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Life sciences · Observational Study
ClinicalTrials.gov · October 20, 2026
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Observational Study.
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Registry record from ClinicalTrials.gov (NCT07883746). This is a study registration, not published results. Lead sponsor: Centre for Addiction and Mental Health. Recruitment status: NOT_YET_RECRUITING. Study type: OBSERVATIONAL. Enrollment: 60 participants (ESTIMATED). Conditions: Mild Cognitive Impairment (MCI), Alzheimer Disease (AD). Primary outcome measures: Participant Completion Rate , Baseline through 12 months; System Usability Scale (SUS) Score , Baseline, 6 months, and 12 months; Participant Acceptability of MonitorCog-AI , Baseline, 6 months, and 12 months; User Burden Scale (UBS) Score , Baseline, 6 months, and 12 months. Brief summary: Memory and thinking problems in older adults, including mild cognitive impairment (MCI) and Alzheimer's disease (AD), can be difficult to assess promptly because evaluation often depends on in-person specialist visits. MonitorCog-AI is a computer-based platform designed to provide a more accessible way to assess and monitor cognitive changes using standard computers or tablets. Artificial intelligence (AI) will be used to analyze the digital information collected by the platform and examine whether combining several types of measures improves assessment of cognitive impairment. The study will include 60 adults aged 50 years and older: 20 healthy adults, 20 adults with MCI, and 20 adults with AD. Participants will be assessed at baseline, 6 months, and 12 months. At each timepoint, participants will complete one clinical assessment and one digital assessment. The clinical assessment will include standard measures of memory and thinking, an assessment of daily functioning, and a brief recording of brain electrical activity. The digital assessment will take approximately 60-80 minutes and will assess five areas: cognition, eye movements measured using a webcam, language, speech, and fine-motor performance measured using a mouse. At baseline, the digital assessment will be completed in the clinic; at 6 and 12 months, it will usually be completed remotely at home. The study has three main goals. First, it will determine whether MonitorCog-AI is feasible, acceptable, easy to use, and not overly burdensome for repeated assessment. Second, it will examine how well AI-based analysis of the digital measures distinguishes healthy adults, adults with MCI, and adults with AD, and whether combining several types of digital information performs better than any single type alone. Third, it will examine how digital measures change over 12 months and whether these changes are related to changes in clinical measures of cognition and daily functioning.