Kinetics and Kinematics of the Free-Weight Back Squat and Loaded Jump Squat

This study investigated the inter-day and intra-device reliability, and criterion validity of six devices for measuring barbell velocity in the free-weight back squat and power clean. In total, 10 competitive weightlifters completed an initial one repetition maximum (1RM) assessment followed by three load-velocity profiles (40–100% 1RM) in both exercises on four separate occasions. Mean and peak velocity was measured simultaneously on each device and compared to 3D motion capture for all repetitions. Reliability was assessed via coefficient of variation (CV) and typical error (TE). Least products regression (LPR) (R2) and limits of agreement (LOA) assessed the validity of the devices. The Gymaware was the most reliable for both exercises (CV < 10%; TE < 0.11 m·s−1, except 100% 1RM (mean velocity) and 90‒100% 1RM (peak velocity)), with MyLift and PUSH following a similar trend. Poorer reliability was observed for Beast Sensor and Bar Sensei (CV = 5.1–119.9%; TE = 0.08–0.48 m·s−1). The Gymaware was the most valid device, with small systematic bias and no proportional or fixed bias evident across both exercises (R2 > 0.42–0.99 LOA = −0.03–0.03 m·s−1). Comparable validity data was observed for MyLift in the back squat. Both PUSH devices produced some fixed and proportional bias, with Beast Sensor and Bar Sensei being the least valid devices across both exercises (R2 > 0.00–0.96, LOA = −0.36–0.46 m·s−1). Linear position transducers and smartphone applications could be used to obtain velocity-based data, with inertial measurement units demonstrating poorer reliability and validity.

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Velocity Loss Thresholds Reliably Control Kinetic and Kinematic Outputs during Free Weight Resistance Training

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17186509 ◽

2020 ◽

Vol 17 (18) ◽

pp. 6509

Author(s):

Madison Pearson ◽

Amador García-Ramos ◽

Matthew Morrison ◽

Carlos Ramirez-Lopez ◽

Nicholas Dalton-Barron ◽

...

Keyword(s):

Resistance Training ◽

Coefficient Of Variation ◽

External Load ◽

Relative Velocity ◽

Peak Power ◽

Crossover Design ◽

Professional Athletes ◽

Back Squat ◽

Free Weight

Exercise velocity and relative velocity loss thresholds (VLTs) are commonly used in velocity-based resistance training. This study aims to quantify the between-day reliability of 10%, 20%, and 30% VLTs on kinetic and kinematic outputs, changes in external load, and repetition characteristics in well-trained athletes. Using a repeated, counter-balanced crossover design, twelve semi-professional athletes completed five sets of the back squat with an external load corresponding to a mean concentric velocity of ~0.70 m·s−1 and a VLT applied. The testing sessions were repeated after four weeks of unstructured training to assess the long-term reliability of each VLT. A coefficient of variation (CV) <10% was used to classify outputs as reliable. Kinetic and kinematic outputs and external load were largely reliable, with only peak power during sets 2–5 within the 10% VLT condition demonstrating a CV >10% (CV: 11.14–14.92%). Alternatively, the repetitions completed within each set showed large variation (CV: 18.92–67.49%). These findings demonstrate that by utilizing VLTs, kinetic and kinematic outputs can be prescribed and replicated across training mesocycles. Thus, for practitioners wishing to reliably control the kinetic and kinematic stimulus that is being applied to their athletes, it is advised that a velocity-based approach is used.

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The effects of a high-intensity free-weight back-squat exercise protocol on postural stability in resistance-trained males

Journal of Sports Sciences ◽

10.1080/02640414.2014.934709 ◽

2014 ◽

Vol 33 (2) ◽

pp. 211-218 ◽

Cited By ~ 3

Author(s):

R.M. Thiele ◽

E.C. Conchola ◽

T.B. Palmer ◽

J.M. DeFreitas ◽

B.J. Thompson

Keyword(s):

High Intensity ◽

Postural Stability ◽

Exercise Protocol ◽

Back Squat ◽

Free Weight ◽

Squat Exercise

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The Reliability of Individualized Load–Velocity Profiles

International Journal of Sports Physiology and Performance ◽

10.1123/ijspp.2017-0610 ◽

2018 ◽

Vol 13 (6) ◽

pp. 763-769 ◽

Cited By ~ 24

Author(s):

Harry G. Banyard ◽

Kazunori Nosaka ◽

Alex D. Vernon ◽

G. Gregory Haff

Keyword(s):

Linear Regression ◽

Peak Velocity ◽

Intraclass Correlation ◽

Velocity Profiles ◽

Mean Velocity ◽

Movement Velocity ◽

Repetition Maximum ◽

Back Squat ◽

Free Weight ◽

Order Polynomial

Purpose: To examine the reliability of peak velocity (PV), mean propulsive velocity (MPV), and mean velocity (MV) in the development of load–velocity profiles (LVP) in the full-depth free-weight back squat performed with maximal concentric effort. Methods: Eighteen resistance-trained men performed a baseline 1-repetition maximum (1-RM) back-squat trial and 3 subsequent 1-RM trials used for reliability analyses, with 48-h intervals between trials. 1-RM trials comprised lifts from 6 relative loads including 20%, 40%, 60%, 80%, 90%, and 100% 1-RM. Individualized LVPs for PV, MPV, or MV were derived from loads that were highly reliable based on the following criteria: intraclass correlation coefficient (ICC) >.70, coefficient of variation (CV) ≤10%, and Cohen d effect size (ES) <0.60. Results: PV was highly reliable at all 6 loads. MPV and MV were highly reliable at 20%, 40%, 60%, 80%, and 90% but not 100% 1-RM (MPV: ICC = .66, CV = 18.0%, ES = 0.10, SEM = 0.04 m·s−1; MV: ICC = .55, CV = 19.4%, ES = 0.08, SEM = 0.04 m·s−1). When considering the reliable ranges, almost perfect correlations were observed for LVPs derived from PV20–100% (r = .91–.93), MPV20–90% (r = .92–.94), and MV20–90% (r = .94–.95). Furthermore, the LVPs were not significantly different (P > .05) between trials or movement velocities or between linear regression versus 2nd-order polynomial fits. Conclusions: PV20–100%, MPV20–90%, and MV20–90% are reliable and can be utilized to develop LVPs using linear regression. Conceptually, LVPs can be used to monitor changes in movement velocity and employed as a method for adjusting sessional training loads according to daily readiness.

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The Effects of 10%, 20%, and 30% Velocity Loss Thresholds on Kinetic, Kinematic, and Repetition Characteristics During the Barbell Back Squat

International Journal of Sports Physiology and Performance ◽

10.1123/ijspp.2018-1008 ◽

2020 ◽

Vol 15 (2) ◽

pp. 180-188 ◽

Cited By ~ 5

Author(s):

Jonathon Weakley ◽

Carlos Ramirez-Lopez ◽

Shaun McLaren ◽

Nick Dalton-Barron ◽

Dan Weaving ◽

...

Keyword(s):

Individual Differences ◽

Resistance Training ◽

Power Output ◽

Crossover Design ◽

Neuromuscular Fatigue ◽

Back Squat ◽

Free Weight ◽

Traditional Resistance Training ◽

Squat Lifting

Purpose: Prescribing resistance training using velocity loss thresholds can enhance exercise quality by mitigating neuromuscular fatigue. As little is known regarding performance during these protocols, we aimed to assess the effects of 10%, 20%, and 30% velocity loss thresholds on kinetic, kinematic, and repetition characteristics in the free-weight back squat. Methods: Using a randomized crossover design, 16 resistance-trained men were recruited to complete 5 sets of the barbell back squat. Lifting load corresponded to a mean concentric velocity (MV) of ∼0.70 m·s−1 (115 [22] kg). Repetitions were performed until a 10%, 20%, or 30% MV loss was attained. Results: Set MV and power output were substantially higher in the 10% protocol (0.66 m·s−1 and 1341 W, respectively), followed by the 20% (0.62 m·s−1 and 1246 W) and 30% protocols (0.59 m·s−1 and 1179 W). There were no substantial changes in MV (−0.01 to −0.02 m·s−1) or power output (−14 to −55 W) across the 5 sets for all protocols, and individual differences in these changes were typically trivial to small. Mean set repetitions were substantially higher in the 30% protocol (7.8), followed by the 20% (6.4) and 10% protocols (4.2). There were small to moderate reductions in repetitions across the 5 sets during all protocols (−39%, −31%, −19%, respectively), and individual differences in these changes were small to very large. Conclusions: Velocity training prescription maintains kinetic and kinematic output across multiple sets of the back squat, with repetition ranges being highly variable. Our findings, therefore, challenge traditional resistance training paradigms (repetition based) and add support to a velocity-based approach.

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Comparison of Kinematics and Muscle Activation in Free-Weight Back Squat With and Without Elastic Bands

The Journal of Strength and Conditioning Research ◽

10.1519/jsc.0000000000001178 ◽

2016 ◽

Vol 30 (4) ◽

pp. 945-952 ◽

Cited By ~ 13

Author(s):

Atle H. Saeterbakken ◽

Vidar Andersen ◽

Roland van den Tillaar

Keyword(s):

Muscle Activation ◽

Back Squat ◽

Free Weight ◽

Elastic Bands

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Influence of Strength Level on the Acute Post-Activation Performance Enhancement Following Flywheel and Free Weight Resistance Training

Sensors ◽

10.3390/s20247156 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7156

Author(s):

Borja Sañudo ◽

Moisés de Hoyo ◽

G Gregory Haff ◽

Alejandro Muñoz-López

Keyword(s):

Resistance Training ◽

Body Mass ◽

Performance Enhancement ◽

Group Analysis ◽

Sprint Performance ◽

Countermovement Jump ◽

Back Squat ◽

Free Weight ◽

Before And After ◽

Positive Effect

This study aimed to compare the post-activation potentiation performance enhancement (PAPE) response to the acute inertial flywheel (FW) and free weight resistance training (TRA) on subsequent countermovement jump (CMJ) and sprint performance (10 m sprint). This study used a randomized crossover design including twenty-eight healthy males that were divided into strong (relative one-repetition maximum (1RM) back squat > 2.0 × body mass) and weak (relative 1RM back squat < 2.0 × body mass) groups. All participants performed the following: (a) three reps at 90% of their 1RM back squat (TRA) and (b) three reps on an inertial FW (plus one repetition to initiate flywheel movement) with an intensity that generated a mean propulsive velocity equal to that achieved with 90% of the 1RM back squat. Before and after the conditioning activity, participants performed two CMJs and two 10 m sprints. Within-group analyses showed significantly greater CMJ (d > 0.9, p < 0.001) and sprint performance (d > 0.5, p < 0.05) in the FW and the TRA group. Between-group analysis showed that sprint changes were significantly greater in the FW-strong group when compared with the TRA (F1,18 = 5.11, p = 0.036, η2p = 0.221—large) group. These results suggest that using a squat activation protocol on a FW may lead to an acute positive effect on jump and sprint performance, especially in stronger individuals.

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Reliability and concurrent validity of the Velowin optoelectronic system to measure movement velocity during the free-weight back squat

International Journal of Sports Science & Coaching ◽

10.1177/1747954118791525 ◽

2018 ◽

Vol 13 (5) ◽

pp. 737-742 ◽

Cited By ~ 15

Author(s):

Amador García-Ramos ◽

Alejandro Pérez-Castilla ◽

Fernando Martín

Keyword(s):

Concurrent Validity ◽

Body Height ◽

Maximum Velocity ◽

Single Session ◽

Optoelectronic System ◽

Mean Velocity ◽

Movement Velocity ◽

Back Squat ◽

Free Weight ◽

Squat Exercise

The objective of this study was to explore the reliability and concurrent validity of the Velowin optoelectronic system to measure movement velocity during the free-weight back squat exercise. Thirty-one men (age = 27.5 ± 3.2 years; body height = 1.76 ± 0.15 m; body mass: 78.3 ± 7.6 kg) were evaluated in a single session against five different loads (20, 40, 50, 60 and 70 kg) and three velocity variables (mean velocity, mean propulsive velocity and maximum velocity) were recorded simultaneously by a linear velocity transducer (T-Force; gold-standard) and a camera-based optoelectronic system (Velowin). The main findings revealed that (1) the three velocity variables were determined with a high and comparable reliability by both the T-Force and Velowin systems (median coefficient of variation of the five loads: T-Force: mean velocity = 4.25%, mean propulsive velocity = 4.49% and maximum velocity = 3.45%; Velowin: mean velocity = 4.29%, mean propulsive velocity = 4.60% and maximum velocity = 4.44%), (2) the maximum velocity was the most reliable variable when obtained by the T-force ( p < 0.05), but no significant differences in the reliability of the variables were observed for the Velowin ( p > 0.05) and (3) high correlations were observed for the values of mean velocity ( r = 0.976), mean propulsive velocity ( r = 0.965) and maximum velocity ( r = 0.977) between the T-Force and Velowin systems. Collectively, these results support the Velowin as a reliable and valid system for the measurement of movement velocity during the free-weight back squat exercise.

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Pooled Versus Individualized Load–Velocity Profiling in the Free-Weight Back Squat and Power Clean

International Journal of Sports Physiology and Performance ◽

10.1123/ijspp.2020-0534 ◽

2020 ◽

pp. 1-9

Author(s):

Steve W. Thompson ◽

David Rogerson ◽

Alan Ruddock ◽

Harry G. Banyard ◽

Andrew Barnes

Keyword(s):

Correlation Coefficient ◽

Intraclass Correlation Coefficient ◽

Regression Models ◽

Intraclass Correlation ◽

Repetition Maximum ◽

Back Squat ◽

Free Weight ◽

Subject Variability ◽

Power Clean ◽

Acceptable Reliability

Purpose: This study compared pooled against individualized load–velocity profiles (LVPs) in the free-weight back squat and power clean. Methods: A total of 10 competitive weightlifters completed baseline 1-repetition maximum assessments in the back squat and power clean. Three incremental LVPs were completed, separated by 48 to 72 hours. Mean and peak velocity were measured via a linear-position transducer (GymAware). Linear and nonlinear (second-order polynomial) regression models were applied to all pooled and individualized LVP data. A combination of coefficient of variation (CV), intraclass correlation coefficient, typical error of measurement, and limits of agreement assessed between-subject variability and within-subject reliability. Acceptable reliability was defined a priori as intraclass correlation coefficient > .7 and CV < 10%. Results: Very high to practically perfect inverse relationships were evident in the back squat (r = .83–.96) and power clean (r = .83–.89) for both regression models; however, stronger correlations were observed in the individualized LVPs for both exercises (r = .85–.99). Between-subject variability was moderate to large across all relative loads in the back squat (CV = 8.2%–27.8%) but smaller in the power clean (CV = 4.6%–8.5%). The power clean met our criteria for acceptable reliability across all relative loads; however, the back squat revealed large CVs in loads ≥90% of 1-repetition maximum (13.1%–20.5%). Conclusions: Evidently, load–velocity characteristics are highly individualized, with acceptable levels of reliability observed in the power clean but not in the back squat (≥90% of 1-repetition maximum). If practitioners want to adopt load–velocity profiling as part of their testing and monitoring procedures, an individualized LVP should be utilized over pooled LVPs.

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