Date of Award

May 2013

Degree Type

Thesis

Degree Name

Master of Science

Department

Kinesiology

First Advisor

Kristian M. O'Connor

Committee Members

Stephen C. Cobb, Jennifer Earl-Boehm

Keywords

Fatigue, Joint Coordination, Principle Components Analysis, Running, Variability

Abstract

About half of all runners sustain a running-related injury in a given year. Less variable joint coordination patterns may be detrimental as stress endured by the same tissue, encountered over many running cycles, could lead to overuse running injuries. The effects of fatigue may contribute to runners' risk of injury by altering joint coordination variability. Since fatigue is task-dependent, it is practical to consider a level of fatigue typically experienced by runners. The purpose of this study was to examine the influence of running in an exerted state on lower extremity joint coordination variability, using Principal Components Analysis (PCA) and traditional analysis methods. Sixteen healthy female runners were recruited. Data collection included three-dimensional motion analyses of the ankle, knee and hip before and after a run designed to mimic the subject's typical training experience. Joint coordination was defined using a vector coding technique for eight pairs of joints and planes of motion (e.g. ankle-frontal/knee-transverse) considered relevant to running injury risk. The within-subject variability for these eight coordination patterns was determined from the standard deviation of the coupling angle, averaged over each 25% of stance phase. A repeated measures MANOVA was used to determine differences in joint coordination variability before and after the run. No significant differences were found for the eight coordination patterns. These results are limited by the analysis method, which requires a priori selection of time periods within stance phase as the dependent variables. PCA is an unbiased way to determine relevant differences in variability among full waveforms, and was used to determine fatigue-related changes in joint coordination variability for each of the eight coupling angle waveforms. A repeated measures MANOVA also did not reveal any differences in joint coordination variability for the eight coordination patterns before and after the run. These results suggest that healthy runners may not experience a change in joint coordination variability during their typical training run. This study established methods for using PCA to quantify changes in joint coordination variability. This can be used in injured populations to test the theory that overuse running injury is associated with low joint coordination variability.

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