Diagnostic Validity of the Smart Aging Serious Game: An Innovative Tool for Digital Phenotyping of Mild Neurocognitive Disorder.


Journal

Journal of Alzheimer's disease : JAD
ISSN: 1875-8908
Titre abrégé: J Alzheimers Dis
Pays: Netherlands
ID NLM: 9814863

Informations de publication

Date de publication:
2021
Historique:
pubmed: 31 8 2021
medline: 17 12 2021
entrez: 30 8 2021
Statut: ppublish

Résumé

The Smart Aging Serious Game (SASG) is an ecologically-based digital platform used in mild neurocognitive disorders. Considering the higher risk of developing dementia for mild cognitive impairment (MCI) and vascular cognitive impairment (VCI), their digital phenotyping is crucial. A new understanding of MCI and VCI aided by digital phenotyping with SASG will challenge current differential diagnosis and open the perspective of tailoring more personalized interventions. To confirm the validity of SASG in detecting MCI from healthy controls (HC) and to evaluate its diagnostic validity in differentiating between VCI and HC. 161 subjects (74 HC: 37 males, 75.47±2.66 mean age; 60 MCI: 26 males, 74.20±5.02; 27 VCI: 13 males, 74.22±3.43) underwent a SASG session and a neuropsychological assessment (Montreal Cognitive Assessment (MoCA), Free and Cued Selective Reminding Test, Trail Making Test). A multi-modal statistical approach was used: receiver operating characteristic (ROC) curves comparison, random forest (RF), and logistic regression (LR) analysis. SASG well captured the specific cognitive profiles of MCI and VCI, in line with the standard neuropsychological measures. ROC analyses revealed high diagnostic sensitivity and specificity of SASG and MoCA (AUCs > 0.800) in detecting VCI versus HC and MCI versus HC conditions. An acceptable to excellent classification accuracy was found for MCI and VCI (HC versus VCI; RF: 90%, LR: 91%. HC versus MCI; RF: 75%; LR: 87%). SASG allows the early assessment of cognitive impairment through ecological tasks and potentially in a self-administered way. These features make this platform suitable for being considered a useful digital phenotyping tool, allowing a non-invasive and valid neuropsychological evaluation, with evident implications for future digital-health trails and rehabilitation.

Sections du résumé

BACKGROUND
The Smart Aging Serious Game (SASG) is an ecologically-based digital platform used in mild neurocognitive disorders. Considering the higher risk of developing dementia for mild cognitive impairment (MCI) and vascular cognitive impairment (VCI), their digital phenotyping is crucial. A new understanding of MCI and VCI aided by digital phenotyping with SASG will challenge current differential diagnosis and open the perspective of tailoring more personalized interventions.
OBJECTIVE
To confirm the validity of SASG in detecting MCI from healthy controls (HC) and to evaluate its diagnostic validity in differentiating between VCI and HC.
METHODS
161 subjects (74 HC: 37 males, 75.47±2.66 mean age; 60 MCI: 26 males, 74.20±5.02; 27 VCI: 13 males, 74.22±3.43) underwent a SASG session and a neuropsychological assessment (Montreal Cognitive Assessment (MoCA), Free and Cued Selective Reminding Test, Trail Making Test). A multi-modal statistical approach was used: receiver operating characteristic (ROC) curves comparison, random forest (RF), and logistic regression (LR) analysis.
RESULTS
SASG well captured the specific cognitive profiles of MCI and VCI, in line with the standard neuropsychological measures. ROC analyses revealed high diagnostic sensitivity and specificity of SASG and MoCA (AUCs > 0.800) in detecting VCI versus HC and MCI versus HC conditions. An acceptable to excellent classification accuracy was found for MCI and VCI (HC versus VCI; RF: 90%, LR: 91%. HC versus MCI; RF: 75%; LR: 87%).
CONCLUSION
SASG allows the early assessment of cognitive impairment through ecological tasks and potentially in a self-administered way. These features make this platform suitable for being considered a useful digital phenotyping tool, allowing a non-invasive and valid neuropsychological evaluation, with evident implications for future digital-health trails and rehabilitation.

Identifiants

pubmed: 34459394
pii: JAD210347
doi: 10.3233/JAD-210347
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't Validation Study

Langues

eng

Sous-ensembles de citation

IM

Pagination

1789-1801

Auteurs

Sara Isernia (S)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan-Florence, Italy.

Monia Cabinio (M)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan-Florence, Italy.

Sonia Di Tella (S)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan-Florence, Italy.
Department of Psychology, Universitá Cattolica del Sacro Cuore, Milan, Italy.

Stefania Pazzi (S)

Consorzio di Bioingegneria e Informatica Medica (CBIM), Pavia, Italy.

Federica Vannetti (F)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan-Florence, Italy.

Filippo Gerli (F)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan-Florence, Italy.

Irene Eleonora Mosca (IE)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan-Florence, Italy.

Gemma Lombardi (G)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan-Florence, Italy.

Claudio Macchi (C)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan-Florence, Italy.

Sandro Sorbi (S)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan-Florence, Italy.
Universitá degli Studi di Firenze, NEUROFARBA, Firenze, Italy.

Francesca Baglio (F)

IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan-Florence, Italy.

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