Using Virtual Reality to Assess Spatial Navigation Ability in Individuals With Mild Cognitive Impairment and Older Adults: Cross-Sectional Study
Bibliographic record
Abstract
Background: Spatial navigation impairment is prevalent in people with Alzheimer disease (AD) and may appear in its initial clinical stage. Detecting this deficit in people at risk may not only help prevent them from getting lost or going missing but also provide a useful clinical aid to accurate diagnosis. Traditional assessments for spatial navigation impairment include questionnaires, paper-and-pencil and maze tests, or video games. While a real-world setting is more valid, direct, and accurate, it is plagued by unpredictable conditions such as weather, obstacles, or accidents. Owing to modern technology, virtual reality (VR) offers a new way to test spatial navigation impairment. Objective: The aims of this study were to test the feasibility of a VR setting to assess sense of location in people with mild cognitive impairment (MCI) and the power of VR to discriminate among groups with different clinical conditions. Methods: We used the Pai-Jan virtual reality (PJVR) device to test spatial navigation ability in those who were cognitively unimpaired (CU) and those who experienced subjective cognitive decline (SCD) and MCI. The PJVR device is the VR version (VIVE Pro Eye head-mounted display) of the Pai-Jan device, which has demonstrated its power to discriminate among CU, AD MCI, and mild AD dementia. With a map provided and using joysticks or handles, participants were asked to reach 5 points on a 660-m path. Linear deviation (LD; in meters) from each target point and vector deviation (in degrees) from the direction to the start point at each location were treated as the variables for comparison. Results: A total of 113 participants provided informed consent to initiate the study. Of these 113 participants, 93 (82.3%) completed the trials, including 22 (24%) who were CU, 39 (42%) with SCD, and 32 (34%) with MCI. In total, 17.7% (20/113) failed the trials due to cybersickness. The mean LD of the CU, SCD, and MCI groups was 38.2 (SD 39.5), 50.4 (SD 40.7), and 100.4 (SD 46.2) meters, respectively (P<.001). The MCI group showed greater vector deviation (mean 63.2, SD 42.4 degrees) than either the SCD (mean 39.4, SD 33.0 degrees) or CU (mean 38.6, SD 37.4 degrees; P=.02) group. The LD of the PJVR device was correlated with the total scores on the caregiver version of the Questionnaire on Everyday Navigational Ability (P<.001), indicating good ecological validity. Conclusions: The PJVR device is feasible for older adults and participants with MCI. It can detect spatial navigation deficits related to AD pathology, and the results show a close correlation with real-world navigation ability.
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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 | 0.000 | 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".