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The Role of Distress Tolerance in Terms of Asthma Outcomes The Role of Distress Tolerance in Terms of Asthma Outcomes A thesis submitted to the Graduate School of the University of Cincinnati in partial fulfillment of the requirements for the degree of Master of Arts in the Department of Psychology of the College of Arts and Sciences 2016 by Talya Alsaid-Habia B.A., University of Nevada, Las Vegas, 2012 Committee Chair: Alison C. McLeish, Ph.D. Committee: Sarah Whitton, Ph.D. & Kristen Jastrowski-Mano, Ph.D. Abstract Asthma is a chronic obstructive lung disease that affects nearly 19 million adults in the United States (CDC, 2015). If not well controlled through medical intervention, asthma can result in significant rates of morbidity and mortality. One important contributor to the negative impact of asthma is the presence of psychopathology, particularly panic psychopathology (Goodwin et al., 2010; McCauley et al., 2007). In order to better understand the association between asthma and panic psychopathology, recent literature has begun examining the role of anxiety-related cognitive risk factors in asthma outcomes. This work has primarily focused on the cognitive risk factor of anxiety sensitivity (AS; fear of arousal-related sensations; McNally, 2002) and found that higher levels of anxiety sensitivity are predictive of poorer asthma outcomes (Avallone et al., 2012; McLeish et al., 2011; McLeish et al. 2016). An important next step in this area of work is to explore associations between asthma and other anxiety-related cognitive risk factors. One such factor to examine in this regard is distress tolerance (DT), defined as an individual’s perceived or behavioral capacity to withstand distress related to aversive affective states (Simons & Gaher, 2005; Zvolensky et al., 2011). Indeed, low DT is associated with increased risk for anxiety disorders as well as greater AS (Keough et al., 2010). Therefore, the aim of the current study was to examine the unique predictive ability of self-reported global and behavioral distress tolerance, as well as tolerance of fear and anxiety in terms of asthma control, asthma- related quality of life and lung function among non-smoking adults with current asthma. (n = 61; 61.9% female, 54.8% African-American, Mage = 34.72, SD = 13.58). Results indicated that, after controlling for the effects of age, race, and anxiety sensitivity, greater self-reported DT significantly predicted better lung function (β = .39, t = 2.80, p < .01), asthma control (β = .31, t = 2.77, p < .01), and asthma quality of life (β = .27, t = 2.53, p < .05). Behavioral DT did not significantly predict asthma outcomes. Greater self-reported tolerance of fear significantly predicted better lung function, (β = .30, t = 2.28, p < .05), asthma control (β = .28, t = 2.70, p < .01), and quality of life (β = .30, t = 3.23, p < .01). Additionally, tolerance of anxiety also ii significantly predicted better lung function (β = .33, t = 2.55, p < .01), asthma control β = .23, t = 2.08, p < .05), and quality of life (β = .28, t = 2.88, p < .01). These findings suggest that developing interventions targeting DT may be an effective way to improve asthma outcomes. iii Page Intentionally Left Blank iv Table of Contents The Role of Distress Tolerance in Terms of Asthma Outcomes ........................................... 1 Asthma .................................................................................................................................... 1 Asthma and Anxiety ............................................................................................................... 2 Distress Tolerance ................................................................................................................. 4 Current Study .......................................................................................................................... 6 Method ......................................................................................................................................... 7 Participants ............................................................................................................................. 7 Measures ................................................................................................................................. 8 Procedure .............................................................................................................................. 11 Data Analytic Plan ................................................................................................................ 12 Results ...................................................................................................................................... 13 Data Screening and Preparation ......................................................................................... 13 Zero-Order Correlations ....................................................................................................... 13 Regressions .......................................................................................................................... 14 Discussion ................................................................................................................................ 16 Limitations and Future Directions ...................................................................................... 17 Clinical Implications ............................................................................................................. 18 References ................................................................................................................................ 19 v List of Tables Table 1. Descriptive Statistics for all Study Variables. ............................................. 30 Table 2. Zero Order Correlations among all Study Variables .................................. 31 Table 3. TNASS-Total Predicting Asthma Outcomes ................................................ 32 Table 4. MTPT-C Predicting Asthma Outcomes ........................................................ 33 Table 5. TNASS-Fear Predicting Asthma Outcomes ................................................. 34 Table 6. TNASS-Fear Predicting Asthma Outcomes ................................................. 35 vi The Role of Distress Tolerance in Terms of Asthma Outcomes Asthma Asthma is a chronic obstructive lung disease that is characterized by inflammation of the airways and episodes of exacerbation in response to certain stimuli (American Lung Association [ALA], 2012). These airway exacerbations, or asthma attacks, consist of increased swelling of the airways, constriction of the muscles surrounding the airways, and increased mucus production, resulting in symptoms of wheezing, shortness of breath, tightness in the chest, and coughing (ALA, 2012). Asthma affects nearly 19 million, or 1 in 12, adults in the Unites States (U.S.; Centers for Disease Control and Prevention [CDC], 2015). Although rates of asthma have been declining, it is estimated that the number of individuals with asthma in the U.S. has increased by 15% in the last decade (CDC, 2015). Most recent estimates suggest that African Americans have the highest prevalence rate of asthma (11.9%), followed by Caucasians (8.1%), American Indian or Native Alaskans (9.4%) and Asian Americans (5.2%). Asthma is also more common among Hispanics than Non-Hispanics (CDC, 2015). Among adults, asthma rates are also higher in females (9.2%) than males (7.0%). If not well controlled through medical intervention, asthma can result in significant rates of morbidity and mortality. For example, individuals with asthma are at higher risk for cardiovascular disease-related death (Iribarren, Tolstykh, Miller, Sobel, & Eisner, 2012) and complications from respiratory illnesses (e.g., influenza, bronchitis, pneumonia; ALA, 2012). As a result, asthma significantly negatively impacts the healthcare system in the U.S. and beyond. Indeed, each year in the U.S. there are approximately 14.2 million physician office visits, 1.8 million emergency department visits, and 14.2 million lost workdays due to asthma (CDC, 2013). Moreover, it is estimated that healthcare expenditures related to asthma (both direct and indirect) cost the U.S. approximately $56 billion per year (CDC, 2013). Other work suggests that 1 the cost of care for asthma from a worldwide perspective exceeds the economic costs associated with treating HIV/AIDS and tuberculosis combined (Braman, 2006). Asthma and Anxiety One important contributor to the high healthcare costs associated with asthma is the presence of psychopathology, particularly anxiety psychopathology. Indeed, anxiety disorders are more common among individuals with asthma than among the general population (Goodwin, Pagura, Cox & Sareen, 2010; Opolski & Wilson, 2005; Zielinski et al., 2000). On average, 34% of individuals with asthma have an anxiety disorder, and this number is primarily driven by increased rates of panic disorder, agoraphobia, and generalized anxiety disorder (Weiser, 2007). Not surprisingly, having a comorbid anxiety disorder diagnosis adversely affects asthma. Research has consistently found that co-occurring anxiety disorders are associated with greater bronchodilator use, shortness of breath, wheezing, ED or doctor’s office visits, and frequency of asthma attacks (Lavoie et al., 2005; Goldney, Ruffin, Fisher, & Wilson, 2003; Strine, Mokdad, Balluz, Berry, & Gonzalez, 2008) as well as poorer quality of life and increased functional limitations (Lavoie et al., 2005; Lavoie et al., 2006; Mancuso, Peterson,
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