The Power of Suggestion: Subjective Satiety Is Affected by Nutrient and Health-Focused Food Labelling with No Effect on Physiological Gut Hormone Release
Bibliographic record
Abstract
Health claims on food labelling can influence peoples’ perception of food without them actually eating it, for example driving a belief that a particular food will make them feel fuller. The aim of this study was to investigate whether nutrient and health claims on food labelling can influence self-reported, and physiological indicators of, satiation. A total of 50 participants attended two visits where they were asked to consume a 380 kcal breakfast (granola and yogurt) labelled as a 500 kcal ‘indulgent’ breakfast at one visit and as a 250 kcal ‘sensible’ breakfast at the other. The order of the breakfast descriptions was randomly allocated. Participants were unaware that the two breakfasts were the same product and that only the food labels differed. At each visit blood samples were collected to measure gut hormone levels (acylated ghrelin, peptide tyrosine-tyrosine and glucagon-like peptide-1) at three time points: 20 min after arrival (baseline), after 60 min (anticipatory, immediately prior to consumption) and after 90 min (post-consumption). Visual analogue scales measuring appetite (hunger, satiety, fullness, quantity and desire to eat) were completed prior to each sample. Between 60 and 90 min, participants consumed the breakfast and rated its sensory appeal. Participants reported a higher mean change in self-reported fullness for the ‘indulgent’ than the ‘sensible’ breakfast from anticipatory to post-consumption (mean difference: 7.19 [95% CI: 0.73, 13.6]; p = 0.030). This change was not observed for the other appetite measures at the other time points or gut hormone levels. This study suggests that nutrient and health claims on food labels may influence satiation as measured by self-reported fullness. It also suggests that the observed differences in satiety scores are not due to changes in the main appetite regulating gut hormones, but are more likely centrally mediated. More high-quality trials are required to confirm these findings.
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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 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".