Abstract
The Metabolic Syndrome (MetS) is a cluster of medical disorders (obesity, hypertension, dyslipidemia, and insulin/resistance/glucose intolerance) that characteristically occur together in individuals. The prevalence of MetS continues to increase in the U.S. and increases the risk of type 2 diabetes, cardiovascular disease (CVD), and mortality. Little research uses a theoretical model to identify direct or indirect contributions to MetS and predictors to MetS. Identifying the factors that influence MetS in a national sample is necessary to understand targets for intervention to prevent the MetS and its sequelae.
This study tested a hypothesized conceptual model of the biopsychosocial factors associated with MetS in adults using a representative sample.
Sigma Membership
Beta Xi, Pi at-Large
Type
Dissertation
Format Type
Text-based Document
Study Design/Type
Cross-Sectional
Research Approach
Quantitative Research
Keywords:
Metabolic Syndrome, Chronic Conditions, Nursing for Adults
Advisor
Erika Friedmann
Degree
PhD
Degree Grantor
University of Maryland, Baltimore
Degree Year
2011
Recommended Citation
Saylor, Jennifer L., "The biopsychosocial model of metabolic syndrome among U.S. adults" (2020). Dissertations. 971.
https://www.sigmarepository.org/dissertations/971
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record.
All permission requests should be directed accordingly and not to the Sigma Repository.
All submitting authors or publishers have affirmed that when using material in their work where they do not own copyright, they have obtained permission of the copyright holder prior to submission and the rights holder has been acknowledged as necessary.
Review Type
None: Degree-based Submission
Acquisition
Proxy-submission
Date of Issue
2020-05-06
Full Text of Presentation
wf_yes
Description
This dissertation has also been disseminated through the ProQuest Dissertations and Theses database. Dissertation/thesis number: 3454814; ProQuest document ID: 871192709. The author still retains copyright.