3640 - AN EMBODIED INTELLIGENT DIGITAL PLATFORM FOR EARLY DETECTION OF NEUROCOGNITIVE DISORDERS

Session: 3904 - COMPREHENSIVE PERSPECTIVES ON AGING: INNOVATIONS IN DETECTION, INFORMAL CARE, AND PUBLIC HEALTH POLICY
AUTHORS:
Marocco Davide (NAC Natural and Artificial Intelligence Laboratory, Department of Humanistic Studies, Federico II University of Naples, Italy ~ Naples ~ Italy) , Simeoli Roberta (NAC Natural and Artificial Intelligence Laboratory, Department of Humanistic Studies, Federico II University of Naples, Italy ~ Naples ~ Italy) , Taurisano Paolo (Department of Translational Biomedicine and Neuroscience (DiBraiN)), University of Bari Aldo Moro, Apulia, Italy ~ Apulia ~ Italy)
Abstract text:
Introduction. Neurocognitive disorders (NCDs), including Mild Cognitive Impairment (MCI)
and Alzheimer's disease, are associated with progressive decline across cognitive, motor, and
functional domains. Early detection remains a major challenge due to fragmented clinical
procedures, limited integration of diagnostic data, and reliance on self-reporting, particularly in
the initial stages.
Purpose. Run2cog is a novel digital health application designed to support clinicians in the
early identification and continuous monitoring of NCDs. By combining embodied technologies
with artificial intelligence (AI), the app aims to provide ecologically valid, cost-effective, and
accessible tools for healthcare systems and patients.
Descriptions. The platform integrates sensorimotor, cognitive, and physiological measures
collected through widely available devices such as smartphones, tablets, and wearable
sensors. Run2cog offers gamified, user-friendly tasks targeting key neurocognitive domains,
including executive functions, working memory, visuospatial abilities, attention, and
psychomotor parameters.
Innovation. Unlike traditional assessments, Run2cog leverages an embodied cognition
perspective, linking cognitive decline to measurable alterations in everyday motor and
behavioral patterns. AI-based algorithms analyze multimodal data in real time, enabling
personalized monitoring, predictive modeling, and adaptive feedback for patients and
clinicians.
Conclusions. By embedding Run2cog within the E-Move project, the initiative represents a
step forward in bridging technology and clinical practice for neurocognitive health. Its
embodied and AI-driven approach provides continuous, non-invasive monitoring that can
enhance diagnostic accuracy, reduce clinical variability, and facilitate timely interventions. By
improving the sustainability and efficiency of healthcare systems, Run2cog has the potential to
significantly impact quality of life for individuals at risk of or living with NCDs.
Keywords: neurocognitive disorders, artificial intelligence, digital health, embodied technology, early
detection