Given the possibilities offered by continuous glucose monitoring (rtCGM), capillary blood glucose measurement (SMBG) may seem somewhat outdated—but it is far from obsolete. Not every patient can use a CGM system permanently, particularly those not undergoing insulin therapy. Ultimately, there is also an economic aspect to consider: the cost of blood glucose test strips is approximately €0.30–€0.50 per measurement, amounting to €1.50–€2.50 per day for five daily tests. In comparison, rtCGM costs range between €5–€10 per day, depending on the system.
Although the use of rtCGM is steadily increasing—currently with over 10 million users worldwide [1]—a large proportion of people with diabetes still measure blood glucose in the traditional way, using blood glucose meters and test strips.
Methods for Determining Glucose in Capillary Blood
As an invasive method, SMBG requires the collection of small blood samples. The glucose contained in the blood is chemically converted using biocatalysts, i.e., specific enzymes [2,3].
The commonly used—and historically established—methods of enzymatic glucose determination can be classified as follows:
Blood Glucose Measurement Using Glucose Oxidase (GOD)
The hexokinase (HK) method is primarily used in laboratory chemistry. Among the follow-up reactions, the amperometric method has become particularly well established—partly because it requires little maintenance.
In the simplest case, when using the enzyme glucose oxidase (GOD), the enzyme catalyzes the oxidation of glucose in an initial reaction. The reaction produces the compounds gluconolactone and hydrogen peroxide (H₂O₂):
When using the electrochemical method, a second reaction takes place in which, in the simplest case, hydrogen peroxide (H₂O₂) is decomposed at an electrode to which a voltage in the millivolt range is applied. This causes electrons to be transferred from the hydrogen peroxide (H₂O₂) to the electrode:
Blood Glucose Measurement Using Glucose Dehydrogenase (GDH)
The enzymatic measurement method has developed significantly over the past decades. The driving force behind this progress has been the practical requirements of self-monitoring under everyday conditions. Nowadays, test strips containing glucose dehydrogenase (GDH) are predominantly used.
In comparison, measurements using glucose oxidase (GOD) can be interfered with by reducing substances and certain medications, such as ascorbic acid, paracetamol, or by the oxygen content of the blood.
In modern systems for capillary blood glucose measurement (SMBG), the required blood volume has been reduced to ≤ 0.5 µl, and measurement times have been shortened to a few seconds (≤ 5 s). Moreover, external influences—such as hematocrit variations, ambient temperature, and oxygen partial pressure—are now compensated for [4–7]. Overall, the handling of these systems has become much simpler.
However, the reaction chemistry involving glucose dehydrogenase (GDH) is more complex, since an additional enzyme (a so-called coenzyme) and often also a mediator are required [7].
As an example, the coenzyme nicotinamide adenine dinucleotide (NAD) may be used, with the following reactions:
Perfect — here’s the complete and polished English version with the standard biochemical notation included:
Glucose + GDH cofactor (oxidized) → Gluconolactone + GDH cofactor (reduced)
In other words:
Glucose, in the presence of glucose dehydrogenase (GDH) and an oxidized cofactor, is converted to gluconolactone, while the cofactor is reduced.
When nicotinamide adenine dinucleotide (NAD⁺) is used as the coenzyme, the reaction can be expressed as follows:
Glucose + NAD⁺ → Gluconolactone + NADH + H⁺
Reduced GDH cofactor → Reduced mediator + 2 electrons
or written out in full text:
The reduced GDH cofactor transfers its electrons to a mediator, resulting in a reduced mediator and the release of two electrons.
GDH cofactor (reduced) → Mediator (reduced) + 2 e⁻
In the second reaction, the reduced coenzyme reacts with a mediator, releasing electrons that flow to the electrode of the test strip. The resulting electric current again depends on the amount of glucose converted during the reaction and thus provides information about the blood glucose level.
An important advantage of this method is that only a very small voltage needs to be applied to the electrode, thereby avoiding side reactions with other substances in the body. Consequently, the specificity for glucose detection is improved, and the desired advancements—such as smaller blood volumes and independence from oxygen concentration—are achieved.
The test procedures used by various blood glucose meter manufacturers differ primarily in the chemical detection method and thus in the test strips. Of course, the devices themselves also vary. Factors such as design, user experience, size of control buttons, and display size play a role.
Apart from these details, the basic handling of blood glucose meters is largely comparable:
The test strip is inserted into the device (which automatically switches on), the applied blood sample is absorbed, additional dosing is possible, and after a few seconds, the glucose value is displayed.
Calibration of Blood Glucose Meters No Longer Required
Questions regarding measurement accuracy, measurement time, and required blood volume are determined primarily by the test strip chemistry. After the enzymatic reaction, an electric current is generated, which is measured by the device’s integrated ammeter and converted into a glucose value.
Most modern blood glucose meters are factory-calibrated by the manufacturer. Calibration using a test chip or similar is now extremely rare. However, the accuracy of a device can be checked by applying a specific control solution (designed for that particular model) to a test strip.
Innovations in Blood Sampling
Innovations in blood glucose measurement also include advances in blood sampling devices. These have been significantly improved:
Together, these developments enable virtually pain-free blood collection and have significantly reduced measurement errors.
Apps and Software for Diabetes Management
Overall, the development of SMBG (self-monitoring of blood glucose) can be considered largely complete. The method now represents the technically optimal standard currently achievable.
However, innovations beyond the measurement principle itself—namely, in the digital evaluation of data—continue to evolve.
Modern blood glucose meters often integrate with software solutions, especially smartphone apps, enabling immediate and user-friendly analysis for therapeutic assessment and management.
Recent examples include smart insulin pens connected with apps, such as the NovoPen 6 and NovoPen Echo (Novo Nordisk), or the InPen (Medtronic) [8,9].
The accuracy of blood glucose monitoring systems is crucial for making therapy decisions and ensuring safe ongoing glucose control.
Manufacturers and distributors of such systems must comply with the accuracy standard ISO 15197:2015 [10].
This standard requires that:
Figure SK 2 illustrates this relationship [11].
Additionally, certain safety and usability requirements must be met, including secure handling and the minimization of external influences.
Fig. SK 2: Example of accuracy assessment of several blood glucose meters according to the system accuracy standard ISO 15197:2015 (modified after [11]).
Assessment Using Error Grid Analysis
To evaluate the accuracy of glucose meters in comparison with a reference glucose measurement, the Error Grid Analysis (EGA) is also used. This is a modified error grid method specifically adapted for glucose data.
It addresses the question of the clinical relevance of deviations between measured glucose values and reference values within the context of diabetes therapy—that is, it assesses the clinical accuracy of the measurement.
The deviation of a measured value from the reference value is evaluated according to its potential clinical consequences for diabetes management.
This specification was first developed by W. Clarke in the 1980s, and therefore the graphical representation is referred to as the Clarke Error Grid Plot (EGP) [12] (see Fig. SK 3, left).
Fig. SK 3: Error Grid Plots according to Clarke [12] (left) and Parkes [13] (center: Type 1 diabetes; right: Type 2 diabetes).
In the grid (Fig. SK 3), paired glucose values from a single sample (glucose concentration measured by the test device and by the reference device) are plotted.
If the measurements are in 100% agreement, the points lie exactly on the line of identity (the bisector of the plot).
A large number of such paired values produces a characteristic distribution pattern in the graph.
The graph is divided into five distinct zones, classified according to their clinical relevance:
Ideally, the majority of data points should lie within Zone A, symmetrically distributed along both sides of the line of identity in the EGP. This is true for most modern blood glucose meters, with only a small proportion of data points typically falling within Zone B.
Additional error grid models exist, such as the Parkes Error Grid developed by J. L. Parkes [13] (Fig. SK 3, center and right). This model addresses certain limitations of the Clarke EGP, such as direct transitions between Zones B and D or B and E, which are clinically illogical.
Characteristics of the Blood Sample in Self-Monitoring of Blood Glucose (SMBG)
Blood is composed of blood cells—erythrocytes, leukocytes, and platelets—and plasma.
Plasma consists of about 90% water, along with proteins, salts, and nutrients, and it contains the blood glucose.
Blood serves as the primary transport medium of the body, carrying gases (oxygen, carbon dioxide, nitrogen, etc.), nutrients (including glucose), and metabolic waste products. It ensures the exchange of substances throughout the organism.
Since self-monitoring of blood glucose is performed using capillary whole blood, the measured blood is effectively “diluted,” meaning the glucose concentration is lower than it would be in plasma measurements (e.g., in laboratory tests).
This effect accounts for approximately 11%.
However, all modern glucose meters now display plasma-calibrated results, i.e., they report the current glucose concentration in blood plasma.
[1] Kelly Close Report 21.01.2025
[2] Hönes J, Müller P, Surridge N. The Technology Behind Glucose Meters: Test Strips. Diabetes Technology & Therapeutics 2008; 10(Suppl.1): S10-S26.
[3] Heinemann L. Measuring glucose concentrations: daily practice, current and future developments. J Diabetes Sci Technol. 2008 Jul;2(4):710-717.
[4] Baumstark A, Liebing C, Jendrike N, Pleus S, Haug C, Freckmann G. Evaluation of Hematocrit Influence on Measurements With a Novel Self-Monitoring of Blood Glucose System Based on ISO 15197:2013. J Diabetes Sci Technol. 2018 Sep;12(5):1078-1079.
[5] Hattemer A, Wardat S. Evaluation of Hematocrit Influence on Self-Monitoring of Blood Glucose Based on ISO 15197:2013: Comparison of a Novel System With Five Systems With Different Hematocrit Ranges. J Diabetes Sci Technol. 2018 Mar;12(2):333-340.
[6] Nerhus K, Rustad P, Sandberg S. Effect of ambient temperature on analytical performance of self-monitoring blood glucose systems. Diabetes Technol Ther. 2011 Sep;13(9):883-892.
[7] Baumstark A, Schmid C, Pleus S, Haug C, Freckmann G. Influence of partial pressure of oxygen in blood samples on measurement performance in glucose-oxidase-based systems for self-monitoring of blood glucose. J Diabetes Sci Technol. 2013 Nov 1;7(6):1513-1521.
[8] Heinemann L, Schnell O, Gehr B, Schloot NC, Görgens SW, Görgen C. Digital Diabetes Management: A Literature Review of Smart Insulin Pens. J Diabetes Sci Technol. 2022 May;16(3):587-595.
[9] MacLeod J, Vigersky RA. A Review of Precision Insulin Management With Smart Insulin Pens: Opening Up the Digital Door to People on Insulin Injection Therapy. J Diabetes Sci Technol. 2023 Mar;17(2):283-289.
[10] DIN EN ISO 15197:2015 DE – Testsysteme für die In-vitro-Diagnostik – Anforderungen an Blutglukosemesssysteme zur Eigenanwendung bei Diabetes mellitus (ISO 15197:2013); Deutsche Fassung EN ISO 15197:2015 (Foreign Standard). Letzter Aufruf: 12.03.2025
[11] Freckmann G, Link M, Schmid C, Pleus S, Baumstark A, Haug C. System Accuracy Evaluation of Different Blood Glucose Monitoring Systems Following ISO 15197:2013 by Using Two Different Comparison Methods. Diabetes Technol Ther. 2015 Sep;17(9):635-48. doi: 10.1089/dia.2015.0085.
[12] Clarke WL, Cox D, Gonder-Frederick LA et.al. Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diabetes Care. 1987; 10(5):622-628.
[13] Parkes JL, Slatin SL, Pardo S, et.al. A new consensus error grid to evaluate the clinical significance of inaccuracies in the measurement of blood glucose. Diabetes Care. 2000; 23(8):1143-1148.