Influence of Nutrition Claims on Appetite Sensations according to Sex, Weight Status, and Restrained Eating
Bibliographic record
Abstract
Nutrition claims may help people to adopt healthier eating habits, but little is known about the potential cognitive effects of such claims on appetite sensations. The main purpose of this study was to evaluate the impact of nutrition claims and individual factors on perceived appetite sensations. According to a three (“healthy” versus “diet” (i.e., satiating) versus “hedonic”) by two (restrained or not restrained) by two (normal-weight or overweight/obese) by two (men versus women) factorial design, 164 males and 188 females aged 18–65 were invited to taste an oatmeal-raisin snack in a blinded andad libitumcontext. Visual analog scales (150 mm) were used to evaluate appetite sensations before and over 1 h after consumption period. BMI and Restraint Scale were used to categorize participants according to their weight and restraint status. No main condition effect was observed for any of the four appetite sensations. However, subgroups analysis revealed significant differences among specific subgroups. A main effect of sex was also observed for all appetite sensations with men reporting higher levels of desire to eat, hunger and prospective food consumption, and lower levels of fullness than women. These findings highlight the importance of considering individual characteristics in interaction when studying appetite sensations.
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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 | 0.003 |
| 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 | 0.000 |
| 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".