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

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 (AUC(S) > 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.

Automated summary

The study confirmed the validity of SASG in detecting MCI and VCI from healthy controls, with high diagnostic sensitivity and specificity. SASG effectively captured the cognitive profiles of MCI and VCI, showing good classification accuracy.