Enhancing Post‐Exercise Oxygen Kinetics Modeling With Physiological Bounds and Manual VO₂__baseline Input: A Novel Approach

This study addresses a critical limitation in existing computational tools for modeling post-exercise oxygen consumption kinetics (V̇O₂). Although exponential modeling provides practical insights into recovery dynamics, the inability to incorporate an individual's pre-exercise baseline oxygen consumption value (V̇O₂__baseline) can lead to inaccurate interpretations. A user-defined baseline allows for more precise modeling by aligning recovery kinetics with the true physiological endpoint, representing the individual's actual recovery target after a sufficient rest. To overcome this limitation, this study employs a customized Python algorithm that incorporates user-defined baseline V̇O₂ and uses both mono-exponential and bi-exponential models, aiming to improve upon existing analytical methods. Twenty-two male amateur soccer players participated in this study and performed a 30-s Wingate test. V̇O₂ was measured continuously before, during, and after exercise via a metabolic gas analyzer. Both mono-exponential and bi-exponential models were used to analyze post-exercise V̇O₂ kinetics. The analysis was performed using Origin software (as the reference tool), GedaeLab (a specialized web-based platform), and a custom-developed Python algorithm. The bi-exponential model demonstrated superior fit compared to the mono-exponential model with higher determination coefficient (R²) values. Specifically, R² values were 0.963 ± 0.013 and 0.805 ± 0.078 for the bi-exponential and mono-exponential models, respectively. The bi-exponential model also provided a more accurate approximation of real post-exercise oxygen consumption integrals at both 5 min and 15 min. Additionally, variations in V̇O_{2_baseline} values had different impacts on key parameters in both models, showing that higher V̇O_{2_baseline} values generally improved the model fit in the mono-exponential model but had minimal impact on the bi-exponential model.