Evaluation of a Novel Mobile Exergame in a School-Based Environment
Bibliographic record
Abstract
Physical inactivity is increasing among children globally and has been directly linked to the growing problems of overweight and obesity. We aim to assess the impact of a new mobile exergame, MobileKids Monster Manor (MKMM), in a school-based setting. MKMM, developed with input from youth to enhance physical activity, is wirelessly connected to an accelerometer-based activity monitor. Forty-two healthy students (11.3 ± 1.2 years old and 0.28 ± 1.29 body-mass index [BMI] z-score) participated in a randomized 4-week crossover study to evaluate the game intervention. The two study arms consisted of week-long baseline, game intervention/control, washout, and control/game intervention phases. All participants were required to wear an activity monitor at all times to record steps and active minutes for the study duration. MKMM was used during each arm's respective intervention week, during which children were asked to play the game at their convenience. When children were exposed to the game, an increase compared with the control phase of 2,934 steps per day (p = 0.0004, 95% CI 1,434-4,434) and 46 active minutes per day (p = 0.001, 95% CI 20-72) from baseline (12,299 steps/day and 190 active minutes/day) was observed. A linear regression model showed that MKMM yielded a greater increase in steps and active minutes per day among children with a higher BMI z-score, showing 10 percent more steps per day and 14 percent more active minutes per day relative to baseline, per unit increase in BMI z-score. In conclusion, MKMM increased steps and active minutes in a school-based environment. This suggests that mobile exergames could be useful tools for schools to promote physical activity and combat obesity in adolescents.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".