Exploring the comorbidity mechanism of internet addiction, insomnia, depression, and suicidality among Chinese college students through network analysis

Exploring the comorbidity mechanism of internet addiction, insomnia, depression, and suicidality among Chinese college students through network analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring the comorbidity mechanism of internet addiction, insomnia, depression, and suicidality among Chinese college students through network analysis Huayu Li, Hongyuan Lv, JiaHui Zhang, Yu Fu, Jianpeng Zhang, Hongmei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7549655/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Internet addiction is a common concern among college students, often resulting in negative mental outcomes such as insomnia, depression, and suicidality. However, the variability in internet addiction patterns among college students and its connection to mental disorders remains insufficiently explored. This study investigated the comorbidity network of internet addiction, insomnia, depression, and suicidality in 3,127 Chinese college students using latent profile analysis (LPA) and network analysis. Methods: A total of 3,127 Chinese college students provided their data on internet use, insomnia, depression, and suicidality using the Internet Addiction Test (IAT), Insomnia Severity Index (ISI), Patient Health Questionnaire-9 (PHQ-9), and Suicide Behavior Questionnaire-Revised (SBQ-R). Latent profile analysis (LPA) was employed to identify subgroups of students exhibiting similar patterns of internet addiction. Network structures relating to internet addiction and its association with mental disorders were constructed among addicted users. The stability of the network was assessed through a case drop bootstrap procedure, and a network comparison test (NCT) was conducted to evaluate differences in network characteristics across the identified subgroups. Results : LPA identified three distinct groups of college students based on their internet usage patterns: regular users, moderate users, and addicted users. Network analysis revealed that the central symptom of internet addiction was “lack of self-control when online”. Furthermore, “trouble sleeping”, “frequency of suicidal ideation over the past year” and “sleep maintenance (middle)” were identified as bridge symptoms, connecting insomnia, depression, and suicidality with internet addiction. The NCT showed no significant gender differences in the global strength of the internet addiction network. Conclusions: Interventions focused on improving self-regulation and addressing sleep problems should be prioritized to help reduce internet addiction, insomnia, depression, and suicidality in this population. Health sciences/Diseases Health sciences/Health care Biological sciences/Psychology Social science/Psychology Health sciences/Risk factors college students internet addiction insomnia depression suicidality network analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Over the past decade, technological advancements have made the internet an integral part of daily life, with the global number of Internet users reaching 4.9 billion in 2021, accounting for more than two-thirds of the world's population (Duc et al., 2024 ). Since its inception, the internet has offered numerous advantages, including enhanced social connectivity, active engagement, and expanded access to information. However, excessive internet use has detrimental effects on health, contributing to issues such as social anxiety, depression, low self-esteem, poor sleep quality, and heightened stress levels (Alonzo et al., 2021 ). Internet addiction refers to the compulsive and excessive use of the internet, characterized by withdrawal symptoms, a persistent urge to engage in online activities, and other related symptoms (Jorgenson et al., 2016 ; Kuss and Lopez-Fernandez, 2016 ). Adolescents, particularly college students, often find themselves in an environment with relatively few restrictions and may lack the necessary self-control, which makes them more vulnerable to developing internet addiction (Liu et al., 2022 ). This heightened vulnerability can be linked to a combination of neurobiological and social influences. For example, many parents provide their children with internet-connected devices during their first year of college to help them stay in touch, offering them easy access to online platforms (Tian et al., 2025 ). Furthermore, the reduced supervision from educators and parents grants university freshmen greater independence in their internet usage. As students transition from a structured academic environment to one that emphasizes self-directed learning, they experience increased flexibility in managing their online time. This stage of life offers more convenient and unrestricted access to the internet, which amplifies the likelihood of developing internet addiction, particularly among those with lower levels of self-control. Additionally, the boarding school setting shifts communication away from familial interactions, placing more emphasis on peer relationships, while daily life demands increased self-sufficiency and collaboration with others. These changes often lead to heightened social interaction needs. Students often avoid face-to-face interactions due to social anxiety (Yuan et al., 2022 ). For these individuals, online communication that does not require direct personal contact provides a more comfortable environment. However, this preference can lead to the development of maladaptive thought patterns, which in turn may increase the risk of internet addiction. The prevalence of internet addiction differs across regions and cultures, largely due to variations in diagnostic criteria and the assessment tools used. A comparative analysis of internet addiction rates in various countries reveals a higher prevalence in certain Asian nations compared to the United States. Data from a study of 8,067 college students aged 18–30 across six Asian countries show that the rates of internet addiction were 12.9% in Japan, 13.8% in China, and 9.3% in Singapore, in contrast to the 8% observed among students in the United States. Additionally, Asian students diagnosed with internet addiction were found to be more susceptible to depression compared to their American counterparts (Tang et al., 2018 ). Although internet addiction is not yet recognized by the Diagnostic and Statistical Manual of Mental Disorders (DSM), it has become a major challenge and public health problem in China nowadays and is receiving increasing attention from psychiatrists and educators. A significant amount of research has demonstrated that internet addiction among college students is strongly linked to a range of mental health disorders, including both psychopathological symptoms and personality disorders (Cerniglia et al., 2020 ). For instance, prior studies have found that internet addiction was associated with conditions such as insomnia (Zhang et al., 2022 ) depression (Yang et al., 2022 ), even suicidality (Herruzo et al., 2023 ). Specifically, research indicates that college students with internet addiction experience notably poorer sleep quality and reduced sleep duration (Shen et al., 2020a ). A meta-analysis revealed that individuals with internet addiction were 2.2 times more likely to suffer from insomnia compared to those without internet addiction and experienced significantly shorter sleep durations (Alimoradi et al., 2019 ). Furthermore, Li et al. (Li et al., 2024 ) investigated the dose-response relationship between the amount of time spent online and depressive symptoms in children and adolescents, finding a significant association between internet addiction behaviors and an increased risk of depression. Meanwhile, research by Shen et al. (Shen et al., 2020 ) showed that individuals with internet addiction exhibit higher rates of suicidal behaviors, even after adjusting for confounding variables like depression. Excessive internet use consumes a significant portion of an individual’s time, leaving less opportunity for real-world social interactions, sleep, and other essential activities (Kumar et al., 2022 ), which in turn contributes to higher levels of depression (Hammad et al., 2024 ). Comorbidity, rather than a single mental health issue, often leads to a worse prognosis, greater dysfunction, and more interference in daily life (Wang et al., 2018 ). Given that insomnia, depression, and suicidality are frequently associated with internet addiction, and these conditions often overlap, it is crucial to explore the independent relationships between internet addiction and other psychological issues. However, existing studies primarily examined the relationship between internet addiction and psychological problems by employing a variable-centered analytical method. This approach treats all samples as subjects of internet addiction, which neglects potential differences within the sample. The potential heterogeneity of the symptoms might also be obscured. As a result, it is possible that users who engage in normal internet use are included in subsequent analyses, which might weaken impact of internet addiction on psychological symptoms. Additionally, the specific pathways how internet addiction influences depression, sleep disorders and suicidality remain unclear. Latent Profile Analysis (LPA) is a statistical approach that focuses on identifying unobserved subgroups or latent profiles within data. The goal is to uncover distinct groups that exhibit different patterns or characteristics across the observed variables (Brusco et al., 2017 ). LPA is commonly applied to explore latent types or subgroups within a population, allowing for a deeper understanding of the data and the development of tailored interventions or treatment strategies (Wang et al., 2024 ). When applied to internet addiction symptoms, this method can help identify various subgroups within the population. Network Analysis (NA) is an emerging analytical method based on dynamic system models, providing fresh insights into the role of specific symptoms within the structure of variables (Borsboom, 2017 ). Unlike traditional approaches, the network theory of mental disorders (NTMD) does not view mental disorders as underlying causes of symptoms; instead, it regards symptoms as integral components of mental disorders themselves (Cramer et al., 2010 ). This framework serves two primary functions. First, it identifies core symptoms by evaluating their relative significance within the disorder. Second, it explains comorbid conditions by examining symptoms that act as bridges, connecting different disorders. A symptom from one disorder can trigger symptoms in another, thereby playing a role in both the onset and continuation of comorbid conditions (Cramer et al., 2010 ). The combination of LPA and NA is widely used to explore the relationship between mental disorders and problematic internet use, particularly in the context of social media. For instance, research by Shan et al. (Shan et al., 2024 ) employed LPA to classify WeChat users into three distinct groups, with moderate and problematic users comprising 52.9% of the overall sample. By applying NA to the problematic users, the researchers found that excessive use of WeChat was linked to depressive symptoms through two bridging symptoms: sadness and pessimism. However, this study primarily focused on the connection between problematic WeChat use and depression, limiting its scope to a single platform and a specific mental health outcome. To the best of our knowledge, there is limited research that specifically examines the relationship between internet addiction and mental disorders such as insomnia, depression, and suicidality using a combination of LPA and NA. In this study, we begin by applying LPA to identify college students with internet addiction. Subsequently, NA is used to pinpoint the central symptoms of internet addiction within this group and to investigate how these symptoms influence psychological outcomes, including insomnia, depression, and suicidality. 2. Methods 2.1. Participants and procedure This study received ethical approval from the School of Public Health, Zhejiang University (Approval No. ZGL201312-1). It was conducted in compliance with the Helsinki Declaration. The participants in this study were freshmen, sophomores, and juniors from various academic disciplines at four universities in Yantai, China. A multistage random cluster sampling method was used to determine the scope of questionnaire distribution, considering different academic years, faculties, and classes. Recruitment was carried out through class advisors, who distributed survey links via the online platform Questionnaire Star ( https://www.wjx.cn/ ) to the WeChat group of each class. Students could then participate by clicking on the survey link. To maintain data integrity, an attention-check item was incorporated into the survey. Participants were informed that their participation was voluntary, and they received detailed information about the study in advance. They were also made aware of their right to withdraw at any time and were required to provide informed consent before the data collection began. 2.2. Measurements Internet addiction test (IAT) The IAT developed by Young in 1998, is a widely used tool for assessing the prevalence and severity of internet addiction. It comprises 20 self-reported items, each rated on a six-point scale from 0 (“not applicable”) to 5 (“always”). (Young, 1998 ). The total score ranges from 0 to 100, with higher scores indicating a greater severity of internet addiction. The IAT evaluates six dimensions, each representing distinct symptom patterns: salience, excessive use, neglect of work, anticipation, lack of control, and neglect of social life (Vieira et al., 2022). Previous studies have demonstrated that the IAT exhibits strong reliability and validity among college students (Hussain et al., 2020 ). In this study, the internal consistency coefficient was calculated to be 0.955. Insomnia severity index (ISI) The presence and severity of insomnia were assessed using the ISI, a 7-item self-report measure designed to evaluate the nature, severity, and impact of insomnia experienced in the past month. Each item is rated on a 5-point Likert scale, ranging from 0 (no problem) to 4 (very severe problem), with total scores ranging from 0 to 28. The ISI score is categorized as follows: no insomnia (0–7), sub-threshold insomnia (8–14), moderate insomnia (15–21), and severe insomnia (22–28) (Bastien et al., 2001 ). In this study, the Cronbach’s α coefficient for the ISI was found to be 0.881. Patient health questionnaire-9 (PHQ-9) Depressive symptoms were assessed using the PHQ-9 (Kroenke et al., 2001 ), which is based on the nine criteria for major depressive disorder as outlined in the DSM-IV. Participants responded to each item using a 4-point Likert scale, with options ranging from 0 (not at all) to 3 (most of the time or always), resulting in a total score between 0 and 27. Higher scores reflect more severe depression, with a score of 5 or higher indicating mild depression. The PHQ-9 has been extensively validated within the Chinese population and is regarded as a reliable measure for assessing depressive symptoms (Wang et al., 2014 ). In this study, the internal consistency coefficient was 0.904. Suicide behavior questionnaire-revised (SBQ-R) Suicide risk factors were assessed using the SBQ-R (Osman et al., 2001 ), a self-report instrument designed to evaluate: (1) the history of suicidal thoughts and attempts, (2) the frequency of suicidal ideation in the past month, (3) the communication of suicidal intentions, and (4) the individual’s self-perceived likelihood of future suicidal behavior. The scores across all items are summed, with total scores ranging from 3 to 18. Higher scores indicate a greater risk, with a score of 7 or above demonstrating strong sensitivity and specificity in predicting suicidal behavior. The SBQ-R is a widely used tool, proven to have robust reliability and validity in assessing suicidal ideation and behaviors among students (Kirlic et al., 2023 ). In this study, the internal consistency was deemed acceptable, with a Cronbach's α of 0.767. 2.3. Data analysis Statistical analyses were conducted with IBM SPSS version 27.0 to generate descriptive statistics. For categorical variables, frequencies ( N ) and percentages (%) were used, whereas continuous variables were expressed as means (𝑥̅) and standard deviations ( SD ). To evaluate associations between variables, Spearman’s correlation analysis was applied. LPA was conducted using Mplus version 8.4 to determine distinct subgroups of internet addiction within a population of college students. LPA, a person-centered methodology, classifies individuals into subgroups based on shared behavioral patterns and subsequently compares the differences among these subgroups, offering a more ecologically valid depiction of real-world situations (Sinha et al., 2021 ). The IAT comprised 20 items that served as observable indicators. Models were developed by incrementally increasing the number of profiles, starting from one, until optimal fit indices were achieved. A log-likelihood test was utilized for model fitting, and common indices—including the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the sample size-adjusted Bayesian Information Criterion (aBIC)—were employed, with lower values signifying a superior model fit. An Entropy value closer to 1 indicates a better fit for the model. Statistical significance was determined when the Lo-Mendell-Rubin likelihood-ratio test (LMRT) and the Bootstrapping likelihood-ratio test (BLRT) both yielded p-values below 0.05, supporting the conclusion that a model with “ k ” categories is superior to a model with “( k -1)” categories. NA was performed using R version 4.4.2. Initially, the qgraph package was utilized to create an undirected correlation network with a spring layout, where nodes with strong connections clustered centrally. In this research, internet addiction and various mental disorders were treated as distinct communities. The Least Absolute Shrinkage and Selection Operator (LASSO) network was implemented, employing statistical regularization to minimize spurious edges and produce a sparse network structure (Epskamp and Fried, 2018 ). To assess the significance of node structure, three centrality indices—strength, closeness, and betweenness—were computed. Strength represents the absolute value of the weights on the edges connected to a given node, betweenness indicates the number of times a node lies on the shortest path among nodes, and closeness refers to the average distance from a node to all other nodes. Core nodes demonstrate numerous connections within the network, and altering or removing these nodes can lead to substantial changes throughout the entire network. Our analysis emphasizes node strength, along with its accuracy and stability, as recent discussions have suggested that closeness and betweenness may exhibit instability (Rodebaugh et al., 2018 ). In addition, we applied the networktools package to reveal bridge nodes reflected by bridge centrality, including bridge strength (i.e., the total connectivity of a node with other disorders), bridge betweenness (i.e., the number of times a node lies on the shortest path between any two nodes of different disorders), and bridge closeness (i.e., the average distance from a node to all other nodes outside of its disorder, with distance based on the inverse of the edge weights in the weighted network). Additionally, bootstrapping was carried out using the bootnet package to evaluate the accuracy and stability of the network. We employed a case-dropping subset bootstrap method (1000 replicates, 8 cores) to compute the correlation stability (CS) coefficient. A CS coefficient (correlation = 0.7) represents the maximum proportion of cases that can be removed while maintaining a correlation of 0.7 in at least 95% of samples. A CS coefficient above 0.25 is acceptable, while values exceeding 0.5 are preferable (Epskamp et al., 2018 ). Non-parametric bootstrapping (1000 replicates, 8 cores) was also performed to estimate 95% confidence intervals (CIs) for edge weights, with narrower CIs indicating higher precision. Lastly, we performed bootstrapped difference tests to investigate variations in edge weights. A network comparison test (NCT) was conducted using the network comparison test package to evaluate network invariance, global strength (GS) invariance, and centrality invariance of internet addiction among different groups of college students (Van Borkulo et al., 2023 ). 3. Results 3.1. Demographic characteristics of participants A total of 4,038 college students were invited to participate in the study. To maintain the quality of responses, one attention check item was included in the survey. Additionally, participants who completed the survey in under 150s were excluded from the analysis. As a result, 910 participants were removed from the dataset, yielding a final response rate of 77.5%. Participants provided demographic information, including age, gender, race, residence, family sibling status, body mass index (BMI), smoking and alcohol use, physical activity, dietary habits, and levels of internet addiction, insomnia, depression, and suicidality. The final valid sample consisted of 3,127 college students, with an average age of 19.47 years (range: 16–24 years). Of these, 45.1% were male and 54.9% were female. Detailed demographic characteristics are provided in Table 1 . The correlation analysis indicated significant positive relationships between internet addiction, insomnia, depression, and suicidality ( Table S1 ). Table 1 Depiction of participant demographic information. Variable Total ( n = 3127) Regular users ( n = 1577) Moderate users ( n = 1100) Addicted users ( n = 450) χ 2 /F P Gender 122.531 < 0.001 Male 1410(45.1) 865(65.9) 390(62.2) 155(58.5) Female 1717(54.9) 712(34.1) 710(37.8) 295(41.5) Race 0.601 0.740 Han 2989(95.6) 1503(95.3) 1055(95.9) 431(95.8) Other 138(4.4) 74(4.7) 45(32.6) 19(4.2) Residence 5.076 0.079 Urban 1042(33.3) 554(35.1) 341(31.0) 147(32.7) Rural 2085(66.7) 1023(64.9) 759(69.0) 303(67.3) Family sibling status 15.056 < 0.001 Only child 929(29.7) 516(32.7) 284(25.8) 129(28.7) Non-only child 2198(70.3) 69(67.3) 816(74.2) 321(71.3) BMI 8.301 0.081 Low weight 471(15.1) 210(13.3) 180(16.4) 81(18.0) Normal 1945(62.2) 1000(63.4) 676(61.5) 269(59.8) Overweight/obesity 711(22.7) 367(23.3) 244(22.2) 100(22.2) Smoking status 13.565 < 0.001 Yes 225(7.2) 140(8.9) 59(5.4) 26(5.8) No 2902(92.8) 1437(91.1) 1041(94.6) 424(94.2) Alcohol status 4.071 0.131 Yes 618(19.8) 334(21.2) 200(18.2) 84(18.7) No 2509(80.2) 1243(78.8) 900(81.8) 366(81.3) Physical activity 156.770 < 0.001 Low 171(5.5) 64(4.1) 58(5.3) 49(10.9) Moderate 2037(65.1) 905(57.4) 797(72.5) 335(74.4) High 919(29.4) 608(38.6) 245(22.3) 66(14.7) Regularity of meals 90.965 < 0.001 Irregular 586(18.7) 245(15.5) 197(17.9) 144(32.0) Generally regular 2165(69.2) 1088(69.0) 799(72.6) 278(61.8) Regular 376(12.0) 244(15.5) 104(9.5) 28(6.2) ISI score 4.66 ± 4.38 3.02 ± 3.57 5.58 ± 3.87 8.16 ± 5.31 337.719 < 0.001 PHQ-9 score 5.56 ± 4.87 3.39 ± 3.91 6.68 ± 4.04 10.44 ± 5.29 559.697 < 0.001 SBQ-R score 4.13 ± 2.06 3.49 ± 1.32 4.46 ± 2.14 5.58 ± 2.89 233.044 < 0.001 Note: BMI, Body mass index; ISI, Insomnia severity index; PHQ-9, Patient health questionnaire-9; SBQ-R, Suicide behavior questionnaire-revised. 3.2. LPA for college students In this study, the 20 items of IAT were used as observed variables, and potential profile models with 1 to 5 profiles were tested. The fit indices for these models are shown in Table 2 . As the number of profiles increased, the information criteria (AIC, BIC, and aBIC) decreased progressively, while the LMRT and BLRT tests remained significant, suggesting an improvement in model fit. However, in both the four-profile and five-profile models, the smallest profile accounted for less than 5% of the sample. Therefore, considering both the fit parameters and practical significance, the three-profile model is determined to be the most optimal for the data. This model yields an entropy value of 0.957, which reflects a high level of classification accuracy. As depicted in Fig. 1 , the first profile, comprising 50.4% of students, shows the lowest average scores on the 20 items and is termed the “regular users”. The second profile, representing 35.2% of students, is characterized by moderate scores across all 20 items of internet addiction and is labeled the “moderate users”. The third profile, consisting of 14.4% of students, exhibits the highest scores, indicating a higher risk of problematic internet use, and is referred to as the “addicted users”. Table 2 Model fit indices for profile solutions. Number of profiles AIC BIC aBIC Entropy LMRT BLRT Proportion 1 187022.324 187264.237 187137.141 - - - - 2 159831.788 160200.705 160006.883 0.963 < 0.001 < 0.001 2132/995 3 150910.870 151406.792 151146.244 0.957 < 0.001 < 0.001 1100/1577/450 4 147519.687 148142.613 147815.340 0.944 0.176 < 0.001 1406/604/967/150 5 145728.288 146478.218 146084.219 0.930 0.084 < 0.001 860/1281/566/352/68 3.3. Cross-Class Comparisons We examined sociodemographic differences among the three groups. Statistically significant variations were found in gender ( χ 2 = 122.531, p < 0.001), family sibling status ( χ 2 = 15.056, p < 0.001), smoking status ( χ 2 = 13.565, p < 0.001), physical activity ( χ 2 = 156.770, p < 0.001) and regularity of meals ( χ 2 = 90.965, p < 0.001). To explore differences in insomnia, depression, and suicidality across the groups, we performed ANOVAs on the total scores of the ISI, PHQ-9, and SBQ-R, with the results shown in Table 1 . Significant differences in the total scores of the three scales emerged across three classes. Post hoc comparisons revealed that the “addicted users” (class 3) had notably higher scores for insomnia, depression, and suicidality (all p < 0.001). 3.4. Network analysis of internet addiction Figure 2 presented the network diagram illustrating the conditional associations among the 20 items of the IAT scale for the “addicted users” group. Each circle in the diagram represents a node corresponding to an item, and the edges indicate the strength of the associations between these nodes. The meaning of each node abbreviation is explained in Table S2 . The edge linking node IAT1 “stay online longer than intended” and node IAT 2 “neglect household chores” has the strongest association (edge weight = 0.416). Nodes IAT16 (“lack of self-control when being online”) demonstrate the highest strength centrality indices, showing a strong positive correlation with other symptoms, which can also be considered a central symptom within the network. Node IAT12 (“life is boring without the internet”) exhibited the highest closeness, indicating its significant role in linking otherwise unrelated symptoms within the network. Additionally, node IAT12 ranked highest in betweenness centrality, highlighting its function as a key mediator, connecting different symptoms and acting as a bridge within the network. The strength values for all nodes in the network was displayed in Fig. S1 , and no statistical difference was discovered for strength between the nodes IAT16 and IAT2. The edge stability estimation exhibited fewer overlaps, indicating good network stability ( Fig. S2 ). The centrality stability was preferable in this network, as the CS coefficients for strength was 0.673 ( Fig. S3 ). 3.5. Network analysis of internet addiction and mental disorders Mihai suggests that for constructing symptom networks with 20 or fewer nodes, a sample size of 250 to 350 participants is typically required (Mihai). However, in this study, the comorbid symptoms of internet addiction and mental disorders involve 40 nodes with highly interrelated connections. The sample of “addicted user” students consists of 450 participants, which is below the recommended sample size for such complex networks with more than 40 nodes. As a result, we focused on the six dimensions of the IAT—salience, excessive use, neglect of work, anticipation, lack of control, and neglect of social life—as observation variables to build the network model. The resulting network structure for internet addiction, insomnia, depression, and suicidality was illustrated in Fig. 3 . The most prominent connection within the insomnia community was between node I6, which represents “noticeability of impairment due to sleep problems”, and node I7, which reflects “level of distress caused by sleep problems” (edge weight = 0.408). Within the depression community, the strongest link was found between node P1, indicating “low interest or pleasure”, and node P4, which denotes “feeling tired or having little energy” (edge weight = 0.288). In the suicidality community, the strongest connection occurred between node S2, representing “frequency of suicidal ideation in the past year”, and node S4, which relates to “self-reported likelihood of death by suicide” (edge weight = 0.273). Node S2 “frequency of suicidal ideation in the past year” and node P9 “suicidal thoughts” showed the strongest association within the mental disorders community (edge weight = 0.289), followed by node I1 “severity of sleep − onset (initial)” and node P3 “trouble sleeping” (edge weight = 0.182). Centrality indices of all nodes were illustrated in Fig. S4 . According to three centrality indices, and node P4 (strength( rank ) = 1, betweenness( rank ) = 5, closeness( rank ) = 2), node I2 (strength( rank ) = 2, betweenness( rank ) = 1, closeness( rank ) = 4) exhibited the most significant position in the network. Therefore, the central factors within the network were node P4 “tired or little energy”, and node I2 “sleep maintenance (middle)”. In addition, the centrality differs test for strength indicated that the strength of node P4 and the node I2 were not statistically different ( Fig. S5 ). Figure 4 displayed the results of the bridge centrality of the network. For the depression community, node P3 (bridge strength( rank ) = 2, bridge betweenness( rank ) = 1, bridge closeness( rank ) = 2) exhibited the highest bridge centrality. For suicidality community, node S2 (bridge strength( rank ) = 4, bridge betweenness( rank ) = 5, bridge closeness( rank ) = 14) had the highest bridge centrality. For insomnia community, node I2 (bridge strength( rank ) = 9, bridge betweenness( rank ) = 2, bridge closeness( rank ) = 3) showed highest bridge centrality comparing to other nodes in the community. Consequently, node P3 (“trouble sleeping”), node S2 (“frequency of suicidal ideation in the past year”), and node I2 (“sleep maintenance (middle)”) were bridge nodes linking mental disorders symptoms with internet addiction. More detailed information of the centrality differs test for bridge strength was summarized in Fig. S6 . Fig. S7 illustrated that the edge weights in the current sample were consistent with those in the bootstrapped samples and the bootstrapped 95% CIs were narrow, indicating a good accuracy of the network model. Fig. S8 and S9 showed that the CS coefficients for strength (traditional centrality) and bridge strength (bridge centrality) were both 0.673, reaching the critical cutoff point (0.5). 3.6. Network comparison We conducted a NCT to further explore the symptom characteristics of networks among college students with varying profiles of internet addiction. Significant differences in the global strength of the internet addiction networks were observed across the three groups, with values of 10.391 for “regular users” (class 1), 11.627 for “moderate users” (class2), and 11.148 for “addicted users”(class3) (class1 vs. class2, P = 0.010; class2 vs. class3, P = 0.208; class1 vs. class3, P = 0.010). Additionally, we compared the symptom networks between subgroups of male (n = 155) and female (n = 295) students, as well as between only child (n = 129) and non-only child (n = 321). No significant differences in global network strength were found between these groups (both p > 0.05). It is worth noting that only covariates with sample sizes greater than 100 were included in the analysis to ensure sufficient statistical power. 4. Discussion To the best of our knowledge, this study is the first to explore the interrelationships of mental disorders symptoms with internet addiction among college students using a combination of LPA and NA. Based on their patterns of internet use, the students were classified into three distinct profiles including “regular users”, “moderate users”, and “addicted users”. Internet addiction and mental disorders symptoms differed significantly across the subgroups. The “addicted users” group exhibited the most pronounced symptoms of insomnia, depression, and suicidality. Additionally, the NA identified IAT 16 (“lack of self-control when being online”) as the central node of the addicted user profile, and P3 (“trouble sleeping”), S2 (“frequency of suicidal ideation in the past year”), and I2 (“sleep maintenance (middle)”) were bridge nodes linking depression, insomnia and suicidality symptoms with internet addiction. Identifying central and bridging symptoms enhances our understanding of the comorbidity between internet addiction and mental disorders. These symptoms be recognized as critical indicators for early detection and targeted intervention. Compared to previous studies, the present study reported a higher prevalence of Internet addiction, and this discrepancy can be attributed to the fact that earlier studies predominantly focused on adolescents populations (Li, 2023 ) and social media use (Peng and Liao, 2023 ) within a broader framework. In contrast, our study specifically targeted college students, which led to variations in the study population, measurement tools, and other influencing factors. The 3-class model of internet addiction observed in the Chinese college student population closely mirrored earlier findings from a large sample of Chinese adolescents (Li et al., 2020 ), implying itself as an appropriate classification for understanding internet use among young Chinese individuals. In our research, although three profiles showed relatively parallel paths, their characteristics were indeed discriminant. The “regular user” profile, representing 50.4% of the participants, had one peak value in item 9 (“alert when asked about online”) and scored lower than the other two profiles in each item. The “moderate users” profile, comprising 35.2% of the participants, had higher scores on item 1 (“stay online longer than intended”) and item 9. The “addicted users” profile, comprising 14.4% of the participants, scored higher than the other profiles on each item. The differences observed among the three profiles suggest that internet addiction among college students is a heterogeneous phenomenon. Interestingly, we observed that “moderate users” spent considerable time online (item 1) and remained alert during their internet activities (item 9), yet they reported relatively low levels of negative emotions (item 20). This finding suggests that, despite their high levels of internet engagement, these individuals did not experience the usual negative effects commonly linked to excessive use, nor did they display more pronounced symptoms of insomnia, depression, or suicidality. This phenomenon could be explained by the evolving role of the internet as an indispensable part of daily life, leading to an increase in both the frequency and duration of its usage. Recent findings by Fournier and colleagues (Fournier et al., 2023 ) align with our research, proposing that extended online hours and concerns about privacy may be secondary factors in internet addiction, rather than core components. Consequently, a greater emphasis should be placed on addressing the negative impacts of internet usage to avoid pathologizing internet engagement. Core symptoms are the most impactful indicators of a disorder, as they have the ability to trigger other symptoms and contribute to the progression of mental health issues (Borsboom and Cramer, 2013 ). Overall, identifying these central symptoms is crucial for effectively directing clinical interventions (McNally, 2016 ). Our study identified IAT16 (“lack of self-control when being online”) and IAT2 (“neglect household chores”) as central symptoms of internet addiction among college students. This contrasts with previous research on internet addiction in adolescents and young adults, which highlighted IAT6 (“grades or school work suffer”) and IAT8 (“job performance or productivity suffer”) as core symptoms (Lu et al., 2022 ). The differences in findings may stem from the distinct focus of our study, which centers on college students, as opposed to the adolescent and young adult populations examined in earlier studies. College students generally have greater access to the internet and more free time compared to adolescents, particularly high school students. In some cases, internet use is even encouraged due to online courses and assignments. Furthermore, college students face less stringent supervision and academic pressure than high school students, and they are not burdened by the financial demands of work. Psychologically, the developmental stage of college students—characterized by a search for identity and the formation of meaningful relationships—may additionally distinguishes them from adolescents. Interestingly, lack of self-control and neglect household chores appeared to play a key role in the development of internet addiction of college students. Previous research has shown that individuals with poor time management and self-control skills often find the internet to be a compelling escape from their demanding routines (Salarvand et al., 2022 ). Additionally, people suffering from internet addiction tend to exhibit low self-esteem, heightened feelings of loneliness, and inadequate social skills (Romero-López et al., 2021 ). The anonymity offered by the internet facilitates easier social interaction, which can be particularly appealing for those with internet addiction, as their social needs are often unmet in real-life situations (Zsido et al., 2020 ). This reliance on the virtual world for social fulfillment can result in individuals neglecting their real-world interactions, leading to excessive internet use and procrastination. Cognitive behavioral therapy (CBT) may prove effective for students struggling with internet addiction, as they often hold the belief that life without online activities will feel dull and unfulfilling, which contributes to their difficulty in controlling internet use. Previous research has demonstrated that CBT can help reduce excessive internet usage, enhance time management skills, and promote emotional stability (Zhu et al., 2023 ). The NA provided new evidence about the relationship between internet addiction and mental disorders from a symptom perspective, which revealed that behaviors associated with sleep (“trouble sleeping” and “sleep maintenance (middle)”) serve as bridges between internet addiction and mental disorders. Consistent with previous research (Lu et al., 2023 ), students with higher levels of internet addiction tended to have shorter sleep durations. According to the displacement hypothesis (Kraut et al., 1998 ), internet use substitutes real-life activities that protect college students’ mental health (such as sleep), leading to insufficient sleep and decreased sleep quality, thereby associating with the risk of psychological symptoms. However, improving sleep maintenance can help mitigate the adverse effects of internet addiction. Notably, promoting an earlier bedtime could extend sleep duration by approximately half an hour per night (Wake and Hiscock, 2022 ). This finding suggests that encouraging earlier bedtimes and allowing for longer recovery sleep on weekends might be effective strategies to counteract the negative impacts of excessive internet use (Kawabe et al., 2019 ). Furthermore, the study identifies a specific symptom of suicidality—frequency of suicidal ideation in the past year, also as bridge between internet addiction and mental disorders. There are several possible explanations for the direct link between internet addiction and suicidal ideation. First, the anonymity provided by the internet increases the likelihood of individuals with internet addiction being exposed to suicidal content or experiences (Mars et al., 2015 ). Repeated exposure to such material may lead individuals to imitate suicidal ideation in the real world. Second, the phenomenon of de-individuation, which is common in online interactions, can diminish self-awareness, reduce concerns about social judgment, and lower the threshold for engaging in harmful behaviors such as suicide (Shen et al., 2020b ). In this environment, individuals may be less aware of or less sensitive to the negative consequences of suicidal actions due to changes in cognitive control processes (Seok et al., 2015 ). Lastly, the factors contributing to pathological behavior often overlap. As mentioned above, poor sleep quality, a common issue in internet addiction, may also exacerbate suicidal ideation among affected students. Our finding highlights the potential role of internet addiction in exacerbating mental disorders by affecting individuals' sleep quality and emotional states. Specifically, prolonged internet addiction can lead to insufficient or disrupted sleep, which in turn increases the risk of depression. Suicidal ideation, in particular, may reflect the emotional distress and psychological pressure experienced by individuals, suggesting that those with internet addiction may lack effective coping mechanisms when faced with real-life challenges (Liu et al., 2024 ). While our study found no significant differences in network strength and connectivity between male and female college students in the addiction group, previous research by Shan et al. (Shan et al., 2024 ) identified gender differences in the central symptoms of problematic WeChat use among college students. However, we emphasize the importance of considering gender differences in internet addiction, as societal expectations and psychological needs differ between males and females. Males tend to prioritize achievement and independence, while females often place greater value on relationship maintenance and emotional expression. Additionally, males typically seek higher levels of stimulation and entertainment, while females are more focused on fulfilling their social needs. 5. Strengths and limitations This study offers several notable strengths. Firstly, stratified random sampling ensured a representative college student sample, enhancing the reliability and generalizability of the findings. Secondly, the survey-based methodology enabled large-scale data collection, encompassing students from a range of academic disciplines. This approach broadens our understanding of the relationship between internet addiction and mental health disorders in this population. Additionally, the use of the IAT questionnaire, which is well-validated for assessing internet addiction in the daily lives of Chinese college students, proved advantageous. Finally, the study addresses a pressing contemporary issue by exploring the connections between internet addiction and mental health, providing valuable insights that contribute to the ongoing dialogue and inform the development of potential interventions. Despite the strengths of this study, there are several limitations that should be addressed in future research. First, the cross-sectional design restricts the ability to draw conclusions about causal relationships between internet addiction and mental disorders. Longitudinal studies would be valuable in understanding how internet addiction may affect mental health over time. Additionally, the reliance on self-report data may introduce social desirability bias and common method bias. To reduce these limitations, future studies could incorporate objective measures alongside self-reports. Lastly, our study examined internet use in a general context. Given the varying functions, features, and recommendation algorithms of different online platforms, future research should investigate problematic usage within specific social media platforms to offer a more nuanced understanding. 6. Conclusion Our study found that 14.4% of college students exhibit signs of internet addiction, with “lack of self-control while online” emerging as the most prominent symptom. To address this, interventions aimed at enhancing self-regulation, improving time management abilities, and offering psychological support could be effective strategies for both preventing and treating internet addiction. Additionally, that behaviors associated with sleep and suicidal ideation serve as bridges between internet addiction and mental disorders. Interventions targeting sleep issues may help mitigate the negative effects of internet addiction, while providing psychological support to reduce suicidal risks could effectively break the harmful cycle between addiction and mental disorders. Therefore, future intervention strategies should focus on these bridging symptoms, offering more comprehensive support to address both internet addiction and its associated mental health issues. Declarations Funding This research was supported by the Zhejiang University Global Partnership Fund. Author Contributions Huayu Li: Conceptualization, Investigation, Data curation, Writing original draft. Hongyuan Lv: Investigation, Data curation, Methodology, Visualization. Jiahui Zhang: Investigation, Data curation, Formal analysis, Visualization. Yu Fu: Data curation, Methodology, Formal analysis. Jianpeng Zhang: Conceptualization, Project administration, Supervision, Writing – review & editing. Hongmei Wang: Conceptualization, Project administration, Supervision, Validation, Writing – review & editing. Acknowledgements We thank the students and teachers who participated in this study and the support of the school principals. Declaration of Competing Interest The authors have no conflicts of interest to declare. Data Availability Statement The data that support the findings of this study are available on request from the corresponding author. Ethics Statement This study received ethical approval from the School of Public Health, Zhejiang University (Approval No. ZGL201312-1). References Alimoradi, Z., Lin, C. Y., Broström, A., Bülow, P. H., Bajalan, Z., Griffiths, M. D., Ohayon, M. M., Pakpour, A. H., 2019. Internet addiction and sleep problems: a systematic review and meta-analysis. Sleep medicine reviews, 47, 51–61. https://doi.org/10.1016/j.smrv.2019.06.004. Alonzo, R., Hussain, J., Stranges, S., Anderson, K. K., 2021. 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02:11:04","extension":"xml","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":186379,"visible":true,"origin":"","legend":"","description":"","filename":"3c0c921c445743b48ee167de17bc5ef41structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7549655/v1/973d864443e1e37f7034073d.xml"},{"id":93728333,"identity":"a681e36d-8260-4b4f-ab35-f0e1c51268e3","added_by":"auto","created_at":"2025-10-17 02:10:55","extension":"html","order_by":26,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":197668,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7549655/v1/5064cf9db4050122db154daa.html"},{"id":93728329,"identity":"61c17a03-99bb-4003-ab1f-c6f0cce43556","added_by":"auto","created_at":"2025-10-17 02:10:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":50495,"visible":true,"origin":"","legend":"\u003cp\u003eThe scoring characteristics of three profiles in 20-item behaviors of internet addiction.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7549655/v1/c738b3a7bbcd00f85c2099f0.png"},{"id":93728338,"identity":"8791ffbe-9184-4388-bba9-a6c4cd501e80","added_by":"auto","created_at":"2025-10-17 02:10:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113851,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork structure of internet addiction. Blue edges constitute positive partial correlations between variables. The edge thickness represents the strength of the association between symptom nodes. Centrality (Z score) of network was shown in right panel.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7549655/v1/5c2a56231686b7610e52e31f.png"},{"id":93728490,"identity":"36b45b9e-7c01-45d9-877d-c471cdd56f1b","added_by":"auto","created_at":"2025-10-17 02:11:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":98081,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork structure of internet addiction and mental disorders. Blue edges constitute positive partial correlations between variables. The edge thickness represents the strength of the association between symptom nodes.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7549655/v1/2ad70e87089fb553d13d944f.png"},{"id":93728373,"identity":"244776b2-296e-4fc6-9346-abcd8ce25616","added_by":"auto","created_at":"2025-10-17 02:11:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":40263,"visible":true,"origin":"","legend":"\u003cp\u003eBridge centrality indices of the network between internet addiction and mental disorders.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7549655/v1/3f105a422bba13d80f23e4c0.png"},{"id":100787877,"identity":"197b41d6-6707-42b4-a2d8-03eb305d4481","added_by":"auto","created_at":"2026-01-21 12:04:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1228092,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7549655/v1/6998d381-871f-4483-b4c1-34caa5a9d5eb.pdf"},{"id":93728381,"identity":"9958c401-b1b4-42dd-a237-15609ea8deb8","added_by":"auto","created_at":"2025-10-17 02:11:02","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1245817,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7549655/v1/f4c09ac1115f1f03d7d6bc1c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the comorbidity mechanism of internet addiction, insomnia, depression, and suicidality among Chinese college students through network analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past decade, technological advancements have made the internet an integral part of daily life, with the global number of Internet users reaching 4.9\u0026nbsp;billion in 2021, accounting for more than two-thirds of the world's population (Duc et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Since its inception, the internet has offered numerous advantages, including enhanced social connectivity, active engagement, and expanded access to information. However, excessive internet use has detrimental effects on health, contributing to issues such as social anxiety, depression, low self-esteem, poor sleep quality, and heightened stress levels (Alonzo et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Internet addiction refers to the compulsive and excessive use of the internet, characterized by withdrawal symptoms, a persistent urge to engage in online activities, and other related symptoms (Jorgenson et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Kuss and Lopez-Fernandez, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Adolescents, particularly college students, often find themselves in an environment with relatively few restrictions and may lack the necessary self-control, which makes them more vulnerable to developing internet addiction (Liu et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This heightened vulnerability can be linked to a combination of neurobiological and social influences. For example, many parents provide their children with internet-connected devices during their first year of college to help them stay in touch, offering them easy access to online platforms (Tian et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Furthermore, the reduced supervision from educators and parents grants university freshmen greater independence in their internet usage. As students transition from a structured academic environment to one that emphasizes self-directed learning, they experience increased flexibility in managing their online time. This stage of life offers more convenient and unrestricted access to the internet, which amplifies the likelihood of developing internet addiction, particularly among those with lower levels of self-control. Additionally, the boarding school setting shifts communication away from familial interactions, placing more emphasis on peer relationships, while daily life demands increased self-sufficiency and collaboration with others. These changes often lead to heightened social interaction needs. Students often avoid face-to-face interactions due to social anxiety (Yuan et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). For these individuals, online communication that does not require direct personal contact provides a more comfortable environment. However, this preference can lead to the development of maladaptive thought patterns, which in turn may increase the risk of internet addiction. The prevalence of internet addiction differs across regions and cultures, largely due to variations in diagnostic criteria and the assessment tools used. A comparative analysis of internet addiction rates in various countries reveals a higher prevalence in certain Asian nations compared to the United States. Data from a study of 8,067 college students aged 18\u0026ndash;30 across six Asian countries show that the rates of internet addiction were 12.9% in Japan, 13.8% in China, and 9.3% in Singapore, in contrast to the 8% observed among students in the United States. Additionally, Asian students diagnosed with internet addiction were found to be more susceptible to depression compared to their American counterparts (Tang et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Although internet addiction is not yet recognized by the Diagnostic and Statistical Manual of Mental Disorders (DSM), it has become a major challenge and public health problem in China nowadays and is receiving increasing attention from psychiatrists and educators. A significant amount of research has demonstrated that internet addiction among college students is strongly linked to a range of mental health disorders, including both psychopathological symptoms and personality disorders (Cerniglia et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). For instance, prior studies have found that internet addiction was associated with conditions such as insomnia (Zhang et al., \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) depression (Yang et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), even suicidality (Herruzo et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Specifically, research indicates that college students with internet addiction experience notably poorer sleep quality and reduced sleep duration (Shen et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). A meta-analysis revealed that individuals with internet addiction were 2.2 times more likely to suffer from insomnia compared to those without internet addiction and experienced significantly shorter sleep durations (Alimoradi et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, Li et al. (Li et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) investigated the dose-response relationship between the amount of time spent online and depressive symptoms in children and adolescents, finding a significant association between internet addiction behaviors and an increased risk of depression. Meanwhile, research by Shen et al. (Shen et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed that individuals with internet addiction exhibit higher rates of suicidal behaviors, even after adjusting for confounding variables like depression. Excessive internet use consumes a significant portion of an individual\u0026rsquo;s time, leaving less opportunity for real-world social interactions, sleep, and other essential activities (Kumar et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which in turn contributes to higher levels of depression (Hammad et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Comorbidity, rather than a single mental health issue, often leads to a worse prognosis, greater dysfunction, and more interference in daily life (Wang et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Given that insomnia, depression, and suicidality are frequently associated with internet addiction, and these conditions often overlap, it is crucial to explore the independent relationships between internet addiction and other psychological issues. However, existing studies primarily examined the relationship between internet addiction and psychological problems by employing a variable-centered analytical method. This approach treats all samples as subjects of internet addiction, which neglects potential differences within the sample. The potential heterogeneity of the symptoms might also be obscured. As a result, it is possible that users who engage in normal internet use are included in subsequent analyses, which might weaken impact of internet addiction on psychological symptoms. Additionally, the specific pathways how internet addiction influences depression, sleep disorders and suicidality remain unclear.\u003c/p\u003e\u003cp\u003eLatent Profile Analysis (LPA) is a statistical approach that focuses on identifying unobserved subgroups or latent profiles within data. The goal is to uncover distinct groups that exhibit different patterns or characteristics across the observed variables (Brusco et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). LPA is commonly applied to explore latent types or subgroups within a population, allowing for a deeper understanding of the data and the development of tailored interventions or treatment strategies (Wang et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). When applied to internet addiction symptoms, this method can help identify various subgroups within the population. Network Analysis (NA) is an emerging analytical method based on dynamic system models, providing fresh insights into the role of specific symptoms within the structure of variables (Borsboom, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Unlike traditional approaches, the network theory of mental disorders (NTMD) does not view mental disorders as underlying causes of symptoms; instead, it regards symptoms as integral components of mental disorders themselves (Cramer et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This framework serves two primary functions. First, it identifies core symptoms by evaluating their relative significance within the disorder. Second, it explains comorbid conditions by examining symptoms that act as bridges, connecting different disorders. A symptom from one disorder can trigger symptoms in another, thereby playing a role in both the onset and continuation of comorbid conditions (Cramer et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The combination of LPA and NA is widely used to explore the relationship between mental disorders and problematic internet use, particularly in the context of social media. For instance, research by Shan et al. (Shan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) employed LPA to classify WeChat users into three distinct groups, with moderate and problematic users comprising 52.9% of the overall sample. By applying NA to the problematic users, the researchers found that excessive use of WeChat was linked to depressive symptoms through two bridging symptoms: sadness and pessimism. However, this study primarily focused on the connection between problematic WeChat use and depression, limiting its scope to a single platform and a specific mental health outcome. To the best of our knowledge, there is limited research that specifically examines the relationship between internet addiction and mental disorders such as insomnia, depression, and suicidality using a combination of LPA and NA. In this study, we begin by applying LPA to identify college students with internet addiction. Subsequently, NA is used to pinpoint the central symptoms of internet addiction within this group and to investigate how these symptoms influence psychological outcomes, including insomnia, depression, and suicidality.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Participants and procedure\u003c/h2\u003e\u003cp\u003eThis study received ethical approval from the School of Public Health, Zhejiang University (Approval No. ZGL201312-1). It was conducted in compliance with the Helsinki Declaration. The participants in this study were freshmen, sophomores, and juniors from various academic disciplines at four universities in Yantai, China. A multistage random cluster sampling method was used to determine the scope of questionnaire distribution, considering different academic years, faculties, and classes. Recruitment was carried out through class advisors, who distributed survey links \u003cem\u003evia\u003c/em\u003e the online platform Questionnaire Star (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wjx.cn/\u003c/span\u003e\u003cspan address=\"https://www.wjx.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to the WeChat group of each class. Students could then participate by clicking on the survey link. To maintain data integrity, an attention-check item was incorporated into the survey. Participants were informed that their participation was voluntary, and they received detailed information about the study in advance. They were also made aware of their right to withdraw at any time and were required to provide informed consent before the data collection began.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Measurements\u003c/h2\u003e\u003cp\u003e\u003cem\u003eInternet addiction test (IAT)\u003c/em\u003e The IAT developed by Young in 1998, is a widely used tool for assessing the prevalence and severity of internet addiction. It comprises 20 self-reported items, each rated on a six-point scale from 0 (\u0026ldquo;not applicable\u0026rdquo;) to 5 (\u0026ldquo;always\u0026rdquo;). (Young, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The total score ranges from 0 to 100, with higher scores indicating a greater severity of internet addiction. The IAT evaluates six dimensions, each representing distinct symptom patterns: salience, excessive use, neglect of work, anticipation, lack of control, and neglect of social life (Vieira et al., 2022). Previous studies have demonstrated that the IAT exhibits strong reliability and validity among college students (Hussain et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In this study, the internal consistency coefficient was calculated to be 0.955.\u003c/p\u003e\u003cp\u003e\u003cem\u003eInsomnia severity index (ISI)\u003c/em\u003e The presence and severity of insomnia were assessed using the ISI, a 7-item self-report measure designed to evaluate the nature, severity, and impact of insomnia experienced in the past month. Each item is rated on a 5-point Likert scale, ranging from 0 (no problem) to 4 (very severe problem), with total scores ranging from 0 to 28. The ISI score is categorized as follows: no insomnia (0\u0026ndash;7), sub-threshold insomnia (8\u0026ndash;14), moderate insomnia (15\u0026ndash;21), and severe insomnia (22\u0026ndash;28) (Bastien et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). In this study, the Cronbach\u0026rsquo;s α coefficient for the ISI was found to be 0.881.\u003c/p\u003e\u003cp\u003e\u003cem\u003ePatient health questionnaire-9 (PHQ-9)\u003c/em\u003e Depressive symptoms were assessed using the PHQ-9 (Kroenke et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), which is based on the nine criteria for major depressive disorder as outlined in the DSM-IV. Participants responded to each item using a 4-point Likert scale, with options ranging from 0 (not at all) to 3 (most of the time or always), resulting in a total score between 0 and 27. Higher scores reflect more severe depression, with a score of 5 or higher indicating mild depression. The PHQ-9 has been extensively validated within the Chinese population and is regarded as a reliable measure for assessing depressive symptoms (Wang et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In this study, the internal consistency coefficient was 0.904.\u003c/p\u003e\u003cp\u003e\u003cem\u003eSuicide behavior questionnaire-revised (SBQ-R)\u003c/em\u003e Suicide risk factors were assessed using the SBQ-R (Osman et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), a self-report instrument designed to evaluate: (1) the history of suicidal thoughts and attempts, (2) the frequency of suicidal ideation in the past month, (3) the communication of suicidal intentions, and (4) the individual\u0026rsquo;s self-perceived likelihood of future suicidal behavior. The scores across all items are summed, with total scores ranging from 3 to 18. Higher scores indicate a greater risk, with a score of 7 or above demonstrating strong sensitivity and specificity in predicting suicidal behavior. The SBQ-R is a widely used tool, proven to have robust reliability and validity in assessing suicidal ideation and behaviors among students (Kirlic et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In this study, the internal consistency was deemed acceptable, with a Cronbach's α of 0.767.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Data analysis\u003c/h2\u003e\u003cp\u003eStatistical analyses were conducted with IBM SPSS version 27.0 to generate descriptive statistics. For categorical variables, frequencies (\u003cem\u003eN\u003c/em\u003e) and percentages (%) were used, whereas continuous variables were expressed as means (\u0026#119909;̅) and standard deviations (\u003cem\u003eSD\u003c/em\u003e). To evaluate associations between variables, Spearman\u0026rsquo;s correlation analysis was applied. LPA was conducted using Mplus version 8.4 to determine distinct subgroups of internet addiction within a population of college students. LPA, a person-centered methodology, classifies individuals into subgroups based on shared behavioral patterns and subsequently compares the differences among these subgroups, offering a more ecologically valid depiction of real-world situations (Sinha et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The IAT comprised 20 items that served as observable indicators. Models were developed by incrementally increasing the number of profiles, starting from one, until optimal fit indices were achieved. A log-likelihood test was utilized for model fitting, and common indices\u0026mdash;including the Akaike Information Criterion (AIC), the Bayesian Information Criterion (BIC), and the sample size-adjusted Bayesian Information Criterion (aBIC)\u0026mdash;were employed, with lower values signifying a superior model fit. An Entropy value closer to 1 indicates a better fit for the model. Statistical significance was determined when the Lo-Mendell-Rubin likelihood-ratio test (LMRT) and the Bootstrapping likelihood-ratio test (BLRT) both yielded p-values below 0.05, supporting the conclusion that a model with \u0026ldquo;\u003cem\u003ek\u003c/em\u003e\u0026rdquo; categories is superior to a model with \u0026ldquo;(\u003cem\u003ek\u003c/em\u003e-1)\u0026rdquo; categories. NA was performed using R version 4.4.2. Initially, the \u003cem\u003eqgraph\u003c/em\u003e package was utilized to create an undirected correlation network with a spring layout, where nodes with strong connections clustered centrally. In this research, internet addiction and various mental disorders were treated as distinct communities. The Least Absolute Shrinkage and Selection Operator (LASSO) network was implemented, employing statistical regularization to minimize spurious edges and produce a sparse network structure (Epskamp and Fried, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To assess the significance of node structure, three centrality indices\u0026mdash;strength, closeness, and betweenness\u0026mdash;were computed. Strength represents the absolute value of the weights on the edges connected to a given node, betweenness indicates the number of times a node lies on the shortest path among nodes, and closeness refers to the average distance from a node to all other nodes. Core nodes demonstrate numerous connections within the network, and altering or removing these nodes can lead to substantial changes throughout the entire network. Our analysis emphasizes node strength, along with its accuracy and stability, as recent discussions have suggested that closeness and betweenness may exhibit instability (Rodebaugh et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In addition, we applied the \u003cem\u003enetworktools\u003c/em\u003e package to reveal bridge nodes reflected by bridge centrality, including bridge strength (i.e., the total connectivity of a node with other disorders), bridge betweenness (i.e., the number of times a node lies on the shortest path between any two nodes of different disorders), and bridge closeness (i.e., the average distance from a node to all other nodes outside of its disorder, with distance based on the inverse of the edge weights in the weighted network). Additionally, bootstrapping was carried out using the \u003cem\u003ebootnet\u003c/em\u003e package to evaluate the accuracy and stability of the network. We employed a case-dropping subset bootstrap method (1000 replicates, 8 cores) to compute the correlation stability (CS) coefficient. A CS coefficient (correlation\u0026thinsp;=\u0026thinsp;0.7) represents the maximum proportion of cases that can be removed while maintaining a correlation of 0.7 in at least 95% of samples. A CS coefficient above 0.25 is acceptable, while values exceeding 0.5 are preferable (Epskamp et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Non-parametric bootstrapping (1000 replicates, 8 cores) was also performed to estimate 95% confidence intervals (CIs) for edge weights, with narrower CIs indicating higher precision. Lastly, we performed bootstrapped difference tests to investigate variations in edge weights. A network comparison test (NCT) was conducted using the network comparison test package to evaluate network invariance, global strength (GS) invariance, and centrality invariance of internet addiction among different groups of college students (Van Borkulo et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Demographic characteristics of participants\u003c/h2\u003e\u003cp\u003eA total of 4,038 college students were invited to participate in the study. To maintain the quality of responses, one attention check item was included in the survey. Additionally, participants who completed the survey in under 150s were excluded from the analysis. As a result, 910 participants were removed from the dataset, yielding a final response rate of 77.5%. Participants provided demographic information, including age, gender, race, residence, family sibling status, body mass index (BMI), smoking and alcohol use, physical activity, dietary habits, and levels of internet addiction, insomnia, depression, and suicidality. The final valid sample consisted of 3,127 college students, with an average age of 19.47 years (range: 16\u0026ndash;24 years). Of these, 45.1% were male and 54.9% were female. Detailed demographic characteristics are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The correlation analysis indicated significant positive relationships between internet addiction, insomnia, depression, and suicidality (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDepiction of participant demographic information.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3127)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRegular users\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1577)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eModerate users\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1100)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAddicted users\u003c/p\u003e\u003cp\u003e(\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;450)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/F\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e122.531\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1410(45.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e865(65.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e390(62.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e155(58.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1717(54.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e712(34.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e710(37.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e295(41.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRace\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.740\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2989(95.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1503(95.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1055(95.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e431(95.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e138(4.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e74(4.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45(32.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e19(4.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResidence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.076\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.079\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1042(33.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e554(35.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e341(31.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e147(32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRural\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2085(66.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1023(64.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e759(69.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e303(67.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFamily sibling status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.056\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOnly child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e929(29.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e516(32.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e284(25.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e129(28.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-only child\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2198(70.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69(67.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e816(74.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e321(71.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e8.301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow weight\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e471(15.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e210(13.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e180(16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e81(18.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1945(62.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1000(63.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e676(61.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e269(59.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverweight/obesity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e711(22.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e367(23.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e244(22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e100(22.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSmoking status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.565\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e225(7.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e140(8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e59(5.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26(5.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2902(92.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1437(91.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1041(94.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e424(94.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlcohol status\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4.071\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.131\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e618(19.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e334(21.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e200(18.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e84(18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2509(80.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1243(78.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e900(81.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e366(81.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhysical activity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e156.770\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e171(5.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e64(4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58(5.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e49(10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2037(65.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e905(57.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e797(72.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e335(74.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e919(29.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e608(38.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e245(22.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e66(14.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegularity of meals\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e90.965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIrregular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e586(18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e245(15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e197(17.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e144(32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenerally regular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2165(69.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1088(69.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e799(72.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e278(61.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRegular\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e376(12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e244(15.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e104(9.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e28(6.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eISI score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.66\u0026thinsp;\u0026plusmn;\u0026thinsp;4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.02\u0026thinsp;\u0026plusmn;\u0026thinsp;3.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.58\u0026thinsp;\u0026plusmn;\u0026thinsp;3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.16\u0026thinsp;\u0026plusmn;\u0026thinsp;5.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e337.719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHQ-9 score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.56\u0026thinsp;\u0026plusmn;\u0026thinsp;4.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;3.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.68\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.44\u0026thinsp;\u0026plusmn;\u0026thinsp;5.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e559.697\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSBQ-R score\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.13\u0026thinsp;\u0026plusmn;\u0026thinsp;2.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.58\u0026thinsp;\u0026plusmn;\u0026thinsp;2.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e233.044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: BMI, Body mass index; ISI, Insomnia severity index; PHQ-9, Patient health questionnaire-9; SBQ-R, Suicide behavior questionnaire-revised.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2. LPA for college students\u003c/h2\u003e\u003cp\u003eIn this study, the 20 items of IAT were used as observed variables, and potential profile models with 1 to 5 profiles were tested. The fit indices for these models are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As the number of profiles increased, the information criteria (AIC, BIC, and aBIC) decreased progressively, while the LMRT and BLRT tests remained significant, suggesting an improvement in model fit. However, in both the four-profile and five-profile models, the smallest profile accounted for less than 5% of the sample. Therefore, considering both the fit parameters and practical significance, the three-profile model is determined to be the most optimal for the data. This model yields an entropy value of 0.957, which reflects a high level of classification accuracy. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the first profile, comprising 50.4% of students, shows the lowest average scores on the 20 items and is termed the \u0026ldquo;regular users\u0026rdquo;. The second profile, representing 35.2% of students, is characterized by moderate scores across all 20 items of internet addiction and is labeled the \u0026ldquo;moderate users\u0026rdquo;. The third profile, consisting of 14.4% of students, exhibits the highest scores, indicating a higher risk of problematic internet use, and is referred to as the \u0026ldquo;addicted users\u0026rdquo;.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eModel fit indices for profile solutions.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of profiles\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eaBIC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEntropy\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLMRT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBLRT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eProportion\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e187022.324\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e187264.237\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e187137.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e159831.788\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e160200.705\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e160006.883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2132/995\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e150910.870\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e151406.792\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e151146.244\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.957\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1100/1577/450\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e147519.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e148142.613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e147815.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.944\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1406/604/967/150\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e145728.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e146478.218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e146084.219\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.084\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e860/1281/566/352/68\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Cross-Class Comparisons\u003c/h2\u003e\u003cp\u003eWe examined sociodemographic differences among the three groups. Statistically significant variations were found in gender (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;122.531, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), family sibling status (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;15.056, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), smoking status (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;13.565, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), physical activity (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;156.770, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and regularity of meals (\u003cem\u003eχ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;90.965, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). To explore differences in insomnia, depression, and suicidality across the groups, we performed ANOVAs on the total scores of the ISI, PHQ-9, and SBQ-R, with the results shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Significant differences in the total scores of the three scales emerged across three classes. \u003cem\u003ePost hoc\u003c/em\u003e comparisons revealed that the \u0026ldquo;addicted users\u0026rdquo; (class 3) had notably higher scores for insomnia, depression, and suicidality (all \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Network analysis of internet addiction\u003c/h2\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presented the network diagram illustrating the conditional associations among the 20 items of the IAT scale for the \u0026ldquo;addicted users\u0026rdquo; group. Each circle in the diagram represents a node corresponding to an item, and the edges indicate the strength of the associations between these nodes. The meaning of each node abbreviation is explained in \u003cb\u003eTable S2\u003c/b\u003e. The edge linking node IAT1 \u0026ldquo;stay online longer than intended\u0026rdquo; and node IAT 2 \u0026ldquo;neglect household chores\u0026rdquo; has the strongest association (edge weight\u0026thinsp;=\u0026thinsp;0.416). Nodes IAT16 (\u0026ldquo;lack of self-control when being online\u0026rdquo;) demonstrate the highest strength centrality indices, showing a strong positive correlation with other symptoms, which can also be considered a central symptom within the network. Node IAT12 (\u0026ldquo;life is boring without the internet\u0026rdquo;) exhibited the highest closeness, indicating its significant role in linking otherwise unrelated symptoms within the network. Additionally, node IAT12 ranked highest in betweenness centrality, highlighting its function as a key mediator, connecting different symptoms and acting as a bridge within the network. The strength values for all nodes in the network was displayed in \u003cb\u003eFig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e, and no statistical difference was discovered for strength between the nodes IAT16 and IAT2. The edge stability estimation exhibited fewer overlaps, indicating good network stability (\u003cb\u003eFig. S2\u003c/b\u003e). The centrality stability was preferable in this network, as the CS coefficients for strength was 0.673 (\u003cb\u003eFig. S3\u003c/b\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Network analysis of internet addiction and mental disorders\u003c/h2\u003e\u003cp\u003eMihai suggests that for constructing symptom networks with 20 or fewer nodes, a sample size of 250 to 350 participants is typically required (Mihai). However, in this study, the comorbid symptoms of internet addiction and mental disorders involve 40 nodes with highly interrelated connections. The sample of \u0026ldquo;addicted user\u0026rdquo; students consists of 450 participants, which is below the recommended sample size for such complex networks with more than 40 nodes. As a result, we focused on the six dimensions of the IAT\u0026mdash;salience, excessive use, neglect of work, anticipation, lack of control, and neglect of social life\u0026mdash;as observation variables to build the network model. The resulting network structure for internet addiction, insomnia, depression, and suicidality was illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe most prominent connection within the insomnia community was between node I6, which represents \u0026ldquo;noticeability of impairment due to sleep problems\u0026rdquo;, and node I7, which reflects \u0026ldquo;level of distress caused by sleep problems\u0026rdquo; (edge weight\u0026thinsp;=\u0026thinsp;0.408). Within the depression community, the strongest link was found between node P1, indicating \u0026ldquo;low interest or pleasure\u0026rdquo;, and node P4, which denotes \u0026ldquo;feeling tired or having little energy\u0026rdquo; (edge weight\u0026thinsp;=\u0026thinsp;0.288). In the suicidality community, the strongest connection occurred between node S2, representing \u0026ldquo;frequency of suicidal ideation in the past year\u0026rdquo;, and node S4, which relates to \u0026ldquo;self-reported likelihood of death by suicide\u0026rdquo; (edge weight\u0026thinsp;=\u0026thinsp;0.273). Node S2 \u0026ldquo;frequency of suicidal ideation in the past year\u0026rdquo; and node P9 \u0026ldquo;suicidal thoughts\u0026rdquo; showed the strongest association within the mental disorders community (edge weight\u0026thinsp;=\u0026thinsp;0.289), followed by node I1 \u0026ldquo;severity of sleep\u0026thinsp;\u0026minus;\u0026thinsp;onset (initial)\u0026rdquo; and node P3 \u0026ldquo;trouble sleeping\u0026rdquo; (edge weight\u0026thinsp;=\u0026thinsp;0.182).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eCentrality indices of all nodes were illustrated in \u003cb\u003eFig. S4\u003c/b\u003e. According to three centrality indices, and node P4 (strength(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;1, betweenness(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;5, closeness(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;2), node I2 (strength(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;2, betweenness(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;1, closeness(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;4) exhibited the most significant position in the network. Therefore, the central factors within the network were node P4 \u0026ldquo;tired or little energy\u0026rdquo;, and node I2 \u0026ldquo;sleep maintenance (middle)\u0026rdquo;. In addition, the centrality differs test for strength indicated that the strength of node P4 and the node I2 were not statistically different (\u003cb\u003eFig. S5\u003c/b\u003e). Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e displayed the results of the bridge centrality of the network. For the depression community, node P3 (bridge strength(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;2, bridge betweenness(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;1, bridge closeness(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;2) exhibited the highest bridge centrality. For suicidality community, node S2 (bridge strength(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;4, bridge betweenness(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;5, bridge closeness(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;14) had the highest bridge centrality. For insomnia community, node I2 (bridge strength(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;9, bridge betweenness(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;2, bridge closeness(\u003csub\u003erank\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;3) showed highest bridge centrality comparing to other nodes in the community. Consequently, node P3 (\u0026ldquo;trouble sleeping\u0026rdquo;), node S2 (\u0026ldquo;frequency of suicidal ideation in the past year\u0026rdquo;), and node I2 (\u0026ldquo;sleep maintenance (middle)\u0026rdquo;) were bridge nodes linking mental disorders symptoms with internet addiction. More detailed information of the centrality differs test for bridge strength was summarized in \u003cb\u003eFig. S6\u003c/b\u003e. \u003cb\u003eFig. S7\u003c/b\u003e illustrated that the edge weights in the current sample were consistent with those in the bootstrapped samples and the bootstrapped 95% CIs were narrow, indicating a good accuracy of the network model. \u003cb\u003eFig. S8 and S9\u003c/b\u003e showed that the CS coefficients for strength (traditional centrality) and bridge strength (bridge centrality) were both 0.673, reaching the critical cutoff point (0.5).\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Network comparison\u003c/h2\u003e\u003cp\u003eWe conducted a NCT to further explore the symptom characteristics of networks among college students with varying profiles of internet addiction. Significant differences in the global strength of the internet addiction networks were observed across the three groups, with values of 10.391 for \u0026ldquo;regular users\u0026rdquo; (class 1), 11.627 for \u0026ldquo;moderate users\u0026rdquo; (class2), and 11.148 for \u0026ldquo;addicted users\u0026rdquo;(class3) (class1 vs. class2, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010; class2 vs. class3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.208; class1 vs. class3, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010). Additionally, we compared the symptom networks between subgroups of male (n\u0026thinsp;=\u0026thinsp;155) and female (n\u0026thinsp;=\u0026thinsp;295) students, as well as between only child (n\u0026thinsp;=\u0026thinsp;129) and non-only child (n\u0026thinsp;=\u0026thinsp;321). No significant differences in global network strength were found between these groups (both \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). It is worth noting that only covariates with sample sizes greater than 100 were included in the analysis to ensure sufficient statistical power.\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo the best of our knowledge, this study is the first to explore the interrelationships of mental disorders symptoms with internet addiction among college students using a combination of LPA and NA. Based on their patterns of internet use, the students were classified into three distinct profiles including \u0026ldquo;regular users\u0026rdquo;, \u0026ldquo;moderate users\u0026rdquo;, and \u0026ldquo;addicted users\u0026rdquo;. Internet addiction and mental disorders symptoms differed significantly across the subgroups. The \u0026ldquo;addicted users\u0026rdquo; group exhibited the most pronounced symptoms of insomnia, depression, and suicidality. Additionally, the NA identified IAT 16 (\u0026ldquo;lack of self-control when being online\u0026rdquo;) as the central node of the addicted user profile, and P3 (\u0026ldquo;trouble sleeping\u0026rdquo;), S2 (\u0026ldquo;frequency of suicidal ideation in the past year\u0026rdquo;), and I2 (\u0026ldquo;sleep maintenance (middle)\u0026rdquo;) were bridge nodes linking depression, insomnia and suicidality symptoms with internet addiction. Identifying central and bridging symptoms enhances our understanding of the comorbidity between internet addiction and mental disorders. These symptoms be recognized as critical indicators for early detection and targeted intervention.\u003c/p\u003e\u003cp\u003eCompared to previous studies, the present study reported a higher prevalence of Internet addiction, and this discrepancy can be attributed to the fact that earlier studies predominantly focused on adolescents populations (Li, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and social media use (Peng and Liao, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) within a broader framework. In contrast, our study specifically targeted college students, which led to variations in the study population, measurement tools, and other influencing factors. The 3-class model of internet addiction observed in the Chinese college student population closely mirrored earlier findings from a large sample of Chinese adolescents (Li et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), implying itself as an appropriate classification for understanding internet use among young Chinese individuals. In our research, although three profiles showed relatively parallel paths, their characteristics were indeed discriminant. The \u0026ldquo;regular user\u0026rdquo; profile, representing 50.4% of the participants, had one peak value in item 9 (\u0026ldquo;alert when asked about online\u0026rdquo;) and scored lower than the other two profiles in each item. The \u0026ldquo;moderate users\u0026rdquo; profile, comprising 35.2% of the participants, had higher scores on item 1 (\u0026ldquo;stay online longer than intended\u0026rdquo;) and item 9. The \u0026ldquo;addicted users\u0026rdquo; profile, comprising 14.4% of the participants, scored higher than the other profiles on each item. The differences observed among the three profiles suggest that internet addiction among college students is a heterogeneous phenomenon. Interestingly, we observed that \u0026ldquo;moderate users\u0026rdquo; spent considerable time online (item 1) and remained alert during their internet activities (item 9), yet they reported relatively low levels of negative emotions (item 20). This finding suggests that, despite their high levels of internet engagement, these individuals did not experience the usual negative effects commonly linked to excessive use, nor did they display more pronounced symptoms of insomnia, depression, or suicidality. This phenomenon could be explained by the evolving role of the internet as an indispensable part of daily life, leading to an increase in both the frequency and duration of its usage. Recent findings by Fournier and colleagues (Fournier et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) align with our research, proposing that extended online hours and concerns about privacy may be secondary factors in internet addiction, rather than core components. Consequently, a greater emphasis should be placed on addressing the negative impacts of internet usage to avoid pathologizing internet engagement.\u003c/p\u003e\u003cp\u003eCore symptoms are the most impactful indicators of a disorder, as they have the ability to trigger other symptoms and contribute to the progression of mental health issues (Borsboom and Cramer, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Overall, identifying these central symptoms is crucial for effectively directing clinical interventions (McNally, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our study identified IAT16 (\u0026ldquo;lack of self-control when being online\u0026rdquo;) and IAT2 (\u0026ldquo;neglect household chores\u0026rdquo;) as central symptoms of internet addiction among college students. This contrasts with previous research on internet addiction in adolescents and young adults, which highlighted IAT6 (\u0026ldquo;grades or school work suffer\u0026rdquo;) and IAT8 (\u0026ldquo;job performance or productivity suffer\u0026rdquo;) as core symptoms (Lu et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The differences in findings may stem from the distinct focus of our study, which centers on college students, as opposed to the adolescent and young adult populations examined in earlier studies. College students generally have greater access to the internet and more free time compared to adolescents, particularly high school students. In some cases, internet use is even encouraged due to online courses and assignments. Furthermore, college students face less stringent supervision and academic pressure than high school students, and they are not burdened by the financial demands of work. Psychologically, the developmental stage of college students\u0026mdash;characterized by a search for identity and the formation of meaningful relationships\u0026mdash;may additionally distinguishes them from adolescents. Interestingly, lack of self-control and neglect household chores appeared to play a key role in the development of internet addiction of college students. Previous research has shown that individuals with poor time management and self-control skills often find the internet to be a compelling escape from their demanding routines (Salarvand et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, people suffering from internet addiction tend to exhibit low self-esteem, heightened feelings of loneliness, and inadequate social skills (Romero-L\u0026oacute;pez et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The anonymity offered by the internet facilitates easier social interaction, which can be particularly appealing for those with internet addiction, as their social needs are often unmet in real-life situations (Zsido et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This reliance on the virtual world for social fulfillment can result in individuals neglecting their real-world interactions, leading to excessive internet use and procrastination. Cognitive behavioral therapy (CBT) may prove effective for students struggling with internet addiction, as they often hold the belief that life without online activities will feel dull and unfulfilling, which contributes to their difficulty in controlling internet use. Previous research has demonstrated that CBT can help reduce excessive internet usage, enhance time management skills, and promote emotional stability (Zhu et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe NA provided new evidence about the relationship between internet addiction and mental disorders from a symptom perspective, which revealed that behaviors associated with sleep (\u0026ldquo;trouble sleeping\u0026rdquo; and \u0026ldquo;sleep maintenance (middle)\u0026rdquo;) serve as bridges between internet addiction and mental disorders. Consistent with previous research (Lu et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), students with higher levels of internet addiction tended to have shorter sleep durations. According to the displacement hypothesis (Kraut et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), internet use substitutes real-life activities that protect college students\u0026rsquo; mental health (such as sleep), leading to insufficient sleep and decreased sleep quality, thereby associating with the risk of psychological symptoms. However, improving sleep maintenance can help mitigate the adverse effects of internet addiction. Notably, promoting an earlier bedtime could extend sleep duration by approximately half an hour per night (Wake and Hiscock, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This finding suggests that encouraging earlier bedtimes and allowing for longer recovery sleep on weekends might be effective strategies to counteract the negative impacts of excessive internet use (Kawabe et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, the study identifies a specific symptom of suicidality\u0026mdash;frequency of suicidal ideation in the past year, also as bridge between internet addiction and mental disorders. There are several possible explanations for the direct link between internet addiction and suicidal ideation. First, the anonymity provided by the internet increases the likelihood of individuals with internet addiction being exposed to suicidal content or experiences (Mars et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Repeated exposure to such material may lead individuals to imitate suicidal ideation in the real world. Second, the phenomenon of de-individuation, which is common in online interactions, can diminish self-awareness, reduce concerns about social judgment, and lower the threshold for engaging in harmful behaviors such as suicide (Shen et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). In this environment, individuals may be less aware of or less sensitive to the negative consequences of suicidal actions due to changes in cognitive control processes (Seok et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Lastly, the factors contributing to pathological behavior often overlap. As mentioned above, poor sleep quality, a common issue in internet addiction, may also exacerbate suicidal ideation among affected students. Our finding highlights the potential role of internet addiction in exacerbating mental disorders by affecting individuals' sleep quality and emotional states. Specifically, prolonged internet addiction can lead to insufficient or disrupted sleep, which in turn increases the risk of depression. Suicidal ideation, in particular, may reflect the emotional distress and psychological pressure experienced by individuals, suggesting that those with internet addiction may lack effective coping mechanisms when faced with real-life challenges (Liu et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhile our study found no significant differences in network strength and connectivity between male and female college students in the addiction group, previous research by Shan et al. (Shan et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) identified gender differences in the central symptoms of problematic WeChat use among college students. However, we emphasize the importance of considering gender differences in internet addiction, as societal expectations and psychological needs differ between males and females. Males tend to prioritize achievement and independence, while females often place greater value on relationship maintenance and emotional expression. Additionally, males typically seek higher levels of stimulation and entertainment, while females are more focused on fulfilling their social needs.\u003c/p\u003e"},{"header":"5. Strengths and limitations","content":"\u003cp\u003eThis study offers several notable strengths. Firstly, stratified random sampling ensured a representative college student sample, enhancing the reliability and generalizability of the findings. Secondly, the survey-based methodology enabled large-scale data collection, encompassing students from a range of academic disciplines. This approach broadens our understanding of the relationship between internet addiction and mental health disorders in this population. Additionally, the use of the IAT questionnaire, which is well-validated for assessing internet addiction in the daily lives of Chinese college students, proved advantageous. Finally, the study addresses a pressing contemporary issue by exploring the connections between internet addiction and mental health, providing valuable insights that contribute to the ongoing dialogue and inform the development of potential interventions.\u003c/p\u003e\u003cp\u003eDespite the strengths of this study, there are several limitations that should be addressed in future research. First, the cross-sectional design restricts the ability to draw conclusions about causal relationships between internet addiction and mental disorders. Longitudinal studies would be valuable in understanding how internet addiction may affect mental health over time. Additionally, the reliance on self-report data may introduce social desirability bias and common method bias. To reduce these limitations, future studies could incorporate objective measures alongside self-reports. Lastly, our study examined internet use in a general context. Given the varying functions, features, and recommendation algorithms of different online platforms, future research should investigate problematic usage within specific social media platforms to offer a more nuanced understanding.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eOur study found that 14.4% of college students exhibit signs of internet addiction, with \u0026ldquo;lack of self-control while online\u0026rdquo; emerging as the most prominent symptom. To address this, interventions aimed at enhancing self-regulation, improving time management abilities, and offering psychological support could be effective strategies for both preventing and treating internet addiction. Additionally, that behaviors associated with sleep and suicidal ideation serve as bridges between internet addiction and mental disorders. Interventions targeting sleep issues may help mitigate the negative effects of internet addiction, while providing psychological support to reduce suicidal risks could effectively break the harmful cycle between addiction and mental disorders. Therefore, future intervention strategies should focus on these bridging symptoms, offering more comprehensive support to address both internet addiction and its associated mental health issues.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Zhejiang University Global Partnership Fund.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuayu Li:\u003c/strong\u003e Conceptualization, Investigation, Data curation, Writing original draft. \u003cstrong\u003eHongyuan Lv:\u003c/strong\u003e Investigation, Data curation, Methodology, Visualization. \u003cstrong\u003eJiahui Zhang:\u003c/strong\u003e Investigation, Data curation, Formal analysis, Visualization. \u003cstrong\u003eYu Fu:\u003c/strong\u003e Data curation, Methodology, Formal analysis. \u003cstrong\u003eJianpeng Zhang:\u003c/strong\u003e Conceptualization, Project administration, Supervision, Writing – review \u0026amp; editing. \u003cstrong\u003eHongmei Wang:\u003c/strong\u003e Conceptualization, Project administration, Supervision, Validation, Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the students and teachers who participated in this study and the support of the school principals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical approval from the School of Public Health, Zhejiang University (Approval No. ZGL201312-1).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlimoradi, Z., Lin, C. Y., Brostr\u0026ouml;m, A., B\u0026uuml;low, P. H., Bajalan, Z., Griffiths, M. D., Ohayon, M. M., Pakpour, A. H., 2019. Internet addiction and sleep problems: a systematic review and meta-analysis. Sleep medicine reviews, 47, 51\u0026ndash;61. https://doi.org/10.1016/j.smrv.2019.06.004.\u003c/li\u003e\n \u003cli\u003eAlonzo, R., Hussain, J., Stranges, S., Anderson, K. K., 2021. Interplay between social media use, sleep quality, and mental health in youth: a systematic review. Sleep Med Rev, 56, 101414. https://doi.org/10.1016/j.smrv.2020.101414.\u003c/li\u003e\n \u003cli\u003eBastien, C. H., Valli\u0026egrave;res, A., Morin, C. M., 2001. Validation of the insomnia severity index as an outcome measure for insomnia research. Sleep Med, 2(4), 297\u0026ndash;307. https://doi.org/10.1016/s1389-9457(00)00065-4.\u003c/li\u003e\n \u003cli\u003eBorsboom, D., 2017. A network theory of mental disorders. 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Psychiatry Res, 292, 113323. https://doi.org/10.1016/j.psychres.2020.113323.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"college students, internet addiction, insomnia, depression, suicidality, network analysis","lastPublishedDoi":"10.21203/rs.3.rs-7549655/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7549655/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Internet addiction is a common concern among college students, often resulting in negative mental outcomes such as insomnia, depression, and suicidality. However, the variability in internet addiction patterns among college students and its connection to mental disorders remains insufficiently explored. This study investigated the comorbidity network of internet addiction, insomnia, depression, and suicidality in 3,127 Chinese college students using latent profile analysis (LPA) and network analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003cem\u003e \u003c/em\u003eA total of 3,127 Chinese college students provided their data on internet use, insomnia, depression, and suicidality using the Internet Addiction Test (IAT), Insomnia Severity Index (ISI), Patient Health Questionnaire-9 (PHQ-9), and Suicide Behavior Questionnaire-Revised (SBQ-R). Latent profile analysis (LPA) was employed to identify subgroups of students exhibiting similar patterns of internet addiction. Network structures relating to internet addiction and its association with mental disorders were constructed among addicted users. The stability of the network was assessed through a case drop bootstrap procedure, and a network comparison test (NCT) was conducted to evaluate differences in network characteristics across the identified subgroups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003cem\u003e:\u003c/em\u003e LPA identified three distinct groups of college students based on their internet usage patterns: regular users, moderate users, and addicted users. Network analysis revealed that the central symptom of internet addiction was “lack of self-control when online”. Furthermore, “trouble sleeping”, “frequency of suicidal ideation over the past year” and “sleep maintenance (middle)” were identified as bridge symptoms, connecting insomnia, depression, and suicidality with internet addiction. The NCT showed no significant gender differences in the global strength of the internet addiction network.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Interventions focused on improving self-regulation and addressing sleep problems should be prioritized to help reduce internet addiction, insomnia, depression, and suicidality in this population.\u003c/p\u003e","manuscriptTitle":"Exploring the comorbidity mechanism of internet addiction, insomnia, depression, and suicidality among Chinese college students through network analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 02:10:06","doi":"10.21203/rs.3.rs-7549655/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b304034-0733-4ffb-b9d0-4b8652e4d2b9","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56197108,"name":"Health sciences/Diseases"},{"id":56197109,"name":"Health sciences/Health care"},{"id":56197110,"name":"Biological sciences/Psychology"},{"id":56197111,"name":"Social science/Psychology"},{"id":56197112,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-01-21T11:55:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 02:10:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7549655","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7549655","identity":"rs-7549655","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}