A Generalized Equation for Prediction of VO2peak from a Step Test

Abstract

This study aimed to develop an equation to reduce variability of VO2peak prediction from a step test and compare VO2peak prediction from the new equation to the Queen’s College Step Test (QCST). The development group (n=86; 21.7±2 years) was utilized to develop the SDState step test equation to predict relative VO2peak. The cross-validation group (n=99; 21.6±2 years) was used to determine the validity of the SDState step test VO2peak prediction equation. A regression analysis was used to identify the best model to predict VO2peak. Analysis of variance (ANOVA) was further used to determine differences among predicted and measured VO2peak values. Forward stepwise multiple regression identified age, sex, abdominal circumference, and active heart rate at the 3-min mark of the step test to be significant predictors of VO2peak (mL·kg−1·min−1). No differences among measured VO2peak (47.3±7.1 mL·kg−1·min−1) and predicted VO2peak (QCST, 46.9±9.3 mL·kg−1·min−1; SDState 48.3±5.7 mL·kg−1·min−1) were found. Pearson correlations, ICC, SEE, TEE, Bland-Altman plots, and Mountain plots indicate the SDState step test equation provides less variation in the prediction of VO2peak compared to the QCST. The SDState step test equation is effective for predicting VO2peak from the YMCA step test in young, healthy adults.

Prediction of peak oxygen consumption using cardiorespiratory parameters from warm-up and submaximal stage of treadmill cardiopulmonary exercise test

“…Our study focused on investigating ML algorithms to predict VO 2peak with the set of features, which could be obtained using simpler techniques than commonly used spirometry, and the significance of incorporating respiration into the prediction process. The presented results are similar or superior compared to some other presented VO 2peak prediction methods like WFI VO 2peak prediction equation, deep-learning model based on 2DE, or regression models from PACER 20-m shuttle run (19,28,49–52). However, in the existing literature, there are also techniques, which managed to obtain better performance like regression models based on submaximal exercise test protocol using a total body recumbent stepper (53–55).…”

“…Actually, thanks to: a) the growing development of aforementioned measurement devices, b) availability of simply field-based tests such as incremental shuttle walk test (15,16) and c) new statistical prediction models and equations, clinicians and/or researchers are able to estimate VO 2peak , and/or VO 2max , based on selected parameters without performing maximal CPET (17)(18)(19)(20)(21)(22). Unfortunately, estimated VO 2peak using, e.g., only 6-min Walk Test distance demonstrated poor agreement with measured VO 2peak from a CPET (23).…”

Prediction of peak oxygen consumption using cardiorespiratory parameters from warmup and submaximal stage of treadmill cardiopulmonary exercise test

“…Our study focused on investigating ML algorithms to predict VO 2peak with the set of features, which could be obtained using simpler techniques than commonly used spirometry, and the significance of incorporating respiration into the prediction process. The presented results are similar or superior compared to some other presented VO 2peak prediction methods like WFI VO 2peak prediction equation, deep-learning model based on 2DE, or regression models from PACER 20-m shuttle run [ 19 , 28 , 50 – 53 ]. However, in the existing literature, there are also techniques, which managed to obtain better performance like regression models based on submaximal exercise test protocol using a total body recumbent stepper [ 54 – 56 ].…”

“…Actually, due to: a) the growing development of aforementioned measurement devices, b) availability of simply field-based tests such as incremental shuttle walk test [15,16] and c) new statistical prediction models and equations, clinicians and/or researchers are able to estimate VO 2peak , and/or VO 2max (it is however not the subject of this study), based on selected parameters without performing maximal CPET [17][18][19][20][21][22]. Unfortunately, estimated VO 2peak using, e.g., only 6-min Walk Test distance demonstrated poor agreement with measured VO 2peak from a CPET [23].…”

The effect of HIIT on body composition, cardiovascular fitness, psychological well-being, and executive function of overweight/obese female young adults

“…The acquired HR was multiplied by 4 to get the beats per minute (bpm), which was then used to estimate the VO2max of female subjects by using the regression equation as follows: VO2max (ml/kg) /min) = 65.81- (0.1847× HR (bpm). The QCST was constructed for college students and has been used effectively in other studies of young adults, including sedentary or non-exercising individuals and overweight or obese female participants ( D'Alonzo et al, 2006 ; Choudhuri et al, 2014 ; Selland et al, 2021 ).…”