Associations between eating patterns, dietary intakes and eating behaviours in premenopausal overweight women
Bibliographic record
Abstract
It has been suggested that consuming a large proportion of energy in the morning is associated with a lower daily energy intake. To our knowledge, no study has yet investigated how the distribution of energy consumed at different times of the day is associated with eating behaviours. Objective To examine associations between eating patterns, energy intakes and eating behaviours in 144 overweight premenopausal women. Methods Women completed a 3‐day food record. The Three‐Factor‐Eating‐Questionnaire was used to assess eating behaviours (dietary restraint, disinhibition, hunger). Results Proportion of energy consumed at breakfast was negatively associated with total daily energy intake (r= −0.22; p< 0.01) and energy density (r= −0.17; p< 0.05) while proportion of energy from snacks was associated with total daily energy intake (r= 0.27; p< 0.01). Proportion of energy consumed at breakfast was associated with flexible restraint (r= 0.24; p< 0.01) while it was negatively correlated with situational susceptibility to disinhibition (r= −0.21; p< 0.05). Finally, proportion of energy consumed in the evening was correlated with hunger (r= 0.23; p< 0.01). Conclusion These results suggest that eating behaviours could be important factors to further consider in order to explain the association observed between a higher proportion of energy consumed earlier in the day and lower total energy intake. Supported by CIHR.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".