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Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors

Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounte...

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Autores principales: Kushioka, Junichi, Sun, Ruopeng, Zhang, Wei, Muaremi, Amir, Leutheuser, Heike, Odonkor, Charles A., Smuck, Matthew
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739785/
https://www.ncbi.nlm.nih.gov/pubmed/36502003
http://dx.doi.org/10.3390/s22239301
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author Kushioka, Junichi
Sun, Ruopeng
Zhang, Wei
Muaremi, Amir
Leutheuser, Heike
Odonkor, Charles A.
Smuck, Matthew
author_facet Kushioka, Junichi
Sun, Ruopeng
Zhang, Wei
Muaremi, Amir
Leutheuser, Heike
Odonkor, Charles A.
Smuck, Matthew
author_sort Kushioka, Junichi
collection PubMed
description Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)—neurogenic diseases, and knee osteoarthritis (KOA)—structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (p = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: p ≤ 0.05; 4th min: p = 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment.
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spelling pubmed-97397852022-12-11 Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors Kushioka, Junichi Sun, Ruopeng Zhang, Wei Muaremi, Amir Leutheuser, Heike Odonkor, Charles A. Smuck, Matthew Sensors (Basel) Article Mobility impairments are a common symptom of age-related degenerative diseases. Gait features can discriminate those with mobility disorders from healthy individuals, yet phenotyping specific pathologies remains challenging. This study aims to identify if gait parameters derived from two foot-mounted inertial measurement units (IMU) during the 6 min walk test (6MWT) can phenotype mobility impairment from different pathologies (Lumbar spinal stenosis (LSS)—neurogenic diseases, and knee osteoarthritis (KOA)—structural joint disease). Bilateral foot-mounted IMU data during the 6MWT were collected from patients with LSS and KOA and matched healthy controls (N = 30, 10 for each group). Eleven gait parameters representing four domains (pace, rhythm, asymmetry, variability) were derived for each minute of the 6MWT. In the entire 6MWT, gait parameters in all four domains distinguished between controls and both disease groups; however, the disease groups demonstrated no statistical differences, with a trend toward higher stride length variability in the LSS group (p = 0.057). Additional minute-by-minute comparisons identified stride length variability as a statistically significant marker between disease groups during the middle portion of 6WMT (3rd min: p ≤ 0.05; 4th min: p = 0.06). These findings demonstrate that gait variability measures are a potential biomarker to phenotype mobility impairment from different pathologies. Increased gait variability indicates loss of gait rhythmicity, a common feature in neurologic impairment of locomotor control, thus reflecting the underlying mechanism for the gait impairment in LSS. Findings from this work also identify the middle portion of the 6MWT as a potential window to detect subtle gait differences between individuals with different origins of gait impairment. MDPI 2022-11-29 /pmc/articles/PMC9739785/ /pubmed/36502003 http://dx.doi.org/10.3390/s22239301 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kushioka, Junichi
Sun, Ruopeng
Zhang, Wei
Muaremi, Amir
Leutheuser, Heike
Odonkor, Charles A.
Smuck, Matthew
Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors
title Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors
title_full Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors
title_fullStr Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors
title_full_unstemmed Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors
title_short Gait Variability to Phenotype Common Orthopedic Gait Impairments Using Wearable Sensors
title_sort gait variability to phenotype common orthopedic gait impairments using wearable sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9739785/
https://www.ncbi.nlm.nih.gov/pubmed/36502003
http://dx.doi.org/10.3390/s22239301
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