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Rest Redistribution Functions as a Free and Ad-Hoc Equivalent to Commonly used Velocity-Based Training Thresholds During Clean Pulls at Different Loads

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Abstract This study determined whether redistributing total rest time into shorter, but more frequent rest periods could maintain velocity and power output during 3 traditional sets of 6 clean pulls… Click to show full abstract

Abstract This study determined whether redistributing total rest time into shorter, but more frequent rest periods could maintain velocity and power output during 3 traditional sets of 6 clean pulls using 80% (TS80), 100% (TS100) and 120% (TS120) of power clean 1RM with 180 seconds of inter-set rest and during 3 “rest redistribution” protocols of 9 sets of 2 clean pulls using 80% (RR80), 100% (RR100) and 120% (RR120) of power clean 1RM with 45 seconds of inter-set rest. The total number of repetitions performed above 10 and 20% velocity loss thresholds, mean and peak velocity maintenance (the average of all 18 repetitions relative to the best repetition; MVM, PVM), and decline (the worst repetition relative to the best repetition; MVD, PVD) were calculated. For MVM, PVM, MVD, and PVD, there were small-to-moderate effect sizes in favour of RR80 and RR100, but large effects favouring RR120, compared to their respective TS protocols. The number of repetitions within a 20% velocity loss threshold was 17.7 ± 0.6 during RR and 16.5 ± 2.4 during TS (effect size 0.69); and the number of repetitions within a 10% velocity loss threshold was about 13.1 ± 3.7 during RR and 10.7 ± 3.6 during TS (effect size 0.66). Therefore, RR generally allowed for a better overall maintenance of velocity and power, especially at heavy loads. Coaches who wish to implement velocity-based training, but who do not wish to purchase or use the associated equipment, may consider rest-redistribution to encourage similar training stimuli.

Keywords: clean pulls; velocity; rest redistribution; based training; velocity based

Journal Title: Journal of Human Kinetics
Year Published: 2019

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