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Background  A newer generation neuropsychological tests can take advantage of touch screen and mobile technology. We have developed a new Android application termed “User eXperience-Trail Making Test (UX-TMT)” for neurocognitive assessment and training. This study investigated the utility, including the reliability and the validity, of the UX-TMT as a screening test for cognitive decline in adults.    Methods  A total of 84 individuals aged 27–86 years were divided into three groups; healthy controls ([HC]  n  = 29), people with Parkinson’s disease (PD;  n  = 28), and people with mild cognitive impairment (MCI) and dementia (MCI&D;  n  = 27). We examined the distributions of the scores and the time required, and the effects of age and group on these distributions. We analyzed internal consistency and convergent validity in all samples and applied receiver operator characteristic (ROC) analysis to determine a cutoff score that could differentiate the MCI & D group from the HC group.    Results  97.6% of the participants completed all of the tasks, and the average total test time required for UX-TMT was 428.8 (± 109.1) s in the HC, 542.0 (± 168.7) s in the PD, and 777.5 (± 256.1) s in the MCI&D groups, respectively. The MCI&D group showed significantly lower UX-TMT scores and longer total time in completing the task than the HC group. In an ROC analysis, a score of 21 showed high sensitivity (.83) and specificity (.92), and the UX-TMT score plus age improved sensitivity to .96. Additionally, the UX-TMT scores showed significant correlation with the Mini-Mental State Examination (Japanese version) scores ( r  = .77,  p  = .001), and Cronbach’s alpha (.71–.83) indicated acceptable internal consistency.    Conclusion  The UX-TMT demonstrated high reliability and validity to detect cognitive decline in Japanese adults, highlighting its utility as a screening tool for epidemiological and clinical research.

bmc psychiatry

A new device-aided cognitive function test, User eXperience-Trail Making Test (UX-TMT), sensitively detects neuropsychological performance in patients with dementia and Parkinson’s disease