The Effect of a Novel Video Game on Young Soccer Players' Sports Performance and Attention: Randomized Controlled Trial
Bibliographic record
Abstract
Background: Currently, the fusion of technology and sports is inevitable. The integration of various systems and devices has brought about significant transformations in established sports practices, impacting not only the rules but also physiological, biomechanical, and even psychological aspects. Objective: The purpose of this study was to analyze the effect of an attention intervention through a video game on young soccer players. Methods: Twelve young male soccer players (age: mean 8.5, SD 1 years) were divided into 2 groups: a control group (CG; n=10) and an experimental group (EG; n=10). During the 6-week training program, the EG received attention training through a video game twice a week for 15 minutes per session. Pre- and postintervention measurements included a specific decision-making soccer test and interviews with coaching staff. Additionally, success in the video game, muscular activity, and sweat levels were monitored. Results: The EG demonstrated a significant improvement in video game success following the intervention program, as indicated by the achieved level (P<.001). However, no significant differences were found between groups regarding electromyographic (EMG) activity (P=.21) and sweating (P=.20). Prior to implementing the attention training program, both groups exhibited similar data for variables related to decision-making and execution mechanisms (≤10%). Only 2 decision-making variables exceeded 10% but remained below 15% (Shot_D=13.35%; Marking_with_Ball_D=-12.64%). Furthermore, changes in attacking action variables were more pronounced in execution-related variables, except for dribbling and fixing. Conversely, in defensive action variables, changes were greater in decision-related variables, except for marking with the ball and marking without the ball. Conclusions: Our findings reveal that incorporating a specific attentional video game into a soccer training program enhances decision-making compared to a program without the video game. Therefore, it is advisable for practitioners to consider using this tool due to its high efficiency in terms of economic and temporal costs, particularly in improving a key psychological variable.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".