Estimated artificial neural network modeling of maximal oxygen uptake based on multistage 10-m shuttle run test in healthy adults

Abstract

We aimed to develop an artificial neural network (ANN) model to estimate the maximal oxygen uptake (VO2max) based on a multistage 10 m shuttle run test (SRT) in healthy adults. For ANN-based VO2max estimation, 118 healthy Korean adults (59 men and 59 women) in their twenties and fifties (38.3 ± 11.8 years, men aged 37.8 ± 12.1 years, and women aged 38.8 ± 11.6 years) participated in this study; data included age, sex, blood pressure (systolic blood pressure (SBP), diastolic blood pressure (DBP)), waist circumference, hip circumference, waist-to-hip ratio (WHR), body composition (weight, height, body mass index (BMI), percent skeletal muscle, and percent body), 10 m SRT parameters (number of round trips and final speed), and VO2max by graded exercise test (GXT) using a treadmill. The best estimation results (R2 = 0.8206, adjusted R2 = 0.7010, root mean square error; RMSE = 3.1301) were obtained in case 3 (using age, sex, height, weight, BMI, waist circumference, hip circumference, WHR, SBP, DBP, number of round trips in 10 m SRT, and final speed in 10 m SRT), while the worst results (R2 = 0.7765, adjusted R2 = 0.7206, RMSE = 3.494) were obtained for case 1 (using age, sex, height, weight, BMI, number of round trips in 10 m SRT, and final speed in 10 m SRT). The estimation results of case 2 (using age, sex, height, weight, BMI, waist circumference, hip circumference, WHR, number of round trips in 10 m SRT, and final speed in 10 m SRT) were lower (R2 = 0.7909, adjusted R2 = 0.7072, RMSE = 3.3798) than those of case 3 and higher than those of case 1. However, all cases showed high performance (R2 ) in the estimation results. This brief report developed an ANN-based estimation model to predict the VO2max of healthy adults, and the model’s performance was confirmed to be excellent.