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NCT Number: NCT07656389

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.

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Key information

Conditions

Mild Cognitive Impairment (MCI) Central Nervous System Diseases Cognition Disorders Cognitive Dysfunction Cranial Nerve Diseases Eye Diseases Eye Movement Disorder Mental Disorders Multimodal Monitoring Nervous System Diseases Neurocognitive Disorders Ocular Motility Disorders Older Adults Virtual Reality

Age range

30 year–85 year

Sex eligibility

All sexes

Study type

Observational

Primary location

National Cheng-Kung University Hospital

Tainan, Taiwan, 704

About this study

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.

Who can participate

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • (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.

Exclusion criteria

  • (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.

Treatment and study plan

Primary outcomes

  1. 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.

Secondary outcomes

  1. Number of Fixations on the Area of Interest

    Time frame: Baseline

    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.

  2. Total Fixation Duration (TFD) on the Area of Interest

    Time frame: Baseline

    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.

  3. Steering Reaction Time

    Time frame: Baseline

    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).

  4. Steering Reversal Rate (SRR)

    Time frame: Baseline

    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.

  5. Speed Change During Driving Tasks

    Time frame: Baseline

    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).

  6. Reaction Time in Speed-Limit Judgment Tasks

    Time frame: Baseline

    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).

  7. Accuracy in Speed-Limit Judgment Tasks

    Time frame: Baseline

    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 (%).

  8. Standard Deviation of Lane Position (SDLP)

    Time frame: Baseline

    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).

  9. Frequency of Driving Errors

    Time frame: Baseline

    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.

  10. Montreal Cognitive Assessment (MoCA) Score

    Time frame: Baseline

    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).

  11. Digit Span Test Score

    Time frame: baseline

    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.

  12. Knox Cube Test-Revised Score

    Time frame: baseline

    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.

  13. Conners Continuous Performance Test 3 (CPT 3) Metrics

    Time frame: baseline

    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.

Study contacts

Contact information is provided by the study sponsor or research team.

Hsiu-Yun Hsu, Ph.D

CONTACT

[email protected]

886-6-2353535 ext. 2669

Sponsors and collaborators

Lead sponsor

National Cheng-Kung University Hospital

Other

Registry information

Official study 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

Important dates

Study start
2026
Primary completion
2029
Study completion
2029
First posted
Jun 18, 2026
Registry last updated
Jun 18, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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