Hajime Takechi1, Hiroshi Yoshino1. 1. Department of Geriatrics and Cognitive Disorders, Fujita Health University School of Medicine, Toyoake, Japan.
Abstract
AIM: This study aimed to assess whether CogEvo, a computerized cognitive assessment and training tool, could distinguish patients with mild Alzheimer's disease and mild cognitive impairment from cognitively normal older people. METHODS: This cross-sectional study enrolled 166 participants with Alzheimer's disease, mild cognitive impairment and cognitively normal older people. In CogEvo, five types of cognitive tasks were carried out, and the z-scores were used as a composite score. Logistic regression and receiver operating characteristics analyses were then carried out to evaluate the usefulness of CogEvo in distinguishing between the three groups. RESULTS: CogEvo and Mini-Mental State Examination scores showed excellent correlation, and could significantly differentiate between the Alzheimer's disease, mild cognitive impairment and cognitively normal older people groups (Mini-Mental State Examination 20.4 ± 3.5, 25.5 ± 1.6 and 27.6 ± 2.0, respectively; CogEvo: -1.9 ± 0.9, -0.8 ± 0.8 and 0.0 ± 1.0, respectively; both P < 0.001 by analysis of variance). Logistic regression analysis adjusted for age, sex and years of education significantly differentiated the mild cognitive dysfunction group (mild cognitive impairment plus mild Alzheimer's disease; n = 78) from the cognitively normal group (n = 88) (P < 0.001), whereas receiver operating characteristics analysis showed moderate accuracy (area under the receiver operating characteristic curve 0.830). CONCLUSIONS: These results suggest that CogEvo, a computerized cognitive assessment tool, is useful for evaluating early-stage cognitive impairment. Further studies are required to assess its effectiveness as a combination assessment and training tool. Geriatr Gerontol Int 2020; ••: ••-••.
AIM: This study aimed to assess whether CogEvo, a computerized cognitive assessment and training tool, could distinguish patients with mild Alzheimer's disease and mild cognitive impairment from cognitively normal older people. METHODS: This cross-sectional study enrolled 166 participants with Alzheimer's disease, mild cognitive impairment and cognitively normal older people. In CogEvo, five types of cognitive tasks were carried out, and the z-scores were used as a composite score. Logistic regression and receiver operating characteristics analyses were then carried out to evaluate the usefulness of CogEvo in distinguishing between the three groups. RESULTS: CogEvo and Mini-Mental State Examination scores showed excellent correlation, and could significantly differentiate between the Alzheimer's disease, mild cognitive impairment and cognitively normal older people groups (Mini-Mental State Examination 20.4 ± 3.5, 25.5 ± 1.6 and 27.6 ± 2.0, respectively; CogEvo: -1.9 ± 0.9, -0.8 ± 0.8 and 0.0 ± 1.0, respectively; both P < 0.001 by analysis of variance). Logistic regression analysis adjusted for age, sex and years of education significantly differentiated the mild cognitive dysfunction group (mild cognitive impairment plus mild Alzheimer's disease; n = 78) from the cognitively normal group (n = 88) (P < 0.001), whereas receiver operating characteristics analysis showed moderate accuracy (area under the receiver operating characteristic curve 0.830). CONCLUSIONS: These results suggest that CogEvo, a computerized cognitive assessment tool, is useful for evaluating early-stage cognitive impairment. Further studies are required to assess its effectiveness as a combination assessment and training tool. Geriatr Gerontol Int 2020; ••: ••-••.