Model for predicting metabolic activity in athletes based on biochemical blood test analysis

Area of Science:

• Sports Science
• Biochemistry
• Exercise Physiology

Background:

• Optimizing athletic performance and preventing overtraining depend on effective training organization.
• Athlete adaptation to physical stress involves specific biochemical and functional changes.
• Predicting metabolic states aids in tailoring training and recovery programs.

Purpose of the Study:

• To develop a diagnostic model for predicting metabolic processes in athletes.
• To utilize standard biochemical blood analysis indicators for this prediction.
• To enhance the organization of the training process for improved efficiency.

Main Methods:

• Involved track and field athletes (n=42).
• Collected capillary blood samples over an annual cycle for biochemical analysis (3,625 tests).
• Derived equations from a linear discriminant function to predict anabolic or catabolic processes.

Main Results:

• Developed a predictive model for athletic metabolic processes with 92.1% information capacity.
• The model's discriminant function showed statistical significance (p < 0.01).
• Confirmed by neuroendocrine system activity, reflecting adaptive regulatory mechanisms.

Conclusions:

• An informative mathematical model reliably predicts metabolic activity phases in athletes.
• The model aids in assessing adaptation, identifying weaknesses, and forecasting performance.
• Enables timely adjustments to training and recovery programs for optimal results.