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

INTRODUCTION. The magnitude of knee flexion angle is a relevant information during clinical examination of the knee, and this item is a significant part of every knee scoring system. It is generally performed by visual analysis or with manual goniometers, but these techniques may be neither precise nor accurate. More sophisticated techniques are only possible in experimental studies. Smartphone technology might offer a new way to perform this measurement with increased accuracy. MATERIAL. 20 patients operated on for unicompartmental or total knee replacement with help of a navigation system participated to the study. There were 13 women and 7 men with a mean age of 72.1 years. METHODS. All patients were operated on for unicompartmental or total knee replacement. All patients were operated on with help of a non-image based navigation system. The navigation system is able to measure very accurately the knee flexion angle. The Smartphone application allows measuring this angle in two steps 1) recording the reference position by putting the Smartphone on the operating table, 2) recording the knee flexion angle by putting the Smartphone against the tibial crest. Two observers participated to the study. The first observer performed three independent navigated measurements followed by three independent Smartphone measurements while positioning the knee under visual control in full extension, at 0°, 30°, 60°, 90° of knee flexion and at maximal flexion; the second observer performed only one set of measurements. The intra- and inter-observer variability was assessed by calculation of the intra-class correlation coefficient. Navigated and Smartphone data were compared by a paired Wilcoxon test and calculation of the Spearman coefficient of correlation at a 5% level of significance. RESULTS. There was no significant difference between paired navigated and Smartphone measurements at any degree of knee flexion. There was a strong correlation between the two data sets. The intra- and inter-observer reproducibility was high. DISCUSSION-CONCLUSION. There was a high agreement between the navigated measurements considered as the reference and the Smartphone measurements. This new technology is easy to use and extensively available. It allows improving significantly the precision and the accuracy of the measurement of the knee flexion angle without technical difficulties. This technique might allow a self-control by the patient of the progression of the post-operative rehabilitation. SUMMARY. The Smartphone application allows improving significantly the precision and the accuracy of the measurement of the knee flexion angle without technical difficulties

INTRODUCTION. Measurement of range of motion is a critical item of any knee scoring system. Conventional measurements used in the clinical settings are not as precise as required. Smartphone technology using either inclinometer application or photographic technology may be more precise with virtually no additional cost when compared to more sophisticated techniques such as gait analysis or image analysis. No comparative analysis between these two techniques has been previously performed. The goal of the study was to compare these two technologies to the navigated measurement considered as the gold standard. MATERIAL. Ten patients were consecutively included. Inclusion criterion was implantation of a TKA with a navigation system. METHODS. Two free angle measurement applications were downloaded to the Smartphone: one using inclinometer technology, the other using camera technology. After navigation assisted TKA and just before wound closure, the operated knee was positioned at full extension, 30±2°, 60±2°, 90±2° and 120±2° according to the navigated measurement. At each step, the knee flexion angle was measured with both Smartphone applications: inclinometer application (figure 1) and camera application (figure 2). For each of the ten patients, 5 navigated, 5 inclinometer and 5 camera measurements were obtained for each patient, giving three sets of 50 repeat measurements. The sample size was calculated to get a significance level of 0.05 and a power of 0.8 to detect a 10° difference. The difference between the three sets of measurements was analyzed with an ANOVA test for repeat measurements, with post-hoc comparisons with a paired Wilcoxon test. The correlation between the three sets of measurements was analyzed with a Kendall test, with post-hoc comparisons with a Spearman test. All tests were performed at a 0.05 level of significance, and post-hoc comparisons were performed at a 0.01 level of significance. RESULTS. The mean paired difference between navigated and camera measurements was 0.7° (SD 1.5°), with one difference greater than 3°. The mean paired difference between navigated and inclinometer measurements was 7.5° (SD 5.3°), with 16 differences greater than 10°. The mean paired difference between inclinometer and camera measurements was −6.8° (SD5.2°), with 7 differences greater than 10°. The ANOVA test for repeat measurements showed a significant difference between the three sets of measurements (p<0.001). The results of post-hoc paired comparisons with the Wilcoxon test are reported in table 2. The Kendall test showed that the distribution of the three sets of measurements was no different. The post-hoc paired correlations with the Spearman test showed a good coherence between all pairs of measurements (R² between 0.02 and 0.12). No pre-operative criteria showed a significant influence on the differences observed. DISCUSSION. Measuring the knee flexion angle with the camera of a smartphone is effective in a routine clinical practice. Accuracy can be better than other conventional measurement techniques. All applications of a smartphone do not have the same precision and must be validated before clinical use. CONCLUSION. Smartphone technology enables a more accurate assessment of the knee range of motion after TKA than conventional measurement techniques. To view tables/figures, please contact authors directly

Background. Smartphones are now a ubiquitous presence within the modern healthcare setting. Uses such as internet, database software and storage of medical textbooks, all contribute to the clinical value of the devices. Within orthopaedics, transmission of digital images via smartphones is now routinely used to obtain instant second opinions of trauma radiographs. However questions remain as to whether smartphone image quality is sufficient for primary diagnosis and secondary consultation. Aim. To assess the accuracy of diagnosis made when radiographs are viewed on a smartphone screen in comparison with a standard digital monitor. Also to assess the diagnostic confidence, diagnostic difficulty, subjective image quality and formulation of management plan. Method. 30 orthopaedic registrars viewed a total of 900 radiographs–450 radiographs were viewed on a 3.5inch, 640×960 PPI smartphone screen and 450 on a standard digital monitor. Likert scales were used to assess the diagnostic accuracy, confidence and difficulty along with image quality and management plan. Initially images were viewed on the smartphone screen and after one week the same images were viewed on the digital monitor. Results were then compared. Results. There was no difference in diagnostic accuracy between smartphone and digital monitors although participants tended to be more confident in their diagnosis when made on the larger screen of the digital monitor. Diagnostic difficulty was higher in the smartphone group and image quality was lower. However there was a high correlation between management plans made after viewing images in both modalities. Discussion. This study demonstrates that although participants found the image quality poorer and found it marginally more difficult to make a diagnosis the same diagnosis was arrived at and the same management plan formulated using a smartphone compared to a standard digital monitor. Therefore smartphone based teleradiology appears to have a valid use in orthopaedic trauma

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

Acetabular cup placement in total hip replacement surgery is often difficult to assess, especially in the lateral position and using the posterior approach. On table control X-Rays are not always accessible, especially in the government sector. Conventional techniques and computer assisted surgery (CAS), are currently the two most popular methods for proper placement of the acetabular cup in Lewinnek's safe zone of orientation (anteversion 15°–10° and lateral inclination 40°±10°). We developed a simple way to get accurate cup placement using Smartphone technology. Methods:. A spirit level application was downloaded to the Smartphone. The acetabulum inclination was measured on the pre-operative X-Rays. The phone is placed in a sterile bag and then used intra-operatively, to measure and set our acetabular cup orientation to our pre-operative measurements. The inclination level was measured before and after final placement of the acetabular cup. This was compared to the acetabular cup inclination in our post-operative X-Rays. Results:. In our series of 50 cup placements we found high accuracy. The results show less than 5° deviation between our pre-, intra- and post-operative measurements. Conclusions:. Smartphone technology proves to be good alternative to conventional methods and CAS, to improve Acetabular Cup placement in Total Hip Arthroplasty

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

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

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

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

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

Aim. Early discharge of patients after joint arthroplasty leaves patients responsible for monitoring their postoperative wound by themselves. This might result in a delayed presentation of postoperative complications. The use of a mobile woundcare app by patients after arthroplasty might result in (1) earlier report of complications, (2) an increase in patient satisfaction and (3) insight in the incidence and duration of postoperative wound leakage. Therefore, the ease of use and perceived usefulness of using a postoperative mobile woundcare app in patients after joint arthroplasty was investigated. Method. A cohort study was conducted in 2017 in 2 Dutch Hospitals. Eligible cases were all consecutive patients that received an arthroplasty and who owned a smartphone. During the first 30 postoperative days, patients filled in daily reviews of their wound and took a photo of the wound. Based on the review, an underlying algorithm calculated daily a score that prompted a mobile alert if needed, which advised patients to contact the hospital. Patients filled in a form on day 30 and day 90 in order to document occurrence of any postoperative wound complication. On day 15 and 30, patients were requested to fill in a questionnaire evaluating the perceived usefulness and the ease of use of the App. Results. Of 127 eligible patients, 30 (24%) did not have a smartphone. Of the remaining 97 patients, 69 patients (71%) were included. Median age was 68 years (range 33–90 years). Forty-one patients (59.4%) used the app until day 30. On average, the app was used for 19.1 days (95% CI 16.6–21.5). Nine patients (13.0%) stopped using the app directly after the first or second day. The overall mean grade on a scale of 1 (strongly disagree) to 5 (strongly agree) was 4.2 for ease of use and 4.1 for perceived usefulness. The scores on day 30 were comparable to day 15. One patient (1.4%) developed a prosthetic joint infection. Conclusions. The introduction of a mobile woundcare app resulted in a high overall satisfaction rate with respect to ease of use and perceived usefulness. Daily use of the app did not lead to more stress. Currently, a nationwide cohort study is set up to implement the mobile woundcare app in Dutch hospitals to improve patient care. The app will then also be used to investigate the correlation between duration of postoperative wound leakage after joint arthroplasty and the development of prosthetic joint infection

Introduction. Recent advances in algorithms developed with passively collected sensor data from smart phones and watches demonstrate new, objective, metrics with the capacity to show qualitative gait characteristics. The purpose of this feasibility study was to assess the recovery of gait quality following primary total hip and knee arthroplasty collected using a smartphone-based care platform. Methods. A secondary data analysis of an IRB approved multicenter prospective trial evaluating the use of a smartphone-based care platform for primary total knee arthroplasty (TKA, n=88), unicondylar knee arthroplasty (UKA, n=28), and total hip arthroplasty (THA, n=82). Subjects were followed from 6 weeks preoperative to 24 weeks postoperative. The group was comprised of 117 females and 81 males with a mean age of 61.4 and BMI of 30.7. Signals were collected from the participants' smartphones. These signals were used to estimate gait quality according to walking speed, step length, and timing asymmetry. Post-operative measures were compared to preoperative baseline levels using a Signed-Rank test (p<0.05). Results. Mean walking speeds were lowest at postoperative week 2 for all three procedures (p<.001). The TKA population stabilized to preoperative speeds by week 17. For UKA cases, mean walking speeds rebounded to preoperative speed consistently by week 9 (p>.05). THA cases returned to preoperative walking speeds with a stable rebound starting at week 6 (p>.05), and improvement was seen at week 14 (p=.025). The average weekly step length was lowest in postoperative week 2 for both TKA and UKA (p<.001), and at week 3 for THA (p<.001). The TKA population rebounded to preoperative step lengths at week 9 (p=0.109), UKA cases at week 7 (p=.123), and THA cases by week 6 (p=.946). For TKA subjects, the change in average weekly gait asymmetry peaked at week 2 postoperatively (p <0.001), returning to baseline symmetry by week 13 (p=.161). For UKA cases, mean gait asymmetry also reached its maximum at week 2 (p =.006), returning to baseline beginning at week 7 (p=0.057). For THA cases mean asymmetry reached its maximum in week 2 (p <0.001) and was returned to baseline values at week 6 (p=.150). Discussion and Conclusion. Monitoring gait quality in real-world patient care following hip and knee arthroplasty using smart phone technology demonstrated recovery curves similar to previously reported curves captured by traditional gait analysis methods and patient reported outcome scores. Capturing such real-world gait quality metrics passively through the phone may also provide the advantage of removing the Hawthorne effect related to typical gait assessments and in-clinic observations, leading to a more accurate picture of patient function

Introduction. The Center for Medicare Services (CMS) recently proposed its phase 3 “Quality metrics” which include a section on patient engagement. CMS uses a fitness monitor as an example of an acceptable way for patients to contribute to the health record. Wearable technology allows measurement of activity, blood glucose, heart rate, sleep, and other health metrics, all of which can be useful in the management of patients in the orthopaedic practice. The purpose of this study is to thoroughly review existing fitness devices; and evaluate their potential uses in orthopaedic practice. Methods. Several fitness devices exist; we focused on the top 27 based on popularity mentioned in reputable tech review articles. Features of each device were reviewed including type, specifications, interfaces, measurable outcomes (HR, steps, distance, sleep, weight, calorie intake), cost to the patient, barriers to compliance and strengths. Ultimately all these factors were taken into consideration to look into potential uses for orthopaedic surgery. The orthopedic applications of these devices were reviewed. Nonsurgical management applications were: compliance with physiotherapy, distance walked and stairs completed, and compliance with activity restrictions. Preoperative optimization included detection of sleep apnea, blood glucose monitoring, preoperative weight, and preoperative activity level. Postoperative outcomes included postoperative activity level, stairs, and distance walked. Results. Twenty-seven devices were reviewed of which 26% were targeted for the beginner, 33% for runners and 41% were multipurpose fitness trackers. Most were designed as either a wrist band (26%) or watch (30%). Several used a smartphone as an interface (33%) while the majority (52%) synced automatically via Bluetooth to either the online, mobile device, smartphone or pc application. The majority (37%) had excellent battery life, over 7 days; all were either waterproof (26%) or water resistant (74%), and some (41%) had GPS tracking. A pedometer was included in 85% of devices, 63% monitored HR of which 26% required a separate chest strap or forearm strap, 7% measured respiratory rate and 7% devices measured temperature. Sleep was recorded in 63% of devices, mostly as length of sleep and quality of sleep based on wrist movement. One device was able to differentiate between sleep phases allowing the application of sleep apnea assessment for preoperative testing. Twenty devices monitored weight, twenty two monitored calorie intake, three could monitor glucose readings, seventeen measured distance walked, whereas five measured both stairs and distance walked. A few devices (15%) are already linked to electronic medical records (EMR), the majority allowed for sharing (67%) and 19% are linked to insurance companies which provide incentivized reimbursement rates. Conclusion. The fitness device technology has yet to be explored or implemented widely in orthopaedic surgery. We demonstrated how fitness devices can assist the orthopaedic surgeon in measurement of basic outcomes and can also assist with preoperative, perioperative and postoperative care. Further research is warranted as the use of these devices increases. Patient privacy issues may come into play as more practices employ these devices for their patients

There is increased awareness of the health benefits of regular exercise, and quantifying daily activity has become popular. Consequently, there are an increasing number of devices for measuring physical activity. Healthcare professionals and the general public should know the accuracy and limitations of these devices to better determine which ones suit their needs. Ten devices were tested: one ankle-based device, StepWatch™ Activity Monitor (SAM); two wrist-based devices, FitBit Force™ and Nike+ Fuelband SE; seven waist-based devices, Omron HJ-321 Pedometer, Sportline 340 Strider Pedometer, FitBit One™, Samsung Galaxy S4 utilizing the two most popular applications (Runtastic and Noom Walk), and the iPhone 5 utilizing the two most popular applications (Runtastic and ARGUS). Thirty healthy volunteers, mean age 25.6 years (range 20–30) and mean body mass index 23.5 (range 17.3–29.0), completed the following protocol: (1) walk briskly around a 400-M track simulating community ambulation (2) jog around a 400-M track (3) walk slowly for 10-M, approximating household or workplace pace (4) ascend 10 steps, and (5) descend 10 steps. Each subject completed 3 trials for each task. Manual count was the gold standard (Champion Sports Tally Counter). Accuracy and mean percent error were calculated to demonstrate overall performance and any tendencies for over or undercounting. An Aggregate Accuracy Score was calculated using the mean accuracy of each activity and multiplying by a corresponding weighted value for a prototypical person: 400-M walk represents community ambulation, weighted 40%; 10-M walk represents household and workplace ambulation, weighted 30%; 400-M jog represents jogging or running, weighted 20%; Stair Ascent and Descent represent community and household stair use, weighted 5% each. Device rank based on the Aggregate Accuracy Score was #1 FitBit One™ (98.0%), #2 Omron HJ-321 (97.0%), #3 StepWatch™ Activity Monitor (93.3%), #4 Runtastic Google App (92.7%), #5 Runtastic iPhone App (89.5%), #6 Fitbit Force™ (88.2%), #7 Argus iPhone App (87.2%), #8 Sportline 340 Strider (85.7%), #9 Nike Fuelband (76.1%), #10 Noom Walk Google App (75.9%). The FitBit One™ was 99.5%, 97.8%, 96.7%, 94.3%, and 96.9% accurate in the 400-M walk, 10-M walk, 400-M jog, 10 stair ascent, and 10 stair descent, respectively. The Omron HJ-321 was 99.3%, 94.9%, 97.9%, 92.2%, and 91.3% accurate, respectively. The SAM performed well (>95% accurate) in all activities except one, consistently undercounting the 400-M jog by about 25% (95% CI: −27.2% – −23.9%). The FitBit ForceTM and Nike+ Fuelband SE wrist devices were ≥90% accurate in the 400-M walk and 400-M jog, but ≤83% accurate for all other activities. Three of the 4 smartphone applications were >97% accurate in the 400-M walk, 1 of 4 was 97.3% accurate in the 400-M jog, but all devices performed poorly (≤90% accurate) for all other activities. Smartphones are very popular, but current technology is less accurate for measuring overall daily activity. The relatively inexpensive FitBit One™ and Omron HJ-321 pedometer are highly accurate for quantifying a variety of activities, including running. The StepWatch™ Activity Monitor performs well in lower cadence, but consistently undercounted jogging. Wrist-based activity devices are not as accurate as waist-based. Next generation technologies, including smartphones, should undergo accuracy testing before recommending them for daily use

Aims

The aim of this study was to develop and evaluate machine-learning-based computerized adaptive tests (CATs) for the Oxford Hip Score (OHS), Oxford Knee Score (OKS), Oxford Shoulder Score (OSS), and the Oxford Elbow Score (OES) and its subscales.

INTRODUCTION. Total Knee Arthroplasty (TKA) survival is directly dependent on precise component placement. As showed by Mason meta-analysis in 2007, only 68.2% of TKAs achieved axis less than 3° with conventional methods versus 91% with Computer Assisted Surgery (CAS). However, if CAS seems to have more accuracy its use is in less than 10% procedures in United States because of its cost, operative time and need of extra pin sites. Smart technology, providing no requirement or arrays for registration, no need of pre operative images and lest cost effective seems to be an encouraging way. OJBECTIVES. We report our experience of a new system that is an accelerometer-based portable navigation with a disposable display console and reference sensor; gyrometer is like smartphone ones. This system permits to realize femoral distal cut, and tibial proximal cut, adjusting varus-valgus, flexion-extension and tibial slope regardless implants used. Goal of the study was to determine accuracy and reliability of the system. METHODS. We've utilized it on 40 patients (28 female, 12 male; 70.4 years old) operated for arthritis and have reviewed all of them at 6 months with goniometry and angle measurements. Predominant pre operative deformity was varus (29 cases) rather than valgus (11 cases). RESULTS. Use of this handled system doesn't lengthen surgery more than 10 minutes. Accuracy for tibial cut was confirmed with a mechanic angle of 90.1° +/− 1.8; all except 2 were less than 2°. Tibial slope had a mean of 3.2° +/− 1.8 but only 60% were less or equal to 3°. All femurs were orthogonal or in valgus with an average angle of 93° +/− 2; mean flexion was 5° +/− 4, independently of pre operative deformity. CONCLUSION. The smart technology studied seems to be convenient and a good device to ensure tibial cut (better in coronal plane) but is less effective for femur with a valgus and flexion tendency. Discussion about software, calibration, landmarks or positioning must be lead

Aims

This study assesses patient barriers to successful telemedicine care in orthopaedic practices in a large academic practice in the COVID-19 era.

Aims

The ongoing COVID-19 pandemic has disrupted and delayed medical and surgical examinations where attendance is required in person. Our article aims to outline the validity of online assessment, the range of benefits to both candidate and assessor, and the challenges to its implementation. In addition, we propose pragmatic suggestions for its introduction into medical assessment.

Virtual encounters have experienced an exponential rise amid the current COVID-19 crisis. This abrupt change, seen in response to unprecedented medical and environmental challenges, has been forced upon the orthopaedic community. However, such changes to adopting virtual care and technology were already in the evolution forecast, albeit in an unpredictable timetable impeded by regulatory and financial barriers. This adoption is not meant to replace, but rather augment established, traditional models of care while ensuring patient/provider safety, especially during the pandemic. While our department, like those of other institutions, has performed virtual care for several years, it represented a small fraction of daily care. The pandemic required an accelerated and comprehensive approach to the new reality. Contemporary literature has already shown equivalent safety and patient satisfaction, as well as superior efficiency and reduced expenses with musculoskeletal virtual care (MSKVC) versus traditional models. Nevertheless, current literature detailing operational models of MSKVC is scarce. The current review describes our pre-pandemic MSKVC model and the shift to a MSKVC pandemic workflow that enumerates the conceptual workflow organization (patient triage, from timely care provision based on symptom acuity/severity to a continuum that includes future follow-up). Furthermore, specific setup requirements (both resource/personnel requirements such as hardware, software, and network connectivity requirements, and patient/provider characteristics respectively), and professional expectations are outlined. MSKVC has already become a pivotal element of musculoskeletal care, due to COVID-19, and these changes are confidently here to stay. Readiness to adapt and evolve will be required of individual musculoskeletal clinical teams as well as organizations, as established paradigms evolve.

Cite this article: Bone Joint Open 2020;1-6:272–280.