Screen Time and Young People: A Systematic Review and Meta-analysis of the Evidence on Self-Harm and Suicidality

Screen Time and Young People: A Systematic Review and Meta-analysis of the Evidence on Self-Harm and Suicidality | 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 Systematic Review Screen Time and Young People: A Systematic Review and Meta-analysis of the Evidence on Self-Harm and Suicidality Kerri M Gillespie, Matthew Morgan, Bridget Weir, Grace Branjerdporn, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8209607/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objectives: The aim of this systematic review and meta-analysis was to investigate the impacts of screen time and screen behaviours on suicidality and non-suicidal self-injury (NSSI) in children and young people. Methods: A systematic search was conducted of the following databases: CINAHL, PubMed, Embase, PsycARTICLES, PsycINFO, Scopus, and Web of Science. The search identified 61 eligible studies comprising 338,472 participants aged up to 18 years, drawn from 16 countries. A random effects meta-analysis of odds ratios was conducted on 15 studies. Results: Meta-analysis revealed that screen time was measured inconsistently across studies, yet frequent screen use – particularly nocturnal smartphone use – was significantly associated with increased odds of NSSI and suicidal behaviours. Internet addiction (IA) showed strong links to suicidal behaviours, often mediated by insomnia, depression, or anxiety. Internet gaming disorder (IGD) also predicted suicidality and NSSI, while mobile phone and social media addiction demonstrated weaker but significant associations. IA was positively associated with NSSI across all seven relevant studies. Structural models identified depression, loneliness, and interpersonal problems as key mediators. Some gender disparities emerged, with females reporting higher NSSI and suicidality, and males showing higher rates of digital addiction. Conclusion: While these findings highlight concerning associations between excessive screen time and suicidality, they are limited by methodological heterogeneity and inconsistency, raising questions about directionality – whether excessive screen time contributes to poor mental health or preexisting vulnerabilities drive increased screen use. Psychiatry Screen time self-injurious behavior self-harm suicide internet addiction internet gaming disorder Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The association between screen time and suicidal or non-suicidal self-injury (NSSI) in young people is gaining increasing attention in academic literature and public policy discourse (Chen et al., 2024; World Health Organization (WHO), 2019). In Australia, the federal government amended the Online Safety Act 2021 to ban users under the age of 16 from having social media accounts. To take effect in late 2025, the legislative change seeks to reduce the impact of social media harms for young people. Although not yet fully understood, the rising frequency of NSSI and suicidal behaviours among young people is a serious concern, particularly as it appears to be increasing in tandem with greater exposure to digital media, especially in the aftermath of the COVID-19 pandemic (Alves et al., 2025). Considering these concerns, this systematic review and meta-analysis of the literature examines the relationship between screen time and suicidal or self-harming behaviours in young people. Such a review is essential to synthesise fragmented evidence, identify gaps in current research, and inform the development of targeted prevention and intervention strategies. Suicide and non-suicidal self-injury Suicidal behaviour and NSSI represent critical public health challenges, ranking among the leading causes of injury and mortality globally (Australian Institute of Health and Welfare (AIHW), 2025a; WHO, 2025). Suicide is defined as the intentional act of ending one’s life, while suicidal behaviours encompass nonfatal thoughts and actions related to suicidal intent, including suicidal ideation (SI - thoughts of ending one’s life), suicide plans, and suicide attempts (Posner et al., 2007; Silverman et al., 2007). NSSI is defined as intentional self-injury without suicidal intent (Zetterqvist et al., 2013). Due to this lack of suicidal intent, it is often excluded from suicide research; however, its role as a significant risk factor for suicide warrants its inclusion (Nock and Kessler, 2006; Nock and Prinstein, 2005). Globally, suicide is the third leading cause of death among individuals aged 15–29, contributing to approximately 727,000 deaths annually (WHO, 2025). Temporal analyses of the WHO Mortality Database indicate alarming increases in youth suicide rates, particularly in the United States, the United Kingdom, and parts of Central and South America (Bertuccio et al., 2024). Rates of suicide in adolescents aged 15-17 have also more than doubled in Australia since 2004, with males more likely to end their lives by suicide than females (AIHW, 2025c). Nearly half of all individuals who die by suicide have a history of NSSI (Hawton et al., 2012; Duarte et al., 2020). The global prevalence of adolescent self-harm is estimated at 17% to 21%, with urban areas reporting higher rates (Lucena et al., 2022). In Australia during 2022-23, children aged 15 to 19 had the highest rate of hospitalisation for NSSI of all age groups at 308 per 100,000, with rates much higher in females than males (AIHW, 2025b). Hospitalisations related to NSSI among females aged 14 and under have increased dramatically – from 19 per 100,000 in 2008-09 to 66 per 100,000 in 2022-23 (AIHW, 2025c). These behaviours are strongly associated with adverse mental health outcomes, psychiatric hospitalisation, and elevated suicide risk (Andreo-Jover et al., 2024; Ghoghre, 2024; Li et al., 2024). Identifying modifiable risk factors for these behaviours in young people is imperative for developing effective preventative strategies. Screen time Screen time, defined as time spent using televisions, smartphones, tablets, and computers, has increased dramatically over the past two decades, with many adolescents surpassing recommended daily limits (Qi et al., 2023; Twenge et al., 2018). It is estimated that 68% to 95% of adolescents have access to a smartphone (Keles et al., 2020; Moreno et al., 2022) and around 91% of UK and US adolescents use social media (Dane and Bhatia, 2023). According to Australian Guidelines (Department of Health and Aged Care, 2021), children under 2 years old should have no screen time, children aged 2 to 5 have no more than one hour per day, and those aged 5-17 should limit recreational screen time to no more than 2 hours per day – excluding screen time used for schoolwork. Despite this, less than 10% of families meet these guidelines for children under 2 years of age (Brushe et al., 2023), and adolescents in the US spend an average of 4.8 hours per day on social media (Rothwell, 2023). While digital connectivity facilitates access to information, learning, and social interaction, excessive screen use has been associated with adverse mental health outcomes, including NSSI, suicidal behaviour, depression, and anxiety (Twenge et al., 2018; Stiglic and Viner, 2019). However, research suggests that different types of screen time may have different impacts on child health. Some forms of screen time may be detrimental, while others act as a supportive mechanism to promote mental well-being (Rothwell, 2023; Hamilton et al., 2024b). Neuroimaging studies of internet and gaming addiction have identified similarities in underlying mechanisms to those of drug addiction (Weinstein and Lejoyeux, 2020; Weinstein et al., 2017). Initial exposures bring an increase in dopaminergic stimulation in mesolimbic and nigrostriatal pathways, known as the reward system of the brain (Volkow et al., 2019). Over time, chronic exposure leads to dysfunction in the reward system, reduced dopaminergic sensitivity, withdrawal, and cravings. Neuroplastic maladaptation associated with addiction is linked to reduced activity in the prefrontal cortex, impaired impulse control and decision-making, depression, and impaired emotion regulation (Goldstein and Volkow, 2011; Adinoff, 2004; Rácz, 2014). Reductions in grey and white matter volume in regions associated with emotion regulation, impulse control, attention, and memory have also been associated with internet and gaming addiction, and increased screen media use (Weinstein and Lejoyeux, 2020; Zhou et al., 2011). Despite this growing body of evidence, significant gaps remain in understanding how screen time influences suicidal behaviours. Variability in measurement methodologies, ranging from self-reported surveys to app-based tracking, hinders cross-study comparability (Chen et al., 2024). Additionally, broad categorisations of screen time often overlook distinctions between active (e.g., gaming) and passive (e.g., streaming) use, or between positive (e.g., educational) and negative (e.g., exposure to harmful content) engagements (Pandya and Lodha, 2021; Mougharbel et al., 2023). While numerous studies examine the relationship between screen time and self-harm or suicide-related outcomes, systematic reviews consolidating these findings are limited and often outdated or narrow in scope. Existing reviews have focused on a singular aspect of screen time or screen behaviour, (Daine et al., 2013; Marchant et al., 2017; Memon et al., 2018), combine various forms of screen behaviours into a singular item (Hoare et al., 2016), or synthesise a wide and disparate age range, incorporating very young children, adolescents, and young adults (Daine et al., 2013). More recent studies have focussed primarily on longitudinal studies, leaving cross-sectional evidence underexplored (Chen et al., 2024; Vasconcellos et al., 2025). Considering the heterogeneous nature and restricted focus of many papers, it is essential to incorporate both longitudinal and cross-sectional studies to gain a comprehensive understanding of the impacts of screen time and screen time behaviours. More importantly, fast-paced technological advancements mean that many of these studies are now outdated, and as a result, many fail to accurately reflect current screen and device use among young people. A systematic review that synthesises cross-sectional and longitudinal studies on screen-based activities and suicidal behaviours would offer critical insights into immediate associations, emerging trends, and diverse demographic patterns, thereby informing timely interventions and policy development. This systematic review aims to bridge these gaps by synthesising findings from all studies that explore the relationships between various types of screen-based activities [e.g., social media use, time spent on screens, and internet addiction (IA)] and suicide-related outcomes (e.g., SI, attempts, and NSSI) among children and adolescents. By consolidating these insights, this review seeks to clarify these associations and inform interventions targeting screen-based risk factors for suicidality. Methods This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, and the MOOSE Reporting Guidelines for Meta-analyses of Observational Studies. The protocol has been registered with PROSPERO (ID: CRD42023493058; available at https://www.crd.york.ac.uk/PROSPERO/view/CRD42023493058). Initial searches were conducted on December 12 th , 2023. An updated search was conducted on January 26 th , 2025. Search strategy A systematic review of the literature was conducted using CINAHL, PubMed, Embase, PsycARTICLES, PsycINFO, Scopus, and Web of Science databases. A title, abstract, and keyword search was completed using terms relevant to screen media, suicide, and self-harm, combined with child or adolescent terms (see Figure 1). MeSH and Emtree terms for suicide, self-injurious behaviour, and automutilation were included where appropriate. Citation chaining and hand searches were conducted to ensure no eligible articles were missed. Figure 1. Search terms Eligibility criteria Studies were included if they examined at least one form of screen media use and NSSI and/or suicidal behaviours were included as dependent or independent variables. Studies that investigated psychiatric samples were excluded. Young people, defined as children and adolescents aged from 0 to 18, were eligible for inclusion to permit the investigation of differences across the developmental spectrum. An upper age limit of 18 years was selected to correspond with the legal age of majority, marking a transition to adult roles and responsibilities that come with their own distinct health, behavioural, and social factors. Studies that focused solely on cyberbullying were excluded from the analysis to ensure consistency and comparability of the included studies. Studies were limited to those available in English and published in 2007 or later to align with the release of the iPhone and to provide a comparable digital environment. The review excluded studies not containing primary data (review papers, systematic reviews, opinion and commentary papers), dissertations, editorials, posters, and conference abstracts. Cross-sectional studies presenting secondary analyses of data, were excluded to prevent duplication of data. Study selection Identified papers were exported to Covidence web-based software (Veritas Health Innovation, 2019) for screening. Screening was conducted in two stages: title and abstract, and full-text screening. Both stages utilised the same screening process: four independent reviewers conducted screening, with one primary reviewer screening all papers to maintain consistency. Disagreements were resolved by a fifth reviewer or by discussion between two or more reviewers. Data extraction Data were extracted from all eligible studies based on a pre-defined data extraction spreadsheet, which included variables relating to population demographics, screen time type, self-harm or suicidal items, outcome measures, and findings. Where data was missing, an email request was sent to the corresponding author. Two independent reviewers extracted data and completed the data extraction worksheet. Where differences in the worksheet were identified, a third reviewer resolved discrepancies. Meta-analysis A random effects meta-analysis of odds ratios, using the Restricted Maximum-Likelihood (REML) heterogeneity estimator, was conducted with cross-sectional studies that investigated addictive behaviours and NSSI or suicidal behaviours. Sub-group analyses were conducted for the different addictive behaviours: IA; mobile phone addiction (MPA); and internet gaming disorder (IGD). Risk of bias assessment Risk of bias was assessed using the Joanna Briggs Institute (JBI) checklist for cross-sectional and cohort studies. These tools include questions relating to sample selection, study design, analytical methods, and reporting. Questions are answered by choosing Yes (1 - little risk of bias); No (0 - risk of bias); Unclear (0.5); or Not applicable (no score). A final score is created by summing all scores and dividing by the number of responses. Based on previous use of this tool (Shu et al., 2024), a score of 70% or higher is considered good quality, 50% - 69% is considered acceptable, and below 50% is considered poor quality. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) guidelines (Atkins et al., 2004). Deviations from the protocol The protocol for this review has been published (Gillespie et al., 2024). Minor alterations have been made to this protocol. Age ranges were initially limited to those under 18 years. However, many studies incorporating high school years up to grade 12 have by necessity included 18-year-olds, which makes up a small percentage of grade 12 classes. This is in keeping with previous literature investigating child and adolescent groups (Madigan et al., 2022). Any study that specifies the inclusion of students or community members aged over 18 years of age has been excluded. The current review has also limited the included papers by excluding those focused solely on cyberbullying, given the large number of cyberbullying studies published, and to provide more reliable results by focusing on more comparable exposures. Results Study Selection See Figure 2 for a flow diagram describing the steps conducted during the search and screening process. The final search obtained 14,542 articles. After removing duplicates, 7203 underwent title and abstract screening, leaving 677 for full-text review. Fourteen papers were unable to be located, and an additional 602 did not meet the inclusion criteria. A total of 61 studies were included in the final analysis. Figure 2. PRISMA flow diagram (Page et al., 2021) Study characteristics The 61 identified studies included a total of 338,472 young people: 160,702 males; 156,019 females; and 18 individuals who identified as non-binary (Marengo et al., 2024) with mean ages ranging from 9.85 (Marengo et al., 2024) to 16.8 years (Oktan, 2015), or in grades one to twelve. A pooled mean and standard deviation were calculated as M pooled = 10.26 and SD pooled = 1. Studies were cross-sectional (n = 44) (see Table 1) or longitudinal cohort (n = 17) designs (see Table 2). Thirty-one of the included studies investigated suicide behaviours, 23 investigated NSSI, and seven studies looked at both outcomes. Studies evaluated one or more of the following independent variables: Problematic Internet Use (PIU) or IA (from here on we will use IA to refer to both of these terms) (n = 25); screen time (frequency or duration of device or internet use) (n = 16); IGD (n = 10); MPA (n = 5); Social media activity (n = 4); Social Media Addiction (n = 2); sexting (n = 2); and lifetime experience of video slot machines (n = 1). The majority of studies were conducted in China (n = 32), and the rest were conducted in Europe (n = 6), the USA (n = 5), Korea (n = 5), Taiwan (n = 3), Israel (n = 2), Turkey (n = 2), Indonesia (n = 1), Colombia (n = 1), Egypt (n = 1), Japan (n = 1), Thailand (n = 1), and Vietnam (n = 1). Rates of suicidal ideation were reported in 22 studies, and ranged from 3% (Taechoyotin et al., 2020) to 37.7% (Teng et al., 2023) of participants. Rates of suicide attempts were reported in six studies, ranging from 1.4% (Nguyen et al., 2020) and 21.8% (Teng et al., 2023). The prevalence of NSSI was reported in 16 studies, ranging from 3.3% (Oshima et al., 2012) to 47.11% (Wang et al., 2022a) among the study samples. There was also a wide discrepancy across rates of IA (ranging from 1.6% (Kim et al., 2006) to 65.6% (Khalil et al., 2022) reported in 9 studies), IGD (from 0.7% (Lan et al., 2022) to 25.1% (Lee and Ham, 2018) in eight studies), social media or facenook addiction (reported as 19% (Dumont et al., 2024) and 92.8% (Khalil et al., 2022)), and MPA (reported as 11.11% (Wang et al., 2022a) and 26.2% (Cheng et al., 2024). Screen time and mental health assessments Screen time frequency was measured inconsistently across seventeen studies: as a mean number of hours; in two-to-three-hour increments; more or less than 90 or 120 minutes; rarely, occasionally, or frequently; or merely defined as ‘high intensity use’ of screens, video games, mobile phones, or apps. Pathological screen time was measured in a majority of studies, using a wide range of validated measures of pathological internet use, internet gaming disorder, MPA, and social media addiction (collectively labelled here as screen-related addictive behaviours). Measuring suicidal ideation, planning, and attempts were most commonly measured using one or more yes/no questions relating to the presence of suicidal thoughts or intent rather than a validated tool. NSSI outcomes included NSSI episodes, NSSI urges, frequency of NSSI, and medical care received for NSSI. NSSI was also frequently measured using yes/no questions relating to the presence or frequency of self-harming acts, but was more likely to be measured using a validated measure of self-harm behaviours. Cross-sectional associations Screen time frequency Thirteen studies investigated the impact of screen time frequency on NSSI and suicidal behaviours, with nine of these finding significant positive associations. Frequent smartphone use was associated with greater odds of NSSI, suicidal ideation, and suicide attempts when controlling for demographic factors (Ai et al., 2025; Chen et al., 2020). Almost daily nocturnal mobile phone use was associated with more than three times the odds of NSSI, and more than double the odds of suicidal feelings and poor mental health (Oshima et al., 2012). However, these odds decreased to between 1.56 and 1.65 times when adjusted for sex, age, substance use, and sleep length. The number of social media apps used was also positively correlated with SI ( r =0.184) and social media usage had a significant direct effect on SI (Marengo et al., 2024). When investigating hours of screen time, more than two (Guo et al., 2024), four (Zhang et al., 2024), or ten hours per day (Chau et al., 2022) was associated with higher rates of NSSI, SI and planning, or suicide attempts, respectively. Dumont et al. (2024) also found that daily screen time significantly increased the odds of SI, however, the method used to measure screen time was not reported. Lan et al. (2022) found that internet use hours per day were positively associated with NSSI scores. Increased school day video game and computer exposure (more than two hours) was also associated with a greater risk of NSSI (Liu et al., 2016). Four studies found no relationship between screen time and NSSI or suicidal behaviours (Wiguna et al., 2021; Martinez-Estevez et al., 2024; Nguyen et al., 2020; Kim et al., 2023). These papers employed a cut-off of two hours or more (Kim et al., 2023; Nguyen et al., 2020), or over 90 minutes’ screen time per day (Martinez-Estevez et al., 2024). Wiguna created a six-point Likert scale in 2-hour increments, from less than two hours to ten hours or more (Wiguna et al., 2021). Screen behaviours The majority of identified studies (n = 32) investigated the impacts of screen-related addictive behaviours. Fifteen studies measured IA and suicidal behaviours, with 14 of these identifying a significant positive relationship between IA and SI, plans, and/or attempts. Only one regression model did not find IA to be a predictor of suicidal thoughts (Amendola and Cerutti, 2023). However, this paper did reveal that viewing content relating to self-harm, suicide, and drug use was associated with suicidal thoughts. They also found that children who had suicidal thoughts were more likely to have IA. Four papers (Kim et al., 2006; Lu et al., 2025; Karabel et al., 2024; Khalil et al., 2022) found suicidal behaviours were more common in those with IA. Seven studies investigated, and found significant positive associations between, IA and NSSI. However, adjusted odds ratios varied greatly; from 1.01 (Hamdan et al., 2022) to 18.84 (Kim et al., 2023). IGD was investigated in eight papers; six investigating suicidal behaviours, and two investigating NSSI. All found a significant positive relationship between IGD and suicidal behaviours or NSSI. Yu et al. (2020) reported that IGD, depression, and insomnia were all significant predictors of SI. Insomnia was also seen to mediate the relationship between IGD and SI. Xie and colleagues (Xie et al., 2023) found the relationship between IGD and SI to be mediated by negative emotions. They also found that SI was higher in the IGD group (6.3%) compared to those not experiencing IGD (5%), though this was not tested for statistical significance. MPA was assessed in three studies. Cheng et al. (2024) identified MPA as a significant predictor of NSSI, SI and suicide attempts, but not for suicide planning. Long et al. (2024) found a weak but significant positive association between MPA and NSSI, while Wang et al. (2022a) found that MPA increased the odds of NSSI by more than four times (aOR = 4.28). Dumont et al. (2024) investigated the association between social media addiction and SI. They found that an increased social media addiction score increased the odds of SI in a univariate analysis (OR = 1.12; 95% CI=1.05, 1.19, p=0.001), though this was not significant when controlling for other variables. Other screen types measured included sexting (Lan et al., 2022) and video slot machines (Mosconi et al., 2024). Experience of both were associated with an increase in NSSI. Gender Fifteen studies identified gender disparities. A slightly higher number of studies (Guo et al., 2024; Long et al., 2024; Wang et al., 2022a) found females to experience a higher prevalence of NSSI compared to studies that found males with a higher prevalence in their sample (Kim et al., 2023; Hamdan et al., 2022). Seven studies observed higher rates of suicidal behaviours in females (Oshima et al., 2012; Kim et al., 2023; Tan et al., 2024; Zhang et al., 2024; Teng et al., 2023; Xie et al., 2023; Dumont et al., 2024). Only one found higher rates in males (Huang et al., 2020). Four studies reported higher prevalences of IA (Lu et al., 2025; Karabel et al., 2024), IGD (Xie et al., 2023), and video slot machine use (Mosconi et al., 2024) in males. Longitudinal associations Five studies investigated the impact of screen time frequency of use on future NSSI and suicidal behaviours, with conflicting findings. Wang et al. (2020) and Chu et al. (2023) utilised self-report data to assess the impact of baseline media use on outcomes at one- and two-year follow-up, respectively. Both found increased use of screen behaviours, such as internet and mobile phone use (Wang et al., 2020), or videos, video games, texting, and video chatting (Chu et al., 2023) led to greater odds of suicidal behaviours and NSSI at follow-up. Both studies found no impact of television hours, and Chu also failed to detect an impact of social networking. Hamilton et al. (2024a) also found no relationship between hours spent on social media and SI over two months, whereas Kim et al. (2017), found that social networking was consistently associated with greater SI at one year. Coyne et al (Coyne et al., 2021) was the only study to employ a subjective measure of screen time (passive sensing) or to investigate longitudinal trends over a ten-year period. Significant results were identified only in girls, where early and high levels of both social media and television, which increased over time, were associated with higher levels of suicide risk in emerging adulthood. Increasing levels of video games were also predictive of suicide risk in girls only. Screen-related addictive behaviours Four studies investigated diverse types of social media behaviours. Negative experiences on social media were associated with greater odds of reporting SI, whereas positive experiences were associated with lower odds (Hamilton et al., 2024a). Individuals who more frequently shared content as younger adolescents, in addition to other social media activities, had significantly higher odds of NSSI one year later than those who engaged in moderate messaging and browsing, but less content sharing (Winstone et al., 2022). The final study highlighted a bidirectional relationship between social media exposure of NSSI, and engaging in NSSI among adolescents (Wu et al., 2024). Only one paper looked at sexting, finding that non-consensual sexting (sending intimate images of others against their will) or being pressured to sext were both positively associated with NSSI (Wright and Wachs, 2024). Nine of the longitudinal studies investigated the impacts of screen-related addictive behaviours over the course of five to 18 months. Five of these studies found a significant positive association between IA (Li et al., 2025; Ma et al., 2023; Pan and Yeh, 2018), IGD (Gao et al., 2025; Gong et al., 2025), MPA (Li et al., 2022) and problematic social media use (Shen et al., 2024) on NSSI and suicidal behaviours. However, Shen et al. (2024) found that problematic social media use only influenced initial levels of NSSI, not NSSI over time, which declined consistently over 12-months. While Gong et al. (2025) identified a correlation between IA and NSSI across all time points, they also found that internalising symptoms fully mediated these relationships. Similarly, Lin et al. (2025) observed no direct relationship between IA and NSSI, but did find that this relationship was mediated by depression and anxiety. One 18-month study found that NSSI was predictive of IA, but IA was not predictive of NSSI . Risk of bias The majority of studies (n = 36) were of high quality, with only three scoring below 50% (see Table 1 for individual scores). However, 36 cross-sectional studies failed to adequately describe their inclusion and exclusion criteria. A common risk of bias for all study types was a lack of validated tools to measure exposure or outcome variables. Around half of all studies used yes/no questions to determine the presence of suicidal or NSSI behaviours, or used binary or limited categorical variables to determine screen time. Failing to identify or adequately control for confounding factors was also commonly identified (n = 15). Twelve longitudinal studies failed to adequately describe, or explore the reasons for, loss to follow-up, or did not utilise strategies to address participant drop-out. According to GRADE guidelines, the quality of evidence for NSSI was high (See Table s1). Quality of evidence was deemed low for suicidal behaviours due to significant heterogeneity and risk of publication bias identified in the meta-analysis. The findings for suicidal behaviours should therefore be interpreted with caution. Meta-analysis Two separate meta-analyses were conducted on cross-sectional papers to investigate the relationships between addictive digital behaviours and NSSI or suicidal ideation (see Figures 3 and 4). One included paper measured suicide risk rather than suicidal ideation (Khalil et al., 2022), however, sensitivity analyses showed that the removal or addition of this individual study had no substantial effect on the results. Subgroup analyses were conducted to identify differences between IA, IGD, or mobile phone and social media addiction (grouped due to small study numbers). Analysis was conducted using a random effects model and the inverse-variance method. REML estimation was chosen due to the high heterogeneity observed. Overall, there was a significant effect of digital addiction on NSSI (OR = 2.74, 95% CI 1.55 to 4.84, p = 0.01). However, subgroup analysis identified no significant effect of IA (OR = 3.04, 95% CI 0.01 to 790.24, p = 0.24) or IGD (OR = 2.93, 95% CI 0.08 to 108.83, p = 0.16) on NSSI. There was also an overall effect of digital addiction on suicidal behaviours (OR = 2.51, 95% CI 1.9 to 3.33, p < 0.001). Subgroup analyses identified a significant effect of both IA (OR = 3.08, 95% CI 1.8 to 5.29, p = 0.002) and MPA or social media addiction (OR = 1.9, 95% CI = 1.69 to 2.15, p = 0.009) on suicidal behaviours. IGD showed a trend toward increasing suicidal behaviours, but fell short of significance (OR = 2.17, 95% CI 0.94 to 4.99, p = 0.06). Longitudinal studies were too heterogeneous to conduct a meta-analysis and were therefore only analysed narratively. Figure 3. Random-effects meta-analysis for studies measuring digital addiction and non-suicidal self-injury. Figure 4. Random-effects meta-analysis for studies measuring addictive digital behaviours and suicidal ideation. Heterogeneity of reported outcomes and study bias In the meta-analysis of digital addiction and NSSI, overall heterogeneity was moderate (I 2 = 49%), but more substantial in the individual subgroups of IA (I 2 = 57%) and IGD (I 2 = 66%) (see Figure 4). Heterogeneity was varied in the suicidal behaviours meta-analysis, with low heterogeneity observed in the MPA and social media addiction subgroup (I 2 = 0%), moderate heterogeneity in IDG (I 2 = 48%), and substantial heterogeneity in the IA subgroup (I 2 = 97%). A visual inspection of the funnel plots (see Figure 5) shows some evidence of asymmetry with respect to IA trials. Egger's test of small study bias showed no risk of bias for NSSI studies ( p = 0.215). However, a risk of bias was observed in the suicidal behaviour meta-analysis ( p <0.001) and in the meta-analyses overall ( p <0.001). This could be due to the substantial variations in digital addiction and screen use existing within different study populations, diverse outcome measurements and cut-offs, age variances, or other methodological differences. Figure 5. Funnel plots for (a) non-suicidal self-injury and (b) suicidal behaviours. Sensitivity analysis A sensitivity analysis was conducted by removing studies, one at a time, to determine their impact on the results. Together, the four studies included in the NSSI analysis showed low heterogeneity (I 2 = 0%) and high significance ( p <0.001). Removal of any individual studies had little effect on these results. When investigating the suicidal behaviours meta-analysis, removing Khalil (2022) and Peng (2021) together reduced heterogeneity for the IA subgroup to I 2 = 0%, but had no impact on overall heterogeneity. The overall effect remained significant regardless of these removals. Similarly, removing Taechoyotin (2020) from the IGD subgroup reduced heterogeneity for the subgroup to 6% but did not affect overall heterogeneity. Removal of Junus (2023) greatly increased subgroup heterogeneity to 74% and further reduced subgroup significance to p =0.33, but did not affect overall results. Discussion This systematic review synthesised existing literature on the relationship between screen time and suicidal and NSSI behaviours among young people in non-clinical settings. Its significance is underscored by increasing global concern regarding the impact of excessive screen use on children and adolescents during critical stages of psychological and emotional development (Joshi and Hinkley, 2021). These concerns have intensified alongside a sharp rise in screen use among young people, which has reached historically high levels since the COVID-19 pandemic (Trott et al., 2022). While this systematic review highlights concerning associations between various forms of excessive screen use and poorer mental health outcomes, the findings are limited by methodological heterogeneity and inconsistencies across the included studies. Significant variation in how screen time is measured hinders the generalisability of the findings. All but one study utilised self-report or parent-reported measures, which are prone to various forms of bias. Studies reporting no adverse effects often used thresholds of 1.5 or 2 hours as cut-off points, which may be too conservative; durations of 2 to 3 hours may not constitute problematic use and may fall within a range considered developmentally normative. Included papers also failed to discuss or interpret findings in the context of age-based guidelines, or incorporate thresholds that reflected existing recommendations (WHO, 2019). Varying results (including significant variations in the prevalence of suicide, NSSI, digital addiction, and screen time) may also reflect the inclusion of participants at different ages and corresponding stages of physical and/or emotional development. Prior research has identified greater mobile phone use (Oshima et al., 2012), suicidal behaviours (Peng et al., 2021; Martinez-Estevez et al., 2024), and NSSI (Westers, 2023), among older adolescents compared to younger children. However, the role of age in this relationship – alongside the potential moderating effects of gender, temperament, pre-existing mental health status, and sexuality – remains largely unexplored in the empirical literature. It is imperative that future research adequately control for these developmental variables to inform evidence-based guidelines on screen time and suicide risk among young people. This knowledge gap is further compounded by a limited understanding of the relationship between screen time addiction and adverse mental health outcomes. The role of dopamine addiction has been extensively examined in the neuroscience literature, where addiction is widely understood as a learned behaviour influenced by the brain’s reward system (Volkow et al., 2019; Rácz, 2014). Dopamine plays a key role by reinforcing reward-seeking actions through repeated exposure to pleasurable stimuli (Wise and Jordan, 2021; Adinoff, 2004). Behavioural addictions, such as excessive screen time use, are often linked with dysregulated dopamine signalling and poorer mental health outcomes (Weinstein and Lejoyeux, 2020). The findings underscore the variability in outcomes across screen time modalities. When specific forms of screen engagement and user experiences were examined independently, conflicting results emerged. Several studies reported that particular activities, such as watching television or communicating socially via social networking apps, had little effect on NSSI or suicidal behaviours (Chu et al., 2023; Liu et al., 2016). Inconsistent findings were evident in studies investigating social networking, potentially due to differences in user experience. For example, one study found that positive experiences on social media reduced the odds of SI, whereas negative experiences were associated with increased odds (Hamilton et al., 2024a). While numerous studies measure the time spent on social media or devices, relatively little information exists regarding the nuanced impacts of content type or exposure characteristics, such as what users are watching, with whom they are interacting, or the nature of the materials they encounter. Gender differences in NSSI, self-harm, and screen usage also indicate the need for gender-specific evaluation and recommendations. The failure to adequately control for gender may obscure our understanding of gender-based needs and potential management strategies. While this systematic review identifies a strong association between excessive screen use or screen addiction and outcomes such as NSSI or suicidality, the direction of this relationship remains unclear. A ‘chicken-and-egg’ dilemma persists: does excessive screen time contribute to the development of maladaptive behaviours, or do pre-existing vulnerabilities lead to increased screen use? Clarifying this relationship requires further investigation into the underlying mechanisms driving behavioural addictions, as well as consideration of confounding variables such as media content, sleep disruption, and exposure to cyberbullying. Several studies have established that NSSI or suicidality are more prevalent among children and adolescents who experience poor family relationships and other adversities (Wang et al., 2022b). Research also indicates that young people from single-parent and reconstituted families tend to engage in more sedentary behaviour and screen time compared to those from nuclear family structures (Langøy et al., 2019). This raises important questions about whether studies on screen time are inaccurately controlling for such confounding variables. This issue is highlighted in mediation and moderation analyses that frequently identified sleep, depression, anxiety, and other social or psychiatric factors reduced, or wholly mitigated, the relationship between screen time and NSSI or suicidal behaviours (Zhang et al., 2024b; Tang et al., 2024; Sami et al., 2018; Miao et al., 2024; Xie et al., 2023; Liu et al., 2024). There is a risk that digital media may be inaccurately positioned as the root cause of mental health concerns, when in fact, underlying vulnerabilities – such as early adversity, emotional dysregulation, insomnia, or disrupted attachment – may predispose young people to both excessive screen use and poorer mental health outcomes. It is therefore plausible that screen-related behaviours (e.g., escapism, compulsive use, internalisation of harmful ideals) may be manifestations of deeper psychosocial distress, rather than independent causal factors. Conversely, research also suggests that excessive screen time may adversely impact familial relationships (Wolfers et al., 2025), potentially increasing risk of NSSI and suicidality. Substituting parent-child interaction with excessive screen use may erode relationship quality, as higher recreational media consumption has been associated with fewer shared activities and weaker parent-child bonds (Wolfers et al., 2025). The enforcement of screen time limits is also frequently cited as a common source of parent-child conflict (Thompson et al., 2023; Evans et al., 2011). These findings may suggest that underlying familial vulnerabilities may not only contribute to increased screen use but also exacerbate suicidal risk in young people by weakening protective family dynamics. This systematic review also found that insomnia and depression were more strongly associated with suicide risk than digital addictions, with MPA and IA showing comparatively lower odds ratios. For example, Lee (Lee and Ham, 2018) identified sleep disturbance and depression as significant predictors of SI in the context of gaming addiction. Similarly, Oshima (Oshima et al., 2012) reported that controlling for sleep substantially reduced the odds of NSSI and suicidal thoughts related to mobile phone use, while Cheng et al., (Cheng et al., 2024) found poor sleep quality explained the link between phone addiction and suicidal behaviours. Regarding depression, Tang (Tang et al., 2024) demonstrated that it fully mediated the relationship between IA and NSSI. A longitudinal study conducted over 18 months found that NSSI was predictive of IA, but IA was not predictive of NSSI (Xiong et al., 2023). This further suggests a potential disparity of impact among different forms of screen time and highlights the crucial need for additional longitudinal studies to investigate these relationships. These findings again raise important questions about causality; specifically, whether insomnia and psychological distress are antecedents to NSSI and suicidality, or whether digital addiction contributes to these conditions, thereby elevating risk. As these concerns have only gained prominence in recent years, efforts to disentangle these complex relationships remain in their infancy. While this research questions the direction of causality between suicide risk and excessive screen time, it does not suggest that excessive screen use is harmless, nor that existing parental guidelines should be disregarded. This is particularly important given the well-established associations between sedentary behaviour, sleep deprivation, and poor mental and physical health (Duncan et al., 2023), as well as substantial evidence from attachment theory highlighting the protective role of strong parent-child bonds (Zhang et al., 2022). Considering most screen time is typically spent in sedentary and socially isolating contexts, excessive use is likely to contribute to reduced physical activity, diminished sleep, weakened family connections, and poorer mental health outcomes (Wang et al., 2019; dos Santos et al., 2024; Carson et al., 2016). However, further research is needed to clarify the proposed direct link between excessive screen time and NSSI or suicidality in young people. Strengths and Limitations The review included 61 studies involving children across 16 different countries, increasing generalisability and strengthening the reliability of the findings. Limitations included the exclusion of studies in languages other than English, which may have led to the omission of important findings. Due to limitations in funding, the translation and interpretation of foreign-language studies were not feasible. Another limitation was that five of the included studies failed to specify the age of their participants, providing only school grade, generally up to year 12. It is possible that some of these year 12 classes may have included adolescents over the age of 18 years. Six studies specified a broad age range (for example, age 12 to 18) or grades one to twelve, and 29 studies included only mean ages that were similarly around 13 to 16 years of age. This meant that the current systematic review was unable to provide comparisons of different age groups in terms of their vulnerability to different digital exposures. Screen time recommendations for different age groups were also rarely considered in the measurement or analysis of screen time effects. Few studies investigated characteristics of screen use (messaging, posting photos, watching short videos) or investigated the impacts of individual behaviours. The variable impacts of these different screen use behaviours would pose a considerable confounding effect on outcomes and should be further investigated and controlled for in future studies. The heterogeneity of included studies was significant. This may be due to sample variance, different measurement tools used, the inclusion or exclusion of ‘mild’ or ‘possible’ IA or IGD in the final prevalence figures, or the varied classifications of suicidal behaviours (such as ideation, thoughts, feelings, or planning that sometimes differentiated and sometimes grouped for analysis). The pooling of different age groups is also likely to have introduced some bias into the results, particularly if age is not adequately controlled for. Findings of the meta-analysis should also be interpreted with caution due to the heterogeneity of measures used to evaluate NSSI and suicidal ideation. Conclusion The systematic review highlights the complex and evolving relationship between screen time and suicidal behaviours or NSSI in children and adolescents. Meta-analyses and narrative syntheses revealed significant associations between excess screen time or digital addiction and child NSSI and suicidality. However, bidirectionality in these relationships was frequently identified. Confounding variables such as sleep quality, LGBTQIA status, psychological distress, lifestyle behaviours, and family structure were found to be mediators and/or predictors of NSSI and suicidality on their own, complicating the interpretation of the findings. It is unclear whether screen time leads to NSSI and suicidality, or whether childhood adversity and mental illness leave children vulnerable to pathological digital behaviours, which may subsequently lead to additional social and emotional impairment and thereby strengthen suicidality and self-harming impulses. Sleep quality, in particular, may be impacted by device use, leading to the exacerbation of poor mental health. Further research examining screen use over time, incorporating familial and lifestyle factors, child mental health outcomes, and consistent, less conservative measures of screen time, is needed to better disentangle these associations. Gender differences in screen use and NSSI and suicidal behaviours suggest further investigation into gender-specific screen use and potential harms to avoid dilution of these findings in total population studies. Overall, the findings suggest a deleterious impact of excessive screen time on child NSSI and suicidality, reinforcing the need for enhanced guidelines around screen use. Findings also support additional research to determine the impact of screen time in children experiencing increased adversity and other vulnerabilities. Declarations Ethical considerations The study was a systematic review and, therefore, did not require ethical approval. Consent to participate Not applicable Consent for publication Not applicable Declaration of conflicting interest The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding statement This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Data availability statement Not applicable. No new data was generated or analysed in this systematic review. References Adinoff B (2004) Neurobiologic processes in drug reward and addiction. Harvard Review of Psychiatry 12(6): 305-320. Ai M, Wang W, Chen J-M, et al. (2025) Multidimensional stress and self-harm in Chinese preadolescents: A cross-sectional study. Journal of Affective Disorders 372: 370-376. Alves MI, Dias Junior SA, Martins T, et al. 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Tables Tables are available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files ScreentimeSupplementaryTables1.docx Table S1. GRADE summary of evidence Tables.docx Cite Share Download PDF Status: Posted Version 1 posted 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. 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young people is gaining increasing attention in academic literature and public policy discourse (Chen et al., 2024; World Health Organization (WHO), 2019). In Australia, the federal government amended the \u003cem\u003eOnline Safety Act 2021\u003c/em\u003e to ban users under the age of 16 from having social media accounts. To take effect in late 2025, the legislative change seeks to reduce the impact of social media harms for young people. Although not yet fully understood, the rising frequency of NSSI and suicidal behaviours among young people is a serious concern, particularly as it appears to be increasing in tandem with greater exposure to digital media, especially in the aftermath of the COVID-19 pandemic (Alves et al., 2025). Considering these concerns, this systematic review and meta-analysis of the literature examines the relationship between screen time and suicidal or self-harming behaviours in young people. Such a review is essential to synthesise fragmented evidence, identify gaps in current research, and inform the development of targeted prevention and intervention strategies. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSuicide and non-suicidal self-injury\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSuicidal behaviour and NSSI represent critical public health challenges, ranking among the leading causes of injury and mortality globally (Australian Institute of Health and Welfare (AIHW), 2025a; WHO, 2025). Suicide is defined as the intentional act of ending one\u0026rsquo;s life, while suicidal behaviours encompass nonfatal thoughts and actions related to suicidal intent, including suicidal ideation (SI - thoughts of ending one\u0026rsquo;s life), suicide plans, and suicide attempts (Posner et al., 2007; Silverman et al., 2007). NSSI is defined as intentional self-injury without suicidal intent (Zetterqvist et al., 2013). Due to this lack of suicidal intent, it is often excluded from suicide research; however, its role as a significant risk factor for suicide warrants its inclusion (Nock and Kessler, 2006; Nock and Prinstein, 2005).\u003c/p\u003e\n\u003cp\u003eGlobally, suicide is the third leading cause of death among individuals aged 15\u0026ndash;29, contributing to approximately 727,000 deaths annually (WHO, 2025). Temporal analyses of the WHO Mortality Database indicate alarming increases in youth suicide rates, particularly in the United States, the United Kingdom, and parts of Central and South America (Bertuccio et al., 2024). Rates of suicide in adolescents aged 15-17 have also more than doubled in Australia since 2004, with males more likely to end their lives by suicide than females (AIHW, 2025c). Nearly half of all individuals who die by suicide have a history of NSSI (Hawton et al., 2012; Duarte et al., 2020). The global prevalence of adolescent self-harm is estimated at 17% to 21%, with urban areas reporting higher rates (Lucena et al., 2022). In Australia during 2022-23, children aged 15 to 19 had the highest rate of hospitalisation for NSSI of all age groups at 308 per 100,000, with rates much higher in females than males (AIHW, 2025b). Hospitalisations related to NSSI among females aged 14 \u0026nbsp;and under have increased dramatically \u0026ndash; from 19 per 100,000 in 2008-09 to 66 per 100,000 in 2022-23 (AIHW, 2025c). These behaviours are strongly associated with adverse mental health outcomes, psychiatric hospitalisation, and elevated suicide risk (Andreo-Jover et al., 2024; Ghoghre, 2024; Li et al., 2024). Identifying modifiable risk factors for these behaviours in young people is imperative for developing effective preventative strategies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScreen time\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eScreen time, defined as time spent using televisions, smartphones, tablets, and computers, has increased dramatically over the past two decades, with many adolescents surpassing recommended daily limits (Qi et al., 2023; Twenge et al., 2018). It is estimated that 68% to 95% of adolescents have access to a smartphone (Keles et al., 2020; Moreno et al., 2022) and around 91% of UK and US adolescents use social media (Dane and Bhatia, 2023). According to Australian Guidelines (Department of Health and Aged Care, 2021), children under 2 years old should have no screen time, children aged 2 to 5 have no more than one hour per day, and those aged 5-17 should limit recreational screen time to no more than 2 hours per day \u0026ndash; excluding screen time used for schoolwork. Despite this, less than 10% of families meet these guidelines for children under 2 years of age (Brushe et al., 2023), and adolescents in the US spend an average of 4.8 hours per day on social media (Rothwell, 2023). While digital connectivity facilitates access to information, learning, and social interaction, excessive screen use has been associated with adverse mental health outcomes, including NSSI, suicidal behaviour, depression, and anxiety (Twenge et al., 2018; Stiglic and Viner, 2019). However, research suggests that different types of screen time may have different impacts on child health. Some forms of screen time may be detrimental, while others act as a supportive mechanism to promote mental well-being (Rothwell, 2023; Hamilton et al., 2024b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNeuroimaging studies of internet and gaming addiction have identified similarities in underlying mechanisms to those of drug addiction (Weinstein and Lejoyeux, 2020; Weinstein et al., 2017). Initial exposures bring an increase in dopaminergic stimulation in mesolimbic and nigrostriatal pathways, known as the reward system of the brain (Volkow et al., 2019). Over time, chronic exposure leads to dysfunction in the reward system, reduced dopaminergic sensitivity, withdrawal, and cravings. Neuroplastic maladaptation associated with addiction is linked to reduced activity in the prefrontal cortex, impaired impulse control and decision-making, depression, and impaired emotion regulation (Goldstein and Volkow, 2011; Adinoff, 2004; R\u0026aacute;cz, 2014). Reductions in grey and white matter volume in regions associated with emotion regulation, impulse control, attention, and memory have also been associated with internet and gaming addiction, and increased screen media use (Weinstein and Lejoyeux, 2020; Zhou et al., 2011).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite this growing body of evidence, significant gaps remain in understanding how screen time influences suicidal behaviours. Variability in measurement methodologies, ranging from self-reported surveys to app-based tracking, hinders cross-study comparability (Chen et al., 2024). Additionally, broad categorisations of screen time often overlook distinctions between active (e.g., gaming) and passive (e.g., streaming) use, or between positive (e.g., educational) and negative (e.g., exposure to harmful content) engagements (Pandya and Lodha, 2021; Mougharbel et al., 2023). While numerous studies examine the relationship between screen time and self-harm or suicide-related outcomes, systematic reviews consolidating these findings are limited and often outdated or narrow in scope. Existing reviews have focused on a singular aspect of screen time or screen behaviour, (Daine et al., 2013; Marchant et al., 2017; Memon et al., 2018), combine various forms of screen behaviours into a singular item (Hoare et al., 2016), or synthesise a wide and disparate age range, incorporating very young children, adolescents, and young adults (Daine et al., 2013). More recent studies have focussed primarily on longitudinal studies, leaving cross-sectional evidence underexplored (Chen et al., 2024; Vasconcellos et al., 2025). Considering the heterogeneous nature and restricted focus of many papers, it is essential to incorporate both longitudinal and cross-sectional studies to gain a comprehensive understanding of the impacts of screen time and screen time behaviours. More importantly, fast-paced technological advancements mean that many of these studies are now outdated, and as a result, many fail to accurately reflect current screen and device use among young people.\u003c/p\u003e\n\u003cp\u003eA systematic review that synthesises cross-sectional and longitudinal studies on screen-based activities and suicidal behaviours would offer critical insights into immediate associations, emerging trends, and diverse demographic patterns, thereby informing timely interventions and policy development. This systematic review aims to bridge these gaps by synthesising findings from all studies that explore the relationships between various types of screen-based activities [e.g., social media use, time spent on screens, and internet addiction (IA)] and suicide-related outcomes (e.g., SI, attempts, and NSSI) among children and adolescents. By consolidating these insights, this review seeks to clarify these associations and inform interventions targeting screen-based risk factors for suicidality.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, and the MOOSE Reporting Guidelines for Meta-analyses of Observational Studies. The protocol has been registered with PROSPERO (ID: CRD42023493058; available at https://www.crd.york.ac.uk/PROSPERO/view/CRD42023493058). Initial searches were conducted on December 12\u003csup\u003eth\u003c/sup\u003e, 2023. An updated search was conducted on January 26\u003csup\u003eth\u003c/sup\u003e, 2025. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSearch strategy\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA systematic review of the literature was conducted using CINAHL, PubMed, Embase, PsycARTICLES, PsycINFO, Scopus, and Web of Science databases. A title, abstract, and keyword search was completed using terms relevant to screen media, suicide, and self-harm, combined with child or adolescent terms (see Figure 1). MeSH and Emtree terms for suicide, self-injurious behaviour, and automutilation were included where appropriate. Citation chaining and hand searches were conducted to ensure no eligible articles were missed. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 1.\u003c/strong\u003e Search terms\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEligibility criteria\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eStudies were included if they examined at least one form of screen media use and NSSI and/or suicidal behaviours were included as dependent or independent variables. Studies that investigated psychiatric samples were excluded. Young people, defined as children and adolescents aged from 0 to 18, were eligible for inclusion to permit the investigation of differences across the developmental spectrum. An upper age limit of 18 years was selected to correspond with the legal age of majority, marking a transition to adult roles and responsibilities that come with their own distinct health, behavioural, and social factors. Studies that focused solely on cyberbullying were excluded from the analysis to ensure consistency and comparability of the included studies. Studies were limited to those available in English and published in 2007 or later to align with the release of the iPhone and to provide a comparable digital environment. The review excluded studies not containing primary data (review papers, systematic reviews, opinion and commentary papers), dissertations, editorials, posters, and conference abstracts. Cross-sectional studies presenting secondary analyses of data, were excluded to prevent duplication of data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudy selection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIdentified papers were exported to Covidence web-based software (Veritas Health Innovation, 2019) for screening. Screening was conducted in two stages: title and abstract, and full-text screening. Both stages utilised the same screening process: four independent reviewers conducted screening, with one primary reviewer screening all papers to maintain consistency. Disagreements were resolved by a fifth reviewer or by discussion between two or more reviewers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData extraction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eData were extracted from all eligible studies based on a pre-defined data extraction spreadsheet, which included variables relating to population demographics, screen time type, self-harm or suicidal items, outcome measures, and findings. Where data was missing, an email request was sent to the corresponding author. Two independent reviewers extracted data and completed the data extraction worksheet. Where differences in the worksheet were identified, a third reviewer resolved discrepancies. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMeta-analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA random effects meta-analysis of odds ratios, using the Restricted Maximum-Likelihood (REML) heterogeneity estimator, was conducted with cross-sectional studies that investigated addictive behaviours and NSSI or suicidal behaviours. Sub-group analyses were conducted for the different addictive behaviours: IA; mobile phone addiction (MPA); and internet gaming disorder (IGD).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRisk of bias assessment\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eRisk of bias was assessed using the Joanna Briggs Institute (JBI) checklist for cross-sectional and cohort studies. These tools include questions relating to sample selection, study design, analytical methods, and reporting. Questions are answered by choosing Yes (1 - little risk of bias); No (0 - risk of bias); Unclear (0.5); or Not applicable (no score). A final score is created by summing all scores and dividing by the number of responses. Based on previous use of this tool (Shu et al., 2024), a score of 70% or higher is considered good quality, 50% - 69% is considered acceptable, and below 50% is considered poor quality. Quality of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) guidelines (Atkins et al., 2004).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDeviations from the protocol\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe protocol for this review has been published (Gillespie et al., 2024). Minor alterations have been made to this protocol. Age ranges were initially limited to those under 18 years. However, many studies incorporating high school years up to grade 12 have by necessity included 18-year-olds, which makes up a small percentage of grade 12 classes. This is in keeping with previous literature investigating child and adolescent groups (Madigan et al., 2022). Any study that specifies the inclusion of students or community members aged over 18 years of age has been excluded. The current review has also limited the included papers by excluding those focused solely on cyberbullying, given the large number of cyberbullying studies published, and to provide more reliable results by focusing on more comparable exposures.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cem\u003eStudy Selection\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSee Figure 2 for a flow diagram describing the steps conducted during the search and screening process. The final search obtained 14,542 articles. After removing duplicates, 7203 underwent title and abstract screening, leaving 677 for full-text review. Fourteen papers were unable to be located, and an additional 602 did not meet the inclusion criteria. A total of 61 studies were included in the final analysis. \u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 2.\u003c/strong\u003e PRISMA flow diagram (Page et al., 2021)\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eStudy characteristics\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe 61 identified studies included a total of 338,472 young people: 160,702 males; 156,019 females; and 18 individuals who identified as non-binary (Marengo et al., 2024) \u0026nbsp;with mean ages ranging from 9.85 (Marengo et al., 2024) \u0026nbsp;to 16.8 years (Oktan, 2015), or in grades one to twelve. A pooled mean and standard deviation were calculated as M\u003cem\u003e\u003csub\u003epooled\u003c/sub\u003e\u003c/em\u003e = 10.26 and SD\u003cem\u003e\u003csub\u003epooled\u003c/sub\u003e\u003c/em\u003e = 1. Studies were cross-sectional (n = 44) (see Table 1) or longitudinal cohort (n = 17) designs (see Table 2). Thirty-one of the included studies investigated suicide behaviours, 23 investigated NSSI, and seven studies looked at both outcomes. Studies evaluated one or more of the following independent variables: Problematic Internet Use (PIU) or IA (from here on we will use IA to refer to both of these terms) (n = 25); screen time (frequency or duration of device or internet use) (n = 16); IGD (n = 10); MPA (n = 5); Social media activity (n = 4); Social Media Addiction (n = 2); sexting (n = 2); and lifetime experience of video slot machines (n = 1). The majority of studies were conducted in China (n = 32), and the rest were conducted in Europe (n = 6), the USA (n = 5), Korea (n = 5), Taiwan (n = 3), Israel (n = 2), Turkey (n = 2), Indonesia (n = 1), Colombia (n = 1), Egypt (n = 1), Japan (n = 1), Thailand (n = 1), and Vietnam (n = 1). Rates of suicidal ideation were reported in 22 studies, and ranged from 3% (Taechoyotin et al., 2020) to 37.7% (Teng et al., 2023) of participants. Rates of suicide attempts were reported in six studies, ranging from 1.4% (Nguyen et al., 2020) and 21.8% (Teng et al., 2023). The prevalence of NSSI was reported in 16 studies, ranging from 3.3% (Oshima et al., 2012) to 47.11% (Wang et al., 2022a) among the study samples. There was also a wide discrepancy across rates of IA (ranging from 1.6% (Kim et al., 2006) to 65.6% (Khalil et al., 2022) reported in 9 studies), IGD (from 0.7% (Lan et al., 2022) to 25.1% (Lee and Ham, 2018) in eight studies), social media or facenook addiction (reported as 19% (Dumont et al., 2024) and 92.8% (Khalil et al., 2022)), and MPA (reported as 11.11% (Wang et al., 2022a) and 26.2% (Cheng et al., 2024). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScreen time and mental health assessments\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eScreen time frequency was measured inconsistently across seventeen studies: as a mean number of hours; in two-to-three-hour increments; more or less than 90 or 120 minutes; rarely, occasionally, or frequently; or merely defined as \u0026lsquo;high intensity use\u0026rsquo; of screens, video games, mobile phones, or apps. Pathological screen time was measured in a majority of studies, using a wide range of validated measures of pathological internet use, internet gaming disorder, MPA, and social media addiction (collectively labelled here as screen-related addictive behaviours). Measuring suicidal ideation, planning, and attempts were most commonly measured using one or more yes/no questions relating to the presence of suicidal thoughts or intent rather than a validated tool. NSSI outcomes included NSSI episodes, NSSI urges, frequency of NSSI, and medical care received for NSSI. NSSI was also frequently measured using yes/no questions relating to the presence or frequency of self-harming acts, but was more likely to be measured using a validated measure of self-harm behaviours. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCross-sectional associations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScreen time frequency\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThirteen studies investigated the impact of screen time frequency on NSSI and suicidal behaviours, with nine of these finding significant positive associations. Frequent smartphone use was associated with greater odds of NSSI, suicidal ideation, and suicide attempts when controlling for demographic factors (Ai et al., 2025; Chen et al., 2020). \u0026nbsp;Almost daily nocturnal mobile phone use was associated with more than three times the odds of NSSI, and more than double the odds of suicidal feelings and poor mental health (Oshima et al., 2012). However, these odds decreased to between 1.56 and 1.65 times when adjusted for sex, age, substance use, and sleep length. The number of social media apps used was also positively correlated with SI (\u003cem\u003er\u003c/em\u003e=0.184) and social media usage had a significant direct effect on SI (Marengo et al., 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen investigating hours of screen time, more than two (Guo et al., 2024), four (Zhang et al., 2024), or ten hours per day (Chau et al., 2022) was associated with higher rates of NSSI, SI and planning, or suicide attempts, respectively. Dumont et al. (2024) also found that daily screen time significantly increased the odds of SI, however, the method used to measure screen time was not reported. Lan et al. (2022) found that internet use hours per day were positively associated with NSSI scores. Increased school day video game and computer exposure (more than two hours) was also associated with a greater risk of NSSI (Liu et al., 2016). Four studies found no relationship between screen time and NSSI or suicidal behaviours (Wiguna et al., 2021; Martinez-Estevez et al., 2024; Nguyen et al., 2020; Kim et al., 2023). These papers employed a cut-off of two hours or more (Kim et al., 2023; Nguyen et al., 2020), or over 90 minutes\u0026rsquo; screen time per day (Martinez-Estevez et al., 2024). Wiguna created a six-point Likert scale in 2-hour increments, from less than two hours to ten hours or more (Wiguna et al., 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScreen behaviours\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe majority of identified studies (n = 32) investigated the impacts of screen-related addictive behaviours. Fifteen studies measured IA and suicidal behaviours, with 14 of these identifying a significant positive relationship between IA and SI, plans, and/or attempts. Only one regression model did not find IA to be a predictor of suicidal thoughts (Amendola and Cerutti, 2023). However, this paper did reveal that viewing content relating to self-harm, suicide, and drug use was associated with suicidal thoughts. They also found that children who had suicidal thoughts were more likely to have IA. Four papers (Kim et al., 2006; Lu et al., 2025; Karabel et al., 2024; Khalil et al., 2022) found suicidal behaviours were more common in those with IA. Seven studies investigated, and found significant positive associations between, IA and NSSI. However, adjusted odds ratios varied greatly; from 1.01 (Hamdan et al., 2022) to 18.84 (Kim et al., 2023). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIGD was investigated in eight papers; six investigating suicidal behaviours, and two investigating NSSI. All \u0026nbsp;found a significant positive relationship between IGD and suicidal behaviours or NSSI. Yu et al. (2020) reported that IGD, depression, and insomnia were all significant predictors of SI. Insomnia was also seen to mediate the relationship between IGD and SI. Xie and colleagues (Xie et al., 2023) found the relationship between IGD and SI to be mediated by negative emotions. They also found that SI was higher in the IGD group (6.3%) compared to those not experiencing IGD (5%), though this was not tested for statistical significance.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMPA was assessed in three studies. Cheng et al. (2024) identified MPA as a significant predictor of NSSI, SI and suicide attempts, but not for suicide planning. Long et al. (2024) found a weak but significant positive association between MPA and NSSI, while Wang et al. (2022a) found that MPA increased the odds of NSSI by more than four times (aOR = 4.28). Dumont et al. (2024) investigated the association between social media addiction and SI. They found that an increased social media addiction score increased the odds of SI in a univariate analysis (OR = 1.12; 95% CI=1.05, 1.19, p=0.001), though this was not significant when controlling for other variables. Other screen types measured included sexting (Lan et al., 2022) and video slot machines (Mosconi et al., 2024). Experience of both were associated with an increase in NSSI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eGender\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFifteen studies identified gender disparities. A slightly higher number of studies (Guo et al., 2024; Long et al., 2024; Wang et al., 2022a) found females to experience a higher prevalence of NSSI compared to studies that found males with a higher prevalence in their sample (Kim et al., 2023; Hamdan et al., 2022). Seven studies observed higher rates of suicidal behaviours in females (Oshima et al., 2012; Kim et al., 2023; Tan et al., 2024; Zhang et al., 2024; Teng et al., 2023; Xie et al., 2023; Dumont et al., 2024). Only one found higher rates in males (Huang et al., 2020). Four studies reported higher prevalences of IA (Lu et al., 2025; Karabel et al., 2024), IGD (Xie et al., 2023), and video slot machine use (Mosconi et al., 2024) in males.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eLongitudinal associations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFive studies investigated the impact of screen time frequency of use on future NSSI and suicidal behaviours, with conflicting findings. Wang et al. (2020) and Chu et al. (2023) utilised self-report data to assess the impact of baseline media use on outcomes at one- and two-year follow-up, respectively. Both found increased use of screen behaviours, such as internet and mobile phone use (Wang et al., 2020), or videos, video games, texting, and video chatting (Chu et al., 2023) led to greater odds of suicidal behaviours and NSSI at follow-up. Both studies found no impact of television hours, and Chu also failed to detect an impact of social networking. Hamilton et al. (2024a) also found no relationship between hours spent on social media and SI over two months, whereas Kim et al. (2017), found that social networking was consistently associated with greater SI at one year. Coyne et al (Coyne et al., 2021) was the only study to employ a subjective measure of screen time (passive sensing) or to investigate longitudinal trends over a ten-year period. Significant results were identified only in girls, where early and high levels of both social media and television, which increased over time, were associated with higher levels of suicide risk in emerging adulthood. Increasing levels of video games were also predictive of suicide risk in girls only. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScreen-related addictive behaviours\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFour studies investigated diverse types of social media behaviours. Negative experiences on social media were associated with greater odds of reporting SI, whereas positive experiences were associated with lower odds (Hamilton et al., 2024a). Individuals who more frequently shared content as younger adolescents, in addition to other social media activities, had significantly higher odds of NSSI one year later than those who engaged in moderate messaging and browsing, but less content sharing (Winstone et al., 2022). The final study highlighted a bidirectional relationship between social media exposure of NSSI, and engaging in NSSI among adolescents (Wu et al., 2024). Only one paper looked at sexting, finding that non-consensual sexting (sending intimate images of others against their will) or being pressured to sext were both positively associated with NSSI (Wright and Wachs, 2024).\u003c/p\u003e\n\u003cp\u003eNine of the longitudinal studies investigated the impacts of screen-related addictive behaviours over the course of five to 18 months. Five of these studies found a significant positive association between IA (Li et al., 2025; Ma et al., 2023; Pan and Yeh, 2018), IGD (Gao et al., 2025; Gong et al., 2025), MPA (Li et al., 2022) and problematic social media use (Shen et al., 2024) on NSSI and suicidal behaviours. However, Shen et al. (2024) found that problematic social media use only influenced initial levels of NSSI, not NSSI over time, which declined consistently over 12-months. While Gong et al. (2025) identified a correlation between IA and NSSI across all time points, they also found that internalising symptoms fully mediated these relationships. Similarly, Lin et al. (2025) observed no direct relationship between IA and NSSI, but did find that this relationship was mediated by depression and anxiety. One 18-month study found that NSSI was predictive of IA, but IA was not predictive of NSSI . \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRisk of bias\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe majority of studies (n = 36) were of high quality, with only three scoring below 50% (see Table 1 for individual scores). However, 36 cross-sectional studies failed to adequately describe their inclusion and exclusion criteria. A common risk of bias for all study types was a lack of validated tools to measure exposure or outcome variables. Around half of all studies used yes/no questions to determine the presence of suicidal or NSSI behaviours, or used binary or limited categorical variables to determine screen time. Failing to identify or adequately control for confounding factors was also commonly identified (n = 15). Twelve longitudinal studies failed to adequately describe, or explore the reasons for, loss to follow-up, or did not utilise strategies to address participant drop-out. According to GRADE guidelines, the quality of evidence for NSSI was high (See Table s1). Quality of evidence was deemed low for suicidal behaviours due to significant heterogeneity and risk of publication bias identified in the meta-analysis. The findings for suicidal behaviours should therefore be interpreted with caution.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMeta-analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTwo separate meta-analyses were conducted on cross-sectional papers to investigate the relationships between addictive digital behaviours and NSSI or suicidal ideation (see Figures 3 and 4). One included paper measured suicide risk rather than suicidal ideation (Khalil et al., 2022), however, sensitivity analyses showed that the removal or addition of this individual study had no substantial effect on the results. Subgroup analyses were conducted to identify differences between IA, IGD, or mobile phone and social media addiction (grouped due to small study numbers). Analysis was conducted using a random effects model and the inverse-variance method. REML estimation was chosen due to the high heterogeneity observed. Overall, there was a significant effect of digital addiction on NSSI (OR = 2.74, 95% CI 1.55 to 4.84, \u003cem\u003ep\u003c/em\u003e = 0.01). However, subgroup analysis identified no significant effect of IA (OR = 3.04, 95% CI 0.01 to 790.24, \u003cem\u003ep\u003c/em\u003e = 0.24) or IGD (OR = 2.93, 95% CI 0.08 to 108.83, \u003cem\u003ep\u003c/em\u003e = 0.16) on NSSI. There was also an overall effect of digital addiction on suicidal behaviours (OR = 2.51, 95% CI 1.9 to 3.33, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Subgroup analyses identified a significant effect of both IA (OR = 3.08, 95% CI 1.8 to 5.29, \u003cem\u003ep\u003c/em\u003e = 0.002) and MPA or social media addiction (OR = 1.9, 95% CI = 1.69 to 2.15, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.009) on suicidal behaviours. IGD showed a trend toward increasing suicidal behaviours, but fell short of significance (OR = 2.17, 95% CI 0.94 to 4.99, \u003cem\u003ep\u003c/em\u003e = 0.06). Longitudinal studies were too heterogeneous to conduct a meta-analysis and were therefore only analysed narratively. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 3.\u003c/strong\u003e Random-effects meta-analysis for studies measuring digital addiction and non-suicidal self-injury.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFigure 4.\u003c/strong\u003e Random-effects meta-analysis for studies measuring addictive digital behaviours and suicidal ideation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003cem\u003eHeterogeneity of reported outcomes and study bias\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the meta-analysis of digital addiction and NSSI, overall heterogeneity was moderate (I\u003csup\u003e2\u003c/sup\u003e = 49%), but more substantial in the individual subgroups of IA (I\u003csup\u003e2\u003c/sup\u003e = 57%) and IGD (I\u003csup\u003e2\u003c/sup\u003e = 66%) (see Figure 4). Heterogeneity was varied in the suicidal behaviours meta-analysis, with low heterogeneity observed in the MPA and social media addiction subgroup (I\u003csup\u003e2\u003c/sup\u003e = 0%), moderate heterogeneity in IDG (I\u003csup\u003e2\u003c/sup\u003e = 48%), and substantial heterogeneity in the IA subgroup (I\u003csup\u003e2\u003c/sup\u003e = 97%). A visual inspection of the funnel plots (see Figure 5) shows some evidence of asymmetry with respect to IA trials. Egger\u0026apos;s test of small study bias showed no risk of bias for NSSI studies (\u003cem\u003ep\u003c/em\u003e = 0.215). However, a risk of bias was observed in the suicidal behaviour meta-analysis (\u003cem\u003ep\u003c/em\u003e \u0026lt;0.001) and in the meta-analyses overall (\u003cem\u003ep\u003c/em\u003e \u0026lt;0.001). This could be due to the substantial variations in digital addiction and screen use existing within different study populations, diverse outcome measurements and cut-offs, age variances, or other methodological differences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure 5.\u003c/strong\u003e Funnel plots for (a) non-suicidal self-injury and (b) suicidal behaviours.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eSensitivity analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA sensitivity analysis was conducted by removing studies, one at a time, to determine their impact on the results. Together, the four studies included in the NSSI analysis showed low heterogeneity (I\u003csup\u003e2\u003c/sup\u003e = 0%) and high significance (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001). Removal of any individual studies had little effect on these results. When investigating the suicidal behaviours meta-analysis, removing Khalil (2022) and Peng (2021) together reduced heterogeneity for the IA subgroup to I\u003csup\u003e2\u003c/sup\u003e = 0%, but had no impact on overall heterogeneity. The overall effect remained significant regardless of these removals. Similarly, removing Taechoyotin (2020) from the IGD subgroup reduced heterogeneity for the subgroup to 6% but did not affect overall heterogeneity. Removal of Junus (2023) greatly increased subgroup heterogeneity to 74% and further reduced subgroup significance to \u003cem\u003ep\u003c/em\u003e=0.33, but did not affect overall results.\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis systematic review synthesised existing literature on the relationship between screen time and suicidal and NSSI behaviours among young people in non-clinical settings. Its significance is underscored by increasing global concern regarding the impact of excessive screen use on children and adolescents during critical stages of psychological and emotional development (Joshi and Hinkley, 2021). These concerns have intensified alongside a sharp rise in screen use among young people, which has reached historically high levels since the COVID-19 pandemic (Trott et al., 2022).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile this systematic review highlights concerning associations between various forms of excessive screen use and poorer mental health outcomes, the findings are limited by methodological heterogeneity and inconsistencies across the included studies. Significant variation in how screen time is measured hinders the generalisability of the findings. All but one study utilised self-report or parent-reported measures, which are prone to various forms of bias. \u0026nbsp;Studies reporting no adverse effects often used thresholds of 1.5 or 2 hours as cut-off points, which may be too conservative; durations of 2 to 3 hours may not constitute problematic use and may fall within a range considered developmentally normative. Included papers also failed to discuss or interpret findings in the context of age-based guidelines, or incorporate thresholds that reflected existing recommendations (WHO, 2019).\u003c/p\u003e\n\u003cp\u003eVarying results (including significant variations in the prevalence of suicide, NSSI, digital addiction, and screen time) may also reflect the inclusion of participants at different ages and corresponding stages of physical and/or emotional development. Prior research has identified greater mobile phone use (Oshima et al., 2012), suicidal behaviours (Peng et al., 2021; Martinez-Estevez et al., 2024), and NSSI (Westers, 2023), among older adolescents compared to younger children. However, the role of age in this relationship \u0026ndash; alongside the potential moderating effects of gender, temperament, pre-existing mental health status, and sexuality \u0026ndash; remains largely unexplored in the empirical literature.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt is imperative that future research adequately control for these developmental variables to inform evidence-based guidelines on screen time and suicide risk among young people. This knowledge gap is further compounded by a limited understanding of the relationship between screen time addiction and adverse mental health outcomes. The role of dopamine addiction has been extensively examined in the neuroscience literature, where addiction is widely understood as a learned behaviour influenced by the brain\u0026rsquo;s reward system (Volkow et al., 2019; R\u0026aacute;cz, 2014). Dopamine plays a key role by reinforcing reward-seeking actions through repeated exposure to pleasurable stimuli (Wise and Jordan, 2021; Adinoff, 2004). Behavioural addictions, such as excessive screen time use, are often linked with dysregulated dopamine signalling and poorer mental health outcomes (Weinstein and Lejoyeux, 2020).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings underscore the variability in outcomes across screen time modalities. When specific forms of screen engagement and user experiences were examined independently, conflicting results emerged. Several studies reported that particular activities, such as watching television or communicating socially via social networking apps, had little effect on NSSI or suicidal behaviours (Chu et al., 2023; Liu et al., 2016). Inconsistent findings were evident in studies investigating social networking, potentially due to differences in user experience. For example, one study found that positive experiences on social media reduced the odds of SI, whereas negative experiences were associated with increased odds (Hamilton et al., 2024a). While numerous studies measure the time spent on social media or devices, relatively little information exists regarding the nuanced impacts of content type or exposure characteristics, such as what users are watching, with whom they are interacting, or the nature of the materials they encounter. Gender differences in NSSI, self-harm, and screen usage also indicate the need for gender-specific evaluation and recommendations. The failure to adequately control for gender may obscure our understanding of gender-based needs and potential management strategies. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile this systematic review identifies a strong association between excessive screen use or screen addiction and outcomes such as NSSI or suicidality, the direction of this relationship remains unclear. A \u0026lsquo;chicken-and-egg\u0026rsquo; dilemma persists: does excessive screen time contribute to the development of maladaptive behaviours, or do pre-existing vulnerabilities lead to increased screen use? Clarifying this relationship requires further investigation into the underlying mechanisms driving behavioural addictions, as well as consideration of confounding variables such as media content, sleep disruption, and exposure to cyberbullying.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeveral studies have established that NSSI or suicidality are more prevalent among children and adolescents who experience poor family relationships and other adversities (Wang et al., 2022b). Research also indicates that young people from single-parent and reconstituted families tend to engage in more sedentary behaviour and screen time compared to those from nuclear family structures (Lang\u0026oslash;y et al., 2019). This raises important questions about whether studies on screen time are inaccurately controlling for such confounding variables. This issue is highlighted in mediation and moderation analyses that frequently identified sleep, depression, anxiety, and other social or psychiatric factors reduced, or wholly mitigated, the relationship between screen time and NSSI or suicidal behaviours (Zhang et al., 2024b; Tang et al., 2024; Sami et al., 2018; Miao et al., 2024; Xie et al., 2023; Liu et al., 2024). There is a risk that digital media may be inaccurately positioned as the root cause of mental health concerns, when in fact, underlying vulnerabilities \u0026ndash; such as early adversity, emotional dysregulation, insomnia, or disrupted attachment \u0026ndash; may predispose young people to both excessive screen use and poorer mental health outcomes. It is therefore plausible that screen-related behaviours (e.g., escapism, compulsive use, internalisation of harmful ideals) may be manifestations of deeper psychosocial distress, rather than independent causal factors.\u003c/p\u003e\n\u003cp\u003eConversely, research also suggests that excessive screen time may adversely impact familial relationships (Wolfers et al., 2025), potentially increasing risk of NSSI and suicidality. Substituting parent-child interaction with excessive screen use may erode relationship quality, as higher recreational media consumption has been associated with fewer shared activities and weaker parent-child bonds (Wolfers et al., 2025). The enforcement of screen time limits is also frequently cited as a common source of parent-child conflict (Thompson et al., 2023; Evans et al., 2011). These findings may suggest that underlying familial vulnerabilities may not only contribute to increased screen use but also exacerbate suicidal risk in young people by weakening protective family dynamics.\u003c/p\u003e\n\u003cp\u003eThis systematic review also found that insomnia and depression were more strongly associated with suicide risk than digital addictions, with MPA and IA showing comparatively lower odds ratios. For example, Lee (Lee and Ham, 2018) identified sleep disturbance and depression as significant predictors of SI in the context of gaming addiction. Similarly, Oshima (Oshima et al., 2012) reported that controlling for sleep substantially reduced the odds of NSSI and suicidal thoughts related to mobile phone use, while Cheng et al., (Cheng et al., 2024) found poor sleep quality explained the link between phone addiction and suicidal behaviours. Regarding depression, Tang (Tang et al., 2024) demonstrated that it fully mediated the relationship between IA and NSSI. A longitudinal study conducted over 18 months found that NSSI was predictive of IA, but IA was not predictive of NSSI (Xiong et al., 2023). This further suggests a potential disparity of impact among different forms of screen time and highlights the crucial need for additional longitudinal studies to investigate these relationships. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings again raise important questions about causality; specifically, whether insomnia and psychological distress are antecedents to NSSI and suicidality, or whether digital addiction contributes to these conditions, thereby elevating risk. As these concerns have only gained prominence in recent years, efforts to disentangle these complex relationships remain in their infancy. While this research questions the direction of causality between suicide risk and excessive screen time, it does not suggest that excessive screen use is harmless, nor that existing parental guidelines should be disregarded. This is particularly important given the well-established associations between sedentary behaviour, sleep deprivation, and poor mental and physical health (Duncan et al., 2023), as well as substantial evidence from attachment theory highlighting the protective role of strong parent-child bonds (Zhang et al., 2022). Considering most screen time is typically spent in sedentary and socially isolating contexts, excessive use is likely to contribute to reduced physical activity, diminished sleep, weakened family connections, and poorer mental health outcomes (Wang et al., 2019; dos Santos et al., 2024; Carson et al., 2016). However, further research is needed to clarify the proposed direct link between excessive screen time and NSSI or suicidality in young people. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe review included 61 studies involving children across 16 different countries, increasing generalisability and strengthening the reliability of the findings. Limitations included the exclusion of studies in languages other than English, which may have led to the omission of important findings. Due to limitations in funding, the translation and interpretation of foreign-language studies were not feasible. Another limitation was that five of the included studies failed to specify the age of their participants, providing only school grade, generally up to year 12. It is possible that some of these year 12 classes may have included adolescents over the age of 18 years. Six studies specified a broad age range (for example, age 12 to 18) or grades one to twelve, and 29 studies included only mean ages that were similarly around 13 to 16 years of age. This meant that the current systematic review was unable to provide comparisons of different age groups in terms of their vulnerability to different digital exposures. Screen time recommendations for different age groups were also rarely considered in the measurement or analysis of screen time effects. Few studies investigated characteristics of screen use (messaging, posting photos, watching short videos) or investigated the impacts of individual behaviours. The variable impacts of these different screen use behaviours would pose a considerable confounding effect on outcomes and should be further investigated and controlled for in future studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe heterogeneity of included studies was significant. This may be due to sample variance, different measurement tools used, the inclusion or exclusion of \u0026lsquo;mild\u0026rsquo; or \u0026lsquo;possible\u0026rsquo; IA or IGD in the final prevalence figures, or the varied classifications of suicidal behaviours (such as ideation, thoughts, feelings, or planning that sometimes differentiated and sometimes grouped for analysis). The pooling of different age groups is also likely to have introduced some bias into the results, particularly if age is not adequately controlled for. Findings of the meta-analysis should also be interpreted with caution due to the heterogeneity of measures used to evaluate NSSI and suicidal ideation.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe systematic review highlights the complex and evolving relationship between screen time and suicidal behaviours or NSSI in children and adolescents. Meta-analyses and narrative syntheses revealed significant associations between excess screen time or digital addiction and child NSSI and suicidality. However, bidirectionality in these relationships was frequently identified. Confounding variables such as sleep quality, LGBTQIA status, psychological distress, lifestyle behaviours, and family structure were found to be mediators and/or predictors of NSSI and suicidality on their own, complicating the interpretation of the findings. It is unclear whether screen time leads to NSSI and suicidality, or whether childhood adversity and mental illness leave children vulnerable to pathological digital behaviours, which may subsequently lead to additional social and emotional impairment and thereby strengthen suicidality and self-harming impulses. Sleep quality, in particular, may be impacted by device use, leading to the exacerbation of poor mental health. Further research examining screen use over time, incorporating familial and lifestyle factors, child mental health outcomes, and consistent, less conservative measures of screen time, is needed to better disentangle these associations. Gender differences in screen use and NSSI and suicidal behaviours suggest further investigation into gender-specific screen use and potential harms to avoid dilution of these findings in total population studies. Overall, the findings suggest a deleterious impact of excessive screen time on child NSSI and suicidality, reinforcing the need for enhanced guidelines around screen use. Findings also support additional research to determine the impact of screen time in children experiencing increased adversity and other vulnerabilities.\u0026nbsp;\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was a systematic review and, therefore, did not require ethical approval.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of conflicting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. 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(2011) Gray matter abnormalities in Internet addiction: A voxel-based morphometry study. \u003cem\u003eEuropean Journal of Radiology\u003c/em\u003e 79(1): 92-95.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Mater Research Institute, University of Queensland","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Screen time, self-injurious behavior, self-harm, suicide, internet addiction, internet gaming disorder","lastPublishedDoi":"10.21203/rs.3.rs-8209607/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8209607/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjectives:\u003c/strong\u003e The aim of this systematic review and meta-analysis was to investigate the impacts of screen time and screen behaviours on suicidality and non-suicidal self-injury (NSSI) in children and young people.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A systematic search was conducted of the following databases: CINAHL, PubMed, Embase, PsycARTICLES, PsycINFO, Scopus, and Web of Science. The search identified 61 eligible studies comprising 338,472 participants aged up to 18 years, drawn from 16 countries. A random effects meta-analysis of odds ratios was conducted on 15 studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Meta-analysis revealed that screen time was measured inconsistently across studies, yet frequent screen use – particularly nocturnal smartphone use – was significantly associated with increased odds of NSSI and suicidal behaviours. Internet addiction (IA) showed strong links to suicidal behaviours, often mediated by insomnia, depression, or anxiety. Internet gaming disorder (IGD) also predicted suicidality and NSSI, while mobile phone and social media addiction demonstrated weaker but significant associations. IA was positively associated with NSSI across all seven relevant studies. Structural models identified depression, loneliness, and interpersonal problems as key mediators. Some gender disparities emerged, with females reporting higher NSSI and suicidality, and males showing higher rates of digital addiction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e While these findings highlight concerning associations between excessive screen time and suicidality, they are limited by methodological heterogeneity and inconsistency, raising questions about directionality – whether excessive screen time contributes to poor mental health or preexisting vulnerabilities drive increased screen use.\u003c/p\u003e","manuscriptTitle":"Screen Time and Young People: A Systematic Review and Meta-analysis of the Evidence on Self-Harm and Suicidality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:40:33","doi":"10.21203/rs.3.rs-8209607/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"29b13fea-c6a8-4b9d-a6c0-1aca8f2f8cae","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":58628571,"name":"Psychiatry"}],"tags":[],"updatedAt":"2025-12-01T08:40:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:40:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8209607","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8209607","identity":"rs-8209607","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}