A Multi-Week Assessment of a Mobile Exergame Intervention in an Elementary School
Bibliographic record
Abstract
BACKGROUND: Exergaming is potentially useful to promote physical activity in children; however, long-term effectiveness is unclear. MobileKids Monster Manor (MKMM) is a mobile exergame developed with the help of young advisors. The game wirelessly transmits physical activity data from an accelerometer to a mobile device. Players' steps are redeemed for in-game rewards, for example, new characters. OBJECTIVE: First, to evaluate whether increased physical activity previously observed in a 1-week intervention is sustained over a 2-week intervention and 1-week follow-up, and second, to compare impact in schools within different socioeconomic environments. METHODS: accelerometer throughout. Linear mixed models were applied to assess sustainability; a second 42-children-based dataset and age-/sex-adjusted linear regression models were used to compare effect across socioeconomic environments. RESULTS: In the first week of intervention, the Game group compared to the Control group showed a greater increase in physical activity (of 1,758 steps/day [95% confidence interval, CI = 133-3,385] and 31 active minutes/day [95% CI = 4-59]), relative to baseline (13,986 steps/day; 231 active minutes/day). However, this was not sustained in the second intervention week or follow-up. The school within a lower socioeconomic status environment showed lower baseline activity and the 1-week intervention resulted in a greater increase relative to baseline (3,633 steps/day more [95% CI = 1,281-5,985]). CONCLUSION: MKMM could be a useful short-term physical activity promotion tool; however, effectiveness may decrease as novelty diminishes.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".