The study aim was to compare different predictive models in one repetition maximum (1RM) estimation from load-velocity profile (LVP) data. Fourteen strength-trained men underwent initial 1RMs in the free-weight back squat, followed by two LVPs, over three sessions. Profiles were constructed via a combined method (jump squat (0 load, 30–60% 1RM) + back squat (70–100% 1RM)) or back squat only (0 load, 30–100% 1RM) in 10% increments. Quadratic and linear regression modeling was applied to the data to estimate 80% 1RM (kg) using 80% 1RM mean velocity identified in LVP one as the reference point, with load (kg), then extrapolated to predict 1RM. The 1RM prediction was based on LVP two data and analyzed via analysis of variance, effect size (g/), Pearson correlation coefficients (r), paired t-tests, standard error of the estimate (SEE), and limits of agreement (LOA). p < 0.05. All models reported systematic bias < 10 kg, r > 0.97, and SEE < 5 kg, however, all linear models were significantly different from measured 1RM (p = 0.015 <0.001). Significant differences were observed between quadratic and linear models for combined (p < 0.001; = 0.90) and back squat (p = 0.004, = 0.35) methods. Significant differences were observed between exercises when applying linear modeling (p < 0.001, = 0.67–0.80), but not quadratic (p = 0.632–0.929, = 0.001–0.18). Quadratic modeling employing the combined method rendered the greatest predictive validity. Practitioners should therefore utilize this method when looking to predict daily 1RMs as a means of load autoregulation.

中文翻译：

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使用负载速度曲线进行 1RM 预测的新方法：模型比较

该研究的目的是根据负载速度曲线 (LVP) 数据在一次最大重复 (1RM) 估计中比较不同的预测模型。14 名接受过力量训练的男性在自由重量后蹲中进行了最初的 1RM，然后是两次 LVP，历时三个疗程。通过组合方法（跳蹲（0 负荷，30-60% 1RM）+ 后蹲（70-100% 1RM））或仅后蹲（0 负荷，30-100% 1RM）以 10% 的增量构建轮廓。将二次和线性回归模型应用于数据以估计 80% 1RM (kg)，使用 LVP 1 中确定的 80% 1RM 平均速度作为参考点，以负载 (kg)，然后外推以预测 1RM。1RM 预测基于 LVP 2 数据，并通过方差分析、效应大小 ( g /)、Pearson 相关系数 ( r)、配对t检验、估计的标准误差 (SEE) 和一致限 (LOA)。p < 0.05。所有模型均报告系统偏差 < 10 kg、r > 0.97 和 SEE < 5 kg，但是，所有线性模型与测量的 1RM 显着不同（p = 0.015 <0.001）。在组合 ( p < 0.001; = 0.90) 和深蹲 ( p = 0.004, = 0.35) 方法的二次和线性模型之间观察到显着差异。应用线性建模时，在练习之间观察到显着差异（p < 0.001，= 0.67–0.80），但不是二次（p= 0.632–0.929，= 0.001–0.18）。采用组合方法的二次建模呈现最大的预测有效性。因此，当希望预测每日 1RMs 作为负载自动调节的一种手段时，从业者应该使用这种方法。