Literature DB >> 33615834

Variation of Mean Absolute Relative Differences of Continuous Glucose Monitoring Systems Throughout the Day.

Stefan Pleus1, Andreas Stuhr2, Manuela Link1, Cornelia Haug1, Guido Freckmann1.   

Abstract

BACKGROUND: There is an increasing use of continuous glucose monitoring (CGM) by people with diabetes. Measurement performance is often characterized by the mean absolute relative difference (MARD). However, MARD is influenced by a number of factors and little is known about whether MARD is stable throughout the day.
MATERIAL AND METHODS: A total of 24 participants with type 1 diabetes were enrolled in the study. The study was performed for seven in-patient days. Participants wore two CGM systems in parallel and performed additional frequent blood glucose (BG) measurements. On two days, glucose excursions were induced.MARD was calculated between pairs of CGM and BG values, with BG values serving as reference values. ARD values calculated from CGM-BG pairs were grouped by hour of the day. Results were analyzed separately for glucose excursion days and for regular days.
RESULTS: Total MARDs for the complete study duration were 12.5% ± 3.6% and 13.2% ± 2.4% (n = 24). Throughout the day marked variability of MARD was observed (8.0% ± 1.3%-16.3% ± 2.9% (G5); 9.1% ± 1.4%-16.3% ± 5.3% (FL), up to n = 157 each). Low(est) MARD values were observed before breakfast and dinner, when subjects were in or near a fasting state. Especially after breakfast and lunch, MARD values were higher than average.
CONCLUSIONS: Analytical performance of the two CGM systems, assessed by MARD, was found to vary markedly throughout the day. Activities of daily life likely triggered these variations. An increasing number of CGM users base therapeutic decisions on CGM values, and they should be aware of these variations of performance throughout the day.

Entities:  

Keywords:  circadian rhythm; continuous glucose monitoring; mean absolute relative difference; type 1 diabetes

Mesh:

Substances:

Year:  2021        PMID: 33615834      PMCID: PMC9294578          DOI: 10.1177/1932296821992373

Source DB:  PubMed          Journal:  J Diabetes Sci Technol        ISSN: 1932-2968


  18 in total

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Authors:  Harald Kirchsteiger; Lutz Heinemann; Guido Freckmann; Volker Lodwig; Günther Schmelzeisen-Redeker; Michael Schoemaker; Luigi Del Re
Journal:  J Diabetes Sci Technol       Date:  2015-09-01

2.  Discrepancies between methods of continuous glucose monitoring in key metrics of glucose control in children with type 1 diabetes.

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Journal:  Pediatr Diabetes       Date:  2019-04-17       Impact factor: 4.866

3.  Time lag of glucose from intravascular to interstitial compartment in type 1 diabetes.

Authors:  Ananda Basu; Simmi Dube; Sona Veettil; Michael Slama; Yogish C Kudva; Thomas Peyser; Rickey E Carter; Claudio Cobelli; Rita Basu
Journal:  J Diabetes Sci Technol       Date:  2014-10-10

4.  Significance and Reliability of MARD for the Accuracy of CGM Systems.

Authors:  Florian Reiterer; Philipp Polterauer; Michael Schoemaker; Guenther Schmelzeisen-Redecker; Guido Freckmann; Lutz Heinemann; Luigi Del Re
Journal:  J Diabetes Sci Technol       Date:  2016-09-25

5.  Rate-of-Change Dependence of the Performance of Two CGM Systems During Induced Glucose Swings.

Authors:  Stefan Pleus; Michael Schoemaker; Karin Morgenstern; Günther Schmelzeisen-Redeker; Cornelia Haug; Manuela Link; Eva Zschornack; Guido Freckmann
Journal:  J Diabetes Sci Technol       Date:  2015-04-07

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

Authors:  Stefan Pleus; Ulrike Kamecke; Delia Waldenmaier; Manuela Link; Eva Zschornack; Nina Jendrike; Cornelia Haug; Guido Freckmann
Journal:  J Diabetes Sci Technol       Date:  2020-06-08

7.  Head-to-head comparison between flash and continuous glucose monitoring systems in outpatients with type 1 diabetes.

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Journal:  J Endocrinol Invest       Date:  2016-06-10       Impact factor: 4.256

8.  HbA1c Levels in Type 1 Diabetes from Early Childhood to Older Adults: A Deeper Dive into the Influence of Technology and Socioeconomic Status on HbA1c in the T1D Exchange Clinic Registry Findings.

Authors:  Kellee M Miller; Roy W Beck; Nicole C Foster; David M Maahs
Journal:  Diabetes Technol Ther       Date:  2020-09       Impact factor: 6.118

9.  Accuracy of two continuous glucose monitoring systems: a head-to-head comparison under clinical research centre and daily life conditions.

Authors:  J Kropff; D Bruttomesso; W Doll; A Farret; S Galasso; Y M Luijf; J K Mader; J Place; F Boscari; T R Pieber; E Renard; J H DeVries
Journal:  Diabetes Obes Metab       Date:  2014-09-10       Impact factor: 6.577

10.  The Performance and Usability of a Factory-Calibrated Flash Glucose Monitoring System.

Authors:  Timothy Bailey; Bruce W Bode; Mark P Christiansen; Leslie J Klaff; Shridhara Alva
Journal:  Diabetes Technol Ther       Date:  2015-07-14       Impact factor: 6.118

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  1 in total

1.  Data Obtained with Early Generations of CGM Sensors: Comment on Pleus et al.

Authors:  Alexander Seibold
Journal:  J Diabetes Sci Technol       Date:  2021-07-21
  1 in total

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