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Updated: Feb 26, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Community-based trials of mobile solutions for the detection and management of cognitive decline
Adele Boyd1, Jonathan Synnott1, Chris Nugent1
1The Computer Science Research Institute and the School of Computing and Mathematics, Ulster University, Newtownabbey, Co. Antrim BT37 0QB, UK.
Abstract:
This study focused on the development and usability evaluation of EnCare diagnostics (ECD) and the brain fit plan (BFP) in healthy older adults, cognitively impaired and physically impaired individuals. ECD is proposed as a novel solution to cognitive assessment based on colour selection. BFP is a novel solution to personalised cognitive stimulation. The study consisted of two trials designed to evaluate the usability of the apps. Trial 1 involved 11 healthy older adults and four older adults with physical impairments who undertook ECD and mini-mental state examination (MMSE) once per month for 4 months with only those with physical impairments also completing the BFP daily. Trial 2 involved eight older adults diagnosed with early stage dementia who completed MMSE and ECD once per month for 6 months. In Trial 1, 10 out of 11 participants enjoyed the trial and managed the usability of the app easily. A 75% drop out was observed in response to the BFP with issues of dexterity and lack of understanding on how to use the technology being the main reasons for lack of compliance. Four out of eight participants completed Trial 2 with most of the participants having no usability issues. This usability study demonstrated that ECD is highly acceptable in both healthy older adults and those with early stage dementia when given the shorter versions to accommodate their diagnosis. The BFP was not suited to this population of participants.
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