Cognitive testing is critical for screening, diagnosing, and monitoring neurocognitive disorders, but current tests are limited by long administration times and the need for trained personnel. Digital cognitive testing offers a convenient and highly repeatable alternative to track cognitive decline, which is essential for evaluating patient care and in clinical trials. To address these limitations, we developed a novel digital spatial working memory test called the D-Cog.
Objective
This study aimed to assess the feasibility, validity, and diagnostic utility of the D-Cog in a clinical population.
Methods
Patients with Alzheimer's disease (AD), Lewy body dementia (DLB), Parkinson's disease (PD), and cognitively normal controls (CN) were included. During clinical visits, a medical assistant administered the D-Cog test, presented as a “serious game” on an iPad.
Results
The D-Cog showed high participant engagement, although engagement decreased with advancing cognitive impairment. A significant difference in the mean D-Cog score was observed between CN and both AD and DLB/PD groups (p < 0.003). Additionally, a significant correlation between the D-Cog score and the Mini-Mental State Examination was identified (r = 0.65, p < 0.001, 95% CI: 0.52–0.75). The D-Cog demonstrated good discrimination between groups, with AUCs of 0.96 (95% CI: 0.91–1.00) for AD versus healthy controls and 0.88 (95% CI: 0.75–0.97) for MCI versus healthy controls.
Conclusions
The D-Cog is a feasible and valid digital cognitive test with potential clinical utility across neurocognitive disorders. Although engagement was lower in advanced dementia, these findings support further development and exploration of the D-Cog, particularly for potential in-home assessments.
TsoiKKFChanJYCHiraiHW, et al.Cognitive tests to detect dementia: a systematic review and meta-analysis. JAMA Intern Med2015; 175: 1450–1458.
2.
WebbKLRyanJWolfeR, et al.Test-retest reliability and minimal detectable change of four cognitive tests in community-dwelling older adults. J Alzheimers Dis2022; 87: 1683–1693.
3.
van DyckCHSwansonCJAisenP, et al.Lecanemab in early Alzheimer’s disease. N Engl J Med2023; 388: 9–21.
4.
Budd HaeberleinSAisenPSBarkhofF, et al.Two randomized phase 3 studies of aducanumab in early Alzheimer’s disease. J Prev Alzheimers Dis2022; 9: 197–210.
5.
RutkoveSBNarayanaswamiPBerishaV, et al.Improved ALS clinical trials through frequent at-home self-assessment: a proof of concept study. Ann Clin Transl Neurol2020; 7: 1148–1157.
6.
PappKVSamarooAChouH-C, et al.Unsupervised mobile cognitive testing for use in preclinical Alzheimer’s disease. Alzheimers Dement (Amst)2021; 13: e12243.
7.
MüllerSHerdeLPreischeO, et al.Diagnostic value of digital clock drawing test in comparison with CERAD neuropsychological battery total score for discrimination of patients in the early course of Alzheimer’s disease from healthy individuals. Sci Rep2019; 9: 3543.
8.
MüllerSPreischeOHeymannP, et al.Increased diagnostic accuracy of digital vs. Conventional clock drawing test for discrimination of patients in the early course of Alzheimer’s disease from cognitively healthy individuals. Front Aging Neurosci2017; 9: 101.
9.
BinacoRCalzarettoNEpifanoJ, et al.Machine learning analysis of digital clock drawing test performance for differential classification of mild cognitive impairment subtypes versus Alzheimer’s disease. J Int Neuropsychol Soc2020; 26: 690–700.
10.
DavoudiADionCAminiS, et al.Classifying non-dementia and Alzheimer’s disease/vascular dementia patients using kinematic, time-based, and visuospatial parameters: the digital clock drawing test. J Alzheimers Dis2021; 82: 47–57.
11.
RentzDMPappKVMayblyumDV, et al.Association of digital clock drawing with PET amyloid and tau pathology in normal older adults. Neurology2021; 96: e1844–e1854.
12.
YuanJAuRKarjadiC, et al.Associations between the digital clock drawing test and brain volume: large community-based prospective cohort (Framingham heart study). J Med Internet Res2022; 24: e34513.
13.
PoosJMvan der HamIJMLeeuwisAE, et al.Short digital spatial memory test detects impairment in Alzheimer’s disease and mild cognitive impairment. Brain Sci2021; 11: 1350.
14.
BockJRHaraJFortierD, et al.Application of digital cognitive biomarkers for Alzheimer’s disease: identifying cognitive process changes and impending cognitive decline. J Prev Alzheimers Dis2021; 8: 123–126.
15.
HicksEBAhsanNBhandariA, et al.Associations of visual paired associative learning task with global cognition and its potential usefulness as a screening tool for Alzheimer’s dementia. Int Psychogeriatr2021; 33: 1135–1144.
16.
Valladares-RodriguezSFernández-IglesiasMJAnido-RifónL, et al.Episodix: a serious game to detect cognitive impairment in senior adults. A psychometric study. PeerJ2018; 6: e5478.
17.
Valladares-RodriguezSPérez-RodriguezRFernandez-IglesiasJM, et al.Learning to detect cognitive impairment through digital games and machine learning techniques. Methods Inf Med2018; 57: 197–207.
18.
NicosiaJAschenbrennerAJBalotaDA, et al.Unsupervised high-frequency smartphone-based cognitive assessments are reliable, valid, and feasible in older adults at risk for Alzheimer’s disease. J Int Neuropsychol Soc2023; 29: 459–471.
19.
SanzCCarrilloFSlachevskyA, et al.Automated text-level semantic markers of Alzheimer’s disease. Alzheimers Dement (Amst)2022; 14: e12276.
20.
GuarinoAFavieriFBoncompagniI, et al.Executive functions in Alzheimer disease: a systematic review. Front Aging Neurosci2019; 10: 437.
21.
JohnsEKPhillipsNABellevilleS, et al.Executive functions in frontotemporal dementia and Lewy body dementia. Neuropsychology2009; 23: 765–777.
22.
McKhannGMKnopmanDSChertkowH, et al.The diagnosis of dementia due to Alzheimer’s disease: recommendations from the national institute on aging-Alzheimer’s association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement2011; 7: 263–269.
23.
PostumaRBBergDSternM, et al.MDS Clinical diagnostic criteria for Parkinson’s disease. Mov Disord2015; 30: 1591–1601.
24.
McKeithIGDicksonDWLoweJ, et al.Diagnosis and management of dementia with Lewy bodies. Neurology2005; 65: 1863–1872.
NasreddineZSPhillipsNABédirianV, et al.The Montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J Am Geriatr Soc2005; 53: 695–699.
27.
SaczynskiJSInouyeSKGuessJ, et al.The Montreal cognitive assessment: creating a crosswalk with the Mini-mental state examination. J Am Geriatr Soc2015; 63: 2370–2374.
28.
BlonieckiVHagmanGRydenM, et al.Digital screening for cognitive impairment - a proof of concept study. J Prev Alzheimers Dis2021; 8: 127–134.
29.
LiNYangXDuW, et al.Exploratory research on key technology of human-computer interactive 2.5-min fast digital early warning for mild cognitive impairment. Comput Intell Neurosci2022; 2022: 2495330.
30.
PerneczkyRWagenpfeilSKomossaK, et al.Mapping scores onto stages: mini-mental state examination and clinical dementia rating. Am J Geriatr Psychiatry2006; 14: 139–144.
31.
SabuncuMRDesikanRSSepulcreJ, et al.The dynamics of cortical and hippocampal atrophy in Alzheimer disease. Arch Neurol2011; 68: 1040–1048.
32.
Arevalo-RodriguezISmailagicNRoqué-FigulsM, et al.Mini-Mental state examination (MMSE) for the early detection of dementia in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev2021; 7: CD010783.
33.
DevenneyEHodgesJR. The Mini-mental state examination: pitfalls and limitations. Pract Neurol2017; 17: 79–80.
34.
GillDJFreshmanABlenderJA, et al.The Montreal cognitive assessment as a screening tool for cognitive impairment in Parkinson’s disease. Mov Disord2008; 23: 1043–1046.
35.
MarcantonioER. Delirium in hospitalized older adults. N Engl J Med2017; 377: 1456–1466.
36.
SaczynskiJSMarcantonioERQuachL, et al.Cognitive trajectories after postoperative delirium. N Engl J Med2012; 367: 30–39.
37.
WitloxJEurelingsLSMde JongheJFM, et al.Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta-analysis. JAMA2010; 304: 443–451.
38.
MorandiADavisDBellelliG, et al.The diagnosis of delirium superimposed on dementia: an emerging challenge. J Am Med Dir Assoc2017; 18: 12–18.
39.
DwyerBMikkelsonJDiaz-PachecoV, et al.The technology use survey: an actionable digital literacy assessment that matches clients to training resources. Community Ment Health J2026; 62: 1–14.
