Comorbid symptoms of internet addiction among adolescents with and without autism spectrum disorder: a comparative study

ABSTRACT

This study investigates the prevalence of internet addiction and compares its related factors among adolescents with and without autism spectrum disorder (ASD). Between January 2017 and December 2019, outpatients (n = 102), aged 12–15, completed the basic information questionnaire, Internet Addiction Test, and Quick Inventory of Depressive Symptomatology Self-Report Japanese version (QIDS-J). Their parents completed the Social Responsiveness Scale-2 (SRS-2) and Attention Deficit Hyperactivity Disorder Rating Scale-IV (ADHD-RS). The scores of adolescents with and without ASD were compared. Internet addiction was prevalent among 40.0% and 23.9% of adolescents in the ASD and non-ASD groups, respectively. No potential risk factors for internet addiction were statistically significant in the ASD group. However, QIDS-J and ADHD-RS scores were significantly correlated with internet addiction in the non-ASD group. Factors related to internet addiction were symptoms of depression and ADHD in the non-ASD group.

KEYWORDS:

• Autism spectrum disorder
• gaming disorder
• internet addiction
• adolescents
• pathological internet use

Introduction

Over the last decade, internet tools have provided a rapidly growing source of recreation, not only among adolescents and young adults but among people of all ages. The internet allows easy, and often free, access to online games. Digital devices enable communication across online communities and product review sites, using instant messaging, voice chat, social network services (SNS), and so on. Recently, many people have started using the internet for virtual communication, as well as social interaction with friends in the real world. Moreover, individuals use SNS and online gaming to alleviate negative feelings, such as sadness, feeling low, and loss of interest in daily activities (Casale, 2021).

Thus, it is clear that the internet is a critical part of our lives; however, it also has several negative effects, such as uncontrollable and problematic use and addiction. Internet addiction is defined as excessive and problematic internet use, with individuals exhibiting clinical features of behavioural addiction: preoccupation, compulsive behaviour, lack of control, and functional impairment (Young, 1996). Internet addiction is associated with various psychosocial issues. Moreover, psychological issues or stressful events may play a role in the aetiology of internet addiction.

Internet addiction has been linked to extreme real-life social withdrawal among adolescents and young adults (Stavropoulos et al., 2019). In addition, problematic internet use for online gaming is the most common cause of internet addiction and a serious issue in many countries (Chia et al., 2020). The American Psychiatric Association (2013) specifies internet addiction as an ‘internet gaming disorder’ in Section III of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The World Health Organization (2018) has also listed ‘gaming disorder’ as a substance use and addictive disorder in the 11th edition of the International Classification of Diseases.

According to a systematic review, the prevalence of internet addiction in the general population is 7.0% across 31 nations (Pan et al., 2020). Another meta-analysis found that the pooled prevalence rate of internet addiction in Southeast Asia was 20.0% (Chia et al., 2020). According to high-quality evidence, internet addiction is moderately associated with attention-deficit/hyperactivity disorder (ADHD) and depression (Normand et al., 2021; Seki et al., 2019; Tokunaga, 2017; Wang et al., 2017; Zhou et al., 2020). A more recent study has found high levels of comorbidity between the symptoms of ADHD and internet addiction (El Archi et al., 2022). Other recent works have reported a higher prevalence of problematic internet use among individuals with autism spectrum disorder (ASD; Murray et al., 2022; Normand et al., 2021). ASD is a group of heterogeneous conditions characterized by deficits in social communication and interactions, and by the presence of restrictive and repetitive patterns of behaviours, interests, or activities (American Psychiatric Association, 2013). Among children with ASD, over 70.4% have been reported to be diagnosed with at least one comorbid psychiatric disorder and 41.1% with multiple comorbidities (Lundström et al., 2015; Simonoff et al., 2008). This suggests that internet addiction could be one of the comorbid psychiatric disorders among people with ASD (Hirota et al., 2021). Although a number of studies have examined the effects of internet use in ASD, limited data are available on the factors related to problematic internet use (Murray et al., 2022; Normand et al., 2021). The first aim of the present study is to examine the relationship between ASD and internet addiction, with the hypothesis that individuals with ASD will present with higher rate of internet addiction in comparison to the group without. The second aim was to investigate the contributing factor of internet addiction in participants both with and without ASD, the hypothesis being that internet use among adolescents with ASD differs from that among adolescents without.

Methods

Aim

To investigate the prevalence of internet addiction among adolescents with ASD and elucidate factors contributing to internet addiction among adolescents with and without ASD.

Setting and design

This study employed a cross-sectional quantitative research design. Participants were recruited using a purposive sampling method. The research was carried out at the Center for Child Health, Behaviour, and Development, Ehime University Hospital. This centre is a specialized psychiatric outpatient clinic for children and adolescents where medical examinations are performed for almost 200 new patients annually. The participants were recruited from this centre. The study period was from January 2017 to December 2019, before the outbreak of the coronavirus pandemic in 2019. There were 631 patients who visited the hospital for the first time during the study period. During recruitment, the selected participants were aware that participation for the study was voluntary, and they were free to decline. No incentive for participation was offered to the participants.

Instruments

Participants completed a basic information questionnaire, Young’s (1996) Internet Addiction Test (IAT), and the Quick Inventory of Depressive Symptomatology Self-Report (Rush et al., 2003) Japanese version (QIDS-J). Simultaneously, parents responded to the Social Responsiveness Scale-2 (SRS-2; Constantino & Gruber, 2012) and ADHD Rating Scale-IV (ADHD-RS; Tani et al., 2010).

Basic information questionnaire

The basic information questionnaire included questions regarding gender, school grade, and usage of electronic devices, such as television, laptops, computers, tablets, smartphones, and video game consoles. Participants also responded to the question: ‘Are electronic devices freely accessible to you?’ with ‘Yes’ or ‘No.’

IAT

The IAT comprises of 20 items and is calibrated with scores between 1 and 5, with total scores ranging from 20 to 100. Higher scores reflect a greater tendency towards addiction. Total scores ≥ 70 are classified as severe internet addiction and 40–69 as possible internet addiction (Young, 1996). In this study, the internet addiction group was classified as those obtaining IAT scores ≥ 50, while the non-internet addiction group was classified as those obtaining IAT scores ≤ 49. This classification was based on several previous studies (Malak et al., 2017; Tateno et al., 2018). In the present study, the Cronbach’s α coefficient was 0.93.

QIDS-J

We used the QIDS-J to evaluate the severity of depression in each patient. The total score ranges from 0 to 27, with higher scores indicating a higher severity of depression. Individual scores are interpreted as 0–5 (none), 6–10 (mild severity), 11–15 (moderate severity), 16–20 (severe), and 21–27 (very severe; Rush et al., 2003). Thus, a cut-off score of 11 is considered to indicate moderate or severe levels of depression. Both the validity and reliability of the QIDS-J have been established previously (Trivedi et al., 2004). In the present study, the Cronbach’s α coefficient was 0.85.

SRS-2

The SRS-2 is a 65-item parent-rated questionnaire that evaluates the child’s autistic traits in terms of social communication, awareness, motivation, cognition, and behaviour flexibility within the past six months. Items are rated on a four-point Likert scale (ranging from 0 to 3), with higher scores indicating more autistic traits (total range from 0 to 195; Constantino & Gruber, 2012). In the research of Japanese child and adolescent population, the cut-off scores for primary screening were 53.5 for boys and 52.5 for girls, and they were 10.9% of the population sample (Kamio et al., 2013). In the present study, the Cronbach’s α coefficient was 0.88.

ADHD-RS

The ADHD-RS is an 18-item questionnaire that reports the frequency of symptoms over the past six months on a four-point Likert scale (ranging from 0 to 3), measuring symptoms of ADHD according to the DSM-IV. The total score ranges from 0 to 54 (Tani et al., 2010). In the present study, the Cronbach’s α coefficient was 0.90.

Sample size

The sample size was calculated based on two-sample t-tests using the G*Power 3.1.9.2 software (Faul et al., 2009). An effect size of 0.5, significance level of α = 0.05, statistical power of 1-β = 0.8, and a 1:2 allocation ratio between the ASD and non-ASD groups were considered. Sample size calculation was performed before initiating recruitment, and we set a total sample size of 114 participants.

Eligibility criteria

The inclusion criteria were as follows: 1) aged 12–15 years, equivalent to being enrolled in 7^th–9^th grade; 2) able to fully comprehend and respond to questionnaires; 3) who provided assent and whose parents provided written informed consent for participation; and 4) who were diagnosed with ASD based on the Autism Diagnostic Observation Schedule-2, Autism Diagnostic Interview-Revised, and DSM-5 criteria (American Psychiatric Association, 2013).

The exclusion criteria were as follows: those 1) who were diagnosed with moderate-to-severe intellectual disabilities or severe psychiatric disorders, such as schizophrenia and anorexia nervosa, as screened by psychiatrists and 2) who were diagnosed with depressive disorders or ADHD based on the DSM-5 criteria (American Psychiatric Association, 2013).

Procedure

The survey was conducted using self-report and parent-report questionnaires. The questionnaires were handed to the participants and their parents by a doctor during their first visit. Before the study, the doctor explained to participants and their parents that: 1) participation was voluntary and 2) strict confidentiality would be maintained. Written informed consent and assent forms were obtained from the parents and participants, respectively. The study was approved by the Institutional Review Board of the Ehime University Graduate School of Medicine (IRB No. 1,507,007) and was conducted in accordance with the Declaration of Helsinki.

After careful data screening, a total of 192 participants who fit the eligibility criteria were enrolled, with a total of 102 participants completing the surveys (53.1% response rate; Figure 1). Participants were divided into two groups: adolescents with and without ASD.

Figure 1. Flowchart of the recruitment process.

ASD, autism spectrum disorder; ADHD, attention-deficit/hyperactivity disorder

Statistical analysis

The results are expressed as mean ± standard deviation for continuous variables and as numbers and percentages for categorical variables. Descriptive statistics were used to show the distribution of participant characteristics. The Mann-Whitney U test was used for the comparison of numerical variables. Chi-square tests were used for categorical variables. We performed multiple linear regression analysis to explore the correlation between the IAT score and scores on the QIDS-J, ADHD-RS, and SRS-2. In order to test the assumption of multicollinearity, the variance inflation factor (VIF) and tolerance values were calculated. VIF values were dispersed between 1.00 and 2.23 and tolerance values ranged from 0.45 to 1.00. Therefore, the assumption of multicollinearity was validated. All tests were two-sided, and the significance level was set at 5%. All data were analysed using SPSS Statistics software (version 23.0; IBM Corp., Armonk, NY, USA) for Windows and R version 4.1.0.

Results

Participants’ demographics and characteristics are presented in Table 1. The SRS-2 and ADHD-RS scores were significantly higher in the ASD group (p < 0.001) than in the non-ASD group (p = 0.02). Based on IAT scores, the prevalence of internet addiction was 40.0% in the ASD group (14/35, 95% CI: 23.9–57.9%) and 23.9% (16/67, 95% CI: 14.3–35.9%) in the non-ASD group. There were no significant differences in the prevalence of internet addiction between the ASD and non-ASD groups (p = 0.09). No significant differences were observed in the number of participants to whom electronic devices were freely accessible between the ASD and non-ASD groups.

Table 1. Demographics and characteristics of participants.

	ASD, n = 35	Without ASD, n = 67	φ or R	P
Gender			−0.19	0.06
Boys	21 (60.0)	27 (40.6)
Girls	14 (40.0)	40 (59.4)
Grade			0.13	0.42
7^th	13 (37.1)	19 (27.5)
8^th	12 (34.3)	32 (47.8)
9^th	10 (28.6)	16 (24.6)
SRS-2	67.83 (29.11)	42.81 (18.87)	−0.40	<0.001***
ADHD-RS	12.11 (8.86)	7.77 (7.93)	−0.24	0.02*
IAT score	46.26 (19.79)	40.96 (14.65)	0.10	0.26
Internet addiction	14 (40.0)	26 (23.9)	0.17	0.09
Severity			0.19	0.17
IAT score; ≥ 70	5 (14.3)	3 (4.5)
IAT score; 40–69	15 (42.9)	27 (40.3)
QIDS-J score	7.57 (5.94)	8.78 (6.39)	0.09	0.37
Depression	7 (20.0)	26 (38.8)	−0.19	0.05
Electronic devices
Television	29 (82.9)	56 (83.6)	−0.07	0.46
Laptop	17 (48.6)	30 (44.8)	0.05	0.61
Tablet	16 (45.7)	27 (40.3)	0.04	0.71
Smartphone	21 (60.0)	45 (67.2)	−0.06	0.56
Video games	27 (77.1)	47 (70.1)	0.09	0.41

Values expressed as n (%) or mean ± standard deviation; internet addiction, IAT score ≥ 50; depression, QIDS-J score ≥ 11; *p-value < 0.05, **p-value < 0.01, ***p < 0.005

ASD, autism spectrum disorder; SRS-2, Social Responsiveness Scale-2; ADHD-RS, ADHD Rating Scale-IV; IAT, Internet Addiction Test; QIDS-J, Quick Inventory of Depressive Symptomatology Self-Report Japanese version

The results of multiple linear regression analysis revealed that no potential risk factors were statistically significant in the ASD group (Table 2).

Table 2. Possible contributing factors to IAT score in the ASD group.

	ASD
Model	B	95% CI	t	p	Tolerance	VIF
Boys	−2.24	−18.67–14.19	−0.28	0.78	0.81	1.23
Grade	−2.02	−11.05–7.00	−0.46	0.65	1.00	1.00
QIDS-J	−0.12	−1.57–1.33	−0.17	0.87	0.74	1.36
SRS-2	0.11	−0.27–0.49	0.60	0.56	0.45	2.23
ADHD-RS	−0.52	−1.74–0.70	−0.87	0.39	0.46	2.16

Multiple linear regression analysis coefficient table

IAT, Internet Addiction Test; ASD, autism spectrum disorder; CI: confidence interval; VIF, variance inflation factor; QIDS-J, Quick Inventory of Depressive Symptomatology Self-Report Japanese version; SRS-2, Social Responsiveness Scale-2; ADHD-RS, ADHD Rating Scale-IV

However, the QIDS-J (B = 0.94, p = .001) and ADHD-RS (B = 0.74, p = 0.003) scores were significantly correlated with IAT scores in the non-ASD group (Table 3).

Table 3. Possible contributing factors to IAT score in the non-ASD group.

	Without ASD
Model	B	95% CI	t	p	Tolerance	VIF
Boys	3.31	−3.79–10.40	0.93	0.36	0.83	1.21
Grade	2.31	−2.22–6.85	1.02	0.31	0.99	1.01
QIDS-J	0.94	0.41–1.47	3.54	0.001***	0.86	1.17
SRS-2	−0.13	−0.33–0.07	−1.28	0.21	0.70	1.43
ADHD-RS	0.74	0.26–1.22	3.09	0.003***	0.70	1.43

***p < 0.005. Multiple linear regression analysis coefficient table

IAT, Internet Addiction Test; ASD, autism spectrum disorder; CI, confidence interval; VIF, variance inflation factor; QIDS-J, Quick Inventory of Depressive Symptomatology Self-Report Japanese version; SRS-2, Social Responsiveness Scale-2; ADHD-RS, ADHD Rating Scale-IV

Discussion

We examined the factors related to internet addiction among junior high school students who visit psychiatric clinics. To our knowledge, this is the first study to investigate the factors of this specific population with and without ASD.

The prevalence of internet addiction found was 40.0% among adolescents with ASD and 23.9% among adolescents without ASD. A previous study reported that the prevalence of internet addiction among junior high school students was 16.3% (86/529, 95% CI: 13.2–19.7%; Kawabe et al., 2016), while another reported that the prevalence of internet addiction among adolescents with ASD was 38.6% based on an IAT cut-off score of 50, (Masi et al., 2021). These results corroborate those found in this study. Kawabe et al. (2019) reported a higher prevalence (45.5%) of internet addiction among adolescents with ASD but included people with comorbid ADHD. Internet addiction has been associated with ADHD diagnosis and traits (Tokunaga, 2017; Wang et al., 2017). Therefore, we excluded participants with ADHD. In the current study, the considerably higher prevalence of internet addiction among participants with ASD than in the general population suggests that adolescents with ASD have a higher rate of internet addiction. This study also showed that the prevalence of internet addiction among adolescents with mild psychiatric disorders such as adjustment disorder, somatic symptom disorder, and sleep disorders was slightly higher than the general population.

The results of multiple regression analyses revealed that no potential risk factors for internet addiction were statistically significant in the ASD group. Self-reported results indicated that participants with ASD tended to be more addicted to the internet than participants without ASD, although factors related to internet addiction changed between the two groups. A recent systematic review indicated that internal factors (gender differences, ASD symptom severity, age) and external factors (social aspects of video gaming: playing in multiplayer mode, access to video game systems, parent-child relationships) are significant predictors of problematic video game use among individuals with ASD (Craig et al., 2021). In terms of gender differences, boys play video games more frequently, whereas girls use SNSs more frequently. Therefore, there is no consensus on whether internet addiction is associated with gender differences (Su et al., 2020). Individuals with ASD have trouble interpreting complex social interactions and intentions and are unable to make quick judgements in social contexts. This further undermines their social interactions with peers (Masten et al., 2011). Individuals with ASD have characteristics such as highly restricted interests and social deficits, which in turn can lead to a greater risk of excessive use of video games (Engelhardt et al., 2017).

In this study, we investigated the severity of ASD symptoms using the SRS-2, which is unrelated to the symptoms of internet addiction in ASD. Although internet addiction is associated with deficits in social communication and avoidance of social contact, factors related to internet addiction might be more diverse in case of individuals with ASD (Jiao et al., 2017). It has been suggested that if an individual with ASD has developed internet addiction, it should be treated as a comorbid diagnosis (Paulus et al., 2020). To date, there is no tool that distinguishes between restricted interests or repetitive behaviours and internet use in individuals with ASD (Craig et al., 2021). Therefore, clinicians should consider the possibility of comorbid internet addiction among people with ASD and use various evaluation scales and diagnostic criteria to assess internet addiction and gaming disorders.

In addition, the association of both depressive states and ADHD scores with internet addiction was observed only in the non-ASD group. Some studies have shown the association between ADHD characteristics, traits, and internet addiction (Tateno et al., 2018; Wang et al., 2017; Zhou et al., 2020), while others have shown the association between depressive states and internet addiction (Tokunaga, 2017). These results support the findings of the present study. Furthermore, according to several epidemiologic studies, there is a strong association between depressive symptoms and internet addiction (Li et al., 2019; Moreno et al., 2022). People who experience loneliness tend to prefer socializing on the internet, which leads to the overuse of SNS (Ndasauka et al., 2016). Peer relationships are also important predictors of internet addiction, although whether pathological social withdrawal creates internet addiction or internet overuse creates poor social relationships remains unclear (Kato et al., 2020; Strittmatter et al., 2016).

Limitations

Our study has several limitations. First, the participants were chosen from a single centre in Japan, which might not be representative of all adolescents. Therefore, the generalizability of our findings is limited. To ascertain the true impact of the effect of ASD on the severity of internet addiction, multicenter registries are needed. Second, the sample size was relatively small (n = 102) and the response rate is low (53.1%). Therefore, there remains a possibility of non-response bias and the prevalence of internet addiction reported herein cannot be directly extrapolated to different institutions and countries. To encourage participation in future studies, using web-based forms or mobile application software when delivering questionnaires may be useful. Third, we did not control for confounding factors, such as economic status and academic performance. Finally, participants with moderate-to-severe psychiatric disorders and ADHD were excluded, which could have introduced a selection bias.

Conclusion

In conclusion, factors related to internet addiction differed for individuals with and without ASD. Future research should carefully diagnose internet addiction in patients with ASD to better understand the comorbidity with ASD.

Acknowledgements

The authors are tremendously grateful to all the individuals and families who enrolled in this study, as well as the staff at the Ehime University Hospital.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by a Grant-in-Aid for Scientific Research [20K18935]. The funding is provided by KAKENHI in JAPAN (https://kaken.nii.ac.jp/ja/grant/KAKENHI-PROJECT-20K18935/).

Notes on contributors

Kiwamu Nakachi

Kentaro Kawabe is an associate professor/lecturer of Department of Neuropsychiatry at Ehime University Graduate School of Medicine, where he also completed his PhD. His work focuses specifically on the internet and gaming addiction of children and adolescents and the methods of prevention.