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Application of the Stability Coefficient for Considering Intrinsic Variability of Postural Sway

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Abstract

Postural sway typically has high intrinsic variability. Due to high intrinsic variability, the reliability of its clinical application is limited. This study proposed the modified stability coefficient considering the intrinsic variability of postural sway for reducing its high level of variability, and calculated the contribution of major sensory systems (somatosensory, visual and vestibular) for its possible clinical application. The subjects of this study were composed of 25 healthy young (HY) adults in their 20 s and 33 healthy older (HO) adults over 65 years of age. Each subject maintained four standing conditions (eyes open and eyes closed on a firm surface, and a foam surface) for 1 min each, and postural sway was measured using the inertial sensor that was attached to their waist. Postural sway was calculated using seven variables that reflect the changes in spatial movements (Mean distance, Root mean square, Path length, Range of acceleration, Mean velocity, Mean frequency, 95% confidence ellipse area). The stability coefficient proposed in this study was calculated using the variables that showed significant difference between groups, and sensory contributions were calculated. The indices on the statistics (p value) and practical significance (effect size: Cohen’s d) between the groups, and the coefficient of variation (CV) within each group were calculated by the calculated sensory contribution. By introducing the stability coefficient, the average CV was reduced to 28.13% in HY and 27.20% in HO with a high level of variation, compared to 36.67% in HY and 39.30% in HO with a very high level of variation. The average CV of sensory ratios was 12.79% in HY and 12.92% in HO with a medium level of variation. As the sensory ratios utilizing stability coefficient show statistical and practical differences of age-related changes in balance and the average CVs with a medium level of variation, these results indicate the possibility of clinical use about the sensory ratios.

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Abbreviations

HY:

Healthy young

HO:

Healthy older

CV:

Coefficient of variation

EO:

Eyes open, firm surface

EC:

Eyes closed, firm surface

EO-foam:

Eyes open, foam surface

EC-foam:

Eyes closed, foam surface

DIST:

Mean distance

RMS:

Root mean square

PATH:

Path length

RANGE:

Range of acceleration

MV:

Mean velocity

MF:

Mean frequency

AREA:

95% confidence ellipse area

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A2A2A03014511 and 2016R1D1A3B03930135).

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Authors and Affiliations

  1. Department of Biomedical Engineering, Konkuk University, 268 Chungwon-daero, Chungju-si, Chungcheongbuk-do, 27478, South Korea

    Dongwon Kang, Jeongwoo Seo, Junggil Kim, Jinsoo Lee, Jinseung Choi & Gyerae Tack

  2. BK21 Plus Research Institute of Biomedical Engineering, Konkuk University, Korea, 268 Chungwon-daero, Chungju-si, Chungcheongbuk-do, 27478, South Korea

    Jinseung Choi & Gyerae Tack

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  1. Dongwon Kang
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  2. Jeongwoo Seo
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Correspondence to Gyerae Tack.

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Kang, D., Seo, J., Kim, J. et al. Application of the Stability Coefficient for Considering Intrinsic Variability of Postural Sway. Int. J. Precis. Eng. Manuf. 20, 443–449 (2019). https://doi.org/10.1007/s12541-019-00024-0

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  • DOI: https://doi.org/10.1007/s12541-019-00024-0

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