A New Analysis Tool for Continuous Glucose Monitor Data

Evan Olawsky, Yuan Zhang, Lynn E. Eberly, Erika S. Helgeson, Lisa S. Chow

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Background: With the development of continuous glucose monitoring systems (CGMS), detailed glycemic data are now available for analysis. Yet analysis of this data-rich information can be formidable. The power of CGMS-derived data lies in its characterization of glycemic variability. In contrast, many standard glycemic measures like hemoglobin A1c (HbA1c) and self-monitored blood glucose inadequately describe glycemic variability and run the risk of bias toward overreporting hyperglycemia. Methods that adjust for this bias are often overlooked in clinical research due to difficulty of computation and lack of accessible analysis tools. Methods: In response, we have developed a new R package rGV, which calculates a suite of 16 glycemic variability metrics when provided a single individual’s CGM data. rGV is versatile and robust; it is capable of handling data of many formats from many sensor types. We also created a companion R Shiny web app that provides these glycemic variability analysis tools without prior knowledge of R coding. We analyzed the statistical reliability of all the glycemic variability metrics included in rGV and illustrate the clinical utility of rGV by analyzing CGM data from three studies. Results: In subjects without diabetes, greater glycemic variability was associated with higher HbA1c values. In patients with type 2 diabetes mellitus (T2DM), we found that high glucose is the primary driver of glycemic variability. In patients with type 1 diabetes (T1DM), we found that naltrexone use may potentially reduce glycemic variability. Conclusions: We present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.

Original languageEnglish (US)
Pages (from-to)1496-1504
Number of pages9
JournalJournal of Diabetes Science and Technology
Issue number6
Early online dateJul 20 2021
StatePublished - Jul 20 2021

Bibliographical note

Funding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the National Institutes of Health (1R01-DK099137). Research reported in this publication was supported by the Healthy Foods Healthy Lives program (17SFR-2YR50LC to LSC), the Academic Health Center (AHC-FRD-17-08 to LSC), the National Institutes of Health (NIH National Center for Advancing Translational Sciences, UL1TR002494 and UL1TR000114), the American Diabetes Association, and Clinical and Translational Science Award 5KL2TR113. Clinical trials registration numbers for the three trials whose data was used in this work are: NCT03129581, NCT01053078, and NCT03481530.

Publisher Copyright:
© 2021 Diabetes Technology Society.


  • R
  • continuous glucose monitoring
  • glycemic variability


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