JP2017063913A - Risk evaluation program, risk evaluation device, and risk evaluation method for neurogenic anorexia - Google Patents

Abstract

PROBLEM TO BE SOLVED: To perform a quantitative evaluation of a risk of neurogenic anorexia onset easily.SOLUTION: A risk evaluation program for neurogenic anorexia causes a computer to output a display in which an image of a subject is enlarged or reduced in the width direction of the subject on a display part, specify a present self-body shape image and an ideal self-body shape image that the subject cherishes based on an operation by the subject to the display, and generate neurogenic anorexia risk evaluation information based on the specified present and ideal self-body shape images.SELECTED DRAWING: Figure 10

Description

  The present invention relates to a technique for evaluating the risk of anorexia nervosa.

  In recent years, there has been a rapid increase in psychiatric disorders called severe eating disorders characterized by severe disturbances in eating behavior. Eating disorders include anorexia nervosa (Anorexia Nervosa, hereafter referred to as AN), bulimia nervosa (Bulimia Nervosa, hereafter sometimes referred to as BN), and others There are three groups. AN develops in young women and is characterized by marked thinness, amenorrhea, and unusual eating behavior, and exhibits various complications depending on the degree.

  Since AN is a disease that is often difficult to treat due to the nature of the disease, AN criteria are defined (see Non-Patent Document 1). Among the judgment criteria, there are judgment items relating to distorted recognition of body weight and body type, and guidelines such as inquiring about the desired body weight and focusing on the behavior of patients trying to maintain low body weight are described. In addition, in the determination criteria, an item of -20% or more of the standard weight is set. On the other hand, Patent Document 1 proposes a method for determining the risk of eating disorder by detecting a gene polymorphism existing in the vicinity of a specific marker.

JP 2008-48730 A

“Guidelines for primary care for anorexia nervosa (2007)”, Ministry of Health, Labor and Welfare, Research Project for Overcoming Intractable Diseases “Survey Research Group on Central Eating Disorders”

  In the method described in Patent Document 1, genetic testing is required. Moreover, the judgment criteria described in Non-Patent Document 1 depend on the way of inquiry and the behavior of the patient, and quantitative judgment is difficult with the criteria.

  This invention is made | formed in view of such a situation, and provides the technique which enables simple quantitative evaluation of the onset risk of anorexia nervosa.

  Each aspect of the present invention employs the following configurations in order to solve the above-described problems.

  The first aspect relates to a risk assessment program for anorexia nervosa. The risk assessment program according to the first aspect outputs a display in which a subject image is enlarged or reduced in the width direction of the subject to the display unit, and the subject holds the current self based on the subject's operation on the display. A body shape image and an ideal self-body shape image are identified, and the computer is configured to generate risk assessment information for anorexia nervosa based on the identified current and ideal self-body shape images.

  The second aspect relates to a risk assessment apparatus for anorexia nervosa, comprising at least one CPU (Central Processing Unit). In the risk evaluation device according to the second aspect, the at least one CPU outputs a display in which the image of the subject is enlarged or reduced in the width direction of the subject to the display unit, and based on the operation of the subject on the display, Identifying a current self-form image and an ideal self-form image held by the subject and generating risk assessment information for anorexia nervosa based on the identified current and ideal self-form images.

  Another aspect of the present invention is a risk assessment method for anorexia nervosa, which is executed when the program according to the first aspect is executed by a CPU. Moreover, the computer-readable recording medium which recorded the risk evaluation program which concerns on a 1st side may be sufficient. This recording medium includes a non-transitory tangible medium.

  According to each said aspect, it enables it to perform quantitative evaluation of the onset risk of anorexia nervosa simply.

It is a figure which shows notionally the apparatus structural example of the risk evaluation apparatus in 1st embodiment. It is a flowchart which shows the operation example of the risk evaluation apparatus in 1st embodiment. It is a flowchart which shows the operation example of the risk evaluation apparatus in 1st embodiment. It is a figure which shows the example of a display screen in the case of image | photographing a test subject with an imaging unit. It is a figure which shows the example of a display of the test subject image in which expansion and contraction is repeated. It is a figure which shows the example of a display after the expansion-contraction stop. It is a flowchart which shows the operation example of the risk evaluation apparatus in 2nd embodiment. It is a flowchart which shows the operation example of the risk evaluation apparatus in 3rd embodiment. It is a flowchart which shows the operation example of the risk evaluation apparatus in 3rd embodiment. It is a flowchart which shows the operation example of the risk evaluation apparatus in a modification. It is a figure which shows the comparison result of a body image of a verification subject. It is a graph which shows the relationship between the difference value (absolute value) between the present and ideal self-form images of the AN group and the obesity degree-group (healthy group) and the obesity degree. It is a table | surface which shows distribution of the difference value (absolute value) between the present and ideal self-form image of AN group and obesity degree-group (healthy group).

  Embodiments of the present invention will be described below. In addition, each embodiment given below is an illustration, respectively, and this invention is not limited to the structure of each following embodiment.

[First embodiment]
Hereinafter, the risk evaluation apparatus and the risk evaluation program in the first embodiment will be described using a plurality of drawings. The risk evaluation apparatus and the risk evaluation program in the first embodiment are an apparatus and a program related to an anorexia nervosa (AN) risk evaluation.

〔Device configuration〕
FIG. 1 is a diagram conceptually illustrating a device configuration example of a risk evaluation device 10 in the first embodiment. The risk evaluation apparatus 10 is a so-called computer, and has a hardware element group as shown in FIG. The risk evaluation apparatus 10 may be a general-purpose computer such as a PC (Personal Computer), a mobile phone, a smartphone, or a tablet terminal, or may be another dedicated computer such as an AN risk evaluation dedicated or a camera. Good. As shown in FIG. 1, the risk evaluation apparatus 10 includes a CPU (Central Processing Unit) 1, a memory 2, a display unit 3, a touch sensor 4, an input / output interface (I / F) unit 5, a communication unit 6, and an imaging unit. 7 etc. The CPU 1 is connected to other units via a communication line such as a bus. The hardware element group illustrated in FIG. 1 can also be collectively referred to as an information processing circuit.

The CPU 1 may be a general CPU, or instead of or in addition to at least one of an application specific integrated circuit (ASIC), a DSP (Digital Signal Processor), a GPU (Graphics Processing Unit), and the like. May be included.
The memory 2 is a RAM (Random Access Memory), a ROM (Read Only Memory), or an auxiliary storage device (such as a hard disk).

The display unit 3 includes a monitor such as an LCD (Liquid Crystal Display) or a CRT (Cathode Ray Tube) display, and performs display processing.
The touch sensor 4 receives an operation input from the user by sensing an external contact. The touch sensor 4 may be a sensor that can detect a proximity state from the outside even in a non-contact state. Further, the display unit 3 and the touch sensor 4 may be realized as a touch panel unit.

The input / output I / F unit 5 is connected to an input device such as a mouse or a keyboard, a microphone, an earphone, a headphone, or the like.
The communication unit 6 performs wireless communication or wired communication, and communicates with other computers and devices. The communication unit 6 may include a USB (Universal Serial Bus) unit. The communication unit 6 can also exchange data with a portable recording medium such as a USB memory, a CD (Compact Disk), a DVD (Digital Versatile Disc), or a Blu-ray disc.
The imaging unit 7 acquires a captured image.

  However, the hardware configuration of the risk evaluation apparatus 10 is not limited to the example shown in FIG. The risk assessment device 10 may include other hardware elements not shown such as vibration sensors, acceleration sensors, geomagnetic sensors, and the like. Further, the risk evaluation apparatus 10 may not include at least one of the display unit 3, the touch sensor 4, the input / output I / F unit 5, and the imaging unit 7. The number of hardware elements is not limited to the example of FIG. For example, the risk evaluation device 10 may have a plurality of CPUs 1.

  A risk evaluation program 11 is stored in the memory 2 of the risk evaluation apparatus 10. The risk evaluation program 11 may be stored in advance in the ROM (memory 2), or from a portable recording medium such as a CD (Compact Disc) or a memory card, or another computer on the network via the communication unit 6. May be installed and stored in the memory 2. That is, the risk evaluation program 11 may be sold in a state of being stored in a recording medium that is readable by a computer, or may be sold in a state of being stored in a server device on the Internet.

[Operation example / Information processing flow]
The CPU 1 loads and executes the risk evaluation program 11 from the memory 2 to realize an information processing flow as shown in FIGS. 2 and 3 in cooperation with other hardware elements.
FIG.2 and FIG.3 is a flowchart which shows the operation example of the risk evaluation apparatus 10 in 1st embodiment. The risk evaluation apparatus 10 in the first embodiment executes the information processing flow illustrated in FIGS. 2 and 3 when the risk evaluation program 11 is executed by the CPU 1 as described above.

  CPU1 acquires the image which imaged the test subject (S21). The CPU 1 may acquire the image from the image file stored in the memory 2 or may acquire the image by capturing a video frame obtained from the imaging unit 7. The image file may be acquired from another computer or a portable recording medium and stored in the memory 2. The acquired image should just contain the test subject image, and may contain the background image with the test subject image. “Subject image” and “subject image” mean images representing the body shape (form) of the subject. The subject image represents the whole body or a part of the body of the subject. The whole body of the subject may be a front view or a back view. The part of the body may be any part as long as it may be distorted and recognized by the symptoms of AN, such as the face, upper body, lower body, lower abdomen, buttocks, and thighs. Hereinafter, the subject image acquired in (S21) is also referred to as an original image.

  FIG. 4 is a diagram illustrating an example of a display screen when the subject is imaged by the imaging unit 7. In the example of FIG. 4, the risk evaluation device 10 is realized by a tablet terminal. A third party such as an evaluator operates the risk evaluation apparatus 10 and images the whole body of the subject from the front. When the third party confirms the captured image and selects “Yes”, the CPU 1 captures the displayed video. In this example, after the image of the subject is acquired by the operation of a third party, the risk evaluation device 10 is operated by the subject.

  First, CPU1 performs the process for making a test subject select the present self-form image which a test subject holds using the test subject's image acquired by (S21). Here, the “current self-body image” means an image representing the current body shape (form) of the subject.

  In the present specification, “expansion / contraction ratio” is used as a word representing the degree of expansion / contraction from the reference image. Therefore, the “expansion / contraction ratio” may be expressed as a ratio of an image expanded / contracted from the reference image to the reference image, or may be expressed as a ratio of an expansion / contraction width from the reference image to the reference image. “Expansion / contraction ratio” is a broad concept including an enlargement ratio, a reduction ratio, an elongation ratio, a shrinkage ratio, and the like. For example, the expansion / contraction rate may be written as “+ 8%”, “−2%”, or the like. In this case, an image with an expansion rate of “8%” indicates an image obtained by enlarging the reference image by 1.08 times, and an image with an expansion rate of “−2%” indicates an image obtained by reducing the reference image by 0.02 times. In the present embodiment, for convenience of explanation, an example in which the “expansion / contraction rate” is expressed by the ratio of the expanded / contracted image to the original image of the subject is given. Therefore, an image obtained by enlarging the reference image by 1.08 times is represented as an image having an expansion / contraction rate of “108%” or “1.08”, and an image obtained by reducing the reference image by 0.02 times is an expansion / contraction rate of “98%”. Or it is described as an image of “0.98”. However, the notation of “stretch rate” is not limited to such an example.

  The CPU 1 randomly determines the maximum expansion / contraction rate and the minimum expansion / contraction rate in the width direction of the subject (S22). The “maximum expansion / contraction rate” to be determined indicates the degree of elongation from the original image in the image in which the original image of the subject is greatly enlarged in the width direction of the subject. On the contrary, the “minimum expansion / contraction rate” to be determined indicates the degree of contraction from the original image in the image in which the original image of the subject is greatly reduced in the width direction of the subject. According to the notation of the present embodiment, the maximum expansion / contraction rate is determined to be a value at which the original image is enlarged (a value greater than 100%), and the minimum expansion / contraction rate is a value at which the original image is reduced (less than 100%). Value).

  Then, CPU1 outputs the display by which the test subject's image acquired by (S21) is expanded / contracted (expanded and reduced) in the test subject's width direction to the monitor of the display unit 3 (S23). At this time, the CPU 1 enlarges the subject's image until the maximum expansion / contraction rate determined in (S22) is reached, and then reduces the image until the maximum reduction rate determined in (S22) is reached. The execution order of enlargement and reduction may be reversed.

  It is desirable that a subject image in the middle of a change from a certain image of the subject to an image with the maximum expansion / contraction rate is displayed so that the subject can easily see the subject image. The same applies to the changing process from an image to an image with a minimum expansion / contraction rate. This is to make it easier for the subject to select the current self-form image in each change process. For example, the CPU 1 displays the subject's image so as to smoothly expand and contract (enlarge and reduce) at a constant speed. The image of the subject at the start of the display may be the original image acquired in (S21), or an image obtained by enlarging or reducing the original image at an arbitrary rate.

  FIG. 5 is a diagram illustrating a display example of a subject image that is repeatedly expanded and contracted. In the example of FIG. 5, in the upper part, an explanatory note for allowing the subject to select the current self-form image is displayed. Furthermore, a subject image in the middle of changing expansion and contraction is displayed, and the displayed subject image is reduced in the width direction of the subject (lateral direction in the drawing) from the original image of the subject illustrated in FIG. In the example of FIG. 5, the background is displayed together with the subject image, but the background may be removed from the display.

  The subject of expansion and contraction executed by the CPU 1 may be only the subject image, or may include the background image together with the subject image. For example, the CPU 1 can specify a subject image from the image acquired in (S21) using existing image recognition technology, and can expand and contract only the subject image. Moreover, CPU1 can also expand-contract both a test subject image and a background image together. It is desirable that the background is removed from the display or only the subject image is stretched. This is because, when the background image is also expanded and contracted together, the subject may recognize the actual self appearance depending on the display state of the background.

  CPU1 detects the test subject's 1st predetermined operation with respect to the display in (S23) (S24). The first predetermined operation is, for example, a tap operation or a click operation. However, the specific content of the first predetermined operation is not limited. In the display of (S23), the CPU 1 does not detect the first predetermined operation while both the enlargement until reaching the maximum expansion ratio and the reduction until reaching the minimum expansion ratio are completed (S24; NO) ), The maximum expansion ratio and the minimum expansion ratio are determined again at random (S22). The CPU 1 executes the display output of (S23) based on the newly determined maximum expansion ratio and minimum expansion ratio.

  In this way, the CPU 1 outputs a display that alternately repeats the enlargement until the subject's image reaches the maximum expansion / contraction ratio and the reduction until the subject's image reaches the minimum expansion / contraction ratio to the monitor of the display unit 3. In addition, the maximum stretch rate and the minimum stretch rate in the width direction of the subject are determined randomly. In this way, by preventing the subject from showing the actual self, the current self-form image held by the subject can be identified as it is without being influenced by the viewed image. In addition, by randomly determining the maximum expansion / contraction rate and the minimum expansion / contraction rate, it is impossible to assume that the middle of the maximum image and the minimum image is an actual self-form.

  When the first predetermined operation is detected (S24; YES), the CPU 1 stops the expansion and contraction of the subject's image at the detection timing (S25). Thereby, CPU1 fixes and displays a test subject image in the expansion state or the reduction state at the detection timing in the expansion / contraction change process. When the detection timing coincides with the timing for displaying the original image that has not been enlarged or reduced, the CPU 1 displays the subject image while fixing the original image without being expanded or contracted.

  The CPU 1 detects the subject's second predetermined operation for this display (S26). The second predetermined operation is, for example, a pinch-in or pinch-out operation. However, the specific content of the second predetermined operation is not limited. When the CPU 1 detects the second predetermined operation (S26; YES), the CPU 1 changes the expansion / contraction ratio in the width direction of the subject's image with the adjustment width corresponding to the detected operation, and the subject's changed expansion / contraction A rate image is displayed (S27). For example, when the subject image displayed at the stop time in (S25) shows an expansion / contraction ratio of 110% in the width direction and the operation width of the pinch-in corresponds to 2%, the CPU 1 displays the image at the stop time in the width direction. The image with the expansion / contraction rate “108%” in the width direction is displayed. When the second predetermined operation is not detected (S26; NO), the CPU 1 does not execute (S27).

  CPU1 detects a test subject's 3rd predetermined operation (S28). The third predetermined operation is, for example, a tap operation or a click operation for a certain menu display. However, the specific content of the third predetermined operation is not limited. When detecting the third predetermined operation (S28; YES), the CPU 1 specifies the current self-form image (S29). Specifically, the CPU 1 does not detect the second predetermined operation (S26; NO), and if it detects the third predetermined operation (S28; YES), the expansion state or the reduction state at the time when the expansion / contraction is stopped (S25). The subject image displayed in is identified as the current self-form image (S29). On the other hand, if the CPU 1 detects the second predetermined operation even once (S26; YES), the CPU 1 specifies the subject image having the expansion / contraction ratio changed in (S27) as the current self-form image (S29).

  Here, “specify the current self-form image” means that the CPU 1 can acquire the expansion / contraction rate in the width direction of the current self-form image selected by the subject with respect to the original image of the subject.

  FIG. 6 is a diagram illustrating a display example after expansion and contraction is stopped. In the example of FIG. 6, in the upper part, an explanatory note for allowing the subject to select the current self-form image is displayed, and in the lower part, an arrow indicating the operation method is displayed. In the example of FIG. 6, the second predetermined operation is a pinch-in or pinch-out operation, and the third predetermined operation is a tap operation for selecting “decision”.

  As illustrated in FIG. 6, it is possible to finely adjust the expansion / contraction rate in the width direction of the subject image displayed when expansion / contraction is stopped. When the subject stops the expansion / contraction by the first predetermined operation and his / her image displayed at that time does not match his / her body image, The subject image can be finely adjusted in the width direction.

  Next, the CPU 1 performs a process for causing the subject to select an ideal self-form image held by the subject. Here, the “ideal self-form image” means an image representing an ideal body shape (form) envisioned by the subject.

The information processing from (S30) to (S36) is the same as the information processing from (S22) to (S28).
Also in (S31), as in the case of (S23), it is desirable that the subject image in the middle of the expansion / contraction change is displayed so that the subject can easily see. This is to make it easier for the subject to select an ideal self-form image in each change process. The subject performs the first predetermined operation when an image close to the ideal self-form image is displayed.

  Also in (S35), it is possible to finely adjust the expansion / contraction rate in the width direction of the subject image displayed when expansion / contraction is stopped. The subject stops the expansion and contraction by the first predetermined operation, and when the image displayed at that time does not match the image of his ideal body, the image of his / her fixed display becomes the image of his ideal body shape. The subject image can be fine-tuned to fit.

  When detecting the third predetermined operation (S36; YES), the CPU 1 specifies an ideal self-form image (S37). Specifically, the CPU 1 does not detect the second predetermined operation (S34; NO), and if it detects the third predetermined operation (S36; YES), the expansion state or the reduction state at the time when the expansion / contraction is stopped (S33). The test subject image displayed in is specified as an ideal self-form image (S37). On the other hand, if the CPU 1 detects the second predetermined operation even once (S34; YES), the CPU 1 specifies the subject image having the expansion / contraction rate changed in (S35) as an ideal self-body image (S37).

  Here, “specifying an ideal self-shaped image” means that the CPU 1 can acquire the expansion / contraction ratio in the width direction of the ideal self-shaped image selected by the subject with respect to the original image of the subject.

The CPU 1 obtains the gap value of the current self-shaped image with respect to the original image of the subject based on the current self-shaped image specified in (S29) (S38).
Further, the CPU 1 acquires the gap value of the ideal self-shaped image with respect to the original image of the subject based on the ideal self-shaped image specified in (S37) (S39).

  The gap value is a stretch rate or a stretch rate difference in the width direction of the subject. The expansion / contraction rate difference means the difference in the width direction between the expansion / contraction rate of the current or ideal self-shaped image and the expansion / contraction rate of the original image of the subject. The expansion / contraction rate difference is represented by a signed value or an absolute value. For example, when the expansion / contraction ratio of the current self-form image is 108%, the expansion / contraction ratio difference is “8%” or “0.08”. When the expansion / contraction ratio of the ideal self-form image is 96%, the expansion / contraction ratio difference is “−4%” or “−0.04” or “4%” or “0.04”.

The CPU 1 generates risk evaluation information including the gap value of the current self-shaped image acquired in (S38) and the gap value of the ideal self-shaped image acquired in (S39), and displays the risk evaluation information on the display unit. 3 is displayed (S40). For example, the following display is output to the monitor.
“Your image of your current figure is 108% (the actual figure is 100%).”
“Your image of the ideal figure is 96% (the actual figure is 100%).”

  As described in Non-Patent Document 1 above, it is known that patients with AN have a distorted perception of body shape. The displayed gap value of the current self-form image indicates the degree of distortion of the subject's recognition regarding the current figure. The gap value of the displayed ideal self-form image indicates the degree of deviation of the ideal form from the current actual form. According to the display of (S40), since the distortion degree of recognition about a test subject's body type can be confirmed quantitatively, the display content can be used for evaluation of the onset risk of AN.

  In this specification, “risk evaluation information” means information related to the evaluation of the risk of developing AN. For example, the “risk evaluation information” may be information used for evaluating risk, information indicating a risk evaluation result, or adbus information corresponding to the risk evaluation result. It may be a combination of two or more of these.

  Further, the execution order of the information processing flow realized by the CPU 1 is not limited to the examples shown in FIGS. The execution order of each information processing can be changed within a range that does not hinder the contents. For example, (S38) and (S39) may be executed in parallel or in reverse order. Further, the information processing flow from (S22) to (S29) and the information processing flow from (S30) to (S37) may be executed in reverse order. In this case, the subject selects the ideal self-form image first, and then selects the current self-form image.

  As a first embodiment, it is possible to realize an AN risk assessment method. The AN risk evaluation method in the first embodiment is executed by at least one computer or CPU, such as the risk evaluation apparatus 10 or CPU 1 described above. For example, when the CPU 1 executes the risk evaluation program 11, the AN risk evaluation method in the first embodiment including the information processing flow shown in FIGS. 2 and 3 can be realized.

[Operation and effect of the first embodiment]
As described above, in the first embodiment, in order for the subject to select his / her current and ideal body image, a display in which the subject's image repeatedly expands and contracts in the width direction of the subject is output. This display prevents the body image recognized by the subject from being affected by viewing the image of the subject. As a result, according to the first embodiment, it is possible to specify the present and ideal body image of the subject that the subject holds in the head as it is.

  Furthermore, in the first embodiment, every time the expansion and contraction is repeated, the maximum expansion ratio and the minimum expansion ratio in the width direction of the subject are determined at random, and the test subject is between the determined maximum expansion ratio and the minimum expansion ratio. Image expands and contracts in the width direction. Thereby, in each of an expansion process and a reduction process, since the reaching point of expansion / contraction changes every time, it can be made difficult to make a test subject recognize a self figure. This is because, when the reaching point of expansion / contraction is constant, the subject may guess that the middle of expansion / contraction is his / her own appearance. As a result, according to the first embodiment, it is possible to specify the present and ideal body image of the subject that the subject holds in the head as it is.

  According to the first embodiment, it is possible to specify the current and ideal body image of the subject that the subject is holding in the head as it is, so that the degree of distortion of recognition about the body shape can be quantified with high accuracy. it can.

  In the first embodiment, expansion / contraction is stopped by the subject's operation on the display, and the expansion / contraction rate in the width direction of the subject's image is finely adjusted by the subject's operation on the display where the expansion / contraction is stopped. Thereby, the displayed subject image can be made closer to the current and ideal body image of the subject held in the head. As a result, the body shape image that the subject holds in the head can be specified as quantitative image data (current and ideal self-body shape images).

  In the first embodiment, based on the identified current and ideal self-form images, the gap values of the current and ideal self-form images with respect to the original image of the subject are acquired, and the risk evaluation includes both gap values. Information is displayed. The displayed gap value of the current self-form image indicates the degree of distortion of the subject's recognition regarding the current figure. The gap value of the displayed ideal self-form image indicates the degree of deviation of the ideal form from the current actual form. Therefore, according to the first embodiment, by displaying both gap values, it is possible to quantitatively present the degree of the subject's recognition distortion about the body type, which can be used for quantitative evaluation of the risk of developing AN. it can.

  Furthermore, according to the first embodiment, the subject only needs to perform a simple operation while viewing his / her own image displayed on the monitor of the display unit 3. Thus, according to the first embodiment, it is possible to easily perform quantitative evaluation of the onset risk of AN.

[Second Embodiment]
The present inventors have found that the difference between the current self-form image and the ideal self-form image identified as described above shows a significantly high value in the AN patient group (described later). See example). Therefore, in the second embodiment, the difference value is further calculated for evaluation of the risk of developing AN. Hereinafter, the risk evaluation apparatus and the risk evaluation program in the second embodiment will be described focusing on the content different from the first embodiment. In the following description, the same contents as those in the first embodiment are omitted as appropriate.

  The device configuration of the risk evaluation device 10 in the second embodiment may be the same as that of the first embodiment shown in FIG. The risk assessment program 11 in the second embodiment is stored in the memory 2 of the risk assessment device 10 as in the first embodiment.

[Operation example / Information processing flow]
The CPU 1 loads and executes the risk evaluation program 11 in the second embodiment from the memory 2 to realize an information processing flow as shown in FIGS. 2 and 7 in cooperation with other hardware elements. .
FIG. 7 is a flowchart showing an operation example of the risk evaluation apparatus 10 in the second embodiment. The risk evaluation apparatus 10 in the second embodiment executes the information processing flow illustrated in FIGS. 2 and 7 when the risk evaluation program 11 is executed by the CPU 1 as described above. 7, the same information processing steps as those in FIG. 3 are denoted by the same reference numerals as those in FIG.

As in the first embodiment, the CPU 1 executes (S21) to (S29) (see FIG. 2) and (S30) to (S39).
The CPU 1 calculates a difference value between the current self-form image and the ideal self-form image based on the gap values of the current and ideal self-form images acquired in (S38) and (S39) (S71). The calculated difference value is a value indicating a difference (absolute value) between the current self-form image and the ideal self-form image. For example, when the gap value is the expansion / contraction rate, the difference value indicates the difference (absolute value) between the two expansion / contraction rates. Further, when the gap value is a difference in expansion / contraction rate (absolute value), the difference value indicates a sum value of the difference between the expansion / contraction rates.

  The CPU 1 displays the risk evaluation information including the difference value calculated in (S71) and the gap values acquired in (S38) and (S39) or the risk evaluation information including only the difference value on the monitor of the display unit 3. (S72).

[Operation and effect of the second embodiment]
As described above, in the second embodiment, a difference value between the current self-form image and the ideal self-form image is calculated, and the difference value is further included in the risk evaluation information. As will be described later in the embodiments, the inventors have proved that the difference value can be used for the determination of the AN. The difference value can be considered as a value obtained by combining the degree of distortion of the subject's recognition regarding the current body shape and the degree of deviation of the ideal body shape from the current body shape. Therefore, according to the second embodiment, it is possible to comprehensively quantify the degree of recognition distortion of the subject's body shape, and to evaluate the risk of developing AN with high accuracy based on the difference value.

[Third embodiment]
As described in Non-Patent Document 1 above, it is known that patients with AN are considerably thinner than the standard weight. Therefore, in the third embodiment, the degree of obesity of the subject is further calculated in order to evaluate the risk of developing AN. Hereinafter, the risk evaluation apparatus and the risk evaluation program in the third embodiment will be described focusing on the contents different from those in the first and second embodiments. In the following description, the same contents as those in the first and second embodiments are omitted as appropriate.

  The device configuration of the risk evaluation device 10 in the third embodiment may be the same as that of the first embodiment shown in FIG. The risk assessment program 11 in the third embodiment is stored in the memory 2 of the risk assessment device 10 as in the first embodiment.

[Operation example / Information processing flow]
The CPU 1 loads and executes the risk evaluation program 11 in the third embodiment from the memory 2, thereby realizing an information processing flow as shown in FIGS. 8 and 9 in cooperation with other hardware elements. .
8 and 9 are flowcharts showing an example of the operation of the risk evaluation apparatus 10 in the third embodiment. The risk evaluation apparatus 10 according to the third embodiment executes the information processing flow illustrated in FIGS. 8 and 9 when the risk evaluation program 11 is executed by the CPU 1 as described above. 8 and 9, the same information processing steps as those in FIGS. 2 and 3 are denoted by the same reference numerals as those in FIGS.

  CPU1 acquires a subject's height data and weight data (S81). The CPU 1 may display the height and weight input screen on the monitor of the display unit 3 and obtain the height data and the weight data input to the input screen. Moreover, CPU1 can also acquire height data and weight data by communication from a height meter and a weight scale, or another computer. The method for acquiring the height data and the weight data is not limited.

The CPU 1 calculates the obesity level of the subject based on the height data and the weight data acquired in (S81) (S82). The degree of obesity of the subject can be calculated by the following equation, for example.
Obesity level of subject = (actually measured body weight−standard body weight) / standard body weight × 100
The standard weight can be determined by various known methods using the height data. For example, coefficients a and b are determined separately for men and women between the ages of 5 and 17, and a technique for calculating the standard weight by subtracting the coefficient b from the product of the coefficient a and the height is known. In addition, in the case of an adult, a method of calculating a standard weight by multiplying the height squared by 22 is known.

  Thereafter, the CPU 1 executes (S21) and subsequent steps, as in the first embodiment.

  When the CPU 1 acquires each gap value of the current and ideal self-form images (S38) and (S39), the risk evaluation information including the gap values and the obesity calculated in (S82) is displayed on the monitor of the display unit 3. It is displayed (S91).

[Operation and effect of the third embodiment]
As described above, in the third embodiment, the subject's height data and weight data are acquired, and the degree of obesity calculated from them is further included in the risk evaluation information. As described above, according to the third embodiment, it is possible to quantitatively confirm the degree of distortion in recognition of the subject's body shape by both gap values, and to quantitatively confirm the degree of leanness according to the degree of obesity. it can. As described above, it is known that the symptoms of AN can be determined by using the degree of thinness as one index. That is, according to the third embodiment, the quantitative values of two scales, that is, the degree of distortion of the recognition of the body type and the degree of skinnyness, can be used for evaluating the risk of developing AN.

[Other examples of risk assessment information]
In each of the above embodiments, the index values that can be used to evaluate the risk of developing AN are listed as gap values between the current and ideal self-form images, difference values between the current and ideal self-form images, and the degree of obesity. It was. All the index values may be included in the risk evaluation information, or only the difference value and the degree of obesity may be included.

  The CPU 1 can also include text data indicating AN care advice or both as a result of AN risk evaluation as well as at least one of the above index values in the risk evaluation information. In this case, the CPU 1 loads and executes the risk evaluation program 11 from the memory 2 to further realize the following information processing in cooperation with other hardware elements. Moreover, the risk evaluation apparatus 10 further executes the following information processing by the risk evaluation program 11 being executed by the CPU 1.

  CPU1 corresponds to only the difference value calculated as described above or the difference value and the degree of obesity from a plurality of types of text data respectively indicating AN care advice or both of them as a result of AN risk assessment. Select the text data to be used. Text data indicating the results of AN risk assessment are, for example, “There seems to be a risk of anorexia nervosa”, “No risk for anorexia nervosa”, etc. The text data indicating the care advice of AN is, for example, “You may consult with a medical institution”.

  For the selection of text data, a threshold value of a difference value is used. Further, when the obesity level is used to select text data, the obesity level threshold is used. These threshold values will be described in detail in examples described later.

[Modification]
In the third embodiment described above, the degree of obesity is calculated as an evaluation index for the risk of developing AN. As indicated by the criteria of Non-Patent Document 1 above, it can be considered that the risk of developing AN in subjects whose obesity is 0% or more (non-skinned type) is low. Therefore, the CPU 1 may further determine whether or not the subject is a subject based on the degree of obesity calculated for the subject. In this case, in FIG. 8, after calculating the obesity degree in (S82), the CPU 1 determines whether or not (S21) is executed for the subject. When the calculated degree of obesity is 0% or more, CPU 1 does not execute (S21) and subsequent steps. If the calculated degree of obesity is less than 0%, CPU 1 executes (S21) and subsequent steps for the subject. In this way, the subject can be narrowed down because an immediate result can be obtained only with the degree of obesity.

  Moreover, in each above-mentioned embodiment, a test subject may image an own image with the imaging unit 7 on the user side (inside) by operating the risk evaluation apparatus 10. In this case, it is desirable to prevent the subject from confirming his / her own appearance during imaging. Therefore, when the subject's image is captured by the subject's own operation, the CPU 1 displays a video in which the subject's image included in the video sequentially obtained from the imaging unit 7 is enlarged or reduced in the width direction of the subject. Sequentially displayed on the monitor. And CPU1 may acquire a test subject's image by capturing the image | video obtained from the imaging unit 7 based on a test subject's predetermined operation (shooting operation).

  Further, in each of the above-described embodiments, after the current self-form image is specified, the process for preventing the subject from seeing his / her captured image may not be executed. This is because, since the current self-form image has already been specified, there is no particular problem even if the subject sees the self-form in the image. Specifically, when causing the subject to select an ideal self-form image, the maximum stretch rate and the minimum stretch rate may not be determined randomly but may be fixed. Further, in that case, the expansion / contraction display may not be performed. In the example of FIG. 3, (S30), (S31), (S32), and (S33) are not executed, and the image of the subject may be displayed in a fixed manner.

In addition, although the convenience is reduced, the display of the subject's image repeatedly expanding and contracting in the width direction of the subject and the change of the expansion / contraction rate in the adjustment width corresponding to the operation of the subject may not be executed.
FIG. 10 is a flowchart illustrating an operation example of the risk evaluation apparatus 10 according to the modification. The risk evaluation apparatus 10 according to the modification executes the information processing flow illustrated in FIG. 10 when the risk evaluation program 11 is executed by the CPU 1 as described above. The CPU 1 loads and executes the risk evaluation program 11 in the modification example from the memory 2, thereby realizing an information processing flow as shown in FIG. 10 in cooperation with other hardware elements.

  The CPU 1 outputs a display in which the image of the subject is enlarged or reduced in the width direction of the subject to the monitor of the display unit 3 (S101), and based on the operation of the subject on the display (S102), Self-form images and ideal self-form images are identified (S103, S104, in no particular order), and AN risk assessment information is generated based on the identified current and ideal self-form images (S105).

  As described above, if a display in which the subject's image is enlarged or reduced in the width direction of the subject is output, it is possible to make it difficult for the subject to recognize his / her own figure even if the subject is not stretched. Moreover, even if it is not possible to change the expansion / contraction rate within the adjustment range corresponding to the operation of the subject, the operability is inferior, but the self-form image can be specified. Also in this modified example, it is possible to easily perform quantitative evaluation of the onset risk of AN.

  Furthermore, as shown in FIG. 10, the CPU 1 does not have to display the risk evaluation information on the monitor. The CPU 1 may transmit (notify) the risk evaluation information to another computer or print it on a printer (not shown). Further, the CPU 1 may store the risk evaluation information as a file in the memory 2. For example, CPU1 can each transmit risk evaluation information to a server apparatus (not shown) with test subject ID for every test subject. If it does in this way, the server apparatus can manage risk evaluation information for every test subject. Further, the server device can display the processed information on the terminal of the subject after processing the risk evaluation information so as to be easily seen by the subject.

  Examples will be given below to explain the above-mentioned contents in more detail. The present invention is not limited in any way by the following examples. The inventors conducted the following experiment and verified the above-described effect. This verification experiment will be described below as an example.

  Ten patients aged 10 to 20 years old who are outpatient (hereinafter referred to as AN group) and 62 healthy children aged 10 to 15 years (hereinafter referred to as healthy group) are subject to verification. It was. And about this healthy group, the obesity degree is computed based on height data and weight data similarly to the above-mentioned 3rd embodiment, Based on this obesity degree, a healthy group is obesity degree-group (obesity degree 0% Less than) and obesity degree + group (obesity degree 0% or more). As a result, the obesity degree group was 35 people, and the obesity degree group was 27 people.

  The risk assessment apparatus 10 of each of the above-described embodiments is used by the verification subject of the AN group 10 people, the obesity level-35 groups, and the obesity level + 27 groups, and the current and ideal self for each verification subject. The gap value of the body image, the difference value between the current and ideal self-body images and the degree of obesity were generated. In this embodiment, the absolute value of the expansion / contraction rate, the signed expansion / contraction rate difference, and the expansion / contraction rate difference was calculated as the gap value.

  FIG. 11 is a diagram illustrating a comparison result of the body image of the person to be verified. The number before “±” indicates the average value of the group, and the number after “±” indicates the standard deviation of the group. According to FIG. 11, since the standard deviation of the AN group is large in the expansion ratio and the signed expansion / contraction ratio difference, the difference between each group is not observed, but the absolute value of the expansion / contraction ratio difference between the AN group and the other groups. There was a difference. That is, it can be seen that the absolute value of the difference in expansion / contraction ratio between the current and ideal self-form images is higher in the AN group than in the other groups. Further, it can be seen that the difference value (absolute value) between the current and ideal self-shaped images is significant. In this example, it was verified that a significant difference was observed at a significance level of 5% between the obesity level-group and the AN group in the absolute value of the difference between the stretch rates of the current and ideal self-form images.

  FIG. 12 is a graph showing the relationship between the difference value (absolute value) between the current and ideal self-form images and the degree of obesity in the AN group and the degree of obesity-group (healthy group). According to FIG. 12, there is no difference in the distribution regarding the degree of obesity in both groups, but there is a large difference in the difference value, that is, the distribution of the difference in body image between the ideal and the reality. For example, half of the AN group showed a difference value of 14% or more, whereas all the healthy groups showed a difference value of less than 14%. Moreover, the person who showed the difference value of 8% or less is 1/5 (2 persons) of the AN group, but more than half of the healthy group.

  FIG. 13 is a table showing the distribution of difference values (absolute values) between the current and ideal self-form images of the AN group and the obesity-group (healthy group). As shown in FIG. 13, when a difference value (absolute value) threshold of 14% is used, the sensitivity can be evaluated at 50% and specificity of 100%, and when the threshold of 12% is used, sensitivity is 70% and specificity is 91. It was verified that the evaluation can be performed at a sensitivity of 80% and the specificity of 74.3% when the threshold value of 8% is used.

  Thus, the usefulness of the risk evaluation information generated in each of the above-described embodiments was demonstrated by this example. For example, if a gap value (stretch rate and stretch rate difference) between the current and ideal self-shaped images is output, a difference value between the current and ideal self-shaped images can be calculated using the gap value. In “another example of generating risk evaluation information”, a value of 8% or more and 14% or less can be used as a threshold value of a difference value used for selecting text data. If the threshold is set to 12%, an evaluation accuracy of 70% sensitivity and 91.4% specificity can be expected.

  In the plurality of flowcharts used in the above description, a plurality of information processing steps are described in order, but the information processing execution order in each embodiment is not limited to the description order. In each embodiment, the illustrated order can be changed within a range that does not hinder the contents. Moreover, each above-mentioned embodiment and each modification can be combined in the range with which the content does not conflict.

  A part or all of each of the above embodiments and modifications may be specified as in the following supplementary notes. However, each embodiment and each modification are not limited to the following description.

(Appendix 1)
A display in which the image of the subject is enlarged or reduced in the width direction of the subject is output to the display unit,
Based on the subject's operation on the display, the subject's current self-form image and ideal self-form image are identified,
Generating anorexia nervosa risk assessment information based on the identified current and ideal self-form images;
A risk assessment program for anorexia nervosa that lets a computer do this.
(Appendix 2)
The output is output to the display unit the display in which the image of the subject repeats expansion and contraction in the width direction of the subject,
The specification specifies the image of the subject after the stop as the current self-form image or the ideal self-form image by stopping expansion and contraction at the detection timing of the operation of the subject with respect to the display.
The risk assessment program for anorexia nervosa according to appendix 1.
(Appendix 3)
When an operation of the subject with respect to the display in which the expansion / contraction is stopped is detected, the expansion / contraction ratio in the width direction of the image of the subject is changed with an adjustment width corresponding to the detected operation.
The computer further executes:
The specifying specifies the subject's image of the changed stretch rate as the current self-form image or the ideal self-form image.
The risk assessment program for anorexia nervosa according to appendix 2.
(Appendix 4)
The output is
For each repetition, the maximum stretch rate and the minimum stretch rate in the width direction are determined randomly,
Outputting the display on the display unit alternately repeating the enlargement until the subject image reaches the maximum expansion / contraction rate and the reduction until reaching the minimum expansion / contraction rate,
The risk evaluation program for anorexia nervosa according to appendix 2 or 3, including the above.
(Appendix 5)
The generation is
Obtaining a gap value for each of the identified current and ideal self-form images relative to the subject's original image,
Including the gap value for the current self-form image and the gap value for the ideal self-form image in the risk assessment information,
The gap value is a stretch rate or a stretch rate difference in the width direction.
The risk assessment program for anorexia nervosa according to any one of appendices 1 to 4.
(Appendix 6)
The generation is
Obtaining a gap value for each of the identified current and ideal self-form images relative to the subject's original image,
Calculating a difference value between the current self-form image and the ideal self-form image based on the obtained gap values of the current and ideal self-form images;
Include the calculated difference value in the risk assessment information,
The gap value is a stretch rate or a stretch rate difference in the width direction.
The risk assessment program for anorexia nervosa according to any one of appendices 1 to 4.
(Appendix 7)
The generation is
Further comprising selecting text data corresponding to the calculated difference from a plurality of types of text data each indicating a result of risk assessment of anorexia nervosa or care advice for anorexia nervosa,
Including the selected text data in the risk assessment information;
The risk assessment program for anorexia nervosa according to appendix 6.
(Appendix 8)
Obtaining the subject's height data and weight data;
Based on the acquired height data and weight data, to calculate the degree of obesity of the subject,
The computer further executes:
The generation further includes the calculated degree of obesity in the risk assessment information.
The risk assessment program for anorexia nervosa according to appendix 5 or 6.
(Appendix 9)
Obtaining the subject's height data and weight data;
Based on the acquired height data and weight data, to calculate the degree of obesity of the subject,
The computer further executes:
The generation is
Selecting the text data corresponding to the calculated difference and the degree of obesity from a plurality of types of text data respectively indicating a result of risk assessment of anorexia nervosa or care advice for anorexia nervosa Including
Including the calculated degree of obesity and the selected text data in the risk assessment information;
The risk assessment program for anorexia nervosa according to appendix 6.
(Appendix 10)
Obtain the subject's height and weight data,
Based on the acquired height data and weight data, the obesity level of the subject is calculated,
Based on the calculated degree of obesity, it is determined whether or not the subject is the subject.
The risk assessment program for anorexia nervosa according to any one of appendices 1 to 9, further causing the computer to execute the following.
(Appendix 11)
The image of the subject included in the video sequentially obtained from the imaging unit is sequentially displayed on the display unit the video that is enlarged or reduced in the width direction of the subject,
Based on a predetermined operation of the subject, capturing a video obtained from the imaging unit,
The risk evaluation program for anorexia nervosa according to any one of appendices 1 to 10, further causing the computer to execute the above.
(Appendix 12)
In a risk assessment apparatus for anorexia nervosa, comprising at least one CPU,
The at least one CPU is
A display in which the image of the subject is enlarged or reduced in the width direction of the subject is output to the display unit,
Based on the subject's operation on the display, the subject's current self-form image and ideal self-form image are identified,
Generating anorexia nervosa risk assessment information based on the identified current and ideal self-form images;
A risk assessment device for anorexia nervosa that performs that.
(Appendix 13)
The output is output to the display unit the display in which the image of the subject repeats expansion and contraction in the width direction of the subject,
The specification specifies the image of the subject after the stop as the current self-form image or the ideal self-form image by stopping expansion and contraction at the detection timing of the operation of the subject with respect to the display.
The risk assessment apparatus for anorexia nervosa according to appendix 12.
(Appendix 14)
The at least one CPU is
When an operation of the subject with respect to the display in which the expansion / contraction is stopped is detected, the expansion / contraction ratio in the width direction of the image of the subject is changed with an adjustment width corresponding to the detected operation.
To do further,
The specifying specifies the subject's image of the changed stretch rate as the current self-form image or the ideal self-form image.
The risk assessment apparatus for anorexia nervosa according to appendix 13.
(Appendix 15)
The output is
For each repetition, the maximum stretch rate and the minimum stretch rate in the width direction are determined randomly,
Outputting the display on the display unit alternately repeating the enlargement until the subject image reaches the maximum expansion / contraction rate and the reduction until reaching the minimum expansion / contraction rate,
The risk assessment apparatus for anorexia nervosa according to appendix 13 or 14, including the above.
(Appendix 16)
The generation is
Obtaining a gap value for each of the identified current and ideal self-form images relative to the subject's original image,
Including the gap value for the current self-form image and the gap value for the ideal self-form image in the risk assessment information,
The gap value is a stretch rate or a stretch rate difference in the width direction.
The risk assessment apparatus for anorexia nervosa according to any one of appendices 12 to 15.
(Appendix 17)
The generation is
Obtaining a gap value for each of the identified current and ideal self-form images relative to the subject's original image,
Calculating a difference value between the current self-form image and the ideal self-form image based on the obtained gap values of the current and ideal self-form images;
Include the calculated difference value in the risk assessment information,
The gap value is a stretch rate or a stretch rate difference in the width direction.
The risk assessment apparatus for anorexia nervosa according to any one of appendices 12 to 15.
(Appendix 18)
The generation is
Further comprising selecting text data corresponding to the calculated difference from a plurality of types of text data each indicating a result of risk assessment of anorexia nervosa or care advice for anorexia nervosa,
Including the selected text data in the risk assessment information;
The risk assessment apparatus for anorexia nervosa according to appendix 17.
(Appendix 19)
The at least one CPU is
Obtaining the subject's height data and weight data;
Based on the acquired height data and weight data, to calculate the degree of obesity of the subject,
To do further,
The generation further includes the calculated degree of obesity in the risk assessment information.
The risk assessment apparatus for anorexia nervosa according to appendix 16 or 17.
(Appendix 20)
The at least one CPU is
Obtaining the subject's height data and weight data;
Based on the acquired height data and weight data, to calculate the degree of obesity of the subject,
To do further,
The generation is
Selecting the text data corresponding to the calculated difference and the degree of obesity from a plurality of types of text data respectively indicating a result of risk assessment of anorexia nervosa or care advice for anorexia nervosa Including
Including the calculated degree of obesity and the selected text data in the risk assessment information;
The risk assessment apparatus for anorexia nervosa according to appendix 17.
(Appendix 21)
The at least one CPU is
Obtain the subject's height and weight data,
Based on the acquired height data and weight data, the obesity level of the subject is calculated,
Based on the calculated degree of obesity, it is determined whether or not the subject is the subject.
The risk assessment apparatus for anorexia nervosa according to any one of appendices 12 to 20, further executing the above.
(Appendix 22)
The at least one CPU is
The image of the subject included in the video sequentially obtained from the imaging unit is sequentially displayed on the display unit the video that is enlarged or reduced in the width direction of the subject,
Based on a predetermined operation of the subject, capturing a video obtained from the imaging unit,
The risk assessment apparatus for anorexia nervosa according to any one of appendices 12 to 21, further executing the above.
(Appendix 23)
In the risk assessment method for anorexia nervosa executed by a computer,
A display in which the image of the subject is enlarged or reduced in the width direction of the subject is output to the display unit,
Based on the subject's operation on the display, the subject's current self-form image and ideal self-form image are identified,
Generating anorexia nervosa risk assessment information based on the identified current and ideal self-form images;
A method for evaluating the risk of anorexia nervosa.

1 CPU
2 Memory 3 Display Unit 4 Touch Sensor 5 Input / Output I / F Unit 6 Communication Unit 7 Imaging Unit 10 Risk Evaluation Device 11 Risk Evaluation Program

Claims (11)

A display in which the image of the subject is enlarged or reduced in the width direction of the subject is output to the display unit,
Based on the subject's operation on the display, the subject's current self-form image and ideal self-form image are identified,
Generating anorexia nervosa risk assessment information based on the identified current and ideal self-form images;
A risk assessment program for anorexia nervosa that lets a computer do this. The output is output to the display unit the display in which the image of the subject repeats expansion and contraction in the width direction of the subject,
The specification specifies the image of the subject after the stop as the current self-form image or the ideal self-form image by stopping expansion and contraction at the detection timing of the operation of the subject with respect to the display.
The risk assessment program for anorexia nervosa according to claim 1. When an operation of the subject with respect to the display in which the expansion / contraction is stopped is detected, the expansion / contraction ratio in the width direction of the image of the subject is changed with an adjustment width corresponding to the detected operation.
The computer further executes:
The specifying specifies the subject's image of the changed stretch rate as the current self-form image or the ideal self-form image.
The risk assessment program for anorexia nervosa according to claim 2. The output is
For each repetition, the maximum stretch rate and the minimum stretch rate in the width direction are determined randomly,
Outputting the display on the display unit alternately repeating the enlargement until the subject image reaches the maximum expansion / contraction rate and the reduction until reaching the minimum expansion / contraction rate,
The risk evaluation program for anorexia nervosa according to claim 2 or 3. The generation is
Obtaining a gap value for each of the identified current and ideal self-form images relative to the subject's original image,
Including the gap value for the current self-form image and the gap value for the ideal self-form image in the risk assessment information,
The gap value is a stretch rate or a stretch rate difference in the width direction.
The risk evaluation program for anorexia nervosa according to any one of claims 1 to 4. The generation is
Obtaining a gap value for each of the identified current and ideal self-form images relative to the subject's original image,
Calculating a difference value between the current self-form image and the ideal self-form image based on the obtained gap values of the current and ideal self-form images;
Include the calculated difference value in the risk assessment information,
The gap value is a stretch rate or a stretch rate difference in the width direction.
The risk evaluation program for anorexia nervosa according to any one of claims 1 to 4. The generation is
Further comprising selecting text data corresponding to the calculated difference from a plurality of types of text data each indicating a result of risk assessment of anorexia nervosa or care advice for anorexia nervosa,
Including the selected text data in the risk assessment information;
The risk assessment program for anorexia nervosa according to claim 6. Obtaining the subject's height data and weight data;
Based on the acquired height data and weight data, to calculate the degree of obesity of the subject,
The computer further executes:
The generation further includes the calculated degree of obesity in the risk assessment information.
The risk assessment program for anorexia nervosa according to claim 5 or 6. Obtaining the subject's height data and weight data;
Based on the acquired height data and weight data, to calculate the degree of obesity of the subject,
The computer further executes:
The generation is
Selecting the text data corresponding to the calculated difference and the degree of obesity from a plurality of types of text data respectively indicating a result of risk assessment of anorexia nervosa or care advice for anorexia nervosa Including
Including the calculated degree of obesity and the selected text data in the risk assessment information;
The risk assessment program for anorexia nervosa according to claim 6. In a risk assessment apparatus for anorexia nervosa, comprising at least one CPU,
The at least one CPU is
A display in which the image of the subject is enlarged or reduced in the width direction of the subject is output to the display unit,
Based on the subject's operation on the display, the subject's current self-form image and ideal self-form image are identified,
Generating anorexia nervosa risk assessment information based on the identified current and ideal self-form images;
A risk assessment device for anorexia nervosa that performs that. In the risk assessment method for anorexia nervosa executed by a computer,
A display in which the image of the subject is enlarged or reduced in the width direction of the subject is output to the display unit,
Based on the subject's operation on the display, the subject's current self-form image and ideal self-form image are identified,
Generating anorexia nervosa risk assessment information based on the identified current and ideal self-form images;
A method for evaluating the risk of anorexia nervosa.

Images