Use of social media in primary school aged children: Concurrent associations with mental health variables

Use of social media in primary school aged children: Concurrent associations with mental health variables | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Use of social media in primary school aged children: Concurrent associations with mental health variables Nza Amir, Alina Marinca, Megan McMahon, Bethany Arkley, Evelyn Staples, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8051391/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Digital screen use has been rising in pre-adolescent children but very little is known about use of social media specifically and how it associates with mental and physical health in this age group. The present study aimed to examine self-reported time spent on social media among primary school-aged children and its associations with indicators of poor mental health. We also explored sex differences in the strengths of these associations. Seven hundred and seventy-three children (ages 8–12 years old) from the DEvelopment of Emotional Resilience (DEER) study reported their screen time use, symptoms of anxiety and depression, sleep quality, somatic complaints, and life satisfaction. Associations between screen time usage and mental health indicators were examined using Structural Equation Modelling. We found that children spend an average of 2 hours on screens on a weekday during term time, with an average of 48% of their time communicating with friends and posting content, and 52% browsing social media feeds. There were no significant differences between boys and girls in estimated time on social media, but older children reported using social media for longer (β = .15, p < .001). Higher social media use was significantly associated with greater symptoms of anxiety (β = .157, p < .001), depression (β = .145, p < .001), poorer sleep habits (β = .09, p = .01), somatic complaints (β = .174, p < .001), and lower life satisfaction (β=–.097, p = .01). Social media usage strongly associated across poor mental health outcomes for girls, but in boys, it only significantly related to anxiety. Our findings suggest cross-sectional associations between measures of social media use and measures of health in pre-adolescent children, particularly girls. Future research should clarify the directions of these associations. Nonetheless, it would be prudent to consider management and regulation of digital technology and social media usage in this age group. Health sciences/Health care Biological sciences/Psychology Social science/Psychology social media developmental psychopathology sex differences pre-adolescence Figures Figure 1 Introduction A 2022 report from Ofcom (2023) [ 1 ] found that 50% of children by the age of 9 years own a mobile phone, and this increases to roughly 85% by the age of 11 years. This same report also highlighted social media usage, with over half (54%) of 8 to 11-year-old children using WhatsApp and over a quarter (26%) using TikTok as platforms to communicate with others. Overlapping with this period when children’s accessibility to mobile devices has risen, there has been a marked decline in mental health observed in children and young people [ 2 ], leading many key stakeholders (mental health professionals, teachers, policy makers and the wider public including parents and children and young people themselves) to speculate over whether the two are related [ 3 ]. Yet, there is little empirical data on whether the amount of time spent on digital technology and social media is linked to worse health outcomes in this age group. This is surprising given that the current generation of pre-adolescent children (8–11 years) was one that was home-schooled in the early phases of primary school during the COVID-19 pandemic, and who may, as a result, have been disproportionally exposed to technology use. Moreover, this is an age group where parents may still be able to retain monitoring and management of healthy time limitations on mobile devices. In this study, we investigated self-reported screen time of 8–12-year-olds and explored its links with mental health. Access to digital technology has revolutionised children and young people’s learning and social opportunities, and there are notable benefits in education, personal growth and development [ 4 , 5 ]. One area of opportunity is the building and nurturing of social connections through social media. Social media is an emerging concept with ever-changing features, but most definitions pinpoint an internet-based system that features user-generated content and permits user interactions both synchronously and asynchronously [ 6 ]. Social media fosters online interactions which can include positive exchanges (peer acceptance) but also negative exchanges (cyberbullying, grooming, social comparisons), as a result it is of particular interest to researchers exploring mental health. Several meta-analyses and systematic reviews have explored its mental health impact in adolescents and young adults, with one systematic review noting that social media use was associated with depression and anxiety in 82.6% and 78.3% of their included studies on adolescents, respectively [ 7 ]. Similarly, Santos et al. (2023) [ 8 ] found that social media usage had a negative association with adolescent mental health, particularly in girls. A meta-analysis of 18 studies of adolescents and young adults (that spanned age ranges from approx. 12 to 30 years) noted moderate but significant correlations between problematic social media use and depression (r = 0.273, P < .001), anxiety (r = 0.348, P < .001), and stress (r = 0.313, P < .001) [ 9 ]. Others also find similar results between social media use and poorer mental health in this age group (16–25 years; [ 10 ]), as well as on other markers of poor mental health such as sleep [ 10 , 11 ]. While syntheses of the literature highlight that social media use amongst adolescents and young adults carry a greater mental health risk, possibly more so in those with pre-existing mental health conditions [ 12 ] some also note that individual characteristics may moderate these responses [ 5 , 13 ]. One of these is gender, with female adolescents spending more time on social media and showing stronger correlations between time spent and depressive symptoms [ 5 ]. Male adolescents, on the other hand, are more likely to be classed as minimal or moderate social media users, where usage is associated with better mental health outcomes [ 14 ]. Another characteristic is age [ 5 ] with some studies indicating that the relationship between social media use and mental health indices is stronger particularly in younger adolescents (10 to 15 years old) [ 15 , 16 , 17 ]. Despite these worrying downward age trends, studies of digital activity in general and social media use, in particular amongst pre-adolescents (8–11 years), are much scarcer. Only a handful of studies have explored associations between social media use and mental health outcomes. Belton and colleagues (2021) [ 18 ] analysed cross-sectional questionnaire responses on well-being (using the KIDSCREEN-27; [ 19 ]) and leisure screen time use (using the Adolescent Sedentary Activity Questionnaire; [ 20 ]) from 879 Irish children, 8–12 years old. They discovered that 69.60% of children had a daily screen time average of less than 2 hours, and such children had scored higher on social support, academics, and physical and parental well-being. Roberston and colleagues (2022) [ 21 ] examined first-wave cross-sectional data from the Adolescent Brain Cognitive Development Study [ 22 ], which included 1,780 American children, aged 9–10 years old to understand whether various forms of screen time, including social media, were associated with internalising disorders. The authors found that children who were social media users were more likely to have depressive disorders (as measured by the Kiddie Schedule for Affective Disorders and Schizophrenia; [ 23 ]) compared to non-users. Unlike the previous study [ 18 ], the association between social media use and depression was greater in girls than boys, suggesting gender differences similar to those found in adolescents. Middle childhood is a transitional period where many children begin to prepare for secondary education, leading to greater independence from parents and potentially, ownership of mobile devices for practical reasons (such as commuting to school). At this age, children also begin placing greater value on their friendships and whilst reciprocated relationships may influence self-worth [ 24 ], having fewer supportive peers has been linked to emotional symptoms relating to anxiety and depression [ 25 ]. It is not yet known whether similar negative associations are found between social media use and indicators of poor mental health in pre-adolescent children. Here, we addressed this gap by exploring pre-adolescent self-reported screen time use and its links to mental health. Based on previous studies, we had the following hypotheses: First , we hypothesised that greater social media use, measured by average hours of use per day, would be associated with more symptoms of anxiety and depression, worse sleep quality, higher levels of somatic complaints, and lower levels of life satisfaction. Second , we expected differences between boys and girls in time spent on social media and in the strength of the associations between social media use and indicators of poor mental health. Methods Participants and design Our participants were 773 primary school children aged 8-12 years old from the DEvelopment of Emotional Resilience (DEER) study [26], conducted across 10 primary schools in East London. The current study used the second wave data collected between February and November 2024. This study received ethical approval from the Queen Mary University Ethics of Research Committee (QMERC22.251) and was conducted in accordance with the principles set forth in the ICH Harmonised Tripartite Guidelines for Good Clinical Practice and the Declaration of Helsinki. Participating children and their parents received information sheets with details of the study. Consent was obtained from parents/carers and assent from children was provided prior to taking part in the study [26]. Testing was conducted in school settings, with children reporting on all outcome measures using Samsung tablets and REDCap [27] electronic data capture tools hosted at QMUL. Demographic information, including ethnicity and being in receipt of free school meals (used as an indicator of socio-economic status [28]) was collected from the school. All other information used in the current study has been self-reported by the children. Participants were on average 9.91 years (SD = 0.89, range [8-12]). Just over half (52%) were female and. Largely consistent with sub-populations in East London [29, 30], the majority of our participants (61.27%) were of Asian/Asian British ethnicity with the rest of the sample consisting of 17.88% White, 8.81% Black African/Caribbean/Black British, 6.48% Mixed/Multiple ethnic groups, 5.05% other ethnic groups, and 0.52% not reported. Additionally, reflecting the high levels of child poverty in East London boroughs [31], 24.4% were reported as having access to free school meals, an index of lower socio-economic status (SES). Measures Screen time and social media use To assess children’s average social media use, we used the BeeWell social media use measure [32] from the Hobbies and Entertainment domain within the Drivers of Wellbeing section of the #BeeWell survey (https://beewellprogramme.org/greater-manchester/), which has previously used in Year 8 and 10 pupils in the UK. This measure includes three items capturing the frequency and context of social media use. For item 1, children reported on their use of social media on a ‘normal weekday during term time’ in hours (0-7 hours or more). Those children who reported using social media > 1 hour were then asked to estimate how they used their time on social media, aiming to explore the rate of passive and active social media use. Children would be required to input a figure from 0-100 indicating the proportion of active time spent on social media compared to passive. However, as children found it difficult to accurately estimate percentages of their activities and struggled to understand this question, we used the first item only (‘total screen time’) in all analyses. Indicators of poor mental health We used the 47-item Revised Child Anxiety and Depression Scale (RCADS; [33]) to assess children’s self-reported symptoms of anxiety and depression. Children reported on how often each item applied to them on a 4-point Likert scale, ranging from 0 ("Never") to 3 (“Always''). Raw scores were converted into T-scores (0–100), with higher scores indicating greater symptom severity. Children’s Sleep problems and Habits were measured with the Sleep Self-report (SSR; [34]). The SSR is a 26-item measure of self-reported sleep habits and common sleep disturbances in children. The scale comprises items that measure bedtime routines, sleep behaviour, and daytime sleepiness on a 3-point Likert scale ranging from ‘rarely’, ‘sometimes’ or ‘usually’, with higher scores indicative of more sleep disturbances. Example SSR questions include: ‘Do you go to bed at the same time every night on school nights?’, ‘Do you fall asleep in about 20 minutes?’. The SSR has been found to correlate with the Children's Sleep Habits Questionnaire (CSHQ), a parent-report sleep measure previously validated in research [34, 35]. The Somatic Complaints List (SCL) [36] is an 11-item measure used to assess the frequency of children’s somatic complaints [37] on a 3-point Likert scale, ranging from 1 (Never) to 3 (Often). Example questions include: ‘I feel tired’, ‘I feel pain in my arms and legs’, ‘I feel nauseous (like I may vomit)’. Higher scores indicate a higher frequency of complaints. The SCL has been shown to be stable for a period of half a year with strong correlations with sadness, fear, and anger reports of children’s somatic complaints [36]. Life satisfaction was measured using the 7-item Student Life Satisfaction Scale (SLSS; [38]), where each item is assessed on a 6-point Likert scale from 1 (“Strongly disagree”) to 6 (“Strongly agree”) [39]. The higher the average score, the greater overall life satisfaction. Example questions include: ‘My life is going well’, ‘I wish I had a different kind of life’, ‘My life is better than most kids’. Previous research has shown that the SLSS is correlated with several other well-being measures including the Andrews and Withey Life Satisfaction Scale [38]. Data analysis Descriptive analyses were first conducted using SPSS (IBM Statistics Version 29). We investigated the average time spent on social media (total screen time) and the average proportions of average and passive social media time (although the latter was not used in further analysis). If sex, age, free school meals and ethnicity were associated with the outcome variables, these were included as covariates in subsequent analysis. To inform structural equation modelling (SEM) analysis, we used Pearson's correlation to explore the links between social media use and mental health indicator variables. We then fitted SEM using R studio (Version 2024.12.0+467). This included direct paths between ‘total screen time’ and variables reflecting child anxiety, depression, poor sleep habits, somatic complaints, and life satisfaction along with relevant covariates. We considered the Comparative Fit Index (CFI) which ranges from 0 to 1, with higher values considered a good fit [40], Standardized Root Mean Square Residual (SRMR) which from a rule of thumb should be less than .05 and Root Mean Square Error of Approximation (RMSEA) with values larger than .10 not acceptable [41]. We also examined sex differences by employing a multigroup SEM analysis. We wanted to establish if these differences stem from structural differences in the path coefficients and not from measurement differences across groups [42, 43]. We tested the configural model, following Byrne (2006) [44], to assess whether the same general factor structure fits across groups (i.e., girls and boys). We used the chi-square (χ²) test to assess model fit, where a non-significant difference would suggest that the same conceptual model is appropriate for both groups, indicating baseline comparability. Results Descriptive analysis Children reported spending on average 2 hours on social media on a weekday during term time, with a mean of 48% of time on active use. Total screen time did not differ significantly between boys and girls. Age was significantly positively correlated with children’s social media use (r = .15, p < .001) and anxiety symptoms (r = .10, p = .002) and negatively correlated with poor sleep habits (r=–.19, p < .001) and somatic complaints (r=–.10, p = .005). Children receiving free school meals (indicative of lower SES) reported spending more time on social media compared with children who did not receive free school meals (Table 1 ). A one-way ANOVA examining differences in social media use across ethnic groups did not find statistically significant differences F(4,751) = 1.28, p = .276). Table 1 Descriptive statistics Mean (SD) Male mean (SD) Female mean (SD) T (df) No free school meals Mean (SD) Yes free school meals Mean (SD) T (df) Correlation with age Variable Social media use 2.00 (1.94) 2.02 (1.98) 1.99 (1.91) 0.19 (669) 1.91 (1.91) 2.31 (2.02) -2.23 (246)* 0.15 *** Anxiety symptoms 46.00 (11.58) 46.63 (12.72) 45.41 (10.39) 1.44(704) 46.17 (11.70) 45.46 (11.22) 0.73 (326) 0.10 ** Depression symptoms 48.62 (11.56) 48.53 (12.16) 48.71 (10.99) -0.21(732) 48.72 (11.32) 48.33 (12.30) 0.38 (294) -0.04 Poor Sleep habits 39.29 (7.20) 38.57 (6.93) 39.94 (7.39) -2.62(752)** 39.28 (7.03) 39.30 (7.73) -0.02 (289) -0.19*** Somatic complaints 17.66 (4.04) 17.33 (3.97) 17.97 (4.07) -2.16(724)* 17.67 (3.89) 17.63 (4.48) 0.11 (268) -0.10** Life satisfaction 32.90 (6.67) 33.15 (6.43) 32.66 (6.89) 1.03(752) 33.04 (6.47) 32.46 (7.24) 0.96 (285) 0.06 *p < .05, **p < .01, ***p < .001 Means and standard deviations for mental health indicators are presented in Table 1 . There were no sex differences in anxiety or depressive symptoms, nor in life satisfaction scores but girls did report worse sleep habits and had more somatic complaints (Table 1 ). Anxiety symptoms, poor sleep habits and somatic complaints were all higher for older children, but correlations were weak (Table 1 ). Prior to running the SEM, we also examined the pattern of correlations between social media use and symptoms of anxiety and depression, poor sleep habits, somatic complaints and life satisfaction. Social media use was positively associated with all measured mental health linked variables, ranging from r = .09 (p < .05) with poor sleep to r = .17 (p < .001) with somatic complaints, and was negatively correlated with life satisfaction (r = –.10, p < .05; Table 2 ). Table 2 Pearson correlations between social media use, anxiety, depression, poor sleep habits, somatic complaints and life satisfaction 1 2 3 4 5 6 1. Social Media Use - .16*** .15*** .095* .17*** − .10* 2. Anxiety - .76*** .59*** .55*** − .51*** 3. Depression - .58*** .57*** − .57*** 4. Poor Sleep habits - .51*** − .46*** 5. Somatic complaints - − .38*** 6. Life satisfaction - Note: *indicates p < .05, **indicates p < .01, ***indicates p < .001 Social media use represents the scores of the time spend on social media, higher scores at SSR (Sleep Self Report) indicate poor sleep problems Structural Equation Model A model that included paths between social media use and anxiety symptoms, depression symptoms, poor sleep habits, somatic complaints, and life satisfaction (Fig. 1 ) showed a good fit to the data: CFI = 0.98, RMSEA = 0.098 with a 90% CI and SRMR = 0.05. This model also included age and free school meals as covariates, though the latter ceased to be a significant effect on social media use and was therefore removed from the model. The analysis with age as a covariate (st.β = 0.15, p < .001) revealed that social media use positively associated with children’s anxiety (st.β = 0.16, p < .001), depression (st.β = 0.15, p < .001), poor sleep habits (st.β = 0.09, p = 0.01), somatic complaints (st. =0.17, p < 0.001) and negatively associated with children’s life satisfaction (st.β=-0.10, p = 0.01). These results suggest that higher media use is linked to poorer mental health variables and lower life satisfaction. Multigroup SEM The multigroup SEM analysis, indicated that the factor structure and loadings were equivalent across sex (metric invariance). However, the chi-square difference test comparing constrained and unconstrained structural paths was significant (Δχ²(6) = 13.67, p = 0.033), suggesting that the effects of social media on mental and physical health outcomes differed between boys and girls. The configural model showed acceptable fit (fit indices: CFI = 0.977, SRMR = 0.05, RMSEA = 0.1) Social media use showed stronger path estimates for all mental health indicators among girls, whereas for boys, social media use was significantly associated only with anxiety (Table 3 ). Table 3 Direct effects of Social Media Use on each outcome in boys’ vs girls' model (multiple group analysis) Outcome Variable Standardised beta for boys Standardised beta for girls Anxiety 0.13* 0.19*** Depression 0.08 0.21*** Poor Sleep Habits 0.04 0.14** Somatic complaints 0.07 0.27*** Life Satisfaction -0.01 -0.17** Note: *p < .05, **p < .01, ***p < .001 Discussion This study examined cross-sectional associations between social media use and numerous indicators of poor mental health in pre-adolescent children. Children who were aged 8–12 years reported spending approximately 2 hours each day on screens, with most of this time being spent on social media platforms. We found that although there were no significant differences between boys and girls in total time spent on social media, greater estimated time spent on social media was associated with symptoms of anxiety and depression, worse sleep quality, higher level of somatic complaints, and lower levels of life satisfaction in girls (regression coefficients ranged from 0.14 to 0.27), while in boys the social media variable only correlated with anxiety (r = 0.13). A recent bibliometric analysis reviewing studies globally from 2000 to 2024 has emphasised the growing concerns about the impacts of digital environments on children's mental health and well-being [ 45 ]. The literature suggests that the rise in digital activity, particularly social media use since the COVID-19 pandemic, has contributed to increased internalising symptoms, poorer well-being, and suicidal behaviours in youth [ 46 , 47 ]. Yet even though many of the current generation of children already own phones and have accounts on social media platforms [ 1 ], there is very little data on social media use in late childhood and early adolescence. Our data from middle to late childhood add to the emerging, though at times mixed, picture that more time spent on social media in adolescence and young adulthood can be worse for mental health. Indeed, previous research highlights the associations between social media and depression symptoms [ 21 ], poorer physical functioning [ 48 ] and sleep quality [ 49 ], and lower life satisfaction throughout late childhood to early adulthood [ 47 , 45 ] – findings that are aligned with ours. Our findings also partly align with existing research on gender differences found in rates of social media usage and the effect of social media use on health. Serenko et al. (2021) [ 50 ] reported that 13- to 15-year-olds girls spent 3.15 hours weekly on social media and slept for 8.55 hours on a school day, compared to boys, who spent 2 hours on social media and 8.70 hours sleeping. We did not find these mean differences in self-reported social media use between pre-adolescent boys and girls, suggesting that these sex differences might emerge later with age. However, we did find that social media use correlated with a wider range of mental health indicators in girls than in boys, and that the range of effect sizes of these relationships were larger in boys than girls. It is becoming increasingly clear that the associations between social media use and poorer mental health are complex and moderated by variables including sex, though it should be noted that in our present study, our variable here reflected biological sex rather than children’s gender identity per se. Overall, our results further emphasise that negative associations between social media and mental health appear in pre-adolescence too. However, our work has several limitations. First, although we adopted a widely used measure, this did rely on self-reported data, which required participants to reflect on their social media usage. This might be difficult for pre-adolescent children, where time perception may be less reliable. Indeed, some participants struggled with conceptualising the percentages of time spent on passive and active social media use. Although we reported these estimates here (and refrained from conducting statistical analysis on these variables), they should still be considered with caution. More detailed measures of social media use that are currently being developed should be considered in future, in combination with other indices such as parental reports and objective screen time records (e.g., https://www.so-me-study.org/our-team ). Second, we were only able to capture children’s total screen time whilst other relevant dimensions were not assessed. We did not measure content viewed (risky versus safe) and other individual differences, such as emotional responsiveness, which might contribute to increased digital risk over and above the total screen time, as recent work suggest might be the case for adolescents [ 51 ]. Finally, as our data are cross-sectional, we cannot infer causality. It therefore remains unclear whether social media use negatively impacts mental health, or whether children with poorer mental health tend to seek social media more. Emerging theoretical frameworks and empirical findings suggest that the relationship between digital technology and mental health is complex and likely reciprocal [ 13 ] but large cohort, longitudinal research or time-sensitive methods such as ecological momentary assessment studies, are needed to address these temporal effects. Regardless, our findings, though formative have several important implications. The observed associations between social media use and markers of poor mental health in children, represent only the first step of research efforts in this area. There is a need for more mechanistic studies of digital technology impact on development. Theoretical and empirical models of digital activity and health suggest complex pathways involving moderators and mediators that warrant further investigation [ 13 ]. In interpreting the findings of this paper, we shared our findings with some young people to discuss their views on what these findings meant, and as seen in Box 1A and 1B, young people themselves also suggest possible mechanisms by which social media can impact mental health, highlighting sleep problems and limited time for physical activities (Box 1A and 1B). Inclusion of these pathways driven by theory, empirical findings, and youth experiences may increase the explanatory power by which social media usage associates with mental health variables. Although it is not yet clear whether reducing social media use will improve mental health for young people, early prevention measures such as improved school policies and/or parental involvement in regulating social media use are to be encouraged. We also shared our findings with a teacher who is a member of a school senior leadership team. He highlighted risks that come from unsupervised digital activities and call for systemic guidelines and policies (Box 1C). In fact, many schools have begun implementing policies to reduce phone use in school-time. Examples include the phones being: handed in on arrival, kept in a secure location which the pupil does not access throughout the school day, and pupils having access to the mobile phone, but ensuring it is not used, seen, or heard [ 52 ]. It is worth noting, however, that in a cross-sectional observational study across 30 secondary schools, Goodyear et al. (2025) [ 53 ] found that although restrictive phone policies resulted in less time spent on phones and social media during school hours, this was not linked to a significant difference in lowering their students’ overall phone use or enhancing their well-being. Interestingly, young people from the community had some views on why school restrictions may not be the only solution, and indeed suggested that parents should be involved in those decisions more (Box 1A). Future studies could potentially explore the conceptual model by Morawska et al. (2023) [ 54 ], which emphasises the importance of parental beliefs surrounding screen usage and self-efficacy in healthy screen use behaviours. It may be that parents' responses are especially important in primary school-aged children at a time when they still spend more time at home and where parent-implemented rules are still followed. Interesting another young person also noted the benefits of social media and that these should not be overlooked when coming to decisions over complete restrictions (Box 1B). BOX 1. Excerpts from Reflections: young people and educators A. Bethany A (age 10) Out of my whole class only me and one other girl don't have a phone yet but I have roblox on my tablet and play with my friends like this. At my school the teacher takes everyone’s phone in the morning and gives them back at the end of the day which I think is a good idea. I don't think using your phone is always bad but if it stops you doing other things like playing with your friends then this is not good and can be boring if you don't want to look at your phone all the time. I think it is the parent's job to decide if a child spends too long looking at their phone, but some parents are more strict than others. B. Evelyn S (age 15) As a young person I often see the day-to-day aspects of social media use and make my own observations and often it is not the use of social media itself which is the key issue but related factors such as the management of time around social media. For example, I can confidently state that a large proportion of students in my age group miss significant amounts of sleep as a result of late-night social media use. This lack of sleep then negatively aspects their capacity for resilience, learning and social interaction. Leading in many cases to frustration, feelings of isolation and generally worse mental health. It is, however, to be noted that this particular issue is as a result of the intrinsically addictive nature of social media as well as poor time management. […] It is important to note that the situation is a lot more complicated than simply removing social media will cause a positive result in mental health as many people of my age group and significantly older have turned to social media in times of emotional difficulty ad stress and now find themselves emotionally reliant on social media as a form of escapism or coping method however unhealthy. It is the unfortunate truth that these people would actively struggle should that crutch of social media be removed. C. Alex Wedgbury (School Deputy Headteacher and Deputy Safeguarding Lead) As a school leader, I see daily the effects of social media on our pupils' wellbeing. While these platforms offer opportunities for connection, the negatives often outweigh the positives. Children as young as four [years old] come into school having viewed inappropriate content, faced online bullying or suffered significant sleep disruption – all of which damage learning and development. Despite our school-based initiatives and restrictions, cases are increasing. Schools, parents and carers require greater support in terms of guidance and regulation; the pace of change is such that tackling these problems in isolation is ineffective. A collective, national strategy is required. Conclusion Taken together, our study adds valuable evidence to the growing call to address social media’s impact on children's mental health and wellbeing. Our findings highlight the importance of effective management and preventive strategies to promote healthier social media use during these critical stages of mental and brain development (WHO, 2024). By identifying young people at risk earlier, we can improve the chances of timely support and reduce the likelihood of negative outcomes during adolescence and young adulthood. Declarations Author contribution statement N.A, A.M, M.M (Queen Mary University of London, UK) made substantial contributions to the conception and design of the work, acquisition, analysis, interpretation, draft, and substantial revision, approved the submitted version, and agree to be personally accountable. I.H, J.M, F.V, D.O made substantial contributions to the conception and substantively revised the work, approved the submitted version, and agree to be personally accountable. B.A, E.S, A.W made contributions to the interpretation of the data, approved the submitted version, and agree to be personally accountable. J.L made substantial contributions to the conception, design, interpretation, draft, and revision of the work, approved the submitted version, and agrees to be personally accountable. Funding declaration This study was funded by a Barts Charity grant of £2.8 million received on 30 th March 2020 (MRC&U0042). 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Impact of mobile phones and wireless devices use on children and adolescents’ mental health: A systematic review. European Child & Adolescent Psychiatry 33, 1621–1651 https://doi.org/10.1007/s00787-022-02012-8 (2022). Benson, V., Hand, C. & Hartshorne, R. How compulsive use of social media affects performance: Insights from the UK by purpose of use. Behaviour & Information Technology 38, 549–563 https://doi.org/10.1080/0144929x.2018.1539518 (2018). Levenson, J. C., Shensa, A., Sidani, J. E., Colditz, J. B. & Primack, B. A. The association between social media use and sleep disturbance among young adults. Preventive Medicine 85, 36–41 https://doi.org/10.1016/j.ypmed.2016.01.001 (2016). Serenko, A., Turel, O. & Bohonis, H. The impact of social networking sites use on health-related outcomes among UK adolescents. Computers in Human Behavior Reports 3, 100058 https://doi.org/10.1016/j.chbr.2021.100058 (2021). Orben, A. Teenagers, screens and social media: A narrative review of reviews and key studies. Social Psychiatry and Psychiatric Epidemiology 55, 407–414 https://doi.org/10.1007/s00127-019-01825-4 (2020). Department for Education. Mobile phones in schools. GOV.UK (2024). Available at: https://assets.publishing.service.gov.uk/media/65cf5f2a4239310011b7b916/Mobile_phones_in_schools_guidance.pdf. (Accessed: 24th August 2025) Goodyear, V. A. et al. School phone policies and their association with mental wellbeing, phone use, and social media use (Smart Schools): A cross-sectional observational study. The Lancet Regional Health - Europe 51, 101211 https://doi.org/10.1016/j.lanepe.2025.101211 (2025). Morawska, A., Mitchell, A. E. & Tooth, L. R. Managing screen use in the under-fives: Recommendations for parenting intervention development. Clinical Child and Family Psychology Review 26, 943–956 https://doi.org/10.1007/s10567-023-00435-6 (2023). Additional Declarations No competing interests reported. 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06:44:19","extension":"html","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":149164,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8051391/v1/ca7acdc89baa4ea704ed2496.html"},{"id":96916973,"identity":"3d19d08a-4b1b-49a8-be31-80add2e3cb7c","added_by":"auto","created_at":"2025-11-27 14:09:06","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130284,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStructural Equation Model Illustrating the Effects of Social Media Use (with age as covariate) on children’s Anxiety, Depression, Sleep habits, Somatic complaints and Life satisfaction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Predictor: MediaUse, Social media use. Outcomes: Anxiety; Depression; Sleep, poor sleep habits; Satisf, life satisfaction; Somatic, Somatic complaints. Covariate: age.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8051391/v1/b095106c4d0a0f469ef8e8fd.jpeg"},{"id":96923002,"identity":"20d31755-7752-4144-b5b4-65f93512b29c","added_by":"auto","created_at":"2025-11-27 14:20:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1054545,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8051391/v1/93bb4f3e-83d0-4f12-9e1a-1f4db3ef1e27.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Use of social media in primary school aged children: Concurrent associations with mental health variables","fulltext":[{"header":"Introduction","content":"\u003cp\u003eA 2022 report from Ofcom (2023) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] found that 50% of children by the age of 9 years own a mobile phone, and this increases to roughly 85% by the age of 11 years. This same report also highlighted social media usage, with over half (54%) of 8 to 11-year-old children using WhatsApp and over a quarter (26%) using TikTok as platforms to communicate with others. Overlapping with this period when children\u0026rsquo;s accessibility to mobile devices has risen, there has been a marked decline in mental health observed in children and young people [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], leading many key stakeholders (mental health professionals, teachers, policy makers and the wider public including parents and children and young people themselves) to speculate over whether the two are related [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Yet, there is little empirical data on whether the amount of time spent on digital technology and social media is linked to worse health outcomes in this age group. This is surprising given that the current generation of pre-adolescent children (8\u0026ndash;11 years) was one that was home-schooled in the early phases of primary school during the COVID-19 pandemic, and who may, as a result, have been disproportionally exposed to technology use. Moreover, this is an age group where parents may still be able to retain monitoring and management of healthy time limitations on mobile devices. In this study, we investigated self-reported screen time of 8\u0026ndash;12-year-olds and explored its links with mental health.\u003c/p\u003e\u003cp\u003eAccess to digital technology has revolutionised children and young people\u0026rsquo;s learning and social opportunities, and there are notable benefits in education, personal growth and development [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. One area of opportunity is the building and nurturing of social connections through social media. Social media is an emerging concept with ever-changing features, but most definitions pinpoint an internet-based system that features user-generated content and permits user interactions both synchronously and asynchronously [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Social media fosters online interactions which can include positive exchanges (peer acceptance) but also negative exchanges (cyberbullying, grooming, social comparisons), as a result it is of particular interest to researchers exploring mental health. Several meta-analyses and systematic reviews have explored its mental health impact in adolescents and young adults, with one systematic review noting that social media use was associated with depression and anxiety in 82.6% and 78.3% of their included studies on adolescents, respectively [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Similarly, Santos et al. (2023) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] found that social media usage had a negative association with adolescent mental health, particularly in girls. A meta-analysis of 18 studies of adolescents and young adults (that spanned age ranges from approx. 12 to 30 years) noted moderate but significant correlations between problematic social media use and depression (r\u0026thinsp;=\u0026thinsp;0.273, P\u0026thinsp;\u0026lt;\u0026thinsp;.001), anxiety (r\u0026thinsp;=\u0026thinsp;0.348, P\u0026thinsp;\u0026lt;\u0026thinsp;.001), and stress (r\u0026thinsp;=\u0026thinsp;0.313, P\u0026thinsp;\u0026lt;\u0026thinsp;.001) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Others also find similar results between social media use and poorer mental health in this age group (16\u0026ndash;25 years; [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]), as well as on other markers of poor mental health such as sleep [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWhile syntheses of the literature highlight that social media use amongst adolescents and young adults carry a greater mental health risk, possibly more so in those with pre-existing mental health conditions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] some also note that individual characteristics may moderate these responses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. One of these is gender, with female adolescents spending more time on social media and showing stronger correlations between time spent and depressive symptoms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Male adolescents, on the other hand, are more likely to be classed as minimal or moderate social media users, where usage is associated with better mental health outcomes [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnother characteristic is age [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] with some studies indicating that the relationship between social media use and mental health indices is stronger particularly in younger adolescents (10 to 15 years old) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Despite these worrying downward age trends, studies of digital activity in general and social media use, in particular amongst pre-adolescents (8\u0026ndash;11 years), are much scarcer. Only a handful of studies have explored associations between social media use and mental health outcomes. Belton and colleagues (2021) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] analysed cross-sectional questionnaire responses on well-being (using the KIDSCREEN-27; [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]) and leisure screen time use (using the Adolescent Sedentary Activity Questionnaire; [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]) from 879 Irish children, 8\u0026ndash;12 years old. They discovered that 69.60% of children had a daily screen time average of less than 2 hours, and such children had scored higher on social support, academics, and physical and parental well-being. Roberston and colleagues (2022) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] examined first-wave cross-sectional data from the Adolescent Brain Cognitive Development Study [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], which included 1,780 American children, aged 9\u0026ndash;10 years old to understand whether various forms of screen time, including social media, were associated with internalising disorders. The authors found that children who were social media users were more likely to have depressive disorders (as measured by the Kiddie Schedule for Affective Disorders and Schizophrenia; [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]) compared to non-users. Unlike the previous study [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], the association between social media use and depression was greater in girls than boys, suggesting gender differences similar to those found in adolescents.\u003c/p\u003e\u003cp\u003e Middle childhood is a transitional period where many children begin to prepare for secondary education, leading to greater independence from parents and potentially, ownership of mobile devices for practical reasons (such as commuting to school). At this age, children also begin placing greater value on their friendships and whilst reciprocated relationships may influence self-worth [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], having fewer supportive peers has been linked to emotional symptoms relating to anxiety and depression [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It is not yet known whether similar negative associations are found between social media use and indicators of poor mental health in pre-adolescent children. Here, we addressed this gap by exploring pre-adolescent self-reported screen time use and its links to mental health. Based on previous studies, we had the following hypotheses: \u003cem\u003eFirst\u003c/em\u003e, we hypothesised that greater social media use, measured by average hours of use per day, would be associated with more symptoms of anxiety and depression, worse sleep quality, higher levels of somatic complaints, and lower levels of life satisfaction. \u003cem\u003eSecond\u003c/em\u003e, we expected differences between boys and girls in time spent on social media and in the strength of the associations between social media use and indicators of poor mental health.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eParticipants and design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur participants were 773 primary school children aged 8-12 years old from the DEvelopment of Emotional Resilience (DEER) study [26], conducted across 10 primary schools in East London. The current study used the second wave data collected between February and November 2024. This study received ethical approval from the Queen Mary University Ethics of Research Committee (QMERC22.251) and was conducted in accordance with the principles set forth in the ICH Harmonised Tripartite Guidelines for Good Clinical Practice and the Declaration of Helsinki. Participating children and their parents received information sheets with details of the study. Consent was obtained from parents/carers and assent from children was provided prior to taking part in the study [26].\u003c/p\u003e\n\u003cp\u003eTesting was conducted in school settings, with children reporting on all outcome measures using Samsung tablets and REDCap [27] electronic data capture tools hosted at QMUL. Demographic information, including ethnicity and being in receipt of free school meals (used as an indicator of socio-economic status [28]) was collected from the school. All other information used in the current study has been self-reported by the children.\u003c/p\u003e\n\u003cp\u003eParticipants were on average 9.91 years (SD = 0.89, range [8-12]). Just over half (52%) were female and. Largely consistent with sub-populations in East London [29, 30], \u0026nbsp;the majority of our participants (61.27%) were of Asian/Asian British ethnicity with the rest of the sample consisting of 17.88% White, 8.81% Black African/Caribbean/Black British, 6.48% Mixed/Multiple ethnic groups, 5.05% other ethnic groups, and 0.52% not reported. Additionally, reflecting the high levels of child poverty in East London boroughs [31], 24.4% were reported as having access to free school meals, an index of lower socio-economic status (SES).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eScreen time and social media use\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo assess children\u0026rsquo;s average social media use, we used the BeeWell social media use measure [32] from the \u003cem\u003eHobbies and Entertainment\u003c/em\u003e domain within the \u003cem\u003eDrivers of Wellbeing\u003c/em\u003e section of the #BeeWell survey (https://beewellprogramme.org/greater-manchester/), which has previously used in Year 8 and 10 pupils in the UK. This measure includes three items capturing the frequency and context of social media use. For item 1, children reported on their use of social media on a \u0026lsquo;normal weekday during term time\u0026rsquo; in hours (0-7 hours or more). Those children who reported using social media \u003cu\u003e\u0026gt;\u003c/u\u003e 1 hour were then asked to estimate \u003cem\u003ehow\u0026nbsp;\u003c/em\u003ethey used their time on social media, aiming to explore the rate of \u003cem\u003epassive\u0026nbsp;\u003c/em\u003eand \u003cem\u003eactive\u0026nbsp;\u003c/em\u003esocial media use. Children would be required to input a figure from 0-100 indicating the proportion of active time spent on social media compared to passive. However, as children found it difficult to accurately estimate percentages of their activities and struggled to understand this question, we used the first item only (\u0026lsquo;total screen time\u0026rsquo;) in all analyses.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIndicators of poor mental health\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eWe used the 47-item Revised Child Anxiety and Depression Scale (RCADS;\u0026nbsp;[33]) to assess children\u0026rsquo;s self-reported symptoms of anxiety and depression. Children reported on how often each item applied to them on a 4-point Likert scale, ranging from 0 (\u0026quot;Never\u0026quot;) to 3 (\u0026ldquo;Always\u0026apos;\u0026apos;). Raw scores were converted into T-scores (0\u0026ndash;100), with higher scores indicating greater symptom severity.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eChildren\u0026rsquo;s Sleep problems and Habits were measured with the Sleep Self-report (SSR; [34]). The SSR is a 26-item measure of self-reported sleep habits and common sleep disturbances in children. The scale comprises items that measure bedtime routines, sleep behaviour, and daytime sleepiness on a 3-point Likert scale ranging from \u0026lsquo;rarely\u0026rsquo;, \u0026lsquo;sometimes\u0026rsquo; or \u0026lsquo;usually\u0026rsquo;, with higher scores indicative of more sleep disturbances. Example SSR questions include: \u0026lsquo;Do you go to bed at the same time every night on school nights?\u0026rsquo;, \u0026lsquo;Do you fall asleep in about 20 minutes?\u0026rsquo;. The SSR has been found to correlate with the Children\u0026apos;s Sleep Habits Questionnaire (CSHQ), a parent-report sleep measure previously validated in research [34, 35].\u003c/p\u003e\n\u003cp\u003eThe Somatic Complaints List (SCL) [36] is an 11-item measure used to assess the frequency of children\u0026rsquo;s somatic complaints [37] on a 3-point Likert scale, ranging from 1 (Never) to 3 (Often). Example questions include: \u0026lsquo;I feel tired\u0026rsquo;, \u0026lsquo;I feel pain in my arms and legs\u0026rsquo;, \u0026lsquo;I feel nauseous (like I may vomit)\u0026rsquo;. Higher scores indicate a higher frequency of complaints. The SCL has been shown to be stable for a period of half a year with strong correlations with sadness, fear, and anger reports of children\u0026rsquo;s somatic complaints [36].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLife satisfaction was measured using the 7-item Student Life Satisfaction Scale (SLSS; [38]), where each item is assessed on a 6-point Likert scale from 1 (\u0026ldquo;Strongly disagree\u0026rdquo;) to 6 (\u0026ldquo;Strongly agree\u0026rdquo;) [39]. The higher the average score, the greater overall life satisfaction. Example questions include: \u0026lsquo;My life is going well\u0026rsquo;, \u0026lsquo;I wish I had a different kind of life\u0026rsquo;, \u0026lsquo;My life is better than most kids\u0026rsquo;. Previous research has shown that the SLSS is correlated with several other well-being measures including the Andrews and Withey Life Satisfaction Scale [38].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive analyses were first conducted using SPSS (IBM Statistics Version 29). We investigated the average time spent on social media (total screen time) and the average proportions of \u003cem\u003eaverage\u003c/em\u003e and \u003cem\u003epassive\u003c/em\u003e social media time (although the latter was not used in further analysis). If sex, age, free school meals and ethnicity were associated with the outcome variables, these were included as covariates in subsequent analysis. To inform structural equation modelling (SEM) analysis, we used Pearson\u0026apos;s correlation to explore the links between social media use and mental health indicator variables. We then fitted SEM using R studio (Version 2024.12.0+467). This included direct paths between \u0026lsquo;total screen time\u0026rsquo; and variables reflecting child anxiety, depression, poor sleep habits, somatic complaints, and life satisfaction along with relevant covariates.\u0026nbsp;We considered\u0026nbsp;the Comparative Fit Index (CFI) which ranges from 0 to 1, with higher values considered a good fit [40], Standardized Root Mean Square Residual (SRMR) which from a rule of thumb should be less than .05 and Root Mean Square Error of Approximation (RMSEA) with values larger than .10 not acceptable [41].\u003c/p\u003e\n\u003cp\u003eWe also examined sex differences by employing a multigroup SEM analysis. We wanted to establish if these differences stem from structural differences in the path coefficients and not from measurement differences across groups [42, 43]. We tested the configural model, following Byrne (2006) [44], to assess whether the same general factor structure fits across groups (i.e., girls and boys). We used the chi-square (\u0026chi;\u0026sup2;) test to assess model fit, where a non-significant difference would suggest that the same conceptual model is appropriate for both groups, indicating baseline comparability.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eDescriptive analysis\u003c/h2\u003e\u003cp\u003eChildren reported spending on average 2 hours on social media on a weekday during term time, with a mean of 48% of time on \u003cem\u003eactive\u003c/em\u003e use. Total screen time did not differ significantly between boys and girls. Age was significantly positively correlated with children\u0026rsquo;s social media use (r\u0026thinsp;=\u0026thinsp;.15, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and anxiety symptoms (r\u0026thinsp;=\u0026thinsp;.10, p\u0026thinsp;=\u0026thinsp;.002) and negatively correlated with poor sleep habits (r=\u0026ndash;.19, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) and somatic complaints (r=\u0026ndash;.10, p\u0026thinsp;=\u0026thinsp;.005). Children receiving free school meals (indicative of lower SES) reported spending more time on social media compared with children who did not receive free school meals (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A one-way ANOVA examining differences in social media use across ethnic groups did not find statistically significant differences F(4,751)\u0026thinsp;=\u0026thinsp;1.28, p\u0026thinsp;=\u0026thinsp;.276).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescriptive statistics\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eMean (SD)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eMale mean (SD)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eFemale mean (SD)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eT (df)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003eNo free school meals Mean (SD)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eYes free school meals Mean (SD)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cem\u003eT (df)\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eCorrelation with age\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSocial media use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.00 (1.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.02 (1.98)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.99 (1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.19 (669)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.91 (1.91)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.31 (2.02)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-2.23 (246)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.15 ***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e46.00\u003c/p\u003e\u003cp\u003e(11.58)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e46.63 (12.72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e45.41 (10.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.44(704)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e46.17 (11.70)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e45.46 (11.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.73 (326)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.10 **\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression symptoms\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48.62\u003c/p\u003e\u003cp\u003e(11.56)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e48.53 (12.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.71 (10.99)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.21(732)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e48.72 (11.32)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e48.33 (12.30)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.38 (294)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor Sleep habits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e39.29\u003c/p\u003e\u003cp\u003e(7.20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e38.57 (6.93)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.94 (7.39)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.62(752)**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e39.28 (7.03)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e39.30 (7.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e-0.02 (289)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.19***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSomatic complaints\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e17.66\u003c/p\u003e\u003cp\u003e(4.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.33 (3.97)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e17.97 (4.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-2.16(724)*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e17.67 (3.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.63 (4.48)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.11 (268)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-0.10**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32.90\u003c/p\u003e\u003cp\u003e(6.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e33.15 (6.43)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.66 (6.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.03(752)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33.04 (6.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e32.46 (7.24)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.96 (285)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eMeans and standard deviations for mental health indicators are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. There were no sex differences in anxiety or depressive symptoms, nor in life satisfaction scores but girls did report worse sleep habits and had more somatic complaints (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Anxiety symptoms, poor sleep habits and somatic complaints were all higher for older children, but correlations were weak (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrior to running the SEM, we also examined the pattern of correlations between social media use and symptoms of anxiety and depression, poor sleep habits, somatic complaints and life satisfaction. Social media use was positively associated with all measured mental health linked variables, ranging from r\u0026thinsp;=\u0026thinsp;.09 (p\u0026thinsp;\u0026lt;\u0026thinsp;.05) with poor sleep to r\u0026thinsp;=\u0026thinsp;.17 (p\u0026thinsp;\u0026lt;\u0026thinsp;.001) with somatic complaints, and was negatively correlated with life satisfaction (r = \u0026ndash;.10, p\u0026thinsp;\u0026lt;\u0026thinsp;.05; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePearson correlations between social media use, anxiety, depression, poor sleep habits, somatic complaints and life satisfaction\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1. Social Media Use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.16***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.15***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.095*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.17***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.10*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2. Anxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e.76***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.59***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.55***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.51***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3. Depression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.58***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.57***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.57***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4. Poor Sleep habits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.51***\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.46***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5. Somatic complaints\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.38***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6. Life satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: *indicates p\u0026thinsp;\u0026lt;\u0026thinsp;.05, **indicates p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***indicates p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eSocial media use represents the scores of the time spend on social media, higher scores at SSR (Sleep Self Report) indicate poor sleep problems\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStructural Equation Model\u003c/h3\u003e\n\u003cp\u003eA model that included paths between social media use and anxiety symptoms, depression symptoms, poor sleep habits, somatic complaints, and life satisfaction (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showed a good fit to the data: CFI\u0026thinsp;=\u0026thinsp;0.98, RMSEA\u0026thinsp;=\u0026thinsp;0.098 with a 90% CI and SRMR\u0026thinsp;=\u0026thinsp;0.05. This model also included age and free school meals as covariates, though the latter ceased to be a significant effect on social media use and was therefore removed from the model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe analysis with age as a covariate (st.β\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;.001) revealed that social media use positively associated with children\u0026rsquo;s anxiety (st.β\u0026thinsp;=\u0026thinsp;0.16, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), depression (st.β\u0026thinsp;=\u0026thinsp;0.15, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), poor sleep habits (st.β\u0026thinsp;=\u0026thinsp;0.09, p\u0026thinsp;=\u0026thinsp;0.01), somatic complaints (st. =0.17, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and negatively associated with children\u0026rsquo;s life satisfaction (st.β=-0.10, p\u0026thinsp;=\u0026thinsp;0.01). These results suggest that higher media use is linked to poorer mental health variables and lower life satisfaction.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eMultigroup SEM\u003c/h2\u003e\u003cp\u003eThe multigroup SEM analysis, indicated that the factor structure and loadings were equivalent across sex (metric invariance). However, the chi-square difference test comparing constrained and unconstrained structural paths was significant (Δχ\u0026sup2;(6)\u0026thinsp;=\u0026thinsp;13.67, p\u0026thinsp;=\u0026thinsp;0.033), suggesting that the effects of social media on mental and physical health outcomes differed between boys and girls. The configural model showed acceptable fit (fit indices: CFI\u0026thinsp;=\u0026thinsp;0.977, SRMR\u0026thinsp;=\u0026thinsp;0.05, RMSEA\u0026thinsp;=\u0026thinsp;0.1)\u003c/p\u003e\u003cp\u003eSocial media use showed stronger path estimates for all mental health indicators among girls, whereas for boys, social media use was significantly associated only with anxiety (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDirect effects of Social Media Use on each outcome in boys\u0026rsquo; vs girls' model (multiple group analysis)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOutcome Variable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStandardised beta for boys\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStandardised beta for girls\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAnxiety\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.13*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.19***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDepression\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoor Sleep Habits\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSomatic complaints\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.27***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLife Satisfaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.17**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: *p\u0026thinsp;\u0026lt;\u0026thinsp;.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined cross-sectional associations between social media use and numerous indicators of poor mental health in pre-adolescent children. Children who were aged 8\u0026ndash;12 years reported spending approximately 2 hours each day on screens, with most of this time being spent on social media platforms. We found that although there were no significant differences between boys and girls in total time spent on social media, greater estimated time spent on social media was associated with symptoms of anxiety and depression, worse sleep quality, higher level of somatic complaints, and lower levels of life satisfaction in girls (regression coefficients ranged from 0.14 to 0.27), while in boys the social media variable only correlated with anxiety (r\u0026thinsp;=\u0026thinsp;0.13).\u003c/p\u003e\u003cp\u003eA recent bibliometric analysis reviewing studies globally from 2000 to 2024 has emphasised the growing concerns about the impacts of digital environments on children's mental health and well-being [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The literature suggests that the rise in digital activity, particularly social media use since the COVID-19 pandemic, has contributed to increased internalising symptoms, poorer well-being, and suicidal behaviours in youth [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Yet even though many of the current generation of children already own phones and have accounts on social media platforms [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], there is very little data on social media use in late childhood and early adolescence. Our data from middle to late childhood add to the emerging, though at times mixed, picture that more time spent on social media in adolescence and young adulthood can be worse for mental health. Indeed, previous research highlights the associations between social media and depression symptoms [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], poorer physical functioning [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and sleep quality [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], and lower life satisfaction throughout late childhood to early adulthood [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] \u0026ndash; findings that are aligned with ours.\u003c/p\u003e\u003cp\u003eOur findings also partly align with existing research on gender differences found in rates of social media usage and the effect of social media use on health. Serenko et al. (2021) [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] reported that 13- to 15-year-olds girls spent 3.15 hours weekly on social media and slept for 8.55 hours on a school day, compared to boys, who spent 2 hours on social media and 8.70 hours sleeping. We did not find these mean differences in self-reported social media use between pre-adolescent boys and girls, suggesting that these sex differences might emerge later with age. However, we did find that social media use correlated with a wider range of mental health indicators in girls than in boys, and that the range of effect sizes of these relationships were larger in boys than girls. It is becoming increasingly clear that the associations between social media use and poorer mental health are complex and moderated by variables including sex, though it should be noted that in our present study, our variable here reflected biological sex rather than children\u0026rsquo;s gender identity per se.\u003c/p\u003e\u003cp\u003eOverall, our results further emphasise that negative associations between social media and mental health appear in pre-adolescence too. However, our work has several limitations. First, although we adopted a widely used measure, this did rely on self-reported data, which required participants to reflect on their social media usage. This might be difficult for pre-adolescent children, where time perception may be less reliable. Indeed, some participants struggled with conceptualising the percentages of time spent on \u003cem\u003epassive\u003c/em\u003e and \u003cem\u003eactive\u003c/em\u003e social media use. Although we reported these estimates here (and refrained from conducting statistical analysis on these variables), they should still be considered with caution. More detailed measures of social media use that are currently being developed should be considered in future, in combination with other indices such as parental reports and objective screen time records (e.g., \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.so-me-study.org/our-team\u003c/span\u003e\u003cspan address=\"https://www.so-me-study.org/our-team\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Second, we were only able to capture children\u0026rsquo;s total screen time whilst other relevant dimensions were not assessed. We did not measure content viewed (risky versus safe) and other individual differences, such as emotional responsiveness, which might contribute to increased digital risk over and above the total screen time, as recent work suggest might be the case for adolescents [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Finally, as our data are cross-sectional, we cannot infer causality. It therefore remains unclear whether social media use negatively impacts mental health, or whether children with poorer mental health tend to seek social media more. Emerging theoretical frameworks and empirical findings suggest that the relationship between digital technology and mental health is complex and likely reciprocal [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] but large cohort, longitudinal research or time-sensitive methods such as ecological momentary assessment studies, are needed to address these temporal effects.\u003c/p\u003e\u003cp\u003eRegardless, our findings, though formative have several important implications. The observed associations between social media use and markers of poor mental health in children, represent only the first step of research efforts in this area. There is a need for more mechanistic studies of digital technology impact on development. Theoretical and empirical models of digital activity and health suggest complex pathways involving moderators and mediators that warrant further investigation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In interpreting the findings of this paper, we shared our findings with some young people to discuss their views on what these findings meant, and as seen in Box 1A and 1B, young people themselves also suggest possible mechanisms by which social media can impact mental health, highlighting sleep problems and limited time for physical activities (Box 1A and 1B). Inclusion of these pathways driven by theory, empirical findings, and youth experiences may increase the explanatory power by which social media usage associates with mental health variables.\u003c/p\u003e\u003cp\u003eAlthough it is not yet clear whether reducing social media use will improve mental health for young people, early prevention measures such as improved school policies and/or parental involvement in regulating social media use are to be encouraged. We also shared our findings with a teacher who is a member of a school senior leadership team. He highlighted risks that come from unsupervised digital activities and call for systemic guidelines and policies (Box 1C). In fact, many schools have begun implementing policies to reduce phone use in school-time. Examples include the phones being: handed in on arrival, kept in a secure location which the pupil does not access throughout the school day, and pupils having access to the mobile phone, but ensuring it is not used, seen, or heard [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. It is worth noting, however, that in a cross-sectional observational study across 30 secondary schools, Goodyear et al. (2025) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] found that although restrictive phone policies resulted in less time spent on phones and social media during school hours, this was not linked to a significant difference in lowering their students\u0026rsquo; overall phone use or enhancing their well-being.\u003c/p\u003e\u003cp\u003eInterestingly, young people from the community had some views on why school restrictions may not be the only solution, and indeed suggested that parents should be involved in those decisions more (Box 1A). Future studies could potentially explore the conceptual model by Morawska et al. (2023) [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e], which emphasises the importance of parental beliefs surrounding screen usage and self-efficacy in healthy screen use behaviours. It may be that parents' responses are especially important in primary school-aged children at a time when they still spend more time at home and where parent-implemented rules are still followed. Interesting another young person also noted the benefits of social media and that these should not be overlooked when coming to decisions over complete restrictions (Box 1B).\u003c/p\u003e\u003cp\u003e\u003cb\u003eBOX 1. Excerpts from Reflections: young people and educators\u003c/b\u003e\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eA. Bethany A (age 10)\u003c/h2\u003e\u003cp\u003e\u003cem\u003eOut of my whole class only me and one other girl don't have a phone yet but I have roblox on my tablet and play with my friends like this. At my school the teacher takes everyone\u0026rsquo;s phone in the morning and gives them back at the end of the day which I think is a good idea.\u003c/em\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eI don't think using your phone is always bad but if it stops you doing other things like playing with your friends then this is not good and can be boring if you don't want to look at your phone all the time. I think it is the parent's job to decide if a child spends too long looking at their phone, but some parents are more strict than others.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eB. Evelyn S (age 15)\u003c/h2\u003e\u003cp\u003e\u003cem\u003eAs a young person I often see the day-to-day aspects of social media use and make my own observations and often it is not the use of social media itself which is the key issue but related factors such as the management of time around social media. For example, I can confidently state that a large proportion of students in my age group miss significant amounts of sleep as a result of late-night social media use. This lack of sleep then negatively aspects their capacity for resilience, learning and social interaction. Leading in many cases to frustration, feelings of isolation and generally worse mental health. It is, however, to be noted that this particular issue is as a result of the intrinsically addictive nature of social media as well as poor time management. [\u0026hellip;] It is important to note that the situation is a lot more complicated than simply removing social media will cause a positive result in mental health as many people of my age group and significantly older have turned to social media in times of emotional difficulty ad stress and now find themselves emotionally reliant on social media as a form of escapism or coping method however unhealthy. It is the unfortunate truth that these people would actively struggle should that crutch of social media be removed.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eC. Alex Wedgbury (School Deputy Headteacher and Deputy Safeguarding Lead)\u003c/h2\u003e\u003cp\u003e\u003cem\u003eAs a school leader, I see daily the effects of social media on our pupils' wellbeing. While these platforms offer opportunities for connection, the negatives often outweigh the positives. Children as young as four [years old] come into school having viewed inappropriate content, faced online bullying or suffered significant sleep disruption \u0026ndash; all of which damage learning and development. Despite our school-based initiatives and restrictions, cases are increasing. Schools, parents and carers require greater support in terms of guidance and regulation; the pace of change is such that tackling these problems in isolation is ineffective. A collective, national strategy is required.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eTaken together, our study adds valuable evidence to the growing call to address social media\u0026rsquo;s impact on children's mental health and wellbeing. Our findings highlight the importance of effective management and preventive strategies to promote healthier social media use during these critical stages of mental and brain development (WHO, 2024). By identifying young people at risk earlier, we can improve the chances of timely support and reduce the likelihood of negative outcomes during adolescence and young adulthood.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eN.A, A.M, M.M (Queen Mary University of London, UK) made substantial contributions to the conception and design of the work, acquisition, analysis, interpretation, draft, and substantial revision, approved the submitted version, and agree to be personally accountable.\u003c/p\u003e\n\u003cp\u003eI.H, J.M, F.V, D.O made substantial contributions to the conception and substantively revised the work, approved the submitted version, and agree to be personally accountable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eB.A, E.S, A.W made contributions to the interpretation of the data, approved the submitted version, and agree to be personally accountable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJ.L made substantial contributions to the conception, design, interpretation, draft, and revision of the work, approved the submitted version, and agrees to be personally accountable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by a Barts Charity grant of \u0026pound;2.8 million received on 30\u003csup\u003eth\u003c/sup\u003e March 2020 (MRC\u0026amp;U0042).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from Professor Jennifer Lau upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOfcom. 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Managing screen use in the under-fives: Recommendations for parenting intervention development. \u003cem\u003eClinical Child and Family Psychology Review\u003c/em\u003e \u003cstrong\u003e26,\u003c/strong\u003e 943\u0026ndash;956 https://doi.org/10.1007/s10567-023-00435-6 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"social media, developmental psychopathology, sex differences, pre-adolescence","lastPublishedDoi":"10.21203/rs.3.rs-8051391/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8051391/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDigital screen use has been rising in pre-adolescent children but very little is known about use of social media specifically and how it associates with mental and physical health in this age group. The present study aimed to examine self-reported time spent on social media among primary school-aged children and its associations with indicators of poor mental health. We also explored sex differences in the strengths of these associations. Seven hundred and seventy-three children (ages 8\u0026ndash;12 years old) from the DEvelopment of Emotional Resilience (DEER) study reported their screen time use, symptoms of anxiety and depression, sleep quality, somatic complaints, and life satisfaction. Associations between screen time usage and mental health indicators were examined using Structural Equation Modelling. We found that children spend an average of 2 hours on screens on a weekday during term time, with an average of 48% of their time communicating with friends and posting content, and 52% browsing social media feeds. There were no significant differences between boys and girls in estimated time on social media, but older children reported using social media for longer (β\u0026thinsp;=\u0026thinsp;.15, p\u0026thinsp;\u0026lt;\u0026thinsp;.001). Higher social media use was significantly associated with greater symptoms of anxiety (β\u0026thinsp;=\u0026thinsp;.157, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), depression (β\u0026thinsp;=\u0026thinsp;.145, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), poorer sleep habits (β\u0026thinsp;=\u0026thinsp;.09, p\u0026thinsp;=\u0026thinsp;.01), somatic complaints (β\u0026thinsp;=\u0026thinsp;.174, p\u0026thinsp;\u0026lt;\u0026thinsp;.001), and lower life satisfaction (β=\u0026ndash;.097, p\u0026thinsp;=\u0026thinsp;.01). Social media usage strongly associated across poor mental health outcomes for girls, but in boys, it only significantly related to anxiety. Our findings suggest cross-sectional associations between measures of social media use and measures of health in pre-adolescent children, particularly girls. Future research should clarify the directions of these associations. Nonetheless, it would be prudent to consider management and regulation of digital technology and social media usage in this age group.\u003c/p\u003e","manuscriptTitle":"Use of social media in primary school aged children: Concurrent associations with mental health variables","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 06:44:14","doi":"10.21203/rs.3.rs-8051391/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-11-13T14:24:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-08T09:29:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-08T09:27:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-06T21:20:06+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"afaa0fa8-1c94-4c4e-9c7f-9451fb4b3759","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58457789,"name":"Health sciences/Health care"},{"id":58457790,"name":"Biological sciences/Psychology"},{"id":58457791,"name":"Social science/Psychology"}],"tags":[],"updatedAt":"2025-11-26T06:44:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 06:44:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8051391","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8051391","identity":"rs-8051391","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}