Date of Award

2021

Embargo Period

8-1-2024

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College

College of Nursing

First Advisor

Gayenell S. Magwood

Second Advisor

Martina Mueller

Third Advisor

Sarah Miller

Fourth Advisor

Sandra B. Dunbar

Abstract

Background: Heart failure (HF) is a prevalent disease with a complex symptom experience. Black women with Heart Failure preserved Ejection Fraction (HFpEF) are especially burdened by symptoms, and their symptom experience is poorly understood. Clustering HF symptoms to understand which symptoms are experienced together is a potential option for helping patients recognize impending exacerbations. Methods: The dissertation's first manuscript is an integrative review to examine the current state of HF symptom cluster literature. The second manuscript explores the feasibility and acceptability of a mixed methods HF symptom cluster study of Black women with HFpEF (N=44) and presents preliminary symptom cluster results. The third manuscript presents symptom experience themes from qualitative interviews (N=15) and integrates those findings with quantitative questionnaire data. Findings: An integrative review of HF symptom cluster literature revealed a need for exploring the symptom experience of Black women with HFpEF using mixed methods. A convergent-parallel mixed methods study protocol met feasibility benchmarks and was deemed acceptable by Black women with HFpEF. Preliminary symptom clusters were identified using quantitative symptom data from symptom questionnaires. Qualitative themes emerged relating to the symptom experience and person, and mixed methods integration provided additional key findings of concordance, discordance, and expansion. Conclusion: This dissertation describes the symptom experience of Black women with HFpEF, provides valuable information on the recruitment of an understudied population and their acceptability of a study protocol, and builds the foundation for conducting a mixed methods HF symptom cluster study with a larger sample in the future.

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