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Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools
As the global burden of dementia continues to plague our healthcare systems, efficient, objective, and sensitive tools to detect neurodegenerative disease and capture meaningful changes in everyday cognition are increasingly needed. Emerging digital tools present a promising option to address many d...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9494215/ https://www.ncbi.nlm.nih.gov/pubmed/36069747 http://dx.doi.org/10.2196/38130 |
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author | Hackett, Katherine Giovannetti, Tania |
author_facet | Hackett, Katherine Giovannetti, Tania |
author_sort | Hackett, Katherine |
collection | PubMed |
description | As the global burden of dementia continues to plague our healthcare systems, efficient, objective, and sensitive tools to detect neurodegenerative disease and capture meaningful changes in everyday cognition are increasingly needed. Emerging digital tools present a promising option to address many drawbacks of current approaches, with contexts of use that include early detection, risk stratification, prognosis, and outcome measurement. However, conceptual models to guide hypotheses and interpretation of results from digital tools are lacking and are needed to sort and organize the large amount of continuous data from a variety of sensors. In this viewpoint, we propose a neuropsychological framework for use alongside a key emerging approach—digital phenotyping. The Variability in Everyday Behavior (VIBE) model is rooted in established trends from the neuropsychology, neurology, rehabilitation psychology, cognitive neuroscience, and computer science literature and links patterns of intraindividual variability, cognitive abilities, and everyday functioning across clinical stages from healthy to dementia. Based on the VIBE model, we present testable hypotheses to guide the design and interpretation of digital phenotyping studies that capture everyday cognition in vivo. We conclude with methodological considerations and future directions regarding the application of the digital phenotyping approach to improve the efficiency, accessibility, accuracy, and ecological validity of cognitive assessment in older adults. |
format | Online Article Text |
id | pubmed-9494215 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-94942152022-09-23 Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools Hackett, Katherine Giovannetti, Tania JMIR Aging Viewpoint As the global burden of dementia continues to plague our healthcare systems, efficient, objective, and sensitive tools to detect neurodegenerative disease and capture meaningful changes in everyday cognition are increasingly needed. Emerging digital tools present a promising option to address many drawbacks of current approaches, with contexts of use that include early detection, risk stratification, prognosis, and outcome measurement. However, conceptual models to guide hypotheses and interpretation of results from digital tools are lacking and are needed to sort and organize the large amount of continuous data from a variety of sensors. In this viewpoint, we propose a neuropsychological framework for use alongside a key emerging approach—digital phenotyping. The Variability in Everyday Behavior (VIBE) model is rooted in established trends from the neuropsychology, neurology, rehabilitation psychology, cognitive neuroscience, and computer science literature and links patterns of intraindividual variability, cognitive abilities, and everyday functioning across clinical stages from healthy to dementia. Based on the VIBE model, we present testable hypotheses to guide the design and interpretation of digital phenotyping studies that capture everyday cognition in vivo. We conclude with methodological considerations and future directions regarding the application of the digital phenotyping approach to improve the efficiency, accessibility, accuracy, and ecological validity of cognitive assessment in older adults. JMIR Publications 2022-09-07 /pmc/articles/PMC9494215/ /pubmed/36069747 http://dx.doi.org/10.2196/38130 Text en ©Katherine Hackett, Tania Giovannetti. Originally published in JMIR Aging (https://aging.jmir.org), 07.09.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Aging, is properly cited. The complete bibliographic information, a link to the original publication on https://aging.jmir.org, as well as this copyright and license information must be included. |
spellingShingle | Viewpoint Hackett, Katherine Giovannetti, Tania Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools |
title | Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools |
title_full | Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools |
title_fullStr | Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools |
title_full_unstemmed | Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools |
title_short | Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools |
title_sort | capturing cognitive aging in vivo: application of a neuropsychological framework for emerging digital tools |
topic | Viewpoint |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9494215/ https://www.ncbi.nlm.nih.gov/pubmed/36069747 http://dx.doi.org/10.2196/38130 |
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