The Prevalence of Impulse Control Disorders and Behavioral Addictions in Eating Disorders: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Aim: Individuals with eating disorders (EDs) may present with impulse control disorders (ICDs) and behavioral addictions (BAs), which may result in additional suffering and treatment resistance. However, the prevalence of ICDs and BAs in EDs has not been systematically examined. Therefore, this systematic review and meta-analysis aimed to assess the prevalence of ICDs and BAs in ED samples. Methods: A comprehensive electronic database search of the peer-reviewed literature was conducted in the following online databases: MEDLINE, PsycINFO, Embase, and CINAHL from their inception to May 2021. We restricted review eligibility to research studies reporting prevalence for ICDs or BAs in individuals with diagnosed EDs. The outcome for this review was the prevalence of ICDs or BAs in individuals with EDs. A series of random-effects meta-analyses were performed on eligible studies to estimate the pooled proportions and 95% confidence intervals (CIs). Results: Thirty-five studies met the inclusion criteria, including a total of 9,646 individuals identified as having an ED, 18 of these studies specifically examined ICDs/BAs in AN, BN, and BED. Random-effects pooled estimates demonstrated that the comorbid prevalence of any ICD was 22%. The prevalence of comorbid pathological/compulsive buying was highest (19%), followed by kleptomania (18%), pathological internet use (12%), intermittent explosive disorder (4%), trichotillomania (3%), and gambling disorder (2%). In addition, the prevalence of stealing/shoplifting behaviors was 30% in those with EDs. Conclusion: This is the first meta-analysis on the comorbid prevalence of EDs and ICDs/BAs. We found a moderate prevalence for these comorbid conditions, with approximately one out of five individuals with an ED also displaying a comorbid ICD/BA. Although causal inferences cannot be drawn, the numbers strongly suggest that clinical screening/monitoring of ICDs/BAs should be part of the clinical routine in cohorts with EDs. ED settings need either the capacity to manage these disorders or adequate access to relevant services. Further investigations are needed to reveal common underlying pathomechanisms. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/ , identifier: CRD42020202044.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.042 |
| Bibliometrics | 0.011 | 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".