The role of Somatization as a clinical marker of Depressive Symptoms and Suicidal Risk associated with Social Media use in Adolescents

The role of Somatization as a clinical marker of Depressive Symptoms and Suicidal Risk associated with Social Media use in Adolescents | 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 The role of Somatization as a clinical marker of Depressive Symptoms and Suicidal Risk associated with Social Media use in Adolescents Grazia Maria Giovanna Pastorino, Antonio Aquino, Roberto Buonaiuto, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9064743/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Purpose Adolescent mental health disorders are increasing worldwide, and suicidal ideation represents a major public health concern. Social media use is a pervasive component of adolescents’ daily lives and may influence psychological well-being through complex behavioral and emotional mechanisms. This study investigated whether patterns of social media engagement and perceived online self-expression are associated with depressive symptoms, somatic complaints, and suicidal ideation in adolescents. Methods A survey was conducted among 1,364 adolescents aged 11–19 years recruited from six schools. Participants completed questionnaires assessing social media use patterns, depressive symptoms (Children’s Depression Inventory-2), and emotional–behavioral problems including somatization and suicidal ideation (Child Behavior Checklist). Correlation analyses, multiple regression models, and sequential mediation analyses were performed to examine direct and indirect associations among variables. Results Daily social media use was reported by 96% of participants, with 20% spending more than four hours per day online. Clinically relevant depressive symptoms were observed in approximately 12% of adolescents, while 9% reported recurrent suicidal ideation. Greater time spent on social media was significantly associated with higher levels of depressive symptoms, somatic complaints, and suicidal ideation. Adolescents reporting greater ease in online self-expression showed increased psychological vulnerability. Sequential mediation analysis indicated that perceived online self-expression was indirectly associated with suicidal ideation through increased time spent online, depressive symptoms, and somatization. Gender-stratified analyses revealed stronger sequential effects in females, whereas in males suicidal ideation was primarily mediated by depressive and somatic symptoms. Conclusions Problematic patterns of social media engagement may represent clinically relevant psychosocial risk markers in adolescents. Somatic symptoms appear to function as a clinical bridge between maladaptive digital behaviors and suicidal vulnerability. Pediatricians should consider screening for digital habits when adolescents present with depressive symptoms and medically unexplained somatic complaints, as early identification of at-risk youths may improve preventive interventions. Social Media Adolescents Depressive symptoms Suicidal Ideation Self-expression Somatiation What is Known – What is New What is Known Excessive social media use is linked to increased rates of depressive symptoms and sleep disturbances Somatic complaints in adolescence often represent manifestations of underlying psychological distress. What is New Perceived "online authenticity" is a key driver of a sequential pathway leading to depressive symptoms and suicidal ideation. Somatic symptoms provide a bridge between dysfunctional social media use and mental health symptoms, suggesting a potential marker for clinicians to identify at-risk youth. 1. Introduction Adolescent mental health disorders have increased markedly over the past decade and currently represent a major global public health concern. Approximately one in seven individuals aged 10–19 years is affected by a mental disorder, with anxiety and depressive conditions ranking among the leading causes of disability in this age group [ 1 , 2 ]. Suicide is now one of the primary causes of death among adolescents and young adults worldwide [ 2 ]. In pediatric clinical practice, this epidemiological trend translates into a growing number of consultations for emotional distress, behavioral dysregulation, and medically unexplained physical symptoms that often conceal underlying psychological vulnerability. Because early psychopathological manifestations frequently persist into adulthood, the identification of modifiable psychosocial risk factors has become a priority for preventive pediatric care. One of the most influential environmental factors shaping contemporary adolescent development is the digital ecosystem. Social media platforms are deeply embedded in youths’ daily lives, with the majority of adolescents reporting daily use [ 3 ]. Although these platforms facilitate social connection and identity exploration, they also expose adolescents to several psychosocial risks. A substantial proportion of youths exhibit patterns consistent with Problematic Social Media Use (PSMU), characterized by impaired control, withdrawal symptoms, and functional impairment [ 3 ]. During adolescence, a developmental stage marked by heightened sensitivity to peer evaluation and identity formation, digital environments become central arenas for social comparison and self-presentation [ 4 ]. Extensive literature has documented associations between intensive social media engagement and internalizing symptoms, particularly depression [ 5 – 7 ]. However, the relationship between digital behaviors and suicidality remains less clearly defined, suggesting the presence of complex mediating mechanisms [ 8 ]. This association is likely bidirectional: excessive social media exposure may exacerbate depressive symptoms through cyberbullying, social comparison, and emotional overstimulation, while psychologically vulnerable adolescents may increasingly rely on digital environments as maladaptive coping strategies [ 9 ]. Beyond psychological outcomes, problematic digital engagement has been increasingly linked to physical health complaints. Growing evidence indicates that adolescents with maladaptive patterns of social media use frequently report somatic symptoms, including headaches, fatigue, and recurrent pain syndromes [ 10 , 11 ]. Somatization may therefore represent a clinically relevant manifestation of psychological distress and a potential bridge between digital stressors and internalizing disorders. Importantly, pediatricians are often the first healthcare professionals to evaluate adolescents presenting with medically unexplained physical complaints, placing them in a strategic position for early detection of underlying psychosocial vulnerability. Recent research further suggests that the qualitative characteristics of digital engagement may be more clinically informative than mere exposure time. In particular, perceived online authenticity — defined as the subjective sense of expressing one’s “true self” more easily online than offline — has emerged as a potentially relevant psychological construct [ 12 , 13 ]. While online environments may initially provide adolescents with a sense of belonging and emotional safety, excessive reliance on digital spaces for identity validation may paradoxically reinforce social withdrawal, emotional dysregulation, and psychological distress. Despite growing societal concern and increasing regulatory efforts aimed at limiting minors’ access to social media platforms, clinical practice requires a deeper understanding of the mechanisms linking specific digital behaviors to severe mental health outcomes. Clarifying these pathways is essential for developing clinically meaningful screening strategies and preventive interventions in pediatric settings. The present study investigates the associations between social media use patterns, perceived online self-expression, depressive symptoms, somatization, and suicidal ideation in a large school-based adolescent population. We tested a sequential mediation model hypothesizing that a preference for online self-expression increases time spent on social media, which in turn is associated with greater depressive symptoms and somatic complaints, ultimately elevating suicidal risk. By identifying clinically observable markers within this pathway, this study aims to provide pediatricians with actionable tools for early risk identification in an increasingly digitized developmental context. 2. Methods 2.1 Ethics approval and consent to participate The study was approved by the Campania Sud Ethics Committee (protocol number = 0033986). All procedures were conducted in accordance with the ethical standards of the responsible institutional committee and with the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all the parents or legal guardians prior to data collection. 2.2 Study design and sample selection We conducted an observational study involving 1436 adolescents recruited from three middle schools and three high schools of the Campania region, in Southern Italy. All the subjects with good compliance to the study were included; the subjects with severe medical or neuropsychiatric conditions that could affect cognitive abilities and compromise the understanding of the questionnaires despite the support were excluded (e.g., severe intellectual disability, etc.). The final sample included 1364 adolescents (653 males = 48%; 696 females = 51%; 15 undeclared = 1%) aged between 11 and 19 years (mean = 13.21 ± 0.43 years).All the parents were preliminarily informed of the purpose and methods of our research and provided their written informed consent. Preliminary meetings were also held with teachers and school principals, in order to illustrate the aims and methods of the study. Data were collected and examined by two neuropsychiatrists and one psychologist. Participants completed anonymously, in paper or digital format via Google Form, the following questionnaires: - a questionnaire investigating personal information (age and gender) , Social Media use - a standardized self-report questionnaire assessing depressive symptoms (Children Depression Inventory - Second Edition (CDI-2) - 20 single questions assessing somatic symptoms and suicidal ideation selected from the standardized self-report questionnaire Child Behavior Checklist 6-18 years old (CBCL 6-18). 2.3Social Media questionnaire (SMQ) We developed a questionnaire to explore quantitative and qualitative use of SM. Questions are listed below: 1. Please indicate your age 2. Please indicate your gender 3. How much time do you spend on Social Media on average each day? 4. Which device do you use most to surf the Internet? 5. What is your main reason for using Internet? 6. Which Social Media do you mainly use? 7. How do you accept new friends on social networks? 8. Have you ever shared personal information or photos with people you met online? If so, what information? 9. Do you ever find yourself staying up late on Social Media? 10. Have you ever been teased/bullied by someone online? And if so, why? 11. What do you think are the main reasons people get bullied on Social Media? 12. Do you talk about private things on the Internet that you wouldn't share with others in person? 13. Do you think it's easier to be yourself on the internet than in person? 14. Can you talk about more things online than you do in person? 2.4 Children’s Depression Inventory, Second Edition (CDI-2) CDI-2 is a standardized questionnaire that assesses depressive symptoms in children and adolescents aged 7-17 years [14]. The CDI-2-Self Report (CDI-2 SR) is self-filled out by the child/adolescent himself and consists of 28 single questions, to which the subject can give a score on a Likert scale ranging from 0 to 3 (0=never, 1=sometimes, 2=often, 3=very often or most of the time). The single questions are grouped and form the following scale: Total Scale, Emotional Problems, Negative Mood/Physical Symptoms, Negative Self-Esteem, Functional Problems, Ineffectiveness and Interpersonal Problems. The scores of the individual scales are expressed inraw score, then converted to T-score. Therefore, higher T-scores (≥60) reflect a higher incidence of depressive symptoms and are in the “pathological range”, in which it is possible to distinguish between medium-high (60-64), high (65-69) and very high (>70) values. 2.5 Child BehaviourCheckList 11-18 The Child Behavior Checklist (CBCL) is a standardized questionnaire [15] that aims to assess emotional and behavioral symptoms in adolescents. The form we used in this study is the self-report form, which is completed by children aged 11-18. The questionnaire includes 113 statements to which the child gives a score using a Likert scale ranging from 0-2: 0 Not true, 1 Sometimes True, 2 Often True. The raw scores are converted into t-scores and constitute three main scales, eight empirical syndrome subscales and six DSM-IV-oriented subscales. For the scales of the "internalization problems", "externalization problems" and "total problems", a t-score ≤59 indicates normal scores, a t-score between 60 and 64 indicates a score that is within a boundary range, and high levels of maladaptive behavior are indicated by t-score ≥65. 2.6 Statistical Analysis Data were expressed as means, standard deviations, and proportions, and were analyzed using descriptive statistics. Data distribution was assessed using the Shapiro–Wilk test. Multiple regression analyses were conducted to examine the associations between demographic and behavioral variables (including age, gender, and time spent on social media) and psychological outcomes, specifically depressive symptoms and suicidal ideation. To test the hypothesized pathways underlying suicidal ideation, a sequential mediation model was performed using the PROCESS macro for SPSS (Model 6). Based on theoretical assumptions and previous literature, time spent on social media, depressive symptoms, and somatization were specified as sequential mediators of the association between ease of online self-expression and suicidal ideation. The order of mediators reflected the sequence in which the constructs were assessed in the questionnaires. Gender-stratified analyses were also conducted to explore potential differences between males and females. 3. Results 3.1 Sample characteristics Subject enrollment was voluntary, and the participation rate in the participating schools was 95%. The questionnaires were administered in a paper format for 1009; instead, 364 questionnaires were filled out on a multimedia platform via Google forms. The main sample characteristics are summarized in Table 1. Please insert Table 1 here 3.2. Descriptive analysis of Social Media Questionnaire, CDI-2 and CBCL. The Social Media Questionnaire showed that 45% of participants reported spending 1–3 hours per day on social media, while 20% spent more than four hours daily and 18% described themselves as being “always connected.” Fourteen percent used social media for less than one hour per day, and only 2.5% reported not using social platforms. Smartphones were the most commonly used devices (90%), whereas personal computers/laptops and tablets were used by 7% and 1% of participants, respectively. Social networking represented the primary reason for internet use (72%), followed by listening to music (51%), school-related activities (30%), and video gaming (24%). Instagram was the most frequently used platform (82%), followed by YouTube (56%). Less commonly used platforms included Facebook (15%), Pinterest (2.5%), Twitter (2.3%), TikTok (0.8%), and Twitch (0.2%). Regarding online social interactions, 69% of adolescents accepted friend requests exclusively from individuals known offline, whereas 18% accepted requests based on selected profile characteristics and 9% accepted requests indiscriminately. Most participants reported not sharing personal information online, while 8% admitted doing so; 34% did not respond to this item. Late-night social media use was frequently reported, with 42% indicating occasional use, 19% frequent use, and 11% very frequent use. Conversely, 26% reported never staying up late due to social media engagement. Most adolescents reported never experiencing cyberbullying (49%), while smaller proportions reported occasional (7.5%), frequent (1%), or very frequent (0.4%) victimization. Non-response for this item was 42%. Reported perceived reasons for cyberbullying included physical appearance (88%), sexual orientation (67%), skin color (56%), clothing style and personal tastes (39%), religious beliefs (21%), academic performance (9%), and political opinions (9%). Concerning online self-expression, 73% of participants did not consider social media a space where expressing personal thoughts was easier than in person. Similarly, 61% reported that it was not easier to be themselves online, whereas 27% and 10% indicated that it was sometimes or often easier, respectively. Discussing a wider range of topics online was reported as easier by 22% of participants sometimes and by 9% often, while 68% did not perceive this difference. On the CDI-2, depressive symptom scores were classified as medium–low in 77% of females and 82% of males, average in 10% of females and 7% of males, high in 6% of both genders, and very high in 7% of females and 5% of males. CBCL self-report data indicated occasional alcohol use in 22% of participants and frequent use in 10%, while drug use was occasional in 4.5% and frequent in 5.5%. No alcohol or drug use was reported by 63% and 86% of adolescents, respectively. Suicidal ideation was absent in 88% of participants, occasional in 5%, and frequent in 4%. Detailed results of the Social Media Questionnaire, CDI-2, and CBCL are presented in Table 2. Please insert Table 2 here 3.4. Multiple regression analysis Two multiple regression models were conducted using CDI-2 Total Score and suicidal ideation as dependent variables, respectively. Both models revealed significant associations between the examined predictors and the outcome measures. Detailed results of the regression analyses are presented in Table 3. Please insert Table 3 here 3.5. Mediations analysis Supporting our hypothesis, bootstrapping with 5,000 resamples indicated a significant indirect effect of ease of online self-expression on suicidal ideation through the hypothesized sequential mediation pathway (b = .01, 95% CI [.001, .003]). The overall model was significant (R² = .43, F(4, 1284) = 70.64, p < .001). Specifically, greater ease of online self-expression predicted increased time spent online, which was associated with higher depressive symptoms and, subsequently, greater somatization. Somatization, in turn, was significantly associated with suicidal ideation. The mediation was partial, as the direct effect remained significant after inclusion of the mediators (t = 2.48, p = .013, 95% CI [.008, .075]). Exploratory gender-stratified analyses revealed distinct patterns. Among females, the sequential mediation model replicated the overall findings, showing a significant indirect effect of ease of online self-expression on suicidal ideation through time spent online, depressive symptoms, and somatization (R² = .18, F(4, 659) = 35.39, p < .001, b = .01, 95% CI [.001, .003]). Partial mediation was again observed, as the direct effect remained significant after accounting for the mediators (t = 2.63, p = .008, 95% CI [.016, .114]). In contrast, the same sequential mediation pathway was not significant among males (b = .000, 95% CI [−.001, .001]). Instead, depressive symptoms and somatization independently mediated the relationship between ease of online self-expression and suicidal ideation (b = .010, 95% CI [.002, .021]). In this subgroup, mediation was full, as the direct effect was no longer significant after inclusion of the mediators (t = .15, p = .878, 95% CI [−.041, .048]). 4. Discussion This study highlights that social media exposure represents a pervasive environmental factor in adolescent development and should be considered a relevant social determinant of mental health. While digital platforms offer opportunities for communication and identity exploration, our findings suggest that specific patterns of engagement—rather than exposure time alone—may serve as clinically meaningful markers of psychological vulnerability and suicide risk. Consistent with previous literature [16–21], most adolescents in our sample reported daily and prolonged online activity. However, from a pediatric clinical perspective, the focus should move beyond quantitative measures of screen time toward qualitative features of digital behavior. Nighttime connectivity, compulsive checking, and persistent online presence may reflect sleep disruption and heightened fear of missing out (FOMO), factors known to exacerbate emotional dysregulation and vulnerability to depressive symptoms [22–25]. Sleep impairment, in turn, represents a well-established risk factor for mood disorders and suicidal ideation during adolescence. Our findings confirm the association between prolonged social media use and depressive symptoms while further suggesting that the motivations underlying digital engagement may carry greater clinical relevance [26-30]. Adolescents who perceive online environments as safer spaces for authentic self-expression may increasingly rely on digital interaction to compensate for offline social difficulties [31,32]. Although this behavior may initially provide emotional relief, prolonged reliance on virtual environments may intensify social comparison, exposure to negative feedback, and emotional overload, thereby worsening psychological well-being [33]. A key clinical contribution of this study is the identification of somatization as a mediating factor linking digital behaviors to suicidal ideation. Pediatricians frequently evaluate adolescents presenting with recurrent headaches, abdominal pain, fatigue, and other medically unexplained physical complaints [34-36]. Our results suggest that these symptoms may not merely represent functional disorders but rather clinically observable manifestations of psychological distress associated with maladaptive digital engagement. Somatic symptoms may therefore function as early warning signals of underlying emotional vulnerability and suicide risk. Gender-specific analyses further refine risk profiling. Female adolescents showed a stronger sequential pathway linking online self-expression, prolonged digital exposure, depressive symptoms, somatization, and suicidal ideation. This pattern may reflect greater sensitivity to social comparison and interpersonal evaluation in digital contexts [37–40]. In contrast, among males, suicidal ideation appeared to be primarily mediated by depressive and somatic symptoms rather than by time spent online. These findings suggest that psychological distress in boys may be less directly expressed through digital behavior patterns and more frequently manifested through internalizing and somatic symptoms, requiring greater clinical vigilance. From a pediatric practice perspective, these findings have direct implications for early identification and prevention. Adolescents presenting with depressive symptoms, sleep disturbances, unexplained somatic complaints, or social withdrawal may benefit from routine assessment of digital habits. Brief screening questions exploring time spent online, motivations for social media use, nighttime connectivity, and reliance on digital environments for emotional expression may help clinicians identify vulnerable youths. Integrating digital behavior assessment into standard psychosocial evaluations, such as the HEEADSSS framework, may improve suicide risk stratification and facilitate timely referral to mental health services. This study has limitations. The observational design precludes causal inference, and reliance on self-report measures may introduce reporting bias. Furthermore, the geographically restricted sample may limit generalizability to other sociocultural contexts. Longitudinal investigations are needed to clarify temporal relationships between maladaptive digital behaviors and psychological outcomes. In conclusion, social media use should be considered an integral component of psychosocial assessment in pediatric care. Qualitative patterns of digital engagement appear closely intertwined with adolescents’ emotional experiences and may provide early clinical indicators of psychological distress. Recognizing somatic symptoms as potential markers of digital-related emotional vulnerability may enhance early detection of at-risk youths and support more effective suicide prevention strategies. Declarations Conflicts of Interest: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethics approval : The study was approved by the Campania Sud Ethics Committee (protocol number = 0033986). Consent to participate: Written informed consent was obtained from all the parents or legal guardians prior to data collection. Funding: The authors received no financial support for this research. Author Contribution Conceptualization, FFO, GMGP, AA; methodology GMGP, AA, RB, GD; formal analysis GMGP, AA, RB; data curation RB, GD, MO; write original draft preparation, GMGP, AA; writing, review and editing GMGP, AA, MO; supervision FFO. All the authors have read and agreed to the published version of the manuscript. Acknowledgement We thank all the young participants for dedicating their time to completing the questionnaires, making this study possible. We thank the school Principal and teachers who supported our project. Data Availability The data that support the findings of this study are openly available in Open Science Framework at [https://osf.io/n3d7e/overview?view\_only=1e76c74aae62430d84d5f31fdd59c427](https:/osf.io/n3d7e/overview?view_only=1e76c74aae62430d84d5f31fdd59c427) Reference Number: n3d7e References Nagata JM, Otmar CD, Shim J, Balasubramanian P, Cheng CM, Li EJ, Al-Shoaibi AAA, Shao IY, Ganson KT, Testa A, Kiss O, He J, Baker FC (2025) Social Media Use and Depressive Symptoms During Early Adolescence. JAMA Netw Open 8(5):e2511704. 10.1001/jamanetworkopen.2025.11704 World Health Organization (2025) Adolescent mental health. Retrieved from https://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health . [accessed 2025-09-06] Nagata JM, Memon Z, Talebloo J, Li K, Low P, Shao IY, Ganson KT, Testa A, He J, Brindis CD, Baker FC (2025) Prevalence and Patterns of Social Media Use in Early Adolescents. 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Front Neurol 15:1395003. 10.3389/fneur.2024.1395003 Tables Table 1 . Sample characteristics. m = mean; SD = standard deviation Total Sample Sample Size 1364 Gender male 653 (48%) female 696 (51%) undeclared 15 (1%) Age - m±SD 13.21 ± 0.43 years Table 2. Social Media Questionnaire How much time do you spend on Social Media on average each day? Never =34 (2.5%) Less than 1 hour = 193 (14%) 1 to 3 hours = 603 (44%) Over 4 hours = 275 (20%) I'm always connected = 246 (18%) Which device do you use most to surf the Internet? Smartphone = 1234 (90%) Personal Computer/Laptop = 92 (7%) Tablet = 16 (1%) What is your main reason for using Internet? (more than one answer) Social Network = 976 (72%) Listen to music = 696 (51%) School research = 415 (30%) Play videogame = 327 (24%) Do you talk about personal things on the Social Media that you wouldn't share with others in person? Never = 998 (73%) Sometimes = 252 (18%) Often = 87 (6%) Do you think it's easier to be yourself on the Social Media than in person? Never = 838 (61%) Sometimes = 370 (27%) Often = 134 (10%) Can you talk about more things on Social Media than you do in person? Never = 924 (68%) Sometimes = 300 (22%) Often = 117 (9%) Do you ever find yourself staying up late on Social Media? Sometimes = 578 (42%) Never = 353 (26%) Often = 260 (19%) Very Often =149 (11%) Which Social Media do you mainly use? (more than one answer) Instagram = 1122 (82%) Youtube = 765 (56%) Facebook = 203 (15%) Pinterest = 35 (2.5%) Twitter = 32 (2.3%) TikTok = 11 (0.8%) Twitch = 2 (0.2%) How do you accept new friends on social networks? Only if I know the person in real life = 946 (69%) Based on gender and profile photo = 247 (18%) From anyone = 123 (9%) Have you ever shared personal information or photos with people you met online? If so, what information? No = 795 (58%) Yes = 107 (8%) Have you ever been teased/bullied by someone online? And if so, why? Never = 662 (49%) Sometimes = 101 (7.5%) Often = 15 (1%) Very Often = 5 (0.4%) What do you think are the main reasons people get bullied on Social Media? (more than one answer) Physical appearance = 1205 (88%) Sexual orientation = 917(67%) Skin color = 759 (56%) Opinions and tastes in clothing = 532 (39%) Religious orientation = 286 (21%) Academic performance = 128 (9%) Politic opinions = 125 (9%) CDI Total Scale mean = 11.84 ± 8.01 CDI classification score (male) very high = 5% high = 6% above average = 7% medium-low = 82% CDI classification score (female) very high = 7% high = 6% above average = 10% medium-low = 77% CBCL Are you thinking about killing yourself? never= 1194 (88 %) sometimes= 66 (5%) often= 53 (4%) Somatization mean=1.99 ± 1.99 Table 3. Multiple regression analysis Dependent variable: CDI-2 Total Score non-standardized coefficients standardized coefficients t p-value B Standard error Beta Independents variants age ,439 ,068 ,160 6,451 ,000 gender -1,515 ,400 -,097 -3,784 ,000 Time on Social Media 1,011 ,198 ,132 5,108 ,000 being yourself online 2,259 ,282 ,194 8,014 ,000 Somatization 1,470 ,100 ,368 14,680 ,000 Dependent variable: suicidal ideation non-standardized coefficients standardized coefficients t p-value B Standard error Beta Independentsvariants age ,004 ,005 ,025 ,878 ,380 gender ,036 ,025 ,042 1,459 ,145 Time on Social Media -,017 ,012 -,040 -1,362 ,173 being yourself online ,035 ,018 ,055 1,993 ,046 Somatization ,038 ,007 ,172 5,602 ,000 CDI-2 Total Score ,017 ,002 ,297 9,293 ,000 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 May, 2026 Reviews received at journal 25 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 25 Mar, 2026 Editor assigned by journal 23 Mar, 2026 Submission checks completed at journal 23 Mar, 2026 First submitted to journal 08 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9064743","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":612425951,"identity":"502ff394-4ccf-49fc-8530-408593c7f8a9","order_by":0,"name":"Grazia Maria Giovanna Pastorino","email":"","orcid":"","institution":"Magna Graecia University of Catanzaro","correspondingAuthor":false,"prefix":"","firstName":"Grazia","middleName":"Maria Giovanna","lastName":"Pastorino","suffix":""},{"id":612425952,"identity":"e93b0b22-5ab6-453f-bf8d-19c64ad1d533","order_by":1,"name":"Antonio Aquino","email":"","orcid":"","institution":"Magna Graecia University of Catanzaro","correspondingAuthor":false,"prefix":"","firstName":"Antonio","middleName":"","lastName":"Aquino","suffix":""},{"id":612425953,"identity":"4cb3df29-0feb-4316-80a3-3b111e9d3568","order_by":2,"name":"Roberto Buonaiuto","email":"","orcid":"","institution":"Scuola Superiore Meridionale","correspondingAuthor":false,"prefix":"","firstName":"Roberto","middleName":"","lastName":"Buonaiuto","suffix":""},{"id":612425954,"identity":"41648ef8-2bf6-403f-9d28-95da66af0d7d","order_by":3,"name":"Giuseppe Diaspro","email":"","orcid":"","institution":"University of Campania “L.Vanvitelli”","correspondingAuthor":false,"prefix":"","firstName":"Giuseppe","middleName":"","lastName":"Diaspro","suffix":""},{"id":612425955,"identity":"15b1395a-cdc0-48de-b624-4bb1523f6c07","order_by":4,"name":"Miriam Olivieri","email":"","orcid":"","institution":"University of Salerno","correspondingAuthor":false,"prefix":"","firstName":"Miriam","middleName":"","lastName":"Olivieri","suffix":""},{"id":612425956,"identity":"dabe2b76-76af-46cf-94f5-28f273b67c03","order_by":5,"name":"Francesca Felicia Operto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYBAC9gYogx+IDwAxD0EtPAfAlAGDZANUC0E9cC0GB2AiBLUwMB978HHPHznjG7kHD/z4wyBjT1gLW7rhjGcGxmY38hIO9vAQ4TB7Bh4zaZ4DBonbbuQYHGaQIMYvIC1/gFo2zwBpMSBWCwNQywYJkJYEYrQwA/3Sc8DYWOLMG4ODPQckeKBhiEcLe/OxBz8OyMnxt+cYf/jxx8YeHrk4ATMDGzJXgpB6MGAjrGQUjIJRMApGNgAAK0Q1SWFUjIMAAAAASUVORK5CYII=","orcid":"","institution":"Magna Graecia University of Catanzaro","correspondingAuthor":true,"prefix":"","firstName":"Francesca","middleName":"Felicia","lastName":"Operto","suffix":""}],"badges":[],"createdAt":"2026-03-08 14:08:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9064743/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9064743/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105728060,"identity":"07ee32dc-d384-4415-bd64-4a217b71aa90","added_by":"auto","created_at":"2026-03-30 11:08:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1035712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9064743/v1/bb7e7db1-f609-4c4c-8140-6482e4ee6f71.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The role of Somatization as a clinical marker of Depressive Symptoms and Suicidal Risk associated with Social Media use in Adolescents","fulltext":[{"header":"What is Known – What is New","content":"\u003cp\u003e\u003cstrong\u003eWhat is Known\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eExcessive social media use is linked to increased rates of depressive symptoms and sleep disturbances\u003c/li\u003e\n \u003cli\u003eSomatic complaints in adolescence often represent manifestations of underlying psychological distress.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eWhat is New\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003ePerceived \"online authenticity\" is a key driver of a sequential pathway leading to depressive symptoms and suicidal ideation.\u003c/li\u003e\n \u003cli\u003eSomatic symptoms provide a bridge between dysfunctional social media use and mental health symptoms, suggesting a potential marker for clinicians to identify at-risk youth.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eAdolescent mental health disorders have increased markedly over the past decade and currently represent a major global public health concern. Approximately one in seven individuals aged 10\u0026ndash;19 years is affected by a mental disorder, with anxiety and depressive conditions ranking among the leading causes of disability in this age group [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Suicide is now one of the primary causes of death among adolescents and young adults worldwide [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In pediatric clinical practice, this epidemiological trend translates into a growing number of consultations for emotional distress, behavioral dysregulation, and medically unexplained physical symptoms that often conceal underlying psychological vulnerability. Because early psychopathological manifestations frequently persist into adulthood, the identification of modifiable psychosocial risk factors has become a priority for preventive pediatric care.\u003c/p\u003e \u003cp\u003eOne of the most influential environmental factors shaping contemporary adolescent development is the digital ecosystem. Social media platforms are deeply embedded in youths\u0026rsquo; daily lives, with the majority of adolescents reporting daily use [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although these platforms facilitate social connection and identity exploration, they also expose adolescents to several psychosocial risks. A substantial proportion of youths exhibit patterns consistent with Problematic Social Media Use (PSMU), characterized by impaired control, withdrawal symptoms, and functional impairment [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. During adolescence, a developmental stage marked by heightened sensitivity to peer evaluation and identity formation, digital environments become central arenas for social comparison and self-presentation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExtensive literature has documented associations between intensive social media engagement and internalizing symptoms, particularly depression [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, the relationship between digital behaviors and suicidality remains less clearly defined, suggesting the presence of complex mediating mechanisms [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This association is likely bidirectional: excessive social media exposure may exacerbate depressive symptoms through cyberbullying, social comparison, and emotional overstimulation, while psychologically vulnerable adolescents may increasingly rely on digital environments as maladaptive coping strategies [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond psychological outcomes, problematic digital engagement has been increasingly linked to physical health complaints. Growing evidence indicates that adolescents with maladaptive patterns of social media use frequently report somatic symptoms, including headaches, fatigue, and recurrent pain syndromes [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Somatization may therefore represent a clinically relevant manifestation of psychological distress and a potential bridge between digital stressors and internalizing disorders. Importantly, pediatricians are often the first healthcare professionals to evaluate adolescents presenting with medically unexplained physical complaints, placing them in a strategic position for early detection of underlying psychosocial vulnerability.\u003c/p\u003e \u003cp\u003eRecent research further suggests that the qualitative characteristics of digital engagement may be more clinically informative than mere exposure time. In particular, perceived online authenticity \u0026mdash; defined as the subjective sense of expressing one\u0026rsquo;s \u0026ldquo;true self\u0026rdquo; more easily online than offline \u0026mdash; has emerged as a potentially relevant psychological construct [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While online environments may initially provide adolescents with a sense of belonging and emotional safety, excessive reliance on digital spaces for identity validation may paradoxically reinforce social withdrawal, emotional dysregulation, and psychological distress.\u003c/p\u003e \u003cp\u003eDespite growing societal concern and increasing regulatory efforts aimed at limiting minors\u0026rsquo; access to social media platforms, clinical practice requires a deeper understanding of the mechanisms linking specific digital behaviors to severe mental health outcomes. Clarifying these pathways is essential for developing clinically meaningful screening strategies and preventive interventions in pediatric settings.\u003c/p\u003e \u003cp\u003eThe present study investigates the associations between social media use patterns, perceived online self-expression, depressive symptoms, somatization, and suicidal ideation in a large school-based adolescent population. We tested a sequential mediation model hypothesizing that a preference for online self-expression increases time spent on social media, which in turn is associated with greater depressive symptoms and somatic complaints, ultimately elevating suicidal risk. By identifying clinically observable markers within this pathway, this study aims to provide pediatricians with actionable tools for early risk identification in an increasingly digitized developmental context.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cem\u003e2.1\u0026nbsp;\u0026nbsp;Ethics approval and consent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Campania Sud Ethics Committee (protocol number = 0033986). All procedures were conducted in accordance with the ethical standards of the responsible institutional committee and with the 1964 Helsinki Declaration and its later amendments. Written informed consent was obtained from all the parents or legal guardians prior to data collection.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2 Study design and sample selection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted an observational study involving 1436 adolescents recruited from three middle schools and three high schools of the Campania region, in Southern Italy. All the subjects with good compliance to the study were included; the subjects with severe medical or neuropsychiatric conditions that could affect cognitive abilities and compromise the understanding of the questionnaires despite the support were excluded (e.g., severe intellectual disability, etc.). The final sample included 1364 adolescents (653 males = 48%; 696 females = 51%; 15 undeclared = 1%) aged between 11 and 19 years (mean = 13.21 ± 0.43 years).All the parents were preliminarily informed of the purpose and methods of our research and provided their written informed consent. Preliminary meetings were also held with teachers and school principals, in order to illustrate the aims and methods of the study. Data were collected and examined by two neuropsychiatrists and one psychologist. Participants completed anonymously, in paper or digital format via Google Form, the following questionnaires:\u003c/p\u003e\n\u003cp\u003e- \u0026nbsp;a questionnaire investigating personal information (age and gender) , Social Media use\u003c/p\u003e\n\u003cp\u003e- a standardized self-report questionnaire assessing depressive symptoms (Children Depression Inventory - Second Edition (CDI-2)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e- 20 single questions assessing somatic symptoms and suicidal ideation selected from the standardized self-report questionnaire Child Behavior Checklist 6-18 years old (CBCL 6-18).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3Social Media questionnaire (SMQ)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe developed a questionnaire to explore quantitative and qualitative use of SM. \u0026nbsp;Questions are listed below:\u003c/p\u003e\n\u003cp\u003e1.\u0026nbsp; \u0026nbsp;\u0026nbsp;Please indicate your age\u003c/p\u003e\n\u003cp\u003e2.\u0026nbsp; \u0026nbsp;\u0026nbsp;Please indicate your gender\u003c/p\u003e\n\u003cp\u003e3.\u0026nbsp; \u0026nbsp;\u0026nbsp;How much time do you spend on Social Media on average each day?\u003c/p\u003e\n\u003cp\u003e4.\u0026nbsp; \u0026nbsp;\u0026nbsp;Which device do you use most to surf the Internet?\u003c/p\u003e\n\u003cp\u003e5.\u0026nbsp; \u0026nbsp;\u0026nbsp;What is your main reason for using Internet?\u003c/p\u003e\n\u003cp\u003e6.\u0026nbsp; \u0026nbsp;\u0026nbsp;Which Social Media do you mainly use?\u003c/p\u003e\n\u003cp\u003e7.\u0026nbsp; \u0026nbsp;\u0026nbsp;How do you accept new friends on social networks?\u003c/p\u003e\n\u003cp\u003e8.\u0026nbsp; \u0026nbsp;\u0026nbsp;Have you ever shared personal information or photos with people you met online? If so, what information?\u003c/p\u003e\n\u003cp\u003e9.\u0026nbsp; \u0026nbsp;\u0026nbsp;Do you ever find yourself staying up late on Social Media?\u003c/p\u003e\n\u003cp\u003e10.\u0026nbsp;\u0026nbsp;Have you ever been teased/bullied by someone online? And if so, why?\u003c/p\u003e\n\u003cp\u003e11.\u0026nbsp;\u0026nbsp;What do you think are the main reasons people get bullied on Social Media?\u003c/p\u003e\n\u003cp\u003e12.\u0026nbsp;\u0026nbsp;Do you talk about private things on the Internet that you wouldn't share with others in person?\u003c/p\u003e\n\u003cp\u003e13.\u0026nbsp;\u0026nbsp;Do you think it's easier to be yourself on the internet than in person?\u003c/p\u003e\n\u003cp\u003e14.\u0026nbsp;\u0026nbsp;Can you talk about more things online than you do in person?\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4 Children’s Depression Inventory, Second Edition (CDI-2)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eCDI-2 is a standardized questionnaire that assesses depressive symptoms in children and adolescents aged 7-17 years [14]. The CDI-2-Self Report (CDI-2 SR) is self-filled out by the child/adolescent himself and consists of 28 single questions, to which the subject can give a score on a Likert scale ranging from 0 to 3 (0=never, 1=sometimes, 2=often, 3=very often or most of the time). The single questions are grouped and form the following scale: Total Scale, Emotional Problems, Negative Mood/Physical Symptoms, Negative Self-Esteem, Functional Problems, Ineffectiveness and Interpersonal Problems. The scores of the individual scales are expressed inraw score, then converted to T-score. Therefore, higher T-scores (≥60) reflect a higher incidence of depressive symptoms and are in the “pathological range”, in which it is possible to distinguish between medium-high (60-64), high (65-69) and very high (\u0026gt;70) values.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.5 Child BehaviourCheckList 11-18\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Child Behavior Checklist (CBCL) is a standardized questionnaire [15] that aims to assess emotional and behavioral symptoms in adolescents. The form we used in this study is the self-report form, which is completed by children aged 11-18. The questionnaire includes 113 statements to which the child gives a score using a Likert scale ranging from 0-2: 0 Not true, 1 Sometimes True, 2 Often True. The raw scores are converted into t-scores and constitute three main scales, eight empirical syndrome subscales and six DSM-IV-oriented subscales. For the scales of the \"internalization problems\", \"externalization problems\" and \"total problems\", a t-score ≤59 indicates normal scores, a t-score between 60 and 64 indicates a score that is within a boundary range, and high levels of maladaptive behavior are indicated by t-score ≥65.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.6 Statistical Analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData were expressed as means, standard deviations, and proportions, and were analyzed using descriptive statistics. Data distribution was assessed using the Shapiro–Wilk test.\u003c/p\u003e\n\u003cp\u003eMultiple regression analyses were conducted to examine the associations between demographic and behavioral variables (including age, gender, and time spent on social media) and psychological outcomes, specifically depressive symptoms and suicidal ideation.\u003c/p\u003e\n\u003cp\u003eTo test the hypothesized pathways underlying suicidal ideation, a sequential mediation model was performed using the PROCESS macro for SPSS (Model 6). Based on theoretical assumptions and previous literature, time spent on social media, depressive symptoms, and somatization were specified as sequential mediators of the association between ease of online self-expression and suicidal ideation. The order of mediators reflected the sequence in which the constructs were assessed in the questionnaires. Gender-stratified analyses were also conducted to explore potential differences between males and females.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cem\u003e3.1 Sample characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSubject enrollment was voluntary, and the participation rate in the participating schools was 95%.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe questionnaires were administered in a paper format for 1009; instead, 364 questionnaires were filled out on a multimedia platform via Google forms. The main sample characteristics are summarized in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlease insert Table 1 here\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2. Descriptive analysis of Social Media Questionnaire, CDI-2 and CBCL.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe Social Media Questionnaire showed that 45% of participants reported spending 1–3 hours per day on social media, while 20% spent more than four hours daily and 18% described themselves as being “always connected.” Fourteen percent used social media for less than one hour per day, and only 2.5% reported not using social platforms.\u003c/p\u003e\n\u003cp\u003eSmartphones were the most commonly used devices (90%), whereas personal computers/laptops and tablets were used by 7% and 1% of participants, respectively.\u003c/p\u003e\n\u003cp\u003eSocial networking represented the primary reason for internet use (72%), followed by listening to music (51%), school-related activities (30%), and video gaming (24%). Instagram was the most frequently used platform (82%), followed by YouTube (56%). Less commonly used platforms included Facebook (15%), Pinterest (2.5%), Twitter (2.3%), TikTok (0.8%), and Twitch (0.2%).\u003c/p\u003e\n\u003cp\u003eRegarding online social interactions, 69% of adolescents accepted friend requests exclusively from individuals known offline, whereas 18% accepted requests based on selected profile characteristics and 9% accepted requests indiscriminately. Most participants reported not sharing personal information online, while 8% admitted doing so; 34% did not respond to this item.\u003c/p\u003e\n\u003cp\u003eLate-night social media use was frequently reported, with 42% indicating occasional use, 19% frequent use, and 11% very frequent use. Conversely, 26% reported never staying up late due to social media engagement.\u003c/p\u003e\n\u003cp\u003eMost adolescents reported never experiencing cyberbullying (49%), while smaller proportions reported occasional (7.5%), frequent (1%), or very frequent (0.4%) victimization. Non-response for this item was 42%. Reported perceived reasons for cyberbullying included physical appearance (88%), sexual orientation (67%), skin color (56%), clothing style and personal tastes (39%), religious beliefs (21%), academic performance (9%), and political opinions (9%).\u003c/p\u003e\n\u003cp\u003eConcerning online self-expression, 73% of participants did not consider social media a space where expressing personal thoughts was easier than in person. Similarly, 61% reported that it was not easier to be themselves online, whereas 27% and 10% indicated that it was sometimes or often easier, respectively. Discussing a wider range of topics online was reported as easier by 22% of participants sometimes and by 9% often, while 68% did not perceive this difference.\u003c/p\u003e\n\u003cp\u003eOn the CDI-2, depressive symptom scores were classified as medium–low in 77% of females and 82% of males, average in 10% of females and 7% of males, high in 6% of both genders, and very high in 7% of females and 5% of males.\u003c/p\u003e\n\u003cp\u003eCBCL self-report data indicated occasional alcohol use in 22% of participants and frequent use in 10%, while drug use was occasional in 4.5% and frequent in 5.5%. No alcohol or drug use was reported by 63% and 86% of adolescents, respectively. Suicidal ideation was absent in 88% of participants, occasional in 5%, and frequent in 4%.\u003c/p\u003e\n\u003cp\u003eDetailed results of the Social Media Questionnaire, CDI-2, and CBCL are presented in Table 2.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlease insert Table 2 here\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.4. Multiple regression analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTwo multiple regression models were conducted using CDI-2 Total Score and suicidal ideation as dependent variables, respectively. Both models revealed significant associations between the examined predictors and the outcome measures. Detailed results of the regression analyses are presented in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlease insert Table 3 here\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.5. Mediations analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSupporting our hypothesis, bootstrapping with 5,000 resamples indicated a significant indirect effect of ease of online self-expression on suicidal ideation through the hypothesized sequential mediation pathway (b = .01, 95% CI [.001, .003]). The overall model was significant (R² = .43, F(4, 1284) = 70.64, p \u0026lt; .001). Specifically, greater ease of online self-expression predicted increased time spent online, which was associated with higher depressive symptoms and, subsequently, greater somatization. Somatization, in turn, was significantly associated with suicidal ideation. The mediation was partial, as the direct effect remained significant after inclusion of the mediators (t = 2.48, p = .013, 95% CI [.008, .075]).\u003c/p\u003e\n\u003cp\u003eExploratory gender-stratified analyses revealed distinct patterns. Among females, the sequential mediation model replicated the overall findings, showing a significant indirect effect of ease of online self-expression on suicidal ideation through time spent online, depressive symptoms, and somatization (R² = .18, F(4, 659) = 35.39, p \u0026lt; .001, b = .01, 95% CI [.001, .003]). Partial mediation was again observed, as the direct effect remained significant after accounting for the mediators (t = 2.63, p = .008, 95% CI [.016, .114]).\u003c/p\u003e\n\u003cp\u003eIn contrast, the same sequential mediation pathway was not significant among males (b = .000, 95% CI [−.001, .001]). Instead, depressive symptoms and somatization independently mediated the relationship between ease of online self-expression and suicidal ideation (b = .010, 95% CI [.002, .021]). In this subgroup, mediation was full, as the direct effect was no longer significant after inclusion of the mediators (t = .15, p = .878, 95% CI [−.041, .048]).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study highlights that social media exposure represents a pervasive environmental factor in adolescent development and should be considered a relevant social determinant of mental health. While digital platforms offer opportunities for communication and identity exploration, our findings suggest that specific patterns of engagement—rather than exposure time alone—may serve as clinically meaningful markers of psychological vulnerability and suicide risk.\u003c/p\u003e\n\u003cp\u003eConsistent with previous literature [16–21], most adolescents in our sample reported daily and prolonged online activity. However, from a pediatric clinical perspective, the focus should move beyond quantitative measures of screen time toward qualitative features of digital behavior. Nighttime connectivity, compulsive checking, and persistent online presence may reflect sleep disruption and heightened fear of missing out (FOMO), factors known to exacerbate emotional dysregulation and vulnerability to depressive symptoms [22–25]. Sleep impairment, in turn, represents a well-established risk factor for mood disorders and suicidal ideation during adolescence.\u003c/p\u003e\n\u003cp\u003eOur findings confirm the association between prolonged social media use and depressive symptoms while further suggesting that the motivations underlying digital engagement may carry greater clinical relevance [26-30]. Adolescents who perceive online environments as safer spaces for authentic self-expression may increasingly rely on digital interaction to compensate for offline social difficulties [31,32]. Although this behavior may initially provide emotional relief, prolonged reliance on virtual environments may intensify social comparison, exposure to negative feedback, and emotional overload, thereby worsening psychological well-being [33].\u003c/p\u003e\n\u003cp\u003eA key clinical contribution of this study is the identification of somatization as a mediating factor linking digital behaviors to suicidal ideation. Pediatricians frequently evaluate adolescents presenting with recurrent headaches, abdominal pain, fatigue, and other medically unexplained physical complaints [34-36]. Our results suggest that these symptoms may not merely represent functional disorders but rather clinically observable manifestations of psychological distress associated with maladaptive digital engagement. Somatic symptoms may therefore function as early warning signals of underlying emotional vulnerability and suicide risk.\u003c/p\u003e\n\u003cp\u003eGender-specific analyses further refine risk profiling. Female adolescents showed a stronger sequential pathway linking online self-expression, prolonged digital exposure, depressive symptoms, somatization, and suicidal ideation. This pattern may reflect greater sensitivity to social comparison and interpersonal evaluation in digital contexts [37–40]. In contrast, among males, suicidal ideation appeared to be primarily mediated by depressive and somatic symptoms rather than by time spent online. These findings suggest that psychological distress in boys may be less directly expressed through digital behavior patterns and more frequently manifested through internalizing and somatic symptoms, requiring greater clinical vigilance.\u003c/p\u003e\n\u003cp\u003eFrom a pediatric practice perspective, these findings have direct implications for early identification and prevention. Adolescents presenting with depressive symptoms, sleep disturbances, unexplained somatic complaints, or social withdrawal may benefit from routine assessment of digital habits. Brief screening questions exploring time spent online, motivations for social media use, nighttime connectivity, and reliance on digital environments for emotional expression may help clinicians identify vulnerable youths. Integrating digital behavior assessment into standard psychosocial evaluations, such as the HEEADSSS framework, may improve suicide risk stratification and facilitate timely referral to mental health services.\u003c/p\u003e\n\u003cp\u003eThis study has limitations. The observational design precludes causal inference, and reliance on self-report measures may introduce reporting bias. Furthermore, the geographically restricted sample may limit generalizability to other sociocultural contexts. Longitudinal investigations are needed to clarify temporal relationships between maladaptive digital behaviors and psychological outcomes.\u003c/p\u003e\n\u003cp\u003eIn conclusion, social media use should be considered an integral component of psychosocial assessment in pediatric care. Qualitative patterns of digital engagement appear closely intertwined with adolescents’ emotional experiences and may provide early clinical indicators of psychological distress. Recognizing somatic symptoms as potential markers of digital-related emotional vulnerability may enhance early detection of at-risk youths and support more effective suicide prevention strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003e \u003cb\u003eEthics approval\u003c/b\u003e:\u003c/h2\u003e \u003cp\u003eThe study was approved by the Campania Sud Ethics Committee (protocol number\u0026thinsp;=\u0026thinsp;0033986).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate:\u003c/strong\u003e \u003cp\u003eWritten informed consent was obtained from all the parents or legal guardians prior to data collection.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThe authors received no financial support for this research.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, FFO, GMGP, AA; methodology GMGP, AA, RB, GD; formal analysis GMGP, AA, RB; data curation RB, GD, MO; write original draft preparation, GMGP, AA; writing, review and editing GMGP, AA, MO; supervision FFO. All the authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank all the young participants for dedicating their time to completing the questionnaires, making this study possible. We thank the school Principal and teachers who supported our project.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the findings of this study are openly available in Open Science Framework at [https://osf.io/n3d7e/overview?view\\_only=1e76c74aae62430d84d5f31fdd59c427](https:/osf.io/n3d7e/overview?view_only=1e76c74aae62430d84d5f31fdd59c427) Reference Number: n3d7e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNagata JM, Otmar CD, Shim J, Balasubramanian P, Cheng CM, Li EJ, Al-Shoaibi AAA, Shao IY, Ganson KT, Testa A, Kiss O, He J, Baker FC (2025) Social Media Use and Depressive Symptoms During Early Adolescence. 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Int J Environ Res Public Health 20(5):3924. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph20053924\u003c/span\u003e\u003cspan address=\"10.3390/ijerph20053924\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNesi J, Prinstein MJ (2015) Using Social Media for Social Comparison and Feedback-Seeking: Gender and Popularity Moderate Associations with Depressive Symptoms. J Abnorm Child Psychol 43(8):1427\u0026ndash;1438. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10802-015-0020-0\u003c/span\u003e\u003cspan address=\"10.1007/s10802-015-0020-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTwenge JM. Increases in Depression, Self-Harm, and, Suicide Among US (2020) Adolescents After 2012 and Links to Technology Use: Possible Mechanisms. Psychiatr Res Clin Pract. ;2(1):19\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1176/appi.prcp.20190015\u003c/span\u003e\u003cspan address=\"10.1176/appi.prcp.20190015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTwenge JM, Martin GN, Campbell WK (2018) Decreases in psychological well-being among American adolescents after 2012 and links to screen time during the rise of smartphone technology. Emotion 18(6):765\u0026ndash;780. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1037/emo0000403\u003c/span\u003e\u003cspan address=\"10.1037/emo0000403\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePastorino GMG, Olivieri M, Viggiano A, Meccariello R, Roccella M, Parisi L, Cerulli Irelli E, Di Bonaventura C, Orsini A, Operto FF (2024) Depressive symptoms in children and adolescents with epilepsy and primary headache: a cross-sectional observational study. Front Neurol 15:1395003. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fneur.2024.1395003\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2024.1395003\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Sample characteristics. m = mean; SD = standard deviation\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"404\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Sample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eSample Size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e1364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e653 (48%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e696 (51%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eundeclared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e15 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAge - m\u0026plusmn;SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e13.21 \u0026plusmn; 0.43 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eSocial Media Questionnaire\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"644\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHow much time do you spend on Social Media on average each day?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eNever =34 (2.5%)\u003c/p\u003e\n \u003cp\u003eLess than 1 hour = 193 (14%)\u003c/p\u003e\n \u003cp\u003e1 to 3 hours = 603 (44%)\u003c/p\u003e\n \u003cp\u003eOver 4 hours = 275 (20%)\u003c/p\u003e\n \u003cp\u003eI\u0026apos;m always connected = 246 (18%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhich device do you use most to surf the Internet?\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eSmartphone = 1234 (90%)\u003c/p\u003e\n \u003cp\u003ePersonal Computer/Laptop = 92 (7%)\u003c/p\u003e\n \u003cp\u003eTablet = 16 (1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhat is your main reason for using Internet?\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(more than one answer)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eSocial Network = 976 (72%)\u003c/p\u003e\n \u003cp\u003eListen to music = 696 (51%)\u003c/p\u003e\n \u003cp\u003eSchool research = 415 (30%)\u003c/p\u003e\n \u003cp\u003ePlay videogame = 327 (24%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you talk about personal things on the Social Media that you wouldn\u0026apos;t share with others in person?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eNever = 998 (73%)\u003c/p\u003e\n \u003cp\u003eSometimes = 252 (18%)\u003c/p\u003e\n \u003cp\u003eOften = 87 (6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you think it\u0026apos;s easier to be yourself on the Social Media than in person?\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eNever = 838 (61%)\u003c/p\u003e\n \u003cp\u003eSometimes = 370 (27%)\u003c/p\u003e\n \u003cp\u003eOften = 134 (10%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCan you talk about more things on Social Media than you do in person?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eNever = 924 (68%)\u003c/p\u003e\n \u003cp\u003eSometimes = 300 (22%)\u003c/p\u003e\n \u003cp\u003eOften = 117 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDo you ever find yourself staying up late on Social Media?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eSometimes = 578 (42%)\u003c/p\u003e\n \u003cp\u003eNever = 353 (26%)\u003c/p\u003e\n \u003cp\u003eOften = 260 (19%)\u003c/p\u003e\n \u003cp\u003eVery Often =149 (11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhich Social Media do you mainly use?\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(more than one answer)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eInstagram = 1122 (82%)\u003c/p\u003e\n \u003cp\u003eYoutube = 765 (56%)\u003c/p\u003e\n \u003cp\u003eFacebook = 203 (15%)\u003c/p\u003e\n \u003cp\u003ePinterest = 35 (2.5%)\u003c/p\u003e\n \u003cp\u003eTwitter = 32 (2.3%)\u003c/p\u003e\n \u003cp\u003eTikTok = 11 (0.8%)\u003c/p\u003e\n \u003cp\u003eTwitch = 2 (0.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHow do you accept new friends on social networks?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eOnly if I know the person in real life = 946 (69%)\u003c/p\u003e\n \u003cp\u003eBased on gender and profile photo = 247 (18%)\u003c/p\u003e\n \u003cp\u003eFrom anyone = 123 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHave you ever shared personal information or photos with people you met online? If so, what information?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eNo = 795 (58%)\u003c/p\u003e\n \u003cp\u003eYes = 107 (8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHave you ever been teased/bullied by someone online? And if so, why?\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eNever = 662 (49%)\u003c/p\u003e\n \u003cp\u003eSometimes = 101 (7.5%)\u003c/p\u003e\n \u003cp\u003eOften = 15 (1%)\u003c/p\u003e\n \u003cp\u003eVery Often = 5 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWhat do you think are the main reasons people get bullied on Social Media? (more than one answer)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003ePhysical appearance = 1205 (88%)\u003c/p\u003e\n \u003cp\u003eSexual orientation = 917(67%)\u003c/p\u003e\n \u003cp\u003eSkin \u0026nbsp;color = 759 (56%)\u003c/p\u003e\n \u003cp\u003eOpinions and tastes in clothing = 532 (39%)\u003c/p\u003e\n \u003cp\u003eReligious orientation = 286 (21%)\u003c/p\u003e\n \u003cp\u003eAcademic performance \u0026nbsp;= 128 (9%)\u003c/p\u003e\n \u003cp\u003ePolitic opinions = 125 (9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDI Total Scale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003emean = 11.84 \u0026plusmn; 8.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDI classification score (male)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003every high = 5%\u003c/p\u003e\n \u003cp\u003ehigh = 6%\u003c/p\u003e\n \u003cp\u003eabove average = 7%\u003c/p\u003e\n \u003cp\u003emedium-low = 82%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCDI classification score (female)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003every high = 7%\u003c/p\u003e\n \u003cp\u003ehigh = 6%\u003c/p\u003e\n \u003cp\u003eabove average = 10%\u003c/p\u003e\n \u003cp\u003emedium-low = 77%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCBCL\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAre you thinking about killing yourself?\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003enever= 1194 (88 %)\u003c/p\u003e\n \u003cp\u003esometimes= 66 (5%)\u003c/p\u003e\n \u003cp\u003eoften= 53 (4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 360px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSomatization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003emean=1.99 \u0026plusmn; 1.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u0026nbsp;\u003c/strong\u003eMultiple regression analysis\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"bottom\" style=\"width: 40.3813%;\"\u003e\n \u003cp\u003eDependent variable:\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCDI-2 Total Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 24.7833%;\"\u003e\n \u003cp\u003enon-standardized coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003estandardized coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7851%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eIndependents variants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6378%;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7851%;\"\u003e\n \u003cp\u003e,439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e,068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e,160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e6,451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6378%;\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7851%;\"\u003e\n \u003cp\u003e-1,515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-,097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-3,784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6378%;\"\u003e\n \u003cp\u003eTime on Social Media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7851%;\"\u003e\n \u003cp\u003e1,011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e,198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e,132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5,108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6378%;\"\u003e\n \u003cp\u003ebeing yourself online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7851%;\"\u003e\n \u003cp\u003e2,259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e,282\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e,194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e8,014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6378%;\"\u003e\n \u003cp\u003eSomatization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7851%;\"\u003e\n \u003cp\u003e1,470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e,100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e,368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e14,680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6378%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7851%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 577px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"bottom\" style=\"width: 40.3813%;\"\u003e\n \u003cp\u003eDependent variable: suicidal ideation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 24.7833%;\"\u003e\n \u003cp\u003enon-standardized coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003estandardized coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003et\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 69px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 11.7851%;\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 93px;\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 98px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"6\" valign=\"top\" style=\"width: 49px;\"\u003e\n \u003cp\u003eIndependentsvariants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6378%;\"\u003e\n \u003cp\u003eage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7851%;\"\u003e\n \u003cp\u003e,004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e,005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e,025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e,878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e,380\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6378%;\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7851%;\"\u003e\n \u003cp\u003e,036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e,025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e,042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e,145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6378%;\"\u003e\n \u003cp\u003eTime on Social Media\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7851%;\"\u003e\n \u003cp\u003e-,017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e,012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-,040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e-1,362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e,173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6378%;\"\u003e\n \u003cp\u003ebeing yourself online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7851%;\"\u003e\n \u003cp\u003e,035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e,018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e,055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1,993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e,046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6378%;\"\u003e\n \u003cp\u003eSomatization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7851%;\"\u003e\n \u003cp\u003e,038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e,007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e,172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e5,602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.6378%;\"\u003e\n \u003cp\u003eCDI-2 Total Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11.7851%;\"\u003e\n \u003cp\u003e,017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e,002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e,297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e9,293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e,000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"european-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpe","sideBox":"Learn more about [European Journal of Pediatrics](https://www.springer.com/journal/431)","snPcode":"431","submissionUrl":"https://submission.nature.com/new-submission/431/3","title":"European Journal of Pediatrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Social Media, Adolescents, Depressive symptoms, Suicidal Ideation, Self-expression, Somatiation","lastPublishedDoi":"10.21203/rs.3.rs-9064743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9064743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eAdolescent mental health disorders are increasing worldwide, and suicidal ideation represents a major public health concern. Social media use is a pervasive component of adolescents\u0026rsquo; daily lives and may influence psychological well-being through complex behavioral and emotional mechanisms. This study investigated whether patterns of social media engagement and perceived online self-expression are associated with depressive symptoms, somatic complaints, and suicidal ideation in adolescents.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA survey was conducted among 1,364 adolescents aged 11\u0026ndash;19 years recruited from six schools. Participants completed questionnaires assessing social media use patterns, depressive symptoms (Children\u0026rsquo;s Depression Inventory-2), and emotional\u0026ndash;behavioral problems including somatization and suicidal ideation (Child Behavior Checklist). Correlation analyses, multiple regression models, and sequential mediation analyses were performed to examine direct and indirect associations among variables.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eDaily social media use was reported by 96% of participants, with 20% spending more than four hours per day online. Clinically relevant depressive symptoms were observed in approximately 12% of adolescents, while 9% reported recurrent suicidal ideation. Greater time spent on social media was significantly associated with higher levels of depressive symptoms, somatic complaints, and suicidal ideation. Adolescents reporting greater ease in online self-expression showed increased psychological vulnerability. Sequential mediation analysis indicated that perceived online self-expression was indirectly associated with suicidal ideation through increased time spent online, depressive symptoms, and somatization. Gender-stratified analyses revealed stronger sequential effects in females, whereas in males suicidal ideation was primarily mediated by depressive and somatic symptoms.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eProblematic patterns of social media engagement may represent clinically relevant psychosocial risk markers in adolescents. Somatic symptoms appear to function as a clinical bridge between maladaptive digital behaviors and suicidal vulnerability. Pediatricians should consider screening for digital habits when adolescents present with depressive symptoms and medically unexplained somatic complaints, as early identification of at-risk youths may improve preventive interventions.\u003c/p\u003e","manuscriptTitle":"The role of Somatization as a clinical marker of Depressive Symptoms and Suicidal Risk associated with Social Media use in Adolescents","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-27 20:09:21","doi":"10.21203/rs.3.rs-9064743/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"272733183277522082694338676079449013069","date":"2026-05-11T14:54:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-25T18:22:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118676062503960567080812717840984621051","date":"2026-04-06T14:16:36+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-25T08:11:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-23T11:29:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-23T10:30:23+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Pediatrics","date":"2026-03-08T14:04:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpe","sideBox":"Learn more about [European Journal of Pediatrics](https://www.springer.com/journal/431)","snPcode":"431","submissionUrl":"https://submission.nature.com/new-submission/431/3","title":"European Journal of Pediatrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e527fe06-f64a-40ad-b6fe-c01aeaa5e958","owner":[],"postedDate":"March 27th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"272733183277522082694338676079449013069","date":"2026-05-11T14:54:54+00:00","index":68,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-27T20:09:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-27 20:09:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9064743","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9064743","identity":"rs-9064743","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}