IRCCS Fondazione Don Carlo Gnocchi ONLUS, Milan-Florence, Italy.
Department of Psychology, Universitá Cattolica del Sacro Cuore, Milan, Italy.
J Alzheimers Dis. 2021;83(4):1789-1801. doi: 10.3233/JAD-210347.
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.
Smart Aging Serious Game(SASG)是一种基于生态的数字平台,用于轻度神经认知障碍。鉴于轻度认知障碍(MCI)和血管性认知障碍(VCI)发展为痴呆的风险较高,对其进行数字表型分析至关重要。借助 SASG 的数字表型分析,对 MCI 和 VCI 的新认识将挑战当前的鉴别诊断,并为定制更个性化的干预措施开辟前景。
确认 SASG 在检测健康对照组(HC)中的 MCI 方面的有效性,并评估其在区分 VCI 和 HC 方面的诊断有效性。
161 名受试者(74 名 HC:37 名男性,75.47±2.66 岁;60 名 MCI:26 名男性,74.20±5.02 岁;27 名 VCI:13 名男性,74.22±3.43 岁)接受了 SASG 测试和神经心理学评估(蒙特利尔认知评估(MoCA)、自由和线索选择性回忆测试、连线测试)。采用多模态统计方法:受试者工作特征(ROC)曲线比较、随机森林(RF)和逻辑回归(LR)分析。
SASG 很好地捕捉到了 MCI 和 VCI 的特定认知特征,与标准神经心理学测量结果一致。ROC 分析显示,SASG 和 MoCA 对 VCI 与 HC 和 MCI 与 HC 情况的诊断具有较高的敏感性和特异性(AUCs>0.800)。MCI 和 VCI 的分类准确性较高(HC 与 VCI;RF:90%,LR:91%。HC 与 MCI;RF:75%,LR:87%)。
SASG 允许通过生态任务并可能以自我管理的方式早期评估认知障碍。这些特点使该平台成为一种有用的数字表型工具,可进行非侵入性和有效的神经心理学评估,对未来的数字健康试验和康复具有明显的意义。