Improvement of Weight and Body Composition in a Group of Women through a Weight Management Program Using Food Supplements with or without a Hypocaloric Diet
Bibliographic record
Abstract
Overweight is an increasing health problem characterised as a higher than normal body weight due to an abnormal increase in body fat. Body weight adequacy is categorized by using body mass index (BMI), however other parameters such as fat mass (FM), waist circumference (Wci) or waist to hip ratio, are relevant. Ideally, body composition should be calculated initially to evaluate changes during a dietary intervention of weight loss. Hunger experience is another parameter to take into account. The aim of this study was to assess the improvement of weight and body composition through the use of food supplements to control hunger, with and without a hypocaloric diet prescription. 252 women who wanted to lose weight were recruited in the program and splitted into two groups and were monitored for 8 weeks. Anthropometric measures (weight, height, body mass index, fat mass, waist and hip circumference) were taken. The mean age was of 36.84±7.29 and most of them were within overweight or obesity values for BMI, FM, Wci and hip circumference. After 8 weeks, both groups got significant results, achieving not only weight loss but also reduction in body mass index, fat mass, and waist and hip circumferences. However, as expected, improvements were better in FS+diet than in FS group. There is a need to tackle overweight and obesity individually, taking into account the personal characteristics of the patient. Food supplements may be useful to manage hunger and professional individualised assessment is critical to succeed.
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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 | 0.000 |
| 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".