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

The first three months following Total Knee Arthroplasty (TKA) provide an early window into a patient's functional outcomes, with the change of function in this time yielding valuable insight. 20 patients due to undergo primary TKA were recruited to the study. Data were recorded at three time points; pre-assessment clinic (PAC) before the operation, 6-weeks-post-operation (6WKs), at 12-weeks-post-operation (12WKs). Functional activity levels were monitored during early post-operative recovery for changes in early functional outcome, and allowed a comparison of metrics at each time point. This included direct functional testing of power output, timed functional performance in clinic, patient reported outcome measures, and multiday activity monitoring devices. Maximal power output symmetry (Power) was similar at 6WKs vs PAC (p = 0.37). At 12WKs, it had increased (p < 0.05). Timed functional performance (Performance) remained similar across all three time points (p = 0.27). Patient reported activities of daily living (ADL) performance significantly increased at 6WKs vs PAC (p < 0.05). At 12WKs, it remained similar (p = 0.10). Patient daily step count significantly decreased at 6WKs vs PAC (p < 0.05). By 12WKs, this had increased to similar levels to PAC (p = 0.30). Within the functional outcome measures, strong post-operative correlations were observed between Power and Performance (r = 0.62), Power and ADL (r = 0.49), and Performance and ADL (r = 0.61). Despite reduced measured step count and similar functional performance, patients report improved ADL at 6WKs. When symmetrical power output and measured step count have improved at 12WKs, patients report similar ADL to that at 6WKs. Multiple measures are required to get a full picture, however this highlights the different aspects measured by different tools

There is controversy regarding the effect of different approaches on recovery after THR. Collecting detailed relevant data with satisfactory compliance is difficult. Our retrospective observational multi-center study aimed to find out if the data collected via a remote coaching app can be used to monitor the speed of recovery after THR using the anterolateral (ALA), posterior (PA) and the direct anterior approach (DAA). 771 patients undergoing THR from 13 centers using the moveUP platform were identified. 239 had ALA, 345 DAA and 42 PA. There was no significant difference between the groups in the sex of patients or in preoperative HOOS Scores. There was however a significantly lower age in the DAA (64,1y) compared to ALA (66,9y), and a significantly lower Oxford Hip Score in the DAA (23,9) compared to PA(27,7). Step count measured by an activity tracker, pain killer and NSAID use was monitored via the app. We recorded when patients started driving following surgery, stopped using crutches, and their HOOS and Oxford hip scores at 6 weeks. Overall compliance with data request was 80%. Patients achieved their preoperative activity level after 25.8, 17,7 and 23.3 days, started driving a car after 33.6, 30.3 and 31.7 days, stopped painkillers after 27.5, 20.2 and 22.5 days, NSAID after 30.3, 25.7, and 24.7 days for ALA, DAA and PA respectively. Painkillers were stopped and preoperative activity levels were achieved significantly earlier favoring DAA over ALA. Similarly, crutches were abandoned significantly earlier (39.9, 29.7 and 24.4 days for ALA, DAA and PA respectively) favoring DAA and PA over ALA. HOOS scores and Oxford Hip scores improved significantly in all 3 groups at 6 weeks, without any statistically significant difference between groups in either Oxford Hip or HOOS subscores. No final conclusion can be drawn as to the superiority of either approach in this study but the remote coaching platform allowed the collection of detailed data which can be used to advise patients individually, manage expectations, improve outcomes and identify areas for further research

Introduction. The ability to walk over various surfaces such as cobblestones, slopes or stairs is a very patient centric and clinically meaningful mobility outcome. Current wearable sensors only measure step counts or walking speed regardless of such context relevant for assessing gait function. This study aims to improve deep learning (DL) models to classify surfaces of walking by altering and comparing model features and sensor configurations. Method. Using a public dataset, signals from 6 IMUs (Movella DOT) worn on various body locations (trunk, wrist, right/left thigh, right/left shank) of 30 subjects walking on 9 surfaces were analyzed (flat ground, ramps (up/down), stairs (up/down), cobblestones (irregular), grass (soft), banked (left/right)). Two variations of a CNN Bi-directional LSTM model, with different Batch Normalization layer placement (beginning vs end) as well as data reduction to individual sensors (versus combined) were explored and model performance compared in-between and with previous models using F1 scores. Result. The Bi-LSTM architecture improved performance over previous models, especially for subject-wise data splitting and when combining the 6 sensor locations (e.g. F1=0.94 versus 0.77). Placement of the Batch Normalization layer at the beginning, prior to the convolutional layer, enhanced model understanding of participant gait variations across surfaces. Single sensor performance was best on the right shank (F1=0.88). Conclusion. Walking surface detection using wearable IMUs and DL models shows promise for clinically relevant real-world applications, achieving high F1 levels (>0.9) even for subject-wise data splitting enhancing the model applicability in real-world scenarios. Normalization techniques, such as Batch Normalization, seem crucial for optimizing model performance across diverse participant data. Also single-sensor set-ups can give acceptable performance, in particular for specific surface types of potentially high clinical relevance (e.g. stairs, ramps), offering practical and cost-effective solutions with high usability. Future research will focus on collecting ground-truth labeled data to investigate system performance in real-world settings

Background. To complement subjective patient-reported outcome measures, objective assessments are needed. Activity is an objective clinical outcome which can be measured with wearable activity monitors (AM). AM's have been validated and used in joint arthroplasty patients to count postures, walking or transfers. However, for demanding patients such as after sports injury, running is an important activity to quantify. A new AM algorithm to distinguish walking from running is trialed in this validation study. Methods. Test subjects (n=9) performed walking and running bouts of 30s duration on a treadmill at fixed speeds (walking: 3, 4, 5, 7km/h, running: 5, 7, 9, 12, 15km/h) and individually preferred speeds (slow, normal, fast, maximum, walk/run transition). Flat and inclined surfaces (8%, 16%), different footwear (soft, hard, barefoot) and running styles (hind/fore-foot) were tested. An AM (3D accelerometer) was worn on the lateral thigh. Previously validated algorithms to classify all gait as walking were adapted to differentiate running from walking, the main criterium being vertical acceleration peaks exceeding 2g within each subsequent 2s-interval. Independently annotated video observation served as reference. Results. A total of 312 events had to be classified. Walking bouts (162) were correctly identified in 158 cases resulting in 97.5% detection accuracy. Running bouts (150) were correctly identified in 146 cases (97.3%). In 8 walking bouts (5.0%), an additional running event was falsely detected. These happened at 7km/h and maximum (>8.6km/h) walking speed and during continuous walk/run transitions at individual transition speeds. In 12 running bouts (8.2%), an additional walking event was falsely detected. These happened during slow running (<7km/h). Timing event duration and step counts were >95% accurate. Conclusions. Thigh-worn AM and a simple algorithm can distinguish walking from running at high accuracy and thus can serve doctors, therapists or coaches to objectify outcomes, decisions about effective and safe exercise intensities or return-to-play

Introduction. Following primary total knee arthroplasty (TKA), patients experience pain relief and report improved physical function and activity. However, there is paucity of evidence that patients are truly more active in daily life after TKA. The aims of this study were: 1) to prospectively measure physical activity with a wearable motion sensor before and after TKA; 2) to compare patient-reported levels of physical activity with objectively assessed levels of physical activity before and after TKA; 3) to investigate whether differences in physical activity after TKA are related to levels of physical function. Methods. 22 patients (age=66.6 ±9.3yrs; m/f= 12/11; BMI= 30.6 ±6.1) undergoing primary TKA (Vanguard, ZimmerBiomet), were measured preoperatively and 1–3 years postoperatively. Patient-reported outcome measures (PROMs) included KOOS-PS and SQUASH for assessment of perceived physical function and activity resp. Physical activity was assessed during 4 consecutive days in patients” home environments while wearing an accelerometer-based activity monitor (AM) at the thigh. All data were analysed using semi-automated algorithms in Matlab. AM-derived parameters included walking time (s), sitting time (s) standing time (s), sit-to-stand transfers, step count, walking bouts and walking cadence (steps/min). Objective physical function was assessed by motion analysis of gait, sit-to-stand (STS) transfers and block step-up (BS) transfers using a single inertial measurement unit (IMU) worn at the pelvis. IMU-based motion analysis was only performed postoperatively. Statistical comparisons were performed with SPSS and a per-protocol analysis was applied to present the results at follow-up. Results. Data were available for 17 of 22 patients at follow-up. PROMs demonstrated significant improvement of perceived physical function (KOOS-PS=68±21 vs. 34±26; p<0.001) and physical activity (SQUASH=2584 ±1945 vs. 3038 ±2228; p<0.001) following TKA. AM-based parameters of physical activity demonstrated no significant differences between pre- and postoperative quantitative outcomes. Only the qualitative outcome of walking cadence significantly changed after TKA (81.41 ±10.86 (steps/min) vs. 94.24 ±7.20 resp.; p<0.001). There were moderate correlations between self-reported and objectively assessed levels of physical activity after TKA (Pearson”s r=0.36–0.43; p<0.05). Outcomes of physical activity after TKA were moderately correlated to IMU-based functional outcome measures (Pearson”s r = 0.31 – 0.48; p<0.05). Conclusion. 1–3 years after TKA, patients demonstrate improved function. However, the self-perceived higher activity level (+18%) after TKA is not supported by any objective data obtained by wearable motion sensors such as steps, transfers or time-on-feet. This may have implications for general health and requires further investigation into patient communication, expectation management or motivational intervention

Introduction. Limited physical activity (PA) is one indication for orthopaedic intervention and restoration of PA a treatment goal. However, the objective assessment of PA is not routinely performed and in particular the effect of spinal pathology on PA is hardly known. It is the purpose of this study using wearable accelerometers to measure if, by how much and in what manner spinal stenosis affects PA compared to age-matched healthy controls. Patients & Methods. Nine patients (m/f= 5/4, avg. age: 67.4 ±7.7 years, avg. BMI: 29.2 ±3.5) diagnosed with spinal stenosis but without decompressive surgery or other musculoskeletal complaints were measured. These patients were compared to 28 age-matched healthy controls (m/f= 17/11, avg. age: 67.4 ±7.6 years, avg. BMI: 25.3±2.9). PA was measured using a wearable accelerometer (GCDC X8M-3) worn during waking hours on the lateral side of the right leg for 4 consecutive days. Data was analyzed using previously validated activity classification algorithms in MATLAB to identify the type, duration and event counts of postures or PA like standing, sitting, walking or cycling. In addition, VAS pain and OSWESTRY scores were taken. Groups were compared using the t-test or Mann-Whitney U-test where applicable. Correlations between PA and clinical scores were tested using Pearson”s r. Results. Spinal stenosis patients showed much lower PA than healthy controls regarding all parameters like e.g. daily step count (2946 vs 8039, −63%, p<0.01) or the relative daily time-on-feet (%) (8.6% vs 28.3%, −70%, p<0.01) which is matched with increased sitting durations (80.3% vs 58.8%, p<0.01). Also qualitative parameters such as walking cadence was reduced in stenosis patients (83.7 vs 97.8 steps/min). With stenosis no patient ever walked >1000 steps without interruption. Also the number of walking bouts between 250–1000 steps was 4.5 times lower than in healthy controls (p<0.01). When the relative distribution of walking bout length was calculated, it became visible that stenosis patients showed more short walking bouts of 10–50 steps (p<0.05). There were no strong and significant correlations between the clinical scores and PA parameters. Discussion & Conclusions. Spinal stenosis greatly reduced physical activity to levels below WHO guidelines (e.g. <5000 steps= sedentary lifestyle) where the risk for general health (overall mortality), cardiovascular or endocrinological health is significantly increased. Activity levels are lower than reported for end-stage hip or knee osteoarthritis. Therefore, spinal stenosis patients should not only receive pain medication, but be made aware of their limited PA and its detrimental health effects, participate in activation programs, or be considered for surgical intervention. The absence of long walking bouts and the relatively more frequent short walking bouts seem indicative of intermittent claudication as typical in spinal stenosis

Besides eliminating pain, restoring activity is a major goal in orthopaedic interventions including joint replacement or trauma surgery following falls in frail elderly, both treatments of highest socio-economic impact. In joint replacement and even more so in frail elderly at risk of falling, turns are assessed in clinical tests such as the TUG (Timed Get-up-and-Go), Tinetti, or SPPB so that classifying turning movements in the free field with wearable activity monitors promises clinically valuable objective diagnostic or outcome parameters. It is the aim of this study to validate a computationally simple turn detection algorithm for a leg-worn activity monitor comprising 3D gyroscopes. A previously developed and validated activity classification algorithm for thigh-worn accelerometers was extended by adding a turn detection algorithm to its decision tree structure and using the 3D gyroscope of a new 9-axis IMU (56×40×15mm, 25g, f=50Hz,). Based on published principles (El-Gohary et al. Sensors 2014), the turn detection algorithm filters the x-axis (thigh) for noise and walking (Butterworth low-pass, 2. nd. order with a cut-off at 4Hz and 4. th. order with a cut-off at 0.3Hz) before using a rotational speed threshold of 15deg/s to identify a turn and taking the bi-lateral zero-crossings as start and stop markers to integrate the turning angle. For validation, a test subject wore an activity monitor on both thighs and performed a total of 57 turns of various types (walking, on-the-spot, fast/slow), ranges (45 to 360deg) and directions (left/right) in free order while being video-taped. An independent observer annotated the video so that the algorithmic counts could be compared to n=114 turns. Video-observation was compared to the algorithmic classification in a confusion matrix and the detection accuracy (true positives) was calculated. In addition, 4-day continuous activity measures from 4 test subjects (2 healthy, 2 frail elderly) were compared. Overall, only 5/114 turns were undetected producing a 96% detection accuracy. No false positives were classified. However, when detection accuracy was calculated for turning angle intervals (45°: 30–67.5°; 90°: 67.5–135°; 180°: 135–270°; 360°: 270–450°), accuracy for all interval classifications combined dropped to 83.3% with equal values for left and right turns. For the 180° and 360°, accuracy was 100% while for the shorter 45° and 90° turns accuracy was 75% and 71% only, mainly because subsequent turns were not separated. Healthy subjects performed between 470 (office worker) and 823 (house wife) turns/day while frail elderly scored 128 (high fall risk) to 487 turns/day (low fall risk). Turns/day and steps/day were not correlated. In healthy subjects ca. 50% of turns were in the 45° category compared to only ca. 35% in frail elderly. Turn detection for a thigh-worn IMU activity monitor using a computationally simple algorithm is feasible with high general detection accuracy. The classification and separation of subsequent short turns can be further improved. In multi-day measurement, turns/day and the distribution of short and long turns seem to be a largely independent activity parameter compared to step counts and may improve objective assessment of fall risk or arthroplasty outcome

Falls and fall-related injuries can have devastating health consequences and form a growing economic burden for the healthcare system. To identify individuals at risk for preventive measures and therapies, fall risk assessment scores have been developed. However, they are costly in terms of time and effort and rely on the subjective interpretation of a skilled professional making them less suitable for frequent assessment or in a screening situation. Small wearable sensors as activity monitor can objectively provide movement information during daily-life tasks. It is the aim of this study is to evaluate whether the activity parameters from wearable monitors correlate with fall risk scores and may predict conventional assessment scores. Physical activity data were collected from nineteen home-dwelling frail elderly (n=19, female=10; age=81±5.6 years, GFI=5.4±1.9, MMSE=27.4±1.5) during waking hours of 4 consecutive days, wearing a wearable 9-axis activity monitor (56×40×15mm, 25g) on the lateral side of the right thigh. The signal was analysed using self-developed, previously validated algorithms (Matlab) producing the following parameters: time spent walking, step count, sit-stand-transfer counts, mean cadence (steps/min), count of stair uses and intensity counts >1.5G. Conventional fall risk assessment was performed using the Tinetti sore (range: 0–28=best), a widely used tool directly determining the likelihood of falls and the Short Physical Performance Battery (SPPB, range: 0–12=best) which measures lower extremity performance as a validated proxy of fall risk. The anxiety to fall during activities of daily living was assessed using the self-reported Short Falls Efficacy Scale-International (FES-I, range: 7–28=worst). Correlations between activity parameters and conventional scores were tested using Pearson's r. The activity parameters (daily means) for the 19 participants were 70.8min (SD=28.7; min-max= 22.8–126.6) of walking, 4427 steps (SD=2344; min-max= 1391–8269) with a cadence 79.3 steps per minute (SD=17.1; min-max=52.8–103.9) and 33.3 sit-stand transfers (SD=9.7; min-max=8.8–48.0). The average Tinetti score was 21.2 (SD=5.1; min-max=10.0–27.0), with SPPB scoring 7.8 (SD=2.4; min-max=3.0–12.0), and FES-I 4.6 (SD=5.1; min-max=7.0–23.0). Strong (r≥0.6) and significant correlations existed between the walking cadence and the Tinetti (r=.60, p=<.01) and SPPB (r=.71, p=<.01) scores. No other correlations were found between the activity parameters and the Tinetti, SPPB and none with the psychological FES-I questionnaire. Conventional fall risk scores and activity data are comparable to literature values and thus representative of home-dwelling frail elderly including a wide range covered for both dimensions. No quantitative activity measure had a predictive value for fall risk assessment. Strongly correlated with Tinetti and SPPB, objectively measured cadence as a qualitative parameter seems a useful parameter for remotely identifying fall risk in frail elderly. The perceived anxiety to falls was not correlated to quantitative and qualitative activity parameters suggesting that this psychological aspect hardly affects activity. Wearable activity monitors seem a valid tool to assess fall risk remotely and thus allow low cost, frequent and large group screening of frail elderly towards a health economically viable tool for a growing societal need. The predictive quality of activity monitored data may be increased by deriving additional qualitative measures from the activity data

Hip simulators have been used for ten years to determine the tribological performance of large-head metal-on-metal devices using traditional test conditions. However, the hip simulator protocols were originally developed to test metal-on-polyethylene devices. We have used patient activity data to develop a more physiologically relevant test protocol for metal-on-metal devices. This includes stop/start motion, a more appropriate walking frequency, and alternating kinetic and kinematic profiles.

There has been considerable discussion about the effect of heat treatments on the wear of metal-on-metal cobalt chromium molybdenum (CoCrMo) devices. Clinical studies have shown a higher rate of wear, levels of metal ions and rates of failure for the heat-treated metal compared to the as-cast metal CoCrMo devices. However, hip simulator studies in vitro under traditional testing conditions have thus far not been able to demonstrate a difference between the wear performance of these implants.

Using a physiologically relevant test protocol, we have shown that heat treatment of metal-on-metal CoCrMo devices adversely affects their wear performance and generates significantly higher wear rates and levels of metal ions than in as-cast metal implants.