A Brief Online Cognitive Dissonance-Based Intervention to Reduce Consideration of Cosmetic Surgery and Improve Body Image Among Chinese Women

DOI

Across many cultures, women are evaluated based on their appearance, with narrow societal beauty ideals as the standard against which they are judged and, eventually, judge themselves. Women who internalize the beauty ideal are more likely to consider cosmetic surgery. Dissonance-based interventions targeting thin-ideal internalization are effective at preventing eating disorders and associated risk factors. In this study, we evaluated an online dissonance-based intervention targeting beautyideal internalization to reduce favorable attitudes toward cosmetic surgery among Chinese women. Chinese women who were dissatisfied with their appearance and who were considering cosmetic surgery were randomized to the intervention (n=127, Mage =35.49) or to the educational brochure control condition (n =98, Mage =32.97). Beauty-ideal internalization, favorable attitudes toward cosmetic surgery, facial appearance concerns, body satisfaction, and body appreciation were assessed at pretest, posttest, and 4-week follow-up. Intention-to-treat analyses showed that the intervention reduced beauty-ideal internalization and favorable attitudes toward cosmetic surgery at posttest, with small-to-medium effect sizes; however, effects were not sustained at follow-up. No effects were found for facial appearance concerns, body satisfaction, and body appreciation. This study provides preliminary evidence for the short-term efficacy of the dissonance-based intervention for reducing beauty-ideal internalization and favorable attitudes toward cosmetic surgery, and points to valuable directions for improvement.

Identifier
DOI https://doi.org/10.34894/KJXGTT
Related Identifier https://doi.org/10.1177/03616843231183946
Metadata Access https://dataverse.nl/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.34894/KJXGTT
Provenance
Creator Wu, Yi ORCID logo; Mulkens, Sandra ORCID logo; Atkinson, Melissa J. ORCID logo; Alleva, Jessica M. ORCID logo
Publisher DataverseNL
Contributor faculty data manager FPN; Wu, Yi; Mulkens, Sandra
Publication Year 2023
Rights info:eu-repo/semantics/restrictedAccess
OpenAccess false
Contact faculty data manager FPN (Maastricht University); Wu, Yi (Maastricht University); Mulkens, Sandra (Maastricht University)
Representation
Resource Type Dataset
Format application/x-spss-sav
Size 199343; 269346
Version 1.0
Discipline Agriculture, Forestry, Horticulture, Aquaculture; Agriculture, Forestry, Horticulture, Aquaculture and Veterinary Medicine; Life Sciences; Social Sciences; Social and Behavioural Sciences; Soil Sciences