JP5835580B2 - Lifestyle improvement support system and analysis method of biological index - Google Patents

Description

  The present invention relates to a system for supporting lifestyle habit improvement guidance.

  In recent years, an increase in lifestyle-related diseases such as diabetes, hypertension, and dyslipidemia has become a problem, and prevention of the onset and aggravation of lifestyle-related diseases has become an important social issue. In order to prevent the onset and aggravation of lifestyle-related diseases, it is important to improve lifestyles such as diet and exercise, as well as treatment with drugs. However, in reality, it is said that it is difficult for the subject to continuously work on improving their lifestyle habits.

  As guidance for improving lifestyle habits, for example, in diabetes, a method of eating a meal with a predetermined nutritional balance using a food exchange table is known. In addition, there are a method using a GI value that indexes the magnitude of the influence of food on blood sugar, a carbo count method that controls the amount of carbohydrates, and the like. In addition, various methods for effectively promoting such guidance have been proposed. For example, as a method of monitoring biometric indicators such as blood glucose of participants in order to measure the effect of improving lifestyle habits, a method using a device that self-measures blood glucose levels, or a urine sugar that monitors blood glucose status without blood sampling There is a method using a meter (for example, see Patent Document 1). In addition, as a method utilizing information processing, a method of estimating insulin sensitivity from the result of self-blood glucose measurement (see, for example, Patent Document 2), and determining whether the amount of carbohydrate input by the participant is correct is obtained from blood glucose levels before and after a meal. And the like (for example, see Patent Document 3) and the method for estimating postprandial blood glucose level from the contents of meal (for example, see Patent Document 4).

JP 2006-153849 A Special table 2010-533038 gazette Special table 2010-535045 gazette JP 2011-65539 A

  In lifestyle improvement guidance, it is said that it is important that the participants of the guidance have self-efficacy, which is the confidence to perform lifestyle improvement behavior, and success is one factor for obtaining self-efficacy. There is an acquisition of experience. Participants themselves are aware of what diets and behaviors improve (or worsen) biometrics such as body weight, blood glucose, blood pressure, and lipids, and gain confidence in improving biometrics as a successful experience , Leading to continued improvement of lifestyle habits. Furthermore, if it is possible to present to the participant what kind of behavior is good (or bad), that is, the relationship between the biometric index, the meal, and the behavior, it is possible to support improvement of lifestyle habits. However, life of individuals such as meals and exercise is different for each individual, and it is difficult to uniformly instruct the participants about appropriate meals and actions. Furthermore, changes (improvement / deterioration) of biometric indices due to meals and behaviors vary among individuals, such as the constitution of participants and the degree of disease progression, and also change over time.

  Patent Documents 1, 2, and 4 described above describe a method for estimating a result from an individual's behavior and displaying the estimation result, but do not describe a method for presenting an appropriate meal and behavior to a participant. . Patent Document 3 describes a method of presenting an error in the amount of input carbohydrate from the test results, but each person has different diet and behavior, and individual differences and temporal changes are taken into account. Absent.

  An object of the present invention is to estimate a relationship between a participant's diet or behavior and a change in a biometric index and to present it to the participant in order to support the lifestyle habit improvement of the participant.

A typical example of the invention disclosed in the present application is as follows. That is, a lifestyle improvement support system that supports lifestyle improvement, a database that stores a participant's biometric index, food intake and action records in time series, a participant's biometric index, A data acquisition unit that acquires the intake and action records from the database, and calculates by multiple regression analysis using the rate of change of the urine sugar value with respect to the intake of food and the presence or absence of action acquired from the database as a biological index coefficient A biometric index coefficient calculating section, an output information generating section for generating data for ranking and outputting the food and action based on the calculated biometric index coefficient, and a urine sugar value of the participant is predetermined. An application determining unit that determines that data satisfying the condition is an object of the multiple regression analysis, and the biometric index coefficient calculating unit includes the number of foods consumed by the participant and When the number of actions performed exceeds the number of days in the target period of the multiple regression analysis, the biometric index coefficient is calculated by multiple regression analysis, and the output information creation unit averages the amount of food taken at one time For each food, ranking the food according to the product of the biometric coefficient for the food calculated by the multiple regression analysis and the average intake of the food, and calculating the average frequency at which the action is performed. was calculated for each action, the following product of the average frequency of the calculated action as biomarker factors for the calculated action by multiple regression analysis, Ru ranking the action.

  According to the typical embodiment of the present invention, it is possible to estimate the relationship between the food intake or behavior of the participant and the change of the biometric index.

It is a block diagram which shows an example of the hardware constitutions of the lifestyle improvement guidance support system of embodiment of this invention. It is a figure explaining an example of a structure of the data stored in the biometric index management part of embodiment of this invention. It is a figure explaining an example of a structure of the data stored in the meal and action information management part of embodiment of this invention. It is a figure explaining an example of a structure of the data stored in the biometric parameter coefficient change pattern management part of embodiment of this invention. It is a figure explaining an example of a structure of the data stored in the application condition management part of embodiment of this invention. It is a figure which shows an example of the functional block diagram at the time of the biometric coefficient coefficient calculation process of the lifestyle improvement guidance support system of embodiment of this invention. It is a flowchart which shows an example of the biological index coefficient calculation process of embodiment of this invention. It is a figure which shows an example of the functional block diagram at the time of the biometric coefficient coefficient calculation process of the lifestyle improvement guidance support system of embodiment of this invention. It is a flowchart which shows an example of the constitution improvement evaluation process of embodiment of this invention. It is an example of the functional block diagram at the time of the constitution improvement evaluation process of the lifestyle improvement guidance support system of embodiment of this invention. It is a flowchart which shows an example of the constitution improvement evaluation process of embodiment of this invention. It is a flowchart which shows an example of the constitution improvement evaluation process of embodiment of this invention. It is a figure explaining an example of the recording input screen of embodiment of this invention. It is a figure explaining an example of the record reference screen of the embodiment of the present invention. It is a figure explaining an example of the meal and action display screen of the embodiment of the present invention. It is a figure explaining an example of the biometric coefficient coefficient display screen of embodiment of this invention.

  An example of an embodiment of the present invention will be described with reference to the drawings. In the present embodiment, a case will be described as an example in which the urine sugar value is used as a biometric index and the amount for each food item is recorded as a meal record in the lifestyle improvement guidance. The biometric index coefficient is a urine sugar coefficient.

  FIG. 1 is a block diagram showing an example of a hardware configuration of a lifestyle habit improvement guidance support system according to an embodiment of the present invention.

  The medical examination information analysis system of this embodiment includes a server 101, a database 102, a participant terminal 104, and a leader terminal 105.

  The server 101 and the database 102 are connected so that the server 101 can access data stored in the database 102. The server 101 is connected to the participant terminal 104 and the instructor terminal 105 via the network 103.

  The server 101 is a computer having a processor (CPU) 111 that executes a program, a memory (not shown) that stores the program, a storage device 112, and a communication interface 113. The server 101 may have a user interface such as an input device (keyboard, mouse, etc.) and an output device (display device, printer, etc.).

  The processor 111 executes a program stored in the memory.

  The memory is a high-speed and volatile storage device such as a dynamic random access memory (DRAM), and stores an operating system (OS) and application programs. When the processor 111 executes the operating system, the basic functions of the server 101 are realized, and when the application program is executed, the functions provided by the server 101 are realized.

  The communication interface 113 connects the server 101 to a network and controls communication with other devices.

  The storage device 112 is a large-capacity nonvolatile storage device such as a magnetic storage device or a flash memory, and stores a program executed by the processor 111 and data used when the program is executed. That is, the program executed by the processor 111 is read from the storage device 112, loaded into the memory, and executed by the processor 111.

  Specifically, the storage device 112 includes a control unit 131, a data acquisition unit 132, a biometric index coefficient calculation unit 133, an application determination unit 134, a biometric index coefficient change extraction unit 135, a biometric index coefficient change pattern processing unit 136, and output information. A program for mounting each unit of the creation unit 137 is stored.

  The control part 131 calls other each part by execution of a predetermined program, and controls the whole operation | movement of the lifestyle improvement guidance support system of this embodiment.

  The data acquisition unit 132 acquires data satisfying a predetermined condition from the database 102 by executing a predetermined program.

  The biometric index coefficient calculation unit 133 performs multiple regression analysis using meal and behavior data by executing a predetermined program, and outputs a multiple regression analysis result.

  The application determination unit 134 determines that the biological index satisfies the application condition (for example, the urine sugar value is equal to or higher than the threshold) by executing a predetermined program, and extracts information on the day that satisfies the application condition.

  The biometric index coefficient change extraction unit 135 calculates changes in the biometric index coefficient for meals and actions based on the results of regression analysis by executing a predetermined program.

  The biometric index coefficient change pattern processing unit 136 performs processing for outputting information that coincides with the change in the biometric index coefficient based on the change in the biometric index coefficient with respect to meals and actions by executing a predetermined program.

  The output information creation unit 137 creates information (for example, screen data) to be presented to a participant (a trainee) or a leader by executing a predetermined program.

  The database 102 is data for the server 101 to support lifestyle improvement guidance, that is, a biometric index management unit 121 (see FIG. 2), a meal / behavior information management unit 122 (see FIG. 3), a biometric coefficient coefficient change pattern. The management unit 123 (see FIG. 4) and the application condition management unit 124 (see FIG. 5) are stored.

  The server 101 may be physically constructed on one computer, or may be physically constructed on a logical partition configured on one or a plurality of computers.

  The program executed by the processor is provided to the computer via a nonvolatile storage medium or a network. For this reason, the computer may include an interface for reading a storage medium (CD-ROM, flash memory, etc.).

  The participant terminal 104 is a computer having a communication interface 141, a processor (CPU) 142 for executing a program, a memory (not shown) for storing the program, an input device 143, an output device 144, and a storage device (not shown). Used by participants to enter data and display analysis results. The participant terminal 104 may be a terminal such as a mobile phone, a smartphone, or a tablet terminal as long as it has a display and input function.

  The communication interface 141 connects the participant terminal 104 to the network 103 and controls communication with other devices.

  The processor 142 executes a program stored in the memory.

  The memory of the participant terminal 104 is a high-speed and volatile storage device such as a DRAM (Dynamic Random Access Memory), and stores an operating system (OS) and application programs. The processor 142 executes the operating system to realize the basic functions of the participant terminal 104, and the application 142 executes the functions provided by the participant terminal 104.

  The input device 143 is a keyboard, a mouse, a touch panel, or the like, and is an interface that receives input from the user. The output device 144 is a display device, a printer, or the like, and outputs the processing result in a format that can be visually recognized by the user.

  The storage device of the participant terminal 104 is a large-capacity and nonvolatile storage device such as a magnetic storage device or a flash memory, and stores a program executed by the processor 142 and data used when the program is executed. That is, the program executed by the processor 142 is read from the storage device, loaded into the memory, and executed by the processor 142.

  The participant terminal 104 only needs to operate an application (for example, a web browser) that displays a calculation result output from the server 101 and receives an input from the user.

  The instructor terminal 105 is a computer having a communication interface 151, a processor (CPU) 152 for executing a program, a memory for storing a program (not shown), an input device 153, an output device 154, and a storage device (not shown). Used by instructors when instructing participants.

  The communication interface 151 connects the instructor terminal 105 to the network 103 and controls communication with other devices.

  The processor 152 executes a program stored in the memory.

  The memory of the instructor terminal 105 is a high-speed and volatile storage device such as a DRAM (Dynamic Random Access Memory), for example, and stores an operating system (OS) and application programs. When the processor 152 executes the operating system, the basic function of the leader terminal 105 is realized, and when the application program is executed, the function provided by the leader terminal 105 is realized.

  The input device 153 is a keyboard, a mouse, a touch panel, or the like, and is an interface that receives input from the user. The output device 154 is a display device, a printer, or the like, and outputs a processing result in a format that can be visually recognized by the user.

  The storage device of the instructor terminal 105 is a large-capacity non-volatile storage device such as a magnetic storage device or a flash memory, and stores a program executed by the processor 152 and data used when the program is executed. That is, the program executed by the processor 152 is read from the storage device, loaded into the memory, and executed by the processor 152.

  The instructor terminal 105 only needs to operate an application (for example, a web browser) that displays a calculation result output from the server 101 and receives an input from the user.

  FIG. 2 is a diagram illustrating an example of the configuration of data stored in the biometric index management unit 121 according to the embodiment of this invention.

  The biometric index management unit 121 is a database that stores biometric information of participants in lifestyle improvement guidance, and includes items 1211 and biometric data 1212.

  The item 1211 is an item of a biometric index obtained from a living body such as a post-breakfast urine sugar value, a post-lunch urine sugar value, a post-dinner urine sugar value, a body weight, and the like. The biometric index data 1212 is biometric index data corresponding to the item 1211 and is recorded every day. Note that the biometric index management unit 121 may include other biometric information.

  FIG. 3 is a diagram illustrating an example of the configuration of data stored in the meal / behavior information management unit 122 according to the embodiment of this invention.

  The meal / behavior information management unit 122 is a database that stores information on meals and actions of participants in lifestyle improvement guidance, and includes a time 1221, food 1222, actions 1223, and data 1224.

  The time 1221 is a time zone when the meal / behavior recorded in the meal / behavior information management unit 122 is performed. The food 1222 is a type of food taken by the participant. The action 1223 is a type of action performed by the participant. Data 1224 is data representing whether the food is ingested and actions are taken. A part of the data 1224 represents the amount of food intake and behavior.

  For example, “rice bowl” of food 1222 indicates how many cups of rice were eaten in the bowl, and “fruit” indicates whether fruit was eaten (“1” if eaten, “0” if not eaten).

  In addition, “exercise after meal” of the action 1223 indicates whether exercise has been performed after meal (“1” when exercise is performed, “0” when exercise is not performed).

  Although the time 1221 shows an example in which it is divided at the time of breakfast, lunch, and dinner, it may be divided by time.

  Further, the food 1222 and the action 1223 may be customized to change the setting contents for each participant.

  FIG. 4 is a diagram illustrating a configuration of data stored in the biometric index coefficient change pattern management unit 123 when urine sugar is used as a biometric index in the embodiment of the present invention.

  The biometric index coefficient change pattern management unit 123 is a database that stores biomarker coefficient change patterns related to urine sugar in the guidance of participants in lifestyle habit improvement guidance. The biometric index coefficient item 1231 and the biometric index coefficient change 1232 are stored in the database. , Biometric index 1233, biometric index change 1234, processing 1235 when the condition is met, and comment 1236 output in the comment output processing.

  The biometric index coefficient 1231 is an item for designating a coefficient of a biometric index to be referred to (for example, urine sugar). The representative food related to urine sugar has a high input frequency in daily recording, and the biometric index coefficient related to urine sugar is Indicates a food (eg, rice) that is large (strongly associated with urine sugar). The method for determining the representative food will be described later. A change 1232 is a change in the biometric index coefficient. For example, the change 1232 indicates a change in the biometric index coefficient when a certain amount of representative food (for example, rice) is consumed in the same amount. The biometric index 1233 is another biometric that may change in relation to the biometric index defined in the biometric index coefficient 1231. Here, the weight is another biometric index. A change 1234 is a change of the biological index 1233 (for example, “decrease” in weight). The process 1235 is a process performed when a change in the biometric index coefficient or the like matches the condition from the biometric index coefficient 1231 to the change 1234. The comment 1236 is a comment that is output when the process 1235 is “comment output”.

  For example, if the bioindicator coefficient for urine sugar for this food is reduced and the body weight is reduced as a result of ingesting a certain amount of representative food, the comment `` There is a possibility that sugar metabolism has been improved by reducing fat. Is output.

  FIG. 5 is a diagram illustrating an example of a configuration of data stored in the application condition management unit 124 according to the embodiment of this invention.

  The application condition management unit 124 is a database that stores biometric index conditions used for lifestyle improvement guidance, and includes data of the biometric index 1241 and application conditions 1242.

  The biometric index 1241 is a biometric index used for lifestyle improvement guidance. The application condition 1242 is a condition to be analyzed by this biological index.

  For example, when the urine sugar value exceeds 150 mg / dl, the correlation between the urine sugar value and the diet or behavior is analyzed.

  FIG. 6 is a functional block diagram during the biometric index coefficient calculation process of the lifestyle habit improvement guidance support system according to the embodiment of the present invention.

  First, the data acquisition unit 132 acquires urine sugar value data from the biometric index management unit 121, acquires meal and behavior data from the meal / behavior information management unit 122, and sends the acquired data to the application determination unit 134. .

  The application determination unit 134 acquires the application condition 1242 related to the urine sugar value from the application condition management unit 124, and extracts the date on which the urine sugar value acquired by the data acquisition unit 132 satisfies the application condition (is greater than or equal to the threshold). The information on the day is sent to the biometric index coefficient calculation unit 133.

  The biometric index coefficient calculation unit 133 performs a multiple regression analysis using the data on the day when the application condition is satisfied among the meal and behavior data acquired by the data acquisition unit 132, and as a result of the multiple regression analysis, a biometric index of the meal or behavior The coefficient is sent to the output information creation unit 137.

  The output information creation unit 137 creates screen data for displaying meals and actions related to urine sugar using the biometric coefficient of meals and actions as a result of multiple regression analysis, and the participant terminal 104 or the leader terminal Sent to 105.

  The output unit 144 of the participant terminal 104 or the output unit 154 of the instructor terminal 105 displays the screen data created by the output information creation unit 137, that is, foods and actions that have a low correlation with urine sugar.

  FIG. 7 is a flowchart of biometric coefficient coefficient calculation processing for urine sugar according to the embodiment of the present invention.

  First, the data acquisition unit 132 acquires urine sugar value data from the biometric index management unit 121 and acquires meal and behavior data from the meal / behavior information management unit 122 (S101). Here, urine sugar at breakfast is targeted.

  Thereafter, the application determination unit 134 acquires the application condition 1242 related to the urine sugar value from the application condition management unit 124, and the data on the day when the urine sugar value acquired by the data acquisition unit 132 satisfies the application condition (is greater than or equal to the threshold). Extract (S102).

  Next, the biometric index coefficient calculation unit 133 determines that the number of days for which the urine sugar value is greater than or equal to the threshold is greater than the number of food and action items input by the participant (S103). This determination may be made by determining that the number of days for which the urine sugar value is equal to or greater than the threshold is greater than the total number of items for food and action, but the number of days for which the urine sugar value is equal to or greater than the threshold is at least the number of items for food and action. The determination may be made depending on whether the number is greater than one. As a result, if the number of days for which the urine sugar value is equal to or greater than the threshold is smaller than the number of items of food / behavior, the data used for the regression analysis is small and the regression analysis cannot be performed accurately, so the urine sugar coefficient calculation process ends.

On the other hand, when the number of food / behavior items is greater than the number of days for which the urine sugar value is equal to or greater than the threshold value, the biometric index coefficient calculation unit 133 performs biometric index for urine sugar of food i by multiple regression analysis using Equation (1) The biometric index coefficient βi for the coefficient αi and the urine sugar of the action i is calculated (S104). For the food i, for example, a variable is set for each type of meal such that meal 1 is rice, meal 2 is bread, and meal 3 is fruit. In addition, the action j has a variable as an action input item of the participant, such that the action 1 exercises after eating, the action 2 chews well, and the action 3 eats a lot of vegetables. In addition, variable selection generally performed in multiple regression analysis is performed for meals and behaviors, and only items that contribute to the biometric index (urine sugar) are extracted.
Urine sugar = Σαi × meal amount i + Σβj × action j (1)

  Next, the output information creation unit 137 calculates the average of the amount taken once for each food (S105), and calculates the food urine sugar coefficient αi × the average food single dose (S106).

  Finally, the output information creation unit 137 displays the type of food for each food category (main food, side dish, snack, liquor, etc.) in the order of αi × average food average amount, and the action type is βj × average action frequency. The screen data for display in ascending order is output (S107). The category information is assumed to manage the relationship between food and category in a separate table (not shown).

  When the participant terminal 104 and the instructor terminal 105 receive the screen data output from the output information creation unit 137, the type and behavior of the food are output from the output unit 144 of the participant terminal 104 or the output unit 154 of the instructor terminal 105. Are output (FIG. 14).

  As described above, in the urine sugar coefficient calculation process of the present embodiment, the lifestyle improvement guidance is appropriately provided by presenting the instructor or the participant with items having a small correlation in consideration of the average intake of food and the average action frequency. Can be done. In addition, an item with a large correlation (food urine sugar coefficient αi, behavior urine sugar coefficient βj) may be displayed without taking into consideration the average intake amount or the action amount.

  As described above, the lifestyle improvement guidance support system of the present embodiment extracts meals and actions that contribute to a biometric index (here, urine sugar) from daily records input by the participants, and the meals and actions are extracted. By presenting, there is an effect that it is possible to specifically show what kind of meals and actions were good for the participants. The instructor can easily find out what kind of meals and behaviors contribute to the increase or suppression of urine sugar for the participant, and can use the information for instruction.

  Participants can also know what diet and behavior contributed to the increase or suppression of urine sugar in their diet and behavior, and know what to do to control urine sugar. it can. In particular, by extracting only meals that contribute to the biometric index (here, urine sugar) by variable selection, and calculating the average of the single dose, showing the product of the bioindex coefficient and the amount of food from the smallest, It is possible to present foods that do not easily raise urine sugar even if you eat them. Also, by showing it by category, for example, even if you eat at the same time in the staple food, how much urine sugar rises for the person when you eat rice and when you eat soba It is possible to indicate which is more difficult to raise urine sugar according to individual performance.

  FIG. 8 is a functional block diagram of the lifestyle habit improvement guidance support system according to the embodiment of the present invention, showing urine sugar coefficient calculation processing.

  First, the data acquisition unit 132 acquires urine sugar value data from the biometric index management unit 121, acquires meal and behavior data from the meal / behavior information management unit 122, and sends the acquired data to the application determination unit 134. .

  The application determination unit 134 acquires the application condition 1242 related to the urine sugar value from the application condition management unit 124, and extracts the date on which the urine sugar value acquired by the data acquisition unit 132 satisfies the application condition (is greater than or equal to the threshold). The information on the day is sent to the biometric index coefficient change extraction unit 135.

  The biometric index coefficient change extraction unit 135 selects a food having the highest food intake frequency during the evaluation calculation period as the index food.

  The biometric index coefficient calculation unit 133 performs a multiple regression analysis using the data on the day when the application condition is satisfied among the daily meal and behavior data during the evaluation calculation period, and extracts the multiple regression analysis result as a biometric coefficient coefficient change extraction. Send to part 135.

  The biometric index coefficient change extraction unit 135 obtains a change (slope) with respect to the passage of time of the food urine sugar coefficient αi of the index food from the result of the multiple regression analysis, and sends the result to the output information creation unit 137.

  The output information creation unit 137 creates screen data for displaying the change (slope) of the food urine sugar coefficient αi of the index food using the multiple regression analysis result, and sends it to the participant terminal 104 or the instructor terminal 105. .

  The output unit 144 of the participant terminal 104 and the output unit 154 of the instructor terminal 105 display the screen data created by the output information creation unit 137, that is, the change (slope) of the urine sugar coefficient of the index food (FIG. 15). .

  FIG. 9 is a flowchart of the constitution improvement evaluation process according to the embodiment of the present invention.

  First, the data acquisition unit 132 acquires urine sugar value data from the biometric index management unit 121 and acquires meal and behavior data from the meal / behavior information management unit 122 (S111).

  Thereafter, the application determination unit 134 acquires the application condition 1242 related to the urine sugar value from the application condition management unit 124, and sets the date of the date on which the urine sugar value acquired by the data acquisition unit 132 satisfies the application condition (is equal to or greater than the threshold). Extract (S112).

  Next, the biometric index coefficient change extraction unit 135 sets an input value for a period for calculating the evaluation as a parameter (S113). This period is input to the input unit (keyboard) 143 of the participant terminal 104 or the input unit (keyboard) 143 of the instructor terminal 105. For example, if the target participant inputs 60 days, the calculation period is the entire 60 days, and the number of days such as 7 days or 10 days is specified as the calculation period. Here, seven days are set, and the first period is set as the period from the first day to the seventh day of the instruction.

  Next, the biological index coefficient change extraction unit 135 determines that the number of days in which the urine sugar value is equal to or greater than the threshold in the number of days to be calculated (60 days of the entire input) is greater than the number of items of food and behavior (S114). . This determination may be made by determining that the number of days for which the urine sugar value is greater than or equal to the threshold is greater than the total number of items for food and action, but the number of days for which the urine sugar value is greater than or equal to the threshold is the number of items for food and action. You may determine by having at least one of these more.

  As a result, if the number of days for which the urinary sugar value is equal to or greater than the threshold value is equal to or less than the number of foods / items, the data used for the regression analysis is small, and the regression analysis cannot be performed accurately.

  On the other hand, when the number of days in which the urine sugar value is equal to or greater than the threshold is greater than the number of items of food / behavior, the biometric index coefficient change extraction unit 135 selects a food having the highest food intake frequency in the calculation period as the index food ( S115). For example, if a participant ingests rice, bread, fruit, ramen, etc. as a meal in 60 days, and rice ingests most frequently among them, the rice is used as the index food.

  Next, the biometric index coefficient calculation unit 133 extracts data on the corresponding day during the calculation period from the data acquired in step S111 (S116). Here, the calculation period is from the first day to the seventh day.

  Next, the biometric index coefficient calculation unit 133 calculates a biometric index coefficient αi for urine sugar of food i and a bioindex coefficient βj for urine sugar of action j by multiple regression analysis using the above-described equation (1) ( S117). This process is the same as S104 in FIG.

  Next, the biometric index coefficient change extraction unit 135 determines whether the calculation has been performed up to the last data in the calculation period (S118). Specifically, it is determined whether the calculation period has reached the 60th day. If there is uncalculated data in the entire period, the calculation target date is moved one day (S123), and the process proceeds to step S116. The shift of the calculation period is to shift the calculation period set from the first day to the seventh day by one day and shift it by one day, such as from the second day to the eighth day.

  On the other hand, if the calculation is completed up to the last data in the calculation period, the biometric index coefficient change extraction unit 135 determines the change (gradient) of the food urine sugar coefficient αi of the index food with the passage of time from the result of the multiple regression analysis. Is obtained (S119).

  Finally, the output information creation unit 137 outputs screen data for displaying the change and inclination of the urine sugar coefficient of the index food (S120).

  When the participant terminal 104 and the instructor terminal 105 receive the screen data output from the output information creation unit 137, the urine sugar of the index food is output from the output unit 144 of the participant terminal 104 or the output unit 154 of the instructor terminal 105. The change (slope) of the coefficient is output (FIG. 15).

  As described above, the lifestyle habit improvement guidance support system according to the present embodiment determines the food with a high intake frequency, and how the contribution to the increase of the biometric index (here, urine sugar) when eating the same food. There is an effect that the change can be presented as a time-series change of the biometric index coefficient. Participants may reduce fat or increase muscles due to dietary restrictions or increased exercise, which may make it difficult for urine sugar to rise even when eating the same food. This can be presented to a leader or a participant by a change in the biometric index coefficient for one food item (decreasing as a slope). Instructors can refer to this information and show that daily efforts may appear as an improvement in their structure. In addition, participants can confirm that urine sugar is difficult to increase even if they eat the same meal by referring to the guidance from the leader and this information directly. By making it an experience, you can work on continuous improvement of lifestyle habits.

  FIG. 10 is a functional block diagram of the lifestyle improvement guidance support system of the embodiment of the present invention, and shows the constitution improvement evaluation process.

  First, the data acquisition unit 132 acquires urine sugar value data from the biometric index management unit 121, acquires meal and behavior data from the meal / behavior information management unit 122, and sends the acquired data to the application determination unit 134. .

  The application determination unit 134 acquires the application condition 1242 related to the urine sugar value from the application condition management unit 124, and extracts the date on which the urine sugar value acquired by the data acquisition unit 132 satisfies the application condition (is greater than or equal to the threshold). The information on the day is sent to the biometric index coefficient change extraction unit 135.

  The biometric index coefficient change extraction unit 135 selects a food having the highest food intake frequency during the evaluation calculation period as the index food.

  The biometric index coefficient calculation unit 133 performs a multiple regression analysis using the data on the day when the application condition is satisfied among the daily meal and behavior data during the evaluation calculation period, and extracts the multiple regression analysis result as a biometric coefficient coefficient change extraction. Send to part 135.

  The biometric index coefficient change extraction unit 135 obtains a change (slope) with respect to the passage of time of the food urine sugar coefficient αi of the index food from the result of the multiple regression analysis, and sends the result to the biometric index coefficient change pattern processing unit 136.

  The biometric index coefficient change pattern processing unit 136 acquires information about the index from the biometric index coefficient change pattern management unit 123, and matches the biometric index coefficient change pattern based on the inclination obtained by the biometric index coefficient change extraction unit 135. Information (comment) is sent to the output information creation unit 137.

  The output information creation unit 137 creates screen data for displaying a comment corresponding to the change obtained from the multiple regression analysis result, and sends the screen data to the participant terminal 104 or the instructor terminal 105.

  The output unit 144 of the participant terminal 104 and the output unit 154 of the instructor terminal 105 display the screen data created by the output information creation unit 137, that is, comments.

  11A and 11B are flowcharts of the constitution improvement evaluation process according to the embodiment of the present invention.

  First, the data acquisition unit 132 acquires urine sugar value data from the biometric index management unit 121 and acquires meal and behavior data from the meal / behavior information management unit 122 (S131).

  Thereafter, the application determination unit 134 acquires the application condition 1242 related to the urine sugar value from the application condition management unit 124, and sets the date of the date on which the urine sugar value acquired by the data acquisition unit 132 satisfies the application condition (is equal to or greater than the threshold). Extract (S132).

  Next, the biometric index coefficient change extraction unit 135 sets an input value of a period for calculating the evaluation as a parameter (S133). This period is input to the input unit (keyboard) 143 of the participant terminal 104 or the input unit (keyboard) 143 of the instructor terminal 105. For example, if the target participant inputs 60 days, the calculation period is the entire 60 days, and the number of days such as 7 days or 10 days is specified as the calculation period. Here, seven days are set, and the first period is set as the period from the first day to the seventh day of the instruction.

  Next, the biometric index coefficient change extraction unit 135 determines that the number of days in which the urine sugar value is equal to or greater than the threshold is greater in the number of items of food and action (S134). This determination may be made by determining that the number of days for which the urine sugar value is equal to or greater than the threshold is greater than the total number of items for food and action, but the number of days for which the urine sugar value is equal to or greater than the threshold is at least the number of items for food and action. You may determine by one having more.

  As a result, when the number of foods / items is equal to or less than the number of days for which the urine sugar value is equal to or greater than the threshold value, the regression analysis cannot be performed accurately, so the constitution improvement evaluation process is terminated.

  On the other hand, when the number of days in which the urine sugar value is equal to or greater than the threshold is greater than the number of items of food / behavior, the biometric index coefficient change extraction unit 135 selects a food having the highest food intake frequency in the calculation period as the index food ( S135). For example, if a participant ingests rice, bread, fruit, ramen, etc. as a meal in 60 days, and the rice is ingested with the highest frequency, the rice is used as the index food.

  Next, the biometric index coefficient calculation unit 133 extracts data of a corresponding day in the calculation period (from day 1 to day 7 at the beginning of the loop) from the data acquired in step S131 (S136).

  Next, the biometric index coefficient calculation unit 133 calculates the biometric index coefficient α i of urine sugar for the food i and the bio index coefficient β j of urine sugar for the action j by the multiple regression analysis using the above-described equation (1) ( S137).

  Next, the biometric index coefficient change extraction unit 135 determines whether calculation has been performed up to the last data of the period (up to data on the 60th day) (S138). If there is uncalculated data in the calculation period, the calculation target date is moved one day (S146), and the process proceeds to step S136. The movement of the calculation period indicates that the calculation period is shifted by one day and moved from the second day to the eighth day.

  On the other hand, if the calculation is completed up to the entire target period at the end of the calculation period, the biological index coefficient change extraction unit 135 determines the change of the food urine sugar coefficient αi of the index food over time from the result of the multiple regression analysis. (Slope) is obtained (S139). For example, here, the calculation period is shifted by one day, and when the calculation period is from the 54th day to the 60th day, the process moves to S139.

  Next, the biometric index coefficient change pattern processing unit 136 acquires information about the index from the biometric index coefficient change pattern management unit 123 (S140), and the change (slope) obtained in step S139 is the biometric index coefficient change pattern. (S141). As shown in FIG. 4, the change pattern of the biometric index coefficient combines the change of the biometric index coefficient of the representative food related to urine sugar and the biometric index change pattern of the body weight. Here, it is assumed that the biometric index coefficient of urine sugar in rice is decreased, and that the weight is decreased between the first day and the 60th day when the change in weight is observed. Then, it is determined that the condition of the first row is met.

  If there is a matching pattern as a result of the determination, the output information creation unit 137 acquires the comment 1236 of the matching pattern (S142), and outputs screen data for displaying the comment (S143). Here, since it corresponds to the first line in FIG. 4, a comment that “there is a possibility that sugar metabolism is improved by reducing fat” is output.

  When the participant terminal 104 and the instructor terminal 105 receive the screen data output from the output information creation unit 137, the participant terminal 104 and the instructor terminal 105 output a comment from the output unit 144 of the participant terminal 104 or the output unit 154 of the instructor terminal 105.

  As described above, the lifestyle habit improvement guidance support system according to the present embodiment includes a biometric index coefficient change pattern management unit, and can branch information to be presented depending on combinations of changes in a plurality of biometric indexes. This can show the participants what changes may be occurring. For example, there is an insulin resistance state as a high-risk state of diabetes. Insulin resistance indicates that the blood sugar level is high due to effects such as an increase in fat, a decrease in muscle mass, and a decrease in sugar uptake ability. Usually, in order to evaluate insulin resistance, it is necessary to measure an insulin level by a blood test. However, since blood collection is necessary, it is difficult to perform it on many people frequently. On the other hand, since urine sugar is not invasive, it can be measured frequently, but it is difficult to directly evaluate insulin resistance itself.

  In this embodiment, using daily recorded urine sugar, meal, and behavior information, a change in the biometric index coefficient indicating the degree of increase in urine sugar when eating the same food, and a change in body weight as another biometric index By combining the relationships, it is possible to simply evaluate and present how the insulin resistance state may change. If the urine sugar does not increase even if the same food is eaten, and the participant is losing weight, it is considered that the insulin resistance may be improved due to the decrease in fat. In addition, when the participant's exercise is gaining weight but the biomarker coefficient of urine sugar is decreased, the insulin resistance is improved by increasing the ability of glucose uptake by attaching muscles to the body. There is a possibility that is happening. Such a possibility can be presented from a combination of a plurality of biometric indices and their coefficients based on the daily records of the participants.

  FIG. 12 is a diagram illustrating an example of the recording input screen 500 according to the embodiment of this invention. A recording input screen 500 shown in FIG. 12 is displayed on the output unit (display device) 144 of the participant terminal 104.

  The record input screen 500 includes a date input field 501 and an elapsed day number input field 502. The date input field 501 can be displayed using the date data held by the participant terminal 104, and the elapsed days input field 502 includes the start date of lifestyle improvement guidance as the participant's personal data. By holding, it is possible to calculate and display using date data held by the participant terminal 104.

  The record input screen 500 includes a weight input field 503, a time zone input field 504, and a urine sugar input field 505. The participant inputs today's weight in the weight input field 503, inputs the time zone in which the data was acquired in the time period input field 504, and inputs the post-meal urine sugar value in the urine sugar input field 505.

  The record input screen 500 includes a food input field 506 and an action input field 507. The participant selects items displayed in the food input field 506 and the action input field 507, and inputs the type and amount of the inoculated food and the performed action.

  The options displayed in the time zone input field 504, food input field 506, and action input field 507 are the options corresponding to the time 1221, food 1222, and action 1223 of the meal / behavior information management unit 122, respectively. .

  Note that the record input screen 500 may be displayed so that foods that are good for improving urine sugar values, bad foods, good behaviors, and bad behaviors can be seen. For example, in order to be able to distinguish the upper predetermined number of foods and actions among the foods and actions displayed on the meal / behavior display screen 700 (FIG. 14) that are difficult to increase in urine sugar level (for example, the color, font, size, etc.) In order to distinguish the top predetermined number of foods and actions that are likely to increase urinary sugar levels (for example, different character colors, fonts, sizes, It may be displayed with a different background color. In this way, when a participant records data using the participant terminal 104, he / she is conscious of the relationship between diet and behavior and urine sugar level, and is strongly motivated to have good food and good behavior. can do.

  The recording input screen 500 includes a registration button 508 and a cancel button 509. When the participant operates the registration button 508, the data input to the recording input screen 500 is transmitted to the server 101 and registered in the biometric index management unit 121 and the meal / behavior information management unit 122. The indicated data is created.

  FIG. 13 is a diagram illustrating an example of the record reference screen 600 according to the embodiment of this invention. A record reference screen 600 shown in FIG. 13 is displayed on the output unit (display device) 154 of the instructor terminal 105. Note that the record reference screen 600 may be displayed on the output unit (display device) 144 of the participant terminal 104.

  The record reference screen 600 is displayed by setting a display period (display date) and a time zone, and includes a post-breakfast urine sugar value display area 601, a carbohydrate amount display area 602, and an action record display area 603.

  The post-breakfast urine sugar value display area 601 displays a change in the urine sugar value measured after breakfast of the day. The carbohydrate amount display area 602 displays the amount of carbohydrate intake during breakfast. A correspondence table relating to the amount of carbohydrate for each meal item, such as 506 in FIG. 12, is prepared in advance, and the amount of carbohydrate in the meal is calculated from the correspondence table and displayed. The action record display area 603 displays a record of the participant performing the action.

  With this screen, the participant and the instructor can grasp the relationship between their diet and behavior and urine sugar.

  FIG. 14 is a diagram illustrating an example of a meal / action display screen 700 according to the embodiment of this invention. A meal / action display screen 700 shown in FIG. 14 is displayed on the output unit (display device) 154 of the instructor terminal 105. The meal / action display screen 700 may be displayed on the output unit (display device) 144 of the participant terminal 104.

The meal / action display screen 700 includes a meal display area 701 and an action display area 702.
The meal display area 701 displays foods having a low correlation with the urine sugar value in the participant, that is, foods in which the participant's urinary sugar value is difficult to rise, from the top for each food category. The action display area 702 displays a predetermined number of actions from the top among actions that have a low correlation with the urine sugar value in the participant, that is, actions that do not easily increase the urine sugar value of the participant.

  In this way, by presenting which meals and actions are difficult to raise urine sugar among the meals and actions that the participants have specifically implemented, the instructor can give specific instructions to the participants Yes, participants can figure out what to do for them.

  FIG. 15 is a diagram illustrating an example of the biometric coefficient display screen 800 according to the embodiment of this invention. A biometric index coefficient display screen 800 shown in FIG. 15 is displayed on the output unit (display device) 154 of the instructor terminal 105. The biometric index coefficient display screen 800 may be displayed on the output unit (display device) 144 of the participant terminal 104.

  The biological index coefficient display screen 800 displays the change and inclination of the urine sugar coefficient of the food serving as the index. For example, on the biometric index coefficient display screen 800 shown in FIG. 15, changes in the correlation (biometric index coefficient) between rice and urine sugar values are displayed in chronological order with the horizontal axis as the date.

  In this way, by following the change in the biometric index coefficient of one food, it is possible to see the change in the constitution of the participant. Here, it is possible to refer to how the degree of increase in urine sugar in the case of a similar meal is changed, and the improvement of the constitution can be easily grasped.

  The embodiment of the present invention has been described with respect to lifestyle improvement guidance using urine sugar levels, but other biological indices (for example, test values such as blood glucose level, blood pressure, neutral fat and LDL cholesterol level, body weight, etc. In addition, the present invention can be similarly applied to lifestyle habit improvement guidance using measured values such as BMI and the like, indicators calculated from these test values and measured values, and the like.

  Up to this point, various functions of the lifestyle habit improvement guidance support system according to the embodiment of the present invention have been described. However, it is not necessary to implement all these functions, and one or more functions may be implemented in various combinations. it can.

  In the embodiment of the present invention described above, the method of setting the equivalent amount of tea bowl or the amount of carbohydrates as a single meal is shown, but it may be the weight of food. Further, in addition to the amount of carbohydrate, it may be used for the calculation by having an amount for each nutrient such as the amount of fat and the amount of salt. For example, it is said that carbohydrate levels are related to blood sugar levels, but neutral fat and cholesterol levels are related to fat content in foods, and blood pressure is said to be related to salinity, which affects each biometric indicator. Nutrients are different. Depending on the biological index to be evaluated, food nutrients can be used separately for each biological index, and the biological index coefficient can be obtained to be applied to various biological indices.

  In the embodiment of the present invention described above, the value of the biometric index is the dependent variable of the multiple regression analysis, but the difference of the biometric index may be the dependent variable. For example, in the case of body weight, it is possible to evaluate what diet or exercise is related to a change in body weight by using the difference from the previous day as to how much the body weight has changed as a storage variable. Moreover, you may use values, such as a change rate and ratio with the previous day instead of a difference.

  In the embodiment of the present invention described above, as a process of the application determination unit 134, a method of using only data having a certain threshold value or more has been described, but another method of use may be used. For example, if there is more than a certain difference in weight with the previous day, it can not be a change in weight of the day, so in the application condition management unit, the difference from the previous day of weight is set to be applied only within 5 kg, It may be used to remove outlier data. As shown in FIG. 12, when data is manually input, there is a possibility that wrong data may be entered. This method can prevent calculation errors due to erroneous input data. Further, the application condition may be applied to a person and excluded from the calculation target by the person. For example, there are people who have high urine sugar values and those who do not have so high urine sugar levels depending on the threshold of the kidneys differing between individuals and the progress of diabetes. Therefore, the application condition management unit 124 defines that the maximum value of postprandial urine sugar is only 150 or more, and the application determination unit 134 evaluates the maximum value of postprandial urine sugar and evaluates it. You may make it exclude from object. Thereby, the urine sugar value does not change due to individual differences, and persons who cannot accurately evaluate the relationship between meals and behaviors and urine sugar can be excluded in advance from the calculation target.

  In the embodiment of the present invention described above, the case where the daily value is used for urine sugar has been described as an example, but the urine sugar value is usually measured three times after breakfast, after lunch, and after dinner. Therefore, three times a day may be used for the calculation, with morning, noon and evening as separate information. In this case, the urine sugar value after breakfast is calculated using the contents of breakfast, and the urine sugar after lunch is calculated using the contents of lunch. These data can be used to calculate the biometric index coefficient. Moreover, although the case where a meal and action were used was demonstrated to the example as a variable used for multiple regression analysis, morning, noon, and evening time zone information may be used as a variable. For example, some people change blood sugar and urine sugar depending on the time of day. In addition, it is said that some people have high blood pressure in the morning. In this way, by combining the time information, it is possible to grasp when the biometric index rises and what kind of meal or behavior the biometric index rises or improves at that time.

  In addition, as in S103 of FIG. 7, the number of items of food or action is compared with the number of days of data, and when the number of data is small, the biometric index coefficient is not calculated. May be. For example, for meals, we explain how to manage meals and breads in the category of staple foods, but if there is little data, calculate the biometric index coefficient by replacing meals with staple foods by category. May be. By doing in this way, even when the number of items of food and action is small in the number of days of data, it can be presented from rough information. Further, when the number of data increases, the details of information may be changed in accordance with information to be used, such as changing from a category to an individual food.

  As described above, in the embodiment of the present invention, among the foods taken by the participants and the actions taken, which foods can be consumed to suppress an increase in urine sugar level (blood sugar level), which action is taken. Then, it can be extracted whether the increase in urine sugar value can be suppressed. In addition, by showing this information to the instructor, the instructor can grasp the behavior of each participant and instruct good meals and behavior. Participants can know what was good in the meals they usually eat and the actions they can take, and can motivate them to improve their lifestyle and lead to the next action .

  In addition, when the blood glucose level is equal to or higher than a predetermined value (individual kidney threshold), sugar is excreted from the kidney into the urine, so the urine sugar level does not have a linear relationship with the diet or behavior. For this reason, when the urine sugar value is less than or equal to the predetermined value, the application determining unit can accurately calculate the biometric index coefficient for urine sugar by excluding it from the multiple regression analysis.

  In addition, it is possible to evaluate whether a participant has a body in which urine sugar (blood sugar) does not easily rise without taking a blood test in consideration of individual differences.

  In addition, the instructor can make an evaluation including the improvement of the constitution of the participant by using the result output from the lifestyle improvement instruction support system of the present embodiment.

  Participants can also see if changes in their lifestyle have led to improvements in their constitution by looking at the results output from the lifestyle improvement guidance support system of this embodiment, and willing to improve further. Can be improved.

  In addition, the reason for the change in the biometric index coefficient can be easily found from the relationship between the change in the biometric index coefficient with respect to the biometric index (urine sugar) and the change in the other biometric index (weight). The instructor can use this information to give appropriate guidance to the participants.

101 Server 102 Database 103 Network 104 Participant terminal 105 Instructor terminal 111 Processor (CPU)
112 storage device 121 biometric index management unit 122 meal / behavior information management unit 123 biometric index coefficient change pattern management unit 124 application condition management unit 131 control unit 132 data acquisition unit 133 biometric index coefficient calculation unit 134 application determination unit 135 biometric coefficient coefficient change Extraction unit 136 Biometric index coefficient change pattern processing unit 137 Output information creation unit

Claims (11)

A lifestyle improvement support system that supports lifestyle improvement,
A database that stores participants' urine sugar levels, food intake, and action records in time series,
A data acquisition unit for acquiring a participant's urine sugar value, food intake, and action record from the database;
A biometric coefficient calculation unit that calculates the change rate of the urine sugar value with respect to the intake of food obtained from the database and the presence or absence of behavior as a biometric coefficient, by multiple regression analysis;
An output information creation unit that creates data for ranking and outputting the food and action based on the calculated biometric index coefficient;
An application determination unit that determines that the data on which the urine sugar value of the participant satisfies a predetermined condition is an object of the multiple regression analysis,
The biological index coefficient calculation unit calculates the biological index coefficient by multiple regression analysis when the number of foods taken by the participant and the number of actions performed exceed the number of days in the target period of the multiple regression analysis,
The output information creation unit
The average of the amount of food taken at one time is calculated for each food, and the food is ranked according to the product of the bioindex coefficient for the food calculated by the multiple regression analysis and the calculated average food intake. With
An average of the frequency at which an action is executed is calculated for each action, and the actions are ranked according to a product of a biometric coefficient for the action calculated by the multiple regression analysis and the calculated average frequency of the action. A lifestyle improvement support system. A lifestyle improvement support system that supports lifestyle improvement,
A database that stores participants' biometrics, food intake, and action records in time series;
A data acquisition unit for acquiring the biometric index of the participant, the ingested food and the action record of the action from the database;
A biological index coefficient calculation unit that calculates a change rate of the biological index with respect to the presence of food intake and behavior obtained from the database as a biological index coefficient by multiple regression analysis;
A biometric index coefficient change extraction unit for obtaining a change in time of the biometric index coefficient of the food and action that are the targets of the multiple regression analysis;
A lifestyle improvement support system comprising: an output information creation unit that outputs a change with time of the biometric index coefficient. A biological index coefficient change pattern management unit for managing a relationship between changes in the biological index coefficient for the ingested food and behavior with time, changes in other biological indices, and processing corresponding to these changes;
A biological index coefficient change pattern processing unit that determines a process corresponding to a pattern managed by the biological index coefficient change pattern management unit and executes the determined process;
The lifestyle improvement support system according to claim 2, wherein the output information creation unit creates output data related to an execution result of the process determined by the biological index coefficient change pattern processing unit.   The biometric index coefficient change extraction unit selects a food with a high average intake during the target period of the multiple regression analysis and a behavior with high frequency of implementation, and depends on the time of the biometric index coefficient for the selected food and behavior The lifestyle improvement support system according to claim 2 or 3, wherein a change is obtained.   The lifestyle improvement support system according to any one of claims 2 to 4, wherein the biological index is a urine sugar value of the participant.   The output information creation unit outputs data for outputting an input screen for selecting the food and the action, and a screen for associating and displaying the input result of the food and action and the biological index. The lifestyle improvement support system according to any one of claims 2 to 5, wherein: The output information creation unit
Create data for outputting an input screen including an input field for selecting the food and the action,
The lifestyle improvement support system according to any one of claims 1 to 5, wherein in the input field, data for outputting the food and the ranking given to the action in an identifiable manner is created. . A method for analyzing a biometric index using a lifestyle improvement support system that supports lifestyle improvement,
In the lifestyle improvement support system, a processor that executes a program, a memory that stores a program executed by the processor, a urine sugar value of a participant, a food intake, and an action record of actions are recorded in time series. A storage device for storing the database,
The analysis method is:
A data acquisition procedure in which the processor acquires a participant's urine sugar value, food intake, and action record from the database, and stores it in the memory;
A biometric coefficient calculation procedure for storing, in the memory, a biometric coefficient calculated by a multiple regression analysis of the rate of change of the urine sugar value with respect to the presence of food intake and behavior obtained by the processor from the database;
An output information creation procedure for creating data for ranking and outputting the food and action based on the calculated biometric index coefficient;
An application determination procedure for determining that data in which the urine sugar value of the participant satisfies a predetermined condition is an object of the multiple regression analysis,
In the biometric coefficient calculation procedure, when the number of foods consumed and the number of actions performed by the participant exceed the number of days in the target period of the multiple regression analysis, the biometric coefficient is calculated by multiple regression analysis,
In the output information creation procedure,
The average of the amount of food taken at one time is calculated for each food, and the food is ranked according to the product of the bioindex coefficient for the food calculated by the multiple regression analysis and the calculated average food intake. With
An average of the frequency at which an action is executed is calculated for each action, and the actions are ranked according to a product of a biometric coefficient for the action calculated by the multiple regression analysis and the calculated average frequency of the action. Analyzing method of biological index.   A method for analyzing a biometric index using a lifestyle improvement support system that supports lifestyle improvement,
  In the lifestyle improvement support system, a processor that executes a program, a memory that stores a memory that is executed by the processor, a biometric index of a participant, a food intake, and an implementation record of actions are recorded in time series A storage device for storing the database;
  The analysis method is:
  A data acquisition procedure in which the processor acquires a participant's biometric index, ingested food and action record of the action from the database, and stores it in the memory;
  A biometric index coefficient calculation procedure for storing, in the memory, a biometric index coefficient calculated by multiple regression analysis of the change rate of the biometric index with respect to the intake of food obtained from the database and the presence / absence of behavior, obtained from the database;
  The processor is a biometric index coefficient change extraction procedure for obtaining a change of the biometric index coefficient with time of the food and action that are the targets of the multiple regression analysis;
  A biometric index analysis method, comprising: an output information creation procedure in which the processor outputs a change with time of the biometric index coefficient.   The analysis method is:
  The processor is a biometric coefficient change pattern management procedure for managing the relationship between changes in the biometric coefficient for the ingested food and behavior over time, changes in other biometric indices, and processing corresponding to these changes,
  The processor is controlled by the biological index coefficient change pattern management procedure
And a biometric coefficient coefficient change pattern processing procedure for executing the determined process,
  The biometric index analysis method according to claim 9, wherein in the output information creation procedure, output data relating to an execution result of the process determined by the biometric index coefficient change pattern processing procedure is created.   In the biometric coefficient coefficient change extraction procedure, a food with a high average intake during the target period of the multiple regression analysis and a behavior with high frequency of operation are selected, and the time of the biometric coefficient coefficient for the selected food and behavior is selected. The method for analyzing a biological index according to claim 9 or 10, wherein a change is obtained.

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