Experimental and Clinical Psychopharmacology 2014, Vol. 22, No. 1, 57– 64
© 2014 American Psychological Association 1064-1297/14/$12.00 DOI: 10.1037/a0034403
Dimensions of Impulsive Behavior and Treatment Outcomes for Adolescent Smokers Millie Harris
Robert B. Penfold
University of Kentucky
Group Health Research Institute, Seattle, Washington and University of Washington
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Ariane Hawkins, Jared Maccombs, and Bryan Wallace
Brady Reynolds University of Kentucky
Research Institute at Nationwide Children’s Hospital, Columbus, Ohio
Adolescent cigarette smoking rates remain a significant public health concern, and as a result there is a continued need to understand factors that contribute to an adolescent’s ability to reduce or quit smoking. Previous research suggests that impulsive behavior may be associated with treatment outcomes for smoking. The current research (N ⫽ 81) explored 3 dimensions of impulsive behavior as predictors of treatment response from a social– cognitive type program for adolescent smokers (i.e., Not On Tobacco; N-O-T). Measures included laboratory assessments of delay discounting, sustained attention, and behavioral disinhibition. A self-report measure of impulsivity was also included. Adolescent smokers who had better sustained attention were more likely to reduce or quit smoking by the end of treatment. No other measures of impulsivity were significantly associated with treatment response. From these findings, an adolescent smoker’s ability to sustain attention appears to be an important behavioral attribute to consider when implementing smoking cessation programs such as N-O-T. Keywords: impulsivity, delay discounting, attention, disinhibition, smoking, adolescents
smokers attempted to quit smoking, but only 12.2% were successful (Malarcher, Jones, Morris, Kann, & Buckley, 2009). Other research suggests that for some smokers the path to cessation occurs as a series of steps toward that ultimate goal. For example, adult smokers who reduce cigarette consumption during a quit attempt are more likely to fully quit at some future date (Carpenter, Hughes, Solomon, & Callas, 2004; Cunningham & Selby, 2010; Hyland et al., 2005; Okuyemi, Thomas, Warren, Guo, & Ahuluwalia, 2010). Identifying factors (e.g., behavioral, social, biological) that influence an adolescent’s ability to quit or reduce smoking is a significant priority in efforts to reduce smoking rates in this age group. Impulsivity (assessed either by questionnaire or laboratorybehavioral methods) is a behavioral attribute that has been linked with a smoker’s ability to reduce or quit smoking (e.g., Cavalca et al., 2013; Krishnan-Sarin et al., 2007; Kvaavik & Rise, 2012; Sheffer et al., 2012). For example, impulsive behavior assessed with a questionnaire is a significant predictor of abstinence during a cognitive– behavioral treatment for smoking cessation (Sheffer et al., 2012). Among adolescent smokers, findings based on selfreport assessments have been similar (e.g., Cavalca et al., 2013; Ryan, MacKillop, & Carpenter, 2013; Wegmann, Bühler, Strunk, Lang, & Nowak, 2012). In contrast to questionnaire methods, laboratory behavioral measures of impulsivity represent a different class of assessment tools that typically isolate more specific behavioral processes. Factor analyses suggest that these measures can be categorized into at
Cigarette smoking trends have declined since the 1980s; however, this pattern has not continued for adolescent smokers (U.S. Department of Health and Human Services, 2012). As of 2009, approximately 20% of high-school students acknowledge smoking in the past month (Johnston, O’Malley, Bachman, & Schulenberg, 2011), and other research indicates that each day up to 1,000 adolescents become daily smokers (Hum, Robinson, Jackson, & Ali, 2011). Many adolescent smokers engage in multiple quit attempts; however, quit rates for adolescents are low when compared with adult smokers (Milton et al., 2004). From the Youth Risk Behavior Survey, approximately 60.9% of high-school daily
This article was published Online First January 13, 2014. Millie Harris, Department of Behavioral Science, University of Kentucky; Robert B. Penfold, Group Health Research Institute, Seattle, Washington, and Department of Health Services Research, University of Washington; Ariane Hawkins, Jared Maccombs, and Bryan Wallace, Research Institute at Nationwide Children’s Hospital, Columbus, Ohio; Brady Reynolds, Department of Behavioral Science, University of Kentucky. All authors report no conflicts of interest. This research was supported by funds from Nationwide Children’s Hospital. All authors contributed in a significant way to the manuscript, and all authors have read and approved the final manuscript. Correspondence concerning this article should be addressed to Brady Reynolds, Department of Behavioral Science, 105 Medical Behavioral Science Building, University of Kentucky, Lexington, KY 40536-0086. E-mail: [email protected] 57
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least three separate dimensions of behavior that include decisionmaking, inattention, and disinhibition (Reynolds, Penfold, & Patak, 2008). These assessments differ from questionnaire methods by using tasks that measure the actual behaviors of interest in the laboratory rather than a participant’s self-reports (or selfperceptions) of these behaviors. Correlations between behavioral and self-report assessments of impulsivity are typically low (e.g., Heyman, & Gibb, 2006; Lane, Cherek, Rhodes, Pietras, & Tcheremissine, 2003) or nonexistent (e.g., Krishnan-Sarin et al., 2007; Reynolds et al., 2008), which may stem from differences in assessment method (measuring actual behavior vs. self-perceptions of behavior) and the breadth/specificity of the behaviors being measured across methods (see Reynolds, Richards, & de Wit, 2006). Related to cigarette smoking, decision-making tasks have been studied the most. These assessments usually require participants to choose between outcomes that are delayed or immediate (i.e., delay discounting). A choice pattern that reflects comparatively more choices for smaller immediate rewards at the expense of larger but delayed rewards indicates a higher level of impulsive decision-making. In the adult literature, delay discounting is associated with treatment outcomes for smoking cessation from cognitive– behavioral and incentive-based (contingency management [CM]) treatment approaches (Dallery & Raiff, 2007; MacKillop & Kahler, 2009; Sheffer et al., 2012). Likewise, delay discounting assessed with a real-time procedure (i.e., Experiential Discounting Task) was associated with outcomes for adolescent smokers taking part in a CM program (Krishnan-Sarin et al., 2007). In all cases, participants who discounted more impulsively by delay were less successful in abstaining from smoking or maintaining abstinence. Behavioral measures of inattention (i.e., ability to maintain vigilance over time) and disinhibition (i.e., ability to inhibit unwanted behaviors) have been less studied in the context of smoking cessation. However, adolescent smokers are more impulsive in terms of both behaviors than their nonsmoking counterparts (e.g., Fields, Collins, Leraas, & Reynolds, 2009; Schepis, McFetridge, Chaplin, Sinha, & Krishnan-Sarin, 2011), thus suggesting the relevance of these behaviors to smoking more generally. Also, impulsive inattention and disinhibition increase during abstinence from smoking, particularly among participants who are less impulsive before abstinence (Ashare & Hawk, 2012; Harrison, Coppola, & McKee, 2009). However, these behaviors may not be related to treatment outcomes. For example, Schepis et al. (2011) found that sustained attention did not predict treatment response in a small sample (N ⫽ 12) of adolescent smokers, and KrishnanSarin et al. (2007) had a similar finding in a slightly larger sample (N ⫽ 30) of adolescent smokers. Notably, both of these reports were based on CM cessation programs; therefore, it is not clear if these findings would extend to social– cognitive programs. For the current study, we assessed all three dimensions of impulsive behavior using laboratory behavioral procedures in adolescent smokers participating in a social– cognitive smoking cessation program, the American Lung Association’s (ALA) Not-OnTobacco (N-O-T) program. We also included a questionnaire assessment of impulsivity for comparison purposes. The goal of this research was to better establish which types of impulsive behavior are associated with outcomes from this widely prescribed treatment approach designed specifically for adolescent smokers.
As described above, each behavioral task indicates a distinct form of behavioral deficit, which may guide treatment program modifications in unique directions on the basis of the specific type(s) of impulsive behavior associated with outcomes. To our knowledge, this is the first research to evaluate three dimensions of impulsive behavior using laboratory behavioral assessments in relation to adolescent smoking outcomes. We hypothesized that participants who were more impulsive in one or more of the three dimensions of behavior assessed would be less successful in reducing or quitting smoking during participation in the N-O-T program.
Method Participants There were 97 adolescent smokers who took part in the N-O-T program. However, 6 did not want to participate in the research, and 10 were later determined to be nonsmokers. Therefore, 81 adolescents (47 females) participated in this research. Selfreported smoking status was biochemically validated through level of breath carbon monoxide (CO) and urinary cotinine content. Participants were required to have a CO level of 9 ppm or greater (Bedfont Instruments, Micro III, U.K.) and/or a quantitative cotinine value of 100 ng/mL or greater. Cotinine content was determined using a homogenous enzyme immunoassay at GrahamMassey Analytical Labs in New Haven, CT. One of the 81 participants had a cotinine level of 95 ng/mL; however, this individual’s CO level was 11 ppm, which was above the criterion for being a smoker.
Procedure The N-O-T program was a voluntary 10-week quit-smoking program offered at Nationwide Children’s Hospital in Columbus, OH. N-O-T consists of a standardized curriculum that specifically targets adolescents by using language and graphics appropriate for teens. This program provides social– cognitive training to improve participants’ social skills, self-management, and psychoeducation about relapse prevention as well as ways to manage withdrawal and peer pressure. The program consists of 50-min weekly group sessions of same-gender participants moderated by a same-gender facilitator. The adult male or female facilitators were fully trained by the ALA, and the N-O-T program was implemented as prescribed by the ALA. Data collection took place in a human-behavior laboratory at the Research Institute at Nationwide Children’s Hospital, Department of Pediatrics, The Ohio State University. Institutional Review Board approved consent and assent forms were reviewed and signed by all participants. Within the 2 weeks immediately before starting the N-O-T program, each participant completed a pretreatment laboratory session. On average, pretreatment sessions occurred 3.70 days (SD ⫽ 8.25) before the first week of the N-O-T program. For these sessions, participants first provided a breath sample to measure CO level and then completed the self-report assessments and laboratory behavioral tasks. Self-report assessments included smoking-related variables about smoking status, nicotine dependence, and readiness to quit smoking. Regarding behavioral tasks, participants received standard instructions and practiced each procedure before beginning. Task order was coun-
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PREDICTORS OF SMOKING TREATMENT OUTCOMES
terbalanced across participants. Participants took a 10-min break after completing the first two tasks, during which they were able to walk around, use the restroom, get some water, and so forth. Urine samples were collected after behavioral tasks in a private bathroom close to the laboratory using specially designed, heat-sensitive specimen containers. Participants were paid between $25 and $35 for completing the pretreatment session. All sessions were conducted individually between the hours of 12:00 p.m. and 7:00 p.m. and were approximately 2 hr in duration. After the 10-week N-O-T program, participants completed a posttreatment session during the 2 weeks immediately after conclusion of N-O-T. These sessions consisted of many of the same assessments as the pretreatment session. Posttreatment sessions were conducted to explore treatment effects on smoking-related variables and impulsivity. However, the posttreatment sample size was small because data were only collected from those who completed the N-O-T program, which excluded all participants who dropped out of the treatment program. Therefore, only smoking stats data are reported here from these posttreatment sessions. Participant posttreatment data were collected within an average of 7.92 days (SD ⫽ 5.27) of the last N-O-T session.
Measures Self-report assessments. Demographic questionnaire. Participants completed a short demographic questionnaire designed for use in this study. Included were queries for age, gender, race, and peer smoking status. There were also questions pertaining to other drugs used by the adolescent and how often these substances were used over the previous 6 months. Smoking related variables. The Fagerström Test for Nicotine Dependence (Heatherton, Kozlowski, Frecker, & Fagerström, 1991; Prokhorov, Pallonen, Fava, Ding, & Niaura, 1996) was given to determine participants’ nicotine dependence; however, only one item from the Fagerström was used to assess dependence (i.e., “How soon after waking do you have your first cigarette?”). Previous research has demonstrated that this particular item is most predictive of nicotine dependence (Fagerström, 2003; Grainge, Shahab, Hammond, O’Connor, & McNeill, 2009). Participants also completed the contemplation ladder (Biener & Abrams, 1991) to assess their willingness to quit smoking. Barratt Impulsiveness Scale–Adolescent. The Barratt Impulsiveness Scale–Adolescent (BIS-11-A; Fossati, Barratt, Acquarini, & Di Ceglie, 2002) is a 30-item self-report questionnaire designed to measure impulsiveness. Items are on a 4-point scale (1 ⫽ rarely/never to 4 ⫽ almost always/always). The BIS-11-A is an adaptation of the adult BIS-11 (Patton, Stanford, & Barratt, 1995). The original adult version of the BIS-11 consisted of three subfactors (motor impulsiveness, nonplanning impulsiveness, and attentional impulsiveness); however, because of the high intercorrelations among the subfactors for adolescents, it has been recommended that total scores are the most appropriate index of impulsivity for this age group. Higher total scores reflect greater impulsivity. Laboratory behavioral assessments. Question-Based Delay-Discounting Measure. For the Question-Based Delay-Discounting Measure (DDQ; Richards, Zhang, Mitchell, & de Wit, 1999), participants were presented
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choices between $10 available after a specified delay (i.e., 1, 2, 30, 180, or 365 days) and a smaller amount available immediately (e.g., “Would you rather have $10 in 30 days or $2 now?”). This task used an adjusting amount procedure (adjusting the immediate amount in increments of $0.50) to derive indifference points between the delayed-standard and immediate-adjusting options. An indifference point reflected the smallest amount of money an individual chose to receive immediately instead of the delayedstandard amount ($10) at a specific delay. The choice questions were presented in a randomized order determined by the computer program. Participants were told that their answers to the questions were important because at the end of the session one question would be selected and honored—resulting in either immediate or delayed money (see Reynolds, Karraker, Horn, & Richards, 2003). Conners’ Continuous Performance Test-II. The Conners’ Continuous Performance Test-II (CPT-II; Conners, 2004) is a computerized task designed to measure sustained attention. Participants were asked to left click a computer mouse as quickly as possible when letters other than the letter X were presented (target stimulus) on the screen and to refrain from responding when the letter X (nontarget stimulus) was presented. The time between each stimulus (target and nontarget) was varied among 1, 2, or 4 s, and the task took approximately 15 min to complete. The outcome measures used in this study were measures designed to indicate inability to sustain attention and included number of omissions, number of commissions, and hit reaction time (RT) (raw scores). High numbers of omission errors (not responding to target stimuli) and/or commission errors (responding to nontarget stimuli) as well as high hit RTs (slow rate of response) reflect inattention. Go/Stop Task. The Go/Stop Task (Dougherty et al., 2003) was designed to assess ability to inhibit prepotent, motoric responses (see Dougherty, Mathias, Marsh, & Jagar, 2005). Participants were presented a series of three-digit numbers on a computer screen (e.g., . . . 436 . . . 256 . . . 256 . . . 822) and were instructed to respond as quickly as possible by left-clicking a computer mouse when a matching number appeared (go signal), which occurred for 50% of the numbers. Participants earned $0.05 for each go-signal response but lost $0.05 for “late” responses that occurred after the number disappeared. The participant lost $0.10 for responses to nonmatching numbers. For a randomly selected 25% of the go-signal trials, the second matching number changed colors from black to red, thus indicating a stop trial. Participants were instructed to inhibit responses when the go-signal numbers changed colors (stop-signal). Participants earned $0.05 for each successfully inhibited response after a color change but lost $0.05 for failures to inhibit. Stop-signal interval lengths decreased (i.e., occurred more quickly during the gosignal) after failures to inhibit, thus making it easier to stop for the next trial; however, interval lengths increased after successful inhibition, thus making it more difficult to stop for the next trial. Stop-signal intervals adjust (within a range of 50 –300 ms) until the participant successfully inhibits on approximately 50% of the stop-signal trials. At this 50% criterion, the stop-signal RT (SSRT) was calculated by subtracting the stop-signal delay from the go RT. From this calculation, longer SSRT values reflected behavioral disinhibition and impulsivity. See Dougherty et al. (2003) for participant instructions. Participants earned between $0 and $4 for completing the Go/Stop Task.
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Treatment Outcomes For purposes of analyses, treatment outcomes were divided into three categories: reduction, nonreduction, and dropout. We did not include an outcome for complete abstinence because too few participants fully quit smoking on the basis of cotinine results (⬍100 ng/mL; n ⫽ 7) to support meaningful analyses. Participants were classified as “reducers” if they completed the N-O-T program and self-reported a smoking decrease of at least 50% from the preto posttreatment laboratory sessions. This classification was based on evidence that smoking reductions of 50% or greater are associated with health benefits, such as a reduction of risk factors for cardiovascular-related issues and lung cancer (see Pisinger & Godtfredsen, 2007). Also, reductions of at least 50% are associated with a greater likelihood of future quit attempts and eventual cessation (e.g., Carpenter et al., 2004; Hughes, 2000). Selfreported reductions in smoking were confirmed by a decrease in cotinine level over the same time period. Participants were considered “nonreducers” if they completed the N-O-T program but did not report at least a 50% reduction in smoking or if a selfreported reduction of more than 50% was not verified by a reduction in cotinine level. Participants were considered “dropouts” if they attended fewer than half of the N-O-T sessions (i.e., ⬍5 sessions) or did not attend one of the last two sessions of the program. Attending one of the last two sessions was taken to indicate sustained participation in the treatment program.
Data Analysis The BIS-11-A had 1.2% missing data, the CPT-II had 3.7% missing data, and the DDQ had no missing data; however, 19.8% the Go/Stop Task data were missing. Missing data for the Go/Stop Task were primarily the result of participants not reaching stable performance of inhibition. Across the treatment outcomes, 11.8% of the reduction group, 18.2% of the nonreduction group, and 25.8% of the dropout group was missing Go/Stop Task data. Multiple imputation was used to improve data completeness for the Go/Stop Task (Heron, Hickman, Macleod, & Munafo, 2011; Reynolds et al., 2008; SAS, 2009; Spratt et al., 2010; Walsh et al., 2010; Yuan, 2010). Multiple imputation involves replacing each missing value with a set of plausible values (each with random error) to reflect the uncertainty associated with the true value to impute (He, 2010). We used a regression approach and information from all covariates with no missing data to impute missing values. We imputed missing data 10 times, which produced 10 corresponding sets of covariate values. Two ordinal logistic regression models were run in SAS (version 9.2) on each of the 10 sets of results. The first model examined whether the four impulsivity measures (DDQ, Go/Stop, CPT-II, and BIS-11-A) predicted treatment outcomes. The second model included the demographic variables age, sex, and race and to determine if results from the assessment model would be maintained after controlling for these other variables. Tests of the proportional hazards assumption indicated that the assumption was valid. Finally, we used the SAS procedure PROC MIANALYZE to aggregate the separate parameter estimates from each of the 10 models (with and without demographic covariates) to produce a single estimate for each parameter (i.e., to account for the variability in estimates between models with different values of imputed data).
Results Treatment Outcomes Approximately 21% (n ⫽ 17) of the participants met the criteria for reduction. These participants on average reduced their selfreported rate of smoking from 7.57 to 1.13 cigarettes per day from pre- to posttreatment and reduced their cotinine levels from 1,514.06 to 732.47 ng/mL over the same time frame (see top panels of Figure 1). Reducers participated in an average of 8.76 (SD ⫽ 1.25) of the 10 N-O-T sessions. For nonreducers, almost 41% (n ⫽ 33) of the participants fell into this category. On average, nonreducers had a self-reported decrease in their rate of smoking from 6.37 to 5.11 cigarettes per day, but their cotinine levels increased from 943.36 to 1,235.39 ng/mL (see bottom panels of Figure 1). These participants attended an average of 7.94 (SD ⫽ 1.22) sessions, which did not differ significantly from reducers. Finally, 38% (n ⫽ 31) of the participants dropped out of the N-O-T program, attending an average of 1.52 (SD ⫽ 1.52) sessions.
Participants Table 1 shows self-reported demographic, substance use, and smoking background for the reducers, nonreducers, and dropouts. There were no significant differences between the groups regarding age, sex, race, or IQ. For smoking-related variables, there were no significant differences in CO level, how many peers or closest friends smoked, the level of nicotine dependence, or the readiness to quit smoking (contemplation ladder). Furthermore, the groups did not differ in use of marijuana or alcohol. There were significant differences for cotinine level, with nonreducers having lower cotinine levels than the other two groups. Odds ratios (OR) are reported in Table 2 from ordinal logistic regression analyses exploring impulsive behavior as a predictor of treatment outcomes. The CPT-II was a significant predictor of treatment outcomes. The Go/Stop Task, although not statistically significant, had a weak association with outcomes (OR ⫽ 1.01, p ⫽ .16). The DDQ and BIS-11-A were not predictive of treatment outcomes. These analyses were rerun to include covariates. The CPT-II remained a robust predictor of treatment outcomes—significantly predicting outcomes above and beyond what can be accounted for by included covariates (see Table 3). With demographic covariates included, the Go/Stop Task was not a significant predictor (OR ⫽ 1.01, p ⫽ .20); however, the Go/Stop Task may have been significant had the same effect been present in a larger study. The DDQ and BIS-11-A were still not significant predictors.
Discussion The current study was conducted to assess three dimensions of impulsive behavior using laboratory behavioral procedures in adolescent smokers participating in a social– cognitive smoking cessation program. There were three treatment outcome groups: reduction, nonreduction, and dropout. From pretreatment assessment, these groups did not differ on many baseline variables, such as demographic data, nicotine dependence, daily number of
PREDICTORS OF SMOKING TREATMENT OUTCOMES
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REDUCERS Cotinine
TLFB 10
1800 1600
8 7 6 5 4 3
*
2
Cotinine Values (n g/ml)
Average Daily Cigarettes
1400 1200
*
1000 800 600 400
1
200
0
0
NONREDUCERS 10
1800
9 1600
8
Co tin in e Values (ng/ml)
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9
7 6 5 4 3 2
1200 1000 800 600 400 200
1 0
1400
0
Pretreatment
Posttreatment
Assessments
Pre treatment
Posttreatment
Assessments
Figure 1. Mean (⫾SEM, denoted by the error bars) number of cigarettes smoked per day and cotinine values assessed before treatment and after treatment for reducers and nonreducers. ⴱ Significant difference from pretreatment assessments at the p ⬍ .05 level.
cigarettes smoked, and CO levels—pointing to other variables being responsible for variability in outcomes. The primary finding to help explain treatment outcomes was that sustained attention robustly predicted outcomes; that is, adolescent smokers who had fewer errors of omission (i.e., better attention) on the CPT-II task were more successful in reducing or quitting smoking during treatment. In earlier studies, Schepis et al. (2011) and Krishnan-Sarin et al. (2007) found that sustained attention did not predict treatment outcomes for adolescent smokers. However, treatment program differences may explain the divergent findings between these previous studies and the current study. For example, the earlier studies used CM programs that monetarily incentivized changes in smoking behavior. The current study was based on a social– cognitive program that did not directly incentivize changes in smoking behavior, which may have required more vigilance on the part of participants to maintain the effort required to change smoking behavior. Delay discounting has been associated with treatment outcomes in prior studies of adult and adolescent smokers; therefore, we
expected a similar pattern of findings. Contrary to expectation, delay discounting did not predict treatment outcomes, nor did the BIS-11-A or the Go/Stop Task. For delay discounting, it is possible again that the social– cognitive program this research was based on contributed to our nonfinding. In addition, the fact that we were working with adolescent smokers may have been a contributing factor to our lack of a delay-discounting finding. Sheffer et al. (2012) reported that delayed discounting predicted outcomes from a cognitive– behavioral program for adults; however, this relationship may not be robust for adolescent smokers because of evidence that delay discounting changes as we age (e.g., Green, Fry, & Myerson, 1994). That is, children and adolescents on average will discount more by delay than adults, which may serve to compress variability and reduce delay discounting’s sensitivity to detect treatment outcome effects for the younger age groups. Consistent with this hypothesis is evidence that—although adolescent smokers do discount significantly more than adolescent nonsmokers—the effect sizes for delay discounting are often smaller between adolescent smokers and nonsmokers than be-
HARRIS ET AL.
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Table 1 Participant Demographics and Baseline Information (N ⫽ 81)
Age (yr; M [SD]) Sex (% female) Race (n; black:white:other) KBIT 2 (IQ standard score; M [SD])a Cigarettes (number per day; M [SD])b Nicotine dependence (M [SD])c Carbon monoxide (ppm; M [SD]) Cotinine (ng/mL; M [SD]) How many friends smoke (M [SD])d Closest/best friend smokes (% reporting yes) Contemplation ladder (scale 0 to 10; M [SD]) Marijuana (M [SD])e Alcohol (M [SD])e
Reducers (n ⫽ 17)
Nonreducers (n ⫽ 33)
Dropouts (n ⫽ 31)
16 (1.00) 64.7 10/7/0 86.41 (10.72) 7.57 (4.65) 1.31 (.48) 11.18 (8.49) 1,514.06 (1,130.19)A 3.53 (1.12) 94.1 7.76 (1.30) 3 (1.97) 1.65 (1.17)
16.24 (1.28) 51.5 13/16/3 85.53 (12.25) 6.37 (5.00) 1.45 (.77) 12.09 (10.14) 943.36 (805.94)B 3.48 (.94) 75.8 7.47 (2.40) 3.03 (1.72) 1.61(1.03)
15.84 (1.57) 61.3 6/20/5 87.27 (11.57) 8.79 (5.69) 1.29 (.53) 11.13 (5.73) 1,413.06 (881.41)A 3.71 (.94) 80.6 6.55 (2.45) 2.17 (1.77) 1.87 (1.04)
Note. Means or percentages in the same row that do not share the same superscript differ at p ⬍ .05. a Kaufman Brief Intelligence Test 2. b Cigarettes per day were calculated using a timeline follow-back calendar to determine cigarettes smoked each day during the past 14 days. c Nicotine dependence was measured using the following question: “How soon after waking do you have your first cigarette?”: 1 ⫽ within first 30 min, 2 ⫽ more than 30 min after waking but before noon, 3 ⫽ in the afternoon, 4 ⫽ in the evening. d Friends who smoke was assessed using the following question: “How many of your friends smoke cigarettes/black & milds?”: 1 ⫽ none, 2 ⫽ some, 3 ⫽ half, 4 ⫽ most, 5 ⫽ all. e Drug use was assessed with the following question: “Thinking about the past 6 months, how often have you used the following substances?”: 1 ⫽ tried it, 2 ⫽ 1–2 times/month, 3 ⫽ once a week, 4 ⫽ 2– 4 times/week, 5 ⫽ 5 or more times a week.
tween adult smokers and nonsmokers (e.g., Reynolds et al., 2003; Reynolds, Richards, Horn, & Karraker, 2004). However, delay discounting has predicted treatment outcomes for adolescent smokers participating in a CM program (Krishnan-Sarin et al., 2007), which, as an incentive-based program, may align better with delay-discounting processes and result in larger effects. There are two main implications of this research. First, the finding that sustained attention was predictive of treatment outcomes and remained so after controlling for certain demographic variables is novel. If replicated, this finding could guide treatment program modifications, which would move the field forward in terms of enhancing treatment options for teen smokers. One might expect that adolescents with less ability to sustain attention may not excel in treatment programs that provide less frequent participation and feedback during the program. Treatment programs such as N-O-T often meet on a weekly basis and involve a concentrated amount of course material in a relatively small amount of time. Comparatively, CM programs usually involve contact with a program facilitator on a daily basis or even multiple times per day, thus requiring shorter periods of independent effort on the part of the adolescent smoker to sustain smoking cessation goals. Perhaps modifications to program design or formatting for
Table 2 Ordinal Logistic Regression and Confidence Interval Results Including Assessment Measures Only Parameter
OR
95% Lower
95% Upper
t
p-values
Intercept 2 Intercept 3 DDQ CPT-II Go/Stop Task BIS-11-A
0.06 0.01 0.77 1.04 1.01 1.01
0.00 0.00 0.11 1.01 1.00 0.97
3.60 0.54 5.29 1.07 1.01 1.06
⫺1.34 ⫺2.25 ⫺0.27 2.87 1.40 0.53
0.18 0.024 0.788 0.004a 0.163 0.595
a
Significance of p ⬍ .05.
programs such as N-O-T that involve more frequent contact between participants and program facilitators would enable less attentive adolescents to be more successful in changing their smoking behavior. Second, the literature suggests that delay discounting is a robust predictor of treatment outcomes. However, this may only serve for certain types of treatment programs and be a more accurate assertion for adults than for adolescents. As described earlier, this study was based on a social– cognitive treatment program, and previous research has largely used incentive-based CM programs to evaluate delayed discounting and treatment outcomes—with the exception of adult smokers studied by Sheffer et al. (2012). For delay discounting, it stands to reason that this assessment of an individual’s choices involving delay to reward would predict outcomes from incentive-based (or reward based) treatment programs. Future research also should examine sustained attention to determine if attention is a behavioral capacity required by programs that do and do not directly incentivize abstinence from smoking. If so, programs could easily implement new components designed to improve sustained attention as part of the treatment approach and Table 3 Ordinal Logistic Regression and Confidence Interval Results Including Demographic Covariates Parameter
OR
95% Lower
95% Upper
t
p-values
Intercept 2 Intercept 3 Age Sex (male) Race DDQ CPT-II Go/Stop Task BIS-11-A
0.12 0.02 0.99 0.86 1.68 0.44 1.05 1.01 1.00
0.00 0.00 0.70 0.56 1.04 0.06 1.02 1.00 0.95
322.82 41.34 1.41 1.34 2.72 3.40 1.08 1.01 1.05
⫺0.52 ⫺1.04 ⫺0.05 ⫺0.66 2.13 ⫺0.78 3.17 1.27 0.07
0.603 0.299 0.957 0.509 0.033a 0.433 0.002a 0.204 0.942
a
Significance of p ⬍ .05.
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PREDICTORS OF SMOKING TREATMENT OUTCOMES
by extension hopefully reduce the effects of inattention on smoking outcomes. This study is not without limitations. For example, the sample size was relatively small, limiting generalizability to the larger population. Another important limitation was the lack of collected follow-up data from participants who did not complete the treatment program, thus precluding pre- to posttreatment analyses for this outcome group. However, even with these limitations in mind, the current study provides initial evidence that a teen’s ability to sustain attention may be an important behavioral attribute to consider in supporting his or her efforts to change smoking behavior while completing social– cognitive-type cessation programs. Future smoking cessation work with these types of programs might include treatment enhancements such as more frequent contact with program facilitators to reduce the effects of inattention on outcomes.
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Okuyemi, K. S., Thomas, J. L., Warren, J., Guo, H., & Ahluwalia, J. S. (2010). Relationship between smoking reduction and cessation among light smokers. Nicotine & Tobacco Research, 12, 1005–1010. doi: 10.1093/ntr/ntq138 Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Factor structure of the Barratt impulsiveness scale. Journal of Clinical Psychology, 51, 768 –774. doi:10.1002/1097-4679(199511)51:6⬍768::AIDJCLP2270510607⬎3.0.CO;2-1 Pisinger, C., & Godtfredsen, N. S. (2007). Is there a health benefit of reduced tobacco consumption? A systematic review. Nicotine & Tobacco Research, 9, 631– 646. doi:10.1080/14622200701365327 Prokhorov, A. V., Pallonen, U. E., Fava, J. L., Ding, L., & Niaura, R. (1996). Measuring nicotine dependence among high-risk adolescent smokers. Addictive Behaviors, 21, 117–127. Reynolds, B., Karraker, K., Horn, K., & Richards, J. B. (2003). Delay and probability discounting as related to different stages of adolescent smoking and non-smoking. Behavioural Processes, 64, 333–344. Reynolds, B., Penfold, R. B., & Patak, M. (2008). Dimensions of impulsive behavior in adolescents: Laboratory behavioral assessments. Experimental and Clinical Psychopharmacology, 16, 124 –131. doi:10.1037/10641297.16.2.124 Reynolds, B., Richards, J. B., & de Wit, H. (2006). Acute-alcohol effects on the Experiential Discounting Task (EDT) and a question-based measure of delay discounting. Pharmacology, Biochemistry and Behavior, 83, 194 –202. doi:10.1016/j.pbb.2006.01.007 Reynolds, B., Richards, J. B., Horn, K., & Karraker, K. (2004). Delay discounting and probability discounting as related to cigarette smoking status in adults. Behavioural Processes, 65, 35– 42. doi:10.1016/S03766357(03)00109-8 Richards, J. B., Zhang, L., Mitchell, S. H., & de Wit, H. (1999). Delay or probability discounting in a model of impulsive behavior: Effect of alcohol. Journal of the Experimental Analysis of Behavior, 71, 121–143. doi:10.1901/jeab.1999.71-121 Ryan, K. K., MacKillop, J., & Carpenter, M. J. (2013). The relationship between impulsivity, risk-taking propensity and nicotine dependence among older adolescent smokers. Addictive Behaviors, 38, 1431–1434. doi:10.1016/j.addbeh.2012.08.013
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Received April 16, 2013 Revision received August 2, 2013 Accepted August 5, 2013 䡲
© 2014 American Psychological Association 1064-1297/14/$12.00 DOI: 10.1037/a0034403
Dimensions of Impulsive Behavior and Treatment Outcomes for Adolescent Smokers Millie Harris
Robert B. Penfold
University of Kentucky
Group Health Research Institute, Seattle, Washington and University of Washington
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Ariane Hawkins, Jared Maccombs, and Bryan Wallace
Brady Reynolds University of Kentucky
Research Institute at Nationwide Children’s Hospital, Columbus, Ohio
Adolescent cigarette smoking rates remain a significant public health concern, and as a result there is a continued need to understand factors that contribute to an adolescent’s ability to reduce or quit smoking. Previous research suggests that impulsive behavior may be associated with treatment outcomes for smoking. The current research (N ⫽ 81) explored 3 dimensions of impulsive behavior as predictors of treatment response from a social– cognitive type program for adolescent smokers (i.e., Not On Tobacco; N-O-T). Measures included laboratory assessments of delay discounting, sustained attention, and behavioral disinhibition. A self-report measure of impulsivity was also included. Adolescent smokers who had better sustained attention were more likely to reduce or quit smoking by the end of treatment. No other measures of impulsivity were significantly associated with treatment response. From these findings, an adolescent smoker’s ability to sustain attention appears to be an important behavioral attribute to consider when implementing smoking cessation programs such as N-O-T. Keywords: impulsivity, delay discounting, attention, disinhibition, smoking, adolescents
smokers attempted to quit smoking, but only 12.2% were successful (Malarcher, Jones, Morris, Kann, & Buckley, 2009). Other research suggests that for some smokers the path to cessation occurs as a series of steps toward that ultimate goal. For example, adult smokers who reduce cigarette consumption during a quit attempt are more likely to fully quit at some future date (Carpenter, Hughes, Solomon, & Callas, 2004; Cunningham & Selby, 2010; Hyland et al., 2005; Okuyemi, Thomas, Warren, Guo, & Ahuluwalia, 2010). Identifying factors (e.g., behavioral, social, biological) that influence an adolescent’s ability to quit or reduce smoking is a significant priority in efforts to reduce smoking rates in this age group. Impulsivity (assessed either by questionnaire or laboratorybehavioral methods) is a behavioral attribute that has been linked with a smoker’s ability to reduce or quit smoking (e.g., Cavalca et al., 2013; Krishnan-Sarin et al., 2007; Kvaavik & Rise, 2012; Sheffer et al., 2012). For example, impulsive behavior assessed with a questionnaire is a significant predictor of abstinence during a cognitive– behavioral treatment for smoking cessation (Sheffer et al., 2012). Among adolescent smokers, findings based on selfreport assessments have been similar (e.g., Cavalca et al., 2013; Ryan, MacKillop, & Carpenter, 2013; Wegmann, Bühler, Strunk, Lang, & Nowak, 2012). In contrast to questionnaire methods, laboratory behavioral measures of impulsivity represent a different class of assessment tools that typically isolate more specific behavioral processes. Factor analyses suggest that these measures can be categorized into at
Cigarette smoking trends have declined since the 1980s; however, this pattern has not continued for adolescent smokers (U.S. Department of Health and Human Services, 2012). As of 2009, approximately 20% of high-school students acknowledge smoking in the past month (Johnston, O’Malley, Bachman, & Schulenberg, 2011), and other research indicates that each day up to 1,000 adolescents become daily smokers (Hum, Robinson, Jackson, & Ali, 2011). Many adolescent smokers engage in multiple quit attempts; however, quit rates for adolescents are low when compared with adult smokers (Milton et al., 2004). From the Youth Risk Behavior Survey, approximately 60.9% of high-school daily
This article was published Online First January 13, 2014. Millie Harris, Department of Behavioral Science, University of Kentucky; Robert B. Penfold, Group Health Research Institute, Seattle, Washington, and Department of Health Services Research, University of Washington; Ariane Hawkins, Jared Maccombs, and Bryan Wallace, Research Institute at Nationwide Children’s Hospital, Columbus, Ohio; Brady Reynolds, Department of Behavioral Science, University of Kentucky. All authors report no conflicts of interest. This research was supported by funds from Nationwide Children’s Hospital. All authors contributed in a significant way to the manuscript, and all authors have read and approved the final manuscript. Correspondence concerning this article should be addressed to Brady Reynolds, Department of Behavioral Science, 105 Medical Behavioral Science Building, University of Kentucky, Lexington, KY 40536-0086. E-mail: [email protected] 57
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least three separate dimensions of behavior that include decisionmaking, inattention, and disinhibition (Reynolds, Penfold, & Patak, 2008). These assessments differ from questionnaire methods by using tasks that measure the actual behaviors of interest in the laboratory rather than a participant’s self-reports (or selfperceptions) of these behaviors. Correlations between behavioral and self-report assessments of impulsivity are typically low (e.g., Heyman, & Gibb, 2006; Lane, Cherek, Rhodes, Pietras, & Tcheremissine, 2003) or nonexistent (e.g., Krishnan-Sarin et al., 2007; Reynolds et al., 2008), which may stem from differences in assessment method (measuring actual behavior vs. self-perceptions of behavior) and the breadth/specificity of the behaviors being measured across methods (see Reynolds, Richards, & de Wit, 2006). Related to cigarette smoking, decision-making tasks have been studied the most. These assessments usually require participants to choose between outcomes that are delayed or immediate (i.e., delay discounting). A choice pattern that reflects comparatively more choices for smaller immediate rewards at the expense of larger but delayed rewards indicates a higher level of impulsive decision-making. In the adult literature, delay discounting is associated with treatment outcomes for smoking cessation from cognitive– behavioral and incentive-based (contingency management [CM]) treatment approaches (Dallery & Raiff, 2007; MacKillop & Kahler, 2009; Sheffer et al., 2012). Likewise, delay discounting assessed with a real-time procedure (i.e., Experiential Discounting Task) was associated with outcomes for adolescent smokers taking part in a CM program (Krishnan-Sarin et al., 2007). In all cases, participants who discounted more impulsively by delay were less successful in abstaining from smoking or maintaining abstinence. Behavioral measures of inattention (i.e., ability to maintain vigilance over time) and disinhibition (i.e., ability to inhibit unwanted behaviors) have been less studied in the context of smoking cessation. However, adolescent smokers are more impulsive in terms of both behaviors than their nonsmoking counterparts (e.g., Fields, Collins, Leraas, & Reynolds, 2009; Schepis, McFetridge, Chaplin, Sinha, & Krishnan-Sarin, 2011), thus suggesting the relevance of these behaviors to smoking more generally. Also, impulsive inattention and disinhibition increase during abstinence from smoking, particularly among participants who are less impulsive before abstinence (Ashare & Hawk, 2012; Harrison, Coppola, & McKee, 2009). However, these behaviors may not be related to treatment outcomes. For example, Schepis et al. (2011) found that sustained attention did not predict treatment response in a small sample (N ⫽ 12) of adolescent smokers, and KrishnanSarin et al. (2007) had a similar finding in a slightly larger sample (N ⫽ 30) of adolescent smokers. Notably, both of these reports were based on CM cessation programs; therefore, it is not clear if these findings would extend to social– cognitive programs. For the current study, we assessed all three dimensions of impulsive behavior using laboratory behavioral procedures in adolescent smokers participating in a social– cognitive smoking cessation program, the American Lung Association’s (ALA) Not-OnTobacco (N-O-T) program. We also included a questionnaire assessment of impulsivity for comparison purposes. The goal of this research was to better establish which types of impulsive behavior are associated with outcomes from this widely prescribed treatment approach designed specifically for adolescent smokers.
As described above, each behavioral task indicates a distinct form of behavioral deficit, which may guide treatment program modifications in unique directions on the basis of the specific type(s) of impulsive behavior associated with outcomes. To our knowledge, this is the first research to evaluate three dimensions of impulsive behavior using laboratory behavioral assessments in relation to adolescent smoking outcomes. We hypothesized that participants who were more impulsive in one or more of the three dimensions of behavior assessed would be less successful in reducing or quitting smoking during participation in the N-O-T program.
Method Participants There were 97 adolescent smokers who took part in the N-O-T program. However, 6 did not want to participate in the research, and 10 were later determined to be nonsmokers. Therefore, 81 adolescents (47 females) participated in this research. Selfreported smoking status was biochemically validated through level of breath carbon monoxide (CO) and urinary cotinine content. Participants were required to have a CO level of 9 ppm or greater (Bedfont Instruments, Micro III, U.K.) and/or a quantitative cotinine value of 100 ng/mL or greater. Cotinine content was determined using a homogenous enzyme immunoassay at GrahamMassey Analytical Labs in New Haven, CT. One of the 81 participants had a cotinine level of 95 ng/mL; however, this individual’s CO level was 11 ppm, which was above the criterion for being a smoker.
Procedure The N-O-T program was a voluntary 10-week quit-smoking program offered at Nationwide Children’s Hospital in Columbus, OH. N-O-T consists of a standardized curriculum that specifically targets adolescents by using language and graphics appropriate for teens. This program provides social– cognitive training to improve participants’ social skills, self-management, and psychoeducation about relapse prevention as well as ways to manage withdrawal and peer pressure. The program consists of 50-min weekly group sessions of same-gender participants moderated by a same-gender facilitator. The adult male or female facilitators were fully trained by the ALA, and the N-O-T program was implemented as prescribed by the ALA. Data collection took place in a human-behavior laboratory at the Research Institute at Nationwide Children’s Hospital, Department of Pediatrics, The Ohio State University. Institutional Review Board approved consent and assent forms were reviewed and signed by all participants. Within the 2 weeks immediately before starting the N-O-T program, each participant completed a pretreatment laboratory session. On average, pretreatment sessions occurred 3.70 days (SD ⫽ 8.25) before the first week of the N-O-T program. For these sessions, participants first provided a breath sample to measure CO level and then completed the self-report assessments and laboratory behavioral tasks. Self-report assessments included smoking-related variables about smoking status, nicotine dependence, and readiness to quit smoking. Regarding behavioral tasks, participants received standard instructions and practiced each procedure before beginning. Task order was coun-
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PREDICTORS OF SMOKING TREATMENT OUTCOMES
terbalanced across participants. Participants took a 10-min break after completing the first two tasks, during which they were able to walk around, use the restroom, get some water, and so forth. Urine samples were collected after behavioral tasks in a private bathroom close to the laboratory using specially designed, heat-sensitive specimen containers. Participants were paid between $25 and $35 for completing the pretreatment session. All sessions were conducted individually between the hours of 12:00 p.m. and 7:00 p.m. and were approximately 2 hr in duration. After the 10-week N-O-T program, participants completed a posttreatment session during the 2 weeks immediately after conclusion of N-O-T. These sessions consisted of many of the same assessments as the pretreatment session. Posttreatment sessions were conducted to explore treatment effects on smoking-related variables and impulsivity. However, the posttreatment sample size was small because data were only collected from those who completed the N-O-T program, which excluded all participants who dropped out of the treatment program. Therefore, only smoking stats data are reported here from these posttreatment sessions. Participant posttreatment data were collected within an average of 7.92 days (SD ⫽ 5.27) of the last N-O-T session.
Measures Self-report assessments. Demographic questionnaire. Participants completed a short demographic questionnaire designed for use in this study. Included were queries for age, gender, race, and peer smoking status. There were also questions pertaining to other drugs used by the adolescent and how often these substances were used over the previous 6 months. Smoking related variables. The Fagerström Test for Nicotine Dependence (Heatherton, Kozlowski, Frecker, & Fagerström, 1991; Prokhorov, Pallonen, Fava, Ding, & Niaura, 1996) was given to determine participants’ nicotine dependence; however, only one item from the Fagerström was used to assess dependence (i.e., “How soon after waking do you have your first cigarette?”). Previous research has demonstrated that this particular item is most predictive of nicotine dependence (Fagerström, 2003; Grainge, Shahab, Hammond, O’Connor, & McNeill, 2009). Participants also completed the contemplation ladder (Biener & Abrams, 1991) to assess their willingness to quit smoking. Barratt Impulsiveness Scale–Adolescent. The Barratt Impulsiveness Scale–Adolescent (BIS-11-A; Fossati, Barratt, Acquarini, & Di Ceglie, 2002) is a 30-item self-report questionnaire designed to measure impulsiveness. Items are on a 4-point scale (1 ⫽ rarely/never to 4 ⫽ almost always/always). The BIS-11-A is an adaptation of the adult BIS-11 (Patton, Stanford, & Barratt, 1995). The original adult version of the BIS-11 consisted of three subfactors (motor impulsiveness, nonplanning impulsiveness, and attentional impulsiveness); however, because of the high intercorrelations among the subfactors for adolescents, it has been recommended that total scores are the most appropriate index of impulsivity for this age group. Higher total scores reflect greater impulsivity. Laboratory behavioral assessments. Question-Based Delay-Discounting Measure. For the Question-Based Delay-Discounting Measure (DDQ; Richards, Zhang, Mitchell, & de Wit, 1999), participants were presented
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choices between $10 available after a specified delay (i.e., 1, 2, 30, 180, or 365 days) and a smaller amount available immediately (e.g., “Would you rather have $10 in 30 days or $2 now?”). This task used an adjusting amount procedure (adjusting the immediate amount in increments of $0.50) to derive indifference points between the delayed-standard and immediate-adjusting options. An indifference point reflected the smallest amount of money an individual chose to receive immediately instead of the delayedstandard amount ($10) at a specific delay. The choice questions were presented in a randomized order determined by the computer program. Participants were told that their answers to the questions were important because at the end of the session one question would be selected and honored—resulting in either immediate or delayed money (see Reynolds, Karraker, Horn, & Richards, 2003). Conners’ Continuous Performance Test-II. The Conners’ Continuous Performance Test-II (CPT-II; Conners, 2004) is a computerized task designed to measure sustained attention. Participants were asked to left click a computer mouse as quickly as possible when letters other than the letter X were presented (target stimulus) on the screen and to refrain from responding when the letter X (nontarget stimulus) was presented. The time between each stimulus (target and nontarget) was varied among 1, 2, or 4 s, and the task took approximately 15 min to complete. The outcome measures used in this study were measures designed to indicate inability to sustain attention and included number of omissions, number of commissions, and hit reaction time (RT) (raw scores). High numbers of omission errors (not responding to target stimuli) and/or commission errors (responding to nontarget stimuli) as well as high hit RTs (slow rate of response) reflect inattention. Go/Stop Task. The Go/Stop Task (Dougherty et al., 2003) was designed to assess ability to inhibit prepotent, motoric responses (see Dougherty, Mathias, Marsh, & Jagar, 2005). Participants were presented a series of three-digit numbers on a computer screen (e.g., . . . 436 . . . 256 . . . 256 . . . 822) and were instructed to respond as quickly as possible by left-clicking a computer mouse when a matching number appeared (go signal), which occurred for 50% of the numbers. Participants earned $0.05 for each go-signal response but lost $0.05 for “late” responses that occurred after the number disappeared. The participant lost $0.10 for responses to nonmatching numbers. For a randomly selected 25% of the go-signal trials, the second matching number changed colors from black to red, thus indicating a stop trial. Participants were instructed to inhibit responses when the go-signal numbers changed colors (stop-signal). Participants earned $0.05 for each successfully inhibited response after a color change but lost $0.05 for failures to inhibit. Stop-signal interval lengths decreased (i.e., occurred more quickly during the gosignal) after failures to inhibit, thus making it easier to stop for the next trial; however, interval lengths increased after successful inhibition, thus making it more difficult to stop for the next trial. Stop-signal intervals adjust (within a range of 50 –300 ms) until the participant successfully inhibits on approximately 50% of the stop-signal trials. At this 50% criterion, the stop-signal RT (SSRT) was calculated by subtracting the stop-signal delay from the go RT. From this calculation, longer SSRT values reflected behavioral disinhibition and impulsivity. See Dougherty et al. (2003) for participant instructions. Participants earned between $0 and $4 for completing the Go/Stop Task.
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Treatment Outcomes For purposes of analyses, treatment outcomes were divided into three categories: reduction, nonreduction, and dropout. We did not include an outcome for complete abstinence because too few participants fully quit smoking on the basis of cotinine results (⬍100 ng/mL; n ⫽ 7) to support meaningful analyses. Participants were classified as “reducers” if they completed the N-O-T program and self-reported a smoking decrease of at least 50% from the preto posttreatment laboratory sessions. This classification was based on evidence that smoking reductions of 50% or greater are associated with health benefits, such as a reduction of risk factors for cardiovascular-related issues and lung cancer (see Pisinger & Godtfredsen, 2007). Also, reductions of at least 50% are associated with a greater likelihood of future quit attempts and eventual cessation (e.g., Carpenter et al., 2004; Hughes, 2000). Selfreported reductions in smoking were confirmed by a decrease in cotinine level over the same time period. Participants were considered “nonreducers” if they completed the N-O-T program but did not report at least a 50% reduction in smoking or if a selfreported reduction of more than 50% was not verified by a reduction in cotinine level. Participants were considered “dropouts” if they attended fewer than half of the N-O-T sessions (i.e., ⬍5 sessions) or did not attend one of the last two sessions of the program. Attending one of the last two sessions was taken to indicate sustained participation in the treatment program.
Data Analysis The BIS-11-A had 1.2% missing data, the CPT-II had 3.7% missing data, and the DDQ had no missing data; however, 19.8% the Go/Stop Task data were missing. Missing data for the Go/Stop Task were primarily the result of participants not reaching stable performance of inhibition. Across the treatment outcomes, 11.8% of the reduction group, 18.2% of the nonreduction group, and 25.8% of the dropout group was missing Go/Stop Task data. Multiple imputation was used to improve data completeness for the Go/Stop Task (Heron, Hickman, Macleod, & Munafo, 2011; Reynolds et al., 2008; SAS, 2009; Spratt et al., 2010; Walsh et al., 2010; Yuan, 2010). Multiple imputation involves replacing each missing value with a set of plausible values (each with random error) to reflect the uncertainty associated with the true value to impute (He, 2010). We used a regression approach and information from all covariates with no missing data to impute missing values. We imputed missing data 10 times, which produced 10 corresponding sets of covariate values. Two ordinal logistic regression models were run in SAS (version 9.2) on each of the 10 sets of results. The first model examined whether the four impulsivity measures (DDQ, Go/Stop, CPT-II, and BIS-11-A) predicted treatment outcomes. The second model included the demographic variables age, sex, and race and to determine if results from the assessment model would be maintained after controlling for these other variables. Tests of the proportional hazards assumption indicated that the assumption was valid. Finally, we used the SAS procedure PROC MIANALYZE to aggregate the separate parameter estimates from each of the 10 models (with and without demographic covariates) to produce a single estimate for each parameter (i.e., to account for the variability in estimates between models with different values of imputed data).
Results Treatment Outcomes Approximately 21% (n ⫽ 17) of the participants met the criteria for reduction. These participants on average reduced their selfreported rate of smoking from 7.57 to 1.13 cigarettes per day from pre- to posttreatment and reduced their cotinine levels from 1,514.06 to 732.47 ng/mL over the same time frame (see top panels of Figure 1). Reducers participated in an average of 8.76 (SD ⫽ 1.25) of the 10 N-O-T sessions. For nonreducers, almost 41% (n ⫽ 33) of the participants fell into this category. On average, nonreducers had a self-reported decrease in their rate of smoking from 6.37 to 5.11 cigarettes per day, but their cotinine levels increased from 943.36 to 1,235.39 ng/mL (see bottom panels of Figure 1). These participants attended an average of 7.94 (SD ⫽ 1.22) sessions, which did not differ significantly from reducers. Finally, 38% (n ⫽ 31) of the participants dropped out of the N-O-T program, attending an average of 1.52 (SD ⫽ 1.52) sessions.
Participants Table 1 shows self-reported demographic, substance use, and smoking background for the reducers, nonreducers, and dropouts. There were no significant differences between the groups regarding age, sex, race, or IQ. For smoking-related variables, there were no significant differences in CO level, how many peers or closest friends smoked, the level of nicotine dependence, or the readiness to quit smoking (contemplation ladder). Furthermore, the groups did not differ in use of marijuana or alcohol. There were significant differences for cotinine level, with nonreducers having lower cotinine levels than the other two groups. Odds ratios (OR) are reported in Table 2 from ordinal logistic regression analyses exploring impulsive behavior as a predictor of treatment outcomes. The CPT-II was a significant predictor of treatment outcomes. The Go/Stop Task, although not statistically significant, had a weak association with outcomes (OR ⫽ 1.01, p ⫽ .16). The DDQ and BIS-11-A were not predictive of treatment outcomes. These analyses were rerun to include covariates. The CPT-II remained a robust predictor of treatment outcomes—significantly predicting outcomes above and beyond what can be accounted for by included covariates (see Table 3). With demographic covariates included, the Go/Stop Task was not a significant predictor (OR ⫽ 1.01, p ⫽ .20); however, the Go/Stop Task may have been significant had the same effect been present in a larger study. The DDQ and BIS-11-A were still not significant predictors.
Discussion The current study was conducted to assess three dimensions of impulsive behavior using laboratory behavioral procedures in adolescent smokers participating in a social– cognitive smoking cessation program. There were three treatment outcome groups: reduction, nonreduction, and dropout. From pretreatment assessment, these groups did not differ on many baseline variables, such as demographic data, nicotine dependence, daily number of
PREDICTORS OF SMOKING TREATMENT OUTCOMES
61
REDUCERS Cotinine
TLFB 10
1800 1600
8 7 6 5 4 3
*
2
Cotinine Values (n g/ml)
Average Daily Cigarettes
1400 1200
*
1000 800 600 400
1
200
0
0
NONREDUCERS 10
1800
9 1600
8
Co tin in e Values (ng/ml)
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9
7 6 5 4 3 2
1200 1000 800 600 400 200
1 0
1400
0
Pretreatment
Posttreatment
Assessments
Pre treatment
Posttreatment
Assessments
Figure 1. Mean (⫾SEM, denoted by the error bars) number of cigarettes smoked per day and cotinine values assessed before treatment and after treatment for reducers and nonreducers. ⴱ Significant difference from pretreatment assessments at the p ⬍ .05 level.
cigarettes smoked, and CO levels—pointing to other variables being responsible for variability in outcomes. The primary finding to help explain treatment outcomes was that sustained attention robustly predicted outcomes; that is, adolescent smokers who had fewer errors of omission (i.e., better attention) on the CPT-II task were more successful in reducing or quitting smoking during treatment. In earlier studies, Schepis et al. (2011) and Krishnan-Sarin et al. (2007) found that sustained attention did not predict treatment outcomes for adolescent smokers. However, treatment program differences may explain the divergent findings between these previous studies and the current study. For example, the earlier studies used CM programs that monetarily incentivized changes in smoking behavior. The current study was based on a social– cognitive program that did not directly incentivize changes in smoking behavior, which may have required more vigilance on the part of participants to maintain the effort required to change smoking behavior. Delay discounting has been associated with treatment outcomes in prior studies of adult and adolescent smokers; therefore, we
expected a similar pattern of findings. Contrary to expectation, delay discounting did not predict treatment outcomes, nor did the BIS-11-A or the Go/Stop Task. For delay discounting, it is possible again that the social– cognitive program this research was based on contributed to our nonfinding. In addition, the fact that we were working with adolescent smokers may have been a contributing factor to our lack of a delay-discounting finding. Sheffer et al. (2012) reported that delayed discounting predicted outcomes from a cognitive– behavioral program for adults; however, this relationship may not be robust for adolescent smokers because of evidence that delay discounting changes as we age (e.g., Green, Fry, & Myerson, 1994). That is, children and adolescents on average will discount more by delay than adults, which may serve to compress variability and reduce delay discounting’s sensitivity to detect treatment outcome effects for the younger age groups. Consistent with this hypothesis is evidence that—although adolescent smokers do discount significantly more than adolescent nonsmokers—the effect sizes for delay discounting are often smaller between adolescent smokers and nonsmokers than be-
HARRIS ET AL.
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Table 1 Participant Demographics and Baseline Information (N ⫽ 81)
Age (yr; M [SD]) Sex (% female) Race (n; black:white:other) KBIT 2 (IQ standard score; M [SD])a Cigarettes (number per day; M [SD])b Nicotine dependence (M [SD])c Carbon monoxide (ppm; M [SD]) Cotinine (ng/mL; M [SD]) How many friends smoke (M [SD])d Closest/best friend smokes (% reporting yes) Contemplation ladder (scale 0 to 10; M [SD]) Marijuana (M [SD])e Alcohol (M [SD])e
Reducers (n ⫽ 17)
Nonreducers (n ⫽ 33)
Dropouts (n ⫽ 31)
16 (1.00) 64.7 10/7/0 86.41 (10.72) 7.57 (4.65) 1.31 (.48) 11.18 (8.49) 1,514.06 (1,130.19)A 3.53 (1.12) 94.1 7.76 (1.30) 3 (1.97) 1.65 (1.17)
16.24 (1.28) 51.5 13/16/3 85.53 (12.25) 6.37 (5.00) 1.45 (.77) 12.09 (10.14) 943.36 (805.94)B 3.48 (.94) 75.8 7.47 (2.40) 3.03 (1.72) 1.61(1.03)
15.84 (1.57) 61.3 6/20/5 87.27 (11.57) 8.79 (5.69) 1.29 (.53) 11.13 (5.73) 1,413.06 (881.41)A 3.71 (.94) 80.6 6.55 (2.45) 2.17 (1.77) 1.87 (1.04)
Note. Means or percentages in the same row that do not share the same superscript differ at p ⬍ .05. a Kaufman Brief Intelligence Test 2. b Cigarettes per day were calculated using a timeline follow-back calendar to determine cigarettes smoked each day during the past 14 days. c Nicotine dependence was measured using the following question: “How soon after waking do you have your first cigarette?”: 1 ⫽ within first 30 min, 2 ⫽ more than 30 min after waking but before noon, 3 ⫽ in the afternoon, 4 ⫽ in the evening. d Friends who smoke was assessed using the following question: “How many of your friends smoke cigarettes/black & milds?”: 1 ⫽ none, 2 ⫽ some, 3 ⫽ half, 4 ⫽ most, 5 ⫽ all. e Drug use was assessed with the following question: “Thinking about the past 6 months, how often have you used the following substances?”: 1 ⫽ tried it, 2 ⫽ 1–2 times/month, 3 ⫽ once a week, 4 ⫽ 2– 4 times/week, 5 ⫽ 5 or more times a week.
tween adult smokers and nonsmokers (e.g., Reynolds et al., 2003; Reynolds, Richards, Horn, & Karraker, 2004). However, delay discounting has predicted treatment outcomes for adolescent smokers participating in a CM program (Krishnan-Sarin et al., 2007), which, as an incentive-based program, may align better with delay-discounting processes and result in larger effects. There are two main implications of this research. First, the finding that sustained attention was predictive of treatment outcomes and remained so after controlling for certain demographic variables is novel. If replicated, this finding could guide treatment program modifications, which would move the field forward in terms of enhancing treatment options for teen smokers. One might expect that adolescents with less ability to sustain attention may not excel in treatment programs that provide less frequent participation and feedback during the program. Treatment programs such as N-O-T often meet on a weekly basis and involve a concentrated amount of course material in a relatively small amount of time. Comparatively, CM programs usually involve contact with a program facilitator on a daily basis or even multiple times per day, thus requiring shorter periods of independent effort on the part of the adolescent smoker to sustain smoking cessation goals. Perhaps modifications to program design or formatting for
Table 2 Ordinal Logistic Regression and Confidence Interval Results Including Assessment Measures Only Parameter
OR
95% Lower
95% Upper
t
p-values
Intercept 2 Intercept 3 DDQ CPT-II Go/Stop Task BIS-11-A
0.06 0.01 0.77 1.04 1.01 1.01
0.00 0.00 0.11 1.01 1.00 0.97
3.60 0.54 5.29 1.07 1.01 1.06
⫺1.34 ⫺2.25 ⫺0.27 2.87 1.40 0.53
0.18 0.024 0.788 0.004a 0.163 0.595
a
Significance of p ⬍ .05.
programs such as N-O-T that involve more frequent contact between participants and program facilitators would enable less attentive adolescents to be more successful in changing their smoking behavior. Second, the literature suggests that delay discounting is a robust predictor of treatment outcomes. However, this may only serve for certain types of treatment programs and be a more accurate assertion for adults than for adolescents. As described earlier, this study was based on a social– cognitive treatment program, and previous research has largely used incentive-based CM programs to evaluate delayed discounting and treatment outcomes—with the exception of adult smokers studied by Sheffer et al. (2012). For delay discounting, it stands to reason that this assessment of an individual’s choices involving delay to reward would predict outcomes from incentive-based (or reward based) treatment programs. Future research also should examine sustained attention to determine if attention is a behavioral capacity required by programs that do and do not directly incentivize abstinence from smoking. If so, programs could easily implement new components designed to improve sustained attention as part of the treatment approach and Table 3 Ordinal Logistic Regression and Confidence Interval Results Including Demographic Covariates Parameter
OR
95% Lower
95% Upper
t
p-values
Intercept 2 Intercept 3 Age Sex (male) Race DDQ CPT-II Go/Stop Task BIS-11-A
0.12 0.02 0.99 0.86 1.68 0.44 1.05 1.01 1.00
0.00 0.00 0.70 0.56 1.04 0.06 1.02 1.00 0.95
322.82 41.34 1.41 1.34 2.72 3.40 1.08 1.01 1.05
⫺0.52 ⫺1.04 ⫺0.05 ⫺0.66 2.13 ⫺0.78 3.17 1.27 0.07
0.603 0.299 0.957 0.509 0.033a 0.433 0.002a 0.204 0.942
a
Significance of p ⬍ .05.
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PREDICTORS OF SMOKING TREATMENT OUTCOMES
by extension hopefully reduce the effects of inattention on smoking outcomes. This study is not without limitations. For example, the sample size was relatively small, limiting generalizability to the larger population. Another important limitation was the lack of collected follow-up data from participants who did not complete the treatment program, thus precluding pre- to posttreatment analyses for this outcome group. However, even with these limitations in mind, the current study provides initial evidence that a teen’s ability to sustain attention may be an important behavioral attribute to consider in supporting his or her efforts to change smoking behavior while completing social– cognitive-type cessation programs. Future smoking cessation work with these types of programs might include treatment enhancements such as more frequent contact with program facilitators to reduce the effects of inattention on outcomes.
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Received April 16, 2013 Revision received August 2, 2013 Accepted August 5, 2013 䡲
