|Year : 2022 | Volume
| Issue : 2 | Page : 149-154
Gaming addiction in children and adolescents with attention-deficit hyperactivity disorder and disruptive behavior disorders
Lavkush Verma1, Vivek Agarwal2, Amit Arya2, Pawan Kumar Gupta2, Pooja Mahour2
1 Department of Psychiatry, Era's Lucknow Medical College, Lucknow, Uttar Pradesh, India
2 Department of Psychiatry, King George's Medical University, Lucknow, Uttar Pradesh, India
|Date of Submission||23-Jun-2021|
|Date of Decision||14-Dec-2021|
|Date of Acceptance||27-Jan-2022|
|Date of Web Publication||19-Aug-2022|
Dr. Pawan Kumar Gupta
Department of Psychiatry, King George's Medical University, Lucknow - 226 003, Uttar Pradesh
Source of Support: None, Conflict of Interest: None
Context: There is a dearth of studies on the risk of gaming addiction (GA) in children and adolescents with disruptive behavior disorders (DBDs) and its comorbidity with attention-deficit hyperactivity disorder (ADHD). Methods: Seventy participants aged 6–16 years diagnosed with ADHD and DBD were included in this cross-sectional, observational study and compared with 40 healthy controls. They were assessed for clinical details of gadget type, duration of use, and purpose on a semi-structured questionnaire. The intensity of video gaming was assessed using Game Addiction Scale (GAS). Behavioral symptoms were assessed on Child Behavior Checklist (CBCL). Descriptive statistics with t-test, analysis of variance, and Pearson's correlational analysis were used as applicable. Results: Use of gadgets for video games for ≥ 4 hours was found to be significantly higher (P = 0.001) in cases (61.5%) than in controls (10%). Most of the cases used Internet for communication (69.4%) and entertainment (58.3%). A significantly higher number of cases (37.1%) fulfilled criteria for video game addiction and the numbers were significantly higher in ADHD + DBD groups as compared to only ADHD or only DBD group. Children with GA had significantly higher scores in all domains of CBCL as compared to those without GA. The GAS score had a significant positive correlation with aggressive behavior, social problems, rule breaking, and attention problem domains of CBCL. Conclusions: GA was significantly higher in ADHD and/or DBD than normal children and adolescents. Comorbidity of ADHD and DBD further increases the risk of GA. Therefore, children with these disorders should be screened routinely for GA.
Keywords: Adolescents, attention-deficit hyperactivity disorder, children, disruptive behavior disorders, gaming addiction, gaming disorders
|How to cite this article:|
Verma L, Agarwal V, Arya A, Gupta PK, Mahour P. Gaming addiction in children and adolescents with attention-deficit hyperactivity disorder and disruptive behavior disorders. Ann Indian Psychiatry 2022;6:149-54
|How to cite this URL:|
Verma L, Agarwal V, Arya A, Gupta PK, Mahour P. Gaming addiction in children and adolescents with attention-deficit hyperactivity disorder and disruptive behavior disorders. Ann Indian Psychiatry [serial online] 2022 [cited 2022 Sep 30];6:149-54. Available from: https://www.anip.co.in/text.asp?2022/6/2/149/354123
| Introduction|| |
Video games have become popular among children and adolescents (referred as children in the present paper) because they are exciting. In addition to the positive effects of these games, the subsequent behavioral disorders in children as a result of addiction to them have led to a lot of studies in recent years. Attention-deficit hyperactivity disorder (ADHD) is one of the most common abnormalities in childhood and is characterized by attention-deficit, impulsivity, and hyperactivity. Moreover, 60% of cases also occur in adulthood. Since impulsivity is one of the fundamental components of attention-deficit disorder, it is reported that there is probably a correlation between addiction to video games and ADHD symptoms. Accordingly, studies show that children with ADHD have a higher score and degree of addiction to video games., Weinstein (2010) argued that addiction to video games was directly related to stress and depression. Complete inactivation of the cortex causes the stimuli to accumulate and ultimately causes a lack of concentration in children with ADHD. Dopamine secretion during the game, immediate rewards, and rapid response cause children with ADHD to be attracted to video games. Children with ADHD are more susceptible to drug abuse due to problems in judgment, impulsive behavior, and tendency to high-risk behaviors. Given the similarity of physiological mechanisms between people with ADHD and addiction to video games, it is hypothesized that the prevalence of addiction to video games is higher among children with ADHD than normal people. It has also been reported that addiction to the Internet and video games can be one of the predictors of ADHD and vice versa.
Disruptive behavior disorders (DBD), i.e., oppositional defiant disorder (ODD) and conduct disorder (CD), are common comorbid psychiatric disorders in children with ADHD. However, there is some evidence that the presence of conduct symptoms is associated with risk of Internet addiction or gaming addiction (GA) in adolescents., but the evidence for its comorbidity with DBDs is still limited. Studies on GA and its comorbidities such a ADHD and DBDs are lacking in the Indian context. Hence, the present study aims to explore the phenomenology of the addiction of video games (online and offline) in children and adolescents with DBD and to see whether comorbid DBD increases the risk of GA in children and adolescents with ADHD using both self-report and parental account.
| Methods|| |
Design and sample
This study is a cross-sectional, observational study conducted at the child and adolescent psychiatry outpatient department (OPD) of a tertiary care hospital. Cases were recruited via the purposive sampling (nonprobability) technique.
Tools and procedure
Subjects aged 6–16 years were screened and diagnosed using Kiddie Schedule for Affective Disorders and Schizophrenia-Present and Lifetime (K-SADS-PL) with ADHD and/or DBD as per DSM-IV-TR at Child and Adolescent Psychiatry OPD from September 2016 to July 2017. Siblings of non-ADHD or non-DBD children attending child and adolescent psychiatry OPD were screened and those who fulfilled selection criteria were included as healthy controls. Subjects with intellectual disability (IQ below 70) and the presence of any psychiatric comorbidity (other than DBD) were excluded. Diagnoses which were not present in K-SADS-PL were excluded by clinical assessment, for example, dissociative and somatoform disorders. The presence of medical comorbidity that required urgent medical attention was also excluded as assessments were not possible. Similarly, in the control group, children and adolescents with any psychiatric or medical illness were excluded.
Information regarding sociodemographic and clinical details was recorded on semi-structured proforma. Intellectual assessment of the child was performed by the consultant clinical psychologist using Colored Progressive Matrices. Information was collected both from subjects and parents about the gadgets use. Information for gadgets use was done through a questionnaire which was designed for the study purpose which included type of electronic gadgets use, duration of gadget use, the purpose of gadget uses and presence of Internet use or not.
Then, an assessment of degree of gaming was done using Game Addiction Scale (GAS). Child Behavior Checklist (CBCL) was applied to assess the severity of behavioral problems in children among the case group.
Diagnosing video game addiction
In this study, 21-item GAS measures adolescents' degree of addiction to computer and video games. This scale is based on the criteria for pathological gambling of the DSM-IV. Three items were created for each of the seven criteria: salience, tolerance, mood modification, relapse, withdrawal, conflict, and problems. In this scale, endorsement of at least 50% of the domain criteria is required for a positive diagnosis. In the current study, the term “video game addiction” was used as per recommended by Lemmens et al. based on the number of domain criteria (4 domains out of 7 domains) met in GAS, and an item was considered met when a subject answered 3 (sometimes) on a 5-point scale, ranging from 1 (never) to 5 (very often), over the last 6 months. The psychometric properties of this scale were tested among two independent group of Dutch adolescent gamers (n = 352 and n = 369). It serves as screening as well as a diagnostic tool. The 21-item scale, as well as a shortened 7-item version, showed high reliabilities. Furthermore, both versions showed good concurrent validity across samples, as indicated by the consistent correlations with usage, loneliness, life satisfaction, social competence, and aggression. The diagnosis of DSM-5 Internet gaming disorder (IGD) was made by consensus of the investigator and either of a consultant psychiatrist (VA, PKG, LV, and AA) involved in the study.
This study was approved by the institutional ethics committee. Written informed consent by parent/guardian and assent from children and adolescents was obtained.
Data were analyzed using the Statistical Package for the Social Sciences (SPSS) version 16.0 (IBM Corp., Armonk, New York, USA). Descriptive statistics were used to calculate frequencies (absolute and cumulative), mean, and standard deviation. Chi-square test was used to compare sociodemographic variables. “t-test” was used to compare CBCL scores between children with and without Internet addiction in the case group. Analysis of variance was used to compare time spent on the gadget and GAS scores between the Group A (ADHD Only), B (DBD Only), C (ADHD + DBD), and D (Control). Pearson's correlational analysis was used to assess the linear correlation between domains of CBCL (problems having pathological T-score) and GAS.
| Results|| |
A total of 70 cases (mean age 11.78 ± 2.3. years) and 40 healthy controls were included in the study. Both the groups were comparable in terms of sociodemographic variables. Case group was categorized under three subgroups: ADHD (n = 31, mean age 10.0 ± 2.25 years), DBD (n = 29, mean age 13.05 ± 1.51 years), and ADHD + DBD (n = 10, mean age 12 ± 1.73 years). There was no significant (P > 0.05) difference between the sociodemographic variables between subgroups and control. Comparative analysis of sociodemographic details is provided in [Table 1].
Duration of gadget use
Screen hours (4 h/day) were found to be significantly higher (P = 0.001) number of cases (61.5%) as compared to controls (10%).
Type of gadget use
All the cases were using mobile phones (n = 70), among which 28.6% were using only mobile phones, 32.9% were using mobile phone and television, 22.9% were using mobile phone and computer, 10% were using other additional gadgets for gaming, and 5.7% were using mobile phones, computer, and television. There was no significant difference found in the type of gadgets being used among cases and controls.
Purpose of gadget use
In the present study, 52% of cases and 52.5% of controls were found to be “internet users,” rest of others were offline users of gadgets, and there was no significant difference between the groups. Among the case group, most subjects used the Internet for communication (69.4%), followed by for entertainment (58.3%) and then for educational purposes (30.6%). But among controls, the purpose of Internet usage was found to be in the reverse order namely educational purposes (66.6%), entertainment (52.3%), and communication (42.9%).
Effect of comorbidities
Most of the children with ADHD + DBD were chatting online (60%) when compared to those with only ADHD (29%; P = 0.01) and with only DBD (34.4%; P = 0.01). Similarly, use of the Internet for online gaming was significantly high in ADHD + DBD cases (50%), as compared to cases with DBD (34.4%; P = 0.02) or ADHD (19.3%; P = 0.01).
[Table 2] shows the proportion of subjects meeting each domain of GAS and cutoff score criteria for GA. Game addiction (GAS score >50) was found to be significantly higher (P = 0.001) in cases (37.1%) than in controls (10%). ADHD + DBD group had significantly higher numbers (80%) of game-addicted children when compared to ADHD group (22.5%; P = 0.001) and DBD group (37.9%; P = 0.004) each.
Subjects with GA had higher scores in all domains of CBCL as compared to the nonproblematic users [Table 3]. Among subjects with video game addiction, a positive correlation was found between the GAS score and aggressive behavior (r = 0.77), social problems (r = 0.80), rule breaking (r = 0.96), and attention problem (r = 0.68) domains of CBCL [Table 4].
|Table 3: Comparison between T-score of Child Behavior Checklist domains of Internet/game-addicted and nonaddicted children in case group|
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|Table 4: Correlation between and Game Addiction Scale score and T-score of Child Behavior Checklist domain among game-addicted cases|
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| Discussion|| |
This study explored the phenomenology of the addiction of video games (online and offline) and its association with behavioral problems in children and adolescents with ADHD and DBD.
In this study, video game users for ≥4 h/day were found to be significantly higher (P = 0.001) in cases (61.5%) than controls. The finding of our study is in line with earlier study which reported a high duration of Internet use in ADHD children as compared to normal children. A study reported that the mean duration of Internet use and offline gaming in adolescents was roughly 3 h per day in the general population and over 6 h per day in ADHD children. Similarly, Mazurek and Engelhardt. reported that video game use was more in ADHD children as compared to control. More time spent on screen was associated with greater attention problems. A correlation between the severity of ADHD symptoms (particularly inattention) and time spent on the Internet in adolescents has also been reported. It was reported that subjects addicted to using the Internet stayed online for 20-15 hours per week. In the present study among the case group, most subjects used the Internet for communication as compared to healthy controls who used it more for educational purposes. Most of the cases who had ADHD + DBD were chatting online significantly when compared to those with only ADHD and only DBD (P = 0.01). Findings in our study are consistent with the study by Vural et al. who reported a similar pattern of Internet use in ADHD population, i.e., primarily for communication (38%) followed by entertainment (26.4%) and academic work (25.4%). Among the cases who had GA had higher scores in all domains of CBCL as compared to nonproblematic user. GA was found to be significantly higher (P = 0.001) in cases. Among children with GA, ADHD + DBD group had significantly higher proportions when compared to the ADHD group and the DBD group each. Findings in our study are consistent with the earlier clinic-based study which reported that problematic video gameplay and GA both were significantly higher in the ADHD group (34%). In the study on school population, unsafe Internet usage was found to be associated with higher clinical scores on Conners' subscale scores in domains of (i) attention deficit, (ii) hyperactivity/impulsivity, (iii) oppositional defiant behavior, and (iv) CD. The above findings thus indicate that the risk of Internet/game addiction increases with the presence of comorbidities. Among children with GA, a positive correlation was found between the GAS score and scores of CBCL domains, i.e., aggressive behavior (r = 0.77), social problems (r = 0.80), rule breaking (r = 0.96), and attention problem (r = 0.68). Converging evidence suggested that problematic video gaming is associated with externalizing symptoms, such as impulsivity, attention-deficit hyperactive disorder, and aggression and hostility.,, It is possible that individuals with behavioral problems (i.e., inattention, impulsivity, aggression, and hostility) are more likely to have poor relational strengths and thus use video games to retreat from difficult in-person social interactions.
In neurobiological studies, there is evidence that GA and aggression share a number of common neural substrates and neuromodulators; key neural substrates of aggression, such as the prefrontal cortex and the limbic system, are located in brain regions that also relate to Internet addiction. In addition, poor family functioning, problems in school bonding, low academic achievement, and deviant peer relationships are among the main risk factors for substance use in adolescents with ADHD + ODD/CD, and the same risk factors may also be linked to GA in these individuals.
Reasons for the above finding could explain that children with ADHD or DBD have shown deficits in behavioral response inhibition. Deficient inhibitory control in subjects with ADHD and DBD may interfere with the self-regulation of Internet use. Dysfunctions of the prefrontal cortex are thought to play an important role in the pathogenesis of DBD. Children with CD exhibit a tendency toward risk-taking and reckless behavior, indicating difficulties with decision-making and impulsivity. They are also more vulnerable to substance abuse, potentially reflecting an altered sensitivity of reward mechanisms and persistent selection of options with short-term benefits, despite negative long-term consequences. A recent review concluded that, on the basis of existing data, one of the most robust motivational markers in ADHD was a preference for smaller sooner over larger later rewards (Delay aversion). It has also been argued that the presence of ODD/CD comorbidity may account for most of the associations between such delay aversion and ADHD.
Strengths of this study are that the use of gadgets was verified by interviewing both parents and subjects, while this study has few limitations too. The cross-sectional design prevents this study from clearly defining the direction of the causal relationship between ADHD, DBD, and GA. Any generalization of results is limited because the sample size is small and most of the patients were receiving medication for their illness which could have affected the finding of CBCL scores. At the time of the study (years 2016–17), DSM has not accepted “GA” as a disorder, and in the scale, it was synonymously used as “pathological gaming;” hence, the finding of the present study should be interpreted accordingly, and the study does not endorse currently accepted criteria for IGD of DSM-5 or gaming disorder of ICD.
| Conclusion|| |
The present study found that GA was significantly higher in DBD and ADHD than healthy controls. Comorbid ADHD and DBD further increase the risk of GA in children and adolescents. Screen use of more than 4 h per day and differences in purpose of screen use among DBD cases as compared to controls may also be contributing toward higher risk of GA. Therefore, it is important to screen children with these disorders routinely for GA because problematic overuse (more than 4 h/day) of Internet or video game occurs at the expense of healthy activities such as playing with peers, academic work, and interaction with family members. It leads to worsening of behavioral problems, disruption of daily routine activities, and conflicts. Findings of the present study may also have implications for future studies to explore common phenomenological and neurobiological underpinning of DBD and GA. With the help of newer diagnostic criteria in DSM-5 IGD and ICD-11 gaming disorder, such studies may help clinicians to identify high-risk phenomenological features to prevent further risk of developing pathological gaming.
The authors acknowledged all the contribution in proofreading by Dr. Praveen Sachan (Junior Resident, Department Of Psychiatry, K.G.M.U, Lucknow) and Dr. Nitika Singh (Junior Resident, Department Of Psychiatry, K.G.M.U, Lucknow).
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
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[Table 1], [Table 2], [Table 3], [Table 4]