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Toward a Multidimensional Understanding of Later Life Disability: A Latent Profile Analysis

Disability is difficult to define succinctly. Current literature on disability has primarily focused on physical functional limitations. However, relying on a single dimension or index cannot accurately represent disability as the experience of disability is nuanced and complex. To address these gap...

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Autores principales: Peterson, Natasha, Lee, Jeongeun, Kahana, Eva
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8682257/
http://dx.doi.org/10.1093/geroni/igab046.778
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author Peterson, Natasha
Lee, Jeongeun
Kahana, Eva
author_facet Peterson, Natasha
Lee, Jeongeun
Kahana, Eva
author_sort Peterson, Natasha
collection PubMed
description Disability is difficult to define succinctly. Current literature on disability has primarily focused on physical functional limitations. However, relying on a single dimension or index cannot accurately represent disability as the experience of disability is nuanced and complex. To address these gaps, this study aims to understand the multidimensional nature of disability among retired, community-dwelling older adults. Using a sample of 414 older adults between the ages of 72 and 106 years (M=84.84, SD=4.56), latent profile analysis was employed to identify classes based on five indicators of disability across three domains. The five indicators of disability included difficulties with activities of daily living (ADLs), cognitive impairment, physical impairment, sensory impairment, and participation restrictions. Three classes were found to represent the data best. The most favorable and highly functioning group comprised the highest number of participants (n=242, 59.5%). The next group, class 2 (n=157, 37.9%), was characterized by high physical impairment and ADL-difficulty. The smallest group, class 3 (n=15, 3.6%), had the highest ADL-difficulty and participation restrictions but drastically lower cognitive and sensory impairment. Multinomial logistic regression revealed that class membership was related to sociodemographic characteristics. Finally, class membership predicted several mental health outcomes such as depressive symptoms, positive affect, and life satisfaction in the expected direction. If supported by future work, these findings could inform practitioners in developing more specific interventions relevant to older adults based on their disability profiles. Understanding various combinations of disablement has potential implications for services and interventions to be tailored to individuals’ distinct disability-related needs.
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spelling pubmed-86822572021-12-17 Toward a Multidimensional Understanding of Later Life Disability: A Latent Profile Analysis Peterson, Natasha Lee, Jeongeun Kahana, Eva Innov Aging Abstracts Disability is difficult to define succinctly. Current literature on disability has primarily focused on physical functional limitations. However, relying on a single dimension or index cannot accurately represent disability as the experience of disability is nuanced and complex. To address these gaps, this study aims to understand the multidimensional nature of disability among retired, community-dwelling older adults. Using a sample of 414 older adults between the ages of 72 and 106 years (M=84.84, SD=4.56), latent profile analysis was employed to identify classes based on five indicators of disability across three domains. The five indicators of disability included difficulties with activities of daily living (ADLs), cognitive impairment, physical impairment, sensory impairment, and participation restrictions. Three classes were found to represent the data best. The most favorable and highly functioning group comprised the highest number of participants (n=242, 59.5%). The next group, class 2 (n=157, 37.9%), was characterized by high physical impairment and ADL-difficulty. The smallest group, class 3 (n=15, 3.6%), had the highest ADL-difficulty and participation restrictions but drastically lower cognitive and sensory impairment. Multinomial logistic regression revealed that class membership was related to sociodemographic characteristics. Finally, class membership predicted several mental health outcomes such as depressive symptoms, positive affect, and life satisfaction in the expected direction. If supported by future work, these findings could inform practitioners in developing more specific interventions relevant to older adults based on their disability profiles. Understanding various combinations of disablement has potential implications for services and interventions to be tailored to individuals’ distinct disability-related needs. Oxford University Press 2021-12-17 /pmc/articles/PMC8682257/ http://dx.doi.org/10.1093/geroni/igab046.778 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of The Gerontological Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Abstracts
Peterson, Natasha
Lee, Jeongeun
Kahana, Eva
Toward a Multidimensional Understanding of Later Life Disability: A Latent Profile Analysis
title Toward a Multidimensional Understanding of Later Life Disability: A Latent Profile Analysis
title_full Toward a Multidimensional Understanding of Later Life Disability: A Latent Profile Analysis
title_fullStr Toward a Multidimensional Understanding of Later Life Disability: A Latent Profile Analysis
title_full_unstemmed Toward a Multidimensional Understanding of Later Life Disability: A Latent Profile Analysis
title_short Toward a Multidimensional Understanding of Later Life Disability: A Latent Profile Analysis
title_sort toward a multidimensional understanding of later life disability: a latent profile analysis
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8682257/
http://dx.doi.org/10.1093/geroni/igab046.778
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