A system and method for supporting blood glucose management of subjects using a 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

Technical Field

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

Background Art

[0002] Diabetes is one of the endocrine diseases characterized by chronic hyperglycemia (such as high fasting blood glucose level, high HbA1c level, etc.). Diabetes is mainly classified into type 1 diabetes, type 2 diabetes, gestational diabetes, diabetes caused by pathological variants of related genes, and diabetes associated with other diseases, etc. Type 2 diabetes, which has the largest number of patients, is mainly caused by a decrease in insulin secretion or insulin resistance. Type 1 diabetes is caused by the destruction of pancreatic β-cells.

[0003] Regardless of the disease type, the basics of treatment are dietary therapy and exercise therapy in addition to drug therapy. Through these treatments, appropriate blood glucose management can be performed, and the risk of complications can be reduced. That is, improvement of lifestyle is important. Furthermore, there is a so-called "pre-diabetes group" or "borderline diabetes" that does not meet the diagnostic criteria for diabetes but has a high blood glucose level. For this type, improvement of lifestyle is important to reduce the risk of developing diabetes. As described above, it is considered that improvement of lifestyle including appropriate diet and exercise is effective for blood glucose management and prevention of diabetes.

[0004] The treatment of chronic diseases such as diabetes is generally said to be difficult. The reasons include that it progresses without obvious symptoms; it is difficult to maintain the motivation for treatment; it is difficult to be cured and lifelong treatment is required; and improvement of lifestyle is essential in addition to drug treatment. These are said to make self-management necessary for treatment difficult (Non-Patent Document 1).

[0005] In fact, some reports suggest that it takes 18 to 254 days, with a median of 66 days, for a new habit to become automatic (Non-Patent Literature 2). Based on this, behavioral change requires two to several months. Generally, some kind of system is needed to maintain motivation for such a long period.

[0006] Typical biomarkers for blood glucose control include 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. In other words, HbA1c levels are too long a feedback loop for behavioral change.

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

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

[0009] However, albumin has a half-life of 17 days, which is longer than a week. Therefore, weekly behavioral changes in subjects do not immediately appear as weekly GA values. To evaluate a behavioral change using GA values, it is necessary to observe GA values ​​over several weeks. [Prior art documents] [Non-patent literature]

[0010] [Non-Patent Document 1] 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-s081 [Non-Patent Document 2] Lally 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 [Overview of the project]

[0011] While the GA value is an excellent blood glucose management marker, users want to know immediately how their weekly efforts are reflected in their weekly GA value. Meeting this need is crucial for maintaining their motivation. The method disclosed herein was invented as a result of the inventors' diligent efforts to solve this problem.

[0012] According to some embodiments of this disclosure, the average blood glucose value converted to a GA value for that period is used based on GA[i] and GA[j] obtained at two time points t[i] and t[j], and the GA value for that period is used as the glucose management index (GMI) of GA. GA [i,j]) is provided as the result of determining this GA glucose management index (GMI). GA Using [i,j]), it is possible to perform glucose management, blood glucose management, lifestyle management, etc.

[0013] In some embodiments, the subject's target is obtained. In some embodiments, the subject's GA value is obtained. In some embodiments, the subject's GA value is obtained at two time points. In some embodiments, based on the two GA values, the GA glucose management index (GMI) is calculated as the average blood glucose level converted to GA value over that period. GA The [i,j]) is determined. In some embodiments, the determined glucose management 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, GMI may be the GMI value of HbA1c (GMI HbA1c ) may also be used as such.

[0015] In one embodiment, the present disclosure may cause a computer system to perform the method. 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 memory connected to the processor. In some embodiments, the computer is caused to acquire the subject's GA values ​​(GA[i],GA[j]) at two points in time (t[i],t[j]). In some embodiments, the computer is caused to calculate a glucose management index (GMI[i,j]) as the average blood glucose level converted to GA values ​​for that period, based on the two acquired GA values ​​(GA[i],GA[j]). In some embodiments, the computer is caused to output the calculated glucose management index to the subject's terminal. [Effects of the Invention]

[0016] The typical modern life cycle is structured in weeks. Therefore, users can more directly recognize how their actions during a given week affected their GA values, which are physiological markers. This is therefore effective, for example, in increasing users' awareness of their lifestyle habits, encouraging them to change their behavior themselves, and maintaining their motivation.

[0017] Furthermore, the effects described above are not necessarily limited, and any of the effects described herein, or any other effects that can be inferred from this specification, may be achieved in conjunction with or in lieu of the effects described above. [Brief explanation of the drawing]

[0018] [Figure 1] A flowchart of a method according to one embodiment is shown. [Figure 2] A flowchart of a method according to one embodiment is shown. [Figure 3A] A flowchart of a method according to one embodiment is shown. [Figure 3B] A flowchart of a method according to one embodiment is shown. [Figure 3C] A flowchart of a method according to one embodiment is shown. [Figure 3D] A flowchart of a method according to one embodiment is shown. [Figure 4A] This shows a network configuration including a system and a target user terminal according to one embodiment. [Figure 4B] This shows a block diagram representing the functional components of a system according to a certain embodiment. [Figure 5A] A graph showing the changes in GA value and WGMI related to one embodiment is shown. [Figure 5B] A graph showing the changes in GA value and WGMI related to one embodiment is shown. [Figure 5C] A graph showing the changes in GA value and WGMI related to one embodiment is shown. [Figure 5D]The graph shows the changes in GA value, WGMI, and HbA1c value in one embodiment. [Figure 6] A graph showing the correlation between CGM and GMI in one embodiment is shown. [Figure 7A] This graph shows the correlation between predicted and actual HbA1c values ​​when using the conventional method. [Figure 7B] This graph shows the correlation between predicted and actual HbA1c values ​​when HbA1c values ​​are predicted using the method of one embodiment of this disclosure. [Figure 7C] This graph shows the correlation between predicted and actual HbA1c values ​​when HbA1c values ​​are predicted using the methods of other embodiments of this disclosure. [Figure 8] This graph (a) shows the time course of GA values, GMIGA, and HbA1c values ​​actually obtained in a clinical study relating to one embodiment, and this graph (b) shows the time course of HbA1c values ​​predicted by two different methods. [Figure 9] The following are scatter plots (a) showing the relationship between RGI and BMI for each subject obtained from clinical research data related to one embodiment, (b) showing the relationship between RGI and waist circumference for each subject, and (c) showing the relationship between RGI and HDL cholesterol levels for each subject. [Modes for carrying out the invention]

[0019] The following description includes numerous specific details to provide a more complete understanding of the invention. However, it will be apparent to those skilled in the art that the invention may be implemented in one or more of these specific details. In other examples, features and procedures well known to those skilled in the art are not described in order to avoid obscuring the invention.

[0020] Several embodiments and examples will be described with reference to the accompanying drawings. Arrows in sequence diagrams indicate the order of information transfer or process (steps). However, between processes not indicated by arrows, the order is not specified, as long as it is not contradictory. This property of arrows also applies to other sequence diagrams. Note that if multiple embodiments and drawings have similar functions, configurations, or steps, one of them may be omitted. They are omitted on the premise that they can be understood by 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 those generally understood by those skilled in the art to which the present invention pertains.

[0022] As used herein, the term "Subject" refers to an entity that manages or should manage blood glucose levels. As used herein, the term "User" refers to an entity that receives output from the System. In some embodiments, the User may be a Subject who manages their own blood glucose levels. In some embodiments, the User may be a person who has the lawful right to independently or in cooperation with the Subject to manage their blood glucose levels, such as a healthcare professional or a family member of the Subject. The User may include multiple entities. "Subject" or "User" includes entities that are currently using, will use, plan to use, or have used the System of this Disclosure.

[0023] In some embodiments, the subject may be human. However, the subject of this disclosure is not limited to human. Examples of subjects include animals other than humans. For example, the subject may be a mammal. Examples of subjects include, but are not limited to, racing animals (such as racehorses), working animals, domesticated animals, pets, and wild animals.

[0024] The subjects may be diabetic patients. For example, diabetic patients may be type 2 diabetic patients. The subjects may also be non-diabetic patients. Non-diabetic patients refer to all people who have not been diagnosed with diabetes. The subjects may also be pre-diabetic and / or healthy individuals.

[0025] computer In this disclosure, the calculation of GMI from acquired GA values, the prediction of future (unmeasured) GA values ​​and / or HbA1c values ​​based on GMI values, the output of information, optimization, and other calculation steps are performed by a computer or computer unit (hereinafter also referred to as the "processing unit") unless otherwise stated.

[0026] system In some embodiments, the system may include an arithmetic unit (e.g., a processor). The system may be configured to include a fixed server or an arithmetic unit. The system may be configured to include a user interface (e.g., a display unit, a fixed or portable electronic terminal used by the user, etc.). The system may include hardware and software for performing the methods of this disclosure. For example, the system may include an application installed on the user's portable electronic terminal.

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

[0028] In some embodiments, the system may include a computing unit (e.g., a processor), sensors, 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 memory connected to the processor.

[0029] In this disclosure, if the entity performing the action is not explicitly stated, it shall be interpreted as the system.

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

[0031] GA value measurement As used herein, the term "GA value" generally refers to the amount obtained by dividing the amount of glycoalbumin in a subject's body fluid by the total amount of albumin in the same body fluid. Alternatively, the GA value may be determined as the ratio of glycated albumin to the total amount of albumin in a body fluid sample, without determining these amounts.

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

[0033] In some embodiments, an enzymatic method may be used as the clinical testing method. Glycoalbumin may be broken down into amino acids with a protease, and the glycoalbumin concentration may be determined by reacting it with ketoamine oxidase, which reacts only with the glycosylated amino acids, and measuring the H2O2 produced by this reaction. Albumin concentration may also 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 relationship formula and conversion formula (conversion from mmol / mol to %) between the analytical value of a glycoalbumin standard sample and the GA value associated with the standard sample may be prepared in advance. The relationship formula may be obtained by linear regression of both. The GA value can be determined by measuring the total albumin concentration and glycoalbumin concentration in the sample and using the relationship formula described above.

[0035] In some embodiments, the concentration of glycoalbumin may be measured using a glycated protein sensor comprising an immobilized protease, an immobilized ketoamine oxidase, and a hydrogen peroxide detection unit.

[0036] For example, by using such a sensor, GA levels can be minimally invasively measured not only in venous blood, but also in minute amounts of blood such as fingertip blood, earlobe blood, heel blood, capillary blood, dry spot blood (DBS), and skin puncture blood. Alternatively, the concentration of glycated albumin in tears or saliva can be measured non-invasively. Therefore, GA levels can be measured without imposing physical or mental stress on the subject, or with minimal stress on the subject.

[0037] Obtaining GA values In some embodiments, the subject may collect their bodily fluids (e.g., blood, saliva, urine, etc.) and transport them in a predetermined manner to a laboratory equipped with equipment for measuring GA values. Transportation may be by mail. The laboratory, upon receiving the subject's bodily fluids, may determine the GA values ​​within those fluids. In some embodiments, the laboratory may transmit the determined GA values ​​to the system's computing unit, thereby allowing the system to obtain the GA values. In some embodiments, the laboratory may store the obtained GA values ​​in predetermined storage, and the system's processor may access this storage to retrieve the GA values.

[0038] In some embodiments, the subject may go to 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 "hospital" or "medical institution"). For example, the subject may go to a medical institution to submit or measure a specimen. The medical institution may collect the subject's bodily fluids and measure the GA value in-house or in an affiliated hospital. The GA value measuring device may be, for example, a Point of Care Testing (POCT) device (hereinafter sometimes simply referred to as "POCT device") or an In Vitro Diagnostic Device (IVD). The medical institution may collect the subject's bodily fluids, transport them to an external laboratory, and have the laboratory measure the GA value. In some embodiments, the medical institution may transmit the requested GA value to the system's computing unit. The system can then obtain the GA value. In some embodiments, the medical institution may store the obtained GA value in a predetermined storage, and the system's processor may access this storage to obtain the GA value.

[0039] In some embodiments, the subject may have a GA value measuring device at home or in their residence. The GA value measuring device may be, for example, a POCT device, an in vitro diagnostic (IVD) device, a home healthcare device, a self-measuring device, or a self-monitoring device. The subject may collect their own bodily fluids or have another person collect them and measure the GA value with the measuring device. In some embodiments, the self-monitoring device may transmit the obtained GA value to the system's computing unit. The system can then acquire the GA value. In some embodiments, the self-monitoring device may store the acquired GA value in a predetermined storage, and the system's processor may access this storage to acquire the GA value.

[0040] The GA value does not necessarily have to be determined by measurement. In some embodiments, the GA value may be arbitrarily set, or it may be calculated or estimated based on other parameters such as glucose management indicators.

[0041] GA value acquisition time As used herein, the terms "GA(t[i])" or "GA[i]" mean the GA value obtained at time t[i]. Unless otherwise defined, time t[i] means the time when the bodily fluid was collected for the purpose of measuring the GA value, or at least for the purpose of measuring the GA value.

[0042] In this specification, expressions such as "time of GA value," "time of GA value measurement," and "time of obtaining / having obtained GA value," or similar expressions relating to time, refer to the time when the glycoalbumin and the body fluid or specimen containing albumin that give rise to the GA value were obtained, rather than the time when the GA value was measured.

[0043] The unit of time may be "days," or more precisely, "hours" or "hours and minutes." Time point t[i] may be, for example, the day blood was drawn or the day saliva was collected. In other words, GA[i] refers to the GA value based on the bodily fluid collected at time point t[i] in a series of consecutive GA value measurements.

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

[0045] GA value acquisition interval For example, the frequency of GA value measurement may be substantially once a month, twice a month, or higher than these values. For example, the frequency may be less than or equal to values ​​such as 20 times a month, 10 times a month, 8 times a month, or once a month. For example, the frequency may be substantially twice a month, three times a month, four times a month, or even 0.5 times a week or once a week.

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

[0047] GA values ​​may be measured at intervals of an average of 7 days, 10 days, 14 days, or longer. GA values ​​may also be measured at intervals of an average of 21 days, 24 days, 28 days, or shorter.

[0048] As used herein, the term "weekly GA value" refers to the GA value of bodily fluids obtained at approximately one-week intervals. The measurement interval may be set to one week (e.g., Monday to Sunday, Sunday to Saturday, Saturday to Friday). Bodily fluids may be collected once at any point during that week. Therefore, the interval for obtaining GA values ​​does 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 according to the subject's convenience. For example, it may not be possible to collect bodily fluids on the same day of the week every week; that is, the day of the week on which bodily fluids are collected may differ over several weeks. For example, the cycle does not have to be one week. Some subjects may have lifestyles that do not operate on a one-week unit. For example, cycles of 5, 6, 8, or 9 days may be adopted. Lifestyles can also change over periods of several weeks or months. Each time this happens, the settings for the interval, cycle, or frequency may be changed.

[0049] In some embodiments, the actual interval between blood collections may be between one and fourteen days. In some embodiments, it may be planned that subjects have blood collected at intervals of one and less than fourteen days, i.e., once every specified week. The actual blood collection interval does not necessarily have to be once every specified week. The blood collection intervals may be gaps or non-equal.

[0050] Glucose Management Index (GMI) This disclosure proposes a new glucose management index (GMI) and a glucose management method using the GMI. The glucose management index (GMI) used herein is expressed as the average blood glucose level converted to GA value, and is represented by the following formula:

number

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

number

[0052] Therefore, the units of the Glucose Management Index (GMI) are the same as those of the GA value, and are expressed in % or mmol / mol.

[0053] In this specification, the interval Δt[i]=t[i]-t[i-1] for obtaining GA values ​​is not limited. In some embodiments, Δt[i]=t[i]-t[i-1] may be one week. For example, Δt[i] may be between one and fourteen days.

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

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

[0056] In some embodiments, the following formula may be used to predict current and future HbA1c based on GMI.

number

[0057] Generalization This disclosure mainly uses GMI based on GA values, but is not limited thereto. GMI can generally be applied to organic substances having a scientifically conceivable half-life in a certain system and those that are modified. Examples of the systems targeted include, without limitation, naturally existing systems such as living organisms and cells, and artificially or industrially formed systems such as in vitro and reactors. Examples of the organic substances targeted include, without limitation, proteins, peptide fragments, lipids, and nucleic acids. A typical example of the modification in this disclosure is glycation, but this disclosure is not limited thereto. Examples of the sugars contributing to glycation include, without limitation, glucose, fructose, and their derivatives. Hereinafter, glycation by glucose will be mainly used for explanation.

[0058] Generally, the glucose management index (GMI) of a generalized glycated protein (GP) is represented by the following formula:

Equation

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

number

[0060] For example, GMI can also be defined based on HbA1c values, as shown in the following formula.

number

[0061] In some embodiments, the GMI (GMI) is based on the degree of glycation (GP1) of the primary protein (P1). GP1 The degree of glycation (GP2) of the secondary protein (P2) can be determined using the following formula: The relationship is expressed by the following formula, which is a generalization of formula 3:

number

[0062] Output of glucose management indicators In some embodiments, once a new GA value is obtained, the previous GA value may be referenced, and the Glucose Management Index (GMI) or Weekly Glucose Management Index (WGMI) may be promptly output to the subject. The subject can then access the latest GMI through a user interface such as an electronic terminal.

[0063] In some embodiments, glucose management indicators may be made accessible to the subject at times other than when they were acquired. Past, current, and projected future glucose management indicators may be made accessible to the subject at any time. The subject can review their GMI at any time and become more aware of their own behavior.

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

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

[0066] In some embodiments, information related to “behavior” (also called “behavior-related information”) may be input from a user interface such as an electronic terminal. Examples of the form of behavior-related information include, but are not limited to, text information, audio information, handwritten information, image information, location information, communication information, biometrics, and the like.

[0067] In some embodiments, behavioral information may be declared or entered by the subject (self-reported information). In some embodiments, behavioral information may be the subject's biometric information.

[0068] In some embodiments, participants may input their selected answers to multiple-choice questions regarding exercise, diet, and medication. In some embodiments, participants may input sentences to open-ended questions.

[0069] In some embodiments, behavioral information (such as self-reported information) may be entered in natural language. For example, behavioral information may be entered in the form of a diary. The participant can record the actions they took that day in their own words, either concisely or in detail. Through the diary, the participant may also ask questions.

[0070] In some embodiments, behavioral information (such as self-reported information) may be entered by the subject themselves. By entering the information themselves, the subject can recognize their own behavior and change it voluntarily rather than compulsively, or the probability of doing so increases. Voluntary behavioral change can contribute to high sustainability and effective blood glucose control.

[0071] In some embodiments, behavioral or self-reported information may be entered by the non-subject. For example, a caregiver (e.g., a spouse, family member, friend, nurse, doctor, lawyer, etc., without limitation) may enter the information. The caregiver may be aware of the subject's wishes. The caregiver may have legitimate rights.

[0072] In some embodiments, behavioral information or biometric information may be obtained by non-invasive devices such as wearable devices and weighing scales, invasive devices such as CGMs, or various measurements in health examinations such as blood tests.

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

[0074] Examples of markers used for targets include, but are not limited to, GA values, HbA1c values, weight, BMI, etc. Participants may set marker values ​​to be achieved or maintained at predetermined future timings or periods. In some embodiments, the marker used for targets is the GA value. Examples of behaviors used for targets include, but are not limited to, diet, exercise, medication, etc. Participants may set the content of diet, exercise, and / or medication to be implemented at predetermined future timings or periods. In some embodiments, the behaviors used for targets are diet, exercise, and medication.

[0075] Examples of exercise goals include, without limitation, the type of exercise (walking, swimming, strolling, climbing stairs, squats, etc.), the time of day (after meals, early morning, etc.), and the amount (frequency, intensity, number of steps, duration, etc.).

[0076] Examples of goals related to "diet" include, without limitation, what to eat / not eat: avoiding sugary drinks such as juice, eliminating snacks, avoiding sugary snacks, eating fiber-rich foods such as vegetables, reducing carbohydrates such as white rice and bread, reducing alcohol consumption; the amount of food; the frequency of meals; the amount of fat, carbohydrates, and salt (GI: glycemic index) or balance (FC balance, FCS balance); the order in which meals are eaten (e.g., eating vegetables first). Examples of goals related to "medication" include, without limitation, taking prescribed medication as directed and not forgetting to take it.

[0077] In some embodiments, the “goal” may be suggested by the system and referred to by the subject. The subject may refer to or ignore the goal suggested by the system and set or enter their own next goal into the system.

[0078] It is believed that allowing individuals to proactively set their own goals, rather than having existing goals set or choosing from multiple options, is effective for behavioral change.

[0079] The “goals” may be changed periodically or irregularly. New goals may be set proactively by the participants. For example, participants may be given the opportunity to change their goals weekly or each time the latest GA values ​​are output from the system. Participants may change their goals when given the opportunity to do so, or they may maintain the same goals without changing them.

[0080] The set "goals" or "goals" 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). The "goals" may be displayed simultaneously with or on the same screen as the GA value or GMI. The latest or updated goals may be provided to the subject. The subject can recognize their latest GA value or GMI and their current "goals." Recognition of changes in the blood glucose biomarker, GA value or GMI, and awareness building of the actions they should take may be linked in the subject's conscious or subconscious mind. This may have a positive impact on behavioral change or improvement of lifestyle.

[0081] Risk assessment As used herein, the term "risk" generally refers to the possibility of medical problems arising in connection with blood glucose management behaviors.

[0082] Examples of medical “risks” include, but are not limited to, having or being able to develop, conditions such as hypoglycemia, complications of diabetes, infections, trauma, neoplastic lesions, and mental illnesses such as depression.

[0083] In some embodiments, it may be determined whether a subject or their behavior is at risk based on a glucose management index (GMI), GA value, trend of GA value, and any combination thereof.

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

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

[0086] Next, the GMI is calculated based on the acquired GA values ​​(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 requested GMI[i-1,i] is output to the subject (S131). By recognizing the GMI[i-1,i], the subject can evaluate their own actions during the period between time points t[i-1] and t[i].

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

[0089] First, the subject's GA value is obtained (S201). For example, the subject's weekly GA value is obtained over several weeks. In order to determine the next GMI, it is necessary to obtain the GA value at at least two points in time. For example, GA[i-1] and GA[i] are obtained, which are the GA values ​​at time points t[i-1] and t[i]. In this embodiment, in step S212 described later, two consecutive weekly GA values ​​are evaluated. 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 values ​​(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 requested GMI[i-1,i] is output to the subject (S231). By recognizing the GMI[i-1,i], the subject can evaluate their own actions during the period between time points t[i-1] and t[i].

[0092] This embodiment further includes a process of interpreting the GMI values ​​and / or trends and providing useful comments to the subject.

[0093] If the GMI[i-1,i] falls below a predetermined threshold (S211), for example, there is a risk of hypoglycemia, so it is judged as "at risk" (S221), and this judgment is output to the subject (S231). A rapid decrease in blood glucose levels or persistently low blood glucose levels indicate a risk of hypoglycemia. If this judgment is made, the subject may be offered suggestions such as changing (relaxing) their target behavior, or seeking a doctor's diagnosis, in addition to the GA value and GMI.

[0094] The determination of "risk present" (S221) may be made based solely on GMI. For example, if the GMI is below 11%, it may be determined that there is "risk present." On the other hand, this determination may also be made considering the GA value and / or trend, age, medication, cognitive function, etc., in addition to GMI. For example, if there is a risk of hypoglycemia, such as when the subject is taking medication or when the subject is elderly, the threshold for determining "risk present" may be set higher.

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

[0096] Next, we determine whether the GMI consistently shows a trend lower than the GA value. In this embodiment, we determine whether the GMI has been lower than the GA value at least two times in a row. That is, the conditions are as follows: GMI[i-2,i-1] <GA[i-1] かつ GMI[i-1,i] <GA[i]。

[0097] For example, if the GMI is consistently maintained at a level lower than the GA value, the GA value will asymptotically approach that GMI value. Therefore, if the GMI is lower than the GA value multiple times, it suggests that the behaviors the subject is taking are effective in reducing a high GA value.

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

[0099] When the condition that the GMI is lower than the GA value for two consecutive times is not satisfied (S212), it may be further determined whether the action in the most recent period was effective based on another criterion.

[0100] For example, when the previous GMI was lower than the GA value and the next GMI exceeded the GA value, it may be determined that the blood glucose management tends to deteriorate. Also, for example, when the previous GMI was higher than the GA value and the next GMI was lower than the GA value, it may be determined that the blood glucose management tends to improve.

[0101] However, in any case, it may be possible to avoid making such a determination, or to reserve the determination itself. This is because such a determination may lead to an incorrect evaluation of the action.

[0102] In some embodiments, the average of the GA values and the average of the GMI for two or more periods may be compared. In FIG. 2, when the average of the GMI is sufficiently smaller than the average of the GA values (GMI average < GA value average - threshold TH), that is, when the difference is greater than a certain threshold (GA value average - GMI average > threshold TH), it may be determined that "the action is effective", and otherwise, it may be determined that "the action is ineffective" (S213). In some embodiments, in the period before those two periods, it may be used as a criterion to determine whether the GA value had a downward trend.

[0103] If the GA value shows a downward trend and then remains nearly constant (S213, Yes), the most recent action is judged to be effective (S222). A continuous decline in the GA value suggests that the subject's actions during that period were effective. If the GA value then remains nearly constant, it suggests that the effect of those actions is sustained. When the GA value is maintained nearly constant, even a slight shift occurs, and the GMI reacts sensitively to that change. Therefore, judgment should not be made solely based on the GMI; judging based on the trend of change in the GA value allows for a more appropriate evaluation of the effectiveness of the subject's actions.

[0104] If the GA value has not shown a downward trend over the preceding period (S213, No), then the most recent action is judged to have been ineffective (S223). For example, if the GA value has remained constant over the preceding period, or if it has shown an upward trend or at least not shown a downward trend, it is difficult to say that the GA value is under control. Therefore, the subject's actions during that period cannot be said to have been effective.

[0105] In the embodiment shown in Figure 2, the decisions in S211, S212, and S213 are performed in serial order in the figure, but are not limited to this. They may be performed simultaneously, in reverse order, or in any order. Similarly, in other figures, embodiments, and examples, the order of the steps should not be interpreted restrictively and can be changed as appropriate or possible.

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

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

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

[0109] If multiple GA values ​​have been obtained, they are referenced to determine if the GA values ​​are within the healthy range (S304). For example, GA values ​​over a period of 4 weeks or 1 month may be referenced to determine if they are all within the healthy range. If the GA values ​​remain within the healthy range for a certain period, there is no need to further improve the behavior, and the current good behavioral pattern should be continued. In such cases, the subject may be supported in a long-term maintenance mode (S310) to continue maintaining the current behavioral pattern.

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

[0111] If the GMI is extremely low or high (within the range where an alert should be issued) (S305), it is determined that there is some kind of risk in the subject's condition, behavior, etc. (S306), and output information indicating "risk present" is generated for the subject (S308) and output (S309). As a non-limiting example, if the GMI is less than a value such as 11% or 10%, GMIIf the value is greater than 24%, for example, it may be considered "risky."

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

[0113] For example, in cases where this method has just been started, or where it has not been implemented for some time and this constitutes a de facto restart, only a GA value for two weeks, i.e., only one GMI, may be obtained. In such cases, the so-called "initial GMI determination" process is performed (S320). If GA values ​​for three weeks or more have been obtained, i.e., two or more GMIs are required, the so-called "continued GMI determination" process is performed (S330). The following describes each process.

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

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

[0116] If the GA value is showing signs of leveling off (S324), the subject may be given suggestions to set more effective behaviors as goals, suggestions to improve target behaviors, or suggestions for specific target behaviors. Target behaviors related to one or more of the following may be improved: diet, exercise, and medication. In some embodiments, if the subject is unable to further improve their diet and / or exercise, they may be informed that they should change their medication.

[0117] On the other hand, if the GA value has decreased sufficiently since the previous measurement (the difference in GA value from the previous measurement, "ΔGA value") is not greater than a predetermined value, and if the initial GMI was not sufficiently small (the initial GMI was smaller than a predetermined threshold) (S321), then it cannot be said that there was an effect. If there is almost no change in the GA value since the previous measurement (S325), then it is judged that the action taken that week was ineffective (S327). If there is some change in the GA value since the previous measurement (S325), even if it cannot be said that there was a sufficient change (S321), then it is not possible to clearly determine whether the action was effective (S323, S324) or ineffective (S327). Therefore, in that case, the judgment is reserved (S326).

[0118] Once the output information is determined (S323, S324, S326, S327), it is output to the target person (S319). Then, the GA value for the following week is obtained (S301). If multiple GMIs are obtained (S307), the "Continued GMI Judgment" process is performed (S330).

[0119] Figure 3D shows a flowchart S330 of the "Continuous GMI Judgment" 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 decreased sufficiently (S333). If the average GMI has not yet reached the healthy range (S332), it is determined that the GA value is showing a downward trend (S334).

[0120] If, on the other hand, the magnitude relationship between the GMI and the GA value has reversed within two weeks (S331), the trend of the GA value is not clear. From just the fact that the GMI and the GA value are close, it cannot be determined that the GA value is trending downward. In such a case, it is possible that the GA value is being maintained by effective actions, or it is possible that the actions are not effective and the GA value has not changed.

[0121] In the embodiment of FIG. 3D, if the magnitude relationship between the GMI and the GA value has reversed within two weeks (S331), a further determination is made based on the average of the GMI and the average of the GA value (S335). If the GMI average is sufficiently lower than the GA value average (GMI average < GA value average - threshold TH), it is determined that the action was effective, but the GA value has a tendency to level off (S334). Otherwise, it is determined that "the action is ineffective" (S336). As a non-limiting example, when the difference between the GMI average and the GA value average (GA value average - GMI average) is greater than a predetermined value (for example, GA value average - GMI average > 1.5%), it may be determined that "the action is effective".

[0122] When the output information is determined (S333, S334, S336), it is output to the subject (S319). Then, the GA value for the next week is obtained (S301). If low GMIs are continuous, the GA value will also follow and become low. If that trend continues (S304), the long-term maintenance mode is implemented (S310).

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

[0124] If the GMI is lower than the GA for two consecutive times (S314), the GA value is judged to be within the healthy range and showing a decreasing trend (S315). On the other hand, if the GMI is higher than the GA for two consecutive times (S316), the GA value is judged to be within the healthy range and showing an increasing trend (S318). In either case (S314, S316), the GA value is judged to be within the healthy range and stable (S317).

[0125] The "healthy range" for GA or GMI may be set based on the subject's wishes, attributes, physiological conditions, medical conditions, external factors, theories, general principles, etc. The "healthy range" for GA or GMI may, for example, be non-restrictive, ranging from 11% to less than 16%.

[0126] A GA score within the healthy range indicates that the subject has achieved effective behavior. However, if support is discontinued at this point, the GA score is often observed to worsen in many subjects. Therefore, the long-term maintenance mode is an effective tool for encouraging subjects to maintain or maintain their motivation for continuous effective behavior.

[0127] The relationship between behavior and GMI In some embodiments, the relationship or correlation (hereinafter, expressions using either term include both) between the content of the actions taken by the subject and / or the subject's target behavior and the GMI may be determined. In some embodiments, the relationship between the content of the actions and / or target behavior and the GMI may be determined for a large number of subjects. In some embodiments, based on the relationship between the actions or target behavior and the GMI that has already been determined, and the GMI of the subject that has been determined, actions that the subject should take or that are suggested to the subject (target behavior) may be generated. The target behavior for the subject may be generated by referring to the GA value and the trend of the GA value in addition to the subject's GMI. The algorithm for generating the target behavior for the subject may be optimized using the GMI or GA value as an evaluation value. For example, the algorithm for generating the target behavior for the subject may be optimized so that the GMI and / or GA value are within a risk-free range.

[0128] In some embodiments, the relationship between data not derived from GA values ​​(also referred to as "non-GA derived data") and GMI may be sought. Non-GA derived data may be classified, exemplarily but non-limitingly, into data groups such as personal data, health-related data, lifestyle data, and population data. Examples of non-GA derived data include, non-limitingly, the following: Personal data: Personal ID (My Number), age, gender, marital status, number of children, address, type of residence, car ownership (car, motorcycle, bicycle), occupation, job type, job title, annual income, genomic information, religious beliefs, health insurance number, fingerprints, voiceprint, facial recognition information, iris recognition information, personal thoughts, emotions, desires, and needs that change over time, information on input and usage of smart devices, etc. • Group data: Race, nationality, region, area, seasonal variation factors in the region (such as seasonal temperature and humidity changes), holiday periods (including year-end and New Year holidays, spring break, summer break, etc.), mode of commuting, religion, etc. • Health-related data: weight, height, body fat, systolic blood pressure, diastolic blood pressure, heart rate, steps taken, activity level, blood oxygen saturation, body temperature, room temperature, solar radiation, UV radiation, average blood glucose levels, standard deviation, and AGP (Ambulatory Glucose Profile) obtained from Continuous Glucose Monitoring (CGM) and Flash Glucose Monitoring (FGM) devices, health checkup data (may include HbA1c values), medical history, prescription medications, data from medical institutions, food and other allergies, medical history and health-related data of family members and relatives, etc. • Lifestyle data: Eating habits (including preference for meat, vegetarianism, etc.), favorite foods, meal menus, photos of meals, photos of snacks, exercise habits, wake-up time, bedtime, sleep duration, sleep depth, sleep score, active or passive rest time, eating speed, chewing, frequency, speed, characteristics, food allergies, dietary rules related to religious beliefs (including Ramadan, etc.), whether or not exercise is performed, type of exercise, exercise duration, exercise frequency, calories burned from exercise, hobbies, etc.

[0129] In some embodiments, the relationship between GMI and one or more of the following may be determined: minimum heart rate, heart rate variability, body temperature (e.g., skin temperature).

[0130] Correlation with CGM In some embodiments, any parameter of continuous glucose monitoring (CGM) may be estimated based on GMI. The correlation between the average blood glucose level obtained from CGM and GMI may be determined. Statistical values ​​such as average blood glucose levels and diurnal variations such as blood glucose spikes can be obtained from CGM. The correlation between GMI and these parameters may be determined. Based on these correlations, the parameters of the subject may be determined from GMI. This reduces the frequency of CGM use, which requires invasive use over long periods, enabling more comfortable and accurate blood glucose management.

[0131] System Configuration and Operation Examples Figure 4A schematically shows a network environment connecting a computing unit 100 and a subject 200 according to one embodiment of this disclosure.

[0132] The arithmetic unit 100 includes a central processing unit (CPU, also referred to herein as "processor" and "computer processor") 101, a storage unit 102, main memory or memory location 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 multiple processors for parallel processing. The CPU 101 may include a computing architecture that processes data signals and implements various architectures or combinations of instruction sets. The CPU has an internal cache (not shown), and the cache is located in the CPU 101. register (Not shown) Data can be communicated at high speed. The cache stores data used by CPU 101, and CPU 101 can process this data at high speed. Access to the cache is not explicitly described below.

[0134] The storage unit or storage 102 is a non-temporary storage medium for storing data to provide the functions described herein. The storage unit 102 may be a data storage unit (or data repository) for storing data. The storage unit 102 stores programs and data that can be used by the arithmetic unit 100. Examples of the storage 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 storage unit 102 include, but are not limited to, hard disks, floppy disks, CD-ROMs, DVD-ROMs, DVD-RAMs, DVD-RWs, flash memory, magnetic tape, 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 the CPU 101 can execute. For example, it may be, without limitation, dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), or flash memory. The CPU 101 can transfer programs and data stored in the memory unit 102 to the main memory 103, execute them, and use them.

[0136] The peripheral device 104 may, for example, not limited to, other memory, data storage, and / or an electronic display adapter. The communication interface 105 may, for example, not limited to, a network adapter. The communication interface 105 can communicate with other devices via the network 400.

[0137] There are multiple ways in which the subject 200 obtains the GA value and provides it to the computing unit 100.

[0138] In some embodiments, the subject 200 can undergo transport testing. The subject 200 may collect their bodily fluids (e.g., blood, saliva, etc.) using a predetermined transport kit 211 and send them to the testing center 220. The testing center 220 can measure the subject 200's GA value using the received bodily fluids. The testing center 220 can communicate with the computing unit 100 via the network 400 through the communication interface 221. The testing center 220 provides the computing unit 100 with the acquired GA value of the subject 200.

[0139] In some embodiments, the subject 200 may go to the medical institution 230 on their own. The subject 200 may have their bodily fluids collected at the medical institution 230 and have their GA value measured. The medical institution 230 can measure the subject 200's GA value using the received bodily fluids. The medical institution 230 can communicate with the computing unit 100 via the network 400 through the communication interface 231. The medical institution 230 provides the computing unit 100 with the acquired GA value of the subject 200.

[0140] In some embodiments, the subject 200 may measure the GA value using a self-monitoring (or household) GA measuring device 240. The subject 200 may introduce their own bodily fluids into the measuring device 240 to measure the GA value. The measuring device 240 can communicate with the arithmetic unit 100 via a built-in or external communication interface (not shown) and a network 400. The measuring device 240 provides the arithmetic unit 100 with the acquired GA value of the subject 200.

[0141] The GA values ​​of subject 200, obtained in illustrated or not illustrated forms, are transmitted to the computing unit 100 via the network 400, along with accompanying information (such as subject 200's ID, date and time of fluid collection, and information related to the measurement), and received by the communication interface 105. The information received by the communication interface 105 is temporarily stored in the main memory 103 and, as needed, moved to the storage 102 for storage.

[0142] The subjects 200 possess a communication-enabled electronic terminal 300 (e.g., a smartphone, tablet device, personal computer (PC), etc.). The electronic terminal 300 is configured to be connectable to or able to communicate with the network 400.

[0143] Subject 200 may initiate, modify, suspend, or terminate the usage agreement for the computing unit 100 via the electronic terminal 300. The computing unit 100 may establish or conduct communication with subject 200 or their electronic terminal 300 only with approved or contractually active subject 200, in accordance with personal information, copyright law, civil law, and other laws, as well as in an ethically and medically appropriate manner. Subject 200, as a legitimate user of the computing unit 100, may communicate with the computing unit 100 via the electronic terminal 300.

[0144] Subject 200 collects or has body fluids collected weekly. The GA value is measured from these fluids and transmitted to the calculation unit 100. The calculation unit 100 transmits the received GA value to the subject 200's electronic terminal 300. This allows the subject 200 to know their own GA value via the electronic terminal 300. If a self-monitoring GA measuring device 240 is used, the subject 200 can know their own GA value on its display or on the electronic terminal 300 connected to the self-monitoring GA measuring device 240.

[0145] Each of the 200 subjects collects their own bodily fluids weekly and measures their GA value, which is then stored in storage 102.

[0146] Example of system functional configuration Figure 4B shows the functional components of a computing unit 100 according to one embodiment of the present disclosure. These functional components do not necessarily have to be physical devices, parts, or units of electronic equipment. Each functional component may consist of one or more physical devices, parts, or units of electronic equipment, or a part thereof, or a combination thereof. The expression "~part" used herein 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, for example, be configured to include, but not limited to, a processor for executing programs, a communication interface, etc. The communication unit 110 has a receiving unit 111 that has the function of receiving data from the network 400, and a transmitting unit 112 that has the function of transmitting data to the network 400. The communication unit 110 has the function of connecting to the network 400 and communicating with each device (also called GA measuring device) 221, 231, 240 that measures or has GA values, a user electronic terminal 300, and a counselor terminal 510.

[0149] The receiving unit 111 can receive GA value-related information transmitted from GA measuring instruments 221, 231, and 240, as well as target, evaluation, 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, for example, be configured to have a storage area of ​​a storage medium, for example. The storage unit 130 can store and accumulate data related to GA values ​​(GA value data) 131 transmitted from GA measuring instruments 221, 231, 240 and the obtained GMI data 132. The storage unit 130 also stores data related to the target of the subject 200 (target data) transmitted from the user electronic terminal 300. 、The system can store and accumulate information related to the subject's behavior (behavioral information data) 133, and data related to the subject's evaluation of the output (evaluation data) 134. The storage unit 130 stores each algorithm 134 used by the control unit 120.

[0151] The control unit 120 may, for example, be configured to have a processor for executing a program, for example, not limited to that. 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, more or all of the following: a behavioral evaluation unit 122, a risk judgment unit 123, a healthy range judgment unit 124, a GA value prediction unit 125, an HbA1c value prediction unit 126, a mode selection unit 127, and a suggestion 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 via the network 400 to the electronic terminal 300 of the target person 200. As a result, the target person 200 can find out their own WGMI via the electronic terminal 300.

[0153] The action evaluation unit 122 refers to the GMI data 132 and evaluates whether the actions of the subject 200 during the period to which the GMI corresponds had an effect. For example, when the GMI is smaller than the GA value continuously two or more times, it may be evaluated that the actions of the subject 200 during the period to which the GMI corresponds had an effect. When the GA value and the GMI are swapped every week, the GA value may be somewhat stable. The effect of the actions may be evaluated by referring to the statistical behavior over a longer period (for example, the average of the GA value, the average of the GMI, etc.). For example, when the average of the GMI is clearly smaller than the average of the GA value (GMI average < GA average - threshold TH), the GA value tends to decrease. Therefore, it may be determined that the actions of the subject 200 during this period had an effect.

[0154] The risk determination unit 123 may refer to the GA value and / or the GMI to determine whether the subject 200 has a medical risk or may have a medical risk. For example, the risk determination unit 123 may, based on that information, determine whether the subject 200 is in a hypoglycemic state or may be in a hypoglycemic state. For example, when the GMI is lower than a predetermined value, it may be determined that there is a risk in the actions during the corresponding period. Generally, the change in the GA value follows the change in the GMI. Therefore, before the GA value drops below a predetermined value, the drop in the GMI can be detected early to sense whether there is a risk in the actions of the subject 200.

[0155] When it is determined that the subject 200 may have a risk, the arithmetic unit 100 may send a message to the effect that the subject 200 should receive human counseling or a diagnosis by a doctor. The arithmetic unit 100 may, simultaneously or separately, send a message to the effect that the arithmetic unit 100 does not bear any responsibility for the health or illness of the subject 200. The subject 200 can receive the message on the electronic terminal 300 and determine on its own responsibility whether to receive human counseling or a diagnosis by a doctor.

[0156] The healthy range determination unit 124 may refer to the GA value data 131 and / or GMI data 132 to determine whether the subject 200 is within 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 one or a combination of the GA value data 131, GMI data 132, behavioral information 133, etc. For example, if the GA value and / or GMI remain within the healthy range for a certain period of time, the CPU 101 may change the support for the subject 200 from a mode that actively promotes behavioral change (behavioral change mode) to a long-term maintenance mode. Conversely, even if the long-term maintenance mode is in operation, if the GA value and / or GMI fall outside the healthy range, the calculation unit 100 may start operating the behavioral change mode.

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

[0159] The HbA1c value prediction unit 126 may predict future HbA1c values ​​based on GMI data 132. For example, if the most recent GMI is maintained, it may predict the HbA1c value at a future point in time. For example, it may predict the HbA1c value at a future point in time based on assumed time-series GMI data (time change of GMI).

[0160] The proposal generation unit 128 generates proposals for the subject 200 based on the GMI data 132, or by referring to other information. The proposal generation unit 128 may generate proposals using a predetermined algorithm 134. For example, the proposal generation unit 128 may generate proposals such as that the subject 200 should maintain their most recent GMI, that the subject 200 should change their behavior to achieve a different GMI from their most recent GMI, the content of the actions the subject 200 should take, and the content of the actions the subject 200 should aim for. In this case, the proposal generation unit 128 may refer to the subject 200's previous GMI data 132, related behavioral information data 133 of the subject 200, and data from people other than the subject 200 (for example, data from people with similar attributes to the subject 200).

[0161] The optimization unit 129 optimizes algorithms used in blood glucose management, such as the behavioral evaluation algorithm performed by the behavioral evaluation unit 122, the selection algorithm taken by the mode selection unit 127, and the algorithm used by the suggestion generation unit 128. For example, the optimization unit 129 may tune the relationship between GMI and behavior based on the accumulated GA values, GMI, and behavioral information of the subjects 200, and optimize the algorithm used to suggest actions that the subjects 200 should take.

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

[0163] The following examples of blood glucose management using the system will be explained with reference to Figures 5A to 5D. Example 1 Figure 5A shows the relationship between GA value and WGMI in a particular 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 actual diabetic patients.

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

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

[0166] As shown in Figure 5A(a), the system referred to 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." Based on this, the system performed an analysis and obtained the result that "the daily calorie intake was 2800 kcal, of which 52% came from carbohydrates, and the amount of exercise was approximately 6000 METs per week." At week 1, the GA value was approximately 19.6% (GA[1]=19.6%).

[0167] The system referred to information about the target behavior entered by the participant and recognized that the participant's target behavior at the weekly stage was set as "start walking and strength training, reduce rice intake by half at each meal, and eat vegetables first." The system referred to the participant's self-reported information and recognized that the participant diligently performed this target behavior every day.

[0168] The system obtained a GA value of approximately 19.0% at week 2 (GA[2]=19.0%). From GA[1]=19.6% and GA[2]=19.0%, the system calculated WGMI[1,2]=17.2%. The system then predicted the change in the GA value if the same behavior continued from week 2, i.e., if the same WGMI was maintained (dotted and dashed lines). 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 ​​weekly and calculated the WGMI. Although the WGMI increased slightly in week 3, it returned to the target value of 17.4% in week 4. The system interpreted this as meaning that the subject continued a certain behavior during this period. In fact, the GA values ​​from week 1 to week 4 showed a decreasing trend, and the GA value in week 3 was also close to the target value. Therefore, the system predicted that if this behavior was maintained, the target GA value would be reached in week 8 (dotted and dashed lines).

[0170] The system, referring to information about target behaviors entered by the participant, recognized that at week 4, the participant had changed their target behavior, increasing the average number of steps walked per day from 7,000 to approximately 12,000, and reducing the amount of snacks to about one piece of candy once a week, compared to the behavior they had been doing from week 2. The system, referring to the participant's self-reported information, recognized that the participant was actually performing this target behavior every day. The system, referring to the participant's self-reported information at week 8, recognized that the participant was consuming 2,000 kcal, with carbohydrates accounting for approximately 45% of the total calories, and was continuing exercise at a rate of 6,400 METs per week.

[0171] As shown in Figure 5A(c), the GA value acquired by the system at week 5 was approximately 17.8% (GA[5]=17.8%). From GA[4]=18.3% and GA[5]=17.8%, the system calculated GMI[5,4]=16.3%. The system then predicted the change in the GA value if the same behavior was continued from week 5, i.e., if the same WGMI was maintained. In this case, the system predicted that the GA value would be approximately 16.9% at week 8. Since the GA[8] was approximately 17.6% when 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 WGMI calculated from it, were again the same as the target 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 referred to the subject's self-reported information and recognized that the subject "actually ate too much on some days and felt a lack of vegetables." The system associated this dietary content with the rise in WGMI and recorded this correlation in memory. The system obtained a GA value of 17.1% at week 8 and calculated a WGMI of 16.1% based on this. The system recognized that the subject had reached the target GA value of 17.1% and achieved their goal, and recorded this information in memory. The system referred to the subject's self-reported information and recognized that "based on the reflection from the previous week, all goals were achieved." The system associated the actual behavior with the decrease in WGMI and recorded this correlation in memory.

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

[0174] Example 2 Figure 5B shows the relationship between GA value and WGMI in a particular 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 actual diabetic patients.

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

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

[0177] The system also obtained self-reported information from participants at the 5-day-a-week mark and recognized an increase in physical activity and a restriction in food intake. The system recognized this as the cause of the sharp drop in WGMI to 18.4%. The system associated this behavioral change with WGMI and recorded the correlation in memory.

[0178] Furthermore, the system determined that the WGMI was 18.4% and the behavior did not pose a medical risk, and therefore did not issue a "risk" alert.

[0179] Example 3 Figure 5C shows the relationship between GA value and WGMI in a particular 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 actual diabetic patients.

[0180] At the 2-week mark, the system obtained 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%. The system predicted that if this WGMI was maintained constant, the GA value after 8 weeks would be 11.7% (dotted and dotted lines (1)).

[0181] However, the system determined that a WGMI of 10.9% was below the lower limit of the healthy range and therefore posed a risk. The system then informed the individual that they should consult a physician. The system also informed the physician that they should comprehensively review the individual's condition and suggested possible adjustments to diet, exercise, and medication. As a result, the physician decided to reduce medication dosage, and the system acquired this information. Accordingly, the system included medication reduction in the individual's target behaviors.

[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 assuming this WGMI was maintained. As a result, the system predicted that the future GA value would be 13.8% in approximately 8 weeks (dotted and dotted lines (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 diary. The system outputted to the subject that the medication dosage change was effective, the WGMI was appropriate, and the future GA value was also appropriate. However, at this point, the doctor's diagnosis revealed some side effects from the medication, and the doctor decided to further reduce the dosage. The doctor's decision and the change in medication were entered into the system by the medical institution, and the system recorded this information in memory.

[0184] The system was monitored until week 9. The GA value obtained by the system showed a slight downward trend but remained almost constant between approximately 15.8% and 16.2%. In contrast, the WGMI calculated by the system varied significantly from approximately 15.3% to 17.4%. Thus, when the GA value is almost constant, or when the WGMI increases or decreases relative to the GA value, the WGMI tends to fluctuate. In such cases, it is not advisable to refer to individual WGMI values ​​for judgment, but rather to use their average value or moving average to recognize the trend of the WGMI.

[0185] The system determined that the WGMI remained within the healthy range during the period of 5 to 9 days per week, and predicted that the GA value would be maintained at approximately 15.9% if the current situation continued (dotted and dashed lines (3)). The prediction of future GA value changes was not changed. The system referred to the subject's self-reported information (diary input) and recognized that the medication dosage was consistent. Based on this, the system outputted to the subject and the physician that the current blood glucose management and the blood glucose management plan for the past few weeks were all appropriate and without problems.

[0186] Example 4 Figure 5D shows the relationship between GA value and WGMI in a particular 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 used to explain the period up to week 7, and Figure 5D(b) is used to explain the period from there to week 13. Note that GA value was not obtained in week 1, but WGMI is calculated correctly even with a 2-week interval.

[0187] As shown in Figure 5D(a), the GA value acquired by the system was 17.4% on week 0, gradually increasing to 17.7% on week 3. The calculated WGMI was 17.9% on week 3, which was also high. The system output these values ​​to the subject. Subsequently, the system referred to information about the subject's target behavior, recognized that the subject had set a new target behavior, and inferred that the subject was working to improve their GA value. This was likely successful, as the WGMI calculated from the GA value acquired by the system decreased to 16.1% on week 4 and 15.3% on week 5. The system associated the newly set target behavior with the inferred actual behavior, concluding that it contributed to improved blood glucose management, and recorded this association in memory. This also shows that WGMI is sensitive to weekly behavior and more accurately reflects the subject's actions.

[0188] Therefore, the system predicted that if the participant maintained a WGMI of 15.3% for 5 days a week, their GA value would be 15.4% after 8 weeks (13 days a week). The system determined that this future GA value was acceptable. Furthermore, the system obtained the participant's self-reported information (diary entries) and recognized that there had been a behavioral change (low-carb diet) during the week of 5 days a week. Referring to the criterion that "rapid low-carb diets should not be recommended," the system output a message recommending that the participant view educational information on proper dietary therapy.

[0189] Based on the GA values ​​obtained by the system, the WGMI calculated for week 6 fell further to 13.2%. The system inferred that this was due to a further change in the subject's behavior. Up to week 6, both the GA values ​​obtained by the system and the calculated WGMI decreased. The system associated the inference of a change in behavior with the decrease in both the GA value and WGMI, and recorded this association in memory. Since 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 issue a risk alert. Therefore, the system predicted that if the WGMI at week 6 was maintained, the GA value would be 13.5% after 7 weeks (week 13).

[0190] Based on the GA value obtained by the system, the WGMI further decreased to 10.3% over seven days a week, falling below the threshold of 11%. Therefore, the system determined that there was a potential risk of medical problems and issued a risk alert to the individual, recommending that they seek medical attention. The system predicted that if the WGMI was maintained over seven days a week, the GA value would fall below 11% in the future. The system output this prediction to the individual and also provided 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 value acquired by the system was 12.9%, exceeding the threshold of 11%. Therefore, the system determined that blood glucose control had improved. At week 9, the WGMI calculated based on the GA value acquired by the system rose further to 14.5%. The system determined that this behavior was "risk-free". At week 10, the WGMI calculated based on the GA value acquired by the system was 15.5%. The system issued a message recommending that the subject continue their current behavior at a WGMI of around 15%. Subsequently, the WGMI calculated based on the GA value acquired by the system showed a slight upward trend but remained below 16%.

[0192] Generally, the normal range for the GA value is considered to be between 11% and 16%, so ideally, the GA value should remain below 16%. However, lifestyle patterns and health conditions are never perfectly constant, and it is possible that the GA value may exceed the normal range. Some strategies involve drastically changing behavior when the GA value exceeds the normal range. On the other hand, it is also possible to lower the GA value again and return it to below 16% by monitoring appropriate lifestyle habits with WGMI and continuing these habits. Therefore, even if the GA value or GMI temporarily exceeds the normal range, it is advisable to continue the same behavior and observe the progress.

[0193] Example 5: Prediction of HbA1c values Figure 5D shows, as an example, the measured GA values ​​for a certain subject from week 0 to week 7, the measured HbA1c value at week 0, the WGMI calculated based on the GA value, 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. Although this value did not exceed the diagnostic criterion of diabetes (6.5%), the system judged it to be 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 at week 3 was 17.9%, which was also high. The system output these values ​​to the subject. Subsequently, the system referred to information on the subject's target behaviors, recognized that the subject had set new target behaviors, and inferred that the subject was working to improve their GA value. This was likely successful, as the WGMI calculated from the GA value obtained by the system decreased to 16.1% at week 4 and 15.3% at week 5. The system associated the newly set target behaviors with the inferred actual behaviors contributing to improved blood glucose management and recorded this association in memory. In fact, the obtained measured GA value and predicted HbA1c value showed a gradual downward trend.

[0195] As is clear from this, the response of GA values ​​to weekly behavioral changes is slow, and the response of HbA1c values ​​is even slower. In contrast, WGMI is more sensitive to weekly behavior and reflects the behavior of the subjects more accurately.

[0196] Therefore, the system calculates that if a WGMI of 15.1% is maintained at 5 days a week, the HbA1c level will be 6 after 8 weeks (13 days a week). % It was predicted to fall below (dotted line (1)).

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

[0198] The WGMI calculated from the GA values ​​acquired by the system fell further to 10.3% over 7 days a week. Therefore, the system predicted that if the WGMI was continued over 7 days a week, the HbA1c value would be 4.6% over 24 days a week (dotted line (3)).

[0199] Thus, using GMI, it is possible not only to 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 the target HbA1c range, the range of HbA1c values ​​to be targeted, the range of HbA1c values ​​to be targeted, etc.) may be set. The range is used interchangeably with an upper and / or lower limit. Either one or both of the upper and lower limits of the target HbA1c value may be set. The target HbA1c range may be set as a function of time. For example, the set range of HbA1c values ​​at the time the HbA1c value is actually tested may be set. Set ranges corresponding to each of the multiple time points may be set.

[0201] In some embodiments, the target HbA1c range may be set according to the guidance of a healthcare professional, such as a physician. In some embodiments, the target HbA1c range may be set or suggested by the system. The target HbA1c range suggested by the system may be approved by the individual or healthcare professional, and the target HbA1c range may be set based on that approval. The target HbA1c range may be set or suggested based on the user's medical conditions. Examples of the user's medical conditions to be considered include, but are not limited to, age, medical history, disease type (type 1, type 2, etc.), and comorbidities. These factors may be taken into consideration when setting or suggesting the range.

[0202] In some embodiments, it may be determined whether the predicted HbA1c value at a certain point in time, based on the GMI, falls within the target HbA1c range at that same point in time. The result of this comparison may be output. If the predicted HbA1c value does not fall within the target HbA1c range, the displacement (time-series data) of the GA value and / or GMI required to bring the future HbA1c value into the target HbA1c range may be calculated and output. Furthermore, an action plan (target action) that results in such a displacement (time-series data) of the GA value and / or GMI may be proposed.

[0203] Example 6: Correlation between CGM and GMI Refer to Figure 6 to explain the correlation between CGM and GMI.

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

[0205] The 95% confidence interval for the regression coefficients shown in Figure 6 had a slope of 0.097 to 0.172 and an intercept of -5.421 to 2.877. This range includes the theoretical formula, GA or WGMI = 0.139 × glucose value - 1.49, and this result is consistent with the theoretical formula.

[0206] Table 1 shows the correlation coefficients between the obtained WGMI and GA values ​​and the time periods and areas of hyperglycemia (time × blood glucose level) obtained from CGM. It was also confirmed that WGMI generally tends to show a higher correlation with various indicators than GA values. [Table 1]

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

[0208] In this embodiment, future HbA1c values ​​were predicted assuming that the GMI would remain constant, but this is not limited to this. The GMI may not be constant or may change. Although conversion formulas between GA values ​​and HbA1c values ​​are known, these formulas only hold true when blood glucose levels or GA values ​​are maintained at a relatively constant level over a long period of time. For example, if there are weekly changes in GMI, which is the actual case, such conversion formulas do not hold true. The method of this disclosure can present to the subject in an easily understandable way what HbA1c value their actions during that week will result in.

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

[0210] 1. Glucose Management Index (GMI) of GA value GA ) calculation Glucose management index (GMI) derived from time-series data of GA values GA To calculate ), use the following formula 8.

number

[0211] This formula calculates a glucose management index, a dynamic indicator based on time-series data, by considering the change in the GA value between time points t[i-1] and t[i], and further adding the previous GA value GA[i-1] as a correction term.

[0212] This glucose management index (GMI) GA The relationship between ) and average blood glucose level (MBG) can be expressed by the following formula 9.

number

[0213] 2. Calculation of the glucose management indicator (GMI HbA1c ) of the HbA1c value Next, H the glucose management indicator (GMI HbA1c ) of the HbA1c value is derived by the following formula 10. This indicator dynamically represents the temporal variation of HbA1c and is corrected using the difference in HbA1c values between time points.

Equation

[0214] Furthermore, the glucose management indicator (GMI HbA1c ) of the HbA1c value defines the relationship with the mean blood glucose level (MBG) by the following formula 11. By clarifying this relationship, it becomes possible to comprehensively understand the relationship between the dynamic changes in HbA1c values and blood glucose levels.

Equation

[0215] 3. Prediction of HbA1c value Obtain the HbA1c value (HbA1c[q]) at time point t[q] as a known value. Then, using the glucose management indicator (GMI HbA1c ) calculated based on the time series data of GA values, predict the HbA1c value (HbA1c[r]) at time point t[r] based on the following formula 12.

Equation

[0216] This formula enables highly accurate prediction of HbA1c values by combining the glucose management indicator (GMI HbA1c ) of HbA1c, the time difference, and the correction coefficient C HbA1c .

[0217] 4. Relationship between MBG and GMI Glucose management indicators (GMI) of average blood glucose (MBG) and GA values. GA The relationship between ( ) and is defined by the following equation 13.

number

[0218] Furthermore, the glucose management index (GMI) of average blood glucose (MBG) and HbA1c is also important. HbA1c The relationship between ( ) and is defined by the following equation 14.

number

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

number

[0220] By using these formulas, it becomes possible to model the indirect relationship between GA values ​​and HbA1c values ​​from the perspective of average blood glucose levels.

[0221] 5. Glycation sensitivity ratio (R GI Definition and calculation method of ) Glycation sensitivity ratio (R GI The glycemic index (GI) of different proteins relative to blood glucose levels is defined by the following equation 16. This ratio serves as an important correction factor in the conversion between GA values ​​and HbA1c values.

number

[0222] (1) When HbA1c levels are stable over time When the HbA1c value is relatively stable, the following equations 17 and 18 may be used. In this case, since there is a certain proportional relationship between the GA value and the HbA1c value, stable R GI calculation is possible.

Number

Number

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

[0224] 2) When the HbA1c value varies over time When the HbA1c value varies over time, it is necessary to consider the influence of the period before time point t[q] for the prediction after a specific time point t[q]. Based on this, correction is performed by the following method.

[0225] First, when HbA1c is obtained at time point t[p] and the next time point t[q] (p < q), the mean blood glucose value (MBG) between time point t[p] and time point t[q] is defined by the following equation 19.

Number

[0226] Next, after obtaining the GA values at time points t[p], t[p + 1], …, t[q], calculate MBG based on the following equation 20.

Number

[0227] Here, GMI GA[i-1,i] is a glucose management index for GA values, GI Alb This represents the glycation sensitivity of albumin. This formula is designed to normalize the change in GA value between time points over time intervals and average the effects from past time point t[p] to time point t[q].

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

number

[0229] These formulas calculate the ratio of albumin to hemoglobin glycation sensitivity and correct it based on past data from time point t[p] to t[q]. This correction further improves the accuracy of HbA1c prediction.

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

number

[0231] Through the methods described above, the present invention enables highly accurate prediction of HbA1c values ​​while appropriately considering the influence of past data.

[0232] This embodiment provides a technology that significantly reduces the frequency of HbA1c testing while accurately predicting HbA1c values ​​using time-series data of GA values. This reduces the burden on patients and healthcare professionals, and enables real-time blood glucose management.

[0233] As one embodiment of this model, Figure 7 shows a comparison of the results obtained using the conventional method and the method of the present invention (Equations 18 and 21). These comparisons were based on data obtained from the Jinnai Clinical Study (2022-2023, https: / / doi.org / 10.1007%2Fs13300-024-01599-2). In this model, the subjects consisted of patients with type 2 diabetes, and they made three hospital visits: an initial visit, an intermediate visit (5 weeks after the initial visit), and a final visit (4 weeks after the intermediate visit). Venous blood was collected at each visit and used to measure HbA1c levels. During that time, 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 model consists of data from 37 subjects out of 44 subjects with type 2 diabetes, excluding 7 subjects who did not perform home measurements in the week of their final hospital visit.

[0234] Figure 7A shows the results of predicting HbA1c values ​​from GA values ​​using the conventional method. This method predicts HbA1c values ​​from GA values ​​using the linear regression results of HbA1c values ​​from venous blood collected at the intermediate hospital visit (t[p]) and home GA measurements during that week.

[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), with an intercept of 5.10 (95% CI: 3.55–6.39). This result indicates a large variability in the predicted values ​​and limits the accuracy.

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

[0237] The correlation coefficient (Pearson's r) between the predicted and measured values ​​was very high at 0.995. 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 is observed that the intercept of the linear approximation deviates from 0.

[0238] Figure 7C shows the results when a method based on Equation 19 is further applied. In this method, the GA measurement values ​​from the first visit (t[p]) to the last visit (t[r]), and the HbA1c values ​​at the first visit (t[p]) and intermediate visits (t[q]) were used to predict the HbA1c value at the last visit (t[r]).

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

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

[0241] Figure 8(a) shows the GA values ​​and GMI actually obtained in this clinical study. GA The graph shows the change in HbA1c levels over time. The square markers (white squares inside) and the solid line represent the measured GA values, and the dashed line represents the GMI calculated from the GA values. GAThe graph shows the measured HbA1c value, indicated by a black circle marker (●). Figure 8(a) shows an example where the GA value and HbA1c value show different trends. In this case, the HbA1c value is increasing despite the GA value decreasing.

[0242] Figure 8(b) is a graph showing the time course of HbA1c values ​​predicted using two different methods, overlaid with the measured HbA1c values ​​based on the data in Figure 8(a). The circles (white-outlined circular markers) and thin dashed lines represent the R values ​​calculated using formula 18 from the HbA1c and GA values ​​at the first hospital visit. GI The HbA1c values ​​predicted using formulas 12 and 15 from subsequent at-home GA measurements are shown. On the other hand, the square markers (white squares inside) and thick dashed lines represent the R values ​​calculated from formula 21 using the two HbA1c values ​​from the initial and intermediate hospital visits, and the at-home GA measurements from week -1 to week 4. GI Using this method, the predicted HbA1c values ​​are shown based on the subsequent at-home GA measurements from week 4 onwards, using equations 12 and 15.

[0243] The difference between these two predicted HbA1c values ​​is calculated using formula 18 from a single HbA1c value and a GA value, respectively. GI And, using two HbA1c values ​​and multiple GA measurements, R was calculated from equation 21. GI This typically illustrates the difference in effectiveness compared to calculations using [another method]. As shown in the example in Figure 8(a), when the HbA1c value increases despite a decrease in the GA value, it is thought that the HbA1c value, with its longer half-life, more strongly reflects the effect of the increase in blood glucose levels before the start of measurement, while the GA value more strongly reflects the recent decrease in blood glucose.

[0244] Equation 18 does not include a half-life term, and the R value obtained from this equation is... GI The calculation does not reflect these differences in half-lives. Therefore, the prediction is that the HbA1c value will decrease in the same way as the GA value. On the other hand, formula 21 includes GMI, which reflects the half-live. Therefore, when using this formula, R is calculated using two HbA1c values ​​and multiple GA measurements. GIBy calculating this, it is possible to remove the influence of past blood glucose fluctuations and obtain a more accurate glycation sensitivity ratio (see Figure 8(b)), thus enabling more accurate prediction of HbA1c values.

[0245] Figure 9(a) shows the R values ​​of each subject obtained from the clinical study data. GI This shows a scatter plot illustrating the relationship between and BMI. Similar to Figures 7(A), (B), and (C), this data consists of data from 37 subjects out of 44 patients with type 2 diabetes, excluding 7 who did not undergo home measurements during their last hospital visit week. GI This tends to increase as BMI increases.

[0246] Figure 9(b) shows the R values ​​of each subject obtained from the same clinical research data as in Figure 9(a). GI This shows a scatter plot illustrating the relationship between waist circumference and body circumference. GI It tends to increase as waist circumference increases.

[0247] These can be used in R, especially for users with a large BMI or waist circumference, using Equation 18 or Equation 21. GI This demonstrates the importance of determining the following: In other words, for users with a large BMI or waist circumference, the commonly known R GI This indicates that the predicted HbA1c value using a value of 0.25 deviates more significantly from the actual measured value. For example, when an actual HbA1c value cannot be obtained, such as when blood has not been drawn, R can be used provisionally. GI HbA1c values ​​may be predicted from GA values ​​measured assuming a value of 0.25. In this case, subjects with a BMI of 25 or higher or a waist circumference of 85 cm or higher may be informed that the predicted HbA1c value may have a large margin of error or be inaccurate.

[0248] Figure 9(c) shows the R values ​​for each subject obtained from the same clinical research data as in Figure 9(a). GI A scatter plot showing the relationship between R and HDL cholesterol levels is shown. GI This tends to increase as HDL cholesterol levels decrease.

[0249] This suggests the possibility of estimating HDL cholesterol levels from GA and HbA1c values. For example, R calculated from GA and HbA1c values GI If the value is greater than a predetermined value (e.g., 0.33), the user or healthcare professional may be notified to check their HDL cholesterol level.

[0250] In the embodiments and examples described above, the time constant τ (the time constant for the decrease of target proteins such as albumin and 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 by selectively or in combination using 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 several weeks, but it may change over the long term due to changes in the individual's health status, disease state, age, etc. Furthermore, τ may be affected by treatment status, such as dialysis.

[0251] This disclosure includes the following embodiments: A001. A method for supporting the blood glucose management of a subject using a computer system, The aforementioned computer system includes a processor, The aforementioned method, The processor obtains the glycated albumin (GA) values ​​of the subject at two time points t[i] and t[j] > t[i], namely GA[i] and GA[j]; The processor determines a glucose management index (GMI[i,j]), which is the average blood glucose level converted to GA values ​​for the period between the two time points t[i] and t[j], based on GA[i] and GA[j]; and The processor generates output information based on the glucose management index (GMI[i,j]) and outputs it to the subject; A method for providing this. A001b. A method for supporting the blood glucose management of a subject using a computer system, The computer system comprises 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), The aforementioned method, The processor obtains GP[i] and GP[j], which are the glycation rates (GP values) of the target protein of the subject at two time points t[i] and t[j]>t[i], via the communication unit; The processor determines a glucose management index (GMI[i,j]), which is the average blood glucose level converted to GP values ​​during the period between the two time points t[i] and t[j], based on GP[i] and GP[j]; and The processor generates output information based on the glucose management index (GMI[i,j]) and outputs it to the subject via the communication unit; A method for providing this. A002. A method for supporting the blood glucose management of a subject using a computer system, The computer system comprises 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), The aforementioned method, The processor obtains 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], via the communication unit; The processor determines a glucose management index (GMI[i-1,i]), which is the average blood glucose level converted to GA values ​​during the period between the two time points t[i-1] and t[i], based on GA[i-1] and GA[i]; and The processor generates output information based on the glucose management index (GMI[i-1,i]) and outputs it to the subject; A method for providing this. A003. A method according to A002, or any embodiment thereof, The interval Δt=t[i]-t[i-1] for acquiring GA values ​​is effectively between 1 day and 1 month. method. A003b. A method according to A003, or any embodiment thereof, The interval Δt=t[i]-t[i-1] for acquiring the aforementioned GA value is effectively between 3 days and 14 days. method. A005. A method according to any one of the claims A001 to A003, or any embodiment thereof, 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), Obtaining the aforementioned GA[i] and GA[j] includes the processor obtaining the aforementioned GA[i] and GA[j] via the communication unit. method. A007. A method according to any one of the claims A001 to A005, or any embodiment thereof, 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), Outputting the aforementioned output information to the subject includes outputting it to the subject via the communication unit. method. A011. A method according to A002 or A003, or any embodiment thereof, Generating the output information includes generating an evaluation of the subject's behavior during the period from time t[i-1] to time t[i], based on the glucose management index (GMI[i-1,i]), method. A012. A011, or a method according to any embodiment, The output information is, When the glucose management index (GMI[i-1,i]) is smaller than the GA value GA[i], the assessment includes that the actions taken by the subject during the relevant period were effective. method. A013. A method according to A011 or any embodiment thereof, The output information is, When the glucose management index (GMI[i-1,i]) is less than the GA value GA[i] for two consecutive times, that is, Condition 1: GMI[i-1,i] <GA[i]、かつ、GMI[i-2,i-1]<GA[i-1] When the conditions are met, the actions taken by the subject during the relevant period are evaluated as having been effective, method. A014. A method according to A013, or any embodiment thereof, The output information is, when the condition 1 is not met, When the average of at least two glucose management indicators is sufficiently smaller than the average of at least two corresponding GA values, i.e., Condition 2: Mean {GMI[i-1,i]} < Mean {GA[i]} - TH (threshold) When this condition is met, the actions taken by the subject during the relevant period are deemed to have been effective, and / or If the above condition 2 is not met, the actions taken by the subject during the relevant period are deemed ineffective, method. A015. A method according to any one of the items A012 to A014, or any embodiment thereof, The output information is, When the GMI is lower than the GMI risk assessment value and / or the GA value is lower than the GA risk assessment value, an alert is issued indicating that the actions taken by the subject during the relevant period pose a health risk and / or that the target behavior should be changed. method. A021. A method according to A001 or any embodiment, To obtain behavioral information regarding the actions taken by the subject during the period between the aforementioned time points t[i] and t[j]; and To determine the relationship between the aforementioned behavioral information and the aforementioned glucose management index (GMI[i,j]); A way to further enhance it. A022. A021, or a method according to any embodiment, The aforementioned behavioral information includes self-reported information by the subject and / or biological information obtained about the subject. method. A023. A method according to A021 or any embodiment, Determining the relationship between the aforementioned behavioral information and the aforementioned glucose management index (GMI[i,j]) involves referring to a database containing behavioral information and GA values ​​of non-subjects, who are not the aforementioned subjects. method. A031. A method according to A011 or any embodiment thereof, Based on the aforementioned relationships, generate target actions that the subject should take in the future. A method for providing this. A032. A031, or a method according to any embodiment, Generating target actions that the subject should take in the future includes, but is not included in, or is removed from, the generated target actions, or is given a lower priority than, actions that were previously ineffective or risky, based on the subject's past behavioral information. method. A035. A method according to A031 or any embodiment, Output to the subject the target actions that the subject should take in the future, A way to further enhance it. A041. A method according to A031, or any embodiment thereof, The algorithm for generating target actions that the subject should take in the future is optimized using the subject's glucose management indicator as an evaluation parameter. method. A101. A method for supporting the blood glucose management of a subject using a computer system, The aforementioned computer system includes a processor, The aforementioned method, (a1) The processor obtains the subject's first GA value GA[m] at the first time point t[m]; (b1) The processor obtains a time-series glucose management index {GMI[i-1,i]} (i=m+1~n) from the first time point t[m] to the second time point t[n]; and (c1) The processor, based on the first GA value GA[m] and the time series glucose management index {GMI[i-1,i]}(i=m+1~n), at the second time point t[n] The To find the GA value GA[n], A method for providing this. A111. A method for supporting the blood glucose management of a subject using a computer system, The aforementioned computer system includes a processor, The aforementioned method, (a2) The processor obtains the subject's first GA value GA[m] at the first time point t[m]; (b) The processor obtains a target second GA value GA[n] at the second time point t[n]; and (c2) The processor determines the time-series glucose management index {GMI[i-1,i]} (i=m+1~n) 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]. A method for providing this. A112. A method according to A111, or any embodiment thereof, Step (c2) includes determining the time series {GMI[i-1,i]}[k] (i=m+1~n) of multiple (k, k≧2) glucose management indices. method. A113. A method according to any one of the items A101 to A112, or any embodiment thereof, 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), Step (a1) and / or step (a2) include the processor obtaining the subject's first GA value GA[m] at a first time point t[m], method. A121. A method for supporting the blood glucose management of a subject using a computer system, The aforementioned computer system includes a processor, The aforementioned method, (a3) The processor obtains the subject's first HbA1c value HbA1c[m] at the first time point t[m]; (b3) The processor obtains a time-series glucose management index {GMI[i-1,i]} (i=m+1~n) from the first time point t[m] to the second time point t[n]; (c3) The processor, based on the first HbA1c value HbA1c[m] and the time-series glucose management index {GMI[i-1,i]}(i=m+1~n), at the second time point t[n] The To determine the HbA1c value HbA1c[n]; (d3) The processor determines the target HbA1c value range at the second time point t[n]; (e3) The processor determines whether the second HbA1c value HbA1c[n] is included in the target HbA1c value range; and (f3) The processor outputs the result of the determination; A method for providing this. A122. A method according to A121, or any embodiment thereof, (g3) The processor calculates a time-series glucose management index {GMI[i-1,i]} (i=m+1~n) such that the second HbA1c value HbA1c[n] at the second time point t[n] falls within the target HbA1c value range. A way to further enhance it.

[0252] C001. Software including computer executable code for assisting in the blood glucose management of a subject, wherein the computer executable code, when executed by a processor, The aforementioned processor, Obtain the GA values ​​GA[i] and GA[j] for the subject at two points in time, t[i] and t[j] > t[i]; Based on the aforementioned GA[i] and GA[j], the glucose management index (GMI[i,j]), which is the average blood glucose level converted to GA values ​​during the period between the two time points t[i] and t[j], is determined; and The system generates output information based on the glucose management index (GMI[i,j]) and outputs it to the target individual; Software configured in such a way. Software described in C002, C001, or any embodiment thereof, Allowing the processor to acquire GA[i] and GA[j] includes allowing the processor to acquire 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 target person, etc.). method. C011. Software containing computer executable code for assisting in the blood glucose management of a subject, The computer executable code is configured, when executed by a processor, to cause the processor to perform any one of A001 to A261, or the method and / or steps described in any embodiment thereof. software. D001. C001 to C 011A non-temporary storage medium for storing any of the software described in any of the embodiments.

[0253] E001. A computer system for supporting blood glucose control in subjects, The aforementioned computer system includes a processor, The aforementioned processor, Obtain the GA values ​​GA[i] and GA[j] for the subject at two points in time, t[i] and t[j] > t[i]; Based on the aforementioned GA[i] and GA[j], the glucose management index (GMI[i,j]), which is the average blood glucose level converted to GA values ​​during the period between the two time points t[i] and t[j], is determined; and The system generates output information based on the glucose management index (GMI[i,j]) and outputs it to the target individual; programmed to do so, Computer system. E005. A computer system according to E001, or any of the embodiments thereof, The aforementioned computer system, The system includes a communication unit connected to the aforementioned processor and capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the aforementioned person, etc.), or It is configured to be connected to a communication unit that can communicate with the aforementioned user interface, To cause the processor to acquire the GA[i] and GA[j] is to cause the processor to acquire the GA[i] and GA[j], which are the GA values ​​of the subject, via the communication unit. Causing the processor to output the output information to the target person includes causing the processor to output the output information to the target person via the communication unit. method. E011. A computer system for supporting blood glucose control in subjects, The aforementioned computer system includes a processor, The processor is programmed to perform any one of the items A001 to A201, or the method and / or steps described in any embodiment thereof. Computer system. E021. A computer system that supports blood glucose control for a subject, Processor; and A non-temporary storage medium for storing software described in any one of the embodiments described in C001 to C201; A computer system equipped with the following features. A computer system as described in E022, E021, or any of the embodiments, A computer system further comprising a communication unit capable of communicating with a user interface (e.g., a display unit, an electronic device of the target user, etc.). E101. A computer system that supports blood glucose control for a subject, A receiving means for obtaining the target person's GA value; GMI calculation means for determining a glucose management index based on multiple acquired GA values; A transmission means for transmitting the glucose management indicator to the subject's terminal; A computer system equipped with the following features. E102. A computer system as described in E101 or any of the embodiments, Behavioral evaluation means for evaluating the subject's behavior based on the glucose management index; Risk assessment means for determining the risk of the subject's behavior based on the glucose management index and / or the GA value; A GA value prediction means that predicts future GA values ​​based on the glucose management indicator; HbA1c value prediction means for predicting future HbA1c values ​​based on the glucose management indicator; Mode selection means for selecting a support mode appropriate to the subject from a plurality of blood glucose management modes based on the glucose management index and / or the GA value; Proposal generation means that generates output information including actions or target actions that the subject should take based on the glucose management index; and Optimization means for optimizing the algorithm that generates the output information; A computer system further comprising at least one or more of the following.

[0254] B201. A system to support blood glucose management, The system comprises a sensor, a processor, a memory unit, and a user interface. The sensor is configured to receive the subject's bodily fluids, measure the subject's glycated albumin (GA) level, and transmit the measured GA level to a processor; The aforementioned processor, The sensor receives GA values ​​(GA[i] and GA[j], where t[j]>t[i]) at different time points. Based on Formula 1, the GA glucose management index (GMI) is the average blood glucose level converted to GA values ​​during the period between two time points t[i] and t[j]. GA Generate [i,j])

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[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 only as examples. The present invention is not intended to be limited by any specific examples provided herein. Any or any aspect of the embodiments disclosed herein can be combined independently, in part or in whole, with any other embodiments and aspects described herein. For example, one, two, or three or more embodiments can be combined in whole or in part.

[0256] As used herein, the terms “including” and “equipped” are intended to mean that configurations and methods include the enumerated elements but do not exclude others. Embodiments defined by each of these transitional terms are within the scope of the invention. Thus, methods and compositions, configurations “including” 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 rather as "and / or" unless otherwise specified. Similarly, a group of items connected by the conjunction "or" should not be read as requiring mutual exclusivity between the items in that group, but rather as "and / or" unless otherwise specified.

[0258] While the present invention has been described with reference to the above specification, the descriptions and figures of the embodiments herein are not intended to be constrained. A number of variations, modifications, and substitutions will be conceivable to those skilled in the art without departing from the present invention. Furthermore, it should be understood that all aspects of the present invention are not limited to the specific descriptions, configurations, or relative proportions described herein, which depend on various conditions and variables. It should be understood that various alternatives to the embodiments of the present invention described herein may be used in carrying out the present invention. Therefore, the present invention is considered to encompass such alternative forms, modifications, variations, or equivalents. The following claims define the scope of the present invention, and methods and structures within these claims, as well as their equivalents, are intended to be covered thereby.

Claims

1. It is a system to support blood glucose management. The system comprises a sensor, a processor, a memory unit, and a user interface. The sensor is configured to receive the subject's bodily fluids, measure the subject's glycated albumin (GA) level, and transmit the measured GA level to a processor; The aforementioned processor, The sensor receives GA values ​​(GA[i] and GA[j], where t[j] > t[i]) at different time points. Based on Formula 1, the GA glucose management index (GMI) is the average blood glucose level converted to GA values ​​during the period between two time points t[i] and t[j]. ＧＡ Generate [i, j], [Math 1] Here, C ＧＡ [i,j]=1-exp[-(t[j]-t[i]) / τ Ａｌｂ ]、 τ Ａｌｂ The albumin decrease time constant; and The aforementioned GA glucose management index (GMI) ＧＡ Generate output information based on [i, j]) and transmit it to the user interface; It is configured in such a way, The user interface is configured to output the output information received from the processor, system.

2. The system according to claim 1, A system in which the interval Δt = t[i] - t[i-1] for acquiring the GA value is substantially between 3 days and 14 days.

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

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

5. The system according to claim 4, The output information is the GA glucose management index (GMI) ＧＡ A system that includes an evaluation that the actions taken by the subject during the period between two time points t[i-1] and t[i] were effective when [i-1, i]) is smaller than the GA value GA[i].

6. The system according to claim 5, The output information is, When the GA glucose management index (GMIGA[i-1,i]) is smaller than the GA value GA[i] for two consecutive times, that is, Condition 1: GMIGA[i-1, i] < GA[i], and GMIGA[i-2, i-1] < GA[i-1] When the condition is met, the evaluation includes that the actions taken by the subject during the period between the two time points t[i-2] and t[i] were effective, system.

7. The system according to claim 6, The output information is, If the above condition 1 is not met, When the average of at least two GA glucose management indicators is sufficiently smaller than the average of at least two corresponding GA values, that is, Condition 2: Mean {GMIGA[i-1,i]} < Mean {GA[i]} - h (threshold) When this condition is met, the actions taken by the subject during the relevant period are deemed to have been effective, and / or If the above condition 2 is not met, the actions taken by the subject during the relevant period are deemed ineffective, system.

8. A system according to any one of claims 5 to 7, The output information is, When the GMI is lower than the GMI risk threshold and / or the GA value is lower than the GA risk threshold, an alert is issued indicating that the actions taken by the subject during the relevant period pose a health risk and / or that the target behavior should be changed. system.

9. The system according to claim 1, The processor acquires behavioral information relating to the actions taken by the subject during the period between time points t[i-1] and t[i]; Determine the relationship between the aforementioned behavioral information and the aforementioned GA glucose management index (GMIGA[i-1,i]); and The relationship between the behavioral information of the subject and the GA glucose management index is included in the output information. It is configured to be so system.

10. The system according to claim 9, The system includes, as the behavioral information, self-reported information by the subject and / or biometric information obtained concerning the subject.

11. The system according to claim 9, A system for determining the relationship between the aforementioned behavioral information and the aforementioned GA glucose management index (GMIGA[i-1,i]) includes referring to a database containing behavioral information and GA values ​​of non-subjects, who are not the subject.

12. The system according to claim 9, The system further comprises a processor configured to generate target actions that the subject should perform in the future based on the association, and to output the generated target actions to be performed by the subject in the future to the subject.

13. The system according to claim 12, A system for generating target actions that the subject should perform in the future, which includes not including or removing from the generated target actions actions that were ineffective or risky in the subject's previous behavioral information, or lowering the priority of actions that were ineffective or risky.

14. The system according to claim 12, A system in which an algorithm for generating target actions that the subject should perform in the future is stored in the memory unit, and the algorithm is optimized using the subject's GA glucose management index as an evaluation parameter.

15. The system according to claim 1, The aforementioned processor performs the following steps: (a1) Obtain the subject's first GA value GA[m] at the first time point t[m] from the sensor; (b1) Time-series GA glucose management index {GMI} from the first time point t[m] to the second time point t[n] ＧＡ To obtain [i-1, i] (i = m+1 to n); and (c1) The first GA value GA [m] and the time-series GA glucose management index {GMI ＧＡ Based on {i-1, i} (i = m+1 to n), the second GA value GA[n] at the second time point t[n] is determined. Configured to perform, system.

16. The system according to claim 1, The aforementioned processor performs the following steps: (a2) Obtain the subject's first GA value GA[m] at the first time point t[m] from the sensor; (b2) Obtaining a target second GA value GA[n] at a second time point t[n] from the sensor; and (c2) Based on the first GA value GA[m] and the second GA value GA[n], the time-series GA glucose management index {GMI ＧＡ [i - 1, i]} (i = m + 1 to n) required to reach the second GA value GA[n] at the second time point t[n] is obtained. Configured to perform, system.

17. The system according to claim 1, The aforementioned processor performs the following steps: (a) Obtaining multiple time-series glycated albumin (GA) values ​​of the subject from time point t[q] to t[r] (r>q); (b) A time-series glucose management index (GMI) obtained from the acquired time-series GA values. ＧＡ ) and the glycation sensitivity ratio R ＧＩ Based on the above, using the following formulas 15 and 16, the GMI of the HbA1c value during the period from t[q] to t[r] is calculated (GMI ＨｂＡ１ｃ Calculate [q, r]: [Number 15] Here, the glycation sensitivity ratio R ＧＩ This is expressed by the following equation 16: [Number 16] (c) Obtain the subject's HbA1c value (HbA1c[q]) at time t[q]; (d) The GMI of the HbA1c value during the period from t[q] to t[r] (GMI ＨｂＡ１ｃ From [q, r] and the subject's HbA1c value at time t[q] (HbA1c[q]), the following formula is used: [Math 12] To estimate the HbA1c value (HbA1c[r]) of the subject at time t[r] (r>q); and (e) Generate output information based on the estimated HbA1c value (HbA1c[r]) at the estimated time t[r] and output it to the user interface; Configured to perform, system.

18. The system according to claim 17, The aforementioned R ＧＩ It can be expressed by the following formula: [Number 18] system.

19. The system according to claim 17, The aforementioned R ＧＩ It can be expressed by the following formula: [Math 21] method.

20. A method for supporting the blood glucose management of subjects using a computer system, The computer system comprises a sensor, a processor, a memory unit, and a user interface. The aforementioned method, The sensor measures the body fluids of the subject and obtains the subject's GA value; To transmit GA values ​​(GA[i] and GA[j], where t[j] > t[i]) at different points in time to the processor; The processor calculates the GA glucose management index (GMI), which is the average blood glucose level converted to a GA value for the period between two time points t[i] and t[j], based on formula 1. ＧＡ To generate [i, j], [Math 1] Here, C ＧＡ [i,j]=1-exp[-(t[j]-t[i]) / τ Ａｌｂ ]、 τ Ａｌｂ This is the time constant for the decrease of albumin; The aforementioned GA glucose management index (GMI) ＧＡ To generate output information based on [i, j]) and transmit it to the user interface; and The user interface outputs the output information received from the processor to the target person. Equipped with, method.

21. A method for supporting blood glucose management of a subject using a computer system, as described in claim 20, The aforementioned method, (a) Using the sensor, measure the body fluids of the subject and obtain multiple time-series glycoalbumin values ​​(GA values) of the subject from time t[q] to t[r] (r>q); (b) The processor provides a time-series glucose management index (GMI) of the GA value obtained from the acquired time-series GA value. ＧＡ ) and the glycation sensitivity ratio R ＧＩ Based on this, the following formula (Eq. 15) is used to calculate the GMI of the HbA1c value during the period from t[q] to t[r] (GMI ＨｂＡ１ｃ Calculate [q, r]; [Number 15] Here, [Number 16] (c) The processor obtains the subject's HbA1c value (HbA1c[q]) at time t[q]; (d) The processor calculates the GMI of the HbA1c value for the period from t[q] to t[r]. ＨｂＡ１ｃ From [q, r] and the subject's HbA1c value at time t[q] (HbA1c[q]), the following formula is used: [Math 12] To estimate the HbA1c value (HbA1c[r]) of the subject at time t[r] (r>q); (e) The processor generates output information based on the estimated HbA1c value (HbA1c[r]) at the estimated time t[r] and transmits it to the user interface; and (f) Outputting the output information received from the processor to the target person via the user interface; A method for providing this.

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