Effects of Health At Every Size® strategies on short- versus long-term weight loss in people with overweight and obesity: a systematic review
Bibliographic record
Abstract
Traditional weight loss interventions based on the principle “eat less, move more” often lead to weight regain in the long-term. The Health-At-Every-Size® (HAES®) is an alternative intervention that focuses on self-acceptance, intuitive eating, and physical activity for overall well-being. However, evidence on the effectiveness of HAES® in terms of weight loss remains sparse and the existing systematic reviews did not compare the short-term versus long-term effects. The aim of this systematic review was to assess the effectiveness of HAES® on short- and long-term weight loss in people with obesity and overweight. Five scientific databases were searched and 11 papers met the inclusion criteria. These studies were conducted in Canada, United States, Brazil, and the United Kingdom, only with female participants, and in a group-setting. Six out of ten studies with short-term follow-ups (<1y) and four out of seven studies with long-term follow-ups (>1y) reported significant weight reductions in the HAES®-group compared to the pre-intervention baseline. There were no consistent weight reduction effects when HAES® was compared to control groups (waiting list, social support, or traditional dieting). Interestingly, some studies found significant benefits of HAES® on health-outcomes, eating behaviors, or psychological functioning. In conclusion, there is only modest evidence that HAES® facilitates weight loss short-term or long-term. However, there are behavioral and psychological benefits of HAES®, which suggests that combining this method with traditional weight loss interventions could result in optimal outcomes.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".