Harnessing serious games to foster healthier and more sustainable food experiences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
• Serious Games can increase nutrition knowledge and support healthy eating • Serious Games enhance engagement and promote reflection on food choices • Well-designed Serious Games can also foster sustainability awareness • Serious Games may complement or substitute traditional in-person training In an era of rapid technological advancement, global food systems continue to fall short at addressing complex challenges such as malnutrition, food waste, and sustainability. While policy interventions have partially succeeded in addressing these challenges, persistent citizen behavior and systemic barriers necessitate innovative approaches. This review analyzes the role of serious games (SGs) as innovative tools to support a healthier and more sustainable food sector. We conducted a literature search identifying a total of 270 publications. Bibliometric analysis underscores the great diversity of SGs and their effectiveness. A total of twenty-seven serious games were described. Educational role-playing games, simulation-based planning tools, and persuasive mobile applications have been leveraged to support food behavioral change, nutritional education, resource conservation, and climate-resilient practices. Documented outcomes include a 29% increase in broccoli and 17% in cauliflower consumption, alongside reductions of 25% in French fries and 21% in candy, following gameplay in a nutrition-focused SG for children. Adolescents showed reduced intake of high-energy snacks, while adult women experienced measurable body mass index reductions over 90 days. Games targeting environmental impact achieved a 23% reduction in diet-related carbon emissions.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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 it