ArticleJournal of eating disorders2021
Testing the discrepancy between actual and ideal body image with the Implicit Relational Assessment Procedure (IRAP).
Article in Journal of eating disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 10 citations in OpenAlex.
- The Body as a Battlefield: Identity Development and Psychosomatic Expression in Eating Disorders Across Childhood and Adolescence.Children (Basel, Switzerland) · 2025Review
- Article
- Article
- Feasibility of a Theory-Based, Online Tailored Message Program to Motivate Healthier Behaviors in College Women.Nutrients · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe discrepancy between actual and ideal body image is considered an index of body dissatisfaction and a risk factor for eating disorders. While discrepancy has been traditionally tested with figural drawing rating scales, in recent times the use of implicit measures has been explored.
methodsThis study employs the Implicit Relational Assessment Procedure (IRAP) to examine actual-ideal body-size discrepancy in a sample of 130 Spanish college students, as well as its utility to predict symptoms of eating disorders and other body-image relevant measures. Participants completed the Contour Drawing Rating Scale (CDRS). The three smallest and the three largest contour drawings of the CDRS were used as target stimuli in two different IRAP tasks: one in combination with the sample phrases "I am" and "I am not" (that assessed implicit actual body image), another in combination with the phrases "I want to be" and "I don't want to be" (that assessed implicit ideal body image). After completing both IRAP tasks, participants completed explicit measures of body-image psychological inflexibility, body dissatisfaction, and symptoms of eating disorders.
resultsResults showed a small implicit bias towards thinness. Participants were faster in affirming than denying that they are thin and that they desire to be thin. They were also faster in affirming than denying that they are fat and that they want to be fat, but to a smaller extent than with thinness. Specifically, the implicit desire to be (or not be) fat emerged as an independent predictor of eating disorder symptoms, psychological inflexibility, and body dissatisfaction that significantly increased the predictive power of CDRS scores.
conclusionsThese findings underscore the need for further research on specific body image implicit beliefs towards fatness, both in subclinical and clinical populations, in order to examine whether willingness to accept the idea that one can have a larger body size can be a suitable target for prevention and intervention in eating disorders.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.