Evidence map›Paper›PMID 40799411›Full record

ArticleMayo Clinic proceedings. Digital health2025

An Automated Mobile Cognitive Test for the Identification of Cognitive Impairment: A Cross-sectional Feasibility and Diagnostic Study.

Louis Y Tee, Li Feng Tan, Santhosh Seetharaman, Lian Leng Low, Zhi Peng Ong, Munirah Bashil, Hock Hai Teo

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Article in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Louis Y TeeDivision of Healthy Ageing, Alexandra Hospital, Singapore.
Li Feng TanDivision of Healthy Ageing, Alexandra Hospital, Singapore.
Santhosh SeetharamanDivision of Healthy Ageing, Alexandra Hospital, Singapore.
Lian Leng LowDepartment of Family Medicine and Continuing Care, Outram Community Hospital, Singapore.
Zhi Peng OngDepartment of Statistics and Data Science, Faculty of Science, National University of Singapore, Singapore.
Munirah BashilDepartment of Information Systems and Analytics, School of Computing, National University of Singapore, Singapore.
Hock Hai TeoDepartment of Information Systems and Analytics, School of Computing, National University of Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To develop Digital Processing Speed Test (DPST), a free, automated, multilingual, artificial intelligence-based cognitive testing application, with the aim to enhance recognition of cognitive impairment in underserved communities by leveraging mobile health to improve cognitive testing's accessibility. Patients and Methods: In this cross-sectional feasibility and diagnostic study, we determined the test performance of DPST for the identification of mild cognitive impairment (MCI) and dementia, compared with traditional cognitive tests, such as Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). The study was conducted from January 19, 2021, to November 12, 2023. In total, 476 adult participants were recruited by consecutive sampling at waiting areas of primary and secondary care clinics. The participants completed MMSE and MoCA with trained assessors and then performed DPST independently on a mobile device. The reference standard was a clinical diagnosis of MCI/dementia by a memory specialist blinded to the DPST score. Results: Area under the receiver operating characteristic curve analyses showed that area under the curves were similar for the 3 tests (MMSE, 0.862; MoCA, 0.888; DPST, 0.861). Likewise, sensitivity (DPST, 85.2%; MMSE, 85.2%; MoCA, 90.2%), negative likelihood ratio (DPST, 0.197; MMSE, 0.193; MoCA, 0.129), specificity (DPST, 75.0%; MMSE, 76.5%; MoCA, 76.2%), and positive likelihood ratio (DPST, 3.41; MMSE, 3.62; MoCA, 3.79) were similar. Conclusion: Digital Processing Speed Test, a free, automated, multilingual cognitive test conducted on a mobile device, has similar test performance to MMSE and MoCA. Nonetheless, DPST does not capture the multidomain cognitive deficits that characterize MCI/dementia. Moreover, test-retest reliability and interrater agreement of artificial intelligence-based handwriting recognition needs further confirmation.

Identifiers

PMID40799411
PMCPMC12341694

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.