Predicting Running Performance and Adaptations from Intervals at Maximal Sustainable Effort.

Area of Science:

• Sports Science
• Exercise Physiology
• Running Performance Analysis

Background:

• Predicting running performance is crucial for training.
• Maximal sustainable effort intervals are a potential tool for performance assessment.
• Understanding training adaptations enhances performance monitoring.

Purpose of the Study:

• To evaluate the predictive accuracy of maximal sustainable effort intervals for 3-km and 10-km running times.
• To investigate the relationship between changes in interval performance and running performance.
• To assess the utility of these intervals in monitoring training adaptations.

Main Methods:

• Recreational runners completed 2-week (3-km group) or 6-week (10-km group) interval training.
• A linear model used initial interval and test run data (T1) to predict performance.
• Model accuracy was validated using final interval and test run data (T2); Pearson correlation analyzed speed changes.

Main Results:

• The predictive model showed a mean absolute percentage error of 2.3% for 3-km and 3.4% for 10-km times at T2.
• Changes in interval running speed and test run speed from T1 to T2 were strongly correlated (r=0.75, p<0.001).
• Maximal sustainable effort intervals demonstrated good accuracy in predicting running performance and training changes.

Conclusions:

• Maximal sustainable effort intervals are a valid and accurate method for predicting 3-km and 10-km running performance.
• These intervals effectively track training-induced adaptations in recreational runners.
• Interval performance monitoring offers practical insights for optimizing running training programs.