Does gamification improve fruit and vegetable intake in adolescents? a systematic review
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
BACKGROUND: Nutrition and diet-related non-communicable diseases are a major cause of death worldwide. Food preferences and eating behaviours are likely to be established during adolescence, making it an important period to promote healthy behaviours. AIM: To review the effectiveness of gamification to improve fruit and vegetable intake in adolescents. METHODS: A systematic search was conducted using eight databases and grey literature sources for articles published to date on the effectiveness of gamification on fruit and vegetable intake in adolescents. Search criteria included articles that were complete and peer reviewed, conducted empirical research, described gamified elements used, focused on individuals between 10 and 24 years, and were available in English. RESULTS: Out of 402 studies identified by the search, 7 were included in the review. Overall, short-term gamified interventions showed promise in improving fruit and vegetable intake in those aged 10 to 24 years old. Gamification was primarily facilitated through extrinsic motivation (i.e. points, badges, vouchers, leaderboard, narration, avatars, challenges) rather than intrinsic motivation (i.e. team-based competition). Studies were moderate in quality and key methodological issues related to non-randomized study design, lack of comparison group, inadequate control for confounding, and small sample size. CONCLUSIONS: Gamification can be an effective tool in changing nutrition-related behaviour in adolescents over the short term. Future research should consider gamified interventions that are of longer duration, incorporate additional intrinsic gamified elements, tailor game elements for population subgroups, and address methodological issues.
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) | 0.003 | 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 | 0.001 |
| 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