identify an interpretable early signature based on CGM-derived digital biomarkers of post-exercise NH risk in children and adolescents with T1D. CGM data from 49 pediatric subjects (DirecNet cohort) were used to extract several CGM metrics across two temporal configurations: (i) Exercise + Cumulative, where features were computed over the exercise window and over an extended window spanning from exercise onset through recovery (16:00-17:00 and 16:00-22:00); and (ii) Exercise + Post-exercise, where features were computed separately over two non-overlapping intervals, capturing the exercise phase and the subsequent recovery phase (16:00-17:00 and 17:00-22:00). A Random Forest classifier was trained within a Leave-One-Out Cross Validation framework, incorporating variance inflation factor (VIF)-based multicollinearity filtering, minimum redundancy-maximum relevance (mRMR) feature selection, and SMOTE-based class balancing. The Exercise + Post-exercise configuration achieved superior performance: balanced accuracy (BA) = 76.9%, F1-score = 0.71, Area Under Receiver Operating Characteristic Curve (ROC-AUC) = 0.75, outperforming the Exercise + Cumulative configuration; this result was achieved using only five features: CONGA-15_EX (short-term glucose variability during exercise) emerged as the most robust predictor, alongside below_54 and above_250 (time spent in hypoglycemic and hyperglycemic ranges), MAG (mean absolute glucose change), and GRADE_hypo (hypoglycemia risk score). The generalizability of the temporal framework was further supported by independent validation on the OhioT1DM free-living cohort, where the Exercise + Post-exercise configuration (BA = 76.3%, ROC-AUC = 0.804) again outperformed the cumulative approach. These results suggest that a small set of interpretable CGM-derived features, extracted from the exercise and recovery windows, can effectively discriminate pediatric T1D subjects at risk of NH, supporting the development of lightweight CGM-only decision support tools for safer exercise management.
    Keywords:  SMOTE; continuous glucose monitoring; exercise; feature engineering; machine learning; nocturnal hypoglycemia; type 1 diabetes
    DOI:  https://doi.org/10.3390/s26123842
  20. Nutr Health. 2026 Jun 25. 2601060261463785
    Precision Nutrition Using Continuous Glucose Monitors: Hype, Hope, and the Road Ahead.
    Waqas Asghar, Komal Javed, Nauman Khalid.
      
    DOI:  https://doi.org/10.1177/02601060261463785
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