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Validation of an automated digital neuropsychological assessment system (DNAS)
Li Liang1, Chetwyn C H Chan2, Tatia M C Lee1
1Laboratory of Neuropsychology and Human Neuroscience, The University of Hong Kong, Hong Kong SAR, China; State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China.
Introduction:
Diagnosing neurocognitive disorders, mild or major, requires a comprehensive assessment of domains of cognitive functions according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR). Conventional neuropsychological assessments, usually conducted on a one-to-one assessment format, are under constant challenge of the demand on manpower and time resources, let alone its limited scalability to serve a larger population. Entering the era of digital healthcare emphasizing scalability, accessibility, and cost-effectiveness in delivering healthcare services, we have developed an automated digital neuropsychological assessment system - the Digital Neuropsychological Assessment System (DNAS), administered in individual or group format with minimal supervision.
Methods:
We conducted a validation study of the DNAS, which encompasses 13 subtests assessing a spectrum of cognitive domains delineated in the DSM-5-TR for diagnosing neurocognitive disorders. These 13 tests fall in the domains of processing speed, attention, working memory, visual and verbal memory, spatial judgment, higher-order cognition, and social cognition tests.
Results:
On a sample of 60 Chinese-speaking younger adults (mean age = 25.4 years old, SD = 3.6 years), we demonstrated the criterion validity (rs or ρs = .30 to .62) substantiated by the significant correlations between the DNAS and conventional assessment results.
Conclusions:
Our findings confirmed that the DNAS is a promising alternative to conventional neuropsychological assessments in the era of digital healthcare. The DNAS contributes to improving the cost-effectiveness of delivering neuropsychological services and research. Its automated design enhances the scalability of neuropsychological services to people whose access to healthcare settings is limited.
