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在体内捕捉认知衰老:新兴数字工具的神经心理学框架应用

Capturing Cognitive Aging in Vivo: Application of a Neuropsychological Framework for Emerging Digital Tools.

作者信息

Hackett Katherine, Giovannetti Tania

机构信息

Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, United States.

出版信息

JMIR Aging. 2022 Sep 7;5(3):e38130. doi: 10.2196/38130.

Abstract

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.

摘要

随着痴呆症的全球负担持续困扰我们的医疗保健系统,越来越需要高效、客观且灵敏的工具来检测神经退行性疾病并捕捉日常认知中的有意义变化。新兴数字工具为解决当前方法的诸多弊端提供了一个有前景的选择,其使用场景包括早期检测、风险分层、预后和结果测量。然而,缺乏指导数字工具假设和结果解释的概念模型,而这些模型对于整理和组织来自各种传感器的大量连续数据是必要的。在此观点中,我们提出一个神经心理学框架,以便与一种关键的新兴方法——数字表型分析——一起使用。日常行为变异性(VIBE)模型基于神经心理学、神经病学、康复心理学、认知神经科学和计算机科学文献中的既定趋势,将个体内变异性模式、认知能力以及从健康到痴呆的各个临床阶段的日常功能联系起来。基于VIBE模型,我们提出可检验的假设,以指导在实际生活中捕捉日常认知的数字表型分析研究的设计和解释。我们最后讨论了关于应用数字表型分析方法以提高老年人认知评估的效率、可及性、准确性和生态效度的方法学考量及未来方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d420/9494215/ff01a7c76580/aging_v5i3e38130_fig1.jpg

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