A randomized study of legume consumption during weight loss: effects on food cravings
Bibliographic record
Abstract
To examine the associations of weight loss and dietary composition with food cravings, we studied n=42 adults (BMI 25–35 kg/m 2 ) who completed a 6‐wk study. Subjects were assigned to consume either LOW (1 Tbsp), medium (MED, 0.5 c), or HIGH legumes (1.8 c (women) and 2.7 c (men)) diet 6 d/wk while reducing energy intake (EI) by 30%. ~50% of EI was provided and contained the requisite amount of legumes, while the remainder of EI was self‐selected. All groups lost weight (p=0.023), with MED losing more than LOW (p=0.032) but not HIGH (p=0.12) (meanΔ±SEM: MED: −3.8±0.5 kg (n=16); HIGH: −2.3±0.6 kg (n=13); LOW: 1.8±0.5 kg (n=13). At baseline, 76% of subjects had food cravings (Hill et al questionnaire, 1991) over the past 3 months, averaging 11.1±1.7 times/mo. The change in the frequency of the strongest food craving differed among groups, decreasing by 5.3±3.9 and 0.7±1.0/mo in LOW and MED, respectively, and increasing by 2.5±2.5/mo in HIGH (p=0.069). The BMI decrease was significantly associated with the decrease in the frequency of the strongest food craving after controlling for legume group, baseline BMI and baseline strongest craving frequency the (p=0.049). All groups consumed the most craved food significantly less often at wk 6 compared to wk 0 (Δ= −15±7% of the time; p=0.013), but acting on cravings was not related to weight loss. Food craving frequency can change with weight loss and changes in dietary composition. [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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".