CN113284578A - Prediction method and device for improving cardio-pulmonary function effect by exercise and storage medium - Google Patents
Prediction method and device for improving cardio-pulmonary function effect by exercise and storage medium Download PDFInfo
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Abstract
The invention relates to a prediction method for improving the effect of heart and lung functions by exercise, which comprises the following steps: obtaining the initial value preVO of the maximum oxygen uptake of healthy adults through a progressive loading exercise scheme2max; recording gender and testing initial body morphology measurement data; providing two high-strength intermittent motion schemes with different load strengths, testing and recording test data after HIIT motion intervention; constructing a cardiopulmonary function effect prediction model according to the gender, the initial maximum oxygen uptake amount, the initial body shape measurement data and the HIIT exercise intervention scheme; before the healthy adults carry out the two HIIT exercise intervention schemes, the cardiopulmonary function improving effect of the healthy adults under the two HIIT exercise intervention schemes is predicted according to the cardiopulmonary function effect prediction model, and a basis is provided for the public to select a personalized and effective exercise and fitness scheme.
Description
Technical Field
The invention relates to the field of exercise health, in particular to a prediction method, a prediction device and a storage medium for improving the cardio-pulmonary function effect by exercise.
Background
Cardiopulmonary function (CRF) refers to the ability of the circulatory system to transport oxygen and nutrients to the body by pushing blood circulation through lung respiration and cardiac activity; oxygen uptake (VO)2) Is the core index reflecting the function of heart and lung. It is presently believed that High-intensity intermittent exercise (HIIT) increases maximum oxygen uptake (VO)2max) is obviously higher than that of the medium-intensity continuous exercise, and one of the methods for reducing the exercise and improving the inefficiency of the CRF is to increase the exercise intensity; but increases VO even with high intensity intermittent motion2The effect of max still has individual difference, namely some people can not effectively improve VO2max, the effect.
Therefore, how to scientifically and effectively predict the effectiveness of the HIIT exercise scheme and select the effective HIIT exercise scheme for the user becomes a technical problem to be solved urgently.
Disclosure of Invention
The invention aims to solve the technical problem of providing a prediction method, a prediction device and a storage medium for improving the cardio-pulmonary function effect by exercise, predicting the exercise of healthy adults in advance to improve the cardio-pulmonary function effect by constructing a prediction model, and providing a basis for the public to select a personalized and effective exercise and fitness scheme.
The technical scheme for solving the technical problems is as follows: a prediction method for improving the effect of cardiopulmonary function by exercise comprises the following steps:
obtaining the standard maximum oxygen uptake initial value preVO of the testee through the increasing load exercise scheme2max；
Recording the sex of the subject and obtaining the body shape measurement index data of the subject;
providing two high-intensity intermittent motion schemes with different load intensities, and recording test data of a subject before and after motion intervention;
constructing a cardiopulmonary function effect prediction model according to the gender, the initial maximum oxygen uptake amount, the body shape measurement index data and different HIIT exercise intervention schemes;
before the healthy adults carry out the two HIIT exercise intervention schemes, the cardiopulmonary function improving effect of the healthy adults under the two HIIT exercise intervention schemes is predicted according to the cardiopulmonary function effect prediction model, so that the healthy adults can select the corresponding exercise intervention schemes.
The invention has the beneficial effects that: the standard maximum oxygen uptake initial value of the subject is acquired through collection, errors caused by estimation are avoided, the accuracy is higher, the model is more reliable, two HIIT motion schemes with different load degrees are provided, and preVO is obtained through the standard maximum oxygen uptake initial value2max, body shape measurement index data and exercise scheme establishing a prediction model, predicting the individual effect of improving the cardiopulmonary function of the healthy adults by exercise before exercise intervention, and predicting VO of the two schemes2The max improves the effect, so that the public can select HIIT schemes with different load intensities more accurately, theoretical basis is provided for making accurate and personalized exercise prescriptions for healthy adults in the future, and the method can be applied to selection and making of healthy public exercise schemes.
On the basis of the technical scheme, the invention can be further improved as follows:
further, the standard maximum oxygen uptake initial value preVO of the subject is obtained through the increasing load exercise scheme2The step of max includes:
recording the exercise regimen of increasing load by using a power bicycle when the subject satisfies at least two of the following four conditionsThe maximum oxygen uptake of the subject is divided by the body weight at the time of the test to obtain the value of preVO2max；
The four VOs2The max determination conditions include:
the respiratory quotient reaches 1.1;
heart rate greater than 90% of the predicted maximum heart rate;
the oxygen uptake appears flat and does not rise along with the increase of the load;
the subjective fatigue index RPE of the tested person is more than or equal to 17.
The beneficial effect of adopting the further scheme is that: adopts gold standard relative value VO2max, and when the subject meets at least two conditions, the maximum oxygen uptake of the subject is recorded, so that inaccuracy caused by estimation is avoided, the accuracy is higher, and the established model is more reliable.
Further, the bicycle incremental loading exercise regimen comprises:
the male is initially loaded with 50 watts W, and the load is increased by 25W every 2 min; the female initial load was 40W, 20W increments every 2min, and the pedaling frequency was controlled at 60 rpm by a metronome.
The beneficial effect of adopting the further scheme is that: the reliability of data acquisition is ensured through different bicycle incremental load exercise schemes for males and females.
Further, the two HIIT exercise intervention schemes include:
the first scheme is as follows: 1-4 weeks is an adaptation stage, the slow-run warmup is carried out for 10min, the high-intensity run is gradually increased from 56 × 15 seconds, 28 × 30 seconds, 14 × 1min to 7 × 2min by one week, and the interval time between groups is the same as that of the high-intensity run;
formal 4 × 4min training is carried out in 5-12 weeks, each training is 38min, the warm-up is carried out in 10min by jogging, and the temperature is 4 × 4min 80-90% VO2max running, interval 3min between groups;
scheme II: 1-4 weeks is adaptation stage, warm-up is performed by jogging in 10min, high-intensity running is performed in 28 × 30s and 14 × 1min in two-week progressive increase, and interval time between groups is same as high-intensity running;
the transition stage is set at 5-8 weeks, the warm-up is performed by jogging for 10min, the high-intensity running is performed at 7 × 2min for 5-6 weeks, the high-intensity running is performed at 4 × 3min for 7-8 weeks, and the interval time between groups is the same as the high-intensity running;
training for 4 × 4min at 9-12 weeks, each training for 38min, warming up by jogging for 10min, and performing 4 × 4min 80-90% VO2max run, 3min pause between groups.
The beneficial effect of adopting the further scheme is that: providing two high-intensity intermittent exercise modes, training for 3 times per week, training for 12 weeks each time, and high-intensity running intensity of 80-90% VO2max, intermittent intensity 50-55% VO2max, the cardiopulmonary improvement effect through two high-intensity intermittent exercise schemes, so that the construction of a prediction model is more personalized, and the feasibility is improved.
Further, the providing two high intensity intermittent exercise regimens with different load intensities and recording test data of the subject before and after exercise intervention comprises:
after the subject passes through the exercise intervention of the scheme I, acquiring an effector Cohen's d according to test data of the scheme I, determining that the heart-lung function CRF of the subject is ineffective when the Cohen's d is smaller than a preset threshold, performing exercise intervention of the scheme II on the subject with ineffective CRF, and recording the test data of the scheme II; subjects who were effective in increasing CRF did not undergo regimen two motor intervention.
The beneficial effect of adopting the further scheme is that: due to the same standardized exercise intervention, the lung function effect has individual difference, and the individual difference has reproducibility, so that the increase of CRF of a subject in a high-load-dose scheme I is ineffective, the load dose is changed, a different high-intensity intermittent training scheme II is carried out, the lung function improvement effect of the subject is further measured, and a basis is provided for the establishment of a subsequent prediction model.
Further, the body morphology measurement indicator data includes: the initial value of pectoralis major thickness preET and the initial value of body fat weight preFAT before the motor intervention.
The beneficial effect of adopting the further scheme is that: the thickness of the pectoralis major and the weight of body fat are obviously improved by the CRF, and the accuracy and reliability of the establishment of a subsequent prediction model are ensured.
Further, the step of constructing a cardiopulmonary function effect prediction model according to the gender, the initial maximum oxygen uptake amount, the physical form measurement index data and the HIIT exercise intervention scheme comprises the following steps:
with preET, preVO2max, preFAT, gender and locomotor intervention scheme as arguments, Δ VO2max is a dependent variable, and a cardiopulmonary function effect prediction model is constructed:
ΔVO2max＝19.978+2.534×preET-0.406×preVO2max-0.223×preFAT
-3.716 x sex-3.394 x exercise intervention program
Wherein, the Δ VO2max is the difference between the maximum oxygen uptake after the prediction of the exercise intervention and the initial value of the maximum oxygen uptake; in the gender, the value of male is 0, and the value of female is 1; in the exercise intervention scheme, a first scheme value is 0, and a second scheme value is 1.
The beneficial effect of adopting the further scheme is that: by testing body fat weight, VO2max, pectoralis major thickness, different load dose HIIT schemes to be selected and gender establishing prediction model, VO under different HIIT schemes can be effectively predicted2max increases the effect.
The method for predicting the cardiopulmonary function improving effect of healthy adults under two HIIT exercise intervention schemes according to the cardiopulmonary function effect prediction model comprises the following steps:
predicting delta VO corresponding to healthy adults according to the cardiopulmonary function effect prediction model2A max value;
obtaining a target delta VO corresponding to the healthy adult2A max value;
according to target Delta VO2max value and predicted Δ VO2The max value recommends a corresponding HIIT exercise intervention program for the healthy adult.
The beneficial effect of adopting the further scheme is that: VO of the two schemes can be predicted through a prediction model before movement2max improves the effect, lets the masses more accurate selection different load intensity HIIT schemes, avoids the invalid motion.
In order to solve the above technical problems, the present invention further provides a device for predicting an effect of improving cardiopulmonary function by exercise, comprising: a processor and a memory;
the processor is configured to execute one or more computer programs stored in the memory to implement the steps of the prediction method for improving the cardiopulmonary function effect by exercise as described above.
In order to solve the above technical problem, the present invention further provides a storage medium, which includes one or more computer programs stored thereon, wherein the one or more computer programs are executable by one or more processors to implement the steps of the prediction method for improving the effect of heart and lung functions through exercise as described above.
Drawings
FIG. 1 is a flowchart illustrating a method for predicting the effect of improving cardiopulmonary function by exercise according to an embodiment of the present invention;
FIG. 2 is a first round of HIIT intervention VO provided by an embodiment of the invention2max individual effect difference plot;
FIG. 3 is a second HIIT intervention VO provided by an embodiment of the invention2max individual effect difference plot;
FIG. 4 is a flowchart illustrating a method for predicting the effect of improving cardiopulmonary function by exercise according to another embodiment of the present invention;
FIG. 5 shows VO of different HIIT schemes of the same subject according to another embodiment of the present invention2max effect is shown in comparison.
Detailed Description
The principles and features of this invention are described below in conjunction with the following drawings, which are set forth by way of illustration only and are not intended to limit the scope of the invention.
Example one
Cardiopulmonary function CRF is associated with cardiovascular disease, all-cause mortality, cancer mortality, and the like, and the gold index oxygen uptake (VO) of CRF2) For every 1 Metabolic Equivalent (MET) increase (3.5ml/min/kg), the risk factor for all-cause death decreased by 12%, the risk factor for cardiovascular disease decreased by 16%, and the risk factor for cancer death decreased by 14%. Thus, many countries recommend that healthy adults should perform moderate physical activity for 150min (minutes) per week, whereas standardized exercise regimens are partially unavailable to achieve the desired improvement in cardiopulmonary functionThe effect is achieved; the implementation provides a prediction method for improving the cardiopulmonary function effect by exercise, and before exercise, the cardiopulmonary function improvement effect of healthy adults can be predicted in advance; as shown in fig. 1, fig. 1 is a flowchart of a method for predicting cardiopulmonary function improvement effect through exercise according to an embodiment of the present invention, where the method for predicting cardiopulmonary function improvement effect through exercise includes:
s1, obtaining the standard maximum oxygen uptake initial value preVO of the subject through the increasing load exercise scheme2max；
S2, recording the sex of the subject and acquiring the body shape measurement index data of the subject;
s3, providing two HIIT exercise intervention schemes with different load intensities, and recording test data of the HIIT exercise intervention schemes of the subjects;
s4, constructing a cardiopulmonary function effect prediction model according to the gender, the initial maximum oxygen uptake amount, the body shape measurement index data and the HIIT exercise intervention scheme;
and S5, before the healthy adult carries out the two HIIT exercise intervention schemes, predicting the cardiopulmonary function improving effect of the healthy adult under the two HIIT exercise intervention schemes according to the cardiopulmonary function effect prediction model, so that the healthy adult can select the corresponding exercise intervention scheme.
In the embodiment, the standard maximum oxygen uptake initial value of the subject is acquired through collection, so that errors caused by estimation are avoided, the accuracy is higher, the model is more reliable, two different HIIT motion schemes are provided, and preVO is obtained through the gender and the initial value of the maximum oxygen uptake2max, body shape measurement index data and exercise scheme establishing a prediction model, predicting the individual effect of improving the cardiopulmonary function of the healthy adults by exercise before exercise intervention, and predicting VO of the two schemes2The max improves the effect, so that the public can select HIIT schemes with different load intensities more accurately, theoretical basis is provided for making accurate and personalized exercise prescriptions for healthy adults in the future, and the method can be applied to selection and making of healthy public exercise schemes.
In this embodiment, S1 specifically includes:
using powerThe bicycle increasing load exercise scheme comprises recording the maximum oxygen uptake of a subject when the subject meets at least two of the following four conditions, and dividing the maximum oxygen uptake by the body weight at the time of test to obtain the value as preVO2max (ml/min/kg); wherein the four conditions include: the respiratory quotient reaches 1.1; heart rate greater than 90% of the predicted maximum heart rate; the oxygen uptake appears flat and does not rise along with the increase of the load; the subjective fatigue index (RPE) of the subject is more than or equal to the threshold of the subjective amount of movement, and optionally, the threshold of the subjective amount of movement is 17, and the RPE ranges from 6 (no fatigue) to 20 (very fatigue). In the embodiment, direct test is performed through a CORTEX gas metabolizer (CORTEX MetaMax 3B), and the test and data judgment method adopts an international standard, so that inaccuracy caused by estimation is avoided, the accuracy is higher, and the model is more reliable. Wherein, the examinee collects the real-time heart rate of the examinee through polar heart rate belt during the exercise, the fatigue index of the examinee is recorded by each stage of load, and the dynamic electrocardiogram is monitored during the test process to check the exercise risk.
In this embodiment, the bicycle incremental loading exercise regimen comprises: male initial load of 50W, 25W increments every 2 min; the female initial load was 40W, 20W increments every 2min, and the pedaling frequency was controlled at 60 rpm by a metronome. That is, during the exercise of male subject or female subject with increasing load on the bicycle, the CORTEX gas metabolism instrument is used to directly measure the maximum oxygen uptake, and the maximum oxygen uptake is measured and the relative value VO is calculated2max (ml/min/kg) as preVO2max。
In this embodiment, the body morphology measurement index data in S2 includes an initial value preet (cm) of the thickness of the pectoralis major muscle before the exercise intervention and an initial value preFAT (kg) of the body fat weight, wherein the body fat weight of the body component of the subject in the fasting state in the morning is measured by using an Inbody230 body component measuring instrument as the initial value preFAT of the body fat weight; the ultrasonic muscle thickness test adopts an American GE portable color ultrasonic diagnostic system LOGIQe to intercept images of a quadriceps femoris cross section and a pectoralis major cross section, the pectoralis major thickness is measured, the thickness of the homonymous muscles at the left side and the right side is averaged, the average thickness (ET, cm) of the pectoralis major is obtained through calculation, and the average thickness of the pectoralis major is used as an initial value preET of the thickness of the pectoralis major.
It should be noted that the two HIIT exercise intervention programs provided in this embodiment are 12-week HIIT, and 8-month elution period elapses between the two-round exercise intervention programs; specifically, the first scheme includes: 1-4 weeks is an adaptation stage, the slow-run warmup is carried out for 10min, the high-intensity run is gradually increased from 56 × 15 seconds, 28 × 30 seconds, 14 × 1min to 7 × 2min by one week, and the interval time between groups is the same as that of the high-intensity run; formal 4 × 4min training is carried out in 5-12 weeks, each training is 38min, the warm-up is carried out in 10min by jogging, and the temperature is 4 × 4min 80-90% VO2max running, interval 3min between groups;
the second scheme comprises the following steps: 1-4 weeks is adaptation stage, warm-up is performed by jogging in 10min, high-intensity running is performed in 28 × 30s and 14 × 1min in two-week progressive increase, and interval time between groups is same as high-intensity running;
the transition stage is set at 5-8 weeks, the warm-up is performed by jogging for 10min, the high-intensity running is performed at 7 × 2min for 5-6 weeks, the high-intensity running is performed at 4 × 3min for 7-8 weeks, and the interval time between groups is the same as the high-intensity running;
training for 4 × 4min at 9-12 weeks, each training for 38min, warming up by jogging for 10min, and performing 4 × 4min 80-90% VO2max run, 3min pause between groups.
Wherein, the examinee wears polar heart rate belt to carry out training intensity monitoring in HIIT exercise process, and the effectiveness and the accuracy of modeling data are guaranteed to be high. Recording test data of the subject before and after the exercise intervention, wherein the test data comprises VO2max, and also includes the quantitative load oxygen uptake (VO)2RE) and/or long running achievements (1000 m for men, 800 m for women), by means of which test data basis for model building is provided.
Specifically, S3 includes: after the subject passes through the exercise intervention of the scheme I, acquiring an effect amount Cohen's d according to test data of the scheme I, determining that the heart-lung function CRF of the subject is ineffective when Cohen's d is smaller than a preset threshold, performing exercise intervention of the scheme II on the subject with ineffective CRF, and recording the test data of the scheme II; subjects who were effective in increasing CRF did not undergo regimen two exercise intervention. Wherein the effect amount
For example, test data is taken as the test before and after the sports interventionVO of test subject2max is an example, the measured data is preVO through an incremental loading motion scheme2max, VO with posterior data for subject protocol-locomotor intervention2max, antecedent standard deviation preVO of all subjects2Standard deviation of max, post-measured standard deviation VO for prognosis of protocol-locomotor intervention in all subjects2max, calculating Cohen's d value, defining Cohen's d < 0.2 as invalid and Cohen's d ≧ 0.2 as valid. Subjects with no effect (including VO)2max or quantitative load oxygen uptake (VO)2RE) or runner-score null) with exercise intervention on regimen two, whereas subjects with increased CRF were not. In other embodiments, the VO can also be passed2Cohen's d values for RE or long run performance judged subjects with ineffective CRF enhancement.
It is noted that S4 in this embodiment specifically includes:
with preET, preVO2max, preFAT, gender and locomotor intervention scheme as arguments, Δ VO2max is a dependent variable, and a regression model is constructed:
ΔVO2max＝19.978+2.534×preET-0.406×preVO2max-0.223 × preFAT-3.716 × gender-3.394 × exercise intervention scheme
Wherein, the Δ VO2max is the difference between the maximum oxygen uptake after the prediction of the exercise intervention and the initial value of the maximum oxygen uptake; in the gender, the value of male is 0, and the value of female is 1; in the motion scheme, the value of the first scheme is 0, and the value of the second scheme is 1. In this example, gender, pectoralis major thickness and VO were significantly correlated with increased CRF2max (significance level p)<0.01), and different protocols as independent variables, analysis of factors related to the training effect of cardiopulmonary function by VIF (variance expansion factor) < 5 and 0<Tolerance of<1 avoid co-linearity between variables with a variable inclusion criterion of p<0.01, a cardiopulmonary function effect prediction model is constructed. In this example, Δ VO is performed2In max multiple linear regression, the contribution rate of each factor of the model can be calculated, and the contribution rate (%) is the variable B value/sigmaAnd (4) model variable B value. 2% contribution rate of initial value of body fat weight, VO2The initial value contribution rate of max is 4%, the initial value contribution rate of pectoralis major thickness is 25%, the contribution rate of different HIIT schemes is 33%, and the gender contribution rate is 36%.
In this embodiment, S5 specifically includes:
predicting delta VO corresponding to healthy adults according to the cardiopulmonary function effect prediction model2A max value; obtaining a target delta VO corresponding to the healthy adult2A max value; according to target Delta VO2max value and predicted Δ VO2The max value recommends a corresponding HIIT exercise intervention scheme for the healthy adult; for example, healthy adult A predicts the first scenario Δ VO through the cardiopulmonary function effect prediction model before performing the first scenario exercise intervention2max is 8ml/min/kg, Δ VO predicted for protocol two2max is 6ml/min/kg, target Δ VO set by this healthy adult A2If the max value is 8, recommending the healthy adult to select the HIIT exercise intervention scheme of the first scheme; namely, when the predicted Delta VO of one scheme exists in the two HIIT motion intervention schemes2max value greater than target Δ VO2If the max value (indicating that the lifting effect is effective), recommending a scheme with effective lifting; as another example, target Δ VO when setting for healthy adult A2If the max value is 9, recommending the healthy adult A to select other exercise schemes; namely when the predicted delta VO of the two HIIT motion intervention schemes2The max values are all smaller than the target delta VO2max, recommending other motion schemes; when the prediction of two HIIT motion intervention schemes is Δ VO2The max values are all larger than the target delta VO2max, which scheme is selected according to the requirements of healthy adults. In other embodiments, the target Δ VO may also be determined2max value and predicted Δ VO2max values determine whether the two HIIT motor intervention programs are effective for a healthy adult, as shown in fig. 2 and 3, the lung function effect of the healthy adult has individual difference and the individual difference has reproducibility for the same standardized motor intervention, and fig. 2 is VO of the first round of HIIT intervention (program one)2max individual effect difference plot, VO indicated below dotted line2max increase is not valid. FIG. 3 shows a second round of HIIT intervention VO2max ofDifference in volume effect, VO indicated below the dotted line2max improvement is not valid, it is understood that the scheme is different, target Δ VO2The max values are different.
Example two
For the convenience of understanding, the present embodiment describes a method for predicting the effect of improving cardiopulmonary function by exercise, in which an adult 345 is organized and an experiment is performed to construct a prediction model, as shown in fig. 4, the method for predicting the effect of improving cardiopulmonary function by exercise includes:
s41, testing by adopting a power bicycle incremental load exercise scheme, and directly testing by a CORTEX gas metabolism instrument to obtain preVO of each subject2max；
S42, testing the weight of the body fat of the subject in the fasting state in the morning by an Inbody230 body composition tester;
s43, testing the average thickness of the pectoralis major of the subject through a LOGIQe system;
s44, providing two different 12-week HIIT exercise schemes for the subject to exercise;
informing the subjects of experiment purposes, processes and possible exercise risks, filling in exercise risk screening questionnaires for survey, and excluding subjects with large exercise risks; the subject has no sports injury, insufficient physical activity, no cardiovascular or metabolic diseases within 6 months and is not taken; subjects signed informed consent, 345 subjects performed the first 12 weeks HIIT round (protocol one). After the first round, after 8 months of elution, those with no CRF improvement (VO) were selected2max or quantitative load oxygen uptake (VO)2RE) or Cohen's d < 0.2 for long-distance running performance), the change exercise regimen was followed by a second 12-week HIIT exercise intervention, 38 were followed by a second HIIT exercise (regimen two), and the subject's VO was tested before and after each exercise intervention2max, quantitative load oxygen uptake (VO)2RE) and long running performance (1000 meters for men, 800 meters for women). Optionally, the test time after the exercise intervention is three days after the last exercise intervention is finished. The test of the cardio-pulmonary function before and after the exercise intervention reaches VO2Data statistics and scoring for the first 295 people and the second 30 people in the max evaluation criteriaAssay, wherein the measurement data are described as mean ± standard deviation.
S45, passing test of body fat weight and VO2Constructing different HIIT schemes for improving VO (VO), such as max, pectoralis major thickness, sex and selection2Prediction model of max effect.
Gender, body fat weight, pectoralis major thickness and preVO with significant correlation to CRF enhancement2max, and various exercise regimens as independent variables, analysis of cardiorespiratory function training effect-related factors, by VIF < 5 and 0<Tolerance of<1 avoid co-linearity between variables with a variable inclusion criterion of p<0.01, constructing a prediction model, wherein the prediction model participates in the first embodiment, and details are not repeated herein.
S46, before the user moves, acquiring the initial value preVO of the maximum oxygen uptake of the user2max, pectoralis major thickness initial value, body fat weight initial value, motion intervention scheme and gender, inputting the prediction model to obtain the predicted VO of the user2max increases the effect.
And S47, accurately recommending different HIIT schemes for the user according to the target delta VO2max of the user.
As shown in FIG. 5, VO of the same subject for different stress intensity HIIT regimens2The max improvement effect is different. The embodiment can predict the invalid of different schemes in advance, namely predicting the delta VO in the load intensity scheme2max and target Δ VO2max does not match; wherein the white lattice represents Δ VO2max (ml/min/kg) is effective, non-white boxes indicate Δ VO2max (ml/min/kg) is not valid, e.g. for a healthy adult N3, the first round (solution one) of motion should be chosen which achieves a valid Δ VO2max (ml/min/kg), therefore, the embodiment can select a personalized, accurate and effective exercise scheme for the persons participating in the exercise before the exercise intervention, so that the exercise intervention is performed after VO2max can be effectively improved, and clinical significance is achieved.
EXAMPLE III
The embodiment provides a prediction device for improving the effect of the heart and lung functions by exercise, which comprises: a processor and a memory;
the processor is configured to execute one or more computer programs stored in the memory to implement the steps of the prediction method for improving cardiopulmonary function effect through exercise in the above embodiments.
It can be understood that the cardiopulmonary function effect prediction apparatus based on exercise can implement the steps of the prediction method for improving cardiopulmonary function effect by exercise in the above embodiments, and details are not repeated here.
The present embodiment also provides a storage medium, where the storage medium includes one or more computer programs stored therein, and the one or more computer programs can be executed by one or more processors to implement the steps of the prediction method for improving cardiopulmonary function effect through exercise in the foregoing embodiments, which are not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment of the present invention.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The technical solutions provided by the embodiments of the present invention are described in detail above, and the principles and embodiments of the present invention are explained in this patent by applying specific examples, and the descriptions of the embodiments above are only used to help understanding the principles of the embodiments of the present invention; the present invention is not limited to the above preferred embodiments, and any modifications, equivalent replacements, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for predicting the effect of improving cardiopulmonary function through exercise, comprising:
obtaining the standard maximum oxygen uptake initial value preVO of the testee through the increasing load exercise scheme2max；
Recording the sex of the subject and obtaining the body shape measurement index data of the subject;
providing two high-intensity intermittent HIIT exercise intervention schemes with different load intensities, and recording test data before and after exercise intervention of a subject;
constructing a cardiopulmonary function effect prediction model according to the gender, the initial maximum oxygen uptake amount, the body shape measurement index data and different HIIT exercise intervention schemes;
before the healthy adults carry out the two HIIT exercise intervention schemes, the cardiopulmonary function improving effect of the healthy adults under the two HIIT exercise intervention schemes is predicted according to the cardiopulmonary function effect prediction model, so that the healthy adults can select the corresponding exercise intervention schemes.
2. The method of claim 1, wherein the exercise regimen is performed with increasing load to obtain preVO, the subject's standard starting value of maximum oxygen uptake2The step of max includes:
adopting a power bicycle incremental load exercise scheme, recording the maximum oxygen uptake of a subject when the subject meets at least two of the following four judgment conditions, and dividing the maximum oxygen uptake by the weight at the time of test to obtain a value as preVO2max；
The four VOs2The max determination conditions include:
the respiratory quotient reaches 1.1;
heart rate greater than 90% of the predicted maximum heart rate;
the oxygen uptake appears flat and does not rise along with the increase of the load;
the subjective fatigue index RPE of the subject in motion is more than or equal to 17, and the current load can not be completed.
3. The method of claim 2, wherein the cycling incremental loading exercise regimen comprises:
the male is initially loaded with 50 watts W, and the load is increased by 25W every 2 min; the female initial load was 40W, 20W increments every 2min, and the pedaling frequency was controlled at 60 rpm by a metronome.
4. The method of claim 1, wherein the two HIIT exercise intervention programs comprise:
the first scheme is as follows: 1-4 weeks is an adaptation stage, the slow-run warmup is carried out for 10min, the high-intensity run is gradually increased from 56 × 15 seconds, 28 × 30 seconds, 14 × 1min to 7 × 2min by one week, and the interval time between groups is the same as that of the high-intensity run;
formal 4 × 4min training is carried out in 5-12 weeks, each training is 38min, the warm-up is carried out in 10min by jogging, and the temperature is 4 × 4min 80-90% VO2max running, interval 3min between groups;
scheme II: 1-4 weeks is adaptation stage, warm-up is performed by jogging in 10min, high-intensity running is performed in 28 × 30s and 14 × 1min in two-week progressive increase, and interval time between groups is same as high-intensity running;
the transition stage is set at 5-8 weeks, the warm-up is performed by jogging for 10min, the high-intensity running is performed at 7 × 2min for 5-6 weeks, the high-intensity running is performed at 4 × 3min for 7-8 weeks, and the interval time between groups is the same as the high-intensity running;
training for 4 × 4min at 9-12 weeks, each training for 38min, warming up by jogging for 10min, and performing 4 × 4min 80-90% VO2max run, 3min pause between groups.
5. The method of claim 4, wherein the providing two high-intensity intermittent exercise intervention programs with different load intensities and recording the test data of the subject before and after the intervention comprises:
after the subject passes through the exercise intervention of the scheme I, acquiring an effector Cohen's d according to test data of the scheme I, determining that the heart-lung function CRF of the subject is ineffective when the Cohen's d is smaller than a preset threshold, performing exercise intervention of the scheme II on the subject with ineffective CRF, and recording the test data of the scheme II; subjects who were effective in increasing CRF did not undergo regimen two motor intervention.
6. The method of claim 5, wherein the morphometric measurement data comprises: the initial value of pectoralis major thickness preET and the initial value of body fat weight preFAT before the motor intervention.
7. The method of claim 6, wherein the step of constructing a model for predicting the effect of improving cardiopulmonary function based on the gender, the initial maximum oxygen uptake, the morphometric index data, and the HIIT exercise intervention program comprises:
with preET, preVO2max, preFAT, gender and locomotor intervention scheme as arguments, Δ VO2max is a dependent variable, and a cardiopulmonary function effect prediction model is constructed:
ΔVO2max＝19.978+2.534×preET-0.406×preVO2max-0.223 × preFAT-3.716 × gender-3.394 × exercise intervention program;
wherein, the Δ VO2max is the difference between the maximum oxygen uptake after the prediction of the exercise intervention and the initial value of the maximum oxygen uptake; in the gender, the value of male is 0, and the value of female is 1; in the exercise intervention scheme, a first scheme value is 0, and a second scheme value is 1.
8. The method of claim 7, wherein the step of predicting the cardiopulmonary function improvement effect of healthy adults under two HIIT exercise intervention schemes according to the cardiopulmonary function effect prediction model comprises:
predicting delta VO corresponding to healthy adults according to the cardiopulmonary function effect prediction model2A max value;
obtaining a target delta VO corresponding to the healthy adult2A max value;
according to target Delta VO2max value and predicted Δ VO2The max value recommends a corresponding HIIT exercise intervention program for the healthy adult.
9. A device for predicting the effect of exercise on improving cardiopulmonary function, comprising: a processor and a memory;
the processor is configured to execute one or more computer programs stored in the memory to implement the steps of the prediction method for improving the cardiopulmonary performance effect by exercise according to any one of claims 1 to 8.
10. A storage medium comprising one or more computer programs stored thereon for execution by one or more processors to perform the steps of the method of any one of claims 1 to 8 for predicting the effect of exercise-enhanced cardiopulmonary function.
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