The Brief Addiction Science Information Source (BASIS)

The ASHES, Vol 6(7) – What’s more important for predicting smoking behavior: How unpleasant you say smoking is or how closely smoking and unpleasant are associated in your mind?

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

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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

ASHES Vol. 6(6) – Danger Ahead! The Effects of Looming Vulnerability on Smoking Cessation Attempts

For decades, public health advocates have developed and tested smoking cessation programs. One method, looming vulnerability, involves a perceived increase of susceptible dangers due to smoking, which heightens state anxiety. Researchers hypothesize that this change will stimulate efforts to quit. This week the ASHES reviews a study that examined the effects of looming vulnerability and smoking cessation (McDonald, D., O’Brien, J., Farr, E., & Haaga, D. A. F. 2010).

Methods

• Researchers used a randomized control study design and surveyed 72 adult smokers.
• At baseline, researchers included questions for demographics, smoking history, nicotine dependence, and state anxiety.
• For both conditions, participants listened to four three-minute audiotape-guided imagery exercises.
• The experimental condition focused on the act of smoking and the associated health consequences. The scene also connected the continuation of smoking with other impending dangers such as riding a conveyor belt that ultimately leads to a diagnosis of lung cancer.  
• The control condition elicited relatively calm images that included movement, but were not associated with smoking, such as riding an escalator in an empty mall.
• Immediately after the manipulation, participants completed measures for state anxiety, motivation, and contemplation to quit.
• During a one-month follow-up interview, participants again completed measures of motivation, contemplation, nicotine dependence, current smoking behavior, and quit attempts.

Table 1: Immediate effects of looming manipulation (adapted from McDonald et al., 2010)

 
Note: For means, standard deviations are given in parentheses. State anxiety = Visual Analogue Scale (0-100). t(70) compares looming and control conditions. NS parameters not reported.

Results

• As Table 1 shows, the post-test anxiety ratings for the looming condition were significantly higher than for the control condition. No significant difference was found between groups in the follow-up interview for motivation, contemplation, nicotine dependence, quit attempts, or attempts to seek formal help for cessation.
• Looming condition participants on average (M = 9.10, SD = 6.72) did smoke fewer cigarettes than control participants (M=12.90, SD = 9.02). ANCOVA on smoking rate, with baseline rate as covariate and experimental condition as independent variable, was significant, F (1, 58) = 4.24, p<.05, partial eta squared = .068.

Limitations

• Self-reported measures.
• Measures of contemplation and motivation to quit were completed post-test only.
• Small sample size limits statistical power.

Discussion

Looming vulnerability was found to successfully increase a participant’s state anxiety immediately after the manipulation. While it did have a significant impact on the number of cigarettes smoked during a one-month follow-up survey, there were no other significant findings. These results illustrate how an induced behavioral change might not penetrate or affect internal motivations for such behavior. The results also suggest that scare tactics are ineffective as a smoking cessation strategy in the long term. A more comprehensive approach that addresses personal and psychological history and physical dependence might result in more successful cessations.

-Aaron Lim

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References

McDonald, D., O’Brien, J., Farr, E., & Haaga, D. A. F. (2010).  Pilot study of inducing smoking cessation attempts by activating a sense of looming vulnerability.  Addictive Behaviors, 35, 599-605.
  
Riskind, J. H., & Williams, N. L., & Joiner, T. E., jr. (2006). The looming cognitive style: A cognitive vulnerability for anxiety disorders. Journal of Social & Clinical Psychology, 25, 2006.

ASHES Vol. 6(5) – Tell me who your friends are and I’ll tell you who you are: Social networks and smoking

A previous BASIS DRAM described a study about the relationship between friendship and addiction-related behavior, specifically, alcohol abuse. Continuing this topic, this week’s ASHES reviews a study that examined how social networks predict long-term changes in smoking behavior (Pollard, Tucker, Green, Kennedy, & Go, 2010). 

Methods

• Pollard et al. used data from the National Longitudinal Study of Adolescent Health (Add Health). The research sample included students who were interviewed three times (i.e., wave I, wave II, wave III) during a six year period. The students were in grades 9 to 11 at the last wave of interviews (N = 837 for the final analysis). 
• First, the researchers calculated the average number of cigarettes smoked during the last 30 days of each wave using the reported frequency of smoking days, that is, the reported number of cigarettes smoked each day. Next, the researchers applied latent class growth analysis to estimate the smoking trajectory for each individual. These trajectories describe how the individual’s smoking behavior changes over time (i.e., whether it decreases, increases or stays steady).    
• Then, the researchers applied a network analysis strategy to classify the wave I network position of an individual (i.e., belongs to smoking group, belongs to a non-smoking group, is a liaison to a smoking group, is a liaison to only nonsmoking groups). They also calculated other network related variables (e.g., number of best friend who smoke). 
• Finally, the researchers built a statistical model to estimate how well the network position of the individual predicted smoking trajectory. The model controlled for other concomitants of smoking behavior (i.e., gender, race/ethnicity, depressed affect, coping, self esteem, parental education, and resident parent who smoked).

Results

• As Figure 1 shows, the study identified 6 unique smoking trajectories ranked from the “lowest” to the “highest” in tobacco use as follows: never smokers, steady lows, delayed uptake to regular use, early uptake, decreasers, and steady highs. 
• Adolescents with a greater number of smoking friends were more likely to belong to the higher use trajectories. 
• Membership in a smoking group and being a liaison of a smoking group increased the likelihood of belonging to the higher use trajectories; this finding was obtained beyond the effect of the perceived number of smoking friends. 

Figure 1. Trajectories of average cigarettes per day: grades 9–11 at Wave 1 in the entire Add Health sample (N = 6096).*

 * Note: The picture is copied from (Pollard et al., 2010) . 

Limitations

• Self-reported measures.
• It is not clear, why the authors defined “decreasers” as higher tobacco use trajectory, compared to the “delayed uptake” and “early uptake”.  
• It is not clear, exactly how the social network affects smoking behavior. The mechanism is unknown. 

Conclusion

The main contribution of this experiment is that the investigators used both network analysis and trajectory analysis within the same study. By using these strategies within the same study, they were able to find that the position within the social network affects the dynamic of smoking behavior. Another important strength is that this study used a nationally representative sample of adolescents, so the findings likely will generalize to other groups of adolescents. The major conclusion –that the peer network is associated with smoking behavior – seems to be intuitive. However, this is the first study to demonstrate that the network position predicts the changes in adolescent’s smoking behavior over time above and beyond simple number of smoking friends. 

References

Pollard, M. S., Tucker, J. S., Green, H. D., Kennedy, D., & Go, M.-H. (2010). Friendship networks and trajectories of adolescent tobacco use. Addictive Behaviors, 35(7), 678-685.

ASHES, Vol. 6(4) - Even better than the real thing? Electronic cigarettes vs. real cigarettes

Despite a lack of research, electronic cigarette (e-cigarette) companies market their products as “safe,” without harmful carcinogens found in cigarettes, and helpful in the cessation process (World Health Organization, 2008, 2009). E-cigarettes are a refillable nicotine cartridge with a heater that vaporizes a nicotine solution (i.e., nicotine, propylene glycol and other chemicals), which then gets absorbed into the lungs when the smoker takes a “puff” (Laugesen, 2009; World Health Organization, 2008). This week the ASHES reviews a study that compares participants’ nicotine and craving levels from smoking their usual cigarette brand to levels produced by two e-cigarettes (Eissenberg, 2010).

Methods

• The researcher used a convenience sample to identify 16 eligible participants.
• Eligibility criteria included:(1) healthy adults aged 18-55 who smoked > 15 cigarettes/day for at least 1 year; (2) no knowledge of e-cigarettes; (3) not trying to quit smoking; and (4) agreed not to use tobacco/nicotine 12 hours prior to each session (confirmed by < 10 ppm expired air CO).
• Participants went to one session for each of the four products (separated by 48 hours): preferred cigarette brand, sham cigarette (puffing on preferred unlit cigarette), and two e-cigarettes (Brand One had a 18mg nicotine cartridge and Brand Two a 16mg cartridge).
• Dependent measures included:
• Blood plasma nicotine levels; and 
• Craving – (1) Tiffany-Drobes Questionnaire of Smoking Urges Brief (QSU Brief); and (2) a 35-item computerized visual analog scale (VAS) consisting of the Hughes-Hatsukami Smoking Withdrawal Scale (Hughes & Hatsukami, 1986).
• The researcher obtained these measures 5 minutes prior to the first puff and 5, 15, 30 and 45 minutes after the first puff (bout 1). After 60 minutes, the product was administered again and the process was repeated (bout 2).

Results

• Figure 1 shows that the lit preferred brand cigarette significantly increased plasma nicotine and decreased cigarette craving at most recorded timepoints (p < 0.05) during bouts 1 and 2.
• The two e-cigarettes failed to significantly increase plasma nicotine levels and decrease cigarette craving at all recorded timepoints during bouts 1 and 2, except Brand One significantly decreased cigarette craving at the 5 minute timepoint during bout 2 (p < 0.05).
• The sham cigarette failed to significantly increase plasma nicotine levels and decrease cigarette craving at all recorded timepoints.

Figure 1 – Mean Plasma Nicotine Level and Craving a Cigarette/Nicotine Score for each Product (from Eissenberg, 2010)

 Note: Arrows indicate the time the product was administered (10 “normal” puffs with 30 second intervals between puffs). Bold symbols indicate a significant difference from the first timepoint following product administration. Asterisks above or below the symbol indicate a significant difference from sham smoking at the given timepoint (p < 0.05).

Limitations

• The sample size is small and from a convenience sampling strategy, making it difficult to generalize to all smokers.
• Vapor content can differ based on the e-cigarette design.
• Nicotine delivery can vary among participants based on their chronic use and intensity of inhalation.

Discussion

Both brands of e-cigarette reduced nicotine use by delivering less nicotine than the products’ claimed, but neither significantly reduced craving. Therefore, smokers might be more likely to revert to smoking their preferred brand of cigarette to satisfy their craving and expose themselves to the harmful chemicals associated with cigarette smoking. If manufacturers want to market e-cigarettes as a harm reduction tool, then these results suggest that e-cigarette companies might want to do additional testing and product revisions to ensure that they effectively reduce cravings.

-Tasha Chandler

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References

Cobb, C. O., Weaver, M. F., & Eissenberg, T. (2009 Online First). Evaluating the acute effects of oral, non-combustible potential reduced exposure products marketed to smokers. Tobacco Control, doi: 10.1136/tc.2008.028993.

Eissenberg, T. (2010). Electronic nicotine delivery devices: Ineffective nicotine delivery and craving suppression after acute administration. Tobacco Control, 19(1), 87-88.

Hughes, J. R., & Hatsukami, D. (1986). Signs and symptoms of tobacco withdrawal. Archives of General Psychiatry, 43(3), 289-294.

Laugesen, M. (2009). Ruyan e-cigarette bench-top tests. Poster presented to the joint conference of the Society for Research on Nicotine and Tobacco and Society for Research on Nicotine and Tobacco-Europe; April 30, 2009.

World Health Organization. (2008). Marketers of electronic cigarettes should halt unproved therapy claims.   Retrieved April 14, 2010, from http://wno.int/mediacentre/news/releases/2008/pr34/en/index.html

World Health Organization. (2009). The scientific basis of tobacco product regulation: Report of a WHO Study Group. Geneva, Switzerland: World Health Organization.

ASHES, Vol. 6(3) - Marking Risk for Relapse: Objective Markers of Ability to Maintain Smoking Cessation

Like most addictive behaviors, relapse is common among individuals who attempt to quit smoking (Shaffer et al., 2004). However, the identification of markers for high risk of smoking relapse could facilitate lasting, or longer, periods of smoking cessation by creating opportunities to intervene (e.g., improve treatment, emphasize relapse prevention). This week, ASHES reviews a recent study, which examined whether pre-cessation reactivity to smoking-related images and words could mark increased risk for smoking relapse (Janes et al., 2010).

Methods

• 21 women who met DSM-IV criteria for current nicotine dependence, smoked at least 10 cigarettes per day during the previous 6 months, expired more than 10 ppmv air CO, and were scheduled to participate immediately in an 8-week smoking cessation clinical trialⁱ, participated in this study.ⁱⁱ
• Participants completed a variety of paper-pencil survey items, as well as two smoking cue reactivity tasks: (1) a Stroop task (i.e., researchers used a computer program to record the accuracy and time required for participants to report the word color for smoking-related and neutral words); and (2) cue-related neuroimaging (i.e., participants viewed smoking-related, neutral, or animal images during fMRI scans).
• Participants reported smoking behavior each week using the Timeline Followback Method and expired CO measurement.
• The researchers operationally defined individuals who quit smoking for 24 hours as abstinent if their study-wide weekly smoking report included CO<9 ppmv and no self-reported smoking; they considered all others (n=9) as “slipped.”

Results
Compared to individuals who did not slip, those who reported that they had slipped showed more baseline fMRI reactivity to smoking cues in multiple brain areas, t(19)=2.86, cluster corrected, p<.01,ⁱⁱⁱ and baseline smoking-related Stroop effects for accuracy, F(1, 16)=7.98, p<.05, and timing, F(1, 16)=7.30, p<.05, of responses, after controlling for assessed nicotine dependence (see Table 1).

Table 1. Baseline mean and standard deviations by post-quit smoking group

Note. Only 8 of 9 individuals who “slipped” completed the Stroop task.

Limitations

• The study included only a small number of women seeking help for quitting smoking, which might affect generalizability of these results.
• The study took place within a co-occurring smoking cessation trial; therefore, the participants are help-seekers, who might be different from smokers more generally. This circumstance also can limit the generalizability of these findings.

Discussion
The identification of objective markers of risk for addiction-related relapse behavior is important. Identifying markers can help guide treatment planning and matching, and smoking prevention efforts. This study represents a preliminary step toward the development of an early warning system for smoking relapse risk. Relapse cue training could become an important treatment component for providers and help-seekers. Additional research should include practical considerations of the use of fMRI in risk assessment, a determination of the durability of such predictive markers over time, and the identification of other possible behavioral markers of risk for relapse.

References
Janes, A. C. et al., (2010 online first). Brain reactivity to smoking cues prior to smoking cessation    predicts ability to maintain tobacco abstinence. Biological Psychiatry.

Shaffer, H. J. et al., (2004). Toward a syndrome model of addiction: Multiple expressions, common etiology. Harvard Review of Psychiatry, 12(6), 367-374.


ⁱ During the 8 week study each participant received individual behavioral intervention, the patch, and nicotine gum or lozenges as needed.
ⁱⁱ Exclusions included: unstable medical illness; pregnancy; recent drug and alcohol use; major depressive disorder; alcohol use disorder in prior 6 months; current psychotropic drug use; or lifetime diagnosis of organic mental or psychotic disorders.
ⁱⁱⁱ Elevated reactivity occurred for the bilateral insula, ACC, posterior cingulated cortex, inferior parietal cortex, paraphippocampal gyrus, thalamus, putamen, cerebellar hemispheres and vermis, prefrontal cortex, and striate and extrastriate corex.

ASHES Vol. 6(2) – Casting a World Wide Web: A Meta-analysis of Interactive Internet Interventions for Smoking Cessation.

Online interventions have potential to help people suffering with addiction. The Internet can reach large, dispersed, and reluctant populations in ways that are impossible for face-to-face interventions. It is possible that interactive features, such as providing immediate personalized feedback, will enhance the effectiveness of online interventions. In this week’s ASHES, we review a meta-analysis of the published literature that compared interactive smoking cessation interventions to minimal, non-interactive, untailored interventions or waitlist controls (Shahab & McEwen, 2009).

Methods

• During December 2008, researchers conducted a systematic search of multiple online databases (e.g., PubMed, PsychINFO, CINHAL Plus) for relevant studies of online support for smoking cessation.
• Researchers included studies meeting the following criteria: published after 1990; participants were current smokers; compared an interactive Internet (i.e., provide personalized information and responses) condition to a minimal, non-interactive, untailored control condition (e.g., booklet or e-mail); at least 1-month follow-up assessing smoking cessation; and randomized control trial design.
• The search strategy yielded 82 studies, but only nine met inclusion criteria; further bibliography searches from the nine selected studies revealed two more eligible papers for a total of 11 studies.
• Researchers analyzed the reviews using Review Manager (i.e., RevMan: a computer program for meta-analyses) and reported rate ratios (i.e., RR) for smoking cessation at follow-up.
• Three of the 11 studies included information from a 6-month follow-up interview. We are reporting only about these three studies because a more limited follow-up period might be too brief an interval for evaluating long-term outcomes like smoking cessation.

Results

• The three meta-analytic studies included 1,203 participants. Researchers conducted one of the three studies in the U.S. and recruited from the general population for the other two studies.
• The researchers reported that, compared to the controls, 6-month abstinence rates were 17% (95% CI: 12%-21%) higher for the interactive interventions.
• As the Total in Figure 1 shows, compared to controls, the rate of 6-month abstinence for participants in the interactive intervention was nearly double (RR = 1.93; 95% CI: 1.44, 2.60).

Figure 1. Comparison of Interactive Internet smoking cessation interventions to minimal, non-interactive, untailored control condition - 6-month Abstinence

Note. Study 1: An et al., 2008 ; Study 2: Brendryen & Kraft, 2008; Study 3: Brendryen et al., 2008. Minimal control, booklet or static website containing information about smoking cessation. RR: Rate Ratio [  ]: 95% Confidence Interval.

Limitations

• The analysis relies on only three studies.
• The three studies did not include many measures of potential confounders (e.g., type of Internet access, socioeconomic status).

Conclusion

This meta-analysis suggests that incorporating interactive Internet interventions in a quit-smoking-plan improves the chances of abstinence at six months by 1.9 times compared to minimal, non-interactive, untailored interventions. The 6-month abstinence rates among the intervention (35%) and control (19%) groups are sizable compared to the ~5% abstinence rate reported by Hughes, Keely, and Naud (2004) among participants attempting to quit without the help of any aids. However, researchers did not measure all possible confounding variables that might affect the results and the available evidence is scant. People who are considering quitting smoking, want to help themselves, and have Internet access, should consider searching the Internet for reputable interventions¹.

-John H Kleschinsky

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References

Hughes,J. R., Keely, J., & Naud, S. (2004). Shape of the relapse curve and long-term abstinence among untreated smokers. Addiction, 99(1), 29-38.

Shahab,L., & McEwen, A. (2009). Online support for smoking cessation: a systematic review of the literature. Addiction, 104(11), 1792-1804.

1. For more information about how to identify and locate reputable self-directed interventions for smoking cessation on the Internet, please see ASHES Vol. 3(4)

ASHES, Vol. 6 (1) –Modest lifetime cigarette use affects women’s breast cancer risk.

There are numerous factors that can lead to breast cancer. These influences include age, family history, exposure to hormones and lifestyle habits such as smoking and drinking alcohol. In this week’s ASHES, we review a study by Croghan et al. (2009). The study compared the prevalence of non-milk duct or lobule related breast cancer among women who reported smoking more than 100 cigarettes during their lifetime and women who smoked less than 100 cigarettes or never smoked.

Methods

• Croghan et al. collected data from 8,927 female patients seen at the Mayo Clinic Breast Clinic between August 1, 1993 and November 31, 2003
• The final sample of 8,097 women included: 
• 831 current smokers;
• 714 former smokers; and
• 6,552 women who never smoked.
• Researchers excluded women who:
• were diagnosed with non-breast-related cancers; 
• were diagnosed with breast cancers originating in the milk-producing ducts or lobules of the breast; or
• could not be linked with the Mayo Clinic Diagnostic Index. 
• Patients completed a self-administered questionnaire. If patients made return visits to the clinic, researchers included only their first completed survey in the analyses.
• To test the effect tobacco smoke has on the probability of a breast cancer diagnosis, researchers compared patients with no cancer to patients with breast cancer using Pearson chi-squared tests and multivariate logistic regression with stepwise elimination. In each case, researchers used a two-sided test with a 0.05 significance level.

Table 1: Predictive Model of Breast Cancer using Logistic Regression (Adapted from Croghan et al, 2009)

 

Results

• Croghan et al., using logistic regression found, after controlling for other factors, that having a personal history of ever smoking more than 100 cigarettes increased the odds of a breast cancer diagnosis by 25% (CI = 1.07, 1.46). 

Limitations

• Self-report of previous smoking behavior provides a limited amount of data.
• Self-report might be affected by recall bias.
• The use of a multivariate model includes shared variance; researchers did not specifically analyze the unique variance* due to cigarette smoking.

Conclusion 

The researchers admit that it is difficult to assess the effects of lifestyle habits such as smoking and drinking. More research is needed to clarify the influence of lifestyle on the development of breast cancer. Nevertheless, Croghan et al. observed that smoking at least 100 cigarettes at any point during life increases a woman’s risk for developing breast cancer. This study supports a cautionary note: it is possible that modest levels of substance use can lead to adverse health issues.

-Ingrid Maurice

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References 

Croghan, I. T., Pruthi, S., Hays, J. T., Cha, S., Johnson, R. E., Kosel, M., et al. (2009). The role of smoking in breast cancer development: an analysis of a Mayo Clinic cohort. The Breast Journal, 15(5), 489-495.

*Note: Researchers did not test the effect of cigarette smoking on the development of breast cancer independently from other variables listed in Table 1.

ASHES, Vol. 5(10) – Crushing virtual cigarettes to aid real world smoking cessation

There are a variety of treatments used to encourage smokers to stop smoking. However, despite the treatment options, many smokers still struggle to quit (Center for Disease Control and Prevention, 1999). Perhaps smoking cessation treatment could benefit by expanding the variety of treatments available; a particularly novel intervention is Virtual Reality (VR) treatment (Baumann & Sayette, 2006; Carter, Bordnick, Traylor, Day, & Paris, 2008). This week the ASHES reviews a study of the benefits of VR in combination with psychosocial treatment to reduce cigarette smoking (Girard, Turcotte, Bouchard, & Girard, 2009).

Methods

• Researchers recruited 91 healthy adults, aged 18-65, from Quebec, Canada via local ads. Participants were not interested in quitting and smoked ≥10 cigarettes per day.
• Researchers provided participants with brief psychosocial treatment and randomly assigned participants to either experimental (i.e., crushing cigarettes; n = 46) or control (i.e., crushing balls, n = 45) VR sessions.

• Clinic visits (weeks 1, 2, 3, 4, 6, 8, 10 and 12): During each 30-minute session, a nurse collected each participant’s smoking journal; performed a carbon monoxide test to confirm abstinence; administered a revised version of the Fagerström test (Heatherton, Kozlowski, Frecker, & Fagerström, 1991) for nicotine dependence (only at weeks 1, 4, and 12); and conducted a brief counseling session.    

• VR sessions (weeks 1, 2, 3 and 4): Additional 30-minute sessions during which participants in each condition repeatedly simulated grasping the assigned object or crushing it.    

• The researchers used an intent-to-treat analysis to analyze all the participants’ results regardless of treatment drop out.

Results

• Researchers tested the program’s impact on the Fagerström test using a repeated measures ANOVA. Figure 1 shows, a significant group (grasping ball or crushing cigarette ) by time (Pretreatment, End of VR, End of program) interaction F (2, 178) = 3.75, p < 0.05.

• Contrast analysis revealed that the Cigarette condition was statistically better than the Ball condition at reducing nicotine dependence between week 4, F (1, 89) = 33.34, p < 0.001, and the end of the study F (1, 89) = 55.18, p < 0.001.    

• At week 4, there was no significant difference for abstinence between the Ball condition (2%) and the Cigarette condition (9%) (Χ² (1) = 1.84, ns). However, the difference was significant at week 12 between the Ball condition (2%) and the Cigarette condition (15%) (Χ² (1) = 4.79, p < 0.05).
• On average, participants in the Ball condition dropped out of the study significantly earlier than participants in Cigarettes condition (5.89 weeks completed, SD = 4.66; 8.26 weeks, SD = 4.17; respectively; t(89) = 2.56, p < 0.05).

Figure 1 - Fagerström test for Nicotine Dependence

Click image to enlarge, or adjust your browser's zoom setting.
*Total number of nicotine dependent behaviors (six items) endorsed for a total score between 0 (minimum dependence) and 10 (maximum dependence).  

Limitations

• The control group had a higher drop-out rate and, thusly, a greater proportion of early final ratings. Without information on the tendency of drop-outs to show greater or less improvement than completers, it is not possible to gauge the extent to which the outcomes were affected by the intent-to-treat procedure.
• The study uses self-report for some of its measures.
• The study does not explore why crushing cigarettes was more effective than grasping balls for smoking cessation.

Discussion

Compared to the control group, participants whose treatment included crushing cigarettes in a VR environment had lower rates of nicotine dependence, higher cessation rates, and were more likely to remain in the treatment program. What remains illusive about these results is why crushing cigarettes in a VR environment helped participants improve. Future studies should analyze the mechanisms that moderate and mediate these outcomes (e.g., the objective or goal of the treatment session or the subjective states stimulated by crushing cigarettes) utilized in VR. However, these results indicate that VR, combined with more traditional treatment programs, can influence treatment outcomes favorably. Mental health professionals might consider adapting their current treatment programs to include a controlled VR environment.

-Tasha Chandler

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References

Baumann, S. B., & Sayette, M. A. (2006). Smoking cues in a virtual world provoke craving in cigarette smokers. Psychology of Addictive Behavior, 20(4), 484-489.

Carter, B. L., Bordnick, P., Traylor, A., Day, S. X., & Paris, M. (2008). Location and longing: The nicotine craving experience in virtual reality. Drug and Alcohol Dependence, 95(1-2), 73-80.

Center for Disease Control and Prevention. (1999). Cigarette smoking among adults: United States. Morbidity & Mortality Weekly Report, 48, 993-996.

Girard, B., Turcotte, V., Bouchard, S., & Girard, B. (2009). Crushing virtual cigarettes reduces tobacco addiction and treatment discontinuation. Cyberpsychology & Behavior, 12(5), 477-483.

Heatherton, T. F., Kozlowski, L. T., Frecker, R. C., & Fagerström, K.-O. (1991). The Fagerström Test for Nicotine Dependence: A revision of the Fagerström Tolerance Questionnaire. British Journal of Addiction, 86(9), 1119-1127.

ASHES, Vol. 5(9) –Thinking of quitting smoking "cold turkey?" Chew on it for a while.

Most smokers interested in quitting do not feel ready for an abrupt cessation, often referred to as “cold turkey.” Most smokers prefer to gradually quit by reducing the number of cigarettes they smoke (Cheong, Yong, & Borland, 2007). This method, however, fails more often than not (Levinson, Shapiro, Schwartz, & Tursky, 1971). It has been hypothesized that nicotine–replacement therapy might aid smoking cessation by permitting smokers to gradually reduce the number of cigarettes they smoke. This week’s ASHES reviews a randomized double-blind placebo-controlled clinical trial that tests the efficacy of nicotine gum to assist cessation (Shiffman, Ferguson, & Strahs, 2009).

Methodology

• Investigators enrolled 3297 participants (57% women) using print and radio advertisements. The investigators randomized participants to nicotine gum or placebo treatment.
• Participants self-selected their nicotine gum dosage (2 mg (n = 1636) vs. 4 mg (n = 1661))
• A user-guide instructed participants to add an additional hour of abstinence and substitute one additional piece of gum daily. Once smokers had quit completely (i.e., for 24 hours), clinicians instructed participants to gradually reduce use of gum consistent with the current FDA-approved labeling.
• The investigators compared the number of smokers who achieved and maintained abstinence—self-reported, verified by two CO readings <= 10 ppm in average—for the placebo condition and for each dose level of nicotine gum.

Results

• As Figure 1 illustrates, smokers using nicotine gum were more likely to achieve initial cessation (i.e., no smoking for 24 hours) compared to the placebo group. They also were more likely to maintain abstinence 28 days and six months later (p < .05).
• Participants using nicotine gum were more likely to experience an adverse event (e.g., nausea, hiccups, heartburn) than participants using placebo gum (48% vs. 37%, p < .001)

Figure 1. Initial cessation and continuous smoking abstinence percentages.

                          Initial abstinence  28-day abstinence   6-month abstinence

Limitations

• Low absolute cessation rates
• Specific sample (i.e., smokers who are ready to quit on a relatively time-limited program) restricts the ability to generalize these results to other groups.
• The absence of a procedural measure of fidelity reveals that it is not clear, how precisely smokers followed the instructions.
• As Table 1 shows, the study encountered non-completers; it is not evident whether these completers were systematically different from completers; therefore, it is not possible to determine whether the treatment yielded differential impact upon the participants both within and across groups.

Table 1. Number of non-completers per condition, summarized across 2mg and 4 mg self-selected groups

	Placebo (N = 1648)	Nicotine gum (N = 1649)
28-day follow-up: number of drop outs	238	262
6-month follow-up: number of drop outs	8	21

Conclusions

Nicotine gum is widely used to help smokers maintain abstinence (Silagy, Lancaster, Stead, Mant, & Fowler, 2004). This study is the first to investigate the effect of nicotine gum on graduated abstinence from smoking. Although the absolute rate of quitting was low, it is comparable to similar “cold turkey” studies (Shiffman et al., 2002). The results suggest that, for smokers quitting gradually, using nicotine gums facilitates tobacco abstinence compared to a placebo. Investigators should conduct more studies to compare its safety and effectiveness against other smoking cessation methods and to include fidelity measure to ensure the participants followed the instructions.

References

Cheong, Y., Yong, H.-H., & Borland, R. (2007). Does how you quit affect success? A comparison between abrupt and gradual methods using data from the International Tobacco Control Policy Evaluation Study. Nicotine & Tobacco Research, 9(8), 801-810.

Levinson, B. L., Shapiro, D., Schwartz, G. E., & Tursky, B. (1971). Smoking elimination by gradual reduction. Behavior Therapy, 2(4), 477-487.

Shiffman, S., Ferguson, S. G., & Strahs, K. R. (2009). Quitting by Gradual Smoking Reduction Using Nicotine Gum: A Randomized Controlled Trial. American Journal of Preventive Medicine, 36(2), 96-104.e101.

Shiffman, S., Rolf, C. N., Hellebusch, S. J., Gorsline, J., Gorodetzky, C. W., Chiang, Y.-K., et al. (2002). Real-world efficacy of prescription and over-the-counter nicotine replacement therapy. Addiction, 97(5), 505-516.

Silagy, C., Lancaster, T., Stead, L., Mant, D., & Fowler, G. (2004). Nicotine replacement therapy for smoking cessation. Cochrane Database Systematic Reviews, 3, CD000146.

 

ASHES, Vol. 5(8) - Lessons Learned from an Evaluation of a Smoking Cessation Program

Adolescent smoking rates in tobacco-growing states are higher than the national average (Eaton et al., 2008). Adolescents from these communities face unique social pressures that deter them from quitting smoking. The Adolescent Cessation of Tobacco: Independent of Nicotine (ACTION) program was designed to help adolescents who live in tobacco-growing communities stop using tobacco by prompting them to reflect on social pressures not to quit. This week’s ASHES reviews a recent study (Stein-Seroussi, Stockton, Brodish, & Meyer, 2009) which evaluates the efficacy of ACTION, and, in doing so, highlights several practical issues that can hamper efforts to evaluate school-based intervention programs. 

Methods
•    Participants were 268 adolescent smokers recruited from 14 schools in Kentucky, North Carolina, and Ohio. 
•    Assignment to a program was by school. Seven schools received the ACTION program and seven received the comparison program. Investigators randomly assigned ACTION to the schools, using stratification within state to assure comparable school assignments.
•    There were three assessment periods: pre-test (up to one week before the programs began); post-test (no more than thee days after the programs ended); and follow-up (three months after the programs ended).
•    261 students contributed useable baseline data; 220 (88%) of them contributed post-test data and 181 (69%) contributed follow-up data. 
•    At each assessment period, students completed a self-report of tobacco abstinence online and provided saliva samples to test for continine (a metabolite of nicotine). Salivary cotinine can be detected up to 72 h after cigarette and smokeless tobacco use.
•    There were two abstinence outcomes: 3-day (self-report of 3-day abstinence confirmed by negative continine sample) and 7-day (self-report of 7-day abstinence confirmed by negative continine sample).

Data Analysis
•    The authors report results from two types of analyses: intent-to-treat (ITT) and using all available data. Here we report results from the latter set of analyses, which are less conservative.
•    The authors used mixed model analyses to account for the intra-class correlation (ICC), or non-independence that resulted from student membership in particular classrooms and schools.

Results
•    Many participants falsely reported abstinence (61% at post-test and 38% at follow-up). Results reported here only include continine-verified abstinence.
•    At post-test, six students in the ACTION condition (5.6%) and nine students in the comparison condition (7.4%) achieved 3-day abstinence. Four students in the ACTION condition (3.7%) and three in the comparison condition (2.5%) achieved 7-day abstinence. Group differences were not significant for 3-day abstinence (OR=1.05; CI = 0.35-3.20) and 7-day abstinence (OR=1.56; CI = 0.32-7.71).
•    At follow-up, nine students in the ACTION condition (11.1%) and four students in the comparison condition (4%) achieved 3-day abstinence. Nine in the ACTION condition (11.1%) and three in the comparison condition (3%) achieved 7-day abstinence. Group differences were significant for both 3-day abstinence (OR=4.46; CI = 1.02-19.52; p < 0.05) and 7-day abstinence (OR=4.44; CI = 1.01-19.49; p < 0.05). See Figure 1.

Figure 1: Proportion of participants who achieved and failed to achieve 3-day verified abstinence at follow-up, as a function of program completed. Based on data provided by Stein-Seroussi et al. (2009).        
Limitations
•    The original protocol called for enrollment of 336 students. However, only 268 students (80%) actually participated. This sample size reduced statistical power and, consequently, the ability to detect significant group differences.
•    Also, according to the original protocol, participants were supposed to be interested in voluntarily participating in a study of smoking cessation. However, in several schools, participation was offered as an alternative to suspension for using tobacco on school grounds. Therefore, roughly 17% of participants indicated at the outset that they were not at all motivated to quit smoking. This might have diminished the effectiveness of both smoking cessation programs and limited the likelihood of detecting between-group differences.
•    Randomizing within classes (rather than schools) would have provided more power to detect significant effects. However, this would come at the expense of potential contamination between groups.
•    Program fidelity was low. Videotapes of the ACTION sessions revealed that on average, teachers only covered about half of the intended content. They also introduced their own content.
•    Results suggest that the effects of the ACTION program might become stronger over time. This claim is difficult to evaluate, however, because there was only one follow-up assessment at 90 days.


Conclusions
The group differences in abstinence rates at follow-up suggest that the ACTION program has promise as a smoking cessation program for adolescents from tobacco-growing communities. Future research in this area should seek to (1) increase statistical power (by enrolling larger samples and decreasing attrition rates), (2) decrease within-group variability (by ensuring program fidelity and standardizing participant enrollment procedures), and (3) increase the length of the follow-up period.

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References

Eaton, D. K., Kann, L., Kinchen, S., Shanklin, S., Ross, J., Hawkins, J., et al. (2008). Youth risk behavior surveillance--United States, 2007. MMWR Surveill Summ, 57(4), 1-131.

Stein-Seroussi, A., Stockton, L., Brodish, P., & Meyer, M. (2009). Randomized controlled trial of the ACTION smoking cessation curriculum in tobacco-growing communities. Addictive Behaviors, 34(9), 737-743.