Journal of Substance Abuse Treatment 35 (2008) 41–52

Regular article

Multidimensional smoker profiles and their prediction of smokingfollowing a pharmacobehavioral intervention

Anil Batra, (M.D.)⁎, Susan E. Collins, (Ph.D.), Iris Torchalla, (Ph.D.),Martina Schröter, (M.A.), Gerhard Buchkremer, (M.D.)

Department of Psychiatry and Psychotherapy, University Hospital Tübingen, D-72076 Tübingen, Germany

Received 17 April 2007; received in revised form 3 August 2007; accepted 8 August 2007

Abstract

The objective of this observational study was to identify multidimensional smoker profiles to aid the development of future tailoredtreatments for different smoker subtypes. Based on findings in the literature, it was hypothesized that smokers reporting higher levels ofnovelty seeking/hyperactivity, depressivity, and nicotine dependence would evince greater odds of postintervention smoking than smokersreporting lower symptom levels across these dimensions. Adult regular smokers (N = 165) in southwest Germany completed self-reportquestionnaires assessing psychological and smoking variables and received a pharmacobehavioral intervention. A k-means cluster analysisinvolving indicators of depressivity, novelty seeking/hyperactivity, and nicotine dependence confirmed the hypothesized smoker profiles.These clusters evinced robustness on cross-validation and predicted smoking on follow-up. Specifically, smokers with depressive, novelty-seeking/hyperactive, and higher dependence profiles evinced significantly greater odds of smoking on follow-up than smokers with low-scoring profiles. © 2008 Elsevier Inc. All rights reserved.

Keywords: Smoker profiles; Cigarette smoking; Nicotine dependence; Behavioral intervention; Smoker subtypes

1. Introduction

Among scientifically reviewed and accepted methods ofsmoking cessation, the combination of behavioral andpharmacological interventions has been found to yieldrelatively high short-term abstinence rates of up to nearly80% (Cinciripini, Cinciripini, Wallfisch, Haque, & VanVunakis, 1996). However, treatment efficacy typicallydecreases after treatment end, such that only 20–35% ofparticipants remain abstinent 1 year following treatment(Alterman, Gariti, & Mulvaney, 2001; Haas, Munoz,Humfleet, Reus, & Hall, 2004). These relatively high ratesof relapse indicate a need to develop new and more effica-cious ways to help smokers maintain long-term abstinence.

Dr. Collins in now at the Addictive Behaviors Research Center,University of Washington, Seattle, WA 98195, USA.

⁎ Corresponding author. Department of Psychiatry and Psychotherapy,University Hospital Tübingen, Osianderstr. 24, D-72076 Tübingen,Germany. Tel.: +49 7071/29 82685; fax: +49 7071/29 5384.

E-mail address: anil.batra@med.uni-tuebingen.de (A. Batra).

0740-5472/08/$ – see front matter © 2008 Elsevier Inc. All rights reserved.doi:10.1016/j.jsat.2007.08.006

Recent studies have endeavored to improve abstinencerates by targeting smokers with psychiatric conditions, suchas major depressive disorder, and physical risk factors, suchas pregnancy, heart disease, or type 1 diabetes (Ranney,Melvin, Lux, McClain, & Lohr, 2006). Smokers with suchrisk factors typically evince a lower ability to achieveabstinence, a higher risk for relapse, and/or an increased riskfor further smoking-related complications than smokerswithout these risk factors (Burgess et al., 2002). However,interventions tailored to these groups have had mixedsuccess in increasing abstinence rates (e.g., Hall, Muñoz,& Reus, 1994; Hall et al., 1996).

Considering these groups' risk for continued smoking,special attention is warranted. However, a focus on singlehealth risk factors, such as heart disease or pregnancy, maynot provide a clinically useful classification for optimaltreatment tailoring. First of all, such smoker subtypes areoften based on factors associated with increased smokingintensity and smoking-related risks, but not on underlyingfactors that may impede achievement and maintenance of

42 A. Batra et al. / Journal of Substance Abuse Treatment 35 (2008) 41–52

abstinence. Although these smokers need attention to avoidthe health-related risks they face, treatments tailored to theunderlying processes of smoking may be more efficacious(Shiffman, 2006). Second, single-factor risk groups may beinefficient because they typically focus on only oneidentifying aspect of the smoker (Tiffany, Conklin, Shiff-man, & Clayton, 2004). Because smokers are a hetero-geneous group with diverse demographic backgrounds andmedical histories, more efforts should be exerted to examinethe universal features of smoking, such as psychologicalfactors, instead of narrowly defined single risk factors(Pomerleau, Marks, & Pomerleau, 2000). Finally, the riskgroups typically targeted with specialized interventions, suchas clinically depressed smokers, represent a comparativelysmall subset of smokers. More research is needed oninterventions that could be applied to smokers in the largergeneral smoking population who may also benefit frominterventions tailored to their needs. For these reasons, it maybe helpful to explore multidimensional smoker risk profilesthat (a) include psychological factors associated withsmoking status, (b) address a larger pool of smokers, and(c) may be used to better tailor pharmacobehavioraltreatments to different smokers' needs (Tiffany et al., 2004).

1.1. Smoker profiles

A few studies have explored smoker profiles orsubgroups in terms of baseline smoking, psychological,and/or personality characteristics (Lesch et al., 2004;Norman, Velicer, Fava, & Prochaska, 1998; Patton, Barnes,& Murray, 1997). Other studies have examined theheterogeneity of withdrawal symptoms following a smok-ing-cessation intervention (Pomerleau et al., 2000) andalong natural recovery trajectories (Furberg et al., 2005).However, no studies to date have explored clinically usefulprofiles in a nonclinical smoker population, as well as theassociation between profile and smoking status following asmoking-cessation intervention.

This study therefore drew on recent findings regardingpotential groups of at-risk smokers, or smokers with higherrates of relapse/smoking at posttreatment, to provide thebasis for smoker profiles for which future interventions maybe tailored. Four smoker profiles served as the focus of thisstudy, including three at-risk smoker profiles (i.e., higherdependence, depressive, and novelty seeking/hyperactive)and one lower risk profile (i.e., relatively low levels ofpsychological and dependence symptoms).

1.1.1. Smokers with a higher dependence profileResearch suggests that smokers with higher nicotine

dependence and craving have lower postintervention successrates than other smokers. For example, an observational studyconducted with smoking abstainers following an interventionshowed that higher levels of morning craving were positivelyassociated with a same-day lapse (Shiffman et al., 1997).Foulds et al. (2006) found that symptoms of higher nicotine

dependence predicted resumed smoking following a phar-macobehavioral treatment program. Moreover, in a clinicaltrial testing bupropion, higher nicotine dependence waspositively associated with the length of time from the initiallapse to the reattainment of abstinence (Wileyto et al., 2005).Thus, higher nicotine dependence and craving appear to berisk factors for resumed smoking and difficulties maintainingabstinence following smoking cessation.

1.1.2. Smokers with a depressive profileNumerous studies have cited depressive symptoms and

negative affect as risk factors for smoking. For example,smokers who are even slightly depressed prior to treatmentmay have greater difficulty attaining and maintainingabstinence over the long term (Burgess et al., 2002;Hitsman, Borelli, & McChargue, 2003). Furthermore,negative affect in early abstinence has been shown topredict relapse (Hall et al., 1996). These findings have led tothe hypothesis that smokers with more depressive symptomsmay use nicotine for its antidepressive qualities and maythus experience increased difficulty maintaining abstinence(Eissenberg, 2004).

1.1.3. Smokers with a novelty-seeking/hyperactive profileStudies have indicated that adults with symptoms of

hyperactivity or attention-deficit/hyperactivity disorderevince higher levels of both novelty seeking and depression(Downey, Pomerleau, & Pomerleau, 1996; Downey, Stelson,Pomerleau, & Giordani, 1997). Furthermore, adults withhigher impulsivity have shown higher negative affect relieffollowing nicotinized versus denicotinized cigarette smok-ing, indicating greater dependence potential (Doran, Van-derVeen, McChargue, Spring, & Cook, 2006). Such findingsindicate that both disinhibition and self-medication may playa role in smoking behavior among smokers with increasedlevels of hyperactivity and novelty seeking.

Elevated impulsivity and hyperactivity have also beenshown to be associated with lower intervention success rates.In one intervention trial, adults with a retrospectivelyassessed history of childhood attention-deficit/hyperactivitydisorder relapsed significantly earlier than adults with lowsymptomatology (Humfleet et al., 2005). A further studywith a nonclinical regular smoking population indicated thatsmokers with higher trait impulsivity experienced a shortertime to relapse than smokers with lower trait impulsivity(Doran, Springs, McChargue, Pergadia, & Richmond, 2004).Based on this evidence, it is estimated that smokers fitting anovelty-seeking/hyperactive profile would be at higher riskfor smoking following an intervention.

1.1.4. Smokers with a low-scoring profileIt was hypothesized that the final group of smokers would

score relatively low on nicotine dependence and psycholo-gical symptoms and would have less difficulty achieving andmaintaining abstinence than the other smokers. This groupserved as a low-risk reference group in this study, whereas the

Table 1

43A. Batra et al. / Journal of Substance Abuse Treatment 35 (2008) 41–52

other groups were referred to as “at-risk” groups, or groups atrisk for postintervention smoking.

1.2. Objectives and hypotheses

The smoking-cessation literature to date indicates thatfurther research is necessary to improve smoking-cessationmethods. One promising approach has been to tailorsmoking-cessation interventions to specific subtypes of at-risk smokers (Ranney et al., 2006). Such treatment tailoring,however, may be more efficacious when founded onmultidimensional profiles that include psychological factorsassociated with smoking. Therefore, this study aimed to: (a)determine whether multidimensional smoker profiles may beidentified in a nonclinical treatment-seeking smoker popula-tion; (b) test the reliability of the profiles; and (c) test theability of the profiles to predict smoking following apharmacobehavioral smoking-cessation intervention. Theaim of this study was to provide the foundation for a futureintervention trial that could compare the efficacy oftreatments tailored to the needs of these at-risk smokersand the efficacy of a standard pharmacobehavioral treatment.

Based on findings on at-risk smokers in the literature, itwas hypothesized that four distinct smoker profiles (higherdependence, depressive, novelty seeking/hyperactive, andlow scoring) would be identified. Smokers with (a) higherdependence profiles would evince relatively high scores onmeasures of dependence and craving; (b) depressive profileswould evince a relatively high level of depressive symptoms;(c) novelty-seeking/hyperactive profiles would have ele-vated hyperactivity, novelty-seeking, and depressive scores;and (d) low-scoring profiles would evince relatively lowscores across all measures. It was also hypothesized thatsmoker profiles would predict smoking for up to a yearfollowing a smoking-cessation intervention, such thatsmokers with at-risk profiles would evince greater odds ofsmoking than smokers with low-scoring profiles.

Descriptive statistics of the sample at baseline

Variables n M SD

Daily cigarette consumption 161 22.12 8.06Smoking duration

(number of years)164 23.66 9.21

Started smoking regularly(age in years)

161 17.62 2.95

FTND-G 165 4.93 2.26QSU-G 165 110.41 36.56BDI 165 7.01 (2.33) 6.16 (1.26)ISE-N 165 35.57 (1.54) 7.77 (0.10)ADHD 165 9.249 (2.83) 6.338 (1.11)BAS 165 3.07 0.35TPQ-NS 165 16.83 4.66

TPQ-NS = Tridimensional Personality Questionnaire-Novelty-seekingScale; BAS = Behavioral Approach System scale; ADHD = ADHDChecklist Questionnaire; ISE-N = Inventory of Self-communication forAdults; BDI = Beck Depression Inventory; QSU = Questionnaire onSmoking Urges-German Version; FTND-G = Fagerström Test of NicotineDependence-German Version.Values in parentheses represent the transformed scores used in the analyses.

2. Methods

2.1. Design

This study was based on a prospective observationaldesign. Subsequent to the baseline data collection, allparticipants received the same 6-week pharmacobehavioralsmoking-cessation intervention (for the treatment manual,see Batra & Buchkremer, 2004). Follow-up assessmentscontinued up to a year after the last treatment session.

2.2. Participants

Participants were smokers living in southwest Germanywho were recruited via local media (radio, newspaperarticles, and notices), a universitywide e-mail campaign,flyers sent to physicians' offices, visitors to the research

group's Web site, and local referrals. Of the 303 smokerswho were screened, 202 qualified for the study, gaveinformed consent to participate, and participated in treat-ment; however, only 165 participants (55% women; n = 91)provided complete data for the cluster analyses and were thusincluded in the current report.

The participants' (N = 165) average age was 41.24 years(SD = 9.51), and the majority of the participants were single(55%). This sample represented a relatively well-educatedgroup: 33% had completed or were completing a universityeducation, 40% had completed an apprenticeship, and 20%had completed the equivalent of a technical/associate degree.Most participants reported being employed: 61% and 17% ofthe participants reported working full time and part time,respectively, whereas only 3% indicated they were unem-ployed. On average, the participants smoked more than apack a day and reported symptoms indicating a moderatelevel of nicotine dependence (for more smoking-relatedstatistics, see Table 1). A large majority of the participantshad already tried to quit smoking before the study withoutlong-term success (n = 133; 81%), and 37% (n = 61) hadbeen advised by their doctors to quit smoking.

Inclusion criteria for participants were as follows: age ofat least 18 years, smoked at least 10 cigarettes/day for thepast 2 years, able to give informed consent to participate, andspoke German well enough to complete the questionnairesand to participate in group sessions. Exclusion criteria wereas follows: health conditions contraindicating nicotinereplacement therapy (NRT) (myocardial infarction in thepast 4 weeks, decompensating heart insufficiency, andunstable angina pectoris), pregnancy or nursing, lifetimediagnosis of a psychotic disorder, current major depressiveepisode, use of antidepressants or neuroleptics, and with-drawal of consent to participate in the study. Finally, only theparticipants who attended at least two treatment sessions

44 A. Batra et al. / Journal of Substance Abuse Treatment 35 (2008) 41–52

were included in this study. This exclusion criterion was usedbecause this study sought to identify the differing abstinencerates among the participants who had received at least sometreatment and was not a randomized clinical trial testing theefficacy of the treatment per se.

2.3. Questionnaires and materials

The Screening Form (SF) determined participants'eligibility for the study (see above inclusion criteria) andwas used in telephone screening.

The Sociodemographic and Smoking Baseline Question-naire (SSBQ) consisted of single items assessing socio-demographic information and baseline smoking frequency,quantity, duration, quit attempts, and typical smokingsituations. The SSBQ was distributed during the informa-tional meeting.

The Fagerström Test of Nicotine Dependence—GermanVersion (FTND-G; Heatherton, Kozlowski, Frecker, &Fagerström, 1991; Rumpf, Hapke, & John, 1995) wasdistributed to participants during the informational sessionand represented their baseline level of nicotine dependence inthe cluster analysis. Previous studies have established thismeasure's reliability and validity in English- and German-speaking samples (Heatherton et al., 1991; Schumann,Rumpf, Meyer, Hapke, & John, 2003). In the current sample,the internal consistency reached an alpha level of .65.

The Questionnaire on Smoking Urges (QSU-G; Mucha &Pauli, 2003; Tiffany & Drobes, 1991) assesses the level ofcurrent smoking craving and consists of 32 items based on a7-point Likert scale. Typically, 21 of the original items areused to form two scales representing desire and craving tosmoke (Tiffany & Drobes, 1991). However, a pilotinvestigation using a principal components analysis withvarimax rotation indicated that participants' responses didnot conform to the two-scale solution and instead formed sixuninterpretable scales. Thus, a summary score of the 32items was used in the cluster analysis to represent overallcraving. The internal consistency of the total QSU-G scoresin the current sample was excellent (α = .95).

The Beck Depression Inventory—German Version (BDI;Beck & Steer, 1987; Hautzinger, Bailer, Worall, & Keller,1994) was used to measure depressive symptoms for thecluster analysis. The scale consists of 21 questions answeredon a 4-point Likert scale, with higher total scores indicating agreater experience of depressive symptoms. The reliabilityand validity of this scale have been established in English-and German-speaking samples (Beck & Steer, 1987;Hautzinger et al., 1994). In this study, reliability reachedan adequate level (α = .82).

The Inventory of Self-Communication for Adults, Nega-tive Scale (ISE-N; Tönnies & Tausch, 1981) consists ofpositive and negative self-communication scales (as well assix subscales). The total of the negative self-communicationscale was used in the cluster analysis as an indicator ofdepressivity. This scale has been found in previous studies to

be reliable and valid (Tönnies & Tausch, 1981). In thecurrent sample, internal consistency was excellent (α = .92).

The ADHD Checklist Questionnaire (ADHD; Heßlinger,Philipsen, & Richter, 2004) is based on Diagnostic andStatistical Manual of Mental Disorders, Fourth Editioncriteria for the diagnosis of attention-deficit/hyperactivitydisorder. This 18-item measure is based on a 3-point Likertscale. Participants may respond that an item describes theirbehavior completely, partially, or not at all. Items weresummed to create the ADHD score, which was used in thecluster analysis. Principal components analysis usingvarimax rotation favored a one-factor solution, whichaccounted for 31% of the variance. The reliability of theitems was good (α = .87).

The Tridimensional Personality Questionnaire, Novelty-Seeking Scale (TPQ-NS; Cloninger, 1987; Weyers, Krebs, &Janke, 1995) was used in this study to assess participants'baseline level of novelty seeking. The 32-item scale isanswered dichotomously (yes/no), and some items arereverse scored. The sum of these items was used in thecluster analysis. Other scales were not included because oftheir potential to act as masking variables (Everitt, Landau,& Leese, 2001) and because the novelty-seeking scale wasdeemed to be most relevant to the hypotheses. Previousresearch has demonstrated the reliability and validity of theTPQ-NS (Weyers et al., 1995), and internal consistency wasadequate in this sample (Kuder-Richardson [K-R] = .70).

The Behavioral Activation System scale of the BehavioralInhibition System/Behavioral Approach System Question-naire (BIS/BAS; Carver & White, 1994; Strobel, Beauducel,Debener, & Brocke, 2001) measures extraversion and “funseeking.” The BAS scale consists of 13 items based on a4-point Likert scale. Items were reversed, as necessary, andsummed to create the BAS total score. This score was thenused as an indicator for the hypothesized novelty-seeking/hyperactive group in the cluster analysis. Reliability andvalidity had been established in previous studies (Carver &White, 1994; Strobel et al., 2001); in this study, internalconsistency reached an adequate level (α = .76).

The Follow-Up Smoking Questionnaire (FUSQ) consistsof single items used to establish postintervention smokingrates and was distributed at 1-, 6-, and 12-month follow-ups.

Carbon monoxide (CO) measurements were conductedat each treatment session and at 1-month follow-up tobiochemically validate participants' self-report of smok-ing. The piCO Smokerlyzer (Bedfont Scientific, Kent,England, UK) was used to establish CO parts per millionin exhaled air. Values of N 9 ppm were considered to beevidence of smoking in this study (West, Hajek, Stead, &Stapleton, 2005).

2.4. Procedures

All procedures were presented to and accepted by theResearch Ethics Committee of the University HospitalTübingen. In May 2002, researchers sent a press release

45A. Batra et al. / Journal of Substance Abuse Treatment 35 (2008) 41–52

about the study to local media (radio, newspaper articles, andnotices) and directly to the public via a universitywide e-mailcampaign and flyers sent to doctors' offices. Referrals fromlocal physicians' offices and others who called during therecruitment period (May 2002–November 2003) wererecruited for the study. Smokers who were interested inparticipation were screened by research assistants using theSF described above. Eligible smokers were placed on awaiting list for an information session, at which theyreceived details regarding their participation, the goals ofthe study, and their rights as participants. The participantsthen gave informed consent and completed the SSBQ andFTND-G. Information sessions were scheduled 1 weekahead of the participants' first group session.

The group intervention used in this study was a 6-weekbehavioral program supplemented by recommendations forNRT, which were adapted to smoking intensity and level of

Table 2Treatment session time line

Session Main treatment/study components

Week 0 • Information on studycontent and time line

• Informed consent• Questionnaires

Week 1 • Greeting/questionnaires a • Group decisional balanceexercise

• Treatment time line,organization

• Psychoeducation:nicotine/tobacco dependence

• Today's agenda a • Explanation of homework a

(first week: self-monitoring)• Partnerinterviews/introductions

• Preview of next week'ssession a

• Introduction of CO test • Feedback round a

• CO test a

Week 2 • Group check-in a • Behavioral tips for the firstsmoke-free day

• Typical smoking situationsand alternatives to smoking

• Introduction of NRT

• Introduction of themotivational statement

• Individual nicotinerecommendations

• Set date for first smoke-freeday

Week 3 • Use of rewards/pleasurableactivities

• Healthy eating/lifestyle

• Course “buddy”/setting upbehavioral contracts and bets

Week 4 • Feedback on positivechanges since smoke-freeday (motivationalenhancement)

• Review smoking alternativesand how these have worked(support self-efficacy)

• Risky situations/solutionsWeek 5 • Review of key intervention

elements• Relaxation training

Week 6 • Review of key interventionelements

• Relapse prevention

• Relapse dynamics: slip,lapse, and relapse

• Crisis plan in case of relapse

• Feedback round/wrap-upa An element repeated in all subsequent weeks.

nicotine dependence and adhered to the guidelines of theDrug Commission of the German Medical Association(Arzneimittelkommission der Deutschen Ärzteschaft, 2001).Participants attended 1.5-hour group sessions on a weeklybasis, and groups consisting of 6–10 participants each wereled by one interventionist. Interventionists (one medicaldoctor, one psychiatric nurse, and two psychologists)conducted the group sessions using a published manual(Batra & Buchkremer, 2004) and received supervision fromthe first author (A.B.). At the first and second group sessions,the participants completed baseline questionnaires andstarted the 6-week treatment program. The treatment wasdivided into three main phases: (1) abstinence preparationand attainment, (2) abstinence stabilization, and (3) relapseprevention (for treatment components, see Table 2).

Follow-up data collection was scheduled 1 month afterthe final treatment session. At this time, participantsprovided CO measurement and completed the FUSQ.Research assistants mailed the FUSQ to the participants at6- and 12-month follow-ups. The participants returned thequestionnaires using prestamped envelopes. In the case thatquestionnaires were not returned, research assistantsreminded the participants via mail or telephone. When thisstrategy was unsuccessful, research assistants documentedabstinence over the telephone. The final wave of follow-upswas completed in December 2004. The participants were notoffered monetary compensation for their participation.Agreement to participate in the study waived usual treatmentcosts, although the participants did have to pay for NRT.

3. Results

3.1. Data preparation and preliminary analyses

3.1.1. Cluster dataWhere appropriate, missing scale items were replaced

using mean substitution to increase statistical power and todecrease potential bias caused by listwise deletion of data. Ifparticipants' data were missing for an entire scale, valueswere not replaced, and the participants were deleted listwisefrom the analyses. Exploratory data analysis indicatedskewed distributions for BDI, ADHD, and ISE-N, andthese variables were subsequently transformed using squareroot (BDI and ADHD) and log10 (ISE-N) transformations.Because the optimization technique used in this study wasscale sensitive and the indicators were scaled differently,continuous indicators were standardized prior to analyses.

3.1.2. Smoking statusAbstinence was directly measured in this study, and the

rates were reversed for the main analyses to correspond tothe goal of this study: to detect at-risk groups with higherodds of smoking following the intervention. Point-preva-lence abstinence (PPA) was defined as reporting 1-dayabstinence at a specific follow-up questionnaire session.

Fig. 1. Standardized cluster centers across indicators. TPQ-NS = Tridimen-sional Personality Questionnaire-Novelty-seeking Scale; BAS = BehavioralApproach System scale; ADHD = ADHD Checklist Questionnaire; ISE-N =Inventory of Self-communication for Adults; BDI = Beck DepressionInventory; QSU = Questionnaire on Smoking Urges; FTND = FagerströmTest of Nicotine Dependence.

46 A. Batra et al. / Journal of Substance Abuse Treatment 35 (2008) 41–52

One-day PPAwas chosen over 7-day PPA because it is mosteasily confirmed using CO tests (Velicer, Prochaska, Rossi,& Snow, 1992). Continuous abstinence (CA) was defined asnot having smoked one cigarette since the end of treatmentand having consistently reported CA throughout the follow-up period. Intent-to-treat (ITT) analyses were used such thatparticipants who did not provide smoking status data atposttest, follow-ups, or both were coded as smoking for thattime point. Smoking status was available for 95%, 91%, and81% of the participants at 1-, 6-, and 12-month follow-ups,respectively; 5% of those with missing data were theparticipants who had discontinued treatment.

3.2. Completer analyses

Participants who did not provide complete data on clusterindicators were deleted listwise from the cluster analyses.Because missing data can introduce bias, the participantswho did (n = 165) and did not (n = 37) provide complete dataon cluster indicators were compared on baseline variablesusing chi-square, Mann–Whitney U, and t tests, asappropriate. The participants who provided complete datatended to smoke less at baseline (M = 22.124, SD = 8.058)than the participants who provided incomplete data (M =25.514, SD = 10.176), U(N = 198) = 2,334, p = .038. Therewere no other significant differences on baseline measures,including gender, age, smoking duration, age at start ofregular smoking, number of quit attempts, QSU-G, BDI,ISE-N, ADHD, TPQ-NS, and BAS (all ps N .10).

Chi-square tests (n = 7) tested differences on posttest andfollow-up abstinence between participants who providedand participants who did not provide complete cluster data.PPA rates in the overall sample (N = 202) were 75.2%,63.9%, 49.0%, and 42.1% at posttest, 1-month, 6-month,and 12-month follow-ups, respectively. CA rates in theoverall sample were 53.0%, 38.1%, and 30.7% at 1-, 6-, and12-month follow-ups, respectively. On the other hand, PPArates for the selected sample (N = 165) were 82.4%, 70.9%,55.2%, and 47.3%, whereas CA rates were 58.8%, 42.4%,and 34.5%, respectively. Results indicated that cluster datacompletion predicted abstinence rates at all time points (allps b .02). The general pattern of results indicated that theparticipants who did not provide complete cluster data weremore often classified as smokers (according to ITT analyses)compared to the participants who provided complete clusterdata. This finding is not surprising considering that ITTanalyses, which classify missing follow-up data as anindication of smoking, may be confounded with theprovision of incomplete data for the cluster variables.

3.3. Treatment compliance

Compliance with the behavioral treatment was defined ashaving attended five of the six treatment sessions. Accordingto a frequency analysis, 81% of participants in this studywere treatment compliant. Chi-square tests indicated that

behavioral treatment compliance was associated with1-month CA and PPA, χ2(1, N = 165) = 7.43, p = .006,and χ2(1, N =165) = 11.12, p = .001, respectively, such thatthe participants who were not treatment compliant reportedsmoking more often than treatment-compliant participants.No other variables, including abstinence rates at other timepoints, gender, and baseline smoking variables, wereassociated with treatment compliance (all ps N .30).

Compliance with the NRT recommendation was also highin this study. On the week following the NRT recommenda-tions (third week of treatment), 81% of participants reportedusing some type of NRT. On the fourth to sixth weeks, 76%,69%, and 68% of the participants reported using NRT,respectively. The top three forms of NRT consistentlyendorsed by the participants in this study were nicotinegum, nicotine patch, and a combination of nicotine gum andpatch. The participants' reported use of NRT on the thirdweek of treatment predicted PPA at posttest, χ2(1, N =160) = 9.54, p = .002, and at 1-month follow-up, χ2(1, N =160) = 4.23, p = .04, such that the reported use of NRT wasassociated with higher abstinence rates. No other abstinencerates or baseline smoking variables were related to thismeasure of treatment compliance (all ps N .05).

3.4. Cluster analyses

A k-means algorithm (SPSS 12.5; SPSS, 2004), whichminimizes the sum of the squared Euclidean distancesbetween items and their cluster centers (within-traceminimization), was used to classify smokers according tothe hypothesized smoker profiles. Indicators were selected torepresent the following constructs: (a) higher dependence

Table 3Standardized (and raw) means and standard deviations of indicator values across profiles

Indicators

Profiles

Low scoring Depressive Higher dependence Novelty seeking/hyperactive

M SD M SD M SD M SD

FTND-G −0.66 (3.55) 0.91 (2.06) −0.79 (3.26) 0.88 (1.99) 0.45 (6.08) 0.64 (1.45) 0.53 (6.25) 0.87 (1.97)QSU-G −0.61 (87.98) 0.85 (32.45) −0.46 (93.74) 0.81 (31.09) 0.60 (134.44) 0.77 (29.39) 0.04 (113.09) 0.81 (31.13)BDI −0.91 (2.26) 0.70 (2.11) 0.81 (11.89) 0.70 (6.00) −0.23 (5.07) 0.74 (3.94) 1.02 (13.47) 0.60 (5.46)ISE-N −0.78 (29.81) 0.82 (5.34) 0.74 (41.52) 0.66 (5.88) −0.17 (34.02) 0.74 (5.76) 0.75 (41.88) 0.83 (7.71)ADHD −0.88 (4.28) 0.83 (2.99) 0.64 (13.07) 0.73 (5.88) −0.11 (7.88) 0.68 (4.48) 0.99 (15.84) 0.68 (5.83)BAS −0.13 (3.03) 0.90 (0.32) −0.86 (2.77) 0.97 (0.34) 0.13 (3.12) 0.85 (0.30) 0.64 (3.30) 0.80 (0.28)TPQ-NS −0.03 (16.79) 0.86 (3.96) −0.13 (16.33) 0.91 (4.21) −0.43 (14.95) 0.96 (4.46) 0.83 (20.78) 0.87 (4.03)

TPQ-NS = Tridimensional Personality Questionnaire-Novelty-seeking Scale; BAS = Behavioral Approach System scale; ADHD = ADHD ChecklistQuestionnaire; ISE-N = Inventory of Self-communication for Adults; BDI-2 = Beck Depression Inventory-Version 2; QSU = Questionnaire on Smoking Urges;FTND = Fagerström Test of Nicotine Dependence-German Version.The standardized values of BDI, ADHD, and ISE-N are based on square root, log10, and square root transformed scores, respectively. The mean nontransformednonstandardized scores and standard deviations are reported in parentheses. Although cutoff values were not used to form the smoker profiles in this study,potentially helpful cutoff values have been established for two of the six indicators: mild depression, BDI ≥ 9 (Pyszczynski, Hamilton, Herring, & Greenberg,1989); high dependence, FTND-G ≥ 6 (Fagerström et al., 1996).

Table 4Cluster prediction of smoking on follow-ups

Predictors ORSemirobustSE

95% confidenceinterval z p

Smoking status based on PPA criteriat 1.274 0.061 1.161–1.398 5.10 b.001t2 0.990 0.003 0.983–0.996 −3.32 .001D 2.525 1.105 1.071–5.956 2.12 .034HD 2.257 0.814 1.113–4.577 2.26 .024NS/H 2.741 1.081 1.265–5.939 2.56 .011

Smoking status based on CA criteriat 1.282 0.061 1.169–1.407 5.25 b.001t2 0.991 0.003 0.986–0.996 −3.37 .001D 2.968 1.299 1.259–6.999 2.49 .013HD 2.302 0.897 1.072–4.939 2.14 .032NS/H 2.451 1.045 1.063–5.653 2.10 .036t × D 0.936 0.031 0.878–0.998 −2.01 .045t × HD 0.973 0.033 0.911–1.039 −0.82 .412t × NS/H 0.967 0.036 0.899–1.039 −0.92 .359

t = linear time predictor; t2 = quadratic time predictor; D = depressiveprofile; HD = higher dependence profile; NS/H = novelty-seeking/hyperactive profile.Groups shown were coded 1, whereas the low-scoring group was coded 0 forthe analyses shown here.Smoking status was coded as 1 = smoking or 0 = abstinent.

47A. Batra et al. / Journal of Substance Abuse Treatment 35 (2008) 41–52

(FTND-G and QSU-G); (b) depressive symptoms (BDI andISE-N); and (c) novelty seeking/hyperactivity (TPQ-NS,BAS, and ADHD). As shown in Fig. 1, the resulting clustersolution produced four interpretable profiles that fit thehypotheses: depressive (16%; n = 27), novelty-seeking/hyperactive (19%; n = 32), higher dependence (36%; n =59), and low-scoring (29%; n = 47) smokers. Clustercentroids also confirmed the expected patterns (for standar-dized/nonstandardized means and standard deviations,see Table 3).

Next, a cross-validation of the results was conducted toconfirm the reliability of the group classification (Everittet al., 2001). First, the sample was divided at random intotwo subsamples. A cluster analysis was conducted on thefirst subsample, and the centers were saved. Next, the secondsubsample was classified (no iterations) using the savedcenters from the previous step as the initial cluster centers.Finally, the second subsample was classified again, this timewithout start values and allowing 10 iterations. Solutionsproduced during these intermediate steps fit the pattern of theoverall cluster solution. To test the robustness of the overallcluster analysis, the resulting classifications for the last tworuns were compared using kappa coefficients. Reliabilityanalyses indicated an acceptable level of agreement (κ = .64;Altman, 1991), and 73% of the sample was reclassifiedcorrectly. These findings indicated adequate robustness ofthe overall cluster solution.

3.5. Smoker profiles and smoking status

Population-averaged generalized estimating equation(PA-GEE; Zeger & Liang, 1986) models were conductedusing STATA 9.2 (StataCorp, 2006) to test smoker profiles aspredictors of smoking status in the follow-up period.Predictors included a linear time variable (t; coded as 0, 1,6, and 12 in the four time points), a quadratic time variable

(t2), and three dummy-coded smoker profile variables (1 =depressive, 0 = others; 1= novelty seeking/hyperactive, 0 =others; 1 = higher dependence, 0 = others). Smoking ratesbased on measures of PPA and CA (1 = smoking, 0 = ab-stinent) served as dependent variables; because thesevariables were dichotomous, the logit link and Bernoullidistribution were specified. Repeated measures on one caseserved as the clustering variable. Because the follow-upsmoking data were longitudinal, unevenly spaced, andvariably correlated, an unspecified correlation structurewas used (Hardin & Hilbe, 2003).

Three sets of linear predictors were fit to the data: amain-effects model, a model adding t × Smoker Profile

48 A. Batra et al. / Journal of Substance Abuse Treatment 35 (2008) 41–52

interactions, and a model adding t and t2 × Smoker Profileinteractions. The best-fitting model was determined by thelowest quasi likelihood under the independence modelinformation criterion (QIC) score (Hardin & Hilbe, 2003).

To test whether NRT use would contribute to theprediction of smoking, NRT use at posttest was added as acovariate to an additional set of models. Although thesemodels were significant (ps b .001), NRT at posttest was nota significant predictor of smoking (all ps N .27). For thisreason and because the data on NRT at posttest wereincomplete (resulting in the listwise deletion of 27 cases),these models will not be further discussed.

The best-fitting model for smoking according to PPAwasthe main-effects model, χ2(8, N = 165) = 58.03, p b .001,QIC = 109.57. As shown in Table 4, t, t2, and the smoker

Fig. 2. Plots of smoking curves. A better model fit corresponds to a relative agreem(obs), and the left panels, displaying probability of smoking as predicted by PA-GEESmoking curves according to CA.

profile variables were significant predictors of smokingfollowing the intervention. The significant t and t2 effectsindicated that an accelerating curvilinear pattern of resumedsmoking over time best fit the data (Fig. 2). At-risk profilespredicted smoking, such that at-risk groups had higherprobabilities of smoking on follow-up compared to the low-scoring group. Models including t × Smoker Profileinteractions (QICs N 109.57) were not superior to themain-effects model, which indicated that group differencesin smoking status were significantly different yet stable andsimilarly patterned over time.

A model including both main-effects and linear interac-tions provided the best prediction of smoking using CA,χ2(8, N =165) = 70.24, p b .001, QIC = 98.33. As shown inTable 4, t, t2, smoker profiles, and the t × Depressive

ent between the right panels, displaying the observed proportion of smokingmodels (pred). Top panel: Smoking curves according to PPA. Bottom panel:

49A. Batra et al. / Journal of Substance Abuse Treatment 35 (2008) 41–52

interaction were significant predictors. The t and t2 effectsindicated that, as with PPA, an accelerating curvilinearpattern of resumed smoking over time best fit the data. At-risk groups had higher probabilities of smoking compared tothe low-scoring group (Fig. 2). The significant depressiveprofile effect was qualified by a significant t × Depressiveinteraction. As shown in Fig. 2, this interaction indicated thatsmoking probability for depressive smokers plateaued overtime compared to low scorers, whose relapse curve remainedrelatively linear and stable over time.

Further post hoc analyses, in which dummy coding wasrotated such that each profile served as the reference group,were conducted to test further group differences (i.e.,hyperactive vs. depressive, hyperactive vs. higher depen-dence, and depressive vs. higher dependence). Analysesindicated no further significant profile differences onsmoking status (coefficient ps N .16).

4. Discussion

The current study drew on previous empirical findings toclassify smokers into clinically relevant smoker profiles.Cluster analyses and cross-validation techniques indicatedthat the four hypothesized smoker profiles could beidentified and validated in a sample of German adultsmokers who had volunteered to take part in a smoking-cessation intervention. The smoker profiles evinced mainlyexpected patterns across psychological and nicotine depen-dence measures.

The overall PPA rates of 82% at posttest and 47% at 1-yearfollow-up were in line with previous findings (Altermanet al., 2001; Cinciripini et al., 1996). Furthermore, as foundin other studies (e.g., Furberg et al., 2005), consideration offollow-up abstinence rates separately by profile showedheterogeneity in ability to maintain abstinence. This findingsupported the potential clinical utility of focusing on certainsmoker subtypes to increase overall abstinence rates amongsmokers voluntarily seeking smoking-cessation treatment.

4.1. Interpretation of smoker profiles

As hypothesized, participants with low-scoring profilesin this study evinced relatively low scores across allmeasures. Not surprisingly, this group of smokers alsoevinced the highest abstinence rates at all time points. Giventheir 62% PPA 1 year following treatment, low-scoringsmokers appear to benefit from combined NRT andbehavioral treatment, and may need little further assistancein achieving and maintaining abstinence.

One unexpected feature of the low-scoring group was itscomparatively high level of novelty seeking, which impliesextraversion and openness to new experiences. Althoughsmoking motives were not directly assessed in this study, it ispossible that the low-scoring group's smoking persistsprimarily due to the learned positive effects of smoking

(Glautier, 2004). Considering the relatively high abstinencerates in the low-scoring group, however, such learnedbehavior appears to be easily amenable to change in theabsence of elevated dependence, psychological symptoms,or both. In fact, the elevated novelty seeking among theseparticipants may have increased their openness to newalternatives to smoking and their ability to work with othersin a group treatment setting.

As predicted, participants who were classified as higherdependence smokers evinced comparatively higher nicotinedependence and craving scores, whereas scores on psycho-logical dimensions were relatively low. These findings are inline with the negative reinforcement theory, which posits thatsmokers with higher levels of dependence would mainlysmoke to suppress, avoid, or suppress and avoid withdrawalsymptoms (Eissenberg, 2004). As predicted, higher depen-dence smokers also evinced a significantly higher smokingprobability than the low-scoring group, which indicated akey role for dependence in predicting smoking statusfollowing an intervention.

Smokers classified into the depressive profile evincedrelatively high scores on scales representing depressivesymptoms and negative self-communication, and relativelylow scores on measures of novelty seeking, nicotine depen-dence, and craving. One surprising finding in the depressiveprofile was the relatively high level of self-reportedhyperactivity symptoms. This finding may have resultedfrom the moderate correlation of the BDI and ADHD scales(r = .55), which may have existed due to overlapping itemsaddressing similar symptoms. Considering that clinical dep-ression and attention-deficit/hyperactivity disorder may sharesimilar symptom constellations, such as difficulty concen-trating, psychomotor agitation, and irritability, this overlapbetween depressive and hyperactive profiles may represent aclinical reality instead of a psychometric flaw (McGough etal., 2005; Steer, Ranieri, Kumar, & Beck, 2003).

Despite this group's relatively low scores on nicotinedependence and craving, smokers with depressive profilesevinced clear difficulties in attaining and maintainingabstinence during the follow-up period. Considering theirlow levels of nicotine dependence, it is conceivable thatsmokers with a depressive profile use cigarettes to obtainnot only pharmacological but also behavioral antidepressiveeffects of smoking, including hand-to-mouth movement,the timeout effect of a smoke break, and the addition of apleasant activity to their daily schedule (Eissenberg, 2004).These motives would contrast with those of smokers withhigher dependence profiles who primarily use cigarettesas a direct means of suppressing or avoiding nicotinewithdrawal symptoms.

The significant Time × Depressive Profile interaction forsmoking indicated that depressive smokers resumed smok-ing more rapidly during early follow-up than the low-scoringparticipants. Later in the follow-up, however, the depressivegroup's smoking curve plateaued, and the absolute differ-ence was no longer observable. Thus, smokers with a

50 A. Batra et al. / Journal of Substance Abuse Treatment 35 (2008) 41–52

depressive profile may be particularly at risk for earlyresumption of smoking following an intervention and mayhave difficulty reattaining abstinence. This clinically rele-vant finding may indicate a potential point of intervention fordepressive smokers. A follow-up study will test correspond-ing postintervention changes in smoking motives, self-efficacy, and decisional balance to elucidate potentialcorrelates of this trajectory.

The novelty-seeking/hyperactive profile was character-ized by relatively high scores on multiple dimensions:dependence, craving, hyperactivity/novelty seeking, nega-tive self-communication, and depressivity. These findingscorrespond to clinical research on hyperactivity, which hasshown high levels of comorbidity among adults with aclinical diagnosis of attention-deficit/hyperactivity disorder(Downey et al., 1997; McGough et al., 2005). Although thisstudy did not include clinical diagnostics, the novelty-seeking/hyperactive group does appear to have bothelevated levels of nicotine dependence and psychologicalsymptomatology. Thus, future tailored interventions aimingto maximize treatment efficacy among smokers withelevated novelty seeking/hyperactivity will need to takeinto account the complexity of the profile to best addressthese smokers' needs.

Participants with novelty-seeking/hyperactive profilesalso evinced notable difficulties in attaining and maintain-ing abstinence. It was hypothesized that novelty-seeking/hyperactive smokers would smoke primarily due to theirimpulsivity and desire for stimulation, as well as to alleviatenegative affect. Indeed, the resulting pattern indicated thatelements of both positive and negative reinforcement mayplay a role in this group's smoking behavior. For example,impulsivity has been associated with increased sensation-and novelty-seeking behaviors (e.g., drug use; Bickel &Marsch, 2001). On the other hand, recent research has alsodemonstrated a negatively reinforcing role for nicotine inreducing negative affect among more impulsive smokers(Doran et al., 2006). Our findings mirror these researchresults and also highlight the difficulties in definingnovelty-seeking/hyperactive smokers. Future studies mayseek to define this group in terms of their personalityfeatures and smoking expectancies, in addition to theirpsychological symptoms.

Current findings showed a reliable statistically signifi-cant difference between low-scoring and at-risk smokergroups in terms of their smoking status trajectories. Thisstudy thus corroborated the findings of previous studiesthat have established these at-risk smoker profiles inclinically affected populations. Furthermore, this studyextends these at-risk profiles to a nonclinical sample oftreatment-seeking smokers and evinces the potentialclinical utility of providing these at-risk groups withtailored treatments to address their needs and therebyincrease abstinence rates.

Despite these findings, however, post hoc analysesindicated no further group differences on the probability of

smoking on follow-up among at-risk smoker profiles. Thisfinding indicates that, although the at-risk groups appear todiffer in terms of their profile scores, these different profilesdo not result in significantly different smoking status reportsfollowing a behavioral intervention. This finding highlightsthe multiple pathways to nicotine dependence and mayindicate that even smokers with low levels of physicaldependence (i.e., depressive profile) may evince psycholo-gical dependence that can similarly hinder the attainment andmaintenance of abstinence.

4.2. Study limitations

This study has some limitations that warrant discussion.First, due to scheduling limitations and oversights, thebaseline participant data in this study were not complete.If participant responses were missing for an entiremeasure, the case was deleted listwise. Although thisprocedure avoided introducing data entirely constructed byimputation, it reduced the sample size and, thereby, thestatistical power for the analyses. Despite this limitation,the current findings fit the hypotheses and providedclinically relevant information that will be replicated in afuture study.

A further potential limitation concerns the use ofbiochemical validation in this study. Due to funding limits,CO validation was discontinued after the first follow-upappointment. For this reason, self-report abstinence rateswere not biochemically validated for the 6- and 12-monthfollow-up periods, which limits the assurance of validity ofthe abstinence self-reports. Fortunately, abstinence ratesappear to be comparable to those in the literature (Altermanet al., 2001; Cinciripini et al., 1996). In the follow-up study,the recent guidelines provided by West et al. (2005) will beimplemented such that a randomly selected subset ofparticipants will be invited to provide CO validation at12-month follow-up.

Finally, the smoker profiles were validated on a smallsample of smokers in a midsized German university town.The high levels of education and employment in this sampleindicate a relatively high socioeconomic level, which raisesquestions as to the external validity of the smoker profiles.Because clustering techniques tend to be sample specific,replication of these profiles in other, perhaps more diversesamples is necessary to confirm the generalizability of thesmoker profiles to other populations.

5. Conclusions and future directions

Despite its limitations, this study identified clinicallyuseful, multidimensional smoker subgroups that are at higherrisk for postintervention smoking. The proposed smokerprofiles included low-scoring, higher dependence, depres-sive, and novelty-seeking/hyperactive smokers. Analysesindicated that smokers could be classified into profiles and

51A. Batra et al. / Journal of Substance Abuse Treatment 35 (2008) 41–52

that these profiles predicted smoking following a pharma-cobehavioral smoking-cessation intervention.

According to the findings in this study, at-risk smokersmay benefit from treatment tailored to their specific profile.Thus, in a future intervention trial, smokers will be classifiedinto the smoker profiles indicated in this study and will beprovided with interventions tailored to their needs. Thisstudy thus serves as the foundation for a later trial, which willcompare the efficacy of tailored interventions with theefficacy of the standard pharmacobehavioral treatment usedin this study.

Acknowledgments

This research was supported by grant 01 EB 0110 fromthe Ministry of Science, Research, and the Arts, and theGerman Federal Ministry of Education and Research to Dr.Anil Batra.

The authors would like to thank Klara Sattler, Dr. PatrickKaspar, and Dr. Hubertus Friedrich for conducting groupinterventions for this study, as well as Katrin Törpisch andSabine Konnerth for entering and managing the data.

References

Alterman, A., Gariti, P., & Mulvaney, F. (2001). Short- and long-termsmoking cessation for three levels of intensity of behavioral treatment.Psychology of Addictive Behaviors, 15, 261−264.

Altman, D. G. (1991). Practical statistics for medical research. London:Chapman & Hall.

Arzneimittelkommission der Deutschen Ärzteschaft. (2001). Empfehlungenzur Therapie von Tabakabhängigkeit [Recommendations for thetreatment of tobacco dependence]. Arzneiverordnung in der Praxis—Sonderheft Therapieempfehlungen Medication Guidelines in Practice—Special Issue for Treatment Recommendations, 1.

Batra, A., & Buchkremer, G. (2004). Tabakabhängigkeit. Ein Leitfaden fürTherapeuten [Tobacco dependence: A manual for therapists]. Stuttgart:Kohlhammer.

Beck, A., & Steer, R. (1987). Beck Depression Inventory—manual. SanAntonio, TX: The Psychological Corporation.

Bickel, W. K., & Marsch, L. A. (2001). Toward a behavioral economicunderstanding of drug dependence: Delay discounting processes. Ad-diction, 96, 73−86.

Burgess, E. S., Brown, R. A., Kahler, C. W., Niaura, R., Abrams, D. B.,Goldstein, M. G., et al. (2002). Patterns of change in depressivesymptoms during smoking cessation: Who's at risk for relapse? Journalof Consulting and Clinical Psychology, 70, 356−361.

Carver, C., & White, T. (1994). Behavioral inhibition, behavioral activationand affective responses to impending reward and punishment: The BIS/BAS scales. Journal of Personality and Social Psychology, 67,319−333.

Cinciripini, P. M., Cinciripini, L. G., Wallfisch, A., Haque, W., & VanVunakis, H. (1996). Behavior therapy and the transdermal nicotinepatch: Effects on cessation outcome, affect and coping. Journal ofConsulting and Clinical Psychology, 64, 314−323.

Cloninger, C. (1987). A systematic method for clinical description andclassification of personality variants: A proposal. Archives of GeneralPsychiatry, 44, 573−588.

Doran, N., Springs, B., McChargue, D., Pergadia, M., & Richmond, M.(2004). Impulsivity and smoking relapse. Nicotine and TobaccoResearch, 6, 641−647.

Doran, N., VanderVeen, J.,McChargue, D., Spring, B., &Cook, J.W. (2006).Effect of nicotine on negative affect among more impulsive smokers.Experimental and Clinical Psychopharmacology, 14, 287−295.

Downey, K. K., Pomerleau, C. S., & Pomerleau, O. F. (1996). Personalitydifferences related to smoking and adult attention deficit hyperactivitydisorder. Journal of Substance Abuse, 8, 129−135.

Downey, K. K., Stelson, F. W., Pomerleau, O. F., & Giordani, B. (1997).Adult attention deficit hyperactivity disorder: Psychological test profilesin a clinical population. Journal of Nervous and Mental Disease, 185,32−38.

Eissenberg, T. (2004). Measuring the emergence of tobacco depen-dence: The contribution of negative reinforcement models. Addic-tion, 99(Suppl 1), 5−29.

Everitt, B. S., Landau, S., & Leese, M. (2001). Cluster analysis, (4th ed.).New York: Oxford University Press.

Fagerström, K. O., Kunze, M., Schoberberger, R., Breslau, N., Hughes, J.,Hurt, R. D., et al. (1996). Nicotine dependence versus smokingprevalence: Comparison among countries and categories of smokers.Tobacco Control, 5, 52−56.

Foulds, J., Gandhi, K. K., Steinberg, M. B., Richardson, D. L., Williams, J.M., Burke, M. V., et al. (2006). Factors associated with quitting smokingat a tobacco dependence treatment clinic. American Journal of HealthBehavior, 30, 400−412.

Furberg, H., Sullivan, P. F., Maes, H., Prescott, C. A., Lerman, C., Bulik, C.,et al. (2005). The types of regular cigarette smokers: A latent classanalysis. Nicotine & Tobacco Research, 7, 351−360.

Glautier, S. (2004). Measures and models of nicotine dependence: Positivereinforcement. Addiction, 99(Suppl 1), 30−50.

Haas, A. L., Munoz, R. F., Humfleet, G. L., Reus, V. I., & Hall, S. M. (2004).Influences of mood, depression history and treatment modality onoutcomes in smoking cessation. Journal of Consulting and ClinicalPsychology, 72, 563−570.

Hall, S. M., Muñoz, R., & Reus, V. (1994). Cognitive–behavioralintervention increases abstinence rates for depressive-history smokers.Journal of Consulting and Clinical Psychology, 62, 141−146.

Hall, S. M., Sees, K. L., Munoz, R., Reus, V. I., Duncan, C., Humfleet, G. L.,et al. (1996). Mood management and nicotine gum in smokingtreatment: A therapeutic contact and placebo-controlled study. Journalof Consulting and Clinical Psychology, 64, 1003−1009.

Hardin, J. W., & Hilbe, J. M. (2003). Generalized estimating equations.Boca Raton, FL: Chapman & Hall/CRC.

Hautzinger, M., Bailer, M., Worall, H., & Keller, F. (1994). Das Beck-Depressions-Inventar (BDI). Bern: Huber.

Heatherton, T. F., Kozlowski, L. T., Frecker, R. C., & Fagerström, K. O.(1991). The Fagerström Test for Nicotine Dependence: A revision of theFagerström Tolerance Questionnaire. British Journal of Addiction, 86,1119−1127.

Heßlinger, B., Philipsen, A., & Richter, R. (2004). Psychotherapie derADHS im Erwachsenalter: Ein Arbeitsbuch [Psychotherapy for adultADHD: A workbook]. Göttingen, Germany: Hogrefe-Verlag.

Hitsman, B., Borelli, B., & McChargue, D. E. (2003). History of depressionand smoking cessation outcome: A meta-analysis. Journal of Consultingand Clinical Psychology, 71, 657−663.

Humfleet, G. L., Prochaska, J. J., Mengis,M., Cullen, J., Munoz, R., Reus, V.I., et al. (2005). Preliminary evidence of the association betweenthe history of childhood attention-deficit/hyperactivity disorder andsmoking treatment failure. Nicotine and Tobacco Research, 7, 453−460.

Lesch, O. M., Dvorak, A., Hertling, I., Klingler, A., Kunze, M., Ramskogler,K., et al. (2004). The Austrian Multicentre Study on Smoking:Subgroups of nicotine dependence and their craving. Neuropsychobiol-ogy, 50, 78−88.

McGough, J. J., Smalley, S. L., McCracken, J. T., Yang, M., Del'Homme,M., Lynn, D. E., et al. (2005). Psychiatric comorbidity in adultattention deficit hyperactivity disorder: Findings from multiplexfamilies. American Journal of Psychiatry, 162, 1621−1627.

Mucha, R. F., & Pauli, P. (2003). Die deutsche Version des Questionniare onSmokingUrges (QSU-G). InA. Glöckner-Rist, F. Rist, &H.Küfner (Eds.),

52 A. Batra et al. / Journal of Substance Abuse Treatment 35 (2008) 41–52

Elektronisches Handbuch zu Erhebungsinstrumenten im Suchtbereich(EHES), Version 3.00 Mannheim: Zentrum für Umfragen, Methoden undAnalysen: Zentrum für Umfragen, Methoden und Analysen.

Norman, G. J., Velicer, W. F., Fava, J. L., & Prochaska, J. O. (1998).Dynamic typology clustering within the stages of change for smokingcessation. Addictive Behaviors, 23, 139−153.

Patton, D., Barnes, G. E., & Murray, R. P. (1997). A personality typology ofsmokers. Addictive Behaviors, 22, 269−273.

Pomerleau, C. S., Marks, J. L., & Pomerleau, O. F. (2000). Who gets whatsymptom? Effects of psychiatric cofactors and nicotine dependence onpatterns of smoking withdrawal symptomatology. Nicotine & TobaccoResearch, 2, 275−280.

Pyszczynski, T., Hamilton, J. C., Herring, F. H., & Greenberg, J. (1989).Depression, self-focused attention, and the negative memory bias.Journal of Personality and Social Psychology, 57, 351−357.

Ranney, L., Melvin, C., Lux, L., McClain, E., & Lohr, K. N. (2006).Systematic review: Smoking cessation intervention strategies for adultsand adults in special populations. Annals of Internal Medicine, 145.

Rumpf, H. J., Hapke, U., & John, U. (1995). Deutsche Übersetzung desFagerström Test for Nicotine Dependence. Greifswald: Institut fürEpidemiologie und Sozialmedizin.

Schumann, A., Rumpf, H. -J., Meyer, C., Hapke, U., & John, U. (2003).Deutsche Version des Fagerström Test for Nicotine Dependence (FTND-G) und des Heaviness of Smoking Index (HSI-G). In A. Glöckner-Rist,F. Rist , & H. Küfner (Eds.), Elektronisches Handbuch zu Erhebungsin-strumenten im Suchtbereich (EHES), Version 3.00 Mannheim: Zentrumfür Umfragen, Methoden und Analysen: Zentrum für Umfragen,Methoden und Analysen.

Shiffman, S. (2006). Reflections on smoking relapse research. Drug andAlcohol Review, 25, 15−20.

Shiffman, S., Engberg, J. B., Paty, J. A., Perz, W. G., Gnys, M., Kassel, J. D.,et al. (1997). A day at a time: Predicting smoking lapse from daily urge.Journal of Abnormal Psychology, 106, 104−116.

Steer, R. A., Ranieri, W. F., Kumar, G., & Beck, A. T. (2003). BeckDepression Inventory-II items associated with self-reported symptoms ofADHD in adult psychiatric outpatients. Journal of PersonalityAssessment, 80, 58−63.

Strobel, A., Beauducel, A., Debener, S., & Brocke, B. (2001). Einedeutschsprachige Version des BIS/BAS-Fragebogens von Carver undWhite. Zeitschrift für Differentielle und Diagnostische Psychologie, 22,216−227.

Tiffany, S. T., Conklin, C. A., Shiffman, S., & Clayton, R. R. (2004). Whatcan dependence theories tell us about assessing the emergence oftobacco dependence? Addiction, 99(Suppl 1), 78−86.

Tiffany, S. T., & Drobes, D. J. (1991). The development and initial validationof a questionnaire on smoking urges. British Journal of Addiction, 86,1467−1476.

Tönnies, S., & Tausch, R. (1981). Inventar zur Selbstkommunikation fürErwachsene (ISE) [Self-communication inventory for adults ISE].Zeitschrift für Klinische Psychologie, 10, 52−63.

Velicer, W. F., Prochaska, J. O., Rossi, J. S., & Snow, M. G. (1992).Assessing outcome in smoking cessation studies. PsychologicalBulletin, 111, 23−41.

West, R., Hajek, P., Stead, L., & Stapleton, J. (2005). Outcome criteria insmoking cessation trials: Proposal for a common standard. Addiction,100, 299−303.

Weyers, P., Krebs, H., & Janke, W. (1995). Reliability and constructvalidity of the German version of Cloniger's Tridimensional Perso-nality Questionnaire. Personality and Individual Differences, 19,853−861.

Wileyto, E. P., Patterson, F., Niaura, R., Epstein, L. H., Brown, R. A.,Audrain, M. J., et al. (2005). Recurrent event analysis of lapse andrecovery in a smoking cessation clinical trial using bupropion. Nicotine& Tobacco Research, 7, 257−268.

Zeger, S. L., & Liang, K. -Y. (1986). Longitudinal data analysis for discreteand continuous outcomes. Biometrics, 42, 121−130.