Active Video Games Using Virtual Reality Influence Cognitive Performance in Sedentary Female University Students: A Randomized Clinical Trial
Bibliographic record
Abstract
Background: Virtual reality (VR) is an emerging technology that is proving to be effective in encouraging physical activity (PA) and improving health. Although regular PA has many advantages, physical inactivity continues to be a significant global health concern. Using an ActivPAL for PA assessment, this study examines the effects of an active video game (AVG) using VR on cognitive function among female university students. Methods: We randomly divided 44 sedentary female university students (mean age 21.3 years, SD 1.12 years) into two groups, the control group and VR group. During the study period, the VR group was required to play the Beat Saber VR game for 20 min, while the control group was required to remain quiet. Their cognitive performance was evaluated using the Montreal Cognitive Assessment (MoCA)—Arabic version pre- and post-test, and the PA level and intensity were tracked using the ActivPAL. Results: There was a significant difference between the MoCA total score pre-test (mean = 22.3, SD = 2.25) and the MoCA total score post-test (mean = 23.4, SD = 2.48), t (23) = 1.87, p = 0.03. The VR game significantly influenced the naming, abstraction, and orientation components of the MoCA scale (all p ≤ 0.05). The intensity of PA generated by the VR game was equivalent to moderate-to-vigorous PA, with a mean of 4.98 metabolic equivalents of task (MET) (SD = 1.20). Conclusions: The VR game improved the cognitive ability compared to the control group, suggesting that VR games have a positive impact on cognitive function. Physically inactive female university students have been found to benefit from VR games in terms of their cognitive function.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".