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

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

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