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

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.

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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

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

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

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, & Charlson,

2000; Goldney, Ruffin, Fisher, & Wilson, 2003; Eisner, Katz, Lactao, & Iribarren, 2005;

Fernandes et al., 2010; Feldman, Lehrer, Borson, Hallstrand, & Siddique, 2005; Kullowatz,

Kanniess, Dahme, Magnussen, & Ritz, 2007; McCauley, Katon, Russo, Richardson, & Lozano,

2007). Further, independent of objective pulmonary function, individuals with anxiety tend to overuse short-acting beta agonist medication, receive more intensive corticosteroid regimens, and have longer and more frequent hospitalizations (Dhalem, Kinsman, & Horton, 1977; Dirks,

Horton, Kinsman, Fross, & Jones, 1978; Dirks et al., 1977; Mawhinney et al., 1993; Fernandes et al., 2010; Cordina, Fenech, Vasallo, & Cacciottolo, 2009).

Although extant research has clearly documented an association between asthma and anxiety disorders, there is evidence for a relatively specific association between asthma and panic psychopathology (i.e., panic attacks, panic disorder, and agoraphobia). Rates of panic 2

attacks and panic disorder are consistently higher among individuals with asthma compared to the general population (Goodwin, Jacobi, & Thefeld, 2003; Goodwin, Pine, & Hoven, 2003); it is estimated that 12% of individuals with asthma have comorbid panic disorder and 25% have a history of panic attacks (Weiser, 2007). Moreover, there appears to be a dose-response relationship, such that with every additional panic symptom an individual experiences the likelihood of asthma increases (Goodwin, et al., 2003; Hasler et al., 2005). The relationship between panic and asthma seems to be bidirectional as a recent 20-year longitudinal study found that asthma was a predictor of future panic disorder (OR = 4.5), and panic disorder predicted later asthma activity (OR = 6.3; Hasler et al., 2005).

More recent work on anxiety and asthma has focused on the role of anxiety-related cognitive-based risk factors. One such factor that has received increasing empirical attention is anxiety sensitivity. Anxiety sensitivity is defined as the fear of arousal-related sensations due to their perceived negative physical, cognitive, and social consequences (McNally, 2002; Reiss &

McNally, 1985). Anxiety sensitivity reflects a relatively stable, albeit malleable, cognitive predisposition that results in an amplification of pre-existing anxiety levels such that a person who is high in anxiety sensitivity will be more reactive to any personally-relevant threat stimulus.

For example, an indivdiual who is high in anxiety sensitivity who experiences heart palpitations will experience an increase in fear because of concerns about having a heart attack and a further amplification in this fear due to this anxious responding to such sensations. A large body of literature has demonstrated that anxiety sensitivity is most strongly associated with panic disorder (Olatunji & Wolitzky-Taylor, 2009). Indeed, anxiety sensitivity is concurrently and prospectively associated with increased risk for panic attacks and panic disorder (Hayward,

Killen, Kraemer, & Taylor, 2000; Li & Zinbarg, 2007; Maller & Reiss, 1992; Marshall, Miles, &

Stewart, 2010; Schmidt, Lerew, & Jackson, 1997; 1999; Schmidt, Zvolensky, & Maner, 2006).

Results from an emerging body of literature on anxiety sensitivity and asthma indicate that, independent of asthma severity, anxiety sensitivity significantly predicts retrospectively

reported anxiety associated with asthma symptoms (Carr, Lehrer, & Hochron, 1995) as well as increased activity limitations due to asthma symptoms and poorer asthma-related physical and emotional health (McCauley, et al., 2007). More recent research has found that the physical concerns domain of anxiety sensitivity, in particular, is associated with poorer asthma control and asthma-related quality of life (Avallone, McLeish, Luberto, & Bernstein, 2012; McLeish,

Zvolensky, & Luberto, 2011) as well as greater physical and emotional reactivity and decreased lung function in response to asthma-like symptoms (McLeish, Luberto, O’Bryan, 2016).

However, treatment for anxiety sensitivity can be especially problematic for individuals with asthma, as it addresses the physical symptoms of anxiety, which could potentially lead to an asthma attack.

Distress Tolerance

Given the important role that anxiety sensitivity plays in asthma, an important next step in this area of work is to explore associations between asthma and other anxiety-related cognitive risk factors. One important factor to consider in this regard is distress tolerance, which is defined as an individual’s perceived or behavioral capacity to withstand distress related to aversive affective, cognitive or physical states (Simons & Gaher, 2005; Zvolensky et al., 2011).

For the purpose of this study, distress tolerance will be conceptualized as the perceived or behavioral capacity to withstand or endure negative emotional states, which is distinct from It is the likelihood of experiencing this emotion and the amount of attention given to it (Bernstein &

Brantz, 2013). This inability to tolerate negative emotions can lead to the use of maladaptive emotion regulation strategies in an attempt to avoid, stop, or replace the negative emotion

(Bernstein & Brantz, 2013). For example, an individual who is low in distress tolerance and experiences anxiety symptoms prior to public speaking would report significant distress related to these feelings of anxiety because of beliefs that they are unable to “handle” or tolerate these symptoms, likely resulting in even further symptoms of anxiety and distress. As a result, the individual might take steps to avoid these anxious feelings by avoiding public speaking 4

situations or engaging in maladaptive coping strategies (e.g., using drugs or alcohol). This behavior may serve to reduce anxiety in the short term, but in the long term would likely maintain or even potentiate their anxiety symptoms.

Consistent with this theory, distress tolerance has been identified as a key factor in the development and maintenance of numerous forms of psychopathology, including depression, bulimia, substance use, and self-harm (Anestis, Pennings, Lavender, Tull & Gratz, 2013;

Anestis, Selby, Fink & Joiner, 2007; Brandon et al., 2003; Brandt, Zvolensky & Bonn-Miller,

2013; Buckner, Keough & Schmidt, 2007; Daughters, Lejuez, Kahler, Strong & Brown, 2005;

Ellis, 2010; Ellis, Vanderlind & Beevers, 2013; Gorka, Ali & Daughters, 2012; Ozdel & Ekinci,

2014; Starr & Davila, 2012; Williams, Thompson & Andrews, 2013; Zvolensky et al., 2009). In terms of anxiety-related disorders, extant research indicates that individuals with anxiety psychopathology have lower levels of distress tolerance than the general population (Mitchell,

Riccardi, Keough, Timpano & Schmidt, 2013). Further, low distress tolerance is associated with symptoms of worry, generalized anxiety disorder, obsessive-compulsive disorder, panic psychopathology, and social anxiety (Allan, Macatee, Norr & Schmidt, 2014; Cougle, Timpano &

Goetz, 2012; Cougle, Timpano, Fitch & Hawkins, 2011; Huang, Szabo & Han, 2009; Laposa,

Collimore, Hawley & Rector, 2015; Keough, Riccardi, Timpano, Mitchell & Schmidt, 2010; Kertz,

Stevens, McHugh & Bjorgvinsson, 2014; Marshall et al., 2008; Norr et al., 2013).

Despite some conceptual overlap between distress tolerance and anxiety sensitivity

(Anestis et al., 2007; Timpano, Buckner, Richey, Murphy & Schimdt, 2009; Zvolensky et al.,

2009), empirical research has found that they do represent distinct constructs. Indeed, distress tolerance demonstrates unique associations with symptoms of panic disorder, social anxiety disorder, obsessive-compulsive disorder, and generalized anxiety disorder above and beyond the effects of anxiety sensitivity (Keough, et al., 2010). Thus, current theoretical models posit that both distress tolerance and anxiety sensitivity are lower order facets of a higher order affect tolerance factor (Bernstein, Zvolensky, Vujanovic & Moos, 2009).

Current Study

Taken together, asthma has been shown to be significantly associated with anxiety psychopathology, including the cognitive risk factor of anxiety sensitivity. However, despite these associations as well as robust associations between anxiety psychopathology and distress tolerance among individuals without asthma, no research, to date, has examined the role of this cognitive risk factor in asthma. Theoretically, an individual low in distress tolerance who experiences anxiety as a result of experiencing shortness of breath or wheezing related to asthma, would likely be unable to withstand or endure this anxiety and take steps to immediately reduce these symptoms. As a result, they may use their short-acting beta agonist medication for immediate relief without first determining whether medication is necessary (e.g., through lung function testing, determining the presence of asthma triggers). Such an individual might then begin over-using their asthma medications to avoid the anxiety that accompanies asthma-related sensations. As physicians typically treat asthma based on self-reported asthma symptoms, this could result in the individual’s asthma being seen as worse than it objectively is and being placed on more medication than is truly warranted. Moreover, short-acting beta agonists can also produce anxiety-like symptoms (e.g., increased heart rate), which could lead to a vicious cycle of increasing anxiety and medication use.

As a first attempt to test this theory, the purpose of the current study was to examine the unique predictive ability of distress tolerance in terms of asthma control, asthma-related quality of life and lung function among non-smoking adults with current asthma. Due to the well- established relationship between smoking and both asthma and distress tolerance, current smokers were excluded from the current study (McLeish & Zvolensky, 2010; Brown et al., 2005;

Brown et al., 2002). It was hypothesized that, after controlling for the effects of race, gender and the physical concerns domain of anxiety sensitivity, lower distress tolerance would be predictive of poorer lung function (forced expiratory volume in 1 second [FEV1]), asthma control, and asthma-related quality of life. Given the strong association between anxiety and asthma, in

addition to examining associations between tolerance of negative affect more generally, a secondary aim of this study was to examine associations between asthma outcomes and the tolerance of fear and anxiety, specifically. It was hypothesized that, after controlling for the effects of race, gender and the physical concerns domain of anxiety sensitivity, lower levels of tolerance of fear and anxiety would be predictive of poorer asthma outcomes (i.e., lung function, asthma control, and asthma-related quality of life).

Method

Participants

Participants were 61 non-smoking adults with current asthma (61.9% female; Mage =

34.72, SD = 13.58, Range = 18-65 years). For inclusion in the study, participants had to (a) be between the age of 18-65; (b) meet biochemical cutoff values for being a nonsmoker, as indexed by expired carbon monoxide levels below 5 ppm (Perkins, Karelitz & Jao, 2013); (c) have received a physician diagnosis of asthma; (d) not have a diagnosis of another chronic lung disease (e.g., Chronic Obstructive Pulmonary Disease); (e) have experienced asthma symptoms in the past six months; (f) have a prescription for an inhaled corticosteroid or similar medication (i.e., more than just a short-acting bronchodilator used on an as-needed basis); (g) meet the cutoff score for an asthma diagnosis on the Asthma Screening Questionnaire (ASQ; i.e., score > 4; Shin et al., 2010) The mean ASQ score for the current sample was 12.13 (SD =

4.38).

In terms of the racial composition of the sample, 54.8% self-identified as African

American, 39.7% as Caucasian and 1.8% of the sample reported Hispanic ethnicity.

Participants were, on average, 16.48 years (SD = 14.13) years of age when diagnosed with asthma. 14.3% of the sample reported daily rescue inhaler use, 76.2% had experienced at least one asthma attack in the past six months, and 45.9% had had at least one emergency department visit due to asthma in the past six months. Participants reported a mean Asthma

Control Test (Nathan et al., 2004) score of 15.98 (SD = 4.54), indicating poorly controlled asthma.

Measures

Expired Carbon Monoxide. Biochemical verification of smoking status was completed by carbon monoxide (CO) analysis of breath samples assessed using a Bedfont Micro 4

Smokerlyzer CO Monitor (Model EC50; coVita, Haddonfield, NJ). Past research has shown that a cutoff score of 5 ppm reliably discriminates non-smoking status (Perkins et al., 2013).

Asthma Screening Questionnaire (ASQ). The ASQ (Shin, Cole, Park, Ledford, &

Lockley, 2010) is a six-item measure that assesses four dimensions of asthma symptoms (i.e., coughing, wheezing, chest tightness, shortness of breath) in four situations that commonly elicit asthma symptoms (i.e., lying down, after exercising, after laughing/crying). Research indicates that a score greater than four on the ASQ reliably discriminates between those with and without asthma (96% sensitivity, 100% specificity; Shin et al., 2010). The ASQ showed good reliability

(a = .81).

Anxiety Sensitivity Index-3 (ASI-3). The ASI-3 (Taylor et al., 2007) is an 18-item self- report measure that assesses the degree to which participants fear the negative consequences associated with anxiety symptoms. Items are rated on a 5-point Likert scale (0 = very little; 4 = very much). The ASI-3 has three lower-order factors: (a) physical concerns (e.g., “When my throat feels tight, I worry that I would choke to death”; (b) social concerns (e.g., “I worry that other people will notice my anxiety”) (c) cognitive concerns (e.g., “When my thoughts seem to speed up, I worry that I might be going crazy”). For the purpose of this study, only the physical concerns subscale was used. The ASI-3 has demonstrated strong psychometric properties

(Taylor et al., 2007). Internal consistency for the physical concerns subscale in the current sample was good (α = .84).

Tolerance of Negative Affective States Scale (TNASS). The TNASS (Bernstein &

Brantz, 2013) is a 25-item self-report measure that assesses the degree to which individuals can withstand or endure specific negative affective states. Participants are given a definition of tolerance and intolerance at the beginning of the measure and then asked to rate how tolerant they are of eight specific emotions using a 5-point Likert-type scale (1 = very intolerant to 5 = very tolerant). Higher scores reflect higher levels of affective tolerance. The TNASS is comprised of one higher-order affective tolerance factor and six lower order factors: (a) Fear-

Distress; (b) Sadness-Depression; (c) Anger; (d) Disgust; (e) Anxious Apprehension; (f)

Negative Social Emotions. In the initial validation study (Bernstein & Brantz, 2013), the TNASS demonstrated discriminant validity, such that an individual’s perceived tolerance of an emotion was not related to the frequency of experiencing the emotion (r = .08), nor to the degree of awareness or attention of the emotion (r = -.02). In the current study, internal consistency was excellent for the total score (a = .96), good for the anxious apprehension subscale (a = .79), and acceptable for the fear (a = .70) subscale. This measure of distress tolerance was chosen over more common self-report measures (i.e., Distress Tolerance Scale; DTS; Simons & Gaher,

2005) because it provides an explicit definition of tolerance to participants and assesses both global distress tolerance as well as tolerance of specific negative emotions, providing an opportunity to determine whether there are differential effects for specific emotions. Moreover, the DTS has been criticized because it indirectly measures tolerance. That is, it does not specifically ask whether or not these emotions are tolerable but rather uses other words to describe this term (e.g., “My feelings of distress or being upset are not acceptable”). Further, the

DTS appears to be measuring emotion regulation (e.g., When I feel distressed or upset I must do something about it immediately) rather than the perceived ability to tolerate emotional distress.

Lung Function. Lung function was assessed using a KoKo Legend portable office spirometer (nSpire Health, Inc., Longmont, CO). Forced expiratory volume in one second [FEV1] was used as the main outcome variable indicative of lung function.

Mirror-Tracing Persistence Task- Computerized (MTPT-C). The MTPT-C (Daughters et al., 2005) is a behavioral measure of distress tolerance. Participants use a computer mouse to trace shapes on a computer screen as if they are viewing them through a mirror. That is, the cursor on the computer screen moves in the opposite direction from the movement of the mouse. The participant is given three different figures of increasing difficulty to trace (i.e., a straight line, two perpendicular lines, and a star). If the participant moves the mouse too slowly, stops moving the mouse, or moves off the outline of the figure, it creates a distressing buzzing sound and the task restarts. The participant must complete the first two figures within thirty seconds. The last figure is more challenging, and the participant is instructed that they have the option to terminate the task at any time. The task automatically terminates if the participant has not ended the task within 10 minutes. The amount of time spent on the last figure before termination is used as an index of distress tolerance (Daughters et al., 2005), with longer task persistence indexing greater distress tolerance. The MTPT-C task has been used in several studies of distress tolerance to successfully induce distress (Leyro et al., 2010). It also is significantly correlated with other behavioral measures of distress tolerance (Marshall-Berenz et al., 2010; Zvolensky, Vujanovic, Bernstein, & Leyro, 2010).

Subjective Units of Distress Scale (SUDS). The SUDS is a self-report scale used to index intensity of distress (Wolpe, 1969). Participants are asked to indicate on a 100-point scale ranging from 0 (no distress) to 100 (extremely distressed) how distressed they feel at the current moment. SUDS ratings are commonly and successfully used in psychopathology research to examine changes current moment distress associated with a laboratory stressor

(e.g., Feldner et al., 2006; Spira, Zvolensky, Eifert, & Feldner, 2004; Schmidt, Maner, &

Zvolensky, 2007). SUDS ratings were obtained before and after the MTPT-C to ensure that it successfully produced distress (i.e., manipulation check).

Asthma Control Test (ACT). The ACT (Nathan et al., 2004) is a five-item self-report measure that assesses level of asthma control over the past four weeks. It measures degree of functional impairment due to asthma symptoms (e.g., How much of the time did your asthma keep you from getting as much done at work, school or at home?) and frequency of asthma symptoms (e.g., How often have you had shortness of breath?), with higher scores indicating better asthma control. Research has shown that the ACT has good reliability and is able to differentiate between different levels of asthma control (Nathan et al., 2004). The ACT was used at the main outcome measure for asthma control. The ACT showed good reliability (a = .86)

Asthma Quality of Life Questionnaire (AQLQ). The AQLQ (Juniper et al., 1992) is a

32-item self-report measure that assesses asthma-related quality of life across four factors: (a) asthma symptoms (How much discomfort or distress have you felt over the last two weeks as a result of chest tightness?); (b) activity limitation (In general, how much of the time in the last two weeks did you feel you had to avoid a situation or environment because of dust?); (c) emotional function (In general, how much of the time in the last two weeks did you feel concerned about having asthma?); (d) environmental stimuli (In general, how much of the time in the last two weeks did you experience asthma symptoms as a result of the weather or air pollution outside?). Participants rate each item on a 7-point Likert-type scale (1 = totally limited, 7 = not limited at all). The AQLQ has demonstrated good internal consistency and discriminant validity

(Juniper et al., 1993). The AQLQ was used as the primary outcome measure for asthma quality of life. The AQLQ showed excellent reliability (a = .97)

Procedure

Participants were recruited from the community via advertisements placed in public areas, healthcare provider waiting rooms, in local newspapers, and on community-oriented

websites (e.g., Craigslist). Interested individuals were first screened for eligibility by phone (i.e., age, smoking status, Asthma Screening Questionnaire). Potentially eligible participants were then scheduled for an individual appointment by a trained research assistant. Upon arrival to the study session, participants first provided informed, written consent. Non-smoking status was then biochemically verified via CO analysis. Eligible participants then completed spirometry to assess lung function and completed the self-report measures described above. Finally, participants completed the MTPT-C. Following completion of the study, participants were compensated $25 for their time and effort. The Institutional Review Board approved all study materials and procedures prior to the collection of data.

Data Analytic Plan

Descriptive statistics and zero-order correlations were examined for all study variables.

Then, the main effect of distress tolerance (TNASS or MTPT-C) for the primary dependent variables (asthma control, asthma quality of life, lung function) was evaluated using a hierarchical multiple regression procedure (Cohen, Cohen, West & Aiken, 2003). Three sets of regressions were conducted: one using the TNASS total score as the measure of distress tolerance, one using the MTPT-C, and one using the two TNASS subscales. For each set, separate models were constructed for predicting asthma control (ACT), quality of life (AQLQ) and lung function (FEV1). In each model, race (coded as 0 = African American and 1 =

Caucasian), age, and anxiety sensitivity-physical concerns were entered simultaneously as covariates in step one in order to control for those theoretically-relevant factors. Given the conceptual similarity between anxiety sensitivity and distress tolerance as well as research demonstrating that anxiety sensitivity, particularly the physical concerns domain, is associated with poorer asthma outcomes (Avallone, McLeish, Luberto, & Bernstein, 2012; McLeish,

Zvolensky, & Luberto, 2011), anxiety sensitivity-physical concerns was chosen as a covariate on an a priori basis to ensure that any significant findings are due to the intolerance of negative affective states rather than the fear of symptoms associated with anxiety. Race and age were 12

chosen as covariates due to their significant associations with lung function, asthma control and asthma-related quality of life. At the second step of the model, distress tolerance (TNASS- total,

MTPT-C, or TNASS subscales) was entered to estimate the amount of variance accounted for by this variable.

Results

Data Screening and Preparation

The data were then examined for the presence of outliers. One univariate outlier, defined as a z-score greater than 3.29 or less than -3.29 (Tabachnik & Fidell, 2001), was identified and excluded from subsequent analyses. There were no multivariate outliers. Then, the data were screened for the assumptions of regression. The assumption of normality was not met for the

MTPT-C. Thus, a logarithmic transformation was used to reduce skewness (Tabachnik & Fidell,

2001), resulting in skewness decreasing from 1.75 to -.17. The transformed MTPT-C variable was used in all subsequent analyses. All other assumptions for linear regression (i.e., normality of residuals, homogeneity of variance, absence of multicollinearity) were met. Results of the within-subjects t-test indicated that the MTPT-C did successfully induce distress [t(59) = -6.352, p < .001]; SUDS scores were higher after completing the task (M = 44.38, SD = 29.86) compared to before the task (M = 24.25, SD = 24.47).

Zero-Order Correlations

Associations between all study variables are presented in Table 1. Race was significantly associated with age, tolerance of disgust, lung function, asthma control and asthma-related quality of life (r = .26 to .54, p < .05). Age was positively associated with anxiety sensitivity (r = .27, p < .01) and negatively associated with tolerance of anger, asthma control, and asthma-related quality of life (r = -.31 to -.53, p < .05). Anxiety sensitivity was negatively associated with asthma control (r = -.47, p < .01) and asthma-related quality of life (r = -.54, p <

.01). The TNASS total score was positively associated with all TNASS subscales (r = .78 to .90,

p < .01), and with lung function, asthma control, and asthma-related quality of life (r = .40 to .42, p < .01). All TNASS subscales were positively associated with each other (r = .52 to .90, p <

.01). All TNASS subscales except for tolerance of fear and tolerance of negative social emotions were positively associated with lung function (r = .32 to .40, p < .05). Each of the

TNASS subscales were also positively associated with both asthma control (r = .26 to .41, p <

.05) and asthma-related quality of life (r = .32 to .42, p < .05). Lung function was positively associated with asthma control (r = .36, p < .01), but not with asthma-related quality of life.

Quality of life and asthma control were positively associated (r = .85, p < .01). The two measures of distress tolerance (i.e., TNASS and MTPT-C) were not associated with each other.

Regressions

Results for the first set of regressions, using the TNASS total score as the measure of distress tolerance, are presented in Table 3. In terms of FEV1, step one of the model was not significant. Step two of the model accounted for 13.8% unique variance, and the TNASS total score was a significant predictor (β = .39, t = 2.80, p < .01). For asthma control, the first step accounted for 42.2% unique variance. Age (β = -.36, t = -2.94, p < .01) and anxiety sensitivity physical concerns (β = -.35, t = -2.86 p < .01) was the only significant predictor at this step. At step two, the TNASS total score was a significant predictor (β = .31, t = 2.77, p < .01), accounting for 8.8% unique variance. In terms of asthma-related quality of life, step one of the model accounted for 55.8% unique variance, and race (β = .35, t = 2.92, p < .01), age (β =-.29 t

= -2.48, p < .05), and anxiety sensitivity physical concerns (β = -.36, t = -3.03, p < .01) were all significant predictors. Specifically, those who were White, younger, and lower in anxiety sensitivity physical concerns had higher quality of life. Step two accounted for 6.7% unique variance, and the TNASS total score was a significant predictor (β = .27, t = 2.53, p < .05).

Results of the regression analyses using the MTPT-C as the measure of distress tolerance are presented in Table 4. For FEV1, neither step one nor step two of the model were significant. In terms of asthma control, step one of the model accounted for 40.5% unique 14

variance, and age (β = -.34, t = -2.89, p < .01) and anxiety sensitivity physical concerns (β = -

.33, t = -2.90, p < .01) were both significant predictors at this step. Step two of the model was not significant. For asthma-related quality of life, step one of the model was significant, accounting for 54.7% unique variance. Race (β = .34, t = 2.93, p < .01), age (β = -.29 t = -2.52, p < .05) and anxiety sensitivity physical concerns (β = -.37, t = -3.37, p < .01) were all significant predictors at step one. Specifically, those who were White, younger, and lower in anxiety sensitivity physical concerns had greater distress tolerance. Step two of the model was not significant.

Results of the regression analyses using the TNASS-Fear subscale as the measure of distress tolerance are presented in Table 5. In terms of FEV1, step one of the model was not significant. Step two of the model accounted for 8.7% unique variance, and TNASS-Fear was a significant predictor (β = .30, t = 2.28, p < .05). For asthma control, the first step accounted for

41.6% unique variance. Age (β = -.35, t = -3.03, p < .01) and anxiety sensitivity physical concerns (β = -.34, t = -2.97 p < .01) were significant predictors at this step, such that younger individuals with lower levels of anxiety sensitivity had better asthma control. At step two,

TNASS-Fear was a significant predictor (β = .28, t = 2.70, p < .01), accounting for 7.4% unique variance. In terms of asthma-related quality of life, step one of the model accounted for 56.4% unique variance, and race (β = .32, t = 2.82, p < .01), age (β =-.31 t = -2.74, p < .01), and anxiety sensitivity physical concerns (β = -.38, t = -3.50, p < .01) were all significant predictors, such that White, young participants with lower levels of anxiety sensitivity had better quality of life. Step two accounted for 8.9% unique variance, and TNASS-Fear was a significant predictor

(β = .30, t = 3.23, p < .01).

Results of the regression analyses using the TNASS-Anxiety subscale as the measure of distress tolerance are presented in Table 6. In terms of FEV1, step one of the model was not significant. Step two of the model accounted for 10.9% unique variance, and TNASS-Anxiety was a significant predictor (β = .33, t = 2.55, p < .01). For asthma control, the first step

accounted for 40.2% unique variance. Age (β = -.33, t = -2.78, p < .01) and anxiety sensitivity physical concerns (β = -.36, t = -3.03 p < .01) were significant predictors at this step, such that younger individuals with lower levels of anxiety sensitivity had better asthma control. At step two, TNASS-Anxiety was a significant predictor (β = .23, t = 2.08, p < .05), accounting for 4.9% unique variance. In terms of asthma-related quality of life, step one of the model accounted for

54.7% unique variance, and race (β = .30, t = 2.58, p < .01), age (β =-.27 t = -2.34, p < .05), and anxiety sensitivity physical concerns (β = -.41, t = -3.57, p < .01) were all significant predictors, such that White, young participants with lower levels of anxiety sensitivity had better quality of life. Step two accounted for 7.9% unique variance, and TNASS-Anxiety was a significant predictor (β = .28, t = 2.88, p < .01).

Discussion

The current study sought to examine the role of the cognitive-based anxiety risk-related risk factor of distress tolerance in terms of asthma outcomes (i.e., lung function, asthma control and asthma-related quality of life) among adults with asthma. Results indicated that, as hypothesized, lower levels of self-reported global distress tolerance as well as tolerance of fear and anxiety, specifically, were significantly predictive of poorer lung function, asthma control, and asthma-related quality of life. It should be noted that these effects were above and beyond the variance accounted for by age, race, and the physical concerns domain of anxiety sensitivity. These results suggest that individuals with asthma who believe that they are unable to tolerate emotional distress, particularly anxiety and fear, have more severe asthma and more functional impairment. It may be that their inability to tolerate the increased anxiety and negative emotions often experienced during asthma exacerbations directly impacts their asthma by affecting inflammatory processes (e.g., cytokines). Previous research has found that stress may affect cytokines which then leads to exacerbation in asthma symptoms (Glasser, 2005; Tsigos &

Chrousos, 2002). An inability to tolerate distress may also impact asthma outcomes indirectly.

Individuals with lower levels of distress tolerance may be less able to tolerate even small increases in anxiety associated with their asthma symptoms, which might result in increased anxiety and fear and likely an increase in asthma symptoms. Because of their low distress tolerance, they also may perceive their asthma symptoms as worse than they really are and may then use their rescue inhaler more than is truly warranted. These short-acting beta agonists also can produce anxiety symptoms, which would further exacerbate both their anxiety and their asthma symptoms.

Contrary to prediction, however, when assessed behaviorally, distress tolerance did not significantly predict asthma outcomes. Such a discrepancy between self-report and behavioral measures of distress tolerance is consistent with previous work (e.g., McHugh et al., 2011;

Leyro et al., 2010) showing that behavioral and self-report indices of distress tolerance do not tend to be significantly correlated with one another. It has been postulated that behavioral measures tap into the actual capacity to withstand emotional distress, while the self-report measures tap into the perceived capacity to tolerate such distress (Simons & Gaher, 2005;

Zvolensky et al., 2010). Thus, it may be that it is the perception of being unable to tolerate negative emotions rather than the actual ability that is what is most problematic for individuals with asthma. This further supports the theory that these individuals may take steps to immediately alleviate their symptoms before it is truly necessary because they believe that they are unable to tolerate these negative experiences. Additionally, behavioral measures of distress tolerance have also been criticized because they appear tap into frustration tolerance specifically, rather than distress tolerance broadly as well as require significant motivation to persist (Leyro et al., 2010; Simon & Gaher, 2005). Thus, the null findings in the current study may also be due to the psychometric properties of the behavioral measure of distress tolerance.

Limitations and Future Directions

Despite these significant findings, there are a number of limitations to the current study that warrant further consideration. First, this study was cross-sectional in nature so no causal 17

inferences can be made. Longitudinal studies are needed to better understand how distress tolerance impacts asthma over time. Second, the sample utilized was relatively small and nonclinical. It will be important for future studies to use larger samples to determine whether these findings generalize to clinical populations. Third, the sample only included individuals who were prescribed an inhaled corticosteroid as opposed to only a short-acting rescue inhaler, limiting the generalizability of the results to individuals with less severe forms of asthma, such as exercise-induced bronchoconstriction.

Clinical Implications

Despite these limitations, there are a number of clinical implications that can be gathered from the findings of the present study. For example, intervention efforts can begin to target distress tolerance in individuals with asthma, as this appears to contribute to negative asthma outcomes above and beyond anxiety sensitivity. Previous efforts targeting anxiety sensitivity are especially problematic in individuals with asthma, as exposure to these physical symptoms could potentially lead to an asthma attack. Distress tolerance interventions could target the emotional symptoms associated with anxiety rather than the physical symptoms. Although it has not been evaluated in individuals with asthma, Dialectical Behavior Therapy may be beneficial for this population in order to increase distress tolerance (Linehan; 1993). It will be important for future work to determine whether distress tolerance interventions are effective in the asthma population, and whether interventions need to be tailored for these individuals (e.g., understanding asthma symptoms, knowing when to use an inhaler).

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Table 1. Descriptive Statistics for all Study Variables.

Observed M SD Range

Race - - -

Age 34.72 13.58 18-65

AS-PC 20.43 7.47 10-44

MTPT-C 1.98 .35 1.11-2.66

TNASS-Total 61.26 17.47 22-105

TNASS-Fear 11.30 3.23 4-18

TNASS-Anxiety 11.62 3.54 4-19

FEV1 84.44 20.63 43-131

ACT 15.98 4.54 6-25

AQLQ 4.49 1.30 1.28-6.97

Note: Race: 0 = African-American, 1 = White; AS-PC: Anxiety Sensitivity Index-3- Physical Concerns subscale (Taylor et al., 2007); MTPT-C: Mirror Tracing Persistence Task-Computerized, log transformed (Daughters et al., 2005); TNASS-Total: Tolerance of Affective States Scale Total Score (Bernstein & Brantz, 2013); TNASS-Fear: Tolerance of Negative Affective States Scale Fear-Distress Subscale (Bernstein & Brantz, 2013); TNASS Anxiety: Tolerance of Negative Affective States Scale-Anxious Apprehension Subscale (Bernstein & Brantz, 2013); FEV1: Forced expiratory volume in one second; ACT: Asthma Control Test (Nathan et al., 2004); AQLQ: Asthma Quality of Life Questionnaire (Juniper et al., 1992).

Table 2. Zero Order Correlations among all Study Variables 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 1. Race - -.29* -.24 -.20 .20 .11 .11 .26* .35** .54** 2. Age - - .27* -.04 -.25 -.14 -.17 -.16 -.51** -.53** 3. AS-PC - - - .17 -.11 -.14 -.06 .01 -.47** -.54** 4. MTPT-C - - - - -.11 -.16 -.03 .01 -.11 -.09 5. TNASS-Total - - - - - .86** .86** .40** .41** .42** 6. TNASS-Fear ------.77** .23 .28* .41** 7. TNASS-Anxiety ------.32* .26* .35*

8. FEV1 ------.36** .25 9. ACT ------.85** 10. AQLQ ------

* p < .05, ** p <.01 Note: Race: 0 = African-American, 1 = White; AS-PC: Anxiety Sensitivity Index-3- Physical Concerns subscale (Taylor et al., 2007); MTPT- C: Mirror Tracing Persistence Task-Computerized, log transformed (Daughters et al., 2005); TNASS-Total: Tolerance of Affective States Scale Total Score (Bernstein & Brantz, 2013); TNASS-Fear: Tolerance of Negative Affective States Scale Fear-Distress Subscale (Bernstein & Brantz, 2013); TNASS Anxiety: Tolerance of Negative Affective States Scale-Anxious Apprehension Subscale (Bernstein & Brantz, 2013); FEV1: Forced expiratory volume in one second; ACT: Asthma Control Test (Nathan et al., 2004); AQLQ: Asthma Quality of Life Questionnaire (Juniper et al., 1992).

Table 3. TNASS-Total Predicting Asthma Outcomes

ΔR2 t β sr2 p

Criterion Variable: FEV1 Step 1 .11 .18 Race 2.06 .31 .09 .05* Age -.58 -.09 .01 .57 AS-PC .95 .14 .02 .35 Step 2 .14 .01** TNASS-Total 2.80 .39 .14 .01** Criterion Variable: Asthma Control Step 1 .42 .00** Race 1.63 .20 .03 .11 Age -2.94 -.36 .11 .01** ASI-PC -2.86 -.35 .11 .01** Step 2 .09 .01** TNASS-Total 2.77 .31 .09 .01** Criterion Variable: Asthma-Related Quality of Life Step 1 .56 .00** Race 2.92 .35 .10 .01** Age -2.48 -.29 .07 .02* ASI-PC -3.03 -.36 .11 .00** Step 2 .07 .02* TNASS-Total 2.53 .27 .07 .02* * p < .05, ** p < .01 Note. β = standardized beta weight; sr2 = squared semi-partial correlation

Table 4. MTPT-C Predicting Asthma Outcomes

ΔR2 t β sr2 p Criterion Variable: FEV1 Step 1 .09 .17 Race 2.17 .31 .09 .03* Age -.19 -.03 .00 .85 AS-PC .09 .09 .01 .55 Step 2 .00 .70 MTPT-C .39 .06 .00 .70 Criterion Variable: Asthma Control Step 1 .41 .00** Race 1.81 .21 .04 .08 Age -2.89 -.34 .10 .01** ASI-PC -2.90 -.33 .14 .01** Step 2 .00 .78 MTPT-C -.28 -.03 .00 .78 Criterion Variable: Asthma-Related Quality of Life Step 1 .55 .00** Race 2.93 .34 .09 .01** Age -2.52 -.29 .07 .02* ASI-PC -3.37 -.37 .12 .00** Step 2 .00 .97 MTPT-C .04 .01 .00 .97 * p < .05, ** p < .01 Note. β = standardized beta weight; sr2 = squared semi-partial correlation.

Table 5. TNASS-Fear Predicting Asthma Outcomes

ΔR2 t β sr2 p

Criterion Variable: FEV1 Step 1 .08 .25 Race 1.91 .27 .07 .06 Age -.32 -.05 .00 .75 ASI-PC .57 .08 .01 .57 Step 2 .09 .03* TNASS-Fear 2.28 .30 .09 .03* Criterion Variable: Asthma Control Step 1 .42 .00** Race 1.84 .21 .04 .07 Age -3.03 -.35 .10 .00** ASI-PC -2.97 -.34 .10 .01** Step 2 .07 .01** TNASS-Fear 2.70 .28 .07 .01** Criterion Variable: Asthma-Related Quality of Life Step 1 .56 .00** Race 2.82 .32 .02 .01** Age -2.74 -.31 .08 .01** ASI-PC -3.50 -.38 .13 .00** Step 2 .09 .00** TNASS-Fear 3.23 .30 .09 .00** * p < .05, ** p < .01 Note. β = standardized beta weight; sr2 = squared semi-partial correlation

Table 6. TNASS-Fear Predicting Asthma Outcomes

ΔR2 t β sr2 p Criterion Variable: FEV1 Step 1 .09 .19 Race 2.03 .29 .09 .05* Age -.53 -.08 .01 .60 ASI-PC .69 .10 .01 .50 Step 2 .11 .01* TNASS-Anxiety 2.55 .33 .11 .01* Criterion Variable: Asthma Control Step 1 .40 .00** Race 1.58 .18 .03 .12 Age -2.78 -.33 .09 .01** ASI-PC -3.03 -.36 .11 .00** Step 2 .05 .04* TNASS-Anxiety 2.08 .23 .05 .04* Criterion Variable: Asthma-Related Quality of Life Step 1 .55 .00** Race 2.58 .30 .08 .00** Age -2.34 -.27 .06 .02* ASI-PC -3.57 -.41 .14 .00** Step 2 .08 .01** TNASS-Anxiety 2.88 .28 .08 .01** * p < .05, ** p < .01 Note. β = standardized beta weight; sr2 = squared semi-partial correlation