Development of an Exergame on Mobile Phones to Increase Physical Activity for Adults with Severe Mental Illness
Bibliographic record
Abstract
Maintaining a certain level of Physical Activity (PA) is important to prevent some chronic pathologies. This is even more important for individuals with severe mental health problems, but they have many barriers that make it very difficult for them to be physically active, including lack of motivation. In this paper, we propose an exergame that aims to help these people integrate PA into their daily lives. This exergame is designed on a smartphone in order to be able to follow the level of activity of the player on a daily basis. It offers game mechanics that allow the player to manage their PA. However, it encourages them to be physically active so that they can progress more easily in the game. The application uses an activity detection algorithm to measure the level of PA. We offer a preliminary study on healthy subjects on a demo version of the game that observes their gaming experience. The results show that the mechanics are globally appreciated and that the game allows each player to manage his PA as he wishes.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".