Physiological complexity and system adaptability: evidence from postural control dynamics of older adults.

The degree of multiscale complexity in human behavioral regulation, such as that required for postural control, appears to decrease with advanced aging or disease. To help delineate causes and functional consequences of complexity loss, we examined the effects of visual and somatosensory impairment on the complexity of postural sway during quiet standing and its relationship to postural adaptation to cognitive dual tasking. Participants of the MOBILIZE Boston Study were classified into mutually exclusive groups: controls [intact vision and foot somatosensation, n = 299, 76 ± 5 (SD) yr old], visual impairment only (<20/40 vision, n = 81, 77 ± 4 yr old), somatosensory impairment only (inability to perceive 5.07 monofilament on plantar halluxes, n = 48, 80 ± 5 yr old), and combined impairments (n = 25, 80 ± 4 yr old). Postural sway (i.e., center-of-pressure) dynamics were assessed during quiet standing and cognitive dual tasking, and a complexity index was quantified using multiscale entropy analysis. Postural sway speed and area, which did not correlate with complexity, were also computed. During quiet standing, the complexity index (mean ± SD) was highest in controls (9.5 ± 1.2) and successively lower in the visual (9.1 ± 1.1), somatosensory (8.6 ± 1.6), and combined (7.8 ± 1.3) impairment groups (P = 0.001). Dual tasking resulted in increased sway speed and area but reduced complexity (P < 0.01). Lower complexity during quiet standing correlated with greater absolute (R = -0.34, P = 0.002) and percent (R = -0.45, P < 0.001) increases in postural sway speed from quiet standing to dual-tasking conditions. Sensory impairments contributed to decreased postural sway complexity, which reflected reduced adaptive capacity of the postural control system. Relatively low baseline complexity may, therefore, indicate control systems that are more vulnerable to cognitive and other stressors.

[1]  Tang,et al.  Self-Organized Criticality: An Explanation of 1/f Noise , 2011 .

[2]  Pamela S. Haibach,et al.  Egomotion and Vection in Young and Elderly Adults , 2009, Gerontology.

[3]  H. Huikuri,et al.  Heart rate dynamics predict poststroke mortality , 2004, Neurology.

[4]  Farzaneh A. Sorond,et al.  The MOBILIZE Boston Study: Design and methods of a prospective cohort study of novel risk factors for falls in an older population , 2008, BMC geriatrics.

[5]  Jeffrey M. Hausdorff,et al.  Fractal dynamics in physiology: Alterations with disease and aging , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Fernando Porté-Agel,et al.  Synthetic turbulence, fractal interpolation, and large-eddy simulation. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

[7]  F. Scheer,et al.  The Endogenous Circadian Pacemaker Imparts a Scale-Invariant Pattern of Heart Rate Fluctuations across Time Scales Spanning Minutes to 24 Hours , 2008, Journal of biological rhythms.

[8]  Kun Hu,et al.  Reduction of scale invariance of activity fluctuations with aging and Alzheimer's disease: Involvement of the circadian pacemaker , 2009, Proceedings of the National Academy of Sciences.

[9]  D. Winter,et al.  Assessment of balance control in humans. , 1990, Medical progress through technology.

[10]  Jeffrey M. Hausdorff,et al.  Gait variability and fall risk in community-living older adults: a 1-year prospective study. , 2001, Archives of physical medicine and rehabilitation.

[11]  Madalena Costa,et al.  Complex dynamics of human red blood cell flickering: alterations with in vivo aging. , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  L. Lipsitz Dynamics of stability: the physiologic basis of functional health and frailty. , 2002, The journals of gerontology. Series A, Biological sciences and medical sciences.

[13]  H V Huikuri,et al.  Effect of cardiac vagal outflow on complexity and fractal correlation properties of heart rate dynamics. , 2003, Autonomic & autacoid pharmacology.

[14]  Robert J. Peterka,et al.  Diabetic neuropathy and surface sway-referencing disrupt somatosensory information for postural stability in stance , 2002, Somatosensory & motor research.

[15]  Ravi Varadhan,et al.  Physiological Complexity Underlying Heart Rate Dynamics and Frailty Status in Community‐Dwelling Older Women , 2008, Journal of the American Geriatrics Society.

[16]  Madalena Costa,et al.  Multiscale entropy analysis of complex physiologic time series. , 2002, Physical review letters.

[17]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Tim Kiemel,et al.  Multisensory fusion and the stochastic structure of postural sway , 2002, Biological Cybernetics.

[19]  K Bandeen-Roche,et al.  Function and visual impairment in a population-based study of older adults. The SEE project. Salisbury Eye Evaluation. , 1997, Investigative ophthalmology & visual science.

[20]  Marjorie H. Woollacott,et al.  Non-visual spatial tasks reveal increased interactions with stance postural control , 2008, Brain Research.

[21]  Jeffrey M. Hausdorff,et al.  Quantifying Fractal Dynamics of Human Respiration: Age and Gender Effects , 2002, Annals of Biomedical Engineering.

[22]  L. Rowell,et al.  Exercise : regulation and integration of multiple systems , 1996 .

[23]  A. Leon,et al.  A Comparison of Multiplicity Adjustment Strategies for Correlated Binary Endpoints , 2005, Journal of biopharmaceutical statistics.

[24]  J. Fleiss,et al.  Power law behavior of RR-interval variability in healthy middle-aged persons, patients with recent acute myocardial infarction, and patients with heart transplants. , 1996, Circulation.

[25]  Dagmar Sternad,et al.  Complexity of human postural control in young and older adults during prolonged standing , 2008, Experimental Brain Research.

[26]  M. Schlossberg The Halstead-Reitan Neuropsychological Test Battery: Theory and Clinical Interpretation. , 1986 .

[27]  C. Peng,et al.  Frailty and the degradation of complex balance dynamics during a dual-task protocol. , 2009, The journals of gerontology. Series A, Biological sciences and medical sciences.

[28]  F. Horak,et al.  Postural Orientation and Equilibrium , 2011 .

[29]  R. Peterka Sensorimotor integration in human postural control. , 2002, Journal of neurophysiology.

[30]  B. E. Maki,et al.  Measuring balance in the elderly: validation of an instrument. , 1992, Canadian journal of public health = Revue canadienne de sante publique.

[31]  Chung-Kang Peng,et al.  Adaptive Data Analysis of Complex Fluctuations in physiologic Time Series , 2009, Adv. Data Sci. Adapt. Anal..

[32]  D. Levy,et al.  Predicting survival in heart failure case and control subjects by use of fully automated methods for deriving nonlinear and conventional indices of heart rate dynamics. , 1997, Circulation.

[33]  H Eugene Stanley,et al.  Non-random fluctuations and multi-scale dynamics regulation of human activity. , 2004, Physica A.

[34]  A. Goldberger,et al.  Loss of 'complexity' and aging. Potential applications of fractals and chaos theory to senescence. , 1992, JAMA.

[35]  M. Folstein,et al.  The Mini-Mental State Examination. , 1983, Archives of general psychiatry.

[36]  C. Peng,et al.  Noise and poise: Enhancement of postural complexity in the elderly with a stochastic-resonance–based therapy , 2007, Europhysics letters.

[37]  K. Torre,et al.  Fractal dynamics of human gait: a reassessment of the 1996 data of Hausdorff et al. , 2009, Journal of applied physiology.

[38]  J. Duysens,et al.  Widespread short-latency stretch reflexes and their modulation during stumbling over obstacles , 1999, Brain Research.

[39]  Stefan Thurner,et al.  Change of Complexity Patterns in Human Posture during Aging , 2002, Audiology and Neurotology.

[40]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[41]  J. J. Collins,et al.  The effects of visual input on open-loop and closed-loop postural control mechanisms , 2004, Experimental Brain Research.

[42]  Z R Struzik,et al.  Autonomic Imbalance Induced Breakdown of Long-range Dependence in Healthy Heart Rate , 2007, Methods of Information in Medicine.