Virtual-Reality Training of Elite Boxers Preparing for the Tokyo 2020 Olympics During the COVID-19 Pandemic: A Case Study
Bibliographic record
Abstract
The COVID-19 pandemic struck right during the Olympic preparation, leading to significant training restrictions such as noncontact practices for combat sports. This case study research describes the application of a complementary virtual-reality (VR) intervention to train elite boxers preparing for Tokyo 2020 during the pandemic. It also addresses the evaluation of broader visuocognitive functions in elite boxers. Six boxers were allocated to two groups: one experimental group trained on a 360° VR (360VR) temporal video-occlusion program, and one active control group trained on a VR game simulation during 11 sessions. Pre- and postevaluations of specific decision-making performance were performed on a 360VR evaluation test. Fundamental visual and visuocognitive functions were assessed at baseline. Greater on-test decision-making improvements were observed in the 360VR-trained group compared with VR game, and 360VR offered self-reported satisfactory, representative, and safe individual training opportunities for the boxers. More research is warranted to explore the applications of 360VR and VR simulation for psycho-perceptual-motor-skill evaluation and training. Superior visuocognitive performance was observed in elite boxers and should also be a topic of further investigation. The methodological approach, implementation, and reflections are provided in detail to guide practitioners toward the applied use of VR in the sporting environment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| 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".