Estimation method of metabolic equivalents during short duration walking and running considering EPOC - Akihiro SUZUKI; Yuki MONIWA; Kenji ISHII; Seisuke FUJIWARA; Ryoichi NAGATOMI (Nihon Kikai Gakkai ronbunshu, 2017)
Amount of daily physical activity is strongly associated with the prevalence of lifestyle-associated diseases, and thus maintaining a certain level of physical activity is recommended. Amount of physical activity, namely energy expenditure, is commonly estimated from linear regression analysis of oxygen uptake during steady-state exercise of different intensities. Short-duration exercise, mainly walking lasting less than 3 minutes, has been reported to account for over 90% of the daily energy expenditure of healthy adults. The estimation of energy expenditure by ACSM Metabolic Equations used well, however, is based on steady-state exercise of more than 5 minutes, and energy consumption of shorter duration exercise is not commonly considered. Oxygen consumption remains elevated for some period of time after exercise. Then, the estimation of energy expenditure for short duration exercise must include excess post-exercise oxygen consumption (EPOC). The amount of EPOC change in duration exercise. Therefore, we attempted to elucidate the energy expenditure for VO2 kinetics. Ten healthy participants aged 19-54 y walked and ran for 1, 3 and 5 minutes on a treadmill at constant speed. Oxygen consumption during and post-exercise was measured using a portable gas analyzer in breath-by-breath mode. A best-fit exponential equation to estimate oxygen consumption from exercise duration and walking speed was generated. The root mean square (RMS) of estimate equations was calculated using Leave-one-out cross validation. The RMS(1.44～2.14 ml/kg/min) was lower, and the results of the Bland-Altman analysis revealed neither fixed nor proportional bias with the exception of the decent walking and running. An equation for the estimation of oxygen consumption at shorter durations of exercise was successfully generated. Accumulation of additional data may further improve the equation.