Chewing gum increases energy expenditure before and after controlled breakfasts
Bibliographic record
Abstract
Chewing has been associated with improved satiation and satiety, but little is known about the metabolic impact of gum chewing. We tested the hypothesis that gum chewing would increase energy expenditure (EE) and reduce respiratory exchange ratio (RER) before and after a controlled test meal. Seventeen males and 13 females (age 21.5 ± 6.6 years, body mass index 23.9 ± 2.8 kg/m(2)) participated in a randomized crossover study in which subjects chewed sugar-free gum for a total of 1 h (3 sessions of 20 min) on the test day (GC) and did not chew gum on a control day (NG). EE and RER were measured by indirect calorimetry after an overnight fast. Subjects consumed a breakfast shake containing 30% of their measured energy needs, and then postprandial EE and RER were measured for 3 h. Blood glucose (GLC) was measured in the fasting and postprandial states at regular intervals. Fasting EE was higher during GC (1.23 ± 0.04 kcal/min; 1 kcal = 4.2 kJ) than during NG (1.17 ± 0.04 kcal/min; p = 0.016). Postprandial EE was also higher during GC (1.46 ± 0.05 kcal/min) than during NG (1.42 ± 0.05 kcal/min; p = 0.037). Fasting and postprandial RER and GLC did not differ between GC and NG. The findings demonstrate that GC is associated with higher fasting and postprandial EE without altering blood glucose or substrate oxidation as measured by RER. These data suggest that gum chewing potentially could influence short-term energy balance in this population; however, longer-term research is needed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".