Smartphone-Based Gait Assessment Captures Functional Recovery Following Total Knee Arthroplasty

Novel smartphone-based methods offer an accessible and promising alternative to traditional tools for performing clinical gait assessments in total knee arthroplasty (TKA) patients. The OneStep app uses the smartphone's sensors and proprietary machine learning algorithms to measure gait parameters of walking trials, including stride length, step length, step width, gait velocity, cadence, and double stance time. The objectives of this study were to (1) validate the reliability of the OneStep app against a traditional motion capture (MoCap) system and (2) use the OneStep app to measure functional recovery of TKA patients pre- and post-operatively. For Objective 1, walking trials using both OneStep and MoCap were conducted with N = 17 healthy adults (9M/8F, aged 22.29 ± 2.08 years). Results showed that of all gait variables, cadence (<i>p</i> < 0.0001) and gait velocity (<i>p</i> < 0.0001) exhibited the strongest correlations between methods indicated by their linear regression results, and step width had the weakest correlation between methods (<i>p</i> = 0.67). For Objective 2, OneStep gait measurements were collected for N = 11 TKA patients (5M/6F, aged 70.91 ± 6.56 years) at their pre-operative, 2-weeks post-operative, and 6-weeks post-operative appointments. Results showed marked declines in gait properties (decreased stride length, step length, cadence, and gait velocity and increased step width and double stance time) of participants relative to pre-operative values at 2-weeks pre-operative, and an increase/surpassing of pre-operative gait measurements 6-weeks post-operative. The greatest differences were observed in gait velocity between pre-operative and 2-weeks post-operative (<i>p</i> = 0.011) and 2-weeks post-operative to 6 weeks post-operative (<i>p</i> = 0.005).