Are Active Video Games Effective at Eliciting Moderate-Intensity Physical Activity in Children, and Do They Enjoy Playing Them?
Bibliographic record
Abstract
Background Despite current physical activity (PA) guidelines, children spend an average of 1-3 hours/day playing video games. Some video games offer physically active components as part of gameplay. We sought to determine if these active video games (AVGs) can elicit at least moderate PA in children, identify game elements important for PA, and determine if they are fun to play. Methods Twenty children aged 8 to 16 years underwent cardiopulmonary exercise testing to determine their heart rate (HR) at ventilatory threshold. Participants played 2 different AVGs, and the gaming time that each participant's HR was above the HR thresholds for moderate and vigorous PA was determined. Gameplay elements that supported or inhibited active gameplay were also identified. Participants also completed questionnaires on physical activity, game engagement, and game experience. Results The Dance Central Spotlight and Kung-Fu for Kinect AVGs produced at least moderate PA, for a mean of 54.3% ± 29.5% and 87.8% ± 21.8% of gameplay time, respectively. Full-body movements, player autonomy, and self-efficacy were observed to be important elements of good AVG design. Although participants enjoyed these AVGs, they still preferred their favorite games (game engagement score of 1.82 ± 0.67 vs 0.95 ± 0.70 [ Dance Central Spotlight ] and 1.39 ± 0.37 [ Kung Fu for Kinect ]). Conclusions AVGs can provide at least moderate PA and are enjoyable to play, but most popular video games do not incorporate active components. The implementation of government policies and a rating system concerning PA in video games may help address the widespread sedentary lifestyle of children.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".