Does gamification improve fruit and vegetable intake in adolescents? a systematic review
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.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".