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. Author manuscript; available in PMC: 2017 Oct 1.
Published in final edited form as: Addict Behav. 2016 May 12;61:74–79. doi: 10.1016/j.addbeh.2016.05.009

Problematic Internet use, well-being, self-esteem and self-control: Data from a high-school survey in China

Songli Mei 1,*, Yvonne H C Yau 2,3,*, Jingxin Chai 1, Jinhua Guo 1, Marc N Potenza 3,4
PMCID: PMC4916009  NIHMSID: NIHMS791152  PMID: 27249805

Abstract

Given the prevalence of Internet use among youth, there is concern that a subset of Internet-using youth may exhibit problematic or addictive patterns of Internet use. The present study examines the association between problematic Internet use (PIU), demographic variables, and health-related measures among Chinese adolescents. Survey data from 1552 adolescents (male = 653, mean age = 15.43 years) from Jilin Province, China, were collected. According to the Young Diagnostic Questionnaire for Internet Addiction (YDQ), 77.8% (n=1,207), 16.8% (n=260), and 5.5% (n=85) showed adaptive, maladaptive, and problematic Internet use, respectively. Multinomial logistic regression analysis revealed that gender and family income per month differed between youth showing problematic and adaptive patterns of Internet use. Well-being, self-esteem, and self-control were related to severity of problematic Internet use, with greater severity typically associated with poorer measures in each domain. The findings that severity of problematic Internet use is associated with specific socio-demographic features and temperamental and well-being measures suggest that specific groups of youth may be particularly vulnerable to developing problematic Internet use. Early prevention/intervention programs targeting at-risk groups may help improve public health.

Keywords: Problematic Internet use, well-being, self-esteem, self-control

Introduction

The use of the Internet has increased dramatically over the past decade, and the Internet has become an important academic, business and recreational tool. Data from the China Internet Network Information Center suggest that China ended 2014 with a total Internet population of 632 million people, of which 24% were adolescents below 19 years of age (CNNIC, 2014). With the growing number of Internet users, there is concern that a subset of individuals may exhibit addictive patterns of engagement. While moderate Internet use may enhance one’s quality of life such as by widening social circles (Wellman and Haythornthwaite, 2001; Willoughby, 2008), diminished control over Internet use may be problematic and impact negatively on daily function, health and well-being (Ko, Yen, et al., 2012; Yau, Potenza, et al., 2012) particularly as behaviors may become habitual and compulsive over time (Brewer and Potenza, 2008; Leeman and Potenza, 2013).

Problematic Internet Use (PIU) or “Internet addiction disorder” was proposed for inclusion in the recently released DSM-5; however, it was concluded that there is currently insufficient evidence to warrant its inclusion in the main section of the text (Petry, 2013; Potenza, 2014). Although there remains no consensus as to what constitutes PIU, most definitions contend that PIU is characterized by excessive or poorly controlled urges and a maladaptive obsession with the Internet (Aboujaoude, Koran et al., 2006; Yau, Crowley, et al., 2012). Noticeably, PIU is a problem that has been observed in different cultures. Globally, it is estimated that 4–12% (Yau, Crowley, et al., 2012) of adolescents may demonstrate PIU, although estimates have varied widely depending on definitions used (Petry, 2013). Prevalence estimates have typically been highest in the Far East with studies reporting rates such as 17.9% among Taiwanese university freshmen (Tsai, Cheng, et al., 2009) and 20.3% among Korean adolescents (Ha, Kim, et al., 2007). Among Chinese adolescent populations, rates have ranged from between 2.4%–5.5% in Hunan Province to 6.4% in Shanxi Province (Cao, Su, et al., 2007; Deng, Hu, et al., 2007).

Multiple demographic features may be associated with or contribute to PIU. For example, males are roughly five times more likely to report PIU (Cao and Su, 2007; Tsai, Cheng, et al., 2009). Family structure, such as having siblings (Low, Shortt, et al., 2012; McCutcheon, Scherrer, et al., 2012), and family socioeconomic status may also contribute to adolescent problem behaviors (e.g. substance use and problem gambling) (Tozzi, Akre, et al., 2013); however, few studies have examined these factors in the context of PIU severity.

Internet addiction has been associated with poor mental health in 74,980 Korean middle-and-high school students (Yoo, Cho, et al., 2013). Affective temperament profiles and emotional are more frequent in Turkish adolescents who have problematic Internet use (Canan, 2013). Cao et al reported that PIU was associated with more happiness but poorer life satisfaction among Chinese students (Cao, Sun, et al., 2011; Canan, 2013; Yoo, Cho, et al., 2013). A study of college freshmen assessed self-reported levels of depression, loneliness and self-esteem and grouped respondents into four latent classes: good psychological well-being, normative, minor-disadvantageous and severe-disadvantageous. Greater PIU severity increased the likelihood of disadvantageous psychological well-being and decreased the probability of good psychological well-being (Chen, 2012). Adolescence represents a developmental stage that may be more prone to both developing PIU and exhibiting poorer self-control (Yau, Crowley, et al., 2012). Increased self-control has been associated with decreased PIU severity and may serve to moderate social relationships with parents and peers (Li et al., 2013; Park et al., 2014). Some data indicate that preferences for online social interactions influence the relationship between self-esteem and adolescent PIU (Fioravanti, Dèttore, et al., 2012), and poorer self-esteem may predict Internet-related problems (Armstrong, Phillips, et al., 2000); however, other data do not find significant relationships between self-esteem and PIU (Caplan, 2006; Fioravanti, Dèttore, et al., 2012; Koronczai, Kökönyei, et al., 2013).

In the current study, we surveyed over 1,500 junior- and senior-high-school students on Internet-use behaviors and other measures including well-being, self-esteem and self-control. Given prior data linking adolescent PIU with poor mental health (Caplan, 2006; Fioravanti, Dèttore, et al., 2012; Liu, et al., 2011; Dèttore et al., 2012; Koronczai, Kökönyei, et al., 2013), we hypothesized that low self-esteem, poorer well-being and diminished self-control would be related to PIU in the sample.

2 Method

2.1 Participants

The present study obtained data between October 2009 and October 2010 from 5 junior-high schools (mean age=13.62 years, SD=1.16) and 5 senior-high schools (mean age=16.81 years, SD=1.02) in Jilin Province located in the northeast of China. Participating high schools were from different districts of the province and five junior-high schools and five senior-high schools were randomly selected. Two classes were then randomly selected from grade 7 to 9 or grade 10 to 12. In each school, we invited all students in the six classes participate in the survey. After gaining approval from school principals, students using the Internet in the past six months were invited to participate and complete a series of self-report questionnaires in class. A member of the research team described the survey and answered any questions prior to implementation. Students were reminded that their participation was voluntary and anonymous. Individuals who did not wish to participate were instructed to sit quietly and complete other work. This procedure received approval from the Jilin University Public Health School’s Institutional Review Board and the Jilin Province Bureau of education. Students who refused participation and/or students with incomplete questionnaires were excluded. The response rate was 77.6%, and the final analytic sample included 1,552 adolescents.

2.2 Socio-demographics

Socio-demographic variables assessed in the present analysis include gender, grade (junior-high school, including grades 7–9, senior-high school, including grades 10–12), only child, single parent, living expense per month, father’s education level, mother’s education level, and family income per month.

2.3 Young’s diagnostic questionnaire for Internet addiction (YDQ)

The YDQ (Young 1998) was adapted from the DSM-IV diagnostic criteria for pathological gambling and was modified to the distinctive features of PIU; it is the most commonly used questionnaire to assess PIU and has demonstrated reliability and validity (Su and Lin, 2014). Consisting of eight “yes” or “no” questions, the YDQ diagnosis is based on patterns of Internet use over the past 6 months. In preference to a dichotomous classification, previous studies using the YDQ have identified three subcategories of PIU severity; this approach is widely used and may allow for greater statistical power (Bakken, Wenzel, et al., 2009; Durkee et al., 2012; Greenfield, 1999; Johansson and Götestam, 2004). Respondents who answered yes to ≥5 of the criteria were categorized as “problematic users,” and those answering yes to 3–4 criteria were categorized as “maladaptive users”. A previous review of the Chinese YDQ indicated that 341 of 713 studies use the YDQ as a measure to assess Internet addiction in China during 2009–2012 (Su and Lin, 2014). The internal consistency of the YDQ in the present study was acceptable with Cronbach’s α = 0.72.

2.4 Self-esteem, well-being and self-control variables

The Rosenberg self-esteem scale (RSES) (Rosenberg, 1989), a ten-item questionnaire with a four-point Likert-type scale, provides evaluation of one’s state self-esteem by asking respondents to reflect on their current feelings. The RSES, including the Chinese version, is considered a reliable and valid quantitative tool for self-esteem assessment (Moksnes and Espnes, 2013; Wang, Jiang, et al., 1999). The Chinese RSES fit better (χ2 /df = 3.72, CFI= 1.00, TLI = 0.99, RMSEA = 0.07) (Yang and Wang, 2007). The internal consistency of the RSES in this study was good with the Cronbach’s α = 0.87. The well-being scale (WBS), based on work by Campbell (1976), consists of eight items that measure subjective well-being on a seven-point Likert scale (ranging from delighted to terrible). The validity and reliability study in China was performed by Wang et al. (Wang, Jiang, et al., 1999). A meta-analysis of the Chinese well-being scale (WBS) indicated that more than 86 studies use the WBS to assess subjective well-being. (Song and Fan, 2013). The internal consistency of the WBS in this study was good with the Cronbach’s α = 0.84. The self-control scale (SCS) (Grasmick et al., 1993) is a 23-item measure of self-control and includes three subscales (Impulsivity, Simple Tasks, Self-Centeredness). Impulsivity assesses tendencies to respond to tangible stimuli in the immediate environment, to have a concrete “here-and-now” orientation (e.g., “I often act on the spur of the moment without stopping to think.”); Simple Tasks assesses lack of diligence, tenacity, or persistence in the course of action (e.g., “I dislike really hard tasks that stretch my abilities to the limit.”); and Self-Centeredness assesses people with low self-control who tend to be self-centered, indifferent, or insensitive to the suffering and needs of others (e.g., “I try to look out for myself first, even if it means making things difficult for other people.”). Each item is scored on a four-point Likert with 1 representing ‘‘strongly disagree’’ and 4 representing ‘‘strongly agree’’. Total scores and each dimension’s score were calculated with higher scores representing lower self-control. Although it has been argued that there is no optimal measure of self-control (Benda, 2005), the SCS has been shown to be a valid and reliable measure of self-control (Longshore et al., 1998). Consistent with a previous study conducted among Turkish adolescents (Ozdemir et al., 2014), the internal consistency of the SCS in this study was good with the Cronbach’s α = 0.89. Adoption of the SCS to Chinese was conducted and the SCS in Chinese fit better (χ2/ df =3.96 , GFI =0.94 , TLI =0.81 ,RMSEA =0.06) (Qu, and Zou. 2009).

2.5 Data analysis

Statistical analyses were conducted using SPSS 19.0 (IBM Corp., NY). Chi-square tests were utilized to evaluate the socio-demographic variables and Kruskal-Wallis H-test were used to test for group differences on health-related variables (i.e., well-being, self-esteem, self-control). To examine the unique effects of PIU severity, multinomial logistic regression analyses were performed to examine maladaptive vs. adaptive use, and problematic vs. adaptive use. We present the odds ratio (ORs) and corresponding 95% confidence intervals (CIs). CIs that do not include 1.0 indicate statistical significant at p<.05. An alpha level of .05 was adopted for all statistical analyses.

3. Results

3.1. Prevalence estimates and socio-demographics

The analytic sample was comprised of 899 girls and 652 boys with an average age of 15.43 years (SD=1.89, range=11–20). Of all Internet users, the percentage of adolescents classified as adaptive, maladaptive, and problematic Internet users were 77.8% (n=1,207), 16.8% (n=260), and 5.5% (n=85), respectively.

The relationships between PIU and socio-demographic variables are provided in Table 1. Chi-square analyses revealed that severity of PIU was significantly associated with gender (X2=7.13, p<.05), grade (X2=6.60, p<.05), having a single parent (X2=7.11, p<.05), monthly living expenses (X2=15.12, p<.05), and monthly family income (X2=45.98, p<.05). No significant relationship was observed between PIU severity and father’s or mother’s education levels and whether the subject was an only child (all p>.05).

Table 1.

Socio-Demographic Data

Types of Internet Use
Adaptive Use n (%) Maladaptive Use n (%) Problematic Use n (%) X2 p
Gender
 Male 489(40.5) 117(45.0) 46(54.1) 7.13 .028
 Female 717(59.5) 143(55.0) 39(45.9)
Grade
 Junior High School 503(41.7) 126(48.5) 44(51.8) 6.60 .037
 Senior High School 704(58.3) 124(51.5) 41(48.2)
Single Child
 Yes 985(81.7) 203(78.1) 71(83.5) 2.14 .343
 No 221(18.3) 57(21.9) 14(16.5)
Single Parent
 Yes 89(7.4) 31(11.9) 10(11.8) 7.11 .029
 No 1118(92.6) 229(88.1) 75(88.2)
Living Expense Per Month
 <500 RMB 310(26.7) 56(22.1) 20(24.7) 15.12 .019
 500–1000 RMB 393(33.9) 74(29.2) 26(32.1)
 1000–1500RMB 242(20.8) 49(19.4) 19(23.5)
 >1500RMB 216(18.6) 74(29.2) 16(19.8)
Father's Education Level
 Primary School Or Below 244(21.2) 57(22.7) 24(29.6) 9.21 .325
 Junior High School 368(32.0) 70(27.9) 21(25.9)
 Senior High School 186(16.2) 43(17.1) 17(21.0)
 Undergraduate 267(23.2) 55(21.9) 14(17.3)
 Post-Graduate Or Above 86(7.5) 26(10.4) 5(6.2)
Mother's Education Level
 Primary School Or Below 240(20.8) 60(23.8) 19(23.8) 3.79 .876
 Junior High School 412(35.7) 77(30.6) 26(32.5)
 Senior High School 193(16.7) 48(19.0) 13(16.3)
 Undergraduate 249(21.6) 52(20.6) 18(22.5)
 Post-Graduate Or Above 61(5.3) 15(6.0) 4(5.0)
Family income per-month
 <1000 RMB 34 (3.0) 15(6.1) 12(15.2) 45.98 .0000
 1000–5000RMB 513(45.2) 81(32.8) 26(32.9)
 5000–10000RMB 406(35.8) 92(37.2) 25(31.6)
 >10000RMB 182(16.0) 59(23.9) 16(20.3)
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3.2 Associations between PIU severity and well-being, self-esteem and self-control

Table 2 shows mean scores of well-being and individual-difference variables among adolescents with adaptive, maladaptive, and problematic Internet users. Significant between-group differences were observed for well-being (X2(2)=80.39, p<.001) and self-esteem (X2(2)=40.33, p<.001), with the highest and lowest mean scores for both variables observed in the adaptive and problematic use groups, respectively. Significant differences were observed between PIU severity and measures of self-control, Impulsivity (X2(2)=52.98, p< .001), Self-Centeredness (X2(2)=73.46, p < .001), and Simple Tasks (X2(2)=37.76, p<.001). The higher scores represent lower self-control. The highest mean scores observed among the problematic use group, which means that problematic Internet users have lower self-control than the other groups (Table 2).

Table 2.

Associations between Problematic Internet Use Severity and Measures of Wellbeing, Self-Esteem, and Self-Control.

Adaptive Use Maladaptive Use Problematic Use F p value
Mean(SD) Mean(SD) Mean(SD)
Well-Being 47.51(10.42) 43.08(9.49) 38.89( 11.29) 42.86 <.001
Self-Esteem 31.11(3.19) 29.95(3.49) 29.26(3.99) 23.52 <.001
 Impulsivity 15.61(3.63) 17.20(3.68) 17.21(3.28) 26.08 <.001
Self-control
 Self-Centered 14.47(3.93) 16.57(4.50) 17.29(4.38) 43.19 <.001
 Simple Task 10.46(2.79) 11.45(2.96) 10.69(2.88) 17.93 <.001
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3.3 Multivariate analysis

Multinomial logistic regression analyses indicated a lower odds of being female in association with problematic versus adaptive use (OR = 0.59, 95%CI: 0.35–0.97). Income-related differences were also noted between groups, with lower odds of higher income associated with maladaptive and problematic patterns of use (Table 3).

Table 3.

Multivariate-adjusted odds ratios

Maladaptive Use vs. Adaptive Use Pathological Use vs. Maladaptive Use Pathological Use vs. Adaptive Use
OR 95% CI OR 95% CI OR 95% CI
Gender
 Male REF --- REF --- REF ---
 Female 0.821 0.606–1.110 0.671 0.389–1.155 0.586* 0.354–0.968
Grade
 Junior High School REF --- REF --- REF ---
 Senior High School 1.010 0.735–1.386 1.163 0.661–2.044 1.062 0.629–1.788
Single Parent
 Yes REF --- REF --- REF ---
 No 0.699 0.424–1.150 1.603 0.643–3.991 0.882 0.379–2.051
Living Expense Per Month
 <500RMB REF --- REF --- REF ---
 500–1000RMB 1.129 0.740–1.721 1.043 0.494–2.197 1.180 0.583–2.389
 1000–1500RMB 1.108 0.688–1.782 1.172 0.493–2.786 1.168 0.529–2.573
 >1500RMB 1.549 0.965–2.484 0.763 0.319–1.822 1.375 0.606–3.117
Family Income Per Month
 <1000RMB REF --- REF --- REF ---
 1000–5000RMB 0.397* 0.190–0.829 0.349* 0.130–0.931 0.189* 0.076–0.464
 5000–10000RMB 0.657 0.308–1.397 0.302* 0.106–0.855 0.249* 0.097–0.638
 >10000RMB 0.810 0.363–1.806 0.352 0.115–1.072 0.334* 0.115–0.963
Wellbeing 0.975* 0.958–0.992 0.972 0.941–1.003 .959* 0.932–0.985
Self-Esteem 0.926* 0.880–0.973 0.987 0.911–1.069 .921 0.848–1.000
Impulsive 1.114* 1.065–1.165 0.990 0.907–1.079 1.092* 1.012–1.176
Self-Centered 1.070* 1.028–1.113 1.038 0.971–1.108 1.116* 1.045–1.190
Simple Task 1.122* 1.062–1.185 1.005 0.915–1.103 1.110* 1.013–1.215
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*

p < 0.05

Maladaptive and problematic users as compared with adaptive users were also generally less likely to report better subjective well-being and higher self-esteem and more likely to report greater Impulsivity, Self-Centeredness and Simple Task scores, with maladaptive and problematic users not showing significant differences in these domains (Table 3).

Discussion

The present study investigated prevalence estimates of PIU among youth Internet users in Jilin province, China. Additionally, associations between PIU, demographic variables, and tendencies relating to self-control were examined.

Prevalence estimates of PIU appear to vary with respect to different target groups and measures used to assess PIU. The present study suggests that 5.5% of students demonstrated PIU, and 22.2% demonstrated maladaptive use. This finding closely resembles prevalence estimates reported in previous studies that have used the YDQ among adolescent populations, which have ranged between 1.45%–4.02% for pathological use, and 13.5%–38.0% for maladaptive use (Durkee et al., 2012; Johansson and Götestam, 2004; Kim et al., 2006). Moreover, this finding appears largely consistent with studies that used different measures to assess PIU, including a 3% prevalence estimate for PIU among Korean middle-school students (Yoo, Cho, et al., 2013), 17.6% of Qatar adolescents (Bener and Bhugra, 2013), and 17.9% of Taiwanese university freshmen (Tsai, Cheng,, et al., 2009). Further studies and standardized diagnostic criteria are needed before direct comparison between studies can be made. Nevertheless, the high prevalence estimates suggest that PIU may be a serious concern with a significant public health impact.

Consistent with previous studies (Tsai, Cheng, et al., 2009; Xiu, Hui, et al., 2010), PIU severity is related to socio-demographic variables. Males may be at higher risk for PIU as compared to females. The exact reasons for observed gender-related differences in PIU-related behaviors are not well understood, and there have been differing results in the literature (Yau, Crowley, et al., 2012; Chiu, Hong, et al., 2013). Differences in preferred online activities may contribute to the findings. For example, males are more likely to view cyber porn (Cooper, Delmonico, et al., 2000) and engage in online gaming (Hellström, Nilsson, et al., 2012; Wang, Ho, Chan, and Tse, 2015). The extent to which there are gender-related differences in patterns and types of online activities warrants further examination. Some differences are observed with family income per month, suggesting that high-income may serve as a protective factor with respect to PIU. This finding is consistent with a prior report (Yoo, Cho, et al., 2013; Mason, Mennis, et al., 2014) stating that adolescents with PIU are more likely to have low familial socioeconomic status.

Though other socio-demographic variables were significantly different when examining across all three PIU groups using Chi-square tests, further analysis using logistic regression to investigate the unique effects of PIU severity found only family income per month to be significant. Interestingly, pathological users were less likely than adaptive users and maladaptive users to have a family income per month higher than our lowest income group (<1,000RMB per month). This result appears to differ from previous findings in adults that suggest Internet use is associated with higher social status (determined by income and education) (Beutel et al., 2011). According to the results of regression analysis, we found that group differences between self-reported well-being, self-esteem, and measures of self-control were observed in the present study. Individuals with PIU or maladaptive use, as compared to those with adaptive use, were less likely to report high subjective well-being. Subjective well-being reflects an important component of an individual’s soundness of mind and body (Pressman, Cohen 2005). Consistent with other surveys, subjective well-being was negatively correlated with PIU (Spada, Langston, et al., 2008; Meerkerk, van den Eijnden, et al., 2010; Akın, 2012), with reductions in positive emotions and life satisfaction also associated with PIU (Wan and Chiou, 2006; Cao, Sun, et al., 2011).

Individuals reporting higher self-esteem were less likely to be maladaptive users, and the association with PIU trended in the same direction, although it was not statistically significant at P < 0.05. Previous research has posited self-esteem as measure of psychological well-being (Meerkerk, van den Eijnden et al. 2010), and low self-esteem may represent a risk factor for PIU (Douglas, Mills et al. 2008; Yang, Tung 2007; Kim, Davis 2009). It is possible that individuals with low self-esteem may have difficulty controlling PIU behavior, facing differences between realities and ideals, and understanding the potential negative consequences of PIU. Low self-esteem may lead individuals to seek temporary relief through mind-altering activities (Greenberg, Lewis, et al., 1999). However, there have been some seemingly conflicting results in the literature, with a report of a positive relationship between self-esteem and PIU among adolescents. Thus, further research is warranted to determine the specific relationships between self-esteem and PIU.

Self-control has been proposed to be a key factor relating to the pathophysiology and treatment of PIU (Dong and Potenza, 2014). The measure of self-control used in the present study assesses multiple domains- Impulsivity, Self-Centeredness, and Simple Task- that may be correlated with PIU. Consistent with previous studies (Li, Li, et al., 2013), individuals with higher self-control are less likely to experience PIU. Self-control has also been previously found to correlate negatively with online gaming problems (Kim, Namkoong, et al., 2008). These results suggest that low self-control may increase the tendency to escape from daily activities (Ozdemir et al., 2014). The above suggest that countermeasures may address these PIU by focusing on the need for better self-control, perhaps involving better time management or emotional regulation (Hormes, Kearns, & Timko, 2014).

There are strengths and limitations in the current study. While associations were observed between PIU and poorer well-being, self-esteem, and self-control, the cross-sectional nature of the study does not permit insight into temporal relationships. Future longitudinal data will be important to understand what factors may increase the risk for development of PIU. The present study also did not differentiate between different types of Internet use (e.g., online gaming, social networking, academics). Internet use is a heterogeneous construct and the contexts may relate differently to various health-related measures and socio-demographic variables. Furthermore, the measures are all self-reported and thus are limited by individuals’ abilities and willingnesses to recall and report information accurately. Additionally, the number of individuals who were not included in the survey due to not using the computer in the last 6 months is not known.

Conclusion

This study provides an initial investigation of the psychological variables relating to PIU among adolescents in Jilin province in China. Psychological characteristics relating to subjective well-being, self-esteem, and self-control are related to PIU. Although more research should be performed to corroborate and extend the findings, the results have the potential to inform policy and prevention efforts to limit the impact of PIU among adolescents and into adulthood.

Highlights.

  1. Problematic Internet use (PIU) is common amongst youth.

  2. PIU is linked to poor well-being, self-esteem and self-control.

  3. These factors may help identify at-risk youth for PIU.

Acknowledgments

Funding: This work was supported in part by Jilin province department of science and technology development program (20130522027JH, 20160414035GH), Science Education Program Project “Twelfth Five- Year Plan” of Jilin Province GH14001, the seed funding of Philosophy and Social Sciences Research of Jilin University 2014ZZ025, R01 DA018647 from the National Institutes of Health, the National Center on Addiction and Substance Abuse (CASAColumbia), the Connecticut State Department of Mental Health and Addiction Services, and a Center of Excellence in Gambling Research Award from the National Center for Responsible Gaming. The funding agencies did not provide input or comment on the content of the manuscript, and the content of the manuscript reflects the contributions and thoughts of the authors and do not necessarily reflect the views of the funding agencies.

Footnotes

Disclosures: The authors report no conflicts of interest with respect to the content of this manuscript. Dr. Potenza has: consulted for and advised Lundbeck, Ironwood, Shire, INSYS, RiverMend Health and Opiant/Lakelight Therapeutics; received research support from the National Institutes of Health, Veteran’s Administration, Mohegan Sun Casino, the National Center for Responsible Gaming, and Pfizer pharmaceuticals; participated in surveys, mailings, or telephone consultations related to drug addiction, impulse control disorders or other health topics; consulted for law offices and the federal public defender’s office in issues related to impulse control disorders; provides 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; has guest-edited 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.

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