Early post-operative walking bouts are associated with improved gait speed and symmetry at 90 days

Osteoarthritis (OA) poses a growing challenge for the aging population, especially in the hip and knee joints, contributing significantly to disability and societal costs. Exploring the integration of wearable technology, this study addresses the limitations of traditional rehabilitation assessments in capturing real-world experiences and dynamic variations. Specifically, it focuses on continuously monitoring physical activity in hip and knee OA patients using automated unsupervised evaluations within the rehabilitation process. We analyzed data from 1144 patients who used a mobile health application after surgery; the activity data were collected using the Garmin Vivofit 4. Several parameters, such as the total number of steps per day, the peak 6-minute consecutive cadence (P6MC) and peak 1-minute cadence (P1M), were computed and analyzed on a daily basis. The results indicated that cadence-based measurements can effectively, and earlier, differ among patients with hip and knee conditions, as well as in the recovery process. Comparisons based on recovery status and type of surgery reveal distinctive trajectories, emphasizing the effectiveness of P6MC and P1M in detecting variations earlier than total steps per day. Furthermore, cadence-based measurements showed a lower inter-day variability (40%) compared to the total number of steps per day (80%). Automated assessments, including P1M and P6MC, offer nuanced insights into the patients’ dynamic activity profiles.

Section: Discussionsupporting

confidence: 90%

Incorporating Wearable Technology for Enhanced Rehabilitation Monitoring after Hip and Knee Replacement

Lebleu,

Daniels,

Pauwels

et al. 2024

“…In 2020, Apple developed gait metric algorithms and made gait metrics available to iPhone owners with iOS 14, and in June 2021, iOS 15 allowed iPhone owners to electronically share their gait metric data with healthcare providers [ 31 ]. Passively collected spatiotemporal data has been used to assess the effect of physical activity on walking speed recovery following TKA [ 32 ] and to describe the recovery trajectory of select functional metrics (steps per day, stairs climbed, and gait speed) at specific time intervals (one month, three months, and six months) following TKA [ 33 ]. However, to our knowledge, continuous monitoring of gait quality recovery during the early post-operative period has not been performed.…”

Section: Introductionmentioning

confidence: 99%

Stepping beyond Counts in Recovery of Total Knee Arthroplasty: A Prospective Study on Passively Collected Gait Metrics

Fary

Cholewa

Abshagen

et al. 2023

Self Cite

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.

“…Smartphone-based IMU data has been used to develop predictive models for adverse post-operative events and frailty detection based on gait in cardiac surgery patients [ 33 , 34 ] and assess abnormal gait in osteoarthritis patients [ 35 , 36 , 37 ]. More recently, passively collected data via a smartphone-based care management platform was used to describe the recovery trajectory of gait metrics following total knee arthroplasty [ 38 , 39 , 40 ]. To our knowledge, only one study has used passively collected, smartphone-based IMU data to investigate recovery trajectories following THA.…”

Section: Introductionmentioning

confidence: 99%

Stepping Beyond Counts in Recovery of Total Hip Arthroplasty: A Prospective Study on Passively Collected Gait Metrics

Fary

Cholewa

Abshagen

et al. 2023

Self Cite

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.