AUTHOR=Rozanski Gabriela , Delgado Andrew , Putrino David 

TITLE=Spatiotemporal parameters from remote smartphone-based gait analysis are associated with lower extremity functional scale categories

JOURNAL=Frontiers in Rehabilitation Sciences

VOLUME=Volume 4 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/rehabilitation-sciences/articles/10.3389/fresc.2023.1189376

DOI=10.3389/fresc.2023.1189376

ISSN=2673-6861

ABSTRACT=Objective: Self-report tools are recommended in research and clinical practice to capture individual perceptions regarding health status; however, only modest correlations are found with performance-based results. The Lower Extremity Functional Scale (LEFS) is one well-validated measure that is compared with objective tests. Few studies incorporate the comprehensive output from motion trackers or mobile gait assessment software for remote monitoring in ecologically valid environments, so more knowledge about how this data relates to subjective scales is needed. Therefore, the association between the LEFS and walking variables collected by a smartphone application was explored.
Methods: Proprietary algorithms extracted spatiotemporal parameters detected by a standard integrated inertial measurement unit from subjects enrolled in physical therapy for orthopedic or neurological rehabilitation. Users initiated ambulation recordings and completed questionnaires through the OneStep digital platform. Discrete categories were created based on LEFS score cut-offs and Analysis of Variance was applied to estimate the difference in gait metrics across functional groups (Low-Medium-High).
Results: The main finding of this cross-sectional retrospective study is that remotely-collected biomechanical walking data are significantly associated with individuals’ self-evaluated function as defined by LEFS categorization (n=132) and many variables differ between groups. Velocity was found to have the strongest effect.
Discussion: When patients are classified according to subjective mobility level, there are significant differences in quantitative measures of ambulation analyzed with smartphone-based technology. Capturing clinically-relevant information about movement in real-time is important to obtain accurate impressions of how therapeutic recovery occurs while understanding the relationship between enacted activity and patient-reported outcomes.