Virtual visits and the use of continuous glucose monitoring for diabetes care in the era of COVID-19

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Patricia Underwood ◽  
Jennifer Hibben ◽  
Jolynn Gibson ◽  
Monica DiNardo
Keyword(s):  
Continuous Glucose Monitoring ◽  
Diabetes Care ◽  
Glucose Monitoring ◽  
Virtual Visits

scholarly journals Response to Comment on Bergenstal et al. Glucose Management Indicator (GMI): A New Term for Estimating A1C From Continuous Glucose Monitoring. Diabetes Care 2018;41:2275–2280

Diabetes Care ◽  
2018 ◽  
Vol 42 (2) ◽  
pp. e29-e30
Author(s):  
Richard M. Bergenstal ◽  
Roy W. Beck ◽  
Kelly L. Close ◽  
George Grunberger ◽  
David B. Sacks ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Diabetes Care ◽  
Glucose Monitoring ◽  
Glucose Management

scholarly journals Accuracy of flash glucose monitoring and continuous glucose monitoring technologies: Implications for clinical practice

2018 ◽  
Vol 15 (3) ◽  
pp. 175-184 ◽  
Author(s):  
Ramzi A Ajjan ◽  
Michael H Cummings ◽  
Peter Jennings ◽  
Lalantha Leelarathna ◽  
Gerry Rayman ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Diabetes Care ◽  
Interstitial Fluid ◽  
Glucose Monitoring ◽  
Relative Difference ◽  
Glucose Sensing ◽  
Sensor Data ◽  
Flash Glucose Monitoring ◽  
Fluid Sensor ◽  
Monitoring Technologies

Continuous glucose monitoring and flash glucose monitoring technologies measure glucose in the interstitial fluid and are increasingly used in diabetes care. Their accuracy, key to effective glycaemic management, is usually measured using the mean absolute relative difference of the interstitial fluid sensor compared to reference blood glucose readings. However, mean absolute relative difference is not standardised and has limitations. This review aims to provide a consensus opinion on assessing accuracy of interstitial fluid glucose sensing technologies. Mean absolute relative difference is influenced by glucose distribution and rate of change; hence, we express caution on the reliability of comparing mean absolute relative difference data from different study systems and conditions. We also review the pitfalls associated with mean absolute relative difference at different glucose levels and explore additional ways of assessing accuracy of interstitial fluid devices. Importantly, much data indicate that current practice of assessing accuracy of different systems based on individualised mean absolute relative difference results has limitations, which have potential clinical implications. Healthcare professionals must understand the factors that influence mean absolute relative difference as a metric for accuracy and look at additional assessments, such as consensus error grid analysis, when evaluating continuous glucose monitoring and flash glucose monitoring systems in diabetes care. This in turn will ensure that management decisions based on interstitial fluid sensor data are both effective and safe.

scholarly journals Continuous Glucose Monitoring Use in Clinical Trials for On-Market Diabetes Drugs

2021 ◽  
Author(s):  
Bradley Q. Fox ◽  
Peninah F. Benjamin ◽  
Ammara Aqeel ◽  
Emily Fitts ◽  
Spencer Flynn ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Clinical Practice ◽  
Continuous Glucose Monitoring ◽  
Diabetes Care ◽  
Glucose Monitoring ◽  
Published Data ◽  
Start Date ◽  
Treatment Of Diabetes ◽  
Pharmacologic Agents ◽  
Over Time

Despite the growing momentum behind a movement to augment adoption of continuous glucose monitoring (CGM) in clinical practice and investigation, to the best of our knowledge, there are no published data on the historical and recent use of CGM in clinical trials of pharmacologic agents used in the treatment of diabetes. We analyzed 2,032 clinical trials of 40 diabetes therapies currently on the market with a study start date between 1 January 2000 and 31 December 2019. According to ClinicalTrials.gov listings, 119 (5.9%) of these trials used CGM. CGM usage in clinical trials has increased over time, rising from <5% before 2005 to 12.5% in 2019. However, it is still low given its inclusion in the American Diabetes’s Association’s latest guidelines and known limitations of A1C for assessing ongoing diabetes care.

Analysis of “Multicenter Evaluation Study Comparing a New Factory-Calibrated Real-Time Continuous Glucose Monitoring System to Existing Flash Glucose Monitoring System”

2021 ◽  
pp. 193229682110460
Author(s):  
Thomas Peyser
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Continuous Glucose Monitoring ◽  
Diabetes Care ◽  
Glucose Monitoring ◽  
Evaluation Study ◽  
Continuous Glucose Monitoring System ◽  
Flash Glucose Monitoring ◽  
Sensor Performance ◽  
Flash Glucose Monitoring System

In an article in the Journal of Diabetes Science and Technology, Ji et al. report on the accuracy of a new factory calibrated continuous glucose monitoring system, the AiDEX CGM (Microtech Medical Company, Ltd., Hangzhou, China). This is the first report from a new manufacturer of a highly accurate factory calibrated CGM. The authors report that the accuracy of the AiDEX CGM is comparable to previous results from Abbott Diabetes Care and Dexcom. However, the study protocol was significantly different from previous studies. This study is the first of numerous studies likely from other manufacturers of CGM technology. It raises the question of how to evaluate sensor performance in the coming era of mass adoption of CGM and increased use of automated insulin delivery systems.

scholarly journals Using Cluster Analysis to Understand Clinician Readiness to Promote Continuous Glucose Monitoring Adoption

2018 ◽  
Vol 12 (6) ◽  
pp. 1108-1115 ◽  
Author(s):  
Molly L. Tanenbaum ◽  
Rebecca N. Adams ◽  
Monica S. Lanning ◽  
Sarah J. Hanes ◽  
Bianca I. Agustin ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Diabetes Care ◽  
Glucose Monitoring ◽  
Patient Characteristics ◽  
Care Providers ◽  
Additional Time ◽  
Chi Square ◽  
Technology Attitudes ◽  
Descriptive Names ◽  
Technology Advances

Background: Many people with type 1 diabetes (T1D) report barriers to using continuous glucose monitoring (CGM). Diabetes care providers may have their own barriers to promoting CGM uptake. The goal of this study was to develop clinician “personas” with regard to readiness to promote CGM uptake. Methods: Diabetes care providers who treat people with T1D (N = 209) completed a survey on perceived patient barriers to device uptake, technology attitudes, and characteristics and barriers specific to their clinical practice. K-means cluster analyses grouped the sample by CGM barriers and attitudes. ANOVAs and chi-square tests assessed group differences on provider and patient characteristics. The authors assigned descriptive names for each persona. Results: Analyses yielded three clinician personas regarding readiness to promote CGM uptake. Ready clinicians (20% of sample; 24% physicians, 38% certified diabetes educators/CDEs) had positive technology attitudes, had clinic time to work with patients using CGM, and found it easy to keep up with technology advances. In comparison, Cautious clinicians (41% of sample; 17% physicians, 53% CDEs) perceived that their patients had many barriers to adopting CGM and had less time than the Ready group to work with patients using CGM data. Not Yet Ready clinicians (40% of sample; 9% physicians; 79% CDEs) had negative technology attitudes and the least clinic time to work with CGM data. They found it difficult to keep up with technology advances. Conclusion: Some diabetes clinicians may benefit from tailored interventions and additional time and resources to empower them to help facilitate increased uptake of CGM technology.

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