Enhancing Post-Exercise Oxygen Kinetics Modeling With Physiological Bounds and Manual V̇O_ Input: A Novel Approach.

This study addresses a critical limitation in existing computational tools for modeling post-exercise oxygen consumption kinetics (V̇O). Although exponential modeling provides practical insights into recovery dynamics, the inability to incorporate an individual's pre-exercise baseline oxygen consumption value (V̇O_) 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 V̇O 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. V̇O 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 V̇O 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 (R) values. Specifically, R 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 values had different impacts on key parameters in both models, showing that higher V̇O values generally improved the model fit in the mono-exponential model but had minimal impact on the bi-exponential model.