Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland.
Finnish Institute of High Performance Sport KIHU, Jyväskylä, Finland.
Int J Sports Med. 2023 Jul;44(9):657-663. doi: 10.1055/a-2024-9490. Epub 2023 Feb 1.
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.
本研究旨在检验以最大可持续努力完成的区间跑预测 3 公里和 10 公里跑时间的预测质量。此外，还研究了区间跑表现的变化及其与跑步表现的关联变化。29 名休闲跑者参加了 6 周（10 公里组）或 2 周（3 公里组）的间歇训练。根据前一周的前 6 个 3 分钟区间跑和测试跑（T1）的跑步速度，为两个组建立了线性模型。用最后一个区间跑和训练期后的测试跑（T2）的跑步速度来检验模型的准确性。用皮尔逊相关分析测试和区间跑中跑步速度的变化之间的关系。在 T2 时，3 公里和 10 公里测试时间的估计平均绝对百分比误差分别为 2.3%和 3.4%。T1 到 T2 时，区间跑和测试跑的跑步速度变化相关（r=0.75，p<0.001），两个数据集都有。因此，最大可持续努力的区间跑可以很好地准确预测 3 公里和 10 公里跑的表现和训练适应性，目前的结果表明，区间跑作为监测过程的一部分具有潜在的用处。
