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cgmquantify: R package for analyzing glucose and glucose variability

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.

User Guide Issue Tracking Installation: Recommended: install_github("marhenriq/cgmquantify")

Coming soon -

Currently only supports Dexcom CGM, more CGM coming soon Integration with food logs, myFitnessPal food logs Machine Learning methods for discovering trends in CGM data

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Version

Install

install.packages('cgmquantify')

Monthly Downloads

273

Version

0.1.0

License

MIT License + file LICENSE

Maintainer

Maria Henriquez

Last Published

February 5th, 2021

Functions in cgmquantify (0.1.0)

TIR

Compute Time Inside Range
MGN

Compute Mean of Normal Glucose
eA1c

Compute Estimated A1c
LBGI_HBGI

Compute Low Blood Glucose Index
intradaycv

Compute Intraday Coefficient of Variation
MGE

Compute Mean of Glycemic Excursions
LBGI

Compute Low Blood Glucose Index
interdaycv

Compute Interday Coefficient of Variation
readfile

Read in Data Frame
POR

Compute Percent of Time Outside Range
summary_glucose

Compute Glucose Summary Statistics
GMI

Compute Glycemic Management Indicator
HBGI

Compute High Blood Glucose Index
plot_glucose

Plot Glucose Data
intradaysd

Compute Intraday Standard Deviation
interdaysd

Compute Interday Standard Deviation
J_index

Compute J-index
TOR

Compute Time Outside Range