Date of Award

2021

Degree Type

Thesis

Degree Name

Master of Arts (MA)

Department

Psychology

Abstract

Despite the high prevalence of menstrual pain (i.e., dysmenorrhea) and the negative impact on quality of life, there is a dearth of research on how menstrual pain changes over time. There is also a critical need to understand psychological and behavioral predictors of these menstrual pain trajectories because dysmenorrhea is a known risk factor for the development of chronic pelvic pain (CPP) and chronic non-pelvic pain (CNPP). Given that current treatments for CPP and CNPP have had limited success, developing preventative strategies for dysmenorrhea is of high importance. Relying on longitudinal survey data collected as part of a larger investigation, the present study delineated menstrual pain trajectories over two years and examined contributions of psychological (e.g., pain catastrophizing and somatic symptoms) and behavioral (e.g., sleep) factors in predicting such pain trajectories among a sample of anatomical females ages 18-45 years of age (n= 157). Four distinct menstrual pain trajectory subgroups were identified using growth mixture modeling. A multinomial logistic regression was used to predict differences between subgroups. Only somatic symptoms were significant when predicting group membership. The study provides a deeper understanding of the complexities of heightened dysmenorrhea and suggests that somatic symptoms may be an area of focus for intervention.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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