Next Article in Journal / Special Issue
Pattern of Smoking Habit among Greek Blue and White Collar Workers
Previous Article in Journal
Biological Effects and Safety in Magnetic Resonance Imaging: A Review
Previous Article in Special Issue
Calibrating Self-Reported Measures of Maternal Smoking in Pregnancy via Bioassays Using a Monte Carlo Approach
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Peer Pressure, Psychological Distress and the Urge to Smoke

Institute of Population Health Sciences / National Health Research Institutes, No.35, Keyan Road, Zhunan Town, Miaoli County 350, Taiwan
School of Nursing / National Yang Ming University / No.155, Sec2, Linong St., Beitou District, Taipei City 112, Taiwan
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2009, 6(6), 1799-1811;
Submission received: 27 April 2009 / Accepted: 5 June 2009 / Published: 10 June 2009
(This article belongs to the Special Issue Smoking and Tobacco Control)




Psychology and addiction research have found that cigarette smokers react with subjective and automatic responses to stimuli associated with smoking. This study examines the association between the number of cigarettes smokers consume per month and their response to cues derived from peer and psychological distress.


We studied 1,220 adult past and current smokers drawn from a national face-to-face interview survey administered in 2004. We defined two types of cues possibly triggering a smoker to have a cigarette: peer cues and psychological cues. We used ordinary least square linear regressions to analyze smoking amount and response to peer and psychological distress cues.


We found a positive association between amount smoked and cue response: peer cues (1.06, 95%CI: 0.74–1.38) and psychological cues (0.44, 95%CI = 0.17–0.70). Response to psychological cues was lower among male smokers (−1.62, 95%CI = −2.26–−0.98), but response to psychological cues were higher among those who had senior high school level educations (0.96, 95%CI = 0.40–1.53) and who began smoking as a response to their moods (1.25, 95%CI = 0.68–1.82).


These results suggest that both peer cues and psychological cues increase the possibility of contingent smoking, and should, therefore, be addressed by anti-smoking policies and anti-smoking programs. More specifically, special attention can be paid to help smokers avoid or counter social pressure to smoke and to help smokers resist the use of cigarettes to relieve distress.

1. Introduction

Tobacco smoking is the world’s largest preventable cause of disease morbidity and mortality, and tobacco control policies play a very important role in reducing the economic cost of tobacco to society and the individual. Although prevention is an important aim of tobacco control, so is cessation. Cessation without assistance is very difficult due to the highly addictive nature of cigarettes and unpleasantness of withdrawing from nicotine [14]. Current treatment guidelines recommend healthcare providers advise every smoker to quit and provide treatment for his or her dependence [5,6].
Although there is an increase in effective and economical modes of treatment for tobacco dependence, including behavioral counseling and pharmacotherapy [5,79], such treatment remains underused [10] and the relapse rates are very high [1113]. Relapse after the 12th month of cession range from 10% to 28.4% [11,13], reducing the apparent effectiveness of various treatments for this addiction [11].
What keeps a smoker from successfully quitting cigarettes is an important question, not only for clinicians but also for policymakers and health economists. Clinically, most treatment protocols adhere to the well-known 5A’s model [5], which is to ask (screen for smoking), advise (provide a quit message), assess (evaluate readiness to quit), assist (provide treatment) and arrange (track cessation progress). Most providers who try to help their patients overcome their addiction to cigarettes prescribe nicotine patches, bupropion, or nicotine replacement therapy [14]. However, long-term relapse rates show that it will take more than pharmaceuticals to overcome the addiction to nicotine. Nicotine has been found to act in the midbrain, where it creates impulses to smoke in response to stimuli [15]. In addition, some studies applied the Pavlovian’s conditioning theory or social learning theories to highlight the role of environmental events or affective/physiological cues that may evoke conditioned reactions to substance use [1620].
In tobacco and addiction research, craving, defined as a subjective automatic response to related stimuli, is thought to contribute to the continuance of smoking and relapse [2125]. The concept of cigarette craving is often measured by using cue reactivity methods as cue reactivity has proven useful in studying addictive behaviors and is used as a treatment strategy for relapse prevention [18,2628]. Previous evidence suggests that affective experiences (i.e. positive or negative emotions, feeling stressed) and social situations (i.e. being in the company of others, with smokers present, while eating and/or drinking) can elicit urges to smoke [2932]. However, most studies have been done based on the laboratory experiments by using “visible” photographs, role-play situation or imagery scripts to investigate smokers’ mood, physiological changes, or self-reported craving [18,31,33,34]. Although laboratory-based approach offers greater potential for experimental manipulation of tobacco-specific cues, this approach is limited to whether there is a meaningful association between the cue-elicited craving and real smoking behavior. Smoking behavior is considered to be a set of complex responses to smoking cues which interact with the given social-environmental context. Some studies have noted that the patterning of smoking cues related to reactivity and the responses to cues vary according to smoking history, gender, and social situations [32,34,35]. The current literature has mainly documented the empirical findings in Western societies, and there is little information on cue responses among non-Western communities. The present study used a population-based sample from Taiwan to investigate the relationship between cue responses and the amount of cigarettes smoked among current and former smokers.

2. Materials and Methods

2.1. Data Collection

This study draws from data obtained in a three-stage random-sampled face-to-face interview survey on cigarette consumption administered in 107 townships and two metropolitan areas in Taiwan during 2004. We surveyed 3,874 non-institutionalized residents (1,908 men and 1,966 women) aged 18 or above. The surveys were reviewed and approved by the Institutional Review Board of National Health Research Institutes. Interviewees were asked to sign an informed consent detailing the purpose of the study, benefits and risks associated with participation and assuring confidentiality.
Of the 3,874 adult subjects surveyed in 2004, 1,104 (57.86%) of the 1,908 men and 116 (5.90%) of the 1,966 women were current smokers or past-smokers. Nine hundred and twenty-seven of the smokers (75.98%) were current smokers. The 2004 prevalence rates were similar to those found in the 2001 National Health Interview Survey (46.5% in men and 4.2% in women) [36].

2.2. Measures

Cue Response (CR). Cue responses consist of six items. The factor analysis confirms two factors (peer cues and psychological cues) which account for approximately 68 percent of variance. Psychometric measurement indicates the overall internal consistency is 0.79. Peer cues (CR_peer) measured how sensitive a smoker was to people smoking around him or her. The higher the value for CR_peer, the more likely it would be that the respondent would smoke when he or she saw someone smoking. Psychological cues (CR_psych) measured how likely it would be for a respondent to smoke when he or she was in a bad mood or stressed. The higher the CR psych value, the stronger the desire to smoker when challenged. The measures of these two CRs were defined based on the answers to the questions regarding the desire to smoke in different circumstances: 0 meaning not at all, 1 little, 2 moderate, 3 very much, and 4 extreme. CR_peer was calculated by summing up the answers for three circumstances: (1) being among smokers in a party or wedding banquet, (2) seeing someone else smoke, and (3) being around friends who are smoking. CR_psych was calculated by summing up the answers for three circumstances: (1) feeling nervous and anxious, (2) feeling angry, and (3) feeling frustrated. Each cue was scored from 0 to 12.
Smoking behavior and independent variables. The dependent variable, current amount smoked (CAS) was measured based on how many packs of cigarettes a smoker consumed per month. The current amount smoked for past smokers was defined as zero. Two variables were defined to represent the reasons people started smoking in the beginning: SOCIAL_MOTIVE and MOOD_MOTIVE. On the survey, smokers were asked to select from a list of eight possible reasons for beginning to smoke, including “to satisfy initial curiosity”, “to stimulate and maintain vitality”, “to enhance positive sensations”, “to control body weight”, or “to alleviate stress” on in response to “a tobacco advertisement” or “social activity”. They could choose more than one reason. They were assigned a SOCIAL_MOTIVE score of 1 if they indicated that they started for reasons of social interaction, and zero if not. They were assigned a MOOD_MOTIVE of 1 if they started as a means of coping with stress and zero if not. Dummy variables representing exposure to cigarette advertising, exposure to family smoke at home, and exposure to second-hand smoke in the workplace were assigned a value of 1 if participants indicated they has been exposed this in one of these ways and zero if not. Sociodemographic factors included gender, age, educational level, and employment status.

2.3. Statistical Methods

Linear regressions were used to analyze the amount smoked (CAS), cue-response to peer (CR_peer) and cue-response to psychological effect (CR_psych). First, ordinary least square (OLS) estimation was used to estimate regressions of CAS, CR_peer, and CR_psych with each of these variables, which were also included as independent variables in the estimations of the others. All models and tests included the total ever-smokers as well on male smokers alone. We also used the Hausman test [37] to check the endogeneity when some independent variables were correlated with the error term. The endogeneity problem may lead the biased estimate in OLS estimation. All statistical operations were performed using STATA version 7.

3. Results

Table 1 shows the descriptive data of the 1,220 smokers: 927 current smokers (836 males, 91 females) and 293 former smokers (268 males, 25 females). Two hundred and sixty-three (21.56%) reported that they first started smoking for social reasons (SOCIAL_MOTIVE) and 143 (11.72%) for stress relief (MOOD_MOTIVE). Most smokers, both former and current, reported being reactive to both peer and psychological cues (96.97% and 91.48%, respectively). Those who tended to smoke most were 35–54 years old (26.30 to 28.61 packs per month), male (26.26, Std = 15.74), and had junior high school levels of education (27.09, Std = 15.78) or below (27.34, Std = 17.45). Female smokers were different from male smokers in a few ways. Most obviously, only 9.48% of the women reported a social motive for first time smoking, in contrast to 22.83% of male smokers, but a higher percentage of women than men reported that they started in an effort to manage their moods (27.89% vs. 10.05%, respectively).
In Table 2, the average CR_peer scores and CR_psych scores were 6.25 (Std = 2.64) and 5.75 (Std = 3.34), respectively. Of the 1,220 smokers, 1,183 (96.97%) reported being reactive to peer cues (CR_peer > 0), mostly men (97.19% vs. 94.83%). The men also reported a higher response to peer cues than females (CR_peer 6.35, Std = 2.62 vs. 5.31, Std = 2.69. respectively). Another group difference in CR_peer was whether the smokers originally started smoking to manage moods. While a higher proportion of those who originally began smoking to manage their moods reported being responsive to positive CR_peer (97.90% vs. 96.84%), they had a lower average (mean = 5.92 vs. 6.29) than those who originally were not mood-motivated to start.
While most smokers (1,116, 91.48%) reported being reactive to psychological cues (CR_psych > 0), there were significant differences in CR_psych scores when analyzed by age, gender and education. Smokers over 55 years old were less likely to report being reactive to psychological cues (97.54% vs. 87.54%) and also had lower mean scores in this measure than younger groups (mean = 4.59 vs. 6.99). A greater proportion of female smokers reported being reactive to psychological cues than male smokers (94.83% vs. 91.12%), and these women also reported a higher mean response (7.48 vs. 5.57). A greater proportion of smokers (94.39%) with senior high school educations reported being reactive to psychological cues, reporting a higher mean CR_psych score (6.49, Std = 3.28) than smokers at other education levels.
Table 3a and Table 3b show the results of single-equation on amount smoked, CR_peer and CR_psych for total smokers and male smokers. The Hausman tests showed insignificant endogeneity in the regression on amount smoked, CR_peer and CR_psych. As can be seen in Table 3a for all smokers, there was a positive association between amount smoked and increased CR_peer values (0.05, 95%CI = 0.04–0.06) and CR_psych values (0.05, 95%CI = 0.03–0.06). There was also a negative association between CR_peer and employment status (−0.52, 95%CI = −0.97–−0.07), and a positive effect between CR_peer and exposure to secondhand smoke in the workplace (0.49, 95%CI = 0.12–0.85). The degree of response to psychological cues was lower for male smokers than female smokers (−1.62, 95%CI = −2.26–−0.98). Smokers who started smoking to cope with mood (MOOD_MOTIVE) tended to report higher reactivity to psychological cues (1.25, 95%CI = 0.68–1.82). Smokers aged 35–44, 45–54 and 55 years or above tended to report lower response to psychological cues than smokers aged 18–24 years (−0.98, 95%CI = −1.66–−0.30, −1.02, 95%CI = −1.74–−0.30 and −1.79, 95%CI = −2.57–−1.01). Smokers with senior high school levels (0.96, 95%CI = 0.40–1.53) reported higher response to psychological cues than those with elementary school or no education. Current smokers had higher smoking amount than former smokers (24.93, 95%CI = 23.06–26.80). Current smoker reported lower response to peer and psychological cues than former smokers (−1.37, 95%CI = −1.84–−0.91 and −1.19, 95%CI = −1.75–−0.63). The estimates for total smokers were very similar to those for male smokers (Table 3b).

4. Discussion and Conclusions

This study used a national sample to investigate the influence of external peer cues and internal psychological cues that might cause a smoker to want to have a cigarette in Taiwan. The findings suggest that response to peer cues plays a very important role in the smoking behavior of men in Taiwan and psychological cues is more important for women who are more likely to use smoking as a means of coping with psychological distress.
This study extends the existing research on cue-reactivity in addiction [27,28,33,38,39]. Most previous studies have investigated the influence of photographs or imagery scripts as cues to test reactivity in subjects who self-reported cravings or mood states or in subjects whose physiological changes such as heart rate or skin conductance were compared. This study measured responses to other variables, such as seeing others smoke and internally emerging psychological stimuli.
We extend and strengthen the current public health and economic research on tobacco control policies [12,39,40]. While most tobacco control polices have been based on price elasticity, regulation, health education aimed at imparting health information and enhancing knowledge regarding the hazards of smoking over the past 40 years, tobacco marketers have paid increasing attention to smokers’ psychological and attitudinal profiles, physical environment, and activities. They have changed from marketing product characteristics to a general public to targeting their markets physchographically. This study provides public health with greater insight into smoking behavior by looking into both environmental and affective cue-triggered smoking behavior and may help current tobacco control policies become more multi-dimensional.
The findings of this study show that response to peer cues plays a very important role in the behavior of smokers. The stronger the urge induced by peer cues, the greater the amount smoked. Most tobacco control literature emphasizes the importance of peer effect with regard to its influence on teenagers and much effort has devoted to educating this high-risk group. These findings indicate that, in addition to nicotine patches and regular health education, more should be done to help the smoker learn to avoid or counter such cues triggering smoking. Current cue-exposure treatments have shown promise in treating some addictive disorders [38,39], and may be useful for relapse prevention in the smoking cessation treatment [41,42]. In terms of the public health prevention aspect, we propose an integration of cue exposure methods to break the cue-elicited association through eliminating environmental cues and regulating cigarette advertisements in future anti-tobacco policies.
Another significant finding of this study regards the influence exerted by psychological cues on amount smoked. The influence of psychological cues can be traced back to the first time a person started smoking. The urge to smoke for some people may be born from an internal need to calm themselves down, not from a need to fit in socially. This study found women who smoked tended to be more reactive to psychological triggers than men, suggesting that women might be more likely to use smoking to temper emotions as a means of coping with stress, anxiety and depression than men. Current smoker tended to be more reactive to peer and psychological cues than former smokers. The findings of this study indicate that more needs to be done to provide anti-tobacco programs and campaigns aimed at detaching the smoking of cigarettes from the soothing of emotions, an approach conspicuously missing in tobacco control programs policy.
This study is subject to the limitations associated with use of survey data: subjective information and recall bias. With self-reported cue response to particular circumstances, there may exist a certain bias derived by subjectivity. Also, because the rate of smoking among women is very low in Taiwan, the study sample of female smokers was very small, limiting the generalization of our results to the general female population. Nevertheless, this population-based study contributes to our understanding of cigarette addiction, especially by reporting that social and psychological cues may induce smoking over diverse sociodemographic backgrounds. Future anti-tobacco prevention and control campaigns should be tailored to fit the psychological and attitudinal profiles of potential and current smokers. In Taiwan, more health education should be given to male smokers to help them learn to resist smoking when challenged by social stress, and more social support or consultation should be provided to women to help them resist smoking caused from psychological stress.


This research was supported by two grants: one from the Bureau of Health Promotion (Grant Number: BHP-92-Anti-Tobacco-2H02), and the other from an intramural grant of National Health Research Institutes (Grant Number: HP-093-PP-10).


  1. Shiffman, S; Patten, C; Gwaltney, C; Paty, J; Gnys, M; Kassel, J; Hickcox, M; Waters, A; Balabanis, M. Natural history of nicotine withdrawal. Addiction 2006, 101, 1822–1832. [Google Scholar]
  2. Hughes, JR. Effects of abstinence from tobacco: Valid symptoms and time course. Nicotine Tob. Res 2007, 9, 315–327. [Google Scholar]
  3. Hughes, JR. Effects of abstinence from tobacco: Etiology, animal models, epidemiology, and significance: A subjective review. Nicotine Tob. Res 2007, 9, 329–339. [Google Scholar]
  4. Hughes, JR; Keely, J; Naud, S. Shape of the relapse curve and long-term abstinence among untreated smokers. Addiction 2004, 99, 29–38. [Google Scholar]
  5. Fiore, MC; Bailey, WC; Cohen, SJ; Dorfman, SF; Goldstein, MG; Gritz, ER; Heyman, RB; Jaén, CR; Kottke, TE; Lando, HA; Mecklenburg, RE; Mullen, PD; Nett, LM; Robinson, L; Stitzer, ML; Tommasello, AC; Villejo, L; Wewers, ME. Treating tobacco use and dependence: clinical practice guideline; Department of Health and Human Services, Public Health Service: Rockville, MD, USA, 2000. [Google Scholar]
  6. Fiore, MC; Hatsukami, DK; Baker, TB. Effective tobacco dependence treatment. JAMA 2002, 288, 1768–1771. [Google Scholar]
  7. Etter, JF; Stapleton, JA. Nicotine replacement therapy for long-term smoking cessation: a metaanalysis. Tob. Control 2006, 15, 280–285. [Google Scholar]
  8. Parrott, S; Godfrey, C. Economics of smoking cessation. BMJ 2004, 328, 947–949. [Google Scholar]
  9. Hughes, JR; Shiffman, S; Callas, P; Zhang, J. A meta-analysis of the efficacy of over-the-counter nicotine replacement. Tob. Control 2003, 12, 21–27. [Google Scholar]
  10. Cokkinides, VE; Ward, E; Jemal, A; Thun, MJ. Under-use of smoking-cessation treatments: Results from the National Health Interview Survey, 2000. Am. J. Prev. Med 2005, 28, 119–122. [Google Scholar]
  11. Stapleton, JA; Sutherland, G; Russell, MAH. How much does relapse after one year erode effectiveness of smoking cessation treatments? Long term follow up of randomised trial of nicotine nasal spray. BMJ 1998, 316, 830–831. [Google Scholar]
  12. Pizacani, BA; Martin, DP; Stark, MJ; Koepsell, TD; Thompson, B; Diehr, P. A prospective study of household smoking bans and subsequent cessation related behaviour: the role of stage of change. Tob. Control 2004, 13, 23–28. [Google Scholar]
  13. Blondal, T; Gudmundsson, LJ; Olafsdottir, I; Gustavsson, G; Westin, A. Nicotine nasal spray with nicotine patch for smoking cessation: randomised trial with six year follow up. BMJ 1999, 318, 285–289. [Google Scholar]
  14. Schnoll, RA; Rukstalis, M; Wileyto, EP; Shields, AE. Smoking Cessation Treatment by Primary Care Physicians: An Update and Call for Training. Am. J. Prev. Med 2006, 31, 233–239. [Google Scholar]
  15. Balfour, DJK. The neurobiology of tobacco dependence: A preclinical perspective on the role of the dopamine projections to the nucleus. Nicotine Tob. Res 2004, 6, 899–912. [Google Scholar]
  16. Laverz, AB; Herzog, TA; Thaddeus, A; Brandon, TH. Classical conditioning of environmental cues: evidence with urge and reaction time. Exp. Clin. Psychopharmacol 1999, 6, 45–53. [Google Scholar]
  17. Carter, BL; Tiffany, ST. Meta-analysis of cue-reactivity in addiction research. Addiction 1999, 94, 327–340. [Google Scholar]
  18. Rohsenow, DJ; Niaura, RS; Childress, AR; Abrams, DB; Monti, PM. Cue reactivity in addictive behaviors: Theoretical and treatment implications. Int. J. Addiction 1990–1991, 25, 957–993. [Google Scholar]
  19. Abrams, DB; Niaura, RS. Social learning theory. In Psychological theories of drinking and alcoholism; Blane, HT, Leonard, KE, Eds.; Guilford: New York, NY, USA, 1987; pp. 131–178. [Google Scholar]
  20. Hammersley, R. Cue exposure and learning theory. Addict. Behav 1992, 17, 297–300. [Google Scholar]
  21. Drummond, DC; Litten, RZ; Lowman, C; Hunt, WA. Craving research: future directions. Addiction 2000, 95, S247–S255. [Google Scholar]
  22. Colby, SM; Tiffany, ST; Shiffman, S; Niaura, RS. Are adolescent smokers dependent on nicotine? A review of the evidence. Drug Alcohol Depend 2000, 59, 83–95. [Google Scholar]
  23. Stanton, WR; Lowe, JB; Gillespie, AM. Adolescents' experiences of smoking cessation. Drug Alcohol Depend 1996, 43, 63–70. [Google Scholar]
  24. Upadhyaya, HP; Drobes, DJ; Wang, W. Reactivity to in vivo smoking cues in older adolescent cigarette smokers. Nicotine Tob. Res 2006, 8, 135–140. [Google Scholar]
  25. Upadhyaya, HP; Drobes, DJ; Thomas, SE. Reactivity to smoking cues in adolescent cigarette smokers. Addict. Behav 2004, 29, 849–856. [Google Scholar]
  26. Marlatt, GA. Cue exposure and relapse prevention in the treatment of addictive behaviors. Addict. Behav 1990, 15, 395–399. [Google Scholar]
  27. Havermans, RC; Jansen, ATM. Increasing the efficacy of cue exposure treatment in preventing relapse of addictive behavior. Addict. Behav 2003, 28, 989–994. [Google Scholar]
  28. Drummond, DC. What does cue-reactivity have to offer clinical research? Addiction 2000, 95, S129–144. [Google Scholar]
  29. Baer, JS; Lichtenstein, E. Classification and prediction of smoking relapse episodes: An exploration of individual differences. J. Counsult. Clin. Psychol 1988, 56, 104–110. [Google Scholar]
  30. Niaura, RS; Rohsenow, DJ; Binkoff, JA; Monti, PM; Pedraza, M; Abrams, DB. Relevance of cue reactivity to understanding alcohol and smoking relapse. J. Abnormal Psychol 1988, 97, 133–152. [Google Scholar]
  31. Carpenter, MJ; Saladin, ME; DeSantis, S; Gray, KM. Laboratory-based, cue-elicited craving and cue reactivity as predictors of naturally occurring smoking behavior. Addict. Behav 2009, 34, 536–541. [Google Scholar]
  32. Burger, J; Gochfeld, M. Natural observation of smoking behavior: Are there sex, age, context or activity-related differences? Addict. Behav 1990, 15, 309–317. [Google Scholar]
  33. LaRowe, SD; Saladin, ME; Carpenter, MJ; Upadhyaya, HP. Reactivity to nicotine cues over repeated cue reactivity sessions. Addict. Behav 2007, 32, 2888–2899. [Google Scholar]
  34. Niaura, R; Abrams, DB; Pedraza, M; Monti, PM; Rohsenow, DJ. Smokers’ reactions to interpersonal interaction and presentation of smoking cues. Addict. Behav 1992, 17, 557–566. [Google Scholar]
  35. Tsai, YW; Tsai, TI; Yang, CL; Kuo, KN. Gender differences in smoking behaviors in an Asian population. J. Women’s Health 2008, 17, 971–978. [Google Scholar]
  36. Cheng, TY; Wen, CP; Tsai, MC; Tsai, SP. The current status of smoking behavior in Taiwan: data analysis from National Health Interview Survey in 2001. Taiwan J. Public Health 2003, 22, 453–464. [Google Scholar]
  37. Hausman, J. Specification tests in econometrics. Econometrica 1978, 46, 1251–1271. [Google Scholar]
  38. Niaura, R; Abrams, DB; Shadel, WG; Rohsenow, DJ; Monti, PM; Sirota, AD. Cue exposure treatment for smoking relapse prevention: a controlled clinical trial. Addiction 1999, 94, 685–695. [Google Scholar]
  39. O'Brien, CP; Childress, AR; McLellan, T; Ehrman, R. Integrating systematic cue exposure with standard treatment in recovering drug dependent patients. Addict. Behav 1990, 15, 55–365. [Google Scholar]
  40. Becker, GS; Murphy, KM. A theory of rational addiction. J. Polit. Econ 1988, 96, 675–700. [Google Scholar]
  41. Keeler, TE; Marciniak, M; Hu, TW. Rational addiction and smoking cessation: An empirical study. J. Socio-Economics 1999, 28, 633–643. [Google Scholar]
  42. Dols, M; Willems, B; van den Hout, M; Bittoun, R. Smokers can learn to influence their urge to smoke. Addict. Behav 2000, 25, 103–108. [Google Scholar]
Table 1. Descriptive statistics of the study sample of 1,220 current and past smokers.
Table 1. Descriptive statistics of the study sample of 1,220 current and past smokers.
Total (n = 1,220)Male smokers (n = 1,104)Female Smokers (n = 116)Monthly Amount Smoked (packs)
Total smokersCurrent smokersMale smokersCurrent male smokers
Current smokers92775.9883675.729178.4525.2815.6326.2615.74
Age (years)
Elementary school or no education30525.0028025.362521.5519.5819.2427.3417.4519.9019.6328.3717.56
Junior high school23719.4321419.382319.8321.6917.8027.0915.7822.4418.2428.2815.93
Senior high school55345.3349244.576152.5919.5516.3724.5314.6220.5516.6225.5514.68
Undergraduate or graduate school12210.0011510.4276.0311.6514.0418.9413.4911.9514.2819.3613.66
Employment Status
Social motive for the first-time smoking
Mood motive for the first-time smoking
Cue response
Exposure to cigarette advertisement
Exposure to second hand smoke in the workplace
Exposure to second hand smoke at home
Std: standard deviation
Table 2. Descriptive statistics on cue response.
Table 2. Descriptive statistics on cue response.
Cue Response on Peer (CR_Peer)Cue Response on Psychological Stimulus (CR_Psych)
Total smokersMale smokersTotal smokersMale smokers
Response (CR_Peer > 0)CR_PeerResponse (CR_Peer > 0)CR_PeerResponse (CR_Psych > 0)CR_PsychResponse (CR_Psych > 0)CR_Psych
Age (years)
Elementary school
or no education29797.386.212.7627497.866.352.7227489.844.703.1325390.364.643.10
Junior high school23097.056.142.6220897.206.162.6221289.455.453.3118988.325.163.22
Senior high school53696.936.342.5747897.156.482.5352294.396.493.2846394.116.263.25
Undergraduate or graduate school11795.906.132.7211095.656.152.7510586.075.583.469885.225.573.49
Employment Status
Social motive for the first-time smoking
Mood motive for the first-time smoking
Exposure to cigarette advertisement
Exposure to second hand smoke in the workplace
Exposure to second hand smoke at home
Std: standard deviation
Table 3a. Regressions on smoking amount, cue-response to peer influence (CR_peer) and cue-response to psychological effect (CR_Psych) for smokers.
Table 3a. Regressions on smoking amount, cue-response to peer influence (CR_peer) and cue-response to psychological effect (CR_Psych) for smokers.
Smoking AmountCR_peerCR_Psych
Coef95% CICoef95% CICoef95% CI
Smoking Amount0.
Age (years) (Ref: 18–24)
Gender (Ref: woman)
Education (Ref: Elementary school or no education)
Junior high school−1.05−3.61.50−0.11−0.620.400.16−0.450.77
Senior high school−3.05−5.42−0.670.15−0.320.630.960.41.53
Undergraduate or graduate school−6.27−9.39−−0.431.07
Employment Status (Ref: No)
Exposure to cigarette advertisement (Ref: No)
Exposure to second hand smoke at home (Ref: No)
Exposure to second hand smoke at workplace (Ref: No)
Social motive for the first-time smoking (Ref: No)
Mood motive for the first-time smoking (Ref: No)
Smoking status (Ref: former smoking)
Current smoking24.9323.0626.80−1.37−1.84−0.91−1.19−1.75−0.63
Hausman Test
χ2 value1.475.2412.91
Table 3b. Regressions on smoking amount, cue-response to peer influence (CR_peer) and cue-response to psychological effect (CR_Psych) for male smokers.
Table 3b. Regressions on smoking amount, cue-response to peer influence (CR_peer) and cue-response to psychological effect (CR_Psych) for male smokers.
Smoking AmountCR_peerCR_Psych
Coef95% CICoef95% CICoef95% CI
Smoking Amount0.
Age (years) (Ref:18–24)
Education (Ref: Elementary school or no education)
Junior high school−0.76−3.481.96−0.23−0.760.30−0.13−0.770.51
Senior high school−3.07−5.60−0.550.14−0.350.630.800.211.39
Undergraduate or graduate school−6.35−9.64−3.06−0.03−0.670.620.30−0.471.07
Employment Status (Ref: No)
Exposure to cigarette advertisement (Ref: No)
Exposure to second hand smoke at home (Ref: No)
Exposure to second hand smoke at workplace (Ref: No)
Social motive for the first-time smoking (Ref: No)
Mood motive for the first-time smoking (Ref: No)
Smoking status (Ref: former smoking)
Current smoking26.1424.1328.15−1.38−1.87−0.9−1.3−1.89−0.7
Hausman Test
χ2 value−940.21−848.21−938.04

Share and Cite

MDPI and ACS Style

Tsai, Y.-W.; Wen, Y.-W.; Tsai, C.-R.; Tsai, T.-I. Peer Pressure, Psychological Distress and the Urge to Smoke. Int. J. Environ. Res. Public Health 2009, 6, 1799-1811.

AMA Style

Tsai Y-W, Wen Y-W, Tsai C-R, Tsai T-I. Peer Pressure, Psychological Distress and the Urge to Smoke. International Journal of Environmental Research and Public Health. 2009; 6(6):1799-1811.

Chicago/Turabian Style

Tsai, Yi-Wen, Yu-Wen Wen, Chia-Rung Tsai, and Tzu-I Tsai. 2009. "Peer Pressure, Psychological Distress and the Urge to Smoke" International Journal of Environmental Research and Public Health 6, no. 6: 1799-1811.

Article Metrics

Back to TopTop