Mangine Gerald T, Feito Yuri, Tankersley Joy E, McDougle Jacob M, Kliszczewicz Brian M
Department of Exercise Science and Sport Management, Kennesaw State University, Kennesaw, GA, USA.
J Hum Kinet. 2021 Mar 31;78:89-100. doi: 10.2478/hukin-2021-0043. eCollection 2021 Mar.
To observe workout repetition and rest interval pacing strategies and determine which best predicted performance during the 2016 CrossFit® Open, five male (34.4 ± 3.8 years, 176 ± 5 cm, 80.3 ± 9.7 kg) and six female (35.2 ± 6.3 years, 158 ± 7 cm, 75.9 ± 19.3 kg) recreational competitors were recruited for this observational, pilot study. Exercise, round, and rest time were quantified via a stopwatch for all competitors on their first attempt of each of the five workouts. Subsequently, pacing was calculated as a repetition rate (repetitions·s) to determine the fastest, slowest, and average rate for each exercise, round, and rest interval, as well as how these changed (i.e., slope, Δ rate / round) across each workout. Spearman's rank correlation coefficients indicated that several pacing variables were significantly (p < 0.05) related to performance on each workout. However, stepwise regression analysis indicated that the average round rate best predicted (p < 0.001) performance on the first (R = 0.89), second (R = 0.99), and fifth (R = 0.94) workouts, while the competitors' rate on their slowest round best predicted workout three performance (R = 0.94, p < 0.001). The wall ball completion rate (R = 0.89, p = 0.002) was the best predictor of workout four performance, which was improved by 9.8% with the inclusion of the deadlift completion rate. These data suggest that when CrossFit Open workouts consist of multiple rounds, competitors should employ a fast and sustainable pace to improve performance. Otherwise, focusing on one or two key exercises may be the best approach.
为了观察训练重复次数和休息间隔节奏策略，并确定哪种策略能最好地预测2016年CrossFit®公开赛期间的表现，招募了五名男性（年龄34.4±3.8岁，身高176±5厘米，体重80.3±9.7千克）和六名女性（年龄35.2±6.3岁，身高158±7厘米，体重75.9±19.3千克）休闲参赛者参与这项观察性的试点研究。在五次训练中的每次训练的首次尝试时，通过秒表对所有参赛者的运动时间、轮次时间和休息时间进行量化。随后，将节奏计算为重复率（重复次数·秒），以确定每项运动、每轮和每个休息间隔的最快、最慢和平均速率，以及这些速率在每次训练中的变化情况（即斜率，Δ速率/轮次）。斯皮尔曼等级相关系数表明，几个节奏变量与每次训练的表现显著相关（p<0.05）。然而，逐步回归分析表明，平均轮次速率最能预测第一次（R=0.89）、第二次（R=0.99）和第五次（R=0.94）训练的表现（p<0.001），而参赛者最慢轮次的速率最能预测第三次训练的表现（R=0.94，p<0.001）。壁球完成率（R=0.89，p=0.002）是第四次训练表现的最佳预测指标，纳入硬拉完成率后，表现提高了9.8%。这些数据表明，当CrossFit公开赛的训练由多个轮次组成时，参赛者应采用快速且可持续的节奏来提高表现。否则，专注于一两个关键练习可能是最佳方法。
