Network analysis of internet gaming disorder,problematic social media use,problematic smartphone use,psychological distress,and meaning in life among adolescents

Abstract

Background

Few studies have examined the connections between psychological distress, meaning in life, internet gaming disorder (IGD), problematic smartphone use (PSU), and problematic social media use (PSMU).

Methods

The central and bridging nodes of IGD, PSMU, and PSU symptoms were investigated using network analysis in China among 742 adolescents (M_age = 15.39, SD = 1.68, range = 12–19; 53.23% female). The relationships between IGD, PSMU, and PSU and associated factors were investigated using a Directed Acyclic Graphs method.

Results

The results suggested that the central symptoms were withdrawal in IGD, mood modification in PSU, and tolerance in PSMU. Mood modification in IGD, mood modification in PSU, withdrawal in PSMU, and functional impairment in PSMU were the bridge symptoms. Males were more likely to experience symptoms of IGD than females, while females were more likely to need meaning than males. Stress is the root factor, while depression, meaning confusion, meaning anxiety, and meaning avoidance were closely associated with IGD, PSMU, and PSU.

Conclusions

The current research improved the understanding of IGD, PSMU, and PSU symptoms in teenagers and demonstrated the potential of dynamic systems perspectives on problematic use behaviors and stress/meaning-focused interventions.

Keywords

problematic smartphone use internet gaming disorder problematic social media use meaning in life psychological distress

Introduction

With the growing use of the internet and smartphone, problematic use has emerged. The researchers focused mainly on internet gaming disorder (IGD),¹ problematic social media use (PSMU),² and problematic smartphone use (PSU).³ Meta-analysis results exhibited a higher prevalence of IGD among adolescents than in other populations.⁴ IGD is positively linked to psychological problems.⁵ A meta-analysis of 41,871 children and adolescents showed that 23.3% reported PSU accompanied by poorer mental health symptoms.⁶ A survey of adolescents from 29 countries showed that the prevalence of PSMU varied from 3.22% to 14.17%, among which adolescents with PSMU were at risk for a lower level of well-being.⁷ Therefore, identifying and understanding the symptoms of problematic technology use in adolescents is essential for promoting appropriate intervention strategies to reduce their mental health problems and improve their well-being.

The symptoms of IGD, PSMU, and PSU could occur simultaneously. In fact, the three problematic technology use might overlap (e.g. using a smartphone to access social media and play games).⁸ Some studies combined IGD, PSMU, and PSU into one model while studying problematic technology use. For example, a longitudinal follow-up of 504 children identified three different levels of IGD, PSMU, and PSU groups: the high-level group scored high on the IGD, PSMU, and PSU measures; the moderate-level group scored moderately on the IGD, PSMU, and PSU measures, and the low-level group scored low on all three IGD, PSMU, and PSU measures.⁹ This does not imply that IGD, PSMU, or PSU are interchangeable. According to the spectrum hypothesis,^10–12 specific problematic use behaviors, such as IGD, PSMU, and PSU, are distinct, and conceptualizing these specific problematic use behaviors under a unitary “umbrella construct” (e.g. problematic Internet use or problematic smartphone use) would neglect the differences between these specific problematic use behaviors. The differences between IGD, PSMU, and PSU make a difference in their effects on the correlated variables and the mechanisms of their effects on the correlated variables. For example, previous studies found that IGD, PSMU, and PSU were linked to self-stigma, sleep quality, and several aspects of quality of life through various pathways.^13,14 In another study, results indicated that IGD was more likely to be correlated with depression, whereas PSMU was more likely to be correlated with anxiety.² Overall, the symptoms and effects of IGD, PSMU, and PSU are thus related and separate from one another. If people with a variety of different problematic use behaviors (e.g. IGD, PSMU, and PSU) are characterized as only exhibiting problematic Internet use or problematic smartphone use, these differences between specific problematic use behaviors would be ignored.¹² However, if the IGD, PSMU, and PSU are separately studied, the comorbidity of these problematic use behaviors would be overlooked. Simultaneously considering the comorbidity and differences of IGD, PSMU, and PSU can clarify and deepen their understanding of them. However, the comorbidity and differences of IGD, PSMU, and PSU have barely been studied.

By allowing the exploration of the dynamic relationships between concurrent symptoms, network analysis has been increasingly used in recent years to identify and examine the associations among psychological symptoms, including technology use addictions.¹⁵ For instance, a recent study¹⁶ used network analysis to investigate the association between neuroticism and PSU among adolescents and found that “worry in losing messages, network or power” is the central symptom of PSU. The “perceived persistent failures for stresses” of neuroticism and “give up hurry-up things” of PSU were the bridge symptoms. However, to date, no studies have used network analysis to investigate the relationships between the symptoms of IGD, PSU, and PSMU. Therefore, the current study aimed to conduct a network analysis to identify the central symptoms and the bridge symptoms in the IGD, PSMU, and PSU symptoms among adolescents.

In addition to the interactions within the IGD, PSMU, and PSU symptoms, they interact with other psychosocial factors. According to Caplan's Cognitive-Behavioral Model¹⁷ and Compensatory Internet Use Theory,¹⁸ people who struggle with psychological distress (e.g. depression, anxiety, and stress) and poor emotional and self-regulation tend to develop maladaptive Internet-related beliefs and actions (e.g. IGD, PSMU, and PSU) to compensate for their emotional and psychological needs, ultimately leading to adverse outcomes. Self-Determination Theory¹⁹ states that unmet basic psychological needs can exacerbate negative emotional experiences. The meaning of life is the essence of a person's existence and provides a personal understanding of the meaning of self-existence.²⁰ Low meaning in life sufferers lack motivation and life goals and believe their lives are empty and worthless. They cannot meet the psychological needs for autonomy and competence, which could increase psychological distress.²¹ When the individual has meaning avoidance and meaning anxiety, the individual may indulge in hedonic happiness (e.g. IGD, PSMU, and PSU) to escape from the pain and distress of pursuing meaning in life.²² Although some studies have explored the association between IGD, PSMU, and PSU and psychological distress,^23,24 IGD, PSMU, and PSU and meaning in life,^25,26 few studies have explored the association between IGD, PSMU, and PSU, psychological distress, and meaning in life, especially in the adolescent population. A recent study demonstrated that the presence of meaning in life could affect PSU by affecting depression in children and adolescents using three waves of longitudinal data.²⁷ However, other potential paths between IGD, PSMU, and PSU, psychological distress, and meaning in life were not investigated in that study, as well as the connection between IGD, PSMU, and PSU, psychological distress, and other aspects of meaning in life (e.g. meaning avoidance). Hence, the current study adopted Directed Acyclic Graphs (DAG), a method that allows algorithms to explore potential causal directions between variables in the cross-sectional data,²⁸ to investigate the potential pathways between psychological distress, meaning in life, and IGD, PSMU, and PSU.

In summary, the current study used network analysis to investigate the core and bridging symptoms of IGD, PSU, and PSMU. Additionally, the DAG approach was employed in this study to explore potential connections between IGD, PSMU, and PSU, psychological distress, and adolescent meaning in life to address gaps in empirical evidence. By identifying central symptoms, bridging symptoms, and potential impact pathways, researchers and clinicians might gain new insights into preventing or mitigating IGD, PSU, PSMU, and IGD, PSMU, and PSU comorbidity.

Methods

Participants and procedures

The Human Subjects Ethics Sub-Committee of the City University of Hong Kong (2020-21-CIR8-3) approved the ethics of this study. A convenience sampling method was used for data collection. A comprehensive high school (including junior and high school students) was selected in Chongqing, China. An online data collection platform was adopted to collect the data because of the coronavirus pandemic.²⁹ Specifically, the research team had the approval and cooperation of the local educational authorities and school administrators. The research team trained the teachers (class tutors and psychology teachers) on collecting data, provided them with thorough information about the survey's objectives, contents, and confidentiality protections, and gave them access to the electronic questionnaire's internet link. The teachers then explained the study to the participants and their parents and forwarded the online link to them via the class's social network groups (i.e., QQ and WeChat). All the participants should access the website link, read the “informed consent,” and then decide whether to complete the survey. The parents of participants under 18 must also read the “informed consent” before allowing their kids to participate in the survey. The official questionnaire page would not appear until the participants and the parents of participants under 18 clicked the “Agree” button.³⁰

A total of 742 students (347 males and 395 females) who were 12 years old or above participated in the survey from 3 June to 10 June 2021. The mean age of students was 15.39 years (SD = 1.68, range = 12–19). About a third (32.48%, 241) of the participants’ parents were not in an original marriage (i.e. divorced, separated, bereaved, cohabited, or remarried), while the rest, 67.52% (501) of the participants’ parents were in the original marriage. During the survey period, one U.S. dollar was equal to approximately CNY 6.40. In terms of the annual per-capita household income, the majority of participants (36.12%) were between CNY4,001 and CNY10,000, followed by those less than CNY4,000 (29.25%) and those between CNY10,001 and CNY30,000 (22.78%). The portions between CNY 30,001 and CNY 50,000 and over CNY 50,000 were 7.55% and 4.31%, respectively.

Measures

Internet gaming disorder

The 9-item Internet Gaming Disorder Scale-Short Form¹ was used. Participants should rate on a five-point Likert scale that ranged from 1 (never) to 5 (very often). The item description and the symptoms are attached in Table S1 in the Supplemental Materials. The 9-item Internet Gaming Disorder Scale-Short Form has been translated into several languages,³¹ and studies of Chinese populations using it have revealed positive psychometric characteristics.^32–35 The Cronbach's alpha was 0.92 in the current study.

Problematic smartphone use

The 6-item Smartphone Application-Based Addiction Scale³ measured the symptoms of problematic smartphone use. Participants were asked to rate on a six-point Likert scale ranging from 1 (strongly disagree) to 6 (strongly agree). The item description and the symptoms are attached in Table S1 in the Supplemental Materials. The 6-item Smartphone Application-Based Addiction Scale has been validated in Chinese populations.^32–35 The Cronbach's alpha was 0.87 in the current study.

Problematic social media use

The 6-item Bergen Social media Addiction Scale² was adopted to assess the degree and the symptoms of problematic social media use. Participants were asked to rate on a five-point Likert scale ranging from 1 (very rarely) to 5 (very often). The item description and the symptoms are attached in Table S1 in the Supplemental Materials. Previous studies showed that the 6-item Bergen Social media Addiction Scale possessed good psychometric characteristics in Chinese populations.^32–35 The Cronbach's alpha was 0.82 in the current study.

Psychological distress

The 21-item Depression, Anxiety, and Stress Scale³⁶ were adopted to assess psychological distress. Each item was reported on a four-point Likert scale ranging from 0 (did not apply to me at all) to 3 (applied to me very much or most of the time). The 21-item Depression, Anxiety, and Stress Scale was often utilized by Chinese populations and showed favorable psychometric properties.^37–40 The Cronbach's alpha of the subscales and the full scale were between 0.88 and 0.96 in the current study.

Meaning in life

The 10-item Meaning in Life Questionnaire²⁰ was used to measure the search for meaning (five items) and the presence of meaning (five items). The 24-item Extended Meaning in Life Questionnaire²² was used to measure participants’ need for meaning (six items), confusion of meaning (six items), meaning-related anxiety (six items), and avoidance of meaning (six items). Response options ranged from 1 (absolutely untrue) to 7 (absolutely true). The 10-item Meaning in Life Questionnaire⁴¹ and 24-item Extended Meaning in Life Questionnaire²² showed good psychometric characteristics in the Chinese population. The Cronbach's alpha of the subscales and the whole scales were between 0.76 and 0.94 in the current study.

Statistical analysis

Descriptive data analysis was conducted in SPSS 26.0.⁴² The absolute skewness ≤ 2.0 and absolute kurtosis ≤ 7.0 were used as the indicators of data normality.⁴³ Network analysis was performed in R (Version 4.1.2).⁴⁴

The Gaussian Graphical Model and the graphical least absolute shrinkage and selection operator estimation algorithm using the Extended Bayesian Information Criterion (EBICglasso) were adopted to estimate the IGD, PSMU, and PSU network because the symptoms were continuous data with a normal distribution.⁴⁵ The strength and expected influence, which are the most emphasized in psychopathology networks,⁴⁶ were adopted to infer which nodes acted as “central” in the network. The bridge strength and bridge expected influence (both 1-step and 2-step) were calculated using the “networktools” package (Version 1.4.0).⁴⁷ The top 20% of bridge symptoms were selected based on the blind 80th percentile cutoff on the scores of bridge strength⁴⁶ and were shown graphically in the network. The accuracy and stability of the edges and centrality metrics were tested by conducting the random 5000 “nonparametric” type and “case-drop” type bootstraps.⁴⁵ The correlation stability coefficient (CS-coefficient) of the edge weights, strength, and expected influence higher than 0.25 (acceptable), higher than 0.5 (good), and higher than 0.7 (excellent) was adopted as the stable network criteria.⁴⁵

DAG was adopted to estimate the potential pathways between variables. Instead of the symptom scores, the sum scores of IGD, PSU, and PSMU were used to reduce the model complexity.⁴⁸ Similarly, the total scores of the three dimensions of psychological distress and the mean scores of the six dimensions of meaning in life were put into the model for estimation. As the relationships between IGD, PSMU, and PSU and other variables can be moderated by demographic factors (e.g. age, gender, and family functioning),⁴⁹ age, gender, parental marital status, and annual per-capita household income were included in the model. A stable PC-algorithm in the “bnlearn” package (Version 4.7) was used to estimate the DAG.^48,50 Given the missing data on age, a multiple imputation method was adopted to impute missing data of age in the “mice” package (Version 3.14.0).⁵¹ All edges towards age and gender, and all edges from age, gender, IGD, PSMU, and PSU, psychological distress, and meaning in life towards parental marital status and income were blacklisted to make the estimation more reasonable. Besides, the algorithm helped to discover all the potential edges because no edges were whitelisted. The 1000 non-parametric bootstraps were conducted to test the estimation's stability and identify all the possible pathways.^48,50

Results

Descriptive statistics

Table 1 shows the results of the descriptive analysis. There were moderate positive correlations between the IGD, PSMU, and PSU. IGD, PSMU, and PSU showed small to moderate positive correlations with psychological distress and the three negative meaning states (i.e. meaning confusion, meaning anxiety, and meaning avoidance). In contrast, all three categories showed small negative correlations with the presence of meaning.

Table 1.

Correlations (Pearson's r), mean (M), standard deviation (SD), skewness, and kurtosis among key variables (n = 742).

Variables	1	2	3	4	5	6	7	8	9	10	11	12
1 Internet game disorder	−
2 Problematic smartphone use	0.52***	−
3 Problematic social media use	0.44***	0.48***	−
4 Anxiety	0.31***	0.27***	0.18***	−
5 Depression	0.35***	0.30***	0.21***	0.85***	−
6 Stress	0.30***	0.31***	0.21***	0.88***	0.88***	−
7 Search for meaning	−0.06	0.08*	−0.03	0.03	−0.06	0.04	−
8 Presence of meaning	−0.10**	−0.11**	−0.11**	−0.05	−0.18***	−0.10**	0.55***	−
9 Need for meaning	−0.07	0.18***	0.02	0.04	−0.05	0.08*	0.76***	0.41***	−
10 Meaning confusion	0.29***	0.52***	0.26***	0.30***	0.35***	0.36***	0.28***	−0.16***	0.40***	−
11 Meaning anxiety	0.27***	0.47***	0.22***	0.34***	0.31***	0.37***	0.48***	0.08*	0.58***	0.80***	−
12 Meaning avoidance	0.42***	0.43***	0.23***	0.28***	0.37***	0.30***	−0.06	−0.12**	0.00	0.59***	0.45***	−
M	18.39	18.25	13.79	8.11	7.35	8.09	4.67	4.24	4.64	3.75	3.84	3.17
SD	6.82	6.05	4.28	5.01	5.35	5.03	1.41	1.11	1.38	1.42	1.33	1.31
Skewness	0.44	−0.07	0.11	0.31	0.35	0.28	−0.16	0.13	−0.24	−0.03	−0.14	0.06
Kurtosis	−0.35	−0.36	−0.34	−0.32	−0.57	−0.41	−0.13	0.65	−0.17	−0.26	0.04	−0.47

Note: *p < 0.05, **p < 0.01, ***p < 0.001.

IGD, PSMU, and PSU network model and the bridge symptoms

The IGD, PSU, and PSMU symptom communities were visualized based on their original symptom groups in the IGD, PSMU, and PSU network structure in Figure 1. According to the centrality estimates (see Figure 2), withdrawal in IGD, mood modification in PSU, tolerance, and withdraw in PSMU had the highest node strength centrality in their original symptom groups, respectively. Among all the IGD, PSMU, and PSU symptoms, the withdrawal in IGD and the mood modification in PSU had the highest node strength centrality (refer to Table S1 in the Supplemental Materials for details).

Figure 1.

EBICglasso network analysis results and the top 20% bridge symptoms of problematic technology use (N = 742). Note: Blue edges represent positive associations, and red edges represent negative associations. Nodes represent problematic technology use symptoms. Yellow nodes represent the top 20% of bridge nodes with large 2-step bridge expected influence values. IGD1 = Preoccupation, IGD2 = Withdrawal, IGD3 = Tolerance, IGD4 = Loss of control, IGD5 = Loss of previous interests, IGD6 = Continuation despite problems, IGD7 = Deception, IGD8 = Mood modification, IGD9 = Jeopardization. SABA1 = Salience, SABA2 = Conflict, SABA3 = Mood modification, SABA4 = Tolerance, SABA5 = Withdrawal, SABA6 = Loss of control. SMA1= Salience, SMA2 = Tolerance, SAM3 = Mood modification, SMA4 = Loss of control, SMA5 = Withdrawal, SMA6 = Functional impairment.

Figure 2.

Centrality metrics (strength, expected influence) for the problematic technology use. Note: Standardized z-scores are plotted for visualization. IGD1 = Preoccupation, IGD2 = Withdrawal, IGD3 = Tolerance, IGD4 = Loss of control, IGD5 = Loss of previous interests, IGD6 = Continuation despite problems, IGD7 = Deception, IGD8 = Mood modification, IGD9 = Jeopardization. SABA1 = Salience, SABA2 = Conflict, SABA3 = Mood modification, SABA4 = Tolerance, SABA5 = Withdrawal, SABA6 = Loss of control. SMA1= Salience, SMA2 = Tolerance, SAM3 = Mood modification, SMA4 = Loss of control, SMA5 = Withdrawal, SMA6 = Functional impairment.

According to the bridge centrality metrics of nodes (see Figure 3), the top 20% scoring nodes were visualized as a new symptom community in Figure 1. Mood modification in IGD bridged with mood modification in PSU. Functional impairment in PSMU was positively associated with loss of control in IGD and conflict in PSU. Withdrawal in PSMU formed bridges with withdrawal in IGD and withdrawal in PSU.

Figure 3.

Bridge centrality metrics (bridge strength, 1-step bridge expected to influence, and 2-step bridge expected influence) for the problematic technology use. Note: IGD1 = Preoccupation, IGD2 = Withdrawal, IGD3 = Tolerance, IGD4 = Loss of control, IGD5 = Loss of previous interests, IGD6 = Continuation despite problems, IGD7 = Deception, IGD8 = Mood modification, IGD9 = Jeopardization. SABA1 = Salience, SABA2 = Conflict, SABA3 = Mood modification, SABA4 = Tolerance, SABA5 = Withdrawal, SABA6 = Loss of control. SMA1= Salience, SMA2 = Tolerance, SAM3 = Mood modification, SMA4 = Loss of control, SMA5 = Withdrawal, SMA6 = Functional impairment.

Network accuracy and stability

The bootstrap analysis results of edge weight bootstrap, strength centrality bootstrap, edge weights significance testing, and strength centrality difference testing can be found in the Supplemental Materials. The edge weight bootstrap results showed that edge weights were accurately estimated (see Figure S1 in the Supplemental Materials). The CS-coefficient was 0.67 for the edge, 0.28 for expected influence, and 0.28 for strength, suggesting that the IGD, PSMU, and PSU network remained stable (see Figures S2 and S3 in the Supplemental Materials).

DAG analysis

The estimated DAG structure and the inclusion proportions of each edge of IGD, PSMU, and PSU, psychological distress, and meaning in life are presented in Figure 4. The model demonstrated that gender would differentiate IGD scores with a higher score in males than females. The use of PSU and PSMU would exacerbate IGD. The algorithm could not distinguish the influence direction between PSU and PSMU. Regarding psychological distress, the model predicted that stress could increase anxiety and depression, and depression could increase anxiety. As to the relationships between IGD, PSMU, and PSU, psychological distress, and meaning in life, the algorithm predicted that PSU could intensify meaning confusion and further intensify meaning avoidance. Likewise, the direction of the relationship between PSU and meaning anxiety could not be distinguished. IGD could intensify meaning avoidance both directly and indirectly through depression.

Figure 4.

Directed acyclic graph (DAG) estimation and bootstrap replication results of problematic technology use, psychological distress, and meaning in life (N = 742). Note: The left panel shows the estimated DAG structure, and the right panel shows the inclusion proportion of each edge based on 1000 bootstrap replications. An undirected edge indicates that the algorithm could not determine the direction of effect. Threshold = 0.25 for the simplicity of the figure. IGD = Internet game disorder. PSU = Problematic smartphone use. PSMU = Problematic social media use. Anx = Anxiety. Dep = Depression. Str = Stress. SM = Search for meaning. PM = Presence of meaning. NM = Need for meaning. MC = Meaning confusion. MAN = Meaning anxiety. MAV = Meaning avoidance.

Discussion

IGD, PSMU, and PSU symptoms conceptually have the potential to coexist and interact. How these symptoms interact with one another is unclear, though. Theoretically, psychological distress and meaning in life correlate with IGD, PSMU, and PSU; however, the pathways by which these components interact are yet unknown, especially in adolescence. In this study, network analysis was utilized to investigate the central and bridging symptoms of IGD, PSMU, and PSU using cross-sectional data. The results indicated that withdrawal in IGD, mood modification in PSU, and tolerance in PSMU were the central symptoms among adolescents. Mood modification in IGD, mood modification in PSU, withdrawal in PSMU, and functional impairment in PSMU were the bridge symptoms. The DAG technique was used in this study to investigate potential pathways between IGD, PSMU, PSU, psychological distress, and meaning in life. Depression, the negative states of meaning confusion, anxiety, and avoidance were more closely associated with IGD, PSMU, and PSU.

Although the findings of this study's network analysis of adolescent data on the central symptoms for IGD, PSMU, and PSU were comparable to^52,53 and different from^54,55 those of other research using adult data, these results support and expanded the spectrum hypothesis.^10,11 The spectrum hypothesis suggests that using the unitary “umbrella construct” (problematic Internet use or problematic smartphone use) to cover a variety of different problematic use behaviors (e.g. IGD and PSMU) would ignore the differences between specific problematic use behaviors.¹² Future studies should prevent using the generalized concept and concentrate on unique problematic use behaviors. Slightly different from the spectrum hypothesis is that the current study discovered PSU is not a generalized “umbrella construct” but a specific problematic use behavior. The present results showed that IGD, PSMU, and PSU had different core symptoms. This implies that PSU is an independent construct with its unique central symptom. Therefore, to extend the spectrum hypothesis, further research is required to examine the parallels and differences between PSU and particular problematic use behaviors.

The discovery of bridging symptoms provides unique insights into the prevention and intervention of IGD, PSMU, and PSU and their comorbidities. First, the bridge symptoms indicated that recovery from negative emotions would be essential to managing IGD and PSU symptoms. Regardless of which kind of IGD, PSMU, and PSU is regulated, adolescents would experience withdrawal symptoms, mainly manifested in the emotional aspect (e.g. anxiety and irritability). Second, in the behavioral aspect, individuals with IGD, PSMU, and PSU are susceptible to functional dysfunction when negative emotions are overwhelming. Third, our findings also shed some light on intervention strategies for adolescents. On the one hand, targeting emotional regulation for adolescents is a priority of the intervention. Different strategies of emotion regulation interventions should be integrated to alleviate negative emotions in IGD, PSMU, and PSU. On the other hand, crude restraint and abstinence are not ideal approaches. Parents and teachers should be taught effective and healthy intervening approaches (e.g. replacing screen use with exercise⁵⁶) to help reduce adolescents’ withdrawal symptoms caused by being banned from the Internet.

The DAG bootstrap results suggested that each of the three constructs—problematic use behavior, psychological distress, and meaning in life—is a dynamic system unto itself. These systems appear to associate with each other in a bidirectional manner as opposed to a unidirectional manner. Stress was the trigger among the three components of psychological distress. Meaning of life itself is a dynamically evolving system.⁵⁷ Meaning anxiety could influence PSU. PSU and PSMU collaborate. PSU could enhance meaning confusion and IGD, which could further influence depression and meaning avoidance. Meaning avoidance, in turn, may have an impact on depression and IGD. These clues support the findings of numerous previous investigations. Recent studies have found the bidirectional relationship between IGD, PSMU, and PSU and psychological distress using long-term follow-up questionnaires at various time points.^58,59 However, the unidirectional impact of life meaning (meaningful existence and meaning seeking) on problematic use behaviors is still the sole focus of current longitudinal studies,^27,60 and no research has attempted to examine the contralateral effect. Hence, theories of problematic use behavior development need to be updated and frame the relationship between IGD, PSMU, and PSU and psychosocial factors, including psychological distress and life meaning from a dynamical systems perspective. Besides, the inclusion of psychological interventions, such as stress-focused and meaning-focused strategies, can be the icing on the cake when intervening with IGD, PSMU, and PSU, in addition to treatment approaches that target problematic use behaviors (e.g. restraint and withdrawal).

The current findings need to be interpreted with caution. First, it should be noted that DAG is not recommended for psychological network analysis because DAG requires the assumption of acyclicity. In contrast, psychological symptoms networks are likely to contain the very cycles.²⁸ However, DAG can be supplementary to the Gaussian Graphical Model to explore the potential direction of causality.⁶¹ Besides, the current study included some exogenous variables (i.e. age, gender, parental marital, and family income) that would not be affected by IGD, PSMU, and PSU, psychological distress, and meaning in life. Hence, DAG estimation was adopted to investigate the potential pathways. The current findings should be interpreted as generating hypotheses rather than verifying the exact causality effects.⁴⁸ In future studies, cross-lagged network analysis using longitudinal data is necessary to verify the causality and the direction of the relationships. Second, although the network stability of the current research met the criteria of network analysis, it has room for improvement. Future research can expand the sample size and enable network comparisons between groups (e.g. age, gender) to improve network stability. Third, the current study only included self-reported IGD, PSMU, and PSU. Future research should be more thorough and objective, that is, include objective data and additional problematic use behaviors (e.g., problematic online shopping). Finally, it would be interesting to carry out this investigation in other groups/cultures to confirm the reproducibility of the results, as the current study concentrated on Chinese adolescents, and prior research has indicated that age and culture influence IGD, PSMU, and PSU.^7,62

Supplemental Material

sj-docx-1-dhj-10.1177_20552076231158036 - Supplemental material for Network analysis of internet gaming disorder, problematic social media use, problematic smartphone use, psychological distress, and meaning in life among adolescents

Supplemental material, sj-docx-1-dhj-10.1177_20552076231158036 for Network analysis of internet gaming disorder, problematic social media use, problematic smartphone use, psychological distress, and meaning in life among adolescents by Yumei Li, Wenlong Mu, Xuying Xie and Sylvia Y.C.L. Kwok in Digital Health

Footnotes

Acknowledgments

Not applicable.

Author contributions

YM and WL researched literature and conceived the study. All authors were involved in protocol development, study design, and research questions. YM and WL were responsible for the data analysis. YM wrote the first draft of the manuscript. All authors reviewed and edited the manuscript and approved the final version of the manuscript.

Data,materials and/or code availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Ethical approval

This study was conducted with the approval of the College Human Subjects Ethics Sub-Committee College of Liberal Arts and Social Sciences of City University of Hong Kong (2020-21-CIR8-3).

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Key Research Institute of Humanities and Social Sciences at Universities, Ministry Education [No. 22JJD860009].

Guarantor

WL.

ORCID iD

Wenlong Mu

Supplemental material

Supplemental material for this article is available online.

References

Pontes

Griffiths

. Measuring DSM-5 internet gaming disorder: development and validation of a short psychometric scale. Comput Human Behav 2015; 45: 137–143.

Andreassen

Billieux

Griffiths

, et al. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behav 2016; 30: 252–262.

Csibi

Griffiths

Cook

, et al. The psychometric properties of the smartphone application-based addiction scale (SABAS). Int J Ment Health Addict 2018; 16: 393–403.

Stevens

MWR

Dorstyn

Delfabbro

, et al. Global prevalence of gaming disorder: a systematic review and meta-analysis. Australian & New Zealand Journal of Psychiatry 2020; 55: 553–568.

Cheng

Cheung

MWL

Wang

H-Y

. Multinational comparison of internet gaming disorder and psychosocial problems versus well-being: meta-analysis of 20 countries. Comput Human Behav 2018; 88: 153–167.

Sohn

Rees

Wildridge

, et al. Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence. BMC Psychiatry 2019; 19: 356.

Boer

van den Eijnden

RJJM

Boniel-Nissim

, et al. Adolescents’ intense and problematic social media use and their well-being in 29 countries. Journal of Adolescent Health 2020; 66: S89–S99.

Rozgonjuk

Sindermann

Elhai

, et al. Associations between symptoms of problematic smartphone, Facebook, WhatsApp, and Instagram use: an item-level exploratory graph analysis perspective. J Behav Addict 2020; 9: 686–697.

Chen

C-Y

Liu

C-H

, et al. Internet addiction and psychological distress among Chinese schoolchildren before and during the COVID-19 outbreak: a latent class analysis. J Behav Addict 2021; 10: 731–746.

10.

Billieux

. Problematic use of the mobile phone: a literature review and a pathways model. Curr Psychiatry Rev 2012; 8: 299–307.

11.

Starcevic

Aboujaoude

. Internet addiction: reappraisal of an increasingly inadequate concept. CNS Spectr 2017; 22: 7–13.

12.

Baggio

Starcevic

Billieux

, et al. Testing the spectrum hypothesis of problematic online behaviors: a network analysis approach. Addict Behav 2022; 135: 107451.

13.

Chen

Chang

K-C

Chang

C-W

, et al. Temporal associations between problematic use of the internet and self-stigma among people with substance use disorders: a cross-lagged model across one year. J Psychiatr Res 2022; 156: 339–348.

14.

Saffari

Chen

H-P

Chang

C-W

, et al. Effects of sleep quality on the association between problematic internet use and quality of life in people with substance use disorder. BJPsych Open 2022; 8: e155.

15.

Berta

Miguel Ángel

Clara

G-S

, et al. A bibliometric analysis of 10 years of research on symptom networks in psychopathology and mental health. Psychiatry Res 2022; 308: 114380.

16.

Wei

Jiang

Wang

, et al. The relationship between components of neuroticism and problematic smartphone use in adolescents: a network analysis. Pers Individ Dif 2022; 186: 111325.

17.

Caplan

. Theory and measurement of generalized problematic internet use: a two-step approach. Comput Human Behav 2010; 26: 1089–1097.

18.

Kardefelt-Winther

. A conceptual and methodological critique of internet addiction research: towards a model of compensatory internet use. Comput Human Behav 2014; 31: 351–354.

19.

Ryan

Deci

. Self-determination theory: Basic psychological needs in motivation, development, and wellness. New York: Guilford Publications, 2017.

20.

Steger

Frazier

Oishi

, et al. The meaning in life questionnaire: assessing the presence of and search for meaning in life. J Couns Psychol 2006; 53: 80–93.

21.

J-B

Salcuni

Delvecchio

. Meaning in life, self-control and psychological distress among adolescents: a cross-national study. Psychiatry Res 2019; 272: 122–129.

22.

Zhang

Sang

Chen

, et al. Need for meaning, meaning confusion, meaning anxiety, and meaning avoidance: additional dimensions of meaning in life. J Happiness Stud 2018; 19: 191–212.

23.

Chen

C-Y

Pakpour

, et al. Problematic internet-related behaviors mediate the associations between levels of internet engagement and distress among schoolchildren during COVID-19 lockdown: a longitudinal structural equation modeling study. J Behav Addict 2021; 10: 135–148.

24.

Chen

I-H

Pakpour

Leung

, et al. Comparing generalized and specific problematic smartphone/internet use: longitudinal relationships between smartphone application-based addiction and social media addiction and psychological distress. J Behav Addict 2020; 9: 410–419.

25.

Zhao

Zhou

, et al. The relationship between bullying victimization and online game addiction among Chinese early adolescents: the potential role of meaning in life and gender differences. Child Youth Serv Rev 2020; 116: 105261.

26.

Blau

Goldberg

Benolol

. Purpose and life satisfaction during adolescence: the role of meaning in life, social support, and problematic digital use. J Youth Stud 2019; 22: 907–925.

27.

Qiu

Liu

, et al. The influence of meaning in life on children and adolescents’ problematic smartphone use: a three-wave multiple mediation model. Addict Behav 2022; 126: 107199.

28.

Epskamp

Waldorp

Mõttus

, et al. The Gaussian graphical model in cross-sectional and time-series data. Multivariate Behav Res 2018; 53: 453–480.

29.

Duan

Kong

Chen

, et al. COVID-19-induced social exclusion and quality of life among Chinese adolescents in the context of family education: the mediating role of perceived control. Educ Psychol. Early Access. DOI: 10.1080/01443410.2022.2099529.

30.

Xie

, et al. The Chinese version of the digital stress scale: evaluation of psychometric properties. Curr Psychol 11. Early Access. DOI: 10.1007/s12144-022-03156-1.

31.

Poon

LYJ

Tsang

HWH

Chan

TYJ

, et al. Psychometric properties of the internet gaming disorder scale–short-form (IGDS9-SF): systematic review. J Med Internet Res 2021; 23: e26821.

32.

Chen

Ahorsu

Pakpour

, et al. Psychometric properties of three simplified Chinese online-related addictive behavior instruments among Mainland Chinese primary school students. Front Psychiatry 2020; 11: 875–875.

33.

Chen

Strong

Lin

Y-C

, et al. Time invariance of three ultra-brief internet-related instruments: smartphone application-based addiction scale (SABAS), Bergen social media addiction scale (BSMAS), and the nine-item internet gaming disorder scale - short form (IGDS-SF9) (study part B). Addict Behav 2020; 101: 105960.

34.

Yam

C-W

Pakpour

Griffiths

, et al. Psychometric testing of three Chinese online-related addictive behavior instruments among Hong Kong University students. Psychiatric Quarterly 2019; 90: 117–128.

35.

Leung

Pakpour

Strong

, et al. Measurement invariance across young adults from Hong Kong and Taiwan among three internet-related addiction scales: Bergen social media addiction scale (BSMAS), smartphone application-based addiction scale (SABAS), and internet gaming disorder scale-short form (IGDS-SF9) (study part A). Addict Behav 2020; 101: 105969.

36.

Lovibond

. Manual for the depression, anxiety and stress scales. 2nd ed. Sydney, Australia: Psychological Foundation of Australia, 1995.

37.

Wang

Shi

H-S

Geng

F-L

, et al. Cross-cultural validation of the depression anxiety stress scale-21 in China. Psychol Assess 2016; 28: e88–e100.

38.

Duan

. Evaluating the construct validity of stress overload scale-short using exploratory structural equation modeling. J Health Psychol 2020; 25: 913–921.

39.

Duan

. Validation of a Chinese version of the stress overload scale-short and its use as a screening tool for mental health status. Qual Life Res 2018; 27: 411–421.

40.

Duan

Xiong

. Cross-cultural adaptation and validation of the physical disability resiliency scale in a sample of Chinese with physical disability. Front Psychol 2020; 11: 602736.

41.

Datu

JAD

Yuen

. Factorial validity of meaning in life questionnaire in Hong Kong secondary school students: a construct validation approach. Couns Psychol Q 2021: 1–14. DOI: 10.1080/09515070.2021.1875989.

42.

IBM Corp. IBM SPSS statistics for windows, version 26.0. Armonk, NY: IBM Corp, 2019.

43.

Curran

West

Finch

. The robustness of test statistics to nonnormality and specification error in confirmatory factor analysis. Psychol Methods 1996; 1: 16–29.

44.

R Core Team. R: a language and environment for statistical computing. 4.2.1 ed. Vienna, Austria: R Foundation for Statistical Computing, 2022.

45.

Epskamp

Borsboom

Fried

. Estimating psychological networks and their accuracy: a tutorial paper. Behav Res Methods 2018; 50: 195–212.

46.

Jones

McNally

. Bridge centrality: a network approach to understanding comorbidity. Multivariate Behav Res 2021; 56: 353–367.

47.

Jones

. Networktools: Tools for identifying important nodes in networks. R package version 1.4.0. ed. 2021.

48.

Liu

Epskamp

Isvoranu

A-M

, et al. Network analysis of physical and psychiatric symptoms of hospital discharged patients infected with COVID-19. J Affect Disord 2021; 294: 707–713.

49.

Busch

McCarthy

. Antecedents and consequences of problematic smartphone use: a systematic literature review of an emerging research area. Comput Human Behav 2021; 114: 106414.

50.

Briganti

Scutari

McNally

. A tutorial on Bayesian Networks for psychopathology researchers. 2021. DOI: 10.31234/osf.io/h4vxa.

51.

van Buuren

Groothuis-Oudshoorn

. Mice: multivariate imputation by chained equations in R. J Stat Softw 2011; 45: 1–67.

52.

Csibi

Griffiths

Demetrovics

, et al. Analysis of problematic smartphone use across different age groups within the ‘components model of addiction’. Int J Ment Health Addict 2021; 19: 616–631.

53.

Liu

Lemmens

Hong

, et al. A network analysis of internet gaming disorder symptoms. Psychiatry Res 2022: 114507. DOI: 10.1016/j.psychres.2022.114507.

54.

Svicher

Fioravanti

Casale

. Identifying the central symptoms of problematic social networking sites use through network analysis. J Behav Addict 2021; 10: 767–778.

55.

Yuan

Shi

Elhai

, et al. Gaming to cope: applying network analysis to understand the relationship between posttraumatic stress symptoms and internet gaming disorder symptoms among disaster-exposed Chinese young adults. Addict Behav 2022; 124: 107096.

56.

Kandola

del Pozo Cruz

Hayes

, et al. Impact on adolescent mental health of replacing screen-use with exercise: a prospective cohort study. J Affect Disord 2022; 301: 240–247.

57.

Schnell

. The psychology of meaning in life. Routledge, 2020.

58.

Chang

Huang

Strong

, et al. Reciprocal relationships between problematic social media use, problematic gaming, and psychological distress among university students: a 9-month longitudinal study. Front Public Health 2022; 10: 858482.

59.

Di Blasi

Salerno

Albano

, et al. A three-wave panel study on longitudinal relations between problematic social media use and psychological distress during the COVID-19 pandemic. Addict Behav 2022; 134: 107430.

60.

Wang

Geng

, et al. Meaning in life and self-control mediate the potential contribution of harsh parenting to adolescents’ problematic smartphone use: longitudinal multi-group analyses. J Interpers Violence 2022; 38: NP2159–NP2181.

61.

McNally

Heerena

Robinaugh

. A Bayesian network analysis of posttraumatic stress disorder symptoms in adults reporting childhood sexual abuse. Eur J Psychotraumatol 2017; 8: 1–10.

62.

Olson

Sandra

Colucci

ÉS

, et al. Smartphone addiction is increasing across the world: a meta-analysis of 24 countries. Comput Human Behav 2022; 129: 107138.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.16 MB