The Prevalence of Excessive Exercise in Eating Disorders: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: Individuals with eating disorders (EDs) often present with maladaptive behaviours such as excessive exercise (EE). The consequences of EE include physical injuries, increased risk of anxiety and depression, and impaired social functioning. No systematic reviews have been conducted on the prevalence of EE in EDs. This study aimed to assess the prevalence of EE in EDs and by ED type. METHOD: An electronic database search of the peer-reviewed literature was conducted from inception to October 2024. Review eligibility was restricted to research studies reporting prevalence data for EE in individuals diagnosed with EDs. RESULTS: Fifty-six studies met the inclusion criteria (n = 21,518; mean age: 22.34 years). The current prevalence of EE in all EDs was 48%. Current prevalence was highest in AN (48%), followed by BN (45%), OSFED (38%), and BED (11%). The lifetime prevalence of EE in all EDs was 63%. Lifetime prevalence was highest in AN (72%), followed by BN (57%) and OSFED (21%). CONCLUSIONS: Nearly half of individuals with an ED engage in EE. High heterogeneity across the included studies likely influenced the prevalence found in this study. Data suggest clinical screening and longitudinal monitoring of EE in those with EDs. Future research into early intervention and treatment for EE in those with EDs is recommended. TRIAL REGISTRATION: PROSPERO: CRD42023464148; Open Science Framework: https://doi.org/10.17605/OSF.IO/MYVXW.
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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 | 0.016 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.008 | 0.008 |
| 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) | 0.004 | 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".