Macht Jordan W, Abel Mark G, Mullineaux David R, Yates James W
1Department of Kinesiology and Health Promotion, University of Kentucky, Lexington, Kentucky; and 2School of Sport, Coaching, and Exercise Science, University of Lincoln, United Kingdom.
J Strength Cond Res. 2016 Oct;30(10):2901-6. doi: 10.1519/JSC.0000000000001385.
Macht, JW, Abel, MG, Mullineaux, DR, and Yates, JW. Development of 1RM prediction equations for bench press in moderately trained men. J Strength Cond Res 30(10): 2901-2906, 2016-There are a variety of established 1 repetition maximum (1RM) prediction equations, however, very few prediction equations use anthropometric characteristics exclusively or in part, to estimate 1RM strength. Therefore, the purpose of this study was to develop an original 1RM prediction equation for bench press using anthropometric and performance characteristics in moderately trained male subjects. Sixty male subjects (21.2 ± 2.4 years) completed a 1RM bench press and were randomly assigned a load to complete as many repetitions as possible. In addition, body composition, upper-body anthropometric characteristics, and handgrip strength were assessed. Regression analysis was used to develop a performance-based 1RM prediction equation: 1RM = 1.20 repetition weight + 2.19 repetitions to fatigue - 0.56 biacromial width (cm) + 9.6 (R = 0.99, standard error of estimate [SEE] = 3.5 kg). Regression analysis to develop a nonperformance-based 1RM prediction equation yielded: 1RM (kg) = 0.997 cross-sectional area (CSA) (cm) + 0.401 chest circumference (cm) - 0.385%fat - 0.185 arm length (cm) + 36.7 (R = 0.81, SEE = 13.0 kg). The performance prediction equations developed in this study had high validity coefficients, minimal mean bias, and small limits of agreement. The anthropometric equations had moderately high validity coefficient but larger limits of agreement. The practical applications of this study indicate that the inclusion of anthropometric characteristics and performance variables produce a valid prediction equation for 1RM strength. In addition, the CSA of the arm uses a simple nonperformance method of estimating the lifter's 1RM. This information may be used to predict the starting load for a lifter performing a 1RM prediction protocol or a 1RM testing protocol.
马赫特，JW；阿贝尔，MG；穆利诺克斯，DR；以及耶茨，JW。中等训练水平男性卧推1RM预测方程的开发。《力量与体能研究杂志》30(10): 2901 - 2906，2016年 - 现已有多种既定的1次重复最大值（1RM）预测方程，然而，极少有预测方程完全或部分使用人体测量特征来估计1RM力量。因此，本研究的目的是利用中等训练水平男性受试者的人体测量和表现特征，开发一个原创的卧推1RM预测方程。60名男性受试者（21.2 ± 2.4岁）完成了一次卧推1RM测试，并被随机分配一个负荷，要求完成尽可能多的重复次数。此外，还评估了身体成分、上身人体测量特征和握力。回归分析用于开发一个基于表现的1RM预测方程：1RM = 1.20×重复重量 + 2.19×疲劳重复次数 - 0.56×肩峰间宽度（厘米）+ 9.6（R = 0.99，估计标准误差[SEE] = 3.5千克）。用于开发一个非基于表现的1RM预测方程的回归分析得出：1RM（千克）= 0.997×横截面积（CSA）（厘米）+ 0.401×胸围（厘米）- 0.385×体脂百分比 - 0.185×臂长（厘米）+ 36.7（R = 0.81，SEE = 13.0千克）。本研究中开发的基于表现的预测方程具有较高的有效性系数、最小的平均偏差和较小的一致性界限。基于人体测量的方程具有中等偏高的有效性系数，但一致性界限较大。本研究的实际应用表明，纳入人体测量特征和表现变量可得出一个有效的1RM力量预测方程。此外，手臂的横截面积采用一种简单的非基于表现的方法来估计举重者的1RM。这些信息可用于预测进行1RM预测方案或1RM测试方案的举重者的起始负荷。
