Int J Sports Physiol Perform. 2021 Jun 1;16(6):825-833. doi: 10.1123/ijspp.2020-0534. Epub 2021 Feb 4.
This study compared pooled against individualized load-velocity profiles (LVPs) in the free-weight back squat and power clean.
A total of 10 competitive weightlifters completed baseline 1-repetition maximum assessments in the back squat and power clean. Three incremental LVPs were completed, separated by 48 to 72 hours. Mean and peak velocity were measured via a linear-position transducer (GymAware). Linear and nonlinear (second-order polynomial) regression models were applied to all pooled and individualized LVP data. A combination of coefficient of variation (CV), intraclass correlation coefficient, typical error of measurement, and limits of agreement assessed between-subject variability and within-subject reliability. Acceptable reliability was defined a priori as intraclass correlation coefficient > .7 and CV < 10%.
Very high to practically perfect inverse relationships were evident in the back squat (r = .83-.96) and power clean (r = .83-.89) for both regression models; however, stronger correlations were observed in the individualized LVPs for both exercises (r = .85-.99). Between-subject variability was moderate to large across all relative loads in the back squat (CV = 8.2%-27.8%) but smaller in the power clean (CV = 4.6%-8.5%). The power clean met our criteria for acceptable reliability across all relative loads; however, the back squat revealed large CVs in loads ≥90% of 1-repetition maximum (13.1%-20.5%).
Evidently, load-velocity characteristics are highly individualized, with acceptable levels of reliability observed in the power clean but not in the back squat (≥90% of 1-repetition maximum). If practitioners want to adopt load-velocity profiling as part of their testing and monitoring procedures, an individualized LVP should be utilized over pooled LVPs.
本研究比较了自由重量深蹲和力量清洁中的综合与个体化负荷-速度曲线（LVPs）。
共 10 名竞技举重运动员在深蹲和力量清洁中完成了基线 1 次重复最大评估。在 48 到 72 小时之间完成了三个递增的 LVP。通过线性位置传感器（GymAware）测量平均和峰值速度。对所有综合和个体化 LVP 数据应用线性和非线性（二次多项式）回归模型。通过变异系数（CV）、组内相关系数、典型测量误差和一致性界限来评估个体间变异性和个体内可靠性。预先定义可接受的可靠性为组内相关系数>.7 和 CV < 10%。
在深蹲（r =.83-.96）和力量清洁（r =.83-.89）中，两种回归模型都表现出非常高到几乎完美的负相关关系；然而，在这两种运动中，个体化 LVPs 显示出更强的相关性（r =.85-.99）。在深蹲中，所有相对负荷的个体间变异性均为中度至较大（CV = 8.2%-27.8%），而在力量清洁中则较小（CV = 4.6%-8.5%）。在所有相对负荷中，力量清洁均符合我们可接受可靠性的标准；然而，在深蹲中，在负荷≥90%的 1 次重复最大负荷时，CV 较大（13.1%-20.5%）。
显然，负荷-速度特征高度个体化，在力量清洁中观察到可接受的可靠性水平，但在深蹲中（≥90%的 1 次重复最大负荷）则不然。如果从业者希望将负荷-速度分析作为其测试和监测程序的一部分，那么应使用个体化 LVP 而不是综合 LVPs。
