Is all risk bad? Young adult cigarette smokers fail to take adaptive risk in a laboratory decision-making test

Department of Psychiatry and Biobehavioral Sciences and Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA.
Psychopharmacology (Impact Factor: 3.88). 02/2011; 215(4):801-11. DOI: 10.1007/s00213-011-2182-y
Source: PubMed


Cigarette smoking has been linked to real-world risky behavior, but this association has been based largely on retrospective self-reports. Limitations of self-report data can be avoided by using laboratory, performance-based measures, such as the Balloon Analogue Risk Task (BART; Lejuez et al., J Exp Psychol Appl 8:75-84, 2002). Initial studies have suggested that smokers display greater risk-taking on this task than nonsmokers, but these studies did not account for drug abuse and psychiatric comorbidities, which are commonplace among smokers.
We sought to examine the performance of smokers and nonsmokers on the BART after excluding drug abuse and psychiatric comorbidities.
We conducted a study of late adolescent/young adult (age 18 to 21) smokers (n = 26) and nonsmokers (n = 38) performing the BART and excluded individuals with positive drug or alcohol toxicology screens, substance abuse or dependence diagnoses, and/or current psychiatric conditions.
Contrary to previous findings, smokers did not display greater risk-taking on the BART than nonsmokers. In fact, when performance was examined trial-by-trial, the nonsmokers displayed progressively greater pumping relative to smokers over time (p < .001), earning them a nonsignificantly greater amount of money than the smokers. Controlling for smoking status, additional analyses revealed that pumping on the BART was positively associated with years of education, nonverbal IQ, and employment.
The results suggest that in young adults, smoking may be associated with a failure to take risks in situations where risk-taking is adaptive.

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    • "Gordon (2007) found BART to be uncorrelated with attitudes towards risky driving. Dean et al. (2011) found that smokers did not display greater risk taking on the BART than nonsmokers . Klassen (2010) found that the BART was unrelated to any of the alcohol consumption measures examined. "
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    ABSTRACT: We compare four different risk-taking propensity measures on their ability to describe and to predict actual risky behavior in the domain of health. The risk-taking propensity measures we compare are: (1) a general measure of risk-taking propensity derived from a one-item survey question (Dohmen et al., 2011), (2) a risk aversion index calculated from a set of incentivized monetary gambles (Holt & Laury, 2002), (3) a measure of risk taking derived from an incentive compatible behavioral task-the Balloon Analog Risk Task (Lejuez et al., 2002), and (4) a composite score of risk-taking likelihood in the health domain from the Domain-Specific Risk Taking (DOSPERT) scale (Weber et al., 2002). Study participants are 351 clients of health centers around Witbank, South Africa. Our findings suggest that the one-item general measure is the best predictor of risky health behavior in our population, predicting two out of four behaviors at the 5% level and the remaining two behaviors at the 10% level. The DOSPERT score in the health domain performs well, predicting one out of four behaviors at the 1% significance level and two out of four behaviors at the 10% level, but only if the DOSPERT instrument contains a hypothetical risk-taking item that is similar to the actual risky behavior being predicted. Incentivized monetary gambles and the behavioral task were unrelated to actual health behaviors; they were unable to predict any of the risky health behaviors at the 10% level. We provide evidence that this is not because the participants had trouble understanding the monetary trade-off questions or performed poorly in the behavioral task. We conclude by urging researchers to further test the usefulness of the one-item general measure, both in explaining health related risk-taking behavior and in other contexts.
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    • "This finding may be dependent on the current sample, as the Fernie and colleagues (2010) study sampled from a population of social drinkers in contrast to the problem drinkers recruited in the present study. However, a similar negative trend was observed between BART performance and risky behaviors in a sample of young adult cigarette smokers, where greater pumping was found to relate to positive traits (e.g., nonsmoking, employment, years of education, and higher IQ; Dean et al., 2011). . The negative relationship observed in the current model, as well as the findings by Dean and colleagues (2011), could potentially be a byproduct of the task, as the range of balloon pumps in the current sample was restricted, resulting in the higher pump values being associated with better outcomes on the task. "
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    ABSTRACT: Higher levels of impulsivity have been implicated in the development of alcohol use disorders. Recent findings suggest that impulsivity is not a unitary construct, highlighted by the diverse ways in which the various measures of impulsivity relate to alcohol use outcomes. This study simultaneously tested the following dimensions of impulsivity as determinants of alcohol use and alcohol problems: risky decision making, self-reported risk-attitudes, response inhibition, and impulsive decision making. Participants were a community sample of nontreatment seeking problem drinkers (n = 158). Structural equation modeling (SEM) analyses employed behavioral measures of impulsive decision making (delay discounting task [DDT]), response inhibition (stop signal task [SST]), and risky decision making (Balloon Analogue Risk Task [BART]), and a self-report measure of risk-attitudes (domain-specific risk-attitude scale [DOSPERT]), as predictors of alcohol use and of alcohol-related problems in this sample. The model fits well, accounting for 38% of the variance in alcohol problems, and identified 2 impulsivity dimensions that significantly loaded onto alcohol outcomes: (i) impulsive decision making, indexed by the DDT; and (ii) risky decision making, measured by the BART. The impulsive decision-making dimension of impulsivity, indexed by the DDT, was the strongest predictor of alcohol use and alcohol pathology in this sample of problem drinkers. Unexpectedly, a negative relationship was found between risky decision making and alcohol problems. The results highlight the importance of considering the distinct facets of impulsivity to elucidate their individual and combined effects on alcohol use initiation, escalation, and dependence.
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    ABSTRACT: The relationship between risk-taking behavior and alcohol use disorder (AUD) symptoms is poorly understood. This study employed a modified version of a behavioral measure of risk-taking, the Balloon Analogue Risk Task (BART), to examine its relationship to alcohol use and related symptoms in a community sample of individuals with or at risk for AUD. A total of 158 (71.9% male) participants completed a testing battery that included the BART, a structured diagnostic interview for AUD, and measures of alcohol use and related problems. Estimates of IQ and working memory were assessed as covariates. Results indicated that the relationship between risk-taking propensity, as assessed by the BART, and alcohol problems was significant and negative. Individuals with higher symptom count made fewer pumps per trial on the BART, indicating less risk-taking. It is important to note that this relationship was attenuated when controlling for estimated IQ and working memory span. Further examination demonstrated that IQ and age mediated the relationship between risk-taking propensity and symptom count. The main negative relationship observed between risk-taking on the BART and alcohol use and AUD symptomatology in this sample stands in contrast to the positive relationships observed in adolescent and nonclinical samples. Together, these findings highlight the need to consider development and the course of addiction to fully elucidate the effects of risky-decision making on AUD liability. Furthermore, our results demonstrate the importance of inclusion of neurocognitive covariates (IQ), as well as demographic variables (age) when using this task.
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