Foot pain and related problems are quite common in the community. It is reported that 24% of individuals older than 45 experienced foot pain. Also, it is stated that at least two thirds of individuals experiences moderate physical disability due to foot problems. In the absence of evaluation of risk factors such as limited ankle dorsiflexion in the early period of the diseases (Plantar fasciitis, Achilles Tendinopathy e.g.) and the lack of mobile systems with portable remote access, foot pain becomes refractory/chronic foot pain, secondary pathologies and ends with workload of 1., 2. and 3rd level healthcare services. In the literature, manuel and dijital methods have been used to analyze the ankle range of motion (ROM). These studies are generally based on placing protractors on the image and / or angle detection from inclination measurement by using the gyroscope sensor of the mobile device. Some of these applications are effective and they are designed to be suitable for measuring in a clinical setting by a physician or physiotherapist. To the best of our knowledge, there is no system developed to measure real-time ankle ROM remotely with collaboration of the patients. In this research, we proposed to develop an ankle ROM analyze system with smart phone application that can be used comfortably by subjects. We present a case of a 22-year-old male with a symptomatic pes planus. The mobile application, which was used for data collection, was designed and implemented for Android devices. Initially, before the mobile application home page is opened, a consent page was submitted to the acceptance of individual within the scope of Law (KVKK) data privacy. Then, the participant was asked to state his sociodemographic characteristics [age, gender, height, weight] and dominant side. No history of foot-ankle injury, trauma, and surgery was recorded. Activity pain of the foot was 6 according to visual anolog scale (VAS) in the mobile application. His ankle dorsiflexion was 15 ° by manuel goniometer. Besides, server was responsible for storing the collected data and ROM measurement. ROM was calculated by processing the foot video which was sent through the mobile application. During the processing phase, a segmentation model was used which was trained with image process and deep learning methods. With the developed system, we obtained the manual goniometric measurement result with 2 degrees deviation. As the application is calibrated, it is expected to approach the actual measurement of ROM. We can conclude that mobile app-goniometer result in dorsiflexion measurement is a novel promising evaluation method for ankle ROM. it will be easy and practical to detect and monitor risk factor of the diseases, decrease medical costs, provide health services in rural areas, and contribution to life quality and to reduce the workload on physicians and physiotherapist

Introduction. Provision of prehabilitation prior to total knee arthroplasty (TKA) through a digital mobile application is a novel concept. The primary aim of our research is to determine whether provision of prehabilitation through a mobile digital application impacts length of stay (LOS), requirement for inpatient rehabilitation and hospital-associated costs after TKA. Our study hypothesis is that a mobile digital application provides a low resource, cost effective method of delivering prehabilitation prior to TKA. Methods. An observational, retrospective analysis was performed on a consecutive case series of 64 patients who underwent TKA by a single surgeon over a 21-month period. Pre operative Knee Osteoarthritis Outcome Score (KOOS) Patient Reported Outcome Measures (PROMs) were collected on all patients. The first group of patients (control) did not undergo prehabilitation, the subsequent group of patients (experimental) were offered prehabilitation through a mobile application called PhysiTrack. The experimental group were provided with progressive quadriceps and hamstring strengthening exercises, and calf and hamstring stretches. Exercises were automatically progressed after 2 weeks unless the patient requested otherwise or a physiotherapist clinically intervened. The non-compliance rate was 33% (n=11), after removing these patients from the analysis, 22 patients remained and these were age matched to 22 patients from the control group. Aside from the access to prehabilitation, all patients underwent TKA using identical surgical technique and peri-operative care regime. Length of stay data for inpatient care and rehabilitation were captured for all patients. Cost was calculated using the inpatient and rehabilitation costs provided by the hospital. Results. 44 patients were included in our final analysis. Pre operative KOOS were collected for all of the experimental group and 18 (81%) of control group. These subscores were not statistically different (p>0.05) reflecting pre operative equivalence. The average inpatient length of stay was statistically different, being 5.04 days for the control group and 4.31 days for the experimental group (p=0.01). The decision for ongoing inpatient rehabilitation (after the immediate post-operative inpatient period) was not statistically different between the groups (chi-quared p=0.07). Rehabilitation length of stay was 9.12 days in the experimental and 10.85 days in the control group (p = 0.25). The remaining outcomes were statistically significant with total length of stay 11.95 days in the control and 7.63 days in the experimental group (p=0.01) and the total cost of the hospital stay $6362.55AUD for the control and $4145.17AUD for the experimental group (p=0.01). This represents an average saving $2217.38 per patient who participated in prehabiliation prior to surgery. Conclusions. Our research shows a significant cost saving with this intervention, as measured by reduction in total length of stay in patients undergoing prehabilitation using PhysiTrack. To our knowledge, this is the first study that analyses the impact of a mobile application providing prehabilitation prior to TKA. Further work is required to determine the effect in a larger, randomised cohort of patients. Future studies should also be directed towards assessing the utility of digital prehabilitation on a per patient basis prior to total knee arthroplasty

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

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

Provision of prehabilitation prior to total knee arthroplasty (TKA) through a digital mobile application is a novel concept. Our research evaluates a resource effective and cost effective method of delivering prehabilitation. The primary aim of our research is to determine whether provision of prehabilitation through a mobile digital application impacts inpatient LOS after TKA. The secondary objective is to understand the effect of digital prehabilitation on hospital costs. An observational, retrospective analysis was performed on a consecutive case series of 64 patients who underwent TKA by a single surgeon over a 21 month period. Exercise provision varied from 3 months to 2 weeks prior to TKA. The outcomes of rehabilitation length of stay, total length of stay and total hospital costs were statistically significantly at p=0.5. The rehabilitation length of stay was 3.79 days in the experimental and 7.33 days in the control group (p = 0.045), the total length of stay was 12.00 days in the control and 8.04 days in the experimental group (p=0.03) and the total cost of the hospital stay was $6357.35AUD for the control and $4343.22AUD for the experimental group (p=0.029). Our research shows a cost saving with this intervention, as measured by a reduction in rehabilitation length of stay. To our knowledge, this is the first piece of research that analyses the impact of the use of a digital mobile application providing prehabilitation prior to TKA

Introduction. Total Knee Arthroplasty (TKA) has been demonstrated to drastically improve a patient's quality of life. The outcomes following TKA are often reported by subjective patient reported outcome measurements (PROMs). However, there are few objective outcome measures following TKA, limiting the amount of information physicians can use to effectively guide a patient's recovery, especially in the first 3 weeks. Newly developed knee sensors have been able to ameliorate this problem by providing the physician with previously unobtainable objective data. Our study aims to evaluate the use of a wearable knee sensor device to measure functional outcomes (range of motion and steps) in real time. Methods. 29 patients who underwent primary, unilateral TKA were recruited for this IRB approved study. Patients were instructed how to use the device and associated mobile phone application preoperatively (Figure 1) and provided knee sensors to wear postoperatively (Figure 2). Patients wore the device for 3 weeks postoperatively to allow for data collection. The device recorded range of motion, number of steps, and percentage of physical therapy exercises completed. Patients were grouped by gender, age (<69 or >=70 years old), and BMI (<30 and >=30 kg/m2) for analysis of functional outcome measurements (maximum flexion, minimum extension, and number of steps). Unpaired two-sample t-tests were used to analyze differences between the groups. Results. Patients were able to tolerate wearing the device without complication and the device collected functional outcome data appropriately as designed. After brief instruction, both patients and physicians were able to monitor patient data via the mobile phone application in real time. The mean maximum flexion and minimum extension did not significantly change from postoperative week 1 to postoperative week 2 and week 3. However, the mean number of steps taken increased from 4,923 steps in postoperative week 1 to 8,163 steps week 2 (p=0.01) and 11,615 steps week 3 (p<0.001) postoperatively. There were no statistically significant differences in maximum flexion, minimum extension, and number of steps between the different gender, age, and BMI groups. Discussion and Conclusion. The knee sensor device used in our study proved to be useful in providing objective functional outcomes following TKA. The device was well tolerated by patients and the mobile phone applications were easy to use for the physicians and the patients. Real time tracking of patients' own range of motion, number of steps, and percentage of exercises completed may motivate them to further their own recovery process. There were wide ranges in the number of steps taken during each postoperative time interval. These results may help identify individuals who are recovering at a faster rate or those who may need more focused physical therapy. Subsequent larger studies can utilize the device to elucidate previously unknown recovery trends among different groups of patients following TKA. In the future, the device's ability to collect real time functional outcome data will allow physicians and other healthcare providers to create individualized physical therapy plans, thereby optimizing the patient's recovery process. For figures, tables, or references, please contact authors directly

Problem. The identification of unknown orthopaedic implants is a crucial step in the pre-operative planning for revision joint arthroplasty. Compatibility of implant components and instrumentation for implant removal is specific based on the manufacturer and model of the implant. The inability to identify an implant correctly can lead to increased case complexity, procedure time, procedure cost and bone loss for the patient. The number of revision joint arthroplasty cases worldwide and the number implants available on the market are growing rapidly, leading to greater difficulty in identifying unknown implants. Solution. The solution is a machine-learning based mobile platform which allows for instant identification of the manufacturer and model of any implant based only on the x-ray image. As more surgeons and implant representatives use the platform, the model should continue to improve in accuracy and number of implants recognized until the algorithm reaches its theoretical maximum of 99% accuracy. Market. Multiple organizations have created small libraries of implant images to assist surgeons with manual identification of unknown implants based on the x-ray, however no automated implant identification system exists to date. One of the most financially successful implant identification tools on the market is a textbook of hip implants which sells for a per unit cost of $200. Several free web-based resources also act as libraries for the manual identification of a limited number of arthroplasty implants. A number of academic and private organizations are working on the development of an automated system for implant identification, however none are available to the public. Product. Implant Identifier is mobile application which uses machine-learning to instantly detect the model and manufacturer of any common arthroplasty implant, based only on x-ray. The beta version offers a large library of implants for manual identification and is currently available for free download on iOS and Android. Its purpose is to further develop the model to its maximal theoretical accuracy, prior to official release. The beta version of the application currently has over 15,000 registered users worldwide and has the largest publicly available arthroplasty library available on the market. Over 200,000 implant images have been submitted by users to date. Timescales. The product was initially released in the form of a closed beta which became available to invited guests around 18 months ago. The current version is an open beta which can be downloaded and used by any individual. It was released roughly 12 months ago. The final rendition of the application will allow for free manual identification using the implant library, as well as subscription-based automated implant identification. The implementation, testing and release of this final subscription product is projected to be completed by Q3 2022. Funding. A small number of early investors have funded the initial research and development of the beta product; however, another round of investment will be beneficial in the final evolution of the product. This additional investment round will allow for completion of development of the identification algorithm, product dissemination, customer support, and lasting sustainability of the venture

In light of recent regulatory initiatives, medical devices now require additional clinical evidence to prove their safety and efficacy. At the same time, patients' own assessment of their devices' function and performance has gained in importance. The collection of these data allows for a more comprehensive picture of clinical outcomes and complications following total knee arthroplasty (TKA). These trends have led researchers to search for new methods of acquiring, interpreting and disseminating patient-reported outcome measurements (PROMs). The current study assesses the feasibility of a digital platform for collecting PROMs that was recently adapted for TKA patients. It sought to determine patient engagement, survey completion rates, and satisfaction with this platform. Eighty-two patients (mean age, 63.7 years, 59% females) scheduled for TKA were enrolled from one US and six UK sites between January 12, 2018 and April 30, 2018. Patients were supplied with a mobile application (app) that collects a variety of PROMs, including four domains based on the Patient-Reported Outcome Information System (PROMIS™): physical function, depression, pain interference and pain behavior. The platform electronically administers questionnaires using computer-adaptive tests (CATs), which reduce the burden on patients by tailoring follow-up questions to account for their previous answers. Satisfaction with the app was assessed in subset of patients who evaluated its ease-of-use (n=45), likelihood that they would recommend it to family/friends (n=35), and whether they successfully used the information it provided during their recovery (n=31). These scores were taken on a 1 to 10 (worst to best) scale. Patients demonstrated regular engagement with the platform, with 73% using the app at least once a week. Weekly engagement remained high throughout the seven-week post-operative period (Figure 1). There was a 69% completion rate of all PROMIS™ CAT surveys during the study. The four PROMIS™ CAT domains had similar survey completion rates (Figure 2). The subset of patients queried regarding their satisfaction with the app gave it favorable mean scores for ease-of-use (8.8), likelihood to recommend to a family member or friend (8.1), and their success at using its information to improve their recovery (7.4). Initial results support this digital platform's potential for successfully and efficiently collecting large volumes of PROMs. Patients reported high levels of engagement and satisfaction. For any figures or tables, please contact authors directly

The Step Holter is a software and mobile application that can be used to easily study gait analysis. The application can be downloaded for free on the App Store and Google Play Store for iOS and Android devices. The software can detect with an easy calibration the three planes to detect the movement of the gait. Before proceeding with the calibration, the smartphone can be placed and fixed with a band or stowed into a long sock with its top edge at the height of the joint line, in the medial side of the tibia. The calibration consists in bending the knee about 20 to 30 degrees and then making a rotation movement, leaving the heel fixed to the ground as a rotation fulcrum. After calibration, the program records data related to lateral flexion, rotation, and bending of the leg. This data can be viewed directly from the smartphone screen or transmitted via a web link to the Step Holter web page . www.stepholter.com. by scanning a personal QR code. The web page allows the users to monitor the test during its execution or view data for tests done previously. By pressing the play button, it is possible to see a simulation of the patient's leg and its movement. With the analyze button, the program is capable of calculating the swing and stance phase of every single step, providing a plot with time and percentages. Finally, with the Get Excel button, test data can be conveniently exported for more in-depth research. The advantage of this application is not only to reduce the costs of a machine for the study of gait analysis but also being able to perform tests quickly, without expensive hardware or software and be used in specific spaces, without specialized personnel. Furthermore, the application can collect important data concerning rotation that cannot be highlighted with the classic gait analysis. The versatility of a smartphone allows tests to be carried out not only during walking but also by climbing or descending stairs or sitting down or getting up from a chair. This software offers the possibility to easily study any kind of patients; Older patients, reluctant to leave their homes for a gait analysis can be tested at home or during an office control visit. Step Holter could be one small step for patients, one giant leap for gait study simplicity. For any figures or tables, please contact authors directly

Venous thromboembolism (VTE) prophylaxis following total joint arthroplasty (TJA) should be individualised in order to maximise the efficacy of prophylactic measures while avoiding the adverse events associated with the use of anticoagulants. At our institution, we have developed a scoring model using the Nationwide Inpatient Sample (NIS) database, which is validated against our institutional data, to stratify patients into low- and high-risk groups for VTE. Low-risk patients are placed on aspirin 81 mg twice daily for four weeks post-operatively, and high-risk patients are placed on either a Vitamin K antagonist (warfarin), low molecular weight heparin, or other oral anticoagulants for four weeks post-operatively. All patients receive sequential pneumatic compression devices post-operatively, and patients are mobilised with physical therapy on the day of surgery. Patients who have a history of peptic ulcer disease or allergy to aspirin are also considered for other types of anticoagulation following surgery. Risk Stratification Criteria. Major comorbid risk factors utilised in our risk stratification model include history of hypercoagulability or previous VTE, active cancer or history of non-cutaneous malignancy, history of stroke, and pulmonary hypertension. We consider patients with any of these risk factors at elevated risk of VTE and therefore candidates for formal anticoagulation. Other minor risk factors include older age, bilateral surgery compared with unilateral, inflammatory bowel disease, varicose veins, obstructive sleep apnea, and history of myocardial infarction, myeloproliferative disorders, and congestive heart failure. Each minor criterion is associated with a score. The cumulative score is compared with a defined threshold and the score that surpasses the threshold indicates that the patient should receive post-operative anticoagulation. To facilitate the use of this scoring system, an iOS mobile application (VTEstimator) has been developed and can be downloaded from the app store

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.

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.

Aims

Optimal exposure through the direct anterior approach (DAA) for total hip arthroplasty (THA) conducted on a regular operating theatre table is achieved with a standardized capsular releasing sequence in which the anterior capsule can be preserved or resected. We hypothesized that clinical outcomes and implant positioning would not be different in case a capsular sparing (CS) technique would be compared to capsular resection (CR).