JP5974348B2 - Blood glucose level prediction method and blood glucose level prediction system - Google Patents

Description

  The present invention relates to a blood sugar level prediction method and a blood sugar level prediction system.

Traditionally, with regard to lifestyle-related diseases caused by daily lifestyle habits, information on health check results (vital data such as blood pressure, blood glucose level, body weight, and body fat percentage), dietary habits, and amount of exercise, etc. It is necessary to set the target intake and target calorie consumption per day due to exercise. In addition, based on these settings, there are methods to compare actual calorie intake and calorie consumption and vital data to encourage subsequent meals and exercises.
As such a method, a method of performing meal and exercise guidance from a menu and nutrient information table, a meal intake information table, a medical examination result, and the like is disclosed (for example, see Patent Document 1). In addition, a meal menu or a health management method provided by a delivery company that provides meals and a medical institution or preventive medical center is disclosed (for example, see Patent Document 2).

JP 2008-310401 A JP 2004-240792 A

  However, hypoglycemia, especially when blood sugar levels are too low, because the dietary intake and exercise exercises after meals do not predict subsequent blood sugar levels, i. There is a problem that it cannot be predicted.

  SUMMARY An advantage of some aspects of the invention is to solve at least a part of the problems described above, and the invention can be implemented as the following forms or application examples.

  [Application Example 1] A blood glucose level prediction method according to this application example includes a meal start time record in which a time when a user starts a meal, a meal provision order record in which the order of the meals is recorded, An input receiving step for receiving an input of a meal information record in which meal information indicating the content is recorded; a meal end time record in which the end time of the meal is recorded; and a first prediction for the calorie intake calculated from the meal information A first prediction algorithm calculating step for calculating a curve; a first model waveform generating step for generating a first model waveform by analyzing a change tendency of a blood glucose level with respect to the calorie intake; the first prediction curve and the first model; A first prediction curve deforming step of comparing the blood glucose level change of the waveform and deforming the first prediction curve, and a calorie consumption due to exercise performed by the user A second prediction algorithm calculation step of calculating a second prediction curve, characterized in that it comprises a.

  According to this application example, it is possible to easily predict accurate blood glucose level by sending meal information such as calorie intake, meal order, meal time, etc., which are particularly important for predicting blood glucose level, to the blood glucose level prediction device through the system. As a result, the user can easily know the change tendency of the blood glucose level without being bothered by the meal time and the order of meals during the meal. In particular, it is possible to avoid a change that lowers the blood sugar level.

  Application Example 2 In the blood sugar level prediction method according to the application example described above, the user position information is transmitted in the information transmission step.

  According to this application example, by transmitting the position information of the user, it is possible to acquire an appropriate meal menu not only at a meal provider who already knows but also at a place where the user is uninformed. As a result, the number of choices increases, the food to be taken can be taken without bias, and the blood glucose level prediction system can be used without getting tired.

  Application Example 3 A blood glucose level prediction system according to this application example includes an operation unit that receives input of a user's blood glucose level, meal information, and exercise information, a storage unit that stores the user's history information, An intake energy acquisition unit that acquires calorie intake for meal information, a first prediction curve that predicts a change in blood glucose level due to the calorie intake, and a second prediction that predicts change in blood glucose level due to calorie consumption for the exercise information A blood sugar level prediction apparatus comprising: a calculation unit for obtaining a curve; a consumption energy acquisition unit for acquiring calorie consumption from the exercise information; and a first communication unit for transmitting optimal calorie intake calculated from the first prediction curve; A second communication unit that transmits the optimal calorie intake from the first communication unit, and a search unit that extracts meal information that matches the optimal calorie intake A meal menu system, a POS system capable of recording a meal start time and a meal end time, and a cooking management system capable of recording the order of meals to be provided And the second communication unit and the first communication unit transmit meal information suitable for the optimal calorie intake to the blood sugar level predicting device.

  According to this application example, by transmitting detailed meal information from the POS system and the cooking management system to the blood sugar level predicting device, a blood sugar level predicting system that is in line with actual meal contents and has high accuracy is provided. be able to.

  Application Example 4 The blood sugar level prediction system according to the application example is characterized in that the blood sugar level prediction apparatus includes a position information acquisition unit.

  According to this application example, since the position information acquisition unit acquires the user's position information, the user's position information is used not only in the already-known meal provider but also in places where the user is uninformed. It is possible to extract meal providers by performing a search. Thereby, a user's meal information can be acquired more simply.

The schematic block diagram which shows the structure of the blood glucose level prediction system which concerns on embodiment. The schematic block diagram which shows each structure of the blood glucose level prediction system which concerns on embodiment. The figure which shows the example of the meal menu database which concerns on embodiment, meal menu information, exercise kind classification, and meal order. The figure which shows the example of the historical information which concerns on embodiment. The figure which shows the function structural example of the blood glucose level prediction apparatus which concerns on embodiment. The figure explaining the 1st prediction curve and 2nd prediction curve which concern on embodiment. The flowchart which shows the operation | movement flow of the blood glucose level prediction method which concerns on embodiment. The flowchart which shows the meal time and order subroutine of the blood glucose level prediction method which concerns on embodiment.

  Hereinafter, an example of a preferred embodiment of a blood sugar level prediction method and a blood sugar level prediction system of the present invention will be described.

(First embodiment)
Hereinafter, the blood sugar level prediction method and the blood sugar level prediction system of the present embodiment will be described with reference to FIGS.

(Overview)
As shown in FIG. 1, a blood sugar level prediction system 1 according to the present embodiment includes blood sugar level prediction devices 10 (10a, 10b, 10n) used by each patient (user), communication lines 20, 22, and meals. The menu system 30, the meal menu database 50, and the meal management system 60 are configured.

  In general, the blood glucose level is often used to determine the blood glucose state, and the blood glucose level (unit: mg / dl) varies depending on the user's state, such as the user's health condition, diet, and exercise. Therefore, even if the blood sugar level is high, the blood sugar state is not bad, or even if the blood sugar level is low, the blood sugar state may be bad. In medical institutions, HbA1c in blood is used as an index for determining the state of blood glucose, and HbA1c is generally examined in medical institutions. HbA1c is considered to represent the state of blood glucose from the time of examination to 1 to 2 months before, and is normal if the value of HbA1c is within a certain range, and is diabetic if it exceeds a certain range. It is judged that there is, and the progress of diabetes can be estimated by HbA1c.

Hereinafter, details of the blood sugar level prediction system 1 according to the present embodiment will be described.
FIG. 2 is a diagram illustrating each configuration of the blood sugar level prediction system 1.
Hereinafter, each part of the blood sugar level prediction device 10, the meal menu system 30, the meal menu database 50, and the meal management system 60 will be described.

(Blood glucose level prediction device 10)
The blood glucose level prediction device 10 is configured to be wearable on a user's wrist or the like, and includes a control unit 110, an activity amount measurement unit 120, an operation unit 130, a storage unit 140, a display unit 150, a timing unit 160, and a position information acquisition unit. 180 and a first communication unit 170. The blood sugar level predicting device 10 predicts a time series change of the user's blood sugar level.
The control unit 110 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), and a RAM (Random Access Memory), and executes a control program stored in the ROM in advance using the RAM as a working area. Each unit connected to the control unit 110 is controlled.

The activity amount measuring unit 120 obtains calorie consumption consumed by the user, and sends the obtained calorie consumption to the control unit 110 as consumption energy information. The activity amount measuring unit 120 has sensors such as an acceleration sensor and a speed sensor for detecting the user's movement state, for example, and defines in advance an output signal from the sensor detected by the user's walking or movement Convert to calories burned using the calculated formula.
Moreover, in order to obtain | require calorie consumption accurately, you may detect biometric data, such as a pulse-wave RR interval, body temperature, blood pressure, and sleep, using optical detection, electrical signal detection, pressure detection, etc.

  The operation unit 130 includes, for example, an operation button group having input keys such as numbers and characters, and sends an operation signal corresponding to the input key operated by the user to the control unit 110. In the present embodiment, in particular, the operation unit 130 receives input of the measured blood glucose level and HbA1c of the user, meal information indicating the meal contents (meal menu and intake calories) consumed by the user, and exercise performed by the user. Accepts input of exercise information data indicating the contents.

The display unit 150 is configured by a display device such as a liquid crystal display, and displays various images such as an input screen for meal information and exercise information, a blood sugar level prediction curve, and the like under the control of the control unit 110.
The position information acquisition unit 180 includes, for example, a GPS (Global Positioning System), and acquires position information of the user wearing the blood glucose level prediction device 10.
The timer 160 counts a predetermined clock and measures time.
The first communication unit 170 is connected to the communication line 20 and establishes communication between the blood glucose level prediction device 10 and the meal menu system 30 based on a preset address under the control of the control unit 110. Various data are transmitted and received between the value prediction apparatus 10 and the meal menu system 30.

The storage unit 140 is configured by a non-volatile storage medium, for example, identification information (hereinafter referred to as a user ID) for identifying a user such as a medical examination ticket number in a medical institution, history information 200 including information on a user's blood glucose level, and A table such as exercise type information 212 is stored. And the detailed meal menu information 213 (refer FIG. 3) provided from the meal menu database 50 is memorize | stored in the meal menu information 211. FIG.
Here, the meal menu database 50, the meal menu information 211, the detailed meal menu information 213, the exercise type information 212, the history information 200, and the meal management system 60 will be described below.

FIG. 3A is a diagram illustrating an example of the meal menu database 50. The meal menu database 50 stores meal menus such as single food names and dish names provided by meal providers such as in-house cafeteria and in-house cafeteria, intake calories corresponding to the meal menu, and the like. The meal menu database 50 is referred to when the user takes a meal.
For example, before a meal, the meal provider can be referred to by the information of the position information acquisition unit 180 in the blood sugar level prediction apparatus 10, so that the meal menu information 211 of the corresponding meal provider can be referred to.
During the meal, the order in which each meal menu is provided is recorded by the cooking management system 610, and the meal start time and meal end time are recorded by the POS system 600.
After the meal, detailed meal menu information 213 is created by adding the meal start time, meal end time, and meal order information to the meal menu information 211.
FIG. 3C is a diagram illustrating an example of the exercise type information 212. The exercise type information 212 stores an exercise type and exercise content. The exercise type information 212 is referred to when the user inputs exercise information.

The history information 200 includes blood glucose level information 200a shown in FIG. 4 (a) and behavior information 200b shown in FIG. 4 (b). The blood glucose level information 200a stores, for example, a blood glucose level during an educational hospitalization period of about 1 to 2 weeks such as when a user has been hospitalized for diabetes in the past, and an action history such as a meal and exercise. In the history information 200, in addition to this behavior history, daily blood glucose levels after an education hospitalization period and behavior histories such as meals and exercise are stored as predicted blood glucose level data.
When the user takes a meal, a detailed meal menu is acquired as described above. When the user exercises, exercise information is input and exercised as described above.
FIG. 4A shows blood glucose level information 200a of a user on February 1, 2010 and February 2, 2010 as an example. A waveform 41 in this figure represents a time-series change in the blood glucose level of the user. “Breakfast”, “walk”, “lunch”, etc. on the time axis indicate action histories such as meals taken by the user and timing of exercise. FIG. 4B shows action information 200b corresponding to each action history in FIG. 4A, corresponding to action contents (meal contents, exercise contents) corresponding to each action history and action contents. Calories (calorie intake, calorie consumption) are associated with each other.
For example, the meal content of the breakfast on February 1, 2010 in FIG. 4A is “Japanese food A” selected from the detailed meal menu information 213 shown in FIG. It shows that the calorie was “500 kcal”. Further, it is shown that the calorie consumption of “walk” performed in the morning of February 1, 2010 in FIG. 4A was “50 kcal”. Thus, in this embodiment, in the history information 200, ingested calories and consumed calories with respect to user behavior are stored together with changes in the user's blood glucose level.

(Functional configuration of control unit 110)
FIG. 5 is a functional configuration diagram centering on the functions of the control unit 110 described above. The control unit 110 includes an intake energy acquisition unit 111, a consumption energy acquisition unit 112, a calculation unit 113, an analysis unit 114, a generation unit 115, and a transmission control unit 116, which are examples of acquisition means.
The intake energy acquisition unit 111 acquires blood glucose level data input by the user and meal information (meal content) from the meal menu system 30 as intake energy information, and meal information based on the acquired meal information and meal menu information 211 Get calorie intake for.
The consumed energy acquisition unit 112 acquires the calorie consumption as the user's consumed energy information from the activity amount measuring unit 120 at regular intervals.

  The calculation unit 113 predicts a change in blood glucose level with respect to the calorie intake (hereinafter referred to as “prediction curve”) based on the calorie intake obtained with the energy intake acquisition unit 111 or the calorie intake with respect to the meal information and a predetermined first prediction algorithm. (Referred to as the first prediction curve). Further, the calculation unit 113 predicts a change in blood sugar level with respect to calorie consumption based on the calorie consumption acquired by the energy consumption acquisition unit 112 and a predetermined second prediction algorithm (hereinafter, second prediction). (Referred to as a curve).

  The analysis unit 114 extracts the history information 200 using the extraction condition for extracting the history information 200 corresponding to the meal information input from the operation unit 130 as the intake energy information, and uses the extracted history information 200 to extract the meal information. Analyzes the change tendency of the user's blood glucose level.

  Specifically, for example, when the meal information selected and input from the detailed meal menu information 213 shown in FIG. 3B is Japanese food A, it represents a change in blood glucose level when the Japanese food A is ingested. As the waveform, waveform data for a period from when the Japanese food A is ingested until the next action (meal or exercise) is performed is extracted. In the example of the blood glucose level information 200a shown in FIG. 4A, the waveform data for the period from the time when the breakfast on February 1, 2010 is ingested until the next action, that is, until the walk is performed, is extracted. If a walk is not performed, waveform data for a period from when breakfast is consumed until lunch is extracted is extracted. In this way, a waveform representing a change in blood glucose level when the same meal content is ingested is extracted as the first model waveform. In addition, when a plurality of waveforms are extracted, the analysis unit 114 performs processing such as averaging the plurality of extracted waveforms, and generates a first model waveform indicating a change tendency of the blood sugar level with respect to the meal information. To do.

  In addition, the analysis unit 114 extracts the history information 200 using the extraction condition for extracting the history information 200 corresponding to the exercise information (exercise content) input from the operation unit 130, and uses the extracted history information 200 to extract the history information 200. Analyzes changes in user's blood glucose level relative to exercise information. In the present embodiment, the calorie consumption of the user is sequentially calculated by the activity amount measuring unit 120, but it is distinguished what operation the calculated calorie consumption is performed. Therefore, for an exercise other than the normal operation, the exercise type is input before the user exercises.

  Specifically, for example, when the input exercise information is walking, a waveform representing a change in blood glucose level when walking is performed as a waveform representing a change in blood glucose level in the blood glucose level information 200a shown in FIG. Waveform data is extracted from the start of walking on the 1st month until the next action, that is, taking a snack. In this way, a waveform representing a change in blood glucose level when the same exercise is performed is extracted as the second model waveform. In addition, when a plurality of waveforms are extracted, the analysis unit 114 performs processing such as averaging the plurality of extracted waveforms, and generates a second model waveform indicating a blood glucose level change tendency with respect to the exercise information. Generate.

The generation unit 115 deforms the first prediction curve and the second prediction curve calculated by the calculation unit 113 based on the analysis result of the analysis unit 114, and integrates the deformed first prediction curve and the second prediction curve. To generate a predictive blood sugar level curve.
Specifically, as illustrated in FIG. 6, the generation unit 115 includes an increase value h11 up to the peak value of the blood glucose level in the increase period d2 of the first prediction curve, and an increase value up to the peak value of the first model waveform. And the coefficient α is adjusted such that the increase value h11 of the first prediction curve becomes the increase value of the first model waveform. Further, the generation unit 115 compares the equilibrium period d3 of the first prediction curve with the continuation period in which the peak value of the blood sugar level continues in the first model waveform, and the difference between the equilibrium period d3 and the continuation period is determined in advance. If it is equal to or greater than the threshold, the equilibrium period d3 is set so that the equilibrium period d3 of the first prediction curve matches the duration. In the first model waveform, it is assumed that the peak value continues during the period when the peak value of the blood glucose level falls within the predetermined threshold range, and the time point when the peak value falls below the threshold range is Judged to be the end.

  In addition, the generation unit 115 compares the amount of decrease (h11) in which the blood sugar level has decreased from the peak value in the falling period d4 of the first prediction curve with the amount of decrease in which the blood sugar level has decreased from the peak value of the first model waveform. When the difference in the amount of decrease is equal to or greater than a predetermined threshold, the coefficient β of the slope s2 is set so that the amount of decrease (h11) in the decrease period d4 of the first prediction curve becomes the amount of decrease in the first model waveform. adjust.

The generation unit 115 deforms the second prediction curve in the same manner as the first prediction curve. Specifically, the generation unit 115 compares the decrease amount Δc per unit time of the blood glucose level in the falling period e2 of the second prediction curve with the decrease amount per unit time of the blood glucose level in the second model waveform, When the difference in the amount of decrease is equal to or greater than a predetermined threshold, the coefficient γ is adjusted so that the amount of decrease Δc of the second prediction curve becomes the amount of decrease in the second model waveform.
The generation unit 115 deforms the first prediction curve and the second prediction curve as described above, generates a predicted blood glucose level curve that integrates the deformed first prediction curve and the second prediction curve, and generates the generated prediction. The blood glucose level curve is stored in the storage unit 140.

Here, with reference to FIG. 6, calculation of the first prediction curve and the second prediction curve in the calculation unit 113 will be described. Fig.6 (a) is a figure which shows an example of the 1st prediction curve in this embodiment.
The first prediction curve is obtained based on the calorie intake and the first prediction algorithm. The first prediction curve has a delay period d1, a rising period d2, an equilibrium period d3, and a falling period d4. Hereinafter, an example of the 1st prediction algorithm which calculates | requires the blood glucose level curve in each period is demonstrated.

  The delay period d1 indicates a period from the start of a meal until the blood glucose level (reference value) C0 at the start of the meal is exceeded. In the delay period d1, a predetermined time (for example, 15 minutes) from the start of the meal is set, and the blood glucose level C0 at the start of the meal is maintained. Note that the blood glucose level C0 at the start of the meal uses the blood glucose level measured by the user at the time, but if the measurement cannot be made, for example, a preset standard value of the user's blood glucose level is used. May be.

The rising period d2 indicates a period starting from the end of the delay period d1 until the blood sugar level starts to rise and reaches a peak value (peak value). The peak value is a value obtained by adding the blood glucose level increase value h11 to the blood glucose level C0 at the start of the meal, with the blood glucose level increasing at the slope s1.
The increase value h11 of the blood glucose level is obtained by, for example, h11 = (calorie intake) × (insulin secretion amount) × (coefficient α). In the present embodiment, the insulin secretion amount and the coefficient α (> 0) are fixed values set in advance according to the user. The insulin secretion amount and the coefficient may be not only fixed values set in advance, but also values or variable values determined according to user attributes (age, sex, height, weight).

  The equilibrium period d3 is a period in which the blood sugar level is maintained at the peak value from the end of the rising period d2, and in this embodiment, a predefined fixed value is set. In addition, for example, the value obtained by multiplying the calorie intake by a coefficient specific to the user is divided by a coefficient corresponding to the difference from the previous calorie intake, and the equilibrium period using the calorie intake and a predetermined arithmetic expression. d3 may be obtained.

  The falling period d4 indicates a period from the end of the equilibrium period d3 until the blood sugar level starts to decrease at the slope s2 and reaches the reference value. That is, the descent period d4 is a period until the blood glucose level returns from the peak value to the reference value (blood glucose level C0 at the start of the meal). The slope s2 is obtained by, for example, s2 = (calorie intake) × (coefficient β). In the present embodiment, the coefficient β is a fixed value (<0) that is predetermined according to the user, but is a value or variable value that is predetermined according to the user's attributes (age, sex, height, weight). It may be.

Next, the second prediction curve will be described. FIG. 6B is a diagram illustrating an example of the second prediction curve in the present embodiment.
The second prediction curve is obtained based on the calorie consumption and the second prediction algorithm. The second prediction curve includes a delay period e1 and a falling period e2. Hereinafter, an example of the 2nd prediction algorithm which calculates | requires the blood glucose level curve in each period is demonstrated.

  The delay period e1 indicates a period from the start of exercise until the blood sugar level starts to decrease, and is a period during which the blood sugar level at the start of exercise is maintained. In the present embodiment, a predetermined period (for example, 2 minutes) is set as the delay period e1. The falling period e2 is a period during which the blood sugar level falls with a slope s3 (the amount of decrease in blood sugar level per unit time Δc) from the end of the delay period e1. The amount of decrease Δc is obtained, for example, by Δc = (calorie consumption) × (insulin secretion amount) × (coefficient γ). The calorie consumption is the user's calorie consumption measured by the activity amount measuring unit 120. In the present embodiment, the user's calorie consumption is calculated by the activity amount measuring unit 120 even during normal operation when the user is not conscious of exercise. Calculated and sequentially input. The amount of insulin secretion is a fixed value preset according to the user, and the coefficient γ (<0) may be a variable value according to the blood glucose level, or a fixed value determined according to the user's attribute It may be.

  As shown in FIG. 3 (a), the meal menu system 30 stores detailed nutrition information such as meal contents (meal menu and calorie intake) held by a meal provider such as an in-house cafeteria, an in-house cafeteria, or a delivery company. Yes.

As shown in FIG. 2, the meal menu system 30 includes a search unit 310, an information acquisition unit 320, a storage unit 350, and a second communication unit 340.
The meal menu system 30 is a system that retrieves detailed meal information (meal content) from a meal provider in cooperation with the meal management system 60.

  The search unit 310 searches for and extracts meal providers from the user's location information.

  The information acquisition unit 320 acquires meal information from the meal menu database 50 (see FIG. 1). Also, the user position information is acquired.

  The storage unit 350 is configured by a non-volatile storage medium, and stores meal information and user location information acquired by the search unit 310 and the information acquisition unit 320.

  The second communication unit 340 is connected to the communication line 22 and establishes communication between the meal menu system 30, the meal menu database 50, and the meal management system 60 based on a preset address, and the meal menu system 30. Various data are transmitted and received between the meal menu database 50 and the meal management system 60.

  As shown in FIG. 2, the meal management system 60 stores detailed meal menu information such as meal start time, meal end time, meal order, and the like in cooperation with each meal provider's internal system.

  The POS system 600 stores the meal time information of the user by storing the time of the user's ordering and accounting.

  The cooking management system 610 stores the order in which meals are provided to the user in cooperation with the kitchen.

(Operation)
Next, the operation of the blood sugar level prediction system 1 according to this embodiment will be described.
First, the operation of the blood sugar level prediction method will be described.
(Glucose level prediction method)
FIG. 7 shows an operation flow of the blood sugar level prediction method. FIG. 8 shows a meal information input processing subroutine in the blood sugar level prediction method. In this embodiment, the blood glucose level is actually measured by the user before breakfast every day. The blood glucose level prediction method uses the actual measurement value to predict a blood glucose level each time a meal is input and meal information is input, and whenever a user's consumed calories are measured, and a predicted blood glucose level curve is output.
The user inputs actually measured blood glucose level data via the operation unit 130 of the blood glucose level prediction apparatus 10. When the control unit 110 receives the blood glucose level data and the input time input via the operation unit 130, the control unit 110 sets the input blood glucose level data as the reference value C0, and executes a blood glucose level data receiving step. Prediction is started (step S11).

  A meal provider extraction step is performed in which the position information of the user acquired from the position information acquisition means such as GPS is transmitted from the blood glucose level prediction apparatus 10 to the meal menu system 30 (step S61). In the meal menu system 30, the meal provider to be used is determined based on the position information.

  In the meal management system 60 of the extracted meal provider, the meal start time recording step of recording the time when the user started the meal by the POS system 600 is performed (step S62). The POS system 600 records the time from the time when the user ordered a meal.

  Next, in the meal management system 60, a meal provision recording step of recording the order of meals performed by the user by the cooking management system 610 is performed (step S63). The order in which meals are provided to the user by the cooking management system 610 is recorded in conjunction with the cooking site.

  Finally, in the meal management system 60, a meal start time recording step of recording the time when the user finished the meal by the POS system 600 is performed (step S64). The POS system 600 records the time from the time when the user accounts for the meal.

When the user performs an operation to display the meal information input screen via the operation unit 130, the control unit 110 displays the detailed meal menu information 213 on the display unit 150 and receives an input of the meal menu from the user. (Step S12).
When the meal menu as the detailed meal menu information 213 is input by the user via the operation unit 130 (step S12: YES), the control unit 110 is input as the intake energy information together with the meal menu. The calorie intake corresponding to the meal menu is acquired from the detailed meal menu information 213. And the 1st prediction algorithm calculation step which calculates the 1st prediction curve with respect to the acquired calorie intake with a 1st prediction algorithm is implemented (Step S13).
The control unit 110 extracts the history information 200 corresponding to the meal menu input in step S12, analyzes the change tendency of the past blood glucose level with respect to the calorie intake as meal information, and generates a first model waveform. A model waveform generation step is performed (step S14).

The control unit 110 compares the blood glucose level change of each period (d1, d2, d3, d4) in the first prediction curve calculated in step S13 with the blood glucose level change of the first model waveform generated in step S14. And the 1st prediction curve deformation | transformation step which deform | transforms a 1st prediction curve according to a comparison result is implemented (step S15).
Moreover, the control part 110 accepts the user's calorie consumption measured by the activity amount measuring unit 120 at regular intervals as the energy consumption information from the activity amount measuring part 120 (step S16), and the second prediction for the accepted calorie consumption. A curve is calculated by the second prediction algorithm (step S17). A second prediction algorithm calculation step of calculating a second prediction curve for calorie consumption due to exercise performed by the user is performed.

When the user performs an operation to display the exercise information input screen via the operation unit 130, the control unit 110 displays the exercise type information 212 on the display unit 150 and accepts an input from the user (step S18).
When the exercise type is input by the user via the operation unit 130 as the exercise information (step S18: YES), the control unit 110 extracts the history information 200 corresponding to the input exercise information, and the past with respect to the exercise information. A second model waveform is generated by analyzing the change tendency of the blood glucose level (step S19).
The control unit 110 compares the blood glucose level change during the falling period e2 in the second prediction curve calculated in step S17 with the blood glucose level change of the second model waveform generated in step S19, and determines the first in accordance with the comparison result. 2 The prediction curve is deformed (step S20).

The control unit 110 generates a predictive blood sugar level curve obtained by integrating the first predictive curve and the second predictive curve on the same time axis, and displays an image indicating the generated predictive blood sugar level curve on the display unit 150 (step 150). S21). In step S12, when the meal information is not input by the user (step S12: NO), the control unit 110 performs the process of step S16. Moreover, when exercise information is not input by the user in step S18 (step S18: NO), the control part 110 performs the process of step S21.
The blood glucose level data, meal information, and exercise information input by the user may be stored as history information 200 in the storage unit 140 by the control unit 110 as information related to the user's past blood glucose level.

  As described above, in the present embodiment, the first prediction curve and the second prediction curve are calculated every time the intake energy information and the consumption energy information are input, and based on the calculated first prediction curve and second prediction curve. Thus, a predictive blood sugar level curve is generated.

  According to this embodiment, the blood sugar level prediction system predicts blood sugar based on the detailed meal menu information 213 having detailed meal information such as the meal start time, end time, and meal order of the user. Compared to the case of predicting the change tendency of blood glucose level only from the calories of the meal, the change tendency of blood glucose level can be analyzed accurately. In addition, the meal management system 60 acquires information such as meal start time, meal end time, and meal order, so that the change tendency of blood glucose level can be easily known in detail without worrying about information acquisition during meals. Can do.

  DESCRIPTION OF SYMBOLS 1 ... Blood glucose level prediction system 10, 10a, 10b, 10n ... Blood glucose level prediction apparatus, 20, 22 ... Communication line, 30 ... Meal menu system, 41 ... Waveform, 50 ... Meal menu database, 60 ... Meal management system, 110 DESCRIPTION OF SYMBOLS ... Control part 111 ... Intake energy acquisition part 112 ... Consumption energy acquisition part 113 ... Calculation part 114 ... Analysis part 115 ... Generation part 116 ... Transmission control part 120 ... Activity amount measurement part 130 ... Operation part , 140 ... storage section, 150 ... display section, 160 ... timing section, 170 ... first communication section, 180 ... location information acquisition section, 200 ... history information, 200a ... blood glucose level information, 200b ... action information, 211 ... meal menu Information, 212 ... exercise type information, 213 ... detailed meal menu information, 310 ... search unit, 320 ... information acquisition unit, 340 ... second communication unit, 350 ... Department, 600 ... POS system, 610 ... cooking management system, s1, s2, s3 ... inclination, e1, d1 ... delay period, e2, d4 ... falling period, d2 ... rising period, d3 ... equilibration period, h11 ... rise value.

Claims (9)

Meal in which the intake energy acquisition unit records the meal start time recorded when the user started the meal, the meal provision order record in which the order of the meal is recorded, and the meal information indicating the content of the meal An input receiving step for receiving an input of an information record and a meal end time record in which the end time of the meal is recorded;
A calculation unit calculates a first prediction algorithm for calculating a first prediction curve for the calorie intake calculated from the meal information, and a second prediction algorithm calculation for calculating a second prediction curve for calories consumed by the exercise performed by the user. Performing steps, and
A first model waveform acquisition step in which an analysis unit acquires a first model waveform generated by analyzing a change tendency of a blood glucose level with respect to the calorie intake;
The generation unit compares the first prediction curve with a blood glucose level change of the first model waveform, deforms the first prediction curve, and based on the deformed first prediction curve and second prediction curve Blood glucose level predicting step for predicting the blood glucose level of the user,
With
The blood glucose level prediction method, wherein the meal end time record is a time when the user performed accounting at the store where the meal was performed. In the blood glucose level prediction method according to claim 1,
The method for predicting blood sugar level, wherein the meal start time record is a time when the user orders the meal. In the blood glucose level prediction method according to any one of claims 1 and 2,
Obtaining the location information of the user,
The intake energy acquisition unit acquires at least one of the meal start time record, the meal provision order record, and the meal end time record based on the position information. In the blood glucose level prediction method according to any one of claims 1 to 3,
The meal start time record is a time when the user orders the meal, and includes the step of acquiring the meal time information of the user based on the meal end time record and the meal start record. To predict blood sugar level. A storage unit that stores history information including first blood glucose level change information representing a relationship between a meal performed by the user in the past and a blood glucose level change;
A location information acquisition unit for acquiring location information of the user;
Based on the location information, the user obtains meal information that the user intends to ingest, and at least obtains a start time when the meal related to the meal information is started; and
From the storage unit, the user corresponding to the meal information obtains the first blood sugar level change information of the meal performed in the past, the first blood sugar level change information, and the management unit obtained from the management unit A control unit for predicting a blood glucose level change of the user based on a start time;
A blood glucose level prediction system comprising: In the blood sugar level prediction system according to claim 5,
The blood glucose level prediction system, wherein the start time is a time when the user orders the meal. In the blood sugar level prediction system according to claim 5 or 6,
The management unit obtains an end time when the meal related to the meal information is finished,
The blood glucose level prediction system according to claim 1, wherein the end time is a time when the user performs accounting related to the meal. In the blood sugar level prediction system according to any one of claims 5 to 7,
The history information includes second blood glucose level change information representing a relationship between exercise performed by the user in the past and blood glucose level change,
An exercise type acquisition unit for acquiring an exercise type to be performed by the user;
The user corresponding to the exercise type acquires second blood glucose level change information of the exercise performed in the past, and the first blood glucose level change information, the second blood glucose level change information, and the management A blood glucose level prediction system for predicting a change in blood glucose level of the user based on the start time and the end time acquired from a unit. The blood sugar level prediction system according to claim 8,
The blood glucose level prediction system, wherein the exercise type is at least one of rest, walk, walking, jogging, and underwater walking.

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