Quantitative gait analysis in Parkinson's disease: comparison with a healthy control group

Arch Phys Med Rehabil. 2005 May;86(5):1007-13. doi: 10.1016/j.apmr.2004.08.012.

Abstract

Objective: To compare gait parameters in Parkinson's disease (PD) during the on-phase of medication cycle with those of healthy elderly control subjects.

Design: A group-comparison study.

Setting: Gait analysis laboratory of a university hospital.

Participants: Fifteen patients with PD and 9 healthy elderly controls.

Interventions: Not applicable.

Main outcome measures: Spatiotemporal, kinematic, and kinetic gait parameters.

Results: The PD spatiotemporal results showed a significant reduction in step length and walking velocity compared with controls. In the kinematics, the major feature of the PD group was a markedly reduced ankle plantarflexion excursion (at 50%-60% of the gait cycle). Most important, the kinetics showed reduced ankle push-off power and hip pull-off power. Unlike the control subjects, the patients with PD did not show any correlation between ankle generation (push-off) power and stride length ( r =.19) or with gait speed ( r =.29). Correction for walking velocity did not result in significant changes in the kinetics between the groups.

Conclusions: Reduced ankle (push-off) power generation and reduced hip flexion (pull-off) power persisted in PD gait despite being tested in the on-phase of the medication cycle. Lack of a correlation between ankle and hip power generation and walking velocity suggests that peripheral and central factors contribute to lack of forward progression. Patients with PD may benefit from intervention strategies that correct the kinematic and the kinetic gait components.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Biomechanical Phenomena
  • Case-Control Studies
  • Female
  • Gait / physiology*
  • Humans
  • Male
  • Middle Aged
  • Parkinson Disease / physiopathology*
  • Parkinson Disease / rehabilitation
  • Statistics, Nonparametric