Objective and Subjective Eating Speeds Are Related to Body Composition and Shape in Female College Students
Bibliographic record
Abstract
Eating speed reportedly relates to body composition and shape. Little is known about the relationship between the objectively assessed eating speed and the body composition and shape. This study examined relationships between eating speed as assessed both objectively and subjectively, and body composition and shape. The following variables of body composition and shape were measured in 84 female college students: body mass, relative body fat mass (%Fat), body mass index (BMI), and circumferences of the waist, abdomen and hip. After measuring the body composition and shape, subjects consumed a 174-kcal salmon rice ball. The following chewing variables were measured by observing videotape recordings of the subjects' faces: number of chews per bite, total number of chews, total meal duration, number of bites, and chewing rate. The subjects were categorized into three groups (fast, moderate and slow) according to their own subjective assessments of the actual eating speed. In objective assessments of the eating speed, the total number of chews and the total meal duration were significantly negatively correlated with the body mass, %Fat, BMI, and circumferences of the waist, abdomen and hip. In subjective eating-speed assessments, the body mass, %Fat, BMI, and circumferences of the waist, abdomen and hip were greater in the fast eating group than in the slow eating group. Both the objectively and subjectively assessed eating speeds are related to the body composition and shape. The present study supports that fast eating may relate to gains in body mass and/or fat mass.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".