JP7432878B2 - Health care methods, devices and programs - Google Patents

Description

The present invention relates to a health management method, device, and program for predicting injuries and poor physical condition with high accuracy and formulating a training plan.

Training and competitions are daily activities for athletes. Safe and effective training and competition are important for athletes. If the load of training or competition is insufficient for the target person, such as an athlete, it may be difficult to obtain the effects even if the training or competition is performed for a long time. On the other hand, if the workload of training or competitions is too high, the risk of injury or poor physical condition increases, and it may become difficult to continue training or competitions. Therefore, it is important to understand the condition of the target person and set the appropriate training and match load according to the purpose.

Rating of perceived exercise intensity (RPE) has been known as an index for understanding the intensity of exercise such as training. RPE is a numerical representation of the subjective burden level of an exerciser, such as an athlete, during exercise. By multiplying the intensity of this exercise by the time, the load of the exercise can be calculated.

In addition, the Acute:Chronic Workload Ratio (hereinafter referred to as "A/C ratio") calculated from RPE is used as an index for setting the appropriate exercise load such as training, and is used to predict injuries and poor physical condition. can do. (For example, see Non-Patent Document 1).

Gabbett, Tim J. “The training-injury prevention paradox: should athletes be training smarter and harder?” Br J Sports Med (2016): bjsports- 2015.

However, since the exercise load obtained by conventional RPE was measured based on a questionnaire, there was a problem in that the accuracy of detecting injury physical condition risk depended on the questionnaire results.

In order to solve the above-mentioned problems, the health management method according to the present invention allows a computer to calculate the ratio of acute exercise load to chronic exercise load calculated from either the measured heart rate or pulse rate and time. The A/C ratio indicating the ratio of the acute exercise load to the chronic exercise load calculated from either the heart rate or pulse rate measured in the past and the time, and the period measured in the past. The present invention is characterized in that injuries and poor physical condition are predicted by making an evaluation using an injury/unwell physical condition prediction graph based on the frequency of injuries and poor physical condition that occur after a certain period of time.

Further, a health management method according to the present invention is a health management method for predicting the possibility of injury or poor physical condition using a health management device including a measuring section, a calculating section, and a predicting section , wherein the measuring section is a step of measuring one of the heart rate and pulse rate and time, and the calculation unit indicates a ratio of acute exercise load to chronic exercise load from the measured heart rate and pulse rate and time. the step of calculating an A/C ratio; and the step of the prediction unit obtaining an injury/poor physical condition prediction index from the A/C ratio based on an injury/poor physical condition prediction graph, The prediction graph is an A/C ratio indicating the ratio of acute exercise load to chronic exercise load calculated from either the heart rate or pulse rate measured in the past and time, and a certain period of the previously measured period. It is characterized by being created based on the frequency of injuries and poor physical condition that occur later.

Further, the health management device according to the present invention is a health management device that predicts the possibility of injury or poor physical condition, and is a health management device that predicts the possibility of injury or poor physical condition, and is a health management device that predicts the possibility of injury or poor physical condition. A storage unit that stores an A/C ratio indicating the ratio of acute exercise load to exercise load, a frequency of injury or poor physical condition that occurs after a certain period of the previously measured period, and a heart rate and a pulse rate. a measuring section that measures the measured heart rate and pulse rate, and a calculating section that calculates an A/C ratio indicating a ratio of acute exercise load to chronic exercise load from either the measured heart rate or pulse rate and time; a prediction unit that obtains an injury/unwellness prediction index from the A/C ratio calculated in the past using an injury/unwellness prediction graph based on the A/C ratio calculated in the past and the frequency of injury/unwellness; Equipped with.

Further, the health management program according to the present invention includes a step of measuring either heart rate or pulse rate and time for a health management device that predicts the possibility of injury or poor physical condition; calculating an A/C ratio indicating the ratio of acute exercise load to chronic exercise load from either heart rate or pulse rate and time;・Performing a process comprising a step of acquiring a poor physical condition prediction index, so that the injury/poor physical condition prediction graph is based on the acute Functioning the health management device, which is created based on the A/C ratio indicating the ratio of exercise load and the frequency of injuries and poor physical condition that occur after a certain period of time measured in the past. .

Further, a health management method according to the present invention is a health management method for formulating a training plan using a health management device including a calculation unit, a prediction unit, and a determination unit, wherein the calculation unit a step of calculating an A/C ratio indicating a ratio of acute exercise load to chronic exercise load from the exercise load in the training plan ; a step of acquiring an injury/poor physical condition prediction index, and a step in which the determination unit compares the injury/poor physical condition prediction index with a reference value, and the injury/poor physical condition prediction graph is based on a heart rate measured in the past. A/C ratio indicating the ratio of acute exercise load to chronic exercise load calculated from either pulse rate or pulse rate and time, and the frequency of injuries and poor physical condition that occur after a certain period of the previously measured period. It is characterized by being created based on.

Further, the health management device according to the present invention is a health management device for formulating a training plan, and the health management device is a health management device for formulating a training plan. A storage unit that stores an A/C ratio indicating a ratio of exercise loads and a frequency of injuries and poor physical condition that occur after a certain period of time measured in the past, and data regarding exercise loads in the training plan are inputted. an exercise load input unit; a calculation unit that calculates an A/C ratio indicating a ratio of acute exercise load to chronic exercise load from either the measured heart rate or pulse rate and time; and the calculated A/C ratio. a prediction unit that obtains an injury/unwellness prediction index from the ratio, using an injury/unwellness prediction graph based on the A/C ratio calculated in the past and the frequency of injury/unwellness. .

Further, the health management program according to the present invention includes a step of inputting data regarding an exercise load in the training plan to a health management device for formulating a training plan, and an acute exercise load relative to a chronic exercise load than the exercise load. a step of calculating an A/C ratio indicating the ratio of a step of comparing with a reference value, and the injury/deterioration prediction graph is calculated based on the acute exercise load relative to the chronic exercise load calculated from either the heart rate or pulse rate measured in the past and time. and the frequency of injuries and poor health that occur after a certain period of time measured in the past.

According to the present invention, it is possible to provide a health management method, device, and program that predict injury and poor physical condition with high accuracy and formulate a training plan.

FIG. 1 is a block diagram showing the configuration of a health management device according to a first embodiment of the present invention. FIG. 2 is a diagram showing an example of an injury/illness prediction graph according to the first embodiment of the present invention. FIG. 3 is an example of an exercise load and injury/illness history data list according to the first embodiment of the present invention. FIG. 4 is a flowchart for explaining the health management method according to the first embodiment of the present invention. FIG. 5 shows an example of an injury/illness prediction graph derived based on RPE (conventional method). FIG. 6 is a block diagram showing the configuration of a health management device according to the second embodiment. FIG. 7 is a flowchart for explaining the health management method according to the second embodiment. FIG. 8 is a block diagram showing an example of Internet connection relationships in the embodiment of the present invention.

<First embodiment>
A health care device and method according to a first embodiment of the present invention will be described with reference to FIGS. 1 to 5. In the health management device and method according to the present embodiment, injury or poor physical condition is predicted.

<Configuration of health management device>
FIG. 1 is a block diagram showing the configuration of a health care device 10 according to the present embodiment. The health management device 10 includes a measurement section 11 , an input section 12 , a storage section 13 , a calculation section 14 , a prediction section 15 , and an output section 16 . The measurement unit 11 includes a heart rate measurement unit 111 and a time measurement unit 112. The calculation unit 14 includes an exercise intensity calculation unit 141, an exercise load calculation unit 142, and an A/C ratio calculation unit 143.

In the health management device 10, the calculation unit 14 calculates the A/C ratio (described later) based on the heart rate or pulse rate (hereinafter referred to as “heart rate, etc.”) data measured by the measurement unit 11, and makes predictions. The unit 15 predicts injury or poor physical condition from the calculated A/C ratio based on the injury/unwell physical condition prediction graph stored in the storage section 13.

<How to create an injury/illness prediction graph>
The injury/illness prediction graph 20 will be explained below. FIG. 2 shows an example of an injury/illness prediction graph 20.

Data is plotted with the horizontal axis (x-axis) as the A/C ratio calculated from previously measured heart rates, etc., and the vertical axis (y-axis) as the injury/illness occurrence index. A solid line 21 is an approximate curve obtained by approximating the plot with a quadratic function, and a dotted line 22 is a 95% prediction interval indicating an error. In Figure 2, the correlation coefficient of the approximate curve is 0.96, and at the A/C ratio that is the minimum value on the vertical axis of the approximation formula, 95% of the data falls within the range of injury/illness occurrence index of ±0.59. is included.

Here, the A/C ratio is the ratio between acute workload (A) and chronic workload (C). The acute load generally utilizes the average exercise load for one week. It is said that the average exercise load for one week represents fatigue. Chronic loading generally utilizes a four-week average exercise load. It is said that the average exercise load for four weeks represents physical strength (Non-Patent Document 1).

On the other hand, the injury/unwellness occurrence index measures the frequency of injuries/unwellness that occur after a certain period of time (or period) when the A/C ratio is calculated, or in other words, when heart rate etc. are measured (or period). show. In this embodiment, the number of injuries and poor physical conditions per 1,000 people per unit time is used as the frequency of injuries and poor physical conditions.

The injury/poor physical condition prediction graph 20 is based on the exercise load data and injury/poor physical condition history data in the exercise load and injury/poor physical condition history data list (hereinafter referred to as "data list") 30 shown as an example in FIG. Created in

In the data list 30, attributes (gender, age, etc.) and sports type are registered for each subject (individual), and each subject's exercise history, injury/physical condition history is input weekly. As the exercise history of each subject, the average value of the total exercise load per day for one week is input. As the injury/physical condition history, if the injury/physical condition is in good physical condition without any injury, "〇" is entered, and if an injury or poor physical condition occurs, "x" is entered.

Here, as the exercise history of each subject, the maximum exercise intensity (heartbeat) and time may be stored separately, rather than the value obtained by multiplying the maximum exercise intensity (heartbeat) by time, or the A/C ratio may be memorized.

Here, the injury status is input as "〇" or "x", but it may also be input as a numerical value based on the degree of injury (5-level evaluation, 10-level evaluation, etc.). Further, the physical condition may be input as a numerical value in five stages, ten stages, etc. based on body temperature, blood pressure, heart rate, blood oxygen concentration, and lactic acid value.

Based on this data list 30, the A/C ratio on the x-axis in the injury/poor physical condition prediction graph 20 can be determined, for example, for an arbitrary subject on an arbitrary day (hereinafter referred to as "measurement date"). Using the data of the most recent week and the data of the most recent 4 weeks, A/C ratio = (Average value of total exercise load per day in the most recent week) / (Average value of total exercise load per day in the most recent 4 weeks) ) can be calculated.

The details of the method for calculating the A/C ratio will be explained below. First, the heart rate is measured by the number of beats per minute using a heart rate monitor or the like. Note that the subject's pulse rate may be measured instead of the heart rate. At the same time, the time at which heart rate and other measurements were taken is also measured.

Next, extract the maximum heart rate within a period (hereinafter referred to as a "session") divided from the measured heart rate and time, and calculate the exercise intensity based on the maximum heart rate using equation (1). .

For example, when measuring heart rate during one training session, a session is a period for each type of exercise performed within one training session. In this case, the heart rate measurement time corresponds to the training time, and the various practice times correspond to the session time.

In this way, a session is a period divided by exercise event or type, such as training, and is determined by a director, coach, data analyst, etc.

Exercise intensity = (maximum heart rate during measured exercise - resting heart rate) / (maximum heart rate - resting heart rate) x 10 (1)

In equation (1), the "measured maximum heart rate during exercise" is a value measured with a heart rate monitor or the like. Furthermore, the "resting heart rate" and the "maximum heart rate" are values actually measured in advance.

Furthermore, the exercise intensity may be calculated using equation (2) based on the average heart rate.

Exercise intensity = (measured average heart rate during exercise - resting heart rate) / (maximum heart rate - resting heart rate) x 10 (2)

In the above equation (2), the "measured average heart rate during exercise" is the average value of the measured values by the heart rate meter. Furthermore, the "resting heart rate" and the "maximum heart rate" are values actually measured in advance.

By multiplying the calculated exercise intensity by the session time, the exercise load is calculated for each session. The daily sum of the exercise loads calculated for each session is calculated as the daily total exercise load. Here, the exercise load is a scale representing a value obtained by multiplying the intensity of exercise based on the physical ability of the subject performing the exercise multiplied by time.

Acute load and chronic load are calculated based on the calculated daily total exercise load. For example, the average value of the total exercise load per day in the most recent week is used for the acute load, and the average value of the total exercise load per day in the most recent four weeks is used for the chronic load.

Finally, the ratio between the calculated acute load (A) and chronic load (C) is calculated as the A/C ratio.

On the other hand, the injury/physical condition occurrence index on the y-axis in the injury/physical condition prediction graph 20 in FIG. Calculate from For example, extract the training time and injury/physical condition history data for one week after the measurement date for 100 arbitrary subjects, and convert it to the average value per hour (training time) per 1000 subjects. Then, calculate the injury/illness occurrence index.

Data is plotted with the A/C ratio obtained as described above as x and the injury/illness occurrence index as y. An approximate curve obtained by approximating this plot using a quadratic function is indicated by a solid line 21. Further, a prediction interval is indicated by a dotted line 22. Here, the approximate curve is expressed as y=17.8x ² -41.2x+24.4.

The approximate curve in this injury/illness prediction graph 20 is derived by the least squares method, and varies depending on the subject, attributes (gender, age, etc.), sport type, and team. Therefore, the coefficient of the quadratic function in the approximate curve differs depending on the subject, attributes (gender, age, etc.), sport type, and team. Therefore, data is calculated separately for each target person, attribute, sport type, and team.

As described above, the injury/unwellness prediction graph 20 is based on the A/C ratio calculated from the heart rate etc. measured in the past and the frequency of injuries/unwellness that occur after a certain period of time measured in the past. Created based on.

In this way, the injury/illness prediction graph 20 is created and stored in the storage unit 13.

<Health management method>
The health management method according to this embodiment will be explained below. FIG. 4 is a flowchart for explaining the health management method according to this embodiment.

First, the heart rate measuring section 111 of the measuring section 11 measures the heart rate of the subject during the period during which the subject exercises, using a heart rate meter or the like (step 41). Furthermore, the period during which the subject exercises may be divided into periods for each type of exercise, such as training.

Further, the time measuring section 112 of the measuring section 11 measures the time at which the heart rate and the like are measured (step 42). Functions provided in the heart rate monitor may also be used.

Next, information such as heart rate, injury and physical condition at the time of time measurement are input to the input unit 12 (step 43).

The storage unit 13 stores data such as the subject's heart rate measured by the heart rate measurement unit 111 and time data measured by the time measurement unit 112. At the same time, the measurement date and time are also stored. The data stored in the storage unit 13 may be data of multiple people or data of multiple days. In addition, the input injury and physical condition are stored (step 44).

Next, the exercise intensity calculation unit 141 extracts the measured heart rate and the maximum heart rate within the session, and calculates the exercise intensity based on the maximum heart rate using equation (1) (step 45).

Furthermore, the exercise intensity may be calculated using equation (2) based on the average heart rate.

Next, the exercise load calculation unit 142 calculates the exercise load for each session by multiplying the calculated exercise intensity by the session time (step 46).

Acute load and chronic load are calculated based on the calculated daily total exercise load. In this embodiment, the average value of the total exercise load per day in the most recent week is used as the acute load, and the average value of the total exercise load per day in the most recent four weeks is used as the chronic load.

Next, the A/C ratio calculation unit 143 calculates the A/C ratio, which is the ratio between the calculated acute load and chronic load (step 47).

Next, the prediction unit 15 reads the injury/poor physical condition prediction graph 20 from the storage unit 13 and compares the calculated A/C ratio with the injury/poor physical condition prediction graph 20. As a result of the comparison, an injury/illness prediction index corresponding to the calculated A/C ratio is obtained (step 48).

Alternatively, an approximate curve of the injury/poor physical condition prediction graph may be used as a prediction formula, and this prediction formula may be used to obtain the injury/poor physical condition prediction index.

In this way, an injury/unwellness prediction index is obtained from the calculated A/C ratio based on the injury/unwellness prediction graph.

For example, according to the injury/poor physical condition prediction graph 20 in FIG. Or, it is predicted that there is a high possibility that poor physical condition will occur.

Furthermore, according to the injury/unwellness prediction graph 20 in FIG. 2, it is predicted that when the A/C ratio is 0.95 to 1.3, the possibility of injury or unwellness occurring in the next week is low.

Finally, the output unit 16 outputs an injury/illness prediction index as the injury/illness prediction result (step 49). Alternatively, the possibility of injury or poor physical condition occurring in the next week may be output based on the injury/unwellness prediction index. The possibility of injury or poor physical condition may be indicated by the presence or absence of the possibility, or may be indicated by a numerical value such as a 5-level evaluation or a 10-level evaluation.

Injury/unwellness prediction graph 20 is created using data such as heart rate, time, injury/unwell condition, exercise intensity, exercise load, A/C ratio, etc. that are stored in the process of predicting injury/unwell health as described above. , updates the data list 30 regularly.

Here, the step of inputting data (step 43) and the step of storing data (step 44) are necessary for updating databases such as the injury/illness prediction graph 20 and the data list 30. Or, it is not necessarily necessary to predict poor physical condition.

Furthermore, if the injury/illness prediction graph 20 is created by classifying the target person's attributes, such as age, gender, sport type, etc., and is used for prediction, the accuracy of prediction can be improved.

In this way, by using the value of the A/C ratio for the actual exercise load, it is possible to predict the possibility of injury or poor physical condition occurring in the next week. Based on this A/C ratio data or graph, it is possible to set an A/C ratio with a lower frequency of occurrence of injury or poor physical condition, and to construct a training plan.

For comparison, FIG. 5 shows an injury/illness prediction graph 50 derived based on RPE, which is a conventional method.

Specifically, the one-week average of the exercise load obtained by multiplying the RPE and the time the exercise was performed is the acute load, and the four-week average of the exercise load obtained by the product of the RPE and the time the exercise was performed. was determined as chronic load, A/C ratio=acute load/chronic load.

As in FIG. 2, the A/C ratio calculated from RPE was plotted as x and the injury/illness occurrence index as y. An approximate curve obtained by approximating this plot using a quadratic function is shown by a solid line. Moreover, the prediction interval is shown by a dotted line.

Comparing the injury/poor physical condition prediction graph 20 (FIG. 2) in this embodiment with the injury/poor physical condition prediction graph 50 (FIG. 5) according to the conventional method (based on RPE), it is found that the approximate curve in this embodiment The correlation coefficient of is 0.96, and that of the conventional method is 0.84. Further, the standard deviation of the approximate curve in this embodiment is 0.59, and that in the conventional method is 0.96.

As described above, the correlation coefficient in the injury/illness prediction graph 20 in this embodiment is high, the prediction interval is narrow, and the data variation is small. Therefore, if the injury/unwellness prediction graph of this embodiment is used, the A/C ratio is calculated using objective data compared to the conventional method, so that injuries/unwellness can be predicted with high accuracy.

According to the prediction device and method according to the present embodiment, by setting the exercise load at the training planning stage and calculating the A/C ratio, it is possible to predict the possibility of injury or poor physical condition occurring in the next week. Therefore, it can be used to design a training schedule that takes exercise load into consideration based on the allowable index of injury and poor physical condition.

In this embodiment, an example is shown in which the A/C ratio is calculated using the data of the most recent week and the data of the most recent four weeks on the measurement date, but the target period is limited to the most recent one week or the most recent four weeks. do not have. It doesn't have to be recent, it can be several species ago, and the period can be 2 weeks or 10 weeks.

Further, although an example has been shown in which the injury/physical condition history data for one week after the measurement date is used to create the injury/physical condition prediction graph to predict the next week's injury/physical condition, the present invention is not limited to this. Injury/physical condition history data for three weeks after the measurement date may be used to predict injury/physical condition three weeks later. By using injury/physical condition history data for a certain period after the measurement date, it is possible to predict injuries/physical condition after a certain period.

<Second embodiment>
A health care device and method according to a second embodiment of the present invention will be described with reference to FIGS. 6 and 7. The health management device and method according to the present embodiment relate to the formulation of a training plan. The health care device and method according to the present embodiment use part of the health care device 10 and method according to the first embodiment.
<Configuration of health management device>

FIG. 6 is a block diagram showing the configuration of health management device 60 according to this embodiment.

The health management device 60 includes an exercise load input section 61 , a storage section 13 , an A/C ratio calculation section 143 , a prediction section 15 , a determination section 62 , and an output section 63 . The storage unit 13, A/C ratio calculation unit 143, and prediction unit 15 are the same as those in the first embodiment (shown within the dotted line in FIG. 6).

<Health management method>
The health management method according to this embodiment will be explained below. FIG. 7 is a flowchart for explaining the health management method according to the present embodiment.

First, the exercise load of the planned training is input to the exercise load input section 61 (step 71). The exercise load input unit 61 may create a list in advance in which training types and exercise loads are associated with each other, and set the exercise load in accordance with the input training type.

Next, the storage unit 13 stores exercise loads from past trainings and games (step 72). For example, similar to the first embodiment, it is stored as an exercise load and injury/illness history data list or injury/illness prediction graph.

Next, the A/C ratio calculation unit 143 calculates the A/C ratio from the exercise load input to the exercise load input unit 61 (step 73).

Next, similarly to the first embodiment, the prediction unit 15 compares the A/C ratio obtained from the input exercise load with the injury/poor physical condition prediction graph read from the storage unit 13. An injury/physical condition occurrence index is calculated (step 74).

Next, the determining unit 62 compares the calculated injury/physical condition occurrence index with a reference value to determine the quality of the training (step 75).

If the reference value is exceeded, the exercise load is changed and the steps of calculating and determining the injury/illness occurrence index are repeated (step 76).

If the injury/physical condition occurrence index is below the reference value, the training plan formulation process is ended and the exercise load in the training plan is determined (step 77).

In this embodiment, the injury/illness occurrence index in the injury/illness prediction graph 20 in FIG. 2 is used as the reference value. The reference value is not limited to this, and may be set depending on the attributes of the user, the type of sport, etc.

Finally, the output unit 63 outputs the exercise load in the determined training plan (step 78).

As described above, according to the health management device and method according to the present embodiment, it is possible to determine an exercise load in a training plan that can suppress the occurrence of injury or poor physical condition. Further, since the prediction unit in the first embodiment is used, it is possible to determine the exercise load in a training plan that can suppress the occurrence of injury or poor physical condition with high accuracy.

<Modification of second embodiment>
Although an example has been shown in which the exercise load in the training plan is input, the type of sport, training content, training time, etc. in the training plan may also be input. The health care device and method in this case are as follows.

In the storage unit 13, exercise loads are stored in advance for each training for each sport type.

For example, in rugby, the maximum heart rate or average heart rate is measured for each practice such as interval training, tackling practice, scrum practice, and game-style practice, and the exercise load is calculated as described above. Memorizes the exercise load for each exercise. Similarly, the exercise load for each practice is memorized for other sports such as soccer, track and field, and swimming.

First, the sport type of training to be planned, training content, training time, etc. (hereinafter referred to as "data related to exercise load" including exercise load) are input into the exercise load input section 61.

Next, the A/C ratio calculation unit 143 reads out the exercise load corresponding to the input sport type and training content from the storage unit 13, and calculates the A/C ratio using the input training time.

Next, similarly to the first embodiment, the prediction unit 15 compares the calculated A/C ratio with the injury/unwellness prediction graph read from the storage unit 13 to determine the injury/unwellness occurrence index. Calculate.

Next, the determination unit 62 compares the injury/illness occurrence index with the reference value. If the standard value is exceeded, the training content and training time are changed and the steps of calculating and determining the injury/poor physical condition occurrence index are repeated again.

When the injury/physical condition occurrence index is below the reference value, the training plan formulation process is ended and the training content and training time in the training plan are determined.

In this modification, the reference value may be set depending on the sport type, training content, etc.

Finally, the output unit 63 outputs the training content and training time in the determined training plan. Alternatively, it may be output as a training plan based on training content and training time.

In this way, according to the health management device and method according to this modification, it is possible to formulate a training plan that can suppress the occurrence of injury or poor physical condition. Further, since the prediction unit in the first embodiment is used, it is possible to formulate a training plan that can suppress the occurrence of injury or poor physical condition with high accuracy.

In the present embodiment, the determination unit determines the injury/illness occurrence index and the reference value calculated by the prediction unit and outputs the training plan and exercise load to the output unit, but the present invention is not limited thereto. The injury/illness occurrence index and the possibility of injury/illness occurrence calculated by the prediction section may be output to the output section without providing the determination section. In this case, the user can judge whether the training plan is good or bad based on the injury/illness occurrence index and the possibility of injury/illness, and use the output results to formulate the training plan.

The health management device 10 according to the first embodiment and the health management device 60 according to the second embodiment can communicate with a user terminal 82 via an Internet communication network 81, for example, as shown in FIG. may be connected to. Here, the health management device may be a cloud server. The user inputs various data from the user terminal via the Internet communication network, and obtains the output prediction results of injuries and poor physical condition and training plans.

The health care device according to the embodiment of the present invention can be realized by a computer including a CPU (Central Processing Unit), a storage device, and an interface, and a program that controls these hardware resources.

The health care device according to the embodiment of the present invention may include a computer inside the device, or may be implemented using an external computer. Further, the storage unit 13 may also use a storage medium external to the apparatus, and may read and execute a measurement program stored in the storage medium. Storage media include various magnetic recording media, magneto-optical recording media, CD-ROMs, CD-Rs, and various memories. Further, the analysis program may be supplied to the computer via a communication line such as the Internet.

In the embodiment of the present invention, an example of the structure of each component has been shown in the configuration of the health management device, method, program, etc., but the present invention is not limited thereto. Any device, method, or program may be used as long as it exhibits the functions and effects of these devices, methods, and programs.

The present invention can be applied to a system that can collectively record and manage information necessary for conditioning and training of athletes, and to services in the sports field using this system.

10 Health management device 11 Measurement section 12 Input section 13 Storage section 14 Calculation section 15 Prediction section 16 Output section

Claims (8)

The computer calculates the A/C ratio indicating the ratio of acute exercise load to chronic exercise load calculated from either the measured heart rate or pulse rate and time,
The A/C ratio indicates the ratio of the acute exercise load to the chronic exercise load calculated from either the heart rate or pulse rate measured in the past and the time, and the injury that occurs after a certain period of the previously measured period. A health management method that predicts injury or poor physical condition by evaluating using a prediction graph of injury or poor physical condition based on the frequency of poor physical condition. A health management method for predicting the possibility of injury or poor physical condition using a health management device comprising a measurement unit, a calculation unit, and a prediction unit, the method comprising:
a step in which the measurement unit measures either heart rate or pulse rate and time;
a step in which the calculation unit calculates an A/C ratio indicating a ratio of acute exercise load to chronic exercise load from either the measured heart rate or pulse rate and time;
The prediction unit obtains an injury/illness prediction index from the A/C ratio based on an injury/illness prediction graph,
The injury/poor physical condition prediction graph includes an A/C ratio indicating the ratio of acute exercise load to chronic exercise load calculated from either the heart rate or pulse rate measured in the past and time, and the previously measured A health management method characterized by being created based on the frequency of injuries and poor physical condition that occur after a certain period of time. A health management device that predicts the possibility of injury or poor physical condition,
A/C ratio indicating the ratio of acute exercise load to chronic exercise load calculated from either the heart rate or pulse rate measured in the past and time, and the injury that occurs after a certain period of the previously measured period.・A memory unit that stores the frequency of poor physical condition;
a measurement unit that measures either heart rate or pulse rate and time;
a calculation unit that calculates an A/C ratio indicating a ratio of acute exercise load to chronic exercise load from either the measured heart rate or pulse rate and time;
Obtaining an injury/unwellness prediction index from the calculated A/C ratio using an injury/unwellness prediction graph based on the previously calculated A/C ratio and the frequency of injury/unwellness. A health management device comprising a prediction unit that performs prediction. For health management devices that predict the possibility of injury or poor physical condition,
a step of measuring either heart rate or pulse rate and time;
Calculating an A/C ratio indicating the ratio of acute exercise load to chronic exercise load from either the measured heart rate or pulse rate and time;
executing a process comprising a step of obtaining an injury/poor physical condition prediction index from the A/C ratio based on an injury/poor physical condition prediction graph;
The injury/poor physical condition prediction graph includes an A/C ratio indicating the ratio of acute exercise load to chronic exercise load calculated from either the heart rate or pulse rate measured in the past and time, and the previously measured A health management program for operating the health management device, characterized in that the program is created based on the frequency of injuries and poor physical condition that occur after a certain period of time. A health management method for formulating a training plan using a health management device including a calculation unit, a prediction unit, and a determination unit, the method comprising:
a step in which the calculation unit calculates an A/C ratio indicating a ratio of acute exercise load to chronic exercise load from exercise load in the input training plan ;
a step in which the prediction unit obtains an injury/poor physical condition prediction index from the A/C ratio based on an injury/poor physical condition prediction graph;
The determination unit includes a step of comparing the injury/poor physical condition prediction index with a reference value,
The injury/poor physical condition prediction graph includes an A/C ratio indicating the ratio of acute exercise load to chronic exercise load calculated from either the heart rate or pulse rate measured in the past and time, and the previously measured A health management method characterized by being created based on the frequency of injuries and poor physical condition that occur after a certain period of time. A health management device for formulating a training plan,
A/C ratio indicating the ratio of acute exercise load to chronic exercise load calculated from either the heart rate or pulse rate measured in the past and time, and the injury that occurs after a certain period of the previously measured period.・A memory unit that stores the frequency of poor physical condition;
an exercise load input unit into which data regarding exercise load in the training plan is input;
a calculation unit that calculates an A/C ratio indicating a ratio of acute exercise load to chronic exercise load from either the measured heart rate or pulse rate and time;
From the calculated A/C ratio, an injury/unwell physical condition prediction index is calculated using an injury/unwell physical condition prediction graph based on the previously calculated A/C ratio and the frequency of injury/unwell physical condition. A health management device comprising: a prediction unit for acquiring information. The health management device according to claim 6, further comprising a determination unit that compares the injury/illness prediction index with a reference value. For health management devices for formulating training plans,
inputting data regarding exercise load in the training plan;
Calculating an A/C ratio indicating a ratio of acute exercise load to chronic exercise load from the exercise load;
Obtaining an injury/illness prediction index from the A/C ratio based on an injury/illness prediction graph;
executing a process comprising a step of comparing the injury/poor physical condition prediction index with a reference value;
The injury/poor physical condition prediction graph includes an A/C ratio indicating the ratio of acute exercise load to chronic exercise load calculated from either the heart rate or pulse rate measured in the past and time, and the previously measured A health management program for operating the health management device, characterized in that the program is created based on the frequency of injuries and poor physical condition that occur after a certain period of time.

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