Research News

Previous studies have demonstrated a positive association between maltreatment during childhood and drinking-related problems later in life (Bensley, Spieker, Van Eenywk, & Schoder, 1999). However, the motivational mechanisms underlying this association have not been studied. This week DRAM reviews a paper that examines why men and women who report experiencing childhood maltreatment abuse alcohol (Goldstein, Flett, & Wekerle, 2010).

Method

• Eighty-six male and 132 female college students answered questions regarding
  • Childhood maltreatment (i.e., physical, sexual and emotional abuse; physical and emotional neglect), assessed with the 28-item Childhood Trauma Questionnaire (CTQ; Bernstein and Fink, 1998).
  • Drinking motives, assessed with the 28-item Modified Drinking Motives Questionnaire-Revised (M-DMQ-R; Grant et al., 2007)o Alcohol consumption and consequences, assessed with the Alcohol Use Disorders Identification Test (AUDIT; Saunders, Aasland, Babor, De La Fuente, & Grant, 2006).
• The researchers built models separately for men and women to examine which drinking motives mediate the effect of childhood maltreatment on alcohol problems.

Results

• Approximately half (51.6%) of the male students and 23.4% of the female students were binge drinking at least monthly.
• Overall, 31% of students reported experiencing any type of abuse and 30% reported any type of neglect
• There was a positive association between being abused (r = .32, p < .01) or neglected (r = .28, p <.05) and having alcohol problems.
• For men, the association between being maltreated (e.g., neglected or abused) and having alcohol problems was mediated by the motive “to feel good and get happy;” this model accounted for 42.12% of the variance in alcohol problems, adjusted R² =0.39.
• For women, this association was mediated by the desire to cope with depression and anxiety (this model accounted for 22.54% of the variance in alcohol problems (adjusted R² = .21)

Table 1 Mediation effects (Z values) of drinking motives on childhood maltreatment to alcohol problems association for both genders.

Coping-anxiety motive

Coping-depression motive

Enhancement motive

Total model

Men

1.15

.31

2.01*

2.48*

Women

-.95

2.56**

2.02*

3.15**

* p < .05; ** p < .01

Limitations

• Retrospective reports might reflect some memory bias. For example, individuals who have drinking problems might be inclined to recall more childhood maltreatment.
• Correlational design prevents causality interpretation.
• Convenience sample (i.e., students) decreases generalizability of the results to other populations.

Conclusion

Men who report experiencing maltreatment during their childhood tend to report drinking to feel good and to get happy. In other words, the enhancement motive mediates the association between childhood maltreatment and alcohol problems among men. For women who were maltreated, problematic drinking is associated with a desire to cope with depression and anxiety. In other words, coping with depression and anxiety mediates the association between problematic drinking and childhood maltreatment among women. Further studies will need to conduct longitudinal studies to establish causality (i.e. to examine if childhood maltreatment indeed leads to future alcohol-related problems). 

-Julia Braverman

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References

Bensley, S. L., Spieker, S. J., Van Eenwyk, J., & Schoder, J. (1999). Self-reported abuse history and adolescent problem behaviours. II. Alcohol and drug use. Journal of Adolescent Health, 24, 173-180.

Bernstein, D. P., & Fink, L. (1998). Childhood Trauma Questionnaire: A retrospective self-report manual. San Antonio, TX: The Psychological Corporation.

Goldstein, A. L., Flett, G. L., & Wekerle, C. (2010). Child maltreatment, alcohol use and drinking consequences among male and female college students: An examination of drinking motives as mediators. Addictive Behaviors, 35(6), 636-639.

Grant, V. V., Stewart, S. H., O’Connor, R. M., Blackwell, E., & Conrod, P. J. (2007).Psychometric evaluation of the five-factor Modified Drinking Motives Questionnaire—Revised in undergraduates. Addictive Behaviors, 32, 2611-2632.

Saunders, J. B., Aasland, O. G., Babor, T. F., De La Fuente, J. R., & Grant, M. (2006).Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative project on early detection of persons with harmful alcohol consumption-II. Addiction, 88, 791-804.

Patients being treated for Parkinson disease (PD) have been reported to have a disproportionately high prevalence of disordered gambling and other impulse control disorders (ICDs) such as compulsive shopping, binge-eating, and hypersexuality (e.g., Bostwick, Hecksel, Stevens, Bower, & Ahlskog, 2009; Evans, Strafella, Weintraub, & Stacy, 2009). The development of these disorders appears to be most associated with dopamine agonists, a type of medication commonly used to treat PD, but estimates of the effect have varied. This week’s WAGER reviews a large cross-sectional study designed to obtain more accurate data about ICD prevalence among those with PD and the association of ICDs with dopamine agonists (Weintraub et al., 2010).

Methods

• The authors recruited participants (n=3,090) from patients receiving their regular care at 46 movement disorder centers in the United States and Canada.
• Individual sites developed their sampling plans (e.g., attempting to recruit every third patient scheduled during a clinic) before starting.  
• Investigators included subjects in the study if they met the following criteria:  
• Between the ages of 30 to 75 years;   
• Had been taking medication for PD for >1 year and had responded to treatment;   
• Had not started or stopped dopamine agonist therapy within the past 6 months.
• Patients were assessed for current ICDs using a semi-structured interview and formal diagnostic tools.
• The Massachusetts Gambling Screen (problem gambling, 3-4 criteria endorsed; pathological gambling, 5 or more criteria) (Shaffer, LaBrie, Scanlan, & Cummings, 1994) identified disordered gambling.  
• Compulsive buying and sexual behavior were assessed by the Minnesota Impulsive Disorders Interview (Christenson, Faber, & deZwaan, 1994).  
• Investigators determined the presence of binge-eating by using the proposed research criteria from the DSM-4 TR (Diagnostic and Statistical Manual of Mental Disorders, 2000).  
• The researchers calculated odds ratios to compare ICD frequencies between PD patients on dopamine agonist therapy and those receiving other treatment; they used several statistical tests to assess the strength of the association (i.e., Cochran-Mantel-Haenszel; Breslow-Day; Wilcoxon-Mann-Whitney).

Results

• Compared to PD patients not taking a dopamine agonist, PD patients prescribed a medication in this class were 2.7 times more likely to be diagnosed with an ICD and 2.8 times more likely to be diagnosed with disordered gambling (see Table 1).
• Overall, 17.1% of patients taking dopamine agonists were diagnosed with one or more ICDs, compared with 6.9% of patients on alternative treatment. The highest relative risk for patients on dopamine agonists was for binge-eating disorder: 5.6% of patients taking dopamine agonists were diagnosed with binge-eating disorder, compared to only 1.7% of patients on alternative treatment.
• The four ICDs measured by this study occurred at similar frequencies among patients taking dopamine agonists, ranging from 4.4% for compulsive sexual behavior to 7.2% for compulsive buying.
• Many patients had more than one disorder: of the 348 subjects on dopamine agonists that were diagnosed with an ICD, the average number of ICDs was 1.38.
• On average, patients diagnosed with an ICD were younger, less likely to be married, more likely to smoke, and more likely to report a family history of alcohol abuse or gambling problems than patients not diagnosed with an ICD. There was no statistically significant association between ICD diagnosis and sex, race, or PD duration or severity.

Table 1: ICD Frequencies by Dopamine Agonist Treatment Status (adapted from Weintraub et al 2010) 

*p < 0.005 for all OR

Limitations

• This study captured ICD prevalence at a particular moment. Because side effects from PD treatment can change over time, this study might underestimate the total number of patients that will eventually develop an ICD with treatment.
• The lack of an untreated PD group or a non-PD control group prevents direct comparison with overall ICD prevalence in PD or with the population as a whole.
• The authors did not assess mediators of impulsive behavior such as motivation, mood or other disorders that could be differentially influenced by dopamine agonist treatment.

Discussion

Weintraub et al (2010) examined the association between dopamine replacement therapy and ICDs in a large sample of patients being treated for PD. Consistent with previous studies, (Bostwick et al., 2009; Evans et al., 2009) patients treated with dopamine agonists were significantly more likely to be diagnosed with one or more ICDs than those on alternative treatments. Although dopamine agonists are associated with elevated risk for ICD, more research is necessary to determine the cause of this increase and which subgroups of patients are most at risk. Clinicians should discuss the risk of ICDs with patients and their families when starting dopamine replacement therapy for PD.

-Liz Joe

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References

Bostwick, J. M., Hecksel, K. A., Stevens, S. R., Bower, J. H., & Ahlskog, J. E. (2009). Frequency of new-onset pathologic compulsive gambling or hypersexuality after drug treatment of idiopathic Parkinson disease. Mayo Clinic Proceedings, 84(4), 310-316.

Christenson, G. A., Faber, R. J., & deZwaan, M. (1994). Compulsive buying: descriptive characteristics and psychiatric comorbidity. Journal of Clinical Psychiatry, 55(1), 5-11.

Diagnostic and Statistical Manual of Mental Disorders. (2000).  (Fourth Edition, Text Revision ed.). Washington, DC: American Psychiatric Association.

Evans, A. H., Strafella, A. P., Weintraub, D., & Stacy, M. (2009). Impulsive and compulsive behaviors in Parkinson’s disease. Movement Disorders, 24(11), 1561-1570.

Shaffer, H. J., LaBrie, R., Scanlan, K. M., & Cummings, T. N. (1994). Pathological gambling among adolescents: Massachusetts gambling screen (MAGS). Journal of Gambling Studies, 10(4), 339-362.

Weintraub, D., Koester, J., Potenza, M. N., Siderowf, A. D., Stacy, M., Voon, V., et al. (2010). Impulse control disorders in Parkinson disease: a cross-sectional study of 3090 patients. Archives of Neurology, 67(5), 589-595.

As described in a previous BASIS, US college students report taking unprescribed ADHD medications, such as Ritalin, Concerta, and Adderall, to improve their academic performance. An open question is whether some use these prescription medications to manage undiagnosed ADHD. This week the STASH reviews a recent article by Peterkin and colleagues (2010) that explores the potential association between the misuse of ADHD medications and undiagnosed ADHD among college students.

Methods

• A convenience sample of college students (n=190) volunteered to participate in an anonymous survey regarding cognitive performance enhancement.
• The survey asked whether participants had ever been diagnosed with ADHD and whether they had ever used medications used to treat ADHD without a prescription.  
• The survey included the World Health Organization Adult ADHD Self-Report Scale, which has 97.9% classification accuracy for identifying the presence of ADHD symptoms (Kessler et al., 2005). 

Results

• Table 1 shows the distribution of participants by ADHD medication use and results of the ADHD symptom screen.
• Participants who used ADHD medication without a prescription were nearly 7 times more likely than participants who did not use ADHD medication to have ADHD symptoms (χ2 = 62.30, p < 0.0001).  
• Approximately 70% of those who used ADHD medications without a prescription screened positive for ADHD, while only 30% of those who did not use ADHD medications screened positive.  

Table 1 – ADHD Medication Use and ADHD Symptom Results (adapted from Peterkin et al.)

*This analysis was based on 180 respondents – 6 surveys were incomplete and 4 participants who reported misusing ADHD medications had a previous ADHD diagnosis.

Limitations

• Because the recruitment procedures employed flyers seeking people who were interested in cognitive performance enhancement, the study likely oversampled students who used medications to improve academic performance. Consequently, the results might exaggerate the prevalence of ADHD medication use in the absence of an ADHD diagnosis.
• The study relies on self-reported behavior. Some respondents might have been unwilling to admit that they take unprescribed medications.

Discussion

The results indicate that, among college students who take unprescribed ADHD medications, some might be self-medicating undiagnosed ADHD symptoms. These results are not unique to the ADHD population. For example, as a recent STASH discussed (STASH 6(5)), some opioid-dependent individuals use medications (e.g., buprenorphine) without a prescription to self-treat their dependence, anxiety, and pain as well as to prevent withdrawal. Regardless of the underlying condition—ADHD or opioid dependence—individuals should consider using medications under a doctor’s supervision rather than taking on the risks associated with self-medication. Colleges should consider facilitating a doctor-patient relationship by providing treatment outreach for all students.

-Tasha Chandler

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References

DeSantis, A. D., Webb, E. M., & Noar, S. M. (2008). Illicit use of prescription ADHD medications on a college campus: A multimethodological approach. Journal of American College Health, 57(3), 315-324.

Kessler, R. C., Adler, L., Ames, M., Demler, O., Faraone, S., Hiripi, E., et al. (2005). The World Health Organization Adult ADHD Self-Report Scale (ASRS): A short screening scale for use in the general population. Psychological Medicine, 35(2), 245-256.

Peterkin, A. L., Crone, C. C., Sheridan, M. J., & Wise, T. N. Cognitive performance enhancement: Misuse or self-treatment? Journal of Attention Disorders, Published Online First: 21 April 2010. doi: 2010.1177/1087054710365980.

Ethically, healthcare professionals are bound to respect participants’ addiction treatment-seeking decisions, and therefore cannot force treatment onto unwilling participants unless they represent a threat to themselves or others. But, what would happen if such a restriction did not exist? A previous Humanities review (Vol 5-8) of A&E’s “The Cleaner” revealed the inaccuracies in the show’s depiction of addiction recovery following an intervention. Similarly, this week’s Addiction & the Humanities examines another A&E show related to addiction recovery, “Intervention.” Within the context of the show, we discuss the efficacy and ethical ramifications of intervention as an attempt to change the substance using patterns of a loved one.

Initially, family members and close friends nominate a candidate for “Intervention” based on his/her risky substance use or addiction-related behavior. The “Intervention” team then invites the candidate to contribute to a documentary series on addiction through a series of interviews. The invitation, in part, is a ruse. The true intention of “Intervention” is to force the candidate into treatment. The candidates, who agree to contribute, do not know that the “Intervention” team trains members of the candidate’s family about intervention tactics while searching for an ideal rehabilitation clinic for the candidate to undergo a 90-day treatment. The show content primary consists of interviews of the candidate and family members, who share their perspectives on the effects of addiction. In each episode, the interventionist typically explains to the family how their own inaction translates into an acceptance of the candidate’s addiction. At the end of the show, the family conducts the surprise intervention, and television cameras capture the candidate’s response in real time. In most cases, the candidate agrees to treatment after the intervention.

Intervention and A&E logos are owned by A&E.

If the episode “Amber” is representative of “Intervention”, it suggests that intervention indeed coerces candidates’ decision to agree to rehabilitation. For example, Amber’s father tells her that she can no longer live in his house if she refuses rehabilitation, while her ex-husband promises to seek sole custody of their daughter if her addiction continues. Facing these threats, Amber seems to have no choice other than to accept treatment, including the condition that her family attends a family clinic with her. Two months into treatment, Amber admits that she is having trouble with recovery and that she is disappointed that her family has not attended the family clinic. The show claims that Amber is sober today, but again, the program does not report its follow-up methods.
 
Amber’s case illustrates the coercive tactics that are characteristic of interventions. Her initial response to confrontation is to leave the intervention and escape the clutches of her family. Eventually she submits to social pressure and undergoes treatment. This separation between personal motivation and rehabilitation entry is a distinct flaw of “Intervention.” For example, several studies have suggested the importance of the association between internal desire to reduce or change addictive behavior and long-term recovery (Shaffer 1992; Laudet, Magura, Vogel, & Knight, 2003; Laudet & White, 2008).

“Intervention” also extends ethical boundaries: the candidate is approached only when family members submit a nomination, when the candidate is subjectively deemed to be “at a point of personal crisis and estranged from their friends and loved ones” (http://www.aetv.com/intervention/about/). The candidate remains unaware of the selection process and the true intentions of the show until the family members conduct the intervention, thereby raising ethical questions regarding these methods of treatment introduction.
 
“Intervention” claims that 71% of the candidates who have appeared on the show and entered a rehabilitation clinic have remained sober. Strangely, it cites 131/161 as the exact number of sober candidates, which actually represents a sober rate of 81%. The Division on Addictions attempted to contact Candy Finnigan, one of the show’s interventionists, to determine how follow-up interviews were structured and to explore this statistical discrepancy, but she has not responded. 

Dramatic portrayals of addiction recovery remain surprisingly popular. One possible appeal of shows such as “Intervention” could be the drama of forcing candidates into treatment against their will. This formula essentially simplifies the complexities of addiction and the recovery process. However, such a portrayal can be detrimental to a more comprehensive and accurate understanding of addiction. Regardless of the results of this television show, as a technique, intervention remains a coercive method of persuasion. Considering that the follow-up methods of “Intervention” are presently unknown, the purported rate of recovery seems exceedingly, if not mistakenly high – particularly compared to the rates of relapse and recovery established by treatment outcome research. Furthermore, there is little research focusing on the efficacy and adverse consequences associated with interventions. Consequently, it is premature to consider intervention as an effective treatment, despite shows’ claims; perhaps just as important, there are important questions about intervention as an ethical component of treatment. Alternatively, motivational enhancement techniques may likely result in more successful recoveries with fewer negative effects. These new resistance reduction approaches (Shaffer, 2001) can diminish the need for “interventions” as portrayed on television.

-Aaron Lim

What do you think?  Please use the comment link below to provide feedback on this article.

References

Intervention: About the show. Retrieved from http://www.aetv.com/intervention/about/

Johnson, V. E. (1980). I’ll quit tomorrow (Revised ed.). New York: Harper & Row.

Johnson, V. E. (1986). Intervention: How to help someone who doesn’t want help. Minneapolis: Johnson Institute Books.

Laudet, A. B., Magura, S., Vogel, H. S., & Knight, E. L. (2003). Participation in 12-Step-Based Fellowships Among Dually-Diagnosed Persons. Alcohol Treat Q, 21(3), 19-39.

Laudet, A. B., & White, W. L. (2008). Recovery capital as prospective predictor of sustained recovery, life satisfaction, and stress among former poly-substance users. Subst Use Misuse, 43(1), 27-54.

Shaffer, H. J. (1992). The psychology of stage change: The transition from addiction to recovery. In J. H. Lowinson, P. Ruiz, R. B. Millman & J. G. Langrod (Eds.), Substance abuse: a comprehensive textbook (Second ed., pp. 100-105). Baltimore: Williams & Wilkins.

Shaffer, H. J., & Simoneau, G. (2001). Reducing resistance and denial by exercising ambivalence during the treatment of addiction. Journal of Substance Abuse Treatment, 20(1), 99-105.

Vargas-Cooper, N. (2010). The rehab show that works. The Daily Beast. Retrieved from http://www.thedailybeast.com/blogs-and-stories/2010-07-21/the-new-ae-reality-show-intervention-with-jeff-vanvonderen-and-candy-finnigan/.

Social psychologists often demonstrate that explicit measures of attitudes (i.e., direct inquiries about one’s thoughts and opinions) are biased and unreliable (Holtgraves, 2004). Therefore, researchers have developed various implicit methods for measuring attitudes. This week’s ASHES reviews a study that examined how implicit and explicit attitudes about smoking predict the probability of being a smoker (Perugini, 2005).

Methods

• Perugini recruited 35 women and 13 men on campus; of these participants, 25 were smokers and 23 non-smokers.
• Participants completed:
• Implicit Association Test (IAT) measuring response times to associations between the concepts, smoking and exercise, and the attributes, pleasant and non-pleasant.
• An explicit measure of smoking-related attitudes – This measure contained questions about smoking-related attitudes on 11 bipolar scales (i.e., bad – good, harmful – harmless, etc.).
• Researchers computed a logistic regression model to examine the ability of explicit and implicit attitudes individually and jointly to predict whether the participants were smokers.

Results

• The central tendencies (means and medians) of both smokers and non-smokers displayed negative implicit and explicit attitudes toward smoking.
• Smokers compared to non-smokers demonstrated significantly less negative implicit (F (1, 47) = 8.17, p < .01) and explicit (F (1, 47) = 31.77, p <.01) attitudes toward smoking. However, after controlling for the explicit attitudes, implicit attitudes were not related to smoking status. 
• A logistic regression model revealed that a measure combining implicit and explicit attitudes improved the overall prediction beyond that of the individual attitudes (R2 change = .5.2%, χ2 = 3.2, p =.07). Figure 1 depicts this interactive pattern by extrapolating the observed regression model to standardized values ranging from -3 to +3 standard deviations around the mean.  Implicit attitudes taken alone do not predict being a smoker. However, even a slight increase in implicit attitude score when combined with a positive explicit attitude score sharply increased the probability of being a smoker.

Figure 1. Interaction between implicit and explicit attitudes in predicting smoking behavior (adapted from Perugini, 2005).

Limitations

• Although some consider IAT to be the most reliable procedure for measuring implicit attitudes, there are methodological issues that might detract from its validity. For example, some research suggests that familiarity, salience and knowledge of cultural norms can influence the results of the IAT (Houben & Wiers, 2007) and obscure interpretation.
• The correlational and retrospective design of this study make it difficult to interpret causality.
• The study’s small sample size limits the generalizability of the results. 
• The interactive pattern was not statistically significant (p < .07).

Conclusion

This study contributes to our understanding of the interplay between implicit and explicit attitudes in predicting smoking status. The main finding is that a combination of less negative explicit attitudes toward smoking and less negative implicit smoking-related associations is related to an estimated greater likelihood of being a smoker. Future studies should use methods other than the IAT to measure implicit attitudes to complement the findings of this study. Longitudinal studies will help to investigate the effect of implicit and explicit attitudes on smoking behavior prospectively rather than retrospectively.

-Julia Braverman

What do you think?  Please use the comment link below to provide feedback on this article.

References

Holtgraves, T. (2004). Social desirability and self-reports: Testing models of socially desirable responding. Personality and Social Psychology Bulletin, 30(2), 161-172.

Houben, K., & Wiers, R. W. (2007). Are drinkers implicitly positive about drinking alcohol? Personalizing the alcohol-IAT to reduce negative extrapersonal contamination. Alcohol and Alcoholism, 42(4), 301-307.

Perugini, M. (2005). Predictive models of implicit and explicit attitudes. British Journal of Social Psychology, 44(1), 29-45