Artificial neural network-based equation for estimating VO2max from the 20 m shuttle run test in adolescents
Objective: To develop an artificial neural network (ANN)-equation to estimate maximal oxygen uptake (VO2max) from 20 m shuttle run test (20mSRT) performance (stage), sex, age, weight, and height in young persons. Methods: The 20mSRT was performed by 193 (122 boys and 71 girls) adolescents aged 13-19 years. All the adolescents wore a portable gas analyzer to measure VO2 and heart rate during the test. The equation was developed and cross-validated following the ANN mathematical model. The neural net performance was assessed through several error measures. Agreement between the measured VO2max and estimated VO2max from Leger's and ANN equations were analysed following the Bland and Altman method. Results: The percentage error was 17.13 and 7.38 for Leger and ANN-equation (P < 0.001), respectively, and the standard error of the estimate obtained with Leger's equation was 4.27 ml/(kg min), while for the ANN- equation was 2.84 ml/(kg min). A Bland-Altman plot for the measured VO2max and Leger-VO2max showed a mean difference of 4.9 ml/(kg min) (P < 0.001), while the Bland-Altman plot for the measured VO2max and ANN-VO2max showed a mean difference of 0.5 ml/(kg min) (P = 0.654). In the validation sample, the percentage error was 21.08 and 8.68 for Leger and ANN-equation (P < 0.001), respectively. Conclusions: In this study, an ANN-based equation to estimate VO2max., from 20mSRT performance (stage), sex, age, weight, and height in adolescents was developed and cross-validated. The newly developed equation was shown to be more accurate than Leger's. The proposed model has been coded in a user-friendly spreadsheet. (C) 2008 Elsevier B.V. All rights reserved.
artificial neural networks maximal oxygen uptake cardiorespiratory fitness aerobic capacity test exercise field test