Health condition estimation method, device, and program

Abstract

To provide an improved method, device, and program for estimating the health condition of a subject.SOLUTION: A health condition estimation method includes the steps of: acquiring a sensory evaluation value 21 related to the taste sense of a subject 98; and estimating the health condition 25 of the subject 98 on the basis of an estimation model 24 that includes the relevance between a health evaluation value in a human population and the sensory evaluation value related to the taste sense and the sensory evaluation value 21.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a method, an apparatus and a program for estimating a health condition of a subject. [Background technology]

[0002] In daily life, people may experience various mental and physical disorders, such as fatigue, insomnia, and depression. In many cases, these disorders gradually improve, but in some cases they may become severe and lead to the onset of mental disorders or lifestyle-related diseases. In order to prevent illness, it is necessary to take appropriate measures at the appropriate time. Various efforts have been made to objectively grasp mental and physical disorders and detect high-risk conditions that may lead to the onset of illness.

[0003] For example, Patent Document 1 below discloses a method for testing depression using a biomarker. Patent Document 2 below discloses a biomarker for determining mental illness. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2017-000063 A [Patent Document 2] International Publication No. 2016 / 167365 Summary of the Invention [Problem to be solved by the invention]

[0005] Genetic testing and intestinal bacteria-based diagnosis of mental illness are screening for conditions that have already developed, and are not technologies that measure the risk of developing the illness before it occurs. In order to maintain the health of many people, there is a need for a method to identify mental and physical disorders that require attention before they develop into illness.

[0006] An object of the present invention is to provide an improved method, device, and program for estimating the health condition of a subject. [Means for solving the problem]

[0007] The present invention for solving the above problems includes, for example, the following aspects.

[0008] (Section 1) A step of obtaining a sensory evaluation value regarding taste by a subject; estimating a health condition of the subject based on an estimation model including a correlation between a health evaluation value and a sensory evaluation value related to taste in a human population and the sensory evaluation value; A health condition estimation method comprising: (Section 2) Item 2. The method according to Item 1, wherein the sensory evaluation value related to taste includes a sensory evaluation value related to at least one selected from the group consisting of taste intensity, taste preference, taste aftertaste, taste detection ability, taste recognition ability, and taste evaluation. (Section 3) Item 3. The method according to item 1 or 2, wherein the health condition includes at least one selected from the group consisting of fatigue, insomnia, and physical and mental health conditions. (Section 4) The method according to any one of items 1 to 3, wherein the estimation model includes a correlation between a fatigue score or an insomnia score and a sensory evaluation value for taste in a human population. (Section 5) The estimation model is A first correlation representing a correlation between the insomnia score and a sensory evaluation value regarding a taste aftertaste; A second correlation represents the correlation between the fatigue score and the sensory evaluation scores for taste detection ability and preference; a third correlation representing the correlation between fatigue scores and taste intensity; Item 5. The method according to item 4, comprising at least one of the following: (Section 6) The method according to any one of items 1 to 5, wherein the estimation model includes a correlation between a mental and physical health score and a sensory evaluation value for taste in a human population. (Section 7) The estimation model is A fourth correlation represents a correlation between the score of the mental and physical health condition and the sensory evaluation value regarding taste preference; A fifth correlation represents a correlation between the score on the physical symptoms of mental and physical health and the sensory evaluation value on the preference for sourness; A sixth correlation represents the correlation between the scores on anxiety and insomnia in terms of mental and physical health and the sensory evaluation score on taste preference; Item 7. The method according to item 6, comprising at least one of the following: (Section 8) The estimation model includes a plurality of associations between health evaluation values ​​and sensory evaluation values ​​related to taste in a human population, The method according to any one of claims 1 to 7, wherein the step of estimating the health state further comprises estimating the degree of the health state. (Section 9) A sensory evaluation value acquisition means for acquiring a sensory evaluation value regarding taste by a subject; a health condition estimation means for estimating a health condition of the subject based on an estimation model including a correlation between a health evaluation value and a sensory evaluation value related to taste in a human population and the sensory evaluation value; A health condition estimation device comprising: (Section 10) A program for causing a computer to execute each step of the method according to any one of items 1 to 8. (Section A) The method according to any one of items 3 to 8, wherein the fatigue includes physical fatigue and mental fatigue as factors. (Section B) The method according to any one of items 3 to 8, wherein the mental and physical health conditions include factors such as physical symptoms, anxiety and insomnia, social activity disorders and depression tendencies. (Section C) The method according to any one of items 1 to 8, wherein the taste includes at least one selected from the group consisting of sweetness, saltiness, sourness, bitterness, and umami. Effect of the Invention

[0009] According to the present invention, it is possible to provide an improved method, device and program for estimating the health condition of a subject. [Brief description of the drawings]

[0010] [Figure 1] FIG. 2 is a diagram showing a usage mode of a health state estimation device according to one embodiment of the present invention, illustrating a usage mode when estimating a health state. [Diagram 2] FIG. 2 is a diagram illustrating a usage mode of a health state estimation device according to one embodiment of the present invention, and is a diagram for explaining a usage mode when conducting a taste test to create an estimation model. [Diagram 3] 1 is a block diagram for explaining functions of a health state estimation device according to an embodiment of the present invention. [Figure 4] 3 is a flowchart illustrating a health condition estimation method according to one embodiment of the present invention. [Diagram 5] FIG. 2 is a diagram showing the concentrations of aqueous solutions used for each taste in the taste test in Example 1. [Figure 6] 1 is a graph showing the results of the taste test in Example 1, and illustrating the results regarding the circadian rhythm of taste. (A) shows the evaluation value of the taste intensity for a sweet aqueous solution. (B) shows the evaluation value of the taste intensity for a bitter aqueous solution. [Figure 7] 1 is a graph showing the results of the taste test in Example 1, and illustrating the results regarding the circadian rhythm of taste. (A) shows the perception threshold (the lowest concentration that could be recognized as bitter) and the acceptable concentration for bitterness. (B) shows the perception threshold (the lowest concentration that could be recognized as umami) and the preferred concentration for umami. [Figure 8] 1 shows the results of the taste test in Example 1, and is a graph for explaining the results regarding taste and poor physical condition. (A) shows the change in aftertaste for sweetness for the healthy group and the insomnia group. (B) shows the change in aftertaste for umami for the healthy group and the insomnia group. (C) shows the change in aftertaste for sourness for the healthy group and the insomnia group. [Figure 9] 1 is a graph showing the results of the taste test in Example 1, illustrating the results relating to taste and poor physical condition. The preferred concentration of sweetness is shown for a healthy group and an insomnia group. [Figure 10] 1 is a graph showing the results of the taste test in Example 1, and is intended to explain the results relating to taste and poor physical condition. (A) shows the taste evaluation scores of bitter chocolate for the healthy group and the insomnia group. (B) shows the taste evaluation scores of dry sausage for the healthy group and the insomnia group. [Figure 11] 1 shows the results of the taste test in Example 1, and is an example of a model showing the influence (large influence) of the evaluator's state on taste and taste preference. [Figure 12] 1 shows the results of the taste test in Example 1, and is an example of a model showing the influence (moderate) of the evaluator's condition on taste and taste preference. [Figure 13] 1 shows the results of the taste test in Example 1, and is an example of a model showing the influence (small influence) of the evaluator's state on taste and taste preference. [Figure 14] 1 is a table showing the analysis results in Example 2, which is a result of a detailed analysis of the results of the taste test in Example 1 with respect to the correlation between the fluctuation of the evaluator's sense of taste and smell and the evaluator's condition (physical condition). (A) shows the correlation coefficient between the lingering aftertaste (evaluation value of aftertaste strength) and the score on the physical condition evaluation. (B) shows the correlation coefficient between the evaluation value of taste strength and the score on the physical condition evaluation. (C) shows the correlation coefficient between desire for five tastes (preference / aversion) and the score on the physical condition evaluation. [Figure 15] 1 is a graph showing the analysis results in Example 2, which are ROC curves when a tendency to insomnia is detected from the time it takes for a gustatory aftertaste to disappear. (A) is the ROC curve for the time it takes for a sweet aftertaste to disappear, and (B) is the ROC curve for the time it takes for a umami aftertaste to disappear. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, an embodiment of the present invention will be described in detail with reference to the accompanying drawings. In the following description and drawings, the same reference numerals denote the same or similar components, and therefore, redundant description of the same or similar components will be omitted.

[0012] [Apparatus and Method Overview] Figures 1 and 2 are diagrams for explaining a usage mode of a health state estimation device according to one embodiment of the present invention. Figure 1 shows a usage mode when estimating a health state, and Figure 2 shows a usage mode when conducting a taste test for creating an estimation model. The taste test shown in Figure 2 is conducted in advance before the health state estimation shown in Figure 1.

[0013] Health status estimation Please refer to Fig. 1. A health condition estimation device 100 (hereinafter also simply referred to as device 100) according to one embodiment is a device that estimates the health condition (e.g., fatigue, insomnia, and mental and physical health conditions) of a subject 98 based on an evaluation value (hereinafter also referred to as a sensory evaluation value) of a sensory evaluation of taste (e.g., sweetness, saltiness, sourness, bitterness, and umami) by the subject 98 and an estimation model 24. The estimation model 24 is an association in a human population, and includes an association between a score related to the health condition (hereinafter also referred to as a health evaluation value) and a sensory evaluation value related to taste.

[0014] As a person's physical or mental health problems increase, their taste and taste preferences change. If the subject 98 is experiencing physical or mental health problems, the sensory evaluation value of the subject 98 regarding taste, which is input to the device 100, changes from the general sensory evaluation value of taste in the human population. In this embodiment, the health condition of the subject 98 is estimated by detecting a change in the sensory evaluation value of taste by the subject 98 from the general sensory evaluation value in the human population. This makes it possible to distinguish a level of physical or mental health problems that may not yet develop into illness but require attention.

[0015] The procedure will be described below. The subject 98 performs a sensory evaluation of taste (e.g., sweetness) of the food or drink 99, and inputs the sensory evaluation value of the taste into the device 100. In this embodiment, the sensory evaluation value of taste is a sensory evaluation value of taste intensity, taste preference (e.g., preferred taste intensity value), aftertaste (e.g., aftertaste intensity every 10 seconds), taste detection ability, taste recognition ability (taste discrimination ability), and taste evaluation. The sensory evaluation value of taste inputted into the device 100 may be at least one of these five types shown as examples.

[0016] The device 100 stores an estimation model 24 including a correlation between a health evaluation value and a sensory evaluation value related to taste in a human population. The estimation model 24 may include a plurality of correlations. Such correlations between health evaluation values ​​and sensory evaluation values ​​related to taste stored in the estimation model 24 are generated in advance by a taste test in a human population, which will be described later with reference to FIG. 2.

[0017] The device 100 estimates the health condition of the subject 98 (e.g., the possibility of chronic fatigue or insomnia) based on the acquired sensory evaluation value of the subject 98 and the estimation model 24. The estimated health condition is displayed on, for example, the display unit 32 of the device 100.

[0018] - Conduct taste tests to create estimation models Please refer to Fig. 2. The estimation model 24 used by the device 100 to estimate the health condition includes a correlation between the health evaluation value and the sensory evaluation value related to taste in a human population. Such a correlation included in the estimation model 24 is generated in advance by conducting a plurality of types of taste tests by a plurality of evaluators 198 targeting a plurality of types of food and drink 199 as taste tests in a human population.

[0019] The evaluator 198 performs a sensory evaluation of taste for each of a plurality of types of food and drink 199, and inputs the sensory evaluation value for taste into the device 100. The taste tests include, for example, a test on taste intensity, a test on taste preference, a test on taste aftertaste, a test on taste detection ability and taste recognition ability (cognitive threshold), and a taste evaluation. The sensory evaluation for taste is performed for each of the five tastes, for example, sweetness, saltiness, sourness, bitterness, and umami.

[0020] The evaluator 198 inputs a health evaluation value related to his / her own health condition (e.g., fatigue, insomnia, and mental and physical health condition) into the device 100 by answering, for example, three types of questions related to health evaluation displayed on, for example, the display unit 32 of the device 100. In this embodiment, the questions related to health evaluation are three types: the Chalder Fatigue Scale, the Athens Sleep Scale, and the General Health Questionnaire. The estimation model creation unit 14 provided in the device 100 generates an association between the health evaluation value and the sensory evaluation value related to taste in a human group based on the sensory evaluation values ​​related to taste and the health evaluation values ​​obtained from the multiple evaluators 198, and creates an estimation model 24 including the generated association.

[0021] [Device configuration] FIG. 3 is a block diagram for explaining functions of a health state estimation device according to one embodiment of the present invention.

[0022] Health state estimation device 100 according to one embodiment includes a data processing unit 10, an auxiliary storage device 20, an input unit 31, a display unit 32, and a communication interface unit (communication I / F unit) 33. Data processing unit 10 is configured as software, while auxiliary storage device 20, input unit 31, display unit 32, and communication I / F unit 33 are configured as hardware. Health state estimation device 100 can be configured using, for example, a general-purpose computer such as a personal computer, a tablet terminal, a smartphone, or the like. Although not shown, health state estimation device 100 further includes, as hardware configuration, a processor such as a CPU that processes data, and a memory that the processor uses as a working area for data processing.

[0023] The data processing unit 10 is a functional block that is realized by a processor executing a health condition estimation program 29 (hereinafter, also simply referred to as the program 29), which will be described later. In this embodiment, the data processing unit 10 is provided as a software functional block.

[0024] The sensory evaluation value acquiring unit 11 acquires data 21 of sensory evaluation values ​​relating to taste by a subject 98. The sensory evaluation value data 21 is acquired, for example, via the input unit 31 or the communication I / F unit 33, and stored in the auxiliary storage device 20. In this embodiment, the sensory evaluation value data 21 is input by the subject 98 via dialog boxes 81, 82, and 83 displayed on the display unit 32 of the device 100, as exemplified in FIG.

[0025] The health evaluation value acquisition unit 13 and the estimation model creation unit 14 function when a taste test is conducted in a human population. The taste test is conducted in advance before estimating the health state. The health evaluation value acquisition unit 13 acquires data 23 of health evaluation values ​​related to the health state of a plurality of evaluators 198. The health evaluation value data 23 is acquired, for example, via the input unit 31 or the communication I / F unit 33, and stored in the auxiliary storage device 20. The estimation model creation unit 14 generates an association between the health evaluation value and the sensory evaluation value related to taste in the human population based on the sensory evaluation values ​​related to taste and the health evaluation values ​​acquired from the plurality of evaluators 198, and creates an estimation model 24 including the generated association. The created estimation model 24 is stored in the auxiliary storage device 20.

[0026] In this embodiment, the evaluator 198 inputs the sensory evaluation value and the health evaluation value to the device 100 while the taste test is being performed, via dialog boxes 71 to 75 displayed on the display unit 32 of the device 100, as exemplified in Fig. 2. The dialog boxes 71 to 74 are dialog boxes for the evaluator 198 to input the sensory evaluation value, and are, in order, a dialog box for a test on taste intensity, a test on taste preference, a test on taste aftertaste, and a dialog box for taste evaluation. The dialog box 75 is a dialog box for the evaluator 198 to input the health evaluation value while the taste test is being performed.

[0027] The health condition estimation unit 15 estimates the health condition of the subject 98 based on the estimation model 24 and the sensory evaluation value data 21 of the subject 98. Estimated health condition data 25 is stored in the auxiliary storage device 20. In this embodiment, the estimated health condition data 25 is displayed on the display unit 32 of the device 100 via a dialog box 84, as exemplified in FIG.

[0028] Furthermore, when the estimation model 24 includes a plurality of correlations between health evaluation values ​​in a human population and sensory evaluation values ​​related to taste, the health condition estimation unit 15 can estimate the degree of the health condition of the subject 98. For example, assume that the health condition estimation unit 15 determines that one or more of the correlations included in the estimation model 24 correspond to the judgment criterion items related to "insomnia". In such a case, the health condition estimation unit 15 can estimate the degree of the health condition of the subject 98 in stages (for example, good, slightly poor, poor = high possibility of insomnia) according to the items determined to correspond.

[0029] The auxiliary storage device 20 is a non-volatile storage device that stores an operating system (OS), various control programs, data generated by the programs, etc., and is configured, for example, by a flash memory, an eMMC (embedded multi media card), an SSD (solid state drive), etc. In this embodiment, the auxiliary storage device 20 stores sensory evaluation value data 21, an estimation model 24, health condition data 25, and a health condition estimation program 29.

[0030] In the present embodiment, the estimation model 24 includes six associations. The first association represents an association between the insomnia score and a sensory evaluation value related to (the transition of) the aftertaste of taste. Preferably, the target taste in the first association includes at least one of sweetness and umami. The second association represents an association between the fatigue score and a sensory evaluation value related to the ability to detect tastes and preference. Preferably, the target taste in the second association includes saltiness. The third association represents an association between the fatigue score and the intensity of taste. Preferably, the target taste in the third association includes saltiness. The fourth association represents an association between the mental and physical health score and a sensory evaluation value related to taste preference. Preferably, the target taste in the fourth association includes sourness. The fifth association represents an association between the score related to the physical symptoms of the mental and physical health and a sensory evaluation value related to the preference for sourness. The sixth correlation represents a correlation between scores related to anxiety and insomnia in mental and physical health and a sensory evaluation value related to taste preference. The tastes targeted in the sixth correlation preferably include at least one of sweetness, saltiness, sourness, bitterness, and umami, and more preferably include at least one of sweetness and sourness. The six correlations included in the estimation model 24 and the examples described later are as follows. The first correlation is based on Fig. 8(A) to Fig. 8(C) and Fig. 14(A). The second correlation is based on Fig. 12 and Fig. 14(C). The third correlation is based on Fig. 11 and Fig. 14(B). The fourth correlation is based on Fig. 14(C). The fifth correlation is based on Fig. 14(B) and Fig. 14(C). The sixth correlation is based on Fig. 14(C). The estimation model 24 can be realized, for example, as a computer program. For example, when the estimation model 24 is implemented as a computer program, the computer program is programmed to show the trends shown in each of the six associations described above.

[0031] The health condition estimation program 29 is a computer program for implementing the units 11 to 15 in the data processing unit 10, which are functional blocks implemented by software. The program 29 can be installed in the device 100 via a network 39, such as the Internet, connected by the communication I / F unit 33. Alternatively, the program 29 can be installed in the device 100 by having the device 100 read a computer-readable, non-transitory, tangible recording medium, such as a memory card, on which the program 29 is recorded.

[0032] The input unit 31 can be configured with, for example, a mouse and a keyboard, and the display unit 32 can be configured with, for example, a liquid crystal display and an organic EL display. The communication I / F unit 33 transmits and receives data to and from an external device via a wired or wireless network. The communication I / F unit 33 can be configured with various wired or wireless connections such as Ethernet (registered trademark), Wi-Fi (registered trademark), and Bluetooth (registered trademark). The input unit 31 and the display unit 32 may be integrated to be realized as a touch panel type display device.

[0033] [Processing Procedure] FIG. 4 is a flowchart showing a health condition estimation method according to one embodiment of the present invention.

[0034] The processing in steps S1 to S2 shown in FIG. 4 is executed by each functional block included in data processing unit 10 (that is, by health state estimation device 100).

[0035] In step S1, sensory evaluation value data 21 relating to taste from a subject 98 is obtained. In step S2, the health condition of the subject 98 is estimated based on the estimation model 24 and the sensory evaluation value data 21 of the subject 98.

[0036] As described above, according to one embodiment of the present invention, an improved method, device, and program for estimating the health condition of a subject can be provided. In one embodiment of the present invention, when estimating the health condition of a subject 98, a change in the sensory evaluation value of taste by the subject 98 from a general sensory evaluation value in a human population is detected. This makes it possible to distinguish mental and physical disorders that are not yet onset of illness but require attention. In this way, according to one embodiment of the present invention, the health condition of the subject 98 can be estimated based on the change in a person's taste or taste preferences that occurs with a person's mental and physical disorders, and it becomes possible to distinguish mental and physical disorders that are not yet onset of illness but require attention. As a result, it becomes possible to take appropriate measures for the subject 98 at the appropriate time, and it becomes possible to prevent illness before it occurs.

[0037] [Other formats] Although the present invention has been described above with reference to specific embodiments, the present invention is not limited to the above-described embodiments.

[0038] In the above embodiment, in the taste test for creating an estimation model, the evaluator 198 inputs a health evaluation value related to his / her own health condition to the device 100 by inputting an answer to a question related to health evaluation to the device 100 via the input unit 31, but the manner in which the health evaluation value is input to the device is not limited to this. The answer to the question related to health evaluation may be, for example, data previously answered by the evaluator 198, which may be acquired from the external server 97 via the network 39. Instead of the answer to the question related to health evaluation, the biometric data of the evaluator 198 (e.g., biorhythm, fatigue level, working hours, sleeping hours, number of steps, heart rate, blood pressure, etc.) may be automatically measured via a wearable smart device (e.g., a smart watch) worn by the evaluator 198, and a part or all of the automatically measured biometric data may be input to the device 100. In this case, the device 100 may appropriately reinterpret or convert the acquired biometric data of the evaluator 198 and use it as the health evaluation value of the evaluator 198.

[0039] In the above embodiment, the tastes to be subjected to the sensory evaluation are five tastes, namely sweetness, saltiness, sourness, bitterness, and umami (five tastes), but the tastes to be subjected to the sensory evaluation are not limited to these five tastes. The tastes to be subjected to the sensory evaluation do not need to include all five tastes, and may be, for example, four tastes, namely, sweetness, saltiness, sourness, and bitterness, or may include at least one of these five tastes. In addition to these exemplified tastes, the taste to be subjected to the sensory evaluation may further include flavor, deliciousness, and aroma. The meaning of taste in this specification includes the usual meaning of taste, as well as the aroma and flavor (smell), appearance (sight), texture (touch), and chewing sound (hearing) that are recognized in conjunction with taste, and should also be understood as a composite sensation (e.g., deliciousness) in which these elements are combined. In the example shown in the examples described later, for example, in the case of bitter chocolate, the aroma is, for example, a sweet aroma or a cacao aroma, and the flavor is, for example, an oily and fat feeling. In the case of dry sausage, for example, the aroma is, for example, a smoked aroma, and the flavor is, for example, a spicy feeling, a meaty flavor, or a greasy feeling.

[0040] In the above embodiment, the questions regarding health evaluation are three types, namely, the Chalder Fatigue Scale, the Athens Sleep Scale, and the General Health Questionnaire, but the questions regarding health evaluation are not limited to these three types. The questions regarding health evaluation may be any content that can determine the health evaluation value regarding the health condition of the subject 98 and the evaluator 198. For example, questions regarding fatigue can be used other than the exemplified Chalder Fatigue Scale. Similarly, questions regarding insomnia can be used other than the exemplified Athens Sleep Scale, and questions regarding physical and mental health conditions can be used other than the exemplified General Health Questionnaire.

[0041] In the above embodiment, health state estimation device 100 is realized as an integrated device, but health state estimation device 100 does not need to be an integrated device, and the processor, memory, auxiliary storage device 20, etc. may be located in separate locations and connected to each other via a network. Input unit 31 and display unit 32 also do not necessarily need to be located in one place, and may be located in separate locations and connected to each other so as to be able to communicate with each other via a network.

[0042] In the above embodiment, each of the functional blocks 11-15 constituting the data processing unit 10 is realized by software, but each of these functional blocks 11-15 may be realized partly or entirely as hardware. The processing of each of the functional blocks 11-15 constituting the data processing unit 10 does not need to be processed by a single processor, and may be distributed and processed by multiple processors. Some or all of the functions of the data processing unit 10 and the data items in the auxiliary storage device 20 may be cloud-based in an external server device 37 connected via the communication I / F unit 33.

[0043] The following examples of the present invention will make the features of the present invention clearer. EXAMPLES

[0044] In Example 1, a taste test was conducted on a human group, and the relationship between the sensory evaluation of taste in the human group, the health evaluation value, and the circadian rhythm of taste was considered. The taste test was conducted on multiple types of food and drink by multiple evaluators. The circadian rhythm of taste is the daily rhythm in which subjective taste sensitivity and preferences change throughout the day.

[0045] <Method> The taste test was conducted three times, in the morning (8:00 AM), afternoon (12:00 PM), and evening (7:00 PM), on 19 research participants (ages 20-50). The amount of saliva produced by the research participants was measured before the taste test. The research participants refrained from eating, drinking coffee, or other leisure items for two hours prior to the start of the test. Five aqueous solutions with five different concentrations, as shown in Figure 5, were used in the test. Five aqueous solutions with five different concentrations were prepared and used for each of the five tastes (sweet, salty, sour, bitter, and umami).

[0046] Taste Intensity, Discrimination Ability, and Preference Tests The research collaborators put the solutions in their mouths in order of decreasing concentration and rated the intensity of the taste they detected from 0 to 5 (0: no taste, 1: very weak taste, 2: weak taste, 3: moderate taste, 4: strong taste, 5: very strong taste). The taste intensity test was rated on a 6-point scale from 0 to 5. If the participants rated the strength as 1 or higher, they answered which of the five taste categories (sweet, salty, sour, bitter, and umami) the solution tasted. Based on the answers, the concentration of the solution with the lowest concentration that allowed them to correctly answer the taste was recorded. The taste discrimination test was rated on a 5-point scale from 1 to 5. Furthermore, the research collaborators selected one solution with the most preferred (or in another embodiment, not unpleasant and acceptable) concentration from the five solutions with five different concentrations. The taste preference test was rated on a 5-point scale from 1 to 5.

[0047] Aftertaste test The research participant placed about 15 ml of the most highly concentrated aqueous solution in Figure 5 into their mouth and tasted it thoroughly with their entire mouth. After about 10 seconds, they spat out the solution, and evaluated the aftertaste every 10 seconds for 120 seconds. The evaluation was done using a 12 cm line scale. The aftertaste test was a continuous evaluation between 0 and 12. The aftertaste test was done for each of the five tastes.

[0048] Physical condition evaluation After the evening taste test, the participants answered three questionnaires about fatigue, insomnia, and mental and physical health over the past week. Fatigue was asked using the Chalder Fatigue Scale (evaluated on a scale of 0 to 33, with a score of 16 or more indicating possible chronic fatigue). Insomnia was asked using the Athens Sleep Scale (evaluated on a scale of 0 to 24, with a score of 6 or more indicating possible insomnia). Mental and physical health was asked using the General Health Questionnaire GHQ-28 (evaluated on a scale of 0 to 28, with a score of 6 or more indicating possible poor mental health). Mental and physical health includes the mental state, which is the state of the mind, and the physical health, and the GHQ-28 can capture four symptoms: physical symptoms, anxiety / insomnia, impaired social activities, and depression.

[0049] Taste evaluation The research participants tasted both the bitter chocolate and the dry sausage, and evaluated the aroma, taste, flavor, and deliciousness of each. The taste evaluation was done on a continuous scale ranging from 0 to 12. A 12 cm (120 mm) line scale was also used for the taste evaluation. The position on the line scale where the research participant made a mark was measured in millimeters (mm) and used as an evaluation score. For example, if the research participant could not smell anything at all, the score would be 0, and if the research participant smelled a very strong aroma, the score would be 120.

[0050] <Result> -Regarding the circadian rhythm of taste In the taste intensity test, circadian rhythms were observed for sweetness and saltiness. The taste intensity rating for a low-concentration sweet solution (concentration 1: 0.125%) was significantly lower at night than during the day (P<0.05, Fig. 6(A)). For bitterness, the intensity ratings for all concentrations were lower at night than during the day (P<0.05, Fig. 6(B)).

[0051] Furthermore, when we looked at the concentration at which the type of taste could be accurately recognized (cognitive threshold), for bitterness, a lower concentration was recognized as a bitter aqueous solution in the daytime compared to the morning and evening (P<0.05, Fig. 7(A) left). This result showed that bitterness sensitivity increased in the daytime, and as a result, the acceptable concentration of bitterness was lower in the daytime compared to the evening (P<0.05, Fig. 7(A) right). On the other hand, for umami, taste sensitivity decreased in the daytime (P<0.05, Fig. 7(B) left), and it was revealed that a higher concentration of umami aqueous solution was preferred (P<0.05, Fig. 7(B) right).

[0052] Regarding taste and physical discomfort Regarding insomnia, 18 subjects were analyzed, excluding 1 subject with incomplete data, and 8 out of 18 subjects were found to have a tendency toward insomnia. When the results of the aftertaste test (daytime data) were compared between the healthy group (10 subjects) and the insomnia group (8 subjects), it was found that the aftertaste of sweetness and umami in the insomnia group tended to disappear more quickly than in the healthy group (P<0.10, Figure 8(A) and Figure 8(B)). For sourness, the strength of the aftertaste after 40 to 80 seconds was significantly lower in the insomnia group (P<0.05, Figure 8(C)). This result suggests that insomnia may have accelerated the disappearance of tastes in the oral cavity. In addition, the insomnia group preferred a lower concentration sweet solution than the healthy group (P<0.05, Figure 9), indicating a decreased preference for sweetness.

[0053] In a taste evaluation of bitter chocolate (approximately 32% fat, approximately 72% cacao) conducted at lunchtime, the insomnia group tended to have lower ratings for saltiness and melting in the mouth (P<0.10, Figure 10(A)). In a taste evaluation of dry sausage, the insomnia group also tended to have lower ratings for saltiness (P<0.10, Figure 10(B)), and lower ratings for bitterness and spiciness (P<0.05, Figure 10(B)).

[0054] <Example of a model showing the effect of evaluator state and evaluation time on taste and taste preference> 11 to 13 are examples of models showing the influence of the evaluator's condition (physical condition) on taste and taste preference. These illustrated models were created by analyzing the results of the taste test in this Example 1. Stepwise multiple regression analysis was used to analyze the relationships between the factors. Factors with a large influence are shown in FIG. 11, those with a medium influence are shown in FIG. 12, and those with a small influence are shown in FIG. 13. In the following description, the ability to detect taste means the ability to detect the presence of a certain taste, and the ability to recognize taste means the ability to accurately recognize which taste the detected taste is, for example, of the five tastes.

[0055] Referring to Figure 11, we consider the model with the greatest influence. When the evaluator is sleepless, the aftertaste disappears more quickly. When the evaluator is mentally fatigued, the ability to detect weak flavors decreases, and the evaluation value of the intensity of strong flavors (subjective taste intensity across the five tastes) also decreases, resulting in a lower taste evaluation value. Note that the path values ​​shown in the model are standard partial regression coefficients, ** :p<0.01.

[0056] Referring to Figure 12, a model with a medium degree of influence will be considered. When the evaluator feels physically tired, their ability to detect flavors increases and the aftertaste tends to linger, leading to a higher taste evaluation value. When the evaluator feels physically tired or feels that their daily activities (work, etc.) are stalled, their preference for saltiness increases. On the other hand, when the evaluator's physical health is declining (headache, fatigue, etc.), their preference for saltiness, sourness, and umami decreases. When their mental health is declining (anxiety, insomnia), their preference for sweetness decreases. Note that the path values ​​shown in the model are standard partial regression coefficients, ** :p<0.01.

[0057] Referring to Figure 13, we consider a model with a small effect. If the evaluator produces a large amount of saliva, the aftertaste disappears more quickly. If the evaluator's body temperature is high, their ability to detect flavors increases, so they can detect even weak flavors (low concentrations). On the other hand, if the evaluator's body temperature is high, their preference for sweetness decreases. If the evaluator is experiencing insomnia, their preference for umami increases. Note that the path values ​​shown in the model are standard partial regression coefficients, * :p<0.05. EXAMPLES

[0058] In Example 2, the results of Example 1 were analyzed in more detail, and the relationship between the fluctuations in the evaluator's sense of taste and smell and the evaluator's condition (physical condition) was considered. Figure 14(A) shows the correlation coefficient between the aftertaste lingering (evaluation value of aftertaste strength) and the score on the physical condition evaluation. Figure 14(B) shows the correlation coefficient between the evaluation value of taste strength and the score on the physical condition evaluation. Figure 14(C) shows the correlation coefficient between desire for five tastes (preference / aversion) and the score on the physical condition evaluation. As a result of the consideration, the changes in taste and taste preference that occur due to mental and physical disorders were as follows:

[0059] Change 1: Insomnia accelerates the disappearance of the aftertaste of sweet and umami tastes (Figures 8(A) to 8(C), Figure 14(A)). Change 2: Physical fatigue increases the ability to detect tastes and also increases the preference for saltiness (Figures 12 and 14(C)). Change 3: Mental fatigue reduces the intensity of taste (Figure 11), and this effect is most evident in salty taste (Figure 14(B)). Change 4: A decline in physical and mental health reduces preference for sour tastes (Figure 14(C)). Change 5: When the decline in mental and physical health is due to physical symptoms such as headaches or fatigue, the sour taste is perceived more strongly, leading to a decrease in preference for sour tastes, i.e., an increase in aversion to sour tastes (Figures 14(B) and 14(C)). Change 6: When the decline in mental and physical health is due to anxiety or irritability, preference for the five tastes (sweet, salty, sour, bitter, and umami) generally decreases. In particular, preference for sweet and sour tastes decreases (Figure 14(C)).

[0060] Based on the above findings, an example of a taste test method for detecting mental and physical disorders is given below. All concentrations of the aqueous solutions are in weight percent.

[0061] Test example 1 (i) Approximately 15 ml of approximately 8% sugar water or approximately 0.24% sodium L-glutamate solution is placed in the mouth and tasted thoroughly for approximately 10 seconds with the whole mouth. (ii) The solution is spat out, and the taste remaining in the mouth is checked while remaining in the mouth in a state of rest for 90 seconds. (iii) If the sweet taste (sugar water) or umami taste (sodium L-glutamate) disappears from the mouth before 90 seconds have passed, insomnia is suspected. ROC curves for detecting insomnia tendency from the time when the aftertaste of sweetness and umami taste disappear are shown in Figures 15(A) and 15(B). In the figure, the TPR (True Positive Rate) on the vertical axis is the true positive rate, and the FPR (False Positive Rate) on the horizontal axis is the false positive rate. The accuracy of this evaluation method estimated from the AUC (Area Under the Curve) calculated from this ROC curve was approximately 81.8% for sweetness and approximately 71.2% for umami.

[0062] Test example 2 (i) If you put an appropriate amount of about 0.1% salt water in your mouth and feel that a higher concentration is more salty, you may be suffering from physical fatigue.

[0063] Test example 3 (i) The subjects alternate between holding two different concentrations of citric acid in their mouths, one at about 0.00375% and the other at about 0.03%, and judge which has the preferred taste intensity. (ii) If the subjects judge that the lower concentration, about 0.00375%, is preferred, this indicates that they are suspected of having a tendency toward depression.

[0064] In addition, although an aqueous solution is used in the taste test methods exemplified as Test Examples 1 to 3, for example, a tablet that dissolves easily in the oral cavity and is flavored can be used in the taste test methods instead of an aqueous solution. According to Test Examples 1 to 3, it is possible to estimate and detect the mental and physical disorders of a subject by simply using an aqueous solution or such a tablet. The exemplified aqueous solutions and tablets are easy to use and have low introduction costs. [Explanation of symbols]

[0065] 10 Data Processing Section 11 Sensory evaluation value acquisition unit 13 Health evaluation value acquisition unit 14 Estimation Model Creation Department 15 Health Status Estimation Department 20 Auxiliary storage 21 Sensory evaluation data 23 Health evaluation data 24 Estimation model 25 Health Status Data 29 Health Status Estimation Program 31 Input section 32 Display section 33 Communication interface section (communication I / F section) 37 Server equipment (cloud server) 39 Network 71-75 Input dialog box for taste testing 81~83 Input dialog box 84 Output Dialog Box 97 Server 98 Subjects 99 Food and drink 100 Health condition estimation device 198 Taste Test Evaluators 199 Food and drink for taste testing

Claims

1. A health condition estimation method executed by an apparatus having a processor, comprising: A step of obtaining a sensory evaluation value regarding taste by a subject; estimating a health condition of the subject based on an estimation model including a correlation between a health evaluation value and a sensory evaluation value related to taste in a human population and the sensory evaluation value; Including, The health condition estimation method, wherein the health condition is a health condition evaluated by a respective questionnaire method, and includes at least one selected from the group consisting of insomnia and mental and physical disorders.

2. The method of claim 1, wherein the sensory evaluation value related to taste includes a sensory evaluation value related to at least one selected from the group consisting of taste intensity, taste preference, taste aftertaste, taste detection ability, taste recognition ability, and taste evaluation.

3. The method of claim 1 , wherein the health condition further comprises fatigue.

4. The method of claim 1 , wherein the prediction model includes an association between fatigue scores or insomnia scores and sensory evaluation values ​​related to taste in a human population.

5. The estimation model is A first correlation representing a correlation between an insomnia score and a sensory evaluation value related to a taste aftertaste; A second correlation representing the correlation between the fatigue score and the sensory evaluation scores related to taste detection ability and taste preference; A third correlation represents the correlation between fatigue score and taste intensity; and The method of claim 4 , comprising at least one of:

6. The method of claim 1 , wherein the estimation model includes an association between a score of a mental and physical disorder and a sensory evaluation value related to taste in a human population.

7. The estimation model is A fourth correlation representing a correlation between the score of the mental and physical discomfort state and the sensory evaluation value regarding taste preference; A fifth correlation representing a correlation between the score on the physical symptoms of mental and physical disorders and the sensory evaluation value on the preference for sourness; A sixth correlation representing a correlation between the scores related to anxiety and insomnia, which are mental and physical disorders, and the sensory evaluation value related to taste preference; The method of claim 6, comprising at least one of:

8. The estimation model includes a plurality of associations between health evaluation values ​​and sensory evaluation values ​​related to taste in a human population, The method of claim 1 , wherein the step of estimating a health condition further comprises estimating a degree of the health condition.

9. A sensory evaluation value acquisition means for acquiring a sensory evaluation value regarding taste by a subject; a health condition estimation means for estimating a health condition of the subject based on an estimation model including a correlation between a health evaluation value and a sensory evaluation value related to taste in a human population and the sensory evaluation value; Equipped with The health condition estimation device, wherein the health condition is a health condition evaluated by a respective questionnaire method, and includes at least one selected from the group consisting of insomnia and mental and physical discomfort.

10. A program for causing a computer to execute each step of the method according to any one of claims 1 to 8.

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