Transfer of balance performance depends on the specificity of balance training.

Abstract

This study investigated whether a 4-wk training on an easy-level (EL) unstable board could induce a transfer of balance performance in a hard-level (HL) unstable board and in an unexpected perturbation-based task. Nonlinear center of pressure (CoP) analysis investigated whether training could induce postural control adaptations in trained and untrained tasks. Thirty-four subjects were divided into a training (TR, N = 17) group and a control (CTRL, N = 17) group. Balance was assessed before (T₀) and after (T₁) a balance training under static and dynamic conditions (EL, HL, and perturbation-based task). A force platform allowed the calculation of CoP displacement while balance performance based on the angular displacement of the unstable boards was assessed with an inertial sensor. From the angular displacement, we calculated three parameters of balance performance: full balance (FB), fine balance (FiB), and gross balance (GB). Stabilogram diffusion analysis (SDA) and sample entropy (SampEn) indirectly assessed neuromuscular control mechanisms. Results showed improvements in the TR from T₀ to T₁ in balance performance for FB (P < 0.001), FiB (P < 0.05), and GB (P < 0.01) on EL and HL boards. In the perturbation-based task, the earliest CoP response consequent to perturbation improved after training (P < 0.01). SampEn and SDA revealed increased automaticity (P < 0.05) and efficiency (P < 0.05) of balance control in the EL and HL tasks after training. Balance training led to highly task-specific adaptations and improvements that can be transferred between functionally similar balance tasks. Postural strategies learned during training seemed barely transferable to a different balance task, as the unexpected perturbation of the base of support.NEW & NOTEWORTHY Our study showed that improvement in balance performance is task-specific, with transfer depending on functional similarities between the trained and the untrained tasks. Computational nonlinear methods highlighted that training could extend the improved efficiency and automaticity of balance control of the trained task to a similar untrained task. Therefore, the benefits of balance training may not generalize to all balance challenges, highlighting the importance of targeted testing and training approaches.