The Effect of Mental and Muscular Fatigue on Movement Variability in Dart Throwing: A UCM Analysis

Muscular fatigue often induces compensatory adjustments-such as increased motor unit recruitment and synchronization-that can elevate kinematic variability. By contrast, mental fatigue alters top-down neural control, modulates corticospinal excitability, and changes prefrontal-motor cortical activity, indirectly influencing muscle activation patterns. These central and peripheral distinctions suggest that mental and muscular fatigue may differently affect movement variability and synergy organization, yet it remains unclear how the structure of variability diverges between the two. The purpose of this study was to examine the effect of mental and muscular fatigue on movement variability during dart throwing using the uncontrolled manifold method. This method decomposes joint kinematic into two components: components of joint variability that either stabilize (parallel variance) or destabilize (orthogonal variance) performance variable. Participants were 28 young individuals (19 females and nine males) aged 25-35 without regular experience in throwing darts. All participants threw darts under three conditions: mental fatigue, muscular fatigue, and nonfatigue. Throwing kinematics data was collected with a motion capture system and quantified movement variability using the uncontrolled manifold approach. We hypothesized that the structure of movement variability would differ across the three conditions-mental fatigue, muscular fatigue, and nonfatigue. Repeated-measures multivariate analysis of variance was used to test these hypotheses. The results showed that the main effect of fatigue has a significant effect on parallel variance (p = .034, partial η2 = .31) but not on orthogonal variance (p = .289, partial η2 = .23) and synergy index (p = .864, partial η2 = .14) at different times of the throwing cycle. Increasing parallel variance is an effective strategy for helping to be more flexible in using degrees of freedom to perform a task. These results suggest that an increase in parallel variance may reflect an adaptive mechanism to maintain stability.