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  • 1 Université de Paris, LPPS, Boulogne-Billancourt, France
  • | 2 Centre Pierre Nicole, Consultation Jeunes Consommateurs, Paris, France
  • | 3 Clinique Dupré, Fondation Santé des étudiants de France, Paris, France
  • | 4 Unité Inserm CESP, Paris, France
Open access

Background and aims

The aim of this study was to evaluate the effects of a prevention intervention on French adolescents’ Internet and video games use and on their beliefs concerning gaming and Internet Gaming Disorder (IGD), in order to adjust prevention programs further.

Methods

The study comprised a prevention intervention group (PIG) and a control group assessed at three times – baseline, post-test, and 4-month follow-up. At baseline, a total of 434 junior high adolescents from five secondary schools were assessed (Mage = 13.2 years; SD = 0.5). The main outcome measures were adolescents’ gaming and Internet use (amount of time spent during the week and the weekend), the number of adolescents with IGD, and beliefs about gaming and IGD.

Results

The results showed significant effects of the prevention intervention on Internet and gaming use (at T2, time spent was significantly lower in the PIG), an important increase of IGD prevalence between baseline and follow-up in the control group, and decreased rates of IGD among adolescents in the PIG between post-intervention and follow-up. Between baseline and follow-up, the control group showed a more significant increase of minutes per day during the week and the weekend on Internet versus during the week on video games. The impact of the prevention intervention on adolescents’ beliefs varied according to gender. Girls had a better understanding generally of the potential dangers of and reasons for IGD.

Discussion

Implications for future research and prevention approaches are discussed in this study.

Abstract

Background and aims

The aim of this study was to evaluate the effects of a prevention intervention on French adolescents’ Internet and video games use and on their beliefs concerning gaming and Internet Gaming Disorder (IGD), in order to adjust prevention programs further.

Methods

The study comprised a prevention intervention group (PIG) and a control group assessed at three times – baseline, post-test, and 4-month follow-up. At baseline, a total of 434 junior high adolescents from five secondary schools were assessed (Mage = 13.2 years; SD = 0.5). The main outcome measures were adolescents’ gaming and Internet use (amount of time spent during the week and the weekend), the number of adolescents with IGD, and beliefs about gaming and IGD.

Results

The results showed significant effects of the prevention intervention on Internet and gaming use (at T2, time spent was significantly lower in the PIG), an important increase of IGD prevalence between baseline and follow-up in the control group, and decreased rates of IGD among adolescents in the PIG between post-intervention and follow-up. Between baseline and follow-up, the control group showed a more significant increase of minutes per day during the week and the weekend on Internet versus during the week on video games. The impact of the prevention intervention on adolescents’ beliefs varied according to gender. Girls had a better understanding generally of the potential dangers of and reasons for IGD.

Discussion

Implications for future research and prevention approaches are discussed in this study.

Introduction

As a pastime practiced by many individuals around the world, Internet-based activity and more specifically Internet gaming mark a cultural and generational split. Unlike other addictive disorders, it is possible that virtually every adolescent from developed countries use the Internet at least occasionally. Although this activity has several well-known benefits like emotion regulation or developing cognition (Bediou et al., 2018; Gaetan, Bréjard, & Bonnet, 2016; Russoniello, O’Brien, & Parks, 2009; Wang et al., 2017), Internet gaming also produces deleterious effects with excessive use. Indeed, it is now commonly admitted that some people develop significant problems related to Internet gaming, that their gaming has certain features of addictive disorders, and that it should be diagnosed as a disorder (Saunders et al., 2017) called Gaming Disorder (ICD-11; World Health Organization, 2018) or Internet Gaming Disorder (IGD) (DSM-5; American Psychiatric Association, 2013). IGD has been associated with many issues, including refusal of social activities, isolation, family conflicts, mood disorders, lower academic achievement or school disconnection, sleep deprivation, day–night reversal, malnutrition, physical inactivity, and higher frequency of gaming expenses (Achab et al., 2011; Bonnaire, Liddle, Har, Nielsen, & Phan, 2019; Bonnaire & Phan, 2017; Brunborg et al., 2013; Mihara, Nakayama, Osaki, & Higuchi, 2016; Wang et al., 2014).

In a recent systematic review of epidemiological studies on IGD, the prevalence of the disorder ranged from 0.7% to 27.5% with higher prevalence among younger people (Mihara & Higuchi, 2017). The definition of IGD generates important debates and a multitude of measuring tools (Feng, Ramo, Chan, & Bourgeois, 2017), the Internet Gaming Disorder Test (Pontes, Király, Demetrovics, & Griffiths, 2014), and the the Internet Gaming Disorder Scale–Short-Form (Pontes & Griffiths, 2015) being the most widely used, and more recently the development of the first psychometric tool to assess gaming disorder using the new diagnostic framework developed by the World Health Organization (the Gaming Disorder Test; Pontes et al., 2019). Although a concern is that this may result in overpathologizing everyday behaviors (Billieux, Schimmenti, Khazaal, Maurage, & Heeren, 2015), it remains that the high prevalence rates of IGD highlight the presence of a problem. IGD and its ensuing issues seem inversely related to age (Wittek et al., 2016). A 2016 study that included five European countries (Germany, Estonia, Italy, Spain, and Romania) also suggested that IGD is not only on the rise (Kaess et al., 2016), but that it is present already in some 10-year olds (Wichstrøm, Stenseng, Belsky, von Soest, & Hygen, 2019). Such studies indicate that IGD among young people is an important public health concern that merits intervention. Thus, there is a emphasizing need for research into and development of preventive approaches to counteract this rising clinical phenomenon (Király et al., 2018; Saunders et al., 2017) and identify best practice guidelines across populations and regions (King et al., 2018). Nonetheless, although some attention has been given to psychological (and pharmacological) treatments (see Zajac, Ginley, Chang, & Petry, 2017 for a recent review) in some countries, prevention of IGD has received less attention in many other countries, despite potential benefits of this approach. Indeed, several authors recommended that the field should focus on enhancing its preventive efforts to help society overcome disordered gaming (Griffiths & Pontes, 2019; Pontes & Griffiths, 2019).

In their review, King et al. (2018) summarize peer-reviewed prevention studies on IGD and Internet addiction. Only 13 quantitative studies in the past decade were identified internationally, none of which were in France, despite the increasing number of requests to address this problem (Obradovic, 2017). Several recommendations emerged in the research literature reviewed, especially those based on school programs (Throuvala, Griffiths, Rennoldson, & Kuss, 2019). While some studies suggested that elementary-school-aged children should be prioritized for selective prevention interventions (Lee, 2013), most researchers also agreed that preventive interventions should focus mainly on and would be more effective for adolescents whose values and standards are in the process of developing (Vitaro & Gagnon, 2003; Vondráčková & Gabrhelík, 2016). Our literature review and clinical experience with adolescents with IGD lean toward the conclusion that junior high-school students (between 13 and 14 years old), at crossroads between recreational and excessive video game use, are an important focus group for IGD. As suggested by Werch and DiClemente (1994) about the McMos model for psychoactive substances, the early teen years are beyond the primary prevention target, but should be classified as a secondary prevention issue. Secondary prevention aims to reduce the impact of a disease or injury that has already occurred – in this case disordered gamers, whereas primary prevention aims to prevent problems or diseases before they manifest (Petry et al., 2018). Thus, the purpose of such a secondary prevention program would be to help teenagers become aware of and thereby reduce the risks associated with possible excessive video gaming. Indeed, it is probable that most teenagers in secondary school are in the precontemplation stage regarding their use habits (Werch & DiClemente, 1994) of gaming (playing for more than 6 months and not imagining to stop playing). Thus, using techniques and messages adapted to the stage of readiness of the individual implies considering both the potential detriments and benefits of Internet and video game use. There are several ways of using video games and Internet-based activities, which are adaptive, productive, and socially significant and which increase the psychological well-being of users (Granic, Lobel, & Engels, 2014). Our intervention is oriented toward harm reduction. Indeed, our goal is not to ban or withdraw the adolescent from screens and video games, but to enhance skills and competencies associated with identifying its risk and institute protective factors (Throuvala et al., 2019).

Another recommended approach would be using interactive and visual materials to enhance self-reflection rather than employing authoritarian anti-gaming messages (Joo & Park, 2010). Providing information about negative consequences of risk behaviors is ineffective. Instead, interactive interventions should aim to change attitudes as well as develop personal skills (with use of Internet and gaming, with coping with stress and emotions, etc.; Vondráčková & Gabrhelík, 2016). Enhancing self-reflection rather than transmitting anti-gaming messages is a more productive goal for prevention programs that does not focus on reducing individual-level use to its lowest possible point, nor imposing unnecessary restrictions upon healthy users (King et al., 2018). Helping gamers think about their motives, expectations, and reasons for repeated use of online games (King & Delfabbro, 2014) could be a key mechanism for preventive programs. By exploring these factors, each gamer “could enhance individual self-control and reflection on their own needs, resulting in functional, responsible gaming behavior as well as in the establishment of alternative coping strategies for everyday life” (Wegmann & Brand, 2018, p. 533). These elements are in line with the idea of social and emotional skill development, a protective factor from developing mental health issues (Catrinel & Mircea, 2010). Promotion of positive youth development is a promising direction for prevention intervention (Shek & Yu, 2016). These perspectives are not only oriented to individuals who present IGD symptoms already, but also to individuals who experience problems without fulfilling all the criteria. This is a core aspect of early intervention (Wegmann & Brand, 2018). Finally, another recommendation is to integrate clinical measures of IGD into prevention programs and not consider the use of Internet and gaming time reduction as main outcome (Throuvala et al., 2019).

Overall, the primary aim of this study is to evaluate a prevention intervention based on the development of psychosocial skills. Psychosocial skills include social skills (e.g., communication, resistance and negotiation, empathy, group collaboration, and advocacy), cognitive skills (e.g., decision-making and problem-solving, critical thinking and self-evaluation, and influence of the media and peers), and emotional skills (e.g., emotion regulation, stress management, and time management). This study seeks to assess its impact on French adolescents’ beliefs of gaming and IGD but also on their gaming and Internet use behaviors. Understanding teenagers’ beliefs and knowledge about video games and IGD will help to adjust future prevention programs better. Theory of Reasoned Action (Ajzen & Fishbein, 1980) shows that beliefs (i.e., positive and negative perceptions of a particular behavior) influence attitudes, which in turn shape intentions in terms of behaviors. For example, research on gambling indicates that teenagers’ attitudes are an important predictor of their gambling behavior (Moore & Ohtsuka, 1999). The second aim of this study is to investigate gender differences. Previous studies on parenting styles and restrictive mediation seem to differ according to the gender of the child (Bonnaire & Phan, 2017; Choo, Sim, Liau, Gentile, & Khoo, 2015; Wallenius & Punamäki, 2008), thus it may be that prevention interventions will have different impacts on boys and girls. Because factors associated with Internet addiction differ by gender, prevention intervention should also fit boys’ and girls’ different needs.

This pilot effectiveness study serves as a first step to design further prevention programs according the adolescents’ needs (boys and girls). It also evaluates the possibility of this type of intervention in France.

Methods

Participants and procedure

Five Parisian suburban schools agreed to participate in the study. Each school comprised between four and five classrooms of 4th grade (junior high-school students between 13 and 14 years olds). In each school, two classrooms were randomly selected to participate in the prevention intervention. All participants were self-selected; inclusion or exclusion criteria were based on informed consent from parents and teenager, and the Game Addiction Scale (GAS) completely filled (Lemmens, Valkenburg, & Peter, 2009). At baseline, a total of 434 secondary-school pupils from 20 classrooms (Mage = 13.2 years; SD = 0.5) were included. The control group (CG) was formed with participants (pupils of the other classrooms) who did not participate in the prevention intervention (see Figure 1 for recruitment procedure).

Figure 1.
Figure 1.

The CONSORT flow diagram of the selection process. GAS: Game Addiction Scale

Citation: Journal of Behavioral Addictions 8, 3; 10.1556/2006.8.2019.54

At the beginning of the school year (between October and November 2016), all the students who agreed to participate in the study and from whom we did not receive parental refusal completed the questionnaire concerning their use and their beliefs (T0). The questionnaire was completed again (T1) just after the prevention session, but only by the adolescents who benefited from the prevention intervention, which took place between November and December. From March to April, all adolescents from both prevention and CGs completed the questionnaire for a third time (T2). The time between T1 and T2 was 4 months to detect maintenance of effects. All evaluations (T0, T1, and T2) and the prevention sessions occurred on the same day in each school to avoid contamination via sharing questions among pupils.

Prevention intervention

The prevention intervention lasted 90 min (see Appendix). The aims were to: (a) increase awareness about the time spent in front of different screens and about the increase in the number of screens present in their daily life; (b) increase reflection about individual “life priorities” (i.e., homework, sport, and interpersonal interaction); (c) raise awareness about the consequences of excessive use of video games on sleep, school investment, and family; and (d) reinforce protective factors like positive use and self-control, and help them think of ways to change their use or reduce negative consequences. Overall, the prevention intervention aimed to increase knowledge and skills development among adolescents. Prevention programs that increase knowledge are more effective if they are combined with a skill-based approach (Hawks, Scott, McBride, Jones, & Stockwell, 2002). It was a one shot action: one prevention intervention during the academic year. For some researchers, there is insufficient evidence to suggest that long-term programs are more effective than short-term ones (Cuijpers, 2002; Gottfredson & Wilson, 2003). Here, the French institutions that agreed to participate in the study did not want the prevention intervention to take too much time from their curriculum.

The intervention was conducted by a prevention officer in an interactive way for the purpose of the adolescents to confront their different beliefs with one another and to understand the varying motivations for using the different screens and video games. The prevention officer asked the questions to the whole class (writing the answers on the board) and asked questions that promoted group dynamics, which means exchanges between teenagers to find common proposals. In the subject area of adolescent drug abuse prevention, the literature shows that interactive programs are more effective than non-interactive programs (Botvin & Griffin 2007; Springer et al. 2004; Tobler et al., 2000). In schools, interactive programs are found to be at least twice as effective as lecture-style programs (Hawks et al., 2002).

Measures

Gaming and Internet use

The questionnaire included four questions about use: amount of time spent on Internet (MSN, Facebook, YouTube, etc.) from Monday to Friday after school, and during the weekend.

Several dichotomous questions (“Yes” or “No” answers) were asked: Do you sleep less to spend more time playing video games? Do you spend more time playing video games than seeing your friends? Do you think that time spent playing video games has an impact on your school marks? Do you think that time spent playing video games affects the time spent with your family?

The questionnaire also incorporated the short version of the GAS, French validation (Khazaal et al., 2016), to evaluate addictive gaming (Lemmens et al., 2009). This 7-item scale is one of the most frequently used instruments for measuring IGD in adolescents (e.g., “Have you thought all day long about playing video games?”). As recommended by Lemmens et al. (2009), four “validated” items (responses indicating sometimes or more) correspond to addictive use of video games.

Gaming beliefs

The participant was asked to rank the following four propositions (products or behaviors) in order of increasing danger for schooling and health: tobacco > alcohol > cannabis > video games; cannabis > alcohol > tobacco > video games; alcohol > cannabis >tobacco > video games; video games > cannabis >alcohol >  tobacco.

The participant was asked to name the type of game that leads to the most dependence (only one answer): shooting games, strategy games, role-playing games, simulation games, management games, and no opinion.

Several dichotomous questions (“Yes” or “No” answers) were asked: Do you think that video games can have negative consequences on education? Do you think that video games can have negative consequences on family time? Do you think that video games can have negative consequences on physical health (e.g., malnutrition and back problems)? Do you think that video games can have negative consequences on mental health (e.g., depression)?

Internet gaming disorder beliefs

One dichotomous question (“Yes” or “No” answers) was asked: Do you think that we can become addicted to video games? The last three questions allowed for selection of multiple answers.

  1. 1.If you think video games can have negative consequences, what do you think they are? Possible response selections: eating problems, sleep problems, vision problems, withdrawal into a virtual world, lack of exercise, conflicts with parents/family, conflicts with friends, loss of the notion of time, lack of school investment, and aggressivity.
  2. 2.In your opinion, what could lead a person to become addicted on video games? Possible response selections: family problems, poor school performance, lack of friends, lack of self-confidence, bad self-image, difficulty making friends, and coincidence.
  3. 3.How would you define someone who is addicted on video games? Possible response selections: number of hours played, bad marks, saying no to all outings, only talking about video games, a person who cannot stop playing, playing instead of fulfilling one’s obligations (e.g., homework, sport, etc.), playing all the time.

Statistical analyses

All statistical analyses were carried out with SPSS software (version 20; New York, NY, USA). Univariate analyses were conducted: the CG and the intervention group were compared. Baseline (T0) and follow-up (T1 and T2) differences between the two groups were tested.

Next, gender differences were analyzed. A one-way analysis of variance was used to assess mean differences in continuous variables. For categorical data, differences in percentages were compared using the χ2 test. The p value <.05 was used as a test of significance. To test the effect sizes, Cohen’s d for the continuous variables and ϕ2 or Cramer’s V for the categorical data were calculated. Because of baseline differences between the two groups (prevention intervention and control) in time spent on Internet (during the week and the weekend) and video game (during the week and the weekend), we conducted an analysis of covariance with the prescore as a covariate. All the prescores were centered and used as a covariate.

Ethics

The study procedures were carried out in accordance with the Declaration of Helsinki. The study was approved by all school principals. Active consent was given by the adolescents and passive consent was obtained from the adolescents’ parents (parents were informed by letter about the study and could refuse their child’s participation by returning the consent). All participants gave their written informed consent. The ethics committee of Paris Descartes University (CERES) approved the study (IRB number: 20133600001072) and it was the subject of a declaration to the CNIL (treatment number 73).

Results

Comparison of the prevention intervention group (PIG) vs. the control group (CG)

At baseline (T0), despite randomization, there were significant differences between the two groups (see Table 1). While there were no significant differences at T0, at T2, there were significantly more IGD gamers in the CG group (ϕc = 0.11; p = .027) and more adolescents who played video games instead of seeing friends (ϕc = 0.15; p = .013). In the PIG, the number of IGD gamers decreased by half between T1 and T2 (ϕc = 0.15; p = .052). At T2, the minutes spent on Internet during the week [F(2) = 17.68; p < .001; d = 0.09] and the weekend [F(2) = 6.90; p = .001; d = 0.10], and the minutes spent on video game during the week [F(2) = 18.57; p < .001; d = 0.42] and the weekend [F(2) = 31.06; p < .001; d = 0.05] were significantly lower in the PIG group in comparison with the CG. Between T0 and T2, the number of minutes spent on Internet during the week increased by 39.4 min (+12.1%) in the CG, whereas it increased by 17.2 min (+5.7%) in the PIG. Furthermore, the number of minutes spent on Internet during the weekend increased by 46 min (+13%) in the CG, whereas it increased by 37.2 min (+11.7%) in the PIG, and the number of minutes spent on video games during the week increased by 35.3 min (+30%) in the CG, whereas it increased by 27.7 min (+22.9%) in the PIG. At T2, in the PIG, there was a significant increase of perceived risk associated with excessive gaming [e.g., they were more likely to think that video games could generate vision problems (ϕc = 0.27; p < .001) or conflicts with friends (ϕc = 0.19; p < .001)]. Furthermore, new awareness about risk factors of IGD development emerged in the PIG group. Finally, the PIGs were more likely to think that not being able to stop playing video game is a characteristic of a person presenting IGD (ϕc = 0.10; p = .039).

Table 1.

Comparison between the prevention intervention group and the control group

Prevention Intervention Group (PIG; n = 190)Control group (CG; n = 194)PIG vs. CG
T0T1T2T0T2
n/mean%/SDn/mean%/SDn/mean%/SDT0 vs. T1T1 vs. T2T0 vs. T2n/mean%/SDn/mean%/SDT0 vs. T2T0T2
Gender
 Girls9751.18845.6
 Boys9348.910654.4
Gaming use
 Nb min/day Internet M-F283.0447.2284.5352.4300.2414.9NSNSNS286.1369.8325.5429.9NSNS<0.001
 Nb min/day Internet WE280.7330.3265.5255.9317.9530.8NSNSNS307.1362.2353.1434.4NSNS0.001
 Nb min/day VG M-F92.1182.6120.6221.2119.8208.3NSNSNS153.6272.6188.9367.3NS0.011<0.001
 Nb min/day VG WE161.5208.9176.4258.3177.8381.9NSNSNS225.9332.6213.1346.9NS0.025<0.001
Less sleep for play
 Yes4020.94322.83619.1NSNSNS5327.54925.3NSNSNS
 No11459.79550.310153.711358.510755.2
 Non-gamer3719.45127.05127.12714.03819.6
Playing instead of seeing friends
 Yes3417.93116.41910.1NSNS0.0133216.63518.1NSNS.013
 No12364.710756.611762.213368.912464.2
 Non-gamer3317.45127.05227.72814.53417.6
Addictive gamers (GAS)
 Yes168.72010.7105.6NS0.053NS2412.62312.1NSNS.027
 No16816717094.416687.416787.9
Gaming beliefs
 Most dangerous for education
  Tobacco > Alcohol > Cannabis > Video game94.92312.2126.50.0060.012NS147.3105.2NSNSNS
  Cannabis > Alcohol > Tobacco > Video game10555.67640.210556.59248.29348.7
  Alcohol > Cannabis > Tobacco > Video game2412.72613.81910.22814.73116.2
  Video game > Cannabis > Alcohol > Tobacco5127.06433.95026.95729.85729.8
 Most dangerous for health
  Tobacco > Alcohol > Cannabis > Video game2211.73719.43317.50.031NSNS3015.72915.2NSNSNS
  Cannabis > Alcohol > Tobacco > Video game12063.81056.511158.711560.212364.4
  Alcohol > Cannabis > Tobacco > Video game3217.02211.52915.33317.32814.7
  Video game > Cannabis > Alcohol > Tobacco147.42412.6168.5136.8115.8
 Impact on education
  Yes8142.67940.96735.3NSNSNS6735.37740.3NSNSNS
  No8243.27940.98243.29650.58544.5
  Non-gamer2714.23518.14121.62714.22915.2
 Impact on family time
  Yes8142.69247.46534.4NS0.034NS6433.35730.2NSNSNS
  No8343.76935.68545.010353.610354.5
  Non-gamer2613.73317.03920.62513.02915.3
 Impact on physical health
  Yes13072.915881.014777.40.038NSNS12062.213972.00.0400.026NS
  No5127.13719.04322.67337.85428.0 
 Impact on mental health
  Yes13370.415780.114174 .20.027NSNS12564.812062.8NSNS.017
  No5629.63919.94925.86835.27137.2
Beliefs about IGD
 Internet gaming disorder?
  Yes15380.517089.016084.70.07NSNS15079.816083.8NSNSNS
  No3719.52111.02915.33820.23116.2
 Type of VG associated with addiction
  Shoot games4523.66532.25629.30.037NSNS6131.44523.20.04NSNS
  Strat games147.3209.92412.6NSNS0.06157.72110.8NSNSNS
  RP games4724.66733.25528.80.039NSNS5226 .85327.3NSNSNS
  Sim games189.43416.83116.20.030NS0.032914.92814.4NSNSNS
  Manag games2613.62713.42714.1NSNSNS168.22311.9NSNSNS
  No opinion4423.03215.84322.50.047NSNS4322 .24322.2NSNSNS
 Risks associated with gaming use
  Food problems5428.313566.810756.0<0.0010.028<0.0014422.77438.10.001NS<0.001
  Sleep problems16083.617486.117390.6NS0NS0.0314976.816384.0NSNS0.037
  Vision problems14978.016983.716485.9NSNS0.0313167.512162.4NS0.021<0.001
  Withdrawal into a virtual world10454.511155.012163.4NS0.0560.049247.411056.7NSNSNS
  Lack of exercise9549.711757.912062.80.06NS0.0077840.29649.5NSNS0.008
  Conflicts with parents/family9750.815074.311359.2<0.0010.0010.068845.410152.1NSNSNS
  Conflicts with friends4523.69145.07438.7<0.001NS0.0013317.04221.6NSNS<0.001
 Loss of time concept10152.913767.813570.70.002NS<0.00111056.710855.7NSNS0.002
  Lack of school investment13671.215476.215078.5NSNS0.0611860.813167.5NS0.0320.015
  Aggressivity11057.613968.812263.90.014NSNS11358.29951.0NSNS0.011
 Reasons for the addiction
  Family problems7639.812059.410756.0<0.001NS0.0017538.79347.9NSNS0.07
  Poor school results7237.79044.68745.5NSNS0.076835.17136.6NSNS0.046
  Lack of friend(s)11560.214471.313972.80.014NS0.0069146.912866.0<0.0010.009NS
  Lack of self-confidence8041.98642.66735.1NS0.078NS6433.07840.2NSNSNS
  Bad self-image6031.47135.17036.6NSNSNS5126.35628.9NSNS0.06
 Difficulty making friends8946.610853.510756.0NSNS0.046131.49850.5<0.0010.002NS
  Chance5830.44924.37137.2NS0.005NS7940.75729.40.0190.034NS
 Characteristics of a IGD gamer
  Number of hours spent playing10856.511355.99951.8NSNSNS10051.511157.2NSNSNS
  Poor school results3819.93919.33518.3NSNSNS2512.93116.0NSNSNS
  Saying no to all outings7036.69245.59449.20.046NS0.0098342.89549.0NSNSNS
  Only talking about video games11962.311657.411660.7NSNSNS11056.712262.9NSNSNS
  Cannot stop playing12967.514672.314777.0NSNS0.02612664.913167.5NSNS0.039
  Playing instead of fulfilling his obligations12967.513767.812766.5NSNSNS13670.111961.3NSNSNS
  Playing all the time13269.114270.614475.4NSNSNS13569.613871.1NSNSNS

Note. Nb min/day Internet M-F: number of minutes per day on Internet from Monday to Friday after school; Nb min/day Internet WE: number of minutes per day on Internet during the weekend; Nb min/day VG M-F: number of minutes per day playing video games from Monday to Friday after school; Nb min/day VG WE: number of minutes per day playing video games during the weekend; VG: video games; Shoot games: shooting games; Strat games: strategic games; RP games: role-playing games; Sim games: simulation games; Manag games: management games; SD: standard deviation; NS: not significant.

Gender comparison

At baseline (T0), there were significant differences between boys and girls (see Table 2). Just after the prevention program (T1), girls spent more time on Internet during the week and the weekend (d = 0.35; p = .018 and d = 0.31; p = .040, respectively), whereas boys spent more time playing video games during the week and the weekend (d = 0.54; p < .001 and d = 0.82; p < .001, respectively). There were more IGD gamers among the boys (ϕc = 0.22; p = .003). There were more boys who thought that gaming had no impact on physical and mental health (ϕc = 0.20; p = .006 and ϕc = 0.15; p = .041, respectively). There were more girls who thought that video games could generate several problems [e.g., conflicts with parents (ϕc =0.25; p < .001) or lack of school investment (ϕc = 0.21; p = .003)]. There were more girls who thought that family problems (ϕc = 0.16; p = .022) and lack of self-confidence (ϕc = 0.16; p = .023) were reasons for developing IGD that playing instead of fulfilling one’s obligations and playing all the time were characteristics of a person presenting IGD (ϕc = 0.16; p = .024 and ϕc = 0.16; p = .023, respectively).

Table 2.

Gender comparison in the prevention intervention group

Prevention Intervention Group (PIG; n = 190)
T0T1T2
♀ (n = 97)♂ (n = 93)♀ (n = 97)♂ (n = 93)♀ (n = 97)♂ (n = 93)
n/mean%/SDn/mean%/SD♀ vs ♂n/mean%/SDn/mean%/SD♀ vs. ♂n/mean%/SDn/mean%/SD♀ vs. ♂T0 vs. T2T0 vs. T2
Gaming use
 Nb min/day Internet M-F257.1368.8311.1519.1NS343.8406.8221.8272.40.018364.0498.0235.0292.60.030NSNS
 Nb min/day Internet WE287.8323.3273.4340.5NS302.8279.8225.2221.80.040282.1299.2346.4693.2NSNSNS
 Nb min/day VG M-F36.952.2150.1242.9<0.00164.2172.3181.3251.0<0.00161.8196.7182.0205.4<0.001NSNS
 Nb min/day VG WE67.589.6262.5248.4<0.00179.7191.0277.3280.8<0.00164.4180.0295.9491.2<0.001NSNS
Less sleep for play
 Yes1212.42830.1<0.0011313.43032.6<0.0011515.52022.7<0.0010.021NS
 No5253.66266.73940.25660.93334.06675.0
 Non-gamer3334.033.24546.466.54950.522.3
Playing instead of seeing friends
 Yes99.32527.2<0.00188.22325.0<0.00144.11517.0<0.0010.010NS
 No5859.86570.74344.36469.64344.37180.7
 Non-gamer3030.922.24647.455.45051.522.3
Addictive gamers (GAS)55.41112.2NS44.21617.60.00333.278.2NSNSNS
Gaming beliefs
 Most dangerous for education
  Tobacco > Alcohol > Cannabis > Video game88.311.1NS1616.377.70.01588.544.4NSNSNS
  Cannabis > Alcohol > Tobacco > Video game4850.05660.92929.64751.64952.15460.0
  Alcohol > Cannabis > Tobacco > Video game1111.51314.11616.31011.077.41213.3
  Video game > Cannabis > Alcohol > Tobacco2930.22223.93737.82729.73031.92022.2
 Most dangerous for health
  Tobacco > Alcohol > Cannabis > Video game1212.51011.0NS2323.51415.10.0461818.81516.7NSNSNS
  Cannabis > Alcohol > Tobacco > Video game5961.56065.94646.96266.75052.15864.4
  Alcohol > Cannabis > Tobacco > Video game2020.81213.21515.377.52121.988.9
  Video game > Cannabis > Alcohol > Tobacco55.299.91414.31010.877.3910.0
 Impact on education
  Yes4142.74043.0<0.0013939.44042.6<0.0013233.03538.9<0.001NSNS
  No3233.35053.83030.34952.12828.95156.7
  Non-gamer2324.033.23030.355.33738.144.4
 Impact on family time
  Yes4142.34043.50.0014848.54446.3<0.0013132.03438.2<0.0010.05NS
  No3536.14852.22222.24749.53030.95258.4
  Non-gamer2121.644.32929.344.23637.133.4
 Impact on physical health
  Yes8285.45459.3<0.0018788.87173.20.0068486.66370.00.006NSNS
  No1414.63740.71111.22626.81313.42730.0
 Impact on mental health
  Yes7678.45661.50.0128585.97274.20.0418183.55763.30.002NSNS
  No2121.63538.51414.12525.81616.53336.7
Beliefs about IGD
 Internet gaming disorder?
  Yes8284.57076.1NS9090.08087.0NS8992.77077.80.004NSNS
  No1515.52223.999.11213.077.32022.2
 Type of VG associated with addiction
  Shoot games1515.52931.20.0102322.84241.60.0042121.63437.40.018NSNS
  Strat games77.277.2NS87.91211.9NS77.21617.60.030NS0.039
  RP games2525.82223.7NS3433.73332.7NS2121.63134.10.041NSNS
  Sim games66.21212.9NS109.92423.80.0081212.41819.8NSNSNS
  Manag games1717.599.7NS1413.91312.9NS1414.41213.2NSNSNS
  Without opinion2626.81819.4NS2524.876.90.0012929.91415.40.018NSNS
 Risks associated with gaming use
  Food problems3132.02223.7NS7675.25958.40.0116162.94549.50.044<0.001<0.001
  Sleep problems8890.77176.30.0079291.18281.20.0429496.97784.60.003NSNS
  Vision problems8587.66468.80.0029089.17978.20.0369395.97076.9<0.0010.037NS
  Withdrawal into a virtual world6364.94043.00.0026463.44746.50.0166870.15257.10.045NS0.038
  Lack of exercise5354.64245.3NS5857.45958.4NS5859.86167.0NSNS0.003
  Conflicts with parents/family5960.83739.80.0048685.16463.4<0.0017173.24145 .1<0.0010.046NS
  Conflicts with friends2525.81920.4NS5150.54039.6NS4344.33033.0NS0.0070.039
  Loss of time concept5960.84144.10.0217574.36261.40.0357880.45661.50.0040.0030.018
  Lack of school investment7981.45660.20.0018685.16867.30.0038789.76167.0<0.001NSNS
  Aggressivity6061.94952.7NS7574.36463.4NS6769.15358.2NSNSNS
 Reasons for the addiction
  Family problems4748.52830.10.0106867.35251.50.0226162.94549.50.0440.0430.007
  Poor school results3839.23436.6NS4544.64544.6NS4546.44044.0NSNSNS
  Lack of friend(s)5960.85559.1NS7473.37069.3NS7274.26571.4NS0.0460.05
  Lack of self-confidence4950.53032.30.0115150.53534.70.0234344.32325.30.006NSNS
  Bad self-image3940.22021.50.0054039.63130.7NS4142.32830.8NSNSNS
  Difficulty making friends5051.53840.9NS5756.45150.5NS5556.75054.9NSNS0.039
  Chance2828.93032.3NS2423.82524.8NS3132.03942.9NSNSNS
 Characteristics of a IGD gamer
  Number of hours spent playing6061.94851.6NS6160.45251.5NS5354.64448.4NSNSNS
  Poor school results1717.52122.6NS1918.82019.8NS1515.52022.0NSNSNS
  Saying no to all outings3738.13234.4NS4645.54645.5NS4647.44751.6NSNS0.018
  Only talking about video games6466.05458.1NS6160.45554.5NS5960.85661.5NSNSNS
  Cannot stop playing6870.16064.5NS7575.27069.3NS7779.46874.7NSNSNS
  Playing instead of fulfilling his obligations6971.16064.5NS7675.26160.40.0246668.06065.9NSNSNS
  Playing all the time6567.06671.0NS7878.06463.40.0237577.36874.7NSNSNS

Note. Nb min/day Internet M-F: number of minutes per day on Internet from Monday to Friday after school; Nb min/day Internet WE: number of minutes per day on Internet during the weekend; Nb min/day VG M-F: number of minutes per day playing video games from Monday to Friday after school; Nb min/day VG WE: number of minutes per day playing video games during the weekend; VG: video games; Shoot games: shooting games; Strat games: strategic games; RP games: role-playing games; Sim games: simulation games; Manag games: management games; SD: standard deviation; NS: not significant; ♀: female; ♂: male.

After the prevention program (T2), girls spent more time on Internet during the week (d = 0.32; p = .030), whereas boys spent more time playing video games during the week and the weekend (d = 0.60; p < .001 and d = 0.63; p < .001, respectively). There were more boys who thought that gaming had no impact on physical and mental health (ϕc =0.20; p = .006 and ϕc = 0.23; p = .002, respectively) and that video games could not generate IGD (ϕc = 0.21; p =.004). There were still more girls who thought that video games could generate several problems. Furthermore, there were still more girls who thought that family problems and lack of self-confidence were reasons for developing IGD. Both in boys and girls, several changes occurred between T0 and T2 in their representations about IGD.

Discussion

This study examined the effects of a unique secondary prevention intervention on adolescent beliefs about gaming and IGD. It also analyzed its effects on Internet gaming and Internet use behaviors and examined gender differences.

Analyses showed a significant effect of the intervention on the time spent on Internet and gaming. The main effect concerned the number of adolescents presenting with IGD. Indeed, at 4-month follow-up, the prevalence of IGD was higher in the CG. Moreover, the number of IGD adolescents in the PIG decreased by half between post-intervention and follow-up, while it remained stable in the CG. In terms of time spent on Internet and video games during the week and the weekend, while an increase was observed in both groups, the increase was higher in the CG. Furthermore, at T2, time spent on Internet and video games were significantly lower in the PIG. This result could suggest a better ability to organize daily time during the week among adolescents from the PIG. Gender comparisons also confirm previous findings (Mihara & Higuchi, 2017) in that video game use, prevalence of IGD, and rise of IGD are all higher among boys. Our results are in line with two comparable studies on secondary students in Korea and Germany indicating a lower increase in amount of excessive gaming in the PIG compared to CG (Joo & Park, 2010; Walther, Hanewinkel, & Morgenstern, 2014). However, the study by Walther et al. (2014) found differences in time spent per day on video games but not on Internet. Beyond the fact that the measure of gaming time was different in this study, the relevance of this unique outcome measure could also be questioned. As previously highlighted (Throuvala et al., 2019), setting Internet time reduction cannot be a primary outcome variable, although less so for gaming (Andrisano et al., 2016; de Leeuw, de Bruijn, de Weert-van Oene, & Schrijvers, 2010; Walther et al., 2014). Focusing only on time spent online does not address sufficiently the individual’s/gamer’s experience. The most important factor is not necessarily the time spent on Internet or gaming but rather the fact that it undermines other areas of the subject’s life such as homework, sleep time, etc. This means that other assessments need to be included in future studies.

In this study, in comparison with gaming time, time spent on Internet was much higher during the week and the weekend. As highlighted by Walther et al. (2014), while Internet gaming is a well-defined activity that can easily be reported, Internet use is much more heterogeneous. de Leeuw et al. (2010) also noted that time spent online is contextual and not generalized. Assessment of online activities is very complex and poses a challenge in the design of prevention programs. There is evidence suggesting that the time individuals spend online should not be the defining variable in excessive or addictive use because time spent online is specifically focused (Griffiths & Szabo, 2014; Pontes, Szabo, & Griffiths, 2015). Thus, it seems important to investigate further the amount of time spent on each specific Internet activity: e.g., social networks, YouTube (especially time spent on watching gaming videos), watching films or TV shows, researching on the Internet for school homework, etc. Furthermore, gender differences seem important because in the two groups, girls spent more time on the Internet than boys. As boys and girls use the Internet differently [i.e., girls tend to spend more time chatting, whereas boys play interactive games (Bernardi & Pallanti, 2009)], gathering more data about these activities is essential.

Overall, it can be said that the prevention intervention had an impact on adolescents’ beliefs regarding IGD. Indeed, there was a significant increase in perceptions of risks associated with excessive use of video games, of the factors related to IGD, and of the characteristics of an IGD gamer.

Some representations changed in both boys and girls. In this respect, data highlighted that: (a) girls generally had better understanding about the potential dangers of and reasons for excessive gaming; (b) the prevention intervention had a greater impact in some aspects for girls; and (c) changes related to the prevention intervention did not always persist over time. Indeed, girls reported more negative consequences than boys and pointed to etiological factors not mentioned by boys, including family conflict, lack of self-confidence, or poor self-image. Thus, to promote psychosocial skills, it seems important to involve girls in interactive prevention programs, which are more effective than lecture programs (Springer et al., 2004). Future studies should also identify specific ways to enhance psychosocial skills in boys. Furthermore, the duration of the prevention program is an important consideration, given the encouraging results of studies of longer duration (Mun & Lee, 2015; Shek & Sun, 2010).

This study has several limitations as well as implications for future research. First, despite randomization, there were baseline differences between the two groups on several measures. As suggested by Walther et al. (2014), it is possible that additional differences existed between the two groups that were not assessed, an aspect that reduces the internal validity of the study. The use of self-reported data (which could underestimate the time spent on the Internet or video games) remains a critical issue in empirical study (Throuvala et al., 2019) as well as social desirability biases (Andrisano et al., 2016). A momentary assessment (behavioral tracking) could be an alternative for a more accurate assessment of the time spent on various online activities. Furthermore, some questions were specifically designed for the study to reflect on the content of the prevention intervention; this may undermine the validity of some assessments.

Another limitation was the number of prevention sessions. It was a unique intervention, so even if the results are contrasted in the literature (Cuijpers, 2002; Gottfredson & Wilson, 2003) and this study showed promising results, a repetition of the method both during the year and over several years could yield more solid conclusions. For example, the 3-year prevention program by Shek and Sun (2010) showed significant positive benefits for youth development and changes in self-restraint using the computer.

The difficulties of engaging this type of research and this type of prevention intervention in France highlight the need for greater involvement of the French Ministry of Education in the promotion of healthy use of Internet and video games. Over the past 2 years, some French school staff have been trained in the early identification of IGD to encourage referrals to specialized addiction treatment centers. This involvement must extend to the entire territory. Thus, one goal of this pilot effectiveness prevention intervention assessment study was to evaluate the possibility of this type of intervention in France.

An important limitation of this study is that it includes only adolescents. The inclusion of parents in a prevention program enables the address of familial influences on gaming use, and thereby may increase the prevention program’s effectiveness (Schneider, King, & Delfabbro, 2017). This being said, a preventive intervention would be very relevant for parents, allowing them to increase their knowledge of video games and sense of personal effectiveness as a parent by providing advice on how to monitor of the use of video games (e.g., playing times, using a connection management system like “Log Me In,” etc.). Simply providing a guide with advice and strategies for regulating video gaming is insufficient (Krossbakken et al., 2018). A large number of empirical studies and meta-analyses have demonstrated the effectiveness of parenting support programs based on a psychoeducational approach (Kaminski & Valle, 2008). Several studies also recommended including the community, school, or work environment in prevention interventions (Throuvala et al., 2019; Vondráčková & Gabrhelík, 2016). Here, again, it is necessary to evaluate the effectiveness of such combined prevention programs. Finally, the prevention of addiction among students in France is conducted by external institutions, a costly endeavor in terms of time and money. Effectiveness may also increase if France were to adopt a prevention program similar to those used in Germany that allows teachers to oversee external interventions.

Conclusions

Despite its limitations, this study provided encouraging conclusions regarding the impact of a single prevention intervention. Digital technologies are practically omnipresent and will continue to mesh with our daily lives. It is therefore essential to encourage healthy use and to prevent excessive use that may cause significant harm or disrupt individual functioning. In this study, the time spent on Internet and video games increased during the school year. Relevant stakeholders need to work together in the public interest to help children and adolescents become aware of potential harm and develop sufficient psychosocial skills to maintain a healthy use of Internet-related activities. Parents must be aware of potential harms and taught best parenting practices concerning digital technologies. There is a need for greater empirical evaluation of prevention approaches as well as use of up-to-date knowledge for defining and promoting best prevention practices. Given the strong habitual nature of the disorder, the effectiveness of prevention initiatives is dependent on significant evidence-based delivery approaches and more longitudinal designs to target attitudes and behaviors consistently. In addition, it is important to consider carefully the aims vis-a-vis the duration of the intervention.

Excessive screen time may pose a significant psychosocial problem for a certain groups of children and adolescents. Difficulties in school are often the first signal of IGD. School staff are limited in effecting early intervention, despite recognizing the necessity. Similarly, although some schools in France already organized conferences/debates on video games for parents, attendance was low and parents present are generally already informed about and involved in monitoring their child’s video game use. Developing prevention programs will enhance school staff skills and early diagnoses of excessive use. In this way, children and adolescents will receive the help they need and the programs will contribute to raise awareness among parents about the importance of screen use education.

Authors’ contribution

CB drafted the first version of the manuscript. All authors have corrected and made suggestions to this first draft. It was sent back and forth with corrections several times between CB and the other authors. All authors agreed to the final version of the manuscript.

Conflict of interest

The authors declare no conflict of interest.

References

  • Achab, S., Nicolier, M., Mauny, F., Monnin, J., Trojak, B., Vandel, P., Sechter, D., Gorwood, P., & Haffen, E. (2011). Massively multiplayer online role-playing games: Comparing characteristics of addict vs. non-addict online recruited gamers in a French adult population. BMC Psychiatry, 11(1), 144. doi:10.1186/1471-244X-11-144

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Upper Saddle River, NJ: Prentice-Hall.

  • American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed., pp. 795798). Washington, DC: American Psychiatric Association.

    • Search Google Scholar
    • Export Citation
  • Andrisano, R., Santoro, E., De Caro, F., Palmieri, L., Capunzo, M., Venuleo, C., & Boccia, G. (2016). Internet addiction: A prevention action-research intervention. Epidemiology, Biostatistics and Public Health, 13(4). doi:10.2427/11817

    • Search Google Scholar
    • Export Citation
  • Bediou, B., Adams, D. M., Mayer, R. E., Tipton, E., Green, C. S., & Bavelier, D. (2018). Meta-analysis of action video game impact on perceptual, attentional, and cognitive skills. Psychological Bulletin, 144(1), 77110. doi:10.1037/bul0000130

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bernardi, S., & Pallanti, S. (2009). Internet addiction: A descriptive clinical study focusing on comorbidities and dissociative symptoms. Comprehensive Psychiatry, 50(6), 510516. doi:10.1016/j.comppsych.2008.11.011

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Billieux, J., Schimmenti, A., Khazaal, Y., Maurage, P., & Heeren, A. (2015). Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. Journal of Behavioral Addictions, 4(3), 119123. doi:10.1556/2006.4.2015.009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonnaire, C., Liddle, H., Har, A., Nielsen, P., & Phan, O. (2019). Why and how to include parents in the treatment of adolescents presenting Internet gaming disorder?. Journal of Behavioral Addictions, 8(2), 201212. doi:10.1556/2006.8.2019.27

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonnaire, C., & Phan, O. (2017). Relationships between parental attitudes, family functioning and Internet gaming disorder in adolescents attending school. Psychiatry Research, 255, 104110. doi:10.1016/j.psychres.2017.05.030

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Botvin, G. J., & Griffin, K. W. (2007). School-based programmes to prevent alcohol, tobacco and other drug use. International Review of Psychiatry, 19(6), 607615. doi:10.1080/09540260701797753

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brunborg, G. S., Mentzoni, R. A., Melkevik, O. R., Torsheim, T., Samdal, O., Hetland, J., Andreassen, C. S., & Pallesen, S. (2013). Gaming addiction, gaming engagement, and psychological health complaints among Norwegian adolescents. Media Psychology, 16(1), 115128. doi:10.1080/15213269.2012.756374

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Catrinel, A. S., & Mircea, M. (2010). Prevention programmes targeting emotional and social development in preschoolers: Current status and future directions. Early Child Development and Care, 180(8), 11031128. doi:10.1080/03004430902830263

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choo, H., Sim, T., Liau, A. K. F., Gentile, D. A., & Khoo, A. (2015). Parental influences on pathological symptoms of video-gaming among children and adolescents: A prospective study. Journal of Child and Family Studies, 24(5), 14291441. doi:10.1007/s10826-014-9949-9

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cuijpers, P. (2002). Effective ingredients of school-based drug prevention programs. A systematic review. Addictive Behavior, 27(6), 10091023. doi:10.1016/S0306-4603(02)00295-2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Leeuw, J. R. J., de Bruijn, M., de Weert-van Oene, G. H., & Schrijvers, A. J. (2010). Internet and game behaviour at a secondary school and a newly developed health promotion programme: A prospective study. BMC Public Health, 10(1), 544. doi:10.1186/1471-2458-10-544

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, W., Ramo, D., Chan, S., & Bourgeois, J. (2017). Internet gaming disorder: Trends in prevalence 1998–2016. Addictive Behaviors, 75, 1724. doi:10.1016/j.addbeh.2017.06.010

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaetan, S., Bréjard, V., & Bonnet, A. (2016). Video games in adolescence and emotional functioning: Emotion regulation, emotion intensity, emotion expression, and alexithymia. Computers in Human Behavior, 61, 344349. doi:10.1016/j.chb.2016.03.027

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gottfredson, D. C., & Wilson, D. B. (2003). Characteristics of effective school-based substance abuse prevention. Prevention Science, 4(1), 2738. doi:10.1023/A:1021782710278

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Granic, I., Lobel, A., & Engels, R. (2014). The benefits of playing video games. American Psychologist, 69(1), 6678. doi:10.1037/a0034857

  • Griffiths, M. D., & Pontes, H. M. (2019). The future of Gaming Disorder research and player protection: What role should the video gaming industry and researchers play? International Journal of Mental Health and Addiction. Advance online publication. doi:10.1007/s11469-019-00110-4

    • Search Google Scholar
    • Export Citation
  • Griffiths, M. D., & Szabo, A. (2014). Is excessive online usage a function of medium or activity? An empirical pilot study. Journal of Behavioral Addictions, 3(1), 7477. doi:10.1556/JBA.2.2013.016

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawks, D., Scott, K., McBride, N., Jones, P., & Stockwell, T. (2002). Prevention of psychoactive substance use. A selected review of what works in the area of prevention. Genève, Switzerland: Organisation mondiale de la santé.

    • Search Google Scholar
    • Export Citation
  • Joo, A., & Park, I. (2010). Effects of an empowerment education program in the prevention of Internet games addiction in middle school students. Journal of Korean Academy of Nursing, 40(2), 255263. doi:10.4040/jkan.2010.40.2.255

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaess, M., Parzer, P., Brunner, R., Koenig, J., Durkee, T., Carli, V., & Wasserman, D. (2016). Pathological Internet use is on the rise among European adolescents. Journal of Adolescent Health, 59(2), 236239. doi:10.1016/j.jadohealth.2016.04.009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaminski, J., & Valle, L. A., (2008). A meta-analytic review of components associated with parent training program effectiveness. Journal of Abnormal Child Psychology, 36(4), 567589. doi:10.1007/s10802-007-9201-9

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khazaal, Y., Chatton, A., Rothen, S., Achab, S., Thorens, S., Zullino, D., & Gmel, G. (2016). Psychometric properties of the 7-item Game Addiction Scale among French and German speaking adults. BMC Psychiatry, 16(1), 132. doi:10.1186/s12888-016-0836-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, D. L., & Delfabbro, P. H. (2014). The cognitive psychology of Internet gaming disorder. Clinical Psychology Review, 34(4), 298308. doi:10.1016/j.cpr.2014.03.006

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, D. L., Delfabbro1, P. H., Yim Doh, Y., Wu, A. M. S., Kuss, D. J., Pallesen, S., Mentzoni, R., Carragher, N., & Sakuma, H. (2018). Policy and prevention approaches for disordered and hazardous gaming and Internet use: An international perspective. Prevention Science, 19(2), 233249. doi:10.1007/s11121-017-0813-1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Király, O., Griffiths, M. D., King, D. L., Lee, H.-K., Lee, S.-Y., Bányai, F., Zsila, Á., Takacs, Z. K., & Demetrovics, Z. (2018). Policy responses to problematic video game use: A systematic review of current measures and future possibilities. Journal of Behavioral Addictions, 7(3), 503517. doi:10.1556/2006.6.2017.050

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krossbakken, E., Torsheim, T., Mentzoni, R. A., King, D. L., Bjorvatn, B., Lorvik, I. M., & Pallesen, S. (2018). The effectiveness of a parental guide for prevention of problematic video gaming in children: A public health randomized controlled intervention study. Journal of Behavioral Addictions, 7(1), 5261. doi:10.1556/2006.6.2017.087

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, S. J. (2013). Parental restrictive mediation of children’s Internet use: Effective for what and for whom? New Media & Society, 15(4), 466481. doi:10.1177/1461444812452412

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemmens, J. S., Valkenburg, P. M., & Peter, J. (2009). Development and validation of a Game Addiction Scale for adolescents. Media Psychology, 12(1), 7795. doi:10.1080/15213260802669458

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mihara, S., & Higuchi, S. (2017). Cross-sectional and longitudinal epidemiological studies of Internet gaming disorder: A systematic review of the literature. Psychiatry and Clinical Neurosciences, 71(7), 425444. doi:10.1111/pcn.12532

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mihara, S., Nakayama, H., Osaki, Y., & Higuchi, S. (2016). Report from Japan. Background paper prepared for the WHO Hong-Kong meeting on policy and program responses to mental and behavioral disorders associated with excessive use of the internet and other communication and gaming platforms. Department of Mental Health and Substance Abuse, World Health Organization, Geneva, Switzerland.

    • Search Google Scholar
    • Export Citation
  • Moore, S. M., & Ohtsuka, K. (1999). Beliefs about control over gambling among young people, and their relation to problem gambling. Psychology of Addictive Behaviors, 13(4), 339347. doi:10.1037/0893-164X.13.4.339

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mun, S. Y., & Lee, B. S. (2015). Effects of an integrated Internet addiction prevention program on elementary students’ self-regulation and Internet addiction. Journal of Korean Academy of Nursing, 45(2), 251261. doi:10.4040/jkan.2015.45.2.251

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Obradovic, I. (2017). Bilan de 10 ans d’activité des consultations jeunes consommateurs (CJC) [Ten years of activty in Youth Care Centers for addictive disorders]. Thérapie, 72(1), 147. doi:10.1016/j.therap.2016.11.006

    • Search Google Scholar
    • Export Citation
  • Petry, N. M., Zajac, K., Ginley, M., Lemmens, J., Rumpf, H.-J., Ko, C.-H., & Rehbein, F. (2018). Policy and prevention efforts for gaming should consider a broad perspective. Commentary on: Policy responses to problematic video game use: A systematic review of current measures and future possibilities. Journal of Behavioral Addictions, 7(3), 543547. doi:10.1556/2006.7.2018.64

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., & Griffiths, M. D. (2015). Measuring DSM-5 Internet gaming disorder: Development and validation of a Short Psychometric Scale. Computers in Human Behavior, 45, 137143. doi:10.1016/j.chb.2014.12.006

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., & Griffiths, M. D. (2019). A new era for gaming disorder research: Time to shift from consensus to consistency. Addictive Behaviors. Advance online publication. doi:10.1016/j.addbeh.2019.106059

    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., Király, O., Demetrovics, Z., & Griffiths, M. D. (2014). The conceptualisation and measurement of DSM-5 Internet Gaming Disorder: The development of the IGD-20 Test. PLoS One, 9(10), e110137. doi:10.1371/journal.pone.0110137

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., Schivinski, B., Sindermann, C., Li, M., Becker, B., Zhou, M., & Montag, C. (2019). Measurement and conceptualization of Gaming Disorder according to the World Health Organization framework: The development of the Gaming Disorder Test. International Journal of Mental Health and Addiction. Advance online publication. doi:10.1007/s11469-019-00088-z

    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., Szabo, A., & Griffiths, M. D. (2015). The impact of Internet-based specific activities on the perceptions of Internet addiction, quality of life, and excessive usage: A cross-sectional study. Addictive Behaviors Reports, 1, 1925. doi:10.1016/j.abrep.2015.03.002

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Russoniello, C. V., O’Brien, K., & Parks, J. M. (2009). The effectiveness of casual video games in improving mood and decreasing stress. Journal of CyberTherapy & Rehabilitation, 2(1), 5366.

    • Search Google Scholar
    • Export Citation
  • Saunders, J. B., Hao, W., Long, J., King, D. L., Mann, K., Fauth-Bülher, M., Rumpf, H. J., Bowden-Jones, H., Rahimi-Movaghar, A., Chung, T., Chan, E., Bahar, N., Achab, S., Lee, H. K., Potenza, M., Petry, N., Spritzer, D., Ambekar, A., Derevensky, J., Griffiths, M. D., Pontes, H. M., Kuss, D., Higuchi, S., Mihara, S., Assangangkornchai, S., Sharma, M., Kashef, A. E., Ip, P., Farrell, M., Scafato, E., & Carragher, N. (2017). Gaming disorders: Its delineation as an important condition for diagnosis, management, and prevention. Journal of Behavioral Addictions, 6(3), 271279. doi:10.1556/2006.6.2017.039

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schneider, L. A., King, D. L., & Delfabbro, P. H. (2017). Family factors in adolescent problematic Internet gaming: A systematic review. Journal of Behavioral Addictions, 6(3), 321333. doi:10.1556/2006.6.2017.035

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shek, D. T., & Sun, R. C. (2010). Effectiveness of the tier 1 program of project PATHS: Findings based on three years of program implementation. The Scientific World Journal, 10, 15091519. doi:10.1100/tsw.2010.122

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shek, D. T. L., & Yu, L. (2016). Adolescent Internet addiction in Hong Kong: Prevalence, change and correlates. Journal of Pediatric and Adolescent Gynecology, 29(1), S22S30. doi:10.1016/j.jpag.2015.10.005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Springer, J. F., Sale, E., Hermann, J., Sambrano, S., Kasim, R., & Nistler, M. (2004). Characteristics of effective substance abuse prevention programs for high-risk youth. The Journal of Primary Prevention, 25(2), 171194. doi:10.1023/B:JOPP.0000042388.63695.3f

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Throuvala, M. A., Griffiths, M. D., Rennoldson, M., & Kuss, D. J. (2019). School-based prevention for adolescent Internet addiction: Prevention is the key. A systematic literature review. Current Neuropharmacology, 17(6), 507525. doi:10.2174/1570159X16666180813153806

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tobler, N. S., Roons, M. R., Ochshorn, P., Marshall, D. G., Streke, A. V., & Stackpole, K. M. (2000). School-based adolescent drug prevention programs: 1998 meta-analysis. The Journal of Primary Prevention, 20(4), 275336. doi:10.1023/A:1021314704811

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitaro, F., & Gagnon, C. (2003). Prevention of adolescence problems in children and adolescents: Volume II, externalized problems. Québec, Canada: Presses de l’Université du Québec.

    • Search Google Scholar
    • Export Citation
  • Vondráčková, P., & Gabrhelík, R. (2016). Prevention of Internet addiction: A systematic review. Journal of Behavioral Addictions, 5(4), 568579. doi:10.1556/2006.5.2016.085

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallenius, M., & Punamäki, R. L. (2008). Digital game violence and direct aggression in adolescence: A longitudinal study of the roles of sex, age, and parent-child communication. Journal of Applied Developmental Psychology, 29(4), 286294. doi:10.1016/j.appdev.2008.04.010

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walther, B., Hanewinkel, R., & Morgenstern, M. (2014). Effects of a brief school-based media literacy intervention on digital media use in adolescents: Cluster randomized controlled trial. Cyberpsychology, Behavior, and Social Networking, 17(9), 616623. doi:10.1089/cyber.2014.0173

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C. W., Chan, C. L. W., Mak, K. K., Ho, S. Y., Wong, P. W. C., & Ho, R. T. H. (2014). Prevalence and correlates of video and Internet gaming addiction among Hong Kong adolescents: A pilot study. The Scientific World Journal, 2014, 874648. doi:10.1155/2014/874648

    • Search Google Scholar
    • Export Citation
  • Wang, P., Liu, H.-H., Zhu, X.-T., Meng, T., Li1, H.-J., & Zuo, X.-N. (2017). Action video game training for healthy adults: A meta-analytic study. Frontiers in Psychology, 7, 907. doi:10.3389/fpsyg.2016.00907

    • Search Google Scholar
    • Export Citation
  • Wegmann, E., & Brand, M. (2018). The imperative of integrating empirical and theoretical considerations when developing policy responses to Internet-gaming disorder. Journal of Behavioral Addictions, 7(3), 531535. doi:10.1556/2006.7.2018.61

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Werch, C. E., & DiClemente, C. C. (1994). A multi-component stage model for matching drug prevention strategies and messages to youth stage of use. Health Education Research, 9(1), 3746. doi:10.1093/her/9.1.37

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wichstrøm, L., Stenseng, F., Belsky, J., von Soest, T., & Hygen, B. W. (2019). Symptoms of Internet gaming disorder in youth: Predictors and comorbidity. Journal of Abnormal Child Psychology, 47(1), 7183. doi:10.1007/s10802-018-0422-x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wittek, C. T., Finserås, T. R., Pallesen, S., Mentzoni, R. A., Hanss, D., Griffiths, M. D., & Molde, H. (2016). Prevalence and predictors of video game addiction: A study based on a national representative sample of gamers. International Journal of Mental Health and Addiction, 14(5), 672686. doi:10.1007/s11469-015-9592-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • World Health Organization. (2018). ICD-11 (Mortality and Morbidity Statistics). Retrieved from https://icd.who.int/dev11/l-m/en

  • Zajac, K., Ginley, M. K., Chang, R., & Petry, N. M. (2017). Treatments for Internet gaming disorder and Internet addiction: A systematic review. Psychology of Addictive Behaviors, 31(8), 979994. doi:10.1037/adb0000315

    • Crossref
    • Search Google Scholar
    • Export Citation

Appendix: Translation of the Prevention Intervention Content

00:00 – Presentation

Presentation of the prevention officer, the “Young Consumer Consultation” service and the theme of the intervention. This introduction time allows for the creation of a climate of trust and establishes the foundations of the speech’s credibility. This credibility is necessary to ensure the success of the prevention program.

During this time, the health care center is presented to the teenagers and they are briefly informed about its functioning. Thus identified, the referral work that can be carried out by the teaching team a posteriori is facilitated.

00:05 – Questions and interactions with the teenagers

The teenagers are asked to answer these questions: what are the different screens they use and what are the differences between these screens? Nowadays, there are 10 screens per household on average.

This part provides an introduction to the topic. By providing information, the speaker seems more reliable. It is also a moment that allows the group to take a step back and contextualize screen use. Information related to the increase in the number of screens per household allows them to become aware of the external stressors that they may face and cannot always avoid. The work on the screen differentiation then makes it possible to get them to think about the different uses of the screens and what that implies in the short and medium term.

00:15 – Promotion of healthy screen use

The prevention officer asks the teenagers to list the positive aspects of screens/video games.

To solidify the trust relationship with the teenagers, the program begins by asking them to make the most comprehensive list possible of the positive aspects of screens and video games. Since it is the teenagers who write the list, these interactions show them that the speaker (and the consultation service by extension) does not perceive the screens and video games, Internet … as a world that should be avoided. It is not taking a negative stance against screens as something evil that must be fought, but rather as a universe rich with possibilities, shared by several generations and upon, which we can establish a conversation.

It appears from the classes in which this program has already taken place that the following ideas are mentioned: Play, De-stress, Relax, Share, Exchange, Inquire, Discover, Work, Learn, Keep in touch, Listen to music/movies, Have fun…

A time is taken to reread the list, and remember that at the beginning of any use there is a positive intention. Therefore, a user’s first intention when starting a video game or checking a Facebook page is a positive one and comes from this list.

00:30 – Excessive Use: definition

Excessive use? What is it?

Notions discussed: Habit × Frequency × Quantity.

Excessive use depends on the context and the consequences (health, social, work, and economic).

During this time, it is demonstrated that words are sometimes misleading. Teenagers are invited to express themselves freely on their representations of the notion of excessive use. Teenager participation is important during this time, as the prevention officer emphasizes the different representations mentioned and emphasizes that one must take all the criteria into consideration and not make hasty judgements.

Through the teenagers’ answers and examples concerning excessive use, the prevention officer shows his compassionate and non-judgmental position, while simultaneously consolidating an expert position that can guide and frame the exchanges. The notions, the context, and the consequences will serve as a guideline for the rest of the intervention.

Funnel diagram: This diagram makes it possible to visualize and summarize the elements previously discussed. It allows to visually anchor the invasion of an activity in a person’s life.

00:50 – My own screen and video games time

The teenagers are asked to complete the following questionnaire on time spent on screens (see tables below).

A time of reflection is taken on the time spent each day in front of the screens.

The difficulty faced by the young people to complete the table is used by the prevention officer as an educational experience, about the loss of the notion of time in front of screens, which is a very common issue.

The table allows everyone to take a step back on their overall use of screens.

Monday to FridaySaturday and Sunday
Screens
 TV or Cinema
 Video game or computer
 Online Gaming
 Social networks
 YouTube
Other activities
 Sports or cultural activities
 Activities with friends without screens
 Activities with the family (meals, games,…)
 School work + any private lessons
Other

1:05 – Excessive use: what are the consequences?

Here, the prevention officer asks the teenagers to list the negative aspects of screens/video games.

This time is interactive since once again, it is the teenagers who fill the list of negative (unwanted) effects of screens and video games. The prevention officer helps the students develop their ideas and for the notions mentioned, the prevention officer provides technical and practical information. For example, if the impact on health or vision is mentioned, a brief explanation will be given as well as advice (recommendations) to reduce this risk.

It appears from the classes in which this program has already been implemented that the following ideas are mentioned: Stress, Isolation, Food, Health, Lack of activity, Feeling nervous, Waste of time, Loss of the notion of time, No life, Family relations…

The prevention officer helps the teenagers think about the link between positive and negative aspects. How does our use of screens go from positive to negative?

This part serves to remind teenagers that the negative consequences are not disconnected from the initial aspirations of a positive use. This time helps to reinforce students’ skills and to question them about the means they can use to modify, limit, or avoid the transition from positive to negative use.

In order to stay in touch with teens’ lives, the prevention officer will use relevant examples that will reflect real-life situations.

1:20 – Awareness of unwanted effects

How much time do we spend in front of the screens? What is the impact on daily life?

The teenagers are asked to fill out a sleep questionnaire: all teenagers write down their bedtime, the time they wake up, and the amount of time they rest.

What is the impact of activities carried out before falling asleep?

To take a step back from the use and to check that certain key information have been correctly captured (e.g., that playing time is not the only determinant), the presenter goes back to the time spent in front of the screens. The prevention officer discusses data from the general population.

The questions regarding sleep brings to their attention an important aspect of the impact their screen use can have, on something like sleep, something that the student can easily perceive and modify individually. The goal is to bring the teenager to increased awareness of their behavior and the effect it can have.

The three rules of the Internet: undecidable/eternal/public.

This last informative part allows the group to address issues concerning the private life of the teenagers and their relation with intimacy. It is also a way of trying to get them to look ahead and to anticipate unwanted consequences that can emerge from what were positive practices at the outset. This part makes it possible, as a final note, to remind them that they have a large amount of control of their lives (real and virtual) and that there are people to listen to them, to advise them, to guide them, and to protect them.

  • Achab, S., Nicolier, M., Mauny, F., Monnin, J., Trojak, B., Vandel, P., Sechter, D., Gorwood, P., & Haffen, E. (2011). Massively multiplayer online role-playing games: Comparing characteristics of addict vs. non-addict online recruited gamers in a French adult population. BMC Psychiatry, 11(1), 144. doi:10.1186/1471-244X-11-144

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Upper Saddle River, NJ: Prentice-Hall.

  • American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed., pp. 795798). Washington, DC: American Psychiatric Association.

    • Search Google Scholar
    • Export Citation
  • Andrisano, R., Santoro, E., De Caro, F., Palmieri, L., Capunzo, M., Venuleo, C., & Boccia, G. (2016). Internet addiction: A prevention action-research intervention. Epidemiology, Biostatistics and Public Health, 13(4). doi:10.2427/11817

    • Search Google Scholar
    • Export Citation
  • Bediou, B., Adams, D. M., Mayer, R. E., Tipton, E., Green, C. S., & Bavelier, D. (2018). Meta-analysis of action video game impact on perceptual, attentional, and cognitive skills. Psychological Bulletin, 144(1), 77110. doi:10.1037/bul0000130

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bernardi, S., & Pallanti, S. (2009). Internet addiction: A descriptive clinical study focusing on comorbidities and dissociative symptoms. Comprehensive Psychiatry, 50(6), 510516. doi:10.1016/j.comppsych.2008.11.011

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Billieux, J., Schimmenti, A., Khazaal, Y., Maurage, P., & Heeren, A. (2015). Are we overpathologizing everyday life? A tenable blueprint for behavioral addiction research. Journal of Behavioral Addictions, 4(3), 119123. doi:10.1556/2006.4.2015.009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonnaire, C., Liddle, H., Har, A., Nielsen, P., & Phan, O. (2019). Why and how to include parents in the treatment of adolescents presenting Internet gaming disorder?. Journal of Behavioral Addictions, 8(2), 201212. doi:10.1556/2006.8.2019.27

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonnaire, C., & Phan, O. (2017). Relationships between parental attitudes, family functioning and Internet gaming disorder in adolescents attending school. Psychiatry Research, 255, 104110. doi:10.1016/j.psychres.2017.05.030

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Botvin, G. J., & Griffin, K. W. (2007). School-based programmes to prevent alcohol, tobacco and other drug use. International Review of Psychiatry, 19(6), 607615. doi:10.1080/09540260701797753

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brunborg, G. S., Mentzoni, R. A., Melkevik, O. R., Torsheim, T., Samdal, O., Hetland, J., Andreassen, C. S., & Pallesen, S. (2013). Gaming addiction, gaming engagement, and psychological health complaints among Norwegian adolescents. Media Psychology, 16(1), 115128. doi:10.1080/15213269.2012.756374

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Catrinel, A. S., & Mircea, M. (2010). Prevention programmes targeting emotional and social development in preschoolers: Current status and future directions. Early Child Development and Care, 180(8), 11031128. doi:10.1080/03004430902830263

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Choo, H., Sim, T., Liau, A. K. F., Gentile, D. A., & Khoo, A. (2015). Parental influences on pathological symptoms of video-gaming among children and adolescents: A prospective study. Journal of Child and Family Studies, 24(5), 14291441. doi:10.1007/s10826-014-9949-9

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cuijpers, P. (2002). Effective ingredients of school-based drug prevention programs. A systematic review. Addictive Behavior, 27(6), 10091023. doi:10.1016/S0306-4603(02)00295-2

    • Crossref
    • Search Google Scholar
    • Export Citation
  • de Leeuw, J. R. J., de Bruijn, M., de Weert-van Oene, G. H., & Schrijvers, A. J. (2010). Internet and game behaviour at a secondary school and a newly developed health promotion programme: A prospective study. BMC Public Health, 10(1), 544. doi:10.1186/1471-2458-10-544

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Feng, W., Ramo, D., Chan, S., & Bourgeois, J. (2017). Internet gaming disorder: Trends in prevalence 1998–2016. Addictive Behaviors, 75, 1724. doi:10.1016/j.addbeh.2017.06.010

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gaetan, S., Bréjard, V., & Bonnet, A. (2016). Video games in adolescence and emotional functioning: Emotion regulation, emotion intensity, emotion expression, and alexithymia. Computers in Human Behavior, 61, 344349. doi:10.1016/j.chb.2016.03.027

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gottfredson, D. C., & Wilson, D. B. (2003). Characteristics of effective school-based substance abuse prevention. Prevention Science, 4(1), 2738. doi:10.1023/A:1021782710278

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Granic, I., Lobel, A., & Engels, R. (2014). The benefits of playing video games. American Psychologist, 69(1), 6678. doi:10.1037/a0034857

  • Griffiths, M. D., & Pontes, H. M. (2019). The future of Gaming Disorder research and player protection: What role should the video gaming industry and researchers play? International Journal of Mental Health and Addiction. Advance online publication. doi:10.1007/s11469-019-00110-4

    • Search Google Scholar
    • Export Citation
  • Griffiths, M. D., & Szabo, A. (2014). Is excessive online usage a function of medium or activity? An empirical pilot study. Journal of Behavioral Addictions, 3(1), 7477. doi:10.1556/JBA.2.2013.016

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawks, D., Scott, K., McBride, N., Jones, P., & Stockwell, T. (2002). Prevention of psychoactive substance use. A selected review of what works in the area of prevention. Genève, Switzerland: Organisation mondiale de la santé.

    • Search Google Scholar
    • Export Citation
  • Joo, A., & Park, I. (2010). Effects of an empowerment education program in the prevention of Internet games addiction in middle school students. Journal of Korean Academy of Nursing, 40(2), 255263. doi:10.4040/jkan.2010.40.2.255

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaess, M., Parzer, P., Brunner, R., Koenig, J., Durkee, T., Carli, V., & Wasserman, D. (2016). Pathological Internet use is on the rise among European adolescents. Journal of Adolescent Health, 59(2), 236239. doi:10.1016/j.jadohealth.2016.04.009

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kaminski, J., & Valle, L. A., (2008). A meta-analytic review of components associated with parent training program effectiveness. Journal of Abnormal Child Psychology, 36(4), 567589. doi:10.1007/s10802-007-9201-9

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Khazaal, Y., Chatton, A., Rothen, S., Achab, S., Thorens, S., Zullino, D., & Gmel, G. (2016). Psychometric properties of the 7-item Game Addiction Scale among French and German speaking adults. BMC Psychiatry, 16(1), 132. doi:10.1186/s12888-016-0836-3

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, D. L., & Delfabbro, P. H. (2014). The cognitive psychology of Internet gaming disorder. Clinical Psychology Review, 34(4), 298308. doi:10.1016/j.cpr.2014.03.006

    • Crossref
    • Search Google Scholar
    • Export Citation
  • King, D. L., Delfabbro1, P. H., Yim Doh, Y., Wu, A. M. S., Kuss, D. J., Pallesen, S., Mentzoni, R., Carragher, N., & Sakuma, H. (2018). Policy and prevention approaches for disordered and hazardous gaming and Internet use: An international perspective. Prevention Science, 19(2), 233249. doi:10.1007/s11121-017-0813-1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Király, O., Griffiths, M. D., King, D. L., Lee, H.-K., Lee, S.-Y., Bányai, F., Zsila, Á., Takacs, Z. K., & Demetrovics, Z. (2018). Policy responses to problematic video game use: A systematic review of current measures and future possibilities. Journal of Behavioral Addictions, 7(3), 503517. doi:10.1556/2006.6.2017.050

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Krossbakken, E., Torsheim, T., Mentzoni, R. A., King, D. L., Bjorvatn, B., Lorvik, I. M., & Pallesen, S. (2018). The effectiveness of a parental guide for prevention of problematic video gaming in children: A public health randomized controlled intervention study. Journal of Behavioral Addictions, 7(1), 5261. doi:10.1556/2006.6.2017.087

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lee, S. J. (2013). Parental restrictive mediation of children’s Internet use: Effective for what and for whom? New Media & Society, 15(4), 466481. doi:10.1177/1461444812452412

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lemmens, J. S., Valkenburg, P. M., & Peter, J. (2009). Development and validation of a Game Addiction Scale for adolescents. Media Psychology, 12(1), 7795. doi:10.1080/15213260802669458

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mihara, S., & Higuchi, S. (2017). Cross-sectional and longitudinal epidemiological studies of Internet gaming disorder: A systematic review of the literature. Psychiatry and Clinical Neurosciences, 71(7), 425444. doi:10.1111/pcn.12532

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mihara, S., Nakayama, H., Osaki, Y., & Higuchi, S. (2016). Report from Japan. Background paper prepared for the WHO Hong-Kong meeting on policy and program responses to mental and behavioral disorders associated with excessive use of the internet and other communication and gaming platforms. Department of Mental Health and Substance Abuse, World Health Organization, Geneva, Switzerland.

    • Search Google Scholar
    • Export Citation
  • Moore, S. M., & Ohtsuka, K. (1999). Beliefs about control over gambling among young people, and their relation to problem gambling. Psychology of Addictive Behaviors, 13(4), 339347. doi:10.1037/0893-164X.13.4.339

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mun, S. Y., & Lee, B. S. (2015). Effects of an integrated Internet addiction prevention program on elementary students’ self-regulation and Internet addiction. Journal of Korean Academy of Nursing, 45(2), 251261. doi:10.4040/jkan.2015.45.2.251

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Obradovic, I. (2017). Bilan de 10 ans d’activité des consultations jeunes consommateurs (CJC) [Ten years of activty in Youth Care Centers for addictive disorders]. Thérapie, 72(1), 147. doi:10.1016/j.therap.2016.11.006

    • Search Google Scholar
    • Export Citation
  • Petry, N. M., Zajac, K., Ginley, M., Lemmens, J., Rumpf, H.-J., Ko, C.-H., & Rehbein, F. (2018). Policy and prevention efforts for gaming should consider a broad perspective. Commentary on: Policy responses to problematic video game use: A systematic review of current measures and future possibilities. Journal of Behavioral Addictions, 7(3), 543547. doi:10.1556/2006.7.2018.64

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., & Griffiths, M. D. (2015). Measuring DSM-5 Internet gaming disorder: Development and validation of a Short Psychometric Scale. Computers in Human Behavior, 45, 137143. doi:10.1016/j.chb.2014.12.006

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., & Griffiths, M. D. (2019). A new era for gaming disorder research: Time to shift from consensus to consistency. Addictive Behaviors. Advance online publication. doi:10.1016/j.addbeh.2019.106059

    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., Király, O., Demetrovics, Z., & Griffiths, M. D. (2014). The conceptualisation and measurement of DSM-5 Internet Gaming Disorder: The development of the IGD-20 Test. PLoS One, 9(10), e110137. doi:10.1371/journal.pone.0110137

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., Schivinski, B., Sindermann, C., Li, M., Becker, B., Zhou, M., & Montag, C. (2019). Measurement and conceptualization of Gaming Disorder according to the World Health Organization framework: The development of the Gaming Disorder Test. International Journal of Mental Health and Addiction. Advance online publication. doi:10.1007/s11469-019-00088-z

    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., Szabo, A., & Griffiths, M. D. (2015). The impact of Internet-based specific activities on the perceptions of Internet addiction, quality of life, and excessive usage: A cross-sectional study. Addictive Behaviors Reports, 1, 1925. doi:10.1016/j.abrep.2015.03.002

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Russoniello, C. V., O’Brien, K., & Parks, J. M. (2009). The effectiveness of casual video games in improving mood and decreasing stress. Journal of CyberTherapy & Rehabilitation, 2(1), 5366.

    • Search Google Scholar
    • Export Citation
  • Saunders, J. B., Hao, W., Long, J., King, D. L., Mann, K., Fauth-Bülher, M., Rumpf, H. J., Bowden-Jones, H., Rahimi-Movaghar, A., Chung, T., Chan, E., Bahar, N., Achab, S., Lee, H. K., Potenza, M., Petry, N., Spritzer, D., Ambekar, A., Derevensky, J., Griffiths, M. D., Pontes, H. M., Kuss, D., Higuchi, S., Mihara, S., Assangangkornchai, S., Sharma, M., Kashef, A. E., Ip, P., Farrell, M., Scafato, E., & Carragher, N. (2017). Gaming disorders: Its delineation as an important condition for diagnosis, management, and prevention. Journal of Behavioral Addictions, 6(3), 271279. doi:10.1556/2006.6.2017.039

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schneider, L. A., King, D. L., & Delfabbro, P. H. (2017). Family factors in adolescent problematic Internet gaming: A systematic review. Journal of Behavioral Addictions, 6(3), 321333. doi:10.1556/2006.6.2017.035

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shek, D. T., & Sun, R. C. (2010). Effectiveness of the tier 1 program of project PATHS: Findings based on three years of program implementation. The Scientific World Journal, 10, 15091519. doi:10.1100/tsw.2010.122

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shek, D. T. L., & Yu, L. (2016). Adolescent Internet addiction in Hong Kong: Prevalence, change and correlates. Journal of Pediatric and Adolescent Gynecology, 29(1), S22S30. doi:10.1016/j.jpag.2015.10.005

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Springer, J. F., Sale, E., Hermann, J., Sambrano, S., Kasim, R., & Nistler, M. (2004). Characteristics of effective substance abuse prevention programs for high-risk youth. The Journal of Primary Prevention, 25(2), 171194. doi:10.1023/B:JOPP.0000042388.63695.3f

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Throuvala, M. A., Griffiths, M. D., Rennoldson, M., & Kuss, D. J. (2019). School-based prevention for adolescent Internet addiction: Prevention is the key. A systematic literature review. Current Neuropharmacology, 17(6), 507525. doi:10.2174/1570159X16666180813153806

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Tobler, N. S., Roons, M. R., Ochshorn, P., Marshall, D. G., Streke, A. V., & Stackpole, K. M. (2000). School-based adolescent drug prevention programs: 1998 meta-analysis. The Journal of Primary Prevention, 20(4), 275336. doi:10.1023/A:1021314704811

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vitaro, F., & Gagnon, C. (2003). Prevention of adolescence problems in children and adolescents: Volume II, externalized problems. Québec, Canada: Presses de l’Université du Québec.

    • Search Google Scholar
    • Export Citation
  • Vondráčková, P., & Gabrhelík, R. (2016). Prevention of Internet addiction: A systematic review. Journal of Behavioral Addictions, 5(4), 568579. doi:10.1556/2006.5.2016.085

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wallenius, M., & Punamäki, R. L. (2008). Digital game violence and direct aggression in adolescence: A longitudinal study of the roles of sex, age, and parent-child communication. Journal of Applied Developmental Psychology, 29(4), 286294. doi:10.1016/j.appdev.2008.04.010

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Walther, B., Hanewinkel, R., & Morgenstern, M. (2014). Effects of a brief school-based media literacy intervention on digital media use in adolescents: Cluster randomized controlled trial. Cyberpsychology, Behavior, and Social Networking, 17(9), 616623. doi:10.1089/cyber.2014.0173

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, C. W., Chan, C. L. W., Mak, K. K., Ho, S. Y., Wong, P. W. C., & Ho, R. T. H. (2014). Prevalence and correlates of video and Internet gaming addiction among Hong Kong adolescents: A pilot study. The Scientific World Journal, 2014, 874648. doi:10.1155/2014/874648

    • Search Google Scholar
    • Export Citation
  • Wang, P., Liu, H.-H., Zhu, X.-T., Meng, T., Li1, H.-J., & Zuo, X.-N. (2017). Action video game training for healthy adults: A meta-analytic study. Frontiers in Psychology, 7, 907. doi:10.3389/fpsyg.2016.00907

    • Search Google Scholar
    • Export Citation
  • Wegmann, E., & Brand, M. (2018). The imperative of integrating empirical and theoretical considerations when developing policy responses to Internet-gaming disorder. Journal of Behavioral Addictions, 7(3), 531535. doi:10.1556/2006.7.2018.61

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Werch, C. E., & DiClemente, C. C. (1994). A multi-component stage model for matching drug prevention strategies and messages to youth stage of use. Health Education Research, 9(1), 3746. doi:10.1093/her/9.1.37

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wichstrøm, L., Stenseng, F., Belsky, J., von Soest, T., & Hygen, B. W. (2019). Symptoms of Internet gaming disorder in youth: Predictors and comorbidity. Journal of Abnormal Child Psychology, 47(1), 7183. doi:10.1007/s10802-018-0422-x

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wittek, C. T., Finserås, T. R., Pallesen, S., Mentzoni, R. A., Hanss, D., Griffiths, M. D., & Molde, H. (2016). Prevalence and predictors of video game addiction: A study based on a national representative sample of gamers. International Journal of Mental Health and Addiction, 14(5), 672686. doi:10.1007/s11469-015-9592-8

    • Crossref
    • Search Google Scholar
    • Export Citation
  • World Health Organization. (2018). ICD-11 (Mortality and Morbidity Statistics). Retrieved from https://icd.who.int/dev11/l-m/en

  • Zajac, K., Ginley, M. K., Chang, R., & Petry, N. M. (2017). Treatments for Internet gaming disorder and Internet addiction: A systematic review. Psychology of Addictive Behaviors, 31(8), 979994. doi:10.1037/adb0000315

    • 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%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 850 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access
Purchase per Title  

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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