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

gait analysis mHealth rehabilitation technology remote patient monitoring subjective function

Journal

Frontiers in rehabilitation sciences
ISSN: 2673-6861
Titre abrégé: Front Rehabil Sci
Pays: Switzerland
ID NLM: 9918227358906676

Informations de publication

Date de publication:
2023
Historique:
received: 21 03 2023
accepted: 12 07 2023
medline: 11 8 2023
pubmed: 11 8 2023
entrez: 11 8 2023
Statut: epublish

Résumé

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 of impairment affecting physical activities that has been compared with objective tests. More recently, mobile gait assessment software can provide comprehensive motion tracking output from ecologically valid environments, but how this data relates to subjective scales is unknown. Therefore, the association between the LEFS and walking variables remotely collected by a smartphone was explored. Proprietary algorithms extracted spatiotemporal parameters detected by a standard integrated inertial measurement unit from 132 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). 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 ( When patients are classified according to subjective mobility level, there are significant differences in quantitative measures of ambulation analyzed with smartphone-based technology. Capturing real-time information about movement is important to obtain accurate impressions of how individuals perform in daily life while understanding the relationship between enacted activity and relevant clinical outcomes.

Identifiants

pubmed: 37565184
doi: 10.3389/fresc.2023.1189376
pmc: PMC10410151
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1189376

Informations de copyright

© 2023 Rozanski, Delgado and Putrino.

Déclaration de conflit d'intérêts

This work was supported by Celloscope Inc. Study sponsors were involved in data collection. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Auteurs

Gabriela Rozanski (G)

Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States.

Andrew Delgado (A)

Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States.

David Putrino (D)

Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States.

Classifications MeSH