How To Treat Anorexic Behavior

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1 Psychology of Addictive Behaviors 2009 American Psychological Association 2009, Vol. 23, No. 2, X/09/$12.00 DOI: /a Changes in Tobacco Use Among Adolescent Smokers in Substance Abuse Treatment Karni Shelef Children s Hospital of Philadelphia Gary M. Diamond Ben Gurion University of the Negev Guy S. Diamond University of Pennsylvania School of Medicine and Children s Hospital of Philadelphia Mark G. Myers Veteran Affairs San Diego Healthcare System, University of California Adolescents with substance use disorders (SUDs) smoke cigarettes more than youth in the general population. Little is known about changes in smoking patterns during and after outpatient SUD treatment. We examined whether receiving SUD treatment had a differential impact on cigarette smoking behaviors of mild ( 10 cigarettes per day [CPD]), moderate (10 19 CPD), and heavy ( 20 CPD) smokers (smoked on 60 days in the past 90). Our sample included 378 adolescents from the Cannabis Youth Treatment study, who were assessed at intake, and after 3, 6, 9, and 12 months. Results indicate that after controlling for the effect of changes in cannabis use, mild smokers decreased days of smoking during treatment and follow-up, whereas moderate and heavy smokers demonstrated a small decrease over treatment, and no change over follow-up. Heavy smokers demonstrated a slight decrease in CPD during the treatment phase. These results suggest that, whereas cigarette smoking may decrease for mild smokers while in SUD treatment, the addition of specialized smoking interventions may be necessary to effect change in cigarette smoking for moderate and heavy smokers. Keywords: cigarette smoking, tobacco use, cannabis use, adolescents, substance abuse treatment Adolescents with substance use disorders (SUDs) smoke cigarettes at substantially higher rates than youth in the general population. Over 80% of adolescents treated for SUDs report current tobacco use (Arria, Dohey, Mezzich, Bukstein, & Van Thiel, 1995), with the majority smoking daily, and at an average of 15 cigarettes per day (CPD) (Myers & Brown, 1994; Myers & MacPherson, 2004). These adolescents are also more susceptible to becoming dependent, long-term smokers (Myers & Brown, 1994; Myers, Doran, & Brown, 2007), and therefore are at higher risk for a variety of smoking-related health problems (United States Department of Health and Human Services, 2004; Wetter et al., 1998). In fact, among long-term substance abusers, smokers have higher mortality rates than nonsmokers (Hser, McCarthy, & Karni Shelef, Department of Psychology, Children s Hospital of Philadelphia, Behavioral Health Center, Philadelphia, Pennsylvania; Guy S. Diamond, Department of Child and Adolescent Psychiatry, University of Pennsylvania School of Medicine, and Children s Hospital of Philadelphia, Department of Psychology, Behavioral Health Center, Philadelphia, Pennsylvania; Gary M. Diamond, Department of Psychology, Ben-Gurion University of the Negev, Beer-Sheva, Israel; Mark G. Myers, Psychology Service, Veteran Affairs San Diego Healthcare System, and Department of Psychiatry, University of California, San Diego, California. The current affiliation for Karni Shelef is Achva Academic College, M.P.O Shikmim 79800, Israel. Correspondence concerning this article should be addressed to Karni Shelef, Achiva Academic College, M.P.O. Shikmim 79800, Israel. kshelef@hotmail.com Anglin, 1994), with mortality more often related to tobacco use itself, than to substance use (Hurt et al., 1996). Given the serious nature of this problem, one question worthy of investigation is whether participation in a SUD program for adolescents that does not directly target cigarette smoking is associated with decreases in cigarette smoking. There are a number of reasons why SUD treatment per se might have a positive impact on adolescents cigarette smoking. First, the treatment foci and interventions designed to reduce substance use may also be relevant to cigarette smoking. Core substance use reduction skills such as enhancing motivation, identifying triggers, learning refusal skills, and improving emotional coping are skills that are also taught in smoking cessation programs (Myers & Brown, 2005; Sussman, 2002). Second, successfully reducing substance use may itself lead to reductions in cigarette smoking. Based on the priming hypothesis, these behaviors may activate each other. This may be especially true for cigarette and marijuana use, where both substances are administered through smoking. Third, a decrease in substance use may promote an increased sense of mastery and increase the likelihood of successfully reducing cigarette smoking (Prochaska, Delucchi, & Hall, 2004). To date, only one study has examined unaided changes over time in smoking behavior among adolescents in SUD treatment. In this study, Myers and Brown (1994) found that although adolescents smoking levels decreased significantly during a 28-day inpatient SUD program that did not target smoking cessation, use remained high (i.e., more than 10 CPD), and smoking outcome was not associated with substance use outcome. 355

2 356 SHELEF, DIAMOND, DIAMOND, AND MYERS This study aims to examine whether adolescents cigarette smoking changed over time in a group of adolescents who participated in outpatient SUD treatment. Our goal was to examine if such changes in smoking occurred for adolescents who received SUD treatment, but were not placed in a controlled environment. We hypothesized that some adolescents would show a decrease in smoking while in SUD treatment, despite the fact that no specific cigarette smoking intervention was provided. We were also interested to see if the impact of SUD treatment on cigarette smoking was different for adolescents who smoked frequently, but at varying levels (i.e., mild, moderate, and heavy). We hypothesized the mild smokers would benefit the most. Data were derived from the Cannabis Youth Treatment (CYT) study (Dennis et al., 2002). Materials and Method Data analyzed in this study were collected as part of the CYT study. Detailed reports have been published on the design and psychometrics (Dennis et al., 2002), client characteristics (Tims et al., 2002), treatment conditions (Diamond et al., 2002), and clinical outcomes and cost effectiveness (Dennis et al., 2004) and will only be summarized here. The analyses in this study focused on the 378 adolescents who reported smoking cigarettes frequently at the intake assessment. Participants Basic inclusion/exclusion criteria. Participants were eligible for CYT if they were: (a) aged 12 to 18; (b) self-reported one or more DSM-IV (American Psychiatric Association, 1994) criteria for cannabis abuse or dependence; (c) had used cannabis in the past 90 days; and (d) had at least one parent willing to participate. Patients were excluded if they: (a) reported use of alcohol 45 or more of the 90 days before intake; (b) reported use of drugs other than cannabis 13 or more of the 90 days before intake; or (c) reported an acute medical or psychological problem that was likely to prohibit full participation in treatment. The study was conducted with the informed consent of the participants, under a federal certificate of confidentiality. We included in our analyses only frequent smokers, defined as having smoked on 60 days or more of the past 90 days. Of the entire CYT sample, 81% (n 484) reported any smoking at intake, of whom 78% (n 378) were frequent smokers. Participants characteristics. Of the 378 clients in this study, 78% (n 295) were male, and their ages ranges from 13 to 18 (M 15.83, SD 1.18). Two thirds of the sample (68%, n 256) were White, 23% (n 88) were African American, 4% (n 14) were Hispanic, and 5% (n 20) identified themselves as mixed or other. Most of these adolescents (88%, n 333) began using alcohol or other drugs before the age of 15, and 76% (n 287) used cannabis weekly or daily. Based on a combined parent and adolescent report, all adolescents met criteria for a cannabis use disorder, 42% (n 159) had alcohol use disorder, and 13% (n 49) had other substance use disorders. There were no differences between treatment conditions on any of these characteristics. Treatment Interventions Five manualized treatments were tested in CYT, representing a full range of treatments typically offered in outpatient treatment. Interventions included: (a) Motivational Enhancement Treatment/ Cognitive Behavior Therapy (MET/CBT5; Sampl & Kadden, 2001); (b) MET/CBT12, which included seven supplemental group CBT sessions (CBT7: Webb, Scudder, Kaminer, Kadden, & Tawfik, 2002); (c) Family Support Network (FSN: Hamilton, Brantley, Tims, Angelovich, & McDougall, 2001), a parent skills training as a supplement to MET/CBT12; (d) The Adolescent Community Reinforcement Approach (ACRA: Godley, Meyers, et al., 2001), an intervention based on behavioral analysis; and (e) Multidimensional Family Therapy (MDFT; Liddle, 2002), a family based model focused on individual, family, and social functioning. None of the treatment manuals included any material targeting cigarette smoking. Instruments Participant characteristics, diagnoses, and primary outcomes were measured with the Global Appraisal of Individual Needs (GAIN; Dennis, 1999) at intake, posttreatment, and at 3-, 6-, and 9-month follow-ups. The GAIN is a standardized clinical assessment battery, covering eight domains of functioning, and has been normed on both adults and adolescents (Dennis, Scott, Godley, & Funk, 1999, 2000). The GAIN is administered as an interview, and during its administration personal anchors (e.g., birthdays, holidays, etc.) are used to pinpoint the 90-day period of interest. For this study, selected variables were used. Days of cigarette smoking, days of cannabis use, and days of alcohol use. These three measures were each based on a single question pertaining to the number of days the client used the corresponding substance in the past 90 days, regardless of amount of use per day. Self report relating to tobacco, cannabis, and alcohol use has been shown to be valid and typically more sensitive to change than alternative methods of data collection, such as collateral reports or biometric measures (Buchan, Dennis, Tims, & Diamond, 2002; Del Boca, & Noll, 2000; Dennis et al., 2002; Patrick et al., 1994; Rouse, Kozel, & Richards, 1985). Number of CPD. This measure was based on a single question referring to the number of CPD or times per day tobacco was used. Smoker subgroup. We identified three subgroups of frequent smokers: Mild smokers were defined as smoking between 1 through 9 CPD. Moderate smokers were defined as smoking between 10 through 19 CPD. Heavy smokers were defined as smoking 20 CPD or more. These criteria were determined empirically by examining the distribution of CPD among all frequent cigarette smokers in our sample. The data evidenced substantial peaks at 10 (23.8%) and 20 (24.6%) CPD. Analytic strategy. The two outcome measures, days of smoking and number of CPD, were analyzed separately across the five time points (i.e., baseline, 3-month, 6-month, 9-month, and 12- month assessments), using multilevel modeling for longitudinal data (Goldstein, 1995; Singer & Willet, 2003) implemented via SPSS (version 12.0). Both models were estimated using an unstructured covariance matrix and coefficients were estimated using maximum likelihood to accommodate for incomplete data, with no restriction on number of follow-up assessments per case. Analysis for each outcome was conducted on a continuous, linear piecewise model with one joint located at the 3-month assessment, denoting the active treatment phase (lasting 3 months) and the follow-up phase (lasting 9 months). For the first model (i.e., days of smok-

3 ADOLESCENT TOBACCO AND SUBSTANCE USE 357 ing), variance of the outcome variable at the baseline assessment was restricted because our analysis included only frequent smokers (i.e., smoked on 60 days or more out of the past 90 days at baseline). Therefore, a fixed, rather than a random intercept, had a better fit. The best fitting model included a fixed intercept, a random active treatment [AT] slope, and a random follow-up [FU] slope. The second model (i.e., for CPD) included a random intercept because there were no restrictions on the outcome variable at baseline, a fixed active AT slope, and a random FU slope. The primary analyses for both outcome measures included one categorical time-invariant predictor (smoker subgroup: mild/ moderate/heavy), continuous and categorical time-invariant covariates (age, gender, race, and treatment condition), and one time-varying continuous covariate (days of cannabis use [DCU], measured at the same time points as the outcome variables). Two sets of dummy variables were created to represent each of the categorical variables in the model (i.e., smoker subgroup and treatment condition). Baseline covariates were controlled for by including their main effects and interactions in the model, none of which had any specific hypothesis attached. Only significant interactions were retained, along with preceding main effects. In addition, to control for the effect of within-person changes in DCU on smoking, DCU was separated into two components: a betweenpersons component [BPdcu] represented each participant s personmean level of cannabis use over time; and a time-varying withinperson component [WPdcu] that represented each participant s variation at each time point from his or her person-mean. We were also interested in examining if the effect of the two components of DCU differed among the three smoking groups (i.e., the 2-way interactions between BPdcu Smoker subgroup and WPdcu Smoker subgroup). Support for our hypothesis that trajectories of change in smoking would differ among the three smoker subgroups above and beyond changes in cannabis use would be garnered providing the 2-way interactions between smoker subgroup and each of the two slopes (i.e., Smoker subgroup AT slope and Smoker subgroup FU slope) were significant. Consequently, we examined a model that included all the aforementioned 2-way interactions. Because smoker subgroup and DCU were central to our hypotheses, their main effects and interactions with each of the two slopes were retained in the model regardless of their significance level. Other nonsignificant interaction terms were removed from the model in a backward manner, such that 2-way interactions were removed before their preceding main effects, and interactions with the FU slope were removed before interactions with the AT slope. Preliminary Analyses Results Baseline (i.e., intake) characteristics of the three subgroups of smokers are presented in Table 1. There was a small yet statistically significant difference between the three subgroups in the number of days smoked at baseline, F(2, 377) 3.21, p.05. The difference, however, did not appear to be clinically significant, as the average number of days of smoking was 87, 88, and 89 days for mild, moderate, and heavy smokers, respectively. There were no differences between the smoker subgroups in number of days they used cannabis or alcohol in the past 90 days ( ps.05). To identify potential confounding variables in our data we examined the relationships between smoker subgroup and gender, age, and race (i.e., race was grouped into two categories: White/minority). Of these, the only significant association found was between smoker subgroup and race, 2 (2, N 378) 60.59, p.001. Minority youth were primarily mild smokers, whereas the majority of White youth were moderate or heavy smokers (see Table 1). Primary Analyses The final multilevel model for days of smoking is presented in Table 2. None of the baseline covariates nor any of their interactions had a significant effect on days of smoking, and were therefore not included in the model. Days of smoking decreased over time at a different rate for each of the three smoking groups, both during the treatment phase and during the follow-up phase (see Figure 1). Tests of simple effects indicated that mild smokers decreased their frequency of smoking by 14 days during the treatment phase ( p.001), and continued to decrease at a rate of Table 1 Baseline Characteristics of the Three Smoker Subgroups Mild (n 137) Moderate (n 142) Heavy (n 99) M SD M SD M SD F (2, 377) Age Days used cannabis a Days used alcohol a n % n % N % 2 Gender Male % % % 1.84 Female % % % Race Minority % % 9 7.4% White % % % a In the past 90 days. p.05. p.001.

4 358 SHELEF, DIAMOND, DIAMOND, AND MYERS Table 2 Multilevel Models for Longitudinal Assessments of Days of Smoking and CPD Variable Days of smoking Unstandardized estimate SE t/f CPD Unstandardized estimate SE t/f Race Tx condition 3.93 Race Tx condition 3.32 Smoker subgroup AT slope FU slope BPdcu WPdcu BPdcu AT slope WPdcu AT slope BPdcu FU slope WPdcu FU slope Smoker subgroup AT slope Smoker subgroup FU slope Note. Only effects included in the final model are presented. Omnibus F test statistics are reported for categorical predictors that were entered as sets of dummy variables. AT active treatment; FU follow up; BPdcu person mean level of cannabis use; WPdcu within person variation from mean level of cannabis use. Tx treatment condition; CPD cigarettes per day. p.05. p days per follow-up assessment ( p.001). Moderate smokers decreased by 7.50 days during the treatment phase ( p.01), and did not continue to decrease significantly over the follow-up phase ( p.05). Heavy smokers did not change their frequency of smoking during either the treatment or the follow-up phases ( ps.05). In addition, during the treatment phase, individual changes in days of cannabis use were associated with changes in days of smoking, such that for every 6 days below their individual average rate of cannabis use, there was a 1-day decrease in days of smoking. Interestingly, the effect of average individual rates of cannabis use (i.e., person-mean rates) during this time period was not significant ( p.05), indicating that it was the decrease, rather than the absolute level of cannabis use, that was associated with rates of smoking. The final multilevel model for CPD is presented in Table 2. Of the baseline covariates entered into the model, only Treatment condition Race had a significant effect, indicating that the overall levels of CPD in each of the treatment conditions differed by race. To control for this variance, the interaction term as well as the main effects were retained in the model. Results indicate that Figure 1. Days of smoking for mild, moderate, and heavy frequent smokers across time.

5 ADOLESCENT TOBACCO AND SUBSTANCE USE 359 changes in amount of CPD varied among the three smoking groups during the treatment phase, above and beyond variations because of treatment condition and race. There were no differences, however, during the follow-up phase (see Figure 2). Tests of simple effects indicate that during the treatment phase heavy smokers decreased their amount of daily smoking by 2.30 CPD ( p.01), whereas moderate smokers demonstrated no change in CPD ( p.05), and mild smokers increased their daily smoking by 2.61 CPD ( p.05). During the follow-up phase there was a slow decrease of.34 CPD per assessment wave ( p.05) across the three smoking groups. Interestingly, mild smokers appeared to decrease their frequency (i.e., days) yet increase their quantity (i.e., CPD) of cigarette smoking during the active treatment phase. However, a closer look at 3-month smoking levels and abstinence rates (see Table 3) revealed that the increase in quantity was accounted for by only 25% of the mild smokers. The remaining 75% either quit, decreased, or sustained their pretreatment smoking levels. In addition, our findings indicate that the proportions of abstainers differed significantly among the three groups at the 12-month assessment, 2 (2, N 36) 16.17, p.01, but not at the 3-month assessment. Discussion This is the first study to examine changes in smoking behavior among adolescents in an outpatient SUD treatment that did not target cigarette smoking. We examined changes in days of smoking and in CPD, over a 3-month active treatment phase, and then over a 9-month follow-up period, for three subgroups of frequent smokers (i.e., mild, moderate, and heavy). Our findings indicate that at the 3-month assessment, 86% of adolescents remained frequent smokers, and only 3% reported abstinence. By the 12-month assessment, 78% were still frequent smokers, and 10% reported abstinence. These results are comparable to those found by Myers and Brown (1994) in a similar study of adolescents who received 28 days of inpatient treatment. Importantly, our findings indicate that above and beyond the impact of decreases in cannabis use on smoking behavior, the three smoker subgroups exhibited different trajectories of change over time. Mild smokers decreased their frequency of smoking over time, with 5% abstinent at 3-months, and 18% abstinent at the 12-month follow-up. In contrast, moderate and heavy smokers frequency of smoking decreased only slightly during the active treatment phase, and remained stable throughout the follow-up period. In fact, only 1% and 4% of heavy smokers were abstinent at the 3-month and the 12-month assessments, respectively. Heavy smokers did evidence a significant reduction in quantity of use during the treatment phase, although at the 3-month assessment they were still smoking nearly a pack of cigarettes a day on average. These findings are consistent with recent studies of smoking reduction, which show that reductions in smoking increase the probability of subsequent cessation (Hughes & Carpenter, 2006). Changes in days of cannabis use also appeared to have an impact on days of smoking during the active treatment phase, but this impact did not differ among the three smoker-subgroups. One reason why mild smokers decreased their cigarette smoking over the course of treatment may be that their smoking behavior was less driven by nicotine dependence, and more related to environmental and external cues (Istvan & Matarazzo, 1984), which are typically targeted in SUD treatment. Therefore, the basic drug refusal and coping skills taught in drug treatment may generalize to smoking behaviors as well. However, for moderate and heavy smokers, who are presumably more nicotine-dependent (McDonald et al., 2000), more targeted interventions may be needed. Alternatively, mild smokers smoking may be more responsive to certain lifestyle changes resulting from SUD treatment, such as less association with deviant peer groups, practicing Figure 2. CPD for mild, moderate, and heavy frequent smokers across time.

6 360 SHELEF, DIAMOND, DIAMOND, AND MYERS Table 3 Frequencies and Percentages of Smoker-Subgroups at Baseline by Smoker-Subgroups at 3-month and 12-month Assessments 3-Month assessment Frequent Nonsmoker a Nonfrequent Mild Moderate Heavy Total Mild 7 5.3% % % % 7 5.3% 133 Moderate 4 2.9% % % % % 138 Heavy 1 1.0% 3 3.1% 6 6.1% % % 98 Total % % % % % Month assessment Mild % % % % 3 2.4% 127 Moderate 9 6.6% 7 5.1% % % % 137 Heavy 4 4.4% 7 7.7% 3 3.3% % % 91 Total % % % % % 355 a Proportions of abstainers were significantly different at the 12-month assessment ( p.01). No differences were found at the 3-month assessment. healthy living, strengthening family communication, or decreasing their substance use. The impact of within-person changes in cannabis use on days of smoking during the treatment phase may be explained by the priming theory. This theory posits that after the paired use of two substances, the use of either one may act as a cue to prime the individual to use the other (Myers & Prochaska, 2008). Thus, a decrease in cannabis use (i.e., the priming cue), may have contributed to a decrease in smoking (i.e., the paired behavior). Alternatively, the pairing of the two behaviors could be related to the social context in which they occur (e.g., deviant peer groups of teens who use drugs and smoke cigarettes). Therefore, a reduction in cannabis use may be associated with less exposure to adolescents who smoke. A number of methodological strengths increase our confidence in the findings reported. The sample size was substantial, data collection was highly standardized and monitored, data were collected several times over a 1 year period and retention rates were high (90% at most waves). Methodological limitations included the lack of a control group, the association between race and smoker-subgroup, and measurement limitations such as single item measurements of cigarette smoking quantity and frequency, lack of biological verification of smoking abstinence, and lack of assessment for nicotine dependence. Although, certainly a control group would strengthen the findings, the precipitous decline in cigarette smoking for mild smokers is not reflective of normative smoking patterns for frequent smokers (Chassin, Presson, Rose, & Sherman, 1996; Chen & Kandel, 1995). Smoker subgroups were confounded with race, such that minority youth were more highly represented among mild smokers. Although we controlled for race statistically in the analyses, future research is needed to replicate our findings within a more diverse sample. Finally, cigarette smoking was not a primary outcome of the original CYT study. More comprehensive measurements of smoking should be utilized in future research. The findings of this study suggest that although mild smokers reduced their smoking to some extent following SUD treatment, moderate and heavy smokers evidenced little or no change. It is likely that an additional intervention to specifically target smoking behavior may be required to reduce both cigarette smoking and substance use, particularly for moderate and heavy smokers. In one study that took this approach (Myers & Brown, 2005; Myers & Prochaska, 2008) the combination of the two interventions produced larger reductions in both target drugs. There is a wealth of research on adults that supports this same finding (Prochaska et al., 2004). Thus, given the serious consequences of youth cigarette smoking, particularly for substance using teens, it is important to pursue the integration of smoking cessation interventions into adolescent drug treatment programs. 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