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Detecting At-Risk Alcohol Drinking B...

Farinelli, Lisa Anne.

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Detecting At-Risk Alcohol Drinking Behavior: Integrating Individual Cardiovascular Signs and Symptom Appraisal.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Detecting At-Risk Alcohol Drinking Behavior: Integrating Individual Cardiovascular Signs and Symptom Appraisal./
Author:	Farinelli, Lisa Anne.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	158 p.
Notes:	Source: Dissertations Abstracts International, Volume: 82-04, Section: B.
Contained By:	Dissertations Abstracts International82-04B.
Subject:	Mental health. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28150043
ISBN:	9798678143907

Detecting At-Risk Alcohol Drinking Behavior: Integrating Individual Cardiovascular Signs and Symptom Appraisal.
Farinelli, Lisa Anne.

Detecting At-Risk Alcohol Drinking Behavior: Integrating Individual Cardiovascular Signs and Symptom Appraisal. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 158 p.

Source: Dissertations Abstracts International, Volume: 82-04, Section: B.

Thesis (Ph.D.)--The University of Arizona, 2020.

This item must not be sold to any third party vendors.

Alcohol use disorder (AUD) is a chronic mental health condition and the third leading preventable cause of death in the United States (U.S.) diagnostically characterized by difficulty controlling alcohol consumption in terms of onset, symptoms of withdrawal, tolerance and termination. There is an established link between excessive alcohol drinking and an increased risk to develop cardiovascular disease, including alcoholic cardiomyopathy. This association warrants further research on the potential utility for precision monitoring using the electrocardiogram (ECG) in the personalized care approach in addiction to promote clinical, statistical and theoretically significant evidence-based nursing interventions and was the catalyst for this study. This data-driven, characterization-based research guided by the National Institutes of Health Symptom Science Model (NIH-SSM) studied important links between sociodemographic and biologically relevant markers with levels of alcohol risk advancing monitoring and early detection for this chronic mental health condition. The primary aim of this research was to evaluate in non-smoking individuals significant sociodemographic characteristics and cardiovascular biomarkers of at-risk alcohol drinking. A secondary aim was to determine the potential predictive value of the sociodemographic characteristics and cardiovascular biomarkers in relation to alcohol related phenotypes including quantity of alcohol intake, alcohol dependence syndrome and alcohol withdrawal symptomatology. Findings were based on a comprehensive review of the literature including a PRISMA-based theoretically guided systematic review as well as a quantitative secondary analysis on de-identified data in a descriptive correlational study design. Empiric associations between quantity and patterns of alcohol intake and key social and physiological variables were established in this study. Specifically, the predictive value of gender, age, race, body mass index (BMI), mean arterial pressure (MAP), QTcF interval, QRS axis and heart rate (HR) has been defined in relation to behavioral and biologically based alcohol related phenotypes employing rigorous correlational and regression analyses. This information may in turn lead to better innovative approaches to optimize personalized and precise AUD screening interventions to close the abysmally low rates of treatment for this chronic mental health condition.

ISBN: 9798678143907Subjects--Topical Terms:

534751
Mental health.
Subjects--Index Terms:

Alcohol use disorder

Detecting At-Risk Alcohol Drinking Behavior: Integrating Individual Cardiovascular Signs and Symptom Appraisal.
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