212
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Trait Body Shame Predicts Menstrual-Related Symptoms: Evidence for Extending the Menstrual Reactivity Hypothesis

Pages 24-42 | Accepted 10 Dec 2021, Published online: 22 Feb 2022
 

Abstract

Reports of menstrual-related symptoms vary greatly among individuals, suggesting that individual differences may influence symptom reports. The menstrual reactivity hypothesis describes how the personality trait anxiety sensitivity may heighten women’s negative perceptions of symptoms that accompany menstruation. Similarly, trait body shame may increase negative focus on menstrual-related symptoms, thus increasing menstrual-related symptom reports. Moreover, this relationship may be explained by distress about bodily changes. These ideas were tested cross-sectionally in undergraduate women (N = 126). Trait body shame predicted increased menstrual-related symptoms regardless of menstrual cycle phase, and distress about bodily changes mediated this relationship. Findings support the extension of the menstrual reactivity hypothesis to include trait body shame.

Disclosure Statement

No potential conflict of interest was reported by the author.

Author’s note

Research conducted by Jean M. Lamont, PhD, in the Department of Psychology, Bellarmine University. The study reported herein was approved by Bellarmine’s institutional review board and all participants provided informed consent prior to participation. The author declares no conflicts of interest. Many thanks to Lauren Deines and Abby Flynn for their invaluable assistance in data collection on this project.

Log in via your institution

Log in to Taylor & Francis Online

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 89.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.