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 (VO(2max)) from 20m shuttle run test (20 mSRT) performance (stage), sex, age, weight, and height in young persons. METHODS The 20 mSRT was performed by 193 (122 boys and 71 girls) adolescents aged 13-19 years. All the adolescents wore a portable gas analyzer to measure VO(2) 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 VO(2max) and estimated VO(2max) from Léger's and ANN equations were analysed following the Bland and Altman method. RESULTS The percentage error was 17.13 and 7.38 for Léger and ANN-equation (P<0.001), respectively, and the standard error of the estimate obtained with Léger'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 VO(2max) and Léger-VO(2max) showed a mean difference of 4.9 ml/(kg min) (P<0.001), while the Bland-Altman plot for the measured VO(2max) and ANN-VO(2max) 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 Léger and ANN-equation (P<0.001), respectively. CONCLUSIONS In this study, an ANN-based equation to estimate VO(2max) from 20 mSRT 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 Léger's. The proposed model has been coded in a user-friendly spreadsheet.