Determining optimal approaches for weight maintenance: a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Weight regain often occurs after weight loss in overweight individuals. We aimed to compare the effectiveness of 2 support programs and 2 diets of different macronutrient compositions intended to facilitate long-term weight maintenance. METHODS: Using a 2 x 2 factorial design, we randomly assigned 200 women who had lost 5% or more of their initial body weight to an intensive support program (implemented by nutrition and activity specialists) or to an inexpensive nurse-led program (involving "weigh-ins" and encouragement) that included advice about high-carbohydrate diets or relatively high-monounsaturated-fat diets. RESULTS: In total, 174 (87%) participants were followed-up for 2 years. The average weight loss (about 2 kg) did not differ between those in the support programs (0.1 kg, 95% confidence interval [CI] -1.8 to 1.9, p = 0.95) or diets (0.7 kg, 95% CI -1.1 to 2.4, p = 0.46). Total and low-density lipoprotein (LDL) cholesterol levels were significantly higher among those on the high-monounsaturated-fat diet (total cholesterol: 0.17 mmol/L, 95% CI 0.01 to 0.33; p = 0.040; LDL cholesterol: 0.16 mmol/L, 95% CI 0.01 to 0.31; p = 0.039) than among those on the high-carbohydrate diet. Those on the high-monounsaturated-fat diet also had significantly higher intakes of total fat (5% total energy, 95% CI 3% to 6%, p < 0.001) and saturated fat (2% total energy, 95% CI 1% to 2%, p < 0.001). All of the other clinical and laboratory measures were similar among those in the support programs and diets. INTERPRETATION: A relatively inexpensive program involving nurse support is as effective as a more resource-intensive program for weight maintenance over a 2-year period. Diets of different macronutrient composition produced comparable beneficial effects in terms of weight loss maintenance.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 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 | 0.003 |
| 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".