Dementia and geriatric cognitive disorders · 2026
Digital Cognitive Assessment for Older Adults: Validation of an Automated Three-Module Tool for Mild Cognitive Impairment and Dementia Screening.
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Pan Dong-Ni, Xie Hailun, Ren Juejing, Shen Ji, Li Jing, Zeng Yanjia, Wang Wen, Fan Dingyao, Xue Gui
Аннотация
INTRODUCTION: With the aging global population, effective screening tools for age-related cognitive disorders are urgently needed. Mild cognitive impairment (MCI), a transitional stage between normal aging and dementia, requires early detection for timely intervention.
METHODS: The current research developed an innovative electronic assessment tool designed with three task modules targeting executive function, memory binding, and spatial navigation to quickly screen for MCI in older adults. A validation study was conducted with 271 older participants, aged 56 to 89, comprising 138 individuals with MCI and 133 cognitively normal controls. An independent dataset from a community hospital was used for further confirmation.
RESULTS: The validation study indicated excellent reliability and validity, achieving 72% accuracy in distinguishing MCI from cognitively normal individuals, with excellent screening power (AUC = 0.807; 95% CI: 0.756-0.858). This performance surpasses that of the paper-and-pencil Mini-Mental State Examination (MMSE). The independent dataset from a community hospital further confirmed that the tool achieved a good accuracy rate of 93% (26/28) in predicting MCI.
CONCLUSION: These results provide strong tool support for the early identification of MCI, enhancing the effective management of cognitive decline in at-risk elderly individuals.
METHODS: The current research developed an innovative electronic assessment tool designed with three task modules targeting executive function, memory binding, and spatial navigation to quickly screen for MCI in older adults. A validation study was conducted with 271 older participants, aged 56 to 89, comprising 138 individuals with MCI and 133 cognitively normal controls. An independent dataset from a community hospital was used for further confirmation.
RESULTS: The validation study indicated excellent reliability and validity, achieving 72% accuracy in distinguishing MCI from cognitively normal individuals, with excellent screening power (AUC = 0.807; 95% CI: 0.756-0.858). This performance surpasses that of the paper-and-pencil Mini-Mental State Examination (MMSE). The independent dataset from a community hospital further confirmed that the tool achieved a good accuracy rate of 93% (26/28) in predicting MCI.
CONCLUSION: These results provide strong tool support for the early identification of MCI, enhancing the effective management of cognitive decline in at-risk elderly individuals.