A Single Bout of Exercise Improves Accuracy in Video Gaming: a Pilot Study
Bibliographic record
Abstract
There are 2.3 billion of video gamers worldwide and this number is expected to grow to more than 2.7 billion by 2021. Research has demonstrated negative associations between the number of hours spent in front of a screen and physical inactivity. Video gamers are thus at a great risk of experiencing long-term health issues associated to excessive sedentarism. Cardiovascular exercise has been proven to be an effective intervention in reducing the risk of cardiometabolic clinical conditions as well as enhancing brain health and function. However, whether exercise has positive effects on video game performance is not known. PURPOSE: To investigate the effects of a single bout of cardiovascular exercise on the performance of “League of Legends” (LoL), a video game played daily by more than 30 million players. METHODS: 14 healthy young (18-28 yo) LoL gamers played an individual LoL task of 20 min preceded by either 15 min of a high-intensity interval exercise or rest. The two conditions were administered on two separate days in a counterbalanced fashion. Video game performance was assessed as the number of targets destroyed, as well as accuracy, defined as the ability to destroy a target with only one attack. Attacks that required more than one attempt to destroy a target were counted as accuracy errors. RESULTS: Exercise improved the capacity of participants to successfully destroy targets, but differences between exercise (119.43 [4.23]) and rest (111.50 [3.98]) did not reach statistical significance (paired t-test; t=1.81; p=0.094). Exercise enhanced accuracy, with fewer errors after exercise than after rest (paired t-test; t=-2.38; p=0.033). Self-reported sitting time was negatively associated with total score after the rest condition (r=-0.55; p=0.040). Neither other variable (cardio-respiratory fitness, BMI, cognitive level) was associated with game performance. CONCLUSION: Exercise performed before playing LoL improves video game performance increasing accuracy. The fact that players with less sitting time showed better performance reinforces the importance of reducing sedentary behaviors in this group. The implementation of exercise routines in video gamers may improve their general health and their gaming performance. Supported by FRQS Junior I Salary Award (MR) and by the McGill Faculty of Medicine (OL).
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".