Association of Screen Time with Attention-Deficit/Hyperactivity Disorder Symptoms and Their Development: The Mediating Role of Brain Structure

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This study aims to examine the association between screen time, ADHD symptoms, and the brain, using large-scale longitudinal samples from the Adolescent Brain Cognitive Development (ABCD) study. Method From the ABCD study, we extracted, data on screen time, ADHD symptoms based on the Child Behavior Checklist, and brain structure measures of 10116 and 7880 children (aged 9–10 years) at baseline and at the 2-year follow-up, respectively. We used the linear mixed-effects model to examine the association between screen time at baseline, and the development of ADHD symptoms and brain structure after two years. We also examined the mediating role of brain structure on the association between screen time and ADHD symptoms. Results Screen time was associated with the development of ADHD symptoms (β = 0.032, p = 0.001) and thickness of some cortical regions (right temporal pole: β=-0.036, false discovery rate (FDR)-corrected p = 0.020; left superior frontal gyrus: β=-0.028, FDR-corrected p = 0.020; and left rostral middle frontal gyrus: β=-0.030, FDR-corrected p = 0.020). Moreover, the total cortical volume partially mediated the relationship between screen time and ADHD symptoms (β = 0.001, p = 0.023) at baseline. Conclusion These results suggest that screen time influences ADHD symptom development and brain structure, providing insight into the mechanisms underlying the association between screen time and ADHD symptoms. Furthermore, interventions to reduce screen time may help improve ADHD symptoms. Health sciences/Diseases/Psychiatric disorders/ADHD Biological sciences/Psychology/Human behaviour Biological sciences/Neuroscience Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Over the years, adolescents’ screen time has increased worldwide, especially since the COVID-19 pandemic [ 1 , 2 ]. Screen time affects lifestyle habits, such as the frequency of physical activity and sleep duration [ 3 , 4 ], and is considered to have a negative impact on mental health, behavior, and brain development [ 5 – 7 ]. Considering that adolescence is a critical developmental period for health, well-being, and brain regions, and is influenced by biological and environmental factors [ 8 ], some academics and researchers recommend screen-time limits for children and adolescents [ 9 , 10 ]. According to the Adolescent Brain Cognitive Development (ABCD) study, adolescents in America have a mean screen time of 7.70 hours/day, implying lengthy screen time for children and adolescents [ 11 ]. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by symptoms of age-inappropriate inattention and/or hyperactivity/impulsiveness. Previous studies found increased screen time to be associated with ADHD symptom severity [ 4 , 6 , 12 – 15 ], and a higher risk of ADHD diagnoses [ 16 , 17 ]. However, most of these studies were cross-sectional; therefore, concluding whether screen time exacerbates ADHD symptoms was difficult. We conducted a longitudinal analyses of screen time and ADHD symptoms, as it could provide evidence of whether screen time can lead to changes in ADHD symptoms. Adolescence—a critical period for brain development—is influenced by both biological and environmental factors [ 8 ]. Previous studies have examined the influence of screen-based activity on the development of brain structure [ 18 – 22 ]. Takeuchi et al., who examined approximately 250 Japanese children, used diffusion-tensor imaging and found that the time spent on video games affected the development of the microstructural brain [ 20 ]. However, another study with a larger sample size reported no association between screen time and white matter microstructure abnormalities [ 21 ]. Takeuchi et al. conducted a longitudinal study, examining the effect of various screen-related activities in terms of brain structure; they found that screen-related activity was associated with a smaller increase in gray and white matter volumes of widespread brain areas, such as orbitofrontal, lateral, prefrontal, and anterior cingulate areas [ 19 ]. Conversely, a study that utilized the ABCD study, and used Mendelian randomization analyses to examine the causal relationships between screen time and brain volume at baseline, did not observe any significant relationship [ 22 ]. Nevertheless, it did not examine the relationship between the development of the brain and screen time, using longitudinal analyses. Thus, the lack of longitudinal studies examining this relationship in a large sample size of children and adolescents necessitates confirming the findings. Previous studies proposed impulsivity and sleep quality as mediators in the relationship between screen time and ADHD [ 4 , 23 ]. However, research has rarely examined the neural mediators between screen time and ADHD. A study on the role of the microstructural brain in the relationship between the polygenic risk of ADHD and screen time [ 24 ], revealed a potential neural overlap between ADHD and screen-related activity. In brain structure studies, ADHD has been associated with delayed cortical maturation, including thickness [ 25 ] and volume [ 26 ]. Moreover, the reduction of gray matter volume in extensive regions of the brain has been found to be associated with ADHD [ 26 ] and screen-related activities [ 18 , 19 ]. However, to the best of our knowledge, no study has directly examined the role of the brain structure in the relationship between screen time and ADHD symptoms. Therefore, we analyzed data from the ABCD study, a database with longitudinal data of nearly 10,000 children aged 9–10 years. Using cross-sectional and longitudinal analyses, we examined: 1) the relationship of screen time with ADHD symptoms and their development; 2) the relationship of screen time with brain structure and its development; and 3) the mediating effect of brain structure on the association of screen time with ADHD symptoms and their development. We used baseline and 2-year follow-up data to examine the relationship between screen time, ADHD symptoms, and brain structure. We proposed the following hypotheses: (1) screen time is associated with ADHD symptoms and their development; (2) screen time is associated with the brain structure and its development; and (3) the brain structure has a mediating effect on the association between screen time and ADHD symptoms and their development. Method Participants The ABCD study is an ongoing project in 21 centers across the United States, following a cohort of 11,878 children aged 9–10 years [ 27 ]. Details of its recruitment and ethics have been published [ 28 , 29 ]. Parents of all participants provided their written consent after the procedures were fully explained to them by the investigators; additionally, the children provided their assent before participating in the ABCD study [ 30 ]. We obtained data on the participants’ brain structure and behavior, as well as their demographic background from the National Institute of Mental Health (NIMH) Data Archive ABCD Data Release 5.0. The University of Fukui’s Research Ethics Committee approved the data analysis (Assurance no. FU-20210067). Participants’ demographic data and covariates are summarized in Table 1 . For the analyses, we included data of 10,116 and 7,880 children at baseline and during the 2-year follow-up, respectively. To maximize the sample size, all models were performed with all the available participants. Table 1 Demographic data and covariates of participants Baseline (n = 10122) 2-year follow up (n = 7880) Age (month) 119.00 ± 7.50 143.69 ± 7.85 Sex Male Female 5276 4846 3740 4139 Parents’ income < 49,999 50,000–74,999 75,000–99,999 100,000–199,999 ≥ 200,000 2928 1394 1469 3135 1196 2090 1109 1207 2530 944 Parents’ education (years) 15.34 ± 2.53 15.46 ± 2.47 Race White Black Hispanic Asian Other 5606 1327 2971 180 1038 4104 756 1322 116 690 Pubertal status (scores) 1.63 ± 0.42 2.15 ± 0.65 Sleep duration (hours) 9.80 ± 1.26 9.32 ± 1.28 Physical activity (times per week) 3.54 ± 2.30 3.77 ± 2.16 Handedness-score rating Right-handed 8132 6239 Left-handed 730 558 Mixed-handed 1361 1083 Total intracranial volume (cm 3 ) 1496.70 ± 143.12 1528.54 ± 146.85 Screen time (baseline) 3.70 ± 3.00 3.60 ± 2.86 ADHD T-score 53.08 ± 5.48 52.93 ± 5.13 Note: ADHD, attention-deficit/hyperactivity disorder Screen time We assessed screen time using a self-reported questionnaire, and computed it as the total amount of time spent using various devices, including playing video games and watching television. The ABCD study provided the total screen time for typical weekdays and weekends, and we calculated a weighted-sum score to represent a daily screen time of 5/7× hours of screen time (weekday) + 2/7× hours of screen time (weekend) [ 31 ]. The data were available for the baseline (11,067 samples). ADHD symptoms To evaluate the level of the children’s ADHD symptoms, we used the ADHD-related DSM-5-oriented syndrome scales of the Child Behavior Checklist (CBCL) [ 32 ]. The data used in our study at baseline and during the 2-year follow-up comprised 10,116 and 6,986 samples, respectively. We recorded all the scores as T-scores, with higher scores representing greater behavioral problems. Brain structure Details of magnetic resonance imaging acquisition and data preprocessing in the ABCD study were published by Casey et al. and Hagler et al. [ 33 , 34 ]. The ABCD study used three Tesla magnetic resonance scanners (Siemens, General Electric 750, and Philips) to obtain high-resolution T1-weighted three-dimensional structural images (1 mm isotropic) and acquisition parameters, as previously described [ 33 ]. The structural data were processed using FreeSurfer (version 5.3.0) with a standardized processing pipeline [ 34 ]. In our study, we used structural data with the Desikan-Killiany atlas-based classification for cortical regions, and atlas-based segmentation for subcortical regions. We included data from participants whose data satisfied the FreeSurfer quality control for structural imaging data for analysis. We included 34 cortical regions and seven subcortical regions for one hemisphere (68 and 14 regions in total) for volume, and 34 cortical regions in each hemisphere (68 regions in total) for thickness. Additionally, as previous studies had found ADHD to be associated with cortical gray matter volume [ 26 , 35 , 36 ] and mean thickness of the cerebral cortex [ 25 ], we included total cortical volume and mean cortical thickness, directly measured by FreeSurfer in the analyses. Demographic variables and covariates Based on previous ABCD-based studies [ 37 – 40 ], we included the variables listed in Table 1 as covariates. We coded sex and race/ethnicity (White, Black, Hispanic, Asian, or others) as dummy variables, and treated parental income as a five-level categorical variable, as in previous studies [ 38 , 39 ]. We included age, parental education levels, pubertal status, total intracranial volume, daily sleep duration, and physical activity as continuous variables, and recorded parental education levels by school year, as in previous studies [ 38 , 39 ]. We assessed pubertal status using the Pubertal Development scale [ 41 ]. Additionally, according to previous studies, as sleep and physical activities are typically included in an analysis of the relationship between screen-related activities and ADHD [ 3 , 4 , 42 ], we included sleep duration and frequency of physical activities as covariates. Statistical analysis In this study, we used R (version 4.3.1; The R Foundation for Statistical Computing, Vienna, Austria) to perform statistical analysis. We created scatter plot figures using R-package “ggplot” and brain mapping figures using Python version 3.6.8 (The Python Software Foundation, USA) with python-package “Pysurfer” to display brain-related statistics. First, we examined the association between screen time and ADHD symptoms. We analyzed data from 10,116 samples at baseline, and 6,986 samples after a 2-year follow-up. We winsorized data (outliers of screen time, brain structure, ADHD symptoms, and all continuous covariates) at three standard deviations from the mean. First, we adapted a linear mixed-effects model to examine cross-sectional relationships between screen time and ADHD symptoms using baseline data. We analyzed this model using R-package “lmertest.” Based on previous studies [ 38 , 39 ], we modeled family ID (used to denote sibling status), multiple data collection, and twin or triplet status, as random effects. For the mixed-effects model with ADHD symptoms as the dependent variable, we considered children’s age, sex, race, pubertal status, household income, parental education, sleep duration, and physical activity as covariates. Second, we conducted a residualized change regression model [ 43 ] of a 2-year change in ADHD symptoms to examine the effect of screen time on the development of ADHD symptoms. Specifically, 2-year follow-up of ADHD symptoms were regressed on baseline screen time, controlling for baseline ADHD symptoms. In this model, we also adopted family ID, multiple data collection, and twin or triplet status as random effects and the abovementioned variables as covariates. Further, we examined the association between screen time and brain structure. We analyzed the data of 9,713 and 6,426 samples at baseline and during the 2-year follow-up, respectively. First, we used linear mixed-effects models to examine the effect of screen time on brain structure. For the mixed-effects model, using brain structure as the dependent variable, we considered multiple data collections and twin or triplet status modeled as random effects. In addition to the covariates of the mixed-effects model of ADHD symptoms, we included handedness and total intracranial volume as covariates for the brain structure, including volume and thickness. The p -values were false discovery rate (FDR)-corrected for multiple testing per structure [ 44 ]. Second, we performed the same residualized change regression for each brain structure measurement. We regressed the brain structures at 2-year follow-up on baseline screen time, controlling for baseline brain structures. In this model, we also adapted family ID, multiple data collection, and twin or triplet status as random effects, and the above-mentioned variables as covariates. Finally, we examined the mediating effect of brain structure on the association of screen time with ADHD symptoms at baseline and the development of ADHD symptoms. We analyzed data from 9,663 samples at baseline, and 5,472 samples after a 2-year follow-up. First, we performed a mediation analysis of the structures that were significantly associated with screen time in the relationship between screen time and ADHD symptoms at baseline. We residualized brain structure measures and ADHD symptoms for study site variables by the linear mixed-effects model, and then converted these measures to z-scores. We used the R-package “lanvnn” to perform a standard three-variable mediation analysis to estimate the significance of the mediating effect by using the bias-corrected bootstrap approach (with 10,000 random samplings). Second, we performed a mediation analysis of the development of structures that were significantly associated with screen time in residualized change regressions on the relationship between screen time and the development of ADHD symptoms. We residualized brain structure measures (2-year follow-up) for the above-mentioned variables and baseline brain structures, using the linear mixed-effects model, and then converted these measures to z-scores. We residualized ADHD symptoms (2-year follow-up) for the above-mentioned variables and baseline ADHD symptoms, using the linear mixed-effects model, and then converted these measures to z-scores. We performed a standard three-variable mediation analysis to estimate the significance of the mediating effect using a bias-corrected bootstrap approach (with 10,000 random samplings). Results Association of screen time with ADHD symptoms We adopted a linear mixed-effects regression model with ADHD symptoms as the dependent variable, and screen time as the independent variable. We found that screen time had a significant main effect (b = 0.199, 95% confidence interval [CI] = 0.160–0.236], β = 0.109, p < 0.001), implying a significant association between screen time and ADHD symptoms at baseline (Fig. 1 A). Then, at the 2-year follow-up, we found screen time to have a significant main effect on ADHD symptoms, controlling baseline ADHD symptoms as covariates (b = 0.055, 95% CI = 0.021–0.089, β = 0.032, p = 0.001; Fig. 1 B). Associations of screen time with brain structure The results of the association between screen time and the volume or thickness of each brain region at baseline are summarized in Table S1 (available online) and Fig. 2 . The results showed that screen time was negatively associated with the volume of right putamen (b = -290.836, 95% CI = -483.750 to 97.921, β = -0.036, FDR-corrected p = 0.005; Fig. 2 C), and with total cortical volume (b = -290.836, 95% CI = -483.751 to -97.921, β = -0.015, p = 0.003; Fig. 2 D). We then examined the association between screen time and the development of brain structures (Table S2 and Fig. 3 ). The results showed that screen time was negatively associated with the thicknesses of the right temporal pole (b = -0.004, 95% CI = -0.006 to 0.001, β = -0.036, FDR-corrected p = 0.020; Fig. 3 C), left superior frontal gyrus (b = -0.001, 95% CI = -0.006 to 0.001, β = -0.028, FDR-corrected p = 0.020; Fig. 3 D), and left rostral middle frontal gyrus (b = -0.001, 95% CI = -0.002 to -0.0005, β = -0.030, FDR-corrected p = 0.020; Fig. 3 E). Additionally, screen time was negatively correlated with the mean cortical thicknesses (b = -0.0004, 95% CI = -0.001 to -0.00007, β = -0.017, p = 0.023). Mediating effect of brain structure on the relationship between screen time and ADHD symptoms We examined the mediating effects of brain structures, which were significantly associated with screen time at baseline, on the relationship between screen time and ADHD symptoms. The results showed that the cortical volume had a significant mediating effect on the relationship between screen time and ADHD symptoms (β = 0.001, 95% CI = 0.000–0.002, p = 0.023; Fig. 4 ). The volume of right putamen had no mediating effect on the relationship between screen time and ADHD symptoms (right putamen volume: β = 0.000, 95% CI = -0.001 to 0.001, p = 0.889). We then examined the mediating effect of the brain structures, whose development was significantly associated with screen time, on the relationship between screen time and ADHD symptom development. No structure had a mediating effect on this relationship (right temporal pole: β = 0.000, 95% CI = -0.001 to 0.001, p = 0.945; left superior frontal gyrus: β = 0.000, 95% CI = -0.001 to 0.002, p = 0.641; left rostral middle frontal gyrus: β = 0.000, 95% CI = -0.001 to 0.001, p = 0.980; and mean cortical thickness: β = 0.000, 95% CI = -0.001 to 0.001, p = 0.789). Discussion This study used a large sample size of children from the ABCD study, and examined the relationship between screen time, ADHD symptoms, and brain structure, especially from the developmental perspective. The results showed that screen time was positively correlated with ADHD symptoms and their development after 2 years, and negatively correlated with: the volumes of right putamen and cortical gray matter at baseline; and the development of the thickness of right temporal pole, left superior frontal gyrus, and left rostral middle frontal gyrus after 2 years. These results are consistent with the hypotheses that longer screen time is associated with both, ADHD and the development of its symptoms, and brain structures and their development. Moreover, using cross-sectional analyses, we found that cortical volume partially mediates the relationship between screen time and ADHD symptoms. This result partially supported the hypothesis that brain structure is mediated by the link between screen time and ADHD symptoms. These findings provide evidence that longer screen time can worsen ADHD symptoms and influence brain structure development and smaller cortical volumes could account for the negative association between screen time and ADHD symptoms. Our results showed that screen time is associated with ADHD symptoms and their development. In line with the results of our study, previous studies have demonstrated a positive correlation between screen time and ADHD symptoms [ 4 , 6 , 12 – 15 ]. However, these studies rarely examined the relationship between screen time and the development of ADHD symptoms, and a causal relationship between screen-based activities and ADHD remained unclear [ 45 ]. In our study, we examined this relationship by controlling for baseline ADHD symptom data. Our study provides evidence that longer screen time is associated with the development of ADHD symptoms two years later in children aged 9–10 years. This result was partially consistent with the finding of Soares et al., that screen time at 18 years was associated with a diagnosis of ADHD at 22 years [ 3 ], and expanded the age-range of conclusion. These findings reveal that longer screen time may worsen ADHD symptoms in children and adolescents, and interventions for screen-time may help reduce later ADHD symptoms. However, as the effect sizes are small, the clinical impact of screen time on ADHD symptoms may be marginal; this should be interpreted with caution. We also examined the relationship between screen time and brain structures at baseline and during the 2-year follow-up. At baseline, the volume of the right putamen was negatively correlated with screen time. The putamen is a part of the striatum, and is involved in the language function, reward, cognitive function, and addiction [ 46 ]. Previous studies found that screen time was associated with the functional connectivity of the frontoparietal network to the putamen [ 47 ], and internet use frequency was associated with the volume of the putamen in young women [ 48 ]; this implies that screen-based activity is associated with the putamen. The relationship between screen time and the putamen may explain the addition of screen-related behavior, in that, screen-based activity can result in children preferring more immediate rewards over delayed outcomes [ 47 ]; our findings of the correlation may provide evidence for this explanation. Our study also examined the effects of screen time on brain structural development, showing that screen time was involved in the development of the right temporal pole, left superior frontal gyrus, and left rostral middle frontal gyrus. These brain regions are associated with cognitive functions [ 49 – 51 ], including working memory, language function, and attention, implying that screen-based activity may influence the development of cognition. This finding is in line with those of previous studies [ 18 , 19 ], which found that screen-related activity influenced the development of extensive areas of the cerebral cortex. Our study strengthened this conclusion by analyzing the data of over 6,000 children. A previous study found that social media usage was associated with a co-development pattern of the thalamus with common structural measures in key brain regions, including the bilateral superior frontal, rostral middle frontal, inferior parietal, and inferior temporal regions [ 52 ]. Our study identified specific brain regions—the right temporal pole, left superior frontal gyrus, and left rostral middle frontal gyrus—whose development was influenced by screen-based activities. The results showed a partial mediating effect of the cortical volume on the relationship between screen time and ADHD symptoms. Specifically, longer screen time was associated with a smaller cortical volume, which in turn, was associated with more severe ADHD symptoms. Cortical volume may partially explain the relationship between screen time and ADHD symptoms. In previous studies, the reduction of gray matter volume, especially cortical gray matter volume [ 26 , 36 ] was observed in children with ADHD, which can be explained by a model of delayed cortical maturation in children with ADHD, compared to those with normal development [ 25 ]. The finding, that longer screen time is associated with smaller cortical volume, may imply that longer screen time is involved in the delay of brain development, and can cause more significant ADHD symptoms. Our findings expand those of prior studies, showing direct evidence of the mediating role of the brain in the association between screen time and ADHD, and provide evidence of the same potential neural mechanism between ADHD and screen-related activity. However, because this result was only shown in the cross-sectional analyses of the ABCD baseline data, causality could not be established. A limitation of our study is that it only examined the relationship with brain structures. Prior studies have reported that screen time is associated with the functional network of the brain [ 47 ] and the microstructural brain [ 20 , 24 ]. Thus, it is possible that brain development of them mediates the relationship between screen time and ADHD symptoms development, although our study did not identify the development of brain structures that mediate this relationship. Therefore, future studies should include the functional network of the brain and the microstructural brain in their analysis to examine the neural relationships that underlie screen time. This study was the first to examine the relationship between screen time, ADHD symptoms, and brain structure, especially from a developmental perspective. The findings provide evidence that longer screen time can worsen ADHD symptoms, and influence brain structural development. Moreover, ours is the first study to find that cortical volume partially mediates the relationship between screen time and ADHD symptoms in cross-sectional analyses, implying that the reduction in cortical volume may explain the negative link between screen time and ADHD symptoms. The findings can help us understand the relationship between screen time and ADHD symptoms, and the mechanism influencing ADHD symptoms, and further suggest that screen time interventions may improve ADHD symptoms. Declarations Acknowledgements This project was funded by the Japan Society for the Promotion of Science through Grants-in-Aid for Scientific Research (KAKENHI) (Grant numbers: 24K16647 to QS, 23K12814 to MY, 21K02380, 24K21453 to YM), Kawano Masanori Memorial Public Interest Incorporated Foundation for Promotion of Pediatrics (AY 2022 to YM), Research Grants from the University of Fukui (AY 2023 to YM and QS), and the Life Science Innovation Center, University of Fukui (AY 2023 to QS). Data used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 years, and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. The ABCD consortium investigators designed and implemented the study or provided data, but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. 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Zhan, High polygenic risk score is a risk factor associated with colorectal cancer based on data from the UK Biobank . PLoS One, 2023. 18(11): p. e0295155. Achenbach, T.M., L.A. Rescorla, and M.Y. Ivanova, International epidemiology of child and adolescent psychopathology I: Diagnoses, dimensions, and conceptual issues . J Am Acad Child Adolesc Psychiatry, 2012. 51(12): p. 1261–72. Casey, B.J., et al., The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites . Dev Cogn Neurosci, 2018. 32: p. 43–54. Hagler, D.J., Jr., et al., Image processing and analysis methods for the Adolescent Brain Cognitive Development Study . NeuroImage, 2019. 202: p. 116091. Vilgis, V., et al., Global and local grey matter reductions in boys with ADHD combined type and ADHD inattentive type . Psychiatry Res Neuroimaging, 2016. 254: p. 119–26. Maier, S., et al., Discrete global but no focal gray matter volume reductions in unmedicated adult patients with attention-deficit/hyperactivity disorder . Biol Psychiatry, 2016. 80(12): p. 905–915. Bernanke, J., et al., Structural brain measures among children with and without ADHD in the Adolescent Brain and Cognitive Development Study cohort: A cross-sectional US population-based study . Lancet Psychiatry, 2022. 9(3): p. 222–231. Hamatani, S., et al., Longitudinal impact of COVID-19 pandemic on mental health of children in the ABCD study cohort . Sci Rep, 2022. 12(1): p. 19601. Hiraoka, D., et al., Effects of prenatal cannabis exposure on developmental trajectory of cognitive ability and brain volumes in the adolescent brain cognitive development (ABCD) study . Dev Cogn Neurosci, 2023. 60: p. 101209. Owens, M.M., et al., Multimethod investigation of the neurobiological basis of ADHD symptomatology in children aged 9–10: baseline data from the ABCD study . Transl Psychiatry, 2021. 11(1): p. 64. Petersen, A.C., et al., A self-report measure of pubertal status: Reliability, validity, and initial norms . J Youth Adolesc, 1988. 17(2): p. 117–33. Healy, S., J. Foley, and J.A. Haegele, Physical activity, screen time, and sleep duration among youth with chronic health conditions in the United States . Am J Health Promot, 2020. 34(5): p. 505–511. Romer, A.L. and D.A. Pizzagalli, Is executive dysfunction a risk marker or consequence of psychopathology? A test of executive function as a prospective predictor and outcome of general psychopathology in the adolescent brain cognitive development study® . Dev Cogn Neurosci, 2021. 51: p. 100994. Hochberg, Y. and Y. Benjamini, More powerful procedures for multiple significance testing . Stat Med, 1990. 9(7): p. 811–8. Beyens, I., P.M. Valkenburg, and J.T. Piotrowski, Screen media use and ADHD-related behaviors: Four decades of research . Proc Natl Acad Sci U.S.A., 2018. 115(40): p. 9875–9881. Ghandili, M. and S. Munakomi, Neuroanatomy, Putamen , in StatPearls . 2023: Treasure Island (FL). Chen, Y.Y., H. Yim, and T.H. Lee, Negative impact of daily screen use on inhibitory control network in preadolescence: A two-year follow-up study . Dev Cogn Neurosci, 2023. 60: p. 101218. Altbacker, A., et al., Problematic internet use is associated with structural alterations in the brain reward system in females . Brain Imaging Behav, 2016. 10(4): p. 953–959. Herlin, B., V. Navarro, and S. Dupont, The temporal pole: From anatomy to function-A literature appraisal . J Chem Neuroanat, 2021. 113: p. 101925. du Boisgueheneuc, F., et al., Functions of the left superior frontal gyrus in humans: a lesion study . Brain, 2006. 129(Pt 12): p. 3315–28. Miller, E.K. and J.D. Cohen, An integrative theory of prefrontal cortex function . Annu Rev Neurosci, 2001. 24: p. 167–202. Zhao, Y., et al., Brain structural covariation linked to screen media activity and externalizing behaviors in children . J Behav Addict, 2022. 11(2): p. 417–426. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files supportinginformation.docx Cite Share Download PDF Status: Published Journal Publication published 31 Oct, 2025 Read the published version in Translational Psychiatry → Version 1 posted Editorial decision: revise 21 Feb, 2025 Review # 3 received at journal 02 Feb, 2025 Reviewer # 3 agreed at journal 12 Jan, 2025 Review # 2 received at journal 10 Nov, 2024 Reviewer # 2 agreed at journal 17 Oct, 2024 Reviewer # 1 agreed at journal 18 Sep, 2024 Reviewers invited by journal 10 Sep, 2024 Submission checks completed at journal 27 Aug, 2024 First submitted to journal 23 Aug, 2024 Editor assigned by journal 23 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4966967","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":352268662,"identity":"46db5547-a618-4c55-8cf8-73034f33b675","order_by":0,"name":"Qiulu Shou","email":"","orcid":"","institution":"University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Qiulu","middleName":"","lastName":"Shou","suffix":""},{"id":352268663,"identity":"3b55a8b4-31f7-4360-b184-6e148843b4cf","order_by":1,"name":"Masatoshi Yamashita","email":"","orcid":"https://orcid.org/0000-0002-8037-7275","institution":"University of Fukui","correspondingAuthor":false,"prefix":"","firstName":"Masatoshi","middleName":"","lastName":"Yamashita","suffix":""},{"id":352268661,"identity":"ac26afb8-8fad-4e3e-aea3-e7b1a9e0181d","order_by":2,"name":"Yoshifumi Mizuno","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYDACZhhDgrHx4YcKkAhzA9Famo0lzoBEGAlogQMJBjYJ3jYQi4AW3Xb2Z9IFFYfl5Gc3N0hIzquN5m8HavlRsQ2nFrPDDGnSM84cNja4c7DBoHDb8dwZhxkbGHvO3Man5Zg0b9vhxA0SiQ0JktuO5TYAtTAztuHTwtgG1jJ/RmLDAd45x3LnE9bCzAbW0nAjsbGBt6EmdwNhLWzM1jxn0o0NbiQ2M0scO5C7EajlIF6/nD/+8DZPhbWc/Iz05z8/1NTlzjt/+OCDHxW4tUBBM4xxGEweIKQeCOowGKNgFIyCUTAK4AAAvMJcrTuoTyUAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0003-2209-352X","institution":"University of Fukui","correspondingAuthor":true,"prefix":"","firstName":"Yoshifumi","middleName":"","lastName":"Mizuno","suffix":""}],"badges":[],"createdAt":"2024-08-24 03:25:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4966967/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4966967/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41398-025-03672-1","type":"published","date":"2025-10-31T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":65375714,"identity":"f6562656-921c-41b9-85bc-cc8dc31f16dc","added_by":"auto","created_at":"2024-09-26 16:15:32","extension":"tif","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":3565204,"visible":true,"origin":"","legend":"\u003cp\u003eThe association of screen time with ADHD symptoms and their development. (A) shows the association between screen time and ADHD symptoms. Screen time was converted to z-scores. ADHD symptoms were controlled for covariates by the linear mixed-effects model and converted to z-scores. (B) shows the association between screen time and the development of ADHD symptoms. Screen time was converted to z-scores. ADHD symptoms at 2-year follow-up were controlled for covariates and ADHD symptoms at baseline by the linear mixed-effects model and converted to z-scores.\u003c/p\u003e\n\u003cp\u003eADHD, attention-deficit/hyperactivity disorder\u003c/p\u003e","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-4966967/v1/813fb774c1ef189c2f80aacf.tif"},{"id":65375198,"identity":"06ce25d5-4719-4e60-bd14-59c3dab420ff","added_by":"auto","created_at":"2024-09-26 16:07:32","extension":"tif","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5062189,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between screen time and brain structure measures at baseline. (A) shows the standard coefficients (β) of screen time on volumes around the cerebral cortex. (B) shows the standard coefficients (β) of screen time on thickness around the cerebral cortex. (C) shows the association between screen time and volume of the right putamen. Screen time was converted to z-scores. The right putamen volume was controlled for covariates and converted to z-scores. (D) shows the association between screen time and the total cortical volume. Screen time was converted to z-scores. The total cortical volume was controlled for covariates and converted to z-scores.\u003c/p\u003e","description":"","filename":"Figure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-4966967/v1/d190dd53d9671ba8e8d2cb4d.tif"},{"id":65375197,"identity":"5a8c190f-0abc-4616-a900-54db5fe20952","added_by":"auto","created_at":"2024-09-26 16:07:32","extension":"tif","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2675652,"visible":true,"origin":"","legend":"\u003cp\u003eThe association between screen time and development of brain structure measures. (A) shows the standard coefficients (β) of screen time on the development of volumes around the cerebral cortex. (B) shows the standard coefficients (β) of screen time on the development of thickness around the cerebral cortex. (C) shows the association between screen time and thickness of the left temporal pole. Screen time was converted to z-scores. The thickness of the left temporal pole was controlled for covariates and the same structure at baseline, and then converted to z-scores. (D) shows the association between screen time and the thickness of the right superior frontal gyrus. Screen time was converted to z-scores. The thickness of the right superior frontal gyrus was controlled for covariates and the same structure at baseline, and then converted to z-scores. (E) shows the association between screen time and the right rostral middle frontal gyrus. Screen time was converted to z-scores. The thickness of the right rostral middle frontal gyrus was controlled for covariates and the same structure at baseline, and then converted to z-scores.\u003c/p\u003e","description":"","filename":"Figure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-4966967/v1/9a9660a43217c90c8bdac462.tif"},{"id":65375195,"identity":"05fb3161-34e7-4968-af4c-78ff59d9b353","added_by":"auto","created_at":"2024-09-26 16:07:32","extension":"tif","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":125868,"visible":true,"origin":"","legend":"\u003cp\u003eThe mediating effect of the total cortical volume on the association between screen time and ADHD symptoms.\u003c/p\u003e\n\u003cp\u003eADHD, attention-deficit/hyperactivity disorder\u003c/p\u003e","description":"","filename":"Figure4.tif","url":"https://assets-eu.researchsquare.com/files/rs-4966967/v1/93b030115ab6618961e4a0fb.tif"},{"id":94904173,"identity":"540a3406-2106-41c5-b816-b966ca7150e4","added_by":"auto","created_at":"2025-11-01 07:09:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8569145,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4966967/v1/fc8356fd-0e2e-4aff-82e2-1930f21ec56e.pdf"},{"id":65375199,"identity":"957edfd8-e635-4732-a45d-0d7aa9811bbd","added_by":"auto","created_at":"2024-09-26 16:07:32","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":106262,"visible":true,"origin":"","legend":"","description":"","filename":"supportinginformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-4966967/v1/eb611e223ecdcac3acd98837.docx"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Association of Screen Time with Attention-Deficit/Hyperactivity Disorder Symptoms and Their Development: The Mediating Role of Brain Structure","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOver the years, adolescents\u0026rsquo; screen time has increased worldwide, especially since the COVID-19 pandemic [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Screen time affects lifestyle habits, such as the frequency of physical activity and sleep duration [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], and is considered to have a negative impact on mental health, behavior, and brain development [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Considering that adolescence is a critical developmental period for health, well-being, and brain regions, and is influenced by biological and environmental factors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], some academics and researchers recommend screen-time limits for children and adolescents [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. According to the Adolescent Brain Cognitive Development (ABCD) study, adolescents in America have a mean screen time of 7.70 hours/day, implying lengthy screen time for children and adolescents [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAttention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by symptoms of age-inappropriate inattention and/or hyperactivity/impulsiveness. Previous studies found increased screen time to be associated with ADHD symptom severity [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and a higher risk of ADHD diagnoses [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, most of these studies were cross-sectional; therefore, concluding whether screen time exacerbates ADHD symptoms was difficult. We conducted a longitudinal analyses of screen time and ADHD symptoms, as it could provide evidence of whether screen time can lead to changes in ADHD symptoms.\u003c/p\u003e \u003cp\u003eAdolescence\u0026mdash;a critical period for brain development\u0026mdash;is influenced by both biological and environmental factors [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Previous studies have examined the influence of screen-based activity on the development of brain structure [\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Takeuchi et al., who examined approximately 250 Japanese children, used diffusion-tensor imaging and found that the time spent on video games affected the development of the microstructural brain [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, another study with a larger sample size reported no association between screen time and white matter microstructure abnormalities [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Takeuchi et al. conducted a longitudinal study, examining the effect of various screen-related activities in terms of brain structure; they found that screen-related activity was associated with a smaller increase in gray and white matter volumes of widespread brain areas, such as orbitofrontal, lateral, prefrontal, and anterior cingulate areas [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Conversely, a study that utilized the ABCD study, and used Mendelian randomization analyses to examine the causal relationships between screen time and brain volume at baseline, did not observe any significant relationship [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Nevertheless, it did not examine the relationship between the development of the brain and screen time, using longitudinal analyses. Thus, the lack of longitudinal studies examining this relationship in a large sample size of children and adolescents necessitates confirming the findings.\u003c/p\u003e \u003cp\u003ePrevious studies proposed impulsivity and sleep quality as mediators in the relationship between screen time and ADHD [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. However, research has rarely examined the neural mediators between screen time and ADHD. A study on the role of the microstructural brain in the relationship between the polygenic risk of ADHD and screen time [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], revealed a potential neural overlap between ADHD and screen-related activity. In brain structure studies, ADHD has been associated with delayed cortical maturation, including thickness [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and volume [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, the reduction of gray matter volume in extensive regions of the brain has been found to be associated with ADHD [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] and screen-related activities [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. However, to the best of our knowledge, no study has directly examined the role of the brain structure in the relationship between screen time and ADHD symptoms.\u003c/p\u003e \u003cp\u003eTherefore, we analyzed data from the ABCD study, a database with longitudinal data of nearly 10,000 children aged 9\u0026ndash;10 years. Using cross-sectional and longitudinal analyses, we examined: 1) the relationship of screen time with ADHD symptoms and their development; 2) the relationship of screen time with brain structure and its development; and 3) the mediating effect of brain structure on the association of screen time with ADHD symptoms and their development. We used baseline and 2-year follow-up data to examine the relationship between screen time, ADHD symptoms, and brain structure. We proposed the following hypotheses: (1) screen time is associated with ADHD symptoms and their development; (2) screen time is associated with the brain structure and its development; and (3) the brain structure has a mediating effect on the association between screen time and ADHD symptoms and their development.\u003c/p\u003e"},{"header":"Method","content":"\u003cp\u003eParticipants\u003c/p\u003e \u003cp\u003eThe ABCD study is an ongoing project in 21 centers across the United States, following a cohort of 11,878 children aged 9\u0026ndash;10 years [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Details of its recruitment and ethics have been published [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Parents of all participants provided their written consent after the procedures were fully explained to them by the investigators; additionally, the children provided their assent before participating in the ABCD study [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We obtained data on the participants\u0026rsquo; brain structure and behavior, as well as their demographic background from the National Institute of Mental Health (NIMH) Data Archive ABCD Data Release 5.0. The University of Fukui\u0026rsquo;s Research Ethics Committee approved the data analysis (Assurance no. FU-20210067). Participants\u0026rsquo; demographic data and covariates are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For the analyses, we included data of 10,116 and 7,880 children at baseline and during the 2-year follow-up, respectively. To maximize the sample size, all models were performed with all the available participants.\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\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDemographic data and covariates of participants\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;10122)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2-year follow up (n\u0026thinsp;=\u0026thinsp;7880)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (month)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119.00\u0026thinsp;\u0026plusmn;\u0026thinsp;7.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143.69\u0026thinsp;\u0026plusmn;\u0026thinsp;7.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003cp\u003eMale\u003c/p\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5276\u003c/p\u003e \u003cp\u003e4846\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3740\u003c/p\u003e \u003cp\u003e4139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParents\u0026rsquo; income\u003c/p\u003e \u003cp\u003e\u0026lt;\u0026thinsp;49,999\u003c/p\u003e \u003cp\u003e50,000\u0026ndash;74,999\u003c/p\u003e \u003cp\u003e75,000\u0026ndash;99,999\u003c/p\u003e \u003cp\u003e100,000\u0026ndash;199,999\u003c/p\u003e \u003cp\u003e\u0026ge;\u0026thinsp;200,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2928\u003c/p\u003e \u003cp\u003e1394\u003c/p\u003e \u003cp\u003e1469\u003c/p\u003e \u003cp\u003e3135\u003c/p\u003e \u003cp\u003e1196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2090\u003c/p\u003e \u003cp\u003e1109\u003c/p\u003e \u003cp\u003e1207\u003c/p\u003e \u003cp\u003e2530\u003c/p\u003e \u003cp\u003e944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParents\u0026rsquo; education (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.34\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.46\u0026thinsp;\u0026plusmn;\u0026thinsp;2.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003cp\u003eWhite\u003c/p\u003e \u003cp\u003eBlack\u003c/p\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003cp\u003eAsian\u003c/p\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5606\u003c/p\u003e \u003cp\u003e1327\u003c/p\u003e \u003cp\u003e2971\u003c/p\u003e \u003cp\u003e180\u003c/p\u003e \u003cp\u003e1038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4104\u003c/p\u003e \u003cp\u003e756\u003c/p\u003e \u003cp\u003e1322\u003c/p\u003e \u003cp\u003e116\u003c/p\u003e \u003cp\u003e690\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePubertal status (scores)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep duration (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.80\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.32\u0026thinsp;\u0026plusmn;\u0026thinsp;1.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical activity (times per week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.54\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHandedness-score rating\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRight-handed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6239\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeft-handed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e558\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed-handed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal intracranial volume (cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1496.70\u0026thinsp;\u0026plusmn;\u0026thinsp;143.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1528.54\u0026thinsp;\u0026plusmn;\u0026thinsp;146.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScreen time (baseline)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.60\u0026thinsp;\u0026plusmn;\u0026thinsp;2.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADHD T-score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.08\u0026thinsp;\u0026plusmn;\u0026thinsp;5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote: ADHD, attention-deficit/hyperactivity disorder\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eScreen time\u003c/p\u003e \u003cp\u003eWe assessed screen time using a self-reported questionnaire, and computed it as the total amount of time spent using various devices, including playing video games and watching television. The ABCD study provided the total screen time for typical weekdays and weekends, and we calculated a weighted-sum score to represent a daily screen time of 5/7\u0026times; hours of screen time (weekday)\u0026thinsp;+\u0026thinsp;2/7\u0026times; hours of screen time (weekend) [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The data were available for the baseline (11,067 samples).\u003c/p\u003e \u003cp\u003eADHD symptoms\u003c/p\u003e \u003cp\u003eTo evaluate the level of the children\u0026rsquo;s ADHD symptoms, we used the ADHD-related DSM-5-oriented syndrome scales of the Child Behavior Checklist (CBCL) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The data used in our study at baseline and during the 2-year follow-up comprised 10,116 and 6,986 samples, respectively. We recorded all the scores as T-scores, with higher scores representing greater behavioral problems.\u003c/p\u003e \u003cp\u003eBrain structure\u003c/p\u003e \u003cp\u003eDetails of magnetic resonance imaging acquisition and data preprocessing in the ABCD study were published by Casey et al. and Hagler et al. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. The ABCD study used three Tesla magnetic resonance scanners (Siemens, General Electric 750, and Philips) to obtain high-resolution T1-weighted three-dimensional structural images (1 mm isotropic) and acquisition parameters, as previously described [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The structural data were processed using FreeSurfer (version 5.3.0) with a standardized processing pipeline [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In our study, we used structural data with the Desikan-Killiany atlas-based classification for cortical regions, and atlas-based segmentation for subcortical regions.\u003c/p\u003e \u003cp\u003e We included data from participants whose data satisfied the FreeSurfer quality control for structural imaging data for analysis. We included 34 cortical regions and seven subcortical regions for one hemisphere (68 and 14 regions in total) for volume, and 34 cortical regions in each hemisphere (68 regions in total) for thickness. Additionally, as previous studies had found ADHD to be associated with cortical gray matter volume [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and mean thickness of the cerebral cortex [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], we included total cortical volume and mean cortical thickness, directly measured by FreeSurfer in the analyses.\u003c/p\u003e \u003cp\u003eDemographic variables and covariates\u003c/p\u003e \u003cp\u003eBased on previous ABCD-based studies [\u003cspan additionalcitationids=\"CR38 CR39\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], we included the variables listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e as covariates. We coded sex and race/ethnicity (White, Black, Hispanic, Asian, or others) as dummy variables, and treated parental income as a five-level categorical variable, as in previous studies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. We included age, parental education levels, pubertal status, total intracranial volume, daily sleep duration, and physical activity as continuous variables, and recorded parental education levels by school year, as in previous studies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. We assessed pubertal status using the Pubertal Development scale [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Additionally, according to previous studies, as sleep and physical activities are typically included in an analysis of the relationship between screen-related activities and ADHD [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], we included sleep duration and frequency of physical activities as covariates.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eIn this study, we used R (version 4.3.1; The R Foundation for Statistical Computing, Vienna, Austria) to perform statistical analysis. We created scatter plot figures using R-package \u0026ldquo;ggplot\u0026rdquo; and brain mapping figures using Python version 3.6.8 (The Python Software Foundation, USA) with python-package \u0026ldquo;Pysurfer\u0026rdquo; to display brain-related statistics.\u003c/p\u003e \u003cp\u003eFirst, we examined the association between screen time and ADHD symptoms. We analyzed data from 10,116 samples at baseline, and 6,986 samples after a 2-year follow-up. We winsorized data (outliers of screen time, brain structure, ADHD symptoms, and all continuous covariates) at three standard deviations from the mean. First, we adapted a linear mixed-effects model to examine cross-sectional relationships between screen time and ADHD symptoms using baseline data. We analyzed this model using R-package \u0026ldquo;lmertest.\u0026rdquo; Based on previous studies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], we modeled family ID (used to denote sibling status), multiple data collection, and twin or triplet status, as random effects. For the mixed-effects model with ADHD symptoms as the dependent variable, we considered children\u0026rsquo;s age, sex, race, pubertal status, household income, parental education, sleep duration, and physical activity as covariates. Second, we conducted a residualized change regression model [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] of a 2-year change in ADHD symptoms to examine the effect of screen time on the development of ADHD symptoms. Specifically, 2-year follow-up of ADHD symptoms were regressed on baseline screen time, controlling for baseline ADHD symptoms. In this model, we also adopted family ID, multiple data collection, and twin or triplet status as random effects and the abovementioned variables as covariates.\u003c/p\u003e \u003cp\u003eFurther, we examined the association between screen time and brain structure. We analyzed the data of 9,713 and 6,426 samples at baseline and during the 2-year follow-up, respectively. First, we used linear mixed-effects models to examine the effect of screen time on brain structure. For the mixed-effects model, using brain structure as the dependent variable, we considered multiple data collections and twin or triplet status modeled as random effects. In addition to the covariates of the mixed-effects model of ADHD symptoms, we included handedness and total intracranial volume as covariates for the brain structure, including volume and thickness. The \u003cem\u003ep\u003c/em\u003e-values were false discovery rate (FDR)-corrected for multiple testing per structure [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Second, we performed the same residualized change regression for each brain structure measurement. We regressed the brain structures at 2-year follow-up on baseline screen time, controlling for baseline brain structures. In this model, we also adapted family ID, multiple data collection, and twin or triplet status as random effects, and the above-mentioned variables as covariates.\u003c/p\u003e \u003cp\u003eFinally, we examined the mediating effect of brain structure on the association of screen time with ADHD symptoms at baseline and the development of ADHD symptoms. We analyzed data from 9,663 samples at baseline, and 5,472 samples after a 2-year follow-up. First, we performed a mediation analysis of the structures that were significantly associated with screen time in the relationship between screen time and ADHD symptoms at baseline. We residualized brain structure measures and ADHD symptoms for study site variables by the linear mixed-effects model, and then converted these measures to z-scores. We used the R-package \u0026ldquo;lanvnn\u0026rdquo; to perform a standard three-variable mediation analysis to estimate the significance of the mediating effect by using the bias-corrected bootstrap approach (with 10,000 random samplings). Second, we performed a mediation analysis of the development of structures that were significantly associated with screen time in residualized change regressions on the relationship between screen time and the development of ADHD symptoms. We residualized brain structure measures (2-year follow-up) for the above-mentioned variables and baseline brain structures, using the linear mixed-effects model, and then converted these measures to z-scores. We residualized ADHD symptoms (2-year follow-up) for the above-mentioned variables and baseline ADHD symptoms, using the linear mixed-effects model, and then converted these measures to z-scores. We performed a standard three-variable mediation analysis to estimate the significance of the mediating effect using a bias-corrected bootstrap approach (with 10,000 random samplings).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eAssociation of screen time with ADHD symptoms\u003c/p\u003e \u003cp\u003eWe adopted a linear mixed-effects regression model with ADHD symptoms as the dependent variable, and screen time as the independent variable. We found that screen time had a significant main effect (b\u0026thinsp;=\u0026thinsp;0.199, 95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;0.160\u0026ndash;0.236], β\u0026thinsp;=\u0026thinsp;0.109, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), implying a significant association between screen time and ADHD symptoms at baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThen, at the 2-year follow-up, we found screen time to have a significant main effect on ADHD symptoms, controlling baseline ADHD symptoms as covariates (b\u0026thinsp;=\u0026thinsp;0.055, 95% CI\u0026thinsp;=\u0026thinsp;0.021\u0026ndash;0.089, β\u0026thinsp;=\u0026thinsp;0.032, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eAssociations of screen time with brain structure\u003c/p\u003e \u003cp\u003eThe results of the association between screen time and the volume or thickness of each brain region at baseline are summarized in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e (available online) and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results showed that screen time was negatively associated with the volume of right putamen (b = -290.836, 95% CI = -483.750 to 97.921, β = -0.036, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), and with total cortical volume (b = -290.836, 95% CI = -483.751 to -97.921, β = -0.015, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then examined the association between screen time and the development of brain structures (Table S2 and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results showed that screen time was negatively associated with the thicknesses of the right temporal pole (b = -0.004, 95% CI = -0.006 to 0.001, β = -0.036, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), left superior frontal gyrus (b = -0.001, 95% CI = -0.006 to 0.001, β = -0.028, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD), and left rostral middle frontal gyrus (b = -0.001, 95% CI = -0.002 to -0.0005, β = -0.030, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Additionally, screen time was negatively correlated with the mean cortical thicknesses (b = -0.0004, 95% CI = -0.001 to -0.00007, β = -0.017, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMediating effect of brain structure on the relationship between screen time and ADHD symptoms\u003c/p\u003e \u003cp\u003eWe examined the mediating effects of brain structures, which were significantly associated with screen time at baseline, on the relationship between screen time and ADHD symptoms. The results showed that the cortical volume had a significant mediating effect on the relationship between screen time and ADHD symptoms (β\u0026thinsp;=\u0026thinsp;0.001, 95% CI\u0026thinsp;=\u0026thinsp;0.000\u0026ndash;0.002, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The volume of right putamen had no mediating effect on the relationship between screen time and ADHD symptoms (right putamen volume: β\u0026thinsp;=\u0026thinsp;0.000, 95% CI = -0.001 to 0.001, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.889).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then examined the mediating effect of the brain structures, whose development was significantly associated with screen time, on the relationship between screen time and ADHD symptom development. No structure had a mediating effect on this relationship (right temporal pole: β\u0026thinsp;=\u0026thinsp;0.000, 95% CI = -0.001 to 0.001, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.945; left superior frontal gyrus: β\u0026thinsp;=\u0026thinsp;0.000, 95% CI = -0.001 to 0.002, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.641; left rostral middle frontal gyrus: β\u0026thinsp;=\u0026thinsp;0.000, 95% CI = -0.001 to 0.001, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.980; and mean cortical thickness: β\u0026thinsp;=\u0026thinsp;0.000, 95% CI = -0.001 to 0.001, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.789).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study used a large sample size of children from the ABCD study, and examined the relationship between screen time, ADHD symptoms, and brain structure, especially from the developmental perspective. The results showed that screen time was positively correlated with ADHD symptoms and their development after 2 years, and negatively correlated with: the volumes of right putamen and cortical gray matter at baseline; and the development of the thickness of right temporal pole, left superior frontal gyrus, and left rostral middle frontal gyrus after 2 years. These results are consistent with the hypotheses that longer screen time is associated with both, ADHD and the development of its symptoms, and brain structures and their development. Moreover, using cross-sectional analyses, we found that cortical volume partially mediates the relationship between screen time and ADHD symptoms. This result partially supported the hypothesis that brain structure is mediated by the link between screen time and ADHD symptoms. These findings provide evidence that longer screen time can worsen ADHD symptoms and influence brain structure development and smaller cortical volumes could account for the negative association between screen time and ADHD symptoms.\u003c/p\u003e \u003cp\u003eOur results showed that screen time is associated with ADHD symptoms and their development. In line with the results of our study, previous studies have demonstrated a positive correlation between screen time and ADHD symptoms [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, these studies rarely examined the relationship between screen time and the development of ADHD symptoms, and a causal relationship between screen-based activities and ADHD remained unclear [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. In our study, we examined this relationship by controlling for baseline ADHD symptom data. Our study provides evidence that longer screen time is associated with the development of ADHD symptoms two years later in children aged 9\u0026ndash;10 years. This result was partially consistent with the finding of Soares et al., that screen time at 18 years was associated with a diagnosis of ADHD at 22 years [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and expanded the age-range of conclusion. These findings reveal that longer screen time may worsen ADHD symptoms in children and adolescents, and interventions for screen-time may help reduce later ADHD symptoms. However, as the effect sizes are small, the clinical impact of screen time on ADHD symptoms may be marginal; this should be interpreted with caution.\u003c/p\u003e \u003cp\u003eWe also examined the relationship between screen time and brain structures at baseline and during the 2-year follow-up. At baseline, the volume of the right putamen was negatively correlated with screen time. The putamen is a part of the striatum, and is involved in the language function, reward, cognitive function, and addiction [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Previous studies found that screen time was associated with the functional connectivity of the frontoparietal network to the putamen [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], and internet use frequency was associated with the volume of the putamen in young women [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]; this implies that screen-based activity is associated with the putamen. The relationship between screen time and the putamen may explain the addition of screen-related behavior, in that, screen-based activity can result in children preferring more immediate rewards over delayed outcomes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]; our findings of the correlation may provide evidence for this explanation.\u003c/p\u003e \u003cp\u003eOur study also examined the effects of screen time on brain structural development, showing that screen time was involved in the development of the right temporal pole, left superior frontal gyrus, and left rostral middle frontal gyrus. These brain regions are associated with cognitive functions [\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], including working memory, language function, and attention, implying that screen-based activity may influence the development of cognition. This finding is in line with those of previous studies [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], which found that screen-related activity influenced the development of extensive areas of the cerebral cortex. Our study strengthened this conclusion by analyzing the data of over 6,000 children. A previous study found that social media usage was associated with a co-development pattern of the thalamus with common structural measures in key brain regions, including the bilateral superior frontal, rostral middle frontal, inferior parietal, and inferior temporal regions [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Our study identified specific brain regions\u0026mdash;the right temporal pole, left superior frontal gyrus, and left rostral middle frontal gyrus\u0026mdash;whose development was influenced by screen-based activities.\u003c/p\u003e \u003cp\u003eThe results showed a partial mediating effect of the cortical volume on the relationship between screen time and ADHD symptoms. Specifically, longer screen time was associated with a smaller cortical volume, which in turn, was associated with more severe ADHD symptoms. Cortical volume may partially explain the relationship between screen time and ADHD symptoms. In previous studies, the reduction of gray matter volume, especially cortical gray matter volume [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] was observed in children with ADHD, which can be explained by a model of delayed cortical maturation in children with ADHD, compared to those with normal development [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The finding, that longer screen time is associated with smaller cortical volume, may imply that longer screen time is involved in the delay of brain development, and can cause more significant ADHD symptoms. Our findings expand those of prior studies, showing direct evidence of the mediating role of the brain in the association between screen time and ADHD, and provide evidence of the same potential neural mechanism between ADHD and screen-related activity. However, because this result was only shown in the cross-sectional analyses of the ABCD baseline data, causality could not be established.\u003c/p\u003e \u003cp\u003eA limitation of our study is that it only examined the relationship with brain structures. Prior studies have reported that screen time is associated with the functional network of the brain [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and the microstructural brain [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Thus, it is possible that brain development of them mediates the relationship between screen time and ADHD symptoms development, although our study did not identify the development of brain structures that mediate this relationship. Therefore, future studies should include the functional network of the brain and the microstructural brain in their analysis to examine the neural relationships that underlie screen time.\u003c/p\u003e \u003cp\u003eThis study was the first to examine the relationship between screen time, ADHD symptoms, and brain structure, especially from a developmental perspective. The findings provide evidence that longer screen time can worsen ADHD symptoms, and influence brain structural development. Moreover, ours is the first study to find that cortical volume partially mediates the relationship between screen time and ADHD symptoms in cross-sectional analyses, implying that the reduction in cortical volume may explain the negative link between screen time and ADHD symptoms. The findings can help us understand the relationship between screen time and ADHD symptoms, and the mechanism influencing ADHD symptoms, and further suggest that screen time interventions may improve ADHD symptoms.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis project was funded by the Japan Society for the Promotion of Science through Grants-in-Aid for Scientific Research (KAKENHI) (Grant numbers: 24K16647 to QS, 23K12814 to MY, 21K02380, 24K21453 to YM), Kawano Masanori Memorial Public Interest Incorporated Foundation for Promotion of Pediatrics (AY 2022 to YM), Research Grants from the University of Fukui (AY 2023 to YM and QS), and the Life Science Innovation Center, University of Fukui (AY 2023 to QS).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9\u0026ndash;10 years, and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health and additional federal partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners.html. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/consortium_members/. The ABCD consortium investigators designed and implemented the study or provided data, but did not necessarily participate in the analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors have reported no biomedical financial interests or potential conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTrott, M., et al., \u003cem\u003eChanges and correlates of screen time in adults and children during the COVID-19 pandemic: A systematic review and meta-analysis\u003c/em\u003e. EClinicalMedicine, 2022. 48: p. 101452.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMajor, D., et al., \u003cem\u003eEffect of school lockdown due to the COVID-19 pandemic on screen time among adolescents in Hungary: a longitudinal analysis\u003c/em\u003e. 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Brown, and A.C. Coordinators, Introduction. Dev Cogn Neurosci, 2018. 32: p. 1\u0026ndash;3.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaravan, H., et al., \u003cem\u003eRecruiting the ABCD sample: Design considerations and procedures\u003c/em\u003e. Dev Cogn Neurosci, 2018. 32: p. 16\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuchter, A.M., et al., \u003cem\u003eA description of the ABCD organizational structure and communication framework\u003c/em\u003e. Dev Cogn Neurosci, 2018. 32: p. 8\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eClark, D.B., et al., \u003cem\u003eBiomedical ethics and clinical oversight in multisite observational neuroimaging studies with children and adolescents: The ABCD experience\u003c/em\u003e. Dev Cogn Neurosci, 2018. 32: p. 143\u0026ndash;154.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, M., V.M. Narasimhan, and F.B. Zhan, \u003cem\u003eHigh polygenic risk score is a risk factor associated with colorectal cancer based on data from the UK Biobank\u003c/em\u003e. PLoS One, 2023. 18(11): p. e0295155.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAchenbach, T.M., L.A. Rescorla, and M.Y. Ivanova, \u003cem\u003eInternational epidemiology of child and adolescent psychopathology I: Diagnoses, dimensions, and conceptual issues\u003c/em\u003e. J Am Acad Child Adolesc Psychiatry, 2012. 51(12): p. 1261\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasey, B.J., et al., \u003cem\u003eThe Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites\u003c/em\u003e. Dev Cogn Neurosci, 2018. 32: p. 43\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHagler, D.J., Jr., et al., \u003cem\u003eImage processing and analysis methods for the Adolescent Brain Cognitive Development Study\u003c/em\u003e. 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Dev Cogn Neurosci, 2021. 51: p. 100994.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHochberg, Y. and Y. Benjamini, \u003cem\u003eMore powerful procedures for multiple significance testing\u003c/em\u003e. Stat Med, 1990. 9(7): p. 811\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeyens, I., P.M. Valkenburg, and J.T. Piotrowski, \u003cem\u003eScreen media use and ADHD-related behaviors: Four decades of research\u003c/em\u003e. Proc Natl Acad Sci U.S.A., 2018. 115(40): p. 9875\u0026ndash;9881.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhandili, M. and S. Munakomi, \u003cem\u003eNeuroanatomy, Putamen\u003c/em\u003e, in \u003cem\u003eStatPearls\u003c/em\u003e. 2023: Treasure Island (FL).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Y.Y., H. Yim, and T.H. Lee, \u003cem\u003eNegative impact of daily screen use on inhibitory control network in preadolescence: A two-year follow-up study\u003c/em\u003e. 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Annu Rev Neurosci, 2001. 24: p. 167\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, Y., et al., \u003cem\u003eBrain structural covariation linked to screen media activity and externalizing behaviors in children\u003c/em\u003e. J Behav Addict, 2022. 11(2): p. 417\u0026ndash;426.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4966967/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4966967/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThe effect of screen time on the development of attention-deficit/hyperactivity disorder (ADHD) symptoms, as well as the brain, and neural mechanisms underlying the association between screen time and ADHD symptoms remain unclear. This study aims to examine the association between screen time, ADHD symptoms, and the brain, using large-scale longitudinal samples from the Adolescent Brain Cognitive Development (ABCD) study.\u003c/p\u003e\u003ch2\u003eMethod\u003c/h2\u003e \u003cp\u003eFrom the ABCD study, we extracted, data on screen time, ADHD symptoms based on the Child Behavior Checklist, and brain structure measures of 10116 and 7880 children (aged 9\u0026ndash;10 years) at baseline and at the 2-year follow-up, respectively. We used the linear mixed-effects model to examine the association between screen time at baseline, and the development of ADHD symptoms and brain structure after two years. We also examined the mediating role of brain structure on the association between screen time and ADHD symptoms.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eScreen time was associated with the development of ADHD symptoms (β\u0026thinsp;=\u0026thinsp;0.032, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) and thickness of some cortical regions (right temporal pole: β=-0.036, false discovery rate (FDR)-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020; left superior frontal gyrus: β=-0.028, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020; and left rostral middle frontal gyrus: β=-0.030, FDR-corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020). Moreover, the total cortical volume partially mediated the relationship between screen time and ADHD symptoms (β\u0026thinsp;=\u0026thinsp;0.001, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.023) at baseline.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese results suggest that screen time influences ADHD symptom development and brain structure, providing insight into the mechanisms underlying the association between screen time and ADHD symptoms. Furthermore, interventions to reduce screen time may help improve ADHD symptoms.\u003c/p\u003e","manuscriptTitle":"Association of Screen Time with Attention-Deficit/Hyperactivity Disorder Symptoms and Their Development: The Mediating Role of Brain Structure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-26 16:07:27","doi":"10.21203/rs.3.rs-4966967/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-02-21T10:01:57+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-02-03T02:09:41+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-01-12T14:27:45+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2024-11-10T05:23:13+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-10-17T22:38:28+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2024-09-18T13:33:58+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2024-09-11T00:49:56+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-27T11:22:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2024-08-24T03:22:51+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-24T03:22:51+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a180cfa7-c1ce-45bd-9d86-174ab7289668","owner":[],"postedDate":"September 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":37411084,"name":"Health sciences/Diseases/Psychiatric disorders/ADHD"},{"id":37411085,"name":"Biological sciences/Psychology/Human behaviour"},{"id":37411086,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2025-11-01T07:09:07+00:00","versionOfRecord":{"articleIdentity":"rs-4966967","link":"https://doi.org/10.1038/s41398-025-03672-1","journal":{"identity":"translational-psychiatry","isVorOnly":false,"title":"Translational Psychiatry"},"publishedOn":"2025-10-31 04:00:00","publishedOnDateReadable":"October 31st, 2025"},"versionCreatedAt":"2024-09-26 16:07:27","video":"","vorDoi":"10.1038/s41398-025-03672-1","vorDoiUrl":"https://doi.org/10.1038/s41398-025-03672-1","workflowStages":[]},"version":"v1","identity":"rs-4966967","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4966967","identity":"rs-4966967","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}