Self-injurious behavior and gambling-related attitudes, perceptions and behaviors in adolescents

Journal of Psychiatric Research 124 (2020) 77–84

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Self-injurious behavior and gambling-related attitudes, perceptions and behaviors in adolescents

T

Luis C. Farhata, Aaron J. Robertob, Jeremy Wamplerc, Marvin A. Steinbergd, Suchitra Krishnan-Sarinb, Rani A. Hoffb,e, Marc N. Potenzab,f,g,h,i,∗ a

Departamento de Psiquiatria da Faculdade de Medicina FMUSP, Universidade de São Paulo, São Paulo, SP, Brazil Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA c Problem Gambling Services, Middletown, CT, USA d Retired, Guilford, CT, USA e Northeast Program Evaluation Center, VA Connecticut Healthcare System, West Haven, CT, USA f Connecticut Mental Health Center, New Haven, CT, USA g Connecticut Council on Problem Gambling, Wethersfield, CT, USA h Child Study Center, Yale School of Medicine, New Haven, CT, USA i Department of Neuroscience, Yale University, New Haven, CT, USA b

A R T I C LE I N FO

A B S T R A C T

Keywords: Adolescent Gambling Risky behaviors Self-injurious behaviors Non-suicidal self-injury

Gambling is prevalent among adolescents and adolescents are vulnerable to experiencing gambling-related problems. Although problem gambling and suicidal behavior have been linked in adults and self-injurious behaviors may predict future suicidality, prior studies have not investigated relationships between problemgambling severity and self-injurious behavior in adolescents. Data from 2234 Connecticut high-school students were analyzed in chi-square tests and logistic regression models to examine self-injurious behaviors in relation to at-risk/problem gambling with respect to sociodemographic characteristics, gambling attitudes and perceptions, and extracurricular and health measures. Individuals who engaged in self-injurious behavior (versus those who did not) reported more permissive views towards gambling and were more likely to exhibit at-risk/problem gambling. Stronger relationships between problem-gambling severity and gambling in casinos (OR 4.85, 95%CI 1.94, 12.12) and non-strategic gambling (1.92, 95%CI 1.01, 3.66) were observed in adolescents who acknowledged engagement in self-injurious behavior versus those who did not. Links between self-injurious behaviors and more permissive gambling attitudes and perceptions and at-risk/problem gambling suggest the need for improved interventions targeting co-occurring self-injurious behaviors and gambling. Stronger relationships between problem-gambling severity and casino and non-strategic gambling among adolescents with self-injurious behaviors suggest adolescents with self-injurious behavior may engage in specific forms of gambling as maladaptive coping strategies to alleviate suffering. Prevention and treatment approaches targeting distress management and improving adaptive coping skills may be important for targeting self-injurious behaviors in adolescents with at-risk/problem gambling.

1. Introduction Gambling is common among adolescents, with 68% of adolescents having wagered during the past year in the US (Welte et al., 2008) and between 0.2% and 12.3% of youth who gamble exhibiting gambling problems (Calado and Griffiths, 2016). Gambling problems in adolescents have been linked to familial, functioning, health and financial problems (Hardoon et al., 2004), and to psychiatric concerns, including substance use and abuse (Lynch et al., 2004; Yip et al., 2011). While

∗

gambling problems have been associated with suicidality in adults (Frank et al., 1991; Karlsson and Hakansson, 2018; Petry and Kiluk, 2002; Ronzitti et al., 2017, 2018, 2019), more limited data suggest similar relationships with suicidal ideation, suicidal attempts and completed suicides in youth (Cook et al., 2015; Ladouceur et al., 1999). Despite associations between problem gambling and suicidal behaviors, prior studies have not investigated self-injurious behavior (SIB) in relation to problem gambling, including in youth. SIB has been linked, often as a precursor, to suicidal behavior. SIB comprises several direct,

Corresponding author. 1 Church Street, Room 726, New Haven, CT, 06510, USA. E-mail address: [email protected] (M.N. Potenza).

https://doi.org/10.1016/j.jpsychires.2020.02.016 Received 4 December 2019; Received in revised form 15 February 2020; Accepted 19 February 2020 0022-3956/ © 2020 Published by Elsevier Ltd.

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L.C. Farhat, et al.

validated, many have been used previously (Kundu et al., 2013; Rahman et al., 2014; Zhai et al., 2019, 2020). Other items have been used in other youth surveys (e.g., the Youth Risk Behavior Survey).

deliberate, violent behaviors that are performed with an aim to damage an individual's own bodily tissues (e.g. burning, cutting, self-hitting) without the intention to die (Klonsky et al., 2014; Peterson et al., 2008). SIB is relatively common among youth (Swannell et al., 2014), is associated with significant economic and familial burden (Kinchin and Doran, 2017) and, perhaps most importantly, is one of the strongest predictors of future suicidal behavior (Hawton et al., 2015; Nock, 2010). Both SIB (Nock et al., 2009) and problem gambling (Blaszczynski and Nower, 2002) have been hypothesized to be performed to alleviate negative suffering, and therefore, it is possible that they co-occur through shared psychological pathways. Although adolescent gambling problems may significantly and negatively impact developmental trajectories (Hammond et al., 2014; Messerlian et al., 2005), parents and teachers infrequently identify gambling problems in adolescents and problem gambling usually goes unnoticed (Potenza et al., 2019). Thus, additional research is needed to identify gambling problems in youth, and this may be facilitated by characterizing high-risk groups. In this way, understanding relationships between SIB and adolescent gambling, particularly at-risk/problem gambling (ARPG), and the factors influencing such relationships may facilitate the identification of vulnerable individuals who might benefit from intervention, both with respect to ARPG and SIB. In other words, characterizing the relationship between SIB and ARPG may help with honing and targeting interventions (prevention and treatment strategies) that may help individuals with these concerning behaviors. To address this gap in the literature, we analyzed data from a highschool survey in Connecticut to investigate relationships between ARPG, gambling attitudes and perceptions, health/functioning measures and gambling behaviors in adolescents who acknowledged or denied past-year engagement in SIB. We hypothesized that SIB would be associated with at-risk/problem gambling and more permissive views regarding parental-related problem-gambling prevention efforts, as adolescents with SIB frequently report unfavorable parent-child relationships associated with poor parental oversight and communication. We also had exploratory hypotheses that SIB would moderate associations between ARPG and: (1) extracurricular measures given that individuals with SIB may be more like likely to isolate (Endo et al., 2017); (2) mental health measures, because of associations between suicidality and mental health concerns among adults with gambling problems (Ronzitti et al., 2017); (3) non-strategic gambling, because of elevated suicidality among individuals with non-strategic gambling problems (Bischof et al., 2016); and, (4) gambling with friends, because of the isolative behaviors among youth with SIB (Endo et al., 2017).

2.2. Measures & procedures Sociodemographic characteristics. Sociodemographic characteristics queried in the survey included age, gender, race/ethnicity, grade level and family structure. Gambling attitudes and perceptions. Respondents’ perceived importance of specific gambling prevention strategies were assessed. Items queried about checking identification for purchasing lottery tickets; hanging out with friends who do not gamble; participating in activities that are fun and free of gambling; fear of losing valuable possessions, friends or relatives to gambling; advertisements that show problems associated with gambling; not having access to internet gambling at home; parent/guardian strictness about gambling; warnings about gambling from adults in the family; warnings about gambling from, or listening to, peers; having parents who do not gamble; learning about the risks of gambling in school; learning about the risks of gambling from parents; learning about the risks of gambling from peers; adults not involving kids in gambling; and parents/guardians not permitting card games (for money) at home. Extracurricular and health measures. Engagement in extracurricular activities involved querying about participation in church activities, community service/volunteer work, school clubs and team sports. Health measures assessed substance use and dysphoria/depression. Lifetime cigarette smoking frequency, lifetime alcohol use, lifetime marijuana use, lifetime other drug use, current alcohol use and current caffeine use were assessed. Dysphoria/depression was defined as endorsement of hopelessness or feelings of extreme sadness that precluded daily activities for almost every day for two weeks or more in a row. Variables were stratified as depicted in the tables. Gambling measures. Gambling items covered age-of-onset of gambling (< 8; 8–11; 12–14; ≥15 years old), gambling types (e.g., lottery/ scratch card; dice/craps; machine gambling; placing bets with a bookie), locations (e.g., internet; casino; school grounds), types of gambling partners (e.g., adults, family, alone, friends or strangers), motivations to gamble (e.g., gambling for excitement/fun; gambling for financial reasons; gambling for escape/to relieve dysphoria; gambling for social reasons) and average time spent gambling (≤1 h; ≥ 2 h) per week. Gambling groups. Youth who did not report gambling over the past year were considered non-gambling (NG). Among those who acknowledged past-year gambling, problem-gambling severity was evaluated through the use of the 12-item Massachusetts Gambling Screen (MAGS) (Shaffer et al., 1994), a validated instrument to assess DSM-IV pathological gambling criteria among adolescents. Cronbach's alpha for the MAGS items was 0.90 in this study. Youth who met at least one criterion for pathological gambling according to the MAGS were classified as exhibiting ARPG while those who met no criterion were considered to exhibit low-risk gambling (LRG). Self-injurious behavior groups. SIB was assessed by the question, “Have you ever intentionally (i.e., on purpose) cut your wrists, arms, or other areas of your body or done anything else to hurt yourself (e.g., burned, bit, or severely scratched) without intending to kill yourself?“. Responses included “yes” or “no”; if they chose the former, they were classified in the SIB group, and if they chose the latter, they were classified in the non-SIB group. To characterize further SIB, youth who acknowledged intentionally hurting themselves were asked to answer additional items which covered frequency of SIB, attempts to reduce SIB, concern of family members about their SIB, missing school, work or other important social activities due to SIB, feeling urges or tension/ anxiety to self-injure and previous hospitalization or medical treatment due to SIB. Youth were also asked about whether they believed their SIB was a problem.

2. Methods 2.1. Survey methodology: recruitment and sample characteristics The current study analyzed cross-sectional data from a state-wide investigation into gambling and other risk behaviors among Connecticut high-school students. The Connecticut high-school survey methodology has been described extensively in previous studies (Desai et al., 2010; Kundu et al., 2013; Leeman et al., 2014; Liu et al., 2011; Potenza et al., 2011; Rahman et al., 2014; Slavin et al., 2013; Weinberger et al., 2015; Yip et al., 2011). Briefly, public four-year and non-vocational or special education high-schools in Connecticut were invited to participate. The final sample was demographically consistent with the 2000 Census data of 14- to 18-year-old Connecticut residents, although this was not a random sample of high schools. Of the 4523 students surveyed, 2234 were included in this study. Reasons to exclude the 2289 adolescents included: (1) lack of adequate data for past-year gambling (n = 168), (2) lack of answers to all 12 items of the MAGS (n = 1871) and (3) lack of response to the SIB question (n = 250). Individuals included in this study provided answers to a 154-item questionnaire comprised of both novel items and standardized measures of gambling. Although most novel items were not previously 78

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2.3. Statistical analysis

Table 1 Sociodemographic characteristics stratified by self-injurious behavior status.

Data were double-entered into an electronic system; random spotchecks of completed surveys and data-cleaning procedures were performed to ensure data were accurate and within range. All statistical analyses were conducted using the SAS system (Cary, NC). Differences between the two SIB groups (i.e., SIB vs non-SIB) in sociodemographic characteristics and gambling attitudes and perceptions were evaluated through Pearson chi-square analysis; comparisons were two-tailed, and a Bonferroni correction was applied to provide a threshold of significance of p < 0.0025. Logistic regression models and multinomial logistic regression models were employed for binary and categorical outcomes, respectively, to provide odds ratios (ORs) with 95% confidence intervals (CIs) as measures of magnitudes of associations. All models were adjusted for gender, race/ethnicity, grade level and family structure. To investigate differences in health measures as well as gambling behaviors between the different gambling groups (NG, LRG, ARPG), similar procedures were conducted. For gambling-related measures, the NG group was excluded from the analyses. To determine whether SIB status moderated associations with ARPG, main effects for SIB and problem-gambling severity, as well as the interaction term (self-injurious-behavior-status-by-problem-gambling-severity), were included in the appropriate logistic or multinomial logistic regression models in an exploratory analysis.

Variable

Gender Male Female Race/ethnicity Caucasian Yes No African American Yes No Asian Yes No Hispanic Yes No Other Yes No Grade 9th 10th 11th 12th Grade Average A's and B's Mostly C's D's or lower Living with One parent Both parents Other

2.4. Ethics The high-school survey was approved by the Yale School of Medicine IRB. Passive consent procedures were adopted for parental consent and procedures were in accordance with the Declaration of Helsinki (2013).

Self-Injurious Behavior

Non-Self-Injurious Behavior

X2 Statistics

N

%

N

%

X2

p

39.14

< .0001

168 236

41.6 58.4

1060 746

58.7 41.3 0.0007

0.9794

298 114

72.33 27.67

1319 503

72.39 27.61 0.2017

0.6533

9.44

0.0021

4.89

0.0270

0.3173

0.5732

5.14

0.1619

31.76

< .0001

20.51

< .0001

47 365

11.41 88.59

194 1628

10.65 89.35

32 380

7.77 92.23

76 1746

4.17 95.83

75 323

18.84 81.16

250 1483

14.43 85.57

70 342

16.99 83.01

289 1533

15.86 84.14

136 100 112 61

33.25 24.45 27.38 14.91

511 483 502 322

28.11 26.57 27.61 17.71

184 125 87

46.46 31.57 21.97

1019 552 209

57.25 31.01 11.74

101 251 44

25.51 63.38 11.11

394 1304 101

21.90 72.48 5.61

3. Results 3.2. Gambling attitudes and perceptions

3.1. Sociodemographic characteristics

Table 2 and Table S3 include information relating to gambling attitudes and perceptions among the two SIB groups. Adolescents who engaged in SIB showed more permissive views towards gambling on all but three of the queried items (participating in leisure activities that are free of gambling; hanging out with friends who do not gamble; parent/ guardian not permitting card games for money at home). Betweengroup differences were observed relating to parental and peer-related factors such as parental oversight (parent/guardian strictness about gambling); warnings from adults in family; learning about the risks of gambling from parents; parental gambling (having parents who do not gamble); adults not involving kids in gambling; peer gambling (warnings from, or listening to, peers); and, learning about the risks of gambling from peers. Between-group differences were also observed relating to other domains (not having access to internet gambling at home, parental disapproval of gambling, worrying about a family member's gambling behavior).

Table 1 and Table S1 display results sociodemographic characteristics of the two SIB groups Among respondents included in analyses (n = 2234), 404 reported engaging in SIB; of these, 236 were female and 298 were white/Caucasian. The SIB group included more girls and people of Asian ethnicity. The SIB group also included a lower percentage of adolescents who acknowledged living with two parents and higher percentages of adolescents who reported living with only one parent or classified their family structure as “other”. Table S2 describes characteristics of SIB among youth who hurt themselves. Most reported feeling urges (58.20%) or anxiety/tension (55.17%) to self-injure and had attempted to reduce SIB (58.33%). Although nearly half (49.74%) had parents who expressed concern regarding their SIB, most did not consider their SIB a problem (73.14%), nor reported that it interfered with school or work (66.84%). Nearly 30% required medical assistance due to an injury resulting from their SIB. Adolescents who engaged in SIB were more likely to gamble as compared to non-SIB adolescents (86.9% vs 81.2%, χ2 = 7.52, p = 0.0106), although this difference did not survive Bonferroni correction. Adolescents with SIB were more likely to exhibit ARPG relative to adolescents without SIB (43.3% vs 31.1%, χ2 = 19.24, p < 0.0001). Adolescents who acknowledged missing school, work or other activity due to SIB or who reported their SIB led to severe injury which required medical treatment included the highest frequencies of ARPG (48.39 vs. 33.16%, χ2 = 11.09, p = 0.0039; 53.27% vs. 30.60%, χ2 = 17.06, p = 0.0002), although the former difference did not survive Bonferroni correction.

3.3. Extracurricular and health measures Table 3 and Table S4 include findings relating to extracurricular and health measures among the gambling groups stratified by SIB status. Among adolescents with SIB, both LRG and ARPG respondents were more likely than NG respondents to smoke regularly and have at least sipped alcohol. ARPG respondents were more likely than NG respondents to report dysphoria/depression. Among adolescents without SIB, both LRG and ARPG respondents were more likely than NG respondents to participate in extracurricular activities and smoke occasionally and regularly. Both LRG and ARPG respondents were also more 79

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Table 2 Gambling perceptions in self-injurious versus non-self-injurious adolescents: Regression analyses. Variables

Self-Injurious vs. Non-Self-Injurious OR

Parent perception about gambling Neither approve nor disapprove Approve Importance for preventing gambling problems in teens Checking identification for purchasing lottery tickets Hanging out with friends who don't gamble Participating in activities that are fun and free of gambling Fear of losing valuable possessions, close friends, and relatives Advertisements that show the problems associated with gambling Not having access to Internet gambling at home Parent/Guardian strictness about gambling Warnings from adults in family Warnings from, or listening to, peers Having parents who don't gamble Learning about the risks of gambling in school Learning about the risks of gambling from parents Learning about the risks of gambling from peers Adults not involving kids in gambling Parent/Guardian not permitting card games (for money) at home Family concern

p .0002

1.16 (0.88–1.55) 2.43 (1.60–3.69) 0.64 0.08 0.66 0.52 0.56 0.66 0.48 0.44 0.56 0.56 0.59 0.52 0.56 0.59 0.75 1.78

.002 .037 .006 .0001 < .0001 .0012 < .0001 < .0001 < .0001 < .0001 .0002 < .0001 < .0001 .0005 .0240 .0006

(0.48–0.85) (0.58–0.98) (0.49–0.89) (0.37–0.72) (0.43–0.73) (0.52–0.85) (0.37–0.64) (0.34–0.58) (0.42–0.74) (0.42–0.74) (0.45–0.78) (0.39–0.69) (0.42–0.74) (0.44–0.80) (0.59–0.96) (1.28–2.48)

Model covariates include: African American, White, Hispanic, Asian, other race, gender, grade, grade average, living with parental figures in household.

their self-reported SIB status. Among adolescents with SIB, ARPG respondents were more likely than LRG respondents to prefer non-strategic and machine gambling; to gamble on the internet, on school grounds and at casinos; to experience pressure and anxiety as triggers to gamble; to gamble for excitement, financial and social reasons and escape; to gamble alone, with strangers and with other adults that were not friends nor family members; to spend more than 1 h gambling per week. In the SIB group, ARPG respondents were less likely than LRG respondents to start gambling at 12–14 years old and at 15 years old or older. Among adolescents without SIB, ARPG respondents were more likely than LRG respondents to prefer strategic, non-strategic and machine gambling; to gamble on the Internet, on school grounds and on casinos; to experience pressure and anxiety as triggers to gamble; to gamble for excitement and social reasons and escape; to gamble alone, with family members, friends, strangers and other adults that were not

likely than NG respondents to have sipped alcohol and to consume moderate and heavy quantities of alcohol, use marijuana and other drugs and consume caffeinated drinks three or more times a day. LRG respondents were more likely than NG respondents to consume caffeinated drinks one or two times a day while ARPG respondents were more likely than NG respondents to report dysphoria/depression. Interaction analyses comparing the SIB versus the non-SIB groups showed weaker associations between both LRG and ARPG status and extracurricular activities, LRG status and use of other drugs and ARPG status and moderate alcohol use. 3.4. Gambling behaviors Table 4 and Table S5 include the results of the differences relative to gambling behaviors among the different gambling groups stratified by

Table 3 Extracurricular and health measures and problem gambling severity in self-injurious versus non-self-injurious adolescents. Variables

Self-injurious

Non-self-injurious

Interaction OR (Self-Injurious vs Non-self-injurious)

LRG vs NG

ARPG vs NG

LRG vs NG

ARPG vs NG

LRG vs NG

ARPG vs NG

OR (95% CI)

OR (95% CI)

OR (95% CI)

OR 95% CI

OR (95% CI)

OR 95% CI

0.94 (0.42–2.09)

1.88 (1.38–2.56)

2.76 (1.88–4.05)

0.41 (0.18–0.93)

2.27 5.27 1.56 3.52

(0.99–5.23) (2.03–13.72) (0.73–3.35) (1.27–9.73)

1.95 1.83 1.83 3.43

(1.37–2.77) (1.10–3.05) (1.32–2.53) (2.39–4.92)

2.98 2.93 2.86 4.78

(2.00–4.44) (1.67–5.14) (1.97–4.13) (2.97–7.70)

0.71 1.72 0.67 3.12

(0.30–1.68) (0.61–4.85) (0.30–1.47) (0.83–11.75)

0.76 1.80 0.55 0.74

(0.31–1.90) (0.61–5.35) (0.24–1.26) (0.25–2.20)

1.16 0.78 5.42 2.36

(0.30–4.40) (0.23–2.61) (0.91–32.41) (0.97–5.71)

1.21 2.58 3.78 3.02

(0.75–1.96) (1.48–4.50) (1.61–8.91) (1.33–6.84)

1.67 3.83 6.89 4.22

(0.96–2.93) (2.05–7.13) (2.75–17.22) (1.79–9.90)

1.14 0.30 0.30 0.24

(0.31–4.23) (0.09–1.02) (0.04–2.17) (0.07–0.80)

0.69 0.20 0.79 0.56

(0.17–2.89) (0.05–0.78) (0.11–5.74) (0.17–1.88)

Extracurricular Any extracurricular activity 0.77 (0.36–1.65) Substance use Smoking (lifetime) (ref: never) Occasionally 1.38 (0.63–3.02) Regularly 3.15 (1.28–7.75) Marijuana (lifetime) 1.22 (0.59–2.51) Alcohol (sipage) 10.70 (2.98–38.4) Alcohol (frequency) (ref: never regular) Light 1.38 (0.41–4.69) Moderate 0.77 (0.26–2.31) Heavy 1.15 (0.20–6.74) Other drug (lifetime) 0.73 (0.31–1.75) Caffeine use (ref: none) 1–2 per day 1.47 (0.62–3.49) 3+ per day 1.61 (0.65–3.97) Mood Dysphoria/Depression 1.66 (0.84–3.27)

0.34 (0.14–0.81)

1.16 (0.47–2.88) 1.63 (0.64–4.17)

1.56 (1.14–2.16) 3.33 (2.15–5.17)

1.36 (0.92–2.02) 4.10 (2.50–6.74)

0.94 (0.38–2.36) 0.48 (0.18–1.32)

0.85 (0.32–2.26) 0.40 (0.14–1.13)

2.77 (1.34–5.75)

1.01 (0.70–1.47)

1.63 (1.06–2.50)

1.64 (0.75–3.55)

1.70 (0.75–3.88)

Model covariates include: African American, White, Hispanic, Asian, other race, gender, grade, grade average, living with. 80

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Table 4 Gambling behaviors and problem gambling severity in self-injurious versus non-self-injurious adolescents. Variables

Self-Injurious Behavior

Non-Self-Injurious Behavior

Interaction OR (Self-Injurious Behavior vs. Non-Self-Injurious Behavior)

ARPG vs LRG

ARPG vs LRG

ARPG vs LRG

OR

95% CI

OR

95% CI

OR

95% CI

0.48–4.54 1.50–4.89 1.20–3.28

11.41 1.41 2.08

2.70–48.10 1.07–1.86 1.62–2.68

0.13 1.92 0.95

0.02–0.78 1.01–3.66 0.55–1.66

2.28–7.37 2.86–8.62 4.77–24.25

2.48 3.89 2.22

1.84–3.34 2.97–5.10 1.42–3.46

1.66 1.28 4.85

0.8–3.18 0.69–2.35 1.94–12.12

4.22–37.65 4.96–62.35

2.91 15.94

1.89–4.49 5.49–46.30

4.33 1.10

1.35–13.94 0.22–5.64

2.08–6.68 2.35–6.51 1.66–4.36 1.69–4.59

3.21 3.54 2.60 1.85

2.35–4.38 2.69–4.66 2.01–3.38 1.43–2.38

1.16 1.11 1.03 1.51

0.61–2.23 0.62–1.96 0.60–1.77 0.87–2.62

0.96–2.45 0.56–1.54 1.22–3.38 1.85–11.41 1.74–7.43

1.58 2.37 2.40 3.91 3.25

1.24–2.02 1.72–3.28 1.82–3.17 2.47–6.20 2.07–5.10

0.97 0.39 0.84 1.18 1.11

0.58–1.64 0.22–0.71 0.48–1.49 0.43–3.23 0.48–2.56

3.37–15.32

4.08

2.85–5.86

1.76

0.76–4.06

0.27–1.57 0.13–0.53 0.16–0.79

1.30 1.17 0.90

0.80–2.13 0.76–1.81 0.57–1.43

0.50 0.22 0.39

0.18–1.35 0.10–0.51 0.16–0.97

Gambling types Strategic 1.48 Non-strategic 2.71 Machine 1.98 Gambling locations Online 4.10 School gambling 4.96 Casino 10.76 Triggers for gambling Pressure 12.61 Anxiety 17.59 Reasons for gambling Excitement 3.73 Financial reasons 3.91 Escape 2.69 Social reasons 2.78 Gambling partners Family 1.54 Friends 0.92 Other adults 2.03 Strangers 4.59 Alone 3.60 Time spent gambling 1 h or less? Only 1 variable listed 7.18 Age of onset of gambling (ref: ≤8 years old) 9–11 years old (2) 0.65 12–14 years old (3) 0.26 ≥ 15 years old (4) 0.35

Model covariates include: African American, white, Hispanic, asian, other race, gender, grade, grade average, living with.

other behavioral problems associated with significant distress and suffering would engage in both disordered levels of gambling and SIB as coping strategies to alleviate their suffering. Individuals often engage in SIB to alleviate negative sensations (Klonsky, 2007; Nock and Prinstein, 2004) and cope with stress (Jacobs, 1986). Blaszczynski and Nower (2002) proposed a pathways model in which some individuals gamble to modulate their mood and/or psychological state. Consistently, adolescents with problem gambling have exhibited less efficient coping strategies (i.e., emotion-based instead of task-focused) in comparison to those without problem gambling (Bergevin et al., 2006; Gupta et al., 2004). They are also more likely than non-problem gambling adolescents to experience feelings of escape and dissociative experiences when gambling (Wood et al., 2004), and such experiences may mediate the relationship between childhood trauma and disordered gambling (Imperatori et al., 2017). It is also possible that some adolescents with ARPG engage in SIB to alleviate suffering related to financial problems, interpersonal and intrafamilial conflicts and poor academic performance associated with their ARPG, and the SIB may escalate to suicidality. Gambling-related problems have been linked to suicidality. Blaszczynski and Farrell (1998) reported 44 cases of completed suicide in which heavy gambling or related financial problems immediately preceded the completed suicides. Ledgerwood et al. (2005) reported that gambling-related suicidality was more likely than non-gambling-related suicidality among 986 individuals calling a gambling helpline. Given the paucity of longitudinal studies, the relationships between gambling-related problems, SIB and suicidality are unclear, and future longitudinal research is needed.

friends nor family members; and to spend more than 1 h gambling per week. Interaction analyses showed stronger associations between ARPG and non-strategic gambling, gambling at casinos and experiencing pressure as a trigger to gamble in the SIB versus the non-SIB group. Interaction analyses also showed a weaker association between ARPG and strategic gambling, gambling with friends, age-of-onset of gambling at 12–14 years old and at 15 years old or older in the SIB versus the non-SIB group. 4. Discussion To our knowledge, this is the first investigation to study sociodemographic characteristics, gambling attitudes and perceptions, health/functioning correlates and gambling behaviors using a large adolescent sample stratified by SIB status. Findings largely supported our a priori hypotheses. Adolescents acknowledging SIB versus those without SIB were more likely to report ARPG and more permissive gambling attitudes. Adolescents with SIB also showed weaker associations between ARPG status and extracurricular participation and heavy alcohol drinking and stronger associations between ARPG and preferences for non-strategic gambling and casino gambling. Implications are discussed below. 4.1. At-risk/problem gambling Considering the association between problem gambling and suicidal behavior in adults (Ronzitti et al., 2017), the association between ARPG and SIB found in adolescents was anticipated. As data were cross-sectional, no definitive conclusions can be drawn as to whether there exists a causal relationship between adolescent ARPG and SIB, if the behaviors co-occurred as both may share similar psychological pathways associated with coping, or some other possibility. For instance, it is possible that some adolescents battling depression, anxiety and/or

4.2. Sociodemographic characteristics Among individuals with SIB, boys were more likely to report ARPG. Boys typically start gambling at an earlier age, gamble more frequently 81

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strategic gambling as well as gambling in casinos in the SIB group in comparison to the non-SIB group. Considering individuals who gamble as a coping strategy may experience more dissociative experiences, it is possible that they may prefer non-strategic forms of gambling like electronic gambling machines that have been linked to a dissociativelike attention-absorbing state termed “dark flow” (Dixon et al., 2019). Dissociation may also interfere with adherence to financial limits when gambling on slot machines (Dixon et al., 2018). Gambling in casinos may also facilitate dissociative experiences while gambling, and dissociative experiences (e.g., losing track of time) are common among individuals with problem gambling who gamble in casinos (Grant and Kim, 2003). Therefore, the current findings suggest that targeting underlying dissociative experiences (and factors associated with dissociation like trauma) with psychotherapeutic techniques may be helpful in treating problematic gambling among individuals who selfinjure. We also observed a weaker relationship between ARPG and gambling with friends in the SIB relative to the non-SIB group. Individuals who self-injure may engage in SIB as a means of escaping from undesirable social situations, which has been termed interpersonal negative reinforcement in SIB (Nock et al., 2009). This process may underlie tendencies of individuals with SIB to isolate (Endo et al., 2017). Considering that social interactions may represent natural reinforcers or rewards (Krach et al., 2010), individuals who engage in SIB as a function of interpersonal negative reinforcement may be vulnerable to engage in addictive behaviors such as gambling to compensate for blunted reward responses obtained from social interactions. Although this notion is speculative and warrants further examination, the finding resonates with the lower likelihood of ARPG associating with extracurricular activities in the SIB relative to the non-SIB group. Together, these findings suggest that increasing social connectedness may help in reducing co-occurring SIB and ARPG. Of note, a higher proportion of individuals with SIB and ARPG reported onset of gambling prior to 8 years of age (38.2%) as compared to those without SIB and with ARPG (12.0%). Given links between early age of gambling onset and ARPG (Rahman et al., 2012), these findings suggest that targeting gambling behaviors early in development may be particularly important for youth with SIB.

and experience more gambling-related problems than girls (Desai et al., 2005; Gupta and Derevensky, 1998). Girls were more likely to report SIB, and this finding does not resonate with results of a recent metaanalysis (Bresin and Schoenleber, 2015). Boys with SIB may be especially vulnerable to exhibiting risky/problematic gambling. Thus, adolescent problem-gambling prevention problem should consider targeting boys who self-injure. 4.3. Gambling attitudes and perceptions Individuals with SIB versus those without were more likely to report permissive views towards gambling. More adolescents in the SIB group reported parental approval of gambling, suggesting a more permissive parental style. Adolescents who engage in SIB typically report unfavorable relationships characterized by lack of support and warmth between them and their parents (Bureau et al., 2010; Hilt et al., 2008). Unfavorable relationships between adolescents and their parents may foster communication barriers. Parental knowledge, monitoring and communication may decrease problems arising from gambling and other risk-taking behaviors such as drinking alcohol (Rahman et al., 2014). While speculative, poor communication may translate into perceived permissiveness towards gambling, which may lead to increased youth involvement with gambling and, therefore, gamblingrelated problems (Leeman et al., 2014). Multi-systemic therapy focused on improving parental functioning has demonstrated efficacy in treating SIB (Huey et al., 2004), and it is possible that such treatment may indirectly influence prevention of gambling-related problems in this vulnerable group, although this is currently speculative. The finding that adolescents with SIB were more likely to report concern with gambling of a family member could be indirectly related with exposure to stress and trauma, which have been associated with both adolescent problem gambling and SIB (Bergevin et al., 2006; Liu et al., 2016) – which could serve as coping strategies to deal with such stress. This speculative pathway suggests that interventions focused on stressmanagement may be effective in treating risky/problematic gambling among individuals with SIB. 4.4. Extracurricular and health measures

4.6. Strengths and limitations

A relationship between extracurricular activities and ARPG was present in the non-SIB group and absent in the SIB group. The finding suggests that social involvement may be less linked to ARPG in the SIB, perhaps due to isolation and less impact of peer influences on risk behaviors like gambling. A weaker association was observed between ARPG and moderate alcohol use in the SIB group versus the non-SIB group. This finding suggests that SIB accounts for some of the variance in the association between alcohol use and ARPG. Alcohol use is widespread among individuals who engage in SIB (Cherpitel et al., 2004). Previous research has demonstrated an association between substance use and SIB; slightly more than 33% of self-injurious acts are performed while misusing alcohol (Ness et al., 2015). Therefore, individuals who engage in SIB and who experience gambling-related problems may benefit from interventions targeting alcohol use. As there are currently no well-established treatments for adolescent problem gambling, focusing treatment strategies on alcohol use in youth who report SIB may be indirectly beneficial for gambling-related problems (Rahman et al., 2014).

Among the strengths of this study are its large sample size, which is similar in composition to Connecticut census data, and the evaluation of a wide range of gambling attitudes and perceptions and health measures (e.g., substance use). Nevertheless, this study also has important limitations. The number of individuals who acknowledged engagement in SIB was relatively small, and this may have limited statistical power. Engagement in SIB was assessed by a single, unvalidated question with a “Yes/No” answer. Although this question may not capture the heterogeneity of SIB as has recently been described in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) (American Psychiatric Association, 2013), the subsequent questions asking about the frequency and extent of SIB help to provide additional information about the impact of the SIB. Future studies should examine SIB in relation to gambling and other measures in more fine detail according to more recent criteria that include self-harm with an intention to regulate negative feelings. Some of the measures, particularly those related to gambling perceptions, have not been validated, although they have been used in multiple prior publications, As the data were collected in the mid-2000s, and there have been changes in gambling and other behaviors (e.g., relating to internet and sports gambling), additional studies are warranted to examine relationships between gambling and SIB in the current environment. This may be particularly relevant for online gambling given tendencies for ARPG online to be done in solitary fashions (Potenza et al., 2011). However, the current study represents an important comparison study against which future results may be

4.5. Gambling behaviors Our results indicated a stronger association between ARPG status and gambling to alleviate pressure among individuals who acknowledge engaging in SIB, which is consistent with the hypothesis that adolescents who self-injure are more likely to gamble as an additional coping strategy to deal with pressures and related suffering. Our results also indicated a stronger association between ARPG status and non82

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compared. As the current study was cross-sectional, it is limited examination of the natures of associations, particularly with respect to longitudinal or potentially causal relationships. Future longitudinal studies are needed to examine relationships between SIB and gambling behaviors and to translate the findings into improved policy, prevention and treatment strategies. Future studies should also examine how other (e.g., gender) may influence the relationships between ARPG and SIB in order to further hone these strategies.

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Funding This work was supported by the National Institutes of Health [grant numbers R01 DA019039]; Connecticut Mental Health Center; the Connecticut State Department of Mental Health and Addiction Services; the Connecticut Council on Problem Gambling; and a Center of Excellence in Gambling Research Award from the National Center for Responsible Gaming (now the International Center for Responsible Gaming). The funding agencies had no role in data collection or analysis or in the decision to submit the paper for publication. CRediT authorship contribution statement Luis C. Farhat: Data curation, Writing - original draft, Writing review & editing. Aaron J. Roberto: Conceptualization, Writing - review & editing. Jeremy Wampler: Writing - review & editing. Marvin A. Steinberg: Writing - review & editing. Suchitra Krishnan-Sarin: Funding acquisition, Writing - review & editing. Rani A. Hoff: Funding acquisition, Writing - review & editing. Marc N. Potenza: Data curation, Writing - original draft, Conceptualization, Funding acquisition, Writing - review & editing. Declaration of competing interest The authors report no conflicts of interest with respect to the content of this manuscript. Dr. Potenza has consulted for and advised Game Day Data, the Addiction Policy Forum, AXA, and Opiant/Lakelight Therapeutics; received research support from the Mohegan Sun Casino and the National Center for Responsible Gaming (now the International Center for Responsible Gaming); participated in surveys, mailings, or telephone consultations related to drug addiction, impulse-control disorders, or other health topics; consulted for legal and gambling entities on issues related to impulse-control and addictive disorders; provided clinical care in the Connecticut Department of Mental Health and Addiction Services Problem Gambling Services Program; performed grant reviews for the National Institutes of Health and other agencies; edited journals and journal sections; given academic lectures in grand rounds, CME events, and other clinical/scientific venues; and generated books or chapters for publishers of mental health texts. The other authors report no disclosures. The views presented in this manuscript represent those of the authors and not necessarily those of the funding agencies. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.jpsychires.2020.02.016. References American Psychiatric Association, 2013. Diagnostic and Statistical Manual of Mental Disorders, fifth ed. American Psychiatric Publishing, Varlington, VA. Bergevin, T., Gupta, R., Derevensky, J., Kaufman, F., 2006. Adolescent gambling: understanding the role of stress and coping. J. Gambl. Stud. 22 (2), 195–208. Bischof, A., Meyer, C., Bischof, G., John, U., Wurst, F.M., Thon, N., Lucht, M., Grabe, H.J., Rumpf, H.J., 2016. Type of gambling as an independent risk factor for suicidal events in pathological gamblers. Psychol. Addict. Behav. 30 (2), 263–269. Blaszczynski, A., Farrell, E., 1998. A case series of 44 completed gambling-related

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