Psychological factor (PF) changes during a randomized trial of legume consumption during weight loss
Bibliographic record
Abstract
Weight loss attempts often fail. Legumes are high in satiety factors and may aid weight loss, but PFs may also influence weight loss and vice‐versa. In a 6wk study, subjects (n= 42; BMI 25–35 kg/m 2 ) were randomized to consume LOW (1T), medium (MED; 0.5c) or HIGH (1.8–2.7c) legumes 6d/wk while reducing energy intake (EI) by 30%. ~50% of the target EI was provided and the remainder was self‐selected. All groups lost weight (2.7±2.3kg; p=0.023), with MED losing more than LOW (p=0.032) but not HIGH (p=0.12). PFs changed over time in all groups: dietary disinhibition, its two subscales (situational susceptibility, uncontrolled eating) and hunger decreased, whereas self‐efficacy, dietary restraint and several restraint subscales (strategic dieting, avoiding fattening foods (AFF), flexible control, rigorous control) increased (p≤0.005). There were significant group by time interaction effects on external hunger (p=0.046) and habitual disinhibition (p=0.003); external hunger increased more in MED compared to LOW (p=0.015) and habitual disinhibition increased more in MED compared to HIGH (p=0.063). Predictors of greater weight loss were a more mindful eating style at baseline (p=0.023) and a decrease in AFF score (p=0.051), independent of baseline AFF, baseline weight and legume treatment. These data suggest that PFs may have helped explain differences in weight loss among legume groups. [Funding: Pulse Canada PIP]
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".