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Citation |
Edginton Bigelow K, Berme N. J. Gerontol. A Biol. Sci. Med. Sci. 2011; 66(2): 228-233. |
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Affiliation |
Department of Mechanical and Aerospace Engineering, University of Dayton, 300 College Park, Dayton, Ohio 45469-0238. kimberly.bigelow@udayton.edu. |
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Copyright |
(Copyright © 2011, Gerontological Society of America) |
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DOI |
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PMID |
21127191 |
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Abstract |
BACKGROUND: The usefulness of posturography in the clinical screening of older adults for fall risk has been limited by a lack of standardization in testing methodology and data reporting. This study determines which testing condition and postural sway measures best differentiate recurrent fallers and nonrecurrent fallers. METHODS: One hundred and fifty older adults were categorized based on their fall status in the past year. Participants performed four quiet-standing tasks, eyes open and eyes closed in both comfortable and narrow stance, for 60 seconds while standing on a force-measuring platform. Traditional and fractal measures were calculated from the center of pressure data. Logistic regression was performed to determine the model for each condition that best discriminated between recurrent fallers and nonrecurrent fallers. RESULTS: The eyes closed comfortable stance condition, with its associated model, best differentiated recurrent fallers and nonrecurrent fallers. Medial-lateral sway velocity, anterior-posterior short-term α-scaling exponent, medial-lateral short-term α-scaling exponent, mean frequency, body mass index, and age were included in this model. Sensitivity of the model was 75%, and specificity was 94%. CONCLUSIONS: This resulting model demonstrates potential to differentiate recurrent fallers and nonrecurrent fallers in an eyes closed comfortable stance condition. The inclusion of traditional sway parameters, fractal measures, and personal characteristics in this model demonstrates the importance of considering multiple descriptions of postural stability together rather than using only a single measure to establish fall risk. Language: en |