The association between knee joint biomechanics and neuromuscular control and moderate knee osteoarthritis radiographic and pain severity.

OBJECTIVE: The objective of this study was to determine the association between biomechanical and neuromuscular factors of clinically diagnosed mild to moderate knee osteoarthritis (OA) with radiographic severity and pain severity separately. METHOD: Three-dimensional gait analysis and electromyography were performed on a group of 40 participants with clinically diagnosed mild to moderate medial knee OA. Associations between radiographic severity, defined using a visual analog radiographic score, and pain severity, defined with the pain subscale of the WOMAC osteoarthritis index, with knee joint kinematics and kinetics, electromyography patterns of periarticular knee muscles, BMI and gait speed were determined with correlation analyses. Multiple linear regression analyses of radiographic and pain severity were also explored. RESULTS: Statistically significant correlations between radiographic severity and the overall magnitude of the knee adduction moment during stance (r²=21.4%, P=0.003) and the magnitude of the knee flexion angle during the gait cycle (r²=11.4%, P=0.03) were found. Significant correlations between pain and gait speed (r²=28.2%, P<0.0001), the activation patterns of the lateral gastrocnemius (r²=16.6%, P=0.009) and the medial hamstring (r²=10.3%, P=0.04) during gait were found. The combination of the magnitude of the knee adduction moment during stance and BMI explained a significant portion of the variability in radiographic severity (R(2)=27.1%, P<0.0001). No multivariate model explained pain severity better than gait speed alone. CONCLUSIONS: This study suggests that some knee joint biomechanical variables are associated with structural knee OA severity measured from radiographs in clinically diagnosed mild to moderate levels of disease, but that pain severity is only reflected in gait speed and neuromuscular activation patterns. A combination of the knee adduction moment and BMI better explained structural knee OA severity than any individual factor alone.

Foot progression angle and the knee adduction moment: a cross-sectional investigation in knee osteoarthritis.

OBJECTIVE: To test the hypothesis that an association exists between the characteristics of the knee adduction moment and foot progression angle (FPA) in asymptomatic individuals and those with mild to moderate and severe knee osteoarthritis (OA). DESIGN: Fifty asymptomatic individuals, 46 patients with mild to moderate and 44 patients with severe knee OA were recruited. Maximum knee adduction moment during late stance and principal component analysis (PCA) were used to describe the knee adduction moment captured during gait. Multiple regression models were used for each of the three group assignments to analyze the association between the independent variables and the knee adduction moment. RESULTS: FPA explained a significant amount of the variability associated with the shape of the knee adduction moment waveform for the asymptomatic and mild to moderate groups (P<0.05), but not for the severe group (P>0.05). Walking velocity alone explained significant variance associated with the shape of the knee adduction moment in the severe OA group (P<0.05). CONCLUSION: A toe out FPA was associated with altered knee adduction moment waveform characteristics, extracted using PCA, in asymptomatic individuals and those with mild to moderate knee OA only. These findings are directly implicated in medial knee compartment loading. This relationship was not evident in those with severe knee OA.

Biomechanical features of gait waveform data associated with knee osteoarthritis: an application of principal component analysis.

This study compared the gait of 50 patients with end-stage knee osteoarthritis to a group of 63 age-matched asymptomatic control subjects. The analysis focused on three gait waveform measures that were selected based on previous literature demonstrating their relevance to knee osteoarthritis (OA): the knee flexion angle, flexion moment, and adduction moment. The objective was to determine the biomechanical features of these gait measures related to knee osteoarthritis. Principal component analysis was used as a data reduction tool, as well as a preliminary step for further analysis to determine gait pattern differences between the OA and the control groups. These further analyses included statistical hypothesis testing to detect group differences, and discriminant analysis to quantify overall group separation and to establish a hierarchy of discriminatory ability among the gait waveform features. The two groups were separated with a misclassification rate (estimated by cross-validation) of 8%. The discriminatory features of the gait waveforms were, in order of their discriminatory ability: the amplitude of the flexion moment, the range of motion of the flexion angle, the magnitude of the flexion moment during early stance, and the magnitude of the adduction moment during stance.

Neuromuscular alterations during walking in persons with moderate knee osteoarthritis.

This paper compared the neuromuscular responses during walking between those with early-stage knee osteoarthritis (OA) to asymptomatic controls. The rationale for studying those with mild to moderate knee OA was to determine the alterations in response to dynamic loading that might be expected before severe pain, joint space narrowing and joint surface changes occur. We used pattern recognition techniques to explore both amplitude and shape changes of the surface electromyograms recorded from seven muscles crossing the knee joint of 40 subjects with knee OA and 38 asymptomatic controls during a walking task. The principal patterns for each muscle grouping explained over 83% of the variance in the waveforms. This result supported the notion that the main neuromuscular patterns were similar between asymptomatic controls and those with OA, reflecting the specific roles of the major muscles during walking. ANOVA revealed significant (p<0.05) differences in the principal pattern scores reflecting both amplitude and shape alterations in the OA group and among muscles. These differences captured subtle changes in the neuromuscular responses of the subjects with OA throughout different phases of the gait cycle and most likely reflected changes in the mechanical environment (joint loading, instability) and pain. The subjects with OA attempted to increase activity of the lateral sites and reduce activity in the medial sites, having minimal but prolonged activity during late stance. Therefore, alterations in neuromuscular responses were found even in this high functioning group with moderate knee OA.

Changes in frontal plane dynamics and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis application of a multidimensional analysis technique.

BACKGROUND: Osteoarthritis of the knee is related to many correlated mechanical factors that can be measured with gait analysis. Gait analysis results in large data sets. The analysis of these data is difficult due to the correlated, multidimensional nature of the measures. METHODS: A multidimensional model that uses two multivariate statistical techniques, principal component analysis and discriminant analysis, was used to discriminate between the gait patterns of the normal subject group and the osteoarthritis subject group. Nine time varying gait measures and eight discrete measures were included in the analysis. All interrelationships between and within the measures were retained in the analysis. FINDINGS: The multidimensional analysis technique successfully separated the gait patterns of normal and knee osteoarthritis subjects with a misclassification error rate of <6%. The most discriminatory feature described a static and dynamic alignment factor. The second most discriminatory feature described a gait pattern change during the loading response phase of the gait cycle. INTERPRETATION: The interrelationships between gait measures and between the time instants of the gait cycle can provide insight into the mechanical mechanisms of pathologies such as knee osteoarthritis. These results suggest that changes in frontal plane loading and alignment and the loading response phase of the gait cycle are characteristic of severe knee osteoarthritis gait patterns. Subsequent investigations earlier in the disease process may suggest the importance of these factors to the progression of knee osteoarthritis.

A multivariate gait data analysis technique: application to knee osteoarthritis.

Modern gait analysis is a powerful non-invasive tool for calculating the mechanical factors involved in pathological processes such as knee osteoarthritis (OA). Although very accurate measurements can be made, the clinical applicability and widespread use of gait analysis have been hindered by a lack of appropriate data analysis techniques for reducing and analysing the resulting large volumes of highly correlated gait data. This paper introduces a multidimensional gait data analysis technique that simultaneously considers multiple time-varying and discrete measures, exploiting the correlation structure between and within the measures. The multidimensional analysis technique was used to detect discriminatory mechanical features of knee OA gait patterns that involved interacting changes in several gait measures, at specific time portions of the gait cycle. The two most discriminatory features described a dynamic alignment difference and a loading response difference with knee OA.

An integrated procedure to assess knee-joint kinematics and kinetics during gait using an optoelectric system and standardized X-rays.

This paper describes a 3-D gait analysis system, which combines optoelectric motion tracking and a standardized X-ray procedure, to calculate the net knee-joint forces and moments of a normal subject group during walking. The optoelectric system collects kinematic data from infra-red LED markers placed at selected skin surface locations and projecting probes attached to the lower limb. A standardized X-ray procedure is used to move surface markers into their designated bony landmarks based on individual bone structure, which reduces the error caused by uncertainty of skin-surface marker locations. Based on moved-in marker information, different joint coordinate systems are proposed for kinematic and kinetic analysis of the knee joint. Normalized data of knee angles, net reaction forces and net moments from 35 young, normal subjects are presented.

A procedure to validate three-dimensional motion assessment systems.

The automation provided by computer-assisted motion-tracking systems allows for three-dimensional motion and force analysis. These systems combined with mathematical modelling are able to analyse quickly the intricate dynamics of human movement. Understanding the limitations of human motion analysis as performed by the present measurement techniques is essential for proper application of the results. It is necessary to validate the analysis system prior to subject testing. This paper provides a validation of an optoelectric motion-tracking system used in a dynamic knee assessment study. While the validation is shown with one particular system only, it is suggested that all systems used in two- or three-dimensional motion analysis should be tested similarly in the actual configuration used. Three simple mechanical representations of the human knee have been used in this validation. The first model provided an understanding of the source and behaviour of the error introduced to the accuracy of defining a vector between the recorded coordinates of two markers. The other two models investigated the effect of processing methods specific to the knee analysis project. Separating the markers by at least 180 mm is recommended to produce stable vectors. Relative joint angles could be calculated in all three planes of rotation. The error in calculating flexion and longitudinal rotation was less than 2.0 degrees, while calculating adduction introduced errors of 4.0 degrees. Force calculations were found to be within 8%. The system behaviour was found to be consistent within the calibrated volume about the force platform. Simple mechanical models combined with straightforward procedures can provide validation in terms of clinically relevant parameters.

Semiautomatic three-dimensional knee motion assessment system.

A semiautomatic three-dimensional knee motion assessment system has been developed based on an optoelectric motion tracking system connected to an IBM-compatible computer. Critical decisions made in implementing the software component of the system include the modelling of the thigh and lower leg segments, calculating the knee angles, reaction forces, and moments; the file structure used and the format of the programs used to process the data are outlined. Once the subject-specific data have been collected, the system of programs requires no other user-intervention during processing. Also, selected curve parameters are automatically extracted from the output and combined with the subject-specific data that include precision X-ray and anthropometric data, which are all added to a knee motion assessment database. The automated portion of the system frees the experimenter from data processing and allows concentration on data analysis.