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Visual Predictors of Postural Sway in Older Adults
PURPOSE: Accurate perception of body position relative to the environment through visual cues provides sensory input to the control of postural stability. This study explored which vision measures are most important for control of postural sway in older adults with a range of visual characteristics....
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Association for Research in Vision and Ophthalmology
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9424966/ https://www.ncbi.nlm.nih.gov/pubmed/36006028 http://dx.doi.org/10.1167/tvst.11.8.24 |
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author | Wood, Joanne M. Killingly, Callula Elliott, David B. Anstey, Kaarin J. Black, Alex A. |
author_facet | Wood, Joanne M. Killingly, Callula Elliott, David B. Anstey, Kaarin J. Black, Alex A. |
author_sort | Wood, Joanne M. |
collection | PubMed |
description | PURPOSE: Accurate perception of body position relative to the environment through visual cues provides sensory input to the control of postural stability. This study explored which vision measures are most important for control of postural sway in older adults with a range of visual characteristics. METHODS: Participants included 421 older adults (mean age = 72.6 ± 6.1), 220 with vision impairment associated with a range of eye diseases and 201 with normal vision. Participants completed a series of vision, cognitive, and physical function tests. Postural sway was measured using an electronic forceplate (HUR Labs) on a foam surface with eyes open. Linear regression analysis identified the strongest visual predictors of postural sway, controlling for potential confounding factors, including cognitive and physical function. RESULTS: In univariate regression models, unadjusted and adjusted for age, all of the vision tests were significantly associated with postural sway (P < 0.05), with the strongest predictor being visual motion sensitivity (standardized regression coefficient, β = 0.340; age-adjusted β = 0.253). In multiple regression models, motion sensitivity (β = 0.187), integrated binocular visual fields (β = −0.109), and age (β = 0.234) were the only significant visual predictors of sway, adjusted for confounding factors, explaining 23% of the variance in postural sway. CONCLUSIONS: Of the vision tests, visual motion perception and binocular visual fields were most strongly associated with postural stability in older adults with and without vision impairment. TRANSLATIONAL RELEVANCE: Findings provide insight into the visual contributions to postural stability in older adults and have implications for falls risk assessment. |
format | Online Article Text |
id | pubmed-9424966 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Association for Research in Vision and Ophthalmology |
record_format | MEDLINE/PubMed |
spelling | pubmed-94249662022-08-31 Visual Predictors of Postural Sway in Older Adults Wood, Joanne M. Killingly, Callula Elliott, David B. Anstey, Kaarin J. Black, Alex A. Transl Vis Sci Technol Artificial Intelligence PURPOSE: Accurate perception of body position relative to the environment through visual cues provides sensory input to the control of postural stability. This study explored which vision measures are most important for control of postural sway in older adults with a range of visual characteristics. METHODS: Participants included 421 older adults (mean age = 72.6 ± 6.1), 220 with vision impairment associated with a range of eye diseases and 201 with normal vision. Participants completed a series of vision, cognitive, and physical function tests. Postural sway was measured using an electronic forceplate (HUR Labs) on a foam surface with eyes open. Linear regression analysis identified the strongest visual predictors of postural sway, controlling for potential confounding factors, including cognitive and physical function. RESULTS: In univariate regression models, unadjusted and adjusted for age, all of the vision tests were significantly associated with postural sway (P < 0.05), with the strongest predictor being visual motion sensitivity (standardized regression coefficient, β = 0.340; age-adjusted β = 0.253). In multiple regression models, motion sensitivity (β = 0.187), integrated binocular visual fields (β = −0.109), and age (β = 0.234) were the only significant visual predictors of sway, adjusted for confounding factors, explaining 23% of the variance in postural sway. CONCLUSIONS: Of the vision tests, visual motion perception and binocular visual fields were most strongly associated with postural stability in older adults with and without vision impairment. TRANSLATIONAL RELEVANCE: Findings provide insight into the visual contributions to postural stability in older adults and have implications for falls risk assessment. The Association for Research in Vision and Ophthalmology 2022-08-24 /pmc/articles/PMC9424966/ /pubmed/36006028 http://dx.doi.org/10.1167/tvst.11.8.24 Text en Copyright 2022 The Authors https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License. |
spellingShingle | Artificial Intelligence Wood, Joanne M. Killingly, Callula Elliott, David B. Anstey, Kaarin J. Black, Alex A. Visual Predictors of Postural Sway in Older Adults |
title | Visual Predictors of Postural Sway in Older Adults |
title_full | Visual Predictors of Postural Sway in Older Adults |
title_fullStr | Visual Predictors of Postural Sway in Older Adults |
title_full_unstemmed | Visual Predictors of Postural Sway in Older Adults |
title_short | Visual Predictors of Postural Sway in Older Adults |
title_sort | visual predictors of postural sway in older adults |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9424966/ https://www.ncbi.nlm.nih.gov/pubmed/36006028 http://dx.doi.org/10.1167/tvst.11.8.24 |
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