Network Analysis of Internet Addiction Symptoms Among a Clinical Sample of Japanese Adolescents with Autism Spectrum Disorder

Sex gaps and age differences in the structure of academic cyberloafing from early to middle adolescence: A network analysis

BACKGROUND

Academic cyberloafing refers to students' engagement in non-learning-related online activities during online courses, which can negatively affect their academic performance. Prior studies investigated cyberloafing primarily in the workplace, neglecting core behaviors and interactions among academic cyberloafing in educational contexts.

AIMS

This study employed network analysis to capture academic cyberloafing as an interactive behavior network to explore the core behavioral patterns of academic cyberloafing and the interactions between these behaviors.

MATERIALS & METHODS

A total of 3537 adolescents (Mage = 12.49; 53.7% boys and 46.3% girls) in China were included in this study.

RESULTS

The findings indicated that "seeking gossip news" and "watching short videos" are central behaviors. Among boys, "browsing nonacademic web pages" and "watching short videos" are central behaviors; "seeking gossip news" is the most central behavior among girls. Furthermore, in early adolescence, central behaviors encompass "chatting privately" and "seeking gossip news"; in middle adolescence, central behaviors include "seeking gossip news" and "watching short videos." Additionally, the comparisons indicated that academic cyberloafing networks (between boys and girls; between early and middle adolescence) show a similar structure and global strength but differ in specific academic cyberloafing associations.

CONCLUSION

As adolescents of different sexes and ages engage in academic cyberloafing differently, tailored education interventions can be implemented to address unregulated cyberloafing.

Network Structure and Clustering Analysis Relating to Individual Symptoms of Problematic Internet Use in a Community Adolescent Population

INTRODUCTION

Problematic internet use (PIU) is a psychopathology that includes multiple symptoms and psychological constructs. Because no studies have considered both network structures and clusters among individual symptoms in the context of PIU in a Korean adolescent population, this study aimed to investigate network structures and clustering in relation to PIU symptoms in adolescents.

METHODS

Overall, 73,238 adolescents were included. PIU severity was assessed using a self-rating scale comprising 20 items and 6 subscales, namely, the Internet Addiction Proneness Scale for Youth-Short Form; KS scale. Network structures and clusters among symptoms were analyzed using a Gaussian graphical model and exploratory graph analysis, respectively. Centrality of strength, closeness, and betweenness scores was also calculated.

RESULTS

Our study identified four clusters: disturbance in adaptive functioning, virtual interpersonal relationships, withdrawal, and tolerance. The symptom of confidence served as a node bridging the cluster of virtual interpersonal relationships and other clusters of withdrawal and disturbances of adaptive function. The symptom of craving served as a bridge between the clusters of withdrawal and tolerance with high betweenness centrality.

CONCLUSION

This study identified network structures and clustering among PIU symptoms in adolescents and revealed that positive experiences derived from online interpersonal relationships were an important mechanism underlying PIU. These are novel insights concerning the interconnection among multiple symptoms and related clustering for the mechanism of adolescent PIU in terms of KS-scale PIU assessment.

The Impact of Long-Term Online Learning on Internet Addiction Symptoms among Depressed Secondary School Students: Insights from a Cross-Panel Network Analysis

BACKGROUND

The COVID-19 pandemic and the shift to online learning have increased the risk of Internet addiction (IA) among adolescents, especially those who are depressed. This study aims to identify the core symptoms of IA among depressed adolescents using a cross-lagged panel network framework, offering a fresh perspective on understanding the interconnectedness of IA symptoms.

METHODS

Participants completed the Internet addiction test and the Patient Health Questionnaire-9. A total of 2415 students were initially included, and after matching, only 342 students (a cutoff score of 8) were retained for the final data analysis. A cross-lagged panel network analysis was conducted to examine the autoregressive and cross-lagged trajectories of IA symptoms over time.

RESULTS

The incidence rate of depression rose remarkably from 14.16% (N = 342) to 17.64% (N = 426) after the four-month online learning. The symptom of "Anticipation" exhibited the highest out-expected influence within the IA network, followed by "Stay online longer" and "Job performance or productivity suffer". Regarding the symptom network of depression, "Job performance or productivity suffer" had the highest in-expected influence, followed by "Life boring and empty", "Snap or act annoyed if bothered", "Check email/SNS before doing things", and "School grades suffer". No significant differences were found in global network strength and network structure between waves 1 and 2.

CONCLUSION

These findings prove the negative effects of online learning on secondary students' mental health and have important implications for developing more effective interventions and policies to mitigate IA levels among depressed adolescents undergoing online learning.

Internet addiction and residual depressive symptoms among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic: a network analysis perspective

To assess the inter-relationships between residual depressive symptoms (RDS) and Internet addiction (IA) using network analysis among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic. RDS and IA were assessed using the Patient Health Questionnaire-9 (PHQ-9) and the Internet Addiction Test (IAT), respectively. Central symptoms and bridge symptoms in the network model were examined. A total of 1,454 adolescents met the study criteria and were included in the analyses. The prevalence of IA was 31.2% (95% CI: 28.8%-33.6%). In the network analysis, the nodes IAT15 ("Preoccupation with the Internet"), PHQ2 ("Sad mood"), and PHQ1 ("Anhedonia") were the most central symptoms in the IA-RDS network model. Bridge symptoms included IAT10 ("Sooth disturbing about your Internet use"), PHQ9 ("Suicide ideation"), and IAT3 ("Prefer the excitement online to the time with others"). Additionally, PHQ2 ("Sad mood") was the main node linking "Anhedonia" to other IA clusters. Internet addiction was common among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic. Core and bridge symptoms identified in this study could be prioritized as targets for the prevention and treatment of IA in this population.

Gender differences in symptom structure of adolescent problematic internet use: A network analysis

BACKGROUND

Gender differences in problematic Internet use (PIU) have long been discussed. However, whether and how female and male adolescents differ in central symptoms and symptom associations are not fully understood.

METHODS

As a national survey in the Chinese mainland, 4884 adolescents (51.6% females; M_age = 13.83 ± 2.41) participated in the present study. This study applies network analysis to identify central symptoms of PIU networks in female and male adolescents and compares whether and how global and local connectivity of PIU networks differ by gender.

RESULTS

Female and male network structures of PIU were different and global strength was stronger in males than females, indicating a higher risk of chronicity of PIU among male adolescents. Specifically, "Reluctant to turn off Internet" exerted the largest effect on both genders. "Increase time online to achieve satisfaction" and "Feel depressed once not online for a while" were particularly critical to female and male adolescents, respectively. Moreover, females scored higher centralities in social withdrawal symptoms and males did so in interpersonal conflicts owing to PIU.

CONCLUSIONS

These findings provide novel insights into gender differential risks and features of adolescent PIU. Differences in the core symptoms of PIU suggest that gender-specific interventions focusing on core symptoms might effectively relieve PIU and maximize treatment effects.

Network analysis of internet addiction and sleep disturbance symptoms

BACKGROUND

Internet addiction (IA) is a behavioral addiction to problematic internet use. IA is associated with poorer sleep quality. Few studies to date, however, have explored the interactions between symptoms of IA and symptoms of sleep disturbance. This study uses network analysis to identify bridge symptoms by analyzing these interactions in a large sample of students.

METHOD

We recruited 1977 university students to participate in our study. Each student completed the Internet Addiction Test (IAT) and the Pittsburgh Sleep Quality Index (PSQI). We used these collected data for network analysis to identify the bridge symptoms in the IAT-PSQI network by calculating the bridge centrality. Furthermore, the closest symptom connected with the bridge symptom was found to identify the comorbidity mechanisms.

RESULTS

The core symptom of IA and the sleep disturbance network was "I08" (Study efficiency suffers due to internet use). The bridge symptoms between IA and sleep disturbance were "I14" (Surfing the internet late instead of sleeping), "P_DD" (Daytime dysfunction), and "I02" (Spending much time online instead of socializing in real life). Among the symptoms, "I14" had the highest bridge centrality. The edge connecting nodes "I14" and "P_SDu" (Sleep duration) had the strongest weight (0.102) around all the symptoms of sleep disturbance. Nodes "I14" and "I15" (Thinking about online shopping, games, social networking, and other network activities when unable to access the internet) had the strongest weight (0.181), connecting all the symptoms of IA.

CONCLUSIONS

IA leads to poorer sleep quality, most likely by shortening sleep duration. Preoccupation with and craving the internet while being offline may lead to this situation. Healthy sleep habits should be learned, and craving may be a good point at which to treat the symptoms of IA and sleep disturbance.

A network analysis of problematic smartphone use in Japanese young adults

Background

We aimed to explore the overall network structure of problematic smartphone use symptoms assessed by smartphone addiction scale-short version (SAS-SV) and to identify which items could play important roles in the network.

Methods

487 college and university students filled out the study questionnaire, including SAS-SV. We constructed a regularized partial correlation network among the 10 items of SAS-SV. We calculated three indices of node centrality: strength, closeness, and betweenness, to quantify the importance of each SAS-SV item.

Results

We identified 34 edges in the estimated network. In the given network, one item pertaining to withdrawal symptom hadthe highest strength and high closeness centrality. Additionally, one item related to preoccupation was also found to have high centrality indices.

Conclusion

Our results indicating the central role of one withdrawal symptom and one preoccupation symptom in the symptom network of problematic smartphone use in young adults were in line with a previous study targeting school-age children. Longitudinal study designs are required to elicit the role of these central items on the formation and maintenance of this behavioral problem.

Internet addiction and its association with quality of life in patients with major depressive disorder: a network perspective

Depressive disorders and internet addiction (IA) are often comorbid. The aims of this study were to examine the network structure of IA in patients with major depressive disorders (MDD) and explore the association between IA and quality of life (QoL) in this population. This was a multicenter, cross-sectional survey. IA and QoL were assessed with the Internet Addiction Test (IAT) and the World Health Organization Quality of Life-brief version, respectively. Node expected influence (EI) was used to identify central symptoms in the network model, while the flow network of QoL was generated to examine its association with IA. A total of 1,657 patients with MDD was included. "Preoccupation with the Internet," "Job performance or productivity suffer because of the Internet," and "Neglect chores to spend more time online" were central symptoms. The symptom "Form new relationships with online users" had the strongest direct positive relation with QoL, while "Spend more time online over going out with others" and "Job performance or productivity suffer because of the Internet" had the strongest direct negative relations with QoL. Neglecting work caused by IA correlated with QoL, while making friends online appropriately was related to better QoL among MDD patients. Appropriate interventions targeting the central symptoms may potentially prevent or reduce the risk of IA in MDD patients.

Problematic Internet use and daily difficulties among adolescents with school refusal behaviors: An observational cross-sectional analytical study

Problematic Internet use (PIU) is common and likely to coexist with mental health problems among adolescents with school refusal behavior. To date, no study has revealed to what extent PIU relates to the daily burden compared with other mental health problems. This study has examined the association between daily difficulties and PIU among adolescents with school refusal behaviors.This cross-sectional study involved all first-visit patients, regardless of diagnosis, aged 10 to 18 years at 2 child/adolescent psychiatric outpatient clinics in Yokohama City, Japan, from April 2016 to March 2018. The Questionnaire-Children with Difficulties (QCD) were obtained from parents. Simultaneously, the severity of PIU was evaluated using the Internet Addiction Test and depressive and anxiety symptoms were evaluated using the Patient Health Questionnaire-9 and General Anxiety Disorder-7 scale in the 2 weeks before the first-visit. From 684 first-visit patients, 227 with school refusal behaviors were enrolled in the study.PIU was observed in 40% of adolescents with school refusal behaviors. The QCD scores among patients with PIU were significantly lower than those in patients without PIU. Linear regression analysis revealed relationships between PIU and lower QCD scores throughout the day (except at night) and the total score of the day, after controlling for confounders such as depressive and anxiety symptoms.In conclusion, among adolescents with school refusal behaviors, PIU may affect their parent-assessed daily difficulties particularly experienced throughout the day.

Mapping network connectivity between internet addiction and residual depressive symptoms in patients with depression

BACKGROUND AND AIMS

Depression often triggers addictive behaviors such as Internet addiction. In this network analysis study, we assessed the association between Internet addiction and residual depressive symptoms in patients suffering from clinically stable recurrent depressive disorder (depression hereafter).

MATERIALS AND METHODS

In total, 1,267 depressed patients were included. Internet addiction and residual depressive symptoms were measured using the Internet Addiction Test (IAT) and the two-item Patient Health Questionnaire (PHQ-2), respectively. Central symptoms and bridge symptoms were identified via centrality indices. Network stability was examined using the case-dropping procedure.

RESULTS

The prevalence of IA within this sample was 27.2% (95% CI: 24.7-29.6%) based on the IAT cutoff of 50. IAT15 ("Preoccupation with the Internet"), IAT13 ("Snap or act annoyed if bothered without being online") and IAT2 ("Neglect chores to spend more time online") were the most central nodes in the network model. Additionally, bridge symptoms included the node PHQ1 ("Anhedonia"), followed by PHQ2 ("Sad mood") and IAT3 ("Prefer the excitement online to the time with others"). There was no gender difference in the network structure.

CONCLUSION

Both key central and bridge symptoms found in the network analysis could be potentially targeted in prevention and treatment for depressed patients with comorbid Internet addiction and residual depressive symptoms.

Relationship Between Gaming Disorder, Self-Compensation Motivation, Game Flow, Time Spent Gaming, and Fear of Missing Out Among a Sample of Chinese University Students: A Network Analysis

Background and Aims: In previous correlational research, the relationship between gaming disorder (GD), compensation motivation, game flow, time spent gaming, and fear of missing out (FoMO) has been examined. However, network analysis has rarely been applied to explore the relationship between GD, self-compensation motivation, game flow, time spent gaming, and FoMO. Therefore, the present study used network analysis to examine the relationship between the aforementioned variables among a sample of gamers. Methods: The present study comprised gamers (N = 1,635) recruited from three Chinese universities, who completed an online survey including the Gaming Disorder Test, Self-Compensation Motivation Questionnaire, Game Flow Questionnaire, and Trait-State Fear of Missing Out Scale, as well as four items related to time spent gaming. Results: Self-compensation motivation, game flow, time spent gaming, and FoMO were all significantly and positively associated with GD. In the domain-level and facet-level networks, weekday gaming hours and weekend gaming hours had the strongest edge intensity. The domain-level, facet-level, and item-level networks analysis also showed that GD was connected with self-compensation motivation, game flow, time spent gaming, and FoMO. The network structure demonstrated a significant difference between males and females (2.33 vs. 2.81, p = 0.001) using the domain-level network comparison test (NCT). Conclusions: The results suggest that GD is closely associated with self-compensation motivation, game flow, time spent gaming, and FoMO. FoMO and gaming motivation (i.e., self-compensation and game flow) may increase time spent gaming and facilitate GD. Therefore, interventions that decrease game immersion and time spent gaming are likely to decrease GD.

The Association Between Internet Addiction and Anxiety in Nursing Students: A Network Analysis

Background: Nursing students who suffer from co-occurring anxiety experience added difficulties when communicating and interacting with others in a healthy, positive, and meaningful way. Previous studies have found strong positive correlations between Internet addiction (IA) and anxiety, suggesting that nursing students who report severe IA are susceptible to debilitating anxiety as well. To date, however, network analysis (NA) studies exploring the nature of association between individual symptoms of IA and anxiety have not been published. Objective: This study examined associations between symptoms of IA and anxiety among nursing students using network analysis. Methods: IA and anxiety symptoms were assessed using the Internet Addiction Test (IAT) and the Generalized Anxiety Disorder Screener (GAD-7), respectively. The structure of IA and anxiety symptoms was characterized using "Strength" as a centrality index in the symptom network. Network stability was tested using a case-dropping bootstrap procedure and a Network Comparison Test (NCT) was conducted to examine whether network characteristics differed on the basis of gender and by region of residence. Results: A total of 1,070 nursing students participated in the study. Network analysis showed that IAT nodes, "Academic decline due to Internet use," "Depressed/moody/nervous only while being off-line," "School grades suffer due to Internet use," and "Others complain about your time spent online" were the most influential symptoms in the IA-anxiety network model. Gender and urban/rural residence did not significantly influence the overall network structure. Conclusion: Several influential individual symptoms including Academic declines due to Internet use, Depressed/moody/nervous only while being off-line, School grades suffering due to Internet use and Others complain about one's time spent online emerged as potential targets for clinical interventions to reduce co-occurring IA and anxiety. Additionally, the overall network structure provides a data-based hypothesis for explaining potential mechanisms that account for comorbid IA and anxiety.

Network Analysis of Internet Addiction Symptoms Among a Clinical Sample of Japanese Adolescents with Autism Spectrum Disorder

In the present study, we employed network analysis that conceptualizes internet addiction (IA) as a complex network of mutually influencing symptoms in 108 adolescents with autism spectrum disorder (ASD) to examine the network architecture of IA symptoms and identify central/influential symptoms. Our analysis revealed that defensive and secretive behaviors and concealment of internet use were identified as central symptoms in this population, suggesting that mitigating these symptoms potentially prevent the development and/or maintenance of IA in adolescents with ASD. Providing adolescents and their caregivers with psychoeducation on the role of central symptoms above in IA can be a salient intervention. Doing so may facilitate nonconflicting conversations between them about adolescents' internet use and promote more healthy adolescents' internet use behavior.