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Assessing the Relationship between the Enhanced Gait Variability Index and Falls in Individuals with Parkinson's Disease


Assessing the Relationship between the Enhanced Gait Variability Index and Falls in Individuals with Parkinson's Disease



Parkinson's Disease 2020: 5813049



ISSN/ISBN: 2090-8083

PMID: 32089816

DOI: 10.1155/2020/5813049

Gait impairment and increased gait variability are common among individuals with Parkinson's disease (PD) and have been associated with increased risk for falls. The development of composite scores has gained interest to aggregate multiple aspects of gait into a single metric. The Enhanced Gait Variability Index (EGVI) was developed to compare an individual's gait variability to the amount of variability in a healthy population, yet the EGVI's individual parts may also provide important information that may be lost in this conversion. We sought to contrast individual gait measures as predictors of fall frequency and the EGVI as a single predictor of fall frequency in individuals with PD. 273 patients (189M, 84F; 68 ± 10 yrs) with idiopathic PD walked over an instrumented walkway and reported fall frequency over three months (never, rarely, monthly, weekly, or daily). The predictive ability of gait velocity, step length, step time, stance time, and single support time and the EGVI was assessed using regression techniques to predict fall frequency. The EGVI explained 15.1% of the variance in fall frequency (p < 0.001, r = 0.389). Although the regression using the combined spatiotemporal measures to predict fall frequency was significant (p=0.002, r = 0.264), none of the components reached significance (gait velocity: p=0.640, step length: p=0.900, step time: p=0.525, stance time: p=0.532, single support time: p=0.480). The EGVI is a better predictor of fall frequency in persons with PD than its individual spatiotemporal components. Patients who fall more frequently have more variable gait, based on the interpretation of the EGVI. While the EGVI provides an objective measure of gait variability with some ability to predict fall frequency, full clinical interpretations and applications are currently unknown.

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Accession: 069842796

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