The DOI matches its Crossref record; 11 of the 12 references it cites were found in a registry or an index.
Optimal Sampling Intervals to Assess Long-Term Glycemic Control Using Continuous Glucose Monitoring
Open accessAbstract
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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“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]
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“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]
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Citations
Cites
Translating the A1C assay into estimated average glucose values
Nathan, D. M., Kuenen, J., Borg, R., Zheng, H., Schoenfeld, D., Heine, R. J., & for the A1c-Derived Average Glucose (ADAG) Study Group (2008) · Diabetes Care · doi:10.2337/dc08-0545 · Cited by 1,812
Continuous glucose monitoring and intensive treatment of type 1 diabetes
(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0805017 · Cited by 1,559
The effect of continuous glucose monitoring in well-controlled type 1 diabetes
Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group (2009) · Diabetes Care · doi:10.2337/dc09-0108 · Cited by 399
Effectiveness of Continuous Glucose Monitoring in a Clinical Care Environment
Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group (2010) · Diabetes Care · doi:10.2337/dc09-1502 · Cited by 313
Bode, B. W., Gross, K. M., Rikalo, N., Schwartz, S. L., Wahl, T. O., Page, C. W., Gross, T. M., & Mastrototaro, J. J. (2004) · Diabetes Technology & Therapeutics · doi:10.1089/152091504773731285 · Cited by 190
Chase, H. P., Beck, R. W., Xing, D., Tamborlane, W. V., Coffey, J., Fox, L. A., Ives, B., Keady, J., Kollman, C., Laffel, L. M., et al. (2010) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2010.0021 · Cited by 142
Wilson, D. M., Beck, R. W., Tamborlane, W. V., Dontchev, M., Kollman, C., Chase, P., Fox, L. A., Ruedy, K. J., Tsalikian, E., WEINZIMER, S. A., et al. (2006) · Diabetes Care · doi:10.2337/dc06-1407 · Cited by 134
The Juvenile Diabetes Research Foundation Continuous Glucose Monitoring Study Group (2009) · Diabetes Care · doi:10.2337/dc09-0846 · Cited by 121
JDRF CGM Study Group (2008) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2007.0302 · Cited by 115
The Diabetes Research in Children Network (DirecNet) Study Group (2005) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2004-2510 · Cited by 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
- 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.
- DexCom Inc. DexCom™ STS™ Continuous Glucose Monitoring System User's Guide. San Diego, CA: DexCom Inc., 2006; 86–102.
- Magee L. R2 measures based on Wald and Likelihood Ratio Joint Significance tests. Am Stat, 1990; 44:250–253.
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Battelino, T., Danne, T., Bergenstal, R. M., Amiel, S. A., Beck, R., Biester, T., Bosi, E., Buckingham, B. A., Cefalu, W. T., Close, K. L., et al. (2019) · Diabetes Care · doi:10.2337/dci19-0028 · Cited by 4,237
International consensus on use of continuous glucose monitoring
Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., Garg, S., Heinemann, L., Hirsch, I., Amiel, S. A., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1600 · Cited by 2,069
Continuous glucose monitoring and metrics for clinical trials: an international consensus statement
Battelino, T., Alexander, C. M., Amiel, S. A., Arreaza-Rubin, G., Beck, R. W., Bergenstal, R. M., Buckingham, B. A., Carroll, J., Ceriello, A., Chow, E., et al. (2023) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(22)00319-9 · Cited by 831
Validation of Time in Range as an Outcome Measure for Diabetes Clinical Trials
Beck, R. W., Bergenstal, R. M., Riddlesworth, T. D., Kollman, C., Li, Z., Brown, A. S., & Close, K. L. (2019) · Diabetes Care · doi:10.2337/dc18-1444 · Cited by 793
The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading
Beck, R. W., Connor, C. G., Mullen, D. M., Wesley, D. M., & Bergenstal, R. M. (2017) · Diabetes Care · doi:10.2337/dc17-0636 · Cited by 477
Self-citation
Metadata
- 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
- 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
- green
- www.ncbi.nlm.nih.gov
- 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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