bims-glumda 2026-10-04 papers

bims-glumda

Biomed News

on CGM data in management of diabetes

Issue of 2026–10–04
twenty-two papers selected by
Mott Given

Toward standardized continuous glucose monitoring validation: lessons from the Sinocare iCan i3 accuracy study.
Updated Calibration Algorithm Improves Analytical Accuracy of a Continuous Glucose Monitoring System.
Accuracy and Safety of the LinX Continuous Glucose Monitoring System in Adults With Diabetes: A Prospective Multicentre Open-Label Clinical Performance Study.
Knowledge and Attitudes Regarding Continuous Glucose Monitors as Predictors of Current Use.
Knowledge, Attitudes, and Practices Toward Continuous Glucose Monitoring Among Diabetes Educators, Nurses, and Pharmacists in Saudi Arabia: A Cross-Sectional Survey.
Exploratory Analysis of Continuous Glucose Monitoring Patterns in First-Degree Relatives with Dysglycemia at Risk of Progression to Stage 3 Type 1 Diabetes.
Perceptions About CGMs Among Primary Care Physicians and Endocrinologists.
Comparison of Blood Glucose Values Used for Impending Non-Severe Hypoglycemia Management with Corresponding Continuous Glucose Monitoring Data in Type 1 Diabetes: A Secondary Analysis of the REVERSIBLE Trial.
GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting.
Real-Time Psychological Factors Using Ecological Momentary Assessment With Continuous Glucose Monitoring in Diabetes Mellitus: Scoping Review.
Utilization and Impact of Diabetes Technology Among Type 1 Diabetes Patients at a Tertiary Hospital in Thailand.
All Hypoglycemia Is Not Created Equal.
Exploring Diabetes Management Behaviors with FreeStyle Libre 2: Insights from Adults with Type 2 Diabetes in Quebec.
Evaluation of continuous glucose monitoring outcomes and health care resource utilization in patients with non-insulin-treated type 2 diabetes.
Factors Associated with HbA1c-Lowering in a Randomized Trial of Continuous Glucose Monitoring in Adults with Non-Insulin-Treated Diabetes: An Observational Analysis.
Remote Glucose Monitoring Engagement and Glycemic Control in Type 2 Diabetes and Prediabetes: Retrospective Cohort Study.
Associations Among Glycemic Variability, Hypomagnesemia, Thyroid Function, and Albuminuria in Patients With Type 2 Diabetes Mellitus: A Cross-Sectional Study.
Diabetes Technology in the Primary Care Setting: An American Diabetes Association Scientific Statement.
A randomized trial comparing supplemental video care and asynchronous remote monitoring among young people with type 1 diabetes and elevated glycemic levels.
Efficacy of the digital therapeutic glucura combined with CGM in the treatment of type 2 diabetes: a randomised controlled trial over 6 months.
Noninvasive Interstitial Glucose Estimation Using Wearables and Machine Learning in Healthy Individuals and Individuals With Obesity: Observational Cohort Study.
Patient-reported outcomes in adults with type 3c diabetes: a scoping review across aetiologies and pancreatic resection extent.

Front Clin Diabetes Healthc. 2026 ;7 1941126

Toward standardized continuous glucose monitoring validation: lessons from the Sinocare iCan i3 accuracy study.

Auda Ameur.

  Continuous glucose monitoring (CGM) has transformed diabetes management by providing real-time glucose measurements and supporting improved glycemic control. Consequently, the methodological quality of CGM accuracy studies has become increasingly important for clinicians, regulatory agencies, and healthcare decision-makers. The recent real-world evaluation of the Sinocare iCan i3 CGM system provides valuable evidence regarding device performance under routine clinical conditions. However, several methodological aspects warrant further consideration before the reported findings can be generalized to clinical practice. This perspective critically examines key aspects of CGM validation, including study design, selection of the reference glucose measurement, statistical methodology, temporal pairing strategies, sensor survival analysis, potential sponsorship-related bias, and interpretation of clinical accuracy metrics. We also discuss additional methodological considerations that were not fully addressed in the original publication, including uncertainty estimation, subgroup analyses, longitudinal sensor performance, missing-data mechanisms, and reproducibility assessment. Finally, we propose recommendations aimed at improving the methodological rigor of future real-world CGM validation studies and facilitating meaningful comparisons across CGM technologies.

Keywords:  MARD; accuracy; blood glucose monitoring; continuous glucose monitoring; diabetes; methodology; real-world evidence; sensor validation

DOI:  https://doi.org/10.3389/fcdhc.2026.1941126
J Diabetes Sci Technol. 2026 Sep 29. 19322968261489630

Updated Calibration Algorithm Improves Analytical Accuracy of a Continuous Glucose Monitoring System.

Julia K Mader, Delia Waldenmaier, Wiebke Mueller-Hoffmann, Katrin Mueller, Michael Angstmann, Gerhard Vogt, Cosima C Rieger, Manuel Eichenlaub, Thomas Forst, Guido Freckmann.

DOI: https://doi.org/10.1177/19322968261489630
Diabetes Obes Metab. 2026 Oct 01.

Accuracy and Safety of the LinX Continuous Glucose Monitoring System in Adults With Diabetes: A Prospective Multicentre Open-Label Clinical Performance Study.

Yang Liu, Yulong Chen, Yimei Chen, Xiaohong Jian, Xingguang Zhang, Yufeng Li, Weiwei Zhu, Jing Shen, Yiming Mu.

   BACKGROUND: Accurate continuous glucose monitoring (CGM) is essential for diabetes management. This study evaluated the accuracy and safety of the LinX CGM system over 15 days in adults with diabetes.
METHODS: In this prospective, multicentre, open-label clinical performance study, 87 adults who wore two LinX sensors were enrolled. Reference glucose was measured using a Biosen analyser. Primary endpoints were the proportions of readings within ±15%/±40% of reference values and mean absolute relative difference (MARD).
RESULTS: Overall MARD was 8.66% (95% CI: 8.27%-9.05%). For glucose concentrations in the range of 3.9-10.0 mmol/L (70-180 mg/dL), 87.2% of readings were within ±15%; for concentrations ≥ 10.0 mmol/L (≥ 180 mg/dL), 90.2% were within ±15%. Within ±40%, agreement rates were 99.8% and 100.0%, respectively. Sensor survival at 15 days was 96.0%. No serious adverse events occurred.
CONCLUSION: The LinX CGM system demonstrated good analytical accuracy and favourable short-term safety over a 15-day wear period in adults with diabetes.
TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR2200064004.

Keywords:  accuracy; clinical trial; continuous glucose monitoring; diabetes mellitus; safety

DOI:  https://doi.org/10.1111/dom.71392
Sci Diabetes Self Manag Care. 2026 Sep 26. 26350106261483745

Knowledge and Attitudes Regarding Continuous Glucose Monitors as Predictors of Current Use.

Skyler P Gallegos, Jennifer H Zohn, Andrea M Hutchins, Leah A Derksen, Melissa J Benton.

   PURPOSE: The purpose of this study was to evaluate the individual influences of knowledge and attitudes about continuous glucose monitors (CGMs) among adults with type 1 or type 2 diabetes.
METHODS: Adults diagnosed with type 1 or type 2 diabetes completed an online survey regarding knowledge and attitudes about CGM use. Knowledge was measured using the benefits subscale of the CGM Satisfaction Scale, and attitudes were measured using the hassles subscale. Binomial regression was used to evaluate the individual influence of knowledge and attitudes and their predictive ability for current CGM use.
RESULTS: A total of 58 adults participated. Of these, 38 (66%) reported current CGM use, and among current users, 90% reported a diagnosis of type 1 diabetes, and 10% reported a diagnosis of type 2 diabetes. Attitudes regarding CGM were statistically better than knowledge. Current use of a CGM correlated with knowledge and attitudes, and therefore, knowledge and attitudes were entered into binomial logistic regression. The model was statistically significant, explaining 36% of the variance in current CGM use. Participants with better attitudes were more likely to use a CGM, while those with greater knowledge were less likely to do so.
CONCLUSIONS: Consistent with the hypothesis, knowledge and attitudes influenced use of CGM differently. Based on these findings, patient education to promote use of CGM should focus on improving attitudes regarding the hassles of CGM as these exert the greatest influence on current use.

Keywords:  CGM Satisfaction Scale; attitudes; continuous glucose monitor; diabetes mellitus; knowledge

DOI:  https://doi.org/10.1177/26350106261483745
J Multidiscip Healthc. 2026 ;19 630123

Knowledge, Attitudes, and Practices Toward Continuous Glucose Monitoring Among Diabetes Educators, Nurses, and Pharmacists in Saudi Arabia: A Cross-Sectional Survey.

Osamah M Alfayez, Omar S Alkhezi, Yara H Sawan, Rahaf M Almutairi, Metab Algeffari.

   Purpose: Continuous glucose monitoring (CGM) is increasingly used in diabetes care. However, data on CGM-related knowledge, attitudes, and practices among non-physician healthcare professionals in Saudi Arabia are limited. This study evaluated these domains among diabetes educators, nurses, and pharmacists.
Patients and Methods: We conducted a cross-sectional web-based survey of licensed diabetes educators, nurses, and pharmacists involved in diabetes care in Saudi Arabia. The survey assessed knowledge of guideline-aligned CGM metrics, attitudes toward CGM use, practice patterns, perceived barriers, and training needs. Bivariate analyses and Firth penalized logistic regression were used to identify predictors of routine ambulatory glucose profile (AGP) review.
Results: Fifty-three healthcare professionals participated, including 24 nurses, 20 diabetes educators, and 9 pharmacists. Knowledge of CGM metrics was modest overall. Correct responses ranged from 24.5% for glucose variability metrics to 53.1% for limitations of glycated hemoglobin interpretation. Only 26.5% achieved adequate knowledge scores. Overall, 61.4% agreed that CGM improves safety and quality of life in older adults using insulin, and 63.6% reported confidence using time in range and trend arrows during patient counseling. Routine AGP review was reported by 76.2% of respondents and was most common among diabetes educators. In multivariable analysis, diabetes educator role remained independently associated with routine AGP review (adjusted odds ratio 23.57; 95% confidence interval 5.32-82.49). Cost and coverage were the most commonly reported barriers to CGM use. Case-based workshops and workplace teaching were the preferred training formats.
Conclusion: Healthcare professionals demonstrated positive attitudes toward CGM, but important knowledge gaps remain. Diabetes educators reported greater confidence and more frequent use of CGM reports. Targeted multidisciplinary training and improved access to CGM may support wider implementation in routine diabetes care. Given the small sample size, these findings should be interpreted cautiously and may not be generalizable to the broader healthcare workforce in Saudi Arabia.

Keywords:  ambulatory glucose profile; continuous glucose monitoring; cross-sectional study; healthcare professionals; time in range

DOI:  https://doi.org/10.2147/JMDH.S630123
Diabetes Technol Ther. 2026 Sep 29. 15209156261492040

Exploratory Analysis of Continuous Glucose Monitoring Patterns in First-Degree Relatives with Dysglycemia at Risk of Progression to Stage 3 Type 1 Diabetes.

INNODIA consortium C Mathieu, P Gillard, K Casteels, L Overbergh, D Dunger, C Wallace, M Evans, A Thankamony, E Hendriks, S Bruggraber, ML Marcovecchio, M Peakman, N Morgan, S Richardson, J Todd, L Wicker, A Mander, C Dayan, M Alhadj Ali, T Pieber, D Eizirik, M Cnop, S Brunak, F Pociot, J Johannesen, P Rossing, Quigley C Legido, R Mallone, R Scharfmann, C Boitard, M Knip, T Otonkoski, R Veijola, R Lahesmaa, M Oresic, J Toppari, T Danne, AG Ziegler, P Achenbach, T Rodriguez-Calvo, M Solimena, E Bonifacio, S Speier, R Holl, F Dotta, F Chiarelli, P Marchetti, E Bosi, S Cianfarani, P Ciampalini, C de Beaufort, K Dahl-Jørgensen, T Skrivarhaug, G Joner, L Krogvold, P Jarosz-Chobot, T Battelino, D Smigoc Schweiger, B Thorens, M Gotthardt, B Roep, T Nikolic, A Zaldumbide, A Lernmark, M Lundgren, G Costeca, T Strube, A Schulte, A Nitsche, M Peakman, J Vela, M von Herrath, J Wesley, A Napolitano-Rosen, M Thomas, N Schloot, A Goldfine, F Waldron-Lynch, J Kompa, A Vedala, N Hartmann, G Nicolas, J van Rampelbergh, N Bovy, S Dutta, J Soderberg, S Ahmed, F Martin, E Latres, G Agiostratidou, A Koralova

   INTRODUCTION: Continuous glucose monitoring (CGM) can characterize subtle glucose changes in early-stage type 1 diabetes (T1D). We assessed whether CGM metrics predict Stage 3 T1D in first-degree relatives (FDRs) with positive islet autoantibodies (IAbs) and dysglycemia participating in INNODIA.
METHODS: We analyzed IAb-positive FDRs with dysglycemia and available CGM data. CGM metrics were analyzed longitudinally and in a restricted analysis limited to the last assessment before Stage 3 T1D in progressors. CGM metrics were compared using linear mixed models. Predictive performance of single and combined CGM metric models was evaluated using receiver operating characteristic curve analyses.
RESULTS: Data from 27 participants (14 females [52%], median [interquartile range] age: 16.8 [11.9-39.8] years) were analyzed. Nine participants progressed to Stage 3 T1D after a median of 163 [129-244] days from CGM assessment. In the restricted analysis, progressors had higher mean glucose (132 vs. 111 mg/dL, P = 0.001), time >140 mg/dL (36% vs. 10%, P = 0.003), and time >180 mg/dL between 9 PM and 2 AM (8.1% vs. 1.0%, P = 0.005), whereas time <70 mg/dL between 4 and 7 AM was lower (0% vs. 1.2%, P = 0.001). Time >140 mg/dL yielded an area under the curve (AUC) of 0.83 (95% confidence interval [CI] 0.66-1.00). Combining time >140 mg/dL with time-specific percentages >180 and <70 mg/dL increased AUC to 0.93 (95% CI 0.84-1.00). A model incorporating time between 70 and 140 mg/dL, mean glucose, early morning <70 mg/dL, and MAGE achieved 100% sensitivity and 86% specificity.
CONCLUSIONS: CGM may help identify FDRs with IAbs and dysglycemia at higher risk of progressing to Stage 3 T1D. Combining CGM metrics improved prediction compared with time >140 mg/dL alone.

Keywords:  continuous glucose monitoring; dysglycemia; early stage; first-degree relatives; islet autoantibodies; prediction; type 1 diabetes

DOI:  https://doi.org/10.1177/15209156261492040
Am J Manag Care. 2026 Sep 01. 32(9): e316-e324

Perceptions About CGMs Among Primary Care Physicians and Endocrinologists.

Consuela Dennis, Eugene Wright, Stephanie Kohn, James R Gavin Iii, Christopher G Parkin, Steven V Edelman.

OBJECTIVES: To identify clinicians' current practice behaviors, attitudes, and obstacles shaping continuous glucose monitoring (CGM) use.
STUDY DESIGN: This structured survey asked clinicians about their practice environments, their views on the value of CGM and the importance of comprehensive training and education, their preferred prescribing channels for CGM, and details specific to each practice type.
METHODS: Survey participants were selected by PureSpectrum, which used the PureScore system to evaluate participant suitability, collect data, and analyze results. The data show the percentage of respondents in each clinician group who answered each question.
RESULTS: Responses from 101 primary care physicians (PCPs) and 106 endocrinologists were obtained for analysis. Levels of familiarity with the newest CGM technologies were consistent among PCPs and endocrinologists. A large percentage of both PCPs and endocrinologists indicated that concerns about patients' ability to afford CGM were a main reason for prescribing traditional blood glucose monitoring. The majority of PCPs (69%) and endocrinologists (73%) who prescribe CGM prefer the durable medical equipment (DME) channel. Most PCPs (92%) and endocrinologists (91%) indicated they would likely prescribe CGM through the DME channel more often if those suppliers offered disease education and coaching. A large percentage of endocrinologists (76%) and a smaller percentage of PCPs (45%) reported feeling pressured by health insurers to prescribe through the pharmacy channel.
CONCLUSION: Clinician prescribing behaviors continue to be shaped by systemic barriers, including insurance pressures and concerns about patient ability/readiness to use CGM technology.

DOI: https://doi.org/10.37765/ajmc.2026.90010
Diabetes Technol Ther. 2026 Sep 30. 15209156261493676

Comparison of Blood Glucose Values Used for Impending Non-Severe Hypoglycemia Management with Corresponding Continuous Glucose Monitoring Data in Type 1 Diabetes: A Secondary Analysis of the REVERSIBLE Trial.

Andréanne Marquis, Ran Cheng, Sara Al-Mahayni, Ahmed Osman, Josephine Molveau, Virginie Messier, Rémi Rabasa-Lhoret, Nadine Taleb.

   BACKGROUND: To assess continuous glucose monitoring (CGM) data corresponding to blood glucose (BG) values used for impending non-severe (NS) hypoglycemia management in people living with type 1 diabetes (PwTID).
METHODS: We performed a secondary analysis of the REVERSIBLE trial, a three-arm open-label crossover study assessing the efficacy of 16 g oral carbohydrates (CHO) for preventing NS hypoglycemia at different BG levels: <72 mg/dL (4 mmol/L, level 1), ≤80 mg/dL (4.5 mmol/L), or ≤90mg/dL (5.0 mmol/L). CGM data (DexcomG6®) were compared with BG to assess their variances and mean absolute relative differences (MARDs).
RESULTS: In all three arms CGM values overestimated BG levels (P < 0.001). MARDs were higher with lower BG values. Despite these differences, we did not observe rebound hyperglycemia (BG > 180 mg/dL; 10 mmol/L).
CONCLUSION: When using CGM, PwTID may need to treat or prevent NS hypoglycemia at a higher threshold than 72 mg/dL (4.0 mmol/L) and 80 mg/dL (4.5 mmol/L), respectively.

Keywords:  continuous glucose monitoring; hypoglycemia; sensors; type 1 diabetes

DOI:  https://doi.org/10.1177/15209156261493676
IEEE J Biomed Health Inform. 2026 Sep 28. PP

GlyRAG: Context-Aware Retrieval-Augmented Framework for Blood Glucose Forecasting.

Shovito Barua Soumma, Hassan Ghasemzadeh.

Accurate blood glucose forecasting using continuous glucose monitoring (CGM) data can support early prediction of dysglycemic risk. However, current neural-network-based forecasting models treat CGM data as a purely numerical sequence without integrating con textual information that the CGM signal morphology contains. Recently, large language models (LLMs) have shown promise for time-series forecasting tasks, yet their role as agentic context extractors in diabetes care remains largely unexplored. In this study, we bridge glucose forecasting and LLM-based contextualization, and develop GlyRAG, a context-aware retrieval-augmented forecasting framework that uses an LLM as a contextualization agent to summarize glucose morphology directly from a timed CGM window. The generated CGM-only narrative is embedded and fused with patch-based glucose representations, and a retrieval module incorporates similar historical training episodes through cross-attention. We evaluate GlyRAG on OhioT1DM and AZT1D datasets for 5-, 30-, and 60-minute forecasting horizons. Against strong CGM-only baselines, GPT-4 GlyRAG significantly improves long-horizon RMSE (Root Mean Square Error) over PatchTST on both datasets. For example, RMSE decreases from 13.8 to 10.6 at 30 minutes and from 23.1 to 20.2 at 60 minutes on OhioT1DM. LLaMA 3.1 shows smaller but significant long-horizon gains, suggesting that the contextualization pipeline is not limited to GPT-4. Clinical error-grid analyses further show that approximately 85% of predictions fall in clinically acceptable Clarke Error Grid Zones A-B. These results suggest that CGM-derived linguistic context and case-based retrieval can improve long-horizon glucose forecasting without requiring additional sensing modalities.

DOI: https://doi.org/10.1109/JBHI.2026.3737468
JMIR Diabetes. 2026 Sep 30. 11 e87688

Real-Time Psychological Factors Using Ecological Momentary Assessment With Continuous Glucose Monitoring in Diabetes Mellitus: Scoping Review.

Kara Mizokami-Stout, Kira Voelker, Melissa DeJonckheere, Lynn Ang, Evan L Reynolds, Joyce M Lee, Rodica Pop-Busui, Brian C Callaghan, Emily Hirschfeld, Jennifer J Iyengar, Dana Albright.

   Background: Ecological momentary assessment (EMA) is a tool that captures emotional states, experiences, and behaviors in real or near-real time. Concurrent EMA and continuous glucose monitoring (CGM) data can provide an in-depth understanding of the impacts of psychosocial factors on momentary glucose levels. However, study methodology varies widely for EMA and CGM data collection and analysis.
Objective: This scoping review aims to summarize the objectives, methodologies, and outcomes of studies analyzing concurrent biopsychosocial EMA and CGM data in diabetes.
Methods: This study was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Original research studies evaluating participants with diabetes, with concurrent collection and analysis of EMA and CGM data, were included in this review. A total of 782 studies were identified from PubMed, Embase, and EBSCOhost from May 2009 to December 2025. From these, duplicates, non-original research articles, and articles not measuring concurrent EMA and CGM data were excluded, resulting in a total of 19 studies included in this review. Methodological data were abstracted and summarized, including study characteristics, EMA protocols and outcomes, CGM outcomes, and integrated EMA and CGM study objectives. Methodological gaps across the included studies were identified and summarized.
Results: Of the 19 studies, 16 primarily recruited adult populations, and the majority of these studies (n=14, 74%) included participants with type 1 diabetes. The median EMA delivery duration was 14 days (range 3-18 days), with a median of 5.5 prompts delivered per day (range 1-28). EMA outcomes included a variety of biopsychosocial factors (emotion, self-care behaviors, interpersonal interactions, symptoms, and cognition). Seventy-four percent analyzed blinded CGM data over a median of 14 (range 3-18) days. Forty-two percent used nonstandardized CGM glucose outcomes (postprandial glucose, eating patterns, and hypoglycemic events). Integrated EMA and CGM data analysis answered a broad array of study objectives, including the impact of psychosocial factors on momentary glucose metrics; the influence of momentary glucose on emotional states, mood, personal behaviors, sleep, and cognition, or vice versa; and study protocol or mobile app optimization, among others. Methodological gaps included lack of standardization of EMA and CGM outcomes, duration of data capture, reporting of statistical analyses, and study population.
Conclusions: This review provides a detailed summary of the methodology and outcomes of original research articles using combined EMA and blood glucose data measured via CGM. These combined methods allow for an opportunity to elucidate relationships between psychosocial factors and momentary glucose, an understanding of which is imperative for the development of future psychological and behavioral interventions in diabetes. However, standardization of protocols and expansion of participant populations for future EMA and CGM data collection and analysis are needed to ensure data accuracy, reproducibility, and generalizability.

Keywords:  behavioral factors; continuous glucose monitoring; diabetes mellitus; ecological momentary assessment; glucose monitoring; psychosocial factors

DOI:  https://doi.org/10.2196/87688
J ASEAN Fed Endocr Soc. 2026 Aug;41(2): 162-166

Utilization and Impact of Diabetes Technology Among Type 1 Diabetes Patients at a Tertiary Hospital in Thailand.

Jirawan Pongrujikorn, Jutipond Jitchana, Nitchakarn Laichuthai.

   Background: While diabetes technologies such as insulin pumps, continuous glucose monitoring (CGM), and automated insulin delivery (AID) systems have demonstrated potential to improve glycemic control and reduce hypoglycemia, their utilization and effectiveness in Thailand remain underdocumented. This study aimed to assess the use and impact of diabetes technologies on glycemic outcomes at King Chulalongkorn Memorial Hospital (KCMH), a tertiary care center in Bangkok, Thailand.
Methodology: A cross-sectional study was conducted among T1D patients receiving care at the endocrinology clinic of KCMH between August 2023 and July 2024. Data were collected at each patient's most recent follow-up visit, and participants were classified into technology users and non-technology users. Data collection focused on demographics, diabetes technology use, and clinical parameters, including HbA1c, hypoglycemia events, and CGM metrics (Time in Range (TIR), Time Above Range (TAR), Time Below Range (TBR)).
Results: A total of 61 patients with T1D were included, of whom 49% were technology users (6% insulin pumps, 57% CGM, 37% AID). Technology users had a significantly higher mean HbA1c (8.1% ± 1.6) than non-technology users (7.2% ± 1.2, p = 0.011). AID users achieved a higher mean TIR (67.6% ± 16%) compared to CGM users (54.0% ± 23.8%), but the difference was not statistically significant. Both AID and CGM users maintained a low incidence of hypoglycemia (<54 mg/dL).
Conclusion: This study found a relatively high rate of diabetes technology adoption at our center compared to other Asian countries, highlighting its potential to reduce the risk of hypoglycemia and improve glycemic stability. However, technology users also had higher HbA1c levels, suggesting that diabetes technology is often prescribed to high-risk patients with poor glycemic control. Future research should focus on longitudinal studies to better understand and address barriers to optimal glycemic outcomes among diabetes technology users.

Keywords:  diabetes technology; glycemic control; type 1 diabetes

DOI:  https://doi.org/10.15605/jafes.041.02.6355
J Am Geriatr Soc. 2026 Sep 26.

All Hypoglycemia Is Not Created Equal.

Alexandra K Lee, Sei J Lee.

  A Proposed Hypoglycemia Taxonomy: Differences in Frequency, Detection, and Evidence of Harms.

Keywords:  continuous glucose monitoring; diabetes in elderly; glucose monitoring technologies; hypoglycemia; risk factors

DOI:  https://doi.org/10.1111/jgs.70709
Can J Diabetes. 2026 Sep 30. pii: S1499-2671(26)00371-0. [Epub ahead of print]

Exploring Diabetes Management Behaviors with FreeStyle Libre 2: Insights from Adults with Type 2 Diabetes in Quebec.

Jean-François Yale, Sylvie Bouchard, Abraham Lee, Yeesha Poon.

   OBJECTIVES: To understand the long-term behavior changes associated with use of the FreeStyle Libre 2 sensor-based glucose monitor by people living with type 2 diabetes (T2D).
METHODS: This was a qualitative study of adults with T2D living in Quebec and using FreeStyle Libre 2. Discussions were moderated by an experienced facilitator using a semi-directed discussion guide. Discussions were transcribed and analyzed thematically.
RESULTS: 16 people living with T2D who had been using FreeStyle Libre 2 for > 6 months participated in two focus groups (non-insulin treatments, six women and three men; basal insulin, two women and five men). There were no material differences in responses or anecdotes between the two groups. Use of FreeStyle Libre 2 was associated with four main themes: 1) Personalized knowledge, including participants' sense of autonomy and control and their ability to see the effect of their actions on glucose levels and to make informed choices, such as modifying their diet; 2) Improved communication with healthcare providers (HCPs), who can access patients' glucose data in real-time if shared via the smartphone app; 3) Reductions in hypoglycemia, particularly due to warnings of low glucose levels; and 4) Long-term benefits, including reductions in stress and improved HRQoL.
CONCLUSION: Long-term users found that FreeStyle Libre 2 improved their diabetes self-management, communication with healthcare providers, HRQoL, and stress levels, and facilitated lifestyle changes. Individuals with T2D described the device empowering ongoing healthy behavior change. This research serves as a person-centered qualitative finding to consider in access decisions.

Keywords:  Continuous glucose monitoring; Diabetes self-management; Hypoglycemia; Lifestyle modification; Qualitative study; Type 2 diabetes

DOI:  https://doi.org/10.1016/j.jcjd.2026.09.008
J Manag Care Spec Pharm. 2026 Oct;32(10): 1215-1226

Evaluation of continuous glucose monitoring outcomes and health care resource utilization in patients with non-insulin-treated type 2 diabetes.

Shannon Gadd, Connor Willis, Ishfaq Rashid, Kyu Yun Park, Kayla Holland, Amy Beal, Carl Asche, Priyanka Ghule, Diana Brixner.

BACKGROUND: Although continuous glucose monitoring (CGM) is well established for insulin-treated diabetes, its utility in non-insulin-treated type 2 diabetes (T2D) remains less studied.
OBJECTIVE: To evaluate the short-term clinical and economic outcomes following personal use of CGM in patients with T2D.
METHODS: This prospective pre-post study enrolled adults with poorly controlled non-insulin-treated T2D (hemoglobin A1c ≥8%; target N = 200) initiating G7 CGM and observed them for 6 months. Primary endpoints were changes in A1c and Audit of Diabetes-Dependent Quality of Life (ADDQoL) scores. Glycemic events, health care utilization, and medication use were evaluated in a subgroup with claims data. Baseline was defined as the 3 months before CGM initiation. Changes at 3 and 6 months were assessed using the Wilcoxon signed-rank test. Multivariable analysis of covariance models evaluated A1c change, adjusting for baseline A1c and clinical covariates. ADDQoL was analyzed using linear mixed-effects models with random intercepts and fixed effects for time, whereas Cuzick's test assessed quality-of-life trends.
RESULTS: Of 217 enrolled patients, a subgroup of 76 had claims data. Most were White (75%) and male (58%), with mean age 58.3±11.5 years. Baseline median A1c was 8.60% (IQR = 8.20-9.10), and mean A1c was 8.96%±1.24%. Prevalent comorbidities included hyperlipidemia (73%), hypertension (70%), and obesity (44%). A1c significantly decreased at 3 and 6 months after CGM (median: -1.10%, -1.40%; mean: -1.28%, -1.42%; P < 0.001). In adjusted models, the adjusted mean change in A1c from baseline was -1.32 percentage points at 3 months (95% CI = -1.48 to -1.15; P < 0.001; n = 153) and -1.48 percentage points at 6 months (95% CI = -1.68 to -1.28; P < 0.001; n = 143). American Diabetes Association (ADA) (<7%) and Healthcare Effectiveness Data and Information Set (HEDIS) (<8%) target attainment improved from 0% at baseline to 26.4% and 50.9% at 6 months (both P < 0.001). Hyperglycemic events declined from 53.7% at baseline to 16.3% at 6 months, although not significantly. No significant changes were observed in health care utilization, medication adherence, or medication burden. Greater CGM use and higher time in range were associated with improved A1c and glucose management indicator outcomes. The proportion reporting good-to-excellent quality of life increased from 24.1% at baseline to 45.8% at 6 months, while weighted ADDQoL scores remained stable.
CONCLUSIONS: CGM use in non-insulin-treated patients with T2D was associated with significant improvements in glycemic control and perceived quality of life, with stable health care utilization and medication therapy. These findings support expanded implementation and reimbursement of CGM in this population, but further research is required to assess the long-term sustainability of these improvements and the specific roles of diet, medication, and adherence.

DOI: https://doi.org/10.18553/jmcp.2026.32.10.1215
Can J Diabetes. 2026 Sep 30. pii: S1499-2671(26)00370-9. [Epub ahead of print]

Factors Associated with HbA1c-Lowering in a Randomized Trial of Continuous Glucose Monitoring in Adults with Non-Insulin-Treated Diabetes: An Observational Analysis.

Jiawei Ryan Zhang, Pratima Singh, Roseanne O Yeung, Donna P Manca, Tawnya Perry, Inga Olu-Jordan, Gulelala Rahim, Evan M Hagen, Darren Lau.

   BACKGROUND: A prior randomized trial showed that CGM lowers HbA1c in adults with type 2 diabetes and elevated glycated hemoglobin (HbA1c > 7.0%) not on insulin.
OBJECTIVES: We examined the effect modifiers of CGM effectiveness, and factors associated with HbA1c-lowering.
METHODS: Secondary analysis of data from a randomized trial. The primary outcome was change in HbA1c from baseline to 12 weeks. Explanatory variables included sociodemographics, diabetes and treatment parameters, and neighbourhood environment characteristics. Variables were assessed for interaction with CGM using linear regression, which was also used to estimate associations between each variable and HbA1c change, and to develop a final multi-variable adjusted-model.
RESULTS: We included 86 individuals. No formal interactions were identified with CGM effectiveness, though noticeable numeric differences in the effect of CGM were seen with sex, recent diagnosis, and neighbourhood walkability. Aside from CGM treatment allocation (β=-0.65, p=0.003 95% CI [-1.07 to -0.22]), multi-variable predictors of HbA1c-lowering were higher baseline HbA1c (β =-0.71, p<0.001 [-0.-90 to -0.53]), diabetes diagnosis < 1 year (β=-1.06, p=0.004 [-1.77 to -0.35]), male sex (β=-0.51, p=0.017 [-0.93 to -0.09]), and not using sodium-glucose co-transporter-2 inhibitor medication at baseline (β=0.62, p=0.006 [0.18 to 1.05]). Neighbourhood walkability was associated with HbA1c-lowering on crude (β=-0.26, p=0.041 [-0.51 to -0.01]), but not adjusted, analysis.
CONCLUSIONS: Further studies are needed to determine the factors influencing the effectiveness of CGM. Individuals with type 2 diabetes, not on insulin, who voluntarily seek CGM, may experience a reduction in HbA1c proportional to their baseline HbA1c, regardless of treatment allocation.

Keywords:  Built Environments; Continuous Glucose Monitoring (CGM); Predictors of Glycemic Control; Type 2 Diabetes

DOI:  https://doi.org/10.1016/j.jcjd.2026.09.007
JMIR Form Res. 2026 Sep 30. 10 e95679

Remote Glucose Monitoring Engagement and Glycemic Control in Type 2 Diabetes and Prediabetes: Retrospective Cohort Study.

Yong Huang, Nitish Nagesh, Ajan Subramanian, Li Wang, Zhiyu Liu, Yichen Sun, Weiyi Hou, Iman Azimi, Amir M Rahmani, Randall S Stafford.

   Background: Suboptimal glycemic control remains a significant public health challenge among adults with type 2 diabetes and prediabetes, with 47.4% of US adults with diagnosed diabetes having hemoglobin A1c (HbA1c) ≥7%. Remote glucose monitoring programs have shown promise for supporting self-management, but real-world evidence on the causal impact of varying patient engagement levels on glycemic outcomes remains limited.
Objective: This study aimed to estimate the causal dose-response relationship between patient engagement, operationalized as weekly glucose monitoring frequency, and glycemic control measured by HbA1c among adults enrolled in a comprehensive primary care-integrated remote monitoring program.
Methods: We conducted a retrospective cohort study of 1436 adults with type 2 diabetes or prediabetes enrolled in the Unika Health program between 2019 and 2024. The program integrated Bluetooth-connected glucose meters, a mobile app, structured lifestyle coaching, and primary care coordination across 74 physician practices. Engagement was defined as mean weekly glucose monitoring frequency during the first 6 months. The causal effect of monitoring frequency on 6-month HbA1c was estimated using marginal structural models (MSMs) with inverse probability weighting to address time-varying confounding. Covariates included age, sex, BMI, baseline HbA1c, comorbidities, medication status, and physical activity level.
Results: The cohort was predominantly older (95.4%, 1370/1436 aged ≥46 years), with 82.6% (1186/1436) having hypertension and 45.9% (659/1436) classified as obese. Overall, HbA1c decreased by a mean of 0.54 (SD 1.47; 95% CI 0.47-0.62) percentage points (P<.001) over 6 months. Cluster analysis identified 3 engagement tiers: low (n=835; mean 2.55, SD 1.35 measurements/week), medium (n=493; mean 6.19, SD 1.46 measurements/week), and high (n=108; mean 12.59, SD 3.03 measurements/week). A monotonic dose-response was observed, with mean HbA1c reductions of 0.38 (SD 1.40; 95% CI 0.29-0.48), 0.71 (SD 1.50; 95% CI 0.58-0.85), and 1.01 (SD 1.67; 95% CI 0.69-1.32) percentage points for the low, medium, and high tiers, respectively (all P<.001 by 2-tailed paired t test). In weighted MSMs, each additional weekly measurement was associated with a 0.05 (95% CI 0.03-0.07) percentage point greater HbA1c reduction (P<.001). Among patients with high baseline HbA1c (≥9%), the high-engagement group achieved a mean reduction of 3.12 (SD 1.97; 95% CI 2.29-3.94) percentage points. Findings were consistent in sensitivity analyses at 3 months (β=-0.04; P<.001) and 12 months (β=-0.03; P=.003) and across alternative weighting specifications.
Conclusions: Higher engagement with a digitally enabled, primary care-integrated remote glucose monitoring program was causally associated with significantly greater HbA1c reductions in adults with type 2 diabetes and prediabetes. These findings support scalable remote patient monitoring strategies that actively foster sustained patient engagement as an effective approach to improving glycemic control and reducing the burden of diabetes-related complications at a population level.

Keywords:  causal inference; digital health; glucose monitoring; glycemic control; marginal structural models; patient-centered care; prediabetic state; remote patient monitoring; type 2 diabetes mellitus

DOI:  https://doi.org/10.2196/95679
Cureus. 2026 Sep;18(9): e117117

Associations Among Glycemic Variability, Hypomagnesemia, Thyroid Function, and Albuminuria in Patients With Type 2 Diabetes Mellitus: A Cross-Sectional Study.

Shoaib Asghar, Kashif Shaikh, Ahmed Nisar, Sameera Aftab, Rabia Qadeer, Abdul Wahab.

  Background Albuminuria is an important marker of diabetic kidney disease (DKD) and is associated with adverse renal and cardiovascular outcomes in people with type 2 diabetes mellitus (T2DM). Although sustained hyperglycemia is a recognized contributor to DKD, glycated hemoglobin (HbA1c) does not fully capture short-term glycemic variability. Emerging evidence suggests that hypomagnesemia and altered thyroid function may be associated with albuminuria, but their combined relationships with albuminuria and the possible statistical mediating role of thyroid hormones remain unclear. This study was conducted to identify the associations of albuminuria with glycemic variability, serum magnesium, and thyroid function in T2DM patients. Materials and methods A cross-sectional study included 330 adults aged 30-84 years with T2DM of more than five years' duration. Continuous glucose monitoring (CGM)-derived measures, including mean glucose, glucose standard deviation (SD), coefficient of variation (CV), time in range (TIR), and mean amplitude of glycemic excursions (MAGE), were assessed alongside HbA1c, serum magnesium, free triiodothyronine (FT3), free thyroxine (FT4), thyroid-stimulating hormone (TSH), and urinary albumin-to-creatinine ratio (UACR). Associations with albuminuria were examined using univariable and multivariable logistic regression, with additional correlation and exploratory regression-based mediation analysis. Ethical approval and written informed consent were obtained. Results Among 330 participants, 208 (63%) had normal UACR, 98 (29.7%) had microalbuminuria, and 24 (7.3%) had macroalbuminuria. Hypomagnesemia was associated with more than twofold higher odds of albuminuria in univariable analysis (odds ratio (OR)=2.26; 95% confidence interval (CI): 1.38-3.70; p=0.001). In Model IV, which was adjusted for demographic variables, higher HbA1c (OR=1.66; 95% CI: 1.34-2.05; p<0.001), SD (OR=1.63; 95% CI: 1.18-2.26; p=0.003), CV (OR=1.06; 95% CI: 1.01-1.10; p=0.016), and MAGE (OR=1.25; 95% CI: 1.07-1.45; p=0.005) were independently associated with higher odds of albuminuria. In contrast, higher FT3 was inversely associated with albuminuria (OR=0.46; 95% CI: 0.28-0.74; p=0.001). TIR, FT4, and TSH were not significantly associated with albuminuria in univariable analysis. HbA1c was negatively correlated with FT3 (ρ=-0.133; p=0.016). Similarly, SD was negatively correlated with FT3 (ρ=-0.227; p<0.001). Exploratory mediation analysis indicated that FT3 statistically accounted for 6.9% of the association between HbA1c and albuminuria and 21.4% of the association between glucose SD and albuminuria. Conclusion Greater glycemic variability and lower FT3 levels were independently associated with albuminuria in patients with T2DM, while hypomagnesemia was associated with albuminuria in univariable analysis. Exploratory mediation analysis suggested that FT3 statistically accounted for a modest proportion of the associations of HbA1c and glucose SD with albuminuria. These findings indicate that CGM-derived variability measures, alongside magnesium status and thyroid function, may provide complementary information in the assessment of DKD. Prospective multicenter studies are required to clarify the temporal relationships and clinical significance of these findings.

Keywords:  continuous glucose monitoring (cgm); diabetic kidney disease (dkd); free thyroxine (ft4); free triiodothyronine (ft3); glucose time in range (tir); glycated hemoglobin (hba1c); mean amplitude of glycemic excursions (mage); thyroid-stimulating hormone (tsh); type 2 diabetes mellitus (t2dm); urinary albumin-to-creatinine ratio (uacr)

DOI:  https://doi.org/10.7759/cureus.117117
J Diabetes Sci Technol. 2026 Sep 28. 19322968261489568

Diabetes Technology in the Primary Care Setting: An American Diabetes Association Scientific Statement.

Osagie Ebekozien, Halis K Akturk, Joseph A Aloi, Natalie Bellini, Benjamin M Bluml, Veronica J Brady, Doreen Cassarino, Stephen C Clement, Carla Demeterco-Berggren, Katherine Claire D'Onfro, Dianna Isaacs, Christopher W Jones, Paige Johnson, Zachary Johnson, David Kerr, Collin Kruczek, Sandra Leal, Janice MacLeod, Thomas W Martens, Eden Miller, Medha N Munshi, Cara E Orr, Sean M Oser, Francisco Prieto, Viral N Shah, Jay Shubrook, Elias S Siraj, Ryan Smith, Pamela L Stamm, Sean Sullivan, Cyril Ubiem, Eugene Wright.

  Most diabetes care is delivered in primary care, yet diabetes technologies remain inconsistently integrated. Workforce, training, time, data infrastructure, and insurance barriers contribute to inequitable access and adoption. This scientific statement provides evidence-informed recommendations for integrating diabetes technology into primary care practices. The American Diabetes Association (ADA) convened an interdisciplinary panel that met in person at the Diabetes Technology Meeting in October 2025. Panelists reviewed available evidence, published between 2020 and 2025, on diabetes technology implementation in primary care, with a focus on clear statements and recommendations addressing technology access and adoption, team-based workflows, continuous glucose monitoring, connected insulin delivery, and implementation barriers and opportunities. Virtual meetings were convened over the next 2 months to further develop and finalize statements and recommendations. Together, the ADA and interdisciplinary panel developed 16 consensus statements and 10 key recommendations. Diabetes technologies can be safely and effectively integrated into primary care practices when supported by standardized workflows, appropriate training, defined team roles, and access to specialty expertise. Equitable implementation also requires addressing coverage, affordability, digital literacy, broadband access, interoperability, and workforce development. Integrating diabetes technology into primary care is feasible and necessary to advance equitable, person-centered, data-informed care. Health systems, practices, payors, professional organizations, manufacturers, and policymakers should align resources, training, workflows, payment, and infrastructure to support sustainable implementation and improve outcomes for people with diabetes.

Keywords:  automated insulin delivery; continuous glucose monitoring; health equity; insulin pen; primary care; technology and diabetes

DOI:  https://doi.org/10.1177/19322968261489568
Pediatr Diabetes. 2026 ;2026 e007

A randomized trial comparing supplemental video care and asynchronous remote monitoring among young people with type 1 diabetes and elevated glycemic levels.

Stephanie Crossen, Salvador Lopez, Carrie Lewis, Daniel Tancredi, Amy Hughes-Lansing, Nicole Glaser.

  Telehealth can enable supplemental care to young people with type 1 diabetes (T1D) in need of frequent guidance or treatment changes. However, the relative benefits of different telehealth modalities (e.g., synchronous video visits versus asynchronous remote patient monitoring [RPM]) for specific populations remain unclear. We conducted a three-arm randomized controlled trial comparing supplemental monthly video visits or monthly RPM versus usual care over 6 months among young people aged 5-18 years with established T1D and hemoglobin A1c (HbA1c) > 8%. The study cohort of 65 participants was 57% publicly insured, with 54% White and 26% Hispanic/Latino, a mean age of 13.4 years, a T1D duration of 5.8 years, a baseline HbA1c of 9.7%, and high baseline use of continuous glucose monitoring (98%) and insulin pumps (77%). Both study arms experienced high rates of attrition (45% among video participants, 35% among RPM participants). Compared with RPM encounters, video visits were longer (mean: 29.8 vs 17.2 min, p < 0.001) and more likely to cover topics beyond insulin dose adjustments, such as diabetes behaviors, technology use, and emotional support. Intention-to-treat analysis demonstrated a trend (p = 0.06) toward 1.0% lower mean HbA1c at study completion among video participants compared with usual care, adjusting for baseline factors; no significant difference was seen for RPM. Feedback about both interventions was highly positive. These findings suggest that among young people with elevated HbA1c despite using diabetes technology, supplemental synchronous, individualized interactions may be more effective at reducing HbA1c than supplemental data review with asynchronous outreach.

Keywords:  Digital health; Remote patient monitoring; Telehealth; Type 1 diabetes

DOI:  https://doi.org/10.48130/pedi-0026-0007
Diabetologia. 2026 Oct 01.

Efficacy of the digital therapeutic glucura combined with CGM in the treatment of type 2 diabetes: a randomised controlled trial over 6 months.

Guido Freckmann, Inke R König, Hanna Grube, Maren Vens, Oliver Witt, Vanessa Kappelmeier, Kim L Pethahn, Christian Sina, Othmar Moser, Torsten Schröder.

   AIMS/HYPOTHESIS: CGM is increasingly recommended for non-insulin-treated type 2 diabetes according to the ADA Standards of Care 2026. However, its clinical benefit in high-quality standard care remains limited. This study evaluated whether addition of the digital therapeutic glucura, which delivers personalised lifestyle intervention and self-management guidance, improves glycaemic outcomes beyond CGM and standard care.
METHODS: The study was a 6-month, multicentre, open-label RCT conducted at 24 sites in Germany (ID: DRKS00032537). Adults (≥18 years) with non-insulin-treated type 2 diabetes (HbA1c 53-97 mmol/mol [7.0-11.0%], BMI≥25 kg/m2) receiving standard care were randomly assigned (1:1) by centralised stratified block randomisation to glucura or a control app. Participants and study personnel were not masked to group assignment. Both groups used intermittent CGM every 3 months during the study. CGM sensors were not used prior to the study. The primary endpoint was the change in HbA1c from baseline to 6 months. Secondary outcomes included weight change and patient-centric measures.
RESULTS: Of 327 randomised participants (intervention: n=165; control: n=162), 320 were analysed (ITT population). The standalone CGM control group improved HbA1c levels by 3.3 mmol/mol (0.30 percentage points); however, the intervention group showed greater reductions of 8.7 mmol/mol (0.80 percentage points) with an adjusted difference of -5 mmol/mol (-0.46 percentage points; 95% CI -7, -3 mmol/mol; p=0.00003). Within the intervention group, more participants achieved HbA1c targets (<53 mmol/mol [<7.0%]: 55% vs 31%, OR 3.38, exploratory p=0.0002; <48 mmol/mol [<6.5%]: 27% vs 7%, OR 6.63, exploratory p=0.0001). The mean weight loss was also greater in the intervention group (4.11% vs 1.79%, p<0.0001). Significant improvements in mental health and diabetes-related distress were also observed. The positive effects were consistent across all subgroups. No device-related serious adverse events were observed.
CONCLUSIONS/INTERPRETATION: In non-insulin-treated type 2 diabetes, CGM plus standard care yielded less metabolic benefit than CGM combined with glucura. The combination of glucura and CGM also translated into improvements beyond glycaemic management. Trial registration German Clinical Trials Register DRKS00032537 Funding The study was funded by Perfood Laboratories GmbH.

Keywords:  CGM; Continuous glucose monitoring; DTx; Digital therapeutic; Glucura; Lifestyle treatment; Low-glycaemic diet

DOI:  https://doi.org/10.1007/s00125-026-06880-6
JMIR Mhealth Uhealth. 2026 Sep 30. 14 e91724

Noninvasive Interstitial Glucose Estimation Using Wearables and Machine Learning in Healthy Individuals and Individuals With Obesity: Observational Cohort Study.

Franziska Schmelter, Xinyu Huang, Annika Heidmann, Paul Beier, Friederike Miriam Rundfeldt, Christina Plöger, Artur Piet, Md Abid Hasan, Yuanheng Zhang, Lennart Jablonski, Oliver Witt, Torsten Schröder, Stefanie Derer, Chris Easthope Awai, Marcin Grzegorzek, Christian Sina.

   Background: Continuous glucose monitoring (CGM) can facilitate weight management and lower the risk of metabolic diseases by providing real-time feedback on glycemic responses, thereby enabling more informed lifestyle decisions. However, current CGM systems remain constrained by invasiveness, cost, and short sensor lifespan, limiting their practicality for guiding individualized postprandial low-glycemic diets.
Objective: Extending earlier proof-of-concept findings, this study aimed to validate an interstitial glucose (IG) machine learning algorithm in real-world environments using multimodal, continuous data collected from wearable sensors and smartwatches. In the long term, we aim to embed a noninvasive, sensor-based algorithm for estimating tissue glucose within mobile health apps and postprandial low-glycemic diet frameworks to enable scalable, personalized prevention strategies.
Methods: We conducted a 2-week study phase during which participants continuously wore 2 noninvasive sensor devices: a scientific sensor wristband (Empatica EmbracePlus) and a commercially available smartwatch (Fitbit Sense 2). As a reference measurement, an invasive CGM sensor (Abbott FreeStyle Libre 3) measured IG levels. For metabolic characterization, 1-point fasting blood and urine samples were collected, and deep phenotyping using state-of-the-art nuclear magnetic resonance spectroscopy and bioelectrical impedance analysis was performed. For participants with overweight and obesity, clinical standard parameters focusing on glucose metabolism were analyzed.
Results: A total of 74 participants, 34 (46%) healthy controls and 40 (54%) metabolically at-risk (MR) individuals, simultaneously used an invasive CGM device together with 2 noninvasive wristbands over 2 weeks. Healthy controls were characterized by a mean age of 24.53 (SD 3.68) years and a mean BMI of 22.36 (SD 2.16) kg/m². In contrast, the MR cohort had a mean age of 55.38 (SD 15.08) years and a mean BMI of 35.38 (SD 4.91) kg/m². Furthermore, the MR cohort showed elevated fasting glucose levels (mean 107.56, SD 19.26 mg/dL), hemoglobin A1c levels (mean 5.67%, SD 0.63%), and an increased homeostatic model assessment of insulin resistance index (mean 4.34, SD 3.49), indicating a disturbed glucose metabolism. The proposed long short-term memory network based on feature vectors obtained the best IG prediction performance, with an average root-mean-squared error of 21.04 (SD 8.32) mg/dL and 98.3% of predictions in zones A and B of the Clarke error grid analysis, representing a high level of predictive accuracy. In addition, based on data from a smartwatch, we achieved a comparable average root-mean-squared error of 23.49 (SD 11.46) mg/dL for the overall cohort.
Conclusions: This study demonstrates that IG levels can be predicted from multimodal, noninvasive wearable sensor data using a machine learning approach under real-world conditions. While further validation in larger and more diverse cohorts is warranted, this approach represents a promising step toward accessible, personalized glycemic monitoring and dietary guidance as a preventive tool in mobile health apps.

Keywords:  continuous glucose monitoring; interstitial glucose; machine learning algorithm; personalized nutrition; wearables

DOI:  https://doi.org/10.2196/91724
Front Endocrinol (Lausanne). 2026 ;17 1963887

Patient-reported outcomes in adults with type 3c diabetes: a scoping review across aetiologies and pancreatic resection extent.

Ana Meden, Nika Aleksandra Kravos Tramšek.

   Background: Type 3c diabetes is a heterogeneous secondary diabetes caused by pancreatic disease, injury or resection. Glycaemic outcomes alone do not capture the combined effects of insulin deficiency, impaired counter-regulation, exocrine insufficiency, pain, altered nutrition and treatment burden. We mapped standardized patient-reported outcome measures (PROMs) across aetiologies and pancreatic resection extent.
Methods: MEDLINE via PubMed, Scopus and Web of Science Core Collection were searched from inception to 23-24 July 2026, supplemented by public trial registries and targeted citation checking. A scoping review framed by Population-Concept-Context followed JBI guidance and PRISMA-ScR. An automation-assisted title/abstract pathway was followed by independent report-level adjudication by two reviewers.
Results: The searches yielded 5,132 records and 3,372 unique database records after deduplication. Fifty-six reports underwent independent report-level adjudication; one was not retrieved, 15 were excluded and 40 were included. In a reproducible post hoc random validation sample of 200 of 3,321 early exclusions, exact and binary reviewer agreement were 100% (Cohen's kappa = 1.00); three records marked unclear by both reviewers were excluded after targeted adjudication, leaving no eligible record in the sample (0/200; Wilson 95% confidence interval 0-1.88%). Twenty-six included reports concerned pancreatic resection or an insulin-treated TPIAT subgroup, 11 cystic-fibrosis-related diabetes, two diabetes secondary to chronic pancreatitis and one fibrocalculous pancreatic diabetes. Generic health and physical function were often impaired after total pancreatectomy, whereas some comparator studies reported similar PAID, ADDQoL or treatment-satisfaction results to type 1 or other insulin-treated diabetes. No report used SARC-F. Only six reports were available to the authors in full text; the favorable technology signals depended on abstract-based reports and were therefore hypothesis-generating.
Conclusions: Patient-reported burden in type 3c diabetes is multidimensional and measurement remains fragmented. Future studies should combine complementary measures and report aetiology, diabetes timing and total versus partial resection explicitly.

Keywords:  continuous glucose monitoring; pancreatectomy; pancreatogenic diabetes; patient-reported outcomes; quality of life; sarcopenia; scoping review; type 3c diabetes

DOI:  https://doi.org/10.3389/fendo.2026.1963887