Treatment for overweight and obesity in adult populations: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Obesity is a major public health issue. This review updates the evidence on the effectiveness of behavioural and pharmacologic treatments for overweight and obesity in adults. METHODS: We updated the search conducted in a previous review. Randomized trials of primary-care-relevant behavioural (diet, exercise and lifestyle) and pharmacologic (orlistat and metformin) with or without behavioural treatments in overweight and obese adults were included if 12-month, postbaseline data were provided for weight outcomes. Studies reporting harms were included regardless of design. Data were extracted and pooled wherever possible for 5 weight outcomes, 6 secondary health outcomes and 4 adverse events categories. RESULTS: We identified 68 studies, most consisted of short-term (≤ 12 mo) treatments using diet (n = 8), exercise (n = 4), diet and exercise (n = 10), lifestyle (n = 19), orlistat (n = 25) or metformin (n = 4). Compared with the control groups, intervention participants had a greater weight loss of -3.02 kg (95% confidence interval [CI] -3.52 to -2.52), a greater reduction in waist circumference of -2.78 cm (95% CI -3.34 to -2.22) and a greater reduction in body mass index of -1.11 kg/m(2) (95% CI -1.39 to -0.84). The relative risk for loss of ≥ 5% body weight was 1.77 (95% CI 1.58-1.99, [number needed to treat 5, 95% CI 4-7]), and the relative risk for loss of ≥ 10% body weight was 1.91 (95% CI 1.69-2.16, [number needed to treat 9, 95% CI 7-12]). Incidence of type 2 diabetes was lower among pre-diabetic intervention participants (relative risk 0.62 [95% CI 0.50-0.77], number needed to treat 17 [95% CI 13-29]). With prevalence rates for type 2 diabetes on the rise, weight loss coupled with a reduction in the incidence of type 2 diabetes could potentially have a significant benefit on population health and a possible reduction in need for drug treatments for glycemic control. INTERPRETATION: There is moderate quality evidence that behavioural and pharmacologic plus behvioural, treatments for overweight and obesity in adults lead to clinically important reductions in weight and incidence of type 2 diabetes in pre-diabetic populations. REGISTRATION: PROSPERO no. CRD42012002753.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".