Interpreting blood GLUcose data with R package iglu

Continuous Glucose Monitoring (CGM) data play an increasing role in clinical practice as they provide detailed quantification of blood glucose levels during the entire 24-hour period. The R package iglu implements a wide range of CGM-derived metrics for measuring glucose control and glucose variabil...

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Veröffentlicht in:PloS one Jg. 16; H. 4; S. e0248560
Hauptverfasser: Broll, Steven, Urbanek, Jacek, Buchanan, David, Chun, Elizabeth, Muschelli, John, Punjabi, Naresh M., Gaynanova, Irina
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 01.04.2021
Public Library of Science (PLoS)
Schlagworte:
Accessibility
Biology and Life Sciences
Blood sugar monitoring
Computer and Information Sciences
Computer programs
Data collection
Data processing
Data visualization
Diabetes
Engineering and Technology
Exact solutions
Glucose
Graphical user interface
Healthcare industry software
Hyperglycemia
Medicine and Health Sciences
Methods
Open source software
Physical Sciences
Programming languages
R (Programming language)
Reliability analysis
Science Policy
Software
Source code
Standard deviation
Technology application
User requirements
United States
United States > US
Texas
Baltimore Maryland
ISSN:1932-6203, 1932-6203
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Zusammenfassung:Continuous Glucose Monitoring (CGM) data play an increasing role in clinical practice as they provide detailed quantification of blood glucose levels during the entire 24-hour period. The R package iglu implements a wide range of CGM-derived metrics for measuring glucose control and glucose variability. The package also allows one to visualize CGM data using time-series and lasagna plots. A distinct advantage of iglu is that it comes with a point-and-click graphical user interface (GUI) which makes the package widely accessible to users regardless of their programming experience. Thus, the open-source and easy to use iglu package will help advance CGM research and CGM data analyses. R package iglu is publicly available on CRAN and at https://github.com/irinagain/iglu .
Bibliographie:ObjectType-Article-1
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Current address: Miller School of Medicine, University of Miami, Miami, FL, United States of America
Competing Interests: The authors have declared that no competing interests exist.
Current address: Department of Statistics and Data Science, Cornell University, Ithaca, NY, United States of America
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0248560