Internet Addiction and Gaming Addiction in Italian children and adolescents: results from a National survey Luca Milani, Serena Grumi, Paola Di Blasio C.Ri.d.e.e. Department and Faculty of Psychology, Catholic University of Milan Workshop Internazionale Approcci di intervento delle neuroscienze cliniche e della neuropsichiatria alle new e old addictions
Problematic Internet Use Problematic Internet Use has been defined as a impulse control disorder that does not involve a substance (Young, 1998). Literature highlighted correlations between PIU and adjustment problems in childhood and adolescence (cfr. Lee et al., 2015; Chen et al., 2015; Durkee et al., 2012; Sim et al., 2012): Depression (Ko et al., 2008; Park et al., 2013: Ozdemir et al., 2014) Anxiety (Lee et al., 2012; Dalbudak et al., 2013); Preference for online social activities (Casale et al., 2011; Huan et al., 2014); Loneliness (Caplan, 2007; Huan et al., 2014); Worse interpersonal relations (Whang et al., 2003; Kuss et al., 2013; Ching et al., 2014; Milani et al., 2009); Attention problems (Yen et al., 2009); Risk taking behaviors (Oktan, 2015) and sensation seeking (Dalbudak et al., 2015); Pathological gaming (Strittmatter, 2015).
Problematic Internet Use: a debated issue Recently, research has debated whether Internet Addiction (or problematic use) can be considered a viable construct (cf. Griffiths et al., 2016). Some authors suggest that the concept of IA should be replaced by addictions to specific online activities (Starcevic, 2013). «The dominant view, which indirectly resulted in the APA's choice to favor the term Internet Gaming Disorder and reject Internet addiction, is that a gaming addict is not addicted to the Internet per se but simply uses it as a medium to engage in the chosen behavior» (Griffiths, 2016, p.194).
Internet gaming disorder Literature confirmed the existence of a kind of behavioral addiction related to videogames (cf. Grüsser, Thalemann, & Griffiths, 2006). However, it s necessary to make a distinction between a normal, high level of game engagement, and actual gaming addiction (Griffiths, 2010). The development of a Video Game disorder appears to be linked not only to the excessive amount of time spent video gaming, but specifically to the use of VGs as a mean to fulfill those needs that individuals do not (or cannot) satisfy in different ways: Video gaming as coping strategy to deal with daily stressors and negative feelings (Cole & Hooley, 2013; Gentile et al., 2017, personal communication); Preference for on-line relationships (Caplan, Williams & Yee, 2009); Search for immersion and dissociation (Snodgrass, Dengah, Lacy & Fagan, 2013).
Internet gaming disorder In the third section of DSM 5 (American Psychiatric Association, 2013), the Internet gaming disorder is defined as persistent and recurrent use of the Internet to engage in games, often with other players, leading to clinically significant impairment or distress as indicated by five (or more) of the following in a 12-month period (p.795): 1. Preoccupation or obsession with Internet games. 2. Withdrawal symptoms when not playing Internet games. 3. A build-up of tolerance more time needs to be spent playing the games. 4. The person has tried to stop or curb playing Internet games, but has failed to do so. 5. The person has had a loss of interest in other life activities, such as hobbies. 6. A person has had continued overuse of Internet games even with the knowledge of how much they impact a person s life. 7. The person lied to others about his or her Internet game usage. 8. The person uses Internet games to relieve anxiety or guilt it s a way to escape. 9. The person has lost or put at risk an academic/job opportunity or relationship because of Internet games.
Comorbidity Problematic VG use seems to be related to: Depression (Van Rooij et al. 2014) Anxiety (Cole & Hooley, 2013; Rehbein et al., 2010) Somatic complaints (Allison et al. 2006; Dworak, Schierl, Bruns, Struder, 2007) Behavior problems (Brunborg et al., 2014) Lower school grades (Jeong & Kim, 2011 )
Risk and Protective factors Risk Factors Male gender Impulsivity Attention Problems Low social skills Low emotional regulation Single parent family Protective Factors Parental monitoring School & class acceptance Well-being at school (Gentile et al., 2011; Gentile et al., 2012; Lemmens, Valkenburg & Peter, 2011; Rehbein & Baier, 2013)
The research [Milani et al. (2017) Internet Gaming Addiction in Adolescence: Risk Factors and Maladjustment Correlates. Int J Ment Health Addiction, doi: 0.1007/s11469-017-9750-2]
Aims 1. Gaining knowledge about Internet and VGs problematic use prevalence in Italy. 2. Investigating differences between adolescents without, with sub-clinical and with clinical problematic use of VG and/or Internet. 3. Identifying risk factors that predict a problematic engagement in Internet and Video gaming.
612 students & parents 47.2% M 52.8 % F M.Age = 13.94yrs (range 9-19) [9 12 years old 31.2%; 13 15 years old 39.2%; 16 19 years old 29.6%] Nationality: 93.8% Italian; 3.8% European (EU); 2.4% Extra-EU SES = middle class Participants
(24.5%) (34.3%) (27.0%) (14.2%) Acknowledgements: Maria Fiore & Margherita Ferrante (CT); Giuseppe La Torre (RM)
Instruments Revised VGA Questionnaire (Gentile, 2012). [α=.71] Problematic VG use IAT - Internet Addiction Test (Young, 1998) [α=.89] Problematic Internet use CCSC - Children s Coping Strategies Checklist (Ayers & Sandler, 1999; Camisasca et al., 2012) [α=.90] TRI - Test delle Relazioni Interpersonali (Bracken, 1996) [α=.95] CBCL - Child Behavior Checklist (Achenbach, 1991; Frigerio, 2001) [α=.95] Parents Coping strategies Interpersonal relations Adjustment problems Avoidance Distraction Active Support Seeking Mother Father M/F Peers Teachers Internalization Externalization (sindromic scales)
Aim 1 - Prevalence
Media use habits Avg. or % Internet 12.92 (0- > 84) VGs (Milani et al. 2015) 6.84 (0 - > 30) Social Networks 69.2% YouTube 53.1% Games 41.7% Wikipedia 49.6% News 30.4% Email 36.9%
Prevalence of VG problematic use VGA 5-item cutoff 2.1% (n=13) VGA 3-item cutoff 15.2% (n=93)
Prevalence of Internet problematic use IAT 50 cutoff 5.9% (n=36) IAT 40 cutoff 16.3% (n=100) Note: 4.5% of the participants presented signs of both.
Gender & prevalence of videogames problematic use M F X 2 p Non pr. 76.8% 92.0% Prob. 23.2% 8.0% 27.10.000
Gender & prevalence of Internet problematic use M F X 2 p Non pr. 83.3% 83.9% Prob. 16.7% 16.1% 0.36 n.s.
Aim 2 Clinical and subclinical addiction
MANOVA 1: Comparisons between non problematic and sub-clinic problematic VG users (VGA >3) Covariates: Age & Gender Fixed Factor: above/below VGA cutoffs Dependent variables: CBCL externalization, internalization and subscales; TRI total quality of relationships and subscales, CCSC-R1 coping strategies scores, IAT score of problematic Internet use Wilks Λ =.381, F = 41.44, p <.001, multivariate η 2 =.619 Non prob. Problematic users VG use F η p Time spent online 12.62 14.58 18.28 083.001 IAT 24.45 33.35 20.44.092.001 CCSC Active 2.38 2.43 9.01.043.001 CSCC Distraction 2.11 2.34 5.17.025.01 CSCC Avoidance 2.10 2.39 9.21.043.001 CCSC Support seeking 2.19 2.18 8.50.040.001 TRI Quality of relationships with male peers 88.55 86.06 14.99.069.001 TRI Quality of relationships with female peers 91.09 83.50 14.55.067.001 TRI Quality of relationships with teachers 83.97 77.59 6.99.033.001 CBCL Withdraw 1.98 2.43 3.77.018.01 CBCL Somatic complaints 1.30 1.12 6.31.030.001 CBCL Social problems 1.42 1.98 7.36.035.01 CBCL Thought problems.49.88 3.82.019.01 CBCL Attention problems 3.01 4.34 7.77.037.001 CBCL Aggressive behavior 4.57 6.05 5.06.024.01 CBCL Externalization 6.48 7.24 4.10.020.01 CBCL Internalization 5.77 7.67 3.75.018.01
MANOVA 3: Comparisons between problematic (VGA > 3) and IGD users (VGA > 5) Covariates: Age & Gender Fixed Factor: above/below VGA cutoffs Dependent variables: CBCL externalization, internalization and subscales; TRI total quality of relationships and subscales, CCSC-R1 coping strategies scores, IAT score of problematic Internet use Wilks Λ =.310, F = 6.87, p <.001, multivariate η2 =.690 Problematic VG use IGD F η p Time spent online 12.59 26.80 5.63.160.001 IAT 31.68 43.58 2.46.077.06 TRI Quality of relationships with male peers TRI Quality of relationships with female peers 85.95 86.76 7.46.201.001 85.13 73.46 5.69.161.001 2-3 Times!
Remarks (1) As the threshold moves from 3 symptoms to 5, areas of maladaptation decrease focusing on interpersonal relations and effect sizes increase A LOT. VG problematic users and addicts tend to relate better with males and worse with females. A male-centred & videogame-mediated social milieu?
MANOVA 4: Comparisons between non problematic and sub-clinical Internet addicts (IAT>40) Covariates: Age & Gender Fixed Factor: above/below IAT cutoffs Dependent variables: CBCL externalization, internalization and subscales; TRI total quality of relationships and subscales, CCSC-R1 coping strategies scores, IAT score of problematic Internet use Wilks Λ =.478, F = 27.80 p <.001, multivariate η2 =.522 Non prob. Sub-clinical users Internet ad. F η p Time spent online 11.21 21.64 37.24.155.001 VGA 1.22 2.04 35.95.151.001 CCSC Active 2.36 2.52 10.25.048.001 CSCC Distraction 2.11 2.34 6.57.031.001 CSCC Avoidance 2.10 2.34 6.32.030.001 CCSC Support seeking 2.18 2.24 8.19.039.001 TRI Quality of relationships with male peers 92.64 95.61 8.16.050.001 TRI Quality of relationships with female peers 95.42 91.92 19.27.110.001 TRI Quality of relationships with teachers 86.35 84.29 8.80.053.001 CBCL Withdraw 2.03 2.16 2.59.013.05 CBCL Somatic complaints 1.24 1.44 6.52.031.001 CBCL Anxiety/depression 3.25 4.12 3.15.015.05 CBCL Social problems 1.45 1.76 7.01.034.001 CBCL Attention problems 3.04 4.07 7.34.035.001 CBCL Aggressive behavior 4.64 5.63 4.41.021.01 CBCL Internalization 6.42 7.57 4.07.020.01 CBCL Externalization 5.85 7.20 3.11.015.05
MANOVA 6: Comparisons between Internet addicts (VGA >50) and problematic users (VGA >40) Covariates: Age & Gender Fixed Factor: above/below IAT cutoffs Dependent variables: CBCL externalization, internalization and subscales; TRI total quality of relationships and subscales, CCSC-R1 coping strategies scores, IAT score of problematic Internet use Wilks Λ =.264, F = 8.83, p <.001, multivariate η2 =.736 Sub-clinical Internet ad. Internet addicts F η p Time spent online 19.04 25.92 5.15.140.001 VGA 1.83 2.53 5.55.149.001 CSCC Distraction 2.27 2.44 2.85.083.05 CCSC Avoidance 2.20 2.28 5.68.152.001 TRI Quality of relationships with male peers TRI Quality of relationships with female peers 88.42 93.19 3.93.110.05 89.58 84.86 2.91.084.05 CBCL Anxiety/depression 3.61 5.00 3.28.094.05 CBCL Delinquent behavior 1.12 2.44 3.07.089.05 CBCL Internalization 6.84 8.88 3.11.090.05
Remarks (2) As the threshold moves from IAT score of 40 to 50, areas of maladaptation decrease BUT effect sizes increase. Internet problematic users and addicts tend to have worse relations with mothers and fathers. Internet problematic users and addicts tend to recur more to distraction and avoidance as coping strategies. Many similarities with VG addiction.
Aim 3 Risk factors for Internet and VG addiction
Path Analysis Model on VG addiction Age -.100* Gender -.249*** CCSC Avoidance.233*** VG addiction TRI teachers CBCL Attention probl. -.134**.123* χ2 = 139.038; df = 52; p <.000 CFI =.946 RMSEA =.059
Path Analysis Model on Internet addiction Age.203*** CCSC Distraction.115* CCSC Avoidance.098 ~ Internet addiction TRI mother CBCL Attention probl. -.172*.125* χ2 = 139.038; df = 52; p <.000 CFI =.945 RMSEA =.059
Conclusions
Results showed that a risk for sub-clinical problematic VG use (VGA >3) seem to be quite widespread among Italian adolescents (15.2%). Adopting a 5-item cutoff, the prevalence drops to 2.1%. What this means? If % are reliable, 2.1% means more than 120.000 minors at risk of videogame addiction (14% of 60M Italians are under 15 y/o). Consistent with many previous correlational studies, the problematic VGs engagement appears to be linked to various dysfunctional outcomes: Withdraw & social problems Attention problems Aggressive behavior Externalization Problematic video gamers tend to preferentially adopt dysfunctional coping strategies as distraction and avoidance, so VGs may represent a mean to cope with problems and difficulties.
Tentative Social rewarding mechanisms in VGs. What about (online) videogames? Intermittent rewarding Immediate feedback Social status Attention issues Lose progress Game continues w/o the player Social pressures
Risk factors for VG addiction: Age Gender Avoidant coping strategies Attention problems Risk factors for Internet addiction: Age Avoidant & distraction coping strategies Attention problems Same disorder with some subtle differences On the plus side: protective factors -> quality of relationships With Teachers > Gaming addiction With Family > Internet addiction
Looking on the bright side We do have protective factors. The need is to implement them: Factor School relations Actions - Media literacy - Increase awareness about health Family relations - Limiting access - Parental monitoring
Limits & future directions
Limitations Cross-sectional methodology. Future directions Implement longitudinal methodology. School grades are missing!! Include school grades in the questionnaire. Limited measures about adolescents adjustment & media habits Supplement assessment with specific measures about: Depression/Anxiety Attention deficits Parental monitoring Other media usage Favourite VGs VG using time VG as coping
Thanks for the attention luca.milani@unicatt.it