Reducing social disparities in child emotional and behavioral problems by hypothetical physical activity and screen time interventions

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Jansen, Jeremy A. Labrecque, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7094490/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Jan, 2026 Read the published version in Social Psychiatry and Psychiatric Epidemiology → Version 1 posted 8 You are reading this latest preprint version Abstract Purpose To estimate how social disparities in child psychiatric symptoms might change following hypothetical interventions targeting sports, outdoor play, and screen time at age 10. Methods We used data from 9,778 children of the Generation R Study, a prospective population-based cohort in Rotterdam, the Netherlands. Social inequality variables included sex, maternal education, and migration background. Primary caregivers filled out the validated Child Behavior Checklist to report on children's internalizing and externalizing symptoms at the age of 13. The hypothetical interventions (i.e., outdoor play, sports participation, and screen time) were parent-reported at age 10. We used sequential G-estimation to estimate the inequality with and without the hypothetical intervention. Results Children with migration backgrounds (46.3%) and low maternal education (53.3%) were associated with relatively more internalizing and externalizing symptoms than peers, with disparities of 0.125 and 0.177 standard deviations, respectively. Girls had more internalizing symptoms (0.106 SD), while boys had more externalizing symptoms (0.154 SD). Increasing sports participation reduced disparities in internalizing symptoms linked to maternal education (β = -0.014; 95% CI: -0.024, -0.003), while outdoor play and screen time interventions showed limited effects. None of the hypothetical interventions significantly reduced any of the social disparities in externalizing symptoms. Conclusions This study underscores the persistence of sex, cultural, and socioeconomic disparities in youth mental health. While sports participation showed a potential effect in reducing disparities in internalizing symptoms, its impact on externalizing symptoms and other interventions was negligible. Future efforts should focus on identifying more effective strategies for addressing these inequalities. Exercise sports mental health social disparities youth childhood 1. Introduction Globally, mental disorders account for 15% of the disease burden among adolescents aged 10 to 19, meaning that one in seven individuals in this age group is affected [ 1 ]. These conditions can have enduring negative consequences, such as school dropout and poor physical health. Notably, the age of onset of most mental disorders, including depression and anxiety, is before mid-adolescence [ 2 ]. This highlights the urgent need for early preventive interventions to safeguard young people's mental health and address potential issues before they emerge. Studies consistently demonstrate that social disparities, particularly those linked to socioeconomic status (SES), significantly heighten the risk of psychiatric symptoms in childhood [ 3 ]. Key SES indicators, such as parental education and migration background, are closely tied to these disparities. For instance, children from families with less-educated parents or from migrant families face higher rates of mental health challenges, underscoring the profound impact of SES on psychological development [ 4 , 5 ]. In addition to these social factors, sex, defined as sex assigned at birth, is another critical risk factor for psychiatric symptoms. Research indicates that girls are more prone to internalizing issues, such as depression and anxiety [ 6 ], while boys are more likely to exhibit externalizing behaviors, such as conduct problems or Attention-Deficit/Hyperactivity Disorder (ADHD)[ 7 ]. Although our study focused on sex as a measure, it is important to acknowledge that gender, which refers to the social and cultural roles people identify with, may have a different association with psychiatric symptoms. Overall, addressing these disparities is essential for improving mental health outcomes among young people from low-education and migrant backgrounds, as well as those affected by sex differences. While factors like stress or limited social support are well-established contributors, further research is needed to identify specific, actionable protective factors in children and adolescents. Such insights will enable the development of targeted interventions to reduce social disparities in youth mental health effectively. Engaging in physical activities, such as sports, serves as a powerful protective factor against the development of psychiatric symptoms in various ways. On a molecular level, it helps to lower cortisol; in the brain, it enhances gray matter volume in critical regions like the amygdala and hippocampus; and behaviorally, it alleviates symptoms of depression, anxiety, and somatic complaints [ 8 , 9 ]. Although the literature is less consistent, evidence suggests that spending less time on screens is associated with better mental health during childhood and adolescence[ 10 ]. Despite these benefits, children from families with lower education levels or migration backgrounds are less likely to participate in sports[ 11 ], and are more prone to excessive recreational screen time compared to their peers from higher socioeconomic backgrounds. Furthermore, patterns of physical activity and screen time often vary between boys and girls [ 12 , 13 ]. Boys tend to engage more in physical activities but also spend more time on screens, with these behaviors potentially influencing psychiatric symptoms differently during childhood [ 14 ]. Overall, this study aimed to estimate how social disparities in child psychiatric symptoms (i.e., internalizing and externalizing symptoms) at age 13 might change following hypothetical interventions targeting physical activity and screen time at age 10, using a sequential G-estimation model. 2. Methods 2.1 Study design and population We used the Generation R Study data, a prospective population-based birth cohort conducted in Rotterdam, the Netherlands. The design is detailed elsewhere[ 15 , 16 ]. Briefly, 9,778 pregnant women from the general population were enrolled in the study, who gave birth to 9,898 children. Their estimated delivery date was between April 2002 and January 2006, and data have been collected since birth from them and their children for around 20 years[ 15 , 16 ]. The current study used data from 9,898 children at two time points around the ages of 10 and 13 years. Missing values were replaced by performing multiple imputations. Complete case analyses were performed as sensitivity analyses to ensure the robustness of the findings. Of the initial 9,898 participants, 3,413 participants provided complete data on internalizing and externalizing problems at the age of 13 years, as well as on the hypothetical interventions (i.e., outdoor play, sports participation, and screen time) at the age of 10, and the social inequality variables (i.e., sex, parental education, and migration background). The Medical Ethics Committee of Erasmus Medical Centre approved all study procedures. 2.2 Social disparities For assessing social disparities, sex assigned at birth, migration background, and maternal education were used. Sex assigned at birth was obtained from medical records. Migration background and maternal education were assessed by questionnaire. The child was considered to have a migration background if one of their parents was born abroad[ 16 ]. Maternal education was defined as the highest completed education. Two categories were formed, indicating low (from no education to high school or vocational training) and high educational attainment (from higher vocational education to university). 2.3 Outdoor play, sports participation, and screen time Information on outdoor play, sports participation, and screen time was obtained from primary caregiver reports administered when children were 10 years old. The questionnaires for the primary caregiver were mostly completed by the mother (97%). To assess the level of physical activity, informants indicated both the frequency (number of days per week) and duration (minutes per day) of the child's engagement in (i) sports participation, and (ii) outdoor play. Sports considered included soccer, hockey, basketball, handball, korfball, tennis, judo, karate, gymnastics, jazz ballet, etc. School sports activities, such as physical education lessons and swimming lessons, were not included in these physical activity variables. The hours per week spent on outdoor play and sports participation were calculated using the following formula: weekly time spent on the activity = (days per week) * (hours per day)[ 17 ]. Respondents were also asked to indicate the number of days and hours per day their child: (i) watches television (including videos/DVDs) and (ii) uses a computer or similar device (including video games). Screen time was assessed separately for weekdays and weekend days, and then combined to estimate the total hours per week spent on each activity. A total weekly screen time score was calculated by adding the hours of playing video games and watching television. We decided on a hypothetical cutoff based on a combination of guidelines, categories available in our questionnaires, and the distribution of the data[ 18 ]. We set screen time, sports participation, and outdoor play to specific levels in each hypothetical intervention. Screen time was set to ≤ 2 hours/day, outdoor play was set to ≥ 2 hours/day, and sports participation was set to ≥ 2 hours/week. 2.4 Mental health Primary caregivers filled out the validated Child Behavior Checklist (CBCL/6–18) around the child aged 10 and 13 years to report on children's mental health problems[ 19 , 20 ]. The CBCL consists of 112 items and evaluates emotional and behavioral symptoms in the preceding 6 months. Items are scored on a 3-point Likert scale (0 = not true, 1 = somewhat or sometimes true, and 2 = very true or often true). We examined the CBCL broadband subscales of internalizing problems (i.e., depression, anxiety, somatic symptoms) and externalizing symptoms (i.e., conduct problems, rule-breaking behavior, ADHD) by averaging the items related to those scores while allowing for 25% missing items. Cronbach’s alphas were 0.84 and 0.87 for internalizing problems and 0.87 and 0.88 for externalizing problems at respectively ages 10 and 13 years[ 21 ]. We additionally examined child self-reported internalizing and externalizing symptoms using the Brief Problem Monitor (BPM) at age 10, and the Youth Self Report (YSR) at age 13 years. The validated BPM is an abbreviated version of the YSR, consisting of 19 items, while the validated YSR contains 112 items[ 22 , 23 ]. Cronbach’s alphas were 0.72 and 0.88 for internalizing symptoms, and 0.63 and 0.82 for externalizing symptoms at respectively ages 10 and 13 years, respectively [ 21 ]. 2.5 Potential confounders At the age of 10, the child’s height and weight were measured at the research center, and body mass index (BMI) was calculated and standardized according to the Dutch reference growth curves ( https://growthanalyser.org )[ 24 ]. At the age of 5, a non-verbal intelligence quotient (IQ) was assessed using the Snijders-Oomen Niet-verbale intelligentie Test- Revisie (SON-R 2.5–7) [ 25 ]. 2.6 Statistical Analysis First, multiple imputations were performed to replace the missing values (ranging from 1.5–55.3%) using multivariate imputation by chained equations[ 26 ]. We created 30 imputed datasets with 100 iterations. Next to our variables of interest, the following variables were added as auxiliary variables to improve imputation because they have a low amount of missing data: household income, parental weight and height, paternal age at inclusion, gestational age at birth, birth weight, parity, and apgar score at 5 minutes[ 27 ]. The socioeconomic (education and migration background) and sex disparities in internalizing and externalizing problems were obtained using linear regression models, adjusted for age. Secondly, we constructed an ‘intervention’ model by estimating the effect of the hypothetical interventions (e.g., outdoor play) on the outcome (e.g., internalizing symptoms) using linear regression while adjusting for potential confounders that were selected using causal graphs and based on the relevant literature. This model requires three causal assumptions: I) Exchangeability, meaning that there is no residual confounding or bias due to selection bias between the proposed intervention and the measure of psychiatric symptoms. Therefore, we adjusted for child age at intervention measurement, child age at outcome measurement, IQ at age 5, standardized body mass index at age 9, and pre-existing mental health symptoms at age 9. We additionally adjusted for maternal education and migration background in the analyses with sex, for maternal education and sex in the analyses with migration background, and for sex and migration background in the analyses with maternal education. To avoid selection bias due to dropout, we used the whole sample (N = 9,898) and imputed all missing values (see Table 1 for several missing data); II) Positivity, meaning that the probability of receiving any level of the intervention (i.e., outdoor time, sports, and screen time) conditional on the covariates must be greater than 0. We estimated the generalized propensity scores on the non-imputed dataset using the estimate_gps function with the non-parametric kernel approach of the CausalGPS R package[ 28 ]. The generalized propensity scores were all greater than 0; III) Consistency assumes that the interventions are well-defined, which is unlikely to be the case for our interventions. For instance, screen time does not account for the content of the activity (e.g., gaming or schoolwork), nor does it specify whether a parent or peer is involved (e.g., gaming with a friend or receiving assistance from a parent during schoolwork). Similarly, this applies to outdoor play and sports participation. Consequently, the observed estimates will depend on how the Generation R cohort defines screen time, outdoor play, or sports participation. Therefore, our estimates show the maximum reduction that could be reached if everyone fully complies with the interventions. Finally, the estimates of the ‘intervention’ model were used to estimate the change in mental health problems if all participants had had the intervention using G-estimation models [ 18 , 21 ]. By comparing the inequality before and after the hypothetical interventions, the absolute change in social inequality was estimated, and the corresponding 95% confidence intervals (CIs) were calculated using bootstrapping with 1000 iterations. Table 1 Characteristics of the cohort. Overall (N = 9,898) Child sex Boy 4,937 (50.7%) Girl 4,808 (49.3%) N 9,745 Migration background No 4,948 (53.7%) Yes 4,273 (46.3%) N 9,221 Maternal education Lower 4,858 (53.3%) Higher 4,264 (46.7%) N 9,122 Child age at psychiatric measurement Mean (SD) 13.56 (0.40) N 4,961 Outdoor play (hrs/week) Mean (SD) 5.63 (4.65) N 4,811 Sports participation (hrs/week) Mean (SD) 2.60 (1.59) N 4,905 Screen time (hrs/week) Mean (SD) 17.29 (11.91) N 4,424 Parent reported internalizing symptoms (sumscore) Mean (SD) 5.63 (5.81) N 4,721 Parent reported externalizing symptoms (sumscore) Mean (SD) 4.22 (5.28) N 4,709 Self-reported internalizing symptoms (sumscore) Mean (SD) 8.78 (7.15) N 4,516 Self-reported externalizing symptoms (sumscore) Mean (SD) 7.03 (5.33) N 4,503 Hrs = Hours. N = Number of participants. SD = Standard deviation. Sensitivity analyses were performed with participants who had complete data on internalizing and externalizing psychiatric symptoms at the age of 13 years, as well as the hypothetical interventions (i.e., outdoor play, sports participation, and screen time) at the age of 10, and the social inequality variables (i.e., sex, mother education, and migration background, N = 3,413). In addition, the analyses were repeated using self-reported symptoms instead of those reported by the primary caregiver for internalizing and externalizing behaviors. All analyses were conducted in R statistical software version 4.2.1. 3. Results 3.1 General characteristics The participants were on average 13.6 (0.4) years old when internalizing and externalizing symptoms were measured. Around half of the participants were male (50.7%), almost half of them had a migration background (46.3%), and more than half had mothers with a low education (vocational training or less, 53.3%). The full general characteristics of the cohort are shown in Table 1 . The social disparities (i.e., stratified by sex, migration background, and maternal education) in outdoor play, sports participation, screen time, and internalizing and externalizing symptoms are shown in Tables S1 , S2 , and S3 , respectively. 3.2 Internalizing symptoms Girls had a 0.106 (95% CI: 0.066, 0.145) standard deviation more internalizing symptoms compared to boys. Similarly, children with a migration background had 0.125 (95% CI: 0.085, 0.165) standard deviation more internalizing symptoms than those without, and children with a lower educated mother had a 0.177 (95% CI: 0.138, 0.216) standard deviation more internalizing symptoms than those with a higher educated mother. In the main analyses, hypothetically increasing outdoor play or reducing screen time did not significantly reduce the disparities in internalizing symptoms (Table 2 ). However, hypothetically increasing sports participation significantly reduced the disparities for maternal education (β = -0.014; 95% CI: -0.024, -0.003) (Table 2 ). In the sensitivity analyses, results were virtually the same in the complete case analyses (β = -0.017; 95% CI: -0.034, -0.000) and when using self-reported internalizing symptoms (β = -0.024; 95% CI: -0.036, -0.012). See Table S4 and Table S5 , respectively. In contrast to the main analyses, the sensitivity analyses showed that hypothetically increasing sports participation also reduced the disparities in self-reported internalizing symptoms for sex (β = -0.016; 95% CI: -0.029, -0.003) and migration background (β = -0.012; 95% CI: -0.025, 0.000). Additionally, hypothetically reducing screen time reduced the disparities in self-reported internalizing symptoms for maternal education (β = 0.030; 95% CI: 0.009, 0.052; Table S5 ). Table 2 Reductions in sex, migration background and maternal education disparities on internalizing and externalizing symptoms at the age of 13 from hypothetical outdoor play, sports participation and screen time interventions in children at age 10. Internalizing symptoms Sex (ref:boy) Migration background (ref:no) Maternal education (ref:high) Intervention = Outdoor play B 95% CI B 95% CI B 95% CI Before intervention 0.106 (0.065, 0.145) 0.125 (0.085, 0.165) 0.177 (0.138, 0.216) After intervention 0.118 (0.050, 0.188) 0.130 (0.063, 0.199) 0.184 (0.117, 0.252) Change 0.013 (-0.045, 0.071) 0.005 (-0.051, 0.063) 0.007 (-0.050, 0.064) Intervention = Sports participation Before intervention 0.106 (0.066, 0.145) 0.125 (0.085, 0.165) 0.177 (0.138, 0.216) After intervention 0.097 (0.058, 0.136) 0.120 (0.081, 0.160) 0.163 (0.125, 0.202) Change -0.009 (-0.020, 0.002) -0.005 (-0.017, 0.006) -0.014 (-0.024 , -0.003) Intervention = Screen time Before intervention 0.106 (0.066, 0.145) 0.125 (0.085, 0.165) 0.177 (0.139, 0.216) After intervention 0.094 (0.052, 0.137) 0.126 (0.083, 0.169) 0.160 (0.117, 0.202) Change -0.011 (-0.032, 0.010) 0.001 (-0.021, 0.023) -0.017 (-0.039, 0.004) Externalizing symptoms Sex (ref:boy) Migration background (ref:no) Maternal education (ref:high) Intervention = Outdoor play B 95% CI B 95% CI lower bound B 95% CI Before intervention -0.154 (-0.193, -0.114) 0.097 (0.057, 0.137) 0.154 (0.116, 0.193) After intervention -0.153 (-0.220, -0.085) 0.076 (0.007, 0.146) 0.165 (0.097, 0.235) Change 0.000 (-0.052, 0.054) -0.021 (-0.075, 0.034) 0.011 (-0.043, 0.066) Intervention = Sports participation Before intervention -0.154 (-0.193, -0.114) 0.097 (0.057, 0.137) 0.154 (0.115, 0.193) After intervention -0.157 (-0.197, -0.118) 0.096 (0.056, 0.137) 0.149 (0.110, 0.189) Change -0.004 (-0.013, 0.006) 0.000 (-0.011, 0.010) -0.005 (-0.015, 0.005) Intervention = Screen time Before intervention -0.154 (-0.193, -0.114) 0.097 (0.057, 0.137) 0.154 (0.115, 0.193) After intervention -0.169 (-0.211, -0.128) 0.095 (0.053, 0.138) 0.140 (0.098, 0.181) Change -0.016 (-0.035, 0.004) -0.002 (-0.023, 0.019) -0.015 (-0.034, 0.005) Ref = reference group. B = standardized beta coefficient. CI = Confidence Interval. 3.3 Externalizing symptoms Boys had a 0.154 (95% CI: 0.114, 0.193) standard deviation more externalizing symptoms compared to girls. Similarly, children with a migration background had a 0.097 (95% CI: 0.057, 0.137) standard deviation more externalizing symptoms than those without, and children with a low-educated mother had a 0.154 (95% CI: 0.115; 0.193) standard deviation more externalizing symptoms. In the main analyses, none of the hypothetical interventions significantly reduced any of the social disparities (Table 2 ). In the sensitivity analyses, hypothetically increasing sports participation and decreasing screen time reduced the disparities for maternal education in the complete case sample (β = -0.020; 95% CI: -0.036, -0.004; and β = -0.034; 95% CI: -0.072, 0.000 respectively; Table S4 ), and the self-reported externalizing symptoms (β = 0.018; 95% CI: 0.006, 0.030; and β = 0.025; 95% CI: 0.003, 0.048 respectively; Table S5 ). Interestingly, hypothetically reducing screen time increased disparities in externalizing symptoms between the sexes in the complete case sample (β = 0.041; 95% CI: 0.014, 0.071) ( Table S4 ), and hypothetically increasing sports participation also increased the disparities in self-reported externalizing symptoms between the sexes ( Table S5 ). 4. Discussion Using a sequential G-estimation model, this study aimed to estimate how social disparities in child mental health at age 13 might change following hypothetical interventions targeting physical activity and screen time at age 10. The study found that children with a migration background and children with a lower educated mother reported more internalizing and externalizing symptoms than children without a migration background and children with a higher-educated mother. In contrast, girls reported more internalizing symptoms than boys, while boys experienced more externalizing symptoms than girls. Hypothetically increasing outdoor play and reducing screen time did not significantly reduce disparities in internalizing symptoms. However, hypothetically increasing sports participation significantly reduced disparities in internalizing symptoms related to maternal education. The study also indicated that increasing sports participation or reducing screen time has varying impacts on social disparities in externalizing symptoms, with certain interventions reducing maternal educational disparities, but increasing sex disparities in some of the sensitivity analyses, denoting inconsistent results when discussing externalizing symptoms. A hypothetical increase in sports participation was found to significantly attenuate disparities in internalizing symptoms associated with maternal education. This finding aligns with our previous research, which demonstrated that sports participation was inversely associated with internalizing symptoms in youth, although the observed effects were modest[ 29 ]. Consistent with other earlier studies, the magnitude of the associations in our research corresponds with findings showing that involvement in sports during childhood is linked to fewer depressive symptoms in early adulthood, although again the effect sizes were small after accounting for confounders[ 10 , 30 , 31 ]. Evidence also suggests that the beneficial impact of physical activity on psychiatric symptoms is more pronounced in controlled clinical samples compared to general population studies[ 30 ]. Despite this, physical activity consistently emerges as a valuable tool contributing to preventing youth from developing clinical or diagnosed mental disorders, particularly in the critical developmental period of adolescence. Home environments characterized by low parental education or low socioeconomic status often act as early life adversities, contributing to the emergence of psychiatric problems in childhood. However, some children in similar circumstances may exhibit greater resilience to the development of psychiatric symptoms, which may be partially explained by intrapersonal factors such as IQ, self-identity, and self-esteem[ 32 – 34 ]. Research suggests that self-esteem can act as a protective factor against the development of psychiatric symptoms in the face of early adversities, and sport-based interventions have been shown to improve self-esteem[ 35 ]. This improvement could play a role in mitigating the risk of internalizing symptoms among youth exposed to early life adversities [ 32 ]. Importantly, not all forms of physical activity appear to offer the same benefits. For instance, outdoor play does not seem to have the same impact as sports, particularly those focused on skill development and teamwork rather than aesthetics[ 29 , 36 ]. Playing sports, especially team sports, has been shown to increase self-esteem, which serves as one of the strongest mechanisms through which youth are protected from internalizing symptoms[ 36 ]. Self-esteem in youth tends to develop through exposure to challenges and overcoming difficulties, and sports provide an ideal environment for practicing resilience, problem-solving, and coping skills—important foundations for the challenges of adolescence and adulthood. The impact of these hypothetical interventions on externalizing symptoms was inconsistent, with non-significant results in the main analyses. However, sensitivity analyses using complete cases and self-reported data revealed that some interventions reduced disparities among children with lower maternal education, while others led to an increase in sex disparities. The inconsistencies observed in the impact of hypothetical interventions on social disparities in externalizing symptoms may be influenced by several factors. First, the relationship between physical activity variables and externalizing symptoms may be non-significant or weak, which could explain some of the negative results observed in our main analyses[ 29 ]. Additionally, the CBCL, a widely used and validated instrument to measure psychopathology in children, may have limitations that affect the accuracy of measuring externalizing symptoms. Specifically, although the majority (87.4%) of CBCL items are not biased by gender, some items related to externalizing symptoms, particularly those assessing aggressive behavior, have been identified as potentially gender-biased in previous studies [ 37 ]. Another potential explanation for the discrepancies found could be the use of sex rather than gender as an inequality measure in our study, as it may not account for the continuous nature of gender identity, potentially affecting our results. Overall, these limitations could help explain our inconsistent findings regarding differences between boys and girls and contribute to some of the discrepancies observed in the results. Furthermore, it is possible that the CBCL measures of externalizing symptoms in youth may not be sensitive enough to detect subtle or nuanced differences in behavior. All in all, addressing these measurement issues could lead to a better understanding of the relationship between physical activity and externalizing symptoms. However, our findings might also be accurate: lifestyle interventions could reduce SES-related disparities in externalizing behavior while unintentionally exacerbating sex disparities. This outcome may reflect differences in how boys and girls respond to interventions due to biological, social, or environmental factors. Careful evaluation and tailoring of interventions are crucial to addressing both SES and sex disparities, ensuring equitable outcomes across groups. This study is among the first to employ this methodology to quantify reductions in social disparities in child mental health at age 10 through hypothetical interventions targeting outdoor play, sports participation, and screen time. Recent advancements in causal inference methodology have significantly improved the study of health disparities, offering tools to directly assess the key parameter of interest: the potential for interventions to reduce these disparities[ 21 , 38 ]. Unlike many studies that adjust for confounders in the relationship between social disparities and health outcomes, this study intentionally avoids such adjustments. The rationale is that unadjusted health disparities are of primary interest, as they represent real-world disparities in health outcomes based on factors like sex, maternal education, or migration background, rather than disparities conditioned on covariates. By focusing on unadjusted health disparities, this approach provides a clearer understanding of how specific interventions could influence social disparities and sets realistic expectations for their effect sizes. Additional strengths of this study include the use of a population-based, multicultural cohort, which enhances the generalizability of the findings, and the application of multiple imputation techniques to minimize selection bias and ensure representative data. These methodological strengths contribute to a more comprehensive understanding of strategies to address social disparities in child mental health. Nonetheless, our findings must be interpreted in the context of several limitations. First, the observational design restricts our ability to infer causality for any of the hypothetical interventions and their outcomes. Second, other potential confounders not included in the model may also contribute to reducing the social disparities explored in this study. Third, we measured both the hypothetical interventions and the outcomes at a single time point, which prevented us from examining the stability of these variables from childhood to adolescence. Fourth, hypothetical interventions were assessed through parental reports, which introduces the possibility of under- or overestimations of behaviors. Furthermore, both the hypothetical interventions and the outcomes were reported by the primary caregivers, which may have inflated the observed relationships due to shared method variance. Sensitivity analyses indicated that when the child reported on the outcomes, some results, particularly those related to externalizing symptoms, showed slight changes, while those related to internalizing symptoms remained consistent. Fifth, this study assesses sex (assigned at birth) rather than gender, which may limit the ability to capture the potential association of gender identity and expression with psychiatric symptoms. Lastly, while we used multiple imputations to ensure all participants were included in the analysis and reduce bias, the results may still be influenced by missing data, especially if the missingness is related to variables not included in the imputation model. We sought to mitigate this by incorporating all variables from the analyses, as well as additional variables associated with missingness, into the imputation process. 4.1 Practical implications and future research Given that many internalizing problems, including depression, anxiety, and somatic symptoms, typically emerge during formative years—especially among young people from low-education and migrant backgrounds, as well as those affected by sex differences —investing in early interventions targeting at-risk youth is essential [ 39 ]. New societal challenges, such as the COVID-19 pandemic, climate change, and the ongoing war in Ukraine, may exacerbate the risk of mental health issues[ 40 ]. In light of these growing concerns, policymakers should prioritize programs that encourage physical activity, particularly sports, as part of a broader strategy to mitigate the escalating mental health burden worldwide. This approach is especially important for youth from lower socioeconomic backgrounds to ensure they have equal opportunities to thrive compared to their peers with higher socioeconomic status. Future intervention studies focusing on these at-risk populations are essential to determine the most cost-effective delivery methods—whether through online, blended, or in-person approaches. These interventions should aim not only to provide young people with access to sports but also to integrate behavioral change strategies that promote a sustained positive impact on mental health. 5. Conclusion In conclusion, this study highlights the presence of social disparities in both internalizing and externalizing symptoms in young people, with sex, migration background, and maternal education playing significant roles. While hypothetical interventions targeting outdoor play and screen time did not uniformly reduce these disparities, increasing sports participation seems consistently effective in reducing disparities in internalizing symptoms related to maternal education. The impact of hypothetical lifestyle interventions on externalizing symptoms was inconsistent, with some interventions showing reductions in disparities for maternal education but increasing sex disparities. These findings suggest that while certain lifestyle interventions can reduce disparities in child mental health, the effects may vary across different types of symptoms and demographic groups, emphasizing the need for tailored approaches in addressing social disparities in mental health. Future research should further explore the differential effects of interventions on internalizing and externalizing symptoms across various social contexts. Declarations Declaration of Interests None of the authors have any conflict of interest. Author Contribution M.R. drafted the main manuscript and M.R. and C.E. conducted the analyses. All authors conceptualized and designed the work, interpreted the data and reviewed it critically. Acknowledgments and Funding María Rodriguez-Ayllon was supported by the Sara Borrell postdoctoral fellowship (CD23/00096- Instituto de Salud Carlos III (ISCIII)). Clair Enthoven was supported by the Erasmus Initiative Vital Cities and Citizens initiative. This study was supported by the Sophia Foundation (S18-20) and the Netherlands Organization for Health Research and Development (ZonMw). Supercomputing resources were supported by the Netherlands Organization for Scientific Research (Exacte Wetenschappen) and SURFsara (Snellius Compute Cluster, www.surfsara.nl ). The Generation R Study is conducted by the Erasmus Medical Center in close collaboration with the School of Law and the Faculty of Social Sciences of the Erasmus University Rotterdam, the Municipal Health Service Rotterdam area, and the Stichting Trombosedienst en Artsenlaboratorium Rijnmond. The general design of the Generation R Study was made possible by financial support from the Erasmus Medical Center, Rotterdam, ZonMw, the Netherlands Organization for Scientific Research, and the Ministry of Health, Welfare, and Sport. The authors gratefully acknowledge the contributions of the participating children and parents, general practitioners, hospitals, midwives, and pharmacies in Rotterdam. Data Availability Data from this study are available upon reasonable request to the director of the Generation R Study ( [email protected] ), subject to local, national and European rules and regulations. References Adolescent mental health https://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health . Accessed 19 Jan 2023 Solmi M, Radua J, Olivola M et al (2022) Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Mol Psychiatry 27:281–295. https://doi.org/10.1038/S41380-021-01161-7 Hong RY, Ding XP, Chan KMY, Yeung WJJ (2024) The influence of socio-economic status on child temperament and psychological symptom profiles. Br J Psychol 115:535–554. https://doi.org/10.1111/BJOP.12701 Kevers R, Spaas C, Colpin H et al (2022) Mental health problems in refugee and immigrant primary school children in Flanders, Belgium. Clin Child Psychol Psychiatry 27:938–952. https://doi.org/10.1177/13591045221105199 Sonego M, Llácer A, Galán I, Simón F (2013) The influence of parental education on child mental health in Spain. Qual Life Res 22:203–211. https://doi.org/10.1007/S11136-012-0130-X Melchior M (2021) Social inequalities in children’s mental health: isn’t it time for action? Eur Child Adolesc Psychiatry 30:1317. https://doi.org/10.1007/S00787-021-01855-X van der Sluis S, Polderman TJC, Neale MC et al (2017) Sex differences and gender-invariance of mother-reported childhood problem behavior. Int J Methods Psychiatr Res 26. https://doi.org/10.1002/mpr.1498 Hillman CH, Erickson KI, Kramer AF (2008) Be smart, exercise your heart: exercise effects on brain and cognition. Nat Rev Neurosci 9:58–65. https://doi.org/10.1038/nrn2298 Erickson KI, Hillman C, Stillman CM et al (2019) Physical Activity, Cognition, and Brain Outcomes: A Review of the 2018 Physical Activity Guidelines. Med Sci Sports Exerc 51:1242–1251 Rodriguez-Ayllon M, Cadenas-Sánchez C, Estévez-López F et al (2019) Role of Physical Activity and Sedentary Behavior in the Mental Health of Preschoolers, Children and Adolescents: A Systematic Review and Meta-Analysis. Sports Med 49:1383–1410. https://doi.org/10.1007/S40279-019-01099-5 Rodriguez-Ayllon M, Cadenas-Sánchez C, Estévez-López F et al (2019) Role of Physical Activity and Sedentary Behavior in the Mental Health of Preschoolers, Children and Adolescents: A Systematic Review and Meta-Analysis. Sports Med 49:1383–1410. https://doi.org/10.1007/s40279-019-01099-5 Guthold R, Stevens GA, Riley LM, Bull FC (2020) Global trends in insufficient physical activity among adolescents: a pooled analysis of 298 population-based surveys with 1·6 million participants. Lancet Child Adolesc Health 4:23–35. https://doi.org/10.1016/S2352-4642(19)30323-2 Nagata JM, Ganson KT, Iyer P et al (2022) Sociodemographic Correlates of Contemporary Screen Time Use among 9- and 10-Year-Old Children. J Pediatr 240:213–220e2. https://doi.org/10.1016/J.JPEDS.2021.08.077 Rodriguez-Ayllon M, Cadenas-Sánchez C, Estévez-López F et al (2019) Role of Physical Activity and Sedentary Behavior in the Mental Health of Preschoolers, Children and Adolescents: A Systematic Review and Meta-Analysis. Sports Med 49:1383–1410 Jaddoe VWv, Mackenbach JP, Moll HA et al (2006) The Generation R Study: Design and cohort profile. Eur J Epidemiol 21:475–484. https://doi.org/10.1007/s10654-006-9022-0 Kooijman MN, Kruithof CJ, van Duijn CM et al (2016) The Generation R Study: design and cohort update 2017. Eur J Epidemiol 31:1243–1264. https://doi.org/10.1007/s10654-016-0224-9 Rodriguez-Ayllon M, Derks IPM, van den Dries MA et al (2020) Associations of physical activity and screen time with white matter microstructure in children from the general population. NeuroImage 205:116258. https://doi.org/10.1016/j.neuroimage.2019.116258 Lara M, Labrecque JA, Van Lenthe FJ, Voortman T (2020) Estimating Reductions in Ethnic Inequalities in Child Adiposity from Hypothetical Diet, Screen Time, and Sports Participation Interventions. Epidemiology 31:736–744. https://doi.org/10.1097/EDE.0000000000001221 Achenback TM, Rescorla La (2003) Manual for the ASEBA School-Age Forms and Profiles. Manual for the ASEBA School -Age Forms & Profiles 99–107 Ivanova MY, Achenbach TM, Rescorla LA et al (2010) Preschool psychopathology reported by parents in 23 societies: Testing the seven-syndrome model of the child behavior checklist for ages 1.55. J Am Acad Child Adolesc Psychiatry 49:1215–1224. https://doi.org/10.1016/j.jaac.2010.08.019 Enthoven CA, Labrecque JA, Koopman-Verhoeff ME et al (2024) Reducing behavior problems in children born after an unintended pregnancy: the generation R study. Soc Psychiatry Psychiatr Epidemiol 59. https://doi.org/10.1007/S00127-024-02693-3 Achenbach TM, VT University of Vermont Department of Psychiatry (1991) Manual for the Youth Self-Report and 1991 Profile. Burlington,. - References - Scientific Research Publishing. https://www.scirp.org/reference/referencespapers?referenceid=1246863 . Accessed 25 Feb 2025 Achenbach T, Burlington SM- (2011) VT undefined, undefined (2011) Manual for the ASEBA brief problem monitor (BPM). documents.acer.orgTM Achenbach, SH McConaughy, MY Ivanova, LA RescorlaBurlington, VT: ASEBA, 2011•documents.acer.org Fredriks AM, van Buuren S, Burgmeijer RJ et al (2000) Continuing positive secular growth change in The Netherlands 1955–1997. Pediatr Res 47:316–323 Tellegen W, Wijnberg-Williams L (2005) Snijders-Oomen Niet-Verbale Intelligentietest: SON-R 2 – 1/2 -to-7. Boom Testu, Ansterdam van Buuren S, Groothuis-Oudshoorn K (2011) mice: Multivariate Imputation by Chained Equations in R. J Stat Softw 45:1–67. https://doi.org/10.18637/JSS.V045.I03 Madley-Dowd P, Curnow E, Hughes RA et al (2024) Analyses using multiple imputation need to consider missing data in auxiliary variables. Am J Epidemiol 00:1–8. https://doi.org/10.1093/AJE/KWAE306 Khoshnevis N, Wu X, Braun D (2023) CausalGPS: An R Package for Causal. Inference With Continuous Exposures Rodriguez-Ayllon M, Neumann A, Hofman A et al (2023) Neurobiological, Psychosocial, and Behavioral Mechanisms Mediating Associations Between Physical Activity and Psychiatric Symptoms in Youth in the Netherlands. JAMA Psychiatry 80:451–458. https://doi.org/10.1001/JAMAPSYCHIATRY.2023.0294 Purgato M, Cadorin C, Prina E et al (2024) Umbrella Systematic Review and Meta-Analysis: Physical Activity as an Effective Therapeutic Strategy for Improving Psychosocial Outcomes in Children and Adolescents. J Am Acad Child Adolesc Psychiatry 63:172–183. https://doi.org/10.1016/J.JAAC.2023.04.017 Biddle SJH, Ciaccioni S, Thomas G, Vergeer I (2019) Physical activity and mental health in children and adolescents: An updated review of reviews and an analysis of causality. Psychol Sport Exerc 42:146–155. https://doi.org/10.1016/J.PSYCHSPORT.2018.08.011 Zinn ME, Huntley ED, Keating DP (2020) Resilience in adolescence: Prospective Self moderates the association of early life adversity with externalizing problems. J Adolesc 81:61–72. https://doi.org/10.1016/J.ADOLESCENCE.2020.04.004 Kim Y, Lee H, Park A (2022) Patterns of adverse childhood experiences and depressive symptoms: self-esteem as a mediating mechanism. Soc Psychiatry Psychiatr Epidemiol 57:331–341. https://doi.org/10.1007/S00127-021-02129-2 Afifi TO, MacMillan HL (2011) Resilience following child maltreatment: a review of protective factors. Can J Psychiatry 56:266–272. https://doi.org/10.1177/070674371105600505 Bruner MW, McLaren CD, Sutcliffe JT et al (2023) The effect of sport-based interventions on positive youth development: a systematic review and meta-analysis. Int Rev Sport Exerc Psychol 16:368–395. https://doi.org/10.1080/1750984X.2021.1875496 Equinet L, Enthoven C, Jansen PW, Rodriguez-Ayllon M (2025) The longitudinal association between sport participation and self-esteem in youth in the Netherlands: The role of sport type. J Sci Med Sport 28:140–146. https://doi.org/10.1016/J.JSAMS.2024.09.008 van der Sluis S, Polderman TJC, Neale MC et al (2017) Sex differences and gender-invariance of mother-reported childhood problem behavior. Int J Methods Psychiatr Res 26. https://doi.org/10.1002/MPR.1498 Dall’Aglio L, Labrecque JA, Schuurmans I et al (2025) Evaluating hypothetical prevention strategies for internalizing symptoms in the general population and at-risk children. J Consult Clin Psychol 93. https://doi.org/10.1037/CCP0000912 Solmi M, Radua J, Olivola M et al (2021) Age at onset of mental disorders worldwide: large-scale meta-analysis of 192 epidemiological studies. Molecular Psychiatry 2021 27:1 27:281–295. https://doi.org/10.1038/s41380-021-01161-7 Poletti M, Preti A, Raballo A (2023) From economic crisis and climate change through COVID-19 pandemic to Ukraine war: a cumulative hit-wave on adolescent future thinking and mental well-being. Eur Child Adolesc Psychiatry 32:1815–1816. https://doi.org/10.1007/S00787-022-01984-X Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eGlobally, mental disorders account for 15% of the disease burden among adolescents aged 10 to 19, meaning that one in seven individuals in this age group is affected [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These conditions can have enduring negative consequences, such as school dropout and poor physical health. Notably, the age of onset of most mental disorders, including depression and anxiety, is before mid-adolescence [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This highlights the urgent need for early preventive interventions to safeguard young people's mental health and address potential issues before they emerge.\u003c/p\u003e\u003cp\u003eStudies consistently demonstrate that social disparities, particularly those linked to socioeconomic status (SES), significantly heighten the risk of psychiatric symptoms in childhood [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Key SES indicators, such as parental education and migration background, are closely tied to these disparities. For instance, children from families with less-educated parents or from migrant families face higher rates of mental health challenges, underscoring the profound impact of SES on psychological development [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition to these social factors, sex, defined as sex assigned at birth, is another critical risk factor for psychiatric symptoms. Research indicates that girls are more prone to internalizing issues, such as depression and anxiety [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], while boys are more likely to exhibit externalizing behaviors, such as conduct problems or Attention-Deficit/Hyperactivity Disorder (ADHD)[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Although our study focused on sex as a measure, it is important to acknowledge that gender, which refers to the social and cultural roles people identify with, may have a different association with psychiatric symptoms. Overall, addressing these disparities is essential for improving mental health outcomes among young people from low-education and migrant backgrounds, as well as those affected by sex differences. While factors like stress or limited social support are well-established contributors, further research is needed to identify specific, actionable protective factors in children and adolescents. Such insights will enable the development of targeted interventions to reduce social disparities in youth mental health effectively.\u003c/p\u003e\u003cp\u003eEngaging in physical activities, such as sports, serves as a powerful protective factor against the development of psychiatric symptoms in various ways. On a molecular level, it helps to lower cortisol; in the brain, it enhances gray matter volume in critical regions like the amygdala and hippocampus; and behaviorally, it alleviates symptoms of depression, anxiety, and somatic complaints [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although the literature is less consistent, evidence suggests that spending less time on screens is associated with better mental health during childhood and adolescence[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite these benefits, children from families with lower education levels or migration backgrounds are less likely to participate in sports[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and are more prone to excessive recreational screen time compared to their peers from higher socioeconomic backgrounds. Furthermore, patterns of physical activity and screen time often vary between boys and girls [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Boys tend to engage more in physical activities but also spend more time on screens, with these behaviors potentially influencing psychiatric symptoms differently during childhood [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOverall, this study aimed to estimate how social disparities in child psychiatric symptoms (i.e., internalizing and externalizing symptoms) at age 13 might change following hypothetical interventions targeting physical activity and screen time at age 10, using a sequential G-estimation model.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study design and population\u003c/h2\u003e\u003cp\u003eWe used the Generation R Study data, a prospective population-based birth cohort conducted in Rotterdam, the Netherlands. The design is detailed elsewhere[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Briefly, 9,778 pregnant women from the general population were enrolled in the study, who gave birth to 9,898 children. Their estimated delivery date was between April 2002 and January 2006, and data have been collected since birth from them and their children for around 20 years[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The current study used data from 9,898 children at two time points around the ages of 10 and 13 years. Missing values were replaced by performing multiple imputations. Complete case analyses were performed as sensitivity analyses to ensure the robustness of the findings. Of the initial 9,898 participants, 3,413 participants provided complete data on internalizing and externalizing problems at the age of 13 years, as well as on the hypothetical interventions (i.e., outdoor play, sports participation, and screen time) at the age of 10, and the social inequality variables (i.e., sex, parental education, and migration background). The Medical Ethics Committee of Erasmus Medical Centre approved all study procedures.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Social disparities\u003c/h2\u003e\u003cp\u003eFor assessing social disparities, sex assigned at birth, migration background, and maternal education were used. Sex assigned at birth was obtained from medical records. Migration background and maternal education were assessed by questionnaire. The child was considered to have a migration background if one of their parents was born abroad[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Maternal education was defined as the highest completed education. Two categories were formed, indicating low (from no education to high school or vocational training) and high educational attainment (from higher vocational education to university).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Outdoor play, sports participation, and screen time\u003c/h2\u003e\u003cp\u003eInformation on outdoor play, sports participation, and screen time was obtained from primary caregiver reports administered when children were 10 years old. The questionnaires for the primary caregiver were mostly completed by the mother (97%). To assess the level of physical activity, informants indicated both the frequency (number of days per week) and duration (minutes per day) of the child's engagement in (i) sports participation, and (ii) outdoor play. Sports considered included soccer, hockey, basketball, handball, korfball, tennis, judo, karate, gymnastics, jazz ballet, etc. School sports activities, such as physical education lessons and swimming lessons, were not included in these physical activity variables. The hours per week spent on outdoor play and sports participation were calculated using the following formula: weekly time spent on the activity = (days per week) * (hours per day)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRespondents were also asked to indicate the number of days and hours per day their child: (i) watches television (including videos/DVDs) and (ii) uses a computer or similar device (including video games). Screen time was assessed separately for weekdays and weekend days, and then combined to estimate the total hours per week spent on each activity. A total weekly screen time score was calculated by adding the hours of playing video games and watching television.\u003c/p\u003e\u003cp\u003eWe decided on a hypothetical cutoff based on a combination of guidelines, categories available in our questionnaires, and the distribution of the data[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. We set screen time, sports participation, and outdoor play to specific levels in each hypothetical intervention. Screen time was set to \u0026le;\u0026thinsp;2 hours/day, outdoor play was set to \u0026ge;\u0026thinsp;2 hours/day, and sports participation was set to \u0026ge;\u0026thinsp;2 hours/week.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Mental health\u003c/h2\u003e\u003cp\u003ePrimary caregivers filled out the validated Child Behavior Checklist (CBCL/6\u0026ndash;18) around the child aged 10 and 13 years to report on children's mental health problems[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The CBCL consists of 112 items and evaluates emotional and behavioral symptoms in the preceding 6 months. Items are scored on a 3-point Likert scale (0\u0026thinsp;=\u0026thinsp;not true, 1\u0026thinsp;=\u0026thinsp;somewhat or sometimes true, and 2\u0026thinsp;=\u0026thinsp;very true or often true). We examined the CBCL broadband subscales of internalizing problems (i.e., depression, anxiety, somatic symptoms) and externalizing symptoms (i.e., conduct problems, rule-breaking behavior, ADHD) by averaging the items related to those scores while allowing for 25% missing items. Cronbach\u0026rsquo;s alphas were 0.84 and 0.87 for internalizing problems and 0.87 and 0.88 for externalizing problems at respectively ages 10 and 13 years[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We additionally examined child self-reported internalizing and externalizing symptoms using the Brief Problem Monitor (BPM) at age 10, and the Youth Self Report (YSR) at age 13 years. The validated BPM is an abbreviated version of the YSR, consisting of 19 items, while the validated YSR contains 112 items[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Cronbach\u0026rsquo;s alphas were 0.72 and 0.88 for internalizing symptoms, and 0.63 and 0.82 for externalizing symptoms at respectively ages 10 and 13 years, respectively [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Potential confounders\u003c/h2\u003e\u003cp\u003eAt the age of 10, the child\u0026rsquo;s height and weight were measured at the research center, and body mass index (BMI) was calculated and standardized according to the Dutch reference growth curves (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://growthanalyser.org\u003c/span\u003e\u003cspan address=\"https://growthanalyser.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. At the age of 5, a non-verbal intelligence quotient (IQ) was assessed using the \u003cem\u003eSnijders-Oomen Niet-verbale intelligentie Test- Revisie (SON-R 2.5\u0026ndash;7)\u003c/em\u003e [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\u003cp\u003eFirst, multiple imputations were performed to replace the missing values (ranging from 1.5\u0026ndash;55.3%) using multivariate imputation by chained equations[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We created 30 imputed datasets with 100 iterations. Next to our variables of interest, the following variables were added as auxiliary variables to improve imputation because they have a low amount of missing data: household income, parental weight and height, paternal age at inclusion, gestational age at birth, birth weight, parity, and apgar score at 5 minutes[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe socioeconomic (education and migration background) and sex disparities in internalizing and externalizing problems were obtained using linear regression models, adjusted for age. Secondly, we constructed an \u0026lsquo;intervention\u0026rsquo; model by estimating the effect of the hypothetical interventions (e.g., outdoor play) on the outcome (e.g., internalizing symptoms) using linear regression while adjusting for potential confounders that were selected using causal graphs and based on the relevant literature. This model requires three causal assumptions: I) Exchangeability, meaning that there is no residual confounding or bias due to selection bias between the proposed intervention and the measure of psychiatric symptoms. Therefore, we adjusted for child age at intervention measurement, child age at outcome measurement, IQ at age 5, standardized body mass index at age 9, and pre-existing mental health symptoms at age 9. We additionally adjusted for maternal education and migration background in the analyses with sex, for maternal education and sex in the analyses with migration background, and for sex and migration background in the analyses with maternal education. To avoid selection bias due to dropout, we used the whole sample (N\u0026thinsp;=\u0026thinsp;9,898) and imputed all missing values (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for several missing data); II) Positivity, meaning that the probability of receiving any level of the intervention (i.e., outdoor time, sports, and screen time) conditional on the covariates must be greater than 0. We estimated the generalized propensity scores on the non-imputed dataset using the estimate_gps function with the non-parametric kernel approach of the CausalGPS R package[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The generalized propensity scores were all greater than 0; III) Consistency assumes that the interventions are well-defined, which is unlikely to be the case for our interventions. For instance, screen time does not account for the content of the activity (e.g., gaming or schoolwork), nor does it specify whether a parent or peer is involved (e.g., gaming with a friend or receiving assistance from a parent during schoolwork). Similarly, this applies to outdoor play and sports participation. Consequently, the observed estimates will depend on how the Generation R cohort defines screen time, outdoor play, or sports participation. Therefore, our estimates show the maximum reduction that could be reached if everyone fully complies with the interventions. Finally, the estimates of the \u0026lsquo;intervention\u0026rsquo; model were used to estimate the change in mental health problems if all participants had had the intervention using G-estimation models [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. By comparing the inequality before and after the hypothetical interventions, the absolute change in social inequality was estimated, and the corresponding 95% confidence intervals (CIs) were calculated using bootstrapping with 1000 iterations.\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\u003eCharacteristics of the cohort.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall (N\u0026thinsp;=\u0026thinsp;9,898)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChild sex\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBoy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,937 (50.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGirl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,808 (49.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,745\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMigration background\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,948 (53.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,273 (46.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,221\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal education\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLower\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,858 (53.3%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,264 (46.7%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9,122\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChild age at psychiatric measurement\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.56 (0.40)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,961\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOutdoor play (hrs/week)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.63 (4.65)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,811\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSports participation (hrs/week)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.60 (1.59)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,905\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eScreen time (hrs/week)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.29 (11.91)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,424\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParent reported internalizing symptoms (sumscore)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.63 (5.81)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,721\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eParent reported externalizing symptoms (sumscore)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4.22 (5.28)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,709\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelf-reported internalizing symptoms (sumscore)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.78 (7.15)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,516\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSelf-reported externalizing symptoms (sumscore)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean (SD)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.03 (5.33)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4,503\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eHrs\u0026thinsp;=\u0026thinsp;Hours. N\u0026thinsp;=\u0026thinsp;Number of participants. SD\u0026thinsp;=\u0026thinsp;Standard deviation.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSensitivity analyses were performed with participants who had complete data on internalizing and externalizing psychiatric symptoms at the age of 13 years, as well as the hypothetical interventions (i.e., outdoor play, sports participation, and screen time) at the age of 10, and the social inequality variables (i.e., sex, mother education, and migration background, N\u0026thinsp;=\u0026thinsp;3,413). In addition, the analyses were repeated using self-reported symptoms instead of those reported by the primary caregiver for internalizing and externalizing behaviors. All analyses were conducted in R statistical software version 4.2.1.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 General characteristics\u003c/h2\u003e\n \u003cp\u003eThe participants were on average 13.6 (0.4) years old when internalizing and externalizing symptoms were measured. Around half of the participants were male (50.7%), almost half of them had a migration background (46.3%), and more than half had mothers with a low education (vocational training or less, 53.3%). The full general characteristics of the cohort are shown in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The social disparities (i.e., stratified by sex, migration background, and maternal education) in outdoor play, sports participation, screen time, and internalizing and externalizing symptoms are shown in \u003cstrong\u003eTables S1\u003c/strong\u003e, \u003cstrong\u003eS2\u003c/strong\u003e, and \u003cstrong\u003eS3\u003c/strong\u003e, respectively.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Internalizing symptoms\u003c/h2\u003e\n \u003cp\u003eGirls had a 0.106 (95% CI: 0.066, 0.145) standard deviation more internalizing symptoms compared to boys. Similarly, children with a migration background had 0.125 (95% CI: 0.085, 0.165) standard deviation more internalizing symptoms than those without, and children with a lower educated mother had a 0.177 (95% CI: 0.138, 0.216) standard deviation more internalizing symptoms than those with a higher educated mother.\u003c/p\u003e\n \u003cp\u003eIn the main analyses, hypothetically increasing outdoor play or reducing screen time did not significantly reduce the disparities in internalizing symptoms (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). However, hypothetically increasing sports participation significantly reduced the disparities for maternal education (\u0026beta; = -0.014; 95% CI: -0.024, -0.003) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In the sensitivity analyses, results were virtually the same in the complete case analyses (\u0026beta; = -0.017; 95% CI: -0.034, -0.000) and when using self-reported internalizing symptoms (\u0026beta; = -0.024; 95% CI: -0.036, -0.012). See \u003cstrong\u003eTable S4\u003c/strong\u003e and \u003cstrong\u003eTable S5\u003c/strong\u003e, respectively. In contrast to the main analyses, the sensitivity analyses showed that hypothetically increasing sports participation also reduced the disparities in self-reported internalizing symptoms for sex (\u0026beta; = -0.016; 95% CI: -0.029, -0.003) and migration background (\u0026beta; = -0.012; 95% CI: -0.025, 0.000). Additionally, hypothetically reducing screen time reduced the disparities in self-reported internalizing symptoms for maternal education (\u0026beta;\u0026thinsp;=\u0026thinsp;0.030; 95% CI: 0.009, 0.052; \u003cstrong\u003eTable S5\u003c/strong\u003e).\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eReductions in sex, migration background and maternal education disparities on internalizing and externalizing symptoms at the age of 13 from hypothetical outdoor play, sports participation and screen time interventions in children at age 10.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003eInternalizing symptoms\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eSex (ref:boy)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMigration background (ref:no)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMaternal education (ref:high)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntervention\u0026thinsp;=\u0026thinsp;Outdoor play\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBefore intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.065,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.085,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.138,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.216)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfter intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.050,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.188)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.063,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.117,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.252)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(-0.045,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.071)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.051,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.063)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.050,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntervention\u0026thinsp;=\u0026thinsp;Sports participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBefore intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.066,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.085,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.138,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.216)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfter intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.058,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.136)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.081,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.125,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(-0.020,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.017,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e(-0.024\u003c/strong\u003e,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e-0.003)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntervention\u0026thinsp;=\u0026thinsp;Screen time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBefore intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.066,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.085,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.139,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.216)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfter intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(0.052,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.083,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.169)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.117,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.202)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e(-0.032,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.021,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.039,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"11\"\u003e\n \u003cp\u003e\u003cstrong\u003eExternalizing symptoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex (ref:boy)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMigration background (ref:no)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal education (ref:high)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntervention\u0026thinsp;=\u0026thinsp;Outdoor play\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI lower bound\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBefore intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.193,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.057,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.116,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfter intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.220,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.085)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.007,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.097,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.235)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.052,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.054)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.075,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.034)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.043,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntervention\u0026thinsp;=\u0026thinsp;Sports participation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBefore intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.193,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.057,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.115,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfter intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.197,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.118)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.056,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.110,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.189)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.013,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.011,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.010)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.015,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntervention\u0026thinsp;=\u0026thinsp;Screen time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBefore intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.193,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.114)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.057,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.115,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfter intervention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.211,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.128)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.053,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.098,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.181)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.035,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.023,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.019)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.034,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.005)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"11\"\u003eRef\u0026thinsp;=\u0026thinsp;reference group. B\u0026thinsp;=\u0026thinsp;standardized beta coefficient. CI\u0026thinsp;=\u0026thinsp;Confidence Interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Externalizing symptoms\u003c/h2\u003e\n \u003cp\u003eBoys had a 0.154 (95% CI: 0.114, 0.193) standard deviation more externalizing symptoms compared to girls. Similarly, children with a migration background had a 0.097 (95% CI: 0.057, 0.137) standard deviation more externalizing symptoms than those without, and children with a low-educated mother had a 0.154 (95% CI: 0.115; 0.193) standard deviation more externalizing symptoms.\u003c/p\u003e\n \u003cp\u003eIn the main analyses, none of the hypothetical interventions significantly reduced any of the social disparities (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). In the sensitivity analyses, hypothetically increasing sports participation and decreasing screen time reduced the disparities for maternal education in the complete case sample (\u0026beta; = -0.020; 95% CI: -0.036, -0.004; and \u0026beta; = -0.034; 95% CI: -0.072, 0.000 respectively; \u003cstrong\u003eTable S4\u003c/strong\u003e), and the self-reported externalizing symptoms (\u0026beta;\u0026thinsp;=\u0026thinsp;0.018; 95% CI: 0.006, 0.030; and \u0026beta;\u0026thinsp;=\u0026thinsp;0.025; 95% CI: 0.003, 0.048 respectively; \u003cstrong\u003eTable S5\u003c/strong\u003e). Interestingly, hypothetically reducing screen time increased disparities in externalizing symptoms between the sexes in the complete case sample (\u0026beta;\u0026thinsp;=\u0026thinsp;0.041; 95% CI: 0.014, 0.071) (\u003cstrong\u003eTable S4\u003c/strong\u003e), and hypothetically increasing sports participation also increased the disparities in self-reported externalizing symptoms between the sexes (\u003cstrong\u003eTable S5\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUsing a sequential G-estimation model, this study aimed to estimate how social disparities in child mental health at age 13 might change following hypothetical interventions targeting physical activity and screen time at age 10. The study found that children with a migration background and children with a lower educated mother reported more internalizing and externalizing symptoms than children without a migration background and children with a higher-educated mother. In contrast, girls reported more internalizing symptoms than boys, while boys experienced more externalizing symptoms than girls. Hypothetically increasing outdoor play and reducing screen time did not significantly reduce disparities in internalizing symptoms. However, hypothetically increasing sports participation significantly reduced disparities in internalizing symptoms related to maternal education. The study also indicated that increasing sports participation or reducing screen time has varying impacts on social disparities in externalizing symptoms, with certain interventions reducing maternal educational disparities, but increasing sex disparities in some of the sensitivity analyses, denoting inconsistent results when discussing externalizing symptoms.\u003c/p\u003e\u003cp\u003eA hypothetical increase in sports participation was found to significantly attenuate disparities in internalizing symptoms associated with maternal education. This finding aligns with our previous research, which demonstrated that sports participation was inversely associated with internalizing symptoms in youth, although the observed effects were modest[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Consistent with other earlier studies, the magnitude of the associations in our research corresponds with findings showing that involvement in sports during childhood is linked to fewer depressive symptoms in early adulthood, although again the effect sizes were small after accounting for confounders[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Evidence also suggests that the beneficial impact of physical activity on psychiatric symptoms is more pronounced in controlled clinical samples compared to general population studies[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Despite this, physical activity consistently emerges as a valuable tool contributing to preventing youth from developing clinical or diagnosed mental disorders, particularly in the critical developmental period of adolescence.\u003c/p\u003e\u003cp\u003eHome environments characterized by low parental education or low socioeconomic status often act as early life adversities, contributing to the emergence of psychiatric problems in childhood. However, some children in similar circumstances may exhibit greater resilience to the development of psychiatric symptoms, which may be partially explained by intrapersonal factors such as IQ, self-identity, and self-esteem[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Research suggests that self-esteem can act as a protective factor against the development of psychiatric symptoms in the face of early adversities, and sport-based interventions have been shown to improve self-esteem[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This improvement could play a role in mitigating the risk of internalizing symptoms among youth exposed to early life adversities [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eImportantly, not all forms of physical activity appear to offer the same benefits. For instance, outdoor play does not seem to have the same impact as sports, particularly those focused on skill development and teamwork rather than aesthetics[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Playing sports, especially team sports, has been shown to increase self-esteem, which serves as one of the strongest mechanisms through which youth are protected from internalizing symptoms[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Self-esteem in youth tends to develop through exposure to challenges and overcoming difficulties, and sports provide an ideal environment for practicing resilience, problem-solving, and coping skills\u0026mdash;important foundations for the challenges of adolescence and adulthood.\u003c/p\u003e\u003cp\u003eThe impact of these hypothetical interventions on externalizing symptoms was inconsistent, with non-significant results in the main analyses. However, sensitivity analyses using complete cases and self-reported data revealed that some interventions reduced disparities among children with lower maternal education, while others led to an increase in sex disparities. The inconsistencies observed in the impact of hypothetical interventions on social disparities in externalizing symptoms may be influenced by several factors. First, the relationship between physical activity variables and externalizing symptoms may be non-significant or weak, which could explain some of the negative results observed in our main analyses[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, the CBCL, a widely used and validated instrument to measure psychopathology in children, may have limitations that affect the accuracy of measuring externalizing symptoms. Specifically, although the majority (87.4%) of CBCL items are not biased by gender, some items related to externalizing symptoms, particularly those assessing aggressive behavior, have been identified as potentially gender-biased in previous studies [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Another potential explanation for the discrepancies found could be the use of sex rather than gender as an inequality measure in our study, as it may not account for the continuous nature of gender identity, potentially affecting our results. Overall, these limitations could help explain our inconsistent findings regarding differences between boys and girls and contribute to some of the discrepancies observed in the results. Furthermore, it is possible that the CBCL measures of externalizing symptoms in youth may not be sensitive enough to detect subtle or nuanced differences in behavior. All in all, addressing these measurement issues could lead to a better understanding of the relationship between physical activity and externalizing symptoms. However, our findings might also be accurate: lifestyle interventions could reduce SES-related disparities in externalizing behavior while unintentionally exacerbating sex disparities. This outcome may reflect differences in how boys and girls respond to interventions due to biological, social, or environmental factors. Careful evaluation and tailoring of interventions are crucial to addressing both SES and sex disparities, ensuring equitable outcomes across groups.\u003c/p\u003e\u003cp\u003eThis study is among the first to employ this methodology to quantify reductions in social disparities in child mental health at age 10 through hypothetical interventions targeting outdoor play, sports participation, and screen time. Recent advancements in causal inference methodology have significantly improved the study of health disparities, offering tools to directly assess the key parameter of interest: the potential for interventions to reduce these disparities[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Unlike many studies that adjust for confounders in the relationship between social disparities and health outcomes, this study intentionally avoids such adjustments. The rationale is that unadjusted health disparities are of primary interest, as they represent real-world disparities in health outcomes based on factors like sex, maternal education, or migration background, rather than disparities conditioned on covariates. By focusing on unadjusted health disparities, this approach provides a clearer understanding of how specific interventions could influence social disparities and sets realistic expectations for their effect sizes. Additional strengths of this study include the use of a population-based, multicultural cohort, which enhances the generalizability of the findings, and the application of multiple imputation techniques to minimize selection bias and ensure representative data. These methodological strengths contribute to a more comprehensive understanding of strategies to address social disparities in child mental health.\u003c/p\u003e\u003cp\u003eNonetheless, our findings must be interpreted in the context of several limitations. First, the observational design restricts our ability to infer causality for any of the hypothetical interventions and their outcomes. Second, other potential confounders not included in the model may also contribute to reducing the social disparities explored in this study. Third, we measured both the hypothetical interventions and the outcomes at a single time point, which prevented us from examining the stability of these variables from childhood to adolescence. Fourth, hypothetical interventions were assessed through parental reports, which introduces the possibility of under- or overestimations of behaviors. Furthermore, both the hypothetical interventions and the outcomes were reported by the primary caregivers, which may have inflated the observed relationships due to shared method variance. Sensitivity analyses indicated that when the child reported on the outcomes, some results, particularly those related to externalizing symptoms, showed slight changes, while those related to internalizing symptoms remained consistent. Fifth, this study assesses sex (assigned at birth) rather than gender, which may limit the ability to capture the potential association of gender identity and expression with psychiatric symptoms. Lastly, while we used multiple imputations to ensure all participants were included in the analysis and reduce bias, the results may still be influenced by missing data, especially if the missingness is related to variables not included in the imputation model. We sought to mitigate this by incorporating all variables from the analyses, as well as additional variables associated with missingness, into the imputation process.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Practical implications and future research\u003c/h2\u003e\u003cp\u003eGiven that many internalizing problems, including depression, anxiety, and somatic symptoms, typically emerge during formative years\u0026mdash;especially among young people from low-education and migrant backgrounds, as well as those affected by sex differences \u0026mdash;investing in early interventions targeting at-risk youth is essential [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. New societal challenges, such as the COVID-19 pandemic, climate change, and the ongoing war in Ukraine, may exacerbate the risk of mental health issues[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. In light of these growing concerns, policymakers should prioritize programs that encourage physical activity, particularly sports, as part of a broader strategy to mitigate the escalating mental health burden worldwide. This approach is especially important for youth from lower socioeconomic backgrounds to ensure they have equal opportunities to thrive compared to their peers with higher socioeconomic status. Future intervention studies focusing on these at-risk populations are essential to determine the most cost-effective delivery methods\u0026mdash;whether through online, blended, or in-person approaches. These interventions should aim not only to provide young people with access to sports but also to integrate behavioral change strategies that promote a sustained positive impact on mental health.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn conclusion, this study highlights the presence of social disparities in both internalizing and externalizing symptoms in young people, with sex, migration background, and maternal education playing significant roles. While hypothetical interventions targeting outdoor play and screen time did not uniformly reduce these disparities, increasing sports participation seems consistently effective in reducing disparities in internalizing symptoms related to maternal education. The impact of hypothetical lifestyle interventions on externalizing symptoms was inconsistent, with some interventions showing reductions in disparities for maternal education but increasing sex disparities. These findings suggest that while certain lifestyle interventions can reduce disparities in child mental health, the effects may vary across different types of symptoms and demographic groups, emphasizing the need for tailored approaches in addressing social disparities in mental health. Future research should further explore the differential effects of interventions on internalizing and externalizing symptoms across various social contexts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eDeclaration of Interests\u003c/h2\u003e\n\u003cp\u003eNone of the authors have any conflict of interest.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eM.R. drafted the main manuscript and M.R. and C.E. conducted the analyses. All authors conceptualized and designed the work, interpreted the data and reviewed it critically.\u003c/p\u003e\n\u003ch2\u003eAcknowledgments and Funding\u003c/h2\u003e\n\u003cp\u003eMar\u0026iacute;a Rodriguez-Ayllon was supported by the Sara Borrell postdoctoral fellowship (CD23/00096- Instituto de Salud Carlos III (ISCIII)). Clair Enthoven was supported by the Erasmus Initiative Vital Cities and Citizens initiative. This study was supported by the Sophia Foundation (S18-20) and the Netherlands Organization for Health Research and Development (ZonMw). Supercomputing resources were supported by the Netherlands Organization for Scientific Research (Exacte Wetenschappen) and SURFsara (Snellius Compute Cluster, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.surfsara.nl\u003c/span\u003e\u003c/span\u003e). The Generation R Study is conducted by the Erasmus Medical Center in close collaboration with the School of Law and the Faculty of Social Sciences of the Erasmus University Rotterdam, the Municipal Health Service Rotterdam area, and the Stichting Trombosedienst en Artsenlaboratorium Rijnmond. The general design of the Generation R Study was made possible by financial support from the Erasmus Medical Center, Rotterdam, ZonMw, the Netherlands Organization for Scientific Research, and the Ministry of Health, Welfare, and Sport. The authors gratefully acknowledge the contributions of the participating children and parents, general practitioners, hospitals, midwives, and pharmacies in Rotterdam.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData from this study are available upon reasonable request to the director of the Generation R Study ([email protected]), subject to local, national and European rules and regulations.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdolescent mental health \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/adolescent-mental-health\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. 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Eur Child Adolesc Psychiatry 32:1815\u0026ndash;1816. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/S00787-022-01984-X\u003c/span\u003e\u003cspan address=\"10.1007/S00787-022-01984-X\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"social-psychiatry-and-psychiatric-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sppe","sideBox":"Learn more about [Social Psychiatry and Psychiatric Epidemiology](http://link.springer.com/journal/127)","snPcode":"127","submissionUrl":"https://submission.nature.com/new-submission/127/3","title":"Social Psychiatry and Psychiatric Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Exercise, sports, mental health, social disparities, youth, childhood","lastPublishedDoi":"10.21203/rs.3.rs-7094490/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7094490/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eTo estimate how social disparities in child psychiatric symptoms might change following hypothetical interventions targeting sports, outdoor play, and screen time at age 10.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe used data from 9,778 children of the Generation R Study, a prospective population-based cohort in Rotterdam, the Netherlands. Social inequality variables included sex, maternal education, and migration background. Primary caregivers filled out the validated Child Behavior Checklist to report on children's internalizing and externalizing symptoms at the age of 13. The hypothetical interventions (i.e., outdoor play, sports participation, and screen time) were parent-reported at age 10. We used sequential G-estimation to estimate the inequality with and without the hypothetical intervention.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eChildren with migration backgrounds (46.3%) and low maternal education (53.3%) were associated with relatively more internalizing and externalizing symptoms than peers, with disparities of 0.125 and 0.177 standard deviations, respectively. Girls had more internalizing symptoms (0.106 SD), while boys had more externalizing symptoms (0.154 SD). Increasing sports participation reduced disparities in internalizing symptoms linked to maternal education (β = -0.014; 95% CI: -0.024, -0.003), while outdoor play and screen time interventions showed limited effects. None of the hypothetical interventions significantly reduced any of the social disparities in externalizing symptoms.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study underscores the persistence of sex, cultural, and socioeconomic disparities in youth mental health. While sports participation showed a potential effect in reducing disparities in internalizing symptoms, its impact on externalizing symptoms and other interventions was negligible. Future efforts should focus on identifying more effective strategies for addressing these inequalities.\u003c/p\u003e","manuscriptTitle":"Reducing social disparities in child emotional and behavioral problems by hypothetical physical activity and screen time interventions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-04 17:14:37","doi":"10.21203/rs.3.rs-7094490/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-03T13:13:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-29T06:39:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"280911938702292847895758816665625854768","date":"2025-08-04T08:47:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"282811214126397997047871155937944447558","date":"2025-08-04T05:25:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-30T15:41:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-14T05:45:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-11T11:43:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Social Psychiatry and Psychiatric Epidemiology","date":"2025-07-10T15:15:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"social-psychiatry-and-psychiatric-epidemiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"sppe","sideBox":"Learn more about [Social Psychiatry and Psychiatric Epidemiology](http://link.springer.com/journal/127)","snPcode":"127","submissionUrl":"https://submission.nature.com/new-submission/127/3","title":"Social Psychiatry and Psychiatric Epidemiology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ae1de60a-0e4e-43f3-833a-234a29465a86","owner":[],"postedDate":"August 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-26T16:02:58+00:00","versionOfRecord":{"articleIdentity":"rs-7094490","link":"https://doi.org/10.1007/s00127-025-03036-6","journal":{"identity":"social-psychiatry-and-psychiatric-epidemiology","isVorOnly":false,"title":"Social Psychiatry and Psychiatric Epidemiology"},"publishedOn":"2026-01-19 15:58:09","publishedOnDateReadable":"January 19th, 2026"},"versionCreatedAt":"2025-08-04 17:14:37","video":"","vorDoi":"10.1007/s00127-025-03036-6","vorDoiUrl":"https://doi.org/10.1007/s00127-025-03036-6","workflowStages":[]},"version":"v1","identity":"rs-7094490","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7094490","identity":"rs-7094490","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}