Real-World Functional Gait Recovery Trajectories After Total Hip and Total Knee Arthroplasty via Smartphone Sensors: Observational Longitudinal Study.
4 affiliations
- 1OneStep (Celloscope Ltd), Nachmani 14, Tel Aviv, 6579424, Israel, 972 547432106.
- 2Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA, United States.
- 3Beth Israel Deaconess Medical Center, Boston, MA, United States.
- 4Harvard Medical School, Harvard University, Boston, MA, United States.
Abstract
Background
Recovery after total hip arthroplasty (THA) and total knee arthroplasty (TKA) is typically assessed with infrequent clinic-based evaluations that may not reflect patients' everyday mobility. Smartphone inertial sensors enable scalable, repeated gait measurement in real-world settings, but the extent to which prompted walking tests reflect daily life walking during recovery is not well defined.Objective
The objective of this study was to characterize week-by-week recovery trajectories after THA and TKA using high-frequency smartphone sensor data and quantify the difference between prompted in-app walking tests and passive daily life background walking.Methods
We analyzed longitudinal smartphone-based gait measurements from 735 patients (THA, n=350; TKA, n=385) who underwent unilateral surgery between July 2022 and October 2025. Nine spatiotemporal gait parameters were monitored over the first 15 postoperative weeks across 2 conditions: prompted "in-app" walks and passively captured "background" walks. Recovery trajectories were evaluated using Kaplan-Meier time-to-baseline analyses, linear mixed-effects models, and comparison of late-stage plateau levels (weeks 11-15) using estimated marginal means of the models.Results
Across all parameters, THA patients returned to preoperative baseline levels faster than TKA patients, with mean trajectories generally approaching baseline by week 4 for THA versus weeks 6 to 8 for TKA. Linear mixed-effects models showed that surgical procedure type significantly influenced recovery trajectory for all parameters except single support of the unaffected leg. All gait parameters except stance asymmetry and single support of the unaffected leg exhibited persistent differences through the late-recovery plateau (weeks 11-15). We also compared prompted "in-app" walks with passively captured background walking. Prompted and passive walking showed systematic divergence in both THA and TKA; prompted assessments generally indicated faster or more complete return to baseline than passive daily life walking. Finally, preoperative gait level was strongly associated with postoperative trajectories, with baseline-stratified analyses showing distinct patterns of recovery between groups.Conclusions
High-frequency smartphone gait assessment quantifies procedure-specific recovery dynamics and demonstrates that prompted tests and passive daily life measurements capture distinct, noninterchangeable functional signals. Parameters from prompted walks plateaued while those from passively captured walks were still changing; prompted testing alone therefore captures only one component of returning function, highlighting the additional information that passive, real-world gait monitoring can provide.
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BibTeX
@article{teitz2026realworld,
title = {{Real-World Functional Gait Recovery Trajectories After Total Hip and Total Knee Arthroplasty via Smartphone Sensors: Observational Longitudinal Study.}},
author = {Levi S Teitz and Einat Granot-Hershkovitz and Pei-An Lee and On-Yee Lo and Brad Manor and Yuval Y Naveh},
journal = {JMIR mHealth and uHealth},
year = {2026},
month = oct,
doi = {10.2196/97657},
url = {https://europepmc.org/article/MED/42961449},
note = {CC BY},
}
RIS
TY - JOUR
TI - Real-World Functional Gait Recovery Trajectories After Total Hip and Total Knee Arthroplasty via Smartphone Sensors: Observational Longitudinal Study.
AU - Levi S Teitz
AU - Einat Granot-Hershkovitz
AU - Pei-An Lee
AU - On-Yee Lo
AU - Brad Manor
AU - Yuval Y Naveh
PY - 2026
DA - 2026/10/30
JO - JMIR mHealth and uHealth
DO - 10.2196/97657
UR - https://europepmc.org/article/MED/42961449
AB - Background Recovery after total hip arthroplasty (THA) and total knee arthroplasty (TKA) is typically assessed with infrequent clinic-based evaluations that may not reflect patients' everyday mobility. Smartphone inertial sensors enable scalable, repeated gait measurement in real-world settings, but the extent to which prompted walking tests reflect daily life walking during recovery is not well defined.Objective The objective of this study was to characterize week-by-week recovery trajectories after THA and TKA using high-frequency smartphone sensor data and quantify the difference between prompted in-app walking tests and passive daily life background walking.Methods We analyzed longitudinal smartphone-based gait measurements from 735 patients (THA, n=350; TKA, n=385) who underwent unilateral surgery between July 2022 and October 2025. Nine spatiotemporal gait parameters were monitored over the first 15 postoperative weeks across 2 conditions: prompted "in-app" walks and passively captured "background" walks. Recovery trajectories were evaluated using Kaplan-Meier time-to-baseline analyses, linear mixed-effects models, and comparison of late-stage plateau levels (weeks 11-15) using estimated marginal means of the models.Results Across all parameters, THA patients returned to preoperative baseline levels faster than TKA patients, with mean trajectories generally approaching baseline by week 4 for THA versus weeks 6 to 8 for TKA. Linear mixed-effects models showed that surgical procedure type significantly influenced recovery trajectory for all parameters except single support of the unaffected leg. All gait parameters except stance asymmetry and single support of the unaffected leg exhibited persistent differences through the late-recovery plateau (weeks 11-15). We also compared prompted "in-app" walks with passively captured background walking. Prompted and passive walking showed systematic divergence in both THA and TKA; prompted assessments generally indicated faster or more complete return to baseline than passive daily life walking. Finally, preoperative gait level was strongly associated with postoperative trajectories, with baseline-stratified analyses showing distinct patterns of recovery between groups.Conclusions High-frequency smartphone gait assessment quantifies procedure-specific recovery dynamics and demonstrates that prompted tests and passive daily life measurements capture distinct, noninterchangeable functional signals. Parameters from prompted walks plateaued while those from passively captured walks were still changing; prompted testing alone therefore captures only one component of returning function, highlighting the additional information that passive, real-world gait monitoring can provide.
SN - 2291-5222
ER -