Effects of a Protein-Rich, Low-Glycaemic Meal Replacement on Changes in Dietary Intake and Body Weight Following a Weight-Management Intervention—The ACOORH Trial
Bibliographic record
Abstract
Although meal replacement can lead to weight reduction, there is uncertainty whether this dietary approach implemented into a lifestyle programme can improve long-term dietary intake. In this subanalysis of the Almased Concept against Overweight and Obesity and Related Health Risk (ACOORH) study (n = 463), participants with metabolic risk factors were randomly assigned to either a meal replacement-based lifestyle intervention group (INT) or a lifestyle intervention control group (CON). This subanalysis relies only on data of participants (n = 119) who returned correctly completed dietary records at baseline, and after 12 and 52 weeks. Both groups were not matched for nutrient composition at baseline. These data were further stratified by sex and also associated with weight change. INT showed a higher increase in protein intake related to the daily energy intake after 12 weeks (+6.37% [4.69; 8.04] vs. +2.48% [0.73; 4.23], p < 0.001) of intervention compared to CON. Fat and carbohydrate intake related to the daily energy intake were more strongly reduced in the INT compared to CON (both p < 0.01). After sex stratification, particularly INT-women increased their total protein intake after 12 (INT: +12.7 g vs. CON: −5.1 g, p = 0.021) and 52 weeks (INT: +5.7 g vs. CON: −16.4 g, p = 0.002) compared to CON. Protein intake was negatively associated with weight change (r = −0.421; p < 0.001) after 12 weeks. The results indicate that a protein-rich dietary strategy with a meal replacement can improve long-term nutritional intake, and was associated with weight loss.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".