Background:. The aim of this study was to investigate the outcome after ACL reconstruction between a group of patients receiving a standardized supervised physiotherapy guided rehabilitation program and a group of patients who followed an un-supervised, home-based rehabilitation program. Methods:. 40 patients with isolated anterior cruciate ligament injuries were allocated to either a supervised physiotherapy intervention group or home-based exercise group. Patients were investigated by an independent examiner pre-operative, 3, 6, 9 and 12 months post-surgery using the following outcome measures: Lysholm Score and Tegner Activity Scale, functional hopping tests, isometric and isokinetic strength assessments. Results:. Both groups improved significantly (p=0.01–0.04) following 12 months after surgery. The median Lysholm score increased from 57 (34–90) to 94 (90–100) in the supervised group and 60 (41–87) to 97 (95–100) in the unsupervised group. The median Tegner Activity Scale increased from 3 (2–8) to 6 (3–8) in the supervised group and 4 (2–8) to 5 (3–10) in the unsupervised group. The combined mean symmetry indices for the hopping tests improved from 77.3+ 18.7 to 86.8+11.1 (supervised) and from 78.1+30.5 to 88.3+10.9 (unsupervised). Isometric and isokinetic strength symmetry indices for knee extension improved from 68.9+23.5 to 82.8+11.9 resp. 63.7+22.8 to 82.7+15.1 in the supervised group and from 73.6+20.5 to 76.5+17.9 resp. 69.5+24.3 to 76.9+16.9 in the unsupervised group. Eccentric strength symmetry indices for knee extension improved from 67.9+27.7 to 87.8+6.8 in the supervised group and from 71.3+17.8 to 82.6+15.6 in the unsupervised group. Conclusion:. This study could not demonstrate a benefit in a rehabilitation program supervised by a physiotherapist in our population compared to an unsupervised cohort

Aims. Knee osteoarthritis (OA) involves a variety of tissues in the joint. Gene expression profiles in different tissues are of great importance in order to understand OA. Methods. First, we obtained gene expression profiles of cartilage, synovium, subchondral bone, and meniscus from the Gene Expression Omnibus (GEO). Several datasets were standardized by merging and removing batch effects. Then, we used unsupervised clustering to divide OA into three subtypes. The gene ontology and pathway enrichment of three subtypes were analyzed. CIBERSORT was used to evaluate the infiltration of immune cells in different subtypes. Finally, OA-related genes were obtained from the Molecular Signatures Database for validation, and diagnostic markers were screened according to clinical characteristics. Quantitative reverse transcription polymerase chain reaction (qRT‐PCR) was used to verify the effectiveness of markers. Results. C1 subtype is mainly concentrated in the development of skeletal muscle organs, C2 lies in metabolic process and immune response, and C3 in pyroptosis and cell death process. Therefore, we divided OA into three subtypes: bone remodelling subtype (C1), immune metabolism subtype (C2), and cartilage degradation subtype (C3). The number of macrophage M0 and activated mast cells of C2 subtype was significantly higher than those of the other two subtypes. COL2A1 has significant differences in different subtypes. The expression of COL2A1 is related to age, and trafficking protein particle complex subunit 2 is related to the sex of OA patients. Conclusion. This study linked different tissues with gene expression profiles, revealing different molecular subtypes of patients with knee OA. The relationship between clinical characteristics and OA-related genes was also studied, which provides a new concept for the diagnosis and treatment of OA. Cite this article: Bone Joint Res 2023;12(12):702–711

Aims. This study was designed to develop a model for predicting bone mineral density (BMD) loss of the femur after total hip arthroplasty (THA) using artificial intelligence (AI), and to identify factors that influence the prediction. Additionally, we virtually examined the efficacy of administration of bisphosphonate for cases with severe BMD loss based on the predictive model. Methods. The study included 538 joints that underwent primary THA. The patients were divided into groups using unsupervised time series clustering for five-year BMD loss of Gruen zone 7 postoperatively, and a machine-learning model to predict the BMD loss was developed. Additionally, the predictor for BMD loss was extracted using SHapley Additive exPlanations (SHAP). The patient-specific efficacy of bisphosphonate, which is the most important categorical predictor for BMD loss, was examined by calculating the change in predictive probability when hypothetically switching between the inclusion and exclusion of bisphosphonate. Results. Time series clustering allowed us to divide the patients into two groups, and the predictive factors were identified including patient- and operation-related factors. The area under the receiver operating characteristic (ROC) curve (AUC) for the BMD loss prediction averaged 0.734. Virtual administration of bisphosphonate showed on average 14% efficacy in preventing BMD loss of zone 7. Additionally, stem types and preoperative triglyceride (TG), creatinine (Cr), estimated glomerular filtration rate (eGFR), and creatine kinase (CK) showed significant association with the estimated patient-specific efficacy of bisphosphonate. Conclusion. Periprosthetic BMD loss after THA is predictable based on patient- and operation-related factors, and optimal prescription of bisphosphonate based on the prediction may prevent BMD loss. Cite this article: Bone Joint Res 2024;13(4):184–192

Aims. We aimed to develop a gene signature that predicts the occurrence of postmenopausal osteoporosis (PMOP) by studying its genetic mechanism. Methods. Five datasets were obtained from the Gene Expression Omnibus database. Unsupervised consensus cluster analysis was used to determine new PMOP subtypes. To determine the central genes and the core modules related to PMOP, the weighted gene co-expression network analysis (WCGNA) was applied. Gene Ontology enrichment analysis was used to explore the biological processes underlying key genes. Logistic regression univariate analysis was used to screen for statistically significant variables. Two algorithms were used to select important PMOP-related genes. A logistic regression model was used to construct the PMOP-related gene profile. The receiver operating characteristic area under the curve, Harrell’s concordance index, a calibration chart, and decision curve analysis were used to characterize PMOP-related genes. Then, quantitative real-time polymerase chain reaction (qRT-PCR) was used to verify the expression of the PMOP-related genes in the gene signature. Results. We identified three PMOP-related subtypes and four core modules. The muscle system process, muscle contraction, and actin filament-based movement were more active in the hub genes. We obtained five feature genes related to PMOP. Our analysis verified that the gene signature had good predictive power and applicability. The outcomes of the GSE56815 cohort were found to be consistent with the results of the earlier studies. qRT-PCR results showed that RAB2A and FYCO1 were amplified in clinical samples. Conclusion. The PMOP-related gene signature we developed and verified can accurately predict the risk of PMOP in patients. These results can elucidate the molecular mechanism of RAB2A and FYCO1 underlying PMOP, and yield new and improved treatment strategies, ultimately helping PMOP monitoring. Cite this article: Bone Joint Res 2022;11(8):548–560

Access to health care, including physiotherapy, is increasingly occurring through virtual formats. At-home adherence to physical therapy programs is often poor and few tools exist to objectively measure low back physiotherapy exercise participation without the direct supervision of a medical professional. The aim of this study was to develop and evaluate the potential for performing automatic, unsupervised video-based monitoring of at-home low back physiotherapy exercises using a single mobile phone camera. 24 healthy adult subjects performed seven exercises based on the McKenzie low back physiotherapy program while being filmed with two smartphone cameras. Joint locations were automatically extracted using an open-source pose estimation framework. Engineered features were extracted from the joint location time series and used to train a support vector machine classifier (SVC). A convolutional neural network (CNN) was trained directly on the joint location time series data to classify exercises based on a recording from a single camera. The models were evaluated using a 5-fold cross validation approach, stratified by subject, with the class-balanced accuracy used as the performance metric. Optimal performance was achieved when using a total of 12 pose estimation landmarks from the upper and lower body, with the SVC model achieving a classification accuracy of 96±4% and the CNN model an accuracy of 97±2%. This study demonstrates the feasibility of using a smartphone camera and a supervised machine learning model to effectively assess at-home low back physiotherapy adherence. This approach could provide a low-cost, scalable method for tracking adherence to physical therapy exercise programs in a variety of settings

There has been limited research examining the effect training of orthopaedic trainees may have on patient outcomes. This paper aims to determine if there is a difference in revision rate and functional outcomes of total hip joint replacement performed by consultants compared to those performed by supervised and unsupervised trainees. We reviewed all patient data since 2000 from the New Zealand National Joint Registry in patients undergoing total hip joint replacement (THJR) comparing the outcomes with the experience of the primary surgeon. The outcome measures were revision hip replacement and the Oxford Hip score at six months. We compared the reason for revision controlling for factors such as ASA, age and the index diagnosis. We also compared the six-month Oxford scores with the experience of the primary surgeon. There were 35415 patients who underwent elective THJR, 30344 of which were performed by a consultant, 2982 by a supervised registrar and 1067 by an unsupervised registrar. There was an overall revision rate (RR) of 0.77 per 100 component years. The RR was 0.75 (95% CI 0.67–0.82) for consultants, 0.97 (95% CI 0.72 – 1.28) for supervised trainees and 0.70 (95% CI 0.36 – 1.22) for unsupervised trainees. There was no significant difference in revision rates between consultants and supervised trainees (p<0.077) or unsupervised trainees (p< 0.30). The most common cause for revision surgery was dislocation, occurring in 39% of cases. This was more common in supervised and unsupervised trainees (48% and 50%) however there was no significant difference between the three groups (p-value 0.24). The other causes for revision were; loosening of the acetabular or femoral component, deep infection, pain and fracture with no significant difference between the three groups. The mean OHS was higher for consultants at 40.7 compared to 38.95 and 38.23 for supervised and unsupervised trainees respectively (p <0.001). The results of this study show no significant difference in the revision rate of THJR performed by trainees when compared to their consultants. Orthopaedic consultants do appear to have slightly better (1–2 points) OHS. These results are reassuring and show orthopaedic training does not adversely compromise patient outcomes

Physiotherapy is a critical element in successful conservative management of low back pain (LBP). The aim of this study was to develop and evaluate a system with wearable inertial sensors to objectively detect sitting postures and performance of unsupervised exercises containing movement in multiple planes (flexion, extension, rotation). A set of 8 inertial sensors were placed on 19 healthy adult subjects. Data was acquired as they performed 7 McKenzie low-back exercises and 3 sitting posture positions. This data was used to train two models (Random Forest (RF) and XGBoost (XGB)) using engineered time series features. In addition, a convolutional neural network (CNN) was trained directly on the time series data. A feature importance analysis was performed to identify sensor locations and channels that contributed most to the models. Finally, a subset of sensor locations and channels was included in a hyperparameter grid search to identify the optimal sensor configuration and the best performing algorithm(s) for exercise classification. Models were evaluated using F1-score in a 10-fold cross validation approach. The optimal hardware configuration was identified as a 3-sensor setup using lower back, left thigh, and right ankle sensors with acceleration, gyroscope, and magnetometer channels. The XBG model achieved the highest exercise (F1=0.94±0.03) and posture (F1=0.90±0.11) classification scores. The CNN achieved similar results with the same sensor locations, using only the accelerometer and gyroscope channels for exercise classification (F1=0.94±0.02) and the accelerometer channel alone for posture classification (F1=0.91±0.03). This study demonstrates the potential of a 3-sensor lower body wearable solution (e.g. smart pants) that can identify proper sitting postures and exercises in multiple planes, suitable for low back pain. This technology has the potential to improve the effectiveness of LBP rehabilitation by facilitating quantitative feedback, early problem diagnosis, and possible remote monitoring

Depletion of Scleraxis-lineage (ScxLin) cells in adult tendon recapitulates age-related decrements in cell density, ECM organization and composition. However, depletion of ScxLin cells improves tendon healing, relative to age-matched wildtype mice, while aging impairs healing. Therefore, we examined whether ScxLin depletion and aging result in comparable shifts in the tendon cell environment and defined the intrinsic programmatic shifts that occur with natural aging, to define the key regulators of age-related healing deficits. ScxLin cells were depleted in 3M-old Scx-Cre+; Rosa-DTRF/+ mice via diphtheria toxin injections into the hindpaw. Rosa-DTRF/+ mice were used as wildtype (WT) controls. Tendons were harvested from 6M-old ScxLin depleted and WT mice, and 21-month-old (21M) C57Bl/6 mice (aged). FDL tendons (n=6) were harvested for single-cell RNAseq, pooled, collagenase digested, and sorted for single cell capture. Data was processed using Cell Ranger and then aligned to the annotated mouse genome (mm10). Filtering, unsupervised cell clustering, and differential gene expression (DEG) analysis were performed using Seurat. Following integration and sub-clustering of the tenocyte populations, five distinct subpopulations were observed. In both ScxLin depletion and aging, ‘ECM synthesizers’ and ‘ECM organizers’ populations were lost, consistent with disruptions in tissue homeostasis and altered ECM composition. However, in ScxLin depleted mice retention of a ‘specialized ECM remodeler’ population was observed, while aging tendon cells demonstrated inflammatory skewing with retention of a ‘pro-inflammatory tenocyte population’. In addition, enrichment of genes associated with protein misfolding clearance were observed in aged tenocytes. Finally, a similar inflammatory skewing was observed in aged tendon-resident macrophages, with this skewing not observed in ScxLin depleted tendons. These data suggest that loss of ‘ECM synthesizer’ populations underpins disruptions in tendon homeostasis. However, retention of ‘specialized remodelers’ promotes enhanced healing (ScxLin depletion), while inflammatory skewing may drive the impaired healing response in aged tendons

Anterior approach total hip arthroplasty (AA-THA) has a steep learning curve, with higher complication rates in initial cases. Proper surgical case selection during the learning curve can reduce early risk. This study aims to identify patient and radiographic factors associated with AA-THA difficulty using Machine Learning (ML). Consecutive primary AA-THA patients from two centres, operated by two expert surgeons, were enrolled (excluding patients with prior hip surgery and first 100 cases per surgeon). K- means prototype clustering – an unsupervised ML algorithm – was used with two variables - operative duration and surgical complications within 6 weeks - to cluster operations into difficult or standard groups. Radiographic measurements (neck shaft angle, offset, LCEA, inter-teardrop distance, Tonnis grade) were measured by two independent observers. These factors, alongside patient factors (BMI, age, sex, laterality) were employed in a multivariate logistic regression analysis and used for k-means clustering. Significant continuous variables were investigated for predictive accuracy using Receiver Operator Characteristics (ROC). Out of 328 THAs analyzed, 130 (40%) were classified as difficult and 198 (60%) as standard. Difficult group had a mean operative time of 106mins (range 99–116) with 2 complications, while standard group had a mean operative time of 77mins (range 69–86) with 0 complications. Decreasing inter-teardrop distance (odds ratio [OR] 0.97, 95% confidence interval [CI] 0.95–0.99, p = 0.03) and right-sided operations (OR 1.73, 95% CI 1.10–2.72, p = 0.02) were associated with operative difficulty. However, ROC analysis showed poor predictive accuracy for these factors alone, with area under the curve of 0.56. Inter-observer reliability was reported as excellent (ICC >0.7). Right-sided hips (for right-hand dominant surgeons) and decreasing inter-teardrop distance were associated with case difficulty in AA-THA. These data could guide case selection during the learning phase. A larger dataset with more complications may reveal further factors

Primary total hip replacement (THR) is a successful and common operation which orthopaedic trainees must demonstrate competence in prior to completion of training. This study aimed to determine the impact of operating surgeon grade and level of supervision on the incidence of 1-year patient mortality and all-cause revision following elective primary THR in a large UK training centre. National Joint Registry (NJR) data for all elective primary THR performed in a single University Teaching Hospital from 2005–2020 were used, with analysis performed on the 15-year dataset divided into 5-year temporal periods (B1 2005–2010, B2 2010–2015, B3 2015–2020). Outcome measures were mortality and revision surgery at one year, in relation to lead surgeon grade, and level of supervision for trainee-led operations. 9999 eligible primary THR were undertaken, of which 5526 (55.3%) were consultant led (CL), and 4473 (44.7%) trainees led (TL). Of TL, 2404 (53.7%) were non-consultant supervised (TU), and 2069 (46.3%) consultant supervised (TS). The incidence of 1-year patient mortality was 2.05% (n=205), and all-cause revision was 1.11% (n=111). There was no difference in 1-year mortality between TL (n=82, 1.8%) and CL (n=123, 2.2%) operations (p=0.20, OR 0.78, CI 0.55–1.10). The incidence of 1-year revision was not different for TL (n=56, 1.3%) and CL (n=55, 1.0%) operations (p=0.15, OR 1.37, CI 0.89–2.09). Overall, there was no temporal change for either outcome measure between TL or CL operations. A significant increase in revision within 1-year was observed in B3 between TU (n=17, 2.7%) compared to CL (n=17, 1.0%) operations (p=0.005, OR 2.81, CI 1.35–5.87). We found no difference in 1-year mortality or 1-year all-cause revision rate between trainee-led primary THR and consultant-led operations over the entire fifteen-year period. However, unsupervised trainee led THR in the most recent 5-year block (2015–2020) has a significantly increased risk of early revision, mainly due to instability and prosthetic joint infection. This suggests that modern surgical training is having a detrimental effect on THR patient outcomes. More research is needed to understand the reasons if this trend is to be reversed

Giant cell tumors of bone (GCTs) are locally aggressive tumors with recurrence potential that represent up to 10% of primary tumors of the bone. GCTs pathogenesis is driven by neoplastic mononuclear stromal cells that overexpress receptor activator of nuclear factor kappa-B/ligand (RANKL). Treatment with specific anti-RANKL antibody (denosumab) was recently introduced, used either as a neo-adjuvant in resectable tumors or as a stand-alone treatment in unresectable tumors. While denosumab has been increasingly used, a percentage of patients do not improve after treatment. Here, we aim to determine molecular and histological patterns that would help predicting GCTs response to denosumab to improve personalized treatment. Nine pre-treatment biopsies of patients with spinal GCT were collected at 2 centres. In 4 patients denosumab was used as a neo-adjuvant, 3 as a stand-alone and 2 received denosumab as adjuvant treatment. Clinical data was extracted retrospectively. Total mRNA was extracted by using a formalin-fixed paraffin-embedded extraction kit and we determined the transcript profile of 730 immune-oncology related genes by using the Pan Cancer Immune Profiling panel (Nanostring). The gene expression was compared between patients with good and poor response to Denosumab treatment by using the nSolver Analysis Software (Nanostring). Immunohistochemistry was performed in the tissue slides to characterize cell populations and immune response in CGTs. Two out of 9 patients showed poor clinical response with tumor progression and metastasis. Our analysis using unsupervised hierarchical clustering determined differences in gene expression between poor responders and good responders before denosumab treatment. Poor responding lesions are characterized by increased expression of inflammatory cytokines as IL8, IL1, interferon a and g, among a myriad of cytokines and chemokines (CCL25, IL5, IL26, IL25, IL13, CCL20, IL24, IL22, etc.), while good responders are characterized by elevated expression of platelets (CD31 and PECAM), coagulation (CD74, F13A1), and complement classic pathway (C1QB, C1R, C1QBP, C1S, C2) markers, together with extracellular matrix proteins (COL3A1, FN1,. Interestingly the T-cell response is also different between groups. Poor responding lesions have increased Th1 and Th2 component, but good responders have an increased Th17 component. Interestingly, the checkpoint inhibitor of the immune response PD1 (PDCD1) is increased ~10 fold in poor responders. This preliminary study using a novel experimental approach revealed differences in the immune response in GCTs associated with clinical response to denosumab. The increased activity of checkpoint inhibitor PD1 in poor responders to denosumab treatment may have implications for therapy, raising the potential to investigate immunotherapy as is currently used in other neoplasms. Further validation using a larger independent cohort will be required but these results could potentially identify the patients who would most benefit from denosumab therapy

A multicentre retrospective study of 879 total hip replacements (THR) was undertaken to investigate any differences in outcome between trainee surgeons and consultants. The effect of trainee supervision was also assessed. The primary outcome measures were survivorship and the Harris Hip Score (HHS). Length of stay was a secondary outcome. Patients were evaluated pre-operatively and at 1, 3, 5, 7 and 10 years post-operatively. Surgical outcome was compared between junior trainees, senior trainees and consultants. The effect of supervision was determined by comparing supervised and unsupervised trainees. There was no significant difference in post-operative HHS among consultants, senior and junior trainees at 1 year (p=0.122), at 3 years (p=0.282), at 5 years (p=0.063), at 7 years (p=0.875), or at 10 years (p=0.924) follow up. Additionally there was no significant difference in HHS between supervised and unsupervised trainees at 1 year (p=0.220), 3 years (p=0.0.542), 5 years (p=0.880), 7 years (p=0.953) and 10-year (p=0.787) follow-up. Comparison of surgical outcome between the supervised and unsupervised trainees also shows no significant difference in hospital stay (p=0.989), or implant survival years (p=0.257). This study provides evidence that when trainees are appropriately supervised, they can obtain equally good results compared with consultants when performing THR

Introduction. There is a paucity of research investigating the effect of the experience of the operating surgeon on the long term function and survivorship of total hip replacements (THR). With the advent of individualised surgeon data being available to patients via the National Joint Registry, the desire to avoid complications and poor performance grows. This potentially reduces the availability of operative opportunities for trainees as consultants seek to ensure good results. Method & Results. A multicentre retrospective study of 879 THR was undertaken to investigate any differences in outcome between trainee surgeons and consultants. The effect of trainee supervision on the surgical outcome was also assessed. The primary outcome measures were survivorship and the Harris Hip Score (HHS). Rates of deep infection and dislocation were also recorded. Patients were evaluated pre-operatively and at 1, 3, 5, 7 and 10 years post-operatively. Surgical outcome was compared between junior trainees, senior trainees and consultants. The effect of supervision on final outcome was determined by comparing supervised and unsupervised trainees. 66.4% of patients were operated by consultants, 15.7% by junior trainees (ST3–5 equivalent) and 16.8% by senior trainees (ST6–8 equivalent). 10 year implant survival rates were; consultants 96.4 %, senior trainees 98.0 % and junior trainees 97.1%. There was no significant difference in post-operative HHS among consultants, senior and junior trainees at 1 year (p=0.122), 3 year (p=0.282), 5 year (p=0.063), 7 year (p=0.875), or at 10 years (p=0.924). There was no significant difference in HHS between supervised and unsupervised trainees at 1 year (p=0.220), 3 year (p=0.0.542), 5 year (p=0.880), 7 year (p=0.953) and 10-years (p=0.787). Comparison of surgical outcome between the supervised and unsupervised trainees also shows no significant difference in implant survival years (p=0.257). Conclusion. This study provides evidence that when trainees are appropriately supervised there is no negative effect on patient outcomes

Introduction and Objective. Hip osteoarthritis (OA) is the leading cause for total hip arthroplasty (THA). Although, being considered as the surgery of the century up to 23% of the patients report long-term pain and deficits in physical function and muscle strength may persist after THA. Progressive resistance training (PRT) appear to improve several outcomes moderately in patients with hip OA. Current treatment selection is based on low-level evidence as no randomised controlled trials have compared THA to non-surgical treatment. The primary objective of this trial is to determine the effectiveness of THA followed by standard care compared to 12 weeks of supervised PRT followed by 12 weeks of optional unsupervised PRT, on changes in hip pain and function, in patients with severe hip OA after 6 months. Materials and Methods. This is a protocol for a multicentre, parallel-group, assessor blinded, randomised controlled superiority trial. Patients aged ≥50 years with clinical and radiographic hip OA found eligible for THA by an orthopaedic surgeon will be randomised to THA or PRT (allocation 1:1). The primary outcome will be change in patient-reported hip pain and function, measured using the Oxford Hip Score. Key secondary outcomes will be change in the Hip disability and Osteoarthritis Outcome Score subscales, University of California Los Angeles Activity Score, 40-meter fast-paced walk test, 30-second chair stand test, and number of serious adverse events. Results. The trial has been approved by The Regional Committees on Health Research Ethics for Southern Denmark (Project-ID: S-20180158) in February 2019 and registration was performed at . ClinicalTrials.gov. (NCT04070027) in August 2019. Recruitment was initiated on the 2. nd. of September 2019 and the final deadline will be on the 30. th. of June 2021, or when a sample size of 120 patients has been accomplished. Conclusions. The results of the current trial are expected to enable evidence-based recommendations, which may be used to facilitate the shared-decision making process in the discussion of treatment strategy for the individual patient with severe hip OA. All results will be presented in peer-reviewed scientific journals and international conferences

Background. While in vivo kinematics and forces in the knee have been studied extensively, these are typically measured during controlled activities conducted in an artificial laboratory environment and often do not reflect the natural day-to-day activities of typical patients. We have developed a novel algorithm that together with our electronic tibial component provide unsupervised simultaneous dynamic 3-D kinematics and forces in patients. Methods. An inverse finite element approach was used to compute knee kinematics from in vivo measured knee forces. In vitro pilot testing indicated that the accuracy of the algorithm was acceptable for all degrees of freedom except knee flexion angle. We therefore mounted an electrogoniometer on a knee sleeve to monitor knee flexion while simultaneously recording knee forces. A finite element model was constructed for each subject. The femur was flexed using the measured knee flexion angle and brought into contact with the fixed tibial insert using the three-component contact force vector applied as boundary conditions to the femoral component, which was free to translate in all directions. The relative femorotibial adduction-abduction and axial rotation were varied using an optimization program (iSIGHT, Simulia, Providence, RI) to minimize the difference between the resultant moments output by the model and the experimentally measured moments. Maximum absolute error was less than 1 mm in anteroposterior and mediolateral translation and was 1.2° for axial rotation and varus-valgus angulation. This accuracy is comparable to that reported for fluoroscopically measured kinematics. We miniaturized the external hardware and developed a wearable data acquisition system to monitor knee forces and kinematics outside the laboratory. Results. Knee forces were monitored in three subjects during unsupervised outdoor walking. The terrain included level ground, varying grade slopes, hiking trails, and hiking off-trail. In general knee forces were higher than those measured in the laboratory (2.2 xBW). Peak knee forces were highest (>3 xBW) when hiking up and down a 10° slope. One subject tripped and recorded over 5 x bodyweight. Conclusions. This method of obtaining combined kinematics and forces with minimal external hardware greatly increases our ability for capturing true kinematics and forces. Unsupervised activities outside the laboratory generated significantly different forces compared to in-laboratory measurements. Clinically relevant data can be obtained for preclinical testing of prostheses as well as for advising patients regarding postoperative rehabilitation and activities. We are now able to continuously monitor data over extended periods of time (days or weeks) and to record naturally occurring events (in contrast to choreographed activity). Since we compute tibiofemoral contact as part of the algorithm to determine the kinematics, the forces and kinematics are already integrated with contact analysis. These data can be used as input into damage and wear models to predict failure or for validation of biomechanical models of the knee, which predict knee forces and kinematics. Continuously monitoring in vivo knee forces and kinematics under daily conditions will identify weaknesses and potential areas of failure in current designs and will provide direction into enhancing the function and durability of total knee arthroplasty

Participation in a physical therapy program is considered one of the greatest predictors for successful conservative management of common shoulder disorders, however, adherence to standard exercise protocols is often poor (around 50%) and typically worse for unsupervised home exercise programs. Currently, there are limited tools available for objective measurement of adherence and performance of shoulder rehabilitation in the home setting. The goal of this study was to develop and evaluate the potential for performing home shoulder physiotherapy monitoring using a commercial smartwatch. We hypothesize that shoulder physiotherapy exercises can be classified by analyzing the temporal sequence of inertial sensor outputs from a smartwatch worn on the extremity performing the exercise. Twenty healthy adult subjects with no prior shoulder disorders performed seven exercises from a standard evidence-based rotator cuff physiotherapy protocol: pendulum, abduction, forward elevation, internal/external rotation and trapezius extension with a resistance band, and a weighted bent-over row. Each participant performed 20 repetitions of each exercise bilaterally under the supervision of an orthopaedic surgeon, while 6-axis inertial sensor data was collected at 50 Hz from an Apple Watch. Using the scikit-learn and keras platforms, four supervised learning algorithms were trained to classify the exercises: k-nearest neighbour (k-NN), random forest (RF), support vector machine classifier (SVC), and a deep convolutional recurrent neural network (CRNN). Algorithm performance was evaluated using 5-fold cross-validation stratified first temporally and then by subject. Categorical classification accuracy was above 94% for all algorithms on the temporally stratified cross validation, with the best performance achieved by the CRNN algorithm (99.4± 0.2%). The subject stratified cross validation, which evaluated classifier performance on unseen subjects, yielded lower accuracies scores again with CRNN performing best (88.9 ± 1.6%). This proof-of concept study demonstrates the feasibility of a smartwatch device and machine learning approach to more easily monitor and assess the at-home adherence of shoulder physiotherapy exercise protocols. Future work will focus on translation of this technology to the clinical setting and evaluating exercise classification in shoulder disorder populations

Objective. Emergence of low-cost wearable systems has permitted extended data collection for unsupervised subject monitoring. Recognizing individual activities performed during these sessions gives context to recorded data and is an important first step towards automated motion analysis. Convolutional neural networks (CNNs) have been used with great success to detect patterns of pixels in images for object detection and recognition in many different applications. This work proposes a novel image encoding scheme to create images from time-series activity data and uses CNNs to accurately classify 13 daily activities performed by instrumented subjects. Methods. Twenty healthy subjects were instrumented with a previously developed wearable sensor system consisting of four inertial sensors mounted above and below each knee. Each subject performed eight static and five dynamic activities: standing, sitting in a chair/cross-legged, kneeling on left/right/both knees, squatting, laying, walking/running, biking and ascending/descending stairs. Data from each sensor were synchronized, windowed, and encoded as images using a novel encoding scheme. Two CNNs were designed and trained to classify the encoded images of both static and dynamic activities separately. Network performance was evaluated using twenty iterations of a leave-one-out validation process where a single subject was left out for test data to estimate performance on future unseen subjects. Results. Using 19 subjects for training and a single subject left out for testing per iteration, the average accuracy observed when classifying the eight static activities was 98.0% ±2.9%. Accuracy dropped to 89.3% ±10.6% when classifying all dynamic activities using a separate model with the same evaluation process. Ascending/descending stairs, walking/running, and sitting on a chair/squatting were most commonly misclassified. Conclusions. Previous related work on activity recognition using accelerometer and/or gyroscope raw signals fails to provide sufficient data to distinguish static activities. The proposed method operating on lower limb orientations has classified eight static activities with exceptional accuracy when tested on unseen subject data. High accuracy was also observed when classifying dynamic activities despite the similarity of the activities performed and the expected variance of individuals’ gait. Accuracy reported in existing literature classifying comparable activities from other wearable sensor systems ranges between 27.84% to 84.52% when tested using a similar leave-one-subject-out validation strategy[1]. It is expected that incorporating these trained models into the previously developed wearable system will permit activity classification on unscripted instrumented activity data for more contextual motion analysis

Trampoline was used by British & American fighter pilots as a training device during World War II. It became popular among Civilian as a recreational sports during 50s and 60s. Trampoline related injuries including quadriplegia and death have been reported from United States and Europe. We are reporting the incidence, type and distribution of trampoline-related injuries in children in a District General Hospital in United Kingdom. Recently we treated three serious injuries – proximal tibial fracture associated with popliteal artery injury, subtrochanteric fracture of femur and cervical fracture-dislocation leading to quadriplegia, that lead to an audit study. This is a retrospective analysis of trampoline-related injuries in children seen in the Accident & Emergency Department over three months period. The casualty cards and admission records were reviewed. The mechanism of injury was fall on or off the trampoline in 98.15%(53/54). All injuries occurred on back-garden or leisure-centre trampoline. 74% of injuries were sustained while unsupervised. The incidence of soft tissue injuries were 59.25%(32/54) and fractures 40.75%(22/54). Soft tissue injuries commonly involved lower limb(16), upper limb(7) and head and neck(4). Fractures commonly involved upper limb(13), lower limb(8) and nasal bone(1). The fractures involved wrist and forearm in eight cases, ankle in five, elbow in four and one case each involving finger, hip, knee and toe. The treatment of trampoline-related injuries varied from reassurance, analgesia, tubigrip bandage to plaster cast. Fourteen(26%) patients were admitted into the wards and ten(18.5%) required surgical treatment. The incidence of trampoline-related injuries in back-garden and leisure-centre when unsupervised was high. The supervision by an adult has not proved to be very effective in preventing the injuries as 26% of trampoline-related injuries occurred under supervision of an adult. The morbidity related to leisure trampolining is high probably due to lack of training and non-compliance to the manufacturer instructions in the use of trampoline. We would recommend banning the routine use of back garden trampoline without proper training and supervision

Nutritional Status and Short-Term Outcomes Following THA; Initial Metal Ion Levels Predict Risk in MoM THA; THA Bearing Surface Trends in the US ‘07- ’14; Dislocation Following Two-Stage Revision THA; Timing of Primary THA Prior to or After Lumbar Spine Fusion; Failure Rate of Failed Constrained Liner Revision; ESR and CRP vs. Reinfection Risk in Two-Stage Revision?; Mechanical Complications of THA Based on Approach; Impaction Force and Taper-Trunnion Stability in THA; TKA in Patients Less Than 50 Years of Age; Post-operative Mechanical Axis and 20-year TKA Survival and Function; Return to Moderate to High-intensity Sports after UKA; “Running Two Rooms” and Patient Safety in TJA; Varus and Implant Migration and Contact Kinematics after TKA; Quadriceps Snips in 321 Revision TKAs; Tubercle Proximalization for Patella Infera in Revision TKA; Anterior Condylar Height and Flexion in TKA; Compression Bandage Following Primary TKA; Unsupervised Exercise vs. Traditional PT After Primary TKA and UKA

Aims

The aim of this study was to determine the effectiveness of home-based prehabilitation on pre- and postoperative outcomes in participants awaiting total knee (TKA) and hip arthroplasty (THA).