Mood, mood regulation expectancies and frontal systems functioning in current smokers versus never-smokers in China and Australia

Addictive Behaviors 38 (2013) 2741–2750

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Addictive Behaviors

Mood, mood regulation expectancies and frontal systems functioning in current smokers versus never-smokers in China and Australia Michael Lyvers ⁎, Cassandra Carlopio, Vicole Bothma, Mark S. Edwards Department of Psychology, Bond University, Gold Coast, Qld 4229, Australia

H I G H L I G H T S • • • • •

In Australia and China smokers report worse moods than nonsmokers. In Australia and China smokers show worse executive function than nonsmokers. Chinese smokers scored worse than Australian smokers on all measures. Results fail to support the “hardening” hypothesis and suggest the opposite. Ease of smoking in China may promote nicotine dependence and “hardening”.

a r t i c l e Keywords: Smoking Nicotine dependence Addiction Self-regulation Hardening hypothesis

i n f o

a b s t r a c t Indices of mood, mood regulation expectancies and everyday executive functioning were examined in adult current smokers and never-smokers of both genders in Australia (N = 97), where anti-smoking campaigns have dramatically reduced smoking prevalence and acceptability, and in China (N = 222), where smoking prevalence and public acceptance of smoking remain high. Dependent measures included the Depression Anxiety Stress Scales (DASS-21), the Negative Mood Regulation (NMR) expectancies scale, the Frontal Systems Behavior Scale (FrSBe), the Fagerström Test for Nicotine Dependence (FTND) and the Alcohol Use Disorders Identification Test (AUDIT). Multivariate analyses of covariance (MANCOVAs) controlling for demographic and recruitment related variables revealed highly significant differences between current smokers and never-smokers in both countries such that smokers indicated worse moods and poorer functioning than never-smokers on all dependent measures. Chinese smokers scored significantly worse on all dependent measures than Australian smokers whereas Chinese and Australian never-smokers did not differ on any of the same measures. Although nicotine dependence level as measured by FTND was significantly higher in Chinese than Australian smokers and was significantly correlated with all other dependent measures, inclusion of FTND scores as another covariate in MANCOVA did not eliminate the highly significant differences between Chinese and Australian smokers. Results are interpreted in light of the relative ease of taking up and continuing smoking in China compared to Australia today. © 2013 Elsevier Ltd. All rights reserved.

1. Introduction Tobacco smoking remains the leading preventable cause of death worldwide. Of approximately one billion current smokers, about 500 million will eventually die from a smoking related illness (World Health Organisation [WHO], 2011). Smoking prevalence varies considerably across countries, with “Anglo” countries such as the United States, Canada and Australia reporting much lower rates of smoking in recent years than East Asian countries such as China, South Korea and Japan. For example, a 2010 survey (Li, Hsia, & Yang, 2011) reported that 46% of adult men in China were current daily smokers, whereas a ⁎ Corresponding author. Tel.: +61 7 559 52565; fax: +61 7 5595 2540. E-mail address: [email protected] (M. Lyvers). 0306-4603/$ – see front matter © 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.addbeh.2013.07.002

2010 survey in Australia (Australian Institute of Health and Welfare [AIHW], 2011) reported that only 14% of adult men were current daily smokers. This large difference in smoking prevalence rates, at least for men (prevalence rates are considerably lower among women in both countries but especially in China), reflect very different community attitudes towards smoking in China versus Australia. “Anglo” countries such as Australia, Canada and the United States have made considerable efforts to decrease cigarette smoking, resulting in a steady decrease in smoking prevalence over the past several decades (AIHW, 2011; Morrell & Cohen, 2006) except perhaps among the population of those suffering from frequent depression, stress or emotional problems (New York State Department of Health, 2012). In Australia, heavy taxation of tobacco products, smoke-free environment legislation, bans on tobacco advertising, and gruesome ads depicting

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horrific health consequences of smoking are some of the approaches taken by anti-smoking campaigns. There has been no comparable effort to date in China, which remains the largest consumer of tobacco products in the world, has one third of the world's smokers and produces nearly half of all cigarettes worldwide (Li et al., 2011; Peto, Zheng-Meng, & Boreham, 2009). Although China is a signatory to the World Health Organization Framework Convention on Tobacco Control, and smoking cessation programs are available in some areas, smoking rates in China appear to be stable or even rising (Hughes, 2012), and public awareness of the adverse health effects of smoking is minimal (Peto et al., 2009). In Australia approximately 40% of smokers attempt to quit each year (Cooper, Borland, & Yong, 2011), though only half of those who attempt to quit are reportedly successful at maintaining abstinence from smoking for a one-month period (AIHW, 2008). Negative affect appears to impact quit attempts, with smokers who report high levels of negative affect tending to be less successful at quitting (Anda et al., 1999; Kassel, Stroud, & Paronis, 2003; Shiffman et al., 1997; Spielberger, Foreyt, Reheiser, & Poston, 1998). Many research investigations have found strong associations between smoking and negative affective states such as depression, anxiety and stress (e.g., Fergusson, Goodwin, & Horwood, 2003; Kassel et al., 2003; Lyvers, Thorberg, Dobie, Huang, & Reginald, 2008; McChargue, Cohen, & Cook, 2004a,b; New York State Department of Health, 2012; Patton et al., 1998, 1996; Pedersen & von Soest, 2009). For example, Patton et al. (1996) found that even after controlling for academic level, gender, alcohol consumption and parental smoking, adolescents reporting high levels of anxiety and depressive symptoms were approximately twice as likely to smoke compared to those reporting low levels of such symptoms. More recent data from two large Australian national household surveys found that current smokers reported higher levels of psychological distress than their ex-smoker and non-smoker peers, particularly if the current smokers smoked a high number of cigarettes per day and had attempted to quit but failed (Leung, Gartner, Dobson, Lucke, & Hall, 2011). Mykletun, Overland, Aarø, Liabø, and Stewart (2008) examined the association between depression, anxiety and smoking in participants aged 20 to 89 years from a population-based health survey in Norway (N = 60,814); smoking levels were highest in participants with comorbid anxiety and depression, followed by anxiety and then depression. Anxiety and depression were more prevalent in current smokers than in ex-smokers or in people who had never smoked. Behavioral signs of frontal lobe dysfunction have also been associated with smoking. Spinella (2003) found that current smokers reported more signs of frontal lobe dysfunction than non-smokers on all three subscales of the Frontal Systems Behavior Scale (FrSBe; Grace & Malloy, 2001). Deficits of frontal lobe functioning as well as structural deficiencies in prefrontal regions are well known in other addictions including alcoholism, cocaine addiction and heroin addiction (Lyvers, 2000). Like those other forms of drug addiction, brain imaging signs of less prefrontal gray matter and smaller prefrontal volume have been observed in heavy smokers compared to nonsmokers (Brody et al., 2004; Zhang et al., 2011). Shared genetic influences may account for part of the link between smoking and depression (Dierker, Avenevolli, Stolar, & Merikangas, 2002), however even when controlling for genetic influences the association of smoking with depression persists (Korhonen et al., 2007). Both directions of causation may underlie the relationships between smoking and indices of negative affect or cognitive deficit. Depression and anxiety appear to increase the risk of initiating smoking (Escobedo, Reddy, & Giovino, 1998; Patton et al., 1998; Polen et al., 2004), suggesting that those suffering from frequent negative moods are more likely to take up and continue smoking as a form of self-medication (Dinn, Aycicegi, & Harris, 2004; Warburton, 1992). Nicotine can have anxiolytic and antidepressant effects and thus may be used by smokers to alleviate negative moods (Morissette, Tull, Gulliver, Kamholz, & Zimering, 2007). Adan, Prat, and Sanchez-Turet (2004) found that while both light and heavy smokers reported more negative

affect than non-smokers, the heavy smoker group reported worse precigarette and post-cigarette moods than the light smokers did. Behavioral traits related to poor executive function such as impulsivity, risk-taking and disinhibition have also been reported to increase the likelihood of smoking (Carton, Jouvent, & Widlocher, 1994; Dinn et al., 2004; Lejuez, Aklin, Bornovalova, & Moolchan, 2005; Spillane, Smith, & Kahler, 2010), with longitudinal research indicating that risk-taking in childhood predicted adult smoking (Burt, Dinh, Peterson, & Sarason, 2000). Perhaps those who are inclined to take risks and/or who have short time horizons are less likely to be influenced by public health campaigns concerning the long-term adverse consequences of smoking. In any case the notion that those who suffer from frequent negative mood states and/or who exhibit traits linked to poor executive function are more likely to take up and continue smoking has ample support from research findings. On the other hand, evidence also indicates that chronic smoking may itself worsen mood and cognitive functioning. Shahab and West (2012) examined self-reported happiness in current smokers, ex-smokers and never-smokers in a large U.K. sample (N = 6923). Ex-smokers who had quit for more than a year reported similar levels of happiness to never-smokers, levels which were significantly higher than in current smokers; ex-smokers who had quit for less than a year reported similar levels of happiness as current smokers. The authors concluded that their findings strengthen the evidence for a causal relationship between smoking and negative affective states, with smoking cessation leading to improvements in mood. Consistent with this idea, longitudinal research indicates that taking up smoking increases the likelihood of experiencing depressive symptoms (Boden, Fergusson, & Horwood, 2010; Kang & Lee, 2010). Cognitive functioning may also be adversely affected by heavy smoking and associated nicotine dependence. Lyvers, Maltzman, and Miyata (1994) found that when chronic heavy smokers were deprived of nicotine for 12 h they performed significantly worse than non-smokers on a well-known neuropsychological test of frontal lobe related cognitive functioning, the Wisconsin Card Sorting Test (WCST); heavy smokers performed at the level of nonsmokers only after smoking a cigarette. The findings were interpreted as reflecting an adverse effect of nicotine addiction on the functioning of the prefrontal cortex, a region heavily innervated by dopaminergic inputs from the ventral tegmental area where nicotine activates dopaminergic neurons (Mihailescu & Drucker-Colin, 2000). Similar performance deficits on the WCST were observed in opioid-addicted methadone maintenance patients who were acutely deprived of methadone compared to those who had been given their daily methadone dose (Lyvers & Yakimoff, 2003). However, the study by Lyvers et al. (1994) could not rule out the possibility that the smokers in their study had deficient executive function even before taking up smoking and that smoking had a cognitive enhancing effect that normalized their performance on the test. Likewise although Lyvers and Miyata (1993) observed a nicotine-reversible deficit in psychophysiological indices of attention during nicotine abstinence in heavy smokers, the possibility that an attention deficit may have predated onset of smoking could not be ruled out. Parrott (2004, 2005, 2006) proposed that whereas the selfmedication model of smoking maintenance is based on the assumption that mood (and perhaps attention and cognition) acutely improves as a result of smoking, as often reported by smokers themselves (Copeland, Brandon, & Quinn, 1995; Shiffman, 1993), nicotine dependence may cause frequent fluctuations in smokers' moods which could be the primary cause of their higher self-reported negative affect compared to nonsmokers. Parrott suggested that the degree of negative affect experienced by smokers is directly related to their level of nicotine dependence, an idea consistent with the view that drug addictions are characterized by “hedonic homeostatic dysregulation” reflecting alteration of anterior brain dopamine systems by frequent drug use (Koob & Le Moal, 1997). Thus anxiety and stress tend to decrease after quitting smoking even after controlling for stressful life events (Carey, Kalra, Carey, Halperin, & Richards, 1993; Chassin, Presson, Sherman, & Kim, 2002; Cohen & Lichtenstein, 1990; Parrott, 2005;

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West & Hajek, 1997). These observed decreases in negative affect are present following the immediate post-quitting period (Parrott, 2005), suggesting that they are due to subsidence of nicotine dependence and a return to normal pre-smoking brain functioning. Additional factors have also been posited to account for the relationships between negative affect or cognitive deficit and smoking. In “Anglo” countries such as Australia and the U.S. where vigorous and widespread anti-smoking campaigns have dramatically reduced smoking prevalence rates (AIHW, 2011; Chassin, Presson, Sherman, & Kim, 2003), the remaining population of smokers may have become “hardened” (Fagerström & Furber, 2008; Ip et al., 2012; Leung et al., 2011; Warner & Burns, 2003) such that smokers today are characterized by higher levels of nicotine dependence and associated poorer mood and general functioning than in the past when smoking was commonplace. Given the current widespread negative stigma against smoking in “Anglo” countries (Chapple, Ziebland, & McPherson, 2004; Stuber, Galea, & Link, 2008), perhaps those who dare to take up smoking now despite knowledge of the adverse long-term health effects and stigma, and those who refuse to quit or feel incapable of quitting, are likely to exhibit more psychopathology than smokers did in the past when smoking was a widely accepted practice. Such considerations would not apply in a country such as China, where anti-smoking stigma is largely absent and smoking is common and socially accepted. The association of negative affect or cognitive dysfunction with smoking may thus to some extent reflect the degree of stigma against smoking in a given society, an idea which has received tentative research support. Fagerström and Furber (2008) compared smoking prevalence and nicotine dependence across several European countries and the U.S.A. using data collected mostly in the 1990s; lower smoking prevalence was associated with higher nicotine dependence as measured by the Fagerström Test for Nicotine Dependence (FTND; Fagerström, 1978; Heatherton, Kozlowski, Frecker, & Fagerström, 1991) consistent with the notion of “hardening” of the diminishing population of smokers as a result of anti-smoking campaigns and stigma. Lyvers, Hall, and Bahr (2009) compared non-“Anglo” foreign exchange students living in Australia to native-born Australians on mood measures, finding that higher anxiety scores were associated with smoking in native Australians but not in the exchange students; however non-“Anglo” smokers living in their home countries were not assessed, and among nonsmokers the foreign students scored nearly twice as high on the anxiety measure as the Australian students. Further, the average number of cigarettes smoked per day was the same in the foreign and Australian student smokers, contrary to the “hardening” hypothesis which predicted heavier smoking by Australian smokers. The general idea of “hardening” is based on the fact that those who continue to smoke today in “Anglo” and some other Western countries such as Sweden do so in spite of restrictions on smoking in public places, the negative stigma associated with smoking, heavy taxation of cigarettes, strong support for smoking cessation efforts, and public education campaigns regarding the health risks of smoking (Warner & Burns, 2003). These remaining smokers may thus be mostly ‘hardened’ or ‘hard-core’ nicotine dependent smokers (Chassin et al., 2003), as lighter or less nicotine dependent smokers tend to quit first (Chaiton, Cohen, & Frank, 2008). The remaining smokers may be more likely to experience psychological disturbance, use nicotine as their primary coping resource, and be more resistant to anti-smoking campaigns (Fagerström & Furber, 2008; New York State Department of Health, 2012). Current attempts to promote quitting smoking focus on informing people about the harmful long-term effects of smoking, enforcing legislation to increase smoke-free areas, displaying warning labels on tobacco products, and implementing generic or ‘plain’ packaging of cigarettes (Hammond, Fong, McNeill, Borland, & Cummings, 2006; WHO, 2011). However, the plateau of declining prevalence rates of smoking in Western countries suggests that current smokers in such countries may be ‘hardened’ such that current anti-smoking strategies may be ineffective at reaching this population.

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There is evidence contrary to the claimed “hardening” effect of anti-smoking campaigns and stigma. O'Connor, Giovono, Kowlowski, Shiffman, and Hyland (2006) reported that as the prevalence of smoking declined in the U.S.A., the average number of cigarettes smoked per day as well as total nicotine intake both declined among American smokers from the period 1988–94 to 1999–2002, presumably in response to antismoking campaigns and stigma. An opposite possibility thus suggests itself, i.e., that widespread public acceptance of smoking may itself promote “hardening.” In a country such as China, where smoking prevalence is high and there is little if any stigma against smoking (at least by men), as well as low financial and social costs of smoking and minimal public awareness of the adverse health consequences of smoking, perhaps those who take up smoking are more likely to become highly dependent on nicotine than in countries where smoking is heavily discouraged. The smoking population in China may thus be more nicotine dependent and exhibit more dependence-related problems with mood and cognitive function in comparison to the relatively small smoker population of a high-stigma country such as Australia. The present study sought to examine these competing possibilities. In the present study indices of everyday mood and executive functioning were assessed in current smokers and never-smokers in Australia, where anti-smoking campaigns have dramatically reduced smoking prevalence and established a widespread negative stigma toward smoking, and in China, where smoking prevalence and public acceptance of smoking remain high. The mood and executive function measures employed had shown highly significant differences between smokers and nonsmokers in previous research (e.g., Lyvers et al., 2009, 2008; Spinella, 2003): the Depression Anxiety Stress Scales (DASS-21; Lovibond & Lovibond, 2002), the Negative Mood Regulation (NMR) expectancies scale (Catanzaro & Mearns, 1990), and the Frontal Systems Behavior Scale (FrSBe; Grace & Malloy, 2001) which has Apathy, Disinhibition and Executive Dysfunction subscales designed to detect deficits associated with anterior cingulate, orbitofrontal and dorsolateral prefrontal cortex dysfunction, respectively. Risky alcohol use was also assessed using the Alcohol Use Disorders Identification Test (AUDIT; Babor, de la Fuente, Saunders, & Grant, 1992) given the commonly reported positive association between smoking and drinking (Biederman et al., 2005; Grucza & Bierut, 2006), and level of nicotine dependence in smokers was assessed via the FTND (Fagerström, 1978; Heatherton et al., 1991). We expected to find that compared to never-smokers, current smokers would show elevated signs of “hedonic homeostatic dysregulation” (Koob & Le Moal, 1997) due to nicotine-induced alteration of frontal brain circuits that regulate mood and cognition. Thus in comparison to never-smokers, current smokers were expected to report more signs of dysfunction on the three subscales of the FrSBe as well as impaired self-regulation of negative moods as assessed by the NMR scale (Lyvers, Thorberg, Ellul, Turner, & Bahr, 2010) and higher levels of depression, anxiety and stress as measured by the DASS-21. Current smokers were also expected to score higher on the AUDIT than never-smokers, and based on Parrott (2006) all the above indices were expected to be significantly correlated with FTND scores in smokers, such that higher nicotine dependence would be associated with evidence of worse mood, mood regulation and frontal systems functioning. Finally, extrapolating from the findings of Fagerström and Furber (2008) in Western countries as discussed above, the expected differences between current smokers and never-smokers were tentatively expected to be significantly greater in the Australian sample than in the Chinese sample based on the notion that the much smaller percentage of smokers in Australia today are mostly “hardened” smokers who are more nicotine dependent and characterized by worse psychopathology compared to smokers in China, where smoking is far more common and accepted. However, for reasons mentioned above the opposite was also considered as a potential outcome, i.e., that the ease of taking up and continuing smoking in China today may itself promote “hardening” of the smoking population.

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2. Method 2.1. Participants Out of 349 volunteers who completed the online survey, 319 provided usable data after deletion of multivariate outliers, those who did not meet criteria for participation and cases with missing data. Of the 319 participants, 104 reported they have never smoked and 215 reported smoking for at least one year up to the time of testing; there were 203 men and 116 women and ages ranged from 19 to 58 years (M = 30.13 years, SD = 8.79). Of these 319 participants, 75% were recruited via eSearch, an online survey company, and were paid US$3.50 to complete the survey online. This appeared to be a sufficient incentive to attract Chinese participants, as 87% of those recruited via eSearch were from China; however, as the U.S. dollar was considerably weaker than the Australian dollar at the time of the study, and given the high costs of living in Australia, the eSearch incentive paid in U.S. dollars may have been too small to recruit enough participants in Australia as suggested by the low percentage (13%) of Australian respondents among those recruited via eSearch. Thus additional Australian participants were recruited via an advertisement in the local newspaper for a $30 (Australian) monetary incentive (12% of overall sample); they also completed the survey online, using another online survey program, Survey Monkey. At the same time, further Australian participants (13% of overall sample) were recruited via a poster and sign-up sheet at Bond University offering a credit point in an undergraduate psychology subject as an incentive for participation; they too completed the survey online using Survey Monkey. All three recruitment methods asked for current smokers who have smoked for at least one year, as well as those who have never smoked (never-smokers), to participate as research subjects in a study of the trait correlates of smoking. Only those who rated themselves as proficient and confident in their understanding of written English qualified for participation. The final sample included 222 respondents from China (45 never-smokers and 177 smokers) and 97 respondents from Australia (59 never-smokers and 38 smokers). All participants were tested online. Given the diverse recruitment methods and resulting subsamples, participant recruitment method was controlled by covariate analysis in addition to controlling for various demographic factors as described below. 2.2. Materials 2.2.1. Demographic questionnaire Participants were asked questions concerning their age, gender, nationality, country of residence, employment status, level of education, proficiency and confidence with English, current smoker or neversmoker status and illicit drug use. These data were used both for screening purposes and to provide control variables for covariate analysis in all group comparisons. 2.2.2. Fagerström Test for Nicotine Dependence (FTND) The FTND (Fagerström, 1978; Heatherton et al., 1991) is a six-item self-report measure of nicotine dependence. The FTND yields a total score ranging from 0 to 10. Scores between 7 and 10 are indicative of a high level of nicotine dependence; scores of 4 to 6 indicate moderate dependence, and scores less than 4 indicate low to no dependence. A number of studies have demonstrated that the FTND has good internal consistency and validity (Colby, Tiffany, Shiffman, & Niaura, 2000) and test–retest reliability (Pomerleau, Carton, Lutzke, Flessland, & Pomerleau, 1994). The FTND has previously been used in crosscultural research (Fagerström & Furber, 2008). 2.2.3. Negative Mood Regulation (NMR) scale The NMR scale (Catanzaro and Mearns, 1990) is a 30-item questionnaire assessing beliefs about one's ability to regulate or alleviate a negative mood state through their own efforts. The questions follow

the stem, “When I'm upset, I believe that…” and ask respondents to indicate on a five point Likert-type scale the degree to which they agree/ disagree with the statements. High scores on the NMR scale are indicative of strong beliefs in one's ability to regulate or alleviate negative moods without pharmacological assistance. The NMR scale has good psychometric properties (Cohen, McChargue, & Morrell, 2007; Hasking, Lyvers, & Carlopio, 2011) and has demonstrated discriminant validity from the Beck Depression Inventory, the Internal External Locus of Control Scale, and the Social Desirability Scale (Catanzaro & Mearns, 1990). The NMR scale typically shows negative correlations with indices of anxiety and depression (Catanzaro & Greenwood, 1994; Kassel, Jackson, Shannon, & Unrod, 2000; Kirsch, Mearns, & Catanzaro, 1990) and with the FrSBe (Lyvers et al., 2010) in line with theoretical expectations. In the current study, the Cronbach alpha coefficient for the NMR scale was .89, indicating strong internal consistency. 2.2.4. Depression Anxiety Stress Scales (DASS-21) The DASS-21(Lovibond & Lovibond, 2002) is a 21-item short form of the DASS-42. The DASS-21 has three scales designed to assess depression, anxiety and stress with seven questions for each mood state. Responses to each item are indicated on a four-point severity scale from 0 (Did not apply to me at all) to 3 (Applied to me very much, or most of the time). Depression scale items include “I couldn't seem to experience any positive feeling at all”; a sample Anxiety scale item is “I experienced trembling” and a sample Stress scale item is “I found it hard to wind down.” The DASS-21 has demonstrated good psychometric properties (Antony, Bieling, Cox, Enns, & Swinson, 1998), with construct validity established in a non-clinical population (Henry & Crawford, 2005). The DASS-21 has been normed in Australia along with other widely-used self-report mood scales including the Beck Anxiety Inventory, the Beck Depression Inventory and the Carroll Rating Scale for Depression (see Crawford, Cayley, Lovibond, Wilson, & Hartley, 2011, for a review). In the current study, the three subscales of the DASS-21 demonstrated strong internal consistency with Cronbach alpha coefficients of .92 for Depression, .92 for Anxiety, and .89 for Stress. 2.2.5. Frontal Systems Behavior Scale (FrSBe) The FrSBe (Grace & Malloy, 2001) is a self-report questionnaire developed to assess three cognitive and behavioral domains of everyday frontal lobe functioning in adults aged 18 to 95 years: Apathy (anterior cingulate dysfunction), Disinhibition (orbitofrontal dysfunction) and Executive Dysfunction (dorsolateral prefrontal dysfunction). The FrSBe contains 46 items scored on a five point Likert scale (almost never to almost always). Scores provided an indication of the degree of dysfunction within the three domains, in addition to yielding an overall frontal lobe dysfunction score. The standard FrSBe Self-Rating form asks for pre- and post-injury ratings; however the present study only asked for current ratings, consistent with previous studies of non-brain-injured individuals (e.g., Lyvers, Duff, Basch, & Edwards, 2012; Spinella, 2003). The FrSBe has a clear three factor structure (Stout, Ready, Grace, Malloy, & Paulsen, 2003) and the corresponding subscales show good validity and reliability (Lane-Brown & Tate, 2009; Velligan, Ritch, Sui, DiCocco, & Huntzinger, 2002). In the present study, high internal consistency was indicated by Cronbach alpha coefficients of .86 for Apathy, .89 for Disinhibition, and .88 for Executive Dysfunction. 2.2.6. Alcohol Use Disorders Identification Test (AUDIT) The AUDIT (Babor et al., 1992) is a widely used 10-item questionnaire designed to identify and screen for risky or problematic alcohol consumption. The AUDIT yields a total score indicating the degree of alcohol-related risk. Scores between 0 and 7 indicate Low Risk alcohol consumption; scores between 8 and 15 are classified as Hazardous alcohol consumption; and scores of 16 or greater indicate Harmful alcohol consumption. The AUDIT shows good psychometric properties according to a large number of studies, with confirmed validity and reliability to identify harmful alcohol use in diverse countries and across

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demographic variables showed important differences between Chinese and Australian samples as well. A large proportion of the Chinese sample (78%) reported attaining an education level above high school (i.e., trade school or university) versus only 47% of the Australian sample, χ2(1) = 35.95, p b .0001. The Chinese sample was also significantly older (M = 31.55 years, SD = 8.13) than the Australian sample (M =27.36 years, SD = 9.40), t(347) = 4.32, p b .0001. There was also a higher proportion of employed (as opposed to unemployed or student) participants in the Chinese sample (82%) versus the Australian sample (57%), χ2(1) = 23.47, p b .0001. Overall the Chinese sample was thus older, more male, more educated, and more employed than the Australian sample. Given the demographic differences between Chinese and Australian samples, age, gender, recruitment method (eSearch Chinese and Australian, local community Australian and local student Australian), education level and employment status were controlled in all comparisons of Chinese and Australian samples via covariate analysis.

a broad age range (de Menes-Gaya, Zuardi, Loureiro, & Crippa, 2009; Leonardson et al., 2005; McCusker, Basquille, Khwaja, Murray-Lyon, & Catalan, 2002; Pal, Jena, & Yadav, 2004; Reinert & Allen, 2007; Rubin et al., 2006). 2.3. Procedure Approval from the Bond University Human Research Ethics Committee (BUHREC) was granted prior to recruitment of participants. As described above, participants were recruited via the online survey administration tool eSearch in China and Australia as well as locally in Australia via advertisements in a local newspaper and by notices posted on campus. Those who responded to the advertisements and notices did so by telephone so that the researcher and volunteer could arrange a testing session at Bond University where they completed the questionnaire battery online via another online survey administration tool, Survey Monkey. Local community participants were paid $30 for their time, whereas local university undergraduates were rewarded with a credit slip towards a psychology subject. ESearch participants were paid US$3.50 to complete the survey online, with the company providing the survey to participants living in China and Australia only. The minimum age for participation was 18 years. All participants read an explanatory statement before completing the questionnaires in a constant order. Participants were instructed to provide no identifying information on the survey in order to preserve their anonymity.

3.2. Primary analyses A country (China, Australia) × smoker status (current smoker, neversmoker) multivariate analysis of covariance (MANCOVA) was conducted on the FrSBe subscales, the DASS-21 scales, the NMR scale, and the AUDIT, with age, gender, recruitment method subsample (eSearch Chinese and Australian, local community Australian and local student Australian), education level and employment status as the covariates. According to Pillai's Trace multivariate effects were highly significant for country, F(8, 288) = 3.96, p b .0001, partial η2 = .10, observed power = .99; smoker status, F(8, 288) = 17.63, p b .0001, partial η2 = .33, observed power = 1; and the country × smoker status interaction, F(8, 288) = 2.77, p = .006, partial η2 = .07, observed power = .94. As shown in Table 1, univariate effects of country, smoker status, and the country X smoker status interaction were highly significant for all dependent measures. As shown in Table 2, current smokers overall scored significantly higher than never-smokers on all three FrSBe frontal lobe dysfunction subscales (Apathy, Disinhibition, Executive Dysfunction), all three DASS-21 scales (Depression, Anxiety, Stress), and the AUDIT, and scored significantly lower than never-smokers on the NMR scale. Contrary to predictions all of these differences between current smokers and never-smokers were larger among Chinese than Australians, resulting in significant interactions for all dependent measures as illustrated in Figs. 1–8. These interactions accounted for the overall main effect of country. But even when the MANCOVA was conducted on the Australian sample alone, smokers scored significantly worse than never-smokers overall according to Pillai's Trace, F(8.85) = 6.38, p b .0001, partial η2 = .38, observed power = 1, and univariate differences were significant for all measures with the single exception of the FrSBe Apathy scale (see Figs. 1–8). To further clarify the findings, separate MANCOVAs were conducted to compare Chinese and Australian never-smokers on all dependent measures while again controlling for age, gender, recruitment method/ incentive, education level and employment status as covariates, and to compare Chinese and Australian current smokers in the same manner. The MANCOVA for never-smokers showed no multivariate difference between Chinese and Australians according to Pillai's Trace, F(8, 77) = .54, p = .82. Further, univariate effects of country did not

3. Results 3.1. Characteristics of Chinese and Australian samples Of the Chinese sample 80% were current smokers compared to only 38% of the Australian sample, χ2(1) = 54.09, p b .0001, as expected based on the much higher percentage of the Chinese population that smokes compared to Australia (AIHW, 2011; Li et al., 2011). The proportions of smokers were larger than those reported for the adult populations of both China and Australia, presumably reflecting the recruitment methods and explanatory statement which described the research as concerning trait correlates of smoking. Among Chinese smokers in the current study, 78% reported smoking every day compared to 87% of Australian smokers in the current study, however the Chinese smokers reported a significantly higher level of nicotine dependence on the FTND overall (M = 5.76, SD = 1.63) than did Australian smokers (M = 4.47, SD = 2.26), t(216) = 4.08, p b .0001. Although both samples scored in the moderately dependent range on average, a significantly higher percentage of the Chinese smokers scored in the highly dependent range (31%) compared to the Australian smokers (18%), χ2(2) = 19.52, p b .0001. Chinese and Australian samples differed in gender breakdown, with 63% of the Australian sample being female versus only 25% of the Chinese sample, χ2(1) = 41.20, p b .0001, again presumably reflecting the recruitment methods of the study given the relationship of gender to smoking in the Chinese versus Australian samples. Among the Chinese smokers 86% were men compared to 50% of Australian smokers in the current study, χ2(1) = 25.40, p b .0001, consistent with the strong cultural sanction against smoking among women in China (Peto et al., 2009) and the much higher relative proportion of female smokers in Australia (AIHW, 2011). Other

Table 1 Country (China vs. Australia), smoker status (current smoker vs. never-smoker) and country × status interaction univariate F and p values (see text for details of measures). FrSBe apathy

Country Status Country ∗ status

FrSBe exec dysfunction

FrSBe disinhibition

NMR scale

DASS depression

DASS anxiety

DASS stress

Total AUDIT

F

p

F

p

F

p

F

p

F

p

F

p

F

p

F

p

10.33 23.27 16.09

.001 b.0001 b.0001

12.70 40.42 11.26

b.0001 b.0001 .001

19.41 51.08 14.18

b.0001 b.0001 b.0001

3.99 45.92 5.44

.047 b.0001 .020

15.29 47.17 10.85

b.0001 b.0001 .001

20.87 94.17 11.03

b.0001 b.0001 .001

16.74 39.86 8.20

b.0001 b.0001 .004

23.23 54.84 14.49

b.0001 b.0001 b.0001

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M. Lyvers et al. / Addictive Behaviors 38 (2013) 2741–2750

Table 2 Means and standard deviations on the FrSBe, DASS-21, NMR scale and AUDIT for current smokers and never-smokers combined across Chinese and Australian samples. Smokers

FrSBe apathy FrSBe dysfunction FrSBe disinhibition DASS depression DASS anxiety DASS stress NMRS AUDIT

Never-smokers

M

SD

M

SD

38.35 48.32 43.25 16.05 16.18 16.74 97.46 28.37

7.78 8.94 8.90 4.51 4.38 4.10 12.07 7.84

28.71 35.40 30.41 10.16 9.04 11.90 114.15 16.89

5.79 7.82 6.56 3.24 3.04 3.98 13.60 5.13

approach significance for any dependent measure; that is, Chinese and Australian never-smokers did not differ on their Depression, Anxiety, Stress, NMR, Apathy, Disinhibition, Executive Dysfunction or AUDIT scores. By contrast the MANCOVA for smokers indicated a highly significant multivariate effect of country according to Pillai's Trace, F(8, 199) = 4.03, p b .0001, partial η2 = .19, observed power = 1. Univariate differences between Chinese and Australian smokers were highly significant (p b .0001) for all dependent measures as shown in Table 3. Thus although Chinese and Australian never-smokers did not differ on any dependent measure, Chinese smokers scored significantly worse on all indices of mood, mood regulation and frontal dysfunction than did Australian smokers. This was contrary to predictions. An obvious question thus warranted further investigation: why were Chinese current smokers worse off on all measures than Australian current smokers given there was no difference between Chinese and Australian never-smokers on the same measures? One possible factor was the level of nicotine dependence in the two samples of current smokers. As reported earlier above, Chinese smokers scored significantly higher than Australian smokers on the FTND index of nicotine dependence, though still within the moderately dependent range. Further, correlational analysis indicated that among smokers across both Chinese and Australian samples, FTND scores were highly significantly correlated with all dependent measures: Depression (r = .36, p b .0001), Anxiety (r = .31, p b .0001), Stress (r = .38, p b .0001), NMR (r = −.23, p = .003), AUDIT (r = .32, p b .0001), Apathy (r = .33, p b .0001), Disinhibition (r = .30, p b .0001), and Executive Dysfunction (r = .25, p = .001). Thus the present results seem to suggest that dysfunction associated with smoking is related to the level of nicotine dependence irrespective of any potential influence of community attitudes toward smoking on the traits of the current smoker population. However when FTND scores were included as one of the covariates in the MANCOVA, all differences between Chinese and Australian smokers reported above remained highly significant

Never Smokers

4. Discussion Current smokers scored significantly worse on all measures compared to never-smokers across both Chinese and Australian samples after controlling for sample differences on age, gender, recruitment method/incentive, education level and employment status via covariate analysis. Further, country of origin significantly interacted with smoker status on all dependent measures. These interactions indicated that although never-smokers from China and Australia did not differ on any dependent measure as revealed by MANCOVA, Chinese smokers scored significantly higher than Australian smokers on all three FrSBe frontal dysfunction subscales (Apathy, Disinhibition, Executive Dysfunction), all three DASS-21 subscales (Depression, Anxiety, Stress), and AUDIT, and scored significantly lower on the mood self-regulation index NMR. Given that the never-smokers from the two countries did not differ on these measures, and that Chinese smokers reported significantly higher nicotine dependence than Australian smokers on the FTND – which was significantly correlated with all dependent measures in smokers – the observed differences between Chinese and Australian smokers seemed potentially attributable to the higher levels of nicotine dependence in the Chinese smokers. However when FTND scores were added as another covariate in the MANCOVA comparing Chinese to Australian smokers, the differences remained highly significant for all dependent measures. Thus nicotine dependence level, to the extent that it was reflected in FTND scores, was unable to account for the differences between Chinese and Australian smokers on indices of negative mood, mood regulation and frontal lobe dysfunction in the present study. The possibility was also examined that by excluding the small percentages of smokers from each country who reported smoking less than daily, the results might differ from those reported above. However the identical analyses using only data from daily smokers yielded all the same highly significant differences as reported above; indeed, the mean differences between Chinese and Australian smokers tended to increase when only daily smokers were assessed. Thus the present findings are contrary to the expectation that Australian smokers would indicate worse functioning than Chinese smokers on all dependent measures as a result of the strong stigma against smoking in Australia, which was hypothesized to have had a “hardening” effect on the remaining

Never Smokers 70

45 40 35 30 25 20 15 10

Smokers

60

Smokers Meand FrSBe Executive Dysfunction Score

Mean FrSBe Apathy Score

50

(all p b .0001). Thus differences in FTND scores could not account for the pronounced differences between Chinese and Australian smokers on mood, mood regulation and frontal lobe dysfunction indices in the current study — differences that were entirely absent in never-smokers.

50 40 30 20 10

5 0

0 Chinese

Australian

Country Fig. 1. Means and standard deviations for FrSBe apathy as a function of country and smoker status.

Chinese

Australian

Country Fig. 2. Means and standard deviations for FrSBe executive dysfunction as a function of country and smoker status.

M. Lyvers et al. / Addictive Behaviors 38 (2013) 2741–2750

Never Smokers

Smokers 25

Mean DASS Anxiety Score

60

Mean FrSBe Disinhibition Score

50

40

30

20

2747

Never Smokers

Smokers

20

15

10

5

10 0

Australian

Chinese

0 Chinese

Country

Australian

Country Fig. 3. Means and standard deviations for FrSBe disinhibition as a function of country and smoker status.

smoking population in Australia compared to China, where smoking is a culturally accepted and common practice. Importantly, never-smokers in China and Australia did not differ on any measure of mood, mood regulation or frontal lobe dysfunction in the present study, whereas smokers in China, where smoking is culturally accepted (at least among men), scored worse on all measures compared to smokers in Australia, where smoking has become stigmatized as a socially undesirable activity targeted by heavy taxation, bans on advertising of tobacco products, smoke-free environment legislation and antismoking campaigns (NSW Cancer Council, 2012). How to explain this finding? In China, where one third of all cigarettes in the world are smoked, there is widespread absence of public awareness concerning the adverse health effects of smoking (Peto et al., 2009). If one assumes a corresponding lack of fear of potential consequences of smoking, then those in China who feel more anxious, depressed, stressed or less able to cope may be more likely to take up and persist with smoking as a way of dealing with negative affect or mild cognitive dysfunction than their counterparts in Australia, where gruesome ads in the media depicting horrific health consequences of smoking are commonplace. Further, as a result of developing nicotine dependence (Parrott, 2006),

Never Smokers

Fig. 5. Means and standard deviations for DASS-21 anxiety as a function of country and smoker status.

adverse mood and cognitive symptoms due to frequent nicotine withdrawal episodes between cigarettes may lead to worsened mood and cognitive functioning overall. Thus in China the relative ease of both taking up smoking and smoking frequently – given the low cost of cigarettes and widespread acceptability of smoking in public – may have paradoxically led to a “hardening” of the smoking population there compared to Australia, where smoking is strongly discouraged by high taxes on cigarettes, bans on public smoking and other anti-smoking strategies as described above. Lyvers et al. (1994) found that chronic heavy smokers performed significantly worse than non-smokers on the Wisconsin Card Sorting Test (WCST) when deprived of nicotine for 12 h, but performed at the level of non-smokers after smoking a cigarette. Given that the WCST has long been considered the gold standard of neuropsychological tests of frontal lobe related executive cognitive functioning (Mountain & Snow, 1993), the findings were interpreted as reflecting an adverse effect of nicotine addiction on the normal functioning of the prefrontal cortex, which is heavily innervated by dopaminergic inputs through which nicotine exerts its rewarding and addictive effects. Similar performance deficits were observed on the WCST in methadone maintenance patients who were acutely deprived of methadone compared to those who had been given their daily methadone dose (Lyvers & Yakimoff, 2003). The present finding of worse self-reported mood and cognitive

Smokers Never Smokers

25

Smokers

20

Mean DASS Stress Score

Mean DASS Depression Score

25

15

10

5

20

15

10

5

0

0 Chinese

Australian

Country Fig. 4. Means and standard deviations for DASS-21 depression as a function of country and smoker status.

Chinese

Australian

Country Fig. 6. Means and standard deviations for DASS-21 stress as a function of country and smoker status.

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M. Lyvers et al. / Addictive Behaviors 38 (2013) 2741–2750

Never Smokers

Smokers

140

Table 3 Means and standard deviations on the FrSBe, DASS-21, NMR scale and AUDIT for the Chinese and Australian smokers.

Mean Total NMR Score

120

Chinese

100 FrSBe apathy FrSBe dysfunction FrSBe disinhibition DASS depression DASS anxiety DASS stress NMRS AUDIT

80 60 40

0 Chinese

Australian

Country Fig. 7. Means and standard deviations for the NMR scale as a function of country and smoker status.

functioning among Chinese smokers compared to Australian smokers may thus reflect a combination of factors — the more positive attitudes toward smoking in China resulting in a greater likelihood of taking up and continuing smoking as a means of acutely alleviating adverse mood (depression, anxiety, stress) or cognitive symptoms (inattention, apathy, etc.), and the ease of frequent smoking leading to stronger nicotine dependence which eventuates in worse outcomes for both mood and cognitive functioning. Limitations of the present study include the diverse recruitment methods and incentives which resulted in groups that differed substantially in size and demographic features and which were not broadly representative of the populations of interest. In all group comparisons we statistically controlled for recruitment/incentive and demographic differences between corresponding samples, however the possibility that there were other differences that were not measured and which might have influenced the findings cannot be ruled out. Further, the Chinese sample was restricted to those who reported being fluent in English. Nevertheless these issues do not undermine the study's test of the “hardening hypothesis” given the complete absence of any differences between Chinese and Australian never-smokers on the same dependent measures that showed highly significant differences between Chinese and Australian smokers. Moreover, the much higher proportion of smokers in the Chinese sample than in the Australian sample, and the much greater proportion of men in the Chinese smoker

Mean Total AUDIT Score

SD

M

SD

40.18 50.28 45.11 16.98 17.11 17.50 95.37 30.04

6.66 7.73 7.91 4.12 3.99 3.75 11.01 6.96

29.58 38.92 34.26 11.45 11.50 12.95 107.45 19.89

6.78 8.19 7.79 3.48 3.19 3.65 11.87 6.66

Note: all mean differences significant at p b .001.

20

40

Australian

M

Never Smokers

sample than in the Australia smoker sample, were consistent with the general pattern of findings from large scale population surveys in the two countries (AIHW, 2011; Li et al., 2011). The different sample sizes did not result in unequal variances as indicated by Levene's Test for most dependent variables, and for the few variables that showed a significant Levene's Test the group differences were of a sufficient magnitude to greatly exceed the more stringent significance level that is warranted in such cases (Tabachnick & Fidell, 2007). Thus although the sizes of the groups that were compared varied substantially, and recruitment methods, incentives and basic demographic factors varied as well, there was no indication that such factors could account for any of the group differences of interest on the dependent measures in the present study. The fact that never-smokers in China did not differ from never-smokers in Australia on any dependent measure strengthens the interpretation that the present findings pertaining to smoking in the two countries reflect true trait correlates of smoking and not confounding differences between samples. The present findings are completely at odds with the prediction of a “hardened” population of current smokers in Australia compared to China, and suggest instead that easy access to cigarettes and widespread social acceptance of smoking may have a “hardening” effect on the smoking population of high smoking prevalence countries such as China by promoting higher levels of nicotine dependence and greater reliance on smoking as a coping mechanism. To clarify these issues further cross-cultural research on the potential influence of community attitudes toward smoking as well as other factors such as socioeconomic status, genetic influences and pre-smoking mental health on the traits of current smoking populations appears warranted. In any case the present findings are highly consistent with previous evidence of strong associations between smoking and negative affect as well as deficient mood regulation and executive cognition, and indicate that chronic tobacco smoking shares these features in common with other addictive behaviors.

Smokers Role of funding sources This research was funded by an internal Bond University School of Humanities & Social Sciences research grant. The funding source had no further role in the study.

35 30

Contributors Michael Lyvers designed the study, obtained funding, and supervised data collection and analyses. Cassandra Carlopio and Vicole Bothma collected the data. Mark Edwards assisted with statistical analyses. All authors co-wrote the manuscript.

25 20 15

Conflict of interest All authors declare that they have no conflict of interest concerning this study.

10 5

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0 Chinese

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Country Fig. 8. Means and standard deviations for the AUDIT as a function of country and smoker status.

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