The efficacy of the load-velocity profile to predict one repetition maximum

THOMPSON, Stephen William (2022). The efficacy of the load-velocity profile to predict one repetition maximum. Doctoral, Sheffield Hallam University.

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Abstract

Autoregulation is the process of acutely manipulating training variables in response to an individual’s fluctuations in strength and fatigue and is vital for optimising programming. Load-velocity profiles (LVPs) have been proposed as effective flexible programming strategies to optimise resistance training load (kg), often through the daily estimation of one repetition maximum (1RM). This PhD, therefore, adopted a pragmatic, mixed methods research design and followed an applied research model (ARMSS) to devise a series of studies to ascertain a novel, efficient, and valid approach to LVP-based 1RM prediction. Prior to choosing an autoregulatory method, strength and conditioning (S&C) practitioners must first determine an appropriate non-flexible programming strategy. A systematic review of literature revealed percentages of 1RM (% 1RM) as the superior method for increasing maximal strength (study one). After thematic analyses (study two) revealed barriers such as inaccurate 1RM predictions, time-costly protocols, and “iPad coaching” to the implementation of LVPs within practice; common velocity-based technology used by coaches; and the combination of ballistic and non-ballistic exercise when profiling, a new LVP method addressing these factors was devised in a key training, but under-researched exercise, the free-weight back squat. The new approach to LVP-based 1RM prediction developed from this thesis utilised the Gymaware linear-position transducer given its superior reliability and validity (study three); individualised profiling due to stronger load-velocity relationships and large between-participant variability observed (study four); ballistic (jump squat) exercise after larger mechanical output was revealed in 0-60% 1RM when compared to non-ballistic (back squat) (study five); a submaximal point of extrapolation (80% 1RM mean velocity) due to poor within-participant reliability of loads > 85% 1RM (study four); quadratic modelling (study four); and as few as four incremental loads. Results revealed this combination to be an effective method for estimating 1RM and autoregulating daily load for S&C coaches.

Item Type:	Thesis (Doctoral)
Contributors:	Thesis advisor - Barnes, Andrew [0000-0001-8262-5132] Thesis advisor - Ruddock, Alan [0000-0002-7001-9845] Thesis advisor - Rogerson, David [0000-0002-4799-9865]
Additional Information:	Director of studies: Dr. Alan Barnes / Supervisors: Dr. Alan Ruddock and David Rogerson.
Uncontrolled Keywords:	Article based PhD
Research Institute, Centre or Group - Does NOT include content added after October 2018:	Sheffield Hallam Doctoral Theses
Identification Number:	https://doi.org/10.7190/shu-thesis-00591
Depositing User:	Colin Knott
Date Deposited:	05 May 2023 14:15
Last Modified:	28 Mar 2024 16:16
URI:	https://shura.shu.ac.uk/id/eprint/31852

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