Literature DB >> 28541383

The Technology Crisis in Neuropsychology.

Justin B Miller1, William B Barr2.   

Abstract

Neuropsychology has fallen reliant on outdated and labor intensive methods of data collection that are slow, highly inefficient, and expensive, and provide relatively data-poor estimates of human behavior despite rapid technological advance in most other fields of medicine. Here we present a brief historical overview of current testing practices in an effort to frame the current crisis, followed by an overview of different settings in which technology can and should be integrated. Potential benefits of laboratory based assessments, remote assessments, as well as passive and high-frequency data collection tools rooted in technology are discussed, along with several relevant examples and how these technologies might be deployed. Broader issues of data security and privacy are discussed, as well as additional considerations to be addressed within each setting. Some of the historical barriers to adoption of technology are also presented, along with a brief discussion of the remaining uncertainties. While by no means intended as a comprehensive review or prescriptive roadmap, our goal is to show that there are a tremendous number of advantages to technologically driven data collection methods, and that technology should be embraced by the field. Our predictions are that the comprehensive assessments of the future will likely entail a combination of lab-based assessments, remote assessments, and passive data capture, and leading the development of these efforts will cement the role of neuropsychology at the forefront of cognitive and behavioral science.
© The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  Assessment; Behavior; Cognition; Computer science; Information technology; Neurology; Neuroscience

Mesh:

Year:  2017        PMID: 28541383     DOI: 10.1093/arclin/acx050

Source DB:  PubMed          Journal:  Arch Clin Neuropsychol        ISSN: 0887-6177            Impact factor:   2.813


  20 in total

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Journal:  Brain Imaging Behav       Date:  2021-01-07       Impact factor: 3.978

9.  A Virtual Kitchen Protocol to Measure Everyday Memory Functioning for Meal Preparation.

Authors:  Michael D Barnett; Lucas G Childers; Thomas D Parsons
Journal:  Brain Sci       Date:  2021-04-29

10.  The Wisconsin Card Sorting Test: Split-Half Reliability Estimates for a Self-Administered Computerized Variant.

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Journal:  Brain Sci       Date:  2021-04-21
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