Evaluation of a Novel Mobile Exergame in a School-Based Environment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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 it