Continuous Glucose Monitoring Time Series Data Analysis: A Time Series Analysis Package for Continuous Glucose Monitoring Data

Jian Shao; Ziqing Liu; Shaoyun Li; Benrui Wu; Zedong Nie; Yuefei Li; Kaixin Zhou

doi:10.1089/cmb.2022.0100

Continuous Glucose Monitoring Time Series Data Analysis: A Time Series Analysis Package for Continuous Glucose Monitoring Data

J Comput Biol. 2023 Jan;30(1):112-116. doi: 10.1089/cmb.2022.0100. Epub 2022 Aug 8.

Authors

Jian Shao¹, Ziqing Liu¹, Shaoyun Li², Benrui Wu³, Zedong Nie⁴, Yuefei Li², Kaixin Zhou¹

Affiliations

¹ Department of Life Sciences, University of Chinese Academy of Sciences, Beijing, China.
² Chongqing Fifth People's Hospital, Chongqing, China.
³ Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
⁴ Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

PMID: 35939283
DOI: 10.1089/cmb.2022.0100

Abstract

The R package Continuous Glucose Monitoring Time Series Data Analysis (CGMTSA) was developed to facilitate investigations that examine the continuous glucose monitoring (CGM) data as a time series. Accordingly, novel time series functions were introduced to (1) enable more accurate missing data imputation and outlier identification; (2) calculate recommended CGM metrics as well as key time series parameters; (3) plot interactive and three-dimensional graphs that allow direct visualizations of temporal CGM data and time series model optimization. The software was designed to accommodate all popular CGM devices and support all common data processing steps. The program is available for Linux, Windows, and Mac at GitHub.

Keywords: CGM data visualization; CGM metrics; continuous glucose monitoring; quality control; time series analysis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Blood Glucose Self-Monitoring* / methods
Blood Glucose* / analysis
Software
Time Factors

Substances

Blood Glucose