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A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease
BACKGROUND: A well-established connection exists between increased gait variability and greater fall likelihood in Parkinson’s disease (PD); however, a portable, validated means of quantifying gait variability (and testing the efficacy of any intervention) remains lacking. Furthermore, although rhyt...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4627774/ https://www.ncbi.nlm.nih.gov/pubmed/26517720 http://dx.doi.org/10.1371/journal.pone.0141694 |
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author | Ellis, Robert J. Ng, Yee Sien Zhu, Shenggao Tan, Dawn M. Anderson, Boyd Schlaug, Gottfried Wang, Ye |
author_facet | Ellis, Robert J. Ng, Yee Sien Zhu, Shenggao Tan, Dawn M. Anderson, Boyd Schlaug, Gottfried Wang, Ye |
author_sort | Ellis, Robert J. |
collection | PubMed |
description | BACKGROUND: A well-established connection exists between increased gait variability and greater fall likelihood in Parkinson’s disease (PD); however, a portable, validated means of quantifying gait variability (and testing the efficacy of any intervention) remains lacking. Furthermore, although rhythmic auditory cueing continues to receive attention as a promising gait therapy for PD, its widespread delivery remains bottlenecked. The present paper describes a smartphone-based mobile application (“SmartMOVE”) to address both needs. METHODS: The accuracy of smartphone-based gait analysis (utilizing the smartphone’s built-in tri-axial accelerometer and gyroscope to calculate successive step times and step lengths) was validated against two heel contact–based measurement devices: heel-mounted footswitch sensors (to capture step times) and an instrumented pressure sensor mat (to capture step lengths). 12 PD patients and 12 age-matched healthy controls walked along a 26-m path during self-paced and metronome-cued conditions, with all three devices recording simultaneously. RESULTS: Four outcome measures of gait and gait variability were calculated. Mixed-factorial analysis of variance revealed several instances in which between-group differences (e.g., increased gait variability in PD patients relative to healthy controls) yielded medium-to-large effect sizes (eta-squared values), and cueing-mediated changes (e.g., decreased gait variability when PD patients walked with auditory cues) yielded small-to-medium effect sizes—while at the same time, device-related measurement error yielded small-to-negligible effect sizes. CONCLUSION: These findings highlight specific opportunities for smartphone-based gait analysis to serve as an alternative to conventional gait analysis methods (e.g., footswitch systems or sensor-embedded walkways), particularly when those methods are cost-prohibitive, cumbersome, or inconvenient. |
format | Online Article Text |
id | pubmed-4627774 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-46277742015-11-06 A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease Ellis, Robert J. Ng, Yee Sien Zhu, Shenggao Tan, Dawn M. Anderson, Boyd Schlaug, Gottfried Wang, Ye PLoS One Research Article BACKGROUND: A well-established connection exists between increased gait variability and greater fall likelihood in Parkinson’s disease (PD); however, a portable, validated means of quantifying gait variability (and testing the efficacy of any intervention) remains lacking. Furthermore, although rhythmic auditory cueing continues to receive attention as a promising gait therapy for PD, its widespread delivery remains bottlenecked. The present paper describes a smartphone-based mobile application (“SmartMOVE”) to address both needs. METHODS: The accuracy of smartphone-based gait analysis (utilizing the smartphone’s built-in tri-axial accelerometer and gyroscope to calculate successive step times and step lengths) was validated against two heel contact–based measurement devices: heel-mounted footswitch sensors (to capture step times) and an instrumented pressure sensor mat (to capture step lengths). 12 PD patients and 12 age-matched healthy controls walked along a 26-m path during self-paced and metronome-cued conditions, with all three devices recording simultaneously. RESULTS: Four outcome measures of gait and gait variability were calculated. Mixed-factorial analysis of variance revealed several instances in which between-group differences (e.g., increased gait variability in PD patients relative to healthy controls) yielded medium-to-large effect sizes (eta-squared values), and cueing-mediated changes (e.g., decreased gait variability when PD patients walked with auditory cues) yielded small-to-medium effect sizes—while at the same time, device-related measurement error yielded small-to-negligible effect sizes. CONCLUSION: These findings highlight specific opportunities for smartphone-based gait analysis to serve as an alternative to conventional gait analysis methods (e.g., footswitch systems or sensor-embedded walkways), particularly when those methods are cost-prohibitive, cumbersome, or inconvenient. Public Library of Science 2015-10-30 /pmc/articles/PMC4627774/ /pubmed/26517720 http://dx.doi.org/10.1371/journal.pone.0141694 Text en © 2015 Ellis et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Ellis, Robert J. Ng, Yee Sien Zhu, Shenggao Tan, Dawn M. Anderson, Boyd Schlaug, Gottfried Wang, Ye A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease |
title | A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease |
title_full | A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease |
title_fullStr | A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease |
title_full_unstemmed | A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease |
title_short | A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease |
title_sort | validated smartphone-based assessment of gait and gait variability in parkinson’s disease |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4627774/ https://www.ncbi.nlm.nih.gov/pubmed/26517720 http://dx.doi.org/10.1371/journal.pone.0141694 |
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