Exercise Improves Video Game Performance: A Win–Win Situation
Bibliographic record
Abstract
PURPOSE: Video gamers exceeding screen-time limits are at greater risk of experiencing health issues associated with physical inactivity. Demonstrating that exercise has positive effects on video game performance could promote physical activity among video gamers. We investigated the short-term effects of a single session of cardiovascular exercise on the performance of the popular video game League of Legends (LoL) and explored psychosocial mechanisms. METHODS: Twenty young video gamers played a customized LoL task preceded by a short bout of high-intensity interval training or a period of rest. The two conditions were administered on two separate days in a randomized counterbalanced fashion. Video game performance was assessed as the total number of targets eliminated as well as accuracy, defined as the ability to eliminate targets using single attacks. Short-term changes in affect after exercise as well as exercise enjoyment were also assessed. RESULTS: Exercise improved (P = 0.027) the capacity to eliminate targets (mean ± SEM, 121.17 ± 3.78) compared with rest (111.38 ± 3.43). Exercise also enhanced accuracy (P = 0.019), with fewer targets eliminated with more than one attack after exercise (1.39 ± 0.39) compared with rest (2.44 ± 0.51). Exercise increased positive affect by 17% (P = 0.007), but neither affect nor exercise enjoyment was associated with total number of targets eliminated or accuracy. CONCLUSION: A short bout of intense cardiovascular exercise before playing LoL improves video game performance. More studies are needed to establish whether these effects are generalizable to other video games, whether repeated bouts have summative effects, and to identify underlying mechanisms.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".