Journal of Diabetes Science and Technology最新文献

{"title":"Telemedicine Decreases No-Show Rates and Achieves Comparable Outcomes in Publicly Insured Patients With Type 2 Diabetes.","authors":"Jay Gandhi, Benjamin Doolittle, Jonathan Weber","doi":"10.1177/19322968251362115","DOIUrl":"10.1177/19322968251362115","url":null,"abstract":"<p><strong>Background: </strong>Patients with type 2 diabetes (T2D) require continuous management to achieve optimal outcomes. Socioeconomic barriers can impede the delivery of optimal diabetes care. During the COVID-19 pandemic, telemedicine likely reduced common barriers to care compared with in-person visits. This study investigated the impact of telemedicine on no-show rates and diabetes outcomes in publicly insured patients and the potential link to reduced socioeconomic barriers.</p><p><strong>Methods: </strong>This retrospective study analyzed the records of 819 patients with T2D at the Yale Medicine Diabetes Center (YMDC). From June 2019 to March 2020, patients had in-person clinic visits, followed by telehealth visits from March to December 2020 due to system-wide COVID-19 isolation mandates. No-show rates, glycemic control metrics, and other biomarkers were compared in the same patients between their in-person and telehealth visit periods.</p><p><strong>Results: </strong>Patients utilizing telemedicine averaged no-show rates of 0.34 ± 0.69 compared with 1.19 ± 1.30 (<i>P</i> < .001) for in-person visits. Average hemoglobin A1c (HbA1c) for telemedicine was 7.93 ± 1.95 compared with 7.91 ± 1.90 (<i>P</i> = .65) for in-person visits. Percentages of patients achieving ≥70% glucose time in range (TIR) on an ambulatory glucose profile (AGP) was 43.90% for telehealth and 45.42% (<i>P</i> = .76) for in-person visits.</p><p><strong>Conclusion: </strong>Publicly insured patients with T2D showed significantly reduced no-show rates and comparable glycemic control during telemedicine visits compared with in-person visits. Telemedicine visits may be associated with lower no-show rates due to a reduction of socioeconomic barriers. Future studies are warranted to further clarify potential associations of specific socioeconomic barriers to missed visits.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251362115"},"PeriodicalIF":3.7,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12307333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144731194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effect of Preanalytical Factors on Capillary Blood Glucose Readings From Point-of-Care Devices.","authors":"Bridget Laming, Shania Smee, Hugh Riddell, Angela L Spence, Carly J Brade, Raymond J Davey","doi":"10.1177/19322968251361163","DOIUrl":"10.1177/19322968251361163","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251361163"},"PeriodicalIF":3.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12301224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144707650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy of Glucose Trends by Subcutaneous Continuous Monitoring vs Intermittent Arterial Measurements in Critically Ill Patients.","authors":"Ola Friman, Marcus Lind, Ragnar Thobaben, Pia Zetterqvist, Anders Perner, Olav Rooijackers, Anders Oldner, Johan Mårtensson","doi":"10.1177/19322968251358830","DOIUrl":"10.1177/19322968251358830","url":null,"abstract":"<p><strong>Background: </strong>Continuous glucose monitoring (CGM) has the potential to improve glucose control in critically ill patients, provided that its trend accuracy is reliable. We evaluated the trend accuracy of a subcutaneous CGM system (Dexcom G6) compared with intermittent arterial blood gas (ABG) measurements in intensive care unit (ICU) patients receiving insulin.</p><p><strong>Methods: </strong>We enrolled 40 adult ICU patients receiving insulin and organ-supportive therapies. We assessed trend accuracy using the Rate Error Grid Analysis (R-EGA) and the Diabetes Technology Society Trend Accuracy Matrix (DTS-TAM), overall, across different ABG levels, and over time from CGM initiation.</p><p><strong>Results: </strong>A total of 2701 paired CGM-ABG trends were analyzed, with a median (IQR) time difference between readings of 83 (65-125) minutes. Overall, 99.7% of trends were classified in R-EGA Zone A and 0.3% in Zone B. On DTS-TAM analysis, 98.6% of trends fell in the No Risk category, while 1.7% were in the adjacent Mild-to-Moderate Risk categories. Trends were more frequently categorized as Mild-to-Moderate Risk when ABG values were <100 mg/dL (5.56 mmol/L) (3.6%) compared with 100 to 180 mg/dL (5.56 to 10 mmol/L) (1.3%) or >180 mg/dL (10 mmol/L) (1.6%). During the first 24 hours of CGM use, 2.9% of trends fell into the Mild-to-Moderate Risk categories, compared with 0.9% beyond 24 hours.</p><p><strong>Conclusions: </strong>In critically ill patients receiving insulin, CGM demonstrated high overall trend accuracy relative to ABG. Trend accuracy was reduced at lower glucose ranges and during the initial 24 hours of CGM use.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251358830"},"PeriodicalIF":3.7,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12289614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144698692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Use of Continuous Glucose Monitoring in Comparison to Self-Monitoring of Blood Glucose in Gestational Diabetes: A Systematic Review.","authors":"Bhavadharini Balaji, Wesley Hannah, Polina V Popova, Uma Ram, Mohan Deepa, Janeline Lunghar, Kumaran Uthra, Haritha Sagili, Sadishkumar Kamalanathan, Ranjit Mohan Anjana, Viswanathan Mohan","doi":"10.1177/19322968251357873","DOIUrl":"10.1177/19322968251357873","url":null,"abstract":"<p><strong>Background: </strong>Continuous glucose monitoring (CGM) has emerged as an important tool for managing gestational diabetes mellitus (GDM), offering real-time glucose data and the potential for improved glycemic control. Unlike traditional self-monitoring of blood glucose (SMBG), which provides intermittent readings, CGM captures continuous glucose fluctuations, including postprandial and nocturnal changes, which are critical in GDM management.</p><p><strong>Objective: </strong>This systematic review aimed to assess the effectiveness of CGM compared with SMBG in managing glycemic control in women with GDM, focusing on key glycemic metrics such as time in range (TIR) and glycemic variability (GV), and exploring their associations with maternal and neonatal outcomes.</p><p><strong>Methods: </strong>A comprehensive search of PubMed and Google Scholar was conducted, adhering to PRISMA guidelines. Studies included randomized controlled trials, observational studies, and prospective cohort studies comparing CGM and SMBG, with 35 studies ultimately reviewed.</p><p><strong>Results: </strong>Compared with SMBG, CGM demonstrated significant improvements in maintaining TIR and reducing GV, which correlated with favorable maternal and neonatal outcomes, including lower rates of large-for-gestational-age (LGA) infants, preterm birth, and NICU (neonatal intensive care unit) admissions. Furthermore, CGM detected more hyperglycemic and hypoglycemic events, particularly nocturnal fluctuations. However, the studies also highlighted the need for standardized metrics to optimize CGM use in GDM management.</p><p><strong>Conclusion: </strong>Continuous glucose monitoring offers substantial advantages over SMBG for managing GDM by providing continuous, real-time glucose data, enabling timely treatment adjustments. These findings support the integration of CGM into clinical practice to improve maternal and neonatal outcomes in GDM. Further research is needed to establish standardized CGM metrics specific to GDM management.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251357873"},"PeriodicalIF":4.1,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12286989/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144690467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robotics Use in the Care and Management of People Living With Diabetes Mellitus: A Scoping Review.","authors":"Amani Al Bayrakdar, Mauro Dragone, Gosha Colquhoun, Alistair McConnell, Maria King, Ruth Paterson","doi":"10.1177/19322968251356298","DOIUrl":"10.1177/19322968251356298","url":null,"abstract":"<p><strong>Background: </strong>Diabetes prevalence is rising and projected to affect 783 million globally by 2045. Effective diabetes self-management relies on diabetes knowledge, lifestyle modifications, and health care support; yet global health care workforce shortages hinder the provision of adequate care. Socially assistive technologies, such as robots or artificial intelligence, are proposed as potential solutions to meet rising demands.</p><p><strong>Aim and methods: </strong>To map the current literature on Socially Assistive Robots for diabetes care, identifying robotic types, barriers and enablers to use, and impact on health-related outcomes. A scoping review using Arskey and O'Malley's Framework was conducted, screening studies published between 2013 and 2025 across key databases and extracting data using COVIDENCE.</p><p><strong>Results: </strong>Twenty-two studies met the inclusion criteria, mostly focused on children with type 1 diabetes. Studies were largely conducted in Europe, cross-sectional, and with small sample sizes. Socially assistive robots demonstrated high acceptability, especially among younger children, positively affecting knowledge acquisition, self-management, and self-efficacy. Personalized interactions, gamified features, and emotional responsiveness were key enablers of engagement. However, engagement waned over time, particularly when participants' practical and emotional expectations were unmet. Barriers included usability challenges, privacy concerns, and lack of customization. Economic and sustainability evaluations were absent.</p><p><strong>Conclusions: </strong>Despite growing evidence for robotics in diabetes care, research remains methodologically limited and focused primarily on younger populations. Future studies should include adults, employ multi-faceted robotics designs, and be adequately powered to assess acceptability and efficacy across diverse groups, facilitating broader application in diabetes care.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251356298"},"PeriodicalIF":3.7,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12279771/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144674930","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Increased Glycemic Variability in Patients With Chronic Pancreatitis and Diabetes Compared to Type 2 Diabetes.","authors":"Line Davidsen, Morten Hasselstrøm Jensen, Thomas Kronborg, Stine Hangaard, Peter Vestergaard, Asbjørn Mohr Drewes, Filip Krag Knop, Simon Lebech Cichosz, Søren Schou Olesen","doi":"10.1177/19322968251356239","DOIUrl":"10.1177/19322968251356239","url":null,"abstract":"<p><strong>Background: </strong>Glycemic variability in diabetes secondary to pancreatic diseases (pancreatic diabetes) remains unclear. We compared glycemic control and variability in patients with pancreatic diabetes and a matched group of individuals with type 2 diabetes using continuous glucose monitoring (CGM).</p><p><strong>Methods: </strong>We included 30 patients with chronic pancreatitis and insulin-treated secondary diabetes and 30 individuals with insulin-treated type 2 diabetes (matched on HbA1c, age, and sex). Participants wore a blinded CGM for 20±2 days. Glycemic variability was assessed using coefficient of variation (CV), standard deviation (SD), mean amplitude of glycemic excursions (MAGE), and continuous overall net glycemic action (CONGA) at 1 and 2-hour intervals. Glycemic control was evaluated based on time spent in predefined glucose ranges: >250 mg/dL, 181 to 250 mg/dL, 70 to 180 mg/dL (target range), 54 to 69 mg/dL, and <54 mg/dL. CGM parameters were compared between groups.</p><p><strong>Results: </strong>All CGM-derived measures of glycemic variability (CV, SD, MAGE, CONGA1, and CONGA2) were significantly higher in patients with chronic pancreatitis and diabetes compared to individuals with type 2 diabetes (<i>P</i> < 0.01). Patients with chronic pancreatitis spent more time with glucose >250 mg/dL (8.8% vs 3.1%, <i>P</i> = 0.008), less time in the target range (70-180 mg/dL; 56.7% vs 68.5%, <i>P</i> = 0.044), and more time at 54-69 mg/dL (0.2% vs 0.0%, <i>P</i> = 0.041). Their glycemia risk index for hyperglycemia was also higher (25.5 vs 16.5, <i>P</i> = 0.033).</p><p><strong>Conclusion: </strong>Patients with pancreatic diabetes have higher glycemic variability than individuals with type 2 diabetes despite comparable levels of HbA1c.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251356239"},"PeriodicalIF":4.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12276207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144667725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recycling Insulin Pens: A Call for Action!","authors":"Lutz Heinemann, Chris Unsöld, Sebastian Friedrich Petry","doi":"10.1177/19322968251358229","DOIUrl":"10.1177/19322968251358229","url":null,"abstract":"<p><p>Insulin pens are widely used by people with diabetes for insulin application. Most often ready-to-use pens are used; however, disposal of these pens generates a considerable amount of plastic waste. Until now such pens have almost always been disposed of with household waste. We recommend to establish a nationwide recycling process for insulin pens in Germany over the next few years (PenDE). Collecting and sorting of the finished pens have several facets, which are discussed in this commentary. It is primarily about recycling the actual product, ie, the finished pens, and not their packaging and packaging inserts. Such a project is only possible in close cooperation with the insulin pen manufacturers. The goal is to establish material cycles, ie, multiple use of materials such as recycled plastic.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251358229"},"PeriodicalIF":4.1,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12267213/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144642697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Is Simplified Meal Announcement an Alternative to Exact Carbohydrate Counting in Patients With Type-1 Diabetes Using an Advanced Hybrid Closed-Loop System? A Systematic Review and Meta-Analysis.","authors":"Cristina Sicorschi Gutu, Paweł Łajczak, Patricia-Maria Anton, Krisztina Schmitz-Grosz, Carsten Sommer-Meyer, Cintia Gonzalez Blanco, Günter Niklewski","doi":"10.1177/19322968251351994","DOIUrl":"10.1177/19322968251351994","url":null,"abstract":"<p><strong>Aims: </strong>Hybrid closed-loop (HCL) systems have become the standard of care for type-1 diabetes patients. However, they require exact carbohydrate counting (ECC), which can be complex. We conducted a meta-analysis to assess whether simplified meal announcement (SMA) is inferior to ECC by comparing the percentage of time spent in the time in range (TIR) 3.9 to 10 mmol/l.</p><p><strong>Materials and methods: </strong>PubMed, EMBASE, and Cochrane Central database were searched for randomized controlled trials (RCTs) that compared ECC to SMA and reported the outcomes of (1) percentage of 3.9 to 10 mmol/l glucose level time; (2) percentage of 3.9 to 7.8 mmol/l glucose level time; (3) total daily insulin units (TDI) per kg; and (4) coefficient of variation (CV) of glucose (%). Heterogeneity was examined with <i>I</i>² statistics. A random-effect generic inverse variance (GIV) method was applied to all analyses.</p><p><strong>Results: </strong>We included 4 RCTs (three of them crossover) with 137 patients, of whom 118 underwent SMA. Follow-up ranged from 2 weeks to 12 months. The pooled mean difference (MD) of percentage of time spent in TIR was -3.28 [-6.00, -0.56], indicating lower proportion with SMA. Two studies reported the percentage of TIR of 3.9 to 7.8 mmol/l, with a pooled effect size MD of -3.36 [-5.80, -0.92]). The pooled MD in TDI units per kg was 0.00 [-0.05, 0.05], and CV of glucose was 0.60 [0.02, 1.18]).</p><p><strong>Conclusions: </strong>Simplified meal announcement may be considered as an alternative to ECC, but further research is needed to confirm its broader applicability.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251351994"},"PeriodicalIF":4.1,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12237926/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144584069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications of Artificial Intelligence and Machine Learning in Prediabetes: A Scoping Review.","authors":"Benjamin Lalani, Rohan Herur, Daniel Zade, Grace Collins, Devin M Dishong, Setu Mehta, Jalene Shim, Yllka Valdez, Nestoras Mathioudakis","doi":"10.1177/19322968251351995","DOIUrl":"10.1177/19322968251351995","url":null,"abstract":"<p><strong>Introduction: </strong>Prediabetes is a prevalent condition in which early detection and lifestyle interventions can prevent or delay progression to diabetes. Artificial intelligence (AI) and machine learning (ML) offer enhanced tools for diagnosis, risk stratification, and scalable delivery of lifestyle interventions. This review synthesizes current applications of AI/ML in patients with prediabetes.</p><p><strong>Methods: </strong>We conducted a scoping review using PubMed, EMBASE, and Web of Science (through May 2025) to identify original studies applying AI/ML to prediabetes prediction or management. Population-level forecasting and models combining prediabetes with other conditions were excluded. Data were extracted via structured REDCap instruments and validated through secondary review. Descriptive statistics summarized findings.</p><p><strong>Results: </strong>Of 2072 records screened, 149 studies met criteria: 118 prediction model studies, 20 intervention studies, and 11 miscellaneous. Machine learning models primarily targeted prediction of prediabetes, progression to diabetes, diabetic complications, and glucose metrics. Overall model performance was favorable (mean C-statistic 0.81), with random forests, neural networks, and support vector machines showing better performance. Only 20 studies reported external validation, few compared ML to standard risk tools, and data/code availability was limited. Six AI-based diabetes prevention programs showed positive clinical outcomes, though randomized controlled trial (RCT) evidence was limited. Three personalized nutrition interventions showed mixed efficacy.</p><p><strong>Conclusion: </strong>Most AI/ML research in prediabetes focused on predictive modeling, which shows promise but limited translation to real-world settings. Artificial intelligence-based interventions may scale behavioral change support but need further evaluation versus standard care. Future efforts should prioritize external validation, assess added value over standard tools, and address barriers to integration into care.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251351995"},"PeriodicalIF":4.1,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12237953/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144584068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Postpartum Continuous Glucose Monitoring to Detect Dysglycemia After Gestational Diabetes.","authors":"Camila Cabrera, Selassie Ogyaadu, Camilla Levister, Leah Kaplan, Aslihan Ipek, Lauren Ferrara, Carol J Levy, Grenye O'Malley","doi":"10.1177/19322968251351819","DOIUrl":"10.1177/19322968251351819","url":null,"abstract":"<p><strong>Background: </strong>Up to one-third of people with gestational diabetes (GDM) will have persistent dysglycemia, and more than half do not complete the recommended postpartum oral glucose tolerance test (OGTT). This study assessed the use of blinded postpartum continuous glucose monitoring (CGM) to detect dysglycemia by assessing return rates, participant experience, and power to predict OGTT results.</p><p><strong>Method: </strong>Blinded CGM was placed on postpartum day 1 to 3 before discharge from the hospital and again at six weeks after pregnancy complicated by GDM and worn at home for up to 10 days. Participants mailed the CGM back and were encouraged to undergo standard of care six-week OGTT.</p><p><strong>Results: </strong>Fifty women (36 ± 6 years old; 40% non-Hispanic white, 24% non-Hispanic black, 22% Asian, 14% Hispanic; 34% Medicaid insured) were consented. First CGM was completed by 86%, second CGM was completed by 60%, and postpartum OGTT was performed by 68%. Mean first sensor glucose was 121.8 ± 14.1 mg/dL. Dysglycemia on OGTT was diagnosed in seven participants: six with impaired glucose tolerance (18%) and one with diabetes (3%). Percent time <96% in the range 70 to 180 mg/dL predicted abnormal OGTT with positive predictive value of 54% and negative predictive value of 100%. The sensitivity and specificity of CGM to predict postpartum dysglycemia were 100% and 78%, respectively. If given a choice, 94% of participants would prefer CGM over OGTT.</p><p><strong>Conclusions: </strong>Postpartum CGM is a reasonable and convenient initial postpartum screen for postpartum dysglycemia with high completion rates, sensitivity, and acceptability ratings. Percent time in range 70 to 180 mg/dL had strong predictive power for OGTT.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"19322968251351819"},"PeriodicalIF":4.1,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12226513/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144553610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}