The Effects of a Computer Game (Healthy Rat King) on Preschool Children’s Nutritional Knowledge and Junk Food Intake Behavior: Nonrandomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: Playing computer-aided games could enhance children's interest in learning about nutritional knowledge and eventually promote healthy dietary intake behavior. OBJECTIVE: This study aims to evaluate the effectiveness of a computer game (Healthy Rat King) in improving the knowledge on nutrition and junk food intake among preschool children in Taiwan. METHODS: This was a quasi-experimental study that utilized the computer game Healthy Rat King as the nutrition education tool. We recruited 104 preschool children (aged 5-6 years) from preschools in central Taiwan, who were assigned to either the experimental group (n=56) or the control group (n=48). In the experimental group, a 1-hour computer-based educational game intervention was included in the course for 4 consecutive weeks. The control group did not receive this intervention. RESULTS: The level of nutritional knowledge for children in the experimental group was significantly higher than those in the control group after 4 weeks (P=.002). Furthermore, the frequency of consumption of chocolate, candies, and ice cream (high-calorie junk food) was reduced in the experimental group. There was also no significant difference in the consumption of candy and chocolate (P=.54), ice cream and ice pops (P=.21), cake (P=.92), biscuit (P=.98), soft drinks (P=.52), and fruit juice and sugary drinks (P=.31) between the 2 groups in the posttest. CONCLUSIONS: Teaching using a computer game could improve children's nutritional knowledge. However, the intake frequency of junk food among children in the experimental group showed no significant difference from those in the control group.
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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) | 0.001 | 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 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".