A Web-Based Weight Loss Programme Including Breakfast Cereals Results in Greater Loss of Body Mass than a Standardised Web-Based Programme in a Randomised Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: To test the hypothesis that promoting breakfast cereal consumption, as part of a web-based programme, results in loss of body mass. METHODS: Single centre, single blind, randomised parallel study. Test group followed a fully interactive website (B) with 'prescribed' breakfast cereals. Control group followed website (A) giving standard advice on weight loss.Study site visits at 0, 4, 12 and 24 weeks for measurements of height, weight, skinfolds, body fat, waist and hip circumference. 180 subjects were randomly allocated to two equal groups.Subjects were in good health and aged 19–50 years, with a BMI ranging from 25–40 kg/m 2 .At baseline there was no difference in mean age or BMI between the two groups. RESULTS: The percentage change in body mass loss was greater when following website B than website A (n = 90; ITT repeated measures p = 0.013). For completers (website A: n = 62, website B: n= 64), the percentage change in body mass loss was also greater for website B than website A (repeated measures p = 0.023). CONCLUSION: The advice and motivation offered by an interactive website, including provision and consumption of breakfast cereals, results in significantly greater loss of body mass compared to the use of a standard website. It is not possible to discern which of the three factors is responsible.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| 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 | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".