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Workout Pacing Predictors of Crossfit ® Open Performance: A Pilot Study.
- Source :
-
Journal of human kinetics [J Hum Kinet] 2021 Mar 31; Vol. 78, pp. 89-100. Date of Electronic Publication: 2021 Mar 31 (Print Publication: 2021). - Publication Year :
- 2021
-
Abstract
- 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 <superscript>-1</superscript> ) 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 <superscript>2</superscript> = 0.89), second (R <superscript>2</superscript> = 0.99), and fifth (R <superscript>2</superscript> = 0.94) workouts, while the competitors' rate on their slowest round best predicted workout three performance (R <superscript>2</superscript> = 0.94, p < 0.001). The wall ball completion rate (R <superscript>2</superscript> = 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 <superscript>®</superscript> 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.<br /> (© 2021 Gerald T. Mangine, Yuri Feito, Joy E. Tankersley, Jacob M. McDougle, Brian M. Kliszczewicz, published by Sciendo.)
Details
- Language :
- English
- ISSN :
- 1640-5544
- Volume :
- 78
- Database :
- MEDLINE
- Journal :
- Journal of human kinetics
- Publication Type :
- Academic Journal
- Accession number :
- 34025867
- Full Text :
- https://doi.org/10.2478/hukin-2021-0043