Time delay of CGM sensors: Relevance, causes, and countermeasures

Günther Schmelzeisen-Redeker, Michael Schoemaker, Harald Kirchsteiger, Guido Freckmann, Lutz Heinemann, Luigi Del Re

Research output: Contribution to journalArticlepeer-review

98 Citations (Scopus)


Background: Continuous glucose monitoring (CGM) is a powerful tool to support the optimization of glucose control of patients with diabetes. However, CGM systems measure glucose in interstitial fluid but not in blood. Rapid changes in one compartment are not accompanied by similar changes in the other, but follow with some delay. Such time delays hamper detection of, for example, hypoglycemic events. Our aim is to discuss the causes and extent of time delays and approaches to compensate for these. Methods: CGM data were obtained in a clinical study with 37 patients with a prototype glucose sensor. The study was divided into 5 phases over 2 years. In all, 8 patients participated in 2 phases separated by 8 months. A total number of 108 CGM data sets including raw signals were used for data analysis and were processed by statistical methods to obtain estimates of the time delay. Results: Overall mean (SD) time delay of the raw signals with respect to blood glucose was 9.5 (3.7) min, median was 9 min (interquartile range 4 min). Analysis of time delays observed in the same patients separated by 8 months suggests a patient dependent delay. No significant correlation was observed between delay and anamnestic or anthropometric data. The use of a prediction algorithm reduced the delay by 4 minutes on average. Conclusions: Prediction algorithms should be used to provide real-time CGM readings more consistent with simultaneous measurements by SMBG. Patient specificity may play an important role in improving prediction quality.

Original languageEnglish
Pages (from-to)1006-1015
Number of pages10
JournalJournal of diabetes science and technology
Issue number5
Publication statusPublished - Sept 2015
Externally publishedYes


  • Accuracy
  • CGM
  • Continuous glucose monitoring
  • MARD
  • Performance comparison
  • Performance evaluation
  • Precision
  • Time delay
  • Blood Glucose Self-Monitoring/instrumentation
  • Biosensing Techniques/instrumentation
  • Humans
  • Middle Aged
  • Male
  • Diabetes Mellitus, Type 1/blood
  • Young Adult
  • Blood Glucose/analysis
  • Algorithms
  • Time Factors
  • Adult
  • Female
  • Hypoglycemic Agents/therapeutic use
  • Insulin Infusion Systems


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