Bibliographic record
Abstract
Research indicates that the two main causes of being overweight and obese are living a sedentary lifestyle and unhealthy eating habits. Influencing people to be active and exercise is an active research area that has resulted in the development of several games both commercially available and for free. The area of influencing people to develop healthy eating habits, on the other hand, still has room for growth. In the current paper, I review existing serious games for healthy nutrition over the past five years and summarize the main findings based on three main themes: the design and development of the game, the evaluation of the game, and the findings from the evaluation. My results indicate that most games are designed in collaboration with a team of experts such as nutritionists, psychologists, HCI designers, and software developers. In addition, most of the games for kids are web-based while most of those for adults are mobile-based. Most games used a self-report approach to evaluation which was carried out over a range of period of 30 minutes to 90 days with between 10 to 531 participants. There were mixed results from the evaluations with most games partially achieving their aim. I conclude by suggesting guidelines for developing serious games for influencing healthy nutrition.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".