Biomechanics is an essential form of measurement in the understanding of the development and progression of osteoarthritis (OA). However, the number of participants in biomechanical studies are often small and there is limited ways to share or combine data from across institutions or studies. This is essential for applying modern machine learning methods, where large, complex datasets can be used to identify patterns in the data. Using these data-driven approaches, it could be possible to better predict the optimal interventions for patients at an early stage, potentially avoiding pain and inappropriate surgery or rehabilitation. In this project we developed a prototype database platform for combining and sharing biomechanics datasets. The database includes methods for importing and standardising data and associated variables, to create a seamless, searchable combined dataset of both healthy and knee OA biomechanics. Data was curated through calls to members of the OATech Network+ (Abstract
Objectives
Methods
The aim of this study was to examine the loading
of the other joints of the lower limb in patients with unilateral osteoarthritis
(OA) of the knee. We recruited 20 patients with no other symptoms
or deformity in the lower limbs from a consecutive cohort of patients
awaiting knee replacement. Gait analysis and electromyographic recordings were
performed to determine moments at both knees and hips, and contraction
patterns in the medial and lateral quadriceps and hamstrings bilaterally.
The speed of gait was reduced in the group with OA compared with
the controls, but there were only minor differences in stance times
between the limbs. Patients with OA of the knee had significant
increases in adduction moment impulse at both knees and the contralateral
hip (adjusted p-values: affected knee: p <
0.01, unaffected knee
p = 0.048, contralateral hip p = 0.03), and significantly increased
muscular co-contraction bilaterally compared with controls (all
comparisons for co-contraction, p <
0.01). The other major weight-bearing joints are at risk from abnormal
biomechanics in patients with unilateral OA of the knee. Cite this article:
Patients with knee osteoarthritis frequently complain that they develop pain in other joints due to over-loading during gait. However, there have been no previous studies examining the effect of knee arthritis on the other weight bearing joints. The aim of this study was to examine the loading of the hips and contra-lateral knee during gait in a cohort of patients pre- and post knee replacement. Twenty patients with single joint osteoarthritis awaiting knee replacement and 20 healthy volunteers were recruited. Gait analysis during level gait and at self selected speed was performed using a 12 camera Vicon motion analysis system. The ground reaction force was collected using EMG electrodes attached to the medial and lateral hamstrings and quadriceps bilaterally. Patients were invited to return 12 months post-operatively. Data was analysed using the Vicon plug-in-gait model and statistical testing was performed with SPSS v16.0 using ANCOVA to account for gait speed.Introduction
Methods
Patients with knee osteoarthritis (OA) often tell us that they put extra load on the joints of the opposite leg as they walk. Multiple joint OA is common and has previously been related to gait changes due to hip OA (Shakoor et al 2002). The aim of this study was to determine whether patients with medial compartment knee OA have abnormal biomechanics of the unaffected knee and both hips during normal level gait. Twenty patients (11 male, 9 female), with severe medial compartment knee OA and no other joint pain were recruited. The control group comprised 20 adults without musculoskeletal pain. Patients were reviewed, x-rays were examined and WOMAC and Oxford knee scores were completed. A 12 camera Vicon (Vicon, Oxford) system was used to collect kinematic data (100Hz) on level walking and the ground reaction force was recorded using three AMTI force plates (1000Hz). Surface electrodes were placed over medial and lateral quadriceps and hamstrings bilaterally to record EMG data (1000Hz). Kinematics and kinetics were calculated using the Vicon ‘plug-in-gait’ model. A co-contraction index was calculated for the EMG signals on each side of the knee, representing the magnitude of the combined readings relative to their maximum contraction during the gait cycle. Statistical comparisons were performed using t-tests with Bonferroni's correction for two variables and ANOVA for more than two variables (SPSS v16).Introduction
Methods
