ABSTRACT Background Gait is an extremely complex motor task; therefore, gait data should encompass as many gait parameters as possible. Objective To provide reference values for gait measurements obtained from a Brazilian group of community-dwelling elderly females between the ages of 65 and 89 years and to apply the PCA-biplot to yield insight into different walking strategies that might occur during the aging process. Method 305 elderly community-dwelling females living in Brazil were stratified into four age groups: 65-69 years (N=103); 70-74 years (N=95); 75-79 years (N=77); and ≥80 years (N=30). Age, height, and BMI were assessed to describe the characteristics of the groups. Gait spatiotemporal and variability data were obtained using the GAITRite® system. Principal component analysis, followed by MANOVA and the PCA-biplot approach were used to analyze the data. Results 95% CI showed that only three components – rhythm, variability, and support - together explained 74.2% of the total variance in gait that were different among the groups. The older groups (75-79 and ≥80 years) walked with lower than average velocity, cadence, and step length and were above average for the variables stance, step, swing, and double support time and the ≥80 year old group presented the highest gait variability compared to the other groups. Conclusion Aging is associated with decreased gait velocity and cadence and increased stance, step time, and variability, but not associated with changes in base of support. In addition, the PCA-biplot indicates a decline towards decreased rhythm and increased variability with aging.

CONTEXTUALIZAÇÃO: A aplicabilidade da análise de marcha foi aprimorada com a introdução da análise de componentes principais (ACP), uma técnica estatística que reduz o volume de dados, permitindo a comparação de todo o ciclo entre grupos de indivíduos. OBJETIVOS: Comparar, por meio da ACP, a cinemática da articulação do joelho durante a marcha nos planos sagital e frontal, entre mulheres idosas sem e com diagnóstico de leve a moderado de osteoartrite (OA). MÉTODOS: Participaram 38 mulheres idosas (69,6±8,1 anos) com OA de joelhos e 40 assintomáticas (70,3±7,7 anos). A cinemática foi obtida usando o sistema Qualisys Pró-reflex. RESULTADOS: O grupo OA apresentou menor velocidade da marcha e comprimento da passada (p<0,05) e caracterizou-se por maior escore de dor no WOMAC. No plano frontal, as componentes não foram significativas entre os grupos. No plano sagital, três componentes explicaram 90,7% da variância dos dados. A análise discriminante indicou que a componente 2 (CP2) e a 3 (CP3) classificaram corretamente 71,8% dos indivíduos. Entretanto, a CP3, que capta a diferença de amplitude do ângulo de flexão do joelho durante a marcha, foi a variável com maior poder de discriminação entre os grupos. CONCLUSÕES: A ACP é uma técnica estatística multivariada, efetiva para a análise das variáveis cinemáticas do ciclo da marcha. A perda de flexão de joelho na marcha do grupo com OA foi apontada como fator discriminante importante entre os grupos, sendo, portanto, uma variável que deve ser considerada na avaliação e tratamento fisioterápico da mulher idosa com OA de joelho.

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.