Knee osteoarthritis alters peri-articular knee muscle strategies during gait.

The primary role of muscles is to move, and control joints. It is therefore important to understand how degenerative joint disease changes this role with the resulting effect on mechanical joint loading. Muscular control strategies can vary depending on strength and coordination which in turn influences joint control and loading. The purpose of this study was to investigate the variation in neuromuscular control mechanisms and joint biomechanics for three subject groups including those with: uni-compartmental knee osteoarthritis (OA), listed for high tibial osteotomy surgery (pre-HTO, n = 10); multi-compartmental knee OA listed for total knee replacement (pre-TKR, n = 9), and non-pathological knees (NP, n = 11). Lower limb kinematics and electromyography (EMG) data for subjects walking at self-selected speed, were input to an EMG-driven musculoskeletal knee model which was scaled and calibrated to each individual to estimate muscle forces. Compared to NP, the peak gastrocnemius muscle force reduced by 30% and 18% for pre-HTO and pre-TKR respectively, and the peak force estimated for hamstring muscle increased by 25% for pre-HTO. Higher quadriceps and hamstring forces suggest that co-contraction with the gastrocnemius could lead to higher joint contact forces. Combined with the excessive loading due to a high external knee adduction moment this may exacerbate joint destruction. An increased lateral muscle co-contraction reflects the progression from NP to uni-compartmental OA (pre-HTO). Pre-TKR patients adopt a different gait pattern to pre-HTO patients. Increased medial muscle co-activation could potentially differentiate between uni- or multi-compartmental OA.

Gait function improvements, using Cardiff Classifier, are related to patient-reported function and pain following hip arthroplasty.

Summarizing results of three-dimensional (3D) gait analysis into a comprehensive measure of overall gait function is valuable to discern to what extent gait function is affected, and later recovered after surgery and rehabilitation. This study aimed to investigate whether preoperative gait function, quantified and summarized using the Cardiff Classifier, can predict improvements in postoperative patient-reported activities of daily living, and overall gait function 1 year after total hip arthroplasty (THA). Secondly, to explore relationships between pre-to-post surgical change in gait function versus changes in patient-reported and performance-based function. Thirty-two patients scheduled for THA and 25 nonpathological individuals were included in this prospective cohort study. Patients were evaluated before THA and 1 year postoperatively using 3D gait analysis, patient-reported outcomes, and performance-based tests. Kinematic and kinetic gait parameters, derived from 3D gait analysis, were quantified using the Cardiff Classifier. Linear regressions investigated the predictive value of preoperative gait function on postoperative outcomes of function, and univariate correlations explored relationships between pre-to-post surgical changes in outcome measures. Preoperative gait function, by means of Cardiff Classifier, explained 35% and 30% of the total variance in change in patient-reported activities of daily living, and in gait function, respectively. Moderate-to-strong correlations were found between change in gait function and change in patient-reported function and pain, while no correlations were found between change in gait function and performance-based function. Clinical significance: Preoperative gait function predicts postsurgical function to a moderate degree, while improvements in gait function after surgery are more closely related to how patients perceive function than their maximal performance of functional tests.

Can activities of daily living contribute to EMG normalization for gait analysis?

This study aims to examine alternative methods of normalization that effectively reflect muscle activity as compared to Maximal Voluntary Contraction (MVC). EMG data recorded from knee flexion-extension muscles in 10 control subjects during the stance phase of the gait cycle were examined by adopting different approaches of normalization: MVC, Mean and Peak Dynamic during gait cycles, (MDM and PDM, respectively), Peak Dynamic during activities of daily living (ADLs), (*PDM), and a combination of ADLs and MVC(**PDM). Intra- and inter-individual variability were calculated to determine reliability and similarity to MCV. **PDM showed excellent reliability across subjects in comparison to MVC, where variance ratio ranged from 0.43-0.99 for **PDM and 0.79-1.08 for MVC. Coefficient of variability showed a similar trend to Variance Ratio, ranging from 0.60-1.25 for **PDM and 1.97-3.92 for MVC. Both MVC and **PDM, and to some extent *PDM, demonstrated good-to-excellent relative amplitude's matching; i.e. root mean square difference and absolute difference were both around 0.08 for Vastus medialis to about 4 for Medial gastrocnemius. It was concluded that **PDM and *PDM were reliable, **PDM mirrored MVC and thus could be used as an alternative to MVC for subjects who are unable to provide the required effort for MVC testing. Where MVC testing is not possible, *PDM is the next preferred option.

Harmonising data collection from osteoarthritis studies to enable stratification: recommendations on core data collection from an Arthritis Research UK clinical studies group.

OBJECTIVE: Treatment of OA by stratifying for commonly used and novel therapies will likely improve the range of effective therapy options and their rational deployment in this undertreated, chronic disease. In order to develop appropriate datasets for conducting post hoc analyses to inform approaches to stratification for OA, our aim was to develop recommendations on the minimum data that should be recorded at baseline in all future OA interventional and observational studies. METHODS: An Arthritis Research UK study group comprised of 32 experts used a Delphi-style approach supported by a literature review of systematic reviews to come to a consensus on core data collection for OA studies. RESULTS: Thirty-five systematic reviews were used as the basis for the consensus group discussion. For studies with a primary structural endpoint, core domains for collection were defined as BMI, age, gender, racial origin, comorbidities, baseline OA pain, pain in other joints and occupation. In addition to the items generalizable to all anatomical sites, joint-specific domains included radiographic measures, surgical history and anatomical factors, including alignment. To demonstrate clinical relevance for symptom studies, the collection of mental health score, self-efficacy and depression scales were advised in addition to the above. CONCLUSIONS: Currently it is not possible to stratify patients with OA into therapeutic groups. A list of core and optional data to be collected in all OA interventional and observational studies was developed, providing a basis for future analyses to identify predictors of progression or response to treatment.

Recommendations for the conduct of efficacy trials of treatment devices for osteoarthritis: a report from a working group of the Arthritis Research UK Osteoarthritis and Crystal Diseases Clinical Studies Group.

OBJECTIVE: There are unique challenges to designing and carrying out high-quality trials testing therapeutic devices in OA and other rheumatic diseases. Such challenges include determining the mechanisms of action of the device and the appropriate sham. Design of device trials is more challenging than that of placebo-controlled drug trials. Our aim was to develop recommendations for designing device trials. METHODS: An Arthritis Research UK study group comprised of 30 rheumatologists, physiotherapists, podiatrists, engineers, orthopaedists, trialists and patients, including many who have carried out device trials, met and (using a Delphi-styled approach) came to consensus on recommendations for device trials. RESULTS: Challenges unique to device trials include defining the mechanism of action of the device and, therefore, the appropriate sham that provides a placebo effect without duplicating the action of the active device. Should there be no clear-cut mechanism of action, a three-arm trial including a no-treatment arm and one with presumed sham action was recommended. For individualized devices, generalizable indications and standardization of the devices are needed so that treatments can be generalized. CONCLUSION: A consensus set of recommendations for device trials was developed, providing a basis for improved trial design, and hopefully improvement in the number of effective therapeutic devices for rheumatic diseases.

Three-dimensional motion analysis of postural adjustments during over-ground locomotion in a rat model of Parkinson's disease.

Postural instability, a symptom of Parkinson's disease (PD) patients, leads to frequent falls and difficulty in forward motion during gait. These motor deficits are mainly caused by neurodegenerative processes in the brain leading to reduced levels of the neurotransmitter dopamine. Postural studies involving animal models of PD are mainly based on movement scores or descriptive approaches to discerning differences in behaviour or function. Our aim was to describe postural adjustments in a rat model of PD utilising a quantitative three dimensional motion analysis technique during gait to investigate the effects of unilateral dopamine depletion on rat locomotion while walking on beams of varying widths (wide, narrow and graduated). Tail orientation, limb positions on the beam, range of motion and kinematic waveforms of the Roll, Pitch and Yaw of male Lister Hooded rats were investigated using passive markers placed in locations that were representative of their body axis. Hemiparkinsonian rats moved on the wide beam with a significantly higher Roll range of motion coupled with a positively biased Roll kinematic waveform during one gait cycle. While walking on the narrow beam they displayed an increased use of the ledge and placed their tail towards the right. These results are brought about by the rats' inability to shift body posture using the impaired limb. Our data demonstrate that marker-based motion capture can provide an effective and simple approach to quantifying postural adjustments for rat models of PD.

An innovative application of a small-scale motion analysis technique to quantify human skin deformation in vivo.

This study highlights a new experimental method developed to measure full-field deformation of human skin in vivo. The technique uses a small-scale Qualisys (Sweden) 3D motion capture system and an array of reflective markers placed on the forearm of five healthy volunteers. A load of up to 1.5N was applied to induce skin deformation by pulling a fine wire attached to the centre of the marker configuration. Loading and marker displacements were recorded simultaneously. 3D marker trajectory data was generated for three different load directions. Tests were repeated to investigate accuracy and repeatability. Calibration results indicate the accuracy of the motion capture system with an average residual of 0.05 mm. The procedure was found to be repeatable and accurate for five repeated tests of measured displacements with a maximum variance of 5%. Experimental data are presented to demonstrate robustness and the ability to produce significant outputs. For all five subjects, at 1N load, the mean and standard deviations of skin axial and lateral displacements were found to be 11.7+/-1.6mm and 12.3+/-3.3mm, respectively. The axial displacements ratio (u(90)/u(0)) ranges from 0.63 to 1.45 with mean+/-standard deviation of 0.982+/-0.34 and 0.982+/-0.32 for left and right arms, respectively. The experiments generated useful and accurate data that can be used to study the viscoelastic, hyperelastic or anisotropic behaviour of human skin. The measured displacements will be analysed further to determine the mechanical properties of skin using inverse Finite Element Analysis and Ogden model.

Digital image correlation and finite element modelling as a method to determine mechanical properties of human soft tissue in vivo.

The mechanical properties of human soft tissue are crucial for impact biomechanics, rehabilitation engineering, and surgical simulation. Validation of these constitutive models using human data remains challenging and often requires the use of non-invasive imaging and inverse finite element (FE) analysis. Post-processing data from imaging methods such as tagged magnetic resonance imaging (MRI) can be challenging. Digital image correlation (DIC), however, is a relatively straightforward imaging method. DIC has been used in the past to study the planar and superficial properties of soft tissue and excised soft tissue layers. However, DIC has not been used to non-invasive study of the bulk properties of human soft tissue in vivo. Thus, the goal of this study was to assess the use of DIC in combination with FE modelling to determine the bulk material properties of human soft tissue. Indentation experiments were performed on a silicone gel soft tissue phantom. A two camera DIC setup was then used to record the 3D surface deformation. The experiment was then simulated using a FE model. The gel was modelled as Neo-Hookean hyperelastic, and the material parameters were determined by minimising the error between the experimental and FE data. The iterative FE analysis determined material parameters (micro=1.80kPa, K=2999kPa) that were in close agreement with parameters derived independently from regression to uniaxial compression tests (micro=1.71kPa, K=2857kPa). Furthermore the FE model was capable of reproducing the experimental indentor force as well as the surface deformation found (R(2)=0.81). It was therefore concluded that a two camera DIC configuration combined with FE modelling can be used to determine the bulk mechanical properties of materials that can be represented using hyperelastic Neo-Hookean constitutive laws.

Reduction, classification and ranking of motion analysis data: an application to osteoarthritic and normal knee function data.

There are certain major obstacles to using motion analysis as an aid to clinical decision making. These include: the difficulty in comprehending large amounts of both corroborating and conflicting information; the subjectivity of data interpretation; the need for visualization; and the quantitative comparison of temporal waveform data. This paper seeks to overcome these obstacles by applying a hybrid approach to the analysis of motion analysis data using principal component analysis (PCA), the Dempster-Shafer (DS) theory of evidence and simplex plots. Specifically, the approach is used to characterise the differences between osteoarthritic (OA) and normal (NL) knee function data and to produce a hierarchy of those variables that are most discriminatory in the classification process. Comparisons of the results obtained with the hybrid approach are made with results from artificial neural network analyses.