scholarly journals Glucose management for exercise using continuous glucose monitoring: should sex and prandial state be additional considerations?

Diabetologia ◽  
2021 ◽  
Author(s):  
Jane E. Yardley ◽  
Ronald J. Sigal
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glucose Management

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 Clinical Need for Continuous Glucose Monitoring in the Hospital

2009 ◽  
Vol 3 (6) ◽  
pp. 1309-1318 ◽  
Author(s):  
Jeffrey I Joseph ◽  
Brian Hipszer ◽  
Boris Mraovic ◽  
Inna Chervoneva ◽  
Mark Joseph ◽  
...  
Keyword(s):  
Critical Care ◽  
Glycemic Control ◽  
Clinical Outcome ◽  
Continuous Glucose Monitoring ◽  
Patient Population ◽  
Glucose Monitoring ◽  
Glucose Measurement ◽  
Hospital Patient ◽  
Critical Care Units ◽  
Glucose Management

Automation and standardization of the glucose measurement process have the potential to greatly improve glycemic control, clinical outcome, and safety while reducing cost. The resources required to monitor glycemia in hospitalized patients have thus far limited the implementation of intensive glucose management to patients in critical care units. Numerous available and up-and-coming technologies are targeted for the hospital patient population. Advantages and limitations of these devices are discussed herewith in.

scholarly journals Diabetes Healthcare Professionals Use Multiple Continuous Glucose Monitoring Data Indicators to Assess Glucose Management

2019 ◽  
Vol 14 (2) ◽  
pp. 271-276 ◽  
Author(s):  
Tong Sheng ◽  
Reid Offringa ◽  
David Kerr ◽  
Mark Clements ◽  
Jerome Fischer ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Healthcare Professionals ◽  
Rank Order ◽  
Glucose Monitoring ◽  
Absolute Error ◽  
Multiple Data ◽  
Time In Range ◽  
Glucose Management ◽  
Diabetes Educators ◽  
Glycemic Management

Background: Continuous glucose monitoring (CGM) offers multiple data features that can be leveraged to assess glucose management. However, how diabetes healthcare professionals (HCPs) actually assess CGM data and the extent to which they agree in assessing glycemic management are not well understood. Methods: We asked HCPs to assess ten de-identified CGM datasets (each spanning seven days) and rank order each day by relative glycemic management (from “best” to “worst”). We also asked HCPs to endorse features of CGM data that were important in making such assessments. Results: In the study, 57 HCPs (29 endocrinologists; 28 diabetes educators) participated. Hypoglycemia and glycemic variance were endorsed by nearly all HCPs to be important (91% and 88%, respectively). Time in range and daily lows and highs were endorsed more frequently by educators (all Ps < .05). On average, HCPs endorsed 3.7 of eight data features. Overall, HCPs demonstrated agreement in ranking days by relative glycemic control (Kendall’s W = .52, P < .001). Rankings were similar between endocrinologists and educators ( R2 = .90, Cohen’s kappa = .95, mean absolute error = .4 [all Ps < .05]; Mann-Whitney U = 41, P = .53). Conclusions: Consensus in the endorsement of certain data features and agreement in assessing glycemic management were observed. While some practice-specific differences in feature endorsement were found, no differences between educators and endocrinologists were observed in assessing glycemic management. Overall, HCPs tended to consider CGM data holistically, in alignment with published recommendations, and made converging assessments regardless of practice.

scholarly journals Time in Specific Glucose Ranges, Glucose Management Indicator, and Glycemic Variability: Impact of Continuous Glucose Monitoring (CGM) System Model and Sensor on CGM Metrics

2020 ◽  
pp. 193229682093182
Author(s):  
Stefan Pleus ◽  
Ulrike Kamecke ◽  
Delia Waldenmaier ◽  
Manuela Link ◽  
Eva Zschornack ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
International Consensus ◽  
Freestyle Libre ◽  
Pairwise Differences ◽  
Assess Quality ◽  
Glucose Management ◽  
Post Hoc ◽  
The Impact

Background: International consensus recommends a set of continuous glucose monitoring (CGM) metrics to assess quality of diabetes therapy. The impact of individual CGM sensors on these metrics has not been thoroughly studied yet. This post hoc analysis aimed at comparing time in specific glucose ranges, coefficient of variation (CV) of glucose concentrations, and glucose management indicator (GMI) between different CGM systems and different sensors of the same system. Method: A total of 20 subjects each wore two Dexcom G5 (G5) sensors and two FreeStyle Libre (FL) sensors for 14 days in parallel. Times in ranges, GMI, and CV were calculated for each 14-day sensor experiment, with up to four sensor experiments per subject. Pairwise differences between different sensors of the same CGM system as well as between sensors of different CGM system were calculated for these metrics. Results: Pairwise differences between sensors of the same model showed larger differences and larger variability for FL than for G5, with some subjects showing considerable differences between the two sensors. When pairwise differences between sensors of different CGM models were calculated, substantial differences were found in some subjects (75th percentiles of differences of time spent <70 mg/dL: 5.0%, time spent >180 mg/dL: 9.2%, and GMI: 0.42%). Conclusion: Relevant differences in CGM metrics between different models of CGM systems, and between different sensors of the same model, worn by the same study subjects were found. Such differences should be taken into consideration when these metrics are used in the treatment of diabetes.

scholarly journals Glucose management for exercise using continuous glucose monitoring: should sex and prandial state be additional considerations? Reply to Yardley JE and Sigal RJ [letter]

Diabetologia ◽  
2021 ◽  
Vol 64 (4) ◽  
pp. 935-938
Author(s):  
Othmar Moser ◽  
Michael C. Riddell ◽  
Max L. Eckstein ◽  
Peter Adolfsson ◽  
Rémi Rabasa-Lhoret ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Glucose Management

scholarly journals The Importance of Time in Range (TIR) for Continuous Glucose Monitoring (CGM) in the Clinical Practice for Diabetes

2021 ◽  
pp. 12-13
Author(s):  
Bando Hiroshi
Keyword(s):  
Clinical Practice ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Coefficient Of Variation ◽  
Hba1c Level ◽  
Glucose Monitoring ◽  
Glycemic Variability ◽  
Glucose Profile ◽  
Time In Range ◽  
Glucose Management

As to the development of treatment for diabetes, Continuous Glucose Monitoring (CGM) has been recently prevalent rapidly. By the analysis of real-time CGM, Ambulatory Glucose Profile (AGP) has been used. It includes time in range (TIR, 70-180 mg/dL), time above range (TAR, >181mg/dL), time below range (TBR, <69 mg/dL), Glycemic Variability (GV), Glucose Management Indicator (GMI), Glycemic variability, Coefficient Of Variation (CV%) and so on. TIR value indicating approximately 70% seems to correlate closely with the HbA1c level of 6.77.0%. Marked discordance of HbA1c values has been found between laboratory HbA1c and estimated HbA1c (eA1c) using GMI from CGM.

scholarly journals Glucose Management during Insulinoma Resection Using Real-Time Subcutaneous Continuous Glucose Monitoring

2018 ◽  
Vol 2018 ◽  
pp. 1-4
Author(s):  
Yuki Sugiyama ◽  
Chiaki Kiuchi ◽  
Maiko Suzuki ◽  
Yuki Maruyama ◽  
Ryo Wakabayashi ◽  
...  
Keyword(s):  
Blood Glucose ◽  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Blood Glucose Measurement ◽  
Glucose Measurement ◽  
Glucose Loading ◽  
Before And After ◽  
Interstitial Glucose ◽  
Glucose Management

Insulinoma is a rare neuroendocrine tumor that causes hypoglycemia due to unregulated insulin secretion. Blood glucose management during insulinoma resection is therefore challenging. We present a case in which real-time subcutaneous continuous glucose monitoring (SCGM) in combination with intermittent blood glucose measurement was used for glycemic control during surgery for insulinoma resection. The SCGM system showed the trends and peak of interstitial glucose in response to glucose loading and the change of interstitial glucose before and after insulinoma resection. These data were helpful for adjusting the glucose infusion; therefore, we think that an SCGM system as a supportive device for glucose monitoring may be useful for glucose management during surgery.

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