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Gihwan Byeon Department of Psychiatry, Kangwon National University Hospital, Chuncheon, Korea

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Sun-Jin Jo Department of Prevention Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea

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Jong-Ik Park Department of Psychiatry, Kangwon National University Hospital, Chuncheon, Korea
Department of Psychiatry, Kangwon National University School of Medicine, Chuncheon, Korea

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Hyunsuk Jeong Department of Prevention Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea

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Hae Kook Lee Department of Psychiatry, College of Medicine, The Catholic University of Korea, Uijeongbu St. Mary's Hospital, Uijeongbu, Korea

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Hyeon Woo Yim Department of Prevention Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea

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Open access

Abstract

Background and aims

Internet gaming disorder (IGD) is known to cause various psychological and physical complications. Through data collected from an adolescent prospective longitudinal cohort, we examined how IGD is related to lifestyle and physical symptoms, as well as the temporal relationship between them.

Methods

This study was conducted as part of iCURE (Internet user Cohort for Unbiased Recognition of gaming disorder in Early Adolescence) in Korea between 2015 and 2019. Sleep and physical activity time, dry eye symptoms, musculoskeletal pain, and near-miss accidents were measured at baseline and followed-up after one year. IGD risk was evaluated using the Internet Game Use – Elicited Symptom Screen (IGUESS). The association between IGD risk and measured variables was analyzed, both at baseline and at follow-up after one year.

Results

At baseline, the IGD risk group had significantly less physical activity time and sleep time and had more dry eye symptoms, musculoskeletal pain, and near-miss accidents than the IGD non-risk group. Additionally, in the IGD risk group at baseline, dry eye symptoms, musculoskeletal pain, and near-miss accidents occurred significantly more after one year of follow-up.

Discussion and conclusion

The results of this study show that IGD is a significant risk factor that increases the probability of physical disease and trauma in adolescents. Therefore, interventions aimed at reducing IGD risk and protecting the physical and mental health of adolescents are imperative.

Abstract

Background and aims

Internet gaming disorder (IGD) is known to cause various psychological and physical complications. Through data collected from an adolescent prospective longitudinal cohort, we examined how IGD is related to lifestyle and physical symptoms, as well as the temporal relationship between them.

Methods

This study was conducted as part of iCURE (Internet user Cohort for Unbiased Recognition of gaming disorder in Early Adolescence) in Korea between 2015 and 2019. Sleep and physical activity time, dry eye symptoms, musculoskeletal pain, and near-miss accidents were measured at baseline and followed-up after one year. IGD risk was evaluated using the Internet Game Use – Elicited Symptom Screen (IGUESS). The association between IGD risk and measured variables was analyzed, both at baseline and at follow-up after one year.

Results

At baseline, the IGD risk group had significantly less physical activity time and sleep time and had more dry eye symptoms, musculoskeletal pain, and near-miss accidents than the IGD non-risk group. Additionally, in the IGD risk group at baseline, dry eye symptoms, musculoskeletal pain, and near-miss accidents occurred significantly more after one year of follow-up.

Discussion and conclusion

The results of this study show that IGD is a significant risk factor that increases the probability of physical disease and trauma in adolescents. Therefore, interventions aimed at reducing IGD risk and protecting the physical and mental health of adolescents are imperative.

Introduction

Internet gaming disorder (IGD) is becoming increasingly prevalent worldwide with the development of technology (Feng, Ramo, Chan, & Bourgeois, 2017). IGD consists of a behavioral pattern associated with persistent and repeated engagement in online and offline games, which results in significant impairment or distress over a period of 12 months (American Psychiatric Association, 2013). According to the diagnostic criteria of IGD, at least 5 of the following 9 must be met: (1) a preoccupation with gaming; (2) withdrawal symptoms when gaming is taken away; (3) tolerance, resulting in the need to spend increasing amounts of time engaged in gaming; (4) unsuccessful attempts to control or quit gaming; (5) loss of interest in previous hobbies and entertainment as a result of, and with the exception of, gaming; (6) continued excessive use of games despite awareness about the psychosocial problems it causes; (7) deceiving family members, therapists, or others regarding the amount of gaming; (8) the use of games to escape or relieve negative moods; and (9) jeopardizing or losing a significant relationship, job, education, or career opportunity due to gaming (American Psychiatric Association, 2013).

Additionally, in 2018, a new category “Gaming Disorder, predominantly online gaming disorder” was added in ICD-11, which provides worldwide criteria for diagnosing diseases. This disorder is characterized by a pattern of persistent or recurrent gaming behavior (“digital gaming” or “video-gaming”) that is primarily conducted over the internet and is manifested by impaired control over gaming (e.g., onset, frequency, intensity, duration, termination, context); increasing priority given to gaming to the extent that gaming takes precedence over other life interests and daily activities; and continuation or escalation of gaming despite the occurrence of negative consequences. The behavior pattern is of sufficient severity to cause significant impairment in personal, family, social, educational, occupational, or other important areas of functioning (World Health Organization (WHO), 2022).

As presented in the diagnostic criteria of DSM and ICD, IGD is an excessive absorption in games, causing restrictions in academic achievement and interpersonal relationships. Therefore, the negative effect of IGD is greater in adolescence, when physical and mental development must be achieved (Sugaya, Shirasaka, Takahashi, & Kanda, 2019). In addition, the prevalence of IGD is higher in adolescence than in other periods as it is characterized by rapid adoption of new technologies and a high tendency to pursue novel developments (Odgers & Jensen, 2020).

Previous studies have shown IGD to be associated with loneliness (Wang, Sheng, & Wang, 2019), anxiety (Wang et al., 2017), depression (Wang, Cho, & Kim, 2018), lower academic achievement (Hawi, Samaha, & Griffiths, 2018), conduct problem (Brunborg, Mentzoni, & Frøyland, 2014), sleep difficulties (Wong et al., 2020), and lower psychosocial well-being (Pappa, Apergi, Ventouratou, Janikian, & Beratis, 2016). Decreased physical activity (Henchoz et al., 2016) and physical symptoms, such as pain in the wrist joints, were also found to be significantly associated with IGD (Gentile, 2009). Studies have also found IGD to be related to obesity (Mallorquí-Bagué et al., 2017), neck pain (Cankurtaran et al., 2022), hearing problems (Bener et al., 2019), and eye discomforts (Lee, Cho, Moon, Kim, & Yu, 2019). IGD has also been linked to impulsivity (Kim et al., 2016), and in the case of augmented reality (AR), which has gained immense popularity in recent times among adolescents, IGD is also associated with the risk of injury or accident by blocking attention from stimuli other than games (Mukhra, Baryah, Krishan, & Kanchan, 2019). According to a study conducted on the cost-benefit of gaming in Korea, the cost of gaming itself (excess game spending, crime, counseling, etc.) accounts for 14% of the total benefit due to negative outcomes. However, in gaming addiction, the ratio increases to 95% (Cho, Kim, Lee, & Park, 2018). Therefore, it is necessary to prevent and manage IGD from a socio-economic point of view.

To reduce the negative effects of IGD, appropriate interventions are needed in adolescence, when IGD is known to occur frequently and is likely to induce long-term effects. To achieve this goal, it is necessary to distinguish between the risk factors that cause IGD and the outcomes of IGD. However, most previous studies including those mentioned above on the association between IGD, sleep, physical activity, and physical symptoms have been based on cross-sectional designs (Cankurtaran et al., 2022; Gentile, 2009; Henchoz et al., 2016; Lee et al., 2019; Mukhra et al., 2019; Pappa et al., 2016; Wong et al., 2020). Therefore, it provides insufficient evidence for temporal relationships and to infer causal relationships. To overcome this limitation, our study intends to identify the temporal relationships between IGD and sleep, physical activity, and physical symptoms through a prospective cohort study of Korean adolescents followed-up after a one-year interval. In this process, information on potential confounding variables was collected and adjusted to minimize unpredicted biases.

Based on these data, we hypothesize that IGD has a significant association with sleep problems, physical inactivity, and physical symptoms and tested the hypotheses. Further, with IGD, these symptoms are expected to increase over time.

Methods

Study design and participants

The Internet user Cohort for Unbiased Recognition of Gaming Disorder in Early Adolescence (iCURE) study is a multidisciplinary, prospective, longitudinal cohort study of adolescents, conducted in Korea, with four waves of annual data collection currently planned. The study was conducted in Seoul and Uijeongbu, Gyeonggi Province, with third, fourth, and seventh-grade students. To proceed with the research, we requested three offices of educational support: the Seoul Southern offices of educational support, the Seoul Northern offices of educational support, and the Gyeonggi Province offices of educational support. We sent an official letter to the elementary and middle schools within the jurisdiction of each Office of Education. Among the 258 elementary/junior high schools that sent the official letter, 6 elementary schools and 15 junior high schools that wanted to participate in the study recruited study subjects (Jeong et al., 2020). If they agreed to participate in the survey, they were enrolled as a cohort. Through this process, 2,319 of 4,794 students from 21 schools were enrolled in the cohort. (Participation rate 48.4%) Using the computer room of the school, each participant used a personal computer to respond to the online self-report questionnaire during available regular class hours. Written parental consent and participant consent is mandatory for participation following the explanation of the nature of the principles of research, including confidentiality and the freedom of choice to participate in the study (Jeong et al., 2020). In the present study, we used the baseline evaluation (in 2015) and one-year follow-up (in 2016) data.

Measures and procedures

Assessment of sociodemographic factors

Demographic characteristics were assessed at baseline. Gender, date of birth, school level, and personal smartphone use were obtained from the student assessment; socioeconomic status and academic records were obtained from the parent or guardian assessments.

Assessment of IGD risk (Internet Game Use – Elicited Symptom Screen: IGUESS)

This instrument was created based on the nine DSM-5 IGD criteria. Students were instructed to respond based on their gaming behavior over the previous 12 months, with each item rated on a 4-point scale: 1 = not at all, 2 = occasionally, 3 = frequently, and 4 = always. The sensitivity and specificity of IGUESS were 87.0% and 86.7%, respectively, for a cut-off score of 10 points, with an area under the curve value of 0.93. Its reliability, as determined by Cronbach's alpha, was 0.94, and the correlation coefficient between IGUESS and Young's Internet Addiction Test was r = 0.902 (Jo et al., 2018). Based on a previous study, participants with a score of 10 or higher on the IGUESS were defined as the IGD risk group.

Assessment of physical health status

We assessed physical pain experiences, such as in the hand, wrist, shoulder, or neck; dry eyes with the 11 symptoms questionnaire. For obtaining data on physical pain experience, Musculoskeletal Disease Symptom Survey tool (Kim, Park, & Kim, 2008) was used to investigate whether pain was experienced in parts of the body that could be affected during repetitive work involving the same movement, such as working on a computer. Through this, when at least one of the body parts (1. hand, finger, or wrist, 2. shoulder, 3. neck) was found to experience pain, and the response was “yes,” the person was considered to experience musculoskeletal pain. Further, a self-check list for dry eye symptoms provided by the Korea Public Health Information Service (https://www.g-health.kr/mobile/bbs/selectBoardArticle.do?bbsId=U00194&nttId=193255&lang=&searchCndSj=&searchCndCt=&searchWrd=&pageIndex=1&vType=&option7=11) was used. Further, 3 or more of 11 symptoms were defined as symptomatic through the expert consensus of 3 ophthalmologists. The 11 symptoms included in the self-check list are as follows: 1. There is a feeling of a foreign body such as a grain of sand having entered the eye. 2. My eyelids feel heavy and my head hurts. 3. My eyes are stiff. 4. It appears as if there is a curtain in front of my eyes. 5. My eyes get tired easily and I can't read for a long time. 6. When the wind blows, my eyes tear up. 7. My eyes become bloodshot frequently for no reason. 8. On waking up from sleep, my eyelids stick together, and it is difficult to open them. 9. My eyes keep closing when there is a glare. 10. I feel like my eyes are pouring (falling out). 11. Thread-like discharge keeps coming out.

3. Physical exercise: We also asked whether participants exercised regularly – two times or more per week and 30 min or more at a given time, excluding activities of the school curriculum based on previous Korean guidelines (Ministry of Health & Welfare of Korea, 2013). School physical education hours in elementary and junior high schools in Korea follow the curriculum stipulated by the Ministry of Education and are excluded from the measurement because they are the same at about 4 h per week in all schools. The rate of lack of physical activity among Korean adolescents has touched 95% in 2020 (Korea Centers for Disease Control and Prevention, 2021). Because most of the urban adolescents in Korea, who are competing under pressure for academic achievement, lack physical activity, it was judged that using the absolute value of the recommended physical activity standard as a cut-off point in this study was limited. The Korean National Health and Nutrition Examination Survey at the time of data collection defined regular exercise as when vigorous physical activity was more than 20 min at a time and 3 times a week (Korea Centers for Disease Control and Prevention, 2022). Data were collected by adjusting the frequency to at least 30 min and at least twice a week, a total of 60 min of physical activity per week. We expected this method to minimize the measurement error, because the authors judged that it is easier to accurately recognize and respond to “30 min or longer” rather than “20 min or longer.”

Information on sleep hours during the week and during weekends was also sought. The average daily sleep time was obtained by calculating (weekday sleep time × 5) + (weekend sleep time × 2)/7. As of 2020, the average amount of sleep derived by junior high school students in Korea was 6.9 h, according to the online survey on health behavior of Korean adolescents (https://www.kdca.go.kr/yhs/). Further, from the results of a previous study, 7 h was suggested as the margin of the appropriate sleep time category for teenagers (Hirshkowitz et al., 2015). In this background, sleep hours were dichotomized into <7 h and ≥7 h on analyses according to the sleep duration recommendation of a previous study (Chaput, Dutil, & Sampasa-Kanyinga, 2018). Additionally, information was sought on whether there were near misses or accident experiences due to smartphone use over the previous 12 months.

Statistical analysis

The adjusting variables, gender, school level (elementary or junior high school), parents' subjective economic status (low/middle/high), and personal smartphone use (yes/no) were categorized, and each number and ratio were calculated. Independent variables, baseline and one-year follow up IGD risks, were also calculated as the number and ratio. The main dependent variables were regular exercise (physical activity at least twice a week and at least 30 min a day), sleep duration on weekdays and weekends (sleep more than 7 h a night or not), dry eye symptoms (3 or more specific symptoms), musculoskeletal pain (yes/no), and near or miss accidents (yes/no) were also categorized to calculate the number and ratio at baseline and one-year follow-up.

Subsequently, multiple logistic regression analyses were performed with baseline IGD risk as an independent variable, and baseline regular exercise, weekdays and weekend sleep duration, dry eye symptoms, musculoskeletal pain symptoms, and near-miss accidents as the dependent variables. The odds ratios (ORs) between positive IGD risk and each dependent variable were calculated with negative IGD risk as a reference. Moreover, in order to see the longitudinal effect of IGD, multiple logistic regression analyses were performed with baseline IGD risk as independent variable and the incidence of new physical symptoms, regular exercise, and sleep duration over 7 h during one year from baseline, as dependent variables. The incidence rate ratio (IRR) was calculated to determine the effect size.

Next, to determine the effect of changes in IGD risk on physical symptoms, physical activity, and sleep; participants were divided into (1) non-IGD → non-IGD, (2) IGD → non-IGD, (3) non-IGD → IGD, and (4) IGD → IGD groups. Multiple logistic regression analyses were performed with the IGD risk change group as the independent variable and with the incidence of new physical symptoms, physical activity, and sleep between the baseline and follow-up as the dependent variables with group (1) as a reference. In these analyses, gender, school level, economic status, personal smartphone usage, and academic records were adjusted. To prevent overfitting, only adjusting variables with a P value of less than 0.10 were used in the final analysis model.

Finally, in order to check the interaction between sleep, physical activity, and IGD risk, after stratifying the participants according to regular exercise, chi-square tests were performed to determine the correlation between sleep time and IGD risk.

Ethics

All procedures performed in studies involving human participants were in accordance with the ethical standards of the Institutional Review Board of the Catholic University of Korea (approval number: MC140NM10085) and with the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Written consent was received from all participants and from one of the participants' parents, or caregivers following an explanation of the study, including confidentiality and freedom of choice to participate.

Results

At baseline we registered 2,319 participants, of which 1951 had played Internet games within the past 12 months. Among them, a one-year follow-up was completed with 1849 participants, of which 1838 were included (excluding 11 missing health outcomes or IGD risk). There was no significant difference in the follow-up rate according to the exposure variable at the base time, that is, the presence of IGD. Further, there was no difference in the distribution of follow-up rates according to each health outcome group at baseline. In the case of follow-up rates by other general characteristics, there were no significant differences in any variable other than the school level. In the case of school level, junior high students showed a slightly higher follow-up rate than elementary school students, but they were treated as adjustment variables during multiple logistic analysis. The risk of selection bias is considered to be minimal (Appendix Table A1). Among the groups where follow-up was completed, 64.3% were male and 81.9% were junior high school students. The group that used personal smartphones accounted for 85.4% of the participants. The IGD risk group evaluated by IGUESS was 8.8% at baseline and 7.8% at one-year follow-up (Table 1).

Table 1.

General characteristics at baseline (N = 1838)

Variable n (%)
Gender
 Female 656 35.7
 Male 1,182 64.3
School level
 Elementary school 332 18.1
 Middle school 1,506 81.9
Parents' subjective economic status
 Low 333 18.1
 Middle 986 53.6
 High 519 28.2
Personal smartphone use
 No 269 14.6
 Yes 1,569 85.4
Subjective school achievement
 Good or very good 920 50.1
 Fair or poor 918 49.9
Internet gaming disorder risk
 Baseline Negative 1,676 91.2
Positive 162 8.8
 After 12-month Negative 1,694 92.2
Positive 144 7.8

The main variable of interest; dry eye symptoms were 22% and 20.2% at baseline and follow-up, respectively. Musculoskeletal pain was observed in 43.8% and 51.4% of patients at baseline and follow-up, respectively. Near-miss accidents were both 16.9% at baseline and at follow-up. Regular exercise at baseline was 58.3% and 48% after one-year of follow-up. The group with sleep less than 7 h per week was 9.2% at baseline and 16.5% at follow-up. On weekends, the baseline and follow-up were 5.5% and 5.2%, respectively (Table 2).

Table 2.

Status of Internet gaming disorder risk and health outcomes at baseline and 12-month follow-up (N = 1,838)

Variable Baseline 12-month follow-up
n (%) n (%)
Regular Exercisea
 No 766 41.7 956 52.0
 Yes 1,072 58.3 882 48.0
Sleeping duration
 Weekday <7hr/night 170 9.2 304 16.5
≥7hr/night 1,668 90.8 1,534 83.5
 Weekend <7hr/night 102 5.5 95 5.2
≥7hr/night 1,736 94.5 1,743 94.8
Dry eye symptoms
 Two symptoms or less 1,433 78.0 1,466 79.8
 Three symptoms or more 405 22.0 372 20.2
Musculoskeletal pain symptom
 No 1,033 56.2 893 48.6
 Yes 805 43.8 945 51.4
Near miss/accident
 No 1,527 83.1 1,528 83.1
 Yes 311 16.9 310 16.9

aTwo times or more per week and 30 min or over per time, except for activities of school curriculum.

Regression analysis with baseline IGD risk as an independent variable and health outcome as a dependent variable, dry eye symptoms (OR = 3.07, 95% CI: 2.18–4.31), musculoskeletal pain (OR = 2.07, 95% CI: 1.49–2.89) and near miss accidents (OR = 3.27, 95% CI: 2.27–4.71) revealed a significantly positive association. On the other hand, in the IGD risk group, the ORs of the group that perform regular exercise (OR = 0.59, 95% CI = 0.42–0.83) and the group that sleeps more than 7 h a day (OR = 0.48, 95% CI: 0.31–0.76) were significantly lower. However, there was no significant association between IGD risk and weekend sleep duration. Regression analysis with baseline IGD risk as an independent variable and new incidences of main interest variables during one-year follow-up as dependent variables, dry eye symptoms (IRR = 2.19, 95% CI: 1.31–3.67), musculoskeletal pain (IRR = 1.81, 95% CI: 1.10–2.98) and near-miss accidents (IRR = 1.94, 95% CI: 1.18–3.17) revealed a significantly positive association. However, there was no significant association between IGD risk, regular exercise, and daytime or weekend sleep (Table 3).

Table 3.

Cross-sectional and longitudinal effect of Internet gaming disorder risk on health outcomes: multiple logistic regression results

Health outcomes IGD risk Regular exercisea Sleep duration-weekdays (≥7hr/night)b Sleep duration-weekend (≥7hr/night)b Dry eye symptomsc Musculoskeletal pain symptomsd Near accidentse
n (%) IRR (95%CI) n (%) IRR (95%CI) n (%) IRR (95%CI) n (%) IRR (95%CI) n (%) IRR (95%CI) n (%) IRR (95%CI)
Cross-sectional effect
 IGD risk Negative 991 (49.1) Ref. 1,534 (91.5) Ref. 1,588 (94.7) Ref. 335 (20.0) Ref. 711 (42.4) Ref. 252 (15.0) Ref.
Positive 81 (50.0) 0.59 (0.42–0.83) 134 (82.7) 0.48 (0.31–0.76) 148 (91.4) 0.74 (0.41–1.34) 79 (43.2) 3.07 (2.18–4.31) 94 (58.0) 2.07 (1.49–2.89) 59 (36.4) 3.27 (2.27–4.71)
Longitudinal effect
 IGD risk Negative 664 (67.0) Ref. 1,344 (87.6) Ref. 1,525 (96.0) Ref. 175 (13.0) Ref. 370 (38.3) Ref. 177 (12.4) Ref.
Positive 51 (63.0) 0.75 (0.46–1.22) 113 (84.3) 0.84 (0.51–1.39) 141 (95.3) 0.96 (0.43–2.14) 23 (25.0) 2.19 (1.31–3.67) 37 (54.4) 1.81 (1.10–2.98) 23 (22.3) 1.94 (1.18–3.17)

a Adjusted for sex, school level, economic status, personal smartphone use; b Adjusted for sex, school level, economic status, personal smartphone use, academic records; c Adjusted for sex, school level, economic status, personal smartphone use, and academic records; d Adjusted for sex, school level, economic status, personal smartphone use, academic records and regular exercise; e Adjusted for sex, school level, economic status, personal smartphone use, academic records.

The associations between the IGD risk change during the follow-up period and the new incidence of main interest variables were compared as stratified groups: 1) non-IGD → non-IGD group, 2) IGD → non-IGD group, 3) non-IGD → IGD group, and 4) IGD → IGD group. At IGD → IGD group, the incidence of dry eye symptoms (IRR = 4.77, 95% CI: 1.84–12.35), and of near miss accidents (IRR = 2.73, 95% CI: 1.24–5.99) increased significantly, whereas there was no significant association with weekday/weekend sleep time and musculoskeletal pain. There was also a significant decrease in regular exercise in the non-IGD → IGD group (IRR = 0.48, 95% CI: 0.27–0.84) but not in the IGD → IGD group (Table 4).

Table 4.

Longitudinal association between change of Internet gaming disorder risk and health outcomes at 12-month follow-up

Health outcomes Change of IGD risk (baseline →12-month f/u) Regular exercisea Sleep duration-weekdays (≥7hr/night)b Sleep duration-weekend (≥7hr/night)b Dry eye symptomsc Musculoskeletal pain symptomsd Near accidentse
n (%) IRR (95%CI) n (%) IRR (95%CI) n (%) IRR (95%CI) n (%) IRR (95%CI) n (%) IRR (95%CI) n (%) IRR (95%CI)
Non-IGD→Non-IGD 633 (67.6) Ref. 1,275 (87.7) Ref. 1,441 (96.1) Ref. 205 (19.0) Ref. 347 (37.7) Ref. 154 (11.4) Ref.
 IGD→Non-IGD 32 (59.3) 0.63 (0.35–1.12) 74 (85.1) 0.88 (0.47–1.63) 97 (96.0) 1.00 (0.36–2.83) 18 (33.3) 2.07 (1.14–3.76) 25 (52.1) 1.67 (0.93–3.00) 14 (20.3) 1.88 (1.01–3.47)
Non-IGD→IGD 31 (56.4) 0.48 (0.27–0.84) 69 (85.2) 0.76 (0.40–1.46) 84 (95.5) 0.91 (0.32–2.59) 25 (49.0) 4.50 (2.51–8.06) 23 (51.1) 1.69 (0.92–3.09) 23 (31.5) 3.48 (2.05–5.90)
 IGD→IGD 19 (70.4) 0.94 (0.40–1.98) 39 (83.0) 0.73 (0.33–1.61) 44 (93.6) 0.64 (0.19–2.12) 9 (50.0) 4.77 (1.84–12.35) 12 (60.0) 2.39 (0.96–5.95) 9 (26.5) 2.73 (1.24–5.99)

a Adjusted for sex, school level, economic status, personal smartphone use; b Adjusted for sex, school level, economic status, personal smartphone use, academic records; c Adjusted for sex, school level, economic status, personal smartphone use, and academic records; d Adjusted for sex, school level, economic status, personal smartphone use, academic records and regular exercise; e Adjusted for sex, school level, economic status, personal smartphone use, academic records.

In the stratified analysis according to regular exercise, IGD risk was lower in the group with short weekday sleep time compared to long weekday sleep time in regular exercisers. (6.8% vs 15.6%, P = 0.006). However, this correlation was not significant in the case of non-regular exercisers (9.7% vs. 17.5%, P = 0.052) (Table 5).

Table 5.

Stratified correlation analysis between IGD risk as sleep duration according to physical activity

Stratified group Sleep duration-weekdays (<7hr/night) Sleep duration-weekdays (≥7hr/night) Sleep duration-weekend (<7hr/night) Sleep duration-weekend (≥7hr/night)
n (%) n (%) P value n (%) n (%) P value
Regular exerciser 14 (15.6%) 67 (6.8%) 0.006 8 (12.1%) 73 (7.3%) 0.149
Non-regular exerciser 14 (17.5%) 67 (9.8%) 0.052 6 (16.7%) 75 (10.3%) 0.259

aRegular exerciser: Two times or more per week and 30 min or over per time, except for activities of school curriculum.

Discussion

In this study, IGD risk had a significantly positive association with baseline physical symptoms (dry eye symptoms, musculoskeletal pain, and near-miss accidents). It also significantly increased new incidence during follow-up. IGD risk was also found to significantly affect sleep time during the week and regular exercise at baseline, but the effects on incidence during follow-up were not significant. When participants were classified into groups according to the change in IGD risk during follow-up, in the group with IGD risk both at baseline and follow-up, the incidence of physical symptoms significantly increased compared to other groups. However, the effects of regular exercise and sleep time during the week were not significant. When we checked the interaction effect between IGD risk, sleep, and exercise; regular exercise was negatively correlated with IGD risk when there was enough sleep time during the week, whereas this correlation was not significant when the sleep time during the week was insufficient.

In previous studies, it was reported that psychiatric symptoms (Hygen et al., 2020) and physical symptoms (Aziz, Nordin, Abdulkadir, & Salih, 2021) were more common in the group with Internet gaming disorder. However, some reports suggest that games are used as self-medication to cope with real life stressors (Bowditch, Naweed, & Chapman, 2019). Other studies also suggest that games enhance creativity and cognitive performance (Jackson et al., 2012; Ruiz-Ariza, Casuso, Suarez-Manzano, & Martínez-López, 2018). Hence, confirmatory evidence on negative health effects of IGD has been lacking until now. Further, some researchers argue that the IGD diagnosis of DSM-5 overpathologizes gaming and lowers the validity of the diagnosis (Király, Griffiths, & Demetrovics, 2015). As evidence supporting this, a study result has shown that many of the people diagnosed with behavioral addiction, a higher concept of IGD, were transient users who improved over time (Konkolÿ Thege, Woodin, Hodgins, & Williams, 2015). To find an answer to these issues, it is necessary to examine the longitudinal effect of excessive game use. In this study, in the IGD risk group, the prevalence of physical symptoms at baseline was high, and the incidence also increased after one-year of follow-up. In addition, the highest IRRs of physical symptoms were observed in the group with IGD risk at both baseline and follow-up, indicating a temporal precedence relationship that IGD risk induces and aggravates negative physical outcomes.

In previous studies, Internet gaming disorder was associated with short sleep duration and lack of exercise (Alshehri & Mohamed, 2019; Bener et al., 2019). This study replicated these results. However, unlike physical symptoms, this association was no longer significant in the longitudinal analyses. Therefore, we could infer that IGD risk, sleep, and physical activity are more likely to interact with each other rather than having an antecedent or causal relationship. Supporting this inference, sleep time and exercise in this study interacted with each other and had a significant effect on IGD risk.

As suggested in previous studies and also confirmed in this study, IGD risk can have a negative effect on the physical health of adolescents. Therefore, assessment of IGD risk and active intervention to prevent and treat IGD are much required (Rumpf et al., 2018). According to the results of this study, sleep and exercise were significantly associated with IGD risk. In particular, sufficient sleep time and regular exercise significantly lowered the IGD risk. Hence, improvement in sleep habits, regular physical activity, and healthy environments are important aspects to be considered when planning IGD prevention and treatment for adolescents.

The strength of this study is that it minimizes selection bias with a retention rate of approximately 94.8% during one-year follow-up as a longitudinal design. Thus, it was possible to clarify the temporal relationship between the variables, and it could further be used as evidence to reveal the causal relationship. In addition, it was possible to draw the conclusion that IGD risk independently increases the risk of physical symptoms by correcting various confounding variables collected from participants and their parents. However, this study has its limitation in that IGD risk was evaluated using a self-reporting questionnaire. Although this study was conducted anonymously, it is known that adolescents tend to underreport their Internet game use (Jeong et al., 2020). Hence, there might be a possibility that IGD risk was underestimated compared to the actual prevalence. However, the IGUESS, which was used to evaluate the IGD risk, has been proven to have sufficient reliability and validity in a previous study targeting Korean adolescents (Jo et al., 2018). Therefore, we can assume that the limitations of the self-report scale have been overcome to some extent. Another limitation of this study is that the target participants were evaluated only at certain schools in Seoul (the capital city of Korea) and nearby areas. Therefore, this may not be sufficiently representative of all adolescents in Korea. In this study design, to increase retention probability, it was difficult to include multiple schools, however, this can be supplemented in future national youth cohort studies. Another limitation is that in this study, outcomes such as exercise, sleep, and physical symptoms were analyzed in dichotomies, and the obtained information was not sufficiently utilized for analysis. However, expressing health status as a dichotomous variable has the advantage of being easy to interpret, and hence, it is widely used in medical research. According to previous studies, “only small differences in power and efficiency were evident. These small differences are related to the relatively large sample size” (Manor, Matthews, & Power, 2000). Considering the number of samples in this study, the effect of such information loss on the results is not significant. Further, in this cohort study, the focus of attention was on whether IGD affects the “occurrence” of the problem rather than “how much change it brings to each physical health state.” This is the reason for dichotomous variables used for each physical health problem.

Conclusion

Internet gaming disorder (IGD) induces various physical symptoms in adolescents has been well established. Sleep and exercise interact with each other and are significantly associated with IGD risks. We expect the results of this study to be useful for clinical intervention in adolescents' IGD and toward establishing a national gaming policy.

Funding sources

This study was supported by a grant from the Korean Mental Health Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (HM14C2603). The funding source had no role in the study design, data collection, data analysis, data interpretation, writing of the manuscript, or the decision to submit it for publication.

Authors’ contribution

Gihwan Byeon, Sun-Jin Jo, and Hyeon-Woo Yim conceived and designed the work as well as drafted the manuscript. They also played important roles in interpreting the results and revised the manuscript. Hyunsuk Jeong, Hae Kook Lee and Jong-Ik Park acquired data, analyzed them, and revised the manuscripts. All authors read and approved the final version.

Conflict of interest

None.

Acknowledgements

We thank to the participants and their carers who cooperated with the study.

References

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Appendix

Table A1.

Follow-up rate according to the baseline characteristics of participants (N = 1,951)

Baseline characteristics Assessed at 12-month (n = 1,838) Lost to follow-up (n =113) P
n (%) n (%)
Exposure
Internet gaming disorder risk
 Negative 1,676 94.1 105 5.9 0.526
 Positive 162 95.3 8 4.7
Outcomes
Regular Exercisea
 No 733 94.2 47 5.8 0.986
 Yes 1,072 94.2 66 5.8
Sleep duration
Weekday <7hr/night 170 95.5 8 4.5 0.452
≥7hr/night 1,668 94.1 104 5.9
Weekend <7hr/night 102 92.7 8 7.3 0.478
≥7hr/night 1,736 94.3 104 5.7
Dry eye symptoms
 Two symptoms or less 1,433 94.6 82 5.4 0.181
 Three symptoms or more 405 92.9 31 7.1
Musculoskeletal pain symptom
 No 1,033 93.4 73 6.6 0.080
 Yes 805 95.3 40 4.7
Near miss/accident
 No 1,527 94.3 93 5.7 0.847
 Yes 311 94.5 18 5.5
General characteristics
Gender
 Female 656 95.1 34 4.9 0.227
 Male 1,072 93.7 79 6.3
School level
 Elementary school 332 91.2 32 8.8 0.007
 Middle school 1,506 94.9 81 5.1
Parents' subjective economic status
 Low 333 93.3 24 6.7 0.288
 Middle 986 95.0 82 5.0
 High 519 93.3 37 6.7
Subjective school achievement
 Good or very good 920 93.6 63 6.4 0.240
 Fair or poor 918 94.8 50 5.2
Personal smartphone use
 No 269 93.1 20 6.9 0.374
 Yes 1,569 94.4 93 5.6

a Two times or more per week and 30 min or over per time, except for activities of school curriculum.

  • Alshehri, A. G. , & Mohamed, A. M. A. S. (2019). The relationship between electronic gaming and health, social relationships, and physical activity among males in Saudi Arabia. American Journal of Men's Health, 13(4), 1557988319873512. https://doi.org/10.1177/1557988319873512.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Arlington, VA: American Psychiatric Association.

    • Search Google Scholar
    • Export Citation
  • Aziz, N. , Nordin, M. J. , Abdulkadir, S. J. , & Salih, M. M. M. (2021). Digital addiction: Systematic review of computer game addiction impact on adolescent physical health. Electronics, 10(9), 996. https://doi.org/10.3390/electronics10090996.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bener, A. , Yildirim, E. , Torun, P. , Çatan, F. , Bolat, E. , Alıç, S. , … Griffiths, M. D. (2019). Internet addiction, fatigue, and sleep problems among adolescent students: A large-scale study. International Journal of Mental Health and Addiction, 17(4), 959969. https://doi.org/10.1007/s11469-018-9937-1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bowditch, L. , Naweed, A. , & Chapman, J. (2019). Escaping into a simulated environment: A preliminary investigation into how MMORPGs are used to cope with real life stressors. Singapore.

    • Search Google Scholar
    • Export Citation
  • Brunborg, G. S. , Mentzoni, R. A. , & Frøyland, L. R. (2014). Is video gaming, or video game addiction, associated with depression, academic achievement, heavy episodic drinking, or conduct problems? Journal of Behavioral Addictions, 3(1), 2732. https://doi.org/10.1556/jba.3.2014.002.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cankurtaran, F. , Menevşe, O. , Namlı, A. , Kızıltoprak, H. , Altay, S. , Duran, M. , … Ekşi, C. (2022). The impact of digital game addiction on musculoskeletal system of secondary school children. Nigerian Journal of Clinical Practice, 25(2), 153159. https://doi.org/10.4103/njcp.njcp_177_20.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chaput, J. P. , Dutil, C. , & Sampasa-Kanyinga, H. (2018). Sleeping hours: What is the ideal number and how does age impact this? Nature and Science of Sleep, 10, 421430. https://doi.org/10.2147/nss.S163071.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cho, D. J. , Kim, H. T. , Lee, J. , & Park, S. H. (2018). Economic cost–benefit analysis of the addictive digital game industry. Applied Economics Letters, 25(9), 638642. https://doi.org/10.1080/13504851.2017.1355528.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gentile, D. (2009). Pathological video-game use among youth ages 8 to 18: A national study. Psychological Science, 20(5), 594602. https://doi.org/10.1111/j.1467-9280.2009.02340.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hawi, N. S. , Samaha, M. , & Griffiths, M. D. (2018). Internet gaming disorder in Lebanon: Relationships with age, sleep habits, and academic achievement. Journal of Behavioral Addictions, 7(1), 7078. https://doi.org/10.1556/2006.7.2018.16.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Henchoz, Y. , Studer, J. , Deline, S. , N'Goran, A. A. , Baggio, S. , & Gmel, G. (2016). Video gaming disorder and sport and exercise in emerging adulthood: A longitudinal study. Behavioral Medicine, 42(2), 105111. https://doi.org/10.1080/08964289.2014.965127.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hirshkowitz, M. , Whiton, K. , Albert, S. M. , Alessi, C. , Bruni, O. , DonCarlos, L. , … Adams Hillard, P. J. (2015). National Sleep Foundation's sleep time duration recommendations: Methodology and results summary. Sleep Health, 1(1), 4043. https://doi.org/10.1016/j.sleh.2014.12.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hygen, B. W. , Skalická, V. , Stenseng, F. , Belsky, J. , Steinsbekk, S. , & Wichstrøm, L. (2020). The co-occurrence between symptoms of internet gaming disorder and psychiatric disorders in childhood and adolescence: Prospective relations or common causes? The Journal of Child Psychology and Psychiatry, 61(8), 890898. https://doi.org/10.1111/jcpp.13289.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jackson, L. A. , Witt, E. A. , Games, A. I. , Fitzgerald, H. E. , von Eye, A. , & Zhao, Y. (2012). Information technology use and creativity: Findings from the children and technology Project. Computers in Human Behavior, 28(2), 370376. https://doi.org/10.1016/j.chb.2011.10.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jeong, H. , Yim, H. W. , Jo, S. J. , Lee, S. Y. , Lee, H. K. , Gentile, D. A. , … Choi, J. S. (2020). Gaming patterns and related symptoms in adolescents using cluster analysis: Baseline results from the internet user cohort for unbiased recognition of gaming disorder in early adolescence (iCURE) study. Environmental Research, 182, 109105. https://doi.org/10.1016/j.envres.2019.109105.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jo, S. J. , Yim, H. W. , Lee, H. K. , Lee, H. C. , Choi, J. S. , & Baek, K. Y. (2018). The internet game use-elicited symptom screen proved to be a valid tool for adolescents aged 10–19 years. Acta Paediatrica, 107(3), 511516. https://doi.org/10.1111/apa.14087.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, N. R. , Hwang, S. S. , Choi, J. S. , Kim, D. J. , Demetrovics, Z. , Király, O. , … Choi, S. W. (2016). Characteristics and psychiatric symptoms of Internet Gaming Disorder among adults using self-reported DSM-5 criteria. Psychiatry Investigation, 13(1), 5866. https://doi.org/10.4306/pi.2016.13.1.58.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kim, D. S. , Park, J. K. , & Kim, K. S. (2008). Development of risk assessment criteria guideline (model for each risk level) for musculoskeletal burden work hazard factor investigation. Occupational Health, 243, 6869.

    • Search Google Scholar
    • Export Citation
  • Király, O. , Griffiths, M. D. , & Demetrovics, Z. (2015). Internet gaming disorder and the DSM-5: Conceptualization, debates, and controversies. Current Addiction Reports, 2(3), 254262. https://doi.org/10.1007/s40429-015-0066-7.

    • Crossref
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The author instruction is available in PDF.
Please, download the file from HERE

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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  • EBSCO
  • GoogleScholar
  • PsycINFO
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  • CABI

2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

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
submission  
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

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
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

  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Luke CLARK (University of British Columbia, Canada)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • 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)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • 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)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • 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 (Humboldt-Universität zu Berlin, Germany)
  • 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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