Translating and Testing a Digital Game Promoting Vegetable Consumption in Young Children: Usability Study
Bibliographic record
Abstract
BACKGROUND: Promoting healthy eating in children is key to preventing chronic diseases, and vegetable consumption is notably lower than recommended in this population. Among the interventions tested, gamification has shown promise in promoting familiarization, increasing knowledge, and potentially increasing vegetable intake. OBJECTIVE: This pilot study aimed first to translate the digital game "Veggies4myHeart" into French and to assess its influence on young children's preferences and willingness to taste vegetables when combined with repeated tasting and education. We also aimed to investigate the acceptability and applicability of the game in 2 classrooms. METHODS: During 5 consecutive weekly sessions, children from 2 elementary classes played the digital game consisting of 5 mini games on different vegetables (lettuce, carrot, red cabbage, cucumber, and tomato) in pairs for 10-15 minutes. In addition, they discussed one of the vegetables and tasted the 5 vegetables in each session. Pretest and posttest food preferences and willingness to taste the vegetables were compared. Teachers participated in a semistructured interview. RESULTS: A total of 45 children aged 5 to 6 years tested the French version of the digital game. The children's declared food preferences were already high for carrot, cucumber, and tomato, with scores higher than 4 out of a maximum of 5. The scores did not change significantly after the intervention, except for red cabbage (pretest: mean 2.52, SD 1.49; posttest: mean 3.29, SD 1.67; P=.006) and a composite score (pretest: mean 3.76, SD 1.06; posttest: mean 4.05, SD 1.03; P=.001). Before the intervention, 18 (44%), 30 (73%), 16 (39%), 29 (71%), and 26 (63%) children out of 41 were willing to taste lettuce, carrot, red cabbage, cucumber, and tomato, respectively. After the intervention, no significant statistical differences were observed, with 23 (51%), 36 (80%), 24 (53%), 33 (73%), and 29 (64%) children out of 45 willing to taste lettuce, carrot, red cabbage, cucumber, and tomato, respectively. Teachers supported this tool combined with repeated tasting and education and highlighted facilitators and barriers that should be anticipated to improve implementation in schools. CONCLUSIONS: In this study, we translated an existing digital game applicable and acceptable to both children and teachers. A larger study is warranted to confirm the effectiveness of interventions using the digital game to promote vegetable preference, willingness to taste, and intake.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".