JP7656904B2 - Method and device for determining indexes related to anti-aging ability based on information before starting exercise - Google Patents

Description

The present invention relates to a method and device for determining anti-aging indicators that counter the decline in vitality caused by aging, based on information obtained before starting exercise.

Patent Document 1 discloses a method for easily calculating the endurance of a subject without placing a high load on the subject. The endurance calculation device, which is detachably attached to the subject, includes an acceleration sensor that measures the acceleration acting on the subject, a CPU that calculates the impulse acting on the subject based on the acceleration value output by the acceleration sensor, and a display unit or speaker that outputs walking information that encourages the subject to walk from a low speed to the maximum speed that the subject can achieve. After the walking information is output by the display unit or speaker, the CPU calculates the endurance of the subject, which is correlated with the maximum value of the calculated impulse, based on the maximum value of the impulse.

JP 2006-238970 A

There is a demand for the establishment of preventive medicine with exercise at its core in a super-aging society. For example, physical strength generally peaks in the 20s, and thereafter declines by about 10% for every 10 years of age. Since this decline in physical strength correlates with medical costs, if physical strength is improved by 10% through exercise prescription, it means that medical costs can be reduced by 20%. In order to aim to improve or prevent decline in functions that counter aging with exercise as the core, it is important to be able to obtain an index that indicates the level of functions that counter aging in a simple manner. For example, physical strength (endurance) is one of the life-sustaining functions and can be considered as one index that indicates the level of functions that counter aging. Therefore, there is a demand for an easier way to evaluate indices related to anti-aging capabilities, such as endurance, without the subject having to walk at a slow to maximum speed.

One aspect of the present invention is a system having a function of acquiring prior information including at least one of the following information immediately before the start of spontaneous exercise of the subject: cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, and tissue oxygen saturation; and a function of estimating an index related to the subject's anti-aging ability (anti-aging function) that correlates with at least the acquired data and age based on acquired data showing the change in the first information of the subject's cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, and tissue oxygen saturation included in the prior information, for a first period from the first time point to the start of spontaneous exercise, relative to data prior to the first time point at which the subject became aware of the start of spontaneous exercise, and the subject's age. This system may be provided as a mobile terminal equipped with the function of the system. It may also be provided as a program (program product, application) having instructions for causing a computer to function as this system, via a network or recorded on an appropriate recording medium.

Another aspect of the present invention is a method comprising: acquiring prior information including at least one of cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, and tissue oxygen saturation immediately before the subject starts spontaneous exercise; and estimating an index related to the subject's anti-aging ability based at least on the acquired data obtained from the prior information and the subject's age. One typical example of an index related to anti-aging ability is an index related to physical ability (physical function), one of which is maximum oxygen intake. Another typical example of an index related to anti-aging ability is an index related to mental ability (mental function), one of which is the result of an evaluation of cognitive function (cognitive ability test). These indices are also useful for determining the maintenance and/or suppression of a decline in the quality of life (QOL) of the subject.

The inventors of the present application have found that cerebral blood flow increases, heart rate increases, and muscle blood vessels dilate before the start of spontaneous exercise, and that prior information before the start of spontaneous exercise, such as cerebral blood flow information (increased cerebral blood flow), heart rate response, and muscle blood vessel dilation, has a certain correlation with an index of the subject's anti-aging ability. Therefore, by obtaining prior information, for example, the state of anti-aging ability can be easily and continuously monitored each time an exercise prescription that suppresses the decline of anti-aging ability is performed (or is about to be performed). Furthermore, the inventors have found that by taking into account the subject's age in addition to the prior information, an index of the subject's anti-aging ability can be estimated (calculated) with even greater accuracy. Therefore, a simple method for measuring at least one of cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, and tissue oxygen saturation immediately before the start of spontaneous exercise can be used to easily monitor the subject's anti-aging ability and changes therein, without requiring the subject to take measures such as walking from a slow speed to maximum speed, or in combination with such a method, and various effects can be obtained, such as evaluating the exercise prescription for the subject, preventing a decline in the subject's quality of life (QOL), and contributing to a reduction in medical costs.

When calculating (estimating) an index related to anti-aging ability, the index In may be calculated by a linear function including acquired data S indicating a change in prior information immediately before the subject starts spontaneous exercise and the subject's age Age as variables. One example of anti-aging ability is the maximum oxygen intake VO2peak, which is one index indicating physical function. Another example of anti-aging ability is the cognitive function index (CFI), which indicates mental ability. One example of a linear function for estimating the index In may be the following formula (1).
In=c1+c2・Age+c3・S...(1)
where c1, c2, and c3 are coefficients. In the above function, a negative correlation may be observed between the index In and the age Age, and the coefficient c2 may be negative.

When estimating the index In of anti-aging ability, the index In may be calculated using a linear function including a movement element Di including a body position of spontaneous exercise as a variable. An example of the linear function may be the following formula (2).
In=c1+c2・Age+c3・S+Σdi・Di...(2)
where di is a coefficient for the i-th motion element Di. For example, the motion element D1 may be a body position, and may be "1" during semi-sitting motion and "0" during standing motion.

The acquired data S indicating the change in the prior information immediately before the start of spontaneous exercise may be calculated by the following formula (3).
S=S1/Sb...(3)
S1 is a first average value of the first information contained in the advance information for the first period from a first point in time (first time, first period) t1, when the subject is given pre-start information indicating a predetermined time until the start of spontaneous movement, to the start of spontaneous movement t0, and Sb is a second average value of the first information contained in the advance information immediately before the first point in time t1.

The system may have a function of notifying the subject of a countdown indicating the start of spontaneous exercise as pre-start information. Furthermore, acquiring advance information may include acquiring advance information of a subject whose spontaneous exercise has been started by a countdown.

The system further has a function of substantially continuously recording physical information including at least one of the subject's cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, and tissue oxygen saturation, and the function of acquiring prior information may include a function of extracting prior information from physical information recorded before the start of spontaneous exercise. The system may also include a function of detecting the start of exercise by the subject, and a function of extracting prior information immediately before the start of exercise from the physical information recorded before the start of exercise by the subject, for calculating an index In related to the subject's anti-aging ability.

FIG. 1 is a block diagram showing an overview of a monitoring system. FIG. 13 is a diagram showing a method for determining maximum oxygen intake VO2peak by three-stage step-up walking. FIG. 13 is a graph showing the correlation between the estimated maximum oxygen uptake VO2peak obtained by three-stage step-up walking and the maximum oxygen uptake VO2peak obtained by the bicycle ergometer load increase method and an exhaled gas analyzer. FIG. 11 is a diagram showing some examples of physical information that serves as prior information. A graph showing data on semi-sitting bicycle position for the young group. A graph showing data on semi-sitting bicycle position for the elderly group. A diagram showing data on upright bicycles for the elderly group. Figure showing gait data for the elderly group. Figure 1 shows the characteristics of the subject group. FIG. 1 illustrates the experimental protocol. FIG. 13 is a diagram showing a typical example of prior information obtained through an experiment. 1 shows all the data obtained from the experiment. FIG. 13 is a graph showing the estimated VO2peak (X-axis) obtained by the regression equation obtained for the semi-sitting bicycle position of the young group and the semi-sitting bicycle position of the elderly group, against the measured VO2peak (Y-axis). 13 is a graph showing the estimated VO2peak (X-axis) obtained by multiple regression analysis including age against the actually measured VO2peak (Y-axis) for the semi-sitting bicycle position of the young group and the semi-sitting bicycle position of the elderly group. A graph showing the estimated VO2peak (X-axis) obtained by multiple regression analysis including age and body position for all data against the actually measured VO2peak (Y-axis). 11 is a flowchart showing a process of acquiring a maximum oxygen intake volume VO2peak by a terminal. This figure shows that when the maximum oxygen intake (VO2peak) of elderly people increases through exercise training, the cognitive function assessment result (CFI) also increases. 17 , this figure shows the results that when the maximum oxygen intake (VO2peak) increases through exercise training in elderly people, the cognitive function assessment result (CFI) also increases. A diagram showing the characteristics of the subject group in the cognitive function test. A graph showing cognitive function assessment results and data on semi-sitting bicycle position for the young group. A graph showing cognitive function assessment results and data on semi-sitting bicycle position for the elderly group. A graph showing cognitive function assessment results and data on upright bicycles for the elderly group. A graph showing cognitive function assessment results and walking data for the elderly group. 1 shows all the data obtained from the experiment. FIG. 13 is a graph showing the estimated cognitive function evaluation results (X-axis) obtained by multiple regression analysis including age and body position for all data, against the actual measured cognitive function evaluation results (Y-axis).

Figure 1 shows an overview of an example of a system for monitoring human activity. This system (monitoring system) 1 includes a portable or wearable terminal (user terminal) 10 worn by a user (subject, test subject, examinee) 2, and a server system 50 connected to the terminal 10 via a cloud 9, for example, a computer network such as the Internet. The activity monitoring system 1 may be provided as the terminal 10 alone, or may be provided as a system in which data is accumulated in the server system 50 via the cloud 9 and later analyzed in more detail or by an updated method. An example of the terminal 10 may be a smartphone, or a portable terminal that can be worn on the body of the user 2. The terminal 10 may be a wearable terminal in the form of a wristwatch or glasses.

The terminal 10 includes a sensor group 11, a processor 12 including a function for processing data obtained from the sensor group 11, a user interface 13 for input and output, a communication unit 14 that can connect to a cloud 9 via a mobile phone network or the like, and a memory 15. The sensor group 11 includes a three-axis acceleration sensor 11a, an altimeter 11b, a pulse meter (heart rate meter) 11c, a thermometer 11d, and the like, but is not limited to these, and may also include sensors that measure cerebral blood flow, total peripheral vascular resistance, and the like. One example of the sensor (first sensor) 11b that measures altitude is a barometer.

The processor 12 provides various functions by deploying an application (software, a program including instructions for executing the processes described below) 15a downloaded to the memory 15. The functions provided by the processor 12 include an activity amount (amount of energy consumed) monitoring function (energy consumption monitoring unit) 20 and an anti-aging ability estimation unit 21.

The monitoring unit (monitor) 20 includes a measurement unit 25 that acquires predetermined measurement data 16 from the sensor group 11 and temporarily stores the measurement data 16 in the memory 15, an oxygen consumption estimation unit (VO2 unit) 27 that determines the type of activity of the user 2 and estimates the oxygen consumption eVO2 using a predetermined estimation formula based on the activity form, an energy consumption estimation unit (CAL unit) 28 that calculates the energy consumption using the estimated oxygen consumption, a unit 29 that provides the measurement data 16, including the estimated values and the measured values of the sensors used for the estimation, to the server system 50 via the communication unit 14, and an output unit 26 that provides the calculated energy consumption, etc. to the user 2 via the user interface 13.

The activity type (activity form) of user 2 may be determined automatically from the measurement results of acceleration sensor 11a, may be selected or input by user 2, or may be determined automatically from user 2's lifestyle. Activity types include, for example, walking 3a, cycling (bicycle upright) 3b, exercising by bicycle in a semi-seated position (bicycle semi-seated position) 3c, traveling by car or other means of transportation, resting, etc., and depending on each activity type, the VO2 unit 27 or anti-aging ability estimation unit 21 may select a movement element, such as a body position, that affects the calculation (estimation) of the index (index value) In of anti-aging ability.

Anti-aging Activity (Function) is the ability to prevent or suppress the decline in vitality caused by aging, and is the same as "vitality" or "life-maintenance function (activity)." High oxygen intake VO2 peak is an example of an index of physical function (activity) of anti-aging activity, and the cognitive function assessment result CFI is an example of an index of mental function (activity).

Another example of an index of physical ability (physical function) is the LSD score (Lifestyle-related disease index, LSD risk factor, Lifestyle-related disease index). The LSD score (Lifestyle-related disease index) is a so-called metabolic index, and if one of the following four diagnostic criteria is met, one point is added. Therefore, if all four items are met, the maximum score is four points.
1) Systolic blood pressure ≥ 130mmHg or diastolic blood pressure ≥ 85mmHg
2) Fasting blood sugar ≧100mg/dl,
3) BMI≧25kg/ ^m2
4) Triglycerides ≧150 mg/dl or HDL cholesterol ≦40 mg/dl

In this example, the cognitive function index (CFI) is used as an evaluation result of cognitive function, and is evaluated using a maximum score of 15 points obtained by the Urakami Dementia Screening Method. However, the evaluation indexes (evaluation results) of depression, anxiety, and insomnia may also be used as indicators of mental ability. Cognitive function may also be evaluated using other methods. For example, the MMSE (Mini-mental state examination, see Folstein, M.F., Folstein, S.E., McHugh, P.R., 1975. "Mini-mental state". A practical method for grading the cognitive state of patients for the clinician. J. Psychiatr. Res. 12 (3), 189-198., Kaufer, D.I., Williams, C.S., Braaten, A.J., Gill, K., Zimmerman, S., Sloane, P.D., 2008. Cognitive screening for dementia and mild cognitive impairment in assisted living: comparison of 3 tests. J. Am. Med. Dir. Assoc. 9 (8), 586-593.) In the Urakami-style dementia simple screening, patients listen to questions played through headphones and answer them verbally or in writing. Question 1 concerns immediate recall (1 point each x 3 questions), question 2 concerns time orientation (1 point each x 4 questions), question 3 concerns delayed recall (2 points each x 3 questions), and question 4 concerns spatial cognition (2 points each x 1 question).

In one example, the anti-aging ability estimation unit 21 functions as a physical fitness measurement unit 21 and includes a function (preliminary information acquisition unit) 22 for acquiring (extracting) physical data 17 required for calculating the maximum oxygen intake, such as heart rate 17a, from the measurement data 16 as prior information 18, a function (VO2peak calculation unit) 23a for calculating (estimating) the maximum oxygen intake (VO2peak, estimated maximum oxygen intake) 19a as one of the indices (evaluations) In19 regarding the anti-aging ability of the user 2 based on the prior information 18, and a spontaneous movement detection unit 24 for determining the start of exercise (activity) of the user 2. The anti-aging ability estimation unit 21 may include a cognitive ability estimation unit 23b for estimating the results of a cognitive function test (CFI, estimated result of cognitive function evaluation) 19b as one of the indices In19 of the anti-aging ability of the user 2 based on the prior information 18. The anti-aging ability estimation unit 21 may function as a physical ability estimation unit or as a mental ability estimation unit.

The spontaneous exercise detection unit 24 may detect the start of spontaneous exercise from the acceleration sensor 11a or the like, and may provide the user 2 with a countdown (CD) via the user interface 13 to inform the user 2 of the start of spontaneous exercise. The advance information 18 may include other information in addition to the heart rate 17a, such as one or more of cerebral blood flow, heart rate, total peripheral vascular resistance (total peripheral vascular resistance), blood pressure, oxygen intake, and tissue oxygen saturation. The memory 15 records user attribute information 150 required for calculating maximum oxygen intake and energy consumption, and is referenced in each calculation process. Examples of attribute information 150 are age and body weight. In this example, age Age is referenced in estimating specific physical and mental abilities such as maximum oxygen intake, and body weight W is referenced in calculating energy consumption.

The server system 50 capable of communicating with the terminal 10 via the cloud 9 includes an interface 51 for receiving the measurement data 16 via the cloud 9, a database 52 for storing the measurement data 16 and the like, a unit 54 for analyzing the activity of the user 2 based on information provided by the terminal 10, for example the measurement data 16, and a service provider (advisor) unit 53 for providing advice on activity plans and the like to the user 2 based on the measurement data 16 and the processing results of the analysis unit 54. The analysis unit 54 may have the functions of the monitoring unit 20 of the terminal 10 and the anti-aging ability estimation unit 21. The anti-aging ability estimation unit 21 may function as a physical ability estimation unit or as a mental ability estimation unit.

The advisor unit 53 may include a function of advising on an exercise prescription for maintaining or improving the anti-aging ability based on the index In related to the estimated anti-aging ability of the subject. That is, the advisor unit (advisor) 53 periodically refers to the index In of the anti-aging ability of each user stored in the database, such as physical strength (maximum oxygen intake) 19a and/or cognitive function 19b, and the history of energy consumption 52a, and provides advice on a prescription for improving or worsening the index In related to the anti-aging ability obtained for each exercise prescription and a schedule planned by each user 2, provides an alarm for excessive or insufficient exercise (energy consumption), and provides advice on the living environment and activity environment based on the anti-aging ability (physical function and mental function).

As mentioned above, physical fitness peaks in the 20s, and thereafter it is said to decline by 10% for every 10 years of age, and it is known that there is a correlation between this decline in physical fitness and medical expenses. Therefore, if physical fitness could be improved by 10% through exercise prescription, medical expenses could be reduced by 20%. With this in mind, up until now, the recommendation has been to walk 10,000 steps a day. However, this method does not prescribe exercise that is suited to the physical fitness of the individual, and does not take into account the intensity of exercise. As a result, it has been pointed out that physical fitness does not improve.

On the other hand, the international standard for improving physical strength is to do machine training at a gym. During this, VO2 peak is measured by exhaled gas analysis to determine the training intensity. However, there are space and time restrictions, and considering the cost of training, this method is not necessarily accessible to everyone. In response, the inventors advocate interval walking training (IWT).

One of the challenges in carrying out IWT and other physical fitness training is determining maximum physical fitness (maximum oxygen intake VO2peak). Conventionally, maximum oxygen intake VO2peak has been routinely determined from the amount of oxygen consumed when an individual is pushed to their maximum value by gradually increasing the load using exercise load devices such as bicycle ergometers and treadmills at a gym. In response to this, the inventors of the present application first performed a three-stage step-up walk in a field such as a gymnasium, and demonstrated that the amount of exercise measured using a portable calorie meter during this walk matched the amount measured using a conventional machine, demonstrating that maximum oxygen intake VO2peak can be easily measured in the field without having to go to the trouble of going to a gym.

Figure 2 shows a method for determining the maximum oxygen intake VO2peak by three-stage step-up walking. In this method, the subject is asked to walk at a slow 111, medium 112, and high speed 113, and the oxygen intake 115 during the last minute of walking at the highest speed is estimated from the accelerometer and taken as the maximum oxygen intake VO2peak. Specifically, in a place with a flat floor such as a gymnasium, the subject is asked to wear a portable calorie meter on his/her waist and is gradually loaded with walking at rest, slow 111, medium 112, and maximum speed 113 for three minutes each, and the energy consumption during the last minute of walking at the highest speed is taken as the individual's maximum oxygen intake (maximum exercise intensity) VO2peak, and the heart rate 116 at that time is taken as the maximum heart rate.

Figure 3 shows the results of plotting the estimated maximum oxygen intake VO2peak obtained by the three-stage step-up walking measured by the inventors of the present application against the maximum oxygen intake VO2peak obtained by the bicycle ergometer load increase method and the breath gas analyzer. It can be seen that the two are highly correlated. The three-stage step-up walking method does not require a breath gas analyzer, and the maximum oxygen intake VO2peak can be easily obtained. Therefore, in the following, the maximum oxygen intake VO2peak obtained by the three-stage step-up walking is compared as an actual measurement value with the estimated maximum oxygen intake VO2peak calculated from the prior information 18.

In other words, the maximum oxygen intake VO2peak obtained by three-stage step-up walking is sufficiently accurate and simpler than conventional methods, but it requires that subject 2 go to a gym or other facility and push himself to the point of exerting his maximum physical strength. Considering the time and effort required for physical fitness testing, it is desirable to be able to obtain maximum oxygen intake VO2peak more easily and without straining himself in everyday life.

In response to this, the inventors of the present application have found that the cerebrocardiovascular response before the start of exercise is associated with the peak aerobic capacity, i.e., maximum oxygen uptake, VO2peak, of each individual subject (user) 2. They found that the countdown (CD) before voluntary exercise induces brain activation and a pressor response, followed by muscular vasodilation, and that these responses are associated with the peak aerobic capacity, VO2peak, of each individual subject 2.

Figure 4 shows the subjects' physical information 17 measured before the start of cycle exercise with CD+ and CD- in 14 young adults. In the CD+ condition, the start of the exercise was signaled by a 30-second countdown (CD1). In the CD- condition, the exercise started without CD. In the CD+ condition, the following additional words CD2 and CD3 were given before starting the exercise. CD2 was given with the words "15 seconds left" and CD3 was given at the start of the exercise with the words "10, 9, 8...1, start exercising." In this example, the physical information 17 includes the subject's cerebral blood flow CBF, heart rate HR, cardiac output CO, mean arterial pressure (MAP), Z transformed value of cross-correlation (Z transformed R(t), ZR(t), where R(t) represents the change in systolic blood pressure (Δsystolic arterial pressure), the change in heart rate (ΔHR), oxygen consumption VO2, total peripheral resistance TPR, and oxygen saturation in the muscle tissue (Oxygen Saturation in the muscle tissue). In Figure 4, the changes are shown as changes (Δ) from baseline, shown by the dashed line, from -90 seconds. The 30-second average and standard error S.E. of each value are shown as bars for each of the 14 subjects, every second. Note that ZR(t) was not determined during the transition phase, ±5 seconds of CD, and ±5 seconds of exercise. Solid lines indicate the CD+ condition, and dotted lines indicate the CD- condition. In addition, "*" in the figure indicates areas with significant differences (P<0.05) relative to the CD- condition. In the interaction effect shown as "(CD+ vs. CD-) x time", significant differences were observed for all variables (all P<0.008).

Figures 5 to 8 show the relationship between the first information of the physical information 17 included in the prior information 18 obtained by an experiment with two groups of different age groups, in this example the acquired data S (X-axis) showing the change (fluctuation) in the heart rate 17a, and the maximum oxygen intake VO2peak (actual VO2peak, Y-axis, ml/kg/min) obtained by three-stage step-up walking. Figure 5 shows the case where the young group performed the bicycle semi-sitting position 3c exercise as spontaneous exercise, Figure 6 shows the case where the elderly group performed the bicycle semi-sitting position 3c exercise as spontaneous exercise, Figure 7 shows the case where the elderly group performed the bicycle upright position 3b exercise as spontaneous exercise, and Figure 8 shows the case where the elderly group performed the walking 3a exercise as spontaneous exercise.

Figure 9(a) shows the characteristics of 27 people in the young group. Figure 9(b) shows the characteristics of 33 people in the elderly group. Figure 10 shows the countdown protocol. In the experiment shown in Figures 5 to 7, the first countdown CD was sent at the first time point (first time (t1), -30 seconds) 30 seconds before the start of exercise (t0), with the message "Please start exercising in 30 seconds" and then 15 seconds before, with the message "Please start in 15 seconds", followed by the call "10 seconds left, 9, 8, ... 1, please start exercising" to start exercise at time (start of spontaneous exercise) t0. In the experiment shown in Figure 8, the first countdown CD was sent at the first time point (first time (t1), -15 seconds) 15 seconds before the start of exercise (t0), with the message "Please start exercising in 15 seconds," followed by the call "10 seconds left, 9, 8, ... 1, please start exercising," which started the exercise at time t0 (the start of spontaneous exercise).

FIG. 11 shows a typical example of the heart rate 17a obtained as the prior information 18. It can be seen that the heart rate increases after the time t1 (-30 seconds) when the first countdown is notified. In this example, the prior information acquisition unit 22 includes a function of calculating the change in the heart rate (first information) 17a included in the prior information 18 as the acquired data S. Specifically, based on the formula (3), a ratio S1/Sb between a first average value S1 of the heart rate 17a from a first time point t1 (-30 seconds) at which the subject is given pre-start information (countdown) indicating a predetermined time until the start of spontaneous exercise t0, for example, 30 seconds, until the start of spontaneous exercise, and a second average value Sb of the heart rate 17a immediately before the first time point t1 is calculated as the acquired data S.
S=S1/Sb...(3)
The first average value S1 is determined by the average value HRs1 of the heart rate from -30 seconds to 0 seconds, and the previous second average value Sb can be determined by the average value HRbase of the heart rate from -35 seconds to -30 seconds. In an experiment in which the notification time of the first countdown (first time t1) is -15 seconds, the first average value S1 is the average value HRs1 of the heart rate from -15 seconds to 0 seconds, and the previous second average value Sb can be determined by the average value HRbase of the heart rate from -20 seconds to -15 seconds.

The acquired data S on the change (variable) of the first information, which is found to have a correlation with a physical or cognitive function such as the heart rate 17a included in the advance information 18, may be data in which the state (value, displacement, fluctuation) of the period (first period) from the time t1 when the pre-start information (countdown) is given to the spontaneous exercise start time t0 is evaluated based on the state (value, baseline BL) of the previous period, and is not limited to the ratio of average values. For example, the acquired data S may be calculated by integrating the area of the curve of the period (first period, in this example, from -30 seconds to 0 seconds) from the time t1 when the pre-start information (countdown) is given to the spontaneous exercise start time t0, and the baseline BL calculated from the previous period (for example, from -90 seconds to -30 seconds), or may be calculated by other methods.

All the data shown in Figures 5 to 8 are shown in Figure 12. The semi-sitting bicycle position for the young group is indicated by ◯, the semi-sitting bicycle position for the elderly group by ×, the upright bicycle position for the elderly group by △, and the walking position for the elderly group by □. Regression analysis of these data shows that there is a positive correlation between the maximum oxygen intake VO2peak (actual VO2peak, Y-axis) and the variation in heart rate 17a S1/Sb, as shown in Figure 12. However, the coefficient of determination ^R2 is 0.2705 (p<0.01).

13, a regression analysis was performed between the maximum oxygen intake VO2peak (actual VO2peak, Y-axis) and the heart rate 17a fluctuation S1/Sb for the young group in the semi-sitting position (◯) and the elderly group in the semi-sitting position (X), and the maximum oxygen intake VO2peak (estimated VO2peak, X-axis) obtained by the following linear function (regression equation) (A) obtained as a result is shown against the maximum oxygen intake VO2peak (actual VO2peak, Y-axis). The confidence interval of the regression equation is shown by a dashed line.
VO2peak = -85.504 + 120.442×S1/Sb...(A)
The unit of the maximum oxygen intake VO2peak is ml/kg/min, and the units of the heart rate 17a averages S1 and Sb are beats/min.

In Fig. 14, for the half-sitting bicycle position of the young group (◯) and the half-sitting bicycle position of the elderly group (X), a multiple regression analysis was performed between the maximum oxygen intake VO2peak (actual VO2peak, Y axis), the fluctuation S1/Sb of the heart rate 17a, and the age of the subject Age, and the maximum oxygen intake VO2peak (estimated VO2peak, X axis) obtained by the following linear function (regression equation, first-order polynomial) (B) is shown against the maximum oxygen intake VO2peak (actual VO2peak, Y axis). The confidence interval of the regression equation is shown by a dashed line.
VO2peak = -25.314 -0.239×Age +71.774×S1/Sb...(B)
The coefficient of determination ^R2 of the correlation between the estimated VO2peak obtained by the regression equation (A) and the measured VO2peak was 0.2901 (p<0.001), whereas the coefficient of determination ^R2 of the estimated VO2peak obtained by the regression equation (B) using multiple regression analysis including age and the measured VO2peak was 0.5256 (p<0.001). Therefore, it can be seen that the maximum oxygen intake VO2peak can be estimated more accurately based on the heart rate 17a fluctuation S1/Sb using the regression equation (B).

Figure 15 shows the maximum oxygen intake VO2peak (measured VO2peak, Y-axis), heart rate 17a fluctuation S1/Sb, subject age Age, and posture D1, which is one of the exercise elements, for the younger group riding a bicycle in a semi-sitting position (◯), the elderly group riding a bicycle in a semi-sitting position (X), the elderly group riding a bicycle in an upright position (△), and the elderly group walking (□). A multiple regression analysis was performed between the maximum oxygen intake VO2peak (measured VO2peak, Y-axis), obtained by the following linear function (regression equation, first-order polynomial) (C), and the maximum oxygen intake VO2peak (estimated VO2peak, X-axis) is plotted against the maximum oxygen intake VO2peak (measured VO2peak, Y-axis).
VO2peak = -25.166 -0.226×Age +62.703×S1/Sb+8.764×D1
... (C)
However, the movement element D1 is "1" for the semi-sitting position and "0" for the others. The coefficient of determination ^R2 between the estimated VO2peak and the measured VO2peak obtained by the regression formula (C) using multiple regression analysis including age and body position is 0.7716 (p<0.01). Therefore, it can be seen that the maximum oxygen intake VO2peak can be estimated more accurately based on the heart rate 17a fluctuation S1/Sb for all data obtained in this experiment using the regression formula (C) that takes age and body position into consideration.

The fact that the coefficient is positive in semi-sitting exercise suggests that for individuals with the same maximum oxygen intake VO2peak, the increase in heart rate during countdown is smaller in semi-sitting compared to standing. This phenomenon is thought to be due to the fact that, during voluntary exercise in a semi-sitting position similar to the supine position, the amount of blood returning from the periphery to the heart increases, stretching the heart and suppressing the secretion of vasopressin. For example, it has been reported in Kazumasa Manabe, et al. “Countdown before voluntary exercise induces muscle vasodilation with decreased muscle sympathetic nerve activity in humans” (The FASEB Journal, 20 Apr 2018) that the reaction to the countdown almost disappears when in the supine position.

It was found that the maximum oxygen intake VO2peak, which is one of the physical function indexes In, can be easily estimated by obtaining the prior information 18 in this way. When calculating the maximum oxygen intake VO2peak, the first information included in the prior information 18 immediately before the subject starts spontaneous exercise, in this example, the acquired data S of the change in heart rate 17a and the subject's age Age are used as variables to accurately estimate the maximum oxygen intake VO2peak for subjects of all ages, not limited to young or elderly subjects. An example of a linear function (first-order polynomial) including age Age as a variable is shown in the following formula (11).
VO2peak=c11+c12・Age+c13・S...(11)
S=S1/Sb...(3)

When heart rate 17a is used as the first information acquired as advance information 18, and equation (3) showing the ratio of the average values before and after countdown is used as the fluctuation data S, examples of coefficients c11 (ml/kg/min), c12 (ml/kg/Year/min) and c13 (ml/kg/min) are as follows:
-85.072<c11<34.445
-0.351<c12<-0.128
16.341<c13<127.208...(B11)

The coefficient c2 of age obtained by the experiment is negative, suggesting that for subjects with similar maximum oxygen intake VO2peak, the heart rate increases more during the countdown in older people than in younger people. Therefore, the method of estimating maximum oxygen intake VO2peak based on prior information 18 is suitable for estimating the physical strength of elderly people, reduces the burden of physical fitness tests for elderly people, and enables more accurate monitoring of changes in physical strength.

Furthermore, when performing spontaneous exercise with different movement elements such as posture (body position), it was found that the maximum oxygen intake VO2peak can be estimated more accurately by obtaining the prior information 18 and taking the posture into consideration. When calculating the maximum oxygen intake VO2peak, a linear function (linear polynomial) including the first information of the prior information 18 immediately before the subject starts spontaneous exercise, for example, the change S in the heart rate 17a, the subject's age Age, and a movement element Di such as body position can be used to accurately estimate the maximum oxygen intake VO2peak without limiting the type of spontaneous exercise in addition to age. An example of a linear function for calculating the maximum oxygen intake VO2peak of the subject from the acquired data S related to the fluctuation of the first information included in the prior information 18 using the movement element Di including the body position of the spontaneous exercise as a variable in addition to age Age is the following formula (12).
VO2peak=c1+c2・Age+c3・S+Σdi・Di...(12)

When heart rate 17a is used as prior information 18, equation (3) showing the ratio of the average values before and after the countdown is used as the fluctuation data S, and posture D11 ("1" for semi-sitting exercise and "0" for standing exercise) is taken into account as the exercise element, an example of coefficients c11, c12, c13, and d11 (ml/kg/min) is as follows:
-64.333<c11<14.001
-0.304<c12<-0.148
26.815<c13<98.591
5.381<d11<12.146...(C12)

Figure 16 shows a flow chart of a process in terminal 10 for determining the maximum oxygen intake VO2peak, one of the physical abilities, as an index In showing anti-aging ability. In step 121, monitoring unit 20 continuously acquires measurement data 16 including physical information 17 and stores it in memory 15. In step 121, it is sufficient that measurement data 16 including physical information 17 is recorded substantially continuously, and all past measurement data 16 may be recorded, or measurement data 16 for a predetermined period may be recorded cyclically. Furthermore, continuous recording also includes measurement data 16 being recorded intermittently at intervals of about several tens of milliseconds.

In step 122, the spontaneous exercise detection unit 24 of the anti-aging ability estimation unit 21 functioning as a physical fitness measurement unit obtains the timing t0 of the start of spontaneous exercise from the acceleration information included in the measurement data 16. The spontaneous exercise detection unit 24 may have a function of notifying the subject 2 of a countdown CD indicating the start of spontaneous exercise.

In step 123, when spontaneous movement is detected, for example, when CD becomes zero, the prior information acquisition unit 22 extracts (acquires) prior information 18 immediately before the start of spontaneous movement, prior to the spontaneous movement start time t0, from the physical information 17 before the start of spontaneous movement recorded in the memory 15. The prior information acquisition unit 22 may have a function of obtaining, as the obtained data S, the ratio S1/Sb between the first information in the prior information 18 for the first period from the first time point (first time) t1 at which the subject is given pre-start information (countdown, CD) indicating a predetermined time until the start of spontaneous movement to the time t0 at which the spontaneous movement starts, in this example the first average value S1 of the heart rate 17a, and the second average value Sb of the prior information immediately before the first time t1 (for example, the average from 5 seconds before the first time t1). In the above, the countdown starts 30 seconds or 15 seconds before, but the start of the countdown is not limited to these periods (seconds, minutes).

In step 124, the VO2peak calculation unit 23a calculates the subject's maximum oxygen intake VO2peak based on the prior information 18. The VO2peak calculation unit 23a may include a first calculation function for calculating the maximum oxygen intake VO2peak using equation (11), or may include a second calculation function for calculating the maximum oxygen intake VO2peak using equation (12). The prior information 18 may include at least any one of the cerebral blood flow CBF, heart rate HR, and total peripheral vascular resistance TPR immediately before the subject 2 starts spontaneous exercise. In the above example, the heart rate HR information 17a is used as the first information, but the maximum oxygen intake VO2peak may be estimated using other information. The prior information 18 may include at least one of the following information: cerebral blood flow CBF, heart rate HR, and total peripheral vascular resistance TPR of the subject 2, as well as cardiac output CO, blood pressure (e.g., mean arterial pressure MAP), oxygen intake VO2, and tissue oxygen saturation (StO2). The application (program, program product) 15a that performs these processes may be provided by recording it on a recording medium, or may be provided via a network.

The above shows a method including obtaining prior information 18 of a subject 2 whose spontaneous exercise has been started by a countdown. Alternatively, or in addition, the terminal 10 may monitor the heart rate HR and the amount of activity during daily activities, and estimate the maximum oxygen intake VO2peak from the degree of increase in HR immediately before the start of the activity. In a person with a high maximum oxygen intake VO2peak, the degree of increase in the heart rate HR17a included in the prior information 18 before the start of exercise is large, whereas in a person with a low maximum oxygen intake VO2peak, the degree of increase in the heart rate HR17a is small. In addition, in the above, the subject is made aware of the start of exercise (start of activity) by performing a countdown, but in daily life or exercise, when the subject is conscious of starting exercise, the cerebral blood flow increases, the heart rate increases, and the total peripheral resistance decreases due to muscular vasodilation, and these can be obtained as prior information 18. Subjects can easily and effortlessly learn their maximum oxygen intake VO2peak in their daily lives, and can continuously record fluctuations in their maximum oxygen intake VO2peak on a daily basis.

The terminal 10 may include an interval walking trainer function (interval walking trainer unit, IWT unit) 30 that works in conjunction with the monitoring unit 20 to guide the subject 2 in performing interval walking training IWT. The IWT unit 30 calculates the oxygen intake VO2 during IWT using the acceleration sensor 11a and the altimeter 11b. The estimation formula (1) for calculating the estimated value VO2 of oxygen consumption during walking is disclosed in "Yamazaki T, Gen-no H, Kamijo Y, Okazaki K, Masuki S, and Nose H. A new device to estimate VO2 during incline walking by accelerometry and barometry. Med Sci Sports Exerc, 41: 2213-2219, 2009" by the inventors of the present application.

The IWT unit 30 may include a first unit 31 that guides normal walking, a second unit 32 that guides fast walking, and a management unit 33 that manages the IWT history and the amount of energy consumed by IWT. The first unit 31 estimates the oxygen consumption VO2 at appropriate intervals of about several tens of milliseconds, manages the duration of normal walking, and outputs a guide to promote transition to fast walking after a predetermined time, for example, 3 minutes, has elapsed. When the calculated oxygen consumption VO2 exceeds 70% of the maximum oxygen intake VO2peak, the second unit 32 uses voice or the like to guide the user to maintain the speed, and then outputs a guide to promote transition to normal walking after a predetermined time, for example, 3 minutes, has elapsed. In this terminal 10, the physical fitness measurement unit (anti-aging ability estimation unit) 21 can measure the maximum oxygen intake VO2peak19a of the subject (user) 2 at any time and update the value. This allows IWT to be guided under conditions that match the condition of subject 2 at that time, allowing subject 2 to perform IWT comfortably and at an intensity that is expected to increase his or her physical strength.

According to the inventors' research, (1) younger (or older) people with higher maximum oxygen intake have a higher heart rate response at the start of spontaneous exercise, and (2) younger (or older) people with higher maximum oxygen intake have a higher degree of increase in cerebral blood flow at the start of spontaneous exercise.

Furthermore, (3) it has been found that when the maximum oxygen intake (VO2peak) increases through exercise training in elderly people, the cognitive function evaluation result (CFI) also increases, and it is possible to estimate the cognitive function of elderly people from the heart rate response and the increase in cerebral blood flow at the start of spontaneous exercise. In other words, the monitoring system 1 is capable of evaluating not only physical ability but also cognitive function, which is an index In of mental ability, from the heart rate response 17a at the start of spontaneous exercise, and the present invention can provide a method for evaluating anti-aging ability, including the evaluation of cognitive function based on the heart rate response, and further provide an evaluation system for anti-aging ability.

Figure 17 shows the change in maximum oxygen intake VO2peak (ΔVO2peak) (Figure 17(a)), change in LSD score (ΔLSD score) (Figure 17(b)), and change in cognitive function (ΔCFI score) (Figure 17(c)) between subjects who performed interval walking (IWT) as exercise training and subjects who did not (CNT). The changes in subjects who performed interval walking (IWT) are shown after five months of interval walking for 30 minutes per day, four days a week (a total of 15 minutes of brisk walking and 15 minutes of normal walking). It can be seen that with IWT, maximum oxygen intake VO2peak increased (increased physical strength), LSD score decreased, and cognitive function improved.

Figure 18 shows the correlation between the change in cognitive function ΔCF score and the change in maximum oxygen intake VO2peak ΔVO2peak (Figure 18(a)), and the correlation between the change in cognitive function ΔCF score and the change in LSD score ΔLSD score (Figure 18(b)). It can be seen that as the maximum oxygen intake VO2peak increases, the cognitive function CFI score improves, and as the LSD score decreases, the cognitive function CFI score tends to improve.

The anti-aging ability estimation unit 21 of this monitoring system 1 includes a cognitive function estimation unit 23b that estimates the evaluation result (CFI) 19b of a cognitive function test related to the mental ability of the user 2 based on the prior information 18. One example of a cognitive function test (cognitive ability test) is the above-mentioned Urakami Dementia Simple Screening, which tests cognitive function on a scale of 15 points.

Figures 20 to 23 show experimental results on cognitive function evaluation obtained by an experiment in which subjects with the characteristics shown in Figure 19 were divided by age group. The experimental results show the relationship between the acquired data S (X-axis) showing the change (fluctuation) in the heart rate 17a in this example, which is any of the first information of the physical information 17 included in the preliminary information 18, and the results of the cognitive function test (CFI, score, 15 points maximum). Figure 20 shows a case where a young group performed the bicycle semi-sitting position 3c exercise as spontaneous exercise, Figure 21 shows a case where an elderly group performed the bicycle semi-sitting position 3c exercise as spontaneous exercise, Figure 22 shows a case where an elderly group performed the bicycle upright position 3b exercise as spontaneous exercise, and Figure 23 shows a case where an elderly group performed walking 3a as spontaneous exercise.

The countdown protocol is the same as that shown in FIG. 10. The acquired data S of the change in heart rate (first information) 17a is calculated based on formula (3) by calculating the ratio S1HR/baseHR between the first average value S1 (shown as S1HR in the cognitive function experiment example) of the heart rate 17a from the first time point t1 (-30 seconds) at which the subject is given pre-start information (countdown) indicating a predetermined time until the start of spontaneous exercise t0, for example 30 seconds, until the start of spontaneous exercise, to the second average value Sb (shown as baseHR in the cognitive function experiment example) of the heart rate 17a immediately before the first time point t1.

All the data shown in Figures 20 to 23 are shown in Figure 24. The semi-sitting position on a bicycle for the younger group is indicated by an x, the semi-sitting position on a bicycle for the elderly group by a ■, the upright bicycle for the elderly group by a ▲, and walking for the elderly group by a ◯. When these data are subjected to regression analysis, as shown in Figure 24, it can be seen that there is a positive correlation between the cognitive function assessment results (actual measurements) and the heart rate 17a fluctuation S1HR/baseHR. However, the coefficient of determination ^R2 is 0.1844 (p<0.001).

FIG. 25 shows the cognitive function evaluation results (estimated CFI, X-axis) obtained by performing a multiple regression analysis between the actual measured values of the cognitive function evaluation results (actual CFI), the heart rate 17a fluctuation S1HR/base, the subject's age Age, and posture D1, which is one of the movement elements, for the younger group in a semi-sitting position on a bicycle, the elderly group in a semi-sitting position on a bicycle, the elderly group in an upright position on a bicycle, and the elderly group in a walking position, against the actual measured values of the cognitive function evaluation results (actual CFI, Y-axis) using the following linear function (regression equation, first-order polynomial) (D).
CFI= 7.154 -0.008×Age +6.0153×S1HR/baseHR+0.921×D1
... (D)
However, the movement element D1 is "1" for the semi-sitting position and "0" for the others. The coefficient of determination ^R2 between the estimated CFI and the measured CFI obtained by regression equation (C) using multiple regression analysis including age and body position is 0.6408 (p<0.001). Therefore, it can be seen that for all data obtained in this experiment, cognitive function can be accurately estimated based on the heart rate 17a fluctuation S1HR/baseHR using regression equation (D) that takes age and body position into consideration.

In this way, it was found that the cognitive function evaluation result CFI can be accurately estimated in the same way as the maximum oxygen intake VO2peak by considering the age of the subject and, further, when performing spontaneous exercise with different movement elements such as posture (body position), by considering the posture. Therefore, when estimating the cognitive function of the subject, by using a linear function (linear polynomial) including the first information of the advance information 18 immediately before the subject starts spontaneous exercise, for example, the acquired data S of the change in the heart rate 17a, the subject's age Age, and the movement element Di such as body position as variables, it can be accurately estimated without limiting the age and the type of spontaneous exercise. An example of a linear function for obtaining the cognitive function evaluation result CFI of the subject from the acquired data S related to the fluctuation of the first information included in the advance information 18 with the movement element Di including the body position of the spontaneous exercise as a variable in addition to the age Age is the following formula (22).
CFI=c21+c22・Age+c23・S+Σdi・Di...(22)

The Urakami Dementia Simple Screening Examination (maximum score of 15 points) is used as the cognitive function evaluation result CFI, the heart rate 17a is used as the prior information 18, the formula (3) showing the ratio of the average values before and after the countdown is used as the fluctuation data S, and the body position D21 ("1" for semi-sitting exercise and "0" for standing exercise) is taken into account as the exercise element. When these are taken into account, examples of coefficients c21 (ml/kg/min), c22 (ml/kg/min/year), c23 (ml/kg/min), and d21 (ml/kg/min) are as follows:
3.255<c21<11.052
-0.17<c22<-0.002
2.560<c23<9.470
0.582<d21<1.260...(C22)

In the terminal 10, the process of obtaining the cognitive function evaluation result CFI as the index In related to the anti-aging ability is common to the process of obtaining the maximum oxygen intake VO2peak shown in FIG. 16. That is, in step 121, the monitoring unit 20 continuously obtains the measurement data 16 including the physical information 17 and stores it in the memory 15. In step 122, the spontaneous movement detection unit 24 of the anti-aging ability estimation unit 21 functioning as the cognitive function evaluation unit obtains the timing t0 of the start of spontaneous movement from the acceleration information included in the measurement data 16. In step 123, when spontaneous movement is detected, for example, when CD becomes zero, the prior information acquisition unit 22 extracts (obtains) prior information 18 immediately before the start of spontaneous movement, prior to the spontaneous movement start time t0, from the physical information 17 before the start of spontaneous movement recorded in the memory 15. The prior information acquisition unit 22 obtains a ratio S1/Sb (S1HR/baseHR) of a first average value S1 (S1HR) of the heart rate 17a to a second average value Sb (baseHR) of the prior information immediately before the first time t1 as the obtained data S. In step 124, the CFI calculation unit 23b, instead of the VO2peak calculation unit 23a, estimates the evaluation result CFI of the subject's cognitive function based on the prior information 18. The CFI calculation unit 23b may include a calculation function for obtaining the evaluation result CFI of the cognitive function by equation (22). The prior information 18 may include at least one of the following information: cerebral blood flow CBF, heart rate HR, and total peripheral vascular resistance TPR immediately before the subject 2 starts spontaneous exercise. Although the above describes an example in which the heart rate HR information 17a is used as the first information, the cognitive function evaluation result CFI can be estimated using other information, as with the maximum oxygen intake VO2peak. In addition to the cerebral blood flow CBF, heart rate HR, and total peripheral vascular resistance TPR of the subject 2, the prior information 18 may include at least one of the following information: cardiac output CO, blood pressure (e.g., mean arterial pressure MAP), oxygen intake VO2, and tissue oxygen saturation (StO2).

By obtaining the prior information 18 in this way, it becomes possible to easily estimate the index (evaluation) In of anti-aging ability every time the subject exercises. One example of the index In of anti-aging ability shown above is the maximum oxygen intake VO2peak related to physical ability, and another example is the evaluation result of cognitive function (cognitive ability test result) CFI related to neurological ability, and it has been shown that these can be easily and accurately estimated. When calculating these indexes In of anti-aging ability, by using a linear function including the first information included in the prior information 18 immediately before the subject starts spontaneous exercise, in this example, the acquired data S of the change in heart rate 17a and the subject's age Age as variables, the index In of anti-aging ability can be accurately estimated for subjects of all ages, not limited to young or elderly people. An example of a linear function (linear polynomial) including age as a variable is the following equation (1). When heart rate 17a is used as the first information included in advance information 18 and equation (3) showing the ratio of the average values before and after countdown is used as the fluctuation data S, an example of coefficients c1, c2, and c3 is shown in equation (B11).
In=c1+c2・Age+c3・S...(1)
S=S1/Sb...(3)

Furthermore, it was shown that even when performing spontaneous exercise with different movement elements such as posture (body position), the index In of anti-aging ability can be easily estimated each time the exercise is performed by obtaining the prior information 18. An example of a linear function for determining the index In of the subject's anti-aging ability from the fluctuation S of the prior information 18 using the movement element Di including the body position of the spontaneous exercise in addition to the age Age as a variable is the following formula (2). When the heart rate 17a is adopted as the prior information 18, the formula (3) showing the ratio of the average value before and after the countdown is adopted as the fluctuation S of the prior information, and the body position D1 ("1" during semi-sitting exercise and "0" during standing exercise) is taken into account as the movement element, examples of the coefficients c1, c2, c3, and d1 are shown in formulas (C12) and (C22).
In=c1+c2・Age+c3・S+Σdi・Di...(2)

As explained above, in order to maintain and/or improve anti-aging ability, it is important to train in a way that is suited to the individual's condition, for example, physical strength and mental strength. With the present invention, it is possible to estimate the anti-aging ability of the subject each time training is performed, and even at the start of training, and it is possible to provide a safe and effective exercise prescription suited to the subject's condition, and to evaluate the results. In particular, it is important to be able to easily estimate (measure) the maximum oxygen intake VO2peak as an index of physical function, and the cognitive function evaluation result CFI as an index of mental ability. As one type of training for improving physical strength, interval walking with an awareness of maximum physical strength (maximum oxygen intake VO2peak) is also useful for improving or maintaining cognitive function, and the device (terminal) 10 provided in the present invention can provide training suited to the condition of each individual user 2, particularly training suitable for improving anti-aging ability.

1 monitoring system, 10 user terminals (devices), 50 server systems

Claims (20)

A function of acquiring prior information including at least any one of information on cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, and tissue oxygen saturation immediately before the subject starts spontaneous exercise;
The system has acquired data showing a change in first information of the subject's cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, and tissue oxygen saturation included in the prior information, compared to data prior to a first time point at which the subject became aware of starting spontaneous exercise, during a first period from the first time point to the start of spontaneous exercise, and a function of estimating an indicator related to the subject's anti-aging ability that is correlated at least with the acquired data and age, based on the subject's age. In claim 1,
The system, wherein the estimation function includes a first calculation function of calculating an index In related to the subject's anti-aging ability using the following formula including the acquired data S of the subject and the subject's age Age:
In=c1+c2・Age+c3・S
where c1, c2 and c3 are coefficients. In claim 2,
The coefficient c2 is negative. In any one of claims 1 to 3,
The system, wherein the estimation function includes a second calculation function of calculating an index In related to the subject's anti-aging ability using the following equation, which includes the acquired data S of the subject, the subject's age Age, and a movement element Di including the posture of the spontaneous exercise.
In=c1+c2・Age+c3・S+Σdi・Di
where c1, c2, and c3 are coefficients, and di is the coefficient for the i-th motion element Di. In any one of claims 2 to 4,
The system, wherein the estimation function includes a function of determining the acquired data S as a ratio S1/Sb of a first average value S1 of the first information for the first period from the first time point when the subject is given pre-start information indicating a predetermined time until the start of the spontaneous movement to the start of the spontaneous movement, to a second average value Sb of the first information immediately before the first time point. In any one of claims 1 to 5,
The system has a function of notifying the subject of a countdown indicating the start of the voluntary exercise. In any one of claims 1 to 6,
a function of substantially continuously recording physical information of the subject, the physical information including at least any one of cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, and tissue oxygen saturation;
The function of acquiring the prior information includes a function of extracting the prior information from the physical information recorded before the start of the spontaneous exercise, the system. In any one of claims 1 to 7,
The system, wherein the estimating function includes a function for obtaining an estimate of maximum oxygen intake related to physical performance as the indicator. In any one of claims 1 to 8,
The system, wherein the estimating function includes a function of obtaining an estimated result of a cognitive function assessment regarding mental ability as the indicator. A function of substantially continuously recording physical information including at least one of information on cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, and tissue oxygen saturation of the subject;
A function for detecting the onset of motion of the subject;
A function of extracting prior information immediately before the start of exercise from the physical information recorded before the start of exercise of the subject;
and estimating an index related to the subject's anti-aging ability that is correlated at least to the acquired data and age, based on acquired data showing changes in first information of the subject's cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, and tissue oxygen saturation included in the prior information, relative to data prior to a first time point at which the subject became aware of starting spontaneous exercise, during a first period from the first time point to the start of spontaneous exercise, and based on the subject's age. In any one of claims 1 to 10,
The system has a function of advising on an exercise prescription for maintaining or improving anti-aging ability based on an index related to the estimated anti-aging ability of the subject. A mobile terminal equipped with the system described in any one of claims 1 to 11. Acquiring prior information including at least any one of cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, and tissue oxygen saturation immediately before the subject starts spontaneous exercise;
and estimating an index related to the subject's anti-aging ability correlated at least to the acquired data and age based on acquired data indicating a change in first information of the subject's cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, or tissue oxygen saturation included in the prior information, relative to data prior to a first time point at which the subject became aware of starting spontaneous exercise, during a first period from the first time point to the start of spontaneous exercise, and the age of the subject. In claim 13,
and recording substantially continuously physical information of the subject, the physical information including at least one of information on cerebral blood flow, heart rate, total peripheral vascular resistance, cardiac output, blood pressure, oxygen intake, and tissue oxygen saturation;
The method, wherein obtaining the prior information includes extracting the prior information from the physical information recorded before the onset of the locomotor activity. In claim 13 or 14,
The method, wherein the prior information includes information indicative of cerebral blood flow increase, heart rate response, and muscle vasodilation immediately prior to the onset of locomotor activity. In any one of claims 13 to 15,
The method, wherein the estimating step includes determining an index In relating to the anti-aging ability of the subject using a linear function including the acquired data S of the subject and the age Age of the subject as variables. In claim 16,
The method, wherein the estimating step further includes determining an index In related to the anti-aging ability of the subject using a linear function including, as a variable, a movement element Di including the body position of the spontaneous exercise. In claim 16 or 17,
The estimating step includes determining the acquired data S as a ratio S1/Sb of a first average value S1 of the first information during the first period from the first time point at which the subject is given pre-start information indicating a predetermined time until the start of the spontaneous movement to the start of the spontaneous movement, to a second average value Sb of the first information immediately before the first time point. In any one of claims 13 to 18,
The method, wherein obtaining the prior information includes obtaining the prior information of the subject whose locomotor activity has been started by a countdown. A program having instructions for causing a computer to function as a system according to any one of claims 1 to 11.

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