The Effect of Asthma on Smoking Behavior and Smoking-Related Cognitive Processes

among Adult Smokers

A thesis submitted to the

Division of Graduate Education and Research

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

July 2011

Kimberly M. Avallone

B.S., University of Florida, 2008

Committee: Alison C. McLeish, Ph.D. (chair)

Christine A. Hovanitz, Ph.D.

Farrah Jacquez, Ph.D.

Abstract

Despite the negative effects of smoking on lung functioning and overall health, smoking is more prevalent among individuals with asthma compared to those without. The purpose of the current study was to replicate and extend past work on asthma and smoking by examining the unique predictive ability of asthma diagnosis in terms of smoking behavior and smoking-related cognitive processes among adult daily smokers. Participants were 251 regular daily smokers:

125 smokers with self-reported, physician-diagnosed asthma (54% male; Mage = 37.66 years,

SD = 12.12) and 126 smokers without asthma (70.4% male; Mage = 36.51 years, SD = 13.05) who completed a battery of self-report measures. Partially consistent with hypotheses, after controlling for gender, race, number of medical problems, and alcohol consumption, asthma diagnosis was a significant predictor of age of regular smoking onset (1.9% unique variance), number of quit attempts (2.5% unique variance), and self-control reasons for quitting (2.6% unique variance). Results for nicotine dependence and daily smoking rate approached significance. Contrary to prediction, asthma diagnosis did not significantly predict smoking motives, reasons for quitting related to health concerns, or barriers to cessation. The primary implication of the present findings is that while there are few differences between smokers with and without asthma in terms of smoking behaviors and smoking-related cognitive processes, the differences that do exist suggest that current smoking cessation interventions may not be as effective for smokers with asthma and targeted, specialized interventions need to be developed.

Keywords: asthma, cigarette smoking, smoking motives, smoking cessation

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Table of Contents Abstract ...... ii Table of Contents...... iv List of Tables...... v Introduction...... 1 Asthma ...... 1 Cigarette Smoking...... 3 Asthma and Smoking Co-occurrence ...... 4 Effects of Smoking on Asthma Severity...... 5 Smoking-Related Cognitive Processes ...... 6 Smoking-Related Cognitive Processes Among Smokers with Asthma...... 8 Conclusions and Limitations of Previous Research...... 10 Present Study...... 11 Method...... 11 Participants ...... 11 Measures ...... 14 Procedure...... 18 Results...... 18 Analytic Approach...... 18 Bivariate Correlations among Covariates and Predictor Variable...... 20 Bivariate Correlations among Covariates and Criterion Variables ...... 20 Bivariate Correlations among Predictor and Criterion Variables ...... 20 Hierarchical Multiple Regression Analyses...... 21 Discussion...... 24 Asthma Diagnosis and Smoking Behavior ...... 24 Asthma Diagnosis and Smoking-Related Cognitive Processes...... 26 Limitations and Future Directions ...... 28 Conclusions ...... 31 References ...... 33

List of Tables

Table 1: Asthma Severity Classification………………………………………………………….44 Table 2: Descriptive Data for Covariates, Predictor, and Criterion Variables……………..45 Table 3: Intercorrelations among Covariates, Predictor, and Criterion Variables………..46 Table 4: Asthma Diagnosis Predicting Smoking Behavior……………………………………47 Table 5: Asthma Diagnosis Predicting Reasons for Smoking (RFS)………………………..48 Table 6: Asthma Diagnosis Predicting Reasons for Quitting (RFQ) and Barriers to Cessation……………………………………………………………………………………………….49

Introduction

Asthma

Over 20 million individuals in the United States currently suffer from asthma, a reversible obstructive airway disease that consists of chronic airway inflammation and episodes of exacerbations in response to certain stimuli (America Lung Association [ALA], 2010a). These airway exacerbations, or asthma attacks, include swelling of the airway lining, tightening of the muscles around the airways, and increased mucus production, resulting in symptoms of wheezing, coughing, chest tightness, and shortness of breath (ALA, 2010b). Common triggers for an asthma attack include allergens and irritants, such as dust mites, pollen, cockroaches, outdoor air pollution, pets, mold, environmental tobacco smoke, exercise, and changes in weather (e.g., increased humidity, cold temperatures; ALA, 2008). Asthma attacks are common among those with asthma; approximately 56% of individuals with asthma experience one or more asthma attacks per year (Center for Disease Control [CDC], 2007). If not well controlled through medical intervention, asthma can result in significant rates of morbidity and mortality

(ALA, 2008). For example, individuals with asthma are at greater risk for experiencing complications from respiratory illnesses (e.g., influenza, bronchitis, pneumonia) due to the chronic airway inflammation associated with the disease (ALA, 2008).

The lifetime prevalence rate of asthma among adults in the United States (U.S.) is

13.3%, and females are 10.5% more likely than males to have ever received an asthma diagnosis (ALA, 2010c). Females also have consistently higher prevalence rates of asthma attacks per year than males (ALA, 2010c). Asthma is generally diagnosed by physician determination of the presence of recurrent episodes of airflow obstruction (usually at least partially reversible) or airway hyperresponsiveness (National Heart, Lung, and Blood Institute

[NHLBI], 2007). The classification of asthma severity is typically completed through physical exam to assess an individual’s lung functioning, frequency of symptoms, and frequency of rescue inhaler use. Lung functioning is assessed via spirometry, which measures peak

expiratory flow rates and forced expiratory volume in one second (FEV; Sims, 2006). In both cases, the lower the number, the more severe the level of impairment. Based on the findings from this assessment, patients can be placed into one of the following categories of severity: intermittent, mild persistent, moderate persistent, or severe persistent (NHLBI, 2007). Please see Table 1 for a more detailed description of asthma severity classification for individuals over

12 years of age.

Although the exact etiology of asthma is currently unknown, a great deal of information is currently available about the pathophysiology of the disease. A number of cells and immune system markers have been implicated in the developmental origins of asthma, such as mast cells, T cells, and Immunoglobulin E (IgE). Indeed, atopy, the genetic predisposition to develop an IgE-related response to common allergens, is the strongest risk candidate for developing asthma (NHLBI, 2008). It is hypothesized that in addition to a genetic vulnerability, exposure to certain allergens, infections, and irritants (e.g., environmental tobacco smoke) early in life may contribute to the developments of asthma (NHLBI, 2008). It is estimated that approximately 34 million Americans have been diagnosed with asthma in their lifetime, with the highest prevalence rates found among 5-17 year olds (106.3 per 1000; ALA, 2008). Asthma is more common among boys during childhood and females during adulthood. The onset of asthma is most frequently in childhood with several possible trajectories: remission, relapse and remission, and persistent (Reed, 2006). The severity of asthma in childhood, degree of lung dysfunction, and the presence of atopy have each been shown to be significant predictors of the extent to which asthma persists into adulthood (Reed, 2006).

Because of information gaps regarding the etiology of asthma, it is noteworthy that clinical focus is generally currently aimed at disease management rather than prevention.

Treatment consists primarily of medication to control inflammation and decreasing exposure to asthma triggers (CDC, 2007). The two classes of medications typically used to control asthma include a controller medication (anti-inflammatory medications, typically inhaled corticosteroids)

and a reliever medication (bronchodilators, such as short-acting beta-agonists). In terms of reducing exposure to environmental triggers, environmental tobacco smoke has been the primary target of research and prevention/intervention efforts. Substantially less research attention, however, has been paid to examining active smoking among individuals with asthma.

Despite the negative impact of smoking on already compromised lung functioning, a substantial proportion of individuals with asthma are active cigarette smokers.

Cigarette Smoking

Although there have been increased efforts to prevent the onset or maintenance of cigarette smoking, smoking remains a significant public health concern (Pleis, Lucas, & Ward,

2009). Currently, approximately 21% of U.S. adults are regular cigarette smokers (Pleis et al.,

2009), and smoking prevalence is higher among men than women, with approximately 23.1% of men and 18.3% of women reporting current daily smoking in the U.S. (CDC, 2009). Although rates of smoking had been steadily decreasing until 2004, this rate of decline seems to have stalled in recent years (CDC, 2010). Each day approximately 1,000 children under the age of 18 and 1,800 adults become regular daily smokers in the U.S. (CDC, 2010b). On average, regular smokers smoke 13 cigarettes per day (Substance Abuse and Mental Health Services

Administration [SAMHSA], 2003), with males smoking, on average, more cigarettes per day than females (14 cigarettes and 12 cigarettes, respectively; SAMHSA, 2003). The onset of daily smoking typically occurs during adolescence, particularly between the ages of 15 and 20 and rarely after 25 years of age (Breslau, Johnson, Hiripi, & Kessler, 2001).

Smoking is the leading preventable cause of death and disability in the U.S., accounting for 1 in 5 deaths each year (CDC, 2008; U.S. Department of Health and Human Services

[USDHHS], 2004). Smoking harms nearly every organ of the body resulting in approximately 8.6 million individuals in the U.S. who suffer from one or more serious illnesses associated with cigarette smoking (e.g., chronic obstructive lung disease [COPD], lung cancer; ALA, 2010d).

Other conditions associated with smoking include coronary heart disease, various types of non-

lung-related cancers (e.g., kidney, bladder, cervical), preterm deliveries, stroke, pneumonia, and asthma (ALA, 2010d). Smokers are more likely to be absent from work, have longer-lasting illnesses, incur more medical costs, have more frequent doctor’s visits, and be admitted to the hospital more often and for longer periods than nonsmokers (USDHHS, 2004). Recent reports estimate that the total costs of cigarette smoking are approximately $193 billion per year or

$10.47 per pack of cigarettes sold in the U.S. (CDC, 2008).

Asthma and Smoking Co-occurrence

Despite the known compromising effects of smoking on lung functioning and health, many individuals with asthma are current smokers. Indeed, extant research indicates that smoking is more prevalent among individuals with asthma compared to those without. For example, Frank, Morris, Hazell, Linehan, and Frank (2006) found that current smokers were more likely to have an asthma diagnosis than both non-smokers (OR = 1.59) and former smokers (OR = 1.79). Similarly, Avila, Soto-Martinez, Soto-Quiros, and Celedon (2005) found that young adults with current asthma were more likely to be ‘regular smokers’ (> 4 days per week) and to be ‘heavier smokers’ (> 10 cigarettes per day) than those without asthma (OR =

4.4). In addition, among individuals reporting physician diagnosed current or lifetime asthma, the odds of being an active smoker were found to be higher than of being a non-smoker (OR = 1.36 and 1.31, respectively; Gwynn, 2004). Similar results were found for odds of being a former smoker (OR = 1.28 and 1.26, respectively).

In terms of temporal patterning, the majority of studies have found that smoking predicts later asthma onset. For example, Plashke and colleagues (2000) conducted a three-year longitudinal study and found that smokers were three times as likely to develop asthma as non- smokers. Similarly, Genuneit and colleagues (2006) conducted a seven-year prospective study and found that adolescents who smoked were over 2.5 times more likely to develop asthma than non-smokers, with risk of asthma incidence increased as number of smoking years increased. While the majority of studies in this area have concluded that smoking precedes the

onset of asthma, two studies have found the reverse to be true. In a longitudinal study of children from birth through adolescence, Oechsli, Selzer, and van den Berg (1987) found that in

90% of the cases of comorbid asthma and smoking, asthma onset preceded smoking onset. In a 22-month longitudinal study, Van De Ven, Engels, Kerstjens, and Van Den Eijnden (2007) found that the greater the asthma symptom severity, the higher the likelihood that the individual would become a regular smoker (OR = 1.75). Furthermore, adolescents with asthma were nearly twice as likely to become regular smokers as opposed to experimental smokers. Thus, there appear to be significant bidirectional associations, or multiple pathways, leading to asthma-smoking co-occurrence.

Effects of Smoking on Asthma Severity

In addition to increased prevalence rates of smoking among individuals with asthma, smoking also affects asthma symptom severity and symptom management. With regard to asthma control, research has consistently found that smoking results in poorer asthma control

(McLeish & Zvolensky, 2010). Indeed, research has found that smokers are 1.5 to 2.6 times more likely than non-smokers to have poorer asthma control and more frequent and severe asthma exacerbations (McCoy et al., 2006; Schatz, Zeiger, Vollmer, Mosen, & Cook, 2006).

Similarly, Pedersen and colleagues (2007) found that patients classified as having not well controlled asthma (NWC) were more likely to be current or former smokers than those with well- controlled asthma and that smoking status had the greatest influence on whether or not patients achieved asthma control. Furthermore, current smokers experienced asthma exacerbations at rates twice that of non-smokers.

Moreover, compared to non-smokers with asthma, smokers with asthma experience increased levels of daily asthma symptoms (Boulet, FitzGerald, McIvor, Zimmerman, &

Chapman, 2008), greater symptom severity for a variety of asthma symptoms (e.g., wheezing, nighttime awakenings; Althuis, Sexton, & Prybylski, 1999), and greater interference with daily activities due to asthma (Beeh, Micke, Ksoll, & Buhl, 2001; Boulet et al., 2008). Further,

smokers diagnosed with asthma were found to be 2.4 times more likely than non-smokers with asthma to experience daily asthma attacks and 5.4 times more likely to report episodes of abnormal breathing in the previous 12 months (Siroux, Pin, Oryszczyn, Le Moual, & Kauffman,

2000). Additionally, smokers with asthma are at a greater risk for asthma-related hospitalization

(OR = 1.86) or another type of hospital-based care for asthma (OR = 1.76; Eisner & Iribarren,

2007). Not only does smoking increase asthma severity and decrease asthma control, but it also decreases the effectiveness of asthma treatment; corticosteroid use results in improved lung functioning and asthma control and decreased asthma symptoms in non-smokers but not in current smokers (Chaudhuri et al., 2002).

Smoking-Related Cognitive Processes

In order to better understand smoking behavior and to improve cessation rates, researchers have investigated smokers’ self-reported reasons or motives for smoking. As defined by Ikard, Green, and Horn (1969), there are six main motives for smoking: (1) habitual

(e.g., smoking cigarettes without awareness); (2) addictive (e.g., smoking to reduce cravings);

(3) negative affect reduction (e.g., smoking to manage negative moods); (4) pleasurable relaxation (e.g., smoking for enjoyment or to relax); (5) stimulation (e.g., smoking to increase concentration; and (6) sensorimotor manipulation (e.g., enjoyment of the physical handling of cigarettes). Research has shown that women are more likely to report smoking to manage negative affect while men report smoking to reduce withdrawal symptoms (Berlin et al., 2003;

Ikard et al., 1969; Livson & Leino, 1988; Lujic, Reuter, & Netter, 2005). Motives related to nicotine dependence (e.g., reduce craving) are more frequently reported than other motives

(e.g., relaxation) and account for nearly two-thirds of all smoking occasions (Piasecki,

Richardson, & Smith, 2007).

There is a growing body of literature examining smoking motives as a way to better understand smoking behavior in general as well as association between smoking and psychopathology. For example, negative affect reduction smoking motives are related to

increased smoking levels, greater nicotine dependence, and heightened risk of relapse to smoking (O’Connell & Shiffman, 1988; Pomerleau, Adkins, & Pertschuk, 1978). More recently, researchers have begun to examine associations between smoking motives and indices of affect vulnerability. One such measure of affect vulnerability, anxiety sensitivity (fear of anxiety- related physical and psychological sensations; McNally, 2002; Reiss & McNally, 1985) has been found to significantly predict negative affect reduction smoking motives (Gonzalez, Zvolensky,

Vujanovic, Leyro, & Marshall, 2009; Gregor, Zvolensky, Bernstein, Marshall, & Yartz, 2007;

Leyro, Zvolensky, Vujanovic, & Bernstein, 2008). Anxiety sensitivity has also been shown predictive ability in terms of habitual, addictive, and stimulation smoking motives (Gonzalez et al., 2009; Gregor et al., 2007; Leyro et al., 2008).

Although smoking increases one’s risk for developing a variety of lethal medical diseases, quitting smoking decreases the risk of developing such problems and may increase the survival time among those persons who have already developed medical problems (Samet,

1991). Approximately 70% of current adult smokers report they are motivated to quit and about

40% of smokers make a quit attempt each year (CDC, 2010b). Among individuals who make a quit attempt without the use of medications or other quit aids (e.g., smoking cessation group), only 4-7% are successful in abstaining from nicotine for at least six months (American Cancer

Society [ACS], 2009). Approximately 25-33% of individuals who use medications as a quit aid are able to abstain from smoking for at least six months (ACS, 2009). Although approximately

45% of regular smokers are ultimately able to quit successfully (APA, 2000), most smokers make an average of five quit attempts before successfully quitting and the majority are unable to quit without some form of treatment (McKenna et al., 2001).

Similar to motives for smoking, reasons for quitting smoking vary on an individual basis and influence smoking cessation behavior and success (Miller & Rollnick, 1991). Research has shown significant gender differences in attitudes towards smoking cessation; men are more likely to report wanting or planning to quit smoking completely, while women are more likely to

report plans to reduce their current smoking behavior (Sorensen & Pechacek, 1987). In addition, men are more likely than women to believe that by quitting, they can escape the negative health effects of smoking. In general, reasons for quitting smoking fall along two broad dimensions

(intrinsic and extrinsic) that each consist of two subdomains (Curry, Wagner, & Grothaus, 1990).

Intrinsic reasons for quitting include health concerns (e.g., worry about shorter life) and self- control (e.g., to like one’s self better) and extrinsic reasons include immediate reinforcement

(e.g., save money) and social influence (e.g., to get others to stop nagging). Research indicates that individuals with extrinsic reasons for quitting are less successful in quitting and those who are high in intrinsic reasons for quitting and low in extrinsic reasons are ultimately the most successful in quitting (Curry et al., 1990; Halpern & Warner, 1993). Additional influences on cessation success are perceived barriers to smoking cessation. Identified barriers to cessation include those related to addiction (e.g., thinking about smoking all the time), external barriers

(e.g., lack of encouragement or support), and internal barriers (e.g., feeling less in control of moods; Macnee & Talsma, 1995).

Smoking-Related Cognitive Processes Among Smokers with Asthma

Although numerous studies show that individuals with asthma are as likely or more likely to smoke than individuals without asthma, there may be different patterns of onset and maintenance between the two groups. The following studies examined differences between those with and without asthma in terms of smoking onset, smoking behavior, and smoking- related cognitions. In terms of smoking onset, Brook and Shiloh (1993) found that adolescents with current asthma reported less positive attitudes towards smoking than those without asthma and were less likely to report an intention to smoke in the future. Similarly, in a longitudinal study of smoking cognitions and smoking behavior, adolescents with asthma reported a lower intention to start smoking, a more negative attitude towards smoking, and greater self-efficacy regarding their ability to resist smoking at baseline (Van de Ven, Engels, Otten, & Van Den

Eijnden, 2007). For adolescents with and without asthma, a more positive attitude towards

smoking, lower self-efficacy to resist smoking, and a perception that parents and friends were in favor of smoking predicted intention to smoke, which, in turn, predicted smoking onset (Van de

Ven et al., 2007). In an examination of reasons for smoking initiation, Precht, Keiding, Nielsen, and Madsen (2006) found that adolescent smokers with asthma were more likely to report starting to smoke due to peer pressure (OR = 1.67), particularly among boys (OR = 2.39).

In terms of smoking behavior, there appears to be no difference between smokers with and without asthma in terms of daily smoking rate, likelihood of making a quit attempt, and stage of change in terms of readiness to quit smoking (Wakefield, Roberts, Ruffin, Wilson, &

Campbell, 1995). Similarly, in a study of adolescent smokers, Zimmerman and colleagues

(2004) found no differences between adolescents with and without asthma in terms of puff volume, puff duration, interpuff interval, and puff velocity. There were also no significant group differences for age at first cigarette, age at daily smoking, time to treatment, and number of quit attempts.

Research on smoking-related cognitions among smokers with asthma is in its infancy and has focused primarily on adolescents. Van de Ven, Van Den Eijnden, and Engels (2006) found that the only difference between adolescent smokers with and without asthma was that smokers with asthma reported a higher perceived risk of having severe lung problems after smoking for a few years. There were no differences in positive attitudes towards smoking, self- efficacy regarding ability to resist smoking, others’ approval of their smoking, perceived prevalence of smoking, risk of death, and risk of addiction. However, adult smokers with asthma are twice as likely as smokers without asthma to report that they believe their health is worse due to smoking and that their health in the future will be worse due to smoking (Wakefield et al.,

1995). In terms of reasons for smoking, there do appear to be significant group differences; adolescents with asthma are more likely to report smoking as a means of weight control (OR =

1.41), and less likely to smoke for social reasons (OR = .78) compared to adolescents without asthma (Precht et al., 2006).

Conclusions and Limitations of Previous Research

Taken together, empirical evidence suggests that: (1) smoking is more prevalent among individuals with asthma than those without; (2) smoking is a risk candidate for the development of asthma; and (3) smoking is associated with decreased asthma control and increased risk of mortality as well as asthma attacks and exacerbations. However, there is currently no overarching theory to explain why asthma and smoking co-occur. The current body of literature primarily documents the co-occurrence of asthma and smoking and provides no explanation for this association. Such a theory would not only guide research, but also provide a framework for understanding how data from various studies fit together. One line of research that would inform such a theory would be to better understand the impact of asthma on smoking-related cognitive processes and behaviors. That is, why do individuals with asthma smoke and what processes underlie such linkages? Exploring the underlying motives for smoking and how they function to maintain smoking behavior will not only help to develop a model for understanding the smoking- asthma association, but also may point to specific targets for prevention and intervention efforts in this population.

Extant research in this area is limited and suggests that despite having an initially more negative view towards smoking, lower intention to start smoking, and greater perceived health risks from smoking, it does not appear, when looking at smoking prevalence rates, that these beliefs actually systemically deter individuals with asthma from smoking. There are, however, two major limitations to previous research on smoking processes among smokers with asthma.

First, the majority of studies examine smoking processes among adolescents with asthma and only one study (Wakefield et al., 1995) has examined these processes among adults. Smoking habits evolve as one continues smoking from adolescence into adulthood, thus smoking processes likely differ between adolescents and adults, limiting the generalizability of previous findings. Second, only a limited number of process variables have been examined to date.

Indeed, the majority of research has focused primarily on smoking initiation rather than factors

that maintain smoking behavior (e.g., perceived barriers to cessation, reasons for quitting smoking).

Present Study

Taken together, the overarching purpose of the present investigation was to replicate and extend past work on asthma and smoking by examining the unique predictive ability of asthma diagnosis in terms of smoking behavior (i.e., age at regular smoking onset, daily smoking rate, level of nicotine dependence, number of quit attempts) and smoking-related cognitive processes (i.e., reasons for smoking, reasons for quitting smoking, perceived barriers to quitting smoking) among adult daily smokers. In terms of smoking behavior, it was hypothesized that, after controlling for the theoretically relevant covariate of alcohol consumption as well as any other variables on which the two participant groups differ (e.g., gender, race, number of medical problems, number of psychiatric diagnoses), having a diagnosis of asthma would be predictive of daily smoking rate, level of nicotine dependence, and number of quit attempts, but not age at regular smoking onset. In terms of smoking processes, it was hypothesized that, after controlling for alcohol consumption and any identified demographic covariates, having a diagnosis of asthma would be predictive of: (1) addiction and habitual motives for smoking; (2) motives for quitting related to health concerns and social influence; and (3) greater perceived barriers to cessation.

Method

Participants

The study sample consisted of 251 regular daily smokers between the ages of 18 and 65 recruited from the greater Cincinnati, OH community: 125 smokers with self-reported, physician- diagnosed asthma and 126 smokers without asthma. Participants were recruited via newspaper ads (online and print) and flyers posted throughout the community. Eligibility criteria for the study were: (1) daily smoking rate greater than 10 cigarettes per day; (2) expired carbon monoxide levels greater than 10 parts per million (ppm; please see Measures section for

details); and (3) regular, daily smoking for at least one year. Individuals in the smokers without asthma group must not have endorsed a current or past diagnosis of asthma. Participants in the smokers with asthma group were required to have: (1) received a physician diagnosis of asthma; (2) a current prescription for asthma medication; and (3) experienced asthma symptoms within the past year.

Smokers with asthma. The smokers with asthma group consisted of 125 daily smokers with asthma (54% male; Mage = 37.66 years, SD = 12.12). The racial composition of the group was 41.5% Caucasian, 54.5% African American, 1.6% Asian, and 2.4% Other. With regard to education level, 24% of smokers with asthma had less than a high school degree, 36.8% had a high school degree, 28.8% completed some college, 3.2% completed a 2-year degree, 4.8% completed a 4-year degree, and 2.4% completed some graduate level education.

Participants in the smokers with asthma group reported a lifetime history of the following medical problems: 63.2% had seasonal allergies, 41.6% had back or neck problems, 30.4% had high blood pressure, 23.2% had arthritis or rheumatism, 20.8% experienced frequent or severe headaches, 17.7% experienced chronic pain, 12.8% had another chronic lung disease in addition to asthma, 8.8% had ulcers, 8.1% had heart disease, 6.4% had diabetes, 4.8% had kidney problems, 4% experienced a heart attack, 4% had cancer, 1.6% had a stroke, 1.6% had epilepsy or seizures, and 0.8% had HIV/AIDS. Overall, smokers with asthma reported a lifetime history of an average of 2.5 (SD = 1.85) medical problems in addition to asthma. Participants were administered the Structured Clinical Interview for DSM-IV Axis I Disorders- Non-Patient

Edition (First, Spitzer, Gibbon, & Williams, 1995) and reported the following history of current or past psychiatric problems: 54.4% had major depressive disorder, 2.4% had bipolar I disorder,

2.4% had bipolar II disorder, 46.3% had experienced one or more panic attacks, 16% had panic disorder, 2.4% had agoraphobia without panic disorder, 15.2% had social phobia, 6.4% had obsessive-compulsive disorder, 19.4% had posttraumatic stress disorder, and 7.2% had generalized anxiety disorder.

Smokers with asthma smoked on average 20.78 cigarettes per day (SD = 12.84), considered themselves regular cigarette smokers by a mean age of 16.52 (SD = 4.21) years, had been smoking on average for a total of 20.31 years (SD = 11.30), and had made an average of 3.52 (SD = 3.28) serious quit attempts since starting smoking. Among smokers with asthma the average expired carbon monoxide (CO) level was 21.39 ppm (SD = 10.28). The average level of nicotine dependence, as indexed by the Fagerström Test for Nicotine

Dependence (FTND; Heatherton, Kozlowski, Frecker, & Fagerström, 1991), was 6.08 (SD =

2.2); this reflects a high level of overall nicotine dependence. On average, smokers with asthma were 17.46 (SD = 12.92) years of age when diagnosed with asthma and reported 1.62 (SD =

.49) hospitalizations due to asthma. In terms of asthma control, 27.2% of smokers with asthma reported troubling asthma symptoms most or all days/nights during the previous two weeks and a mean Asthma Control Test (Nathan et al., 2004) score of 15.48 (SD = 4.21), indicating difficulties with asthma control.

Smokers without asthma. The smokers without asthma group consisted of 126 regular, daily smokers with no history of asthma (70.4% male; Mage = 36.51 years, SD = 13.05). The racial composition of the group was 69.8% Caucasian, 26.2% African American, and 4% Other.

With regard to education level, 14.3% of smokers without asthma had less than a high school degree, 31.7% had a high school degree, 32.5% completed some college, 11.1% completed a

2-year degree, 7.9% completed a 4-year degree, and 2.4% completed some graduate level education.

Participants in the smokers without asthma group reported a lifetime history of the following medical problems: 24.6% had back or neck problems, 20.6% had seasonal allergies,

15.1% had arthritis or rheumatism, 12.7% had high blood pressure, 12.7% experienced chronic pain, 10.3% experienced frequent or severe headaches, 4.8% had kidney problems, 3.2% experienced an ulcer, 3.2% had diabetes, 3.2% had cancer, 2.4% had epilepsy or seizures,

2.4% had a chronic lung disease other than asthma, 0.8% had heart disease, 0.8% experienced

a heart attack, 0.8% had a stroke, and 0.8% had HIV/AIDS. Overall, smokers without asthma reported a lifetime history of an average of 1.18 (SD = 1.35) medical problems. Participants were administered the Structured Clinical Interview for DSM-IV Axis I Disorders- Non-Patient

Edition (First et al., 1995) and reported the following history of current or past psychiatric problems: 38.9% had major depressive disorder, 5.6% had bipolar I disorder, 1.6% had bipolar

II disorder, 31% had experienced one or more panic attacks, 9.5% had panic disorder, 0.8% had agoraphobia without panic disorder, 18.3% had social phobia, 11.1% had obsessive- compulsive disorder, 16.7% had posttraumatic stress disorder, and 9.5% had generalized anxiety disorder.

Smokers without asthma smoked on average 18.52 cigarettes per day (SD = 8.69), considered themselves regular cigarette smokers by a mean age of 17.51 (SD = 5.14) years, had been smoking on average for a total of 19.23 years (SD = 12.83), and had made an average of 2.48 (SD = 2.41) serious quit attempts since starting smoking. Among smokers without asthma, the average expired carbon monoxide (CO) level was 21.94 ppm (SD = 11.27).

The average level of nicotine dependence, as indexed by the Fagerström Test for Nicotine

Dependence (FTND; Heatherton et al., 1991), was 5.50 (SD = 2.10); this reflects a medium level of overall nicotine dependence.

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; Bedfont Scientific USA, Williamsburg, VA). Research indicates that 10 ppm is an optimal cutoff score for reliably discriminating smoking status

(Corcores, 1993). Obtained values above this cutoff were considered indicative of smoking.

Asthma diagnosis and severity. To identify individuals with an asthma diagnosis, participants were first asked whether or not a physician has ever diagnosed them with asthma.

Those who endorsed an asthma diagnosis and who reported a current prescription for an

asthma-related medication and asthma-related symptoms within the past 12 months were considered to have 'current asthma.' This strategy has been successfully employed in previous research in our laboratory (McLeish, Zvolensky, & Luberto, 2011). Information was also gathered regarding age at diagnosis, number of hospitlizations, and recent symptoms and medication use.

Structured Clinical Interview for DSM-IV Axis I Disorders- Non-Patient Edition

(SCID-NP). The SCID-NP (First, Spitzer, Gibbon, & Williams, 1995) is a well-established diagnostic interview for psychiatric problems. The SCID-NP exhibits good internal reliability as well as good validity for anxiety and other psychiatric disorders (Kranzler, Kadden, Babor,

Tennen, & Rounsaville, 1996). A modified version of the original SCID-NP was used that included assessment of mood disorders (major depressive disorder and bipolar disorder I and II) and all anxiety disorders excluding specific phobia. The SCID-NP was used to provide descriptive information on the samples and to determine any group differences in terms of number of psychiatric diagnoses. The SCID-NP was administered to participants by trained interviewers (either a graduate- or an undergraduate-level research technician trained in administering the SCID-NP). Reliability ratings by an independent rater (ACM) were completed on a random selection of 10% of the protocols with only two cases of disagreement being noted.

These disagreements (one obsessive-compulsive disorder diagnosis and one social phobia diagnosis) were resolved via consensus after being reviewed by an additional independent rater

(CML).

Asthma Control Test (ACT). The ACT (Nathan et al., 2004) is a five-item self-report measure that assesses asthma control. The ACT measures frequency of symptoms (e.g., “How often have you had shortness of breath?”) and functional impairment due to symptoms (e.g.,

“How much of the time did your asthma keep you from getting as much done at work or at home?”) within the past 4 weeks. Scores range from 5-25 with higher scores indicating better control. Scores at or above 19 indicate that an individual’s asthma is well-controlled. The ACT

shows good reliability and is able to discriminate between groups of patients with different levels of asthma control (Nathan et al., 2004). The ACT was used for descriptive purposes only and was not employed in the analyses.

Alcohol Use Disorders Identification Test (AUDIT). The AUDIT is a 10-item screening measure developed by the World Health Organization to identify individuals with alcohol problems (Babor, de la Fuente, Saunders, & Grant, 1992). Most items are rated on a 5-point

Likert scale (0 = never to 4 = daily or almost daily). Scores range from 0-40 with a score of 8 indicating a likelihood of alcohol use problems. Major areas of problematic drinking that are assessed include: alcohol consumption, drinking behavior (dependence), adverse psychological reactions, and alcohol-related problems. The AUDIT has evidenced good reliability and validity among both college and clinical samples (Allen, Litten, Fertig, & Babor, 1997; Saunders et al.,

1993). Because of the significant association between alcohol use and smoking (Chen et al.,

2002; Epstein, Botvin, Baker, & Diaz, 1999; Koopmans, van Doornen, & Boomsma, 1997), alcohol consumption (frequency x quantity composite) was employed as a covariate in the regression analyses.

Smoking History Questionnaire (SHQ). Smoking history and pattern was assessed with the SHQ (Brown, Lejuez, Kahler, & Strong, 2002), which includes items pertaining to smoking rate, age of onset at initiation, and years of being a daily smoker. The SHQ has been successfully used in previous studies and has been identified as a psychometrically sound descriptive measure of smoking history (Zvolensky et al., 2005).

Fagerström Test for Nicotine Dependence (FTND). The FTND is a six-item scale designed to assess gradations in tobacco dependence (Heatherton, Kozlowski, Frecker, &

Fagerström, 1991). Scores range from 0-11 with higher scores suggesting greater levels of nicotine dependence. The FTND has shown good internal consistency (r = .61; Heatherton et al., 1991) as well as high degrees of test-retest reliability (Hudmon, Pomerleau, Brigham, Javitz,

& Swan, 2005; Pomerleau, Carton, Lutzke, Flessland, & Pomerleau, 1994).

Reasons for Smoking Questionnaire (RFS). The RFS is 23-item self-report measure that assesses smoking motives (Ikard et al., 1969). The RFS consists of six subscales: addictive

(e.g., “Between cigarettes, I get a craving only a cigarette can satisfy”), habitual (e.g., “I’ve found a cigarette in my mouth and didn’t remember putting it there”), pleasurable relaxation (e.g., “I find cigarettes pleasurable”), negative affect reduction (e.g., “When I feel uncomfortable or upset about something, I light up a cigarette”), stimulation (e.g., “I like smoking when I am busy and working hard”), and sensorimotor manipulation (e.g., “Part of the enjoyment of smoking a cigarette comes from the steps I take to light up”). Items are rated on a 5-point Likert-type scale

(1 = never to 5 = always) The RFS demonstrates good internal consistency and test-retest reliability (Ikard et al.; Shiffman, 1993). More recently, this measure’s psychometric properties have been examined using a college-aged sample and found to have moderate to high internal consistency with reliability coefficients ranging from .62-.91 (Fiala, D’Abundo, & Marinaro,

2010).

Reasons for Quitting Questionnaire (RFQ). The RFQ (Curry et al., 1990) is a 20-item self-report measure that assesses motivation to quit smoking. Participants were asked to indicate, on a 4-point Likert-type scale ranging from 1 (not at all true) to 4 (extremely true), the extent to which different reasons for quitting smoking apply to them. The RFQ assesses four domains of motivation to quit smoking, which fall along two broad domains: intrinsic and extrinsic. The dimensions that fall under intrinsic motivation to quit smoking are health concerns

(e.g., “I am concerned about illness”) and self-control (e.g., I want to show myself or others I can quit”). The remaining two subscales, immediate reinforcement (e.g., “I will save money on cigarettes”) and social influence (e.g., “I want people to stop nagging me”) are extrinsic reasons to quit smoking. The RFQ has demonstrated good internal consistency, adequate convergent and discriminant validity, and satisfactory predictive validity (Curry et al., 1990).

Barriers to Cessation Scale (BCS). The BCS (Macnee & Talsma, 1995) is a 19-item self-report measure that assesses barriers associated with smoking cessation. Items are rated

on a 4-point Likert-type scale (0 = not a barrier to 3 = large barrier) and respondents indicate the extent to which they identify with each barrier. Scores range from 0-57 with higher scores indicating greater perceived barriers to cessation. The BCS consists of three subscales: addiction barriers (e.g., thinking about smoking all the time), external barriers (e.g., lack of encouragement or support), and internal barriers (e.g., feeling less in control of moods). The

BCS has been found to have good psychometric properties (Macnee & Talsma, 1995). In the present study, the BCS total score was used as an index of general perceived barriers to cessation.

Procedure

First, participants provided informed, written consent. Next, participants’ smoking status was biochemically verified via expired CO analysis. Then, participants were administered a demographic interview to obtain information on key demographic variables and medical history as well as the SCID-NP to determine the presence of current or past psychiatric diagnoses.

Participants in the smokers with asthma group were also asked additional questions regarding their asthma diagnosis, current asthma medications, and asthma symptoms. Lastly, participants completed the following self-report measures: Asthma Control Test, Alcohol Use Disorders

Identification Test, Smoking History Questionnaire, Fagerström Test for Nicotine Dependence,

Reasons for Smoking Questionnaire, Reasons for Quitting Questionnaire, and Barriers to

Cessation Scale. Participants received $30 as compensation for their time and effort.

Results

Analytic Approach

Identification of covariates. Independent samples t-tests (or Chi-square tests as appropriate) were used to determine if there were any significant group differences in terms of demographic variables (i.e., gender, education, race, age) and other variables that might have influenced the results (i.e., number of psychiatric diagnoses, number of medical problems).

There were significant differences between smokers with and without asthma with regard to

gender [χ2(1) = 7.10, p < .01] and race [χ2(5) = 23.88, p < .01], with more males and fewer

African Americans in the smokers without asthma group. There were also significant group differences in terms of number of medical problems [t(227) = 6.42, p < .01], with smokers with asthma reporting significantly more comorbid medical problems compared to smokers without asthma (M = 2.5 and M = 1.18, respectively). The groups did not differ significantly in terms of age, level of education, or number of psychiatric diagnoses. Thus, gender, race, and number of medical problems were added as covariates in all subsequent analyses. Alcohol consumption was also identified as a covariate a priori due to the high co-occurrence of smoking and alcohol use (Chen et al., 2002; Epstein et al., 1999; Koopmans et al., 1997).

Correlation analyses. The first step to understand the nature of the data was to compute a series of conceptually-relevant zero-order correlations for all variables. Here, zero- order correlations were computed to assess the associations between the primary predictor variable (asthma diagnosis), the covariates, and the primary dependent variables.

Regression analyses. The main effect of asthma diagnosis for the primary dependent variables was evaluated using a hierarchical multiple regression procedure (Cohen & Cohen,

1983). For smoking behavior, separate models were constructed for predicting: (1) age at regular smoking onset; (2) daily smoking rate; (3) level of nicotine dependence; and (4) number of quit attempts. Gender, race, number of medical problems, and alcohol consumption were entered simultaneously as covariates at step one in the model. Asthma diagnosis was entered at the second step of the model. Similar models were constructed for predicting (1) smoking motives (using the Reasons for Smoking Questionnaire subscales: habitual, addictive, negative affect reduction, pleasurable relaxation, stimulation, and sensorimotor manipulation); (2) cessation motives (using the Reasons for Quitting Questionnaire subscales: health concerns, immediate reinforcement, self-control, and social influence); and (3) barriers to cessation (using the Barriers to Cessation Scale).

Bivariate Correlations among Covariates and Predictor Variable

Descriptive data for all study variables are presented in Table 2. The associations among covariates, predictor, and criterion variables are presented in Table 3. Asthma diagnosis was significantly correlated with gender (r = -.17, p < .01), race (r = -.20, p < .01), and number of medical problems (r = -.38, p < .01), but not alcohol consumption (r = -.06, p = .39). Gender was significantly associated with alcohol consumption (r = -.22, p < .01) and number of medical problems (r = -.21, p < .01), but not race (r = .08, p = .19). Race was not significantly correlated with number of medical problems (r = .07, p = .25) or alcohol consumption (r = -.03, p = .69).

Number of medical problems was significantly associated with alcohol consumption (r = -.14, p

< .05).

Bivariate Correlations among Covariates and Criterion Variables

Gender was significantly associated with all Reasons for Smoking subscales except stimulation and sensorimotor manipulation (range: .14 to .24). Number of medical problems was significantly associated with number of quit attempts (r = .16, p < .05) and Reasons for Quitting-

Health Concerns (r = .13, p < .05). Alcohol consumption was significantly correlated with

Reasons for Smoking- Pleasurable Relaxation (r = .13, p < .05), and Reasons for Smoking-

Sensorimotor Manipulation (r = .18, p < .01).

Bivariate Correlations among Predictor and Criterion Variables

Asthma diagnosis was significantly correlated with nicotine dependence (r = -.14, p <

.05), number of quit attempts (r = -.18, p < .01), Reasons for Quitting- Self-Control (r = -.14, p <

.05), and Reasons for Quitting- Social Influence (r = -.14, p < .05). Age at regular smoking onset was significantly associated with Reasons for Smoking- Habitual, Reasons for Smoking-

Addictive, and Reasons for Smoking- Negative Affect Reduction (range: -.21 to -.13). Cigarettes per day was significantly associated with nicotine dependence (r = .32, p < .01), Reasons for

Smoking- Habitual (r = .20, p < .01), Reasons for Smoking- Addictive (r = .18, p < .01), and

Barriers to Cessation (r = .18, p < .01). Nicotine dependence was significantly correlated with

age at regular smoking onset (r = -.33, p < .01), all Reasons for Smoking subscales except sensorimotor manipulation (range: .12 to .52), Barriers to Cessation (r = .30, p < .01), and

Reasons for Quitting- Immediate Reinforcement (r = .13, p < .01). Number of quit attempts was significantly associated with Barriers to Cessation (r = .14, p < .05), and all subscales except the immediate reinforcement subscale of the Reasons for Quitting Questionnaire (range: .10 to .28).

As expected, all subscales of the Reasons for Smoking Questionnaire were significantly associated with one another (range: .16 to .68) and all subscales of the Reasons for Quitting

Questionnaire were significantly associated with one another (range: .19 to .44). Reasons for

Quitting- Health Concerns was significantly associated with Reasons for Smoking- Habitual,

Reasons for Smoking- Addictive, and Reasons for Smoking- Negative Affect Reduction (range:

.17 to .28). Reasons for Quitting- Immediate Reinforcement was significantly associated with

Reasons for Smoking- Habitual, and Reasons for Smoking- Negative Affect Reduction, and

Reasons for Smoking- Stimulation (Range: .14 to .18). Reasons for Quitting- Self-Control was significantly associated with Reasons for Smoking- Habitual, and Reasons for Smoking-

Negative Affect Reduction, and Reasons for Smoking- Stimulation (Range: .17 to .28). Reasons for Quitting- Social Influence was significantly associated with all Reasons for Smoking subscales except Reasons for Smoking- Primary Reinforcement (Range: .14 to .24). Barriers to

Cessation was significantly associated with all Reasons for Smoking subscales and all Reasons for Quitting subscales (range: .22 to .50).

Hierarchical Multiple Regression Analyses

Smoking behavior. Data for the smoking behavior variables hierarchical regression analyses are presented in Table 4. In terms of age of regular smoking onset, the first step accounted for 3.7% of the variance. Gender (t = -1.99, β = -.13, p < .05) was the only significant predictor at step one. Step two of the model predicted 1.9% of unique variance and asthma diagnosis was a significant predictor (t = 2.21, β = .15, p < .05).

For daily smoking rate, the first step accounted for 2.7% of the variance. There were no significant predictors at step one. Step two of the model accounted for 1.5% of unique variance and asthma diagnosis was not a significant predictor; however, it did approach significance (t = -

1.85, β = -.13, p = .065).

With regard to nicotine dependence, the first step accounted for 4.2% of the variance.

Race (t = -2.51, β = -.16, p < .05) was the only significant predictor at step one. Step two of the model accounted for 1.5% of unique variance and asthma diagnosis was not a significant predictor; however, it did approach significance (t = -1.91, β = -.14, p = .057).

In terms of number of quit attempts, the first step accounted for 3.9% of the variance.

Number of medical problems (t = 2.05, β = .13, p < .05) was the only significant predictor at step one. Step two of the model predicted 2.5% of unique variance and asthma diagnosis was a significant predictor (t = -2.52, β = -.18, p < .05).

Reasons for smoking. Data for the Reasons for Smoking Questionnaire subscales hierarchical regression analyses are presented in Table 5. For the habitual subscale, the first step accounted for 5.4% of the variance. Gender (t = 2.65, β = .17, p < .01) and race (t = -2.42,

β = -.15, p < .05) were the only significant predictors at step one. Step two of the model accounted for 0.6% of unique variance and asthma diagnosis was not a significant predictor.

In terms of the addiction subscale, the first step accounted for 6.0% of the variance.

Gender (t = 3.45, β = .23, p < .01) was the only significant predictor at step one. Step two of the model accounted for 0% of unique variance and asthma diagnosis was not a significant predictor.

In terms of the negative affect reduction subscale, the first step accounted for 7.8% of the variance. Gender (t = 4.31, β = .28, p < .01) was the only significant predictor at step one.

Step two of the model accounted for 0.2% of unique variance and asthma diagnosis was not a significant predictor.

For the pleasurable relaxation subscale, the first step accounted for 4.6% of the variance. Gender (t = 2.83, β = .19, p < .01) and alcohol consumption (t = 2.25, β = .15, p < .05) were the only significant predictors at step one. Step two of the model accounted for 1.1% of unique variance and asthma diagnosis was not a significant predictor.

With regard to the stimulation subscale, the first step accounted for 2.0% of the variance.

There were no significant predictors at step one. Step two of the model accounted for 0.1% of unique variance and asthma diagnosis was not a significant predictor.

In terms of the sensorimotor manipulation subscale, the first step accounted for 3.1% of the variance. Alcohol consumption (t = 2.29, β = .15, p < .05) was the only significant predictor at step one. Step two of the model accounted for 0.2% of unique variance and asthma diagnosis was not a significant predictor.

Reasons for quitting. Data for the Reasons for Quitting Questionnaire subscales hierarchical regression analyses are presented in Table 6. For the health concerns subscale, the first step accounted for 6.0% of the variance. Race (t = -3.25, β = -.21, p < .01) was the only significant predictor at step one. Step two of the model accounted for 0% of unique variance and asthma diagnosis was not a significant predictor.

With regard to the self-control subscale, the first step accounted for 2.5% of the variance. There were no significant predictors at step one. Step two of the model accounted for

2.6% of unique variance and asthma diagnosis (t = -2.54, β = -.18, p < .05) was a significant predictor.

In terms of the immediate reinforcement subscale, the first step accounted for 0.4% of the variance. There were no significant predictors at step one. Step two of the model accounted for 0.2% of unique variance and asthma diagnosis was not a significant predictor.

For the social influence subscale, the first step accounted for 4.8% of the variance.

Gender (t = 2.94, β = .20, p < .01) was the only significant predictor at step one. Step two of the

model accounted for 1.0% of unique variance and asthma diagnosis was not a significant predictor.

Barriers to cessation. Data for the Barriers to Cessation Scale hierarchical regression analyses are presented in Table 6. With regard to barriers to cessation, the first step accounted for 3.0% of the variance. There were no significant predictors at step one. Step two of the model accounted for

0.2% of unique variance and asthma diagnosis was not a significant predictor.

Discussion There has been a growing level of clinical and research interest in examining associations between cigarette smoking and asthma. This interest is driven by the fact that individuals with asthma are more likely to smoke than those without asthma as well as the significant negative health effects of smoking, particularly among individuals whose lungs are already compromised (McLeish & Zvolensky, 2010). Despite this interest, however, currently no overarching theory exists that explains why this asthma-smoking association occurs. In order to inform such a theory, the present study examined the effect of having a diagnosis of asthma on smoking behaviors (i.e., smoking rate, nicotine dependence, number of quit attempts, age at regular smoking onset) and smoking-related cognitive processes (i.e., reasons for smoking, reasons for quitting, barriers to cessation) among adult daily smokers.

Asthma Diagnosis and Smoking Behavior

In terms of smoking behavior, the results partially supported our hypotheses. As hypothesized, asthma was a significant predictor of number of quit attempts, accounting for

2.5% of unique variance. Contrary to prediction, asthma diagnosis was also a significant predictor of age at regular smoking onset, accounting for 1.9% of unique variance. These significant effects were above and beyond the variance accounted for by the covariates of gender, race, number of medical problems, and alcohol consumption. Contrary to prediction, asthma was not a significant predictor of daily smoking rate or level of nicotine dependence. It

should be noted, however, that the effect of asthma diagnosis on both daily smoking rate (1.5% unique variance, p = .065) and nicotine dependence (1.5% unique variance, p = .057) approached significance.

The current study extends previous smoking-asthma work in adolescents to an adult sample. Similar to the findings of Wakefield and colleagues (1995), but in contrast to findings of

Avila and colleagues (2005), asthma was not associated with daily smoking rate; however, there was a non-significant trend for asthma diagnosis predicting greater daily smoking rate and nicotine dependence. In addition to being more likely to smoke in general, individuals with asthma may be at risk for smoking at heavier rates and, as a result, greater nicotine dependence.

In contrast to findings among adolescents (Zimmerman et al., 2004), in the current study, a diagnosis of asthma was associated with age of regular smoking onset. One possible explanation for these discrepant findings is that Zimmerman and colleagues used a sample of adolescents while the current sample was a sample of adults. Thus, participants in the

Zimmerman et al. study may have been able to more accurately report when they became a regular smoker as it had occurred more recently than in our sample. Thus, our findings may be due to inaccurate recall rather than a true association between asthma diagnosis and smoking onset. If so, these results suggest that smoking prevention programs may need to start earlier in order to target children with asthma before they initiate smoking. Alternatively, the participants in the Zimmerman et al. study were nicotine dependent, which is relatively uncommon in adolescent smokers (Colby, Tiffany, Shiffman, & Niaura, 2000). Thus, the sample in this study may not have been representative of most adolescent smokers with asthma.

The finding that asthma was a significant predictor of a greater number of quit attempts is also contrary to previous work among adolescent samples (Zimmerman et al., 2004). It is possible that as they get older, smokers with asthma may be experiencing, not only greater difficulties achieving and maintaining asthma control, but also greater overall negative health

effects from smoking than smokers without asthma. As a result, they may become more concerned about their overall health status and make more attempts to quit smoking. While certainly plausible, in the current study, asthma diagnosis was not associated with cessation motives due to health concerns. Alternatively, they may be getting advice to stop smoking every time they see their healthcare provider, resulting in more cessation attempts.

Unfortunately, despite the greater number of quit attempts, smokers with asthma are not only still smoking, but may be smoking at higher rates than smokers without asthma. Thus, while smokers with asthma appear to have the motivation to quit smoking, as evidenced by a greater number of quit attempts, they do not seem to be able to quit successfully. It may be that current cessation treatments are not as effective in this population and more targeted interventions need to be developed. Further, given the trend towards greater smoking rates and increased levels of nicotine dependence, it may be that their difficulties stem from more severe withdrawal symptoms. Thus, use of nicotine replacement therapy would be a critical component in any cessation intervention in this population.

Asthma Diagnosis and Smoking-Related Cognitive Processes

In terms of smoking-related cognitive processes, the results did not support our hypotheses. That is, asthma diagnosis did not predict unique variance in terms of addiction or habitual motives for smoking, motives for quitting related to health concerns or social influence, or greater perceived barriers to cessation. Further, contrary to prediction, asthma diagnosis was a significant predictor of self-control reasons for quitting, accounting for 2.6% of unique variance beyond the effects of gender, race, number of medical problems, and alcohol consumption.

Overall, these findings suggest asthma diagnosis is not associated with specific smoking motives or perceived barriers to cessation. These findings are contrary to the previous examination of smoking motives in an adolescent sample, which found that adolescents were more likely to smoke as a form of weight control and less likely to smoke for social reasons

(Precht et al., 2006). The current study, however, utilized a measure of smoking motives that did

not assess the exact same motives as in the Precht et al. study, thus a direct comparison of the results is not possible. Regardless, the pattern of results for the current study suggests that smoking motives may change from adolescence to adulthood. It may be that the as duration of smoking increases, differences in reasons for smoking decrease such that all smokers eventually are smoking for similar reasons.

In terms of smoking cessation motives, our hypotheses also were not supported. Asthma diagnosis was associated with an intrinsic reason for quitting; however, it was quitting for self- control reasons rather than quitting due to health concerns, as originally hypothesized.

Logically, one would expect that because of their already compromised lung functioning and the improvements in lung functioning that can be seen as early as 24 hours after cessation

(Fennerty et al., 1987), that health concerns would be the primary motivation for quitting smoking for this population. Indeed, Wakefield and colleagues (1995) found that smokers with asthma were twice as likely as smokers without asthma to report that they believed their health was worse due to smoking and that their health in the future would be worse due to smoking. It may be that smokers with asthma believe that their health is already compromised due to asthma and do not see their health improving substantially with smoking cessation. These findings suggest that current smoking cessation interventions for smokers with asthma may need to be tailored to place greater focus on the negative health consequences of continued smoking and the health benefits of quitting that are specific to smokers with asthma.

Although not associated with motivation to quit because of health reasons, asthma diagnosis was associated with quitting for self-control reasons. That is, smokers with asthma were more likely to endorse that one of their reasons for quitting was to prove to themselves and others that they could quit successfully. It may be the case that because smokers with asthma are making more unsuccessful quit attempts, their primary motivation for quitting is to prove that they can successfully quit after all of their previous failed attempts. Thus, smoking cessation interventions for smokers with asthma might benefit from focusing on this reason for

quitting to increase motivation to make a quit attempt. These results also indicate that physicians should continue to recommend smoking cessation to smokers with asthma at each visit as these smokers may be motivated to quit to prove to their doctor they can do so successfully.

Asthma also was not associated with perceived barriers to cessation; smokers with asthma expect and experience similar difficulties when quitting smoking as smokers without asthma. This finding is somewhat unexpected given that asthma was associated with a greater number of quit attempts. If smokers with asthma are making more quit attempts and are ultimately unsuccessful, as they are still smoking, one would expect that they would perceive a greater number of barriers to quitting given this lack of success. One possible explanation for this finding is that the assessment of barriers to cessation, the Barriers to Cessation Scale

(BCS), was inadequate; smokers with asthma may experience unique barriers to cessation, because of their asthma diagnosis, that are not included in the BCS. Further, the BCS contains only very general items related to nicotine withdrawal (“withdrawal symptoms”, “being addicted to cigarettes”) and no items related to specific physical withdrawal symptoms. Given the results described above of a trend towards an association between nicotine dependence and asthma diagnosis and extant research indicating that one of the reasons for relapsing to smoking during a quit attempt is due to withdrawal symptoms (Fennerty, Banks, Ebden, & Bevan, 1987), it would be useful for future studies to include a more detailed assessment of addiction- or withdrawal-related barriers to cessation.

Limitations and Future Directions

Although the present study adds to extant literature on smoking and asthma in a unique manner, there are a number of interpretive caveats that deserve further comment. First, asthma diagnosis was based on self-report and was not verified or corroborated by objective measures.

Although efforts were made to verify asthma diagnosis using proxy indicators (i.e., prescriptions for asthma medications, experiencing symptoms in the past year), it is possible that our sample

consisted of some individuals who did not truly have asthma. Similarly, individuals with COPD were not excluded from the current study. Thus, it is unclear whether some participants had

COPD only or COPD and asthma. There are likely differences in smoking behavior and smoking-related cognitions among those with asthma only compared to those with COPD only.

Therefore, future studies should utilize objective measures of lung functioning (e.g., spirometry) and physician exam to verify asthma diagnosis and exclude individuals with COPD. However, only 12.8% of smokers with asthma in the current study endorsed having another chronic lung disease, attenuating concerns about COPD to some extent.

Along these same lines, no distinction was made between smokers with asthma who were diagnosed with asthma first and started smoking later and those who started smoking first and were diagnosed with asthma later. 57% of the smokers with asthma in the current study reported having an asthma diagnosis prior to the onset of smoking, while 41% began smoking first and received an asthma diagnosis later. It is unclear whether these individuals who started smoking first had asthma prior to smoking onset that had not yet been diagnosed or whether they later developed asthma, likely due to their smoking. Future research efforts should explore these associations among individuals with asthma who “chose” to start smoking despite being diagnosed with a chronic lung disease. It is this subset of smokers with asthma who likely differ from smokers in general in terms of smoking behavior and smoking-related cognitions.

Understanding the characteristics of this subgroup would be an important step in developing appropriate prevention programs to prevent smoking initiation among individuals with asthma.

Such an approach would be particularly prudent in light of the current findings that once they begin smoking, individuals with asthma have more difficulty quitting.

Third, the sample, by design, consisted of regular daily smokers who smoked more than

10 cigarettes per day, limiting the generalizability of the results to individuals who smoke less than that or do not smoke on a daily basis. Both asthma and cigarette smoking are more common among African Americans compared to Caucasians (ALA, 2010e; NHLBI, 1999).

Interestingly, while African American smokers tend to smoke fewer cigarettes than their

Caucasian counterparts (Royce, Hymowitz, Corbett, Hartwell, & Orlandi, 1993), there is some indication that nicotine is metabolized differently among African Americans making it more harmful at lower doses (Perez-Stable, Herrera, Jacob, & Benowitz, 1998). Future work will need to more closely examine these racial differences as well as differences in smoking processes among individuals with various patterns of smoking behavior.

Fourth, self-report measures were utilized as the primary assessment methodology for many of the key constructs. The utilization of self-report methods does not fully protect against reporting errors and may be influenced by shared method variance. Thus, future studies could build on the present work by utilizing a multi-method assessment approach to address this concern. Specifically, the use of laboratory-based assessments or ecological momentary assessment would provide information about smoking behavior in “real time.” Fifth, the present cross-sectional design does not permit causal-oriented hypothesis testing. Future studies examining how smoking behavior and processes changes over time are warranted as well as prospective studies of quit attempts to begin to examine why smokers with asthma have such difficulty quitting smoking.

Lastly, while there were some significant associations between asthma and smoking behavior and smoking-related cognitive processes, asthma diagnosis only accounted for a small amount of overall variance in these variables. Therefore it is likely that there are other key factors involved in the association between smoking and asthma. Other than comparing the two smoking groups on psychiatric diagnoses, psychological symptoms and psychopathology was largely ignored. However, there is a large body of research indicating that psychiatric symptoms and disorders, in particular anxiety symptoms and disorders, are associated with smoking (e.g.,

Lasser et al., 2000; Morissette, Tull, Gulliver, Kamholz, & Zimering, 2007). One promising line of inquiry has focused on exploring the role of smoking in the severity, onset, and maintenance of panic psychopathology. This corpus of work has indicated that daily smoking is associated with

future risk of developing panic-spectrum problems (panic attacks, panic disorder, and agoraphobia) as well as more severe concurrent expressions of such problems (Zvolensky,

Feldner, Leen-Feldner, & McLeish, 2005). In support of this line of thinking, while there were no differences between smokers with and without asthma in number of psychiatric diagnoses in the current study, smokers with asthma were more likely to experience panic attacks than smokers without asthma. One cognitive risk factor that has been found to moderate this smoking-panic association is anxiety sensitivity (e.g., McLeish, Zvolensky, & Bucossi, 2007; Zvolensky, Kotov,

Bonn-Miller, Schmidt, & Antipova, 2007), a trait-like cognitive characteristic defined as the fear of arousal-related physical and psychological sensations (McNally, 2002; Reiss & McNally,

1985). Anxiety sensitivity is also associated with panic in response to symptoms of asthma as well as poorer asthma control (Carr, Lehrer, Rausch, & Hochron, 1994; McLeish, Zvolensky, &

Luberto, 2011). Thus, some of the association between asthma and smoking may be due to anxiety sensitivity and further research is needed to examine the role of anxiety-related cognitive vulnerability factors in this association.

Conclusions

Taken together, the present findings suggest that, among smokers, a diagnosis of asthma significantly predicts becoming a regular smoker at an earlier age, a greater number of quit attempts, motives for quitting related to self-control, and possibly greater daily smoking rate and nicotine dependence. The primary implication of the present findings is that while there are few differences between smokers with and without asthma in terms of smoking behaviors and smoking-related cognitive processes, the differences that do exist suggest that current smoking cessation interventions may not be as effective for smokers with asthma and targeted, specialized interventions need to be developed. Further, since there is currently no theory regarding the association between asthma and smoking, the findings from the current study provide important novel information regarding this association and thus, bridges a critical gap in the literature. The development of a theory that explains this association will enable treatment

and prevention interventions to be tailored to this at-risk population. Future research should continue to focus on attempting to understand this association in order to inform such a theory.

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Table 1. Asthma Severity Classification (National Institutes of Health, National Asthma Education Program, 2007, p. 43)

Intermittent Mild Persistent Moderate Persistent Severe Persistent Symptom > 2 days/week, Throughout the < 2 days/week Daily Frequency < daily day

Interference with extreme none minor limitations some limitations normal activity limitations

Nighttime > 1x/week, < 2x/month 3-4x/month Nightly awakenings < nightly

> 2 days/week, Short-acting < 2 days/week < daily, not Daily Several times/day beta -agonist use 2 >1x/day

· Normal FEV1 between attacks • FEV1 > 80% • FEV > 60%, predicted • FEV > 80% 1 • FEV < 60% 1 but < 80% 1 • FEV1/FVC predicted predicted Lung functioning predicted normal • FEV /FVC • FEV /FVC 1 • FEV /FVC 1 normal 1 reduced > 5% reduced 5%

Note: FEV1 = forced expiratory volume in one second; FVC = forced vital capacity; Normal FEV1/FVC for ages 8-19 yrs = 85%, 20-39 yrs = 80%, 40-59 yrs = 75%, 60-80 yrs = 70%

Table 2. Descriptive Data for Covariates, Predictor, and Criterion Variables Variable Range M SD Asthma - - - Gender - - - Race - - - Medical Problems 0-8 1.84 1.74 Alcohol 0-20 4.07 4.77 Onset Age 8-48 17.00 4.72 CPD 1-81 19.57 10.85 FTND 0-10 5.79 2.17 Quit Attempts 0-18 3.00 2.91 RFS-HA 1-5 2.29 .83 RFS-AD 1-5 3.33 .89 RFS-NA 1-5 3.43 .92 RFS-PR 1-5 3.83 .97 RFS-ST 1-5 2.48 1.04 RFS-SM 1-5 2.29 1.02 RFQ-HC 1-4 2.81 .82 RFQ-IR 1-4 3.07 .73 RFQ-SC 1-4 2.94 .81 RFQ-SI 1-4 1.91 .81 BCS 0-57 27.00 11.90 Note. Asthma Diagnosis: 1 = yes, 2 = no; Gender: 1 = male, 2 = female; Race: 1 = Caucasian, 2 = African American, 3 = Asian, 4 = Other; Medical Problems: number of medical problems; Alcohol: alcohol consumption frequency x quantity composite from the Alcohol Use Disorders Identification Test (AUDIT; Babor, de la Fuente, Saunders, & Grant, 1992); Onset Age: age at regular smoking onset (Smoking History Questionnaire; Brown et al., 2002); CPD: cigarettes per day (Smoking History Questionnaire; Brown et al., 2002); FTND: Fagerström Test for Nicotine Dependence (Heatherton et al.,1991); Quit Attempts: number of quit attempts (Smoking History Questionnaire; Brown et al., 2002); RFS-HA: Reasons for Smoking Questionnaire-Habitual subscale (Ikard et al., 1969); RFS-AD: Reasons for Smoking Questionnaire-Addictive subscale (Ikard et al., 1969); RFS-NA: Reasons for Smoking Questionnaire-Negative Affect Reduction subscale (Ikard et al., 1969); RFS-PR: Reasons for Smoking Questionnaire- Pleasurable Relaxation subscale (Ikard et al., 1969); RFS-ST: Reasons for Smoking Questionnaire-Stimulation subscale (Ikard et al., 1969); RFS-SM: Reasons for Smoking Questionnaire-Sensorimotor Manipulation subscale (Ikard et al., 1969); RQS-HC: Reasons for Quitting Questionnaire-Health Concerns subscale (Curry et al., 1990); RQS-IR: Reasons for Quitting Questionnaire- Immediate Reinforcement subscale (Curry et al., 1990); RQS-SC: Reasons for Quitting Questionnaire-Self-Control subscale (Curry et al., 1990); RQS-SI: Reasons for Quitting Questionnaire-Social Influence subscale (Curry et al., 1990); BCS: Barriers to Cessation Scale (Macnee & Talsma, 1995).

Table 4. Asthma Diagnosis Predicting Smoking Behavior

ΔR2 t (each predictor) β sr2 p Criterion Variable: Age at Regular Smoking Onset Step 1 .04 ns Gender -1.99 -.13 .02 <.05 Race 1.58 .10 .01 ns Medical Problems 1.06 .07 .00 ns Alcohol Consumption -1.74 -.11 .01 ns Step 2 .02 <.05 Asthma Diagnosis 2.21 .15 .02 <.05

Criterion Variable: Daily Smoking Rate Step1 .02 ns Gender -1.04 -.07 .00 ns Race -1.71 -.11 .01 ns Medical Problems .37 .03 .00 ns Alcohol Consumption -1.14 -.08 .01 ns Step 2 .01 ns Asthma Diagnosis -1.17 -.08 .01 ns

Criterion Variable: Nicotine Dependence Step1 .04 <.05 Gender 1.35 .09 .01 ns Race -2.51 -.16 .03 <.05 Medical Problems 1.29 .09 .01 ns Alcohol Consumption -.22 -.02 .00 ns Step 2 .02 ns Asthma Diagnosis -1.91 -.14 .02 ns

Criterion Variable: Number of Quit Attempts Step1 .04 ns Gender .08 .01 .00 ns Race 1.09 .07 .00 ns Medical Problems 2.05 .13 .04 <.05 Alcohol Consumption -1.49 -.10 .01 ns Step 2 .03 <.05 Asthma Diagnosis -2.52 -.18 .03 <.05 Note. β = standardized beta weight; sr2 = squared semi-partial correlation

Table 5. Asthma Diagnosis Predicting Reasons for Smoking (RFS)

ΔR2 t (each predictor) β sr2 p Criterion Variable: RFS-Habitual Step1 .05 <.05 Gender 2.65 .17 .03 <.01 Race -2.42 -.15 .02 <.05 Medical Problems .59 .04 .00 ns Alcohol Consumption 1.35 .09 .01 ns Step 2 .01 ns Asthma Diagnosis -1.25 -.09 .01 ns

Criterion Variable: RFS-Addiction Step1 .06 <.01 Gender 3.45 .23 .05 <.01 Race -1.91 -.12 .01 ns Medical Problems -.28 -.02 .00 ns Alcohol Consumption .16 .01 .00 ns Step 2 .00 ns Asthma Diagnosis .15 .01 .00 ns

Criterion Variable: RFS-Negative Affect Reduction Step1 .08 <.01 Gender 4.31 .28 .07 <.01 Race -1.39 -.09 .01 ns Medical Problems -.89 -.06 .00 ns Alcohol Consumption .82 .05 .00 ns Step 2 .00 ns Asthma Diagnosis .62 .04 .00 ns

Criterion Variable: RFS-Pleasurable Relaxation Step 1 .05 <.05 Gender 2.83 .19 .03 <.01 Race .22 .01 .00 ns Medical Problems -.86 -.06 .00 ns Alcohol Consumption 2.25 .15 .02 <.05 Step 2 .01 ns Asthma Diagnosis 1.63 .11 .01 ns

Criterion Variable: RFS-Stimulation Step 1 .02 ns Gender 1.58 .11 .01 ns Race .05 .00 .00 ns Medical Problems -1.39 -.09 .01 ns Alcohol Consumption 1.11 .07 .01 ns Step 2 .00 ns Asthma Diagnosis -.39 -.03 .00 ns

Criterion Variable: RFS-Sensorimotor Manipulation Step 1 .03 ns Gender -.13 -.01 .00 ns Race 1.47 .09 .01 ns Medical Problems .38 .03 .00 ns Alcohol Consumption 2.29 .15 .02 <.05 Step 2 .00 ns Asthma Diagnosis -.68 -.05 .00 ns Note. β = standardized beta weight; sr2 = squared semi-partial correlation

Table 6. Asthma Diagnosis Predicting Reasons for Quitting (RFQ) and Barriers to Cessation

ΔR2 t (each predictor) β sr2 p Criterion Variable: RFQ-Health Concerns Step1 .06 <.01 Gender 1.34 .09 .01 ns Race -3.25 -.21 .04 <.01 Medical Problems 1.45 .10 .01 ns Alcohol Consumption -.50 -.03 .00 ns Step 2 .00 ns Asthma Diagnosis .10 .01 .00 ns

Criterion Variable: RFQ-Self Control Step1 .03 ns Gender 1.82 .12 .01 ns Race -.47 -.03 .00 ns Medical Problems -.82 -.05 .00 ns Alcohol Consumption -1.20 -.08 .01 ns Step 2 .03 <.05 Asthma Diagnosis -2.54 -.18 .03 <.05

Criterion Variable: RFQ-Immediate Reinforcement Step 1 .00 ns Gender .59 .04 .00 ns Race .47 .03 .00 ns Medical Problems .28 .02 .00 ns Alcohol Consumption -.04 -.00 .00 ns Step 2 .00 ns Asthma Diagnosis -.68 -.05 .00 ns

Criterion Variable: RFQ-Social Influence Step 1 .05 <.05 Gender 2.94 .20 .04 <.01 Race 1.33 .09 .01 ns Medical Problems .30 .02 .00 ns Alcohol Consumption .87 .06 .00 ns Step 2 .01 ns Asthma Diagnosis -1.53 -.11 .01 ns Criterion Variable: Barriers to Cessation Step1 .03 ns Gender 1.56 .11 .01 ns Race -1.11 -.07 .01 ns Medical Problems 1.50 .10 .01 ns Alcohol Consumption 1.29 .09 .01 ns Step 2 .00 ns Asthma Diagnosis -.62 -.05 .00 ns Note. β = standardized beta weight; sr2 = squared semi-partial correlation