Abstract

Background

     There has been growing evidence of comorbidity between internet addiction and depression in youth during the COVID-19 period. According to the network theory, this may arise from the interplay of symptoms shared by these two mental disorders. Therefore, we examined this underlying process by measuring the changes in the central and bridge symptoms of the co-occurrence networks across time.

Methods

     A total of 852 Chinese college students were recruited during two waves (T1: August 2020; T2: November 2020), and reported their internet addiction symptoms and depressive symptoms. Network analysis was utilized for the statistical analysis.

Results

     The internet addiction symptoms “escape” and “irritable,” and depression symptoms “energy” and “guilty” were the central symptoms for both waves. At the same time, “guilty” and “escape” were identified as bridge symptoms. Notably, the correlation between “anhedonia” and “withdrawal” significantly increased, and that between “guilty” and “escape” significantly decreased over time.

Conclusions

     This study provides novel insights into the central features of internet addiction and depression during the two stages. Interestingly, “guilty” and “escape,” two functions of the defense mechanism, are identified as bridge symptoms. These two symptoms are suggested to activate the negative feedback loop and further contribute to the comorbidity between internet addiction and depression. Thus, targeting interventions on these internalized symptoms may contribute to alleviating the level of comorbidity among college students.

Methods

Participants and procedure

     From August to November 2020, using a cluster sampling method, 1,162 college students completed a survey via a Chinese online questionnaire at T1 (August 2020). The second wave (T2: November 2020) of data collection included 1,082 participants. Missing complete at random (MCAR) proposed by Little (1988) was utilized to assess whether the missing data were random. It is worth noting that during T1, college students were preparing to return to school, and T2 took place three months later to allow students to settle in.

     A total of 852 students who completed the questionnaire during both waves—through the matching of their phone numbers—were included in the final analysis. No missing data needed rejection because all items were required to be answered before submission. The research collected information including demographics, age, gender, family structure, current location, whether the participants had siblings or not, depression, and IA. The sample consisted of 300 (35.21%) males (Meanage = 20.22, SD = 2.07) and 552 (64.79%) females (Meanage = 20.79, SD = 2.15), with ages ranging from 17 to 28.

Conclusion

     To our knowledge, this study is the first to investigate network structure and its dynamic changes between IA and depression in Chinese college students. The results revealed that the IA symptom “escape” and depression symptom “guilty,” two functions of the defense mechanism, showed both central and bridge characteristics. These two symptoms were suggested to activate the negative feedback loop and to further contribute to the comorbidity between IA and depression. These bridge symptoms have an enlightening effect on interventions and treatments of comorbidity of IA and depression.

Abstract

Background

     Previous studies have reported that Internet addiction (IA) is an important predictor of aggression. However, little is known whether aggression is a risk factor of IA. This study aims to explore whether IA can predict total and sub-types of aggression, whether aggression also can predict IA, and the potential sex difference.

Method

     A total of 15,977 adolescents aged 12 to 18 were recruited though a stratified cluster sampling across five representative provinces in mainland of China. Young’s Internet Addiction Test (IAT) and Buss and Warren’s Aggression Questionnaire (BWAQ) were used to measure IA and aggression, respectively.

Results

     The prevalence of IA and total aggression was 16.8 % and 25.0 %, respectively. Binary logistic regression analysis indicated that IAT score was significantly associated with total and five sub-types of aggression (P < 0.001). However, scores of physical aggression, hostility, and indirect aggression were positively associated with IA (P < 0.001), while verbal aggression and anger were not significant (P > 0.05). Moreover, the interaction of sex and IA was significant for aggression. Specifically, females with IA had higher risks of anger and indirect aggression but had lower risks of physical and verbal aggression compared to males with IA.

Limitations

     Cross-sectional design, self-reported information, and only Chinese rural adolescents.

Conclusions

     IA is a strong risk factor of total and five sub-types of aggression, while not all sub-types of aggression are predictors of IA. Prevention programs for adolescents’ IA and aggression should consider different aggression sub-types and sex.

Introduction

     Internet accessibility has increasing rapidly in recent years, especially for developing countries (Agbaria, 2021; Hahn and Kim, 2014). Although there are various benefits of Internet, a loss of control and excessive Internet use has been proposed as a novel psycho-behavioral disorder named Internet addiction (IA) (Lee et al., 2020; Yen et al., 2011), which is first conceptualized by Young (1996). The symptoms or signs of IA include an excessive occupation with Internet, a felt need to use Internet, repeated attempts to stop Internet use, a loss of time spent away from Internet, spending more time than planned on Internet, and feeling uneasy when being off Internet (Shaw and Black, 2008; Young, 1998a, 1998b).

     According to recent reviews, the prevalence of IA is higher in SouthEast Asia than that in other regions of the world and IA is considered as a major public health issue in China, Japan, and South Korea (Kuss et al., 2014; Kuss et al., 2021; Pan et al., 2020). In China, the prevalence of IA has an increasing trend year by year (Shao et al., 2018), and the pooled overall prevalence among college students was 11.3 % (95 % CI: 10.1 %– 12.5 %) (Li et al., 2018). However, compared to ample previous studies for Chinese college students (Chou et al., 2018; Shen et al., 2020; Shen et al., 2021; Shi and Du, 2019), the characteristics of IA among Chinese adolescents, such as junior and senior high-school students, have not been clearly delineated (Lin et al., 2018). Adolescents are the most vulnerable group for developing IA (Arrivillaga et al., 2020), since they generally have changeability in cognitive control and boundary setting skills (Agbaria, 2021; Lee et al., 2020; Obeid et al., 2019). Therefore, it has great significance to explore the prevalence and characteristics of IA among adolescents, which is particularly important to prevent IA and its adverse outcomes.

Conclusion

     This study not only contributes novel theoretical understandings of the relationship between IA and aggression, but also provides several practical implications for preventing IA and aggression. The findings suggest that IA plays a prominent role in total and five sub-types of aggression among Chinese adolescents. Therefore, IA intervention seems a promising strategy for preventing adolescents’ aggressive behaviors. However, not all sub-types of aggression are predictors of IA, and early intervention efforts for IA should target to those adolescents who presented hostility, physical aggression, and indirect aggression. Moreover, sex is a key influencing factor of both IA and aggression, and males and females with and without IA have different risks of aggression. Thus, different strategies based on sex could be more effective and economic than one-size-fits-all method in developing prevention programs for adolescents’ addictive and aggressive behaviors.

Habit and addiction are two distinct drivers of information technology (IT) use that nonetheless bear increasing resemblance in how they are conceptualized and modeled in the information systems (IS) literature. The purpose of this study is to aid the further growth of these heretofore-independent streams of research by developing a comparative framework that allows us to distinguish between them. Drawing on the theories of automaticity and incentive-sensitization, we developed a theoretical model that describes the nature, antecedents, and consequences of habit and addiction of IT use in the context of social networking services. The proposed model was tested based on the data collected from 420 actual users of Twitter. We found that habit was indeed influenced by its proposed unique antecedents of routine seeking and cognitive rigidity, whereas addiction was not. Similarly, addiction was influenced by its proposed unique antecedents of focused immersion and concern for social acceptance, whereas habit was not. Looking at their outcomes, we found that although both drivers positively affected goal-congruent outcomes, only habit enhanced goal-congruent usage and addiction had a negative impact. Overall, this study contributes to the IS literature by offering a robust conceptual framework that allows us to observe the profound differences between these superficially similar drivers of routine IT use.

Introduction

In recent years, online services have become integral elements of our lifestyle that we constantly reach for throughout the day. An exemplary case is seen in the ubiquitous use of social networking sites (SNS): Almost a third of Facebook users and a fifth of Twitter users returning to their SNS several times a day (Hampton, Goulet, Rainie, & Purcell, 2011; Thadani & Cheung, 2011). In terms of time spent on SNS, 17.3% of users spend more than 10 h a week on their preferred service, and 3.3% spend up to 25 h a week (Salaway, Caruso, & Nelson, 2008). Information systems (IS) researchers have recognized that frequent use of information technology (IT) is often habitual and involves markedly different psychological states and behavioral consequences than those that over time occur with merely continued use (Guinea & Markus, 2009; Kim, 2009; Kim & Malhotra, 2005; Limayem, Hirt, & Cheung, 2007; Polites & Karahanna, 2012). But for some users, online services such as SNS might be more an addiction than a habit. The IS discipline has already shown keen interest in both forms of the use of IT applications. In particular, Kim and Malhotra (2005) found that prior use of certain online services is a strong predictor of future use, and thus indicative of habitual tendencies. Limayem et al. (2007) similarly showed that past use strengthens habit, which eventually drives continued use at the postadoption stage. In addition, Polites and Karahanna (2012) demonstrated that habitual behavior tends to prevent IT users from adopting other applications. Several pioneering works related to IT addiction have also recently been published (Turel, Serenko, & Bontis, 2011a; Turel & Serenko, 2012). These studies paint an unequivocal picture of the harmful effects of obsessive IT use driven by addiction. Looking at mobile e-mail use among workers, Turel et al. (2011a) found that addiction increases technology-family conflict and work overload. In a similar vein, Turel, Serenko, and Giles (2011b) showed that addiction to online auctions distorts individuals’ beliefs in the perceived usefulness and perceived enjoyment of IT.

Internet addiction (IA) is prevalent and associated with negative measures of health functioning, with males appearing more vulnerable than females. However, little is known about gender-related differences in the effect sizes of IA globally. This multinational meta-analysis addresses this gap in knowledge by providing estimates of effect sizes of gender-related differences in IA tendencies across jurisdictions and how they relate to global national indexes including gross domestic product (GDP) per capita, internet penetration, gender-related gaps in economies, internet penetration, alcohol consumption, smoking prevalence, life satisfaction and suicide rates. One-hundred-and-one studies consisting of 115 independent samples involving 204,352 participants from 34 countries/regions were identified. The average effect size of gender-related differences in IA in a random-effects model was small at g = 0.145. The highest gender-related effect sizes were in Asia with g = 0.208, and the lowest were in the North America with g = −0.049, Africa with g = 0.092 and Europe with g = 0.114. Metaregression revealed that smaller effect sizes were observed in nations with greater GDP per capita and higher internet penetration. As operationalized, both the internet availability and social norms hypotheses were supported by effect sizes being positively related to gender-related differences in economic measures, internet penetration, smoking prevalence and alcohol consumption. The Psychological Well-being hypothesis was largely not supported, since the effect sizes were unrelated to gender-related differences in life satisfaction and negatively related to gender-related differences in suicide rates. Findings suggest economic factors, internet availability, social norms and some addiction-related health factors may relate importantly to gender-related differences in IA tendencies across countries.

Introduction

Internet addiction (IA) is characterized by excessive or poorly-controlled preoccupations, urges or behaviors regarding internet use that lead to impairment or distress (Shaw & Black, 2008). IA is associated with suicidality even after adjusting for potential confounding variables including depression (Cheng et al., 2018). Such information dovetails with ongoing efforts of the WHO to promote public health and prevent internet-use-related harms and disorders, including with respect to the inclusion of hazardous gaming and gaming disorder in the eleventh edition of the International Classification of Diseases (ICD-11) (King et al., 2018; Potenza, 2018; Rumpf et al., 2018). IA is a clinically relevant phenomenon that may affect both women and men (Anderson, Steen, & Stavropoulos, 2017; Liang, Zhou, Yuan, Shao, & Bian, 2016). The global prevalence of IA was estimated to be 6.0% (95% CI: 5.1–6.9) according to a meta-analysis from 31 countries spanning multiple global regions (Cheng et al., 2014). Despite multiple publications suggesting that males have higher prevalence estimates of IA (Mei, Yau, Chai, Guo, & Potenza, 2016; Tsitsika et al., 2014), relatively little is known regarding gender-related differences at a global level and gender-specific relationships with other measures across nations.

YouTube is a popular video-sharing platform where viewers can watch videos made by media performers called YouTubers. YouTube is a social media site conducive to the development of parasocial relationships, which consists in asymmetrical relationships between media users and media performers. The aim of this study is to identify the determinants of YouTube addiction by examining the relationships between social anxiety, parasocial relationships with YouTubers and YouTube addiction based on a cognitive-behavioral theoretical framework. Data from 932 participants were collected through an online survey. Multiple regression analyses and structural equation modeling using a bootstrap procedure reveal that 1) social anxiety and 2) parasocial relationships with YouTubers are predictors of YouTube addiction, 3) social anxiety is a predictor of parasocial relationships, 4) social anxiety moderates the relation between parasocial relationships and YouTube addiction, 5) parasocial relationships mediate the relation between social anxiety and YouTube addiction and 6) social anxiety also moderates this mediated effect. The proposed moderated-mediation model of YouTube addiction fits well the data. The findings of this study contribute to the literature on the parasocial compensation hypothesis while providing useful information for prevention and intervention for YouTube addiction and social anxiety disorder.

Introduction

Launched in 2005, YouTube is a popular video-sharing platform with over one billion users and one billion hours of video viewed every day (YouTube, 2017). It is the second most visited website in the world after Google and ahead of Facebook (Alexa, 2019). YouTube is the most popular social media platform in the US with 73% of American adults using it (Pew Research Center, 2018). Social media sites can be characterized as internet applications that permit users to create and exchange content with others (Kaplan & Haenlein, 2010). They all have their unique architecture, norms and culture (Smith, Fischer, & Yongjian, 2012). Among them, social networking sites (SNS), and especially Facebook, are the most popular and the most studied (Kuss & Griffiths, 2017). SNS are characterized by the fact that users create personal profiles and use the platform to interact with real-life friends or to meet new people (Kaplan & Haenlein, 2010; Kuss & Griffiths, 2017). YouTube does not possess all the social networking functionalities and is best categorized as a content community within the scope of social media sites (Kaplan & Haenlein, 2010; Kuss & Griffiths, 2017). Whereas SNS are more focused on relationships between users, YouTube is focusing on content viewing (Khan, 2017). As in the case of SNS, the success of YouTube comes along with the development of addictive behaviors toward the website (Balakrishnan & Griffiths, 2017; Klobas, McGill, Moghavvemi, & Paramanathan, 2018; Masters, 2015). Regarding SNS, social networking functions seem to act as reinforcement of online addictive behaviors through the enjoyment they bring to the users (Turel & Serenko, 2012). However, as YouTube architecture differs from SNS architecture, determinants of YouTube addiction may also differ from the determinants of SNS addiction. Based on a cognitive-behavioral framework of online addiction (Davis, 2001), the aim of the present study is to identify the specific determinants of YouTube addiction.

In recent years, published studies have examined the construct of problematic smartphone use. Problematic smartphone use is often defined as excessive use of a smartphone, with social or occupational functional impairment, and including dependence and symptoms seen in addictive disorders such as withdrawal and tolerance (Billieux et al., 2015a). Research has examined relations between psychopathology and problematic smartphone use and/or the continuum of smartphone use frequency. Depression and anxiety severity in particular are well-supported in association with problematic smartphone use and use frequency (reviewed in Elhai et al., 2017a). However, little research has explored the role of more contemporary transdiagnostic constructs of psychopathology in studying these relationships. The most commonly studied psychopathology constructs in relation to problematic smartphone use and usage frequency include variables measuring levels of depression, anxiety, stress and low self-esteem (reviewed in Elhai et al., 2017a). Depression and anxiety severity have demonstrated moderate and small links (respectively) to levels of problematic smartphone use and usage frequency, with effect sizes of .30 to .50 for depression severity (recently in Demirci et al., 2015; Lu et al., 2011; Smetaniuk, 2014), and approximately .20 for anxiety severity (recently, Demirci et al., 2015; Elhai et al., 2016; Kim, R et al., 2015; Lee, Y-K et al., 2014). These findings generally parallel those from the literature on internet addiction (but not specifically smartphone use) (Ho et al., 2014; Prizant-Passal et al., 2016). However, effect sizes are nonetheless small on average for anxiety, and in some studies for depression severity (Augner and Hacker, 2012; Elhai et al., 2016, 2017b; Kim, J et al., 2015). Some evidence suggests a bidirectional relationship, whereby problematic smartphone use can lead to depression and anxiety severity, and vice-versa (van Deursen et al., 2015; Yen et al., 2012). Theory and empirical evidence demonstrate a dual system process that underlies addictive disorders, involving a balance between impulsive reward seeking and reflection/inhibition (Bechara, 2005; Volkow and Fowler, 2000). This theory has been supported in research on problematic use of technology, and suggests that deficits in brain circuitry may be responsible for such problematic use (Turel and Qahri-Saremi, 2016; Turel et al., 2016). Relevant to the present paper, depression and anxiety severity, and rumination, may be caused by the same types of brain circuitry deficits found in the addictive disorders. Furthermore, research on problematic smartphone use thus far has not examined more contemporary “transdiagnostic” psychopathology constructs – that is, constructs which cut across numerous mental disorders. Such constructs are increasingly important in understanding mechanisms involved in the etiology and maintenance of psychopathology (Mansell et al., 2008). Mediating and moderating variables between psychopathology and problematic internet use (albeit not specific to smartphone) have been tested and supported recently (Brand et al., 2016; Jiang, 2014).