. 2020 Jul 23;9(3):497–533. doi: 10.1556/2006.2020.00045

Table 2.

Summary of research articles investigating the association between conduct problems and problem gambling and gaming

Authors	Country	Design and sampling method	Sample population	Sample characteristics	PG and PVG measure	CP measure	Findings
Problem gambling
Barnes, Welte, Hoffman, and Tidwell (2011)	United States	Cross-sectional survey	Representative household sample of adolescents and young adults	N = 2,274	SOGS-RA	DISC-C	CP were correlated with PG (r = 0.31, P < 0.001). Youth who endorsed 3 or more symptoms of conduct disorder (CD) were 4 times more likely to report PG compared to those not meeting the criteria for CD (31% vs. 8%).
		Randomly selected telephone sample from a sampling frame of all working telephone blocks in the United States		Gender NR			Logistic regression results controlling for gender, age, socioeconomic status, and race/ethnicity indicated that CD increased the odds of being a problem gambler by 4.4 times (P < 0.001). When alcohol, tobacco and marijuana problems were added to the model, CD remained significant, increasing the odds of being a problem gambler by 2.9 times (P < 0.001).
				M_age = NR, range 14–21
Brunelle et al. (2012a)	Canada (Quebec)	Cross-sectional survey	High-school students	N = 1,870	DSM-IV-MR-J (French version)	MASPAQ	Male and female problem gamblers had higher average scores in all domains of CP including severe delinquency, fraud and theft, and vandalism and interpersonal violence compared to non-gamblers and non-problem gamblers (all significant at P < 0.001).
		Convenience sampling		54.1% female
		Convenience sampling		M_age = 15.43 (SD = 0.97, range 14–18)
Brunelle et al. (2012b)	Canada (Quebec)	Cross-sectional survey	High-school students	N = 1,870	DSM-IV-MR-J (French version)	MASPAQ	In both Internet and non-Internet gamblers, CP were associated with a greater severity of PG (β = 0.29, P < 0.05; β = 0.15, P < 0.001, respectively).
		Convenience sampling		54.1% female
		Convenience sampling		M_age = 15.43 (SD = 0.97, range 14–18)
Cheung (2014)	China	Cross-sectional survey	High-school students	N = 4,734	DSM-IV-MR-J	Delinquency scale	Correlations between delinquency and gambling variables (problems, frequency, permissiveness) ranged from r = 0.22 to r = 0.28 (P < 0.001).
		Stratified random sampling		50.7% male			Logistic regression predicting PG indicated that delinquency predicted PG (AOR = 1.20 95% CI [1.17, 1.23]) while controlling for age, gender, SES, familial status.
		Stratified random sampling		M_age = 16.39 (SD = 1.73, range 12–23)			Delinquency remained significant in the model that also included tobacco and alcohol use (AOR = 1.11 95% CI [1.08, 1.15]).
Cook et al. (2015)	Canada (Ontario)	Cross-sectional survey	High-school students	N = 4,851	SOGS-RA	Delinquency scale (violent and non-violent acts)	Correlation between delinquency and PG was significant (r = 0.24, P < 0.001). Violent and non-violent delinquent behaviors were more common in PG than non-PG, with ORs ranging from 3.4 to 19.6 depending on the delinquent act. Overall delinquency scores indicated 11.35 times (P < 0.001) higher likelihood of PG.
		Stratified (region and school type), two-stage (school, class) cluster sampling		53% female			A multivariate logistic regression indicated that higher overall delinquency resulted in youth being 5.9 times (P < 0.001) more likely to meet the criteria for PG compared to less delinquent youth when controlling for hazardous drinking, cannabis dependency, suicide attempt(s) and psychological distress.
				M_age = 14.6 (SD = NR, range NR)
Hayatbakhsh, Clavarino, Williams, Bor, and Najman (2013)	Australia	Cross-sectional study	Young adults	N = 3,512	PGSI	CBCL Young Adult Self-Report	Individuals in the top 10% of externalizing problems had a greater likelihood of being categorized as at-risk for problem gambling compared to being categorized as non-gamblers (OR = 5.4, 95% CI [3.1, 9.4]).
		Convenience sampling		47% male
		Convenience sampling		M_age = 20.6 (SD = 0.8), range 18–23)
Martins et al., (2013)	United States	14-year longitudinal study	Urban males from predominantly low SES neighborhoods	N = 310	Age 17, 19, & 20: SOGS-RA	Grade 1–3: Childhood aggressive behaviors (Teacher Observation of Classroom Adaptation-Revised)	General growth mixture modeling based on the longitudinal development of CP indicated that those who had chronically high CP throughout childhood were 2.6 times more likely (95% CI [1.06, 6.38]) to meet the criteria for at-risk or PG.
		Convenience sampling		100% male		Grade 6–10:
		Convenience sampling		M_age = NR (range 6–20)		Adolescent aggressive behaviors (Teacher Report of Classroom Behavior-Checklist Form)	Those with chronically high CP throughout adolescence were 3.19 times more likely (95% CI [1.18, 8.64]) to meet the criteria for at-risk or PG.
Martins et al. (2014)	United States	17-year longitudinal study	Urban youth from predominantly low SES neighborhood	N = 617	Age 17, 19, & 20: SOGS-RA	Age 13–17: DISC-C	A greater proportion of problem gamblers (65%) were arrested before the age of 23 compared to social (38%) and non-gamblers (24%). PG was significantly associated with the hazard of first arrest by age 23 in both the unadjusted (HR = 3.6, P < 0.001) and adjusted (covarying for gambling status, race, household structure, lunch status, intervention status, theft/property damage, illegal drug use; AHR = 1.6, P = 0.05) models
		Convenience sampling		53% male		Age 17–23: Arrest history
		Convenience sampling		M_age = NR (range 6–23)		Age 17–23: Arrest history
Pace, Schimmenti, Zappulla, and Maggio (2013)	Italy	Cross-sectional study	High-school students	N = 268	SOGS	CBCL Youth Self-Report	The group of at-risk and pathological gamblers compared to non-gamblers did not endorse higher levels of CP (P > 0.05).
		Convenience sampling		100% male			In the discriminant function analysis, higher CP was one of the variables that best differentiated at-risk gamblers from pathological gamblers (pathological gamblers having slightly more CP) but did not differentiate between non-gamblers and at-risk gamblers.
		Convenience sampling		M_age = 16.23 (SD = 0.39, range 15–17)
Terrone et al. (2018)	Italy	Cross-sectional study	High-school students	N = 94	SOGS	CBCL Youth Self-Report	Utilizing attachment style as a moderator, there was a significant positive association between CP and PG only among the dismissing-detached group (P = 0.04), but not among the fearful-avoidant group.
		Convenience sampling		65.96% male
		Convenience sampling		M_age = 17.51 (SD = 0.82, range 16–20)
Vitaro, Brendgen, Ladouceur, and Tremblay (2001)	Canada (Quebec)	5-year longitudinal study (2 year period reporting CP and PG)	Adolescent boys from disadvantaged neighborhoods	N = 717	Age 16–17: SOGS-RA	Age 16–17: Self-Reported Delinquency Scale	Delinquency at age 16 was positively correlated with PG at age 16 (r = 0.29, P < 0.001) and this association remained significant at age 17 (r = 0.31, P < 0.001). The path model accounting for gambling frequency, PG and drug/alcohol use at age 16 and 17, indicated that delinquency at age 16 did not significantly predict PG one year later.
		Convenience sampling		100% male
		Convenience sampling		M_age = NR (range 13–17)
Wanner, Vitaro, Carbonneau, and Tremblay (2009)	Canada (Quebec)	7-year longitudinal study	Sample 1: Low SES youth	Sample 1:	SOGS-RA	Sample 1:	In both samples, there were significant correlations (P < 0.05) between CP and PG at age 16 (r = 0.22–0.25), age 23 (r = 0.21–0.31), and age 16 and 23 (r = 0.07(ns)-0.13).
		Convenience sampling	Sample 2: Community youth	N = 502		Self-Report Delinquency Questionnaire	Investigating the cross-lagged models, CP at age 16 were not prospectively linked to PG at age 23, when accounting for gambling participation, PG, and substance use at age 16.
				100% male		Sample 2:
				Time 1:		DISC-C (delinquency)
				M_age = 16.2 (SD = 0.6, range NR)
				Time 2:
				M_age = 22.8 (SD = 0.6, range NR)
				Sample 2:
				N = 663
				100% male
				Time 1:
				M_age = 16.2 (SD = 0.5, range NR)
				Time 2:
				M_age = 22.5 (SD = 0.5, range NR)
Welte, Barnes, Tidwell, and Hoffman (2009)	United States	Cross-sectional study	United States residents	N = 2,258	SOGS-RA	DISC-C	Those who had current CP had a 6.1% rate of current PG (vs. 1.7% in non-CP) and a 22.9% rate of current at-risk/PG (vs. 5.2% in non-CP).
		Stratified sample by county and telephone block within county across the United States		Gender NR			In the logistic regression, with each additional DISC-C symptom, odds of at-risk/PG increased (OR = 1.4 (95% CI [1.3, 1.6]). This effect was most striking for those aged 14–15, with an odds ratio of 1.8 (95% CI [1.3, 2.2]). By age 20–21, this relationship was no longer significant (P > 0.05).
				M_age = NR (range 14–21)			In the multinomial logistic regression predicting at-risk/PG age of onset, each additional DISC-C symptom increased the odds that one would have a gambling problem before age 14 (OR = 1.6, 95% CI [1.4, 1.8]), and age 15 and later (OR = 1.2, 95% CI [1.0, 1.4]).
Widinghoff et al. (2019)	Sweden	Cross-sectional study	Violent offenders in prison	N = 264	SCID DSM-IV	SCID Conduct Disorder	Rates of gambling disorder were not higher among those with a conduct disorder (P = 0.15).
		Convenience sampling		100% male
		Convenience sampling		M_age = 22.3 (SD = NR, range 18–25)
Willoughby, Chalmers, and Busseri (2004)	Canada (Ontario)	Cross-sectional study	High-school students	N = 7,290	SOGS-RA	Delinquency (minor and major) and aggression (direct and indirect)	Correlations between CP and PG were significant ranging from r = 0.16–0.18 (P < 0.001). Results from the confirmatory factor analysis indicated a three-factor model with a delinquency factor including major delinquency (β = 0.50, P < 0.001), minor delinquency (β = 0.57, P < 0.001), and gambling (β = 0.30, P < 0.001). This factor was significantly correlated with the aggression factor (r = 0.63). Lastly, there was a consistent co-occurrence of CP and PG across levels of severity (risk ratios ranging between 1.01 and 3.45, all P < 0.001).
		Convenience sampling		47.7% male
		Convenience sampling		M_age = 15.58 (SD = 1.33, range 13–18)
Problem video gaming
Kim et al. (2018)	South Korea	Cross-sectional study	First year middle-school students	N = 402	IGUESS	BPAQ	Correlation analyses indicated a positive correlation between CP and PVG (r = 0.32, P < 0.001). A mediation model was created with father-adolescent communication as a mediating variable between CP and PVG. CP were directly related to PVG (β = 0.29, P < 0.001), with a significant partial indirect effect through poorer father-adolescent communication (β = 0.19, P < 0.001). The total effect of the model was significant (β = 0.42, P < 0.001).
		Convenience sampling		55.5% male
		Convenience sampling		M_age = 13.0 (SD = 0.40, range NR)
Ong, Peh, and Guo (2016)	Singapore	Cross-sectional study	Adolescents presenting at an addiction treatment center (for substance or behavioral addictions)	N = 260	Pathological gaming based on DSM-IV-R-PG;	Delinquent behavior based on violent and non-violent crimes	Adolescents with a history of delinquency were less likely to report PVG compared to adolescents without a history of delinquency (P = 0.001).
		Convenience sampling		81.2% male	PIUQ;
		Convenience sampling		M_age = 15.48 (SD = 1.93, range NR-19)	GAS
Tejeiro, Gómez-Vallecillo, Pelegrina, Wallace, and Emberley (2012)	Spain	Cross-sectional study	High-school students	N = 737	PVP	Anti-Social Illegal Behaviors Questionnaire	The cluster analysis indicated three clusters in the data; 1) comorbid-PVG, 2) social-PVG and 3) non-PVG. The comorbid-PVG cluster, had significantly higher levels of CP compared to the non-PVG group (P < 0.001).
		Convenience sampling		52% male
		Convenience sampling		M_age = 14 (SD = 1.12, range 12–17)
Wartberg et al. (2017)	Germany	Cross-sectional study	Family dyads (adolescent and related caregiver). 98.8% of caregivers were biological parents (85% mothers)	N = 1,095	IGDS	RAASI subscale for antisocial behavior	Two regression models were conducted (linear and logistic) controlling for gender, anger control problems, self-esteem problems, hyperactivity/inattention, parental depression and anxiety.
		Convenience sampling		50.8% male			In the linear regression model, CP predicted PVG (β = 0.14, P < 0.001), and CP also predicted PVG in the logistic regression model (OR = 1.11, 95% CI [1.00, 1.22], P < 0.05).
		Convenience sampling		M_age = 12.99 (SD = 0.82, range 12–14)
Wartberg, Kriston, Zieglmeier, Lincoln, and Kammerl (2019)	Germany	1-year longitudinal study	Family dyads (adolescent and related caregiver). 98.8% of caregivers were biological parents (85% mothers)	N = 985	IGDS	RAASI subscale for antisocial behavior	CP and PVG were correlated at Time 1 (r = 0.44, P < 0.01), Time 2 (r = 0.46, P < 0.01) and between Time 1 and Time 2 (r = 0.28–0.30, P < 0.01).
		Convenience sampling		50.7% male			In the cross-lagged panel design (controlling for anger control problems, emotional distress, self-esteem, hyperactivity/inattention, parental depression and anxiety), CP at Time 1 did not predict PVG at Time 2 (P > 0.05).
				Time 1:
				M_age = 12.99 (SD = 0.82, range 12–14)
				Time 2:
				M_age = 13.89 (SD = 0.89, range NR)

Note. BPAQ = Buss-Perry Aggression Questionnaire, CBCL = Child Behavior Checklist, DISC-C = Diagnostic Interview Schedule for Children for Conduct Disorder, DSM-IV-R-PG = Diagnostic and Statistical Manual-IV-Revised-Pathological Gambling, CP = conduct problems, GAS = Game Addiction Scale, IGDS = Internet Gaming Disorder Scale, IGUESS = Internet Game Use-Elicited Symptom Screen, MASPAQ = Mesure de l'adaptation sociale et personnelle pour adolescents Quebecois, PIUQ = Problematic Internet Use Questionnaire, PG = problem gambling, PGSI = Problem Gambling Severity Index, PVG = problem video gaming, PVP = Problem Video Game Playing, RAASI = Reynolds Adolescent Adjustment Screening Inventory, SCID = Structured Clinical Interview for DSM-IV, SOGS = South Oaks Gambling Screen, SOGS-RA = South Oaks Gambling Screen – Revised Adolescent.