Sleeping Habits Predict the Magnitude of Fat Loss in Adults Exposed to Moderate Caloric Restriction
Bibliographic record
Abstract
OBJECTIVE: To verify whether sleep quantity and quality at baseline predict the magnitude of fat loss in adults subjected to moderate caloric restriction. METHODS: A total of 123 overweight and obese men and women (age, 41.1 ± 6.0 years; BMI, 33.2 ± 3.6 kg/m2 (mean ± SD)) underwent a weight loss intervention consisting of a targeted 600-700 kcal/day decrease in energy intake supervised by a dietician. The length of the intervention varied between 15 and 24 weeks. Body fat mass (dual-energy X-ray absorptiometry), sleep quality (total Pittsburgh sleep quality index score) and sleep duration (h/night, self-reported from the Pittsburgh sleep quality index) were assessed at both baseline and at the end of the weight loss program. RESULTS: The mean weight loss over the dietary intervention was 4.5 ± 3.9 kg, 76% of which came from fat stores. Using a multiple linear regression analysis, we observed a significant positive relationship between sleep duration and the loss of body fat, both in absolute (adjusted β = 0.72 kg/h; p < 0.05) as well as in relative terms (adjusted β = 0.77%/h; p < 0.01), after adjusting for age, sex, baseline BMI, length of the intervention, and change in total energy intake. Furthermore, we observed that a better sleep quality at baseline was associated with greater fat mass loss. CONCLUSION: This study provides evidence that sleeping habits can influence the success of a weight loss intervention and should be taken into consideration when one decides to start a diet.
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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".