Is the timing of food intake a potential indicator of low weight loss responders? A secondary analysis of three weight loss studies
Why this work is in the frame
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Bibliographic record
Abstract
Summary Individual variability in weight loss in response to a weight loss intervention is commonly observed. Recently, the timing of food intake has been identified as one possible factor implicated in obesity and weight loss. The objective of this study was to further characterize low weight loss responders by assessing the pre‐diet distribution of daily energy and macronutrient intakes. A pooled cohort of men and women (n = 122; aged 39.1 ± 8.2 years; body mass index [BMI] 33.1 ± 3.8 kg/m 2 ) who participated in a 12 to 15 week energy‐restricted intervention (−500 to −700 kcal/d) were included in this study. Participants were categorized into two weight loss groups (ie, low [−1.3 ± 2.3 kg] and high [−6.1 ± 2.1 kg] weight loss). Food intake and distribution of energy and macronutrient intakes were assessed using a 3‐day food record at baseline. The daily distribution of energy intake (% of total energy intake) was similar in the two weight loss groups with the exception of the low weight loss group who consumed a slightly lower proportion of their total energy intake before 9:00 am compared with the high weight loss group (12.5% ± 5.8% vs 15.0% ± 6.6%, respectively, P = .03). In the low weight loss group, the percentage of energy intake consumed after 8:00 pm was positively associated with total energy intake ( r = 0.27, P = .04). The results of this study suggest that the timing of food intake measured prior to a weight loss intervention does not permit the characterization of low weight loss responders.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it