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Driver Speed Profiles Index Warning Signs of MCI

GOAL: Use driver behavior profiles to screen and index early warnings of cognitive decline and Alzheimer’s disease (AD). Hypothesis: Real-world driver speed behavior profiles discriminate mild cognitive impairment (MCI). METHODS: Sensors were installed in personal vehicles of 74 legally-licensed, ac...

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Autores principales: Merickel, Jennifer, Wu, Ruiqian, Rizzo, Matthew, Zhang, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8682412/
http://dx.doi.org/10.1093/geroni/igab046.489
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author Merickel, Jennifer
Wu, Ruiqian
Rizzo, Matthew
Zhang, Ying
author_facet Merickel, Jennifer
Wu, Ruiqian
Rizzo, Matthew
Zhang, Ying
author_sort Merickel, Jennifer
collection PubMed
description GOAL: Use driver behavior profiles to screen and index early warnings of cognitive decline and Alzheimer’s disease (AD). Hypothesis: Real-world driver speed behavior profiles discriminate mild cognitive impairment (MCI). METHODS: Sensors were installed in personal vehicles of 74 legally-licensed, active drivers (age: 65-90 years, μ = 75.85) who completed 2, 3-month real-world driving assessments, including demographic and cognitive assessments, 1 year apart (244,564 miles driven). MCI status was indexed using 8 neuropsychological tests (spanning executive function, visuospatial skills, processing speed, and memory), relevant to MCI and driving. Driving environment was indexed from state speed limit (SL; roadway type: residential, commercial, interstate) and sunrise-sunset databases (time of day: day vs. night). Models: Data were randomly split into training (66%) and validation (33%) sets. An optimal mixed effects logistic regression model was determined from validation data AUC values. RESULTS: MCI drivers drove slower with optimal discrimination (estimated for every 5 mph decrease in speeding) in 1) residential roads (SL 25-35 mph; MCI odds increased by 6% [95% CI: 2-11%]), 2) interstate roads (SL >55 mph; MCI odds increased by 14% [95% CI: 8-20%]), and 3) night environments (MCI odds increased by 7% [95% CI: 2-12%]). CONCLUSION: Quantitative indices of real-world driver data provide “ground truth” for screening and indexing phenotypes of cognitive decline, in line with ongoing efforts to link driver behavior with age-related cognitive decline and AD biomarkers. Behavioral biomarkers for diagnosing early warnings of dementia could ultimately bolster our ability to detect and intervene in early AD.
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spelling pubmed-86824122021-12-17 Driver Speed Profiles Index Warning Signs of MCI Merickel, Jennifer Wu, Ruiqian Rizzo, Matthew Zhang, Ying Innov Aging Abstracts GOAL: Use driver behavior profiles to screen and index early warnings of cognitive decline and Alzheimer’s disease (AD). Hypothesis: Real-world driver speed behavior profiles discriminate mild cognitive impairment (MCI). METHODS: Sensors were installed in personal vehicles of 74 legally-licensed, active drivers (age: 65-90 years, μ = 75.85) who completed 2, 3-month real-world driving assessments, including demographic and cognitive assessments, 1 year apart (244,564 miles driven). MCI status was indexed using 8 neuropsychological tests (spanning executive function, visuospatial skills, processing speed, and memory), relevant to MCI and driving. Driving environment was indexed from state speed limit (SL; roadway type: residential, commercial, interstate) and sunrise-sunset databases (time of day: day vs. night). Models: Data were randomly split into training (66%) and validation (33%) sets. An optimal mixed effects logistic regression model was determined from validation data AUC values. RESULTS: MCI drivers drove slower with optimal discrimination (estimated for every 5 mph decrease in speeding) in 1) residential roads (SL 25-35 mph; MCI odds increased by 6% [95% CI: 2-11%]), 2) interstate roads (SL >55 mph; MCI odds increased by 14% [95% CI: 8-20%]), and 3) night environments (MCI odds increased by 7% [95% CI: 2-12%]). CONCLUSION: Quantitative indices of real-world driver data provide “ground truth” for screening and indexing phenotypes of cognitive decline, in line with ongoing efforts to link driver behavior with age-related cognitive decline and AD biomarkers. Behavioral biomarkers for diagnosing early warnings of dementia could ultimately bolster our ability to detect and intervene in early AD. Oxford University Press 2021-12-17 /pmc/articles/PMC8682412/ http://dx.doi.org/10.1093/geroni/igab046.489 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
Merickel, Jennifer
Wu, Ruiqian
Rizzo, Matthew
Zhang, Ying
Driver Speed Profiles Index Warning Signs of MCI
title Driver Speed Profiles Index Warning Signs of MCI
title_full Driver Speed Profiles Index Warning Signs of MCI
title_fullStr Driver Speed Profiles Index Warning Signs of MCI
title_full_unstemmed Driver Speed Profiles Index Warning Signs of MCI
title_short Driver Speed Profiles Index Warning Signs of MCI
title_sort driver speed profiles index warning signs of mci
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8682412/
http://dx.doi.org/10.1093/geroni/igab046.489
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