Effects of dietary recommendations for reducing free sugar intakes, on free sugar intakes, dietary profiles and anthropometry: a randomised controlled trial
Bibliographic record
Abstract
Abstract Free sugar intakes are currently higher than recommended for health, yet effective strategies for reducing consumption are yet to be elucidated. This work investigated the effects of different dietary recommendations for reducing free sugar (FS) intakes, on relevant outcomes, in UK adults consuming > 5 % of total energy intake (TEI) from FS. Using a randomised controlled parallel-group design, 242 adults received nutrient-based (n 61), nutrient- and food-based (n 60), nutrient-, food- and food-substitution-based (n 63) or no (n 58) recommendations for reducing FS at a single timepoint, with effects assessed for the following 12 weeks. Primary outcomes were FS intakes as a percentage of TEI (%FS) and adherence to the recommendations at week 12. Secondary outcomes included TEI, diet composition, sugar-rich and low-calorie-sweetened food consumption and anthropometry. In intention-to-treat analyses adjusted for baseline measures, %FS reduced in intervention groups (%FSchange = –2·5 to −3·3 %) compared with control (%FSchange = –1·2 %) (smallest B = –0·573, P = 0·03), with effects from week 1 until week 12 and no differences between interventions (largest B = 0·352, P = 0·42). No effects of the interventions were found in dietary profiles, but change in %FS was associated with change in %TEI from non-sugar carbohydrate (B = 0·141, P < 0·01) and from protein (B = –0·171, P = 0·02). Body weight was also lower at week 12 in intervention groups compared with control (B = –0·377, P < 0·05), but associations with %FS were weak. Our findings demonstrate the benefit of dietary recommendations for reducing FS intakes in UK adults. Limited advantages were found for the different dietary recommendations, but variety may offer individual choice.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".