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

More smart phone tech for PD monitoring...

PLoS One. 2015 Oct 30;10(10):e0141694. doi: 10.1371/journal.pone.0141694. eCollection 2015.

Ellis RJ, Ng YS, Zhu S, Tan DM, Anderson B, Schlaug G, Wang Y.

http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141694

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.