Peng Wang
Jun Wang
Yun Yan
Yingdong Si
Xiangping Zhan
Yu Tian- Department of Psychology, Shandong Normal University, Jinan, China
This study aimed to explore the mediating effects of internet gaming disorder, social network use, and generalized pathological internet use (GPIU) on the association between loneliness and depression. A total of 2211 junior high school students completed questionnaires regarding loneliness, internet gaming disorder, social network use, GPIU, and depression (aged 10–16 years). The results of a structural equation model revealed that (a) the path coefficient of loneliness to depression was significantly positive, (b) loneliness could not predict depression through GPIU directly, but (c) loneliness could predict depression through internet gaming disorder to GPIU, (d) loneliness could predict depression through social network use to GPIU, and (e) loneliness could not predict depression through internet gaming disorder to social network use to GPIU. These results provided significant implications for the prevention and reduction of depression in Chinese junior high school students.
Introduction
Junior high school students are faced with such problems as high academic pressure and monotonous life, which may easily lead to loneliness, depression, and other mental health problems (Elizabeth and John, 2000; Zhang, 2000; Xu, 2012). In recent years, many studies have proved that depression is a mental illness with high prevalence, high chronic recurrence rate, high disease burden, and high suicide mortality (Birk et al., 2019), and there was a high correlation between loneliness and depression (Ren et al., 2020). Studies have demonstrated that adolescents with generalized pathological internet use (GPIU) usually suffer from loneliness (Sukenick, 2012) and depression (Zou et al., 2007). A number of studies have focused on the research of internet gaming disorder and social networking disorder (Bouna-Pyrrou et al., 2015; Wartberg et al., 2020). This study aims at studying the mediating effects of internet gaming disorder, social network use, and GPIU on the association between loneliness and depression.
Depression
World Health Organization research worldwide shows that depression is affecting physical and mental health in the 21st century (Holden, 2000). From a psychological point of view, depression is the consequence of an ineffective response to life stress, and emotional disorders are the core characteristic, including worthlessness, helplessness and despair, and decreased levels of physical activity (Hammen, 1992). Depression affects emotions as well as thinking, motivation, attention, imagination, behavior, social relationships, and physical conditions, resulting in people feeling lonely and unwell (Jean-Paul et al., 2001; Garcia-Retamero et al., 2015). Junior high school students are in a critical period of physical and mental development and social maturity (Zhang, 2017). The contradiction between ideal and reality often leads to negative emotions, mainly manifested by depression and anxiety, which hinders the healthy development of junior high school students’ mental and physical development (Emamjomeh and Bahrami, 2015). Studies have shown that adolescents’ depressive symptoms increase significantly in frequency and generality (Lewinsohn et al., 1993; Yang et al., 2010; Ferguson, 2020). Many factors affect depression, including both external risk factors and individual susceptibility (Tao, 2006).
Loneliness and Depression
Clinical and related statistics have demonstrated that loneliness is a common issue of modern people (Huang, 2000; Naama et al., 2019). Adolescence is a particularly vulnerable stage for experiencing feelings of loneliness, which is a significant factor for adolescent health and quality of life (Carvajal-Carrasca and Caro-Castillo, 2009; Danneel et al., 2019). Peplau and Perlman (1979) pointed that loneliness occurred when a person’s social network made him/her less satisfied than he/she expected. Some authors have reported that when social quality declines, the original network of relationships (loss or loss of loved ones, relocation) or lack of social skills (personality factors) could lead to strong loneliness (Vazquez and Garcia, 1997; Teunisse et al., 1999; Tijhuis et al., 1999; Huang, 2000; Van et al., 2017). Hence, loneliness is a subjective feeling of unpleasant suffering caused by social defects (Liu, 1995), and long-term or severe loneliness may trigger certain emotional disorders and reduce mental health (Naama et al., 2019).
Studies found that there was a high correlation between loneliness and depression (Cacioppo et al., 2006; Demir and Kutlu, 2016; Ren et al., 2020). On the one hand, among the negative effects of depression, loneliness is the most common (Ling, 2009). When individuals have negative emotions such as anxiety, pessimism, and disappointment, they tend to experience more feelings of loneliness, loss of help, and desire to be understood. However, on the other hand, loneliness is very detrimental to mental health; a high level of the feeling of loneliness is thought to stimulate depressive symptoms (Wei et al., 2005; Demir and Kutlu, 2016). Qualter et al. (2010) found that the interactive effect of loneliness at 5 and 9 years old predicted depressive symptoms at age 13. Cacioppo et al. (2010) published a recent 5-year longitudinal study that found that loneliness predicted an increase in depressive symptoms at yearly intervals, while depressive symptoms did not predict an increase in loneliness at the same time. A longitudinal study also found a gender-dependent impact of loneliness on depressive symptoms. For females, loneliness could significantly predict the increased depressive symptoms, while for males, loneliness could not (Liu et al., 2019). Therefore, our study will focus on the relationship between loneliness and depression. Based on the above literature discussion, we proposed the following Hypothesis 1: Loneliness could positively predict depression.
Mediating Roles of GPIU
Pathological internet use (PIU) refers to a phenomenon of obvious social and psychological damage caused by excessive use of the internet, which means people are unable to control their internet usage (Musetti and Corsano, 2018; Starcevic et al., 2018). Based on the literature, Davis (2001) proposed a cognitive behavioral model of PIU, which highlighted the important role of maladaptive cognition in the development of PIU. The cognitive–behavioral model provided a theoretical explanation for the origin and pathogenesis of GPIU and specific pathological internet use (SPIU) in identifying the etiology of PIU. Davis (2001) proposed two types of PIU (Figure 1): SPIU and GPIU. SPIU is a reliance on the special features of the internet, such as internet gaming disorder and social networks use; GPIU refers to the global set of online behaviors, which contains general, multifaceted internet overuse that perhaps includes no clear destination killing time online (Lopez-Fernandez, 2018). Davis (2001) believed that psychopathology was the distal necessary cause of GPIU/SPIU symptoms. The underlying psychopathology itself did not cause the symptoms of GPIU/SPIU, but it was a necessary factor in its etiology. The key factor in the occurrence of GPIU/SPIU was maladaptive cognition, which was its proximal and sufficient reason.
Figure 1. Cognitive behavioral model of pathological internet use (PIU). Adapted from Davis (2001).
Generalized pathological internet use, characterized by excessive or compulsive internet use and a preoccupation with and loss of control over this use, results in negative personal, professional consequences (Davis, 2001; Caplan, 2002), and may be detrimental (Greenfield and Yan, 2006). Studies found that adolescents with GPIU usually suffered from loneliness (Young and Rogers, 1998; Engelberg and Sjoberg, 2004; Sukenick, 2012), depression (Jia, 2005; Zou et al., 2007), shyness (Eroglu et al., 2013), poor interpersonal relationships (Sanders et al., 2000; Odaci and Çikrikçi, 2014), cognitive distortion (Lu and Yeo, 2015), and other decreases in well-being (Liu et al., 2012). According to the cognitive behavioral model (Davis, 2001), pre-existing psychosocial problems (depression, or low levels of social support) predispose an individual to GPIU cognitions, behaviors, and negative outcomes. Therefore, investigating the detailed relationships of these variables and GPIU is necessary.
Internet addiction was positively associated with loneliness and depression (Kraut et al., 1998; Young and Rogers, 1998; McKenna and Bargh, 2000; Li et al., 2001; Nie et al., 2002; Young and Kimberly, 2007; Ko et al., 2009). Loneliness is more common among internet addicts (Fan and Yuan, 2018), and loneliness in children and adolescents is one of the predictors of internet addiction (Pontes et al., 2014). Tian et al. (2017) found that the association between loneliness and GPIU was dynamic and bidirectional, and Gao et al. (2018) found that loneliness could predicted GPIU. Then, Demir and Kutlu (2016) noted that loneliness was a significant predictor of internet addiction among college students at ages 17–31. In addition, a study found that teenagers in an internet addiction group had more experiences of online anxiety and depression (Jia, 2005; Zou et al., 2007). Akin and İskender (2011) pointed that if individuals could reduce their internet addiction, they would be able to reduce their depression level. Studies applying longitudinal designs pointed that the GPIU caused depression (Regina et al., 2011), and Park (2009) found that use of the internet tended to increase depression by a longitudinal study. Hence, we will focus on the mediating roles of GPIU between loneliness and depression. So, we proposed Hypothesis 2: Loneliness could predict depression through GPIU.
Mediating Role of Internet Gaming Disorder and Social Network Use Between Loneliness and GPIU
To improve prevention and intervention of GPIU, its inner working process must be explored, such as an investigation of the intermediary mechanism, which helps to explain how loneliness affects GPIU. The internet gaming disorder and problematic use of social networks belong to SPIU. Young and Kimberly (1998, 2007) conducted a series of studies on the behavioral characteristics of internet addicts. The results suggest that internet addicts tend to prefer online chat and online interactive games. Therefore, Young and Rogers (1998) posited that the internet was not addictive, but special network applications contributed to the occurrence of internet addiction. Gross (2004) research emphasized that the internet was very important for today’s youth and that computer games and the internet communication may lead to social isolation by replacing friends of children or young people. Also, more internet-addicted students indulge in new and exciting online games and two-way interactive online chat (Yao and Yang, 2014; Zhang and Lei, 2015). In recent years, a number of studies have proposed that the concept of internet addiction lacks specificity and focused on the research of internet gaming disorder and social networking disorder (Bouna-Pyrrou et al., 2015; Wartberg et al., 2020).
First, we proposed social network use as a mediator between loneliness and GPIU. Morahan-Martin and Schumacher (2000) found that individuals with high loneliness were more likely to seek social satisfaction on the network to compensate for social deficiencies in real life. Doane (2008) also found individuals’ loneliness experience was an important psychological factor that affected interpersonal communication, and individuals with high loneliness and social anxiety often cannot establish stable interpersonal relationships in real life and often search for alternatives through the network. Błachnio et al. (2016) also found that individuals with high loneliness used mobile social networks more often than individuals with low loneliness. In addition, some studies have asserted that online social networking can reduce more social cues and less direct evaluation by others than offline social networking. People prefer to self-present through social networks, establish and maintain their positive image, and reduce and avoid the level of anxiety that exists in real life (Lee et al., 2014). Thus, teenagers with loneliness are more likely to choose online socializing, which is manifested as pathological social network use.
Another mediator variable is internet gaming disorder. Internet games are a new form of entertainment combining traditional games with the internet, which have strong interactivity and virtuality, and will lead to many behavioral problems among teenagers (Chen and Fu, 2012), such as tired of school, truancy, dropping out of school, and even cybercrime. The emotional deficiency of lonely individuals in real life can be satisfied through social activities and emotional communication through games in the virtual world (Griffiths et al., 2004), which will make lonely people spend more time on online games, and leads to internet gaming disorder. Bozoglan et al. (2013) pointed that loneliness, self-esteem, and life satisfaction were affirmed to account for 38% of the total variance in internet gaming addiction, and loneliness was the most important variable predicting internet addiction. Lee et al. (2019) also proposed the relationship between loneliness and online game addiction. So, we presented Hypothesis 3: Loneliness could predict depression through social network use to GPIU; and Hypothesis 4: Loneliness could predict depression through internet gaming disorder to GPIU.
What is more, at present, more and more people use internet games to socialize online, but there are few relevant studies. Therefore, this study will also explore the chain mediating effect of internet gaming disorder to social network use in loneliness to GPIU. So, we further put forward Hypothesis 5: Loneliness could predict depression through internet gaming disorder to social network use to GPIU.
Materials and Methods
This study conformed to the code of ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans and was approved by the Ethics Committee of Shandong Normal University. Additionally, our research obtained written informed consent from the parents of the participants.
Participants
The participants were from a junior high school in Eastern China, which is an ordinary school and whose students are representative. Students in the first through third grades were included. After eliminating incomplete and repetitive questionnaires, a total of 2211 valid questionnaires were received. Overall, 1087 (49.2%) participants were male and 1124 (50.8%) were female. Their mean age was 13.04 years (aged 10–16 years, M = 13.04, SD = 1.226).
Procedures
We obtained informed consent from the school administrators and students before data collection. The paper questionnaire in Chinese was used in this study. To maintain the quality of the investigation, the junior high school students were gathered in a large assembly room to complete their questionnaires with the help of two researchers. The other grades finished the questionnaires with the help of two researchers during one full class period of 45 min.
Measures
UCLA Loneliness Scale
The UCLA Loneliness Scale comprises 20 items, for example, “I am unhappy doing so many things alone.” The items were rated on a four-point scale for frequency (1 = often to 4 = never). The final score was calculated by the total score of all the items, and higher scores indicated greater levels of loneliness. We also conducted CFA, with χ2/df = 3.846, RMSEA = 0.023, and CFI = 0.993. Cronbach’s alpha for the UCLA Loneliness Scale was 0.855.
Center for Epidemiologic Studies Depression Scale (CES-D)
The CES-D Scale was used to measure the students’ depression, which was adapted to the Chinese language and culture and comprised 20 items, for example, “I felt that I could not shake off the blues even with help from my family or friends.” The students rated each item on a four-point scale from 1 = rarely or none of the time (less than 1 day) to 4 = most or all of the time (5–7 days). To calculate the final CES-D score, the scores of all the items were added. A very high score meant severe depression. We also conducted CFA, with χ2/df = 3.510, RMSEA = 0.022, CFI = 0.994, and Cronbach’s alpha was 0.894.
Generalized Pathological Internet Use Scale (GPIUS)
To measure the students’ generalized pathological use of the internet, Patricia Gomez’s GPIUS was adapted (Gómez et al., 2017). A total of 11 items were comprised in this version of the GPIUS, for example, “You connected to the internet even though you knew it could get you in trouble.” The items on the GPIUS were rated on a seven-point Likert scale (1 = completely disagree to 7 = completely agree). In the end, the scores of all the items were added to obtain the total GPIUS score. The higher the score, the more serious the GPIU situation was. We also conducted CFA, with χ2/df = 3.794, RMSEA = 0.023, and CFI = 0.998. Cronbach’s alpha for the GPIUS was 0.883.
Ten-Item Internet Gaming Disorder Test
The Ten-Item Internet Gaming Disorder Test (IGDT-10) comprised 10 items and assesses levels of IGD (Orsolya et al., 2017). In an attempt to operationalize IGD, this instrument used the nine DSM-5 criteria in a brief and simple manner and adopted clear, unambiguous wording for each item. The diagnostic criteria of IGD based on the DSM-5 were strictly followed while considering Petry et al. (2014) recommendations to increase content validity. Each criterion was operationalized using a single item, except for the last criterion, referring to “jeopardy or losing a significant relationship, job, or educational or career opportunity because of participation in internet games” (Orsolya et al., 2017). This criterion was operationalized through two items given its complexity and description of more than one construct. Response options for the 10 items were never, sometimes, and often instead of yes and no. Consequently, the composite score of IGDT-10 was from 0 to 9, and higher scores indicated more severe cases of IGD. The CFA demonstrates that the fitting index is acceptable: χ2/df = 11.5, RMSEA = 0.045, and CFI = 0.994. Cronbach’s alpha of the scale was 0.68.
Social Network Use Intensity Scale
The original scale used the social strength website questionnaire compiled by Ellison, Steinfield, and Lampe to assess the intensity of youth social networking use (Ellison and Steinfield, 2006). The questionnaire comprises eight items, and the first two items use a self-reporting method to measure the number of individual social networking friends and the average daily spending on social networking sites. The latter six items use the seven-point Likert review method to measure the emotional connection strength of individuals and social networking sites and the extent to which social networking sites integrate individual life. This questionnaire removes the first two items. This measure includes two self-reported assessments of the Chinese network tools QQ and WeChat. Twelve questions measure the intensity of QQ and WeChat use. Six questions are related to WeChat, and QQ is the same. One sample item is as follows: Social networking sites are part of my daily activities. Participants rated each item on a five-point scale, from 1 (I really disagree) to 5 (I very much agree). All scores were standardized and added together, which is the intensity score for social networking sites. The higher the score, the greater the intensity of social networking sites. We also conducted CFA, with χ2/df = 35.99, RMSEA = 0.138, CFI = 0.865, and Cronbach’s alpha was 0.877.
Statistical Analysis
Liu pointed that the coefficient test results of normal distribution should be combined with the sample size. In large samples (n ≥ 200), the influence of non-normal would be reduced; that is, the assumption of normal distribution in large samples could be slightly violated (Liu, 2019). In this study, the test of the normality of variables was carried out by means of graphic test (Q–Q graph). First of all, as shown in the Figure 2, the Q–Q graph was used to test the normality of each variable, and the variables basically presented a normal distribution. The missing values were replaced by means. To prevent measurement errors caused by common method bias, we used Harman single factor analysis (Podsakoff et al., 2003). Next, descriptive and correlation analyses were used. Subsequently, a structure equation model was used to evaluate a multiple mediation model for the roles of social network use, internet gaming disorder, and GPIU in the relationship between loneliness and depression.
Figure 2. The Q–Q graph of each variable.
Moreover, parceling strategy was adopted to improve the quality of the model fit and the indicators (Tian et al., 2017). Firstly, we executed a factor analysis. Then, we sorted the items of each observed variable from highest to lowest according to factor loading size (Rogers and Schmitt, 2004). We sequentially assigned the highest and lowest remaining items to each package in turn and alternated the package until all items were completed.
The fits of the model were assessed using the chi-square (χ2) test, the RMSEA, the CFI, and the TLI. Considering the χ2 test is easily affected by the size of samples, model fit indices were used as the major standard to assess the model fit (Gao et al., 2018). The CFI and TLI ranged from 0 to 1, with values above 0.90 representing sufficient model fit (Hoyle, 1995). A criterion of thumb for the RMSEA is that values ≥0.10 represent poor fit, values between 0.05 and 0.08 represent a reasonable error of approximation, and values ≤0.05 represent close approximation (Cudeck and Browne, 1992).
We used the structural equation model to investigate the relationship of these variables through Mplus 7.0. The bootstrapping method was conducted to test the mediation effects. According to the mediation effect test procedure, the direct effect of loneliness on depression was first tested, and then the significance of the path coefficient and the fitting of the model after the addition of the mediator variables social network use, internet gaming disorder, and GPIU were tested. From 1000 resamples of the data, this method produced 95% bias-corrected confidence intervals of these effects. When the confidence interval did not contain zero, it meant that there was a significant effect at p < 0.05.
We conducted the common variance analysis to measure whether common method biases existed in this study. The χ2 test of Bartlett’s test of sphericity was significant. Then, we extracted 12 eigenvalues greater than 1 after a principal component analysis. The first factor to explain the variance was 19.305%. The results were less than the critical standard of 40% (Podsakoff et al., 2003), indicating that these instruments had no problem with the common method biases.
Results
Descriptive Statistics and Correlation Analysis
Table 1 shows the descriptive statistics and Pearson correlation of social network use, loneliness, internet gaming disorder, depression, and GPIU. The results indicated that loneliness had a weak negative correlation with social network use, and there was a significant positive correlation between other variables.
Table 1. Descriptive statistics and correlation matrix of all variables.
Testing for Multiple Mediating Roles of Social Network Use, Internet Gaming Disorder, and GPIU
First, the finding demonstrated that the path coefficient of loneliness to depression was significantly positive (Figure 3). The fits of this model were χ2/df = 5.78, RMSEA = 0.051, NFI = 0.978, CFI = 0.975, and TLI = 0.968, which indicated that the model was appropriate and acceptable.
Figure 3. Total effect model. Path values are the path coefficients. ∗∗p < 0.01. The latent variables loneliness and depression were divided into four packages through the packaging strategy.
Second, the indirect effects of internet gaming disorder, social network use, and GPIU between loneliness and depression were explored (Figure 4). The path coefficients of loneliness to internet gaming disorder/social network use, internet gaming disorder to social network use, internet gaming disorder/social network use to GPIU, and GPIU to depression were significantly positive. Specifically, the direct path coefficients of loneliness to GPIU were not significant.
Figure 4. Multiple mediation model. ∗p < 0.05, ∗∗p < 0.01, ∗∗∗p < 0.001. The latent variables loneliness and depression were divided into four packages through the packaging strategy, and the latent variables social network use, internet gaming disorder, and GPIU were divided into two packages through the packaging strategy.
Third, most of the mediated effects paths were significant (Table 2). Specifically, loneliness could predict depression through social network/internet gaming disorder to GPIU. However, loneliness could not predict depression through GPIU. The direct effect of loneliness on depression was significant, but path loneliness to GPIU was not significant after the addition of two mediator variables. What is more, loneliness could not predict depression through internet gaming disorder to social network use to GPIU.
Table 2. Test of the total effect model and mediation effect model.
Discussion
The purpose of this study was to explore the relationship between loneliness and depression and the mediating effects of social network use, internet gaming disorder, and GPIU, in order to further explore the underlying mechanisms by which loneliness affects depression. The total effect model indicated that loneliness could positively predict depression, and Hypothesis 1 was supported. The multiple mediation model showed that loneliness did not affect depression through GPIU, which did not support Hypothesis 2. The model also indicated that the two mediators of internet gaming disorder and social network use parallel mediated the relationship between loneliness and depression through GPIU, which supported Hypotheses 3 and 4. However, the two mediator variables could not have a sequential relationship between loneliness and depression, which did not support Hypothesis 5.
Loneliness and Depression
The result showed that the path coefficients of loneliness to depression were significantly positive, which was consistent with previous studies (Wei et al., 2005; Wang et al., 2008; Cacioppo et al., 2010; Demir and Kutlu, 2016). Studies have shown that loneliness is still significantly associated with depressive symptoms after controlling for demographic information (including gender, age, financial income, marital status, etc.) and risk factors that jointly influence loneliness and depression (including hostility, social support, stressors, etc.) (Cacioppo et al., 2006; Demir and Kutlu, 2016; Ren et al., 2020). Loneliness is associated with personality disorders, psychiatric disorders, and suicide, which can also impair executive control and increase depressive symptoms (Richman and Sokolove, 1992; Neeleman and Power, 1994; Deniro and Dorothy, 1995; Cacioppo et al., 2006; Mushtaq et al., 2014).
Several reasons have been proposed to explain why loneliness is connected with the development of depression. First, lonely individuals always have negative perceptions of things, are susceptible to negative emotions, and often show hostility, and hostility is significantly related to depression (Doering et al., 2009; Li Y. L. et al., 2019). Second, students affected by short-term loneliness and long-term loneliness are more likely to be depressed and unwilling to communicate with others. If these students do not find a proper channel for catharsis, this situation may not improve and may be exacerbated (Van et al., 2017; Sun and Liu, 2018). In addition, De Jong-Giervald (1987) defined loneliness as a subjective social isolation, accompanied by personal perceptions, unacceptable painful experiences resulting from isolation or lack of contact with others, and long-term loneliness that has an adverse effect on the health of lonely patients and can cause emotional disorders such as depression. Long-term loneliness can have a negative impact on their blood pressure and immunity, leading to poor health and emotional disorders such as depression (Dai, 2017; Naama et al., 2019).
However, there is a co-existence between depression and loneliness (Cacioppo et al., 2006; Demir and Kutlu, 2016; Ren et al., 2020); people with depression are more likely to feel lonely due to actively avoiding people (Xue, 2017). What is more, individuals with depression tend to lack confidence and have low self-evaluation, which may lead to feelings of loneliness (Guo et al., 2016). Therefore, further longitudinal studies are needed to determine the causal relationship between loneliness and depression.
The Key Mediating Role of GPIU
Loneliness, internet gaming disorder, and social network use were significantly associated with GPIU, and GPIU was significantly related to depression. The results of this study supported the theory by Kraut et al. (1998), which pointed that when researchers controlled possible mediating variables, loneliness, depression, and daily stress were positively correlated with greater internet usage. The important finding was that further use of the internet was related to an increase in depression over a subsequent period of time (Kraut et al., 1998), which was found in more later studies (Jia, 2005; Zou et al., 2007; Park, 2009; Akin and İskender, 2011; Regina et al., 2011). Several studies have mentioned the theoretical basis for the link between loneliness, depression, and internet addiction (Davis, 2001; Chung, 2013).
This study found that loneliness was not directly related to depression through GPIU, which was not consistent with previous assumptions. Previous studies supported the idea that some people used the internet to cope with negative emotions such as sadness, anxiety, or loneliness (Scherer, 1997; Muñoz-Rivas et al., 2010; Zhou et al., 2017) or to escape psychological problems (Morahan-Martin and Schumacher, 2000). Affected by Davis (2001) cognitive behavioral model, Caplan (2002) argued that people with psychological problems were more likely to choose to communicate online than face to face in order to compensate for their social skills. However, if people use the internet excessively or inappropriately, it will have a bad effect on body and mind (Li, 2020).
The possible reason about this study result is that loneliness could actually affect depression through GPIU, but as a result of SPIU (internet gaming disorder and pathological social network use) as a mediation variable in the model, the direct effect of loneliness on GPIU is weakened. According to Davis’ cognitive behavior model of PIU (Davis, 2001), SPIU and GPIU have a side-by-side relationship, but based on the literature (Bouna-Pyrrou et al., 2015; Wartberg et al., 2020), we speculate that psychopathology and maladaptive cognition could directly lead to SPIU (e.g., internet gaming disorder and pathological social network use) in some cases and then lead to GPIU. The key to the occurrence of PIU is the emergence of social network use and internet gaming disorder, which is a sufficient cause of PIU symptoms, and which ultimately leads to the development of PIU symptoms, such as depression. Previous studies revealed that pathologic internet use could be associated with depression, substance-related disorders, obsessive–compulsive symptoms, low self-esteem, and attention deficits (Shapira et al., 2000; Tsai and Lin, 2003; Kim et al., 2006). What is more, Liang et al. (2016) pointed that in female adolescents, internet addiction was found to significantly predict subsequent depression, indicating that internet addiction leads to depression and supporting the social displacement hypothesis. The possible reason is that teenagers with internet addiction are addicted to the virtual world, which interferes with their interpersonal relationship in the real world, resulting in the lack of face-to-face communication with others and lack of social support in the real world, so they are prone to become depressed (Kraut et al., 1998; Yang, 2016).
Mediating Roles of Social Network Use and Internet Gaming Disorder
In the mediation process of loneliness to internet gaming disorder/social network use to GPIU to depression, the results of this research were consistent with other related research (Morahan-Martin and Schumacher, 2000; Yan, 2009). A possible reason for this similarity is that loneliness is a factor that has been frequently associated with GPIU (Morahan-Martin and Schumacher, 2000; Davis, 2001; Caplan, 2007; Ceyhan and Ceyhan, 2008; Kim et al., 2009; Dowling and Brown, 2010; Odaci and Kalkan, 2010; Ang et al., 2012; Barthakur and Sharma, 2012). Additionally, social network use and internet gaming disorder are two important forms of internet addiction, and people maintain social connections through these two forms. Loneliness occurs when social connections are cut off (Blazer, 1983; Lv, 2016), and the emergence of loneliness makes people try to reconnect or establish new connections (Weiss and Bowlby, 1975; Cagan, 2009). Social networking chat and internet gaming are critical ways for interpersonal interactions and relaxation (Li, 2015). It is worth noting that it is wise to distinguish between GPIU and specific network-related behavior in question. Research on specific behaviors that individuals engage in on the internet shows that individuals are not addicted to the internet media itself but to the specific behaviors or content they engage in or access (Starcevic, 2013; Brand et al., 2016).
Social networks are an emerging network of communication media used primarily to maintain existing relationships (Antheunis et al., 2010; Boyd and Ellison, 2010). Notably, supplementations to offline interpersonal communication are important (Kujath, 2011). Behavioral experiments have also shown that social site status updates have become an important aspect of social networking sites, and lonely people can significantly reduce the loneliness of personal experiences through social networking sites (Deters and Mehl, 2013). Positive online feedback may be one of the reasons why social network use reduces individual loneliness. Social networks based on acquaintances make it easier for individuals to obtain supportive feedback from, for example, friends and classmates. From a theoretical point of view, Social compensation model theory believes that individuals with less social connections may use the internet to obtain compensation for interpersonal interactions, to meet the psychological needs that are lacking in real life, and to obtain satisfactory social relationships (Kraut et al., 1998). In addition, Suler (1999) found that the need for social interaction is one of the needs of internet addiction. When the satisfaction brought by the internet to the lonely individual is stronger than the satisfaction in real life, the lonely individual may be addicted to the internet, which is consistent with the satisfaction theory. However, if the lonely individual relies too much on network compensation and without restraint, it may become a PIU (Zhang et al., 2017). What is more, the model of the poor becoming rich is derived from the theory of social compensation, which believes that the network can enhance the connection between individuals and others and promote the formation of intimate relationships (Tan, 2015). The social lack of lonely individuals in real life can be satisfied through the interactive nature of online games and supportive interpersonal communication (Valkenburg et al., 2005).
What is more, the concealment of the network and the irritability of internet games greatly satisfy the self-demand that they cannot achieve in reality. Internet games have also become a passive lazy escape response to problems. According to the reinforcement theory (Villere and Hartman, 1991), the increasing use and reinforcement of technology are the elements of internet gaming disorder. The improvement of game technology will make individuals psychologically satisfied and thereby escape and eliminate the discomfort brought to them by real life sense. Through continuous learning and upgrading skills in the game, teenagers show their extraordinary wisdom through superb technology, win the attention of all players, and enjoy the sense of accomplishment brought by self-realization (Kuang, 2017). Due to lack of social support, lonely individuals are more likely to produce negative self-awareness, such as self-denial and inferiority (Hou, 2013). The improvement of technology in online games will help lonely people to generate positive self-evaluation and achieve a sense of accomplishment, which in turn increases their dependence on online games, leading to addictive behaviors (Bozoglan et al., 2013; Lee et al., 2019).
Implications for Prevention of Depression
The results of this study were of great significance to depression prevention and intervention strategies for Chinese students. First of all, junior high school students under great study pressure and housing pressure were very prone to depression. Therefore, depressed students deserve more attention from teachers and parents. Teachers and school doctors are required to improve their quality, pay attention to the psychological status of students, and treat students with a positive attitude. Parents should also strive to provide good parenting for their children. Through appropriate expectations and a harmonious family environment, parents can provide children a benign stimulus so that they can adopt a positive coping style to apply to various contradictions and prevent the formation of adverse emotions (Zhu et al., 2003; Li T. H. et al., 2019). Furthermore, two strategies can help students cope with depression. One strategy is to overcome the behavioral symptoms of internet addiction, starting with reducing the use of social networking and internet gaming disorder (SPIU). In this regard, the cognitive behavior model is useful (Davis, 2001). Moreover, other interventions could focus on initial variables such as loneliness because loneliness is very unfavorable to mental health and promotes depression (Wei et al., 2005). Thus, regular group counseling activities should be organized for junior high school students to promote interaction among students and reduce loneliness, SPIU, GPIU, and depression, respectively.
Limitations and Future Directions
Although this study was conducive to understanding the relationship between loneliness and depression in Chinese students, there were still some limitations. First, this study was a cross-sectional study, and its results could not provide a causal relationship. Therefore, further research could explore the causal relationship between these variables through experiments or longitudinal studies, such as loneliness and depression. Another limitation was that the questionnaire survey used in this study may cause some errors. For example, self-reported questionnaires were influenced by social desirability. Hence, further research may apply more professional surveys (e.g., Survey Monkey or Google Forums) to directly record time spent, eliminating coding data errors and controlling the answers at random. In addition, the convenience sampling may limit the generalizability of the result, so if there is an opportunity, we will expand the sample and increase the randomization level of the sample. What is more, other mediators of the relationship between loneliness and depression (such as meditation), as well as how social network use and internet gaming disorders predict depression, and how the two-way relationship needs to be explored. Last but not least, this study was conducted in the context of Chinese culture, so the cross-cultural applicability of the conclusions must be further verified.
Conclusion
In this study, we discussed the mediating effects of internet gaming disorder, social network use, and GPIU on loneliness and depression, and the conclusions were as follows: (a) loneliness could positively predict depression; (b) loneliness could not predict depression through GPIU directly; (c) loneliness could predict depression through internet gaming disorder to GPIU; (d) loneliness could predict depression through social network use to GPIU; and (e) loneliness could not predict depression through internet gaming disorder to social network use to GPIU.
Data Availability Statement
The datasets for this manuscript are not publicly available because the datasets are used only for the team of this article by the permission of the guardians. Requests to access the datasets should be directed to PW, MTIyMzk0MTA4QHFxLmNvbQ==.
Ethics Statement
The studies involving human participants were reviewed and approved by the Ethics Committee of Shandong Normal University. The guardians of the participants provided written informed consent to let them participate in this study.
Author Contributions
PW was the research designer. JW and YY were in charge of writing. XZ and YT participated in the discussion and offered suggestions. YS was the corresponding author. All authors contributed to the article and approved the submitted version.
Funding
This work was supported by the 2018 Shandong Graduate Education Quality Improvement Plan Project under grant number SDYJD18020, fund for Projects of Shandong Social Science Planning under grant number 18CQXJ06, and fund for Projects of Shandong Natural Science under grant number ZR2020MF158.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Acknowledgments
The authors would like to thank the reviewers whose suggestions and comments greatly helped to improve and clarify this manuscript.
References
Akin, A., and İskender, M. (2011). internet addiction and depression, anxiety and stress. IOJES 20, 138–148.
Ang, R. P., Chong, W., Chye, S., and Huan, V. S. (2012). Loneliness and generalized problematic internet use: parents’ perceived knowledge of adolescents’ online activities as a moderator. Comp. Hum. Behav. 28, 1342–1347. doi: 10.1016/j.chb.2012.02.019
Antheunis, M. L., Valkenburg, P. M., and Peter, J. (2010). Getting acquainted through social network sites: testing a model of online uncertainty reduction and social attraction. Comp. Hum. Behav. 26, 100–109. doi: 10.1016/j.chb.2009.07.005
Barthakur, M. M., and Sharma, M. K. (2012). Problematic internet use and mental health problems. Asian J. Psychiatry 5, 279–280. doi: 10.1016/j.ajp.2012.01.010
Birk, J. L., Kronish, I. M., Moise, N., Falzon, L., Yoon, S., and Davidson, K. W. (2019). Depression and multimorbidity: considering temporal characteristics of the associations between depression and multiple chronic diseases. Health Psychol. 38, 802–811. doi: 10.1037/hea0000737
Błachnio, A., Przepiorka, A., Boruch, W., and Bałakier, E. (2016). Self-presentation styles, privacy, and loneliness as predictors of Facebook use in young people. Personal. Individ. Diff. 94, 26–31. doi: 10.1016/j.paid.2015.12.051
Blazer, D. (1983). Loneliness: a source book of current theory, research and therapy. J. Behav. Therapy Exp. Psychiatry 14:281. doi: 10.1016/0005-7916(83)90066-90066
Bouna-Pyrrou, P., Mühle, Christiane, Kornhuber, J., and Lenz, B. (2015). Internet gaming disorder, social network disorder and laterality: handedness relates to pathological use of social networks. J. Neural Transm. 122, 1187–1196. doi: 10.1007/s00702-014-1361-1365
Boyd, D. M., and Ellison, N. B. (2010). Social network sites: definition, history, and scholarship. J. Comp.-Med. Commun. 13, 210–230. doi: 10.1111/j.1083-6101.2007.00393.x
Bozoglan, B., Demirer, V., and Sahin, I. (2013). Loneliness, self-esteem, and life satisfaction as predictors of internet addiction: a cross-sectional study among Turkish university students. Scand. J. Psychol. 54, 313–319. doi: 10.1111/sjop.12049
Brand, M., Young, K. S., Laier, C., Wölfling, K., and Potenza, M. N. (2016). Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: an Interaction of Person-AffectCognition-Execution (I-PACE) model. Neurosci. Biobehav. Rev. 71, 252–266. doi: 10.1016/j.neubiorev.2016.08.033
Cacioppo, J. T., Hawkley, L. C., Ernst, J. M., Burleson, M., Berntson, G. G., Nouriani, B., et al. (2006). Loneliness within a nomological net: an evolutionary perspective. J. Res. Personal. 40, 1054–1085. doi: 10.1016/j.jrp.2005.11.007
Cacioppo, J. T., Hawkley, L. C., and Thisted, R. A. (2010). Perceived social isolation makes me sad: 5-year cross-lagged analyses of loneliness and depressive symptomatology in the chicago health, aging, and social relations study. Psychol. Aging 25, 453–463. doi: 10.1037/a0017216
Cagan, G. (2009). Loneliness: human nature and the need for social connection. Am. J. Peychiatry 166, 375–376. doi: 10.1176/appi.ajp.2008.08091320
Caplan, S. E. (2002). Problematic internet use and psychosocial well-being: development of a theory-based cognitive-behavioral measurement instrument. Comp. Hum. Behav. 18, 553–575. doi: 10.1016/s0747-5632(02)00004-3
Caplan, S. E. (2007). Relations among loneliness, social anxiety, and problematic internet use. CyberPsychol. Behav. 10, 234–242. doi: 10.1089/cpb.2006.9963
Carvajal-Carrasca, G., and Caro-Castillo, C. V. (2009). Loneliness during adolescence: analysis of the concept. Aquichan 9, 281–296.
Ceyhan, A. A., and Ceyhan, E. (2008). Loneliness, depression, and computer self-efficacy as predictors of problematic internet use. Cyberpsychol. Behav. 11, 699–701. doi: 10.1089/cpb.2007.0255
Chen, H. L., and Fu, C. L. (2012). The relationship between loneliness, self-esteem and college students’ online game addiction. J. Campus Life Mental Health 10, 374–376. doi: 10.3969/j.issn.1673-1662.2012.06.004
Chung, J. E. (2013). Social interaction in online support groups: preference for online social interaction over offline social interaction. Comp. Hum. Behav. 29, 1408–1414. doi: 10.1016/j.chb.2013.01.019
Cudeck, R., and Browne, M. W. (1992). Constructing a covariance matrix that yields a specified minimizer and a specified minimum discrepancy function value. Psychometrika 57, 357–369. doi: 10.1007/BF02295424
Dai, G. (2017). The effect of college students’ sense of loneliness on depression. China J. Health Psychol. 25, 297–299. doi: 10.13342/j.cnki.cjhp.2017.02.036
Danneel, S., Nelemans, S., Spithoven, A., Bastin, M., Bijttebier, P., Colpin, H., et al. (2019). Internalizing problems in adolescence: linking loneliness, social anxiety symptoms, and depressive symptoms over time. J. Abnormal Child Psychol. 47, 1691–1705. doi: 10.1007/s10802-019-00539-530
Davis, R. A. (2001). A cognitive-behavioral model of pathological internet use. Comp. Hum. Behav. 17, 187–195. doi: 10.1016/s0747-5632(00)00041-48
De Jong-Giervald, J. (1987). Developing and testing a model of loneliness. J. Pers. Soc. Psychol. 53, 119–128. doi: 10.1037/0022-3514.53.1.119
Demir, Y., and Kutlu, M. (2016). The relationship between loneliness and depression: mediation role of internet addiction. Educ. Process. Int. J. 5, 97–105. doi: 10.12973/edupij.2016.52.1
Deniro, D. A., and Dorothy, A. (1995). Perceived alienation in individs with residual-type schizophrenia. Issues Mental Health Nurs. 16, 185–200. doi: 10.3109/01612849509006934
Deters, F. G., and Mehl, M. R. (2013). Does posting facebook status updates increase or decrease loneliness: an online social networking experiment. Soc. Psychol. Personal. Sci. 4, 579–586. doi: 10.1177/1948550612469233
Doane, L. D. (2008). Loneliness, Stress and Depression Over the Transition to Adulthood: Interpersonal and Physiological Pathways. Ph.D. dissertation, thesis Evanston, IL: Northwestern University.
Doering, L. V., Moser, D. K., and Dracup, K. (2009). Correlates of anxiety, hostility, depression, and psychosocial adjustment in parents of NICU infants. Neonatal Network:J. Neonatal Nurs. 19, 15–23. doi: 10.1891/0730-0832.19.5.15
Dowling, N. A., and Brown, M. (2010). Commonalities in the psychological factors associated with problem gambling and internet dependence. Cyberpsychol. Behav. Soc. Network. 13, 437–441. doi: 10.1089/cyber.2009.0317
Elizabeth, G., and John, C. (2000). Depression on symptoms and cigarette smoking Among teens. Pediatrics 106, 748–755. doi: 10.1542/peds.106.4.748
Ellison, N. B., and Steinfield, C. W. (2006). “Spatially bounded online social networks and social capital: the role of Facebook,” in Proceedings of the Annual Conference of the International Communication Association (ICA), (Dresden), doi: 10.1117/12.343711
Emamjomeh, S. M., and Bahrami, M. (2015). Effect of a supportive-educative program in the math class for stress, anxiety, and depression in female students in the third level of junior high school: an action research. J. Educ. Health Promot. 4:10. doi: 10.4103/2277-9531.151916
Engelberg, E., and Sjoberg, L. (2004). internet use, social skills, and adjustment. CyberPsychol. Behav. 7, 41–47. doi: 10.1089/109493104322820101
Eroglu, M., Pamuk, M., and Pamuk, K. (2013). Investigation of problematic internet usage of university students with psychosocial levels at different levels. Procedia -Social Behav. Sci. 103, 551–557. doi: 10.1016/j.sbspro.2013.10.372
Fan, Q., and Yuan, W. (2018). The correlation between Internet addiction and interpersonal relationship and loneliness among college students. J. Psychiatr. 31, 51–53. doi: 10.4018/978-1-5225-8900-6.ch022
Ferguson, C. J. (2020). Links between screen use and depressive symptoms in adolescents over 16 years: is there evidence for increased harm? Dev. Sci. 24:e13008. doi: 10.1111/desc.13008
Gao, F. Q., Guo, Z. X., Tian, Y., Si, Y. D., and Wang, P. (2018). Relationship between shyness and generalized pathological internet use among chinese school students: the serial mediating roles of loneliness, depression, and self-esteem. Front. Psychol. 9:1822. doi: 10.3389/fpsyg.2018.01822
Garcia-Retamero, R., Okan, Y., and Maldonado, A. (2015). The impact of depression on self–other discrepancies in decision making. J. Behav. Decis. Making 28, 89–100. doi: 10.1002/bdm.1833
Gómez, P., Harris, S. K., Barreiro, C., Isorna, M., and Rial, A. (2017). Profiles of internet use and parental involvement, and rates of online risks and problematic internet use among spanish adolescents. Comp. Hum. Behav. 75, 826–833. doi: 10.1016/j.chb.2017.06.027
Greenfield, P., and Yan, Z. (2006). Children, adolescents, and the internet: a new field of inquiry in developmental psychology. Dev. Psychol. 42, 391–394. doi: 10.1037/0012-1649.42.3.391
Griffiths, M. D., Davies, M. N. O., and Chappell, D. (2004). Online computer gaming: a comparison of adolescent and adult gamers. J. Adolescence 27, 87–96. doi: 10.1016/j.adolescence.2003.10.007
Gross, E. F. (2004). Adolescent internet use: what we expect, what teens report. J. Appl. Dev. Psychol. 25, 633–649. doi: 10.1016/j.appdev.2004.09.005
Guo, Q., Ren, Y., Peng, J. Y., Zhang, H. L., and Yang, H. (2016). Structural equation model analysis of influencing factors of self-confidence in patients with depression. Chinese Remedies Clin. 16, 65–67.
Hammen, C. (1992). Cognitive, life stress, and interpersonal approaches to a developmental psychopathology model of depression. Dev. Psychopathol. 4, 189–206. doi: 10.1017/s0954579400005630
Holden, C. (2000). Global survey examines impact of depression. Science 288, 39–40. doi: 10.1126/science.288.5463.39
Hou, H. X. (2013). The influence of self-consciousness and social support on loneliness of college students. Educ. Higher Educ. Res. 24, 39–40. doi: 10.3969/j.issn.1674-9510.2013.24.031
Hoyle, R. H. (1995). Structural Equation Modeling: Concepts, Issues, and Applications. Thousand Oaks, CA: Sage Publication, doi: 10.1086/602877
Huang, J. (2000). Humanistic research on loneliness. Health Psychol. 8, 29–30. doi: 10.3969/j.issn.1005-1252.2000.01.018
Jean-Paul, M., Laurent, B., Jean-Marie, S., Christian, H., Jacques-Charles, B., and Yves, P. (2001). Depression, anxiety and quality of life among scheduled cosmetic surgery patients: multicentre prospective study. J. Craniomaxillofac Surg. 29, 177–180. doi: 10.1054/jcms.2001.0213
Jia, W. L. (2005). Causes, behavioral manifestations and social hazards of internet addiction. J. Shenyang Agricult. University (Social Sciences Edition) 7, 504–505. doi: 10.3969/j.issn.1008-9713.2005.04.040
Kim, J., LaRose, R., and Peng, W. (2009). Loneliness as the cause and the effect of problematic internet use: the relationship between internet use and psychological well-being. Cyberpsychol. Behav. 12, 451–455. doi: 10.1089/cpb.2008.0327
Kim, K., Ryu, E., Chon, M. Y., Yeun, E. J., Choi, S. Y., Seo, J. S., et al. (2006). Internet addiction in korean adolescents and its relation to depression and suicidal ideation: a questionnaire survey. Int. J. Nurs. Stud. 43, 185–192. doi: 10.1016/j.ijnurstu.2005.02.005
Ko, C. H., Yen, J. Y., Chen, C. S., Yeh, Y. C., and Yen, C. F. (2009). Predictive values of psychiatric symptoms for internet addiction in adolescents: a 2-year prospective study. Arch. Pediatr. Adolesc. Med. 163, 937–943. doi: 10.1001/archpediatrics.2009.159
Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukopadhyay, T., and Scherlis, W. (1998). internet paradox. a social technology that reduces social involvement and psychological well-being?. Am. Psychol. 53, 1017–1031. doi: 10.1037//0003-066X.53.9.1017
Kuang, W. B. (2017). Why are teenagers addicted to online games. People’s Forum 22, 122–123. doi: 10.3969/j.issn.1004-3381.2017.22.056
Kujath, C. L. (2011). Facebook and myspace: complement or substitute for face-to-face interaction? Cyberpsychol. Behav. Soc. Network. 14, 75–78. doi: 10.1089/cyber.2009.0311
Lee, J., Ko, D. W., and Lee, H. (2019). Loneliness, regulatory focus, inter-personal competence, and online game addiction. Int. Res. 29, 381–394. doi: 10.1108/intr-01-2018-2020
Lee, S., Tam, C. L., and Chie, Q. T. (2014). Mobile phone usage preferences: the contributing factors of personality. Soc. Anxiety Loneliness Soc. Indicat. Res. 118, 1205–1228. doi: 10.1007/s11205-013-0460-462
Lewinsohn, P. M., Hops, H., Roberts, R. E., Seeley, J. R., and Andrews, J. A. (1993). Adolescent psychopathology: prevalence and incidence of depression and other dsm–r disorders in high school students. J. Abnormal Psychol. 102, 133–144. doi: 10.1037/0021-843X.102.4.517
Li, H. L., Lei, L., and Wang, Z. Y. (2001). The psychological impact of the internet on people. Psychol. Dynam. 4, 376–381. doi: 10.3969/j.issn.1671-3710.2001.04.014
Li, T. H., Xian, Y. Y. M., Yang, Z. H., Li, Z. Y., and Kang, Z. Y. (2019). A study on the effects of parental rearing patterns and family environments on adolescent depression-adolescent coping style as a mediator. Adv. Psychol. 9, 1652–1661. doi: 10.12677/AP.2019.99201
Li, Y. L., Liu, L. Y., and Yang, J. (2019). Relationship between problem behaviors of left-behind college students and negative emotions and positive personality traits. Chinese J. Mental Health 33, 318–320.
Li, X. (2020). An Analysis of the Relationship between adolescent attachment and internet overuse. Educ. Observ. 9, 77–79.
Li, M. S. (2015). The relationship between online Games and students’ psychological development. Literacy Numeracy (teachers’ edition): Qual. Educ. Forum 25, 190–190.
Liang, L., Zhou, D., Yuan, C., Shao, A., and Bian, Y. (2016). Gender differences in the relationship between internet addiction and depression: a cross-lagged study in chinese adolescents. Comp. Hum. Behav. 63, 463–470. doi: 10.1016/j.chb.2016.04.043
Ling, Y. (2009). The effect of adolescents’ loneliness on depression and the moderating effects of social support. Chin. J. Behav. Med. Brain Sci. 18, 551–553.
Liu, H., Zhang, M., Yang, Q., and Yu, B. (2019). Gender differences in the influence of social isolation and loneliness on depressive symptoms in college students: a longitudinal study. Soc. Psychiatry Psychiatric Epidemiol. 55, 251–257. doi: 10.1007/s00127-019-01726-1726
Liu, Q. X., Fang, X. Y., Deng, L. Y., and Zhang, J. T. (2012). Parent-adolescent communication, parental internet use and internet-specifific norms and pathological internet use among chinese adolescents. Comp. Hum. Behav. 28, 1269–1275. doi: 10.1016/j.chb.2012.02.010
Liu, Y. L. (1995). A preliminary study on the relationship between loneliness, self-esteem and depression. Chinese J. Mental Health 9, 115–116.
Lopez-Fernandez, O. (2018). Generalised versus specific internet use-related addiction problems: a mixed methods study on internet, gaming, and social networking behaviours. Int. J. Environ. Res. Public Health 15:2913. doi: 10.3390/ijerph15122913
Lu, X., and Yeo, K. J. (2015). Pathological internet use among Malaysia University students: risk factors and the role of cognitive distortion. Comp. Hum. Behav. 45, 235–242. doi: 10.1016/j.chb.2014.12.021
Lv, P. F. (2016). A brief analysis of the impact of the internet on the growth of teenagers. Exam Week 63, 161–161. doi: 10.3969/j.issn.1673-8918.2016.63.141
McKenna, K. Y. A., and Bargh, J. A. (2000). Plan 9 from cyberspace: the implications of the internet for personality and social psychology. Personal. Soc. Psychol. Bull. 4, 57–75. doi: 10.1207/s15327957pspr0401_6
Morahan-Martin, J., and Schumacher, P. (2000). Incidence and correlates of pathological internet use among college students. Comp. Hum. Behav. 16, 13–29. doi: 10.1016/S0747-5632(99)00049-47
Muñoz-Rivas, M. J., Fernández, L., and Gámez-Guadix, M. (2010). Analysis of the indicators of pathological internet use in Spanish University students. Spanish J. Psychol. 13, 697–707. doi: 10.1017/S1138741600002365
Musetti, A., and Corsano, P. (2018). The internet is not a tool: reappraising the model for internet-addiction disorder based on the constraints and opportunities of the digital environment. Front. Psychol. 9:558. doi: 10.3389/fpsyg.2018.00558
Mushtaq, R., Shoib, S., Shah, T., and Mushtaq, S. (2014). Relationship between loneliness, psychiatric disorders and physical health ? a review on the psychological aspects of loneliness. J. Clin. Diag. Res. JCDR 8, WE01–WE04. doi: 10.7860/JCDR/2014/10077.4828
Naama, S., Dikla, S. K., and Yuval, P. (2019). Close social relationships and loneliness: the role of subjective age. Int. Psychogeriatr. 11, 1–5. doi: 10.1017/S1041610219001790
Neeleman, J., and Power, M. J. (1994). Social support and depression in three groups of psychiatric patients and a group of medical controls. Soc. Psychiatry Psychiatric Epidemiol. 29, 46–51. doi: 10.1007/bf00796448
Nie, N. H., Hillygus, D. S., and Erbring, L. (2002). “Internet use, interpersonal relations, and sociability: a time diary study. internet,” in Everyday Life, eds W. Barry and H. Caroline (Hoboken, NJ: Wiley online Library), 213–243.
Odaci, H., and Çikrikçi, O. (2014). Problematic internet use in terms of gender, attachment styles and subjective well-being in university students. Comp. Hum. Behav. 32, 61–66. doi: 10.1016/j.chb.2013.11.019
Odaci, H., and Kalkan, M. (2010). Problematic internet use, loneliness and dating anxiety among young adult university students. Comp. Educ. 55, 1091–1097. doi: 10.1016/j.compedu.2010.05.006
Orsolya, K., Sleczka, P., Pontes, H. M., Róbert, U., and Demetrovics, Z. (2017). Validation of the ten-item internet gaming disorder test (igdt-10) and evaluation of the nine dsm-5 internet gaming disorder criteria. Add. Behav. 64, 253–260. doi: 10.1016/j.addbeh.2015.11.005
Park, S. (2009). The association between internet use and depressive symptoms among south korean adolescents. J. Special. Pediatric Nurs. 14, 230–238. doi: 10.1111/j.1744-6155.2009.00191.x
Peplau, L. A., and Perlman, D. (1979). “Blueprint for a social psychological theory of loneliness,” in Love and attraction, eds L. A. Peplau, D. Perlman, M. Cook, and G. Wilson (Pergamon: Oxford), 99–108.
Petry, N. M., Rehbein, F., Gentile, D. A., Lemmens, J. S., Rumpf, H., and Mößle, T. (2014). An international consensus for assessing internet gaming disorder using the new dsm-5 approach. Addiction 109, 1399–1406. doi: 10.1111/add.12457
Podsakoff, P. M., Mackenzie, S. B., Lee, J. Y., and Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. J. Appl. Psychol. 88, 879–903. doi: 10.1037/0021-9010.88.5.879
Pontes, H. M., Griffiths, M. D., and Patrão, I. M. (2014). Internet addiction and loneliness among children and adolescents in the education setting: an empirical pilot study. Aloma: Revista de Psicologia, Ciències de l Educació i de l Esport 32, 91–99.
Qualter, P., Brown, S. L., Munn, P., and Rotenberg, K. J. (2010). Childhood loneliness as a predictor of adolescent depressive symptoms: an 8-year longitudinal study. Eur. Child Adolescent Psychiatry 19, 493–501. doi: 10.1007/s00787-009-0059-y
Regina, V. D. E., Dike, V. D. M., Vet, R., and Vermulst, A. (2011). Alcohol-specific parenting and adolescents’ alcohol-related problems: the interacting role of alcohol availability at home and parental rules. J. Stud. Alcohol Drugs 72, 408–417. doi: 10.15288/jsad.2011.72.408
Ren, L., Han, X., Li, D., Hu, F., and Liu, J. (2020). The association between loneliness and depression among chinese college students: affinity for aloneness and gender as moderators. Eur. J. Dev. Psychol. 1, 1–14. doi: 10.1080/17405629.2020.178986
Richman, N. E., and Sokolove, R. L. (1992). The experience of aloneness, object representation, and evocative memory in borderline and neurotic patients. Psychoanalytic Psychol. 9, 77–91. doi: 10.1037/h0079323
Rogers, W. M., and Schmitt, N. (2004). Parameter recovery and model fit using multidimensional composites: a comparison of four empirical parceling algorithms. Multivariate Behav. Res. 39, 379–412. doi: 10.1207/s15327906mbr3903_1
Sanders, C. E., Field, T. M., Diego, M., and Kaplan, M. (2000). The relationship of internet use to depression and social isolation among adolescents. Adolescence 35, 237–242. doi: 10.1016/S0001-6918(00)00038-X
Scherer, A. (1997). How to profit from. the internet. as handy as a swiss army knife. Dental Economics - Oral Hygiene 87, 54–55. doi: 10.1093/ejo/cjp016
Shapira, N. A., Goldsmith, T. D., Keck, P. E. Jr., Khosla, U. M., and McElroy, S. L. (2000). Psychiatric features of individuals with problematic internet use. J. Affect. Disord. 57, 267–272. doi: 10.1016/S0165-0327(99)00107-X
Starcevic, V. (2013). Is Internet addiction a useful concept? Aust. N. Z. J. Psychiatry 47, 16–19. doi: 10.1177/0004867412461693
Starcevic, V., Billieux, J., and Schimmenti, A. (2018). Selfitis, selfie addiction, twitteritis:irresistibleappealofmedicalterminologyforproblematicbehaviours in the digital age. Aust. N. Z. J. Psychiatry 52, 408–409. doi: 10.1177/0004867418763532
Sukenick, S. (2012). Alone together: why we expect more from technology and less from each other by turkle, sherry. J. Anal. Psychol. 57, 128–129. doi: 10.1080/02650533.2013.769209
Suler, J. R. (1999). To get what you need: healthy and pathological internet use. Cyberpsychol. Behav. 2, 385–393. doi: 10.1089/cpb.1999.2.385
Sun, M. Y., and Liu, K. (2018). Analysis of mediating effect of loneliness on social anxiety and depressive symptoms in middle school students. Chinese Health Statist. 35, 926–928.
Tan, Z. M. (2015). The Influence of social networking platforms on college students’ interpersonal relationship. China Adult Educ. 82–83. doi: 10.7666/d.D484885
Tao, S. (2006). Optimism, pessimism and depression: the relations and differences by stress level and gender. Acta Psychol. Sinica 38, 886–901. doi: 10.1097/00024382-200610001-200610089
Teunisse, R. J., Cruysberg, J. R., Hoefnagels, W. H., Kuin, Y., Verbeek, A. L., and Zitman, F. G. (1999). Social and psychological characteristics of elderly visually handicapped patients with the charles bonnet syndrome. Comprehens. Psychiatry 40, 315–319. doi: 10.1016/S0010-440X(99)90133-90135
Tian, Y., Bian, Y., Han, P., Gao, F., and Wang, P. (2017). Associations between psychosocial factors and generalized pathological internet use in chinese university students: a longitudinal cross-lagged analysis. Comp. Hum. Behav. 72, 178–188. doi: 10.1016/j.chb.2017.02.048
Tijhuis, M. A., De, J. J., Feskens, E. J., and Kromhout, D. (1999). Changes in and factors related to loneliness in older men. the zutphen elderly study. Age Ageing 28, 491–495. doi: 10.1093/ageing/28.5.491
Tsai, C. C., and Lin, S. S. J. (2003). internet addiction of adolescents in taiwan: an interview study. CyberPsychol. Behav. 6, 649–652. doi: 10.1089/109493103322725432
Valkenburg, P. M., Schouten, A. P., and Peter, J. (2005). Adolescents’ identity experiments on the internet. New Media Soc. 7, 383–402. doi: 10.1177/1461444805052282
Van, D. B. R. H. S., Schutter, N., Hanssen, D. J. C., Elzinga, B. M., Rabeling-Keus, I. M., Stek, M. L., et al. (2017). Prognostic significance of social network, social support and loneliness for course of major depressive disorder in adulthood and old age. Epidemiol. Psychiatric Sci. 27, 266–277. doi: 10.1017/S2045796017000014
Vazquez, A. J., and Garcia, R. J. (1997). The Mannheim interview on social support psychometric characteristics of spanish version. Soc. Psychiatry Psychiatric Epidemiol. 32, 208–221. doi: 10.1007/BF00788240
Villere, M. F., and Hartman, S. S. (1991). Reinforcement theory: a practical tool. Leadership Organ. Dev. J. 12, 27–31. doi: 10.1108/01437739110138039
Wang, Y. L., Wang, J. P., and Fu, D. D. (2008). Epidemiological survey of internet users’ addiction in primary and middle school students. Chinese J. Mental Health 22, 678–682. doi: 10.3321/j.issn:1000-6729.2008.09.014
Wartberg, L., Kriston, L., and Thomasius, R. (2020). Internet gaming disorder and problematic social media use in a representative sample of german adolescents: prevalence estimates, comorbid depressive symptoms and related psychosocial aspects. Comp. Hum. Behav. 103, 31–36. doi: 10.1016/j.chb.2019.09.014
Wei, M., Russell, D. W., and Zakalik, R. A. (2005). Adult attachment, social self-efficacy, self-disclosure, loneliness, and subsequent depression for freshman college students: a longitudinal study. J. Counsel. Psychol. 52, 602–614. doi: 10.1037/0022-0167.52.4.602
Weiss, R. S., and Bowlby, J. (1975). Loneliness: the experience of emotional and social isolation. Contemporary Sociol. 25, 39–41. doi: 10.2307/2062224
Xu, H. Y. (2012). Experimental Research on middle School Students’ Mental Health, Attribution Style and Attribution Training Doctoral dissertation, thesis China: Qufu Normal University.
Xue, Y. (2017). The role of introverted personality and social support in the mediating effects of depression and loneliness for medical students. Chin. J. Behav. Med. Brain Sci. 26, 820–824.
Yan, H. (2009). Analysis of the causes of college students’ internet game addiction and its strategy research. Stud. Ideol. Educ. 11, 106–108.
Yang, C., Wang, W. X., and Sun, J. H. (2010). Social adaptability of internet addiction teenagers. Chin. Special Educ. 8, 85–90. doi: 10.3969/j.issn.1007-3728.2010.08.017
Yang, J. (2016). The influence of the online virtual world on individual psychology. Sci. Educ. Article Collects 1, 151–152.
Yao, L. H., and Yang, D. L. (2014). Analysis of the status of internet addiction of college students and their emotional characteristics. Value Eng. 326–328.
Young, K., and Kimberly, S. (2007). Cognitive behavior therapy with internet addicts: treatment outcomes and implications. CyberPsychol. Behav. 10, 671–679. doi: 10.1089/cpb.2007.9971
Young, K. S., and Kimberly, S. (1998). internet addiction: the emergence of a new clinical disorder. CyberPsychol. Behav. 1, 237–244. doi: 10.1089/cpb.1998.1.237
Young, K. S., and Rogers, R. C. (1998). The relationship between depression and internet addiction. Cyberpsychol. Behav. 1, 178–183. doi: 10.1089/cpb.1998.1.25
Zhang, G. H., and Lei, L. (2015). The relationship between online game experience and online game addiction among adolescents: a cross–lagged regression analysis. J. Psychol. Sci. 38, 883–888.
Zhang, M. Y. (2000). Thoughts on chinese psychiatry in the 21st century. Shanghai Psychiatry 12, 1–2.
Zhang, Y. Q., Liu, Y. C., Zhang, H., and Zhou, X. Z. (2017). The relationship between Social support and internet Addiction under the theory of loss compensation: the chain mediating effect of basic psychological needs and coping styles. Eng. Res. 11, 16–18. doi: 10.3969/j.issn.1671-3818.2017.11.005
Zhang, Z. Y. (2017). Psychological problems and performance of junior high school students and counseling strategies. Educ. Innovat. 2:20.
Zhou, F., Liu, R. D., Guo, M. J., and Jiang, S. Y. (2017). Negative affect of teenagers and internet addiction: modulating effect of orientations to happiness. Chin. J. Clin. Psychol. 25, 208–212. doi: 10.16128/j.cnki.1005-3611.2017.02.003
Zhu, W. H., Gu, Y. Q., and Huang, X. T. (2003). The impact of family support on coping styles of junior middle school students. Chin. J. Clin. Psychol. 1, 305–306. doi: 10.3969/j.issn.1005-3611.2003.04.024
Keywords: loneliness, internet gaming disorder, social network use, depression, multiple mediating effects, generalized pathological internet use
Citation: Wang P, Wang J, Yan Y, Si Y, Zhan X and Tian Y (2021) Relationship Between Loneliness and Depression Among Chinese Junior High School Students: The Serial Mediating Roles of Internet Gaming Disorder, Social Network Use, and Generalized Pathological Internet Use. Front. Psychol. 11:529665. doi: 10.3389/fpsyg.2020.529665
Received: 26 January 2020; Accepted: 23 December 2020;
Published: 12 February 2021.
Edited by:
Claudio Longobardi, University of Turin, ItalyReviewed by:
Hengliang Yeh, Taipei Veterans General Hospital, TaiwanNiko Männikkö, Oulu University of Applied Sciences, Finland
Copyright © 2021 Wang, Wang, Yan, Si, Zhan and Tian. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yingdong Si, MTAyMTg2NTkyMUBxcS5jb20=
†These authors share first authorship