Asthma and its relation to smoking behavior and cessation motives among adult daily smokers

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 asthma. The purpose of this study was to examine the predictive ability of asthma diagnosis in terms of smoking behavior and reasons for quitting. Participants were 251 regular daily smokers: 125 smokers with self-reported, physician-diagnosed asthma and 126 smokers without asthma. Asthma diagnosis significantly predicted age of regular smoking onset, number of quit attempts, and reasons for quitting related to self-control suggesting that smokers with asthma may have more difficulty quitting and unique reasons for quitting.

Keywords

asthma beliefs nicotine smoking smoking cessation

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 (American Lung Association, 2010). Despite the known compromising effects of smoking on lung functioning and health, smoking is more prevalent among individuals with asthma compared to those without asthma (Frank et al., 2006; Gwynn, 2004). Furthermore, individuals with asthma are more likely to be regular smokers (>4 days per week) and to be heavier smokers (>10 cigarettes per day) than those without asthma (Avila et al., 2005).

Research also has consistently found that smoking results in significantly poorer asthma control (McCoy et al., 2006; McLeish and Zvolensky, 2010; Schatz et al., 2006). Indeed, compared to nonsmokers with asthma, smokers with asthma experience increased levels of daily asthma symptoms (Boulet et al., 2008), greater symptom severity for a variety of asthma symptoms (e.g. wheezing, nighttime awakenings; Althuis et al., 1999), and greater interference with daily activities due to asthma (Beeh et al., 2001; Boulet et al., 2008). Moreover, smoking decreases the effectiveness of corticosteroids, a common medication used in the treatment of asthma (Chaudhuri et al., 2003).

Although smoking is more common among individuals with asthma and associated with poorer asthma control, relatively little is known about the role of asthma in terms of smoking behavior and reasons for quitting smoking among this population. To our knowledge, only three studies have directly examined these issues. First, Wakefield et al. (1995) examined smoking behaviors and beliefs among Australian adult smokers with and without a self-reported diagnosis of asthma. Results indicated that smokers with and without asthma did not differ in terms of daily smoking rate, readiness to quit, or likelihood of making a quit attempt. However, smokers with asthma were twice as likely to report believing that their health was worse due to smoking and that their health in the future would be worse due to smoking.

In another study, Zimmerman et al. (2004) compared smoking topography among adolescents with and without a self-reported asthma diagnosis requesting smoking cessation treatment. Results indicated that there were 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. Finally, Van Zundert et al. (2008) evaluated readiness to quit smoking among adolescent regular daily smokers with and without a self-reported physician diagnosis of asthma and found no group differences in terms of pros of both smoking and quitting, nicotine dependence, self-efficacy to resist smoking, and craving. However, adolescent smokers with asthma reported greater readiness to quit smoking, particularly among those who perceived greater pros of quitting.

Although promising, there are a number of limitations to extant research. First, only one study has examined asthma–smoking interrelations among adults (Wakefield et al., 1995). Thus, empirical knowledge about asthma–smoking behavior relations is highly limited among adults. Second, previous work has thus far explored only a relatively limited range of smoking behaviors (e.g. smoking rate). It would therefore be advisable to further examine the role of asthma in terms of a broader range of smoking variables among adult smokers (e.g. reasons for quitting). Together, the overarching purpose of the present investigation was to uniquely extend past work on asthma and smoking by examining the role of asthma in terms of numerous aspects of smoking behavior and factors related to smoking cessation (i.e. age at regular smoking onset, daily smoking rate, level of nicotine dependence, number of quit attempts, smoking cessation motives) among adult daily smokers. It was hypothesized that, after controlling for any variables on which the two participant groups differ (e.g. gender, race, number of medical problems, number of psychiatric diagnoses, alcohol consumption), having a diagnosis of asthma would be predictive of number of quit attempts and reasons for quitting related to health concerns.

Method

Participants

The study sample consisted of 251 regular daily smokers between the ages of 18 and 65 recruited from the greater Cincinnati, Ohio community: 125 smokers with self-reported, physician-diagnosed asthma and 126 smokers without a history of 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 (CO) levels greater than 10 parts per million (ppm), and (3) regular, daily smoking for at least 1 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 the following: (1) received a physician diagnosis of asthma, (2) a current prescription for asthma medication, and (3) experienced asthma symptoms within the past year. On average, smokers with asthma were 17.46 (standard deviation (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 percent of smokers with asthma reported troubling asthma symptoms most or all days/nights during the previous 2 weeks and a mean Asthma Control Test (ACT; Nathan et al., 2004) score of 15.48 (SD = 4.21), indicating difficulties with asthma control. Please see Table 1 for additional demographic information.

Table 1.

Additional demographic information for smokers with and without asthma.

	Smokers with asthma	Smokers without asthma
Gender (%, male)	54	70.4
Age (M (SD))	37.7 (12.1)	36.5 (13.1)
Race (%)
Caucasian	41.5	69.8
African American	54.5	26.2
Asian	1.6	0
Other	2.4	4
Education (%)
< High school diploma	24	14.3
High school diploma	36.8	31.7
Some college	28.8	32.5
2-year college degree	3.2	11.1
4-year college degree	4.8	7.9
Some graduate level	2.4	2.4
Medical problems (%)
Seasonal allergies	63.2	20.6
Back or neck problems	41.6	24.6
High blood pressure	30.4	12.7
Arthritis/rheumatism	23.2	15.1
Frequent or severe headaches	20.8	10.3
Chronic pain	17.7	12.7
Chronic lung disease (other than asthma)	12.8	2.4
Ulcers	8.8	3.2
Heart disease	8.1	.8
Diabetes	6.4	3.2
Kidney problems	4.8	4.8
Heart attack	4	.8
Cancer	4	3.2
Stroke	1.6	.8
Epilepsy or seizures	1.6	2.4
HIV/AIDS	.8	.8

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, USA). 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 regular smoking.

Asthma diagnosis and severity

To identify individuals with an asthma diagnosis, participants were first asked whether a physician has ever diagnosed them with asthma. Those who endorsed (1) an asthma diagnosis, (2) having a current prescription for an asthma-related medication, and (3) experiencing 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 et al., 2011). Information was also gathered regarding age at diagnosis, number of hospitalizations, and recent symptoms and medication use.

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. The ACT shows good reliability and is able to discriminate between groups of patients with different levels of asthma control (Nathan et al., 2004). Internal consistency for the ACT in the current sample was acceptable (α = .78).

Smoking History Questionnaire (SHQ)

Smoking history and pattern was assessed with the SHQ (Brown et al., 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).

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). Internal consistency for the health concerns, self-control, and social influence subscales in the current sample were acceptable (range: .74–.80). Internal consistency for the immediate reinforcement subscale, however, was marginally questionable (Cronbach’s α = .67).

Fagerström Test for Nicotine Dependence (FTND)

The FTND is a six-item scale designed to assess gradations in tobacco dependence (Heatherton et al., 1991). The FTND has shown good internal consistency, positive relations with key smoking variables (e.g. cotinine; Heatherton et al., 1991; Payne et al., 1994) as well as high degrees of test–retest reliability (Pomerleau et al., 1994). Internal consistency for the FTND in this study was somewhat questionable (Cronbach’s α = .62).

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 et al., 1992). There is a large body of literature indicating that the AUDIT has strong and well-established psychometric properties (Saunders et al., 1993). Internal consistency for the AUDIT in this study was excellent (Cronbach’s α = .91).

Procedure

Participants responding to community-based advertisements for a research study focused on smoking were scheduled for an individual appointment by a trained research assistant. After providing informed, written consent, participants’ smoking status was biochemically verified via expired CO analysis. Participants were then administered a demographic and medical history interview, and participants in the smokers-with-asthma group were also asked additional questions regarding their asthma. Finally, participants completed a packet of self-report measures. Participants received $30 as compensation for their time and effort. The Institutional Review Board approved all study procedures and materials prior to data collection.

Analytic approach

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 also were significant group differences in terms of number of medical problems (t(227) = 6.42, p < .01), with smokers with asthma reporting significantly more co-occurring 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. Because of the significant association between alcohol use and smoking (Chen et al., 2002; Epstein et al., 1999; Koopmans et al., 1997), group differences in alcohol consumption were examined; however, no significant group differences were found. Thus, gender, race, and number of medical problems were added as covariates in all subsequent analyses. The main effect of asthma diagnosis for the primary dependent variables was evaluated using a hierarchical multiple regression procedure (Cohen and Cohen, 1983). Separate models were constructed for predicting age at regular smoking onset, daily smoking rate, level of nicotine dependence, number of previous quit attempts, and the four reasons for quitting subscales (i.e. health concerns, self-control, immediate reinforcement, social influence). Gender, race, and number of medical problems were entered simultaneously as covariates at step 1 in the model. Asthma diagnosis was entered at the second step of the model.

Results

Descriptive smoking behavior data

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 years (SD = 4.21), 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 CO level was 21.39 ppm (SD = 10.28). The average level of nicotine dependence, as indexed by the FTND, was 6.08 (SD = 2.2); this score reflects a high level of overall nicotine dependence.

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 years (SD = 5.14), 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 CO level was 21.94 ppm (SD = 11.27). The average level of nicotine dependence, as indexed by the FTND (Heatherton et al., 1991), was 5.50 (SD = 2.10); this score reflects a moderate level of overall nicotine dependence.

Zero-order associations

See Table 2 for descriptive data and associations among covariates, predictor, and criterion variables. Asthma diagnosis was significantly correlated with gender (r = .17, p < .01), number of medical problems (r = .38, p < .01), 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).

Table 2.

Descriptive data and intercorrelations among covariates, predictor, and criterion variables.

	1	2	3	4	5	6	7	8	9	10	11	Range	M	SD
1. Asthma	–	.17**	.38**	–.11	.10	.14*	.18**	.01	.07	.14*	.14*	–	–	–
2. Gender	–	–	.08	−.10	−.05	.10	.04	.10	.04	.12	.19**	–	–	–
3. Medical problems	–	–	–	.04	.04	.10	.16	.13*	.02	−.01	.05	0–8	1.84	1.74
4. Age of onset	−	−	−	−	−.06	−.33**	.01	−.01	.07	−.01	−.01	8–48	17.00	4.72
5. CPD	–	–	–	–	–	−.32**	.05	.05	.01	−.03	−.02	1–81	19.57	10.85
6. FTND	–	–	–	–	–	–	.02	.10	.13*	.00	.09	0–10	5.79	2.17
7. Number of quit attempts	–	–	–	–	–	–	–	.13*	.10	.28**	.24**	0–18	3.00	2.91
8. RQS-HC	–	–	–	–	–	–	–	–	.28**	.43**	.19**	1–4	2.81	.82
9. RQS-IR	–	–	–	–	–	–	–	–	–	.44**	.35**	1–4	3.07	.73
10. RQS-SC	–	–	–	–	–	–	–	–	–	–	.41**	1–4	2.94	.81
11. RQS-SI	–	–	–	–	–	–	–	–	–	–	–	1–4	1.91	.81

CPD: cigarettes per day; FTND: Fagerström Test for Nicotine Dependence (Heatherton et al.,1991); RFQ-HC: Reasons for Quitting Questionnaire–Health Concerns subscale (Curry et al., 1990); RFQ-IR: Reasons for Quitting Questionnaire–Immediate Reinforcement subscale (Curry et al., 1990); RFQ-SC: Reasons for Quitting Questionnaire–Self-Control subscale (Curry et al., 1990); RFQ-SI: Reasons for Quitting Questionnaire–Social Influence subscale (Curry et al., 1990).

Asthma: 0 = no, 1 = yes; gender: 1 = male, 2 = female; medical problems: number of medical problems; age of onset: age at regular smoking onset.

Correlation is significant at .05 level; **Correlation is significant at .01 level.

Regression analyses

Data for the smoking behavior variables hierarchical regression analyses are presented in Table 3. In terms of age of regular smoking onset, the first step accounted for 2.5 percent of the variance. There were no significant predictors at step 1. Step 2 of the model predicted 2.1 percent of unique variance, and asthma diagnosis was a significant predictor (t = −2.28, β = −.16, p < .05). Specifically, having a diagnosis of asthma was related to a younger age of regular smoking onset.

Table 3.

Asthma diagnosis predicting smoking behavior and smoking cessation motives.

	ΔR²	t (each predictor)	β	sr ²	p
Criterion variable: age at regular smoking onset
Step 1	.03				NS
Gender		−1.72	−.11	.01	NS
Race		1.62	.10	.01	NS
Medical problems		1.23	.08	.01	NS
Step 2	.02				<.05
Asthma diagnosis		−2.28	−.15	.02	<.05
Criterion variable: daily smoking rate
Step 1	.02				NS
Gender		−.67	−.05	.00	NS
Race		−1.86	−.12	.02	NS
Medical Problems		1.05	.07	.00	NS
Step 2	.01				NS
Asthma diagnosis		1.74	.13	.01	NS
Criterion variable: nicotine dependence
Step 1	.04				<.05
Gender		1.48	.10	.01	NS
Race		−2.44	−.16	.02	<.05
Medical problems		1.46	.10	.01	NS
Step 2	.01				NS
Asthma diagnosis		1.879	.13	.01	=.061
Criterion variable: number of quit attempts
Step 1	.03				NS
Gender		.06	.00	.00	NS
Race		1.11	.07	.01	NS
Medical problems		2.29	.15	.02	<.05
Step 2	.02				<.05
Asthma diagnosis		2.13	.15	.02	<.05
Criterion variable: RFQ-SC
Step 1	.02				NS
Gender		2.02	.13	.02	<.05
Race		−.38	−.02	.00	NS
Medical problems		−.68	−.04	.00	NS
Step 2	.02				<.05
Asthma diagnosis		2.36	.17	.02	<.05

RFQ-SC: Reasons for Quitting Questionnaire–Self-Control subscale; β: standardized beta weight; sr²: squared semi-partial correlation.

For daily smoking rate, the first step accounted for 2.1 percent of the variance. There were no significant predictors at step 1. Step 2 of the model accounted for 1.3 percent of unique variance, and asthma diagnosis was not a significant predictor.

With regard to nicotine dependence, the first step accounted for 4.2 percent of the variance. Race (t = −2.44, β = −.16, p < .05) was the only significant predictor at step 1. Step 2 of the model accounted for 1.4 percent of unique variance and asthma diagnosis was not a significant predictor; however, there was a nonsignificant trend for individuals with an asthma diagnosis to report greater nicotine dependence (t = 1.88, β = .13, p = .061).

In terms of number of quit attempts, the first step accounted for 2.9 percent of the variance. Number of medical problems (t = 2.29, β = .15, p < .05) was the only significant predictor at step 1. Step 2 of the model predicted 1.8 percent of unique variance, and asthma diagnosis was a significant predictor (t = 2.13, β = .15, p < .05). Specifically, having a diagnosis of asthma was related to a greater number of quit attempts.

In contrast to our hypothesis, in terms of Reasons for Quitting, asthma diagnosis was only a significant predictor for the self-control subscale. The first step accounted for 1.7 percent of the variance. Gender (t = 2.02, β = .13, p < .05) was the only significant predictor at step 1. Step 2 of the model accounted for 2.3 percent of unique variance and asthma diagnosis was a significant predictor (t = 2.36, β = .17, p < .05). Specifically, having a diagnosis of asthma was related to greater reasons for quitting related to self-control.¹

Discussion

This study examined the effect of having a diagnosis of asthma on smoking behaviors and reasons for quitting smoking among adult daily smokers. With regard to smoking behaviors, asthma was a significant predictor of number of quit attempts, accounting for 1.8 percent of unique variance, and retrospectively reported age at regular smoking onset, accounting for 2.1 percent of unique variance. Although the size of these statistically significant effects was small, they were incremental—that is, above and beyond the variance accounted for by the covariates of gender, race, number of medical problems, and alcohol consumption. These results suggest that asthma may be associated with earlier smoking onset and more frequent failed quit attempts among adults.

Although this study cannot address the factors underlying these findings, it is possible adult smokers with asthma learn to rely on smoking as a coping strategy for negative emotional experiences or life stressors, and therefore, are more prone to maintain cigarette use. By virtue of their diagnosis, individuals with asthma may be burdened with additional demands and stressors (e.g. symptom monitoring, medication adherence, activity limitation) that results in increased levels of stress (Sandberg et al., 2000; Smyth et al., 1999). As a result, these individuals may begin smoking cigarettes at an earlier age in an attempt to cope with increased stress and negative affect. Smokers with asthma may lack other, more effective strategies to cope with the increased demands of having a chronic medical condition and, as a result, experience more difficulties with smoking cessation.

Notably, asthma was not a statistically significant predictor of daily smoking rate or level of nicotine dependence, although there was a nonsignificant trend for nicotine dependence (1.4 percent unique variance, p = .061). These findings are broadly consistent with past work that has not found differences in smoking rate, puff volume, or severity of tobacco use among smokers with and without asthma (e.g. Zimmerman et al., 2004). The present findings, in conjunction with past work (Zimmerman et al., 2004), strengthen confidence that smokers with asthma do not necessarily have greater levels of nicotine dependence or engage in higher smoking rates than smokers without asthma. As such, greater cessation difficulties among smokers with asthma cannot be attributed to differences in smoking rate or degree of dependence.

In terms of reasons for quitting smoking, asthma significantly predicted only quitting for self-control reasons, accounting for 2.3 percent of unique variance. That is, smokers with asthma were more likely to endorse that one of their primary reasons for quitting was to gain greater degrees of self-control. However, asthma was not associated with greater reasons for quitting for other reasons. It may be that smokers with asthma are particularly apt to rely on smoking to cope with life stress and, therefore, believe quitting could help them gain more self-control or regulation over their behavior. One personality factor that may increase one’s likelihood of using smoking as a form of affect regulation is neuroticism or the tendency to experience negative emotions (Terracciano and Costa, 2004). This need to regain self-control may help explain the nonsignificant finding for reasons for quitting related to health concerns. It may be that smokers with asthma are higher in neuroticism and, thus, may be more willing to engage in risky health behaviors and due to the perceived benefits of smoking (e.g. increased sense of control, decreased negative affect; Loerbroks et al., 2009; Otten et al., 2008).

There are a number of interpretive caveats that deserve further comment. First, asthma diagnosis was based on self-report and was not objectively verified, although efforts were made to verify asthma diagnosis using proxy indicators (i.e. prescriptions for asthma medications, experiencing symptoms in the past year). Future studies should utilize objective measures of lung functioning (e.g. spirometry) and physician exam to verify asthma diagnosis. Second, data on asthma severity ratings were not collected, thus, future research should include assessments of asthma severity in order to determine whether asthma severity impacts smoking behaviors and smoking-related cognitive processes.

Third, 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 later diagnosed with asthma. However, secondary analyses were conducted using the same sample of smokers without asthma and a subsample of 68 smokers with asthma who reported an asthma diagnosis prior to the onset of smoking. Results of these analyses indicated that similar to the full sample, asthma diagnosis was a significant predictor of number of previous quit attempts (4.1% unique variance) and self-control reasons for quitting smoking (3.1% unique variance). Results for age of regular smoking onset were not significant, though we were underpowered to detect small effect sizes. Thus, the results of these secondary analyses increase our confidence that our results can be generalized to smokers with asthma in general, regardless of temporal patterning of smoking and asthma onset. Notably, despite the smaller sample size, asthma diagnosis accounted for a greater amount of unique variance among those who began smoking after the onset of asthma, suggesting that these effects may be stronger among those who are diagnosed with asthma prior to smoking onset.

Finally, 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 multimethod assessment approach to address this concern. Finally, the present cross-sectional design does not permit causal-oriented hypothesis testing. Future studies examining how smoking behavior 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.

In addition to addressing the limitations of this study, future research should begin examining so-called third variables (i.e. moderating and mediating processes) that would allow researchers to further refine our understanding of the smoking–asthma co-occurrence. While there were some significant associations between asthma and smoking behavior and smoking cessation motives in this study, asthma diagnosis only accounted for a small amount of unique variance in these variables indicating that there are likely other key factors involved. Other than comparing the two smoking groups on prevalence of psychiatric diagnoses, the impact of psychological symptoms and psychopathology on this association was largely ignored. However, there is a large body of research indicating that anxiety symptoms and disorders are associated with both smoking (e.g. Lasser et al., 2000; Morissette et al., 2007) and asthma (Avallone et al., 2012; Goodwin et al., 2003; Hasler et al., 2005). Thus, some of the association between asthma and smoking may be due to anxiety psychopathology, and future research should examine the role of anxiety in this association.

Together, the present findings suggest that, among smokers, a diagnosis of asthma significantly predicts retrospectively reported age of regular smoking onset, a greater number of quit attempts, and self-control reasons for quitting. The primary implication of the present findings is smokers with asthma compared to those without may have more difficulty quitting and unique reasons for quitting. Such differences suggest that specialized interventions may need to be developed for this segment of the smoking population. Specifically, due to earlier age of smoking onset, smoking prevention programs may need to start earlier in order to target children with asthma before they initiate smoking. Additionally, smokers with asthma may experience increased difficulties with smoking cessation and may need individualized smoking cessation interventions that address difficulties that may be unique to this population (e.g. increased stress as a result of having a chronic illness). Given the finding that smokers with asthma are not more likely to be motivated to quit due to health concerns, it will be important for physicians and other treatment providers to assess an individual’s unique smoking cessation motives in order to increase cessation success. Since individuals with asthma appear to be more motivated to quit due to self-control reasons, incorporating education and knowledge about self-control motives for quitting as well as incorporating coping skills training into interventions may serve to increase motivation to make a quit attempt as well as improve smoking cessation success.

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes

References

Althuis

Sexton

Prybylski

(1999) Cigarette smoking and asthma symptom severity among adult asthmatics. Journal of Asthma 36: 257–264.

American Lung Association (2010) Asthma. Available at: http://www.lungusa.org/associations/charters/midland-states/program-information/asthma/ (accessed 20 April 2010).

Avallone

McLeish

Luberto

. (2012) Anxiety sensitivity, asthma control, and quality of life in adults with asthma. Journal of Asthma 49: 57–62.

Avila

Soto-Martinez

Soto-Quiros

. (2005) Asthma, current wheezing, and tobacco use among adolescents and young adults in Costa Rica. Journal of Asthma 43: 543–547.

Babor

T. F.

de la Fuente

J. R.

Saunders

Grant

(1992). The Alcohol Use Disorders Identification Test: Guidelines for use in primary care. WHO Publication No. 92.4, World Health Organization, Geneva.

Beeh

Micke

Ksoll

. (2001) Cigarette smoking, but not sensitization to Alternaria, is associated with severe asthma in urban patients. Journal of Asthma 38: 41–49.

Boulet

FitzGerald

McIvor

. (2008) Influence of current or former smoking on asthma management and control. Canadian Respiratory Journal 15: 275–279. Available at: http://www.pulsus.com/journals/index.jsp?origPg=journalHome.jsp&;jnlKy=4&/home.htm

Brown

Lejuez

Kahler

. (2002) Distress tolerance and duration of past smoking cessation attempts. Journal of Abnormal Psychology 111: 180–185.

Chaudhuri

Livingston

McMahon

. (2003) Cigarette smoking impairs the therapeutic response to oral corticosteroids in chronic asthma. American Journal of Respiratory and Critical Care Medicine 168: 1308–1311.

10.

Chen

Unger

Palmer

. (2002) Prior cigarette smoking initiation predicting current alcohol use: Evidence for a gateway drug effect among California adolescents from eleven ethnic groups. Addictive Behaviors 27: 799–817.

11.

Cohen

(1983) Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.

12.

Corcores

(1993) Nicotine dependence: Diagnosis and treatment. Psychiatric Clinics of North America 16: 49–60. Available at: http://www.psych.theclinics.com/

13.

Curry

Wagner

Grothaus

(1990) Intrinsic and extrinsic motivation for smoking cessation. Journal of Consulting and Clinical Psychology 63: 105–1014.

14.

Epstein

Botvin

Baker

. (1999) Impact of social influences and problem behavior on alcohol use among inner-city Hispanic and Black adolescents. Journal of Studies on Alcohol 60: 595–604. Available at: http://www.collegedrinkingprevention.gov/supportingresearch/journalstudiesalcohol.aspx

15.

Frank

Morris

Hazell

. (2006) Smoking, respiratory symptoms and likely asthma in young people: Evidence from postal questionnaire surveys in the Wythenshawe Community Asthma Project (WYCAP). BMC Pulmonary Medicine 6: 10–15.

16.

Goodwin

Jacobi

Thefeld

(2003) Mental disorders and asthma in the community. Archives of General Psychiatry 60: 1125–1130.

17.

Gwynn

(2004) Risk factors for asthma in US adults: Results from the 2000 Behavioral Risk Factor Surveillance System. Journal of Asthma 41: 91–98.

18.

Hasler

Gergen

Kleinbaum

. (2005) Asthma and panic in young adults: A 20-year prospective community study. American Journal of Respiratory and Critical Care Medicine 171: 1224–1230.

19.

Heatherton

Kozlowski

Frecker

. (1991) The Fagerström Test for Nicotine Dependence: A revision of the Fagerström Tolerance Questionnaire. British Journal of Addiction 86: 1119–1127.

20.

Koopmans

Van Doornen

Boomsma

(1997) Association between alcohol use and smoking in adolescent and young adult twins: A bivariate genetic analysis. Alcoholism: Clinical and Experimental Research 21: 537–546.

21.

Lasser

Boyd

Woolhandler

. (2000) Smoking and mental illness: A population-based prevalence study. Journal of the American Medical Association 284: 2606–2610.

22.

Loerbroks

Apfelbacher

Thayer

. (2009) Neuroticism, extraversion, stressful life events and asthma: A cohort study of middle-aged adults. Allergy 64: 1444–1450.

23.

McCoy

Shade

Irvin

. (2006) Predicting episodes of poor asthma control in treated patients with asthma. Journal of Allergy and Clinical Immunology 118: 1226–1233.

24.

McLeish

Zvolensky

(2010) Asthma and cigarette smoking: A review of the empirical literature. Journal of Asthma 47: 345–361.

25.

McLeish

Zvolensky

Luberto

(2011) The role of anxiety sensitivity in terms of asthma control: A pilot test among young adult asthmatics. Journal of Health Psychology 16: 439–444.

26.

Morissette

Tull

Gulliver

. (2007) Anxiety, anxiety disorders, tobacco use, and nicotine: A critical review of interrelationships. Psychological Bulletin 133: 245–272.

27.

Nathan

Sorkness

Kosinski

. (2004) Development of the asthma control test: A survey for assessing asthma control. Journal of Allergy and Clinical Immunology 113: 59–65.

28.

Otten

Engels

RCME

Van Den Eijnden

RJJM

(2008) Smoking behavior in asthmatic and non-asthmatic adolescents: The role of smoking models and personality. Substance Use and Misuse 43: 341–360.

29.

Payne

Smith

McCracken

. (1994) Assessing nicotine dependence: A comparison of the Fagerstrom Tolerance Questionnaire (FTQ) with the Fagerstrom Test of Nicotine Dependence (FTND) in a clinical sample. Addictive Behaviors 19(3): 307–317.

30.

Pomerleau

Carton

Lutzke

. (1994) Reliability of the Fagerström Tolerance Questionnaire and the Fagerström Test for Nicotine Dependence. Addictive Behaviors 19: 33–39.

31.

Sandberg

Paton

Ahola

. (2000) The role of acute and chronic stress in asthma attacks in children. The Lancet 356: 982–987.

32.

Saunders

Aasland

Babor

. (1993) Development of the Alcohol Use Disorder Identification Test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction 88: 791–803.

33.

Schatz

Zeiger

Vollmer

. (2006) Determinants of future long-term asthma control. Journal of Allergy and Clinical Immunology 118: 1048–1053.

34.

Smyth

Soefer

Hurewitz

. (1999) Daily psychosocial factors predict levels and diurnal cycles of asthma symptomatology and peak flow. Journal of Behavioral Medicine 22: 179–193.

35.

Terracciano

Costa

Jr (2004) Smoking and the five-factor model of personality. Addiction 99: 472–481.

36.

Van Zundert

RMP

Engels

RCME

Kleinjan

. (2008) An integration of parents’ and best friends’ smoking, smoking-specific cognitions, and nicotine dependence in relation to readiness to quit smoking: A comparison between adolescents with and without asthma. Journal of Pediatric Psychology 33: 821–832.

37.

Wakefield

Roberts

Ruffin

. (1995) Smoking-related beliefs and behaviour among adults with asthma in a representative population sample. Australian and New Zealand Journal of Medicine 25: 12–17.

38.

Zimmerman

Sehnert

Epstein

. (2004) Smoking topography and trajectory of asthmatic adolescents requesting cessation treatment. Preventive Medicine 39: 940–942.

39.

Zvolensky

Schmidt

Antony

. (2005) Evaluating the role of panic disorder in emotional sensitivity processes involved with smoking. Journal of Anxiety Disorders 19: 673–686.