Effects of Self-Awareness of Eating Behaviors and Differences in Daily Habits Among Japanese University Students on Changes in Weight and Metabolism
Bibliographic record
Abstract
Background: In addition to daily weight measurements and regular exercise, not skipping breakfast, refraining from eating at night, and not overconsuming soft drinks have been reported to suppress the onset and progression of obesity and metabolic syndrome in adulthood. However, few studies have examined the associations between these daily lifestyle habits and the types of eating behaviors (e.g., food preferences, conception of eating, eating habits) among university students. Methods: We investigated the characteristics of eating behaviors based on backgrounds and lifestyle factors in association with changes in weight and metabolism using blood sampling data, a questionnaire on eating behaviors conducted during clinical training, and data from regular health examinations of 100 fifth-grade students at the Oita University Faculty of Medicine in Japan. Results: Characteristic eating behaviors, including daily self-weighing, regular exercise, skipping breakfast, frequently eating late at night, and excess soft drink consumption, were observed for each lifestyle. In addition, three eating behaviors (fast eating, eating late-night snacks, and not eating breakfast) were extracted as factors that cause weight gain of 3% or more from the weight at the time of admission to university. Self-awareness of fast eating was significantly associated with higher body mass index in the fifth grade (P < 0.001), and systolic blood pressure and fasting plasma glucose tended to be higher in students who were strongly aware that they would not have breakfast (P = 0.071 and P = 0.053, respectively). Conclusions: The results indicated that the habits of “fast eating” and “not eating breakfast” respectively increase weight and may cause metabolic disorders, regardless of current weight. Thus, it is important for students to be self-aware of unhealthy eating behaviors in daily life. Although it was developed for the medical treatment of obese patients, the questionnaire on eating behaviors may be useful for helping university students learn eating behavior habits and peculiarities as well as health education. J Endocrinol Metab. 2020;10(5):131-139 doi: https://doi.org/10.14740/jem687
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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".