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

Introduction. The purpose of this study was to characterize the recovery of physical activity following knee arthroplasty by means of step counts and flight counts (flights of stairs) measured using a smartphone-based care platform. Methods. This is a secondary data analysis on the treatment cohort of a multicenter prospective trial evaluating the use of a smartphone-based care platform for primary total and unicondylar joint arthroplasty. Participants in the treatment arm that underwent primary total or unicondylar knee arthroplasty and had at least 3 months of follow-up were included (n=367). Participants were provided the app with an associated smart watch for measuring several different health measures including daily step and flight counts. These measures were monitored preoperatively, and the following postoperative intervals were selected for review: 2–4 days, 1 month, 1.5 month, 3 months and 6 months. The data are presented as mean, standard deviation, median, and interquartile range (IQR). Signed rank tests were used to assess the difference in average of daily step counts over time. As not all patients reported having multiple stairs at home, a separate analysis was also performed on average flights of stairs (n=214). A sub-study was performed to evaluate patients who returned to preoperative levels at 1.5 months (step count) and 3 months (flight count) using an independent samples T test or Fisher's Exact test was to compare demographics between patients that returned to preoperative levels and those that did not. Results. The mean age of the step count population was 63.1 ± 8.3 years and 64.31% were female, 35.69% were male. The mean body mass index was 31.1 ± 5.9 kg/m. 2. For those who reported multiple stairs at home, the mean age was 62.6 ± 8.3 years and 62.3% were female. The mean body mass index was 30.7 ± 5.4 kg/m. 2. . As expected, the immediate post-op (2–4 days) step count (median 1257.5 steps, IQR 523 – 2267) was significantly lower than preop (median 4160 steps, IQR 2669 – 6034, p < 0.001). Approximately 50% of patients returned to preoperative step counts by 1.5 months postoperatively with a median 4,504 steps (IQR, 2711, 6121, p=0.8230, Figure 1). Improvements in step count continued throughout the remainder of follow-up with the 6-month follow-up visit (median 5517 steps, IQR 3888 – 7279) showing the greatest magnitude (p<0.001). In patients who reported stairs in their homes, approximately 64% of subjects returned to pre-op flight counts by 3 months (p=0.085), followed similar trends with significant improvements at 6 months (p=0.003). Finally, there was no difference in age, sex, BMI, or operative knee between those that returned to mean preoperative step or flight counts by 1.5 months and 3 months, respectively. Discussion and Conclusion. These data demonstrated a recovery curve similar to previously reported curves for patient reported outcome measures in the arthroplasty arena. Patients and surgeons may use this information to help set goals for recovery following total and unicondylar knee arthroplasty using objective activity measures. For any figures or tables, please contact the authors directly

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

Introduction. A smartphone-based care platform allows a customizable educational and exercise interface with patients, allowing many to recover after surgery without the need for formal physical therapy (PT). Furthermore, advances in wearable technology to monitor physical activity (PA) provides patients and physicians quantifiable metrics of the patient's recovery. The purpose of this study is to determine the feasibility of a smartphone-based exercise educational platform after primary knee arthroplasty as well as identifying factors that may predict the need for formal physical therapy. Methods. This study is part of a multi-institution, prospective study of patients after primary total knee arthroplasty (TKA) and partial knee arthroplasty (PKA) enrolled in a smartphone with smartwatch-based episode of care platform that recorded multimodal PA (steps, kcal, stairs). Postoperatively, all patients initially followed the smartphone-based exercise program. At the surgeon's discretion, patients were prescribed therapy if needed. The outcome of this study was the need for PT outside the app-based exercise program as well as time to return to preoperative step count. Variables assessed were preoperative weekly step counts (steps/day), weekly postoperative activity level (weekly step count compared to preoperative level), compliance with the exercise program (>75% completion) and patient demographic data including gender, age, BMI and narcotic use. One hundred eighty-eight patients were included in analysis: 45 PKA (24%) and 143 TKA (76%). Step count data was available on 135 patients and physical therapy data on 174. Results. Overall educational compliance was 91% and exercise compliance 34%. By 4-weeks postop, 45.6% of patients reached or exceeded their preoperative step count, including 60% of PKA and 41% of TKA (p=0.05). There was no significant difference in reaching step count based on gender (p=0.7), BMI <40kg/m2 (p=0.9) or age <65-years old (p=0.67). Sixty-three percent of patients that were compliant with the exercise program reached the step count compared to 40% of patients that weren't complaint (p=0.01). One hundred thirty-three patients (76.4%) completed the app-based exercise program without the need for PT, which included 81.4% of PKA patients and 75% of TKA patients (p=0.38). Weekly compliance with the exercise program (>75%) was significantly associated with not needing PT (p<0.001). Other factors that were significantly associated with the need for PT were a high physical activity level in postoperative week 1 (p<0.001) and a low physical activity level in postoperative week 2 (p=0.002). Conclusion. A high percentage of patients after primary knee arthroplasty were able to successfully complete the smartphone-based exercise program without the need for PT. Compliance with the exercise program was an important predictor of success. Postoperative activity level may also indicate the need for therapy as patients who were very active in the first postoperative week and then saw a decline in activity in the second week were more likely to be prescribed PT. With this platform, surgeons can monitor a patient's exercise compliance and postoperative activity level allowing many to recover at home, while being able to identify those within the first few weeks who may need structured physical therapy. For figures, tables, or references, please contact authors directly

Introduction. Ambulation in the postoperative period following TKR is a marker of speed of recovery and, potentally, longer term outcomes. However, patient lifestyle factors are a major confounder. This study sought to develop a model of expected patient step count taking into account preoperative condition and demographics in order to benchmark recovery at a patient specific level. Method. 94 patients were recruited to the study. BMI, demographics, the Short Form 12 (SF-12) and the Knee injury and Osteoarthritis Outcome Score (KOOS) were all captured preoperatively. Step count was measured using commercially available Fitbit devices preoperatively, immediately postoperatively and at 6 weeks postoperatively. Stepwise multiple linear regression models were developed using the preoperative information to define a predictive model of the postoperative step count levels. Spearman's Rho correlations for all relevant data series were also calculated. Results. Of the personal and clinical characteristics, BMI and the SF-12 physical component score had the strongest correlations with outcome. Prior step count periods all had significant correlations with later step count periods. The most significant correlations occurred between the 6 week postoperative step count period and the preoperative period (0.709), while correlations with the period immediately following surgery were weaker (0.389 and 0.536 for preoperative and 6 week postoperative step counts respectively.) All are significantly different from 0 (to p < 0.01.) Likewise, BMI had a significantly negative relationship with step count (−0.526, −0.346 and −0.553 for the preoperative, immediate postoperative and 6 week postoperative periods, see Figure 2), as did the KOOS activities of daily living score and the SF-12 physical health component score. Males were significantly less mobile than females during recovery. A multiple linear regression model of 6 week step count using prior data had an adjusted R. 2. of 0.754, explaining much of the variation, but the immediate postoperative period performed poorly. Predictors in the 6 week model were gender, preoperative SF-12 score, preoperative and immediate postoperative step count. Conclusions. Patient specific factors, including but not limited to that from prior step count periods need to be considered if using step count as a means of benchmarking patient recovery after surgery. The variation in recovery at 6 weeks is more readily explained with the data collected than in the immediate postoperative period, where variations in specific care received, anaesthetic response or surgical outcomes might be more expected to have an impact. Reporting patient performance against customised goals on an individual patient basis could provide a means to drive greater patient mobility and appropriate activity levels during postoperative recovery

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

Introduction. The purpose of this study was to demonstrate the feasibility of passively collecting objective data from a commercially available smartphone-based care management platform (sbCMP) and robotic assisted total knee arthroplasty (raTKA). Methods. Secondary data analysis was performed using de-identified data from a commercial database that collected metrics from a sbCMP combined with intraoperative data collection from raTKA. Patients were included in this analysis if they underwent unilateral raTKA between July 2020 and February 2021, and were prescribed the sbCMP (n=131). The population consisted of 76 females and 55 males, with a mean age of 64 years (range, 43 – 81). Pre-operative through six-week post-operative data included step counts from the sbCMP, as well as administration of the KOOS JR. Intraoperative data included surgical times, the hip-knee-ankle angle (HKA), and medial and lateral laxity assessments from the robotic assessment. Data are presented using descriptive statistics. Comparisons were performed using a paired samples t-test, or Wilcoxon Signed-rank test, with significance assessed at p<0.05. A minimal detectable change (MDC) in the KOOS JR score was considered ½ standard deviation of the preoperative values. Results. KOOS JR scores improved from a preoperative mean of 51.5 ± 11.5 to a 6-week postoperative mean of 64 ± 10.04 (p<0.001). An MDC of 5.75 units was achieved. Step counts decreased initially and returned to preoperative values by week 6 (Figure 1, p=0.196). When evaluating time requirements from landmarking to completed surgical cuts, the median surgical time was 40.2 minutes (IQR, 29.4 – 52.0). The median absolute deformity for HKA preoperatively was 6.9 degrees (IQR, 4.1 – 10.1) and the final intraoperative median HKA was 0.9 degrees (IQR, 0.1 – 3, p<0.001). There was a difference in medial and lateral joint laxity in flexion and extension at the initial intraoperative evaluation (p<0.01). At the final evaluation there was no difference in medial and lateral joint laxity in extension (p=0.239); however, a slight difference in flexion was noted (p=0.001). Given the median values of 1.2mm (0.8 – 2.4) medially vs. 1.4mm (0.9 – 3) laterally, this difference is not likely clinically relevant. Patients who had <1 mm of medial laxity in flexion had significantly fewer step counts at week 6 post-operatively (p=0.035). There was no difference in KOOS JR scores associated with tightness (p>0.05). Discussion. The use of passively collected objective measures in a commercial database across the episode of care was feasible and demonstrated associations between intraoperative and post-operative metrics. To our knowledge, this is the first integrated data collection and reporting platform to report on these measures in a commercial population. Future research is needed in order to understand the benefit of displaying these metrics, as well as the role of variations in alignment and gap balance on function. Conclusions. Contemporary data platforms may be used to improve the understanding of individual recovery paths through real-time passive data collection throughout the episode of care. For any figures or tables, please contact the authors directly

Increasing pressure to use rapid recovery care pathways when treating patients undergoing total hip arthroplasty (THA) is evident in current health care systems for numerous reasons. Patient autonomy and health care economics has challenged the ability of THA implants to maintain functional integrity before achieving bony union. Although collared stems have been shown to provide improved axial stability, it is unclear if this stability correlates with activity levels or results in improved early function to patients compared to collarless stems. This study aims to examine the role of implant design on patient activity and implant fixation. The early follow-up period was examined as the majority of variation between implants is expected during this time-frame. Patients (n=100) with unilateral hip OA who were undergoing primary THA surgery were recruited pre-operatively to participate in this prospective randomized controlled trial. All patients were randomized to receive either a collared (n=50) or collarless (n=50) cementless femoral stem. Patients will be seen at nine appointments (pre-operative, < 2 4 hours post-operation, two-, four-, six-weeks, three-, six-months, one-, and two-years). Patients completed an instrumented timed up-and-go (TUG) test using wearable sensors at each visit, excluding the day of their surgery. Participants logged their steps using Fitbit activity trackers and a seven-day average prior to each visit was recorded. Patients also underwent supine radiostereometric analysis (RSA) imaging < 2 4 hours post-operation prior to leaving the hospital, and at all follow-up appointments. Nineteen collared stem patients and 20 collarless stem patients have been assessed. There were no demographic differences between groups. From < 2 4 hours to two weeks the collared implant subsided 0.90 ± 1.20 mm and the collarless implant subsided 3.32 ± 3.10 mm (p=0.014). From two weeks to three months the collared implant subsided 0.65 ± 1.54 mm and the collarless implant subsided 0.45 ± 0.52 mm (p=0.673). Subsidence following two weeks was lower than prior to two weeks in the collarless group (p=0.02) but not different in the collared group. Step count was reduced at two weeks compared to pre-operatively by 4078 ± 2959 steps for collared patients and 4282 ± 3187 steps for collarless patients (p=0.872). Step count increased from two weeks to three months by 6652 ± 4822 steps for collared patients and 4557 ± 2636 steps for collarless patients (p=0.289). TUG test time was increased at two weeks compared to pre-operatively by 4.71 ± 5.13 s for collared patients and 6.54 ± 10.18 s for collarless patients (p=0.551). TUG test time decreased from two weeks to three months by 7.21 ± 5.56 s for collared patients and 8.38 ± 7.20 s for collarless patients (p=0.685). There was no correlation between subsidence and step count or TUG test time. Collared implants subsided less in the first two weeks compared to collarless implants but subsequent subsidence after two weeks was not significantly different. The presence of a collar on the stem did not affect patient activity and function and these factors were not correlated to subsidence, suggesting that initial fixation is instead primarily related to implant design

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. There is increasing impetus to use rapid recovery care pathways when treating patients undergoing total hip arthroplasty (THA). The direct anterior (DA) approach is a muscle sparing technique that is believed to support these new pathways. Implants designed for these approaches are available in both collared and collarless variations and understanding the impact each has is important for providing the best treatment to patients. Purpose/Aim of Study. This study aims to examine the role of implant design on implant fixation and patient recovery. Materials and Methods. Patients (n=50) with unilateral hip OA who were undergoing primary DA THA surgery were recruited pre-operatively to participate in this prospective randomized controlled trial. All patients were randomized to receive either a collared (n=25) or collarless (n=25) cementless, fully hydroxyapatite coated femoral stem. Patients were seen at nine appointments (pre-operative, <24 hours post-operation, two-, four-, six-weeks, three-, six-months, one-, and two-years). Patients underwent supine radiostereometric analysis (RSA) imaging <24 hours post-operation prior to leaving the hospital, and at all follow-up appointments. Patients also completed an instrumented timed up-and-go (TUG) test using wearable sensors at each visit, excluding the day of their surgery. Participants logged their steps using Fitbit activity trackers and a seven-day average prior to each visit was recorded. Findings/Results. Twenty-two patients that received a collared stem and 27 patients that received a collarless stem have been assessed. There were no demographic differences between groups. From <24 hours to two weeks the collared implants subsided 0.90 ± 1.20 mm and the collarless implants subsided 3.80 ± 3.37 mm (p=0.001). From two weeks to three months the collared implants subsided 0.67 ± 1.61 mm and the collarless implants subsided 0.45 ± 0.46 mm (p=0.377). Step count was reduced at two weeks to 3108 ± 1388 steps for collared patients and 2340 ± 1685 steps for collarless patients (p=0.072). Step count was increased at three months to 8939 ± 3494 steps for collared patients and 6114 ± 2529 steps for collarless patients (p=0.034). TUG test time was increased at two weeks compared to pre-operatively by 3.45 ± 6.01 s for collared patients and 2.29 ± 4.92 s for collarless patients (p=0.754). TUG test time decreased from two weeks to three months by 6.30 ± 6.05 s for collared patients and 5.68 ± 4.68 s for collarless patients (p=0.922). Conclusions. Collared implants subsided less in the first two weeks compared to collarless implants but subsequent subsidence after two weeks was not significantly different. Presence of a collar on the stem impacted patient activity but not function. This suggests that both the implant design as well as the surgical technique may play a role in the patient's early post-operative experience

Purpose: The purpose of this study was to investigate the relationship between self reported disability, physical performance testing (PPT) and everyday physical activity in people with Chronic Low Back Pain (CLBP). Background: Disability is currently assessed using self-report and PPT. Little is known about the relationship between these two constructs and everyday physical activity. Increased knowledge of the relationship may enhance understanding of disability, and lead to the development of more robust methods of disability measurement. Methods: A group of 30 (20f10m) people with non-specific CLBP completed the Roland Morris Disability questionnaire (RMDQ) [self-report], and performed two PPTs (5min walk test, 50ft walk test). Each participant then wore a physical activity monitor for a one week period and mean daily step count was calculated. Correlations were performed between self-report, performance testing and activity monitoring. Results: Relatively weak but statistically significant relationships were found between the three measurement techniques. The strongest relationship existed between the RMDQ and step count (r= −0.494, p=0.006). Step count was also related to performance on the 50ft walk test (r=−.393, (p=0.032). While the relationship between the overall RMDQ score and physical performance did not reach significance, a significant relationship did exist between the 50ft walk test and the third question in the RMDQ (r=0.369, p=0.045), which specifically questions perceived walking behaviour. Conclusion: Everyday physical activity is related to self-reported disability and physical performance capacity. As such, activity monitoring may be a useful objective adjunct to current techniques used to assess disability in people with CLBP

Background and Purpose: Current clinical guidelines recommend supervised exercise as a first-line treatment in the management of low back pain (LBP). To date studies have not used objective forms of measuring changes in free-living physical activity (FLPA). The aim of this study was to compare FLPA between two groups who received either supervised exercise and auricular acupuncture (EAA) or exercise alone (E). Methods: 51 patients with non-specific LBP [mean±SD=42.8±12.4 years] wore an accelerometer for 7 days at baseline, end of the intervention (week 8) and follow up (week 25). FLPA variables were extracted: % time (hours) spent in postures; daily step count and cadence. Data were analysed using SPSS (v15). Repeated measures ANCOVA were performed using a mixed linear model. Results: There was no difference in daily step count between the two groups at any time point (E, mean±SD, week 1, 8197±2187; week 8, 8563±2438, week 25, 8149±2800; EAA, mean±SD, week 1, 8103±1942; week 8, 8010±2845, week 25, 8139±1480, p=0.9) or cadence. No differences in postures were noted, apart from time sitting/lying which was shorter at week 25 in the E group (p=0.006). Conclusions & Implications: Supervised exercise classes, with or without acupuncture, do not produce changes in FLPA in the short term or longer term in people with LBP. This suggests more effective ways should be sought to encourage the patient to incorporate activity into their daily lives. These findings have informed the design of two walking intervention trials for LBP patients. Conflict of Interest: None. Sources of Funding: Research and Development Office, Northern Ireland, Strategic Priority Fund, Department of Employment and Learning, Northern Ireland

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

Introduction. The goal of total hip arthroplasty (THA) is to reduce pain, restore function but also activity levels for general health benefits or social participation. Thus evaluating THA patient activity can be important for diagnosis, indication, outcome assessment or biofeedback. Methods. Physical activity (PA) of n=100 primary THA patients (age at surgery 63 ±8yrs; 49M/51F; 170 ±8cm, 79.8 ±14.0kg) was measured at 8 ±3yrs follow-up. A small 3D accelerometer was worn for 4 successive days during waking hours at the non-affected lateral upper leg. Data was analyzed using validated algorithms (Matlab) producing quantitative (e.g. #steps, #transfers, #walking bouts) and qualitative (e.g. cadence, temporal distribution of events) activity parameters. An age matched healthy control group (n=40, 69 ±8yrs, 22M/18F) served as reference. Results. Daily steps were only 13% lower (n.s) for patients (avg. ±SD: 5989 ±3127) than controls (6890 ±2803). However, the Nr. of walking bouts (187 ±85 vs 223 ±78, −16%) and sit-stand transfers (35 ±14 vs 48 ±15, −27%) were sign. less in patients (p<0.05, Mann-Whitney). Patients showed equal amounts of walking bouts in medium duration (30–60s, 1–5min) but sign. less (−25%) short (<10s, 10–30s) and less (−43%) long events (>5min). This corresponds with sign. less (−32%) short sitting periods (>10min) in patients. Also cadence was sign. lower in patients (93.8 ±11.7 vs 98.9 ±7.3 steps/min). Conclusions. PA varies widely in patients with a substantial proportion (35%) being more active than average controls. Thus, THA must not per se reduce or limit PA. Only 17% of controls and 11% of patients reached the WHO target (10,000 steps/day) suggesting that the THA related drop in activity may inflate the risks for cardiovascular, metabolic or mental disease associated with low activity. Patients avoided short and long activities, both associated with effort (transfers, fatigue) and walked more slowly. Targeted interventions may address this behaviour. Objective clinical outcome assessment must focus on these parameters and not, as commercial fitness trackers may imply, total step counts alone

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