Recently, several smartphone applications (apps) have been developed and validated for ankle ROM measurement tools like the universal goniometer. This is the first innovative study introduces a new smartphone application to measure ankle joint ROM as a remote solution. This study aimed to assess the correlation between smartphone ROM and universal goniometer measurements, and also report the evaluation of the DijiA app by users. The study included 22 healthy university students (14F/8M; 20.68±1.72 years) admitted to Yeditepe University. Fourty four feet was measured by both the universal goniometer (UG) and DijiA app. The datas were analyzed through using the intraclass correlation coefficient (ICC). The DijiA app was evaluated by usability testing with representative users. Pearson correlation coefficient test showed moderate correlation between the DijiA and UG for dorsiflexion (DF) and plantar flexion (PF) measurements (Pearson correlation coefficient: r=0.323, for DF; r=0.435 for PF 95% confidence interval). The application usability was found as high with 76.5 average score and users liked it. The DijiA app may be a more convenient and easy way to measure ankle DF and PF-ROM than UG. It can be used to evaluate ROM in clinical practice or home using as a personal smartphone

Introduction. Orthopedics is experiencing a significant transformation with the introduction of technologies such as robotics and apps. These, integrated into the post-operative rehabilitation process, promise to improve clinical outcomes, patient satisfaction, and the overall efficiency of the healthcare system. This study examines the impact of an app called Mymobility and intra-operative data collected via the ROSA® robotic system on the functional recovery of patients undergoing robot-assisted knee arthroplasty. Method. The study was conducted at a single center from 2020 to 2023. Data from 436 patients were included, divided into “active” patients (active users of Mymobility) and “non-active” patients. Clinical analyses and satisfaction surveys were carried out on active patients. The intra-operative parameters recorded by ROSA® were correlated with the Patient-Reported Outcome Measures (PROMs) collected via Mymobility. Result. Intra-operative data showed significant correlations with PROMs for the 48 active patients, highlighting the importance of parameters such as medial joint space and ligament laxity. No significant differences were observed between the sexes, but a positive correlation was detected between age and PROMs. The data analysis indicated that an increased medial joint space and reduced ligament laxity are associated with better PROMs. The adoption of Mymobility remained limited, with only 10% of patients fully utilizing the app. Critical factors have been identified to improve recruitment, engagement, and overall experience with the platform. Conclusion. The integration of technologies such as Mymobility and ROSA® in post-operative rehabilitation offers numerous advantages, including the objectification of data, active patient involvement, and personalized care. Challenges remain related to costs, patient compliance, and demographic limitations. Nevertheless, these technologies represent a milestone in modern peri-operative management, being able to improve clinical outcomes and the quality of care

There is controversy regarding the effect of different approaches on recovery after THR. Collecting detailed relevant data with satisfactory compliance is difficult. Our retrospective observational multi-center study aimed to find out if the data collected via a remote coaching app can be used to monitor the speed of recovery after THR using the anterolateral (ALA), posterior (PA) and the direct anterior approach (DAA). 771 patients undergoing THR from 13 centers using the moveUP platform were identified. 239 had ALA, 345 DAA and 42 PA. There was no significant difference between the groups in the sex of patients or in preoperative HOOS Scores. There was however a significantly lower age in the DAA (64,1y) compared to ALA (66,9y), and a significantly lower Oxford Hip Score in the DAA (23,9) compared to PA(27,7). Step count measured by an activity tracker, pain killer and NSAID use was monitored via the app. We recorded when patients started driving following surgery, stopped using crutches, and their HOOS and Oxford hip scores at 6 weeks. Overall compliance with data request was 80%. Patients achieved their preoperative activity level after 25.8, 17,7 and 23.3 days, started driving a car after 33.6, 30.3 and 31.7 days, stopped painkillers after 27.5, 20.2 and 22.5 days, NSAID after 30.3, 25.7, and 24.7 days for ALA, DAA and PA respectively. Painkillers were stopped and preoperative activity levels were achieved significantly earlier favoring DAA over ALA. Similarly, crutches were abandoned significantly earlier (39.9, 29.7 and 24.4 days for ALA, DAA and PA respectively) favoring DAA and PA over ALA. HOOS scores and Oxford Hip scores improved significantly in all 3 groups at 6 weeks, without any statistically significant difference between groups in either Oxford Hip or HOOS subscores. No final conclusion can be drawn as to the superiority of either approach in this study but the remote coaching platform allowed the collection of detailed data which can be used to advise patients individually, manage expectations, improve outcomes and identify areas for further research

Artificial Intelligence (AI) is becoming more powerful but is barely used to counter the growth in health care burden. AI applications to increase efficiency in orthopedics are rare. We questioned if (1) we could train machine learning (ML) algorithms, based on answers from digitalized history taking questionnaires, to predict treatment of hip osteoartritis (either conservative or surgical); (2) such an algorithm could streamline clinical consultation. Multiple ML models were trained on 600 annotated (80% training, 20% test) digital history taking questionnaires, acquired before consultation. Best performing models, based on balanced accuracy and optimized automated hyperparameter tuning, were build into our daily clinical orthopedic practice. Fifty patients with hip complaints (>45 years) were prospectively predicted and planned (partly blinded, partly unblinded) for consultation with the physician assistant (conservative) or orthopedic surgeon (operative). Tailored patient information based on the prediction was automatically sent to a smartphone app. Level of evidence: IV. Random Forest and BernoulliNB were the most accurate ML models (0.75 balanced accuracy). Treatment prediction was correct in 45 out of 50 consultations (90%), p<0.0001 (sign and binomial test). Specialized consultations where conservatively predicted patients were seen by the physician assistant and surgical patients by the orthopedic surgeon were highly appreciated and effective. Treatment strategy of hip osteoartritis based on answers from digital history taking questionnaires was accurately predicted before patients entered the hospital. This can make outpatient consultation scheduling more efficient and tailor pre-consultation patient education

The Severity Scoring System (SSS) is a guide to interpreting findings across clinical, functional, and radiological findings, used by qualified, specially trained physiotherapists in the advanced practice role in order to provide consistency in determining the severity of the patient's condition and need for surgical consultation. The system has been utilized for over 14 years as a part of standardized assessment and management care and was incorporated into virtual care in 2020 following the pandemic restrictions. The present study examined the validity of the modified SSS in virtual care. Patients who were referred to the Rapid Access Clinic (RAC), were contacted via phone by two experienced advanced practice practitioners (APPs) from May to July 2020, when in-person care was halted due to the pandemic. The virtual interview included taking history, completing self-reported measures for pain and functional ability and reviewing the radiological reports. A total of 63 patients were interviewed (mean age 68, SD=9), 34 (54%) females. Of 63 patients, 33 (52%) were considered a candidate for total knee arthroplasty (TKA). Men and women were comparable in age, P4 and LEFS scores. The TKA candidates had a significantly higher SSS (p<0.0001) and pain scores (p=0.024). The variability of the total SSS score explained by the functional, clinical and radiological components of the tool were 55%, 48% and 4% respectively, highlighting the more important role of patient's clinical history and disability in the total SSS. The virtual SSS is a valid tool in directing patients for surgical management when used by highly trained advanced practice physiotherapists. A large component of the SSS is based on clinical data and patient disability and the APP's skillset rather than severity of pathology found on imaging

Reduction of length of stay (LOS) without compromising quality of care is a trend observed in orthopaedic departments. To achieve this goal the pathway needs to be optimised. This requires team work than can be supported by e-health solutions. The objective of this study was to assess the impact of reduction in LOS on complications and readmissions in one hospital where accelerated discharge was introduced due to the pandemic. 317 patients with primary total hip and total knee replacements treated in the same hospital between October 2018 and February 2021 were included. The patients were divided in two groups: the pre-pandemic group and the pandemic group. The discharge criteria were: patient feels comfortable with going back home, patient has enough support at home, no wound leakage, and independence in activities of daily living. No face-to-face surgeon or nurse follow-up was planned. Patients’ progress was monitored via the mobile application. The patients received information, education materials, postoperative exercises and a coaching via secure chat. The length of stay (LOS) and complications were assessed through questions in the app and patients filled in standard PROMs preoperatively, at 6 weeks and 3 months. Before the pandemic, 64.8% of the patients spent 3 nights at hospital, whereas during the pandemic, 52.0% spent only 1 night. The median value changed from 3 days to 1 day. The complication rate before the pandemic of 15% dropped to 9 % during the pandemic. The readmission rate remained stable with 4% before the pandemic and 5 % during the pandemic. No difference were observed for PROMS between groups. The results of this study showed that after a hip and knee surgery, the shortening of the LOS from three to one night resulted in less complications and a stable rate of readmissions. These results are in line with literature data on enhanced recovery after hip and knee arthroplasty. The reduction of LOS for elective knee and hip arthroplasty during the pandemic period proved safe. The concept used in this study is transferable to other hospitals, and may have economic implications through reduced hospital costs

Abstract. OBJECTIVES. Application of deep learning approaches to marker trajectories and ground reaction forces (mocap data), is often hampered by small datasets. Enlarging dataset size is possible using some simple numerical approaches, although these may not be suited to preserving the physiological relevance of mocap data. We propose augmenting mocap data using a deep learning architecture called “generative adversarial networks” (GANs). We demonstrate appropriate use of GANs can capture variations of walking patterns due to subject- and task-specific conditions (mass, leg length, age, gender and walking speed), which significantly affect walking kinematics and kinetics, resulting in augmented datasets amenable to deep learning analysis approaches. METHODS. A publicly available (. https://www.nature.com/articles/s41597-019-0124-4. ) gait dataset (733 trials, 21 women and 25 men, 37.2 ± 13.0 years, 1.74 ± 0.09 m, 72.0 ± 11.4 kg, walking speeds ranging from 0.18 m/s to 2.04 m/s) was used as the experimental dataset. The GAN comprised three neural networks: an encoder, a decoder, and a discriminator. The encoder compressed experimental data into a fixed-length vector, while the decoder transformed the encoder's output vector and a condition vector (containing information about the subject and trial) into mocap data. The discriminator distinguished between the encoded experimental data from randomly sampled vectors of the same size. By training these networks jointly using the experimental dataset, the generator (decoder) could generate synthetic data respecting specified conditions from randomly sampled vectors. Synthetic mocap data and lower limb joint angles were generated and compared to the experimental data, by identifying the statistically significant differences across the gait cycle for a randomly selected subset of the experimental data from 5 female subjects (73 trials, aged 26–40, weighing 57–74 kg, with leg lengths between 868–931 mm, and walking speeds ranging from 0.81–1.68 m/s). By conducting these comparisons for this subset, we aimed to assess the synthetic data generated using multiple conditions. RESULTS. We visually inspected the synthetic trials to ensure that they appeared realistic. The statistical comparison revealed that, on average, only 2.5% of the gait cycle showed significantly differences in the joint angles of the two data groups. Additionally, the synthetic ground reaction forces deviated from the experimental data distribution for an average of 2.9% of the gait cycle. CONCLUSIONS. We introduced a novel approach for generating synthetic mocap data of human walking based on the conditions that influence walking patterns. The synthetic data closely followed the trends observed in the experimental data, also in the literature, suggesting that our approach can augment mocap datasets considering multiple conditions, an approach unfeasible in previous work. Creation of large, augmented datasets allows the application of other deep learning approaches, with the potential to generate realistic mocap data from limited and non-lab-based data. Our method could also enhance data sharing since synthetic data does not raise ethical concerns. You can generate and download virtual gait data using our GAN approach from . https://thisgaitdoesnotexist.streamlit.app/. . Declaration of Interest. (b) declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported:I declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research project

The relevance of physical activity (PA) for general health and the value of assessing PA in the free-living environment especially for assessing orthopaedic conditions and outcome are discussed. Available methods for assessing PA such as self-reports, trackers, phone apps and clinical grade monitors are introduced. An overview of devices such as accelerometers for research quality assessments is given and aspects for choosing them such as wear location, usability or study population are reviewed. Basic principles to derive mobility parameters from the PA related sensor signals are presented. The symposium explains mobility parameters, their types, definitions, validity, analysis and those with particular relevance to assess orthopaedic conditions. The application of activity monitors is orthopaedic patient studies is demonstrated in various examples such as knee and hop osteoarthritis and total joint arthroplasty, in frail elderly subjects at fall risk or patients with shoulder pathologies