Continuous glucose monitoring (CGM) systems provide real-time, dynamic glucose information by tracking interstitial glucose values throughout the day. Glycemic variability, also known as glucose variability, is an established risk factor for hypoglycemia (Kovatchev) and has been shown to be a risk factor in diabetes complications. Over 20 metrics of glycemic variability have been identified. Here, we provide functions to calculate glucose summary metrics, glucose variability metrics (as defined in clinical publications), and visualizations to visualize trends in CGM data. Cho P, Bent B, Wittmann A, et al. (2020) <https://diabetes.diabetesjournals.org/content/69/Supplement_1/73-LB.abstract> American Diabetes Association (2020) <https://professional.diabetes.org/diapro/glucose_calc> Kovatchev B (2019) <doi:10.1177/1932296819826111> Kovdeatchev BP (2017) <doi:10.1038/nrendo.2017.3> Tamborlane W V., Beck RW, Bode BW, et al. (2008) <doi:10.1056/NEJMoa0805017> Umpierrez GE, P. Kovatchev B (2018) <doi:10.1016/j.amjms.2018.09.010>.
Version: 0.1.0 Depends: R (≥ 2.10) Imports: dplyr, tidyverse, ggplot2, hms, stats, magrittr Suggests: testthat (≥ 2.0.0), knitr, rmarkdown Published: 2021-02-05 DOI: 10.32614/CRAN.package.cgmquantify Author: Maria Henriquez [aut, com, cph, cre, trl], Brinnae Bent [aut, cph, dtc] Maintainer: Maria Henriquez <marhenriq at gmail.com> License: MIT License + file LICENSE NeedsCompilation: no Materials: README CRAN checks: cgmquantify results Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=cgmquantify to link to this page.
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