title: Validity of a Commercial Linear Encoder to Estimate Bench Press 1 RM from the Force-Velocity Relationship.
abstract: The aim of this study was to assess the validity and accuracy of a commercial linear encoder (Musclelab, Ergotest, Norway) to estimate Bench press 1 repetition maximum (1RM) from the force - velocity relationship. Twenty seven physical education students and teachers (5 women and 22 men) with a heterogeneous history of strength training participated in this study. They performed a 1 RM test and a force - velocity test using a Bench press lifting task in a random order. Mean 1 RM was 61.8 ± 15.3 kg (range: 34 to 100 kg), while 1 RM estimated by the Musclelab's software from the force-velocity relationship was 56.4 ± 14.0 kg (range: 33 to 91 kg). Actual and estimated 1 RM were very highly correlated (r = 0.93, p<0.001) but largely different (Bias: 5.4 ± 5.7 kg, p < 0.001, ES = 1.37). The 95% limits of agreement were ±11.2 kg, which represented ±18% of actual 1 RM. It was concluded that 1 RM estimated from the force-velocity relationship was a good measure for monitoring training induced adaptations, but also that it was not accurate enough to prescribe training intensities. Additional studies are required to determine whether accuracy is affected by age, sex or initial level. Key pointsSome commercial devices allow to estimate 1 RM from the force-velocity relationship.These estimations are valid. However, their accuracy is not high enough to be of practical help for training intensity prescription.Day-to-day reliability of force and velocity measured by the linear encoder has been shown to be very high, but the specific reliability of 1 RM estimated from the force-velocity relationship has to be determined before concluding to the usefulness of this approach in the monitoring of training induced adaptations.
authors.forename: Laurent
authors.initials: L
authors.lastname: Bosquet
authors.forename: Jeremy
authors.initials: J
authors.lastname: Porta-Benache
authors.forename: Jérôme
authors.initials: J
authors.lastname: Blais
keywords: Muscle strength diagnosis; innovative technology; performance prediction