Jukic, I, García-Ramos, A, Malecek, J, Omcirk, D, and Tufano, JJ. Validity of load–velocity relationship to predict 1 repetition maximum during deadlifts performed with and without lifting straps: The accuracy of six prediction models. J Strength Cond Res 36(4): 902–910, 2022—This study aimed to compare the accuracy of six 1 repetition maximum (1RM) prediction models during deadlifts performed with (DLw) and without (DLn) lifting straps. In a counterbalanced order, 18 resistance-trained men performed 2 sessions that consisted of an incremental loading test (20-40-60-80-90% of 1RM) followed by 1RM attempts during the DLn (1RM = 162.0 ± 26.9 kg) and DLw (1RM = 179.0 ± 29.9 kg). Predicted 1RMs were calculated by entering both group and individualized mean concentric velocity of the 1RM (V1RM) into an individualized linear and polynomial regression equations, which were derived from the load–velocity relationship of 5 ([20-40-60-80-90% of 1RM], i.e., multiple-point method) or 2 ([40 and 90% of 1RM] i.e., 2-point method) incremental warm-up sets. The predicted 1RMs were deemed highly valid if the following criteria were met: trivial to small effect size, practically perfect r, and low absolute errors (<5 kg). The main findings revealed that although prediction models were more accurate during the DLn than DLw, none of the models provided an accurate estimation of the 1RM during both DLn (r = 0.92–0.98; absolute errors: 6.6–8.1 kg) and DLw (r = 0.80–0.93; absolute errors: 12.4–16.3 kg) according to our criteria. Therefore, these results suggest that the 1RM for both DLn and DLw should not be estimated through the recording of movement velocity if sport professionals are not willing to accept more than 5 kg of absolute errors.

Jukic, I, García-Ramos, A, Malecek, J, Omcirk, D, and Tufano, JJ. The use of lifting straps alters the entire load-velocity profile during the deadlift exercise. J Strength Cond Res 34(12): 3331–3337, 2020—This study aimed to compare the one repetition maximum (1RM) and load-velocity (LV) profile between deadlifts performed with (DLw) and without (DLn) lifting straps. The full individual LV relationship of 20 men (age: 24.3 ± 2.4 years; body height: 180.6 ± 6.9 cm; body mass: 85.8 ± 8.0 kg) was randomly evaluated during 2 separate sessions for the DLw and DLn via an incremental loading test. One repetition maximum was greater (p < 0.001; g = 0.56, 95% confidence interval = [0.32, 0.79]) for DLw (177.0 ± 28.9 kg) compared with DLn (160.6 ± 26.0 kg). A highly linear relationship between mean velocity (MV) and %1RM was observed for both conditions (R 2 > 0.95; SEE < 6.18 %1RM for pooled data and R 2 > 0.98; SEE < 3.6 %1RM for individual data). However, MV associated with each %1RM was greater for DLn, and these differences were accentuated as the loading magnitude increased (g = 0.30–1.18). One repetition maximum was strongly associated between both conditions (r = 0.875 [0.71, 0.95]), whereas MV at 1RM (r = 0.21 [−0.25, 0.60]) was unrelated between conditions. The slope of the LV profiles (r = 0.845 [0.64, 0.94]) was correlated, but differed (g = 0.41 [0.16, 0.66]) between DLw and DLn, whereas the mean test velocity of all loads was unrelated (r = 0.270 [−0.20, 0.64]). An individual LV profile should be created for each athlete in the same condition that are going to be used in training to obtain a more precise estimation of the submaximal relative loads.

This study investigated redistributing long inter-set rest intervals into shorter but more frequent intervals at 2 different concentric velocities. Resistance-trained men performed 4 randomised isokinetic unilateral knee extension protocols, 2 at 60°·s−1 and 2 at 360°·s−1. At each speed, subjects performed 40 repetitions with 285 s of rest using traditional sets (TS; 4 sets of 10 with 95 s of inter-set rest) and rest-redistribution (RR; 20 sets of 2 with 15 s inter-set rest). Before and at 2, 5, and 10 min after exercise, tensiomyography (TMG) and oxygenation (near-infrared spectroscopy; NIRS) were measured. NIRS was also measured during exercise, and rating of perceived exertion (RPE) was recorded after every 10 repetitions. At both speeds, RR displayed greater peak torque, total work, and power output during latter repetitions, but there were no differences between TS or RR when averaging all 40 repetitions. The RPE was less during RR at both speeds (p < 0.05). RR increased select muscle oxygen saturation and blood flow at both speeds. There were no effects of protocol on TMG, but effect sizes favoured a quicker recovery after RR. RR was likely beneficial in maintaining performance compared with the latter parts of TS sets and limiting perceived and peripheral fatigue. Novelty Although effective at slow velocities, rest-redistribution was likely more effective during high-velocity movements in this study. Rest-redistribution maintained the ability to produce force throughout an entire range of motion. Rest-redistribution reduced RPE during both high-velocity and high-force movements.

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Achieving the maximum possible impact force of the front kick can be related to the isokinetic lower limb muscle strength. Therefore, we aimed to determine the regression model between kicking performance and the isokinetic peak net moment of hip rotators, flexors, and hip extensors and flexors at various speeds of contraction. Twenty-five male soldiers (27.7 ± 7.2 yrs, 83.8 ± 6.1 kg, 180.5 ± 6.5 cm) performed six barefoot front kicks, where impact forces (N) and kick velocity (m∙s-1) were measured. The 3D kinematics and isokinetic dynamometry were used to estimate the kick velocity, isokinetic moment of kicking lower limb hip flexors and extensors (60, 120, 240, 300°∙s-1), and stance lower limb hip internal and external rotators (30, 90°∙s-1). Multiple regression showed that a separate component of the peak moment concentric hip flexion and extension of the kicking lower limb at 90°∙s-1 can explain 54% of the peak kicking impact force variance (R2 = 0.54; p < 0.001). When adding the other 3 components of eccentric and concentric hip internal and external rotations at 30°∙s-1, the internal and external hip rotation ratios at 30°∙s-1 on the stance limb and the concentric ratio of kicking limb flexion and extension at 300°∙s-1 that explained the variance of impact force were 75% (p = 0.003). The explosive strength of kicking limb hip flexors and extensors is the main condition constraint for kicking performance. The maximum strength of stance limb internal and external rotators and speed strength of kicking limb hip flexors and extensors are important constraints of kicking performance that should be considered to improve the front kick efficiency.

Purpose: Assisted jumping can supplement resistance training and traditional plyometric training to increase vertical jump performance. However, as coaches may choose to make field-based decisions based on lab-based research, this study determined whether a field-based assisted jumping set-up results in different ground contact times (CT), take off forces (TOF), flight times (FT), and impact forces (IF) compared to a lab-based set-up.Methods: Eighteen active males (24.8 ± 3.0 yr; 178.8 ± 7.8 cm; 77.8 ± 7.8 kg) performed two sessions of assisted jumping: one with each hand holding a commercially available resistance band (1m) that was attached to a pull-up bar (FIELD), and the other with assistance from a custom-built system of ropes, pulleys, and long (3 m) elastic bands (LAB). With each set-up, subjects performed five sets of five countermovement jumps on a force plate. Each set was performed with either bodyweight (BW), 90, 80, 70, or 60% of BW, which was achieved by either grabbing higher or lower on the bands during FIELD, or by being pulled upward via a full-body harness during LAB. The order of each visit was counter-balanced, and the order of jumps within each visit was quasi-randomized. Data from the 90, 80, 70, and 60% trials for each set-up were then expressed relative to the data of BW jumps, and these relative values were then used for analysis.Results: CTFIELD was less than CTLAB at 80, 70, and 60%. FTFIELD was greater than FTLAB at 90 and 80%, but FTLAB became greater at 60%. TOF and IF remained unchanged during LAB, but TOFFIELD was consistently less than TOF during BW, with IFFIELD generally being greater than IFLAB.Conclusion: If the purpose of assisted jumping is to spend less time on the ground without decreasing force, systems with finite adjustments and longer bands like LAB should be used. However, shorter bands similar to FIELD may also be used; but due to the larger variability of assistance throughout the range of motion, such systems may alter the neuromuscular characteristics of the jump in other ways that should be investigated in future research.

Background Exercise training is crucial for maintaining physical and mental health in aging populations. However, as people participate in structured exercise training, they tend to behaviorally compensate by decreasing their non-exercise physical activity, thus potentially blunting the benefits of the training program. Furthermore, physical activity of older adults is substantially influenced by physical feelings such as fatigue. Nevertheless, how older people react to day-to-day fluctuations of fatigue and whether fatigue plays a role in non-exercise physical activity compensation is not known. Thus, the purpose of this study was twofold: (1) To explore whether the volume and intensity of habitual physical activity in older adults were affected by morning fatigue. (2) To investigate the effect of attending power and resistance exercise sessions on the levels of non-exercise physical activity later that day and the following day. Methods Twenty-eight older adults wore an accelerometer during a 4-week low-volume, low-intensity resistance and power training program with three exercise sessions per week and for 3 weeks preceding and 1 week following the program. During the same period, the participants were prompted every morning, using text messages, to rate their momentary fatigue on a scale from 0 to 10. Results Greater morning fatigue was associated with lower volume (p = 0.002) and intensity (p = 0.017) of daily physical activity. Specifically, one point greater on the fatigue scale was associated with 3.2 min (SE 1.0) less moderate-to-vigorous physical activity. Furthermore, attending an exercise session was associated with less moderate-to-vigorous physical activity later that day by 3.7 min (SE 1.9, p = 0.049) compared to days without an exercise session. During the next day, the volume of physical activity was greater, but only in participants with a body mass index up to 23 (p = 0.008). Conclusions Following low-volume exercise sessions, fit and healthy older adults decreased their non-exercise physical activity later that day, but this compensation did not carry over into the next day. As momentary morning fatigue negatively affects daily physical activity, we suggest that the state level of fatigue should be monitored during intensive exercise programs, especially in less fit older adults with increased fatigability.

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.