Quantifying and Comparing Training Load Metrics in Cycling: A Methodology Review.

Area of Science:

• Sports Science
• Exercise Physiology
• Performance Analytics

Background:

• Accurate training load monitoring is crucial for optimizing cyclist performance and preventing maladaptation or injury.
• Quantifying training load aids in personalized nutrition planning, adjusting energy and macronutrient needs based on exercise demands.
• Students and practitioners face challenges in selecting, calculating, and interpreting various training load metrics.

Purpose of the Study:

• To provide a methodology for computing three key cycling training load metrics: training stress score, Edwards' training impulse, and session rating of perceived exertion.
• To offer practical, illustrated examples for evaluating and predicting these metrics using competitive cyclist data.
• To guide the interpretation of relationships between metrics and their application in nutrition planning.

Main Methods:

• Step-by-step guidance on calculating training stress score, Edwards' training impulse, and session rating of perceived exertion.
• Visualization and interpretation of metric relationships using scatterplots and regression analyses (linear, curvilinear, with/without intercepts).
• Application of partial correlation to analyze metric associations, controlling for exercise duration.

Main Results:

• Demonstrated practical calculation and interpretation of training load metrics.
• Illustrated the derivation of session-specific carbohydrate and energy targets from training data.
• Provided spreadsheet instructions and R script for replication and integration into practice.

Conclusions:

• The outlined methods enhance understanding of training load monitoring for students and practitioners.
• Enables the development of tailored training and nutrition strategies for individual cyclists.
• Facilitates evidence-based decision-making in training and nutrition periodization.