JP6236862B2 - How to calculate geriatric disorder risk - Google Patents

Description

  The present invention relates to a method and system for evaluating the likelihood of occurrence of geriatric disorders such as knee pain (hereinafter also referred to as geriatric disorder risk) based on parameters measured by a walking action.

  Elderly people often fall with daily walking movements. A fall causes a fracture of the femur and the like, and tends to cause bedridden life. Therefore, a system has been proposed that supports the prevention of falls by measuring the stride in daily walking motions of the subject, determining the fall risk and walking ability (Patent Documents 1 and 2).

JP 2002-345786 A JP 2009-261595 A

  On the other hand, falling is not the only thing that interferes with the daily life of the elderly. Geriatric disorders such as knee pain are also a problem. Therefore, it is desirable to predict the risk of occurrence of senile disabilities other than falls and to avoid the occurrence.

  On the other hand, the problem to be solved by the present invention relates to making it possible to easily evaluate the risk of occurrence of senile disorders other than falls from walking action.

  Among the geriatric disorders, the present inventors used knee walking, low back pain, urinary incontinence, dementia and sarcopenia using specific walking parameters that can measure the risk of occurrence from daily walking activities, The present invention has been completed by finding that it can be predicted with a high accuracy rate.

  That is, the present invention is an evaluation method for geriatric disorder risk that evaluates the likelihood of senile disability based on walking parameters measured by a walking action, and includes cadence, stride, walking ratio, stride, step, as walking parameters. Using knee or back pain, urinary incontinence, dementia and sarcopenia, using two or more selected from gap, walking angle, toe angle, stride left / right difference, step difference left / right difference, walking angle left / right difference, and left / right difference between both legs To provide a method for assessing geriatric disorder risk for geriatric disorder risk for selected geriatric disorders.

  In addition, the present invention is selected from cadence, stride, walking ratio, stride, step, walking angle, toe angle, stride left / right difference, step left / right difference, walking angle left / right difference, and left / right difference in both leg support periods, depending on the walking action. Gait parameter measuring means for measuring two or more walking parameters, and senile disorders selected from knee pain, low back pain, urinary incontinence, dementia and sarcopenia based on the two or more walking parameters measured by the walking parameter measuring means There is provided an evaluation system for geriatric disorder risk, comprising calculation means for evaluating a failure risk, and display means for displaying a geriatric disorder risk evaluated by the calculation means.

  According to the evaluation method or the evaluation system of the present invention, the likelihood of the present and future occurrence of geriatric disorder selected from knee pain, low back pain, urinary incontinence, dementia and sarcopenia is determined with a high correct answer rate using specific walking parameters. Can be evaluated.

  Moreover, since this specific walking parameter can be easily obtained by a simple operation of the subject's walking action, the subject can easily use the evaluation method or the evaluation system of the present invention.

  Therefore, the evaluation method or the evaluation system of the present invention is useful in taking appropriate measures such as exercise and use of jigs for individual senile disorders such as knee pain and back pain.

FIG. 1 is a schematic configuration diagram of an embodiment of an evaluation system for geriatric disorder risk. FIG. 2 is a diagram in which walking parameters are added to the pressure distribution diagram during walking detected by the seat type pressure sensor. FIG. 3 is an explanatory diagram of a display mode of an evaluation result of geriatric disorder risk. FIG. 4 is a diagram illustrating a correspondence relationship between the risk of senile disorder (occurrence rate) calculated in the example and the actual degree of senile disorder.

  Hereinafter, the present invention will be described in detail with reference to the drawings. In each figure, the same numerals indicate the same or equivalent components.

  FIG. 1 is a schematic configuration diagram of a geriatric disorder risk evaluation system 1 that implements a geriatric disorder risk evaluation method according to an embodiment of the present invention.

  This evaluation system 1 evaluates the risk of geriatric disorder selected from knee pain, low back pain, urinary incontinence, dementia, and sarcopenia based on specific walking parameters measured from the walking behavior of the subject. There is no particular limitation on the age of the subject who evaluates the risk of geriatric disorder using this evaluation system 1, and a person in their 20s and 30s can also be a subject. However, since the evaluation content is a geriatric disorder risk, the subject is preferably Is an elderly person who is 60 years or older, more preferably 65 years or older, and still more preferably 70 years old or older, who has not yet developed at least one of the above-mentioned geriatric disorders. In addition, when evaluating the risk of aging disorder by multivariate analysis as a premise of the present invention, the data for calculating the regression equation is preferably 60 years old or more, more preferably 65 years old or more, and even more preferably 70 years old or more. Collect from the elderly. Moreover, it is preferable that the age group collecting data for calculating the regression equation corresponds to the age group of the subject who evaluates the risk of senile disability.

  Specific evaluation contents of the senile disorder risk in this evaluation system 1 include at least prediction of the risk of occurrence, frequency and pain of senile disorder selected from knee pain, low back pain, urinary incontinence, dementia and sarcopenia. The degree of prediction can be raised. Note that the evaluation system 1 can also evaluate the risk of occurrence of a fall risk or the like that can be evaluated based on a walking action.

  Here, sarcopenia refers to a weak state in which muscle mass has decreased due to aging. In this example, BMI is 18.5 or less for men and grip strength is 25 kg or less, and BMI is 18.5 or less for women. The grip strength is 20kg or less. As an evaluation content of sarcopenia risk, it is possible to predict whether or not there is a risk of becoming a weak state like this guideline.

  In the present invention, dementia refers to mild cognitive impairment. Judgment of dementia can be made by a score of a commonly known dementia test such as “MMS” (Mini-Mental State). For example, the “MMS” test method is based on an inquiry such as answering today's date or address, answering a simple calculation, or memorizing a simple word. Results are obtained. Generally, if the result of the “MMS” test is 20 points or less, it is determined that the cognitive disorder is present. For example, 23 points or less can be evaluated as having a risk of dementia and mild cognitive symptoms “MCI” (Mild cognitive impairment). In the examples of the present invention described later, geriatric disorder data on dementia was obtained by the “MMS” test.

  The evaluation system 1 of the present embodiment includes a seat-type pressure sensor 2 that can measure a foot pressure distribution during a walking action as a walking parameter measurement unit in order to enable evaluation of the above-mentioned senile disorder risk. Moreover, it has the personal computer 3 as an arithmetic unit which evaluates a geriatric disorder risk based on the specific gait parameter measured with the seat type pressure sensor 2, and is integrated with the personal computer 3 as a display means for evaluating the geriatric disorder risk. It has the display 4 which becomes. Furthermore, it is preferable that the evaluation system 1 includes a video camera 5 to take a posture during walking as necessary. When the video camera 5 is provided, it is preferably connected to the personal computer 3.

  Here, as the seat type pressure sensor 2, at least a cadence, a stride, a walking ratio, a stride, a step, a walking angle, a toe angle, a stride left / right difference, a step left / right difference, a walking angle left / right difference, and a left / right difference in both leg support periods It is preferable to be able to measure a specific walking parameter and further measure a walking parameter such as a walking speed.

Further, as the seat type pressure sensor 2, as shown in FIG. 2, it is preferable to be able to measure the pressure distribution during walking and to measure the above-described walking parameters from the pressure distribution. This makes it possible to easily obtain walking parameters. Further, the sheet pressure sensor 2 is preferably a pressure sensor having an area of at least 240 cm × 60 cm, a resolution of 1 cm ² or less, and an analysis frequency of 60 Hz or more. As such a seat type pressure sensor, for example, a seat type leg weight meter series Walk Way manufactured by Anima Co., Ltd., a floor reaction force meter manufactured by AMTI, or the like can be used.
The walking distance of the subject when the walking parameter is measured by the seat type pressure sensor is preferably 5 to 15 m, and more preferably 5 to 10 m.

  In the present invention, as the walking parameter measurement means, in addition to the seat-type pressure sensor, a device that measures a walking parameter by analyzing a motion of image information captured by a plurality of video cameras with a marker attached to the foot may be provided. Specifically, a plurality of video cameras and a three-dimensional motion analysis system (3D motion analysis system VICON manufactured by Interliha) can be used. Further, the walking parameter measuring means may be a measuring means that measures the distance or angle by the footprint by putting ink on the sole of the foot, or may be a measuring means that combines analysis of time by a video image. Among these, a seat type pressure sensor is preferable from the viewpoints of cost, burden on the subject, and accuracy.

As described above, the walking parameter measurement means is at least a cadence, a stride, a walking ratio, a stride, a step, a walking angle, a toe angle, a stride left / right difference, a step left / right difference, a walking angle left / right difference, and a bilateral leg support difference. Specific walking parameters are measured. Here, cadence refers to the number of steps (step / min) within a unit time. The cadence can be calculated by the following equation.
Cadence = (60 / average) x 2
In the formula, average represents one cycle of right walking or one cycle of left walking (sec / 2 step).

  The stride is the distance from the ground contact on the left and right sides to the ground contact on the other side again, and uses a standard value divided by height. As the stride, left and right stride can be used as walking parameters, respectively. The stride difference is the difference between the right and left stride and the other stride, which is normalized by dividing by the height. The toe angle difference is the difference between the left and right toe angles and the other toe angle.

  The walking ratio is a value obtained by dividing the step length (m) by the pitch (times / minute). The walking ratio indicates walking efficiency, approaching 0.006 for adults and lower for infants and the elderly.

  As shown in FIG. 2, the step distance is a horizontal distance from the contact of the left and right heels to the contact of the other left and right heels again, and a value divided by height is used as a standard. In addition, the right and left of the step are determined by the left and right of the foot that is the axis foot. For example, when the right foot is swallowed with the axis foot as the left foot, the distance between the left and right feet is the left step. As walking parameters, the left and right steps can be used as walking parameters, respectively.

  As shown in FIG. 2, the walking angle is an angle (°) formed by a straight line connecting one heel to the other heel with the traveling direction. In addition, the right and left of the walking angle shall be determined by the left and right of the foot that is the axis foot. For example, the left walking angle is defined as an angle formed by a straight line connecting the left foot and the right foot with the axial foot as the left foot and the traveling direction. As walking parameters, the left and right walking angles can be used as walking parameters, respectively.

  As shown in FIG. 2, the toe angle is an angle (°) formed by a straight line connecting the heel and the toe with the traveling direction, the outside is plus, and the inside is minus. As walking parameters, the left and right toe angles can be used as walking parameters, respectively.

  The stride is the distance from one heel contact to the left to the other heel contact, and uses a value that is divided by height and normalized. As the stride, the left and right strides can be used as walking parameters, respectively.

  The stride left / right difference is the difference between the distance from the ground contact of the left foot to the ground contact and the distance from the ground contact of the other foot to the ground contact again, and is divided by the height. Use what you did.

  The step difference is the difference between the step distance when the left and right foot is used as the axis foot and the other foot is stepped on, and the step distance when the axis foot is reversed left and right, and is normalized by dividing by the height Use what you did.

  The walking angle left / right difference is the difference between the left and right walking angle and the other walking angle.

  The walking speed is the speed (cm / sec) of walking a certain distance.

  In addition, walking speed, step length difference, and toe angle difference can be used as walking parameters.

  Also, the time related to walking can be used as a parameter. Specifically, the stance phase, the swing phase, the both-leg support phase, the stance phase left-right difference, the swing phase left-right difference, and the both-leg support phase left-right difference can be used. The stance phase is the time when one of the left and right legs is on the ground, and the left and right stance phases can be used as walking parameters, respectively. The free leg period is the time during which one of the left and right legs is in the air, and the left and right free leg periods can be used as walking parameters, respectively. The both-leg support period is the time that the left and right legs are on the ground before the left and right legs are separated from the ground, and the left and right both-leg support periods can be used as walking parameters for both legs. The left-right difference in stance is the difference between one stance phase on the left and right and the other stance phase. The difference between the left and right swing leg periods is the difference between the left and right swing leg periods and the other swing leg period. The difference between the left and right leg support periods is the difference between the left and right leg support periods and the other leg support period.

  On the other hand, the personal computer 3 used as a computing device evaluates the above-mentioned senile disorder risk using two or more of the above-mentioned specific walking parameters output from the seat type pressure sensor 2. In this case, a regression equation for calculating the risk of geriatric disorder is obtained in advance by multivariate analysis, which will be described later, the regression equation is stored in the personal computer 3, and the personal computer 3 uses the regression equation for any subject. It is preferable to be able to calculate a geriatric disorder risk from walking parameters. In addition to the specific walking parameters described above, walking parameters such as stride length and walking speed may be added as parameters for evaluating the senile disorder risk. In addition, it is preferable to add one or more basic data such as data relating to age, sex, and physique of BMI.

  The personal computer 3 displays the evaluation results of the individual subjects calculated using the regression equation on the display 4. As the display mode, the probability value obtained by the regression equation may be displayed as a numerical value as it is, and it is displayed step by step with words such as “danger”, “caution”, “relief” according to the probability value. Also good. At that time, the correct answer rate may be written in consideration of the psychology of the subject.

Further, the personal computer 3 can evaluate and display a plurality of types of geriatric disorder risk based on walking parameters measured by one walking action. In this case, the evaluation results may be displayed sequentially, as shown in FIG. 3, may be displayed as a radar chart, or may be displayed in combination with the radar chart and individual evaluation results.
The evaluation result may be displayed on the display 4, printed, provided to the subject, or transmitted to the subject via a communication line.

  The evaluation method for geriatric disorder risk of the present invention is preferably the above-mentioned cadence, stride, walking ratio, stride, step, walking angle, toe angle, stride left / right difference, step left / right difference, walking angle left / right difference, and both legs. Two or more gait parameters selected from the left and right support phase are used, and if necessary, other gait parameters and basic data are used to select from knee pain, back pain, urinary incontinence, dementia and sarcopenia. It is a method for evaluating the risk of geriatric disorder, and more specifically, the risk of geriatric disorder described above (for example, whether or not geriatric disorder occurs, whether or not geriatric disorder occurs with a certain degree of pain, Multi-variate analysis that includes two or more of the above-mentioned specific walking parameters as explanatory variables, and using the obtained regression equation as a probability value Geriatric disorder It is a method of evaluating the likelihood of senile disorders by calculating the click.

  As multivariate analysis to calculate geriatric disorder risk, logistic regression analysis, linear regression analysis, etc. can be performed, and among them, multiple types of walking parameters are confounded and considered to contribute to geriatric disorder risk. It is preferable to use logistic regression analysis because the risk of geriatric disorder can be predicted by a numerical value between 0 and 1.

A method for obtaining a regression equation for evaluating a geriatric disorder risk using logistic regression analysis is realized, for example, by performing the following steps (B) to (G). It is also preferable to perform the following step (A).
For multiple subjects,
(A) obtaining one or more basic data related to physique such as age, gender and BMI;
(B) Two or more walking parameters selected from cadence, stride, walking ratio, stride, step, walking angle, toe angle, stride left / right difference, step left / right difference, walking angle left / right difference, and left / right difference in both leg support periods Steps to measure,
(C) obtaining geriatric disorder data on the presence or absence or degree of geriatric disorder selected from knee pain, low back pain, urinary incontinence, dementia and sarcopenia;
(D) a step of extracting from the geriatric disorder data those that clearly have geriatric disorder and those that do not have geriatric disorder;
(E) The extracted senile disorder data is a binary objective variable, and the measurement data of the two or more walking parameters of the subject related to the senile disorder data is included in the explanatory variable, or the one or more basic data is also explained. For calculating the regression formula of logistic regression analysis included in the variable, and for any subject,
(F) obtaining a walking parameter or further obtaining basic data;
(G) A step of calculating a geriatric disorder risk from the regression equation calculated in (E) based on walking parameters or walking parameters and basic data

Here, the basic data of step (A) can be acquired by an inquiry or measurement.
The walking parameter of (B) step can be measured by the walking parameter means provided with the above-mentioned seat type pressure sensor.
(C) Step-by-step senile disorder data can be obtained through interviews and measurements.

  In the (D) step, in order to calculate the regression equation in the (E) step, from the senile disorder data acquired in the (C) step in advance, those that are clearly senile disorder and those that are not senile disorder are extracted.

  For example, based on the interview data of multiple subjects, for each age-related disability of the subject, knee pain, low back pain, urinary incontinence, dementia and sarcopenia, a disability standard was established for each senile disability. ”,“ Clearly disability ”,“ Slightly disability ”, and according to the geriatric disability data of multiple subjects,“ No disability ”and“ Clearly disability ” To extract.

  More specifically, regarding knee pain and low back pain, if the above-mentioned disability criterion is “medium pain”, classify as “no disability” when there is no pain, and there is strong or moderate pain. When there is a degree of pain, it is classified as “obviously impaired”, and when there is minor pain, it is classified as “slightly impaired”.

  In the case of urinary incontinence, if the standard of disability is “There is urinary incontinence at least once a day”, it is classified as “no disability” when there is no urinary incontinence, and at least once a day When there is urinary incontinence, it is classified as “obviously impaired”, and classified as “having slight disorder” 1 to 3 times a month or 1 to several times a week.

  In addition, the standard of a disorder | damage | failure uses what was determined based on the relationship between each senile disorder level and each gait parameter, the correct answer rate obtained from the result of multivariate analysis, preferably logistic regression analysis, etc. The criteria for obstacles and the levels and gait parameters of each geriatric disorder used to determine them are stored as a database.

In step (E), “no obstacle” or “obviously impaired” geriatric disorder data extracted in step (D) is used as a binary objective variable with one being 0 and the other being 1. The measurement data of the two or more walking parameters of the subject is used as the explanatory variable, or the regression analysis is performed by adding the measurement data of the walking parameter and the reference data to the explanatory variable.
In this case, it is preferable to establish a regression equation for each gender.

Further, the number of subjects for obtaining the regression equation is preferably 50 or more, more preferably 100 or more from the viewpoint of the accuracy of the regression equation.
Commercial statistical software (IBM) can be used to calculate the regression equation.

  In addition, when performing multivariate analysis, preferably logistic regression analysis, the parameters finally used as explanatory variables are the variable increase method, the variable decrease method, or both the variable increase method and the variable decrease method. Perform a regression analysis while changing, and select the one with a higher correct answer rate.

  After obtaining the regression equation, check the reliability of the regression equation by omnibus test, Hosmer and Lemeshow test, etc., and those with the specified reliability, more specifically, the omnibus test result is less than 0.05, Hosmer and When the Lemeshow test result is 0.05 or more, the regression equation is used to calculate the risk of senile disorder for any subject in steps (F) and (G).

Moreover, as a regression equation, the one with a correct answer rate of 65% or more is preferable.
Here, the correct answer rate is the ratio of the number of persons with the same age-related disability data and the age-related disability risk obtained by the regression equation for a plurality of elderly subjects.
For example, senile disability data acquired in step (C) is classified into three groups: “no disabilities”, “obviously disabilities”, “slight disabilities”, as in step (D). The “slightly impaired” group is treated as “no failure”. That is, in calculating the correct answer rate, all data of old-age disability data including “slight disability” that was not used when obtaining the regression equation is used.
On the other hand, the geriatric disorder risk (occurrence rate of geriatric disorder) obtained from the regression equation obtained in step (E) can be obtained as 0-1. Therefore, the case where the risk of senile disability obtained from the regression equation is 0.5 or more is “has a disability”, and the case where it is less than 0.5 is “no disability”.
Then, the results of `` has a disability '' or `` no disability '' when the old age disability data acquired in step (C) is classified as described above, and whether there is a `` disability '' obtained by the regression equation. The number of subjects with the same “no obstacle” result is taken as the correct answer rate.

  In the present invention, in evaluating the likelihood of specific senile disorders such as knee pain, low back pain, urinary incontinence, dementia or sarcopenia, cadence, walking ratio, stride, step, walking angle, toe angle, stride, Two or more selected from specific walking parameters such as stride left / right difference, step difference left / right difference, walking angle left / right difference, and bilateral leg support right / left difference are used. As a result of conducting multivariate analysis to determine the incidence using basic data, and narrowing down the parameters by variable increase method or variable decrease method, about senile disorder such as knee pain, low back pain, urinary incontinence, dementia or sarcopenia, Cadence, walking ratio, stride, step, walking angle, toe angle, stride left / right difference, step left / right difference, walking angle left / right difference, and left / right difference in both legs support period When two or more selected from specific walking parameters are included in the explanatory variable, the present inventors have found that the incidence of geriatric disorder calculated from the regression equation has a high correlation with the actual incidence of geriatric disorder. Based on what you found.

  According to the evaluation method and the evaluation system of the present invention, the elderly person or a caregiver thereof can easily obtain the status of geriatric disorder or the risk of geriatric disorder for various geriatric disorders, and appropriately deal with geriatric disorders. And it is easy to take useful measures to avoid old age disorders.

The evaluation method and evaluation system of the present invention can take various forms.
For example, the computing device stores measures such as exercises and jigs for avoiding old-age obstacles for each type and degree of old-age failure risk, which are displayed when the results are displayed and provided to the subject. May be.

  Further, in the evaluation system of the present invention, the arithmetic unit not only stores a regression equation for calculating the risk of senile disability from walking parameters and the like, but also the basic data of a large number of people used to derive the regression equation, walking Parameters and interview data are stored as a database, and each time a subject newly uses the evaluation system of the present invention, the database is updated by accumulating the subject's basic data, walking parameters, and interview data in the database. Also good. Thereby, the correct answer rate of the regression equation obtained from a database can be improved.

  Hereinafter, the present invention will be described specifically by way of examples.

  Based on an interview with aged 72-91 years old (129 men, 196 women), basic data including age and BMI, and senile disorder data on urinary incontinence, low back pain, sarcopenia and dementia (existence and extent of occurrence) Got.

  Moreover, as a seat type pressure sensor, walking parameter measurement means equipped with a seat type lower limb weight meter series walk way made by Anima Co., Ltd. is used, and the cadence, stride, walking ratio, stride, step, walking angle of these elderly people. The toe angle, the stride left / right difference, the step difference left / right difference, the walking angle left / right difference, and the left / right difference between both legs were measured.

Next, when the frequency of urinary incontinence is higher than the standard, based on the criteria of “there is urinary incontinence at least once a day”, “obviously there is urinary incontinence”, and when there is no urinary incontinence, “no urinary incontinence” From the urinary incontinence data obtained through the interviews, we extracted “obviously urinary incontinence” and “no urinary incontinence”.
Then, in setting whether or not urinary incontinence is the objective variable, 1 is set for “obviously urinary incontinence” and 0 is set for “no urinary incontinence”. Logistic regression analysis using the walking parameters, age, and BMI of elderly people as explanatory variables, and as shown in Table 1, the regression equation for calculating the geriatric disorder risk log p _x / (1−p _x ) is obtained. It was. Here, p _x is the incidence of senile disorders of the elderly x, take the numerical value of 0 to 1.

  Similarly, for low back pain, sarcopenia, knee pain, and dementia, regression equations for calculating the respective senile disorder risks were obtained. When acquiring the regression formula, a combination of basic parameters such as walking parameters and age used in the regression formula was selected so that the correct answer rate of the senile disorder risk (incidence rate) calculated from the regression formula would be high.

  From the values of the regression equation in Table 1, it is possible to determine the likelihood of failure in the present and future. In other words, 0.5-1 is a case where there is a symptom or a case where there is no symptom, but there is actually a symptom. Is possible. Specifically, if it is mild to 0.5 or more and less than 0.66, or if there is no symptom, caution is required.If 0.6 or more and less than 0.83 is moderate or if there is no symptom, it is necessary to consider measures to suppress the onset. It is determined that preparation is necessary.

  Similarly, when there is no symptom, the risk of onset can be determined in three stages. Specifically, 0 to less than 0.16 is considered safe, 0.16 to less than 0.33 is cautioned, and 0.33 to less than 0.5 is considered cautionary. By assigning stages in this way, current symptoms and future risks can be easily displayed for each old age disorder using a spider chart or the like.

  For each age-related disorder of knee pain, low back pain, urinary incontinence, sarcopenia, and dementia, it is calculated from the incidence of age-related disorders calculated from the regression equation in Table 1 and the presence or absence of age-related disorders in the current situation (at the initial interview) The percentage of correct answers was: knee pain (male) 88%, knee pain (female) 67%, back pain (male) 76%, back pain (female) 69%, urinary incontinence (female) 81%, sarcopenia (male) 91%, sarcopenia (Female) 80%, Dementia (male) 81%, Dementia (female) 86%.

  Furthermore, for geriatric disorders such as urinary incontinence (female), low back pain (male and female), and sarcopenia (male and female), the same interview was repeated for the same subject, and the changes in symptoms were followed. The correct answer rate was calculated from

As a result, most of those who answered that the initial incidence of urinary incontinence calculated from the regression equation was 0.5 or higher and that there was no urinary incontinence in the interview, or that there was urinary incontinence below the above-mentioned criteria, after 3 years Responded that there was urinary incontinence. On the other hand, among those who responded that the initial incidence of urinary incontinence calculated from the regression equation was less than 0.5 and there was no urinary incontinence in the interview, there were a few who responded that there was urinary incontinence above the standard after 3 years. there were. More specifically, the incidence of urinary incontinence (female) after 3 years was 71.3% when the regression equation value was 0.5 or more and there was no symptom at the beginning.
Similarly, the incidence of back pain (male and female) after 3 years was 60.9%.
The incidence of sarcopenia after 3 years was 56.5%.

  FIG. 4 shows the relationship between the risk of senile disorder (incidence) calculated from the regression equation and the degree of senile disorder at the time of the initial interview for urinary incontinence, back pain and sarcopenia. From FIG. 4, for all of urinary incontinence, low back pain, and sarcopenia, the higher the actual level of senile disorder, the higher the risk of senile disorder calculated by the regression equation. The accuracy of the estimation is shown.

From the above, it can be seen that based on the incidence of geriatric disorder calculated from the regression equation in Table 1, it is possible to predict the occurrence of geriatric disorder not only at the present time but also in the future within several years.
Table 1 also shows that the incidence of low back pain, urinary incontinence, sarcopenia, and dementia is high when it is obtained from regression equations that use walking parameters and basic data as explanatory variables for each gender.

  In addition to the regression equation shown above, for example, the following regression equation using time parameters relating to walking can be used in each of the following senile disorders.

Knee pain (male)
Walking speed * (-0.125) + right step * 1.219 + left walking angle * (-0.535) + right walking period * (-10.103) + right swing leg period * 0.171 + left both leg support period * 0.327 + step difference * 1.284 + 10.539
(Woman)
Walking speed * (-0.035) + Walking angle left / right difference * 0.289 + Left and right support difference between both legs * 0.267 + 3.923

Backache (female)
Left stride * (-0.083) + left walking angle * 0.136 + left / right difference between both legs support * 0.373 + age * 0.102-6.473

In relation to the above-described aspects of the present invention, the present invention further discloses the following aspects.
<1>
A method for evaluating the risk of geriatric disorder that evaluates the likelihood of occurrence of geriatric disorder (i.e., geriatric disorder risk) based on the walking parameters measured by the walking action, and includes cadence, stride, walking ratio, stride, Use two or more selected from gait, walking angle, toe angle, stride left / right difference, gait left / right difference, walking angle left / right difference, and bilateral leg support difference, knee pain, back pain, urinary incontinence, dementia and sarcopenia Geriatric disorder risk evaluation method for evaluating geriatric disorder risk for geriatric disorders selected from.

<2>
The evaluation method for geriatric disorder risk according to <1>, wherein the geriatric disorder risk is calculated by multivariate analysis including the geriatric disorder risk as an objective variable and the two or more walking parameters included in the explanatory variables.
<3>
The evaluation method for geriatric disorder risk according to <2>, wherein the explanatory variable further includes one or more of (BMI) basic data relating to age, sex and physique.
<4>
The evaluation method for geriatric disorder risk according to <2> or <3>, wherein a regression equation is established with the objective variable being a binary value of 0 or 1.
<5>
The method for evaluating a geriatric disorder risk according to any one of <2> to <4>, wherein logistic regression analysis is performed as multivariate analysis.
<6>
For multiple subjects,
Measuring the two or more walking parameters;
Obtaining geriatric disorder data relating to the presence or absence or degree of geriatric disorder;
Extracting from the geriatric disability data what is clearly geriatric and non-senile,
A step of calculating a regression equation of logistic regression analysis including the extracted senile disorder data as a binary objective variable and the measurement data of the two or more walking parameters of the subject related to the senile disorder data as explanatory variables; and an arbitrary subject <5> The method for evaluating a geriatric disorder risk according to <5>, further comprising a step of acquiring a gait parameter and calculating a geriatric disorder risk from a regression equation based on the gait parameter.
<7>
For a plurality of subjects, the method further comprises obtaining one or more of basic data on age, gender and physique,
In the step of calculating the regression equation, one or more of the basic data is included in the explanatory variables,
The evaluation method for geriatric disorder risk according to <6>, wherein a geriatric disorder risk is calculated from a regression equation based on two or more walking parameters and one or more basic data for any subject.
<8>
<6> or <7> The evaluation method for geriatric disorder risk according to <6> or <7>, wherein the regression equation is calculated for each gender in the step of calculating the regression equation.
<9>
For the plurality of subjects, the geriatric disorder risk is calculated based on the regression equation, the correct rate of the geriatric disorder risk calculation value is calculated from the geriatric disorder risk calculation value and the geriatric disorder data, and the correct answer rate is 65% or more. In this case, the senile disorder risk evaluation method according to any one of <6> to <8>, wherein an aging disorder risk is calculated using the regression formula for an arbitrary subject.
<10>
The method for evaluating a geriatric disorder risk according to any one of <1> to <9>, wherein a plurality of types of geriatric disorder risk are evaluated based on a walking parameter measured by a single walking action.
<11>
The method for evaluating a risk of geriatric disorder according to any one of <1> to <10>, wherein the walking parameter is a walking parameter obtained by measuring a foot pressure distribution during a walking action using a seat type pressure sensor.
<12>
Two or more walking parameters selected from cadence, stride, walking ratio, stride, step, walking angle, toe angle, stride left / right difference, step left / right difference, walking angle left / right difference, and left / right difference during both leg support depending on the walking action Walking parameter measuring means for measuring
Based on the two or more walking parameters measured by the walking parameter measuring means, the computing means for evaluating the senile disorder risk for the senile disorder selected from knee pain, low back pain, urinary incontinence, dementia and sarcopenia, and the computing means were evaluated. An evaluation system for geriatric disorder risk comprising a display means for displaying geriatric disorder risk.
<13>
The evaluation system for geriatric disorder risk according to <12>, wherein the computing means calculates geriatric disorder risk by multivariate analysis including the two or more walking parameters as explanatory variables.
<14>
The calculation means is a binary expression of the regression data of multivariate analysis, the data of geriatric disability related to the presence or absence or degree of geriatric disability obtained from multiple subjects that are clearly senile and have no senile disability As a variable, a regression equation based on logistic regression analysis including measurement data of two or more walking parameters of the subject related to the geriatric disorder data is included in the explanatory variable, and for any subject, the two or more walking parameters are stored. The senile disorder risk evaluation system according to <13>, wherein the senile disorder risk is calculated based on the regression equation.
<15>
The regression equation stored by the calculation means includes one or more basic data on the age, sex and physique of the subject as explanatory variables, and the regression based on the two or more walking parameters and the one or more basic data for any subject The evaluation system for geriatric disorder risk according to <14>, wherein geriatric disorder risk is calculated from a formula.
<16>
The regression equation stored by the computing means is that the senile disorder is calculated from the regression equation for a plurality of subjects including a subject with obvious senile dysfunction, a subject without senile dysfunction, and a subject whose aging disorder is intermediate between them. The evaluation system for geriatric disorder risk according to any one of <12> to <15>, wherein a correct answer rate for risk geriatric disorder data is 65% or more.
<17>
The evaluation system for geriatric disorder risk according to any one of <12> to <16>, wherein the computing unit calculates a plurality of types of geriatric disorder risk based on the gait parameters measured by the gait parameter measuring unit in a single walking action. .
<18>
The evaluation system for geriatric disorder risk according to any one of <12> to <17>, wherein the walking parameter measurement means measures a walking parameter by measuring a foot pressure distribution during a walking action with a seat type pressure sensor.

1 Evaluation system for geriatric disorder risk 2 Seat type pressure sensor 3 Personal computer 4 Display 5 Video camera

Claims (10)

Likelihood of geriatric disorders (hereinafter, also referred to as "old age Failure Risk"), and a method of calculating the geriatric disorders risk calculated using the walking parameters measured by the walking act, vinegar Toraido, walking ratio, stride length, walking Includes two or more walking parameters selected from the gap, walking angle, toe angle, stride left / right difference, step difference left / right difference, walking angle left / right difference, and bilateral leg support right / left difference as explanatory variables , knee pain, low back pain, urinary incontinence, the method of calculating the geriatric disorders risk for calculating the geriatric disorder risks selected from dementia and sarcopenia the old year failure risk multivariate analysis of interest variable. The method for calculating a geriatric disorder risk according to claim 1, wherein the geriatric disorder risk is selected from knee pain, back pain and dementia. The method for calculating the risk of geriatric disorder according to claim 2, wherein logistic regression analysis is performed as multivariate analysis. For multiple subjects,
Measuring the two or more walking parameters;
Obtaining geriatric disorder data relating to the presence or absence or degree of geriatric disorder;
Extracting from the geriatric disability data what is clearly geriatric and non-senile,
A step of calculating a regression equation of logistic regression analysis including the extracted senile disorder data as a binary objective variable and the measurement data of the two or more walking parameters of the subject related to the senile disorder data as explanatory variables; and an arbitrary subject for acquires walking parameter calculation method senile failure risks according to claim 1, comprising the step of calculating the old fault risks from the regression equation based on the walking parameter. For the plurality of subjects, the geriatric disorder risk is calculated based on the regression equation, the correct rate of the geriatric disorder risk calculation value is calculated from the geriatric disorder risk calculation value and the geriatric disorder data, and the correct answer rate is 65% or more. In this case, the senile disorder risk calculation method according to claim 4, wherein the senile disorder risk is calculated for an arbitrary subject using the regression equation. The walking action, scan Toraido, walking ratio, stride, Fu隔, walking angle, toe angle, stride laterality, walking隔左right difference, walking angular difference between right and left, and two or more walking parameters selected from the two-leg supporting period laterality Walking parameter measuring means for measuring,
Based on the walking parameter measuring means the two or more walking parameters were measured, knee pain, lower back pain, urinary incontinence, dementia and old age disorders risks selected from sarcopenia, a walking parameter of the two or more pre-stored description A calculation system for geriatric disorder risk comprising calculation means for calculating from a regression equation of multivariate analysis included in a variable , and display means for displaying a geriatric disorder risk calculated by the calculation means. The calculation means is a binary expression of the regression data of multivariate analysis, the data of geriatric disability related to the presence or absence or degree of geriatric disability obtained from multiple subjects that are clearly senile and have no senile disability As a variable, a regression equation based on logistic regression analysis including measurement data of two or more walking parameters of the subject related to the geriatric disorder data is included in the explanatory variable, and for any subject, the two or more walking parameters are stored. The geriatric disorder risk calculation system according to claim 6, wherein geriatric disorder risk is calculated from the regression equation based on the regression equation. The regression equation stored by the calculation means includes one or more basic data on the age, sex and physique of the subject as explanatory variables, and the regression based on the two or more walking parameters and the one or more basic data for any subject geriatric disorders risk calculation system according to claim 7, wherein calculating the old fault risk from the equation. The senile disorder risk calculation system according to any one of claims 6 to 8 , wherein the calculation means calculates a plurality of types of senile disorder risk based on the gait parameters measured by the gait parameter measurement unit in a single walking action. The system for calculating a risk of geriatric disorder according to any one of claims 6 to 9 , wherein the walking parameter measuring means measures the walking parameter by measuring a foot pressure distribution during a walking action with a seat type pressure sensor.

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