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Reliability and validity of velocity measures and regression methods to predict maximal strength ability in the back-squat using a novel linear position transducer.

Kilgallon, Jonathan and Cushion, Emily and Joffe, Shaun and Tallent, Jamie (2022) 'Reliability and validity of velocity measures and regression methods to predict maximal strength ability in the back-squat using a novel linear position transducer.' Proceedings of the Institution of Mechanical Engineers Part P: Journal of Sports Engineering and Technology. ISSN 1754-3371 (In Press)

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Abstract

Purpose: to examine the reliability of load-velocity profiles (LVPs) and validity of 1-repetition maximum (1-RM) prediction methods in the back-squat using the novel Vitruve linear position transducer (LPT). Methods: twenty-five men completed a back-squat 1-RM assessment followed by 2 LVP trials using 5 incremental loads (20%-40%-60%-80%-90% 1-RM). Mean propulsive velocity (MPV), mean velocity (MV), and peak velocity (PV) were measured via a (LPT). Linear and polynomial regression models were applied to the data. The reliability and validity criteria were defined a-priori as intraclass correlation coefficient (ICC) or Pearson correlation coefficient (r) > 0.70, coefficient of variation (CV) ≤ 10%, and effect size (ES) < 0.60. Bland-Altman analysis and heteroscedasticity of errors (r2) were also assessed. Results: the main findings indicated MPV, MV and PV were reliable across 20- 13 90% 1-RM (CV < 8.8%). The secondary findings inferred all prediction models had acceptable reliability (CV < 8.0%). While the MPV linear and MV linear models demonstrated the best estimation of 1-RM (CV < 5.9%), all prediction models displayed unacceptable validity and a tendency to overestimate or underestimate 1-RM. Mean systematic bias (-7.29 to 2.83 kg) was detected for all prediction models, along with little to no heteroscedasticity of errors for linear (r2 < 0.04) and polynomial models (r2 < 0.08). Furthermore, all 1-RM estimations were significantly different from each other (p < 0.03). Conclusions: MPV, MV, and PV can provide reliable LVPs and repeatable 1-RM predictions. However, prediction methods may not be sensitive enough to replace direct assessment of 1-RM. Polynomial regression is not suitable for 1-RM prediction.

Item Type: Article
Uncontrolled Keywords: Velocity-Based Training; Load-Velocity Relationship; Relative Load; Regression; Linear Position Transducer
Divisions: Faculty of Science and Health
Faculty of Science and Health > Sport, Rehabilitation and Exercise Sciences, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 07 Mar 2022 17:11
Last Modified: 07 Mar 2022 17:11
URI: http://repository.essex.ac.uk/id/eprint/32455

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