Improving adolescent fitness attitudes with a mobile fitness game to combat obesity in youth
Bibliographic record
Abstract
We present the tests results of the first phase of a multi-phase study to combat deteriorating adolescent fitness, reflected in the growth in adolescent obesity rates, by creating a fitness game deployed on mobile devices. The objective of this first phase was to test an initial mobile fitness prototype's efficacy in generating positive attitudinal changes towards fitness activity using strong socialization components within the game. The basic premise being that game play with a strong social aspect is an attractive support mechanism for sustained interest and providing positive reinforcement to users within our fitness application. Such sustained interest and positive reinforcement is vital for producing long-term fitness improvements. Our mobile fitness game prototype included 13 exercises with functionality to socialize with friends regarding exercise progress and collaboration. A subject pool of 12 adolescents aged 15 to 17 used the prototype for a six week period. Results indicate that the application's socialization features were able to improve subjects' views on fitness activities. Increasing online social networking tendency correlated with improving views on the fitness exercises that were most often engaged in with the application.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".