Estimating excess post-exercise oxygen consumption using multiple linear regression in healthy Korean adults: a pilot...

Area of Science:

• Exercise Physiology
• Human Metabolism
• Biostatistics

Background:

• Excess post-exercise oxygen consumption (EPOC) is a key indicator of metabolic recovery after physical activity.
• Accurate estimation of EPOC is crucial for understanding energy expenditure and optimizing training regimens.
• Previous EPOC estimation models often require complex measurements, limiting their practical application.

Purpose of the Study:

• To develop and validate a regression model for estimating EPOC in Korean adults.
• To identify easily measurable variables that can predict EPOC across different exercise modalities.
• To provide a practical tool for assessing post-exercise energy expenditure in a specific population.

Main Methods:

• A pilot study involving 75 healthy Korean adults (31 males, 44 females).
• Measurement of EPOC and potential predictor variables including fat-free mass (FFM) and heart rate sum (HR_sum).
• Application of stepwise regression analysis to develop predictive models for continuous exercise (CEx), interval exercise (IEx), and accumulated short-duration exercise (AEx).

Main Results:

• Fat-free mass (FFM) and heart rate sum (HR_sum) were identified as significant predictors for EPOC across all exercise types.
• The developed regression models demonstrated high explanatory power: R2 values ranged from 83.1% (IEx) to 91.3% (AEx).
• Predicted EPOC values showed no significant difference compared to those measured using a metabolic gas analyzer, indicating model accuracy.

Conclusions:

• A validated regression model was successfully developed to estimate EPOC in healthy Korean adults.
• The model utilizes readily available anthropometric (FFM) and physiological (HR_sum) data, enhancing its clinical utility.
• The derived equations provide a reliable method for estimating EPOC for CEx, IEx, and AEx, contributing to exercise science research and practice.