Method and device for predicting the effect of HIIT on improving exercise heart rate based on the whole genome

Abstract

The present invention relates to a method and device for predicting the effect of HIIT on improving exercise heart rate based on the whole genome. The method comprises: obtaining multiple target single nucleotide polymorphism sites of a user to be tested; determining a first gene prediction score of the user to be tested based on the multiple target single nucleotide polymorphism sites; determining the quantitative load exercise heart rate improvement effect level of the user to be tested through a pre-trained first prediction model based on the first gene prediction score; and determining the quantitative load exercise heart rate improvement effect value of the user to be tested after exercise based on basic information, initial exercise heart rate and the first gene prediction score through a pre-trained second prediction model or a third prediction model. Through the method of the present invention, the quantitative load exercise heart rate improvement effect level and the quantitative load exercise heart rate improvement effect value of the user to be tested can be predicted before the start of exercise, so as to formulate a suitable exercise plan based on the prediction results.

Description

Technical Field

[0001] The present invention relates to the fields of sports science and sports health technology. Specifically, the present invention relates to a method and device for predicting the effect of HIIT on improving exercise heart rate based on the whole genome. Background Art

[0002] Heart rate is a predictor of cardiovascular and all-cause mortality. Resting heart rate, exercise heart rate, heart rate recovery, and heart rate variability are significantly associated with all-cause mortality and sudden cardiac death. The duration of a higher heart rate (150 beats / minute) during exercise can effectively predict the increase in cardiac troponin, a marker of exercise-induced myocardial injury. Even in adults without heart disease, heart rate is still significantly positively correlated with the risk of all-cause mortality and cardiovascular events. Regular exercise can effectively reduce plasma catecholamine levels, improve the regulation of the sympathetic vagal nervous system, improve heart rate variability, reduce resting heart rate and quantitative load exercise heart rate, and accelerate heart rate recovery after exercise. However, the effect of exercise on heart rate depends to some extent on genetics. However, the effect and differences of long-term high intensity intercourse (HIIT) in reducing exercise heart rate under quantitative load, as well as the influence of genetic factors on the effect of improving exercise heart rate, remain unclear.

[0003] Therefore, in the existing technology, there is an urgent need for an effective genome-wide based method for predicting the effect of HIIT on improving exercise heart rate. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and device for predicting the effect of HIIT on improving exercise heart rate based on the whole genome, aiming to solve at least one of the above technical problems.

[0005] The present invention solves the above-mentioned technical problem with the following technical solution: a genome-wide HIIT-based prediction method for improving exercise heart rate, comprising:

[0006] Obtaining basic information of the user to be tested, initial exercise heart rate before exercise, and multiple target single nucleotide polymorphism sites;

[0007] Determining a first gene prediction score of the user to be tested based on the plurality of target single nucleotide polymorphism sites, wherein the first gene prediction score represents an innate genetic characteristic of the user to be tested that is related to an improvement in exercise heart rate;

[0008] Determining, based on the first gene prediction score, a level of improvement in the heart rate of the user to be tested using a pre-trained first prediction model, wherein the first prediction model is trained based on gene prediction scores of different levels of improvement in the heart rate of the user to be tested;

[0009] Determining the quantitative load exercise heart rate improvement effect value of the user to be detected after exercise by a pre-trained second prediction model based on the basic information, the initial exercise heart rate, and the first gene prediction score, or determining the quantitative load exercise heart rate improvement effect value of the user to be detected after exercise by a pre-trained third prediction model based on the basic information and the initial exercise heart rate;

[0010] Among them, the second prediction model is obtained by training based on the basic information, initial exercise heart rate, gene prediction score and quantitative load exercise heart rate improvement effect value after exercise of different users, and the third prediction model is obtained by training based on the basic information, initial exercise heart rate and quantitative load exercise heart rate improvement effect value after exercise of different users.

[0011] The beneficial effects of the present invention are: based on the first gene prediction score of the user to be tested, the quantitative load exercise heart rate improvement effect level of the user to be tested can be predicted through the pre-trained first prediction model; according to the basic information, the initial exercise heart rate and the first gene prediction score, the pre-trained second prediction model is used, or according to the basic information and the initial exercise heart rate, the pre-trained third prediction model is used to determine the quantitative load exercise heart rate improvement effect value of the user to be tested after exercise; the quantitative load exercise heart rate improvement effect level and the quantitative load exercise heart rate improvement effect value can reflect the improvement effect of the exercise heart rate of the user to be tested after performing quantitative load exercise, providing a basis for the formulation of personalized exercise plans.

[0012] On the basis of the above technical solution, the present invention can also be improved as follows.

[0013] Furthermore, the above-mentioned determination of the first gene prediction score of the user to be tested based on the plurality of target single nucleotide polymorphism sites includes:

[0014] A first gene prediction score of the user to be detected is determined according to the multiple target single nucleotide polymorphism sites and the weight of each target single nucleotide polymorphism site.

[0015] The beneficial effect of adopting the above further scheme is that, taking into account the different degrees of influence of different single nucleotide polymorphism sites on the first gene prediction score, the first gene prediction score of the user to be tested can be determined more accurately based on the multiple target single nucleotide polymorphism sites and the weight of each target single nucleotide polymorphism site.

[0016] Furthermore, the first prediction model is trained in the following way:

[0017] Obtaining a first exercise heart rate before exercise, a second exercise heart rate after quantitative load exercise, and a plurality of single nucleotide polymorphism sites for each of a plurality of sample users;

[0018] For each of the sample users, determining an exercise heart rate difference according to the first exercise heart rate and the second exercise heart rate;

[0019] Determining, based on each of the exercise heart rate differences, an effect amount corresponding to each of the exercise heart rate differences, wherein the effect amount represents an improvement effect of the quantitative load exercise heart rate;

[0020] According to each effect amount, each of the sample users is grouped to obtain multiple groups of sample users, each group of sample users corresponds to a quantitative load exercise heart rate improvement effect level;

[0021] For each of the sample users, determining a second gene prediction score of the sample user based on the multiple single nucleotide polymorphism sites;

[0022] Training the first initial model according to the second gene prediction score of each sample user in each group of the sample users to obtain the first prediction model;

[0023] The second prediction model is trained in the following way:

[0024] Obtaining basic information of each of the sample users;

[0025] Training the second initial model according to the basic information, exercise heart rate difference, second gene prediction score and first exercise heart rate of each of the sample users to obtain the second prediction model;

[0026] The third prediction model is trained in the following way:

[0027] The third initial model is trained according to the basic information, the exercise heart rate difference and the first exercise heart rate of each of the sample users to obtain the third prediction model.

[0028] The beneficial effect of adopting the above further scheme is that the first prediction model obtained by training based on multiple single nucleotide polymorphism sites (determined in the absence of exercise) can predict the quantitative load exercise heart rate improvement effect in a non-exercise ability test scenario, and the second prediction model obtained based on the basic information, exercise heart rate difference, second gene prediction score and first exercise heart rate training of each of the sample users can predict the quantitative load exercise heart rate improvement effect value after exercise. The third initial model can also be trained based on the basic information, exercise heart rate difference and first exercise heart rate of each of the sample users to obtain a third prediction model different from the second prediction model to meet more practical needs.

[0029] Furthermore, the multiple single nucleotide polymorphism sites corresponding to each of the multiple sample users are obtained in the following manner:

[0030] Determine the significance level of each gene locus of the sample user and the physical location of each gene locus by genome-wide association analysis based on the exercise heart rate difference corresponding to the sample user;

[0031] Screening candidate gene loci from each of the gene loci according to the significance level and significance level threshold of each of the gene loci;

[0032] According to the physical position of each candidate gene site, target gene sites are screened out from each candidate gene site as the multiple single nucleotide polymorphism sites.

[0033] The beneficial effect of adopting the above further scheme is that, in the process of determining multiple single nucleotide polymorphism sites for each sample user, combined with the whole genome association method, the most representative target gene sites with higher significance levels can be screened out from each gene site as multiple target single nucleotide polymorphism sites, so that the results obtained by prediction based on multiple single nucleotide polymorphism sites are more accurate, and the data processing efficiency can also be improved.

[0034] Furthermore, for each of the sample users, the second exercise heart rate is determined in the following manner:

[0035] For each of the sample users, after the sample user performs high-intensity interval training for a second set period of time after the first set period of time, the exercise heart rate of the sample user during the quantitative load exercise is obtained as the second exercise heart rate.

[0036] The beneficial effect of adopting the above further scheme is that after the sample user undergoes high-intensity interval training, the sample user is placed in a set state, so that the exercise heart rate of the sample user during quantitative load exercise is obtained as the second exercise heart rate, which has more practical reference value.

[0037] Furthermore, the sample users are grouped according to the effect amounts to obtain multiple groups of sample users, including:

[0038] According to each of the effect amounts and the preset effect amount grouping range, each of the sample users is grouped to obtain multiple groups of sample users, and the effect amount grouping range includes a first range, a second range, a third range, a fourth range and a fifth range in which the quantitative load exercise heart rate improvement effect decreases in sequence.

[0039] The beneficial effect of adopting the above further scheme is that each sample user is grouped according to each effect amount, and different groups of sample users correspond to different quantitative load exercise heart rate improvement effect levels. The second gene prediction scores corresponding to different groups of sample users are used as training data to achieve accurate training of the model.

[0040] Furthermore, the above method further includes:

[0041] An exercise plan is recommended for the user to be detected according to the quantitative load exercise heart rate improvement effect level of the user to be detected or the quantitative load exercise heart rate improvement effect value after exercise.

[0042] The beneficial effect of adopting the above further scheme is that after determining the quantitative load exercise heart rate improvement effect of the user to be detected, an exercise plan can be recommended to the user to be detected based on the quantitative load exercise heart rate improvement effect of the user to be detected, thereby realizing the demand for personalized customized exercise plans.

[0043] In a second aspect, in order to solve the above technical problems, the present invention further provides a device for predicting the effect of HIIT on improving exercise heart rate based on the whole genome, the device comprising:

[0044] A data acquisition module is used to obtain basic information of the user to be tested, the initial exercise heart rate before exercise, and multiple target single nucleotide polymorphism sites;

[0045] a gene prediction score determination module, configured to determine a first gene prediction score of the user to be tested based on the plurality of target single nucleotide polymorphism sites, wherein the first gene prediction score represents an innate genetic characteristic of the user to be tested that is related to an improvement in exercise heart rate;

[0046] an effect level determination module, configured to determine, based on the first gene prediction score and a pre-trained first prediction model, a quantitative load exercise heart rate improvement effect level of the user to be tested, wherein the first prediction model is trained based on the gene prediction scores of different effect levels of quantitative load exercise heart rate improvement;

[0047] an improvement effect value determination module, configured to determine, based on the basic information, the initial exercise heart rate, and the first gene prediction score, a quantitative load exercise heart rate improvement effect value of the user to be detected after exercise using a pre-trained second prediction model, or to determine, based on the basic information and the initial exercise heart rate, a quantitative load exercise heart rate improvement effect value of the user to be detected after exercise using a pre-trained third prediction model;

[0048] Among them, the second prediction model is obtained by training based on the basic information, initial exercise heart rate, gene prediction score and quantitative load exercise heart rate improvement effect value after exercise of different users, and the third prediction model is obtained by training based on the basic information, initial exercise heart rate and quantitative load exercise heart rate improvement effect value after exercise of different users.

[0049] In the third aspect, 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 runnable on the processor. When the processor executes the computer program, the whole genome-based HIIT method for predicting the effect of improving exercise heart rate is implemented.

[0050] In a fourth aspect, in order to solve the above-mentioned technical problems, the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the whole genome-based HIIT method for predicting the effect of improving exercise heart rate is implemented.

[0051] Additional aspects and advantages of the present application will be given in part in the following description, which will become apparent from the following description, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments of the present invention.

[0053] Figure 1 A schematic flow chart of a genome-wide HIIT-based method for predicting the effect of improving exercise heart rate, provided in one embodiment of the present invention;

[0054] Figure 2 A schematic diagram of an association analysis result provided by one embodiment of the present invention;

[0055] Figure 3 A schematic diagram of PPS scores corresponding to sample users in different groups provided by one embodiment of the present invention;

[0056] Figure 4 A schematic diagram of PPS scores corresponding to different exercise heart rate differences provided by one embodiment of the present invention;

[0057] Figure 5 A schematic diagram of individual differences in the improvement effect of exercise heart rate after a quantitative load exercise provided by one embodiment of the present invention;

[0058] Figure 6 A schematic diagram of a genome-wide HIIT heart rate improvement effect prediction device provided by one embodiment of the present invention;

[0059] Figure 7 The present invention provides a schematic structural diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The principles and features of the present invention are described below. The examples given are only used to explain the present invention and are not used to limit the scope of the present invention.

[0061] The following describes in detail the technical solution of the present invention and how the technical solution of the present invention solves the above-mentioned technical problems using specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The following embodiments of the present invention are described in conjunction with the accompanying drawings.

[0062] The solution provided by the embodiments of the present invention can be applied to any application scenario requiring genome-wide prediction of the effects of HIIT on improving heart rate. The solution provided by the embodiments of the present invention can be executed by any electronic device, for example, a user's terminal device, including at least one of the following: a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smart watch, smart TV, or smart car device.

[0063] The embodiment of the present invention provides a possible implementation method, such as Figure 1 As shown, a flowchart of a method for predicting the effect of HIIT on improving exercise heart rate based on the whole genome is provided. The solution can be executed by any electronic device, for example, a terminal device, or by a terminal device and a server. For the convenience of description, the method provided by the embodiment of the present invention will be described below using the terminal device as the execution subject as an example. Figure 1 As shown in the flowchart, the method may include the following steps:

[0064] Step S110, obtaining basic information of the user to be tested, the initial exercise heart rate before exercise, and multiple target single nucleotide polymorphism sites;

[0065] Step S120, determining a first gene prediction score of the user to be tested based on the plurality of target single nucleotide polymorphism sites, wherein the first gene prediction score represents an innate genetic characteristic of the user to be tested that is related to an improvement in exercise heart rate;

[0066] Step S130, determining the quantitative load exercise heart rate improvement effect level of the user to be tested using a pre-trained first prediction model based on the first gene prediction score, wherein the first prediction model is trained based on the gene prediction scores of different quantitative load exercise heart rate improvement effect levels;

[0067] Step S140, determining the quantitative load exercise heart rate improvement effect value of the user to be tested after exercise using a pre-trained second prediction model based on the basic information, the initial exercise heart rate, and the first gene prediction score, or determining the quantitative load exercise heart rate improvement effect value of the user to be tested after exercise using a pre-trained third prediction model based on the basic information and the initial exercise heart rate;

[0068] Among them, the second prediction model is obtained by training based on the basic information, initial exercise heart rate, gene prediction score and quantitative load exercise heart rate improvement effect value after exercise of different users, and the third prediction model is obtained by training based on the basic information, initial exercise heart rate and quantitative load exercise heart rate improvement effect value after exercise of different users.

[0069] Through the method of the present invention, based on the first gene prediction score of the user to be tested, the quantitative load exercise heart rate improvement effect level of the user to be tested can be predicted through the pre-trained first prediction model, and according to the basic information, initial exercise heart rate and the first gene prediction score, the pre-trained second prediction model is used, or according to the basic information and the initial exercise heart rate, the pre-trained third prediction model is used to determine the quantitative load exercise heart rate improvement effect value of the user to be tested after exercise; the quantitative load exercise heart rate improvement effect level and the quantitative load exercise heart rate improvement effect value can reflect the improvement effect of the exercise heart rate of the user to be tested after performing quantitative load exercise, providing a basis for the formulation of a personalized exercise plan.

[0070] The present invention is further described below with reference to the following specific examples. In this example, a genome-wide HIIT-based method for predicting the effect of improving exercise heart rate may include the following steps:

[0071] Step S110 , obtaining basic information of the user to be detected, the initial exercise heart rate before exercise, and multiple target single nucleotide polymorphism sites.

[0072] The aforementioned basic information refers to the user's basic physical information and personal attributes, including weight, gender, etc., and can be obtained in advance from the user to be tested. The target single nucleotide polymorphism sites are different for each person. Optionally, each person's target single nucleotide polymorphism sites can be obtained through genotyping testing.

[0073] Step S120, determining a first gene prediction score of the user to be tested based on the plurality of target single nucleotide polymorphism sites, wherein the first gene prediction score represents the innate genetic characteristics of the user to be tested that are related to the exercise heart rate improvement effect, that is, the exercise heart rate improvement effect can be represented by the first gene prediction score;

[0074] Optionally, the above-mentioned determination of the first gene prediction score of the user to be tested based on the plurality of target single nucleotide polymorphism sites includes:

[0075] A first gene prediction score of the user to be detected is determined according to the multiple target single nucleotide polymorphism sites and the weight of each target single nucleotide polymorphism site.

[0076] The weight of each target single nucleotide polymorphism site can be pre-set. The above-mentioned determination of the first gene prediction score of the user to be tested based on the multiple target single nucleotide polymorphism sites and the weight of each target single nucleotide polymorphism site can be achieved by the following formula:

[0077] PPS=β1×n1+β2×n2…β i ×n i (2)

[0078] Where PPS represents the first gene prediction score, n1 to n i Represents multiple target single nucleotide polymorphism sites, β1 to β i represents the weight of each target single nucleotide polymorphism site. The above formula (2) can be called the PPS model.

[0079] Step S130, based on the first gene prediction score, determine the quantitative load exercise heart rate improvement effect of the user to be tested through a pre-trained first prediction model, wherein the first prediction model is trained based on the first gene prediction score with different improvement effects on the quantitative load exercise heart rate.

[0080] Optionally, the first prediction model is trained in the following manner:

[0081] Obtaining a first exercise heart rate before exercise, a second exercise heart rate after quantitative load exercise, and a plurality of single nucleotide polymorphism sites for each of a plurality of sample users;

[0082] For each of the sample users, determining an exercise heart rate difference according to the first exercise heart rate and the second exercise heart rate;

[0083] Determining, based on each of the exercise heart rate differences, an effect amount corresponding to each of the exercise heart rate differences, wherein the effect amount represents an improvement effect of the quantitative load exercise heart rate;

[0084] According to each of the effect amounts, each of the sample users is grouped to obtain multiple groups of sample users, each group of sample users corresponding to a quantitative load exercise heart rate improvement effect level;

[0085] For each of the sample users, determining a second gene prediction score of the sample user based on the multiple single nucleotide polymorphism sites;

[0086] The first initial model is trained according to the second gene prediction score of each sample user in each group of sample users to obtain the first prediction model.

[0087] In particular, multiple sample users may be pre-trained with quantitative load training. The magnitude of the quantitative load may also vary depending on the gender of the sample users. For example, the quantitative load for male sample users is 100W, and the rated load for female sample users is 60W. Before and after each sample user performs the quantitative load, a first exercise heart rate before exercise and a second exercise heart rate after exercise are obtained for each sample user. Optionally, for each sample user, the second exercise heart rate is determined by the following method:

[0088] For each of the sample users, after the sample user performs high-intensity interval training for a second set time period after the first set time period, the exercise heart rate of the sample user during the quantitative load exercise is obtained as the second exercise heart rate.

[0089] Each sample user may be allowed to perform high-intensity interval training (HIIT) for a first set duration (e.g., 12 weeks). The specific training courses for high-intensity interval training can be found in Table 1:

[0090] Table 1

[0091]

[0092]

[0093] The test time after the 12-week intervention is three days after the last exercise training (the second set duration), and the quantitative load exercise heart rate is tested using a power bicycle, a CORTEX gas metabolism meter and a Polar heart rate belt. The sample users exercise with a preset load power, for example, pedaling for 5 minutes. The quantitative load of male sample users is 100W, and the rated load of female sample users is 60W, and the pedaling frequency is controlled at 60 rpm by a metronome. When collecting data, the stable exercise heart rate in the last three minutes of the quantitative load test is selected as the second exercise heart rate, or the average of multiple exercise heart rates obtained in the last three minutes is taken as the second exercise heart rate.

[0094] Optionally, the multiple single nucleotide polymorphism sites corresponding to each of the multiple sample users are obtained in the following manner:

[0095] Determine the significance level of each gene locus of the sample user and the physical location of each gene locus by genome-wide association analysis based on the exercise heart rate difference corresponding to the sample user;

[0096] Screening candidate gene loci from each of the gene loci according to the significance level and significance level threshold of each of the gene loci;

[0097] According to the physical position of each candidate gene site, target gene sites are screened out from each candidate gene site as the multiple single nucleotide polymorphism sites.

[0098] The second exercise heart rate may be the expected exercise heart rate of the sample user after a quantitative load exercise. The above exercise heart rate difference may be determined by the following formula:

[0099] ΔHR=HR after intervention-HR before intervention (1)

[0100] Wherein, ΔHR represents the difference in exercise heart rate, HR after intervention represents the second exercise heart rate, and HR before intervention represents the first exercise heart rate.

[0101] The difference in exercise heart rate can reflect the improvement effect of quantitative load exercise heart rate. The specific implementation process of determining the significance level of each gene locus of the sample user and the physical location of each gene locus through the whole genome association analysis method based on the exercise heart rate difference is as follows: Based on the exercise heart rate difference, the association analysis is performed through the whole genome association analysis method. The association analysis results can be found in Figure 2 , Figure 2 The significance level of each gene locus in Figure 2 The horizontal axis represents the chromosome number where each gene locus is located, and the vertical axis represents the significance level. The gene loci above the straight line parallel to the horizontal axis represent higher significance levels, and the gene loci below the straight line represent lower significance levels. First, based on the exercise heart rate difference, the association analysis was performed using the whole genome association analysis method to obtain the expansion coefficient λ of the GWAS association results. Based on the expansion coefficient λ, the bias of the GWAS association results and whether it was affected by population stratification were evaluated. Then, the GWAS association results were represented using the Manhattan plot drawn by R studio. Figure 2 A Manhattan plot can be drawn using R studio.

[0102] Optionally, the significance level threshold of the candidate gene loci included in the gene prediction score can be set based on actual needs. In this application, it can be 1×10 -5The above method selects candidate gene loci from each gene locus based on the significance level and significance level threshold of each gene locus. Specifically, the method selects gene loci within the 10KB region of the gene loci whose significance level is less than the significance level threshold in each gene locus as candidate gene loci. Figure 2 The above-mentioned candidate gene loci can also be called Lead SNPs. Optionally, 12 SNPs can be selected as candidate gene loci, namely rs9399743, rs1362357, rs10868262, rs6500617, rs4443189, rs3923313, rs2278556, rs74851348, rs4397048, rs12354689, rs1796113 and rs58416265.

[0103] Then, based on the physical position of each of the candidate gene sites, target gene sites are screened from each of the candidate gene sites as multiple single nucleotide polymorphism sites, wherein, because the physical positions of two gene sites are very close, it indicates that the two gene sites may have a linkage association, and redundant gene sites can be removed based on this principle to obtain multiple single nucleotide polymorphism sites. In the present application, 2 redundant SNPs are removed from 12 SNPs to obtain 10 gene sites as multiple single nucleotide polymorphism sites, which can also be described as 10 SNPs. The 10 SNPs can be rs9399743, rs10868262, rs6500617, rs4443189, rs3923313, rs2278556, rs4397048, rs12354689, rs1796113 and rs58416265.

[0104] Optionally, the above-mentioned determination of the effect size (Cohen's d effect size, ES) corresponding to each exercise heart rate difference value based on each exercise heart rate difference value can be implemented based on existing technologies and will not be repeated here. Then, based on each effect size, each sample user is grouped to obtain multiple groups of sample users, each group of sample users corresponding to a level of exercise-induced cardiopulmonary endurance improvement effect; Optionally, based on each effect size, each sample user is grouped to obtain multiple groups of sample users, including:

[0105] According to each of the effect amounts and the preset effect amount grouping range, each of the sample users is grouped to obtain multiple groups of sample users, and the effect amount grouping range includes a first range, a second range, a third range, a fourth range and a fifth range in which the quantitative load exercise heart rate improvement effect decreases in sequence.

[0106] As an example, according to each of the effect amounts, the sample users are divided into 5 groups, and the corresponding effect amounts of each group are: ES<-0.8 (first range), -0.8≤ES<-0.5 (second range), -0.5≤ES<-0.2 (third range), -0.2≤ES<0 (fourth range) and ES≥0 (fifth range). The larger the effect amount, the worse the improvement effect of the quantitative load exercise heart rate, that is, the quantitative load exercise heart rate improvement effect corresponding to the first range is the best, and the higher the level, the worse the quantitative load exercise heart rate improvement effect corresponding to the fifth range, and the lower the level.

[0107] As an example, for multiple sample users, the second gene prediction score (PPS score) of the sample users is determined based on the multiple single nucleotide polymorphism sites, such as Figure 3 and Figure 4 As shown, the PPS score can be used to effectively distinguish different ES effect groups. When the PPS score is higher than 11.45 points (95% CI: 8.719-14.190, CI: 8.719-14.190 represents the confidence interval in statistics, which represents the 95% confidence interval here), HIIT is completely ineffective in reducing exercise heart rate, and the corresponding group is: ES≥0; when the PPS score is lower than -0.527 points (95% CI: -3.808-2.755), the exercise heart rate improvement effect is better, and the corresponding group is ES4: 0.8≤ES<-0.5; when the PPS score is lower than -7.728 points (95% CI: -11.520--3.938), HIIT has the best effect on improving exercise heart rate, and the corresponding group is: ES5: ES<-0.8. Figure 3 In the figure, the horizontal axis represents the ES1, ES2, ES3, ES4, ES5 and ES6 groups, respectively, representing ES ≥ 0, -0.2 ≤ ES < 0, -0.5 ≤ ES < -0.2, -0.8 ≤ ES < -0.5 and ES < -0.8, and the vertical axis represents the PPS score. Figure 4 In the figure, the horizontal axis represents the difference in heart rate during different exercises, and the vertical axis represents the PPS score. Figure 3 It can be seen that the PPS scores of sample users in different groups are different. Figure 4 It can be seen that different exercise heart rate differences correspond to different PPS scores. Therefore, the effect of quantitative load exercise heart rate improvement can be characterized by different exercise heart rate differences, and the effect of quantitative load exercise heart rate improvement can also be characterized by PPS scores. Different sample users will have individual differences in the effect of quantitative load exercise heart rate improvement after exercise, with some having better results and some not having better results.

[0108] In addition, due to individual differences, some people's quantitative load exercise heart rate does not improve after exercise intervention, while others' quantitative load exercise heart rate improves. As an example, see Figure 5As shown in the figure, after 12 weeks of HIIT intervention, one user's heart rate under a fixed load significantly decreased from 144 ± 16 beats / min to 137 ± 16 beats / min, corresponding to an ES of -0.74. Experimental verification showed that 27% of users did not improve their heart rate under a fixed load (ES ≥ 0). Figure 5 The horizontal axis represents each subject, and the vertical axis represents the exercise heart rate difference (exercise heart rate change). Figure 5 It can be seen that after 12 weeks of HIIT exercise intervention, the improvement effect of quantitative load exercise heart rate has individual differences.

[0109] It should be noted that, for each of the sample users, the second gene prediction score of the sample user can be determined based on the multiple single nucleotide polymorphism sites in the same manner as the first gene prediction score of the user to be tested based on the multiple target single nucleotide polymorphism sites described above, and will not be repeated here.

[0110] It should be noted that the first initial model is trained according to the second gene prediction score of each sample user in each group of sample users, and the first prediction model is obtained by training the first initial model according to the second gene prediction score of each sample user to obtain the predicted quantitative load exercise heart rate improvement effect level corresponding to each sample user; based on the actual quantitative load exercise heart rate improvement effect level and the predicted quantitative load exercise heart rate improvement effect level when grouping, the loss value of the first initial model can be determined. When the loss value meets the preset training end condition, the first initial model that meets the training end condition is used as the first prediction model. When the loss value does not meet the preset training end condition, the model parameters of the first initial model are adjusted, and the first initial model is re-trained based on the adjusted model parameters until the loss value meets the training end condition.

[0111] In addition, the first initial model can also be trained based on the second gene prediction scores corresponding to sample users with different quantitative load exercise heart rate improvement effects to obtain the predicted quantitative load exercise heart rate improvement effect corresponding to each sample user. Based on each true quantitative load exercise heart rate improvement effect and each predicted quantitative load exercise heart rate improvement effect, the loss value of the first initial model can be determined. That is, the input of the first initial model is the second gene prediction score corresponding to each sample user, and the output is the predicted quantitative load exercise heart rate improvement effect corresponding to each sample user.

[0112] Step S140, determining the quantitative load exercise heart rate improvement effect value of the user to be tested after exercise using a pre-trained second prediction model based on the basic information, the initial exercise heart rate, and the first gene prediction score, or determining the quantitative load exercise heart rate improvement effect value of the user to be tested after exercise using a pre-trained third prediction model based on the basic information and the initial exercise heart rate;

[0113] The second prediction model is obtained by training based on the basic information, initial exercise heart rate, gene prediction score and quantitative load exercise heart rate improvement effect value after exercise of different users, and the third prediction model is obtained by training based on the basic information, initial exercise heart rate and quantitative load exercise heart rate improvement effect value after exercise of different users.

[0114] Optionally, the second prediction model is trained in the following manner:

[0115] Obtaining basic information of each of the sample users;

[0116] The second initial model is trained according to the basic information, exercise heart rate difference, second gene prediction score and first exercise heart rate of each of the sample users to obtain the second prediction model.

[0117] Among them, for each sample user, the exercise heart rate difference represents the actual quantitative load exercise heart rate improvement effect value of the sample user, and the exercise heart rate difference can be determined by the second formula below. Similarly, the above-mentioned second prediction model can be expressed by the second formula. Then, based on the basic information, the initial exercise heart rate and the first gene prediction score, the above-mentioned quantitative load exercise heart rate improvement effect value of the user to be tested after exercise is determined by the pre-trained second prediction model, specifically:

[0118] Based on the basic information, the initial exercise heart rate, and the first gene prediction score, a second formula corresponding to a pre-trained second prediction model is used to determine the quantitative load exercise heart rate improvement effect value of the user to be tested after exercise, wherein the second formula is:

[0119] Quantitative load exercise heart rate improvement effect value = 18.412 + 0.540 × PPS - 0.363 × initial exercise heart rate + 8.113 × gender + 1.074 × age

[0120] Here, 18.412 represents the intercept (constant) of the regression model, PPS represents the first gene prediction score, 0.540 represents the weight of PPS, 0.363 represents the weight of initial exercise heart rate, 8.113 represents the weight of gender, and 1.074 represents the weight of age. For males, the gender term in the formula is 0, and for females, the gender term is 1.

[0121] Based on the second prediction model, the third prediction model can be trained in the following way:

[0122] The third initial model is trained according to the basic information, the exercise heart rate difference and the first exercise heart rate of each of the sample users to obtain the third prediction model.

[0123] The difference between the second and third prediction models is that the input data of the models is different. The input data of the second prediction model includes gene prediction scores, while the input data of the third prediction model does not include gene prediction scores. The trained third prediction model can be expressed by the third formula:

[0124] Quantitative load exercise heart rate improvement effect value = 13.086-0.359×initial exercise heart rate+8.654×gender+1.366×age

[0125] Here, 13.086 represents the intercept (constant) of the regression model, 0.359 is the weight for initial exercise heart rate, 8.654 is the weight for gender, and 1.366 is the weight for age. For males, the gender term in the formula is 0; for females, the gender term in the formula is 1.

[0126] The third and second formulas are both used to predict the improvement effect of quantitative load exercise heart rate after exercise, but after experimental verification, the prediction accuracy of the second formula is higher than that of the third formula. 2 The R of the second formula is only 0.245. 2 Reaching 0.625, R 2 is the model explanation in linear regression, R 2 The larger the value, the higher the prediction accuracy.

[0127] Optionally, the method further includes:

[0128] An exercise plan is recommended for the user to be detected according to the quantitative load exercise heart rate improvement effect level of the user to be detected or the quantitative load exercise heart rate improvement effect value after exercise.

[0129] The solution of this application has the following beneficial effects compared to the prior art:

[0130] (1) This program provides the first prediction model for the effect of 12 weeks of HIIT on improving exercise heart rate. It can also predict the effect of HIIT on improving exercise heart rate based on genetic markers before the start of exercise, and formulate a suitable exercise program for the population. When the PPS score is higher than 11.45 points, HIIT is completely ineffective in reducing exercise heart rate. This group of people is not suitable for 12 weeks of HIIT to improve exercise heart rate, and other exercise programs can be selected. When the PPS score is lower than -7.728 points, HIIT is most effective in improving exercise heart rate, and HIIT exercise training is recommended.

[0131] (2) This model is a predictor of the effectiveness of HIIT in improving exercise heart rate before the start of exercise intervention, and the model is stable. After stepwise regression, the included variable p < 0.01 and the excluded variable p > 0.05, indicating good model stability. Because genetic molecular markers are included, the genetic markers are stable and not easily changed, and have long-term application value.

[0132] Based on Figure 1 The present invention also provides a genome-wide HIIT-based prediction device 20 for improving exercise heart rate. Figure 6 As shown in , the whole genome-based HIIT improvement exercise heart rate effect prediction device 20 may include a data acquisition module 210, a gene prediction score determination module 220 and an effect level determination module 230, wherein:

[0133] The data acquisition module 210 is used to acquire multiple target single nucleotide polymorphism sites of the user to be detected;

[0134] A gene prediction score determination module 220 is configured to determine a first gene prediction score of the user to be tested based on the plurality of target single nucleotide polymorphism sites, wherein the first gene prediction score represents an innate genetic characteristic of the user to be tested that is related to an improvement in exercise heart rate;

[0135] an effect level determination module 230 for determining, based on the first gene prediction score and a pre-trained first prediction model, a quantitative load exercise heart rate improvement effect level of the user to be tested, or, based on the basic information and the initial exercise heart rate and a pre-trained third prediction model, determining a quantitative load exercise heart rate improvement effect value of the user to be tested after exercise;

[0136] Among them, the first prediction model is obtained by training based on the gene prediction score of different levels of quantitative load exercise heart rate improvement effect, and the third prediction model is obtained by training based on the basic information, initial exercise heart rate and quantitative load exercise heart rate improvement effect value of different users after exercise.

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

[0138] A first gene prediction score of the user to be detected is determined according to the plurality of target single nucleotide polymorphism sites and the weight of each target single nucleotide polymorphism site.

[0139] Optionally, the first prediction model is obtained by training through the following first training module:

[0140] The first training module is used to obtain a first exercise heart rate before exercise, a second exercise heart rate after quantitative load exercise, and multiple single nucleotide polymorphism sites for each of a plurality of sample users; for each of the sample users, determining an exercise heart rate difference based on the first exercise heart rate and the second exercise heart rate; determining an effect amount corresponding to each exercise heart rate difference based on each of the exercise heart rate differences, wherein the effect amount represents an improvement effect of the quantitative load exercise heart rate; grouping the sample users based on each of the effect amounts to obtain multiple groups of sample users, each group of sample users corresponding to a quantitative load exercise heart rate improvement effect level; for each of the sample users, determining a second gene prediction score for the sample user based on the multiple single nucleotide polymorphism sites; training the first initial model based on the second gene prediction score of each of the sample users in each group of the sample users to obtain the first prediction model;

[0141] The second prediction model is obtained by training the following second training module:

[0142] A second training module is configured to obtain basic information of each of the plurality of sample users; and train a second initial model based on the basic information of each of the sample users, the exercise heart rate difference, the second gene prediction score, and the first exercise heart rate to obtain the second prediction model;

[0143] The third prediction model is obtained by training the following third training module:

[0144] The third training module is used to train the third initial model according to the basic information, exercise heart rate difference and first exercise heart rate of each of the sample users to obtain the third prediction model.

[0145] Optionally, the multiple single nucleotide polymorphism sites corresponding to each of the multiple sample users are obtained in the following manner:

[0146] Determine the significance level of each gene locus of the sample user and the physical location of each gene locus by genome-wide association analysis based on the exercise heart rate difference corresponding to the sample user;

[0147] Screening candidate gene loci from each of the gene loci according to the significance level and significance level threshold of each of the gene loci;

[0148] According to the physical position of each candidate gene site, target gene sites are screened out from each candidate gene site as the multiple single nucleotide polymorphism sites.

[0149] Optionally, for each sample user, the second exercise heart rate is determined by:

[0150] For each of the sample users, after the sample user performs high-intensity interval training for a second set period of time after the first set period of time, the exercise heart rate of the sample user during the quantitative load exercise is obtained as the second exercise heart rate.

[0151] Optionally, when the training module groups the sample users according to the effect amounts to obtain multiple groups of sample users, it is specifically configured to:

[0152] According to each of the effect amounts and the preset effect amount grouping range, each of the sample users is grouped to obtain multiple groups of sample users, and the effect amount grouping range includes a first range, a second range, a third range, a fourth range and a fifth range in which the quantitative load exercise heart rate improvement effect decreases in sequence.

[0153] Optionally, the device further includes:

[0154] The exercise program recommendation module is used to recommend an exercise program for the user to be detected based on the quantitative load exercise heart rate improvement effect level of the user to be detected or the quantitative load exercise heart rate improvement effect value after exercise.

[0155] The whole genome-based HIIT exercise heart rate improvement effect prediction device of the embodiment of the present invention can execute the whole genome-based HIIT exercise heart rate improvement effect prediction method provided by the embodiment of the present invention. The implementation principle is similar. The actions performed by each module and unit in the whole genome-based HIIT exercise heart rate improvement effect prediction device in each embodiment of the present invention correspond to the steps in the whole genome-based HIIT exercise heart rate improvement effect prediction method in each embodiment of the present invention. For the detailed functional description of each module of the whole genome-based HIIT exercise heart rate improvement effect prediction device, please refer to the description of the corresponding whole genome-based HIIT exercise heart rate improvement effect prediction method shown in the previous text, which will not be repeated here.

[0156] Among them, the above-mentioned whole genome-based HIIT-based exercise heart rate improvement effect prediction device can be a computer program (including program code) running in a computer device. For example, the whole genome-based HIIT-based exercise heart rate improvement effect prediction device is an application software; the device can be used to execute the corresponding steps in the method provided in the embodiment of the present invention.

[0157] In some embodiments, the whole genome-based HIIT exercise heart rate improvement effect prediction device provided by the embodiment of the present invention can be implemented in a combination of software and hardware. As an example, the whole genome-based HIIT exercise heart rate improvement effect prediction device provided by the embodiment of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the whole genome-based HIIT exercise heart rate improvement effect prediction method provided by the embodiment of the present invention. For example, the processor in the form of a hardware decoding processor can adopt 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.

[0158] In other embodiments, the genome-wide HIIT-based prediction device for improving exercise heart rate provided by the embodiments of the present invention can be implemented in software. Figure 6 A whole-genome-based HIIT-improving exercise heart rate effect prediction device stored in a memory is shown. The device can be software in the form of a program and a plug-in, and includes a series of modules, including a data acquisition module 210, a gene prediction score determination module 220, and an effect level determination module 230, for implementing the whole-genome-based HIIT-improving exercise heart rate effect prediction method provided in an embodiment of the present invention.

[0159] The modules involved in the embodiments of the present invention may be implemented in software or hardware, wherein the name of a module does not necessarily limit the module itself.

[0160] Based on the same principle as the method shown in the embodiments of the present invention, an electronic device is also provided in the embodiments of the present invention, which may include but is not limited to: a processor and a memory; the memory is used to store computer programs; the processor is used to execute the method shown in any embodiment of the present invention by calling the computer program.

[0161] In an alternative embodiment, an electronic device is provided, such as Figure 7 As shown, Figure 7 The electronic device 4000 shown 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 exchange between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, the number of transceivers 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0162] 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 may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the present disclosure. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.

[0163] Bus 4002 may include a path for transmitting information between the above components. Bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. Bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0164] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0165] The memory 4003 is used to store application code (computer program) for executing the solution of the present invention, and is controlled by the processor 4001. The processor 4001 is used to execute the application code stored in the memory 4003 to implement the content shown in the above method embodiment.

[0166] Among them, the electronic device can also be a terminal device, Figure 7 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0167] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer-readable storage medium is run on a computer, the computer can execute the corresponding contents of the aforementioned method embodiment.

[0168] 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 HIIT-based prediction method for improving exercise heart rate provided in the various implementations described above.

[0169] Computer program code for performing the operations of the present invention may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on the remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0170] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and computer program products according to various embodiments of the present invention. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0171] The computer-readable storage medium provided by the embodiments of the present invention may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or component, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present invention, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or device.

[0172] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device executes the method shown in the above embodiment.

[0173] The above description is merely a preferred embodiment of the present invention and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also includes other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present invention.

Claims

1. A genome-wide method for predicting the effect of HIIT on improving exercise heart rate, characterized in that: The following steps are involved: Obtaining basic information of the user to be tested, initial exercise heart rate before exercise, and multiple target single nucleotide polymorphism sites; Determining a first gene prediction score of the user to be tested based on the plurality of target single nucleotide polymorphism sites, wherein the first gene prediction score represents an innate genetic characteristic of the user to be tested that is related to an improvement in exercise heart rate; Determining, based on the first gene prediction score, a level of improvement in the heart rate of the user to be tested using a pre-trained first prediction model, wherein the first prediction model is trained based on gene prediction scores of different levels of improvement in the heart rate of the user to be tested; Determining the quantitative load exercise heart rate improvement effect value of the user to be detected after exercise by a pre-trained second prediction model based on the basic information, the initial exercise heart rate, and the first gene prediction score, or determining the quantitative load exercise heart rate improvement effect value of the user to be detected after exercise by a pre-trained third prediction model based on the basic information and the initial exercise heart rate; Among them, the second prediction model is obtained by training based on the basic information, initial exercise heart rate, gene prediction score and quantitative load exercise heart rate improvement effect value after exercise of different users, and the third prediction model is obtained by training based on the basic information, initial exercise heart rate and quantitative load exercise heart rate improvement effect value after exercise of different users.

2. The method according to claim 1, characterized in that Determining the first gene prediction score of the user to be tested based on the plurality of target single nucleotide polymorphism sites includes: A first gene prediction score of the user to be detected is determined according to the 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 is trained in the following way: Obtaining a first exercise heart rate before exercise, a second exercise heart rate after quantitative load exercise, and a plurality of single nucleotide polymorphism sites for each of a plurality of sample users; For each of the sample users, determining an exercise heart rate difference according to the first exercise heart rate and the second exercise heart rate; Determining, based on each of the exercise heart rate differences, an effect amount corresponding to each of the exercise heart rate differences, wherein the effect amount represents an improvement effect of the quantitative load exercise heart rate; According to each of the effect amounts, each of the sample users is grouped to obtain multiple groups of sample users, each group of sample users corresponding to a quantitative load exercise heart rate improvement effect level; For each of the sample users, determining a second gene prediction score of the sample user based on the multiple single nucleotide polymorphism sites; Training the first initial model according to the second gene prediction score of each sample user in each group of the sample users to obtain the first prediction model; The second prediction model is trained in the following way: Obtaining basic information of each of the sample users; Training the second initial model according to the basic information, exercise heart rate difference, second gene prediction score and first exercise heart rate of each of the sample users to obtain the second prediction model; The third prediction model is trained in the following way: The third initial model is trained according to the basic information, the exercise heart rate difference and the 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 are obtained in the following manner: Determine the significance level of each gene locus of the sample user and the physical location of each gene locus by genome-wide association analysis based on the exercise heart rate difference corresponding to the sample user; Screening candidate gene loci from each of the gene loci according to the significance level and significance level threshold of each of the gene loci; According to the physical position of each candidate gene site, target gene sites are screened out from each candidate gene site as the 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 is determined by: For each of the sample users, after the sample user performs high-intensity interval training for a second set time period after the first set time period, the exercise heart rate of the sample user during the quantitative load exercise is obtained as the second exercise heart rate.

6. The method according to claim 3, characterized in that The sample users are grouped according to the effect amounts to obtain multiple groups of sample users, including: According to each of the effect amounts and the preset effect amount grouping range, each of the sample users is grouped to obtain multiple groups of sample users, and the effect amount grouping range includes a first range, a second range, a third range, a fourth range and a fifth range in which the quantitative load exercise heart rate improvement effect decreases in sequence.

7. The method according to any one of claims 1 to 6, characterized in that The method further comprises: An exercise plan is recommended for the user to be detected according to the quantitative load exercise heart rate improvement effect level of the user to be detected or the quantitative load exercise heart rate improvement effect value after exercise.

8. A genome-wide HIIT-based prediction device for improving exercise heart rate, characterized in that: include: A data acquisition module is used to obtain basic information of the user to be tested, the initial exercise heart rate before exercise, and multiple target single nucleotide polymorphism sites; a gene prediction score determination module, configured to determine a first gene prediction score of the user to be tested based on the plurality of target single nucleotide polymorphism sites, wherein the first gene prediction score represents an innate genetic characteristic of the user to be tested that is related to an improvement in exercise heart rate; an effect level determination module, configured to determine, based on the first gene prediction score and a pre-trained first prediction model, a quantitative load exercise heart rate improvement effect level of the user to be tested, wherein the first prediction model is trained based on the gene prediction scores of different effect levels of quantitative load exercise heart rate improvement; an improvement effect value determination module, configured to determine, based on the basic information, the initial exercise heart rate, and the first gene prediction score, a quantitative load exercise heart rate improvement effect value of the user to be detected after exercise using a pre-trained second prediction model, or to determine, based on the basic information and the initial exercise heart rate, a quantitative load exercise heart rate improvement effect value of the user to be detected after exercise using a pre-trained third prediction model; Among them, the second prediction model is obtained by training based on the basic information, initial exercise heart rate, gene prediction score and quantitative load exercise heart rate improvement effect value after exercise of different users, and the third prediction model is obtained by training based on the basic information, initial exercise heart rate and quantitative load exercise heart rate improvement effect value after exercise of different users.

9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method according to any one of claims 1 to 7 when executing the computer program.

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

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