The Effects of Acute Virtual Reality Exergaming on Mood and Executive Function: Exploratory Crossover Trial
Bibliographic record
Abstract
BACKGROUND: Virtual reality (VR) exergaming is a new intervention strategy to help humans engage in physical activity to enhance mood. VR exergaming may improve both mood and executive function by acting on the prefrontal cortex, expanding the potential benefits. However, the impact of VR exergaming on executive function has not been fully investigated, and associated intervention strategies have not yet been established. OBJECTIVE: This study aims to investigate the effects of 10 minutes of VR exergaming on mood and executive function. METHODS: A total of 12 participants played the exergame "FitXR" under 3 conditions: (1) a VR exergame condition (ie, exercise with a head-mounted display condition [VR-EX]) in which they played using a head-mounted display, (2) playing the exergame in front of a flat display (2D-EX), and (3) a resting condition in which they sat in a chair. The color-word Stroop task (CWST), which assesses executive function; the short form of the Profile of Mood States second edition (POMS2); and the short form of the Two-Dimensional Mood Scale (TDMS), which assess mood, were administered before and after the exercise or rest conditions. RESULTS: =3.53, P=.006), suggesting that VR exergaming enhanced mood. Conversely, there was no effect on CWST performance in either the 2D-EX or VR-EX conditions. Interestingly, the VR-EX condition showed a significant positive correlation between changes in CWST arousal and reaction time (r=0.58, P=.046). This suggests that the effect of exergaming on improving executive function may disappear under an excessively increased arousal level in VR exergaming. CONCLUSIONS: Our findings showed that 10 minutes of VR exergaming enhanced mood but did not affect executive function. This suggests that some VR content may increase cognitive demands, leading to psychological fatigue and cognitive decline as an individual approaches the limits of available attentional capacity. Future research must examine the combination of exercise and VR that enhances both brain function and mood.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".