Injury rates among runners are high, with the knee injured most frequently. The interaction of running experience and running mechanics is not well understood but may be important for understanding relative injury risk in low vs higher mileage runners. The study aim was to apply a principal component analysis (PCA) to test the hypothesis that differences exist in kinematic waveforms and coordination between higher and low mileage groups. Gait data were collected for 50 subjects running at 3.5 m/s assigned to either a low (< 15 miles/wk) or higher (> 20 miles/wk, 1 year experience) mileage group. A PCA was performed on a matrix of trial vectors of all force, joint kinematic, and center of pressure data. The projection of the subjects’ trial vectors onto the linear combination of PC7, PC10, PC13, and PC19 was significantly different between the higher and lower mileage groups (d = 0.63, P = .012). This resultant PC represented variation in transverse plane pelvic rotation, hip internal rotation, and hip and knee abduction and adduction angles. These results suggest the coordination of lower extremity segment kinematics is different for lower and higher mileage runners. The adopted patterns of coordinated motion may explain the lower incidence of overuse knee injuries for higher mileage runners.
Katherine A. Boyer, Julia Freedman Silvernail and Joseph Hamill
Julia Freedman Silvernail, Richard E.A. van Emmerik, Katherine Boyer, Michael A. Busa and Joseph Hamill
The development of a methodology to assess movement coordination has provided gait researchers a tool to assess movement organization. A challenge in analyzing movement coordination using vector coding lies within the inherent circularity of data garnered from this technique. Therefore, the purpose of this investigation was to determine if accurate group comparisons can be made with varying techniques of vector coding analyses. Thigh–shank coordination was analyzed using a modified vector coding technique on data from 2 groups of runners. Movement coordination was compared between groups using 3 techniques: (1) linear average completed with compressed data (0°–180°) and noncompressed data (0°–360°), (2) coordination phase binning analysis; and (3) a circular statistics analysis. Circular statistics (inferential) analysis provided a rigorous comparison of average movement coordination between groups. In addition, the binning analysis provided a metric for detecting even small differences in the time spent with a particular coordination pattern between groups. However, the linear analysis provided erroneous group comparisons. Furthermore, with compressed data, linear analysis led to misclassification of coordination patterns. While data compression may be attractive as a means of simplifying statistical analysis of inherently circular data, recommendations are to use circular statistics and binning methods on noncompressed data.