Physical activity, eating traits and weight in young adulthood: a cross‐sectional and longitudinal study
Bibliographic record
Abstract
Summary Objective To investigate the association between eating traits (e.g. dietary restraint or opportunistic eating) and weight – both cross‐sectionally and longitudinally – and whether physical activity (PA) moderates these associations. Methods Two‐hundred seventy young adults (21–35 years; BMI: 25.40 kg/m2 [SD = 3.90 kg/m2]; 48.90% female) participated in this 12‐month observational cohort study. Cognitive Restraint (CR), Disinhibition (DI) and Hunger (HU) were measured using the Three‐Factor Eating Questionnaire at baseline and 12 months. Participants were measured at quarterly intervals for objectively measured PA and anthropometrics. Cross‐sectional and longitudinal models determined if eating traits were associated with weight or weight change, and whether these associations were moderated by PA. Results At baseline, higher CR (B = 0.429, p < 0.01) and DI (B = 0.942, p < 0.01) were associated with higher weight. The associations of DI (B = −0.008 p = 0.02) and HU (B = −0.006, p = 0.04) with weight were moderated by PA at baseline. The longitudinal model for CR determined PA altered the relationship between change in CR and weight change (B = 0.004, p < 0.01). Conclusions Eating traits and PA are associated with weight and weight change. However, to elucidate how PA and eating traits directly affect weight changes, future weight loss interventions should investigate whether improving eating traits and concomitantly increasing PA amplify weight loss.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".