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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....

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Autores principales: Wood, Joanne M., Killingly, Callula, Elliott, David B., Anstey, Kaarin J., Black, Alex A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Association for Research in Vision and Ophthalmology 2022
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.
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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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