A genome-wide method and device for predicting the effects of exercise on cardiorespiratory endurance.

Abstract

This invention relates to a method and apparatus for predicting the effect of exercise on improving cardiorespiratory endurance based on whole-genome sequencing. The method includes: acquiring basic information of a user to be tested, an initial cardiorespiratory endurance value before exercise, and multiple target single nucleotide polymorphism (SNP) sites; determining a first gene prediction score for the user to be tested based on the multiple target SNP sites; determining the level of the effect of exercise on improving cardiorespiratory endurance based on the first gene prediction score using a pre-trained first prediction model; and determining the target cardiorespiratory endurance effect value after exercise based on the basic information, the initial cardiorespiratory endurance value, and the first gene prediction score using a pre-trained second prediction model. This invention allows for the prediction of the effect of exercise on improving cardiorespiratory endurance in both scenarios with and without cardiorespiratory endurance testing.

Description

Technical Field

[0001] This invention relates to the fields of sports science and exercise health technology. Specifically, this invention relates to a method and device for predicting the effect of exercise on improving cardiorespiratory endurance based on whole genome. Background Technology

[0002] Cardiorespiratory function (CRF) is associated with cardiovascular disease, all-cause mortality, and cancer mortality. The gold standard for CRF, VO2max, is associated with a 1 MET (3.5 ml / min / kg) increase, which reduces the all-cause mortality risk by 12%. Many countries advocate that healthy adults should engage in 150 minutes of moderate-intensity physical activity per week. However, standardized exercise programs fail to achieve the expected benefits for improving cardiorespiratory function in some individuals. Therefore, there is an urgent need for a genome-wide predictive method for improving cardiorespiratory endurance through exercise. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method for predicting the effect of exercise on improving cardiorespiratory endurance based on whole genome, and aims to solve at least one of the above-mentioned technical problems.

[0004] The technical solution of this invention to solve the above-mentioned technical problems is as follows: a genome-wide method for predicting the effect of exercise on improving cardiorespiratory endurance, the method comprising:

[0005] Obtain basic information about the user to be tested, initial cardiorespiratory endurance value before exercise, and multiple target single nucleotide polymorphism sites;

[0006] Based on multiple target single nucleotide polymorphism sites, a first gene prediction score is determined for the user to be tested. The first gene prediction score characterizes the innate genetic characteristics of the user to be tested that are related to the effect of exercise on improving cardiorespiratory endurance.

[0007] Based on the first gene prediction score, the level of the exercise-induced cardiorespiratory endurance improvement effect of the user to be tested is determined by a pre-trained first prediction model. The first prediction model is trained based on the first gene prediction score of the cardiorespiratory endurance improvement effect level of different exercises.

[0008] Based on the basic information, the initial cardiorespiratory endurance value, and the first gene prediction score, the target cardiorespiratory endurance effect value of the user to be tested after exercise is determined by a pre-trained second prediction model. The second prediction model is trained based on the basic information, initial cardiorespiratory endurance value, gene prediction score, and target cardiorespiratory endurance effect value of different users after exercise.

[0009] The beneficial effects of this invention are as follows: Based on the first gene prediction score of the user to be tested, a pre-trained first prediction model can predict the level of improvement in cardiorespiratory endurance after exercise for the user to be tested. This level of improvement in cardiorespiratory endurance reflects the improvement in cardiorespiratory endurance after exercise. Furthermore, based on the user's basic information, the initial cardiorespiratory endurance value, and the first gene prediction score, a pre-trained second prediction model can predict the target cardiorespiratory endurance value after exercise for the user to be tested. This target cardiorespiratory endurance value reflects the improvement in cardiorespiratory endurance after exercise. Through the two prediction models of this application, the prediction of the improvement in cardiorespiratory endurance before exercise can be completed under different exercise conditions.

[0010] Based on the above technical solution, the present invention can be further improved as follows.

[0011] Furthermore, the determination of the first gene prediction score of the user to be tested based on multiple target single nucleotide polymorphism sites includes:

[0012] A first gene prediction score is determined for the user to be tested based on multiple target single nucleotide polymorphism sites and the weight of each target single nucleotide polymorphism site.

[0013] The beneficial effect of adopting the above-mentioned further scheme is that, considering that different single nucleotide polymorphism sites have different degrees of influence on the first gene prediction score, the first gene prediction score of the user to be tested can be determined more accurately based on multiple target single nucleotide polymorphism sites and the weight of each target single nucleotide polymorphism site.

[0014] Furthermore, the first prediction model mentioned above was trained in the following way:

[0015] Obtain the first cardiorespiratory endurance value before exercise, the second cardiorespiratory endurance value after exercise, and multiple single nucleotide polymorphism sites for each of the multiple sample users;

[0016] For each of the sample users, a first cardiopulmonary endurance difference is determined based on the first cardiopulmonary endurance value and the second cardiopulmonary endurance value;

[0017] Based on each of the first cardiopulmonary endurance differences, the effect quantity corresponding to each first cardiopulmonary endurance difference is determined, and the effect quantity characterizes the improvement effect of cardiopulmonary endurance;

[0018] Based on the various effect quantities, the sample users are grouped to obtain multiple groups of sample users, and each group of sample users corresponds to a level of exercise to improve cardiorespiratory endurance.

[0019] For each of the sample users, a second gene prediction score is determined based on multiple single nucleotide polymorphism sites.

[0020] The first initial model is trained based on the second gene prediction score of each of the sample users in each group to obtain the first prediction model.

[0021] The beneficial effect of adopting the above-mentioned further approach is that the first prediction model, trained based on multiple single nucleotide polymorphism sites (determined in the absence of exercise), can predict the effect of exercise on improving cardiorespiratory endurance in the absence of exercise capacity testing scenarios.

[0022] Furthermore, the multiple single nucleotide polymorphism sites corresponding to each of the aforementioned sample users were obtained through the following method:

[0023] Based on the first cardiopulmonary endurance difference value corresponding to the sample users, the significance level of each gene locus and the physical location of each gene locus of the sample users are determined by genome-wide association analysis.

[0024] Candidate gene loci are selected from each gene locus based on the significance level and significance level threshold of each gene locus.

[0025] Based on the physical location of each candidate gene locus, target gene loci are selected from the candidate gene loci as multiple single nucleotide polymorphism sites.

[0026] The beneficial effect of adopting the above-mentioned further scheme is that, in the process of determining multiple single nucleotide polymorphism sites, combining the genome-wide association method, the most representative target gene sites with high significance levels can be screened from each gene site as multiple single nucleotide polymorphism sites, making the prediction results based on multiple single nucleotide polymorphism sites more accurate, and improving data processing efficiency.

[0027] Furthermore, the second prediction model mentioned above was trained in the following way:

[0028] Obtain basic information for each of the multiple sample users;

[0029] For each of the sample users, the second cardiorespiratory endurance difference is determined based on the sample user's second gene prediction score, first cardiorespiratory endurance value, and basic information.

[0030] Based on the basic information of each of the sample users, the second cardiorespiratory endurance difference, the second gene prediction score, and the first cardiorespiratory endurance value, the second initial model is trained to obtain the second prediction model.

[0031] The beneficial effect of adopting the above-mentioned further scheme is that, based on the basic information of each of the sample users, the second cardiorespiratory endurance difference (determined in the case of exercise testing), the second gene prediction score and the first cardiorespiratory endurance value trained by the second prediction model, it is possible to predict the effect of exercise on improving cardiorespiratory endurance in the case of exercise ability testing.

[0032] Furthermore, for each of the sample users, the aforementioned first cardiorespiratory endurance value and second cardiorespiratory endurance value were determined in the following manner:

[0033] For each of the sample users, multiple training parameters are obtained before and after the sample user performs high-intensity interval training for a set duration. The multiple training parameters include respiratory quotient, heart rate, oxygen uptake and RPE.

[0034] For each of the sample users, when at least two of the training parameters meet the corresponding preset conditions, the obtained cardiorespiratory endurance value is used as the first cardiorespiratory endurance value and the second cardiorespiratory endurance value.

[0035] The beneficial effect of adopting the above-mentioned further scheme is that, during the high-intensity interval training of sample users, selecting training parameters that meet the preset conditions to determine the first and second cardiopulmonary endurance values ​​can make the first and second cardiopulmonary endurance values ​​more practically valuable.

[0036] Furthermore, for each of the sample users, the preset conditions include the maximum oxygen uptake before and after a set duration of high-intensity interval training.

[0037] When at least two of the training parameters satisfy the corresponding preset conditions, the obtained cardiorespiratory endurance value is used as the first cardiorespiratory endurance value and the second cardiorespiratory endurance value, including:

[0038] Obtain the weight of the sample users;

[0039] Based on the training parameters and the preset conditions, determine the maximum oxygen uptake before and after high-intensity interval training of a set duration.

[0040] The first cardiorespiratory endurance value is determined based on the maximum oxygen uptake before the high-intensity interval training of a set duration and the weight of the sample user.

[0041] The second cardiorespiratory endurance value is determined based on the maximum oxygen uptake after a set duration of high-intensity interval training and the weight of the sample user.

[0042] The beneficial effect of adopting the above-mentioned further scheme is that, considering that the weight of each sample user is different, and that weight directly affects a person's true cardiorespiratory endurance effect before and after exercise, the determination of the first and second cardiorespiratory endurance values ​​can be made more accurate by taking into account both the maximum oxygen uptake before and after exercise and the weight.

[0043] Secondly, to solve the above-mentioned technical problems, the present invention also provides a genome-wide exercise-based device for predicting the effects of exercise on improving cardiorespiratory endurance, the device comprising:

[0044] The data acquisition module is used to acquire the basic information of the user to be tested, the initial cardiorespiratory endurance value before exercise, and multiple target single nucleotide polymorphism sites;

[0045] The gene prediction score determination module is used to determine the first gene prediction score of the user to be tested based on multiple target single nucleotide polymorphism sites. The first gene prediction score characterizes the innate genetic characteristics of the user to be tested that are related to the effect of exercise on improving cardiorespiratory endurance.

[0046] The effect level determination module is used to determine the effect level of the exercise on improving cardiorespiratory endurance of the user under test based on the first gene prediction score and through a pre-trained first prediction model. The first prediction model is trained based on the first gene prediction score of the effect level of different exercises on improving cardiorespiratory endurance.

[0047] The target cardiorespiratory endurance effect value determination module is used to determine the target cardiorespiratory endurance effect value of the user to be tested after exercise based on the basic information, the initial cardiorespiratory endurance value and the first gene prediction score, through a pre-trained second prediction model. The second prediction model is trained based on the basic information, initial cardiorespiratory endurance value, gene prediction score and target cardiorespiratory endurance effect value of different users after exercise.

[0048] Thirdly, in order to solve the above-mentioned technical problems, the present invention also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method of the present application for predicting the effect of exercise on improving cardiorespiratory endurance based on whole genome.

[0049] Fourthly, in order to solve the above-mentioned technical problems, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the genome-wide exercise-based method for predicting the effect of exercise on improving cardiorespiratory endurance.

[0050] Additional aspects and advantages of this application will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of this application. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments of the present invention will be briefly introduced below.

[0052] Figure 1 This is a flowchart illustrating a method for predicting the effects of exercise on improving cardiorespiratory endurance based on whole-genome sequencing, as provided in one embodiment of the present invention.

[0053] Figure 2 This is a schematic diagram of an association analysis result provided in one embodiment of the present invention;

[0054] Figure 3 A schematic diagram illustrating the PPS model and the effect of exercise on improving cardiorespiratory endurance (VO2max) according to an embodiment of the present invention;

[0055] Figure 4 This is a schematic diagram illustrating the individual differences in the effect of exercise on improving cardiorespiratory endurance (VO2max) according to an embodiment of the present invention;

[0056] Figure 5 This is a schematic diagram of a device for predicting the effect of exercise on improving cardiorespiratory endurance (VO2max) based on whole-genome scoring, provided as an embodiment of the present invention.

[0057] Figure 6 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present invention. Detailed Implementation

[0058] The principles and features of the present invention are described below. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0059] The technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0060] The solution provided in this invention can be applied to any application scenario requiring prediction of the effects of exercise on improving cardiorespiratory endurance based on whole-genome sequencing. The solution provided in this invention can be executed by any electronic device, such as a user's terminal device, including at least one of the following: smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, smart TV, or smart in-vehicle device.

[0061] This invention provides a possible implementation, such as... Figure 1 As shown, a flowchart of a method for predicting the effect of exercise on improving cardiorespiratory endurance based on whole-genome sequencing is provided. This method can be executed by any electronic device, such as a terminal device, or by a terminal device and a server. For ease of description, the method provided in this embodiment will be described below using a terminal device as the execution subject as an example. Figure 1 The flowchart shown indicates that the method may include the following steps:

[0062] Step S110: Obtain the basic information of the user to be tested, the initial cardiorespiratory endurance value before exercise, and multiple target single nucleotide polymorphism sites;

[0063] Step S120: Based on multiple target single nucleotide polymorphism sites, determine the first gene prediction score of the user to be tested. The first gene prediction score characterizes the innate genetic characteristics of the user to be tested that are related to the effect of exercise on improving cardiorespiratory endurance.

[0064] Step S130: Based on the first gene prediction score, the exercise-enhanced cardiorespiratory endurance level of the user to be tested is determined by a pre-trained first prediction model. The first prediction model is trained based on the first gene prediction score of different exercise-enhanced cardiorespiratory endurance levels.

[0065] Step S140: Based on the basic information, the initial cardiorespiratory endurance value, and the first gene prediction score, determine the target cardiorespiratory endurance effect value of the user to be tested after exercise using a pre-trained second prediction model. The second prediction model is trained based on the basic information, initial cardiorespiratory endurance value, first gene prediction score, and target cardiorespiratory endurance effect value of different users after exercise.

[0066] The method of this invention, based on the first gene prediction score of the user to be tested, and through a pre-trained first prediction model, can predict the level of improvement in cardiorespiratory endurance caused by exercise for the user to be tested. This level of improvement in cardiorespiratory endurance reflects the improvement in cardiorespiratory endurance after exercise. Furthermore, based on the user's basic information, the initial cardiorespiratory endurance value, and the first gene prediction score, and through a pre-trained second prediction model, can predict the target cardiorespiratory endurance effect value after exercise for the user to be tested. This target cardiorespiratory endurance effect value reflects the improvement in cardiorespiratory endurance caused by exercise. Through the two prediction models of this application, the prediction of the improvement in cardiorespiratory endurance before exercise can be completed under different exercise conditions.

[0067] The following specific embodiments further illustrate the solution of the present invention. In these embodiments, the method for predicting the effect of exercise on improving cardiorespiratory endurance based on whole-genome sequencing may include the following steps:

[0068] Step S110: Obtain the basic information of the user to be tested, the initial cardiorespiratory endurance value before exercise, and multiple target single nucleotide polymorphism sites.

[0069] The aforementioned basic information refers to the user's basic physical information and personal attributes, including weight and gender, which can be obtained in advance from the user to be tested. Optionally, the target single nucleotide polymorphism site for each individual can be obtained through genotyping testing.

[0070] Step S120: Based on multiple target single nucleotide polymorphism sites, determine the first gene prediction score of the user to be tested. The first gene prediction score characterizes the innate genetic characteristics of the user to be tested that are related to the effect of exercise on improving cardiorespiratory endurance.

[0071] Optionally, the determination of the first gene prediction score of the user to be tested based on multiple target single nucleotide polymorphism sites includes:

[0072] A first gene prediction score is determined for the user to be tested based on multiple target single nucleotide polymorphism sites and the weight of each target single nucleotide polymorphism site.

[0073] The weight of each target single nucleotide polymorphism (SNP) site can be preset. The determination of the first gene prediction score of the user to be tested based on multiple target SNP sites and the weight of each target SNP site can be achieved through the following formula:

[0074] PPS=β1×n1+β2×n2…β i ×n i (2)

[0075] Where PPS represents the first gene prediction score, n1 to n i Representing multiple target single nucleotide polymorphism sites, β1 to β i This represents the weight of each target single nucleotide polymorphism site. The above formula (2) can be called the PPS model.

[0076] Step S130: Based on the first gene prediction score, the exercise-enhanced cardiorespiratory endurance level of the user to be tested is determined by a pre-trained first prediction model. The first prediction model is trained based on the first gene prediction score of different exercise-enhanced cardiorespiratory endurance levels.

[0077] Step S140: Based on the basic information, the initial cardiorespiratory endurance value, and the first gene prediction score, determine the target cardiorespiratory endurance effect value of the user to be tested after exercise using a pre-trained second prediction model. The second prediction model is trained based on the basic information, initial cardiorespiratory endurance value, gene prediction score, and target cardiorespiratory endurance effect value of different users.

[0078] Optionally, the first prediction model is trained in the following way:

[0079] Obtain the first cardiorespiratory endurance value before exercise, the second cardiorespiratory endurance value after exercise, and multiple single nucleotide polymorphism sites for each of the multiple sample users;

[0080] For each of the sample users, a first cardiopulmonary endurance difference is determined based on the first cardiopulmonary endurance value and the second cardiopulmonary endurance value;

[0081] Based on each of the first cardiopulmonary endurance differences, the effect quantity corresponding to each first cardiopulmonary endurance difference is determined, and the effect quantity characterizes the improvement effect of cardiopulmonary endurance;

[0082] Based on the various effect quantities, the sample users are grouped to obtain multiple groups of sample users, and each group of sample users corresponds to a level of exercise to improve cardiorespiratory endurance.

[0083] For each of the sample users, a second gene prediction score is determined based on multiple single nucleotide polymorphism sites.

[0084] The first initial model is trained based on the second gene prediction score of each of the sample users in each group to obtain the first prediction model.

[0085] In this process, multiple sample users can undergo the same training in advance to obtain a first cardiorespiratory endurance value before exercise and a second cardiorespiratory endurance value after exercise for each sample user. Optionally, for each sample user, the first and second cardiorespiratory endurance values ​​are determined in the following way:

[0086] For each of the sample users, multiple training parameters of the sample user before and after performing high-intensity interval training for a set duration are obtained. The multiple training parameters include respiratory quotient, heart rate, oxygen uptake and RPE.

[0087] For each of the sample users, when at least two of the training parameters meet the corresponding preset conditions, the obtained cardiorespiratory endurance value is used as the first cardiorespiratory endurance value and the second cardiorespiratory endurance value.

[0088] This allows each sample user to undergo High Intensity Interval Training (HIIT) for a set duration (e.g., 12 weeks). Specific HIIT training courses are shown in Table 1.

[0089] Table 1

[0090]

[0091] During high-intensity interval training for each sample user, various training parameters were directly measured using a Cortex MetaMax 3B gas metabolism analyzer. Real-time heart rate was collected using a polar heart rate monitor during exercise, and fatigue index was recorded for each load level. Holter monitoring was conducted throughout the test to assess exercise risks.

[0092] Different training parameters correspond to different preset conditions. The preset condition for respiratory quotient is that the respiratory quotient reaches 1.1. The preset condition for heart rate is that the heart rate is greater than 90% of the expected maximum heart rate. The preset condition for oxygen uptake is that oxygen uptake plateaus and no longer increases with the increase of load. The preset condition for RPE is that RPE ≥ 17, indicating that the current load cannot be completed.

[0093] For each of the sample users, when at least two of the training parameters meet the corresponding preset conditions, the obtained cardiorespiratory endurance value is used as the first cardiorespiratory endurance value and the second cardiorespiratory endurance value.

[0094] Optionally, for each of the sample users, the preset conditions include the maximum oxygen uptake before and after high-intensity interval training (HIIT) of a set duration; then, when at least two of the training parameters satisfy the corresponding preset conditions, the obtained cardiorespiratory endurance value is used as the first cardiorespiratory endurance value and the second cardiorespiratory endurance value, including:

[0095] Obtain the weight of the sample users;

[0096] Based on the training parameters and the preset conditions, determine the maximum oxygen uptake before and after high-intensity interval training of a set duration.

[0097] The first cardiorespiratory endurance value is determined based on the maximum oxygen uptake before the high-intensity interval training of a set duration and the weight of the sample user. Specifically, the ratio of the maximum oxygen uptake to the weight before the high-intensity interval training of a set duration can be used as the first cardiorespiratory endurance value.

[0098] The second cardiorespiratory endurance value is determined based on the maximum oxygen uptake after a set duration of high-intensity interval training and the weight of the sample user. Specifically, the ratio of maximum oxygen uptake to body weight after a set duration of high-intensity interval training can be used as the second cardiorespiratory endurance value.

[0099] Optionally, the multiple single nucleotide polymorphism sites corresponding to each of the aforementioned sample users are obtained in the following manner:

[0100] Based on the first cardiopulmonary endurance difference value corresponding to the sample users, the significance level of each gene locus and the physical location of each gene locus of the sample users are determined by genome-wide association analysis.

[0101] Candidate gene loci are selected from each gene locus based on the significance level and significance level threshold of each gene locus.

[0102] Based on the physical location of each candidate gene locus, target gene loci are selected from the candidate gene loci as multiple single nucleotide polymorphism sites.

[0103] The second cardiorespiratory endurance value can be the expected cardiorespiratory endurance value after long-term exercise. The difference between the first and second cardiorespiratory endurance values ​​can be determined by the following formula:

[0104] △VO2max=Post-intervention VO2max -Pre-intervention VO2max (1)

[0105] Wherein, △VO2max represents the first cardiopulmonary endurance difference, VO2max after intervention represents the second cardiopulmonary endurance value, and VO2max before intervention represents the first cardiopulmonary endurance value.

[0106] The first cardiorespiratory endurance difference can reflect the effect of exercise on improving cardiorespiratory endurance. The specific process of determining the significance level and physical location of each gene locus in the sample users based on the first cardiorespiratory endurance difference using genome-wide association analysis is as follows: Based on the first cardiorespiratory endurance difference, a genome-wide association analysis is performed. The association analysis results can be found in [reference needed]. Figure 2 , Figure 2 The significance level of each gene locus in the study. Figure 2 The x-axis represents the chromosome number of each gene locus, and the y-axis represents the significance level. Gene loci located above the line parallel to the x-axis indicate a higher significance level, while gene loci below the line indicate a lower significance level. First, based on the first cardiopulmonary endurance difference, a genome-wide association analysis (GWAS) was performed to obtain the expansion coefficient λ of the GWAS association results. The bias of the GWAS association results and whether it was affected by population stratification were evaluated based on the expansion coefficient λ. Then, a Manhattan plot was generated using R Studio to represent the GWAS association results. Figure 2 This can be used to create a Manhattan plot using Rstudio.

[0107] Optionally, the significance level threshold for candidate gene loci included in the gene prediction score can be set based on actual needs; in this application, it can be 1×10⁻⁶. -5 The above-mentioned selection of candidate gene loci based on the significance level and significance level threshold of each gene locus can specifically be as follows: Gene loci within a 10KB region of gene loci with significance levels less than the significance level threshold are selected as candidate gene loci. This can be understood as... Figure 2Each gene locus above the middle line. The above candidate gene loci can also be referred to as Lead SNPs. Then, according to the physical positions of each of the candidate gene loci, target gene loci are screened out from each of the candidate gene loci as the multiple single nucleotide polymorphism loci. Among them, when the physical positions of two gene loci are very close, it indicates that there may be a linkage association between the two gene loci. Based on this principle, redundant gene loci can be removed to obtain the multiple single nucleotide polymorphism loci. In the solution of this application, 8 gene loci can be selected as the multiple single nucleotide polymorphism loci, which can also be described as 8 SNPs. The 8 SNPs can be rs474377, rs9365605, rs17116985, rs4784714, rs77878025, rs36173935, rs12362449 and rs56797499 respectively.

[0108] Optionally, determining the effect size (Cohen's d effect size, Effect Size, abbreviated as ES) corresponding to each of the first cardiorespiratory endurance differences according to each of the first cardiorespiratory endurance differences can be implemented based on the prior art and will not be elaborated here. Then, according to each of the effect sizes, the sample users are grouped to obtain multiple groups of sample users, and each group of sample users corresponds to a cardiorespiratory endurance improvement effect level; as an example, according to each of the effect sizes, the sample users are divided into 5 groups, and the effect sizes corresponding to each group are: ES≥0.8, 0.5≤ES<0.8, 0.2≤ES<0.5, 0<ES<0.2 and ES≤0. The larger the effect size, the better the cardiorespiratory endurance effect, and the higher the corresponding cardiorespiratory endurance improvement effect level.

[0109] It should be noted that for each sample user, determining the second gene prediction score of the sample user according to the multiple single nucleotide polymorphism loci can be determined in the same way as determining the first gene prediction score of the to-be-detected user based on multiple target single nucleotide polymorphism loci described above, and will not be elaborated here.

[0110] It should be noted that the first initial model is trained based on the second gene prediction score of each sample user in each group of sample users to obtain the first prediction model. The first prediction model is trained based on the second gene prediction score of each sample user to obtain the predicted exercise improvement cardiorespiratory endurance effect level for each sample user. Based on the actual exercise improvement cardiorespiratory endurance effect level and the predicted exercise improvement cardiorespiratory endurance effect level when grouping, the first loss value of the first initial model can be determined. When the first loss value meets the preset first training termination condition, the first initial model that meets the first training termination condition is used as the first prediction model. When the first loss value does not meet the preset first training termination condition, the model parameters of the first initial model are adjusted, and the first initial model is retrained based on the adjusted model parameters until the first loss value meets the first training termination condition.

[0111] The first prediction model obtained through the above training can effectively distinguish users with different training effects. For example... Figure 3 As shown, the PPS score (gene prediction score) can effectively distinguish different ES effect groups. When the PPS score is higher than 1.757 (95% CI: 1.030 to 2.485), HIIT has the best effect on improving VO2max (ES≥0.8); when the PPS score is lower than -3.712 (95%: -5.603 to -1.821), HIIT has no effect on improving VO2max (ES<0.2).

[0112] Optionally, the second prediction model described above is trained in the following way:

[0113] Obtain basic information about each of the multiple sample users, including gender and height;

[0114] For each of the sample users, the second cardiorespiratory endurance difference is determined based on the sample user's second gene prediction score, first cardiorespiratory endurance value, and basic information.

[0115] Based on the basic information of each of the sample users, the second cardiorespiratory endurance difference, the second gene prediction score, and the first cardiorespiratory endurance value, the second initial model is trained to obtain the second prediction model.

[0116] For each sample user, the second cardiorespiratory endurance difference represents the user's true cardiorespiratory endurance performance value, which can be determined by the third formula below. Similarly, the second prediction model described above can be expressed by the third formula. Therefore, based on the basic information, the initial cardiorespiratory endurance value, and the first gene prediction score, the pre-trained second prediction model determines the target cardiorespiratory endurance performance value of the user to be tested after exercise, specifically as follows:

[0117] Based on the basic information, the initial cardiorespiratory endurance value, and the first gene prediction score, the target cardiorespiratory endurance effect value of the user after exercise is determined using the third formula, wherein the third formula is:

[0118] ΔVO2max = 21.879 + 0.581 × PPS - 5.162 × gender - 0.340 ×

[0119] VO2max-0.056×body weight

[0120] Where ΔVO2max represents the target cardiorespiratory endurance effect value, 21.879 represents the intercept (constant) of the regression model, PPS represents the first gene prediction score, VO2max represents the initial cardiorespiratory endurance value, 0.581 is the weight of PPS, 5.162 is the weight of gender, and 0.056 is the weight of body weight. For males, the gender term in the formula is 0; for females, the gender term in the formula is 1.

[0121] To better illustrate and understand the principle of the method provided by this invention, the following description uses an optional specific embodiment to illustrate the solution of this invention. With the same standardized exercise intervention, the lung function effects in healthy adults exhibit individual differences. The data from this solution shows good stability, high scientific validity, and reproducibility. For example... Figure 4 As shown, Figure 4 The left-hand graph shows the individual differences in the effect of exercise on improving cardiorespiratory endurance. The horizontal axis represents each subject, and the vertical axis represents the cardiorespiratory endurance effect value. Figure 4 The right-hand graph shows the horizontal axis representing the effect of different exercises on improving cardiorespiratory endurance, and the vertical axis representing the cardiorespiratory endurance effect value. Figure 4 The two graphs illustrate that the improvement in cardiorespiratory endurance after exercise varies from person to person; some people experience better results, while others do not.

[0122] Furthermore, the effect of exercise on improving VO2max was correlated with genome-wide genetic information, with an inflation coefficient λ = 1.008. The genomic data were not stratified by population, and there was no significant bias between the actual and expected values ​​(λ < 1.1). Ten lead SNPs were significantly associated with the improvement in VO2max (P < 1 × 10⁻⁵).

[0123] The solution proposed in this application has the following advantages compared to the prior art:

[0124] (1) This method provides two prediction models under different environments, and through this patent, the effect of exercise on improving VO2max can be predicted in two scenarios: with and without exercise ability testing.

[0125] (2) This patent is a predictive model that can predict the effectiveness of HIIT in improving VO2max before the start of exercise intervention. The model has good stability. After stepwise regression, the included variable p < 0.01 and the removed variable p > 0.05, indicating good model stability. Among them, the PPS model and the comprehensive model of PPS combined phenotype have long-term application value because they include genetic molecular markers, which are stable and not easily changed.

[0126] Based on and Figure 1 The method shown in the example follows the same principle. This embodiment of the invention also provides a genome-wide prediction device 20 for predicting the effect of exercise on improving cardiorespiratory endurance, such as... Figure 5 As shown, the genome-wide exercise-based cardiorespiratory endurance enhancement effect prediction device 20 may include a data acquisition module 210, a gene prediction score determination module 220, an effect level determination module 230, and a target cardiorespiratory endurance effect value determination module 240, wherein:

[0127] The data acquisition module 210 is used to acquire the basic information of the user to be tested, the initial cardiorespiratory endurance value before exercise, and multiple target single nucleotide polymorphism sites;

[0128] The gene prediction score determination module 220 is used to determine a first gene prediction score of the user to be tested based on multiple target single nucleotide polymorphism sites. The first gene prediction score characterizes the innate genetic characteristics of the user to be tested that are related to the effect of exercise on improving cardiorespiratory endurance.

[0129] The effect level determination module 230 is used to determine the effect level of the exercise on improving cardiorespiratory endurance of the user under test based on the first gene prediction score and through a pre-trained first prediction model. The first prediction model is trained based on the first gene prediction score of the effect level of different exercises on improving cardiorespiratory endurance.

[0130] The target cardiorespiratory endurance effect value determination module 240 is used to determine the target cardiorespiratory endurance effect value of the user to be tested after exercise based on the basic information, the initial cardiorespiratory endurance value and the first gene prediction score, through a pre-trained second prediction model. The second prediction model is trained based on the basic information, initial cardiorespiratory endurance value, gene prediction score and target cardiorespiratory endurance effect value of different users after exercise.

[0131] Optionally, the aforementioned multiple target single nucleotide polymorphism sites are obtained through the following methods:

[0132] Obtain the target cardiorespiratory endurance value of the user to be tested after exercise;

[0133] The first cardiopulmonary endurance difference is determined based on the initial cardiopulmonary endurance value and the target cardiopulmonary endurance value.

[0134] Based on the first cardiopulmonary endurance difference, the significance level of each gene locus and the physical location of each gene locus of the user to be tested are determined by genome-wide association analysis.

[0135] Candidate gene loci are selected from each gene locus based on the significance level and significance level threshold of each gene locus.

[0136] Based on the physical location of each candidate gene locus, target gene loci are selected from the candidate gene loci as multiple target single nucleotide polymorphism sites.

[0137] Optionally, when determining the first gene prediction score of the user to be tested based on multiple target single nucleotide polymorphism sites, the gene prediction score determination module 220 is specifically used for:

[0138] A first gene prediction score is determined for the user to be tested based on multiple target single nucleotide polymorphism sites and the weight of each target single nucleotide polymorphism site.

[0139] Optionally, the first prediction model described above is trained in the following way:

[0140] Obtain the first cardiorespiratory endurance value before exercise, the second cardiorespiratory endurance value after exercise, and multiple single nucleotide polymorphism sites for each of the multiple sample users;

[0141] For each of the sample users, a first cardiopulmonary endurance difference is determined based on the first cardiopulmonary endurance value and the second cardiopulmonary endurance value;

[0142] Based on each of the first cardiopulmonary endurance differences, the effect quantity corresponding to each first cardiopulmonary endurance difference is determined, and the effect quantity characterizes the improvement effect of cardiopulmonary endurance;

[0143] Based on the various effect quantities, the sample users are grouped to obtain multiple groups of sample users, and each group of sample users corresponds to a level of exercise to improve cardiorespiratory endurance.

[0144] For each of the sample users, a second gene prediction score is determined based on multiple single nucleotide polymorphism sites.

[0145] The first initial model is trained based on the second gene prediction score of each of the sample users in each group to obtain the first prediction model.

[0146] Optionally, the second prediction model described above is trained in the following way:

[0147] Obtain basic information for each of the multiple sample users;

[0148] For each of the sample users, the second cardiorespiratory endurance difference is determined based on the sample user's second gene prediction score, first cardiorespiratory endurance value, and basic information.

[0149] Based on the basic information of each of the sample users, the second cardiorespiratory endurance difference, the second gene prediction score, and the first cardiorespiratory endurance value, the second initial model is trained to obtain the second prediction model.

[0150] Optionally, for each of the sample users, the aforementioned first cardiorespiratory endurance value and second cardiorespiratory endurance value are determined by the cardiorespiratory endurance value determination module:

[0151] For each of the sample users, the cardiorespiratory endurance value determination module is used to acquire multiple training parameters of the sample user during a high-intensity interval training process of a set duration. The multiple training parameters include respiratory quotient, heart rate, oxygen uptake and RPE. When at least two of the training parameters meet the corresponding preset conditions, the acquired cardiorespiratory endurance value is used as the first cardiorespiratory endurance value and the second cardiorespiratory endurance value.

[0152] Optionally, for each of the sample users, the above-mentioned preset conditions include the maximum oxygen uptake before and after the high-intensity interval training for a set duration.

[0153] When the aforementioned cardiorespiratory endurance value determination module satisfies at least two of the training parameters according to the corresponding preset conditions, and uses the obtained cardiorespiratory endurance value as the first cardiorespiratory endurance value and the second cardiorespiratory endurance value, it is specifically used for:

[0154] Obtain the weight of the sample users;

[0155] Based on the training parameters and the preset conditions, determine the maximum oxygen uptake before and after high-intensity interval training of a set duration.

[0156] The first cardiorespiratory endurance value is determined based on the maximum oxygen uptake before the high-intensity interval training of a set duration and the weight of the sample user.

[0157] The second cardiorespiratory endurance value is determined based on the maximum oxygen uptake after a set duration of high-intensity interval training and the weight of the sample user.

[0158] The genome-wide exercise-based cardiorespiratory endurance enhancement effect prediction device of this invention can execute the genome-wide exercise-based cardiorespiratory endurance enhancement effect prediction method provided in this invention. The implementation principle is similar. The actions performed by each module and unit in the genome-wide exercise-based cardiorespiratory endurance enhancement effect prediction device in each embodiment of this invention correspond to the steps in the genome-wide exercise-based cardiorespiratory endurance enhancement effect prediction method in each embodiment of this invention. For detailed functional descriptions of each module of the genome-wide exercise-based cardiorespiratory endurance enhancement effect prediction device, please refer to the descriptions in the corresponding genome-wide exercise-based cardiorespiratory endurance enhancement effect prediction methods shown above, which will not be repeated here.

[0159] The aforementioned device for predicting the effect of exercise on improving cardiorespiratory endurance based on whole-genome sequencing can be a computer program (including program code) running on a computer device. For example, the device for predicting the effect of exercise on improving cardiorespiratory endurance based on whole-genome sequencing is an application software. The device can be used to execute the corresponding steps in the method provided in the embodiments of the present invention.

[0160] In some embodiments, the whole-genome-based exercise-based cardiorespiratory endurance enhancement prediction device provided in this invention can be implemented using a combination of hardware and software. As an example, the whole-genome-based exercise-based cardiorespiratory endurance enhancement prediction device provided in this invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the whole-genome-based exercise-based cardiorespiratory endurance enhancement prediction method provided in this invention. For example, the hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0161] In other embodiments, the genome-wide exercise-based prediction device for improving cardiorespiratory endurance provided in this invention can be implemented in software. Figure 5A genome-wide exercise-based cardiorespiratory endurance enhancement effect prediction device is shown, which can be software in the form of programs and plug-ins, and includes a series of modules, including a data acquisition module 210, a gene prediction score determination module 220, an effect level determination module 230, and a target cardiorespiratory endurance effect value determination module 240, for implementing the genome-wide exercise-based cardiorespiratory endurance enhancement effect prediction method provided in the embodiments of the present invention.

[0162] The modules described in the embodiments of the present invention can be implemented in software or hardware. The names of the modules are not, in some cases, limiting the scope of the module itself.

[0163] Based on the same principles as the methods shown in the embodiments of the present invention, the embodiments of the present invention also provide an electronic device, which may include, but is not limited to: a processor and a memory; the memory for storing computer programs; and the processor for executing the methods shown in any embodiment of the present invention by invoking the computer programs.

[0164] In one alternative embodiment, an electronic device is provided, such as Figure 6 As shown, Figure 6 The illustrated electronic device 4000 includes a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, for example, via a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as sending and / or receiving data. It should be noted that in practical applications, the transceiver 4004 is not limited to one type, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0165] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. Processor 4001 may also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0166] Bus 4002 may include a pathway for transmitting information between the aforementioned components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 4002 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0167] The memory 4003 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0168] The memory 4003 stores the application code (computer program) for executing the present invention, and its execution is controlled by the processor 4001. The processor 4001 executes the application code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0169] Among these, electronic devices can also be terminal devices. Figure 6 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0170] This invention provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0171] According to another aspect of the present invention, a computer program product or computer program is also provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the genome-wide exercise-based method for predicting cardiorespiratory endurance improvement provided in the various embodiments described above.

[0172] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—and conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0173] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0174] The computer-readable storage medium provided in this invention can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0175] The aforementioned computer-readable storage medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the method shown in the above embodiments.

[0176] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

Claims

1. A genome-wide method for predicting the effect of HIIT on improving exercise heart rate, characterized in that, Includes the following steps: The system obtains basic information about the user to be tested, their initial heart rate before exercise, and multiple target single nucleotide polymorphism sites. The basic information includes weight and gender, and this basic information is obtained from the user to be tested in advance. Based on multiple target single nucleotide polymorphism sites, a first gene prediction score is determined for the user to be tested, the first gene prediction score characterizing the innate genetic characteristics of the user to be tested related to the improvement effect of exercise heart rate; Based on the first gene prediction score, the level of improvement in the heart rate of the user under test is determined by a pre-trained first prediction model. The first prediction model is trained based on gene prediction scores of different levels of improvement in the heart rate of the user under test. Based on the basic information, the initial exercise heart rate, and the first gene prediction score, the quantitative load exercise heart rate improvement effect value of the user under test after exercise is determined by a pre-trained second prediction model; or, based on the basic information and the initial exercise heart rate, the quantitative load exercise heart rate improvement effect value of the user under test after exercise is determined by a pre-trained third prediction model. The second prediction model is trained based on the basic information of different users, initial exercise heart rate, gene prediction score and the improvement effect value of quantitative load exercise heart rate after exercise, and the third prediction model is trained based on the basic information of different users, initial exercise heart rate and the improvement effect value of quantitative load exercise heart rate after exercise.

2. The method according to claim 1, characterized in that, The step of determining the first gene prediction score of the user to be tested based on multiple target single nucleotide polymorphism sites includes: A first gene prediction score is determined for the user to be tested based on multiple target single nucleotide polymorphism sites and the weight of each target single nucleotide polymorphism site.

3. The method according to claim 1, characterized in that, The first prediction model was trained in the following way: The first exercise heart rate before exercise, the second exercise heart rate after quantitative exercise, and multiple single nucleotide polymorphism sites were obtained for each of the multiple sample users. For each of the sample users, the difference in exercise heart rate is determined based on the first exercise heart rate and the second exercise heart rate; Based on each of the exercise heart rate differences, the effect quantity corresponding to each exercise heart rate difference is determined, and the effect quantity characterizes the improvement effect of quantitative load exercise heart rate; Based on the various effect quantities, the sample users are grouped to obtain multiple groups of sample users, and each group of sample users corresponds to a quantitative load exercise heart rate improvement effect level. For each of the sample users, a second gene prediction score is determined based on multiple single nucleotide polymorphism sites. The first initial model is trained based on the second gene prediction score of each of the sample users in each group to obtain the first prediction model; The second prediction model was trained in the following way: Obtain basic information for each of the multiple sample users; Based on the basic information, exercise heart rate difference, second gene prediction score and first exercise heart rate of each of the sample users, the second initial model is trained to obtain the second prediction model. The third prediction model was trained in the following way: The third initial model is trained based on the basic information, exercise heart rate difference, and first exercise heart rate of each of the sample users to obtain the third prediction model.

4. The method according to claim 3, characterized in that, The multiple single nucleotide polymorphism sites corresponding to each of the multiple sample users were obtained in the following manner: Based on the heart rate differences of the sample users, the significance level of each gene locus and the physical location of each gene locus of the sample users were determined by genome-wide association analysis. Candidate gene loci are selected from each gene locus based on the significance level and significance level threshold of each gene locus. Based on the physical location of each candidate gene locus, target gene loci are selected from the candidate gene loci as multiple single nucleotide polymorphism sites.

5. The method according to claim 3, characterized in that, For each of the sample users, the second exercise heart rate was determined in the following manner: For each of the sample users, after a second set time following a first set time of high-intensity interval training, the heart rate of the sample user during quantitative load exercise is obtained as the second exercise heart rate.

6. The method according to claim 3, characterized in that, The process involves grouping the sample users according to each effect measure to obtain multiple groups of sample users, including: Based on the various effect quantities and the preset effect quantity grouping ranges, the sample users are grouped to obtain multiple groups of sample users. The effect quantity grouping ranges include a first range, a second range, a third range, a fourth range, and a fifth range where the improvement effect of quantitative load exercise heart rate decreases sequentially.

7. The method according to any one of claims 1 to 6, characterized in that, The method further includes: Based on the level of improvement in heart rate during quantitative exercise or the value of improvement in heart rate after quantitative exercise, an exercise program is recommended for the user under test.

8. A genome-wide HIIT-based device for predicting the effect of improved exercise heart rate, characterized in that, include: The data acquisition module is used to acquire the basic information of the user to be tested, the initial exercise heart rate before exercise, and multiple target single nucleotide polymorphism sites. The basic information includes weight and gender, and the basic information is obtained from the user to be tested in advance. The gene prediction score determination module is used to determine a first gene prediction score of the user to be tested based on multiple target single nucleotide polymorphism sites. The first gene prediction score characterizes the innate genetic characteristics of the user to be tested that are related to the improvement effect of exercise heart rate. The effect level determination module is used to determine the effect level of the quantitative load exercise heart rate improvement of the user under test based on the first gene prediction score and through a pre-trained first prediction model. The first prediction model is trained based on gene prediction scores of different effect levels of quantitative load exercise heart rate improvement. The improvement effect value determination module is used to determine the quantitative load exercise heart rate improvement effect value of the user under test after exercise by using a pre-trained second prediction model based on the basic information, the initial exercise heart rate and the first gene prediction score, or to determine the quantitative load exercise heart rate improvement effect value of the user under test after exercise by using a pre-trained third prediction model based on the basic information and the initial exercise heart rate. The second prediction model is trained based on the basic information of different users, initial exercise heart rate, gene prediction score and the improvement effect value of quantitative load exercise heart rate after exercise, and the third prediction model is trained based on the basic information of different users, initial exercise heart rate and the improvement effect value of quantitative load exercise heart rate after exercise.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the method of any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1-7.

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