ArticleHuman movement science2021
Nonlinear analysis of the movement variability structure can detect aging-related differences among cognitively healthy individuals.
Article in Human movement science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Identifying cognitive impairment in older adults using machine learning on combined fNIRS and motion data during an upper extremity dual task function.Research square · 2026Article
- Unveiling Learning Strategies in the Mirror-Drawing Task: A Single-Case Study of Movement Stability and Complexity Using Entropy.Entropy (Basel, Switzerland) · 2025Article
- Frailty identification using a sensor-based upper-extremity function test: a deep learning approach.Scientific reports · 2025Article
- Brain function complexity during dual-tasking is associated with cognitive impairment and age.Journal of neuroimaging : official journal of the American Society of Neuroimaging · 2022Article
- Frailty assessment using a novel approach based on combined motor and cardiac functions: a pilot study.BMC geriatrics · 2022Article
- Reliability of Automatic Computer Vision-Based Assessment of Orofacial Kinematics for Telehealth Applications.Digital biomarkers · 2022Article
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Authors and funding
6 authors.
Funding
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 30 years) and older adults (65 years 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.
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