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  • 1 Charles University in Prague, Czech Republic
  • | 2 National Monitoring Centre for Drugs and Addiction, Office of the Government of the Czech Republic, Czech Republic
  • | 3 National Institute of Mental Health, Czech Republic
  • | 4 Charles University and General University Hospital in Prague, Czech Republic
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Background and aims

Gambling in adolescence is often related to licit and illicit substance use. Some evidence shows that teenage smokers gamble more than non-smokers. The aim of the study is to analyze the relationship between problem gambling and smoking among Czech adolescents.

Methods

Data on 6,082 adolescents (50.1% boys and 49.9% girls) aged 15–19 years were collected as part of the ESPAD study in the Czech Republic in 2015. Logistic regression and linear regression models were used to test the hypothesis that the early onset of daily smoking increases the risk of problem gambling.

Results

The age of initiation of daily smoking seems to be a more reliable marker of the risk of problem gambling than smoking status or intensity of smoking. More than 20% of smokers who started smoking daily at the age of 12 years or earlier are at risk of problem gambling, which shows a significantly increased probability compared to non-smokers (OR = 2.7). Other factors that increase the chances of becoming a problem gambler include being male, of higher age, and a student of a secondary school.

Discussion and conclusions

The relationship between adolescent smoking and gambling is complex and is likely to be influenced by other underlying factors. Early daily smokers and at-risk gamblers tend in a similar way to risky behavior as a result of impulsivity. Interventions targeting early smoking and other substance-use behavior should not only aim at quitting smoking but could also include preventing smokers from developing problem gambling.

Abstract

Background and aims

Gambling in adolescence is often related to licit and illicit substance use. Some evidence shows that teenage smokers gamble more than non-smokers. The aim of the study is to analyze the relationship between problem gambling and smoking among Czech adolescents.

Methods

Data on 6,082 adolescents (50.1% boys and 49.9% girls) aged 15–19 years were collected as part of the ESPAD study in the Czech Republic in 2015. Logistic regression and linear regression models were used to test the hypothesis that the early onset of daily smoking increases the risk of problem gambling.

Results

The age of initiation of daily smoking seems to be a more reliable marker of the risk of problem gambling than smoking status or intensity of smoking. More than 20% of smokers who started smoking daily at the age of 12 years or earlier are at risk of problem gambling, which shows a significantly increased probability compared to non-smokers (OR = 2.7). Other factors that increase the chances of becoming a problem gambler include being male, of higher age, and a student of a secondary school.

Discussion and conclusions

The relationship between adolescent smoking and gambling is complex and is likely to be influenced by other underlying factors. Early daily smokers and at-risk gamblers tend in a similar way to risky behavior as a result of impulsivity. Interventions targeting early smoking and other substance-use behavior should not only aim at quitting smoking but could also include preventing smokers from developing problem gambling.

Introduction

The increased availability of gambling as a result of widespread access to the Internet and the high level of adolescents’ participation in gambling in developed countries are raising the importance of adolescent gambling as a public health issue (Volberg, Gupta, Griffiths, Olason, & Delfabbro, 2010). A recent review of worldwide research showed a high prevalence of both participation in gambling (Delfabbro, King, & Derevensky, 2016) and problem gambling (Calado, Alexandre, & Griffiths, 2017) in some European countries, suggesting that a substantial proportion of Czech adolescents might also be engaged in gambling activities. Data from the latest wave of the ESPAD study carried out in 2015 showed that 9% of 16-year-old Czech adolescents reported having gambled for money in the past 12 months (15% of the boys and 3% of the girls; Chomynová, Csémy, & Mravčík, 2016), or up to 18% if both online and offline gambling activities are considered (Molinaro et al., 2018). In the European context, the Czech Republic, together with Malta, Austria, Netherlands, Ukraine, Norway, Sweden, Liechtenstein, Lithuania, and Iceland, is one of the countries with a low prevalence of gambling (Molinaro et al., 2018).

Adolescents are more vulnerable to risky behaviors such as gambling than adults (Derevensky, Gupta, & Winters, 2003). Although adolescents should have limited access to gambling activities, a majority of adolescents have gambled for money at least once in their lifetime (Delfabbro et al., 2016). Although most adolescents who have gambled do not subsequently suffer from severe problems, some are at risk of developing a gambling disorder. Involvement in problem gambling in adolescence increases the severity of gambling-related problems in young adulthood, including indebtedness, psychosocial impacts, and suicidal behavior (Richard, Blaszczynski, & Nower, 2014). From the point of view of public health policy, identifying the predictors of adolescent problem gambling may be of great importance for prevention and early intervention programs.

Smoking, and especially daily smoking, is one of the most widespread forms of substance use among adolescents, representing a serious public health concern in itself, also because the majority of adult smokers started smoking in adolescence (United States Department of Health and Human Services, 2014). A positive correlation between problem gambling and smoking has been established in the literature both in adults (Hayatbakhsh, Clavarino, Williams, Bor, & Najman, 2012) and adolescents (Kong et al., 2013; Molinaro et al., 2018; Volberg et al., 2010; Weinberger et al., 2015a, 2015b).

Analyzing the relationship between adolescent gambling and smoking in a sample of Connecticut secondary school students, Weinberger et al. (2015b) showed evidence that smokers gambled more severely than non-smokers. A second study based on the same sample found out that gamblers at risk are more likely to be regular smokers, start smoking at a younger age, and smoke with greater intensity (Weinberger et al., 2015a); these findings are in line with the findings from a study among Spanish students (Míguez & Becona, 2015). These results are consistent with recognized theories of adolescent substance use such as Jessor’s problem–behavior theory (Donovan, Jessor, & Costa, 1991; Jessor, 1991) or general deviance syndrome theory (McGee & Newcomb, 1992), claiming that various forms of risk behavior and attitudes coexist among adolescents.

Although the existing evidence clearly shows a positive relationship between problem gambling and smoking in adolescence, to date, very little is known about causality and severity interactions. This study aims to analyze the relationship between smoking and gambling among Czech adolescents, with a focus on the relation between the intensity of smoking, early initiation into smoking, and the intensity of gambling and the risk of the development of problem gambling.

Data and Methods

Sample

Data were collected as part of the European School Survey Project on Alcohol and Other Drugs (ESPAD) 2015, a cross-sectional questionnaire survey focusing on substance use among 15- to 16-year-old students that was carried out in 35 European countries (Chomynová et al., 2016; The ESPAD Group, 2016).

The ESPAD target population is defined as regular students who turn 16 years in the calendar year of the data collection (students born in 1999 for the 2015 survey), who are present in the classroom on the day of the survey (Hibell, 2014). The ESPAD study covers all grades containing at least 10% of the target population (i.e., the ninth grade of middle schools, first grade of secondary schools, and relevant grades of 8-year grammar schools). A stratified random sample of schools from all regions of the Czech Republic was prepared to ensure a representative sample of schools according to the region and type of school. The participation of the students was voluntary; students had the right to refuse to fill in the questionnaire; altogether eight students refused to participate. The average time needed to complete the questionnaire was 46 min; younger students in middle schools needed more time compared to grammar school students. The Czech version of the ESPAD questionnaire was pilot-tested in one selected school prior to the survey data collection.

In the Czech Republic, a total of 6,707 self-completed questionnaires were collected at 209 randomly selected middle and secondary schools of all types in all regions of the country. For the purpose of the international ESPAD comparisons, only nationally representative samples of students reaching 16 years in the year of data collection are used (N = 2,738 in the Czech Republic in 2015); for the purpose of our analysis, we included all 6,082 valid observations from all students aged 15–19 years present in selected classes on the date of the data collection (50.1% boys and 49.9% girls). The aim of our analysis was to investigate risk factors related to problem gambling among Czech adolescents. Enlarging the data set to more than double significantly improves the precision of the results, especially when the subpopulation at risk is relatively small, and it allows the effect of age to be inspected, which would not be possible only among the subpopulation of 15- to 16-year-olds.

Measures

Logistic regression was used to identify the risk factors of problem gambling. The following independent variables entered the analysis: age, gender, school type, family structure (both parents = 1/different family structure = 0), three smoking status variables (occasional smoker = 1/other = 0; moderate daily smoker smoking up to 10 cigarettes a day = 1/other = 0; heavy daily smoker smoking 11+ cigarettes a day = 1/other = 0), and two variables of early initiation of daily smoking (at the age of 12 or earlier = 1/other = 0; at the age of 13 or 14 = 1/other = 0). The dependent variable of problem gambling was based on the Lie/Bet scale (Johnson et al., 1997) consisting of two items: “Have you ever had to lie to people important to you about how much you gambled?” (yes = 1/no = 0) and “Have you ever felt the need to bet more and more money?” (yes = 1/no = 0); students scoring 0 points for both questions were considered non-risky (0), whereas students scoring 1 or 2 points were considered to be at-risk gamblers (1).

A linear regression model was carried out on the subsample of students at risk according to the Lie/Bet scale in order to inspect the effect of the intensity of smoking on the intensity of gambling. The Consumption Screen for Problem Gambling (CSPG; Rockloff, 2012) was used to measure the intensity of gambling. The CSPG score entered the model as a dependent variable with the same explanatory variables as presented in the previous model. The CSPG consists of three questions measuring: (a) gambling frequency: “How often (if ever) have you gambled money in the last 12 months?” reported on a scale “I have not gambled money” = 0, “monthly or less” = 1, “2–4 times a month” = 2, “2–3 times a week” = 3, “4–5 times a week” = 4, and “6 or more times a week” = 5; (b) time spent on gambling: “How much time did you spend gambling on a typical day in which you gambled in the last 12 months?” reported on a scale “I have not gambled money” = 0 and “less than 30 min” = 0, “between 30 min and 1 hr” = 1, “between 1 and 2 hr” = 2, “between 2 and 3 hr” = 3, and “3 hr or more” = 4; and (c) gambling intensity: “How often did you spend more than 2 hr gambling (on a single occasion) in the last 12 months?” reported on a scale “I have not gambled money” = 0 and “never” = 0, “less than monthly” = 1, “monthly” = 2, “weekly” = 3, and “daily or almost daily” = 4. Summing up the scores on the three questions, the respondents can gain 0–13 points on the scale; respondents scoring 0–1 points were considered at no risk, those with a sum of 2–3 points were considered at low risk, and those scoring 4+ points were considered at high risk of problem gambling. The cut-off points were calculated according to the literature to provide results comparable to those of other surveys and/or countries (Rockloff, 2012). For the purposes of this paper, the terms “gamblers at risk of problem gambling” and “problem gamblers” are used interchangeable.

Hypotheses

A model was conducted to test for the following hypotheses:

  1. 1.Smoking status is a risk factor for the development of problem gambling.
  2. 2.The relationship between smoking and gambling is more pronounced when intensity is taken into account.
  3. 3.The age of smoking initiation plays a significant role in the development of problem gambling than smoking status itself.

Missing values

The majority of the students (78%) left some answers in the questionnaire blank. On average, three answers were missing per completed questionnaire. Only 124 students (2%) did not answer one or more questions analyzed in the models. These missing values were treated as non-risky behavior for the analysis as students reporting no risk behavior in a given time frame skipped questions asking about the frequency or intensity of such behavior in more detail. The elimination of these observations did not lead to statistically different coefficients in the model.

Ethics

The ESPAD study is a cross-sectional questionnaire survey carried out in randomly selected schools; the emphasis is placed on anonymity and voluntary participation. No ethical committee approval was required for the ESPAD study in the Czech Republic in 2015, as it was already the sixth wave of data collection within this international project. As the participating respondents were aged over 15 years, no parental consent for the students’ participation was required. The researchers followed all relevant legislation in the Czech Republic with regard to personal data protection, that is, no personal data identifying individual students were requested. The participants returned their questionnaires in sealed envelopes in order to protect their anonymity, and the mass processing of the data guaranteed their anonymity.

Results

Prevalence of problem gambling among Czech adolescents

The proportion of students at risk of problem gambling on the basis of the Lie/Bet scale was nearly 8%. A higher proportion of students at risk of problem gambling were observed among secondary school students (8%) compared to middle school students (5%). The significant difference between male and female students (13% of the males at risk, compared to 2% of the females) is in line with findings observed in previous studies on adolescent gambling.

Only a slight difference between occasional and moderate daily smokers and current non-smokers in terms of the prevalence of problem gambling was observed (Table 1). A significantly higher prevalence of problem gambling was found among heavy smokers, who showed a prevalence rate double that of non-smokers.

Table 1.

Prevalence of problem gambling among Czech adolescents

Lie/Bet score (distribution in %)At risk (1 or 2 points in % Lie/Bet)[95% CI]Total (N)
012
Total92.346.680.997.66[7.0–8.3]6,082
Gender
 Male87.0711.321.6112.93[11.7–14.1]3,047
 Female97.632.010.362.37[1.8–2.9]3,035
Type of school
 Middle95.014.120.874.99[7.6–9.1]1,263
 Secondary91.647.351.028.36[3.8–6.2]4,819
Current smoking status
 Non-smoker92.866.450.697.14[6.4–7.9]4,187
 Occasional smoker91.767.171.088.24[6.4–10.1]837
 Moderate daily smoker92.656.021.347.35[5.5–9.2]748
 Heavy daily smoker86.1310.003.8713.87[10.0–17.7]310
First cigarette
 Never93.256.250.506.75[5.7–7.8]2,016
 At 9 years or earlier87.139.653.2212.87[9.6–16.1]404
 At 10–12 years90.577.701.739.43[7.8–11.0]1,273
 At 13 or 14 years93.076.350.586.93[5.7–8.2]1,544
 At 15 years or later93.965.330.716.04[4.4–7.6]845
Initiation of daily smoking
 Never92.886.370.767.12[6.4–7.9]4,492
 At 9 years or earlier65.2226.098.7034.78[15.3–54.2]23
 At 10–12 years81.8812.755.3718.12[11.9–24.3]149
 At 13 or 14 years90.008.491.5110.00[7.4–12.6]530
 At 15 years or later93.475.630.906.53[4.9–8.2]888

Note. CI: confidence interval.

As regards, the age at which the first cigarette was smoked, the highest prevalence of problem gambling was observed among the youngest smokers (starting at the age of nine or younger), while starting smoking at 13 years or older did not increase the risk of problem gambling as compared with non-smokers.

The age of initiation into daily smoking seems to be a more reliable marker of the risk of problem gambling than smoking status or the age at which the first cigarette was smoked. One fifth of the students who smoked daily at the age of 12 or earlier were at risk of problem gambling. With postponement of the initiation into daily smoking to 13 or 14 years, the prevalence rate decreased sharply to 10%, and further to 7% among those who started smoking daily at 15 years or later.

When the population of daily smokers is sorted according to the age at which they initiated with smoking, a clear positive relationship can be observed between the risk of problem gambling and the age of daily smoking initiation (Figure 1).

Figure 1.
Figure 1.

Prevalence of problem gambling by initiation into daily smoking. Note. The black lines represent 95% confidence intervals

Citation: Journal of Behavioral Addictions J Behav Addict 8, 1; 10.1556/2006.8.2019.04

The increased prevalence of problem gambling among early smokers may also be explained by the fact that early smokers are often heavy smokers, leading to a correlation between heavy smoking and problem gambling. However, when the age of initiation of daily smoking was controlled for by excluding students who started to smoke daily before the age of 15 years, no significant differences were observed between different levels of intensity of smoking. This suggests that it is the early initiation of daily smoking rather than intensity of smoking that increases the risk of problem gambling (Figure 2).

Figure 2.
Figure 2.

Prevalence of problem gambling in a subsample excluding daily smokers starting before the age of 15 years. Note. The black lines represent 95% confidence intervals

Citation: Journal of Behavioral Addictions J Behav Addict 8, 1; 10.1556/2006.8.2019.04

Problem gambling intensity and smoking intensity

The relationship between the intensity of gambling and smoking among the subgroup of adolescent gamblers at risk of problem gambling was further analyzed using the CSPG.

When the means of the CSPG score between smoking and non-smoking gamblers at risk were compared, a significantly higher score was observed among smoking gamblers (Figure 3). According to the CSPG, non-smoking individuals gamble with lower intensity and face a lower risk of problem gambling than their smoking peers.

Figure 3.
Figure 3.

Smoking status and average CSPG score of gamblers at risk. Note. The black lines represent 95% confidence intervals

Citation: Journal of Behavioral Addictions J Behav Addict 8, 1; 10.1556/2006.8.2019.04

Gamblers who smoke up to 10 cigarettes a day, or smoke occasionally, achieve similar CSPG scores (around 3 points) and do not differ significantly from non-smokers. A significantly higher intensity of gambling is associated with heavy smoking (11+ cigarettes a day). On average, heavy smokers scored 5+ points on the CSPG scale, which suggests a high intensity of gambling with a high risk of developing problem gambling.

The hypothesis that early initiation into daily smoking increases the risk of problem gambling was further tested in a logistic regression model. In line with the hypothesis, smokers who start smoking daily at an early age should be at higher risk of problem gambling even when other explanatory variables such as gender, age, the type of the school (middle or secondary school), or family composition are controlled for. The logistic regression model showed that the variables of early initiation into daily smoking are significant – students who started smoking daily at the age of 12 years or earlier have 2.7 times higher odds of problem gambling than smokers who started later or non-smokers. Those who start smoking at the age of 13 or 14 years have odds ratios (ORs) around 1.6. As already suggested, the variables of the intensity of smoking are not significant (OR close to 1). As expected, being male especially, older, and a student of a secondary school (rather than middle school) increases the chances of being a problem gambler, while a complete family composition acts as a protective factor (Table 2).

Table 2.

Predictors of problem gambling – Logistic regression

BSEz statisticspOdds ratio [95% CI]
Intercept−6.5871.174−5.613.000
Male1.7740.13213.451.0005.9 [4.6–7.7]
Age0.1810.0702.589.0101.2 [1.0–1.4]
Middle school−0.3450.162−2.135.0330.7 [0.5–1.0]
Daily smoker from 13 or 14 years0.4490.1782.529.0111.6 [1.1–2.2]
Daily smoker at the age of 12 years or earlier1.0000.2144.679.0002.7 [1.8–4.1]
Living with both parents−0.2210.102−2.165.0300.8 [0.7–1.0]
Occasional smoker0.1220.1460.836.4031.1 [0.8–1.5]
Daily smoker – up to 10 cigarettes a day−0.2350.170−1.379.1680.8 [0.6–1.1]
Daily smoker – more than 10 cigarettes a day0.2630.1991.321.1871.3 [0.9–1.9]

Note. Model: Observations = 6,082; Akaike information criterion = 2,981; Nagelkerke’s R2 = .126; SE: standard error; CI: confidence interval.

In addition, gamblers at risk of problem gambling were inspected in order to show how the intensity of smoking affects the intensity of gambling. To understand which of the two factors – the intensity of smoking or early initiation into daily smoking – predicts a higher CSPG score, a linear regression model with the logarithm of the CSPG score as a dependent variable was created, using the same explanatory variables as in the previous model (Table 3). On average, male gamblers scored about 60% higher than their female peers. In line with previous analysis, heavy smoking increases the CSPG score by 75%. The age of initiation into daily smoking is an important factor for the intensity of gambling – gamblers smoking daily at the age of 12 years or earlier scored 84% higher.

Table 3.

Predictors of CSPG score – Linear regression on a subsample of gamblers in risk

BSEt statisticsp% change [95% CI]
Intercept−0.8391.030−0.815.416−83.9 [−289.8–122.0]
Male0.4740.1233.868.00060.7 [25.7–105.3]
Age0.0710.0631.136.2577.1 [−5.4–19.6]
Middle school0.0260.1660.155.8772.6 [−26.4–43.0]
Daily smoker from 13 or 14 years0.1490.1730.858.39116.0 [−17.9–64.1]
Daily smoker at the age of 12 years or earlier0.6100.2052.968.00384.0 [22.0–177.5]
Living with both parents−0.0460.102−0.453.651−4.5 [−22.0–17.0]
Occasional smoker0.1210.1450.832.40612.8 [−15.6–50.8]
Daily smoker – up to 10 cigarettes a day0.0870.1620.540.5909.1 [−21.0–50.7]
Daily smoker – more than 10 cigarettes a day0.5590.2172.582.01075.0 [13.4–169.9]

Note. Model: Observations = 466; R2 = .087; Adjusted R2 = .069; CSPG: Consumption Screen for Problem Gambling; SE: standard error; CI: confidence interval.

Discussion

This study represents the first attempt to investigate the relationship between problem gambling and smoking among adolescents in the Czech Republic. Using the ESPAD data, we presented the relationship between problem gambling and smoking, which shows that early initiation into daily smoking increases the risk of problem gambling as well as the intensity of gambling in problem gamblers. Interestingly, and contradictory to Weinberger et al. (2015a), no clear evidence for an association between the intensity of smoking and the risk of problem gambling was shown. On the other hand, among gamblers who are already at risk, a high intensity of smoking is associated with a higher intensity of gambling.

Slutske, Moffitt, Poulton, and Caspi (2012) argued that the individual risk of being addicted to pathological behavior depends strongly on the individual’s temperament, which is observable as soon as at the age of 3 years; and both impulsivity and sensation-seeking play an important role in the development of risk behaviors such as substance use and gambling. Working on the presumption, it means that students with low behavioral and emotional self-control (e.g., with increased impulsivity) tend to engage in risky behavior, such as daily smoking, binge drinking, use of other substances, or gambling. Since cigarettes are one of the most accessible addictive substances in the Czech environment, early initiation into daily smoking may represent an important indicator for general vulnerability for the development of risky behavior, including problem gambling. However, as some authors (Canale, Scacchi, & Griffiths, 2016; Leeman, Hoff, Krishnan-Sarin, Patock-Peckham, & Potenza, 2014; Malmberg et al., 2013) have argued, part of the relationship between impulsivity, sensation-seeking, and participation in gambling might be influenced by involvement in a part-time job that provided adolescents with money available for gambling activities.

Adolescent problem gambling is strongly associated with impulsivity and delay discounting (Cosenza & Nigro, 2015; Nigro & Cosenza, 2016), and the same cognitive distortions are also connected to smoking (Friedel, Dehart, Madden, & Odum, 2014). Individuals with impulsivity and steep delay discounting may be regarded as a vulnerable group suitable for prevention and intervention programs targeted at all kinds of risk behaviors, focusing on the most prevalent risk activities. In general, adolescents “living in the now” (Nigro & Cosenza, 2016) might be at greater risk of any problem behavior. Our finding that earlier initiation into smoking is associated with the development of problem gambling some years later might also be explained by the common risk factor of impulsivity and delay discounting.

Empirical evidence also shows that gambling in adolescence is associated not only with the use of tobacco, but also with other substances such as alcohol, illicit drugs (especially cannabis, but also cocaine or non-medical use of prescription drugs), or energy drinks (Canale et al., 2017; Cook et al., 2015; Vieno et al., 2018). It is also associated with risky sexual behavior (Martins, Lee, Kim, Letourneau, & Storr, 2014; Räsänen, Lintonen, Joronen, & Konu, 2015) and other health risks and various forms of anti-social behavior such as driving under the influence of alcohol, being involved in a fight, or carrying a weapon (Chaumeton, Ramowski, & Nystrom, 2011; Mishra, Lalumière, Morgan, & Williams, 2011; Proimos, DuRant, Pierce, & Goodman, 1998).

In this study, adolescents already at risk of problem gambling gambled more intensively if they had started smoking at the age of 12 years or earlier and/or if they smoked 11 or more cigarettes a day. Even when heavy smoking itself does not significantly increase the risk of problem gambling as measured by the Lie/Bet screen, it predicts a higher intensity of gambling in gamblers at risk. Such results argue in favor of the problem behavior theory (Jessor, 1991) suggesting that impulsivity increases the risk of smoking and gambling addiction and that impulsive behavior leads to more intensive smoking and gambling. At the moment, we are not aware of any comparable study in another country that may confirm or refute these findings. However, we believe that our findings may be generalized to other (European) countries, as they try to describe a general relationship between two different forms of risk behavior among adolescents. More research is needed to understand the multifaceted association between smoking and gambling as they coexist in adolescence, being also associated with other forms of risk behaviors from very early ages.

These findings may have implications for prevention policy. In this study, early initiation into daily smoking in late childhood has proved to be a strong predictor of problem gambling in adolescence. Early interventions targeted at young smokers should aim not only at their quitting smoking but also at preventing smokers from developing the risk of problem gambling and other risk behaviors.

Limitations of this study

Several limitations of this study exist. The ESPAD study’s target population is the cohort of 15- to 16-year-old students attending regular schools that are randomly selected in order to reach a representative sample on a national level. In order to investigate risk factors related to problem gambling among Czech students, we decided, for the purpose of our analysis, to include all valid observations collected within the project. Similarly to other cross-sectional surveys, the ESPAD data do not allow testing for the causality of the relationship between different forms of risky behaviors, so including students of other ages may help to shed light on the associations between the variables analyzed. Another limitation of the study lies in the self-reporting design of the ESPAD questionnaire; students might overestimate, as well as underestimate, their responses. Moreover, school surveys like ESPAD use cluster sampling, which would increase the size of the confidence intervals. Still, the ESPAD study is considered to be a valuable source of data on adolescent risky behavior (e.g., Király et al., 2014; Molinaro et al., 2014; Vorobjov, Saat, & Kull, 2014), including participation in gambling activities (Molinaro et al., 2018).

Conclusions

Early initiation of daily smoking increases the risk of problem gambling and the intensity of gambling in problem gamblers. However, the relationship between adolescent smoking and gambling seems to be more complex than just simply positively correlated. On the basis of the results of this study, it is highly probable that the relationship is rather indirect. Early daily smoking, as well as other substance use and problem gambling, is likely to arise from common psychological traits expressed as impulsive behavior.

The identification of pre-adolescent daily smokers and prevention programs targeting those not only at risk of smoking, but also of other forms of substance use and risk behavior, including excessive gambling, could significantly reduce the prevalence of adverse consequences in adolescents and help them to pass through adolescence in a better health and social state.

Authors’ contribution

PCh and VM were responsible for the survey data collection and ESPAD survey methodology compliance. MŠ drafted the paper and performed statistical analyses. All authors contributed to literature review and the final revision of the manuscript. All authors had full access to the survey data and read and approved the final manuscript.

Conflict of interest

The authors declare no conflict of interest.

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  • Delfabbro, P., King, D. L., & Derevensky, J. L. (2016). Adolescent gambling and problem gambling: Prevalence, current issues, and concerns. Current Addiction Reports, 3(3), 268274. doi:10.1007/s40429-016-0105-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derevensky, J. L., Gupta, R., & Winters, K. (2003). Prevalence rates of youth gambling problems: Are the current rates inflated? Journal of Gambling Studies, 19(4), 405425. doi:10.1023/A:1026379910094

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donovan, J. E., Jessor, R., & Costa, F. M. (1991). Adolescent health behavior and conventionality-unconventionality: An extension of problem-behavior theory. Health Psychology, 10(1), 5261. doi:10.1037/0278-6133.10.1.52

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Friedel, J. E., Dehart, W. B., Madden, G. J., & Odum, A. L. (2014). Impulsivity and cigarette smoking: Discounting of monetary and consumable outcomes in current and non-smokers. Psychopharmacology, 231(23), 45174526. doi:10.1007/s00213-014-3597-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayatbakhsh, M. R., Clavarino, A., Williams, G. M., Bor, W., & Najman, J. M. (2012). Young adults’ gambling and its association with mental health and substance use problems. Australian and New Zealand Journal of Public Health, 36(2), 160166. doi:10.1111/j.1753-6405.2011.00815.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hibell, B. (2014). Overview of the ESPAD Project: Background, methodology and organisation. The ESPAD Handbook, Section 2. Lisbon, Portugal: EMCDDA.

    • Search Google Scholar
    • Export Citation
  • Jessor, R. (1991). Risk behavior in adolescence: A psychological framework for understanding and action. Journal of Adolescent Health, 12(8), 597605. doi:10.1016/1054-139X(91)90007-K

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, E. E., Hamer, R., Nora, R. M., Tan, B., Eisenstein, N., & Engelhart, C. (1997). The Lie/Bet Questionnaire for screening pathological gamblers. Psychological Reports, 80, 8388. doi:10.2466/pr0.1997.80.1.83

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Király, O., Griffiths, M. D., Urbán, R., Farkas, J., Kökönyei, G., Elekes, Z., Tamás, D., & Demetrovics, Z. (2014). Problematic Internet use and problematic online gaming are not the same: Findings from a large nationally representative adolescent sample. Cyberpsychology, Behavior, and Social Networking, 17(12), 749754. doi:10.1089/cyber.2014.0475

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kong, G., Tsai, E., Pilver, C. E., Tan, H. S., Hoff, R. A., Cavallo, D. A., Krishnan-Sarin, S., Steinberg, M. A., Rugle, L., & Potenza, M. N. (2013). Differences in gambling problem severity and gambling and health/functioning characteristics among Asian-American and Caucasian high-school students. Psychiatry Research, 210(3), 10711078. doi:10.1016/j.psychres.2013.10.005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leeman, R. F., Hoff, R. A., Krishnan-Sarin, S., Patock-Peckham, J. A., & Potenza, M. N. (2014). Impulsivity, sensation-seeking, and part-time job status in relation to substance use and gambling in adolescents. Journal of Adolescent Health, 54(4), 460466. doi:10.1016/j.jadohealth.2013.09.014

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Malmberg, M., Kleinjan, M., Overbeek, G., Vermulst, A. A., Lammers, J., & Engels, R. C. (2013). Are there reciprocal relationships between substance use risk personality profiles and alcohol or tobacco use in early adolescence? Addictive Behaviors, 38(12), 28512859. doi:10.1016/j.addbeh.2013.08.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martins, S. S., Lee, G. P., Kim, J. H., Letourneau, E. J., & Storr, C. L. (2014). Gambling and sexual behaviors in African-American adolescents. Addictive Behaviors, 39(5), 854860. doi:10.1016/j.addbeh.2014.02.002

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGee, L., & Newcomb, M. D. (1992). General deviance syndrome: Expanded hierarchical evaluations at four ages from early adolescence to adulthood. Journal of Consulting and Clinical Psychology, 60(5), 766776. doi:10.1037/0022-006X.60.5.766

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Míguez, M. C., & Becona, E. (2015). Do cigarette smoking and alcohol consumption associate with cannabis use and problem gambling among Spanish adolescents? Addiciones, 27(1), 816. doi:10.20882/adicciones.189

    • Search Google Scholar
    • Export Citation
  • Mishra, S., Lalumière, M. L., Morgan, M., & Williams, R. J. (2011). An examination of the relationship between gambling and antisocial behavior. Journal of Gambling Studies, 27(3), 409426. doi:10.1007/s10899-010-9217-x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molinaro, S., Benedetti, E., Scalese, M., Bastiani, L., Fortunato, L., Cerrai, S., Canale, N., Chomynova, P., Elekes, Z., Feijão, F., Fotiou, A., Kokkevi, A., Kraus, L., Rupšienė, L., Monshouwer, K., Nociar, A., Strizek, J., & Urdih Lazar, T. (2018). Prevalence of youth gambling and potential influence of substance use and other risk factors throughout 33 European countries: First results from the 2015 ESPAD study. Addiction, 113(10), 18621873. doi:10.1111/add.14275

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molinaro, S., Canale, N., Vieno, A., Lenzi, M., Siciliano, V., Gori, M., & Santinello, M. (2014). Country- and individual-level determinants of probable problematic gambling in adolescence: A multi-level cross-national comparison. Addiction, 109(12), 20892097. doi:10.1111/add.12719

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nigro, G., & Cosenza, M. (2016). Living in the now: Decision-making and delay discounting in adolescent gamblers. Journal of Gambling Studies, 32(4), 11911202. doi:10.1007/s10899-016-9595-9

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Proimos, J., DuRant, R. H., Pierce, J. D., & Goodman, E. (1998). Gambling and other risk behaviors among 8th- to 12th-grade students. Pediatrics, 102(2), e23. doi:10.1542/peds.102.2.e23

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Räsänen, T., Lintonen, T., Joronen, K., & Konu, A. (2015). Girls and boys gambling with health and well-being in Finland. Journal of School Health, 85(4), 214222. doi:10.1111/josh.12246

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richard, D. C. S., Blaszczynski, A., & Nower, L. (2014). The Wiley-Blackwell handbook of disordered gambling. Chichester, UK: John Wiley & Sons, Ltd.

    • Search Google Scholar
    • Export Citation
  • Rockloff, M. J. (2012). Validation of the Consumption Screen for Problem Gambling (CSPG). Journal of Gambling Studies, 28(2), 207216. doi:10.1007/s10899-011-9260-2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slutske, W. S., Moffitt, T. E., Poulton, R., & Caspi, A. (2012). Undercontrolled temperament at age 3 predicts disordered gambling at age 32: A longitudinal study of a complete birth cohort. Psychological Science, 23(5), 510516. doi:10.1177/0956797611429708

    • Crossref
    • Search Google Scholar
    • Export Citation
  • The ESPAD Group. (2016). ESPAD report 2015. Results from the European School Survey Project on Alcohol and Other Drugs (p. 99). Luxembourg: Publications Office of the European Union.

    • Search Google Scholar
    • Export Citation
  • United States Department of Health and Human Services. (2014). The health consequences of smoking – 50 years of progress. A report of the Surgeon General. Rockville, MD: United States Department of Health and Human Services.

    • Search Google Scholar
    • Export Citation
  • Vieno, A., Canale, N., Potente, R., Scalese, M., Griffiths, M. D., & Molinaro, S. (2018). The multiplicative effect of combining alcohol with energy drinks on adolescent gambling. Addictive Behaviors, 82, 713. doi:10.1016/j.addbeh.2018.01.034

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Volberg, R. A., Gupta, R., Griffiths, M. D., Olason, D. T., & Delfabbro, P. (2010). An international perspective on youth gambling prevalence studies. International Journal of Adolescent Medicine and Health, 22(1), 338. doi:10.1515/IJAMH.2010.22.1.3

    • Search Google Scholar
    • Export Citation
  • Vorobjov, S., Saat, H., & Kull, M. (2014). Social skills and their relationship to drug use among 15-16-year-old students in Estonia: An analysis based on the ESPAD data. Nordic Studies on Alcohol and Drugs, 31(4), 401412. doi:10.2478/nsad-2014-0031

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weinberger, A. H., Franco, C. A., Hoff, R. A., Pilver, C., Steinberg, M. A., Rugle, L., Wampler, J., Cavallo, D. A., Krishnan-Sarin, S., & Potenza, M. N. (2015a). Cigarette smoking, problem-gambling severity, and health behaviors in high-school students. Addictive Behaviors Reports, 1, 4048. doi:10.1016/j.abrep.2015.01.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weinberger, A. H., Franco, C. A., Hoff, R. A., Pilver, C. E., Steinberg, M. A., Rugle, L., Wampler, J., Cavallo, D. A., Krishnan-Sarin, S., & Potenza, M. N. (2015b). Gambling behaviors and attitudes in adolescent high-school students: Relationships with problem-gambling severity and smoking status. Journal of Psychiatric Research, 65, 131138. doi:10.1016/j.jpsychires.2015.04.006

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Calado, F., Alexandre, J., & Griffiths, M. D. (2017). Prevalence of adolescent problem gambling: A systematic review of recent research. Journal of Gambling Studies, 33(2), 397424. doi:10.1007/s10899-016-9627-5

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Canale, N., Scacchi, L., & Griffiths, M. D. (2016). Adolescent gambling and impulsivity: Does employment during high school moderate the association? Addictive Behaviors, 60, 3741. doi:10.1016/j.addbeh.2016.04.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Canale, N., Vieno, A., Billieux, J., Lazzeri, G., Lemma, P., & Santinello, M. (2017). Is medicine use for nervousness associated with adolescent at-risk or problem gambling? European Addiction Research, 23(4), 171176. doi:10.1159/000479001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chaumeton, N. R., Ramowski, S. K., & Nystrom, R. J. (2011). Correlates of gambling among eighth-grade boys and girls. Journal of School Health, 81(7), 374385. doi:10.1111/j.1746-1561.2011.00605.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chomynová, P., Csémy, L., & Mravčík, V. (2016). Evropská školní studie o alkoholu a jiných drogách (ESPAD) 2015 [The European School Survey Project on Alcohol and Other Drugs (ESPAD) 2015]. Zaostřeno, 14(5), 116. Retrieved from https://www.drogy-info.cz/data/obj_files/32196/734/zaostreno_2016-05_v03.pdf

    • Search Google Scholar
    • Export Citation
  • Cook, S., Turner, N. E., Ballon, B., Paglia-Boak, A., Murray, R., Adlaf, E. M., Ilie, G., den Dunnen, W., & Mann, R. E. (2015). Problem gambling among Ontario students: Associations with substance abuse, mental health problems, suicide attempts, and delinquent behaviours. Journal of Gambling Studies, 31(4), 11211134. doi:10.1007/s10899-014-9483-0

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cosenza, M., & Nigro, G. (2015). Wagering the future: Cognitive distortions, impulsivity, delay discounting, and time perspective in adolescent gambling. Journal of Adolescence, 45, 5666. doi:10.1016/j.adolescence.2015.08.015

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Delfabbro, P., King, D. L., & Derevensky, J. L. (2016). Adolescent gambling and problem gambling: Prevalence, current issues, and concerns. Current Addiction Reports, 3(3), 268274. doi:10.1007/s40429-016-0105-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Derevensky, J. L., Gupta, R., & Winters, K. (2003). Prevalence rates of youth gambling problems: Are the current rates inflated? Journal of Gambling Studies, 19(4), 405425. doi:10.1023/A:1026379910094

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Donovan, J. E., Jessor, R., & Costa, F. M. (1991). Adolescent health behavior and conventionality-unconventionality: An extension of problem-behavior theory. Health Psychology, 10(1), 5261. doi:10.1037/0278-6133.10.1.52

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Friedel, J. E., Dehart, W. B., Madden, G. J., & Odum, A. L. (2014). Impulsivity and cigarette smoking: Discounting of monetary and consumable outcomes in current and non-smokers. Psychopharmacology, 231(23), 45174526. doi:10.1007/s00213-014-3597-z

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hayatbakhsh, M. R., Clavarino, A., Williams, G. M., Bor, W., & Najman, J. M. (2012). Young adults’ gambling and its association with mental health and substance use problems. Australian and New Zealand Journal of Public Health, 36(2), 160166. doi:10.1111/j.1753-6405.2011.00815.x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hibell, B. (2014). Overview of the ESPAD Project: Background, methodology and organisation. The ESPAD Handbook, Section 2. Lisbon, Portugal: EMCDDA.

    • Search Google Scholar
    • Export Citation
  • Jessor, R. (1991). Risk behavior in adolescence: A psychological framework for understanding and action. Journal of Adolescent Health, 12(8), 597605. doi:10.1016/1054-139X(91)90007-K

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, E. E., Hamer, R., Nora, R. M., Tan, B., Eisenstein, N., & Engelhart, C. (1997). The Lie/Bet Questionnaire for screening pathological gamblers. Psychological Reports, 80, 8388. doi:10.2466/pr0.1997.80.1.83

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Király, O., Griffiths, M. D., Urbán, R., Farkas, J., Kökönyei, G., Elekes, Z., Tamás, D., & Demetrovics, Z. (2014). Problematic Internet use and problematic online gaming are not the same: Findings from a large nationally representative adolescent sample. Cyberpsychology, Behavior, and Social Networking, 17(12), 749754. doi:10.1089/cyber.2014.0475

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kong, G., Tsai, E., Pilver, C. E., Tan, H. S., Hoff, R. A., Cavallo, D. A., Krishnan-Sarin, S., Steinberg, M. A., Rugle, L., & Potenza, M. N. (2013). Differences in gambling problem severity and gambling and health/functioning characteristics among Asian-American and Caucasian high-school students. Psychiatry Research, 210(3), 10711078. doi:10.1016/j.psychres.2013.10.005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Leeman, R. F., Hoff, R. A., Krishnan-Sarin, S., Patock-Peckham, J. A., & Potenza, M. N. (2014). Impulsivity, sensation-seeking, and part-time job status in relation to substance use and gambling in adolescents. Journal of Adolescent Health, 54(4), 460466. doi:10.1016/j.jadohealth.2013.09.014

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Malmberg, M., Kleinjan, M., Overbeek, G., Vermulst, A. A., Lammers, J., & Engels, R. C. (2013). Are there reciprocal relationships between substance use risk personality profiles and alcohol or tobacco use in early adolescence? Addictive Behaviors, 38(12), 28512859. doi:10.1016/j.addbeh.2013.08.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Martins, S. S., Lee, G. P., Kim, J. H., Letourneau, E. J., & Storr, C. L. (2014). Gambling and sexual behaviors in African-American adolescents. Addictive Behaviors, 39(5), 854860. doi:10.1016/j.addbeh.2014.02.002

    • Crossref
    • Search Google Scholar
    • Export Citation
  • McGee, L., & Newcomb, M. D. (1992). General deviance syndrome: Expanded hierarchical evaluations at four ages from early adolescence to adulthood. Journal of Consulting and Clinical Psychology, 60(5), 766776. doi:10.1037/0022-006X.60.5.766

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Míguez, M. C., & Becona, E. (2015). Do cigarette smoking and alcohol consumption associate with cannabis use and problem gambling among Spanish adolescents? Addiciones, 27(1), 816. doi:10.20882/adicciones.189

    • Search Google Scholar
    • Export Citation
  • Mishra, S., Lalumière, M. L., Morgan, M., & Williams, R. J. (2011). An examination of the relationship between gambling and antisocial behavior. Journal of Gambling Studies, 27(3), 409426. doi:10.1007/s10899-010-9217-x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molinaro, S., Benedetti, E., Scalese, M., Bastiani, L., Fortunato, L., Cerrai, S., Canale, N., Chomynova, P., Elekes, Z., Feijão, F., Fotiou, A., Kokkevi, A., Kraus, L., Rupšienė, L., Monshouwer, K., Nociar, A., Strizek, J., & Urdih Lazar, T. (2018). Prevalence of youth gambling and potential influence of substance use and other risk factors throughout 33 European countries: First results from the 2015 ESPAD study. Addiction, 113(10), 18621873. doi:10.1111/add.14275

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molinaro, S., Canale, N., Vieno, A., Lenzi, M., Siciliano, V., Gori, M., & Santinello, M. (2014). Country- and individual-level determinants of probable problematic gambling in adolescence: A multi-level cross-national comparison. Addiction, 109(12), 20892097. doi:10.1111/add.12719

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nigro, G., & Cosenza, M. (2016). Living in the now: Decision-making and delay discounting in adolescent gamblers. Journal of Gambling Studies, 32(4), 11911202. doi:10.1007/s10899-016-9595-9

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Proimos, J., DuRant, R. H., Pierce, J. D., & Goodman, E. (1998). Gambling and other risk behaviors among 8th- to 12th-grade students. Pediatrics, 102(2), e23. doi:10.1542/peds.102.2.e23

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Räsänen, T., Lintonen, T., Joronen, K., & Konu, A. (2015). Girls and boys gambling with health and well-being in Finland. Journal of School Health, 85(4), 214222. doi:10.1111/josh.12246

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Richard, D. C. S., Blaszczynski, A., & Nower, L. (2014). The Wiley-Blackwell handbook of disordered gambling. Chichester, UK: John Wiley & Sons, Ltd.

    • Search Google Scholar
    • Export Citation
  • Rockloff, M. J. (2012). Validation of the Consumption Screen for Problem Gambling (CSPG). Journal of Gambling Studies, 28(2), 207216. doi:10.1007/s10899-011-9260-2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Slutske, W. S., Moffitt, T. E., Poulton, R., & Caspi, A. (2012). Undercontrolled temperament at age 3 predicts disordered gambling at age 32: A longitudinal study of a complete birth cohort. Psychological Science, 23(5), 510516. doi:10.1177/0956797611429708

    • Crossref
    • Search Google Scholar
    • Export Citation
  • The ESPAD Group. (2016). ESPAD report 2015. Results from the European School Survey Project on Alcohol and Other Drugs (p. 99). Luxembourg: Publications Office of the European Union.

    • Search Google Scholar
    • Export Citation
  • United States Department of Health and Human Services. (2014). The health consequences of smoking – 50 years of progress. A report of the Surgeon General. Rockville, MD: United States Department of Health and Human Services.

    • Search Google Scholar
    • Export Citation
  • Vieno, A., Canale, N., Potente, R., Scalese, M., Griffiths, M. D., & Molinaro, S. (2018). The multiplicative effect of combining alcohol with energy drinks on adolescent gambling. Addictive Behaviors, 82, 713. doi:10.1016/j.addbeh.2018.01.034

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Volberg, R. A., Gupta, R., Griffiths, M. D., Olason, D. T., & Delfabbro, P. (2010). An international perspective on youth gambling prevalence studies. International Journal of Adolescent Medicine and Health, 22(1), 338. doi:10.1515/IJAMH.2010.22.1.3

    • Search Google Scholar
    • Export Citation
  • Vorobjov, S., Saat, H., & Kull, M. (2014). Social skills and their relationship to drug use among 15-16-year-old students in Estonia: An analysis based on the ESPAD data. Nordic Studies on Alcohol and Drugs, 31(4), 401412. doi:10.2478/nsad-2014-0031

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weinberger, A. H., Franco, C. A., Hoff, R. A., Pilver, C., Steinberg, M. A., Rugle, L., Wampler, J., Cavallo, D. A., Krishnan-Sarin, S., & Potenza, M. N. (2015a). Cigarette smoking, problem-gambling severity, and health behaviors in high-school students. Addictive Behaviors Reports, 1, 4048. doi:10.1016/j.abrep.2015.01.001

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weinberger, A. H., Franco, C. A., Hoff, R. A., Pilver, C. E., Steinberg, M. A., Rugle, L., Wampler, J., Cavallo, D. A., Krishnan-Sarin, S., & Potenza, M. N. (2015b). Gambling behaviors and attitudes in adolescent high-school students: Relationships with problem-gambling severity and smoking status. Journal of Psychiatric Research, 65, 131138. doi:10.1016/j.jpsychires.2015.04.006

    • Crossref
    • Search Google Scholar
    • Export Citation
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Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

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2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
sumbission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

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Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Publication
Programme
2021 Volume 10
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • Anikó MARÁZ (Humboldt University of Berlin, Germany)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)
  • Attila SZABÓ (ELTE Eötvös Loránd University, Hungary)
  • Róbert URBÁN (ELTE Eötvös Loránd University, Hungary)
  • Aviv M. WEINSTEIN (Ariel University, Israel)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Beáta BÖTHE (University of Montreal, Canada)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Eötvös Loránd University, Hungary)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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