Blood glucose level management system and blood glucose level management method

Abstract

A blood glucose level management system according to the present disclosure comprises: a blood glucose level fluctuation learning unit that learns blood glucose level fluctuations of a user on the basis of biological data including at least blood glucose level fluctuations of the user and meal data including at least a meal menu, a meal amount, a meal start time, and a required meal time of the user, and constructs a blood glucose level fluctuation model; a blood glucose level fluctuation prediction unit that predicts, before the user starts a meal, blood glucose level fluctuations of the user after the user has ingested the meal on the basis of the meal data regarding the meal about to be ingested and the blood glucose level fluctuation model, and calculates a predicted blood glucose level fluctuation value; and a result presentation unit that presents the predicted blood glucose level fluctuation value to the user.

Description

Blood glucose level management system and blood glucose level management method

[0001] The present disclosure relates to a blood glucose level management system, and more particularly to a blood glucose level management system that manages blood glucose levels based on biological data and dietary data.

[0002] Diet has a significant impact on health, but the effects of dietary content and eating behavior on the body are difficult to understand. Therefore, several technologies that provide advice on healthy eating are being considered.

[0003] For example, in Patent Document 1, a user's behavior is estimated based on behavioral information and biometric information, the estimated behavior results are applied to metabolic models to estimate blood glucose levels, and recommended behaviors are presented to the user based on the estimated blood glucose levels.

[0004] JP 2012-235869 A

[0005] Blood glucose levels fluctuate, and it is important to estimate and present time-series changes in order to encourage users to manage their blood glucose levels. Furthermore, factors that affect blood glucose level fluctuations include multiple conditions such as diet, an individual's constitution, health condition, and exercise history, and the technology disclosed in Patent Document 1 has the problem of not being able to adequately manage blood glucose levels.

[0006] The present disclosure has been made to solve the above-mentioned problems, and aims to provide a blood glucose level management system that allows users to adequately manage their blood glucose levels.

[0007] The blood glucose level management system according to the present disclosure includes a blood glucose level fluctuation learning unit that learns the blood glucose level fluctuations of a user based on biometric data including at least the blood glucose level fluctuations of the user and dietary data including at least the user's meal menu, meal amount, meal start time, and meal duration, and constructs a blood glucose level fluctuation model; a blood glucose level fluctuation prediction unit that, before the user starts eating, predicts the blood glucose level fluctuations of the user after consuming the meal based on the dietary data of the meal to be ingested and the blood glucose level fluctuation model, and calculates a blood glucose level fluctuation prediction value; and a result presentation unit that presents the blood glucose level fluctuation prediction value to the user.

[0008] According to the blood glucose management system of the present disclosure, the blood glucose fluctuations of the user are learned based on the user's biometric data and dietary data, a blood glucose fluctuation model is constructed, and a prediction of the user's blood glucose fluctuations after consuming a meal is made based on the dietary data of the meal to be consumed and the blood glucose fluctuation model, thereby making it possible to make highly accurate blood glucose predictions and effectively encourage the user to manage their blood glucose levels.

[0009] 1 is a functional block diagram showing the configuration of a blood glucose level management system of embodiment 1. FIG. 2 is a flowchart explaining processing in the blood glucose level management system of embodiment 1. FIG. 3 is a flowchart explaining a method for creating a blood glucose level fluctuation model. FIG. 4 is a flowchart explaining a method for predicting blood glucose level fluctuations. FIG. 5 is a diagram showing the results of calculations using a blood glucose level fluctuation model as a table. FIG. 6 is a diagram showing the results of calculations using a blood glucose level fluctuation model as a graph. FIG. 7 is a diagram showing predicted blood glucose levels as a table. FIG. 8 is a diagram showing predicted blood glucose levels as a graph. FIG. 9 is a diagram showing re-calculated predicted results of postprandial blood glucose level fluctuations as a table. FIG. 10 is a diagram showing an example of a display screen in a result presentation unit. FIG. 11 is a functional block diagram showing the configuration of a blood glucose level management system of embodiment 2. FIG. 12 is a flowchart explaining processing in the blood glucose level management system of embodiment 2. FIG. 13 is a diagram showing an example of a display screen in a result presentation unit. FIG. 14 is a functional block diagram showing the configuration of a blood glucose level management system of embodiment 3. FIG. 15 is a flowchart explaining processing in the blood glucose level management system of embodiment 3. FIG. 16 is a diagram showing an example of a display screen in a result presentation unit. FIG. 17 is a functional block diagram showing the configuration of a blood glucose level management system of embodiment 4. FIG. 18 is a flowchart explaining processing in the blood glucose level management system of embodiment 4. FIG. 19 is a functional block diagram showing the configuration of a blood glucose level management system of embodiment 5. FIG. 19 is a flowchart explaining processing in the blood glucose level management system of embodiment 5.

[0010] <First Embodiment> <System Configuration> Fig. 1 is a functional block diagram showing the configuration of a blood glucose level management system 100 according to a first embodiment of the present disclosure. As shown in Fig. 1, the blood glucose level management system 100 includes a biological data collection unit 1, a dietary data collection unit 2, a blood glucose level fluctuation learning unit 3, a blood glucose level fluctuation model storage unit 4, a blood glucose level fluctuation prediction unit 5, a dietary method advice creation unit 6, and a result presentation unit 7.

[0011] The biometric data collection unit 1 sequentially collects biometric data of a specific user (hereinafter referred to as "user") to be managed. The biometric data collected from the user includes at least blood glucose fluctuations [mg / dL], and may also include life log data related to daily life and health checkup data measured during health checkups.

[0012] Examples of life log data include the number of steps [steps], sleep time [hours], sleep score, heart rate [bpm], whether or not oral care was performed, stress level, and working hours.

[0013] Examples of health checkup data include height [cm], weight [kg], BMI [kg / m2], waist circumference [cm], blood pressure [mmHg], triglycerides [mg / dL], cholesterol [mg / dL], HbA1c [%], and smoking status.

[0014] Examples of methods for inputting biometric data include manual input, as well as input methods using invasive sensors, non-invasive sensors, wearable devices, cameras, and video cameras.

[0015] An example of manual input is a method in which weight, blood pressure, and blood glucose levels are recorded and saved on a personal computer using spreadsheet software, and then uploaded to a server computer via a public communication network such as the Internet, and then downloaded from there to the blood glucose level management system 100. Blood glucose levels can be measured using a commercially available personal blood glucose meter or the like that measures the blood glucose level of blood obtained by pricking a fingertip or the like with an attached measuring needle.

[0016] An example of an input method using an invasive sensor is to attach a wearable invasive sensor equipped with a measuring needle to the arm on a daily basis, store the blood glucose data obtained from the sensor as a file in the measuring device, and then upload the file to a server computer.

[0017] An example of an input method using a non-invasive sensor is a method in which blood glucose levels are measured non-invasively using laser technology in a commercially available stationary sensor, the acquired blood glucose data is saved as a file in the meter, and the file is then uploaded to a server computer.

[0018] An example of an input method using a wearable device is to use a commercially available wristwatch-type sensor to acquire daily activity data such as heart rate and number of steps, as well as sleep recording data, export the acquired data, and upload the output data file to a server computer.

[0019] An example of an input method using a camera is to take a picture of the display showing the measurement results of a scale and a blood glucose sensor on a smartphone, read the numbers using the character recognition function, save them to a file, and then upload the file to a server computer.

[0020] An example of an input method using a video camera is to take a video of a smartphone displaying the blood glucose sensor measurement results or a video of the user exercising, and then use a character recognition function or a behavior recognition service that analyzes the subject's biometric data and behavior from the video to read the numbers and exercise history, save them in a file, and upload them to a server computer.

[0021] The meal data collection unit 2 collects meal data for one meal. The meal data includes at least the meal menu (a list of the items in each dish), the amount of food eaten (g, servings), the time the meal started, and the time required to eat (min), and can also include data such as the nutritional value of the meal, energy (kcal), and the people who ate with the meal.

[0022] Examples of nutritional values ​​include carbohydrates [g], protein [g], fat [g], sugars [g], dietary fiber [g], salt [g] and GI (Glycemic Index) value.

[0023] Examples of methods for inputting meal data include manual input, as well as input methods using wearable devices, cameras, and video cameras.

[0024] An example of manual input is a method in which a meal menu, calories, and meal start time are entered into an input app on a smartphone, and the app outputs the data to a file, which is then uploaded to a server computer.

[0025] An example of a wearable device is a method that analyzes eating speed from arm movements measured with a wristwatch-type sensor, outputs the data to a file, and uploads the file to a server computer.In addition, research is being conducted on technology that estimates meal content from acceleration data from a wristwatch-type sensor worn on the dominant hand, and this technology could also be used.

[0026] An example of an input method using a camera is to take a photo of food with a smartphone, analyze the menu and calories of the food in the photo using an app, record it in a file, and upload the file to a server computer. Services that analyze food data from camera images are commonly used.

[0027] An example of an input method using a video camera is to film the eating process with a smartphone camera, analyze the food content and eating speed from the video, record the results in a file, and upload the file to a server.

[0028] The blood glucose fluctuation learning unit 3 uses each user's past meal data and past biological data to extract features that have a large impact on blood glucose fluctuations, and uses these features to construct a blood glucose fluctuation model for each user. The construction of the blood glucose fluctuation model will be described in more detail later.

[0029] The blood glucose level fluctuation model storage unit 4 is a storage device that accumulates the blood glucose level fluctuation model constructed by the blood glucose level fluctuation learning unit 3.

[0030] Before starting a meal, the blood glucose fluctuation prediction unit 5 predicts the user's blood glucose fluctuation after consuming the meal using the meal data of the meal to be consumed and the blood glucose fluctuation model stored in the blood glucose fluctuation model storage unit 4, and calculates a blood glucose fluctuation prediction value. For example, when the required meal time, meal menu, and meal amount are input, the time change in blood glucose level is output as a graph of the blood glucose fluctuation prediction value. Note that the required meal time for the meal data cannot be obtained before the meal starts, so it is input as an estimate. The method of predicting blood glucose fluctuation will be explained further below.

[0031] If the predicted blood glucose fluctuation value deviates from a preset reference value based on the predicted postprandial blood glucose fluctuation value predicted by the blood glucose fluctuation prediction unit 5, the dietary advice creation unit 6 creates dietary advice that prevents the predicted blood glucose fluctuation value from deviating from the reference value. An example of dietary advice will be given later.

[0032] The result presentation unit 7 presents to the user the dietary advice created by the dietary advice creation unit 6, the blood glucose fluctuation predicted value predicted by the blood glucose fluctuation prediction unit 5 when the dietary advice is not applied, and the blood glucose fluctuation predicted value recalculated by the blood glucose fluctuation prediction unit 5 when the dietary advice is applied.

[0033] <Processing in the System> Figure 2 is a flowchart explaining the processing in the blood glucose level management system 100 of embodiment 1 shown in Figure 1. When the blood glucose level management system 100 starts processing, the meal data collection unit 2 collects meal data (step S1), and the biological data collection unit 1 collects biological data (step S2). These processes can be performed in either order, or simultaneously. Furthermore, these processes are data collection for building a blood glucose level fluctuation model of the user, and the biological data also includes data on blood glucose level fluctuations before and after the user's meal.

[0034] Next, a blood glucose fluctuation model is created in the blood glucose fluctuation learning unit 3 (step S3). Here, a method for creating the blood glucose fluctuation model will be described with reference to FIG.

[0035] 3 is a flowchart explaining a method for creating a blood glucose level fluctuation model. As shown in FIG. 3, the blood glucose level fluctuation learning unit 3 first analyzes data items that affect blood glucose level fluctuations from the collected biological data and dietary data (step S31). The analysis method uses statistical techniques or machine learning, such as inputting the biological data and dietary data and correlating them with blood glucose level fluctuations before and after meals.

[0036] For example, blood glucose level fluctuations, meal data A and B, life log data C, D and E, and health check data F, G, H, and I are input as input data, and it is output that blood glucose level fluctuations are strongly influenced by meal data A, life log data C, and health check data H.

[0037] Next, the blood glucose fluctuation learning unit 3 constructs a blood glucose fluctuation model using data items with a high degree of influence (step S32).

[0038] For example, a fluctuation model is assumed in which a predicted blood glucose level F(t) at time t after a meal is expressed by the following equation (1).

[0039]

[0040] In the above formula (1), A(t) is the value of the dietary data A at time t, C(t) is the value of the life log data C at time t, H(t) is the value of the health check data H, and J is an arbitrary constant. The above formula (1) shows a case where the fluctuation model is expressed as a nonlinear model, but it may also be expressed as a linear model. Note that AI (artificial intelligence) or machine learning can be used to create the blood glucose level fluctuation model.

[0041] Returning now to the explanation of the flowchart in Fig. 2, after the blood glucose level fluctuation model is created in step S3, dietary data of the meal that the user will now take is collected in the dietary data collection unit 2 (step S4).

[0042] Next, the blood glucose fluctuation prediction unit 5 predicts the user's postprandial blood glucose fluctuation using the blood glucose fluctuation model created by the blood glucose fluctuation learning unit 3 (step S5). Here, the method for predicting blood glucose fluctuation will be described with reference to FIG.

[0043] 4 is a flowchart illustrating a method for predicting blood glucose fluctuations. As shown in Fig. 4, the blood glucose fluctuation prediction unit 5 first receives data on meals that the user plans to take from the meal data collection unit 2 (step S51).

[0044] An example of meal data that a user plans to eat is a meal menu of katsudon (pork cutlet rice bowl), cauliflower, pumpkin, boiled green beans, and tea, with the meal starting time at 12:30, the meal time (planned) being 20 minutes, and the calories being 983 kcal.

[0045] Next, the blood glucose fluctuation model created by the blood glucose fluctuation learning unit 3 is used to predict postprandial blood glucose fluctuation (step S52).

[0046] For example, a fluctuation model is assumed in which a predicted blood glucose level f(t) at time t after a meal is expressed by the following equation (2).

[0047]

[0048] In the above formula (2), a(t) is the calorie intake per unit of time elapsed since the meal (calorie intake / meal time) [kcal], b(t) is the number of steps since the previous meal [thousand steps], c(t) is the muscle percentage, and d(t) is the average blood glucose level at rest.

[0049] The results of calculations using the above formula (2) are shown in the table of Figure 5 and as a graph in Figure 6. In Figures 5 and 6, the time unit of time t is 5 minutes, time t=0 is the start of the meal, and time t=-1 indicates the time immediately before the start of the meal. In this case, since a(t) is 0, the average resting blood glucose value of 77 is the predicted blood glucose value f(t).

[0050] 5 and 6 show that the predicted blood glucose level peaked at 156 mg / dL 15 minutes after the start of the meal. Also, at time t=4, a(t)=0, indicating that the meal ended 20 minutes after the start of the meal.

[0051] Returning now to the explanation of the flowchart in Fig. 2, after predicting postprandial blood glucose fluctuations in step S5, the blood glucose fluctuation prediction unit 5 determines whether the blood glucose fluctuations deviate from the reference value based on the prediction results (step S6).

[0052] Here, the reference value is an arbitrarily set value, and can be, for example, 140 mg / dL, above which a blood glucose spike is determined, or 126 mg / dL, which is the lower limit of fasting blood glucose used as an index of diabetes.

[0053] In step S6, if it is determined that the value deviates from the standard value (if Yes), the process proceeds to step S7 in the dietary advice creation unit 6, and if it is determined that the value does not deviate from the standard value (if No), the process proceeds to step S8.

[0054] Here, the reference value used is 140 mg / dL, which is the standard for determining a blood glucose spike. Figure 7 is a table showing the predicted blood glucose level f(t) in the table shown in Figure 5, and Figure 8 is a graph of this. Figure 8 shows the reference value of 140 mg / dL, and it can be seen that it exceeds 140 mg / dL when t = 3.

[0055] In step S7, the blood glucose fluctuation model used for the prediction is referenced, and values ​​of changeable items that affect blood glucose fluctuation are reset.

[0056] That is, the items used in the blood glucose level fluctuation model are checked, and changeable items, i.e., items related to the food data to be consumed and items related to the life log, are identified. Here, the "calorie intake per unit of elapsed time of meal," a(t) used in formula (2), is used.

[0057] Then, the value of "calorie intake per unit of elapsed time after meal" is changed and input to the blood glucose fluctuation prediction unit 5, and the prediction of postprandial blood glucose fluctuation is performed again in step S5.

[0058] Figure 9 is a table showing the recalculated predicted postprandial blood glucose fluctuations. Figure 9 shows the recalculated predicted postprandial blood glucose level f(t), calorie intake per unit of time elapsed after eating a(t), meal time, and calorie intake. Figure 9 shows that the blood glucose level is 114 mg / dL at t=3. This indicates that taking more time to eat lowers the peak blood glucose level.

[0059] After predicting postprandial blood glucose fluctuations in step S5, a determination is again made in step S6 as to whether the blood glucose fluctuations deviate from the reference value. As shown in Fig. 9, the blood glucose peak is 114 mg / dL, so it is determined in step S6 that the blood glucose fluctuations do not deviate from the reference value, and the process proceeds to step S8 in the blood glucose fluctuation prediction unit 5.

[0060] In step S8, it is determined whether the prediction has been made two or more times, i.e., whether the prediction has been redone. If the prediction has been made once (No), no dietary advice is necessary, and the series of processes is terminated without creating any advice.

[0061] On the other hand, if it is determined in step S8 that prediction has been performed two or more times (Yes), the items changed during the re-prediction, i.e., the improvement values, are converted into advice for the user, and dietary advice is created in the dietary advice creation unit 6 (step S9).

[0062] That is, as can be seen from FIG. 9, by changing the calorie intake a(t) per unit of elapsed time after eating, the blood glucose peak will not deviate from the reference value, and therefore the eating method advice creating unit 6 creates eating method advice such as "Take 25 minutes to eat your meal."

[0063] Returning now to the explanation of the flowchart in Fig. 2, after the dietary advice has been prepared in step S9, the result presentation unit 7 presents the blood glucose fluctuations before and after the dietary improvement and the dietary advice to the user (step S10).

[0064] Figure 10 shows the display screen of a smartphone as an example of the result presentation unit 7. The display screen displays an image of the meal the user is about to eat, taken with the smartphone, and the eating advice "Take 25 minutes to eat it," as well as a graph of blood glucose level fluctuations before the eating method was improved, when the food was eaten as is (dashed line), and a graph of blood glucose level fluctuations after the eating method was improved, when the food was eaten as advised (solid line), along with the reference value of 140 mg / dL.

[0065] <Effects> By using the blood glucose level management system 100 of embodiment 1 described above, it is possible to construct a blood glucose level fluctuation model that takes into account individual differences in factors that affect blood glucose level fluctuations, and to perform highly accurate blood glucose level predictions, thereby encouraging the user to manage their blood glucose levels effectively.

[0066] <Embodiment 2> <System configuration> Fig. 11 is a functional block diagram showing the configuration of a blood glucose level management system 200 according to embodiment 2 of the present disclosure. As shown in Fig. 11, the blood glucose level management system 200 is basically the same as the blood glucose level management system 100 shown in Fig. 1, but is configured so that the blood glucose fluctuation prediction unit 5 also receives as input the biological data collected by the biological data collection unit 1.

[0067] That is, in the blood glucose level management system 200 , the biological data collected by the biological data collection unit 1 while the user is eating is input to the blood glucose level fluctuation prediction unit 5 .

[0068] The biometric data collected by the biometric data collection unit 1 and the collection method thereof are basically the same as the biometric data and collection method described in embodiment 1, but also include items that can only be obtained during a meal, such as the number of chews (number of times chewed), intake amount, and intake speed.

[0069] Then, by inputting biological data while the user is eating into the blood glucose fluctuation prediction unit 5, blood glucose fluctuation prediction is repeated even while the user is eating. For example, by adding the number of chews, the chewing side (i.e., which side of the teeth is used for chewing), the heart rate during the meal, and the amount of food per mouthful to the blood glucose fluctuation model, if a small number of chews per mouthful affects blood glucose fluctuations, the eating method advice creation unit 6 can create eating method advice such as "chew your food more thoroughly."

[0070] In addition, if postprandial blood glucose levels are predicted before a meal and estimated values ​​of dietary data and biological data during the meal are used in the prediction, a process is also envisioned in which the estimated values ​​are replaced with actual measured values ​​obtained during the meal.

[0071] <Processing in the System> Figure 12 is a flowchart explaining the processing in the blood glucose level management system 200 of embodiment 2 shown in Figure 11. As shown in Figure 12, the processing in the blood glucose level management system 200 is basically the same as the processing in the blood glucose level management system 100 shown in Figure 2, but after the processing of step S10 in the result presenting unit 7, processing of step S12 has been added in which the biological data collecting unit 1 collects biological data of the user while the user is consuming a meal and inputs the data to the blood glucose fluctuation predicting unit 5.

[0072] Figure 13 shows the display screen of a smartphone as an example of the result presentation unit 7. The display screen displays an image of the meal the user is about to eat, taken with the smartphone, and the eating advice "chew your food 20 to 30 times," as well as a graph of blood glucose level fluctuations before the eating method was improved, when the food was eaten as is (dashed line), and a graph of blood glucose level fluctuations after the eating method was improved, when the food was eaten as advised (solid line), along with the reference value of 140 mg / dL.

[0073] <Effects> By using the blood glucose level management system 200 of the second embodiment described above, in addition to the effects of the blood glucose level management system 100 of the first embodiment, it is possible to give advice to the user on their behavior during meals by repeating blood glucose level predictions even during meals.

[0074] <Embodiment 3> <System configuration> Fig. 14 is a functional block diagram showing the configuration of a blood glucose level management system 300 according to embodiment 3 of the present disclosure. As shown in Fig. 14, the blood glucose level management system 300 includes a biological data storage unit 8 that accumulates biological data collected by the biological data collection unit 1, in addition to the configuration of the blood glucose level management system 200 shown in Fig. 11.

[0075] That is, in the blood glucose level management system 300 , the biological data collected by the biological data collection unit 1 while the user is eating is input to the blood glucose level fluctuation prediction unit 5 and stored in the biological data storage unit 8 .

[0076] The biological data collected by the biological data collection unit 1 while the user is eating includes data on meals that took 25 minutes or more, and this data is stored as biological data for the past few days in the biological data storage unit 8. This biological data for the past few days is read out by the dietary advice creation unit 6, processed so that it can be used as reference information, and presented to the user by the result presentation unit 7 together with dietary advice.

[0077] <Processing in the System> Figure 15 is a flowchart explaining the processing in the blood glucose level management system 300 of embodiment 3 shown in Figure 14. As shown in Figure 15, in addition to the processing in the blood glucose level management system 200 shown in Figure 12, the processing in the blood glucose level management system 300 further includes, following step S2, a process of storing biological data in the biological data storage unit 8 (step S21), and in step S10, in addition to the blood glucose level fluctuations before and after dietary method improvement and dietary method advice, past biological data is also presented as reference information.

[0078] Figure 16 shows the display screen of a smartphone as an example of the result presentation unit 7. The display screen displays an image of the meal the user is about to eat, taken with the smartphone, the eating advice "Take 25 minutes to eat," a graph of blood glucose level fluctuations before the eating method improvement, when the food is eaten as is (dashed line), and a graph of blood glucose level fluctuations after the eating method improvement, when the food is eaten as advised (solid line), as well as a record of compliance with the dietary improvement suggestions for the past five days.

[0079] The dietary improvement suggestion compliance record shows the dates of the last five days and whether or not the suggestion was implemented using a symbol. For example, on April 1st (4 / 1), the suggestion was not implemented and is marked with an X, but on April 2nd (4 / 2), April 3rd (4 / 3), and April 3rd (4 / 3), the suggestion was implemented and is marked with a O. Also, on April 4th (4 / 4), the suggestion was not implemented sufficiently and is marked with a △.

[0080] In this way, the user can flexibly choose their actions by presenting the results of past advice execution along with dietary advice on the result presentation unit 7. For example, after eating healthy meals for three days in accordance with dietary improvement suggestions, the user can select, "Today, I'll eat as much as I want of the foods I particularly like."

[0081] <Effects> By using the blood glucose level management system 300 of the third embodiment described above, in addition to the effects of the blood glucose level management system 100 of the first embodiment, it is possible to more flexibly support the user's dietary behavior selection by presenting the results of past dietary improvement suggestions to the user. Furthermore, it is possible to keep the user motivated to improve their diet.

[0082] <Fourth embodiment> <System configuration> Fig. 17 is a functional block diagram showing the configuration of a blood glucose level management system 400 according to a fourth embodiment of the present disclosure. As shown in Fig. 17, the blood glucose level management system 400 includes, in addition to the configuration of the blood glucose level management system 100 shown in Fig. 1, a similar blood glucose level fluctuation model selection unit 9 for selecting a blood glucose level fluctuation model of another user having similar biometric data to that of the user.

[0083] The biological data collected by the biological data collection unit 1 is input to the blood glucose fluctuation learning unit 3 and also to the similar blood glucose fluctuation model selection unit 9. The similar blood glucose fluctuation model selection unit 9 selects a blood glucose fluctuation model of another user who has similar biological data to the inputted user's biological data, and inputs the selected model to the blood glucose fluctuation prediction unit 5 as a similar blood glucose fluctuation model.

[0084] That is, instead of generating one's own blood glucose level fluctuation model, the blood glucose level model of another user with similar biometric data to one's own is used to predict blood glucose level fluctuations. This makes it possible to accurately predict blood glucose level fluctuations even before one's own blood glucose level fluctuation model is constructed.

[0085] Here, the similarity is determined by focusing on, for example, items measured in a regular health checkup or characteristic items that deviate from the average value among the biometric data acquired from the user. A blood glucose level model of another user with similar items is selected as the similar blood glucose fluctuation model. In addition, methods for determining similarity can use, for example, the magnitude of the Euclidean distance of the biometric data or the magnitude of the correlation coefficient of the items used to determine similarity.

[0086] The blood glucose level management system according to the present disclosure is not a system for managing the blood glucose levels of only a specific user, but a system for managing the blood glucose levels of multiple users, and stores the biometric data of multiple users and blood glucose fluctuation models generated based on the data, from which a similar blood glucose level fluctuation model can be selected. The biometric data of multiple users can be stored in a storage unit provided inside the similar blood glucose level fluctuation model selection unit 9, and the blood glucose level fluctuation models of multiple users can also be stored in a storage unit provided inside the similar blood glucose level fluctuation model selection unit 9. The blood glucose level fluctuation models of multiple users can be read out and selected from the blood glucose level fluctuation model storage unit 4.

[0087] To generate a blood glucose level fluctuation model, at least one correct answer data, i.e., the actual measured value of blood glucose level fluctuation after eating, is used, so it is necessary to record blood glucose level fluctuation after eating at least once, which takes time. Furthermore, to improve the accuracy of the blood glucose level fluctuation model, it is necessary to collect a lot of correct answer data, and since many meals are eaten, it takes even more time.

[0088] However, by using the similar blood glucose fluctuation model, it becomes possible to start managing one's own blood glucose level without ever actually measuring blood glucose fluctuations after meals, thereby increasing the convenience of the blood glucose management system.

[0089] The initial blood glucose level management uses the similar blood glucose level fluctuation model, but after constructing the own blood glucose level fluctuation model, the similar blood glucose level fluctuation model is not used and the own blood glucose level fluctuation model is used instead.

[0090] The blood glucose fluctuation models of others stored in the blood glucose fluctuation model memory unit 4 are constructed by performing actual measurements of blood glucose fluctuations multiple times, so there is a lot of learning data and they are highly accurate, but the initial blood glucose fluctuation model of oneself is constructed at a stage when there are few actual measurements of blood glucose fluctuations, so there is little learning data and they are low in accuracy, so a similar blood glucose fluctuation model is used for initial blood glucose management.

[0091] However, since a blood glucose fluctuation model of oneself, constructed by actually measuring blood glucose fluctuations multiple times, is more accurate than a blood glucose fluctuation model of another person, it is desirable to ultimately use one's own blood glucose fluctuation model for blood glucose management. Therefore, in this embodiment, the blood glucose fluctuation model to be used is changed.

[0092] <Processing in the System> Figure 18 is a flowchart explaining the processing in the blood glucose level management system 400 of embodiment 4 shown in Figure 17. As shown in Figure 18, in addition to the processing in the blood glucose level management system 100 shown in Figure 2, the processing in the blood glucose level management system 400 further includes, following step S2, a process of selecting a similar blood glucose level fluctuation model in the similar blood glucose level fluctuation model selection unit 9 if a blood glucose level fluctuation model has not been constructed (step S22).

[0093] The display on the result presentation unit 7 is the same as the display on the result presentation unit 7 of the blood glucose level management system 100 of the first embodiment shown in FIG.

[0094] <Effects> By using the blood glucose level management system 400 of the fourth embodiment described above, in addition to the effects of the blood glucose level management system 100 of the first embodiment, a new user can perform blood glucose level predictions even before creating their own blood glucose level fluctuation model, and can receive suggestions for improving their diet.

[0095] <Fifth embodiment> <System configuration> Fig. 19 is a functional block diagram showing the configuration of a blood glucose level management system 500 according to a fifth embodiment of the present disclosure. As shown in Fig. 19, the blood glucose level management system 500 has basically the same configuration as the blood glucose level management system 400 shown in Fig. 17, except that the function of the blood glucose level fluctuation learning unit 3 has changed to a blood glucose level fluctuation learning unit 31.

[0096] In other words, the blood glucose management system 500 is the same as the blood glucose management system 400 of embodiment 4 in that it uses a similar blood glucose fluctuation model to predict blood glucose fluctuations instead of generating its own blood glucose fluctuation model, but differs in that the blood glucose fluctuation learning unit 31 re-learns the similar blood glucose fluctuation model based on the results of analyzing the user's own biological data.

[0097] That is, the blood glucose fluctuation learning unit 31 does not construct a blood glucose fluctuation model from scratch, but if there is a similar blood glucose fluctuation model selected by the similar blood glucose fluctuation model selection unit 9, it uses the similar blood glucose fluctuation model for initial blood glucose management, and then re-learns the similar blood glucose fluctuation model to obtain a blood glucose fluctuation model that is more suited to the user. The blood glucose fluctuation model obtained by re-learning is stored in the blood glucose fluctuation model storage unit 4.

[0098] To re-learn the similar blood glucose fluctuation model, AI or machine learning is used to analyze data items that affect blood glucose fluctuations from one's own biological data and dietary data, and based on the analysis results, the similar blood glucose fluctuation model is re-learned and used as one's own blood glucose fluctuation model.

[0099] <Processing in the System> Figure 20 is a flowchart explaining the processing in the blood glucose level management system 500 of embodiment 5 shown in Figure 19. As shown in Figure 20, the processing in the blood glucose level management system 500 is such that the processing in step S3 in the blood glucose level management system 400 shown in Figure 18 is replaced by processing in step S23 in which, if a similar blood glucose level fluctuation model has already been selected, the model is re-learned using the user's own actual measurement value data.

[0100] The display on the result presentation unit 7 is the same as the display on the result presentation unit 7 of the blood glucose level management system 100 of the first embodiment shown in FIG.

[0101] <Effects> By using the blood glucose level management system 500 of embodiment 5 described above, in addition to the effects of the blood glucose level management system 100 of embodiment 1, new users can perform blood glucose level predictions even before creating their own blood glucose level fluctuation model, and by relearning similar blood glucose level fluctuation models, they can manage their blood glucose levels using a blood glucose level fluctuation model that is more suited to them.

[0102] In addition, since there is no need to build a blood glucose fluctuation model from scratch, the cost of building a blood glucose fluctuation model can be reduced. Furthermore, by storing the retrained blood glucose fluctuation model in the blood glucose fluctuation model storage unit 4, the number of blood glucose fluctuation models available to users who will be using the blood glucose management system 500 for the first time will increase. This makes it possible to select a blood glucose fluctuation model that is more suited to the user.

[0103] <Usage of Blood Glucose Level Management System> In the blood glucose level management systems 100 to 500 of the first to fifth embodiments described above, components other than the biological data collection unit 1, the dietary data collection unit 2, and the result presentation unit 7 can be constructed on a server computer that constitutes a cloud environment. Then, biological data and dietary data are acquired from the user of the system via a communication network such as the Internet, a blood glucose fluctuation model is constructed and blood glucose level prediction is performed on the server computer, and dietary advice, predicted blood glucose fluctuation values, etc. are presented via the communication network to the result presentation unit 7, for example, the display screen of the user's smartphone. Note that other usage modes are also possible.

[0104] The above description is illustrative in all respects, and it is understood that countless variations not illustrated can be envisioned.

[0105] It is possible to freely combine the embodiments, and to modify or omit the embodiments as appropriate.

[0106] The present disclosure described above will be summarized as an appendix.

[0107] (Supplementary Note 1) A blood glucose management system comprising: a blood glucose fluctuation learning unit that learns the blood glucose fluctuations of the user based on biometric data including at least blood glucose fluctuations of the user and meal data including at least the user's meal menu, meal amount, meal start time, and meal duration, and constructs a blood glucose fluctuation model; a blood glucose fluctuation prediction unit that, before the user starts eating, predicts the blood glucose fluctuations of the user after consuming the meal based on the meal data of the meal to be ingested and the blood glucose fluctuation model, and calculates a blood glucose fluctuation prediction value; and a result presentation unit that presents the blood glucose fluctuation prediction value to the user.

[0108] (Appendix 2) The blood glucose management system according to Appendix 1 further comprises a dietary advice creation unit that creates dietary advice to prevent the blood glucose fluctuation prediction value from deviating from a predetermined reference value when the blood glucose fluctuation prediction value deviates from the reference value, and the result presentation unit presents to the user the dietary advice, the blood glucose fluctuation prediction value before the dietary improvement based on the dietary advice, and the blood glucose fluctuation prediction value after the dietary improvement based on the dietary advice.

[0109] (Appendix 3) The blood glucose level management system according to appendix 1 or appendix 2, wherein the blood glucose level fluctuation learning unit uses data items contained in the dietary data and the biometric data that have a large impact on the blood glucose level fluctuation of the user as features, and constructs the blood glucose level fluctuation model using the features.

[0110] (Appendix 4) The blood glucose management system according to Appendix 2, wherein the dietary method advice creation unit, when the predicted blood glucose fluctuation value deviates from a preset reference value, creates the dietary method advice by referring to the blood glucose fluctuation model used to predict the user's blood glucose fluctuation, and resetting values ​​of changeable data items among data items that affect the blood glucose fluctuation and are included in the dietary data and the biological data.

[0111] (Appendix 5) The blood glucose level management system described in Appendix 2, wherein the blood glucose level fluctuation prediction unit calculates the blood glucose level fluctuation prediction value even during the meal based on the biometric data of the user during the meal, and the dietary method advice creation unit creates the dietary method advice based on the blood glucose level fluctuation prediction value calculated during the meal.

[0112] (Supplementary Note 6) A blood glucose level management system according to Supplementary Note 2, further comprising a biometric data storage unit that stores the biometric data of the user during the meal, and the dietary method advice creation unit processes the biometric data stored in the biometric data storage unit to present it to the user as reference information.

[0113] (Appendix 7) A blood glucose management system as described in Appendix 2, further comprising a similar blood glucose fluctuation model selection unit that selects a similar blood glucose fluctuation model of another user having biometric data similar to that of the user, and the blood glucose fluctuation prediction unit uses the similar blood glucose fluctuation model when calculating the blood glucose fluctuation prediction value for the first time.

[0114] (Appendix 8) The blood glucose level management system according to Appendix 7, wherein the blood glucose level fluctuation learning unit re-learns the similar blood glucose level fluctuation model using the biometric data and dietary data of the user, and sets the similar blood glucose level fluctuation model as the blood glucose level fluctuation model of the user.

[0115] (Supplementary Note 9) The blood glucose level management system according to Supplementary Note 2, further comprising a blood glucose level fluctuation model storage unit that stores the blood glucose level fluctuation model constructed by the blood glucose level fluctuation learning unit.

[0116] (Supplementary Note 10) A blood glucose management method comprising: (a) a step of learning the blood glucose fluctuations of the user based on biometric data including at least the blood glucose fluctuations of the user and meal data including at least the user's meal menu, meal amount, meal start time, and meal duration, and constructing a blood glucose fluctuation model; (b) a step of predicting the blood glucose fluctuations of the user after consuming the meal based on the meal data of the meal to be ingested and the blood glucose fluctuation model, before the user starts eating, and calculating a blood glucose fluctuation prediction value; and (c) a step of presenting the blood glucose fluctuation prediction value to the user.

[0117] (Appendix 11) (d) A blood glucose management method as described in Appendix 10, further comprising a step of creating dietary advice to prevent deviation from a predetermined reference value when the predicted blood glucose fluctuation value deviates from the reference value, wherein step (c) comprises a step of presenting to the user the dietary advice, the predicted blood glucose fluctuation value before the dietary improvement based on the dietary advice, and the predicted blood glucose fluctuation value after the dietary improvement based on the dietary advice.

[0118] (Appendix 12) The blood glucose management method according to Appendix 10 or 11, wherein step (a) comprises a step of using data items contained in the dietary data and the biometric data that have a large impact on the blood glucose fluctuations of the user as features, and constructing the blood glucose fluctuation model using the features.

[0119] (Appendix 13) The blood glucose management method according to Appendix 11, wherein step (d) creates the dietary advice by, when the predicted blood glucose fluctuation value deviates from a pre-set reference value, referring to the blood glucose fluctuation model used to predict the user's blood glucose fluctuation, and resetting values ​​of the data items that are changeable and that affect the blood glucose fluctuation and are included in the dietary data and the biological data.

[0120] (Supplementary Note 14) The blood glucose level management method according to Supplementary Note 11, wherein the step (d) creates the dietary advice when the blood glucose level fluctuation prediction in the step (b) is performed two or more times.

Claims

1. A blood glucose level management system comprising: a blood glucose level fluctuation learning unit that learns the blood glucose level fluctuations of a user based on biometric data including at least the blood glucose level fluctuations of the user and meal data including at least the user's meal menu, meal amount, meal start time, and meal duration, and constructs a blood glucose level fluctuation model; a blood glucose level fluctuation prediction unit that, before the user starts eating, predicts the blood glucose level fluctuations of the user after consuming the meal based on the meal data of the meal to be ingested and the blood glucose level fluctuation model, and calculates a blood glucose level fluctuation prediction value; and a result presentation unit that presents the blood glucose level fluctuation prediction value to the user.

2. A blood glucose management system as described in claim 1, further comprising a dietary advice creation unit that creates dietary advice to prevent deviation from a predetermined standard value when the predicted blood glucose fluctuation value deviates from the standard value, and the result presentation unit presents to the user the dietary advice, the predicted blood glucose fluctuation value before the dietary improvement based on the dietary advice, and the predicted blood glucose fluctuation value after the dietary improvement based on the dietary advice.

3. A blood glucose level management system as described in claim 1 or claim 2, wherein the blood glucose level fluctuation learning unit uses data items contained in the dietary data and the biometric data that have a large impact on the blood glucose level fluctuations of the user as features, and constructs the blood glucose level fluctuation model using the features.

4. A blood glucose management system as described in claim 2, wherein, when the predicted blood glucose fluctuation value deviates from a predetermined reference value, the dietary method advice creation unit creates the dietary method advice by referring to the blood glucose fluctuation model used to predict the user's blood glucose fluctuation and resetting values ​​of the data items that are changeable and that affect the blood glucose fluctuation and are included in the dietary data and the biological data.

5. A blood glucose management system as described in claim 2, wherein the blood glucose fluctuation prediction unit calculates the blood glucose fluctuation prediction value during the meal based on the biometric data of the user during the meal, and the dietary advice creation unit creates the dietary advice based on the blood glucose fluctuation prediction value calculated during the meal.

6. A blood glucose level management system as described in claim 2, further comprising a biometric data storage unit that stores the biometric data of the user during the meal, and wherein the dietary method advice creation unit processes the biometric data stored in the biometric data storage unit so as to present it to the user as reference information.

7. A blood glucose management system as described in claim 2, further comprising a similar blood glucose fluctuation model selection unit that selects a similar blood glucose fluctuation model of another user having biometric data similar to that of the user, and the blood glucose fluctuation prediction unit uses the similar blood glucose fluctuation model when calculating the blood glucose fluctuation prediction value for the first time.

8. A blood glucose level management system as described in claim 7, wherein the blood glucose level fluctuation learning unit re-learns the similar blood glucose level fluctuation model using the user's biological data and dietary data, and uses the similar blood glucose level fluctuation model as the user's blood glucose level fluctuation model.

9. The blood glucose level management system according to claim 2, further comprising a blood glucose level fluctuation model storage unit that stores the blood glucose level fluctuation model constructed by the blood glucose level fluctuation learning unit.

10. A blood glucose management method comprising: (a) a step of learning the blood glucose fluctuations of a user based on biometric data including at least the user's blood glucose fluctuations and meal data including at least the user's meal menu, meal amount, meal start time, and meal duration, and constructing a blood glucose fluctuation model; (b) a step of predicting the user's blood glucose fluctuations after consuming the meal based on the meal data of the meal to be consumed and the blood glucose fluctuation model before the user starts eating, and calculating a blood glucose fluctuation prediction value; and (c) a step of presenting the blood glucose fluctuation prediction value to the user.

11. A blood glucose management method as described in claim 10, further comprising: (d) a step of creating dietary advice to prevent deviation from a predetermined standard value when the predicted blood glucose fluctuation value deviates from the standard value, wherein step (c) includes a step of presenting to the user the dietary advice, the predicted blood glucose fluctuation value before the dietary improvement based on the dietary advice, and the predicted blood glucose fluctuation value after the dietary improvement based on the dietary advice.

12. A blood glucose management method as described in claim 10 or claim 11, wherein step (a) comprises a step of using data items contained in the dietary data and the biometric data that have a large impact on the blood glucose fluctuations of the user as features, and constructing the blood glucose fluctuation model using the features.

13. A blood glucose management method as described in claim 11, wherein step (d) creates the dietary advice by, if the predicted blood glucose fluctuation value deviates from a predetermined reference value, referring to the blood glucose fluctuation model used to predict the user's blood glucose fluctuation, and resetting values ​​for changeable data items among data items that affect the blood glucose fluctuation and are included in the dietary data and biological data.

14. The blood glucose level management method according to claim 11, wherein said step (d) creates said dietary advice when said blood glucose level fluctuation prediction in said step (b) is performed two or more times.

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