Using the lab to understand adolescent risk taking
Carl Lejuez received his PhD in 2000 from West Virginia University. After completing a clinical internship at Brown University’s Brown Clinical Psychology Training Consortium, he joined the Clinical Psychology Program at the University of Maryland in 2001. His research is translational in nature using laboratory methods to understand real world clinical phenomena and to develop novel assessment and treatment strategies. His research spans the clinical domains of addictions, personality pathology, and mood disorders, and he is most interested in the common processes across these conditions. He is the founding editor of the APA journal Personality Disorders: Theory, Research, and Treatment.
In their classic book, Jessor and Jessor (1977) defined risk taking as engagement in “behavior that is socially defined as a problem, a source of concern, or as undesirable by the norms of conventional society and the institutions of adult authority, and its occurrence usually elicits some kind of social control response” (p.33). Definitions of risk taking also have focused on the balancing of potential for harm or danger to the individual with potential achievement or reward (Byrnes, Miller, and Schafer, 1999; Leigh, 1999). Thus in addition to a focus on potential negative consequences, this approach allows for a focus on potential gains. Such gains may take the form of positive reinforcement as well as negative reinforcement (such as alleviation of negative affect or avoidance of external aversive stimuli). This approach can also capture the opportunity costs associated with an unwillingness to take risks. Research on risk taking has encompassed a variety of behaviors including alcohol consumption, tobacco use, risky sexual activity, dangerous driving, interpersonal aggression, and delinquent behaviors (Boyer, 2006). It is broadly acknowledged that many of these types of risk-taking behaviors emerge, increase, and eventually peak in adolescence (e.g., Finer & Henshaw, 2006; Windle et al., 2008) and that the factors that underlie their development must be understood in order to develop effective intervention or prevention programs.
Large-scale prevention programs aimed at providing accurate information regarding the negative consequences of risk behavior have led to a greater awareness of such consequences but have had relatively limited success in preventing or reducing engagement in risk taking behaviors among adolescents (e.g., Malow, Kershaw, Sipsma, Rosenberg, & Devieux, 2007). For example, a review of universal prevention strategies for alcohol misuse in young people (i.e., programs provided to all adolescents; Foxcroft, Ireland, Lister-Sharp, Lowe, & Breen, 2002) concluded that there is little support for the efficacy of drug education programs. In contrast to these nomothetic, one-size-fits-all programs, recent individualized, skill-based prevention programs have proven considerably more effective in reducing risk taking behaviors (Ingram, Flannery, Elkavich, & Rotheram-Borus, 2008). Of particular interest are approaches aimed at addressing the particular personality characteristics that make some youth vulnerable to risk behavior ( Conrod, Castellanos, & Mackie; 2008; Conrod, Stewart, Comeau, & Maclean, 2006; Conrod, Stewart, Pihl, Cote, Fontaine, & Dongier, 2000 ). While these programs show promise, there is a recognized need for continued investigation of the factors that increase the likelihood of health risk behavior engagement and apply this work to the development of tailored interventions. Towards this end, there is great promise in the development of behavioral measures that allow for the evaluation of the processes underlying risk behavior across individuals.
We have developed the Balloon Analogue Risk Task (BART; Lejuez et al., 2002) which is a computerized measure of risk taking propensity. In this task, the participant is presented with a display of a small balloon and asked to pump the balloon by clicking a button on the screen. With each click, the balloon inflates a small amount and actual money is added to the participant’s temporary winnings. At any point, the participant has the option to press a button labeled “Collect $$$,” which deposits the amount in temporary winnings to the bank (i.e., it can no longer be lost) and ends the trial, at which point a new trial begins. However, each balloon is programmed to pop somewhere between 1 and 128 pumps, with an average breakpoint of 64 pumps. If the participant fails to press “Collect $$$” before the balloon pops, all earnings for that balloon are lost and the next balloon is presented. Risk-taking is defined as the average number of pumps on un-popped balloons), with higher scores indicating greater risk-taking. In the original version of the task each pump was worth $.05 and there were 30 total balloons. Participants were given this information, but were not given any information about the breakpoints, which allowed for the examination of participants’ initial response to the task and changes as they experienced the contingencies related to payout collections and balloon explosions. More recent studies have examined the effects of differing payouts per pump and the provision of information about the contingencies of the task in order to understand how these aspects of the task affect risk behavior (e.g., Bornovalova et al., 2009; Pleskac, Wallsten, Wang, & Lejuez, 2008).
Beginning with a young adult sample (ages 18-25), Lejuez et al., (2002) sought to evaluate the psychometric properties of the task, including reliability, as well as its link to real world risk behavior engagement. Results indicated that the BART evidenced sound psychometric properties, and riskiness on the BART was correlated with scores on measures of sensation seeking, impulsivity, and deficiencies in behavioral constraint. Also, riskiness on the BART was correlated with the self-reported occurrence of addictive, health, and safety risk behaviors, with the task accounting for variance in these behaviors beyond that accounted for by demographics and self-report measures of risk-related constructs. These results provided the first evidence that the BART may be a useful tool in the assessment of risk taking propensity. Subsequent research with adults has provided further evidence for a correlation between BART scores and substance use in community and clinical samples (Bornovalova et al., 2005; Lejuez et al., 2003; Pleskac et al., 2008), and between BART scores and risky sexual behavior (Lejuez, Simmons, Aklin, Daughters & Dvir, 2004).
Following this work, the BART was extended to middle adolescents (ages 14-17). The first series of studies indicated that riskiness on the BART was related to a variety of real world risk behaviors including substance use, gambling, delinquency behaviors, and risky sexual behavior (Aklin, Lejuez, Zvolensky, Kahler, & Gwadz, 2005; Lejuez et al., 2005; Lejuez et al., 2007). A second study in this developmental period compared 20 adolescent patients in a program treating conduct disorder and substance use disorder and 20 highly matched community controls ( Crowley , Raymond, Mikulich-Gilbertson, Thompson, & Lejuez, 2005). All were substance free for at least seven days and underwent substance-use, psychological, and social assessments. Data indicated higher BART scores for the patient group, with group differences stable throughout engagement in the task. These data suggest the utility of the task for assessing risk taking in both community and clinical samples.
Although the BART is useful for identifying older adolescents and young adults who are already engaging in risk behaviors, questions remain concerning its utility for younger youth -- both for identifying those currently engaging in risk behaviors and for predicting future risk behaviors. My colleagues and I are conducting a five-year longitudinal study funded by the National Institute on Drug Abuse studying emerging adolescents starting at ages 10-12. Initial data indicate that riskiness on the BART is related to currently occurring risk behavior (MacPherson, Reynolds, et al., in press). Moreover, across the first three years of assessment, BART scores were correlated with the specific risk behavior of alcohol use, with increases in BART scores each year associated with greater likelihood of alcohol use at subsequent assessments, controlling for other key risk-related variables including sensation seeking (MacPherson, Magidson, et al., in press ). Thus, early evidence suggests that changes in BART scores over time correspond with changes in alcohol use over time, indicating that behavior on the task is not static and that the changes are important indicators of risk behavior changes. We are currently exploring prospective relationships to determine the extent to which the BART shows utility for prospective prediction. Results from this work could have important implications for identifying youth with a vulnerability to risk taking before such behavior becomes habitual and changes are harder to engender.
Future Directions and Implications for Research, Intervention, and Policy
In moving forward using the BART as a measure of risk taking propensity, it becomes important to progress from purely descriptive studies to studies that also improve our understanding of the processes underlying the development of risk taking behavior. Specifically, teasing apart the relative contributions of the reward and punishment aspects of the task may have important implications for intervention. For example, a child who is overly focused on potential gain would pump the balloon up to levels of high risk for very different reasons than a child driven by insensitivity to the punishment associated with exploded balloons. Although not targeted to this specific question, recent work using EEG (Fein & Chang, 2008) and fMRI (Rao, Korczykowski, Pluta, Hoang, & Detre, 2008) suggests potential approaches for determining the differing neural mechanisms associated with reward and punishment. As a complement to neurobehavioral assessment, Wallsten, Pleskac, & Lejuez (2005) and Pleskac (2008) developed a set of mathematical models to understand the cognitive processes underlying learning and sequential choice in the BART. These models include a single parameter that indexes the relative strength of risk seeking to risk aversion, as well as two other parameters related to subjective probability and to choice sensitivity. Further work with such models may ultimately lead to a precise characterization of the processes underlying risky decisions on the task and in real world settings.
In addition to enabling progress in understanding fundamental risk processes, the BART may also be used to conduct controlled laboratory studies for understanding the role of emotional and contextual factors in risk behavior. In a recent study (Lighthall, Mather, & Gorlick, 2009), participants played the BART fifteen minutes after completing a stress challenge or control task. Stress increased risk taking among men but decreased it among women, with the authors positing evolutionary principles that may explain these results. My colleagues and I have recently collected data, from a sample of low income drug users in residential treatment, which closely replicated these results. Additionally, current studies in our laboratory are considering the impacts of other contextual variables on risk taking, including effects of the ways in which risk information is framed and effects of peer presence and encouragement. Knowing how risk taking may differ across different environments and in different emotional states also will be crucial for targeting prevention programs with these factors in mind.
Current empirical support suggests that the BART may be a useful tool for assessing risk vulnerabilities and for understanding risk processes that may underlie those vulnerabilities. Descriptive studies have shown strong concurrent relationships between BART scores and real world risk behavior across a range of samples from early adolescence through adulthood. Current work is exploring long-term prediction of risk behavior as well as examining contextual changes and neurobehavioral responses in order to understand how risk processes develop and change over time and how they affect real world risk behavior. This understanding may be used to inform individualized skill based prevention programs by identifying at-risk youth and the particular risk vulnerabilities that would be most effectively targeted.
Aklin, W.M., Lejuez, C.W., Zvolensky, M.J., Kahler, C.W., & Gwadz, M. (2005). Evaluation of behavioral measures of risk taking propensity with inner city adolescents. Behavior Research and Therapy, 43, 215-228.
Bornovalova, M., Cashman-Rolls, A, O'Donnell, J., Ettinger, K., Richards, J., deWit, H., & Lejuez, C. (2009). Risk taking differences on a behavioral task as a function of potential reward/loss magnitude and individual differences in impulsivity and sensation seeking.Pharmacology, Biochemistry, and Behavior, 93(3), 258-262.
Bornovalova, M., Daughters, S., Hernandez, G., Richards, J., & Lejuez, C. (2005). Differences in impulsivity and risk-taking propensity between primary users of crack cocaine and primary users of heroin in a residential substance-use program. Experimental and Clinical Psychopharmacology, 13(4), 311-318.
Boyer, T. (2006). The development of risk-taking: A multi-perspective review. Developmental Review, 26(3), 291-345.
Byrnes, J., Miller, D., & Schafer, W. (1999). Gender differences in risk taking: A meta-analysis. Psychological Bulletin, 125(3), 367-383.
Conrod, P., Castellanos, N., & Mackie, C. (2008). Personality-targeted interventions delay the growth of adolescent drinking and binge drinking. Journal of Child Psychology and Psychiatry, 49(2), 181-190.
Conrod, P.J, Stewart, S.H., Comeau, N., & Maclean, A.M. (2006). Preventative efficacy of cognitive behavioral strategies matched to the motivational bases of alcohol misuse in at-risk youth. Journal of Clinical Child and Adolescent Psychology, 35, 550–563.
Conrod, P.J, Stewart, S.H., Pihl, R.O, Cote, S., Fontaine, V., & Dongier, M. (2000). Efficacy of brief coping skills interventions that match different personality profiles of female substance abusers. Psychology of Addictive Behaviors, 14, 231–242.
Crowley , T. J., Raymond, K.M., Mikulich-Gilbertson, S.K., Thompson, L.T., & Lejuez, C.W. (2006). A risk-taking “set” in a novel task among adolescents with serious conduct and substance problems. Journal of the American Academy of Child and Adolescent Psychiatry, 45, 175-183.
Fein, G., & Chang, M. (2008). Smaller feedback ERN amplitudes during the BART are associated with a greater family history density of alcohol problems in treatment-naïve alcoholics. Drug and Alcohol Dependence, 92, 141-148.
Finer, L.B., & Henshaw, S.K. (2006). Disparities in rates of unintended pregnancy in the United States, 1994 and 2001. Perspectives on Sexual and Reproductive Health, 38(2), 90-96.
Foxcroft, D.R., Ireland, D., Lister-Sharp, D.J., Lowe, G., & Breen, R. (2003). Longer-term primary prevention for alcohol misuse in young people: A systematic review. Addiction, 98(4), 397 411
Ingram, B.L., Flannery, D., Elkavich, A., & Rotheram-Borus, M.J. (2008). Common processes in evidence-based adolescent HIV prevention programs. AIDS Behavior, 12, 374-83.
Jessor, R., & Jessor, S. L. (1977). Problem behavior and psychosocial development: A longitudinal study of youth . New York: Academic Press.
Leigh, B. (1999). Peril, chance, adventure: Concepts of risk, alcohol use and risky behavior in young adults. Addiction, 94(3), 371-383.
Lejuez, C., Aklin, W., Bornovalova, M., & Moolchan, E. (2005). Differences in risk-taking propensity across inner-city adolescent ever- and never-smokers. Nicotine & Tobacco Research, 7(1), 71-79.
Lejuez, C.W., Aklin, W., Daughters, S., Zvolensky, M., Kahler, C., & Gwadz, M. (2007). Reliability and validity of the youth version of the Balloon Analogue Risk Task (BART-Y) in the assessment of risk-taking behavior among inner-city adolescents. Journal of Clinical Child & Adolescent Psychology, 36, 106-11.
Lejuez C.W., Aklin, W.M., Jones, H.A., Richards, J.B., Strong, D.R., Kahler, C.W., Read, J.P. (2003). The Balloon Analogue Risk Task (BART) differentiates smokers and nonsmokers. Experimental and Clinical Psychopharmacology, 11, 26-33.
Lejuez, C.W., Read, J.P., Kahler, C.W., Richards, J.B., Ramsey, S.E., Stuart, G.L., Strong, D.R., & Brown, R.A. (2002). Evaluation of a behavioral measure of risk taking: the Balloon Analogue Risk Task (BART). Journal of Experimental Psychology: Applied, 8, 75-84.
Lejuez, C., Simmons, B., Aklin, W., Daughters, S., & Dvir, S. (2004). Risk-taking propensity and risky sexual behavior of individuals in residential substance use treatment. Addictive Behaviors, 29(8), 1643-1647.
Lighthall, N.R., Mather, M., Gorlick, M.A. (2009). Acute Stress Increases Sex Differences in Risk Seeking in the Balloon Analogue Risk Task. PLoS ONE, 4, e6002. doi:10.1371/journal.pone.0006002
MacPherson, L., Magidson, J. F., Reynolds, E. K., Kahler, C. W., & Lejuez, C. W. (in press). Change in risk-taking propensity and sensation seeking predicts increases in alcohol use among early adolescents. Alcoholism: Clinical and Experimental Research.
MacPherson, L., Reynolds, E. K., Daughters, S. B., Wang, F., Cassidy, J., Mayes, L. C., & Lejuez , C. W. (in press). Positive and negative reinforcement underlying risk behavior in early adolescents. Prevention Science.
Malow, R.M., Kershaw, T., Sipsma, H., Rosenberg, R., & Devieux, J.G. (2007). HIV preventive interventions for adolescents: A look back and ahead. Current HIV/AIDS Report, 4, 173-80.
Pleskac, T., Wallsten, T., Wang, P., & Lejuez, C. (2008). Development of an automatic response mode to improve the clinical utility of sequential risk-taking tasks. Experimental and Clinical Psychopharmacology, 16(6), 555-564.
Rao, H., Korczykowski, M., Pluta, J., Hoang, A., & Detre, J.A. (2008). Neural correlates of voluntary and involuntary risk taking in the human brain: an fMRI study of the Balloon Analogue Risk Task (BART). Neuroimage, 42(2), 902-910.
Wallsten, T., Pleskac, T., & Lejuez, C. (2005). Modeling behavior in a clinically diagnostic sequential risk-taking task. Psychological Review, 112(4), 862-880.
Windle, M., Spear, L., Fuligni, A., Angold, A., Brown, J., Pine, D., et al. (2008). Transitions into underage and problem drinking: Developmental processes and mechanisms between 10 and 15 years of age. Pediatrics, 121(Suppl4), S273-S289.