Effect of Lumbosacral orthosis on the gait dynamics in chronic non-specific recurrent low back pain patients
Low back pain (LBP) is one of the most common musculoskeletal problems with the lifetime prevalence up to 84%. About 90% of all low back pain classified as chronic non-specific LBP which is defined as LBP not attributable to a recognizable, known specific pathology (1, 2). Lumbosacral orthoses (LSO) are widely used for management of chronic LBP despite the lack of adequate scientific documentation for their function (3). Although there is several presumed mechanism of action of lumbar support, one steady finding of researches is that lumbar supports increase passively trunk stiffness (4-8). In the presence of any perturbation, some co-contraction of trunk antagonist muscles provides structural stiffness of lumbar spine (9, 10). Additional stiffness provided by lumbar support help to stabilize the lumbar spine and consequently decrease the demand of antagonist trunk muscles co-contraction.
A question then arises that CNS can perceive the passive stiffness provided by LSO and make any motor control adaptations to this enhancement of spine stability? To address this question, body and CNS must be viewed as a dynamic system in which LSO as a whole may interact with this dynamical system.
Human locomotor system, as a dynamical system, integrates input from central nervous system and peripheral feedback from visual, vestibular and proprioception sensors to produced controlled command that result in coordinated body movement. The main concept to system dynamics is how all the objects in a system interact with one another in which a change in one variable affects other variables over time. As human gait consist of consecutive stride, assessment of stride-to-stride fluctuations and their changes over time, gait dynamics, can help to determine motor control facts and related alternations of locomotor system (11). Traditional studies focused on the amount of gait variables such as mean, median and SD. They ignored the stride-to-stride fluctuations and considered them to be uncorrelated noise. Recent studies have shown that the presumed noise reveals important information about intrinsic gait dynamics. Any alternations in the gait dynamics can reveal disease severity and response to the therapeutic interventions (12-14). Stride-to-stride fluctuations in healthy adults exhibit long-range, fractal-like correlation (15). However, stride dynamics exhibits more random behavior in older adults and patients with Huntington’s and Parkinson’s disease (12, 16). These findings demonstrate that the gait variability may be a useful tool to understand the organization and interaction of the locomotor system.
A few studies have investigated the fractal nature in neuromuscular diseases. Roberts et al. (17) showed that the stride-interval pattern in the LBP patients became more correlated after receiving nerve block. Newell et al. (18) compared the fractal components of the gait in LBP patients with healthy matched controls and showed that the LBP patients had lower fractal scaling indices or reduced complexity during walking than the control subjects. It appears that the assessment of the physiological time series may reveal valuable information about system complexity and the mechanism of the system’s control. Also, stride-to-stride fluctuations are amenable to change with aging, disease and therapeutic intervention. Moreover, stride interval can be considered as a final output of neuromuscular system, evaluating of stride dynamics may be able to help us to recognize the LSO interaction with locomotor system.
Our aim is to determine any differences in the stride-to-stride fluctuations in LBP patient while wearing LSO.
To achieve this goal, LBP subjects will be randomly allocated to control and intervention groups. The control subjects will receive physical therapy (exercises and electrotherapy) and intervention group will receive physical therapy as well as a semi-rigid LSO for 6 weeks, 6-8 hour a day. The data collection will do at first day of treatment and repeat at 3 weeks and 6 weeks after treatment. Subjects will be followed one month after the end of treatment. All participants walk on the treadmill at a preferred walking speed while footswitches are inserted in their shoes to detect the gait events. Detrended fluctuation analysis (DFA) will be used to analyze the fractal nature of the stride interval’s time series. DFA quantifies the scale-free fluctuations in time series by a scaling exponent (). This scaling component may identify alternations of the system’s behavior. α, an emergent property of a system, may reveal that any event in time series has self-correlations or it is random.
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