Interventions to Reduce Spasticity and Improve Function in People With Chronic Incomplete Spinal Cord Injury: Distinctions Revealed by Different Analytical Methods.

Background. Spinal cord injury (SCI) results in impaired function, and ankle joint spasticity is a common secondary complication. Different interventions have been trialed with variable results. Objective. We investigated the effects of pharmacological and physical (locomotor training) interventions on function in people living with incomplete motor function loss caused by SCI and used different analytical techniques to understand whether functional levels affect recovery with different interventions. Methods. Participants with an incomplete SCI were assigned to 3 groups: no intervention, Lokomat, or tizanidine. Outcome measures were the 10-m walk test, 6-minute walk test, and the Timed Up and Go. Participants were classified in 2 ways: (1) based on achieving an improvement above the minimally important difference (MID) and (2) using growth mixture modeling (GMM). Functional levels of participants who achieved the MID were compared and random coefficient regression (RCR) was used to assess recovery in GMM classes. Results. Overall, walking speed and endurance improved, with no difference between interventions. Only a small number of participants achieved the MID. Both MID and GMM-RCR analyses revealed that tizanidine improved endurance in high-functioning participants. GMM-RCR classification also showed that speed and mobility improved after locomotor training. Conclusions. Improvements in function were achieved in a limited number of people with SCI. Using the MID and GMM techniques, differences in responses to interventions between high-and low-functioning participants could be identified. These techniques may, therefore, have potential to be used for characterizing therapeutic effects resulting from different interventions

Balance and gait adaptations in patients with early knee osteoarthritis

AbstractGait adaptations in people with severe knee osteoarthritis (OA) have been well documented, with increased knee adduction moments (KAM) the most commonly reported parameter. Neuromuscular adaptations have also been reported, including reduced postural control. However these adaptations may be the result of morphological changes in the joint, rather than the cause. This study aimed to determine if people with early OA have altered gait parameters and neuromuscular adaptations. Gait and postural tasks were performed by 18 people with early medial knee OA and 18 age and gender-matched control subjects. Parameters measured were kinematics and kinetics during gait and postural tasks, and centre of pressure and electromyographic activity during postural tasks. OA subjects showed no differences in the gait parameters measured, however they demonstrated postural deficits during one-leg standing on both their affected and unaffected sides and altered hip adduction moments compared with controls. Increased activity of the gluteus medius of both sides (p<0.05), and quadriceps and hamstrings of the affected side (p<0.05) during one-leg standing compared with controls were also noted. This study has demonstrated that gait adaptations commonly associated with OA do not occur in the early stages, while neuromuscular adaptations are evident. These results may be relevant for early interventions to delay or prevent osteoarthritis in its early stages

Knee moments of anterior cruciate ligament reconstructed and control participants during normal and inclined walking

Objectives: Prior injury to the knee, particularly anterior cruciate ligament (ACL) injury, is known to predispose one to premature osteoarthritis (OA). The study sought to explore if there was a biomechanical rationale for this process by investigating changes in external knee moments between people with a history of ACL injury and uninjured participants during walking: (1) on different surface inclines and (2) at different speeds. In addition we assessed functional differences between the groups. Participants: 12 participants who had undergone ACL reconstruction (ACLR) and 12 volunteers with no history of knee trauma or injury were recruited into this study. Peak knee flexion and adduction moments were assessed during flat (normal and slow speed), uphill and downhill walking using an inclined walkway with an embedded Kistler Force plate, and a ten-camera Vicon motion capture system. Knee injury and Osteoarthritis Outcome Score (KOOS) was used to assess function. Multivariate analysis of variance (MANOVA) was used to examine statistical differences in gait and KOOS outcomes. Results: No significant difference was observed in the peak knee adduction moment between ACLR and control participants, however, in further analysis, MANOVA revealed that ACLR participants with an additional meniscal tear or collateral ligament damage (7 participants) had a significantly higher adduction moment (0.33±0.12 Nm/kg m) when compared with those with isolated ACLR (5 participants, 0.1±0.057 Nm/kg m) during gait at their normal speed ( p<0.05). A similar (nonsignificant) trend was seen during slow, uphill and downhill gait. Conclusions: Participants with an isolated ACLR had a reduced adductor moment rather an increased moment, thus questioning prior theories on OA development. In contrast, those participants who had sustained associated trauma to other key knee structures were observed to have an increased adduction moment. Additional injury concurrent with an ACL rupture may lead to a higher predisposition to osteoarthritis than isolated ACL deficiency alone

The Complexity of Human Walking: A Knee Osteoarthritis Study

This study proposes a framework for deconstructing complex walking patterns to create a simple principal component space before checking whether the projection to this space is suitable for identifying changes from the normality. We focus on knee osteoarthritis, the most common knee joint disease and the second leading cause of disability. Knee osteoarthritis affects over 250 million people worldwide. The motivation for projecting the highly dimensional movements to a lower dimensional and simpler space is our belief that motor behaviour can be understood by identifying a simplicity via projection to a low principal component space, which may reflect upon the underlying mechanism. To study this, we recruited 180 subjects, 47 of which reported that they had knee osteoarthritis. They were asked to walk several times along a walkway equipped with two force plates that capture their ground reaction forces along 3 axes, namely vertical, anterior-posterior, and medio-lateral, at 1000 Hz. Data when the subject does not clearly strike the force plate were excluded, leaving 1–3 gait cycles per subject. To examine the complexity of human walking, we applied dimensionality reduction via Probabilistic Principal Component Analysis. The first principal component explains 34% of the variance in the data, whereas over 80% of the variance is explained by 8 principal components or more. This proves the complexity of the underlying structure of the ground reaction forces. To examine if our musculoskeletal system generates movements that are distinguishable between normal and pathological subjects in a low dimensional principal component space, we applied a Bayes classifier. For the tested cross-validated, subject-independent experimental protocol, the classification accuracy equals 82.62%. Also, a novel complexity measure is proposed, which can be used as an objective index to facilitate clinical decision making. This measure proves that knee osteoarthritis subjects exhibit more variability in the two-dimensional principal component space

Analysis of knee osteoarthritis ground reaction vertical force during stair ascent: A neural network approach

Osteoarthritis (OA) is the second leading cause of pain and disability, affecting more than 250 million people worldwide. Here, we focus on knee OA, the most common form of OA. To collect data, subjects were asked to ascend a custom-based stair with a force plate (Kistler Type 9286B, Kistler Instrumente AG, Winterthur, Switzerland). Each subject was barefoot and provided 3 trials. We consider the strike of the right foot on the force plate. Trial data where subjects did not cleanly strike the force plate was excluded from the analysis, so a total of 272 trials were recorded. The signal from the force plate was recorded at a sampling rate of 1000 Hz, then normalised to the subject’s body weight (N/kg), and time-normalised to the entire gait cycle using linear interpolation. We retain the ground reaction force over the vertical plane. Out of the 96 subjects, 37 have OA at the one knee, 11 at both knees and the remaining 48 are control subjects. To automatically classify the motion patterns into three categories, i.e. normal, knee OA at one knee, and knee OA at both knees, a probabilistic neural network (PNN) was employed. The PNN is based on the theory of Bayesian classification. Regarding the PNN structure, it is a feed-forward neural network with high degree of parallelism. The PNN classifier is a non-parametric classification approach, since we have no guarantee that the data follows a Gaussian distribution. The effectiveness of the PNN for detecting knee OA has been verified previously, but the data analysed were radiographic images, rather that ground reaction forces. Results for 5-fold cross-validation can be seen in the Table below. To conclude, the PNN is able to effectively handle locomotion data that exploit great variability both inter- and intra-subject. Also, the PNN can detect approximately 16% of subjects that claim not to have knee OA, but they present gait patterns similar to those of subjects that suffer knee OA

Predicting knee osteoarthritis risk in injured populations

Background Individuals who suffered a lower limb injury have an increased risk of developing knee osteoarthritis. Early diagnosis of osteoarthritis and the ability to track its progression is challenging. This study aimed to explore links between self-reported knee osteoarthritis outcome scores and biomechanical gait parameters, whether self-reported outcome scores could predict gait abnormalities characteristic of knee osteoarthritis in injured populations and, whether scores and biomechanical outcomes were related to osteoarthritis severity via Spearman's correlation coefficient. Methods A cross-sectional study was conducted with asymptomatic participants, participants with lower-limb injury and those with medial knee osteoarthritis. Spearman rank determined relationships between knee injury and outcome scores and hip and knee kinetic/kinematic gait parameters. K-Nearest Neighbour algorithm was used to determine which of the evaluated parameters created the strongest classifier model. Findings Differences in outcome scores were evident between groups, with knee quality of life correlated to first and second peak external knee adduction moment (0.47, 0.55). Combining hip and knee kinetics with quality of life outcome produced the strongest classifier (1.00) with the least prediction error (0.02), enabling classification of injured subjects gait as characteristic of either asymptomatic or knee osteoarthritis subjects. When correlating outcome scores and biomechanical outcomes with osteoarthritis severity only maximum external hip and knee abduction moment (0.62, 0.62) in addition to first peak hip adduction moment (0.47) displayed significant correlations. Interpretation The use of predictive models could enable clinicians to identify individuals at risk of knee osteoarthritis and be a cost-effective method for osteoarthritis screening

Towards predicting the effectiveness of knee surgery for knee osteoarthritis patients

Osteoarthritis (OA) is the commonest form of musculoskeletal disability. Surgery is usually used to manage the end stage of the disease taking the form of either total joint or unicompartmental replacement. The outcome of such surgeries however could be disappointing. This work aims to exploit machine learning [1] to predict the effectiveness of surgery with respect to return to normal activities, as assessed using the Tegner activity score

Detecting knee osteoarthritis and its discriminating parameters using random forests

T his paper tackles the problem of automatic detection of knee osteoarthritis. A computer system is built that takes as input the body kinetics and produces as output not only an estimation of presence of the knee osteoarthritis , as previous ly do ne in the literature , but also the most discriminating parameters along with a set of rules on how this decision was reached. This fills the gap of interpretability between the medical and the engineering approaches . W e collected locomotion data from 47 su bjects with knee osteoart hritis and 47 healthy subjects. Osteoarthritis subjects were recruited from hospital clinics and GP surgeries, and age and sex matched heathy subjects from the local community . S ubjects walked on a walkway equipped with two force p lates with piezoelectric 3 - component force sensors . Parameters of the vertical, anterior - pos terior, and medio - lateral ground reaction forces, such as mean value, push - off time, and slope , were extracted. Then r andom forest regressors map those parameters v ia rule induction to the degree of kne e osteoarthritis. To boost generalisation ability , a subject - independent protocol is employed. The 5 - fold cross - validated accuracy is 72.61%± 4.2 4%. W e show that with 3 steps or less a reliable clinical measure can be extracted in a rule - based approach when the dataset is analysed appropriately