Kristian M O'Connor1, Michael C Bottum. 1. Department of Human Movement Sciences, Neuromechanics Laboratory, University of Wisconsin-Milwaukee, Milwaukee, WI 53201l, USA. krisocon@uwm.edu
Abstract
PURPOSE: The increased number of women participating in sports has led to a higher knee injury rate in women compared with men. Analysis of injury risk is limited to identification of discrete-dependent variables, but analysis of the entire waveform using principal components analysis (PCA) may provide greater insight. The purpose of this study was to examine gender differences in cutting knee mechanics using PCA and to compare these findings to those based on traditional discrete measures. METHODS: Sixteen male and 17 female recreational athletes were recruited to perform unanticipated run and cutting tasks. Three-dimensional joint dynamics were recorded, and discrete variables were extracted. PCA analyses were also performed on the angle and moment waveforms in all three planes. The PCA used an eigenvalue analysis on the data covariance matrix. Gender differences in the principal component (PC) scores generated by the PCA were assessed using a MANOVA (P < 0.05). RESULTS: On the basis of the discrete variables, flexion range of motion for females was less than for males. From the PCA analysis, females were less internally rotated during late stance and exhibited a relatively greater peak adduction moment that was not apparent in the original time series. This peak moment correlated with a greater abduction oscillation during early stance. There was also less variability for females in the sagittal and frontal plane moment PC. CONCLUSIONS: The PCA analysis did not significantly detect the decreased flexion, but PCA did reveal gender differences in movement patterns and variability that were not apparent in the discrete variables. The results of this study demonstrate the potential of PCA to provide deeper understand of movement dynamics that may help in detecting injury risk factors.
PURPOSE: The increased number of women participating in sports has led to a higher knee injury rate in women compared with men. Analysis of injury risk is limited to identification of discrete-dependent variables, but analysis of the entire waveform using principal components analysis (PCA) may provide greater insight. The purpose of this study was to examine gender differences in cutting knee mechanics using PCA and to compare these findings to those based on traditional discrete measures. METHODS: Sixteen male and 17 female recreational athletes were recruited to perform unanticipated run and cutting tasks. Three-dimensional joint dynamics were recorded, and discrete variables were extracted. PCA analyses were also performed on the angle and moment waveforms in all three planes. The PCA used an eigenvalue analysis on the data covariance matrix. Gender differences in the principal component (PC) scores generated by the PCA were assessed using a MANOVA (P < 0.05). RESULTS: On the basis of the discrete variables, flexion range of motion for females was less than for males. From the PCA analysis, females were less internally rotated during late stance and exhibited a relatively greater peak adduction moment that was not apparent in the original time series. This peak moment correlated with a greater abduction oscillation during early stance. There was also less variability for females in the sagittal and frontal plane moment PC. CONCLUSIONS: The PCA analysis did not significantly detect the decreased flexion, but PCA did reveal gender differences in movement patterns and variability that were not apparent in the discrete variables. The results of this study demonstrate the potential of PCA to provide deeper understand of movement dynamics that may help in detecting injury risk factors.