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

This study examined the predictive quality of intervals performed at maximal sustainable effort to predict 3-km and 10-km running times. In addition, changes in interval performance and associated changes in running performance were investigated. Either 6-week (10-km group, n=29) or 2-week (3-km group, n=16) interval training periods were performed by recreational runners. A linear model was created for both groups based on the running speed of the first 6×3-min interval session and the test run of the preceding week (T1). The accuracy of the model was tested with the running speed of the last interval session and the test run after the training period (T2). Pearson correlation was used to analyze relationships between changes in running speeds during the tests and interval sessions. At T2, the mean absolute percentage error of estimate for 3-km and 10-km test times were 2.3% and 3.4%, respectively. The change in running speed of intervals and test runs from T1 to T2 correlated (r=0.75, p<0.001) in both datasets. Thus, the maximal sustainable effort intervals were able to predict 3-km and 10-km running performance and training adaptations with good accuracy, and current results demonstrate the potential usefulness of intervals as part of the monitoring process.