Citation

Gangwani R, Dusane S, Wang S, Kannan L, Wang E, Fung J, Bhatt T. J. Neurol. Phys. Ther. 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, Neurology Section, American Physical Therapy Association)

DOI

10.1097/NPT.0000000000000331

PMID

32815890

Abstract

BACKGROUND AND PURPOSE: Considering the multifactorial nature and the often-grave consequences of falls in people with chronic stroke (PwCS), determining measurements that best predict fall risk is essential for identifying those who are at high risk. We aimed to determine measures from the domains of the International Classification of Functioning, Disability and Health (ICF) that can predict laboratory-induced slip-related fall risk among PwCS.

METHODS: Fifty-six PwCS participated in the experiment in which they were subjected to an unannounced slip of the paretic leg while walking on an overground walkway. Prior to the slip, they were given a battery of tests to assess fall risk factors. Balance was assessed using performance-based tests and instrumented measures. Other fall risk factors assessed were severity of sensorimotor impairment, muscle strength, physical activity level, and psychosocial factors. Logistic regression analysis was performed for all variables. The accuracy of each measure was examined based on its sensitivity and specificity for fall risk prediction.

RESULTS: Of the 56 participants, 24 (43%) fell upon slipping while 32 (57%) recovered their balance. The multivariate logistic regression analysis model identified dynamic gait stability, hip extensor strength, and the Timed Up and Go (TUG) score as significant laboratory-induced slip-fall predictors with a combined sensitivity of 75%, a specificity of 79.2%, and an overall accuracy of 77.3%.

DISCUSSION AND CONCLUSIONS: The results indicate that fall risk measures within the ICF domains-body, structure, and function (dynamic gait stability and hip extensor strength) and activity limitation (TUG)-could provide a sensitive laboratory-induced slip-fall prediction model in PwCS.Video Abstract available for more insights from the authors (see the Video, Supplemental Digital Content 1, available at: http://links.lww.com/JNPT/A323).

Language: en