The Brief Addiction Science Information Source (BASIS)

ASHES, Vol. 5(6) – Resisting Tempting Foods and Cigarette Smoking.

Many smokers are concerned about gaining weight if they quit smoking. Doctors recommend that individuals concentrate on smoking cessation rather than dieting until they are certain that they will not relapse (Fiore et al., 2008). Imagine you are a cigarette smoker in front of a table full of tempting sweets that you are not allowed to eat. When you finally leave the room, will you be tempted to have a cigarette? This week the ASHES provides a critical review of a study by Shmueli and Prochaska (2009) that raises a similar question and then explains why it might be difficult to moderate food consumption and avoid smoking at the same time.

Method

• Investigators measured participants’ expired alveolar air to assess baseline levels of carbon monoxide (i.e., CO); CO levels serve as an indicator of recent tobacco smoking.
• Experimenters randomly assigned 97 smokers to two independent groups: one exposed to sweets and the other exposed to vegetables. An experimenter brought each participant to a room, where there was a plate of either sweets (i.e., cookies, and brownies) or vegetables (i.e., radishes and broccoli).
• Researchers required the participants to sit at the table for five minutes and did not allow them to eat anything from the plate. During the five-minute period, a bell rang at random intervals and at each ring the participant had to smell the plate of food and think about the temptation of eating the food.
• After a 10 minute break, participants provided investigators with another CO sample and also answered if they smoked during the break.

Results

• The agreement level between self-reported smoking and CO test was good, but not perfect (k = .88). All but one of the discrepancy cases involved participants who reported smoking during the break, but the CO reading did not confirm this.
• Participants who resisted sweets were more likely to smoke (based on an increase in CO level) during the break (n = 25, 53%) than those who resisted vegetables (n = 17, 34%), X²(97) = 3.65, p < .05. However, the effect size was small (i.e., φ =.19) and the self reported smoking rates of 51.1% and 42.0% for the sweets and vegetables groups were not significantly different.

Limitations

• Although statistically significant, the effect of “sweets” vs. “vegetables” resistance was small and was not supported by self-reported smoking.
• Participants in the study were not trying to inhibit smoking; therefore the self-control strength model was not directly tested.

Conclusion
The authors of this paper explain the results in terms of a self-control strength model (Muraven & Baumeister, 2000). According to this model, a person has limited resources for self-control. Therefore, after resisting temptation to eat a plate of food it becomes more difficult to resist smoking. This study supports the advice not to quit smoking and attempt a diet at the same time. However, the observed effect was not substantial and not supported by the self-reported results. Other studies (e.g., Drobes, 2002) have shown successful concurrent treatment for alcoholism and smoking. Despite the reported limitations, Shmueli and Prochaska present an interesting methodology for studying self-control and developing effective interventions for promoting quitting behavior. 

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

References

Drobes, D. J. (2002). Concurrent Alcohol and Tobacco Dependence: Mechanisms and Treatment. Alcohol Research & Health, 26(2), 136-142.

Fiore, M. C., Jean, C. R., Baker, T. B., Bailey, W. C., Benowitz, N. L., & Curry, S. J. (2008). Treating tobacco use and dependence: 2008 update.

Muraven, M., & Baumeister, R. F. (2000). Self-regulation and depletion of limited resources: does self-control resemble a muscle? Psychol Bull, 126(2), 247-259.

Shmueli, D., & Prochaska, J. J. (2009). Resisting tempting foods and smoking behavior: Implications from a self-control theory perspective. Health Psychology, 28(3), 300-306.

 

ASHES, Vol. 5(5) – The Effects of Smoking and Smoking Abstinence on Verbal and Visuospatial Working Memory Capacity

Smoking can cause numerous health problems. However, despite this fact, many people start and continue to smoke cigarettes. Often, smokers argue that smoking actually helps them concentrate and think more clearly. Is it actually so? Working memory is one of the mental systems involved in the thinking process. This week the ASHES reviews a study by Greenstein & Kassel (2009), which examines the effect of smoking on working memory. Working memory is a complex mental system that is involved in performing tasks that require both storage and processing information. How does smoking and smoking abstinence affect working memory capacity? Previous studies examining this question have produced inconsistent results (e.g., showing higher or lower performance for smokers and nonsmokers on different tasks) depending on the task itself. The study under review uses highly reliable, well-validated working memory tasks to examine the effect of smoking and smoking abstinence on verbal and visiospatial working memory capacity.

Methods

• Researchers recruited 49 male middle-aged participants, 26 smokers and 23 nonsmokers.
• All participants completed two experimental sessions (1 week apart).
  • During one session, smokers abstained from smoking for 12 hours or more. During the next session, investigators instructed smokers to smoke as they usually would.  
  • During each session, participants completed two tasks to measure their memory capacity.  
    1. OSPAN – a measure of verbal working memory capacity.
       •  Participants solved a math problem while simultaneously trying to recall a series of letters.   
       • Investigators calculated their performance as a proportion of correct responses multiplied by 100 to get an index that ranged from 0 to 100.   
    2. SSPAN – a measure of spatial working memory capacity. 
    • Participants recalled sequences of red-squared locations while performing a symmetry judgment task (deciding whether the presented figures were symmetrical or asymmetrical along the vertical axis).
• In addition, researchers measured demographic characteristics and cognitive abilities to assure the initial similarities in basic academic skills between smokers and nonsmokers (no differences were found).      

Results

• Investigators removed 15 participants (9 smokers and 6 nonsmokers) from the analyses, because of low performance in math or symmetry tasks (less than 80% accuracy). This ensured that the analyses included only those participants who were attempting to solve both tasks.
• Smokers manifested better OSPAN performance (verbal working memory task) when they were abstinent compared with when they were not abstinent. Also, nonsmokers performed better on this task than did smokers (see Figure 1).
• There were no significant differences between any groups for the SSPN (spatial working memory task) performance.
  

Figure 1. OSPAN (Operation Span Task) performance (* p < .05).

 
Limitations

• Is working memory actually the same as “concentration”? These two concepts are not necessary identical. 
• The study used a limited sample size.
• Participants were males only.

Conclusion

The study claims to test the conventional wisdom that smoking helps concentration. Rather then testing concentration, however, the authors examined the more specific effects of smoking on working memory capacity. The results demonstrate that smoking might cause different effects on different working memory domains. Specifically, (a) smokers’ verbal working memory performance is less effective compared to nonsmokers’, (b) smokers’ verbal working memory capacity is less effective after smoking than during abstinence, and (c) smoking does not affect visiospatial working memory span. These results suggest that smoking actually can impair some of the memory functions necessary for complex tasks (e.g., comprehension, learning, and reasoning). The authors also interpret their results as a demonstration that there is no evidence that smoking helps the ability to concentrate. This particular conclusion, however, seems to contradict the “conventional wisdom” and other scientific results. For example, a recent study (Zabala et al. 2009) showed that smoking facilitated cognitive functioning (including attention and working memory tasks performance) among psychotic patients. The contradiction between these studies might be due to the differences between psychotic and non-psychotic participants. To test this hypothesis and to resolve the contradiction, we need additional studies.

-Julia Braverman

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

References

 Greenstein, J., & Kassel, J. (2009). The effects of smoking and smoking abstinence on verbal and visuospatial working memory capacity. Experimental and Clinical Psychopharmacology, 17(2), 78-90.

Zabala, A., Eguiluz, J., Segarra, R., Enjuto, S., Ezcurra, J., Pinto, A., et al. (2009). Cognitive performance and cigarette smoking in first-episode psychosis. European Archives of Psychiatry and Clinical Neuroscience, 259(2), 65-71

ASHES, Vol. 5(4) – To Pee or not to Pee? Testing Urine to Predict Lung Cancer.

Some lifelong smokers do not develop lung cancer (Peto et al., 2000). The risk of developing lung cancer might vary as a function of smokers’ uptake and metabolism of carcinogens in tobacco smoke. NNK, a known carcinogen in rodents, is metabolically converted to NNAL, a human carcinogen (Hecht, 1998). Another common tobacco metabolite found in humans is cotinine; researchers often measure cotinine to determine exposure to tobacco smoke. This week’s ASHES reviews a study that examines the relationship between the urine levels of two carcinogen metabolites (NNAL and cotinine) and the risk for developing lung cancer (Yuan et al., 2009).

Methods:

• Participants were drawn from two cohorts:

          1. Shanghai Cohort (Ross et al., 1992):

• At baseline, researchers assessed tobacco use and collected urine samples among 18,244 participants. 
• 259 participants were diagnosed with lung cancer.  
• Participants with lung cancer (cases) were matched to cancer free participants (controls) based on the following criteria: age within 2 years, date of urine collection within 1 month, and residential neighborhood at baseline.  

          2. Singapore Cohort (Hankin et al., 2001):

• At baseline, researchers assessed tobacco use among 63, 257 participants. The current research team requested urine samples from all surviving participants.
  • 99 participants were diagnosed with lung cancer.
  • Controls were matched to cases based on the following criteria: gender, dialect group, baseline age within 3 years, date of baseline interview within 2 years, and date of urine collection within 6 months.

• Table 1 shows the exclusion of participants for the current study.
• Cases had a mean age of 65.6 (SD=6.8) years; controls matched at the time of cases’ cancer diagnosis had a mean age of 65.5 (SD=6.9) years.
  • Urine samples were taken pre-lung cancer diagnosis, with an average of 4.0 (SD=2.5) years prior. 
• The dependent measures were NNAL and cotinine levels.  

Table 1: Study Sample Cases and Matched Controls.
 

*Click image to enlarge, or adjust your browser's zoom setting.

Results:

• Figure 1 shows the odds ratios of the joint effects of cotinine and NNAL levels on the risk of developing lung cancer.
  • Compared to the low NNAL-low cotinine tertile, all odds ratios showed that the other tertiles were at a significantly increased risk for developing lung cancer. 
  • Participants in the highest tertile (high NNAL and cotinine levels) had an 8.47 fold (95% CI, 3.69-19.46) increased risk for developing lung cancer compared to participants in the lowest tertile (low NNAL and cotinine levels). 

Figure 1: Odds Ratios of NNAL and Cotinine Levels Risk for Developing Lung Cancer (adapted from Yuan et al., 2009). 

*Click image to enlarge, or adjust your browser's zoom setting.
Note. Cot stands for cotinine. Ca stands for cases. Co stands for controls.
The tertile cutoff values of NNAL for the Shanghai cohort were ≤0.105, 0.106 to 0.209, and ≥ 0.210 pmol/mg creatinine; for the Singapore cohort, ≤ 0.468, 0.469 to 0.819, and ≥ 0.820 pmol/mg creatinine.
ORs were adjusted for age, year of interview, year of sample collection, gender and dialect group, cohort location, number of curettes smoked per day, and number of years smoking.

Limitations:

• This study uses self-report to determine smoking history.
• The concentration of NNK varies in cigarette brands (e.g., lower NNK concentrations in cigarettes from the Shanghai cohort), and brands were not controlled in this study.
• In the Shanghai sample, there was significantly less intense smoking behavior in the control group compared with the case group at baseline; consequently, the results might emerge from smoking intensity and not metabolic activity.

Discussion:

The results of this study show that the extensive animal data (Hecht, 1998) suggesting a link between NNK and cancer also might apply to humans. However, more research is needed to confirm the relationship between the metabolites reviewed above and the risk for developing lung cancer. This study suggests that measuring metabolite levels might offer a more accurate way to assess the risk of developing lung cancer for smokers than an assessment based on self-reported smoking history.

-Tasha Chandler

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

References:

Hankin, J. H., Stram, D. O., Arakawa, K., Park, S., Low, S. H., Lee, H. P., et al. (2001). Singapore Chinese Health Study: Development, validation, and calibration of the quantitative food frequency questionnaire. Nutrition and Cancer, 39(2), 187-195.

Hecht, S. S. (1998). Biochemistry, biology, and carcinogenicity of tobacco-specific N-nitrosamines. Chemical Research in Toxicology, 11(6), 559-603.

Peto, R., Darby, S., Deo, H., Silcocks, P., Whitley, E., & Doll, R. (2000). Smoking, smoking cessation, and lung cancer in the UK since 1950: Combination of national statistics with two case-control studies. BMJ (British Medical Journal), 321(7257), 323-329.

Ross, R. K., Yuan, J. M., Yu, M. C., Wogan, G. N., Qian, G. S., Tu, J. T., et al. (1992). Urinary aflatoxin biomarkers and risk of hepatocellular carcinoma. Lancet, 339(8799), 943-946.

Yuan, J. M., Koh, W. P., Murphy, S. E., Fan, Y., Wang, R., Carmella, S. G., et al. (2009). Urinary levels of tobacco-specific nitrosamine metabolites in relation to lung cancer development in two prospective cohorts of cigarette smokers. Cancer Research, 69(7), 2990-2995.

ASHES, Vol. 5(3) - The No-Nicotine Blues: Investigating Why Some Smokers Feel Depressed While Trying to Quit.

Recent research has identified a correlation between nicotine withdrawal and depression. Scientists suggest that this association is related to changes in the function of serotonin transporter proteins, known to play a role in regulating mood, during periods of nicotine abstinence.  This week’s ASHES reviews an animal study investigating both the biological mechanisms underlying the appearance of depressive signs, and a potential treatment (Semba & Wakuta, 2008).

Methods

• Researchers implanted adult male rats with a pump that administered either nicotine or a neutral solution (control) at a constant rate for a period of 12 days.
• A separate group of rats also received bupropion treatment at 15 or 30mg/kg/day in addition to the nicotine or control treatments.
• Researchers removed the pumps and either (1) decapitated rats on the last day of treatment or (2) allowed the rats to enter into a withdrawal state, which lasted for an additional two days.
• Researchers prepared slides of rats’ brain tissue, including the dorsal raphe nuclei.
• in situ hybridization was performed to locate the mRNA that codes for serotonin transporter proteins.
• Researchers used computer software to create photographic images from the in situ slides.
• Scientists measured the relative optical density of the slides; this distinguishes areas positive for serotonin transporter mRNA from the rest of the brain tissue because this transporter appears darker than areas that do not contain it.
• Scientists compared the relative optical densities of the experimental and control groups’ slides using a t-test as the statistical tool.

Results

• As Figure 1 shows, expression of serotonin transporter coding mRNA in the dorsal raphe nuclei of rats exposed to nicotine for 12 days was significantly decreased (-24%) when compared to the control, t (15) = 3.18, p <0.01, immediately after nicotine administration was stopped.
• Two days following the termination of nicotine administration scientists identified a lesser, but still significant decrease (-14.9%), expression compared to the control, t (14) = 2.36, p<0.05.
• As Figure 2 shows, rats who were co-administered bupropion showed smaller decreases in serotonin transporter coding mRNA; the higher the dose, the smaller the decrease observed.

Figure 1: Expression of serotonin transporter mRNA in the dorsal raphe nucleus of male rats--both after twelve days of either saline or nicotine treatment, and after two days withdrawal from treatment (Adapted from Semba & Wakuta, 2008).

Figure 2: Effects of Bupropion on expression of serotonin transporter mRNA in the dorsal raphe nucleus of male rats (Adapted from Semba & Wakuta, 2008).

 

Limitations

• Findings from animal models do not always generalize to human beings.  The way nicotine effects the expression of serotonin transporter mRNA might vary across species.
• The synthesis of mRNA, which codes for serotonin transporter proteins, and the actual expression of these proteins is not always the same.
• Several similar studies have found no significant difference between control and experimental conditions. However, these studies used different tissue preparations and/or adolescent rats instead of adult rats. These differences in design could account for differences in results observed.
• Researchers are unsure how long the decrease in the expression of transporter coding mRNA lasts, and whether or not the change is permanent due to the fact that the in situ hybridization was not performed in live tissue.
• Biological processes and subjective states might not be consistent. In other words, reduced serotonin function might not cause depressed mood in rats as it does in humans.

Conclusions
Chronic exposure to nicotine can reduce the amount of serotonin transporter protein coding mRNA present in the dorsal raphe nuclei of rats. This reduction might underlie the feelings of depression that humans sometimes report while trying to abstain from smoking, making it very difficult for them to stop using products containing nicotine. Co-administration of Bupropion appears to help relieve this circumstance, and might be a useful tool to help smokers quit.

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

References

Semba, Jun'ichi; Wakuta, Mak (2008). Chronic effect of nicotine of serotonin transporter mRNA in the raphe nuclei of rats: Reversal by co-administration of bupropion. Psychiatry and Clinical Neurosciences, 62(4), 435-441.

ASHES, Vol. 5(2) - Advertising the Smoking while Drinking Phenomena

The tendency of people, especially young adults, to smoke while drinking is well documented in the scientific literature (e.g., Falk, Yi, & Hiller-Sturmhofel, 2006).  The tobacco industry suggested using the associations between smoking and drinking to “leverage …the correlation… in our marketing efforts” (p.1; Troutman & R.J. Reynolds Tobacco Co, 1990).  This week’s ASHES reviews a study that explores how frequently tobacco companies link alcohol to cigarettes in their advertisements. 

Methods
Belstock, Connolly, Carpenter, & Tucker (2008) reviewed each 2003 and 2004 issue of Maxim, FHM, Cosmopolitan, and Ebony. 

• The target audience of these magazines is 18-24 year olds for each magazine was (40.6%, 42.8%, 30.9%, and 18.9% of readers respectively).

The authors examined all alcohol and cigarette advertisements, using content analysis (κ = 0.79–1.0) to determine whether the advertisement:

• Was for alcohol, and pictured a bar or a club in the advertisement,
• Was for cigarettes, and pictured a bar or a club in the advertisement,
• Was for cigarettes, and contained a reference to drinking (e.g., a drink in the model’s hand) in the advertisement, or
• Was for alcohol, and contained a reference to smoking (e.g., a cigarette in the model’s hand) in the advertisement.

Results

• There were 114 unique cigarette advertisements and 317 unique alcohol advertisements.
• Of the cigarette advertisements, 32 (28.1%) referred to alcohol.  Of the alcohol advertisements, none referred to cigarettes.
• Table 1 shows that cigarette and alcohol advertisements were not statistically significant regarding their likelihood to show a bar or nightclub in the picture that appeared in the magazine.

Table 1. Characteristics of Cigarette and Alcohol Advertisements

Adapted from Belstock et al. (2008).
Click image to enlarge, or adjust your browser's zoom setting.

Limitations

• The researchers only reviewed four magazines during a two-year period. In addition to the limited magazine sample, tobacco companies use other advertising media, so the data reviewed might not be a representative sample of tobacco marketing.

Conclusion
Some tobacco advertisements referred to alcohol, but no alcohol advertisements referred to cigarettes.  This imbalanced relationship might be because alcohol advertisers consider an alcohol-cigarette pairing unfavorable, while tobacco advertisers consider it favorable.  It might be more favorable for tobacco advertisers because people smoke more while drinking (Falk et al., 2006), and unfavorable for alcohol advertisers because tobacco is less effective at changing people’s opinion of alcohol.

What do you think?  Comments can be addressed to Leslie Bosworth.

References
Belstock, S. A., Connolly, G. N., Carpenter, C. M., & Tucker, L. (2008). Using alcohol to sell cigarettes to young adults: A content analysis of cigarette advertisements. Journal of American College Health, 56(4), 383-389.

Falk, D. E., Yi, H.-Y., & Hiller-Sturmhofel, S. (2006). An epidemiologic analysis of co-occuring alcohol and tobacco use and disorders: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions. Alcohol Research & Health, 29(3), 162-171.

ASHES, Vol. 5(1) - Adolescent smoking trajectories: Do all adolescents fit the same pattern?

Cigarette smoking patterns typically become established during adolescence (Surgeon General, 1994). This week’s ASHES reports a trajectory study about the smoking behavior of adolescents grouped by early behavior (Bernat, Erickson, Widome, Perry, & Forster, 2008).

Methods

• The researchers used a random digit dialing procedure to recruit 3,637 Minnesota youths between the ages of 12 and 16 at baseline.
• The researchers interviewed participants every six months for a three year period and used a cohort-sequential design to identify changes in smoking behavior and smoking patterns.
• Researchers created smoking stages from the participants’ reports of cigarette use.
• Using semiparametric, group based methods developed by (Nagin, 1999), researchers grouped individuals who developed similar trajectories of smoking behaviors.
• Researchers used multivariate logit models to identify the association between baseline measures and smoking trajectory; the investigators included age at baseline, community type, race, and family structure as covariates during these analyses.

Results

• Figure 1 shows the typical smoking stage for six distinct patterns of tobacco use across each age of the sample; these patterns are nonsmokers (54%), triers (17%), occasional users (10%), early established (7%), late established (8%), and decliners (4%).
• Researchers did not find a group who regularly smoked throughout the length of the study. One of the interesting trajectory patterns was among the decliner group, which showed an exposure adaptation trend during the length of the study. The early and late established groups continued smoking into early adulthood and became regular smokers.

Figure 1 Smoking stages by age for identified Adolescent smoking trajectories (adapted from Bernate et al., 2008). 

Note. Smoking stages: 1 – never smoker: never smoked; 2 – trier: smoked one whole cigarette or less; 3 – less than a monthly smoker: smoked more than one cigarette, but not in the last 30 days; 4 – experimental smoker: smoked at least once in the last 30 days; 5 - regular smoker: smoked at least once in the last week; 6 – established smoker: smoked daily/almost daily

Limitations

• The study uses self report.
• The 58.5% response rate is low; more than one third of potential adolescents opted not to participate in the study. However, researchers were able to maintain a high follow-up rate (93.1%) for this limited sample.
• The study was conducted in Minnesota and 84% of the participants were Caucasian; this sample makes it difficult to generalize these findings to other populations.
• The three year follow-up period does not allow us to project these trajectories to their completion.

Conclusion
The researchers explored smoking-related factors and how this impacted smoking trajectory throughout adolescence. The investigators identified specific adolescent smoking trajectories. These patterns illustrate that teen smokers do not always follow a pattern of increasing tobacco use. This study shows that smoking patterns change often throughout adolescent years. Based on this research, prevention specialists need to design programs for specific trajectories.

What do you think?  Comments can be addressed to Tasha Chandler.

References

Bernat, D. H., Erickson, D. J., Widome, R., Perry, C. L., & Forster, J. L. (2008). Adolescent smoking trajectories: results from a population-based cohort study. Journal of Adolescent Health, 43(4), 334-340.

Nagin, D. S. (1999). Analyzing developmental trajectories: A semi-parametric, group-based approach. Psychological Methods, 4(2), 139-177.

Surgeon General. (1994). Preventing tobacco use among young people. A report of the Surgeon General. Executive summary. Morbidity and Mortality Weekly Report Recommendations and Reports, 43(RR-4), 1-10.

ASHES Vol. 4 (12) - Underestimating the Burning Desire to Smoke

    For cigarette smokers, the experience of craving is a powerful motivator. When people are craving cigarettes, they think about more positive aspects of smoking, they think their craving will intensify over time (even though it doesn’t), and they experience a slowing down of time (a sense of “extended now”) (as reviewed by Sayette et al., 2008). This all contributes to a visceral desire to smoke. However, when smokers are not in a craving state, they might have trouble estimating how powerful the urge to smoke will be during future craving episodes. In other domains, underestimating the extent to which visceral factors will impact future behavior is known as the “cold-to-hot empathy gap” (Loewenstein, 1996) This week’s ASHES reviews a study that documents the role of the cold-to-hot empathy gap in cigarette craving (Sayette et al., 2008).

Participants:
•    Participants were male and female adult cigarette smokers (N = 98) recruited from newspaper advertisements not currently interested in quitting but willing to abstain from smoking for up to 2 days.

Measures:
•    At Time 1, participants predicted the minimum amount of money they would require to postpone smoking for 5 minutes at Time 2, when they would be craving a cigarette.
•    Participants made this prediction either while in a craving state (i.e., hot) or a non-craving state (i.e., cold). Those who were randomly assigned to the craving state had abstained from smoking for at least the past 12 hours and were exposed to smoking cues while making the prediction. Those in a non-craving state had smoked as they normally would and were not exposed to smoking cues while making the prediction. The experimental groups did not differ in age, ethnicity, gender, martial status, number of cigarettes smoked per day, or nicotine dependence.
•    At Time 2, all participants had abstained from smoking for at least the past 12 hours. This was confirmed by measuring participants’ carbon monoxide levels. While holding a lit cigarette, participants were asked to indicate the minimum amount of money they would require to delay smoking for 5 minutes “right now.” Investigators did not report the study attrition rates.
•    Then, all participants were allowed to smoke immediately.
•    The main dependent measures were the amounts provided at Time 1 and Time 2.
•    The researchers hypothesized that participants who made their predictions while in a non-craving state would underestimate how much money they would require to postpone smoking at Time 2, but that no such difference would be found among those who made their predictions in a craving state.

Results:
•    A Group (craving, non-craving) by Time (1, 2) ANOVA (with repeated measures on the second factor) was significant, F (1, 72) = 7.83, p<.01.
•    Participants in the non-craving group showed a significant increase in the amount required to postpone smoking from Time 1 (M = $8.18, SD = 5.27) to Time 2 (M = $9.33, SD = 5.34), p < .05. This result corresponds to a small-to-medium effect size. No such increase was found for participants in the craving group (Time 1 M = $9.53, SD = 6.99; Time 2 M = $8.77, SD = 6.05).

Figure 1: Minimum amount of money required to postpone smoking for 5 minutes, as a function of Group during initial prediction and Time. Adapted from Sayette et al. (2008).

Limitations:
•    The researchers are making the assumption that the minimum amount of money required to postpone smoking for 5 minutes is a valid proxy for how strongly participants want to smoke. It is unclear whether this assumption is valid. However, participants also indicated their urge to smoke and the results using this measure were consistent with the monetary prediction results. In future research, it would be interesting to supplement these self-reports with confirmatory physiological indicators reflecting the visceral urge to smoke.
•    Investigators artificially created the craving state in this study. It is unclear to what extent this state is similar to the cravings smokers experience in everyday life, such as the craving brought on by stress or habits. 
•    It is not clear whether these results might generalize to other types of craving.

Conclusions:
    Smokers who were in a non-craving state failed to anticipate how powerful the urge to smoke would be when they were craving cigarettes. This result has at least two implications. First, non-smokers, who are always in a non-craving state, might not appreciate how difficult it would be for them to quit if they were to become nicotine dependent. This tendency to underestimate the power of craving might hamper efforts to prevent young people from smoking in the first place. Second, many times smokers decide to quit while they are in a non-craving state. Because smokers’ underestimate of how difficult it will be to abstain in the future, we can better understand why they so often fail to quit.  This brings up an interesting question: should clinicians advise smokers to wait until they are in a craving state before deciding whether to try quitting? If so, they might more accurately predict how difficult it will be to abstain in the future and they might be more likely to avoid high-risk situations that increase the urge to smoke. On the other hand, knowing how difficult it will be to quit smoking might make them less likely to attempt quitting.

What do you think? Comments can be addressed to Heather Gray.

References:

Loewenstein, G. (1996). Out of control visceral influences on behavior. Organizational Behavior and Human Decision Processes, 65, 272-292.

Sayette, M. A., Loewenstein, G., Griffin, K. M., & Black, J. J. (2008). Exploring the cold-to-hot empathy gap in smokers. Psychological Science, 19, 926-932.

ASHES Vol. 4 (11) – Cannabis and Tobacco Use: More than the sum of the smoke

The substances adolescents use correlates with unhealthy psychosocial functioning (e.g., Kirby, 2002). Information about the possible added effect of multiple substance use would help to clarify the association.  This week’s ASHES reports a study that compared the prevalence of unhealthy psychosocial functioning markers among adolescents who use only cannabis to the prevalence among adolescents who use both cannabis and tobacco. 

Method

• A Swedish national survey (Suris, Akre, Berchtold, Jeannin, & Michaud, 2007) queried 7548 students aged 16-20 about their substance use behavior and markers of psychosocial functioning, such as their academic performance.
• Researchers excluded from analyses: former tobacco smokers (n=342); occasional smokers (n=826); and daily smokers who have not used cannabis (n=1,117). 
• Among cannabis users (n = 2158), the study compared adolescents who used tobacco (78.9%) to adolescents who did not (21.1%).

Results
Table 1 shows that, compared to adolescents who used cannabis and tobacco, adolescents who only used cannabis, were:

• More likely to be male and were slightly but statistically significantly younger;
• More likely to have good grades, play sports; and
• Less likely to report having been drunk recently or having used cannabis frequently.

Table 1. Multivariate analysis comparing tobacco smokers to non-smokers among cannabis users 

Note: Significant differences between all variables (2-sample test of proportions at the 95% level).(Adapted from Suris et al., 2007)

Limitations

• The study uses self report.
• The study does not include adolescents who were not in school the day the researchers administered the survey. These missing people are likely school dropouts or truants who might have more substance use behaviors and poorer psychosocial markers. This suggests that these findings are conservative estimates of cannabis and tobacco use prevalence and sequelae.
• Using proxies for healthy psychosocial functioning, such as academic performance, might not be as sensitive as formal clinical assessment measures.
• The study is not longitudinal, so we cannot determine whether substance use initiation occurs in a progression.  Older adolescents might be more likely to use tobacco and cannabis compared to younger adolescents.
• The time since onset of cannabis use could correlate with markers of poor functioning.  Therefore, this study might actually assess differences in psychosocial markers as a function of age and tobacco use is a marker for age. 
• This study, conducted in Sweden, might not generalize to youth in other, culturally dissimilar countries.

Conclusion
Although tobacco is easily available and more socially acceptable, when used with cannabis, it is an indicator of potential poor psychosocial functioning.  Teachers, parents, and clinicians should be aware of the potential higher risk of poor functioning signaled by multiple substance use.

What do you think?  Comments can be addressed to Leslie Bosworth
References

Kirby, J. B. (2002). The influence of parental separation on smoking initiation in adolescents. Journal of Health and Social Behavior, 43(1), 56-71.

Suris, J. C., Akre, C., Berchtold, A., Jeannin, A., & Michaud, P.-A. (2007). Some go without a cigarette: Characteristics of cannabis users who have never smoked tobacco. Archives of Pediatric & Adolescent Medicine, 161(11), 1042-1047.

ASHES Vol. 4(10) - All the cool kids are doing it: An increase in waterpipe tobacco smoking?

The popularity of waterpipe tobacco smoking (i.e., hookah, shisha, hubble bubble, argileh), originating in Eastern Mediterranean countries, continues to grow internationally. Those using waterpipes as an alternative to standard cigarettes and cigars associate it with lower health risks (Smith-Simone, Maziak, & Ward, in press). However, these users are still ingesting tobacco, nicotine, and other associated toxins. Thus, it is important to monitor changes in waterpipe use prevalence and the long term effects such use has on both casual and regular users. Eissenberg et al. reported one of the first survey studies of a small sample of college students to explore the prevalence of waterpipe tobacco smoking (Eissenberg, Ward, Smith-Simone, & Maziak, 2008).

Participants / Methods

• Surveyed 1,194 students in an Introductory Psychology courses at Virginia Commonwealth University (VCU)
  • N = 744 (62.3% response rate)
• Researchers used a cross-sectional Internet survey to gather information about students’ demographics and waterpipe use.
  • Investigators also asked students to disclose information about their use, risk perceptions, influences, and perceived social acceptability of other tobacco products (e.g., cigarettes, cigars, cigarillos).

Results

• 151 participants (20.4%) reported smoking a waterpipe in the past 30 days.
  • The majority of these participants (see Figure 1) also smoked cigarettes, cigars, and cigarillos during the 30 days prior to participating in this study.
• Researchers also compared perceptions between users and non-users:
  • Waterpipe users were more likely to perceive waterpipe use as less harmful than cigarettes (OR = .31, CI = (.16-.58)).

Figure 1. Comparing characteristics of non-user and past 30-day waterpipe smokers
(adapted from Eissenberg, Ward, Smith-Simone, & Maziak, 2008)

Limitations

• The data represents a self-report survey completed on the Internet.
• The completion rate was low (37.7 % did not fill out the Internet survey).
• This student sample is a convenience sample that limits the generalizability of the results.

Discussion
Eissenberg et al. introduce worthwhile research ideas that relate directly to the effects of waterpipe tobacco smoking. Currently, researchers know that waterpipe smokers are exposed to more toxins than cigarette smokers (Shihadeh & Saleh, 2005) but the long-term health consequences have not yet been established. For example, we need to better understand the effect of waterpipe smoke on the cardiovascular system, the likelihood that waterpipe tobacco smoking causes addiction and its related problems, and the effectiveness of interventions to decrease smoking on waterpipe users. Additional research should address and clarify whether waterpipe smoking might serve as a gateway to tobacco, nicotine, and other psychoactive products because of the perception that waterpipe use is less harmful than cigarette use.  All of these important areas of inquiry examining waterpipe tobacco smoking will help researchers and clinicians create, improve, and subsequently implement the right prevention methods and treatment resources.

For more BASIS articles about waterpipe use please see:

Addictions and the Humanities, Volume 2(7) - Hooked on Hookah

What do you think? Comments can be addressed to Ingrid R. Maurice

References

Eissenberg, T., Ward, K. D., Smith-Simone, S., & Maziak, W. (2008). Waterpipe Tobacco Smoking on a US College Campus: Prevalence and Correlates. Journal of Adolescent Health, 42, 526-529.

Shihadeh, A., & Saleh, R. (2005). Polycycli aromatic hydrocarbons, carbon monoxide, "tar", and nicotine in the mainstream smoke aerosol of the narghile water pipe. Food and Chemical Toxicology, 43, 655-661.

Smith-Simone, S., Maziak, W., & Ward, K. (in press). Waterpipe tobacco smoking: Knowledge, attitudes, beliefs, and behavior in two US samples. Nicotine and Tobacco Research.

ASHES 4(9) - Life Transitions Inspire Smoking Cessation

Major life transitions might present the opportunity, perhaps even serve as the stimulus, to accomplish other transitions, such as changing identities from a smoker to a non-smoker.  This week’s ASHES examines whether people who transition from worker to retiree quit smoking at a higher rate than people who remain employed (Lang, Rice, Wallace, Guralnik, & Melzer, 2007). 

Methods

• The authors analyzed data from a sub-sample of the English Longitudinal Study of Ageing (ELSA), a UK-based prospective study of the general population.
• The study sample included 1,712 people who were smokers when they participated in the Health Survey for England (HSE) during 1998, 1999, or 2001, and who were 50 years old or older during the 2002 follow-up survey.  The participants reported their current work status during each interview.

Measures

• Follow-up smoking status: persistent smokers (n=1,148); non-smokers (n=564)
• Cotinine levels corroborated self-reported smoking status
• Employment status: 
  
  • Employed at baseline, employed at follow-up (Employed-Employed)
  • Employed at baseline, retired at follow-up (Employed-Retired)
  • Retired at baseline, retired at follow-up (Retired-Retired) and,
  • Other (e.g., long-term disability, full-time caregiver)

Results
Figure 1.  Smoking cessation rates by baseline and follow-up work status, all ages

Adapted from Lang et al., 2007.

• About one third of the sample quit smoking (32.9%) between baseline and follow-up interviews.
• The odds ratios for quitting smoking, among people aged 55-70 and excluding people who retired for health reasons, in a fully adjusted model with a sensitivity analysis, reflected a statistically significant increased rate of quitting among people who were transitioning their status from employed to retired:
  • Employed-employed    1.00 (referent)
  • Employed-retired        2.33 (95% CI 1.24-4.38)
  • Retired-retired           1.26 (95% CI 0.66-2.42)

Limitations

• Self report methodology
• The researchers did not investigate concomitant changes that might explain some of the difference in quit rates between the three groups.  For example, people working in certain occupations might have been less likely to quit smoking than people working in other occupations.
• We cannot determine how many people relapsed, and when they might have relapsed, after the follow-up interview.

Conclusion
More people quit smoking after they retired compared to people who remained working.  Although quitting smoking as soon as possible is important for the health of all smokers, smokers and health professionals can emphasize retiring as a developmental opportunity to change smoking patterns. This also might provide an opportunity to change other substance use patterns as well.  Future research will need to identify the reasons people quit smoking after they retire. Health professionals can use this information to encourage prospective retirees to follow suit.

What do you think?  Comments can be addressed to Leslie Bosworth.

Reference
Lang, I. A., Rice, N. E., Wallace, R. B., Guralnik, J. M., & Melzer, D. (2007). Smoking cessation and transition into retirement: Analyses from the English Longitudinal Study of Ageing. Age and Ageing, 36, 638-643.