Aims

The primary aim of this study was to assess the feasibility of recruiting and retaining patients to a patient-blinded randomized controlled trial comparing corticosteroid injection (CSI) to autologous protein solution (APS) injection for the treatment of subacromial shoulder pain in a community care setting. The study focused on recruitment rates and retention of participants throughout, and collected data on the interventions’ safety and efficacy.

Aims

Paediatric fractures are highly prevalent and are most often treated with plaster. The application and removal of plaster is often an anxiety-inducing experience for children. Decreasing the anxiety level may improve the patients’ satisfaction and the quality of healthcare. Virtual reality (VR) has proven to effectively distract children and reduce their anxiety in other clinical settings, and it seems to have a similar effect during plaster treatment. This study aims to further investigate the effect of VR on the anxiety level of children with fractures who undergo plaster removal or replacement in the plaster room.

Introduction. With advances in mobile application, digital health is being increasingly used for remote and personalised care. Patient education, self-management and tele communication is a crucial factor in optimising outcomes. Aims. We explore the use of a smartphone app based orthopaedic care management system to deliver personalised surgical experience, monitor patient engagement and functional outcomes of patients undergoing knee arthroplasty. Results. Over a 12-month period, 124 patients listed for knee arthroplasty were offered access to the app. Average patient age was 65.4 years (range 49 to 86). 13(10.4%) patients were over 80 years. Compliance with app usage was 86.4%. Compliance with post-operative exercises increased following a message through the app. The mean Oxford knee score improved from a pre-op value of 17 to 35 at a mean follow-up of 6 months. Mean numeric rating scale pain score reduced from 7 pre-operatively to 3 at the latest follow-up. 58 patients (46.7%) used the communication feature on the app (text messages, photos, video consultations), reducing telephone calls and patient foot fall in the hospital. Patient satisfaction with the app was very high. Conclusion. We found the virtual care system to be effective in providing patient education, prehabilitation and post-operative rehabilitation along with being an effective channel of communication between patients and the hospital team. Patient satisfaction and compliance was very high

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

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

In absence of available quantitative measures, the assessment of fracture healing based on clinical examination and X-rays remains a subjective matter. Lacking reliable information on the state of healing, rehabilitation is hardly individualized and mostly follows non evidence-based protocols building on common guidelines and personal experience. Measurement of fracture stiffness has been demonstrated as a valid outcome measure for the maturity of the repair tissue but so far has not found its way to clinical application outside the research space. However, with the recent technological advancements and trends towards digital health care, this seems about to change with new generations of instrumented implants – often unfortunately termed “smart implants” – being developed as medical devices. The AO Fracture Monitor is a novel, active, implantable sensor system designed to provide an objective measure for the assessment of fracture healing progression (1). It consists of an implantable sensor that is attached to conventional locking plates and continuously measures implant load during physiological weight bearing. Data is recorded and processed in real-time on the implant, from where it is wirelessly transmitted to a cloud application via the patient's smartphone. Thus, the system allows for timely, remote and X-ray free provision of feedback upon the mechanical competence of the repair tissue to support therapeutic decision making and individualized aftercare. The device has been developed according to medical device standards and underwent extensive verification and validation, including an in-vivo study in an ovine tibial osteotomy model, that confirmed the device's capability to depict the course of fracture healing as well as its long-term technical performance. Currently a multi-center clinical investigation is underway to demonstrate clinical safety of the novel implant system. Rendering the progression of bone fracture healing assessable, the AO Fracture Monitor carries potential to enhance today's postoperative care of fracture patients

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.

Aims

The aim of this study was to identify the information topics that should be addressed according to the parents of children with developmental dysplasia of the hip (DDH) in the diagnostic and treatment phase during the first year of life. Second, we explored parental recommendations to further optimize the information provision in DDH care.

Aims

In the UK, the agricultural, military, and construction sectors have stringent rules about the use of hearing protection due to the risk of noise-induced hearing loss. Orthopaedic staff may also be at risk due to the use of power tools. The UK Health and Safety Executive (HSE) have clear standards as to what are deemed acceptable occupational levels of noise on A-weighted and C-weighted scales. The aims of this review were to assess the current evidence on the testing of exposure to noise in orthopaedic operating theatres to see if it exceeds these regulations.

Aims

Reimers migration percentage (MP) is a key measure to inform decision-making around the management of hip displacement in cerebral palsy (CP). The aim of this study is to assess validity and inter- and intra-rater reliability of a novel method of measuring MP using a smart phone app (HipScreen (HS) app).

Aims. Disorders of bone integrity carry a high global disease burden, frequently requiring intervention, but there is a paucity of methods capable of noninvasive real-time assessment. Here we show that miniaturized handheld near-infrared spectroscopy (NIRS) scans, operated via a smartphone, can assess structural human bone properties in under three seconds. Methods. A hand-held NIR spectrometer was used to scan bone samples from 20 patients and predict: bone volume fraction (BV/TV); and trabecular (Tb) and cortical (Ct) thickness (Th), porosity (Po), and spacing (Sp). Results. NIRS scans on both the inner (trabecular) surface or outer (cortical) surface accurately identified variations in bone collagen, water, mineral, and fat content, which then accurately predicted bone volume fraction (BV/TV, inner R. 2. = 0.91, outer R. 2. = 0.83), thickness (Tb.Th, inner R. 2. = 0.9, outer R. 2. = 0.79), and cortical thickness (Ct.Th, inner and outer both R. 2. = 0.90). NIRS scans also had 100% classification accuracy in grading the quartile of bone thickness and quality. Conclusion. We believe this is a fundamental step forward in creating an instrument capable of intraoperative real-time use. Cite this article: Bone Jt Open 2023;4(4):250–261

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

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

Aims

Periprosthetic hip-joint infection is a multifaceted and highly detrimental outcome for patients and clinicians. The incidence of prosthetic joint infection reported within two years of primary hip arthroplasty ranges from 0.8% to 2.1%. Costs of treatment are over five-times greater in people with periprosthetic hip joint infection than in those with no infection. Currently, there are no national evidence-based guidelines for treatment and management of this condition to guide clinical practice or to inform clinical study design. The aim of this study is to develop guidelines based on evidence from the six-year INFection and ORthopaedic Management (INFORM) research programme.

Smartphone-based apps that measure step-count and patient reported outcomes (PROMs) are being increasingly used to quantify recovery in total hip arthroplasty (THA). However, optimum patient-specific activity level before and during THA early-recovery is not well characterised. This study investigated 1) correlations between step-count and PROMs and 2) how patient demographics impact step-count preoperatively and during early postoperative recovery. Smartphone step-count and PROM data from 554 THA patients was retrospectively reviewed. Mean age was 64±10yr, BMI was 29±13kg/m2, 56% were female. Mean daily step count was calculated over three time-windows: 60 days prior to surgery (preop), 5–6 weeks postop (6wk), and 11–12 weeks postop (12wk). Linear correlations between step-count and HOOS12 Function and UCLA activity scores were performed. Patients were separated into three step-count levels: low (<2500steps/day), medium (2500-5500steps/day), and high (>5500steps/day). Age >65years, BMI >30, and sex were used for demographic comparisons. Student's t-tests determined significant differences in mean step-counts between demographic groups and in mean PROMs between step-count groups. UCLA correlated with step-count at all time-windows (p<0.01). HOOS12 Function correlated with step-count preoperatively and at 6wk (p<0.01). High vs low step count individuals had improved UCLA scores preoperatively (∆1.8,p<0.001), at 6wk (∆1.1,p<0.05), and 12wk (∆1.6,p<0.01), and improved HOOS12 Function scores preoperatively (∆8.4,p<0.05) and at 6wk (∆8.8,p<0.001). Younger patients had greater step-count preoperatively (4.1±3.0k vs 3.0±2.5k, p<0.01) and at 12wk (5.1±3.3k vs 3.6±2.9k, p<0.01). Males had greater step-count preoperatively (4.1±3.0k vs. 3.0±2.7k, p<0.001), at 6wk (4.5±3.2k vs 2.6±2.5k, p<0.001), and at 12wk (5.2±3.6k vs. 3.4±2.5k, p<0.001). Low BMI patients had greater step-count at 6wk (4.3±3.3k vs. 2.6±2.7k, p<0.01) and 12wk (5.0±3.6k vs. 3.6±2.6k, p<0.05). Daily step-count is significantly impacted by patient demographics and correlates with PROMs, where patients with high step count exhibit improved PROMs. Generic recovery profiles may therefore not be appropriate for benchmarking across diverse populations

Advances in algorithms developed with sensor data from smart phones demonstrates the capacity to passively collect qualitative gait metrics. The purpose of this feasibility study was to assess the recovery of these metrics following joint reconstruction. A secondary data analysis of an ethics approved global, multicenter, prospective longitudinal study evaluating gait quality data before and after primary total knee arthroplasty (TKA, n=476), partial knee arthroplasty (PKA, n=139), and total hip arthroplasty (THA, n=395). A minimum 24 week follow-up was required (mean 45±12, range 24 - 78). Gait bouts and gait quality metrics (walking speed, step length, timing asymmetry, and double support percentage) were collected from a standardized smartphone operating system. Pre- and post-operative values were compared using paired-samples t-tests (p<0.05). A total of 595 females and 415 males with a mean age of 61.9±9.3 years and mean BMI of 30.2±6.1 kg/m. 2. were reviewed. Walking speeds were lowest at post-operative week two (all, p<.001). Speeds exceeded pre-operative means consistently by week 21 (p=0.015) for PKA, and week 13 (p=0.007) for THA. The average weekly step length was lowest in post-operative week two (all, p<0.001). PKA and THA cases achieved pre-operative step lengths by week seven (p=0.064) and week 9 (p=0.081), respectively. The average weekly gait asymmetry peaked at week two post-operatively (all, p <0.001). Return to pre-operative baseline asymmetry was achieved by week 11 (p=0.371) for TKA, week six (p=0.541) for PKA, and week eight (p=.886) for THA. Double limb support percentages peaked at week two (all, p<0.001) and returned to pre-operative levels by week 24 (p=0.089) for TKA, week 12 (p=0.156) for PKA, and week 10 (p=0.143) for THA. Monitoring gait quality in real-world settings following joint reconstruction using smartphones is feasible, and may provide the advantage of removing the Hawthorne effect related to typical gait assessments and in-clinic observations

Passive smartphone-based apps are becoming more common for measuring patient progress after total knee arthroplasty (TKA). Optimum activity levels during early TKA recovery haven't been well documented. This study investigated correlations between step-count and patient reported outcome measures (PROMs) and how demographics impact step-count preoperatively and during early post-operative recovery. Smartphone capture step-count data from 357 TKA patients was retrospectively reviewed. Mean age was 68±8years. 61% were female. Mean BMI was 31±6kg/m2. Mean daily step count was calculated over three time-windows: 60 days prior to surgery (preop), 5-6 weeks postop (6wk), and 11-12 weeks postop (12wk). Linear correlations between step-count and KOOS12-function and UCLA activity scores were performed. Patients were separated into three step-count levels: low (<1500steps/day), medium (1500-4000steps/day), and high (>4000steps/day). Age >65years, BMI >30kg/m2, and sex were used for demographic comparisons. Student's t-tests determined significant differences in mean step-counts between demographic groups, and in mean PROMs between step-count groups. UCLA correlated with step-count at all time-windows (p<0.01). KOOS12-Function correlated with step-count at 6wk and 12wk (p<0.05). High step-count individuals had improved PROMs compared to low step-count individuals preoperatively (UCLA: ∆1.4 [p<0.001], KOOS12-Function: ∆7.3 [p<0.05]), at 6wk (UCLA: ∆1 [p<0.01], KOOS12-Function: ∆7 [p<0.05]), and at 12wk (UCLA: ∆0.8 [p<0.05], KOOS12-Function: ∆6.5 [p<0.05]). Younger patients had greater step-count preoperatively (3.8±3.0k vs. 2.5±2.3k, p<0.01), at 6wk (3.1±2.9k vs. 2.2±2.3k, p<0.05) and at 12wk (3.9±2.6k vs. 2.8±2.6k, p<0.01). Males had greater step-count preoperatively (3.7±2.6k vs. 2.5±2.6k, p<0.001), at 6wk (3.6±2.6k vs. 1.9±2.4k, p<0.001), and at 12wk (3.9±2.3 vs. 2.8±2.8k, p<0.01). No differences in step-count were observed between low and high BMI patients at any timepoint. High step count led to improved PROMs scores compared to low step-count. Early post-operative step-count was significantly impacted by age and sex. Generic recovery profiles may not be appropriate across a diverse population

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.