An unsupervised remote cognitive assessment predicts mild cognitive impairment and associates to amyloid status

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Publicat a:Alzheimer's & Dementia vol. 21 (Dec 1, 2025)
Autor principal: Porta‐Mas, Clàudia
Altres autors: Brugulat‐Serrat, Anna, Corbett, Anne, Suárez‐Calvet, Marc, Gispert, Juan Domingo, Salvadó, Gemma, Grau‐Rivera, Oriol, Sánchez‐Benavides, Gonzalo
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John Wiley & Sons, Inc.
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Accés en línia:Citation/Abstract
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022 |a 1552-5260 
022 |a 1552-5279 
024 7 |a 10.1002/alz70861_108840  |2 doi 
035 |a 3286014404 
045 0 |b d20251201 
100 1 |a Porta‐Mas, Clàudia  |u Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain 
245 1 |a An unsupervised remote cognitive assessment predicts mild cognitive impairment and associates to amyloid status 
260 |b John Wiley & Sons, Inc.  |c Dec 1, 2025 
513 |a Journal Article 
520 3 |a Background The use of digital biomarkers to assess cognition in Alzheimer’s disease (AD) offers scalable, efficient alternatives to paper‐and‐pencil tests. Validating these tools against clinical and biomarker‐defined groups is critical for their adoption in research, clinical trials and clinical contexts. This study evaluates the performance of a remote, unsupervised cognitive assessment (FLAME‐Factors of Longitudinal Attention, Memory and Executive Function) in distinguishing cognitive profiles across diagnostic categories and amyloid status in two cohorts from BBRC. Method Cognitively normal (CN) participants from ALFA+ cohort and subjective cognitive decline (SCD) or mild cognitive impairment (MCI) patients from Beta‐AARC cohort were invited via email to FLAME remote and unsupervised assessment. 249 participants completed FLAME tasks, that include working memory (Self Ordered Search Score, Paired Associate Learning Score, Digit Span Score), episodic memory (Picture Recognition Accuracy), attention (Digit Vigilance Accuracy, Digit Vigilance False Alarms, Digit Vigilance Reaction Time Mean, Choice Reaction Time Accuracy) and executive function (Verbal Reasoning Accuracy). Analysis of covariance (ANCOVA) with post‐hoc (Tukey) were used to examine differences by clinical and amyloid status. Logistic regression models were employed to evaluate if the digital tasks predicted MCI. All analyses were adjusted for age, sex, and education. Result MCI group showed reduced performance in paired associate learning score, attention variables, picture recognition accuracy and verbal reasoning compared to CN and SCD participants. Digit vigilance false alarms, picture recognition accuracy and verbal reasoning accuracy were able to significantly distinguish between CN and SCD groups (Figure 1). Several cognitive variables significantly predicted MCI, including paired associate learning score (OR=1.93,95%CI[1.09‐3.51],p=0.02), digit vigilance accuracy (OR=1.17,95%CI[1.02‐1.35],p=0.02) and false alarms (OR=1.4,95%CI[1.13‐1.76],p=0.002), and accuracy from choice reaction time task (OR=1.23,95%CI[1.03‐1.47],p=0.01), picture recognition (OR=1.39,95%CI[1.17‐1.72],p<0.001), and verbal reasoning (OR=1.05,95%CI [1.01‐1.11],p=0.03). Additionally, self ordered search score and picture recognition accuracy were significantly lower in amyloid‐positive individuals (Figure 2). Conclusion A remote unsupervised assessment reliably differentiates diagnostic and AD biomarker‐defined groups and predicts MCI, underscoring its promise value for research and clinical contexts. 
653 |a Episodic memory 
653 |a Email 
653 |a Memory 
653 |a Analysis of covariance 
653 |a Clinical trials 
653 |a Self evaluation 
653 |a Cognition 
653 |a Cognitive ability 
653 |a Cognitive impairment 
653 |a Cognition & reasoning 
653 |a Paired associate learning 
653 |a Variables 
653 |a Learning 
653 |a Biological markers 
653 |a Attention 
653 |a Verbal reasoning 
653 |a Reaction time 
653 |a Alarms 
653 |a Patients 
653 |a False alarms 
653 |a Sex education 
653 |a Alternative approaches 
653 |a Academic achievement 
653 |a Short term memory 
653 |a Evaluation 
653 |a Alzheimer's disease 
653 |a Accuracy 
653 |a Subjectivity 
653 |a Recognition 
653 |a Clinical research 
653 |a Vigilance 
653 |a Acknowledgment 
653 |a Reasoning 
653 |a Executive function 
653 |a Biomarkers 
653 |a Groups 
700 1 |a Brugulat‐Serrat, Anna  |u Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain 
700 1 |a Corbett, Anne  |u College of Medicine and Health, University of Exeter, Exeter, UK 
700 1 |a Suárez‐Calvet, Marc  |u Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain 
700 1 |a Gispert, Juan Domingo  |u Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain 
700 1 |a Salvadó, Gemma  |u Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain 
700 1 |a Grau‐Rivera, Oriol  |u Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain 
700 1 |a Sánchez‐Benavides, Gonzalo  |u Barcelonaβeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona, Spain 
773 0 |t Alzheimer's & Dementia  |g vol. 21 (Dec 1, 2025) 
786 0 |d ProQuest  |t Consumer Health Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3286014404/abstract/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3286014404/fulltext/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3286014404/fulltextPDF/embedded/Q8Z64E4HU3OH5N8U?source=fedsrch