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The Relationship between CrossFit® Performance and Laboratory-Based Measurements of Fitness

机译:Crossfit®性能与实验室的健身测量的关系

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摘要

To date, research has examined the physiological determinants of performance in standardized CrossFit (CF) workouts but not without the influence of CF familiarity. Therefore, the purpose of this present study was to examine the predictive value of aerobic fitness, body composition, and total body strength on performance of two standardized CF workouts in CF-naïve participants. Twenty-two recreationally trained individuals (males = 13, females = 9) underwent assessments of peak oxygen consumption (VO peak), ventilatory thresholds, body composition, and one repetition maximum tests for the back squat, deadlift, and overhead press in which the sum equaled the CF Total. Participants also performed two CF workouts: a scaled version of the CF Open workout 19.1 and a modified version of the CF Benchmark workout Fran to determine scores based on total repetitions completed and time-to-completion, respectively. Simple Pearson’s r correlations were used to determine the relationships between CF performance variables (19.1 and modified Fran) and the independent variables. A forward stepwise multiple linear regression analysis was performed and significant variables that survived the regression analysis were used to create a predictive model of CF performance. Absolute VO peak was a significant predictor of 19.1 performance, explaining 39% of its variance (adjusted R = 0.39, = 0.002). For modified Fran, CF Total was a significant predictor and explained 33% of the variance in performance (adjusted R = 0.33, = 0.005). These results suggest, without any influence of CF familiarity or experience, that performance in these two CF workouts could be predicted by distinct laboratory-based measurements of fitness.
机译:迄今为止,研究检测了标准化的Crossfit(C​​F)锻炼中的性能的生理决定因素,但并非没有CF熟悉的影响。因此,本研究的目的是检查有氧健身,身体组成和总体强度的预测值,就CF-Naïve参与者的两个标准化CF锻炼的性能进行了预测值。二十二次休闲训练的个体(男性= 13,女性= 9)经历了峰值氧消耗(VO峰),通风阈值,身体组成的评估,以及后蹲,硬拉和架空压力机的一次重复最大测试总和等于CF总数。参与者还执行了两个CF锻炼:CF开放式锻炼19.1的缩放版本和CF基准锻炼Fran的修改版本,以分别根据总重复完成和完成时间。简单的Pearson的R相关性用于确定CF性能变量(19.1和修改的FRAN)与独立变量之间的关系。向前逐步多次线性回归分析,并使用过回归分析的显着变量来创建CF性能的预测模型。绝对VO峰是19.1性能的重要预测因子,解释其差异的39%(调整r = 0.39,= 0.002)。对于修改的Fran,CF总数是重要的预测因子,并解释了33%的性能方差(调整后的r = 0.33,= 0.005)。这些结果表明,没有CF熟悉或经验的任何影响,这两个CF锻炼中的性能可以通过不同的基于实验室的健身测量来预测。

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