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A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease

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  • Robert J Ellis
  • Yee Sien Ng
  • Shenggao Zhu
  • Dawn M Tan
  • Boyd Anderson
  • Gottfried Schlaug
  • Ye Wang

Abstract

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.

Suggested Citation

  • Robert J Ellis & Yee Sien Ng & Shenggao Zhu & Dawn M Tan & Boyd Anderson & Gottfried Schlaug & Ye Wang, 2015. "A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
  • Handle: RePEc:plo:pone00:0141694
    DOI: 10.1371/journal.pone.0141694
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    References listed on IDEAS

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    1. Robert J Ellis & Zhiyan Duan & Ye Wang, 2014. "Quantifying Auditory Temporal Stability in a Large Database of Recorded Music," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-24, December.
    2. Marietta Kirchner & Patric Schubert & Magnus Liebherr & Christian T Haas, 2014. "Detrended Fluctuation Analysis and Adaptive Fractal Analysis of Stride Time Data in Parkinson's Disease: Stitching Together Short Gait Trials," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-6, January.
    3. Jochen Klucken & Jens Barth & Patrick Kugler & Johannes Schlachetzki & Thore Henze & Franz Marxreiter & Zacharias Kohl & Ralph Steidl & Joachim Hornegger & Bjoern Eskofier & Juergen Winkler, 2013. "Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-9, February.
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    Cited by:

    1. Chae Young Lee & Seong Jun Kang & Sang-Kyoon Hong & Hyeo-Il Ma & Unjoo Lee & Yun Joong Kim, 2016. "A Validation Study of a Smartphone-Based Finger Tapping Application for Quantitative Assessment of Bradykinesia in Parkinson’s Disease," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-11, July.

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