How Exergaming with Virtual Reality Enhances Specific Cognitive and Visuo‐Motor Abilities: An Explorative Study
Bibliographic record
Abstract
Virtual reality (VR) is the computer simulation of a three-dimensional environment that a person can interact with using special electronic equipment, such as a headset with an integrated display. Often coupled with VR, exergames are video games that involve physical exercise. Little is known regarding the chronic effects of exergaming through VR chon cognitive functions. Eleven young participants were enrolled in this crossover exploratory study. They had to follow two trainings of 5 consecutive days, 15 min per day, interspaced by a 1-month washout period. Trainings were performed in a random order: (1) a video training using shadow boxing fitness videos (SBV) and (2) a VR training using a three-dimensional game where the aim is to cut moving cubes with a sword in each hand. Before and after each training period, a battery of cognitive tests was performed to assess executive functions, such as attention (change blindness), reaction time, response inhibition (go/no-go, Stroop task), or flexibility (trail making test). Fine motor skills were also evaluated through a Fitt's task. No effect of the SBV training was observed on any of the cognitive functions tested. On the contrary, a significant increased performance in selective attention and observation tests was found after VR training, as well as in inhibitory processes (Stroop and go/no-go). Other performances were unaffected by either VR or SBV training. The present study argues that VR exergaming is a promising tool to promote cognitive enhancement but targets specific functions according to the type of interface/game that is used.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".