Associations Between Computer Game Addictionand Attention Deficit And Hyperactivity Disorder - An Emprical Study
Abstract
The aim of this study is to compare the video game addiction levels and habit of playing computer games
between children with attention deficit hyperactivity disorder (ADHD) and healthy children. The study
group consisted of 100 children aged between 10 and 13 years who were diagnosed with ADHD and who
applied to the psychiatry clinic in the province of Istanbul. The control group consisted of 100 healthy
children between the ages of 10 and 13 years with no psychiatric diagnoses and were matched with the
study group in terms of sociodemographic characteristics. The Personal Information Questionnaire and
the Computer Game Addiction Scale for Children (CGASC) were applied to the participants. Children
with ADHD had significantly higher levels of video game addiction than healthy children. Video game
addiction levels were found to be higher in children who play action, shooter, and racing games than those
who did not play them. Furthermore, online gaming has been found to have a significant effect on the level
of video game addiction. Keywords: Attention deficit hyperactivity disorder, video game addiction, game genre
Associations between Computer Game Addiction
and Attention Deficit and Hyperactivity Disorder -
An Emprical Study
ORCID iDs of the authors: Ş.S.B. 0000-0002-5205-0275; B.B. 0000-0002-3056-4769; M.G.L. 0000-0002-0430-9289.
122 DOI: 10.5152/ADDICTA.2020.20142
Cite this article as: Başgül, Ş.S., Bekar, B., & Luş, M.G. (2020). Associations between computer game addiction and attention deficit and hyperactivity
disorder - An emprical study. Addicta: The Turkish Journal on Addictions, 7(2), 122-128.
Şaziye Senem Başgül1 , Bengi Bekar1 , Melek Gözde Luş2
1Department of Psychology, Hasan Kalyoncu University School of Economics, Administrative and Social Sciences, Gaziantep,
Turkey
2Department of Child and Adolescent Psychiatry, University of Health Sciences, Haydarpaşa Numune Training and Research
Hospital, İstanbul, Turkey
Corresponding author:
Melek Gözde Luş E-mail:
gozdelus@yahoo.com
Received: 7.02.2020 Revision: 4.05.2020 Accepted: 9.05.2020
Introduction
Internet gaming disorder (IGD) is a result of excessive
and prolonged internet gaming with negative
consequences such as impaired real-life relationships
or academic performance (Kuss, 2013).
The Diagnostic and Statistical Manual of Mental
Disorders, Fifth Edition (DSM-5) proposed the diagnostic
criteria of IGD to define addiction to internet
gaming. It is classified under the conditions for
further study of Section III, and it is suggested that
more evidence is necessary before it is included as a
standard disorder in the DSM system (APA, 2013).
Playing a computer game helps a person to cope
with negative emotions such as frustration, stress,
and fear (Weinstein & Weizman, 2012). It is also
one of the preferred activities of leisure. Playing
computer games improves visual-spatial skills and
attention. Educational games increase the interest
of the students in the lessons and are known to improve
the ability to grasp a concept and the rate of
ORIGINAL RESEARCH
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retention (Granic et al., 2014). However, some researchers have reported issues due to computer games. The inability to control the desire to play the game leads to unsuccessful attempts to control the behavior, and changes in emotions, thoughts and social life result; the problem or the dependence of the problem is diagnosed (Griffiths & Davies, 2005; Young, 2009).
Impulsivity, defined by the neurobiological basis of computer addiction, and acting without considering the consequences of actions, similarly forms the basis of gambling and substance addictions (Choi et al., 2014). At the same time, gambling, playing a computer game, and using certain substances are associated with the dopaminergic system in the brain and stimulates the brain’s reward mechanism (Blum et al., 2000). Based on these similarities, risk factors for gambling and drug addiction could pose a risk for computer gaming addiction. According to previous studies, prognosis for IGD is associated with the presence of underlying disorders such as attention-deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD) (Forrest et al., 2017; Han et al., 2012). Baseline depression and ADHD symptoms are also inversely related to long-term IGD recovery (Han et al., 2018). As suggested in the literature, these comorbid conditions may be predisposing factors for the development and maintenance of IGD (Brand et al., 2016). In the light of increasing evidence, it can be said that there is a strong relationship between ADHD and IGD (Kim et al., 2017; Yen et al., 2017). Nevertheless, it needs to be emphasized that IGD is conceptually different from internet addiction. The criteria for IGD proposed in the DSM-5 classification include nine items: preoccupation, withdrawal, tolerance, unsuccessful attempts to control, loss of interest, continued excessive use despite psychosocial problems, deceit, escape, and functional impairment (APA, 2013). Several scales have been developed in order to evaluate computer game addiction such as the short form of Young’s Internet Addiction Test (YIAT-SF) (Young, 2004) and the Digital Game Addiction Scale (DGAS-7) (Lemmens et al., 2009). Given that ADHD is a risk factor for IGD, it is important to determine and deal with computer game addiction levels of children diagnosed with ADHD.
In our study, we investigated whether there is a significant difference between the levels of computer game addiction in children with and without the diagnosis of ADHD. We hypothesized that children with ADHD have significantly higher levels of video game addiction than healthy children.
Methods
Participants and Procedures
The sample for the study group consisted of 100 children aged between 10 and 13 years who were admitted to the child and adolescent psychiatry clinic in İstanbul and were diagnosed with ADHD. The control group of the study consisted of 100 secondary school students aged between 10 and 13 years who did not have any psychiatric diagnosis and who were matched with the study group in terms of age, gender, and socioeconomic characteristics. The children selected for the study group and the control group were chosen using the purposive sampling technique. The study was carried out with children who were examined at five different child psychiatry clinics in İstanbul.
Procedures for Data Collection
The Personal Information Questionnaire and Computer Game Addiction Scale for Children (CGASC) were applied to the participants. For the control group data, permits were obtained from the principals of two secondary schools. Children and their families were informed that they had the right to withdraw from the study if they wanted to do so. The scale was applied to a total of 247 people, but the data of 29 participants were not included in the evaluation due to incomplete information on the form, and 18 participants were excluded from the study because they had excessive extremes and disrupted normal distribution. In total, the data of 200 participants were evaluated.
Instruments
Personal Information Questionnaire: The Personal Information Form prepared by the researchers is an interview form which includes the gender, age, the place where the child plays computer games, how many years the child has played computer games, and the type of games that they play.
CGASC: In order to measure the levels of computer game addiction of the children participating in the study, the CGASC developed by Horzum, Ayas and Balta (2008) was used. This 5-point Likert-type scale consists of 21 items and has a four-factor structure such as “failure to quit playing a computer game,” associating the game with life,” “the disrupting of tasks due to game playing,” and preferring game playing to other activities.” Each item on the scale scores between 1 and 5, the lowest possible score is 21, and the highest possible score is 105. The increase in the total score indicates that the child’s level of computer addiction is high. Exploratory factor analysis was used to examine the construct validity of the scale. In order to measure the reliability of the scale, Cronbach’s internal consistency coefficient was used. Accordingly, the internal consistency coefficient of the sub-factor of the failure to quit playing a computer game was 0.83, the internal consistency coefficient of the sub-factor of the association of computer game with real life was 0.60, the internal consistency coefficient of the sub-factor of the disruption to tasks due to game playing was 0.50, and the internal consistency coefficient of the sub-factor of preferring game playing to other activities was found to be 0.50.
Statistical Analysis
Data were analyzed using the the Statistical Package for Social Sciences version 25.0 (IBM SPSS Corp.; Armonk, NY, USA) program. The significance level was selected as p<0.05 in hypothesis testing. As the data obtained were normal distribution, parametric analysis methods were used. According to the parametric test assumptions, independent samples t-test analysis was used for comparison of the binary groups from continuous data, and categorical variables were compared with Chi-squared test. Bi-directional or multivariate analysis of variance was used to examine the effect of two or more independent variables on multiple dependent variables.
Results
The frequency and percentage distributions of the sociodemographic information of 200 participants that are the sample of the study are given in Table 1. Eighteen (18%) female and 82 (82%) male children diagnosed with ADHD were included in the study, and the mean age was 11.53±1.03 years. The control group included 40 (40%) girls and 60 (60%) boys who were not
123
The Turkish Journal on Addictions, 7, 122-128
diagnosed with ADHD, and the mean age was 11.83±0.98 years. Twenty eight (28%) of the children with ADHD had been playing computer games for less than one year, 21 (21%) had been playing computer games for 1-2 years, and 51 (51%) had been playing computer games for more than 2 years. While families of 67 children set a time limit for computer games, 33 children had no time limit. At the same time, 26 children were playing less than 1 hour per day, 37 children were playing 1-2 hours per day, 16 children were playing 2-3 hours per day, and 21 children (21%) played more than 3 hours per day. In addition, 70 children were playing online games, and 30 children were not (Table 1).
Table 2 shows the results of the analysis of the CGASC and the subscale mean scores of children with and without ADHD. According to the results of independent samples t-test analysis, the total CGASC mean scores of children with and without ADHD was found to be statistically significantly different (t (198)=5.652, p<0.001, d=0.799). The mean scores of the subscales of the inability to stop playing computer games, associating the game with life, and the disrupting tasks due to the game were also found to be significantly different between children with and without ADHD (Table 2).
As the result of a multivariate analysis of variance, as shown in Table 3, according to the age of the children (ƛ=0.99, F=0.161, p>0.05, Ƞ²=0.003) and group x age interaction (ƛ=0.95, F=0.901, p>0.05, Ƞ²=0.019), the mean score of CGASC and their subscales do not differ significantly. Table 4 shows that, as a result of a multivariate analysis of variance, the CGASC and subscale scores differ statistically according to the online game-playing variable (ƛ=0.93, F=3.749, p<0.01, Ƞ²=0.072) while the scale mean scores do not have a statistically significant difference according to the group x online game play interaction (p>0.05). In the advanced analysis to determine the dependent variables upon which online game playing is effective, there was a significant difference between children playing online and children not playing online in terms of the inability to stop playing the computer game, associating the game with life, disrupting tasks due to the game subscales mean scores, and the total CGASC mean scores; this difference was in favor of children playing online games.
Independent samples t-test analysis was performed in order to test whether the mean scores of the total CGASC and the subscales differ significantly according to the type of games played by children with ADHD. The results of the analysis are given in Table 5. Children with ADHD who prefer action-type games have significantly higher scores on the inability to stop playing the computer game and associating the game with life subscales of the CGASC than those of children who did not play action-type games, and their total score of CGASC is also higher than for children who did not prefer action type games (Table 5).
Children with ADHD who preferred shooter type games had significantly higher mean scores on the subscales of inability to stop playing the computer game, associating the game with life, and preferring game playing to other activities, according to the results of the independent samples t-test analysis (Table 5). Total score of the CGASC was also higher than for children who did not prefer shooter type games (Table 5). In addition to these results, although children with ADHD who prefer simulation type games had significantly higher mean scores on the subscale of associating the game with life, children who did not play training type games had higher mean scores on the subscale of the disruption to tasks due to game playing (Table 5). The children with ADHD who preferred racing type games also had significantly higher mean scores on the subscales of inability to stop playing the computer game, disruption of tasks due to game playing, and preferring game playing to other activities and the total score of the CGASC (Table 5).
124
Table 1.
Sociodemographic Characteristics of the Sample
Children with ADHD
Children without ADHD
M
SD
M
SD
Age
11.53
1.03
11.83
0.98
N
%
N
%
Gender
Girls
18
18
40
40
Boys
82
82
60
60
Age
10
19
19
8
8
11
30
30
33
33
12
30
30
27
27
13
21
21
32
32
Grade
5.grade
37
37
27
27
6.grade
33
33
41
41
7.grade
30
30
32
32
Period of playing time
Less than 1 year
28
28
25
25
1-2 years
21
21
25
25
More than 2 years
51
51
50
50
Family's limiting of time
Yes
67
67
82
82
No
33
33
18
18
Frequency of game playing
1 time
76
76
82
82
2 times
13
13
13
13
3 times or more
11
11
5
5
Daily total playing time
Less than 1 hour
26
26
29
29
1-2 hours
37
37
49
49
2-3 hours
16
16
12
12
More than 3 hours
21
21
10
10
Online game playing
Yes
70
70
74
74
No
30
30
26
26
Using credit card for game
Yes
22
22
17
17
No
78
78
83
83
M: mean; SD: standard deviation
Başgül et al. Game Addiction, Attention Deficit and Hyperactivity Disorder
Discussion
Familiarity with computer games provides an opportunity to identify its negative effects and risk factors. Determining the susceptibility to addiction is necessary in order to take precautions and prescribe treatment. In this study, we found that children with ADHD had higher levels of computer game addiction than healthy children. In the literature, it has been reported that the scores of children diagnosed with ADHD on the problematic video game play scale are significantly higher than those of the children who had not been diagnosed (Bae et al., 2016; Bioulac et al., 2007). Hyun et al. (2015) found that ADHD symptoms were more common in computer game addicts compared to healthy controls (Hyun et al., 2015). In another study, when the clinical sample and healthy sample of problematic computer game players were compared, it was found that the rate of ADHD was 42.3% in the clinical sample and 21.3% in the healthy sample, and the difference was reported to be significant (Vadlin et al., 2016). As the results of our study and the studies in the literature, it can be stated that since an ADHD diagnosis provides a predisposition to addiction, it may also be a predisposition to gaming addiction. Follow-up studies with children with ADHD indicate that smoking, alcohol dependence, and behavioral addictions are more likely to be observed in these children (Kieling & Rohde, 2011; Madsen, 2014).
Studies showed that adolescents with IGD have higher scores on the Barratt Impulsivity Scale (Ding et al. 2014) and young adults with IGD had similar results regarding self-reported impulsivity (Ko et al., 2015). Based on these findings, it can be argued that the association between impulsivity and IGD could make individuals yield to the rewarding effects of gaming and contribute to a vulnerability to IGD (Yen et al., 2016). It is possible that the need for reward in a child with ADHD who gradually loses his/her confidence due to constant difficulties in school and at home is resolved in this way. In this study, it was concluded that the mean scores of children from the CGASC total and all other sub-dimensions did not show a significant difference according to the age variable.
This study is partially compatible with Horzum’s study (2011) that compared the grade level and computer gaming addiction of third, fourth, and fifth grade students (Horzum, 2011). In addition, in our study, the CGASC total and all subscale scores of online game players were found to be significantly higher than of those who played offline. Müller et al. (2015) stated that, in a study of 12,938 adolescents aged 14-17 years from European countries, among the types of games that were a risk factor for computer game addiction, online games were the strongest predictor (Müller et al., 2015). Similarly, another study that examined the effect of adult gaming preferences on computer gaming addiction has shown that online game players are more likely to meet the criteria for computer gaming addiction compared to offline game players (Na et al., 2017).
In the literature, there are no studies investigating the computer game preferences of children with ADHD. In our study, when we compared the types of computer games that children with ADHD and without ADHD were playing, we found that there was no significant difference between the levels of playing games such as ac125
Table 2.
Independent Samples t-test Analysis of CGASC and Subscale Mean Scores of Children with and without ADHD
Children with ADHD
Children without ADHD
M
SD
M
SD
t
p
Cohen's d
Inability to stop playing computer games
28.65
9.19
20.94
7.37
6.546
0.000
0.926
Associating the game with life
8.59
3.73
7.05
2.98
3.225
0.001
0.456
Disruption of tasks due to game playing
6.00
2.56
4.60
1.72
4.539
0.000
0.642
Preferring game playing to other activities
8.75
3.76
7.84
3.57
1.755
0.081
0.248
Total
51.99
15.85
40.43
12.93
5.652
0.000
0.799
M: mean; SD: standard deviation
Table 3.
Results of a Multivariate Analysis of Variance According to Age of the Children and Group x Age Interaction
Effect
Wilks' Lambda Value
F
df
Error df
p
Ƞ²
Age
0.99
0.161
12
500
0.999
0.003
Group x age
0.95
0.901
12
500
0.546
0.019
Table 4.
Results of a Multivariate Analysis of Variance According to the Age of the Children and Group x Age Interaction
Effect
Wilks' Lambda Value
F
df
Error df
p
Ƞ²
Playing online games
0.93
3.749
4
193
0.006
0.072
Group x playing online games
0.97
1.735
4
193
0.144
0.035
The Turkish Journal on Addictions, 7, 122-128
tion, adventure, strategy, gunner, simulation, sports, racing, puzzle, and role-playing. In addition, we observed that children without ADHD played more educational games than those with ADHD.
Considering the types of games, those that are fast-moving, similar to real life, competitive, and aggressive are attractive not only for the children with ADHD who lack rewards in other areas of life but also for healthy children. It was observed that children who play action games have significantly higher mean scores on the subscales of “inability to stop playing the computer game” and “associating the game with life” subscales and total CGASC mean scores. This result is similar to some studies in the literature; the findings of a study conducted with young adults show that action type games are among the most frequently played games by problematic computer game players (Elliott et al., 2012). The reasons that action-type games are preferred by children with ADHD who need stimulus and reward include the fact that they are rich in audio-visual stimulation, the action provides constant change, and a prize is won as a result of struggle. Children with ADHD who prefer shooter-type games have significantly higher mean scores on the subscales of inability to stop playing the computer game, associating the game with life, and preferring game to other activities. The total mean score of CGASC is also higher than for children with ADHD who do not prefer shooter type games. Shooter games are computer games where participants aim to kill or injure each other. Similar studies can be found in the literature. In a study conducted by Ream, Elliott, & Dunlap (2014), shooters games were mentioned as one of the most preferred game played by problematic computer game
126
Table 5.
Independent Samples t-test Analysis of the Mean Scores of Total CGASC and Subscales According to the Type of Games Played by Children with ADHD
Yes
No
M
SD
M
SD
t
p
Cohen's d
Action
Inability to stop playing computer games
31.38
8.60
24.88
8.72
3.710
0.000
-0.751
Associating the game with life
9.53
3.91
7.29
3.05
3.104
0.002
-0.641
Disruption of tasks due to game playing
6.41
2.72
5.43
2.22
1.927
0.057
-0.397
Preferring game playing to other activities
9.31
3.97
7.98
3.35
1.769
0.080
-0.363
Total
56.64
15.47
45.57
14.18
3.656
0.000
-0.746
Adventure
Inability to stop playing computer games
29.40
8.98
27.81
9.44
0.861
0.391
-0.172
Associating the game with life
9.15
3.86
7.96
3.51
1.610
0.111
-0.324
Disruption of tasks due to game playing
5.96
2.58
6.04
2.56
0.156
0.876
0.031
Preferring game playing to other activities
8.89
3.88
8.60
3.66
0.385
0.701
-0.077
Total
53.40
15.41
50.40
16.34
0.349
0.349
-0.188
Strategy
Inability to stop playing computer games
28.47
9.16
28.80
9.29
0.180
0.858
0.036
Associating the game with life
8.87
3.97
8.36
3.53
0.669
0.505
-0.134
Disruption of tasks due to game playing
5.73
2.67
6.22
2.47
0.942
0.348
0.189
Preferring game playing to other activities
8.36
3.16
9.07
4.19
0.975
0.332
0.193
Total
51.42
15.56
52.45
16.21
0.323
0.748
0.065
Shooter
Inability to stop playing computer games
31.46
8.82
27.00
9.06
2.399
0.018
-0.499
Associating the game with life
9.97
3.93
7.78
3.38
2.951
0.004
-0.599
Disruption of tasks due to game playing
6.57
2.95
5.67
2.26
1.717
0.089
-0.343
Preferring game playing to other activities
9.92
3.64
8.06
3.69
2.440
0.016
-0.506
Total
57.92
15.42
48.51
15.15
2.979
0.004
-0.616
Simulation
Inability to stop playing computer games
30.28
9.49
27.73
8.95
1.334
0.185
-0.276
Associating the game with life
9.92
3.97
7.84
3.39
2.756
0.007
-0.561
Disruption of tasks due to game playing
6.08
2.87
5.95
2.39
0.243
0.808
-0.049
Preferring game playing to other activities
9.56
3.78
8.30
3.70
1.619
0.109
-0.336
Total
55.83
16.63
49.83
15.09
1.841
0.069
-0.378
Racing
Inability to stop playing computer games
32.07
8.38
26.17
9.01
3.327
0.001
-0.678
Associating the game with life
9.17
4.11
8.17
3.40
1.321
0.190
-0.264
Disruption of tasks due to game playing
6.67
2.75
5.52
2.32
2.263
0.026
-0.452
Preferring game playing to other activities
9.93
3.83
7.90
3.50
2.753
0.007
-0.554
Total
57.83
15.05
47.76
15.16
3.290
0.001
-0.667
M: mean; SD: standard deviation
Başgül et al. Game Addiction, Attention Deficit and Hyperactivity Disorder
player adults between the ages of 18 and 29 (Ream et al., 2014). Another study with adolescents shows that one of the most powerful game types predicting computer game addiction are shooter games (Müller et al. 2015). In addition to these results, children with ADHD who prefer simulation type games have significantly higher mean scores on the subscale of associating the game with life. The content of these simulation games enables children to use life-like elements in the game or experience similar things in the game. The fact that children play this game as one person, that the game does not contain much movement and change, and the game progresses uniformly can be shown to be non-addictive features of the game. As we have seen in our study, the children with ADHD who prefer racing type games have also significantly higher mean scores on the subscales of the inability to stop playing the computer game, experience disruption of tasks due to game playing, and prefer game playing to other activities and the total score of CGASC. Racing games played with a computer or person are based on competition and are ranked by success. It is thought that being first is the biggest motivation in this game as it is in the category of games containing other competitive elements, so it is difficult for children to give up the game.
Another observation was that children who did not play training type games had higher mean scores on the subscale of disruption of tasks due to the game play. Educational games are based on thinking, strategy development, and problem solving that aim to teach the player different concepts. When the literature related to computer gaming addiction was examined, there were no studies examining the relationship of education with games. Because educational games are intended for a specific teaching purpose, they may seem boring to young children and could serve as reminders of school assignments.
Limitations and Suggestions for Future Research
A limitation of the study is that it was conducted only in Istanbul and with a narrow age group. It is believed that studies carried out in different provinces with larger sample sizes and wider age groups with parental participation will shed further light on this area.
In this study, it was found that children with ADHD had higher levels of computer game addiction than healthy children. ADHD is the most common neurodevelopmental disorder, and there are a limited number of studies conducted in our country on how ADHD affects computer gaming addiction. Our study is important in terms of showing that an ADHD diagnosis and some playing habits are risk factors for computer game addiction.
Ethics Committee Approval: Ethics committee approval was received for this study from the Ethics Committee of Hasan Kalyoncu University (Approval no: 2017/34 - Date: 27.10.2017).
Informed Consent: Written informed consent was obtained from the parents’ of the participants.
Peer-review: Externally peer-reviewed.
Author Contributions: Concept - S.Ş.B, B.B.; Design - S.Ş.B., B.B., M.G.L.; Supervision - S.Ş.B.; Resources - B.B, M.G.L.; Materials - B.B., M.G.L.; Data Collection and/or Processing - S.Ş.B, B.B.; Analysis and/or Interpretation - S.Ş.B, B.B.; Literature Search - M.G.L, B.B.; Writing Manuscript - M.G.L., B.B.; Critical Review - S.Ş.B.
Conflict of Interest: The authors have no conflicts of interest to declare.
Financial Disclosure: The authors declared that this study has received no financial support.
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The aim of this study is to compare the video game addiction levels and habit of playing computer games
between children with attention deficit hyperactivity disorder (ADHD) and healthy children. The study
group consisted of 100 children aged between 10 and 13 years who were diagnosed with ADHD and who
applied to the psychiatry clinic in the province of Istanbul. The control group consisted of 100 healthy
children between the ages of 10 and 13 years with no psychiatric diagnoses and were matched with the
study group in terms of sociodemographic characteristics. The Personal Information Questionnaire and
the Computer Game Addiction Scale for Children (CGASC) were applied to the participants. Children
with ADHD had significantly higher levels of video game addiction than healthy children. Video game
addiction levels were found to be higher in children who play action, shooter, and racing games than those
who did not play them. Furthermore, online gaming has been found to have a significant effect on the level
of video game addiction. Keywords: Attention deficit hyperactivity disorder, video game addiction, game genre
Associations between Computer Game Addiction
and Attention Deficit and Hyperactivity Disorder -
An Emprical Study
ORCID iDs of the authors: Ş.S.B. 0000-0002-5205-0275; B.B. 0000-0002-3056-4769; M.G.L. 0000-0002-0430-9289.
122 DOI: 10.5152/ADDICTA.2020.20142
Cite this article as: Başgül, Ş.S., Bekar, B., & Luş, M.G. (2020). Associations between computer game addiction and attention deficit and hyperactivity
disorder - An emprical study. Addicta: The Turkish Journal on Addictions, 7(2), 122-128.
Şaziye Senem Başgül1 , Bengi Bekar1 , Melek Gözde Luş2
1Department of Psychology, Hasan Kalyoncu University School of Economics, Administrative and Social Sciences, Gaziantep,
Turkey
2Department of Child and Adolescent Psychiatry, University of Health Sciences, Haydarpaşa Numune Training and Research
Hospital, İstanbul, Turkey
Corresponding author:
Melek Gözde Luş E-mail:
gozdelus@yahoo.com
Received: 7.02.2020 Revision: 4.05.2020 Accepted: 9.05.2020
Introduction
Internet gaming disorder (IGD) is a result of excessive
and prolonged internet gaming with negative
consequences such as impaired real-life relationships
or academic performance (Kuss, 2013).
The Diagnostic and Statistical Manual of Mental
Disorders, Fifth Edition (DSM-5) proposed the diagnostic
criteria of IGD to define addiction to internet
gaming. It is classified under the conditions for
further study of Section III, and it is suggested that
more evidence is necessary before it is included as a
standard disorder in the DSM system (APA, 2013).
Playing a computer game helps a person to cope
with negative emotions such as frustration, stress,
and fear (Weinstein & Weizman, 2012). It is also
one of the preferred activities of leisure. Playing
computer games improves visual-spatial skills and
attention. Educational games increase the interest
of the students in the lessons and are known to improve
the ability to grasp a concept and the rate of
ORIGINAL RESEARCH
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T H E T U R K I S H J O U R N A L O N A D D I C T I O N S
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Yeşilay Cemiyeti (Turkish
Green Crescent Society) -
Available online at www.
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retention (Granic et al., 2014). However, some researchers have reported issues due to computer games. The inability to control the desire to play the game leads to unsuccessful attempts to control the behavior, and changes in emotions, thoughts and social life result; the problem or the dependence of the problem is diagnosed (Griffiths & Davies, 2005; Young, 2009).
Impulsivity, defined by the neurobiological basis of computer addiction, and acting without considering the consequences of actions, similarly forms the basis of gambling and substance addictions (Choi et al., 2014). At the same time, gambling, playing a computer game, and using certain substances are associated with the dopaminergic system in the brain and stimulates the brain’s reward mechanism (Blum et al., 2000). Based on these similarities, risk factors for gambling and drug addiction could pose a risk for computer gaming addiction. According to previous studies, prognosis for IGD is associated with the presence of underlying disorders such as attention-deficit hyperactivity disorder (ADHD) and major depressive disorder (MDD) (Forrest et al., 2017; Han et al., 2012). Baseline depression and ADHD symptoms are also inversely related to long-term IGD recovery (Han et al., 2018). As suggested in the literature, these comorbid conditions may be predisposing factors for the development and maintenance of IGD (Brand et al., 2016). In the light of increasing evidence, it can be said that there is a strong relationship between ADHD and IGD (Kim et al., 2017; Yen et al., 2017). Nevertheless, it needs to be emphasized that IGD is conceptually different from internet addiction. The criteria for IGD proposed in the DSM-5 classification include nine items: preoccupation, withdrawal, tolerance, unsuccessful attempts to control, loss of interest, continued excessive use despite psychosocial problems, deceit, escape, and functional impairment (APA, 2013). Several scales have been developed in order to evaluate computer game addiction such as the short form of Young’s Internet Addiction Test (YIAT-SF) (Young, 2004) and the Digital Game Addiction Scale (DGAS-7) (Lemmens et al., 2009). Given that ADHD is a risk factor for IGD, it is important to determine and deal with computer game addiction levels of children diagnosed with ADHD.
In our study, we investigated whether there is a significant difference between the levels of computer game addiction in children with and without the diagnosis of ADHD. We hypothesized that children with ADHD have significantly higher levels of video game addiction than healthy children.
Methods
Participants and Procedures
The sample for the study group consisted of 100 children aged between 10 and 13 years who were admitted to the child and adolescent psychiatry clinic in İstanbul and were diagnosed with ADHD. The control group of the study consisted of 100 secondary school students aged between 10 and 13 years who did not have any psychiatric diagnosis and who were matched with the study group in terms of age, gender, and socioeconomic characteristics. The children selected for the study group and the control group were chosen using the purposive sampling technique. The study was carried out with children who were examined at five different child psychiatry clinics in İstanbul.
Procedures for Data Collection
The Personal Information Questionnaire and Computer Game Addiction Scale for Children (CGASC) were applied to the participants. For the control group data, permits were obtained from the principals of two secondary schools. Children and their families were informed that they had the right to withdraw from the study if they wanted to do so. The scale was applied to a total of 247 people, but the data of 29 participants were not included in the evaluation due to incomplete information on the form, and 18 participants were excluded from the study because they had excessive extremes and disrupted normal distribution. In total, the data of 200 participants were evaluated.
Instruments
Personal Information Questionnaire: The Personal Information Form prepared by the researchers is an interview form which includes the gender, age, the place where the child plays computer games, how many years the child has played computer games, and the type of games that they play.
CGASC: In order to measure the levels of computer game addiction of the children participating in the study, the CGASC developed by Horzum, Ayas and Balta (2008) was used. This 5-point Likert-type scale consists of 21 items and has a four-factor structure such as “failure to quit playing a computer game,” associating the game with life,” “the disrupting of tasks due to game playing,” and preferring game playing to other activities.” Each item on the scale scores between 1 and 5, the lowest possible score is 21, and the highest possible score is 105. The increase in the total score indicates that the child’s level of computer addiction is high. Exploratory factor analysis was used to examine the construct validity of the scale. In order to measure the reliability of the scale, Cronbach’s internal consistency coefficient was used. Accordingly, the internal consistency coefficient of the sub-factor of the failure to quit playing a computer game was 0.83, the internal consistency coefficient of the sub-factor of the association of computer game with real life was 0.60, the internal consistency coefficient of the sub-factor of the disruption to tasks due to game playing was 0.50, and the internal consistency coefficient of the sub-factor of preferring game playing to other activities was found to be 0.50.
Statistical Analysis
Data were analyzed using the the Statistical Package for Social Sciences version 25.0 (IBM SPSS Corp.; Armonk, NY, USA) program. The significance level was selected as p<0.05 in hypothesis testing. As the data obtained were normal distribution, parametric analysis methods were used. According to the parametric test assumptions, independent samples t-test analysis was used for comparison of the binary groups from continuous data, and categorical variables were compared with Chi-squared test. Bi-directional or multivariate analysis of variance was used to examine the effect of two or more independent variables on multiple dependent variables.
Results
The frequency and percentage distributions of the sociodemographic information of 200 participants that are the sample of the study are given in Table 1. Eighteen (18%) female and 82 (82%) male children diagnosed with ADHD were included in the study, and the mean age was 11.53±1.03 years. The control group included 40 (40%) girls and 60 (60%) boys who were not
123
The Turkish Journal on Addictions, 7, 122-128
diagnosed with ADHD, and the mean age was 11.83±0.98 years. Twenty eight (28%) of the children with ADHD had been playing computer games for less than one year, 21 (21%) had been playing computer games for 1-2 years, and 51 (51%) had been playing computer games for more than 2 years. While families of 67 children set a time limit for computer games, 33 children had no time limit. At the same time, 26 children were playing less than 1 hour per day, 37 children were playing 1-2 hours per day, 16 children were playing 2-3 hours per day, and 21 children (21%) played more than 3 hours per day. In addition, 70 children were playing online games, and 30 children were not (Table 1).
Table 2 shows the results of the analysis of the CGASC and the subscale mean scores of children with and without ADHD. According to the results of independent samples t-test analysis, the total CGASC mean scores of children with and without ADHD was found to be statistically significantly different (t (198)=5.652, p<0.001, d=0.799). The mean scores of the subscales of the inability to stop playing computer games, associating the game with life, and the disrupting tasks due to the game were also found to be significantly different between children with and without ADHD (Table 2).
As the result of a multivariate analysis of variance, as shown in Table 3, according to the age of the children (ƛ=0.99, F=0.161, p>0.05, Ƞ²=0.003) and group x age interaction (ƛ=0.95, F=0.901, p>0.05, Ƞ²=0.019), the mean score of CGASC and their subscales do not differ significantly. Table 4 shows that, as a result of a multivariate analysis of variance, the CGASC and subscale scores differ statistically according to the online game-playing variable (ƛ=0.93, F=3.749, p<0.01, Ƞ²=0.072) while the scale mean scores do not have a statistically significant difference according to the group x online game play interaction (p>0.05). In the advanced analysis to determine the dependent variables upon which online game playing is effective, there was a significant difference between children playing online and children not playing online in terms of the inability to stop playing the computer game, associating the game with life, disrupting tasks due to the game subscales mean scores, and the total CGASC mean scores; this difference was in favor of children playing online games.
Independent samples t-test analysis was performed in order to test whether the mean scores of the total CGASC and the subscales differ significantly according to the type of games played by children with ADHD. The results of the analysis are given in Table 5. Children with ADHD who prefer action-type games have significantly higher scores on the inability to stop playing the computer game and associating the game with life subscales of the CGASC than those of children who did not play action-type games, and their total score of CGASC is also higher than for children who did not prefer action type games (Table 5).
Children with ADHD who preferred shooter type games had significantly higher mean scores on the subscales of inability to stop playing the computer game, associating the game with life, and preferring game playing to other activities, according to the results of the independent samples t-test analysis (Table 5). Total score of the CGASC was also higher than for children who did not prefer shooter type games (Table 5). In addition to these results, although children with ADHD who prefer simulation type games had significantly higher mean scores on the subscale of associating the game with life, children who did not play training type games had higher mean scores on the subscale of the disruption to tasks due to game playing (Table 5). The children with ADHD who preferred racing type games also had significantly higher mean scores on the subscales of inability to stop playing the computer game, disruption of tasks due to game playing, and preferring game playing to other activities and the total score of the CGASC (Table 5).
124
Table 1.
Sociodemographic Characteristics of the Sample
Children with ADHD
Children without ADHD
M
SD
M
SD
Age
11.53
1.03
11.83
0.98
N
%
N
%
Gender
Girls
18
18
40
40
Boys
82
82
60
60
Age
10
19
19
8
8
11
30
30
33
33
12
30
30
27
27
13
21
21
32
32
Grade
5.grade
37
37
27
27
6.grade
33
33
41
41
7.grade
30
30
32
32
Period of playing time
Less than 1 year
28
28
25
25
1-2 years
21
21
25
25
More than 2 years
51
51
50
50
Family's limiting of time
Yes
67
67
82
82
No
33
33
18
18
Frequency of game playing
1 time
76
76
82
82
2 times
13
13
13
13
3 times or more
11
11
5
5
Daily total playing time
Less than 1 hour
26
26
29
29
1-2 hours
37
37
49
49
2-3 hours
16
16
12
12
More than 3 hours
21
21
10
10
Online game playing
Yes
70
70
74
74
No
30
30
26
26
Using credit card for game
Yes
22
22
17
17
No
78
78
83
83
M: mean; SD: standard deviation
Başgül et al. Game Addiction, Attention Deficit and Hyperactivity Disorder
Discussion
Familiarity with computer games provides an opportunity to identify its negative effects and risk factors. Determining the susceptibility to addiction is necessary in order to take precautions and prescribe treatment. In this study, we found that children with ADHD had higher levels of computer game addiction than healthy children. In the literature, it has been reported that the scores of children diagnosed with ADHD on the problematic video game play scale are significantly higher than those of the children who had not been diagnosed (Bae et al., 2016; Bioulac et al., 2007). Hyun et al. (2015) found that ADHD symptoms were more common in computer game addicts compared to healthy controls (Hyun et al., 2015). In another study, when the clinical sample and healthy sample of problematic computer game players were compared, it was found that the rate of ADHD was 42.3% in the clinical sample and 21.3% in the healthy sample, and the difference was reported to be significant (Vadlin et al., 2016). As the results of our study and the studies in the literature, it can be stated that since an ADHD diagnosis provides a predisposition to addiction, it may also be a predisposition to gaming addiction. Follow-up studies with children with ADHD indicate that smoking, alcohol dependence, and behavioral addictions are more likely to be observed in these children (Kieling & Rohde, 2011; Madsen, 2014).
Studies showed that adolescents with IGD have higher scores on the Barratt Impulsivity Scale (Ding et al. 2014) and young adults with IGD had similar results regarding self-reported impulsivity (Ko et al., 2015). Based on these findings, it can be argued that the association between impulsivity and IGD could make individuals yield to the rewarding effects of gaming and contribute to a vulnerability to IGD (Yen et al., 2016). It is possible that the need for reward in a child with ADHD who gradually loses his/her confidence due to constant difficulties in school and at home is resolved in this way. In this study, it was concluded that the mean scores of children from the CGASC total and all other sub-dimensions did not show a significant difference according to the age variable.
This study is partially compatible with Horzum’s study (2011) that compared the grade level and computer gaming addiction of third, fourth, and fifth grade students (Horzum, 2011). In addition, in our study, the CGASC total and all subscale scores of online game players were found to be significantly higher than of those who played offline. Müller et al. (2015) stated that, in a study of 12,938 adolescents aged 14-17 years from European countries, among the types of games that were a risk factor for computer game addiction, online games were the strongest predictor (Müller et al., 2015). Similarly, another study that examined the effect of adult gaming preferences on computer gaming addiction has shown that online game players are more likely to meet the criteria for computer gaming addiction compared to offline game players (Na et al., 2017).
In the literature, there are no studies investigating the computer game preferences of children with ADHD. In our study, when we compared the types of computer games that children with ADHD and without ADHD were playing, we found that there was no significant difference between the levels of playing games such as ac125
Table 2.
Independent Samples t-test Analysis of CGASC and Subscale Mean Scores of Children with and without ADHD
Children with ADHD
Children without ADHD
M
SD
M
SD
t
p
Cohen's d
Inability to stop playing computer games
28.65
9.19
20.94
7.37
6.546
0.000
0.926
Associating the game with life
8.59
3.73
7.05
2.98
3.225
0.001
0.456
Disruption of tasks due to game playing
6.00
2.56
4.60
1.72
4.539
0.000
0.642
Preferring game playing to other activities
8.75
3.76
7.84
3.57
1.755
0.081
0.248
Total
51.99
15.85
40.43
12.93
5.652
0.000
0.799
M: mean; SD: standard deviation
Table 3.
Results of a Multivariate Analysis of Variance According to Age of the Children and Group x Age Interaction
Effect
Wilks' Lambda Value
F
df
Error df
p
Ƞ²
Age
0.99
0.161
12
500
0.999
0.003
Group x age
0.95
0.901
12
500
0.546
0.019
Table 4.
Results of a Multivariate Analysis of Variance According to the Age of the Children and Group x Age Interaction
Effect
Wilks' Lambda Value
F
df
Error df
p
Ƞ²
Playing online games
0.93
3.749
4
193
0.006
0.072
Group x playing online games
0.97
1.735
4
193
0.144
0.035
The Turkish Journal on Addictions, 7, 122-128
tion, adventure, strategy, gunner, simulation, sports, racing, puzzle, and role-playing. In addition, we observed that children without ADHD played more educational games than those with ADHD.
Considering the types of games, those that are fast-moving, similar to real life, competitive, and aggressive are attractive not only for the children with ADHD who lack rewards in other areas of life but also for healthy children. It was observed that children who play action games have significantly higher mean scores on the subscales of “inability to stop playing the computer game” and “associating the game with life” subscales and total CGASC mean scores. This result is similar to some studies in the literature; the findings of a study conducted with young adults show that action type games are among the most frequently played games by problematic computer game players (Elliott et al., 2012). The reasons that action-type games are preferred by children with ADHD who need stimulus and reward include the fact that they are rich in audio-visual stimulation, the action provides constant change, and a prize is won as a result of struggle. Children with ADHD who prefer shooter-type games have significantly higher mean scores on the subscales of inability to stop playing the computer game, associating the game with life, and preferring game to other activities. The total mean score of CGASC is also higher than for children with ADHD who do not prefer shooter type games. Shooter games are computer games where participants aim to kill or injure each other. Similar studies can be found in the literature. In a study conducted by Ream, Elliott, & Dunlap (2014), shooters games were mentioned as one of the most preferred game played by problematic computer game
126
Table 5.
Independent Samples t-test Analysis of the Mean Scores of Total CGASC and Subscales According to the Type of Games Played by Children with ADHD
Yes
No
M
SD
M
SD
t
p
Cohen's d
Action
Inability to stop playing computer games
31.38
8.60
24.88
8.72
3.710
0.000
-0.751
Associating the game with life
9.53
3.91
7.29
3.05
3.104
0.002
-0.641
Disruption of tasks due to game playing
6.41
2.72
5.43
2.22
1.927
0.057
-0.397
Preferring game playing to other activities
9.31
3.97
7.98
3.35
1.769
0.080
-0.363
Total
56.64
15.47
45.57
14.18
3.656
0.000
-0.746
Adventure
Inability to stop playing computer games
29.40
8.98
27.81
9.44
0.861
0.391
-0.172
Associating the game with life
9.15
3.86
7.96
3.51
1.610
0.111
-0.324
Disruption of tasks due to game playing
5.96
2.58
6.04
2.56
0.156
0.876
0.031
Preferring game playing to other activities
8.89
3.88
8.60
3.66
0.385
0.701
-0.077
Total
53.40
15.41
50.40
16.34
0.349
0.349
-0.188
Strategy
Inability to stop playing computer games
28.47
9.16
28.80
9.29
0.180
0.858
0.036
Associating the game with life
8.87
3.97
8.36
3.53
0.669
0.505
-0.134
Disruption of tasks due to game playing
5.73
2.67
6.22
2.47
0.942
0.348
0.189
Preferring game playing to other activities
8.36
3.16
9.07
4.19
0.975
0.332
0.193
Total
51.42
15.56
52.45
16.21
0.323
0.748
0.065
Shooter
Inability to stop playing computer games
31.46
8.82
27.00
9.06
2.399
0.018
-0.499
Associating the game with life
9.97
3.93
7.78
3.38
2.951
0.004
-0.599
Disruption of tasks due to game playing
6.57
2.95
5.67
2.26
1.717
0.089
-0.343
Preferring game playing to other activities
9.92
3.64
8.06
3.69
2.440
0.016
-0.506
Total
57.92
15.42
48.51
15.15
2.979
0.004
-0.616
Simulation
Inability to stop playing computer games
30.28
9.49
27.73
8.95
1.334
0.185
-0.276
Associating the game with life
9.92
3.97
7.84
3.39
2.756
0.007
-0.561
Disruption of tasks due to game playing
6.08
2.87
5.95
2.39
0.243
0.808
-0.049
Preferring game playing to other activities
9.56
3.78
8.30
3.70
1.619
0.109
-0.336
Total
55.83
16.63
49.83
15.09
1.841
0.069
-0.378
Racing
Inability to stop playing computer games
32.07
8.38
26.17
9.01
3.327
0.001
-0.678
Associating the game with life
9.17
4.11
8.17
3.40
1.321
0.190
-0.264
Disruption of tasks due to game playing
6.67
2.75
5.52
2.32
2.263
0.026
-0.452
Preferring game playing to other activities
9.93
3.83
7.90
3.50
2.753
0.007
-0.554
Total
57.83
15.05
47.76
15.16
3.290
0.001
-0.667
M: mean; SD: standard deviation
Başgül et al. Game Addiction, Attention Deficit and Hyperactivity Disorder
player adults between the ages of 18 and 29 (Ream et al., 2014). Another study with adolescents shows that one of the most powerful game types predicting computer game addiction are shooter games (Müller et al. 2015). In addition to these results, children with ADHD who prefer simulation type games have significantly higher mean scores on the subscale of associating the game with life. The content of these simulation games enables children to use life-like elements in the game or experience similar things in the game. The fact that children play this game as one person, that the game does not contain much movement and change, and the game progresses uniformly can be shown to be non-addictive features of the game. As we have seen in our study, the children with ADHD who prefer racing type games have also significantly higher mean scores on the subscales of the inability to stop playing the computer game, experience disruption of tasks due to game playing, and prefer game playing to other activities and the total score of CGASC. Racing games played with a computer or person are based on competition and are ranked by success. It is thought that being first is the biggest motivation in this game as it is in the category of games containing other competitive elements, so it is difficult for children to give up the game.
Another observation was that children who did not play training type games had higher mean scores on the subscale of disruption of tasks due to the game play. Educational games are based on thinking, strategy development, and problem solving that aim to teach the player different concepts. When the literature related to computer gaming addiction was examined, there were no studies examining the relationship of education with games. Because educational games are intended for a specific teaching purpose, they may seem boring to young children and could serve as reminders of school assignments.
Limitations and Suggestions for Future Research
A limitation of the study is that it was conducted only in Istanbul and with a narrow age group. It is believed that studies carried out in different provinces with larger sample sizes and wider age groups with parental participation will shed further light on this area.
In this study, it was found that children with ADHD had higher levels of computer game addiction than healthy children. ADHD is the most common neurodevelopmental disorder, and there are a limited number of studies conducted in our country on how ADHD affects computer gaming addiction. Our study is important in terms of showing that an ADHD diagnosis and some playing habits are risk factors for computer game addiction.
Ethics Committee Approval: Ethics committee approval was received for this study from the Ethics Committee of Hasan Kalyoncu University (Approval no: 2017/34 - Date: 27.10.2017).
Informed Consent: Written informed consent was obtained from the parents’ of the participants.
Peer-review: Externally peer-reviewed.
Author Contributions: Concept - S.Ş.B, B.B.; Design - S.Ş.B., B.B., M.G.L.; Supervision - S.Ş.B.; Resources - B.B, M.G.L.; Materials - B.B., M.G.L.; Data Collection and/or Processing - S.Ş.B, B.B.; Analysis and/or Interpretation - S.Ş.B, B.B.; Literature Search - M.G.L, B.B.; Writing Manuscript - M.G.L., B.B.; Critical Review - S.Ş.B.
Conflict of Interest: The authors have no conflicts of interest to declare.
Financial Disclosure: The authors declared that this study has received no financial support.
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