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


该研究的目的是比较负载速度曲线 (LVP) 数据的一次重复最大 (1RM) 估计中的不同预测模型。 14 名接受过力量训练的男子在自由重量后深蹲中接受了最初的 1RM 训练，随后进行了 3 次 LVP 训练。通过组合方法（跳深蹲（0 负载，30–60% 1RM）+ 后蹲（70–100% 1RM））或仅后蹲（0 负载，30–100% 1RM）以 10% 的增量构建配置文件。对数据应用二次和线性回归模型，使用 LVP 1 中确定的 80% 1RM 平均速度作为参考点，负载 (kg) 来估计 80% 1RM (kg)，然后外推以预测 1RM。 1RM 预测基于 LVP 两个数据，并通过方差分析、效应大小 ( g /)、皮尔逊相关系数 ( r )、配对t检验、估计标准误差 (SEE) 和一致性极限 (LOA) 进行分析）。 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，所有线性模型均与测量的 1RM ( p = 0.015 <0 id=36>p < 0.001; = 0.90) 和后蹲有显着差异( p = 0.004, = 0.35) 方法。应用线性模型时，练习之间观察到显着差异（ p < 0.001，= 0.67–0.80），但不是二次模型（ p = 0.632–0.929，= 0.001–0.18）。采用组合方法的二次建模提供了最大的预测有效性。因此，从业者在寻求预测每日 1RM 作为负载自动调节的手段时应使用此方法。