ArticleJournal of diabetes science and technology2022
A New Analysis Tool for Continuous Glucose Monitor Data.
Article in Journal of diabetes science and technology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Trial
- Time-Restricted Eating in Adults With Metabolic Syndrome : A Randomized Controlled Trial.Annals of internal medicine · 2024Trial
- Continuous glucose monitoring in patients with post-bariatric hypoglycaemia reduces hypoglycaemia and glycaemic variability.Diabetes, obesity & metabolism · 2023Trial
- Continuous Glucose Monitoring Metrics for Predicting Adverse Neonatal Outcomes in Individuals Undergoing Gestational Diabetes Screening.Journal of diabetes science and technology · 2026Article
- Time-restricted eating in adults with type 2 diabetes mellitus on concomitant glucagon-like peptide-1 receptor agonists: case report.Frontiers in clinical diabetes and healthcare · 2026Article
- Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin ManagementJournal of clinical research in pediatric endocrinology · 2025Article
- The metabolic and circadian signatures of gestational diabetes in the postpartum period characterised using multiple wearable devices.Diabetologia · 2025Observational
- Development and Validation of an Electronic Health Record-Based Risk Assessment Tool for Hypoglycemia in Patients With Type 2 Diabetes Mellitus.Journal of diabetes science and technology · 2025Article
- From data to insights: a tool for comprehensive Quantification of Continuous Glucose Monitoring (QoCGM).PeerJ · 2025Article
- AGATA: A Toolbox for Automated Glucose Data Analysis.Journal of diabetes science and technology · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
backgroundWith 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.
methodsIn 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.
resultsIn 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.
conclusionsWe present a new R package and accompanying web app to facilitate quick and easy computation of a suite of glycemic variability metrics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.