Analysis of the Load-Velocity Relationship in Deadlift Exercise
The aim of this study was to analyze the relationship between movement velocity and relative load (%1RM) in the deadlift exercise. Fifty men (age = 23.8 ± 3.6 years, body mass = 78.2 ± 8.3 kg, height = 1.78 ± 0.06 m) performed a first evaluation (T1) consisting of a one-repetition maximum (1RM) test...
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Veröffentlicht in: | Journal of sports science & medicine 2020-09, Vol.19 (3), p.452-459 |
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Sprache: | eng |
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Zusammenfassung: | The aim of this study was to analyze the relationship between movement velocity and relative load (%1RM) in the deadlift exercise. Fifty men (age = 23.8 ± 3.6 years, body mass = 78.2 ± 8.3 kg, height = 1.78 ± 0.06 m) performed a first evaluation (T1) consisting of a one-repetition maximum (1RM) test. Forty-two subjects performed a second evaluation (T2) after 6 weeks. Mean (MV), mean propulsive (MPV) and peak (PV) velocity measures of the concentric phase were analyzed. Load-velocity relationships were studied by fitting first order equations to the data using loads from 30-100% of 1RM. A comprehensive set of statistics for assessing bias and level of agreement to estimate the 1RM value from the different models was used. Stability of these relationships was assessed using the coefficient of variation (CV) and the intraclass correlation coefficient (ICC). General load-velocity equations provided good adjustments (R
~; 0.91-0.93), however individual load-velocity regressions provided better adjustments (R
~; 0.97). Individual estimations also showed higher agreement and more regular variation than general equations. Moreover, MPV showed smaller bias than the other velocity parameters (MV and PV). The stability analysis of the load-velocity relationships resulted in ICC values higher than 0.82 and CV lower than 3.0%. Monitoring repetition velocity allows estimation of the %1RM in the deadlift exercise. More accurate predictions of relative load can be obtained when using individualized regression equations instead of general equations. |
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ISSN: | 1303-2968 1303-2968 |