The Effects of Sport-Specific Virtual Reality Conditions on Attention and Pain in Healthy Baseball Athletes
Bibliographic record
Abstract
Virtual reality is no longer a dream, the future is here. Virtual Reality (VR) offers endless potential for individualized rehabilitation for patients and previous research has established use- cases for multiple sclerosis, spinal cord injuries, burn victims and stroke patients. With respect to sports, VR studies have primarily been focused on training skills or rehabilitation for injured soccer athletes. There is a lack of research on VR’s use as a rehabilitation tool for ball sport athletes. This study aimed to investigate whether health care professionals could use VR for injury rehabilitation as a pain management, immersion and flow tool on these athletes. We conducted a within-subjects design to investigate the effectiveness of VR as a distractor from chemically induced pain by Capsaicin, mimicking real injuries, using a sample of Canadian baseball athletes. Our research questions focused on investigating to what extent being immersed in a sport-specific activity had on how much flow and immersion was experienced, thereby reducing pain even greater than a non-sport specific activity. We randomized the order of three tasks, a non-baseball computer condition (Two-Back), an easy VR baseball practice condition and a challenging hard VR baseball game condition. While the results showed a decrease in pain, they were not statistically or clinically significant. However, our results did show that VR conditions produced a statistically significantly higher and comparable level of immersion and flow for both difficulties, when compared to the Two-Back task. For clinicians who want to immerse an athlete in their sport outside of the field of play, this research shows that Virtual Reality is a valid option.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".