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Orthopaedic Proceedings
Vol. 99-B, Issue SUPP_1 | Pages 2 - 2
1 Jan 2017
Wesseling M Meyer C Corten K Desloovere K Jonkers I
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Several studies have shown that gait kinematics[1–3] and hip contact forces (HCFs)[4, 5] of patients following total hip arthroplasty (THA) do not return to normal, although improvements in kinematics are found compared to the pre-surgery. However, the evolution of HCFs after surgery has not been investigated. The goal of this study is to evaluate HCFs during gait in OA patients before and at 2 evaluation moments post-THA.

Fourteen unilateral hip OA patients before and 3- and 12-months post-THA surgery walked at self-selected speed, as well as 18 healthy control subjects. 3D marker trajectories were captured using Vicon (Oxford Metrics, UK) and force data was measured using two AMTI force platforms (Watertown, MA). A musculoskeletal model consisting of 14 segments, 19 degrees of freedom and 88 musculotendon actuators and including wrapping surfaces around the hip joint was used[6]. All analyses were performed in OpenSim 3.1[7]. The model was scaled to the dimensions of each subject using the marker positions of a static pose. A kalman smoother procedure was used to calculate joint angles[8]. Muscle forces were calculated using static optimization, minimizing the sum of squared muscle activations. HCFs were calculated and normalized to body weight (BW). First and second peak HCFs were determined and used for statistical analysis. To determine differences between HCFs of OA patients at the different evaluation moments, a Friedman test was used. In case of a significant difference, post-hoc rank-based multiple comparison tests with a Bonferonni adjustment was used. To compare controls and patients at each evaluation moment separate Man-Whitney U tests were used. Differences in HCFs between the affected and non-affected legs were expressed by a symmetry index (SI), i.e. the ratio between the HCFs of the affected leg over the non-affected leg, averaged over the stance phase of the gait cycle.

At the first and second HCF peaks, no significant differences were found between pre-, 3- and 12-months post-surgery (first peak average HCF: 2.68, 2.72 and 2.78BW respectively; second peak average HCF: 3.21, 3.83 and 3.77BW respectively). Compared to controls, significant differences are found for all evaluation moments at the first and second HCF peaks (average HCF controls: 3.43 and 5.15BW respectively). The SI was below 1 pre- and 3-months post-surgery (0.88 and 0.85 respectively), indicating decreased loading of the affected compared to the non-affected leg. At 12-months post-surgery SI was close to 1 (0.98).

As reported before[4, 5], first or second peak HCFs do not return to normal after THA. Although HCFs increase after THA compared to pre-surgery, significant differences with controls remain. Surprisingly, no significant differences are found between the different evaluation moments of the patients, indicating no clear improvements are found after THA. Further, average HCF peaks at 3- and 12-months post-surgery are similar, indicating no further improvements are found 3-months post-surgery. However, the SI was above or close to 1 at 12-months post-surgery, indicating hip loading evolved to a more symmetrical loading 12-months post-surgery.


Orthopaedic Proceedings
Vol. 94-B, Issue SUPP_XL | Pages 162 - 162
1 Sep 2012
Scheys L Wong P Callewaert B Leffler J Franz A Vandenneucker H Labey L Leardini A Desloovere K
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INTRODUCTION

In patients with neural disorders such as cerebral palsy, three-dimensional marker-based motion analysis has evolved to become a well standardized procedure with a large impact on the clinical decision-making process. On the other hand, in knee arthroplasty research, motion analysis has been little used as a standard tool for objective evaluation of knee joint function. Furthermore, in the available literature, applied methodologies are diverse, resulting in inconsistent findings [1]. Therefore we developed and evaluated a new motion analysis framework to enable standardized quantitative assessment of knee joint function.

MATERIALS AND METHODS

The proposed framework integrates a custom-defined motion analysis protocol with associated reference database and a standardized post-processing step including statistical analysis. Kinematics are collected using a custom-made marker set defined by merging two existing protocols and combine them with a knee alignment device. Following a standing trial, a star-arc hip motion pattern and a set of knee flexion/extension cycles allowing functional, subject-specific calibration of the underlying kinematic model, marker trajectories are acquired for three trials of a set of twelve motor tasks: walking, walking with crossover turn, walking with sidestep turn, stair ascent, stair descent, stair descent with crossover turn, stair descent with sidestep turn, trunk rotations, chair rise, mild squat, deep squat and lunge. This specific set of motor tasks was selected to cover as much as possible common daily life activities. Furthermore, some of these induce greater motion at the knee joint, thus improving the measurement-to-error ratio. Kinetics are acquired by integrating two forceplates in the walkway. Bilateral muscle activity of 8 major muscles is monitored with a 16 channel wireless electromyography (EMG) system. Finally, custom-built software with an associated graphical user interface was created for automated and flexible analysis of gait lab data, including repeatability analysis, analysis of specific kinematic, kinetic and spatiotemporal parameters and statistical comparisons.