Optimal effort investment for overcoming the weakest point: New insights from a computational model of neuromuscular adaptation

Oggie Arandelovic*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

The occurrence of so-called sticking points in a lift is pervasive in weight training practice. Biomechanically complex exercises often exhibit multi-modal variation of effective force exerted against the load as a function of the elevation and velocity of the load. This results in a variety of possible loci for the occurrence of sticking points and makes the problem of designing the optimal training strategy to overcome them challenging. In this article a case founded on theoretical grounds is made against a purely empirical method. It is argued that the nature of the problem considered and the wide range of variables involved limit the generality of conclusions which can be drawn from experimental studies alone. Instead an alternative is described, whereby a recently proposed mathematical model of neuromuscular adaptation is employed in a series of computer simulations. These are used to examine quantitatively the effects of differently targeted partial range of motion (ROM) training approaches. Counter-intuitively and in contrast to common training practices, the key novel insight inferred from the obtained results is that in some cases the most effective approach for improving performance in an exercise with a sticking point at a particular point in the ROM is to improve force production capability at a different and possibly remote position in the lift. In the context of the employed model, this result is explained by changes in the neuromuscular and biomechanical environment for force production.

Original languageEnglish
Pages (from-to)1715-1723
Number of pages9
JournalEuropean Journal of Applied Physiology
Volume111
Issue number8
DOIs
Publication statusPublished - Aug 2011

Keywords

  • Mathematical model
  • Sticking point
  • Strength
  • Weight training

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