COMPUTER PROGRAM AND DATA CARRIER TEHERFOR

TECHNICAL FIELD

The invention relates to a data processing method for blood glucose measurement, to a blood glucose meter, a blood glucose measurement system, and a computer program and data carrier therefor; particularly but not exclusively in the field of biosensor (electrochemical) measurement technology.

BACKGROUND ART

In biosensor measurements the blood glucose meter indicates the change in electric parameters that occurs during the reaction applied for the measurement. A drop of the solution to be tested is placed on a disposable test strip impregnated with a reagent. The two most widely applied methods for determining glucose concentration are colorimetry and amperometry. In the amperometry method the solution is drawn into a capillary fixed to the end of the test strip. The test strip itself also contains an organic, enzyme-based reagent, by way of example glucose dehydrogenase. In the presence of the enzyme a chemical reaction occurs in the glucose, generating free electrons. The electric charge passing through the electrode can be measured, the current being proportional to the glucose concentration of the solution. During the reaction the ambient temperature and other parameters affecting the measurement also have to be measured in order that the temperature- and hematocrit dependence of the chemical reaction rate can be compensated. Most blood glucose meters operate based on this principle.

The main biochemical sensor is constituted by the test strip, upon which the capillary sample is placed. There are electrodes disposed on the surface of the test strip. The free electrons generated during the chemical reaction are collected by the so-called working electrode that is connected to a current-to-voltage converter operational amplifier. The so-called reference electrode has to be kept at a constant voltage with respect to the working electrode. The third electrode is the so-called counter electrode that is adapted for supplying current to the working electrode. In most glucose meters only the reference and working electrodes are applied. An accurately adjusted reference voltage is applied to the reference electrode that provides the reference potential to the operational amplifier. In this manner an accurately adjusted potential difference can be maintained between the working and reference electrodes. The output current of the test strip - from which it is possible to calculate the number of the free electrons generated during the chemical reaction - is "driven" by this voltage.

A blood glucose meter according to the above is disclosed e.g. in US 2014/0284223 A1.

HbA1c or glycohemoglobin (also: glycated or glycolized hemoglobin) is a form of hemoglobin - found in red blood cells and responsible for the red colour of blood - in which glucose (sugar) is bonded to hemoglobin. The ratio of glucose-bonded (glycated) hemoglobin to whole hemoglobin (expressed as a percentage) is an important laboratory parameter for the treatment of diabetes. It allows for establishing the patient's average blood glucose level over the past three months (long-term blood glucose).

The higher the blood glucose level, the more hemoglobin subunits bond sugar (glucose). The bond is unstable for a while but stabilizes in a few hours and becomes irreversible: the sugar can no longer detach from the hemoglobin. Consequently, the lifetime of the HbA1c form depends on the lifetime of red blood cells. Bonding of glucose to hemoglobin occurs without the help of enzymes, and various glycated hemoglobin forms can be generated. In order that the measurements and thus the treatment of diabetes can be carried out under standardized circumstances all over the world, HbA1c was chosen by a working group of the IFCC (Institute for Collaborative Classification) in Miinster as the parameter to be measured because this glycated form is stable. In HbA1c, glucose binds to the N-terminal of the beta chain of hemoglobin (which is valine, an amino acid).

The HbA1c value of diabetes patients is checked every three months. The HbA1c value cannot be used if the red blood cells have died due to a hemolytic disease, such as due to infections, to toxins or in some hereditary diseases.

Diabetes treatment aims at keeping HbA1c below 6.5% (but at least below 7%) such that the long-term complications of diabetes occur only later or do not occur at all. The HbA1c value cannot be applied for diagnosing diabetes but a diagnosis is supported by values above 6.5%. Since the bond remains unstable for a period of time and stabilizes only in a few hours, HbA1c values can be barely applied for detecting transient high blood glucose values. In contrast to that, they reflect past average blood glucose values very well. In healthy persons the value is 4-6%. Since the normal range changes from laboratory to laboratory, the threshold values applied by the given lab must be included with laboratory findings. The process of hemoglobin glycation can be seen in Fig. 1. As indicated in the diagram, the blood glucose values of the last 30 days are responsible for 50% of the HbA1c value, while the period from 30 to 90 days before the measurement is responsible for 40%, with the period from 90 to 120 days prior the measurement being responsible for only 10% of the HbA1c value. Because red blood cells have a lifetime of nearly 120 days, days before a 120-day period before a measurement do not have a role in the process relevant from the aspect of the invention.

The costs of laboratory tests aiming at establishing the HbA1c values of diabetes mellitus patients are reimbursed by the national healthcare service a few times a year (by way of example, 4 times a year in Hungary). Certain medications and medicinal devices are reimbursed by the healthcare service on the condition that a laboratory measured HbA1c value is provided. It is therefore especially important that patients are informed about their estimated HbA1c values or ranges between the relatively rare laboratory measurements so that they can adjust their diet or insulin intake if necessary. No technical solution related to that has been available in prior art blood glucose meters, systems, methods and computer programs associated therewith.

Another parameter desired to be predicted in the case of users suffering from diabetes is the number of so-called low-blood glucose or hypoglycemic events expected in the near future. During a blood glucose drop first the vegetative nervous system is triggered, which results in symptoms of agitation normally associated with an urge to consume food and thereby increase the blood glucose level: hunger, anxiety, stress, sweating, and malaise. If no food is taken in and the blood glucose level continues to drop, the symptoms of glucose deficiency in the brain start to dominate. This may result in blurred vision, slurred speech, difficulty to concentrate, or potentially confusion. A still lower blood glucose level may lead to sleepiness or, conversely, aggressive behaviour. In very serious cases of prolonged hypoglycemia loss of consciousness can occur. Due to these symptoms such events have to be prevented if possible, and the number of hypoglycemic events expected in the near future has to be provided to the user. A technical solution related to that has also not been available in prior art blood glucose meters, systems, methods and computer programs associated therewith. DESCRIPTION OF THE INVENTION

The object of the invention is to improve conventional blood glucose meters, and systems and methods based thereon, as well as computer programs adapted for operating those in such a manner that reduces or eliminates the drawbacks of prior art technical solutions. The object of the invention is in particular to provide a blood glucose meter, system and method and a computer program adapted for operating those which, allowing for intelligent and flexible operation and the entry of additional information, are capable of providing an estimated HbA1c value or range, and/or preferably an expected number of hypoglycemic events for the subsequent time period.

The objects of the invention have been achieved by providing the data processing method for blood glucose measuring according to claim 1 , the blood glucose meter according to claim 10, the blood glucose measurement system according to claim 11 , the computer program according to claim 12, and the data carrier according to claim 13. Preferred embodiments are defined in the dependent claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the invention are described below by way of example with reference to the following drawings, where

Fig. 1 is a schematic diagram illustrating the glycation process,

Fig. 2 is a composite diagram illustrating the correlation between special averages of glucose values measured between visits and the HbA1c values measured during subsequent visits,

Fig. 3 is a diagram illustrating the correlation between the special average and the HbA1c value and also showing measurement values and a correlation function, Fig. 4 is a composite diagram illustrating the correlation between laboratory-measured HbA1c values (Hbalovi) and HbA1c values estimated (predicted) according to the invention (HbAlcp), Fig. 5 is an ROC diagram showing a comparison of measured and estimated HbA1c values in the test according to Fig. 4,

Fig. 6 is a diagram showing the correlation between the estimated and measured number of hypoglycemic events,

Fig. 7 shows an ROC diagram illustrating the specificity/sensitivity test of the monthly number of hypoglycemic events obtained by the test according to Fig. 6,

Fig. 8 is a diagram showing the results of a Hosmer-Lemeshow test of the results of the test according to Fig. 6,

Fig. 9 is a schematic diagram of a blood glucose measurement system according to the invention,

Fig. 10 is a schematic diagram illustrating the operation of an electronic log according to the invention, and

Fig. 11 schematically illustrates a user interface of a mobile application according to the invention.

MODES FOR CARRYING OUT THE INVENTION

An estimated HbA1c value or range is provided according to the invention in order to allow for calculating - utilizing regularly measured blood glucose data - and displaying, either in a blood glucose meter, in a mobile application, or in an online blood glucose measurement log, such information that, provided that a specific measurement period has passed and provided that the measurements have been carried out in a balanced manner, allows a user to receive information on the trends of his/her HbA1c values or ranges.

According to the invention it has been recognised that in order to realise the new, preferred functionalities, measured blood glucose values provided by users in an arbitrary manner are not suitable, but rather measurements carried out according to appropriate conditions, and/or an average calculated therefrom have to be applied. The application of such a special average is not described in the state of the art, and it forms a new, as yet unutilized basis for implementing new functionalities linked to blood glucose measurement. The reason for that is that according to the invention it has been recognised that the special average can be applied for inferring HbA1c values, based on the correlation calculated from the measurements, only in the case when the conditions applied for calculating the special average are fulfilled. The minimum requirements for the special average according to the invention are the following:

- the length of the measurement period is at least 7 days,

- at least one measurement is performed each day,

- additional information indicating that the values are preprandial is assigned to at least 30% of the measured blood glucose values, and

- additional information indicating that the values are postprandial is assigned to at least 30% of the measured blood glucose values.

In case any of the conditions of computing the special average specified above is not fulfilled, the average will differ from the average applicable for providing an estimation, and thereby the HbA1c values cannot be properly inferred or the HbA1c values or ranges cannot be estimated in advance. Our research leading up to the invention yielded the finding that if the measurements are taken during a period that is shorter than seven days, or there is even a single day in the range utilized for calculation when no measurement was taken, or less than 30% of the measurements are preprandial or less than 30% thereof are postprandial, then the calculated average value will not be capable of predicting the HbA1c value. Our research leading up to the invention indicated that, in a surprising manner, in case any of the conditions specified for the special average is violated the HbA1c values can no longer be estimated. However, if the conditions for the special average are all fulfilled, the HbA1c values and range can be estimated appropriately.

Of course, the more measurement values are assigned additional information as described above, the easier it is to estimate (i.e. predict) the HbA1c values and range; and therefore the lower limit can preferably be set to 40-40% or more preferably to 45-45% or even more preferably to 50-50%; intermediate values being also potentially applicable. It is particularly preferable if the limit values are identical, i.e. the preprandial lower limit is identical with that of the postprandial one; thereby a balanced average can be obtained. It is preferred if blood glucose values are measured by the user in preprandial-postprandial pairs, to which the device preferably calls his/her attention in order to provide the most balanced circumstances possible. This notification can be given by way of example in such a manner that, when a predetermined or user-adjustable period of time has elapsed after the assignment of additional information corresponding to a preprandial measurement, the user is notified by the device to take the postprandial measurement and to assign thereto the additional information corresponding to a postprandial measurement.

It is preferred if the user is motivated to enter the additional information also in a way that after entering the measured blood glucose value the cursor automatically jumps to the entry field for the additional information (e.g. note).

The preprandial character preferably means that the measurement is performed directly before a meal; this criterion can be considered to be fulfilled if measurement data are entered preferably less than 0.5 hours before a meal, more preferably less than 10 minutes, and most preferably less than 5 minutes before a meal. A measurement is preferably considered postprandial if it is performed near the maximum of the postprandial blood glucose rise, i.e. approximately one and a half hours after a meal; the data entry period corresponding to this criterion being preferably set to between 0.5 and 2.5 hours, more preferably between 1 and 2 hours, most preferably between 1.25 and 1.75 hours after the end of a meal.

The required uninterrupted sequence of measurement days utilized for calculating the average can also be longer, i.e. preferably 10 days, more preferably 20 days, still more preferably even 30 days, with intermediate values being also potentially applicable.

With the above described regular and balanced blood glucose measurement regime, therefore, the preferred blood glucose meters according to the invention also provide a special average in addition to, or instead of, calculating normal average values. The word "SPEC" (or other words) preferably shown in a display of the device refers to the fact that in the given time period the user has performed measurements every day in a regular fashion, with the preprandial and postprandial measurement results being recorded in a balanced manner, and therefore the special average adapted for realizing the functionality according to the invention is available.

The considerations leading to the invention were also based on performed a clinical trial (study). This clinical trial was carried out as follows:

Primary trial objective: Testing the correlation between a special average blood glucose value measured every 30 days and HbA1c. Secondary trial objective: testing the correlation between the hypoglycemia risk index (HRI) and the occurrence of hypoglycemic events.

Participants:

Criteria for participation:

- T1 DM (type 1 diabetes mellitus) and T2DM (type 2 diabetes mellitus) treated with insulin (>2 times a day)

- age: between 18-75 years

- duration of diabetes: 5-60 years

Exclusion criteria:

- decompensated carbohydrate metabolism (HbA1c>10%, ketonuria)

- all conditions associated with questionable HbA1c values

- severe hypoglycemic events more frequent than 1/month

- deficiency of sense of hypoglycemia

- pregnancy, lactation As can be seen from the relation between the special average AVspec of blood glucose values measured between visits and the HbA1c values measured during subsequent visits (Fig. 2) there is good (Pearson-type) correlation between the two results.

The diagram shown in the top left square of Fig. 2 was obtained from measurements performed during the trial leading to the invention. The diagram shows the distribution of the special average values of the blood glucose values measured between visits. The diagram in the lower right square (of Fig. 2) illustrates the distribution of HbA1c values taken during visits subsequent to measurements. The measured special average and the HbA1c values measured during the visits are plotted in a diagram shown in the bottom left square in the figure, where the horizontal axis represents the range of the special average values, the vertical axis represents the range of the HbA1c values, the individual dots representing the measured values. In the diagram there can be seen that there is an appropriate correlation between these values, which allows for making estimations of the HbA1c values. The correlation coefficient of the measurement results is shown in the top right square. Fig. 3 illustrates the correlation of the special average and HbA1c values with the measurement results and with the correlation function. In the clinical trial applied for calculating the estimated HbA1c value or range the relationship of HbA1c and the participants' glucose values was studied by breaking down data in multiple ways, thereby examining the correlations that can be detected over the entire HbA1c range or the individual partial ranges. The Pearson-type linear correlation analysis has detected significant and strong correlation for all visit periods between the special average (as an average blood glucose value) and the HbA1c values measured at the end of the 30-day and 90-day visit periods.

We have also studied the correlation between the pre- and postprandial average blood glucose values and the HbA1c values, but a weaker correlation was found in all cases compared to the average values of the aggregate blood glucose data measured according to the above described conditions, and therefore the entire blood glucose data set was applied for making a HbA1c estimate.

As far as answering the question related to the primary objective of the trial is concerned, it can be maintained that a close linear correlation can be detected between the special average (as an average blood glucose value) and the HbA1c value measured at the end of the given trial period, in this case a period of 30 days.

A mathematical analysis has been performed also on the entire database applying the so-called "mixed" model for providing as comprehensive data analysis as possible.

In Fig. 3 the correlation between the special average and the HbA1c value is shown in more detail. The heavy straight line shown in the diagram was obtained by linear approximation of the data of the measurements performed during the trial leading to the invention, for which a 95% confidence interval was also calculated. This confidence interval is indicated in the two lighter lines shown beside the heavy line in the diagram. As it can be seen in the diagram, a clear correlation - which can be utilized for fulfilling the objectives of the invention - can be detected between the two types of values.

This close linear relationship was further studied by making a model. A model was made utilizing the data of the first 30-day visit period (training period). This model reflected the relationship between the average blood glucose values measured during the given period and the laboratory-measured HbA1c average value taken at the end of the period. The model equation thus obtained was not affected by the duration of diabetes, age, or location of the trial. Using this formula an estimation was made of the HbA1c value that could be derived from the formula based on the blood glucose values of the next 30-day period (SPEC averages, testing phase), and then this was compared with the HbA1c results measured in a laboratory at the end of the second period. The formula thus obtained also showed a strong correlation that is illustrated in Fig. 4.

In Fig. 4 the analysis of the correlation between the laboratory-measured HbAlcM values and the HbAlcp values predicted applying the calculation according to the invention (described in detail later on) is shown. The visualisations shown in the figure are similar to Fig. 2. The distribution of measured values in shown in the top left square, with the distribution of the predicted values being shown in the bottom right square. In the bottom left square the two distributions are visualised diagrammatically, showing the straight line of linear correlation separately. In the top right square the correlation value is shown. Fig. 5 shows a ROC (Receiver Operating Characteristic) analytic diagram that is frequently applied for diagnostic tests. The literature describing this test is included in the state of the art and therefore it is not described in detail here. In the diagram, the true positive rate, i.e. the sensitivity (vertical axis) is plotted against the false positive rate, i.e. the specificity (horizontal axis). The diagram adequately represents the quality of estimation with respect to the measured and calculated HbA1c values.

The data processing method for blood glucose measurement according to the invention therefore comprises the steps of receiving user-measured blood glucose values, processing the measured blood glucose values, and calculating therefrom values relating to the user's condition. The invention is characterised by

- also receiving additional information that can be optionally assigned to the measured blood glucose values by the user, said additional information specifying whether the given measured blood glucose value is a preprandial or a postprandial value,

- calculating, upon the fulfilment of the following conditions, a special average from the measured blood glucose values, the special average being the arithmetic average of the measured blood glucose values:

- the length of the measurement period was at least 7 days, - at least one measurement was performed each day,

- additional information indicating that the value is preprandial is assigned to at least 30% of the measured blood glucose values, and - additional information indicating that the value is postprandial is assigned to at least 30% of the measured blood glucose values,

- and calculating an estimated glycohemoglobin (HbA1 c) range from the special average based on the following formula:

where

HbAl crange is the estimated glycohemoglobin range

AVspec is the calculated special average in mmol/l,

c ₁ is a constant between 0.35 and 0.49,

c ₂ is a constant between 2.8 and 4.7, and

c ₃ is a constant between 0 and 0.4. The reason why ranges are given for the constantsc ₁ and c ₂ is that their exact values have to be chosen corresponding to the given population, dietary habits and genetic pool, by way of example as a result of a correlation analysis based on the above described clinical trials. In the light of European conditions the approximate values of ci=0.43 and c ₂ =3.6 were found appropriate, with the values ci=0.4279 and c ₂ =3.5908 being the most preferable for Hungarian conditions (and presumably also for the majority of the world's countries). Our tests have indicated that outside the widest range specified above the estimation no longer yields meaningful results.

The constant C3 is expediently chosen corresponding to the confidence interval of the correlation (expediently it is 95%). The 95% confidence intervals (CI) for particular ranges yielded by the trials leading up to the invention are included in Table 1 below.

To a good approximation, the above values can be rounded to the value C3=0.2, implying that it is expediently this value that can be added to or subtracted from the calculated HbAlc values, thus specifying the estimated range. It is of course also possible according to the invention to provide the calculated HbAlc value (C3=0), but in actual practice it turned out to be more viable to generate a rage, which indicates also for the user that the values delimiting it are estimates. According to the invention largest value of C3 can be 0.4, above which value the information provided by the function can no longer be regarded as informative.

Based on the above the estimation included in Table 2 below can be obtained:

The expected number of hypoglycemic events is an informative value that can be determined from the average of the blood glucose data and shows how many hypoglycemic events can be expected by the user during the next 30 days. In order to determine the expected number of hypoglycemic events for the next 30 days from the blood glucose data obtained by the above described clinical trial a previously published (Ferencz V. et al. Diabetol Hung. 23 (Suppl 1 ):19-21 , 2015) hypoglycemia risk index (HRI) evaluation method was applied. As is known from the literature, the deviation (SD, standard deviation) in itself or in combination with the variation coefficient does not provide a sufficient basis for estimating hypoglycemia risk, which is why the HRI derived from earlier clinical results and the number of hypoglycemic events that can be obtained from the blood glucose data of the present clinical trial were utilized for risk estimation.

In the clinical trial, measured blood glucose values of 3.9 mmol/l or less were considered hypoglycemic values. The number of hypoglycemic events was established applying the so-called "generalized estimated equation" method (Poisson distribution, log link). Individuals with high hypoglycemia risk were also analysed. Risk predictors were established applying the "generalized estimating equation" method, with discrimination being performed utilizing an ROC analysis and calibration being carried out with the help of the Hosmer-Lemeshow test. The allowed monthly number of hypoglycemic events - which is important from the aspect of hypoglycemia risk - was established utilizing the number specified in the relevant international and domestic recommendations for type 1 diabetes patients, i.e. two mild hypoglycemic events per week, that is, maximum 8 hypoglycemic events per month. This was considered the threshold value for high hypoglycemia risk.

Examining the clinical predictors of hypoglycemia risk - utilizing type 1 diabetes patients as a reference - it was found that the type of diabetes and the HbA1c values can be regarded as important predictors (from a clinical aspect) for hypoglycemia risk.

The mode by which hypoglycemia risk can be estimated applying the hypoglycemia risk index already tested in hospitalized patients was also studied. The results indicated a correlation similar to the previous tests, wherein it was possible to highlight the different hypoglycemia risk parameters that could be associated with type 1 and type 2 diabetes patients. As with the HbA1c estimation, a model was generated for assessing whether the expected number of hypoglycemic events during the subsequent period (in this case, 30 days) can be estimated based on the measured number of hypoglycemic events.

In the course of the method according to the invention, an expected number of hypoglycemic events for the next 30 days is preferably calculated from the measured blood glucose values based on the following formula: HN _expected = up ro toun thdeed nearest integer(e ^{(- 7.673+19.298x/HRI-8.44xHRI xHRI -0.773)} ) for type 2 diabetes mellitus, and

HN _expected = rounded up to the nearest mteger(e ^{(- 7.673+19.298x/HRI-8.44xHRI xHRI -0.773)} ) for type 1 diabetes mellitus,

where

HNexpected is the number of hypoglycemic events expected during the next 30 days and HRI is the hypoglycemia risk index that is obtained applying the following formula:

where

HRI is the hypoglycemia risk index in %,

BGM are the measured blood glucose values,

SD(BGM) is the standard deviation of the measured blood glucose values, AV stands for calculating an average, and

BGMhypo is an individual hypoglycemia threshold which by default is 3.5 mmol/l and can be adjusted between 2.9-3.9 mmol/l depending on the user, preferably in an empirical manner.

From the aspect of the accuracy of the calculations it has proven advantageous to calculate the hypoglycemia risk index according to the following conditions:

- if more than 100 measurements were carried out in 2 weeks then all the blood glucose values measured during the 2-week period are used for the calculation,

- if less than 100 measurements were carried out in 2 weeks then the last 100 blood glucose values measured no earlier than 45 days are used for the calculation, and - if less than 100 measurements were carried out during the last 45 days, a hypoglycemia risk index is not calculated.

It is suggested by the obtained results that the estimated and measured number of hypoglycemic events show a strong correlation that is illustrated in Fig. 6. In Fig. 6 the monthly number of hypoglycemic events (vertical axis) is plotted against the hypoglycemia risk index (HRI, horizontal axis). In the table included above the plot "B" represents the base for reference and "SE" represents the standard error of the average (the deviation of the average values), while "p" stands for the theoretical population correlation coefficient and "r" for the sample correlation coefficient (Pearson-type).

In the case of high hypoglycemia risk, i.e. an expected number of greater or equal than 8 hypoglycemic events per month the values were estimated applying an ROC analysis, examining specificity and sensitivity. In both cases the results suggested very favourable values illustrated in Fig. 7 showing the analysis of the specificity and sensitivity (discrimination) of the monthly number of hypoglycemic events applying the parameters indicated in the table.

The reliability of the model concerning the measured and estimated number of hypoglycemic events was tested applying the Hosmer-Lemeshow test which yielded the results shown in Fig. 8. Detailed information on the values and parameter names shown in the diagram according to Fig. 8 can be found in literature related to the test, and therefore they are not described in detail here.

According to the algorithm designed on the basis of the results above the so-called "expected number of hypoglycemic events" estimated for the next 30 days was derived from the blood glucose data measured during a predetermined period (with a length of e.g. 2 weeks or 30 days).

Applying the international and domestic recommendations, the risk is marked as low if the expected number of hypoglycemic events <4. The risk is marked as average if the expected number of hypoglycemic events is between 4 and 7. The risk is marked as high if the expected number of hypoglycemic events is≥8.

In Fig. 9 the schematic diagram of the blood glucose measurement system according to the invention can be seen. The above calculations are preferably performed in a blood glucose meter 10, the results of the calculations being displayed to the user on a display of the blood glucose meter 10. The results of the calculations can also be transferred into a hardware device - by way of example, a mobile phone 11 - or into an internet application 12 in communication with the blood glucose meter 10 and can be displayed on a display of a mobile phone 11 or on a user interface of the internet application 12.

Optionally, at least a part of the calculations are performed utilizing an application running on a hardware device that is in communication with the blood glucose meter 10, the results of the calculations being showed to the user on a display of the hardware device or on an internet interface in communication with the hardware device.

Fig. 10 schematically illustrates the operation of an electronic log 14 according to the invention. From the blood glucose meter 10 the data or the calculation results are preferably transferred to a laptop computer 13, from where they are transferred to the electronic log 14 via an internet link.

The mobile application allows for recording by way of example insulin and carbohydrate intake and physical activity, which can also be visualised on the web interface of the electronic log 14. By giving a more complete picture these data help the patient and (if authorised) the treating physician better understands the patient's metabolic situation and facilitates maintaining balance. The measured data may optionally be uploaded automatically to the mobile application by the blood glucose meter.

The data stored in the memory of the blood glucose meter are uploaded by the system to an internet database and are then plotted diagrammatically and are listed in easily accessible tables. Uploaded data can also be printed out or can be shared electronically with the treating physician. The advantages of the electronic log 14 are that it eases the work of both the physician and the patient, and that it performs statistical calculations, displays diagrams and tables indicating trends that allow for better understanding the patient's metabolic situation and for keeping metabolic balance. Fig. 11 shows user interfaces of a mobile application according to the invention. The user interface allows for entering a wide range of additional information in relation to the measurement data, the results provided by the functionality according to the invention also being displayed in the user interface as shown in the drawing. The invention also relates to a blood glucose meter and blood glucose measurement system which comprises means carrying out the above described method.

The invention further relates to a computer program for a data processing method based on blood glucose measurement, comprising instructions which, when the program is executed by one or more computer, cause the computer to carry out the above method.

The invention further relates to a data carrier storing a computer program adapted for carrying out a data processing method based on blood glucose measurement, the stored data and instructions carrying out the above method when the program is run on one or more computer. In addition to blood glucose results, in a preferred embodiment of the invention it is possible to record dietary information and data related to physical activity. The device assesses the risk of the occurrence of hypoglycemic events based on the stored measurement results, and displays the estimated HbA1c range.

An exemplary preferred device may also comprise display- and test strip illumination. In addition to that, the plastic casing of the device may comprise an antibacterial agent that prevents biological contaminants from spreading over the surface and potentially causing an infection.

An exemplary preferred blood glucose meter is characterised by the following specifications:

- Measurement principle: biosensor

- Blood sample: 0.6 μΙ of capillary whole blood

- Measurement range: 0.6 - 33.3 mmol/l

- Measurement duration: 5 seconds

- Encoding: not encoded

- Memory size: 700 data items

- Display: graphical dot matrix display - Interface: USB interface, Bluetooth

- Blood glucose average calculation: from measurement data of the last 7, 14, 30, 60 or 90 days

- Battery: 3V CR2032 batteries (2 pes)

- Operating temperature: 8 - 44 °C

- Storage temperature: -20 - (+50) °C

- Operating and storage humidity: <85% RH

The in vitro diagnostic blood glucose measurement system preferably performs measurements applying a test strip that is based on an electrochemical operating principle. The system is adapted for self-testing, i.e. its basic functionality is to allow patients to easily check their current blood glucose value from capillary whole blood without being assisted by a medical professional. After the measurement is completed the measured blood glucose result is automatically uploaded via a wireless link (Bluetooth) to the mobile application by the blood glucose meter. In addition to blood glucose data the user may also utilize the application for recording information on insulin intake, the amount of consumed carbohydrate, and the times of physical activity. These data can be synchronized with the online therapy system of the electronic log.

The invention is, of course, not limited to the preferred embodiments and modes of realization described above, but further modifications and developments are possible within the scope of protection determined by the claims. Any and all combinations of the above listed advantageous technical solutions are meant to fall into the scope of protection of the invention, with the functionalities being optionally realized in a blood glucose meter or by other electronic means (online log, application) allowing for visualising the results in any desired form and utilizing them for further purposes.