Mathematical Comparison of Time-Based and Stroke-Based Fatigue Models in Elite Sprint Cycling: Convergence Analysis...

Area of Science:

• Exercise Physiology
• Sports Biomechanics
• Performance Analytics

Background:

• Cyclical power decline during maximal sprints is a key performance determinant.
• Existing models like Parallel Shift Approach (PASA) and Pedal Stroke-Based Approach (PESA) offer different frameworks for fatigue modeling.
• Understanding model accuracy and applicability is crucial for optimizing training and performance.

Purpose of the Study:

• To compare the predictive accuracy, convergence, and applicability of PASA and PESA in elite track cyclists.
• To evaluate model performance across varying sprint durations and cadences.
• To guide the selection of appropriate fatigue modeling techniques based on research or practical needs.

Main Methods:

• Twelve elite track cyclists performed 45-second maximal sprints at a fixed cadence.
• Individual force-velocity (F-v) profiles were used to calibrate both PASA and PESA models.
• Root Mean Square Error (RMSE) and R-squared (R²) were employed to evaluate predictive accuracy.
• Model convergence was analyzed across different sprint phases and cadence ranges.

Main Results:

• Both models showed excellent fit to sprint power data (R² > 0.98).
• PASA demonstrated lower prediction errors (RMSE: 32 ± 11 W) compared to standard PESA (RMSE: 57-60 W).
• Optimizing PESA's parameter significantly improved its accuracy (RMSE: 35 ± 12 W).
• Model agreement was high during early sprint phases and moderate cadences, but diverged later in sprints and at extreme cadences.

Conclusions:

• PASA provides higher precision for detailed fatigue analysis during cycling sprints.
• PESA, especially when optimized, offers a computationally efficient alternative for practical applications.
• Model choice depends on specific analytical goals and constraints, with PASA preferred for in-depth fatigue research.
• Further research should explore model performance under variable-cadence conditions and refine underlying physiological assumptions.