Journal of Affective Disorders 274 (2020) 553–567
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Journal of Aﬀective Disorders journal homepage: www.elsevier.com/locate/jad
Negative aﬀect among daily smokers: A systematic review and meta-analysis a,⁎
Mehdi Akbari , Jafar Hasani , Mohammad Seydavi a b c
Clinical Psychology, Faculty of Psychology and Education, Department of Clinical Psychology, Kharazmi University, Tehran, Iran Psychology, Kharazmi University, Tehran, Iran Clinical Psychology, Kharazmi University, Tehran, Iran
A R T I C LE I N FO
A B S T R A C T
Keywords: Smoking Negative aﬀect Daily smokers Meta-analysis Systematic review
Background: Negative Aﬀect (NA), as a personality trait is a tendency towards experiencing a more negative emotion. The body of research suggests that NA encourages smoking relapse and smoking as a reason for NA reduction, though. The likelihood of this connection does not seem to be bright yet. The present study critically reviews researches to synthesize the existing literature to determine the strength of this linkage. Methods: Key-word related research was systematically searched in PubMed, PsychINFO, Science Direct and Google Scholar for studies conducted from 1980 to 2019, followed by, the assessment and selection of retrieved studies based on deﬁned inclusion criteria. A random-eﬀects meta-analysis model was used to examine the prospective relationship between NA and smoking. Meta-regression was also used to dig for possible explanations of heterogeneity. Furthermore a multi-moderators model and sub-group analyses examined the moderating factors. Results: Forty eﬀect-sizes comprising 12 cross-sectional studies, 28 longitudinal studies and 24,913 participants were included in the meta-analysis. The forest plot of the pooled correlation eﬀect size in the random model indicates a signiﬁcant eﬀect size of the relationship between NA and smoking (r = 0.11; 95%CI 0.071–0.15, P = 0.001) in the meta-analysis with high heterogeneity (Q = 473.916; df=39; P = 0.001; I2=91.77%). Also, the pooled eﬀect size was obtained as 0.143 (95%CI 0.071–0.214) for light-to-moderate and 0.112 (95%CI 0.057–0.166) for moderate-to-heavy smokers, with the eﬀect size ranging from 0.061 to 0.195 which was signiﬁcant among all subtypes, though this trend seem higher among adolescents, males, and longitudinal studies than in adults, females, and cross-sectional studies. Limitations: The review was limited to English articles, and the heterogeneity of the studies were high. Conclusion: These results support the notion that NA was positively and weakly linked to smoking and this linkage is stronger in light-to-moderate smokers, males, and adolescents. Theoretical and clinical implications are discussed with the aim of extending future directions on NA and smoking.
1. Introduction Tobacco smoking is one of the notable public health threats killing, So that each year, tobacco kills seven million people. The number of deaths attributable to smoking is projected to increase to 8.3 million by 2030, with the largest increase in low-and middle-income countries (World Health Organization, 2019). Furthermore, around 1.2 million of death only caused by exposure to smoke, namely second-hand smokers (World Health Organization, 2019). By searching the databases about smoking, a vast amount of papers will retrieve, which shows a variety of countries and researchers concerns this matter (Robinson et al., 2019). Negative aﬀect (NA) is posited to play a central role in the underlying motivation for smoking (Green et al., 2016; Rogers et al., 2019),
making NA one of the most exciting subjects in this ﬁeld which has received much research (Robinson et al., 2017; Gibbons et al., 2018; Rogers et al., 2019). 1.1. Negative aﬀect conceptualization Negative aﬀect, has been deﬁned by Watson et al. (1988) as a “general dimension of subjective distress and unpleasurable engagement that subsumes a variety of aversive mood states, including anger, contempt, disgust, guilt, fear, and nervousness, with low NA being a state of calmness and serenity” (p. 1063). It is also called negative affectivity, measured by factors such as neuroticism, and it is impossible to distinguish from a personality trait operationally (Steptoe, 1998). It
Corresponding author. E-mail address: [email protected]
https://doi.org/10.1016/j.jad.2020.05.063 Received 20 January 2020; Received in revised form 2 April 2020; Accepted 13 May 2020 Available online 26 May 2020 0165-0327/ © 2020 Elsevier B.V. All rights reserved.
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high negative aﬀectivity, i.e., NA-trait of personality, becomes anxious, afraid, irritable, hyper-reactive, and emotionally labile more easily (Watson and Pennebaker, 1989). So, with an increase in subjective distress (Watson et al., 1988), the person would seek strategies to cope or deal with the unpleasantness of stress (Lazarus and Folkman, 1984). Then, when the strategy is used, e.g., smoking, it might have a negative reinforcement eﬀect, encouraging reusing the coping mechanism. Since people with high negative aﬀectivity or neuroticism are more sensitive to punishment and reward (Kropotov, 2016) and experience more negative emotions following subjective or objective distress (Watson et al., 1988), using cigarettes to be released from the negative emotions seems immediately reasonable for smokers (Gibbons et al., 2018).
has been measured by using the Positive and Negative Aﬀect Schedule (PANAS) or by other scales that assess neuroticism (Eysenck and Eysenck, 1985; Watson et al., 1988). Although the PANAS is involved with the assessment of NA when the clinician or the researcher instructs the participants to ﬁll the questionnaire based on how they generally feel, the score will be a representation of NA as a trait. That is synonymous with neuroticism as a higher rate of aﬀective traits such as anger, anxiety, depression (NEO; Costa and McCrae, 1995) and negative aﬀectivity (Stringer, 2013) because it considers the assessment of a stable tendency to experience negative emotions and a variety of “aversive mood states” including anger, contempt, disgust, guilt, fear, and nervousness (Watson and Clark, 1984; Watson et al., 1988). As follow, cognition, self-concept, and worldview might be inﬂuenced by the degree of this trait-NA; a higher level is blended with urging some people to feel more negative feelings, even though the underlying stressor is not present (Hairston, 2015). Hofmann and colleagues (2012) presented a transdiagnostic model stating that mood (and anxiety) disorders result from dysregulated NA, coupled with a deﬁciency in PA (Hofmann et al., 2012). This model of transdiagnostic is supported by recent studies suggesting that increased and dysregulated NA is positively associated with elevated stress levels (Dua, 1993) and, depressive symptoms (Aldao et al., 2010; Young and Dietrich, 2015), and may predict the onset of depression (Nolen-Hoeksema et al., 2008). This is also in line with the Research Domain Criteria (RDoC) initiative by National Institute of Mental Health (NIMH) (Insel et al., 2010) that aims at establishing a dimensional approach to mental disorders, links psychological conditions directly to psychopathological mechanisms. The RDoC initiative identiﬁed ﬁve initial candidate domains including PA (reward seeking, learning, and habit formation) and NA (fear, distress, and aggression) (Sanislow et al., 2010). Neither very high nor very low levels of NA, are safe. Because people with a very low NA dimension, in contrast to high-NA, might be unable to see themselves and their problems realistically (Byrne, 1961, 1964; Cited in Watson and Clark 1984). Although it suggests that there might be an optimal level of NA, and it is not entirely wrong. Still, a growing body of research suggests that general NA is a fundamental component of various forms of psychopathology (Stanton and Watson, 2014). By reviewing 175 studies, Kotov et al. (2010) found that patients diagnosed with depression, social anxiety, panic disorder, agoraphobia, generalized anxiety disorder, obsessive-compulsive disorder, post-traumatic stress disorder, speciﬁc phobia, and substance use disorders obtained high scores on NA-trait. Also, in the case of smokers, NA interferes with coping skills learning, cessation, and abstinence maintenance (Brandon, 1994).
1.3. Negative aﬀect and smoking Due to an instinctual and natural desire of the human being to seek pleasure and avoid all unpleasant conditions, drug users credit their use to the ability of a drug to relieve NA (Kassel et al., 2007), which is the case with depressed people where the desire to alleviate NA might tempt them to smoke (Chaiton et al., 2010). According to this theoretical perspective, the degree to which smoking alleviates aﬀective distress is a critical determinant of the negatively reinforcing power of smoking. NA demonstrates unique relations to cessation outcomes, with negative aﬀect states often cited as common antecedents to smoking lapse and relapse. There is also some evidence suggesting that smoking may serve to reduce NA and improve negative mood states following cigarette administration (Green et al., 2016; Gibbons et al., 2018). Above all, there is no debates between smokers and researchers on the role of smoking as a reliever from NA (Perkins et al., 2010; Kassel et al., 2003; Baker et al., 2004). And this relation is apparent. Among quitters, NA is negatively correlated with not smoking (Garey et al., 2016; Bakhshaie et al., 2016) and positively related to smoking (Wills Et Al., 1999; Johnson et al., 2008), which is consistent with studies indicating that tobacco use after a period of abstinence could decrease NA and increase the level of Positive Aﬀect (PA) (McGovern et al., 2014; Leventhal et al., 2013). NA has been shown to independently increase puﬀ count and lessen the ability to resist smoking (Conklin and Perkins, 2005; McKee et al., 2012), while, regular smoking is negatively associated with PA (Wills et al., 1999). However, relationship between PA and smoking is still under research, so, reaching a conclusion is not possible yet. Although NA reduction motives are frequently conceptualized as an explanatory underlying mechanism for nicotine addiction and the maintenance of smoking, the magnitude of the association of NA and smoking, despite the broad literature, varies across diﬀerent studies, ranging from r = 0.09 (weak correlation; Miklus et al., 2012) to r = 0.18 (moderate correlation; Johnson et al., 2008) and r = 0.48 (strength correlation; Doran et al., 2011). Then, there is the question of to what extent is NA related to smoking? Perkins et al. (2010) showed that this association is restricted to smoking dependence, with smoking reliving NA induced by smoking abstinence. So, smoking might not relive NA from other kinds of sources, such as environmental stressors.
1.2. Trait-Negative aﬀect: biological and psychological background Diﬀerences in the levels of NA might be embodied in almost ﬁfty percent inheritability of neuroticism, which is a symbol of genetic forces in high or low stable experiencing of negative emotions (Petty, 2007). So, the role of the biological components is not negligible, and it seems in the case of smoking and NA, the structural bases of the brain must be given attention. The right hemisphere structures such as the hippocampus, dorsal anterior cingulated cortex, dorsolateral prefrontal cortex, and especially the amygdala are essential to negative aﬀectivity (Whittle et al., 2006). Due to the adverse eﬀects and impairments of smoking on the inhibitory response system (Adams et al., 2017) through a reduction in this control system, behaviors (e.g., smoking) may be driven by instant reward and punishment pulses encoded in the amygdala and NA (Kropotov, 2016). Moreover, cortical asymmetry in the right prefrontal activation is more related to the prediction of a person's responses toward negative emotion tasks (Davidson, 1998). Diving deeper into biology is beyond the scope of this paper, but it is clear that people are born with a certain level of susceptibility to negative aﬀectivity. Therefore, psychologically, people would be aﬀected, based on their biological background. A person with
1.4. Gender and age-related diﬀerences So far, we reviewed the literature on smoking and NA. It seems that men and women faced smoking and cessation diﬀerently (Bauer et al., 2007). Women have more diﬃculties in quitting (Smith et al., 2016), which might be due to social injustice and distress, e.g., sexual victimization and harassment that women face (Breiding et al., 2011). Moreover, women are more inclined towards experiencing NA (Del Castillo-Aparicio et al., 2009) and rating events more than men, which could cause re-experiencing of NA (Eaton and Bradley, 2008). Other diﬀerences are ages in both genders, and earlier initiation which is associated with more physical health threats (Ajdacic-Gross et al., 2009; 554
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(WHO, 019), signiﬁes the importance of studying NA as a critical component of psychopathology (Stanton and Watson, 2014), smoking cessation, and abstinence maintenance (Brandon, 1994). However, there is no meta-analysis showing the overall eﬀect-size of the relationship between smoking and NA to date, making this the ﬁrst study ever aimed at estimating this eﬀect size. With respect to the bidirectional linkage of NA and smoking, we aim to focus on NA and smoking, and to examine variations in the relationship by smoking rate (light, moderate, or heavy) among males and females, as well as variations by mean age from adolescence to adulthood.
Huxley and Woodward, 2011). It is acknowledged that adolescence is the time of smoking establishment (Elders et al., 1994), but smoking initiation of men in their late adolescence exceeds that of women, and it increases the likelihood of major depressive disorder and generalized anxiety disorder in women more than men (Thompson et al., 2016). Above all, age increases is expected to be associated with a decrease in the level of NA (Charles et al., 2001), which is a ﬁnding of a longitudinal study conducted over 23 years follow ups by Charles et al., but on the other hand, Cobb-Clark and Schurer (2012) found that such personality traits remained stable after four years of follow-ups. So, in the case of NA, whether it eventually remains stable over time or not remains a subject of debate. And, if it has changed in some cases, what are the underlying mechanisms? An answer to this question might be cigarette smoking. Stephan et al. (2019), by following 15,572 smokers aged between 20 and 92 years old, ﬁgured out that personality can change over time due to smoking. Although the authors did not explain the underlying mechanisms of this change, they found that smoking is associated with increases in neuroticism and decrease in extroversion, openness, agreeableness, and conscientiousness parts of the NEO personality scale.
2. Method This meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (Moher et al., 2009). 2.1. Literature search strategy Regarding the guidelines of databases, we searched PubMed, PsycINFO, Science Direct, and Google Scholar from 1/1/1980 to 12/ 31/2019 to ﬁnd relevant and eligible papers concerning the relationship between smoking and NA. Due to overlapping between aﬀective states, the keywords were deﬁned based on the Aﬀective Process (Gross, 2007). Similar to applied routines, we used the database MeSH terms or related strategies to support the essential synonyms of our keywords, and in the end, these layers were combined using the “AND” Boolean operator. It should also be noted that we were focused on the total papers that were published and included the keywords in their title or abstract. Therefore, we did not use the words age or male/female in the search strategy, and for this reason, the retrieved papers will embody diﬀerent ages and genders. Interested keywords are as follow: "Smok*" OR "tobacco" OR "cigar*" OR "smoking" OR "tobacco smoking" OR "cigar-smoking” OR "cigarette smoking” OR "tobacco use disorder" OR "nicotine." AND "Aﬀect" OR "Aﬀect regulation" OR "coping” OR "emotionality" OR "emotion reactivity" OR "emotional coping” OR "emotional impulsivity" OR "Emotion regulation" OR "emotion dysregulation" OR "expressed emotion" OR "emotional ﬂexibility" OR "emotional stability" OR "emotional adjustment" OR "mood" OR "negative aﬀect" OR "neuroticism" OR "Negative Aﬀectivity" OR "personality" OR “ﬁve-factor model” OR “big-ﬁve” OR “NEO”.
1.5. Previous meta-analyses and current study Regarding personality traits and smoking, we found ﬁve metaanalyses of which tried to shed light on some part of this relationship. Given the association of the Big-Five personality traits with smoking, Malouﬀ et al. (2006) did a meta-analysis on nine studies with 4730 participants and found that smoking is related to low conscientiousness, low agreeableness, and high neuroticism. In another study, Munafo et al. (2007) did a meta-analysis on the diﬀerences in smoking status to ﬁnd the possible diﬀerences between smokers and non-smokers in extraversion and neuroticism. Based on twenty-two studies, they found a signiﬁcant diﬀerence between smokers and non-smokers on both extraversion (d = 0.19) and neuroticism (d = 0.12) traits. Also, Hakulinen et al. (2015), used the Five-factor model as exposure, aimed at conducting a meta-analysis on the nine cohort studies to investigate the relationship between smoking status and personality traits. Consistent with Malouﬀ et al. (2006) ﬁndings, the results demonstrated higher extraversion and neuroticism and lower conscientiousness among current smokers. Two other studies focused on impulsivity traits and smoking. The meta-analysis of ninety-seven studies by Kale et al. (2018) to examine the relationship of impulsivity-related traits with smoking status, reported that smoking status and the severity of nicotine dependence are associated with impulsivity-related traits except for reward sensitivity, which was consistent with the ﬁndings of Bos et al. (2019) who found a positive association between impulsive personality traits and cigarette consumption in adolescents. Overall, all of these studies were concerned with smoking and personality traits. Except for the last two studies, the three studies mentioned earlier were concerned with studies that used the NEO measurement for assessment of neuroticism. By reviewing these studies, it seems that no meta-analysis broadens the scope to study related synonyms of neuroticism, such as negative aﬀectivity, that might be assessed by PANAS. Moreover, these studies were concerned with the smoking status and mean (SD) diﬀerences in personality traits in smokers and non-smokers. So, there is a necessity to study this relationship in current smokers regarding the estimation of the eﬀect size of this association based on correlational studies. In this regard, smokers attribute smoking to its ability to alleviate NA (Kassel et al., 2007), reporting greater tendency and urges to smoke in more puﬀ and duration and when faced with NA inducing situations (Conklin and Perkins, 2005; McKee et al., 2012). On the other hand, NA interferes with cessation, treatment, and coping skills training after abstinence (Brandon, 1994). Furthermore, the increased death risk of smoking for smokers and second-hand smokers, coupled with the rising trend of millions of possible smoking-related-deaths from now to 2030
2.2. Inclusion and exclusion criteria In this meta-analysis review, we deﬁned a range of inclusion criteria prior to study selection, which are as follows: 1) Published in the English language, 2) Design empirically, based on quantitative statistics and peer-reviewed original research, 3) Suﬃcient sample size of at least 100 participants, 4) Reporting the family of correlation coeﬃcient (r) of NA as a predictive and smoking rate per day or smoking as a criterion, and if the eﬀect size was not reported directly, they had to have the required statistics to calculate it indirectly, and 5) Reporting NA as a Trait, not a state, i.e., NA scores should not be gathered after manipulating the mood. Then, studies were excluded if they met on of the following criteria: 1) The study included other types of publications (e.g., qualitative studies, review papers, case reports, book chapters, conference abstracts, thesis/dissertation), 2) The study included other types of nicotine use, "e.g., hookah, nicotine gum, e-cigarettes/vaping", 3) Participants were treatment-seekers or quitters, 4) Smoking measured by the urge to smoke or nicotine dependence or smoking as a reason for reducing NA, and 5) NA was used as an adjustment variable (e.g., Degenhardt et al., 2001; Kauﬀman et al., 2017). 2.3. Outcome measures Despite using diﬀerent measures for NA scoring, all included studies 555
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value and sample size or the correlation and standard error were used to reach the Z score. Then, scores were back-transformed to the pooled correlation coeﬃcients. After running the analysis, we used the random model to compute the eﬀect size based on the result of the Funnel plot and asymmetry test, and then, we ﬁt this by adding the moderators to determining if there are diﬀerences in correlation eﬀect when the analysis includes covariate variables or not. Also, heterogeneity about the Z- scores was investigated by using the I-squared whose value ranges from zero to 100%, with zero being equal to no, 25% to low, 50% to moderate, and 75% equal to high. Higher heterogeneity is representative of more possible explanations for the observed variation than chance. In order to investigate potential explanations for the study variation, we ran a meta-regression by adding the covariates as the predictors of the pooled eﬀect size. Moreover, the publication biases were assessed in the funnel plots and Egger's test to identify the likelihood of asymmetry in the ﬁnal result, which is a test for the Y intercept = 0 from a linear regression of normalized eﬀect estimate (estimate divided by its standard error) against precision (reciprocal of the standard error of the estimate). Furthermore, when it was possible, we categorized the data based on covariates to subgroups for calculating the pooled measure of the association of NA and smoking by various factors. Comprehensive Meta-Analysis Software (CMA) version2 (Borenstein et al., 2011) was used to perform the statistical analysis, while CMA version3 (trial version) was our choice for conducting the meta-regression (Borenstein et al., 2013) regarding the Z- scores, depending on the items of quality assessment, the study regions, and the characteristics of the population.
used scales for measuring NA as a trait and not as a state. After screening the full-text, we made sure that no one has manipulated the mood of participants before recording the NA score. They used “NEO”, “Eysenck Personality Inventory”, and “Goldberg's Neuroticism Scale” to measure NA-trait by the neuroticism scale. Other studies used “Positive and Negative Aﬀect Schedule, Child Behavior Checklist” (Internalizing scale) and “The Center for Epidemiological Studies Depression Scale”, asking the participants to ﬁll the scales regarding how they generally felt, and smoking was measured by the rate of cigarette uses per day or by status coding to 1=yes or 0= no. The participants themselves selfadministered and provided the data, and given the measures, a high correlation means a high relationship between NA-trait and smoking and vice versa. 2.4. Methodological quality assessment The standard quality assessment criteria for evaluating primary research papers from a variety of ﬁelds (Kmet et al., 2004) with 14 criteria were used to assess the quality of eligible studies. For example, “Is the Study design evident and appropriate?”; “Are the Outcome and (if applicable) exposure measure(s) well deﬁned and robust against measurement/misclassiﬁcation bias? Is the Means of assessment reported?”; “Are analytic methods described/justiﬁed and appropriate?” The answers might be yes, partially, no, or Not should Be Asked (N/A) about this paper. The overall score will be 28, which indicates a high quality of the study. Due to the nature of correlational studies, three criteria of interventional studies (items number 5, 6, 7) were scored as N/A. After bypassing these three criteria, the high score will be 22. By the consensus of the authors, the quality of studies were classiﬁed as low risk of bias (i.e., the score equal or more than 18) and moderate risk of bias (i.e., the score between 16 and 17). For this purpose, two reviewers independently assessed the quality of the papers, and both assigned the same overall score to the studies. After calculating the total score, if a paper had scored lower than the cutoﬀ point of 0.70, it would get excluded from the meta-analysis by the consensus of the authors.
3. Results 3.1. Description of relevant studies The search strategy returned 35,826 studies, and the ﬂow diagram of the selection process is depicted in (Fig. 1). After removing duplicate research, 25,524 papers remained that were reviewed by title and abstract, leaving us with. 132 studies that were potentially eligible for inclusion in the analysis, which were retrieved for full-text review. 101 papers got excluded because there were less than 100 participants, such as (Adams et al., 2013; McLeish et al., 2011; Paulus et al., 2018; McGovern et al., 2014), the urge to smoke or nicotine dependence was used for assessing smoking (e.g., Lechner et al., 2018; Leventhal et al., 2013), or the participants were treatment (cessation) seekers (e.g., Garey et al., 2016; Bakhshaie et al., 2016). Finally, we included 31 papers (40 eﬀect size) that met our eligibility criteria. Nevertheless, Doran et al. (2011) and Wills et al. (1999) were reported 2, and 4 correlations, respectively, and Kashdan et al. (2005), Conner et al. (2014), McChargue et al. (2004), and Burch et al. (2008) reported 2, 3, 2, and 2 correlations, respectively, which were added as separate studies to the analysis. It should be noted that although four studies, i.e., Miklus et al. (2012), Cohen et al., (2012), Magid et al. (2009), and Doran et al., (2011) used scales other than PANAS to measure NA, their conceptualization remained consistent with measuring NA as a personality trait. Also, it should be mentioned that only two studies (Kawakami et al., 2000; Kleinjan et al., 2012) reported Spearman correlation, whose addition, we found after running a meta-regression, caused no signiﬁcant diﬀerences (R2=2.45, Q = 11.63, df=1). So, we decided to keep them in the ﬁnal analysis. Therefore, fourteen studies were included in computing the pooled correlation measure eﬀect.
2.5. Data extraction and coding of studies Data extraction was done based on Lipsey and Wilson (2001). If the inclusion criteria were satisﬁed, the full-text of each study was coded by the ﬁrst author as follows. (a) Authors and the year of publication; (b) Study design (i.e., cross-sectional or longitudinal); (c) Sample size; (d) Gender (i.e., percentage of females); (e) Mean age (in years); (f) Mean of smoking (per day); (g) Mean of negative aﬀect; (h) Name of NA measurement scale (e.g., PANAS; EPI or NEO); (i) Quality of the study; (l) Smoking category (i.e., light-to-moderate= 1–11 cigarette per day; moderate to heavy= 12 or more). 2.6. Statistical analysis To calculate the pooled correlation eﬀect measure, we used the correlation coeﬃcient when it was available. Moreover, other types of data were used in some cases to estimate the correlation. First and foremost, we transformed correlations using Fisher's method to Zscores, then pooled them under a random eﬀect model, which is the updated version of the DerSimonian and Laird method (1986) that assumes diﬀerent studies are estimating diﬀerent, yet related, eﬀects. All included studies have separate samples from each other, but if the point is about studies including independent eﬀect sizes, it should be noted that Wills et al. (1999) is a longitudinal study that tracked 1702 students through four years; therefore, we added four correlations as four independent studies. Also, in Doran et al. (2011), the authors have not reported the proportion of the sample size for the two reported correlations; so we used the correlation and standard error and added them as separate studies. Moreover, when determining which correlation belongs to which sample was not possible in Magid et al. (2009), the T-
3.2. Characteristics of the ﬁnally included studies Table 1 is a summary of the common characteristic features of the included studies in the analysis. After a full-text review, we found that the studies could be classiﬁed as cross-sectional (n = 12) and longitudinal studies (n = 28), with their year of publication ranging from1984 (Koskenvuo et al.) to 2017 (Kauﬀman et al.), and the number 556
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Fig. 1. Flow diagram of the selection process of related studies.
studies with high heterogeneities (Q = 473.916, df=39, P = 0.001, I2=91.77%). The eﬀect size, ranging from 0.061 to 0.195, was signiﬁcant among all subtypes according to smoking rate (light-to-moderate and moderate-to-heavy and the quality of studies), gender proportion, number of participants, study design, mean age, and measurement of NA and smoking rate based on gender. Table 2 shows the point estimate of the pooled correlation meta-analyses based on various factors, as described below:
of participants (n = 24,913; male=15,413 and female=9500) ranging from 12 to 74 years with a mean age of almost equal to 27.79 (SD=12.72). Moreover, the mean of smoking among participants was 13.19 (SD=7.46) cigarettes per day and NA average score was 15.07 (SD= 8.38). 3.3. Quality assessment summary As shown in additional Table s1, of 31 papers included in the analysis, four articles had a moderate risk of bias (Magid et al., 2009; Pettit and Kline, 2001; Sale et al., 2014; McChargue et al., 2004), and the rest of the studies, had a high quality and low risk of bias judged by the authors. Also, the mean average study quality rating of the included studies was 19.58 (SD = 1.60; range 0 to 22). It should be noted that all of the studies that met the inclusion criteria, satisﬁed the cutoﬀ point constraint of > 0.70 based on an inter-rater agreement among two independent reviewers.
3.4.1. Longitudinal versus cross-sectional study design Based on the study design, we also calculated the point estimate of NA and smoking relationship eﬀect-size (ES). ES in cross-sectional studies (n = 28; participants=13,819; mean age= 29.08 (SD=12.54), female= 3421; I2= 93.06%) was 0.109 (CI 0.046 - 0.172) and lower than 0.114 (CI 0.072 - 0.155) which is the ES of longitudinal studies with lower participants and heterogeneity (n = 12; participants=11,094; mean age= 21.85 (SD=11.76); female=5219; I2= 82.25%).
3.4. Synthesis of results: the main analysis 3.4.2. Light-to-moderate versus moderate-to-heavy smokers, and sample size The interesting ﬁnding of this subset analysis reveals a higher ES= 0.143 (CI 0.071 - 0.214) among light-to-moderate smokers than ES= 0.112 (CI 0.057- 0.166) among moderate-to-heavy smokers, although the heterogeneity of studies with moderate-to-heavy smokers was
The results of the main analysis are summarized in Table 2 (see Fig. 2 for forest plot). The forest plot of the pooled correlation eﬀect size based on the included studies in the random model indicates a signiﬁcant and weak eﬀect size of the relation between NA and smoking (r = 0.11; 95%CI 0.071–0.15, P = 0.001) in the meta-analysis of forty 557
Cross-Sectional Cross-Sectional Cross-Sectional Cross-Sectional Cross-Sectional Longitudinal Longitudinal Cross-Sectional Longitudinal Longitudinal Cross-Sectional Cross-Sectional Cross-Sectional Cross-Sectional Cross-Sectional Cross-Sectional Cross-Sectional Longitudinal Cross-Sectional Cross-Sectional Longitudinal Cross-Sectional Cross-Sectional Cross-Sectional
Cross-Sectional Cross-Sectional Cross-Sectional Longitudinal Cross-Sectional Cross-Sectional Cross-Sectional
Johnson et al. (2008) Kauﬀman et al.(2017) Miklus et al. (2012) Schlauch et al. (2013) Tart et al. (2010) Wills et al. (1999) Cohen et al. (2002) Doran et al. (2011) Magid et al. (2009) Armon et al. (2013) Zvolensky et al. (2009) Pettit & Kline (2001) Chapman et al. (2009) Kashdan et al. (2005) Gregor et al. (2007) Zvolensky et al. (2006) Aarstad et al. (2007) Conner et al. (2014) Sale et al. (2014) Garey et al. (2016) Kleinjan et al. (2012) Koskenvuo et al. (1984) Feldner et al. (2007) McChargue et al. (2004)
McLeish et al. (2009) Kawakami et al. (2000) Burch et al. (2008) Huppert et al. (1997) Gonzalez et al. (2008) Garey –b; et al. (2016) Bonn-Miller et al. (2005)
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25 26 27 28 29 30 31
222 136 182 6096 171 179 202
202 113 286 116 270 1702 121 1688 633 923 189 140 2429 222 275 924 200 553 187 448 796 4965 206 137
22.45 (8.08) 54.6 (11.8) 22.7 (4.99) 18–64 25.67 (10.54) 41.17 (12.55) 22.5 (7.9)
23.78 (9.69) 22.81 (2.13) 37.25 (12.83) 42.0 18–65 22.4 (9) 12–15 19.69 (1.50) 15.8 (1.2) 18–19 45.56 (9.64) 24.97 (9.78) – 46.81 (12.92) 19.03 (0.79) 25.12 (10.37) 33.36 (9.90) 61.00 (11.00) 12–18 24.00 (9.00) 37.2 (13.5) 12.88 (0.76) >20 14.88 (1.49) 19
Age Mean(SD) or Range
55.40% 100%males 68.13% 50% 47.95% 48% 49.50%
44.6% 53.13% 43.7% 37.7% 52.6% 47% 45% 51% 47% 31% 46% 65.71% 45.90 52.7% 45.1% 50% 18% 50% 57.21 47.8% 56.8% 100%males 52.42 100%males
17.59 (7.03) 30.4 (13.6) 7.81 (6.32) – 16.20 (3.20) 18.42 (7.64) 10.9 (06.2)
15.9 (8.01) 12.1 (10.51) 19.3 (9.33) 22.5 (11.1) 12.99 (7.61) Y/N 7–11 1–100 per month 3.7 Y/N 16.44 (12.84) – – 2.65 (1.20) 15.33 (7.30) 24.52 (14.86) – Y/N Y/N 16.6 (9.90) 3.63 (15.10) – – 4.75 (2.57)
Smoking status(Yes/No) OR Rate Per day Mean (SD)
PANAS (1988) EPI NEO & EPQ EPI PANAS (1988) PANAS (1988) PANAS (1988)
PANAS(1988) PANAS(1988) PANAS(1988) PANAS(1988) PANAS(1988) PANAS(1988) CBCL (1983) CEDS (1977) CEDS (1977) NEO PANAS (1988) PANAS (1988) NEO NEO PANAS (1988) GNS (1992) EPI NEO EPI NEO NEO EPI PANAS (1988) NEO & PANAS (1988)
Negative Aﬀect Measurement
Low Low Low Low Low Low Low Low Moderate Low Low Moderate Low Low Low Low Low Low Moderate Low Low Low Low Moderate
– 25.00 (8.06) 19.35 (7.46) 24.2 (8.7) 17.85 (5.67) 27.52 (11.28) 8.46 (8.49) Male 5.7 (3.9) Female 8.1 (3.8) – 3.54 (1.22) 20.35 (8.31) 20.79 (6.02) 2.26 (0.67) 21.23 (7.53) 18.89 (7.80) 20.22 (5.98) – 3.00 (0.65) 2.23 (0.54) 22.84 (6.73) 3.72 (0.98) 4.1 (1.12) 14.96 (6.40) NEO=10.42 (5.21) PANAS=23.10 (22.22) 19.5 (7.34) 4.5 (2.9) NEO=24.03 (8.98) EPQ=13.24 (5.52) – 18.06 (6.86) 18.88 (7.39) 17.2 (05.5)
Low Low Low Low Low Low Low
Risk of bias
Negative AﬀectMean (SD)
Note: PANAS= Positive and Negative Aﬀect Schedule; CBCL= Child Behavior Checklist (Internalizing scale); EPI= Eysenck personality Inventory; GNS= Goldberg's Neuroticism Scale; CEDS= The Center for Epidemiological Studies Depression Scale.
Authors & Year
Table 1 Characteristics of the ﬁnal included studies in the meta-analysis (n = 31).
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Table 2 The eﬀect-size of Negative Aﬀect and Smoking Rate in the subgroup meta-analyses by various factors. Factors
No. of studies
Longitudinal studies 12 Cross-sectional studies 28 All 40 Smoking Rate Light-to-Moderate 20 Moderate-to-Heavy 12 Gender Proportion Female >50% 19 Male >50% 13 No. of participants Small< 1000 31 Large> 1000 9 Participants by Mean Age adolescence 12–20 16 Adults >20 23 Gender by Mean Age; Male>50% VS. Female >50% Female>50%–Adolescence 12–20 6 Female>50%-Adults >20 6 Male>50%–Adolescence 12–20 7 Male>50%-Adults >20 12 Smoking Rate based on Gender: Male<50% VS. Female <50% M <50% (Light-to-Moderate) 9 F <50% (Light-to-Moderate) 8 M <50% (Moderate-to-Heavy) 2 F <50% (Moderate-to-Heavy) 7 Smoking measurement Status (Yes/No) 6 Cigarettes per Day 31 Negative Aﬀect Measurement PANAS 19 NEO 11 EPI 5
Correlation Measure Eﬀect 95% CI
Heterogeneity1, I2 (%)
0.114 (0.072 - - 0.155) 0.109 (0.046 - - 0.172) 0.113 (0.071 - - 0.153)
82.25 93.06 91.77
0.143 (0.071 - - 0.214) 0.112 (0.057 - - 0.166)
0.067 (0.016 - - 0.118) 0.172 (0.067 - - 0.274)
0.094 (0.047 - - 0.140) 0.163 (0.082 - - 0.241)
0.133 (0.050 - - 0.214) 0.092 (0.057 - - 0.127)
0.195 0.151 0.061 0.063
(0.027 - - 0.352) (0.044 - - 0.254) (0.014 - - 0.120) (−0.034 - - 0.157)
97.28 70.48 93.10 69.41
0.176 0.103 0.190 0.082
(0.013 (0.043 (0.152 (0.004
0.330) 0.161) 0.227) 0.160)
96.01 83.14 00.00 57.65
0.140 (0.100 - - 0.180) 0.102 (0.053 - - 0.150)
0.125 (0.045 - - 0.203) 0.100 (0.055 - - 0.146) 0.096 (0.031 - - 0.159)
94.72 61.77 83.37
Note: All of heterogeneities and Eﬀect sizes were signiﬁcant at P <0.0003.
moderate (I2=68.37%) while was very high (I2=93.39%) in light-tomoderate smokers It means that ES might better represent the real ES among moderate-to-heavy smokers because it indicates the possible homogeneity of the included studies. Moreover, ES, based on sample sizes of over or lower than 1000; indicates that sample size might inﬂuence ES. Although the heterogeneity among both of these classiﬁcations was high, ES was calculated 0.094 (CI 0.047 - 0.140) from studies with lower than 1000 participants, and regarding the conﬁdence interval, it was lower than ES= 0.163 (CI 0.082 - 0.241) in studies with more than 1000 participants.
representation of non-signiﬁcant ES in adult males. Alternatively, it might be a description of the negative association between negative aﬀect and smoking that was reported by some of the studies included in the meta-analysis, though, theoretically, the latter possibility seems more plausible and explainable. Moreover, based on the proportion of both genders across the included studies and despite the range of smoking and high heterogeneity, I2 >75%, in studies with a female percentage of over 50, ES=0.067 (CI 0.016 - 0.118) seems decidedly weaker than ES=0.172 (CI 0.067 - 0.274) from studies with a male percentage of over 50.
3.4.3. Age and gender diﬀerences Regarding age, ES=0.133 (CI 0.050 - 0.214) among adolescents (n = 16, I2=95.33%) ranging from 12 to 20 years old was stronger than ES=0.092 (CI 0.057 - 0.127) among adult participants (n = 23, I2= 74.56%). However, regarding age and gender, ES=0.195 (CI 0.027 - 0.352) in female adolescents (n = 6, I2=97.28%) was higher than ES=0.061 (0.014 - 0.120) in male adolescents (n = 7, I2=93.10%), but it has remained stable at ES=0.151 (0.044 - 0.254) in female adults (n = 6, I2=70.48%) and ES=0.063 (CI −0.034 - 0.157) in male adults (n = 6, I2=69.41%). Although ES seems higher in studies with a female percentage of over 50, and even across ages, it is higher than ES in males. Nonetheless, based on the boundary of conﬁdence intervals, it seems that ES among female adolescents (0.027 - 0.352) is twice greater than ES among male adolescents (0.014 - 0.120) but, the conﬁdence intervals changed with age. Based on conﬁdence interval, (0.044 0.254) ES decreased in adult females with age. On the other hand, ES among adult males increase with age from (0.014 −0.120) in adolescents to (−0.034 - 0.157) in adults. Also, the negative boundary of the conﬁdence interval of ES among male adults must be paid attention to since it might be a clue of the diﬀerent eﬀects of smoking on these people in comparison to male adolescents and adults or female adolescents or, because of CI boundary that includes zero, it might be a
3.4.5. Gender diﬀerences by diﬀerent smoking rates Considering the proportion of both genders, ES was calculated based on studies categorized by the percentage of males or females being less than 50 percent. Despite the low ES in studies with females >50%, diﬀerent ES was obtained when calculated based on the smoking rate categories. However, regardless of being in the light-to-moderate or moderate-to-heavy smokers category, ES in studies with a higher percentage of females (i.e., males<50%) was more signiﬁcant than the same classiﬁcation for males (Females<50%). Therefore, it seems that ES among females, i.e., light-to-moderate=0.176 and moderate-toheavy=0.190, is more substantial than in males from light-to-moderate= 0.103 and moderate-to-heavy smokers= 0.082. Also, it should be noted that due to the low heterogeneity of studies with moderate-toheavy smokers (I2 <75%), the ES of those studies is more considerable than the ES of studies with light-to-moderate smokers. 3.4.6. Smoking measurement Six studies measured smoking by the coding of 1 as yes and 0 as no. Based on these studies with zero heterogeneity, ES= 0.140 (CI 0.100 0.180) was more magniﬁcent and considerable than ES= 0.102 (CI 0.053 - 0.150) from the rest of the studies with a substantial heterogeneity of I2=93.58% that measured the smoking by cigarettes used 559
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Fig. 2. the pooled estimation of the association of Negative Aﬀect and Smoking. Note: C]Cross-sectional; L= Longitudinal.
3.5. Heterogeneity, risk of publication bias and sensitivity analysis
3.4.7. Negative aﬀect measurement ES= 0.125 (CI 0.045 - 0.203) among studies (n = 19) with substantial heterogeneity(I2=94.72%) that used the Positive and Negative Aﬀect Schedule (PANAS) was higher than those who (n = 5) used Eysenck Personality Inventory (EPI) with ES=0.096 (CI 0.031 - 0.159) and a considerable heterogeneity of I2=83.37 than whom (n = 11) were used the NEO (ES= 0.100, CI 0.055 - 0.146) by moderate heterogeneity of I2= 61.77%. Due to moderate I2 among studies that were used NEO, it might be more considerable than higher ES achieved by PANAS. On the other hand, it might be the representation of diﬀerent conceptualizations of NA-trait.
The present meta-analysis revealed a very high degree of heterogeneity I2=91.77% (Q = 473.916, df=39, P = 0.001). Publication bias based on funnel plot (Fig. 3) was very unlikely (the Begg's funnel plot was symmetric). Despite the very high heterogeneity, the Egger's test with a 95% CI for bias yielded a p-value of 0.476 which also indicates the unlikeliness of asymmetry between forty studies and the likeliness of consistency between the included studies. Moreover, we also used the classic fail-safe N test as another publication bias test to examine the number of studies with non-signiﬁcant eﬀect sizes to be included in the meta-analysis to bring the p-value to a value greater than alpha. This meta-analysis incorporates data from 40 studies, which 560
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3.7. Retrospective power analysis The power of the current study was calculated to investigate if it is of a reasonable statistical power and whether the correlation exists or not, using the proposed formula by Valentine et al. (2010). Based on the summary of eﬀect size, the average number per group, and the number of eﬀect sizes, the result indicates an outstanding power of 0.9991regarding the high heterogeneity of studies. Therefore, considering the obtained value of 0.009 for p, we conclude more conﬁdently that the number of the included studies is large enough to ensure the statistical signiﬁcance and meaningfulness of the overall given pooled correlation. 4. Discussion 4.1. Summary of results Fig. 3. Funnel Plot for pooled correlation of NA and Smoking.
This study aimed to assess the meta-analysis of the overall association between NA and smoking, to do which 31 papers (with 40 effect-sizes and their publication years ranging from 1984 to 2019) that met our eligibility criteria were included. These studies were 12 crosssectional studies, while 28 studies were longitudinal. Furthermore, the mean of smoking among participants was ≈ 13.19 (SD=7.46) cigarettes per day, and NA average score was ≈ 15.07 (SD= 8.38). Four articles among the selected studies had a moderate risk of bias and the rest had a lower risk of bias. The results indicated a signiﬁcant eﬀect size of the relationship between NA and smoking (r = 0.11) with a high heterogeneity (I2=91.77%). Eﬀect size ranged from 0.061 to 0.195 among all subtypes according to smoking rate (light-to-moderate and moderate-to-heavy, and quality of studies), gender proportion, number of participants, study design, mean age, measurement of NA, and smoking rate based on gender. In addition, the moderation-based analysis showed that covariate factors of NA measurements, proportion of females, sample size, quality of studies, year, smoking subtypes (Light-moderate-heavy), and sample category (more or less than 1000) together explained 76.88% of the heterogeneity.
yields a z-value of 17.69006 and a corresponding 2-tailed p-value of 0.00000. The fail-safe N is 3219, which means that we would need to locate and include 3219 'null' studies in order for the combined 2-tailed p-value to exceed 0.050. Put another way, there would need to be 80.5 missing studies for every observed study for the eﬀect to be nulliﬁed. Moreover, the Begg and Mazumdar rank correlation test, consistent with the rest of the publication bias test, indicates that the bias of the publication is very unlikely. In this case, Kendall's Tau b (corrected for ties, if any) is 0.09114, with a 1-tailed p-value of 0.20406, which supports the unlikelihood of publication bias. Also, a sensitivity analysis was conducted for the possible elimination of heterogeneity to examine the robustness of the eﬀect-size. It is vital to determine the impact of a diﬀerent decision (i.e., random or ﬁxed-eﬀects model, include or exclude based on sample sizes, methodological quality, or variances) on results, for which we used the onestudy removed method to identify a possible study that would be responsible for the heterogeneity. The forest plot in Fig. 4 displays no obvious outlier, which means it is not possible that one study, by itself, could twist or shift the point estimation of eﬀect size signiﬁcantly in any direction. The meta-regression and multiple moderation models were used to ﬁnd possible explanations for the very high degree of variation amongst the ﬁnally included studies. The result implicates that covariate factors of NA measurements, proportion of females, sample size, quality of studies, year, smoking subtypes (Light-moderate-heavy), and sample category (more or less than 1000) together explained 76.88% (R2=0.46, Q = 103.81,df=24, P = 0.001) of the heterogeneity while14.89% remained unexplained. The explanation graph of variation of studies is depicted in Fig. 5. Moreover, Table 3 is a representation of the moderation models that ran separately across studies.
4.2. Theoretical implications Observed in moderate-to-heavy smokers, a signiﬁcant relationship between NA and cigarette smoking, was found. In line with these ﬁndings, early evidence indicates that individuals continue to smoke despite knowing the recognizable negative consequences of cigarette smoking, which has directed researchers to examine NA in smokers. In this line, NA has been recognized as an essential component in smokers (Carroll et al., 2019; Kassel, Stoud, & Paronis, 2003; Bakhshaie et al., 2018; Green et al., 2016; Johnson, and McLeish, 2016; Vinci et al., 2015). Previous studies have also proven a positive relationship between negative aﬀect and smoking relapse (e.g., Baker et al., 2004; Shiﬀman and Waters, 2004; Messer et al., 2018). Accordingly, many smokers believe that smoking will reduce their stress and NA (Copeland et al., 1995). NA has also been associated with cessation failure and relapse (Cinciripini et al., 2003; Kenford et al., 2002; Messer et al., 2018), with nicotine dependence being prevalent in individuals with NA-based disorders such as social anxiety (Piper et al., 2011), major depression (Breslau et al., 1991; Breslau et al., 1998; Brown et al., 1996; Leventhal et al., 2012; Stubbs et al., 2018), dysthymic disorder (Weinberger et al., 2012), panic disorder (Piper et al., 2011), posttraumatic stress disorder (PTSD; Zvolensky et al., 2008; Alexander et al., 2019), schizophrenia (Dalack et al., 1998; Castle et al., 2019; Hunter et al., 2020), schizotypal and borderline personality traits (Kolliakou, and Joseph, 2000; Dettenborn et al., 2016) coronary artery disease, (Brummett et al., 2002) and generalized anxiety disorder (GAD; Piper et al., 2011). In addition, nicotine dependence is one of the comorbidity modes (Breslau, 1995; Upadhyaya et al., 2002; John et al., 2004; Valentine et al., 2018; Farris et al., 2017; Parker et al., 2019) and vulnerabilities (Breslau et al., 1993; Allegrini et al., 2019; Clark et al.,
3.6. Additional moderator analyses The additional meta-regression was run to seek possible moderators of high heterogeneity among cross-sectional and longitudinal studies that were included in the ﬁnal analysis regarding all of the extracted data. A multi-moderators model for cross-sectional studies of 1) the mean of age, 2) smokers type, i.e., light-to-moderate and moderate-toheavy, and 3) quality of studies, overall, explained 91.71% (R2=0.45, Q = 313.75, df=28, P = 0.001) of the heterogeneity while only 1.35% remained unexplained. Additionally, the proportion of the total variance in longitudinal studies, explained by a multi-moderators model of 1) the proportion of females, 2) type of smoking measurement (status: yes/no), and 3) the lack of reporting the type of smokers (light-tomoderate or moderate to heavy), taken together, explained 64.87% (R2=0.51, Q = 19.93, df=7, P = 0.001) of the heterogeneity and 17.38% remained unexplained. 561
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Fig. 4. the forest Plot for pooled correlation of NA and Smoking based on one-removed study method.
role in smoking underlying motivations (Wellman et al., 2016). According to this theoretical perspective, the degree to which smoking alleviates aﬀective distress is a critical determinant of the negatively reinforcing power of smoking (Baker et al., 2004). In addition, NA demonstrates unique relationships to cessation outcomes (McCarthy et al., 2006; Piper et al., 2011) and smoking lapse and relapse (Shiﬀman, and Waters, 2004). There is also some evidence
2018; Leventhal, and Zvolensky, 2015) in psychiatry literature. The evidence also shows that neurotic patients smoke more cigarettes (Turiano et al., 2015; Taylor et al., 2020) and drink more alcohol (Larkins and Sher, 2006; Mandic-Gajic et al., 2017). In fact, smokers report greater NA compared to nonsmokers and react more strongly to NA cues (see Cameron et al., 2013; Farris et al., 2015). From a theoretical perspective, NA has been posited to play a crucial
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(e.g., Field and Duka, 2004; Komiyama et al., 2018; Lynch et al., 2002; Nesic and Duka, 2006; Tosun et al., 2018; Sterling et al., 2004), the results of the current research inspecting gender diﬀerences indicate that males and females respond diﬀerently to various reinforcing eﬀects of tobacco smoking (Perkins et al., 1999), with males experiencing greater positive reinforcement from the pharmacological properties of nicotine than females (Cepeda-Benito et al., 2004). In opposition, women experience greater reinforcement of smoking and relief from NA from the subjective eﬀects of contexts in which they smoke (File et al., 2001; Leventhal et al., 2007). With regards to the moderator eﬀect, these data invite a theorydriven explanation in the future on the relationships between neuroticism, smoking, and NA. According to the ﬁndings of the present study, it can be claimed that age smoking onset, the number of cigarettes consumed per day, and sex are important moderator factors for the linkage between smoking and negative aﬀect that inﬂuence motivational behaviors that may be important in the development and maintenance of smoking. Although these moderator mechanisms remain unclear, such ﬁndings are roughly consistent with theoretical models of mood-smoking comorbidity (Levanthal et al., 2014; Levanthal and Zvolensky, 2015; Leyro et al., 2008; Zvolensky and Bernstein, 2005; Zvolensky et al., 2018) and more generally emotion-regulation perspective of drug use (Baker et al., 2004). These models predict that neuroticism and NA should be related to reﬂexive smoking behavior aimed at achieving aﬀect management functions.
Fig. 5. Explanation of heterogeneity by covariate.
suggests smoking may serves to reduce negative aﬀect and improve negative mood states following cigarette administration (Beckham et al., 2007; Carter et al., 2008; Perkins et al., 2010). In the context of smoking, two theoretical models have been proposed by the authors, the self- medication model and the orbitofrontal \disinhibition model (Dinn et al., 2004). According to the self- medication model, a signiﬁcant number of smokers are therapists themselves because the nicotine present in a cigarette has the pleasurable characteristics. According to this model, smokers will experience a high degree of mental stress compared to non-smokers. Therefore, the relationship between social behaviors, NA and relevant personality traits, and cigarette consumption may reﬂect failure in the eﬀective use of signs of punishment and reward in directing behavior (see Duehlmeyer et al., 2018; Bernstein, 2017; Vanderschuren et al., 2017). The orbitofrontal\disinhibition model predicts that smokers will be weaker than non - smokers in neuropsychological tasks, which measure sensitivity to the dysfunction of the prefrontal cortex, and will signiﬁcantly gain higher scores in behavioral inhibition and antisocial personality (Dinn et al., 2004; Weywadt et al., 2017; Neuhaus et al., 2008). In this regard, cognitive impairment is also associated with smoking (Sabia et al., 2012; Stirland et al., 2018), leading to higher neuroticism and NA (Lechner et al., 2018). Biological pathways may partly explain this association regarding neuroticism. In this line, Eysenck (1988) believed that smoking and personality constitute a synergistic relationship, and Gilbert and Gilbert (1995) claimed that personality, psychopathology, and nicotine response mediate the genetics of smoking. In Eysenck's model (Eysenck, 1980), smoking is seen as a means by which individuals modify their levels of cortical arousal. Neurotics In particular, were more likely to smoke than emotionally stables individuals. With respect to our ﬁndings, the mechanisms that can explain the relationship between negative aﬀect and smoking include models of negative reinforcement (Baker et al., 2004), aﬀect regulation (Schleicher et al., 2009), and classical conditioning (Brandon, 1994). In line with these models, especially negative reinforcement models (Baker et al., 2004), individuals may smoke to cope with their negative aﬀect.
4.4. Clinical implications The clinical implications of the current meta-analysis are ﬁvefold. First, it may be advisable to screen smokers with higher levels of neuroticism and NA in the context of smoking cessation who might be at risk of smoking cessation problems and other aspect of smoking based interventions. Second, it is possible that smoking-based programs may be enhanced via developing speciﬁc treatment approaches for smokers with higher levels of NA and neuroticism, especially in transdiagnostic prevention modules for smokers with comorbid psychopathology. Third, by gaining a better understanding of how NA inﬂuences smoking behavior across sex and diﬀerent ages, it may be possible to improve our ability to implement more eﬀective interventions for smoking prevention and cessation. Finally, with aﬀective interventions, we can take important steps in reducing cigarette smoking and interventions based on smoking. 4.5. Limitations and future directions There are a range of limitations that should be noted. First, most participants of the current met-analysis are American, it is not clear whether the results are applicable universally. Second, since this study is limited to articles written in English, diﬀerent relationships may be established between NA and smoking in other cultures. Third, this study only considered correlation designs, but experimental designs may tell a diﬀerent narrative. Fourth, in this study, only one dimension of personality, for its correlation with NA, has been considered while, it is also possible to say that other personality dimensions, particularly
4.3. Moderators of eﬀect sizes We observed that, based on gender, the eﬀect-size in the subtype of light-to-moderate smokers among men was stronger than women, but, there were moderating eﬀects. Although research on gender diﬀerences in reactivity to nicotine and other drug has shown inconsistent results Table 3 Meta-regression and moderation models by study design. Random eﬀect models Study design Total (n = 40) Cross-sectional (n = 28) Longitudinal (n = 12)
Goodness of ﬁt Tau2 Tau
NA measurements, Proportion of females, sample size, quality of studies, year, smoking subtypes (Light-moderate-heavy), sample category (more or less than 1000) Mean age, quality of studies, subtypes (Light-moderate-heavy)
Proportion of females, smoking status (Yes/Not), Not reporting the smokers subtype
Models by a set of covariate moderators
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CRediT authorship contribution statement
personality approaches such the Big Five and RST, have diﬀerent relationships with smoking. Fifth, in this study, NA has been examined in general whereas, in recent aﬀective neurosciences theories, such as the Gross modal model (2013), the structure of aﬀect has witnessed some tangible changes. Therefore, addressing these issues can broaden researchers’ horizons. Sixth, this study was based on participants self-report, and therefore, method variance could inﬂuence the results. Sixth, in all included studies for meta-analysis, there was no measurement of nicotine dependence. Thus, future studies could take this opportunity for modeling the relationship of NA and personality with nicotine dependence and other diversity indices of smoking. Seventh, it is also important to note that many of the eﬀect sizes in this meta-analysis were moderate. Thus, the clinical signiﬁcance of this study should be judged in the context of its limitations and the applied naturalistic design applied. Finally, it is noteworthy that we did not have data on participant's psychiatric condition. Therefore, there is necessarily some caution that must be applied to the comorbidity of smoking and psychological disorders. There are also several directions for future investigations as studies examining the relationships between NA and personality traits. Future investigations could usefully build upon the current study by attempting to explicate mediating and moderating processes involved in linkages between aﬀective process (mood, emotion, stress, and impulses), personality traits and smoking behaviors. Multilevel approaches would be a useful methodological next-step in exploring personality- aﬀective process- smoking modeling over time. Overall, the ﬁndings of the current meta-analysis suggest that NA is associated with lifetime cigarette consumption and that neuroticism plays an important role in many aspects of smoking and its persistence over time. However, there was little prospective evidence that other aspects of aﬀective processes and personality dimensions were related to the examined smoking outcomes.
Mehdi Akbari: Conceptualization, Data curation, Formal analysis, Validation, Writing - original draft, Writing - review & editing. Jafar Hasani: Conceptualization, Data curation, Writing - review & editing. Mohammad Seydavi: Data curation, Formal analysis, Validation, Writing - review & editing. Declaration of Competing Interest None. Acknowledgements All persons who have made substantial contributions to the work reported in the manuscript (e.g., technical help, writing and editing assistance, general support), but who do not meet the criteria for authorship, are named in the Acknowledgements and have given us their written permission to be named. If we have not included an Acknowledgements, then that indicates that we have not received substantial contributions from non-authors. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2020.05.063. References Aarstad, A.K.H., Aarstad, H.J., Olofsson, J., 2007. Quality of life, drinking to cope, alcohol consumption and smoking in successfully treated HNSCC patients. Acta Otolaryngol. 127 (10), 1091–1098. Adams, S., Mokrysz, C., Attwood, A.S., Munafò, M.R., 2017. Resisting the urge to smoke: inhibitory control training in cigarette smokers. R. Soc. Open Sci. 4 (8), 170045. Ajdacic-Gross, V., Landolt, K., Angst, J., Gamma, A., Merikangas, K.R., Gutzwiller, F., Rossler, W, 2009. Adult versus adolescent onset of smoking: how are mood disorders and other risk factors involved? Addiction 104 (8), 1411–1419. Aldao, A., Nolen-Hoeksema, S., Schweizer, S., 2010. Emotion-regulation strategies across psychopathology: a meta-analytic review. Clin. Psychol. Rev. 30, 217–237. Alexander, A.C., Ward, K.D., Forde, D.R., Stockton, M., 2019. Are posttraumatic stress and depressive symptoms pathways to smoking relapse after a natural disaster? Drug Alcohol Depend. 195, 178–185. Allegrini, A.G., Verweij, K.J., Abdellaoui, A., Treur, J.L., Hottenga, J.J., Willemsen, G., … &, Vink, J.M., 2019. Genetic vulnerability for smoking and cannabis use: associations with e-cigarette and water pipe use. Nicotine Tobacco Res. 21 (6), 723–730. *Armon, G., Melamed, S., Shirom, A., Berliner, S., Shapira, I., 2013. The associations of the Five Factor Model of personality with inﬂammatory biomarkers: a four-year prospective study. Pers. Individ. Dif. 54 (6), 750–755. Audrain-McGovern, J., Wileyto, E.P., Ashare, R., Cuevas, J., Strasser, A.A., 2014. Reward and aﬀective regulation in depression-prone smokers. Biol. Psychiatry 76 (9), 689–697. Baker, T.B., Brandon, T.H., Chassin, L., 2004. Motivational inﬂuences on cigarette smoking. Annu. Rev. Psychol. 55, 463–491. Bakhshaie, J., Ditre, J.W., Langdon, K.J., Asmundson, G.J., Paulus, D.J., Zvolensky, M.J., 2016. Pain intensity and smoking behavior among treatment seeking smokers. Psychiatry Res. 237, 67–71. Bakhshaie, J., Rogers, A.H., Kauﬀman, B.Y., Fasteau, M., Buckner, J.D., Schmidt, N.B., Zvolensky, M.J., 2018. Situational fears: association with negative aﬀect-related smoking cognition among treatment seeking smokers. Addict. Behav. 85, 158–163. Bauer, T., Göhlmann, S., Sinning, M., 2007. Gender diﬀerences in smoking behavior. Health Econ. 16 (9), 895–909. Beckham, J.C., Dennis, M.F., McClernon, F.J., Mozley, S.L., Collie, C.F., Vrana, S.R., 2007. The eﬀects of cigarette smoking on script-driven imagery in smokers with and without posttraumatic stress disorder. Addict. Behav. 32 (12), 2900–2915. Bernstein, D.A., 2017. The Modiﬁcation of Smoking Behavior: an Evaluative Review 1. In: Learning Mechanisms in Smoking. Routledge, pp. 3–41. *Bonn-Miller, M.O., Zvolensky, M.J., Leen-Feldner, E.W., Feldner, M.T., Yartz, A.R., 2005. Marijuana use among daily tobacco smokers: relationship to anxiety-related factors. J. Psychopathol. Behav. Assess. 27 (4), 279–289. Borenstein, M., Hedges, L.V., Higgins, J.P.T., Rothstein, H.R., 2011. Comprehensive Metaanalysis (Version 2.2.064). Biostat, Englewood, NJ Computer software. Borenstein, M., Hedges, L.V., Higgins, J.P.T., Rothstein, H.R., 2013. Comprehensive Metaanalysis (Version 3). Biostat, Englewood, NJ Computer software. Bos, J., Hayden, M.J., Lum, J.A., Staiger, P.K., 2019. UPPS-P impulsive personality traits and adolescent cigarette smoking: a meta-analysis. Drug Alcohol Depend. 197, 335–343.
4.6. Conclusion In sum, the underlying mechanisms governing the relationship between negative aﬀect and cigarette smoking are unknown. Research on negative aﬀect in the context of nicotine addiction has focused mainly on core aﬀect, and less emphasis has been placed on the experience of discrete negative emotions, such as anger, sadness, frustration, and the role they play among smokers. Examining the experience of discrete negative emotions with respect to other aﬀective processes and personality traits could open a new window into understanding not only the valence of the emotions smokers feel (pleasant vs. unpleasant) but also how they experience those emotions. To enhance smoking-based prevention and treatment success, programs are increasingly designed such that interventions match individual and social risk factors for smoking.
Statement All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated suﬃciently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. Furthermore, each author certiﬁes that this material or similar material has not been and will not be submitted to or published in any other publication before its appearance in the Journal of Aﬀective Disorders.
Funding This research received no speciﬁc grant from any funding agency in the public, commercial, or not-for-proﬁt sectors. 564
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Further reading *Conner, M., Grogan, S., Fry, G., Gough, B., Higgins, A.R., 2009. Direct, mediated and moderated impacts of personality variables on smoking initiation in adolescents. Psychol. Health 24 (9), 1085–1104. *Garey, L., Bakhshaie, J., Vujanovic, A.A., Reitzel, L.R., Schmidt, N.B., Zvolensky, M.J., 2016. Posttraumatic stress symptom severity and cognitive-based smoking processes among trauma-exposed treatment-seeking smokers: the role of perceived stress. Addict. Behav. 60, 84–89. *Leen-Feldner, E.W., Zvolensky, M.J., van Lent, J., Vujanovic, A.A., Bleau, T., Bernstein, A., …&, Feldner, M.T., 2007. Anxiety sensitivity moderates relations among tobacco smoking, panic attack symptoms, and bodily complaints in adolescents. J. Psychopathol. Behav. Assess. 29 (2), 69. *Peasley-Miklus, C.E., McLeish, A.C., Schmidt, N.B., Zvolensky, M.J., 2012. An examination of smoking outcome expectancies, smoking motives and trait worry in a sample of treatment-seeking smokers. Addict. Behav. 37 (4), 407–413. *Pettit, J.W., Kline, J.P., Gencoz, T., Gencoz, F., Joiner Jr, T.E., 2001. Are happy people healthier? The speciﬁc role of positive aﬀect in predicting self-reported health symptoms. J. Res. Pers. 35 (4), 521–536. *Zvolensky, M.J., Gonzalez, A., Bonn-Miller, M.O., Bernstein, A., Goodwin, R.D., 2008b. Negative reinforcement/negative aﬀect reduction cigarette smoking outcome expectancies: incremental validity for anxiety focused on bodily sensations and panic attack symptoms among daily smokers. Exp. Clin. Psychopharmacol. 16 (1).