Gender differences in gait kinematics for patients with knee osteoarthritis

BMC Musculoskeletal Disorders

Research Article

Angkoon Phinyomark¹  Dylan Kobsar¹  Reed Ferber²  Sean T. Osis³  Blayne A. Hettinga³ 

[1] Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada;Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada;Faculty of Nursing, University of Calgary, Calgary, AB, Canada;Running Injury Clinic, University of Calgary, 2500 University Drive NW, T2N 1N4, Calgary, AB, Canada;Faculty of Kinesiology, University of Calgary, Calgary, AB, Canada;Running Injury Clinic, University of Calgary, 2500 University Drive NW, T2N 1N4, Calgary, AB, Canada;

关键词: Gait;    Biomechanics;    Kinematics;    Knee;    Osteoarthritis;    Sex differences;    Principal component analysis;    Support vector machine;   

DOI  :  10.1186/s12891-016-1013-z

 received in 2015-08-19, accepted in 2016-04-07,  发布年份 2016

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Phinyomark et al. 2016