The Effects of Multiple Sets of Squats and Jump Squats on Mechanical Variables

Postactivation performance enhancement in the vertical jump using loads above or below the optimum-power load for a jump squat

BACKGROUND

Postactivation performance enhancement (PAPE) is an acute response of increased muscle performance following a conditioning activity (CA), generally prescribed based on the percentage of a one-repetition maximum. However, it is unknown how the PAPE response is affected when the CA is performed near the optimum power zone. The purpose of this study was to examine PAPE using loads that were 20% above or below the optimum-power load (OPL).

METHODS

Fifteen recreationally trained subjects, with at least one-year resistance training experience participated in this study. First, the OPL for the JS was determined. Then, subjects performed two protocols in a counterbalanced order: 20% above (+20%OPL) or below (-20%OPL). To examine PAPE on performance, the vertical jump was performed prior to and seven times following each jump squat protocol, with a 2-min rest interval between trials.

RESULTS

The two-way ANOVA revealed main effects for condition (F=4.978; P<0.001) for jump height and jump power (F=2.589; P=0.017), but post-hoc comparisons did not show differences. Between baseline and the best trial following each jump squat protocol, two-way ANOVA did not reveal main effects (F=3.048; P=0.103) or interactions (F=0.304; P=0.590). Paired t-tests did not show significant differences between conditions for relative changes in jump height (P=0.543) or jump power (P=0.233).

CONCLUSIONS

This study revealed similar results between 20% above or below the OPL on subsequent vertical jump performance.

Balancing Injury Risk and Power Development by Weighted Jump Squat Through Controlling Eccentric Loading

ABSTRACT

Songsupap, T, Newton, RU, and Lawsirirat, C. Balancing injury risk and power development by weighted jump squat through controlling eccentric loading. J Strength Cond Res XX(X): 000-000, 2021-Weighted jump squat (WJS) training is highly effective for increasing neuromuscular power but entails higher injury risk than traditional resistance training because of the impact of landing. Braking mechanisms can be used to control the landing impact; however, the optimal eccentric loading condition that balances injury risks and power output is still unclear. The purpose of this study was to assess different eccentric braking conditions. Twenty-two male varsity basketball players aged 20.8 ± 1.1 years and a 1 repetition maximum (1RM) of back squat-to-body mass ratio of 2.0 ± 0.2 participated in the study. The subjects performed 2 sets of WJS of 6 repetitions with additional 30% of 1RM load under 4 randomly assigned conditions: (a) traditional load, no braking (B0), (b) 25% braking load reduction during landing (B25), (c) 50% braking load reduction during landing (B50), and (d) 100% braking load reduction during landing with release at touchdown (B100R). A repeated measures analysis of variance was used to determine differences of dependent variables: peak power output, peak force, peak velocity, and impulse. B100R resulted in statistically lower eccentric peak force and impulse for the first 50 milliseconds than the other 3 conditions (p < 0.05), but the largest concentric peak power. Furthermore, B0 resulted in statistically lower concentric peak power and peak velocity than the other 3 conditions (p < 0.05). We suggest that B100R was a more favorable loading condition that balanced injury risk and power production in WJS.

A Novel Approach to 1RM Prediction Using the Load-Velocity Profile: A Comparison of Models

The study aim was to compare different predictive models in one repetition maximum (1RM) estimation from load-velocity profile (LVP) data. Fourteen strength-trained men underwent initial 1RMs in the free-weight back squat, followed by two LVPs, over three sessions. Profiles were constructed via a combined method (jump squat (0 load, 30-60% 1RM) + back squat (70-100% 1RM)) or back squat only (0 load, 30-100% 1RM) in 10% increments. Quadratic and linear regression modeling was applied to the data to estimate 80% 1RM (kg) using 80% 1RM mean velocity identified in LVP one as the reference point, with load (kg), then extrapolated to predict 1RM. The 1RM prediction was based on LVP two data and analyzed via analysis of variance, effect size (g/ηp2), Pearson correlation coefficients (r), paired t-tests, standard error of the estimate (SEE), and limits of agreement (LOA). p < 0.05. All models reported systematic bias < 10 kg, r > 0.97, and SEE < 5 kg, however, all linear models were significantly different from measured 1RM (p = 0.015 <0.001). Significant differences were observed between quadratic and linear models for combined (p < 0.001; ηp2 = 0.90) and back squat (p = 0.004, ηp2 = 0.35) methods. Significant differences were observed between exercises when applying linear modeling (p < 0.001, ηp2 = 0.67-0.80), but not quadratic (p = 0.632-0.929, ηp2 = 0.001-0.18). Quadratic modeling employing the combined method rendered the greatest predictive validity. Practitioners should therefore utilize this method when looking to predict daily 1RMs as a means of load autoregulation.