Perception of Bar Velocity Loss in Resistance Exercises: Accuracy Across Loads and Velocity Loss Thresholds in the Bench Press

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Antonio Dello Iacono School of Health and Life Sciences, Institute for Clinical Exercise and Health Science, University of the West of Scotland, Hamilton, United Kingdom

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Kevin Watson School of Health and Life Sciences, Institute for Clinical Exercise and Health Science, University of the West of Scotland, Hamilton, United Kingdom
Strength and Conditioning Department, Glasgow School of Sport, Glasgow, United Kingdom

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Milan Marinkovic School of Health and Life Sciences, Institute for Clinical Exercise and Health Science, University of the West of Scotland, Hamilton, United Kingdom

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Israel Halperin School of Public Health, Tel Aviv University, Tel Aviv, Israel
Sylvan Adams Sports Institute, Tel Aviv University, Tel Aviv, Israel

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Purpose: Velocity-based training is used to prescribe and monitor resistance training based on velocity outputs measured with tracking devices. When tracking devices are unavailable or impractical to use, perceived velocity loss (PVL) can be used as a substitute, assuming sufficient accuracy. Here, we investigated the accuracy of PVL equal to 20% and 40% relative to the first repetition in the bench-press exercise. Methods: Following a familiarization session, 26 resistance-trained men performed 4 sets of the bench-press exercise using 4 different loads based on their individual load–velocity relationships (∼40%–90% of 1-repetition maximum [1RM]), completed in a randomized order. Participants verbally reported their PVL at 20% and 40% velocity loss during the sets. PVL accuracy was calculated as the absolute difference between the timing of reporting PVL and the actual repetition number corresponding to 20% and 40% velocity loss measured with a linear encoder. Results: Linear mixed-effects model analysis revealed 4 main findings. First, across all conditions, the absolute average PVL error was 1 repetition. Second, the PVL accuracy was not significantly different between the PVL thresholds (β = 0.16, P = .267). Third, greater accuracy was observed in loads corresponding to the midportion of the individual load–velocity relationships (∼50%–60% 1RM) compared with lighter (<50% 1RM, β = 0.89, P < .001) and heavier loads (>60% 1RM, 0.63 ≤ β ≤ 0.84, all P values < .001). Fourth, PVL accuracy decreased with consecutive repetitions (β = 0.05, P = .017). Conclusions: PVL can be implemented as a monitoring and prescription method when velocity-tracking devices are impractical or absent.

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