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 (V̇O 2 ). Although exponential modeling provides practical insights into recovery dynamics, the inability to incorporate an individual's pre‐exercise baseline oxygen consumption value (V̇O 2 _ 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 V̇O 2 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 2 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 2 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 2 ) values. Specifically, R 2 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.