Gender differences in gait kinematics for patients with knee osteoarthritis

Abstract

BackgroundFemales have a two-fold risk of developing knee osteoarthritis (OA) as compared to their male counterparts and atypical walking gait biomechanics are also considered a factor in the aetiology of knee OA. However, few studies have investigated sex-related differences in walking mechanics for patients with knee OA and of those, conflicting results have been reported. Therefore, this study was designed to examine the differences in gait kinematics (1) between male and female subjects with and without knee OA and (2) between healthy gender-matched subjects as compared with their OA counterparts.MethodsOne hundred subjects with knee OA (45 males and 55 females) and 43 healthy subjects (18 males and 25 females) participated in this study. Three-dimensional kinematic data were collected during treadmill-walking and analysed using (1) a traditional approach based on discrete variables and (2) a machine learning approach based on principal component analysis (PCA) and support vector machine (SVM) using waveform data.ResultsOA and healthy females exhibited significantly greater knee abduction and hip adduction angles compared to their male counterparts. No significant differences were found in any discrete gait kinematic variable between OA and healthy subjects in either the male or female group. Using PCA and SVM approaches, classification accuracies of 98–100 % were found between gender groups as well as between OA groups.ConclusionsThese results suggest that care should be taken to account for gender when investigating the biomechanical aetiology of knee OA and that gender-specific analysis and rehabilitation protocols should be developed.

Reproducibility of in vivo magnetic resonance imaging T1rho and T2 relaxation time measurements of hip cartilage at 3.0T in healthy volunteers

“…Such gender differences have been previously described in the knee. 30 Differences in gait biomechanics (greater knee abduction and hip adduction in females), 31 and perfusion of the hip, 32 could account for these higher relaxometry values. They might reflect the higher risk of cartilage degeneration in women.…”

Artificial Intelligence for Clinical Gait Diagnostics of Knee Osteoarthritis: An Evidence - based Review and Analysis

“…However, analysis of mocap data heavily relies on adequate experimental setup, pre-and post-processing methods, e.g., gap filling between marker trajectories, adequate band-pass filter design and thresholds. Although statistical tools are widely used in gait analysis, given the nonlinearity and high dimensionality of gait data [7], they are only analytical and lack predictive capability to generalise to unseen data.…”

“…Whilst unsupervised AI learning-based algorithms, such as the self-organising map (SOM) [11,12], Random Forest (RF) [13,14], can classify gait data used as inputs without preliminarily knowing their true classes, supervised classifiers instead, such as the multi-layer perceptron (MLP) [15], the radial basis function (RBF) networks [16] and the Support Vector Machine (SVM) [14] require that the true classes of the input data are preliminarily known. Amongst AI-based methods, Machine Learning (ML) [7,[17][18][19] and Artificial Neural Networks (ANN) have proven to be accurate when dealing with gait-related data on patients with knee OA [2,5,6,20,21].…”

The use of real-time feedback to improve kinematic marker placement consistency among novice examiners

“…Frontal plane kinematics of the knee and hip have been identified in research surrounding the aetiology of knee osteoarthritis (OA) and the difference in prevalence between males and females (Phinyomark, Osis, Hettinga, Kobsar, & Ferber, 2016). Again working with a sample of individuals with knee OA, research has shown difference in hip frontal plane kinematics between those that were classified as high-responders to treatment as compared to those that were grouped into the low-or non-responder category (Kobsar, Osis, Hettinga, & Ferber, 2015).…”

“…Previous research has shown that gender differences exist in lower extremity alignment and lower extremity kinematics during the stance phase of running (Ferber, Davis, & Williams, 2003;Horton & Hall, 1989;Phinyomark et al, 2016). Thus, as an alternative methodology to incorporate a second runner, future research may implement a protocol that uses one male runner and one female runner.…”