Weight Management and Fruit and Vegetable Intake Among US High School Students*
Bibliographic record
Abstract
BACKGROUND: Consumption of fruits and vegetables is often recommended to promote healthy weight. The purpose of this study was to examine associations between fruit and vegetable intake and common weight management behaviors among US high school students who were trying to lose or stay the same weight. METHODS: Data from the 1999, 2001, and 2003 national high school Youth Risk Behavior Surveys were combined and the analyses stratified by gender (females, N = 16,709; males, N = 10,521). We considered 3 common weight management strategies--being physically active (ie, moderate activity for 30 minutes on 5 or more days per week or vigorous activity for 20 minutes on 3 or more days per week), eating a reduced calorie or fat diet, and limiting TV viewing. Sufficient fruit and vegetable intake was defined as eating 5 or more servings per day. Odds ratios (ORs) were calculated using logistic regression. RESULTS: Only 21.3% of females and 24.7% of males ate sufficient fruits and vegetables. Being physically active was associated with sufficient fruit and vegetable intake. Eating a reduced calorie or fat diet and limiting TV viewing (among males) were associated with sufficient fruit and vegetable intake only among physically active students. The odds of sufficient fruit and vegetable intake were greatest among female (OR = 3.01) and male (OR = 2.91) students who combined all 3 strategies (31.5% of females, 21.6% of males). CONCLUSIONS: Interventions that promote fruit and vegetable intake within the context of healthy weight management may be more effective if they combine nutrition and physical activity strategies. Further research is needed to test this approach.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".