Advancing Cognitive–Motor Assessment: Reliability and Validity of Virtual Reality-Based Testing in Elite Athletes
Bibliographic record
Abstract
Emerging virtual reality (VR) technologies provide objective and immersive methods for assessing cognitive–motor function, particularly in elite sport. This study evaluated the reliability and validity of VR-based cognitive–motor assessments in a large sample of elite male athletes (n = 829). Ten cognitive–motor tests, delivered via Oculus Quest 2 headsets, were used, covering four domains: Balance and Gait (BG), Decision-Making (DM), Manual Dexterity (MD), and Memory (ME). A Confirmatory Factor Analysis (CFA) was conducted to establish a four-factor model and generate data-driven weights for domain-specific composite scores. The results demonstrated that the composite scores for BG, MD, ME, and a Global Cognitive–Motor (CM) score were all normally distributed. However, the DM score significantly deviated from normality, exhibiting a pronounced ceiling effect. Test–retest reliability was high across all cognitive–motor domains. In summary, VR assessments offer ecologically valid and precise measurements of cognitive–motor abilities by capitalising on high-fidelity motion tracking and standardised test delivery. In particular, the Global CM Score offers a robust metric for parametric analyses. While future work should address the DM ceiling effect and validate these tools in diverse populations, this approach holds significant potential for enhancing the precision and sensitivity of psychological and clinical assessment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".