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. 2018 May 24;18(6):1692.
doi: 10.3390/s18061692.

Dynamical Properties of Postural Control in Obese Community-Dwelling Older Adults †

Christopher W Frames  1   2 , Rahul Soangra  3 , Thurmon E Lockhart  4 , John Lach  5 , Dong Sam Ha  6 , Karen A Roberto  7 , Abraham Lieberman  8
Affiliations

Dynamical Properties of Postural Control in Obese Community-Dwelling Older Adults †

Christopher W Frames et al. Sensors (Basel). .

Abstract

Postural control is a key aspect in preventing falls. The aim of this study was to determine if obesity affected balance in community-dwelling older adults and serve as an indicator of fall risk. The participants were randomly assigned to receive a comprehensive geriatric assessment followed by a longitudinal assessment of their fall history. The standing postural balance was measured for 98 participants with a Body Mass Index (BMI) ranging from 18 to 63 kg/m², using a force plate and an inertial measurement unit affixed at the sternum. Participants' fall history was recorded over 2 years and participants with at least one fall in the prior year were classified as fallers. The results suggest that body weight/BMI is an additional risk factor for falling in elderly persons and may be an important marker for fall risk. The linear variables of postural analysis suggest that the obese fallers have significantly higher sway area and sway ranges, along with higher root mean square and standard deviation of time series. Additionally, it was found that obese fallers have lower complexity of anterior-posterior center of pressure time series. Future studies should examine more closely the combined effect of aging and obesity on dynamic balance.

Keywords: nonlinear; obesity; postural control.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
(a) Median over all the center of pressure (COP) AP time series; (b) The lowest curve is obtained for m = 3 as it shows a minimum that is lower than 0.05. This minimum is reached for r = 0.25 for both AP and ML directions.
Figure 2
Figure 2
Linear measures of postural stability (a) Mean Velocity; (b) Mean Radius; (c) Circular area; (d) Elliptical area; (e) COP Path length.
Figure 3
Figure 3
Forceplate signal based non-linear analysis showing (a) approximate entropy; the graph highlights (dashed red line) obese fallers have significantly lower complexity in anterior posterior direction with eyes open condition; (b) scaling exponent α and; the graph highlights (dashed red line) that obese fallers have significantly higher scaling exponents in anterior posterior direction during eyes open condition (c) sample entropy is significantly lower for AP time series derived from forceplate for fallers with obesity in eyes open condition (red dashed lines).
Figure 4
Figure 4
Inertial measurement units (IMU) based nonlinear analysis showing (a) approximate entropy; The graph highlights (dashed red-line) approximate entropy is significantly lower in obese fallers in anterior posterior direction during eyes open double limb stance; (b) scaling exponent α and; The graph highlights (dashed red-line) that the scaling exponent is significantly higher for obese fallers in anterior posterior direction during eyes open double limb stance; (c) sample entropy; The graph highlights (red dashed line) that fallers who were non-obese showed significantly lower complexity (measured by sample entropy)for AP time series derived from IMU.
Figure 5
Figure 5
Ensemble patterns of postural stability of fallers and non-fallers exhibiting fallers with larger area of sway with lower sample entropy.
Figure 6
Figure 6
Radar plot of significant discriminative parameters.
See this image and copyright information in PMC

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References

    1. Mokdad A.H., Bowman B.A., Ford E.S., Vinicor F., Marks J.S., Koplan J.P. The continuing epidemics of obesity and diabetes in the United States. JAMA. 2001;286:1195–1200. doi: 10.1001/jama.286.10.1195. - DOI - PubMed
    1. Wang Y., Beydoun M.A. The Obesity Epidemic in the United States Gender, Age, Socioeconomic, Racial/Ethnic, and Geographic Characteristics: A Systematic Review and Meta-Regression Analysis. Epidemiol. Rev. 2007;29:6–28. doi: 10.1093/epirev/mxm007. - DOI - PubMed
    1. Lockhart T., Frame C., Soangra R., Lach J. Fall Risk Prediction Using Wearable Wireless Sensors. SPIE Newsroom; Bellingham, WA, USA: 2014.
    1. Ogden C.L., Carroll M.D., Curtin L.R., McDowell M.A., Tabak C.J., Flegal K.M. Prevalence of overweight and obesity in the United States, 1999–2004. JAMA. 2006;295:1549–1555. doi: 10.1001/jama.295.13.1549. - DOI - PubMed
    1. Wilson P.W., D’Agostino R.B., Sullivan L., Parise H., Kannel W.B. Overweight and obesity as determinants of cardiovascular risk: The Framingham experience. Arch. Intern. Med. 2002;162:1867–1872. doi: 10.1001/archinte.162.16.1867. - DOI - PubMed