Effects of interactive video game cycling on overweight and obese adolescent health
Bibliographic record
Abstract
The purpose of this study was to examine the efficacy of interactive video game stationary cycling (GameBike) in comparison with stationary cycling to music on adherence, energy expenditure measures, submaximal aerobic fitness, body composition, and cardiovascular disease risk markers in overweight and obese adolescents, using a randomized controlled trial design. Thirty overweight (with at least 1 metabolic complication) or obese adolescents aged 12-17 years were stratified by gender and randomized to video game or music condition, with 4 participants (2 per group) failing to complete the twice weekly 60 min sessions of the 10-week trial. The music group had a higher rate of attendance compared with the video game group (92% vs. 86%, p < 0.05). Time spent in minutes per session at vigorous intensity (80%-100% of predicted peak heart rate) (24.9 ± 20 min vs. 13.7 ± 12.8 min, p < 0.05) and average distance (km) pedaled per session (12.5 ± 2.8 km vs. 10.2 ± 2.2 km, p < 0.05) also favoured the music group. However, both interventions produced significant improvements in submaximal indicators of aerobic fitness as measured by a graded cycle ergometer protocol. Also, when collapsed, the exercise modalities reduced body fat percentage and total cholesterol. The present study indicates that cycling to music was just as effective as stationary cycling while playing video games at improving fitness, body composition, and cholesterol profiles in overweight and obese teens, and resulted in increased attendance, vigorous intensity of physical activity, and distance pedaled. Therefore, our data support the superiority of cycling to music and indicate investing in the more expensive GameBike may not be worth the cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".