Ability of static and statistical mechanics posturographic measures to distinguish between age and fall risk

doi:10.1016/j.jbiomech.2004.06.014

Journal of Biomechanics

Volume 38, Issue 6, June 2005, Pages 1263-1272

https://doi.org/10.1016/j.jbiomech.2004.06.014 Get rights and content

Abstract

Traditional posturographic analysis and four statistical mechanics techniques were applied to center-of-pressure (COP) trajectories of young, older “low-fall-risk” and older “high-fall-risk” individuals. Low-fall-risk older adults were active 3 days per week in a cardiac rehabilitation program, while high-fall-risk older adults were diagnosed with perilymph fistula. Subjects diagnosed with perilymph fistula must have experienced two of the following vestibular findings: constant disequilibrium, positional vertigo and/or a positive fistula test. Non-parametric statistical tests were used to determine whether the posturographic measures could detect differences between the young and older “low-fall-risk” groups (age comparison) and between the older “low-” and “high-risk” groups (risk of falling comparison). The statistical mechanics techniques were more sensitive than the traditional measures: detecting significant differences between the young and older “low-risk” groups, while none of the traditional measures were significantly different. In addition, interpretation of the statistical mechanics techniques may offer more insight into the nature of the process controlling the COP trajectories. However, the methods offered slightly different explanations. For instance, the Hurst rescaled range analysis suggests that the movement of the COP is governed solely by anti-persistent behavior, whereas the stabilogram diffusion analysis suggests a short-term persistence balanced by a long-term anti-persistence. These discrepancies highlight the need for a model that incorporates the biological systems responsible for maintaining balance and experimental methods to directly quantify their status and roles. Until such a model exists, however, the statistical mechanics techniques appear to have some advantages over traditional posturographic measures for studying balance control.

Section snippets

Introduction and background

Falling due to a failure in the postural control system because of aging or a specific pathology is a major problem facing the burgeoning population of older adults (Kannus et al., 1999; Rubenstein et al., 1994). Techniques to determine if an individual is at an elevated risk of falling due to deterioration of their postural control system that are sensitive to small deviations from the norm would provide a means for early detection and intervention. Additionally, the effectiveness of remedial

Description of the three groups in the study

We examined three groups of individuals able to maintain unassisted upright stance: young healthy active adults (n: 10; age: 21–29 years; mean: 24.6), older healthy active adults at a “low-risk” of falling (n: 10; age: 68–79 years; mean: 72.6) and older adults at a “high-risk” of falling (n: 10; age: 57–80; mean: 69.1). The young and “low-risk” groups were recruited from the campus of Wake Forest University (WFU) and WFU Cardiac Rehabilitation program, respectively. These subjects had no

Traditional static posturographic analysis

Four traditional parameters were calculated for each subject (averaged over the 10 trials). The reader is referred to Murray et al. (1975), Prieto et al. (1996) and Sokal and Rohlf (1981) for more detailed descriptions of the parameters.

Statistical analysis

COP trajectories were analyzed separately for the AP and ML direction. Parameters describing the COP trajectories were compiled for each subject. Averages and standard deviations of the parameters for each group were calculated. For all the tests, statistical significance was defined as $p$ ⩽0.05. Because the parameters for the older adults at “high-risk” often lead to a violation of the homogeneity of variance assumption required for ANOVA, we used non-parametric statistical tests. The

Traditional static posturographic analysis

The averages, standard deviations and results of the statistical analyses for the traditional measures are presented in Table 2. The Kruskal–Wallis tests revealed a significant group main effect for all the traditional measures. In the follow-up tests for age (young compared to “low-risk”), none of the four measures were different, whereas all follow-up tests for fall risk (“low-risk” compared to “high-risk”) were significant.

SDA

The averages of the stabilogram diffusion plots for the three groups

Ability of traditional and statistical mechanics techniques to distinguish between groups

First, we examined the ability of the techniques to distinguish age-related differences by comparing the young and “low-risk” groups. There were no significant differences between the young and “low-risk” groups for any of the traditional measures. However, all four of the statistical mechanics techniques had at least one parameter that exhibited a significant difference between the young and “low-risk” groups. This suggests that statistical mechanics techniques are more sensitive to

Acknowledgments

We would like to thank Didier Delignières for sharing Excel macros. Thanks are also due to Jim Collins and Andrea Stamp who shared Matlab code for SDA. We would like to recognize the help of Pete Santago for discussions on statistical mechanics. Lastly, thanks are due to Jim Norris for additional statistical support.

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Reductions in the AP Path and 95%EA during the AP condition are indicators of improved postural sway. Indeed, larger paths and confidence ellipse areas of the CoP have been linked to increased risk of falling, and are construed as the high-risk situation exhibiting impaired postural control [39,40]. Correlational analyses revealed moderate to strong relationships between the baseline performance measures of APPF50, MLPF50, MLPF95, and EA obtained during the non-stimulation and change due to TS and LS variables.
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During AP perturbations, participants showed higher frequencies, longer paths, and larger ellipse areas. TS caused lower APPF50, MLPF50, MLPF95, APPath and EA while LS caused lower MLPF50 and EA. During ML perturbations, TS reduced APPF50 and both LS and TS caused reduction of MLPF95. Higher instability following AP perturbations was associated with greater effects of TS and LS.
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Do plantar hyperkeratoses affect balance in people older than 65 years old?
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Analyse how plantar hyperkeratosis debridement affects static body balance in subjects of 65 years of age or older.
In order to analyse the impact of hyperkeratoses on balance, 50 older people took part in this study. Pain caused by plantar hyperkeratoses was measured on a visual analogue scale. Static balance was assessed on a pressure sensitive platform. The treatment was scalpel debridement of hyperkeratoses.
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Ability of static and statistical mechanics posturographic measures to distinguish between age and fall risk

Abstract

Section snippets

Introduction and background

Description of the three groups in the study

Traditional static posturographic analysis

Statistical analysis

Traditional static posturographic analysis

SDA

Ability of traditional and statistical mechanics techniques to distinguish between groups

Acknowledgments

Neuroscience Letters

Clinical Biomechanics (Bristol, Avon.)

Gait & Posture

Archives of Physical Medicine and Rehabilitation

Neuroscience Letters

Lancet

A prospective study of laboratory and clinical measures of postural stability to predict community-dwelling fallers

Journal of Gerontology Series A

Open-loop and closed-loop control of posturea random-walk analysis of center-of-pressure trajectories

Experimental Brain Research

The effects of visual input on open-loop and closed-loop postural control mechanisms

Experimental Brain Research

Upright, correlated random walksa statistical-biomechanics approach to the human postural control system

Chaos

Age-related changes in open-loop and closed-loop postural control mechanisms

Experimental Brain Research

A methodological note on nonlinear time series analysisis the open- and closed-loop model of Collins and De Luca (1993) a statistical artifact?

Journal of Motor Behavior