A previous review concluded that postural sway is increased in patients with low back pain (LBP). However, more detailed analysis of the literature shows that postural deficit may be dependent on experimental conditions in which patients with LBP have been assessed. The research question to be answered in this review was: " Is there any difference in postural sway between subjects with and without LBP across several sensory manipulation conditions?" A literature search in Pubmed, Scopus, Embase and PsychInfo was performed followed by hand search and contact with authors. Studies investigating postural sway during bipedal stance without applying external forces in patients with specific and non-specific LBP compared to healthy controls were included. Twenty three articles fulfilled the eligibility criteria. Most studies reported an increased postural sway in LBP, or no effect of LBP on postural sway. In a minority of studies, a decreased sway was found in LBP patients. There were no systematic differences between studies finding an effect and those reporting no effect of LBP. The proportion of studies finding between-group differences did not increase with increased complexity of sensory manipulations. Potential factors that may have caused inconsistencies in the literature are discussed in this systematic review.
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Patients with non-specific low back pain (LBP) may use postural control strategies that differ from healthy subjects. To study these possible differences, we measured the amount and structure of postural sway, and the response to muscle vibration in a working cohort of 215 subjects. Subjects were standing on a force plate in bipedal stance. In the first trial the eyes were open, no perturbation applied. In the following 6 trials, vision was occluded and subjects stood under various conditions of vibration/no vibration of the lumbar spine or m. Triceps Surae (TSM) on firm surface and on foam surface. We performed a factor analysis to reduce the large amount of variables that are available to quantify all effects. Subjects with LBP showed the same amount of sway as subjects without LBP, but the structure of their sway pattern was less regular with higher frequency content. Subjects with LBP also showed a smaller response to TSM vibration, and a slower balance recovery after cessation of vibration when standing on a solid surface. There was a weak but significant association between smaller responses to TSM vibration and an irregular, high frequency sway pattern, independent from LBP. A model for control of postural sway is proposed. This model suggests that subjects with LBP use more co-contraction and less cognitive control, to maintain a standing balance when compared to subjects without LBP. In addition, a reduced weighting of proprioceptive signals in subjects with LBP is suggested as an explanation for the findings in this study.
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Background Exergames are becoming an increasingly popular tool for training balance ability, thereby preventing falls in older adults. Automatic, real time, assessment of the user's balance control offers opportunities in terms of providing targeted feedback and dynamically adjusting the gameplay to the individual user, yet algorithms for quantification of balance control remain to be developed. The aim of the present study was to identify movement patterns, and variability therein, of young and older adults playing a custom-made weight-shifting (ice-skating) exergame. Methods Twenty older adults and twenty young adults played a weight-shifting exergame under five conditions of varying complexity, while multi-segmental whole-body movement data were captured using Kinect. Movement coordination patterns expressed during gameplay were identified using Self Organizing Maps (SOM), an artificial neural network, and variability in these patterns was quantified by computing Total Trajectory Variability (TTvar). Additionally a k Nearest Neighbor (kNN) classifier was trained to discriminate between young and older adults based on the SOM features. Results Results showed that TTvar was significantly higher in older adults than in young adults, when playing the exergame under complex task conditions. The kNN classifier showed a classification accuracy of 65.8%.Conclusions Older adults display more variable sway behavior than young adults, when playing the exergame under complex task conditions. The SOM features characterizing movement patterns expressed during exergaming allow for discriminating between young and older adults with limited accuracy. Our findings contribute to the development of algorithms for quantification of balance ability during home-based exergaming for balance training. Copyright:
