System and method for predicting exercise effect

Abstract

To provide a system for predicting an effect when a training is continued, the training being for continuing an exercise in which a time for reaching a predetermined intensity reaches a predetermined week-average time.SOLUTION: A system 10 includes: an input interface for acquiring a prediction target item related to physical strength improvement or lifestyle-related disease improvement; a first prediction unit 11 which, if the prediction target item is included in an exercise intensity-dependent group, acquires sex, an initial characteristic of a user of the prediction target item, a week-average time scheduled during the training, and an exercise strength scheduled during the training and which outputs a prediction effect by using an exercise intensity-dependent estimation formula that includes them as elements; and a second prediction unit 12 which, if the prediction target item is included in an exercise intensity-non-dependent group, acquires the initial characteristic and the week-average time, and which outputs a prediction effect by using an exercise intensity-non-dependent estimation formula that includes them as elements.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a system and method for predicting exercise effect. [Background technology]

[0002] Patent Document 1 proposes an exercise effect prediction system that accurately predicts how much measured values ​​such as body weight, muscle strength, and blood sugar level will improve if a predetermined exercise is performed continuously for a certain period of time. In this exercise effect prediction system, an initial measurement value measured before the exercise is prescribed or immediately before and an average weekly fast walking time are input into an initial condition input screen. An initial attribute group determination means determines which of the initial attribute groups classified based on statistical data the input initial measurement value belongs to. When the initial attribute group is determined, an effect prediction means substitutes the average weekly fast walking time into an effect estimation formula prepared in advance for each initial attribute group to calculate the amount of change (effect) in the initial measurement value after about five months. The effect estimation formula used to calculate the effect is obtained by analyzing statistical data for each initial attribute group. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2008-073339 A Summary of the Invention [Problem to be solved by the invention]

[0004] It is known that exercise is effective in improving physical fitness and preventing lifestyle-related diseases. However, it is not clear to what extent exercise time and intensity are effective in improving physical fitness and preventing lifestyle-related diseases. For example, even if increasing exercise intensity helps improve physical fitness, the dangers and disadvantages of increasing exercise intensity too much are known, and on the other hand, there is a possibility that no effect will be obtained if the exercise intensity is not increased. In addition, even if the exercise intensity is increased, there is a possibility that no effect will be obtained if the exercise time is insufficient, so it is important to provide an appropriate exercise prescription that is suitable for the user's current situation and future (goal). [Means for solving the problem]

[0005] One aspect of the present invention is a system for predicting the effect of continuing training in which the time during which a predetermined intensity is reached reaches a predetermined time on average per week. The inventors of the present application have found that, by analyzing the exercise effect of a user who has continued this training, the effects of prediction target items related to improving physical fitness or improving lifestyle-related diseases include items that depend on exercise intensity (exercise intensity-dependent group) and items that do not depend on exercise intensity (exercise intensity-independent group). In this specification, the definition of "independent of exercise intensity" or "independent of exercise intensity" means that the exercise intensity reaches a "predetermined intensity" of the exercise specified as the target training, and is not dependent on or independent of exercise intensity equal to or higher than this "predetermined intensity."

[0006] Based on this new viewpoint, the system of the present invention has an input interface for acquiring a prediction target item related to improving physical fitness or lifestyle-related diseases, a first prediction unit for acquiring, via the input interface, at least an initial characteristic of the user for the prediction target item, a weekly average time planned during continued training, and an exercise intensity planned during continued training, if the prediction target item is included in an exercise intensity-dependent group, and outputting a predicted effect of the prediction target item after continuing training for a predetermined period of time using an exercise intensity-dependent estimation equation including at least the initial characteristic, the weekly average time, and the exercise intensity as elements, if the prediction target item is included in an exercise intensity-independent group, and a second prediction unit for acquiring, via the input interface, at least an initial characteristic of the user for the prediction target item and a weekly average time planned during continued training, and outputting a predicted effect of the prediction target item after continuing training for a predetermined period of time using an exercise intensity-independent estimation equation including at least the initial characteristic and the weekly average time as elements.

[0007] Furthermore, the inventors have found that the effect of the predicted item in the exercise intensity-dependent group is related to the gender of the user. Therefore, the first prediction unit may obtain the gender of the user through the input interface, and output a predicted effect of the predicted item after continuing training for a predetermined period of time using an exercise intensity-dependent estimation equation that includes at least the gender, initial characteristics, average weekly time, and exercise intensity as elements.

[0008] Examples of prediction target items included in the exercise intensity-dependent group are maximum oxygen intake, body mass index, and minimum blood pressure, and examples of prediction target items included in the exercise intensity-independent group are blood glucose level, HDL cholesterol, and arteriosclerosis index. An example of training is interval walking training with a predetermined intensity set to 70% of maximum oxygen intake and a predetermined weekly average time set to 60 minutes.

[0009] The system may further include a first interface that provides the user with information including that the predicted effect depends on exercise intensity when the prediction target item is included in the exercise intensity-dependent group. The system may also include a second interface that provides the user with information including that the predicted effect does not depend on exercise intensity when the prediction target item is included in the exercise intensity-independent group. By clearly indicating to the user whether the effect of the prediction target item depends on exercise intensity, for example, by explicitly adding or omitting an input item for exercise intensity, or clearly indicating that the effect depends or does not depend on exercise intensity, it is possible to encourage the user to actively increase exercise intensity or not to increase exercise intensity unnecessarily.

[0010] Another aspect of the present invention is a method for predicting the effect of continuing training in which the time during which a predetermined intensity is reached reaches a predetermined time on a weekly average, using an effect prediction system. The effect prediction system has an input interface for acquiring prediction target items related to improving physical fitness or improving lifestyle-related diseases, and a library that stores, for prediction target items included in an exercise intensity-dependent group, exercise intensity-dependent estimation formulas including at least the initial characteristics of the user of the prediction target items, the weekly average time planned during the continuation of the training, and the exercise intensity planned during the continuation of the training as elements, and stores, for prediction target items included in an exercise intensity-independent group, exercise intensity-independent estimation formulas including at least the initial characteristics and the weekly average time as elements.

[0011] The method includes, if the item to be predicted is included in the exercise intensity-dependent group, acquiring at least initial characteristics, weekly average time, and exercise intensity via an input interface, and outputting a predicted effect of the item to be predicted in the exercise intensity-dependent group after continuing training for a predetermined period of time using an exercise intensity-dependent estimation equation that includes at least them as elements; and, if the item to be predicted is included in the exercise intensity-independent group, acquiring at least initial characteristics and weekly average time via the input interface, and outputting a predicted effect of the item to be predicted in the exercise intensity-independent group after continuing training for a predetermined period of time using an exercise intensity-independent estimation equation that includes at least them as elements.

[0012] The library may store, for a prediction target item included in the exercise intensity-dependent group, an exercise intensity-dependent estimation equation including at least the user's gender, the user's initial characteristics of the prediction target item, the average weekly time planned during the continuation of training, and the exercise intensity planned during the continuation of training as elements. Outputting the predicted effect of the prediction target item in the exercise intensity-dependent group may include outputting a predicted effect of the prediction target item after continuing training for a predetermined period of time using the exercise intensity-dependent estimation equation including at least the gender, the initial characteristics, the average weekly time, and the exercise intensity as elements.

[0013] This method may include, if the item to be predicted is included in the exercise intensity-dependent group, providing the user with information including that the predicted effect depends on the exercise intensity, and, if the item to be predicted is included in the exercise intensity-independent group, providing the user with information including that the predicted effect does not depend on the exercise intensity.

[0014] Another aspect of the present invention is a method for predicting the effect of continuing training involving exercise in which the time at which a predetermined intensity is reached reaches a predetermined time on average per week, the method comprising the steps of: Obtaining prediction items related to improving physical fitness or improving lifestyle-related diseases. If the prediction target item is included in the exercise intensity dependent group, at least the user's initial characteristics for the prediction target item, the average weekly time planned during the continuation of the training, and the exercise intensity planned during the continuation of the training are acquired via the input interface, and a predicted effect of the prediction target item after continuing the training for a predetermined period is output using an exercise intensity dependent estimation equation that includes at least the initial characteristics, the average weekly time, and the exercise intensity as elements. If the prediction target item is included in the exercise intensity independent group, at least the user's initial characteristics of the prediction target item and the weekly average time planned during the continuation of training are acquired via the input interface, and a predicted effect of the prediction target item after continuing training for a predetermined period is output using an exercise intensity independent estimation equation that includes at least the initial characteristics and the weekly average time as elements.

[0015] Another aspect of the present invention is a program for predicting, by a computer, an effect of continuing training involving exercise in which the time during which a predetermined intensity is reached reaches a predetermined time on weekly average, the program having instructions for the computer to execute the following: acquiring a prediction target item related to improving physical fitness or ameliorating lifestyle-related diseases; if the prediction target item is included in an exercise intensity-dependent group, acquiring at least initial characteristics of the user for the prediction target item, the weekly average time planned during the continuation of the training, and the exercise intensity planned during the continuation of the training via an input interface, and outputting a predicted effect of the prediction target item after continuing training for a predetermined period of time using an exercise intensity-dependent estimation equation including at least the initial characteristics, the weekly average time, and the exercise intensity as elements; and if the prediction target item is included in an exercise intensity-independent group, acquiring at least initial characteristics of the user for the prediction target item and the weekly average time planned during the continuation of the training via the input interface, and outputting a predicted effect of the prediction target item after continuing training for a predetermined period of time using an exercise intensity-independent estimation equation including at least the initial characteristics and the weekly average time as elements. [Brief description of the drawings]

[0016] [Figure 1] FIG. 1 is a block diagram showing an overview of an exercise effect prediction system. [Diagram 2] A diagram showing an overview of interval walking training. [Diagram 3] A graph showing the effects of interval walking training on knee extension strength, knee flexion strength, and endurance index (peak oxygen uptake). [Figure 4] A graph showing the effects of interval walking training on systolic and diastolic blood pressure. [Diagram 5] 1 is a flowchart showing an analysis procedure. [Figure 6] A graph showing characteristic values ​​of subjects included in the determinant identification group and the accuracy validation group. [Figure 7] FIG. 13 is a diagram showing an estimated equation obtained by analysis. [Figure 8] Results of analysis including change in peak oxygen intake ΔVO2peak, change in body mass index ΔBMI, change in diastolic blood pressure ΔDBP, change in blood glucose level ΔFBG, change in blood cholesterol ΔHDL, and change in arteriosclerosis index Δ(LDL / HDL) total walking time as independent variables. [Figure 9] Analysis results including weekly brisk walking time as independent variables for change in peak oxygen intake ΔVO2peak, change in body mass index ΔBMI, change in diastolic blood pressure ΔDBP, change in blood glucose level ΔFBG, change in blood cholesterol ΔHDL, and change in arteriosclerosis index Δ(LDL / HDL). [Figure 10] Analysis results including weekly brisk walking time and brisk walking exercise intensity as independent variables for peak oxygen intake change ΔVO2peak, body mass index change ΔBMI, diastolic blood pressure change ΔDBP, blood glucose change ΔFBG, blood cholesterol change ΔHDL, and arteriosclerosis index change Δ(LDL / HDL). [Figure 11] Analysis results including change in body mass index ΔBMI, change in diastolic blood pressure ΔDBP, change in blood glucose level ΔFBG, change in blood cholesterol level ΔHDL, change in arteriosclerosis index Δ(LDL / HDL) and change in estimated maximum oxygen intake ΔVO2peak as independent variables. [Figure 12] Evaluation results of the actual and estimated values ​​for (a) the group in which the determinants of the estimation formula that includes the exercise intensity of the maximum oxygen uptake change ΔVO2peak as an independent variable and (b) the accuracy verification group. [Figure 13] Evaluation results of the actual and estimated values ​​for the group (a) that identified the determinants of the estimation formula that does not include exercise intensity of the maximum oxygen uptake change ΔVO2peak as an independent variable and the group (b) that verified the accuracy. [Figure 14] Evaluation results of the actual and estimated values ​​for the group (a) identifying the determinants of the equation for estimating body mass index change ΔBMI and the group (b) verifying its accuracy. [Figure 15] Evaluation results of the actual and estimated values ​​for the group in which the determinants of the equation for diastolic blood pressure change ΔDBP were identified (a) and the group in which accuracy was verified (b). [Figure 16] Evaluation results of the actual and estimated values ​​for the group in which the determinants of the equation for estimating blood glucose change ΔFBG were identified (a) and the group in which accuracy was verified (b). [Figure 17] Evaluation results of the actual and estimated values ​​for the group (a) in which the determinants of the estimated formula for blood cholesterol change ΔHDL were identified and the group (b) in which accuracy was verified. [Figure 18] Evaluation results of the actual and estimated values ​​of the determinant factor identification group (a) and the accuracy verification group (b) of the estimation formula for the arteriosclerosis index change Δ(LDL / HDL). [Figure 19] 1 is a flowchart showing an overview of the processing of the effect prediction system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0017] Fig. 1 shows an example of a system for predicting the effect of continuing a predetermined training. In this system 1, a user 6 uses a terminal 5 such as a PC or a smartphone to access a server (exercise effect prediction information providing system, effect prediction system) 10 of a provider that provides a service of providing exercise effect prediction information via the Internet 9, and inputs the predetermined user's information (user information) 7 to obtain exercise effect prediction information 8. The exercise effect prediction provided by the effect prediction system 10 is a predicted effect when training is continued in which the time at which a predetermined intensity is reached reaches a predetermined time on average per week, and an example of such training is interval walking training (hereinafter, interval walking) in which the predetermined intensity is set to 70% of the maximum oxygen intake and the predetermined time on average per week is set to 60 minutes or more.

[0018] The effect prediction system (information provision system) 10 includes computer resources such as a CPU and memory. The effect prediction system 10 includes an input / output interface 15 that acquires user information 7 and provides predicted effect information 8. The user information 7 includes prediction target items related to improving physical fitness or improving lifestyle-related diseases. The information provision system 10 includes a library 20 that stores estimation formulas for predicting exercise effects for each prediction target item. The library 20 stores, for prediction target items included in an exercise intensity-dependent group 21, an exercise intensity-dependent estimation formula that includes at least the initial characteristics of the user for the prediction target item, the average weekly time planned during the continuation of training, and the exercise intensity planned during the continuation of training as elements, and, for prediction target items included in an exercise intensity-independent group 22, an exercise intensity-independent estimation formula that includes at least the initial characteristics and the average weekly time as elements.

[0019] Examples of prediction target items included in the exercise intensity dependent group 21 are maximum oxygen intake, body mass index and minimum blood pressure, and the library 20 includes estimation equations that estimate a maximum oxygen intake change ΔVO2peak as the predicted effect of maximum oxygen intake, a body mass index change ΔBMI as the predicted effect of body mass index, and a minimum blood pressure change ΔDBP as the predicted effect of minimum blood pressure.

[0020] Examples of prediction target items included in the exercise intensity independent group 22 are blood glucose level, HDL cholesterol, and atherosclerosis index, and the library 20 includes estimation equations for estimating blood glucose level change ΔFBG as the predicted effect of blood glucose level, HDL cholesterol change ΔHDL as the predicted effect of HDL cholesterol, and atherosclerosis index change Δ(LDL / HDL) as the predicted effect of atherosclerosis index. In this specification, exercise intensity independent means that exercise at a specified intensity specified to obtain the effect of training is performed, and the effect of training does not depend on exercise intensity exceeding the specified intensity, and does not allow exercise intensity below the specified intensity.

[0021] The effect prediction system 10 includes a first prediction unit 11 that predicts the predicted effect of a target item in the exercise intensity-dependent group 21 using an estimation formula, and a second prediction unit 12 that predicts the predicted effect of a target item in the exercise intensity-independent group 22 using an estimation formula. If the prediction target item is included in the exercise intensity-dependent group 21, the first prediction unit 11 acquires user information 7 including the initial characteristics of the user of the prediction target item, the average weekly time planned during the continuation of training, and the exercise intensity planned during the continuation of training via the input / output interface 15, and outputs the predicted effect of the prediction target item after continuing training for a predetermined period, in this example, five months, using the estimation formula of the exercise intensity-dependent group 21 including these as elements. If the prediction target item is included in the exercise intensity-independent group 22, the second prediction unit 12 acquires the initial characteristics of the user of the prediction target item and the average weekly time planned during the continuation of training via the input interface 15, and outputs the predicted effect of the prediction target item after continuing training for a predetermined period, using the estimation formula of the exercise intensity-independent group 22 including these as elements.

[0022] The estimation formula for the exercise intensity-dependent group 21 may include the user's gender S as an element, in which case the first prediction unit 11 acquires user information 7 including the user's gender S via the input interface 15, and uses the estimation formula for the exercise intensity-dependent group 21 including the user information 7 to output a predicted effect of the item to be predicted after continuing training for a specified period of time.

[0023] The input / output interface 15 includes a first interface 16 that provides the user with information including that the predicted effect depends on exercise intensity when the prediction target item is included in the exercise intensity-dependent group, and a second interface 17 that provides the user with information including that the predicted effect does not depend on exercise intensity (exercise intensity equal to or greater than a predetermined exercise intensity (minimum exercise intensity) required for training) when the prediction target item is included in the exercise intensity-independent group. These interfaces 16 and 17 clearly indicate to the user 6 whether the predicted effect of the prediction target item depends on exercise intensity (exercise intensity equal to or greater than a predetermined exercise intensity (minimum exercise intensity) required for training). For example, these interfaces 16 and 17 explicitly add or omit the input item of exercise intensity, or clearly indicate that the information depends or does not depend on exercise intensity, thereby encouraging the user 6 to actively increase the exercise intensity in order to obtain the predicted effect, or not to increase the exercise intensity unnecessarily. Only the first interface 16 or the second interface 17 may be provided.

[0024] FIG. 2 shows an overview of interval walking training (IWT). Interval walking training is training in which a predetermined intensity (exercise intensity) is set to 70% of the maximum oxygen intake VO2peak, and a predetermined weekly average time is set to 60 minutes or more. As shown in FIG. 2, the training is performed by repeating walking at a predetermined intensity or less and fast walking at a predetermined intensity or more at appropriate intervals, for example, 5-minute intervals, for 15 minutes a day, 4 days a week or more, so that the average weekly fast walking time (weekly average time) is 60 minutes or more. Therefore, the weekly average time (planned weekly average time) Wa planned by the user 6 included in the user information 7 of this effect prediction system 10 is the weekly average time of fast walking in the interval walking training, and the exercise intensity (planned exercise intensity) Wi planned during the training is the ratio of the maximum oxygen intake VO2peak of fast walking in the interval walking training. For this reason, the weekly average time Wa included in the user information 7 is assumed to be 60 minutes or more, and the exercise intensity Wi is assumed to be 70% or more.

[0025] It is known that the maximum oxygen intake (VO2peak), which is an index of training, can be estimated by several methods. Of course, it can also be an exhaled gas analysis method using equipment for measuring physical fitness such as a bicycle ergometer or a treadmill. One method for calculating the maximum oxygen intake (VO2peak) is the three-stage step-up walking method, in which the load is gradually increased for three minutes each while walking at rest, slowly, moderately, and at the fastest speed, and the energy consumption (oxygen intake) during the last three minutes of the fastest walking is calculated from the output of a three-axis accelerometer and an altimeter. This method significantly simplifies the measurement of the maximum oxygen intake and allows physical fitness to be measured with sufficient accuracy. The oxygen intake per unit time during walking (eVO2W) can be estimated by the following formula. The estimation formula (7) for calculating the estimated value eVO2W of oxygen intake 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. eVO2W=w1·VM+w2·Hu+w3·Hd···(1) VM is the vector norm of the 3-axis acceleration sensor, i.e., the cumulative acceleration per unit time (G / min, G is the gravitational acceleration) obtained by accumulating the acceleration in each direction, Hu is the amount of rise per unit time (ascent speed, m / min), and Hd is the amount of descent per unit time (descent speed, m / min). When a barometer is used, these are the ascent speed and descent speed H (amount of rise and descent per unit time). w1, w2, and w3 are coefficients.

[0026] The value (estimated value) of oxygen uptake VO2 and the walking time during the interval walking training can be automatically recorded by a measuring device 2 worn by the user 6 or a smartphone app, etc. Therefore, the user 6 can perform the interval walking training so as to satisfy the weekly average time Wa and exercise intensity Wi that he or she has set (planned).

[0027] Figures 3 and 4 show an example of changes in physical fitness and lifestyle-related disease improvement confirmed by the inventors when interval walking training was continued for five months. White indicates the trend for men, diagonal lines indicate the trend for women, and black indicates the overall trend. Interval walking training is effective in improving physical fitness and improving lifestyle-related diseases, such as knee extension strength, knee flexion strength, endurance index, systolic blood pressure, and diastolic blood pressure, and is found to be more effective than exercising 10,000 steps a day, which has traditionally been said to be effective.

[0028] According to the inventors' analysis, five months of interval walking training (IWT) for middle-aged and elderly people improves the maximum oxygen intake (VO2peak) by an average of 10% and improves the symptoms of lifestyle-related diseases by 20%. Furthermore, in the present invention, it was clarified to what extent the initial characteristics of each subject and the intensity and amount of exercise during interval walking training affect the effect. In other words, the factors that determine the effect of interval walking training were identified and their reliability was verified.

[0029] Figure 5 shows the flow of the analysis. Among the participants who joined the interval walking training project (this project) from 2005 to 2016, 485 middle-aged and elderly people (up to 64 years old, determinant factor identification group) who joined this project in 2005 and performed interval walking training for five months were included as the determinant factor identification group. The initial characteristics of the subjects and the intensity and amount of exercise during the intervention were used as candidates for independent variables, and multiple regression analysis was performed with each effect parameter as a dependent variable to identify the independent variable that gave the highest correlation between the estimated value and the actual measured value by the analysis (Analysis 1). Furthermore, the reliability was verified for another 609 middle-aged and elderly people (up to 65 years old, accuracy verification group) who joined this project from 2006 to 2016 and performed interval walking training for five months (Analysis 2).

[0030] Figure 6 shows the average values ​​of pre-training (pre), post-training (post) and change (Δ) of each characteristic value of the subjects in the determinant factor identification group and accuracy verification group that were the subject of the analysis. The changes over 5 months in the determinant factor identification group were analyzed (Analysis 1) for maximum oxygen intake (VO2peak), body mass index (BMI), diastolic blood pressure (DBP), blood glucose level (FBG), HDL cholesterol (HDL) and arteriosclerosis index (LDL / HDL), and the initial values ​​were involved as determining factors of all effects. Furthermore, the intensity and amount of fast walking were identified as determining factors for maximum oxygen intake (VO2peak) and body mass index (BMI), the amount of fast walking was identified as determining factors for blood glucose level (FBG), HDL and arteriosclerosis index (LDL / HDL), and the change in maximum oxygen intake (ΔVO2peak) due to interval fast walking training was identified as determining factors for diastolic blood pressure (DBP). In addition, the 95% prediction intervals were calculated from the estimated values ​​using each regression equation calculated in the determinant factor identification group and the actual values ​​(Analysis 2), and these were almost consistent with those in the determinant factor identification group.

[0031] Therefore, it was found that it is possible to estimate (predict) the changes (Δ, exercise effect) in maximum oxygen intake VO2peak, body mass index BMI, diastolic blood pressure DBP, blood glucose level FBG, HDL cholesterol HDL, and arteriosclerosis index (LDL / HDL) after five months of continuing interval walking training. In other words, when the exercise effects are maximum oxygen intake change ΔVO2peak, body mass index change ΔBMI, diastolic blood pressure change ΔDBP, blood glucose level change ΔFBG, HDL cholesterol change ΔHDL, and arteriosclerosis index change Δ(LDL / HDL), it was found that the exercise effects can be predicted (estimated) by acquiring specific user information 7 suitable for predicting the effects of these prediction items.

[0032] Figure 7 shows the estimation formula (prediction formula) of the exercise effect (item to be predicted) that changes due to training. The estimation formula for the maximum oxygen intake change ΔVO2peak and the body mass index change ΔBMI include the gender S (male = 1, female = 0), the initial value (initial characteristic), the weekly fast walking time (weekly average time) Wa, and the exercise intensity of fast walking Wi as determining factors (parameters). The estimation formula for the minimum blood pressure change ΔDBP includes the initial value (initial characteristic) and the maximum oxygen intake change ΔVO2peak estimated above as determining factors (parameters). It was found that the estimation formula for the blood glucose change ΔFBG, the HDL cholesterol change ΔHDL, and the arteriosclerosis index change Δ(LDL / HDL) include the initial value (initial characteristic) and the weekly fast walking time (weekly average time) Wa as determining factors (parameters). Therefore, as the exercise effects to be predicted, the change in maximum oxygen intake ΔVO2peak, the change in body mass index ΔBMI, and the change in diastolic blood pressure ΔDBP were found to be included in the exercise intensity-dependent group 21, while the change in blood glucose level ΔFBG, the change in HDL cholesterol ΔHDL, and the change in atherosclerosis index Δ(LDL / HDL) were found to be included in the exercise intensity-independent group 22.

[0033] Figures 8 to 11 show the process of identifying the determinants in the estimation equation (multiple regression equation) for the exercise effect of each target item. From the initial values ​​and training records of the subjects included in the determinant factor identification group, factors that determine the change (Δ, exercise effect) before and after training for each measured parameter (each prediction target item) were identified. An example of a multiple regression equation is shown in the following equation (2). yj=a(j,1)·x1+a(j,2)·x2+··· +a(j,i)·xi+cj (2) Here, the candidates for independent variables xi are age, sex, initial value of each measured parameter, 5-month training results (total weekly walking time, weekly fast walking time, fast walking exercise intensity), and maximum change in oxygen content (ΔVO2peak). The dependent variables yj are the changes in each measured parameter before and after training (ΔVO2peak, body mass index (ΔBMI), systolic blood pressure (ΔSBP), diastolic blood pressure (ΔDBP), mean blood pressure (ΔMBP), blood glucose level (ΔFBG), blood cholesterol (ΔHDL, ΔLDL), blood triglyceride (ΔTG), and arteriosclerosis index (Δ(LDL / HDL)).

[0034] Next, for each dependent variable, candidates for independent variables were substituted into the regression equation, and the correlation coefficient (r) between the estimated value and the actual measured value was determined to be significant and the highest value, a(j,i). Furthermore, the following linear regression equation (equation (3)) between the estimated value and the actual measured value was determined, and the 95% prediction interval for the reliability of the estimation equation was calculated. yi'=mj·yj+nj (3) In addition, when a Bland-Altman analysis was performed between the estimated values ​​and the actual measured values, a bias in the error occurred, so the estimated values ​​were corrected using the Brace method, which takes into account the variation on both the X-axis and Y-axis.

[0035] Figure 8 shows the analysis results when total walking time was included as an independent variable, with the dependent variables (amount of change) selected as the change in maximum oxygen intake ΔVO2peak, change in body mass index ΔBMI, change in diastolic blood pressure ΔDBP, change in blood glucose level ΔFBG, change in blood cholesterol ΔHDL, and change in arteriosclerosis index Δ(LDL / HDL) after five months of training. Figure 9 shows the analysis results when weekly brisk walking time was included as an independent variable, Figure 10 shows the analysis results when weekly brisk walking time and brisk walking exercise intensity were included as independent variables, and Figure 11 shows the analysis results when estimated maximum oxygen intake change ΔVO2peak was included as an independent variable.

[0036] For the change in peak oxygen intake, ΔVO2peak, correlations were found with gender, initial value, total weekly walking time, weekly fast walking time, and fast walking exercise intensity, but the estimation equation with gender, initial value, weekly fast walking time, and fast walking exercise intensity as independent variables was found to be the most accurate. For the change in body mass index, ΔBMI, correlations were found with gender, initial value, total weekly walking time, weekly fast walking time, fast walking exercise intensity, and estimated maximum oxygen intake change, but the estimation equation with gender, initial value, weekly fast walking time, and fast walking exercise intensity as independent variables was found to be the most accurate. For the change in diastolic blood pressure, ΔDBP, correlations were found with initial value, weekly fast walking time, and estimated maximum oxygen intake change, but the estimation equation with initial value and estimated maximum oxygen intake change as independent variables was found to be the most accurate. Note that because exercise intensity is necessary to estimate the change in maximum oxygen intake, ΔVO2peak, the change in diastolic blood pressure, ΔDBP, is also included in the exercise intensity-dependent group 21.

[0037] There was a correlation between the initial values ​​and weekly brisk walking time for blood glucose change ΔFBG, blood cholesterol change ΔHDL, and arteriosclerosis index change Δ(LDL / HDL), but no correlation was found for other parameters. Therefore, these changes (exercise effects, predicted items) are included in the exercise intensity-independent group 22.

[0038] Figures 8 to 11 also show the 95% confidence intervals of the coefficients for each independent variable, and the following can be used as the estimation equation for each predicted target item. Here, S is gender (male = 1, female = 0), Wa is the average weekly time (weekly brisk walking time, min / week), and Wi is exercise intensity (brisk walking exercise intensity, %VO2peak). The initial value of the target item is the value of the target item at the start of training for the user to be estimated, for example, the maximum oxygen intake at the start of training. Note that the units of each coefficient are omitted, but they match the independent and dependent variables.

[0039] Peak oxygen intake change ΔVO2peak (ml / kg / min) ΔVO2peak=a11·S+a12·VO2peak initial value+a13·Wa +a14·Wi+c1 (11) -1.873 <a11<-0.495 -0.301 <a12<-0.149 0.006 <a13< 0.014 0.091 <a14< 0.152 -6.469 <c1 < 0.542 The median of each coefficient is shown in Figure 7. The same applies to the following coefficients.

[0040] Change in body mass index ΔBMI (kg / m 2 ) ΔBMI = a21·S + a22·initial BMI + a23·Wa + a24·Wi +c2 (12) 0.051 <a21< 0.306 -0.079 <a22<-0.041 -0.003 <a23<-0.001 -0.011 <a24<-0.001 1.014 <c2 < 2.227

[0041] Diastolic blood pressure change ΔDBP (mmHg) ΔDBP = a32 · DBP initial value + a35 · ΔVO2peak + c3 (13) -0.564 <a32<-0.427 -0.657 <a35<-0.072 31.587 <c3 <42.612

[0042] Blood glucose change ΔFBG (mg / dl) ΔFBG = a42 · FBG initial value + a43 · Wa + c4 (14) -0.256 <a42<-0.150 -0.029 <a43<-0.005 13.483 <c4 <25.261

[0043] Change in blood cholesterol ΔHDL (mg / dl) ΔHDL = a52 · HDL initial value + a53 · Wa + c5 (15) -0.147 <a52<-0.058 0.002 <a53< 0.017 3.829 <c5 < 9.989

[0044] Arteriosclerosis index change Δ(LDL / HDL) Δ(LDL / HDL)=a62·(LDL / HDL)initial value+a63·Wa +c6 (16) -0.170 <a62<-0.079 -0.001 <a63< 0.000 0.124 <c6 < 0.343

[0045] Next, in analysis 2, the independent variables of the accuracy verification group were substituted into the estimated equation for the predictive effect of each prediction target item determined above using the determinant factor identification group to obtain estimates, which were then plotted against the actual measured values ​​of the accuracy verification group to calculate the 95% prediction intervals of the estimated equations. Finally, the obtained 95% prediction intervals were compared between the determinant factor identification group and the accuracy verification group.

[0046] Figures 12 and 13 show the evaluation results of the actual measured values ​​and estimated values ​​of the determinant identification group and accuracy verification group of the maximum oxygen intake change ΔVO2peak. Figure 12 shows the evaluation results of the estimation formula that includes the above-mentioned exercise intensity as an independent variable, and Figure 13 shows, for reference, the evaluation results of the estimation formula that does not include exercise intensity as an independent variable (including only gender, initial value, and weekly fast walking time). It can be seen that the correlation coefficient r of the estimation formula that includes exercise intensity as an independent variable shown in Figure 12 is higher than the correlation coefficient of the estimation formula shown in Figure 13, and the exercise effect can be estimated with higher accuracy.

[0047] FIG. 14 shows the evaluation results of the actual and estimated values ​​of the body mass index change ΔBMI in the determinant identification group and the accuracy verification group. FIG. 15 shows the evaluation results of the actual and estimated values ​​of the diastolic blood pressure change ΔDBP in the determinant identification group and the accuracy verification group. FIG. 16 shows the evaluation results of the actual and estimated values ​​of the blood glucose change ΔFBG in the determinant identification group and the accuracy verification group. FIG. 17 shows the evaluation results of the actual and estimated values ​​of the blood cholesterol change ΔHDL in the determinant identification group and the accuracy verification group. FIG. 18 shows the evaluation results of the actual and estimated values ​​of the arteriosclerosis index change Δ(LDL / HDL) in the determinant identification group and the accuracy verification group. For all prediction target items, the determinant identification group and the accuracy verification group showed high correlations, and furthermore, in the accuracy verification group, when the 95% prediction intervals were calculated from the estimated values ​​and actual values ​​using each regression equation (estimation equation) calculated in the determinant identification group, they almost matched those in the determinant identification group. Therefore, it is shown that the exercise effect (predicted effect) of each prediction target item can be predicted with high accuracy using each of the estimation equations described above.

[0048] Figures 12 to 18 show the linear regression equation (equation (3)) between the estimated values ​​and actual values ​​for each forecast target item. The accuracy of the estimated values ​​can be improved by correcting each coefficient and each constant in the estimation equation for each forecast target item shown in equations (11) to (16) using the Brace method as follows. Note that Δ' indicates the corrected value, and the number in parentheses indicates the median.

[0049] Peak oxygen intake change ΔVO2peak (ml / kg / min) (Estimated formula (11)) Δ´VO2peak=1.75ΔVO2peak-2.10 -3.278 <a11(-2.07)<-0.866 -0.527 <a12(-0.394)<-0.261 0.011 <a13( 0.018)< 0.025 0.159 <a14( 0.214)< 0.266 -13.421 <c1( -7.28) < -1.152

[0050] Change in body mass index ΔBMI (kg / m 2 )(estimation formula (12)) Δ´BMI=2.55ΔBMI+0.65 0.130 <a21( 0.456)< 0.780 -0.201 <a22(-0.153)<-0.105 -0.008 <a23(-0.005)<-0.003 -0.028 <a24(-0.015)<-0.003 3.236 <c2( 4.78) < 6.329

[0051] Diastolic blood pressure change ΔDBP (mmHg) (Estimation formula (13)) Δ´DBP=1.79ΔDBP+2.51 -1.010 <a32(-0.886)<-0.764 -1.176 <a35(-0.653)<-0.129 59.051 <c3( 68.9)<78.785

[0052] Blood glucose change ΔFBG (mg / dl) (Estimation formula (14)) Δ´FBG=2.76ΔFBG+7.18 -0.707 <a42(-0.560)<-0.414 -0.080 <a43(-0.047)<-0.014 44.393 <c4( 60.7) <76.900

[0053] Change in blood cholesterol ΔHDL (mg / dl) (Estimated formula (15)) Δ´HDL=4.22ΔHDL-4.08 -0.620 <a52(-0.430)<-0.245 0.008 <a53( 0.040)< 0.072 12.078 <c5( 25.1) < 38.074

[0054] Arteriosclerosis index change Δ(LDL / HDL) (estimation formula (16)) Δ´(LDL / HDL)=3.73Δ(LDL / HDL)+0.22 -0.634 <a62(-0.466)<-0.295 -0.004 <a63(-0.002)< 0.000 0.683 <c6( 1.09) < 1.499

[0055] 19 is a flowchart showing an outline of the process for determining predicted effects for predicted effect items in the effect prediction system 10. This process may be provided as a program (program product) including computer-executable instructions via a computer-readable medium such as a recording medium. The library 20 of the effect prediction system 10 includes, as the exercise intensity-dependent group 21, the above estimation formulas (11) to (13) determined for each of the maximum oxygen intake change ΔVO2peak in the predicted effect of the maximum oxygen intake VO2peak, the body mass index change ΔBMI (kg / m2) in the predicted effect of the body mass index BMI, and the diastolic blood pressure change ΔDBP (mmHg) in the predicted effect of the diastolic blood pressure DBP. The library 20 also includes, for the exercise intensity-independent group 22, the above estimation equations (14) to (16) obtained for the blood glucose change ΔFBG (mg / dl) as the predicted effect of blood glucose FBG, the blood cholesterol change ΔHDL (mg / dl) as the predicted effect of HDL cholesterol, and the atherosclerosis index change Δ(LDL / HDL) as the predicted effect of the atherosclerosis index.

[0056] First, in step 51, the effect prediction system 10 acquires an item for predicting an exercise effect (an item to be predicted) via the input / output interface 15. In step 52, if the item to be predicted is an exercise intensity dependent group 21, i.e., at least one of maximum oxygen intake, body mass index, and diastolic blood pressure, the input / output interface 15 or the first prediction unit 11 acquires user information 7 including gender S, the user's initial value (initial characteristic) of the item to be predicted, the average weekly time Wa planned during the continuation of training, and the exercise intensity Wi planned during the continuation of training via the input / output interface 15 in step 53. At this time, in step 53a, information including the fact that the predicted effect (exercise effect) of the item to be predicted that is being inquired about depends on the exercise intensity may be provided to the user 6 via the first interface 16. It is possible to show the user 6 that the exercise effect is likely to be obtained by actively increasing the exercise intensity.

[0057] On the other hand, in step 52, if the prediction target item is at least one of the exercise intensity independent group 22, i.e., blood glucose level, HDL cholesterol, and arteriosclerosis index, the input / output interface 15 or the second prediction unit 12 acquires user information 7 including the user's initial value (initial characteristic) of the prediction target item and the weekly average time Wa planned during the continuation of training via the input / output interface 15 in step 54. At this time, in step 54a, information including that the predicted effect (exercise effect) of the prediction target item being inquired about does not depend on the exercise intensity may be provided to the user 6 via the second interface 17. That is, if the exercise intensity is a predetermined intensity (70% or more of maximum physical strength (VO2peak)) required as training, information including that it does not depend on the exercise intensity may be provided. It is possible to show the user 6 that the exercise effect can be obtained by securing the weekly average time even if the exercise intensity is not increased to a predetermined intensity or more (70% or more of maximum physical strength).

[0058] In step 55, the first prediction unit 11 and the second prediction unit 12 estimate the predicted effect of the queried prediction target item based on the user information 7 using an estimation equation for the exercise intensity-dependent group 21 or the exercise intensity-independent group 22 pre-stored in the library 20, and in step 56 provide the predicted effect (expected exercise effect) to the terminal 5 of the user 6 via the Internet 9, etc.

[0059] The LSD score (Lifestyle-related disease index, LSD risk factor, Lifestyle-related disease) is known as one of the indexes for lifestyle-related diseases. The LSD score (Lifestyle-related disease index) is a so-called metabolic index, and one point is added if one of the following four diagnostic criteria is met. 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 ≥ 25 kg / m 2 , 4) Triglycerides ≧150 mg / dl or HDL cholesterol ≦40 mg / dl.

[0060] For example, when a user 6 tries to improve his / her LSD score, if 1) or 3) applies, it is desirable to improve the body mass index and / or diastolic blood pressure. When the user is thinking of improving the applicable items through interval walking training, it is possible to predict the exercise effect using the effect prediction system 10. In this case, the body mass index and diastolic blood pressure are exercise intensity dependent groups 21, and by providing the effect prediction system 10 with user information 7 including the planned exercise intensity Wi in addition to the initial value and the planned weekly average time Wa, the predicted training effect, in this example, the body mass index change ΔBMI and the diastolic blood pressure change ΔDBP, is estimated and provided to the user 6, and a training program including a predicted training effect and the exercise intensity for obtaining the training effect desired by the user can be generated.

[0061] On the other hand, when the LSD score corresponds to 4), it is desirable to improve HDL cholesterol. HDL cholesterol is in the exercise intensity independent group 22, and by providing user information 7 including the initial value and the planned weekly average time Wa to the effect prediction system 10, the predicted training effect, in this example, the HDL cholesterol change ΔHDL, can be estimated and provided to the user 6, and a training program including a predicted training effect and the exercise intensity for obtaining the training effect desired by the user can be generated. In this case, it can be provided to the user 6 that the exercise effect is almost unchanged even if the exercise intensity Wi is increased, and that it is important to increase the weekly average training time Wa as training. [Explanation of symbols]

[0062] 10. Effect Prediction System

Claims

1. A system for predicting an effect of continuing training in which an average time during which a predetermined intensity is reached reaches a predetermined time per week, comprising: an input interface for acquiring prediction target items related to improving physical fitness or improving lifestyle-related diseases; a first prediction unit that, if the prediction target item is included in an exercise intensity dependent group, acquires at least an initial characteristic of the user for the prediction target item, a weekly average time planned during the continuation of the training, and an exercise intensity planned during the continuation of the training via the input interface, and outputs a predicted effect of the prediction target item after the training is continued for a predetermined period using an exercise intensity dependent estimation equation that includes at least the initial characteristic, the weekly average time, and the exercise intensity as elements; and a second prediction unit that, if the item to be predicted is included in an exercise intensity-independent group, acquires via the input interface at least the user's initial characteristics for the item to be predicted and the weekly average time planned during the continuation of the training, and outputs a predicted effect of the item to be predicted after the training has been continued for a predetermined period using an exercise intensity-independent estimation equation that includes at least the initial characteristics and the weekly average time as elements.

2. In claim 1, The system includes a first prediction unit that acquires the gender of the user via the input interface, and outputs a predicted effect of the item to be predicted after the training is continued for a predetermined period of time using an exercise intensity-dependent estimation equation that includes at least the gender, the initial characteristics, the average weekly time, and the exercise intensity as elements.

3. In claim 1, A system comprising a first interface that, if the item to be predicted is included in the exercise intensity dependent group, provides the user with information including that the predicted effect depends on exercise intensity.

4. In claim 1, The system includes a second interface that, if the item to be predicted is included in the exercise intensity-independent group, provides the user with information including that the predicted effect is independent of exercise intensity.

5. In claim 1, The training includes interval walking training with the predetermined intensity set to 70% of maximum oxygen intake and the predetermined duration set to 60 minutes on a weekly average.

6. In claim 5, The prediction target items included in the exercise intensity dependent group include maximum oxygen intake, body mass index, and minimum blood pressure, The prediction target items included in the exercise intensity-independent group include blood glucose level, HDL cholesterol, and an arteriosclerosis index.

7. A method for predicting an effect of continuing training in which an exercise duration during which a predetermined intensity is reached reaches a predetermined time on average per week, using an effect prediction system, comprising: The effect prediction system includes an input interface for acquiring prediction target items related to physical fitness improvement or lifestyle-related disease improvement; a library that stores an exercise intensity-dependent estimation equation for the prediction target item included in an exercise intensity-dependent group, the estimation equation including at least an initial characteristic of a user of the prediction target item, a weekly average time planned during the continuation of the training, and an exercise intensity planned during the continuation of the training as elements, and a library that stores an exercise intensity-independent estimation equation for the prediction target item included in an exercise intensity-independent group, the estimation equation including at least an initial characteristic and the weekly average time as elements, If the prediction target item is included in the exercise intensity-dependent group, at least the initial characteristics, the weekly average time, and the exercise intensity are acquired via the input interface, and a predicted effect of the prediction target item of the exercise intensity-dependent group after the training is continued for a predetermined period is output using the exercise intensity-dependent estimation equation including at least the initial characteristics, the weekly average time, and the exercise intensity as elements. If the item to be predicted is included in the exercise intensity-independent group, at least the initial characteristics and the weekly average time are acquired via the input interface, and a predicted effect of the item to be predicted in the exercise intensity-independent group after continuing the training for a predetermined period is output using the exercise intensity-independent estimation equation that includes at least these as elements.

8. In claim 7, the library stores, for the prediction target item included in the exercise intensity dependent group, an exercise intensity dependent estimation equation including at least the user's gender, an initial characteristic of the user for the prediction target item, an average weekly time planned during the continuation of the training, and an exercise intensity planned during the continuation of the training as elements; The method, wherein outputting the predicted effect of the predicted item of the exercise intensity-dependent group includes outputting the predicted effect of the predicted item after continuing the training for a predetermined period of time using an exercise intensity-dependent estimation equation that includes at least the gender, the initial characteristics, the average weekly time, and the exercise intensity as elements.

9. In claim 7, The method includes, if the item to be predicted is included in the exercise intensity dependent group, providing the user with information including that the predicted effect depends on exercise intensity.

10. In claim 7, A method comprising, if the item to be predicted is included in the exercise intensity-independent group, providing the user with information including that the predicted effect is independent of exercise intensity.

11. In claim 7, The method of claim 1, wherein the training comprises interval walking training, the predetermined intensity being set to 70% of maximum oxygen intake, and the predetermined duration being set to 60 minutes on a weekly average.

12. In claim 11, The prediction target items included in the exercise intensity dependent group include maximum oxygen intake, body mass index, and minimum blood pressure, A method according to claim 1, wherein the prediction target items included in the exercise intensity-independent group include blood glucose level, HDL cholesterol, and an atherosclerosis index.

13. In claim 12, The library includes a peak oxygen intake change ΔVO2peak (ml / kg / min) as a predicted effect of the peak oxygen intake, a body mass index change ΔBMI (kg / m ２ ), a diastolic blood pressure change ΔDBP (mmHg) as the predicted effect of the diastolic blood pressure, a blood glucose level change ΔFBG (mg / dl) as the predicted effect of the blood glucose level, a blood cholesterol change ΔHDL (mg / dl) as the predicted effect of the HDL cholesterol, and an atherosclerosis index change Δ(LDL / HDL) as the predicted effect of the atherosclerosis index. ΔVO2peak=a11·S+a12·VO2peak initial value+a13·Wa +a14・Wi+c1 ΔBMI = a21·S + a22·BMI initial value + a23·Wa + a24·Wi + c2 ΔDBP = a32 · DBP initial value + a35 · ΔVO2peak + c3 ΔFBG = a42 · FBG initial value + a43 · Wa + c4 ΔHDL = a52 · HDL initial value + a53 · Wa + c5 Δ(LDL / HDL)=a62·(LDL / HDL) initial value+a63·Wa+c6 where S is the gender (male = 1, female = 0), Wa is the weekly average time (min / week), Wi is the exercise intensity (%VO2peak), and a11 to a63 and c1 to c6 are coefficients in the following ranges. -3.278<a11<-0.866 -0.527<a12<-0.261 0.011<a13<0.025 0.159<a14<0.266 -13.421<c1<-1.152 0.130<a21<0.780 -0.201<a22<-0.105 -0.008<a23<-0.003 -0.028<a24<-0.003 3.236<c2<6.329 -1.010<a32<-0.764 -1.176<a35<-0.129 59.051<c3<78.785 -0.707<a42<-0.414 -0.080<a43<-0.014 44.393<c4<76.900 -0.620<a52<-0.245 0.008<a53<0.072 12.078<c5<38.074 -0.634<a62<-0.295 -0.004<a63<0.000 0.683<c6<1.499

14. A method for predicting, by a computer, an effect of continuing training in which an exercise time during which a predetermined intensity is reached reaches a predetermined time on average per week, comprising: The computer acquires prediction target items related to improving physical strength or improving lifestyle-related diseases via an input interface; If the prediction target item is included in an exercise intensity dependent group, at least an initial characteristic of the user for the prediction target item, an average weekly time planned during the continuation of the training, and an exercise intensity planned during the continuation of the training are acquired via the input interface, and a predicted effect of the prediction target item after the training is continued for a predetermined period is output using an exercise intensity dependent estimation equation including at least the initial characteristic, the average weekly time, and the exercise intensity as elements; If the item to be predicted is included in an exercise intensity-independent group, the method comprises: acquiring, via the input interface, at least the user's initial characteristics for the item to be predicted and the weekly average time planned during the continuation of the training; and outputting a predicted effect of the item to be predicted after continuing the training for a predetermined period of time using an exercise intensity-independent estimation equation that includes at least the initial characteristics and the weekly average time as elements.

15. A program for predicting, by a computer, an effect of continuing training in which an exercise time during which a predetermined intensity is reached reaches a predetermined time on average per week, comprising: The computer acquires prediction target items related to improving physical strength or improving lifestyle-related diseases via an input interface; If the prediction target item is included in an exercise intensity dependent group, at least an initial characteristic of the user for the prediction target item, an average weekly time planned during the continuation of the training, and an exercise intensity planned during the continuation of the training are acquired via the input interface, and a predicted effect of the prediction target item after the training is continued for a predetermined period is output using an exercise intensity dependent estimation equation including at least the initial characteristic, the average weekly time, and the exercise intensity as elements; If the item to be predicted is included in an exercise intensity-independent group, then via the input interface, at least the user's initial characteristics for the item to be predicted and the weekly average time planned during the continuation of the training, and outputting a predicted effect of the item to be predicted after continuing the training for a predetermined period of time using an exercise intensity-independent estimation equation that includes at least the initial characteristics and the weekly average time as elements.

Images