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cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data
Continuous glucose monitoring (CGM) is an essential part of diabetes care. Real-time CGM data are beneficial to patients for daily glucose management, and aggregate summary statistics of CGM measures are valuable to direct insulin dosing and as a tool for researchers in clinical trials. Yet, the var...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788735/ https://www.ncbi.nlm.nih.gov/pubmed/31603912 http://dx.doi.org/10.1371/journal.pone.0216851 |
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author | Vigers, Tim Chan, Christine L. Snell-Bergeon, Janet Bjornstad, Petter Zeitler, Philip S. Forlenza, Gregory Pyle, Laura |
author_facet | Vigers, Tim Chan, Christine L. Snell-Bergeon, Janet Bjornstad, Petter Zeitler, Philip S. Forlenza, Gregory Pyle, Laura |
author_sort | Vigers, Tim |
collection | PubMed |
description | Continuous glucose monitoring (CGM) is an essential part of diabetes care. Real-time CGM data are beneficial to patients for daily glucose management, and aggregate summary statistics of CGM measures are valuable to direct insulin dosing and as a tool for researchers in clinical trials. Yet, the various commercial systems still report CGM data in disparate, non-standard ways. Accordingly, there is a need for a standardized, free, open-source approach to CGM data management and analysis. A package titled cgmanalysis was developed in the free programming language R to provide a rapid, easy, and consistent methodology for CGM data management, summary measure calculation, and descriptive analysis. Variables calculated by our package compare well to those generated by various CGM software, and our functions provide a more comprehensive list of summary measures available to clinicians and researchers. Consistent handling of CGM data using our R package may facilitate collaboration between research groups and contribute to a better understanding of free-living glucose patterns. |
format | Online Article Text |
id | pubmed-6788735 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-67887352019-10-25 cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data Vigers, Tim Chan, Christine L. Snell-Bergeon, Janet Bjornstad, Petter Zeitler, Philip S. Forlenza, Gregory Pyle, Laura PLoS One Research Article Continuous glucose monitoring (CGM) is an essential part of diabetes care. Real-time CGM data are beneficial to patients for daily glucose management, and aggregate summary statistics of CGM measures are valuable to direct insulin dosing and as a tool for researchers in clinical trials. Yet, the various commercial systems still report CGM data in disparate, non-standard ways. Accordingly, there is a need for a standardized, free, open-source approach to CGM data management and analysis. A package titled cgmanalysis was developed in the free programming language R to provide a rapid, easy, and consistent methodology for CGM data management, summary measure calculation, and descriptive analysis. Variables calculated by our package compare well to those generated by various CGM software, and our functions provide a more comprehensive list of summary measures available to clinicians and researchers. Consistent handling of CGM data using our R package may facilitate collaboration between research groups and contribute to a better understanding of free-living glucose patterns. Public Library of Science 2019-10-11 /pmc/articles/PMC6788735/ /pubmed/31603912 http://dx.doi.org/10.1371/journal.pone.0216851 Text en © 2019 Vigers et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Vigers, Tim Chan, Christine L. Snell-Bergeon, Janet Bjornstad, Petter Zeitler, Philip S. Forlenza, Gregory Pyle, Laura cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data |
title | cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data |
title_full | cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data |
title_fullStr | cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data |
title_full_unstemmed | cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data |
title_short | cgmanalysis: An R package for descriptive analysis of continuous glucose monitor data |
title_sort | cgmanalysis: an r package for descriptive analysis of continuous glucose monitor data |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788735/ https://www.ncbi.nlm.nih.gov/pubmed/31603912 http://dx.doi.org/10.1371/journal.pone.0216851 |
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