4.5 Article

Gait patterns in association with underlying impairments in polio survivors with calf muscle weakness

期刊

GAIT & POSTURE
卷 58, 期 -, 页码 146-153

出版社

ELSEVIER IRELAND LTD
DOI: 10.1016/j.gaitpost.2017.07.107

关键词

Postpoliomyelitis syndrome; Gait patterns; Gait biomechanics; Muscle weakness; Impairments; Clustering

资金

  1. Dutch International Society of Prosthetics and Orthotics

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The objective was to identify gait patterns in polio survivors with calf muscle weakness and associate them to underlying lower extremity impairments, which are expected to help in the search for an optimal orthosis. Unilaterally affected patients underwent barefoot 3D-gait analyses. Gait pattern clusters were created based on the ankle and knee angle and ankle moment shown in midstance of the affected limb. Impairment clusters were created based on plantarflexor and knee-extensor strength, and ankle and knee joint range-of-motion. The association between gait patterns and underlying impairments were examined descriptively. The Random Forest Algorithm and regression analyses were used to predict gait patterns and parameters. Seven gait patterns in 73 polio survivors were identified, with two dominant patterns: one with a mildly/non-deviant ankle angle, ankle moment and knee angle (n = 23), and one with a strongly deviant ankle angle and a mildly/non-deviant ankle moment and knee angle (n = 18). Gait pattern prediction from underlying impairments was 49% accurate with best prediction performance for the second dominant gait pattern (sensitivity 78% and positive predictive value 74%). The underlying impairments explained between 20 and 32% of the variance in individual gait parameters. Polio survivors with calf muscle weakness who present a similar impairment profile do not necessarily walk the same. From physical examination alone, the gait pattern nor the individual gait parameters could be accurately predicted. The patient's gait should therefore be measured to help in the prescription and evaluation of orthoses for these patients.

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