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SART and Individual Trial Mistake Thresholds: Predictive Model for Mobility Decline

The Sustained Attention to Response Task (SART) has been used to measure neurocognitive functions in older adults. However, simplified average features of this complex dataset may result in loss of primary information and fail to express associations between test performance and clinically meaningfu...

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Autores principales: Rizzo, Rossella, Knight, Silvin Paul, Davis, James R. C., Newman, Louise, Duggan, Eoin, Kenny, Rose Anne, Romero-Ortuno, Roman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8482118/
https://www.ncbi.nlm.nih.gov/pubmed/34562986
http://dx.doi.org/10.3390/geriatrics6030085
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author Rizzo, Rossella
Knight, Silvin Paul
Davis, James R. C.
Newman, Louise
Duggan, Eoin
Kenny, Rose Anne
Romero-Ortuno, Roman
author_facet Rizzo, Rossella
Knight, Silvin Paul
Davis, James R. C.
Newman, Louise
Duggan, Eoin
Kenny, Rose Anne
Romero-Ortuno, Roman
author_sort Rizzo, Rossella
collection PubMed
description The Sustained Attention to Response Task (SART) has been used to measure neurocognitive functions in older adults. However, simplified average features of this complex dataset may result in loss of primary information and fail to express associations between test performance and clinically meaningful outcomes. Here, we describe a new method to visualise individual trial (raw) information obtained from the SART test, vis-à-vis age, and groups based on mobility status in a large population-based study of ageing in Ireland. A thresholding method, based on the individual trial number of mistakes, was employed to better visualise poorer SART performances, and was statistically validated with binary logistic regression models to predict mobility and cognitive decline after 4 years. Raw SART data were available for 4864 participants aged 50 years and over at baseline. The novel visualisation-derived feature bad performance, indicating the number of SART trials with at least 4 mistakes, was the most significant predictor of mobility decline expressed by the transition from Timed Up-and-Go (TUG) < 12 to TUG ≥ 12 s (OR = 1.29; 95% CI 1.14–1.46; p < 0.001), and the only significant predictor of new falls (OR = 1.11; 95% CI 1.03–1.21; p = 0.011), in models adjusted for multiple covariates. However, no SART-related variables resulted significant for the risk of cognitive decline, expressed by a decrease of ≥2 points in the Mini-Mental State Examination (MMSE) score. This novel multimodal visualisation could help clinicians easily develop clinical hypotheses. A threshold approach to the evaluation of SART performance in older adults may better identify subjects at higher risk of future mobility decline.
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spelling pubmed-84821182021-10-01 SART and Individual Trial Mistake Thresholds: Predictive Model for Mobility Decline Rizzo, Rossella Knight, Silvin Paul Davis, James R. C. Newman, Louise Duggan, Eoin Kenny, Rose Anne Romero-Ortuno, Roman Geriatrics (Basel) Article The Sustained Attention to Response Task (SART) has been used to measure neurocognitive functions in older adults. However, simplified average features of this complex dataset may result in loss of primary information and fail to express associations between test performance and clinically meaningful outcomes. Here, we describe a new method to visualise individual trial (raw) information obtained from the SART test, vis-à-vis age, and groups based on mobility status in a large population-based study of ageing in Ireland. A thresholding method, based on the individual trial number of mistakes, was employed to better visualise poorer SART performances, and was statistically validated with binary logistic regression models to predict mobility and cognitive decline after 4 years. Raw SART data were available for 4864 participants aged 50 years and over at baseline. The novel visualisation-derived feature bad performance, indicating the number of SART trials with at least 4 mistakes, was the most significant predictor of mobility decline expressed by the transition from Timed Up-and-Go (TUG) < 12 to TUG ≥ 12 s (OR = 1.29; 95% CI 1.14–1.46; p < 0.001), and the only significant predictor of new falls (OR = 1.11; 95% CI 1.03–1.21; p = 0.011), in models adjusted for multiple covariates. However, no SART-related variables resulted significant for the risk of cognitive decline, expressed by a decrease of ≥2 points in the Mini-Mental State Examination (MMSE) score. This novel multimodal visualisation could help clinicians easily develop clinical hypotheses. A threshold approach to the evaluation of SART performance in older adults may better identify subjects at higher risk of future mobility decline. MDPI 2021-08-31 /pmc/articles/PMC8482118/ /pubmed/34562986 http://dx.doi.org/10.3390/geriatrics6030085 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rizzo, Rossella
Knight, Silvin Paul
Davis, James R. C.
Newman, Louise
Duggan, Eoin
Kenny, Rose Anne
Romero-Ortuno, Roman
SART and Individual Trial Mistake Thresholds: Predictive Model for Mobility Decline
title SART and Individual Trial Mistake Thresholds: Predictive Model for Mobility Decline
title_full SART and Individual Trial Mistake Thresholds: Predictive Model for Mobility Decline
title_fullStr SART and Individual Trial Mistake Thresholds: Predictive Model for Mobility Decline
title_full_unstemmed SART and Individual Trial Mistake Thresholds: Predictive Model for Mobility Decline
title_short SART and Individual Trial Mistake Thresholds: Predictive Model for Mobility Decline
title_sort sart and individual trial mistake thresholds: predictive model for mobility decline
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8482118/
https://www.ncbi.nlm.nih.gov/pubmed/34562986
http://dx.doi.org/10.3390/geriatrics6030085
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