Effect of Mid‐morning Snacks on Subjective Appetite, Food intake, and Glucose and Insulin Responses in Healthy Men and Women
Bibliographic record
Abstract
Snacking contributes a significant proportion of daily energy intake. Altering the macronutrient composition of snacks may increase satiety and promote lower food intake throughout the day, which could play an important role in weight management. The objective of this study was to compare the effects of mid‐morning gel‐based snacks fortified with different macronutrients on subjective appetite, short‐term food intake, and glycaemia in healthy young adults. In a repeated measures design, 23 healthy weight adults (BMI: 23.7 ± 0.8 kg/m2) received in random order five gel snacks (238 g) containing whey‐protein (202 kcal), steel cut oats (276 kcal), coconut oil (276 kcal), maltodextrin (272 kcal), or a control snack (186 kcal) 2 h after a standardized breakfast and 2 h prior to an ad libitum pizza meal at which food intake was measured. Glucose and insulin in capillary blood samples and subjective appetite were measured at baseline and at 15, 30, 45, 60, 90 and 120 min. Compared to snack skipping, all test treatments similarly reduced subjective appetite (p=0.0026), but only the snacks containing steel cut oats (p=0.0322) and coconut oil (p=0.0307) significantly decreased test meal food intake. Blood glucose was higher after maltodextrin (p<0.0001) compared with the other gel snacks. Cumulative insulin was higher after maltodextrin (p<0.01) snack consumption compared to the coconut oil snack and snack skipping. Blood glucose and insulin responses had a strong positive linear correlation (R2 = 0.7897). In conclusion, gel snacks differed in their effects on food intake, blood glucose and insulin responses. Both macronutrient composition and calorie content were primary determinants of food intake but macronutrient composition was the main factor in glycaemic and insulin responses. Support or Funding Information This work was funded by the Hershey Company.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".