Rapid yet reliable: Optimising mobile cognitive assessment tool for older adults

Rapid yet reliable: Optimising mobile cognitive assessment tool for older adults | 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 Rapid yet reliable: Optimising mobile cognitive assessment tool for older adults Hyun Jeong Ko, Jin Sung Kim, Whani Kim, Byung Hun Yun, So Yoon Park, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6223882/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Growing dementia prevalence underscores the need for efficient screening methods, but lengthy digital assessments often cause fatigue among older adults. To address this, we developed and validated the Digital Assessment of Cognitive Impairment (DACI), a brief mobile application designed to accurately identify cognitive impairment (CI). Initially, 304 older adults (272 healthy, 32 cognitively impaired) completed both a pencil-and-paper Cognitive Impairment Screening Test (CIST) and a full-length DACI. The best-performing CatBoost model achieved an area under the curve (AUC) of 0.813, with sensitivity of 0.903, requiring an average completion time of 321 seconds. Subsequent feature selection identified two essential subtests for a compact DACI. This compact version was then validated with an additional 297 participants (227 healthy, 70 cognitively impaired), achieving improved diagnostic performance (AUC = 0.871) in only 91 seconds. DACI effectively distinguishes cognitively impaired older adults with minimal test duration, potentially reducing fatigue and improving adherence. By providing rapid, accurate remote assessment without additional equipment, DACI may significantly enhance accessibility of cognitive screening among older adults. Future studies are warranted to validate DACI's feasibility and performance in unsupervised home settings. Health sciences/Biomarkers Biological sciences/Neuroscience/Cognitive ageing Physical sciences/Mathematics and computing/Computer science Physical sciences/Mathematics and computing/Software Health sciences/Neurology/Neurological disorders/Dementia/Alzheimers disease remote cognitive assessment cognitive impairment mobile device optimal test design digital health Full Text Additional Declarations No competing interests reported. Supplementary Files ScientificReportsSupplementary.docx Cite Share Download PDF Status: Published Journal Publication published 19 Aug, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 May, 2025 Reviews received at journal 07 May, 2025 Reviews received at journal 30 Apr, 2025 Reviewers agreed at journal 25 Apr, 2025 Reviewers agreed at journal 13 Apr, 2025 Reviewers invited by journal 20 Mar, 2025 Editor assigned by journal 20 Mar, 2025 Editor invited by journal 17 Mar, 2025 Submission checks completed at journal 14 Mar, 2025 First submitted to journal 14 Mar, 2025 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. 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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-6223882","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":432881040,"identity":"a5b1c479-a2c7-4b48-92cc-30ae37de2ace","order_by":0,"name":"Hyun Jeong Ko","email":"","orcid":"","institution":"HAII Corp","correspondingAuthor":false,"prefix":"","firstName":"Hyun","middleName":"Jeong","lastName":"Ko","suffix":""},{"id":432881041,"identity":"f363c6e6-0750-4c02-a6ac-927a79130f5a","order_by":1,"name":"Jin Sung Kim","email":"","orcid":"","institution":"Sangmyung 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