Craving in eating disorders: Mapping the concept through a systematic review
Bibliographic record
Abstract
PURPOSE: Craving, long considered a hallmark of addictive disorders, has increasingly been recognized as a clinically significant phenomenon in eating disorders (ED). Yet, its conceptualization, measurement, and role in ED pathology remain inconsistent and fragmented. This review aimed to map existing knowledge through a systematic review. METHODS: Searches were conducted in July 2025 in Embase, PsycInfo, and Web of Science. Eligible records were peer-reviewed studies including adults clinically diagnosed with ED. Fifty studies and fifteen reviews met the inclusion criteria. RESULTS: Most studies examined bulimia nervosa (BN) and binge-eating disorder (BED), while anorexia nervosa (AN) and non-food-related cravings (e.g., exercise, vomiting, purging) were rarely addressed. Definitions of craving varied, sometimes conflating strong desire with loss of control or subsequent behaviors. Theoretical models were inconsistent, often borrowed from addiction research, and rarely integrated neurobiological findings. Craving assessment relied mainly on visual analogue scales (VAS) and the Food Cravings Questionnaire (FCQ), with limited use of qualitative, psychophysiological, or neurocognitive methods. Interventions specifically targeting craving were scarce. Cue exposure therapy (including virtual reality), neurofeedback, and non-invasive brain stimulation-repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS)-showed encouraging but mixed effects. Across ED, craving was consistently associated with binge eating, with trait craving emerging as a stronger predictor than state craving. CONCLUSIONS: Craving is central yet conceptually elusive in ED. Establishing a consensual definition, developing theory-driven and transdiagnostic assessment tools, and expanding research beyond food and binge-related disorders are priorities to advance understanding and improve interventions.
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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| 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".