Stress, Loneliness, Depression, Anxiety and Problematic Smartphone Use Among a Sample of Syrian Refugee Adolescents: A Network Approach

Stress, Loneliness, Depression, Anxiety and Problematic Smartphone Use Among a Sample of Syrian Refugee Adolescents: A Network Approach | 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 Research Article Stress, Loneliness, Depression, Anxiety and Problematic Smartphone Use Among a Sample of Syrian Refugee Adolescents: A Network Approach Onat Yetim, Lut Tamam, Ayşegül Efe, İlham Sebea Alleil, Resul Çakır This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5857108/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 Previous studies have demonstrated the existence of complex relationships between stress, loneliness, depression, anxiety, and smartphone addiction in adolescents. However, the paucity of studies evaluating the relevant relationships in migrant adolescents necessitates the elimination of uncertainty in a sample of adolescents exposed to trauma and chronic stressors. Method This study capitalizes on network analysis to identify the central factors and possible bridging paths among these variables. Employing 836 Syrian refugee adolescents, we obtained a stable network of the above variables. The central components and the stability of this network were also identified. Results Within this network, generalized anxiety disorder and panic disorder were the most central nodes, making them the most influential nodes in the development of the network. Stress stands out as the node with the highest connectivity. Conclusion In our study, stress paves the way to smartphone addiction in addition to its significant relationship with psychopathologies. These findings provide a further understanding of the specific roles of stress and related psychopathologies among Syrian refugee adolescents. The identified nodes may be promising targets for prevention and intervention. Syrian refugee adolescent stress loneliness depression anxiety problematic smartphone use Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction As a result of the ongoing civil war in Syria for over a decade, millions of Syrians have been forced to leave their homes. Turkey has emerged as the most important destination for Syrians who have been forced to flee their homeland following the war [ 1 ]. In this context, Turkey is hosting 3,648,983 Syrian refugees [ 2 ]. Additionally, many Syrian refugees worldwide and in Turkey consist of children and adolescents [ 3 , 4 ]. Although settling in a different country affects the entire family, children and adolescents tend to attach more emotions and significance to this experience than adults, and they are more greatly impacted by it [ 5 , 6 ]. Adolescents who are forced to migrate may experience stressors related to relocation [ 7 ], acculturation [ 8 ], and transitioning to a new school environment [ 9 ]. In addition to the stressful experiences they faced in their own countries and the everyday stress of adolescence, factors such as family traumas in the host country, differences in the education system, and language barriers contribute to significant challenges and increased vulnerability for refugees after resettlement [ 10 , 11 , 12 ]. Al-Shatanawi et al. [ 13 ] state that social isolation and loneliness are among the observed primary psychiatric disorders in Syrian refugee adolescents. Many researchers in the literature state that loneliness impacts adolescents' development and well-being [ 14 , 15 ]. For example, it is well known that there is a negative relationship between social support and internalizing symptoms such as depression and anxiety during adolescence [ 16 , 17 ]. Many researchers in the literature, both in Turkey and in different countries, have conducted studies on Syrian refugee adolescents and have noted that at least half of these adolescents exhibit symptoms of anxiety [ 18 , 19 , 20 , 21 ]. Similarly, studies comparing refugee adolescent samples with local samples have found that refugee adolescents have higher rates of anxiety and depression compared to the local sample [ 22 , 23 , 24 , 25 , 26 ]. Several studies revealed that smartphone usage has increased among young people, especially adolescents [ 27 , 28 ]. For this reason, they are assumed to be an at-risk group for demonstrating problematic behaviors about internet addiction [ 29 ] and problematic smartphone usage [ 30 ]. This is probably because they are attempting to solve social and psychological problems they face in this period, such as identity, gender, and individualization [ 31 ]. Problematic smartphone use (PSU) is defined in the scientific literature as the excessive frequency of smartphone use with impairment in academic, occupational, and/or social functioning PSU is a complex phenomenon comprising diverse dysfunctional manifestations [ 29 ]. For instance, loneliness [ 32 ], a low level of self-regulation [ 33 ], anxiety [ 34 ], stress, and depression [ 35 ] were found to be the sources of smartphone addiction for adolescents. Loneliness and PSU Loneliness refers to the distress experienced when there are inconsistencies between expected personal interactions and actual social relationships [ 34 ]. A lack of peer support contributes to increased smartphone usage [ 36 , 37 ]. Individuals who feel lonely frequently use smartphones to counteract loneliness [ 38 ]. Previous studies of loneliness and smartphone use have reported a positive correlation between loneliness and smartphone addiction [ 37 , 39 ], and loneliness has been reported to be an important antecedent of problematic smartphone use and smartphone addiction [ 33 ]. Loneliness in youths seems to be closely related to problematic smartphone use, as well as depression and anxiety [ 29 , 34 ]. Vulnerable youths seem to use their smartphones to distract themselves from a negative mood caused by loneliness and social isolation and are predisposed by depression, anxiety, and vulnerability to stress [ 40 ]. Social cognitions (e.g., loneliness) alongside psychopathology (e.g., depression, anxiety) and vulnerability to stress contribute toward a person's specific cognitions and expectations about smartphone use and reinforce the use of the smartphone as dysfunctional coping [ 41 ]. This reinforcement can result in a conditioned relationship that makes it difficult for the individual to cognitively control smartphone use. Stress and PSU Among various factors that predict smartphone addiction, stress is one of the significant factors that is related to problematic smartphone usage and smartphone addiction [ 27 , 42 ]. Adolescents' daily stress (parent-related stress, home environment stress, and academic stress) was significantly associated with smartphone addiction [ 43 , 44 ]. Adolescents with high stress levels tend to excessively use smartphones to escape from life problems and alleviate stress [ 45 , 46 ]. Individuals' perceived stress affects their cognitive processes, and they may use the Internet to cope with the effects of stress on cognitive processing [ 47 ]. Perceived stress refers to the degree to which an individual perceives an external event as stress. Whether the objective stress affects the individual depends on the individual interpretation and perception of the stress event [ 48 ]. Abundant studies have shown that perceived stress is positively correlated with depression and anxiety [ 49 , 50 ]. Stress is one of the most critical risks leading to mental health problems. Jun and Choi [ 51 ] found that depression and anxiety play a mediating role in the link between academic stress and internet addiction. Relevant studies have shown that mood regulation (defined as reducing negative emotions such as stress, anxiety, and depression) could reduce the occurrence of smartphone addiction among youths [ 50 , 52 ]. Emotional self-regulation is an essential determinant of problematic smartphone use among adolescents [ 53 ]. Anxiety, Depression and PSU Mounting evidence points to a positive correlation between excessive use of smartphones and depression and anxiety [ 40 , 41 ]. Recent studies find PSU severity mildly to moderately correlated with anxiety and depression severity in participants from different countries [ 54 , 55 , 56 ]. Anxiety and depression scores emerged as independent positive predictors of smartphone addiction [ 57 ]. Depression and anxiety make people more vulnerable to PSU as a kind of maladaptive coping which may, in turn, exacerbate existing mental health problems [ 50 ]. The duration of time spent on social media is predictive of scoring higher than the clinical benchmark for anxiety-related disorders in youths [ 58 ]. Generally, individuals with higher baseline anxiety tend to be more drawn to using social media and are also more likely to experience elevated levels of anxiety afterward [ 58 , 59 ]. Individuals who spent more time on social media daily were shown to be more predisposed to anxiety symptoms and developing anxiety-related disorders [ 50 , 60 ]. Adolescents' stress is associated with the experience of anxiety, which leads to a higher frequency of smartphone use and, subsequently, more experiences of problematic smartphone use. This suggests that turning to the smartphone to relieve anxiety may be a maladaptive coping mechanism - since it leads them to experience more problematic use [ 61 ]. Studies showed that there is a significant positive relationship between depression and smartphone addiction and also indicated that depression can predict and account for smartphone addiction among students [ 55 , 57 ]. Adolescents may utilize smartphones as a coping mechanism to mitigate their depressive symptoms and alleviate boredom or frustration [ 62 ]. Using devices provides them with fun and reduces distress so that they can shift their attention from other problems in life [ 27 ]. Such behavior may temporarily help adolescents feel relieved and provide them with an escape from their problems. However, it is not beneficial in the long term, as the problems remain unsolved. Network Relationship Between Loneliness, Stress, Anxiety, Depression and PSU The network analysis quantifies and visualizes complex interactions to elucidate the fine-grained relationships among variables [ 63 ]. The network theory of psychopathology suggests that psychopathological phenomena should be conceptualized as more complex and dynamic systems composed of interacting factors and operating as a network [ 63 , 64 ]. From a network theory perspective, mental disorders emerge from active interactions between symptoms or non-symptoms rather than just passive reflections of latent variables [ 63 , 64 ]. Therefore, researchers have developed a network analysis method to determine the functional roles and importance of specific symptoms in maintaining disorders [ 64 , 65 ], e.g., to determine the core symptoms in disordered networks, which will benefit the treatment and intervention of disorders [ 66 , 67 , 68 ]. According to previous studies [ 64 , 69 ], the dimensions of psychopathological constructs are represented as nodes, and the interactions between different dimensions are depicted as edges using network analysis. The network approach [ 65 ] can be regarded as a method that depicts a network graph of interconnected nodes with edges, whose thickness represents the intensity of the connection. Therefore, the network can tell the relative position of one node to all other nodes, namely, the most core or central node in the whole network [ 65 , 67 , 70 ]. Several centrality indexes (e.g., expected influence, strength) can help determine the core position of nodes. Specifically, nodes with high centrality are referred to as central symptoms, and nodes connecting core variables are referred to as bridging symptoms that are critical to maintaining the co-occurrence of variables and transmitting the influence of one variable on another [ 66 , 71 ]. Previous studies explored the network structure of PSU in various samples [ 34 , 68 , 72 ]. Although these studies explored the PSU network and related variables, the dimension-level network of loneliness, stress, anxiety, depression, and PSU has not been studied in the refugee adolescent population. The Current Study Based on the above literature review, this study aimed to construct a network model to investigate the relationship between loneliness, anxiety, depression, stress, and PSU with a sample of 836 Syrian refugee adolescents. Specifically, the research objectives were as follows: (1) exploring the network structure of these five variables and their centrality components, (2) identifying the critical bridge nodes that facilitate the transmission of loneliness or stress or internalizing symptoms on PSU, and (3) determining the stability of this network. Method Participants Participants included 836 Syrian adolescents whose ages ranged between 12 and 18, with a mean age of 14.20 years (SD = 2.07). Of the total participants, 469 were female, and 367 were male. The data collection process was conducted across three different cities in Turkey: Hatay, Kahramanmaraş, and Mersin. Procedures An exploratory quantitative research design guided the cross-sectional sampling method to recruit adolescents from diverse non-governmental organizations in Turkey. The selection of the research design was based on the strengths of the established quantitative methods, allowing for flexible adoption of the method. Relying on convenience sampling, non-governmental organizations (NGOs) founded by Syrians in these provinces were approached and invited to participate in the research. Written informed consent was obtained from a parent or legal guardian, followed by written assent of the participant if they were younger than 18. Written consent was obtained from the participants at least 18 years of age. Students who completed the consent form knew how to read and write in their preferred language (Turkish or Arabic) and volunteered to participate in the study were included in the data collection process. As a result, the participation rate in the research was calculated as 86%. Considering the recommendations of Bryman and Cramer [ 73 ] and Tabachnick et al. [ 74 ] to calculate the sample size according to the number of scale items, 917 adolescents who agreed to participate in the study were included. After invalid data was checked, the number of participants decreased to 836. Notably, a researcher fluent in Arabic and Turkish facilitated the data collection process for adolescents. Turkish and Arabic scales were applied to the participants in the data collection process according to adolescents' preferred language. The present study complied with the regulations stipulated by the University Ethics Committee, and data collection was performed between February and April 2024. Measurements Screen for Child Anxiety Related Disorders Screen for Child Anxiety Related Disorders (SCARED) was used in the current study to assess anxiety symptoms. The SCARED was developed by Birmaher et al. [ 75 ] to evaluate anxiety disorder symptoms in children and adolescents as well as for screening purposes. The SCARED scale consists of 41 items (e.g., I worry about how well I am doing things) that are scored on a 3-point Likert scale (0 = Not true, 2 = Very true or often true) and includes five subscales measuring panic disorder, somatic disorder, generalized anxiety disorder, separation anxiety, and social anxiety. The total score ranges from 0 to 82, with higher scores indicating higher levels of the corresponding trait. However, a score of 25 or higher on the SCARED indicates a warning for anxiety disorders. The scale was adapted to Turkish culture by Cakmakci [ 76 ]. In this study, Cronbach's alpha internal consistency coefficient of the scale was reported to be between 0.74 and.93. The scale was adapted to Arabic culture by Hariz et al. [ 77 ]. In this study, Cronbach's alpha internal consistency coefficient of the scale was reported to be between 0.65 and 0.89. In Birmaher et al.'s [ 74 ] study, Cronbach's alpha reliability coefficient for the scale and subscales ranged from 0.74 to 0.93, and the test-retest reliability coefficient ranged from 0.70 to 0.90. In the current study, Cronbach's alpha reliability coefficient for SCARED was calculated as 0.87 for the Turkish, 0.86 for the Arabic form of the scale, and 0.86 for the whole. Kutcher Adolescent Depression Scale The Kutcher Adolescent Depression Scale-11 (KADS-11) is an eleven-item self-report instrument, which was constructed based on core symptoms of depression that measure the frequency of depressive symptoms [ 78 ]. The six-item KADS is a 6-item depression screening scale derived from KADS-11, developed to determine the risk of depression for young people aged 12–22 years [ 78 ]. Item examples are "Feelings of worthlessness, hopelessness, letting people down, not being a good person" and "Feeling tired, feeling fatigued, low in energy, hard to get motivated, have to push to get things done, want to rest or lie down a lot ."Every item is scored from 0–3, where zero is "Hardly Ever" and three is "All of the time." Values range from 0 to 18, with higher scores indicating high depression levels. The scale has one factor and no subscales. The original English form of the KADS-6 obtained an internal consistency Cronbach's alpha of 0.80 [ 78 ]. In a study with Turkish adolescents, the internal consistency coefficient (Cronbach's alpha) was reported to be 0.83 [ 79 ]. In another study with Arab adolescents, the internal consistency coefficient (Cronbach's alpha) was reported to be 0.83 [ 80 ]. In the current study, Cronbach's alpha reliability coefficient for KADS-6 was calculated as 0.83 for the Turkish, 0.80 for the Arabic form of the scale, and 0.81 for the whole. Ucla Lonileness Scale The original scale included 20 statements, while the Ucla Lonileness Scale-6 (ULS-6) consists of a subset of six items [ 81 ]. The ULS-6 has been used chiefly with adolescents (Neto, 1992) and college students [ 82 ]. Six items of the UCLA-R constitute the ULS-6. Five are formulated negatively, and one in a positive way [ 81 ]. Sample items for the ULS-6 include: ''I feel isolated from others'' and ''People are around me but not with me''. The participants answered the items on a 4-point Likert scale ranging from (1 = never to 4 = often). Responses are summed over the six items after reversing the score on the positive item to produce a ULS-6 total score, which ranges from 0 to 24, with higher scores indicating higher loneliness. The scale has one factor and no subscales. Cronbach's alpha internal consistency coefficient of the Arabic form of the scale was reported to be between 0.76 and 0.78 [ 83 , 84 ]. The Cronbach's alpha test for the Turkish form of the scale suggested acceptable reliability, and Cronbach's alpha was reported to be 0.77 [ 85 ]. In the current study, Cronbach's alpha reliability coefficient for ULS-6 was calculated as 0.82 for the Turkish, 0.79 for the Arabic form of the scale, and 0.80 for the whole. Perceived Stress Scale Perceived Stress Scale (PSS) was developed by Cohen et al. [ 48 ] to measure the extent to which situations in one's life are appraised as stressful. Several alternate PSS versions exist, varying in the number of items used to describe perceived stress. The three versions of the PSS are the PSS-14 items, PSS-10 items, and the PSS-4 items [ 86 ]. The original English version of the PSS-4 had one factorial structure, whereas the PSS-10 and PSS-14 had two factors [ 86 ]. The two factors were labeled as perceived self-efficacy and perceived helplessness. The PSS-10 demonstrated the best psychometric evidence compared to PSS-14 and PSS-4 [ 87 ]. Sample items for the PSS-10 include: ''In the last month, how often have you felt that you were unable to control the important things in your life''. Each item is rated on a 5-point Likert-type scale with response options of 0 (never) to 4 (very often). Responses are summed over the ten items after reversing the scores on four positive items to produce a PSS-10 total score, which ranges from 0 to 40, with higher scores indicating higher perceived stress. The internal consistency reliability coefficient (Cronbach's alpha) of 0.84 was reported for a sample of Turkish college students [ 88 ]. In another study conducted with Turkish students [ 87 ], Cronbach's alpha reliability coefficient of the total PSS-10 scale was reported to be 0.82. In this study, Cronbach's alpha internal consistency coefficient of the subscales was reported to be 0.80 for helplessness and 0.69 for self-efficacy [ 87 ]. The internal consistency reliability coefficient (Cronbach's alpha) of the Arabic form of the PSS-10 was reported to be 0.67 for a sample of Arab college students [ 89 ]. In this study, Cronbach's alpha internal consistency coefficient of the subscales was reported to be 0.86 for helplessness and 0.79 for self-efficacy. In the current study, Cronbach's alpha reliability coefficient for PSS-10 was calculated as 0.78 for the Turkish, 0.77 for the Arabic form of the scale, and 0.77 for the whole. Smartphone Addiction Scale-Short Version The short version of the smartphone addiction scale (SAS-SV) was developed to measure the risk of smartphone addiction in adolescents [ 90 ] and is widely used to measure problematic smartphone use (PSU). The scale comprises ten items and is assessed on a six-point Likert scale (1 = strongly disagree, 6 = strongly agree). Item examples are "Feeling impatient and fretful when I am not holding my smartphone" and "Using my smartphone longer than I had intended." Scale scores range between 10–60. The higher the score obtained from the test, the higher the risk for addiction. The scale has one factor and no subscales. The SAS-SV's internal consistency reliability coefficient (Cronbach's alpha) was reported to be 0.86 for a sample of Turkish college students [ 91 ]. In a study with Arab adolescents, the internal consistency coefficient (Cronbach's alpha) ranged from 0.74 to 0.89 for all items [ 92 ]. In this study, the internal consistency reliability coefficient (Cronbach's alpha) for all scales was reported to be 0.90. In the current study, Cronbach's alpha reliability coefficient for SAS-SV was calculated as 0.85 for the Turkish, 0.88 for the Arabic form of the scale, and 0.87 for the whole. Statistical Analysis Descriptive statistics were estimated by SPSS 26.0, while correlation matrix and visual network analysis were conducted on R software (version 4.4.0). The network construction was based on the EBICglasso model, which can penalize incorrect Spearman partial correlations to address the issue of overestimating associations between the components and generate a parsimonious network that displays only the significant associations [ 93 ]. Our network was further evaluated on the total score of the scale or dimensions rather than the item level, which would prevent the aggregation of highly relevant items and avoid overloading the network with much information. Besides showing patterns of interactions between nodes, this approach can also provide information about possible mediating pathways. The tuning parameter was adjusted to 0.5 to obtain a more parsimonious and explainable network (i.e., fewer edges, higher specificity, and sensitivity) [ 94 ]. The EBICglasso model was based on the Extended Bayesian Information Criterion (EBIC) [ 95 ] and a graphical most minor absolute shrinkage and selection operator (LASSO) [ 96 ] regularization. The network was estimated via the Gaussian graphical model (GGM) [ 97 ]. The network system included nodes and edges. A network can also be described statistically in terms of edge weights (indicating the correlation or partial correlation between nodes) and centrality (indicating the relative importance of the individual nodes in the network). The centrality of nodes in the network was calculated, including betweenness, closeness, strength (also called degree), and expected influence [ 98 ]. When investigating centrality at a low but common sample size in social science, the EBICglasso estimator gave the most confidence in interpreting centrality indices [ 93 ]. We used expected influence as measures of centrality in the study. Moreover, bridge EI is calculated as a comparable index of bridge centrality to identify crucial bridge nodes using the networktools package. The expected influence for a node is the absolute sum of edge weights associated with it, considering negative nodes [ 69 ]. Additionally, bridge expected influence was used to explore the connectivity between specific communities in the network; it is defined as the sum of the value of all edges between a node and all nodes that are not in the same community [ 99 ]. A network must also be evaluated for its accuracy and stability. The network accuracy was examined through edge-weight accuracy, centrality stability, and testing for significant differences in nodes and edges [ 69 ]. To test the stability of edge and centrality values in terms of EI and bridge EI we used the R package bootnet (version 1.6) [ 97 ] to perform 10,000 bootstraps of the EI/bEI for every node and edge across progressively smaller subject subsets. Case-dropping subset bootstrap (95% confidence intervals) examines if the order of centrality indices remains the same after re-estimating the network with fewer cases (or nodes), quantified in terms of correlation stability coefficient [ 69 ]. The correlation stability coefficient (CS-coefficient) is additionally adopted to estimate stability. This coefficient reflects the correlation between the original centrality indices (based on the complete data) and the correlation obtained from the subset of data representing different percentages of the overall sample. Although a correlation stability coefficient of 0.7 or higher has been suggested as the threshold, Epskamp et al. [ 97 ] have suggested that the correlation stability coefficient should be at least 0.5. Results Participants The sample of the study consist of 836 individuals between the ages of 12–18 (M = 14.20, SD = 2.07). Our participants are Syrian adolescents who migrated to Turkey due to the war. The data collection process was conducted across there different cities in Turkey, namely Hatay, Kahramanmaraş, and Mersin. Data collection was carried out in different provinces of Turkey, including Hatay, Kahramanmaraş and Mersin, and through non-governmental organizations (NGOs) founded by Syrians in these provinces. Of the total participants, 469 (56.1%) were female, and 367 (43.9%) were male (See Table 1 for details). Table 1 Sample characteristics Variables Groups n % 1.Gender Female 469 56.1 Male 367 43.9 2.Attendance status Attending 774 93.1 Not Attending 54 6.9 3.Age 12 250 31.0 13 118 14.6 14 112 13.9 15 87 10.8 16 83 10.3 17 53 6.6 18 103 12.8 4. Family socioeconomic status Very low 21 2.5 Low 123 14.8 Middle 353 42.5 High 202 24.3 Very high 131 15.8 5. Number of smartphone checks per day 40 115 14.2 7. Time spent on a smartphone per day 6 hour 65 7.9 8.Employment status Employed 109 13.3 Unemployed 724 86.9 9.Turkish proficiency Low proficiency 45 5.4 Moderate proficiency 301 36.2 High proficiency 485 58.4 Preliminary Analysis Independent samples t-test and one-way analysis of variance (ANOVA) were carried out to test whether the age and gender of the participants made a difference in terms of the research variables. In this regard, independent samples t-test for gender and one-way analysis of variance (ANOVA) for age were conducted (see Table 2 , 3 ). According to the results of these two analyses, it was concluded that SCARED, KADS-6, ULS-6 and PSS-10 variables differed significantly according to both gender and age, while SAS-SV variable differed significantly only according to age (all p values < .05). Table 2 Independent sample t-test for participants’ gender Variables Group n Mean SD t df p SCARED Boy 364 23,8516 11,93278 -5.873 831 < .000 Girl 469 29,2026 13,84312 KADS-6 Boy 360 5,3778 3,78187 -3.019 818 < .000 Girl 460 6,2109 4,02729 ULS-6 Boy 362 11,9641 3,85417 -4.247 828 < .000 Girl 468 13,0406 3,43063 PSS-10 Boy 362 17,1934 7,14443 -3.514 826 < .000 Girl 466 18,8627 6,48559 SAS-SV Boy 365 30,6986 10,47011 -1.265 832 .206 Girl 469 31,6439 10,89181 Note: SCARED; Screen for Child Anxiety Related Disorders KADS-11: The Kutcher Adolescent Depression Scale-11 ULS-6: UCLA Lonileness Scale-6; PSS: Perceived Stress Scale; SAS-SV Smartphone Addiction Scale Table 3 One-way analysis of variance participants’ age Variables Sum of Square df Mean of Square f p SCARED 6920,933 6 1153,489 6,794 ,000 KADS-6 457,268 6 76,211 5,097 ,000 ULS-6 220,519 6 36,753 2,779 ,011 PSS-10 1471,416 6 245,236 5,428 ,000 SAS-SV 3707,364 6 617,894 5,544 ,000 Note: SCARED; Screen for Child Anxiety Related Disorders KADS-11: The Kutcher Adolescent Depression Scale-11 ULS-6: UCLA Lonileness Scale-6; PSS: Perceived Stress Scale; SAS-SV Smartphone Addiction Scale Network Estimation We conducted the analyses in R (version 4.4.0; R Core Team, 2024). To visualize regularized partial correlation networks, we imported the R package qgraph (version 1.9.8) [ 98 ] and networktools (version 1.5.2) [ 100 ] to estimate it. Graphical LASSO . We used graphical Lasso (Least Absolute Shrinkage and Selection Operator) to visualize regularized partial correlation networks [ 96 ]. Lasso algorithm minimizes the residual sum of squares subject to the sum of the absolute values of the coefficients being less than a constant. This introduces sparsity by shrinking some coefficients to exactly zero. By shrinking some coefficients to zero, Lasso performs variable selection, keeping only the most important predictors in the model [ 101 ]. We used the EBICglasso (Extended Bayesian Information Criterion Graphical Lasso) procedure, which combines the graphical lasso algorithm with the Extended Bayesian Information Criterion (EBIC) for model selection. This method is useful to control the complexity of the model by penalizing the number of non-zero entries in the precision matrix. We set the hyperparameter value to default (λ = 0.5). Expected Influence (EI). We measured centrality by calculating one-step expected influence (EI) [ 100 ]. Expected influence provides a centrality of the network nodes. It illustrates the influenciality of a node in terms of its ability to affect other nodes in the network. In terms of psychopathology, central symptoms are associated with many other symptoms and are potentially critical to the development of the whole network as it is most likely that other symptoms are caused by the central symptom [ 64 , 65 ]. Bridge expected influence (bEI). We also calculated the bridge expected influence (bEI). bEI refers to a node’s sum connectivity with other disorders [ 99 ] and is a useful tool to understand the centrality of nodes in a network, and identify nodes that act as bridges between communities. Greater and more frequent associations are represented by higher bEI values. Stability To test the stability of edge and centrality values in terms of EI and bEI, we used the R package bootnet (version 1.6) [ 97 ] to perform 10,000 bootstraps of the EI/bEI for every node and edge across progressively smaller subject subsets. The means, SDs and labels of the anxiety, depression, loneliness, stress and smartphone addiction dimensions are shown in Table 4 . Table 4 Item scores on the Screen for Child Anxiety Related Disorders (SCARED; Birmaher et al., 1999), The Kutcher Adolescent Depression Scale-11 (KADS-11; Brooks et al., 2003), UCLA Lonileness Scale-6 (ULS-6; Neto, 1992), Perceived Stress Scale (PSS; Cohen et al., 1983), Smartphone Addiction Scale (SAS-SV; Kwon et al., 2013) and network labels. Item Mean(SD) Label SCARED 1. Panic disorder 7.71(5.57) PanDis 2. Generalized anxiety disorder 6.36(4.07) GenAnxDis 3. Seperation anxiety disorder 6.03(3.34) SepAnxDis 4. Social anxiety disorder 5.87(3.18) SocAnxDis 5. School avoidance 2.29(1.86) ScAv KADS-6 1. Depression 5.91(3.96) DPRS ULS-6 1. Loneliness 12.57(3.63) LON PSS-10 1. Stress 18.08(6.75) STR SAS-SV 1. Smartphone addiction 31.3(10.70) SPA Figure 1 . illustrates the regularized partial correlation network and EI and bEI values for each node. While blue edges indicate positive partial correlations, red edges indicate negative partial correlations. The bolder and more saturated the edges are, the stronger the connection will be. We used multidimensional scaling to visualize the data to interpret the results meaningfully [ 100 ]. In general, almost every edge indicates positive correlations with the notable exceptions of school avoidance ( ScAv ) and loneliness ( LON ; r = .02), and separation anxiety disorder ( SepAnxDis ) and depression ( DPRS ; r = .08). Among positive edges, the strongest ones appeared within anxiety clusters: Panic disorder ( PanDis ) and generalized anxiety disorder ( GenAnxDis ; r = .38), and panic disorder ( PanDis ) and school avoidance ( ScAv ; r = .36). Between anxiety symptoms and others (depression, loneliness, stress, smartphone addiction), few edges did emerge notably such as generalized anxiety disorder ( GenAnxDis ) and Stress ( STR ; r = .14), school avoidance ( ScAv ) and depression ( DPRS ; r = .12), and social anxiety disorder ( SocAnxDis ) and smartphone addiction( SPA ; r = .06). Among other symptoms, smartphone addiction ( SPA ) and stress( STR ; r = .29) loneliness ( LON ) and stress ( STR ; r = .27) showed greater correlations. Looking specifically at EI, generalized anxiety disorder ( GenAnxDis ) was the most central node, which makes it the most influential node in terms of the development of the network. Panic disorder ( PanDis ) was the other high centrality node. On the other hand, smartphone addiction ( SPA ), separation anxiety disorder ( SepAnxDis ), and school avoidance ( ScAv ) were the least important nodes in the network. For the current network, the bootstrapped difference test showed that the EI indices of generalized anxiety disorder ( GenAnxDis ) and panic disorder ( PanDis ) were significantly different from those of most other nodes (P < 0.05, see Fig. 2 ). Focusing on bEI, Stress ( STR ), Loneliness ( LON ) and Depression (DPRS) had relatively high bEI values, which means they have a strong influence on other disorders or symptoms across different communities. Anxiety symptoms had lower bEI values compared to others except for generalized anxiety disorder ( GenAnxDis ), having a moderate bEI value. Disorders with higher values can be considered key disorders that bridge multiple communities and targeting these disorders for intervention could have a broader impact on the network. The EI and bEI for each node had high stability, they were both reported as [CS] coefficient = 0.75. A CS coefficient of 0.75 means that up to 75% of the sample can be dropped and the centrality measures (EI and bEI) for the nodes will still be highly correlated (usually r > 0.7) with those obtained from the full sample. Figures 3 and 4 illustrate the evaluation of the stability of the EI and bEI, respectively, using case-dropping bootstrapping. Discussion Expected influence provides a centrality of the network nodes. It illustrates the influenciality of a node in terms of its ability to affect other nodes in the network. Regarding the clinical implications, central nodes are critical for activating or inhibiting other nodes and contribute significantly to the development and maintenance of the overall network [ 64 , 65 ], and interventions targeting the central nodes could disrupt the entire network and mitigate the severity of psychiatric symptoms, facilitating the treatment and prevention [ 102 , 103 ]. In our study, our central nodes were generalized anxiety disorder and panic disorder. Besides their strong association with other anxiety disorders, such as social anxiety disorder, central nodes also appear to be associated with stress, loneliness, and depression. In a study of 183 Syrian and Iraqi immigrant children and adolescents in which anxiety disorders were evaluated with the SCARED scale, the rate of panic disorder was 23%, and the rate of generalized anxiety disorder was 17.5% [ 104 ]. In this study, similar to our study, anxiety disorders were observed more in girls. In a study in which Syrian immigrant children and adolescents were evaluated using a culturally and situationally sensitive structured interview tool (MINI Kid), the prevalence of generalized anxiety disorder and panic disorder symptoms, in addition to posttraumatic stress disorder, was emphasized [ 105 ]. Among the internalizing psychopathologies associated with trauma and chronic stress, anxiety disorders, posttraumatic stress disorder, and depression stand out [ 106 ]. In our study of 2336 Syrian and Turkish adolescents [ 12 ], we observed the adverse effects of anxiety disorders on life satisfaction and peer relationships in Syrian immigrant adolescents. There is a need for individual, family, school, and community-based approaches [ 107 , 108 , 109 ] for stress-related anxiety disorders in Syrian immigrant adolescents. With the results of our study, we think that interventions for generalized anxiety disorder are necessary. In our study, stress, loneliness, and depression come to the forefront as bridge nodes. Bridge nodes are critical for understanding mental comorbidities or co-occurring psychopathological constructs. As they transmit the negative of one construct to another, they are considered targets for prevention and interventions [ 68 , 99 , 110 ]. The findings of this study are in line with past research on the mental health of Syrian refugee children and adolescents that found a high level of distress [ 106 ], loneliness [ 12 ], and depression [ 25 ]. In our study, a strong correlation was observed between depression and loneliness. Considering that loneliness plays a central role in depression and suicidal thoughts in adolescents [ 71 , 111 ], there is a need for approaches to improve the social relationships of Syrian immigrant adolescents. In addition, improving Syrian immigrant adolescents' peer relationships [ 12 ], resilience, and self-esteem [ 21 ] will also have a protective effect against the negative effects of psychopathologies on life satisfaction. In our study, similar to other studies [ 26 ], depression was observed more in female immigrant adolescents. We also observed that suicidal thoughts in the depression scale we used in our study were common in female adolescents with depression symptoms. Pharmacological treatment [ 112 ] and CBT [ 107 ] are prominent for depression symptoms in migrant adolescents. Migrant adolescents with moderate to severe symptoms of depression should have easier access to individualized treatment. Stress or perceived stress stands out as the node with the highest connectivity. In our study, stress paves the way to smartphone addiction in addition to its significant relationship with psychopathologies. In this sense, our study supports other studies [ 35 , 43 ] that found strong connectivity between stress and smartphone addiction in young people. However, no strong relationship between smartphone addiction and psychiatric symptoms other than stress was found. In studies conducted with adolescents, although studies are showing a significant correlation between social anxiety disorder [ 28 ], depression [ 50 ] and loneliness [ 34 ], and smartphone addiction, there are also studies showing a weaker correlation [ 113 ]. Our study includes immigrant adolescents who came to our country after the civil war in Syria and were exposed to chronic stressors such as acculturation stress [ 114 ]. Immigrants are primarily exposed to culture shock in their host country and experience feelings of confusion, denial, and anger due to acculturation stress. In this context, it is not surprising that stress was closely related to both psychopathologies and smartphone addiction in our sample. Our study is the first study to evaluate the relationship between psychopathologies, loneliness, and smartphone addiction in Syrian migrant adolescents living in Turkey. In the results of the network analysis we applied, perceived stress, which is the bridge node, stood out as a critical factor for the development of smartphone addiction and psychopathologies. The importance of interventions for developing stress-coping skills in Syrian immigrant adolescents is a significant result of our study. In this context, in studies conducted with migrant youth, active coping strategies are associated with reduced risk for externalizing and internalizing symptoms [ 115 ]. Also, activities (such as practicing sports and meeting friends) might function as positive coping strategies and could reduce levels of internalizing symptoms and loneliness. Betancourt et al. [ 6 ] found that the protective factors against acculturation stress and settlement stress in immigrant adolescents are healthy family interaction, support networks, and peer support. Additionally, psychosocial interventions often use techniques from psychotherapy but do not follow complete standard treatment protocols and often include additional elements such as creative expressive techniques (e.g., drama, music), relaxation exercises, psychoeducation, and counseling [ 115 ]. Limitations This study has some limitations that need to be considered when interpreting the findings. Firstly, measures of adolescent anxiety, depression, stress, loneliness, and smartphone addiction were only assessed through self-reporting. Self-reporting measures may be subject to response bias or social desirability bias. Therefore, the results may not replace direct assessments by mental health professionals, and caution should be exercised in interpreting the results. Secondly, The study's cross-sectional design does not allow us to conclude the direction of the relationships or the causal effects of the variables on each other. Therefore, future studies should use longitudinal designs to establish temporal relationships among variables and examine causal relationships. Another limitation of our study is that there were more female adolescents than male adolescents in our sample. This situation negatively affects the generalizability of the results of our study. In addition, our study was conducted with 836 migrant adolescents in three cities of Turkey through non-governmental organizations. There is a need for comprehensive studies, including those of other provinces of Turkey. Finally, our study includes adolescents who have access to smartphones. Psychiatric symptoms and other addictions should be examined in future studies in the sample where the economic status of the families prevents adolescents from using smartphones. Conclusion Using network analysis, we conducted a detailed assessment of stress, loneliness, smartphone addiction, and psychopathologies in Syrian migrant adolescents. Stress plays a central role in the pathway to both smartphone addiction and internalizing disorders. Generalized anxiety disorder is at the center of the network and is at the forefront of treatment and interventions. In addition, in youths with depression accompanied by loneliness, associations with other psychopathologies and suicidal thoughts are observed. The results of our study support the urgent need for individual, family, school, and community-based approaches in Syrian immigrant adolescents. Declarations Data Availability The data that support the findings of this study are available from the first author upon request. Ethics approval and consent to participate This study was conducted in accordance with the permission obtained from the ethics committee of a Toros University (2023/145). The study procedures adhered to the Declaration of Helsinki. All participants and one of their guardians completed an informed voluntary consent form. Subjects participated voluntarily and were free to withdraw at any time. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Clinical trial number Not applicable. Funding The authors did not receive support from any organization for the submitted manuscript. Author Contribution All authors were responsible for the study concept and design. OY, RÇ and İSA undertook the data management. OY and RÇ undertook the statistical analyses. All authors interpreted the results. OY and AE drafted the initial manuscript. LT critically reviewed the manuscript. All authors approved the submitted version. Acknowledgements We would like to express our sincere appreciation to all the participants who generously shared their time and provided valuable insights for this study. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5857108","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":408465570,"identity":"02d2de5f-0085-48c7-b6b8-6698b8eea829","order_by":0,"name":"Onat Yetim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBADHgb2BiBlYEGUakagWgMeBp4DIC0SxGthYJBIAHGI0MIv3fz8MU/FHxndmc+vbvhRIMHA396dgFeL5Jxjhs08Zwx4zG7nlN3sATpM4szZDXi1GNxIMGzmbQNrSbvBA9RiIJFLSEv6x2bef0AtN8+k3fxDnJYcoC0NQC032I/dJsoWyRk5hTPnHDPmMTuTw3ZbxkCCh6Bf+CXSN3x4UyNnb3b8+LObb/7YyPG39+LXAgJMPGCKxwBMElQOAow/wBT7A6JUj4JRMApGwcgDAPUdRu0O2D5nAAAAAElFTkSuQmCC","orcid":"","institution":"University of Health Sciences","correspondingAuthor":true,"prefix":"","firstName":"Onat","middleName":"","lastName":"Yetim","suffix":""},{"id":408465571,"identity":"7835ed27-1c05-4f7d-8fb9-c13f06dc5e99","order_by":1,"name":"Lut Tamam","email":"","orcid":"","institution":"Çukurova University Balcalı Campus","correspondingAuthor":false,"prefix":"","firstName":"Lut","middleName":"","lastName":"Tamam","suffix":""},{"id":408465572,"identity":"ae8b24c6-82c6-41fa-baac-29e43150c14b","order_by":2,"name":"Ayşegül Efe","email":"","orcid":"","institution":"University of Health Sciences","correspondingAuthor":false,"prefix":"","firstName":"Ayşegül","middleName":"","lastName":"Efe","suffix":""},{"id":408465574,"identity":"02c62bc5-3633-4fef-901a-d858aff0eaf8","order_by":3,"name":"İlham Sebea Alleil","email":"","orcid":"","institution":"Toros University Bahçelievler district Mersin","correspondingAuthor":false,"prefix":"","firstName":"İlham","middleName":"Sebea","lastName":"Alleil","suffix":""},{"id":408465576,"identity":"fc2ab4e6-de73-4340-b349-b4e7d74488e8","order_by":4,"name":"Resul Çakır","email":"","orcid":"","institution":"Toros University Bahçelievler district Mersin","correspondingAuthor":false,"prefix":"","firstName":"Resul","middleName":"","lastName":"Çakır","suffix":""}],"badges":[],"createdAt":"2025-01-18 23:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5857108/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5857108/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":77711822,"identity":"a444c506-7ea6-4103-bbdf-344e4bef6f71","added_by":"auto","created_at":"2025-03-04 13:09:16","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":145773,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Network constructed by using the LASSO algorithm. The edges represent regularized partial correlations between the assessed symptoms: Screen for Child Anxiety Related Disorders (SCARED; Birmaher et al., 1999), The Kutcher Adolescent Depression Scale-11 (KADS-11; Brooks et al., 2003), UCLA Lonileness Scale-6 (ULS-6; Neto, 1992), Perceived Stress Scale (PSS; Cohen et al., 1983), Smartphone Addiction Scale (SAS-SV; Kwon et al., 2013). \u003cstrong\u003eB. \u003c/strong\u003eExpected influence (EI; left graph) and bridge expected influence (bEI; right graph) for every node. For each plot, \u003cem\u003ey\u003c/em\u003e axis represent the nodes and \u003cem\u003ex\u003c/em\u003e axis represent the EI and bEI.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5857108/v1/5dc1e47bf7dddbfb3d13f13a.jpeg"},{"id":77711069,"identity":"e49d8964-484e-444f-8b7e-8d38c3a222c3","added_by":"auto","created_at":"2025-03-04 13:01:16","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":142937,"visible":true,"origin":"","legend":"\u003cp\u003eBootstrapped difference test for node expected influences in the network. Gray boxes indicate node expected influences that do not differ significantly from one another, while black boxes indicate node expected influences that do differ significantly. The numbers in the white boxes (i.e., diagonal line) represent the values of node expected influences.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5857108/v1/9033ed4629306d98e27e50d1.png"},{"id":77711063,"identity":"d371d53b-1c34-4216-b60a-b7341b949f6d","added_by":"auto","created_at":"2025-03-04 13:01:16","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":135352,"visible":true,"origin":"","legend":"\u003cp\u003eStability of node expected influences in the network. The red bar represents the average correlation between node expected influences in the full sample and subsample with the red area depicting the 2.5th quantile to the 97.5th quantile.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-5857108/v1/75e6429239d48be5f754fd58.png"},{"id":77711824,"identity":"5dcb21be-e1f6-428b-ab65-09519574c51e","added_by":"auto","created_at":"2025-03-04 13:09:16","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":136301,"visible":true,"origin":"","legend":"\u003cp\u003eStability of node bridge expected influences in the network. The red bar represents the average correlation between node bridge expected influences in the full sample and subsample with the red area depicting the 2.5th quantile to the 97.5th quantile.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-5857108/v1/bc677ca4a3ea9741bb84207e.png"},{"id":79750069,"identity":"3ff4807b-41c1-4747-8dc7-d675aae8b68c","added_by":"auto","created_at":"2025-04-02 09:17:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1562081,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5857108/v1/4e456e8b-8e32-454b-9436-f61ea47c3a09.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Stress, Loneliness, Depression, Anxiety and Problematic Smartphone Use Among a Sample of Syrian Refugee Adolescents: A Network Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs a result of the ongoing civil war in Syria for over a decade, millions of Syrians have been forced to leave their homes. Turkey has emerged as the most important destination for Syrians who have been forced to flee their homeland following the war [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In this context, Turkey is hosting 3,648,983 Syrian refugees [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Additionally, many Syrian refugees worldwide and in Turkey consist of children and adolescents [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Although settling in a different country affects the entire family, children and adolescents tend to attach more emotions and significance to this experience than adults, and they are more greatly impacted by it [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdolescents who are forced to migrate may experience stressors related to relocation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], acculturation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], and transitioning to a new school environment [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. In addition to the stressful experiences they faced in their own countries and the everyday stress of adolescence, factors such as family traumas in the host country, differences in the education system, and language barriers contribute to significant challenges and increased vulnerability for refugees after resettlement [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAl-Shatanawi et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] state that social isolation and loneliness are among the observed primary psychiatric disorders in Syrian refugee adolescents. Many researchers in the literature state that loneliness impacts adolescents' development and well-being [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For example, it is well known that there is a negative relationship between social support and internalizing symptoms such as depression and anxiety during adolescence [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany researchers in the literature, both in Turkey and in different countries, have conducted studies on Syrian refugee adolescents and have noted that at least half of these adolescents exhibit symptoms of anxiety [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Similarly, studies comparing refugee adolescent samples with local samples have found that refugee adolescents have higher rates of anxiety and depression compared to the local sample [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral studies revealed that smartphone usage has increased among young people, especially adolescents [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For this reason, they are assumed to be an at-risk group for demonstrating problematic behaviors about internet addiction [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and problematic smartphone usage [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. This is probably because they are attempting to solve social and psychological problems they face in this period, such as identity, gender, and individualization [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eProblematic smartphone use (PSU) is defined in the scientific literature as the excessive frequency of smartphone use with impairment in academic, occupational, and/or social functioning PSU is a complex phenomenon comprising diverse dysfunctional manifestations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For instance, loneliness [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], a low level of self-regulation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], anxiety [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], stress, and depression [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] were found to be the sources of smartphone addiction for adolescents.\u003c/p\u003e\n\u003ch3\u003eLoneliness and PSU\u003c/h3\u003e\n\u003cp\u003eLoneliness refers to the distress experienced when there are inconsistencies between expected personal interactions and actual social relationships [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. A lack of peer support contributes to increased smartphone usage [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Individuals who feel lonely frequently use smartphones to counteract loneliness [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Previous studies of loneliness and smartphone use have reported a positive correlation between loneliness and smartphone addiction [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], and loneliness has been reported to be an important antecedent of problematic smartphone use and smartphone addiction [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLoneliness in youths seems to be closely related to problematic smartphone use, as well as depression and anxiety [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Vulnerable youths seem to use their smartphones to distract themselves from a negative mood caused by loneliness and social isolation and are predisposed by depression, anxiety, and vulnerability to stress [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Social cognitions (e.g., loneliness) alongside psychopathology (e.g., depression, anxiety) and vulnerability to stress contribute toward a person's specific cognitions and expectations about smartphone use and reinforce the use of the smartphone as dysfunctional coping [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. This reinforcement can result in a conditioned relationship that makes it difficult for the individual to cognitively control smartphone use.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStress and PSU\u003c/h2\u003e \u003cp\u003eAmong various factors that predict smartphone addiction, stress is one of the significant factors that is related to problematic smartphone usage and smartphone addiction [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Adolescents' daily stress (parent-related stress, home environment stress, and academic stress) was significantly associated with smartphone addiction [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Adolescents with high stress levels tend to excessively use smartphones to escape from life problems and alleviate stress [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Individuals' perceived stress affects their cognitive processes, and they may use the Internet to cope with the effects of stress on cognitive processing [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePerceived stress refers to the degree to which an individual perceives an external event as stress. Whether the objective stress affects the individual depends on the individual interpretation and perception of the stress event [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Abundant studies have shown that perceived stress is positively correlated with depression and anxiety [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Stress is one of the most critical risks leading to mental health problems.\u003c/p\u003e \u003cp\u003eJun and Choi [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] found that depression and anxiety play a mediating role in the link between academic stress and internet addiction. Relevant studies have shown that mood regulation (defined as reducing negative emotions such as stress, anxiety, and depression) could reduce the occurrence of smartphone addiction among youths [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Emotional self-regulation is an essential determinant of problematic smartphone use among adolescents [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnxiety, Depression and PSU\u003c/h3\u003e\n\u003cp\u003eMounting evidence points to a positive correlation between excessive use of smartphones and depression and anxiety [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Recent studies find PSU severity mildly to moderately correlated with anxiety and depression severity in participants from different countries [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Anxiety and depression scores emerged as independent positive predictors of smartphone addiction [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Depression and anxiety make people more vulnerable to PSU as a kind of maladaptive coping which may, in turn, exacerbate existing mental health problems [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe duration of time spent on social media is predictive of scoring higher than the clinical benchmark for anxiety-related disorders in youths [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Generally, individuals with higher baseline anxiety tend to be more drawn to using social media and are also more likely to experience elevated levels of anxiety afterward [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Individuals who spent more time on social media daily were shown to be more predisposed to anxiety symptoms and developing anxiety-related disorders [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Adolescents' stress is associated with the experience of anxiety, which leads to a higher frequency of smartphone use and, subsequently, more experiences of problematic smartphone use. This suggests that turning to the smartphone to relieve anxiety may be a maladaptive coping mechanism - since it leads them to experience more problematic use [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStudies showed that there is a significant positive relationship between depression and smartphone addiction and also indicated that depression can predict and account for smartphone addiction among students [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Adolescents may utilize smartphones as a coping mechanism to mitigate their depressive symptoms and alleviate boredom or frustration [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Using devices provides them with fun and reduces distress so that they can shift their attention from other problems in life [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Such behavior may temporarily help adolescents feel relieved and provide them with an escape from their problems. However, it is not beneficial in the long term, as the problems remain unsolved.\u003c/p\u003e\n\u003ch3\u003eNetwork Relationship Between Loneliness, Stress, Anxiety, Depression and PSU\u003c/h3\u003e\n\u003cp\u003eThe network analysis quantifies and visualizes complex interactions to elucidate the fine-grained relationships among variables [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]. The network theory of psychopathology suggests that psychopathological phenomena should be conceptualized as more complex and dynamic systems composed of interacting factors and operating as a network [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. From a network theory perspective, mental disorders emerge from active interactions between symptoms or non-symptoms rather than just passive reflections of latent variables [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Therefore, researchers have developed a network analysis method to determine the functional roles and importance of specific symptoms in maintaining disorders [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], e.g., to determine the core symptoms in disordered networks, which will benefit the treatment and intervention of disorders [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAccording to previous studies [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e], the dimensions of psychopathological constructs are represented as nodes, and the interactions between different dimensions are depicted as edges using network analysis. The network approach [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] can be regarded as a method that depicts a network graph of interconnected nodes with edges, whose thickness represents the intensity of the connection. Therefore, the network can tell the relative position of one node to all other nodes, namely, the most core or central node in the whole network [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Several centrality indexes (e.g., expected influence, strength) can help determine the core position of nodes. Specifically, nodes with high centrality are referred to as central symptoms, and nodes connecting core variables are referred to as bridging symptoms that are critical to maintaining the co-occurrence of variables and transmitting the influence of one variable on another [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Previous studies explored the network structure of PSU in various samples [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Although these studies explored the PSU network and related variables, the dimension-level network of loneliness, stress, anxiety, depression, and PSU has not been studied in the refugee adolescent population.\u003c/p\u003e\n\u003ch3\u003eThe Current Study\u003c/h3\u003e\n\u003cp\u003eBased on the above literature review, this study aimed to construct a network model to investigate the relationship between loneliness, anxiety, depression, stress, and PSU with a sample of 836 Syrian refugee adolescents. Specifically, the research objectives were as follows: (1) exploring the network structure of these five variables and their centrality components, (2) identifying the critical bridge nodes that facilitate the transmission of loneliness or stress or internalizing symptoms on PSU, and (3) determining the stability of this network.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eParticipants included 836 Syrian adolescents whose ages ranged between 12 and 18, with a mean age of 14.20 years (SD\u0026thinsp;=\u0026thinsp;2.07). Of the total participants, 469 were female, and 367 were male. The data collection process was conducted across three different cities in Turkey: Hatay, Kahramanmaraş, and Mersin.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcedures\u003c/h3\u003e\n\u003cp\u003eAn exploratory quantitative research design guided the cross-sectional sampling method to recruit adolescents from diverse non-governmental organizations in Turkey. The selection of the research design was based on the strengths of the established quantitative methods, allowing for flexible adoption of the method. Relying on convenience sampling, non-governmental organizations (NGOs) founded by Syrians in these provinces were approached and invited to participate in the research. Written informed consent was obtained from a parent or legal guardian, followed by written assent of the participant if they were younger than 18. Written consent was obtained from the participants at least 18 years of age. Students who completed the consent form knew how to read and write in their preferred language (Turkish or Arabic) and volunteered to participate in the study were included in the data collection process. As a result, the participation rate in the research was calculated as 86%. Considering the recommendations of Bryman and Cramer [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e] and Tabachnick et al. [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] to calculate the sample size according to the number of scale items, 917 adolescents who agreed to participate in the study were included. After invalid data was checked, the number of participants decreased to 836. Notably, a researcher fluent in Arabic and Turkish facilitated the data collection process for adolescents. Turkish and Arabic scales were applied to the participants in the data collection process according to adolescents' preferred language. The present study complied with the regulations stipulated by the University Ethics Committee, and data collection was performed between February and April 2024.\u003c/p\u003e\n\u003ch3\u003eMeasurements\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eScreen for Child Anxiety Related Disorders\u003c/h2\u003e \u003cp\u003eScreen for Child Anxiety Related Disorders (SCARED) was used in the current study to assess anxiety symptoms. The SCARED was developed by Birmaher et al. [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] to evaluate anxiety disorder symptoms in children and adolescents as well as for screening purposes. The SCARED scale consists of 41 items (e.g., I worry about how well I am doing things) that are scored on a 3-point Likert scale (0\u0026thinsp;=\u0026thinsp;Not true, 2\u0026thinsp;=\u0026thinsp;Very true or often true) and includes five subscales measuring panic disorder, somatic disorder, generalized anxiety disorder, separation anxiety, and social anxiety. The total score ranges from 0 to 82, with higher scores indicating higher levels of the corresponding trait. However, a score of 25 or higher on the SCARED indicates a warning for anxiety disorders.\u003c/p\u003e \u003cp\u003eThe scale was adapted to Turkish culture by Cakmakci [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. In this study, Cronbach's alpha internal consistency coefficient of the scale was reported to be between 0.74 and.93. The scale was adapted to Arabic culture by Hariz et al. [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. In this study, Cronbach's alpha internal consistency coefficient of the scale was reported to be between 0.65 and 0.89. In Birmaher et al.'s [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] study, Cronbach's alpha reliability coefficient for the scale and subscales ranged from 0.74 to 0.93, and the test-retest reliability coefficient ranged from 0.70 to 0.90. In the current study, Cronbach's alpha reliability coefficient for SCARED was calculated as 0.87 for the Turkish, 0.86 for the Arabic form of the scale, and 0.86 for the whole.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eKutcher Adolescent Depression Scale\u003c/h2\u003e \u003cp\u003eThe Kutcher Adolescent Depression Scale-11 (KADS-11) is an eleven-item self-report instrument, which was constructed based on core symptoms of depression that measure the frequency of depressive symptoms [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. The six-item KADS is a 6-item depression screening scale derived from KADS-11, developed to determine the risk of depression for young people aged 12\u0026ndash;22 years [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Item examples are \"Feelings of worthlessness, hopelessness, letting people down, not being a good person\" and \"Feeling tired, feeling fatigued, low in energy, hard to get motivated, have to push to get things done, want to rest or lie down a lot .\"Every item is scored from 0\u0026ndash;3, where zero is \"Hardly Ever\" and three is \"All of the time.\" Values range from 0 to 18, with higher scores indicating high depression levels. The scale has one factor and no subscales.\u003c/p\u003e \u003cp\u003eThe original English form of the KADS-6 obtained an internal consistency Cronbach's alpha of 0.80 [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. In a study with Turkish adolescents, the internal consistency coefficient (Cronbach's alpha) was reported to be 0.83 [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. In another study with Arab adolescents, the internal consistency coefficient (Cronbach's alpha) was reported to be 0.83 [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. In the current study, Cronbach's alpha reliability coefficient for KADS-6 was calculated as 0.83 for the Turkish, 0.80 for the Arabic form of the scale, and 0.81 for the whole.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eUcla Lonileness Scale\u003c/h2\u003e \u003cp\u003eThe original scale included 20 statements, while the Ucla Lonileness Scale-6 (ULS-6) consists of a subset of six items [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. The ULS-6 has been used chiefly with adolescents (Neto, 1992) and college students [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Six items of the UCLA-R constitute the ULS-6. Five are formulated negatively, and one in a positive way [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Sample items for the ULS-6 include: ''I feel isolated from others'' and ''People are around me but not with me''. The participants answered the items on a 4-point Likert scale ranging from (1\u0026thinsp;=\u0026thinsp;never to 4\u0026thinsp;=\u0026thinsp;often). Responses are summed over the six items after reversing the score on the positive item to produce a ULS-6 total score, which ranges from 0 to 24, with higher scores indicating higher loneliness. The scale has one factor and no subscales.\u003c/p\u003e \u003cp\u003eCronbach's alpha internal consistency coefficient of the Arabic form of the scale was reported to be between 0.76 and 0.78 [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. The Cronbach's alpha test for the Turkish form of the scale suggested acceptable reliability, and Cronbach's alpha was reported to be 0.77 [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e]. In the current study, Cronbach's alpha reliability coefficient for ULS-6 was calculated as 0.82 for the Turkish, 0.79 for the Arabic form of the scale, and 0.80 for the whole.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePerceived Stress Scale\u003c/h2\u003e \u003cp\u003ePerceived Stress Scale (PSS) was developed by Cohen et al. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] to measure the extent to which situations in one's life are appraised as stressful. Several alternate PSS versions exist, varying in the number of items used to describe perceived stress. The three versions of the PSS are the PSS-14 items, PSS-10 items, and the PSS-4 items [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. The original English version of the PSS-4 had one factorial structure, whereas the PSS-10 and PSS-14 had two factors [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e]. The two factors were labeled as perceived self-efficacy and perceived helplessness. The PSS-10 demonstrated the best psychometric evidence compared to PSS-14 and PSS-4 [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Sample items for the PSS-10 include: ''In the last month, how often have you felt that you were unable to control the important things in your life''. Each item is rated on a 5-point Likert-type scale with response options of 0 (never) to 4 (very often). Responses are summed over the ten items after reversing the scores on four positive items to produce a PSS-10 total score, which ranges from 0 to 40, with higher scores indicating higher perceived stress.\u003c/p\u003e \u003cp\u003eThe internal consistency reliability coefficient (Cronbach's alpha) of 0.84 was reported for a sample of Turkish college students [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e]. In another study conducted with Turkish students [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e], Cronbach's alpha reliability coefficient of the total PSS-10 scale was reported to be 0.82. In this study, Cronbach's alpha internal consistency coefficient of the subscales was reported to be 0.80 for helplessness and 0.69 for self-efficacy [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. The internal consistency reliability coefficient (Cronbach's alpha) of the Arabic form of the PSS-10 was reported to be 0.67 for a sample of Arab college students [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. In this study, Cronbach's alpha internal consistency coefficient of the subscales was reported to be 0.86 for helplessness and 0.79 for self-efficacy. In the current study, Cronbach's alpha reliability coefficient for PSS-10 was calculated as 0.78 for the Turkish, 0.77 for the Arabic form of the scale, and 0.77 for the whole.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSmartphone Addiction Scale-Short Version\u003c/h2\u003e \u003cp\u003eThe short version of the smartphone addiction scale (SAS-SV) was developed to measure the risk of smartphone addiction in adolescents [\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e] and is widely used to measure problematic smartphone use (PSU). The scale comprises ten items and is assessed on a six-point Likert scale (1\u0026thinsp;=\u0026thinsp;strongly disagree, 6\u0026thinsp;=\u0026thinsp;strongly agree). Item examples are \"Feeling impatient and fretful when I am not holding my smartphone\" and \"Using my smartphone longer than I had intended.\" Scale scores range between 10\u0026ndash;60. The higher the score obtained from the test, the higher the risk for addiction. The scale has one factor and no subscales.\u003c/p\u003e \u003cp\u003eThe SAS-SV's internal consistency reliability coefficient (Cronbach's alpha) was reported to be 0.86 for a sample of Turkish college students [\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e]. In a study with Arab adolescents, the internal consistency coefficient (Cronbach's alpha) ranged from 0.74 to 0.89 for all items [\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e]. In this study, the internal consistency reliability coefficient (Cronbach's alpha) for all scales was reported to be 0.90. In the current study, Cronbach's alpha reliability coefficient for SAS-SV was calculated as 0.85 for the Turkish, 0.88 for the Arabic form of the scale, and 0.87 for the whole.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were estimated by SPSS 26.0, while correlation matrix and visual network analysis were conducted on R software (version 4.4.0). The network construction was based on the EBICglasso model, which can penalize incorrect Spearman partial correlations to address the issue of overestimating associations between the components and generate a parsimonious network that displays only the significant associations [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. Our network was further evaluated on the total score of the scale or dimensions rather than the item level, which would prevent the aggregation of highly relevant items and avoid overloading the network with much information. Besides showing patterns of interactions between nodes, this approach can also provide information about possible mediating pathways. The tuning parameter was adjusted to 0.5 to obtain a more parsimonious and explainable network (i.e., fewer edges, higher specificity, and sensitivity) [\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe EBICglasso model was based on the Extended Bayesian Information Criterion (EBIC) [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e] and a graphical most minor absolute shrinkage and selection operator (LASSO) [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e] regularization. The network was estimated via the Gaussian graphical model (GGM) [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e]. The network system included nodes and edges. A network can also be described statistically in terms of edge weights (indicating the correlation or partial correlation between nodes) and centrality (indicating the relative importance of the individual nodes in the network).\u003c/p\u003e \u003cp\u003eThe centrality of nodes in the network was calculated, including betweenness, closeness, strength (also called degree), and expected influence [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e]. When investigating centrality at a low but common sample size in social science, the EBICglasso estimator gave the most confidence in interpreting centrality indices [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e]. We used expected influence as measures of centrality in the study. Moreover, bridge EI is calculated as a comparable index of bridge centrality to identify crucial bridge nodes using the \u003cem\u003enetworktools\u003c/em\u003e package. The expected influence for a node is the absolute sum of edge weights associated with it, considering negative nodes [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. Additionally, bridge expected influence was used to explore the connectivity between specific communities in the network; it is defined as the sum of the value of all edges between a node and all nodes that are not in the same community [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA network must also be evaluated for its accuracy and stability. The network accuracy was examined through edge-weight accuracy, centrality stability, and testing for significant differences in nodes and edges [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. To test the stability of edge and centrality values in terms of EI and bridge EI we used the R package \u003cem\u003ebootnet\u003c/em\u003e (version 1.6) [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e] to perform 10,000 bootstraps of the EI/bEI for every node and edge across progressively smaller subject subsets. Case-dropping subset bootstrap (95% confidence intervals) examines if the order of centrality indices remains the same after re-estimating the network with fewer cases (or nodes), quantified in terms of correlation stability coefficient [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The correlation stability coefficient (CS-coefficient) is additionally adopted to estimate stability. This coefficient reflects the correlation between the original centrality indices (based on the complete data) and the correlation obtained from the subset of data representing different percentages of the overall sample. Although a correlation stability coefficient of 0.7 or higher has been suggested as the threshold, Epskamp et al. [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e] have suggested that the correlation stability coefficient should be at least 0.5.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe sample of the study consist of 836 individuals between the ages of 12\u0026ndash;18 (M\u0026thinsp;=\u0026thinsp;14.20, SD\u0026thinsp;=\u0026thinsp;2.07). Our participants are Syrian adolescents who migrated to Turkey due to the war. The data collection process was conducted across there different cities in Turkey, namely Hatay, Kahramanmaraş, and Mersin. Data collection was carried out in different provinces of Turkey, including Hatay, Kahramanmaraş and Mersin, and through non-governmental organizations (NGOs) founded by Syrians in these provinces. Of the total participants, 469 (56.1%) were female, and 367 (43.9%) were male (See Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for details).\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\u003eSample characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003en\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003e%\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.Gender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e56.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.Attendance status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttending\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e774\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNot Attending\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e3.Age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e4. Family socioeconomic status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery low\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVery high\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e5. Number of smartphone checks per day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e7. Time spent on a smartphone per day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;3 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u0026ndash;4 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;5 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;6 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;6 hour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8.Employment status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e86.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e9.Turkish proficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow proficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModerate proficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh proficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e485\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003ePreliminary Analysis\u003c/h2\u003e \u003cp\u003eIndependent samples t-test and one-way analysis of variance (ANOVA) were carried out to test whether the age and gender of the participants made a difference in terms of the research variables. In this regard, independent samples t-test for gender and one-way analysis of variance (ANOVA) for age were conducted (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e,\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). According to the results of these two analyses, it was concluded that SCARED, KADS-6, ULS-6 and PSS-10 variables differed significantly according to both gender and age, while SAS-SV variable differed significantly only according to age (all \u003cem\u003ep\u003c/em\u003e values\u0026thinsp;\u0026lt;\u0026thinsp;.05).\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\u003eIndependent sample t-test for participants\u0026rsquo; gender\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=\"left\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eGroup\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSCARED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23,8516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11,93278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-5.873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGirl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29,2026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13,84312\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKADS-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,3778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,78187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-3.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGirl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,2109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4,02729\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eULS-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11,9641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,85417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-4.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e828\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGirl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e468\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13,0406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3,43063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePSS-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17,1934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7,14443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-3.514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGirl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18,8627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,48559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSAS-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e365\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30,6986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,47011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e832\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e.206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGirl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31,6439\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10,89181\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: SCARED; Screen for Child Anxiety Related Disorders KADS-11: The Kutcher Adolescent Depression Scale-11 ULS-6: UCLA Lonileness Scale-6; PSS: Perceived Stress Scale; SAS-SV Smartphone Addiction Scale\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOne-way analysis of variance participants\u0026rsquo; age\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSum of Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean of Square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ef\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003eSCARED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6920,933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1153,489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6,794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKADS-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e457,268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76,211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eULS-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e220,519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36,753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2,779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSS-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1471,416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e245,236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAS-SV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3707,364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e617,894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5,544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: SCARED; Screen for Child Anxiety Related Disorders KADS-11: The Kutcher Adolescent Depression Scale-11 ULS-6: UCLA Lonileness Scale-6; PSS: Perceived Stress Scale; SAS-SV Smartphone Addiction Scale\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eNetwork Estimation\u003c/h2\u003e \u003cp\u003eWe conducted the analyses in R (version 4.4.0; R Core Team, 2024). To visualize regularized partial correlation networks, we imported the R package \u003cem\u003eqgraph\u003c/em\u003e (version 1.9.8) [\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e] and \u003cem\u003enetworktools\u003c/em\u003e (version 1.5.2) [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e] to estimate it.\u003c/p\u003e \u003cp\u003e \u003cem\u003eGraphical LASSO\u003c/em\u003e. We used graphical Lasso (Least Absolute Shrinkage and Selection Operator) to visualize regularized partial correlation networks [\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e]. Lasso algorithm minimizes the residual sum of squares subject to the sum of the absolute values of the coefficients being less than a constant. This introduces sparsity by shrinking some coefficients to exactly zero. By shrinking some coefficients to zero, Lasso performs variable selection, keeping only the most important predictors in the model [\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e]. We used the EBICglasso (Extended Bayesian Information Criterion Graphical Lasso) procedure, which combines the graphical lasso algorithm with the Extended Bayesian Information Criterion (EBIC) for model selection. This method is useful to control the complexity of the model by penalizing the number of non-zero entries in the precision matrix. We set the hyperparameter value to default (λ\u0026thinsp;=\u0026thinsp;0.5).\u003c/p\u003e \u003cp\u003e \u003cem\u003eExpected Influence (EI).\u003c/em\u003e We measured centrality by calculating one-step expected influence (EI) [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. Expected influence provides a centrality of the network nodes. It illustrates the influenciality of a node in terms of its ability to affect other nodes in the network. In terms of psychopathology, central symptoms are associated with many other symptoms and are potentially critical to the development of the whole network as it is most likely that other symptoms are caused by the central symptom [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cem\u003eBridge expected influence (bEI).\u003c/em\u003e We also calculated the bridge expected influence (bEI). bEI refers to a node\u0026rsquo;s sum connectivity with other disorders [\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e] and is a useful tool to understand the centrality of nodes in a network, and identify nodes that act as bridges between communities. Greater and more frequent associations are represented by higher bEI values.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eStability\u003c/h2\u003e \u003cp\u003eTo test the stability of edge and centrality values in terms of EI and bEI, we used the R package bootnet (version 1.6) [\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e] to perform 10,000 bootstraps of the EI/bEI for every node and edge across progressively smaller subject subsets. The means, SDs and labels of the anxiety, depression, loneliness, stress and smartphone addiction dimensions are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eItem scores on the Screen for Child Anxiety Related Disorders (SCARED; Birmaher et al., 1999), The Kutcher Adolescent Depression Scale-11 (KADS-11; Brooks et al., 2003), UCLA Lonileness Scale-6 (ULS-6; Neto, 1992), Perceived Stress Scale (PSS; Cohen et al., 1983), Smartphone Addiction Scale (SAS-SV; Kwon et al., 2013) and network labels.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean(SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabel\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCARED\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Panic disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.71(5.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePanDis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2. Generalized anxiety disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.36(4.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGenAnxDis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3. Seperation anxiety disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.03(3.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSepAnxDis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4. Social anxiety disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.87(3.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSocAnxDis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5. School avoidance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.29(1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eScAv\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKADS-6\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.91(3.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eDPRS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eULS-6\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Loneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.57(3.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eLON\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSS-10\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.08(6.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSTR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAS-SV\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. Smartphone addiction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.3(10.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSPA\u003c/em\u003e\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\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. illustrates the regularized partial correlation network and EI and bEI values for each node. While blue edges indicate positive partial correlations, red edges indicate negative partial correlations. The bolder and more saturated the edges are, the stronger the connection will be. We used multidimensional scaling to visualize the data to interpret the results meaningfully [\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e]. In general, almost every edge indicates positive correlations with the notable exceptions of school avoidance (\u003cem\u003eScAv\u003c/em\u003e) and loneliness (\u003cem\u003eLON\u003c/em\u003e; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.02), and separation anxiety disorder (\u003cem\u003eSepAnxDis\u003c/em\u003e) and depression (\u003cem\u003eDPRS\u003c/em\u003e; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.08). Among positive edges, the strongest ones appeared within anxiety clusters: Panic disorder (\u003cem\u003ePanDis\u003c/em\u003e) and generalized anxiety disorder (\u003cem\u003eGenAnxDis\u003c/em\u003e; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.38), and panic disorder (\u003cem\u003ePanDis\u003c/em\u003e) and school avoidance (\u003cem\u003eScAv\u003c/em\u003e; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.36). Between anxiety symptoms and others (depression, loneliness, stress, smartphone addiction), few edges did emerge notably such as generalized anxiety disorder (\u003cem\u003eGenAnxDis\u003c/em\u003e) and Stress (\u003cem\u003eSTR\u003c/em\u003e; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.14), school avoidance (\u003cem\u003eScAv\u003c/em\u003e) and depression (\u003cem\u003eDPRS\u003c/em\u003e; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.12), and social anxiety disorder (\u003cem\u003eSocAnxDis\u003c/em\u003e) and smartphone addiction(\u003cem\u003eSPA\u003c/em\u003e; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.06). Among other symptoms, smartphone addiction (\u003cem\u003eSPA\u003c/em\u003e) and stress(\u003cem\u003eSTR\u003c/em\u003e; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.29) loneliness (\u003cem\u003eLON\u003c/em\u003e) and stress (\u003cem\u003eSTR\u003c/em\u003e; \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.27) showed greater correlations.\u003c/p\u003e \u003cp\u003eLooking specifically at EI, generalized anxiety disorder (\u003cem\u003eGenAnxDis\u003c/em\u003e) was the most central node, which makes it the most influential node in terms of the development of the network. Panic disorder (\u003cem\u003ePanDis\u003c/em\u003e) was the other high centrality node. On the other hand, smartphone addiction (\u003cem\u003eSPA\u003c/em\u003e), separation anxiety disorder (\u003cem\u003eSepAnxDis\u003c/em\u003e), and school avoidance (\u003cem\u003eScAv\u003c/em\u003e) were the least important nodes in the network. For the current network, the bootstrapped difference test showed that the EI indices of generalized anxiety disorder (\u003cem\u003eGenAnxDis\u003c/em\u003e) and panic disorder (\u003cem\u003ePanDis\u003c/em\u003e) were significantly different from those of most other nodes (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFocusing on bEI, Stress (\u003cem\u003eSTR\u003c/em\u003e), Loneliness (\u003cem\u003eLON\u003c/em\u003e) and Depression \u003cem\u003e(DPRS)\u003c/em\u003e had relatively high bEI values, which means they have a strong influence on other disorders or symptoms across different communities. Anxiety symptoms had lower bEI values compared to others except for generalized anxiety disorder (\u003cem\u003eGenAnxDis\u003c/em\u003e), having a moderate bEI value. Disorders with higher values can be considered key disorders that bridge multiple communities and targeting these disorders for intervention could have a broader impact on the network.\u003c/p\u003e \u003cp\u003eThe EI and bEI for each node had high stability, they were both reported as [CS] coefficient\u0026thinsp;=\u0026thinsp;0.75. A CS coefficient of 0.75 means that up to 75% of the sample can be dropped and the centrality measures (EI and bEI) for the nodes will still be highly correlated (usually \u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.7) with those obtained from the full sample. Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrate the evaluation of the stability of the EI and bEI, respectively, using case-dropping bootstrapping.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eExpected influence provides a centrality of the network nodes. It illustrates the influenciality of a node in terms of its ability to affect other nodes in the network. Regarding the clinical implications, central nodes are critical for activating or inhibiting other nodes and contribute significantly to the development and maintenance of the overall network [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], and interventions targeting the central nodes could disrupt the entire network and mitigate the severity of psychiatric symptoms, facilitating the treatment and prevention [\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e]. In our study, our central nodes were generalized anxiety disorder and panic disorder. Besides their strong association with other anxiety disorders, such as social anxiety disorder, central nodes also appear to be associated with stress, loneliness, and depression.\u003c/p\u003e \u003cp\u003eIn a study of 183 Syrian and Iraqi immigrant children and adolescents in which anxiety disorders were evaluated with the SCARED scale, the rate of panic disorder was 23%, and the rate of generalized anxiety disorder was 17.5% [\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e]. In this study, similar to our study, anxiety disorders were observed more in girls. In a study in which Syrian immigrant children and adolescents were evaluated using a culturally and situationally sensitive structured interview tool (MINI Kid), the prevalence of generalized anxiety disorder and panic disorder symptoms, in addition to posttraumatic stress disorder, was emphasized [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e]. Among the internalizing psychopathologies associated with trauma and chronic stress, anxiety disorders, posttraumatic stress disorder, and depression stand out [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e]. In our study of 2336 Syrian and Turkish adolescents [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], we observed the adverse effects of anxiety disorders on life satisfaction and peer relationships in Syrian immigrant adolescents. There is a need for individual, family, school, and community-based approaches [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e] for stress-related anxiety disorders in Syrian immigrant adolescents. With the results of our study, we think that interventions for generalized anxiety disorder are necessary.\u003c/p\u003e \u003cp\u003eIn our study, stress, loneliness, and depression come to the forefront as bridge nodes. Bridge nodes are critical for understanding mental comorbidities or co-occurring psychopathological constructs. As they transmit the negative of one construct to another, they are considered targets for prevention and interventions [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. The findings of this study are in line with past research on the mental health of Syrian refugee children and adolescents that found a high level of distress [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e], loneliness [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and depression [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In our study, a strong correlation was observed between depression and loneliness. Considering that loneliness plays a central role in depression and suicidal thoughts in adolescents [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e], there is a need for approaches to improve the social relationships of Syrian immigrant adolescents. In addition, improving Syrian immigrant adolescents' peer relationships [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], resilience, and self-esteem [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] will also have a protective effect against the negative effects of psychopathologies on life satisfaction. In our study, similar to other studies [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], depression was observed more in female immigrant adolescents. We also observed that suicidal thoughts in the depression scale we used in our study were common in female adolescents with depression symptoms. Pharmacological treatment [\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e] and CBT [\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e] are prominent for depression symptoms in migrant adolescents. Migrant adolescents with moderate to severe symptoms of depression should have easier access to individualized treatment.\u003c/p\u003e \u003cp\u003eStress or perceived stress stands out as the node with the highest connectivity. In our study, stress paves the way to smartphone addiction in addition to its significant relationship with psychopathologies. In this sense, our study supports other studies [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] that found strong connectivity between stress and smartphone addiction in young people. However, no strong relationship between smartphone addiction and psychiatric symptoms other than stress was found. In studies conducted with adolescents, although studies are showing a significant correlation between social anxiety disorder [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], depression [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] and loneliness [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and smartphone addiction, there are also studies showing a weaker correlation [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e]. Our study includes immigrant adolescents who came to our country after the civil war in Syria and were exposed to chronic stressors such as acculturation stress [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. Immigrants are primarily exposed to culture shock in their host country and experience feelings of confusion, denial, and anger due to acculturation stress. In this context, it is not surprising that stress was closely related to both psychopathologies and smartphone addiction in our sample.\u003c/p\u003e \u003cp\u003eOur study is the first study to evaluate the relationship between psychopathologies, loneliness, and smartphone addiction in Syrian migrant adolescents living in Turkey. In the results of the network analysis we applied, perceived stress, which is the bridge node, stood out as a critical factor for the development of smartphone addiction and psychopathologies. The importance of interventions for developing stress-coping skills in Syrian immigrant adolescents is a significant result of our study. In this context, in studies conducted with migrant youth, active coping strategies are associated with reduced risk for externalizing and internalizing symptoms [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e]. Also, activities (such as practicing sports and meeting friends) might function as positive coping strategies and could reduce levels of internalizing symptoms and loneliness. Betancourt et al. [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] found that the protective factors against acculturation stress and settlement stress in immigrant adolescents are healthy family interaction, support networks, and peer support. Additionally, psychosocial interventions often use techniques from psychotherapy but do not follow complete standard treatment protocols and often include additional elements such as creative expressive techniques (e.g., drama, music), relaxation exercises, psychoeducation, and counseling [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eThis study has some limitations that need to be considered when interpreting the findings. Firstly, measures of adolescent anxiety, depression, stress, loneliness, and smartphone addiction were only assessed through self-reporting. Self-reporting measures may be subject to response bias or social desirability bias. Therefore, the results may not replace direct assessments by mental health professionals, and caution should be exercised in interpreting the results.\u003c/p\u003e \u003cp\u003eSecondly, The study's cross-sectional design does not allow us to conclude the direction of the relationships or the causal effects of the variables on each other. Therefore, future studies should use longitudinal designs to establish temporal relationships among variables and examine causal relationships.\u003c/p\u003e \u003cp\u003eAnother limitation of our study is that there were more female adolescents than male adolescents in our sample. This situation negatively affects the generalizability of the results of our study. In addition, our study was conducted with 836 migrant adolescents in three cities of Turkey through non-governmental organizations. There is a need for comprehensive studies, including those of other provinces of Turkey.\u003c/p\u003e \u003cp\u003eFinally, our study includes adolescents who have access to smartphones. Psychiatric symptoms and other addictions should be examined in future studies in the sample where the economic status of the families prevents adolescents from using smartphones.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUsing network analysis, we conducted a detailed assessment of stress, loneliness, smartphone addiction, and psychopathologies in Syrian migrant adolescents. Stress plays a central role in the pathway to both smartphone addiction and internalizing disorders. Generalized anxiety disorder is at the center of the network and is at the forefront of treatment and interventions. In addition, in youths with depression accompanied by loneliness, associations with other psychopathologies and suicidal thoughts are observed. The results of our study support the urgent need for individual, family, school, and community-based approaches in Syrian immigrant adolescents.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe data that support the findings of this study are available from the first author upon request.\u003c/p\u003e \u003c/div\u003e\u003cp\u003e \u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e \u003cp\u003e This study was conducted in accordance with the permission obtained from the ethics committee of a Toros University (2023/145). The study procedures adhered to the Declaration of Helsinki. All participants and one of their guardians completed an informed voluntary consent form. Subjects participated voluntarily and were free to withdraw at any time.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical trial number\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThe authors did not receive support from any organization for the submitted manuscript.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors were responsible for the study concept and design. OY, R\u0026Ccedil; and İSA undertook the data management. OY and R\u0026Ccedil; undertook the statistical analyses. All authors interpreted the results. OY and AE drafted the initial manuscript. LT critically reviewed the manuscript. All authors approved the submitted version.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe would like to express our sincere appreciation to all the participants who generously shared their time and provided valuable insights for this study. We also extend our gratitude to the President and staff of the Freedom Schooner Association for their support in facilitating the data collection process.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eUNHCR. Global Trends. Forced Displacement in 2018. Field Information and Coordination Support: Section Division of Programme Support and Management. Case Postale. 2018;2500:1211.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeneral Directorate of Migration Management. Annual Report T.C. Ministry of Interior, Directorate General of Migration Management: Ankara. 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Rutter's child and adolescent psychiatry Chichester, UK. Wiley Blackwell; 2015. pp. 874\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBuyukbayraktar CG. Predictive relationships between social anxiety, internet addiction and alexithymia in adolescents. JEL. 2020;9(2):222.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYetim O. Effects of acculturation and ethnic identity on immigrant adolescents\u0026rsquo; mental health. Psikiyatr Guncel Yaklasimlar. 2024;16(4):628\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDangmann C, Dybdahl R, Solberg \u0026Oslash;. Mental health in refugee children. Curr Opin Psychol. 2022;48:101460.\u003c/span\u003e\u003c/li\u003e\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":"Syrian refugee adolescent, stress, loneliness, depression, anxiety, problematic smartphone use","lastPublishedDoi":"10.21203/rs.3.rs-5857108/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5857108/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrevious studies have demonstrated the existence of complex relationships between stress, loneliness, depression, anxiety, and smartphone addiction in adolescents. However, the paucity of studies evaluating the relevant relationships in migrant adolescents necessitates the elimination of uncertainty in a sample of adolescents exposed to trauma and chronic stressors.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eThis study capitalizes on network analysis to identify the central factors and possible bridging paths among these variables. Employing 836 Syrian refugee adolescents, we obtained a stable network of the above variables. The central components and the stability of this network were also identified.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWithin this network, generalized anxiety disorder and panic disorder were the most central nodes, making them the most influential nodes in the development of the network. Stress stands out as the node with the highest connectivity.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn our study, stress paves the way to smartphone addiction in addition to its significant relationship with psychopathologies. These findings provide a further understanding of the specific roles of stress and related psychopathologies among Syrian refugee adolescents. The identified nodes may be promising targets for prevention and intervention.\u003c/p\u003e","manuscriptTitle":"Stress, Loneliness, Depression, Anxiety and Problematic Smartphone Use Among a Sample of Syrian Refugee Adolescents: A Network Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-04 13:01:11","doi":"10.21203/rs.3.rs-5857108/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":"131ec3cb-54db-4a9c-b9ff-5283016fde09","owner":[],"postedDate":"March 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-04-02T09:08:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-04 13:01:11","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5857108","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5857108","identity":"rs-5857108","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}