Recently, some smart media devices including portable accelerometers have been used to measure objective physical activity (OPA) after total hip arthroplasty (THA). The aim of this study was to longitudinally assess OPA changes in patients who underwent THA using a compact triaxial accelerometer and to investigate the impact of this recovery process on patient-reported outcomes. This prospective cohort study involved 163 consecutive patients who had unilateral osteoarthritis of the hip and were followed up for 12 months after THA. There were 132 women and 31 men with average age of 66 years. OPA was measured using a compact triaxial accelerometer preoperatively and at 1, 3, 6, and 12 months postoperatively. This study investigated the recovery process of OPA in four patient groups classified by the median of age and preoperative activity levels (younger and higher activity (YH), younger and lower activity (YL), older and higher activity (OH), and older and lower activity (OL)), and examined its impact on patient-reported outcomes, including forgotten joint score-12 (FJS-12). The target period for regaining preoperative activity levels was approximately 3 months for patients with lower preoperative activity, and about 6 months for those with higher preoperative activity. The OPA at 12 months postoperatively was higher in the patients with higher preoperative activity levels than in those with lower preoperative activity levels. In patients with higher preoperative activity levels, FJS-12 scores significantly increased between 6 and 12 months postoperatively (p=0.018). FJS-12 at 12 months postoperatively was best in YH (81.7±18.9), followed by YL (73.5±22.9), OH (73.2±17.4), and OL (66.3±21.8). Differences in the recovery process of postoperative activity levels impacted the duration required for improvement in FJS-12 scores. These results can serve as indicators for setting activity goals in patients undergoing THA

Aims

Conventional patient-reported surveys, used for patients undergoing total hip arthroplasty (THA), are limited by subjectivity and recall bias. Objective functional evaluation, such as gait analysis, to delineate a patient’s functional capacity and customize surgical interventions, may address these shortcomings. This systematic review endeavours to investigate the application of objective functional assessments in appraising individuals undergoing THA.

Gait measurements can vary due to various intrinsic and extrinsic factors, and this variability becomes more pronounced using inertial sensors in a free-living environment. Therefore, identifying and quantifying the sources of variability is essential to ensure measurement reliability and maintain data quality. This study aimed to determine the variability of daily accelerations recorded by an inertial sensor in a group of healthy individuals. Ten participants, four males and six females, with a mean age of 50 years (range: 29–61) and BMI of 26.9 kg/m. 2. (range: 21.4–36.8), were included. A single accelerometer continuously recorded lower limb accelerations over two weeks. We extracted and analyzed the accelerations of three consecutive strides within walking bouts if the time difference between the bouts was more than two hours. Multivariate mixed-effects modeling was performed on both the discretized acceleration waveforms at 101 points (0–100) and the harmonics of the signals in the frequency domain to determine the variance components for different subjects, days, bouts, and steps as the random effect variables. Intraclass correlation coefficients (ICCs) were calculated for between-day, between-bout, and between-step comparisons. The results showed that the ICCs for the between-day, between-bout, and between-step comparisons were 0.73, 0.82, 0.99 for the vertical axis; 0.64, 0.75, 0.99 for the anteroposterior axis; and 0.55, 0.96, 0.97 for the mediolateral axis. For the signal harmonics, the respective ICCs were 0.98, 0.98, 0.99 for the vertical axis; 0.54, 0.93, 0.98 for the anteroposterior axis; and 0.69, 0.78, 0.95 for the mediolateral axis. Overall, this study demonstrated that accelerations recorded continuously for multiple days in a free-living environment exhibit high variability, mainly between days, and some variability arising from differences between walking bouts during different times within days. However, reliable and repeatable gait measurements can be obtained by identifying and quantifying the sources of variability

Wearable inertial sensors can detect abnormal gait associated with knee or hip osteoarthritis (OA). However, few studies have compared sensor-derived gait parameters between patients with hip and knee OA or evaluated the efficacy of sensors suitable for remote monitoring in distinguishing between the two. Hence, our study seeks to examine the differences in accelerations captured by low-frequency wearable sensors in patients with knee and hip OA and classify their gait patterns. We included patients with unilateral hip and knee OA. Gait analysis was conducted using an accelerometer ipsilateral with the affected joint on the lateral distal thighs. Statistical parametric mapping (SPM) was used to compare acceleration signals. The k-Nearest Neighbor (k-NN) algorithm was trained on 80% of the signals' Fourier coefficients and validated on the remaining 20% using 10-fold cross-validation to classify the gait patterns into hip and knee OA. We included 42 hip OA patients (19 females, age 70 [63–78], BMI of 28.3 [24.8–30.9]) and 59 knee OA patients (31 females, age 68 [62–74], BMI of 29.7 [26.3–32.6]). The SPM results indicated that one cluster (12–20%) along the vertical axis had accelerations exceeding the critical threshold of 2.956 (p=0.024). For the anteroposterior axis, three clusters were observed exceeding the threshold of 3.031 at 5–19% (p = 0.0001), 39–54% (p=0.00005), and 88–96% (p = 0.01). Regarding the mediolateral axis, four clusters were identified exceeding the threshold of 2.875 at 0–9% (p = 0.02), 14–20% (p=0.04), 28–68% (p < 0.00001), and 84–100% (p = 0.004). The k-NN model achieved an AUC of 0.79, an accuracy of 80%, and a precision of 85%. In conclusion, the Fourier coefficients of the signals recorded by wearable sensors can effectively discriminate the gait patterns of knee and hip OA. In addition, the most remarkable differences in the time domain were observed along the mediolateral axis

Abstract. Objectives. The aim of this study was to investigate whether mechanical loading induced by physical activity can reduce risk of sarcopenia in middle-aged adults. Methods. This was a longitudinal study based on a subset of UK Biobank data consisting of 1,918 participants (902 men and 1,016 women, mean age 56 years) who had no sarcopenia at baseline (assessed between 2006 and 2010). The participants were assessed again after 6 years at follow-up, and were categorized into no sarcopenia, probable sarcopenia, or sarcopenia according to the definition and algorithm developed in 2018 by European Working Group on Sarcopenia in Older People (EWGSOP). Physical activity was assessed at a time between baseline and follow-up using 7-day acceleration data obtained from wrist worn accelerometers. Raw acceleration data were then analysed to study the mechanical loading of physical activity at different intensities (i.e. very light, light, moderate-to-vigorous). Multinominal logistic regression was employed to examine the association between the incidence of sarcopenia and physical activity loading, between baseline and follow up, controlled for other factors at baseline including age, gender, BMI, smoking status, intake of alcohol, vitamin D and calcium, history of rheumatoid arthritis, osteoarthritis, secondary osteoporosis, and type 2 diabetes. Results. Among the 1918 participants with no sarcopenia at baseline, 230 (69 men and 161 women) developed probable sarcopenia and 37 (14 men and 23 women) developed sarcopenia at follow-up. Physical activity loading at moderate-to-vigorous intensity was higher in men (p<0.05), while women had higher physical activity loading at very light intensity (p<0.05). No significant difference was found in physical activity loading at light intensity between men and women (p>0.05). Logistic regression models showed that increase in physical activity loading at moderate-to-vigorous intensity significantly reduced the risk of sarcopenia (odds ratio = 0.368, p<0.05), but not probable sarcopenia (odds ratio = 0.974, p>0.05), while loading at light or very light activity intensity were not associated with the risk of sarcopenia or probable sarcopenia (p>0.05). Conclusion. Loading of physical activity at moderate-to-vigorous intensity could reduce risk of sarcopenia in middle-aged adults. 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

Aims

A core outcome set for adult, open lower limb fracture has been established consisting of ‘Walking, gait and mobility’, ‘Being able to return to life roles’, ‘Pain or discomfort’, and ‘Quality of life’. This study aims to identify which outcome measurement instruments (OMIs) should be recommended to measure each core outcome.

In this work, we propose a new quantitative way of evaluating acute compartment syndrome (ACS) by dynamic mechanical assessment of soft tissue changes. First, we have developed an animal model of ACS to replicate the physiological changes during the condition. Secondly, we have developed a mechanical assessment tool for quantitative pre-clinical assessment of ACS. Our hand-held indentation device provides an accurate method for investigations into the local dynamic mechanical properties of soft tissue and for in-situ non-invasive assessment and monitoring of ACS. Our compartment syndrome model was developed on the cranial tibial and the peroneus tertius muscles of a pig's leg (postmortem). The compartment syndrome pressure values were obtained by injecting blood from the bone through the muscle. To enable ACS assessment by a hand-held indentation device we combined three main components: a load cell, a linear actuator and a 3-axis accelerometer. Dynamic tests were performed at a frequency of 0.5 Hz and by applying an amplitude of 0.5 mm. Another method used to observe the differences in the mechanical properties inside the leg was a 3D Digital Image Correlation (3D-DIC). Videos were taken from two different positions of the pig's leg at different pressure values: 0 mmHg, 15 mmHg and 40 mmHg. Two strains along the x axis (Exx) and y axis (Eyy) were measured. Between the two pressure cases (15 mmHg and 40 mmHg) a clear deformation of the model is visible. In fact, the bigger the pressure, the more visible the increase in strain is. In our animal model, local muscle pressures reached values higher than 40 mmHg, which correlate with observed human physiology in ACS. In our presentation we will share our dynamic indentation results on this model to demonstrate the sensitivity of our measurement techniques. Compartment syndrome is recognised as needing improved clinical management tools. Our approach provides both a model that reflects physiological behaviour of ACS, and a method for in-situ non-invasive assessment and monitoring

The tendency towards using inertial sensors for remote monitoring of the patients at home is increasing. One of the most important characteristics of the sensors is sampling rate. Higher sampling rate results in higher resolution of the sampled signal and lower amount of noise. However, higher sampling frequency comes with a cost. The main aim of our study was to determine the validity of measurements performed by low sampling frequency (12.5 Hz) accelerometers (SENS) in patients with knee osteoarthritis compared to standard sensor-based motion capture system (Xsens). We also determined the test-retest reliability of SENS accelerometers. Participants were patients with unilateral knee osteoarthritis. Gait analysis was performed simultaneously by using Xsens and SENS sensors during two repetitions of over-ground walking at a self-selected speed. Gait data from Xsens were used as an input for AnyBody musculoskeletal modeling software to measure the accelerations at the exact location of two defined virtual sensors in the model (VirtualSENS). After preprocessing, the signals from SENS and VirtualSENS were compared in different coordinate axes in time and frequency domains. ICC for SENS data from first and second trials were calculated to assess the repeatability of the measurements. We included 32 patients (18 females) with median age 70.1[48.1 – 85.4]. Mean height and weight of the patients were 173.2 ± 9.6 cm and 84.2 ± 14.7 kg respectively. The correlation between accelerations in time domain measured by SENS and VirtualSENS in different axes was r = 0.94 in y-axis (anteroposterior), r = 0.91 in x-axis (vertical), r = 0.83 in z-axis (mediolateral), and r = 0.89 for the magnitude vector. In frequency domain, the value and the power of fundamental frequencies (F. 0. ) of SENS and VirtualSENS signals demonstrated strong correlation (r = 0.98 and r = 0.99 respectively). The result of test-retest evaluation showed excellent repeatability for acceleration measurement by SENS sensors. ICC was between 0.89 to 0.94 for different coordinate axes. Low sampling frequency accelerometers can provide valid and reliable measurements especially for home monitoring of the patients, in which handling big data and sensors cost and battery lifetime are among important issues

Falls in adults are a major problem and can lead to injuries and death. In order to better understand falls and successful recoveries, identifying kinematics, kinetics, and muscle forces during recovery from loss of balance is crucial. To obtain reactive gait patterns, participants must be subjected to unexpected perturbations such as trips and slips. Previous researchers have reported kinetics recovery data following stumbling; however, the muscle force recovery patterns remain unknown. To better target exercises to reduce the risk of falls, we must first understand which muscles, their magnitude, and their coordination patterns, play a role in a successful recovery from a trip and a slip. Additionally, knowing the successful patterns of lower limb function can help with the diagnosis of faulty movements. A total of 20 healthy adults in their twenties with similar athletic backgrounds were perturbed on a split-belt treadmill using Computer-Assisted Rehabilitation Environment (Motkforce Link) at a preset speed of 1.1m/s. Two kinds of perturbations were administered: slip and trip. Slips were simulated by accelerating one belt, whereas trips were simulated by decelerating one belt. Both perturbations had similar intensity and only differed in the direction. Computational modeling was used to obtain lower-limb function during the compensatory step. SPM paired t-test was used to compare differences in recovery strategies between slip and trip through magnitude and patterns of joints. There were no significant differences in joint angles post tripping vs post-slipping. Results of net joint moments showed that compensating for the loss of balance due to tripping required a higher ankle plantarflexion moment than slipping (at 22-52%; 1.2± 0.3vs0.4±0.2, p<0.001). Additionally, larger gluteus maximus (at 40-50%;8.7±3.8vs2.7±1.1N/kg, p=0.001), gluteus medius (at23~33%; 22.6±5.7vs6.8±3.6N/kg, p<0.001) were generated than post-slipping, respectively. These findings suggested that greater GMAX and GMED forces are required post-trip recovery than slip. Future analysis of trip recovery showed the importance of ankle joint in recovering from forward and backward fall. These results can be used as references in remote diagnosis of joint and muscle weakness and assessment of the risk of falls with the use of accelerometers

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

Aims

An objective technological solution for tracking adherence to at-home shoulder physiotherapy is important for improving patient engagement and rehabilitation outcomes, but remains a significant challenge. The aim of this research was to evaluate performance of machine-learning (ML) methodologies for detecting and classifying inertial data collected during in-clinic and at-home shoulder physiotherapy exercise.

We aim to explore the potential technologies for monitoring and assessment of patients undergoing arthroplasty by examining selected literature focusing on the technology currently available and reflecting on possible future development and application. The reviewed literature indicates a large variety of different hardware and software, widely available and used in a limited manner, to assess patients’ performance. There are extensive opportunities to enhance and integrate the systems which are already in existence to develop patient-specific pathways for rehabilitation.

Cite this article: Bone Joint J 2022;104-B(10):1104–1109.

Aims

The Patient-Reported Outcomes Measurement Information System (PROMIS) has demonstrated faster administration, lower burden of data capture and reduced floor and ceiling effects compared to traditional Patient Reported Outcomes Measurements (PROMs). We investigated the suitability of PROMIS Mobility score in assessing physical function in the sequelae of childhood hip disease.

Aims

The purpose of this study is to evaluate early outcomes with the use of a smartphone-based exercise and educational care management system after total hip arthroplasty (THA) and demonstrate decreased use of in-person physiotherapy (PT).

PROBLEM. Since the COVID-19 pandemic of 2020, there has been a marked rise in the use of telemedicine to evaluate patients following total knee arthroplasty (TKA). Telemedicine is helpful to maintain patient contact, but it cannot provide objective functional TKA data. External monitoring devices can be used, but in the past have had mixed results due to patient compliance and data continuity, particularly for monitoring over numerous years. This novel stem is a translational product with an embedded sensor that can remotely monitor patient activity following TKA. SOLUTION. The Canturio™ TE∗ System (Canary Medical) functions structurally as a tibial extension for the Persona® cemented tibial plate (Zimmer Biomet). The stem is instrumented with internal motion sensors (3-D accelerometer and gyroscope) and telemetry that collects and transmits kinematic data. Raw data is converted by analytics into clinically relevant gait metrics using a proprietary algorithm. The Canturio™ TE∗ will monitor the patient's gait daily for the first year and then with lower frequency thereafter to conserve battery power enabling the potential for 20 years of longitudinal data collection and analysis. A base station in the OR activates the device and links the stem and data to the patient. A base station in the patient's home collects and uploads data to the Cloud Based Canary Data Management Platform (Canary Medical). The Canary Cloud is structured as an FDA regulated and HIPPA-compliant database with cybersecurity protocols integrated into the architecture. A third base station is an accessory used in the health care professional's office to perform an on-demand gait analysis of a patient. A dashboard allows the health care professional and patient to monitor objective data of the patient's activity and progress post treatment. MARKET. The early target market for this device includes total joint surgeons who are early adopters of technology and currently utilize technology in their practice. The kinematic data provided by the Canturio™ TE∗ System will enable clinicians to augment patient care by reviewing their objective gait metrics. In the future, this data has the potential to be integrated with other Zimmer Biomet technologies, such as the Rosa™ Knee robotic platform, mymobility™, and sensored devices like iAssist™, to provide the surgeon with a complete pre-surgical functional assessment, intraoperative data, and post-operative functional data. PRODUCT. Persona IQ will be the combination of the proven Persona personalized total knee system with the Canary Medical Canturio™ TE∗. TIMING AND FUNDING. The Canturio™ TE is currently under De Novo FDA review for market clearance; it is not yet available for commercial distribution. The plan is to launch the product in 2021 pending regulatory De Novo grant. This effort is a partnership between Zimmer Biomet and Canary Medical. ∗ The Canturio™ - TE is currently under De Novo FDA review for market clearance; it is not yet available for commercial distribution

Aims

The purpose is to determine the non-inferiority of a smartphone-based exercise educational care management system after primary knee arthroplasty compared with a traditional in-person physiotherapy rehabilitation model.

Aims. The reduction in mobility due to hip diseases in children is likely to affect their physical activity (PA) levels. Physical inactivity negatively influences quality of life and health. Our aim was to objectively measure PA in children with hip disease, and correlate it with the Patient-Reported Outcomes Measurement Information System (PROMIS) Mobility Score. Methods. A total of 28 children (12 boys and 16 girls) with hip disease aged between 8and 17 years (mean 12 (SD 3)) were studied between December 2018 and July 2019. Children completed the PROMIS Paediatric Item Bank v. 2.0 – Mobility Short Form 8a and wore a hip accelerometer (ActiGraph) for seven consecutive days. Sedentary time (ST), light PA (LPA), moderate to vigorous PA (MVPA), and vigorous PA were calculated from the accelerometers' data. The PROMIS Mobility score was classified as normal, mild, and moderate functions, based on the PROMIS cut scores on the physical function metric. A one-way analysis of covariance (ANCOVA) was used to assess differences among mobility (normal; mild; moderate) and measured PA and relationships between these variables were assessed using bivariate Pearson correlations. Results. Children classified as normally functioning on the PROMIS had less ST (p = 0.002), higher MVPA, (p = 0.002) and VPA (p = 0.004) compared to those classified as mild or moderate function. A moderate correlation was evident between the overall PROMIS score and daily LPA (r = 0.462, n = 28; p = 0.013), moderate-to-vigorous PA (r = 0.689, n = 28; p = 0.013) and vigorous PA (VPA) (r = 0.535, n = 28; p = 0.013). No correlation was evident between the mean daily ST and overall PROMIS score (r = -0.282, n = 28; p = 0.146). Conclusion. PROMIS Pediatric Mobility tool correlates well with experimentally measured levels of physical activity in children with hip disease. We provide external validity for the use of this tool as a measure of physical activity in children. Cite this article: Bone Joint J 2021;103-B(2):405–410

The iASSIST system is a portable, accelerometer base with electronic navigation used for total knee arthroplasty (TKA) which guides the surgeon to align and validate bone resection during the surgical procedure. The purpose of this study was to compare the radiological outcome between accelerometer base iASSIST system and the conventional system. Method. A prospective study between two group of 36 patients (50 TKA) of primary osteoarthritis of the knee who underwent TKA using iASSIST ™ or conventional method (25 TKA in each group) from January 2018 to December 2019. A single surgeon performs all operations with the same instrumentation and same surgical approach. Pre-operative and postoperative management protocol are same for both groups. All patients had standardized scanogram (full leg radiogram) performed post operatively to determine mechanical axis of lower limb, femoral and tibial component alignment. Result. There was no significant difference between the 2 groups for Age, Gender, Body mass index, Laterality and Preoperative mechanical axis(p>0.05). There was no difference in proportion of outliers for mechanical axis (p=0.91), Coronal femoral component alignment angle (p=0.08), Coronal tibial component alignment angle (p=1.0). The mean duration of surgery, postoperative drop in Hb, number of blood transfusion didn't show significant difference between 2 groups (p>0.05). Conclusion. Our study concludes that despite being a useful guidance tool during TKA, iASSIST does not show any difference in limb alignment (mechanical axis), Tibial and femoral component alignment when compared with the conventional method