Abstract:IntroduçãoO consumo máximo de oxigênio (VO 2 máx) juntamente com o limiar de lactato (LL) são considerados os melhores indicadores da aptidão aeróbia, sendo ambos amplamente utilizados para a predição da performance, para a avaliação dos efeitos do treinamento e identificação da adequada intensidade de esforço (BILLAT, 1996;BILLAT et al., 1999;NICHOLSON;SLEIVERT, 2001;BASSET;HOWLEY, 2000;PAPADOPOULOS et al., 2006). O VO 2 máx é uma medida que reflete a quantidade máxima de energia que pode ser produzida pelo m… Show more
“…Comparing the obtained results with other papers where models were calculated only for males, it may be observed that the parameter value does not deviate significantly from the published results ( Table 6). The optimal model for males calculated in the study generated greater errors than the models presented by Machado and Demadai [67] (4.10 [mL•kg −1 •min −1 ]) and Bandyopdhyay [68] (1.41 [mL•kg −1 •min −1 ]). The error obtained is still smaller than the error generated using the Costa model [69]…”
This study presents mathematical models for predicting VO2max based on a 20 m shuttle run and anthropometric parameters. The research was conducted with data provided by 308 young healthy people (aged 20.6 ± 1.6). The research group includes 154 females (aged 20.3 ± 1.2) and 154 males (aged 20.8 ± 1.8). Twenty-four variables were used to build the models, including one dependent variable and 23 independent variables. The predictive methods of analysis include: the classical model of ordinary least squares (OLS) regression, regularized methods such as ridge regression and Lasso regression, artificial neural networks such as the multilayer perceptron (MLP) and radial basis function (RBF) network. All models were calculated in R software (version 3.5.0, R Foundation for Statistical Computing, Vienna, Austria). The study also involved variable selection methods (Lasso and stepwise regressions) to identify optimum predictors for the analysed study group. In order to compare and choose the best model, leave-one-out cross-validation (LOOCV) was used. The paper presents three types of models: for females, males and the whole group. An analysis has revealed that the models for females ( RMSE C V = 4.07 mL·kg−1·min−1) are characterised by a smaller degree of error as compared to male models ( RMSE C V = 5.30 mL·kg−1·min−1). The model accounting for sex generated an error level of RMSE C V = 4.78 mL·kg−1·min−1.
“…Comparing the obtained results with other papers where models were calculated only for males, it may be observed that the parameter value does not deviate significantly from the published results ( Table 6). The optimal model for males calculated in the study generated greater errors than the models presented by Machado and Demadai [67] (4.10 [mL•kg −1 •min −1 ]) and Bandyopdhyay [68] (1.41 [mL•kg −1 •min −1 ]). The error obtained is still smaller than the error generated using the Costa model [69]…”
This study presents mathematical models for predicting VO2max based on a 20 m shuttle run and anthropometric parameters. The research was conducted with data provided by 308 young healthy people (aged 20.6 ± 1.6). The research group includes 154 females (aged 20.3 ± 1.2) and 154 males (aged 20.8 ± 1.8). Twenty-four variables were used to build the models, including one dependent variable and 23 independent variables. The predictive methods of analysis include: the classical model of ordinary least squares (OLS) regression, regularized methods such as ridge regression and Lasso regression, artificial neural networks such as the multilayer perceptron (MLP) and radial basis function (RBF) network. All models were calculated in R software (version 3.5.0, R Foundation for Statistical Computing, Vienna, Austria). The study also involved variable selection methods (Lasso and stepwise regressions) to identify optimum predictors for the analysed study group. In order to compare and choose the best model, leave-one-out cross-validation (LOOCV) was used. The paper presents three types of models: for females, males and the whole group. An analysis has revealed that the models for females ( RMSE C V = 4.07 mL·kg−1·min−1) are characterised by a smaller degree of error as compared to male models ( RMSE C V = 5.30 mL·kg−1·min−1). The model accounting for sex generated an error level of RMSE C V = 4.78 mL·kg−1·min−1.
“…Maximal exercise tests (Bandyopadhyay, 2011;Machado & Denadai, 2013;Silva et al, 2012;Veronese da Costa et al, 2013). and sub maximal exercise tests (Billinger, Swearingen, McClain, Lentz, & Good, 2012;Tonis, Gorter, Vollenbroek-Hutten, & Hermens, 2012)are usually performed on a treadmill, ergometer or a track.…”
Aerobic endurance describes the ability of the body's cardio-respiratory system to perform physical activity for an extended period of time and resist fatigue. Standard tests to determine aerobic endurance involves measuring the maximum volume of oxygen (VO 2 max) an athlete uses up while exercising at maximal capacity. Given that the tests of direct measurement of VO 2 max needs expensive equipment, a great deal of time, and trained staff with expertise, many researchers have attempted to find indirect and simpler ways of predicting VO 2 max based on prediction equations. The aim of this study is to establish new prediction equations for estimating the VO 2 max from gender, age, height, weight, body mass index (BMİ), maximal heart rate (HRmax) and test time (TT) for college-aged students in Turkey. Particularly, 18 students from the College of Physical Education and Sports at Gazi University volunteered for this study. Gender has been used as a common predictor variable in all prediction models. By using different combinations of the rest of predictor variables together with the common predictor variable, twelve VO 2 max prediction equations have been established with the help of Multiple Linear Regression (MLR). The performance of the prediction equations have been evaluated using two well-known metrics, namely standard error of estimate (SEE) and multiple correlation coefficient (R). The results reveal that the regression equation, VO 2 max =-(12.
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