Do men become addicted to internet gaming and women to social media? A meta-analysis examining gender-related differences in specific internet addiction

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Highlights

  • Gender differences exist globally in specific internet use behaviors/disorders.

  • Higher levels of internet gaming disorder (IGD) exist in males.

  • Higher levels of social media addiction (SMA) exist in females.

  • The moderating influence of region existed in IGD and SMA.

  • Studies of IGD and SMA may unmask features lost in studies of internet addiction.

Abstract

Males have been proposed to be more vulnerable to internet addiction (IA) than females. However, males and females may differ with respect to specific patterns and types of internet usage and related IA. To investigate further, a meta-analysis was conducted to investigate gender-related differences in internet gaming disorder (IGD) and social media addiction (SMA). The current meta-analysis aimed to quantify gender-related effect sizes relating to IGD and SMA, examine potential moderating influences of regions and other possible confounds, and compare the findings with generalized IA at the country level. The meta-analysis comprised 53 effect sizes with 82,440 individuals from 21 countries/regions for IGD, and 41 effect sizes with 58,336 individuals from 22 countries/regions for SMA. A random-effects model confirmed important gender-specific distinctions as men were more likely to exhibit IGD than women (g = 0.479) and less likely to exhibit SMA than women (g = −0.202). Additional moderator analyses revealed that effect sizes of IGD and SMA were larger in Europe and the Americas than in Asia. Further analyses indicated that the effect sizes of gender-related differences in IGD and SMA at the country-level were significantly larger than those in generalized IA, which suggests that gender-related differences in specific IAs may be underestimated in the “umbrella” of generalized IA. Results have implications for explaining why males and females may become addicted to internet use through different pathways.

Introduction

The internet has become an indispensable part of people's lives. By June 2019, there were more than 4.5 billion internet users worldwide, and the internet penetration rate reached 58.8% (Internet World Stats, 2019). The internet provides a wide range of information and facilitates communication, and it also brings potential problems, including the phenomenon of internet addiction (IA). IA has been defined as “a psychological dependence on the internet” (Kandell, 1998), and it is characterized by excessive or poorly controlled preoccupations, urges, or behaviors regarding internet usage that lead to impairment or distress (Shaw & Black, 2008; Weinstein & Lejoyeux, 2010). Some other labels like “Internet Addiction Disorder,” “Pathological Internet Use,” “Problematic Internet Use,” “Excessive Internet Use” and “Compulsive Internet Use” have all been used in the literature to describe similar concepts relating to IA (Widyanto & Griffiths, 2006). Although it is still controversial whether IA constitutes a behavioral addiction since it was not specifically acknowledged in DSM-5 or ICD-11, research into IA has proliferated in recent decades, and multiple reports highlight the potential negative consequences of IA and its psychiatric comorbidities (Bisen & Deshpande, 2018). For example, IA has been related to anxiety, depression, and poor sleep quality (Lam, 2014; Liang et al., 2016; Malak et al., 2017), as well as reported problems in relationship, academy, and occupation (Bisen & Deshpande, 2018). A multinational meta-analysis, including 31 countries, reported the IA prevalence over the world was as high as 6.0% (Cheng & Li, 2014), indicating it's an important emerging public health problem.

In the process of evaluating factors related to IA from a multidimensional perspective, demographic factors are often first considered by researchers (Yen et al., 2009). Among them, “gender is a crucial factor to explain why people are addicted to the internet through different pathways” (Tang et al., 2017). Gender-related differences in addictive behaviors and their motivations have been reported, and these have been considered important for understanding IA (Liang et al., 2016). Males have often been reported to have higher levels of IA than females (Anderson et al., 2017; Chen et al., 2015), but there exist some contrary results (Rukuye Aylaz, 2015; Sun et al., 2012). To investigate further, Su et al. (2019) conducted a meta-analysis involving 34 global jurisdictions and found that males were more likely than females to experience IA with a small effect size of 0.145 (Hedges' g). According to the study, the gender-related effect sizes of IA were larger in Asia and smaller in other regions, which may be explained by both economic and cultural factors (Su et al., 2019).

Previous studies of generalized IA have demonstrated the importance of how gender may influence online behaviors. However, gender may relate differently to specific types of IA. Young et al. (1999) classified IA into five types, including cybersex, cyber-relationships, online stock trading or gambling, information overload, and computer games. Davis (2001) proposed a cognitive-behavior model to distinguish between “generalized pathological internet use” and “specific pathological internet use.” Specific pathological internet use refers to the condition in which an individual pathologically uses the internet for a particular purpose (e.g., online shopping or online gambling), whereas generalized pathological internet use defines as “a general, multidimensional overuse of the internet” (Davis, 2001; Montag et al., 2015). Differences between generalized and specific IAs were supported by a cross-cultural empirical study (Montag et al., 2015). Thus, it appears important to distinguish between specific forms of IA when considering gender-related differences. With respect to specific IAs, internet gaming disorder (IGD) and social media addiction (SMA) have been described particularly frequently (Mérelle et al., 2017). As examples of specific types of online behaviors, Spilkova et al. (2017) commented, “there is a need to distinguish between online gaming and social media use, and perceive them as separate concepts that should not be combined into a unique category of generalized internet use.” Therefore, we focused our analytical efforts on IGD and SMA in this study.

According to research on IGD and SMA over the last decade, extensive findings suggest that females are more prone to problems with online communication and social media use, while internet gaming is more prevalent among males (Bouna-Pyrrou et al., 2015; Spilkova et al., 2017; van den Eijnden et al., 2018), and these findings may differ for generalized IA. For example, in the study by Tang et al. (2017) involving a Chinese sample, the male-related effect size for generalized IA was small at 0.15, but it was 0.67 for IGD and −0.10 for SMA; similarly, in results from their United States sample, no significant male-related effect size was found in generalized IA (g = −0.03), but there was a positive medium effect size for IGD (g = 0.58) and negative effect size for SMA (g = −0.42). It is possible that males consider internet gaming and females consider social media when completing IA assessments; thus, the effects of the two gender groups may “cancel” each other (Hawi & Samaha, 2019). As such, it is important to distinguish gender-related differences in specific IA subtypes, especially IGD and SMA.

IGD has increasingly been considered an important research topic given the increasing prevalence of gaming and associations between IGD and psychological distress and impaired functioning (Lemos et al., 2016). IGD has been defined as, “persistent and recurrent use of the internet to engage in games, often with other players, leading to impairment or clinically significant distress” and categorized as a “condition for further study” in the DSM-5 Section III (American Psychiatric Association, 2013). Moreover, gaming disorder including online variants has been officially included in the 11th edition of the International Classification of Diseases (ICD-11) (WHO, 2019).

Males typically spend more time gaming per day than females (Chang et al., 2018), and this has been observed across jurisdictions in adults (Laconi et al., 2017) and adolescents (Wichstrom et al., 2019). Not surprisingly, males are more prone to develop IGD than females across age groups (Choliz & Marco, 2011; De Pasquale et al., 2018; Stavropoulos, Adams, et al., 2019). In German adolescents, the prevalence of IGD in boys was 5.9% and in girls was 1.0% (Wartberg et al., 2020). In the Netherlands, boys scored higher on IGD measures than girls, and the effect size was moderate in magnitude (Cohen's d = 0.57) (Kokonyei et al., 2019). In adults, Amendola et al. (2019) reported that males were at greater risk of IGD than females. Similarly, according to the study by Bonnaire and Baptista (2019), most young adult participants in a problematic gaming group were male (78.4%). Several possible explanations have been forwarded regarding gender-related differences in IGD. With respect to neural mechanisms, gaming cues elicit higher cravings in male subjects and exhibit different neural correlates (Dong et al., 2019; Dong, Zheng, et al., 2018). Competitive structures in games may be more attractive to males, who may gain feelings of success and achievement from gaming (Hamlen, 2010; Wartberg et al., 2020). From a sociocultural perspective, male aggressiveness may be considered more socially acceptable, and some game designers set males as the target users of violent and adventurous online games (Barua & Barua, 2012; Bryce & Rutter, 2003). Such gaming may then link to adverse behaviors in individuals with specific temperamental tendencies (e.g., those high in sensation-seeking) (Zhai et al., 2020).

Although multiple studies support a higher prevalence of IGD in males, there exist some contrary findings (Scerri et al., 2019, Stavropoulos et al., 2019, Stavropoulos et al., 2019). In a sample from India, girls scored higher on IGD measures than boys (Rajanna et al., 2016). Consequently, whether males are more likely than females to exhibit internet-gaming IA warrants additional systematic research.

The development of the internet and the popularity of smartphones have increased the use of social media, which allows people to develop, initiate, and maintain existing relationships and new ones (Abbasi, 2019). The umbrella term ‘social media’ comprises multiple types of websites including social networks, messengers, and blogs(van den Eijnden, Lemmens, & Valkenburg, 2016). SMA has been defined as an “inability to regulate the use of social networks, which leads to negative personal outcomes” (Larose et al., 2010; Ryan et al., 2014). As there still exist ongoing debates, relatively limited research, and less direct evidence of clinically significant impairment, SMA as well as other non-substance addictions (except gambling disorder and IGD) have not been officially included in the DSM-5 (Petry et al., 2014). However, it has been proposed that the category of “other specified disorders due to addictive behaviors” may be used to diagnose SMA (Brand et al., 2020).

Multiple studies have investigated gender-related differences in SMA (Andreassen et al., 2017), with girls spending more time on social media than boys and being younger when first exposed to social media (Chae et al., 2018). Females may be more vulnerable to developing SMA than males (Andreassen et al., 2017; Martinez-Ferrer et al., 2018; Romero-Abrio et al., 2019). According to Demircioğlu and Göncü Köse (2018), females score significantly higher than males on measures of SMA. Females in general may have higher tendencies for SMA related to interpersonal relationship orientations (Chae et al., 2018). Females often value relationships more than males and tend to use social media as interaction tools (Fujimori et al., 2015; Kim et al., 2010). Gender-related differences in motivation to use social media exist, as males primarily use social media for entertainment and recreation and females use social media mainly for information and interpersonal communication (Noguti et al., 2019). Females are more likely to use social media in response to feelings of emptiness when their social needs are not fully met in real life, which may increase the likelihood of SMA (Chae et al., 2018).

Although data suggest that SMA is more prevalent among females than males, there exist some contrary findings. For instance, males score higher on SMA measures than females in some studies (Araujo Robles, 2016; Cam & Isbulan, 2012). As such, systematic studies of gender-related differences in SMA are warranted.

As research on gender-related differences in IGD and SMA has increased recently, systematic evaluation of accumulated data is needed. While multiple reports suggest that males more frequently experience IGD and females SMA, contrary research findings also exist, and a meta-analysis may help to resolve such apparent discrepancies (Gurevitch et al., 2018). Meta-analyses provide a more powerful and less biased means for clarifying, quantifying and disproving (or confirming) hypotheses than do many other approaches (Murad & Montori, 2013). The primary aim of the present study is to synthesize findings from research on gender-related differences in IGD and SMA and to identify factors (e.g., geographic region) that may influence the relationships. The current study sought to investigate specific IAs (IGD and SMA), and we suspected that in a meta-analysis investigating specific IAs that we would observe more pronounced gender-related differences than have typically been reported for generalized IA. The following hypotheses were proposed:

H1

Males will demonstrate higher levels of IGD (i.e., demonstrate a positive effect size), and females will demonstrate higher levels of SMA (i.e., demonstrate a negative effect size).

H2

Generalized IA will have a “masking effect” on gender-related differences in IGD and SMA, with the effect size in generalized IA being significantly smaller than the absolute value of the effect sizes of IGD and SMA.

Section snippets

Literature search

For the meta-analysis, the databases searched included ScienceDirect, Springer, Wiley, MEDLINE, ProQuest, and Web of Science. As the internet and how people use it has been changing over time, the current study focused on the most recent 10 years from January 2010 to August 2019 (including online-first publications). For IGD, search terms were: (“internet gaming disorder” OR “internet gaming addiction” OR “computer game addiction” OR “online game addiction” OR “problematic internet game use” OR

Effect size and homogeneity tests

The dataset included 53 independent effect sizes of gender-related differences in IGD tendencies from 82,440 participants of 21 countries/regions. The sample sizes ranged from 104 to 25,573. About half (53.2%) of participants were male. Features, including sample characteristics, measures, and effect sizes, were recorded for each sample (see Table 1 for descriptive statics and Supplementary Table S1 for details).

The results showed a high level of heterogeneity (96.84%) in the IGD studies. The Q

Discussion

The main purpose of this meta-analytic study was to synthesize empirical evidence on gender-related differences in levels of IGD and SMA, as well as to identify possible moderators and relationship with generalized IA. This study clarifies important gender-specific distinctions in IGD and SMA from a global perspective, a point that has not yet been adequately established. A total of 53 independent samples from 21 countries/regions were identified for IGD, and 41 independent samples from 22

Conclusion

In conclusion, this study is the first to systematically examine effect sizes of gender-related differences in IGD and SMA. The current meta-analysis involving 140,776 participants and 94 independent samples across the world provides an accurate effect size of gender-related differences from a global perspective, revealing differences in regions, thus extending descriptions from non-systematic reviews of this topic. The findings reveal important gender-specific distinctions as men were more

Funding

Dr. Su's work was funded by the China Scholarship Council (Grant No. 201706655002). Dr. Potenza's involvement was supported by the Connecticut Council on Problem Gambling and the National Center for Responsible Gaming.

CRediT authorship contribution statement

Wenliang Su: Conceptualization, Methodology, Formal analysis, Visualization, Writing - original draft, Writing - review & editing, Project administration, Funding acquisition. Xiaoli Han: Methodology, Investigation, Data curation, Formal analysis, Visualization, Writing - original draft, Writing - review & editing. Hanlu Yu: Investigation, Data curation. Yiling Wu: Investigation, Data curation. Marc N. Potenza: Supervision, Writing - review & editing.

Declaration of competing interest

No conflicts of interests are present among the authors of this paper. Dr. Potenza reports the following disclosures. Dr. Potenza has received financial support or compensation for the following: Dr. Potenza has consulted for and advised RiverMend Health, Opiant Pharmaceuticals, the Addiction Policy Forum and AXA; has received research support from the Mohegan Sun Casino and the National Center for Responsible Gaming; and has consulted for or advised law offices and gambling entities on issues

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