Effects of Active Video Games on Health-Related Physical Fitness and Motor Competence in Children and Adolescents With Overweight or Obesity: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Childhood obesity is one of the most important public health problems. Active video games (AVGs) have been proposed as an attractive alternative to increase energy expenditure and are being investigated to determine their effectiveness against childhood obesity. OBJECTIVE: The aim of this study is to summarize the existing research and draw conclusions about the effects of AVGs on health-related physical fitness and motor competence in children and adolescents with overweight and obesity. METHODS: The search strategy was applied to PubMed, MEDLINE, Web of Science, and SPORTDiscus, including randomized and nonrandomized controlled trials investigating the effects of AVG programs on health-related physical fitness and motor competence in children and adolescents with overweight and obesity. To measure the risk of bias in randomized and nonrandomized controlled trials, 2 different quality assessment tools were used. In total, 15 articles met the inclusion criteria, and the variables of interest were BMI, body fat percentage, cardiorespiratory fitness (CRF), waist circumference, fat-free mass, muscular fitness, and motor competence. A meta-analysis was performed. RESULTS: Positive effects were found for BMI and body fat percentage, favoring the AVG group compared with a control group with no intervention (mean difference -0.209; 95% CI -0.388 to -0.031 vs mean difference -0.879; 95% CI -1.138 to -0.602). Positive effects seem to be observed for CRF. The effects of AVG interventions on muscular fitness, fat-free mass, waist circumference, and motor competence are unclear. CONCLUSIONS: AVG programs showed positive effects on BMI, body fat percentage, and CRF. AVG could be a good strategy to combat childhood obesity.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.026 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".