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. Methods. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were applied. Eligible studies of THA patients that conducted at least one type of objective functional assessment both pre- and postoperatively were identified through Embase, Medline/PubMed, and Cochrane Central database-searching from inception to 15 September 2023. The assessments included were subgrouped for analysis: gait analysis, motion analysis, wearables, and strength tests. Results. A total of 130 studies using 15 distinct objective functional assessment methods (FAMs) were identified. The most frequently used method was instrumented gait/motion analysis, followed by the Timed-Up-and-Go test (TUG), 6 minute walk test, timed stair climbing test, and various strength tests. These assessments were characterized by their diagnostic precision and applicability to daily activities. Wearables were frequently used, offering cost-effectiveness and remote monitoring benefits. However, their accuracy and potential discomfort for patients must be considered. Conclusion. The integration of objective functional assessments in THA presents promise as a progress-tracking modality for improving patient outcomes. Gait analysis and the TUG, along with advancing wearable sensor technology, have the potential to enhance patient care, surgical planning, and rehabilitation. Cite this article: Bone Joint J 2024;106-B(8):764–774

Smartphones are often equipped with inertial sensors capable of measuring individuals' physical activities. Their role in monitoring the patients' physical activities in telemedicine, however, needs to be explored. The main objective of this study was to explore the correlation between a participant's daily step counts and the daily step counts reported by their smartphone. This prospective observational study was conducted on patients undergoing lower limb orthopedic surgery and a group of non-patients. The data collection period was from 2 weeks before until four weeks after the surgery for the patients and two weeks for the non-patients. The participants' daily steps were recorded by physical activity trackers employed 24/7, and an application recorded the number of daily steps registered by the participants' smartphones. We compared the cross-correlation between the daily steps time-series taken from the smartphones and physical activity trackers in different groups of participants. We also employed mixed modeling to estimate the total number of steps. Overall, 1067 days of data were collected from 21 patients (11 females) and 10 non-patients (6 females). The cross-correlation coefficient between the smartphone and physical activity tracker was 0.70 [0.53–0.83]. The correlation in the non-patients was slightly higher than in the patients (0.74 [0.60–0.90] and 0.69 [0.52–0.81], respectively). Considering the ubiquity, convenience, and practicality of smartphones, the high correlation between the smartphones and the total daily step time-series highlights the potential usefulness of smartphones in detecting the change in the step counts in remote monitoring of the patient's physical activity

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

Aims

Scoliosis is a lateral curvature of the spine with associated rotation, often causing distress due to appearance. For some curves, there is good evidence to support the use of a spinal brace, worn for 20 to 24 hours a day to minimize the curve, making it as straight as possible during growth, preventing progression. Compliance can be poor due to appearance and comfort. A night-time brace, worn for eight to 12 hours, can achieve higher levels of curve correction while patients are supine, and could be preferable for patients, but evidence of efficacy is limited. This is the protocol for a randomized controlled trial of ‘full-time bracing’ versus ‘night-time bracing’ in adolescent idiopathic scoliosis (AIS).

Abstract. Rehabilitation exercise is critical for patients’ recovery after knee injury or post-surgery. Unfortunately, adherence to exercise is low due to a lack of positive feedback and poor self-motivation. Therefore, it is crucial to monitor their progress and provide supervision. Inertial measurement unit (IMUs) based sensing technology can provide remote patient monitoring functions. However, most current solutions only measure the range of knee motion in one degree of freedom. The current IMUs estimate the orientation-angle based on the integrated raw data, which might lack accuracy in measuring knee motion. This study aims to develop an IMU-based sensing system using the absolute measured orientation-angle to provide more accurate comprehensive monitoring by measuring the knee rotational angles. An IMU sensing system monitoring the knee joint angles, flexion/extension (FE), adduction/abduction (AA), and internal/external (IE) was developed. The accuracy and reliability of FE measurements were validated in human participants during squat exercise using measures including root mean square error (RMSE) and correlation coefficient. The RMSE of the three knee angles (FE, AA, and IE) were 0.82°, 0.26°, and 0.11°, which are acceptable for assessing knee motion. The FE measurement was validated in human participants and showed excellent accuracy (correlation coefficient of 0.99°). Further validation of AA and IE in human participants is underway. The sensing system showed the capability to estimate three knee rotation angles (FE, AA, and IE). It showed the potential to provide comprehensive continuous monitoring for knee rehabilitation exercises, which can also be used as a clinical assessment tool

The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction.

Cite this article: Bone Joint Res 2023;12(7):447–454.

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

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

Patient-reported outcome measures (PROMs) have failed to highlight differences in function or outcome when comparing knee replacement designs and implantation techniques. Ankle-worn inertial measurement units (IMUs) can be used to remotely measure and monitor the bi-lateral impact load of patients, augmenting traditional PROMs with objective data. The aim of this study was to compare IMU-based impact loads with PROMs in patients who had undergone conventional total knee arthroplasty (TKA), unicompartmental knee arthroplasty (UKA), and robotic-assisted TKA (RA-TKA). 77 patients undergoing primary knee arthroplasty (29 RA-TKA, 37 TKA, and 11 UKA) for osteoarthritis were prospectively enrolled. Remote patient monitoring was performed pre-operatively, then weekly from post-operative weeks two to six using ankle-worn IMUs and PROMs. IMU-based outcomes included: cumulative impact load, bone stimulus, and impact load asymmetry. PROMs scores included: Oxford Knee Score (OKS), EuroQol Five-dimension with EuroQol visual analogue scale, and the Forgotten Joint Score. On average, patients showed improved impact load asymmetry by 67% (p=0.001), bone stimulus by 41% (p<0.001), and cumulative impact load by 121% (p=0.035) between post-operative week two and six. Differences in IMU-based outcomes were observed in the initial six weeks post-operatively between surgical procedures. The mean change scores for OKS were 7.5 (RA-TKA), 11.4 (TKA), and 11.2 (UKA) over the early post-operative period (p=0.144). Improvements in OKS were consistent with IMU outcomes in the RA-TKA group, however, conventional TKA and UKA groups did not reflect the same trend in improvement as OKS, demonstrating a functional decline. Our data illustrate that PROMs do not necessarily align with patient function, with some patients reporting good PROMs, yet show a decline in cumulative impact load or load asymmetry. These data also provide evidence for a difference in the functional outcome of TKA and UKA patients that might be overlooked by using PROMs alone

Aims

To review the evidence and reach consensus on recommendations for follow-up after total hip and knee arthroplasty.

Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article: Bone Joint J 2022;104-B(12):1292–1303

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

Deep surgical site infection (SSI) is common after lower limb fracture. We compared the diagnosis of deep SSI using alternative methods of data collection and examined the agreement of clinical photography and in-person clinical assessment by the Centers for Disease Control and Prevention (CDC) criteria after lower limb fracture surgery.

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).

Aims

The purpose of this study was to determine the access to and ability to use telemedicine technology in adult patients undergoing total hip arthroplasty (THA) and total knee arthroplasty (TKA), and to determine associations with the socioeconomic characteristics of the patients, including age, sex, race, and education. We also sought to understand the patients’ perceived benefits, risks, and preferences when dealing with telemedicine.

Aims

Spinal deformity surgery carries the risk of neurological injury. Neurophysiological monitoring allows early identification of intraoperative cord injury which enables early intervention resulting in a better prognosis. Although multimodal monitoring is the ideal, resource constraints make surgeon-directed intraoperative transcranial motor evoked potential (TcMEP) monitoring a useful compromise. Our experience using surgeon-directed TcMEP is presented in terms of viability, safety, and efficacy.

Introduction. Gait analysis systems have enjoyed increasing usage and have been validated to provide highly accurate assessments for range of motion. Size, cost, need for marker placement and need for complex data processing have remained limiting factors in uptake outside of what remains predominantly large research institutions. Progress and advances in deep neural networks, trained on millions of clinically labelled datasets, have allowed the development of a computer vision system which enables assessment using a handheld smartphone with no markers and accurate range of motion for knee during flexion and extension. This allows clinicians and therapists to objectively track progress without the need for complex and expensive equipment or time-consuming analysis, which was concluded to be lacking during a recent systematic review of existing applications. Method. A smartphone based computer vision system was assessed for accuracy with a gold standard comparison using a validated ‘traditional’ infra-red motion capture system which had a defined calibrated accuracy of 0.1degrees. A total of 22 subjects were assessed simultaneously using both the computer vision smartphone application and the standard motion capture system. Assessment of the handheld system was made by comparison to the motion capture system for knee flexion and extension angles through a range of motion with a simulated fixed-flexion deformity which prevented full extension to assess the accuracy of the system, repeating movements ten times. The peak extension angles and also numerous discrete angle measurements were compared between the two systems. Repeatability was assessed by comparing several sequential cycles of flexion/extension and comparison of the maximum range of motion in normal knees and in those with a simulated fixed-flexion deformity. In addition, discrete angles were also measured on both legs of three cadavers with both skin and then bone implanted fiducial markers for ground truth reliability accounting for skin movement. Data was processed quickly through an automated secure cloud system. Results. The smartphone application was found to be accurate to 1.47±1.05 degrees through a full range of motion and 1.75±1.56 degrees when only peak extension angles were compared, demonstrating excellent reliability and repeatability. The cadaveric studies despite limitations which will be discussed still showed excellent accuracy with average errors as low as 0.29 degrees for individual angles and 4.09 degrees for an average error in several measurement. Conclusion. This novel solution offers for the first time a way to objectively measure knee range of motion using a markerless handheld device and enables tracking through a range of assessments with proven accuracy and reliability even accounting for traditional issues with the previous marker based systems. Repeatability for both computer vision and motion capture have greater extrinsic than intrinsic error, particularly with marker placement - another benefit of a markerless system. Clinical applications include pre-operative assessment and post-operative follow-up, paired with surgical planning (including with robots) and remote monitoring after knee surgery, with outcomes guiding treatment and rehabilitation and leading to reduced need for manipulation under anaesthesia and greater satisfaction

Aims. Total hip arthroplasty (THA) has well known subjective benefits, but little is known objectively about the recovery of mobility in the early post-operative period. Patients and Methods. A total of 33 patients aged > 60 years who underwent elective primary THA had their activity monitored for 30 days post-operatively using an at-home (Fitbit) ankle accelerometer. Their mean age was 70.7 years (61 to 86); 15 (45.5%) were female. The rate of compliance and the mean level of activity were determined. Comparisons between subgroups based on age, body mass index (BMI), surgical approach, and the destination of the patients when discharged were also performed. Results. The mean compliance over the 30 days was 26.7 days (16 to 30; 89%) of use. The mean number of steps increased from 235 (5 to 1152) to 2563 (87 to 7280) (p < 0.001) between the first and the 30th post-operative day. Age < 70 years and an anterior surgical approach were significantly associated with higher levels of activity (1600 to 2400 (p = 0.016 to 0.031) and 1000 to 1800 (p = 0.017 to 0.037) more steps per day, respectively) between the second and the fourth week post-operatively. There was also a trend towards higher levels of activity in those who were discharged to their home rather than to a nursing facility (a mean of 1500 more steps per day, p = 0.02). BMI greater or less than 30 kg/m. 2. was not predictive of activity (p = 0.45 to 0.98). Conclusion. At-home remote mobility monitoring using existing commercially available technology is feasible in patients who have undergone THA. It showed a clear trend towards increased activity with the passage of time. Additionally, the remote device was able to detect differences in levels of activity clearly between patients in relation to variables of interest including age, BMI, surgical approach, and the destination of the patient at the time of discharge from hospital. Such monitoring may allow for the early identification and targeted intervention in patients who recover slowly. Cite this article: Bone Joint J 2016;98-B:1450–4

Introduction. Measured outcomes from knee joint arthroplasty (TKA) have primarily focused on surgeon-directed criteria, such as alignment, range of motion measured in the clinic, and implant durability, rather than on functional outcomes. There is strong evidence that subjective reporting by patients fails to capture objective real-life function. 1,2. We believe that the recent emphasis on clinical outcomes desired by the patient, as well as the need to demonstrate value, requires a new approach to patient outcomes that directly monitors ambulatory activity after surgery. We have developed and tested a system that: 1) autonomously identifies patients who are not progressing well in their recovery from TKA surgery; 2) characterizes patient activity profiles; 3) automatically alerts health care providers of patients who should be seen for additional follow-up. We anticipate that such a system could decrease secondary procedures such as manipulation under anesthesia (MUA) and reduce hospital re-admission rates thereby resulting in significant cost savings to the patient, the care providers, and insurers. Methods. The components of the system include: 1) A sensor package that is mounted correctly in relation to the knee joint (Figure 1a) and is suitable for long term use; 2) An application that runs under the Android operating system to communicate with the sensor and to gather subjective information (pain, satisfaction, perceived stability etc. together with a photograph of the surgical site (Figure 1b); 3) Software to upload the data from the phone to a remote server; 4) An analysis and reporting package that generates, among other metrics, a profile describing the patient's activity throughout the day, trends in the recovery process, and alerts for abnormal findings (Figure 1c). The system was pilot tested on 12 patients (7 females) who underwent TKA. Complete days of data collection were scheduled for each patient every two weeks until 12 weeks, starting during the second week after surgery. Results. Patients tolerated the system well and datasets of up to 13 hours long were recorded. There was a considerable variation between patients in the use of the prosthetic knee joint at a given time point after surgery. At 6 weeks post-surgery, for example, some relatively inactive subjects had less than 50 excursions per hour while active subjects exhibited more than 750 excursions per hour. It was notable that, in activities of daily living, subjects rarely used the extremes of the flexion range that had been measured during post-operative clinic visits. Examples of activity recognition during free-living will be presented. Discussion. A remote knee monitoring system has been designed and successfully tested in an outpatient setting. The system has revealed discrepancies between knee function measured during clinic visits and that measured remotely during free living. Remote monitoring after orthopaedic procedures adds an important new dimension to the assessment of patient outcome. Acknowledgments. This work was supported by grants from the Washington Research Foundation and The Wallace H. Coulter Translational Partnership at the University of Washington