Is the timing of food intake a potential indicator of low weight loss responders? A secondary analysis of three weight loss studies
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/m2) 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.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".