Changes in Body Composition, Energy Expenditure, and Energy Intake during Four Years of University—A Follow-Up Study
Bibliographic record
Abstract
Purpose: The transition to university is often accompanied by the adoption of negative lifestyle habits, which may result in weight and fat gain. While this has been demonstrated during 1st year, little is known about subsequent years. We investigated changes in body composition, energy expenditure, and dietary/energy intake from 1st to 4th year university. Methods: Thirty-eight students (14 males, 24 females) completed a lifestyle questionnaire and had their body mass, fat mass, lean body mass (LBM), and body fat percentage (%BF) measured three times: at the beginning and end of 1st year, and end of 4th year. Results: During 1st year, body mass, fat mass, LBM, and %BF increased (+3.2 ± 3.8 kg, +2.5 ± 3.0 kg, +0.7 ± 2.1 kg, +2.3 ± 4.9%, respectively; p < 0.01), while daily energy intake and expenditure decreased (−359 ± 1019 kcal·d−1 and −434 ± 786 kcal·d−1, respectively; p < 0.01). Between the end of 1st year and end of 4th year, body mass, LBM, and energy expenditure increased (+3.2 ± 3.8 kg, +1.3 ± 2.9 kg, +209 ± 703 kcal·d−1, respectively; p ≤ 0.05), while %BF, fat mass, and energy intake did not change. Conclusions: Although %BF and fat mass remained stable from the end of 1st year to the end of 4th year in this group of university students, the positive increase in energy expenditure was not enough to reverse the weight and fat gained during 1st year.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".