Drug and Alcohol Dependence 135 (2014) 152–155
Contents lists available at ScienceDirect
Drug and Alcohol Dependence journal homepage: www.elsevier.com/locate/drugalcdep
Short communication
Delay discounting by depressed and non-depressed adolescent smokers and non-smokers Sarah Imhoff a,1 , Millie Harris b,2 , Jason Weiser a,3 , Brady Reynolds b,∗ a b
Department of Psychology, The Ohio State University, United States Department of Behavioral Science, University of Kentucky, United States
a r t i c l e
i n f o
Article history: Received 7 August 2013 Received in revised form 16 September 2013 Accepted 9 November 2013 Available online 25 November 2013 Keywords: Delay discounting Depression Smoking Adolescents
a b s t r a c t Background: Both delay discounting and depression are risk factors for cigarette smoking during adolescence. However, very little research has explored associations between these variables in adolescent smokers and non-smokers. Methods: Eighty adolescents were recruited based on depression status (depressed and non-depressed) and smoking status (smokers and non-smokers) to form four groups (n = 20 per group). All participants completed a computerized monetary delay discounting task and a measure of depression. Results: Delay discounting and depression were significantly correlated. Also, smokers (both depressed and non-depressed) and depressed non-smokers all discounted significantly more than non-smokers who were not depressed. Depressed non-smokers and both groups of smokers did not differ in rate of delay discounting. Conclusions: Adolescent non-smokers who are depressed discount similarly to adolescents who smoke and more than non-smokers who are not depressed. Future research should explore the unique versus shared roles of delay discounting and depression as risk factors for smoking during adolescence. © 2013 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Persistently high national rates of cigarette smoking during adolescence (United States Department of Health and Human Services, 2012) highlight the importance of continued efforts to identify risk factors for smoking during this developmental period. Two factors that appear to increase risk of smoking are delay discounting (e.g., Audrain-McGovern et al., 2009a; Reynolds and Fields, 2012) and depression (e.g., Audrain-McGovern et al., 2009b); however, little research has evaluated associations between these variables in adolescent smokers and non-smokers. Delay discounting refers to the subjective devaluation that occurs for delayed outcomes or events as a function of the delay. An extreme tendency to discount value by delay is often considered an aspect of impulsivity. That is, the behavior of individuals who discount excessively by delay may not be adequately regulated
∗ Corresponding author at: Department of Behavioral Science, 105 Medical Behavioral Science Building, University of Kentucky, Lexington, KY 40536-0086, United States. Tel.: +1 859 323 1457; fax: +1 859 323 5350. E-mail address: [email protected] (B. Reynolds). 1 Address: 600 New Jersey Avenue NW, Washington, DC 20001, United States. 2 Address: Department of Behavioral Science, University of Kentucky, Lexington, KY 40536-0086, United States. Tel.: +1 614 216 5153. 3 Address: 376 Merrimac Trail, #311, Williamsburg, VA 23185, United States. 0376-8716/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.drugalcdep.2013.11.014
by delayed outcomes (e.g., the long-term health consequences of smoking) and instead be much more controlled by immediate circumstances and outcomes (e.g., the positive experience of smoking a cigarette). Consistent with this interpretation, addicted populations (including cigarette smokers) often discount more by delay than matched, non-addicted control participants (see Reynolds, 2006, for review). Additionally, delay discounting has been linked with a smoker’s ability to reduce or quit smoking during treatment, with more extreme discounting associated with less successful treatment outcomes (e.g., Krishnan-Sarin et al., 2007; Sheffer et al., 2012). Alternatively, depression is a chronic and multifaceted disorder associated with at-risk behaviors, which affects an individual on biological, cognitive, and emotional levels. Symptoms such as depressed or irritable mood, changes in sleep and energy, and believing one is worthless are examples of the clinical criteria for major depressive disorder (MDD; American Psychological Association, 2002). Prevalence of depression is low from childhood to adolescence; however, up to 9% of teens are diagnosed with MDD and another 18% have a combination of depressive symptoms but do not meet criteria for diagnosis. As in adults, female teens have higher rates of depression than males, and approximately 50% of teens with MDD carry the disorder into adulthood (Chaiton et al., 2013). Depression during adolescence is associated with poor school performance, co-occurring externalizing
S. Imhoff et al. / Drug and Alcohol Dependence 135 (2014) 152–155
Delay Discounting 0.85 0.80
Reverse Coded AUC
behaviors, and higher rates of suicide attempts (Maurizi et al., 2013; McCarty et al., 2013; Pisani et al., 2013). More relevant to the current study, increased depressive symptoms are correlated with smoking initiation and more intense smoking (e.g., Costello et al., 1999; Fleming et al., 2002; Audrain-McGovern et al., 2004). The current study was designed to explore relationships between delay discounting and depression in a cross-sectional sample of depressed and non-depressed adolescent smokers and non-smokers (four groups). Little previous work has considered both delay discounting and symptoms of depression in adolescent smokers. However, Rezvanfard et al. (2010) reported that more dependent smokers tend to have higher levels of depression and discounted most impulsively by delay, suggesting there is a positive association between delay discounting and depression in this population. Based on this finding and previous findings that smokers discount more by delay than non-smokers, we hypothesized that (a) rate of delay discounting and depression scores would be correlated, (b) that both depressed and non-depressed adolescent smokers would discount more than non-depressed non-smokers, and that (c) depressed non-smokers also would discount more by delay than non-depressed non-smokers (based on an anticipated positive association between delay discounting and depression).
153
0.75 0.70 0.65 0.60 0.55 Depressed 0.50
Non-Depressed
0.45
Smoker
Non-Smoker
Smoking Status Fig. 1. Group average reverse coded area under the curve (AUC) values (with ±S.E.M.) for depressed smokers, non-depressed smokers, depressed non-smokers, and non-depressed non-smokers. Larger values along y-axis indicate greater discounting by delay.
2.3. Analyses 2. Method 2.1. Participants A community sample of adolescent smokers and non-smokers participated in this research (N = 80). Potential participants were prescreened by phone using selected items from the Beck Depression Inventory to specifically recruit depressed and non-depressed smokers and non-smokers (n = 20 per group). All participants were recruited from the central Ohio area from local high schools, advertisement posters, and word-of-mouth referrals.
IBM SPSS Statistics (Version 21) was used for all statistical analyses. One-way analyses of variance (ANOVA) were used to compare participant demographic data across the four groups. An exception was ethnicity, a categorical variable, which was analyzed using a Chi Square test. Data from the delay discounting measure were log10 transformed to normalize AUC values, which the transformation did accomplish. A Pearson correlation was run between the DDQ and BDI. Group DDQ data were analyzed using a between-subjects two-way ANOVA. For these analyses, depression and smoking status were the grouping variables and delay discounting was the dependent variable. Additionally, an ANCOVA was used to re-evaluate any significant interaction or main effects while controlling for demographic group differences as covariates.
2.2. Procedure 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. Participants and their parents or legal guardians reviewed and signed assent and consent forms approved by the Research Institute IRB. Participation involved a single laboratory session consisting of demographic and drug-use questionnaires, assessment of IQ (Kaufman and Kaufman, 2004), an assessment of depression, and a laboratory behavioral measure of delay discounting. Participants provided samples of breath (for carbon monoxide) and urine (for cotinine) to verify cigarette smoking status. Depression was assessed using the Beck’s Depression Inventory: Second Edition (BDI-11; Beck et al., 1996). The BDI-11 is a 21 item self-report questionnaire that assesses the symptoms and severity of depression according to the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (American Psychological Association, 2002). According to Beck et al. (1996), total scores of 0–13 reflect normal to minimal depression; scores of 14–19 represent mild depression; scores of 20–28 moderate depression; and scores of 29–63 severe depression. This study used a score of 14 on the BDI-11 as the minimal score for a participant to be classified as depressed. Delay discounting was assessed for all participants with a computerized question based delay discounting task (DDQ; Richards et al., 1999). For this measure, participants were presented choices between $10 available after a specified delay (i.e., 1, 2, 30, 180, 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 $.50) to derive indifference points between the delayed standard and immediate adjusting options for each of the five delays assessed. An indifference point reflected the smallest amount of money an individual chose to receive immediately instead of the delayed standard amount ($10) at the specific delay. All indifference points were evaluated using an algorithm proposed by Johnson and Bickel (2008) to determine if discounting was systematic, and these data were determined to be adequately systematic. Discounting data were then characterized using an area under the curve method (Myerson et al., 2001), with smaller area values indicating greater discounting by delay and greater impulsivity. However, for the current report these scores were reverse coded so that higher scores reflected more impulsive discounting. One of the participants’ choices was randomly selected and honored—resulting in either immediate or delayed money. See Reynolds et al. (2003) for participant instructions for the DDQ. Participants earned between $0 and $10 from the DDQ.
3. Results Participant demographic information is presented in Table 1. As expected, smokers had significantly higher CO and cotinine levels than non-smokers, providing verification of smoking status. Regarding depression, depressed smokers had significantly higher levels of depression than the depressed non-smokers. There were group differences in self-reported drug use, age, and median household income. Smokers self-reported significantly more marijuana and alcohol use than non-smokers, regardless of depression status. There was a significant correlation between the DDQ and BDI of .220 (p = .049). More impulsive discounting by delay was associated with higher depression scores. The two-way ANOVA revealed a significant main effect of smoking status, F(1,76) = 5.574, p = .021; 2 = .068 and also a significant interaction between depression and smoking status, F(1,76) = 4.751, p = .032; 2 = .059. From Fig. 1, the significant interaction is accounted for by the non-depressed non-smokers discounting less than participants in the other three groups, while these other three groups did not differ substantially. The ANCOVA analyses controlling for group differences in age, median household income, and marijuana and alcohol use revealed that the main effect of smoking status was no longer statistically significant while these variables were controlled. However, the interaction effect remained, F(1,69) = 4.736, p = .033; 2 = .065. 4. Discussion There were two primary findings from the current study. First, delay discounting and depression had a modest but statistically significant association. That is, greater discounting by delay was
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S. Imhoff et al. / Drug and Alcohol Dependence 135 (2014) 152–155
Table 1 Participant demographics and baseline information (N = 80).
Age (years; M [SD]) Sex (n female) Race (n; black:white:other) KBIT 2 (IQ Standard Score; M [SD]) Cigarettes (number per day; M [SD])a Carbon monoxide (ppm; M [SD]) Cotinine (ng/ml; M [SD]) Beck Depression Inventory (M [SD]) Annual Household Income ($ Median) Marijuana (M [SD])b Alcohol (M [SD])b
Depressed smokers (n = 20)
Non-depressed smokers (n = 20)
Depressed non-smokers (n = 20)
Non-depressed non-smokers (n = 20)
15.90 (.78)A,B 17 5:13:2
16.40 (.88)B 14 8:11:1
15.30 (1.13)A 19 6:12:2
15.85 (.93)A,B 14 12:6:2
89.85 (13.31)
87.80 (12.61)
91.90 (8.75)
94.30 (11.97)
A
A
B
7.07 (5.48)
8.06 (5.58)
.00 (.00)
.00 (.00)B
8.10 (4.62)A
10.00 (4.70)A
1.95 (2.21)B
1.80 (1.20)B
1047.70 (512.01)A
1346.30 (884.39)A
.68 (2.98)B
1.30 (5.81)B
25.85 (8.01)A
6.20 (3.99)B
20.85 (5.75)C
4.35 (3.17)B
51,694A,B
48,193A
67,609B
59,644.05A,B
2.25 (1.48)A
1.55 (1.15)A
.00 (.00)B
.05 (.22)B
1.65 (1.27)A
1.35 (1.14)A
.47 (.84)B
.40 (.75)B
Note: Means or percentages in the same row that do not share the same superscript differ at p < .05. a Cigarettes per day were calculated using a timeline follow back calendar to determine cigarettes smoked each day during the past 14 days. b Drug use was assessed with the following question: “Thinking about the past six 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.
associated with higher depression scores. This is consistent with an earlier finding that individuals who discount more by delay have elevated depressive symptoms (e.g., Rezvanfard et al., 2010). Second, group level analyses revealed that non-smokers who were depressed discounted similarly to smokers (both depressed and non-depressed) and discounted more than non-depressed nonsmokers. Interestingly, depressed smokers did not discount more than non-depressed smokers or depressed non-smokers, suggesting that even though smoking and depressive symptoms are both linked with greater delay discounting their effects may not be additive. However, there may be characteristics of these data that limited our potential to detect additive effects. For example, Green et al. (1994) found that delay discounting decreases as we age. That is, children and adolescents on average will discount more by delay than adults, which in our sample may have compressed variability and increased likelihood of a ceiling effect. Also, based on scores from the BDI-11, the depressed smokers and non-smokers were on average in the moderately depressed range. Additive effects may have been more likely had more severely depressed participants been recruited. Of primary interest is the finding that depressed non-smokers discounted by delay similarly to smokers. This finding leads to questions about the possible unique or shared effects of delay discounting and depression as risk factors for smoking during adolescence. For example, previous research has demonstrated that delay discounting mediates the relationship between perceived stress and cigarette smoking status among adolescents (Fields et al., 2009). Similarly, delay discounting may mediate the relationship between depression and risk for smoking. Though the current study was not designed to support such mediation analyses, future research might specifically explore this important possibility. This study potentially holds implications for working with depressed youths. For example, the discounting pattern observed among the depressed non-smokers indicates that these adolescents may underestimate long-term consequences and instead be more behaviorally governed by immediate circumstances. In efforts to improve at-risk behaviors associated with depression, such as smoking, poor school performance or externalizing behaviors, it is important to recognize that highlighting the long-term negative consequences of these behaviors may not be very motivating or
effective for generating behavior change in this population. Alternatively, tailoring communications and program content to focus more on immediate consequences (e.g., the effects of smoking on acne and negative peer perceptions) may be more effective for depressed adolescents given their tendency to discount the future. Similarly, these implications may extend to other conditions associated with depression, such as suicidal ideation or anhedonia. However, research exploring relationships between delay discounting and attempted suicide or anhedonia is limited and somewhat inconsistent. For example, suicide attempters who attempt by low-lethality methods appear to discount more by delay than high-lethality attempters (Dombrovski et al., 2011), and one study has shown that college students with higher ratings of anhedonia discounted less impulsively by delay than participants with lower ratings of anhedonia (Lempert and Pizzagalli, 2010). More research is needed to better understand the association between delay discounting and these behaviors and conditions related to depression. This study is not without limitations. For example, the study sample was small and therefore may be limited in terms of broad-reaching generalizability. Also, the depressed sample was predominantly female. However, recruitment was matched on this variable such that the comparison groups did not differ significantly for gender. The extent to which these effects extend to male smokers and non-smokers is still not clear. However, even with these limitations, the current study provides new insight into the relationship between delay discounting and depression among adolescent smokers and non-smokers. Role of funding source Support for this research was provided by research awards from the Social and Behavioral Sciences Department and by an award from the Arts and Sciences Department, The Ohio State University. This research was completed in partial fulfillment of the first author’s Senior Honors Thesis project. Contributors Authors Imhoff and Reynolds designed the study and wrote the protocol. Authors Harris and Weiser managed the literature
S. Imhoff et al. / Drug and Alcohol Dependence 135 (2014) 152–155
searches and summaries of previous related work, and author Weiser contributed to data collection and management. Author Harris undertook the statistical analysis and wrote the first draft of the manuscript. All authors contributed to and have approved the final manuscript. Conflict of interest All authors declare that they have no conflicts of interest. References American Psychiatric Association, 2002. Diagnostic and Statistical Manual of Mental Disorders, 4th ed., text rev. APA, Washington, DC. Audrain-McGovern, J., Rodriguez, D., Epstein, L.H., Cuevas, J., Rodgers, K., Wileyto, E.P., 2009a. Does delay discounting play an etiological role in smoking or is it a consequence of smoking? Drug Alcohol Depend. 103, 99–106. Audrain-McGovern, J., Rodriguez, D., Kassel, J.D., 2009b. Adolescent smoking and depression: evidence for self-medication and peer smoking mediation. Addiction 104, 1743–1756. Audrain-McGovern, J., Lerman, C., Wileyto, E.P., Rodriguez, D., Shields, P.C., 2004. Interacting effects of genetic predisposition and depression on adolescent smoking progression. Am. J. Psychiatry 161, 1224–1230. Beck, A.T., Steer, R.A., Brown, G.K., 1996. Manual for Beck Depression Inventory-II. Psychological Corporation, San Antonio, TX. Chaiton, M., Contreras, G., Brunet, J., Sabiston, C.M., O’Loughlin, E., Low, N., Karp, I., Barnett, T.A., O’Loughlin, J., 2013. Heterogeneity of depressive symptom trajectories through adolescence: predicting outcomes in young adulthood. J. Can. Acad. Child Adolesc. Psychiatry 22, 96–105. Costello, E.J., Erkanli, A., Federman, E., Angold, A., 1999. Development of psychiatric comorbidity with substance abuse in adolescents: effects of timing and sex. J. Clin. Child Psychol. 23, 298–311. Dombrovski, A.Y., Szanto, K., Siegle, G.J., Wallace, M.L., Forman, S.D., Sahakian, B., Reynolds 3rd, C.F., Clark, L., 2011. Lethal forethought: delayed reward discounting differentiates high- and low-lethality suicide attempts in old age. Biol. Psychiatry 70, 138–144. Fields, S., Leraas, K., Collins, C., Reynolds, B., 2009. Delay discounting as mediator of the relationship between perceived stress and cigarette smoking status in adolescents. Behav. Pharmacol. 20, 406–455. Fleming, C.B., Kim, H., Harachi, T.W., Catalano, R.F., 2002. Family processes for children in early elementary school as predictors of smoking initiation. J. Adolesc. Health 30, 184–189.
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Green, L., Fry, A.F., Myerson, J., 1994. Discounting of delayed rewards: a life-span comparison. Psychol. Sci. 5, 33–36. Johnson, M.W., Bickel, W.K., 2008. An algorithm for identifying nonsystematic delaydiscounting data. Exp. Clin. Psychopharmacol. 16, 264–274. Kaufman, A.S., Kaufman, N.L., 2004. Kaufman Brief Intelligence Test-Second Edition. American Guidance Services, Circle Pines, MN. Krishnan-Sarin, S., Reynolds, B., Duhig, A.M., Smith, A., Liss, T., McFetridge, A., Cavallo, D.A., Carroll, K.M., Potenza, M.N., 2007. Behavioral impulsivity predicts treatment outcome in a smoking cessation program for adolescent smokers. Drug Alcohol Depend. 88, 79–82. Lempert, K.M., Pizzagalli, D.A., 2010. Delay discounting and future-directed thinking in anhedonic individuals. J. Behav. Ther. Exp. Psychiatry 41, 258–264. Maurizi, L.K., Grogan-Kaylor, A., Granillo, M.T., Delva, J., 2013. The role of social relationships in the association between adolescent’s depressive symptoms and academic achievement. Child Youth Serv. Rev. 35, 618–625. McCarty, C.A., Wymbs, B.T., Mason, W.A., Kin, K.M., McCauley, E., Baer, J., Stoep, A.V., 2013. Early adolescent growth in depression and conduct problem symptoms as predictors of later substance use impairment. J. Abnorm. Child Psychol. 41, 1041–1051. Myerson, J., Green, L., Warusawitharana, M., 2001. Area under the curve as a measure of discounting. J. Exp. Anal. Behav. 76, 235–243. Pisani, A.R., Wyman, P.A., Petrova, M., Schmeelk-Cone, K., Goldston, D.B., Xia, Y., Gould, M.S., 2013. Emotion regulation difficulties, youth-adult relationships, and suicide attempts among high school students. J. Youth Adolesc. 42, 807–820. Reynolds, B., Karraker, K., Horn, K., Richards, J.B., 2003. Delay of probability discounting as related to different stages of adolescent smoking and non-smoking. Behav. Processes 64, 333–344. Reynolds, B., 2006. A review of delay-discounting research with humans: relations to drug use and gambling. Behav. Pharmacol. 17, 651–667. Reynolds, B., Fields, S., 2012. Delay discounting by adolescents experimenting with cigarette smoking. Addiction 107, 417–424. Richards, J.B., Zhang, L., Mitchell, S., de Wit, H., 1999. Delay and probability discounting in a model of impulsive behavior: effect of alcohol. J. Exp. Anal. Behav. 71, 121–143. Rezvanfard, M., Ekhtiari, H., Azarakhsh, M., Djavid, G.E., Kaviani, H., 2010. Psychological and behavioral traits in smokers and their relationship with nicotine dependence level. Arch. Iran. Med. 13, 395–405. Sheffer, C., MacKillop, J., McGeary, J., Landes, R., Carter, L., Yi, R., Jones, B., Christensen, D., Stitzer, M., Jackson, L., Bickel, W., 2012. Delay discounting, locus of control, and cognitive impulsiveness independently predict tobacco dependence treatment outcomes in a highly dependent, lower socioeconomic group of smokers. Am. J. Addict. 21, 221–232. United States Department of Health and Human Services, 2012. Preventing Tobacco Use Among Youth and Young Adults: A Report of the Surgeon GeneralCenters for Disease Control and Prevention. National Center for Chronic Disease Prevention and Health Promotion, Office on Smoking and Health, Atlanta, GA.
Contents lists available at ScienceDirect
Drug and Alcohol Dependence journal homepage: www.elsevier.com/locate/drugalcdep
Short communication
Delay discounting by depressed and non-depressed adolescent smokers and non-smokers Sarah Imhoff a,1 , Millie Harris b,2 , Jason Weiser a,3 , Brady Reynolds b,∗ a b
Department of Psychology, The Ohio State University, United States Department of Behavioral Science, University of Kentucky, United States
a r t i c l e
i n f o
Article history: Received 7 August 2013 Received in revised form 16 September 2013 Accepted 9 November 2013 Available online 25 November 2013 Keywords: Delay discounting Depression Smoking Adolescents
a b s t r a c t Background: Both delay discounting and depression are risk factors for cigarette smoking during adolescence. However, very little research has explored associations between these variables in adolescent smokers and non-smokers. Methods: Eighty adolescents were recruited based on depression status (depressed and non-depressed) and smoking status (smokers and non-smokers) to form four groups (n = 20 per group). All participants completed a computerized monetary delay discounting task and a measure of depression. Results: Delay discounting and depression were significantly correlated. Also, smokers (both depressed and non-depressed) and depressed non-smokers all discounted significantly more than non-smokers who were not depressed. Depressed non-smokers and both groups of smokers did not differ in rate of delay discounting. Conclusions: Adolescent non-smokers who are depressed discount similarly to adolescents who smoke and more than non-smokers who are not depressed. Future research should explore the unique versus shared roles of delay discounting and depression as risk factors for smoking during adolescence. © 2013 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Persistently high national rates of cigarette smoking during adolescence (United States Department of Health and Human Services, 2012) highlight the importance of continued efforts to identify risk factors for smoking during this developmental period. Two factors that appear to increase risk of smoking are delay discounting (e.g., Audrain-McGovern et al., 2009a; Reynolds and Fields, 2012) and depression (e.g., Audrain-McGovern et al., 2009b); however, little research has evaluated associations between these variables in adolescent smokers and non-smokers. Delay discounting refers to the subjective devaluation that occurs for delayed outcomes or events as a function of the delay. An extreme tendency to discount value by delay is often considered an aspect of impulsivity. That is, the behavior of individuals who discount excessively by delay may not be adequately regulated
∗ Corresponding author at: Department of Behavioral Science, 105 Medical Behavioral Science Building, University of Kentucky, Lexington, KY 40536-0086, United States. Tel.: +1 859 323 1457; fax: +1 859 323 5350. E-mail address: [email protected] (B. Reynolds). 1 Address: 600 New Jersey Avenue NW, Washington, DC 20001, United States. 2 Address: Department of Behavioral Science, University of Kentucky, Lexington, KY 40536-0086, United States. Tel.: +1 614 216 5153. 3 Address: 376 Merrimac Trail, #311, Williamsburg, VA 23185, United States. 0376-8716/$ – see front matter © 2013 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.drugalcdep.2013.11.014
by delayed outcomes (e.g., the long-term health consequences of smoking) and instead be much more controlled by immediate circumstances and outcomes (e.g., the positive experience of smoking a cigarette). Consistent with this interpretation, addicted populations (including cigarette smokers) often discount more by delay than matched, non-addicted control participants (see Reynolds, 2006, for review). Additionally, delay discounting has been linked with a smoker’s ability to reduce or quit smoking during treatment, with more extreme discounting associated with less successful treatment outcomes (e.g., Krishnan-Sarin et al., 2007; Sheffer et al., 2012). Alternatively, depression is a chronic and multifaceted disorder associated with at-risk behaviors, which affects an individual on biological, cognitive, and emotional levels. Symptoms such as depressed or irritable mood, changes in sleep and energy, and believing one is worthless are examples of the clinical criteria for major depressive disorder (MDD; American Psychological Association, 2002). Prevalence of depression is low from childhood to adolescence; however, up to 9% of teens are diagnosed with MDD and another 18% have a combination of depressive symptoms but do not meet criteria for diagnosis. As in adults, female teens have higher rates of depression than males, and approximately 50% of teens with MDD carry the disorder into adulthood (Chaiton et al., 2013). Depression during adolescence is associated with poor school performance, co-occurring externalizing
S. Imhoff et al. / Drug and Alcohol Dependence 135 (2014) 152–155
Delay Discounting 0.85 0.80
Reverse Coded AUC
behaviors, and higher rates of suicide attempts (Maurizi et al., 2013; McCarty et al., 2013; Pisani et al., 2013). More relevant to the current study, increased depressive symptoms are correlated with smoking initiation and more intense smoking (e.g., Costello et al., 1999; Fleming et al., 2002; Audrain-McGovern et al., 2004). The current study was designed to explore relationships between delay discounting and depression in a cross-sectional sample of depressed and non-depressed adolescent smokers and non-smokers (four groups). Little previous work has considered both delay discounting and symptoms of depression in adolescent smokers. However, Rezvanfard et al. (2010) reported that more dependent smokers tend to have higher levels of depression and discounted most impulsively by delay, suggesting there is a positive association between delay discounting and depression in this population. Based on this finding and previous findings that smokers discount more by delay than non-smokers, we hypothesized that (a) rate of delay discounting and depression scores would be correlated, (b) that both depressed and non-depressed adolescent smokers would discount more than non-depressed non-smokers, and that (c) depressed non-smokers also would discount more by delay than non-depressed non-smokers (based on an anticipated positive association between delay discounting and depression).
153
0.75 0.70 0.65 0.60 0.55 Depressed 0.50
Non-Depressed
0.45
Smoker
Non-Smoker
Smoking Status Fig. 1. Group average reverse coded area under the curve (AUC) values (with ±S.E.M.) for depressed smokers, non-depressed smokers, depressed non-smokers, and non-depressed non-smokers. Larger values along y-axis indicate greater discounting by delay.
2.3. Analyses 2. Method 2.1. Participants A community sample of adolescent smokers and non-smokers participated in this research (N = 80). Potential participants were prescreened by phone using selected items from the Beck Depression Inventory to specifically recruit depressed and non-depressed smokers and non-smokers (n = 20 per group). All participants were recruited from the central Ohio area from local high schools, advertisement posters, and word-of-mouth referrals.
IBM SPSS Statistics (Version 21) was used for all statistical analyses. One-way analyses of variance (ANOVA) were used to compare participant demographic data across the four groups. An exception was ethnicity, a categorical variable, which was analyzed using a Chi Square test. Data from the delay discounting measure were log10 transformed to normalize AUC values, which the transformation did accomplish. A Pearson correlation was run between the DDQ and BDI. Group DDQ data were analyzed using a between-subjects two-way ANOVA. For these analyses, depression and smoking status were the grouping variables and delay discounting was the dependent variable. Additionally, an ANCOVA was used to re-evaluate any significant interaction or main effects while controlling for demographic group differences as covariates.
2.2. Procedure 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. Participants and their parents or legal guardians reviewed and signed assent and consent forms approved by the Research Institute IRB. Participation involved a single laboratory session consisting of demographic and drug-use questionnaires, assessment of IQ (Kaufman and Kaufman, 2004), an assessment of depression, and a laboratory behavioral measure of delay discounting. Participants provided samples of breath (for carbon monoxide) and urine (for cotinine) to verify cigarette smoking status. Depression was assessed using the Beck’s Depression Inventory: Second Edition (BDI-11; Beck et al., 1996). The BDI-11 is a 21 item self-report questionnaire that assesses the symptoms and severity of depression according to the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (American Psychological Association, 2002). According to Beck et al. (1996), total scores of 0–13 reflect normal to minimal depression; scores of 14–19 represent mild depression; scores of 20–28 moderate depression; and scores of 29–63 severe depression. This study used a score of 14 on the BDI-11 as the minimal score for a participant to be classified as depressed. Delay discounting was assessed for all participants with a computerized question based delay discounting task (DDQ; Richards et al., 1999). For this measure, participants were presented choices between $10 available after a specified delay (i.e., 1, 2, 30, 180, 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 $.50) to derive indifference points between the delayed standard and immediate adjusting options for each of the five delays assessed. An indifference point reflected the smallest amount of money an individual chose to receive immediately instead of the delayed standard amount ($10) at the specific delay. All indifference points were evaluated using an algorithm proposed by Johnson and Bickel (2008) to determine if discounting was systematic, and these data were determined to be adequately systematic. Discounting data were then characterized using an area under the curve method (Myerson et al., 2001), with smaller area values indicating greater discounting by delay and greater impulsivity. However, for the current report these scores were reverse coded so that higher scores reflected more impulsive discounting. One of the participants’ choices was randomly selected and honored—resulting in either immediate or delayed money. See Reynolds et al. (2003) for participant instructions for the DDQ. Participants earned between $0 and $10 from the DDQ.
3. Results Participant demographic information is presented in Table 1. As expected, smokers had significantly higher CO and cotinine levels than non-smokers, providing verification of smoking status. Regarding depression, depressed smokers had significantly higher levels of depression than the depressed non-smokers. There were group differences in self-reported drug use, age, and median household income. Smokers self-reported significantly more marijuana and alcohol use than non-smokers, regardless of depression status. There was a significant correlation between the DDQ and BDI of .220 (p = .049). More impulsive discounting by delay was associated with higher depression scores. The two-way ANOVA revealed a significant main effect of smoking status, F(1,76) = 5.574, p = .021; 2 = .068 and also a significant interaction between depression and smoking status, F(1,76) = 4.751, p = .032; 2 = .059. From Fig. 1, the significant interaction is accounted for by the non-depressed non-smokers discounting less than participants in the other three groups, while these other three groups did not differ substantially. The ANCOVA analyses controlling for group differences in age, median household income, and marijuana and alcohol use revealed that the main effect of smoking status was no longer statistically significant while these variables were controlled. However, the interaction effect remained, F(1,69) = 4.736, p = .033; 2 = .065. 4. Discussion There were two primary findings from the current study. First, delay discounting and depression had a modest but statistically significant association. That is, greater discounting by delay was
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Table 1 Participant demographics and baseline information (N = 80).
Age (years; M [SD]) Sex (n female) Race (n; black:white:other) KBIT 2 (IQ Standard Score; M [SD]) Cigarettes (number per day; M [SD])a Carbon monoxide (ppm; M [SD]) Cotinine (ng/ml; M [SD]) Beck Depression Inventory (M [SD]) Annual Household Income ($ Median) Marijuana (M [SD])b Alcohol (M [SD])b
Depressed smokers (n = 20)
Non-depressed smokers (n = 20)
Depressed non-smokers (n = 20)
Non-depressed non-smokers (n = 20)
15.90 (.78)A,B 17 5:13:2
16.40 (.88)B 14 8:11:1
15.30 (1.13)A 19 6:12:2
15.85 (.93)A,B 14 12:6:2
89.85 (13.31)
87.80 (12.61)
91.90 (8.75)
94.30 (11.97)
A
A
B
7.07 (5.48)
8.06 (5.58)
.00 (.00)
.00 (.00)B
8.10 (4.62)A
10.00 (4.70)A
1.95 (2.21)B
1.80 (1.20)B
1047.70 (512.01)A
1346.30 (884.39)A
.68 (2.98)B
1.30 (5.81)B
25.85 (8.01)A
6.20 (3.99)B
20.85 (5.75)C
4.35 (3.17)B
51,694A,B
48,193A
67,609B
59,644.05A,B
2.25 (1.48)A
1.55 (1.15)A
.00 (.00)B
.05 (.22)B
1.65 (1.27)A
1.35 (1.14)A
.47 (.84)B
.40 (.75)B
Note: Means or percentages in the same row that do not share the same superscript differ at p < .05. a Cigarettes per day were calculated using a timeline follow back calendar to determine cigarettes smoked each day during the past 14 days. b Drug use was assessed with the following question: “Thinking about the past six 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.
associated with higher depression scores. This is consistent with an earlier finding that individuals who discount more by delay have elevated depressive symptoms (e.g., Rezvanfard et al., 2010). Second, group level analyses revealed that non-smokers who were depressed discounted similarly to smokers (both depressed and non-depressed) and discounted more than non-depressed nonsmokers. Interestingly, depressed smokers did not discount more than non-depressed smokers or depressed non-smokers, suggesting that even though smoking and depressive symptoms are both linked with greater delay discounting their effects may not be additive. However, there may be characteristics of these data that limited our potential to detect additive effects. For example, Green et al. (1994) found that delay discounting decreases as we age. That is, children and adolescents on average will discount more by delay than adults, which in our sample may have compressed variability and increased likelihood of a ceiling effect. Also, based on scores from the BDI-11, the depressed smokers and non-smokers were on average in the moderately depressed range. Additive effects may have been more likely had more severely depressed participants been recruited. Of primary interest is the finding that depressed non-smokers discounted by delay similarly to smokers. This finding leads to questions about the possible unique or shared effects of delay discounting and depression as risk factors for smoking during adolescence. For example, previous research has demonstrated that delay discounting mediates the relationship between perceived stress and cigarette smoking status among adolescents (Fields et al., 2009). Similarly, delay discounting may mediate the relationship between depression and risk for smoking. Though the current study was not designed to support such mediation analyses, future research might specifically explore this important possibility. This study potentially holds implications for working with depressed youths. For example, the discounting pattern observed among the depressed non-smokers indicates that these adolescents may underestimate long-term consequences and instead be more behaviorally governed by immediate circumstances. In efforts to improve at-risk behaviors associated with depression, such as smoking, poor school performance or externalizing behaviors, it is important to recognize that highlighting the long-term negative consequences of these behaviors may not be very motivating or
effective for generating behavior change in this population. Alternatively, tailoring communications and program content to focus more on immediate consequences (e.g., the effects of smoking on acne and negative peer perceptions) may be more effective for depressed adolescents given their tendency to discount the future. Similarly, these implications may extend to other conditions associated with depression, such as suicidal ideation or anhedonia. However, research exploring relationships between delay discounting and attempted suicide or anhedonia is limited and somewhat inconsistent. For example, suicide attempters who attempt by low-lethality methods appear to discount more by delay than high-lethality attempters (Dombrovski et al., 2011), and one study has shown that college students with higher ratings of anhedonia discounted less impulsively by delay than participants with lower ratings of anhedonia (Lempert and Pizzagalli, 2010). More research is needed to better understand the association between delay discounting and these behaviors and conditions related to depression. This study is not without limitations. For example, the study sample was small and therefore may be limited in terms of broad-reaching generalizability. Also, the depressed sample was predominantly female. However, recruitment was matched on this variable such that the comparison groups did not differ significantly for gender. The extent to which these effects extend to male smokers and non-smokers is still not clear. However, even with these limitations, the current study provides new insight into the relationship between delay discounting and depression among adolescent smokers and non-smokers. Role of funding source Support for this research was provided by research awards from the Social and Behavioral Sciences Department and by an award from the Arts and Sciences Department, The Ohio State University. This research was completed in partial fulfillment of the first author’s Senior Honors Thesis project. Contributors Authors Imhoff and Reynolds designed the study and wrote the protocol. Authors Harris and Weiser managed the literature
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