Background: Smoking behaviour is a major public health problem worldwide. Several sources have confirmed the implication of genomic factors in smoking behaviour. These factors interact both with environmental factors and interventions to develop a certain behaviour. Objectives: Describing the environmental and genomic factors as well as the interventions influencing smoking cessation (SC) and developing a working model incorporating the different factors influencing SC were our main objectives. Methods: Two systematic reviews were conducted using articles in English from the Cochrane library, PubMed and HuGENet from January 2000 to September 2012: (1) a systematic review of systematic reviews and meta-analyses and (2) a systematic review of original research for genomic factors. The proposed working model was developed by making use of previous models of SC and applying an iterative process of discussion and re-examination by the authors. Results: We confirmed the importance of the 4 main factors influencing SC: (1) environmental factors, (2) genomic factors, (3) gene-environment interactions, and (4) evidence-based interventions. The model demonstrates the complex network of factors influencing SC. Conclusion: The working model of SC proposed a global view of factors influencing SC, warranting future research in this area. Future testing of the model will consolidate the understanding of the different factors affecting SC and will help to improve interventions in this field.

Smoking is a major public health problem worldwide and the most preventable cause of morbidity and mortality. Five million people die from tobacco consumption every year [1]. Successful smoking cessation (SC) reduces the incidence of comorbidities as well as mortality among smokers [2,3,4].

Smoking behaviour (smoking initiation, dependence and cessation) is influenced by a high number of both genomic and non-genomic factors. According to twin studies, around 60-70% of the variance of nicotine dependence is explained by inherited factors [5,6]. Genomic factors are explored in 3 types of studies: candidate association studies, genome-wide association studies (GWAS) and ‘omics' studies (including transcriptomic, proteomic and metabolomic studies).To assess the correlation of a particular locus with smoking behaviour in candidate association studies, 2 main hypotheses are used: (1) the cascade theory of reward and (2) the nicotine metabolism. First, based on the ‘cascade theory of reward', a number of target genes can be identified that are related to the central behaviour control system. Many different neurophysiological processes are controlled by the modulation of 4 interlinked compounds: serotonin, opioids, gamma-aminobutyric acid (GABA), and dopamine. In short, serotonin (5-HT) release activates opioids secretion which inhibits the release of GABA. Due to GABA decrease, the release of dopamine is stimulated [7]. Genetic changes in the pathways controlling the synthesis of these compounds may be involved in the susceptibility to smoking. Second, modifications in genes that influence the ‘nicotine metabolism' may also have an impact on smoking behaviour as they define the degradation and the level of nicotine in the body.

Non-genomic factors, also called environmental factors, encompass a broad range of aspects, including cultural, economic, psychological, nutritional, and social factors [8]. SC is reported to be influenced by sociodemographic characteristics, psychological factors and policy decisions [9,10].

Epigenetic mechanisms, such as methylation, histone modification, miRNAS, and chromatin remodelling are induced by tobacco components. Up to now, epigenetic studies have mostly focussed on the development of smoking-related disorders, such as lung cancer, chronic obstructive pulmonary disease and cardiovascular disease [11]. However, some methylations appear to be correlated with smoking status and increase with smoking intensity (e.g. RARB and FHIT) [11].

Every year, around 40% of smokers try to quit smoking for at least one day. Unfortunately, most of them relapse due to nicotine dependence [12,13]. This high rate of relapse supports the need to further improve care for people who want to stop smoking. In the last decades, plenty of interventions have been implemented in SC (e.g. pharmacotherapy and counselling). However, the rate of relapse after SC is still high. A knowledge synthesis of all factors and interventions influencing SC will give a more global view of this public health problem. A visual overview of the current knowledge, through the development of a conceptual or a working model, is particularly relevant. This visual overview illustrates the complex relationships at multiple levels (e.g. psychological, biological and macro-social level) and the relation between the various factors that are involved [14]. Each factor influencing SC is a single component of a causal mechanism, but a given disorder or trait (e.g. SC) may be the result of more than one causal mechanism. Most causes of SC are neither sufficient nor necessary [15].

The models on SC that have been developed up until now had 4 different focuses: (1) application of a general model of behavioural changes on SC [16]; (2) impact of tobacco policies [9]; (3) social classes [17], and (4) environmental factors and interventions [10,18]. These models mostly targeted specific populations (e.g. women, adolescents) and environmental factors. Only one model considered genomic factors focussing mainly on pharmacogenetics [18]. However, the conceptual model assessing tobacco use and dependence includes both non-genomic and genomic factors [19]. The introduction of genomic factors led to a more comprehensive and global model of SC and thus improved the knowledge of both providers and smokers.

In this paper, we present the results of a literature review on factors involved in SC. In the last decade, the dramatic increase of the number of molecular analyses at the genetic level has led to recent GWAS and other ‘genetic test'-based approaches that could shed a new light on the role of particular factors in SC. The aims of this paper were (1) to review the environmental and the genomic factors as well as the interventions influencing SC reported in the recent literature, and (2) to develop a working model of SC integrating all relevant factors based on the existing data and the current models.

In a first step, we conducted 2 systematic reviews of factors influencing SC: the first one based on systematic reviews and meta-analyses (the main review), the second one on original research (review on genomic factors). In the latter review, the rationale to focus specifically on genomic factors was based on the hypothesis that most genomic studies were new and, therefore, more often missed out in reviews that were not very recent. Both systematic reviews were used to develop our working model of SC.

Literature Review of Factors Influencing SC


SC was defined in the Medical Subject Heading (MeSH) index as the discontinuation of the habit of smoking, the inhaling and exhaling of tobacco smoke. This could be self-reported and/or biochemically verified.

Eligibility Criteria

For the main review, eligible studies consisted of systematic reviews and meta-analyses published in English. The target population included adults from the general population. Studies had to be prospective with a follow-up period of at least 6 months.

The review of genomic factors was restricted to prospective studies in English with a 6-months follow-up period. To avoid confounding, we restricted this review to populations of European ancestries. People from the same ethnicity share allelic variations of their genes. Consequently, frequencies of genetic variants differ among different ethnicities [20].

Articles restricted to a population with a specific disorder (e.g. alcohol addiction or cardiovascular disease) other than smoking were excluded from both reviews. This was also the case for hospitalized patients and pregnant women.

Search Strategy (fig. 1)

For the main review, we searched the Cochrane library and PubMed for articles from January 2000 until September 2012 using the term: ‘smoking cessation' (online suppl. table 1; for all supplementary material, see For the review on genomic factors influencing SC, we searched in PubMed for the same period of time using the terms: ‘smoking cessation', ‘genetic', ‘genomic', ‘trancriptomic', ‘proteomic', ‘metabolomic', ‘methylome', and ‘epigenome' (online suppl. table 1). In addition, we searched for ‘smoking cessation' in HuGENet, and we manually reviewed the reference lists of relevant articles and reviews.

Fig. 1

Flow chart of the study selection process. a Flow chart of the study selection process of factors influencing SC in reviews, meta-analyses and guidelines. b Flow chart of the study selection process of genomic factors influencing SC in original studies.

Fig. 1

Flow chart of the study selection process. a Flow chart of the study selection process of factors influencing SC in reviews, meta-analyses and guidelines. b Flow chart of the study selection process of genomic factors influencing SC in original studies.

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One author (S.D.V.) screened the title and electronic abstract identified by the search for relevance and eligibility criteria. Articles that passed this initial screening were further examined.

To make analyses manageable, choices had to be made in the eligibility criteria and the search strategy. Nevertheless, all factors influencing SC were expected to be included in this systematic review, and we presume that the inclusion of other databases would not have contributed to the identification of other factors.

Data Extraction

Factors influencing SC were extracted from reviews and then classified, based on existing models, as follows [9,10,16,17,18]: smoking behaviour, demographic factors, social factors, socioeconomic status (SES), psychological factors, biological factors, health factors, genomic factors, and interventions.

For the interventions, we distinguished 3 target levels: the individual, the neighbourhood (e.g. household or workplace) and the society (e.g. community or national level). Target levels interact with each other and are at the same time influenced by environmental and genomic factors.

For the factors identified in the meta-analyses, the overall risk ratio (RR) or odds ratio (OR), the 95% confidence interval (CI) and the I² statistics (indicating a level of heterogeneity) were extracted (table 1).

Table 1

Overall results of the selected meta-analyses

Overall results of the selected meta-analyses
Overall results of the selected meta-analyses

From eligible papers on genomic factors of SC, we extracted information, where available, on the study design, inclusion and exclusion criteria, sample size, interventions, characteristics of participants, outcome length of follow-up, and Hardy-Weinberg equilibrium (HWE). The HWE assesses the consistency of the genotype frequency of a specific single nucleotide polymorphism (SNP) and other genetic variants (e.g. copy number variation) in the population from generation to generation.

Model Formulation

Building upon the classification of factors extracted from the literature review, we developed the working model of SC (fig. 2). The development of this working model was an iterative process based on existing models of SC and discussion and re-examination by the authors.

Fig. 2

Conceptual model of SC. Based on the literature review, we developed a conceptual model of SC consisting of 3 components: (1) factors influencing SC including both genomic and non-genomic factors and interactions (the different factors are interacting as indicated by the light grey background), (2) interventions based on the 3 levels of population (individual, neighbourhood and society level), and (3) SC (the actual outcome). Interventions are always part of the success of SC. At least unconsciously, interventions at the societal level play a role, as they are part of our daily life. These 3 components are linked together (indicated by the different arrows) and evolve with time (indicated by the timeline at the bottom of the figure). NRT = Nicotine replacement therapy.

Fig. 2

Conceptual model of SC. Based on the literature review, we developed a conceptual model of SC consisting of 3 components: (1) factors influencing SC including both genomic and non-genomic factors and interactions (the different factors are interacting as indicated by the light grey background), (2) interventions based on the 3 levels of population (individual, neighbourhood and society level), and (3) SC (the actual outcome). Interventions are always part of the success of SC. At least unconsciously, interventions at the societal level play a role, as they are part of our daily life. These 3 components are linked together (indicated by the different arrows) and evolve with time (indicated by the timeline at the bottom of the figure). NRT = Nicotine replacement therapy.

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Literature Reviews

Description of Studies

Regarding the main review, among the 636 publications screened in the Cochrane library, PubMed and hand search, 92 met our inclusion criteria (fig. 1a). None of the systematic reviews and meta-analyses discussed genomic factors influencing SC. Actually, meta-analyses mainly assessed the effect of interventions on SC. The overall results (OR, RR and CI) of the meta-analyses for the different categories of factors are presented in table 1.

Regarding the systematic review of genomic factors influencing SC, out of 293 publications, 34 studies met our inclusion criteria (fig. 1b). Four were based on GWAS, and the other 30 were genetic association studies. None of the selected studies reported an ‘omics' association with SC.

Environmental Factor 1 - Smoking Behaviour

Factors determining smoking behaviour such as nicotine dependence or past cessation attempts are leading factors influencing the success of SC. Men initiating smoking before the age of 16 have a reduced SC rate in comparison to men starting at a later age (OR = 2.10, CI = 1.40-3.00) [21,22,23]. Higher nicotine dependence, indicated for example in those who start smoking within 30 min after waking up, reduces SC success [21,22,23,24]. Moreover, a longer duration or a higher number of past cessation attempts is positively associated with SC [21,24]. Compared to abrupt cessation, smoking reduction doubles the probability of SC [23,25]. A positive association with SC is reported for intention-to-quit smoking and the confidence level in quitting [22,24,26,27]. Negative beliefs about smoking and health benefit expectancy increase cessation [22,23,24]. By contrast, a negative impact on SC is reported when enjoying smoking [24] and having easy access to cigarettes [22,23].

Environmental Factor 2 - Demographic Factors

Gender does not influence SC (OR = 1.33, CI = 0.91-1.95) [21], despite hypotheses that women might have lower SC rates due to, for example, more weight concerns or emotional sensitivity [21,22,24]. Regarding age, although in the review of Vangeli et al. [24] 5 studies did not yield significant results, 2 other studies presented a positive association between age and SC. Gender and age might be moderated by other factors such as e.g. menstrual phase, age at smoking initiation and mortality.

Environmental Factor 3 - Social Factors

Social factors are especially important due to transmission of social norms between peers. In most studies, being married, couples living together [21,22,24] or having children at home [22,23,24] has no impact on SC. However, living with both biological parents is suggested to enhance SC [22]. Not having friends who smoke and the absence of other smokers in the household are positively associated with SC [21,22,23].

Environmental Factor 4 - Socioeconomic Status

SES is mainly assessed through education, employment (e.g. employed vs. unemployed, blue vs. white collar) and income. A lower SES may be associated with reduced SC due to limited information about health concerns, lower access to SC therapy, increased risk of daily stress, and weaker social unacceptability of smoking [10]. However, the relation between SC and the 3 aforementioned socioeconomic factors does not prove to be statistically significant in most studies [23,24]. Even a meta-analysis was not able to demonstrate an impact of low income on SC either in men (RR = 1.58, CI = 0.79-3.14) or in women (RR = 1.28, CI = 0.96-1.72) [28].

Environmental Factor 5 - Psychological Factors

Psychological factors such as anxiety (OR = 2.2 for males and 2.6 for females) are likely to reduce SC ability [21]. By contrast, both self-efficacy and intrinsic motivation to quit smoking improve SC [21,24,29]. Therefore, mood and stress management programmes are thought to improve SC [30].

Environmental Factor 6 - Biological Factors

Smoking is known to increase energy expenditure and to reduce appetite. Therefore, a higher weight and more weight concerns may lower SC [30]. Weight concern is linked to psychological and biological factors. A low physical activity is also negatively associated with SC [23]. For women, choosing the target quit day in relation to the menstrual phase, more specifically during the follicular phase, improves SC [30].

Environmental Factor 7 - Health Factors

Health factors, such as alcohol addiction or depression, may have an important impact on SC due to a reduction of the dopamine release. Indeed, smoking induces the release of dopamine. Alcohol use is negatively associated with SC [21,23]. A better perceived mental and physical health status enhances SC [22,23]. However, in most studies there is no significant relation with improved knowledge of health risks associated with smoking [23,24].

Genomic Factors (table 2 and online suppl. table 2)

Regarding the cascade theory of reward, no study reports a significant impact of serotonin and GABA on SC [31,32,33,34]. In opioids, contradictory results are obtained concerning the OPRM1 A118G variant. One study reports that the G-allele improved SC [35]; another confirms this in women but reports an increased SC in men with AA-genotype [36]. Dopamine influences SC through some variants, but some results could not be replicated in different studies [37,38,39,40,41,42,43,44,45,46,47]. The A1-allele of DRD2 Taq1A [38] and the long-allele of DRD4 VNTR [48] appear to reduce SC. By contrast, the Del-allele of DRD2-141C [41], the TT-genotype of DRD2 C957T [41], the 9-repeats of SLC6A3 VNTR [40], and the MetMet-genotype of COMT Val108/158Met [49,50] improve SC. Gene-gene interactions are reported between DRD2 Taq1A-SLC6A3 VNTR [40] and DRD2 Taq1A-CYP2B6 C1459T [47]. The variant CHRNB2 rs2072661, which encodes for a nicotinic receptor is associated with SC [51,52]. However, this finding is not replicated in the study of Spruell et al. [53].

Table 2

Genes influencing SC and reported interactions with interventions

Genes influencing SC and reported interactions with interventions
Genes influencing SC and reported interactions with interventions

Regarding the nicotine metabolism, only one study reports a significant association with CYP2B6*6 in a population of European ancestry [54].

Different genes that belong neither to the cascade of reward nor to the nicotine metabolism are also associated with SC: the C-allele of GALR1(rs2717162) decreases SC [55] and the T-allele of HINT1 (rs3852209) increases SC at 6-month follow-up [56].

In none of the GWAS [57,58,59,60] a significant association was found (level of significance in GWAS is α = 5 × 10-8). Also in 3 (non-GWAS) studies that included many different SNPs, no significant result was found after correction for multiple testing [61,62,63].


Individual Level

At the individual level, 2 kinds of interventions can be distinguished: (1) pharmacological interventions and (2) nonpharmacological interventions.

Meta-analyses (table 1) indicate that various pharmacological treatments improve SC: nicotine replacement therapy, substitutes of nicotine [64,65,66,67,68,69], bupropion and nortriptyline (antidepressants) [67,70,71,72,73,74], and varenicline, cytisine and mecamylamine (agonists of the nicotine acetylcholine receptors) [67,75,76,77,78]. Naltrexone, an opioid antagonist, has no significant impact on SC [79]. Cannabinoid type I receptors (rimonabant and taranabant) appear to improve SC at 12-month follow-up, but no result is observed at 6-month follow-up [80].

Different types of nonpharmacological interventions indicate a substantial improvement in SC: individual counselling (although in contrast with 2 other meta-analyses, Coleman et al. [68] did not report any benefit of individual counselling, I² = 68%) [68,81,82], group counselling [82,83], web-based interventions [84,85,86,87], telephone counselling (with pro-active telephone counselling being even more effective than simple telephone counselling) [82,88,89,90,91], motivational interviewing [92,93,94,95], and genetic notification of smoking-related disease risk [96,97]. Other interventions described in the meta-analyses do not demonstrate statistical significant differences. It concerns: self-help material [98,99], biomedical risk assessment (e.g. CO or spirometry measurement) [100,101,102], incentive-based interventions [85,103,104], and hypnosis (even if men were more likely to respond than women) [105,106,107]. Positive associations are observed for acupuncture [107,108], increased physical activity [23,30,109,110] and aversive therapy [107,111], but the results are not significant. Improvement of SC is also related with the type of provider; e.g. physicians nearly doubled SC in comparison to control [99,112,113], and this is also the case for nurses [99,114,115], psychologists [99] and trained health professionals [116].

Combining pharmacological and/or nonpharmacological interventions appears to improve SC in comparison to no intervention [117,118,119] or to monotherapy (RR = 1.54, CI = 1.19-2.00) [118].

Genetic factors may influence the response to an intervention and, consequently, the success in SC. Some gene-treatment interactions have been reported in European ancestries. Interactions with nicotine replacement therapy are observed for OPRM1 A118G [36], DRD4 VNTR [48], COMT Val108/158Met [43], CHRNB2 rs2072661 [52], and GALR1 rs2717162 [55]. Bupropion interacts with SLC6A4 5-HTTLPR [34], DRD2 TaqIA [42,47], DRD2-141C [41], DRD4 VNTR [45], CYP2B6*6 [120], and GALR1 rs2717162 [55]. Finally, rimonabant interacts with DRD2 Taq1A [44] (table 2).

Neighbourhood Level

At the neighbourhood level, only nonpharmacological interventions are available. Enhancing partner support does not demonstrate any efficacy in improving SC [121,122]. However, the use of a buddy (when someone is appointed to support a smoker) may be of some benefit in the context of a smokers' clinic [123]. Home smoking restrictions have a positive impact on SC [24]. At workplace, no conclusion can be drawn regarding the efficacy of the interventions, due to their heterogeneity of the interventions [124].

Society Level

Policy-makers launch different actions to reduce smoking prevalence at the society level. Increased taxation [23,125] and mass media campaigns [23,125,126,127] appear to improve SC. However, in lower socioeconomic groups, mass media are more effective when also other interventions are included such as free nicotine replacement therapy and telephone counselling or there is a policy to change the social and structural context of cigarette use [127]. Other interventions such as bans (e.g. banning advertising and sponsorship or smoking in public places) [125,128] and health warnings [125] could help to modify SC through health belief modification.

SC Working Model

Based on the literature review, we developed a working model of SC consisting of 3 components (fig. 2): (1) factors influencing SC, (2) interventions and (3) SC. Each factor in the 2 first components may influence SC success. Therefore, when attempting to quit smoking all these factors and interventions need to be considered.

(1) Factors influencing SC - 8 dimensions are included: smoking behaviour, demography, SES, psychology, social situation, health, biology, and genomics. The 8 dimensions cover all factors influencing SC described before, except for the interventions. They constantly evolve and influence one another (indicated by the light grey box covering all factors). For example, smoking induces epigenetic modifications (e.g. methylations) which could influence health [129].

(2) Interventions - 3 target levels are defined to classify the interventions: the individual, neighbourhood and society level. Even if smokers do not want to receive specific interventions, some interventions based on the neighbourhood and society level (e.g. smoke-free workplace and taxation) are present in the daily life of smokers. Interventions are the most important link between factors influencing SC and SC (represented by the broad arrow).

(3) SC - the actual outcome. SC dynamically interacts with the previous boxes (indicated by the dashed arrows). The success of SC by an individual depends on the different factors influencing SC, neighbourhood and the society in which he lives (e.g. social unacceptability, availability of treatments and bans).

Additionally, the model also takes into consideration the evolution of the environmental and the genomic factors and the interventions over time (represented by the time-line).

To our knowledge, this study is the first to propose a literature review-based working model of SC taking into account environmental factors, genomic factors and interventions. Compared to other models focussing on specific factors such as environmental factors [10] or policy [9], our model is more comprehensive.

Conceptual models are useful for summarising and integrating knowledge. The model demonstrates the importance of research that studies the impact of environmental factors, genomic factors and interventions on SC in the general population in an integrated way. It assumes that SC is a dynamic phenomenon depending on many factors, including interventions at the 3 population levels that we described and environmental factors. For example, a health campaign to stop smoking (intervention) leads to changes in the social acceptability (environmental factor), which could further influence the use of interventions and thus also SC itself.

Our actual model focussed on the general population. In a further step, it would be interesting to adapt the model to more specific populations such as smokers with comorbidity or mental illness. The major factors influencing SC will probably be the same, but the effect size might differ. For example, in pregnant women, social pressure will most likely be an important factor. Moreover, for some populations, additional factors appear. In hypertensive smokers, for example, these could be, e.g. treatment of hypertension and other cardiovascular risks.

Factors included in the working model of SC were quite similar to the one used in the integrative model of tobacco use and dependence developed by Swan et al. [19] in 2003. In that model, 7 dimensions were used to consider tobacco use and dependence: (1) environmental risk factors (e.g. SES and peer smoking); (2) vulnerability factors (e.g. family history of smoking and early smoking experiences); (3) tobacco use trajectory features (e.g. speed of transition to regular use and use of other tobacco substances); (4) motivations (e.g. psychosocial and addictive); (5) nicotine dependence (e.g. the Fageström test for nicotine dependence and nicotine metabolism); (6) nicotine reinforcement (e.g. positive reinforcement and relief from withdrawal), and (7) genetic risk factors in relevant biological pathways (e.g. dopamine and serotonin). Although the 7 dimensions used in the integrative model of tobacco use and dependence were classified in a different way than in the working model of SC, the included variables were mostly the same. The main difference between the 2 models is the ‘interventions' component. This component is not included in the model of tobacco use and dependence, as dependence does not involve an intervention.

Some factors in the working model could be considered as moderators or confounders (e.g. age could be a confounder in the relation between age at smoking initiation and SC). However, we decided that all factors should be taken into account, as they are part of the causal mechanism leading to SC [15]. A future statistical validation of the model could help to verify this.

A statistical validation of the model is indeed needed. This validation should assess the interactions between the different factors and the size of the effect of each factor. A way to validate this model would be to develop a prospective cohort of smokers that are willing to stop smoking and to observe them for at least 6 months after having stopped. The statistical technique to deal with this is structural equation modelling, in which the number of observations is based on the number of variables. In the proposed working model, around 40 variables are included, which implies that the minimum number of observations should be 820. The strength of that kind of model would be that it allows evaluating the effect of each single factor and consequently quantifying the effect of the components [15]. Model testing through simulations under different conditions will also help in understanding complex system behaviour in an unexpected fashion [14].

We observe in table 1 that most significant factors influencing SC had a RR or an OR between 1.26 and 2.56. This indicates that none of these factors had a predominant effect on SC, and therefore, all factors may be taken into account when someone wants to quit smoking. Probability helps in the assessment of the contribution of each single component of a causal mechanism. Therefore, it gives a better approximation and more support in public health interventions [130]. However, the interpretation of probabilities often reflects our ignorance about the causal mechanism of a disorder or a trait [130].

Regarding the selection of genomic studies, we focussed on European populations because allele frequencies differ among ethnicities. For example, CYP2A6*2 and CYP2A6*3 alleles are much more frequent in Asian than in Caucasian populations [131]. Environmental factors are also modified by ethnicity; this is, e.g. the case for tobacco use, perception of health risk, or social unacceptability [132]. However, in non-genomic studies, ethnicity is usually not considered as a factor of interest; either mixed ethnicities are used or there is no information on it.

A positive correlation has been reported between the time since quitting and methylation at 2 different loci (F2RL3 and GPR15), even though no epigenetic study reported an effect on SC. Methylations are found to be rapidly reversible, and therefore, remethylations are suggested after SC [133]. Not only exposure to smoking during childhood and adulthood might influence smoking behaviour. Also exposure to smoking during intra-uterine life is known to induce methylations that increase the risk of disorders in later life [134]. These methylations might also influence smoking behaviour in a later stage.

The link between psychology and genomic factors influencing SC could be enhanced as the same genomic pathways are implicated. Munafo et al. [135] reported in their meta-analysis that: (1) a genetic variant of the serotonin (5-HTTLPR) was associated with avoidance and aggression traits; (2) dopamine genes were associated with approach traits (DRD3) and avoidance traits (DRD4). This supports the hypothesis that some personalities are more at risk of having problems to quit smoking.

Multiple interactions (gene-gene, gene-environment and environment-environment interactions) influence individual chances to achieve SC. Because of these complex interactions and the association of the same genomic and environmental factors with other disorders, it is still difficult to recognise the influence individual genes have on SC. Even if some studies demonstrated an association of genomic factors with SC, there is still a lack of replication in the literature. Moreover, up to now, only a few studies assessed the multiple interactions (environment-environment, gene-gene and gene-environment interactions) influencing SC. For example, gene-tobacco control policy interactions have already been suggested in tobacco use. The evolution of tobacco control policies moderates tobacco use by individual genotype [136]. This observation might be applied to SC in the future.

One important limitation of our research is that we restricted the search to prospective studies including 6-month follow-up. This criterion reduced the number of included publications on genomic studies because most original papers and most meta-analyses included retrospective and cross-sectional studies. The same applied for interventions at the society level. For example, even if experts believe that plain packaging will lead to a decline in smoking prevalence [137], no systematic review exists that studied the impact of this on SC.

This review presents various factors influencing SC and provides a framework to further analyse factors influencing SC. Future studies should analyse the interactions and the intensity of the relationship between the different variables of the working model. It will be necessary to validate the model and to assess its quality. Working models are dynamic and constantly changing by the emergence of new evidence. Therefore, the assessment of the quality of this model and the emergence of new developments (e.g. epigenetics, proteomics or vaccines) will lead to modification.

We believe that, in the future, the working model of SC may help to have a global view of factors influencing SC. This will be a key to target interventions on individual smokers and, consequently, improve SC success. The proposed model may also be helpful in the elaboration of future research, aiming to understand the different mechanisms linked to SC.

This work is supported by a grant from the Belgian Federal Science Policy Office and the European Commission PHGEN II (Duration period: May 2009-June 2012, EU-Project No. 20081302).

World Health Organization: WHO Report on the global tobacco epidemic, implementing smoke-free environments. Geneva, World Health Organization, 2009.
Godtfredsen NS, Osler M, Vestbo J, Andersen I, Prescott E: Smoking reduction, smoking cessation, and incidence of fatal and non-fatal myocardial infarction in Denmark 1976-1998: a pooled cohort study. J Epidemiol Community Health 2003;57:412.
White WB: Smoking-related morbidity and mortality in the cardiovascular setting. Prev Cardiol 2007;10(2 suppl 1):1-4.
Willemse BW, Postma DS, Timens W, ten Hacken NH: The impact of smoking cessation on respiratory symptoms, lung function, airway hyperresponsiveness and inflammation. Eur Respir J 2004;23:464-476.
Broms U, Silventoinen K, Madden PA, Heath AC, Kaprio J: Genetic architecture ofsmoking behavior: a study of Finnish adult twins. Twin Res Hum Genet 2006;9:64-72.
Kendler KS, Thornton LM, Pedersen NL: Tobacco consumption in Swedish twins reared apart and reared together. Arch Gen Psychiatry 2000;57:886-892.
Du Y, Wan YJ: The interaction of reward genes with environmental factors in contribution to alcoholism in Mexican Americans. Alcohol Clin Exp Res 2009;33:2103-2112.
Plomin R, Defries JC, McClearn GE, McGuffin P: Behavioral Genetics. New York, Worth Publishers, 2008.
Fong GT, Cummings KM, Borland R, Hastings G, Hyland A, Giovino GA, Hammond D, Thompson ME: The conceptual framework of the International Tobacco Control (ITC) Policy Evaluation Project. Tob Control 2006;15(suppl 3):iii3-iii11.
Manfredi C, Cho YI, Crittenden KS, Dolecek TA: A path model of smoking cessation in women smokers of low socio-economic status. Health Educ Res 2007;22:747-756.
Talikka M, Sierro N, Ivanov NV, Chaudhary N, Peck MJ, Hoeng J, Coggins CR, Peitsch MC: Genomic impact of cigarette smoke, with application to three smoking-related diseases. Crit Rev Toxicol 2012;42:877-889.
Centers for Disease Control and Prevention: Cigarette smoking among adults - United States, 2007. MMWMR 2008;57:1221-1226.
Hughes JR, Keely J, Naud S: Shape of the relapse curve and long-term abstinence among untreated smokers. Addiction 2004;99:29-38.
Galea S, Riddle M, Kaplan GA: Causal thinking and complex system approaches in epidemiology. Int J Epidemiol 2010;39:97-106.
Rothman KJ, Greenland S: Causation and causal inference in epidemiology. Am J Public Health 2005;95(suppl 1):S144-S150.
Hovell MF, Hughes SC: The behavioral ecology of secondhand smoke exposure: a pathway to complete tobacco control. Nicotine Tob Res 2009;11:1254-1264.
Honjo K, Tsutsumi A, Kawachi I, Kawakami N: What accounts for the relationship between social class and smoking cessation? Results of a path analysis. Soc Sci Med 2006;62:317-328.
Lerman CE, Schnoll RA, Munafò MR: Genetics and smoking cessation improving outcomes in smokers at risk. Am J Prev Med 2007;33(suppl 6):S398-S405.
Swan GE, Hudmon KS, Jack LM, Hemberger K, Carmelli D, Khroyan TV, Ring HZ, Hops H, Andrews JA, Tildesley E, McBride D, Benowitz N, Webster C, Wilhelmsen KC, Feiler HS, Koenig B, Caron L, Illes J, Cheng LS: Environmental and genetic determinants of tobacco use: methodology for a multidisciplinary, longitudinal family-based investigation. Cancer Epidemiol Biomarkers Prev 2003;12:994-1005.
Race, Ethnicity, and Genetics Working Group: The use of racial, ethnic, and ancestral categories in human genetics research. Am J Hum Genet 2005;77:519-532.
Caponnetto P, Polosa R: Common predictors of smoking cessation in clinical practice. Respir Med 2008;102:1182-1192.
Cengelli S, O'Loughlin J, Lauzon B, Cornuz J: A systematic review of longitudinal population-based studies on the predictors of smoking cessation in adolescent and young adult smokers. Tob Control 2012;21:355-362.
Bader P, Travis HE, Skinner HA: Knowledge synthesis of smoking cessation among employed and unemployed young adults. Am J Public Health 2007;97:1434-1443.
Vangeli E, Stapleton J, Smit ES, Borland R, West R: Predictors of attempts to stop smoking and their success in adult general population samples: a systematic review. Addiction 2011;106:2110-2121.
Wang D, Connock M, Barton P, Fry-Smith A, Aveyard P, Moore D: ‘Cut down to quit' with nicotine replacement therapies in smoking cessation: a systematic review of effectiveness and economic analysis. Health Technol Assess 2008;12:iii-xi, 1-135.
Cahill K, Lancaster T, Green N: Stage-based interventions for smoking cessation. Cochrane Database Syst Rev 2010;CD004492.
Riemsma RP, Pattenden J, Bridle C, Sowden AJ, Mather L, Watt IS, Walker A: Systematic review of the effectiveness of stage based interventions to promote smoking cessation. BMJ 2003;326:1175-1177.
Bryant J, Bonevski B, Paul C, McElduff P, Attia J: A systematic review and meta-analysis of the effectiveness of behavioural smoking cessation interventions in selected disadvantaged groups. Addiction 2011;106:1568-1585.
Gwaltney CJ, Metrik J, Kahler CW, Shiffman S: Self-efficacy and smoking cessation: a meta-analysis. Psychol Addict Behav 2009;23:56-66.
Torchalla I, Okoli CT, Bottorff JL, Qu A, Poole N, Greaves L: Smoking cessation programs targeted to women: a systematic review. Women Health 2012;52:32-54.
David SP, Munafò MR, Murphy MF, Walton RT, Johnstone EC: The serotonin transporter 5-HTTLPR polymorphism and treatment response to nicotine patch: follow-up of a randomized controlled trial. Nicotine Tob Res 2007;9:225-231.
David SP, Johnstone EC, Murphy MF, Aveyard P, Guo B, Lerman C, Munafò MR: Genetic variation in the serotonin pathway and smoking cessation with nicotine replacement therapy: new data from the Patch in Practice trial and pooled analyses. Drug Alcohol Depend 2008;98:77-85.
Munafò MR, Johnstone EC, Wileyto EP, Shields PG, Elliot KM, Lerman C: Lack of association of 5-HTTLPR genotype with smoking cessation in a nicotine replacement therapy randomized trial. Cancer Epidemiol Biomarkers Prev 2006;15:398-400.
Quaak M, van Schayck CP, Postma DS, Wagena EJ, van Schooten FJ: Genetic variants in the serotonin transporter influence the efficacy of bupropion and nortriptyline in smoking cessation. Addiction 2012;107:178-187.
Lerman C, Wileyto EP, Patterson F, Rukstalis M, Audrain-McGovern J, Restine S, Shields PG, Kaufmann V, Redden D, Benowitz N, Berrettini WH: The functional mu opioid receptor (OPRM1) Asn40Asp variant predicts short-term response to nicotine replacement therapy in a clinical trial. Pharmacogenomics J 2004;4:184-192.
Munafò MR, Elliot KM, Murphy MF, Walton RT, Johnstone EC: Association of the mu-opioid receptor gene with smoking cessation. Pharmacogenomics J 2007;7:353-361.
Munafò MR, Johnstone EC, Murphy MF, Aveyard P: Lack of association of DRD2 rs1800497 (Taq1A) polymorphism with smoking cessation in a nicotine replacement therapy randomized trial. Nicotine Tob Res 2009;11:404-407.
Styn MA, Nukui T, Romkes M, Perkins K, Land SR, Weissfeld JL: The impact of genetic variation in DRD2 and SLC6A3 on smoking cessation in a cohort of participants 1 year after enrollment in a lung cancer screening study. Am J Med Genet B Neuropsychiatr Genet 2009;150B:254-261.
Swan GE, Valdes AM, Ring HZ, Khroyan TV, Jack LM, Ton CC, Curry SJ, McAfee T: Dopamine receptor DRD2 genotype and smoking cessation outcome following treatment with bupropion SR. Pharmacogenomics J 2005;5:21-29.
Lerman C, Shields PG, Wileyto EP, Audrain J, Hawk LH Jr, Pinto A, Kucharski S, Krishnan S, Niaura R, Epstein LH: Effects of dopamine transporter and receptor polymorphisms on smoking cessation in a bupropion clinical trial. Health Psychol 2003;22:541-548.
Lerman C, Jepson C, Wileyto EP, Epstein LH, Rukstalis M, Patterson F, Kaufmann V, Restine S, Hawk L, Niaura R, Berrettini W: Role of functional genetic variation in the dopamine D2 receptor (DRD2) in response to bupropion and nicotine replacement therapy for tobacco dependence: results of two randomized clinical trials. Neuropsychopharmacology 2006;31:231-242.
David SP, Strong DR, Munafò MR, Brown RA, Lloyd-Richardson EE, Wileyto PE, Evins EA, Shields PG, Lerman C, Niaura R: Bupropion efficacy for smoking cessation is influenced by the DRD2 Taq1A polymorphism: analysis of pooled data from two clinical trials. Nicotine Tob Res 2007;9:1251-1257.
Johnstone EC, Elliot KM, David SP, Murphy MF, Walton RT, Munafò MR: Association of COMT Val108/158Met genotype with smoking cessation in a nicotine replacement therapy randomized trial. Cancer Epidemiol Biomarkers Prev 2007;16:1065-1069.
Wilcox CS, Noble EP, Oskooilar N: ANKK1/DRD2 locus variants are associated with rimonabant efficacy in aiding smoking cessation: pilot data. J Investig Med 2011;59:1280-1283.
Leventhal AM, David SP, Brightman M, Strong D, McGeary JE, Brown RA, Lloyd-Richardson EE, Munafò M, Uhl GR, Niaura R: Dopamine D4 receptor gene variation moderates the efficacy of bupropion for smoking cessation. Pharmacogenomics J 2012;12:86-92.
Berrettini WH, Wileyto EP, Epstein L, Restine S, Hawk L, Shields P, Niaura R, Lerman C: Catechol-O-methyltransferase (COMT) gene variants predict response to bupropion therapy for tobacco dependence. Biol Psychiatry 2007;61:111-118.
David SP, Brown RA, Papandonatos GD, Kahler CW, Lloyd-Richardson EE, Munafò MR, Shields PG, Lerman C, Strong D, McCaffery J, Niaura R: Pharmacogenetic clinical trial of sustained-release bupropion for smoking cessation. Nicotine Tob Res 2007;9:821-833.
David SP, Munafò MR, Murphy MF, Proctor M, Walton RT, Johnstone EC: Genetic variation in the dopamine D4 receptor (DRD4) gene and smoking cessation: follow-up of a randomised clinical trial of transdermal nicotine patch. Pharmacogenomics J 2008;8:122-128.
Colilla S, Lerman C, Shields PG, Jepson C, Rukstalis M, Berlin J, DeMichele A, Bunin G, Strom BL, Rebbeck TR: Association of catechol-O-methyltransferase with smoking cessation in two independent studies of women. Pharmacogenet Genomics 2005;15:393-398.
Munafò MR, Johnstone EC, Guo B, Murphy MF, Aveyard P: Association of COMT Val108/158Met genotype with smoking cessation. Pharmacogenet Genomics 2008;18:121-128.
Conti DV, Lee W, Li D, Liu J, Van Den Berg D, Thomas PD, Bergen AW, Swan GE, Tyndale RF, Benowitz NL, Lerman C; Pharmacogenetics of Nicotine Addiction and Treatment Consortium: Nicotinic acetylcholine receptor beta2 subunit gene implicated in a systems-based candidate gene study of smoking cessation. Hum Mol Genet 2008;17:2834-2848.
Perkins KA, Lerman C, Mercincavage M, Fonte CA, Briski JL: Nicotinic acetylcholine receptor beta2 subunit (CHRNB2) gene and short-term ability to quit smoking in response to nicotine patch. Cancer Epidemiol Biomarkers Prev 2009;18:2608-2612.
Spruell T, Colavita G, Donegan T, Egawhary M, Hurley M, Aveyard P, Johnstone EC, Murphy MF, Munafò MR: Association between nicotinic acetylcholine receptor single nucleotide polymorphisms and smoking cessation. Nicotine Tob Res 2012;14:993-997.
Lerman C, Shields PG, Wileyto EP, Audrain J, Pinto A, Hawk L, Krishnan S, Niaura R, Epstein L: Pharmacogenetic investigation of smoking cessation treatment. Pharmacogenetics 2002;12:627-634.
Gold AB, Wileyto EP, Lori A, Conti D, Cubells JF, Lerman C: Pharmacogenetic association of the galanin receptor (GALR1) SNP rs2717162 with smoking cessation. Neuropsychopharmacology 2012;37:1683-1688.
Ray R, Jepson C, Wileyto EP, Dahl JP, Patterson F, Rukstalis M, Pinto A, Berrettini W, Lerman C: Genetic variation in mu-opioid-receptor-interacting proteins and smoking cessation in a nicotine replacement therapy trial. Nicotine Tob Res 2007;9:1237-1241.
Uhl GR, Liu QR, Drgon T, Johnson C, Walther D, Rose JE: Molecular genetics of nicotine dependence and abstinence: whole genome association using 520,000 SNPs. BMC Genet 2007;8:10.
Drgon T, Johnson C, Walther D, Albino AP, Rose JE, Uhl GR: Genome-wide association for smoking cessation success: participants in a trial with adjunctive denicotinized cigarettes. Mol Med 2009;15:268-274.
Uhl GR, Liu QR, Drgon T, Johnson C, Walther D, Rose JE, David SP, Niaura R, Lerman C: Molecular genetics of successful smoking cessation: convergent genome-wide association study results. Arch Gen Psychiatry 2008;65:683-693.
Uhl GR, Drgon T, Johnson C, Ramoni MF, Behm FM, Rose JE: Genome-wide association for smoking cessation success in a trial of precessation nicotine replacement. Mol Med 2010;16:513-526.
Heitjan DF, Guo M, Ray R, Wileyto EP, Epstein LH, Lerman C: Identification of pharmacogenetic markers in smoking cessation therapy. Am J Med Genet B Neuropsychiatr Genet 2008;147B:712-719.
Ray R, Mitra N, Baldwin D, Guo M, Patterson F, Heitjan DF, Jepson C, Wileyto EP, Wei J, Payne T, Ma JZ, Li MD, Lerman C: Convergent evidence that choline acetyltransferase gene variation is associated with prospective smoking cessation and nicotine dependence. Neuropsychopharmacology 2010;35:1374-1382.
Lee W, Bergen AW, Swan GE, Li D, Liu J, Thomas P, Tyndale RF, Benowitz NL, Lerman C, Conti DV: Gender-stratified gene and gene-treatment interactions in smoking cessation. Pharmacogenomics J 2012;12:521-532.
Stead LF, Perera R, Bullen C, Mant D, Hartmann-Boyce J, Cahill K, Lancaster T: Nicotine replacement therapy for smoking cessation. Cochrane Database Syst Rev 2012;11:CD000146.
Cepeda-Benito A, Reynoso JT, Erath S: Meta-analysis of the efficacy of nicotine replacement therapy for smoking cessation: differences between men and women. J Consult Clin Psychol 2004;72:712-722.
Moore D, Aveyard P, Connock M, Wang D, Fry-Smith A, Barton P: Effectiveness and safety of nicotine replacement therapy assisted reduction to stop smoking: systematic review and meta-analysis. BMJ 2009;338:b1024.
Eisenberg MJ, Filion KB, Yavin D, Bélisle P, Mottillo S, Joseph L, Gervais A, O'Loughlin J, Paradis G, Rinfret S, Pilote L: Pharmacotherapies for smoking cessation: a meta-analysis of randomized controlled trials. CMAJ 2008;179:135-144.
Coleman T, Agboola S, Leonardi-Bee J, Taylor M, McEwen A, McNeill A: Relapse prevention in UK Stop Smoking Services: current practice, systematic reviews of effectiveness and cost-effectiveness analysis. Health Technol Assess 2010;14:1-152.
Woolacott NF, Jones L, Forbes CA, Mather LC, Sowden AJ, Song FJ, Raftery JP, Aveyard PN, Hyde CJ, Barton PM: The clinical effectiveness and cost-effectiveness of bupropion and nicotine replacement therapy for smoking cessation: a systematic review and economic evaluation. Health Technol Assess 2002;6:1-245.
Hughes JR, Stead LF, Lancaster T: Antidepressants for smoking cessation. Cochrane Database Syst Rev 2007;CD000031.
Gourlay SG, Stead LF, Benowitz NL: Clonidine for smoking cessation. Cochrane Database Syst Rev 2004;CD000058.
Hughes JR, Stead LF, Lancaster T: Nortriptyline for smoking cessation: a review. Nicotine Tob Res 2005;7:491-499.
Holmes S, Zwar N, Jiménez-Ruiz CA, Ryan PJ, Browning D, Bergmann L, Johnston JA: Bupropion as an aid to smoking cessation: a review of real-life effectiveness. Int J Clin Pract 2004;58:285-291.
Zwar N, Richmond R: Bupropion sustained release. A therapeutic review of Zyban. Aust Fam Physician 2002;31:443-447.
Cahill K, Stead LF, Lancaster T: Nicotine receptor partial agonists for smoking cessation. Cochrane Database Syst Rev 2012;4: CD006103.
Zierler-Brown SL, Kyle JA: Oral varenicline for smoking cessation. Ann Pharmacother 2007;41:95-99.
Etter JF: Cytisine for smoking cessation: a literature review and a meta-analysis. Arch Intern Med 2006;166:1553-1559.
Lancaster T, Stead LF: Mecamylamine (a nicotine antagonist) for smoking cessation. Cochrane Database Syst Rev 2000;CD001009.
David S, Lancaster T, Stead LF, Evins AE: Opioid antagonists for smoking cessation. Cochrane Database Syst Rev 2006;CD003086.
Cahill K, Ussher MH: Cannabinoid type 1 receptor antagonists for smoking cessation. Cochrane Database Syst Rev 2011;CD005353.
Lancaster T, Stead LF: Individual behavioural counselling for smoking cessation. Cochrane Database Syst Rev 2005;CD001292.
Mottillo S, Filion KB, Bélisle P, Joseph L, Gervais A, O'Loughlin J, Paradis G, Pihl R, Pilote L, Rinfret S, Tremblay M, Eisenberg MJ: Behavioural interventions for smoking cessation: a meta-analysis of randomized controlled trials. Eur Heart J 2009;30:718-730.
Stead LF, Lancaster T: Group behaviour therapy programmes for smoking cessation. Cochrane Database Syst Rev 2005;CD001007.
Myung SK, McDonnell DD, Kazinets G, Seo HG, Moskowitz JM: Effects of Web- and computer-based smoking cessation programs: meta-analysis of randomized controlled trials. Arch Intern Med 2009;169:929-937.
Cahill K, Perera R: Competitions and incentives for smoking cessation. Cochrane Database Syst Rev 2011;CD004307.
Hutton HE, Wilson LM, Apelberg BJ, Tang EA, Odelola O, Bass EB, Chander G: A systematic review of randomized controlled trials: Web-based interventions for smoking cessation among adolescents, college students, and adults. Nicotine Tob Res 2011;13:227-238.
Civljak M, Sheikh A, Stead LF, Car J: Internet-based interventions for smoking cessation. Cochrane Database Syst Rev 2010;CD007078.
Stead LF, Perera R, Lancaster T: Telephone counselling for smoking cessation. Cochrane Database Syst Rev 2006;3:CD002850.
Stead LF, Perera R, Lancaster T: A systematic review of interventions for smokers who contact quitlines. Tob Control 2007;16(suppl 1): i3-i8.
Tzelepis F, Paul CL, Walsh RA, McElduff P, Knight J: Proactive telephone counseling for smoking cessation: meta-analyses by recruitment channel and methodological quality. J Natl Cancer Inst 2011;103:922-941.
Pan W: Proactive telephone counseling as an adjunct to minimal intervention for smoking cessation: a meta-analysis. Health Educ Res 2006;21:416-427.
Hettema JE, Hendricks PS: Motivational interviewing for smoking cessation: a meta-analytic review. J Consult Clin Psychol 2010;78:868-884.
Heckman CJ, Egleston BL, Hofmann MT: Efficacy of motivational interviewing for smoking cessation: a systematic review and meta-analysis. Tob Control 2010;19:410-416.
Bodner ME, Dean E: Advice as a smoking cessation strategy: a systematic review and implications for physical therapists. Physiother Theory Pract 2009;25:369-407.
Mantler T, Irwin JD, Morrow D: Motivational interviewing and smoking behaviors: a critical appraisal and literature review of selected cessation initiatives. Psychol Rep 2012;110:445-460.
de Viron S, Van der Heyden J, Ambrosino E, Arbyn M, Brand A, Van Oyen H: Impact of genetic notification on smoking cessation: systematic review and pooled-analysis. Plos One 2012;7:e40230.
Smerecnik C, Grispen JE, Quaak M: Effectiveness of testing for genetic susceptibility to smoking-related diseases on smoking cessation outcomes: a systematic review and meta-analysis. Tob Control 2012;21:347-354.
Lancaster T, Stead LF: Self-help interventions for smoking cessation. Cochrane Database Syst Rev 2005;CD001118.
Mojica WA, Suttorp MJ, Sherman SE, Morton SC, Roth EA, Maglione MA, Rhodes SL, Shekelle PG: Smoking-cessation interventions by type of provider: a meta-analysis. Am J Prev Med 2004;26:391-401.
Bize R, Burnand B, Mueller Y, Rege WM, Cornuz J: Biomedical risk assessment as an aid for smoking cessation. Cochrane Database Syst Rev 2009;CD004705.
Bize R, Burnand B, Mueller Y, Cornuz J: Effectiveness of biomedical risk assessment as an aid for smoking cessation: a systematic review. Tob Control 2007;16:151-156.
McClure JB: Are biomarkers a useful aid in smoking cessation? A review and analysis of the literature. Behav Med 2001;27:37-47.
Leeks KD, Hopkins DP, Soler RE, Aten A, Chattopadhyay SK, Task Force on Community Preventive Services: Worksite-based incentives and competitions to reduce tobacco use. A systematic review. Am J Prev Med 2010;38(suppl 2):S263-S274.
Cahill K, Perera R: Quit and Win contests for smoking cessation. Cochrane Database Syst Rev 2008;CD004986.
Green JP, Jay Lynn S, Montgomery GH: A meta-analysis of gender, smoking cessation, and hypnosis: a brief communication. Int J Clin Exp Hypn 2006;54:224-233.
Green JP, Lynn SJ, Montgomery GH: Gender-related differences in hypnosis-based treatments for smoking: a follow-up meta-analysis. Am J Clin Hypn 2008;50:259-271.
Tahiri M, Mottillo S, Joseph L, Pilote L, Eisenberg MJ: Alternative smoking cessation aids: a meta-analysis of randomized controlled trials. Am J Med 2012;125:576-584.
White AR, Rampes H, Campbell JL: Acupuncture and related interventions for smoking cessation. Cochrane Database Syst Rev 2011;CD000009.
Ussher MH, Taylor A, Faulkner G: Exercise interventions for smoking cessation. Cochrane Database Syst Rev 2012;1:CD002295.
Ussher MH, Taylor AH, West R, McEwen A: Does exercise aid smoking cessation? A systematic review. Addiction 2000;95:199-208.
Hajek P, Stead LF: Aversive smoking for smoking cessation. Cochrane Database Syst Rev 2004;CD000546.
Stead LF, Bergson G, Lancaster T: Physician advice for smoking cessation. Cochrane Database Syst Rev 2008;CD000165.
Zbikowski SM, Magnusson B, Pockey JR, Tindle HA, Weaver KE: A review of smoking cessation interventions for smokers aged 50 and older. Maturitas 2012;71:131-141.
Rice VH, Stead L: Nursing intervention and smoking cessation: meta-analysis update. Heart Lung 2006;35:147-163.
Rice VH, Stead LF: Nursing interventions for smoking cessation. Cochrane Database Syst Rev 2008;CD001188.
Carson KV, Verbiest ME, Crone MR, Brinn MP, Esterman AJ, Assendelft WJ, Smith BJ: Training health professionals in smoking cessation. Cochrane Database Syst Rev 2012;5:CD000214.
Asfar T, Ebbert JO, Klesges RC, Relyea GE: Do smoking reduction interventions promote cessation in smokers not ready to quit? Addict Behav 2011;36:764-768.
Shah SD, Wilken LA, Winkler SR, Lin SJ: Systematic review and meta-analysis of combination therapy for smoking cessation. J Am Pharm Assoc (2003) 2008;48:659-665.
Papadakis S, Mcdonald P, Mullen KA, Reid R, Skulsky K, Pipe A: Strategies to increase the delivery of smoking cessation treatments in primary care settings: asystematic review and meta-analysis. Prev Med 2010;51:199-213.
Lee AM, Jepson C, Hoffmann E, Epstein L, Hawk LW, Lerman C, Tyndale RF: CYP2B6 genotype alters abstinence rates in a bupropion smoking cessation trial. Biol Psychiatry 2007;62:635-641.
Park EW, Tudiver F, Schultz JK, Campbell T: Does enhancing partner support and interaction improve smoking cessation? A meta-analysis. Ann Fam Med 2004;2:170-174.
Park EW, Schultz JK, Tudiver F, Campbell T, Becker L: Enhancing partner support to improve smoking cessation. Cochrane Database Syst Rev 2012;CD002928.
May S, West R: Do social support interventions (‘buddy systems') aid smoking cessation? A review. Tob Control 2000;9:415-422.
Cahill K, Moher M, Lancaster T: Workplace interventions for smoking cessation. Cochrane Database Syst Rev 2008;CD003440.
Wilson LM, Avila TE, Chander G, Hutton HE, Odelola OA, Elf JL, Heckman-Stoddard BM, Bass EB, Little EA, Haberl EB, Apelberg BJ: Impact of tobacco control interventions on smoking initiation, cessation, and prevalence: a systematic review. J Environ Public Health 2012;2012:961724.
Bala M, Strzeszynski L, Cahill K: Mass media interventions for smoking cessation in adults. Cochrane Database Syst Rev 2008; CD004704.
Niederdeppe J, Kuang X, Crock B, Skelton A: Media campaigns to promote smoking cessation among socioeconomically disadvantaged populations: what do we know, what do we need to learn, and what should we do now? Soc Sci Med 2008;67:1343-1355.
Hopkins DP, Razi S, Leeks KD, Priya KG, Chattopadhyay SK, Soler RE; Task Force on Community Preventive Services: Smokefree policies to reduce tobacco use. A systematic review. Am J Prev Med 2010;38(suppl 2): S275-S289.
Alegría-Torres JA, Baccarelli A, Bollati V: Epigenetics and lifestyle. Epigenomics 2011;3:267-277.
Parascandola M: Causes, risks, and probabilities: probabilistic concepts of causation in chronic disease epidemiology. Prev Med 2011;53:232-234.
Pianezza ML, Sellers EM, Tyndale RF: Nicotine metabolism defect reduces smoking. Nature 1998;393:750.
Mermelstein R: Ethnicity, gender and risk factors for smoking initiation: an overview. Nicotine Tob Res 1999;1(suppl 2):S39-S43.
Wan ES, Qiu W, Baccarelli A, Carey VJ, Bacherman H, Rennard SI, Agusti A, Anderson W, Lomas DA, Demeo DL: Cigarette smoking behaviors and time since quitting are associated with differential DNA methylation across the human genome. Hum Mol Genet 2012;21:3073-3082.
Flom JD, Ferris JS, Liao Y, Tehranifar P, Richards CB, Cho YH, Gonzalez K, Santella RM, Terry MB: Prenatal smoke exposure and genomic DNA methylation in a multiethnic birth cohort. Cancer Epidemiol Biomarkers Prev 2011;20:2518-2523.
Munafò MR, Clark TG, Moore LR, Payne E, Walton R, Flint J: Genetic polymorphisms and personality in healthy adults: a systematic review and meta-analysis. Mol Psychiatry 2003;8:471-484.
Fletcher JM: Why have tobacco control policies stalled? Using genetic moderation to examine policy impacts. PLoS One 2012;7:e50576.
Pechey R, Spiegelhalter D, Marteau TM: Impact of plain packaging of tobacco products on smoking in adults and children: an elicitation of international experts' estimates. BMC Public Health 2013;13:18.
Hughes JR, Shiffman S, Callas P, Zhang J: A meta-analysis of the efficacy of over-the-counter nicotine replacement. Tob Control 2003;12:21-27.
Perkins KA, Scott J: Sex differences in long-term smoking cessation rates due to nicotine patch. Nicotine Tob Res 2008;10:1245-1250.
Shiffman S, Ferguson SG: Nicotine patch therapy prior to quitting smoking: a meta-analysis. Addiction 2008;103:557-563.
Lindson N, Aveyard P: An updated meta-analysis of nicotine preloading for smoking cessation: investigating mediators of the effect. Psychopharmacology (Berl) 2011;214:579-592.
Cahill K, Stead LF, Lancaster T: Nicotine receptor partial agonists for smoking cessation. Cochrane Database Syst Rev 2011;2: CD006103.
Aveyard P, Begh R, Parsons A, West R: Brief opportunistic smoking cessation interventions: a systematic review and meta-analysis to compare advice to quit and offer of assistance. Addiction 2012;107:1066-1073.
Shahab L, McEwen A: Online support for smoking cessation: a systematic review of the literature. Addiction 2009;104:1792-1804.
Smedslund G, Fisher KJ, Boles SM, Lichtenstein E: The effectiveness of workplace smoking cessation programmes: a meta-analysis of recent studies. Tob Control 2004;13:197-204.
Munafò MR, Johnstone EC, Walther D, Uhl GR, Murphy MF, Aveyard P: CHRNA3 rs1051730 genotype and short-term smoking cessation. Nicotine Tob Res 2011;13:982-988.
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