Citation

Montero-Odasso M, Oteng-Amoako A, Speechley M, Gopaul K, Beauchet O, Annweiler C, Muir-Hunter SW. J. Gerontol. A Biol. Sci. Med. Sci. 2014; 69(11): 1415-1421.

Affiliation

Gait and Brain Lab, Parkwood Hospital, Lawson Health Research Institute, Schulich School of Medicine and Dentistry, Department of Medicine and Division of Geriatric Medicine and.

Copyright

(Copyright © 2014, Gerontological Society of America)

DOI

10.1093/gerona/glu155

PMID

25182601

Abstract

BACKGROUND: Early motor changes associated with aging predict cognitive decline, which suggests that a "motor signature" can be detected in predementia states. In line with previous research, we aim to demonstrate that individuals with mild cognitive impairment (MCI) have a distinct motor signature, and specifically, that dual-task gait can be a tool to distinguish amnestic (a-MCI) from nonamnestic MCI.

METHODS: Older adults with MCI and controls from the "Gait and Brain Study" were assessed with neurocognitive tests to assess cognitive performance and with an electronic gait mat to record temporal and spatial gait parameters. Mean gait velocity and stride time variability were evaluated under simple and three separate dual-task conditions. The relationship between cognitive groups (a-MCI vs nonamnestic MCI) and gait parameters was evaluated with linear regression models and adjusted for confounders.

RESULTS: Ninety-nine older participants, 64 MCI (mean age 76.3±7.1 years; 50% female), and 35 controls (mean age 70.4±3.9 years; 82.9% female) were included. Forty-two participants were a-MCI and 22 were nonamnestic MCI. Multivariable linear regression (adjusted for age, sex, physical activity level, comorbidities, and executive function) showed that a-MCI was significantly associated with slower gait and higher dual-task cost under dual-task conditions.

CONCLUSION: Participants with a-MCI, specifically with episodic memory impairment, had poor gait performance, particularly under dual tasking. Our findings suggest that dual-task assessment can help to differentiate MCI subtyping, revealing a motor signature in MCI.

Language: en