Predictive validity of four nicotine dependence measures in a college sample

Transcription

1 Drug and Alcohol Dependence 87 (2007) Predictive validity of four nicotine dependence measures in a college sample Eve M. Sledjeski a,, Lisa C. Dierker a, Darcé Costello a, Saul Shiffman b, Eric Donny c, Brian R. Flay d, Tobacco Etiology Research Network (TERN) a Wesleyan University, Psychology Department, 207 High Street, Middletown, CT 06459, United States b Department of Psychology, University of Pittsburgh, 130 N. Bellefield Avenue, Suite 510, Pittsburgh, PA 15260, United States c Department of Psychology, University of Pittsburgh, 4119 Sennott Square, 210 South Bouquet Street, Pittsburgh, PA 15260, United States d Department of Public Health, Oregon State University, College of Health and Human Sciences, 254 Waldo, Corvallis, OR 97331, United States Received 27 February 2006; received in revised form 5 July 2006; accepted 5 July 2006 Abstract Background: The present study compared the predictive and incremental validity of four commonly used dependence measures (Diagnostic and Statistical Manual-IV [] nicotine dependence criteria, Fagerstrom Test for Nicotine Dependence [FTND], Hooked On Nicotine Checklist [HONC], Nicotine Dependence Syndrome Scale [NDSS]) in a first year college sample reporting light smoking patterns. Methods: Nicotine dependence measures were administered at the end of the first semester and follow-up smoking behavior (i.e. continued smoking, quantity, frequency, and length of abstinence) was assessed at the end of the first and second academic years. Results: Higher levels of dependence as measured by the HONC and predicted smoking behavior at both follow-up assessments. While higher scores on some of the NDSS factors predicted heavier smoking behavior during follow-up assessments, higher scores on other NDSS factors predicted lighter smoking behavior. The, NDSS-priority, and HONC measures provided some evidence for incremental validity. Higher dependence scores on all four measures were related to shorter lengths of smoking abstinence. Conclusions: The four dependence measures were differentially related to smoking behavior outcomes in a light smoking sample. These findings suggest that nicotine dependence can predict a variety of smoking behaviors in light smokers Elsevier Ireland Ltd. All rights reserved. Keywords: Smoking; Nicotine dependence; Predictive validity; Incremental validity; Light smokers 1. Introduction Nicotine dependence has been shown to predict smoking maintenance and unsuccessful quit attempts in adulthood (Colby et al., 2000a,b). Four commonly used nicotine dependence measures are the Diagnostic and Statistical Manual-IV () nicotine dependence criteria (APA, 1994; WHO, 1994), Fagerstrom Tolerance Questionnaire (FTQ) and its modified forms (mftq) and Fagerstrom Test for Nicotine Dependence (FTND: Corresponding author. Tel.: ; fax: address: esledjeski@wesleyan.edu (E.M. Sledjeski). Fagerstrom, 1978; Heatherton et al., 1991; Prokhorov et al., 1996), Hooked On Nicotine Checklist (HONC: DiFranza et al., 2000, 2002a,b; O Loughlin et al., 2002a,b,c), and the Nicotine Dependence Syndrome Scale (NDSS: Shiffman et al., 2004). Despite some overlap, these measures tend to assess different aspects of nicotine dependence. The FTQ and its modified forms focus on smoking intensity and the amount of effort used to maintain desired blood nicotine levels while the taps more of the cognitive and behavioral components of dependence (Colby et al., 2000b; Fagerstrom, 1978; Strong et al., 2003). The HONC measures nicotine dependence by assessing an individual s loss of autonomy over smoking behavior resulting in difficulty quitting (DiFranza et al., 2000, 2002a,b), and the /$ see front matter 2006 Elsevier Ireland Ltd. All rights reserved. doi: /j.drugalcdep

2 E.M. Sledjeski et al. / Drug and Alcohol Dependence 87 (2007) NDSS is a new measure designed to assess multiple dimensions of nicotine dependence: drive, tolerance, priority, stereotypy and continuity (Shiffman et al., 2004). Recent cross-sectional and longitudinal research has suggested that the FTQ/FTND, NDSS, and HONC were related to smoking quantity and frequency (Cohen et al., 2002; Kandel et al., 2005; Prokhorov et al., 1998; Shiffman and Sayette, 2005; Shiffman et al., 2004), failed quit attempts (Breslau and Johnson, 2000; Haddock et al., 1999; Shiffman et al., 2004; Wellman et al., 2005), and biochemical markers of nicotine (Chen et al., 2002; Prokhorov et al., 2000; Shiffman et al., 2004) in adolescent and adult moderate to heavy smokers. While there is some evidence of validity for each of these dependence measures, few studies have examined more than one measure of nicotine dependence, making direct comparisons difficult. Among studies that have employed more than a single dependence measure, comparisons suggest that some measures may be superior at predicting smoking behavior. For example, three studies have found the mftq to be a better predictor of smoking quantity/frequency compared to the in adolescents (Cohen et al., 2002; Strong et al., 2003) and adults (Hughes et al., 2004). In addition, two studies have shown the NDSS to continue to predict 1 year follow-up smoking behavior in adolescents and adult smokers after controlling for FTQ scores, suggesting that the NDSS accounted for additional variance not measured by the FTQ (Clark et al., 2005; Shiffman et al., 2004). Aside from the dearth of comparative data, few longitudinal studies have been conducted to examine the ability of dependence measures to predict smoking quantity and frequency over and above initial smoking behavior (incremental validity: Clark et al., 2005; Etter et al., 1999; Shiffman et al., 2004; Wellman et al., 2005). Longitudinal studies controlling for initial smoking behavior have suggested that the mftq/ftnd fails to predict smoking behavior while the NDSS and HONC continue to predict. For example, Etter et al. (1999) found no relationship between the FTND and continued smoking 7 months later after controlling for initial smoking quantity, suggesting that the FTND may represent a proxy measure for the number of cigarettes smoked per day and may not be related to subsequent change in smoking behavior. In a more recent study, the HONC continued to predict smoking cessation at the 6- and 12-month follow-ups after controlling for baseline smoking frequency and quantity while the mftq did not (Wellman et al., 2006). In addition, the NDSS has demonstrated incremental validity over and above initial smoking behavior when predicting follow-up smoking quantity in adolescent daily smokers (Clark et al., 2005) and difficultly with abstaining in adult daily smokers (Shiffman et al., 2004). More longitudinal research is needed to examine the predictive validity of these measures as well as the independent contribution of nicotine dependence when compared to easily obtainable indices of smoking behavior (i.e. quantity and frequency). Finally, although nicotine dependence measures have demonstrated some validity among samples of established smokers, few studies have examined the utility of these measures in smokers with irregular and/or light smoking patterns. Notable exceptions have included studies of validity of the HONC, which have examined samples of adolescents with a range of smoking behaviors, the majority of whom were relatively light non-daily smokers (DiFranza et al., 2002a,b; O Loughlin et al., 2003, 2002c; Wellman et al., 2006; Wheeler et al., 2004). In these studies, HONC scores were associated with smoking frequency (O Loughlin et al., 2003, 2002c; Wellman et al., 2006; Wheeler et al., 2004) and predicted continued smoking at follow-up, daily use, and experiencing a failed quit attempt (DiFranza et al., 2002b). The predictive validity of other nicotine dependence measures is yet to be examined within a light smoking sample. The present study compared the predictive and incremental validity of four commonly used dependence measures in a sample of first year college students who have reported relatively light smoking patterns (i.e. 78% non-daily, average of <5 cigarettes per day). Our goals were to (1) determine whether nicotine dependence measures administered at the end of the first college semester predict follow-up smoking behavior at the end of the first and second college year (predictive validity) and (2) assess whether dependence measures continue to predict smoking behavior after controlling for baseline smoking behavior (incremental validity). 2. Methods 2.1. Participants The selection and retention of participants for the present study have been previously reported in detail (Clayton, 2004; Dierker et al., 2006a; Tiffany et al., unpublished). Briefly, eligible participants were selected from a pool of first year college students who completed a screening survey during an orientation program in the summer of 2002 at Purdue University (n = 4690, 71% response rate). From these, 2001 first year students who had atleast some prior experience with smoking (i.e. one or more puffs lifetime) were invited to participate in the study. In total, 912 students (45%) agreed to participate in the baseline survey and weekly web-based surveys throughout their first year and one survey during their second year. Participants who reported smoking during the past week completed the nicotine dependence measures at the end of the first semester (n = 112). The present analyses included 95 participants who completed the web-based surveys at the end of their first semester and the follow-up survey at the end of their first year (retention rate = 85%). In addition, 55 participants completed the follow-up survey at the end of the second year (retention rate = 58%). All participants lived in on-campus housing their freshman year and 42% continued to live on-campus during their sophomore year. The present sample was 48% female and 94% Caucasian. Attrition rates were similar for males and females and did not differ based on FTND, HONC, NDSS-T, NDSSdrive, NDSS-priority, NDSS-continuity and continuous and diagnosis scores. However, participants who completed the end of the first year assessment reported lower NDSS-tolerance scores and participants who completed the end of the second year assessment reported lower NDSS-stereotypy scores and lower smoking frequency compared to participants who did not complete the follow-up assessments (p < 0.05) Measures Smoking behavior. Smoking behavior was assessed using a 7-day timeline follow-back report. This procedure had participants think back over the past 7 days and report the number of cigarettes they smoked each day. Continued smoking at the end of the first and second years was assessed by these 7-day retrospective reports of cigarette smoking using a web-based protocol. Aggregate past week smoking variables addressing both the quantity (total number of cigarettes smoked this past week) and frequency (number of days smoked

3 12 E.M. Sledjeski et al. / Drug and Alcohol Dependence 87 (2007) during the past week) were created from these responses. In addition, an objective measure of length of abstinence was created to assess the longest period of consecutive days that participants reported not smoking. This variable was created by using the continuous timeline follow-back reports of smoking from the end of the first semester to the end of the first year Nicotine dependence Fagerstrom Test for Nicotine Dependence (FTND). The FTND is a modified version of the Fagerstrom Tolerance Questionnaire (FTQ: Fagerstrom, 1978) consisting of six items designed to assess nicotine dependence (Heatherton et al., 1991). Item scoring was based on procedures developed by Heatherton et al. (1991) and items were summed to yield a total score (possible range = 0 10). The smoking quantity item (i.e. average number of cigarettes per day) on the FTND was excluded in analyses concerning quantity and frequency to avoid confounding (possible range = 0 7: Cohen et al., 2002; Lichtenstein and Mermelstein, 1986; Prokhorov et al., 1998). One item assessing whether the participant smoked more frequently during the first hours after waking was not included in reliability analyses since all participants responded no. The FTND demonstrated low reliability in the present sample (Cronbach s α = 0.585). Prior research has also suggested poor internal consistency using the FTQ, FTND and other modified versions in adolescent and adult samples (Burling and Burling, 2003; Cohen et al., 2002; Heatherton et al., 1991; Lichtenstein and Mermelstein, 1986; Payne et al., 1994; Pomerleau et al., 1994) Hooked On Nicotine Checklist (HONC). The HONC is a 10-item measure rated on a dichotomous scale (i.e. yes or no) designed to test an individual s loss of autonomy over tobacco use (DiFranza et al., 2002a,b). Two methods of scoring have been proposed for the HONC including a continuous measure consisting of the sum of endorsed responses (degree of lost autonomy) and a dichotomous measure categorizing participants into loss (endorsement of one or more items) versus no loss of autonomy groups (DiFranza et al., 2002a). In the present sample only 11 participants reported no loss of autonomy; therefore, the continuous HONC scoring method was used (possible range = 0 10). The measure demonstrated acceptable reliability in the present sample (Cronbach s α = 0.877) Diagnostic and Statistical Manual of mental disorders-iv (). A self-administered version of the Composite International Diagnostic Interview Tobacco Module (CIDI: WHO, 1994) was used to assess the seven criteria of nicotine dependence as specified by the (APA, 1994): tolerance (two items), withdrawal (nine items), smoking in larger amounts or longer than intended (two items), persistent desire or unsuccessful efforts to cut down (one item), great deal of time spent to obtain, use or recover from smoking (one item), activities given up or reduced (one item), and continued use despite physical or psychological problems caused or exacerbated by smoking (two items). The complete questionnaire can be found in Dierker et al. (2006). Given our interest in the association between a broad range of smoking quantity and frequency and the endorsement of nicotine dependence criteria, daily use of nicotine was not required for the assessment of symptoms. Further, unlike instruments that assess withdrawal symptoms only among smokers who have tried to quit or cut down, we assessed withdrawal among all smokers based on any periods in which smoking behavior has been limited for any reason. Based on the difficulty in self-reporting decreased heart rate, this withdrawal symptom was not assessed. Though craving is not listed as a symptom of nicotine dependence or withdrawal in (APA, 1994), craving is the most frequently reported withdrawal symptom among young smokers thus it was assessed (Colby et al., 2000a; DiFranza et al., 2000). Response categories included not at all, a little bit, somewhat, and quite a bit. Symptoms were coded as present if reported at any level (i.e. a little bit through quite a bit). An individual was classified as dependent if he/she experienced atleast three of the seven dependence criteria associated with their smoking behavior. Although the DSM was developed to provide a dichotomous measure of nicotine dependence, modern theories maintain, explicitly or implicitly, that dependence varies on a continuum (Tiffany et al., 2004). That continuum is linked in turn to a trajectory of smoking behavior, with the basic elements of dependence processes evident even in the early episodes of cigarette use. Given that we were interested in nicotine dependence among light irregular smokers, we computed a continuous score in addition to the traditional dependence diagnosis. Similar to prior research, the number of criteria met were summed to yield a continuous dependence score (possible range = 0 7) (Cohen et al., 2002). The continuous measure demonstrated acceptable reliability in the present sample (Cronbach s α = 0.752) Nicotine Dependence Syndrome Scale (NDSS). The NDSS is a 19-item multidimensional measure consisting of five factors (drive, priority, continuity, stereotypy, and tolerance) that assess nicotine dependence (Shiffman et al., 2004). Drive measures craving and withdrawal symptoms while tolerance assesses reduced sensitivity to tobacco products. Priority assesses the preference for smoking over other reinforcers. Continuity assesses the regularity of smoking while stereotypy measures the sameness of smoking contexts. Items were rated on a scale from one (not at all true) to five (extremely true). The total score (NDSS-T) and five factor scores were computed using the regression-based algorithms described in Shiffman et al. (2004). These algorithms were designed to reduce the intercorrelations among the five factors as well as standardizing the scores (mean = 0, S.D. = 1 on the normative sample). The total scores and most factors demonstrated acceptable reliability (Cronbach s α = ); however, reliability for the stereotypy subscale was lower (Cronbach s α = 0.686) Statistical analyses Chi-square analysis (dichotomous measures) and analysis of variance (ANOVA: continuous measures) were used to examine group differences between participants that completed the follow-up assessments and those that did not as well as gender differences in smoking behavior and nicotine dependence. Logistic regression analyses were used to assess the relationship between dependence measures and the dichotomous measures of continued smoking at the follow-up assessments. Linear regression analyses for smoking quantity, frequency, and length of abstinence were used to assess the relationships between the dependence measures and smoking behavior. Hierarchical linear regressions (continuous) were used to examine the incremental validity of the dependence measures in predicting future smoking quantity and frequency after controlling for baseline smoking quantity and frequency (Hunsley and Meyer, 2003). Hierarchical linear regression analyses were used to examine the interaction between nicotine dependence levels and baseline smoking levels in predicting follow-up smoking quantity and frequency. The Bonferroni method was used to adjust p values for measures employing multiple scoring procedures (i.e. NDSS and ). Significance levels were set at for the NDSS and its factor scores and for continuous and diagnosis scores. 3. Results 3.1. Prevalence of nicotine dependence and smoking behavior Table 1 displays descriptive statistics for the four dependence measures and smoking behaviors at each of the three waves of assessment (baseline and end of year 1 and 2). Participants reported relatively light and irregular smoking patterns. On average at baseline, participants smoked (S.D. = 34.88) cigarettes during the past week and the majority were non-daily smokers (M = 4.58, S.D. = 2.21 days; 22% daily smokers). Males and females showed similar rates of nicotine dependence and smoking quantity and frequency (p s > 0.05). Dependence levels as measured by the FTND and NDSS were low. However, 64% of participants met diagnostic criteria based on the and average HONC scores indicated moderate levels of nicotine dependence.

4 E.M. Sledjeski et al. / Drug and Alcohol Dependence 87 (2007) Table 1 Descriptive statistics for nicotine dependence measures and smoking behavior, mean (S.D.) and frequency (%) Baseline assessment Total (n = 95) Female (%) 48 Caucasian (%) 94 Dependence measures FTND 0.71 (1.26) Continuous 3.57 (2.03) Diagnosis 61 (64%) NDSS-total 1.04 (0.886) Drive 1.67 (1.02) Priority (0.502) Tolerance (1.11) Continuity 1.12 (1.25) Stereotypy (0.752) HONC 4.78 (3.13) Smoking behavior Quantity (34.88) Frequency 4.58 (2.21) Second semester Total (n = 95) Continued smoking 58 (61%) Quantity (31.85) Frequency 3.39 (3.06) Length of abstinence (days) 4.63 (4.21) Second year Total (n = 55) Female (%) 54 Caucasian (%) 98 Continued smoking 39 (71%) Quantity (39.58) Frequency 3.75 (3.00) Note: quantity = number of cigarettes smoked in the past week; frequency = number of days smoked in the past week Dependence measures and continued smoking Continued smoking was reported by 58 (61%) participants during the second semester follow-up and 39 (71%) participants during the second year follow-up. Logistic regression analyses were used to evaluate the relationship between the dependence measures (assessed at the end of the first college semester) and continued smoking (yes/no) at the end of the first and second year of college. A separate regression was conducted for each dependence measure with the dependence measure/subscale serving as the predictor and continued smoking serving as the outcome variable in the model. The HONC, NDSS-stereotypy, and DSM- IV continuous score and diagnosis predicted continued smoking at the end of the first year and the measures continued to predict smoking at the end of sophomore year (see Table 2). More specifically, for each unit increase in the HONC score there was an associated 21% increase in the likelihood of continued smoking for the first year follow-up and 20% increase for the second year follow-up. In addition, for each unit increase in the score there was an associated 40% increase in the likelihood of continued smoking for the first year follow-up and 62% increase for the second year follow-up. Finally, for each unit increase in the NDSS-stereotypy score there was an associated 60% decrease in the likelihood of continued smoking for the first year follow-up Dependence measures and smoking quantity and frequency Separate linear regression analyses were conducted to assess the relationship between the dependence measures and smoking quantity and frequency at the follow-up assessments (see Tables 3 and 4). Higher scores on the HONC, NDSS-T, NDSSdrive, NDSS-tolerance, and measures predicted a greater number of cigarettes smoked at the end of the first year while lower scores on the NDSS-priority predicted higher quantity at the end of the first year. In addition, the HONC, NDSSdrive, and measures predicted smoking frequency at the end of the first year. The HONC, NDSS-T, NDSS-tolerance and measures predicted second year quantity and frequency while the FTND and NDSS-drive predicted quantity only. Separate hierarchical linear regressions were used to assess the incremental validity of the dependence measures (see Tables 3 and 4). Only NDSS-priority scores predicted quantity at the end of the first year after controlling for baseline smoking behavior. In addition, the HONC and measures continued to predict frequency at the end of the first year after controlling for baseline smoking behavior. Only DSM- IV continuous scores and diagnosis predicted frequency at the end of the second year after controlling for baseline smoking behavior Interaction between baseline smoking behavior and nicotine dependence Separate hierarchical linear regressions were conducted to test for an interaction between baseline quantity and frequency and dependence levels on all four measures. These analyses were conducted to examine whether the relationship between nicotine dependence and follow-up smoking quantity and frequency varied depending on initial smoking levels. The only significant interaction was between continuous scores and baseline smoking quantity predicting smoking quantity at the end of the first year. continuous scores and baseline smoking quantity were entered in the first step and the interaction between scores and quantity was entered in the second step of the model. Smoking quantity at the first year follow-up served as the dependent variable. The interaction between dependence scores and baseline smoking quantity significantly added to the model, F change (1, 91) = 8.66, p = Decomposition of the interaction revealed that at low levels of baseline smoking (1 S.D. below the mean, 5 cigarettes per week), higher dependence levels ( 5 dependence criteria) predicted higher smoking quantity at first follow-up, t(91) = 2.74, p = (see Fig. 1). In contrast, the did not predict smoking quantity when baseline smoking quantity was high (1 S.D. above the mean, 60 cigarettes per week), t(91) = 1.63, p =

5 14 E.M. Sledjeski et al. / Drug and Alcohol Dependence 87 (2007) Table 2 Logistic regressions: dependence measures predicting continued smoking at the end of the first and second college year Predictor First year (n = 95) Second year (n = 55) B p OR (CI) B p OR (CI) FTND ( ) ( ) HONC ( ) * ( ) + NDSS-T ( ) ( ) + Drive ( ) ( ) + Priority ( ) ( ) Tolerance ( ) ( ) Continuity ( ) ( ) Stereotypy ( ) * ( ) Continuous ( ) * ( ) * Diagnosis ( ) * ( ) * Note: Separate logistic regressions were conducted for each dependence measure/subscale. OR, odds ratio; CI, 95% confidence interval. + Marginally significant, p < 0.1. * Significant at 0.05 or Bonferroni corrected level. Table 3 Linear regressions: dependence measures predicting end of first year smoking quantity and frequency (n = 95) Univariate analyses Hierarchical analyses a β t p R 2 β t p R 2 Quantity FTND HONC * NDSS-T * Drive * Priority * * Tolerance * Continuity Stereotypy Continuous * Diagnosis * Frequency FTND HONC * * NDSS-T Drive * Priority Tolerance Continuity Stereotypy Continuous * * Diagnosis * * Note: R 2 = adjusted R 2. + Marginally significant, p < 0.1. * Significant at 0.05 or Bonferroni corrected level. a Controlling for baseline quantity and frequency Dependence measures and smoking abstinence Results of linear regression analyses evaluating the relationship between dependence measures and the longest period of abstinence are presented in Table 5. Univariate linear regressions were conducted separately for each dependence measure/subscale. A longer length of abstinence was related to lower FTND, HONC, NDSS-drive, NDSS-tolerance, and DSM- IV diagnosis and continuous scores. 4. Discussion The present study examined the predictive and incremental validity of four nicotine dependence measures in a sample of first

6 E.M. Sledjeski et al. / Drug and Alcohol Dependence 87 (2007) Table 4 Linear regressions: dependence measures predicting second year smoking quantity and frequency (n = 55) Univariate analyses Hierarchical analyses a β t p R 2 β t p R 2 Quantity FTND * HONC * NDSS-T * Drive * Priority Tolerance * Continuity Stereotypy Continuous * Diagnosis * Frequency FTND HONC * NDSS-T * Drive Priority Tolerance * Continuity Stereotypy Continuous * * Diagnosis * * Note: R 2 = adjusted R 2. + Marginally significant, p < 0.1. * Significant at 0.05 or Bonferroni corrected level. a Controlling for baseline smoking quantity and frequency. year college students. The sample was comprised of relatively light, mostly non-daily (78%) smokers, smoking an average of 30 cigarettes during the past week. Higher levels of dependence as measured by the HONC and continuous scores and diagnosis predicted continued smoking, smoking quantity and smoking frequency at the end of the first academic year. Both the HONC and measures continued to predict smoking quantity and frequency during the second year follow-up. While higher scores on the NDSS-T, NDSS-drive, and NDSStolerance predicted heavier smoking behavior during follow-up assessments, higher scores on the NDSS-priority and NDSSstereotypy subscales predicted lighter smoking behavior. Higher dependence scores on all four measures were related to shorter Table 5 Linear regressions: dependence measures predicting length of abstinence (n = 95) Length of abstinence β t p R 2 Fig. 1. Interaction between continuous scores and baseline smoking quantity predicting end of first year smoking quantity. Quantity represents the number of cigarettes smoked during the past week. dependence predicted follow-up smoking quantity only at lighter baseline smoking levels. FTND * HONC * NDSS-T * Drive * Priority Tolerance * Continuity Stereotypy Continuous * Diagnosis * Note: R 2 = adjusted R 2. + Marginally significant, p < 0.1. * Significant at 0.05 or Bonferroni corrected level.

7 16 E.M. Sledjeski et al. / Drug and Alcohol Dependence 87 (2007) lengths of smoking abstinence. In addition,, HONC, and NDSS-priority measures provided some evidence for incremental validity. Finally, continuous scores predicted future smoking quantity when baseline smoking quantity was low, but not when baseline smoking quantity was high Predictive validity of nicotine dependence The HONC was designed to measure the onset of nicotine dependence by assessing lost autonomy over smoking behavior in neophyte smokers (DiFranza et al., 2002a). Our findings support previous research demonstrating the predictive validity of the HONC in adolescents reporting light smoking patterns (DiFranza et al., 2002b; Wellman et al., 2006). Mean HONC (M = 4.78, S.D. = 3.13) scores among our predominantly light smoking sample were similar to prior research assessing dependence in adolescent smokers across a range of smoking patterns (M = ; O Loughlin et al., 2003). Despite our small sample size, the odds ratios for HONC scores predicting continued smoking for the first and second year follow-ups were almost identical to those reported by Wellman et al. (2006). Based on prior research and the present findings, the HONC appears to be a valuable measure of nicotine dependence and subsequent smoking behavior among less experienced smokers. Despite our light smoking sample, continuous scores and diagnosis demonstrated predictive validity during follow-up assessments. Our findings support prior research demonstrating a relationship between measures of nicotine dependence and concurrent daily smoking quantity in adolescents (Kandel et al., 2005). In addition, mean dependence scores (M = 3.57, S.D. = 2.03) were similar to prior research assessing dependence in adolescent and adult smokers (M = ; Hughes et al., 2004; Strong et al., 2003). To our knowledge, this is the first study to support the predictive validity of measures of nicotine dependence in a sample of relatively light smokers and highlights the utility of using criteria in college-age samples. The NDSS-stereotypy was negatively associated with continued smoking during the first year while the NDSS-T, NDSSdrive, NDSS-priority, and NDSS-tolerance factors predicted quantity and NDSS-drive predicted smoking frequency at the end of the first year. Only the NDSS-T, NDSS-drive, and NDSStolerance continued to predict smoking quantity and frequency during the second year. While the NDSS-T, NDSS-drive, and NDSS-tolerance scores demonstrated the expected positive relationship with smoking behavior, higher levels of dependence on the NDSS-stereotypy and NDSS-priority factors predicted lower levels of smoking behavior. Stereotypy is a measure of the invariance of smoking which reflects the idea that nicotine dependence would be characterized by smoking patterns that are not particularly affected by the time of day, day of the week, or different situations/emotions. Priority represents a measure of one s preference for smoking over other reinforcers (Shiffman et al., 2004). These findings may reflect the unstable nature of these factors in light smoking samples which is also evidenced by the poor internal consistency of the stereotypy subscale in the present sample. However, one recent study found positive relationships between the stereotypy and priority subscales and several measures of smoking behavior within a group of adult light smokers ( chippers, Shiffman and Sayette, 2005). It is also possible that social networks played a role in decreasing smoking behavior at follow-up. For example, individuals who scored high on priority and stereotypy but lacked a supportive smoking environment may have been more likely to reduce their smoking behavior at follow-up. Numerous studies have shown peers smoking status to impact adolescents smoking behavior (Mayhew et al., 2000). Despite the unexpected relationship between stereotypy, priority and smoking behavior, these findings support the use of separate factor scores when using the NDSS to measure nicotine dependence. With the exception of second year smoking quantity, the FTND failed to predict smoking behavior in both univariate and hierarchical analyses. In the present sample, the internal consistency of the FTND was quite low (α = 0.585); however, this low reliability has been reported in a number of studies (Cohen et al., 2002; Heatherton et al., 1991; Lichtenstein and Mermelstein, 1986; Payne et al., 1994; Pomerleau et al., 1994). Researchers have suggested that the low internal consistency could be due to the small number of items (6), limited response range of the items, or the presence of two separate constructs (Colby et al., 2000b; Etter, 2005; Haddock et al., 1999; Payne et al., 1994). In addition, FTND scores (M = 0.71, S.D. = 1.26) in the present sample were much lower compared to rates reported in previous research (M = ; (Burling and Burling, 2003; Etter et al., 1999; Haddock et al., 1999; Hughes et al., 2004). Sixty-four (67%) participants did not endorse any FTND item resulting in a greatly skewed distribution of scores, while scores on the other measures of dependence were normally distributed. Although cross-sectional studies have demonstrated a relationship between FTQ/FTND/mFTQ scores and smoking behavior (Cohen et al., 2002; Fagerstrom and Schneider, 1989; Prokhorov et al., 2000, 1998, 1996; Strong et al., 2003), our results mirror the findings reported by other longitudinal research demonstrating the inability of the FTND and modified versions to predict smoking behavior even prior to adjusting for baseline smoking behavior (Etter et al., 1999; Wellman et al., 2006). These authors have suggested that the FTND may act as a proxy for smoking quantity. While the FTND was developed for and typically used in samples of heavy smokers, our findings confirm that it may not be an appropriate measure of nicotine dependence among light non-daily smokers. Lower dependence scores on all four measures predicted longer periods of abstinence, suggesting that more dependent individuals have a harder time abstaining and tend to report more regular smoking habits. These findings support prior research demonstrating a relationship between the number of failed quit attempts and nicotine dependence (Breslau and Johnson, 2000; DiFranza et al., 2000; Haddock et al., 1999; Shiffman et al., 2004; Wellman et al., 2006). The use of smoking abstinence, as opposed to quit attempts, may be more appropriate for light smoking samples given the inherent variability in smoking patterns among non-daily smokers. In addition, retrospective reports of quit attempts may be biased due to problems with recall and a lack of an operational definition of a quit attempt (e.g.

8 E.M. Sledjeski et al. / Drug and Alcohol Dependence 87 (2007) defined period of time without smoking). By using 7 day timeline follow-back reports, our study allowed an objective measure of smoking abstinence by examining the longest period of time participants reported not smoking between the end of the first semester and end of the first college year Incremental validity of nicotine dependence To ensure that the measures of dependence were not merely assessing cigarette use, we examined the incremental validity of each (Colby et al., 2000b; Hunsley and Meyer, 2003; Tiffany et al., unpublished). When controlling for baseline quantity and frequency, lower NDSS-priority scores predicted higher smoking quantity while higher dependence scores on the HONC and predicted higher smoking frequency at the end of the first year. The bivariate relationships between end of first year quantity and the HONC,, NDSS-drive, and NDSStolerance scores were no longer significant after adjusting for baseline smoking behavior. By the end of the second year, only the added incremental validity when predicting smoking frequency. Notably, a hierarchical linear regression predicting end of first year smoking quantity demonstrated an interaction between scores and baseline smoking quantity. Decomposition of the interaction revealed that dependence scores predicted smoking quantity at follow-up but only in low level smokers. It has been historically assumed that nicotine dependence is present only in heavy daily smokers (APA, 1994). However, more recent research has contradicted this assumption, finding the presence of dependence symptoms in new adolescent smokers (DiFranza et al., 2000, 2002a,b). Our findings build on this research by establishing the importance of dependence symptoms among light smokers in predicting future smoking over and above number of cigarettes smoked. Research into the emergence of nicotine dependence is necessary in order to identify individual differences that predict sensitivities to nicotine and consequent chronic use (DiFranza et al., 2000; Shiffman, 1991) Strengths and limitations Since most dependence research has been limited to daily smokers, the major strength of our study was the assessment of nicotine dependence and its ability to predict later smoking behavior across a continuum of use including very light and non-daily smoking. Typically dependence criteria are assessed in individuals meeting an arbitrary threshold of use (e.g. daily use). In order to better understand the emergence of nicotine dependence and predict future smoking behavior, it is necessary to assess dependence in all current smokers (Colby et al., 2000a,b; Strong et al., 2003). In addition, current nicotine dependence measures have largely been validated in adult heavy smokers, which raises questions regarding their utility in light smoking samples (Tiffany et al., 2004). Given that research has begun to focus on the emergence of nicotine dependence, it is particularly important to assess dependence at low levels of use to determine the validity of current measures. The present study administered multiple measures of nicotine dependence and assessed smoking behavior at several time points allowing for the examination of both predictive and incremental validity of the measures in a light smoking sample. Future research is needed to better understand the emergence of nicotine dependence as well as the individual differences present in smoking and dependence trajectories. Finally, smoking behavior was assessed weekly based on retrospective recall of past week behavior. While the accuracy of these reports have been questioned, research has shown selfreported smoking behavior to be a valuable index of smoking heaviness, being positively correlated with biochemical measures of tobacco use (Heatherton et al., 1989). Further, accuracy of smoking behavior was maximized by limiting recall to the past week, rather than several weeks or even lifetime use. The present results should be interpreted within the context of study limitations. First, our relatively homogenous sample (i.e. first year college students with the majority being 18 years of age and predominantly Caucasian) limited our ability to generalize to more diverse populations. However, increased smoking rates among college students highlight the importance of assessing smoking behavior and nicotine dependence in this vulnerable population (Kear, 2002). Second, nicotine dependence was only assessed among students who reported smoking within the past 7 days. Given the non-daily smoking behavior in this sample, light smokers who did not smoke during the previous week may have been excluded from completing these measures. Third, including subscales and scoring methods, we examined the validity of ten dependence scales increasing our chance of Type I error. A more conservative interpretation of the results would be to use a p value of Fourth, our inability to find relationships between dependence measures and second year smoking could be due to our small sample size during the follow-up (n = 55). Despite these limitations, to our knowledge this study is the first to administer four dependence questionnaires to mostly nondaily, light smokers. While not all measures performed well, there was evidence of nicotine dependence as well as predictive and incremental validity in a sample of light smokers. Although smoking initiation typically begins in adolescence (SAMHSA, 2002), little research has examined the importance of emerging dependence in light smokers and its consequent role in smoking maintenance into adulthood (Colby et al., 2000a,b; DiFranza et al., 2000; Tiffany et al., 2004). Our findings suggest that nicotine dependence measures, in particular the dependence criteria, could be used as a tool to examine the emergence of dependence as well as predict smoking behavior prior to the development of more established smoking patterns. Given that current measures of nicotine dependence were developed for adult heavy smokers, Colby et al. (2000b) have suggested that they may not be tapping the appropriate constructs that would predict smoking trajectories in light smokers. Thus, more qualitative research (e.g. focus groups, individual interviews) may be warranted to determine factors related to smoking maintenance among light smokers (Nichter et al., 2002, 1997). Additional prospective studies employing multiple measures of nicotine dependence are needed to identify factors that consistently predict smoking persistence among light smokers.

9 18 E.M. Sledjeski et al. / Drug and Alcohol Dependence 87 (2007) Acknowledgments This research was sponsored by the Robert Wood Johnson Foundation, Tobacco Etiology Research Network (TERN). Data analyses were supported by grant K01 DA from the National Institute of Drug Abuse (Dierker) and an Investigator Award from the Patrick & Catherine Weldon Donaghue Medical Research Foundation (Dierker). The Tobacco Etiology Research Network (TERN) includes Richard Clayton, David Abrams, Robert Balster, Linda Collins, Ronald Dahl, Brian Flay, Gary Giovino, Jack Henningfield, George Koob, Robert McMahon, Kathleen Merikangas, Mark Nichter, Saul Shiffman, Stephen Tiffany, Dennis Prager, Melissa Segress, Christopher Agnew, Craig Colder, Lisa Dierker, Eric Donny, Lorah Dorn, Thomas Eissenberg, Brian Flaherty, Lan Liang, Nancy Maylath, Mimi Nichter, Elizabeth Richardson, William Shadel, and Laura Stroud. References APA, Diagnostic and Statistical Manual of Mental Disorders. American Psychiatric Association, Washington, DC. Breslau, N., Johnson, E.O., Predicting smoking cessation and major depression in nicotine-dependent smokers. Am. J. Public Health 90, Burling, A.S., Burling, T.A., A comparison of self-report measures of nicotine dependence among male drug/alcohol-dependent cigarette smokers. Nicotine Tob. Res. 5, Chen, X., Zheng, H., Sussman, S., Gong, J., Stacy, A., Xia, J., Gallaher, P., Dent, C., Azen, S., Shan, J., Unger, J.B., Johnson, C.A., Use of the Fagerstrom Tolerance Questionnaire for measuring nicotine dependence among adolescent smokers in China: a pilot test. Psychol. Addict. Behav. 16, Clark, D.B., Wood, D., Martin, C.S., Cornelius, J.R., Lynch, K.G., Shiffman, S., Multidimensional assessment of nicotine dependence in adolescents. Drug Alcohol Depend. 77, Clayton, R.R., The importance of adolescence in the development of nicotine dependence: introduction to part V. Ann. NY Acad. Sci. 1021, Cohen, L.M., Myers, M.G., Kelly, J.F., Assessment of nicotine dependence among substance abusing adolescent smokers: a comparison of the criteria and the modified Fagerstrom Tolerance Questionnaire. J. Psychopathol. Behav. 24, 225. Colby, S.M., Tiffany, S.T., Shiffman, S., Niaura, R.S., 2000a. Are adolescent smokers dependent on nicotine? A review of the evidence. Drug Alcohol Depend. 59, 83. Colby, S.M., Tiffany, S.T., Shiffman, S., Niaura, R.S., 2000b. Measuring nicotine dependence among youth: a review of available approaches and instruments. Drug Alcohol Depend. 59, S23 S39. Dierker, L., Lloyd-Richardson, E., Stolar, M., Flay, B., Tiffany, S., Collins, L., Bailey, S., Nichter, M., Nichter, M., Clayton, R., 2006a. The proximal association between smoking and alcohol use among first year college students. Drug Alcohol Depend. 81, 1 9. Dierker, L.C., Donny, E., Tiffany, S., Colby, S.M., Perrine, N., Clayton, R., TERN, The association between cigarette smoking and DSM- IV nicotine dependence among first year college students. Drug Alcohol Depend. DiFranza, J.R., Rigotti, N.A., McNeill, A.D., Ockene, J.K., Savageau, J.A., Cyr, D.S., Coleman, M., Initial symptoms of nicotine dependence in adolescents. Tob. Control 9, DiFranza, J.R., Savageau, J.A., Fletcher, K., Ockene, J.K., Rigotti, N.A., McNeill, A.D., Coleman, M., Wood, C., 2002a. Measuring the loss of autonomy over nicotine use in adolescents: the DANDY (Development and Assessment of Nicotine Dependence in Youths) study. Arch. Pediatr. Adolesc. Med. 156, DiFranza, J.R., Savageau, J.A., Rigotti, N.A., Fletcher, K., Ockene, J.K., McNeill, A.D., Coleman, M., Wood, C., 2002b. Development of symptoms of tobacco dependence in youths: 30-month follow-up data from the DANDY study. Tob. Control 11, Etter, J.-F., A comparison of the content-, construct- and predictive validity of the cigarette dependence scale and the Fagerstrom Test for Nicotine Dependence. Drug Alcohol Depend. 77, Etter, J.-F., Duc, T.V., Perneger, T.V., Validity of the Fagerstrom Test for Nicotine Dependence and of the heaviness of smoking index among relatively light smokers. Addiction 94, Fagerstrom, K.-O., Measuring degree of physical dependence to tobacco smoking with reference to individualization of treatment. Addict. Behav. 3, Fagerstrom, K.O., Schneider, N.G., Measuring nicotine dependence: a review of the Fagerstrom Tolerance Questionnaire. J. Behav. Med. 12, 159. Haddock, C., Lando, H., Klesges, R.C., Talcott, G., Renaud, E.A., A study of the psychometric and predictive properties of the Fagerstrom Test for Nicotine Dependence in a population of young smokers. Nicotine Tob. Res. 1, Heatherton, T.F., Kozlowski, L.T., Frecker, R.C., Fagerstrom, K.-O., The Fagerstrom Test for Nicotine Dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br. J. Addict. 86, Heatherton, T.F., Kozlowski, L.T., Frecker, R.C., Rickert, W., et al., Measuring the heaviness of smoking: using self-reported time to the first cigarette of the day and number of cigarettes smoked per day. Br. J. Addict. 84, Hughes, J.R., Oliveto, A.H., Riggs, R., Kenny, M., Liguori, A., Pillitteri, J.L., MacLaughlin, M.A., Concordance of different measures of nicotine dependence: two pilot studies. Addict. Behav. 29, Hunsley, J., Meyer, G.J., The incremental validity of psychological testing and assessment: conceptual, methodological, and statistical issues. Psychol. Assess. 15, Kandel, D., Schaffran, C., Griesler, P., Samuolis, J., Davies, M., Galanti, R., On the measurement of nicotine dependence in adolescence: comparisons of the mftq and a -based scale. J. Pediatr. Psychol. 30, Kear, M.E., Psychosocial determinants of cigarette smoking among college students. J. Community Health Nurs. 19, Lichtenstein, E., Mermelstein, R.J., Some methodological cautions in the use of the Tolerance Questionnaire. Addict. Behav. 11, Mayhew, K.P., Flay, B.R., Mott, J.A., Stages in the development of adolescent smoking. Drug Alcohol Depend. 59, Nichter, M., Nichter, M., Thompson, P.J., Shiffman, S., Moscicki, A.-B., Using qualitative research to inform survey development on nicotine dependence among adolescents. Drug Alcohol Depend. 68, 41. Nichter, M., Nichter, M., Vuckovic, N., Quintero, G., Ritenbaugh, C., Smoking experimentation and initiation among adolescent girls: qualitative and quantitative findings. Tob. Control 6, O Loughlin, J., DiFranza, J., Tarasuk, J., Meshefedjian, G., McMillan-Davey, E., Paradis, G., Tyndale, R.F., Clarke, P., Hanley, J., 2002a. Assessment of nicotine dependence symptoms in adolescents: a comparison of five indicators. Tob. Control 11, O Loughlin, J., DiFranza, J., Tyndale, R.F., Meshefedjian, G., McMillan-Davey, E., Clarke, P.B.S., Hanley, J., Paradis, G., Nicotine-dependence symptoms are associated with smoking frequency in adolescents. Am. J. Prev. Med. 25, O Loughlin, J., Kishchuk, N., DiFranza, J., Tremblay, M., Paradis, G., 2002b. The hardest thing is the habit: a qualitative investigation of adolescent smokers experience of nicotine dependence. Nicotine Tob. Res. 4, O Loughlin, J., Tarasuk, J., Difranza, J., Paradis, G., 2002c. Reliability of selected measures of nicotine dependence among adolescents. Ann. Epidemiol. 12, 353. Payne, T.J., Smith, P.O., McCracken, L.M., McSherry, W.C., Antony, M.M., Assessing nicotine dependence: a comparison of the Fagerstrom Tolerance Questionnaire (FTQ) with the Fagerstrom Test for Nicotine Dependence (FTND) in a clinical sample. Addict. Behav. 19, 307.