Gender and Age-Related Differences in Bilateral Lower Extremity Mechanics during Treadmill Running

Abstract

Female runners have a two-fold risk of sustaining certain running-related injuries as compared to their male counterparts. Thus, a comprehensive understanding of the sex-related differences in running kinematics is necessary. However, previous studies have either used discrete time point variables and inferential statistics and/or relatively small subject numbers. Therefore, the first purpose of this study was to use a principal component analysis (PCA) method along with a support vector machine (SVM) classifier to examine the differences in running gait kinematics between female and male runners across a large sample of the running population as well as between two age-specific sub-groups. Bilateral 3-dimensional lower extremity gait kinematic data were collected during treadmill running. Data were analysed on the complete sample (n = 483: female 263, male 220), a younger subject group (n = 56), and an older subject group (n = 51). The PC scores were first sorted by the percentage of variance explained and we also employed a novel approach wherein PCs were sorted based on between-gender statistical effect sizes. An SVM was used to determine if the sex and age conditions were separable and classifiable based on the PCA. Forty PCs explained 84.74% of the variance in the data and an SVM classification accuracy of 86.34% was found between female and male runners. Classification accuracies between genders for younger subjects were higher than a subgroup of older runners. The observed interactions between age and gender suggest these factors must be considered together when trying to create homogenous sub-groups for research purposes.

Do intermediate- and higher-order principal components contain useful information to detect subtle changes in lower extremity biomechanics during running?

“…To examine classification rates, PC scores were sorted in two ways: first, by their respective variances and second, by the Cohen's d effect sizes (Phinyomark, Hettinga, Osis, & Ferber, 2014a;von Tscharner, Enders, & Maurer, 2013). As the second method has the disadvantage that the evaluation of features is dependent upon classifier type, two classifiers, the SVM with a linear kernel (Janssen et al, 2011;Wu, Wang, & Liu, 2007) and the LDA (Lee, Roan, Smith, & Lockhart, 2009), were used to determine if gender, age, and exercise conditions were classifiable based on the PC scores.…”

Gender classification of running subjects using full-body kinematics

“…Other common values used for features include joint angles, moments and dynamic variables such as those used by Eskofier et al [26] . In contrast to Ferber et al [15] , who focused primarily on the lower body mechanics, and Phinyomark et al [27] , this paper examines features extracted from movement of the subject's entire body.…”

Sex Differences in Lower Limb Biomechanics During a Single-Leg Cut with Body Borne Load

“…Greater hip joint flexion values are documented in female runners throughout stance phase 23,39 when compared to males, but this difference may be attributed to elevated values of anterior pelvic tilt documented in female runners 39,69 . Conversely, more extended knee joint postures are documented in females when compared to their male counterparts 70,76 .…”

“…When running, females are documented to have more range of motion in the frontal and transverse planes at both the hip and the knee 23,37,39,[69][70][71][72] . Specifically, females exhibit significantly higher values of hip adduction and internal rotation, knee abduction and external rotation 23,73 .…”

“…Additionally, load resulted in an increase of mediolateral impulse, resulting in less stable gait patterns 55 , which may exacerbate the unstable kinematic patterns of the unloaded female 70,72 and increase risk of musculoskeletal injury. While running with load, Xu et al…”