Nonlinear analysis of the movement variability structure can detect aging-related differences among cognitively healthy individuals

Abstract

Studying the dynamics of nonlinear systems can provide additional information about the variability structure of the system. Within the current study, we examined the application of regularity and local stability measures to capture motor function alterations due to dual-tasking using a previously validated upper-extremity function (UEF). We targeted young (ages 18 and 30years) and older adults (65years or older) with normal cognition based on clinical screening. UEF involved repetitive elbow flexion without counting (ST) and while counting backward by one (DT1) or three (DT3). We measured the regularity (measured by sample entropy (SE)), local stability (measured by the largest Lyapunov exponent (LyE)), as well as conventional peak-dependent variability measures (coefficient of variation of kinematics parameters) to capture motor dynamic alterations due to dual-tasking. Within both groups, only SE showed significant differences between all pairs of UEF condition comparisons, even ST vs DT1 (p=0.007, effect size=0.507), for which no peak-dependent parameter showed significant difference. Among all measures, the only parameter that showed a significant difference between young and older adults was LyE (p<0.001, effect size=0.453). Current findings highlight the potential of nonlinear analysis to detect aging-related alterations among cognitively healthy participants.