A Pilot Study on the Effects of Competitive Exergames on Life Satisfaction among Overweight and Obese Male Adolescents in Fujian, China
Bibliographic record
Abstract
This pilot study examined the effects of competitive exergames on life satisfaction among overweight and obese male adolescents in Fujian, China. Twenty-four participants aged 12 to 15 years were randomly divided into three groups: a peer involvement competitive exergames group (PICE), a single-player competitive exergames group (SPCE), and a control group physical education class (PEC). The intervention lasted for two weeks, three times a week. Life satisfaction was assessed using the Multidimensional Student Life Satisfaction Scale (MSLSS). The results showed that the PICE group was significantly better than the control group in friend satisfaction (p = 0.017, d = 1.443) and self-satisfaction (p = 0.027, d = 1.009). Other dimensions also showed positive trends, especially in the PICE group, although these trends did not reach statistical significance. These findings suggest that peer involvement in competitive exergame interventions can have a positive impact on adolescents' social and psychological well-being. This study provides preliminary evidence that competitive exergame can be a feasible and effective method to improve life satisfaction in overweight and obese adolescents. Intervention duration and exercise intensity may need to be adjusted in the future to verify and expand these effects and feasibility.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".