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Mixed Methods to Assess Sex Differences in Weight Bias Internalization Among Patients With Obesity
Marianne O Olaniran1, Jackson Francis2, Sitapriya Neti3
1Department of Health Promotion and Behavioral Sciences School of Public Health University of Texas Health Science Center at Houston Dallas Texas USA.
Background:
Individuals with obesity frequently encounter weight bias, which can contribute to the internalization of negative weight-related attitudes. This study examined sex differences in self-reported weight bias internalization among a racially and ethnically diverse sample of adults with obesity engaged in treatment.
Methods:
Mixed method approach was used. Weight bias internalization was assessed quantitatively using the 11-item weight bias internalization scale (WBIS) and qualitatively through in-depth interviews. Sex differences were examined with univariate and multivariate linear regressions. Interviews were thematically analyzed to explore sex differences.
Results:
Quantitative analysis (n = 60, 62% female) revealed that non-Hispanic White individuals with obesity exhibited significantly greater weight bias internalization than non-Hispanic Black individuals with obesity. There was no significant difference in mean WBIS scores between males (4.15 ± 1.34; p = 0.13) and females (3.68 ± 1.02; p = 0.13). Qualitative analysis (n = 24, 50% female) identified themes such as childhood trauma, self-esteem, health challenges, discrimination, and social interactions. No major conceptual differences emerged in the internalization of weight bias between male and female participants. However, female participants mostly described weight bias internalization as contributing to social avoidance and negatively impacting their career prospects.
Conclusion:
Quantitative analyses indicated no statistically significant sex differences in weight bias internalization, and qualitative findings revealed no substantial conceptual differences between male and female participants. Future research should explore socioecological factors such as race/ethnicity, relationship status, and employment to identify populations at greater risk and inform targeted strategies for improving health outcomes.
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