Information processing device, information processing method, and program

Abstract

To provide technology that helps heighten maintenance improvement and eagerness for walking ability and thereby contribute to extension of healthy lifespan and reduction of caregiving and medical costs.SOLUTION: A first parameter acquisition unit 51 acquires data related to each of three or more first parameters pertaining to a user including at least a walking speed, age, and sex, as first parameter data. A walking age calculation unit 52 generates walking ability evaluation data of the user on the basis of the first parameters acquired by the first parameter acquisition unit 51. A second parameter acquisition unit 53 acquires data pertaining to each of one or more second parameters that affect the walking ability other than the foregoing first parameters pertaining to the user, as second parameter data. A walking age correction unit 54 corrects the evaluation data on the basis of the second parameters.SELECTED DRAWING: Figure 5

Description

[Technical Field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] With the advent of a super-aging society and the accompanying increase in medical and nursing care costs, there is a demand for extending healthy lifespan. Patent Document 1 listed below discloses a health condition evaluation method for use in evaluating the effect of extending healthy lifespan. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-174715 Summary of the Invention [Problem to be solved by the invention]

[0004] It is known that "walking ability" is important for extending healthy life expectancy, but the above-mentioned conventional technology does not evaluate "walking ability." As a result, it has been difficult to maintain and improve the walking ability of subjects and increase their motivation, thereby contributing to extending healthy life expectancy and reducing nursing care and medical costs.

[0005] The present invention was made in consideration of these circumstances, and aims to provide technology that can maintain and improve walking ability and increase motivation, thereby contributing to extending healthy life expectancy and reducing nursing care and medical expenses. [Means for solving the problem]

[0006] In order to achieve the above object, an information processing device according to one aspect of the present invention comprises: a first parameter acquiring means for acquiring data on each of three or more first parameters related to the user, including at least walking speed, age, and sex, as first parameter data; evaluation means for generating evaluation data of the user's walking ability based on the first parameter data; a second parameter acquisition means for acquiring data on one or more second parameters other than the first parameter related to the user that affect the walking ability as second parameter data; evaluation correction means for correcting the evaluation data based on the second parameter data; Equipped with.

[0007] An information processing method and a program corresponding to the information processing device according to one aspect of the present invention are also provided as an information processing method and a program according to one aspect of the present invention. [Effects of the Invention]

[0008] According to the present invention, it is possible to maintain and improve walking ability and increase motivation, thereby contributing to extending healthy life expectancy and reducing nursing care and medical expenses. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a schematic diagram showing an example of an outline of a service provided using an information processing device of the present invention. [Figure 2] FIG. 2 is a diagram for explaining a model relating to walking speed and walking age used in the present service of FIG. 1. [Figure 3] FIG. 2 is a schematic diagram showing a specific example of the present service of FIG. 1. [Figure 4] 1 is a block diagram showing an example of a hardware configuration of an information processing device according to the present invention; [Figure 5] 5 is a functional block diagram showing an example of a functional configuration of the information processing device of FIG. 4. FIG. [Figure 6] FIG. 10 is a diagram relating to test case 1 for this service, showing some of the input items and an evaluation of current walking ability. [Figure 7] FIG. 7 is a diagram showing an example of a graph and output quantifying the evaluation of future walking ability in test case 1 of FIG. 6. [Figure 8]FIG. 10 is a diagram relating to test case 2 for this service, showing some of the input items and an evaluation of current walking ability. [Figure 9] FIG. 9 is a diagram showing an example of a graph and output quantifying the evaluation of future walking ability in test case 2 of FIG. 8. [Figure 10] FIG. 10 is a diagram relating to test case 3 for this service, showing some of the input items and an evaluation of current walking ability. [Figure 11] FIG. 11 is a diagram showing an example of a graph and output quantifying the evaluation of future walking ability in test case 3 of FIG. 10. [Figure 12] This is a diagram illustrating five exercises that improve walking ability. [Figure 13] FIG. 10 is a diagram illustrating an example of risk factors and recommendations. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment of the present invention will be described with reference to the drawings.

[0011] FIG. 1 is a schematic diagram showing an example of an outline of a service (hereinafter referred to as the present service) provided using an information processing device of the present invention.

[0012] This service can help maintain and improve the walking ability of the target person (user) and increase their motivation, thereby contributing to extending healthy life expectancy and reducing nursing care and medical expenses. In this type of service, the target audience includes insurers, medical professionals, caregivers, and patients themselves, as well as the entire population.

[0013] This service uses an individual's physical data (age, gender, height, weight, etc.), medical history (diabetes, knee osteoarthritis, etc.), events that may affect future walking ability (history of falls, history of hospitalization, etc.), and current walking ability (walking speed, etc.) to predict an assessment of current walking ability (equivalent to what age's walking ability) and future walking ability (the walking speed at which daily life is hindered and the number of years until independent walking becomes difficult), and provides this to the user. In addition, this service provides each user (individually) with an explanation of factors that will affect their future walking ability and recommended exercise information for improvement.

[0014] With regard to "walking ability," which is important for extending healthy life expectancy, this service takes a two-stage, two-model approach to generating and correcting walking age, which is generated as evaluation data for walking ability, and then provides the corrected walking age to the user. As will be explained in more detail later, this service employs two different models for men and women. Furthermore, in this service, the walking age initially calculated in the first stage is corrected (corrected, adjusted) in the second stage based on, for example, 19 health-related input items. Furthermore, this service adjusts the weight of each input item based on evidence.

[0015] This service collects walking speed data from over 5,000 people and health condition data from over 6,000 people, and this collected health condition data serves as evidence. In addition, this service conducts walking speed tests for Japanese and non-Japanese people by age group, and the test results serve as evidence. The number of tests was not particularly limited, but was as follows: four tests for Japanese people in their 20s, three tests for non-Japanese people in their 20s, four tests for Japanese people in their 30s, three tests for non-Japanese people in their 30s, four tests for Japanese people in their 40s, three tests for non-Japanese people in their 40s, three tests for Japanese people in their 50s, two tests for non-Japanese people in their 50s, nine tests for Japanese people in their 60s, seven tests for non-Japanese people in their 60s, 14 tests for Japanese people in their 70s, 10 tests for non-Japanese people in their 70s, eight tests for Japanese people in their 80s, and eight tests for non-Japanese people in their 80s.

[0016] Although not shown in the figure, it is assumed that the relative adjustment effects of patient characteristics and medical findings on the initial calculated walking age (first calculated walking age), which will be described later, are obtained in advance. It is also assumed that the weighting of input items for calculating the walking age is obtained in advance. Major medical findings included Parkinson's disease, multiple sclerosis, stroke, depression, lower limb arthritis, diabetes, cognitive impairment, joint replacement surgery, hospitalization in the past year, history of falls (over the past two years), and history of fractures (over the past two years). Input items include hospitalization, BMI, diabetes, Parkinson's disease, falls / fractures, joint replacement, arthritis, multiple sclerosis, depression, cognitive impairment, back pain, leg pain, polypharmacy, walking speed, stroke, etc. In weighting the input items, walking speed is the most important.

[0017] The service employs local regression and locally weighted smoothing regression techniques, specifically adapted to the Japanese dataset to ensure that walking age predictions fit various age groups, and employs techniques that reveal nonlinear relationships within the data. This service uses a model that can accurately predict ages from 20 to 91.

[0018] In Figure 1, in this service, the first step is to input walking speed, age, and gender by the user, and then generate an initial calculated walking age (first calculated walking age). In other words, the initial calculated walking age is generated with the minimum number of parameters. The next approach taken in the second stage is to adjust (correct) the walking age of the initial calculation value mentioned above based on health-related input items (health status input items (parameters other than the parameters in the first stage mentioned above)), and provide the user with the adjusted walking age.

[0019] Regarding the generation of the walking age mentioned above, two models shown in Figure 2 are used. FIG. 2 is a diagram for explaining the model relating to walking speed and walking age used in the present service of FIG.

[0020] This service uses two models to generate walking age: a female standard model and a male standard model. In both the female standard model and the male standard model, the vertical axis is walking speed (m / sec) and the horizontal axis is age (years), and the data is plotted so that it is clear whether the person is Japanese or non-Japanese, and the walking age can be determined from the approximation curve. These two models are used in this service. Furthermore, since data on non-Japanese people is also included, the two models can be considered global data. The female standard model and male standard model not only show that walking speed slows with age, but also allow us to determine walking age from walking speed (walking age can be calculated from the models).

[0021] A specific example of the service shown in FIG. 1, in which the above two models are used, will be described with reference to FIG. FIG. 3 is a schematic diagram showing a specific example of the present service of FIG.

[0022] In this service, the user inputs walking speed, age, and gender in the first step described above, and then in the second step, they input information related to their health, and a specified output is provided to the user.

[0023] Specifically, in this service, an initial calculated walking age (baseline walking age) is generated based on the walking speed and gender of the above-mentioned input items entered by the user and the above-mentioned two models. Furthermore, the above-mentioned initial calculated walking age is adjusted (corrected) based on the health condition input items entered by the user, and the adjusted walking age and information, recommendations, etc. regarding the user's future walking ability based on this are provided to the user.

[0024] The initial calculated walking age is adjusted (corrected) based on factors such as BMI, pain (back or leg), medical findings, and the use of five or more medications. The adjustment factors are designed to increase the walking age from the initial calculated value. That is, the adjustment by the adjustment factor "BMI" is performed based on the formula "walking age = walking age + (Japanese standard BMI - BMI) x β". In addition, adjustment by the adjustment factor "pain (back or lower limbs)" is performed based on the formula "walking age = walking age + pain assessment × β". In addition, the adjustment by the adjustment factor "medical findings" is performed based on the formula "walking age = walking age + medical findings × β". In addition, the adjustment factor "taking five or more medications" is adjusted based on the formula "walking age = walking age + medication × β."

[0025] As the above-mentioned output, for example, "Your walking age is 78 years old," "4 years until you will have difficulty walking 400 m," "6 years until you will not be able to walk 400 m," and "12 years until you will need assistance" are displayed on the information processing device 1 operated by the user. The information processing device 1 also displays a button related to "recommendations," and by operating this button, for example, an explanation of factors that will affect future walking ability and recommended exercise information for improvement are provided.

[0026] By allowing users to know their own walking ability based on their walking age as described above, and by providing users with an explanation of factors that will affect their future walking ability and recommended exercise information for improvement, this service can help users maintain and improve their walking ability and increase their motivation, thereby contributing to extending healthy life expectancy and reducing nursing care and medical costs.

[0027] FIG. 4 is a block diagram showing an example of the hardware configuration of an information processing device according to the present invention.

[0028] The information processing device 1 is an information processing device in which a predetermined application program is installed and used by a user, and is configured as a smartphone, tablet, personal computer, etc. Note that, although a predetermined application program is installed here, the service may be provided by a predetermined website managed by a server.

[0029] The information processing device 1 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a bus 14, an input / output interface 15, an input unit 16, an output unit 17, a memory unit 18, a communication unit 19, and a drive 20.

[0030] The CPU 11 executes various processes according to a program recorded in the ROM 12 or a program loaded from the storage unit 18 into the RAM 13 . The RAM 13 also stores data and the like necessary for the CPU 11 to execute various processes.

[0031] The CPU 11, ROM 12, and RAM 13 are connected to one another via a bus 14. An input / output interface 15 is also connected to this bus 14. An input unit 16, an output unit 17, a storage unit 18, a communication unit 19, and a drive 20 are connected to the input / output interface 15.

[0032] The input unit 16 is configured with, for example, a keyboard and is used to input various information. The output unit 17 is configured with a display such as a liquid crystal display, a speaker, etc., and outputs various information as images and sounds. The storage unit 18 is configured with a DRAM (Dynamic Random Access Memory) or the like, and stores various data. The communication unit 19 communicates with other devices via a network N including the Internet.

[0033] Removable media 30, such as a magnetic disk, optical disk, magneto-optical disk, or semiconductor memory, is appropriately attached to the drive 20. A program read from the removable media 30 by the drive 20 is installed in the storage unit 18 as needed. Furthermore, the removable medium 30 can also store various data stored in the storage unit 18 in the same manner as the storage unit 18.

[0034] FIG. 5 is a functional block diagram illustrating an example of the functional configuration of the information processing device in FIG.

[0035] As shown in FIG. 5, the CPU 11 of the information processing device 1 functions as a first parameter acquisition unit 51, a walking age calculation unit 52, a second parameter acquisition unit 53, a walking age correction unit 54, a future walking ability prediction unit 55, and a walking ability improvement suggestion unit 56. In addition, a walking age calculation model 81 and a user information DB 82 are provided in one area of ​​the storage unit 18 of the information processing device 1.

[0036] The first parameter acquisition unit 51 acquires data on three or more first parameters related to the user, including at least walking speed, age, and gender, as first parameter data. In this embodiment, the first parameter data is acquired via the input unit 16 based on input from the user, but may also be acquired via the communication unit 19. The first parameter data acquired by the first parameter acquisition unit 51 is stored in the user information DB 82.

[0037] The walking age calculation unit 52 generates evaluation data of the user's walking ability based on the first parameter data acquired by the first parameter acquisition unit 51. The evaluation data of walking ability here is the walking age at the first stage described above. In generating the walking ability evaluation data, the walking age calculation unit 52 uses the walking age calculation model 81. The walking age calculation model 81 stores two models, the female standard model and the male standard model, and either the female standard model or the male standard model is selected based on the first parameter data related to gender, and then the walking age at the first stage (the walking age of the initial calculated value described above) is generated by the walking age calculation unit 52. The walking age generated by the walking age calculation unit 52 (the walking age of the initial calculated value described above) is stored in the user information DB 82.

[0038] The second parameter acquisition unit 53 acquires data on one or more second parameters that affect the user's walking ability other than the above-mentioned first parameters as second parameter data. In this embodiment, the data is acquired via the input unit 16 based on a user's input, but it may also be acquired via the communication unit 19. The second parameter data acquired by the second parameter acquisition unit 53 is stored in the user information DB 82.

[0039] The parameters that can be the first and second parameters mentioned above are listed below (these parameters are to be selected appropriately from the following parameters. In the above, the number of input items is 19, but this number is just an example). Walking speed, age, sex, Parkinson's disease, multiple sclerosis, stroke, depression, lower limb arthritis, diabetes, cognitive impairment, joint replacement surgery, hospitalization in the past year, fall history (2 years), fracture history (2 years), BMI, number of medications taken, initial medical examination, pain score, knee lifting ability, leg lifting ability, side walking ability, step climbing ability, standing ability, heel-to-toe standing / walking ability, vegetable intake, etc., average number of steps per day, snacking habits, living area, family structure, occupation / industry, smoking history, smoking of cohabitants, drinking tendency, alcohol amount, high-intensity exercise habits, moderate-intensity exercise habits , low-intensity exercise habits, presence or absence of exercise habits, average sleep duration, presence or absence of stress, intention to improve lifestyle habits, frequency of collecting health information, frequency of absence from work, waist circumference, systolic blood pressure, diastolic blood pressure, HDL cholesterol, LDL cholesterol, triglycerides, liver function test values, glucose metabolism test values, kidney function test values, body fat percentage, hemoglobin content, red blood cell count, hematocrit value, visual acuity, food record, salt intake, water intake, number of meals per day, number of meals per day, medical history of close relatives, daily calories burned, daily exercise amount, daily metabolic rate, heart rate, blood oxygen content, number of stairs climbed, distance traveled, stress level, stride length, sleep Sleep state (REM, NREM, interrupted, etc.), wake-up time, sleep duration, resting heart rate, stride length, heart rate variability, body temperature, respiratory rate, working hours, car driving frequency, motorcycle driving frequency, risky hobbies, asthma, high blood pressure, heart disease, pneumonia, liver disease, cancer, surgical history, continuous walking time, performance status, weight, working style (sitting, standing, etc.), cadence, body composition (fat to muscle ratio), grip strength, dynamic gait index score (balance measurement), extends depression diagnosis to mental health parameters such as stress and social connectedness, and cognitive function test scores (memory, attention, etc.) Expanding the diagnosis of cognitive impairment, such as: history of lower limb ligament injury (Achilles tendon, ACL, MCL, PC, etc.), history of sciatica, history of rheumatoid arthritis (not just osteoarthritis), history of dizziness or balance problems, diagnosis of movement disorders other than Parkinson's disease (MSA, PSP, DLB, Huntington's disease, ataxia, tardive dyskinesia, restless legs syndrome, etc.), knee pain, use of walking aids (e.g., canes), heart rate variability, past exercise experience, current exercise habits (e.g., 30 minutes or more per day, sweating more than twice a week), 6-minute walk test, TUG / timed up and go test,These include one-leg stance test, daily sleep time, Vo2Max, resting heart rate, fasting blood glucose level, HbA1c, bone density, blood pressure, visual acuity, intraocular pressure, hearing, lung capacity %, smoking amount / habits, weight change since age 20, weight change over the past year, eating speed, eating dinner within 2 hours before bedtime, number of days skipping breakfast, level of care required, overtime hours worked, and medical history.

[0040] The walking age correcting unit 54 corrects the above-mentioned evaluation data, that is, the walking age generated by the walking age calculating unit 52 (the walking age of the above-mentioned initial calculated value) based on the second parameter data. In this correction, the walking age correction unit 54 uses a walking age calculation model 81. In addition to the two models, the female standard model and the male standard model, the walking age calculation model 81 also stores the above-mentioned "relative adjustment effect of patient characteristics and medical findings on the walking age of the initial calculation value (first calculated walking age)", "weighting of input items for walking age calculation", etc. Furthermore, as adjustment factors, formulas for adjustment for the above-mentioned BMI, pain (back or lower limbs), medical findings, and taking five or more medications are also stored. The walking age corrected by the walking age correcting unit 54 is stored in the user information DB 82.

[0041] The future walking ability prediction unit 55 predicts the user's future walking ability based on the evaluation data corrected by the walking age correction unit 54, i.e., the walking age at the second stage described above. The future walking ability prediction unit 55 uses, for example, a walking age calculation model 81 for predicting the future walking ability. Note that the walking age calculation model 81 stores quantified data required for predicting the future walking ability (this data may be stored separately from the walking age calculation model 81). The walking age corrected by the walking age correcting unit 54 is stored in the user information DB 82.

[0042] The walking ability improvement suggestion unit 56 generates a proposal for improving the user's walking ability based on at least some of the three or more first parameters and one or more second parameters described above, and the walking age corrected by the walking age correction unit 54, and outputs the proposal to execute control to suggest to the user. The proposal for improving the user's walking ability is stored in the user information DB 82. An example of the proposal for improving the user's walking ability will be described later with reference to FIGS.

[0043] The test cases for this service are explained below. FIG. 6 is a diagram relating to test case 1 for this service, showing some of the input items and the current evaluation of walking ability. FIG. 7 is a diagram showing an example of a graph and output that quantifies the evaluation of future walking ability in test case 1 of FIG.

[0044] 6, the user of test case 1 is a 60-year-old male with a walking speed of 1.3 m / s, and these are input to the information processing device 1. In addition, a height of 170 cm and a weight of 70 kg are also input to the information processing device 1. In addition, the following facts are also input to the information processing device 1: no diabetes, no pain, no taking five or more medications, no cognitive impairment, no depression, etc.

[0045] By the user of test case 1 making the above inputs, the information processing device 1 generates and adjusts the walking age of the user of test case 1 and predicts the future walking ability of the user of test case 1, as shown in the graph of Figure 7. Then, the information processing device 1 outputs the following: "Your current age: 60 years," "Your current walking age: 61 years and 8 months," "Predicted period until you will have difficulty walking 400 m: 20 years and 1 month," "Predicted period until you will be unable to walk 400 m: 28 years and 11 months," and "Predicted period until you will require assistance: 37 years and 8 months."

[0046] FIG. 8 is a diagram relating to test case 2 for this service, showing some of the input items and an evaluation of the current walking ability. FIG. 9 is a diagram showing an example of a graph and output that quantifies the evaluation of future walking ability in test case 2 of FIG.

[0047] 8, the user of test case 2 is a 55-year-old male with a walking speed of 1.2 m / s, and these information are input to the information processing device 1. In addition, a height of 165 cm and a weight of 82 kg are also input to the information processing device 1. In addition, the fact that the user has diabetes, pain, is not taking five or more medications, does not have cognitive impairment, has depression, etc. are also input to the information processing device 1.

[0048] By the user of test case 2 making the above inputs, the information processing device 1 generates and adjusts the walking age of the user of test case 2 and predicts the future walking ability of the user of test case 2, as shown in the graph of Figure 9. Then, the information processing device 1 outputs the following: "Your current age: 55 years," "Your current walking age: 87 years 7 months," "Predicted time until you will have difficulty walking 400 m: 0 years," "Predicted time until you will be unable to walk 400 m: 2 years 11 months," and "Predicted time until you will require assistance: 11 years 9 months."

[0049] FIG. 10 is a diagram relating to test case 3 for this service, showing some of the input items and an evaluation of the current walking ability. FIG. 11 is a diagram showing an example of a graph and output that quantifies the evaluation of future walking ability in test case 3 of FIG.

[0050] 10, the user of test case 3 is a 70-year-old woman with a walking speed of 1.2 m / s, and these information are input to the information processing device 1. In addition, a height of 155 cm and a weight of 40 kg are also input to the information processing device 1. In addition, other information such as no diabetes, no pain, taking five or more medications, cognitive impairment, and no depression are also input to the information processing device 1.

[0051] When the user of test case 3 makes the above input, the information processing device 1 generates and adjusts the walking age of the user of test case 3 and predicts the future walking ability of the user of test case 3, as shown in the graph of Figure 11. Then, the information processing device 1 outputs "Your current age: 70 years," "Your current walking age: 96 years 7 months," "Predicted time until you will have difficulty walking 400 m: 0 years," "Predicted time until you will be unable to walk 400 m: 0 years," and "Predicted time until you will require assistance: 2 years 10 months."

[0052] Explain recommended exercise information provided to improve future walking ability. FIG. 12 is a diagram illustrating five exercises that improve walking ability.

[0053] The recommended exercise information provided for improving walking ability in the future includes five exercises (recommended actions) for improving walking ability, as shown in FIG. That is, the exercises include "knee lifting exercise," "step up and down exercise," "standing up exercise," "side leg lifting / walking sideways," and "standing / walking with heel and toe in a straight line." FIG. 12 shows the strength score, balance score, and flexibility score as improvements in walking ability due to the above-mentioned five exercises, and these scores are also provided to the user.

[0054] Risk factors and recommendations for improving the user's walking ability are discussed here. FIG. 13 is a diagram illustrating an example of risk factors and recommendations.

[0055] If the risk factor is "slower than average walking speed," the recommendation is "Increase physical activity and consider exercises and coordination training that improve strength, balance, and timing." Also, if the risk factor is "low BMI," the recommendation is "Improve your diet and consider using nutritional supplements to gain weight. Consult your doctor about what is best for you." Also, if the risk factor is "high BMI," the recommendation is "lose weight. Consult your doctor about appropriate methods." Also, if the risk factor is "high pain score," the recommendation is "Make sure that appropriate pain management is being carried out. Consult your doctor about appropriate methods." Also, if the risk factor is "taking five or more medications," the recommendation is "review the medications you are taking. Consult your doctor to see if they are appropriate." Also, if the risk factor is "Parkinson's disease," the recommendation is "Make sure that your Parkinson's disease is being managed appropriately. Consult your doctor about appropriate methods."

[0056] Also, if the risk factor is "multiple sclerosis," the recommendation is "Make sure that your multiple sclerosis is being managed appropriately. Consult your doctor about appropriate methods." Also, if the risk factor is "stroke," the recommendation is "Make sure that the effects of stroke are being managed appropriately. Consult your doctor about appropriate methods." Also, if the risk factor is "depression," the recommendation is "Make sure that the depression is being managed appropriately. Consult your doctor about appropriate methods." Also, if the risk factor is "lower limb arthritis," the recommendation is "Make sure that your arthritis is being managed appropriately. Consult your doctor about appropriate methods." Also, if the risk factor is "diabetes," the recommendation is "Make sure your diabetes is being managed appropriately. Consult your doctor about appropriate methods." Also, if the risk factor is "cognitive impairment," the recommendation is "Make sure that memory impairment and confusion are being managed appropriately. Consult your doctor about appropriate methods."

[0057] Although not specifically illustrated here, we will summarize the above and explain below "the time when nursing care will be required / walking will become difficult as predicted from walking speed" (a new evidence-based platform (this service)). <Aging of Japanese society> Japan is aging more rapidly than other countries, with an estimated one-third of the population being 65 years or older by 2050. Frailty is a well-recognized problem among older adults and is associated with poor outcomes such as disability and hospitalization and mortality. Therefore, interventions aimed at improving physical capabilities to promote healthy aging and prevent the progression to disability and the need for care are needed. <Why walking speed is important> Walking speed has been shown to affect quality of life, activities of daily living, disease risk, and life expectancy. A decrease in walking speed increases the risk of falls, fractures, injuries, hospitalization, and death. However, it is also important to note that improving walking speed can improve health outcomes. <What is the purpose of this platform?> The aim is to build an evidence-based tool that can calculate and predict the following: (1) calculate the patient's "walking age" by combining various parameters, (2) predict when the patient will require nursing care or become bedridden if no intervention is made, and (3) present step-by-step measures to improve walking age. <Evidence used in platform development> We conducted a systematic review and landscape analysis to examine factors that influence walking speed. A total of 3544 publications were identified and included if they described factors related to gait and movement, or the impact of gait disorders on physical functioning in daily life, survival, and mortality. We also consulted experts in physiology, exercise therapy, and epidemiology. <Evidence search results> <Walking speed and age> Walking speed declines with age, but this is not limited to aging, and can also be caused by underlying health conditions. Furthermore, a decrease in walking speed is associated with an increased risk of falls, fractures, and injuries. <Utilization of wearable devices> Wearable devices can be worn in normal living environments, allowing patients to measure their walking speed over long periods of time. Wearable devices are suitable for measuring walking speeds that vary widely, such as "habitual walking." (The 6-meter walking test is recommended for measuring walking speed, as it is the simplest and most accurate method.) <Major related factors> Various factors related to walking age and transition to a state requiring nursing care were suggested. These were classified into neurological factors (dementia and stroke, etc.), musculoskeletal factors (history of fractures and falls, etc.), and others (depression, diabetes, number of medications, etc.).

[0058] <Algorithm Fundamentals and Structure> The algorithm was developed using walking data from over 5,000 people and health-related data from over 6,000 people. Different output models are used for men and women, and the output is adjusted for each factor based on evidence. The algorithm calculates walking age and other outputs according to a standard normal curve (based on age and gender). Walking age and standard walking speed: Walking speed, age, and gender are input into the model, and "walking age" and standard walking speed for the actual age are calculated from walking speed. Trajectory model: By inputting a wide range of health information into the interface, the age at which a person will be predicted to require care or become unable to walk is identified. Factors included are diabetes, hospitalization, and leg pain, which have relatively small effects on the model, and stroke, multiple medications, and back pain, which have large effects on the model. <Platform output> Once all the information has been collected, the following output will be obtained: That is, walking age (years), difference from actual age, standard walking speed for actual age, predicted number of years until it becomes difficult to walk 400m, predicted number of years until it becomes necessary to require nursing care, and recommendations for improving walking age, including exercise therapy and improving health status. Each recommendation is evidence-based and the output is based on what the expert advisors consider to be useful, important and achievable.

[0059] <Summary> An individual's walking speed affects their quality of life, ability to perform daily activities, and even their risk of disease and death. This new, evidence-based platform calculates each individual's "walking age" based on various factors, and uses the same information to predict when their walking ability will be impaired or when they will require care. It also provides recommendations for measures to improve their walking age.

[0060] Although the embodiments of the present invention have been described above, the present invention is not limited to the above-described embodiments. Furthermore, the effects described in the present embodiments are merely a list of the most preferable effects resulting from the present invention, and the effects of the present invention are not limited to those described in the present embodiments.

[0061] For example, the above series of processes can be executed by hardware or software. In other words, the functional configuration shown in FIG. 5 is merely an example, and is not particularly limited to only the functional configuration of FIG. That is, it is sufficient if the information processing device 1 has the function of executing the above series of processes as a whole, and the type of functional block used to realize this function is not limited to the examples in Figures 1 to 13. Furthermore, the location of the functional block is not particularly limited to those in Figures 1 to 13, and may be arbitrary. For example, the functional block and database of the information processing device 1 may be transferred to a server or the like. Furthermore, one functional block and database may be configured as a single piece of hardware, a single piece of software, or a combination thereof.

[0062] When a series of processes is executed by software, the programs that make up the software are installed into a computer or the like from a network or a recording medium. The computer may be a computer built on dedicated hardware. The computer may also be a computer capable of executing various functions by installing various programs, such as a server, a general-purpose smartphone, or a personal computer.

[0063] The recording medium containing such a program may be composed of not only a removable medium (not shown) that is distributed separately from the device main body in order to provide the program to users, etc., but also a recording medium that is provided to users, etc. in a state where it is pre-installed in the device main body.

[0064] In this specification, the steps describing the program to be recorded on the recording medium include not only processes that are performed in chronological order, but also processes that are not necessarily performed in chronological order but are performed in parallel or individually. In addition, in this specification, the term "system" refers to an overall device that is made up of a plurality of devices, a plurality of means, etc.

[0065] In other words, the information processing device to which the present invention is applied is sufficient as long as it has the following configuration, and can take on a variety of different embodiments. That is, an information processing device to which the present invention is applied (for example, the information processing device 1 in FIG. 5) a first parameter acquiring means (for example, a first parameter acquiring unit 51 in FIG. 5) for acquiring data on each of three or more first parameters (for example, the required input items in FIG. 1) related to the user, including at least walking speed, age, and sex, as first parameter data; evaluation means (for example, the walking age calculation unit 52 in FIG. 5) that generates evaluation data of the user's walking ability (for example, the walking age at the first stage in FIG. 1) based on the first parameter data; a second parameter acquiring means (e.g., a second parameter acquiring unit 53 in FIG. 5) that acquires, as second parameter data, data on one or more second parameters (e.g., health-related input items in FIG. 1) that affect the walking ability other than the first parameter related to the user; evaluation correction means (for example, walking age correction unit 54 in FIG. 5) that corrects the evaluation data based on the second parameter data; It is enough to have this. According to the present invention, it is possible to maintain and improve walking ability and increase motivation, thereby contributing to extending healthy life expectancy and reducing nursing care and medical expenses.

[0066] The device may further include a prediction means (for example, future walking ability prediction unit 55 in FIG. 5) for predicting the user's future walking ability based on the evaluation data corrected by the evaluation correction means.

[0067] In addition, the device may further include an improvement suggestion means (e.g., walking ability improvement suggestion unit 56 in Figure 5) that suggests a plan to improve the walking ability of the user based on at least some of the three or more first parameters and the one or more second parameters, and the evaluation data corrected by the evaluation correction means.

[0068] In addition, the evaluation means can generate walking age as the evaluation data based on a model for each gender (e.g., the two models in Figure 2) obtained from a predetermined population and showing the relationship between age and walking speed.

[0069] The second parameters may include height and weight or a parameter calculated from these (for example, BMI in FIG. 3).

[0070] The second parameter may also include medical history (eg, diabetes, knee osteoarthritis, etc.).

[0071] The second parameter may also include events that will affect the walking ability in the future (for example, a history of falls, a history of hospitalization, etc.). [Explanation of symbols]

[0072] 1. Information processing device, 11. CPU, 18. Memory unit, 19. Communication unit, 51. First parameter acquisition unit, 52. Walking age calculation unit, 53. Second parameter acquisition unit, 54. Walking age correction unit, 55. Future walking ability prediction unit, 56. Walking ability improvement proposal unit, 81. Walking age calculation model, 82. User information DB

Claims

1. a first parameter acquiring means for acquiring data on each of three or more first parameters relating to the user, including at least walking speed, age, and gender, as first parameter data; evaluation means for generating evaluation data of the user's walking ability based on the first parameter data; a second parameter acquisition means for acquiring, as second parameter data, data on one or more second parameters other than the first parameter related to the user that affect the walking ability; evaluation correction means for correcting the evaluation data based on the second parameter data; An information processing device comprising:

2. a prediction means for predicting the user's future walking ability based on the evaluation data corrected by the evaluation correction means; The information processing device according to claim 1 , further comprising:

3. an improvement suggestion means for suggesting a plan to improve the walking ability of the user based on at least a part of the three or more first parameters and the one or more second parameters, and the evaluation data corrected by the evaluation correction means; The information processing device according to claim 1 , further comprising:

4. the evaluation means generates a walking age as the evaluation data based on a model for each sex that indicates a relationship between the age and the walking speed, the model being obtained from a predetermined population. The information processing device according to claim 1 .

5. The second parameter includes height and weight or a parameter calculated therefrom. The information processing device according to claim 1 .

6. The second parameter includes a medical history. The information processing device according to claim 1 .

7. The second parameter includes a future event that will affect the walking ability. The information processing device according to claim 1 .

8. An information processing method executed by an information processing device, a first parameter acquisition step of acquiring data on each of three or more first parameters related to the user, including at least walking speed, age, and gender, as first parameter data; an evaluation step of generating evaluation data of the user's walking ability based on the first parameter data; a second parameter acquisition step of acquiring, as second parameter data, data on one or more second parameters other than the first parameter related to the user that affect the walking ability; an evaluation correction step of correcting the evaluation data based on the second parameter data; An information processing method including:

9. On the computer, a first parameter acquisition step of acquiring data on each of three or more first parameters related to the user, including at least walking speed, age, and gender, as first parameter data; an evaluation step of generating evaluation data of the user's walking ability based on the first parameter data; a second parameter acquisition step of acquiring, as second parameter data, data on one or more second parameters other than the first parameter related to the user that affect the walking ability; an evaluation correction step of correcting the evaluation data based on the second parameter data; A program that executes control processing including:

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