Trajectories in cigarette dependence as a function of anxiety: a multilevel analysis

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Trajectories in cigarette dependence as a function of anxiety: a multilevel analysis

HALLER, Chiara S., ETTER, Jean-François, COURVOISIER, Delphine

Abstract

BACKGROUND: We assessed the association of anxiety with cigarette dependence over time, depending on smoking status (daily, occasional or ex-smoker); and the association of anxiety with (a) smoking cessation, (b) reduction, and (c) relapse. METHODS: A prospective Internet survey of 1967 ever smokers was assessed three times at 2 weeks interval, in 2007-2010. Cigarette dependence was assessed using the cigarette dependence scale.

Predictors included time, smoking status (daily, occasional or ex-smoker) and anxiety. All measures were assessed at each time point. RESULTS: Dependence decreased over time (slope=-0.21, p

HALLER, Chiara S., ETTER, Jean-François, COURVOISIER, Delphine. Trajectories in cigarette dependence as a function of anxiety: a multilevel analysis. Drug and Alcohol Dependence , 2014, vol. 139, p. 115-120

PMID : 24703608

DOI : 10.1016/j.drugalcdep.2014.03.016

Available at:

http://archive-ouverte.unige.ch/unige:123432

Disclaimer: layout of this document may differ from the published version.

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Drug and Alcohol Dependence

j o u r n a l h o m e p a g e :w w w . e l s e v i e r . c o m / l o c a t e / d r u g a l c d e p

Trajectories in cigarette dependence as a function of anxiety:

A multilevel analysis

Chiara S. Haller ^a,b,c,d,∗ , Jean-Franc¸ois Etter ^e , Delphine Sophie Courvoisier ^f

aDepartment of Psychology, Harvard University, 33 Kirkland Street, Cambridge, MA 02138, USA

bHarvard Medical School, Boston, MA 02115, USA

cDivision of Public Psychiatry, Massachusetts Mental Health Center, 75 Fenwood Road, Boston, MA 02115, USA

dDepartment of Psychiatry, Beth Israel Deaconess Medical Center, 330 Brookline Avenue, Boston, MA 02215, USA

eFaculty of Medicine, University of Geneva, IMSP-CMU, 1 rue Michel-Servet, CH-1211 Geneva 4, Switzerland

fDivision of Clinical Epidemiology, University of Geneva and Geneva University Hospitals, 4 rue Perret-Gentil, 1205 Geneva, Switzerland

a r t i c l e i n f o

Article history:

Received 8 November 2013

Received in revised form 9 March 2014 Accepted 10 March 2014

Available online 22 March 2014

Keywords:

Cigarette Dependence Prospective Anxiety

a b s t r a c t

Background:We assessed the association of anxiety with cigarette dependence over time, depending on smoking status (daily, occasional or ex-smoker); and the association of anxiety with (a) smoking cessation, (b) reduction, and (c) relapse.

Methods:A prospective Internet survey of 1967 ever smokers was assessed three times at 2 weeks inter- val, in 2007–2010. Cigarette dependence was assessed using the cigarette dependence scale. Predictors included time, smoking status (daily, occasional or ex-smoker) and anxiety. All measures were assessed at each time point.

Results:Dependence decreased over time (slope =−0.21,p< 0.001), as did feeling prisoner of cigarettes (slope =−0.25,p< 0.001). Both decreased faster between week 0 and week 2 then between week 2 and week 4 (slopes = 0.25, and 0.13;p< 0.01). Differences in anxiety across individuals were associated with dependence (slope = 0.28,p= 0.001), feeling prisoner of cigarettes (slope = 0.38,p <0.001), cessation (OR = 0.42,p< 0.001), relapse (OR = 1.81,p< 0.01), but not with smoking reduction (OR = 0.85,p= 0.35).

Change over time in anxiety (within individuals) was associated with dependence (slope =−0.11,p= 0.04), nor feeling prisoner of cigarettes (slope =−0.21,p= 0.02), predicted smoking cessation (OR = 0.51, p< 0.001), smoking reduction (OR = 0.67,p= 0.047), and relapse (OR = 1.52,p= 0.03).

Conclusions:Cross-sectionally, cigarette dependence, feeling prisoner of cigarettes, and smoking cessation were associated with anxiety; whereas prospectively, smoking cessation, reduction, and relapse were predicted by state anxiety. Thus, anxiety is an important factor that is associated with smoking behavior.

Implications for treatment are discussed.

Published by Elsevier Ireland Ltd.

1. Introduction

Understanding factors that influence the variability of smoking behavior can be useful for clinicians who treat dependent smokers.

Studies of day-to-day variation in smoking behavior, in particular studies using ecological momentary assessment (EMA) have shown that the number of cigarettes smoked is quite stable (Hughes et al., 1991; Shiffman et al., 2008; Stone and Shiffman, 1994). However,

∗ Corresponding author at: Harvard University, Department of Psychology, 33 Kirkland Street, Cambridge; Harvard Medical School, Beth Israel Deaconess Medical Center, and Massachusetts Mental Health Center, 75 Fenwood Road, MMHC 612, Boston, MA 02115, USA. Tel.: +1 617 754 1258; fax: +1 617 754 1250.

E-mail addresses:cshaller@bidmc.harvard.edu,haller@fas.harvard.edu (C.S. Haller).

the predictors of the variability of smoking behavior and tobacco dependence over time are incompletely documented. Determi- nants of a specific smoking event include negative affect regulation (Colvin and Mermelstein, 2010; Hedeker et al., 2009; Shiffman, 2009; Shiffman and Waters, 2004), mood variability (i.e., different ratings of mood across time), mood regulation (i.e., using strategies to modulate a personal mood state), and depression (Weinstein and Mermelstein, 2013). Mood variability is closely connected to variability in anxiety, in that difficulties to use certain strategies to regulate ones mood, may be related to difficulties to regulate ones anxiety. This, in turn, may predict variability in smoking behavior.

Dependent smokers seek to maintain a satisfactory blood level of nicotine. However, chronic administration of nicotine leads to an increase in the number of nicotinic receptors, which in turn reinforce nicotine withdrawal symptoms (Dani and De Biasi, 2001)

0376-8716/Published by Elsevier Ireland Ltd.

116 C.S. Haller et al. / Drug and Alcohol Dependence 139 (2014) 115–120

that include anxiety and anxiogenic responses (Costall et al., 1989).

Smokers could also be smoking predominantly for pleasure and relaxation effects that approach the effects produced by anxiolyt- ics (Juniper et al., 2005). Thus, smoking behavior may be stable over time, due to the entrenched habits, the short half-life of nico- tine and the possibility of reduced withdrawal symptoms, but it may also be variable, due to its association with anxiety and stress, which vary over time (Huang, 2011; Iversen, 2009). People may for example smoke occasionally to reduce anxiety in given moments, but since nicotine has been shown to be anxiety provoking, they may momentarily reduce anxiety, only to then find themselves with greater anxiety levels (Moylan et al., 2012).

Risk factors for being a smoker include depression, anxiety (McClave et al., 2009), and stress (Childs and De Wit, 2010;

Parrott, 1999; Parrott and Murphy, 2012). The immediate posi- tive reinforcement of smoking includes the reduction of anxiety, increased alertness and concentration, whereas smoking with- drawal evokes anxiety, restlessness, impatience, and depressed mood (Hughes et al., 1991). Furthermore, the prevalence of smok- ing is high among people with psychiatric disorders, (Leonard, 2002; Lichlyter, 2010; McKenzie et al., 2010), including those with anxiety disorders (Kenfield et al., 2010; McKenzie et al., 2010).

However, this association between anxiety and smoking could be due to anxious individuals being more prone to smoking (this effect could be called an inter-individual effect of anxiety). But increased anxiety and/or stress within individuals over time could also lead to increased smoking (intra-individual effect). The cur- rent study therefore investigated both inter-individual differences and within-individual fluctuations in anxiety, and how these were related to smoking behavior.

2. Methods

Between 2007 and 2010, all visitors of a smoking cessation website were invited to answer the survey. Previous research showed that visitors of this website were more motivated to quit than smokers in the general population (Wang and Etter, 2004).

The Internet self-report questionnaire, in French, is available here:

http://www.stoptabac.ch/fr/TabHum/q-delphine-200-08-06.htm. Participants who provided an e-mail address were invited to complete two follow-up surveys, 2 and 4 weeks after their initial participation. Participants were not paid but received feedback on their level of dependence after the completion of the last survey at 4 weeks. Analyses were restricted to participants who answered at least two surveys, because we needed at least two data points in order to calculate cessation, reduction, and relapse. The study was approved by the ethics committee of the Faculty of Psychology of the University of Geneva. The participants were informed of the purpose of the study and had an option to decline to have their data stored on file.

2.1. Measurements at each time point

2.1.1. The cigarette dependence scale (CDS).The CDS is a 12-item self-report measure of dependence intended for current smokers, which measures craving, cigarettes smoked per day, loss of control, time allocation, neglect of other activities, and per- sistence of use despite harm, as defined by the DSM-IV (American Psychological Association, 1994) and the ICD-10 (World Health Organization, 1992). Most items are answered on a 5-point Likert scale fromtotally disagree(coded 1) tofully agree (coded 5). The CDS has high test retest reliability (r≥0.83), and high internal con- sistency (˛≥0.90;Courvoisier and Etter, 2010; Etter et al., 2003; Etter, 2005), even though the internal consistency of CDS-12 is lower among light smokers (Okuyemi et al., 2007).

2.1.2. Feeling prisoner to cigarettes (“prisoner”).This CDS item assesses whether par- ticipants feel prisoners to cigarettes (psychological dependence) on a 5-point Likert scale fromnot at all(coded 1) tocompletely(coded 5), and is the only CDS item that can be answered by both current and former smokers.

2.1.3. The State-Trait Anxiety Inventory – State (STAI-S).The STAI-S (Spielberger et al., 1983) is a 20-item inventory that assesses the temporary or emotional state anxiety in adults. The essential qualities evaluated by the STAI-S-Anxiety scale are feelings of nervousness, tension, apprehension, and worry. Items are answered on a 4-point Likert scale fromnot at all(1) tocompletely(4) with higher scores reflecting greater levels of anxiety. The STAI-S has a high degree of internal consistency (˛= 0.86;

Spielberger, 2005).

2.1.4. Smoking status.Smoking status was assessed by a question asking partici- pants if they were (1) daily, (2) occasional or (3) ex-smokers.

2.1.5. Smoking cessation.Based on the question above, we compared baseline daily smokers who were still smoking at follow-up (n= 717) with baseline smokers who had quit smoking at the 2- or 4-week follow-up (n= 249).

2.1.6. Smoking relapse.We compared baseline ex-smokers who were still abstinent at the 2- or 4-week follow-up (n= 474) with baseline ex-smokers who relapsed to smoking at the 2- or 4-week follow-up (n= 73).

2.1.7. Smoking reduction.We compared baseline daily smokers who maintained their cigarette consumption at the 2- or 4-week follow-up (n= 286) versus baseline daily smokers who reduced cigarettes/day by at least 50% (n= 136).

2.2. Analysis

We used mixed linear models with an identity link to examine the effect of anx- iety on (1) cigarette dependence, and (2) feeling prisoner to cigarettes. Mixed linear models allow the assessment of individual characteristics that vary among indi- viduals over time (e.g., level of dependence). For example, individual differences in baseline dependence may be affected by these varying characteristics across dif- ferent individuals (random intercept model). They also allow estimating differences in change over time in dependence ratings across different individuals (random slope model). The usefulness of including a random intercept and random slope was assessed using likelihood ratio tests. Multilevel modeling is also more appropriate for clinical longitudinal data, because it does not require listwise deletion and allows using all available data (i.e., different number of measures for each individual). Resid- ual plots were examined to check for conditional normality and homogeneity of variance.

To examine the effect of both inter-individual differences and intra-individual differences in anxiety, we separated anxiety into a mean level over the three time points (mean anxiety score) and a deviation from that mean level for each individual at each time point (deviation anxiety score) and included these two repeated mea- sure variables as fixed effects predictors (Neuhaus and Kalbfleisch, 1998). Mean level in anxiety would allow us to test the hypothesis that more anxious individuals could be inclined to smoke more (effect of inter-individual differences). Deviation in anx- iety would test the hypothesis that an individual who is more anxious at a specific moment could be inclined to smoke more at that moment (effect of intra-individual differences).

The independent repeated measure variables were (1) time and time squared, to estimate a (potentially non linear) trend over time, (2) mean anxiety and deviation anxiety, and (3) smoking status. Time and time squared were centered around their mean to avoid multicollinearity problems. To examine whether the impact of intra- individual anxiety on subjects’ trajectories of dependence differ for daily versus occasional smokers, we included the following two-way interactions: time and smoking status (to investigate whether the effect of smoking status on dependence is dependent on time), time and intra- as well as inter-individual anxiety (to inves- tigate whether the effect of anxiety on dependence is dependent on time), smoking status and intra- as well as inter-individual anxiety (to investigate whether the effect of smoking status on dependence is dependent on intra-individual anxiety).

To control for age and sex, those variables were added to the model as covariates.

Since the cigarette dependence scale consists of questions that cannot be answered by ex-smokers (e.g., how many cigarettes they smoke a day), we excluded ex-smokers from this analysis. Because feeling prisoner to cigarettes is a CDS item that can be answered by both current and former smokers, the mixed linear model withprisoneras the outcome included also ex-smokers in the analysis.

We used generalized linear mixed models with a logit link for the effect of anx- iety on (1) smoking cessation, (2) smoking reduction, and (3) smoking relapse. For each outcome, the fixed effects were the same: time difference (0–2 weeks and 0–4 weeks), mean anxiety, and deviation in anxiety. Again age, and sex were added as covariates to be controlled for. We did not include any interactions in these models.

Analyses were performed using SPSS v.21.0 (IBM Corp., Armonk, NY, USA).

3. Results

3.1. Characteristics of participants

A total of 1967 individuals completed the initial survey, 889 completed the second survey (45%) and 637 the third survey (32%;

Table 1). Response rates to the 2-week and 4-week surveys were

similar in daily, occasional and ex-smokers (data not shown). Two

thirds of the participants were female and the mean age at baseline

was 39 years (SD: 11.4). Most participants at baseline (69%) were

daily smokers and 25% were ex-smokers (mean duration of absti-

nence: 174 days, SD = 887 days). Participants showed a large range

of dependence and anxiety scores (Table 1). Many participants quit

Table1 Samplecharacteristics. VariablesInitialsurvey(N=1967)p-Value2-Weekfollow-up(N=886,1117missing)p-Value4-Weekfollow-up(N=633,1370missing)p-Value SmokingstatusDailyOccasionalExDailyOccasionalExDailyOccasionalEx N(%)1382(69%)73(3.6%)502(25.1%)<0.001409(20.4%)65(3.2%)412(46.5%)<0.001259(40.9%)46(7.3%)328(51.8%)<0.001 Age,mean(SD)34.05(11.73)29.55(12.91)37.81(10.64)<0.00138.17(11.68)34.09(11.57)37.79(10.46)0.02239.78(11.86)34.95(11.04)38.42(10.79)0.022 Sex,N(%) Male102(31.5%)6(50.0%)66(29.5%)<0.01140(34.6%)24(36.9%)129(31.6%)0.55183(32.2%)12(26.1%)106(32.7%)0.665 Female222(68.5%)6(50.0%)158(70.7%)265(65.4%)41(63.1%)279(68.4%)175(67.8%)34(73.9%)218(67.3%) Dependence,mean(SD)3.90(0.71)2.30(0.71)3.00(1.35)<0.0013.70(0.73)2.35(0.83)2.09(1.19)<0.0013.63(0.61)2.27(0.85)1.75(1.04)<0.001 Prisoner,mean(SD)4.16(1.08)2.39(1.41)–<0.0014.01(1.05)2.54(1.27)–<0.0014.01(0.95)2.69(1.33)–<0.001 TraitAnxiety,mean(SD)2.23(0.66)2.06(0.71)3.00(1.35)<0.012.14(0.59)2.09(0.58)2.00(0.53)<0.0012.14(0.59)2.09(0.50)1.95(0.49)<0.001 StateAnxiety,mean(SD)0.05(0.21)0.06(0.25)0.07(0.30)0.156−0.04(0.29)−0.01(0.30)0.07(0.33)0.1910.06(0.31)0.13(0.38)0.11(0.37)0.076 Note:MultiplecomparisonsBonferronicorrected.

smoking or switched to occasional smoking during the study, as can be expected from visitors of a smoking cessation website (Table 1).

3.2. Cigarette dependence

Data are shown in Table 2. In current smokers, CDS scores decreased over time (significant effect of time), quickly between week 0 and week 2 then more slowly between week 2 and week 4 (significant effect of time squared). Neither the linear nor the quadratic decrease varied between daily and occasional smokers (no significant interactions). In current smokers, anxiety ratings were associated with CDS ratings, both when looking at inter-individual differences, and when looking at intra-individual differences. At the inter-individual level, one unit difference in anx- iety (range: 1–4) across different persons was associated with 0.28 points higher CDS scores (range: 1–5) (p < 0.0001), independently of smoking status (no significant interaction). At the intra-individual level, trajectories of dependence over time were associated with 0.11 points decrease in anxiety (p = 0.040), independently of smok- ing status (no significant interactions).

3.3. Feeling prisoner of cigarettes

As expected, occasional, and ex-smokers felt significantly less prisoner to cigarettes than daily smokers, (ps < 0.001; Table 3). Feel- ing prisoner to cigarettes’ scores decreased over time (significant effect of time;

< 0.01), quickly between week 0 and week 2 then more slowly between week 2 and week 4 (significant effect of time squared;

status (p interaction time by smoking status: <0.0001; Fig. 1) in that stable ex-smokers showed a significantly greater decrease than sta- ble daily smokers over time (difference in slope =

0.44,

< 0.0001).

Anxiety ratings were associated with feeling prisoner to cigarette, both when looking at inter-individual differences (p < 0.0001), and when looking at intra-individual differences (p= 0.02). A one unit difference in anxiety (range: 1–4) across different persons was associated with 0.38 points greater scores of feeling prisoner to cigarettes (range: 1–5). This increase varied across smoking status (p interaction smoking status by anxiety: <0.01) in that ex- smokers showed a significantly greater increase in feeling prisoner to cigarettes as their anxiety increased than daily smokers (slope 0.36,

< 0.01). At the intra-individual level, a one unit increase in anxiety was associated with 0.21 points smaller scores in feeling prisoner, independently of smoking status (no significant interac- tions).

3.4. Smoking cessation

Smoking cessation rates were different across the 2-week and 4- week follow-up (Table 4) in that individuals showed 0.64 smaller odds for cessation between baseline and 2 weeks than between baseline and 4 weeks. Anxiety ratings were associated with smok- ing cessation, when looking at inter-individual differences: a one unit difference in anxiety across different persons was associated with 0.42 smaller odds in smoking cessation (p < 0.001). At the intra-individual level, trajectories of smoking cessation over time depended on individual deviation in anxiety. In other words, an increase of one unit in intra-individual anxiety led to 0.51 smaller odds of smoking cessation (p < 0.001).

3.5. Smoking reduction

The only significant association involved intra-individual differ-

ences in anxiety: a one unit difference in deviation in anxiety within

a person was associated with 0.67 points smaller odds of smoking

118 C.S. Haller et al. / Drug and Alcohol Dependence 139 (2014) 115–120 Table 2

Main effects of multilevel analysis for CDS.

Variable Category CDS scores^∗

B p 95% CI

Smoking status Daily 0

Occasional −1.28 <0.0001 −1.67 −0.90

Time −0.21 <0.0001 −0.32 −0.10

Time² 0.07 <0.001 0.03 0.12

Mean anxiety 0.28 <0.0001 0.21 0.34

Deviation anxiety −0.11 0.04 −0.21 −0.01

Age 0.01 <0.0001 0.008 0.014

Sex 0.04 0.24 −0.03 0.11

* Note: TotalNat baseline = 1465, totalNafter 2 weeks = 474, and totalNafter 4 weeks = 305. Daily smokers = reference group.

Table 3

Main effects of multilevel analysis for prisoner.

Variable Category Prisoner scores^∗

B p 95% CI

Smoking status Daily 0

Occasional −1.31 <0.0001 −1.97 −0.67

Ex −2.08 <0.0001 −2.50 −1.65

Time −0.25 <0.01 −0.44 −0.07

Time² 0.13 <0.01 0.05 0.20

Mean anxiety 0.38 <0.0001 0.27 0.49

Deviation anxiety −0.21 0.02 −0.40 −0.03

Age 0.02 <0.0001 0.01 0.02

Sex 0.24 <0.0001 0.14 0.34

* Note: TotalNat Baseline = 1967, totalNafter 2 weeks = 886, and totalNafter4 weeks = 633. Daily smokers = Reference group.

Fig. 1.Evolution of feeling prisoner to cigarettes across smoking status (pinteraction time by smoking status: <0.0001).

reduction (p = 0.047). Neither anxiety at the inter-individual level, nor time had significant effects on smoking reduction.

3.6. Smoking relapse

Smoking relapse rates were different across the 2-week and 4-week follow-up. In baseline ex-smokers, significant association

involved anxiety ratings with smoking relapse, when looking at

inter-individual differences: a one unit difference in anxiety across

individuals was associated with 1.81 greater odds of smoking

relapse (p = 0.014). At the intra-individual level, trajectories of

smoking relapse over time depended on individual deviation in

anxiety. In other words, an increase of one unit in intra-individual

anxiety led to 1.52 greater odds of smoking relapse (p = 0.041).

Table 4

Main effects of generalized linear models for each dependence outcome.

Variable Cessation Reduction Relapse

OR p 95% CI OR p 95% CI OR p 95% CI

Time 0.64 <0.01 0.47 1.87 0.90 0.64 0.59 1.38 0.71 0.18 0.43 1.17

Mean anxiety 0.42 <0.001 0.31 0.58 0.85 0.35 0.59 1.21 1.81 <0.01 1.13 2.90

Deviation anxiety 0.51 <0.001 0.39 0.69 0.67 0.047 0.45 1.00 1.52 0.03 1.05 2.18

Age 0.99 0.03 0.97 1.00 0.99 0.17 0.97 1.01 0.73 0.04 0.95 1.00

Sex 1.02 0.89 0.74 1.42 0.88 0.56 0.57 1.35 0.98 0.94 0.55 1.75

Note: TotalNcessation = 966 (no change = 717, change = 249), totalNreduction = 422 (no change = 286, change = 136), and totalNrelapse = 547 (no change = 474, change = 73).

Note: OR = odds ratio.

4. Discussion

In this prospective study of smokers interested in quitting or having just quit, we examined associations between anxiety and cigarette dependence, and we assessed whether change over time in anxiety within individuals was associated with cigarette depend- ence.

With respect to trajectories of smoking, smokers decreased their dependence levels over time, but indicators of dependence (e.g., feeling prisoners of cigarettes) decreased much faster in ex- smokers than in occasional or daily smokers, probably because these ex-smokers had quit smoking very recently. These findings contrast with previous studies showing a stability of dependence over time (e.g., French and Sutton, 2010; Hughes et al., 1991), but are in line with other studies of cigarette consumption (e.g., Yong et al., 2012). This is probably due to the fact that visitors of this study’s website were interested in quitting and were actively trying to quit or to reduce their cigarette consumption (Wang and Etter, 2004). Alternatively, the decrease over time in dependence ratings could be due to a reactivity effect; i.e., repeated self-monitoring may alter the measurement of symptoms or behaviors (French and Sutton, 2010). This effect could also be explained by self-selection, as, like in most Internet studies, we observed a substantial loss to follow-up (Table 1; Cook et al., 2000; French and Sutton, 2010).

Anxiety was associated with dependence levels, both when looking at inter- as well as intra-individual differences in anxiety.

Interestingly, our results showed that an increase in mean anxi- ety predicted an increase in dependence, whereas an increase in anxiety compared to one’s own usual level was associated with a

in dependence. Change over time in anxiety within, and between individuals was not related to trajectories in dependence for neither smoking status. Although empirical studies indicate that anxiety is associated with smoking behavior (Brown et al., 2001;

Gregor et al., 2008; Zvolensky et al., 2006, 2008), our results of intra- and inter-individual differences were in line with those findings.

In this sample only daily, and occasional smokers were included, thus there may not have been enough variability to find differ- ences. However, this result is in line with the assumption that more generally anxious people are more prone to smoking (e.g., Parrot, 1999).

Differences in anxiety across individuals and intra-individual change in anxiety levels over time showed a significant influence on smoking cessation. Overall, individuals less anxious than other individuals, and less anxious than their own usual anxiety levels had more chance to stop smoking, or to reduce number of cigarettes per day. They also had less chance to relapse. This negative associ- ation between smoking cessation and anxiety is in line with results from a study following smokers’ anxiety levels just after their ces- sation (West and Hajek, 1997), which found that anxiety levels decrease after cessation. Additionally, as time goes on, the prob- ability for cessation increases. This could be due to the short time of follow-up, but it also speaks to the proneness to cessation across time. Smoking cessation is not less probable after 4 weeks than it is after 2 weeks. It remains very relevant to further investigate

this trend in a longer-term follow-up, to see whether those lapses turn into relapses. The results on smoking relapse and smoking reduction differed from the ones on smoking cessation in that they showed no differences in time, and smoking reduction only showed a significant influence of intra-individual anxiety. In other words, intra-individual anxiety but not inter-individual anxiety sig- nificantly predicted smoking reduction. Smoking relapse however, was predicted by both inter-individual, and intra-individual anxi- ety. Thus both generally anxious persons, as persons who are more anxious compared to their usual level are more prone to relapse, and less prone to stop smoking, whereas only persons who are less anxious compared to their usual level are more prone to smoking reduction.

4.1. Implications for treatment

These results suggest that smokers may benefit from treatment approaches that specifically target these anxiety-related processes for the purpose of facilitating quitting and preventing relapse (Goodwin et al., 2011a,b; Zvolensky et al., 2006). Even if smok- ing cessation decreases anxiety (West and Hajek, 1997), a greater decrease seems to reduce the risk of relapse. At least two main types of treatment can be considered. First, anti-anxiolytics drugs will reduce anxiety overall, as well as momentary peaks in anxiety.

Second, self-help techniques, such as deep breathing and mindful- ness exercises, may be an intervention strategy that can be used to allow smokers to lower their anxiety levels when they need it most.

4.2. Strengths and limitations

Strengths included the large sample of current and former

smokers from various countries, prospective study design with

repeated measurements, and validated instruments of measure. On

the other hand, the present sample is composed of a self-selected

group of Internet users, who were more motivated to quit and

more dependent than smokers in the general population (French

and Sutton, 2010). In addition, as is the case in most Internet sur-

veys (Etter, 2008), the response rates at follow-up were relatively

low. The self-selection of participants and the low response rates

may lead the level of dependence to be underestimated, if more

dependent smokers were more often lost to follow-up (for anal-

ysis of attrition see Table 1). Similarly, participants may be more

or less anxious than a general population of smokers. However,

this may not affect the relationship between anxiety and cigarette

dependence, which was the main goal in this study, if anxiety

is not related to loss to follow-up. A third limitation concerns

potential confounders. In particular, we did not collect informa-

tion on medication and treatments, including smoking cessation

treatment. Additional factors such as self-efficacy, social support,

life-satisfaction, comorbid mental illness, or psychiatric history,

for example, may also confound the relationship between anxiety

and smoking behavior. Thus, this association may not be consid-

ered as causal, but does indicate how anxiety and dependence and

smoking behavior co-vary in time. Also, since we did not measure

120 C.S. Haller et al. / Drug and Alcohol Dependence 139 (2014) 115–120

lapses in smoking among quitters, it is not possible to examine the

impact of lapses on anxiety. Finally, the study lasted only 4 weeks.

Future research may want to address these issues over a longer time period (e.g., to address the cessation-lapse-relapse circle).

4.3. Conclusions

In conclusion, the present findings underline the difference in trajectories of dependence between daily, occasional and ex- smokers. Furthermore, this study highlights the importance of considering both stable and momentary anxiety to predict cigarette dependence.

Role of funding source

This work was not supported by any funding source.

Contributors

Dr. Courvoisier designed the study and protocol. Dr. Etter advised on the background to the study. Dr. Haller conducted the background literature search. Dr. Courvoisier collected the data. Dr.

Haller conducted the data analysis. Dr. Courvoisier assisted in data analysis. Dr. Haller wrote the manuscript. All authors contributed to the manuscript revisions.

Conflict of interest

There are no conflicts of interest.

Acknowledgement

We thank Mr. Vincent Baujard from the Health On The Net Foun- dation, who kindly managed the data collection software.

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