JP7229083B2 - Health prediction systems and programs - Google Patents

Description

The present invention relates to a health condition prediction system and program.

For example, Patent Literature 1 discloses estimating the physical information and appearance of a health care subject predicted in the future based on the health care subject's behavioral pattern analyzed by an analysis unit.
For example, Patent Literature 2 discloses receiving an input of health-related information from a user and presenting future predictions for both cases of life improvement and non-improvement based on the received health-related information. .

WO2017/022013 JP 2010-157123 A

Dietary advice tailored to each user based on the user's age, gender, and ingested meals, etc., and future predictions are being made. However, there is no attempt to predict what kind of health condition the user will have if he or she continues to live as it is based on actual examples of the health condition of a specific user whose life is related to this user's life. I didn't.

An object of the present invention is to predict a user's future health condition based on an example of the health condition of a specific user whose life is related to one user's life.

According to the first aspect of the invention, there is provided an acquisition means for acquiring dietary information relating to dietary habits and health condition information relating to health conditions of a plurality of users; , when a second user who is older than the first user has a relationship that matches the eating habits that the second user has continued since they were the same age as the current age of the first user, the second user's prediction means for predicting the future health condition of the first user based on the health condition information ; the dietary information of the first user; Based on the diet information of a third user whose health condition was the same as the current age of the first user, but at least one of diet and health condition improved. extracting means for extracting improvement points to be improved in the eating habits, wherein the predicting means predicts the future health condition of the first user when the improvement points extracted by the extracting means are improved; A health condition prediction system characterized by predicting .
The invention according to claim 2 is characterized in that the prediction means predicts the health condition of the first user when the points to be improved are improved based on the health condition information of the third user. The health condition prediction system according to claim 1 .
In the information acquired by the acquisition means , the health condition of the first user when the current age and the health condition of the first user match. and for displaying on a terminal used by the first user, among the health condition information of related users who are older than the first user, the health condition information for those older than the first user The health condition prediction system of claim 1, further comprising:
In the invention according to claim 4, the control means can cause the terminal to display, for each of the plurality of related users, the health condition information at an age older than the first user, and a plurality of 4. The health condition prediction system according to claim 3 , wherein, for each related user, the eating habits information at an age higher than that of the first user can be displayed on the terminal.
The invention according to claim 5 is characterized in that, in a computer, an acquisition function for acquiring dietary life information and health condition information concerning the health conditions of a plurality of users, and the information acquired by the acquisition function includes: If there is a relationship in which the eating habits and the eating habits that the second user , who is older than the first user, have been following since they were at the same age as the current age of the first user, the second user Based on the user's health condition information, a prediction function for predicting the future health condition of the first user , the dietary information of the first user, and a person who is older than the first user and who is the first user Based on the dietary habit information of a third user whose health condition was the same as that of the first user when he was the same age as the current age of the first user, but at least one of the dietary habit and the health condition has improved, An extraction function for extracting improvement points to be improved in a first user's eating habits, and a function for predicting the future health condition of the first user when the improvement points extracted by the extraction function are improved. It is a program to realize it.

According to the present invention, it is possible to predict the user's future health condition when the eating habits are improved based on the actual eating habits and health condition of a specific user who is older than one user. can.

It is a figure which shows the whole structural example of a health condition prediction system. 3 is a diagram illustrating an example hardware configuration of a server and a terminal; FIG. It is the figure which showed the functional structural example of a server. It is the figure which showed the user information management table. It is the flowchart which showed the flow of a health condition prediction process. It is the figure which showed the prediction result screen. It is the figure which showed the improvement screen. It is the figure which showed the improvement point screen. It is the figure which showed the same user list screen.

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
<Configuration of health condition prediction system>
FIG. 1 is a diagram showing an example of the overall configuration of a health condition prediction system 1 according to this embodiment.
A health condition prediction system 1 according to the present embodiment is a system that predicts a user's future health condition based on information about the user's eating habits. The information about eating habits is hereinafter referred to as eating habits information.
The health condition prediction system 1 includes a server 10 and a terminal 20 owned by a user. The server 10 and the terminal 20 are connected via a network.

The server 10 predicts the user's future health condition and provides information about the predicted result. More specifically, the server 10 acquires the user's eating habits information and the user's health condition information from the user's terminal 20 for each of a plurality of users. Information about health status is hereinafter referred to as health status information. In addition, the server 10 extracts, from among a plurality of users, users whose eating habits are related to the eating habits of the user whose health condition is to be predicted. A user whose health condition is to be predicted is hereinafter referred to as a target user. Then, the extracted health condition information of the user is transmitted to the terminal 20 possessed by the target user as a prediction result of the future health condition of the target user.

The server 10 is implemented by, for example, a computer. The server 10 may be configured by a single computer, or may be implemented by distributed processing by a plurality of computers. Also, the server 10 may be implemented on virtual hardware provided by cloud computing.

The terminal 20 has a display section 21 that displays information. The display unit 21 displays the health condition information as the predicted result of the health condition received from the server 10 .
The terminal 20 is implemented by, for example, a computer, tablet information terminal, or other information processing device. The terminal 20 may be, for example, a wearable terminal worn on the user's body or a smart phone. That is, the terminal 20 may be any type of terminal.

The type of network used to connect the server 10 and the terminal 20 is not particularly limited as long as data can be transmitted and received. For example, the Internet, LAN (Local Area Network), WAN (Wide Area Network), etc. . A communication line used for data transmission/reception may be wired or wireless. Further, the configuration may be such that each device is connected via a plurality of networks or communication lines.

Although three terminals 20 are shown in the example shown in FIG. 1, the number of terminals 20 is not limited to three as shown. The health condition prediction system 1 may be provided with two terminals 20 or with four or more terminals 20 .

<Hardware configuration example>
FIG. 2 is a diagram showing a hardware configuration example of the server 10 and the terminal 20. As shown in FIG.
As shown in FIG. 2, the server 10 and the terminal 20 are provided with a CPU (Central Processing Unit) 100a as computing means and a memory 100c as main storage means. Each apparatus also includes, as external devices, a nonvolatile recording device 100g, a network interface 100f, a display mechanism 100d, an audio mechanism 100h, and an input device 100i such as a keyboard and mouse.

The memory 100c and the display mechanism 100d are connected to the CPU 100a via the system controller 100b. Network interface 100f, non-volatile recording device 100g, audio mechanism 100h and input device 100i are also connected to system controller 100b via bridge controller 100e. Each component is connected by various buses such as a system bus and an input/output bus.

A program for realizing each function is stored in the nonvolatile recording device 100g. Various functions are realized by loading this program into the memory 100c and executing processing based on this program by the CPU 100a. Examples of the nonvolatile recording device 100g include semiconductor memories such as SSDs (Solid State Drives) and magnetic disk devices such as HDDs (Hard Disk Drives).

<Server functional configuration>
Next, the functional configuration of the server 10 will be described.
FIG. 3 is a diagram showing an example of the functional configuration of the server 10. As shown in FIG.
The server 10 includes a registration reception unit 11, a storage unit 12, an output control unit 13, a diet information reception unit 14, a health information reception unit 15, an attribute determination unit 16, a user extraction unit 17, improvements and an extraction unit 18 .

The registration reception unit 11 receives registration of users as users of the health condition prediction system 1 . For example, upon receiving a registration application from the user's terminal 20, the registration reception unit 11 receives registration of the user's name, gender, age, and the like. Further, the registration reception unit 11 assigns a login ID to the terminal 20 and receives registration of a personal identification number from the terminal 20 . Then, the user who operates the terminal 20 to which the login ID and password have been input is registered as the user of the health condition prediction system 1 .

The registration reception unit 11 transmits user identification information for identifying a user registered as a user to the storage unit 12 in association with information regarding the sex and age of the user. When a user is newly registered as a user of the health condition prediction system 1 via the terminal 20, the server 10 holds information transmitted from the terminal 20 as information related to the newly registered user. will come to A user who has registered as a user of the health condition prediction system 1 is hereinafter referred to as a registered user.

The storage unit 12 stores user identification information, information about the sex and age of registered users, and the like. The contents stored in the storage unit 12 will be detailed later.
The output control unit 13, which is an example of control means, causes the terminal 20 to output information. More specifically, by transmitting information to the terminal 20, the output control unit 13 causes the display unit 21 of the terminal 20 to display the transmitted information, or causes the terminal 20 to output the transmitted information by voice. or

The dietary information reception unit 14 acquires dietary information from the terminal 20 of the registered user. More specifically, the dietary information reception unit 14 causes the terminal 20 of the registered user to display a screen on which the dietary information can be input via the output control unit 13 . Then, the dietary information reception unit 14 acquires the dietary information input to the terminal 20 .

Dietary information includes, for example, information on the food and drink eaten by the registered user for each meal, information on calories and nutrients ingested by the registered user, information on meal times, and information on the registered user's responses to questionnaires on eating habits. etc. Dietary information includes, for example, information on food allergies possessed by registered users.
Dietary information may be information of any aspect. The dietary information may be, for example, character information input to the terminal 20 by the registered user. Also, the dietary information may be information created by the server 10 analyzing image data showing food and drink, for example. This image data is created, for example, by photographing food and drink before the registered user eats and drinks using the terminal 20 , and is transmitted from the terminal 20 to the server 10 .

Each time the dietary information reception unit 14 acquires the dietary information, the acquired dietary information is associated with the user identification information of the registered user who has the dietary habit specified from the dietary information, and is stored in the storage unit 12. be memorized. As a result, the registered user's daily eating habits information is accumulated in the storage unit 12 .

The health information reception unit 15 acquires health condition information from the terminal 20 of the registered user. More specifically, the health information reception unit 15 causes the terminal 20 of the registered user to display, via the output control unit 13, a screen on which health condition information can be input. Then, the health information reception unit 15 acquires the health condition information input to the terminal 20 .

Health condition information includes, for example, height, weight, body fat percentage, BMI, information on the presence or absence of specific symptoms, and presence or absence of illness, information on the results of physical examinations received by registered users, and doctor's diagnosis of registered users. Examples include information on results, information on answers to questionnaires on health conditions of registered users, and the like. Also, the health condition information includes, for example, information as to whether or not the registered user is a worker. The health information may be, for example, daily information.
Health condition information may be information of any aspect. The health information may be, for example, character information input to the terminal 20 by the registered user. Further, in the present embodiment, image data created by photographing the appearance of the registered user is transmitted to the server 10 as the health condition information. In this case, the server 10 may analyze the image data transmitted from the terminal 20 to create the health condition information from the appearance of the registered user shown in the image data.

Each time the health information receiving unit 15 acquires health condition information, the health information receiving unit 15 associates the acquired health condition information with the user identification information of the registered user whose health condition is specified from the health condition information, and stores the information in the storage unit. Store in 12. Thereby, the registered user's health condition information is accumulated in the storage unit 12 .
The dietary habits information reception unit 14 and the health information reception unit 15 are regarded as acquisition means.

The attribute determination unit 16 determines attributes to which registered users belong. More specifically, the attribute determination unit 16 determines, for each registered user, the eating habits to which the registered user belongs based on the eating habits information of the registered users. Also, the attribute determining unit 16 determines the health condition to which the registered user belongs based on the registered user's health condition information for each registered user.

Dietary attributes include, for example, problematic dietary habits and non-problematic dietary habits. Problematic eating habits include, for example, a eating habit having a problem with the calories ingested by the registered user, a eating habit having a problem with the nutrients ingested by the registered user, and the like. Examples of eating habits that have problems related to the calories ingested by registered users include a high-calorie eating habit, a calorie-deficient eating habit, and the like. Also, examples of eating habits that have problems related to nutrients ingested by registered users include eating habits with excessive intake of nutrients, eating habits with insufficient nutrients, and the like.

Attributes of health conditions include, for example, health conditions with problems, health conditions without problems, and the like. Problematic health conditions include, for example, the registered user's physical shape-related health condition, the registered user's physical condition-related health condition, and the health condition of illness. Examples of health conditions that have problems related to the body shape of the registered user include the health condition of obesity and the health condition of thinness. Health conditions that have problems related to the physical condition of the registered user include, for example, health conditions with specific symptoms, such as headache health conditions and high blood pressure health conditions. Conditions with illnesses include, for example, conditions with particular illnesses, such as cold conditions and diabetes conditions.

The attribute determining unit 16 determines the eating habits of the registered user from the eating habits information accumulated in the storage unit 12 for each predetermined period. For example, the attribute determination unit 16 sets a reference value for the dietary information for each attribute of the dietary habit, and determines whether or not the registered user's dietary information reaches the reference value in a predetermined period. It may be determined whether the registered user is on a particular diet during the period. For example, the attribute determining unit 16 determines whether or not the average daily calorie intake of the registered user during the predetermined period reaches 2500 kilocalories. Decide if you belong to a high calorie diet. When the registered user's dietary information reaches any of a plurality of reference values provided for each of a plurality of dietary attributes, the attribute determining unit 16 determines that the registered user belongs to any of the plurality of dietary habits. decide.

Also, the attribute determination unit 16 determines the health condition to which the registered user belongs from the health condition information accumulated in the storage unit 12 for each predetermined period. For example, the attribute determining unit 16 sets a reference value for the health condition information for each attribute of the health condition, and determines whether or not the health condition information of the registered user reaches the reference value in a predetermined period. It may be determined whether a registered user belongs to a particular health condition in a period of time. As an example, whether or not the registered user's BMI reaches 25 on the last day of the predetermined period determines whether the registered user belongs to the "obesity health condition" in this predetermined period. do. If the registered user's health condition information reaches any of a plurality of reference values provided for each of a plurality of health condition attributes, the attribute determination unit 16 determines that the registered user belongs to any of the plurality of health conditions. decide. In addition, when the health condition information for a predetermined period indicates a specific symptom or a specific disease, the attribute determination unit 16 determines whether the registered user has the health condition information for the predetermined period. It is determined that a particular symptom belongs to a health condition or a particular disease health condition.

The predetermined period may be any period. The predetermined period is, for example, one year.
After determining the attribute to which the registered user belongs, the attribute determination unit 16 stores the attribute information indicating this attribute in the storage unit 12 in association with the period to which the registered user belongs to the determined attribute and the user identification information of the registered user. Let As a result, the attribute information to which the registered user belongs is accumulated in the storage unit 12 for each predetermined period.

The user extraction unit 17, which is an example of a prediction unit, extracts one registered user from among a plurality of registered users, and uses the extracted registered user's health condition as a prediction result of the target user's future health condition. More specifically, the user extraction unit 17 first extracts registered users who are older than the target user from among all registered users specified from the user identification information stored in the storage unit 12. . A registered user who is older than the target user is hereinafter referred to as an older user. Next, the user extraction unit 17 extracts registered users who belonged to the health condition to which the target user currently belongs when they were the same age as the target user's current age from the extracted older users. . A registered user who belonged to the health condition to which the target user currently belongs when he/she was the same age as the target user's current age is hereinafter referred to as the same health condition user. Further, the user extracting unit 17 extracts registered users belonging to the diet to which the target user currently belongs, from the extracted users with the same health condition, continuously from when they were the same age as the current age of the target user. do. A registered user who belongs to the diet to which the target user currently belongs continuously from the time when the target user was the same age as the current age of the target user is hereinafter referred to as the same diet user. The user extracting unit 17 uses the health condition specified from the extracted health condition information of the user with the same eating habit as a prediction result of the future health condition of the target user.

The user extracting unit 17 uses the health condition of the user with the same health condition at the age to which the user with the same health condition belongs to the current eating habits of the target user as the predicted result of the health condition at the future age of the target user. For example, if a 40-year-old user with the same diet belongs to a diet currently belonging to a 30-year-old target user from the ages of 30 to 35, is the predicted result of the health condition of the target user from 31 to 35 years old. That is, the period during which the health condition of the user with the same eating habits is valid as a prediction result of the future health condition of the target user continues from the time when the target user is the same age as the current age of the target user. This is the period in which the same eating habit user belongs to the eating habit.

When the target user belongs to a plurality of health conditions, the user extracting unit 17 may extract a registered user who belongs to at least one of the plurality of health conditions as the same health condition user. Alternatively, only registered users who belonged to a plurality of health conditions may be extracted as users with the same health condition.
Further, when the target user belongs to a plurality of eating habits, the user extracting unit 17 may extract a registered user who belonged to at least one of the plurality of eating habits as the same eating habit user. Alternatively, only registered users who belonged to a plurality of eating habits may be extracted as the same eating habit users.
The user extraction unit 17 causes the display unit 21 of the terminal 20 of the target user to display information about the prediction result via the output control unit 13 .

The improvement point extracting unit 18, which is an example of an extraction unit, extracts points to be improved in the current eating habits of the target user. More specifically, the point-to-be-improved extraction unit 18 selects, from among the users with the same health condition extracted by the user extraction unit 17, among the users who are older than the target user's current age, Extract registered users with few problems. A registered user who is older than the target user's current age and has fewer problems than the target user's current eating habits is hereinafter referred to as an improved user. The point-to-be-improved extracting unit 18 is, for example, a user who is older than the current age of the target user and belongs to a diet that does not have a problem, or a user who is older than the current age of the target user and has a problem. A user with the same health condition whose number of eating habits belonging to the subject user is smaller than that of the target user is extracted as an improved user.

The point-to-be-improved extracting unit 18 extracts, for example, differences between the eating habits information of the user to be improved and the eating habits information of the target user as an extraction result of points to be improved in the eating habits of the target user through the output control unit 13. Displayed on the display unit 21 of the terminal 20 of the user. In addition, the improvement point extracting unit 18 displays, through the output control unit 13, the health condition information of the user to be improved as information related to the predicted result of the future health condition of the target user when the eating habits of the target user are improved. display on the unit 21.

Further, the improvement point extracting unit 18 extracts the points to be improved most in the target user's eating habits based on the target user's eating habits information and the improved user's eating habits information. The point-to-be-improved extracting unit 18 extracts, for example, eating habits information that causes the target user to belong to a eating habit to which the user to be improved does not belong, as points to be most improved. For example, when the target user belongs to the “high-calorie diet” and the improvement user belongs to the “non-problematic diet”, the improvement point extraction unit 18 Extract the information on the food with the highest calorie content as the point that should be improved the most. The improvement point extraction unit 18 displays the extracted information on the display unit 21 of the target user's terminal 20 via the output control unit 13 .

<Contents stored in storage unit>
Next, the contents of information stored in the storage unit 12 will be described.
FIG. 4 is a diagram showing a user information management table. The user information management table is a table for managing information about registered users.

In the user information management table, a registered user name is shown in "user". In addition, the "sex" indicates the sex of the "user". Also, the current age of the "user" is shown in "age".

Further, in the user information management table, the "dietary habit attribute" indicates the diet to which the "user" belongs for each predetermined period. Here, "no problem" means that the "user" belongs to a diet with no problems during the relevant period. Also, "high calorie" means that the "user" belongs to a high calorie diet during the relevant period. In addition, "nutrition deficiency" means that the "user" belongs to a nutrient-deficient diet during the relevant period.
Further, in the user information management table, the "dietary habits information" indicates the "user"'s dietary habits information.

Further, in the user information management table, the health condition to which the "user" belongs is shown in the "health attribute" for each predetermined period. Here, "no problem" means that the "user" belongs to a health condition in which there is no problem during the corresponding period. Also, "obesity" means that the "user" belongs to a health condition of obesity in the corresponding period. Also, "hypertension" means that the "user" belongs to a hypertensive health condition during the corresponding period.
Further, in the user information management table, the health condition information of the “user” is shown in the “health condition information”.

<Health Prediction Processing>
Next, the flow of health condition prediction processing will be described. The health condition prediction process is a process in which the server 10 predicts the future health condition of the target user.
FIG. 5 is a flowchart showing the flow of health condition prediction processing.

First, the user extraction unit 17 determines whether or not a request to predict the future health condition of the registered user has been received from the terminal 20 of the registered user (S101). The user extracting unit 17 makes the above determination based on, for example, whether or not request information transmitted from the terminal 20 by the registered user operating the terminal 20 is acquired. The request information is information indicating that there has been a request to predict the future health condition of the registered user. While the negative result continues, the user extraction unit 17 repeats the determination operation of step 101 . On the other hand, when the user extraction unit 17 acquires the request information, a positive result is obtained and the process proceeds to step 102 .

The user extraction unit 17 extracts older users from among all registered users registered as users of the health condition prediction system 1, and further extracts the same health condition users from among the extracted older users. (S102).
The user extraction unit 17 extracts users with the same eating habits from the extracted users with the same health condition (S103).
The output control unit 13 causes the display unit 21 of the terminal 20 of the target user to display the health condition information of the same eating habit user extracted by the user extraction unit 17 as information related to the prediction result of the future health condition of the target user. (S104).

As described above, in the present embodiment, the user extracting unit 17, in the information acquired by the dietary habits information receiving unit 14 and the health information receiving unit 15, The future health condition of the target user is predicted based on the registered user's health condition information when the health condition has a predetermined first relationship with the eating habits.
In this case, the future health condition of the target user can be predicted based on an example of the health condition of a specific registered user whose life is related to the life of the target user.

Also, the first relationship is a relationship defined between the eating habits of the target user and the eating habits of registered users older than the age of the target user. More specifically, the first relationship is a relationship in which the eating habits to which the target user belongs and the eating habits to which the registered users have belonged since they were the same age as the current age of the target user are the same. .
In this case, it is possible to predict the future health condition of the target user when the target user continues the dietary habit based on the actual example of the dietary habits of the registered users older than the target user's age.

<Display example of information on prediction results>
Next, a display example of information regarding the prediction result of the future health condition of the target user will be described. A display example of the information regarding the prediction result of the future health condition of user "A" (see FIG. 4), who is the target user, will be described below. Information about the prediction result is displayed on the display unit 21 of the terminal 20 by the output control unit 13 . It should be noted that the target user "A" currently belongs to "good health condition".

FIG. 6 is a diagram showing a prediction result screen 30. As shown in FIG. The prediction result screen 30 is a screen showing the prediction result of the future health condition of the target user. The prediction result screen 30 includes a target age display portion 31, a future user image 32, a future user state display portion 33, a state explanation portion 34, a lower age shift button 35, an older age shift button 36, and an improvement screen. A display button 37 is displayed.

The target age display section 31 displays the target age of the prediction result of the future health condition of the target user. The age for which the future health condition prediction result of the target user is targeted is hereinafter referred to as the prediction target age. In the illustrated example, the target age display section 31 displays "Predict 40-year-old Mr. has been completed." message is displayed.

In the future user image 32, an image showing the appearance of the target user predicted by the user extraction unit 17 as the target user at the predicted target age is displayed. The future user image 32 is an appearance of the same eating habit user extracted by the user extracting unit 17, and is shown in the image data created by photographing the same eating habit user at the prediction target age. It is an image imitating the figure of a living user.

The future user condition display section 33 displays health condition information and the health condition to which the target user belongs as information on the current target user and information on the target user at the predicted target age. The health information includes body weight and body fat percentage. The information about the target user at the predicted target age is information about the result predicted by the user extraction unit 17 as the health condition of the target user at the predicted target age. This prediction result is the health condition information at the prediction target age of the same eating habit user extracted by the user extracting unit 17 and the health condition to which the same eating habit user belongs.

In the illustrated example, as a result of predicting the health condition of the target user "A" at the age of 40, it is shown that the body weight increases and the body fat percentage increases more than the present. In addition, as a prediction result of the health condition at the age of 40 of the target user “A” who currently belongs to the “health condition with no problem”, the target user “A” has “obesity health condition” and “high blood pressure health condition”. ” belongs to

The state explanation section 34 displays the content predicted as the health state of the target user at the predicted target age. More specifically, the state explanation section 34 displays the content of the health state that will change from the current state when the target user reaches the predicted target age. In the illustrated example, a message is displayed that says, "If you continue your current eating habits, your weight and body fat percentage will increase, leading to obesity and hypertension."

When the user selects the lower age shift button 35, a prediction result screen 30 for a prediction target age younger than the prediction result screen 30 being displayed is displayed. In the illustrated example, when the user selects the transition to lower age button 35, the prediction result screen 30 for the target user "A" aged 35 who is 5 years younger than the prediction result screen 30 being displayed is displayed.
When the user selects the advanced age transition button 36, a prediction result screen 30 for a prediction target age that is older than the prediction result screen 30 being displayed is displayed. In the illustrated example, when the user selects the transition to advanced age button 36, the prediction result screen 30 for the target user "A" aged 45 who is 5 years older than the currently displayed prediction result screen 30 is displayed. be.
Note that the prediction target age at which the prediction result by the user extracting unit 17 can be displayed on the display unit 21 is the same eating habit user's age during the period when the health condition of the same eating habit user is effective as the prediction result of the future health condition of the target user. is the age of

When the user selects the improvement screen display button 37, the display unit 21 displays an improvement screen 40 as shown in FIG. The improvement screen 40 is a screen showing a prediction result of the health condition of the target user at the predicted target age when the target user's eating habits are improved. On the improvement screen 40, a target age display section 41, an improvement information display section 42, a return button 45, and an improvement point display button 46 are displayed.

A predicted target age is displayed in the target age display section 41 . In the illustrated example, the target age display section 41 displays a message "I predicted Mr. A who is 40 years old" indicating the predicted target age of the target user "A" (see FIG. 4) who is 30 years old. is displayed.

The improvement information display section 42 displays information about the current target user, information about the target user when the target user's eating habits are not improved, and information about the target user when the target user's eating habits are improved. ing. Information about the current target user and information about the target user when the target user's eating habits are not improved are displayed in the future user image 32 and the future user state display section 33 on the prediction result screen 30 (see FIG. 6). It is the same content as the information An improved user image 43 and an improved user status display section 44 are displayed in the improvement information display section 42 .

In the improved user image 43, an image showing the appearance of the target user predicted by the improvement point extraction unit 18 as the target user when the eating habits are improved is displayed. The improved user image 43 is an image of the improved user extracted by the improvement point extraction unit 18, and is an image of the improved user shown in image data created by photographing the improved user at the age to be predicted. This is a simulated image.

The improved user condition display portion 44 displays the health condition information of the target user and the health condition to which the target user belongs when the eating habits are improved. The health information includes body weight and body fat percentage. The information displayed in the improved user condition display section 44 is information related to the result predicted by the improvement point extraction section 18 as the health condition of the target user when the eating habits are improved. This prediction result is the health condition information at the predicted target age of the improved user extracted by the improvement point extraction unit 18 and the health condition to which the improved user belongs.

In the illustrated example, as a result of predicting the health condition of target user "A" at the age of 40 when his eating habits are improved, the weight is 65 kg and the body fat percentage is 17%. This value is lower than the target user "A" predicted by the user extracting unit 17, that is, the weight and body fat percentage of the target user "A" when the eating habits are not improved. In addition, as a result of predicting the health condition of the target user "A" at the age of 40 when the eating habits are improved, it is shown that the target user "A" belongs to the "health condition without problems". This health condition is a health condition to which the current target user "A" belongs, and a health condition to which the target user "A" does not belong if the eating habits are not improved.

When the user selects the return button 45, the prediction result screen 30 (see FIG. 6) is displayed again.
When the user selects the improvement point display button 46, the display unit 21 displays an improvement point screen 50 as shown in FIG. The improvement point screen 50 is a screen showing improvement points in the eating habits of the target user. The improvement point screen 50 includes an improvement point display section 51 , a morning selection button 52 , a lunch selection button 53 , an evening selection button 54 , a selection all button 55 , a proposal display section 56 , and an improved user detail display button 57 . , and a return button 58 are displayed.

The improvement point display portion 51 displays information extracted by the improvement point extraction portion 18 as points to be improved in the eating habits of the target user. More specifically, the improvement point display section 51 displays the target user's eating habits information and the improvement user's eating habits information as an improvement policy. The target user's eating habits information displayed in the improvement point display section 51 is, for example, the target user's eating habits information for the most recent day. Further, the dietary habit information of the improvement user displayed in the improvement point display section 51 is, for example, the dietary habit information of the day on which the improvement user ingested the calorie closest to the average calorie intake per day at the prediction target age. is.
The dietary information of the target user displayed in the improvement point display section 51 is the calorie closest to the average daily calorie ingested by the target user during the period corresponding to the diet to which the target user currently belongs. Dietary information on the day of collection may also be used. The mode of extraction of dietary information displayed in the point-to-be-improved display section 51 is not limited to the above example.

In the improvement point display section 51, as eating habits information, "calories" obtained from meals taken by the user, "meal time", "staple food" eaten by the user, "side dishes" eaten by the user, and "side dishes" eaten by the user "Alcoholic beverages" and "snacks" eaten by the user are displayed. Also, the amount ingested by the user is displayed in each item of "staple food", "side dish", "alcoholic beverage", and "appetizer".

The morning selection button 52 is a button for displaying dietary information for breakfast on the point-to-be-improved display section 51 .
The lunch selection button 53 is a button for displaying dietary information for lunch on the point-to-be-improved display section 51 .
The dinner selection button 54 is a button for displaying dietary information for dinner on the improvement point display section 51 . In the illustrated example, by selecting the dinner selection button 54 , dietary information for dinner is displayed in the improvement point display section 51 .
The select all button 55 is a button for causing the improvement point display section 51 to display eating habits information for all of breakfast, lunch, and dinner.

Further, in the illustrated example, among the dietary information displayed in the improvement point display section 51, the item of "alcoholic beverages" in the dietary information of the target user is displayed differently from the items of the other dietary information. It is displayed according to the mode. More specifically, the item "alcoholic beverage" in the target user's eating habits information is displayed with a background color different from that of the other items of the eating habits information. The items displayed with different background colors are items of eating habits information extracted by the improvement point extracting unit 18 as the points that should be most improved in the eating habits of the target user.

The suggestion display portion 56 displays information about the points that should be most improved in the target user's eating habits. The suggestion display section 56 displays the dietary information extracted by the improvement point extracting section 18 as the point that should be most improved in the target user's eating habits. In the illustrated example, the suggestion display section 56 displays a message that "The most important point for Mr. A to improve is the amount and type of alcoholic beverages he drinks."

When the user selects the improved user detail display button 57, the display unit 21 displays, for example, detailed dietary information of the improved user.
When the user selects the return button 58, the display unit 21 displays the improvement screen 40 (see FIG. 7) again.

As described above, in the present embodiment, the improvement point extraction unit 18, based on the dietary information of the target user and the dietary information of registered users who are older than the target user, Extract improvements. Then, the improvement point extraction unit 18 predicts the future health condition of the target user when the extracted improvement points are improved. Therefore, in a broad sense, the improvement point extraction unit 18 can also be regarded as a prediction means.
In this case, based on actual examples of eating habits of registered users who are older than the target user, it is possible to predict the future health condition when the eating habits of the target user are improved.

Furthermore, in the present embodiment, the point-to-be-improved extraction unit 18 predicts the health condition of the target user when the points to be improved are improved based on the registered user's health condition information.
In this case, it is possible to predict the future health condition of the target user when the eating habits of the target user are improved based on the example of the eating habits and the health condition of one registered user who is older than the target user.

<Modification>
Next, a display example as a modified example of the information on the prediction result of the target user's health condition will be described.
FIG. 9 is a diagram showing the same user list screen 60. As shown in FIG. The same user list screen 60 is a screen showing a list of users with the same health condition extracted by the user extraction unit 17 . The user list screen 60 includes a user notification portion 61, a user display portion 62, a selection button 67 for one year later, a selection button 68 for five years later, a selection button 69 for ten years later, and a selection for twenty years later. A button 70, a weight limit section 71, a health limit section 72, a labor limit section 73, and a narrowing down button 74 are displayed.

The user notification portion 61 displays information for notifying that information about the health condition user is displayed on the user list screen 60 . In the illustrated example, the user notification section 61 displays a message "Information about a user who had the same health condition when Mr. A was the same age as Mr. A's present age."

The same user display section 62 shows a list of the same health condition users. The user display section 62 shows the weight and body fat percentage for each health condition user.
A health mark 63 is also displayed on the user display section 62 . The health mark 63 is a mark indicating that the same health condition user belongs to "health condition without problem". The health mark 63 is displayed in association with the same health condition user belonging to the "health condition without problems".
Also, a labor mark 64 is displayed on the user display section 62 . The labor mark 64 is a mark indicating that the same health condition user is a worker. The labor mark 64 is displayed in association with the same health condition user who is a worker.

The prediction mark 65 is a mark indicating that the same health condition user is a registered user whose health condition is extracted by the user extraction unit 17 as the future health condition of the target user, that is, a user who has the same eating habits. The prediction mark 65 is displayed in association with the same eating habit user. In other words, among the users with the same health condition displayed on the user display section 62, registered users who are not displayed in association with the prediction mark 65 are registered users who are not users with the same eating habits. Here, among the users with the same health condition, registered users who are not users with the same eating habits are related users whose health condition has a predetermined second relationship with the health condition of the target user and who is older than the target user. can be taken as Here, the second relationship is, for example, the relationship that the health condition to which the target user belongs is the same as the health condition to which the registered user belonged when the target user was the same age as the current age. .

Also, the celebrity mark 66 is a mark indicating that the displayed weight, body fat percentage, and name are celebrity information. This celebrity information and celebrity mark 66 are displayed regardless of whether or not this celebrity is the same health condition user.

The one-year-after selection button 67 is a button for setting the target of the information displayed on the user display section 62 to the information of the same health condition user when he/she was one year older than the age of the target user. be.
The 5 years later selection button 68 is a button for making the target of the information displayed on the same user display section 62 the information of the same health condition user when he was five years older than the age of the target user. be. In the illustrated example, when the 5 years later selection button 68 is selected, the information of the same health condition user when he was 5 years older than the age of the target user is displayed on the user display section 62. there is
The 10 years later selection button 69 is a button for making the target of the information displayed on the user display section 62 the information of the same health condition user when he/she was 10 years older than the age of the target user. be.
The 20 years later selection button 70 is a button for setting the target of the information displayed on the user display section 62 to the information of the same health condition user when he/she was 20 years older than the age of the target user. be.
In this way, in the user display section 62, it is possible to browse the information of the same health condition user in each age set in a plurality of stages.

The weight limiting section 71 is provided so as to be able to input the range of weight displayed on the user display section 62 as a condition for the user with the same health condition.
The health restriction section 72 is provided so as to be able to input that the health condition user belongs to a health condition with no problem as a condition of the same health condition user displayed on the user display part 62 .
The labor limiting section 73 is provided so that the condition that the user with the same health condition is a worker can be input as a condition of the user with the same health condition displayed on the user display unit 62 .
When the user selects the narrowing down button 74 , only the same health condition users who meet the conditions input in the weight restriction section 71 , the health restriction section 72 , and the work restriction section 73 are displayed on the user display section 62 .

For example, when the target user selects one user displayed in the user display unit 62, the improvement point screen 50 (see FIG. 8) that uses the selected user's eating habits information as the target user's eating habits improvement policy ) is displayed.

Here, for example, when the target user belongs to the health condition of headache, the same health condition user displayed with the health mark 63 on the user display section 62 belongs to the health condition of headache in the past and has had a headache since then. A registered user who no longer belongs to a health condition. Therefore, the target user selects the same health condition user and browses the eating habits information of the selected same health condition user, thereby grasping the eating habits information necessary to cure the headache.

Also, for example, when the 10 years later selection button 69 is selected, the same health condition user displayed with the labor mark 64 on the same user display section 62 is 10 years older than the current age of the target user. A registered user who was a worker when of age. Therefore, the target user selects this user with the same health condition and browses the eating habits information of the selected user with the same health condition, thereby grasping the eating habits information necessary for the target user to be a worker 10 years later. can do.

As described above, in the present embodiment, the output control unit 13 selects health condition information at a higher age than the target users of the related users in the information acquired by the dietary information reception unit 14 and the health information reception unit 15. is displayed on the terminal 20 used for
In this case, the target user can grasp actual examples of the health conditions of registered users whose health conditions are related to the target user's health conditions at a higher age than the target user.

In addition, in the present embodiment, the output control unit 13 can cause the terminal 20 to display the health condition information at a higher age than the target user for each of the plurality of related users, and for each of the plurality of related users, It is possible to cause the terminal 20 to display the eating habits information at an age older than the target user.
In this case, for each of the plurality of related users, the target user comprehends actual examples of health conditions and eating habits at an older age than the target user for registered users whose health conditions are related to the target user's health condition. be able to.

Although the embodiments of the present invention have been described above, the technical scope of the present invention is not limited to the scope described in the above embodiments. It is clear from the scope of claims that various modifications or improvements to the above embodiment are also included in the technical scope of the present invention.

In the present embodiment, the user extraction unit 17 extracts registered users based on the eating habits and health conditions of the registered users, but the information used by the user extraction unit 17 for extraction is not limited to this.
For example, the user extraction unit 17 may extract a user predicted as the future health condition of the target user using lifestyle information related to the user's lifestyle. The lifestyle information includes, for example, information on exercise performed by the user, information on sleep, information on smoking, and the like. The server 10 acquires the lifestyle information input to the terminal 20, for example. In this case, the attribute determining unit 16 determines the lifestyle to which the registered user belongs based on the acquired lifestyle information. Then, the user extraction unit 17 selects registered users belonging to the lifestyle to which the target user belonged when the target user was at the same age as the target user's current age, as targets of prediction results as the future health condition of the target user. You may extract as a registered user. Furthermore, as described above, the user extraction unit 17 extracts registered users using health condition information. That is, the prediction of the future health condition of the target user by the user extraction unit 17 may use not only dietary information but also health condition information and lifestyle information. Moreover, lifestyle information may be used as health condition information. That is, in a broad sense, lifestyle information is also an example of health condition information.

Also, the registered user's lifestyle information extracted by the user extraction unit 17 and the registered user's lifestyle information extracted by the improvement point extraction unit 18 may be displayed on the display unit 21 of the terminal 20 . That is, the information displayed on the display unit 21 is not limited to dietary information and health condition information.

Further, for example, the user extraction unit 17 extracts a registered user whose attributes such as gender, height, weight, etc. are the same as those of the target user as registered users whose future health conditions are to be predicted. good too.

Further, in the present embodiment, the user extracting unit 17 performs prediction processing when requested by the terminal 20 to predict the future health condition of the registered user, but the present invention is not limited to this.
For example, the user extraction unit 17 may periodically predict the future health condition of each registered user.

Further, in the present embodiment, the user extracting unit 17 predicts the future health condition of the target user based on one registered user, but the present invention is not limited to this.
For example, the user extraction unit 17 may predict the future health condition of the target user based on two or more registered users.

Further, in the present embodiment, the improvement point extracting unit 18 extracts registered users who have fewer problems than the current eating habits of the target user, but is not limited to this.
For example, the point-to-be-improved extracting unit 18 identifies users older than the age of the target user who belong to the same health condition with no problem, or who belong to the health condition with a problem, and the number of users who belong to the health condition with a problem is smaller than that of the target user. Users with the same health condition may be extracted. Then, differences between the extracted eating habits information of the same health condition user and the eating habits information of the target user may be used as extraction results of improvement points in the eating habits of the target user. Moreover, based on the extracted eating habits information of the user and the eating habits information of the target user, points to be improved most in the eating habits of the target user may be extracted.

Further, when two or more users with the same eating habit are extracted by the user extracting unit 17, the server 10 displays the prediction result screen 30 only for one user with the same eating habit among the two or more extracted users with the same eating habit. You may make it display on the display part 21 of the terminal 20. FIG. Moreover, the prediction result screen 30 may be displayed on the display unit 21 for each of the extracted two or more same eating habit users.
Further, when two or more improvement users are extracted by the improvement point extraction unit 18, the server 10 causes the display unit 21 to display the improvement screen 40 for only one improvement user among the extracted two or more improvement users. You may do so. Further, the improvement screen 40 may be displayed on the display unit 21 for each of the two or more extracted improvement users.

Further, in the present embodiment, it has been described that an image simulating the appearance of the user is displayed as the future user image 32 (see FIG. 6) and the improved user image 43 (see FIG. 7), but the present invention is not limited to this.
For example, when the permission of the user shown in the image used for the future user image 32 or the improved user image 43 is obtained, the image itself showing the image of the user may be displayed.

Further, when the user extraction unit 17 provides a new health condition prediction result to one target user to whom the future health condition prediction result has already been provided, this prediction result is the same as the previous prediction result. Information on how it has changed since it was provided may be provided. Furthermore, if the target user's eating habits continue, information may be provided as to how the prediction result of this future health condition will change in the future.

In addition, the improvement point extraction unit 18 determines not only the health condition when the target user's eating habits are improved, but also how the medical expenses change when the target user receives treatment at a medical institution, for example. and other information may be provided. Additionally, information about events to improve eating habits may be provided.

Further, in the present embodiment, the server 10 is configured to predict the future health condition of the target user, but the present invention is not limited to this.
For example, the terminal 20 may have the functions of the server 10 . In other words, the terminal 20 includes the registration reception unit 11, the storage unit 12, the output control unit 13, the diet information reception unit 14, the health information reception unit 15, the attribute determination unit 16, the user extraction unit 17, and the improvement extraction unit of the server 10. It is good also as providing the function of part 18 grade.

Programs that implement the embodiments of the present invention can be stored in computer-readable recording media such as magnetic recording media (magnetic tapes, magnetic disks, etc.), optical recording media (optical disks, etc.), magneto-optical recording media, and semiconductor memories. It can be provided as stored. Moreover, it is also possible to provide using communication means, such as the internet.

Reference Signs List 1 health condition prediction system 10 server 11 registration reception unit 12 storage unit 13 output control unit 14 diet information reception unit 15 health information reception unit 16 attribute determination unit 17 ... user extraction unit, 18 ... improvement point extraction unit, 20 ... terminal, 21 ... display unit

Claims (5)

Acquisition means for acquiring dietary life information and health condition information concerning health conditions of a plurality of users;
In the information acquired by the acquisition means, the eating habits of the first user and the eating habits that the second user who is older than the first user has been eating since they were the same age as the current age of the first user Prediction means for predicting the future health condition of the first user based on the health condition information of the second user when having a matching relationship with life;
The eating habits information of the first user and the health condition of the first user when the first user was older than the first user and at the same age as the first user's current age extracting means for extracting improvement points to be improved in the diet of the first user based on the diet information of the third user who has improved at least one of life and health;
with
The health condition prediction system, wherein the prediction means predicts the future health condition of the first user when the improvements extracted by the extraction means are improved. 2. The health condition prediction according to claim 1 , wherein the prediction means predicts the health condition of the first user when the points to be improved are improved based on the health condition information of the third user. system. In the information acquired by the acquisition means , the first user has a relationship in which the health condition when the first user was at the same age as the current age matches the health condition of the first user, and The method further comprises control means for displaying on a terminal used by the first user, among the health condition information of related users who are older than the first user, the health condition information of those older than the first user. Item 1. The health condition prediction system according to item 1. The control means is capable of displaying, on the terminal, the health condition information at a higher age than the first user for each of the plurality of related users, and for each of the plurality of related users, displaying the health condition information of the first 4. The health condition prediction system according to claim 3 , wherein said terminal can display said eating habits information at a higher age than said user. to the computer,
an acquisition function for acquiring dietary life information and health condition information concerning the health conditions of a plurality of users;
In the information acquired by the acquisition function, the eating habits of the first user and the eating habits of the second user who is older than the first user since they were the same age as the current age of the first user a predicting function that predicts the future health condition of the first user based on the health condition information of the second user when there is a matching relationship with life ;
The eating habits information of the first user and the health condition of the first user when the first user was older than the first user and at the same age as the first user's current age an extraction function for extracting improvement points to be improved in the diet of the first user based on the diet information of the third user who has improved at least one of life and health;
A program for realizing a function of predicting the future health condition of the first user when the improvements extracted by the extraction function are improved.

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