Spatiotemporal gait parameter fluctuations in older adults affected by mild cognitive impairment: comparisons among three cognitive dual-task tests

Abstract

Backgrounds Gait disorder is associated with cognitive functional impairment, and this disturbance is more pronouncedly when performing additional cognitive tasks. Our study aimed to characterize gait disorders in mild cognitive impairment (MCI) under three dual tasks and determine the association between gait performance and cognitive function. Methods A total of 260 participants were enrolled in this cross-sectional study and divided into MCI and cognitively normal control. Spatiotemporal and kinematic gait parameters (31 items) in single task and three dual tasks (serial 100-7, naming animals and words recall) were measured using a wearable sensor. Baseline characteristics of the two groups were balanced using propensity score matching. Important gait features were filtered using random forest method and LASSO regression and further described using logistic analysis. Results After matching, 106 participants with MCI and 106 normal controls were recruited. Top 5 gait features in random forest and 4 ~ 6 important features in LASSO regression were selected. Robust variables associating with cognitive function were temporal gait parameters. Participants with MCI exhibited decreased swing time and terminal swing, increased mid stance and variability of stride length compared with normal control. Subjects walked slower when performing an extra dual cognitive task. In the three dual tasks, words recall test exhibited more pronounced impact on gait regularity, velocity, and dual task cost than the other two cognitive tests. Conclusion Gait assessment under dual task conditions, particularly in words recall test, using portable sensors could be useful as a complementary strategy for early detection of MCI.

Machine Learning Model for Mild Cognitive Impairment Stage Based on Gait and MRI Images

“…However, in elderly individuals with no cognitive impairment, executive function and processing speed had a stronger association with temporal parameters than with spatial parameters [34]. Furthermore, temporal parameters were better at distinguishing between non-MCI elderly groups and MCI groups compared to spatial parameters in previous research [32]. Considering that temporal parameters were predominantly included as the top features in our research results, it may be speculated that temporal parameters may better reflect the degree of cognitive impairment in MCI.…”

“…Gait variability is recognised as a reliable indicator of cognitive function, which could explain why SD emerged as the top feature associated with late-stage MCI used in machine learning with gait data in our study. Reports have suggested that gait variability is a better predictor of cognitive decline in healthy elderly individuals and in patients with MCI [31,32]. Previous research has also demonstrated associations between double support phase variability and executive function, processing speed, and visuospatial ability, as well as a correlation between swing time standard deviation and global cognitive function, memory, attention, language, and visuospatial function [33].…”