Different neuromuscular control mechanisms regulate static and dynamic balance: A center-of-pressure analysis in young adults |
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| Institution: | 1. Department of Biomedical Sciences, University of Padova, Padova, Italy;2. Department of Industrial Engineering, University of Padova, Italy;3. Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Switzerland;1. Department of Physical Therapy, Faculty of Medical Science, Fukuoka International University of Health and Welfare, Fukuoka, Japan;2. Department of Occupational Therapy, Faculty of Medical Science, Fukuoka International University of Health and Welfare, Fukuoka, Japan;1. The Biorobotics and Biomechanics Lab, University of Maine, United States of America;2. Husson University, United States of America;1. RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion, University of Oslo, P.O. Box 1133, Blindern, Oslo 0318, Norway;2. Department of Psychology, University of Oslo, P.O. Box 1094, Blindern, Oslo 0317, Norway;3. Department of Psychology, Oslo New University College, Lovisenberggata 13, Oslo 0456, Norway;4. Department of Musicology, University of Oslo, P.O. Box 1017, Blindern, Oslo 0315, Norway;1. Neuroplasticity, Imagery, and Motor Behaviour Laboratory, Department of Psychology, University of British Columbia, Okanagan Campus, Kelowna V1V1V7, British Columbia, Canada;2. Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver V6T1Z3, British Columbia, Canada |
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| Abstract: | The analysis of the center of pressure (CoP) trajectory, derived from force platforms, is a widely accepted measure to investigate postural balance control. The CoP trajectory could be analyzed as a physiological time-series through a general stochastic modeling framework (i.e., Stabilogram Diffusion Analysis (SDA)). Critical point divides short-term from long-term regions and diffusion coefficients reflect the level of stochastic activity of the CoP. Sample Entropy (SampEn) allows quantifying the CoP complexity in terms of regularity. Thus, this study aimed to understand whether SDA and SampEn could discriminate the neuromuscular control mechanisms underpinning static and dynamic postural tasks. Static balance control and its relationship with dynamic balance control were investigated through the CoP velocity (Mean Velocity) and the area of the 95th percentile ellipse (Area95). Balance was assessed in 15 subjects (age: 23.13 ± 0.99 years; M = 9) over a force platform under two conditions: static (ST) and dynamic, both in anterior-posterior (DAP) and medio-lateral (DML) directions. During the DAP and DML, subjects stood on an unstable board positioned over a force platform. Short-term SDA diffusion coefficients and critical points were lower in ST than in DAP and DML (p < 0.05). SampEn values resulted greater in ST than in DAP and DML (p < 0.001). As expected, lower values of Area95 (p < 0.001) and Mean Velocity (p < 0.001) were detected in the easiest condition, the ST, compared to DAP and DML. No significant correlations between static and dynamic balance performances were detected. Moreover, differences in the diffusion coefficients were detected comparing DAP and DML (p < 0.05). In the anterior-posterior direction, the critical point occurred at relatively small intervals in DML compared to DAP (p < 0.001) and ST (p < 0.001). In the medio-lateral direction, the critical point differed only between DAP and DML (p < 0.05). Overall, SDA analysis pointed out a less tightly regulated neuromuscular control system in the dynamic tasks, with closed-loop corrective feedback mechanisms called into play at different time intervals in the three conditions. SampEn results reflected more attention and, thus, less automatic control mechanisms in the dynamic conditions, particularly in the medio-lateral task. The different neuromuscular control mechanisms that emerged in the static and dynamic balance tasks encourage using both static and dynamic tests for a more comprehensive balance performance assessment. |
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