Treating Binge Eating Disorder With Physical Exercise: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: This review aimed to collect evidence about the effectiveness of exercise programs for managing binge eating disorder (BED) (recurrent binge eating episodes). METHODS: Meta-analysis was developed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses protocol. Articles were searched in PubMed, Scopus, Web of Science, and Cochrane Library. Randomized controlled trials were eligible for inclusion if they reported the effect of an exercise-based program on BED symptoms in adults. Outcomes were changes in binge eating symptom severity, measured through validated assessment instruments, after an exercise-based intervention. Study results were pooled using the Bayesian model averaging for random and fixed effects meta-analysis. RESULTS: Of 2,757 studies, 5 trials were included, with 264 participants. The mean age was 44.7 ± 8.1 years for the intervention group and 46.6 ± 8.5 years for the control group. All participants were female. A significant improvement was observed between groups (standardized mean difference, 0.94; 95% credibility interval, -1.46 to -0.31). Patients obtained significant improvements either following supervised exercise programs or home-based exercise prescriptions. IMPLICATIONS FOR RESEARCH AND PRACTICE: These findings suggest that physical exercise, within a multidisciplinary clinical and psychotherapeutic approach, may be an effective intervention for managing BED symptoms. Further comparative studies are needed to clarify which exercise modality is associated with greater clinical benefits.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".