Towards Combating Youth Obesity with a Mobile Fitness Application
Bibliographic record
Abstract
This paper presents the results of the first phase in a multi-phase study developing an educational fitness application on mobile devices to help combat the growing levels of obesity among youth age 11 to 17. This first phase studies demographic differences across age and gender with regards to physical activities, computer games and mobile technology usage in order to develop an adaptive mobile fitness application that requires physical movement by the user while maintaining interest and enjoyment levels. Results indicate significant differences in gender in social activities versus action oriented type games but little difference in types of fitness activities. Across age groups there were some significant differences in terms of their interest in games involving strategy, action and violence. Also of note was that youth in the range of 14-15 tended to view physical activities as more difficult versus those both older and younger than them for the ages we studied. Youth also differed significantly in the type of mobile facilities they used with those older than 13 tending to use G.P.S., music and video facilities much more than those aged 11 to 13. These results have been incorporated into an initial prototype fitness application that will be tested on subjects in the next study phase.
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.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".