Screen patterns and neurodevelopmental delay in preschoolers: an intelligent monitoring study

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Objectives This study aims to identify the screen exposure patterns in preschoolers by intelligent technology, and to examine their associations with their neurodevelopment. Methods This cross-sectional study enrolled preschool children from two kindergartens in Shanghai. Screen time and content types were monitored over 7 consecutive days using a validated intelligent monitoring technology. Neurodevelopmental outcomes were assessed using the Ages and Stages Questionnaire, Third Edition (ASQ-3). K-means clustering analysis identified screen exposure patterns, and binary logistic regression was applied to examine associations between screen exposure patterns and neurodevelopmental outcomes. Results Of 355 preschool children included, 204 were boys (57.5%) and 251 (70.7%) were aged between 34.5 months and 50.5 months. K-means cluster analysis yielded 4 screen exposure patterns: restrictive use, moderately educational, noneducational, and educational-dominant pattern. Binary logistic regression showed the moderately educational pattern linked to gross motor abnormalities (OR = 2.530, 95% CI: 1.089–5.875, P = 0.031), and non - educational to fine motor abnormalities (OR = 3.172, 95% CI: 1.122–8.968, P = 0.029). Conclusion This monitoring study identified heterogeneous screen exposure patterns in preschool-aged children, revealing that excessive use of moderately educational content and noneducational content was associated with lower gross motor and fine motor skills. When limiting total screen time, parents should focus on content selection for preschool-aged children. Future research should focus on the objective measurement of different types of screen content. Neurodevelopment Preschool Digital media Digital technology Screen time Screen content Figures Figure 1 Figure 2 What is Known--What is New What is Known: Few studies have analyzed preschoolers’ screen exposure patterns (especially combined time and content) or their neurodevelopmental impacts, with scarce research using objective measures of both. What is New: Using validated intelligent monitoring, we identified heterogeneous patterns and found excessive moderately educational/noneducational content linked to lower motor skills. Introduction Neurodevelopmental delay refers to significant lag in the development of communication, motor, problem solving or social skills relative to age-normative standards, represents a precursor to persistent neurodevelopmental disorders such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) [ 1 , 2 ]. The preschool period (3–5 years) constitutes a critical window for synaptic pruning and neural circuit refinement[ 3 , 4 ], during which children exhibit heightened sensitivity to environmental input quality[ 5 ]. The majority of preschool children possessed their own mobile devices, making them the fast-growing users for screen exposure duration[ 6 , 7 ]. Across the globe, nearly two-thirds of preschool children exceed 1 hour of daily screen time[ 8 ]. Observational studies have indicated that excessive screen time is associated with delays in language, motor, and cognitive development[ 9 – 11 ]. Furthermore, experimental research has demonstrated that increased screen exposure is linked to reduced microstructural integrity of brain white matter, as well as decreased cortical thickness and sulcal depth in preschool children[ 4 , 12 ]. In this context, screen exposure has emerged as a prevalent environmental risk factor. These findings are consistent with consensus guidelines from the World Health Organization, the United States, Australia, and Canada, which recommend a maximum of 1 hour per day for 2-5-year-olds, prioritizing interactive activities over passive viewing to promote optimal development[ 13 – 16 ]. Beyond screen time, screen content constitutes a critical factor influencing children’s neurodevelopment[ 17 ]. Previous research indicated that distinct types of screen content exhibit differential associations with neurodevelopmental outcomes. For example, Anderson et al found that exposure to educational programming during the preschool years was associated with improved academic performance, increased engagement in reading activities, and enhanced creative abilities[ 18 ]. Takeuchi et al found that increased time of videogame play is associated with the developmental delay of the microstructure in brain[ 19 ]. LeBlanc et al examined that increased TV was associated with poor cognitive development outcomes[ 20 ]. However, the limitations of these studies should be acknowledged. First, most studies focused solely on either total screen time or time in specific content types, with limited evaluation of the comprehensive effects between screen time and screen content [ 21 ]. In addition, previous studies are constrained by the lack of precise measurement tools[ 22 , 23 ]. Most research employs parental reports to investigate children’s screen exposure, a method that relies on parents’ recollection of screen time duration and their evaluation of program content [ 24 ]. On one hand, parental reports frequently contain inaccuracies due to recall bias[ 25 , 26 ]. On the other hand, most preschool children have already started to use mobile screen devices independently[ 27 ], the portable and personalized nature of these devices poses significant challenges for parents to monitor the content[ 28 ]. We aim to address these limitations by conducting a cross-sectional monitoring study of kindergarten children in Shanghai, China. Utilizing intelligent monitoring technology that accurately tracks both the duration and content of screen exposure, this study identifies screen exposure patterns among preschool children. Furthermore, we conducted an exploratory analysis to examine the association between these screen exposure patterns and neurodevelopmental delays across different domains in children. Methods Study Design and Participants This cross-sectional monitoring study was conducted in two kindergartens located in Shanghai, from March 2023 to July 2023. Using a cluster sampling method, 535 preschool children across three grade levels were recruited. The eligibility criteria of parents and children were as follows: (1) children aged between 3 to 6 years old; (2) parents would permit their children to use tablet; (3) parents could read and fully understand the questionnaire and provide consent. To track context-specific screen exposure, tablets equipped with intelligent screen monitoring software were provided to the children for a duration of 7 days. During this period, children were instructed to maintain their typical screen usage habits, and the provided tablets restricted to exclusive use by the child participants to ensure accurate data collection. Data were excluded if parents failed to complete the questionnaires, the tablets were not used, or other individuals accessed the tablets. After applying these criteria, data from 355 children were included in the final analysis.” Measures Screen time and screen content The “Healthy Screen Viewing for Children (HSVC)” system, an intelligent technology screen exposure monitoring software developed by Huawei Corporation, employs two core data acquisition modalities: 1) Real-time detection of foreground applications with precise timestamping of initiation and termination events; 2) Continuous capture of screen content through automated one-minute interval screenshot sampling. The comprehensive architecture of this data collection procedure is detailed in the in Figure S1 in Multimedia Appendix 1. According to pre-survey data, tablet-based screen viewing characteristics of preschool children recorded via the HSVC system showed moderate agreement (Cohen’s κ = 0.61) with diary-based records across all screen devices. In this study, a total of 138.98 thousand screen records and 41,798 screenshots were collected. Screen time duration was calculated as the time difference between the application’s termination and initiation timestamps recorded by the HSVC system. Screen content was categorized by analyzing the visual information captured in the screenshots, with classification based on educational characteristics into three distinct dimensions: Educational content: media designed to facilitate children’s acquisition of knowledge, practical skills, and fundamental concepts. Representative examples include instructional programs covering literacy development (e.g., phonics lessons), safety education (e.g., earthquake preparedness), and strategic thinking (e.g., Go). Moderately educational content: media intended for noneducational purposes, yet possessing inherent elements that may contribute to the enhancement of children’s developmental domains, particularly in cognitive and linguistic capacities (e.g., nursery rhymes). Noneducational content: media created for recreational and entertainment purposes, without explicit educational objectives or measurable benefits in knowledge acquisition or skill development (e.g., entertainment programs and video games). Neurodevelopment The neurodevelopmental levels of children were assessed using the Ages & Stages Questionnaires – Third Edition (ASQ-3), a widely recognized parent-reported screening tool for developmental delays[ 29 – 31 ], which has demonstrated good psychometric properties[ 32 ]. Consistent with previous studies[ 33 , 34 ], children’s neurodevelopmental levels were categorized into three groups based on cutoff scores: (1) low risk (scores above 1 SD below the mean); (2) monitor (scores below 1 SD but above 2 SDs below the mean); and (3) severe developmental delay (scores below 2 SDs below the mean). In this study, as children with severe developmental delay accounted for less than 5% of the sample, they were grouped into two categories: normal (low risk) and abnormal (monitor or severe developmental delay). The questionnaire was distributed and completed via the Wenjuanxing platform, an online survey tool. Parents were instructed to assess their children in a quiet environment before completing the questionnaire online. Covariates The covariates included in this study comprised child’s age, gender, maternal education attainment, and annual family income. Child’s age was stratified into five developmental stages based on the selected ASQ-3 questionnaire version: 34 months and 16 days to 38 months and 30 days, 39 months and 0 days to 44 months and 30 days, 45 months and 0 days to 50 months and 30 days, 51 months and 0 days to 56 months and 30 days, and 57 months and 0 days to 66 months and 0 days. Gender was classified as male or female, while maternal education attainment was categorized into high school or below (≤ High school) and junior college or above (≥ College). Annual family income was divided into three ranges: less than or equal to 79,000 yuan (≤ 79,000 RMB), 80,000 to 199,000 yuan (80,000–199,000 RMB), and greater than or equal to 200,000 yuan (≥ 200,000 RMB). Statistical Analysis In the descriptive analysis of sociodemographic characteristics and neurodevelopmental outcomes, continuous variables were summarized as mean ± standard deviation, while categorical variables were expressed as frequencies and percentages. Chi-square tests were applied to examine differences in sociodemographic characteristics between the included sample and the overall population, as well as across different clusters in terms of sociodemographic features and ASQ-3 domain scores. Screen exposure patterns were identified using K-means clustering analysis, with the optimal number of clusters determined by the elbow method[ 35 ]. The external validity of the clustering results was assessed using the silhouette coefficient[ 36 ]. Binary logistic regression was employed to investigate the association between screen exposure patterns and neurodevelopmental outcomes, adjusting for child’s gender, maternal educational attainment, annual family income. All statistical analyses were conducted using R 4.4.2, except for the K-means clustering, which was performed in Python. A p-value < 0.05 was considered statistically significant. Results There were no significant differences in the distribution of child gender, age, maternal education attainment, and annual family income between the sample included in the analysis and the total sample of 533 children (Table S1 ). The study included preschool children, comprising 204 boys (57.5% of the sample) and 151 girls (42.5% of the sample), and 251 (70.7%) were aged between 34 months and 16 days and 50 months and 30 days. Among the children, 46.2% had mothers with an education level of high school or lower. Furthermore, 55.2% of the children came from families with an annual income exceeding 200,000 RMB (Table 1 ). Table 1 Demographic Characteristics of Participants Variable Participants, No. (%) (n = 355) Gender Male 204 (57.5) Female 151 (42.5) Age 34 months 16 days ~ 50 months 30 days 251(70.7) 51 months 0 days ~ 56 months 30 days 74 (20.8) 57 months 0 days ~ 66 months 0 days 30 (8.5) Maternal education attainment ≤High school 164 (46.2) ≥College 191 (53.8) Annual Family Income a ≤ 79000 RMB 42 (11.8) 80000-199000RMB 117 (33.0) ≥ 200000RMB 196 (55.2) Abnormal development in ASQ – 3 domains Communication Normal 307(86.5) Abnormal 48(13.5) Gross motor Normal 285(80.3) Abnormal 70(19.7) Fine motor Normal 307(86.5) Abnormal 48(13.5) Problem solving Normal 296(83.4) Abnormal 59(16.6) Personal social Normal 321(90.4) Abnormal 34(9.6) a To convert RMB to US dollars, multiply by 0.15. Based on the elbow method (Figure S2), the optimal solution for the cluster analysis was 4 clusters. The children's screen exposure patterns were clustered into four different groups according to the duration of educational, non - educational, and moderately educational content they watched on tablets within seven days. The result of this clustering analysis is presented in Fig. 1 . Moreover, the clustering analysis boasted a silhouette coefficient of 0.68, indicating a relatively good clustering quality. As shown in Fig. 2 ,Cluster 1 (n = 281, 79.15%) was characterized by the lowest screen time across all content categories, with mean daily durations of 5.2 minutes for educational, 5.2 minutes for moderately educational, and 11.6 minutes for non-educational content. This cluster was classified as the restrictive use pattern. Cluster 2 (n = 29, 8.17%) demonstrated distinct exposure characteristics, featuring the highest proportion of moderately educational screen time (83.1 minutes/day), compared to 7.6 minutes of educational and 26.7 minutes of non-educational content. This configuration was designated as the moderately educational pattern. Cluster 3 (n = 20, 5.63%) exhibited marked non-educational screen engagement (139.5 minutes/day), significantly exceeding educational (5.0 minutes) and moderately educational (10.6 minutes) exposures. This cluster was categorized as the non-educational pattern. Cluster 4 (n = 25, 7.04%) displayed dominant educational screen time (53.3 minutes/day), surpassing both non-educational (14.7 minutes) and moderately educational (14.2 minutes) durations. This profile was classified as the educational-dominant pattern. Table 2 presents a comparison of the descriptive characteristics among children in four screen exposure patterns. Children in the noneducational pattern are more likely to come from low – to – middle – income families, and their mothers are more likely to have an educational attainment of high school or less. Moreover, these children have the highest rate of abnormal gross motor development. Table 2 Characteristics of Preschoolers in Different Screen exposure patterns Screen exposure patterns, n(%) Characteristic Restrictive use(n = 281) Moderately educational (n = 29) Noneducational (n = 20) Educational-dominant (n = 25) P value Gender Male 158 (56.2) 18 (62.1) 11 (55.0) 17 (68.0) 0.657 Female 123 (43.8) 11 (37.9) 9 (45.0) 8 (32.0) Age 34 months 16 days ~ 50 months 30 days 197 (70.1) 21 (72.4) 16 (80.0) 17 (68.0) 0.947 51 months 0 days ~ 56 months 30 days 58 (20.6) 7 (24.1) 3 (15.0) 6 (24.0) 57 months 0 days ~ 66 months 0 days 26 (9.25) 1 (3.45) 1 (5.00) 2 (8.00) Maternal education attainment ≤High school 125 (44.5) 17 (58.6) 14 (70.0) 8 (32.0) 0.033 ≥College 156 (46.3) 12 (41.4) 6 (30.0) 17 (68.0) Annual Family Income a ≤ 79000 RMB 34 (12.1) 1 (3.45) 4 (20.0) 3 (12.0) 0.018 80000-199000RMB 85 (30.2) 15 (51.7) 11 (55.0) 6 (24.0) ≥ 200000RMB 162 (57.7) 13 (44.8) 5 (25.0) 16 (64.0) Abnormal development in ASQ – 3 domains Communication Normal 245(87.2) 24(82.8) 17(85.0) 21(84.0) 0.787 Abnormal 36(12.8) 5(17.2) 3(15.0) 4(16.0) Gross motor Normal 232(82.8) 18(62.1) 16(80.0) 19(76.0) 0.069 Abnormal 49(17.2) 11(37.9) 4(20.0) 6(24.0) Fine motor Normal 245(87.2) 25(86.2) 13(65.0) 24(96.0) 0.029 Abnormal 36(12.8) 4(13.8) 7(35.0) 1(4.0) Problem solving Normal 235(83.6) 23(79.3) 17(85.0) 21(84.0) 0.914 Abnormal 46(16.4) 6(20.7) 3(15.0) 4(16.0) Personal social Normal 256(91.1) 26(89.7) 16(80.0) 23(92.0) 0.399 Abnormal 25(8.9) 3(10.3) 4(20.0) 2(8.0) After adjusting for the child’s gender, maternal educational attainment, and annual family income, the results of the binary logistic regression analysis between screen exposure patterns and the neurodevelopmental abnormalities in each domain of the ASQ-3 are shown in Table 3 . This study indicated that, compared with the restrictive use pattern, the moderately educational pattern was associated with a higher likelihood of neurodevelopmental abnormalities in the gross motor domain (OR = 2.530, 95% CI: 1.089–5.875, P = 0.031). The noneducational pattern was associated with a higher likelihood of neurodevelopmental abnormalities in the fine motor domain (OR = 3.172, 95% CI: 1.122–8.968, P = 0.029). Table 3 Association between Screen exposure patterns and Neurodevelopmental Abnormality Risk for Abnormality in Neurodevelopment Variable Communication Gross motor Fine motor Problem solving Personal social OR95%CI P OR95%CI P OR95%CI P OR95%CI P OR95%CI P Screen exposure patterns Restrictive use 1(Reference) 1(Reference) 1(Reference) 1(Reference) 1(Reference) Moderately educational 1.220(0.424–3.514) .712 2.530(1.089–5.875) .031 0.951(0.302–2.994) .931 1.141(0.426–3.053) .793 1.000(0.273–3.659) .998 Noneducational 0.941(0.255–3.463) .927 0.879(0.273–2.829) .829 3.172(1.122–8.968) .029 0.676(0.186–2.463) .553 1.950(0.582–6.531) .279 Educational-dominant 1.387(0.436–4.410) .580 1.614(0.597–4.359) .345 0.254(0.033–1.974) .190 1.041(0.332–3.265) .945 0.881(0.191–4.069) .816 Discussion By using intelligent technology to objectively measure both screen time and screen content of preschool children, we discovered that there are distinct screen exposure patterns among them. K-means cluster analysis revealed four patterns: restrictive use pattern, moderate educational pattern, noneducational pattern, and educational-dominant pattern. Moderately educational pattern was associated with a higher risk of gross motor abnormalities, while noneducational pattern was associated with a higher risk for fine motor abnormalities after controlling for all covariates. The restrictive use pattern, as the largest cluster among preschool children, demonstrated uniformly low daily screen exposure across all content categories. Previous research suggests that this balanced and moderate usage pattern is significantly associated with a substantial increase in children's engagement in physical activity 24 . Regarding the remaining three patterns, while proportionally similar, they exhibited significant differentiation in content preference and exposure intensity: the educational-dominant pattern featured predominantly educational content with the lowest total screen time. The noneducational pattern prioritized noneducational content with the highest total screen time, predominantly observed among children from middle-to-low-income families and those with lower maternal education attainment; consistent with previous studies, these socioeconomic correlates may heighten the likelihood of prolonged screen exposure and of entertainment content in disadvantaged households. The moderately educational pattern displayed moderately educational content, recording a screen time of 117.4 minutes per day. This content - specific preference indicates a notable structural imbalance in preschoolers' screen use. There was an association between moderately educational as well as noneducational patterns and motor skill developmental abnormalities in preschool-aged children. This finding underscores the importance of a time-content integrated screen use model. The results can be explained through screen time impacts and screen content impacts. In terms of screen time, children with moderately educational pattern averaged 117.4 minutes of daily screen exposure, whereas those engaging in noneducational pattern reached 155 minutes daily. Previous research has established that excessive screen time adversely impacts both gross and fine motor skill development in children. The motor developmental abnormalities may be attributed to screen time displacing physical activity opportunities during critical neurodevelopmental stages, as reduced physical activity time impairs cerebellar and vestibular maturation necessary for motor coordination[ 37 – 39 ], while physical activity correlates with enhanced fundamental motor skills. Beyond the influence of time factors, content characteristics have differential impacts on different motor domains. Moderately educational content, often having insufficient movement – oriented design elements, may struggle to cultivate the motivation for physical activity. Since the motivation for physical activity is a known predictor of participation frequency[ 40 ], moderately educational content can contribute to the abnormality in the development of gross motor skills. There is an association between noneducational content and the abnormality in fine motor development, which stems from the interdependence between cognition and movement. Studies have shown that noneducational media can alter cognitive functions[ 41 , 42 ], and motor control shares neural substrates with cognitive processes, such as the cerebellar network through functional connectivity[ 43 , 44 ]. Our research reveals that the screen content pattern dominated by moderately educational content, which is commonly watched by preschoolers yet often overlooked by parents, may have an impact on children's neurodevelopment. It should be noted that moderately educational content such as nursery rhymes demonstrates educational benefits but remains inadequately studied in preschoolers. As for the educational-dominant pattern, there was no association with the neurodevelopmental abnormalities in this study. This finding may stem from the educational-dominant pattern’s daily average screen exposure duration of 81.7 minutes, which exceeded the WHO-recommended threshold of 60 minutes. Although parents frequently select educational programs to enhance cognitive development[ 45 ], its potential benefits may be counterbalanced by the adverse effects of overexposure[ 21 ]. Thus, even high-quality content may fail to confer developmental advantages under excessive exposure, underscoring the necessity to balance educational content quality with exposure duration. Strengths and Limitations To our knowledge, this appears the first study utilizing an objective measurement by reliable coding protocol to examine screen content accessed by preschool-aged children. Through frame – by – frame annotation of screenshots captured via the HSVC system, we obtained precise measurements of screen exposure patterns to assess their association with the neurodevelopment in preschool children. Importantly, our study addresses the limitation in prior research on neurodevelopment in preschool children, where screen time and screen content were often examined separately. By measuring both factors with precision, we provide new evidence on their combined effects during this critical developmental period. Our study also has several limitations. First, the relatively small sample size may compromise the statistical power of the results, particularly in the clustering analysis. This limited capacity to detect meaningful differences across screen exposure patterns may affect the robustness of our inferences. Second, given that this study aimed to preliminarily explore potential associations between screen exposure patterns and neurodevelopmental domains, we did not apply multiple comparison corrections to the numerous statistical tests performed, to avoid overly stringent corrections that might obscure valuable preliminary trends. While some reported associations could be interpreted as noteworthy initial signals, it is important to note that their statistical robustness would require further validation under more stringent correction thresholds, underscoring the need for cautious interpretation. Third, the cross-sectional design precludes causal inferences between screen exposure patterns and neurodevelopment; longitudinal studies are needed to validate these associations. Fourth, although tablet-based screen time was objectively monitored via the HSVC, children may have used other screen media devices (e.g., smartphones, televisions) during the sampling period, resulting in a conservative estimate of total screen exposure. Fifth, despite instructions for habitual use, the Hawthorne effect might have temporarily altered children’s natural screen behaviors during the 7-day monitoring period. Sixth, existing literature indicates that screen exposure timing, particularly before bedtime, can negatively impact sleep, which in turn affects neurodevelopment[ 46 ]. However, our study did not account for the timing of screen use or its potential interaction with sleep quality, which may limit the comprehensiveness of our analysis of screen exposure’s effects on neurodevelopment. Future studies should integrate monitoring technologies into children’s personal devices to capture their total real-world usage, including timing of screen exposure and sleep-related interactions, while employing larger samples and rigorous correction methods to enhance statistical reliability. Future studies should integrate monitoring technologies into children’s personal devices to capture their total real-world usage, including timing of screen exposure and sleep-related interactions, while employing larger samples and rigorous correction methods to enhance statistical reliability. Conclusions This monitoring study identified heterogeneous screen exposure patterns in preschool-aged children, revealing that excessive use of moderately educational content and noneducational content was associated with lower gross motor and fine motor skills. When limiting total screen time, parents should focus on content selection for preschool-aged children. Future research should focus on the objective measurement of different types of screen content. Abbreviations ASQ-3: Ages & Stages Questionnaires – Third Edition Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Ethics approval This study was was conducted in accordance with the Declaration of Helsinki. The study received formal approval from the Ethics Committee for Medical Research at the School of Public Health, Fudan University (IRB#2023-11-1088).Written consent was obtained from all parents prior to participants' involvement in the study. All records underwent anonymization processes with participant identification maintained solely through unique coded identifiers. As a participation acknowledgement, children received art supply kits valued at $ 15 USD per set. Consent to participate Written informed consent for the use of their personal data has been obtained from the child's parent or legal guardian. Funding This study was supported by the National Natural Science Foundation of China (Grant No. 82304260), the China Postdoctoral Science Foundation (Grant No. 2023M730626), and the Shanghai Municipal Health Commission (Grant No. GWV-10.2-YQ2). Author Contribution YJ had full access to all of the data in the study, took responsibility for the integrity of the data and the accuracy of the data analysis, participated in concept and design, critical review of the manuscript for important intellectual content, obtained funding, provided administrative, technical, or material support, and supervised the study. HJ participated in concept and design, acquisition, analysis, or interpretation of data, critical review of the manuscript, provided administrative, technical, or material support, and supervised the study.YS participated in concept and design, acquisition, analysis, or interpretation of data, drafting of the initial manuscript, critical review of the manuscript, and statistical analysis.HC participated in concept and design, acquisition, analysis, or interpretation of data, critical review of the manuscript, statistical analysis, obtained funding, and supervised the study. YZ and CL participated in data investigation, statistical analysis, and critical review of the manuscript.All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work. Acknowledgement The authors would like to express their gratitude to the relevant funding agencies for their financial support in conducting this research. 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Dec 2015;136(6):1044-50. doi:10.1542/peds.2015-2151 Domoff SE, Radesky JS, Harrison K, Riley H, Lumeng JC, Miller AL. A Naturalistic Study of Child and Family Screen Media and Mobile Device Use. J Child Fam Stud . Feb 2019;28(2):401-410. doi:10.1007/s10826-018-1275-1 Shuffrey LC, Firestein MR, Kyle MH, et al. Association of Birth During the COVID-19 Pandemic With Neurodevelopmental Status at 6 Months in Infants With and Without In Utero Exposure to Maternal SARS-CoV-2 Infection. JAMA Pediatr . Jun 1 2022;176(6):e215563. doi:10.1001/jamapediatrics.2021.5563 Hessami K, Norooznezhad AH, Monteiro S, et al. COVID-19 Pandemic and Infant Neurodevelopmental Impairment: A Systematic Review and Meta-analysis. JAMA Netw Open . Oct 3 2022;5(10):e2238941. doi:10.1001/jamanetworkopen.2022.38941 Zhou L, Qiu W, Wang J, et al. Effects of vaginal microbiota transfer on the neurodevelopment and microbiome of cesarean-born infants: A blinded randomized controlled trial. Cell Host Microbe . Jul 12 2023;31(7):1232-1247.e5. doi:10.1016/j.chom.2023.05.022 Sheldrick RC, Marakovitz S, Garfinkel D, Carter AS, Perrin EC. Comparative Accuracy of Developmental Screening Questionnaires. JAMA Pediatr . Apr 1 2020;174(4):366-374. doi:10.1001/jamapediatrics.2019.6000 Muthusamy S, Wagh D, Tan J, Bulsara M, Rao S. Utility of the Ages and Stages Questionnaire to Identify Developmental Delay in Children Aged 12 to 60 Months: A Systematic Review and Meta-analysis. JAMA Pediatr . Oct 1 2022;176(10):980-989. doi:10.1001/jamapediatrics.2022.3079 Kuehn LM, Jones A, Helmkamp L, Knudtson M, Domek GJ, Allison MA. Socioemotional Development of Infants and Toddlers During the COVID-19 Pandemic. JAMA Pediatr . Feb 1 2024;178(2):151-159. doi:10.1001/jamapediatrics.2023.5684 Liu F, Yang D, Liu Y, et al. Use of latent profile analysis and k-means clustering to identify student anxiety profiles. BMC Psychiatry . Jan 5 2022;22(1):12. doi:10.1186/s12888-021-03648-7 Gao CX, Dwyer D, Zhu Y, et al. An overview of clustering methods with guidelines for application in mental health research. Psychiatry Res . Sep 2023;327:115265. doi:10.1016/j.psychres.2023.115265 Glickstein M, Sultan F, Voogd J. Functional localization in the cerebellum. Cortex . Jan 2011;47(1):59-80. doi:10.1016/j.cortex.2009.09.001 Glickstein M, Strata P, Voogd J. Cerebellum: history. Neuroscience . Sep 1 2009;162(3):549-59. doi:10.1016/j.neuroscience.2009.02.054 Bégel V, Bachrach A, Dalla Bella S, et al. Dance Improves Motor, Cognitive, and Social Skills in Children With Developmental Cerebellar Anomalies. Cerebellum . Apr 2022;21(2):264-279. doi:10.1007/s12311-021-01291-2 K BO, Smith J, Lubans DR, Ng JY, Lonsdale C. Self-determined motivation and physical activity in children and adolescents: a systematic review and meta-analysis. Prev Med . Oct 2014;67:270-9. doi:10.1016/j.ypmed.2014.07.033 McNeill J, Howard SJ, Vella SA, Cliff DP. Longitudinal Associations of Electronic Application Use and Media Program Viewing with Cognitive and Psychosocial Development in Preschoolers. Acad Pediatr . Jul 2019;19(5):520-528. doi:10.1016/j.acap.2019.02.010 Christakis DA, Zimmerman FJ, DiGiuseppe DL, McCarty CA. Early television exposure and subsequent attentional problems in children. Pediatrics . Apr 2004;113(4):708-13. doi:10.1542/peds.113.4.708 Xue A, Kong R, Yang Q, et al. The detailed organization of the human cerebellum estimated by intrinsic functional connectivity within the individual. J Neurophysiol . Feb 1 2021;125(2):358-384. doi:10.1152/jn.00561.2020 Buckner RL, Krienen FM, Castellanos A, Diaz JC, Yeo BT. The organization of the human cerebellum estimated by intrinsic functional connectivity. J Neurophysiol . Nov 2011;106(5):2322-45. doi:10.1152/jn.00339.2011 Kirkorian HL, Wartella EA, Anderson DR. Media and young children's learning. Future Child . Spring 2008;18(1):39-61. doi:10.1353/foc.0.0002 Pickard H, Chu P, Essex C, et al. Toddler Screen Use Before Bed and Its Effect on Sleep and Attention: A Randomized Clinical Trial. JAMA Pediatr . Dec 1 2024;178(12):1270-1279. doi:10.1001/jamapediatrics.2024.3997 Additional Declarations No competing interests reported. Supplementary Files supplementarymaterials.docx Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2026 Read the published version in European Journal of Pediatrics → Version 1 posted Editorial decision: Revision requested 31 Oct, 2025 Reviews received at journal 30 Oct, 2025 Reviewers agreed at journal 18 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviews received at journal 12 Sep, 2025 Reviewers agreed at journal 11 Sep, 2025 Reviewers invited by journal 11 Sep, 2025 Editor assigned by journal 11 Sep, 2025 Submission checks completed at journal 10 Sep, 2025 First submitted to journal 09 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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10:01:09","extension":"html","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":143927,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7571507/v1/0d4a278b07814d3584bd39c4.html"},{"id":91842553,"identity":"afbfcc9b-51e4-4543-9c93-24754f24af85","added_by":"auto","created_at":"2025-09-22 10:01:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":236922,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eK-means Cluster Analysis Results\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7571507/v1/f0c3848a7f552c7df5631f2d.png"},{"id":91844327,"identity":"8a6c0071-2c7a-470d-aed3-59b02a6e3c9a","added_by":"auto","created_at":"2025-09-22 10:09:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":150223,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreen Time of Content Categories in Different Screen exposure patterns\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7571507/v1/a3ea62aa4725924a808161e3.png"},{"id":101691116,"identity":"a7c864d5-2511-4918-b8c1-8fff28b22e85","added_by":"auto","created_at":"2026-02-02 16:12:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1376782,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7571507/v1/dfdfafa8-e8f1-4185-87e3-856e04a50aa7.pdf"},{"id":91842543,"identity":"d054ee9e-5b6f-4a2a-884d-5dc90bd70ad5","added_by":"auto","created_at":"2025-09-22 10:01:09","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":390132,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7571507/v1/237b90375dd89ba446a72542.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Screen patterns and neurodevelopmental delay in preschoolers: an intelligent monitoring study","fulltext":[{"header":"What is Known--What is New","content":"\u003cp\u003e\u003cstrong\u003eWhat is Known:\u0026nbsp;\u003c/strong\u003eFew studies have analyzed preschoolers’ screen exposure patterns (especially combined time and content) or their neurodevelopmental impacts, with scarce research using objective measures of both.\u003cbr\u003e\u003cstrong\u003eWhat is New:\u003c/strong\u003e Using validated intelligent monitoring, we identified heterogeneous patterns and found excessive moderately educational/noneducational content linked to lower motor skills.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eNeurodevelopmental delay refers to significant lag in the development of communication, motor, problem solving or social skills relative to age-normative standards, represents a precursor to persistent neurodevelopmental disorders such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The preschool period (3\u0026ndash;5 years) constitutes a critical window for synaptic pruning and neural circuit refinement[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], during which children exhibit heightened sensitivity to environmental input quality[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The majority of preschool children possessed their own mobile devices, making them the fast-growing users for screen exposure duration[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Across the globe, nearly two-thirds of preschool children exceed 1 hour of daily screen time[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Observational studies have indicated that excessive screen time is associated with delays in language, motor, and cognitive development[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Furthermore, experimental research has demonstrated that increased screen exposure is linked to reduced microstructural integrity of brain white matter, as well as decreased cortical thickness and sulcal depth in preschool children[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In this context, screen exposure has emerged as a prevalent environmental risk factor. These findings are consistent with consensus guidelines from the World Health Organization, the United States, Australia, and Canada, which recommend a maximum of 1 hour per day for 2-5-year-olds, prioritizing interactive activities over passive viewing to promote optimal development[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBeyond screen time, screen content constitutes a critical factor influencing children\u0026rsquo;s neurodevelopment[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Previous research indicated that distinct types of screen content exhibit differential associations with neurodevelopmental outcomes. For example, Anderson et al found that exposure to educational programming during the preschool years was associated with improved academic performance, increased engagement in reading activities, and enhanced creative abilities[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Takeuchi et al found that increased time of videogame play is associated with the developmental delay of the microstructure in brain[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. LeBlanc et al examined that increased TV was associated with poor cognitive development outcomes[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eHowever, the limitations of these studies should be acknowledged. First, most studies focused solely on either total screen time or time in specific content types, with limited evaluation of the comprehensive effects between screen time and screen content [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In addition, previous studies are constrained by the lack of precise measurement tools[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Most research employs parental reports to investigate children\u0026rsquo;s screen exposure, a method that relies on parents\u0026rsquo; recollection of screen time duration and their evaluation of program content [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. On one hand, parental reports frequently contain inaccuracies due to recall bias[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. On the other hand, most preschool children have already started to use mobile screen devices independently[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], the portable and personalized nature of these devices poses significant challenges for parents to monitor the content[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eWe aim to address these limitations by conducting a cross-sectional monitoring study of kindergarten children in Shanghai, China. Utilizing intelligent monitoring technology that accurately tracks both the duration and content of screen exposure, this study identifies screen exposure patterns among preschool children. Furthermore, we conducted an exploratory analysis to examine the association between these screen exposure patterns and neurodevelopmental delays across different domains in children.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Participants\u003c/h2\u003e\u003cp\u003eThis cross-sectional monitoring study was conducted in two kindergartens located in Shanghai, from March 2023 to July 2023. Using a cluster sampling method, 535 preschool children across three grade levels were recruited. The eligibility criteria of parents and children were as follows: (1) children aged between 3 to 6 years old; (2) parents would permit their children to use tablet; (3) parents could read and fully understand the questionnaire and provide consent. To track context-specific screen exposure, tablets equipped with intelligent screen monitoring software were provided to the children for a duration of 7 days. During this period, children were instructed to maintain their typical screen usage habits, and the provided tablets restricted to exclusive use by the child participants to ensure accurate data collection. Data were excluded if parents failed to complete the questionnaires, the tablets were not used, or other individuals accessed the tablets. After applying these criteria, data from 355 children were included in the final analysis.\u0026rdquo;\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eScreen time and screen content\u003c/h2\u003e\u003cp\u003eThe \u0026ldquo;Healthy Screen Viewing for Children (HSVC)\u0026rdquo; system, an intelligent technology screen exposure monitoring software developed by Huawei Corporation, employs two core data acquisition modalities: 1) Real-time detection of foreground applications with precise timestamping of initiation and termination events; 2) Continuous capture of screen content through automated one-minute interval screenshot sampling. The comprehensive architecture of this data collection procedure is detailed in the in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e in Multimedia Appendix 1. According to pre-survey data, tablet-based screen viewing characteristics of preschool children recorded via the HSVC system showed moderate agreement (Cohen\u0026rsquo;s κ\u0026thinsp;=\u0026thinsp;0.61) with diary-based records across all screen devices. In this study, a total of 138.98 thousand screen records and 41,798 screenshots were collected. Screen time duration was calculated as the time difference between the application\u0026rsquo;s termination and initiation timestamps recorded by the HSVC system. Screen content was categorized by analyzing the visual information captured in the screenshots, with classification based on educational characteristics into three distinct dimensions:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eEducational content: media designed to facilitate children\u0026rsquo;s acquisition of knowledge, practical skills, and fundamental concepts. Representative examples include instructional programs covering literacy development (e.g., phonics lessons), safety education (e.g., earthquake preparedness), and strategic thinking (e.g., Go).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eModerately educational content: media intended for noneducational purposes, yet possessing inherent elements that may contribute to the enhancement of children\u0026rsquo;s developmental domains, particularly in cognitive and linguistic capacities (e.g., nursery rhymes).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eNoneducational content: media created for recreational and entertainment purposes, without explicit educational objectives or measurable benefits in knowledge acquisition or skill development (e.g., entertainment programs and video games).\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eNeurodevelopment\u003c/h3\u003e\n\u003cp\u003eThe neurodevelopmental levels of children were assessed using the Ages \u0026amp; Stages Questionnaires \u0026ndash; Third Edition (ASQ-3), a widely recognized parent-reported screening tool for developmental delays[\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], which has demonstrated good psychometric properties[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Consistent with previous studies[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], children\u0026rsquo;s neurodevelopmental levels were categorized into three groups based on cutoff scores: (1) low risk (scores above 1 SD below the mean); (2) monitor (scores below 1 SD but above 2 SDs below the mean); and (3) severe developmental delay (scores below 2 SDs below the mean). In this study, as children with severe developmental delay accounted for less than 5% of the sample, they were grouped into two categories: normal (low risk) and abnormal (monitor or severe developmental delay). The questionnaire was distributed and completed via the Wenjuanxing platform, an online survey tool. Parents were instructed to assess their children in a quiet environment before completing the questionnaire online.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThe covariates included in this study comprised child\u0026rsquo;s age, gender, maternal education attainment, and annual family income. Child\u0026rsquo;s age was stratified into five developmental stages based on the selected ASQ-3 questionnaire version: 34 months and 16 days to 38 months and 30 days, 39 months and 0 days to 44 months and 30 days, 45 months and 0 days to 50 months and 30 days, 51 months and 0 days to 56 months and 30 days, and 57 months and 0 days to 66 months and 0 days. Gender was classified as male or female, while maternal education attainment was categorized into high school or below (\u0026le;\u0026thinsp;High school) and junior college or above (\u0026ge;\u0026thinsp;College). Annual family income was divided into three ranges: less than or equal to 79,000 yuan (\u0026le;\u0026thinsp;79,000 RMB), 80,000 to 199,000 yuan (80,000\u0026ndash;199,000 RMB), and greater than or equal to 200,000 yuan (\u0026ge;\u0026thinsp;200,000 RMB).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eIn the descriptive analysis of sociodemographic characteristics and neurodevelopmental outcomes, continuous variables were summarized as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while categorical variables were expressed as frequencies and percentages. Chi-square tests were applied to examine differences in sociodemographic characteristics between the included sample and the overall population, as well as across different clusters in terms of sociodemographic features and ASQ-3 domain scores. Screen exposure patterns were identified using K-means clustering analysis, with the optimal number of clusters determined by the elbow method[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The external validity of the clustering results was assessed using the silhouette coefficient[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Binary logistic regression was employed to investigate the association between screen exposure patterns and neurodevelopmental outcomes, adjusting for child\u0026rsquo;s gender, maternal educational attainment, annual family income. All statistical analyses were conducted using R 4.4.2, except for the K-means clustering, which was performed in Python. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThere were no significant differences in the distribution of child gender, age, maternal education attainment, and annual family income between the sample included in the analysis and the total sample of 533 children (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The study included preschool children, comprising 204 boys (57.5% of the sample) and 151 girls (42.5% of the sample), and 251 (70.7%) were aged between 34 months and 16 days and 50 months and 30 days. Among the children, 46.2% had mothers with an education level of high school or lower. Furthermore, 55.2% of the children came from families with an annual income exceeding 200,000 RMB (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDemographic Characteristics of Participants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParticipants,\u003c/p\u003e\u003cp\u003eNo. (%) (n\u0026thinsp;=\u0026thinsp;355)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e204 (57.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e151 (42.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e34 months 16 days\u0026thinsp;~\u0026thinsp;50 months 30 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e251(70.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51 months 0 days\u0026thinsp;~\u0026thinsp;56 months 30 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74 (20.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e57 months 0 days\u0026thinsp;~\u0026thinsp;66 months 0 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30 (8.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal education attainment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;High school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e164 (46.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;College\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e191 (53.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual Family Income\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;79000 RMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e42 (11.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e80000-199000RMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e117 (33.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;200000RMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e196 (55.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAbnormal development in ASQ \u0026ndash; 3 domains\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCommunication\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e307(86.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48(13.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGross motor\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e285(80.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70(19.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFine motor\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e307(86.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e48(13.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProblem solving\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e296(83.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e59(16.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePersonal social\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e321(90.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34(9.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003csup\u003ea\u003c/sup\u003e To convert RMB to US dollars, multiply by 0.15.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBased on the elbow method (Figure S2), the optimal solution for the cluster analysis was 4 clusters. The children's screen exposure patterns were clustered into four different groups according to the duration of educational, non - educational, and moderately educational content they watched on tablets within seven days. The result of this clustering analysis is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Moreover, the clustering analysis boasted a silhouette coefficient of 0.68, indicating a relatively good clustering quality.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e ,Cluster 1 (n\u0026thinsp;=\u0026thinsp;281, 79.15%) was characterized by the lowest screen time across all content categories, with mean daily durations of 5.2 minutes for educational, 5.2 minutes for moderately educational, and 11.6 minutes for non-educational content. This cluster was classified as the restrictive use pattern. Cluster 2 (n\u0026thinsp;=\u0026thinsp;29, 8.17%) demonstrated distinct exposure characteristics, featuring the highest proportion of moderately educational screen time (83.1 minutes/day), compared to 7.6 minutes of educational and 26.7 minutes of non-educational content. This configuration was designated as the moderately educational pattern. Cluster 3 (n\u0026thinsp;=\u0026thinsp;20, 5.63%) exhibited marked non-educational screen engagement (139.5 minutes/day), significantly exceeding educational (5.0 minutes) and moderately educational (10.6 minutes) exposures. This cluster was categorized as the non-educational pattern. Cluster 4 (n\u0026thinsp;=\u0026thinsp;25, 7.04%) displayed dominant educational screen time (53.3 minutes/day), surpassing both non-educational (14.7 minutes) and moderately educational (14.2 minutes) durations. This profile was classified as the educational-dominant pattern.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents a comparison of the descriptive characteristics among children in four screen exposure patterns. Children in the noneducational pattern are more likely to come from low \u0026ndash; to \u0026ndash; middle \u0026ndash; income families, and their mothers are more likely to have an educational attainment of high school or less. Moreover, these children have the highest rate of abnormal gross motor development.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of Preschoolers in Different Screen exposure patterns\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eScreen exposure patterns, n(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRestrictive use(n\u0026thinsp;=\u0026thinsp;281)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerately educational\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;29)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNoneducational (n\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eEducational-dominant (n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGender\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e158 (56.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (62.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (55.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e17 (68.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.657\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e123 (43.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e8 (32.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e34 months 16 days\u0026thinsp;~\u0026thinsp;50 months 30 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e197 (70.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e21 (72.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16 (80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e17 (68.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.947\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e51 months 0 days\u0026thinsp;~\u0026thinsp;56 months 30 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58 (20.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (24.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3 (15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e6 (24.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e57 months 0 days\u0026thinsp;~\u0026thinsp;66 months 0 days\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26 (9.25)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (5.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2 (8.00)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMaternal education attainment\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;High school\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125 (44.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (58.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14 (70.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e8 (32.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;College\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e156 (46.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (41.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (30.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e17 (68.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAnnual Family Income\u003c/b\u003e \u003csup\u003e\u003cb\u003ea\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;79000 RMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34 (12.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (3.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4 (20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e3 (12.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e0.018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e80000-199000RMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e85 (30.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (51.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e11 (55.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e6 (24.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026ge;\u0026thinsp;200000RMB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e162 (57.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (44.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (25.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e16 (64.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAbnormal development in ASQ \u0026ndash; 3 domains\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCommunication\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e245(87.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24(82.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17(85.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e21(84.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.787\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36(12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5(17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3(15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e4(16.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGross motor\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e232(82.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18(62.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16(80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e19(76.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49(17.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11(37.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4(20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e6(24.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFine motor\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e245(87.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e25(86.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13(65.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e24(96.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.029\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36(12.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4(13.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7(35.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e1(4.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eProblem solving\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e235(83.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23(79.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17(85.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e21(84.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.914\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46(16.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6(20.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3(15.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e4(16.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePersonal social\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e256(91.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26(89.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16(80.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e23(92.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e0.399\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbnormal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25(8.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3(10.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4(20.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003e2(8.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter adjusting for the child\u0026rsquo;s gender, maternal educational attainment, and annual family income, the results of the binary logistic regression analysis between screen exposure patterns and the neurodevelopmental abnormalities in each domain of the ASQ-3 are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. This study indicated that, compared with the restrictive use pattern, the moderately educational pattern was associated with a higher likelihood of neurodevelopmental abnormalities in the gross motor domain (OR\u0026thinsp;=\u0026thinsp;2.530, 95% CI: 1.089\u0026ndash;5.875, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031). The noneducational pattern was associated with a higher likelihood of neurodevelopmental abnormalities in the fine motor domain (OR\u0026thinsp;=\u0026thinsp;3.172, 95% CI: 1.122\u0026ndash;8.968, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eAssociation between Screen exposure patterns and Neurodevelopmental Abnormality\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"15\" nameend=\"c16\" namest=\"c2\"\u003e\u003cp\u003eRisk for Abnormality in Neurodevelopment\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCommunication\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eGross motor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e\u003cp\u003eFine motor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e\u003cp\u003eProblem solving\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c16\" namest=\"c15\"\u003e\u003cp\u003ePersonal social\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOR95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eOR95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eOR95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eOR95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eOR95%CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eScreen exposure patterns\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRestrictive use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1(Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModerately educational\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.220(0.424\u0026ndash;3.514)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.530(1.089\u0026ndash;5.875)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.951(0.302\u0026ndash;2.994)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.141(0.426\u0026ndash;3.053)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.793\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1.000(0.273\u0026ndash;3.659)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e.998\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNoneducational\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.941(0.255\u0026ndash;3.463)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.927\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.879(0.273\u0026ndash;2.829)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.829\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.172(1.122\u0026ndash;8.968)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.029\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.676(0.186\u0026ndash;2.463)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1.950(0.582\u0026ndash;6.531)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e.279\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEducational-dominant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.387(0.436\u0026ndash;4.410)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.614(0.597\u0026ndash;4.359)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003e.345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.254(0.033\u0026ndash;1.974)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.190\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.041(0.332\u0026ndash;3.265)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e.945\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e0.881(0.191\u0026ndash;4.069)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e.816\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBy using intelligent technology to objectively measure both screen time and screen content of preschool children, we discovered that there are distinct screen exposure patterns among them. K-means cluster analysis revealed four patterns: restrictive use pattern, moderate educational pattern, noneducational pattern, and educational-dominant pattern. Moderately educational pattern was associated with a higher risk of gross motor abnormalities, while noneducational pattern was associated with a higher risk for fine motor abnormalities after controlling for all covariates.\u003c/p\u003e\u003cp\u003eThe restrictive use pattern, as the largest cluster among preschool children, demonstrated uniformly low daily screen exposure across all content categories. Previous research suggests that this balanced and moderate usage pattern is significantly associated with a substantial increase in children's engagement in physical activity\u003csup\u003e24\u003c/sup\u003e. Regarding the remaining three patterns, while proportionally similar, they exhibited significant differentiation in content preference and exposure intensity: the educational-dominant pattern featured predominantly educational content with the lowest total screen time. The noneducational pattern prioritized noneducational content with the highest total screen time, predominantly observed among children from middle-to-low-income families and those with lower maternal education attainment; consistent with previous studies, these socioeconomic correlates may heighten the likelihood of prolonged screen exposure and of entertainment content in disadvantaged households. The moderately educational pattern displayed moderately educational content, recording a screen time of 117.4 minutes per day. This content - specific preference indicates a notable structural imbalance in preschoolers' screen use.\u003c/p\u003e\u003cp\u003eThere was an association between moderately educational as well as noneducational patterns and motor skill developmental abnormalities in preschool-aged children. This finding underscores the importance of a time-content integrated screen use model. The results can be explained through screen time impacts and screen content impacts. In terms of screen time, children with moderately educational pattern averaged 117.4 minutes of daily screen exposure, whereas those engaging in noneducational pattern reached 155 minutes daily. Previous research has established that excessive screen time adversely impacts both gross and fine motor skill development in children. The motor developmental abnormalities may be attributed to screen time displacing physical activity opportunities during critical neurodevelopmental stages, as reduced physical activity time impairs cerebellar and vestibular maturation necessary for motor coordination[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], while physical activity correlates with enhanced fundamental motor skills. Beyond the influence of time factors, content characteristics have differential impacts on different motor domains. Moderately educational content, often having insufficient movement \u0026ndash; oriented design elements, may struggle to cultivate the motivation for physical activity. Since the motivation for physical activity is a known predictor of participation frequency[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], moderately educational content can contribute to the abnormality in the development of gross motor skills. There is an association between noneducational content and the abnormality in fine motor development, which stems from the interdependence between cognition and movement. Studies have shown that noneducational media can alter cognitive functions[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], and motor control shares neural substrates with cognitive processes, such as the cerebellar network through functional connectivity[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Our research reveals that the screen content pattern dominated by moderately educational content, which is commonly watched by preschoolers yet often overlooked by parents, may have an impact on children's neurodevelopment. It should be noted that moderately educational content such as nursery rhymes demonstrates educational benefits but remains inadequately studied in preschoolers.\u003c/p\u003e\u003cp\u003eAs for the educational-dominant pattern, there was no association with the neurodevelopmental abnormalities in this study. This finding may stem from the educational-dominant pattern\u0026rsquo;s daily average screen exposure duration of 81.7 minutes, which exceeded the WHO-recommended threshold of 60 minutes. Although parents frequently select educational programs to enhance cognitive development[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], its potential benefits may be counterbalanced by the adverse effects of overexposure[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Thus, even high-quality content may fail to confer developmental advantages under excessive exposure, underscoring the necessity to balance educational content quality with exposure duration.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStrengths and Limitations\u003c/h2\u003e\u003cp\u003eTo our knowledge, this appears the first study utilizing an objective measurement by reliable coding protocol to examine screen content accessed by preschool-aged children. Through frame \u0026ndash; by \u0026ndash; frame annotation of screenshots captured via the HSVC system, we obtained precise measurements of screen exposure patterns to assess their association with the neurodevelopment in preschool children. Importantly, our study addresses the limitation in prior research on neurodevelopment in preschool children, where screen time and screen content were often examined separately. By measuring both factors with precision, we provide new evidence on their combined effects during this critical developmental period.\u003c/p\u003e\u003cp\u003eOur study also has several limitations. First, the relatively small sample size may compromise the statistical power of the results, particularly in the clustering analysis. This limited capacity to detect meaningful differences across screen exposure patterns may affect the robustness of our inferences. Second, given that this study aimed to preliminarily explore potential associations between screen exposure patterns and neurodevelopmental domains, we did not apply multiple comparison corrections to the numerous statistical tests performed, to avoid overly stringent corrections that might obscure valuable preliminary trends. While some reported associations could be interpreted as noteworthy initial signals, it is important to note that their statistical robustness would require further validation under more stringent correction thresholds, underscoring the need for cautious interpretation. Third, the cross-sectional design precludes causal inferences between screen exposure patterns and neurodevelopment; longitudinal studies are needed to validate these associations. Fourth, although tablet-based screen time was objectively monitored via the HSVC, children may have used other screen media devices (e.g., smartphones, televisions) during the sampling period, resulting in a conservative estimate of total screen exposure. Fifth, despite instructions for habitual use, the Hawthorne effect might have temporarily altered children\u0026rsquo;s natural screen behaviors during the 7-day monitoring period. Sixth, existing literature indicates that screen exposure timing, particularly before bedtime, can negatively impact sleep, which in turn affects neurodevelopment[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. However, our study did not account for the timing of screen use or its potential interaction with sleep quality, which may limit the comprehensiveness of our analysis of screen exposure\u0026rsquo;s effects on neurodevelopment. Future studies should integrate monitoring technologies into children\u0026rsquo;s personal devices to capture their total real-world usage, including timing of screen exposure and sleep-related interactions, while employing larger samples and rigorous correction methods to enhance statistical reliability. Future studies should integrate monitoring technologies into children\u0026rsquo;s personal devices to capture their total real-world usage, including timing of screen exposure and sleep-related interactions, while employing larger samples and rigorous correction methods to enhance statistical reliability.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis monitoring study identified heterogeneous screen exposure patterns in preschool-aged children, revealing that excessive use of moderately educational content and noneducational content was associated with lower gross motor and fine motor skills. When limiting total screen time, parents should focus on content selection for preschool-aged children. Future research should focus on the objective measurement of different types of screen content.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eASQ-3:\u003c/strong\u003eAges \u0026amp; Stages Questionnaires \u0026ndash; Third Edition\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests\u003c/h2\u003e\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eEthics approval\u003c/h2\u003e\u003cp\u003e This study was was conducted in accordance with the Declaration of Helsinki. The study received formal approval from the Ethics Committee for Medical Research at the School of Public Health, Fudan University (IRB#2023-11-1088).Written consent was obtained from all parents prior to participants' involvement in the study. All records underwent anonymization processes with participant identification maintained solely through unique coded identifiers. As a participation acknowledgement, children received art supply kits valued at \u003cspan\u003e$\u003c/span\u003e15 USD per set.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003cp\u003e Written informed consent for the use of their personal data has been obtained from the child's parent or legal guardian.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (Grant No. 82304260), the China Postdoctoral Science Foundation (Grant No. 2023M730626), and the Shanghai Municipal Health Commission (Grant No. GWV-10.2-YQ2).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYJ had full access to all of the data in the study, took responsibility for the integrity of the data and the accuracy of the data analysis, participated in concept and design, critical review of the manuscript for important intellectual content, obtained funding, provided administrative, technical, or material support, and supervised the study. HJ participated in concept and design, acquisition, analysis, or interpretation of data, critical review of the manuscript, provided administrative, technical, or material support, and supervised the study.YS participated in concept and design, acquisition, analysis, or interpretation of data, drafting of the initial manuscript, critical review of the manuscript, and statistical analysis.HC participated in concept and design, acquisition, analysis, or interpretation of data, critical review of the manuscript, statistical analysis, obtained funding, and supervised the study. YZ and CL participated in data investigation, statistical analysis, and critical review of the manuscript.All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors would like to express their gratitude to the relevant funding agencies for their financial support in conducting this research.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKobayashi S, Itoh S, Miyashita C, et al. Impact of prenatal exposure to mercury and selenium on neurodevelopmental delay in children in the Japan environment and Children\u0026apos;s study using the ASQ-3 questionnaire: A prospective birth cohort. \u003cem\u003eEnviron Int\u003c/em\u003e. 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A Naturalistic Study of Child and Family Screen Media and Mobile Device Use. \u003cem\u003eJ Child Fam Stud\u003c/em\u003e. Feb 2019;28(2):401-410. doi:10.1007/s10826-018-1275-1\u003c/li\u003e\n\u003cli\u003eShuffrey LC, Firestein MR, Kyle MH, et al. Association of Birth During the COVID-19 Pandemic With Neurodevelopmental Status at 6 Months in Infants With and Without In Utero Exposure to Maternal SARS-CoV-2 Infection. \u003cem\u003eJAMA Pediatr\u003c/em\u003e. Jun 1 2022;176(6):e215563. doi:10.1001/jamapediatrics.2021.5563\u003c/li\u003e\n\u003cli\u003eHessami K, Norooznezhad AH, Monteiro S, et al. COVID-19 Pandemic and Infant Neurodevelopmental Impairment: A Systematic Review and Meta-analysis. \u003cem\u003eJAMA Netw Open\u003c/em\u003e. Oct 3 2022;5(10):e2238941. doi:10.1001/jamanetworkopen.2022.38941\u003c/li\u003e\n\u003cli\u003eZhou L, Qiu W, Wang J, et al. 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Media and young children\u0026apos;s learning. \u003cem\u003eFuture Child\u003c/em\u003e. Spring 2008;18(1):39-61. doi:10.1353/foc.0.0002\u003c/li\u003e\n\u003cli\u003ePickard H, Chu P, Essex C, et al. Toddler Screen Use Before Bed and Its Effect on Sleep and Attention: A Randomized Clinical Trial. \u003cem\u003eJAMA Pediatr\u003c/em\u003e. Dec 1 2024;178(12):1270-1279. doi:10.1001/jamapediatrics.2024.3997\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpe","sideBox":"Learn more about [European Journal of Pediatrics](https://www.springer.com/journal/431)","snPcode":"431","submissionUrl":"https://submission.nature.com/new-submission/431/3","title":"European Journal of Pediatrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Neurodevelopment, Preschool, Digital media, Digital technology, Screen time, Screen content","lastPublishedDoi":"10.21203/rs.3.rs-7571507/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7571507/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eFew studies have analyzed preschoolers\u0026rsquo; screen exposure patterns, especially combined screen time and content, and their neurodevelopmental impacts.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e\u003cp\u003eThis study aims to identify the screen exposure patterns in preschoolers by intelligent technology, and to examine their associations with their neurodevelopment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis cross-sectional study enrolled preschool children from two kindergartens in Shanghai. Screen time and content types were monitored over 7 consecutive days using a validated intelligent monitoring technology. Neurodevelopmental outcomes were assessed using the Ages and Stages Questionnaire, Third Edition (ASQ-3). K-means clustering analysis identified screen exposure patterns, and binary logistic regression was applied to examine associations between screen exposure patterns and neurodevelopmental outcomes.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOf 355 preschool children included, 204 were boys (57.5%) and 251 (70.7%) were aged between 34.5 months and 50.5 months. K-means cluster analysis yielded 4 screen exposure patterns: restrictive use, moderately educational, noneducational, and educational-dominant pattern. Binary logistic regression showed the moderately educational pattern linked to gross motor abnormalities (OR\u0026thinsp;=\u0026thinsp;2.530, 95% CI: 1.089\u0026ndash;5.875, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), and non - educational to fine motor abnormalities (OR\u0026thinsp;=\u0026thinsp;3.172, 95% CI: 1.122\u0026ndash;8.968, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.029).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis monitoring study identified heterogeneous screen exposure patterns in preschool-aged children, revealing that excessive use of moderately educational content and noneducational content was associated with lower gross motor and fine motor skills. When limiting total screen time, parents should focus on content selection for preschool-aged children. Future research should focus on the objective measurement of different types of screen content.\u003c/p\u003e","manuscriptTitle":"Screen patterns and neurodevelopmental delay in preschoolers: an intelligent monitoring study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-22 10:01:04","doi":"10.21203/rs.3.rs-7571507/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-10-31T18:45:35+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-30T23:44:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47563156253224765301984715982289816798","date":"2025-09-18T13:47:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"10148464338373094445122887444615906893","date":"2025-09-17T08:42:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-12T14:31:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"186495014138424617275314354038588286972","date":"2025-09-11T21:25:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-11T19:44:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-11T19:43:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-11T02:44:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Pediatrics","date":"2025-09-09T08:35:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ejpe","sideBox":"Learn more about [European Journal of Pediatrics](https://www.springer.com/journal/431)","snPcode":"431","submissionUrl":"https://submission.nature.com/new-submission/431/3","title":"European Journal of Pediatrics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"749bd950-6ba8-4256-84a0-a18021a153eb","owner":[],"postedDate":"September 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:09:22+00:00","versionOfRecord":{"articleIdentity":"rs-7571507","link":"https://doi.org/10.1007/s00431-026-06749-1","journal":{"identity":"european-journal-of-pediatrics","isVorOnly":false,"title":"European Journal of Pediatrics"},"publishedOn":"2026-01-27 15:59:26","publishedOnDateReadable":"January 27th, 2026"},"versionCreatedAt":"2025-09-22 10:01:04","video":"","vorDoi":"10.1007/s00431-026-06749-1","vorDoiUrl":"https://doi.org/10.1007/s00431-026-06749-1","workflowStages":[]},"version":"v1","identity":"rs-7571507","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7571507","identity":"rs-7571507","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}