Mild Cognitive Impairment (MCI)MonitorCog-AINCT07883746
Remote Digital Assessments for Cognitive Disorders
An observational study of Mild Cognitive Impairment (MCI) and Alzheimer Disease (AD), run by Centre for Addiction and Mental Health at 1 site in Canada.
Trial 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. Read more about this study
- Who is it for?
- Ages 50 years and olderHealthy volunteers can take part
- Conditions the study is for
- Mild Cognitive Impairment (MCI), Alzheimer Disease (AD)
- How many people
- 60 participants
- Where
- 1 siteCanada
- Study dates
- 1 Nov 2026to 1 Apr 2028
- How long will I take part?
- 12 Months
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 50 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study will enroll 60 adults aged 50 years or older across three groups: 20 healthy controls, 20 individuals with mild cognitive impairment (MCI), and 20 individuals with Alzheimer's disease (AD). Participants will be recruited from CAMH Geriatric Mental Health Services clinics, existing research registries, and the community through clinician referral and advertising. Diagnostic group assignment will be determined by the principal investigator or another qualified clinician using cognitive and functional assessments, clinical information, available informant information, and DSM-5 criteria.
Who may be able to join
- Age 50 years or older.
- Sufficient English literacy to complete study assessments.
- Capacity to provide informed consent.
- Ability to attend in-person study assessments.
- Healthy controls: Montreal Cognitive Assessment (MoCA) score >26 and no history of a DSM-5 neurocognitive disorder.
- Mild cognitive impairment (MCI) group: meets DSM-5 criteria for mild neurocognitive disorder based on clinical adjudication.
- Alzheimer's disease (AD) group: meets DSM-5 criteria for major neurocognitive disorder due to AD based on clinical adjudication.
Who cannot join
- Major psychiatric, neurological, or unstable medical condition that may interfere with study participation.
- Recent psychotropic medication changes likely to affect cognition.
- Current or recent substance-use disorder.
- Uncorrectable visual impairment that would interfere with computerized assessment.
- Motor impairment preventing use of a mouse or touchpad.
- Participation in a conflicting interventional clinical trial.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Canada1 site
Centre for Addiction and Mental Health (CAMH)
Toronto, Ontario, M6J 1H1
About this study
Official title: A Remote, AI-enabled Multimodal Digital Platform for Timely Detection and Monitoring of Cognitive Disorders: a Study Protocol (MonitorCog-AI)
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.
Read the detailed description
Current approaches to assessing mild cognitive impairment (MCI) and Alzheimer's disease (AD) rely largely on periodic clinic-based cognitive testing, which may provide only a limited view of change over time. There is a need for practical approaches that can repeatedly capture complementary aspects of cognitive and behavioural function outside the clinic. MonitorCog-AI combines digital measures of cognition, language, speech, eye movements, and motor function to address this need.
MonitorCog-AI is a computer-based platform that integrates five types of digital assessment: cognition, webcam-based eye movements, language, speech, and mouse-based fine-motor performance. Artificial intelligence (AI) and machine-learning methods will be used to analyze and combine the digital information collected by the platform.
The study will enroll 60 adults aged 50 years or older: 20 healthy adults, 20 adults with MCI, and 20 adults with AD. Participants will complete assessments at baseline, 6 months, and 12 months. At each timepoint, participants will complete a clinical assessment and a digital assessment. The clinical assessment will include standard measures of memory and thinking, daily functioning, and a brief electroencephalogram (EEG) recording. The digital assessment will include the five MonitorCog-AI modules. 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 objectives and corresponding hypotheses are as follows:
Objective 1: To evaluate the feasibility, acceptability, usability, and participant-perceived burden of repeated MonitorCog-AI assessments.
Hypothesis 1a: At least 80% of participants will complete the baseline assessment and at least one follow-up assessment.
Hypothesis 1b: At least 75% of participants will report an overall satisfaction rating of 4 or higher on a 5-point scale.
Objective 2: To evaluate the preliminary diagnostic performance of the digital measures for distinguishing healthy adults, adults with MCI, and adults with AD, and to determine whether combining multiple digital modalities improves discrimination compared with individual modalities.
Hypothesis 2a: The multimodal model will achieve an area under the receiver operating characteristic curve (AUC) of at least 0.75 for distinguishing healthy adults from adults with MCI and MCI from AD, and at least 0.85 for distinguishing healthy adults from adults with AD.
Hypothesis 2b: The multimodal model will improve AUC by at least 0.05 compared with the best-performing single-modality model.
Objective 3: To examine changes in digital measures over 12 months and their relationships with changes in clinical measures of cognition and daily functioning.
Hypothesis 3: Changes in digital measures over 12 months will be associated with changes in standard clinical measures of cognition and daily functioning.
- Keywords
- Mild Cognitive Impairment, Alzheimer's Disease, Cognitive Assessment, Remote Assessment, Speech Analysis, Eye Tracking, Artificial Intelligence, Digital Health
- Medical subject headings
- Cognitive Dysfunction, Alzheimer Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Healthy Controls | Adults aged 50 years or older without mild cognitive impairment or Alzheimer's disease who complete the study assessments at baseline, 6 months, and 12 months. | |
| Mild Cognitive Impairment | Adults aged 50 years or older with mild cognitive impairment (MCI) who complete the study assessments at baseline, 6 months, and 12 months. | |
| Alzheimer's Disease | Adults aged 50 years or older with Alzheimer's disease (AD) who complete the study assessments at baseline, 6 months, and 12 months. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Participant Completion Rate
Proportion of enrolled participants who complete the baseline assessment and at least one follow-up assessment at 6 or 12 months. A completion rate of at least 80% is the prespecified feasibility benchmark.
Measured: Baseline through 12 months
System Usability Scale (SUS) Score
Usability of MonitorCog-AI will be measured using the 10-item System Usability Scale (SUS). Item responses are scored and transformed to a total score ranging from 0 to 100, with higher scores indicating better perceived system usability. Total scores will be summarized at baseline, 6 months, and 12 months.
Measured: Baseline, 6 months, and 12 months
Participant Acceptability of MonitorCog-AI
Participant acceptability will be measured as the percentage of participants reporting an overall satisfaction rating of 4 or 5 on a study-specific 5-point satisfaction scale. Scores range from 1 to 5, with higher scores indicating greater satisfaction. At least 75% reporting a rating of 4 or higher is the prespecified acceptability benchmark.
Measured: Baseline, 6 months, and 12 months
User Burden Scale (UBS) Score
Participant-perceived burden of completing MonitorCog-AI will be measured using the 20-item User Burden Scale (UBS), which assesses difficulty of use, physical burden, time and social burden, mental and emotional burden, privacy burden, and financial burden. Each item is scored from 0 to 4, yielding a total score ranging from 0 to 80, with higher scores indicating greater perceived burden. Total scores will be summarized at baseline, 6 months, and 12 months.
Measured: Baseline, 6 months, and 12 months
Secondary outcomes (2)
Diagnostic Performance of MonitorCog-AI
Diagnostic performance of the AI-based multimodal model will be evaluated for healthy controls versus mild cognitive impairment (MCI), healthy controls versus Alzheimer's disease (AD), and MCI versus AD. Performance will be summarized using area under the receiver operating characteristic curve (AUC) with 95% confidence intervals, sensitivity, specificity, and calibration measures where estimable.
Measured: Baseline
Incremental Performance of the Multimodal AI Model
The diagnostic performance of the multimodal AI model will be compared with the best-performing single-modality model for each diagnostic contrast. Differences in area under the receiver operating characteristic curve (AUC) will be estimated with 95% confidence intervals.
Measured: Baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Oct 2026
- Study start1 Nov 2026 (estimated)
- Primary completion31 Mar 2028 (estimated)
- Study completion1 Apr 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Mild Cognitive Impairment (MCI)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 60 participants (estimated)
- Target duration
- 12 Months
Sponsor and investigators
- Lead sponsor
- Centre for Addiction and Mental Health
- Sponsor type
- University, hospital or other
- Collaborators
- AFP Innovation Fund
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07883746
- Sponsor's protocol ID
- 2025/137
- Acronym
- MonitorCog-AI
- Status last verified
- Sep 2026
- First submitted
- 1 Oct 2026
- Last updated
- 20 Oct 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 20 Oct 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Remote, AI-enabled Multimodal Digital Platform for Timely Detection and Monitoring of Cognitive Disorders: a Study Protocol (MonitorCog-AI)
- ClinicalTrials.gov ID
- NCT07883746
- Acronym / sponsor ID
- MonitorCog-AI / 2025/137
- Keywords
- Mild Cognitive Impairment, Alzheimer's Disease, Cognitive Assessment, Remote Assessment, Speech Analysis, Eye Tracking, Artificial Intelligence, Digital Health
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 60 participants (estimated)
- Lead sponsor
- Centre for Addiction and Mental HealthUniversity, hospital or other
- Collaborators
- AFP Innovation Fund
- Study start
- 1 Nov 2026 (estimated)
- Primary completion
- 31 Mar 2028 (estimated)
- Study completion
- 1 Apr 2028 (estimated)
- First posted
- 20 Oct 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Current approaches to assessing mild cognitive impairment (MCI) and Alzheimer's disease (AD) rely largely on periodic clinic-based cognitive testing, which may provide only a limited view of change over time. There is a need for practical approaches that can repeatedly capture complementary aspects of cognitive and behavioural function outside the clinic. MonitorCog-AI combines digital measures of cognition, language, speech, eye movements, and motor function to address this need.
MonitorCog-AI is a computer-based platform that integrates five types of digital assessment: cognition, webcam-based eye movements, language, speech, and mouse-based fine-motor performance. Artificial intelligence (AI) and machine-learning methods will be used to analyze and combine the digital information collected by the platform.
The study will enroll 60 adults aged 50 years or older: 20 healthy adults, 20 adults with MCI, and 20 adults with AD. Participants will complete assessments at baseline, 6 months, and 12 months. At each timepoint, participants will complete a clinical assessment and a digital assessment. The clinical assessment will include standard measures of memory and thinking, daily functioning, and a brief electroencephalogram (EEG) recording. The digital assessment will include the five MonitorCog-AI modules. 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 objectives and corresponding hypotheses are as follows:
Objective 1: To evaluate the feasibility, acceptability, usability, and participant-perceived burden of repeated MonitorCog-AI assessments.
Hypothesis 1a: At least 80% of participants will complete the baseline assessment and at least one follow-up assessment.
Hypothesis 1b: At least 75% of participants will report an overall satisfaction rating of 4 or higher on a 5-point scale.
Objective 2: To evaluate the preliminary diagnostic performance of the digital measures for distinguishing healthy adults, adults with MCI, and adults with AD, and to determine whether combining multiple digital modalities improves discrimination compared with individual modalities.
Hypothesis 2a: The multimodal model will achieve an area under the receiver operating characteristic curve (AUC) of at least 0.75 for distinguishing healthy adults from adults with MCI and MCI from AD, and at least 0.85 for distinguishing healthy adults from adults with AD.
Hypothesis 2b: The multimodal model will improve AUC by at least 0.05 compared with the best-performing single-modality model.
Objective 3: To examine changes in digital measures over 12 months and their relationships with changes in clinical measures of cognition and daily functioning.
Hypothesis 3: Changes in digital measures over 12 months will be associated with changes in standard clinical measures of cognition and daily functioning.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 12 Months
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Healthy Controls | – | –Adults aged 50 years or older without mild cognitive impairment or Alzheimer's disease who complete the study assessments at baseline, 6 months, and 12 months. |
| Mild Cognitive Impairment | – | –Adults aged 50 years or older with mild cognitive impairment (MCI) who complete the study assessments at baseline, 6 months, and 12 months. |
| Alzheimer's Disease | – | –Adults aged 50 years or older with Alzheimer's disease (AD) who complete the study assessments at baseline, 6 months, and 12 months. |
Who may qualify
- Ages
- 50 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study will enroll 60 adults aged 50 years or older across three groups: 20 healthy controls, 20 individuals with mild cognitive impairment (MCI), and 20 individuals with Alzheimer's disease (AD). Participants will be recruited from CAMH Geriatric Mental Health Services clinics, existing research registries, and the community through clinician referral and advertising. Diagnostic group assignment will be determined by the principal investigator or another qualified clinician using cognitive and functional assessments, clinical information, available informant information, and DSM-5 criteria.
Key inclusion criteria (7 listed)
- Age 50 years or older.
- Sufficient English literacy to complete study assessments.
- Capacity to provide informed consent.
- Ability to attend in-person study assessments.
- Healthy controls: Montreal Cognitive Assessment (MoCA) score >26 and no history of a DSM-5 neurocognitive disorder.
Key exclusion criteria (6 listed)
- Major psychiatric, neurological, or unstable medical condition that may interfere with study participation.
- Recent psychotropic medication changes likely to affect cognition.
- Current or recent substance-use disorder.
- Uncorrectable visual impairment that would interfere with computerized assessment.
- Motor impairment preventing use of a mouse or touchpad.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Participant Completion Rate
Time frame: Baseline through 12 months
Proportion of enrolled participants who complete the baseline assessment and at least one follow-up assessment at 6 or 12 months. A completion rate of at least 80% is the prespecified feasibility benchmark.
System Usability Scale (SUS) Score
Time frame: Baseline, 6 months, and 12 months
Usability of MonitorCog-AI will be measured using the 10-item System Usability Scale (SUS). Item responses are scored and transformed to a total score ranging from 0 to 100, with higher scores indicating better perceived system usability. Total scores will be summarized at baseline, 6 months, and 12 months.
Participant Acceptability of MonitorCog-AI
Time frame: Baseline, 6 months, and 12 months
Participant acceptability will be measured as the percentage of participants reporting an overall satisfaction rating of 4 or 5 on a study-specific 5-point satisfaction scale. Scores range from 1 to 5, with higher scores indicating greater satisfaction. At least 75% reporting a rating of 4 or higher is the prespecified acceptability benchmark.
User Burden Scale (UBS) Score
Time frame: Baseline, 6 months, and 12 months
Participant-perceived burden of completing MonitorCog-AI will be measured using the 20-item User Burden Scale (UBS), which assesses difficulty of use, physical burden, time and social burden, mental and emotional burden, privacy burden, and financial burden. Each item is scored from 0 to 4, yielding a total score ranging from 0 to 80, with higher scores indicating greater perceived burden. Total scores will be summarized at baseline, 6 months, and 12 months.
2 secondary outcomes
Diagnostic Performance of MonitorCog-AI
Time frame: Baseline
Incremental Performance of the Multimodal AI Model
Time frame: Baseline
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Oct 2026.
- Individual participant dataSharing planned: Undecided
Related trials for Mild Cognitive Impairment (MCI)
RecruitingPhase 2
Alzheimer's Tau Platform: Master Protocol
Preclinical Alzheimer's Disease, Alzheimer Disease
RecruitingPhase 1
PET Imaging of Phosphodiesterase-4 (PDE4) in Volunteers With Alzheimer Disease (AD) or Mild Cognitive Impairment (MCI)
Alzheimer s Disease, Mild Cognitive Impairment
RecruitingObservational study
Fuzhou Aging Brain Health Initiative
Dementia, Alzheimer Dementia (AD)
Recruiting
Choose Myself (Before) Losing Myself
Dementia (Diagnosis), Mild Cognitive Impairment
RecruitingObservational study
Use of Wearables for Identifying Factors Associated With Mild Cognitive Impairment and Early-Stage Alzheimer's Disease
Mild Cognitive Impairment, Alzheimer's Disease
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Mild Cognitive Impairment (MCI) and Alzheimer Disease (AD), run by Centre for Addiction and Mental Health at 1 site in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 7 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 20 Oct 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Sanjeev Kumar, MD (416-535-8501, sanjeev.kumar@camh.ca), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Participant Completion Rate (time frame: Baseline through 12 months). The study lists 4 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 Oct 2026.