Centralized Remote Monitoring of Continuous Glucose Monitoring Data at Diabetes Camp Mitigates Hypoglycemia

2021 ◽  
pp. 193229682110095
Author(s):  
Sarah Gleich ◽  
Nate Gibson ◽  
Sarah Puhr ◽  
Tomas C. Walker ◽  
Dan Caruso
Keyword(s):  
Continuous Glucose Monitoring ◽  
Remote Monitoring ◽  
Glucose Monitoring ◽  
Monitoring Data ◽  
Diabetes Camp

948-P: Analysis of Key Metrics for Continuous Glucose Monitoring Data in Adults without Diabetes

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 948-P
Author(s):  
GUIDO FRECKMANN ◽  
STEFAN PLEUS ◽  
SINA ULBRICH ◽  
DELIA WALDENMAIER ◽  
ANNETTE BAUMSTARK ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Monitoring Data

860-P: Benefits of Real-Time Sharing and Following of Continuous Glucose Monitoring Data from Youth

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 860-P
Author(s):  
ANDREW PARKER ◽  
MARK DERDZINSKI ◽  
SARAH PUHR ◽  
JOHN WELSH ◽  
TOMAS C. WALKER ◽  
...  
Keyword(s):  
Real Time ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Monitoring Data ◽  
Time Sharing

280-OR: Machine Learning to Predict Hypoglycemic Events from Continuous Glucose Monitoring Data

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 280-OR
Author(s):  
PRATIK AGRAWAL ◽  
LINGTAO CAO ◽  
YUAN-CHI CHANG ◽  
GRETCHEN P. JACKSON ◽  
SARAH KEFAYATI ◽  
...  
Keyword(s):  
Machine Learning ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Monitoring Data

scholarly journals Continuous glucose monitoring system: dawn period calibration does not change accuracy of the method

2009 ◽  
Vol 53 (4) ◽  
pp. 425-428
Author(s):  
Gustavo A. Augusto ◽  
André G. P. Sousa ◽  
Marcela N. A. Perazo ◽  
Maria L. C. Correa-Giannella ◽  
Marcia Nery ◽  
...  
Keyword(s):  
Monitoring System ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Monitoring Data ◽  
Continuous Glucose Monitoring System ◽  
Absolute Difference ◽  
Group A ◽  
Capillary Glucose ◽  
Glucose Meter ◽  
Group B

INTRODUCTION: Continuous glucose monitoring system is a valuable instrument to measure glycemic control, which uses a retrospective calibration based upon 3 to 4 capillary glucose meter values inserted by the patient each day. OBJECTIVE: We evaluated the interference of calibration during the dawn period in the system accuracy. METHODS: The monitoring data were retrospectively divided into two groups: with (Group A) or without (Group B) the dawn period calibration (between 1:00 and 5:00 AM). Accuracy of the method was expressed by relative absolute difference. RESULTS: Thirty-four continuous glucose monitoring data were evaluated comprising a total of 112 nights. A total of 289 paired readings were analyzed - 195 in Group A and 94 in Group B. We did not find a difference in relative absolute difference (RAD%) in any analyzed period of day by adding dawn calibration. CONCLUSIONS: These data suggest that dawn calibration does not alter accuracy of method.

Analysis of Continuous Glucose Monitoring Data from Non-Diabetic and Diabetic Children: A Tale of Two Algorithms

2003 ◽  
Vol 5 (3) ◽  
pp. 375-380 ◽  
Author(s):  
Stuart A. Weinzimer ◽  
Maria C. DeLucia ◽  
Elizabeth A. Boland ◽  
Amy Steffen ◽  
William V. Tamborlane
Keyword(s):  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Monitoring Data ◽  
Diabetic Children

A Dynamic Risk Measure from Continuous Glucose Monitoring Data

2011 ◽  
Vol 13 (8) ◽  
pp. 843-852 ◽  
Author(s):  
Stefania Guerra ◽  
Giovanni Sparacino ◽  
Andrea Facchinetti ◽  
Michele Schiavon ◽  
Chiara Dalla Man ◽  
...  
Keyword(s):  
Continuous Glucose Monitoring ◽  
Risk Measure ◽  
Glucose Monitoring ◽  
Monitoring Data ◽  
Dynamic Risk Measure ◽  
Dynamic Risk
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