Risk evaluation device, risk evaluation program, and risk evaluation method

Abstract

Provided is a risk evaluation device capable of easily, quickly, and accurately evaluating the risk of locomotive syndrome.　A risk evaluation device 10 according to a first embodiment of the present invention comprises: an acquisition unit 101 that acquires first information correlated with the amount of fat of a body and second information correlated with the amount of muscle thereof; and a determination unit 102 that determines the risk of locomotive syndrome on the basis of at least the first information and the second information. According to the risk evaluation device 10, it is possible to provide a risk evaluation device capable of easily, quickly, and accurately evaluating the risk of locomotive syndrome.

Description

Risk assessment device, risk assessment program, and risk assessment method

[0001] The present invention relates to a risk assessment device, a risk assessment program, and a risk assessment method for assessing the risk of locomotive syndrome.

[0002] In recent years, with the trend toward a super-aging society, the issue of locomotive syndrome has come into sharp focus. Locomotive syndrome (abbreviated as locomotive syndrome) is defined by the Japanese Orthopaedic Association as "a condition in which mobility is impaired due to disorders of the musculoskeletal system." As locomotive syndrome progresses, there is a risk that a person may become dependent on nursing care in the future, so assessing the risk of locomotive syndrome is extremely important.

[0003] Conventionally, a technique for assessing the risk of locomotive syndrome using a plurality of wearable sensors is known (see, for example, Patent Document 1).

[0004] Japanese Patent Application Laid-Open No. 2018-114021

[0005] However, conventional techniques require the subject to wear multiple sensors and walk, leaving room for improvement in terms of simply, quickly, and accurately assessing the risk of locomotive syndrome.

[0006] Therefore, a main object of the present invention is to provide a risk assessment device that can assess the risk of locomotive syndrome simply, quickly, and accurately.

[0007] The present invention provides a risk assessment device comprising: an acquisition unit that acquires first information correlating with a body fat amount and second information correlating with a muscle amount; and a determination unit that determines a risk of locomotive syndrome based on at least the first information and the second information. The fat may be visceral fat, and the muscle may be the iliopsoas muscle. The acquisition unit may acquire the first information and the second information from body image information in which an internal structure of the body is captured. The body image information is a two-dimensional image captured at a predetermined position of the body, and the acquisition unit may calculate an area X of the visceral fat as the first information from the two-dimensional image and an area Y of the iliopsoas muscle as the second information from the two-dimensional image, and the determination unit may make the determination based on the area X and the area Y. The determination unit may make the determination using an area ratio between the area X and the area Y. The predetermined position may be the navel of the body. The two-dimensional image may be a body tomography image obtained by CT (Computed Tomography). The determination unit may classify the risk into multiple categories using multiple category indices based on the area ratio and the degree of obesity of the body and determine the risk. The multiple category indices may include the following four category indices: (1) Low obesity level, X / Y low; (2) High obesity level, X / Y low; (3) Low obesity level, X / Y high; and (4) High obesity level, X / Y high. The risk assessment device may further include a memory unit that stores improvement method information for the risk that matches each of the multiple categories, and output the improvement method information that matches the category classified by the determination unit. The acquisition unit may acquire the first information and the second information from measurement results of the body using a body composition meter. The first information may include body fat percentage. The second information may include leg muscle mass and / or SMI. The acquisition unit may receive the first information and the second information, or information including the first information and the second information, via a network. The determination unit may transmit a result of the risk determination via a network.The determination unit may determine the risk of lifestyle-related diseases in addition to determining the risk of locomotive syndrome. The present invention also provides a risk assessment program that causes a computer to execute the steps of: acquiring first information correlated with the amount of visceral fat in the body and second information correlated with the amount of iliopsoas muscle; and determining the risk of locomotive syndrome based on at least the first information and the second information. The present invention also provides a risk assessment method that includes the steps of: acquiring first information correlated with the amount of visceral fat in the body and the second information correlated with the amount of iliopsoas muscle; and determining the risk of locomotive syndrome based on at least the first information and the second information.

[0008] 1 is a diagram for explaining the correlation between the results of the locomotive syndrome test and the parameters of the subject. FIG. 2 is a diagram for explaining the correlation between the results of the standing up test and the parameters of the subject. FIG. 3 is a diagram for explaining the correlation between the results of the 2-step test and the parameters of the subject. FIG. 4 is a diagram for explaining the correlation between the results of the Locomotive Syndrome 25 and the parameters of the subject. FIG. 5 is a diagram showing the degree of correlation between the results of the locomotive syndrome test and the parameters of the subject. FIG. 6 is a diagram showing the statistical distribution of visceral fat / iliopsoas muscle. FIG. 7 is a diagram for explaining the correlation between the results of the locomotive syndrome test, actual age, and visceral fat / iliopsoas muscle. FIG. 8 is a diagram for explaining the correlation between the results of the standing up test, actual age, and visceral fat / iliopsoas muscle. FIG. 9 is a diagram for explaining the correlation between the results of the 2-step test, actual age, and visceral fat / iliopsoas muscle. FIG. 10 is a diagram for explaining the correlation between the results of the Locomotive Syndrome 25, actual age, and visceral fat / iliopsoas muscle. FIG. 11 is a diagram for explaining an example of a locomotive syndrome category classification index and category classification. FIG. 12 is a diagram showing the proportion of people with impaired glucose tolerance by age group when classified by obesity level and locomotive syndrome level (qualitative). 1 is a diagram showing the proportion of people with impaired glucose tolerance by age group when classified by obesity level and visceral fat / iliopsoas muscle (quantitative). FIG. 1 is a block diagram showing the configuration of a risk assessment device according to a first embodiment of the present invention. FIG. 2 is a diagram showing an example of body image information. FIG. 3 is a skeletal muscle diagram illustrating an example of a body part including the psoas major muscle. FIG. 4 is a flowchart for explaining the operation of a risk assessment device according to a first embodiment of the present invention. FIG. 5 is a block diagram showing the configuration of a risk assessment device according to a third embodiment of the present invention. FIG. 6 is a flowchart for explaining the operation of a risk assessment device according to a third embodiment of the present invention. FIG. 7 is a block diagram showing the configuration of a risk assessment device according to a fourth embodiment of the present invention. FIG. 8 is a block diagram showing the configuration of a risk assessment device according to a fifth embodiment of the present invention. FIG. 9 is a diagram for explaining other examples of locomotive category classification indices and category classifications. FIG. 10 is a diagram showing the relationship between locomotive 25 and the measurement results of a body composition monitor. FIG. 11 is a diagram showing the relationship between a stand-up test and the measurement results of a body composition monitor. FIG. 12 is a diagram showing the relationship between a 2-step test and the measurement results of a body composition monitor.

[0009] Preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Note that in this specification and the drawings, components having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted. The embodiments described below are representative embodiments of the present invention, and should not be construed as narrowing the scope of the present invention. Even when the present specification describes that the risk assessment device, risk assessment program, and risk assessment method according to the present invention each achieve multiple effects, it is sufficient that the risk assessment device, risk assessment program, and risk assessment method according to the present invention each achieve at least one effect. The effects described in this specification are merely exemplary and are not limiting, and other effects may also be achieved.

[0010] The description will be given in the following order: 0. Overview of the present invention 1. Risk assessment device according to a first embodiment of the present invention 2. Risk assessment device according to a second embodiment of the present invention 3. Risk assessment device according to a third embodiment of the present invention 4. Risk assessment device according to a fourth embodiment of the present invention 5. Risk assessment device according to a fifth embodiment of the present invention 6. Modifications of the present invention

[0011] <0. Overview of the Invention> Locomotive syndrome (LOCOMO) is a condition in which mobility is impaired due to disorders of the musculoskeletal system, which includes bones, joints, muscles, and nerves, and is one of the causes of requiring nursing care. It has also been noted that LOCOMO is associated with lifestyle-related diseases such as high blood pressure and diabetes. Therefore, preventing LOCOMO and preventing its progression, or preferably improving it, is crucial for extending healthy lifespan (the period during which one can live a healthy daily life). The LOCOMO Risk Test, recommended by the Japanese Orthopaedic Association, is a known means of assessing LOCOMO risk (the risk associated with LOCOMO ranging from a physical state without LOCOMO to a physical state with LOCOMO that is a stage prior to requiring nursing care; hereinafter, also referred to as "LOCOMO risk"). Locomotive syndrome tests include the Standing Up Test, the Two-Step Test, and the Locomotive 25 Test. Of these, the Standing Up Test and the Two-Step Test require the subject to move their body, making them more objective (reliable) than the Locomotive 25 Test, in which the subject answers 25 questions about musculoskeletal disorders. However, because physical exercise tests require the subject to actually move their body in the presence of an examiner (scorer) at a testing facility, there is room for improvement in terms of simplicity and speed. Also, because the tests can be affected by the subject's physical condition at the time of the test, there is room for improvement in terms of accuracy.

[0012] Therefore, the inventors have conducted extensive research into the relationship between locomotive syndrome (LOCOMO) and subject parameters, and have discovered that LOCOMO is closely related to certain subject parameters (specifically, fat mass (e.g., visceral fat mass) and muscle mass (e.g., iliopsoas muscle mass)). This is a novel finding made by the inventors.

[0013] The inventors have developed the risk assessment device according to the present invention as a technical concept that embodies this new finding. The risk assessment device according to the present invention makes it possible to assess locomotive syndrome simply, quickly, and accurately.

[0014] The inventors have further developed a risk assessment program and a risk assessment method according to the present invention as a technical concept that embodies the above-mentioned new findings.

[0015] The present invention is an invention that can easily, quickly, and accurately evaluate locomotive syndrome risk, and is a groundbreaking invention that may coexist with or even replace locomotive syndrome tests in the future. The inventors have high hopes that the widespread use of this invention in medical institutions and other settings will promote the early detection and improvement of locomotive syndrome risk, and ultimately provide an opportunity to extend healthy life expectancy in a super-aging society.

[0016] The inventors thoroughly analyzed which parameters of a subject are involved in the locomotive syndrome test results and to what extent, and then fed back the results of that analysis to complete the present invention. First, the results of the analysis of the relationship between the locomotive syndrome test results and the subject's parameters will be explained.

[0017] (Relationship between Locomotor Stress Test Results and Subject Parameters) Figure 1 shows the relationship between the results (locomotor stress) of the general locomotor stress test and the subject's parameters. The locomotor stress test can determine locomotor stress risk: no locomotor stress, locomotor stress level 1 (locomotor stress level 1), locomotor stress level 2 (locomotor stress level 2), or locomotor stress level 3 (locomotor stress level 3), indicating that locomotor stress has progressed to the point where it interferes with social participation. From data compiled from correlations between the results of locomotor stress tests administered to many subjects in the past and the subject's parameters, as shown in Figure 1 , it has been found that the results of the general locomotor stress test are related to the subject's actual age, subcutaneous fat area, visceral fat area, PMI value, gender, and average CT value (locomotor stress factors). In Figure 1 , the larger the logarithmic value of a parameter and the smaller the P value of a parameter, the stronger the correlation with the locomotor stress test results. From Figure 1, it can be seen that the results of the Locomotion Test (general) are significantly affected by the subject's actual age, subcutaneous fat area, visceral fat area, and PMI value. Here, the PMI value is the area of ​​the iliopsoas muscle in a tomographic image (e.g., a CT image) of the subject's navel (near the third lumbar vertebra) divided by the square of the subject's height (same below). The subcutaneous fat area is the area of ​​subcutaneous fat in the tomographic image (same below). The visceral fat area is the area of ​​visceral fat in the tomographic image (same below). The average CT value is the average value of the visceral fat area of ​​all subjects (same below).

[0018] (Relationship Between Standing Up Test Results and Subject Parameters) Figure 2 is a diagram for explaining the relationship between the results of the standing up test and the parameters of the subject. The standing up test is a test that measures how high a platform a subject can stand up from using one or both legs. In Figure 2, the larger the logarithmic value of a parameter and the smaller the P value of a parameter, the higher the relationship with the results of the standing up test. Figure 2 shows that the results of the standing up test are particularly significantly affected by the subject's actual age, PMI value, subcutaneous fat area, and visceral fat area.

[0019] (Relationship between the results of the two-step test and the parameters of the subject) Figure 3 is a diagram for explaining the relationship between the results of the two-step test and the parameters of the subject. The standing test is a test that measures the distance walked by two steps with as wide a stride as possible. In Figure 3, the larger the logarithmic value of a parameter and the smaller the P value of a parameter, the higher the relationship with the results of the two-step test. Figure 3 shows that the results of the two-step test are particularly significantly affected by the actual age, sex, and visceral fat area of ​​the subject.

[0020] (Relationship between Locomo 25 results and subject parameters) Figure 4 is a diagram for explaining the relationship between Locomo 25 results and subject parameters. As mentioned above, Locomo 25 is a test in which subjects answer 25 questions regarding musculoskeletal disorders. In Figure 4, the larger the logarithmic value of a parameter and the smaller the P value, the stronger the relationship with Locomo 25 results. Figure 4 shows that the results of Locomo 25 are particularly significantly affected by the subject's actual age, subcutaneous fat area, and visceral fat area.

[0021] (Summary: Strength of correlation between locomotive syndrome test results and subject's parameters) Figure 5 is a diagram showing the strength of correlation between locomotive syndrome test results and subject's parameters. In Figure 5, ◯ indicates high correlation, △ indicates moderately high correlation, × indicates low correlation, and ▲ indicates moderately low correlation. Figure 5 shows that the results of the locomotive syndrome test (locomotive syndrome) are significantly influenced by the subject's age, as well as visceral fat and PMI value in particular. Therefore, locomotive syndrome can also be defined as a decline in mobility caused by a combination of aging, metabolic syndrome (dependent on visceral fat mass), and sarcopenia (dependent on PMI value, which depends on iliopsoas muscle mass).

[0022] (Relationship between visceral fat, iliopsoas muscle, and locomotor syndrome) High visceral fat leads to increased weight, making it difficult to carry one's body farther and resulting in smaller steps. This leads to lower scores on the two-step test (increased locomotor syndrome). Thinner iliopsoas muscle reduces the ability to maintain posture. This leads to lower scores on the stand-up test (increased locomotor syndrome). Therefore, it can be said that visceral fat mass is a factor that increases locomotor syndrome risk, while iliopsoas muscle mass is a factor that decreases locomotor syndrome risk. Therefore, the ratio of visceral fat mass to iliopsoas muscle mass makes it possible to quantitatively assess locomotor syndrome risk. For example, the greater the visceral fat mass / iliopsoas muscle mass, the higher the locomotor syndrome risk, and the lower the ratio, the lower the risk. For example, the greater the iliopsoas muscle mass / visceral fat mass, the lower the locomotor syndrome risk, and the lower the risk, the higher the locomotor syndrome risk.

[0023] (Visceral fat / iliopsoas muscle) Figure 6 is a diagram showing the statistical distribution of visceral fat / iliopsoas muscle (more specifically, visceral fat mass / iliopsoas muscle mass, the same applies below). From the left and right diagrams in Figure 6, it can be seen that visceral fat / iliopsoas muscle (logarithmic values) exhibits a normal distribution, there is almost no difference between men and women, and it increases with age. Note that visceral fat / iliopsoas muscle fluctuates not only with age but also with lifestyle habits such as eating habits and exercise habits.

[0024] (Relationship between locomotive syndrome test results and visceral fat / iliopsoas muscle) Figure 7 is a diagram for explaining the relationship between locomotive syndrome test results (general), actual age, and visceral fat / iliopsoas muscle. In Figure 7, the larger the logarithmic value of a parameter and the smaller the P value of a parameter, the stronger the relationship with locomotive syndrome test results. Figure 7 shows that the relationship between visceral fat / iliopsoas muscle and locomotive syndrome test results (general) is as strong as that with actual age.

[0025] (Relationship between Standing Up Test Results and Visceral Fat / Iliopsoas Muscle) Figure 8 is a diagram for explaining the relationship between Standing Up Test results, actual age, and visceral fat / iliopsoas muscle. In Figure 8, the larger the logarithmic value of a parameter and the smaller the P value of a parameter, the stronger the relationship with the Standing Up Test results. Figure 8 shows that the relationship between visceral fat / iliopsoas muscle and Standing Up Test results is quite high, although not as high as that with actual age.

[0026] (Relationship between the results of the 2-step test and visceral fat / iliopsoas muscle) Figure 9 is a diagram for explaining the relationship between the results of the 2-step test, actual age, and visceral fat / iliopsoas muscle. In Figure 9, the larger the logarithmic value of a parameter and the smaller the P value of a parameter, the stronger the relationship with the results of the 2-step test. Figure 9 shows that the relationship between the results of the 2-step test and visceral fat / iliopsoas muscle is much stronger than that of actual age.

[0027] (Relationship between Locomotor 25 results and visceral fat / iliopsoas muscle) Figure 10 is a diagram for explaining the relationship between Locomotor 25 results, actual age, and visceral fat / iliopsoas muscle. In Figure 10, the larger the logarithmic value of a parameter and the smaller the P value of a parameter, the stronger the relationship with Locomotor 25 results. Figure 10 shows that the relationship between visceral fat / iliopsoas muscle and Locomotor 25 results is stronger than that with actual age.

[0028] As explained above, it can be seen that the correlation between visceral fat / iliopsoas muscle and the results of the locomotive syndrome test is as high as that between visceral fat and iliopsoas muscle and the results of the locomotive syndrome test.

[0029] (Locomotive syndrome categorization) Fig. 11 is a diagram showing locomotor syndrome categorization indices and categorization examples. As shown in the left diagram of Fig. 11, locomotor syndrome categories can be determined by the obesity level and the ratio of visceral fat area to iliopsoas muscle area (X / Y), and a categorization indices for classifying locomotor syndrome risk can be obtained, for example, as follows. The obesity level is defined by, for example, the BMI (Body Mass Index) value. (1) Low obesity level, X / Y low (2) High obesity level, X / Y low (3) Low obesity level, X / Y high (4) High obesity level, X / Y high

[0030] Each of the four quadrants in the right diagram of Figure 11 corresponds one-to-one to each of the four quadrants in the left diagram of Figure 11. As an example, locomotor risk is classified as non-locomotor (not applicable) by the above category indicator (1), as metabolic locomotor by the above category indicator (2), as sarcopenia locomotor by the above category indicator (3), and as M / S (metabolic syndrome / sarcopenia) combined locomotor by the above category indicator (4). Metabolic syndrome is characterized by failure of the two-step test, increased visceral fat, and overnutrition. Sarcopenia locomotor is characterized by failure of the standing test, muscle wasting, malnutrition, and low activity. M / S combined locomotor is characterized by failure of the standing test, failure of the two-step test, muscle wasting, increased visceral fat, high nutrition, and low activity.

[0031] (Relationship between Obesity Level, Locomotor Symptom Level, and Impaired Glucose Tolerance) Fig. 12 is a diagram illustrating the proportion of people with impaired glucose tolerance by age group when classified by obesity level and locomotor symptom level (qualitative). Obesity level is represented by BMI (Body Mass Index).

[0032] As can be seen from the left graph of Figure 12, in the group in their 40s with visceral fat below the standard value, the incidence of impaired glucose tolerance (DM) is 0% for those without locomotor disorders and about 5% for those with locomotor disorders. As can be seen from the left graph of Figure 12, in the obese group in their 40s with visceral fat above the standard value, the incidence of impaired glucose tolerance (DM) is about 10% regardless of whether or not they have locomotor disorders.

[0033] As can be seen from the right graph of Figure 12, in the group in their 50s with visceral fat below the standard value, the incidence of impaired glucose tolerance (DM) was about 5% for those without locomotor symptoms and about 10% for those with locomotor symptoms. As can be seen from the right graph of Figure 12, in the group in their 50s with obesity and visceral fat above the standard value, the incidence of impaired glucose tolerance (DM) was about 15% for those without locomotor symptoms and about 20% for those with locomotor symptoms.

[0034] (Relationship between obesity level, visceral fat / iliopsoas muscle, and impaired glucose tolerance) Figure 13 is a diagram illustrating the proportion of people with impaired glucose tolerance by age group when classified by obesity level and visceral fat / iliopsoas muscle (quantitative).

[0035] As can be seen from the left graph of Figure 13, in the group in their 40s with visceral fat below the standard value, the incidence of impaired glucose tolerance (DM) was 0% when the visceral fat / iliopsoas muscle ratio was low or high, but was about 2% when it was normal. As can be seen from the left graph of Figure 13, in the obese group in their 40s with visceral fat above the standard value, the incidence of impaired glucose tolerance (DM) was about 5% when the visceral fat / iliopsoas muscle ratio was normal, and about 20% when it was high.

[0036] 13, in the group in their 50s with visceral fat below the standard value, the incidence of impaired glucose tolerance (DM) was 0% when the visceral fat / iliopsoas ratio was low, about 5% when it was normal, and about 15% when it was high. From the right graph of FIG. 13, in the obese group in their 50s with visceral fat above the standard value, the incidence of impaired glucose tolerance (DM) was about 10% when the visceral fat / iliopsoas ratio was normal, and about 30% when it was high.

[0037] Comparing the examples of Figures 12 and 13, it can be seen that the combination of obesity level and visceral fat / iliopsoas muscle has the same or better discriminative ability for determining whether or not impaired glucose tolerance is present, compared to the combination of obesity level and sarcopenia level (results of the sarcopenia level test).

[0038] Hereinafter, the risk assessment device according to the present invention will be described in detail with reference to several embodiments.

[0039] <1. Risk assessment device according to a first embodiment of the present invention> (Configuration of the risk assessment device) The configuration of the risk assessment device 10 according to the first embodiment of the present invention will be described below. Fig. 14 is a block diagram showing the configuration of the risk assessment device 10 according to the first embodiment of the present invention. Fig. 15 is a diagram showing an example of body image information. Fig. 16 is a musculoskeletal diagram showing an example of a body part including the psoas major muscle.

[0040] The risk assessment device 10 is installed in a medical institution, for example.

[0041] 14, the risk assessment device 10 includes an acquisition unit 101 that acquires first information correlated with the amount of visceral fat in the body and second information correlated with the amount of the iliopsoas muscle, and a determination unit 102 that determines the risk of locomotive syndrome (locomotor risk) based on at least the first information and the second information. The acquisition unit 101 is realized by hardware such as a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a chipset, etc. The determination unit 102 is realized by hardware such as a comparator, a chipset, etc.

[0042] As an example, the acquisition unit 101 acquires the first information and the second information from body image information in which the internal structure of the body is captured.

[0043] The body image information is, for example, a two-dimensional image (more specifically, two-dimensional image data) captured at a predetermined position on the body (see FIG. 15 ). The predetermined position is, for example, the position of the navel. More specifically, the predetermined position is, for example, a position within a range from the upper end of the second lumbar vertebra to the lower end of the fourth lumbar vertebra, and preferably a position within a range from the upper end to the lower end of the third lumbar vertebra, as shown in FIG. 16 . As an example, the two-dimensional image shown in FIG. 15 shows the psoas major muscle, which is part of the iliopsoas muscle, and the third lumbar vertebra.

[0044] The two-dimensional image is, for example, a body tomographic image (CT image) obtained by CT (Computed Tomography). Note that the two-dimensional image may also be a body tomographic image (MRI image) obtained by MRI (Magnetic Resonance Imaging).

[0045] As an example, the acquisition unit 101 calculates the area X of visceral fat as the first information from the two-dimensional image, and calculates the area Y of the iliopsoas muscle as the second information from the two-dimensional image. The acquisition unit 101 further calculates the ratio of the areas X and Y. As the area ratio, X / Y or Y / X may be calculated. As an example, the acquisition unit 101 is configured to include an image processing unit and a calculation unit. In the acquisition unit 101, the image processing unit identifies the ranges of the visceral fat and the iliopsoas muscle in the two-dimensional image by image processing such as edge extraction and contour extraction, and the calculation unit calculates the area X of the visceral fat and the area Y of the iliopsoas muscle.

[0046] The determination unit 102 determines the risk of locomotion based on the area X and the area Y. Specifically, the determination unit 102 determines the risk of locomotion using the area ratio between the area X and the area Y, and outputs the determination result.

[0047] Specifically, as an example, determination unit 102 compares X / Y with a predetermined threshold value using a comparator, and determines that there is no locomotive syndrome if X / Y is equal to or less than the predetermined threshold value (if the output of the comparator is Low), and determines that there is locomotive syndrome if X / Y exceeds the threshold value (if the output of the comparator is High).In other words, this determination is a simple determination of the presence or absence of locomotive syndrome.

[0048] (Operation of Risk Assessment Device) The operation of the risk assessment device 10 according to the first embodiment of the present invention will now be described. Fig. 17 is a flowchart for explaining the operation of the risk assessment device 10 according to the first embodiment of the present invention. An example of a risk assessment method according to the present invention can be implemented using the risk assessment device 10. The flowchart in Fig. 17 corresponds to a risk assessment program, which is a program executed by the CPU (computer) of the risk assessment device 10.

[0049] In the first step S1, the acquisition unit 101 calculates the area X of visceral fat and the area Y of the iliopsoas muscle from a body tomographic image. The body tomographic image is, for example, a CT image of the subject's navel provided directly by the subject or via a medical institution. Specifically, the acquisition unit 101 extracts the visceral fat and the iliopsoas muscle from the CT image using an image processing unit, and calculates the area X of the visceral fat and the area Y of the iliopsoas muscle using a calculation unit.

[0050] In the next step S2, the acquisition unit 101 calculates the ratio of the areas X and Y. Specifically, the acquisition unit 101 calculates, for example, X / Y using the calculation unit.

[0051] In the final step S3, the determination unit 102 determines the risk of locomotor dysfunction based on the ratio of areas X and Y (e.g., X / Y) and outputs the determination result. Specifically, the determination unit 102 compares X / Y with a predetermined threshold using a comparator, and outputs a determination result of no locomotor dysfunction if X / Y is equal to or less than the threshold, and outputs a determination result of the presence of locomotor dysfunction if X / Y exceeds the threshold. Methods for outputting the locomotor risk determination result by the determination unit 102 include monitor output, audio output, printout, etc.

[0052] (Effects of risk assessment device, risk assessment algorithm, and risk assessment method) The risk assessment device 10 according to the first embodiment described above comprises an acquisition unit 101 that acquires first information that correlates with the amount of visceral fat in the body and second information that correlates with the amount of iliopsoas muscle, and a judgment unit 102 that judges the risk of locomotive syndrome (locomotor risk) based on at least the first information and the second information.

[0053] The risk assessment device 10 can determine the risk of sarcopenia based on the first information and the second information, and therefore, compared to sarcopenia tests or conventional technologies that use multiple wearable sensors (e.g., Patent Document 1), it is possible to accurately assess the risk of sarcopenia of a subject without having the subject actually exercise at an examination facility.

[0054] As a result, the risk assessment device 10 can provide a risk assessment device that can assess locomotive syndrome risk simply, quickly, and accurately.

[0055] Furthermore, for example, introducing locomotor risk assessment using the risk assessment device 10 into the examination items of a medical checkup is extremely beneficial in that subjects of the medical checkup can also receive locomotor risk assessment results. In this case, for example, by using CT images taken in a CT examination during a medical checkup for locomotor risk assessment, it is efficient and possible to provide the subject with locomotor risk assessment results (assessment results that are meaningful to the subject) from the latest CT images. Furthermore, for example, during a medical checkup, locomotor risk assessment results can be obtained easily and quickly, which is extremely beneficial in that it eliminates the need for waiting times and examination times to obtain a locomotor risk assessment.

[0056] The risk assessment device 10 according to the first embodiment of the present invention operates by a risk assessment program that causes a computer to execute the steps of acquiring first information that correlates with the amount of visceral fat in the body and second information that correlates with the amount of iliopsoas muscle, and determining the risk of locomotive syndrome based on at least the first information and the second information.

[0057] According to this risk assessment program, the risk assessment device 10 can assess locomotive syndrome risk simply, quickly, and accurately.

[0058] A risk assessment method is implemented using a risk assessment device 10 according to a first embodiment of the present invention, which includes the steps of acquiring first information that correlates with the amount of visceral fat in the body and second information that correlates with the amount of iliopsoas muscle, and determining the risk of locomotive syndrome based on at least the first information and the second information.

[0059] According to this risk assessment method, it is possible to assess locomotive syndrome risk simply, quickly, and accurately.

[0060] 2. Risk assessment device according to a second embodiment of the present invention> A risk assessment device according to a second embodiment of the present invention will now be described with reference to Fig. 18. Fig. 18 is a block diagram showing the configuration of a risk assessment device 20 according to the second embodiment of the present invention.

[0061] The risk assessment device 20 has the same configuration as the risk assessment device 10 according to the first embodiment, except that it further includes a memory unit 103 .

[0062] The storage unit 103 includes at least one storage medium such as a ROM (Read Only Memory), a RAM (Radio Access Memory), a flash memory, or a hard disk.

[0063] In the risk assessment device 20, the determination unit 102 classifies and determines the locomotor risk into a plurality of risk categories using a plurality of category indices based on the area ratio of X and Y and the body's obesity level (e.g., BMI value) (see FIG. 11). The plurality of category indices include, for example, the following four category indices (1) to (4): (1) Low obesity level, X / Y low (2) High obesity level, X / Y low (3) Low obesity level, X / Y high (4) High obesity level, X / Y high

[0064] Here, the determination unit 102 has a first comparator that compares the obesity level with a first threshold value Th1 and a second comparator that compares X / Y with a second threshold value Th2. The first comparator outputs Low (low obesity level) when the obesity level is equal to or less than the first threshold value Th1, and outputs High (high obesity level) when the obesity level exceeds the first threshold value Th1. The second comparator outputs Low (X / Y low) when X / Y is equal to or less than the second threshold value Th2, and outputs High (X / Y high) when the X / Y exceeds the second threshold value Th2. The determination unit 102 reads out a risk category corresponding to the category index determined by the outputs of the first and second comparators from a first memory table 103a in the memory unit 103.

[0065] The obesity level (for example, BMI value) is preferably the most recent value provided by the subject directly or via a medical institution.

[0066] A plurality of (e.g., four) risk categories (e.g., first to fourth risk categories 103a1 to 103a4) are stored (stored) in association with a plurality of (e.g., four) category indices in the first storage table 103a of the storage unit 103. As an example, the first to fourth risk categories 103a1 to 103a4 are four risk categories (non-locomotor disorder, metabolic syndrome-type locomotor disorder, sarcopenia-type locomotor disorder, and M / S (metabolic syndrome / sarcopenia) combined locomotor disorder; see the right diagram in Figure 11) that individually correspond to the above-mentioned four category indices (1) to (4), respectively.

[0067] In the risk assessment device 20, as an example, the determination unit 102 classifies and determines the locomotive syndrome risk of the subject into first to fourth risk categories 103a1 to 103a4 based on the above four category indicators (1) to (4).

[0068] For example, when the obesity level is equal to or less than the first threshold value Th1 and X / Y is equal to or less than the second threshold value Th2 (corresponding to category index (1)), the determination unit 102 reads out the first risk category 103a1 (non-locomotor disorder) corresponding to the category index (1) from the first storage table 103a, and outputs it as the locomotor risk determination result.

[0069] For example, when the obesity level is greater than the first threshold value Th1 and X / Y is equal to or less than the second threshold value Th2 (corresponding to category index (2)), the determination unit 102 reads out the second risk category 103a2 (metabolic syndrome type locomotive syndrome) corresponding to the category index (2) from the first storage table 103a, and outputs it as the locomotive syndrome risk determination result.

[0070] For example, when the obesity level is equal to or less than the first threshold value Th1 and X / Y is greater than the second threshold value Th2 (corresponding to category index (3)), the determination unit 102 reads out the third risk category 103a3 (sarcopenia-type locomotive syndrome) corresponding to the category index (3) from the first storage table 103a, and outputs it as the locomotive syndrome risk determination result.

[0071] For example, when the obesity level is greater than the first threshold value Th1 and X / Y is greater than the second threshold value Th2 (corresponding to category index (4)), the determination unit 102 reads out the fourth risk category 103a4 (M / S combined locomotor syndrome) corresponding to category index (4) from the first storage table 103a, and outputs it as the locomotor risk determination result.

[0072] According to the risk assessment device 20 described above, it is possible to obtain the same effects as the risk assessment device 10 according to the first embodiment, and also to provide the subject with more detailed locomotive risk information.

[0073] Furthermore, a risk assessment program for operating the risk assessment device 20 and a risk assessment method implemented using the risk assessment device 20 can also be provided.

[0074] 3. Risk Assessment Device According to a Third Embodiment of the Present Invention> (Configuration of the Risk Assessment Device) A risk assessment device 30 according to a third embodiment of the present invention will be described below with reference to Fig. 19. Fig. 19 is a block diagram showing the configuration of the risk assessment device 30 according to the third embodiment of the present invention.

[0075] The risk assessment device 30 has the same configuration as the risk assessment device 20 of the second embodiment, except that it further has an output unit 104 and the memory unit 103 has a second memory table 103b in addition to the first memory table 103a.

[0076] The first storage table 103a and the second storage table 103b may be stored in different storage areas of the same storage medium, or may be stored in different storage media.

[0077] The second storage table 103b stores (memorizes) improvement method information for locomotor risk that is compatible with each of a plurality of (e.g., four) risk categories. Specifically, the second storage table 103b stores first improvement method information 103b1 that is compatible with the first risk category 103a1, second improvement method information 103b2 that is compatible with the second risk category 103a2, third improvement method information 103b3 that is compatible with the third risk category 103a3, and fourth improvement method information 103b4 that is compatible with the fourth risk category 103a4.

[0078] A specific example of the first improvement method information 103b1 may be a recommendation that although a person does not currently suffer from locomotive syndrome, it would be preferable for the person to review their eating and exercise habits in order to avoid the risk of developing locomotive syndrome in the future.

[0079] As a specific example of the second improvement method information 103b2, since the patient is currently classified as having metabolic syndrome, possible suggestions for improving eating habits (for example, setting a daily calorie intake limit) and exercise habits (for example, performing aerobic exercise for several tens of minutes to several hours every day or every other day) may be considered.

[0080] As a specific example of the third improvement method information 103b3, since the patient currently has sarcopenia-type locomotive syndrome, specific suggestions for improving eating habits (for example, setting an upper limit on calorie intake) and specific suggestions for improving current exercise habits (for example, performing anaerobic exercise for several tens of minutes to several hours every day or every other day) are conceivable.

[0081] As a specific example of the fourth improvement method information 103b4, since the patient is currently suffering from M / S combined locomotor syndrome, specific suggestions for improving eating habits (for example, setting upper and lower limits for calorie intake) and specific suggestions for improving current exercise habits (for example, performing aerobic exercise and anaerobic exercise for several tens of minutes to several hours every day or every other day) are conceivable.

[0082] The determination unit 102 transmits the locomotive risk determination result (for example, one of the first to fourth risk categories 103a1 to 103a4) to the output unit 104.

[0083] The output unit 104 outputs the risk category classified by the determination unit 102 and improvement method information that matches the risk category. Examples of methods for outputting the risk category and improvement method information by the output unit 104 include monitor output, audio output, and printout. Note that the determination unit 102 may perform at least a part of the functions of the output unit 104.

[0084] (Operation of Risk Assessment Device) The operation of the risk assessment device 30 according to the third embodiment of the present invention will now be described. Fig. 20 is a flowchart for explaining the operation of the risk assessment device 30 according to the third embodiment of the present invention. An example of a risk assessment method according to the present invention can be implemented using the risk assessment device 30. The flowchart in Fig. 20 corresponds to a risk assessment program, which is a program executed by the CPU (computer) of the risk assessment device 30.

[0085] In the first step S11, the acquisition unit 101 calculates the area X of visceral fat and the area Y of the iliopsoas muscle from a body tomographic image. The body tomographic image is, for example, a CT image of the subject's navel provided directly by the subject or via a medical institution. Specifically, the acquisition unit 101 extracts the visceral fat and the iliopsoas muscle from the CT image using an image processing unit, and calculates the area X of the visceral fat and the area Y of the iliopsoas muscle using a calculation unit.

[0086] In the next step S12, the acquisition unit 101 calculates the ratio of the areas X and Y. Specifically, the acquisition unit 101 calculates, for example, X / Y using the calculation unit.

[0087] In the next step S13, the determination unit 102 determines the locomotor risk based on the obesity level and the ratio of areas X and Y. Specifically, the determination unit 102 determines the locomotor risk based on the ratio of areas X and Y (e.g., X / Y). More specifically, the determination unit 102 compares the obesity level with a first threshold value Th1 using a first comparator, and compares X / Y with a second threshold value Th2 using a second comparator. The determination unit 102 then reads out from the first storage table 103 a a risk category corresponding to the category index determined by the comparison results, and transmits the risk category to the output unit 104.

[0088] In the final step S14, the output unit 104 reads out the improvement method information that matches the risk category from the determination unit 102 from the second storage table 103b, and outputs the risk category and the improvement method information.

[0089] The risk assessment device 30 of the third embodiment described above can achieve the same effects as the risk assessment device 20 of the second embodiment, and also provides the subject with information on improvement methods, allowing the subject to take specific measures against sarcopenia risk.

[0090] Furthermore, a risk assessment program for operating the risk assessment device 30 and a risk assessment method implemented using the risk assessment device 30 can also be provided.

[0091] 4. Risk Assessment Device According to a Fourth Embodiment of the Present Invention> A risk assessment device 40 according to a fourth embodiment of the present invention will now be described with reference to Fig. 21. Fig. 21 is a block diagram showing the configuration of the risk assessment device 40 according to the fourth embodiment of the present invention.

[0092] The risk assessment device 40 has the same configuration as the risk assessment device 30 according to the third embodiment, except that it can transmit and receive data to and from an information communication terminal 200 (e.g., a personal computer, a smartphone, etc.) via a network (e.g., the Internet; the same applies below). The risk assessment device 40 has an input / output interface connected to the network.

[0093] The risk assessment device 40 receives, via a network, the first information (e.g., area X) and the second information (e.g., area Y), or information including the first information and the second information (e.g., a body tomographic image), from, for example, an information communication terminal 200 of a subject or a medical institution acting on behalf of the subject. Examples of the information communication terminal 200 include a personal computer and a smartphone.

[0094] In the risk assessment device 40, the output unit 104 transmits the locomotor risk assessment result determined by the assessment unit 102 to, for example, the information communication terminal 200 of the subject or a medical institution via a network (e.g., the Internet).

[0095] According to the risk assessment device 40 described above, the risk assessment device 40 and the information communication terminal 200 can send and receive data via a network, so that the subject can request a locomotor risk assessment and receive the assessment results without going to a medical institution or the like where the risk assessment device 40 is installed. Note that the risk assessment device 40 may be capable of only sending or receiving data via the network with the information communication terminal 200. For example, if only sending is possible, it will only be possible to request a locomotor risk assessment via the network, and if only receiving is possible, it will only be possible to receive the locomotor risk assessment results via the network.

[0096] Furthermore, a risk assessment program for operating the risk assessment device 40 and a risk assessment method implemented using the risk assessment device 40 can also be provided.

[0097] 5. Risk Assessment Device According to a Fifth Embodiment of the Present Invention> A risk assessment device 50 according to a fifth embodiment of the present invention will now be described with reference to Fig. 22. Fig. 22 is a block diagram showing the configuration of the risk assessment device 50 according to the fifth embodiment of the present invention.

[0098] The risk assessment device 50 has the same configuration as the risk assessment device 20 according to the second embodiment, except that the assessment unit 102 assesses the risk of lifestyle-related diseases in addition to assessing the risk of locomotive syndrome. Here, lifestyle-related diseases include diabetes, dyslipidemia, hypertension, cancer, stroke, heart disease, etc.

[0099] In the risk assessment device 50, the judgment unit 102 has a locomotive syndrome risk judgment unit 102a and a lifestyle-related disease risk judgment unit 102b.

[0100] The locomotive risk assessment unit 102a assesses the locomotive risk based on the obesity level and X / Y, in the same manner as the assessment unit 102 of the risk assessment device 20 according to the second embodiment, and outputs one of the first to fourth risk categories 103a1 to 103a4 as the locomotive risk assessment result.

[0101] For example, when the locomotive syndrome risk determination unit 102a outputs the first risk category 103a1 as the locomotive syndrome risk determination result, the lifestyle-related disease risk determination unit 102b outputs a determination result of low lifestyle-related disease risk.

[0102] For example, when the locomotive syndrome risk assessment unit 102a outputs the second risk category 103a2 as the locomotive syndrome risk assessment result, the lifestyle-related disease risk assessment unit 102b outputs a lifestyle-related disease risk assessment result of medium.

[0103] For example, when the locomotive syndrome risk assessment unit 102a outputs the third risk category 103a3 as the locomotive syndrome risk assessment result, the lifestyle-related disease risk assessment unit 102b outputs a lifestyle-related disease risk assessment result of medium.

[0104] For example, when the fourth risk category 103a4 is output as the locomotive syndrome risk determination result from the locomotive syndrome risk determination unit 102a, the lifestyle-related disease risk determination unit 102b outputs a determination result of high lifestyle-related disease risk.

[0105] In this manner, in the risk assessment device 50, the determination unit 102 outputs a risk category as a result of the locomotive syndrome risk determination and a lifestyle-related disease risk determination result corresponding to the risk category.

[0106] Furthermore, a risk assessment program for operating the risk assessment device 50 and a risk assessment method implemented using the risk assessment device 50 can also be provided.

[0107] 6. Modifications of the Present Invention The present invention is not limited to the above-described embodiments and can be modified as appropriate. For example, parts of the configurations of the risk assessment devices according to the above-described embodiments may be combined to the extent that they are not mutually contradictory.

[0108] In each of the above embodiments, first information (e.g., area X) correlating with the amount of visceral fat and second information (e.g., area Y) correlating with the amount of iliopsoas muscle are obtained from body image information (e.g., body tomographic image). However, instead of this, for example, a belt-shaped current-carrying / resistance measuring member may be wrapped around the body at the navel, and electricity may be passed through the body to measure the distribution of electrical resistance values, thereby obtaining the amount of visceral fat and the amount of iliopsoas muscle.

[0109] In the second, third, and fifth embodiments, there are four types of category classification indices and four types of locomotive syndrome category classifications. However, this is not limiting and, for example, as shown in FIG. 23 , nine types each may be used. In FIG. 23 , the left diagram shows locomotive syndrome category classification indices, and the right diagram shows an example of locomotive syndrome category classification. In the example of FIG. 23 , as shown in the left diagram of FIG. 23 , in addition to four types of category classification indices, (1) low obesity, X / Y low, (2) high obesity, X / Y low, (3) low obesity, X / Y high, and (4) high obesity, X / Y high, there are five types of category classification indices, (5) medium obesity, X / Y low, (6) medium obesity, X / Y medium, (7) medium obesity, X / Y high, (8) low obesity, X / Y medium, and (9) high obesity, X / Y medium. (1) Low obesity, X / Y low corresponds to the athletic slim figure shown in the right diagram of FIG. 23 . (2) High obesity and low X / Y correspond to the athletic hypertrophy shown in the right diagram of Figure 23. (3) Low obesity and high X / Y correspond to the sarcopenia type shown in the right diagram of Figure 23. (4) High obesity and high X / Y correspond to the M / S combined type shown in the right diagram of Figure 23. (5) Medium obesity and low X / Y correspond to the athletic standard type shown in the right diagram of Figure 23. (6) Medium obesity and medium X / Y correspond to the standard type shown in the right diagram of Figure 23. (7) Medium obesity and high X / Y correspond to the hidden obesity and metabolic syndrome shown in the right diagram of Figure 23. (8) Low obesity and medium X / Y correspond to the underweight type shown in the right diagram of Figure 23. (9) High obesity and medium X / Y correspond to the obesity and metabolic syndrome shown in the right diagram of Figure 23.

[0110] As explained in the above embodiments, it is possible to determine and evaluate locomotor risk based on the body's visceral fat mass and iliopsoas muscle mass. Therefore, it is expected that this technical concept can be expanded to determine and evaluate locomotor risk based on the body's fat mass and muscle mass.

[0111] For example, it may be possible to determine and evaluate locomotive syndrome risk by measuring the balance between fat and muscle mass using a body composition analyzer.

[0112] Therefore, we conducted a locomotive risk assessment using a body composition scale as follows.

[0113] (Evaluation Subjects) Subjects who underwent a medical checkup within one year and underwent a locomotive syndrome test and measurements using a body composition analyzer (for example, 641 men and women) were used as subjects for the evaluation of locomotive syndrome risk.

[0114] (Body composition monitor) A multi-frequency body composition monitor (MC-780A-N, TANITA) was used as the body composition monitor.

[0115] (Measurement items) Weight, body composition, percent body water, total body water, fat-free mass, estimated bone mass, percent body fat, visceral fat level, muscle mass, limb skeletal muscle mass, right arm muscle mass, left arm muscle mass, right leg muscle mass, left leg muscle mass, total body muscle mass score, left-right hand balance, left-right foot balance, leg score, SMI [limbs skeletal muscle mass (kg) / height] 2 (m 2 )], MM / H^2 [muscle mass (kg) / height 2 (m 2 The measurement items were ASM / BW [limbs skeletal muscle mass (kg) / body weight (kg)], MM / BW [muscle mass (kg) / body weight (kg)], basal metabolic rate, and basal metabolic rate assessment.

[0116] FIG. 24 is a diagram showing the relationship between Locomotor Disorder 25 and the measurement results of a body composition monitor. FIG. 24 shows the logarithmic value and P value for each factor (factor that could be a factor in Locomotor Disorder) obtained from the measurement results of the body composition monitor. In FIG. 24, the larger the logarithmic value and the smaller the P value, the stronger the relationship with Locomotor Disorder 25. FIG. 24 shows that, among the factors obtained from the measurement results of the body composition monitor, the left-right balance of the legs and the muscle mass of the right leg are significantly involved in Locomotor Disorder 25. This suggests that correcting the imbalance in the left-right balance of the legs (more specifically, the balance in muscle mass between the left and right legs) and / or increasing the muscle mass of the right leg can improve the test results of Locomotor Disorder 25.

[0117] FIG. 25 shows the correlation between the stand-up test and the measurement results of a body composition monitor. FIG. 25 shows the logarithmic value and P-value for each factor (potential locomotor factor) obtained from the measurement results of the body composition monitor. In FIG. 25, the larger the logarithmic value and the smaller the P-value, the stronger the correlation with the stand-up test. FIG. 25 shows that, among the factors obtained from the measurement results of the body composition monitor, body fat percentage, SMI, and left leg muscle mass are significantly involved in the stand-up test. This suggests that a decrease in body fat percentage and / or an increase in SMI and / or an increase in left leg muscle mass may improve the results of the stand-up test.

[0118] Figure 26 is a diagram showing the relationship between the 2-step test and the measurement results of a body composition monitor. Figure 26 shows the logarithmic value and P value for each factor (factor that could be a contributing factor to locomotive syndrome) obtained from the measurement results of the body composition monitor. In Figure 26, the larger the logarithmic value and the smaller the P value, the stronger the relationship with the 2-step test. Figure 26 shows that none of the factors obtained from the measurement results of the body composition monitor are significantly involved in the 2-step test.

[0119] Based on the above considerations, the measurement results of a body composition monitor include factors (measurement items) that are significantly related to at least the Locomo 25 and Standing Up Tests of the Locomotor Stress Test, and these factors can be used to determine and evaluate the risk of locomotor syndrome. That is, it is possible to determine and evaluate the risk of locomotor syndrome based on information (first information) that correlates with the amount of fat in the body, such as the body fat percentage, and information (second information) that correlates with the amount of muscle in the body, such as lower limb muscle mass (muscle mass of the right leg and / or muscle mass of the left leg) and SMI. Furthermore, it is possible to provide a risk assessment device, a risk assessment program, and a risk assessment method that embody this technical concept.

[0120] The risk assessment device is a risk assessment device that includes an acquisition unit that acquires first information that correlates with the amount of fat in the body and second information that correlates with the amount of muscle in the body, and a judgment unit that judges the risk of locomotive syndrome (locomotor risk) based on at least the first information and the second information.

[0121] The acquisition unit may acquire first information and second information from the measurement results of the body using a body composition monitor. The determination unit may determine the risk of locomotor syndrome based on a ratio between the first information and the second information. The first information may include body fat percentage. The second information may include leg muscle mass (muscle mass of one leg or muscle mass of both legs) and / or SMI.

[0122] The risk assessment program causes a computer to execute the steps of acquiring first information that correlates with the amount of fat in the body and second information that correlates with the amount of muscle in the body, and determining the risk of locomotive syndrome (locomotor risk) based on at least the first information and the second information.

[0123] The obtaining step may obtain first information and second information from the measurement results of the body using a body composition monitor. The determining step may determine the risk of locomotor syndrome based on a ratio between the first information and the second information. The first information may include body fat percentage. The second information may include leg muscle mass (muscle mass of one leg or muscle mass of both legs) and / or SMI.

[0124] The risk assessment method includes a step of acquiring first information that correlates with the amount of fat in the body and second information that correlates with the amount of muscle in the body, and a step of determining the risk of locomotive syndrome (locomotor risk) based on at least the first information and the second information.

[0125] The obtaining step may obtain first information and second information from the measurement results of the body using a body composition monitor. The determining step may determine the risk of locomotor syndrome based on a ratio between the first information and the second information. The first information may include body fat percentage. The second information may include leg muscle mass (muscle mass of one leg or muscle mass of both legs) and / or SMI.

[0126] 10, 20, 30, 40, 50: Risk assessment device 101: Acquisition unit 102: Determination unit 103: Storage unit 103a1: First risk category (category) 103a2: Second risk category (category) 103a3: Third risk category (category) 103a4: Fourth risk category (category) 103b1: First improvement method information (improvement method information) 103b2: Second improvement method information (improvement method information) 103b3: Third improvement method information (improvement method information) 103b4: Fourth improvement method information (improvement method information) 104: Output unit

Claims

1. A risk assessment device comprising: an acquisition unit that acquires first information that correlates with the amount of body fat and second information that correlates with the amount of muscle; and a judgment unit that judges the risk of locomotive syndrome based on at least the first information and the second information.

2. The risk assessment device according to claim 1, wherein the fat is visceral fat, and the muscle is the iliopsoas muscle.

3. A risk assessment device as described in claim 2, wherein the acquisition unit acquires the first information and the second information from body image information in which the internal structure of the body is captured.

4. The risk assessment device according to claim 3, wherein the body image information is a two-dimensional image captured at a predetermined position of the body, the acquisition unit calculates an area X of the visceral fat as the first information from the two-dimensional image, and calculates an area Y of the iliopsoas muscle as the second information from the two-dimensional image, and the determination unit makes the determination based on the area X and the area Y.

5. The risk assessment device according to claim 4, wherein the determination unit makes the determination using an area ratio between the area X and the area Y.

6. A risk assessment device as described in claim 4, wherein the predetermined location is the navel of the body.

7. A risk assessment device according to claim 4, wherein the two-dimensional image is a body tomographic image obtained by CT (Computed Tomography).

8. A risk assessment device according to claim 5, wherein the determination unit classifies and determines the risk into a plurality of categories using a plurality of category indices based on the area ratio and the degree of obesity of the body.

9. The risk assessment device according to claim 8, wherein the plurality of category indices include the following four category indices: (1) low obesity, low X / Y (2) high obesity, low X / Y (3) low obesity, high X / Y (4) high obesity, high X / Y 10. A risk assessment device as described in claim 8, further comprising a memory unit for storing improvement method information for the risks that fit each of the multiple categories, and outputting the improvement method information that fits the category classified by the judgment unit.

11. A risk assessment device as described in claim 1, wherein the acquisition unit acquires the first information and the second information from measurement results of a body composition scale for the body.

12. The risk assessment device according to claim 11, wherein the first information includes a body fat percentage.

13. The risk assessment device of claim 11, wherein the second information includes leg muscle mass and / or SMI.

14. A risk assessment device described in any one of claims 1 to 13, which receives the first information and the second information, or information including the first information and the second information, via a network.

15. A risk assessment device according to any one of claims 1 to 13, which transmits the risk assessment results via a network.

16. A risk assessment device as described in any one of claims 1 to 13, wherein the judgment unit judges the risk of lifestyle-related diseases in addition to judging the risk of locomotive syndrome.

17. A risk assessment program that causes a computer to execute the steps of: acquiring first information that correlates with the amount of body fat and second information that correlates with the amount of muscle; and determining the risk of locomotive syndrome based on at least the first information and the second information.

18. A risk assessment method comprising the steps of: acquiring first information correlating with an amount of body fat and second information correlating with an amount of muscle; and determining a risk of locomotive syndrome based on at least the first information and the second information.

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