Gait measurement in chronic mild traumatic brain injury: A model approach

Samuel Stuart*, Lucy Parrington, Rosie Morris, Douglas N. Martini, Peter C. Fino, Laurie A. King

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

24 Citations (Scopus)
10 Downloads (Pure)


INTRODUCTION: Mild traumatic brain injury (mTBI) can impact gait, with deficits linked to underlying neural disturbances in cognitive, motor and sensory systems. Gait is complex as it is comprised of multiple characteristics that are sensitive to underlying neural deficits. However, there is currently no clear framework to guide selection of gait characteristics in mTBI. This study developed a model of gait in chronic mTBI and replicated this in a separate group of controls, to provide a comprehensive and structured methodology on which to base gait assessment and analysis.

METHODS: Fifty-two people with chronic mTBI and 59 controls completed a controlled laboratory gait assessment; walking for two minutes back and forth over a 13 m distance while wearing five wirelessly synchronized inertial sensors. Thirteen gait characteristics derived from the inertial sensors were selected for entry into the principle component analysis based on previous literature, robustness and novelty. Principle component analysis was then used to derive domains (components) of gait.

RESULTS: Four gait domains were derived for our chronic mTBI group (variability, rhythm, pace and turning) and this was replicated in a separate control cohort. Domains totaled 80.8% and 77.4% of variance in gait for chronic mTBI and controls, respectively. Gait characteristic loading was unambiguous for all features, with the exception of gait speed in controls that loaded on pace and rhythm domains.

CONCLUSION: This study contributes a four component model of gait in chronic mTBI and controls that can be used to comprehensively assess and analyze gait and underlying mechanisms involved in impairment, or examine the influence of interventions.

Original languageEnglish
Article number102557
Pages (from-to)102557
JournalHuman Movement Science
Early online date26 Nov 2019
Publication statusPublished - 1 Feb 2020


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