A comparison of methods to quantify control of the spine

Eric Bourdon, Ryan B. Graham, Jaap van Diëen*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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Low back pain (LBP) affects many individuals worldwide. The established association between LBP and spine motor control has led to the development of many control assessment techniques. To understand spine control and LBP, it is essential to know the relationship between assessment techniques. Systems identification (SI) and local dynamic stability (LDS) are two methods of quantifying spine control. SI provides a detailed description of control but uses linearity assumptions, whereas LDS provides a "black box" non-linear assessment during dynamic movements. Therefore, the purpose of this project was to compare control outcomes of SI and LDS. 15 participants completed two tasks (SI and LDS) in a random order. For the SI task, participants were seated and ventrally perturbed at the 10th thoracic vertebrae. They were instructed to resist the perturbations (resist condition) or to relax the trunk (relax condition). Admittance was computed, and a neuromuscular control model quantified lumbar stiffness, damping and muscle spindle feedback gains. For the LDS task, participants completed three repetitive movement blocks consisting of flexion/extension, axial rotation, and complex movements. In each block, the maximum finite-time Lyapunov exponent (λmax) was estimated. A stepwise linear regression determined that λmax during the rotation task was best predicted by SI outcomes in the relax condition (adjusted R2 = 0.83). Many conditions demonstrated no relationship between λmax and SI outcomes. These findings outline the importance of a consistent framework for the assessment of spine control. This could clarify research comparisons and the understanding of the cause/effect role of LBP on spine control.

Original languageEnglish
Article number109344
Pages (from-to)1-7
Number of pages7
JournalJournal of Biomechanics
Early online date17 Sept 2019
Publication statusPublished - 11 Nov 2019

Bibliographical note

Copyright © 2019 Elsevier Ltd. All rights reserved.


  • Biomechanics
  • Local dynamic stability
  • Lyapunov exponents
  • Movement
  • Systems identification


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