Electronic Cognitive Screen Technology for Screening Older Adults With Dementia and Mild Cognitive Impairment in a Community Setting: Development and Validation Study

BackgroundA digital cognitive test can be a useful and quick tool for the screening of cognitive impairment. Previous studies have shown that the diagnostic performance of digital cognitive tests is comparable with that of conventional paper-and-pencil tests. However, the use...

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Main Authors: Chan, Joyce Y C, Wong, Adrian, Yiu, Brian, Mok, Hazel, Lam, Patti, Kwan, Pauline, Chan, Amany, Mok, Vincent C T, Tsoi, Kelvin K F, Kwok, Timothy C Y
Format: Article
Language:English
Published: JMIR Publications 2020-12-01
Series:Journal of Medical Internet Research
Online Access:http://www.jmir.org/2020/12/e17332/
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spelling doaj-099dd1caf9f44e9eb1c9b5a0dc4981b02021-04-02T18:40:41ZengJMIR PublicationsJournal of Medical Internet Research1438-88712020-12-012212e1733210.2196/17332Electronic Cognitive Screen Technology for Screening Older Adults With Dementia and Mild Cognitive Impairment in a Community Setting: Development and Validation StudyChan, Joyce Y CWong, AdrianYiu, BrianMok, HazelLam, PattiKwan, PaulineChan, AmanyMok, Vincent C TTsoi, Kelvin K FKwok, Timothy C Y BackgroundA digital cognitive test can be a useful and quick tool for the screening of cognitive impairment. Previous studies have shown that the diagnostic performance of digital cognitive tests is comparable with that of conventional paper-and-pencil tests. However, the use of commercially available digital cognitive tests is not common in Hong Kong, which may be due to the high cost of the tests and the language barrier. Thus, we developed a brief and user-friendly digital cognitive test called the Electronic Cognitive Screen (EC-Screen) for the detection of mild cognitive impairment (MCI) and dementia of older adults. ObjectiveThe aim of this study was to evaluate the performance of the EC-Screen for the detection of MCI and dementia in older adults. MethodsThe EC-Screen is a brief digital cognitive test that has been adapted from the Rapid Cognitive Screen test. The EC-Screen uses a cloud-based platform and runs on a tablet. Participants with MCI, dementia, and cognitively healthy controls were recruited from research clinics and the community. The outcomes were the performance of the EC-Screen in distinguishing participants with MCI and dementia from controls, and in distinguishing participants with dementia from those with MCI and controls. The cohort was randomly split into derivation and validation cohorts based on the participants’ disease group. In the derivation cohort, the regression-derived score of the EC-Screen was calculated using binomial logistic regression. Two predictive models were produced. The first model was used to distinguish participants with MCI and dementia from controls, and the second model was used to distinguish participants with dementia from those with MCI and controls. Receiver operating characteristic curves were constructed and the areas under the curves (AUCs) were calculated. The performances of the two predictive models were tested using the validation cohorts. The relationship between the EC-Screen and paper-and-pencil Montreal Cognitive Assessment-Hong Kong version (HK-MoCA) was evaluated by the Pearson correlation coefficient. ResultsA total of 126 controls, 54 participants with MCI, and 63 participants with dementia were included in the study. In differentiating participants with MCI and dementia from controls, the AUC of the EC-Screen in the derivation and validation cohorts was 0.87 and 0.84, respectively. The optimal sensitivity and specificity in the derivation cohorts were 0.81 and 0.80, respectively. In differentiating participants with dementia from those with MCI and controls, the AUC of the derivation and validation cohorts was 0.90 and 0.88, respectively. The optimal sensitivity and specificity in the derivation cohort were 0.83 and 0.83, respectively. There was a significant correlation between the EC-Screen and HK-MoCA (r=–0.67, P<.001). ConclusionsThe EC-Screen is suggested to be a promising tool for the detection of MCI and dementia. This test can be self-administered or assisted by a nonprofessional staff or family member. Therefore, the EC-Screen can be a useful tool for case finding in primary health care and community settings.http://www.jmir.org/2020/12/e17332/
collection DOAJ
language English
format Article
sources DOAJ
author Chan, Joyce Y C
Wong, Adrian
Yiu, Brian
Mok, Hazel
Lam, Patti
Kwan, Pauline
Chan, Amany
Mok, Vincent C T
Tsoi, Kelvin K F
Kwok, Timothy C Y
spellingShingle Chan, Joyce Y C
Wong, Adrian
Yiu, Brian
Mok, Hazel
Lam, Patti
Kwan, Pauline
Chan, Amany
Mok, Vincent C T
Tsoi, Kelvin K F
Kwok, Timothy C Y
Electronic Cognitive Screen Technology for Screening Older Adults With Dementia and Mild Cognitive Impairment in a Community Setting: Development and Validation Study
Journal of Medical Internet Research
author_facet Chan, Joyce Y C
Wong, Adrian
Yiu, Brian
Mok, Hazel
Lam, Patti
Kwan, Pauline
Chan, Amany
Mok, Vincent C T
Tsoi, Kelvin K F
Kwok, Timothy C Y
author_sort Chan, Joyce Y C
title Electronic Cognitive Screen Technology for Screening Older Adults With Dementia and Mild Cognitive Impairment in a Community Setting: Development and Validation Study
title_short Electronic Cognitive Screen Technology for Screening Older Adults With Dementia and Mild Cognitive Impairment in a Community Setting: Development and Validation Study
title_full Electronic Cognitive Screen Technology for Screening Older Adults With Dementia and Mild Cognitive Impairment in a Community Setting: Development and Validation Study
title_fullStr Electronic Cognitive Screen Technology for Screening Older Adults With Dementia and Mild Cognitive Impairment in a Community Setting: Development and Validation Study
title_full_unstemmed Electronic Cognitive Screen Technology for Screening Older Adults With Dementia and Mild Cognitive Impairment in a Community Setting: Development and Validation Study
title_sort electronic cognitive screen technology for screening older adults with dementia and mild cognitive impairment in a community setting: development and validation study
publisher JMIR Publications
series Journal of Medical Internet Research
issn 1438-8871
publishDate 2020-12-01
description BackgroundA digital cognitive test can be a useful and quick tool for the screening of cognitive impairment. Previous studies have shown that the diagnostic performance of digital cognitive tests is comparable with that of conventional paper-and-pencil tests. However, the use of commercially available digital cognitive tests is not common in Hong Kong, which may be due to the high cost of the tests and the language barrier. Thus, we developed a brief and user-friendly digital cognitive test called the Electronic Cognitive Screen (EC-Screen) for the detection of mild cognitive impairment (MCI) and dementia of older adults. ObjectiveThe aim of this study was to evaluate the performance of the EC-Screen for the detection of MCI and dementia in older adults. MethodsThe EC-Screen is a brief digital cognitive test that has been adapted from the Rapid Cognitive Screen test. The EC-Screen uses a cloud-based platform and runs on a tablet. Participants with MCI, dementia, and cognitively healthy controls were recruited from research clinics and the community. The outcomes were the performance of the EC-Screen in distinguishing participants with MCI and dementia from controls, and in distinguishing participants with dementia from those with MCI and controls. The cohort was randomly split into derivation and validation cohorts based on the participants’ disease group. In the derivation cohort, the regression-derived score of the EC-Screen was calculated using binomial logistic regression. Two predictive models were produced. The first model was used to distinguish participants with MCI and dementia from controls, and the second model was used to distinguish participants with dementia from those with MCI and controls. Receiver operating characteristic curves were constructed and the areas under the curves (AUCs) were calculated. The performances of the two predictive models were tested using the validation cohorts. The relationship between the EC-Screen and paper-and-pencil Montreal Cognitive Assessment-Hong Kong version (HK-MoCA) was evaluated by the Pearson correlation coefficient. ResultsA total of 126 controls, 54 participants with MCI, and 63 participants with dementia were included in the study. In differentiating participants with MCI and dementia from controls, the AUC of the EC-Screen in the derivation and validation cohorts was 0.87 and 0.84, respectively. The optimal sensitivity and specificity in the derivation cohorts were 0.81 and 0.80, respectively. In differentiating participants with dementia from those with MCI and controls, the AUC of the derivation and validation cohorts was 0.90 and 0.88, respectively. The optimal sensitivity and specificity in the derivation cohort were 0.83 and 0.83, respectively. There was a significant correlation between the EC-Screen and HK-MoCA (r=–0.67, P<.001). ConclusionsThe EC-Screen is suggested to be a promising tool for the detection of MCI and dementia. This test can be self-administered or assisted by a nonprofessional staff or family member. Therefore, the EC-Screen can be a useful tool for case finding in primary health care and community settings.
url http://www.jmir.org/2020/12/e17332/
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