Impact of rest-redistribution on fatigue during maximal eccentric knee extensions

Redistributing long inter-set rest intervals into shorter but more frequent rest intervals generally maintains concentric performance, possibly due to improved energy store maintenance. However, eccentric actions require less energy than concentric actions, meaning that shorter but more frequent sets may not affect eccentric actions to the same degree as concentric actions. Considering the increased popularity of eccentric exercise, the current study evaluated the effects of redistributing long inter-set rest periods into shorter but more frequent rest periods during eccentric only knee extensions. Eleven resistance-trained men performed 40 isokinetic unilateral knee extensions at 60°·s-1 with 285 s of total rest using traditional sets (TS; 4 sets of 10 with 95 s inter-set rest) and rest-redistribution (RR; 20 sets of 2 with 15 s inter-set rest). Before and during exercise, muscle oxygenation was measured via near-infrared spectroscopy, and rating of perceived exertion (RPE) was recorded after every 10th repetition. There were no differences between protocols for peak torque (RR, 241.58±47.20 N; TS, 231.64±48.87 N; p=0.396) or total work (RR, 215.26±41.47 J; TS, 209.71±36.02 J; p=0.601), but moderate to large effect sizes existed in later repetitions (6,8,10) with greater peak torque during RR (d=0.66-1.19). For the entire session, RR had moderate effects on RPE (RR, 5.73±1.42; TS, 6.09±1.30; p=0.307; d=0.53) and large effects on oxygen saturation (RR, 5857.4±310.0; TS, 6495.8±273.8; p=0.002, d=2.13). Therefore, RR may maintain peak torque or total work during eccentric exercise, improve oxygen utilization at the muscle, and reduce the perceived effort

Punch Trackers: Correct Recognition Depends on Punch Type and Training Experience

To determine the ability of different punch trackers (PT) (Corner (CPT), Everlast (EPT), and Hykso (HPT)) to recognize specific punch types (lead and rear straight punches, lead and rear hooks, and lead and rear uppercuts) thrown by trained (TR, n = 10) and untrained punchers (UNTR, n = 11), subjects performed different punch combinations, and PT data were compared to data from video recordings to determine how well each PT recognized the punches that were actually thrown. Descriptive statistics and multilevel modelling were used to analyze the data. The CPT, EPT and HPT detected punches more accurately in TR than UNTR, evidenced by a lower percentage error in TR (p = 0.007). The CPT, EPT, and HPT detected straight punches better than uppercuts and hooks, with a lower percentage error for straight punches (p < 0.001). The recognition of punches with CPT and HPT depended on punch order, with earlier punches in a sequence recognized better. The same may or may not have occurred with EPT, but EPT does not allow for data to be exported, meaning the order of individual punches could not be analyzed. The CPT and HPT both seem to be viable options for tracking punch count and punch type in TR and UNTR