Background: Osteoarthritis is a heterogeneous condition characterised by a wide variety of factors and represents a worldwide healthcare challenge. There are multiple clinical and research specialisms involved in the diagnosis, prognosis and treatment of osteoarthritis, and there may be opportunities to share or pool data which are currently not being utilised. However, there are challenges to doing so which require carefully structured solutions and partnership working. Methods: Interviews were conducted with nine experts from various fields within osteoarthritis research. A semistructured approach was used, and thematic analysis applied to the results. Results: Generally, osteoarthritis researchers were supportive of data sharing, provided it is done responsibly and without impacting data integrity. Benefits identified included increasing typically low-powered data, the potential for machine learning opportunities, and the potential for improved patient outcomes. However, a number of challenges were identified, relating to: data security, data harmonisation, storage costs, ethical considerations and governance. Conclusions: There is clear support for increased data sharing and partnership working in osteoarthritis research. Further investigation will be required to navigate the complex issues identified; however, it is clear that collaborative opportunities should be better facilitated and there may be innovative ways to do this. It is also clear that nomenclature within different disciplines could be better streamlined, to improve existing opportunities to harmonise data.

Background Patient recovery can be quantified objectively, via gait analysis, or subjectively, using patient reported outcome measures. Association between these measures would explain the level of disability reported in patient reported outcome measures and could assist with therapeutic decisions. Methods Total knee replacement outcome was assessed using objective classification and patient-reported outcome measures (Knee Outcome Survey and Oxford Knee Scores). A classifier was trained to distinguish between healthy and osteoarthritic characteristics using knee kinematics, ground reaction force and temporal gait data, combined with anthropometric data from 32 healthy and 32 osteoarthritis knees. For the osteoarthritic cohort, classification of 20 subjects quantified changes at up to 3 timepoints post-surgery. Findings Osteoarthritic classification was reduced for 17 subjects when comparing pre- to post-operative assessments, however only 6 participants achieved non-pathological classification and only 4 of these were classified as non-pathological at 12 months. In 15 cases, the level of osteoarthritic classification did not decrease between every post-operative assessment. For an individual's recovery, classification outputs correlated (r > 0.5) with knee outcome survey for 75% of patients and oxford knee score for 78% of patients (based on 20 and 9 subjects respectively). Classifier outputs from all visits of the combined total knee replacement sample correlated moderately with knee outcome survey (r > 0.4) and strongly with oxford knee score (r > 0.6). Interpretation Biomechanical deficits existed in most subjects despite improvements in Patient Reported Outcome Measures, with larger changes reported subjectively as compared to measured objectively. Objective Classification provides additional insight alongside Patient Reported Outcomes when reporting recovered outcomes.

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.

Medial knee OA effects approximately 4.1 million people in England. Non-surgical strategies to lower knee joint loading is commonly researched in the knee OA literature as a method to alleviate pai...

ObjectiveThere are few guidelines for clinical trials of interventions for prevention of post-traumatic osteoarthritis (PTOA), reflecting challenges in this area. An international multi-disciplinary expert group including patients was convened to generate points to consider for the design and conduct of interventional studies following acute knee injury.DesignAn evidence review on acute knee injury interventional studies to prevent PTOA was presented to the group, alongside overviews of challenges in this area, including potential targets, biomarkers and imaging. Working groups considered pre-identified key areas: eligibility criteria and outcomes, biomarkers, injury definition and intervention timing including multi-modality interventions. Consensus agreement within the group on points to consider was generated and is reported here after iterative review by all contributors.ResultsThe evidence review identified 37 …

OBJECTIVES:To review the literature regarding gait retraining to reduce knee adduction moments and their effects on hip and ankle biomechanics.DATA SOURCES:Twelve academic databases were searched from inception to January 2019. Key words "walk*" OR "gait," "knee" OR "adduction moment," "osteoarthriti*" OR "arthriti*" OR "osteo arthriti*" OR "OA," and "hip" OR "ankle" were combined with conjunction "and" in all fields.STUDY SELECTION:Abstracts and full-text articles were assessed by 2 individuals against a predefined criterion.DATA SYNTHESIS:Of the 11 studies, sample sizes varied from 8-40 participants. Eight different gait retraining styles were evaluated: hip internal rotation, lateral trunk lean, toe-in, toe-out, increased step width, medial thrust, contralateral pelvic drop, and medial foot weight transfer. Using the Black and Downs tool, the methodological quality of the included studies was fair to moderate ranging between 12 of 25 to 18 of 28. Trunk lean and medial thrust produced the biggest reductions in first peak knee adduction moment. Studies lacked collective sagittal and frontal plane hip and ankle joint biomechanics. Generally, studies had a low sample size of healthy participants with no osteoarthritis and assessed gait retraining during 1 laboratory visit while not documenting the difficulty of the gait retraining style.CONCLUSIONS:Gait retraining techniques may reduce knee joint loading; however, the biomechanical effects to the pelvis, hip, and ankle is unknown, and there is a lack of understanding for the ease of application of the gait retraining styles.

Purpose: There is strong evidence to suggest altered knee biomechanics and aberrant joint tissue biology are associated with the development and progression of knee osteoarthritis (OA), however our understanding of the link between mechanics and biology in humans with knee pathology is lacking. Human biomechanics studies have shown a link of medial knee OA to knee varus malalignment and increased medial knee compartment loading. Furthermore, changes in bone structure and inflammation are thought to be responsible for long term joint deterioration. We hypothesized that joint inflammation and altered bone remodeling in the degenerative knee are a consequence of medial knee overloading. Therefore, this study aimed to determine if indicators of dynamic frontal plane knee loading or knee varus alignment are associated with synovial fluid biomarkers of joint inflammation, altered bone turnover or bone mechanobiology in subjects with medial knee degeneration. Methods: Subjects with medial knee OA (KL grade II-IV) undergoing high tibial osteotomy surgery or isolated medial knee ICRS grade III focal cartilage defects undergoing microfracture surgery had synovial fluid (SF) aspirated from the affected knee and underwent 3D motion capture analysis within a 4-week period. Kinematic marker data and ground reaction forces from level barefoot walking at a self-selected pace were measured using a 12 infra-red camera (Qualisys) and 6 force plate (Bertec) set up, with a modified Helen Hayes marker-set. A six-degrees-of-freedom musculoskeletal model with 8 segments was built (Visual 3D) to calculate early- and late-stance peak knee adduction moments (1st peak and 2nd peak KAMs, respectively) representative of peak medial knee compartmental loading, knee adduction angles during KAM peaks (1st peak and 2nd peak KAAs) representative of dynamic joint alignment during peak loads, and the knee adduction angular impulse (KAAI) representative of cumulative medial knee loading over stance-phase. SF aspirated from the affected knee was centrifuged at 5000G for 15m to remove cells and stored at -80°C. Multiplex chemiluminescence (Mesoscale Discovery) or enzyme-linked immunosorbent assays quantified the concentrations of 10 SF molecules relating to inflammation (pro-inflammatory: TNF-α, IL-6, IL-8; anti-inflammatory: IL-10), bone remodeling (formation: ALP, OPG; resorption: CTX-I, RANKL) and bone mechanobiology (glutamate, sclerostin). The RANKL:OPG ratio was also calculated as an indication of osteoclast activity. Normality of biomarker data was tested using Shapiro Wilks Test. Non-normal biomarker data was log-transformed prior to regression to satisfy assumptions. Multiple regression was applied to predict biomarker levels from biomechanical parameters, whilst controlling for age and BMI as covariates. Results: Biomechanical and biological data was collected from 10 medial knee pathology subjects (all male; mean (SD) age = 50.3 (6.2) years, BMI = 28.7 (3.9) kg/m2). For the effect of knee mechanics on inflammatory markers, 2nd peak KAMs significantly (p≤0.05) explained TNF-α, IL-6 and IL-8 variances, whereas the KAAI significantly (p≤0.05 to ≤0.01) predicted IL-6 and IL-8 (Figure 1). IL-6 levels were also significantly (p≤0.05) predicted by 1st peak KAAs. In contrast, IL-10 levels negatively associated with loading parameters. For the effect of knee loading on bone remodeling, the variance of CTX-I levels was significantly (p≤0.05 to ≤0.01) predicted by 2nd peak KAMs and 1st and 2ndpeak KAAs, but ALP levels were weakly inversely correlated. Variances in OPG levels were significantly (p≤0.05 to ≤0.01) predicted by all knee function parameters except for 2nd peak KAAs. However, the RANKL:OPG ratio representing osteoclast activation was overall not influenced by knee loading. Neither glutamate or sclerostin concentrations significantly correlated with biomechanical parameters. Conclusions: These findings suggest the magnitude of dynamic knee mechanical loading or varus malalignment influences pro-inflammatory activity and bone remodeling in the degenerative joint. The KAM 2nd peak predicted variances of all 5 mechano-sensitive biomarkers indicating that it may be the strongest predictor of medial knee tissue loading. Increased IL-6, IL-8, TNF-α and decreased IL-10 concomitant with higher knee loading is consistent with a catabolic environment. Moreover, the positive association of knee loading with CTX-I and OPG levels indicates increased bone remodeling with higher knee loads. However, this effect was not reflected by RANKL or ALP concentrations. Unexpectedly, there were no clear influences of peak or cumulative knee loads on mechanically-regulated molecules glutamate or sclerostin levels in the joint, which is evidence for altered bone regulation in the pathological knee. This study demonstrates that identifying and correcting dynamic mechanical axis malalignment or joint overloading could reduce inflammation and bone remodeling to halt the progression of medial knee degeneration in the OA knee.

Objective: To examine functional performance differences using kinematic and kinetic analysis between participants with and without knee osteoarthritis (OA) to determine which outcomes best characterize persons with and without knee OA. Methods: Participants with unilateral moderate knee OA (Kellgren-Lawrence grades 2 or 3) and controls without knee pain were matched for age, gender, and body mass index. Primary outcomes included temporal parameters, joint rotations and moments, and ground reaction forces assessed via 3D motion capture during walking and ascending/descending stairs. Secondary outcomes included timed functional activities (sit to stand; tying shoelaces), 48 hrs lower limb activity monitoring, and patient-reported outcome measures (Knee Injury and Osteoarthritis Outcome Score, Western Ontario and McMaster Universities Osteoarthritis Index, European Quality of Life-5 Dimensions). Results: Eight matched pairs were analyzed. Compared with controls, OA participants exhibited significant reductions in peak frontal hip and sagittal knee moments, and decreased peak anterior ground reaction force with the affected limb while walking. Ascending stairs, OA participants had slower speed, fewer strides per minute, longer cycle and stance times, and increased trunk range of motion (ROM) in assessments of both limbs; longer swing time and reduced ankle ROM in the affected limb; and increased knee frontal ROM in the unaffected limb. Descending stairs, OA participants had fewer strides per minute and decreased trunk transverse ROM in assessments of both limbs; increased knee frontal ROM in the affected limb; and longer strides, shorter stance and cycle times, increased trunk sagittal and decreased knee transverse ROMs in the unaffected limbs vs controls. Compared with controls, OA participants had slower walking cadence (120-130 vs 100-110 steps/min, respectively), took significantly longer on timed functional measures, and had significantly worse scores in patient-reported outcomes. Conclusion: Several objectives and patient-reported measures examined in this study could potentially be considered as outcomes in pharmacologic or physical therapy OA trials.

Background: Low back pain (LBP) classification systems are used to deliver targeted treatments matched to an individual profile, however, distinguishing between different subsets of LBP remains a clinical challenge. Methods: A novel application of the Cardiff Dempster-Shafer Theory Classifier was employed to identify clinical subgroups of LBP on the basis of repositioning accuracy for subjects performing a sitting and standing posture task. 87 LBP subjects, clinically subclassified into flexion (n = 50), passive extension (n = 14), and active extension (n = 23) motor control impairment subgroups and 31 subjects with no LBP were recruited. Thoracic, lumbar and pelvic repositioning errors were quantified. The Classifier then transformed the error variables from each subject into a set of three belief values: (i) consistent with no LBP, (ii) consistent with LBP, (iii) indicating either LBP or no LBP. Findings: In discriminating LBP from no LBP the Classifier accuracy was 96.61%. From no-LBP, subsets of flexion LBP, active extension and passive extension achieved 93.83, 98.15% and 97.62% accuracy, respectively. Classification accuracies of 96.8%, 87.7% and 70.27% were found when discriminating flexion from passive extension, flexion from active extension and active from passive extension subsets, respectively. Sitting lumbar error magnitude best discriminated LBP from no LBP (92.4% accuracy) and the flexion subset from no-LBP (90.1% accuracy). Standing lumbar error best discriminated active and passive extension from no LBP (94.4% and 95.2% accuracy, respectively). Interpretation: Using repositioning accuracy, the Cardiff Dempster-Shafer Theory Classifier distinguishes between subsets of LBP and could assist decision making for targeted exercise in LBP management.

Purpose: A recent systematic review concluded that there was strong evidence of a negative association between pre-operative function and short- and long-term functional outcomes following total hip arthroplasty (THA). Of the 17 studies included, only two included an objective measurement of function, one of which found no significant association. Whilst of value, patient-reported outcome measures (PROMs) have been shown to relate poorly to objectively measured changes in function. A proposed explanation is that the presence or absence of pain may affect perceptions of functional capacity. Pre-operative gait analysis has previously been shown to predict functional outcomes following Total Knee Replacement surgery. A biomechanical index/summary-measure was objectively determined using a classification technique known as the Cardiff Classifier. This study aims to adopt this technique to explore the relationship between pre-operative gait biomechanics and functional outcome following THA. Methods: This study was a retrospective analysis of a prospective cohort study. Ethical approval was obtained from Stockholm’s regional ethical review board (Dnr: 2010/1014-31/1). Motion analysis was initially performed on 25 healthy and 32 subjects with end-stage hip osteoarthritis (OA) at baseline (mean: 23 days prior) and 12 months (mean: 12.3 months) following THA surgery. Subjects were asked to walk barefoot at a self-selected pace across a 10m walkway. Marker trajectories and ground reaction forces were measures using eight MX40 cameras (Vicon Inc, Centennial, USA) and two force platforms (Kistler, Winterthur, Switzerland). Joint kinematics and kinetics were calculated using the Plug-in Gait Full-Body model within Nexus 1.8.5. On the day of the assessment, participants were also asked to complete the Hip disability and Osteoarthritis Outcome Score (HOOS). Perceived functional outcome was measured using the Activities of Daily Living Scale (HOOS-ADLS) Lower-limb joint biomechanics were objectively classified from ankle, knee and joint kinematics and kinetics using a previously reported combined approach of principal component analysis and Dempster-Shafer theory classification. In total, 18 biomechanical features were identified which accurately discriminated between healthy and hip OA gait biomechanics. These features were used to ‘train’ the classifier to discriminate between hip OA and healthy individuals. This mathematically determines the relationship between lower-limb biomechanics and the belief in OA, belief in healthy, and uncertainty - termed B(OA), B(H) and U respectively. The trained classifier was then used to quantify the pre-operative B(OA), and change in B(OA) in patients undergoing THA. Pearson’s correlation coefficients were used to assess the relationship between pre-operative biomechanics, and perceived and biomechanical outcome following THA. A linear regression was used to test the predictive value of B(OA) in combination two previously-reported predictors of functional outcome; age and body-mass index (BMI). Results: The gait analysis index, B(OA), at baseline was moderately correlated to post-operative change in (r=0.37 p=0.037) and change in B(OA) (r=-0.363, p=0.041), but not to absolute post-operative HOOS-ADL (r=0.17, p=0.353) nor B(OA) (r=0.295, p=0.101). The results of the two subsequent linear regression analyses are shown in Table 1. B(OA) in combination with age and BMI explained 34.8% of the total variance in the change in HOOS-ADLS and 30.1% of variance of the change in B(OA) following THA. Conclusions: This study found that poor pre-operative biomechanics was predictive of a greater biomechanical (negative change in B(OA)) and patient-reported (positive change in HOOS-ADLS) improvement following THA. Pre-operative biomechanics, however, was not indicative of absolute long-term post-operative functional status. The latter finding disagrees with other studies which measured only perceived function. Further research, using both subjective and objective outcome measures, is required to establish whether patients with poor pre-operative function should expect to reach equivalent functional capacity following surgery.Tabled 1Table 1 Linear regression analysis - predicting change in HOOS-ADL and B(OA) following surgery usinDependant variablePredictors (pre-op)R squaredUnstandardized CoefficientsStandardized CoefficientsSig.BStd. ErrorBetaChange in HOOS-ADL(Constant)0.348114.040.0.008*Belief (OA)52.521.5.373.021*Age-.930.339-.465.010*BMI-1.87.806-.393.028*Change in B(OA)(Constant)0.301-1.01.450.034*Belief (OA)-.562.243-.366.028*Age.010.004.435.019*BMI.016.009.315.083* Statistically significant p<0.05 Open table in a new tab

The purpose of this study was to quantify changes in knee loading in the three clinical planes, compensatory gait adaptations and patient-reported outcome measures (PROMS) resulting from opening wedge high tibial osteotomy (HTO). Gait analysis was performed on 18 participants (19 knees) with medial osteoarthritis (OA) and varus alignment pre- and post-HTO, along with 18 controls, to calculate temporal, kinematic and kinetic measures. Oxford Knee Score, Knee Outcome Survey and visual analogue pain scores were collected. Paired and independent sample tests identified changes following surgery and deviations from controls. HTO restored frontal and transverse plane knee joint loading to that of the control group, while reductions remained in the sagittal plane. Elevated frontal plane trunk sway (p = 0.031) and reduced gait speed (p = 0.042), adopted as compensatory gait changes pre-HTO, were corrected by the surgery. PROMs significantly improved (p ≤ 0.002). Centre of pressure (COP) was lateralised relative to the knee post-HTO (p < 0.001). Energy absorbed in the sagittal plane significantly increased post-HTO (p = 0.007), whilst work done in the transverse plane reduced (p ≤ 0.008). Pre-operative gait deviations from the control group that were retained post-HTO included smaller sagittal (p = 0.003) knee range of motion during gait, greater stance duration (p = 0.008) and altered COP location (anterior to the knee) in early stance (p = 0.025). HTO surgery restored frontal and transverse plane knee loading to normal levels and improved PROMs. Gait adaptations known to reduce knee loading employed pre-HTO were not retained post-HTO. Some gait features were found to differ between post-HTO subjects and controls. II

Background Gait analysis can be used to measure variations in joint function in patients with knee osteoarthritis (OA), and is useful when observing longitudinal biomechanical changes following Total Knee Replacement (TKR) surgery. The Cardiff Classifier is an objective classification tool applied previously to examine the extent of biomechanical recovery following TKR. In this study, it is further developed to reveal the salient features that contribute to recovery towards healthy function. Methods Gait analysis was performed on 30 patients before and after TKR surgery, and 30 healthy controls. Median TKR follow-up time was 13 months. The combined application of principal component analysis (PCA) and the Cardiff Classifier defined 18 biomechanical features that discriminated OA from healthy gait. Statistical analysis tested whether these features were affected by TKR surgery and, if so, whether they recovered to values found for the controls. Results The Cardiff Classifier successfully discriminated between OA and healthy gait in all 60 cases. Of the 18 discriminatory features, only six (33%) were significantly affected by surgery, including features in all three planes of the ground reaction force (p<0.001), ankle dorsiflexion moment (p<0.001), hip adduction moment (p = 0.003), and transverse hip angle (p = 0.007). All but two (89%) of these features remained significantly different to those of the control group after surgery. Conclusions This approach was able to discriminate gait biomechanics associated with knee OA. The ground reaction force provided the strongest discriminatory features. Despite increased gait velocity and improvements in self-reported pain and function, which would normally be clinical indicators of recovery, the majority of features were not affected by TKR surgery. This TKR cohort retained pre-operative gait patterns; reduced sagittal hip and knee moments, decreased knee flexion, increased hip flexion, and reduced hip adduction. The changes that were associated with surgery were predominantly found at the ankle and hip, rather than at the knee.

Whilst home-based exercise rehabilitation plays a key role in determining patient outcomes following orthopaedic intervention (e.g. total knee replacement), it is very challenging for clinicians to...

Valerie Sparkes Gemma M Whatling Paul Biggs Nidal Khatib Mohammad Al-Amri David Williams Rebecca Hemming Martina Hagen Ishaak Saleem Ramesh Swaminathan Cathy Holt 1School of Healthcare Sciences, Biomechanics and Bioengineering Research Centre Versus Arthritis, College of Biomedical and Life Sciences, Cardiff University, Cardiff CF24 0AB, UK; 2School of Engineering, College of Physical Sciences and Engineering, Cardiff University, Cardiff CF24 3AA, UK; 3Arthritis Research UK Biomechanics and Bioengineering Centre, Cardiff University, Cardiff CF10 3AT, UK; 4Medical Affairs, Pain Relief Category, GSK Consumer Healthcare S.A., Nyon 1260, Switzerland Objective: To examine functional performance differences using kinematic and kinetic analysis between participants with and without knee osteoarthritis (OA) to determine which outcomes best characterize persons with and without knee OA. Methods: Participants with unilateral moderate knee OA (Kellgren–Lawrence grades 2 or 3) and controls without knee pain were matched for age, gender, and body mass index. Primary outcomes included temporal parameters, joint rotations and moments, and ground reaction forces assessed via 3D motion capture during walking and ascending/descending stairs. Secondary outcomes included timed functional activities (sit to stand; tying shoelaces), 48 hrs lower limb activity monitoring, and patient-reported outcome measures (Knee Injury and Osteoarthritis Outcome Score, Western Ontario and McMaster Universities Osteoarthritis Index, European Quality of Life–5 Dimensions). Results: Eight matched pairs were analyzed. Compared with controls, OA participants exhibited significant reductions in peak frontal hip and sagittal knee moments, and decreased peak anterior ground reaction force with the affected limb while walking. Ascending stairs, OA participants had slower speed, fewer strides per minute, longer cycle and stance times, and increased trunk range of motion (ROM) in assessments of both limbs; longer swing time and reduced ankle ROM in the affected limb; and increased knee frontal ROM in the unaffected limb. Descending stairs, OA participants had fewer strides per minute and decreased trunk transverse ROM in assessments of both limbs; increased knee frontal ROM in the affected limb; and longer strides, shorter stance and cycle times, increased trunk sagittal and decreased knee transverse ROMs in the unaffected limbs vs controls. Compared with controls, OA participants had slower walking cadence (120–130 vs 100–110 steps/min, respectively), took significantly longer on timed functional measures, and had significantly worse scores in patient-reported outcomes. Conclusion: Several objectives and patient-reported measures examined in this study could potentially be considered as outcomes in pharmacologic or physical therapy OA trials.

Purpose: Exercise prescription plays a fundamental role in the treatment of knee pathologies such as knee osteoarthritis, where over 90,000 total knee replacement patients receive regular post-surgery physiotherapy each year in the UK. Physiotherapists rely on home-based exercise prescription yet have limited knowledge of patient engagement at home and find it difficult to objectively monitor patient progress, attribute functional improvement (or lack of) to adherence/non-adherence and prescribe personalised interventions. The research vision is to facilitate unobtrusive sensor driven home monitoring/feedback of knee rehabilitation exercises. As a first step, this study sought to fine tune a machine learning algorithm to classify between different knee exercises and understand the impact of different feature selection parameters on classification performance. Methods: 8 volunteers (4 healthy, 4 with self-reported history of knee pain/pathology but not receiving treatment) performed 15 repetitions of 4 knee rehabilitation exercises (sit to stand (STS), knee flexion (KFL), knee extension (KEX) and weight shifting (WSH)) whilst wearing lower limb Xsens inertial sensors sampling at 60 Hz. A total of 85 features were extracted per exercise repetition from tri-axial accelerometer data provided by sensors placed on the foot, shank, thigh and pelvis. These were defined as the 90th percentile spectral edge frequency ((SEF) in X,Y,Z axis) and signal mean, max min, variance, skewness, and kurtosis (all in the X,Y,Z axis) per sensor in addition to repetition length. Participants were split into training and testing datasets using a Leave-One-Group-Out cross validation (cv) where a participant with multiple repetitions represented a group. Within each of the 8 cv-folds, features were scaled, a univariate feature selection method was implemented to reduce the feature set to top ranking features and a linear support vector machine (SVM) classifier was performed to determine how well 4 different knee rehabilitation exercises could be distinguished from each other. This process was repeated using 3 different score function parameters (Mutual Information Classification (MIC), F-value Classification (F-Class) and Chi2) with the number of features being selected ranging from 1 - 10 to determine the optimal score function and number of features whilst optimising classification performance. The F1 score (weighted harmonic mean of precision and recall with a best score of 1) was computed within each cv-fold as an average F1 score across each of the exercises classes and then combined across cv-folds as a median and 1st - 3rd interquartile range. Results: Feature selection methods using the MIC and the F-Class score functions consistently outperformed that using the Chi2 (Figure 1). The MIC approach was chosen as the optimal feature selection method as the F-Class algorithm was marginally outperformed using fewer features with the optimal number of features (ie. fewest whilst retaining classifier performance) being 3. All 8 cv-folds using this method selected Z-axis acceleration 90th percentile SEF of the foot and shank sensors whilst the third feature varied slightly between cv-folds (mean Z-axis acceleration of the foot sensor (n=3/8 cv-folds); Y-axis mean acceleration of the thigh sensor (n=2/8 cv-folds), Z-axis acceleration variance of the foot (n=1/8 cv-folds) or shank (n=1/8 cv-folds) sensors and Z-axis mean acceleration of the shank sensor (n=1/8 cv-folds). Inspection of the individual F1-scores for each exercise using the optimal feature selection method revealed a reduced ability to classify KEX and KFL in comparison to STS and WSH exercises (Figure 2). Conclusions: Changing parameter settings within a univariate feature selection algorithm that identifies top ranking features was found to alter the performance of a linear SVM seeking to classify between 4 knee rehabilitation exercises. This finding confirms the impact that incorrect feature selection parameter settings may have on classification performance. From the parameters and feature selection methods considered, the combination of an MIC score function and the selection of the top 3 features appear optimal for this dataset. Whilst the 3 features selected appeared to consistently classify STS and WSH exercises well, this was not the case for KEX and KFL exercises with both varying considerably between the cv-folds and warrants further investigation. Future work will consider additional features that may improve the ability to discriminate between KEX and KFL exercises. Additional feature selection methods such as recursive feature elimination and removing features with low variance will be investigated to see whether SVM classification performance can be further enhanced.

BACKGROUND:Total Knee Replacement (TKR) surgery is being utilised in a younger, more active population with greater functional expectations. Understanding whether patient-perceived measures of function reflect objective biomechanical measures is critical in understanding whether functional limitations can be adequately captured within a clinical setting. RESEARCH QUESTION:Do changes in objective gait biomechanics measures reflect patient-reported outcome measures at approximately 12 months following TKR surgery? METHODS:Three-dimensional gait analysis was performed on 41 patients with OA who were scheduled for TKR surgery, 22 of which have returned for a (9-24 month) follow-up assessment. Principal Component Analysis was used to define features of variation between OA subjects and an additional 31 non-pathological control subjects. These were used to train the Cardiff Classifier, an objective classification technique, and subsequently quantify changes following TKR surgery. Patient-perceived changes were also assessed using the Oxford Knee Score (OKS), Knee Outcome Survey (KOS), and Pain Audit Collection System scores (PACS). Pearson and Spearman correlation coefficients were calculated to establish the relationship between changes in objectively-measured and perceived outcome. RESULTS:Objective measures of biomechanical change were strongly correlated to changes in OKS(r=-0.695, p < 0.001) and KOS(r=-.810, p < 0.001) assessed outcomes. Pain (PACS) was only related to biomechanical function post-operatively (r=-.623, p = 0.003). SIGNIFICANCE:In this biomechanics study, the relationship between changes in objective function and patient-reported measures pre to post TKR surgery is stronger than in studies which did not include biomechanics metrics. Quality of movement may hold more significance for a patient's perception of improvement than functional measures which consider only the time taken or distance travelled during functional activities.

Purpose: The external knee adduction moment (KAM) has been studied as a surrogate measure of medial compartment load. A high KAM has been associated with medial knee osteoarthritis (OA) disease progression. Additionally, the knee adduction angular impulse (KAAI) has been studied as a dynamic representation of medial knee load. Methods to offload the medial compartment include gait adaptation and surgical re-alignment. This study aims to identify differences in medial knee joint loading parameters between wide stance gait (WS) and high tibial osteotomy (HTO). These treatments aim to offload the medial compartment with dynamic or anatomical compensation of varus knee deformity respectively. This study aims to identify if any benefit in reducing KAM can be attained with WS after HTO. Methods: This was a controlled cohort study. Healthy volunteers participated as control participants and were compared to a patient cohort who underwent HTO. Healthy volunteers were recruited, with no current knee symptoms and no previous knee surgery. Patients were diagnosed with medial compartment knee OA based on history, clinical examination and plain radiographs for disease severity. Varus deformity was confirmed on full leg weightbearing radiographs. The study was part of ongoing research associated with the Arthritis Research UK, Biomechanics and Bioengineering Centre. Three-dimensional motion analysis and ground reaction forces (GRF) was performed on 17 patients with medial compartment knee OA and varus deformity prior to undergoing HTO, 12 patients were assessed approximately 12 months post-operatively, and results compared to 12 healthy controls. Participants walked along the walkway at self-selected speeds. Participants performed 6 walking trials with satisfactory force plate strikes with their natural step width. A verbal and visual demonstration of WS was provided and participants were then asked to walk with their feet wider apart compared to their normal gait feet width, to a comfortable distance. The feet width achieved was within the boundaries of what would be achievable and tolerated by patients in clinical practice. Data was processed using Visual 3D to compute kinetic and kinematic data, where a bespoke model and analysis pipeline was applied to each participants’ static measurement and dynamic trials.Metrics were calculated from individual trials, and then averaged across the six walking trials for each participant, for WS and natural gait. Student's t-test was performed to compare means from two groups. Analysis of variance (ANOVA) testing was performed where there were more than two groups, in which case post hoc tests were performed to identify significant differences between the groups. These were performed using SPSS v25.0. Assumptions of normality were confirmed, and a P value of 0.05 or less was considered a statistically significant result. Results: Table 1 outlines the group demographics and Table 2 outlines results for stance width, gait speed, first and second KAM, KAAI, peak knee flexion moment and peak knee flexion angle. Pre-HTO, patients had a significantly higher first and second peak KAM, and KAAI, compared to healthy controls irrespective of gait style. Wide stance gait did not significantly reduce first peak KAM or KAAI, compared to pre-HTO NG. Pre-HTO, patients had a significantly less peak knee flexion angle compared with the healthy cohort, with WS having a significantly less peak flexion angle than pre-HTO NG. Post-HTO patients had a significantly lower first and second peak KAM, and KAAI, compared to pre-HTO patients irrespective of gait style. Additionally, with WS gait, frontal plane loading parameters were significantly reduced further compared to post-HTO NG. Post-HTO, patients had a significantly lower peak knee flexion moment compared to pre-HTO NG. However, with WS, peak knee flexion moment significantly increased compared to post-HTO NG. Post-HTO, patients walked with a significantly increased peak knee flexion angle compared with pre-HTO. However, with WS peak knee flexion angle significantly reduced from post-HTO NG. Conclusions: Increasing step width from 0.16m (natural) to 0.25m in isolation did not significantly alter first peak KAM and KAAI. These findings suggest that WS has less effect on medial joint loading in OA patients with varus malalignment. Adopting WS following surgical correction, however, complimented the effects of surgery by further reducing medial joint loading parameters and so the present data suggests that WS might be a suitable mechanism to prolong the medial knee joint unloading benefits of HTO surgery. Therefore, future research should assess the clinical benefit of utilising WS gait retraining to compliment HTO surgery.View Large Image Figure ViewerDownload Hi-res image Download (PPT)

Purpose: Over 90,000 osteoarthritis (OA) related TKR surgeries take place across the UK annually, with patients undergoing regular post-surgery physiotherapy that is reliant on home-based exercise rehabilitation and driven by personalised self-management. With poor patient adherence that is difficult to ascertain, clinicians who are challenged to optimise patient outcomes are unable to determine whether improvements (or lack of) can be attributed to an exercise intervention or (non) adherence. There is a clear need for enhanced forms of objectively monitoring patient adherence to home based exercise rehabilitation, providing valuable biomechanical knowledge to clinicians to guide personalised exercise prescription. This could provide rigorous adherence measurements, optimise the rehabilitation process, reduce NHS burden and improve patient satisfaction. This research aims to determine whether the performance of 4 rehabilitation exercises, routinely prescribed to OA patients following TKR, can be objectively distinguished using inertial measurement sensors (IMU's) placed on the lower limbs. Methods: 5 healthy participants (4 males, 1 female; mean age 32.6 ± 11.1 years, height 1.79 ± 0.14 m and mass 82.88 ± 15.93 kg) performed a battery of early phase knee rehabilitation exercises based on the Taxonomy for RehAbilitation of Knee conditions (TRAK). Data was collected for multiple exercises with participants wearing a range of time synchronised biomechanical measurement systems. This study focused on the performance of 1) Knee Flexion in sitting 2) Knee Extension 3) Single Step Down and 4) Sit to Stand, with each participant performing 4 repetitions per exercise and the data collected using lower body IMU sensors. These were placed on the pelvis and bilateral thigh, shank and feet (Xsens, Holland; sampling at 60 Hz). Anthropometric measurements for each participant were combined with IMU data during a static calibration to define the biomechanical model (MVN Studio). 3D hip, knee and ankle joint angles were calculated using the Euler sequence ZXY using the ISB based coordinate system. Joint angle data were processed in Python, with exercise repetitions defined using a detect peaks algorithm. 3D angle data were formatted in Excel, time normalised to 101 points and then Principal Component Analysis (PCA) was performed (Matlab, Mathworks), reducing all joint angle waveforms into new uncorrelated principal components via an orthogonal transformation. Scatterplots of PC1 versus PC2 were used to visually inspect for clustering between the PC values for the 4 exercise groups. A one way ANOVA (SPSS, IBM) was performed on the first 3 PC values (ranked by percentage variance accounted for) for the 9 variables under analysis, with an a priori alpha level of significance set at 0.05. Games-Howell post hoc tests identified variables that were significantly different between exercises. Results: The PC scatterplot representing the hip flexion-extension waveforms produced the most prominent clustering, with all 4 exercise groups easily distinguishable (Fig.1). Whilst multiple statistically significant differences were found between pairs of exercises for individual PC values, only one PC value was statistically different across all exercise pairings (PC1, knee flexion-extension waveform). Conclusions: This study demonstrates the potential to objectively distinguish between different knee rehabilitation exercises using IMU sensors and PCA. It would appear that flexion-extension angles at the hip and knee are most suited for accurate exercise classification and require further investigation. Future work will focus on increasing the healthy cohort sample size and generating a post TKR patient cohort to identify whether similar differentiation between exercises can be established in a pathological cohort, and whether there are functional difference between healthy and post-TKR patients that could be used to map patient progress.

Risk factors for poor outcomes after total knee replacement (TKR) have been identified, but the underlying causes are not fully understood. The aim of this research was to establish the relationship between measurable gait parameters and patients' subjective function, pre and post total knee replacement. 25 subjects underwent gait analysis, before and one year following total knee replacement. Patient reported function was investigated using the Activities of Daily Living Scale of the Knee Outcome Survey (KOS). Gait analysis was performed using infrared cameras and reflective marker clusters. Correlation between motion analysis data and patient reported function was investigate. Whilst multiple gait parameters correlated with KOS score preoperatively, there was no correlation after TKR. Three preoperative measurements correlated with the improvement in score a subject achieved following surgery: These were preoperative rate of extension in swing, total range of flexion from heel strike and time point of m...

Varus knees are corrected into valgus with HTO to redistribute mechanical forces and delay medial arthritis progression. Knee adduction angular impulse (KAAI) and external knee adduction moment (EKAM) measured during gait analysis, predict load distribution across the tibial plateau and are suggested biomechanical markers for osteoarthritis. This study explores relationships between varus knee alignment and dynamic loading in patients with medial osteoarthritis, prior to HTO.