Enhancing Post-Exercise Oxygen Kinetics Modeling With Physiological Bounds and Manual V̇O₂__baseline Input: A Novel...

Area of Science:

• Exercise Physiology
• Computational Biology
• Sports Science

Background:

• Accurate modeling of post-exercise oxygen consumption (V̇O2) kinetics is crucial for understanding recovery.
• Existing computational tools often fail to incorporate individual pre-exercise baseline V̇O2 (V̇O2_baseline), limiting model precision.
• A user-defined baseline is essential for aligning recovery kinetics with the true physiological endpoint.

Purpose of the Study:

• To develop and validate a customized Python algorithm for modeling V̇O2 kinetics incorporating user-defined V̇O2_baseline.
• To compare the analytical performance of mono-exponential and bi-exponential models in post-exercise V̇O2 analysis.
• To assess the impact of V̇O2_baseline variations on model parameters.

Main Methods:

• Twenty-two male amateur soccer players underwent a 30-s Wingate test.
• Continuous V̇O2 measurements were taken pre-, during, and post-exercise using a metabolic gas analyzer.
• A custom Python algorithm was developed to analyze V̇O2 kinetics using mono-exponential and bi-exponential models, with comparisons to Origin and GedaeLab.

Main Results:

• The bi-exponential model demonstrated a significantly superior fit (R² = 0.963 ± 0.013) compared to the mono-exponential model (R² = 0.805 ± 0.078).
• The bi-exponential model provided a more accurate approximation of post-exercise V̇O2 integrals at 5 and 15 minutes.
• Higher V̇O2_baseline values generally improved mono-exponential model fit but had minimal impact on the bi-exponential model.

Conclusions:

• The developed Python algorithm effectively incorporates user-defined V̇O2_baseline for more precise V̇O2 kinetics modeling.
• The bi-exponential model offers superior accuracy and fit for analyzing post-exercise V̇O2 recovery compared to the mono-exponential model.
• This approach enhances the physiological relevance of V̇O2 recovery modeling in athletes.