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Optimal sampling intervals to assess long-term glycemic control using continuous glucose monitoring

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Xing, Dongyuan, Kollman, Craig, Beck, Roy W., Tamborlane, William V., Laffel, Lori, Buckingham, Bruce A., Wilson, Darrell M., Weinzimer, Stuart +2 more·2011·Diabetes Technology & Therapeutics
DOI 10.1089/dia.2010.0156PMC PMC6468940
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Abstract

AIMS AND HYPOTHESIS: The optimal duration and frequency of short-term continuous glucose monitoring (CGM) to reflect long-term glycemia have not been determined. The Juvenile Diabetes Research Foundation CGM randomized trials provided a large dataset of longitudinal CGM data for this type of analysis. METHODS: The analysis included 185 subjects who had 334 3-month intervals of CGM data meeting specific criteria. For various glucose indices, correlations (r²) were computed for the entire 3-month interval versus selected sampling periods ranging from 3 to 15 days. Other computed agreement measures included median relative absolute difference, values within ± 10% and ± 20% of full value, and median absolute difference. RESULTS: As would be expected, the more days of glucose data that were sampled, the higher the correlation with the full 3 months of data. For 3 days of sampling, the r² value ranged from 0.32 to 0.47, evaluating mean glucose, percentage of values 71-180 mg/dL, percentage of values > 180 mg/dL, percentage of values ≤ 70 mg/dL, and coefficient of variation; in contrast, for 15 days of sampling, the r² values ranged from 0.66 to 0.75. The results were similar when the analysis intervals were stratified by age group (8-14, 15-24, and ≥ 25 years), by baseline hemoglobin A1c level (< 7.0% and ≥ 7.0%), and by CGM device type. CONCLUSIONS AND INTERPRETATION: Our data suggest that a 12-15-day period of monitoring every 3 months may be needed to optimally assess overall glucose control. Shorter periods of sampling can be useful, but the correlation with 3-month measures of glycemic control is lower.

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  • Correlations between CGM use and hypoglycemia/glycemic variability have not been shown to increase with longer sampling periods.

    “In individuals with type 1 diabetes, correlations are weaker for hypoglycemia and glycemic variability; however, these correlations have not been shown to increase with longer sampling periods (42).”

    From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [42]

    The Results (Table 2; page unavailable) show that correlations with the full three-month data increased as sampling lengthened for both hypoglycemia and glycemic variability. From 3 to 15 days, r² rose from 0.32 to 0.66 for values ≤70 mg/dL and from 0.36 to 0.70 for coefficient of variation—the opposite of the attributed claim. (LLM, conf=0.93)

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  • In individuals with type 1 diabetes, correlations between >70% CGM use over 14 days and hypoglycemia and glycemic variability are weaker.

    “In individuals with type 1 diabetes, correlations are weaker for hypoglycemia and glycemic variability; however, these correlations have not been shown to increase with longer sampling periods (42).”

    From Clinical Targets for Continuous Glucose Monitoring Data Interpretation: Recommendations From the International Consensus on Time in Range (2019) · cites this paper as [42]

    In type 1 diabetes, the Results (Table 2; page unavailable) show weaker correlations for hypoglycemia and glycemic variability than for mean glucose at 15 days. But they also show that both correlations increase as sampling extends from 3 to 15 days, contrary to the citing sentence’s claim that an increase with longer sampling has not been shown. The paper does not frame this analysis as >70% CGM use over 14 days. (LLM, conf=0.88)

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1 of the 12 references couldn’t be matched to a known paper.

Research works printed but not linked: 3

  1. [1]Bugler J. An estimation of the amount of data required to measure glycemic variability. Presented at the Advanced Technologies and Treatment for Diabetes Conference; Prague, Czech Republic. 2008. [Google Scholar]
  2. [11]DexCom Inc. DexCom™ STS™ Continuous Glucose Monitoring System User's Guide. San Diego, CA: DexCom Inc.; 2006. pp. 86–102. [Google Scholar]
  3. [14]Magee L. R2 measures based on Wald and Likelihood Ratio Joint Significance tests. Am Stat. 1990;44:250–253. [Google Scholar]

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Authors
Xing, D., Kollman, C., Beck, R. W., Tamborlane, W. V., Laffel, L., Buckingham, B. A., Wilson, D. M., Weinzimer, S., Fiallo-Scharer, R., & Ruedy for the Juvenile Diabetes Research Foundation Continuo, K. J.
Affiliations
  • Jaeb Center for Health Research (US)
  • Yale University (US)
  • Joslin Diabetes Center (US)
  • Stanford University (US)
  • Barbara Davis Center
  • Jaeb Center for Health Research
Corresponding author
Xing, Dongyuan
ORCID
Venue
Diabetes Technology & Therapeutics
Publisher
SAGE Publications
Published
2011-03
Volume
13
Issue
3
Pages
351-358
ISSN
  • 1520-9156 (ISSN-L)
  • 1557-8593
DOI
10.1089/dia.2010.0156
PMC
PMC6468940
OpenAlex
W2007986610
Cited by
131
References
12
Citations by year
2026: 10 · 2025: 7 · 2024: 11 · 2023: 15 · 2022: 18 · 2021: 21 · 2020: 15 · 2019: 10 · 2018: 4 · 2017: 6 · 2016: 4 · 2015: 1 · 2014: 1 · 2013: 1 · 2012: 5
FWCI
2.10
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Diabetes Technology & Therapeutics (publishedVersion)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
Eunice Kennedy Shriver National Institute of Child Health and Human Development (U10HD041908)
Topics
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Education
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
Keywords
continuous glucose monitoring, glycemic control, sampling interval, HbA1c, mean blood glucose, hypoglycemia, hyperglycemia, coefficient of variation, type 1 diabetes
MeSH terms
Adolescent, Adult, Aged, Blood Glucose, Child, Diabetes Mellitus, Type 1, Female, Glycated Hemoglobin, Humans, Longitudinal Studies, Male, Middle Aged, Time Factors, Blood Glucose Self-Monitoring, Young Adult

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