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Review
. 2024 Apr 24;39(3):290-304.
doi: 10.1093/arclin/acae016.

Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies

Che Harris  1   2 , Yingfei Tang  1   2 , Eliana Birnbaum  1 , Christine Cherian  1 , Dinesh Mendhe  1 , Michelle H Chen  1   2
Affiliations
Review

Digital Neuropsychology beyond Computerized Cognitive Assessment: Applications of Novel Digital Technologies

Che Harris et al. Arch Clin Neuropsychol. .

Abstract

Compared with other health disciplines, there is a stagnation in technological innovation in the field of clinical neuropsychology. Traditional paper-and-pencil tests have a number of shortcomings, such as low-frequency data collection and limitations in ecological validity. While computerized cognitive assessment may help overcome some of these issues, current computerized paradigms do not address the majority of these limitations. In this paper, we review recent literature on the applications of novel digital health approaches, including ecological momentary assessment, smartphone-based assessment and sensors, wearable devices, passive driving sensors, smart homes, voice biomarkers, and electronic health record mining, in neurological populations. We describe how each digital tool may be applied to neurologic care and overcome limitations of traditional neuropsychological assessment. Ethical considerations, limitations of current research, as well as our proposed future of neuropsychological practice are also discussed.

Keywords: Digital biomarkers; Digital phenotyping; Ecological momentary assessment; Technology; Teleneuropsychology; mHealth.

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Conflict of interest statement

None declared.

Figures

Fig. 1
Fig. 1
The Variability in Everyday Behavior (VIBE) model proposed by Hackett and Giovannetti (2022). According to this model, cognitive abilities decrease gradually in early stages of decline and more rapidly in later stages. Intraindividual variability follows an inverse U-shape pattern and peaks during the MCI stage. As it is often difficult to detect subtle cognitive changes during the prodromal and MCI stages, the authors described the advantage of high dimensional data from digital sources in characterizing cognitive functioning and variability in the real world through repeated data points (e.g., EMA, passive sensors). Figure reprinted from open-access article published by (Hackett & Giovannetti, 2022), distributed under the creative commons attribution license, which permits unrestricted use, distribution, and reproduction in any medium.
See this image and copyright information in PMC

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