Effects of a Diet‐based Weight‐reducing Intervention in Obese Women Resistant to Weight Loss
Bibliographic record
Abstract
The aim of this study was to examine the effects of a diet‐based weight‐reducing intervention on energy, macronutrient and micronutrient intakes, resting metabolic rate, appetite sensations and sleep habits in obese women resistant to weight loss. Obese women (n = 75; aged 39 ± 8 years; BMI: 33 ± 4 kg/m 2 ) were counselled to reduce energy intake by 500‐700 kcal/d over 12‐16 weeks and were classified in tertiles of weight loss (high, moderate and low responders) a posteriori . Post‐intervention, women reduced their body weight and fat mass by 3.2 kg ( p < 0.001) and 2.8 kg ( p < 0.001), respectively, increased protein intakes (16%, p < 0.001) and reduced their intake of total fat (15%, p < 0.001); however, resting metabolic rate remained unchanged. High and moderate responders had greater reductions in body weight and fat mass (6.2 kg and 3.4 kg, respectively; p < 0.001), energy intakes, total and saturated fat intakes compared with the low responders ( p < 0.001) who did not lose weight. Protein, carbohydrate, dietary fibre and micronutrient intakes and resting metabolic rates were not significantly different between the tertiles; however, low responders had higher fasting and postprandial hunger in response to the dietary intervention. High responders reported an improvement in sleep quality and duration compared with low and moderate responders ( p < 0.001). In conclusion, the differences observed in body weight between tertiles in response to dietary supervision may be explained, in part, by variations in energy and fat intakes, appetite sensations and sleep habits.
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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) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".