Related Experiment Video
Updated: Jul 19, 2025
08:56
Clinical Assessment of Spatiotemporal Gait Parameters in Patients and Older Adults
Published on: November 7, 2014
13.9K
Spatiotemporal parameters from remote smartphone-based gait analysis are associated with lower extremity functional
Gabriela Rozanski1, Andrew Delgado1, David Putrino1
1Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States.
Frontiers in Rehabilitation Sciences
|August 11, 2023
Summary
Smartphone gait analysis is linked to self-reported function. This study found significant differences in walking metrics based on Lower Extremity Functional Scale (LEFS) scores, highlighting the value of mobile health technology.
Area of Science:
- Biomechanics and Rehabilitation Engineering
- Digital Health and Mobile Sensing
- Clinical Outcome Measurement
Background:
- Self-report measures like the Lower Extremity Functional Scale (LEFS) are crucial for assessing perceived health status but show modest correlation with objective performance.
- While LEFS is validated against objective tests, the relationship between smartphone-based gait analysis and subjective functional scales remains unexplored.
- Mobile gait assessment software offers detailed motion tracking in real-world settings, presenting an opportunity to bridge the gap between subjective and objective functional assessments.
Purpose of the Study:
- To investigate the association between the Lower Extremity Functional Scale (LEFS) and gait variables obtained from smartphone-based remote monitoring.
- To determine if remotely collected walking data can differentiate functional levels defined by LEFS score categories.
Main Methods:
- A cross-sectional retrospective analysis was conducted on 132 subjects undergoing physical therapy.
- Spatiotemporal gait parameters were extracted using proprietary algorithms from smartphone inertial measurement units via the OneStep digital platform.
- Analysis of Variance (ANOVA) was used to compare gait metrics across functional groups categorized by LEFS score cut-offs.
Main Results:
- Remotely collected biomechanical walking data demonstrated significant associations with self-evaluated function based on LEFS categorization.
- Several gait variables showed significant differences between functional groups (Low-Medium-High LEFS scores).
- Walking velocity exhibited the strongest effect size among the analyzed variables.
Conclusions:
- Smartphone-based gait analysis can effectively identify significant differences in quantitative ambulation measures when patients are stratified by subjective mobility levels.
- This technology provides valuable real-time movement information, enhancing the understanding of daily functional performance and its relation to clinical outcomes.
- Integrating mobile sensing with subjective scales offers a promising approach for comprehensive patient assessment in rehabilitation and clinical practice.
