Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the...

[Full title: Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement]

Citation: Ho, I.M.K.; Weldon, A.; Yong, J.T.H.; Lam, C.T.T.; Sampaio, J Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement Int. J. Environ Res. Public Health 2023 , 20 , 5881 https://doi.org/10.3390/ ijerph 20105881 Academic Editors: Athos Trecroci, Alessio Rossi, Luca Cavaggioni, Fernando Manuel Lourenço Martins, M á rio Ant ó nio Cardoso Marques and Paul B. Tchounwou Received: 29 December 2022 Revised: 13 March 2023 Accepted: 17 May 2023 Published: 19 May 2023 Copyright: © 2023 by the authors Licensee MDPI, Basel, Switzerland This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/) International Journal of Environmental Research and Public Health Article Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement Indy Man Kit Ho 1,2, * , Anthony Weldon 3 , Jason Tze Ho Yong 1 , Candy Tze Tim Lam 1 and Jaime Sampaio 4 1 Department of Sports and Recreation, Technological and Higher Education Institute of Hong Kong (THEi), Chai Wan, Hong Kong, China 2 The Asian Academy for Sports and Fitness Professionals, Chai Wan, Hongkong, China 3 Centre for Life and Sport Sciences, Birmingham City University, Birmingham B 15 3 TN, UK 4 Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, CreativeLab Research Community, 5000-801 Vila Real, Portugal * Correspondence: indymankit@hotmail.com Abstract: To solve the research–practice gap and take one step forward toward using big data with real-world evidence, the present study aims to adopt a novel method using machine learning to pool findings from meta-analyses and predict the change of countermovement jump. The data were collected through a total of 124 individual studies included in 16 recent meta-analyses. The performance of four selected machine learning algorithms including support vector machine, random forest (RF) ensemble, light gradient boosted machine, and the neural network using multi-layer perceptron was compared. The RF yielded the highest accuracy (mean absolute error: 0.071 cm; R 2 : 0.985). Based on the feature importance calculated by the RF regressor, the baseline CMJ (“Pre- CMJ”) was the most impactful predictor, followed by age (“Age”), the total number of training sessions received (“Total number of training_session”), controlled or non-controlled conditions (“Control (no training)”), whether the training program included squat, lunge, deadlift, or hip thrust exercises (“Squat_Lunge_Deadlift_Hipthrust_True”, “Squat_Lunge_Deadlift_Hipthrust_False”), or “Plyometric (mixed fast/slow SSC)”, and whether the athlete was from an Asian pacific region including Australia (“Race_Asian or Australian”). By using multiple simulated virtual cases, the successful predictions of the CMJ improvement are shown, whereas the perceived benefits and limitations of using machine learning in a meta-analysis are discussed Keywords: artificial intelligence; sports science; sports medicine; research–practice gap; sports performance; strength and conditioning 1. Introduction Practitioners in sports science and sports medicine are encouraged to adopt evidenceinformed practices, assuring safe and effective training and treatment methods. However, it is common to find research–practice gaps in these disciplines [ 1 – 3 ]. Reade et al. reported that 25% of Canadian high-performance coaches did not find the literature fit for their sport-specific needs, whereas 35% of coaches agreed that the research was irrelevant to their daily professional problems [ 4 ]. Meanwhile, over 50% of these coaches considered the research findings to be difficult to use. According to Keegan et al., practitioners in various disciplines encounter unstructured, messy, complex, and uncontrolled situations in the real world, and hence, they are required to frequently solve problems with poorly defined variables [ 2 ]. In general, research is often conducted under very confined conditions, which may be considered less relevant to real-world practice. In sports science, many studies were conducted in lab-based settings in highly controlled conditions, compared to strength and conditioning practices in the field with high dynamics, complexity, and interactions Int. J. Environ. Res. Public Health 2023 , 20 , 5881. https://doi.org/10.3390/ijerph 20105881 https://www.mdpi.com/journal/ijerph

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 2 of 15 Therefore, it is questionable whether the findings from the published literature are useful and applicable to real-life situations To bridge the research–practice gap, researchers from different disciplines proposed new and innovative solutions [ 1 , 5 , 6 ]. For example, Sandbakk et al. suggested that journals should publish more case studies from practitioners so that findings can be more comprehensible by coaches [ 1 ]. Meanwhile, Blonde et al. and Kim et al. proposed using real-world evidence to reflect actual clinical scenarios and practice to supplement traditional randomized controlled trials (RCT) and provide a more realistic and complete evidence-informed picture [ 5 , 6 ]. However, before getting real-world evidence-based studies to become more commonplace, various privacy and legal issues in different countries need to be overcome first. Moreover, such studies using unstructured real-world data with high complexity require extremely large datasets. Practitioners are required to adopt administrative workflows that accommodate the collection and analysis of big data [ 7 ]. Currently, most studies rely on small sample sizes, which are often not statistically powerful enough to draw definitive conclusions for making decisions. Therefore, metaanalysis procedures that pool data from multiple studies may be regarded as a potentially useful research method in sports science and medicine [ 8 , 9 ]. In addition to conventional pair-wise meta-analysis, network meta-analysis was proposed and adopted to further allow the comparison of the effects of different interventions to handle more complex real-world conditions [ 10 ]. Although these sophisticated research methods may allow researchers to produce more meaningful results, practitioners may not be able to use those findings to replicate or simulate the results on their athletes. One issue that practitioners may raise is the between-subjects heterogeneity and individual responses within studies [ 11 ]. This may be the case as data are commonly pooled and aggregated, only presenting mean values for a given population. Therefore, it is highly questionable whether the findings from the literature can accurately reflect and predict the potential effects or results in the real world using similar or mixed modalities on their athletes or patients with different backgrounds and conditions In this regard, the use of artificial intelligence techniques in performing predictive analytics can be a novel yet practical way to pool data from complicated datasets with multi-dimensional variables in producing algorithms for providing simulated results for real-world but unseen conditions. Nowadays, machine learning (ML), as one of the artificial intelligence (AI) methods, is widely used in various disciplines and industries for both predictive and prescriptive analytics in solving real-world problems on different scales, and therefore, it has also recently gained an increased importance in sports science domains. Several machine learning algorithms, such as random forest (RF) ensembles, support vector machine (SVM), gradient boosting model (GBM), and multi-layer perceptron regression (MLPR), used for neural networking in deep learning, can take advantage of pooling both categorical and numerical variables with a linear or non-linear relationship to produce models for predictive purposes. Although complicated machine learning techniques such as neural networks are usually regarded as “black boxes” and difficult to understand the mechanisms, the relevant importance or impact of each variable in the process of model generation is detectable in many machine learning algorithms [ 12 – 15 ]. Ideally, performing ML using big data from real cases to build predictive models can represent a broader population and reflect the true effect of mixed and complex interventions. However, this approach requires extremely expensive platforms and time-consuming primary data collection processes. Moreover, the quality assurance and monitoring of proper data entry, and the cleaning and processing of data from strength and conditioning practitioners in different places are difficult, whereas the meaningfulness and quality of the data greatly impact the results and ML performance. Therefore, predictive model development using pre-existing datasets (i.e., large database studies and secondary analyses) can also be a feasible approach [ 16 ]. Nevertheless, there is no single golden rule to assess the quality of the data from individual studies, whereas the meta-analysis is regarded as high-level evidence. Consequently, it is interesting and seems worth investigating whether

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 3 of 15 the use of machine learning models, as powerful AI tools, can pool data from original individual studies used in meta-analyses to make accurate predictions on a target variable To the best of our knowledge, this is the first study to adopt such a novel research idea, and therefore, we only chose the studies included from meta-analyses as the pilot. With the use of machine learning in the meta-analysis (MLMA) to predict the target variable, the results may be more definitive, practical, and understandable for practitioners to apply. Since biased conclusions from inaccurate findings in medical research may lead to serious injuries or lethal risks, instead of predicting the treatment effects for medical conditions, using performance-related variables to explore the strengths and limitations of this novel research idea can be safer and more feasible. For example, the countermovement jump has always been a key test for assessing power performance and monitoring neuromuscular status, and therefore, is extensively studied [ 17 – 20 ]. Although traditional meta-analyses have provided certain insights regarding the training effects and potential benefits, the findings and experimental procedures of the included studies were not always consistent The mixed results or conflicting findings were not always fully explained, as the discrepancies observed in those studies may originate from general (population type, intervention duration, and intensity of the activity performed) and/or specific factors (e.g., jump height, peak power, relative peak power, relative power, mean power, peak velocity, peak force, mean force, rate of force development, eccentric time/concentric time, flight time/eccentric time, and flight time/contraction time using an unloaded and/or loaded CMJ) [ 20 ]. Therefore, the purpose of this study is to examine the accuracy of selected machine learning models in predicting the performance change of the countermovement jump (CMJ) from predictors available in interventional studies included in the selected meta-analyses. By pooling the data from a large amount of the literature included in the meta-analyses with diversified subjects, training backgrounds, programs, and modalities, it was expected to be able to generate accurate predictive models using machine learning techniques. After selecting the most appropriate ML algorithm, feature importance was analyzed to further understand the most relevant and impactful variables affecting CMJ performance. Lastly, by fitting several simulated case data into the model to make some real predictions, the current work can serve as predictive analytics to extrapolate the results to many unknown conditions using artificial intelligence. Finally, the characteristics of the predicted outcomes and potential limitations of this new research idea are discussed 2. Methods 2.1. Source of Data To include sufficiently large datasets, a thorough search of the literature was performed by one of the authors (JTHY) via Sport Discus, PubMed, and Google Scholar from 2007 to February 2020, with the following Boolean operators: (“Meta-analysis” OR “Meta-analyses” OR “Meta-Analysis” OR “Meta-Analyses” AND “Countermovement jump” OR “Counter Movement Jump” OR “Counter-movement Jump” AND published after Jan 2000)”. A total of 25 meta-analysis-related studies were initially identified. After the exclusion of nontraining-related information in the title and abstract screening, 16 meta-analyses assessing changes in CMJ performance after a period of a systematic training program were found to fulfill the initial criteria [ 17 – 32 ]. Among these 16 selected meta-analyses, 15 followed a PRISMA protocol (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) or performed a quality check using the Physiotherapy Evidence Database (PEDro) scale, Consolidated Standard of Reporting Trials (CONSORT), the examination of funnel plots, recommendations from International Society of Musculoskeletal and Neuronal Interactions (ISMNI) or guidelines specific for lower back pain, whereas only 1 meta-analysis did not report relevant items. After that, individual studies from selected meta-analyses were included in this study if they fulfilled the following selection criteria: (1) participants undertook an organized training program; (2) CMJ height was measured preand post-test as a dependent variable; (3) gender of subjects were specified and were not mixed-gender subject groups; (4) subjects involved were healthy and physically active or trained in a

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 4 of 15 particular sport; and (5) the exact values for the CMJ height was presented for preand post-test. The 16 selected meta-analyses included a total of 406 individual papers. Studies that could not be accessed for full papers or those that did not meet the inclusion criteria were excluded. Consequently, 124 studies were finally deemed eligible for further machine learning modeling. Since no previous similar study provides clear inclusion and exclusion criteria for developing an accurate and successful model, this study uses a pragmatic method in which all relevant meta-analyses with the keywords matched and within the selected time frame are included for performing further ML analytics. Among several ML regression performance indicators, the coefficient of determination (R 2 ) is more definitive in indicating how good the model is, whereas 0.8 can be used to clearly indicate a very good regression model performance [ 33 ]. If none of the ML models could achieve the R 2 of ≥ 0.8 after retuning the hyper-parameters and reiterations of ML models, the inclusion criteria would be revised and loosened (e.g., expand the time frame) to increase the size of datasets for better ML performance 2.2. Identification of Predictors Before starting the model development, the candidate predictors potentially influencing CMJ performance were identified. Since an excessively large number of variables or subcategories within variables would substantially increase the model complexity, such as dimension and cardinality, the features were selected according to the literature support [ 34 , 35 ]. These variables included age, gender, ethnicity, sport, level of sport participation, the total number of training sessions, training methods, the use of periodization, the use of strength training movements (squat, lunge, deadlift, and hip thrust), the training volume, and the use of intraset rest (Focke et al., 2013; Gonz á lez-Garc í a et al., 2019; McCurdy et al., 2005; Mujika et al., 2018; Nigro and Bartolomei, 2020; Oliver et al., 2013; Perez-Gomez and Calbet, 2013; Rouis et al., 2014; Slimani, Paravlic, and Granacher, 2018) [ 26 , 36 – 43 ]. All variables and their definitions are presented in Table 1 while the supplementary information are presented in Table A 1 . Due to inconsistent presentations of training intensities from various training methods such as “6 repetition maximum (RM)” in strength work, “85% of 1 RM” in weightlifting, or “bodyweight” in plyometrics, the “intensity” was discarded, but instead, the input of multiple training methods was allowed such that lower limb strength training represents training intensity with the use of at least 80% 1 RM in no less than two weeks of training. Since the type of sports background of subjects were diverse in the selected studies, they were summarized as “vertical based sports”, “horizontal based sports”, and “other sports” based on the characterized nature of sports movements to avoid an imbalanced dataset or cardinality issues [ 44 ]. Furthermore, training programs of intervention studies varied training volumes in different phases or periods. Instead of using the numerical format, volume was discretized from “volume per week” into a categorical format. A recent metaanalysis concluded that optimal CMJ improvements occurred when 3 sets per exercise, 5 repetitions per set, and 2 sessions per week were prescribed, while on average, 6 exercises per session were used in the studies. Therefore, a total of 180 repetitions per week was selected as the cut-off point, and those who adopted training with 180 repetitions or above were regarded as high volume [ 24 ]. As the selected studies had varied sample sizes, and original individual data for each subject were not accessible, to avoid the excessively small and imbalanced class size of features and to better reflect the weighted contribution from each study, an oversampling technique using synthetic data was performed. Rows of data were duplicated according to the sample size of a particular study. For example, for a study that presented data with a mean ± standard deviation of 10 subjects, 9 additional rows of identical data were synthesized (i.e., total of 10) to adjust proper weighting for machine learning modeling. Subsequently, a total of 3915 rows of data were included for further modeling.

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 5 of 15 Table 1. Descriptions and formats of selected variables Variable Description Format CMJ change Raw difference of CMJ before and after intervention or control in cm by post-training CMJ–pre-training CMJ Numerical Age Age of subjects Numerical Gender Sex of subjects Categorical Ethnicity Subjects’ ethnicity including American, Asian, Australian, African, and European Categorical Type of sport Major movement directions or involvements in sports including horizontal based, vertical based, and other sports that are mixed or not well defined Categorical Level of sport participation Level of subjects including league team, junior team, or recreational/amateur/collegiate Categorical Total training sessions Total sessions completed by frequency × number of weeks. For the control group that received no intervention, 0 was inputted Numerical Method Training methods or interventions involved in the study including control (no training), normal sports training, core stability or training-related, lower limb resistance, lower limb strength, plyometric (mixed fast/slow SSC), weightlifting, upper limb resistance, upper limb strength, flexibility training, balance training, sprint-related, agility/quickness training, cardio or aerobic-related, and whole-body vibration Categorical Special training drills To indicate if training programs included squat, lunge, deadlift, or hip thrust movements or variants by marking true or false Categorical Periodization To indicate if any periodization strategies were adopted within programs including no periodization, linear periodization, or undulating periodization Categorical Volume per week To indicate the overall weekly training volume. Number of repetitions ≥ 180 per week indicated as high, while those <180 indicated as low Categorical Intraset rest To indicate if intraset rest or cluster sets were adopted in the program by marking true or false Categorical Baseline CMJ Indicated as the pre-CMJ value (cm) before intervention as the baseline Numerical 2.3. Developing Machine Learning Models Before building the models, one-hot encoding was applied to handle features containing multiple categorical values [ 45 ]. K-fold cross-validation was used by splitting the data into 5 independent sets of observations (K = 5) for each model development to avoid

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 6 of 15 over-fitting and inflated results when the model complexity increased [ 46 ]. Eighty percent of data from the dataset were used to train the model, while 20% were unseen data for cross-validation. The dataset was further normalized using MinMaxScaler for providing enhanced model performance (i.e., data were scaled into the range (0, 1)) [ 47 ]. The dataset in this study was composed of both numerical and categorical variables; therefore, machine learning algorithms selected must be regressors capable of using both features to accurately predict numerical values. Since each machine learning algorithm may best perform under specific circumstances with a different number and format variables, multiple machine learning regression algorithms were employed including random forest (RF) ensemble, support vector machine (SVM), light gradient boosting method (LightGBM), and multi-layer perceptron regression (MLPR). This was to compare the model performance for selecting the most accurate model to produce the most sensible prediction of CMJ performance changes The RF, an ensemble method using a large number of decision trees to split the response based on binary nodes, was used as it was shown to be an accurate regression algorithm to predict numerical outcomes [ 14 ]. Meanwhile, SVM, another popular supervised machine learning method, is robust against over-fitting issues and able to fit linear regression functions into high-dimensional feature spaces to predict values with good accuracy [ 15 ]. The MLPR is a class of feedforward artificial neural networks that simulate the structure and operation of the human brain. It contains the input, hidden, and output layers, and permits the use of both numerical and nominal variables to produce classification or regression models. When compared to traditional multiple linear regression in solving complex tasks (e.g., climate prediction), MLPR was shown to produce higher accuracy [ 13 ]. The LightGBM is a recently enhanced method of the gradient boosting algorithm and is recommended for both high efficiency and accuracy [ 12 ]. All ML models were developed with the Scikit-Learn package in Python 3 using Jupyter Notebook 2.4. Metrics and Model Tuning The performance of the models was evaluated using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R 2 ). Absolute percentage error (MAPE) was not used due to the frequent report of infinite or undefined values when errors are close to zero [ 6 ]. The formulae for the calculation of MAE, RMSE, and R 2 are as follows: RMSE = r 1 N ∑ N i = 1 ( O i − P i ) 2 (1) MAE = 1 N ∑ N i = 1 | ( O i − P i ) | (2) R 2 = 1 − ∑ ( O i − P i ) 2 ∑ ( O i − P m ) 2 (3) where N is the number of data points, while O i and P i are the observed and predicted values, respectively, and P m is the mean of P i values. The final training and testing performances in terms of MAE, RMSE, and R 2 values were obtained by averaging the corresponding values from 5 sets of cross-validation. Training accuracy was obtained through the average values of MAE and RMSE of 5 sets of cross-validation. When the training accuracy of a model was lower (higher error values) than the validation one, it could be considered potentially underfitting, since an underfit model has not sufficiently learned the patterns of the training data well, and usually, the accuracy of both training and testing sets is low. In this case, further reiteration was performed after the retuning of hyper-parameters. Conversely, models were deemed to be overfitting if the validation accuracy was lower (higher error values) than the training accuracy. The model with the highest testing accuracy without underfitting and overfitting was regarded as optimum for further comparison and analyses [ 48 ]. A recent study highlighted that manual search could be more efficient to produce

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 7 of 15 results with similar or even better model optimization than grid search, and therefore, the optimum prediction models of RF, MLPR, and LightGBM were obtained using random manual search with multiple reiterations after hyper-parameters were tuned [ 49 ]. Since each model development for SVM was time-consuming, hyper-parameter optimization was achieved using grid search For RF, the optimal model was achieved with the following parameters: max_depth = 50, n_estimatros = 500, max_leaf_nodes = 800, max_features = “sqrt”, bootstrap = False, and random_state = 0. Regarding SVM, the best model happened with the following: kernel = “poly”, C = 0. 1, epsilon = 1, and gamma = 0.1. For MLPR, the best model was yielded with the following: activation = “relu”, solver = “lbfgs”, learning_rate = “adaptive”, alpha = 0.0001, hidden_layer_sizes = (40, 80), learning_rate_init = 0.001, batch_size = “auto”, max_iter = 5000, and random_state = 0. In using LGBM, the model performed best with the following: num_leaves = 130, min_data_in_leaf = 3, max_depth = 15, learning_rate = 0.15, n_estimators = 200, lambda_l 1 = 0.1, feature_fraction = 1, and xgboost_dart_mode = True 3. Results 3.1. Prediction Accuracy The model performance shown in Table 2 did not show any underfitting or overfitting problems, as both the MAE and RMSE of the training and testing sets were highly comparable in all models. Among the four selected machine learning algorithms, the RF model yielded the most accurate prediction with excellent testing accuracy (MAE: 0.071 and RMSE: 0.300), followed by the LightGBM, MLPR, and SVM models. Meanwhile, the RF and LightGBM models produced the highest R 2 values (0.985). Therefore, the RF model was selected for further analysis due to the lowest error and highest R 2 value Table 2. Performance of RF, SVM, MLPR, and LightGBM models for prediction of CMJ improvement RF SVM MLPR LightGBM Metrics Training Testing Training Testing Training Testing Training Testing MAE 0.069 0.071 1.18 1.20 0.134 0.141 0.072 0.074 RMSE 0.296 0.300 1.66 1.69 0.354 0.365 0.296 0.300 R 2 0.985 0.985 0.523 0.510 0.978 0.977 0.985 0.985 3.2. Feature Importance Figure 1 displays the feature importance score computed from RF for all 40 variables. The total number of features shown was expanded because of the use of one-hot encoding for the variables with multiple categorical values. The values of feature importance are the averaged impurity weighted in the trees of the forest. The top three features with the most predictive power in the RF model include the “Pre-CMJ”, “Age”, and “Total number of training_session”. These are followed by five other relatively important features including “Control (no training)”, “Squat_Lunge_Deadlift_Hipthrust_True”, “Squat_Lunge_Deadlift_Hipthrust_False”, “Plyometric (mixed fast/slow SSC)”, and “Race_Asian or Australian”. Interestingly, the features ranked ninth (“Level_junior team”) to twenty-eighth (“Level_Recreational/amateur/collegiate”) are similar and somewhat impactful on the prediction of CMJ improvement using the RF ensemble. However, the influence from the last two features, including the use of agility or quickness-related training programs (“Agility/Quickness training”) and other non-specific training methods not addressed explicitly in other features (“Other general resistance”), is almost negligible. The complete ranking and relative arbitrary score of feature importance are shown in Figure 1 as well.

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 8 of 15 Int. J. Environ. Res. Public Health 2023 , 20 , x 8 of 16 “Squat_Lunge_Deadlift_Hipthrust_False”, “Plyometric (mixed fast/slow SSC)”, and “Race_Asian or Australian”. Interestingly, the features ranked ninth (“Level_junior team”) to twenty-eighth (“Level_Recreational/amateur/collegiate”) are similar and somewhat impactful on the prediction of CMJ improvement using the RF ensemble. However, the in fl uence from the last two features, including the use of agility or quickness-related training programs (“Agility/Quickness training”) and other non-speci fi c training methods not addressed explicitly in other features (“Other general resistance”), is almost negligible. The complete ranking and relative arbitrary score of feature importance are shown in Figure 1 as well. Figure 1. Feature importance of random forest on predicting improvement of countermovement jump. 4. Discussion Predicting the performance change for athletes using certain strength and conditioning training programs is important to allow coaches to foresee the e ff ects of their training programs based on the background of athletes and help avoid using sub-optimal programs. Currently, practitioners and athletes have to spend time and e ff ort to realize the e ff ectiveness of training programs through training monitoring, testing, and evaluation. When the performance of athletes plateaus, acute training variables or exercise prescriptions are further adjusted to optimize performance. Therefore, performance coaches are encouraged to use evidence-based training methods [4]. Nevertheless, due to experimental limitations, biased studies, individual responses, controlled experimental environments, and the heterogeneity of subjects within research studies, it is uncertain whether the observed outcomes can be applied in real-world se tt ings [2,11]. Therefore, the current study explores the feasibility of a novel MLMA method to pool data used from a su ffi - ciently large amount of the literature with diversi fi ed subjects and training methods to generate a more accurate prediction. The fi nal production model using the RF ensemble yielded an extremely high accuracy with only 0.071 cm of MAE and low RMSE values compared to the changes in the predicted and actual CMJ performance. The novel concept of using MLMA to “intelligentize” pooled data to provide real-world performance predictions seems feasible. Figure 1. Feature importance of random forest on predicting improvement of countermovement jump 4. Discussion Predicting the performance change for athletes using certain strength and conditioning training programs is important to allow coaches to foresee the effects of their training programs based on the background of athletes and help avoid using sub-optimal programs Currently, practitioners and athletes have to spend time and effort to realize the effectiveness of training programs through training monitoring, testing, and evaluation. When the performance of athletes plateaus, acute training variables or exercise prescriptions are further adjusted to optimize performance. Therefore, performance coaches are encouraged to use evidence-based training methods [ 4 ]. Nevertheless, due to experimental limitations, biased studies, individual responses, controlled experimental environments, and the heterogeneity of subjects within research studies, it is uncertain whether the observed outcomes can be applied in real-world settings [ 2 , 11 ]. Therefore, the current study explores the feasibility of a novel MLMA method to pool data used from a sufficiently large amount of the literature with diversified subjects and training methods to generate a more accurate prediction. The final production model using the RF ensemble yielded an extremely high accuracy with only 0.071 cm of MAE and low RMSE values compared to the changes in the predicted and actual CMJ performance. The novel concept of using MLMA to “intelligentize” pooled data to provide real-world performance predictions seems feasible The selected RF model in the present study identified the importance of a group of selected numerical and categorical features to predict the change in CMJ performance, while CMJ is consensually described as a key performance indicator [ 20 ]. Unfortunately, unlike simple transparent models such as decision tree or multiple linear regression, the underlying mechanism of the RF model in acquiring the relative feature importance for each predictor is somehow still regarded as a black box, which means people cannot fully interpret how the prediction is made [ 50 ]. Therefore, inputting data from simulated cases to observe the change in outcomes concerning the manipulation of important features may help us further understand the relationship between predictors and dependent variables The initial simulation referred to an 18-year-old male American recreational athlete without performing any training. All training-related variables were set to 0 or “False”, except the “Control (no training)”, “Volume_per_week_Low”, “Sport Other Sports”, Peri-

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 9 of 15 odization_No periodized program used”, “Squat_Lunge_Deadlift_Hipthrust_False”, and “Intraset_rest_False” were set to 1, and the baseline CMJ value was set as 10 cm, and the predicted CMJ change was − 0.19 cm, which was approximate to a 2% change. As shown in Table 3 , without manipulating the other variables, by adjusting the most impactful predictor, “Pre-CMJ”, to 20, 30, 40, 50, 60, and 70 cm, the predicted CMJ changes were − 0.24, − 0.39, − 0.02, − 0.15, 0.31, and 0.30 cm, respectively. No further change was noticed when the value was either smaller than 10 or larger than 70 cm. Similarly, when the second most important predictor, “Age”, was changed from 18 to 10, 15, 20, 30, and 50 years, the predicted CMJ changes were 0.36, 0.10, 0.20, 0.28, and 0.27 cm, respectively. No further changes were observed when the “Age” was either smaller than 10 or larger than 50 years Table 3. The prediction of CMJ performance after changing “Pre-CMJ” value or “Age” Pre-CMJ Value (cm) Age Predicted CMJ Change (cm) 10 * 18 * − 0.19 20 18 − 0.24 30 18 − 0.39 40 18 − 0.02 50 18 − 0.15 60 18 0.31 70 or above 18 0.30 10 10 0.36 10 15 0.10 10 20 0.20 10 30 0.28 10 50 or above 0.27 * represents the baseline value for simulation The RF model produced a non-linear prediction, whereas when producing the ensemble of decision trees, each one works like a bunch of if-else conditions. Therefore, the predicted outcomes from the RF model were not generated by mathematical formulas, but instead, only produced when certain conditions of decision trees were met. Based on the mechanism of the RF ensemble, it is possible that individuals with a particular age or baseline CMJ value can increase or decrease the CMJ performance under different circumstances when all other predictors are concurrently manipulated. Technically, the RF ensemble can enhance the model performance by building and aggregating multiple weak “learners” or “models” with specific strategies such as bootstrap aggregation or bagging It is based on the parallel ensemble idea such that multiple base learners (decision trees) are constructed simultaneously and become a large forest. Therefore, the predicted values are within those seen in the training dataset, whereas this method is robust to noises or outliners. Conversely, traditional multiple linear regression allows the extrapolation of values that fall outside the training dataset. In this regard, machine learning using RF can protect users from obtaining nonsensical, extreme, or abnormal outcomes. However, it can also be a potential limitation that predictive RF models cannot be used to anticipate non-typical conditions such as excessively young or old individuals. It is also worth noting that in the current study, the LightGBM has shown highly comparable performance (with the same R 2 and RMSE) to the RF as it is also one of the ensemble-based ML methods The LightGBM is a gradient boosting decision tree method optimized for both high speed and accuracy. The base concept of LightGBM is one of the boosting methods for building sequential decision trees. Each subsequent tree is built to reduce the errors of the previous one. In contrast, RF focuses on the use of the bagging method in which deep trees are combined as a large forest. Therefore, theoretically, RF is robust in producing a model with a reduced variance, while the LightGBM focuses more on reducing bias. A recent study has shown a very similar result that both RF and LightGBM outperformed most other ML methods, while RF had a very slight advantage in the accuracy of the classification task [ 51 ].

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 10 of 15 When the initially simulated condition was further modified to another simulated subject who completed 4 weeks of lower limb strength training (i.e., “Control (no training)”) = 0 and “Lower Limb Strength” = 1), the predicted CMJ performance was greatly improved from − 0.19 to 0.87 cm. Interestingly, the optimum CMJ improvement of 1.16 cm was yielded when the third most important feature, “Training_sessions”, was set to 16. Similarly, when the predictors ranked from fifth to tenth shown in Figure 1 were adjusted one by one while keeping the previously modified features, the simulated results were generated and are presented in Table 4 . After adjusting the 10 most important features shown in Figure 1 , the new simulated condition could be described as an Asian or Australian junior male athlete who has completed 16 sessions of periodized strength training including the squat/lunge/deadlift/hip thrust variants as well as plyometric exercises. When compared with the initial simulated subject without the training intervention, the CMJ performance change in the new condition would substantially increase by 4.69 cm Table 4. The prediction of CMJ performance based on the particular simulated condition Variable The Change Made Predicted CMJ Change (cm) Squat_Lunge_Deadlift_Hipthrust_True 1 1.61 Squat_Lunge_Deadlift_Hipthrust_False 0 2.08 Plyometric (mixed fast/slow SSC) 1 2.49 Race_Asian or Australian (meanwhile turn off “Race_American”) 1 4.04 Level_Junior team (meanwhile turn off “Level_Recreational/amateur/collegiate”) 1 4.36 Periodization_No periodized program used (meanwhile turn on “Periodization_Linear periodization used”) 0 4.69 In the present study, it was found that the baseline CMJ performance, age, and the number of training sessions completed were the top three most important predictors. Considering the diminishing return with the continuing increase in performance, it is commonly believed that individuals with lower baseline values should have a larger improvement window when compared with those highly trained athletes [ 52 ]. Nevertheless, multiple studies show that individuals with different training levels could have varied training responses including CMJ performance [ 53 – 55 ]. Moreover, it is worth noting that the baseline CMJ values can be potentially influenced by the level of athletes [ 17 , 27 , 31 ], genes and races [ 39 ], gender [ 56 ], as well as the nature of the sport in which the athlete participates [ 31 , 39 ]. Given such high complexity and interrelated factors between the baseline CMJ performance and the other variables, the non-linear results observed when we only tuned the “Pre-CMJ” feature are not surprising. Similarly, despite “Age” being the second most important feature, athletes of different ages may also have varied training responses. For example, maturation or puberty in adolescents and sarcopenia in aged individuals both potentially induce a certain impact on power and jumping improvement [ 57 , 58 ]. Furthermore, the effect of young or old age on CMJ performance could be masked or boosted when certain conditions are met. Therefore, unlike traditional studies investigating the impact of age in a particular controlled and unique circumstance, the impact of subject age on CMJ performance change could vary non-linearly and disproportionately in different simulated scenarios Since the machine (computer) was trained using a sufficiently large dataset based on the RF algorithm to “learn” and “understand” a massive amount of genuine and synthetic conditions produced from the selected literature, the predictions made theoretically reflected all possible scenarios within the “boundary” of the observations. As several studies highlighted the positive impact of using the squat, deadlift, hip thrust, and lunge exercise variants, plyometric exercises, and periodized training programs on power de-

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 11 of 15 velopment [ 24 , 36 – 38 , 42 , 43 ], it is unsurprising that when switching on all these variables (set these predictors to 1 while the opposite predictors are set to 0), a substantial increase in CMJ performance was observed. Interestingly, an increase in CMJ improvement was identified when changing the race from “American” to “Asian or Australian”. Although it is well known that genetic factors contribute to athletic performance including power output [ 39 , 40 ], to the best knowledge of the authors, no previous study has directly compared the difference in CMJ improvement between Americans and Asians or Australians. Therefore, it is speculated that both the smaller sample size from the literature using Asian or Australian subjects, and coincidentally, better CMJ improvement observed in those studies contributed to such unexpected differences between the predictions Undoubtedly, the novel MLMA method using machine learning and the literature data to perform real-world predictions can potentially bridge the research–practice gap. Nevertheless, the present study using MLMA is not without limitations. The primary issue is the limited accessibility to a large amount of original raw data. Although the RF ensemble demonstrated extremely high model accuracy in our study, oversampling with synthetic data based on the sample size of individual studies was not equivalent to using true individual data points. The combination of using authentic and synthetic data for machine learning is somewhat similar to meta-analyses of aggregated data. We believed that such oversampling techniques would somewhat affect the final prediction and ranking of the feature importance. Moreover, using one-hot encoding substantially increased the dimensionality of the problem, whereas combining classes of certain categorical features and discretizing numerical predictors into categorical variables might lead to information loss, and subsequently affect the model performance. For instance, no clues were identified to understand and differentiate the differences between Asian and Australian individuals when these two classes were combined. Similarly, with a limited number of data points from different sports such as basketball, volleyball, soccer, rugby, and sprinting athletes, the individuals were re-categorized based on movement vectors. Given that the exercise intensity and volume were reported in the literature using different formats, while some programs included varied volume or non-linear patterns in different training phases, accurate and consistent data input for subsequent machine learning was almost impossible Transforming the numerical training volume to either high or low with 180 repetitions per week as a cut-off point might unavoidably lead to a potential bias and neglect the impact of different training volumes on the dependent variable. The MLMA method used in our study was not a true typical meta-analysis, whereas all the data used for the modeling must have been used in the meta-analysis. Therefore, another limitation of our study is that some papers that failed to meet the inclusion criteria within these selected meta-analyses or those papers published before or after the given search timeframe may have been missed. Lastly, a standardized quality check was not completed for all selected papers; therefore, there is a possibility that low-quality meta-analyses, biased predictions, and false accuracies may have been included in this study 5. Conclusions The use of the RF ensemble machine learning for predicting CMJ performance change showed excellent accuracy and sensible prediction. By using a sufficiently large amount of data to train the selected model, the baseline CMJ performance, age, and the number of training sessions completed were found to be the top three most important features Apart from the RF ensemble, LightGBM can also be a good ML alternative to produce accurate models for similar tasks. To better facilitate the use of machine learning for pooling data used in meta-analyses, full access to original raw data and a consistent presentation format of the variables in the individual literature are needed. It is proposed that future similar studies applying machine learning methods may also be used for predicting other performance indicators when large datasets are available with good transparency.

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 12 of 15 Author Contributions: I.M.K.H. performed the conceptualization of the study, statistical and data analysis, and manuscript writing (introduction, methods, results, and discussion). A.W. performed the manuscript writing (introduction) and revision of the paper. J.T.H.Y. performed the data collection and manuscript writing (methodology), C.T.T.L. performed the data collection, and J.S. performed the revision and editing of the manuscript. All authors have read and agreed to the published version of the manuscript Funding: This research received no external funding Institutional Review Board Statement: Not applicable Informed Consent Statement: Not applicable Data Availability Statement: Not applicable Conflicts of Interest: The authors declare no conflict of interest Appendix A Table A 1. The supplementary information regarding the range and frequency of variables of the dataset Variable Range Frequency (By Groups If Applicable) * CMJ change − 4.57 to 9.7 N/A Age 10.1 to 81.1 N/A Gender N/A Male: 247 Female: 75 Ethnicity N/A African: 35 American: 60 Asian: 12 Australian: 22 European: 193 Type of sport N/A American football: 4 Baseball: 3 Basketball: 23 Dancing: 2 Distance running: 12 General conditioning: 5 Golf: 4 Gymnastics: 2 Handball: 12 Mixed or not well defined: 101 Rugby: 3 Skiing: 2 Soccer: 109 Sprinting: 5 Swimming: 3 Tennis: 7 Track and field: 3 Volleyball: 15 Water polo: 7 Level of sport participation Junior team: 121 League game: 84 Recreational/amateur/collegiate: 117

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 13 of 15 Table A 1. Cont Variable Range Frequency (By Groups If Applicable) * Total training sessions 0 to 144 sessions 0 session: 91 1 to 10 sessions: 18 11 to 20 sessions: 117 21 to 30 sessions: 57 31 to 40 sessions: 26 41 to 50 sessions: 4 51 to 60 sessions: 1 61 to 70 sessions: 0 71 to 80 sessions: 1 81 to 90 sessions: 1 91 to 100 sessions: 3 Above 100: 3 Methods No training: 99 Normal sports training: 215 Balance: 3 Cardio or related: 11 Core stability or related: 25 Flexibility: 5 Lower limb strength/resistance: 153 Olympic weightlifting: 8 Plyometric: 123 Sprint/agility/quickness: 28 Upper limb strength/resistance: 44 Other general resistance: 1 Whole body vibration: 8 Special training drills involved (squat, lunge, deadlift, or hip thrust) Yes: 178 No: 144 Periodization No periodization: 183 Linear: 102 Non-linear: 37 Volume per week 180 repetitions or above: 97 Below 180 repetitions: 225 Intraset rest used Yes: 34 No: 288 Baseline CMJ 5.6 to 67.8 cm N/A * Multiple groups are available in most individual studies References 1 Sandbakk, Ø. Let’s Close the Gap Between Research and Practice to Discover New Land Together! Int. J. Sports Physiol. Perform 2018 , 13 , 961. [ CrossRef ] [ PubMed ] 2 Keegan, R.J.; Cotteril, S.; Woolway, T.; Appaneal, R.; Hutter, V. Strategies for bridging the research-practice ‘gap’ in sport and exercise psychology Rev. Psicol. Del Deporte 2017 , 26 , 75–80 3 Mallonee, S.; Fowler, C.; Istre, G.R. Bridging the gap between research and practice: A continuing challenge Inj. Prev 2006 , 12 , 357–359. [ CrossRef ] 4 Reade, I.; Rodgers, W.; Hall, N. Knowledge transfer: How do high performance coaches access the knowledge of sport sci-entists? Int. J. Sport Sci. Coach 2008 , 3 , 319–334. [ CrossRef ] 5 Blonde, L.; Khunti, K.; Harris, S.B.; Meizinger, C.; Skolnik, N.S. Interpretation and Impact of Real-World Clinical Data for the Practicing Clinician Adv. Ther 2018 , 35 , 1763–1774. [ CrossRef ] [ PubMed ] 6 Kim, S.; Kim, H. A new metric of absolute percentage error for intermittent demand forecasts Int. J. Forecast 2016 , 32 , 669–679 [ CrossRef ] 7 Mallappallil, M.; Sabu, J.; Gruessner, A.; Salifu, M. A review of big data and medical research SAGE Open Med 2020 , 8 . [ CrossRef ] 8 Haidich, A.B. Meta-analysis in medical research Hippokratia 2010 , 14 (Suppl. S 1), 29–37 9 Hagger, M. Meta-analysis in sport and exercise research: Review, recent developments, and recommendations Eur. J. Sport Sci 2006 , 6 , 103–115. [ CrossRef ]

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 14 of 15 10 Faltinsen, E.G.; Storebø, O.J.; Jakobsen, J.C.; Boesen, K.; Lange, T.; Gluud, C. Network meta-analysis: The highest level of medical evidence? BMJ Evid. Based Med 2018 , 23 , 56–59. [ CrossRef ] 11 Walker, E.; Hernandez, A.V.; Kattan, M. Meta-analysis: Its strengths and limitations Clevel. Clin. J. Med 2008 , 75 , 431–439 [ CrossRef ] [ PubMed ] 12 Zhang, J.; Mucs, D.; Norinder, U.; Svensson, F. LightGBM: An Effective and Scalable Algorithm for Prediction of Chemical Toxicity–Application to the Tox 21 and Mutagenicity Data Sets J. Chem. Inf. Model 2019 , 59 , 4150–4158. [ CrossRef ] [ PubMed ] 13 Choubin, B.; Khalighi-Sigaroodi, S.; Malekian, A.; Ki¸si, Ö. Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals Hydrol. Sci. J 2016 , 61 , 1001–1009 [ CrossRef ] 14 Jeong, J.H.; Resop, J.P.; Mueller, N.D.; Fleisher, D.H.; Yun, K.; Butler, E.E.; Timlin, D.J.; Shim, K.-M.; Gerber, J.S.; Reddy, V.R.; et al. Random Forests for Global and Regional Crop Yield Predictions PLoS ONE 2016 , 11 , e 0156571. [ CrossRef ] [ PubMed ] 15 Balfer, J.; Bajorath, J. Systematic Artifacts in Support Vector Regression-Based Compound Potency Prediction Revealed by Statistical and Activity Landscape Analysis PLoS ONE 2015 , 10 , e 0119301. [ CrossRef ] [ PubMed ] 16 Shipe, M.E.; Deppen, S.A.; Farjah, F.; Grogan, E.L. Developing prediction models for clinical use using logistic regression: An overview J. Thorac. Dis 2019 , 11 , S 574–S 584. [ CrossRef ] 17 Pagaduan, J.; Pojskic, H. A Meta-Analysis on the Effect of Complex Training on Vertical Jump Performance J. Hum. Kinet 2020 , 71 , 255–265. [ CrossRef ] 18 Bauer, P.; Uebellacker, F.; Mitter, B.; Aigner, A.J.; Hasenoehrl, T.; Ristl, R.; Tschan, H.; Seitz, L.B. Combining higher-load and lower-load resistance training exercises: A systematic review and meta-analysis of findings from complex training studies J. Sci Med. Sport 2019 , 22 , 838–851. [ CrossRef ] 19 Petrigna, L.; Karsten, B.; Marcolin, G.; Paoli, A.; D’antona, G.; Palma, A.; Bianco, A. A Review of Countermovement and Squat Jump Testing Methods in the Context of Public Health Examination in Adolescence: Reliability and Feasibility of Current Testing Procedures Front. Physiol 2019 , 10 , 1384. [ CrossRef ] 20 Claudino, J.G.; Cronin, J.; Mez ê ncio, B.; McMaster, D.T.; McGuigan, M.; Tricoli, V.; Amadio, A.C.; Serr ã o, J.C. The countermovement jump to monitor neuromuscular status: A meta-analysis J. Sci. Med. Sport 2017 , 20 , 397–402. [ CrossRef ] 21 Pagaduan, J.; Schoenfeld, B.J.; Pojski´c, H. Systematic Review and Meta-Analysis on the Effect of Contrast Training on Vertical Jump Performance Strength Cond. J 2019 , 41 , 63–78. [ CrossRef ] 22 Milanovi´c, Z.; Panteli´c, S.; ˇ Covi´c, N.; Sporiš, G.; Mohr, M.; Krustrup, P. Broad-spectrum physical fitness benefits of recreational football: A systematic review and meta-analysis Br. J. Sports Med 2018 , 53 , 926–939. [ CrossRef ] [ PubMed ] 23 Berton, R.; Lixandr ã o, M.E.; Pinto E Silva, C.M.; Tricoli, V. Effects of weightlifting exercise, traditional resistance and plyometric training on countermovement jump performance: A meta-analysis J. Sports Sci 2018 , 36 , 2038–2044. [ CrossRef ] [ PubMed ] 24 Slimani, M.; Paravlic, A.; Granacher, U. A Meta-Analysis to Determine Strength Training Related Dose-Response Relationships for Lower-Limb Muscle Power Development in Young Athletes Front. Physiol 2018 , 9 , 1155. [ CrossRef ] 25 Moran, J.J.; Sandercock, G.R.; Ram í rez-Campillo, R.; Meylan, C.M.; Collison, J.A.; Parry, D.A. Age-related variation in male youth athletes’ countermovement jump following plyometric training J. Strength Cond. Res 2017 , 31 , 552–565. [ CrossRef ] [ PubMed ] 26 Slimani, M.; Paravli´c, A.; Bragazzi, N.L. Data concerning the effect of plyometric training on jump performance in soccer players: A meta-analysis Data Brief 2017 , 15 , 324–334. [ CrossRef ] 27 Stojanovic, E.; Risti´c, V.; McMaster, D.; Milanovi´c, Z. Effect of Plyometric Training on Vertical Jump Performance in Female Athletes: A Systematic Review and Meta-Analysis Sports Med 2016 , 47 , 975–986. [ CrossRef ] 28 Taylor, J.; Macpherson, T.; Spears, I.; Weston, M. The Effects of Repeated-Sprint Training on Field-Based Fitness Measures: A Meta-Analysis of Controlled and Non-Controlled Trials Sports Med 2015 , 45 , 881–891. [ CrossRef ] 29 Manimmanakorn, N.; Hamlin, M.; Ross, J.J.; Manimmanakorn, A. Long-Term Effect of Whole Body Vibration Training on Jump Height J. Strength Cond. Res 2014 , 28 , 1739–1750. [ CrossRef ] 30 Osawa, Y.; Oguma, Y.; Ishii, N. The effects of whole-body vibration on muscle strength and power: A meta-analysis J. Musculoskelet. Neuronal Interact 2013 , 13 , 380–390 31 de Villarreal, E.S.-S.; Kellis, E.; Kraemer, W.J.; Izquierdo, M. Determining Variables of Plyometric Training for Improving Vertical Jump Height Performance: A Meta-Analysis J. Strength Cond. Res 2009 , 23 , 495–506. [ CrossRef ] [ PubMed ] 32 Markovic, G.; Newton, R.U. Does plyometric training improve vertical jump height? A meta-analytical review Br. J. Sports Med 2007 , 41 , 349–355. [ CrossRef ] [ PubMed ] 33 Chicco, D.; Warrens, M.J.; Jurman, G. The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation PeerJ Comput. Sci 2021 , 7 , e 623. [ CrossRef ] [ PubMed ] 34 Roe, K.D.; Jawa, V.; Zhang, X.; Chute, C.G.; Epstein, J.A.; Matelsky, J.; Shpitser, I.; Taylor, C.O. Feature engineering with clinical expert knowledge: A case study assessment of machine learning model complexity and performance PLoS ONE 2020 , 15 , e 0231300. [ CrossRef ] 35 Zhang, B.; Cao, P. Classification of high dimensional biomedical data based on feature selection using redundant removal PLoS ONE 2019 , 14 , e 0214406. [ CrossRef ] 36 Nigro, F.; Bartolomei, S. A comparison between the squat and the deadlift for lower body strength and power training J. Hum Kinet 2020 , 73 , 145–152. [ CrossRef ]

Int. J. Environ. Res. Public Health 2023 , 20 , 5881 15 of 15 37 Gonz á lez-Garc í a, J.; Morencos, E.; Balsalobre-Fern á ndez, C.; Cu é llar-Rayo, Á .; Romero-Moraleda, B. Effects of 7-Week Hip Thrust Versus Back Squat Resistance Training on Performance in Adolescent Female Soccer Players Sports 2019 , 7 , 80. [ CrossRef ] 38 Mujika, I.; Halson, S.; Burke, L.M.; Balagu é , G.; Farrow, D. An Integrated, Multifactorial Approach to Periodization for Optimal Performance in Individual and Team Sports Int. J. Sports Physiol. Perform 2018 , 13 , 538–561. [ CrossRef ] 39 Rouis, M.; Attiogb é , E.; Vandewalle, H.; Jaafar, H.; Noakes, T.D.; Driss, T. Relationship between vertical jump and maximal power output of legs and arms: Effects of ethnicity and sport Scand. J. Med. Sci. Sports 2014 , 25 , e 197–e 207. [ CrossRef ] 40 Focke, A.; Strutzenberger, G.; Jekauc, D.; Worth, A.; Woll, A.; Schwameder, H. Effects of age, sex and activity level on countermovement jump performance in children and adolescents Eur. J. Sport Sci 2013 , 13 , 518–526. [ CrossRef ] 41 Oliver, J.M.; Jagim, A.R.; Sanchez, A.C.; Mardock, M.A.; Kelly, K.A.; Meredith, H.J.; Smith, G.L.; Greenwood, M.; Parker, J.L.; Riechman, S.E.; et al. Greater Gains in Strength and Power with Intraset Rest Intervals in Hypertrophic Training J. Strength Cond Res 2013 , 27 , 3116–3131. [ CrossRef ] [ PubMed ] 42 Perez-Gomez, J.; Calbet, J.A.L. Training methods to improve vertical jump performance J. Sports Med. Phys. Fit 2013 , 53 , 339–357 43 McCurdy, K.W.; Langford, G.A.; Doscher, M.W.; Wiley, L.P.; Mallard, K.G. The Effects of Short-Term Unilateral and Bilateral Lower-Body Resistance Training on Measures of Strength and Power J. Strength Cond. Res 2005 , 19 , 9–15. [ CrossRef ] [ PubMed ] 44 Sharifai, G.A.; Zainol, Z. Feature Selection for High-Dimensional and Imbalanced Biomedical Data Based on Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm Genes 2020 , 11 , 717. [ CrossRef ] 45 Lin, Y.-W.; Zhou, Y.; Faghri, F.; Shaw, M.J.; Campbell, R.H. Analysis and prediction of unplanned intensive care unit readmission using recurrent neural networks with long short-term memory PLoS ONE 2019 , 14 , e 0218942. [ CrossRef ] 46 De Rooij, M.; Weeda, W. Cross-Validation: A method every psychologist should know Adv. Methods Pract. Psychol. Sci 2020 , 3 , 248–263. [ CrossRef ] 47 Rotich, N.K.; Backman, J.; Linnanen, L.; Daniil, P. Wind Resource Assessment and Forecast Planning with Neural Networks J. Sustain. Dev. Energy Water Environ. Syst 2014 , 2 , 174–190. [ CrossRef ] 48 Ho, I.M.K.; Cheong, K.Y.; Weldon, A. Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques PLoS ONE 2021 , 16 , e 0249423. [ CrossRef ] 49 Bergstra, J.; Bengio, Y. Random search for hyper-parameter optimization J. Mach. Learn 2012 , 13 , 281–305. Available online: http://dl.acm.org/citation.cfm?id=2503308.2188395 (accessed on 28 January 2020) 50 McGovern, A.; Lagerquist, R.; Gagne, D.J.; Jergensen, G.E.; Elmore, K.L.; Homeyer, C.R.; Smith, T. Making the Black Box More Transparent: Understanding the Physical Implications of Machine Learning Bull. Am. Meteorol. Soc 2019 , 100 , 2175–2199 [ CrossRef ] 51 Li, Y.; Chen, W. A Comparative Performance Assessment of Ensemble Learning for Credit Scoring Mathematics 2020 , 8 , 1756 [ CrossRef ] 52 Winters-Stone, K.M.; E Neil-Sztramko, S.; Campbell, K.L. Attention to principles of exercise training: A review of exercise studies for survivors of cancers other than breast Br. J. Sports Med 2013 , 48 , 987–995. [ CrossRef ] [ PubMed ] 53 Vikmoen, O.; Raastad, T.; Ellefsen, S.; Rønnestad, B.R. Adaptations to strength training differ between endurance-trained and untrained women Eur. J. Appl. Physiol 2020 , 120 , 1541–1549. [ CrossRef ] [ PubMed ] 54 Wetmore, A.B.; Moquin, P.A.; Carroll, K.M.; Fry, A.C.; Hornsby, W.G.; Stone, M.H. The Effect of Training Status on Adaptations to 11 Weeks of Block Periodization Training Sports 2020 , 8 , 145. [ CrossRef ] [ PubMed ] 55 Hortob á gyi, T.; Lesinski, M.; del Olmo, M.F.; Granacher, U. Small and inconsistent effects of whole body vibration on athletic performance: A systematic review and meta-analysis Eur. J. Appl. Physiol 2015 , 115 , 1605–1625. [ CrossRef ] 56 McMahon, J.J.; Rej, S.J.E.; Comfort, P. Sex Differences in Countermovement Jump Phase Characteristics Sports 2017 , 5 , 8 [ CrossRef ] 57 Hunter, G.R.; Singh, H.; Carter, S.J.; Bryan, D.R.; Fisher, G. Sarcopenia and Its Implications for Metabolic Health J. Obes 2019 , 2019 , 8031705. [ CrossRef ] 58 Asadi, A.; Ramirez-Campillo, R.; Arazi, H.; de Villarreal, E.S. The effects of maturation on jumping ability and sprint adaptations to plyometric training in youth soccer players J. Sports Sci 2018 , 36 , 2405–2411. [ CrossRef ] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

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Age, Mae, Artificial intelligence, Exercise prescription, Mathematical Formula, Medical condition, Maturation, Medical research, Inclusion criteria, Exclusion criteria, Sample size, Clinical Practice, Treatment effect, Meta analysis, Sports medicine, Systematic Review, Intervention duration, Machine Learning, Individual response, Genetic factor, Sarcopenia, Clinical scenario, Innovative solutions, Multiple linear regression, Support Vector Machine, Experimental procedure, Linear Relationship, Athletic performance, Sports performance, High accuracy, Validation accuracy, Predictive Model, Big Data, Plyometric training, Predictive Analytics, Sex difference, Training accuracy, Training Dataset, Metabolic Health, Decision tree, Pooled Data, Muscle loss, Real world evidence, Training program, Dependent variable, Training session, Countermovement jump, Categorical variable, Whole body vibration, Manual search, Data collection processes, Neural Network, Coefficient of determination, Primary data collection, Small effect, Root Mean Square Error, Machine learning algorithm, Model performance, Sports science, Athlete, Model complexity, Boolean operator, Training effect, Numerical variable, Accurate prediction, Injury to people, Injury to property, Real-world setting, Training method, Training intensity, Real life situation, Training level, Training volume, Plyometric exercise, Model accuracy, Non-linear relationship, Training modalities, Experimental limitations, Mean absolute error, MDPI, Target variable, Feature importance, Grid search, Training intensities, Model optimization, Youth soccer players, Data normalization, LightGBM, Classification Task, Synthetic data, Gradient Boosting Decision Tree, Legal Issue, Performance indicator, Hyper-parameter optimization, Supervised machine learning, K-fold cross validation, Multi-layer perceptron, RMSE value, Ensemble method, Absolute percentage error, Key Performance Indicator, Bagging method, Scikit-learn package, Categorical features, Strength and conditioning, Over-fitting, Gradient boosting algorithm, Prescriptive Analytics, Boosting method, Numerical Features, Unseen data, Artificial intelligence technique, Complex Datasets, Individual Studies, ML Regression, Literature Support, Oversampling Technique, One-Hot Encoding, Gradient Boosting Model, MinMaxScaler, Hyper-Parameter Tuning, Linear Periodization, Performance change.