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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.
Afiliación
  • Ellis RJ; School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Singapore.
  • Ng YS; Department of Rehabilitation Medicine, Singapore General Hospital, Outram Rd, Singapore, 169608, Singapore.
  • Zhu S; NUS Graduate School for Integrative Sciences and Engineering, 28 Medical Drive, Singapore, 117456, Singapore.
  • Tan DM; Department of Rehabilitation Medicine, Singapore General Hospital, Outram Rd, Singapore, 169608, Singapore.
  • Anderson B; School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Singapore.
  • Schlaug G; Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Avenue, Palmer 127, Boston, MA, 02215, United States of America.
  • Wang Y; School of Computing, National University of Singapore, Computing 1, 13 Computing Drive, Singapore, 117417, Singapore; NUS Graduate School for Integrative Sciences and Engineering, 28 Medical Drive, Singapore, 117456, Singapore.
PLoS One ; 10(10): e0141694, 2015.
Article en En | MEDLINE | ID: mdl-26517720
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.
Asunto(s)

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Desempeño Psicomotor / Marcha Idioma: En Revista: PLoS One Año: 2015 Tipo del documento: Article País de afiliación: Singapur

Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Enfermedad de Parkinson / Desempeño Psicomotor / Marcha Idioma: En Revista: PLoS One Año: 2015 Tipo del documento: Article País de afiliación: Singapur