Predicting Running Performance and Adaptations from Intervals at Maximal Sustainable Effort

Abstract

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 6x3-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 data sets. 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.

Monitoring fatigue state with heart rate‐based and subjective methods during intensified training in recreational runners

“…Slightly surprisingly, the running speed to perceived exertion‐ratio did not respond to training load systematically and did not differ between the RESP and OR individuals. Thus, impaired subjective recovery at rest was not similarly converted to exercise performance during an overload period, although changes in effort‐based interval running performance have previously aligned with the changes in distance running performance (Nuuttila et al., 2023). As Coates, Hammond, & Burr, 2018 have discussed, especially in recreationally trained individuals, the performance itself, paradoxically, might not be an optimal marker to monitor the fatigue state, because significant improvements in fitness can mask the decrements in performance.…”

“…The average running speed and the average HR were determined as the average of all intervals. The average speed of the intervals has previously correlated strongly with the 3000 m running performance and its change (Nuuttila et al., 2023).…”

“…The running time, average and peak HR, and blood lactate concentration (post 2 min) were analyzed for each test. The 3000 m running test was chosen as an indicator of endurance performance due to its feasibility, previously reported as low coefficient of variation (CV) (1.41%) in recreational runners (Nuuttila et al., 2022b) and strong associations with incremental treadmill test variables (Nuuttila et al., 2023).…”