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. 2025 May;19(3):787-809.
doi: 10.1177/19322968231221803. Epub 2024 Jan 5.

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review

Mikkel Thor Olsen  1 , Carina Kirstine Klarskov  1 , Arnold Matovu Dungu  2 , Katrine Bagge Hansen  3 , Ulrik Pedersen-Bjergaard  1   4 , Peter Lommer Kristensen  1   4
Affiliations

Statistical Packages and Algorithms for the Analysis of Continuous Glucose Monitoring Data: A Systematic Review

Mikkel Thor Olsen et al. J Diabetes Sci Technol. 2025 May.

Abstract

Background: Continuous glucose monitoring (CGM) measures glucose levels every 1 to 15 minutes and is widely used in clinical and research contexts. Statistical packages and algorithms reduce the time-consuming and error-prone process of manually calculating CGM metrics and contribute to standardizing CGM metrics defined by international consensus. The aim of this systematic review is to summarize existing data on (1) statistical packages for retrospective CGM data analysis and (2) statistical algorithms for retrospective CGM analysis not available in these statistical packages.

Methods: A systematic literature search in PubMed and EMBASE was conducted on September 19, 2023. We also searched Google Scholar and Google Search until October 12, 2023 as sources of gray literature and performed reference checks of the included literature. Articles in English and Danish were included. This systematic review is registered with PROSPERO (CRD42022378163).

Results: A total of 8731 references were screened and 46 references were included. We identified 23 statistical packages for the analysis of CGM data. The statistical packages could calculate many metrics of the 2022 CGM consensus and non-consensus CGM metrics, and 22/23 (96%) statistical packages were freely available. Also, 23 statistical algorithms were identified. The statistical algorithms could be divided into three groups based on content: (1) CGM data reduction (eg, clustering of CGM data), (2) composite CGM outcomes, and (3) other CGM metrics.

Conclusion: This systematic review provides detailed tabular and textual up-to-date descriptions of the contents of statistical packages and statistical algorithms for retrospective analysis of CGM data.

Keywords: continuous glucose monitoring; statistical algorithms; statistical packages; statistics; systematic review.

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Conflict of interest statement

Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
PRISMA diagram. Abbreviations: CGM, continuous glucose monitoring; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.
See this image and copyright information in PMC

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