The applicability of gait analysis has been implemented with the introduction of the principal component analysis (PCA), a statistical data reduction technique that allows the comparison of the whole cycle between groups of individuals.
Applying PCA, to compare the kinematics of the knee joint during gait, in the frontal and sagittal planes, between a group of elderly women with and without diagnosis in the initial and moderate stages of Osteoarthritis (OA).
A total of 38 elderly women (69.6±8.1 years) with knee OA and 40 asymptomatic (70.3±7.7 years) participated on this study. The kinematics was obtained using the Qualisys Pro-reflex system.
The OA group showed decreased gait velocity and stride length (p<0.05) and was characterized with higher WOMAC pain score. In the frontal plane, the between-group differences of the components were not significant. In the sagittal plane, three principal components explained 99.7% of the data variance. Discriminant analysis indicated that component 2 and 3 could classify correctly 71.8% of the individuals. However, CP3, which captures the difference in the flexion knee angle magnitude during gait, was the variable with higher discrimination power between groups.
PCA is an effective multivariate statistical technique to analyse the kinematic gait waveform during the gait cycle. The smaller knee flexion angle in the OA group was appointed as a discriminatory factor between groups, therefore, it should be considered in the physical therapy evaluation and treatment of elderly women with knee OA.