Substitute or Supplement? The Role of Multimodal Digital Biomarkers in Mobile Cognitive Impairment Assessment Tools

Substitute or Supplement? The Role of Multimodal Digital Biomarkers in Mobile Cognitive Impairment Assessment Tools | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Substitute or Supplement? The Role of Multimodal Digital Biomarkers in Mobile Cognitive Impairment Assessment Tools Whani Kim, Jin Sung Kim, So Yoon Park, Hyun Jeong Ko, Byung Hun Yun, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6590583/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Digital biomarkers (DBMs) have emerged as promising tools for the detection of cognitive impairment (CI). While many studies have examined their diagnostic value individually, few studies have examined how they fare in multimodal settings. To that end, we developed a digital screening tool that integrates three types of DBMs: Neuropsychological, eye movement, and voice. We evaluated classification performance using the area under the receiver operating characteristic curve (AUC) and assessed feature contributions using SHapley Additive exPlanations (SHAP) values. The multimodal model, which incorporated all three DBMs, achieved an AUC of 0.83, outperforming models using only eye movement DBM (AUC = 0.76) and voice DBM (AUC = 0.71). Our feature importance analysis revealed that eye movement features made the greatest contribution in conjunction with the traditional neuropsychological assessment to classification performance across all models. The predictive models using Our results suggest that DBMs are good supplements to enhance the classification performance rather than a substitution of classical means. In other words, using individual DBMs to build predictive models did not achieve the same classification capacity as the model which incorporated all DBMs coupled with the neuropsychological. However, eye movement DBM showed high potential when used as a single input, achieving classification accuracy comparable to the complete model with all three DBMs. These findings warrant further investigation into eye movement as a digital biomarker for detecting cognitive impairment. Physical sciences/Mathematics and computing/Computer science Biological sciences/Neuroscience/Cognitive ageing Health sciences/Biomarkers/Predictive markers Health sciences/Health care/Geriatrics Health sciences/Neurology/Neurological disorders/Neurodegeneration digital biomarkers cognitive impairment diagnostic tool neuropsychological eye movement voice Full Text Additional Declarations No competing interests reported. Supplementary Files supplementary.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6590583","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":451772283,"identity":"7608cbff-0e57-4e72-9166-20484df57ee5","order_by":0,"name":"Whani Kim","email":"","orcid":"","institution":"HAII Corp","correspondingAuthor":false,"prefix":"","firstName":"Whani","middleName":"","lastName":"Kim","suffix":""},{"id":451772284,"identity":"0c057607-7283-40b6-b810-749eda4fb0fb","order_by":1,"name":"Jin Sung Kim","email":"","orcid":"","institution":"Sangmyung 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