Effectiveness of active video games in overweight and obese adolescents: a systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
PURPOSE: The purpose of this review was to assess the effect of active video games among overweight and obese adolescents. METHODS: A systematic review and meta-analysis were conducted using records from the English-language electronic databases MEDLINE, the Web of Science, and PubMed. These databases were searched from January 2010 to December 2020 using the keywords (adolescent*) AND (overweight OR obese *) AND (active video games OR exergaming * OR video game*). RESULTS: Five articles met the inclusion criteria. Four studies were conducted in the United States of America and 1 study was conducted in Canada. In addition, all included articles had a randomized controlled trial study design. It was determined that the sample size of the studies was 30-46 participants and there were a total of 195 overweight and obese adolescents across the included studies. Active video gaming was negatively associated with changes in body mass index percentile (mean difference [MD], -1.77; 95% confidence interval [CI], -2.55 to -0.99; p<0.001) and total cholesterol (MD, -11.16; 95% CI, -16.64 to -5.68; p<0.001). CONCLUSION: Playing active video games can reduce both the body mass index percentile and total cholesterol in overweight and obese adolescents. Active video games can provide a different method for combating childhood obesity. High-quality randomized controlled trials are recommended to assess the impact of game-based interventions.
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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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.028 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".