Search

Background: The collection of patient-reported outcome measures (PROMs) has historically been reported as costly and time-consuming, with low compliance rates that may impact reimbursement. Little research has reported the effects of mobile applications to support PROMs collection following arthroplasty., Methods: Secondary analysis of data from a multicenter randomized controlled trial was performed. Patients undergoing knee and hip arthroplasty were randomized to utilize a smartphone-based care management platform (app) for self-directed rehabilitation and completed joint-specific PROMs (Hip Dysfunction and Osteoarthritis Outcome Score, Joint Replacement or Knee Injury and Osteoarthritis Score, Joint Replacement) via the application at prescribed intervals or on paper during clinic visits. Control patients received practice standard of care, and completed PROMs via emailed hyperlink or during clinic visits following lower limb arthroplasty. Overall, 455 patients underwent knee arthroplasty procedures (245 control, 210 app group) and 380 underwent total hip arthroplasty (206 control, 174 app group). Compliance with expected PROMs completion was calculated through one year postoperatively., Results: Compliance was higher in the app group preoperatively in both knee (98.1 versus 86.9%, P < .0001) and hip cohorts (96.0 versus 88.4%, P = .008), and postoperatively, including at one year (knees, 72.2 versus 53.7%, P < .0001; hips, 71.1 versus 49.2%, P < .0001). On log-binomial regressions, intervention arm was the strongest predictor of completion of all PROMs, where app users undergoing knee (Relative Risk 2.039, 95% confidence interval (CI) 1.595 to 2.607, P < .000) and hip arthroplasty (2.268 95% CI 1.742 to 2.953, P < .0001) were more likely to be compliant at all timepoints. The majority of patients in the app group, including those over 65 years of age, completed PROMs using the application as opposed to paper methods., Conclusions: A smartphone mobile application that engages patients during recovery after knee and hip joint arthroplasty improved compliance with completion of preoperative and postoperative PROMs compared to other electronic and paper methods., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background: Range of motion (ROM) following total knee replacement (TKR) has been associated with patient satisfaction and knee function, and is also an early indicator of a successful procedure. Robotic-assisted TKR (raTKR) is considered to reproduce more precise resections, and, as a result, may be associated with improved early patient satisfaction compared to manual TKR (mTKR). The purpose of this study was to evaluate the early postoperative active ROM (aROM) between raTKR and mTKR., Methods: A total of 216 mTKR patients were propensity-matched, in terms of age, gender, comorbidities, and BMI, to 216 raTKR cases. Intraoperative and immediate postoperative adverse events were collected. Knee flexion and extension aROM were measured preoperatively and at one- and three months after operation., Results: Changes in flexion aROM were significantly greater in raTKR vs. mTKR at one- (6.9°, 95% CI: 3.5, 10.4°) and three months (4.9°, 95% CI: 2.1, 7.7°). Flexion aROM was greater at three postoperative months compared to preoperative aROM only in the raTKR group, and raTKR patients had higher odds of achieving ≥ 90° of flexion at one month after operation (OR: 2.15, 95% CI: 1.16, 3.99). There were no significant differences between groups in intraoperative (P > 0.999) or postoperative adverse events., Conclusions: Compared with mTKR, raTKR resulted  in less loss of aROM immediately after operation and a faster recovery of aROM within three months after operation., Trial Registration: Clinicaltrials.gov (NCT# 03737149)., (© 2023. The Author(s).)

Gait quality parameters have been used to measure recovery from total hip arthroplasty (THA) but are time-intensive and previously could only be performed in a lab. Smartphone sensor data and algorithmic advances presently allow for the passive collection of qualitative gait metrics. The purpose of this prospective study was to observe the recovery of physical function following THA by assessing passively collected pre- and post-operative gait quality metrics. This was a multicenter, prospective cohort study. From six weeks pre-operative through to a minimum 24 weeks post-operative, 612 patients used a digital care management application that collected gait metrics. Average weekly walking speed, step length, timing asymmetry, and double limb support percentage pre- and post-operative values were compared with a paired-sample t -test. Recovery was defined as the post-operative week when the respective gait metric was no longer statistically inferior to the pre-operative value. To control for multiple comparison error, significance was set at p < 0.002. Walking speeds and step length were lowest, and timing asymmetry and double support percentage were greatest at week two post-post-operative ( p < 0.001). Walking speed (1.00 ± 0.14 m/s, p = 0.04), step length (0.58 ± 0.06 m/s, p = 0.02), asymmetry (14.5 ± 19.4%, p = 0.046), and double support percentage (31.6 ± 1.5%, p = 0.0089) recovered at 9, 8, 7, and 10 weeks post-operative, respectively. Walking speed, step length, asymmetry, and double support all recovered beyond pre-operative values at 13, 17, 10, and 18 weeks, respectively ( p < 0.002). Functional recovery following THA can be measured via passively collected gait quality metrics using a digital care management platform. The data suggest that metrics of gait quality are most negatively affected two weeks post-operative; recovery to pre-operative levels occurs at approximately 10 weeks following primary THA, and follows a slower trajectory compared to previously reported step count recovery trajectories.

Advances in algorithms developed from sensor-based technology data allow for the passive collection of qualitative gait metrics beyond step counts. The purpose of this study was to evaluate pre- and post-operative gait quality data to assess recovery following primary total knee arthroplasty. This was a multicenter, prospective cohort study. From 6 weeks pre-operative through to 24 weeks post-operative, 686 patients used a digital care management application to collect gait metrics. Average weekly walking speed, step length, timing asymmetry, and double limb support percentage pre- and post-operative values were compared with a paired-samples t -test. Recovery was operationally defined as when the respective weekly average gait metric was no longer statistically different than pre-operative. Walking speed and step length were lowest, and timing asymmetry and double support percentage were greatest at week two post-operative ( p < 0.0001). Walking speed recovered at 21 weeks (1.00 m/s, p = 0.063) and double support percentage recovered at week 24 (32%, p = 0.089). Asymmetry percentage was recovered at 13 weeks (14.0%, p = 0.23) and was consistently superior to pre-operative values at week 19 (11.1% vs. 12.5%, p < 0.001). Step length did not recover during the 24-week period (0.60 m vs. 0.59 m, p = 0.004); however, this difference is not likely clinically relevant. The data suggests that gait quality metrics are most negatively affected two weeks post-operatively, recover within the first 24-weeks following TKA, and follow a slower trajectory compared to previously reported step count recoveries. The ability to capture new objective measures of recovery is evident. As more gait quality data is accrued, physicians may be able to use passively collected gait quality data to help direct post-operative recovery using sensor-based care pathways.