The Association Between Time Spent in Vigorous Physical Activity and Dietary Patterns in Adolescents: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Physical activity interventions targeting weight status have yielded mixed results. This variability may be attributed to compensatory changes in dietary patterns after increasing physical activity (PA) levels. Therefore, we sought to determine whether dietary patterns varied with time spent in vigorous-intensity PA in youth. METHODS: Cross-sectional analysis of 330 youth enrolled in a school-based prospective cohort in central Alberta. Physical activity was assessed with waist mounted accelerometers (Actical) worn for 7 days. Main outcomes included consumption of unhealthy foods and the unhealthy food index obtained from a validated web-based 24-hour dietary recall instrument. Secondary outcomes included macronutrient intake, food group (Canada's Food Guide to Healthy Eating) intake, and diet quality. RESULTS: Compared with youth participating in < 7 min/ day of vigorous physical activity, those achieving ≥ 7 min/day displayed no change in healthy or unhealthy food consumption. However, linear regression suggests a modest association between diet quality and vigorous-intensity PA. CONCLUSION: These data demonstrate that in this cohort of Canadian youth, time spent being physically active is associated with healthier dietary patterns and not with increased consumption of unhealthy foods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".