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Load-velocity relationship 1RM predictions: A comparison of Smith machine and free-weight exercise.

Liam J HughesJeremiah J PeifferBrendan R Scott
Published in: Journal of sports sciences (2020)
This study aimed to determine differences in the validity and reliability of 1RM predictions made using load-velocity relationships in Smith machine and free-weight exercise. Twenty well-trained males attended six sessions, comprising the Smith machine and free-weight squat, bench press, prone row and overhead press. Load-velocity relationship-based 1RM predictions were performed using minimal velocity threshold (1RMMVT), load at zero velocity (1RMLD0) and force-velocity (1RMFV) methods, with 5- or 7-loads. Measured 1RM did not differ from 1RMMVT or 1RMLD0 for any of the Smith machine exercises, while it was higher than 1RMFV for all exercises except the prone row. For the free-weight variations all 1RM predictions differed from measured 1RM for the squat and overhead press, while measured and predicted 1RM did not differ in the bench press and prone row. No differences were observed between 7-and 5-load predictions. 1RMMVT was the most reliable and valid of the methods. Smith machine exercises resulted in more reliable predictions than free weight exercises. 1RMMVT provides valid and reliable predictions for the Smith machine, squat, bench press, prone row and overhead press and free-weight bench press and prone row. Practitioners must be aware of the poor validity of free-weight squat and overhead press predictions.
Keyphrases
  • physical activity
  • body mass index
  • weight loss
  • weight gain
  • resistance training
  • deep learning
  • blood flow
  • body weight
  • high intensity
  • primary care
  • body composition
  • machine learning