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

Abstract

According to one embodiment of the present disclosure, provided is a method for assisting blood glucose management of a subject using a computer system provided with: a processor; and a communication unit connected to the processor and capable of communicating with an electronic terminal accessible to the subject, the method comprising: the processor acquiring GP [i] and GP [j], which are the saccharification rates (GP values) of the subject protein at two time points t [i] and t [j] > t [i], via the communication unit; the processor obtains a glucose management index (GMI [i, j]), which is an average blood glucose value converted from a GP value in a period between two time points t [i] and t [j], on the basis of GP [i] and GP [j]. And the processor generates output information based on the glucose management index (GMI [i, j]) and outputs the output information to the subject via the communication unit.

Description

Technical Field

[0001] This disclosure relates to techniques for using computer systems to assist in blood glucose management in subjects. Background Technology

[0002] Diabetes mellitus is an endocrine disorder characterized by chronic hyperglycemia (high fasting blood glucose, high HbA1c, etc.). It 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. Type 2 diabetes, which affects the largest number of patients, is primarily caused by decreased insulin secretion or insulin resistance. Type 1 diabetes is caused by the destruction of pancreatic beta cells.

[0003] Regardless of the specific type of diabetes, treatment generally involves medication combined with dietary and exercise therapies. These treatments enable appropriate blood glucose management and reduce the risk of complications. In other words, lifestyle modifications are crucial. Furthermore, there exists a group of people who do not meet the diagnostic criteria for diabetes but have high blood glucose levels, known as the "prediabetic population" or "borderline diabetes." In this group, lifestyle modifications are essential to reduce the risk of developing diabetes. As mentioned above, lifestyle modifications, including appropriate diet and exercise, are considered effective for blood glucose management and prevention of diabetes.

[0004] Treating chronic diseases like diabetes is generally considered difficult. Reasons for this include: the absence of noticeable symptoms; difficulty in maintaining motivation to treat; the difficulty in curing the condition, requiring lifelong treatment; and the need for lifestyle modifications in addition to medication. These factors are believed to make the self-management required for treatment difficult (Non-Patent Literature 1).

[0005] In fact, there are reports that it takes 18 to 254 days for a new habit to become automatic, with a median of 66 days (Non-Patent Literature 2). Based on this, changes in behavior require two to several months. Typically, some kind of system is needed to maintain motivation during this period.

[0006] Typical examples of biomarkers used for blood glucose control are serum glucose levels, HbA1c levels, and glucose tolerance (GA). However, fasting blood glucose levels are not necessarily a good reflection of lifestyle habits. HbA1c is considered to reflect blood glucose levels over the past two to three months. However, over such a long period, it is difficult to maintain motivation for self-management. In other words, HbA1c levels provide too much feedback on changes in behavior.

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

[0008] On the other hand, GA (glycated albumin) is the degree of glycation of proteins such as albumin. Because GA is a relative value, such as the amount of glycated albumin relative to the total albumin amount, techniques for high-precision measurement have been developed. Furthermore, albumin has a blood half-life of approximately 17 days, which is close to a week in a person's activity cycle. By checking their GA levels weekly, individuals are more motivated to maintain improvements in their lifestyle habits. Therefore, in recent years, GA has received considerable attention as a biomarker for blood glucose management.

[0009] Nevertheless, albumin has a half-life of 17 days, longer than a week. Therefore, weekly changes in a subject's behavior will not immediately translate into weekly GA values. To evaluate a change in behavior using GA values, it is necessary to observe GA values ​​over a period of several weeks.

[0010] Existing technical documents

[0011] Non-patent literature

[0012] Non-patent literature 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

[0013] Non-patent literature 2: Lally P, van Jaarsveld C, Potts H, Wardle J. How are habits formed: Modelling habit formation in the real world. Eur J SocPsychol. 2009;40(6):998-1009. doi:10.1002 / ejsp.674 Summary of the Invention

[0014] While GA (glycemic index) is an excellent marker for blood glucose management, users want to know immediately how their week's efforts are reflected in their weekly GA values. Meeting this requirement is important for maintaining personal motivation. The method disclosed herein was invented as a result of in-depth research conducted by the inventors to address this problem.

[0015] According to some embodiments of this disclosure, an average blood glucose level (GMI) is provided as a glucose management indicator for GA, calculated based on GA[i] and GA[j] obtained at two time points t[i] and t[j], during which the GA value is converted. GA [i, j]) is obtained. In some implementations, the GA glucose management index (GMI) is provided. GA [i, j]) to manage glucose, blood sugar, and lifestyle habits.

[0016] In some implementations, the target of the subject is obtained. In some implementations, the GA value of the subject is obtained. In some implementations, the GA value of the subject is obtained at two time points. In some implementations, the GA glucose management index (GMI) is calculated based on the two GA values ​​as the average blood glucose value converted from the GA value during that period. GA [i, j]). In some implementations, the calculated glucose management index is output to the target.

[0017] In some implementations, the glucose management index GMI can be more generally defined as the average blood glucose level calculated based on the glycation percentage of proteins. For example, GMI can be used as the GMI value for HbA1c (GMI... HbA1c ).

[0018] In one embodiment, this disclosure enables a computer system to perform the method. In some embodiments, the computer system may also include: a processor; a communication unit connected to the processor, capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the user); and a memory connected to the processor. In some embodiments, the computer obtains the GA values ​​(GA[i], GA[j]) of the user at two time points (t[i], t[j]). In some embodiments, based on the two GA values ​​(GA[i], GA[j]) obtained by the computer, an average blood glucose level (GMI[i, j]) is calculated as the GA value for that period. In some embodiments, the computer outputs the calculated GMI to the user's terminal.

[0019] Invention Effects

[0020] Modern life cycles are typically based on weeks. Therefore, users can more directly understand how their actions during a particular week affect physiological markers, such as GA (growth activity levels). Thus, for example, it is effective to improve users' awareness of their lifestyle habits by encouraging them to initiate and maintain positive behavioral changes.

[0021] Furthermore, the effects described above are not necessarily limiting; they may be used in conjunction with or in place of the effects described above to achieve any of the effects shown in this specification, or other effects that can be achieved according to this specification. Attached Figure Description

[0022] Figure 1 A flowchart of a method according to one embodiment is shown.

[0023] Figure 2 A flowchart of a method according to one embodiment is shown.

[0024] Figure 3A A flowchart of a method according to one embodiment is shown.

[0025] Figure 3B A flowchart of a method according to one embodiment is shown.

[0026] Figure 3C A flowchart of a method according to one embodiment is shown.

[0027] Figure 3D A flowchart of a method according to one embodiment is shown.

[0028] Figure 4A A network structure including a system and a target terminal is shown in one embodiment.

[0029] Figure 4B A block diagram representing the functional structural elements of a system according to one embodiment is shown.

[0030] Figure 5A A graph is shown representing the changes in GA value and WGMI according to one embodiment.

[0031] Figure 5B A graph is shown representing the changes in GA value and WGMI according to one embodiment.

[0032] Figure 5C A graph is shown representing the changes in GA value and WGMI according to one embodiment.

[0033] Figure 5D A graph is shown representing the changes in GA, WGMI, and HbA1c values ​​involved in one embodiment.

[0034] Figure 6 A graph showing the correlation between CGM and GMI in one embodiment is shown.

[0035] Figure 7A The graph shows the correlation between predicted and measured values ​​when using existing methods to predict HbA1c values.

[0036] Figure 7B A graph showing the correlation between predicted and measured values ​​when using a method according to an embodiment of the present disclosure to predict HbA1c values ​​is shown.

[0037] Figure 7C A graph showing the correlation between predicted and measured values ​​when using methods from other embodiments of this disclosure to predict HbA1c values ​​is shown.

[0038] Figure 8 This illustration shows an embodiment of GA values ​​and GMI values ​​obtained through clinical studies. GA The graph shows the time-dependent changes in HbA1c values ​​(a) and the graph shows the time-dependent changes in HbA1c values ​​predicted by the two methods (b).

[0039] Figure 9 An embodiment is shown, representing the R values ​​of each subject obtained from clinical study data. GI Scatter plot of the relationship between R and BMI (a), representing the R of each subject GI Scatter plot of the relationship between waist circumference and waist circumference (b), and R value for each subject. GI Scatter plot showing the relationship between HDL cholesterol levels (c). Detailed Implementation

[0040] In the following description, several specific details are set forth to provide a more complete understanding of the invention. However, those skilled in the art will understand that the invention can be practiced through one or more of these specific details. In other examples, features and processes well known to those skilled in the art have not been described in order to avoid obscuring the invention.

[0041] Some embodiments and examples are described with reference to the accompanying drawings. Arrows in the timing diagrams indicate the sequence of information transmission or processes (steps). However, the order is not specified between processes not indicated by arrows, as long as they do not contradict each other. This nature of the arrows also applies to other timing diagrams. Furthermore, when multiple embodiments and drawings have the same function, structure, or steps, one of them is sometimes omitted. This omission is understood by those skilled in the art. This does not necessarily mean that the function, structure, or steps in the multiple embodiments and drawings are the same.

[0042] Unless otherwise defined, all technical and scientific terms used in this specification have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0043] As used in this specification, the term "target" refers to the subject performing or required to perform blood glucose management. The term "user" refers to the subject receiving output from the system. In some embodiments, the user may also be the target performing their own blood glucose management. In some embodiments, the user may be a person with legal authority, such as a healthcare professional or a family member of the target, who independently or collaboratively manages the target's blood glucose. Users may include multiple subjects. "Target" or "user" includes those who currently use the system of this disclosure, will use, plan to use, or have used the system of this disclosure.

[0044] In some implementations, the object may be a human. However, the object of this disclosure is not limited to humans. Examples of objects include animals other than humans. For example, the object may also be a mammal. Examples of objects, without limitation, include racing livestock (racehorses, etc.), animals for specific purposes, domestic animals, pet animals, and wild animals.

[0045] The target population can also include people with diabetes. For example, people with diabetes can have type 2 diabetes. The target population can also include people without diabetes. People without diabetes are all individuals who have not been diagnosed with diabetes. The target population can include people with prediabetes and / or healthy individuals.

[0046] computer

[0047] Unless otherwise specified, the calculation of GMI based on the obtained GA value, the prediction of future (unmeasured) GA value and / or HbA1c value based on the GMI value, the output of information, optimization, and other calculation steps in this disclosure are performed by a computer or computer unit (hereinafter also referred to as "processing unit").

[0048] system

[0049] In some embodiments, the system may include a computing unit (e.g., a processor). The system may also be configured to include a fixed server or computing unit. The system may also be configured to include a user interface (e.g., a display unit, a fixed or portable electronic terminal used by the user). The system may have hardware and software for performing the methods of this disclosure. For example, the system may include an application program installed on a user's portable electronic terminal.

[0050] In some implementations, the system may include a computing unit (e.g., a processor) and a sensor. The sensor may be configured to measure a user's bodily fluid sample and calculate the glycated albumin (GA) value. Specifically, for example, the sensor may be configured to measure the total albumin and glycated albumin levels in the bodily fluid sample, or it may be configured to directly calculate the ratio of glycated albumin to total albumin in the bodily fluid sample without calculating these quantities.

[0051] In some implementations, the system may include a communication unit connected to a computing unit (e.g., a processor), a sensor, a communication unit that is connected to the processor and capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the user), and a memory connected to the processor.

[0052] In this disclosure, where the subject of the act is not explicitly stated, it is interpreted as the system.

[0053] The system can also be configured to grant access to the GA value of the target user, input information from the target user, and output information to the target user only to the target user or other persons with legitimate rights, and to block access to the information by other persons without legitimate rights. In the embodiments or examples of this disclosure, the input and output of information between a single target user and the system are mainly described, but the invention is not limited thereto. The methods, systems, etc., of this disclosure can also be configured to provide services to multiple or a large number of target users. Information processing and optimization, secondary processing, and output information generation related to each target user can be performed by referring to input information from a large number of target users, their information processing and optimization, secondary processing data processing, and output information to a large number of target users.

[0054] Measurement of GA value

[0055] The term "GA value" used in this specification is typically calculated by dividing the amount of glycated albumin in the subject's bodily fluids by the total amount of albumin in those fluids. Alternatively, the GA value may be calculated as the ratio of glycated albumin to total albumin in the bodily fluid sample, rather than by calculating these amounts.

[0056] 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 devices, and mass spectrometers (MS devices) can be used as devices or sensors for measuring GA values.

[0057] In some implementations, enzymatic methods can also be used as clinical examination methods. Glycated albumin can be broken down into amino acids using a protease, allowing ketoamine oxidase, which reacts only with the glycated amino acids, to act. The concentration of glycated albumin is then determined by measuring the H₂O₂ produced during this process. Albumin concentration can also be determined by reacting with bromocresol purple (BCP) reagent and observing the change in absorbance of the blue conjugate.

[0058] For example, the GA value can be calculated as [glycated albumin concentration / albumin concentration] × 100 (%). Alternatively, the analytical values ​​of glycated albumin standard samples and the correlation formula and conversion formula (from mmol / mol to %) associated with the standard samples can be prepared in advance. The correlation formula can also be obtained through linear regression of the two. The GA value can be obtained by measuring the total albumin concentration and glycated albumin concentration in the sample and based on the above correlation formula.

[0059] In some embodiments, a glycated protein sensor equipped with an immobilized protease, an immobilized ketoamine oxidase, and a hydrogen peroxide detection unit can be used to measure the concentration of glycated albumin.

[0060] For example, by using such a sensor, it is possible not only to measure venous blood, but also to measure GA values ​​in minute amounts of blood, such as fingertip blood, earlobe blood, heel blood, capillary blood, dried spot blood (DBS), and skin puncture blood, with minimal invasiveness. Alternatively, it is possible to non-invasively measure the concentration of glycated albumin in tears or saliva. Therefore, GA values ​​can be measured without exerting physical or psychological stress on the subject, or with such stress load minimized.

[0061] Obtaining GA value

[0062] In some embodiments, the subject may also obtain their own bodily fluids (e.g., blood, saliva, urine, etc.) and transport them to an examination center equipped with a device for measuring GA values ​​using a prescribed method. Transportation may also be by mail. The examination center receiving the subject's bodily fluids can calculate the GA value within the fluids. In some embodiments, the examination center may also send the calculated GA value to the system's processing unit. Thus, the system can obtain the GA value. In some embodiments, the examination center may store the obtained GA value in a designated storage device, and the system's processor can access this storage device to obtain the GA value.

[0063] In some implementations, the subject may also go to a medical institution (hereinafter sometimes referred to as "hospital" or "medical institution") such as a hospital, pharmacy, school, convenience store, gym, government agency, community center, or other public facility. For example, the subject may go to a medical institution for the submission or measurement of a specimen. The medical institution may also obtain the subject's bodily fluids and measure the GA value at its internal or affiliated hospital. GA value measuring devices may include, for example, point-of-care testing (POCT) devices (hereinafter sometimes referred to as "POCT devices") or in vitro diagnostic medical devices (IVD). The medical institution may also obtain the subject's bodily fluids and transport them to an external testing facility for GA value measurement. In some implementations, the medical institution may send the calculated GA value to the system's processing unit. Thus, the system can obtain the GA value. In some implementations, the medical institution may store the obtained GA value in a designated storage device, and the system's processor may access the storage device to obtain the GA value.

[0064] In some embodiments, the subject may also have a GA value measuring device at home or in their residence. GA value measuring devices may include, for example, point-of-care testing (POCT) devices, in vitro diagnostic medical devices (IVD), home medical devices, self-measurement devices, and self-monitoring devices. The subject may also obtain their own bodily fluids or have others obtain them, and use the measuring device to measure the GA value. In some embodiments, the self-monitoring device can send the calculated GA value to the system's processing unit. Thus, the system can obtain the GA value. In some embodiments, the self-monitoring device can store the obtained GA value in a designated storage device, and the system's processor can access the storage device to obtain the GA value.

[0065] The GA value does not necessarily need to be determined through measurement. In some implementations, the GA value can be set arbitrarily, or it can be calculated or estimated based on other parameters such as glucose management indicators.

[0066] When to obtain GA value

[0067] As used in this specification, “GA(t[i])” or “GA[i]” refers to the GA value obtained at time point t[i]. Unless otherwise defined, time point t[i] refers to the time point at which body fluids are collected for the purpose of measuring the GA value, or at least for one of the purposes of measuring the GA value.

[0068] The expressions “time of GA value,” “time of measurement of GA value,” and “time of obtaining / acquiring GA value,” or similar time-related expressions used in this specification, unless otherwise defined, are not based on the time of measurement of GA value, but rather on the time of acquisition of the body fluid or specimen containing glycated albumin and albumin, which forms the basis of that GA value.

[0069] The unit of time can be "day", or more precisely "hour" or "hour / minute". Time point t[i] can be, for example, the day of blood collection or the day of saliva collection. That is, GA[i] refers to the GA value of the body fluid collected at time point t[i] in multiple consecutive GA value measurements.

[0070] Unless otherwise defined, in this specification, GA measurements or the collection of bodily fluids used for GA measurements are performed discontinuously or discretely. Therefore, t[i], GA[i], etc., denoted by square brackets, refer to discrete time-series data with i as an integer. On the other hand, GA(t[i]), etc., denoted by parentheses, refer to functions of the independent variable at time t, etc.

[0071] Interval for obtaining GA values

[0072] For example, the frequency of GA value measurement can be substantially 1 time / month, 2 times / month, or higher. For example, the frequency can be below or even lower than values ​​such as 20 times / month, 10 times / month, 8 times / month, 1 time / month, etc. For example, the frequency can be substantially 2 times / month, 3 times / month, 4 times / month, or 0.5 times / week, 1 time / week.

[0073] In some implementations, the frequency can be practically once every 17 days, while in others it can be around once every two weeks.

[0074] GA values ​​can be measured at intervals of 7 days, 10 days, 14 days or more. GA values ​​can also be measured at intervals of 21 days, 24 days, 28 days or less.

[0075] The term "weekly GA value" used in this specification refers to the GA value of bodily fluids obtained at approximately one-week intervals. The unit of measurement interval can also be one week (Monday to Sunday, Sunday to Saturday, Saturday to Friday, etc.). Bodily fluids can be collected once at a specific location within that week. Therefore, the interval for obtaining GA values ​​is not necessarily constant. In some embodiments, GA values ​​can be obtained once per predetermined period (e.g., once a week). The interval, period, or frequency can be adjusted or varied according to the individual's circumstances. For example, bodily fluids cannot be collected on the same day of the week; that is, the day of the week for collecting bodily fluids can vary between multiple weeks. For example, the period may not be one week. There are also individuals whose lifestyle is not measured in weeks. For example, periods of 5 days, 6 days, 8 days, 9 days, etc., can be used. Lifestyles can also vary in units of several weeks, several months, etc. The setting of the interval, period, or frequency can also be changed each time.

[0076] In some implementations, the actual blood collection interval may be more than one day and less than 14 days. In some implementations, it may also be planned to collect blood from the subjects at intervals of more than one day and less than 14 days, i.e., once every week as specified. The actual blood collection interval does not necessarily have to be once a week. The blood collection interval may be incomplete or unequal.

[0077] Glucose Management Index (GMI)

[0078] This disclosure presents a new glucose management index (GMI) and a glucose management method using GMI. The glucose management index (GMI) used in this specification is the average blood glucose value converted from GA values, expressed by the following formula:

[0079] [Mathematical Expression 1]

[0080]

[0081] in,

[0082] GA[i] and GA[j] are the GA values ​​(% or mmol / mol) at time points t[i] and t[j] > t[i], respectively.

[0083] C GA [i,j]=1-exp[-(t[j]-t[i]) / τ Alb ],

[0084] τ Alb It is the time constant for albumin reduction (-half-life / LN(0.5) ~ 24.5 days).

[0085] 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.

[0086] [Mathematical Expression 2]

[0087]

[0088] Therefore, the unit of glucose management index (GMI) is the same as that of GA value, expressed as % or mmol / mol.

[0089] In this specification, the interval for obtaining the GA value is not limited to Δt[i] = t[i] - t[i-1]. In some implementations, Δt[i] = t[i] - t[i-1] can be one week. For example, Δt[i] can be more than one day but less than 14 days.

[0090] In this specification, the glucose management index (GMI) based on two consecutive weekly GA values ​​will be referred to as the "weekly glucose management index (WGMI)".

[0091] HbA1c prediction

[0092] In some implementations, GMI can be used to predict current and future HbA1c levels.

[0093] In some implementations, the following mathematical formula can also be used to predict current and future HbA1c based on GMI.

[0094] [Mathematical Expression 3]

[0095]

[0096] in,

[0097] ha[i] is the corrected HbA1c value at time t[i] (>t[0]).

[0098] HA[i] is the HbA1c value (NGSP value) at time t[i].

[0099] C HbA1c = 1 - exp[- (t[i] - t[0]) / τ Hb ],

[0100] a and b are coefficients determined experimentally; generally, a = 0.25 and b = 2.0.

[0101] τ Hb It is the time constant of hemoglobin reduction (-half-life / LN(0.5)).

[0102] t[0] is the time point at which the previous measured or baseline value of HbA1c was measured.

[0103] ha[0] is the measured or referenced corrected HbA1c value at time t[0].

[0104] HbA1c values ​​can be converted from NGSP values ​​to IFCC values ​​using generally known conversion formulas.

[0105] generalization

[0106] This disclosure primarily uses GMI based on GA values, but is not limited thereto. GMI is generally an organic substance with a scientifically conceivable half-life in a system, which can be applied to the substance being modified. Examples of the target system include, but are not limited to, naturally occurring systems such as organisms and cells, and artificially or industrially formed systems such as invitros and reactors. Examples of the target organic substance include, but are not limited to, proteins, peptide fragments, lipids, and nucleic acids. A typical example of modification in this disclosure is glycosylation, but this disclosure is not limited thereto. Examples of sugars that facilitate glycosylation include, but are not limited to, glucose, fructose, and their derivatives. Hereinafter, glucose-based glycosylation will be used as an example.

[0107] The general glucose management index (GMI) for glycosylated proteins (GPs) is typically expressed by the following formula:

[0108] [Mathematical Expression 4]

[0109]

[0110] in,

[0111] GP[i] and GP[j] are the glycation rates (% or mmol / mol) of the target proteins at time points t[i] and t[j] > t[i], respectively.

[0112] C GP [i,j]=1-exp[-(t[j]-t[i]) / τ P ],

[0113] τ P It is the decrease time constant of the target protein (-half-life / LN(0.5)).

[0114] The glycation rate, GP[i-1], and GP[i] of the target protein are measured at two consecutive actual or hypothetical time points t[i-1] and t[i]. The glucose management index (GMI[i-1,i]) from t[i-1] to t[i] is expressed by the following formula.

[0115] [Mathematical Expression 5]

[0116]

[0117] For example, GMI can also be defined based on HbA1c values, as shown in the following formula.

[0118] [Mathematical Expression 6]

[0119]

[0120] in,

[0121] HbA1c[i] and HbA1c[j] are the HbA1c values ​​(% or mmol / mol) at time points t[i] and t[j] > t[i], respectively.

[0122] C HbA1c [i,j]=1-exp[-(t[j]-t[i]) / τ Hb ],

[0123] τ Hb The time constant for the decrease in hemoglobin is (-half-life / LN (0.5) ~ 49.9 days).

[0124] In some implementations, GMI (Glycosyl Glycation Index) based on the degree of glycosylation (GP1) of the first protein (P1) can be used. GP1 Calculate the degree of glycosylation (GP2) of the second protein (P2). The relationship is expressed by the following generalized expression from equation 3:

[0125] [Mathematical Expression 7]

[0126]

[0127] in,

[0128] GMI GP1 [i-1, i] is the GMI based on GP1, equivalent to the period from time point t[i-1] to time point t[i].

[0129] gp2[i] is the corrected GP2 value at time t[i] (>t[0]).

[0130] GP2[i] is the GP2 value (NGSP value) at time t[i].

[0131] C GP2 = 1 - exp[- (t[i] - t[0]) / τ GP2 ],

[0132] τ GP2 It is the decrease time constant of the second protein (P2) (-half-life / LN(0.5)).

[0133] a and b are coefficients determined experimentally.

[0134] t[0] is the previous measured value or baseline value of GP2.

[0135] gp2[0] is the measured or referenced correction GP2 value at time t[0].

[0136] Output of glucose management indicators

[0137] In some implementations, if a new GA value is obtained, the previous GA value can be used as a reference to quickly output the glucose management index (GMI) or weekly glucose management index (WGMI) to the subject. The subject can obtain the latest GMI through a user interface such as an electronic terminal.

[0138] In some implementations, glucose management indicators (GMIs) can also be provided to the subject at times other than the previously scheduled acquisition points. Past, recent, and future projected GMIs can also be provided in a manner accessible to the subject at any time. This allows the subject to check their GMI at any time, enhancing their awareness of their own actions.

[0139] Action-related information

[0140] The term "behavior" as used in this manual generally refers to lifestyle habits, activities, or behaviors associated with so-called lifestyle-related diseases, including diabetes. "Behavior" may affect lifestyle-related indicators such as blood glucose levels, HbA1c levels, GA levels, or their corresponding or associated values ​​or data (e.g., CGM data).

[0141] In some implementations, "actions" include exercise, diet, and / or medication. Exercise, diet, and medication all affect biomarkers such as blood glucose levels, HbA1c levels, and GA levels.

[0142] In some implementations, information related to the "action" (also referred to as "action-related information") can be input from a user interface such as an electronic terminal. Examples of the form of action-related information, without limitation, include text information, voice information, handwritten information, image information, location information, communication information, biometrics, etc.

[0143] In some implementations, action-related information can be declared or entered by the subject (self-declaration information). In some implementations, action-related information can be the subject's biometric information.

[0144] In some implementations, for multiple-choice questions about exercise, diet, and medication, the user can enter their selected answers. In some implementations, for open-ended questions about essays, the user can enter an essay.

[0145] In some implementations, action-related information (such as self-reported information) can also be input in natural language. For example, action-related information can also be input in the form of a diary. The subject can record the actions performed that day in their own words, either concisely or in detail. Through the diary, the subject can also ask questions.

[0146] In some implementations, action-related information (such as self-reported information) can also be input by the subject themselves. Through this input, the subject can recognize their own actions and voluntarily change them rather than compulsively, or increase their probability. This proactive change in action can contribute to highly consistent and effective blood sugar control.

[0147] In some implementations, action-related information or self-reported information can be entered by non-recipients. For example, caregivers (e.g., non-limited, spouses, family members, friends, nurses, doctors, lawyers, etc.) can also enter the information. Caregivers can also understand the recipient's intentions. Caregivers can also have legitimate rights.

[0148] In some implementations, action-related information or biological information can be obtained through non-invasive devices such as wearable devices and scales, invasive devices such as CGM, and various measurements in health diagnoses such as blood tests.

[0149] Target

[0150] The term "target" as used in this manual generally refers to the value or state that the subject should achieve in biological information and / or actions (including target actions) associated with lifestyle-related diseases.

[0151] Examples of markers used for the goal include, but are not limited to, GA values, HbA1c values, weight, BMI, etc. The target individual may also set values ​​for the markers that should be achieved or maintained at a specified future time or period. In some embodiments, the marker used for the goal is the GA value. Examples of actions used for the goal include, but are not limited to, diet, exercise, medication, etc. The target individual may also set specific dietary, exercise, and / or medication practices to be implemented at a specified future time or period. In some embodiments, the actions used for the goal are diet, exercise, and medication.

[0152] Examples of goals related to "exercise" can be non-limited to include the type of exercise: walking, swimming, strolling, climbing stairs, squatting, etc.; the time period: after meals, in the morning, etc.; and the amount: frequency, intensity, number of steps, time, etc.

[0153] Examples of goals related to "diet" include, without limitation, the following: consuming / not consuming: avoiding sugary drinks such as fruit juice; stopping snacking; avoiding sweets during snacks; consuming fiber such as vegetables; reducing sugar intake such as white rice and bread; reducing alcohol consumption; the amount of food consumed; the frequency of food consumption; the amount and balance of lipids, carbohydrates, and salt (GI: glycemic index) or (FC balance, FCS balance); and the order of food intake (e.g., eating vegetables first). Examples of goals related to "medication" include, without limitation, taking prescribed medications as directed to avoid forgetting to take them.

[0154] In some implementations, the "goal" can be proposed by the system and referenced by the target. The target can also refer to or ignore the goal proposed by the system, set its own next goal, or input it into the system.

[0155] It is believed that setting goals proactively by the target rather than setting existing goals or choosing from multiple options is effective for changing actions.

[0156] Goals can be changed periodically or irregularly. New goals can also be set proactively by the stakeholders. For example, stakeholders can be given the opportunity to change their goals weekly or each time the latest GA value is output from the system. Stakeholders can change their goals on these occasions, or they can maintain the same goals without making changes.

[0157] The set "goals," or the "goals" to be presented to the subject, can also be provided to the subject through a user interface (e.g., a display unit, an electronic terminal used by the subject, etc.). The "goals" can be displayed simultaneously with GA values ​​or GMI, or displayed on the same screen. The latest or updated goals can also be provided to the subject. The subject can recognize their latest GA values ​​or GMI and their current "goals." The recognition of changes in blood glucose biomarkers such as GA values ​​or GMI, and the awareness of actions that should be taken ("awareness building"), can potentially be linked in the subject's conscious or subconscious mind. This may have a positive impact on behavioral changes or improvements in lifestyle habits.

[0158] Risk assessment

[0159] The term “risk” as used in this instruction manual generally refers to the possibility of medical problems arising from actions related to blood glucose management.

[0160] Examples of "risks" in medicine, without limitation, include the likelihood of developing or developing diseases such as hypoglycemia, complications of diabetes, infections, trauma, neoplastic lesions, and mental illnesses such as depression.

[0161] In some implementations, the risk of a subject or their actions can be determined based on glucose management index (GMI), GA value, trends in GA value, and any combination thereof.

[0162] Implementation Method 1

[0163] Reference Figure 1 The process S100 describes the glucose management method involved in a certain implementation.

[0164] First, obtain the GA value of the target (S101). For example, obtain the weekly GA value of the target over multiple weeks. In order to calculate the next GMI, the GA value at at least two time points needs to be calculated. For example, obtain the GA values ​​at time points t[i-1] and t[i], namely GA[i-1] and GA[i].

[0165] Next, the GMI is calculated based on the obtained GA value (S102). For example, based on GA[i-1] and GA[i], the GMI (GMI[i-1, i]) for the period between time points t[i-1] and t[i] is calculated.

[0166] The calculated GMI[i-1, i] is output to the object (S131). By recognizing GMI[i-1, i], the object can evaluate its own actions taken during the period between time points t[i-1] and t[i].

[0167] Implementation Method 2

[0168] Reference Figure 2 The process S200 describes the glucose management method involved in a certain implementation.

[0169] First, the GA value of the target is obtained (S201). For example, the weekly GA value of the target is obtained over multiple weeks. In order to calculate the next GMI, the GA value at at least two time points needs to be calculated. For example, the GA values ​​at time points t[i-1] and t[i] are GA[i-1] and GA[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.

[0170] Next, the GMI is calculated based on the obtained GA value (S202). For example, based on GA[i-1] and GA[i], the GMI[i-1, i] for the period between time points t[i-1] and t[i] is calculated.

[0171] The calculated GMI[i-1, i] is output to the object (S231). By recognizing GMI[i-1, i], the object can evaluate its own actions taken during the period between time points t[i-1] and t[i].

[0172] In this embodiment, there is also a process of interpreting the GMI value and / or tendency to provide helpful comments on the object.

[0173] If GMI[i-1, i] is below a specified threshold (S211), for example, it may be hypoglycemia, so it is judged as "risky" (S221), and this judgment is output to the subject (S231). There is a risk of hypoglycemia when blood glucose levels drop sharply or remain low. In the case of this judgment, in addition to GA value and GMI, suggestions such as changes to the target action (mitigation) or a doctor's diagnosis can also be made to the subject.

[0174] The "risk" judgment (S221) can also be based solely on GMI. For example, if the GMI is below 11%, it can be judged as "risky". On the other hand, in addition to GMI, this judgment can also consider the GA value and / or predisposition, age, medication, cognitive function, etc. For example, in cases where there is a risk of hypoglycemia, such as when the subject is taking medication or is an elderly person, the threshold for the "risk" judgment can be set higher.

[0175] If GMI[i-1, i] is not lower than the specified threshold (S211), for example, there is no possibility of hypoglycemia, and therefore it is judged as "no risk" or not "risk".

[0176] Next, it is determined whether the GMI consistently indicates a tendency to be lower than the GA value. In this embodiment, it is determined at least twice consecutively whether the GMI is lower than the GA value. That is, the conditions are as follows:

[0177] GMI[i-2,i-1]<GA[i-1] and

[0178] GMI[i-1, i] <GA[i]。

[0179] For example, if the GMI remains constant at a level below the GA value, the GA value gradually approaches the GMI value. Therefore, multiple instances of GMI being below the GA value suggest that the actions taken by the subject to lower the high GA value are effective.

[0180] exist Figure 2In the case where GMI is lower than GA value twice in a row (S212), it is determined that the actions from t[i-1] to t[i] are valid (S222), and the judgment is output to the target (S231).

[0181] Even if the condition that the GMI is lower than the GA value twice consecutively is not met (S212), other benchmarks can be used to further determine whether the actions taken in the most recent period were effective.

[0182] For example, if the previous GMI was lower than the GA value, and the next GMI is higher than the GA value, it can be judged as a tendency for blood glucose management to worsen. Conversely, if the previous GMI was higher than the GA value, and the next GMI is lower than the GA value, it can be judged as a tendency for blood glucose management to improve.

[0183] However, in any situation, such a judgment can be avoided, or it can be retained to make the judgment itself. This is because such a judgment may lead to an incorrect evaluation of the action.

[0184] In some implementations, the average of GA values ​​over two or more periods can also be compared with the average of GMI. Figure 2 In this process, when the average GMI is significantly less than the average GA value (average GMI < average GA value - threshold TH), i.e., the difference is greater than a certain threshold (average GA value - average GMI > threshold TH), it can be determined that "the action is effective"; otherwise, it can be determined that "the action is ineffective" (S213). In some implementations, the GA value may also be used as the criterion for judgment during the period preceding these two periods, based on whether the GA value tends to decrease.

[0185] If the GA value, after showing a downward trend, remains approximately constant (S213, yes), then the recent "action was effective" (S222). A sustained decrease in the GA value is considered an indication that the subject's actions were effective during that period. If the GA value subsequently remains approximately constant, the effect of the action is considered to continue. If a subtle shift occurs while the GA value remains approximately constant, the GMI will move sensitively to this change. Therefore, by judging not only based on the GMI but also on the trend of changes in the GA value, the effectiveness of the subject's actions can be evaluated more appropriately.

[0186] If the GA value did not show a decreasing trend in the preceding period (S213, No), then the recent action is judged to be ineffective (S223). For example, if the GA value was constant in the preceding period, or if the GA value showed an increasing trend or at least did not show a decreasing trend, it is difficult to say that the GA value was controlled. Therefore, it cannot be said that the actions of the subject during that period were effective.

[0187] exist Figure 2In the illustrated embodiment, the determinations of S211, S212, and S213 are performed sequentially in the figure, but are not limited to this. They can also be performed simultaneously, in reverse order, or in any other order. Similarly, in other figures, embodiments, and examples, the order of steps should not be interpreted restrictively and can be arbitrarily changed as long as it is reasonable or possible.

[0188] Implementation Method 3

[0189] Reference Figures 3A-3D The process S300 describes the glucose management method involved in a certain implementation.

[0190] The basic model in this embodiment is process S300, which is used to prompt the subject to improve (change) their actions. Figure 3A First, the GA value of the subject is obtained (S301). If the GA value is extremely low or extremely high (within the range where an alarm should be issued), it is determined that the subject's condition, such as illness or movement, poses a certain risk (S302), and the output information "at risk" is output to the subject (S308) (S309). As a non-limiting example, it is also possible to determine "at risk" if the GA value is lower than 11% or 10%, or if the GA value is higher than 24%.

[0191] If the GA value indicates no risk (S302), confirm whether the previous GA value required to calculate GMI has been obtained (S303). If the previous GA value has not been obtained, i.e., if multiple GA values ​​have not been obtained (S303), then the next GA value must be obtained (S301).

[0192] If multiple GA values ​​are obtained, refer to them to determine whether the GA values ​​are within a healthy range (S304). For example, you can also refer to the GA values ​​for 4 weeks or 1 month to determine whether they are all within a healthy range. If the GA values ​​are within a healthy range for a certain period, no further improvement actions are needed, and the current good action pattern should be continued. In this case, the recipient can also be supported by a long-term maintenance model (S310) that continues the current action pattern.

[0193] If at least the previous GA value and the latest GA value are obtained, then the GMI is calculated based on them (S305).

[0194] When the GMI is extremely low or extremely high (within the range where an alarm should be triggered) (S305), it is determined that the condition of the person, such as the patient or their actions, presents a certain risk (S306), and an output message such as "at risk" is generated for the person (S308) and output (S309). As a non-limiting example, when the GMI is less than 11% or 10%, or when the GA value is greater than 24%, it can be determined that there is a "risk".

[0195] If the GMI determines that there is "no risk" (S306), the actions of the person being evaluated are based on the GMI (S320, S330).

[0196] For example, in cases where this method is just beginning or is being implemented temporarily, essentially a restart, there may be situations where only two weeks' worth of GA values ​​are obtained, i.e., only one GMI is obtained. In such cases, the so-called "initial GMI judgment" process (S320) is performed. If more than three weeks' worth of GA values ​​are obtained, i.e., more than two GMIs are calculated, the so-called "continued GMI judgment" process (S330) is performed. The following explains each process.

[0197] Figure 3C Flowchart S320 illustrates the "initial GMI determination" process according to a certain implementation. If the GA value has sufficiently decreased since the last time, 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 within the error range, it can be determined that the GA value has sufficiently decreased. 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 within the error range, it can be determined that the initial GMI is sufficiently small.

[0198] exist Figure 3C In the illustrated implementation, the evaluation of the action's effectiveness is categorized based on the GMI value. Specifically, if the GMI is within a healthy range (S322), it is determined that the GA value has sufficiently decreased (S323). On the other hand, if the GMI has not yet reached a healthy range (S322), it is determined that the GA value is showing a tendency to stop decreasing (S324).

[0199] If the GA value shows a tendency to stop declining (S324), a proposal to set a more effective action as the goal, a proposal to improve the goal action, or a proposal for a specific goal action may also be provided to the subject. It is also possible to improve one or more goal actions related to diet, exercise, and medication. In some implementations, if the subject is unable to further improve their diet and / or exercise, they may be notified that medication should be changed.

[0200] On the other hand, in cases where the GA value has not decreased sufficiently since the last time (the difference in GA values ​​since the last time, "ΔGA value") is greater than a specified value, or the initial GMI is sufficiently small (the initial GMI is less than a specified threshold) (S321), it cannot be said that there is an effect. If the GA value has hardly changed since the last time (S325), it is judged that the action for that week was ineffective (S327). Even if the change in the GA value since the last time cannot be said to be sufficient (S321) but is somewhat present (S325), it cannot be clearly judged whether the action is effective (S323, S324) or ineffective (S327). Therefore, in this case, the judgment is reserved (S326).

[0201] If the output information is determined (S323, S324, S326, S327), it is output to the target (S319). Then, the GA value for the following week is obtained (S301). If multiple GMIs are obtained (S307), the "Continue GMI Judgment" process is implemented (S330).

[0202] Figure 3D Flowchart S330 illustrates the "continued GMI determination" process according to a certain implementation. If the GMI is lower than the GA value twice consecutively (S331), it is determined that the actions taken that week were effective (S333, S334). Figure 3D In the illustrated embodiment, further, if there are two or more GMIs, and the average of these multiple GMIs is within a healthy range (S332), it is determined that the GA value has sufficiently decreased (S333). If the average of the GMIs has not yet reached a healthy range (S332), it is determined that the GA value is showing a tendency to stop decreasing (S334).

[0203] On the other hand, when the relative magnitudes of GMI and GA values ​​reverse over a two-week period (S331), the trend of the GA value becomes unclear. It is impossible to determine whether the GA value is trending downwards simply because GMI and GA values ​​are close. In this situation, the GA value may be maintained through effective action, or it may remain unchanged rather than through effective action.

[0204] exist Figure 3DIn the implementation, if the relationship between GMI and GA values ​​is reversed between two weeks (S331), further judgment is made based on the average of GMI and the average of GA values ​​(S335). If the average GMI is significantly lower than the average GA value (average GMI < average GA value - threshold TH), the action is judged to be effective, but the GA value is showing a tendency to stop declining (S334). Otherwise, it is judged to be "ineffective" (S336). As a non-limiting example, if the difference between the average GMI and the average GA value (average GA value - average GMI) is greater than a predetermined value (e.g., average GA value - average GMI > 1.5%), it can be judged to be "effective".

[0205] If the output information is determined (S333, S334, S336), it is output to the target (S319). Then, the GA value for the following week is obtained (S301). If the low GMI continues, the GA value also decreases. If this tendency persists (S304), a long-term maintenance mode is implemented (S310).

[0206] Figure 3B Flowchart S310 illustrates the "long-term maintenance mode" involved in a certain implementation. 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 implemented (S300). If the GA value is within the healthy range (S312), the long-term maintenance mode continues. Next, the GMI is calculated (S313).

[0207] 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 tending to decrease (S315). On the other hand, if the GMI is greater than the GA for two consecutive times (S316), the GA value is judged to be within the healthy range and tending to increase (S318). If neither of these two cases applies (S314, S316), the GA value is judged to be within the healthy range and stable (S317).

[0208] The "healthy range" of GA value or GMI can be set based on the individual's intentions, attributes, physiological conditions, medical conditions, external factors, theories, general principles, etc. For example, the "healthy range" of GA value or GMI can be non-limitingly defined as above 11% and less than 16%.

[0209] A healthy GA value is proof that an individual is capable of effective action. However, if support ceases at this point, a deterioration in the GA value is observed in many individuals. Therefore, long-term maintenance models are an effective tool for enabling individuals to maintain sustained effective action or sustain their motivation.

[0210] The Relationship Between Action and GMI

[0211] In some implementations, the content of the action performed by the target and / or the correlation or relationship between the target's target action and the GMI can be determined (in the following, any expression using any term includes both expressions). In some implementations, the content of the action and / or the correlation between the target action and the GMI can be determined for a number of targets. In some implementations, the action that the target should take or the action proposed to the target can be generated (target action) based on the determined correlation between the action or target action and the GMI and the determined target's GMI. The target action for the target can also be generated by referring to the GA value and the trend of the GA value in addition to the target's GMI. The algorithm for generating the target action for the target can also be optimized using the GMI or GA value as evaluation values. For example, the algorithm for generating the target action for the target can also be optimized in a way that makes the GMI and / or GA value within a risk-free range.

[0212] In some implementations, the correlation between data not derived from GA values ​​(also known as "non-GA source data") and GMI can also be determined. Non-GA source data can be categorized, but is not limited to, into data groups such as personal data, health-related data, lifestyle data, and group data. Examples of non-GA source data include, but are not limited to:

[0213] Personal data includes: Personal ID (personal identification number), age, gender, marital status, number of children, address, housing type, ownership of a car, motorcycle, or bicycle, occupation, occupation type, job title, annual income, genomic information, beliefs, health insurance number, fingerprints, voiceprints, facial recognition information, iris recognition information, personal thoughts / emotions / desires / requirements that change over time, and information on smart device input and usage, etc.

[0214] • Group data: race, nationality, place, region, seasonal variations in region (temperature and humidity changes during the seasons), vacation period (including year-end and New Year holidays, spring break, summer vacation, etc.), commuting method, religion, etc.

[0215] • Health-related data: weight, height, body fat, maximum blood pressure, minimum blood pressure, heart rate, steps, activity level, blood oxygen saturation, body temperature, room temperature, sunshine duration, UV radiation, average blood glucose level and standard deviation obtained from continuous glucose monitoring (CGM) or flash glucose monitoring (FGM) devices, AGP (Ambulatory Glucose Profile), health diagnostic data (which may also include HbA1c values), medical history, prescription medications, data from medical institutions, food and other allergies, family and relatives' medical history, etc.

[0216] • Lifestyle data: Dietary habits (including meat preference, vegetarianism, etc.), hobbies, meal menus, food photos, snack photos, 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, religious dietary rules (including Ramadan, etc.), presence or absence of exercise, type of exercise, exercise time, exercise frequency, calories burned during exercise, interests, etc.

[0217] In some implementations, the correlation between any one or more of the following—minimum heart rate, heart rate variability, body temperature (e.g., skin temperature)—and GMI can be determined.

[0218] Related to CGM

[0219] In some implementations, any parameter of continuous glucose monitoring (CGM) can be inferred based on GMI. The correlation between the average blood glucose value obtained from CGM and GMI can also be calculated. Statistical values ​​such as average blood glucose values ​​and intraday fluctuations such as blood glucose spikes can be obtained from CGM. The correlation between GMI and these parameters can also be calculated. Based on their correlation, parameters for the individual can be calculated from GMI. This reduces the frequency of CGM use, which requires prolonged invasive procedures, enabling more comfortable and accurate blood glucose management.

[0220] System structure and examples of its operation

[0221] Figure 4A The diagram schematically illustrates a network environment that connects the computing unit 100 to the object 200 according to one embodiment of the present disclosure.

[0222] The arithmetic unit 100 includes a central processing unit (CPU, also referred to herein as a "processor" and "computer processor") 101, a storage unit (storage device) 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.

[0223] CPU 101 can be a single-core or multi-core processor, or multiple processors for parallel processing. CPU 101 may include a computing architecture that processes data signals and executes combinations of various architectures or instruction sets. The CPU has an internal cache (not shown), which can communicate with the CPU 101's registry (not shown) at high speed. The cache stores data used by the CPU 101, which can process this data at high speed. Access to the cache will not be described further.

[0224] Storage unit or storage device 102 is a non-transitory storage medium that stores data used to provide the functions described in this specification. Storage unit 102 may be a data storage unit (or data repository) for storing data. Storage unit 102 holds programs and data that can be used by arithmetic unit 100. Examples of storage unit 102 include, but are not limited to, dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, and other storage devices. Other examples of storage unit 102 include, but are not limited to, hard disks, floppy disks, CD-ROMs, DVD-ROMs, DVD-RAMs, DVD-RWs, flash memory, magnetic tapes, and any other high-capacity storage devices, non-volatile memories, or similar permanent storage devices and media used to store information in a permanent standard.

[0225] Main memory 103 can store commands or data that CPU 101 can execute. For example, but not limited to, dynamic random access memory (DRAM), static random access memory (SRAM), read-only memory (ROM), and flash memory. CPU 101 can transfer programs and data stored in storage unit 102 to main memory 103 for execution and use.

[0226] Peripheral device 104 may be, for example, other memory, data storage device, and / or electronic display adapter, etc., without limitation. Communication interface 105 may also be, for example, a network adapter, etc., without limitation. Communication interface 105 can communicate with other devices via network 400.

[0227] There are multiple ways for object 200 to obtain the GA value and provide it to the arithmetic unit 100.

[0228] In some embodiments, subject 200 may undergo a transport test. Subject 200 may also collect its own bodily fluids (e.g., blood, saliva, etc.) using a prescribed transport kit 211 and send them to the testing center 220. The testing center 220 can use the received bodily fluids from subject 200 to measure subject 200's GA value. The testing center 220 can communicate with the processing unit 100 via communication interface 221 and via network 400. The testing center 220 provides the obtained GA value of subject 200 to the processing unit 100.

[0229] In some implementations, the subject 200 may go to the medical facility 230 on their own. The subject 200 may also collect their own bodily fluids at the medical facility 230 to measure their GA value. The medical facility 230 can use the received bodily fluids from the subject 200 to measure their GA value. The medical facility 230 can communicate with the computing unit 100 via communication interface 231 and network 400. The medical facility 230 provides the obtained GA value of the subject 200 to the computing unit 100.

[0230] In some embodiments, the subject 200 may also use a self-monitoring (or home-use) GA measuring device 240 to measure the GA value. The subject 200 may also introduce their own bodily fluids through the measuring device 240 to measure the GA value. The measuring device 240 may communicate with the processing unit 100 via a built-in or external communication interface (not shown) and a network 400. The measuring device 240 provides the obtained GA value of the subject 200 to the processing unit 100.

[0231] The GA value of the subject 200, obtained through methods shown or not shown, along with accompanying information (the subject 200's ID, the date and time of bodily fluid collection, and information related to the measurement, etc.), is sent to the processing unit 100 via the network 400 and received by the communication interface 105. The information received by the communication interface 105 is temporarily stored in the main memory 103 and may be moved to the storage device 102 for storage at any time.

[0232] The object 200 has an electronic terminal 300 capable of communication (e.g., a smartphone, tablet, personal computer (PC), etc.). The electronic terminal 300 is configured to connect to or communicate with the network 400.

[0233] User 200 can initiate, modify, terminate, or cancel a usage contract with computing unit 100 via electronic terminal 300. Computing unit 100 can establish or execute communication with user 200 or their electronic terminal 300, solely with the recognized or contractually valid user 200, in accordance with appropriate legal, ethical, and medical methods, including those related to personal information, copyright law, civil law, etc. User 200 can communicate with computing unit 100 via electronic terminal 300 as a legitimate user of computing unit 100.

[0234] Subject 200 collects bodily fluids weekly or is instructed to collect bodily fluids. The GA value is measured and sent to the processing unit 100. The processing unit 100 sends the received GA value to the subject 200's electronic terminal 300. Thus, subject 200 can know their own GA value via the electronic terminal 300. When using the self-monitoring GA measuring device 240, subject 200 can know their own GA value via their display or the electronic terminal 300 connected to the self-monitoring GA measuring device 240.

[0235] Subject 200 collects their own bodily fluids weekly and measures their GA value, thereby storing the GA value information in memory 102.

[0236] Examples of system functional structures

[0237] Figure 4B The present disclosure illustrates the functional structural elements (functional structural elements) of a computing unit 100 according to one embodiment. These functional structural elements may not necessarily correspond to physical devices, components, or units of an electronic device. Each functional structural element may also be constituted by one or more physical devices, components, or units of an electronic device, or a part thereof, or a combination thereof. The term "~part" as used in this specification may also be replaced by "~mechanism" or "~unit".

[0238] The arithmetic unit 100 includes a communication unit 110, a control unit 120, and a storage unit 130.

[0239] The communication unit 110 may be configured, for example, non-limitingly, as a processor with an executable program, a communication I / F, etc. The communication unit 110 includes: a receiving unit 111, which has the function of receiving data from the network 400; and a transmitting unit 112, which has the function of transmitting data to the network 400. The communication unit 110 is connected to the network 400 and has the function of communicating with various devices that measure or have GA values ​​(also called GA measurement devices) 221, 231, 240, the user electronic terminal 300, and the consultant terminal 510.

[0240] The receiving unit 111 is capable of receiving GA value-related information sent from GA measurement devices 221, 231, and 240, as well as target, evaluation, and other input information sent from the user terminal 300. The transmitting unit 112 is capable of sending the output information generated by the control unit 120 to a designated terminal via the network 400.

[0241] Storage unit 130 may, for example, be configured as a storage area with a storage medium, for example, without limitation. Storage unit 130 can store and accumulate data related to GA values ​​(GA value data) 131 transmitted from GA measurement devices 221, 231, and 240, as well as data 132 of the calculated GMI. Storage unit 130 can store and accumulate data related to the target of the subject 200 (target data) 132, information related to the subject's actions (action information data) 133, and data related to the subject 200's evaluation of the output (evaluation data) 134 transmitted from the user electronic terminal 300. Storage unit 130 stores various algorithms 134 used in control unit 120.

[0242] The control unit 120 may, for example, be configured as a processor having an execution program. In some embodiments, the control unit 120 may also include a GMI calculation unit 121. The control unit 120 may also include any one, at least one, multiple, or all of the following: an action evaluation unit 122, a risk assessment unit 123, a health range assessment unit 124, a GA value prediction unit 125, an HbA1c value prediction unit 126, a mode selection unit 127, and a proposal generation unit.

[0243] The GMI calculation unit 121 calculates the WGMI for the most recent period based on the previous GA value and the latest GA value. Alternatively, the GMI calculation unit 121 calculates the GMI or WGMI (hereinafter referred to as "WGMI") based on any two GA values ​​from the accumulated GA value data. The calculated GMI or WGMI data 132 is stored in the storage unit 130. The transmission unit 112 of the communication unit 110 transmits the calculated WGMI. The WGMI is transmitted to the electronic terminal 300 of the subject 200 via the network 400. Thus, the subject 200 can obtain its own WGMI via the electronic terminal 300.

[0244] The Action Evaluation Department 122, referring to the GMI data 132, evaluates whether the actions of the subjects 200 during the period corresponding to the GMI were effective. For example, if the GMI is less than the GA value for two or more consecutive periods, the actions of the subjects 200 during the period corresponding to the GMI can also be evaluated as effective. When the GA value and GMI alternate weekly, the GA value may stabilize to some extent. The effectiveness of the actions can also be evaluated by referring to statistical trends over a longer period (such as the average GA value, the average GMI, etc.). For example, if the average GMI is significantly less than the average GA value (average GMI < average GA - threshold TH), the GA value tends to decrease. Therefore, it can also be judged that the actions of the subjects 200 during that period were effective.

[0245] The risk assessment unit 123 can also refer to the GA value and / or GMI to determine whether the subject 200 has a medical risk or is likely to have a medical risk. For example, the risk assessment unit 123 can also use this information to determine whether the subject 200 is in a state of hypoglycemia or whether such a possibility exists. For example, if the GMI is below a specified value, it can also be determined that there is a risk in the corresponding period. Generally, changes in the GA value follow changes in the GMI. Therefore, by detecting the decrease in GMI before the GA value drops below the specified value, it is possible to detect the risk of the subject 200's actions at an early stage.

[0246] If the processing unit 100 determines that the subject 200 may be at risk, it may also send a message to the subject 200 indicating that they should seek consultation or a doctor's diagnosis. The processing unit 100 may also simultaneously or separately send a message indicating that it assumes no responsibility whatsoever for the subject 200's health or illness. The subject 200 can receive this message through the electronic terminal 300 and determine, based on their own responsibility, whether to seek consultation or a doctor's diagnosis.

[0247] The health range assessment unit 124 can also refer to 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.

[0248] The mode selection unit 127 comprehensively refers to any one or more of the following: GA value data 131, GMI data 132, and action information 133, to select or change the support mode for the target 200. For example, if the GA value and / or GMI are within a healthy range for a certain period, the CPU 101 can change the support for the target 200 from a mode that actively promotes action change (action change mode) to a long-term maintenance mode. Conversely, even when using the long-term maintenance mode, if the GA value and / or GMI deviate from a healthy range, the processing unit 100 can begin using the action change mode.

[0249] The GA value prediction unit 125 predicts future GA values ​​based on GMI data 132. For example, the GA value prediction unit 125 can also predict the GA value at a future point in time while maintaining the most recent GMI. For example, the GA value prediction unit 125 can also predict the GA value at a future point in time based on assumed time series GMI data (time variation of GMI).

[0250] The HbA1c prediction unit 126 can also predict future HbA1c values ​​based on GMI data 132. For example, while maintaining the most recent GMI, it is also possible to predict the HbA1c value at a future point in time. For example, it is also possible to predict the HbA1c value at a future point in time based on assumed time-series GMI data (time variations of GMI).

[0251] The proposal generation unit 128 generates a proposal to the target 200 based on the GMI data 132 or by further referring to other information. The proposal generation unit 128 may also use a prescribed algorithm 134 to generate the proposal. For example, the proposal generation unit 128 may generate proposals such as: the target 200 should maintain the most recent GMI; the target 200 should change its actions to achieve a different GMI than the most recent GMI; the actions the target 200 should perform; and the actions the target 200 should be the target of. In this case, the proposal generation unit 128 may also refer to the target 200's previous GMI data 132, the associated action information data 133 of the target 200, and data from sources other than the target 200 (e.g., data from people with similar attributes to the target 200).

[0252] The optimization unit 129 optimizes the algorithms used in blood glucose management, such as the action evaluation algorithm performed by the action evaluation unit 122, the selection algorithm adopted by the mode selection unit 127, and the algorithm used by the proposal generation unit 128. For example, the optimization unit 129 can also adjust the correlation between GMI and action based on the accumulated GA value, GMI, action information, etc. of the target 200, and optimize the algorithm used in the proposal of actions that the target 200 should take.

[0253] exist Figure 4A and Figure 4BIn this document, the electronic terminal 300 of the user 200 is described as another unit capable of communicating with the computing unit 100. In some embodiments, the computing unit 100 may be, for example, a physical server via a cloud, and the electronic terminal 300 may be the user 200's smartphone or tablet terminal, both of which may be configured to communicate. However, this configuration is only one example and should not be interpreted restrictively. In some embodiments, the user 200 may communicate directly with the computing unit 100. The physical interface and the computing unit may also be integrated. For example, the computing unit 100 may also be part of the user 200's electronic terminal 300. For example, the electronic terminal 300 may be the user 200's smartphone, tablet terminal, laptop computer, or desktop computer, with the computing unit 100 assembled inside.

[0254] The following is for reference Figures 5A to 5D This illustrates an example of using a systematic blood glucose management system.

[0255] Example 1

[0256] Figure 5A This indicates the correlation between GA values ​​and WGMI in a particular embodiment. The horizontal axis represents weeks, and the vertical axis represents GA values ​​and WGMI. This data is based on a hypothetical case study of actual diabetic patients.

[0257] For example, week "1" is the time point when blood is collected for GA value determination. In this embodiment, the representation "week n" means, depending on the situation, the time point when the blood is collected, and also means the period between time point "week n-1" and time point "week n".

[0258] The GA value was obtained by analyzing the transfused self-puncture-based finger-prick blood using HPLC. GMI[0,1] as WGMI was calculated using mathematical formula 2 based on GA[0] and GA[1].

[0259] like Figure 5A As shown in (a), the system, referring to the self-reported information of the subjects, recognized that "the subjects ate one bowl of rice for dinner, ate snacks five times a week, and also had habits such as playing golf and walking during commuting" during the period from week 0 to week 1. Based on this, the system analyzed and obtained the result that "the daily calorie intake was 2800 kcal, of which 52% came from carbohydrates, and the weekly exercise volume was about 6000 METs". At the time point of week 1, the GA value was about 19.6% (GA[1]=19.6%).

[0260] Based on the information input by the subject regarding the target action, the system recognized that the subject's target action at a specific time on Monday was set as "start walking and muscle training, halve the amount of rice consumed at each meal, and eat vegetables first." Based on the subject's self-reported information, the system recognized that the subject diligently and actually performed this target action every day.

[0261] The system obtained a GA value of approximately 19.0% at time point Tuesday (GA[2] = 19.0%). Based on GA[1] = 19.6% and GA[2] = 19.0%, the system calculated WGMI[1,2] = 17.2%. Therefore, the system predicted the change in GA value (dotted and dashed lines) for the scenario where the same actions continue from time point Tuesday, i.e., maintaining the same WGMI. In this case, the GA value was predicted to be approximately 17.5% on week 8.

[0262] like Figure 5A As shown in (b), the system obtains the GA value weekly and calculates the WGMI. The WGMI rises slightly in week 3, but returns to the predetermined value of 17.4% in week 4. The system interprets this as meaning the subject maintained constant action during that period. In fact, the GA values ​​from week 1 to week 4 show a decreasing trend, and the GA value in week 3 is also roughly at the predetermined value. Therefore, the system predicts that if this action is maintained, the predetermined GA value (single-dotted line and dashed line) can be reached in week 8.

[0263] Based on the information input by the subject regarding the target action, the system recognized that the subject changed the target action on Thursday, compared to the action taken from Tuesday onwards: "increasing the number of steps walked from an average of 7,000 steps per day to approximately 12,000 steps, and reducing the amount of snacks to about once a week and about one piece of sugar." Based on the subject's self-reported information, the system recognized that the subject actually performed the target action daily. Based on the subject's self-reported information on Thursday, the system recognized that the subject's "calorie intake was 2000 kcal, with carbohydrates accounting for approximately 45% of total meal intake, and sustained 6400 METs of exercise per week."

[0264] like Figure 5AAs shown in (c), the GA value obtained by the system at time point 5 of week 5 is approximately 17.8% (GA[5] = 17.8%). Based on GA[4] = 18.3% and GA[5] = 17.8%, the system calculates GMI[5, 4] = 16.3%. Therefore, the system predicts the change in GA value if the same action continues from time point 5 of week 5, i.e., if the same WGMI is maintained. In this case, the system predicts that the GA value will be approximately 16.9% on week 8. Since GA[8] = approximately 17.6% if the action continues from week 2 to week 4, it can be clearly understood that increased effort in the target action will contribute to a further reduction in the GA value in the future.

[0265] The system's GA value for Saturday and the resulting WGMI were again in line with the predetermined values. The system's GA value for Saturday was 17.6%, higher than predicted, and the calculated WGMI rose to 17.9%. Referring to the subject's self-reported information, the system recognized that the subject "actually had days of overeating and felt a lack of vegetables." The system correlated this dietary content with the increase in WGMI and recorded this correlation in memory. The system's GA value for Saturday was 17.1%, and based on this, the WGMI was calculated to be 16.1%. The system recognized that the subject had reached the target GA value of 17.1%, achieving their personal goal, and recorded this information in memory. Referring to the subject's self-reported information, the system recognized that "based on last week's reflection, all goals were achieved." The system correlated the actual actions taken with the decrease in WGMI and recorded this correlation in memory.

[0266] This means that WGMI reflects weekly actions or efforts more accurately than GA values.

[0267] Example 2

[0268] Figure 5B This indicates the correlation between GA values ​​and WGMI in a particular embodiment. The horizontal axis represents weeks, and the vertical axis represents GA values ​​and WGMI. This data is based on a hypothetical case study of actual diabetic patients.

[0269] like Figure 5B As shown, the system obtains the GA values ​​up to the time point of week 4 and calculates the WGMI based on them. The WGMI is relatively constant between 20.0% and 20.6%, and the GA value also decreases smoothly from 23% to 21.2%. Therefore, the system predicts that if the WGMI is kept constant, the GA value after week 4 will be approximately 20.6% (single-dot and dashed lines (1)).

[0270] At Friday, the system obtained a GA value of 20.5% and calculated a WGMI of 18.4%. Based on this, the system predicts that if the GA value and the WGMI are kept constant, the GA value after 8 weeks will be approximately 18.6% (single-dot and dashed lines (2)).

[0271] In addition, the system obtained self-reported information from participants at Friday's time and identified an increase in physical activity and a restriction in dietary intake. The system recognized this as the reason for the sharp drop in WGMI to 18.4%. The system then correlated this change in behavior with WGMI and recorded the correlation in its memory.

[0272] In addition, the system determined that WGMI = 18.4% and that the action was not medically risky, so no "risky" alert was issued.

[0273] Example 3

[0274] Figure 5C This indicates the correlation between GA values ​​and WGMI in a particular embodiment. The horizontal axis represents weeks, and the vertical axis represents GA values ​​and WGMI. This data is based on a hypothetical case study of actual diabetic patients.

[0275] At the time point of Tuesday, the system obtained the GA value and calculated the WGMI. The WGMI was 10.9%, and the GA value also dropped sharply from 21.4% to 18.8%. The system predicted that if the WGMI is kept constant, the GA value after 8 weeks will be 11.7% (single-dot and dashed lines (1)).

[0276] However, the system determined that a WGMI of 10.9% was below the lower limit of the healthy range, indicating a risk. Therefore, the system notified the subject to consult a doctor. The system then notified the subject's doctor to comprehensively assess the subject's condition and suggested adjustments to diet, exercise, and medication. As a result, the doctor determined to reduce the medication dosage, and the system obtained this information. Consequently, the system included a reduction in medication dosage in the subject's target actions.

[0277] By Friday, the system had obtained a GA value of 15.8%, but the calculated WGMI remained roughly constant at 13.6%. Based on these values, the system recalculated the future GA value while maintaining the WGMI. As a result, the system predicted that the future GA value would be 13.8% in about 8 weeks (dotted and dashed lines (2)).

[0278] The system determined that a future GA value of 13.8% was appropriate (risk-free). Furthermore, the system obtained self-reported information from the subject and recognized that their diary also showed a reduction in medication dosage. The system output to the subject indicating the effect of the dosage change, the appropriateness of the WGMI, and the appropriateness of the future GA value. However, through the doctor's diagnosis at that point in time, some side effects caused by the medication were confirmed, and therefore, based on the doctor's judgment, further dosage reduction was implemented. The doctor's judgment and the medication change were input from the medical institution to the system, and the system recorded these contents in its memory.

[0279] The system was observed up to week 9. The GA value obtained by the system showed some decreasing tendency, but remained roughly constant between approximately 15.8% and 16.2%. In contrast, the WGMI calculated by the system varied more significantly, ranging from approximately 15.3% to 17.4%, compared to the GA value. Thus, when the GA value remains roughly constant, or when the WGMI increases or decreases relative to the GA value, the WGMI tends to fluctuate. In such cases, it is not advisable to rely on individual WGMI values ​​for judgment; instead, it is better to use their average or moving average to understand the trend of the WGMI.

[0280] During the period from Friday to September, the system determined that the WGMI was within a healthy range. If the status quo continued, the system predicted that the GA value would remain at approximately 15.9% (dotted and dashed lines (3)). The prediction of future GA value changes remained unchanged. The system recognized that the medication dosage was constant based on the subject's self-reported information (diary input). Based on this, the system output to the subject and doctor that the current status of blood glucose management and the blood glucose management plan for the past few weeks were both appropriate and without problems.

[0281] Example 4

[0282] Figure 5D This indicates the correlation between GA values ​​and WGMI in a particular embodiment. The horizontal axis represents weeks, and the vertical axis represents GA values ​​and WGMI. This data is based on a hypothetical case study of actual diabetic patients. Figure 5D (a) For the purpose of illustrating the period up to the 7th of the week, Figure 5D (b) Refer to this for the purpose of explaining the period up to week 13. Additionally, GA values ​​were not obtained for week 1, but WGMI calculated them accurately even with the interval set to 2 weeks.

[0283] like Figure 5DAs shown in (a), the GA value obtained 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, referring to information related to the subject's goal actions, the system recognized that the subject had set new goal actions, inferring that the subject was making efforts to improve the GA value. Perhaps because of this, the WGMI calculated from the system's obtained GA value decreased to 16.1% on week 4 and decreased to 15.3% on week 5. The system established a correlation between the newly set goal actions and the inferred actual actions that helped improve blood glucose management, and recorded this correlation in its memory. Thus, it can be seen that the WGMI sensitively judges weekly actions and more accurately reflects the subject's actions.

[0284] Therefore, the system predicts that if the WGMI is maintained at 15.3% on Friday, the GA value will reach 15.4% after 8 weeks (week 13). The system judges that this future GA value is not problematic. Furthermore, the system obtains the subject's self-reported information (diary input), recognizing that there was an action change on Friday (carbohydrate-removing diet). Referring to the benchmark that "drastic carbohydrate-removing diets are not recommended," the system outputs a message recommending that the subject watch educational information related to accurate dietary therapy.

[0285] The WGMI calculated based on the GA value obtained by the system further decreased to 13.2% on Saturday. The system speculated that this was due to further changes in the behavior of the target. Up to Saturday, both the GA value and the calculated WGMI decreased. The system correlated the speculation of changed behavior with the decreases in both the GA value and WGMI, recording this correlation in memory. With a GA value of 15.9% and a calculated WGMI of 13.2%, the system determined these values ​​were acceptable and did not issue any risk warnings. Therefore, the system predicted that if the WGMI remained at Saturday's level, the GA value would reach 13.5% after 7 weeks (week 13).

[0286] The WGMI calculated based on the GA value obtained by the system further decreased to 10.3% on week 7, below the threshold of 11%. Therefore, the system judged that a medical problem was likely (risk) and issued a risk warning to the subject, suggesting that they go to the hospital. The system predicted that if the WGMI remained at the level of week 7, the GA value would be less than 11% in the future. The system output this prediction to the subject and also output additional educational information (video) on diet and exercise therapy, urging them to watch or listen.

[0287] like Figure 5DAs shown in (b), at time point 8, the WGMI calculated based on the GA value obtained by the system was 12.9%, exceeding the threshold of 11%. Therefore, the system judged that blood glucose management had improved. In week 9, the WGMI calculated based on the GA value obtained by the system further increased to 14.5%. The system judged this action as "no risk". In week 10, the WGMI calculated based on the GA value obtained by the system was 15.5%. The system output to the subject recommending that the current action be continued at around WGMI = 15%. Then, the WGMI calculated based on the GA value obtained by the system showed a slight upward trend, but remained below 16%.

[0288] Typically, the baseline for GA (Gross Scale) values ​​is between 11% and 16%, so ideally, GA values ​​should remain below 16%. However, lifestyle patterns and health conditions are not always consistent, and sometimes GA values ​​exceed the baseline range. There are also strategies that drastically change actions when GA values ​​exceed the baseline. On the other hand, by using WGMI to monitor appropriate lifestyle habits and maintaining those habits, it is possible to lower GA values ​​back to below 16%. Therefore, even if GA values ​​or GMI temporarily exceed the baseline range, it is recommended to continue the same actions and observe the process.

[0289] Example 5: Prediction of HbA1c values

[0290] exist Figure 5D As an example, this example shows the measured GA value of a subject from week 0 to week 7, the measured HbA1c value in week 0, the WGMI calculated based on the GA value, the predicted HbA1c value calculated based on the WGMI, and the prediction of HbA1c value changes based on three WGMI patterns. In this embodiment, the same GA value as in Example 4 above is used.

[0291] The HbA1c value obtained by the system was 6.4% on week 0. This value did not exceed the diagnostic benchmark of 6.5% for diabetes, but the system judged it to be relatively high. The GA value obtained by the system was 17.4% on week 0, gradually increasing to 17.7% on week 3. The calculated WGMI on week 3 was 17.9%, which was also high. The system output these values ​​to the subject. Subsequently, referring to information related to the subject's goal actions, the system recognized that the subject had set new goal actions, inferring that the subject was working to improve the GA value. Perhaps because of this, the WGMI calculated from the system's obtained GA value decreased to 16.1% on week 4 and decreased to 15.3% on week 5. The system established a correlation between the newly set goal actions and the inferred actual actions that helped improve blood glucose management, and recorded this correlation in memory. In fact, the obtained measured GA value and predicted HbA1c value showed a gradual decreasing trend.

[0292] This indicates that the GA value responds slowly to weekly changes in behavior, while the HbA1c value responds even less. In contrast, the WGMI makes a more sensitive assessment of weekly behavior, more accurately reflecting the subject's actions.

[0293] Therefore, the system predicts that if the WGMI remains at 15.1% on Friday, the HbA1c value will be lower than 6 after 8 weeks (week 13) (dashed line (1)).

[0294] The calculated WGMI further decreased to 13.2% on Saturday. The system speculated that this was due to further changes in the behavior of the target. Therefore, the system predicted that while maintaining the WGMI in Saturday, the HbA1c value would be below 6% in Saturday, becoming 5.3% (dashed line (2)).

[0295] The WGMI calculated based on the GA value obtained by the system further decreased to 10.3% on week 7. Therefore, the system predicts that if the WGMI continues in week 7, the HbA1c value will be 4.6% on week 24 (dashed line (3)).

[0296] In this way, not only can the HbA1c value at the current time point be obtained using GMI, but the future HbA1c value can also be predicted.

[0297] In some implementations, a specified range of HbA1c values ​​can also be set (also referred to as the target HbA1c value range, the range of HbA1c values ​​that should be the target, the range of HbA1c values ​​that should be the target, etc.). The upper and / or lower limits of the range can be used interchangeably. Either or both of the upper and lower limits of the target HbA1c value can also be set. The target HbA1c value range can also be set as a function of time. For example, a set range of HbA1c values ​​at the actual time points when HbA1c values ​​are checked can also be set. A set range corresponding to each time point can also be set at multiple time points.

[0298] In some implementations, the target HbA1c range can be set based on guidance from physicians or other healthcare professionals. In some implementations, the target HbA1c range can be set by the system or proposed. Alternatively, the target HbA1c range proposed by the system can be approved by the individual or a healthcare professional, and set based on this approval. The target HbA1c range can also be set or proposed based on the user's medical conditions. Examples of user medical conditions to consider include, but are not limited to, age, medical history, disease type (e.g., type 1, type 2, etc.), and complications. These primary factors can also be considered when setting or proposing the range.

[0299] In some implementations, it can also be determined whether the predicted HbA1c value based on GMI at a certain time point is included within the range of the target HbA1c value at the same time point. The result of this comparison can also be output. If the predicted HbA1c value is not included within the range of the target HbA1c value, the shifts (time series data) of the GA value and / or GMI used to ensure that future HbA1c values ​​are included within the range of the target HbA1c value can be calculated and output. Furthermore, action plans (target actions) can be proposed to form such shifts (time series data) of the GA value and / or GMI.

[0300] Implementation Method 6: Correlation between CGM and GMI

[0301] Reference Figure 6 This illustrates the correlation between CGM and GMI.

[0302] Figure 6 The plot shows the correlation between mean glucose levels based on CGM (referred to as "CGM mean blood glucose level" in this specification) and WGMI, measured from five healthy volunteers. They show a high correlation coefficient of 0.825. The solid line in the plot represents the regression line GA or WGMI = 0.126 × glucose level - 0.552 obtained through Passing-Bablok regression (a nonparametric regression), and the dashed line represents its 95% confidence interval.

[0303] Figure 6 The slope of the 95% confidence interval for the regression coefficients shown ranges from 0.097 to 0.172, and the intercept ranges from -5.421 to 2.877. This range includes the theoretical formula, GA, or WGMI = 0.139 × glucose value - 1.49, indicating that the results are consistent with the theoretical formula.

[0304] Table 1 shows the correlation coefficients between the obtained WGMI and GA values ​​and the time of hyperglycemia and the area of ​​hyperglycemia (time × blood glucose value) obtained from CGM. It was also confirmed that, in general, WGMI tends to show a higher correlation with various indicators compared to GA values.

[0305] [Table 1]

[0306]

[0307] Therefore, in some implementations, the mean blood glucose level in the CGM can be predicted based on GMI or WGMI. Alternatively, GMI or WGMI can be used to replace a portion of the values, parameters, statistics, etc., derived from the CGM. For example, the correlation between the number of blood glucose spikes occurring in the CGM and WGMI can also be determined.

[0308] In this embodiment, it is assumed that the future GMI will be constant, and the future HbA1c value is predicted, but this is not limited to this. GMI may not be constant, or it may vary. The conversion formula between GA and HbA1c values ​​is known, but it holds true only when blood glucose or GA values ​​remain constant over a long period. For example, there are cases where GMI changes on a weekly basis, which is a real situation, but such a conversion formula does not hold true. The method disclosed herein can indicate to the subject what HbA1c value should be achieved for the week in a form that is easily understood by the subject.

[0309] As one embodiment, it is possible to predict glycated hemoglobin (HbA1c) values ​​with high accuracy using time-series data of glycated albumin (GA) values. The structure of this embodiment will be described in detail below.

[0310] 1. Glucose Management Index (GMI) GA ) calculation

[0311] To calculate the Glucose Management Index (GMI) based on time-series data of GA values GA ), use the following mathematical formula 8.

[0312] [Mathematical Expression 8]

[0313]

[0314] This mathematical formula considers the change in GA value between time points t[i-1] and t[i], and then adds the previous GA value GA[i-1] as a correction term, thereby calculating the dynamic index based on time series data, namely the glucose management index.

[0315] The glucose management index (GMI) GA The relationship between the mean blood glucose level (MBG) and the mean blood glucose level can be expressed by the following mathematical formula 9.

[0316] [Mathematical Expression 9]

[0317]

[0318] Among them, GI Alb This is the glycation index of albumin. Under the assumption that glycemic management is constant and unchanging, the glycation index (GA) is calculated as GA = GI. ALB The premise is that a relationship like ×MBG is valid.

[0319] 2. HbA1c level as a glucose management indicator (GMI) HbA1c ) calculation

[0320] Next, the glucose management index (GMI) calculated based on GA values ​​was used. GAThe glucose management index (GMI) of HbA1c value is derived through the following mathematical formula 10. HbA1c This indicator dynamically reflects the time-series changes in HbA1c, and is corrected using the difference in HbA1c values ​​between time points.

[0321] [Mathematical Expression 10]

[0322]

[0323] Furthermore, HbA1c levels are a glucose management indicator (GMI). HbA1c The relationship between HbA1c and mean blood glucose (MBG) is defined by the following mathematical formula 11. By clarifying this relationship, we can comprehensively understand the relationship between the dynamic changes in HbA1c and blood glucose levels.

[0324] [Mathematical Expression 11]

[0325]

[0326] Among them, GI Hb It is the glycation coefficient of albumin.

[0327] 3. Prediction of HbA1c values

[0328] The HbA1c value at time point t[q] (HbA1c[q]) is obtained as a known value. Then, the glucose management index (GMI) is calculated using time series data based on GA values. HbA1c Based on the following mathematical formula 12, predict the HbA1c value (HbA1c[r]) at time point t[r].

[0329] [Mathematical Expression 12]

[0330]

[0331] This mathematical formula combines HbA1c glucose management indicators (GMI). HbA1c ) and time difference, and correction factor C HbA1c It can predict HbA1c values ​​with high accuracy.

[0332] 4. The relationship between MBG and GMI

[0333] Mean blood glucose (MBG) and glucose management index (GMI) GA The relationship between is defined by the following mathematical expression 13.

[0334] [Mathematical Expression 13]

[0335]

[0336] In addition, mean blood glucose (MBG) and HbA1c glucose management index (GMI) HbA1c The relationship between them is defined by the following mathematical expression 14.

[0337] [Mathematical Expression 14]

[0338]

[0339] Therefore, the glucose management index (GMI) for HbA1c HbA1c It can be calculated based on mathematical expressions 13 and 14 as in mathematical expression 15 below.

[0340] [Mathematical Expression 15]

[0341]

[0342] By using these mathematical formulas, it is possible to model the indirect relationship between GA and HbA1c values ​​from the perspective of average blood glucose levels.

[0343] 5. Glycation Sensitivity Ratio (R) GI Definition and calculation method of )

[0344] Glycation sensitivity ratio (R) GI Glycation ease (GI) is the ratio of the glycation ease of different proteins relative to the blood glucose value, defined by the following mathematical formula 16. This ratio functions as an important correction factor in the conversion between GA and HbA1c values.

[0345] [Mathematical Expression 16]

[0346]

[0347] (1) The case where HbA1c is stable over time

[0348] When the HbA1c value is relatively stable, formulas 17 and 18 can also be used. In this case, since there is a certain proportional relationship between the GA value and the HbA1c value, a stable R-value can be obtained. GI calculate.

[0349] [Mathematical Expression 17]

[0350]

[0351] [Mathematical Expression 18]

[0352]

[0353] This mathematical formula can be used, for example, when multiple HbA1c values ​​are not available, and only one HbA1c value is available.

[0354] 2) How HbA1c values ​​change over time

[0355] When the HbA1c value changes over time, predictions after a specific time point t[q] need to consider the influence of the period before time point t[q]. Based on this, the following correction method is used.

[0356] First, when HbA1c is obtained at time point t[p] and its next time point t[q] (p < q), the average blood glucose value (MBG) between time point t[p] and time point t[q] is defined by the following mathematical formula 19.

[0357] [Mathematical Expression 19]

[0358]

[0359] Among them, GMI HbA1c [p, q] are glucose management indicators based on HbA1c values ​​from time point t[p] to time point t[q]. Hb This indicates the glycation sensitivity of hemoglobin.

[0360] Next, based on the GA values ​​obtained at time points t[p], t[p+1], ..., t[q], MBG is calculated based on the following mathematical formula 20.

[0361] [Mathematical Expression 20]

[0362]

[0363] Among them, GMI GA [i-1, i] is the glucose management index of GA value, GI Alb This represents the glycation sensitivity of albumin. The mathematical formula normalizes the change in GA values ​​between time points by time intervals, averaging the effect from past time point t[p] to time point t[q].

[0364] Then, the glycation sensitivity ratio (R) is calculated using the following mathematical formula 21. GI ).

[0365] [Mathematical Expression 21]

[0366]

[0367] In these mathematical formulas, the ratio of albumin to hemoglobin glycation sensitivity is calculated, based on data corrected from past time points t[p] to t[q]. This correction further improves the prediction accuracy of HbA1c values.

[0368] Equation 21 uses two consecutive HbA1c values, but more HbA1c values ​​can also be used. This further improves accuracy. The glycation sensitivity ratio (R0) in this case... GI It can be calculated using the following mathematical formula 22.

[0369] [Mathematical Expression 22]

[0370]

[0371] Using the methods described above, the present invention is able to predict HbA1c values ​​with high accuracy, taking into account the influence of past data.

[0372] According to this embodiment, a technique is provided that can predict HbA1c values ​​with high accuracy using time-series data of GA values ​​while significantly reducing the frequency of HbA1c testing. This reduces the burden on patients and healthcare professionals and enables real-time blood glucose management.

[0373] As an example of this implementation, Figure 7 shows a comparison of the use of existing methods and the methods of the present invention (Mathematical Formulas 18 and 21). These comparisons are based on data obtained from an in-situ clinical study (2022-2023, https: / / doi.org / 10.1007%2Fs13300-024-01599-2). In this embodiment, the subjects consisted of patients with type 2 diabetes who underwent three 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 for HbA1c measurement. During this period, subjects collected finger-prick blood weekly at home and mailed it to the hospital for weekly GA measurement. The data shown in this embodiment consists of data from 37 out of 44 subjects with type 2 diabetes, excluding the 7 who did not perform home measurements during the final week of hospital visits.

[0374] Figure 7A This represents the results of predicting HbA1c values ​​based on GA values ​​using existing methods. In this method, HbA1c values ​​are predicted based on GA values ​​using linear regression results of HbA1c values ​​from venous blood collected at an intermediate visit (t[p]) and GA measurements taken at home during that week.

[0375] The Pearson correlation coefficient (R) between the predicted and measured values ​​was 0.629, and the slope obtained by 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 that the predicted values ​​have a large bias and limited accuracy.

[0376] Figure 7B This indicates the result when the method of the present invention is applied, using the method based on mathematical formula 18. In this method, the glycation coefficient ratio is calculated based on the HbA1c and GA values ​​measured at an intermediate visit (t[q]), and the HbA1c value at the final visit (t[r]) is predicted using the GA value during the period from the intermediate visit (t[q]) to the final visit (t[r]).

[0377] The correlation coefficient (Pearson's r) between the predicted and measured values ​​is very high at 0.995, with a slope of 1.05 (95% CI: 0.988–1.22) and an intercept of -0.40 (95% CI: -1.62–0.02) obtained by Passing-Bablok regression. The high correlation coefficient indicates a significant improvement in prediction accuracy compared to existing methods, but a deviation from 0 in the intercept, a characteristic of linear approximation, is observed.

[0378] Figure 7C The results show the outcome when the method based on mathematical formula 19 is further applied. In this method, the HbA1c value at the final visit (t[r]) is predicted using the GA measurement from the initial visit (t[p]) to the final visit (t[r]) and the HbA1c values ​​at the initial visit (t[p]) and intermediate visits (t[q]).

[0379] The Pearson correlation coefficient (R) between the predicted and measured values ​​was 0.974, with a slope of 1.00 (95% CI: 0.909–1.11) and an intercept of -0.01 (95% CI: -0.81–0.63) based on the Passing-Bablok regression. Additionally, in relation to… Figure 7B In the case of comparison, Figure 7C The intercept of the linear approximation shows a value very close to 0, notably coinciding with the origin (0, 0).

[0380] These results confirm that the method of the present invention significantly improves the prediction accuracy of HbA1c values ​​compared to existing methods. In particular, in the method using Formula 19, the linear approximation between the predicted and measured values ​​is approximately consistent with the origin (0, 0), demonstrating excellent accuracy and theoretical fit. This indicates that the method of the present invention has a superior ability to capture minute variations, enabling highly reliable blood glucose management.

[0381] Figure 8 (a) Shows the actual GA and GMI values ​​obtained in this clinical study. GA A graph showing the change of HbA1c values ​​over time. The squares (□) and solid lines represent the measured GA values, while the dashed lines represent the GMI calculated from the GA values. GA● (a circle marked in black in the middle) indicates the measured HbA1c value. Figure 8 (a) shows an example where GA and HbA1c values ​​show different tendencies. That is, in this case, although the GA value decreases, the HbA1c value increases.

[0382] Figure 8 (b) indicates that with Figure 8 (a) is a graph based on the data, overlaid with measured HbA1c values, showing the time-dependent changes in HbA1c values ​​predicted by the two methods. ○ (a blank circle) and the thin dashed line represent the R calculated using Formula 18 based on the HbA1c and GA values ​​at the initial visit. GI HbA1c values ​​were predicted using mathematical formulas 12 and 15 based on subsequent at-home GA measurements. On the other hand, □ (a blank quadrilateral) and thick dashed lines represent the use of two HbA1c values ​​at the initial and intermediate visits, and at-home GA measurements from week 1 to week 4, using R calculated according to mathematical formula 21. GI HbA1c values ​​were predicted using formulas 12 and 15, based on home GA measurements taken after Thursday.

[0383] The difference between these two predicted HbA1c values ​​typically represents the difference between R calculated using Equation 18 based on a single HbA1c value and a single GA value. GI R is calculated using two HbA1c values ​​and multiple GA measurements according to mathematical formula 21. GI The difference in the results of the calculations. In, for example... Figure 8 In the case of example (a), where the GA value decreases but the HbA1c value increases, it is believed that the HbA1c value with a longer half-life more strongly reflects the effect of the increase in blood glucose value before the measurement, while the GA value more strongly reflects the recent decrease in blood glucose.

[0384] Equation 18 does not contain a term with a half-life; based on this equation, R... GI The calculation does not reflect the differences in these individual half-lives. Therefore, it becomes a prediction that HbA1c values ​​decrease in the same way as GA values. On the other hand, equation 21 includes GMI, which reflects the half-life. Therefore, when using this equation, R is calculated by using two HbA1c values ​​and multiple GA measurements. GI This can eliminate the influence of past blood glucose fluctuations and yield a more accurate glycation sensitivity ratio (refer to...). Figure 8 (b) Therefore, it indicates that more accurate HbA1c value prediction is possible.

[0385] Figure 9 (a) represents the R of each subject obtained from the data of this clinical study. GIScatter plot of the relationship with BMI. This data, like in Figures 7(A), (B), and (C), consists of data from 37 out of 44 participants with type 2 diabetes, excluding the 7 who did not undergo home measurements during their final week of hospitalization. R GI It shows a tendency for BMI to increase as it increases.

[0386] Figure 9 (b) indicates from and Figure 9 (a) The same clinical study data described above, representing the R values ​​for each subject. GI A scatter plot showing the relationship between waist circumference and other measurements. GI It shows the tendency for waist circumference to increase as it increases.

[0387] These representations are particularly useful for users with large BMIs or waist circumferences, using formula 18 or formula 21 to calculate R. GI The importance of this. That is, it indicates that for users with a large BMI or waist circumference, the generally known R... GI The discrepancy between the predicted and measured HbA1c values ​​becomes larger at a value of 0.25. For example, when measured HbA1c values ​​are unavailable due to lack of blood collection, a temporary assumption of R can be made. GI The value is 0.25, and the HbA1c value is predicted based on the measured GA value. In this case, for individuals with a BMI of 25 or higher or a waist circumference of 85cm or higher, it can be indicated that the predicted HbA1c value has a large error or may be inaccurate.

[0388] Figure 9 (c) shows the representation by and Figure 9 (a) The same R values ​​obtained from the above clinical study data for each subject GI A scatter plot showing the relationship between R and HDL cholesterol levels. GI It shows a tendency for the level to increase as HDL cholesterol levels decrease.

[0389] This demonstrates the possibility of estimating HDL cholesterol levels from GA and HbA1c values. For example, in R calculated based on GA and HbA1c values... GI If the value is greater than the specified value (e.g., 0.33), the user or medical professional can be notified to confirm the HDL cholesterol value.

[0390] In the above embodiments and examples, the time constant τ (the reduction time constant of target proteins such as albumin and hemoglobin) is set as a general constant. However, in some embodiments, this time constant τ can also be set as an individual-specific parameter. For example, multiple HbA1c and GA values ​​can be used to define τ as an individual parameter (an individualized parameter). The individualized time constant τ can also be optimized selectively or in combination using individual attribute / personal data parameters, measurement data (GA value, HbA1c value, CGM data, or a combination thereof). τ may not be a parameter that changes significantly within a few weeks, but may change over a long period depending on changes in an individual's health status, disease status, age, etc. In addition, τ may be affected by treatment status, such as dialysis.

[0391] The present invention includes the following embodiments:

[0392] A001. A method for using a computer system to assist in blood glucose management in subjects, wherein,

[0393] The computer system has a processor.

[0394] The method includes:

[0395] The processor obtains the glycated albumin (GA) values, namely GA[i] and GA[j], for objects at two time points t[i] and t[j] > t[i].

[0396] The processor calculates the average blood glucose level, i.e., the glucose management index (GMI[i,j]), based on the GA[i] and GA[j] during the period between the two time points t[i] and t[j]; and

[0397] The processor generates output information based on the glucose management index (GMI[i,j]) and outputs it to the object.

[0398] A001b. A method for using a computer system to assist in blood glucose management in subjects, wherein,

[0399] The computer system includes: a processor; and a communication unit connected to the processor, capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the user, etc.).

[0400] The method includes:

[0401] The processor obtains the glycation rate (GP value) of the target protein of the target at two time points t[i] and t[j] > t[i] via the communication unit, namely GP[i] and GP[j].

[0402] The processor calculates the average blood glucose level, i.e., the glucose management index (GMI[i,j]), based on the GP[i] and GP[j] during the period between the two time points t[i] and t[j].

[0403] The processor generates output information based on the glucose management index (GMI[i,j]) and outputs it to the subject via the communication unit.

[0404] A002. A method for using a computer system to assist in blood glucose management in subjects, wherein,

[0405] The computer system includes: a processor; and a communication unit connected to the processor, capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the user, etc.).

[0406] The method includes:

[0407] The processor obtains the GA values ​​of the objects at two consecutive time points t[i-1] and t[i] > t[i-1] via the communication unit, namely GA[i-1] and GA[i].

[0408] The processor calculates the average blood glucose level, i.e., the glucose management index (GMI[i-1, i]), based on the GA[i-1] and GA[i] during the period between the two time points t[i-1] and t[i].

[0409] The processor generates output information based on the glucose management index (GMI[i-1, i]) and outputs it to the subject.

[0410] A003. The method according to A002 or any embodiment, wherein,

[0411] The interval Δt = t[i] - t[i-1] for obtaining the GA value is essentially more than 1 day and less than 1 month.

[0412] A003b. The method according to A003 or any embodiment, wherein,

[0413] The interval Δt = t[i] - t[i-1] for obtaining the GA value is essentially more than 3 days and less than 14 days.

[0414] A005. The method according to any one or any embodiment of A001 to A003, wherein,

[0415] The computer system further includes a communication unit connected to the processor, capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the user, etc.).

[0416] Obtaining GA[i] and GA[j] includes: the processor obtaining GA[i] and GA[j] via the communication unit.

[0417] A007. The method according to any one or any embodiment of A001 to A005, wherein,

[0418] The computer system further includes a communication unit connected to the processor, capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the user, etc.).

[0419] Outputting the output information to the target includes outputting it to the target via the communication unit.

[0420] A011. The method according to A002 or A003 or any embodiment, wherein,

[0421] Generating the output information includes: based on the glucose management index (GMI[i-1, i]), generating an evaluation of the subject's actions as a period from time point t[i-1] to time point t[i].

[0422] A012. The method according to A011 or any embodiment, wherein,

[0423] The output information includes an evaluation of the effectiveness of the actions taken by the subject during the corresponding period when the glucose management index (GMI[i-1, i]) is less than the GA value GA[i].

[0424] A013. The method according to A011 or any embodiment, wherein,

[0425] The output information includes the following evaluation: when the glucose management index (GMI[i-1, i]) is less than the GA value GA[i] for two consecutive times, the following condition is met.

[0426] Condition 1: When GMI[i-1, i]<GA[i] and GMI[i-2, i-1]<GA[i-1],

[0427] The actions performed by the subject during the corresponding period are effective.

[0428] A014. The method according to A013 or any embodiment, wherein,

[0429] The output information includes the following evaluation: when condition 1 is not met.

[0430] When the average of at least two glucose management indicators is sufficiently lower than the average of the corresponding at least two GA values, that is, when the condition is met.

[0431] Condition 2: When the average value {GMI[i-1, i]} < the average value {GA[i]} - TH (threshold),

[0432] The actions performed by the subject during the corresponding period are effective, and / or,

[0433] If condition 2 is not met, the actions performed by the object during the corresponding period have no effect.

[0434] A015. The method according to any one or any embodiment of A012 to A014, wherein,

[0435] The output information includes: an alert indicating that the actions taken by the subject during the corresponding period pose a health risk and / or a notification indicating that the target actions should be changed when the GMI is lower than the GMI risk assessment value and / or the GA value is lower than the GA risk assessment value.

[0436] A021. The method according to A001 or any embodiment, further comprising:

[0437] Obtain action information relating to the actions performed by the object during the period between said time points t[i] and t[j]; and

[0438] Determine the correlation between the action information and the glucose management index (GMI[i,j]).

[0439] A022. The method according to A021 or any embodiment, wherein,

[0440] The action information includes: the self-reported information of the subject, and / or, biological information obtained about the subject.

[0441] A023. The method according to A021 or any embodiment, wherein,

[0442] Determining the correlation between the action information and the glucose management index (GMI[i,j]) involves referring to a database containing action information and GA values ​​of non-target individuals other than the target individuals.

[0443] A031. The method according to A011 or any embodiment, wherein it comprises:

[0444] Based on the aforementioned correlation, the target action that the object generator should perform in the future is determined.

[0445] A032. The method according to A031 or any embodiment, wherein,

[0446] The target actions that the object generator should perform in the future include: in the information on actions previously performed by the object generator, excluding or removing ineffective or risky actions from the generated target actions, or reducing the priority of ineffective or risky actions.

[0447] A035. The method according to A031 or any embodiment, further comprising:

[0448] Output the target action that the generated object should perform in the future to the object.

[0449] A041. The method according to A031 or any embodiment, wherein,

[0450] The algorithm for generating the target actions that the subject should perform in the future is optimized using the subject's glucose management index as an evaluation parameter.

[0451] A101. A method for using a computer system to assist in blood glucose management in subjects, wherein,

[0452] The computer system has a processor.

[0453] The method includes:

[0454] (a1) The processor obtains the first GA value GA[m] of the object at the first time point t[m];

[0455] (b1) The processor acquires the 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

[0456] (c1) The processor calculates the second GA value GA[n] at the second time point t[n] based on the first GA value GA[m] and the time series glucose management index {GMI[i-1, i]} (i = m+1~n).

[0457] A111. A method for using a computer system to assist in blood glucose management in subjects, wherein,

[0458] The computer system has a processor.

[0459] The method includes:

[0460] (a2) The processor obtains the first GA value GA[m] of the object at the first time point t[m];

[0461] (b2) The processor obtains the second time point t[n] as the target second GA value GA[n];

[0462] (c2) The processor calculates the time series glucose management index {GMI[i-1, i]} (i = m+1 to n) required to reach the second GA value GA[n] at the second time point t[n] based on the first GA value GA[m] and the second GA value GA[n].

[0463] A112. The method according to A111 or any embodiment, wherein,

[0464] Step (c2) includes: obtaining the time series {GMI[i-1, i]}[k] of multiple (k, k≥2) glucose management indicators.

[0465] A113. The method according to any one or any embodiment of A101 to A112, wherein,

[0466] The computer system further includes a communication unit connected to the processor, capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the user, etc.).

[0467] The step (a1) and / or the step (a2) includes: the processor obtaining the first GA value GA[m] of the object at the first time point t[m].

[0468] A 121. A method for using a computer system to assist in blood glucose management in subjects, wherein,

[0469] The computer system has a processor.

[0470] The method includes:

[0471] (a3) The processor obtains the first HbA1c value HbA1c[m] of the object at the first time point t[m];

[0472] (b3) The processor obtains the 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);

[0473] (c3) The processor calculates the second HbA1c value HbA1c[n] at the second time point t[n] based on the first HbA1c value HbA1c[m] and the time series glucose management index {GMI[i-1, i]} (i = m+1~n);

[0474] (d3) The processor calculates the target HbA1c value range at the second time point t[n];

[0475] (e3) The processor determines whether the second HbA1c value HbA1c[n] is included within the range of the target HbA1c value; and

[0476] (f3) The processor outputs the result of the judgment.

[0477] A122. The method according to A121 or any embodiment, further comprising:

[0478] (g3) The processor calculates the second HbA1c value HbA1c[n] at the second time point t[n] such that the time series glucose management index {GMI[i-1, i]} (i = m+1~n) is included in the target HbA1c value range.

[0479] C001. Software comprising computer-executable code for assisting in blood glucose management in a subject, wherein, when executed by a processor,

[0480] The processor is configured as follows:

[0481] Obtain the GA values ​​of objects at two time points t[i] and t[j] > t[i], namely GA[i] and GA[j].

[0482] Based on GA[i] and GA[j], the average blood glucose level (GMI[i,j]) is calculated by converting the GA values ​​during the period between the two time points t[i] and t[j].

[0483] Generate output information based on the glucose management index (GMI[i,j]) and output it to the subject.

[0484] C002. The software according to C001 or any embodiment, wherein,

[0485] Acquiring GA[i] and GA[j] for the processor includes: acquiring GA[i] and GA[j] for the processor via a communication unit connected to the processor, the communication unit being able to communicate with a user interface (e.g., a display unit, an electronic terminal accessible to the user, etc.).

[0486] C011. Software comprising computer-executable code for assisting in blood glucose management in a subject, wherein,

[0487] When the computer-executable code is executed by a processor, it is configured to cause the processor to perform the method and / or steps described in any one of A001 to A261 or any of the embodiments.

[0488] D001. A non-transitory storage medium for storing software according to any one of C001 to C201 or any embodiment thereof.

[0489] E001. A computer system for assisting in blood glucose control in a subject, wherein,

[0490] The computer system has a processor.

[0491] The computer system is programmed to cause the processor to:

[0492] Obtain the GA values ​​of objects whose time points t[i] and t[j] > t[i], namely GA[i] and GA[j].

[0493] Based on GA[i] and GA[j], the average blood glucose level (GMI[i,j]) is calculated by converting the GA values ​​during the period between the two time points t[i] and t[j].

[0494] Generate output information based on the glucose management index (GMI[i,j]) and output it to the subject.

[0495] E005. The computer system according to E001 or any embodiment thereof, wherein,

[0496] The computer system includes: a communication unit connected to the processor, capable of communicating with a user interface (e.g., a display unit, an electronic terminal accessible to the user, etc.), or

[0497] The computer system is configured to be connected to a communication unit capable of communicating with the user interface.

[0498] Enabling the processor to obtain the GA[i] and GA[j] includes enabling the processor to obtain the GA[i] and GA[j] as the GA value of the object via the communication unit.

[0499] The processor outputs the output information to the object, including the processor outputting the output information to the object via the communication unit.

[0500] E011. A computer system for assisting in blood glucose control in a subject, wherein,

[0501] The computer system has a processor.

[0502] The computer system is programmed to cause the processor to perform the method and / or steps described in any one of A001 to A201 or any of the embodiments.

[0503] E021. A computer system for assisting in blood glucose control in a user, comprising:

[0504] Processor; and

[0505] A non-transitory storage medium that stores the software described in any one of C001 to C201 or any embodiment thereof.

[0506] E022. The computer system according to E021 or any embodiment.

[0507] It also has a communication unit that can communicate with a user interface (e.g., a display unit, an electronic terminal of the user, etc.).

[0508] E 101. A computer system for assisting in blood glucose control in a subject, comprising:

[0509] The receiving unit obtains the GA value of the object;

[0510] The GMI calculation unit calculates glucose management indicators based on the obtained multiple GA values;

[0511] The sending unit sends the glucose management indicators to the recipient's terminal.

[0512] E102. The computer system according to E101 or any embodiment, further comprising at least one or more of the following:

[0513] The action evaluation unit evaluates the actions of the subject based on the glucose management indicators;

[0514] The risk assessment unit assesses the risk of the subject's actions based on the glucose management indicators and / or the GA value.

[0515] The GA value prediction unit predicts future GA values ​​based on the glucose management indicators.

[0516] The HbA1c value prediction unit predicts future HbA1c values ​​based on the glucose management indicators.

[0517] The mode selection unit selects an auxiliary mode suitable for the subject from multiple blood glucose management modes based on the glucose management index and / or the GA value.

[0518] The proposal generation unit, based on the glucose management indicators, generates output information containing the actions or target actions that the recipient should take; and

[0519] The optimization unit optimizes the algorithm for generating the output information.

[0520] B201. A system for assisting in blood glucose management, wherein,

[0521] The system includes sensors, a processor, a storage unit, and a user interface.

[0522] The sensor is configured to receive bodily fluids from the subject, measure the subject's glycated albumin (GA) value, and send the measured GA value to the processor;

[0523] The processor is configured as follows:

[0524] The sensor receives GA values ​​(GA[i] and GA[j], where t[j] > t[i]) at different time points.

[0525] Based on mathematical formula 1, the average blood glucose level (GMI) is calculated by converting the GA values ​​between two time points t[i] and t[j]. GA [i, j]),

[0526] [Mathematical Expression 1]

[0527]

[0528] in,

[0529] C GA [i,j]=1-exp[-(t[j]-t[i]) / τ Alb ],

[0530] τ Alb The time constant representing the decrease in albumin; and

[0531] Generate the Glucose Management Index (GMI) based on the aforementioned GA GA The output information of [i, j] is sent to the user interface;

[0532] The user interface is configured to output the output information received from the processor.

[0533] B211. The system according to B201 or any embodiment, wherein,

[0534] The different time points are two time points t[i-1] and t[i] that are consecutive in time (where t[i] > t[i-1]).

[0535] B212. The system according to B201, B211, or any of the embodiments, wherein,

[0536] The interval Δt = t[i] - t[i-1] for obtaining the GA value is essentially more than 3 days and less than 14 days.

[0537] B221. The system according to B201 or any embodiment, wherein,

[0538] The system also includes a communication unit connected to the processor, which is capable of communicating with the sensors and the user interface.

[0539] B231. The system according to B201, B211, or any of the embodiments, wherein,

[0540] The output information includes an evaluation of the actions of the subject during the period from time point t[i-1] to time point t[i].

[0541] B232. The system according to B231 or any embodiment, wherein,

[0542] The output information includes the glucose management index (GMI) in the GA. GA When [i-1, i]) is less than the GA value GA[i], the evaluation of the effectiveness of the actions performed by the subject during the corresponding period.

[0543] B233. The system according to B232 or any embodiment, wherein,

[0544] The output information includes the following evaluation: when the glucose management index (GMI[i-1, i]) is less than the GA value GA[i] twice consecutively, it satisfies the following condition.

[0545] Condition 1: When GMI[i-1, i]<GA[i] and GMI[i-2, i-1]<GA[i-1],

[0546] The actions performed by the subject during the corresponding period are effective.

[0547] B234. The system according to B233 or any embodiment, wherein,

[0548] When condition 1 is not met, the following evaluation is included:

[0549] When the average of at least two glucose management indicators is sufficiently lower than the average of the corresponding at least two GA values, that is, when the condition is met.

[0550] Condition 2: When the average value {GMI[i-1, i]} < the average value {GA[i]} - h (threshold),

[0551] The actions performed by the subject during the corresponding period are effective, and / or,

[0552] If condition 2 is not met, the actions performed by the object during the corresponding period have no effect.

[0553] B235. The system according to any one or any embodiment of B232 to B234, wherein,

[0554] When the GMI is lower than the GMI risk assessment value and / or the GA value is lower than the GA risk assessment value, the output information includes an alert indicating that the actions taken by the subject during the corresponding period pose a health risk and / or a notification indicating that the target actions should be changed.

[0555] B241. The system according to B201 or any embodiment, wherein,

[0556] The processor is configured to acquire action information relating to the actions performed by the object during the period between the time points t[i-1] and t[i]; and

[0557] Determine the correlation between the action information and the glucose management index (GMI[i-1, i]).

[0558] B242. The system according to B241 or any embodiment, wherein,

[0559] The action information includes the subject's self-reported information and / or biological information obtained about the subject.

[0560] B243. The system according to B241 or any embodiment, wherein,

[0561] Determining the correlation between the action information and the glucose management index (GMI[i-1, i]) involves referring to a database containing action information and GA values ​​of non-target individuals other than the target individuals.

[0562] B244. The system according to B241 or any embodiment, wherein,

[0563] The processor is further configured to generate, based on the association, a target action that the object should perform in the future.

[0564] B245. The system according to B244 or any embodiment, wherein,

[0565] The target actions that the object generator should perform in the future include: excluding or removing ineffective or risky actions from the generated target actions, or reducing the priority of ineffective or risky actions, based on the information of actions previously performed by the object generator.

[0566] B246. The system according to B244 or any embodiment, wherein,

[0567] The processor is also configured to output the generated target action that the object should perform in the future to the object.

[0568] B247. The system according to B244 or any embodiment thereof, wherein,

[0569] An algorithm for generating the target actions that the object should perform in the future is stored in the storage unit, and the algorithm is optimized using the object's glucose management index as an evaluation parameter.

[0570] B251. The system according to B201 or any embodiment, wherein,

[0571] The processor is configured to perform the following steps:

[0572] (a1) Obtain the first GA value GA[m] of the object at the first time point t[m] from the sensor;

[0573] (b1) Obtain the time series GA glucose management index {GMI} from the sensor from the first time point t[m] to the second time point t[n]. GA [i-1, i]} (i=m+1~n); and

[0574] (c1) Based on the first GA value GA[m] and the time-series GA glucose management index {GMI} GA Given [i-1, i]} (i = m+1~n), find the second GA value GA[n] at the second time point t[n].

[0575] B252. The system according to B201 or any embodiment, wherein,

[0576] The processor is configured to perform the following steps:

[0577] (a2) Obtain the first GA value GA[m] of the object at the first time point t[m] from the sensor;

[0578] (b2) Obtain the second GA value GA[n] of the target at the second time point t[n] from the sensor; and

[0579] (c2) Based on the first GA value GA[m] and the second GA value GA[n], calculate the time-series GA glucose management index {GMI} required to reach the second GA value GA[n] at the second time point t[n]. GA [i-1, i]} (i = m+1~n).

[0580] B253. The system according to B252 or any embodiment, wherein,

[0581] Step (c2) includes: calculating the time series of multiple (k, k≥2) glucose management indicators {GMI} GA [i-1, i]}[k] (i=m+1~n).

[0582] B261. The system according to B201 or any embodiment, wherein,

[0583] The processor is configured to perform the following steps:

[0584] (a) Obtain the GA value of the subject and the glycosylated albumin values ​​(GA values) of multiple time series from time point t[q] to t[r] (q>r);

[0585] (b) Glucose Management Index (GMI) based on the time series of GA values ​​obtained from the time series GA values ​​described above. GA ) and glycation sensitivity ratio R GI Using the following mathematical expressions 15 and 16, calculate the GMI (GMI) of HbA1c values ​​from t[q] to t[r]. HbA1c [q, r]):

[0586] [Mathematical Expression 15]

[0587]

[0588] in,

[0589] [Mathematical Expression 16]

[0590]

[0591] (c) Obtain the HbA1c value (HbA1c[q]) of the object at time point t[q];

[0592] (d) GMI (GMI) based on the HbA1c value during the period from t[q] to t[r]. HbA1c[q, r]) and the HbA1c value (HbA1c[q]) of the object at time point t[q], using the following mathematical formula,

[0593] [Mathematical Expression 15]

[0594]

[0595] Estimate the HbA1c value (HbA1c[r]) of the object at time point t[r] (r > q); and

[0596] (e) Generate output information of HbA1c value (HbA1c[r]) based on the predicted time point t[r], and output it to the user interface.

[0597] B262. The system according to B261 or any embodiment, wherein,

[0598] The R GI It is expressed by the following formula.

[0599] [Mathematical Expression 18]

[0600]

[0601] B263. The system according to B261 or any embodiment, wherein,

[0602] The R GI It is expressed by the following formula.

[0603] [Mathematical Expression 21]

[0604]

[0605] A201. A method for assisting in blood glucose management using a computer system, wherein,

[0606] The computer system includes sensors, a processor, storage units, and a user interface.

[0607] The method includes:

[0608] The GA value of the subject is obtained by measuring the body fluids of the subject using the sensor.

[0609] Send the GA values ​​(GA[i] and GA[j], where t[j]>t[i]) at different time points to the processor;

[0610] Using the processor, based on mathematical formula 1, the average blood glucose level (GMI) is calculated by converting the GA values ​​between two time points t[i] and t[j], which is the Glucose Management Index (GMI). GA[i, j]),

[0611] [Mathematical Expression 1]

[0612]

[0613] in,

[0614] C GA [i,j]=1-exp[-(t[j]-t[i]) / τ Alb ]、

[0615] τ Alb This represents the time constant for albumin reduction;

[0616] Generate the Glucose Management Index (GMI) based on the aforementioned GA GA The output information of [i, j] is sent to the user interface; and

[0617] The user interface is used to output the output information received from the processor to the object.

[0618] A261. A method for assisting a subject in blood glucose management using a computer system, according to A001 or any embodiment, wherein...

[0619] The computer system includes sensors, a processor, storage units, and a user interface.

[0620] The method includes:

[0621] (a) The body fluids of the subject are measured by the sensor to obtain the GA value of the subject and multiple time series glycated albumin values ​​(GA values) from time point t[q] to t[r] (q>r);

[0622] (b) Through the processor, a time-series glucose management index (GMI) based on the GA value obtained according to the acquired time-series GA value. GA ) and glycation sensitivity ratio R GI The GMI (Governing Momentum of HbA1c) for the period from t[q] to t[r] is calculated using the following formula (Eq.18). HbA1c [q, r]);

[0623] [Mathematical Expression 18]

[0624]

[0625] in,

[0626] [Mathematical Expression 19]

[0627]

[0628] (c) Obtain the HbA1c value (HbA1c[q]) of the object at time point t[q] using the processor;

[0629] (d) Through the processor, the GMI (GMI) is calculated based on the HbA1c value during the period from t[q] to t[r]. HbA1c The HbA1c value (HbA1c[q]) of the object at time point t[q] and the object's HbA1c value (HbA1c[q]) at the time point t[q] are obtained using the following formula:

[0630] [Mathematical Expression 15]

[0631]

[0632] Estimate the HbA1c value (HbA1c[r]) of the object at time point t[r] (r > q); and

[0633] (d) The processor generates output information based on the inferred HbA1c value (HbA1c[r]) at time point t[r] and sends it to the user interface;

[0634] (e) Output the output information received from the processor to the object through the user interface.

[0635] While preferred embodiments of the invention have been shown and described in this specification, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. The invention is not intended to be limited to the specific examples provided herein. Any one or any scheme of the embodiments disclosed herein may be combined independently, partially or entirely with other embodiments and schemes described herein in any manner. For example, all or part of one, two, or more embodiments may be combined.

[0636] When used in this specification, the terms "comprising" and "possessing" are intended to indicate that the elements listed in the structures and methods are included, but do not exclude other elements. The embodiments defined by these transitional terms are within the scope of the invention. Therefore, methods and compositions, structures may "comprise" additional steps and structural elements.

[0637] Groups of items connected by the conjunction "and" should not be read as requiring all of these items to be present in the group; unless otherwise specified, they should be read as "and / or". Similarly, groups of items connected by the conjunction "or" should not be read as requiring mutual exclusivity between the items in the group; unless otherwise specified, they should be read as "and / or".

[0638] The present invention has been described with reference to the foregoing specification; however, the description of embodiments and accompanying drawings are not intended to be interpreted in a limiting sense. Many modifications, alterations, and substitutions will occur to those skilled in the art without departing from the invention. Furthermore, it should be understood that all aspects of the invention are not limited to the specific descriptions, structures, or relative proportions set forth in this specification, depending on various conditions and variables. It should be understood that various alternative forms of embodiments of the invention described in this specification can be used to implement the invention. Therefore, the invention is considered to also cover such alternatives, corrections, modifications, or equivalents. The scope of the claims defines the scope of the invention, and the methods and structures within the scope of these claims, as well as their equivalents, are intended to be covered thereto.

Claims

1. A system for assisting in blood glucose management, wherein, The system includes sensors, a processor, a storage unit, and a user interface. The sensor is configured to receive the body fluids of the subject, measure the glycated albumin value (GA value) of the subject, and send the measured GA value to the processor; The processor is configured as follows: The sensor receives GA values ​​at different time points, namely GA[i] and GA[j], where t[j] > t[i]. Based on mathematical formula 1, the GA glucose management index, i.e., GMI, is generated as the average blood glucose value calculated from the GA value during the period between two time points t[i] and t[j]. GA [i, j], [Mathematical Expression 1] Among them, C GA [i,j]=1-exp[-(t[j]-t[i]) / τ Alb ], τ Alb The time constant for albumin reduction; and Generate the glucose management index (GMI) based on the GA. GA The output information of [i, j] is sent to the user interface; The user interface is configured to output the output information received from the processor.

2. The system according to claim 1, wherein, The interval Δt = t[i] - t[i-1] for obtaining the GA value is essentially more than 3 days and less than 14 days.

3. The system according to claim 1, wherein, The system also includes a communication unit connected to the processor, which is capable of communicating with the sensors and the user interface.

4. The system according to claim 1, wherein, The output information includes an evaluation of the actions of the subject during the period from time point t[i-1] to time point t[i].

5. The system according to claim 3, wherein, The output information includes the following evaluation: in the GA glucose management index, i.e., GMI GA When [i-1, i] is less than the GA value GA[i], the action performed by the object during the corresponding period is effective.

6. The system according to claim 5, wherein, The output information includes the following evaluation: when the glucose management index, i.e., GMI[i-1, i], is less than the GA value GA[i] for two consecutive times, the following condition is met. Condition 1: When GMI[i-1, i]<GA[i], and GMI[i-2, i-1]<GA[i-1], The actions performed by the subject during the corresponding period are effective.

7. The system according to claim 6, wherein, When condition 1 is not met, the following evaluation is included: The condition is satisfied when the average of at least two glucose management indicators is sufficiently lower than the average of the corresponding at least two GA values. Condition 2: When the average value {GMI[i-1, i]} < the average value {GA[i]} - h, where h is the threshold value. The actions performed by the subject during the corresponding period are effective, and / or, If condition 2 is not met, the actions performed by the object during the corresponding period have no effect.

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

9. The system according to claim 1, wherein, The processor is configured as follows: Obtain action information related to the actions taken by the subject during the period between time points t[i-1] and t[i]; and Determine the correlation between the action information and the glucose management index, GMI[i-1, i].

10. The system according to claim 9, wherein, The action information includes self-reported information of the subject and / or biological information obtained about the subject.

11. The system according to claim 9, wherein, Determine the correlation between the action information and the glucose management index, GMI[i-1, i], by referring to a database containing action information of non-targets other than the target and GA values.

12. The system according to claim 9, wherein, The processor is further configured to generate a target action that the object should perform in the future based on the correlation, and output the generated target action that the object should perform in the future to the object.

13. The system according to claim 12, wherein, The target actions that the object generator should perform in the future include: excluding ineffective or risky actions from the generated target actions or removing them from the generated target actions, or reducing the priority of ineffective or risky actions, based on the information of actions previously performed by the object generator.

14. The system according to claim 12, wherein, An algorithm for generating the target actions that the object should perform in the future is stored in the storage unit, and the algorithm is optimized using the object's glucose management index as an evaluation parameter.

15. The system according to claim 1, wherein, The processor is configured to perform the following steps: (a1) Obtain the first GA value GA[m] of the object at the first time point t[m] from the sensor; (b1) Obtain the time series GA glucose management index {GMI} from the sensor from the first time point t[m] to the second time point t[n]. GA [i-1, i]}, where i = m+1~n; and (c1) Based on the first GA value GA[m] and the time-series GA glucose management index {GMI} GA Given [i-1, i]}, where i = m+1~n, find the second GA value GA[n] at the second time point t[n].

16. The system according to claim 1, wherein, The processor is configured to perform the following steps: (a2) Obtain the first GA value GA[m] of the object at the first time point t[m] from the sensor; (b2) Obtain the second GA value GA[n] of the target at the 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], calculate the time-series GA glucose management index {GMI} required to reach the second GA value GA[n] at the second time point t[n]. GA [i-1, i]}, where i=m+1~n.

17. The system according to claim 1, wherein, The processor is configured to perform the following steps: (a) Obtain the GA value of the subject and the GA value of multiple time series glycated albumin values ​​from time point t[q] to t[r], where q>r; (b) Glucose Management Index (GMI) based on the time series of GA values ​​obtained from the time series GA values ​​described above. GA and glycation sensitivity ratio R GI Using the following mathematical expressions 15 and 16, calculate the GMI of HbA1c values ​​during the period from t[q] to t[r]. HbA1c [q, r]; [Mathematical Expression 15] in, [Mathematical Expression 16] (c) Obtain the HbA1c value of the object at time point t[q], i.e., HbA1c[q]; (d) GMI based on the HbA1c value during the period from t[q] to t[r] HbA1c [q, r] and the HbA1c value of the object at time point t[q], i.e., HbA1c[q], are expressed using the following mathematical formula. [Mathematical Expression 15] The estimated HbA1c value of the object at time point t[r] is HbA1c[r], where r > q; and (e) Generate output information based on the predicted HbA1c value at time point t[r], i.e. HbA1c[r], and output it to the user interface.

18. The system according to claim 17, wherein, The R GI It can be represented by the following mathematical expression, [Mathematical Expression 18] 。 19. The system according to claim 17, wherein, The R GI It can be represented by the following mathematical expression, [Mathematical Expression 21] 。 20. A method for assisting in blood glucose management of a subject using a computer system, wherein, The computer system includes sensors, a processor, storage units, and a user interface. The method includes: The GA value of the subject is obtained by measuring the body fluids of the subject using the sensor. Send the GA values ​​at different time points, namely GA[i] and GA[j], to the processor, where t[j] > t[i]. Based on mathematical formula 1, the processor generates the GA glucose management index (GMI), which is the average blood glucose value calculated from the GA value during the period between two time points t[i] and t[j]. GA [i, j], [Mathematical Expression 1] Among them, C GA [i,j]=1-exp[-(t[j]-t[i]) / τ Alb ], τ Alb This represents the time constant for the decrease in albumin. Generate the glucose management index (GMI) based on the GA. GA The output information of [i, j] is sent to the user interface; and The user interface is used to output the output information received from the processor to the object.

21. The method for assisting a subject in blood glucose management using a computer system according to claim 20, wherein, The method includes: (a) The body fluids of the subject are measured by the sensor to obtain the GA value of the subject and the GA value is a multiple time series of glycated albumin values ​​from time point t[q] to t[r], where q > r; (b) The glucose management index, or GMI, obtained by the processor based on the time series of GA values ​​derived from the acquired time series GA values. GA and glycation sensitivity ratio R GI Using the following mathematical formula, Eq. 18, calculate the GMI of HbA1c values ​​from t[q] to t[r]. HbA1c [q, r]; [Mathematical Expression 18] in, [Mathematical Expression 19] (c) Using the processor, obtain the HbA1c value of the object at time point t[q], i.e., HbA1c[q]; (d) Through the processor, the GMI is determined based on the HbA1c value during the period from t[q] to t[r]. HbA1c [q, r] and the HbA1c value of the object at time point t[q], i.e., HbA1c[q], are expressed using the following mathematical formula. [Mathematical Expression 15] The estimated HbA1c value of the object at time point t[r] is HbA1c[r], where r > q; and (d) The processor generates output information based on the predicted HbA1c value at time point t[r], i.e., HbA1c[r], and sends it to the user interface; (e) Output the output information received from the processor to the object through the user interface.

Images