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. 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

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

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