System and method for supporting blood glucose management of subject using computer system

Abstract

One embodiment of the present disclosure provides a method for supporting blood glucose management of a subject using a computer system, the computer system comprising a processor and a communication unit that is connected to the processor and is capable of communicating with an electronic terminal accessible by the subject, wherein the method includes: the processor acquiring, via the communication unit, GP [i] and GP [j], which are glycation rates (GP values) of a target protein of the subject at two time points t [i] and t [j] > t [i]; the processor obtaining, on the basis of GP [i] and GP [j], a glucose management index (GMI [i, j]), which is an average blood glucose level converted to a GP value for the period between the two time points t [i] and t [j]; and the processor generating output information based on the glucose management index (GMI [i, j]) and outputting the output information to the subject via the communication unit.

Description

System and method for supporting blood glucose management in a subject using a computer system

[0001] The present disclosure relates to a technology for supporting blood glucose management of a subject using a computer system.

[0002] Diabetes is an endocrine disease characterized by chronically high blood glucose levels (high fasting blood glucose levels, high HbA1c levels, etc.). Diabetes is mainly classified into type 1 diabetes, type 2 diabetes, gestational diabetes, diabetes caused by pathogenic variants in related genes, and diabetes associated with other diseases. Type 2 diabetes, which has the largest number of patients, is mainly caused by decreased insulin secretion or insulin resistance. Type 1 diabetes is caused by the destruction of pancreatic beta cells.

[0003] Regardless of the disease type, the basis of treatment is dietary therapy and exercise therapy in addition to drug therapy. These treatments allow for appropriate blood sugar management and reduce the risk of complications. In other words, improving lifestyle habits is important. Furthermore, there are so-called "pre-diabetes" or "borderline diabetes" types who do not meet the diagnostic criteria for diabetes but have high blood sugar levels. For this type, improving lifestyle habits is important to reduce the risk of developing diabetes. As described above, improving lifestyle habits, including appropriate diet and exercise, is thought to be effective in managing and preventing blood sugar in diabetes.

[0004] Diabetes is a chronic disease that is generally considered difficult to treat. The reasons for this include the fact that the disease progresses without any noticeable symptoms, it is difficult to maintain motivation for treatment, complete recovery is difficult, and treatment must be continued for life, and that not only drug treatment but also lifestyle modification is essential. These factors are said to make the self-management required for treatment difficult (Non-Patent Document 1).

[0005] In fact, it has been reported that it takes 18 to 254 days, with a median of 66 days, for a new habit to become automatic (Non-Patent Document 2). Based on this, behavioral change takes anywhere from two to several months. Generally, some kind of system is required to maintain motivation for such a long period of time.

[0006] Typical examples of biomarkers for blood glucose control are blood glucose levels, HbA1c levels, and GA levels. However, fasting blood glucose levels do not necessarily reflect lifestyle habits. HbA1c is said to reflect blood glucose levels over the past two to three months. However, it is difficult to maintain motivation for self-management over such a long period of time. In other words, HbA1c levels provide too long a period of feedback for behavioral change.

[0007] Recently, the large amounts of data obtained by continuous glucose monitoring (CGM) have also been used. CGM is a technology that continuously measures glucose levels in interstitial fluid at 15-minute intervals for approximately 14 days. This technology visualizes blood glucose trends, blood glucose spikes such as postprandial hyperglycemia, and nocturnal hypoglycemia. However, it has been pointed out that absolute values ​​are not always accurate, and that the large amounts of data obtained can be difficult for patients and medical professionals such as doctors to handle.

[0008] On the other hand, the GA value is the degree of glycation of a protein called albumin. Because the GA value is a relative value, i.e., the amount of glycated albumin relative to the total amount of albumin, techniques for measuring it with high accuracy have been developed. Furthermore, albumin has a half-life in the blood of approximately 17 days, which is close to the human behavioral cycle of one week. By checking their own GA value every week, subjects can more easily maintain their motivation to improve their lifestyle habits. For this reason, the GA value has recently attracted considerable attention as a marker for blood glucose management.

[0009] However, the half-life of albumin is 17 days, which is longer than one week. Therefore, a subject's weekly behavioral changes are not immediately reflected in the weekly GA value. To evaluate a behavioral change using the GA value, it is necessary to observe the GA value over several weeks.

[0010] American Diabetes Association, Diagnosis and Classification of Diabetes Mellitus, Diabetes Care. 2014; 37 Suppl 1:S81-90. doi: 10.2337 / dc14-S081. , https: / / doi. org / 10.2337 / dc14-s081Lally P, van Jaarsveld C, Potts H, Wardle J. How are habits formed: Modeling habit formation in the real world. Eur J Soc Psychol. 2009;40(6):998-1009. doi:10.1002 / ejsp. 674

[0011] Although the GA value is an excellent marker for blood glucose control, users want to know immediately how their weekly efforts are reflected in their weekly GA value. Meeting this need is important for maintaining the user's motivation. The method of the present disclosure was invented as a result of the inventor's diligent efforts to solve this problem.

[0012] According to some embodiments of the present disclosure, the average blood glucose value converted into a GA value for that period is calculated based on the GA[i] and GA[j] obtained at two time points t[i] and t[j] as a glucose management index (GMI) of GA. ＧＡ In some embodiments, the GA glucose control index (GMI) is calculated as ＧＡ [i, j]) to perform glucose management, blood sugar management, lifestyle management, etc.

[0013] In some embodiments, a goal is obtained for the subject. In some embodiments, a GA value is obtained for the subject. In some embodiments, GA values ​​for the subject are obtained at two time points. In some embodiments, a GA glucose management index (GMI) is calculated based on the two GA values ​​as the average blood glucose level converted to a GA value for that time period. ＧＡ [i, j]) is calculated. In some embodiments, the calculated glucose control index is output to the subject.

[0014] In some embodiments, the glucose management index GMI may be more generally defined as the average blood glucose level calculated from the glycation rate of proteins. For example, the GMI may be calculated by the GMI value of HbA1c (GMI ＨｂＡ１ｃ ) may also be used.

[0015] In one embodiment, the present disclosure may include a method implemented by a computer system. In some embodiments, the computer system may include a processor; a communication unit connected to the processor and capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the subject, etc.); and a memory connected to the processor. In some embodiments, the computer acquires GA values ​​(GA[i], GA[j]) of a subject at two time points (t[i], t[j]). In some embodiments, the computer calculates a glucose control index (GMI[i,j]) as an average blood glucose level converted to the GA value for that period based on the two acquired GA values ​​(GA[i], GA[j]). In some embodiments, the computer outputs the calculated glucose control index to the subject's terminal.

[0016] A typical modern life cycle is based on a week as a unit of time. Therefore, users can more directly recognize how their actual actions in a given week affected their GA value, a physiological marker. This is effective, for example, in raising users' awareness of their lifestyle habits, encouraging them to change their behavior, and maintaining their motivation.

[0017] The above effects are not necessarily limiting, and any of the effects described in this specification, or other effects that can be understood from this specification, may be achieved in addition to or instead of the above effects.

[0018] 1 shows a flowchart of a method according to an embodiment. 2 shows a flowchart of a method according to an embodiment. 3 shows a flowchart of a method according to an embodiment. 4 shows a flowchart of a method according to an embodiment. 5 shows a flowchart of a method according to an embodiment. 6 shows a network configuration including a system and a subject terminal according to an embodiment. 7 shows a block diagram showing functional components of a system according to an embodiment. 8 shows a graph showing changes in GA value and WGMI according to an embodiment. 9 shows a graph showing changes in GA value and WGMI according to an embodiment. 10 shows a graph showing changes in GA value and WGMI according to an embodiment. 11 shows a graph showing changes in GA value, WGMI, and HbA1c value according to an embodiment. 12 shows a graph showing the correlation between CGM and GMI according to an embodiment. 13 shows a graph showing the correlation between predicted values ​​and actual measured values ​​when HbA1c values ​​are predicted using a conventional method. 14 shows a graph showing the correlation between predicted values ​​and actual measured values ​​when HbA1c values ​​are predicted using a method according to an embodiment of the present disclosure. 15 shows a graph showing the correlation between predicted values ​​and actual measured values ​​when HbA1c values ​​are predicted using a method according to another embodiment of the present disclosure. In one embodiment, the GA value and GMI actually obtained in a clinical study ＧＡ 1 shows a graph (a) showing the change in HbA1c value over time, and a graph (b) showing the change in HbA1c value over time predicted by two different methods. ＧＩ Scatter plot showing the relationship between BMI and R ＧＩ Scatter diagram (b) showing the relationship between the waist circumference and the R ＧＩ and HDL cholesterol levels.

[0019] In the following description, numerous specific details are set forth to provide a more thorough understanding of the present invention. However, it will be apparent to one skilled in the art that the present invention may be practiced with one or more of these specific details. In other instances, features and procedures well known to those skilled in the art are not described in order to avoid obscuring the present invention.

[0020] Some embodiments and examples will be described with reference to the accompanying drawings. Arrows in sequence diagrams represent the transmission of information or the order of processes (steps). However, the order between processes not defined by arrows is not specified unless there is a contradiction. This nature of arrows also applies to other sequence diagrams. Note that when multiple embodiments and drawings have similar functions, configurations, or steps, some of them may be omitted. These omissions are assumed to be understandable to those skilled in the art. This does not necessarily mean that the functions, configurations, or steps in the multiple embodiments and drawings are the same.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0022] As used herein, the term "subject" refers to an entity that performs or should perform glycemic management. As used herein, the term "user" refers to an entity that receives output from the system. In some embodiments, a user may be a subject that performs their own glycemic management. In some embodiments, a user may be a person that has the legal right to perform the subject's glycemic management independently or in collaboration with the subject, such as a medical professional or a family member of the subject. A user may include multiple entities. "Subject" or "user" includes a subject that is currently using, should use, is planned to use, or has used a system of the present disclosure.

[0023] In some embodiments, the subject may be a human. However, the subjects of the present disclosure are not limited to humans. Examples of subjects include non-human animals. For example, the subject may be a mammal. Examples of subjects include, but are not limited to, sport animals (such as racehorses), working animals, farm animals, companion animals, and wild animals.

[0024] The subject may be a diabetic. For example, the diabetic may be a type 2 diabetic. The subject may be a non-diabetic. A non-diabetic refers generally to a person who has not been diagnosed with diabetes. The subject may be pre-diabetic and / or healthy.

[0025] Computer In the present disclosure, calculation of GMI from the obtained GA value, prediction of future (unmeasured) GA value and / or HbA1c value based on the GMI value, output of information, optimization, and other calculation steps are performed by a computer or computer unit (hereinafter also referred to as "computing unit") unless otherwise specified.

[0026] System In some embodiments, the system may include a computing unit (e.g., a processor). The system may include a stationary server or a computing unit. The system may include a user interface (e.g., a display unit, a stationary or portable electronic device used by a user, etc.). The system may include hardware and software for performing the methods of the present disclosure. For example, the system may include an application installed on a user's portable electronic device.

[0027] In some embodiments, the system may include a computing unit (e.g., a processor) and a sensor. The sensor may be configured to measure a bodily fluid sample of a user and calculate a glycated albumin (GA) value. Specifically, for example, the sensor may be configured to measure the amount of total albumin and the amount of glycated albumin in the bodily fluid sample, or may be configured to directly determine the ratio of the amount of glycated albumin to the amount of total albumin in the bodily fluid sample without determining these amounts.

[0028] In some embodiments, the system may include a computing unit (e.g., a processor), a sensor, a communication unit connected to the processor and capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the subject, etc.); and a memory connected to the processor.

[0029] In this disclosure, if the subject of an action is not specified, it is understood that the subject is the system.

[0030] The system may be configured to allow only the subject or other persons with legitimate rights to access the subject's GA value, input information from the subject, and output information to the subject, and to block access to such information by other persons without legitimate rights. The embodiments and examples of the present disclosure mainly describe the input and output of information between a single subject and the system, but the present disclosure is not limited thereto. The methods, systems, etc. disclosed herein may be configured to provide services to multiple or many subjects. Information processing and optimization, secondary processing data processing, output information generation, etc. for each subject may be performed by referring to input information from multiple subjects, information processing and optimization, secondary processing data processing, output information to multiple subjects, etc.

[0031] Measurement of GA Levels As used herein, the term "GA level" generally refers to the amount of glycated albumin in a subject's body fluid divided by the total amount of albumin in the body fluid. Alternatively, the GA level may be determined as the ratio of the amount of glycated albumin to the total amount of albumin in a body fluid sample, without determining the amounts.

[0032] Examples of methods for measuring GA values ​​include, but are not limited to, enzymatic methods, high performance liquid chromatography (HPLC) methods, mass spectrometry, isotope dilution mass spectrometry, etc. As devices or sensors for measuring GA values, enzyme sensors, HPLC devices, mass spectrometers (MS devices), etc. can be used.

[0033] In some embodiments, an enzymatic method may be used as a clinical test method. Glycated albumin is decomposed into amino acids with a protease, and then ketoamine oxidase, which reacts only with glycated amino acids, is activated. ２ O ２ The albumin concentration may be determined by reacting it with bromocresol purple (BCP) reagent and measuring the change in absorbance of the blue conjugate.

[0034] For example, the GA value can be calculated as [glycated albumin concentration / albumin concentration] × 100 (%). Alternatively, a correlation equation and a conversion equation (conversion from mmol / mol to %) between the analytical value of a glycated albumin standard specimen and the GA value associated with the standard specimen may be prepared in advance. The correlation equation may be determined by linear regression of the two. The GA value can be determined by measuring the total albumin concentration and glycated albumin concentration in a specimen and based on the correlation equation.

[0035] In some embodiments, a glycated protein sensor comprising an immobilized protease, an immobilized ketoamine oxidase, and a hydrogen peroxide detector may be used to measure the concentration of glycated albumin.

[0036] For example, by using such a sensor, it is possible to measure GA levels in minute amounts of blood, such as venous blood, fingertip blood, earlobe blood, heel blood, capillary blood, dry spot blood (DBS), and skin puncture blood, in a minimally invasive manner. Alternatively, it is possible to non-invasively measure the concentration of glycated albumin in tears or saliva. Therefore, it is possible to measure GA levels without imposing physical or mental stress on the subject or by minimizing the burden of such stress on the subject.

[0037] Acquisition of GA Values ​​In some embodiments, a subject may obtain a bodily fluid (e.g., blood, saliva, urine, etc.) and transport it in a predetermined manner to a laboratory equipped with equipment for measuring GA values. Transport may be by mail. Upon receiving the subject's bodily fluid, the laboratory may determine the GA value in the bodily fluid. In some embodiments, the laboratory may transmit the determined GA value to a computing unit of the system, allowing the system to obtain the GA value. In some embodiments, the laboratory may store the obtained GA value in predetermined storage, and the processor of the system may access the storage to obtain the GA value.

[0038] In some embodiments, a subject may personally visit a medical institution such as a hospital, pharmacy, school, convenience store, fitness club, government office, community center, or other public facility (hereinafter, sometimes simply referred to as a "hospital" or "medical institution"). For example, the subject may visit a medical institution to submit or measure a sample. The medical institution may collect the subject's bodily fluid and measure the GA value internally or at an affiliated hospital. The GA value measurement device may be, for example, a point-of-care testing (POCT) device (hereinafter, sometimes simply referred to as a "POCT device") or an in vitro diagnostic medical device (IVD). The medical institution may collect the subject's bodily fluid, transport it to an external laboratory, and have the laboratory measure the GA value. In some embodiments, the medical institution may transmit the calculated GA value to a computing unit of the system, thereby allowing the system to acquire the GA value. In some embodiments, the medical institution may store the acquired GA value in a predetermined storage device, and the processor of the system may access the storage device to acquire the GA value.

[0039] In some embodiments, the subject may have a GA value measuring device in their home or residence. The GA value measuring device may be, for example, a POCT device, an in vitro diagnostic medical device (IVD), a home medical device, a self-meter, or a self-monitoring device. The subject may collect their own bodily fluid or have someone collect it and measure the GA value with the measuring device. In some embodiments, the self-monitoring device may transmit the calculated GA value to a computing unit of the system, which may then retrieve the GA value. In some embodiments, the self-monitoring device may store the retrieved GA value in a predetermined storage device, and the system processor may access the storage device to retrieve the GA value.

[0040] The GA value does not necessarily have to be measured, and in some embodiments, the GA value may be set arbitrarily, calculated, or estimated based on other parameters, such as a glucose control index.

[0041] As used herein, the term "GA(t[i])" or "GA[i]" refers to a GA value obtained at time t[i]. Unless otherwise defined, time t[i] refers to the time at which a body fluid is collected for the purpose of, or at least in part for, measuring a GA value.

[0042] As used herein, the expressions "time of GA value," "time of measurement of GA value," "time of obtaining / taking a GA value," and similar time-related expressions are based on the time of obtaining a body fluid or specimen containing glycated albumin and albumin that is the basis of the GA value, and not the time of measurement of the GA value, unless otherwise defined.

[0043] The unit of time may be "days," or more precisely, "hours" or "hours and minutes." Time t[i] may be, for example, the day blood is drawn or the day saliva is collected. In other words, GA[i] means the GA value based on the body fluid collected at time t[i] in multiple consecutive GA value measurements.

[0044] Unless otherwise defined, in this specification, GA measurement or collection of body fluids for GA measurement is performed discontinuously or discretely. Therefore, expressions such as t[i] and GA[i] expressed in square brackets indicate discrete time series data, where i is an integer. On the other hand, expressions such as GA(t[i]) expressed in small brackets indicate a function of an argument such as time t.

[0045] For example, the frequency of measuring the GA value may be substantially once per month, twice per month, or higher than these values. For example, the frequency may be equal to or lower than a value such as 20 times per month, 10 times per month, 8 times per month, or once per month. For example, the frequency may be substantially twice per month, 3 times per month, or 4 times per month, or may be 0.5 times per week or once per week.

[0046] In some embodiments, the frequency may be substantially once every 17 days, and in some embodiments, as little as once every two weeks.

[0047] The GA value may be measured at intervals of an average of 7 days, an average of 10 days, an average of 14 days, or more. The GA value may be measured at intervals of an average of 21 days, an average of 24 days, an average of 28 days, or less.

[0048] The term "weekly GA value" as used herein refers to a GA value of a body fluid obtained at intervals of approximately one week. The measurement interval may be set to one week (Monday to Sunday, Sunday to Saturday, Saturday to Friday, etc.). Body fluid may be collected once during that week. Therefore, the intervals for obtaining GA values ​​do not necessarily have to be constant. In some embodiments, GA values ​​may be obtained at a frequency of once per predetermined cycle (e.g., one week). The interval, cycle, or frequency may be adjusted or varied depending on the subject's convenience. For example, body fluid may not be collected on the same day every week, i.e., the day on which body fluid is collected may vary over multiple weeks. For example, the cycle does not have to be one week. Some subjects may have lifestyles that are not measured in units of one week. For example, cycles of five, six, eight, or nine days may be used. Lifestyles may also change in units of weeks, months, etc., and the interval, cycle, or frequency may be changed accordingly.

[0049] In some embodiments, the actual interval between blood draws may be one day or more but less than 14 days. In some embodiments, a subject may plan to draw blood at intervals of one day or more but less than 14 days, i.e., once each specified week. The actual interval between blood draws does not necessarily have to be once per specified week. The interval between blood draws may be irregular or non-uniform.

[0050] Glucose Management Index (GMI) In the present disclosure, a new glucose management index (GMI) and a glucose management method using the GMI are proposed. The glucose management index (GMI) used in this specification is an average blood glucose level converted into a GA value and is expressed by the following formula: where GA[i] and GA[j] are the GA values ​​(% or mmol / mol) at time t[i] and t[j]>t[i], respectively; C ＧＡ [i,j]=1-exp[-(t[j]-t[i]) / τ Ａｌｂ ], τ Ａｌｂis the time constant of albumin decline (-half-life / LN(0.5) ~ 24.5 days).

[0051] Using the GA values ​​GA[i-1] and GA[i] at two consecutive actual or virtual measurement times t[i-1] and t[i], the glucose control index (GMI[i-1,i]) for the period from t[i-1] to t[i] is expressed by the following formula:

[0052] Therefore, the units of the glucose management index (GMI) are the same as the GA value and are expressed as % or mmol / mol.

[0053] In this specification, the interval Δt[i]=t[i]-t[i-1] for obtaining the GA value is not limited. In some embodiments, Δt[i]=t[i]-t[i-1] may be one week. For example, Δt[i] may be one day or more and less than 14 days.

[0054] In this specification, a glucose control index (GMI) based on two consecutively obtained weekly GA values ​​is referred to as a "weekly glucose control index (WGMI)."

[0055] Predicting HbA1c In some embodiments, current and future HbA1c may be predicted based on the GMI.

[0056] In some embodiments, the following formula may be used to predict current and future HbA1c based on GMI: Here, ha[i] is the corrected HbA1c value at time t[i] (>t[0]), HA[i] is the HbA1c value (NGSP value) at time t[i], C ＨｂＡ１ｃ =1-exp[-(t[i]-t[0]) / τ Ｈｂ ], a and b are experimentally determined coefficients, and generally, a=0.25, b=2.0, τ Ｈｂ is the time constant of hemoglobin decrease (-half-life / LN(0.5)), t[0] is the time point when the previous actual measurement value or reference value of HbA1c was measured, and ha[0] is the corrected HbA1c value measured or used as the reference value at time t[0]. The HbA1c value can be converted from the NGSP value to the IFCC value using a commonly known conversion formula.

[0057] Generalization: This disclosure primarily uses GMI based on GA values, but is not limited to this. GMI can generally be applied to organic substances that have a scientifically conceivable half-life and undergo modification in a certain system. Examples of target systems include, but are not limited to, naturally occurring systems such as living organisms and cells, and artificial or industrially formed systems such as in vitro and reactors. Examples of target organic substances include, but are not limited to, proteins, peptide fragments, lipids, and nucleic acids. A typical example of modification in this disclosure is glycation, but this disclosure is not limited thereto. Examples of sugars that contribute to glycation include, but are not limited to, glucose, fructose, and their derivatives. The following explanation uses glycation by glucose as a representative example.

[0058] Generally, the generalized glycated protein (GP) glucose management index (GMI) is expressed by the following formula: where GP[i] and GP[j] are the glycation rates (% or mmol / mol) of the target protein at the time points t[i] and t[j]>t[i], respectively; C ＧＰ [i,j]=1-exp[-(t[j]-t[i]) / τ Ｐ ], τ Ｐ is the decay time constant of the protein of interest (−half-life / LN(0.5)),

[0059] Using the glycation rates of the target protein, GP[i-1] and GP[i], at two consecutive actual or virtual measurement times, t[i-1] and t[i], the glucose control index (GMI[i-1,i]) for the period from t[i-1] to t[i] is expressed by the following formula:

[0060] For example, GMI can be defined based on HbA1c values ​​as follows: Here, HbA1c[i] and HbA1c[j] are the HbA1c values ​​(% or mmol / mol) at the time points t[i] and t[j]>t[i], respectively; C ＨｂＡ１ｃ [i,j]=1-exp[-(t[j]-t[i]) / τ Ｈｂ ], τ Ｈｂis the time constant of hemoglobin decline (-half-life / LN(0.5) ~ 49.9 days),

[0061] In some embodiments, the GMI (GMI) based on the glycosylation degree (GP1) of the first protein (P1) is ＧＰ１ ) may be used to determine the degree of glycosylation (GP2) of the second protein (P2). The relationship is expressed by the following equation, which is a generalization of Equation 3: Here, GMI ＧＰ１ [i-1, i] is the GMI corresponding to the period from time t[i-1] to time t[i] based on GP1, gp2[i] is the corrected GP2 value at time t[i] (>t[0]), GP2[i] is the GP2 value (NGSP value) at time t[i], C ＧＰ２ =1-exp[-(t[i]-t[0]) / τ ＧＰ２ ], τ ＧＰ２ is the decay time constant of the second protein (P2) (-half-life / LN(0.5)), a and b are experimentally determined coefficients, t[0] is the previous measured value or reference value of GP2, and gp2[0] is the corrected GP2 value measured or referenced at time t[0].

[0062] In some embodiments, when a new GA value is obtained, the glucose management index (GMI) or weekly glucose management index (WGMI) may be output to the subject immediately by referring to the previous GA value. The subject can view the latest GMI sent via a user interface such as an electronic terminal.

[0063] In some embodiments, the glucose control indicators may be provided for access by the subject at times other than when they were obtained. Past, current, and future predicted glucose control indicators may be provided for access by the subject at any time. The subject may review the GMI at any time to increase awareness of their own behavior.

[0064] Behavior-Related Information As used herein, the term "behavior" generally refers to lifestyle habits, activities, or actions related to so-called lifestyle-related diseases, including diabetes. "Behavior" can affect lifestyle-related disease-related indicators such as blood glucose levels, HbA1c levels, GA levels, or corresponding or related values ​​or data (e.g., CGM data).

[0065] In some embodiments, "behavior" includes exercise, diet, and / or medication, all of which affect biomarkers such as blood glucose levels, HbA1c levels, and GA levels.

[0066] In some embodiments, information related to "activities" (also referred to as "activity-related information") may be input through a user interface such as an electronic device. Examples of forms of activity-related information include, but are not limited to, text information, audio information, handwritten information, image information, location information, communication information, biometrics, etc.

[0067] In some embodiments, the behavior-related information may be reported or entered by the subject (self-reported information). In some embodiments, the behavior-related information may be biometric information of the subject.

[0068] In some embodiments, subjects may enter selected answers to multiple-choice questions about exercise, diet, and medication. In some embodiments, subjects may enter sentences to open-ended sentence questions.

[0069] In some embodiments, the behavior-related information (e.g., self-reported information) may be entered in natural language. For example, the behavior-related information may be entered in the form of a diary. The subject may write down the activities they performed that day in their own words, either briefly or in detail. Through the diary, the subject may also pose questions.

[0070] In some embodiments, behavior-related information (e.g., self-reported information) may be entered by the subject. By entering information, the subject can recognize their own behavior and voluntarily, rather than compulsively, change their behavior, or the likelihood of doing so increases. Voluntary behavioral change may contribute to more sustainable and effective glycemic control.

[0071] In some embodiments, the behavior-related information or self-reported information may be entered by a non-subject. For example, the information may be entered by a caregiver (such as, but not limited to, a spouse, family member, friend, nurse, doctor, lawyer, etc.). The caregiver may be aware of the subject's wishes. The caregiver may have legitimate rights.

[0072] In some embodiments, behavior-related information or biometric information may be obtained by a wearable device, a non-invasive device such as a weighing scale, an invasive device such as a CGM, or measurements taken during a health check such as a blood test.

[0073] Goal As used herein, the term "goal" generally refers to a value or state that a subject should reach in terms of biological information and / or behavior (including target behavior) related to lifestyle-related diseases.

[0074] Examples of markers used for goals include, but are not limited to, GA value, HbA1c value, weight, BMI, etc. The subject may set a marker value to be achieved or maintained at a predetermined timing or period in the future. In some embodiments, the marker used for the goal is a GA value. Examples of behaviors used for goals include, but are not limited to, diet, exercise, medication, etc. The subject may set the details of diet, exercise, and / or medication to be performed at a predetermined timing or period in the future. In some embodiments, the behaviors used for the goal are diet, exercise, and medication.

[0075] Examples of goals related to "exercise" include, but are not limited to, type of exercise: walking, swimming, strolling, climbing stairs, squats, etc.; time of day: after meals, early morning, etc.; amount: frequency, intensity, number of steps, duration, etc.

[0076] Examples of "dietary" goals include, but are not limited to, what to eat / not to eat: avoiding sugary drinks such as juice, stopping snacking, avoiding sweet snacks, eating fibrous foods such as vegetables, eating less sugary foods such as white rice and bread, reducing alcohol consumption; meal size; meal frequency; amount or balance of fat, carbohydrates, and salt (GI: Glycemic Index) (FC balance, FCS balance), meal order (eating vegetables first, etc.). Examples of "medication" goals include, but are not limited to, taking prescribed medication as directed and not forgetting to take it.

[0077] In some embodiments, a "goal" may be suggested by the system and referenced by the subject, who may then set or input their own next goal into the system, either by referring to or ignoring the system-suggested goal.

[0078] It is believed that having subjects set their own goals, rather than having pre-defined goals or choosing from multiple goal options, is an effective way to change behavior.

[0079] The "goal" may be changed periodically or irregularly. A new goal may be set proactively by the subject. For example, the subject may be given an opportunity to change the goal every week or each time the latest GA value is output from the system. The subject may change the goal at the opportunity, or may maintain the same goal without changing it.

[0080] The set "goal" or a "goal" to be proposed to the subject may be provided to the subject through a user interface (e.g., a display unit, an electronic terminal used by the subject, etc.). The "goal" may be displayed simultaneously with or on the same screen as the GA value or GMI. The latest or updated goal may be provided to the subject. The subject can recognize their latest GA value or GMI and their current "goal." Recognition of changes in blood glucose biomarkers, such as GA value or GMI, may lead to awareness building of the actions the subject should take ("awareness building" in English), either consciously or subconsciously. This may have a positive impact on behavioral change or lifestyle improvement.

[0081] Risk Assessment As used herein, the term "risk" generally refers to the likelihood of a medical problem occurring in association with a glycemic control behavior.

[0082] Examples of medical "risks" include, but are not limited to, the development or likelihood of developing a disease such as hypoglycemia, complications from diabetes, infection, trauma, neoplastic lesions, or psychiatric disorders such as depression.

[0083] In some embodiments, a determination may be made as to whether a subject or a subject's behavior is at risk based on the glucose management index (GMI), GA values, trends in GA values, and any combination thereof.

[0084] Embodiment 1 Referring to FIG. 1, a process S100 of a glucose management method according to an embodiment will be described.

[0085] First, the GA value of the subject is obtained (S101). For example, the subject's weekly GA value is obtained over multiple weeks. To calculate the next GMI, it is necessary to obtain GA values ​​at at least two points in time. For example, GA[i-1] and GA[i], which are the GA values ​​at points in time t[i-1] and t[i], are obtained.

[0086] Next, the GMI is calculated based on the acquired GA value (S102). For example, the GMI (GMI[i-1,i]) for the period between time points t[i-1] and t[i] is calculated based on GA[i-1] and GA[i].

[0087] The calculated GMI[i-1,i] is output to the subject (S131). By recognizing the GMI[i-1,i], the subject can evaluate his / her own behavior during the period between time points t[i-1] and t[i].

[0088] Embodiment 2 Referring to FIG. 2, a process S200 of a glucose management method according to an embodiment will be described.

[0089] First, the subject's GA value is obtained (S201). For example, the subject's weekly GA values ​​are obtained over multiple weeks. To calculate the next GMI, it is necessary to obtain GA values ​​at at least two points in time. For example, GA[i-1] and GA[i], which are the GA values ​​at points in time t[i-1] and t[i], are obtained. In this embodiment, two consecutive weekly GA values ​​are evaluated in step S212, which will be described later. Therefore, in addition to GA[i-1] and GA[i], GA[i-2] is also used.

[0090] Next, the GMI is calculated based on the acquired GA value (S202). For example, the GMI[i-1,i] for the period between time points t[i-1] and t[i] is calculated based on GA[i-1] and GA[i].

[0091] The calculated GMI[i-1,i] is output to the subject (S231). By recognizing the GMI[i-1,i], the subject can evaluate his / her own behavior during the period between time points t[i-1] and t[i].

[0092] This embodiment further includes a process for interpreting the GMI and providing useful comments to the subject based on the value and / or trend of the GMI.

[0093] If GMI[i-1,i] falls below a predetermined threshold (S211), for example, there is a risk of hypoglycemia, so it is determined that there is "risk" (S221), and this determination is output to the subject (S231). A sudden drop in blood glucose level or persistently low blood glucose level indicates a risk of hypoglycemia. When this determination is made, in addition to the GA value and GMI, suggestions may be made to the subject, such as changing (relaxing) the target behavior or seeking a doctor's diagnosis.

[0094] The "at risk" determination (S221) may be based solely on the GMI. For example, if the GMI is below 11%, the subject may be determined to be "at risk." On the other hand, the determination may also be made taking into account the value and / or trend of the GA value, age, medication details, cognitive function, and the like, in addition to the GMI. For example, if the subject is taking medication, is elderly, or otherwise at risk of hypoglycemia, the threshold for determining "at risk" may be set higher.

[0095] If GMI[i-1, i] does not fall below a predetermined threshold (S211), for example, there is no risk of hypoglycemia, so it is determined that the patient is not at "no risk" or "at risk."

[0096] Next, it is determined whether the GMI continues to show a tendency to be lower than the GA value. In this embodiment, it is determined whether the GMI is lower than the GA value at least twice in a row. That is, the conditions are as follows: GMI[i-2, i-1]<GA[i-1] and GMI[i-1, i]<GA[i].

[0097] For example, if the GMI is maintained at a constant level lower than the GA, the GA will gradually approach the GMI. Thus, if the GMI is lower than the GA on multiple occasions, this suggests that the subject's behavior is effective in reducing the high GA.

[0098] In FIG. 2, if the GMI is lower than the GA value for two consecutive times (S212), it is determined that the actions taken from t[i-1] to t[i] were effective (S222), and this determination is output to the subject (S231).

[0099] If the condition that the GMI is lower than the GA value for two consecutive times is not met (S212), it may be determined whether the behavior in the most recent period was effective or not using a different criterion.

[0100] For example, if the previous GMI is lower than the GA value and the next GMI exceeds the GA value, it may be determined that glycemic control is tending to worsen.Alternatively, if the previous GMI is higher than the GA value and the next GMI is lower than the GA value, it may be determined that glycemic control is tending to improve.

[0101] In either case, however, one may avoid making such judgments or even withhold judgments altogether, since such judgments may lead to misleading assessments of behavior.

[0102] In some embodiments, the average GA value and the average GMI may be compared for two or more periods. In Figure 2, if the average GMI is sufficiently smaller than the average GA value (average GMI < average GA value - threshold TH), that is, if the difference is greater than a certain threshold (average GA value - average GMI > threshold TH), it may be determined that the behavior is effective; otherwise, it may be determined that the behavior is ineffective (S213). In some embodiments, the criterion for the determination may be whether the GA value was on a downward trend in the period prior to the two periods.

[0103] If the GA value remains at a substantially constant value after showing a downward trend (S213, Yes), the most recent "behavior is effective" is determined (S222). A continuous decline in the GA value indicates that the subject's behavior during that period was effective. If the GA value remains substantially constant thereafter, the effectiveness of the behavior is considered to be sustained. If the GA value fluctuates slightly while remaining substantially constant, the GMI responds sensitively to the change. Therefore, the effectiveness of the subject's behavior can be more appropriately evaluated by judging based on the trend in the change in the GA value, rather than by judging based solely on the GMI.

[0104] If the GA value has not shown a downward trend in the previous period (S213, No), the most recent behavior is determined to be ineffective (S223). For example, if the GA value has remained constant in the previous period, or if the GA value shows an upward trend or at least does not show a downward trend, it is difficult to say that the GA value is being controlled. Therefore, it cannot be said that the subject's behavior during that period was effective.

[0105] In the embodiment shown in FIG. 2, the determinations of S211, S212, and S213 are performed serially in the figure, but this is not limited to this order. They may be performed simultaneously, in the reverse order, or in any other order. Similarly, in other figures, embodiments, and examples, the order of the steps should not be construed as limiting, and may be changed arbitrarily if reasonable or possible.

[0106] Embodiment 3 Referring to FIGS. 3A-3D, a process S300 of a glucose management method according to an embodiment will be described.

[0107] The basic mode in this embodiment is a process S300 for encouraging a subject to improve (change) their behavior ( FIG. 3A ). First, the subject's GA value is acquired ( S301 ). If the GA value is extremely low or high (within a range where an alert should be issued), it is determined that there is some risk in the subject's condition, such as medical condition or behavior ( S302 ), and output information indicating "risk present" ( S308 ) is output to the subject ( S309 ). As a non-limiting example, if the GA value is less than 11%, 10%, or the like, or if the GA value is greater than 24%, or the like, it may be determined that there is "risk present."

[0108] If it is determined that there is no risk based on the GA value (S302), it is checked whether the previous GA value required to calculate the GMI has been acquired (S303). If the previous GA value has not been acquired, i.e., if multiple GA values ​​have not been acquired (S303), the next GA value must be acquired (S301).

[0109] If multiple GA values ​​have been acquired, they are referenced to determine whether the GA values ​​are within the normal range (S304). For example, GA values ​​over a four-week or one-month period may be referenced to determine whether all of them are within the normal range. If the GA values ​​are within the normal range for a certain period of time, there is no need to further improve behavior, and the current good behavior pattern should be continued. In this case, the subject may be supported in a long-term maintenance mode (S310) that continues to maintain the current behavior pattern.

[0110] If at least the previous GA value and the latest GA value have been acquired, the GMI is calculated based on them (S305).

[0111] If the GMI is extremely low or high (within a range where an alert should be issued) (S305), it is determined that there is some risk in the subject's condition, such as medical condition or behavior (S306), and output information indicating "at risk" is generated (S308) and output to the subject (S309). As a non-limiting example, if the GMI is smaller than a value such as 11% or 10%, or if the GA value is larger than a value such as 24%, it may be determined that there is "at risk."

[0112] If it is determined that there is "no risk" based on the GMI (S306), the subject's behavior is evaluated based on the GMI (S320, S330).

[0113] For example, when the method has just started, or when the method has not been used for a while and is essentially being restarted, only two weeks of GA values, i.e., only one GMI, may be obtained. In such cases, the so-called "initial GMI determination" process is carried out (S320). If three or more weeks of GA values ​​have been obtained, i.e., two or more GMIs are required, the so-called "continuous GMI determination" process is carried out (S330). Each process will be described below.

[0114] 3C shows a flowchart S320 of the "initial GMI determination" process according to one embodiment. If the GA value has sufficiently decreased since the previous time, or if the initial GMI is sufficiently small (S321), it is determined that the actions of that week were effective (S323, S324). As a non-limiting example, the GA value may be determined to have decreased sufficiently if the change (or decrease) in the GA value is 2.5% or more of the previous GA value and is within the margin of error. As a non-limiting example, the initial GMI may be determined to have been sufficiently small if the difference between the GMI and the GA value (GMI - GA value) is 10% or more of the previous GA value and is within the margin of error.

[0115] In the embodiment shown in Figure 3C, the evaluation of the effectiveness of the behavior is classified according to the GMI value. That is, if the GMI is within the normal range (S322), it is determined that the GA value has sufficiently decreased (S323). On the other hand, if the GMI has not yet reached the normal range (S322), it is determined that the GA value is bottoming out (S324).

[0116] If the GA value is showing signs of bottoming out (S324), the subject may be provided with suggestions for setting more effective behaviors as goals, suggestions for improving the target behavior, or suggestions for specific target behaviors. The subject may also be provided with suggestions for improving one or more of the target behaviors related to diet, exercise, and medication. In some embodiments, if the subject is unable to further improve their diet and / or exercise, the subject may be notified that they should change their medication.

[0117] On the other hand, if the GA value has dropped sufficiently since the previous time (if the difference in GA value from the previous time, "ΔGA value," is greater than a predetermined value), or if the initial GMI is not sufficiently small (if the initial GMI is smaller than a predetermined threshold value) (S321), it cannot be said that the behavior was effective. If there is almost no change in the GA value since the previous time (S325), it is determined that the behavior for that week was ineffective (S327). If the GA value has changed somewhat (S325), even if it cannot be said that there has been a sufficient change since the previous time (S321), it cannot be clearly determined that the behavior was effective (S323, S324) or ineffective (S327). Therefore, in this case, judgment is withheld (S326).

[0118] Once the output information is determined (S323, S324, S326, S327), it is output to the subject (S319). Then, the GA value for the next week is obtained (S301). Once multiple GMIs are obtained (S307), the "continuation GMI determination" process is executed (S330).

[0119] Figure 3D shows a flowchart S330 of the "Continuous GMI Determination" process according to one embodiment. If the GMI is lower than the GA value for two consecutive weeks (S331), it is determined that the actions taken that week were effective (S333, S334). In the embodiment shown in Figure 3D, if the average value of two or more GMIs is within the healthy range (S332), it is determined that the GA value has sufficiently decreased (S333). If the average GMI value has not yet reached the healthy range (S332), it is determined that the GA value is bottoming out (S334).

[0120] On the other hand, if the magnitude relationship between the GMI and the GA value changes over the two weeks (S331), the trend of the GA value is unclear. It cannot be determined that the GA value is on a downward trend simply because the GMI and the GA value are close. In such cases, the GA value may be maintained by effective behavior, or the behavior may be ineffective and the GA value may not change.

[0121] In the embodiment of Figure 3D, if the magnitude relationship between the GMI and the GA value has changed over the two weeks (S331), a further determination is made based on the average GMI and the average GA value (S335). If the average GMI is sufficiently lower than the average GA value (average GMI < average GA value - threshold TH), it is determined that the behavior was effective, but that the GA value is bottoming out (S334). Otherwise, it is determined that the behavior is ineffective (S336). As a non-limiting example, if the difference between the average GMI and the average GA value (average GA value - average GMI) is greater than a predetermined value (e.g., average GA value - average GMI > 1.5%), it may be determined that the behavior is effective.

[0122] Once the output information is determined (S333, S334, S336), it is output to the subject (S319). The GA value for the next week is then obtained (S301). If a low GMI continues, the GA value will follow suit and become lower. If this trend continues (S304), the long-term maintenance mode is implemented (S310).

[0123] 3B shows a flowchart S310 of the "long-term maintenance mode" according to one embodiment. First, the subject's GA value is obtained (S311). If the GA value is not within the healthy range (S312), the "behavior improvement mode" is immediately executed (S300). If the GA value is within the healthy range (S312), the long-term maintenance mode continues. Next, the GMI is calculated (S313).

[0124] If the GMI is lower than the GA for two consecutive times (S314), the GA value is determined to be within the normal range and to be decreasing (S315). On the other hand, if the GMI is greater than the GA for two consecutive times (S316), the GA value is determined to be within the normal range and to be increasing (S318). If neither of these conditions is met (S314, S316), the GA value is determined to be within the normal range and stable (S317).

[0125] The "healthy range" of the GA value or GMI may be set based on the subject's will, attributes, physiological conditions, medical conditions, external factors, theories, general opinions, etc. The "healthy range" of the GA value or GMI may be, for example, but not limited to, 11% or more and less than 16%.

[0126] A GA value within the normal range is evidence that the subject has achieved effective behavior. However, if support is discontinued at that point, the GA value will worsen in many subjects. Therefore, the long-term maintenance mode is an effective tool for encouraging or motivating subjects to continue effective behavior.

[0127] Association Between Behavior and GMI In some embodiments, an association or correlation (hereinafter, expressions using either term include both expressions) between the content of the behavior performed by a subject and / or the target behavior of the subject and the GMI may be determined. In some embodiments, an association between the content of the behavior and / or the target behavior and the GMI may be determined for a large number of subjects. In some embodiments, an action (target behavior) to be taken by a subject or suggested to the subject may be generated based on the previously determined association between the behavior or target behavior and the GMI and the determined GMI of the subject. The target behavior for a subject may be generated by referring to the GA value and the trend of the GA value in addition to the GMI of the subject. An algorithm for generating a target behavior for a subject may be optimized using the GMI or GA value as an evaluation value. For example, an algorithm for generating a target behavior for a subject may be optimized so that the GMI and / or GA value is in a risk-free range.

[0128] In some embodiments, data not derived from GA values ​​(also referred to as "non-GA derived data") may be correlated with the GMI. Non-GA derived data may be classified into data groups such as, by way of example and not limitation, personal data, health-related data, lifestyle data, and population data. Examples of non-GA-derived data include, but are not limited to: Personal data: personal ID (My Number), age, sex, marital status, number of children, address, type of residence, car ownership, motorcycle ownership, bicycle ownership, occupation, job type, job title, annual income, genome information, religion, health insurance number, fingerprints, voiceprints, face recognition information, iris recognition information, time-varying personal thoughts, emotions, desires, and wishes, input and usage information for smart devices, etc. Group data: race, nationality, region, regional area, regional seasonal variation factors (seasonal changes in temperature and humidity, etc.), holiday periods (including New Year's holidays, spring break, summer vacation, etc.), commuting method, religion, etc. Health-related data: weight, height, body fat, systolic blood pressure, diastolic blood pressure, heart rate, number of steps, activity level, blood oxygen saturation, body temperature, room temperature, solar radiation, UV radiation, continuous blood glucose measurement (Continuous Glucose Monitoring (CGM), Flash Average blood glucose level, standard deviation and AGP (Ambulatory Glucose Profile) obtained from a Glucose Monitoring (FGM) device, health checkup data (which may include HbAlc values), medical history, prescribed medications, data from medical institutions, food and other allergies, medical history and health-related data of family members, etc. Lifestyle data: eating habits (including meat-eating preference, vegetarianism, etc.), favorite foods, meal menu, meal photos, snack photos, exercise habits, wake-up time, bedtime, sleep duration, sleep depth, sleep score, active or passive rest time, eating speed, chewing, number of times, speed and characteristics, food allergies, dietary rules related to religious beliefs (including Ramadan, etc.), whether or not you exercise, type of exercise, exercise duration, exercise frequency, calories burned during exercise, hobbies, etc.

[0129] In some embodiments, the correlation between GMI and one or more of minimum heart rate, heart rate variability, body temperature (eg, skin temperature), etc. may be determined.

[0130] Correlation with CGM In some embodiments, any parameter of continuous glucose monitoring (CGM) may be estimated based on the GMI. A correlation may be determined between the average blood glucose value obtained from the CGM and the GMI. Statistics such as the average blood glucose value and diurnal fluctuations such as blood glucose spikes can be obtained from the CGM. A correlation may be determined between the GMI and these parameters. Based on these correlations, the subject's parameters may be determined from the GMI. This reduces the frequency of use of CGM, which requires invasive use over long periods of time, allowing for more comfortable and accurate blood glucose management.

[0131] Example of System Configuration and Operation FIG. 4A is a schematic diagram illustrating a network environment connecting the computing unit 100 and the subject 200 according to an embodiment of the present disclosure.

[0132] The computing unit 100 includes a central processing unit (CPU, also referred to herein as a "processor" or "computer processor") 101, a memory unit (storage) 102, a main memory or memory locations 103, peripheral devices 104, and a communication interface 105. These components communicate with the CPU 101 via a communication bus 106, such as a motherboard.

[0133] The CPU 101 may be a single-core or multi-core processor, or may have multiple processors for parallel processing. The CPU 101 may process data signals and include a computing architecture that implements various architectures or combinations of instruction sets. The CPU has an internal cache (not shown), which can quickly communicate data with a registry (not shown) of the CPU 101. The cache stores data used by the CPU 101, allowing the CPU 101 to quickly process it. Access to the cache is not specified below.

[0134] The memory unit or storage 102 is a non-transitory storage medium that stores data to provide the functionality described herein. The memory unit 102 may be a data storage unit (or data repository) for storing data. The memory unit 102 stores programs and data that may be used by the computing unit 100. Examples of the memory unit 102 include, but are not limited to, dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, and other memory devices. Other examples of the memory unit 102 include, but are not limited to, hard disks, floppy disks, CD-ROMs, DVD-ROMs, DVD-RAMs, DVD-RWs, flash memory, magnetic tapes, and any other mass storage devices, non-volatile memory, or similar permanent storage devices and media for storing information on a permanent basis.

[0135] The main memory 103 can store instructions or data that can be executed by the CPU 101. For example, but not limited to, the main memory 103 can be a dynamic random access memory (DRAM), a static random access memory (SRAM), a read-only memory (ROM), or a flash memory. The CPU 101 can transfer, execute, and use the programs and data stored in the storage unit 102 to the main memory 103.

[0136] The peripheral device 104 may be, for example, without limitation, other memory, data storage, and / or an electronic display adapter, etc. The communication interface 105 may be, for example, without limitation, a network adapter, etc. The communication interface 105 may communicate with other devices via the network 400.

[0137] There are several ways in which the subject 200 can obtain the GA value and provide it to the computing unit 100.

[0138] In some embodiments, the subject 200 can undergo a transport test. The subject 200 may use a predetermined transport kit 211 to collect his / her own bodily fluid (e.g., blood, saliva, etc.) and send it to the testing center 220. The testing center 220 can measure the GA value of the subject 200 using the received bodily fluid of the subject 200. The testing center 220 can communicate with the computing unit 100 via the communication interface 221 and the network 400. The testing center 220 provides the obtained GA value of the subject 200 to the computing unit 100.

[0139] In some embodiments, the subject 200 may go to the medical institution 230 on his / her own. The subject 200 may have his / her bodily fluid sampled at the medical institution 230 and have the GA value measured. The medical institution 230 can measure the GA value of the subject 200 using the bodily fluid of the subject 200 that it receives. The medical institution 230 can communicate with the computing unit 100 via the communication interface 231 and the network 400. The medical institution 230 provides the acquired GA value of the subject 200 to the computing unit 100.

[0140] In some embodiments, subject 200 may measure the GA value using a self-monitoring (or home) GA measurement device 240. Subject 200 may introduce their own bodily fluid and have the GA value measured by measurement device 240. Measurement device 240 can communicate with computing unit 100 via an internal or external communication interface (not shown) and network 400. Measurement device 240 provides the acquired GA value of subject 200 to computing unit 100.

[0141] The GA value of the subject 200 acquired in a manner shown or not shown, together with accompanying information (such as the ID of the subject 200, the date and time of body fluid collection, and information related to the measurement), is transmitted to the computing unit 100 via the network 400 and received by the communication interface 105. The information received by the communication interface 105 is temporarily stored in the main memory 103, and is then moved to and stored in the storage 102 as needed.

[0142] The subject 200 has a communicable electronic terminal 300 (for example, a smartphone, a tablet device, a personal computer (PC), etc.). The electronic terminal 300 is configured to be connectable to or communicable with a network 400.

[0143] The subject 200 can start, change, suspend, or cancel the usage contract for the computing unit 100 via the electronic terminal 300. The computing unit 100 can establish or carry out communication with the subject 200 or his / her electronic terminal 300 only with authorized or validly contracted subjects 200, in accordance with personal information, copyright law, civil law, and other laws, as well as in an ethically and medically appropriate manner. The subject 200 can communicate with the computing unit 100 via the electronic terminal 300 as a legitimate user of the computing unit 100.

[0144] Subject 200 collects or has collected body fluids every week. The GA value is measured from the collected body fluids and transmitted to computing unit 100. Computing unit 100 transmits the received GA value to electronic terminal 300 of subject 200. This allows subject 200 to know his / her own GA value via electronic terminal 300. When using self-monitoring GA measuring device 240, subject 200 can know his / her own GA value on the display of the device or on electronic terminal 300 connected to self-monitoring GA measuring device 240.

[0145] The subject 200 collects his / her own body fluids and measures the GA value every week, and the information on the GA value is accumulated in the storage 102.

[0146] Example of System Functional Configuration Figure 4B shows functional components (functional components) of the computing unit 100 according to an embodiment of the present disclosure. These functional components do not necessarily correspond to physical devices, parts, or units of electronic equipment. Each functional component may be configured as one or more physical devices, parts, or units of electronic equipment, or as a part thereof, or a combination thereof. The expression "part" used in this specification may be replaced by "means" or "unit."

[0147] The arithmetic unit 100 includes a communication unit 110, a control unit 120, and a storage unit 130.

[0148] The communication unit 110 may be configured to include, for example, but not limited to, a processor that executes a program, a communication I / F, etc. The communication unit 110 includes a receiving unit 111 that has a function of receiving data from the network 400, and a transmitting unit 112 that has a function of transmitting data to the network 400. The communication unit 110 is connected to the network 400 and has a function of communicating with each device (also referred to as a GA measuring device) 221, 231, 240 that measures or has a GA value, the user electronic terminal 300, and the counselor terminal 510.

[0149] The receiving unit 111 can receive GA value-related information transmitted from the GA measuring devices 221, 231, and 240, and goals, evaluations, and other input information transmitted from the user terminal 300. The transmitting unit 112 can transmit output information generated by the control unit 120 to a predetermined terminal via the network 400.

[0150] The storage unit 130 may be configured to have, for example, but not limited to, a storage area of ​​a storage medium. The storage unit 130 can save and accumulate data 131 related to GA values ​​(GA value data) transmitted from the GA measuring devices 221, 231, and 240 and data 132 of calculated GMIs. The storage unit 130 can save and accumulate data 132 related to the target of the subject 200 (target data), information related to the subject's behavior (behavioral information data) 133, and data 134 related to the subject's evaluation of the output (evaluation data), transmitted from the user electronic terminal 300. The storage unit 130 stores each algorithm 134 used by the control unit 120.

[0151] The control unit 120 may be configured to include, for example and without limitation, a processor that executes a program. In some embodiments, the control unit 120 may include a GMI calculation unit 121. The control unit 120 may further include one, at least one, a plurality, or all of a behavior evaluation unit 122, a risk determination unit 123, a healthy range determination unit 124, a GA value prediction unit 125, an HbA1c value prediction unit 126, a mode selection unit 127, and a proposal generation unit.

[0152] The GMI calculation unit 121 calculates the WGMI for the most recent period from the previous GA value and the latest GA value. Alternatively, the GMI calculation unit 121 calculates the GMI or WGMI (hereinafter simply referred to as "WGMI") from any two GA values ​​from the accumulated GA value data. The calculated GMI or WGMI data 132 is stored in the storage unit 130. The transmission unit 112 of the communication unit 110 transmits the calculated WGMI. The WGMI is transmitted to the electronic terminal 300 of the subject 200 via the network 400. This allows the subject 200 to know his or her own WGMI via the electronic terminal 300.

[0153] The behavior evaluation unit 122 refers to the GMI data 132 to evaluate whether the behavior of the subject 200 during the period corresponding to the GMI was effective. For example, if the GMI is smaller than the GA value two or more times in a row, the behavior of the subject 200 during the period corresponding to the GMI may be evaluated as effective. If the GA value and the GMI alternate weekly, the GA value may be somewhat stable. The effectiveness of the behavior may also be evaluated by referring to statistical behavior over a longer period (e.g., average GA value, average GMI, etc.). For example, if the average GMI is clearly smaller than the average GA value (average GMI < average GA - threshold TH), the GA value tends to decrease. Therefore, it may be determined that the behavior of the subject 200 during this period was effective.

[0154] The risk determination unit 123 may refer to the GA value and / or GMI to determine whether the subject 200 is at or may be at risk for a medical condition. For example, the risk determination unit 123 may determine, based on the information, whether the subject 200 is in a state of hypoglycemia or whether there is a possibility of such a condition. For example, if the GMI is lower than a predetermined value, it may be determined that the behavior during the corresponding period is at risk. Generally, changes in the GA value follow changes in the GMI. Therefore, by detecting the decrease in GMI before the GA value falls below a predetermined value, it is possible to detect early whether the behavior of the subject 200 is at risk.

[0155] If it is determined that the subject 200 may be at risk, the computing unit 100 may send the subject 200 a message urging the subject 200 to receive counseling or a diagnosis by a doctor. At the same time or separately, the computing unit 100 may also send a message stating that the computing unit 100 bears no responsibility whatsoever for the health or illness of the subject 200. The subject 200 receives the message on the electronic terminal 300 and can decide at his / her own responsibility whether to receive counseling or a diagnosis by a doctor.

[0156] The healthy range determination unit 124 may refer to the GA value data 131 and / or the GMI data 132 to determine whether the subject 200 is in the healthy range in terms of blood glucose management.

[0157] The mode selection unit 127 selects or changes the support mode for the subject 200 by referring to any one or a combination of the GA value data 131, the GMI data 132, the behavior information 133, etc. For example, if the GA value and / or GMI has been within the healthy range for a certain period of time or more, the CPU 101 may change the support for the subject 200 from a mode that actively encourages behavioral change (behavior change mode) to a long-term maintenance mode. Conversely, for example, even if the long-term maintenance mode is in operation, if the GA value and / or GMI falls outside the healthy range, the calculation unit 100 may start operating the behavior change mode.

[0158] The GA value prediction unit 125 predicts a future GA value based on the GMI data 132. For example, the GA value prediction unit 125 may predict a GA value at a certain point in the future if the most recent GMI is maintained. For example, the GA value prediction unit 125 may predict a GA value at a certain point in the future based on assumed time-series GMI data (changes in GMI over time).

[0159] The HbA1c value prediction unit 126 may predict a future HbA1c value based on the GMI data 132. For example, if the most recent GMI is maintained, the HbA1c value at a certain point in time in the future may be predicted. For example, the HbA1c value at a certain point in time in the future may be predicted based on assumed time-series GMI data (changes in GMI over time).

[0160] The suggestion generator 128 generates a suggestion for the subject 200 based on the GMI data 132 or by further referring to other information. The suggestion generator 128 may generate a suggestion using a predetermined algorithm 134. For example, the suggestion generator 128 may generate a suggestion that the subject 200 should maintain their most recent GMI, a suggestion that the subject 200 should change their behavior so that their GMI is different from their most recent GMI, content of behavior that the subject 200 should perform, content of behavior that the subject 200 should aim for, etc. In doing so, the suggestion generator 128 may refer to the subject's 200's previous GMI data 132, the subject's 200's related behavior information data 133, data on people other than the subject 200 (for example, data on people with attributes similar to those of the subject 200), etc.

[0161] The optimization unit 129 optimizes algorithms used in blood glucose management, such as the behavior evaluation algorithm performed by the behavior evaluation unit 122, the selection algorithm used by the mode selection unit 127, and the algorithm used by the proposal generation unit 128. For example, the optimization unit 129 may tune the association between GMI and behavior based on the accumulated GA value, GMI, behavioral information, etc. of the subject 200, and optimize the algorithm used to suggest behaviors that the subject 200 should perform.

[0162] In FIGS. 4A and 4B , the electronic terminal 300 of the subject 200 is depicted as a separate unit capable of communicating with the computing unit 100. In some embodiments, the computing unit 100 may be a physical server, for example, via the cloud, and the electronic terminal 300 may be a smartphone or tablet terminal of the subject 200, and the two may be configured to be capable of communicating with each other. However, this configuration is merely an example and should not be interpreted as limiting. In some embodiments, the subject 200 may communicate directly with the computing unit 100. A physical interface and a unit that performs calculations may be integrated. For example, the computing unit 100 may be configured as part of the electronic terminal 300 of the subject 200. For example, the electronic terminal 300 may be a smartphone, tablet terminal, laptop computer, or desktop computer of the subject 200, and the computing unit 100 may be incorporated therein.

[0163] Examples of blood glucose management using the system will be described below with reference to Figures 5A to 5D. Example 1 Figure 5A shows the relationship between GA value and WGMI according to one example. The horizontal axis shows weeks, and the vertical axis shows GA value and WGMI. This data is a hypothetical case based on the progress of an actual diabetic patient.

[0164] For example, week "1" is the time when blood was drawn for the GA value. In this example, the expression "week n" may refer to the time when the blood was drawn, as well as the period between time "week n-1" and time "week n."

[0165] The GA value was determined by analyzing the transported fingertip blood obtained by self-puncture using HPLC. The WGMI, GMI[0,1], was calculated using Equation 2 based on GA[0] and GA[1].

[0166] As shown in Figure 5A(a), the system referenced the subject's self-reported information and recognized that during the period from week 0 to week 1, "the subject ate one bowl of rice for dinner, ate sweets five times a week, and also had habits such as playing golf and walking to work," and performed an analysis based on this, obtaining the result that "daily calorie intake was 2,800 kcal, of which 52% came from carbohydrates, and the amount of exercise was approximately 6,000 METs per week." At week 1, the GA value was approximately 19.6% (GA[1] = 19.6%).

[0167] The system referenced the information about the target behavior entered by the subject and recognized that the subject's target behavior as of week 1 was set to "start walking and strength training, cut the amount of rice consumed at each meal in half, and eat vegetables first." The system also referenced the subject's self-reported information and recognized that the subject had diligently and actually performed this target behavior every day.

[0168] The GA value obtained by the system at week 2 was approximately 19.0% (GA[2] = 19.0%). The system calculated WGMI[1,2] = 17.2% from GA[1] = 19.6% and GA[2] = 19.0%. The system then predicted the change in GA value if the subject continued the same behavior from week 2, i.e., maintained the same WGMI (dash-dotted line and dashed line). In this case, the GA value was predicted to be approximately 17.5% at week 8.

[0169] As shown in Figure 5A(b), the system obtained GA values ​​every week and calculated WGMI. Although WGMI increased slightly in week 3, it returned to the expected value of 17.4% in week 4. The system interpreted this as meaning that the subject maintained a consistent behavior during this period. In fact, GA values ​​from week 1 to week 4 showed a decreasing trend, and the GA value in week 3 was also close to the expected value. Therefore, the system predicted that if this behavior was maintained, the expected GA value would be reached by week 8 (dash-dotted line and dashed line).

[0170] The system referenced the information regarding the target behavior entered by the subject and recognized that the subject changed the target behavior at week 4, increasing the number of walking steps from an average of 7,000 steps per day to approximately 12,000 steps, and reducing the amount of snacking to approximately one candy once a week, in comparison to the behavior he had been performing since week 2. The system referenced the subject's self-reported information and recognized that the subject was actually performing this target behavior every day. The system referenced the subject's self-reported information at week 8 and recognized that the subject "had a calorie intake of 2,000 kcal, with approximately 45% of his total calories coming from carbohydrates, and was continuing to exercise 6,400 METs per week."

[0171] As shown in Figure 5A(c), the GA value obtained by the system at week 5 was approximately 17.8% (GA[5] = 17.8%). The system calculated GMI[5,4] = 16.3% from GA[4] = 18.3% and GA[5] = 17.8%. The system then predicted the change in GA value if the same behavior were continued from week 5, i.e., if the same WGMI were maintained. In this case, the system predicted that the GA value would be approximately 16.9% at week 8. Since GA[8] = approximately 17.6% if the behavior from week 2 to week 4 was continued, it is clear that increased effort in the target behavior will contribute to a further decrease in the GA value in the future.

[0172] The GA value obtained by the system at week 6 and the calculated WGMI were again the same as the expected value. The GA value obtained by the system at week 7 was 17.6%, higher than the predicted value, and the calculated WGMI rose to 17.9%. The system referenced the subject's self-reported information and recognized that the subject "actually ate too much on some days and felt that he / she was not getting enough vegetables." The system correlated this dietary content with the increase in WGMI and recorded this correlation in memory. The system obtained a GA value of 17.1% at week 8 and, based on this, calculated a WGMI of 16.1%. The system recognized that the subject had reached the target GA value of 17.1% and achieved his / her goal, and recorded this information in memory. The system referenced the subject's self-reported information and recognized that "based on reflection from the previous week, all goals were achieved." The system correlated actual behavior with the decrease in WGMI and recorded this correlation in memory.

[0173] Thus, the WGMI was shown to more accurately reflect weekly behavior or effort than the GA value.

[0174] Example 2 Figure 5B shows the relationship between GA value and WGMI according to an example. The horizontal axis represents weeks, and the vertical axis represents GA value and WGMI. This data is a hypothetical case based on the course of an actual diabetic patient.

[0175] As shown in Figure 5B, the system obtained GA values ​​up to week 4 and calculated WGMI from them. WGMI remained relatively constant between 20.0% and 20.6%, and GA steadily decreased from 23% to 21.2%. Therefore, if WGMI remained constant, the system predicted that the GA value after 4 weeks would be approximately 20.6% (dash-dotted line and dotted line (1)).

[0176] At week 5, the system obtained a GA of 20.5% and a WGMI of 18.4%. Based on this, the system predicted that if the GA and WGMI remained constant, the GA would be approximately 18.6% after 8 weeks (dash-dotted and dotted lines (2)).

[0177] The system also retrieved the subject's self-reported information at week 5 and recognized that the subject had increased their physical activity and restricted their food intake. The system recognized this as the cause of the steep decline in WGMI to 18.4%. The system correlated this behavioral change with WGMI and recorded the correlation in memory.

[0178] Furthermore, the system determined that the WGMI was 18.4% and the behavioral content was not medically risky, and did not output an alert indicating "risk present."

[0179] Example 3 Figure 5C shows the relationship between GA value and WGMI according to an example. The horizontal axis shows weeks, and the vertical axis shows GA value and WGMI. This data is a hypothetical case based on the course of an actual diabetic patient.

[0180] At week 2, the system retrieved the GA value and calculated the WGMI. The WGMI was 10.9%, and the GA value also showed a sharp decline from 21.4% to 18.8%. If the WGMI remained constant, the system predicted that the GA value would be 11.7% after 8 weeks (dash-dotted line and dotted line (1)).

[0181] However, the system determined that WGMI = 10.9% was lower than the lower limit of the healthy range and posed a risk. Therefore, the system notified the subject that they should consult a doctor. The system also notified the subject's doctor that they should comprehensively consider the subject's condition and suggested possible adjustments to diet, exercise, and medication. As a result, the doctor determined that the subject's medication dosage should be reduced, and the system acquired this information. Accordingly, the system included reducing medication in the subject's target behavior.

[0182] By week 5, the GA value obtained by the system had decreased to 15.8%, but the calculated WGMI remained constant at approximately 13.6%. Based on these values, the system recalculated the future GA value if this WGMI was maintained. As a result, the system predicted that the future GA value would be 13.8% in approximately 8 weeks (dash-dotted line and dotted line (2)).

[0183] The system determined that a future GA value of 13.8% was appropriate (no risk). The system also obtained the subject's self-reported information and recognized a reduction in medication dosage in the subject's diary. The system output to the subject that the medication change had been effective, the WGMI was appropriate, and the future GA value was also appropriate. However, the doctor's diagnosis at this point indicated some side effects from the medication, so the doctor decided to further reduce the dosage. The medical institution input the doctor's decision and medication change into the system, and the system recorded the information in memory.

[0184] The system was used for follow-up observations up to week 9. The GA values ​​obtained by the system showed a slight downward trend but remained roughly constant between approximately 15.8% and approximately 16.2%. In contrast, the WGMI calculated by the system varied more than the GA value, between approximately 15.3% and approximately 17.4%. As such, when the GA value remains roughly constant or when the WGMI increases or decreases relative to the GA value, the WGMI tends to fluctuate. In such cases, judgments should not be made based on individual WGMIs, but rather it is better to recognize the WGMI trend using its average value or moving average line.

[0185] The system determined that the WGMI was within the normal range from week 5 to week 9, and predicted that if the current condition continued, the GA value would be maintained at approximately 15.9% (dash-dotted line and dotted line (3)). The prediction of future GA value changes was not changed. The system referenced the subject's self-reported information (diary entry) and recognized that the medication intake was stable. Based on this, the system output to the subject and physician that there were no problems with the current blood glucose management and the blood glucose management plan for the past few weeks, and that everything was appropriate.

[0186] Example 4 Figure 5D shows the relationship between GA value and WGMI in one example. The horizontal axis represents weeks, and the vertical axis represents GA value and WGMI. This data is a hypothetical case based on the course of an actual diabetic patient. Figure 5D(a) is referenced for explanation of the period up to week 7, and Figure 5D(b) is referenced for explanation of the period from then until week 13. Note that although a GA value was not obtained in week 1, WGMI was correctly calculated even if the interval was set to 2 weeks.

[0187] As shown in Figure 5D(a), the GA value obtained by the system was 17.4% at week 0, gradually increasing to 17.7% at week 3. The calculated WGMI was 17.9% at week 3, also high. The system output these values ​​to the subject. The system then referenced information about the subject's goal behavior, recognized that the subject had set a new goal behavior, and inferred that the subject was working to improve their GA value. This may have been successful, as the WGMI obtained from the system's GA value decreased to 16.1% at week 4 and 15.3% at week 5. The system correlated the newly set goal behavior with the estimated actual behavior, indicating that it contributed to improved glycemic control, and recorded this correlation in memory. This also demonstrates that the WGMI is sensitive to weekly behavior and more accurately reflects the subject's behavior.

[0188] Therefore, the system predicted that if the WGMI at week 5 was maintained at 15.3%, the GA value would be 15.4% after 8 weeks (week 13). The system determined that this future GA value was not problematic. Furthermore, the system obtained the subject's self-reported information (diary entry) and recognized that there had been a behavioral change (low-carbohydrate diet) during week 5. Based on the criterion that "a drastic low-carbohydrate diet should not be recommended," the system output a message to the subject recommending that they watch educational information regarding proper dietary therapy.

[0189] The WGMI calculated based on the GA values ​​obtained by the system further decreased to 13.2% at week 6. The system inferred that this was due to further changes in the subject's behavior. As the GA values ​​obtained by the system decreased by week 6, the calculated WGMI also decreased. The system correlated the inference of a change in behavior with the decrease in both the GA value and the WGMI and recorded this correlation in memory. Because the GA value obtained by the system was 15.9% and the calculated WGMI was 13.2%, the system determined that these values ​​were acceptable and did not output a risk alert. Therefore, the system predicted that if the WGMI at week 6 was maintained, the GA value would be 13.5% seven weeks later (week 13).

[0190] The WGMI calculated based on the GA value obtained by the system further decreased to 10.3% at week 7, below the threshold of 11%. Therefore, the system determined that there was a possibility of a medical problem occurring (at risk) and output a risk alert to the subject and a recommendation to visit a doctor. The system predicted that if the WGMI at week 7 was maintained, the GA value would fall below 11% in the future. The system output this prediction to the subject, and also output additional educational information (video) about diet and exercise therapy, encouraging them to watch it.

[0191] As shown in Figure 5D(b), at week 8, the WGMI calculated based on the GA values ​​acquired by the system was 12.9%, exceeding the threshold of 11%. Therefore, the system determined that glycemic control had improved. At week 9, the WGMI calculated based on the GA values ​​acquired by the system further increased to 14.5%. The system determined that this behavior was "risk-free." At week 10, the WGMI calculated based on the GA values ​​acquired by the system was 15.5%. The system output a recommendation to the subject to continue their current behavior with a WGMI of approximately 15%. Thereafter, the WGMI calculated based on the GA values ​​acquired by the system showed a slight upward trend but remained below 16%.

[0192] Generally, the standard range for GA is considered to be between 11% and 16%, so maintaining a GA below 16% is ideal. However, lifestyle patterns and health conditions are never completely stable, and it is possible for GA values ​​to exceed the standard range. One strategy is to suddenly change behavior just because GA values ​​exceed the standard range. On the other hand, by continuing to monitor appropriate lifestyle habits using WGMI, it is possible to lower GA values ​​again and return them to 16% or below. Therefore, even if GA values ​​or GMI temporarily exceed the standard range, it is recommended to continue the same behavior and monitor the progress.

[0193] Example 5: Prediction of HbA1c Values ​​Figure 5D shows, as an example, the measured GA values ​​of a certain subject from week 0 to week 7, the measured HbA1c value at week 0, the WGMI calculated based on the GA values, the predicted HbA1c value calculated based on the WGMI, and the predicted change in HbA1c value based on three WGMI patterns. In this example, the same GA values ​​as in Example 4 above are used.

[0194] The HbA1c value obtained by the system was 6.4% at week 0. While this value did not exceed the 6.5% diagnostic standard for diabetes, the system deemed it relatively high. The GA value obtained by the system was 17.4% at week 0 and gradually increased to 17.7% at week 3. The calculated WGMI was 17.9% at week 3, also high. The system output these values ​​to the subject. The system then referenced information regarding the subject's goal behavior, recognized that the subject had set a new goal behavior, and inferred that the subject was working to improve their GA value. This may have been successful, as the WGMI obtained from the system's GA value decreased to 16.1% at week 4 and 15.3% at week 5. The system correlated the newly set goal behavior with the estimated actual behavior, indicating that it contributed to improved glycemic control, and recorded this correlation in memory. In fact, the measured GA value and predicted HbA1c value showed a gradual downward trend.

[0195] As is clear from this, the response of the GA value to changes in weekly behavior is slow, and the response of the HbA1c value is even slower. In contrast, the WGMI is sensitive to weekly behavior and more accurately reflects the subject's behavior.

[0196] Therefore, the system predicted that if WGMI = 15.1% at week 5 was maintained, the HbA1c value would fall below 6 after 8 weeks (week 13) (dotted line (1)).

[0197] The calculated WGMI further decreased to 13.2% at week 6. The system assumed this was due to further changes in the subject's behavior. Therefore, the system predicted that if the WGMI at week 6 was maintained, the HbA1c level would fall below 6% to 5.3% at week 24 (dotted line (2)).

[0198] The WGMI calculated from the GA value obtained by the system further decreased to 10.3% at week 7. Therefore, the system predicted that if the WGMI at week 7 was maintained, the HbA1c value would be 4.6% at week 24 (dotted line (3)).

[0199] In this way, the GMI can be used to not only determine the current HbA1c value, but also to predict future HbA1c values.

[0200] In some embodiments, a predetermined range of HbA1c values ​​(also referred to as a target HbA1c value range, a range of HbA1c values ​​to be targeted, a range of HbA1c values ​​to be targeted, etc.) may be set. The term "range" is used interchangeably with "upper limit" and / or "lower limit." Either or both of the upper and lower limits of the target HbA1c value may be set. The target HbA1c value range may be set as a function of time. For example, a set value range for HbA1c values ​​at the time when the HbA1c value is actually tested may be set. Set value ranges corresponding to multiple time points may also be set.

[0201] In some embodiments, the target HbA1c value range may be set under the guidance of a medical professional such as a doctor. In some embodiments, the target HbA1c value range may be set or suggested by the system. The target HbA1c value range suggested by the system may be approved by the user or a medical professional, and the target HbA1c value range may be set based on the approval. The target HbA1c value range may be set or suggested based on the user's medical condition. Examples of the user's medical condition to be taken into consideration include, but are not limited to, age, medical history, disease type (e.g., type 1, type 2), complications, etc. The target HbA1c value range may be set or suggested taking these factors into consideration.

[0202] In some embodiments, a determination may be made as to whether a predicted HbA1c value at a certain time based on the GMI is within a target HbA1c value range for that time. The comparison result may be output. If the predicted HbA1c value is not within the target HbA1c value range, a change in the GA value and / or GMI (time-series data) that will bring the future HbA1c value into the target HbA1c value range may be calculated and output. Furthermore, an action plan (target action) that will bring the change in the GA value and / or GMI (time-series data) into that range may be proposed.

[0203] Example 6: Correlation between CGM and GMI The correlation between CGM and GMI will be explained with reference to FIG.

[0204] Figure 6 shows a correlation plot between the average CGM glucose values ​​(referred to herein as "average CGM blood glucose values") measured by five healthy volunteers and WGMI. These showed a high correlation with a correlation coefficient of 0.825. The solid line in the figure shows the regression line GA or WGMI = 0.126 × glucose value - 0.552 obtained by Passing-Bablok regression, and the dotted line shows its 95% confidence interval.

[0205] The 95% confidence intervals of the regression coefficients shown in Figure 6 were 0.097 to 0.172 for the slope and -5.421 to 2.877 for the intercept. This range included the theoretical formula, GA or WGMI = 0.139 × glucose value -1.49, and it was found that this result was consistent with the theoretical formula.

[0206] The correlation coefficients between the obtained WGMI and GA values ​​and the time when hyperglycemia was observed and the area of ​​hyperglycemia (time x blood glucose level) obtained from CGM are shown in Table 1. It was also confirmed that WGMI generally tends to show a higher correlation with various indices than GA values.

[0207] Therefore, in some embodiments, the CGM average blood glucose level may be predicted from the GMI or WGMI. Alternatively, the GMI or WGMI may replace some of the numerical values, parameters, statistics, etc. obtained by the CGM. For example, the correlation between the number of blood glucose spikes observed by the CGM and the WGMI may be determined.

[0208] In this example, future HbA1c values ​​were predicted assuming that the GMI would remain constant in the future, but this is not limited to this. The GMI may not be constant or may change. A conversion formula between GA values ​​and HbA1c values ​​is known, but this only applies when blood glucose or GA values ​​remain constant over a relatively long period of time. For example, when GMI changes on a weekly basis, which is the actual case, such a conversion formula does not apply. The method disclosed herein can present the subject with an easily understandable indication of the HbA1c value that the subject's behavior will lead to that week.

[0209] In one embodiment, the glycosylated hemoglobin (HbA1c) value can be predicted with high accuracy using time-series data of the glycosylated albumin (GA) value. The configuration of this embodiment will be described in detail below.

[0210] 1. Glucose Management Index (GMI) ＧＡ Calculation of the glucose management index (GMI) from the time series data of GA values ＧＡ ) the following equation 8 is used:

[0211] This formula calculates the glucose control index, which is a dynamic index based on time series data, by taking into account the amount of change in the GA value between time points t[i-1] and t[i] and adding the previous GA value GA[i-1] as a correction term.

[0212] This glucose management index (GMI) ＧＡ ) can be expressed in relation to the mean blood glucose level (MBG) by the following Equation 9: Here, G.I. Ａｌｂ is the glycation coefficient of albumin. Assuming that blood glucose control status is constant and does not fluctuate, there is a relationship between the average blood glucose level MBG and the GA value, where GA value = GI ＡＬＢ × MBG.

[0213] 2. Glucose Management Index (GMI) of HbA1c ＨｂＡ１ｃ Next, the glucose management index (GMI) was calculated based on the GA value. ＧＡ ) and the glucose management index (GMI) of HbA1c value ＨｂＡ１ｃ ) is derived using the following Equation 10. This index dynamically expresses the time-series fluctuation of HbA1c, and is corrected using the difference in HbA1c values ​​between time points.

[0214] Furthermore, the glucose management index (GMI) of HbA1c ＨｂＡ１ｃ The relationship between HbA1c and mean blood glucose (MBG) is defined by the following equation 11. By clarifying this relationship, it becomes possible to comprehensively understand the relationship between dynamic changes in HbA1c and blood glucose levels. Here, G.I. Ｈｂ is the glycation coefficient of albumin.

[0215] 3. Prediction of HbA1c Value The HbA1c value (HbA1c[q]) at time t[q] is acquired as a known value. Then, the glucose management index (GMI) calculated based on the time series data of the GA value is used as a predictor. ＨｂＡ１ｃ ) is used to predict the HbA1c value (HbA1c[r]) at time t[r] based on the following Equation 12.

[0216] This formula is the glucose management index (GMI) of HbA1c. ＨｂＡ１ｃ ) and the time difference, and the correction coefficient C ＨｂＡ１ｃ By combining these, it is possible to predict HbA1c values ​​with high accuracy.

[0217] 4. Relationship between MBG and GMI The glucose management index (GMI) of average blood glucose level (MBG) and GA value ＧＡ ) is defined by the following Equation 13:

[0218] In addition, the mean blood glucose level (MBG) and the glucose management index (GMI) of HbA1c were ＨｂＡ１ｃ ) is defined by the following equation 14:

[0219] Therefore, the glucose management index (GMI) of HbA1c ＨｂＡ１ｃ ) can be calculated from Equations 13 and 14 as shown in Equation 15 below.

[0220] Using these equations, it is possible to model the indirect relationship that exists between GA and HbA1c values ​​in terms of average blood glucose levels.

[0221] 5. Glycation sensitivity ratio (R ＧＩ ) Definition and calculation method of glycation susceptibility ratio (R ＧＩ ) is the ratio of glycation susceptibility (GI) to blood glucose level of different proteins, and is defined by the following Equation 16. This ratio serves as an important correction factor in the conversion between GA and HbA1c values.

[0222] (1) When HbA1c is stable over time When the HbA1c value is relatively stable, the following formulas 17 and 18 may be used. In this case, a constant proportional relationship exists between the GA value and the HbA1c value, so a stable R ＧＩ It is possible to calculate.

[0223] This formula can be used, for example, when multiple HbA1c values ​​cannot be obtained and only one HbA1c value is available.

[0224] 2) When HbA1c values ​​fluctuate over time When HbA1c values ​​fluctuate over time, it is necessary to consider the influence of the period before time t[q] on predictions after a specific time t[q]. Based on this, correction is performed using the following method.

[0225] First, when HbA1c is obtained at time t[p] and the next time t[q] (p<q), the mean blood glucose level (MBG) between time t[p] and time t[q] is defined by the following equation 19. Here, GMI ＨｂＡ１ｃ [p, q] is a glucose control index based on the HbA1c value from time t[p] to time t[q], and GI Ｈｂ indicates the glycation susceptibility of hemoglobin.

[0226] Next, the GA values ​​at the times t[p], t[p+1], . . . , t[q] are obtained, and then the MBG is calculated based on the following Equation 20.

[0227] Here, GMI ＧＡ [i-1, i] is the glucose control index of the GA value, and GI Ａｌｂ represents the glycation susceptibility of albumin. This formula is designed to normalize the change in GA value between time points by the time interval and average the effect from past time point t[p] to time point t[q].

[0228] Furthermore, the glycation susceptibility ratio (R ＧＩ ) is calculated using the following formula 21.

[0229] These formulas calculate the ratio of glycation susceptibility of albumin to hemoglobin, and then correct it based on past data from t[p] to t[q]. This correction further improves the accuracy of predicting HbA1c values.

[0230] Although Equation 21 uses two consecutive HbA1c values, more HbA1c values ​​may be used. This will further improve accuracy. In that case, the glycation susceptibility ratio (R ＧＩ ) can be calculated using the following formula 22.

[0231] By using the above method, the present invention makes it possible to predict HbA1c values ​​with high accuracy while appropriately taking into account the influence of past data.

[0232] This embodiment provides a technology that can predict HbA1c values ​​with high accuracy using time-series data of GA values ​​while significantly reducing the frequency of HbA1c tests, thereby reducing the burden on patients and medical professionals and enabling real-time blood glucose management.

[0233] As an example of this embodiment, Figure 7 shows the comparison results between the conventional method and the method of the present invention (Equation 18 and Equation 21). These comparisons were based on data obtained from the Jinnouchi Clinical Study (2022-2023, https: / / doi.org / 10.1007%2Fs13300-024-01599-2). In this example, the subjects were type 2 diabetes patients and underwent three hospital visits: the first visit, an intermediate visit (five weeks after the first visit), and a final visit (four weeks after the intermediate visit). At each visit, venous blood was collected and used to measure HbA1c levels. During this period, the subjects collected fingertip blood at home every week, mailed it to the hospital, and used it to measure weekly GA levels. The data shown in this example are from 37 of the 44 type 2 diabetes patients, excluding seven who did not undergo home testing during their final visit week.

[0234] Figure 7A shows the results of predicting HbA1c values ​​from GA values ​​using a conventional method, which uses the HbA1c value from venous blood collected at an intermediate visit (t[p]) and the results of a linear regression of home GA measurements for that week to predict HbA1c values ​​from GA values.

[0235] The Pearson correlation coefficient (Pearson's r) between the predicted and measured values ​​was 0.629, and the slope obtained from the Passing-Bablok regression was 0.11 (95% CI: 0.03-0.21) and the intercept was 5.10 (95% CI: 3.55-6.39). These results indicate that the predicted values ​​varied widely and had limited precision.

[0236] 7B shows the results when the method of the present invention was applied and a technique based on Equation 18 was used. In this method, the glycation coefficient ratio was calculated from the HbA1c value and GA value measured at the intermediate visit (t[q]), and the HbA1c value at the final visit (t[r]) was predicted using the GA value during the period from the intermediate visit (t[q]) to the final visit (t[r]).

[0237] The correlation coefficient (Pearson's r) between the predicted and measured values ​​was very high at 0.995, and the slope obtained from the Passing-Bablok regression was 1.05 (95% CI: 0.988-1.22) and the intercept was -0.40 (95% CI: -1.62--0.02). The high correlation coefficient indicates a significant improvement in prediction accuracy compared to conventional methods, but it was observed that the intercept of the linear approximation deviated from 0.

[0238] 7C shows the results when the method based on Equation 19 was further applied. In this method, the HbAlc value at the final visit (t[r]) was predicted using the GA measurement values ​​between the first visit (t[p]) and the final visit (t[r]) and the HbAlc values ​​at the first visit (t[p]) and the intermediate visit (t[q]).

[0239] The Pearson correlation coefficient (r) between the predicted and measured values ​​was 0.974, and the slope of the Passing-Bablok regression was 1.00 (95% CI: 0.909-1.11), with an intercept of -0.01 (95% CI: -0.81-0.63). It is noteworthy that the intercept of the linear approximation in Figure 7C is very close to 0, almost coincident with the origin (0,0), compared to Figure 7B.

[0240] These results confirm that the method of the present invention significantly improves the prediction accuracy of HbA1c values ​​compared to conventional methods. In particular, the method using Equation 19 is characterized by the fact that the linear approximation of the predicted value and the actual measured value nearly coincides with the origin (0, 0), and is excellent in both accuracy and theoretical consistency. This indicates that the method of the present invention is excellent in its ability to capture minute fluctuations and enables highly reliable blood glucose management.

[0241] Figure 8(a) shows the GA and GMI values ​​actually obtained in this clinical study. ＧＡ , a graph showing the change in HbA1c value over time. The squares (white square markers) and the solid line represent the measured GA values, and the dashed line represents the GMI calculated from the GA values. ＧＡ The black circle indicates the measured HbA1c value. Figure 8(a) shows an example in which the GA value and the HbA1c value show different trends. That is, in this example, the HbA1c value increases even though the GA value decreases.

[0242] Figure 8(b) shows a graph over time of the HbA1c values ​​predicted by the two methods, superimposed on the measured HbA1c values ​​based on the data in Figure 8(a). The circles (circular markers with white spaces inside) and thin dashed lines represent the R values ​​calculated using Equation 18 from the HbA1c values ​​and GA values ​​at the first hospital visit. ＧＩ The HbA1c values ​​predicted from the subsequent home GA measurements using Equation 12 and Equation 15 are shown. On the other hand, the squares (white square markers) and the thick dashed lines represent the R calculated from Equation 21 using the two HbA1c values ​​at the first and intermediate visits and the home GA measurements from week -1 to week 4. ＧＩ The HbA1c value predicted using Equation 12 and Equation 15 from the home GA measurement values ​​from week 4 onwards is shown.

[0243] The difference between these two predicted HbA1c values ​​is calculated using Equation 18 from a single HbA1c value and a GA value. ＧＩ and R calculated from Equation 21 using two HbA1c values ​​and multiple GA measurement values. ＧＩ In the example of Figure 8(a), when the HbA1c value is increasing despite the GA value decreasing, it is thought that the HbA1c value, which has a longer half-life, more strongly reflects the influence of the increase in blood glucose level before the start of measurement, while the GA value more strongly reflects the most recent decrease in blood glucose.

[0244] Formula 18 does not include a half-life term, and R ＧＩThe calculation does not reflect the difference in half-life of each. Therefore, the HbA1c value is predicted to decrease in the same way as the GA value. On the other hand, Equation 21 includes GMI, which reflects the half-life. Therefore, when using this equation, R is calculated using two HbA1c values ​​and multiple GA measurements. ＧＩ By calculating the above, it is possible to remove the influence of such past blood glucose fluctuations and obtain a more accurate glycation susceptibility ratio (see FIG. 8(b)), and therefore it is possible to predict HbA1c values ​​with higher accuracy.

[0245] Figure 9(a) shows the R of each subject obtained from the clinical research data. ＧＩ This figure shows a scatter plot of the relationship between BMI and RI. As with Figures 7(A), (B), and (C), this data is composed of data from 37 of the 44 type 2 diabetes patients, excluding 7 who did not undergo home measurements during the final week of their visit. ＧＩ shows a tendency to increase as BMI increases.

[0246] FIG. 9(b) shows the R of each subject obtained from the same clinical research data as FIG. 9(a). ＧＩ The figure shows a scatter plot of the relationship between R and waist circumference. ＧＩ tends to increase as waist circumference increases.

[0247] These are particularly useful for users with large BMI or waist circumferences, using Equation 18 or Equation 21 to calculate R ＧＩ This shows the importance of finding the commonly known R ＧＩ This shows that when the value of 0.25 is used, the predicted HbA1c value deviates more from the actual measured value. For example, when the actual HbA1c value cannot be obtained, such as when blood is not collected, the R ＧＩ The HbA1c value may be predicted from the measured GA value assuming a value of 0.25. In this case, subjects with a BMI of 25 or more or a waist circumference of 85 cm or more may be notified that the predicted HbA1c value may have a large error or may be inaccurate.

[0248] FIG. 9(c) shows the R of each subject obtained from the same clinical research data as FIG. 9(a).ＧＩ A scatter plot showing the relationship between R and HDL cholesterol levels is shown. ＧＩ tends to increase as the HDL cholesterol level decreases.

[0249] This indicates the possibility that the HDL cholesterol level can be estimated from the GA value and HbA1c value. For example, ＧＩ If the value is greater than a predetermined value (eg, 0.33), the user or medical professional may be notified to check the HDL cholesterol level.

[0250] In the above embodiments and examples, the time constant τ (the time constant for the decrease of a target protein, such as albumin or hemoglobin) is set as a general constant. However, in some embodiments, this time constant τ may be set as an individual-specific parameter. For example, τ may be defined as an individual parameter (individualized parameter) using multiple HbA1c values ​​and GA values. The individualized time constant τ may be optimized using, selectively or in combination, the individual's attribute / profile data parameters, measurement data (GA values, HbA1c values, CGM data, or a combination thereof), etc. τ may not necessarily be a parameter that changes significantly over a period of several weeks, but may change over the long term due to changes in the individual's health condition, disease state, age, etc. τ may also be affected by treatment conditions, such as dialysis.

[0251] The present disclosure includes the following embodiments: A001. A method for supporting blood glucose management of a subject using a computer system, wherein the computer system includes a processor, the method comprising: the processor acquiring GA[i] and GA[j], which are the subject's glycoalbumin levels (GA values) at two time points t[i] and t[j] > t[i]; the processor calculating a glucose control index (GMI[i,j]), which is the average blood glucose level converted to a GA value for the period between the two time points t[i] and t[j], based on GA[i] and GA[j]; and the processor generating output information based on the glucose control index (GMI[i,j]) and outputting it to the subject. A001b. A method for assisting blood glucose management of a subject using a computer system, the computer system comprising: a processor; and a communication unit connected to the processor and capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the subject, etc.), the method comprising: the processor acquiring, via the communication unit, GP[i] and GP[j], which are the glycation rates (GP values) of a target protein of the subject at two time points t[i] and t[j]>t[i]; the processor calculating, based on GP[i] and GP[j], a glucose control index (GMI[i,j]), which is an average blood glucose level converted to a GP value for the period between the two time points t[i] and t[j]; and the processor generating output information based on the glucose control index (GMI[i,j]), and outputting the information to the subject via the communication unit.A002. A method for supporting blood glucose management of a subject using a computer system, wherein the computer system includes a processor and a communication unit connected to the processor and capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the subject, etc.), the method comprising: the processor acquiring, via the communication unit, GA[i-1] and GA[i], which are the subject's GA values ​​at two consecutive time points t[i-1] and t[i]>t[i-1]; the processor determining, based on GA[i-1] and GA[i], a glucose control index (GMI[i-1,i]), which is the average blood glucose level converted into a GA value for the period between the two time points t[i-1] and t[i]; and the processor generating output information based on the glucose control index (GMI[i-1,i]) and outputting it to the subject. A003. A002. The method according to any one of the embodiments, wherein the interval Δt = t[i] - t[i-1] for acquiring the GA value is substantially one day or more and one month or less. A003b. The method according to A003 or any one of the embodiments, wherein the interval Δt = t[i] - t[i-1] for acquiring the GA value is substantially three days or more and 14 days or less. A005. The method according to any one of A001 to A003 or any one of the embodiments, wherein the computer system further comprises a communication unit connected to the processor and capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the subject, etc.), and acquiring the GA[i] and GA[j] includes the processor acquiring the GA[i] and GA[j] via the communication unit. A007. A method according to any one of A001 to A005 or any embodiment, wherein the computer system further comprises a communication unit connected to the processor and capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the subject, etc.), and outputting the output information to the subject includes outputting to the subject via the communication unit.A011. The method according to A002 or A003, or any of the embodiments, wherein generating the output information includes generating an evaluation of the subject's behavior during a period from time t[i-1] to time t[i], which is a period of time, based on the glucose control index (GMI[i-1,i]). A012. The method according to A011, or any of the embodiments, wherein the output information includes an evaluation that the behavior performed by the subject during the period of time was effective when the glucose control index (GMI[i-1,i]) is smaller than the GA value GA[i]. A013. A011. The method according to any one of the embodiments, wherein the output information includes an evaluation that the behavior performed by the subject during the relevant period was effective when the glucose control index (GMI[i-1,i]) is smaller than the GA value GA[i] for two consecutive times, i.e., when condition 1: GMI[i-1,i]<GA[i] and GMI[i-2,i-1]<GA[i-1] is satisfied. A014. A013. The method according to any one of A013 to A014 or any one of the embodiments, wherein the output information includes an evaluation that the behavior performed by the subject during the relevant period was effective when Condition 1 is not met, or when the average of at least two glucose control indices is sufficiently smaller than the average of the at least two relevant GA values, i.e., when Condition 2: average {GMI[i-1,i]}<average {GA[i]}-TH(threshold) is met, and / or an evaluation that the behavior performed by the subject during the relevant period was ineffective when Condition 2 is not met. A015. The method according to any one of A012 to A014 or any one of the embodiments, wherein the output information includes an alert that the behavior performed by the subject during the relevant period poses a health risk and / or a notification that the target behavior should be changed when the GMI is lower than a GMI risk judgment value and / or the GA value is lower than a GA risk judgment value.A021. The method described in A001 or any of the embodiments, further comprising: acquiring behavioral information regarding behaviors performed by the subject during the period between the times t[i] and t[j]; and determining an association between the behavioral information and the glucose control index (GMI[i,j]). A022. The method described in A021 or any of the embodiments, wherein the behavioral information includes self-reported information by the subject and / or biological information acquired about the subject. A023. The method described in A021 or any of the embodiments, wherein determining an association between the behavioral information and the glucose control index (GMI[i,j]) includes referencing a database containing behavioral information and GA values ​​of non-subjects other than the subject. A031. A011. The method according to any one of the embodiments, comprising generating a target behavior to be performed by the subject in the future based on the association. A032. The method according to A031 or any one of the embodiments, wherein generating a target behavior to be performed by the subject in the future includes excluding or deleting from the generated target behaviors an ineffective or risky behavior in information of behaviors previously performed by the subject, or lowering the priority of the ineffective or risky behavior. A035. The method according to A031 or any one of the embodiments, further comprising outputting to the subject the generated target behavior to be performed by the subject. A041. The method according to A031 or any one of the embodiments, wherein an algorithm for generating a target behavior to be performed by the subject in the future is optimized using the subject's glucose control index as an evaluation parameter.A101. A method for assisting blood glucose management of a subject using a computer system, the computer system including a processor, the method comprising: (a1) the processor acquiring a first GA value GA[m] of the subject at a first time point t[m]; (b1) the processor acquiring a time-series glucose control index {GMI[i-1,i]} (i = m + 1 to n) from the first time point t[m] to a second time point t[n]; and (c1) the processor determining the second GA value GA[n] at the second time point t[n] based on the first GA value GA[m] and the time-series glucose control index {GMI[i-1,i]} (i = m + 1 to n). A111. A112. A method for assisting a subject in blood glucose management using a computer system, the computer system comprising a processor, the method comprising: (a2) the processor acquiring a first GA value GA[m] of the subject at a first time point t[m]; (b2) the processor acquiring a target second GA value GA[n] at a second time point t[n]; and (c2) the processor calculating a time series glucose control index {GMI[i-1,i]} (i = m + 1 to n) required to reach the second GA value GA[n] at the second time point t[n] based on the first GA value GA[m] and the second GA value GA[n]. A113. The method according to A111 or any embodiment, wherein step (c2) includes calculating a time series of glucose control indexes {GMI[i-1,i]}[k] (i = m + 1 to n) of a plurality (k, k > 2) of glucose control indices. A113. The method according to any one of A101 to A112 or any embodiment, wherein the computer system further comprises a communication unit connected to the processor and capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the subject, etc.), and wherein step (a1) and / or step (a2) comprises the processor obtaining a first GA value GA[m] of the subject at a first time point t[m].A method for assisting blood glucose management of a subject using a computer system, the computer system including a processor, the method comprising: (a3) ​​the processor acquiring a first HbA1c value HbA1c[m] of the subject at a first time point t[m]; (b3) the processor acquiring a time-series glucose control index {GMI[i-1,i]} (i = m + 1 to n) from the first time point t[m] to a second time point t[n]; (c3) the processor calculating the second HbA1c value HbA1c[n] at the second time point t[n] based on the first HbA1c value HbA1c[m] and the time-series glucose control index {GMI[i-1,i]} (i = m + 1 to n); (d3) the processor calculating a target HbA1c value range at the second time point t[n]; (e3) (f3) the processor determining whether the second HbA1c value HbA1c[n] is within the target HbA1c value range; and (f4) the processor outputting a result of the determination. A122. The method of A121 or any embodiment, further comprising: (g3) the processor calculating a time-series glucose control index {GMI[i-1,i]} (i = m + 1 to n) such that the second HbA1c value HbA1c[n] at the second time point t[n] is within the target HbA1c value range.

[0252] C001. Software including computer-executable code for assisting a subject in blood glucose management, the computer-executable code, when executed by a processor, is configured to: cause the processor to acquire GA[i] and GA[j], which are the subject's GA values ​​at two time points t[i] and t[j] > t[i]; determine a glucose control index (GMI[i,j]), which is the average blood glucose level converted into a GA value for the period between the two time points t[i] and t[j], based on the GA[i] and GA[j]; and generate output information based on the glucose control index (GMI[i,j]), and output it to the subject. C002. C001. The software of any one of claims 1 to 5, wherein causing the processor to obtain GA[i] and GA[j] includes causing the processor to obtain GA[i] and GA[j] via a communication unit connected to the processor and capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the subject, etc.). C011. Software for assisting a subject's glycemic management, comprising computer-executable code, the computer-executable code being configured, when executed by a processor, to cause the processor to perform the method and / or steps of any one of claims 1 to 5, or any embodiment. D001. A non-transitory storage medium storing the software of any one of claims 1 to 5, or any embodiment.

[0253] E001. A computer system for supporting blood glucose control in a subject, the computer system comprising a processor, the processor being programmed to: acquire GA[i] and GA[j], which are the subject's GA values ​​at two time points t[i] and t[j] > t[i]; calculate a glucose control index (GMI[i, j]), which is the average blood glucose level converted into a GA value for the period between the two time points t[i] and t[j], based on the GA[i] and GA[j]; and generate output information based on the glucose control index (GMI[i, j]), and output it to the subject. E005. A computer system according to E001 or any of the embodiments, wherein the computer system comprises a communication unit connected to the processor and capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the subject, etc.), or is configured to be connected to a communication unit capable of communicating with the user interface, wherein causing the processor to acquire the GA[i] and GA[j] comprises causing the processor to acquire the GA[i] and GA[j], which are the subject's GA values, via the communication unit, and causing the processor to output the output information to the subject comprises causing the processor to output the output information to the subject via the communication unit. E011. A computer system for supporting glycemic control in a subject, wherein the computer system comprises a processor, and wherein the computer system is programmed to cause the processor to perform the methods and / or steps described in any one of A001 to A201, or any of the embodiments. E021. A computer system for assisting a subject in glycemic control, comprising: a processor; and a non-transitory storage medium storing the software of any one of C001 to C201, or any embodiment.E022. The computer system according to E021 or any of the embodiments, further comprising a communication unit capable of communicating with a user interface (e.g., a display unit, a subject's electronic terminal, etc.). E101. A computer system for supporting glycemic control in a subject, comprising: a receiving means for acquiring a GA value of the subject; a GMI calculation means for calculating a glucose management index based on the acquired multiple GA values; and a transmitting means for transmitting the glucose management index to the subject's terminal. E102. E101 or the computer system described in any of the embodiments, further comprising at least one or more of: a behavior evaluation means for evaluating the behavior of the subject based on the glucose control index; a risk judgment means for determining the risk of the behavior of the subject based on the glucose control index and / or the GA value; a GA value prediction means for predicting a future GA value based on the glucose control index; an HbA1c value prediction means for predicting a future HbA1c value based on the glucose control index; a mode selection means for selecting an assistance mode adapted to the subject from a plurality of blood glucose control modes based on the glucose control index and / or the GA value; a suggestion generation means for generating output information including a behavior to be taken or a target behavior by the subject based on the glucose control index; and an optimization means for optimizing an algorithm for generating the output information.

[0254] B201. A system for supporting blood glucose management, comprising a sensor, a processor, a storage unit, and a user interface, wherein the sensor is configured to receive a body fluid of a subject, measure a glycoalbumin level (GA value) of the subject, and transmit the measured GA value to a processor; the processor receives GA values ​​(GA[i] and GA[j], where t[j]>t[i]) at different times from the sensor, and calculates a GA glucose control index (GMI), which is an average blood glucose level converted into a GA value for a period between two times t[i] and t[j], based on Equation 1. ＧＡ [i,j]), Here, C ＧＡ [i,j]=1-exp[-(t[j]-t[i]) / τ Ａｌｂ ], τ Ａｌｂ is the albumin decline time constant; and the GA glucose control index (GMI ＧＡ B211. The system of B201 or any embodiment, wherein the different points in time are two consecutive points in time t[i-1] and t[i], where t[i] > t[i-1]. B212. The system of B201, B211, or any embodiment, wherein the interval Δt = t[i] - t[i-1] for acquiring the GA value is substantially 3 days or more and 14 days or less. B221. The system of B201 or any embodiment, further comprising a communication unit connected to the processor and capable of communicating with the sensor and the user interface. B231. B231. The system of any of B201, B211, or any of the embodiments, wherein the output information includes an evaluation of the subject's behavior during the period from time t[i-1] to time t[i]. B232. The system of any of B231, or any of the embodiments, wherein the output information includes the GA Glucose Management Index (GMI) ＧＡB232. The system of B232 or any embodiment, wherein the output information includes an evaluation that the behavior performed by the subject during the relevant period was effective when the glucose control index (GMI[i-1,i]) is smaller than the GA value GA[i] for two consecutive times, that is, when Condition 1: GMI[i-1,i]<GA[i] and GMI[i-2,i-1]<GA[i-1] is satisfied. B233. The system of B232 or any embodiment, wherein the output information includes an evaluation that the behavior performed by the subject during the relevant period was effective when the glucose control index (GMI[i-1,i]) is smaller than the GA value GA[i] for two consecutive times, that is, when Condition 1: GMI[i-1,i]<GA[i] and GMI[i-2,i-1]<GA[i-1] is satisfied. B234. B233. The system according to any one of B233 to B234 or any of the embodiments, wherein, when Condition 1 is not met, the system includes an evaluation that the behavior performed by the subject during the relevant period was effective when the average of the glucose control indexes for at least two measurements is sufficiently smaller than the average of the GA values ​​for the at least two relevant measurements, i.e., when Condition 2: average {GMI[i-1,i]}<average {GA[i]}-h(threshold) is met, and / or when Condition 2 is not met, the system includes an evaluation that the behavior performed by the subject during the relevant period was ineffective. B235. The system according to any one of B232 to B234 or any of the embodiments, wherein, when the GMI is lower than a GMI risk judgment value and / or the GA value is lower than a GA risk judgment value, the output information includes an alert that the behavior performed by the subject during the relevant period poses a health risk and / or a notification that the target behavior should be changed. B241. and determining a correlation between the behavioral information and the glucose management index (GMI[i-1,i]). B242. The system of B241 or any embodiment, wherein the behavioral information includes self-reported information by the subject and / or biometric information acquired about the subject.B243. The system of B241 or any embodiment, wherein determining the association between the behavioral information and the glucose control index (GMI[i-1,i]) includes referencing a database containing behavioral information and GA values ​​of non-subjects other than the subject. B244. The system of B241 or any embodiment, wherein the processor is further configured to generate a target behavior to be performed in the future by the subject based on the association. B245. The system of B244 or any embodiment, wherein generating a target behavior to be performed in the future by the subject includes excluding from the generated target behavior or deleting from the generated target behavior an ineffective or risky behavior previously performed by the subject, or lowering the priority of the ineffective or risky behavior. B246. The system of B244 or any embodiment, wherein the processor is further configured to output to the subject the generated target behavior to be performed in the future. B247. The system of B244 or any embodiment, wherein an algorithm for generating a target behavior to be performed by the subject in the future is stored in the memory unit, and the algorithm is optimized using the glucose control index of the subject as an evaluation parameter. B251. The system of B201 or any embodiment, wherein the processor performs the following steps: (a1) acquiring from the sensor a first GA value GA[m] of the subject at a first time point t[m]; (b1) acquiring from the sensor a time series GA glucose control index {GMI} from the first time point t[m] to a second time point t[n]. ＧＡ (c1) obtaining the first GA value GA[m] and the time-series GA glucose control index {GMI[i−1, i]} (i=m+1 to n); and ＧＡ[i-1, i]} (i=m+1 to n), where i=m+1 to n. B252. The system of B201 or any embodiment, wherein the processor is configured to perform the following steps: (a2) acquiring from the sensor a first GA value GA[m] of the subject at the first time point t[m]; (b2) acquiring from the sensor a second target GA value GA[n] at the second time point t[n]; and (c2) calculating a time-series GA glucose control index {GMI} necessary to reach the second GA value GA[n] at the second time point t[n] based on the first GA value GA[m] and the second GA value GA[n]. ＧＡ [i-1, i]} (i=m+1 to n). B253. The system according to B252 or any embodiment, wherein step (c2) comprises: determining a plurality (k, k≧2) of GA glucose control index time series {GMI ＧＡ [i-1,i]}[k] (i=m+1 to n). B261. The system of B201 or any embodiment, wherein the processor performs the following steps: (a) acquiring a plurality of time-series glycoalbumin values ​​(GA values) of the subject at time points t[q] to t[r] (q>r), the GA values ​​being GA values ​​of the subject; (b) calculating a time-series glucose management index (GMI) of the GA values ​​obtained from the acquired time-series GA values. ＧＡ ) and glycation susceptibility ratio R ＧＩ Based on this, the GMI (GMI) of the HbA1c value during the period from t[q] to t[r] is calculated using the following formulas 15 and 16. ＨｂＡ１ｃ [q, r]): where: (c) obtaining the subject's HbA1c value (HbA1c[q]) at time t[q]; (d) obtaining the GMI (GMI ＨｂＡ１ｃ [q, r]) and the subject's HbA1c value (HbA1c[q]) at time t[q], using the following formula: (e) generating and outputting output information based on the estimated HbA1c value (HbA1c[r]) at time t[r] (r>q) of the subject. ＧＩ is expressed by the following formula: B263. The system of B261 or any embodiment, wherein the R ＧＩ is expressed by the following formula: A201. A method for supporting blood glucose management of a subject using a computer system, the computer system comprising a sensor, a processor, a storage unit, and a user interface, the method comprising: measuring the subject's body fluids using the sensor to obtain the subject's GA value; transmitting the GA values ​​(GA[i] and GA[j], where t[j]>t[i]) at different time points to the processor; and calculating a GA glucose control index (GMI), which is the average blood glucose level converted into the GA value for the period between two time points t[i] and t[j], based on Equation 1, by the processor. ＧＡ [i,j]), Here, C ＧＡ [i,j]=1-exp[-(t[j]-t[i]) / τ Ａｌｂ ], τ Ａｌｂ is the time constant of albumin decrease; the GA glucose control index (GMI ＧＡ[i,j]) and transmitting the output information received from the processor to the user interface; and outputting the output information received from the processor to the subject by the user interface. A261. A method for supporting glycemic control of a subject using a computer system according to A001 or any embodiment, wherein the computer system comprises a sensor, a processor, a storage unit, and a user interface, the method comprising: (a) measuring the subject's body fluids using the sensor to obtain a plurality of time-series glycoalbumin values ​​(GA values) of the subject from time points t[q] to t[r] (q>r), the GA values ​​being GA values ​​of the subject; (b) calculating a time-series glucose management index (GMI) of the GA values ​​obtained from the obtained time-series GA values ​​by the processor. ＧＡ ) and glycation susceptibility ratio R ＧＩ Based on this, the GMI (GMI) of the HbA1c value for the period from t[q] to t[r] is calculated using the following equation (Eq. 18). ＨｂＡ１ｃ [q, r]); where: (c) obtaining the subject's HbA1c value (HbA1c[q]) at time t[q] by the processor; (d) obtaining the GMI (GMI) of the HbA1c value for the period from t[q] to t[r] by the processor. ＨｂＡ１ｃ [q, r]) and the subject's HbA1c value (HbA1c[q]) at time t[q], using the following formula: (d) generating output information based on the estimated HbA1c value (HbA1c[r]) at time t[r] (r>q) of the subject by the processor and transmitting the output information to the user interface; (e) outputting the output information received from the processor to the subject by the user interface.

[0255] While preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. The present invention is not intended to be limited by the specific examples provided within. Any or any aspect of the embodiments disclosed herein may be independently, partially, or in whole combined in any manner with other embodiments and aspects described herein. For example, one, two, or three or more embodiments may be combined in whole or in part.

[0256] As used herein, the terms "comprises" and "comprising" are intended to mean that compositions and methods include the recited elements, but do not exclude others. Embodiments defined by each of these transition terms are within the scope of the present invention. Thus, methods, compositions, and compositions can "comprise" additional steps and components.

[0257] A group of items connected by the conjunction "and" should not be read as requiring all of those items to be present in the group, but should be read as "and / or" unless otherwise stated. Similarly, a group of items connected by the conjunction "or" should not be read as requiring mutual exclusivity between the items in the group, but should be read as "and / or" unless otherwise stated.

[0258] Although the present invention has been described with reference to the above specification, the descriptions and illustrations of the embodiments herein are not intended to be construed in a limiting sense. Numerous variations, changes, and substitutions will occur to those skilled in the art without departing from the invention. Furthermore, it should be understood that all aspects of the invention are not limited to the specific depictions, configurations, or relative proportions set forth herein, which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be used in practicing the invention. Accordingly, it is contemplated that the present invention shall encompass all such alternatives, modifications, variations, or equivalents. The following claims define the scope of the invention, and it is intended that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

1. A system for supporting blood glucose management, the system comprising a sensor, a processor, a storage unit, and a user interface, wherein the sensor is configured to receive a body fluid of a subject, measure the glycoalbumin level (GA value) of the subject, and transmit the measured GA value to a processor; the processor receives GA values (GA[i] and GA[j], where t[j]>t[i]) at different times from the sensor, and calculates a GA glucose control index (GMI), which is an average blood glucose level converted into a GA value for the period between two times t[i] and t[j], based on Equation 1. ＧＡ [i,j]), Here, C ＧＡ [i,j]=1-exp[-(t[j]-t[i]) / τ Ａｌｂ ], τ Ａｌｂ is the albumin decline time constant; and the GA glucose control index (GMI ＧＡ [i,j]) and sending the output information to the user interface; wherein the user interface is configured to output the output information received from the processor.

2. A system according to claim 1, wherein the interval Δt=t[i]−t[i−1] for acquiring the GA value is substantially not less than 3 days and not more than 14 days.

3. The system of claim 1, further comprising a communication unit connected to said processor and capable of communicating with said sensor and said user interface.

4. The system of claim 1, wherein the output information includes an evaluation of the subject's behavior during the period from time t[i-1] to time t[i].

5. The system according to claim 3, wherein the output information is the GA glucose management index (GMI) ＧＡ When the GA value GA[i] is less than the GA value GA[i-1, i], the system evaluates that the behavior performed by the subject during the relevant period was effective.

6. A system according to claim 5, wherein the output information includes an evaluation that the behavior performed by the subject during the relevant period was effective when the glucose management index (GMI[i-1,i]) is smaller than the GA value GA[i] for two consecutive times, i.e., when condition 1: GMI[i-1,i]<GA[i] and GMI[i-2,i-1]<GA[i-1] is satisfied.

7. The system according to claim 6, wherein when condition 1 is not met, the system includes an evaluation that the behavior performed by the subject during the relevant period was effective when the average of at least two glucose control indices is sufficiently smaller than the average of at least two relevant GA values, i.e., when condition 2: average {GMI[i-1,i]} < average {GA[i]} - h (threshold) is met, and / or when condition 2 is not met, the system includes an evaluation that the behavior performed by the subject during the relevant period was ineffective.

8. A system according to any one of claims 5 to 7, wherein the output information includes, when the GMI is lower than the GMI risk judgment value and / or the GA value is lower than the GA risk judgment value, an alert that the behavior performed by the subject during the relevant period poses a health risk and / or a notification that the target behavior should be changed.

9. The system of claim 1, wherein the processor is configured to: acquire behavioral information regarding behaviors performed by the subject during a period between the time points t[i-1] and t[i]; and determine an association between the behavioral information and the glucose management index (GMI[i-1,i]).

10. The system of claim 9, wherein the behavioral information includes self-reported information by the subject and / or biometric information obtained about the subject.

11. The system according to claim 9, wherein determining the association between the behavioral information and the glucose management index (GMI[i-1, i]) includes referencing a database containing behavioral information and GA values of non-subjects other than the subject.

12. A system according to claim 9, wherein the processor is further configured to generate a target behavior to be performed by the subject in the future based on the association, and to output the generated target behavior to be performed by the subject in the future to the subject.

13. A system as described in claim 12, wherein generating a target behavior to be performed by the subject in the future includes not including or deleting from the generated target behavior an ineffective or risky behavior that was previously performed by the subject, or lowering the priority of an ineffective or risky behavior.

14. The system according to claim 12, wherein an algorithm for generating a target behavior to be performed by the subject in the future is stored in the memory unit, and the algorithm is optimized using the subject's glucose control index as an evaluation parameter.

15. The system of claim 1, wherein the processor performs the following steps: (a1) acquiring a first GA value GA[m] of the subject at a first time point t[m] from the sensor; (b1) acquiring a time series GA glucose control index {GMI} from the sensor from the first time point t[m] to a second time point t[n]. ＧＡ (c1) obtaining the first GA value GA[m] and the time-series GA glucose control index {GMI[i−1, i]} (i=m+1 to n); and ＧＡ and determining a second GA value GA[n] at the second time point t[n] based on {i−1, i]} (i=m+1 to n).

16. The system according to claim 1, wherein the processor performs the following steps: (a2) acquiring from the sensor a first GA value GA[m] of the subject at a first time point t[m]; (b2) acquiring from the sensor a target second GA value GA[n] at a second time point t[n]; and (c2) calculating a time-series GA glucose control index {GMI} necessary to reach the second GA value GA[n] at the second time point t[n] based on the first GA value GA[m] and the second GA value GA[n]. ＧＡ [i-1, i]} (i=m+1 to n).

17. The system according to claim 1, wherein the processor performs the following steps: (a) acquiring a plurality of time-series glycoalbumin values (GA values) of a subject at time points t[q] to t[r] (q>r), which are GA values of the subject; (b) calculating a time-series glucose management index (GMI) of the GA values obtained from the acquired time-series GA values. ＧＡ ) and glycation susceptibility ratio R ＧＩ Based on this, the GMI (GMI) of the HbA1c value during the period from t[q] to t[r] is calculated using the following formulas 15 and 16. ＨｂＡ１ｃ [q, r]): where: (c) obtaining the subject's HbA1c value (HbA1c[q]) at time t[q]; (d) obtaining the GMI (GMI ＨｂＡ１ｃ [q, r]) and the subject's HbA1c value (HbA1c[q]) at time t[q], using the following formula: (e) generating output information based on the estimated HbA1c value (HbA1c[r]) at time t[r] (r>q) of the subject, and outputting the output information to the user interface.

18. The system according to claim 17, wherein the R ＧＩ is expressed by the following formula: system.

19. The system of claim 17, wherein the R ＧＩ is expressed by the following formula: method.

20. A method for supporting blood glucose management of a subject using a computer system, the computer system comprising a sensor, a processor, a storage unit, and a user interface, the method comprising: measuring the subject's body fluids using the sensor to obtain the subject's GA value; transmitting the GA values (GA[i] and GA[j], where t[j]>t[i]) at different time points to the processor; and calculating a GA glucose control index (GMI), which is the average blood glucose level converted into the GA value for the period between two time points t[i] and t[j], based on Equation 1, by the processor. ＧＡ [i,j]), Here, C ＧＡ [i,j]=1-exp[-(t[j]-t[i]) / τ Ａｌｂ ], τ Ａｌｂ is the time constant of albumin decrease; the GA glucose control index (GMI ＧＡ generating and sending to the user interface output information based on the inputs of the subject and the inputs of the subject; and outputting, by the user interface, the output information received from the processor to the subject.

21. A method for supporting blood glucose management of a subject using a computer system according to claim 20, comprising: (a) measuring the subject's body fluids with the sensor and acquiring a plurality of time-series glycoalbumin values (GA values) of the subject from time points t[q] to t[r] (q>r), the GA values being the subject's GA values; and (b) calculating, by the processor, a time-series glucose management index (GMI) of the GA values obtained from the acquired time-series GA values. ＧＡ ) and glycation susceptibility ratio R ＧＩ Based on this, the GMI (GMI) of the HbA1c value for the period from t[q] to t[r] is calculated using the following equation (Eq. 18). ＨｂＡ１ｃ [q, r]); where: (c) obtaining the subject's HbA1c value (HbA1c[q]) at time t[q] by the processor; (d) obtaining the GMI (GMI) of the HbA1c value for the period from t[q] to t[r] by the processor. ＨｂＡ１ｃ [q, r]) and the subject's HbA1c value (HbA1c[q]) at time t[q], using the following formula: (d) generating output information based on the estimated HbA1c value (HbA1c[r]) at time t[r] (r>q) of the subject by the processor and transmitting the output information to the user interface; (e) outputting the output information received from the processor to the subject by the user interface.

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