Non-linear measures of movement variability in multiple sclerosis: a clinical narrative review of Lyapunov exponent and entropy applications in balance and gait

Human movement variability, the natural fluctuation in motor performance across repeated tasks, is a fundamental characteristic of healthy biological systems, and its alteration is a hallmark of neurological dysfunction. The use of non-linear measures provides a powerful suite of complementary tools for capturing the complexity of this variability, revealing patterns in motor control that traditional linear metrics, based on time and distance, often miss. By quantifying aspects such as stability, adaptability, and the predictability of movement, these methods provide critical insights into neuromuscular function reflected in the dynamic variability of observed movements. This is especially valuable in multiple sclerosis (MS), where disruptions in sensorimotor pathways cause changes in movement patterns that can signal early dysfunction and may be able to guide targeted interventions. The purpose of this narrative review is to discuss the existing evidence for the clinical use of non-linear measures of walking and balance in detecting subtle changes, monitoring disease progression, and evaluating treatment effectiveness in MS.