The effects of time-restricted eating vs. standard dietary advice on weight, metabolic health and the consumption of processed food: A pragmatic randomised controlled trial in community-based adults
Bibliographic record
Abstract
Abstract Weight loss is key to control the increasing prevalence of metabolic syndrome (MS) and its components, i.e. central obesity, hypertension, prediabetes, and dyslipidaemia. We characterised the relationships between eating duration, unprocessed and processed food consumption, and metabolic health. During 4 weeks of observation, 213 adults used a smartphone application to record food and drink consumption, which was annotated for food processing levels following the NOVA classification. Consumption of unprocessed food items showed the highest number of significant associations with MS components after age and sex. In a pragmatic randomised controlled trial, we compared the metabolic benefits of 12h time-restricted eating (TRE) to standard dietary advice (SDA) in 54 adults with an eating duration >14h and at least one MS component. After 6 months, those randomised to TRE lost 1.6% of initial body weight (SD 2.9, p=0.01), compared to the absence of weight loss with SDA (−1.1%, SD 3.5, p=0.19). There was no significant difference in weight loss between TRE and SDA (between-group difference −0.88%, 95% confidence interval −3.1 to 1.3, p=0.43). Our results show the potential of smartphone records to predict metabolic health and highlight that further research is needed to understand individual responses to TRE.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".