Network Analysis of Childhood Maltreatment and Internet Addiction in Adolescents with Major Depressive Disorder

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Methods Conducted across seven hospitals in Anhui Province, China, involving 332 adolescents, it employs the Childhood Trauma Questionnaire - Short Form (CTQ-SF) and the Internet Addiction Test (IAT) to measure CM and the symptoms of IA, respectively. Results Using network analysis, the CM-IA network were constructed to identify the most central symptoms and the bridge symptoms within the networks. "Depress/moody/nervous being offline", " Request an extension for longer time", "Sleep loss due to late-night logines", and " emotional abuse " were identified as the central symptoms of CM-IA network analysis. Bridge symptoms, notably "emotional abuse", "sexual abuse", and "complaints about online time", were significant in linking CM and IA. Conclusion These results underscore the complex relationship between childhood trauma and IA, emphasizing the role of specific symptoms in understanding and addressing internet addiction in adolescents. Internet addition childhood maltreatment Major Depressive Disorder adolescents network analysis Figures Figure 1 Figure 2 Figure 3 1. Introduction Adolescence is a critical period for rapid physical and brain maturation, accompanied by significant hormonal changes and neural restructuring [ 1 , 2 ]. During this phase, adolescents are particularly susceptible to mental disorders, with depression being notably prominent [ 3 ] and becoming one of the most common mental disorders globally [ 4 ]. The "Global Burden of Disease Study 2020" [ 5 ] indicated that during adolescence, major depressive disorder (MDD) significantly increases the Disability-Adjusted Life Years (DALY), becoming a major influencing factor. DALYs are a measure that focuses on the impact of diseases, injuries, or premature death on healthy life years. In China, MDD has become one of the leading causes of disability and life loss [ 6 ]. Adolescents with MDD face greater challenges in social and academic areas and are more likely to experience incomplete education (OR 1.76) [ 7 ], Internet addiction (IA) [ 8 ], self-harm, and suicidal behavior [ 9 ]. MDD in adolescents is significantly associated with depression, anxiety, and suicide in later adulthood [ 10 ], underscoring the need for greater attention to adolescent mental health. IA lacks a widely accepted definition and has not been formally recognized as a diagnosis by modern psychiatric classification systems. Its core features include excessive internet use, an inability to control online impulses, and resulting functional impairment [ 11 ]. According to Young [ 12 ], IA is categorized into five subtypes: cyber gaming, communication, information seeking, technical (like online gambling, shopping, or trading), and pornography addiction. IA negatively impacts the brain development of children and adolescents [ 13 , 14 ], disrupts decision-making and emotional control [ 15 , 16 ], and leads to anxiety, depression, and behavioral problems [ 17 – 19 ]. This dependency can also damage sleep patterns [ 20 , 21 ], further impacting academic and social abilities [ 22 , 23 ]. Risk factors for IA include depressive symptoms, high neuroticism, and poor parent-child relationships [ 24 ]. Childhood maltreatment (CM), such as sexual, physical, and emotional abuse, and neglect, is considered a significant predictor of IA [ 25 , 26 ]. Adolescents who experienced these traumas may develop insecure attachments, leading to behavioral disorders like IA [ 27 ]. From a neurophysiological perspective, CM can disrupt the stress response system in adolescents, altering neural structures and functions, and increasing susceptibility to IA [ 27 , 28 ]. In recent years, numerous studies have focused on the relationship between CM and IA. For instance, a meta-analysis involving 21,398 adolescents across 19 studies [ 29 ] revealed a significant positive correlation between CM and IA (r = 0.395). Further analysis [ 30 ] showed that emotional neglect (OR = 3.062) and physical neglect (OR = 2.328) are independently associated with IA. Although regression analysis has been valuable in assessing the relationship between specific predictor and outcome variables, it only examines linear relationships and does not fully capture the interdependence and complex interactions among multiple variables. Recently, Network Analysis (NA) has been touted as a cutting-edge approach in the study of mental disorders [ 31 ]. In NA, nodes represent symptoms, while edges reveal the connections between them. This method not only uncovers the relationships between individual symptoms but also helps identify central and bridge symptoms through network centrality measures, offering a more comprehensive perspective to explore the link between CM and IA. Therefore, this study aims to use network analysis to reveal the close relationships between core symptoms of adolescent depression-related IA and subtypes of CM, providing a foundation for subsequent prevention measures and targeted intervention strategies for IA in adolescents. 2. Methods 2.1 Study design and participants A cross-sectional study was conducted in four general hospitals (Chaohu, Bengbu, Suzhou, Bozhou) and three psychiatric hospitals (Hefei, Ma’anshan, Fuyang) located in Anhui Province, China, between January and July 2021. Adolescent participants, aged 12 to 18, diagnosed with MDD by two psychiatrists employing the DSM-V criteria, were consecutively enlisted from psychiatric outpatients and inpatients in these hospitals. Exclusion criteria comprised individuals with other psychiatric or neurological disorders, and/or intellectual disabilities. Out of 356 invited adolescents, 332 completed the assessment, yielding a robust participation rate of 93.3%. All eligible participants and their guardians provided informed consent after being briefed on the study's objectives and procedures. The study protocol (202009-kyxm-04) was approved by the Medical Ethics Committee of Chaohu Hospital of Anhui Medical University prior to commencement. 2.2 Measurement tools 2.2.1 The Childhood Trauma Questionnaire - Short Form (CTQ-SF) The CTQ-SF is a widely used questionnaire designed to assess CM in individuals aged 12 and older [ 32 ]. It consists of 28 items, with 25 dedicated to evaluating various forms of maltreatment experienced during childhood, such as emotional abuse (EA), physical abuse (PA), sexual abuse (SA), emotional neglect (EN), and physical neglect (PN). The scoring for each item ranges from 1 (“never”) to 5 (“often”), and the total score for each subscale is the sum of scores for its five items, with higher scores indicating more severe abuse [ 33 ]. It is important to note that the scores for 7 items (item 2, 5, 7, 13, 19, 26, and 28) are to be reverse coded. As a result, the total score for each subscale ranges from 5 to 25. In previous research, the Chinese version of the CTQ-SF demonstrated commendable reliability [ 34 , 35 ]. Cronbach’s alpha in our cohort was 0.801. 2.2.2 Internet Addiction Test (IAT) The evaluation of IA utilized the authenticated Chinese version of the Internet Addiction Test (IAT), as reported by Chin and Leung [ 36 ] and Lai et al. [ 37 ]. This instrument consists of 20 questions and employs a 5-point Likert scale for respondents to assess the frequency of their internet-related symptoms, ranging from 1 (indicating very rarely) to 5 (indicating very often). The cumulative score from this test serves as an indicator of the severity of an individual's internet addiction, with higher scores reflecting more severe addiction levels. Additionally, the Cronbach's alpha coefficient for the Chinese version of the IAT was found to be 0.93, illustrating its high reliability and precision in assessing internet addiction [ 37 ]. 2.3 Network estimation Data analysis in this study was performed using the R language within the RStudio environment, version 4.3.2 [ 38 ]. Network construction was accomplished through the EBICglasso function and Spearman correlation, creating a sparse Gaussian graphical model. This method, guided by the Extended Bayesian Information Criterion (EBIC), streamlined the network structure and enhanced interpretability [ 39 ]. The analysis primarily focused on strength and the expected influence (EI) index, due to their suitability for psychopathological networks [ 40 ]. Bridge Strength and the bridge expected influence (BEI) index were computed to identify bridge symptoms, utilizing the bridge function in the networktools R package [ 41 ]. Other centrality metrics such as closeness and betweenness were not used due to their limited effectiveness in revealing psychological variables [ 42 ]. Graphical representation of the network was generated using the qgraph R package (version 1.9.8) [ 43 ], where nodes (circles) were connected by edges (lines), with the thickness of the edges indicating interaction strength [ 44 ]. Positive correlations were depicted in blue, while negative correlations were shown in red. The Fruchterman-Reingold force-directed algorithm was used to cluster nodes with strong associations and place those with weaker associations on the periphery [ 45 ]. The R 2 predictability index, calculated using the mgm R package (1.2–14) [ 46 ], was visually represented by the size of a semi-circular area around each node, quantifying the variance each node explained in relation to others in the network. 2.4 Estimation of network accuracy and stability The bootnet R package (version 1.5.6) [ 47 ] was used to conduct 1000 bootstrap iterations to assess the accuracy and stability of network edges, with edge precision evaluated by examining the 95% confidence interval of bootstrap edge weights; narrower intervals indicated higher accuracy. The robustness of centrality measures was further assessed by comparing centrality indices from the complete sample with those from a 70% reduced sample. The Centrality Stability coefficient (CS coefficient) was calculated to evaluate network robustness, with a value of ≥ 0.5 indicating high reliability, 0.25 to 0.5 denoting moderate reliability, and < 0.25 indicating less robustness. 2.5. Comparisons of network characteristics by gender We performed gender-based network comparisons using the NetworkComparisonTest package (version 2.2.2) [ 48 ]. Effect sizes were determined using Bootstrap-based Spearman correlation, and the average correlation coefficients were reported from 1000 resampled associations. By comparing two networks and visualizing the differences in a corPlot diagram, we effectively identified key distinctions. 3. Results 3.1 Network structure and centrality measures analysis Figure 1 illustrates the network structure of CM-IA. Out of 300 edges in this network, 131 edges (43.7%) have non-zero weights, indicating a dense interconnectivity between CM and IA. In terms of node predictability, on average, 43.0% of the variance can be explained by neighboring nodes. The node "Preoccupation with the Internet" (IAT-15) has the highest predictability in the network, reaching 61.5%. This is closely followed by "Depress/moody/nervous being offline " (IAT-20) and "Request an extension for longer time " (IAT-16), with predictabilities of 61.3% and 58.9%, respectively. The predictability of all other nodes in the network is detailed in Table 1 . Table 1 Descriptive statistics of measurement items. Item abbreviation Item content Mean (SD) Strength a EI a Predictability b CTQ-SF EA Emotional abuse 11.20 (4.66) 1.028 1.028 0.428 PA Physical abuse 7.37 (3.19) 0.560 0.560 0.284 SA Sexual abuse 5.81 (2.17) 0.358 0.358 0.12 EN Emotional neglect 15.60 (5.12) 0.746 0.746 0.432 PN Physical neglect 10.55 (3.34) 0.632 0.632 0.388 IAT IAT-1 Stay online longer 3.06 (1.36) 0.672 0.672 0.378 IAT-2 Neglect chores to spend more time online 2.74 (1.31) 0.966 0.966 0.492 IAT-3 Prefer the excitement online 2.90 (1.51) 0.856 0.856 0.502 IAT-4 Form new relationship 2.05 (1.22) 0.421 0.421 0.249 IAT-5 Others complain about your time 3.44 (1.37) 0.980 0.980 0.414 IAT-6 School grades suffer 2.46 (1.28) 0.889 0.889 0.455 IAT-7 Academic efficiency declines 2.11 (1.27) 0.650 0.650 0.306 IAT-8 Check email/SNS before doing things 1.94 (1.07) 0.799 0.799 0.383 IAT-9 Become defensive/secretive Internet use 2.65 (1.43) 0.456 0.456 0.166 IAT-10 Sooth disturbing thoughts 3.23 (1.46) 0.908 0.908 0.403 IAT-11 Anticipation for future online activities 2.52 (1.37) 1.015 1.015 0.563 IAT-12 Fear that is boring and empty without the Internet 2.81 (1.37) 0.889 0.889 0.483 IAT-13 Snap or act annoyed if bothered without being online 2.35 (1.20) 0.928 0.928 0.547 IAT-14 Sleep loss due to late-night logines 2.54 (1.39) 1.069 1.069 0.567 IAT-15 Preoccupation with the Internet 2.63 (1.30) 1.027 1.027 0.615 IAT-16 Request an extension for longer time 2.59 (1.41) 1.132 1.132 0.589 IAT-17 Failure to cut down the time spend online 2.21 (1.32) 0.856 0.856 0.484 IAT-18 Conceal the amount of time spend online 2.10 (1.32) 0.699 0.699 0.357 IAT-19 Spend more time online over going out with others 2.51 (1.48) 0.850 0.850 0.521 IAT-20 Depress/moody/nervous being offline. 2.12 (1.33) 1.139 1.139 0.613 Note: a The values of Strength and EI (Expected Influence) were raw data generated from the network; b These relationships align with the predictability measures obtained by R 2 , which are displayed as a bar on the edge of the node’s circle. In the CM community, the connection between the nodes EN "Emotional Neglect" and PN "Physical Neglect" is the most direct and strong, with a weight of 0.398. The node EA "Emotional Abuse" also has a significant direct connection with PA "Physical Abuse", with a weight of 0.376. Additionally, there is a notable link between EA "Emotional Abuse" and EN "Emotional Neglect", with a weight of 0.252. In terms of IA symptoms, the nodes IAT-3 "Prefer the excitement online" and IAT-19 "Spend more time online over going out with others" are the most closely connected, with a weight of 0.464, representing the tightest bond in the entire network. Following closely, IAT-1 "Stay online longer" and IAT-2 "Neglect chores to spend more time online" have a close relationship with a weight of 0.365; and IAT-16 "Request an extension for longer time" and IAT-17 "Failure to cut down the time spend online" are also closely linked, with a weight of 0.284. Notably, the differences in these edge weights are statistically significant (refer to Figure S1 ). Detailed information about the weights of other edges in the CM-IA network can be found in Table S1 . As depicted in Fig. 2 , within the CM-IA network, IAT-20 "Depress/moody/nervous being offline" emerges as the node with the highest centrality, followed by IAT-16 "Request an extension for longer time", IAT-14 " Sleep loss due to late-night logines", and EA "Emotional Abuse". Additionally, SA "Sexual Abuse" holds the highest bridging value, succeeded by IAT-5 "Others complain about your time" and EA "Emotional Abuse". Specifically, the strongest link is observed between EA "Emotional Abuse" and IAT-5 " Others complain about your time". The centrality and bridge symptoms for all nodes are accessible in Table 1 . Figure 3 A illustrates satisfactory accuracy, evidenced by the convergence of the black and red lines. Furthermore, the narrow gray band indicates minimal variability during the resampling process. Regarding the stability of the network analysis, the centrality and bridging metrics exhibit excellent stability levels with CS = 0.75 and CS = 0.675, respectively, as shown in Fig. 3 B. 3.2 Gender differences Figure S4 presents the network structures generated for both genders. When comparing the networks generated for females and males, no significant differences were found in terms of global network strength (females: 10.045 vs males: 9.473; S = 0.573, p = 0.713) or the distribution of edge weights in the network structure (M = 0.277, p = 0.119). 4. Discussion This article employs network analysis to explore the intricate relationship between CM and IA. The study reveals a dense network of connections between CM and IA, with 43.7% of the edges showing non-zero weights, indicating a close interaction between the two. This dense interconnectivity highlights the complex and multifaceted interactions between various aspects of CM and the symptoms of IA. The CM-IA network identifies core symptoms including "Depress/moody/nervous being offline" (IAT-20), "Request an extension for longer time" (IAT-16), "Sleep loss due to late-night logines" (IAT-14), and " Preoccupation with the Internet" (IAT-15). These findings elaborate on the definition of IA as a behavioral disorder where individuals develop an excessive reliance on and lack of control over internet use, leading to significant impairment in psychological, social, academic, or occupational functioning [ 49 ]. Additionally, our results corroborate previous network analysis findings on IA among Chinese adolescents [ 50 ]. Notably, when unable to access the internet, adolescents may experience negative emotional reactions such as depression, anxiety, or irritability [ 51 , 52 ], indicating that the internet can serve not only as an escape from reality but also as a means to manage or regulate emotions in some contexts [ 53 ]. While such emotional regulation can be beneficial to some extent, excessive and compulsive internet use can lead to delayed sleep and disrupted sleep cycles, worsening the individual’s real-life situation. Our study also highlights "Emotional Abuse" (EA) as another central symptom in the CM-IA model. Emotional abuse can have long-lasting effects on an individual’s ability to regulate emotions and interpersonal relationships [ 54 ], undermine their basic sense of safety and trust [ 55 , 56 ], and increase their tendency to escape reality and seek emotional fulfillment later in life [ 57 ]. This drives individuals to use the internet as a coping mechanism to avoid the hardships and emotional pain of real life [ 58 ]. In our composite model, we identified three key bridge symptoms that link IA with CM: Sexual Abuse (SA), Emotional Abuse (EA), and "Others complain about your time" (IAT-5). Notably, there is a significant connection between "Emotional Abuse" (EA) and "Others complain about your time" (IAT-5). Consistent with the findings of Taş [ 59 ], we observed a correlation between emotional abuse and IA. Furthermore, a study in South Korea by Kim et al. [ 60 ] indicated a positive correlation between sexual abuse and IA (β = 0.20). As covert forms of CM, sexual and emotional abuse are often difficult to detect and can persist over time, leading to a relatively high incidence rate [ 61 ]. The challenges in identifying, defining, and legally substantiating emotional abuse increase the risk of children remaining in harmful environments [ 62 ]. These forms of abuse have a lasting negative impact on children's psychological development, manifesting in heightened levels of depression, anxiety, stress, and neuroticism [ 63 ]. Particularly, sexual abuse profoundly affects children's psychological state and can severely damage their self-esteem and identity [ 64 ]. Due to these psychological impacts, such as self-loathing, intense dislike for one's body and emotions, and resultant social isolation, victims may seek solace and escape in the virtual world. The relative anonymity and control afforded by the internet provide these children with a means to distance themselves from the painful experiences of real life. In this space, children and adolescents may find the support, understanding, and acceptance they lack, temporarily alleviating their inner turmoil. However, prolonged internet use can evolve into a mechanism for escaping reality, ultimately leading to excessive dependence on the internet and the development of IA. To address the link between CM and IA, comprehensive intervention measures should include cognitive-behavioral therapy to improve emotional regulation and coping strategies, particularly for individuals experiencing "Depress/moody/nervous being offline" (IAT-20). Furthermore, for those affected by "Emotional Abuse" (EA) and "Sexual Abuse" (SA), trauma-focused therapy and trauma-informed care should be provided to help them process past traumatic experiences. Establishing supportive social networks, enhancing family communication quality, and educating individuals on recognizing and expressing emotions are also crucial strategies for reducing internet dependency and preventing addictive behaviors. This multifaceted intervention approach aims to fundamentally address the issue of IA while tackling the underlying emotional and psychological factors. The limitations of this study warrant attention. Firstly, the observed network structure might be influenced by the specific survey tools used, and different assessment methods could yield varying results. Secondly, despite utilizing self-report tools with high reliability and validity, participant responses could be compromised by recall bias, potentially affecting the objectivity of the findings. Additionally, the study's cross-sectional design does not offer a longitudinal perspective. Future research should expand the sample size and collect data across multiple dimensions to thoroughly analyze the relationship between childhood abuse experiences and internet addiction (IA) in adolescents with varying levels of depression. This will provide a foundation for a more comprehensive understanding and effective prevention of internet addiction. Declarations Acknowledgments We would like to express our heartfelt gratitude to the hospital administrators for their invaluable assistance in enabling this survey, and we extend our thanks to the participants for their unwavering collaboration throughout this research study. Ethics approval and consent to participate The study protocol (202009-kyxm-04) was approved by the Medical Ethics Committee of Chaohu Hospital of Anhui Medical University prior to commencement. All eligible participants and their guardians provided informed consent after being briefed on the study's objectives and procedures. Consent for publication All participants were given participant information prior to starting the survey and gave informed consent for the publication of the study's results. Availability of data and materials The datasets generated and analyzed during the current study are not publicly available due to potential privacy concerns or academic implications, but can be obtained from the corresponding author upon reasonable request. Competing interests The authors declare no competing interests. Funding A multimodal integrated study of the pathogenesis and clinical intervention of adolescent depression (2022zhyx-B01). Authors' contributions Huanzhong Liu and Lei Xia were responsible for the study's design. Song Wang and Feng Geng conducted a comprehensive literature review. Mengyue Gu, Yudong Shi, and Jingyang Gu collected the data. Song Wang, Feng Geng, Mengyue Gu, Jingyang Gu, and Yudong Shi performed the data analysis and were responsible for data interpretation. Song Wang wrote the first daft of the paper. Huanzhong Liu critically revised the manuscript. All authors have reviewed and approved the final version of the manuscript. 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Brand M, Wegmann E, Stark R, Müller A, Wölfling K, Robbins TW, Potenza MN: The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors . Neuroscience and biobehavioral reviews 2019, 104 :1-10. Cai H, Bai W, Sha S, Zhang L, Chow IHI, Lei SM, Lok GKI, Cheung T, Su Z, Hall BJ et al : Identification of central symptoms in Internet addictions and depression among adolescents in Macau: A network analysis . Journal of affective disorders 2022, 302 :415-423. Kaptsis D, King DL, Delfabbro PH, Gradisar M: Withdrawal symptoms in internet gaming disorder: A systematic review . Clinical psychology review 2016, 43 :58-66. Yen J-Y, Lin P-C, Wu H-C, Ko C-H: The withdrawal-related affective, gaming urge, and anhedonia symptoms of internet gaming disorder during abstinence . Journal of behavioral addictions 2022. Kosa M, Uysal A: Four pillars of healthy escapism in games: Emotion regulation, mood management, coping, and recovery . Game user experience and player-centered design 2020:63-76. Burns EE, Jackson JL, Harding HG: Child maltreatment, emotion regulation, and posttraumatic stress: The impact of emotional abuse . Journal of Aggression, Maltreatment & Trauma 2010, 19 (8):801-819. Riggs SA: Childhood emotional abuse and the attachment system across the life cycle: What theory and research tell us . In: The effect of childhood emotional maltreatment on later intimate relationships. edn.: Routledge; 2019: 5-51. Huh HJ, Kim KH, Lee HK, Chae JH: The relationship between childhood trauma and the severity of adulthood depression and anxiety symptoms in a clinical sample: The mediating role of cognitive emotion regulation strategies . Journal of affective disorders 2017, 213 :44-50. Wang L, Chen Y, Li Z, Zhou Y, Li J, Lv X, Yu Z, Gao X: The Influences of Adverse Childhood Experiences and Social Support on Male Teenagers’ Gaming Motivation: A Moderated Network Analysis . Journal of Pediatric Health Care 2024. Guo Y-Y, Gu J-J, Gaskin J, Yin X-Q, Zhang Y-H, Wang J-L: The association of childhood maltreatment with Internet addiction: the serial mediating effects of cognitive emotion regulation strategies and depression . Child Abuse & Neglect 2023, 140 :106134. Taş İ: The relationship between parental emotional abuse and interpersonal competence and digital game addiction: A path analysis . Indian journal of psychiatry 2023, 65 (1):45-51. Kim B-N, Park S, Park M-H: The relationship of sexual abuse with self-esteem, depression, and problematic internet use in Korean adolescents . Psychiatry investigation 2017, 14 (3):372. Gilbert R, Widom CS, Browne K, Fergusson D, Webb E, Janson S: Burden and consequences of child maltreatment in high-income countries . The lancet 2009, 373 (9657):68-81. Rees C: Understanding emotional abuse . Archives of disease in childhood 2010, 95 (1):59-67. Dye HL: Is Emotional Abuse As Harmful as Physical and/or Sexual Abuse? Journal of Child & Adolescent Trauma 2020, 13 (4):399-407. Fergusson DM, Mullen PE: Childhood sexual abuse , vol. 40: Sage; 1999. Additional Declarations No competing interests reported. Supplementary Files NAadolescentsIAsupplementarymaterials.docx Cite Share Download PDF Status: Published Journal Publication published 05 Nov, 2024 Read the published version in BMC Psychiatry → Version 1 posted Editorial decision: Revision requested 26 Sep, 2024 Reviews received at journal 26 Sep, 2024 Reviews received at journal 25 Sep, 2024 Reviewers agreed at journal 21 Sep, 2024 Reviewers agreed at journal 21 Sep, 2024 Reviews received at journal 10 Aug, 2024 Reviewers agreed at journal 10 Aug, 2024 Reviewers agreed at journal 07 Aug, 2024 Reviews received at journal 02 Jun, 2024 Reviewers agreed at journal 26 May, 2024 Reviewers invited by journal 21 Apr, 2024 Editor assigned by journal 13 Apr, 2024 Editor invited by journal 10 Apr, 2024 Submission checks completed at journal 10 Apr, 2024 First submitted to journal 06 Apr, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Geng","suffix":""},{"id":290811299,"identity":"41bcccb6-eac9-438c-8ea1-02e0a2774722","order_by":2,"name":"Mengyue Gu","email":"","orcid":"","institution":"Chaohu Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengyue","middleName":"","lastName":"Gu","suffix":""},{"id":290811300,"identity":"19e524e4-6f3e-41a7-a7d2-ccabe178ab3c","order_by":3,"name":"Jingyang Gu","email":"","orcid":"","institution":"Chaohu Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingyang","middleName":"","lastName":"Gu","suffix":""},{"id":290811301,"identity":"fc0e76ff-af75-4aee-98e5-10e75208cefb","order_by":4,"name":"Yudong Shi","email":"","orcid":"","institution":"Chaohu Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yudong","middleName":"","lastName":"Shi","suffix":""},{"id":290811302,"identity":"478a2d2e-049f-4b80-8465-7267017ce01e","order_by":5,"name":"Yating Yang","email":"","orcid":"","institution":"Chaohu Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yating","middleName":"","lastName":"Yang","suffix":""},{"id":290811303,"identity":"dd6e8d93-ae99-49e0-a720-54c884629cd5","order_by":6,"name":"Ling Zhang","email":"","orcid":"","institution":"Chaohu Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Zhang","suffix":""},{"id":290811304,"identity":"bfb63825-e5e1-4aec-93a2-5499098e2100","order_by":7,"name":"Mengdie Li","email":"","orcid":"","institution":"Chaohu Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengdie","middleName":"","lastName":"Li","suffix":""},{"id":290811305,"identity":"35dba1e9-8bf4-4935-96db-6142a6f05814","order_by":8,"name":"Lei Xia","email":"","orcid":"","institution":"Chaohu Hospital of Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Xia","suffix":""},{"id":290811306,"identity":"67a5ae46-e698-4666-aca1-a73aebb1fe09","order_by":9,"name":"Huanzhong Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACAxDB2AAk2IHkB4YDYFEJ4rQwMzYwziBRCxDxEKPFXCL52cOfO2zy5J2Z26Rtau5EGxxgPnibh8EuD5cWyxlp5gaSZ9KKDQ8ztknnHHuWu+EAW7I1D0NyMU6H3UgwkzBsO5y4sRmkhe0wUAuPmTTQhYkNOLWkf5NIhGmx+AfSwv+NgJYcM4mDQC3zmYFaGNvAtrDh13LmTZlkY1ta4gZmxmbL3r7DuTMPsxlbzjFIxq3lePo2yZ9tNonz29sf3vjx7XBu3/HmhzfeVNjh1ILQewDGYgZzCakHAnmCho6CUTAKRsGIBQBZYF03FqmrUwAAAABJRU5ErkJggg==","orcid":"","institution":"Chaohu Hospital of Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Huanzhong","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-04-07 02:44:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4229258/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4229258/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12888-024-06224-x","type":"published","date":"2024-11-05T15:57:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":54709804,"identity":"56132a5c-ad39-4ea3-8f6d-7b9b2ebb083a","added_by":"auto","created_at":"2024-04-15 14:25:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":329900,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive Network Model Depicting the Association between Childhood Maltreatment and Internet Addiction in Adolescents with Major Depressive Disorder (MDD).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-4229258/v1/e3251021fb27aac1c80291a0.png"},{"id":54709803,"identity":"877c873e-8c5d-48f9-84c1-0e1a19839e8e","added_by":"auto","created_at":"2024-04-15 14:25:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":90081,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of Centrality and Bridge Indices within the Network Structure Connecting Childhood Maltreatment and Internet Addiction. Note. EA: Emotional abuse, PA: Physical abuse, SA: Sexual abuse, EN: Emotional neglect, PN: Physical neglect, IAT-1: Stay online longer, IAT-2: Neglect chores to spend more time online, IAT-3: Prefer the excitement online, IAT-4: Form new relationship, IAT-5: Others complain about your time, IAT-6: School grades suffer, IAT-7: Academic efficiency declines, IAT-8: Check email/SNS before doing things, IAT-9: Become defensive/secretive Internet use, IAT-10: Sooth disturbing thoughts, IAT-11: Anticipation for future online activities, IAT-12: Fear that is boring and empty without the Internet, IAT-13: Snap or act annoyed if bothered without being online, IAT-14: Sleep loss due to late-night logines, IAT-15: Preoccupation with the Internet, IAT-16: Request an extension for longer time, IAT-17: Failure to cut down the time spend online, IAT-18: Conceal the amount of time spend online, IAT-19: Spend more time online over going out with others,IAT-20: Depress/moody/nervous being offline.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-4229258/v1/3311234d80ca54a67acd7f4f.png"},{"id":54709802,"identity":"a10a37b5-83c8-4940-931c-d73a98ddee73","added_by":"auto","created_at":"2024-04-15 14:25:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":79173,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork robustness and accuracy.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-4229258/v1/b6e079a603b3cff7603e50a1.png"},{"id":68750083,"identity":"abc0a9d3-22e8-4c01-b487-0b3d2b72f0ad","added_by":"auto","created_at":"2024-11-11 16:09:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2520211,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4229258/v1/a3128e91-a6e0-4bd2-9a49-71a7856f2fb8.pdf"},{"id":54709805,"identity":"e7856f64-ad7f-4a35-9bc2-dff118fc491c","added_by":"auto","created_at":"2024-04-15 14:25:03","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2671092,"visible":true,"origin":"","legend":"","description":"","filename":"NAadolescentsIAsupplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-4229258/v1/c6666d5405c519cb0ead59ff.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network Analysis of Childhood Maltreatment and Internet Addiction in Adolescents with Major Depressive Disorder","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAdolescence is a critical period for rapid physical and brain maturation, accompanied by significant hormonal changes and neural restructuring [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. During this phase, adolescents are particularly susceptible to mental disorders, with depression being notably prominent [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and becoming one of the most common mental disorders globally [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The \"Global Burden of Disease Study 2020\" [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] indicated that during adolescence, major depressive disorder (MDD) significantly increases the Disability-Adjusted Life Years (DALY), becoming a major influencing factor. DALYs are a measure that focuses on the impact of diseases, injuries, or premature death on healthy life years. In China, MDD has become one of the leading causes of disability and life loss [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Adolescents with MDD face greater challenges in social and academic areas and are more likely to experience incomplete education (OR 1.76) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], Internet addiction (IA) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], self-harm, and suicidal behavior [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. MDD in adolescents is significantly associated with depression, anxiety, and suicide in later adulthood [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], underscoring the need for greater attention to adolescent mental health.\u003c/p\u003e \u003cp\u003eIA lacks a widely accepted definition and has not been formally recognized as a diagnosis by modern psychiatric classification systems. Its core features include excessive internet use, an inability to control online impulses, and resulting functional impairment [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. According to Young [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], IA is categorized into five subtypes: cyber gaming, communication, information seeking, technical (like online gambling, shopping, or trading), and pornography addiction. IA negatively impacts the brain development of children and adolescents [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], disrupts decision-making and emotional control [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], and leads to anxiety, depression, and behavioral problems [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This dependency can also damage sleep patterns [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], further impacting academic and social abilities [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Risk factors for IA include depressive symptoms, high neuroticism, and poor parent-child relationships [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Childhood maltreatment (CM), such as sexual, physical, and emotional abuse, and neglect, is considered a significant predictor of IA [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Adolescents who experienced these traumas may develop insecure attachments, leading to behavioral disorders like IA [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. From a neurophysiological perspective, CM can disrupt the stress response system in adolescents, altering neural structures and functions, and increasing susceptibility to IA [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, numerous studies have focused on the relationship between CM and IA. For instance, a meta-analysis involving 21,398 adolescents across 19 studies [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] revealed a significant positive correlation between CM and IA (r\u0026thinsp;=\u0026thinsp;0.395). Further analysis [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] showed that emotional neglect (OR\u0026thinsp;=\u0026thinsp;3.062) and physical neglect (OR\u0026thinsp;=\u0026thinsp;2.328) are independently associated with IA. Although regression analysis has been valuable in assessing the relationship between specific predictor and outcome variables, it only examines linear relationships and does not fully capture the interdependence and complex interactions among multiple variables. Recently, Network Analysis (NA) has been touted as a cutting-edge approach in the study of mental disorders [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In NA, nodes represent symptoms, while edges reveal the connections between them. This method not only uncovers the relationships between individual symptoms but also helps identify central and bridge symptoms through network centrality measures, offering a more comprehensive perspective to explore the link between CM and IA. Therefore, this study aims to use network analysis to reveal the close relationships between core symptoms of adolescent depression-related IA and subtypes of CM, providing a foundation for subsequent prevention measures and targeted intervention strategies for IA in adolescents.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and participants\u003c/h2\u003e \u003cp\u003eA cross-sectional study was conducted in four general hospitals (Chaohu, Bengbu, Suzhou, Bozhou) and three psychiatric hospitals (Hefei, Ma\u0026rsquo;anshan, Fuyang) located in Anhui Province, China, between January and July 2021. Adolescent participants, aged 12 to 18, diagnosed with MDD by two psychiatrists employing the DSM-V criteria, were consecutively enlisted from psychiatric outpatients and inpatients in these hospitals. Exclusion criteria comprised individuals with other psychiatric or neurological disorders, and/or intellectual disabilities. Out of 356 invited adolescents, 332 completed the assessment, yielding a robust participation rate of 93.3%. All eligible participants and their guardians provided informed consent after being briefed on the study's objectives and procedures. The study protocol (202009-kyxm-04) was approved by the Medical Ethics Committee of Chaohu Hospital of Anhui Medical University prior to commencement.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurement tools\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 The Childhood Trauma Questionnaire - Short Form (CTQ-SF)\u003c/h2\u003e \u003cp\u003eThe CTQ-SF is a widely used questionnaire designed to assess CM in individuals aged 12 and older [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. It consists of 28 items, with 25 dedicated to evaluating various forms of maltreatment experienced during childhood, such as emotional abuse (EA), physical abuse (PA), sexual abuse (SA), emotional neglect (EN), and physical neglect (PN). The scoring for each item ranges from 1 (\u0026ldquo;never\u0026rdquo;) to 5 (\u0026ldquo;often\u0026rdquo;), and the total score for each subscale is the sum of scores for its five items, with higher scores indicating more severe abuse [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. It is important to note that the scores for 7 items (item 2, 5, 7, 13, 19, 26, and 28) are to be reverse coded. As a result, the total score for each subscale ranges from 5 to 25. In previous research, the Chinese version of the CTQ-SF demonstrated commendable reliability [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Cronbach\u0026rsquo;s alpha in our cohort was 0.801.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Internet Addiction Test (IAT)\u003c/h2\u003e \u003cp\u003eThe evaluation of IA utilized the authenticated Chinese version of the Internet Addiction Test (IAT), as reported by Chin and Leung [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and Lai et al. [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This instrument consists of 20 questions and employs a 5-point Likert scale for respondents to assess the frequency of their internet-related symptoms, ranging from 1 (indicating very rarely) to 5 (indicating very often). The cumulative score from this test serves as an indicator of the severity of an individual's internet addiction, with higher scores reflecting more severe addiction levels. Additionally, the Cronbach's alpha coefficient for the Chinese version of the IAT was found to be 0.93, illustrating its high reliability and precision in assessing internet addiction [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Network estimation\u003c/h2\u003e \u003cp\u003eData analysis in this study was performed using the R language within the RStudio environment, version 4.3.2 [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Network construction was accomplished through the EBICglasso function and Spearman correlation, creating a sparse Gaussian graphical model. This method, guided by the Extended Bayesian Information Criterion (EBIC), streamlined the network structure and enhanced interpretability [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The analysis primarily focused on strength and the expected influence (EI) index, due to their suitability for psychopathological networks [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Bridge Strength and the bridge expected influence (BEI) index were computed to identify bridge symptoms, utilizing the bridge function in the networktools R package [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Other centrality metrics such as closeness and betweenness were not used due to their limited effectiveness in revealing psychological variables [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGraphical representation of the network was generated using the qgraph R package (version 1.9.8) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], where nodes (circles) were connected by edges (lines), with the thickness of the edges indicating interaction strength [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Positive correlations were depicted in blue, while negative correlations were shown in red. The Fruchterman-Reingold force-directed algorithm was used to cluster nodes with strong associations and place those with weaker associations on the periphery [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The R\u003csup\u003e2\u003c/sup\u003e predictability index, calculated using the mgm R package (1.2\u0026ndash;14) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], was visually represented by the size of a semi-circular area around each node, quantifying the variance each node explained in relation to others in the network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Estimation of network accuracy and stability\u003c/h2\u003e \u003cp\u003eThe bootnet R package (version 1.5.6) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] was used to conduct 1000 bootstrap iterations to assess the accuracy and stability of network edges, with edge precision evaluated by examining the 95% confidence interval of bootstrap edge weights; narrower intervals indicated higher accuracy. The robustness of centrality measures was further assessed by comparing centrality indices from the complete sample with those from a 70% reduced sample. The Centrality Stability coefficient (CS coefficient) was calculated to evaluate network robustness, with a value of \u0026ge;\u0026thinsp;0.5 indicating high reliability, 0.25 to 0.5 denoting moderate reliability, and \u0026lt;\u0026thinsp;0.25 indicating less robustness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Comparisons of network characteristics by gender\u003c/h2\u003e \u003cp\u003eWe performed gender-based network comparisons using the NetworkComparisonTest package (version 2.2.2) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Effect sizes were determined using Bootstrap-based Spearman correlation, and the average correlation coefficients were reported from 1000 resampled associations. By comparing two networks and visualizing the differences in a corPlot diagram, we effectively identified key distinctions.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003e3.1 Network structure and centrality measures analysis\u003c/h2\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the network structure of CM-IA. Out of 300 edges in this network, 131 edges (43.7%) have non-zero weights, indicating a dense interconnectivity between CM and IA. In terms of node predictability, on average, 43.0% of the variance can be explained by neighboring nodes. The node \"Preoccupation with the Internet\" (IAT-15) has the highest predictability in the network, reaching 61.5%. This is closely followed by \"Depress/moody/nervous being offline \" (IAT-20) and \"Request an extension for longer time \" (IAT-16), with predictabilities of 61.3% and 58.9%, respectively. The predictability of all other nodes in the network is detailed in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eDescriptive statistics of measurement items.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eItem abbreviation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eItem content\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMean (SD)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStrength \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eEI \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePredictability \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTQ-SF\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmotional abuse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e11.20 (4.66)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.028\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.428\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical abuse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e7.37 (3.19)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.560\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.560\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.284\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSexual abuse\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e5.81 (2.17)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.358\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.358\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.12\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEmotional neglect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e15.60 (5.12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.746\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.432\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePhysical neglect\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e10.55 (3.34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.632\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.632\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.388\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStay online longer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.06 (1.36)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.672\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.672\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.378\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeglect chores to spend more time online\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.74 (1.31)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.966\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.492\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrefer the excitement online\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.90 (1.51)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.502\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eForm new relationship\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.05 (1.22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.421\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.421\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.249\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOthers complain about your time\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.44 (1.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.980\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.980\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.414\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSchool grades suffer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.46 (1.28)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.889\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.889\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.455\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAcademic efficiency declines\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.11 (1.27)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.650\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.650\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.306\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCheck email/SNS before doing things\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.94 (1.07)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.799\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.799\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.383\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBecome defensive/secretive Internet use\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.65 (1.43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.456\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.456\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.166\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSooth disturbing thoughts\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e3.23 (1.46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.908\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.908\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.403\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAnticipation for future online activities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.52 (1.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.563\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFear that is boring and empty without the Internet\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.81 (1.37)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.889\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.889\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.483\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSnap or act annoyed if bothered without being online\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.35 (1.20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.928\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.928\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.547\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSleep loss due to late-night logines\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.54 (1.39)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.069\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.567\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePreoccupation with the Internet\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.63 (1.30)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.027\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.615\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRequest an extension for longer time\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.59 (1.41)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.589\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFailure to cut down the time spend online\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.21 (1.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.484\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eConceal the amount of time spend online\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.10 (1.32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.699\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.699\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.357\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpend more time online over going out with others\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.51 (1.48)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.850\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.850\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.521\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIAT-20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDepress/moody/nervous being offline.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e2.12 (1.33)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.139\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.139\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.613\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eNote: \u003csup\u003ea\u003c/sup\u003e The values of Strength and EI (Expected Influence) were raw data generated from the network; \u003csup\u003e\u003cstrong\u003eb\u003c/strong\u003e\u003c/sup\u003e These relationships align with the predictability measures obtained by R\u003csup\u003e2\u003c/sup\u003e, which are displayed as a bar on the edge of the node\u0026rsquo;s circle.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the CM community, the connection between the nodes EN \"Emotional Neglect\" and PN \"Physical Neglect\" is the most direct and strong, with a weight of 0.398. The node EA \"Emotional Abuse\" also has a significant direct connection with PA \"Physical Abuse\", with a weight of 0.376. Additionally, there is a notable link between EA \"Emotional Abuse\" and EN \"Emotional Neglect\", with a weight of 0.252. In terms of IA symptoms, the nodes IAT-3 \"Prefer the excitement online\" and IAT-19 \"Spend more time online over going out with others\" are the most closely connected, with a weight of 0.464, representing the tightest bond in the entire network. Following closely, IAT-1 \"Stay online longer\" and IAT-2 \"Neglect chores to spend more time online\" have a close relationship with a weight of 0.365; and IAT-16 \"Request an extension for longer time\" and IAT-17 \"Failure to cut down the time spend online\" are also closely linked, with a weight of 0.284. Notably, the differences in these edge weights are statistically significant (refer to Figure \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e). Detailed information about the weights of other edges in the CM-IA network can be found in Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e\n\u003cp\u003eAs depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, within the CM-IA network, IAT-20 \"Depress/moody/nervous being offline\" emerges as the node with the highest centrality, followed by IAT-16 \"Request an extension for longer time\", IAT-14 \" Sleep loss due to late-night logines\", and EA \"Emotional Abuse\". Additionally, SA \"Sexual Abuse\" holds the highest bridging value, succeeded by IAT-5 \"Others complain about your time\" and EA \"Emotional Abuse\". Specifically, the strongest link is observed between EA \"Emotional Abuse\" and IAT-5 \" Others complain about your time\". The centrality and bridge symptoms for all nodes are accessible in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA illustrates satisfactory accuracy, evidenced by the convergence of the black and red lines. Furthermore, the narrow gray band indicates minimal variability during the resampling process. Regarding the stability of the network analysis, the centrality and bridging metrics exhibit excellent stability levels with CS\u0026thinsp;=\u0026thinsp;0.75 and CS\u0026thinsp;=\u0026thinsp;0.675, respectively, as shown in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003e3.2 Gender differences\u003c/h2\u003e\n\u003cp\u003eFigure S4 presents the network structures generated for both genders. When comparing the networks generated for females and males, no significant differences were found in terms of global network strength (females: 10.045 vs males: 9.473; S\u0026thinsp;=\u0026thinsp;0.573, p\u0026thinsp;=\u0026thinsp;0.713) or the distribution of edge weights in the network structure (M\u0026thinsp;=\u0026thinsp;0.277, p\u0026thinsp;=\u0026thinsp;0.119).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis article employs network analysis to explore the intricate relationship between CM and IA. The study reveals a dense network of connections between CM and IA, with 43.7% of the edges showing non-zero weights, indicating a close interaction between the two. This dense interconnectivity highlights the complex and multifaceted interactions between various aspects of CM and the symptoms of IA.\u003c/p\u003e \u003cp\u003eThe CM-IA network identifies core symptoms including \"Depress/moody/nervous being offline\" (IAT-20), \"Request an extension for longer time\" (IAT-16), \"Sleep loss due to late-night logines\" (IAT-14), and \" Preoccupation with the Internet\" (IAT-15). These findings elaborate on the definition of IA as a behavioral disorder where individuals develop an excessive reliance on and lack of control over internet use, leading to significant impairment in psychological, social, academic, or occupational functioning [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Additionally, our results corroborate previous network analysis findings on IA among Chinese adolescents [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Notably, when unable to access the internet, adolescents may experience negative emotional reactions such as depression, anxiety, or irritability [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], indicating that the internet can serve not only as an escape from reality but also as a means to manage or regulate emotions in some contexts [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. While such emotional regulation can be beneficial to some extent, excessive and compulsive internet use can lead to delayed sleep and disrupted sleep cycles, worsening the individual\u0026rsquo;s real-life situation. Our study also highlights \"Emotional Abuse\" (EA) as another central symptom in the CM-IA model. Emotional abuse can have long-lasting effects on an individual\u0026rsquo;s ability to regulate emotions and interpersonal relationships [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], undermine their basic sense of safety and trust [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], and increase their tendency to escape reality and seek emotional fulfillment later in life [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. This drives individuals to use the internet as a coping mechanism to avoid the hardships and emotional pain of real life [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn our composite model, we identified three key bridge symptoms that link IA with CM: Sexual Abuse (SA), Emotional Abuse (EA), and \"Others complain about your time\" (IAT-5). Notably, there is a significant connection between \"Emotional Abuse\" (EA) and \"Others complain about your time\" (IAT-5). Consistent with the findings of Taş [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e], we observed a correlation between emotional abuse and IA. Furthermore, a study in South Korea by Kim et al. [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] indicated a positive correlation between sexual abuse and IA (β\u0026thinsp;=\u0026thinsp;0.20). As covert forms of CM, sexual and emotional abuse are often difficult to detect and can persist over time, leading to a relatively high incidence rate [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The challenges in identifying, defining, and legally substantiating emotional abuse increase the risk of children remaining in harmful environments [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. These forms of abuse have a lasting negative impact on children's psychological development, manifesting in heightened levels of depression, anxiety, stress, and neuroticism [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. Particularly, sexual abuse profoundly affects children's psychological state and can severely damage their self-esteem and identity [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Due to these psychological impacts, such as self-loathing, intense dislike for one's body and emotions, and resultant social isolation, victims may seek solace and escape in the virtual world. The relative anonymity and control afforded by the internet provide these children with a means to distance themselves from the painful experiences of real life. In this space, children and adolescents may find the support, understanding, and acceptance they lack, temporarily alleviating their inner turmoil. However, prolonged internet use can evolve into a mechanism for escaping reality, ultimately leading to excessive dependence on the internet and the development of IA.\u003c/p\u003e \u003cp\u003eTo address the link between CM and IA, comprehensive intervention measures should include cognitive-behavioral therapy to improve emotional regulation and coping strategies, particularly for individuals experiencing \"Depress/moody/nervous being offline\" (IAT-20). Furthermore, for those affected by \"Emotional Abuse\" (EA) and \"Sexual Abuse\" (SA), trauma-focused therapy and trauma-informed care should be provided to help them process past traumatic experiences. Establishing supportive social networks, enhancing family communication quality, and educating individuals on recognizing and expressing emotions are also crucial strategies for reducing internet dependency and preventing addictive behaviors. This multifaceted intervention approach aims to fundamentally address the issue of IA while tackling the underlying emotional and psychological factors.\u003c/p\u003e \u003cp\u003eThe limitations of this study warrant attention. Firstly, the observed network structure might be influenced by the specific survey tools used, and different assessment methods could yield varying results. Secondly, despite utilizing self-report tools with high reliability and validity, participant responses could be compromised by recall bias, potentially affecting the objectivity of the findings. Additionally, the study's cross-sectional design does not offer a longitudinal perspective. Future research should expand the sample size and collect data across multiple dimensions to thoroughly analyze the relationship between childhood abuse experiences and internet addiction (IA) in adolescents with varying levels of depression. This will provide a foundation for a more comprehensive understanding and effective prevention of internet addiction.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our heartfelt gratitude to the hospital administrators for their invaluable assistance in enabling this survey, and we extend our thanks to the participants for their unwavering collaboration throughout this research study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol (202009-kyxm-04) was approved by the Medical Ethics Committee of Chaohu Hospital of Anhui Medical University prior to commencement. All eligible participants and their guardians provided informed consent after being briefed on the study\u0026apos;s objectives and procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were given participant information prior to starting the survey and gave informed consent for the publication of the study\u0026apos;s results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are not publicly available due to potential privacy concerns or academic implications, but can be obtained from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multimodal integrated study of the pathogenesis and clinical intervention of adolescent depression (2022zhyx-B01).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuanzhong Liu and Lei Xia were responsible for the study\u0026apos;s design. Song Wang and Feng Geng conducted a comprehensive literature review. Mengyue Gu, Yudong Shi, and Jingyang Gu collected the data. Song Wang, Feng Geng, Mengyue Gu, Jingyang Gu, and Yudong Shi performed the data analysis and were responsible for data interpretation. Song Wang wrote the first daft of the paper. Huanzhong Liu critically revised the manuscript. All authors have reviewed and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBlakemore SJ, Burnett S, Dahl RE: \u003cstrong\u003eThe role of puberty in the developing adolescent brain\u003c/strong\u003e. \u003cem\u003eHuman brain mapping \u003c/em\u003e2010, \u003cstrong\u003e31\u003c/strong\u003e(6):926-933.\u003c/li\u003e\n\u003cli\u003eGoddings AL, Beltz A, Peper JS, Crone EA, Braams BR: \u003cstrong\u003eUnderstanding the role of puberty in structural and functional development of the adolescent brain\u003c/strong\u003e. \u003cem\u003eJournal of Research on Adolescence \u003c/em\u003e2019, \u003cstrong\u003e29\u003c/strong\u003e(1):32-53.\u003c/li\u003e\n\u003cli\u003eMerikangas KR, He J-p, Burstein M, Swanson SA, Avenevoli S, Cui L, Benjet C, Georgiades K, Swendsen J: \u003cstrong\u003eLifetime prevalence of mental disorders in US adolescents: results from the National Comorbidity Survey Replication\u0026ndash;Adolescent Supplement (NCS-A)\u003c/strong\u003e. \u003cem\u003eJournal of the American Academy of Child \u0026amp; Adolescent Psychiatry \u003c/em\u003e2010, \u003cstrong\u003e49\u003c/strong\u003e(10):980-989.\u003c/li\u003e\n\u003cli\u003e\u003cstrong\u003eAdolescent mental health \u003c/strong\u003e[https://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health]\u003c/li\u003e\n\u003cli\u003eVos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, Abbasi-Kangevari M, Abbastabar H, Abd-Allah F, Abdelalim A: \u003cstrong\u003eGlobal burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019\u003c/strong\u003e. \u003cem\u003eThe lancet \u003c/em\u003e2020, \u003cstrong\u003e396\u003c/strong\u003e(10258):1204-1222.\u003c/li\u003e\n\u003cli\u003eLu J, Xu X, Huang Y, Li T, Ma C, Xu G, Yin H, Xu X, Ma Y, Wang L: \u003cstrong\u003ePrevalence of depressive disorders and treatment in China: a cross-sectional epidemiological study\u003c/strong\u003e. \u003cem\u003eThe Lancet Psychiatry \u003c/em\u003e2021, \u003cstrong\u003e8\u003c/strong\u003e(11):981-990.\u003c/li\u003e\n\u003cli\u003eClayborne ZM, Varin M, Colman I: \u003cstrong\u003eSystematic Review and Meta-Analysis: Adolescent Depression and Long-Term Psychosocial Outcomes\u003c/strong\u003e. \u003cem\u003eJournal of the American Academy of Child and Adolescent Psychiatry \u003c/em\u003e2019, \u003cstrong\u003e58\u003c/strong\u003e(1):72-79.\u003c/li\u003e\n\u003cli\u003eLeo K, Kewitz S, Wartberg L, Lindenberg K: \u003cstrong\u003eDepression and social anxiety predict internet use disorder symptoms in children and adolescents at 12-month follow-up: Results from a longitudinal study\u003c/strong\u003e. \u003cem\u003eFrontiers in psychology \u003c/em\u003e2021, \u003cstrong\u003e12\u003c/strong\u003e:787162.\u003c/li\u003e\n\u003cli\u003eMullen S: \u003cstrong\u003eMajor depressive disorder in children and adolescents\u003c/strong\u003e. \u003cem\u003eMental Health Clinician \u003c/em\u003e2018, \u003cstrong\u003e8\u003c/strong\u003e(6):275-283.\u003c/li\u003e\n\u003cli\u003eJohnson D, Dupuis G, Piche J, Clayborne Z, Colman I: \u003cstrong\u003eAdult mental health outcomes of adolescent depression: A systematic review\u003c/strong\u003e. \u003cem\u003eDepression and anxiety \u003c/em\u003e2018, \u003cstrong\u003e35\u003c/strong\u003e(8):700-716.\u003c/li\u003e\n\u003cli\u003eWeinstein A, Feder LC, Rosenberg KP, Dannon P: \u003cstrong\u003eInternet addiction disorder: Overview and controversies\u003c/strong\u003e. \u003cem\u003eBehavioral addictions \u003c/em\u003e2014:99-117.\u003c/li\u003e\n\u003cli\u003eYoung KS: \u003cstrong\u003ePsychology of computer use: XL. 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Adolescent Trauma \u003c/em\u003e2020, \u003cstrong\u003e13\u003c/strong\u003e(4):399-407.\u003c/li\u003e\n\u003cli\u003eFergusson DM, Mullen PE: \u003cstrong\u003eChildhood sexual abuse\u003c/strong\u003e, vol. 40: Sage; 1999.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Internet addition, childhood maltreatment, Major Depressive Disorder, adolescents, network analysis","lastPublishedDoi":"10.21203/rs.3.rs-4229258/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4229258/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIn this study, network analysis was used to explore the relationship between childhood maltreatment (CM) and Internet Addiction (IA) in adolescents with Major Depressive Disorder (MDD).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eConducted across seven hospitals in Anhui Province, China, involving 332 adolescents, it employs the Childhood Trauma Questionnaire - Short Form (CTQ-SF) and the Internet Addiction Test (IAT) to measure CM and the symptoms of IA, respectively.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUsing network analysis, the CM-IA network were constructed to identify the most central symptoms and the bridge symptoms within the networks. \"Depress/moody/nervous being offline\", \" Request an extension for longer time\", \"Sleep loss due to late-night logines\", and \" emotional abuse \" were identified as the central symptoms of CM-IA network analysis. Bridge symptoms, notably \"emotional abuse\", \"sexual abuse\", and \"complaints about online time\", were significant in linking CM and IA.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese results underscore the complex relationship between childhood trauma and IA, emphasizing the role of specific symptoms in understanding and addressing internet addiction in adolescents.\u003c/p\u003e","manuscriptTitle":"Network Analysis of Childhood Maltreatment and Internet Addiction in Adolescents with Major Depressive Disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-15 14:24:58","doi":"10.21203/rs.3.rs-4229258/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-26T10:35:47+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-26T05:01:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-25T12:14:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149064671887556896590069774291669300258","date":"2024-09-21T11:06:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41949396248010645553179369050603123744","date":"2024-09-21T07:27:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-10T13:57:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"48794560195453474101634283544155828735","date":"2024-08-10T12:35:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160966535945979486001393895162615954989","date":"2024-08-07T15:00:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-06-02T20:05:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"245225192300736329189174453797419311773","date":"2024-05-26T20:02:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-21T20:37:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-04-13T12:43:13+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-04-10T07:19:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-04-10T07:18:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychiatry","date":"2024-04-07T02:42:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychiatry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bpsy","sideBox":"Learn more about [BMC Psychiatry](http://bmcpsychiatry.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bpsy/default.aspx","title":"BMC Psychiatry","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6eec2672-88da-49d6-abc5-d45ee2f72fb4","owner":[],"postedDate":"April 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-11-11T16:04:20+00:00","versionOfRecord":{"articleIdentity":"rs-4229258","link":"https://doi.org/10.1186/s12888-024-06224-x","journal":{"identity":"bmc-psychiatry","isVorOnly":false,"title":"BMC Psychiatry"},"publishedOn":"2024-11-05 15:57:19","publishedOnDateReadable":"November 5th, 2024"},"versionCreatedAt":"2024-04-15 14:24:58","video":"","vorDoi":"10.1186/s12888-024-06224-x","vorDoiUrl":"https://doi.org/10.1186/s12888-024-06224-x","workflowStages":[]},"version":"v1","identity":"rs-4229258","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4229258","identity":"rs-4229258","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}