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. 2015 Jun;24(6):1305-11.
doi: 10.1016/j.jstrokecerebrovasdis.2015.02.004. Epub 2015 Apr 13.

Accelerometry-based gait characteristics evaluated using a smartphone and their association with fall risk in people with chronic stroke

Takuya Isho  1 , Hideyuki Tashiro  2 , Shigeru Usuda  3
Affiliations

Accelerometry-based gait characteristics evaluated using a smartphone and their association with fall risk in people with chronic stroke

Takuya Isho et al. J Stroke Cerebrovasc Dis. 2015 Jun.

Abstract

Background: The smartphone, which contains inertial sensors, is currently available and affordable device and has the potential to provide a self-assessment tool for health management. The aims of this study were to use a smartphone to record trunk acceleration during walking and to compare accelerometry variables between poststroke subjects with and without a history of falling.

Methods: This cross-sectional study was conducted in 2 day care centers for elderly adults. Twenty-four community-dwelling adults with chronic stroke (mean age, 71.6 ± 9.7 years; mean time since stroke, 68.5 ± 38.7 months) were enrolled. Acceleration of the trunk during walking was recorded in the anteroposterior and mediolateral directions and quantified using the autocorrelation coefficient, harmonic ratio, and interstride variability (coefficient of variation of root mean square acceleration). Fall history in the past 12 months was obtained by self-report.

Results: Eleven participants (45.8%) reported at least one fall in the past 12 months and were classified as fallers. Fallers exhibited significantly higher interstride variability of mediolateral trunk acceleration than nonfallers. In the logistic regression analysis, interstride variability of mediolateral trunk acceleration was significantly associated with fall history (adjusted odds ratio, 1.462; 95% confidence interval, 1.009-2.120). The area under the receiver operating characteristic curve for interstride variability of mediolateral trunk acceleration to discriminate fallers from nonfallers was .745 (95% confidence interval, .527-.963).

Conclusions: The results suggest that quantitative gait assessment using a smartphone can provide detailed and objective information about subtle changes in the gait pattern of stroke subjects at risk of falling.

Keywords: Accelerometry; accidental falls; cerebrovascular disorders; gait; postural balance; walking.

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