Impact of Weight Reduction on Eating Behaviors and Quality of Life: Influence of the Obesity Degree
Bibliographic record
Abstract
BACKGROUND: To examine the effects of a short-term weight reducing program on body composition, eating behaviors, and health-related quality of life (HRQL) of sedentary obese women characterized by different obesity degrees. METHODS: 44 women with a BMI under 34.9 kg/m(2) and 39 women with a BMI above 35 kg/m(2) were studied. Fat mass and lean mass (electrical bioimpedance), eating behaviors (Three-Factor Eating Questionnaire), and HRQL (36-item short form, SF-36, questionnaire) were determined before and after weight loss. RESULTS: Disinhibition and hunger scores and their subscales decreased after weight loss in both groups (0.0001 < p < 0.04). Restriction increased after weight reduction in all women (p = 0.02). Among the five restriction subscales, flexible restriction increased in women with a BMI above 35 kg/m(2) (p = 0.008), whereas rigid restraint and avoidance of fattening foods increased in both groups (0.006 < p < 0.02). SF-36 Mental Component Score increased after weight loss in all women (p < 0.0001). CONCLUSION: A 3week weight reducing program changes selected eating behaviors and components of HRQL, irrespective of women's obesity degree. Data suggest that women with a BMI above 35 kg/m(2) could have a better weight control in the long term because of their higher flexible restriction after weight loss when compared to those whose BMI was under 34.9 kg/m(2).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".