• Morteza Asgari, Mohammad Ali Sanjari, Hamid Reza Mokhtarinia, Samaneh Moeini Sedeh, Kinda Khalaf, and Mohamad Parnianpour.
• Department of Mechanical Engineering, Sharif University of Technology, Tehran, Iran. Electronic address: morteza_asgari@alum.sharif.edu.
• Clin Biomech (Bristol, Avon). 2015 Aug 1; 30 (7): 682-8.

BackgroundComparison of the kinematic variability and dynamic stability of the trunk between healthy and low back pain patient groups can contribute to gaining valuable information about the movement patterns and neuromotor strategies involved in various movement tasks.MethodsFourteen chronic low back pain patients with mild symptoms and twelve healthy male volunteers performed repeated trunk flexion-extension movements in the sagittal plane at three different speeds: 20 cycles/min, self-selected, and 40 cycles/min. Mean standard deviations, coefficient of variation and variance ratio as variability measures; maximum finite-time Lyapunov exponents and maximum Floquet multipliers as stability measures were computed from trunk kinematics.FindingsHigher speed significantly reduced the kinematic variability, while it increased short-term Lyapunov exponents. Long-term Lyapunov exponents were higher at self-selected speed and lower in low back pain patients as compared to control volunteers. Floquet multipliers were larger at self-selected speed and during higher pace trunk movements.InterpretationOur findings suggest that slower pace flexion-extension trunk movements are associated with more motor variation as well as local and orbital stability, implying less potential risk of injury for the trunk. Individuals with and without low back pain consistently recruited a closed-loop control strategy towards achieving trunk stability. Chronic low back pain patients exhibited more stable trunk movements over long-term periods, indicating probable temporary pain relief functional adaption strategies. These results may be used towards the development of more effective personalized rehabilitation strategies and quantitative spinal analysis tools for low back pain detection, diagnosis and treatment, as well as improvement of workspace and occupational settings.Copyright © 2015 Elsevier Ltd. All rights reserved.

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