Internet use and Problematic Internet Use: a systematic review of longitudinal research trends in adolescence and emergent adulthood

Figures & data

Table 1. Quality of methodologies of studies (reported reliability at each time point).

	Instrument	Reliability (α)
Study 1	Internet Addiction Test (IAT)	‘Strong internal reliability’**
Study 2	IAT (Chinese adaption)	.79 (T1)
		.80 (T2)
		.81 (T3)
Study 3	IAT	.91
Study 4	Compulsive Internet Use Scale (CIUS)	.88 (T1)
		.88 (T2)
Study 5	Measure of Frequency of Computer Game Use	–
Study 6	Measure of Frequency of Computer Game Use	–
Study 7	Generalized Problematic Internet Use Scale 2 (GPIUS2)	.90
Study 8	CIUS (Shortened version)	.85 (T1)
		.90 (T2)
Study 9	Measure of pathological video game use	.71 (T1)
		.77 (T2)
		.79 (T3)
Study 10	CIUS	.89 (T1)
		.89 (T2)
Study 11	Problematic Video Game Playing Test (Adaption of IAT)	.92
Study 12	Measure of Frequency of Internet Use	– (Server logged)
Study 13	IAT	–
Study 14	CIUS	.84 (T1)
		.87 (T2)
Study 15	CIUS	.82 (T1)
		.94 (T2)
Study 16	Measure of Problematic Gaming	.84
	Measure of Frequency of Internet Use	–
Study 17	Chen Internet Addiction Scale (CIAS)	.79–.93**
Study 18	Davis Scale for Problematic Internet Use (adaption)	.84 (Chinese Sample)
		.80 (USA Sample)
Study 19	GPIUS2	.76–.90
Study 20	CIAS	.79–.93**
Study 21	IAT	.93
Study 22	Measure of Frequency of Computer Game Use	–
Study 23	Measure of Pathological Video Game Use	–
Study 24	Measure of Pathological Video Game Use	.91 (T1)
		.91 (T3)
Study 25	CIAS	.79–.93**
Study 26	GPIUS2	.78–.90
Study 27	Measure of Frequency of Computer Game Use	–
Study 28	CIAS	.79–.93**
Study 29	CIUS (Shortened version)	.88 (T1)
		.89 (T2)
		.89 (T3)
		.90 (T4)

*  = Reliability not recorded in research article, instead provided by author of research article.

**  = Reliability data recorded in study refers to known reliability of measure in other studies.

^–  = No reliability data recorded.

Figure 1. PRISMA flowchart of primary study selection.

Table 2. Basic information of studies and summary of findings related to IU and/or PIU.

	Title	Author/year	Country	N	Age group	Time	Findings
1	Internet addiction, reality substitution and longitudinal changes in psychotic-like experiences in young adults	Mittal et al., (2013)	US	170	Adult	2m	Both groups reported similar level of IA and Reality Substitute at baseline Psychotic-like experience (PLE)-Improved/Constant group showed declines in PIU, and reported higher quality interactions PLE-Increase group reported PIU remained constant Moderate correlations between PLE and domains of PIU Association with Reality Substitute for the PLE-Increase group increased over time Aspects associated with PSE may also increase risk for PIU
2	Internet addiction in Hong Kong adolescents: A three-year longitudinal study	Yu & Shek, (2013)	HK	2,667	Adol	3yr	Age did not predict IA Males showed more PIU than females Immigration status and parent’s marital status did not longitudinally influence IA behaviour Low socioeconomic status predicted low IA score High family function at Time 1 predicted low IA at Time 3 Positive youth development scores at Time 1 negatively predicted IA at Time 3
3	Does psychopathology in childhood predict Internet addiction in male adolescents?	Cho et al., (2013)	KR	524	Adol boys	7yr	High percentage (3.6) of participants had IA Significant relationship between those who experiencing withdrawal, anxiety/depression, and future IA
4	Compulsive Internet use: the role of online gaming and other Internet applications	van Rooij et al., (2010)	NL	4,753	Adol	1yr	Online gaming has strongest association with CIU at Time 1 and Time 2 Online gaming, MSN, social networking, Habbo Hotel and chatting are all associated with CIU No relationship was found between CIU and surfing or emailing
5	A short-term longitudinal study of Internet and computer game use by adolescent boys and girls: prevalence, frequency of use, and psychosocial predictors	Willoughby, (2008)	CA	1,591	Adol	21m	Being male, lower parental education, and greater frequency of IU in early high school were significant predictors of computer game and IU in late high school Higher friendship quality and less positive parental relationships found to be predictive of higher frequency IU Moderate IU associated with a more positive academic orientation than non-use or high levels of IU
6	A group-based modelling approach to estimating longitudinal trajectories of Korean adolescents’ on-line game time	Hong et al., (2014)	KR	3,449	Adol	4yr	Males spend more time gaming than females Low self-control associated with ‘chronic’ IU Higher self-esteem associated with less IU No association between aggression and IU Poorer emotional regulation associated with gradual increase in time spent playing online Stress about school work can help decrease online play time Devoted students likely to spend less time gaming No association between social skills and time spent gaming
7	Longitudinal and reciprocal relations of cyber bullying with depression, substance use and problematic Internet use among adolescents	Gámez-Guadix et al., (2013)	ES	845	Adol	6m	CB victimization at Time 1 predicted depressive symptoms and problematic Internet use at Time 2 CB and higher depressive symptoms and more substance use at Time 1 predicted more CB victimization at Time 2 CB predicting substance use and PIU were not significant, but substance use predicted increase in CB victimization PIU did not predict CB Being a bully-victim increased probability of depression, substance use and PIU compared to those who were just victims at Time 1 and Time 2
8	Online communication, compulsive Internet use, and psychosocial well-being among adolescents: A longitudinal study	van den Eijnden et al., (2008)	NL	663	Adol	6m	Instant messenger use and chatting in chat rooms found to be positively related to CIU at Time 2 Instant messenger use found to be positively associated with depression at Time 2 Loneliness negatively related to instant messenger use at Time 2
9	Pathological video game use among youths: A two-year longitudinal study	Gentile et al., (2011)	SG	3,034	Child/Adol	2yr	Males had higher levels of OVG play at each time point Impulsivity, low social skills and poor emotional regulation are risk factors for becoming a pathological gamer and impulsivity worsens after a youth becomes a pathological gamer Amount of gaming and impulsivity significantly predict the number of pathological symptoms at T1 Those with lower social skills and greater impulsivity exhibit increases in their pathological gamer symptoms More pathological symptoms at Time 1 related to higher levels of depression, anxiety, poorer academic grades and social phobia at Time 2. This reduces if cease playing
10	Predicting compulsive Internet use: It’s all about sex	Meerkerk et al., (2006)	NL	229	Adult	1yr	Large differences in the time spent on various Internet applications Large differences in the correlation between time spent on the applications and CIU Positive associations at Time 1 for gaming, chatting, and erotica Negative association between age and CIU Significant correlations between chatting, gaming, dating, buying, and erotica at Time 1, and CIU at Time 2 Spending a lot of time searching for erotic stimulation predicts an increase in CIU 1 year later No other application factors reached significance
11	Trajectories of problem video gaming among adult regular gamers: An 18-month longitudinal study	King, Delfabbro, et al.,(2013)	AU	117	Adult	18m	Gamers who self-identified as having problem OVG use at baseline reported more severe problem gaming symptoms than regular gamers at all time points Both groups experienced a significant decline in problem gaming symptoms over an 18-month period, controlling for ages, gaming activity, and psychopathological symptoms
12	Personality, cognitive style, demographic characteristics and Internet use – Findings from the HomeNetToo project	Jackson et al., (2003)	US	117	Adult	6m	Extraversion was related to IU at Time 1 Neuroticism was negatively related to IU at Time 1 At Time 2 there were no relationships between IU and personality traits Weak predictive relationships between cognitive style and IU. At Time 1 high preference for perceiving abstractly, more time spent online, and number of online sessions. At Time 2, stronger visual preference related to less time spent on-line, and stronger preference for global/relational processing related to more emails sent at Time 2
13	Precursor or sequela: Pathological disorders in people with Internet addiction disorder	Dong et al., (2011)	CN	59	Adult	1yr	Higher than normal obsessive-compulsive (OC) behaviour prior to IA. OC considered a predictor of IA OC scores did not change significantly People with IAD scored significantly higher on OC, depression (DEP), anxiety (ANX) and hostility (HOS) EP, ANX, and HOS increased after IA; are more likely to be outcomes than precursors for IAD No relation between IAD and somatization, interpersonal sensitivity (INT), phobic anxiety, paranoid ideation, psychotic experiences (PSY) and additional items INT and PSY outcomes of IAD: changed significantly at Time 2 from Time 1
14	Compulsive Internet use among adolescents: Bi-directional parent–child relationships	van den Eijnden et al., (2010)	NL	510	Adol	6m	Prevalence of CIU decreased from 8% at Time 1 to 6.5% at Time 2 Higher quality parental communication regarding IU at Time 1 decreased development of CIU at Time 2
15	Adolescents’ compulsive Internet use and depression: A longitudinal study	Thorsteinsson & Davey, (2014)	AU	41	Adol	1yr	Social Internet use at Time 1 associated with decreased levels of depression at Time 2 High support satisfaction, and social Internet use at Time 1 associated with lower increase in depression at Time 2, and lower CIU. CIU at Time 1 had a small association with depression at Time 2 and change in depression from Time 1 to Time 2
16	Assessing problematic video gaming using the Theory of Planned Behaviour: A longitudinal study of Dutch young people	Haagsma et al., (2013)	NL	810	Adol/Adult	6m	Theory of Planned Behaviour variables accounted for significant amount of variance in PVGU at Time 1 but not Time 2 Perceived behavioural control important in predicting PVGU Males spent more time playing video games than females, and had higher levels of PVGU
17	ADHD and autistic traits, family function, parenting style, and social adjustment for Internet addiction among children and adolescents in Taiwan: A longitudinal study	Chen et al., (2015)	TW	1,153	Child/Adol	4m	Prevalence of IA decreased from 11.4% at Time 1 to 10.6% at Time 2 High ADHD-related symptoms and low Autistic traits associated with increased risk and severity of IA ADHD-related symptoms predictive of IA Poor academic performance, male gender, poor social support, and less protective parenting were independent predictors for IA Participants with high grades reported high frequency and duration of IU, but not IA Participants with high hyperactivity/impulsivity higher gaming IU
18	Concurrent and predictive relationships between compulsive Internet use and substance use: Findings from vocational high school students in China and the USA	Sun et al., (2012)	CHN USA	1,761 1,182	Adol	1yr	CIU was not positively related with substance use at baseline Substance use did not predict an increase in CIU Relationships between concurrent changes in CIU and substance use were found among female, but not male students. No reciprocally predictive relationship found between CIU and substance use Positive predictive relationship between baseline CIU and change in substance use among females, but not males Substance use reduced CIU among USA sample. No significant relationship between substance use and change in CIU among Chinese sample
19	Depressive symptoms and problematic Internet use among adolescents: Analysis of the longitudinal relationships from the Cognitive–Behavioural Model	Gámez-Guadix, (2014)	ESP	699	Adol	1yr	Prior psychological distress is a risk factor for development of PIU Depressive symptoms at Time 1 predicted increase in preference for online relationships, IU for mood regulation, and negative outcomes of IU at Time 2 Negative outcomes of IU at Time 1 predicted increase in depressive symptoms at Time 2 No significant gender differences in the relationship between depression and PIU
20	Factors predictive for incidence and remission of Internet addiction in young adolescents: A prospective study	Ko et al., (2007)	TW	517	Adol	1yr	High exploratory excitability, low reward dependence, low self-esteem, low family function, and online gaming were found to predispose IA Low hostility and reduced levels of interpersonal anxiety were found to predict IA remission
21	MMORPG gaming and hostility predict Internet addiction symptoms in adolescents: An empirical multilevel longitudinal study	Stavropoulos et al., (2015)	GRK	648	Adol	2yr	More hostile adolescents were found to present higher IA symptom severity MMORPG playing increases IA symptoms Higher classroom percentage of MMORPG players was found to decrease individuals’ IA symptoms
22	Parental influences on pathological symptoms of video-gaming among children and adolescents: A prospective study	Choo et al., (2015)	SGP	2,974	Child/Adol	1yr	High parent–child closeness at Wave 1 related to decreased number of PVGU symptoms at Wave 2. Effect stronger for males Parental restriction of video-gaming at Wave 1 had no main effect
23	Physiological indicators of pathologic video game use in adolescence	Coyne et al., (2015)	USA	374	Adol	1yr	Adolescents who don’t find cognitive tasks stimulating (less RSA) or who show physiological signs of stress (less RSA) during family problem solving have a greater severity of PVGU GSC activation during family problem solving was related to higher PVGU symptoms for females only
24	Predicting adolescent problematic online game use from teacher autonomy support, basic psychological needs satisfaction, and school engagement: A 2-year longitudinal study	Yu et al., (2015)	CHN	356	Adol	2yr	Higher perceived teacher autonomy support found to be a protective predictor of adolescent POGU with mediating factors of basic psychological needs satisfaction and school engagement
25	Predictive values of psychiatric symptoms for Internet addiction in adolescents: A 2-year prospective study	Ko, Yen, et al., (2009)	TW	2,293	Adol	2yr	Depression, ADHD, social phobia, and hostility were found to predict IA Most significant predictor of IA in males was hostility, and in females was ADHD
26	Problematic Internet use and problematic alcohol use from the cognitive–behavioural model: A longitudinal study among adolescents	Gámez-Guadix et al., (2015)	ESP	801	Adol	6m	Low self-regulation at Time 1 predicted higher preference for online interactions, mood regulation, and negative consequences of the Internet at Time 2 No gender differences between longitudinal relationships of PIU and PIU and problematic alcohol use Negative consequences of PIU at Time 1 predicted increase in problematic alcohol use at Time 2 Low self-regulation related to IU found to maintain PIU
27	Psychosocial causes and consequences of online video game play	Kowert, Vogelgesang, Festl, and Quandt (2015)	GER	110	Adol	2yr	Being a member of the online game playing community was found to increase reported life-satisfaction Adolescent OVG players reported to have higher life satisfaction at Time 2 than non-online players No inverse relationships found between OVG play and psychosocial outcomes among adolescents No significant cross-lagged relationships found between OVG play and self-esteem, loneliness or psychosocial variables and OVG play frequency
28	The bi-directional interactions between addiction, behaviour approach and behaviour inhibition systems among adolescents in a prospective study	Yen et al., (2012)	TW	2,293	Adol	1yr	Higher BAS and fun-seeking predicted IA IA adolescents decreased more on BAS and BIS than the non-addiction group
29	The development of compulsive Internet use and mental health: A four-year study of adolescence	Ciarrochi et al., (2016)	AU	2,068	Adol	4yr	CIU predicted development of poor mental health; poor mental health did not predict CIU development Females and males increased CIU and mental health problems across the high school Females had higher CIU and worse mental health than males

Notes: IA = Internet Addiction, PIU = Problematic Internet Use, IU = Internet Use, CB = Cyber-bullying, CIU = Compulsive Internet Use, PVGU = Pathological Video.

Game Use, IAD = Internet Addiction Disorder, MMORPG = Massive Multiplayer Online Role Playing Games, OVG = Online Video Game POGU = Problematic.

Online Game Use, RSA = Respiratory Sinus Arrhythmia, GSC = Galvanic Skin Conductance, BIS = Behaviour Inhibition System, BAS = Behaviour Approach.

System, ADHD = Attention Deficit Hyperactivity Disorder.

Table 3. Definitions and terminology used in studies regarding Internet use

		Compulsive Internet Use	Internet addiction	Problematic Internet Use	Online game addiction	Internet Use (high/low)	Pathological video game use	Problem gaming	Online video game play	Problematic online game use
1	Internet addiction, reality substitution and longitudinal changes in psychotic-like experiences in young adults	○	●	●	○	○	○	○	○	○
2	Internet addiction in Hong Kong adolescents: A three-year longitudinal study	○	●	○	○	○	○	○	○	○
3	Does psychopathology in childhood predict Internet addiction in male adolescents?	○	●	○	○	○	○	○	○	○
4	Compulsive Internet use: The role of online gaming and other Internet applications	●	○	○	○	○	○	○	○	○
5	A short-term longitudinal study of Internet and computer game use by adolescent boys and girls: Prevalence, frequency of use, and psychosocial predictors	○	○	○	○	●	○	○	○	○
6	A group-based modelling approach to estimating longitudinal trajectories of Korean adolescents’ on-line game time	○	○	○	●	○	○	○	○	○
7	Longitudinal and reciprocal relations of cyber bullying with depression, substance use, and problematic Internet use among adolescents	○	○	●	○	○	○	○	○	○
8	Online communication, compulsive Internet use, and psychosocial well-being among adolescents: A longitudinal study	●	○	○	○	○	○	○	○	○
9	Pathological video game use among youths: A two-year longitudinal study	○	○	○	○	○	●	○	○	○
10	Predicting compulsive Internet use: It’s all about sex!	●	○	○	○	○	○	○	○	○
11	Trajectories of problem video gaming among adult regular gamers: An 18-month longitudinal study	○	○	○	○	○	○	●	○	○
12	Personality, cognitive style, demographic characteristics and Internet use – Findings from the HomeNetToo project	○	○	○	○	●	○	○	○	○
13	Precursor or sequela: Pathological disorders in people with Internet addiction disorder	○	●	○	○	○	○	○	○	○
14	Compulsive Internet use among adolescents: Bi-directional parent–child relationships	●	○	○	○	○	○	○	○	○
15	Adolescents’ compulsive Internet use and depression: A longitudinal study	●	○	○	○	○	○	○	○	○
16	Assessing problematic video gaming using the Theory of Planned Behaviour: A longitudinal study of Dutch young people	○	○	○	○	○	○	●	○	○
17	ADHD and autistic traits, family function, parenting style, and social adjustment for Internet addiction among children and adolescents in Taiwan: A longitudinal study	○	●	○	○	●	○	○	○	○
18	Concurrent and predictive relationships between compulsive Internet use and substance use: Findings from vocational high school students in China and the USA	●	●	○	○	○	○	○	○	○
19	Depressive symptoms and problematic Internet use among adolescents: Analysis of the longitudinal relationships from the cognitive–behavioural model	○	○	●	○	○	○	○	○	○
20	Factors predictive for incidence and remission of Internet addiction in young adolescents: A prospective study	○	●	○	○	○	○	○	○	○
21	MMORPG gaming and hostility predict Internet addiction symptoms in adolescents: An empirical multilevel longitudinal study	○	●	○	○	○	○	○	○	○
22	Parental influences on pathological symptoms of video-gaming among children and adolescents: A prospective study	○	○	○	○	○	●	○	○	○
23	Physiological indicators of pathologic video game use in adolescence	○	○	○	○	○	●	○	○	○
24	Predicting adolescent problematic online game use from teacher autonomy support, basic psychological needs satisfaction, and school engagement: A 2-year longitudinal study	○	○	○	○	○	○	○	○	●
25	Predictive values of psychiatric symptoms for Internet addiction in adolescents: A 2-year prospective study	○	●	○	○	○	○	○	○	○
26	Problematic Internet use and problematic alcohol use from the cognitive–behavioural model: A longitudinal study among adolescents	○	○	●	○	○	○	○	○	○
27	Psychosocial causes and consequences of online video game play	○	○	○	○	○	○	○	●	○
28	The bi-directional interactions between addiction, behaviour approach and behaviour inhibition systems among adolescents in a prospective study	○	●	○	○	○	○	○	○	○
29	The development of compulsive Internet use and mental health: A four-year study of adolescence	●	○	○	○	○	○	○	○	○
	Total	7	10	4	1	3	3	2	1	1

○ = Definition not used, ● = Definition used.

Table 4. Instruments used in each study to measure Internet use variable.

Instrument	Internet Addiction Test (Young, 1998)	Problematic Video Game Playing Test (Adaption of IAT)	Internet Addiction Test – Chinese Adaption	Compulsive Internet Use Scale	Compulsive Internet Use Scale – Shortened Version	Frequency Computer Game Use	Frequency Computer Game Use	Generalized Problematic Internet Use Scale 2	Davis Scale for Problematic Internet Use (adaption)	Frequency Internet Use	Frequency Internet Use	Pathological Video Game Use	Pathological Video Game Use	Problematic Gaming	Chen Internet Addiction Scale
Items	20	*20	10	14	10	–	1–2	15	4	1–8	6	10	11	7	26
Scale	5-point Likert	*5-point Likert	5-point Likert	5-point Likert	5-point Likert	Number of Hours	5-point Likert	6-point Likert	5-point Likert	5–8-point Likert	Yes/No	3-point Likert	3-point Likert	5-point Likert	4-point Likert
Study 1	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 2	○	○	●	○	○	○	○	○	○	○	○	○	○	○	○
Study 3	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 4	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○
Study 5	○	○	○	○	○	●	○	○	○	○	○	○	○	○	○
Study 6	○	○	○	○	○	○	●	○	○	○	○	○	○	○	○
Study 7	○	○	○	○	○	○	○	●	○	○	○	○	○	○	○
Study 8	○	○	○	○	●	○	○	○	○	○	○	○	○	○	○
Study 9	○	○	○	○	○	○	○	○	○	○	○	●	○	○	○
Study 10	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○
Study 11	○	●	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 12	○	○	○	○	○	○	○	○	○	●	○	○	○	○	○
Study 13	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 14	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○
Study 15	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○
Study 16	○	○	○	○	○	○	○	○	○	○	○	○	○	●	○
Study 17	○	○	○	○	○	○	○	○	○	○	●	○	○	○	●
Study 18	○	○	○	○	○	○	○	○	●	○	○	○	○	○	○
Study 19	○	○	○	○	○	○	○	●	○	○	○	○	○	○	○
Study 20	○	○	○	○	○	○	○	○	○	○	○	○	○	○	●
Study 21	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 22	○	○	○	○	○	●	○	○	○	○	○	○	○	○	○
Study 23	○	○	○	○	○	○	○	○	○	○	○	○	●	○	○
Study 24	○	○	○	○	○	○	○	○	○	○	○	○	●	○	○
Study 25	○	○	○	○	○	○	○	○	○	○	○	○	○	○	●
Study 26	○	○	○	○	○	○	○	●	○	○	○	○	○	○	○
Study 27	○	○	○	○	○	●	○	○	○	○	○	○	○	○	○
Study 28	○	○	○	○	○	○	○	○	○	○	○	○	○	○	●
Study 29	○	○	○	○	●	○	○	○	○	○	○	○	○	○	○
Total	4	1	1	4	2	3	1	3	1	1	1	1	2	1	4

○ = Instrument not used, ^● = Instrument used.

* Details not recorded in research article, instead provided by author of research article.

Table 5. Individual, contextual and activity-related variables measured in relation to IU-PIU.

	A	C	I	I	I	I	I	C	I	I	I	I	I	C	I	I	I	I	I	I	C	I	I	I	I	I	I
	Medium of Internet use	Parenting/family factors	Withdrawal	Gender	Psychotic experiences	Depression	Hostility/aggression	Cyber-bullying	Substance use	Self-control/ impulsivity	Social skills/competence	Emotional regulation	Academic disposition	Friendship quality/ social support	Loneliness	Well-being/life satisfaction	Anxiety	Social anxiety	Obsessive compulsive traits	Personality traits	Classroom environment	Physiological Indicators	ADHD	Self-esteem	Sensitivity to reward/punishment	Autism spectrum	Cognitive Style
Study 1	○	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 2	○	●	○	●	○	○	○	○	○	○	○	○	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○
Study 3	○	○	●	○	○	●	○	○	○	○	○	○	○	○	○	○	●	○	○	○	○	○	○	○	○	○	○
Study 4	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 5	○	●	○	●	○	○	○	○	○	○	○	○	●	●	○	●	○	○	○	○	○	○	○	○	○	○	○
Study 6	○	○	○	●	○	○	●	○	○	●	●	●	●	○	○	○	○	○	○	○	○	○	○	●	○	○	○
Study 7	○	○	○	○	○	●	○	●	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 8	●	○	○	○	○	●	○	○	○	○	○	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○	○
Study 9	○	○	○	●	○	●	○	○	○	●	●	●	●	○	○	○	●	●	○	○	○	○	○	○	○	○	○
Study 10	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 11	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 12	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	●	○	○	○	○	○	○	●
Study 13	○	○	○	○	●	●	●	○	○	○	●	○	○	○	○	○	●	○	●	○	○	○	○	○	○	○	○
Study 14	○	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 15	●	○	○	○	○	○	○	○	○	○	○	○	○	●	○	●	○	○	○	○	○	○	○	●	○	○	○
Study 16	○	○	○	●	○	○	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 17	○	●	○	●	○	○	○	○	○	○	●	○	●	●	○	○	○	○	○	○	○	○	●	○	○	●	○
Study 18	○	○	○	●	○	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 19	○	○	○	●	○	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 20	●	●	○	○	○	○	●	○	○	○	○	○	○	○	○	●	○	●	○	●	○	○	○	●	●	○	○
Study 21	●	○	○	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○	○	○	●	○	○	○	○	○	○
Study 22	○	●	○	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 23	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	●	○	○	○	○	○
Study 24	○	○	○	○	○	○	○	○	○	○	○	○	●	○	○	●	○	○	○	○	●	○	○	○	○	○	○
Study 25	○	○	○	●	○	●	●	○	○	○	○	○	○	○	○	○	○	●	○	○	○	○	●	○	○	○	○
Study 26	○	○	○	●	○	○	○	○	●	●	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○
Study 27	○	○	○	○	○	○	○	○	○	○	●	○	○	○	●	●	○	○	○	○	○	○	○	●	○	○	○
Study 28	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	○	●	○	○
Study 29	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○	●	○	○	○	○	○	○	○	○	○	○	○
Total	7	6	1	13	2	7	5	1	3	4	5	2	5	3	2	7	3	3	1	2	2	1	2	4	2	1	1

● = assessed, ○ = not assessed.

I = individual factor C = contextual factor A = activity-related factor, ADHD = attention deficit hyperactivity disorder.

Table 6. Predictors of Internet Use/Abuse/Addiction.

Variable	Studies	Total N	Ratio of statistical significance (%)
Medium of Internet use	4, 8, 10, 11, 15, 20, 21	6968	100
Parenting/family factors	2, 5, 14, 17, 20, 22	9412	100
Withdrawal	3	524	100
Gender	2, 5, 6, 9, 16, 17, 19, 22, 23, 25, 26, 29	21,913	83.3
Psychotic experiences	1, 13	229	50
Depression	3, 7, 8, 9, 13, 19, 25	8117	57.1
Hostility/aggression	6, 13, 20, 21, 25	6966	60
Cyber-bullying	7	845	100
Substance use	7, 18, 26	4589	0
Self-control/impulsivity	6, 9, 16, 26	8094	100
Social skills/competence	6, 9, 13, 17, 27	7805	20
Emotional regulation	6, 9	6483	100
Academic disposition	5, 6, 9, 17, 24	9583	80
Friendship quality/social support	5, 15, 17	2785	100
Loneliness	8, 27	773	50
Well-being/life satisfaction	2, 5, 15, 20, 24, 27, 29	7350	28.5
Anxiety	3, 9, 13	3675	33.3
Social anxiety	9, 20, 25	5844	66.6
Obsessive compulsive traits	13	59	100
Personality traits	12, 20	634	50
Classroom environment	21, 24	1004	100
Physiological indicators	23	374	100
ADHD	17, 25	3446	100
Self-esteem	6, 15, 20, 27	4117	50
Sensitivity to reward/punishment	20, 28	2810	100
Autism	17	1153	100
Cognitive style	12	117	100

Notes: ADHD = Attention Deficit Hyperactivity Disorder. Personality traits included Extroversion/Introversion, Neuroticism & Exploratory Excitability. The ratio of statistical significance is derived by the number of studies resulting to statistically significant results divided by the number of the studies that examined the variable of reference.

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