Department of Physical Education, Sport and Human Movement, Autonomous University of Madrid, Madrid, Spain.
Sport Studies Centre, King Juan Carlos University, Madrid, Spain.
Sports Biomech. 2022 Jan;21(1):41-55. doi: 10.1080/14763141.2019.1640278. Epub 2019 Jul 29.
There is a growing interest in the analysis of different methods for monitor fatigue during resistance training sessions. This study aimed to (1) analyse the relationships between the percentage of performed repetitions with respect to the maximum possible number (%REP), RPE and magnitude of velocity loss (VL), and (2) examine whether a multiple regression analysis with the RPE and VL as predictor variables could improve the goodness of fit to predict %REP in the bench press exercise performed in a Smith machine. Seven men performed a repetition maximum test, on 3 separate testing sessions, against 3 different absolute loads based on a target mean velocity (MV) according to an individual load-velocity profile (≈1.00, ≈0.70, and ≈0.50 m/s). MV, VL, %REP and RPE were collected and used for analysis. Based upon quadratic polynomial regression analysis strong relationships were reported between the RPE and %REP (= 0.89 and SEE = 9.85%) and between the VL and %REP (= 0.91 and SEE = 9.85%). Multiple regression analysis with the RPE and VL as predictor variables improved the goodness of fit ( = 0.94 and SEE = 7.18%) of the model to predict %REP. These results suggest that both RPE and VL are useful variables to accurately estimate %REP in the bench press exercise.
人们对分析抗阻训练过程中不同的疲劳监测方法越来越感兴趣。本研究旨在：（1）分析与完成的重复次数百分比（%REP）、RPE 和速度损失幅度（VL）相关的关系；（2）检验以 RPE 和 VL 为预测变量的多元回归分析是否可以提高对史密斯机卧推运动中 %REP 的预测拟合度。7 名男性在 3 次不同的测试中，根据个体的速度-负荷曲线，对 3 种不同的绝对负荷（基于目标平均速度 MV）进行重复最大测试（约 1.00、0.70 和 0.50 m/s）。收集了 MV、VL、%REP 和 RPE 并进行分析。基于二次多项式回归分析，RPE 和 %REP 之间（=0.89，SEE=9.85%）以及 VL 和 %REP 之间（=0.91，SEE=9.85%）存在较强的关系。以 RPE 和 VL 为预测变量的多元回归分析提高了模型预测%REP 的拟合度（=0.94，SEE=7.18%）。这些结果表明，RPE 和 VL 都是准确估计卧推运动中%REP 的有用变量。
