JP5949847B2 - Calorie information determination method and blood sugar level prediction method - Google Patents

Description

  The present invention relates to a blood glucose level prediction apparatus.

  In Patent Document 1, a prediction model for predicting a user's blood glucose level is created in advance based on the history data of the calorie intake of the user and the history data of the calorie consumption of the user. A technique for predicting a user's blood glucose level from calorie intake and user calorie consumption is disclosed.

JP 2005-328924 A

By the way, recently, using services such as SNS (Social Networking Service)
Increasing number of people trying to communicate with other users by making the diary of the food they have eaten and making it available to other users who use the same service. Many users have entered details in detail. Some people who use services such as SNS require attention to fluctuations in blood glucose levels. Since the change in blood glucose level varies depending on the ingested meal, etc., when the blood glucose level is predicted using the above-described conventional technology, the calorie intake must be acquired by inputting the content of the meal ingested by the user himself / herself. .
The present invention provides a technique for easily predicting blood sugar levels by easily acquiring information about calories consumed by a user who uses a service such as SNS.

In order to solve the above problem, a blood glucose level prediction system according to claim 1 of the present invention provides a first device that provides input screen information representing a screen for allowing a user to input predetermined service information, and the input A second device for each user that inputs the service information using screen information, wherein the first device receives the service information from the second device, and the first receiving device. Storage means for storing the service information received by the user together with identification information for identifying the user for each user, and intake information relating to meals consumed by the user included in the service information received by the first reception means And specifying means for specifying the corresponding intake energy based on the service information stored in the storage means,
First transmission means for transmitting information indicating the intake energy specified by the specification means to the second device, wherein the second device accepts input of the service information for the input screen information. And second transmission means for transmitting the service information received by the reception means together with the identification information to the first device, and information on the intake energy corresponding to the service information transmitted by the second transmission means. A second receiving means for receiving from the first device, and a calculating means for calculating a predicted blood sugar level curve predicting a time-series change in the blood sugar level of the user by using the intake energy received by the second receiving means. And output means for outputting the predictive blood sugar level curve calculated by the calculating means. According to this configuration, it is possible to easily obtain the intake energy from the data input on the input screen when the user uses the service and predict the blood sugar level.

The blood sugar level prediction system according to claim 2 is the blood sugar level prediction system according to claim 2, wherein the intake information includes the contents of meals taken by the user, and the storage means further stores attribute information indicating attributes for each user. The specifying unit extracts a data group corresponding to the attribute information of the user who transmitted the intake information from the data group stored in the storage unit, and the meal content of the intake information based on the extracted data group It is characterized by specifying the intake energy for. According to this configuration, since the blood sugar level can be predicted using the intake energy obtained using the data of another user corresponding to the user attribute, the blood sugar level prediction accuracy can be improved.

  The blood sugar level predicting system according to claim 3 is the blood sugar level predicting system, wherein the intake information includes environmental information indicating the environment when the user ingests a meal and the content of the meal, A data group corresponding to the environmental information of the intake information and the meal content is extracted from the data group stored in the storage means, and the intake energy for the meal content of the intake information is specified based on the extracted data group It is characterized by that. According to this configuration, since the blood sugar level can be predicted using the intake energy obtained using the data of other users corresponding to the environment and the content of the meal when the user took the meal, The prediction accuracy can be further improved.

It is a figure showing the whole blood sugar level prediction system composition concerning an embodiment. It is a figure which shows the structure of the blood glucose level prediction apparatus and server which concern on embodiment. (A) And (b) is a figure which shows the example of the user information which concerns on embodiment. (A)-(c) is a figure which shows the example of the input master information which concerns on embodiment. It is a figure which shows the function structural example of the blood glucose level prediction apparatus which concerns on embodiment. (A) is a figure explaining the 1st prediction curve concerning an embodiment. (B) is a figure explaining the 2nd prediction curve concerning an embodiment. It is a figure which shows the function structure of the server which concerns on embodiment. (A) And (b) is a figure which shows the example of the accumulation | storage information which concerns on embodiment. It is an operation | movement flowchart which shows the whole operation | movement of the blood glucose level prediction apparatus which concerns on embodiment. It is an operation | movement flow which shows the 1st prediction curve calculation process in the case of the server mode in embodiment. (A) And (b) is a screen example of the SNS service in the embodiment. It is an operation | movement flow of the process of the analysis service in embodiment. It is a figure which shows the example of the user input information in embodiment. It is an operation | movement flowchart of the 1st prediction curve calculation process which concerns on embodiment. (A)-(c) is a figure explaining the deformation | transformation process of the 1st prediction curve which concerns on embodiment. It is an operation | movement flowchart of the 2nd prediction curve calculation process which concerns on embodiment. (A)-(c) is a figure explaining the deformation | transformation process of the 2nd prediction curve which concerns on embodiment.

(Overview)
As shown in FIG. 1, a blood glucose level prediction system 1 according to the present embodiment includes a blood glucose level prediction device 10 (10a, 10b... 10n) (first device) used by each user, and a communication line 20 such as the Internet. And a server 30 (second device) that provides services such as SNS and is connected to each blood glucose level prediction device 10 via the network.
The blood sugar level prediction device 10 of the blood sugar level prediction system 1 is based on the blood sugar level that the user actually measures once a day, the intake energy according to the meal that the user ingested, and the energy consumed when the user exercises. It predicts the blood glucose level of the user and outputs a blood glucose level curve in which changes in the predicted blood glucose level (hereinafter referred to as predicted blood glucose level) are represented in time series.
In the present embodiment, in the blood sugar level prediction apparatus 10, an input mode and a server mode are prepared as modes for acquiring ingested energy relating to a meal ingested by a user.
The input mode is a mode in which the calorie intake predicting device 10 calculates the calorie intake according to the content of the meal input by the user to the blood glucose predicting device 10. In the server mode, when a user uses an SNS service (hereinafter referred to as an SNS service) provided by the server 30 using the blood glucose level prediction device 10, the data input by the user is transmitted to the server 30. 30 is a mode in which the result of analyzing the intake energy (calorie intake) based on the transmission data is transmitted to the blood glucose level prediction apparatus 10. That is, in the server mode, the blood glucose level prediction apparatus 10 acquires the intake calories from the server 30.
In the input mode, the blood sugar level predicting device 10 predicts the blood sugar level using the calorie intake corresponding to the meal content every time the user inputs the meal content. In the server mode, the blood glucose level predicting device 10 The blood sugar level is predicted using the calorie intake obtained from 30.
The server 30 provides an SNS service to a user who is registered in advance, and also provides a service (hereinafter referred to as an analysis service) that analyzes the transmission data from the blood glucose level prediction device 10 and transmits the intake calories. . In addition, as an example of the SNS service in the present embodiment, an input screen for inputting a diary (service information) is provided for each user, the diary information input by the user using the input screen is accumulated, and the like. The diary information is disclosed to other users, or the input of comments on the diary information of other users is accepted and released.

(overall structure)
FIG. 2 is a diagram illustrating a configuration for predicting a blood sugar level in the blood sugar level predicting apparatus according to the embodiment. The blood glucose level prediction apparatus 10 includes a control unit 110, an activity amount measurement unit 120, an operation unit 130, a storage unit 140, a display unit 150, a timer unit 160, and a communication unit 170.

The control unit 110 includes a CPU (Central Processing Unit) and a ROM (Read Only Memory).
And a RAM (Random Access Memory) memory, and by executing a control program stored in advance in the ROM using the RAM as a working area, the blood sugar level is predicted using the calorie intake corresponding to the intake energy acquisition mode. The blood glucose level prediction processing function to be performed is executed, and each unit connected to the control unit 110 is controlled. The blood glucose level prediction processing function is a process for predicting a blood glucose level using blood glucose level actually measured by the user, calorie intake (intake energy) acquired according to the acquisition mode, and calorie consumption (energy consumption) for exercise performed by the user. It is.

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 exercise state, for example, and an output signal from the sensor detected by an operation such as the user's exercise is defined in advance. Convert to calories burned using a formula.
In addition, in order to accurately determine calorie consumption, biological data such as pulse wave RR intervals, body temperature, blood pressure, and sleep may be detected using optical detection, electrical signal detection, pressure detection, and the like.

  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 is an operation for inputting the measured blood glucose level data of the user, an operation for selecting an acquisition mode, an input operation for a meal content in the input mode, and an exercise indicating the exercise content performed by the user. Various operations such as an information input operation and an operation for using an SNS service are accepted.

The storage unit 140 is configured by a non-volatile storage medium, and stores data such as user information 200 and input master information 300 including information related to the user's past blood glucose level.
Here, the user information 200 and the input master information 300 will be described. The user information 200 includes blood glucose level information 200a shown in FIG. 3A and behavior information 200b shown in FIG.

  The blood glucose level information 200a stores, for example, daily blood glucose levels and behavioral history (meal and exercise) measured during an educational hospitalization period, such as when the user has been hospitalized for diabetes in the past. FIG. 3A shows blood glucose level information 200a of a user on February 1st and February 2nd 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. 3B shows behavior information 200b corresponding to each behavior history in FIG. 3A, and information regarding behavior (meal information, exercise information) corresponding to each behavior history is stored. For example, in the meal information corresponding to the breakfast on February 1, 2010 in FIG. 3A, “Japanese food A” is stored as the meal content, and “500 kcal” is stored as the calorie intake. In addition, “50 kcal” is stored as calorie consumption for the walk in the exercise information corresponding to “walk” performed in the morning of February 1, 2010 in FIG.

  Next, the input master information 300 will be described. The input master information 300 includes calorie information 3100 shown in FIG. 4A, exercise type information 3200 shown in FIG. 4B, and meal timing information 3300 shown in FIG. 4C.

  The calorie information 3100 exemplified in FIG. 4A stores intake calories for a preset meal menu, and the exercise type information 3200 exemplified in FIG. Is remembered. Moreover, the meal timing information 3300 illustrated in FIG. 4C stores meal timings corresponding to the respective codes. The calorie information 3100 and the meal timing information 3300 are referred to when the user inputs meal information in the input mode. The exercise type information 3200 is referred to when the user inputs exercise information.

  Returning to FIG. 2, the description of the configuration is continued. The display unit 150 is configured by a display device such as a liquid crystal display, and displays various images such as an SNS service WEB page screen, an input screen for meal information and exercise information, and a blood sugar level prediction curve under the control of the control unit 110. To do. The timer 160 counts a predetermined clock and measures time. The communication unit 170 is connected to the communication line 20 such as the Internet, is connected to the WEB site of the SNS service provided by the server 30 under the control of the control unit 110, and transmits / receives data to / from the server 30.

(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 consumed energy acquisition unit 112, a calculation unit 113, an analysis unit 114, and a generation unit 115.
The intake energy acquisition unit 111 acquires intake calories for meals taken by the user. That is, the intake energy acquisition unit 111 acquires the intake calories from the calorie information 3100 for meal information input by the user from the operation unit 130 in the input mode, and is analyzed by the server 30 in the server mode. The calorie intake is acquired via the communication unit 170. 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 acquires blood glucose level data input by the user from the operation unit 130, and based on the intake calorie acquired by the intake energy acquisition unit 111 and a predetermined first prediction algorithm, the blood glucose level for the intake calorie A prediction curve (hereinafter referred to as a first prediction curve) that predicts a change in the above is obtained. Further, the calculation unit 113 predicts a change in blood sugar level relative to the 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).

  Here, calculation of the first prediction curve and the second prediction curve 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 sugar level increase h11 to the blood sugar level C0 at the start of the meal, with the blood sugar level increasing at the slope s1.
The blood glucose level increase amount h11 is obtained, for example, by 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.

  Returning to FIG. 5, the description will be continued. The analysis unit 114 extracts the user information 200 using the extraction condition for extracting the user information 200 corresponding to the meal information input from the operation unit 130, and uses the extracted user information 200 for the user's blood glucose level for the meal information Analyze value changes. Specifically, for example, when the input meal information is Japanese food A, the next action (meal or exercise) from the time when the Japanese food A is ingested as a waveform representing a change in blood glucose level when the Japanese food A is ingested. Waveform data for a period until it is performed is extracted. In the example of the blood glucose level information 200a shown in FIG. 3A, the waveform data for the period from the time when the breakfast on February 1, 2010 is ingested until the next action, that is, walking is extracted. If walking 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.

  Further, the analysis unit 114 extracts the user information 200 using the extraction condition for extracting the user information 200 corresponding to the exercise information input from the operation unit 130, and uses the extracted user information 200 for the user for the exercise information. Analyzes changes in blood glucose levels. 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 2010 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. Generate a predictive blood glucose curve. Specifically, the generation unit 115 compares the amount of increase h11 up to the peak value of the blood glucose level during the increase period d2 of the first prediction curve with the amount of increase up to the peak value of the first model waveform, and When the difference is equal to or greater than a predetermined threshold, the coefficient α is adjusted so that the increase amount h11 of the first prediction curve becomes the increase amount 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, the peak value of the blood glucose level is set to a predetermined threshold value Δc.
It is assumed that the peak value continues during the period falling within the range of th, and the time point that falls below the threshold value Δcth is determined as the end of the continuous period.

  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 output to the display unit 150.

  In the present embodiment, the process of deforming the prediction curve based on the analysis result is to extract a parameter indicating the feature quantity of the user's blood glucose level change from the waveform (model waveform) based on the analysis result, (1 prediction curve, second prediction curve) is a process of extracting a parameter corresponding to this feature amount and bringing the prediction curve parameter closer to the analysis result parameter.

The functional configuration of the control unit 110 in the present embodiment is as described above. Returning to FIG. 2, the server 30 according to the present embodiment will be described.
(Server 30)
As illustrated in FIG. 2, the server 30 includes a control unit 310, a communication unit 320, and a storage unit 330. The control unit 310 includes a CPU, a ROM, and a RAM, and a function that performs processing related to the SNS service and a function that performs processing related to the analysis service by executing a control program stored in the ROM in advance using the RAM as a working area. Is executed to control each unit connected to the control unit 310. The process related to the SNS service is a process of transmitting a WEB page related to the SNS service, that is, a WEB page (input screen information) constituting an input screen for inputting a diary in response to a request from a user registered in advance. In addition, a process of receiving data input on a diary input screen or the like and storing it in the server 30 is included. In addition, the process related to the analysis service is based on the data transmitted from the blood sugar level prediction device 10 of the user who uses the SNS service, and specifies the calorie intake for the meal estimated to have been ingested by the user. Is transmitted to the blood glucose level prediction device 10 of the user. Details of the processing related to the analysis service will be described later.

  The communication unit 320 is connected to the communication line 20 and communicates with the blood glucose level prediction device 10 of the user who uses the SNS service under the control of the control unit 310 to transmit and receive data. The storage unit 330 is configured by a storage medium such as a hard disk, and stores a database such as accumulated information 331. Details of the accumulated information 331 will be described later.

Next, a description is given of a functional configuration of the control unit 3 10 of the server 30. Figure 7 shows a functional block diagram centering on the control unit 3 10. The control unit 3 10 includes received data analysis unit 311, the data extraction unit 312, and the calorie determination unit 313.

The received data analysis unit 311 performs morphological analysis on data (hereinafter referred to as diary data) received by the communication unit 3 20 for a user's diary input screen (hereinafter referred to as diary input screen) using the analysis service. Analytical processing such as structural analysis and semantic analysis is performed, and a character string that matches a character string (hereinafter referred to as an extraction target character string) such as a food name, a dish name, and a menu defined as a character string to be extracted in advance If it is included, the character string is extracted as information (hereinafter referred to as ingestion information) about the meal ingested by the user.

  Note that the extraction target character strings are stored in advance in a food database (not shown) in the storage unit 330 in units of groups indicating food classifications. For example, “Beef Curry”, “Chicken Curry”, “Vegetable Curry” etc. are remembered as “Curry and Rice” group, and “Soy sauce Ramen”, “Miso Ramen”, “Salt” "Ramen" etc. are memorized. In this embodiment, the standard calorie corresponding to the extraction target character string is also stored in the food database. The diary data includes a user ID for identifying a user who has transmitted the diary data, time information, and diary contents.

  The data extraction unit 312 extracts information about calories (hereinafter referred to as calorie information) corresponding to the intake information extracted by the reception data analysis unit 311 from the accumulated information 331 and sends it to the calorie identification unit 313. Here, the accumulation information 331 will be described. The accumulated information 331 includes service user information 331a shown in FIG. 8A and user input information 331b shown in FIG. 8B.

  The service user information 331a includes a user ID uniquely given to a user who uses the SNS service, a user name of the user, and an analysis target indicating whether the user is a use target of the analysis service (1: Analysis target person, 0: non-analysis target person), and attribute information (gender, age, height, weight, hobbies, ...) of the user are stored in association with each other. Each record of the service user information 331a is registered when the user starts using the SNS service. Further, the user input information 331b stores a user ID for each user, a date (time) when the user inputs data on the diary input screen, and content data indicating the content of the diary in association with each other. In this example, the content data mainly includes information about meals eaten by the user, but also includes data other than meals.

  For example, when “ramen” is extracted as ingestion information from the analysis target user's diary data, the data extraction unit 312 extracts calories corresponding to “ramen” from the content data of the user input information 331b. In this example, the character string “ramen” is included in the record 3310 of the user ID 102 and the record 3311 of the user ID 121. As a calorie corresponding to “ramen”, the record 3311 includes a character string “700 kcal”, but the record 3310 does not include a character string indicating calorie. Therefore, in this case, “700 kcal” of the user ID 121 is extracted as the calorie for the intake information “ramen”.

  In addition, since the intake information is an example of “ramen”, calories corresponding to “soy sauce ramen” are extracted. For example, when the intake information is “Miso ramen”, the data extraction unit 312 In the input information 331b, if there is a character string that completely matches the character string of the intake information, calories for that character string are extracted. If there is no completely matched character string, the user inputs the calories for the character string that matches the extraction target character string in the “ramen” group to which “Miso Ramen” belongs in the food database (not shown) in the storage unit 330. Extracted from information 331b.

As a result of the analysis processing of the received data analysis unit 311, the data extraction unit 312 includes a character string indicating the position of the character string and a calorie consisting of a character string of “kcal” and the character string in the sentence including the character string of the intake information When the positional relationship with the position (hereinafter referred to as calorie data) satisfies a predetermined relationship (for example, the calorie is located within several bits from the last character string of the intake information), the calorie The user ID and the calorie data associated with the content data including the data are extracted as calorie information.

Returning to FIG. 7, the description will be continued. Calorie determination unit 313, based on the calorie information extracted by the data extraction unit 312 specifies a corresponding caloric intake and information in the diary data, to the communication unit 3 2 0 the identified calories as calories information for diary data Send it out.
For example, in the above example, when “700 kcal” of the user ID 102 is extracted as the calorie information, the attribute information of the user of the user ID 102 matches the attribute information of the analysis target user under certain conditions. The calorie information is specified using the calorie information of the user ID 102. The condition that the attribute information matches may be, for example, a condition that calorie information that matches the gender of the attribute information of the analysis target user is used, and the difference from the age of the user is within a predetermined threshold. It is good also as conditions on using calorie information. Moreover, it is good also considering using the calorie information whose difference with the BMI value of the analysis object user calculated using height and weight is within a predetermined threshold, or combining a plurality of conditions. Good.

When there are a plurality of pieces of calorie information that matches the attribute information of the analysis target user, the calorie specifying unit 313 specifies the average, intermediate value, mode, etc. of the calorie data of the calorie information as calories for the intake information. In addition, when there is no calorie information that matches the attribute information, the calorie specifying unit 313 specifies the average value, the intermediate value, the mode value, and the like of the calorie data of the extracted calorie information as the calories for the intake information of the received data. To do. Moreover, the calorie specific | specification part 313 specifies the calorie with respect to intake information from a food database (not shown), if calorie information is not extracted.
As described above, in this embodiment, calorie data for the intake information of the analysis target user is extracted from the content data of the diary, and the calorie data of other users having attribute information with content close to that of the analysis target user The calorie with respect to the ingestion information of the analysis target user is specified.

The functional configuration of the control unit 310 of the server 30 according to the present embodiment is as described above. Next, the operation of the blood sugar level prediction system 1 will be described.
(Operation)
FIG. 9 shows an operation flow showing the overall operation of the blood glucose level prediction apparatus 10 in the present embodiment. In the following description of the operation, an example will be described in which the user B in the service user information 331a in FIG. 8A acquires ingested calorie information from the server 30 using the SNS service. Further, it is assumed that an input mode is set in advance in the storage unit 140 as an intake energy acquisition mode in the blood sugar level prediction apparatus 10.

When the user B performs an operation of switching the acquisition mode to the server mode via the operation unit 130 (step S100: YES), the control unit 110 changes the input mode stored in the storage unit 140 to the server mode. (Step S200). If the user B does not perform an operation for switching to the server mode (step S100: NO), the control unit 110 performs the process of step S300.
The user B inputs data obtained by actually measuring the blood glucose level once a day via the operation unit 130 of the blood glucose level prediction apparatus 10. When control unit 110 accepts the blood glucose level data and the input time input via operation unit 130, control unit 110 sets the input blood glucose level data as reference value C0, and starts blood glucose level prediction processing (step S300).
The control unit 110 performs a process of calculating the first prediction curve using the reference value C0 input in step S300 and the intake energy information acquired in the server mode set in the storage unit 140 (step S400). ).

  Here, the process of acquiring the intake energy in the server mode will be described using the operation flow of the blood sugar level predicting device 10 and the server 30. FIG. 10 is an operation flow showing an intake energy acquisition process in the blood glucose level prediction apparatus 10 in the server mode. As described above, in the case of the server mode, the user B acquires the intake energy information from the server 30 using the diary data input when using the SNS service. The user B accesses the SNS service WEB site of the server 30 using the blood sugar level predicting apparatus 10 and inputs the meal contents he / she eats on the diary input screen.

When the URL of the SNS service WEB site provided by the server 30 is input via the operation unit 130, the control unit 110 accesses the SNS service WEB site via the communication unit 170 (step S111).
When the user B performs an operation of inputting the user ID “102” via the operation unit 130 on the SNS service WEB site, the control unit 110 inputs the input user ID “102” via the communication unit 170. It transmits to the server 30 and receives a response from the server 30 that permits login to the WEB site and logs in to the WEB site (step S112).
When the user B designates a WEB page indicating his / her diary input screen set in advance in the SNS service via the operation unit 130, the control unit 110 designates the WEB page designated via the communication unit 170 as the server 30. Are displayed on the display unit 150 (step S113). FIG. 11A shows an example of a diary input screen displayed on the display unit 150. In this example, on the diary input screen, the input date 151, the input field 152 for inputting the diary content data, the OK button 153 for instructing to transmit the diary data to the server 30, and the deletion without transmitting the input data. A cancel button 154 for instructing to do so is displayed.
As shown in FIG. 11B, the user B inputs data related to a meal he / she eats and other data through the operation unit 130 in the input field 152, and performs an operation of pressing an OK button 153. The control unit 110 transmits the diary data input on the diary input screen to the server 30 via the communication unit 170 (step S114). And the control part 110 receives the intake calorie information with respect to the diary data transmitted in step S114 from the server 30 during access to a SNS service (step S115). Note that if the intake calorie information cannot be received from the server 30 while accessing the SNS service, the controller 110 may receive the intake calorie information from the server 30 the next time the SNS service is accessed.

Here, an operation of the calorie intake analysis service for the diary data transmitted from the blood glucose level prediction apparatus 10 in the server 30 will be described. FIG. 12 is a diagram showing an operation flow showing the processing of the analysis service of the server 30.
When the control unit 310 receives the diary data transmitted from the blood glucose level prediction device 10 of the user B via the communication unit 320 (step S311: YES), the user input information in the storage unit 330 is shown in FIG. In 331b, as the record 3320 corresponding to the user ID “102”, the reception time and the content data are accumulated, and the analysis target of the service user information 331a is referred to and whether or not the user B is the analysis target person is determined. Judgment is made (step S312).
Control unit 310, because it is analyzed to "1" is stored in the user B in the service user information 331 a, it determines that the user B is analyzed (Step S312: YES), the received diary Analysis processing is performed on the data, and it is determined whether or not ingestion information that matches the extraction target character string defined in the food database in the storage unit 330 is included in the diary data (step S313). The control unit 310 determines that the character string of “curry” that matches the character string to be extracted is included in the diary data (step S313: YES), and extracts the character string of “curry” as intake information (step S313). S314).
The control unit 310 determines whether or not the user input information 331b in the storage unit 330 includes data including the character string of the intake information “curry” extracted in step S314 (step S315). In the user input information 331b illustrated in FIG. 13, the control unit 310 includes a character string “curry” in each content data of the record 3321 corresponding to the user ID 101, the record 3322 corresponding to the user ID 120, and the record 3323. (Step S315: YES), it is determined whether or not these contents data include calorie data corresponding to “curry” (step S316).
The control unit 310 determines that the calorie data “750 kcal” and “600 kcal” are included after the character string “curry” in the contents data of the records 3321 and 3322 (step S316: YES). The calorie data included in the respective content data 3321 and the record 3322 and the user ID associated with the content data are extracted as calorie information (step S317).
The control unit 310 specifies calories for the ingestion information “curry” of the user B using the calorie information of the user having attribute information that matches the attribute information of the user B among the calorie information extracted in step S317 (step S31). S318).

  In this embodiment, calorie information of a user who matches the sex of the attribute information of the user B and whose difference from the age of the user B is less than 5 years old is used. In this example, the sex of the user B is “male” and the age is “38”. Of the calorie information extracted in step S317, the sex of the user A corresponding to the user ID 101 is “male” and the age is “35”, and the sex of the user C corresponding to the user ID 120 is “female”, age Is “34”. Therefore, in this case, the calorie data “750 kcal” of the calorie information of the user A that matches the sex and age of the user B is specified as the calorie for the ingestion information of the user B. In this example, there is only one calorie information that matches the attribute information of the user B. However, when there are a plurality of calorie information, the average value of the calorie data included in the plurality of calorie information is calculated. It is specified as a calorie with respect to the intake information of the user B.

In addition, the control unit 310 estimates the time when the ingestion information “curry” is ingested in the diary data of the user B received at step S311 (step S319). For example, when a sentence including a character string indicating intake information includes a predetermined character string (breakfast, lunch, dinner, etc.) indicating the intake timing of a meal, the date of reception of the diary data and the character string are If the character string indicating the intake timing is not included, the time when the diary data is received is estimated as the intake time.

  In this example, as shown in the record 3320 in FIG. 13, in the sentence including the character string of the intake information “curry”, the character string “lunch” is included before the character string “curry”. Unit 310 generates intake calorie information in which intake time information “lunch”, intake information “curry”, and calorie “750 kcal” corresponding to the intake information specified in step S318 are associated with each other via communication unit 320. The ingested calorie information is transmitted to the blood glucose level prediction device 10 of the user B (step S320).

Note that in step S311, the control unit 310 does not receive diary data from the user via the communication unit 320 (step S311: NO), or in step S312, the user who transmitted the diary data is subject to analysis. If it is not a person (step S312: NO), or if intake information is not included in the diary data in step S313 (step S313: NO), the process ends.
In step S315, when the data including the character string of the intake information is not accumulated in the user input information 331b (step S315: NO), or step S316
If it is determined that the calorie data is not included (step S316: N
O) The control unit 110 extracts the calorie corresponding to the intake information from the food database in the storage unit 330, and specifies the calorie corresponding to the intake information (step S321). When the intake information is a character string representing a food group, the calories of the food contained in the group are extracted. In this case, the average value of the calories in the group is specified as the calories corresponding to the intake information.

  The processing of the analysis service of the server 30 is as described above. Returning to FIG. 10, the description of the operation of the blood sugar level prediction apparatus 10 will be continued. Control unit 110 calculates a first prediction curve based on the calorie intake information received from server 30 in step S115 (step S116). That is, the control unit 110 calculates the first prediction curve based on the intake information “curry” and the calorie “750 kcal” included in the intake calorie information at the time corresponding to the date included in the intake calorie information and the intake timing “lunch”. To do. In addition, when the intake time information includes the intake timing “lunch”, a preset lunch time (for example, 12:00 pm) is applied as the time corresponding to lunch, and “breakfast” is included. If it is, apply a preset breakfast time (for example, 8:00 am) as the time corresponding to breakfast, and if “dinner” is included, the time corresponding to dinner is set in advance Apply dinner time (eg 8pm).

  The control part 110 calculates the 1st prediction curve based on the calorie contained in intake calorie information about the blood glucose level in lunch time using the 1st prediction algorithm, for example, the 1st prediction curve shown in Drawing 14 (a). Is generated. In addition, the control unit 110 extracts user information 200 corresponding to the intake information “curry” included in the intake calorie information received in step S115, analyzes past blood glucose level changes with respect to the intake information, and generates a first model waveform. Generate (step S117). Specifically, the control unit 110 extracts waveform data until the next action when “curry” is ingested in the waveform of the blood sugar level information 200a, and averages the extracted waveform data, for example, A first model waveform shown in FIG. 14B is generated. If the blood glucose level information 200a does not include a waveform when the same meal content as the intake information is ingested, the process ends without performing the first model waveform generation process.

  The control unit 110 deforms the first prediction curve generated in step S116 based on the first model waveform generated as the analysis result in step S117 (step S118). Specifically, the control unit 110 increases the blood sugar level increase amount h11 in the rising period d2 of the first prediction curve shown in FIG. 14A and the blood sugar level rise of the first model waveform shown in FIG. The amount h21 (C4-C1) is compared, and if the difference in the amount of increase is equal to or greater than the threshold, the first prediction algorithm is set so that the amount of increase h11 in the first prediction curve is h21 (C4-C1). The coefficient α at is adjusted. In addition, after t2 when the blood sugar level of the first model waveform reaches the peak, the control unit 110 performs the duration d31 (t3−t2) from the peak value to t3 within the threshold Δcth range, and the first prediction curve. Is compared with the equilibrium period d3. When the difference between the continuation period d31 and the equilibrium period d3 is greater than or equal to the threshold value, the control unit 110 sets the continuation period d31 as the equilibrium period d3. In addition, the control unit 110 compares the amount of decrease in blood glucose level (h12) in the descending period d4 of the first prediction curve with the amount of decrease in blood glucose level h22 (C4-C2) of the first model waveform. When the difference is greater than or equal to the threshold, the coefficient β in the first prediction algorithm is adjusted so that the amount of decrease h12 becomes the amount of decrease h22 in the first model waveform. FIG. 14C shows the result of deforming the first prediction curve based on the first model waveform in this way.

  The calculation process of the 1st prediction curve in this embodiment is as above. Returning to FIG. 9, the description will be continued. The control part 110 performs the process which calculates a 2nd prediction curve based on the consumption energy measured in the active mass measurement part 120, and the exercise | movement information input by the user B (step S500).

  Here, details of the second prediction curve calculation processing will be described according to the operation flow of FIG. The control unit 110 receives, as energy consumption information, the user's calorie consumption measured at regular intervals by the activity amount measurement unit 120 from the activity amount measurement unit 120 (step S131), and a second prediction curve for the received calorie consumption. Calculation is performed using the second prediction algorithm, and a second prediction curve illustrated in FIG. 16A is generated (step S132).

  When the user performs an operation for displaying an exercise information input screen (not shown) via the operation unit 130, the control unit 110 displays the exercise type information 3200 on the display unit 150 and accepts an input from the user (step S133). ). When the exercise type is input by the user via the operation unit 130 as the exercise information (step S133: YES), the control unit 110 stores the input exercise information in the user information 200 and also adds the input exercise information to the exercise information. Corresponding user information 200 is extracted, a past blood glucose level change with respect to exercise information is analyzed, and a second model waveform is generated (step S134).

  For example, if the input exercise information is the exercise type “3” and the exercise content “walking”, in the example of the blood glucose level information 200a in FIG. 3A, “walking” was performed on February 1, 2010 Waveform data for the period from the start of walking until the next action “snack” is ingested is extracted. In this way, the control unit 110 extracts and averages the waveform data when “walking” is performed from the blood glucose level information 200a, and generates the second model waveform shown in FIG. If the waveform when the same exercise as the exercise information is not included in the blood glucose level information 200a, the process is terminated without generating the second model waveform.

  The control unit 110 transforms the second prediction curve generated in step S132 based on the second model waveform generated as the analysis result in step S134 (step S135). Specifically, the control unit 110 reduces the blood glucose level decrease Δc during the falling period e2 of the second prediction curve shown in FIG. 16A and the blood glucose level reduction of the second model waveform shown in FIG. The quantity Δc1 is compared. The amount of decrease Δc1 of the second model waveform is the amount of decrease per unit time T0 when the blood glucose level decreases by h31 from the blood glucose level at the start of exercise between times t1 and t2. When the difference in the amount of decrease per unit time T0 is equal to or larger than the threshold, the control unit 110 adjusts the coefficient γ in the second prediction algorithm so that Δc in the second prediction curve becomes Δc1. FIG. 16C shows the result of deforming the second prediction curve based on the second model waveform in this way.

  The calculation process of the second prediction curve in the present embodiment is as described above. Returning to FIG. 9, the description will be continued. The control unit 110 generates a predictive blood sugar level curve in which the first predictive curve generated in step S400 and the second predictive curve generated in step S500 are integrated along the same time axis, and the generated predictive blood sugar level curve is generated. The displayed image is displayed on the display unit 150 (step S600).

When the intake energy acquisition mode is the input mode, in the blood sugar level prediction apparatus 10, the control unit 110 displays an input screen (not shown) for inputting meal timing and meal content on the display unit 150, Data input for these items is accepted via the operation unit 130. When the user performs a meal timing input operation, the control unit 110 reads out meal timing data in the meal timing information 3300 in the storage unit 140 and displays the meal timing data on the display unit 150. Accept. When the user performs an operation for inputting meal contents, the control unit 110 reads out meal menu data in the calorie information 3100 in the storage unit 140 and displays the meal menu data on the display unit 150. Accept.
The control unit 110 identifies the calorie intake for the meal content input in the input mode from the calorie information 3100, stores the meal information including the specified intake calorie, the input intake timing and the meal content in the user information 200, and Using the meal information, the same processing as in steps S116 to S118 of FIG. 10 described above is performed, and a first prediction curve based on the intake energy is calculated.

  In this embodiment, when the user uses the SNS service, other users stored in the server 30 that provides the SNS service can enter other information stored in the server 30 that provides the SNS service by inputting ingestion information regarding the meal that he / she has consumed. The calorie corresponding to the intake information is specified from the written diary data. For this reason, even if the user does not input the content of the meal ingested by the blood sugar level prediction device 10, the calorie intake information necessary for predicting the blood sugar level by writing the intake information on the diary input screen provided by the SNS service. And the accuracy of blood glucose level prediction can be improved as compared with the case where there is no calorie intake data necessary for blood glucose level prediction.

  Moreover, the calorie specified in the server 30 is specified based on the calorie ingested by the user who matches the attribute information of the user to be analyzed under a certain condition. For example, even if the same menu is consumed by men and women, the amount of intake (such as large servings) of the menu may differ between men and women. Therefore, the data written by the user whose attribute information matches to some extent is likely to reflect the tendency of the user to be analyzed to eat, and blood glucose level suitable for the user can be predicted.

  In the example of the present embodiment, the user input information 331b is an example in which calorie data is not included in the diary data input in the past by the user who is the target of blood sugar level prediction. If the diary data includes intake information and calorie data, the user's calorie data may be prioritized and included in the user's calorie data and other user's input data. The calorie data to be extracted may be extracted.

<Modification>
The present invention is not limited to the above-described embodiment, and may be carried out by being modified as follows. Further, the following modifications may be combined.

(1) In the above-described embodiment, when the intake information is included in the data transmitted to the server 30 by the analysis target user, the calorie data for the intake information is included and matches the attribute information of the user. This is an example in which the calorie information for the intake information is specified using the user's calorie information, but not only the user's attribute information but also the user's information about the environment in which the user took the meal (hereinafter referred to as environmental information) The calorie information may be specified using the calorie information. Environmental information includes information that indicates where the user to be analyzed has eaten (for example, store name, address, telephone number, etc.), information that indicates other users who have eaten together with the user to be analyzed (user name, etc.) ) Etc. In the case of environment information representing other users, it is a condition that the user is using the same service as the SNS service used by the analysis target user. Relationship information indicating other users having a relationship is stored.

If the analysis target user sends the diary data sent to the server 30, for example, if the sentence “I ate a hamburger at Y store at dinner” is included in the diary data, the control unit 310 of the server 30 The data is analyzed, and “XX Hamburg” is extracted as intake information, and “Y store” is extracted as environmental information indicating a place. Then, the control unit 310 extracts input data including the intake information “XX Hamburg” from the user input information 331b, and extracts input data including the environmental information “Y store” from the extracted data. . The controller 310 extracts the calorie information for “XX Hamburg” from the extracted input data, extracts the calorie information of the user that matches the attribute information of the user to be analyzed from the extracted calorie information, and the calorie information Is used to identify the calorie for “XX Hamburg”. If there is no content data including environmental information, the calorie is specified based on intake information and user attribute information, as in the embodiment.

  In addition, if the diary data transmitted to the server 30 by the analysis target user includes, for example, the sentence “I ate chicken curry with user A today”, the control unit of the server 30 310 analyzes the diary data and extracts “chicken curry” as intake information and environmental information “user A” representing other users. Then, the control unit 310 extracts input data including the intake information “chicken curry” in the user input information 331b. In addition, the control unit 310 refers to the relationship information of the analysis target user and determines whether “user A” is included. If “user A” is included in the relational information, the diary data of “user A” is extracted from the extracted data, and the diary data of user A corresponding to the received date of the analysis target user's diary data is extracted. When the calorie data for the intake information is included, the calorie data is specified as the calorie for the “chicken curry”. When there is no user A's diary data corresponding to the reception date of the analysis target user's diary data, or when calorie data is not included, the control unit 310, as in the embodiment, The calorie is specified based on the attribute information.

  As described above, in this modification, not only the intake information and the attribute information of the user, but also the calorie data included in the diary data corresponding to the environment when the user ingests the meal, the calorie for the intake information is calculated. Since it can be specified, a more appropriate calorie is specified as the calorie to the meal ingested by the user, and the prediction accuracy of the blood glucose level can be improved.

(2) In the above-described embodiment, an example in which the calorie corresponding to the intake information included in the diary data transmitted from the blood sugar level predicting device 10 to the server 30 is specified. Information may be specified, and the server 30 may specify calories for the intake information. For example, when the blood glucose level prediction apparatus 10 is provided with a schedule function for inputting a schedule or the like, the blood sugar level prediction apparatus 10 extracts intake information from the schedule data input by the user using the schedule function, and extracts the information. The intake information may be transmitted to the server 30, and the calorie corresponding to the intake information may be specified by the server 30. In this case, the control unit 110 of the blood sugar level predicting apparatus 10 accumulates schedule data in which the schedule content input using the schedule function and the time information of the schedule are combined in a predetermined area of the storage unit 140. The control unit 110 determines whether or not intake information is included in the schedule data every time the schedule data is input. When the intake information is included, the control unit 110 accesses the server 30 when the user uses the SNS service or whenever the schedule data is input, and the intake information A request for specifying the calorie is transmitted to the server 30.

  In response to the request transmitted from the blood sugar level predicting device 10, the server 30 specifies calories for the intake information by the same method as in the embodiment, and transmits calorie information indicating the specified calories to the blood sugar level predicting device 10. The blood glucose level prediction apparatus 10 calculates a first prediction curve based on calorie information transmitted from the server 30, time information of schedule data including the intake information, and the intake information, and predicts a blood glucose level. In addition, when the blood sugar level prediction device 10 determines whether or not intake information is included in the schedule data, the storage unit 140 stores a predetermined extraction target character string such as a food name or a dish name. Configure as follows.

(3) In the above-described embodiment, when the user re-enters the meal contents using the input mode after the calorie intake is input in the server mode, the input meal contents and the meal contents are displayed. The predicted blood glucose level curve may be recalculated using the corresponding calorie intake. In this case, for example, a past date and meal timing can be input on the input mode screen, and meal information corresponding to the input past date and meal timing is extracted from the behavior information 200b and displayed. To be configured. Similarly to the embodiment, when the user performs an operation of selecting meal contents, the intake calories corresponding to the meal contents are specified, and the input meal contents and intake calories are stored in the behavior information 200b. At the same time, the predictive blood sugar level curve is recalculated based on the calorie intake.

(4) In the above-described embodiment, a food database in which a character string to be extracted of intake information is associated with a standard calorie is stored in the storage unit 330 of the server 30, and the intake is obtained from the user input information 331b. When calorie information corresponding to information could not be extracted, it was an example of specifying calories using calories in the food database. For example, calories corresponding to intake information from other server devices on the Internet May be obtained.

(5) In the above-described embodiment, the calorie consumption of the user is sequentially measured by the activity amount measurement unit 120. However, when the measurement unit that measures the pulse is provided, the consumption energy acquisition unit 112 You may make it convert the pulse measured using the calculation formula defined beforehand which converts a pulse into calorie consumption into calorie consumption.

(6) In the above-described embodiment, the blood sugar level prediction curve is generated using a predefined blood sugar level prediction algorithm. However, using a technique such as nonlinear regression analysis or time series analysis, The blood glucose level may be predicted by applying data such as meal information and exercise information input by the user to the model estimated by these methods.

(7) In the above-described embodiment, the user information 200 is an example in which the blood glucose level and the action history measured during the past education hospitalization period of the user are stored. Any other data may be used. For example, it may be time series data of a user's blood glucose level and behavior history measured at home, or time series data of blood glucose level and behavior history of other diabetic patients having the same characteristics (such as medical condition and constitution) as the user. There may be.

(8) In the above-described embodiment, the user's calorie consumption is sequentially measured by the activity amount measurement unit 120. However, in the case where a means for measuring a pulse is provided, the consumption energy acquisition unit 112 You may make it convert the measurement result measured in the means which measures a pulse using the calculation formula defined beforehand which converts data into calories burned into calories burned.

(9) In the above-described embodiment, an example of the first prediction algorithm using calorie intake has been described. However, a prediction algorithm using a sugar mass, a GI value, a lipid amount, etc. included in the meal content is applied. May be. In this case, a table in which components such as a sugar mass, a GI value, and a lipid amount corresponding to meal contents are defined in advance may be stored in the storage unit 140, or meal information is input by the user. At this time, the intake energy acquisition unit 111 may be configured to acquire component data corresponding to the meal information from the outside by wired or wireless communication.

(10) In the above-described embodiment, the SNS service has been described as an example. However, a service such as a bulletin board, a chat, a blog, and Twitter (registered trademark) may be used. While providing an input screen for inputting data related to these services (service information) to the blood glucose level prediction apparatus 10, calories corresponding to intake information included in the data input using the input screen May be specified using data accumulated by users who use these services.

(11) In the above-described embodiment, the example in which the patient inputs the exercise type via the operation unit 130 is used. For example, by using an activity amount measuring unit alone or an acceleration sensor, the blood sugar level prediction apparatus May determine the type of exercise.

  DESCRIPTION OF SYMBOLS 1 ... Blood glucose level prediction system, 10 ... Blood glucose level prediction apparatus, 30 ... Server, 110, 310 ... Control part, 111 ... Intake energy acquisition part, 112 ... Consumption energy acquisition part, 113 ... Calculation part, 114 ... Analysis part, 115 ... generating part, 120 ... activity amount measuring part, 130 ... operation part, 140, 330 ... storage part, 150 ... display part, 160 ... timing part, 170, 320 ... communication part, 311 ... received data analysis part, 312 ... data Extraction unit, 313 ... Calorie identification unit

Claims (10)

The server device acquires first service information including first intake information related to the meal of the first user and first calorie information corresponding to the first intake information from the storage unit ,
The server device acquires the second service information including the second intake information regarding the meal of the second user from the storage unit ,
When the server device determines that the first intake information and the second intake information satisfy the first condition , the server device determines the first calorie information as calorie information corresponding to the second intake information. Calorie information determination method.   2. The calorie information determination method according to claim 1, wherein the first intake information or the second intake information includes a content of a meal taken by the first user or the second user.   The calorie information according to claim 1 or 2, wherein the first condition is that a character string included in the first intake information matches a character string included in the second intake information. Decision method. In the server device, when the first attribute information of the first user and the second attribute information of the second user satisfy a second condition, the first intake information and the second intake information are It is judged whether 1 conditions are satisfied, The calorie information determination method as described in any one of Claims 1-3 characterized by the above-mentioned.   5. The calorie information determination method according to claim 4, wherein the first attribute information and the second attribute information are at least one of age, sex, height, weight, and BMI value.   The first condition is that the first environment information related to the first intake information matches the second environment information related to the second intake information. The calorie information determination method described.   7. The calorie information determination method according to claim 6, wherein the first environment information and the second environment information are information representing a place or information representing another user who has eaten together. The blood sugar level predicting apparatus performs blood sugar level prediction of the first user using the first intake information and the first calorie information. Calorie information determination method. The calorie information determination method according to any one of claims 1 to 7,
A blood sugar level predicting device acquires the activity information of the second user;
The blood sugar level prediction method, wherein the blood sugar level prediction apparatus performs blood sugar level prediction of the second user based on the activity information and the first calorie information. The blood sugar level prediction according to claim 9 , wherein the blood sugar level prediction device performs blood sugar level prediction of the second user using the second intake information, the first calorie information, and the activity information. Method.

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