The relative reinforcing value of snack food is a significant predictor of fat loss in women with overweight or obesity
Bibliographic record
Abstract
Reinforcing Relative Value (RRV) of food and impulsivity are associated with energy intake and obesity. This study investigated the degree to which changes in RRV and impulsivity independently or interactively predict changes in body weight and composition in women with overweight or obesity engaged in either fast or slow weight loss programs. Body weight, body composition, impulsivity (Barratt Impulsiveness Scale), and RRV snack (computerized Behavioural Choice Task) were measured at baseline and post-intervention in 30 women with obesity undergoing either slow (n = 14, –500 kcal/day, 20 weeks) or fast (n = 16, –1000 kcal/day, 10 weeks) weight reduction. No group × time effects were noted on body composition, impulsivity, or RRV, so participants from both groups were pooled for analysis. Multiple regression analyses indicated that none of the impulsivity variables predicted weight or fat mass (FM) loss. However, ΔRRV snack predicted ΔFM (r = 0.40, p = 0.046), whereby greater increases in RRV snack were associated with less FM loss. The results indicate that different rates of weight loss do not differentially affect RRV snack or impulsivity traits. However, changes in RRV snack predicted FM loss, suggesting that dietary interventions that either mitigate increases or foster reductions in the RRV snack may yield greater reductions in adiposity. Trial Registration clinicaltrials.gov identifier: NCT04866875. Novelty: No differences in RRV of food were noted between fast and slow weight loss. Weight loss from combined fast and slow groups led to a moderate-sized reduction in total impulsivity. Greater diet-induced increases in RRV snacks were associated with less body fat 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".