A Design and Feasibility Study of Virtual Navigation Tasks for Early-Stage Alzheimer's Disease Detection
Bibliographic record
Abstract
Early detection of Alzheimer's disease (AD) is critical, with spatial navigation impairment being a promising biomarker. This study validates the feasibility of using immersive navigation tasks in Virtual Reality (VR) and Mixed Reality (MR) for cognitive screening. By integrating design thinking into task construction, the study also examines how narrative framing and spatial layout influence user comprehension and engagement. Eleven healthy young adults completed the Montreal Cognitive Assessment (MoCA) and performed egocentric and allocentric navigation tasks across three difficulty levels on Meta Quest 3. Results demonstrated high task accuracy in both environments, with MR showing significantly lower motion sickness and higher immersion. Although no significant correlation emerged between MoCA scores and navigation metrics (e.g., time, path deviation), a negative trend was observed, indicating a link between cognitive performance and task completion time. Task difficulty modulation showed limited effectiveness, though Level 2's complex layouts modestly increased allocentric demands. The study identifies MR's advantages in terms of ecological validity and user comfort, while highlighting design refinements needed for enhanced sensitivity, including obstacle placement optimisation and diverse participant sampling. These findings establish a methodological foundation for deploying immersive navigation tasks in clinical AD screening.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".