Adoption Of Continuous Glucose Monitoring Research Articles

5040 Leveraging Continuous Glucose Monitoring Data as an Additional Source for Glucagon Prescription Behavior

Abstract Disclosure: J. Ambalavanan: None. J. Rusticelli: None. D. Isaacs: Consulting Fee; Self; Medtronic Diabetes, Lilly USA, LLC, Novo Nordisk, Insulet Corporation, Abbott Laboratories, Dexcom. H. Xiao: None. J. Bena: None. Z. Jin: None. C. Babiuch: None. M. Lansang: Consulting Fee; Self; Glooko. Research Investigator; Self; Abbott Laboratories, Dexcom, Xeris. Background: Hypoglycemia is a major source of anxiety in, and can be life-threatening for, patients with diabetes (DM). Even though the American Diabetes Association (ADA) recommends glucagon be made available to all individuals taking insulin or at high risk for hypoglycemia (level E evidence), few patients have access to this life-saving medication. Given the rapid adoption of continuous glucose monitoring (CGM) in the management of DM, we aimed to 1) evaluate glucagon prescription by identifying patients with hypoglycemia on CGM reports, and 2) deliver an educational letter to the providers. Methods: Our study had a retrospective chart review and a quality improvement (QI) component. From Mar-Oct 2023, we identified adult patients in the Cleveland Clinic Ohio health system with type 1 or type 2 DM on insulin (including insulin pumps), sulfonylurea, or meglitinide, and hypoglycemia defined as time below range (TBR) ≥4% on CGM reports. We evaluated the percentages of pre-existing and incident glucagon prescription among those patients. For the QI component, we contacted providers individually via letter if the patients did not have a glucagon prescription and shared with them the ADA Standards of Care on hypoglycemia along with information about various forms of glucagon. Whether to prescribe glucagon was left to the clinical judgement of the providers. Information was collected 4 weeks later to see whether there was any change in the prescription practice. Results: Patients numbered 1543, with170 patients having TBR ≥4%. Among patients with hypoglycemia, 37% had pre-existing prescription and 14% incident glucagon prescription, compared with patients without hypoglycemia (p<0.001). Significant (p<0.001) risk factors for hypoglycemia included type 1 DM, insulin-only therapy, use of conventional insulin pumps, and lower A1c. Among patients with TBR ≥4%, 66 (39%) had level 1 hypoglycemia (54 to <70 mg/dl), 88 (52%) had level 2, and 16 (9%) had level 3 (hospitalization or ER admission for hypoglycemia). Pre-existing or incident glucagon prescription was seen in 28% without hypoglycemia, 38% with level 1 hypoglycemia, 49% with level 2, and 63% with level 3 (p<0.001 level 1 vs level 3; level 2 vs no hypoglycemia; level 3 vs no hypoglycemia). Among 70 patients whose providers received education letters, 27 (39%) prescribed glucagon after education. Glucagon emergency kit, glucagon autoinjector, and inhaled glucagon were the top prescription choices. A greater proportion of advanced practice providers responded to our education than physicians (p=0.021). Conclusion: Glucagon prescription remains suboptimal though better than in other studies even among patients with documented hypoglycemia on CGM reports. Effective provider education and communication can increase glucagon prescription. Provider and patient factors should be studied next to determine steps for further improvement. Presentation: 6/1/2024

12420 Effects Of A Continuous Glucose Monitoring (CGM) Optimization Program On Prescribing Behavior And Glycemic Outcomes

Abstract Disclosure: A. Sterbenz: None. J. Fuentes Rosales: None. S. Abraham: None. P. Siroya: None. R. Middelbeek: None. J. Mitri: None. F. Rossitto: None. E. Cabana: None. A. Gharse: None. L. Collins: None. L. Aliaga: None. A. Millan Ferro: None. J. Cifuni: None. A. Schwartz: None. E. Kelley: None. C. Gomez: None. M. Aballay: None. Introduction: Continuous glucose monitoring (CGM) improves glucose control in patients with diabetes, as demonstrated in clinical trials. There is limited information available on the real-world application of CGM adoption in a resource-limited setting or safety net outpatient hospital setting. This intervention's aim was to assess the impact of a multifaceted approach on improving CGM utilization and on glycemic control among patients with diabetes. Methods: Wyckoff Heights Medical Center, located in Brooklyn, NY is a resource-limited setting that in partnership with the Joslin Diabetes Center, Boston, MA launched a CGM Clinical Care Optimization Program. This quality improvement (QI) initiative included establishing a diabetes technology team (DTT) which met weekly (n=24 sessions) to establish a highly specialized interprofessional group. The DTT activities included: 1) Establishing a CGM workflow; 2) Expanding Medical Assistant responsibilities related to diabetes technology and training; 3) Creating five bilingual (English-Spanish) patient handouts; 4) Building CGM vendor relationships; 5) Developing a CGM manual for data download, billing and coding, and education to increase CGM knowledge across the system. Data were collected prior to CGM initiation and at most recent visit. Results: The intervention occurred between February and November 2023. Prior to this intervention, 49 patients had a CGM prescription. After 9 months, CGM prescriptions were written for 333 patients (680% increase) with the following baseline characteristics (mean ± SD): age (61.2 ± 14.7 yrs.); 56% female; 44% male; 71% Latino; Race: 67% Other (63% Hispanic or Latino), 25% Black or African American, 4% White, Asian 2%, American Indian or Alaska Native 2%; 38% Spanish-speaking, 59% English-Speaking, 3% Other; 81% government insurance (63% Medicaid, 18% Medicare), 19% commercial insurance. The majority of patients had been diagnosed with type 2 diabetes (91%) and 9% had type 1 diabetes. Baseline HbA1C was 9.7 ± 1.5%. Of note, prior to CGM, 34% of patients had A1C>9%. The impact of CGM use was evaluated on 116 patients that met the following criteria: baseline A1C value within 60 days before or up-to 30 days after prescription, and a A1C >90 days after. After an average duration of 154 days of CGM use, HbA1c decreased significantly by -0.51% (p=0.001), demonstrating improved glycemic control post-CGM use. In those with A1c >9%, HbA1c decreased from 10.5% to 8.8% (p<0.01). Conclusion: Creating a CGM optimization program in a resource-limited setting is critical in improving patient self-awareness and provider insight in glycemic control in patients diagnosed with type 1 and type 2 diabetes. These data demonstrate the feasibility of implementing a CGM optimization program in a resource-limited setting, which is critically important in improving clinical care for people with diabetes. Presentation: 6/2/2024

Using continuous glucose monitoring to measure and improve quality metrics: Updates on the Healthcare Effectiveness Data and Information Set 2024 Glucose Management Indicator measure.

Value-based diabetes care is a proactive approach to providing quality care to individuals with diabetes. This approach focuses on improving clinical outcomes rather than the volume of services provided. Implementation of value-based diabetes care requires an established set of standardized quality measures against which all stakeholders can assess and benchmark their performance. The National Committee for Quality Assurance recently added the Glucose Management Indicator to its Healthcare Effectiveness Data and Information Set. The Glucose Management Indicator can be used as a measure of glucose control. This article discusses the benefits of value-based care, the importance of diabetes quality measures, and how the rapidly increasing adoption of continuous glucose monitoring is impacting these measures while improving the lives of individuals with diabetes.

The association of continuous glucose monitoring with glycemic parameters in patients with uncontrolled type 2 diabetes: A prospective observational study.

Uncontrolled glycemic parameters in type 2 diabetes mellitus (T2DM) are a major concern. The present study aimed to evaluate the effectiveness of continuous glucose monitoring (CGM) on glycemic control in type 2 diabetics on insulin therapy. This prospective observational study was done in the Outpatient Department of General Medicine from January 1, 2021 till December 31, 2021 on patients with confirmed T2DM and on insulin therapy. Patients underwent detailed history and physical examination. The CGM device was inserted to record blood glucose levels throughout the day and night for monitoring. Parameters like glycosylated hemoglobin (HbA1c), fasting blood sugar (FBS), post-paradial blood sugar (PPBS), and lipid profile parameters [cholesterol, triglyceride (TG), and low-density lipoprotein (LDL)] were compared at baseline and after a follow-up of 3 months. P-value < 0.05 was used to indicate significant difference. Of 107 patients screened, 100 were included in the study and seven were excluded. The mean age of the patients was 60.6 ± 11.1 years. Fifty-six (56%) of the patients were males, and 44 (44%) were females. The mean body mass index (BMI) was 22.9 ± 2.4 kg/m2. Compared to baseline values, after 3 months of CGM, there was significantly decreased HbA1c (9.41 ± 0.83 vs 9.87 ± 1.16 g%, P < 0.001), FBS (194.640 ± 22.4587 vs 205.10 ± 35.7758 mg/dl, P = 0.002), PPBS (271.160 ± 29.1235 vs 299.180 ± 42.3798, P < 0.001), cholesterol (184.470 ± 28.5192 vs 198.430 ± 38.8367 mg/dl, P < 0.001), LDL (102.410 ± 22.8973 vs 112.040 ± 30.8859, P < 0.001), and TG (140.890 ± 18.0979 vs 146.730 ± 20.8665 mg/dl, P < 0.001). There was a significant improvement in the glycemic parameters and lipid profile parameters with the adoption of CGM. Overall, CGM is a novel method for practical use for management of patients with T2DM.

Generalization of a Deep Learning Model for Continuous Glucose Monitoring-Based Hypoglycemia Prediction: Algorithm Development and Validation Study.

Predicting hypoglycemia while maintaining a low false alarm rate is a challenge for the wide adoption of continuous glucose monitoring (CGM) devices in diabetes management. One small study suggested that a deep learning model based on the long short-term memory (LSTM) network had better performance in hypoglycemia prediction than traditional machine learning algorithms in European patients with type 1 diabetes. However, given that many well-recognized deep learning models perform poorly outside the training setting, it remains unclear whether the LSTM model could be generalized to different populations or patients with other diabetes subtypes. The aim of this study was to validate LSTM hypoglycemia prediction models in more diverse populations and across a wide spectrum of patients with different subtypes of diabetes. We assembled two large data sets of patients with type 1 and type 2 diabetes. The primary data set including CGM data from 192 Chinese patients with diabetes was used to develop the LSTM, support vector machine (SVM), and random forest (RF) models for hypoglycemia prediction with a prediction horizon of 30 minutes. Hypoglycemia was categorized into mild (glucose=54-70 mg/dL) and severe (glucose<54 mg/dL) levels. The validation data set of 427 patients of European-American ancestry in the United States was used to validate the models and examine their generalizations. The predictive performance of the models was evaluated according to the sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). For the difficult-to-predict mild hypoglycemia events, the LSTM model consistently achieved AUC values greater than 97% in the primary data set, with a less than 3% AUC reduction in the validation data set, indicating that the model was robust and generalizable across populations. AUC values above 93% were also achieved when the LSTM model was applied to both type 1 and type 2 diabetes in the validation data set, further strengthening the generalizability of the model. Under different satisfactory levels of sensitivity for mild and severe hypoglycemia prediction, the LSTM model achieved higher specificity than the SVM and RF models, thereby reducing false alarms. Our results demonstrate that the LSTM model is robust for hypoglycemia prediction and is generalizable across populations or diabetes subtypes. Given its additional advantage of false-alarm reduction, the LSTM model is a strong candidate to be widely implemented in future CGM devices for hypoglycemia prediction.

A Podcast on the Use of CGM in Optimizing Type 2 Diabetes Management with Non-intensive Insulin Treatment in the Primary Care Setting.

Type 2 diabetes is a widespread health concern with significant implications for patient well-being. Poor glycaemic control can lead to long-term complications, hypoglycaemia and glycaemic variability, highlighting the importance of setting treatment goals. This podcast, "The use of CGM in optimizing type 2 diabetes management with non-intensive insulin treatment in the primary care setting", introduces non-intensive insulin treatment and continuous glucose monitoring (CGM) as crucial tools in achieving these goals. The advantages of CGM over blood glucose monitoring (BGM) are explored, emphasizing its real-time glucose data provision and how it empowers patients to make informed treatment decisions. Drawing on randomized controlled trials (RCTs), the compelling evidence of CGM's effectiveness in patients with type 2 diabetes on basal insulin treatment are discussed. Additionally, the real-world evidence, comparing outcomes between insulin-treated and non-insulin-treated patients are also addressed. The podcast examines the link between glycaemic control and acute complications requiring hospitalizations and how CGM contributes to a better quality of life for patients with type 2 diabetes. Empowering patients is central to this podcast, with a focus on education, engagement and strategies for integrating CGM data into treatment plans. The pivotal role of healthcare providers in supporting patients on non-intensive insulin treatment and CGM in the primary care setting is addressed. Addressing challenges and barriers in CGM adoption, including cost considerations, technology accessibility and patient concerns, is vital to its widespread use. There is also a consideration of the cost-effectiveness of CGM in type 2 diabetes management. The podcast provides insights into when to consider CGM, including intermittent use and data integration with other health technologies. It emphasizes the potential for improved patient outcomes and a reduced burden of type 2 diabetes. Practical tips for interpreting the Ambulatory Glucose Profile (AGP) report are shared, benefitting primary care healthcare professionals new to CGM. The podcast "The use of CGM in optimizing type 2 diabetes management with non-intensive insulin treatment in the primary care setting" highlights the transformative potential of CGM in type 2 diabetes care. It encourages patients and healthcare providers to consider CGM as an integral part of treatment plans, ultimately improving the lives of those living with type 2 diabetes. Podcast Video (MP4 261831 KB).

Overcoming Barriers to Diabetes Management in Young Adults with Type 1 Diabetes by Leveraging Telehealth: A Pilot Study

ObjectiveThe LIFT-YA (leveraging intensive follow-up treatment in young adults) quality improvement program was developed to address clinical and social barriers in young adults (YA) with type 1 diabetes (T1D), using telehealth visits to promote clinic attendance and improve diabetes care. MethodsLIFT-YA enrolled YA aged 18-30 with T1D and HbA1c >8% (64 mmol/mol) who had established adult care in our diabetes clinic. The 6-month, 7-visit hybrid program was facilitated by a case manager serving as the liaison between participants and the care team. The primary end-points were within-group and between-group changes from the baseline in HbA1c at the last visit and adoption of continuous glucose monitoring (CGM). ResultsOf the 57 eligible YA, 24 were enrolled and 33 were unable to participate (UTP). Thirteen of the enrolled participants attended at least 4/7 visits (“completers”, C), whereas 11 were noncompleters (NC). HbA1c at the end of the program was significantly lower in the C versus UTP group [median −1.0; IQR (−0.6, −2.5) vs −0.25 (0.2, −1.0) in UTP; P < .05]. The percentage of CGM users significantly increased by 70% in the C group (P < .05), but did not change in the NC and UTP groups. Limited access to telehealth and the high cost of frequent visits were the main hurdles preventing enrollment into or completion of the program. ConclusionsThe LIFT-YA pathway was associated with a significant HbA1c reduction and an increase in the adoption of CGM. Policy changes are necessary to expand access to LIFT-YA and other programs for high-risk YA with T1D in underserved communities and across all backgrounds.

Roadmap to Continuous Glucose Monitoring Adoption and Improved Outcomes in Endocrinology: The 4T (Teamwork, Targets, Technology, and Tight Control) Program.

Glucose monitoring is essential for the management of type 1 diabetes and has evolved from urine glucose monitoring in the early 1900s to home blood glucose monitoring in the 1980s to continuous glucose monitoring (CGM) today. Youth with type 1 diabetes struggle to meet A1C goals; however, CGM is associated with improved A1C in these youth and is recommended as a standard of care by diabetes professional organizations. Despite their utility, expanding uptake of CGM systems has been challenging, especially in minoritized communities. The 4T (Teamwork, Targets, Technology, and Tight Control) program was developed using a team-based approach to set consistent glycemic targets and equitably initiate CGM and remote patient monitoring in all youth with new-onset type 1 diabetes. In the pilot 4T study, youth in the 4T cohort had a 0.5% improvement in A1C 12 months after diabetes diagnosis compared with those in the historical cohort. The 4T program can serve as a roadmap for other multidisciplinary pediatric type 1 diabetes clinics to increase CGM adoption and improve glycemic outcomes.

Importance of continuous glucose monitoring in the treatment of diabetes mellitus

Background: Emerging evidence suggests that controlling both glycemic variability and hemoglobin A1c is necessary to prevent complications associated with diabetes mellitus. Hence, continuous glucose monitoring (CGM) is crucial for effectively managing diabetes.Current Concepts: There are two primary types of CGM. Retrospective CGM only allows the reviewal of glycemic data after the monitoring period, whereas personal CGM allows real-time monitoring. Personal CGM can be further categorized into real-time CGM and intermittently scanned CGM. To interpret CGM data, time in range (TIR) is considered the standard parameter. A TIR of 70–180 mg/dL for more than 70% of the period has been established as a typical target for both type 1 and type 2 diabetes. Other parameters such as time below range, time above range, coefficient of variation, and glucose management indicator should also be reviewed. Importantly, numerous clinical studies have demonstrated the efficacy of CGM in both type 1 and type 2 diabetes.Discussion and Conclusion: A wealth of clinical evidence supports the application of CGM in diabetes, confirming its effectiveness across various treatment stages. CGM has emerged as a compelling therapeutic option in instances when other treatment choices remain limited. With a growing body of clinical evidence, the widespread adoption of CGM in diabetes management appears inevitable. However, challenges related to user comfort, cost, the need for extensive data interpretation, and necessary system improvements remain unaddressed. Further research is required to validate the appropriate usage and frequency of CGM through costeffectiveness analyses.

Clinically-Similar Clusters of Daily CGM Profiles: Tracking the Progression of Glycemic Control Over Time.

The adoption of CGM results in vast amounts of data, but their interpretation is still more art than exact science. The International Consensus on Time in Range (TIR) proposed the widely accepted TIR system of metrics, which we now take forward by introducing a finite and fixed set of clinically-similar clusters (CSCs), such that the TIR metrics of the daily CGM profiles within a cluster are homogeneous. CSC definition and validation used 204,710 daily CGM profiles in health, type 1 and type 2 diabetes (T1D, T2D), on different treatments. The CSCs were defined using 23,916 daily CGM profiles (Training set), and the final fixed set of CSCs was obtained using another 37,758 profiles (Validation set). The Testing set (143,036 profiles) was used to establish the robustness and generalizability of the CSCs. The final set of CSCs contains 32 clusters. Any daily CGM profile was classifiable to a single CSC which approximated common glycemic metrics of the daily CGM profile, as evidenced by regression analyses with 0 intercept (R-squares≥0.83, e.g., correlation≥0.91), for all TIR and several other metrics. The CSCs distinguished CGM profiles in health, T2D, and T1D on different treatments, and allowed tracking of the daily changes in a person's glycemic control over time. Daily CGM profiles can be classified into one of 32 prefixed CSCs, which enables a host of applications, e.g. tabulated data interpretation and algorithmic approaches to treatment, database indexing, pattern recognition, and tracking disease progression.

Beyond A1C: exploring continuous glucose monitoring metrics in managing diabetes.

Hemoglobin A1c (HbA1c) has long been considered a cornerstone of diabetes mellitus (DM) management, as both an indicator of average glycemia and a predictor of long-term complications among people with DM. However, HbA1c is subject to non-glycemic influences which confound interpretation and as a measure of average glycemia does not provide information regarding glucose trends or about the occurrence of hypoglycemia and/or hyperglycemia episodes. As such, solitary use of HbA1c, without accompanying glucose data, does not confer actionable information that can be harnessed to guide targeted therapy in many patients with DM. While conventional capillary blood glucose monitoring (BGM) sheds light on momentary glucose levels, in practical use the inherent infrequency of measurement precludes elucidation of glycemic trends or reliable detection of hypoglycemia or hyperglycemia episodes. In contrast, continuous glucose monitoring (CGM) data reveal glucose trends and potentially undetected hypo- and hyperglycemia patterns that can occur between discrete BGM measurements. The use of CGM has grown significantly over the past decades as an ever-expanding body of literature demonstrates a multitude of clinical benefits for people with DM. Continually improving CGM accuracy and ease of use have further fueled the widespread adoption of CGM. Furthermore, percent time in range correlates well with HbA1c, is accepted as a validated indicator of glycemia, and is associated with the risk of several DM complications. We explore the benefits and limitations of CGM use, the use of CGM in clinical practice, and the application of CGM to advanced diabetes technologies.

Diabetes Technology and the Human Factor.

Diabetes Technology and the Human Factor.

ISPAD Clinical Practice Consensus Guidelines 2022: Glycemic targets and glucose monitoring for children, adolescents, and young people with diabetes.

ISPAD Clinical Practice Consensus Guidelines 2022: Glycemic targets and glucose monitoring for children, adolescents, and young people with diabetes.

GlucoseML Mobile Application for Automated Dietary Assessment of Mediterranean Food.

Over the years and with the help of technology, the daily care of type 1 diabetes has been improved significantly. The increased adoption of continuous glucose monitoring, the continuous subcutaneous insulin injection and the accurate behavioral monitoring mHealth solutions have contributed to this phenomenon. In this study we present a mobile application for automated dietary assessment of Mediterranean food images as part of the GlucoseML system. Based on short-term predictive analysis of the glucose trajectory, GlucoseML is a type-1 diabetes self-management system. A computer vision approach is used as main part of the GlucoseML dietary assessment system calculating food carbohydrates, fats and proteins, relying on: (i) a deep learning subsystem for food image classification, and (ii) a 3D food image reconstruction subsystem for the volume estimation of food. The deep learning subsystem achieves 82.4% and 97.5% top-1 and top-5 accuracy, respectively, for food image classification while the subsystem for volume estimation of food achieves a mean absolute percentage error 10.7% for the four main categories of MedGRFood dataset.

Expert Roundtable on Continuous Glucose Monitoring

ObjectiveThe objective of this study was to define an expert opinion on continuous glucose monitoring (CGM) in persons with type 2 diabetes mellitus, including its advantages, barriers, and best clinical practices for initiation, patient-clinician communication, and data management. MethodsA series of virtual discussions was held to recommend improvements to clinical practice and design clinical tools for primary care clinicians. Participants included endocrinologists, primary care physicians, physician assistants, advanced practice nurses, and diabetes care and education specialists. ResultsThe expert panels recommended CGM as a supplement to blood glucose monitoring and hemoglobin A1c for managing diabetes in persons with diabetes (PWDs). CGM can help predict potential pitfalls in glycemic management, including hypo and hyperglycemic excursions, which directly influence lifestyle changes, medication initiation, and dosing decisions. A toolkit was designed with practical guidance on the integration of CGM into clinical practice, interpretation of results, clinical guidelines, a patient action plan, and other useful management tools. ConclusionThis review summarizes the findings from a roundtable discussion with endocrinology and primary care clinicians, a discussion of the advantages and challenges of CGM, and clinical approaches to improving the care of PWDs. CGM offers more detailed tracking of glucose levels than blood glucose monitoring or hemoglobin A1c, and it can detect asymptomatic hypoglycemia. Specialized education of providers, the cost to patients and providers, and data management are barriers to the widespread adoption of CGM for PWDs.

Predicting Glycated Hemoglobin Through Continuous Glucose Monitoring in Real-Life Conditions: Improved Estimation Methods.

The adoption of continuous glucose monitoring (CGM) already helps to improve glycemic control in diabetes. When coupled with appropriate data analysis techniques, CGM also provides dependable estimates for significant metrics, like glycated hemoglobin (HbA1c). Findings from the REALISM-T1D study can boost HbA1c estimation methods in diabetes care and stimulate their use in clinical practice. Continuous glucose monitoring data of 27 adults affected by type-1 diabetes were acquired by means of G6 (Dexcom, San Diego, CA) sensors for a time span of 120 days. Glycated hemoglobin laboratory assays were performed during the concluding follow-up visits. Data were then analyzed to derive estimates of assay results, taken as the gold standard. Bland-Altman (BA) plots show that smart interpolation to patch missing data and a wise choice of interstitial glucose (IG) weighting function, besides a proper mean interstitial glucose (MIG) to HbA1c regression equation, improve HbA1c estimation quality with respect to methods relying on MIG alone. A decrease in the BA plot-related variance of differences with respect to the gold standard confirms the improvement. Wilcoxon signed-rank tests on the bias-compensated mean squared error (MSE) with respect to conventional MIG-based methods show that the improvement is statistically significant with a confidence level better than 95% (P = .0179). Improved HbA1c estimation methods result in better HbA1c prediction quality with respect to those based on MIG alone, thus providing quick, but still relatively accurate feedback to diabetologists. They alleviate the discordances reported in literature and, with further improvements, may become a viable complement/alternative to HbA1c assays.

65-LB: Real-World Continuous Glucose Monitoring Data on Time-in-Range from a U.S. Population, 2015–2019

Aims: Improvements in technology and access have led to growing adoption of continuous glucose monitoring (CGM). Targets for glucose metrics including time in range (TIR) have been published. We aimed to investigate i) the proportion of people with ≥70% TIR and ii) the proportion with ≥70% TIR and Methods: Data were collected from 2015 to 2019 from the Cornerstone4Care (C4C) database, a freely available patient support program for people with both type 1 diabetes (T1D) and type 2 diabetes (T2D) on any treatment type. CGM traces were divided into 14-day periods according to the ambulatory glucose profile (AGP)-reporting system. Only profiles with data aligned with these standards were included. Patient data are presented using the first or the mean of all AGP profiles. Results: In total, 484 persons uploaded CGM-data to the database (4727 AGPs); 242 had T1D and 74 had T2D (self-reported), the rest were unknown (Table). People uploaded between 1 and 75 AGPs (mean=10). Average TIR (70-180 mg/dL) based on mean profiles was 63%, 68% and 64% for T1D, T2D and all, respectively. Conclusions: Less than half of the population achieved ≥70% TIR, ~30% with ≥70% TIR and Disclosure R. M. Bergenstal: Advisory Panel; Self; Abbott Diabetes, Eli Lilly and Company, Novo Nordisk, Onduo LLC., Roche, Sanofi, United Healthcare, Consultant; Self; Abbott Diabetes, Ascensia, Dexcom, Inc., Eli Lilly and Company, Hygieia, Johnson & Johnson, Medtronic, Novo Nordisk, Onduo LLC., Roche, Sanofi, United Healthcare, Other Relationship; Self; HealthPartners Institute, Research Support; Self; Abbott Diabetes, Dexcom, Inc., Eli Lilly and Company, Helmsley Charitable Trust, Hygieia, Johnson & Johnson, Medtronic, NIDDK, Novo Nordisk, Onduo LLC., Roche, Sanofi, United Healthcare. E. Hachmann-nielsen: Employee; Self; Novo Nordisk A/S. J. Tarp: Employee; Self; Novo Nordisk. K. Kvist: Employee; Self; Novo Nordisk A/S. J. B. Buse: Consultant; Self; Cirius Therapeutics, CSL Behring, Fortress Biotch, Mellitus Health, Moderna, Pendulum Therapeutics, Praetego Inc., Stability Health, Zealand Pharma A/S, Other Relationship; Self; AstraZeneca, Eli Lilly and Company, Novo Nordisk, vTv Therapeutics, Research Support; Self; NovaTarg Therapeutics, Novo Nordisk, Sanofi, Tolerion, Inc., vTv Therapeutics, Stock/Shareholder; Self; Mellitus Health, Pendulum Therapeutics, PhaseBio Pharmaceuticals, Inc., Stability Health. Funding Novo Nordisk A/S

Reductions in Management Distress Following a Randomized Distress Intervention Are Associated With Improved Diabetes Behavioral and Glycemic Outcomes Over Time.

OBJECTIVETo explore associations between reductions in diabetes distress (DD) and improvements in glycemic outcomes among adults with type 1 diabetes (T1D) in the context of a DD randomized clinical trial.RESEARCH DESIGN AND METHODSAdults with T1D (N = 301) participated in a two-arm trial aimed at reducing DD (DD-focused OnTrack group vs. education-oriented KnowIt group). Mean age was 45.1 years; mean baseline HbA1c was 8.8% (73 mmol/mol). Individuals were assessed at baseline and 9 months later on DD, self-care, HbA1c, and frequency of hypoglycemia. Structural equation models evaluated hypothesized pathways among changes in DD, self-care, and glycemic outcomes in the total sample and by intervention group.RESULTSReductions in DD were significantly and independently associated with better self-care, including fewer missed insulin boluses, more frequent insulin adjustment, improved problem-solving skills, more blood glucose monitoring, and greater adoption of continuous glucose monitoring (all P < 0.05). In turn, better self-care was linked with better glycemic outcomes, including fewer episodes of hypoglycemia and improved HbA1c over time. Fit indices indicated good fit of the model to the data (confirmatory fit index = 0.94, root mean square error of approximation = 0.05), with stronger and more meaningful associations for OnTrack than for KnowIt.CONCLUSIONSIn the context of an intervention to reduce DD for adults with T1D, results indicate that reductions in DD do not affect glycemic outcomes directly but through improvements in self-care behavior. Findings support the importance of integrating disease management with DD interventions to maximize improvements in glycemic outcomes.

Time in Range in Pregnancy: Is There a Role?

Continuous glucose monitoring (CGM) is being used with increasing frequency as an adjunct to self-monitoring of blood glucose in pregnancy, and novel targets based on CGM data are becoming standardized. This adoption of CGM is the result of its improving accuracy, patient preference, and evolving data demonstrating associations of novel targets such as time in range (TIR) with pregnancy and neonatal outcomes. A greater understanding of the relationship of various CGM metrics to outcomes in pregnancy complicated by diabetes is needed. It is clear that TIR parameters need to be uniquely lower for pregnant women than for nonpregnant individuals. CGM technology is also an integral part of hybrid closed-loop insulin delivery systems. These insulin delivery systems will be a significant advance in the management of diabetes during pregnancy if they can achieve the pre- and postprandial targets required for pregnancy and optimize TIR.

The Prognostic Value of Time in Range in Type 2 Diabetes.

Continuous glucose monitoring (CGM) is the recommended approach to the assessment of hypoglycemia and glycemic variability in the setting of type 1 diabetes (1). The latest generation of CGM systems measure glucose every minute or every few minutes, with devices providing ∼1,300 to up to ∼20,000 measurements per person for a 14-day wear period. With this many measurements per person, the question arises of how to summarize the data in the most clinically useful manner and best communicate the results to patients. Time in range has emerged as a CGM metric with clinical utility in the management of type 1 diabetes (2,3). Time in range is a relatively simple CGM metric, defined as the percent of time spent with glucose values within a target range, usually 70–180 mg/dL (3.9–10 mmol/L), although the target can be individualized. Time in range is automatically calculated and included in CGM system reports, along with percentage of time spent in hypo- and hyperglycemic glucose ranges (i.e., below or above target), average glucose, standard deviation, and coefficient of variation. As costs have come down and device accuracy has improved, the adoption of CGM has increased, with up to 20–30% of some U.S. patient populations with type 1 diabetes currently using the technology (4). In patients with type 2 diabetes, the benefits and role of CGM in clinical management are less clear than in patients with type 1 diabetes. American Diabetes Association guidelines state that CGM may have a role in selected patients with type 2 diabetes, primarily those who are taking multiple daily injections of insulin (5). There …