The impact of a family web-based nutrition intervention to increase fruit, vegetable, and dairy intakes: a single-blinded randomized family clustered intervention
Bibliographic record
Abstract
Abstract Background The importance of adopting healthy eating habits at a young age to prevent obesity and chronic diseases justifies the need for effective interventions. Objective This study evaluated the impact of a family web-based nutrition intervention on vegetable and fruit (V/F) and dairy product (DP) consumption, nutrient intakes, diet quality and BMI or BMI z-scores. Methods Forty-three families with children aged 8–16 years were randomized to either the family web-based intervention, or web-based general nutrition guidelines (control) over 8 weeks. Nutritional variables were assessed with three-day dietary records while anthropometry (body weight and height) was assessed with standardized measures at baseline (PRE), immediately after the intervention (POST 1) and 3–6 months after the intervention (POST 2). Linear mixed models for repeated measures were used to assess the main effects and their interactions followed by post hoc tests. Results The intervention had an effect on DP, total sugar, potassium, magnesium, and calcium in children (Group x Time, P = 0.02 to 0.03) and on DP, V/F juice, carbohydrates, total sugar, saturated fat, protein and calcium in parents (Group x Time, P = 0.01 to 0.03). Post hoc tests revealed children in the intervention group increased their DP intakes immediately after the intervention (POST1) but decreased at follow-up (POST2). No effect of the intervention on V/F, diet quality or BMI was observed. Conclusion Compared to general nutrition guidelines, this family web-based nutrition intervention had a modest effect on nutrient intakes, but beneficial effect on DP intakes in the short term. Trial registration ClinicalTrials.gov , NCT03798808 , Registered 10 january 2019 - Retrospectively registered.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".