BACKGROUND: 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. OBJECTIVES: 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). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Principal component analysis; gait; kinematics; osteoarthritis
