Impact of a Web-Based Nutrition Intervention on Eating Behaviors and Body Size Preoccupations among Adolescents
Bibliographic record
Abstract
We aimed to evaluate the impact of a web-based school nutrition intervention on eating behavior traits, body weight concern, body size perception and body size dissatisfaction in adolescents. Ten classes of secondary students in Canada (13.6 ± 0.8 years) were randomized into an intervention (n = 162 students) or control group (n = 75 students). Adolescents in the intervention, conducted between 2011 and 2013, participated in an online nutrition challenge to increase their consumption of vegetables, fruits and dairy products using a web-based platform over six weeks. Measurements were taken at baseline (PRE) and post-intervention (POST). No significant negative changes were observed between the intervention and control groups for eating behavior traits, body weight concern, body size perception and dissatisfaction. However, results suggest a trend for a positive effect of the intervention on susceptibility to hunger in boys (group × time interaction, p = 0.10). Specifically, boys experienced a reduction in their susceptibility to hunger in response to the intervention (PRE: 6.1 ± 3.8, POST: 4.8 ± 3.7, p = 0.009). An intervention aimed at improving the eating habits of adolescents did not negatively influence body size preoccupations. In response to the intervention, boys tended to show a lower susceptibility to hunger, which might help them to prevent overeating and adopt healthy eating habits.
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.
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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".