Mild Cognitive Impairment (MCI)NCT07656389
An AI-Based Prediction of Cognitive Capacity in Older Adults and Individuals With Mild Cognitive Impairment During Virtual Reality Driving Tasks
An observational study of Mild Cognitive Impairment (MCI) and Virtual Reality, run by National Cheng-Kung University Hospital at 1 site in Taiwan.
Trial summary
Driving ability in older adults is essential for independent mobility and social participation, yet declines under high cognitive load or distraction often lead to visual attention failures such as "look-but-fail-to-see," increasing crash risk. Read more about this study
- Who is it for?
- Ages 30 to 85 years
- Conditions the study is for
- Mild Cognitive Impairment (MCI), Virtual Reality, Eye Movement Disorder
- How many people
- 192 participants
- Where
- 1 siteTaiwan
- Study dates
- 20 Jun 2026to 31 Dec 2029
- 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 30 to 85 years
- All sexes
- Adult, Older adult
Study population: The study population will include healthy young adults aged 30-39 years, as well as cognitively healthy older adults and individuals with mild cognitive impairment (MCI) aged 65-85 years.
Who may be able to join
- (1) a score of 23 or higher on the Montreal Cognitive Assessment; (2) a Clinical Dementia Rating score of 0 for cognitively healthy participants or 0.5 for participants with mild cognitive impairment (MCI); (3) healthy young adults aged between 30 and 39 years, and cognitively healthy older adults and participants with MCI aged between 65 and 85 years; (4) right-hand dominance; and (5) adequate visual function to complete VR and eye-tracking tasks, defined as corrected binocular visual acuity of at least 0.5 without severe visual field deficits.
Who cannot join
- (1) the presence or history of major psychiatric disorders or central nervous system diseases; (2) significant ocular diseases, such as untreated cataracts, active retinal diseases, moderate-to-severe or poorly controlled glaucoma, or marked visual field deficits beyond a specified level; (3) epilepsy; and (4) severe dizziness or VR-induced motion sickness.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: From Eye Movements to Visuomotor Coupling: An AI-Based Prediction of Cognitive Capacity in Older Adults and Individuals With Mild Cognitive Impairment During Virtual Reality Driving Tasks
Driving ability in older adults is essential for independent mobility and social participation, yet declines under high cognitive load or distraction often lead to visual attention failures such as "look-but-fail-to-see," increasing crash risk. Older adults and individuals with mild cognitive impairment (MCI) show impairments in visual attention, executive control, and visuomotor integration, which are not adequately captured by conventional assessments. Virtual reality (VR) integrated with eye-tracking and upper-limb motion analysis enables ecologically valid simulation of driving scenarios and precise quantification of visuomotor behavior. However, current studies are limited by single-scenario designs, unimodal AI models, and insufficient integration of action-related data.
This study proposes a multi-phase framework: Year 1 develops an eye-movement-based AI model for MCI identification; Year 2 integrates multimodal data in VR driving tasks; and Year 3 establishes an explainable AI system with longitudinal validation. The study aims to advance cognitive assessment and develop a digital tool for early MCI detection and driving risk prediction.
Read the detailed description
Driving ability in older adults is closely associated with independent mobility and social participation. However, under conditions of high cognitive load or distraction, visual attention failures-such as the "look-but-fail-to-see" phenomenon-frequently occur and substantially increase crash risk. Previous studies have demonstrated that older adults and individuals with mild cognitive impairment (MCI) exhibit declines in visual attention allocation, executive control, and visuomotor transformation efficiency. These dynamic regulatory processes are difficult to capture using conventional paper-and-pencil or static neuropsychological assessments, underscoring the need for ecologically valid and dynamic evaluation approaches.
Virtual reality (VR), when integrated with eye-tracking and upper-limb motion analysis, enables the simulation of realistic driving environments under safe and controlled conditions. This approach facilitates precise quantification of visual search behavior, hazard detection, and visuomotor coupling, thereby offering a novel framework for cognitive assessment and driving risk prediction. Despite these advances, three critical gaps remain in the current literature: (1) a lack of systematic investigations focusing on older adults and individuals with MCI across diverse VR driving scenarios to examine visual search and attentional control; (2) artificial intelligence (AI) models that are predominantly limited to single tasks or single modalities, restricting their ability to generalize across contexts; and (3) insufficient integration of upper-limb operational data to fully characterize the dynamic interactions among vision, action, and cognition.
To address these gaps, this study adopts a multi-phase, multi-level research design. In Year 1, a VR-based eye-movement system incorporating controllable cognitive load will be developed to examine pro-saccade and anti-saccade performance among young adults, cognitively healthy older adults, and individuals with MCI. This phase will establish a high-sensitivity, eye-movement-based AI model for MCI identification. In Year 2, the framework will be extended to multi-scenario VR driving tasks through the synchronous integration of eye-tracking and upper-limb operational data, enabling characterization of visuomotor coupling under varying cognitive demands. External validation, transfer learning, and multimodal fusion techniques will be applied to enhance cross-scenario generalizability. In Year 3, multimodal datasets will be integrated to develop an explainable artificial intelligence (XAI) prediction system. Longitudinal follow-up will be conducted to evaluate its prognostic validity for changes in cognitive and driving performance, ultimately leading to a clinically applicable decision-support prototype.
At the theoretical level, this study aims to elucidate the mechanisms underlying visual attention, working memory, executive control, and visuomotor coupling in older adults and individuals with MCI under dynamic conditions. At the clinical and practical levels, it seeks to develop a non-invasive and repeatable digital cognitive screening tool for early MCI detection and older-driver risk assessment, as well as to provide evidence-based support for traffic safety policy development.
- Conditions
- Mild Cognitive Impairment (MCI), Virtual Reality, Eye Movement Disorder, Multimodal Monitoring, Older Adults
- Medical subject headings
- Cognitive Dysfunction, Ocular Motility Disorders
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 |
|---|---|---|
| Young Adults | Healthy young adults (aged 30-39 years) with no history of neurological or psychiatric disorders. Participants will complete standardized virtual reality (VR) driving tasks with integrated eye-tracking and upper-limb motion recording to establish normative visuomotor and cognitive performance benchmarks. | |
| Cognitively Healthy Older Adults | Community-dwelling older adults (≥65 years) with normal cognitive function based on standardized screening (e.g., MoCA within normal range). Participants will undergo the same VR-based assessments to examine age-related changes in visual attention, executive control, and visuomotor coupling. | |
| Mild Cognitive Impairment (MCI) | Older adults clinically identified with mild cognitive impairment according to established diagnostic criteria. Participants will complete identical VR driving tasks to investigate alterations in visual search behavior, attentional control, and visuomotor integration, and to support development of predictive AI models for cognitive decline and driving risk. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Time to First Fixation (TFF) within the Area of Interest
Time to First Fixation (TFF) is defined as the time interval from event onset (t₀) to the participant's first fixation within the predefined Area of Interest (AOI). A shorter TFF indicates faster attentional orienting. Units of Measure: milliseconds (ms) for each predefined AOI/event condition.
Measured: Baseline
Secondary outcomes (13)
Number of Fixations on the Area of Interest
Number of Fixations is defined as the total count of fixations within the predefined Area of Interest (AOI) during task performance. A higher number of fixations indicates greater visual search activity toward target stimuli. Unit of Measure: count.
Measured: Baseline
Total Fixation Duration (TFD) on the Area of Interest
Total Fixation Duration (TFD) is defined as the cumulative fixation time within the predefined Area of Interest (AOI) during the task. Longer fixation duration indicates greater attentional engagement with the target stimulus. Unit of measure: Milliseconds.
Measured: Baseline
Steering Reaction Time
Steering Reaction Time is defined as the interval from event onset (t₀) to the initial steering wheel deviation of ≥5°. This measure reflects motor initiation speed during driving tasks. Steering Reaction Time will be calculated separately for each driving scenario or curve type. Unit of Measure: Milliseconds (ms).
Measured: Baseline
Steering Reversal Rate (SRR)
Steering Reversal Rate (SRR) is defined as the number of steering direction changes per minute during curve driving, using a steering-wheel angle threshold of 0.5°. Higher SRR values indicate increased steering corrections and may reflect reduced motor control stability and increased driving workload. Unit of Measure: Reversals/minute.
Measured: Baseline
Speed Change During Driving Tasks
Speed Change is defined as the difference in average vehicle speed before and after event onset during driving tasks. This measure reflects adaptive driving behavior in response to traffic events or environmental changes, with larger changes indicating greater speed adjustment. Speed Change will be calculated separately for each driving condition. Unit of Measure: Kilometers per hour (km/h).
Measured: Baseline
Reaction Time in Speed-Limit Judgment Tasks
Reaction Time in Speed-Limit Judgment Tasks is defined as the elapsed time between the presentation of a speed-limit stimulus and the participant's response. This measure assesses the speed of driving-related decision-making under varying cognitive demands, with longer reaction times indicating greater cognitive processing demands. Reaction time will be calculated for each task condition. Unit of Measure: Milliseconds (ms).
Measured: Baseline
Accuracy in Speed-Limit Judgment Tasks
Accuracy in Speed-Limit Judgment Tasks is defined as the percentage of correct responses to speed-limit judgment tasks performed during driving. This measure assesses cognitive performance under dual-task conditions, with higher accuracy indicating better task performance and cognitive processing. Accuracy will be calculated for each task condition. Unit of Measure: Percentage (%).
Measured: Baseline
Standard Deviation of Lane Position (SDLP)
Standard Deviation of Lane Position (SDLP) is defined as the variability of the vehicle's lateral lane position during driving tasks. SDLP reflects lane-keeping ability and driving stability under different driving conditions. The lower SDLP mean better driving stability. Unit of Measure: Meters (m).
Measured: Baseline
Frequency of Driving Errors
Driving Errors are defined as the number of adverse driving events occurring during the driving task, including lane departures, collisions, and incorrect responses to secondary tasks. This measure reflects overall driving performance and safety. Error frequencies will be calculated for each driving condition, and error types will be analyzed separately. Unit of Measure: Count.
Measured: Baseline
Montreal Cognitive Assessment (MoCA) Score
The MoCA is used to assess global cognitive function. Participants complete a standardized set of tasks covering attention, memory, language, visuospatial abilities, and executive functions. The total MoCA score reflects overall cognitive performance.Unit of Measure: Points (0-30).
Measured: Baseline
Digit Span Test Score
The Digit Span Test evaluates working memory capacity by requiring participants to recall sequences of numbers in forward and backward order. The highest correctly recalled sequence length is recorded separately for forward and backward trials. Unit of Measure: Number of digits correctly recalled.
Measured: baseline
Knox Cube Test-Revised Score
The Knox Cube Test-Revised assesses visuospatial sequential memory. Participants must replicate sequences of cube taps demonstrated by the examiner. The total number of correctly recalled sequences reflects visuospatial memory performance. Unit of Measure: Number of sequences correctly recalled.
Measured: baseline
Conners Continuous Performance Test 3 (CPT 3) Metrics
The Conners CPT 3 assesses sustained attention and inhibitory control. Primary metrics include reaction time, omission errors, commission errors, and detectability (d'). Each metric will be reported separately for each test condition. Unit of Measure: Reaction Time: milliseconds (ms); Omission Errors: count; Commission Errors: count; and Detectability (d'): unitless index.
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Jun 2026
- Study start20 Jun 2026 (estimated)
- Primary completion31 Dec 2029 (estimated)
- Study completion31 Dec 2029 (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.
- More trials run by National Cheng-Kung University HospitalThe sponsor's other registered studies.
- 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
- Case-control
- Time perspective
- Prospective
- Enrolment
- 192 participants (estimated)
- Target duration
- 12 Months
Sponsor and investigators
- Lead sponsor
- National Cheng-Kung University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07656389
- Sponsor's protocol ID
- A-ER-115-039
- Status last verified
- Jun 2026
- First submitted
- 19 May 2026
- Last updated
- 18 Jun 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 Jun 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- From Eye Movements to Visuomotor Coupling: An AI-Based Prediction of Cognitive Capacity in Older Adults and Individuals With Mild Cognitive Impairment During Virtual Reality Driving Tasks
- ClinicalTrials.gov ID
- NCT07656389
- Acronym / sponsor ID
- A-ER-115-039
- Conditions
- Mild Cognitive Impairment (MCI), Virtual Reality, Eye Movement Disorder, Multimodal Monitoring, Older Adults
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 192 participants (estimated)
- Lead sponsor
- National Cheng-Kung University HospitalUniversity, hospital or other
- Study start
- 20 Jun 2026 (estimated)
- Primary completion
- 31 Dec 2029 (estimated)
- Study completion
- 31 Dec 2029 (estimated)
- First posted
- 18 Jun 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.
Driving ability in older adults is essential for independent mobility and social participation, yet declines under high cognitive load or distraction often lead to visual attention failures such as "look-but-fail-to-see," increasing crash risk. Older adults and individuals with mild cognitive impairment (MCI) show impairments in visual attention, executive control, and visuomotor integration, which are not adequately captured by conventional assessments. Virtual reality (VR) integrated with eye-tracking and upper-limb motion analysis enables ecologically valid simulation of driving scenarios and precise quantification of visuomotor behavior. However, current studies are limited by single-scenario designs, unimodal AI models, and insufficient integration of action-related data.
This study proposes a multi-phase framework: Year 1 develops an eye-movement-based AI model for MCI identification; Year 2 integrates multimodal data in VR driving tasks; and Year 3 establishes an explainable AI system with longitudinal validation. The study aims to advance cognitive assessment and develop a digital tool for early MCI detection and driving risk prediction.
Detailed description
Driving ability in older adults is closely associated with independent mobility and social participation. However, under conditions of high cognitive load or distraction, visual attention failures-such as the "look-but-fail-to-see" phenomenon-frequently occur and substantially increase crash risk. Previous studies have demonstrated that older adults and individuals with mild cognitive impairment (MCI) exhibit declines in visual attention allocation, executive control, and visuomotor transformation efficiency. These dynamic regulatory processes are difficult to capture using conventional paper-and-pencil or static neuropsychological assessments, underscoring the need for ecologically valid and dynamic evaluation approaches.
Virtual reality (VR), when integrated with eye-tracking and upper-limb motion analysis, enables the simulation of realistic driving environments under safe and controlled conditions. This approach facilitates precise quantification of visual search behavior, hazard detection, and visuomotor coupling, thereby offering a novel framework for cognitive assessment and driving risk prediction. Despite these advances, three critical gaps remain in the current literature: (1) a lack of systematic investigations focusing on older adults and individuals with MCI across diverse VR driving scenarios to examine visual search and attentional control; (2) artificial intelligence (AI) models that are predominantly limited to single tasks or single modalities, restricting their ability to generalize across contexts; and (3) insufficient integration of upper-limb operational data to fully characterize the dynamic interactions among vision, action, and cognition.
To address these gaps, this study adopts a multi-phase, multi-level research design. In Year 1, a VR-based eye-movement system incorporating controllable cognitive load will be developed to examine pro-saccade and anti-saccade performance among young adults, cognitively healthy older adults, and individuals with MCI. This phase will establish a high-sensitivity, eye-movement-based AI model for MCI identification. In Year 2, the framework will be extended to multi-scenario VR driving tasks through the synchronous integration of eye-tracking and upper-limb operational data, enabling characterization of visuomotor coupling under varying cognitive demands. External validation, transfer learning, and multimodal fusion techniques will be applied to enhance cross-scenario generalizability. In Year 3, multimodal datasets will be integrated to develop an explainable artificial intelligence (XAI) prediction system. Longitudinal follow-up will be conducted to evaluate its prognostic validity for changes in cognitive and driving performance, ultimately leading to a clinically applicable decision-support prototype.
At the theoretical level, this study aims to elucidate the mechanisms underlying visual attention, working memory, executive control, and visuomotor coupling in older adults and individuals with MCI under dynamic conditions. At the clinical and practical levels, it seeks to develop a non-invasive and repeatable digital cognitive screening tool for early MCI detection and older-driver risk assessment, as well as to provide evidence-based support for traffic safety policy development.
Study design
- Observational model
- Case-control
- Time perspective
- Prospective
- Target follow-up
- 12 Months
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Young Adults | – | –Healthy young adults (aged 30-39 years) with no history of neurological or psychiatric disorders. Participants will complete standardized virtual reality (VR) driving tasks with integrated eye-tracking and upper-limb motion recording to establish normative visuomotor and cognitive performance benchmarks. |
| Cognitively Healthy Older Adults | – | –Community-dwelling older adults (≥65 years) with normal cognitive function based on standardized screening (e.g., MoCA within normal range). Participants will undergo the same VR-based assessments to examine age-related changes in visual attention, executive control, and visuomotor coupling. |
| Mild Cognitive Impairment (MCI) | – | –Older adults clinically identified with mild cognitive impairment according to established diagnostic criteria. Participants will complete identical VR driving tasks to investigate alterations in visual search behavior, attentional control, and visuomotor integration, and to support development of predictive AI models for cognitive decline and driving risk. |
Who may qualify
- Ages
- 30 to 85 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- The study population will include healthy young adults aged 30-39 years, as well as cognitively healthy older adults and individuals with mild cognitive impairment (MCI) aged 65-85 years.
Key inclusion criteria (1 listed)
- (1) a score of 23 or higher on the Montreal Cognitive Assessment; (2) a Clinical Dementia Rating score of 0 for cognitively healthy participants or 0.5 for participants with mild cognitive impairment (MCI); (3) healthy young adults aged between 30 and 39 years, and cognitively healthy older adults and participants with MCI aged between 65 and 85 years; (4) right-hand dominance; and (5) adequate visual function to complete VR and eye-tracking tasks, defined as corrected binocular visual acuity of at least 0.5 without severe visual field deficits.
Key exclusion criteria (1 listed)
- (1) the presence or history of major psychiatric disorders or central nervous system diseases; (2) significant ocular diseases, such as untreated cataracts, active retinal diseases, moderate-to-severe or poorly controlled glaucoma, or marked visual field deficits beyond a specified level; (3) epilepsy; and (4) severe dizziness or VR-induced motion sickness.
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
Time to First Fixation (TFF) within the Area of Interest
Time frame: Baseline
Time to First Fixation (TFF) is defined as the time interval from event onset (t₀) to the participant's first fixation within the predefined Area of Interest (AOI). A shorter TFF indicates faster attentional orienting. Units of Measure: milliseconds (ms) for each predefined AOI/event condition.
13 secondary outcomes
Number of Fixations on the Area of Interest
Time frame: Baseline
Total Fixation Duration (TFD) on the Area of Interest
Time frame: Baseline
Steering Reaction Time
Time frame: Baseline
Steering Reversal Rate (SRR)
Time frame: Baseline
Speed Change During Driving Tasks
Time frame: Baseline
Reaction Time in Speed-Limit Judgment Tasks
Time frame: Baseline
Accuracy in Speed-Limit Judgment Tasks
Time frame: Baseline
Standard Deviation of Lane Position (SDLP)
Time frame: Baseline
Frequency of Driving Errors
Time frame: Baseline
Montreal Cognitive Assessment (MoCA) Score
Time frame: Baseline
Digit Span Test Score
Time frame: baseline
Knox Cube Test-Revised Score
Time frame: baseline
Conners Continuous Performance Test 3 (CPT 3) Metrics
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 18 Jun 2026.
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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 Virtual Reality, run by National Cheng-Kung University Hospital at 1 site in Taiwan.
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 1 inclusion and 1 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 18 Jun 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 Taiwan. 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 Hsiu-Yun Hsu, Ph.D (886-6-2353535 ext. 2669, hyhsu@mail.ncku.edu.tw), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Time to First Fixation (TFF) within the Area of Interest (time frame: Baseline). 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 18 Jun 2026.