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平均最佳最小速度阈值:一种实用变量,可提高自由重量深蹲中一次重复最大估计的准确性。

Average optimal minimum velocity threshold: A practical variable to increase the accuracy of one-repetition maximum estimation during the free-weight back squat.

机构信息

Neuromuscular Research Lab, Faculdade de Motricidade Humana, Universidade de Lisboa, Dafundo, Portugal.

CIPER - Centro Interdisciplinar para o Estudo da Performance Humana, Faculdade de Motricidade Humana, Universidade de Lisboa, Dafundo, Portugal.

出版信息

J Sports Sci. 2024 Sep;42(18):1767-1775. doi: 10.1080/02640414.2024.2410589. Epub 2024 Oct 2.

Abstract

The prediction of one-repetition maximum (1RM) using the submaximal load-velocity relationship (LVR) is highly relevant for the field of strength and conditioning. The optimal minimum velocity threshold (MVT) was recently proposed to increase the accuracy of 1RM predictions. However, using the average optimal MVT would allow for more practical estimations. LVRs of the free-weight back squat were obtained in 53 participants, throughout 2 sessions. LVRs were obtained using the multi- and two-point methods. Estimations of 1RM were made based on the average actual MVT (1RM velocity) and the average optimal MVT. The accuracy of 1RM predictions was examined using absolute-percent error and Bland-Altman plots. Cross-validation was performed using a leave-one-out approach. The number of selected loads did not affect the slope, y-intercept, optimal MVT or the accuracy of 1RM predictions. Predictions based on the average optimal MVT displayed greater accuracy than those obtained with the average actual MVT (6% vs. ~8% absolute-percent error, respectively). However, wide 95% limits of agreement (LoA) were found between actual and estimated 1RM using both approaches (13%1RM). The average optimal MVT offers more accurate 1RM estimations than the average actual MVT. However, errors prove substantial, making it challenging to precisely track minor changes in 1RM.

摘要

使用亚最大负荷-速度关系(LVR)预测最大重复次数(1RM)在力量和体能训练领域具有重要意义。最近提出了最佳最小速度阈值(MVT),以提高 1RM 预测的准确性。然而,使用平均最佳 MVT 可以进行更实际的估计。在 2 个会话中,对 53 名参与者进行了自由重量深蹲的 LVR 测量。使用多点和两点方法获得了 LVR。根据平均实际 MVT(1RM 速度)和平均最佳 MVT 对 1RM 进行了估计。使用绝对百分比误差和 Bland-Altman 图检查了 1RM 预测的准确性。使用留一法进行了交叉验证。所选负荷的数量不会影响斜率、y 截距、最佳 MVT 或 1RM 预测的准确性。基于平均最佳 MVT 的预测比基于平均实际 MVT 的预测更准确(分别为6%和8%的绝对百分比误差)。然而,两种方法都发现实际和估计的 1RM 之间存在较大的 95%一致性界限(LoA)(~13%1RM)。平均最佳 MVT 提供比平均实际 MVT 更准确的 1RM 估计。然而,误差很大,使得精确跟踪 1RM 的微小变化变得具有挑战性。

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