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Objectives: Postural control in elderly people is impaired by degradations of sensory, motor, and higher-level adaptive mechanisms. Here, we characterize the effects of a progressive balance training programon these postural control impairments using a brain network model based on system identification techniques. Methods and Material: We analyzed postural control of 35 healthy elderly subjects and compared findings to data from 35 healthy young volunteers. Eighteen elderly subjects performed a 10 week balance training conducted twice per week. Balance training was carried out in static and dynamic movement states, on support surfaces with different elastic compliances, under different visual conditions and motor tasks. Postural control was characterized by spontaneous sway and postural reactions to pseudorandom anterior-posterior tilts of the support surface. Data were interpreted using a parameter identification procedure based on a brain network model. Conclusion: Balance training reduced overactive proprioceptive feedback and restored vestibular orientation in elderly. Based on the assumption of a linear deterioration of postural control across the life span, the training effect can be extrapolated as a juvenescence of 10 years. This study points to a considerable benefit of a continuous balance training in elderly, even without any sensorimotor deficits.
Background: In health research, indicators of socioeconomic status (SES) are often used interchangeably and often lack theoretical foundation. This makes it difficult to compare results from different studies and to explore the relationship between SES and health outcomes. To aid researchers in choosing appropriate indicators of SES, this article proposes and tests a theory-based selection of SES indicators using chronic back pain as a health outcome. Results: Chronic back pain intensity was best predicted by the multidimensional index (beta = 0.31, p < 0.05), followed by job position (beta = 0.29, p < 0.05) and education (beta = -0.29, p < 0.05); whereas, income exerted no significant influence. Back pain disability was predicted strongest by education (beta = -0.30, p < 0.05) and job position (beta = 0. 29, p < 0.05). Here, multidimensional index and income had no significant influence. Conclusions: The choice of SES indicators influences predictive power on both back pain dimensions, suggesting SES predictors cannot be used interchangeably. Therefore, researchers should carefully consider prior to each study which SES indicator to use. The introduced framework can be valuable in supporting this decision because it allows for a stable prediction of SES indicator influence and their hierarchy on a specific health outcomes.