Screen Time Patterns and Cognitive Screening Outcomes (MoCA-Ina) in Adolescents: A Retrospective Study

Screen Time Patterns and Cognitive Screening Outcomes (MoCA-Ina) in Adolescents: A Retrospective Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Screen Time Patterns and Cognitive Screening Outcomes (MoCA-Ina) in Adolescents: A Retrospective Study Pricilla Yani Gunawan, Andraina Andraina, Jeremiah Hilkiah Wijaya, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8838273/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Background Adolescent screen exposure is increasing, yet clinically interpretable thresholds for cognitive risk are unclear. This study examined associations between daily screen time and cognitive function and derived a screen-time cutoff linked to cognitive impairment. Methods We conducted an observational cross-sectional study (March–April 2022) at a private junior high school in Indonesia during online learning. Students completed digital questionnaires reporting educational and recreational screen time and a directly reported overall estimate; a computed overall (educational + recreational) was generated to assess reporting consistency. Results Sixty-seven adolescents were included (34 girls, 50.7%; 33 boys, 49.3%), with median age 13.0 years (12.0–16.0) and median MoCA-Ina 25.0 (19.0–31.0). MoCA-Ina did not differ by sex (girls 25.0 [19.0–31.0] vs boys 26.0 [20.0–30.0]; p = 0.244). Recreational screen time correlated inversely with MoCA-Ina (ρ=−0.446, p < 0.001), as did overall screen time (ρ=−0.360, p < 0.01), whereas educational screen time was not associated (ρ=−0.061, p = 0.624). In adjusted regression, overall screen time remained negatively associated with MoCA-Ina (β=−0.24 per hour/day; 95% CI − 0.41 to − 0.07; p = 0.007), while age was positively associated (β = 0.96; 95% CI 0.07 to 1.85; p = 0.034). ROC analysis showed fair discrimination (AUC 0.66) with an optimal cutoff > 8.97 h/day (sensitivity 83.3%, specificity 48.8%, PPV 47.6%, NPV 84.0%); risk of impairment was higher above the cutoff (RR 2.98; 95% CI 1.15–7.72; p = 0.010; OR 4.77; 95% CI 1.40–16.31). Conclusions High daily screen exposure was associated with poorer cognitive screening performance, and a > 8.97-hour/day threshold may help identify adolescents at elevated likelihood of cognitive impairment. Trial Registration 071/K-LKJ/ETIK/II/2022 Adolescent Cognition ROC Curve Screen time Figures Figure 1 Figure 2 Figure 3 Background Digital media has become a near-continuous feature of adolescent life, with population surveillance and nationally representative surveys showing that many youths spend substantial portions of the day on screens outside of schoolwork.[ 1 ] In Indonesia, internet access is now widespread; the Indonesian Internet Service Providers Association (APJII) reported national internet penetration of 79.5% in 2024 (221.6 million users).[ 2 ] Among children and adolescents, UNICEF Online Knowledge and Practice baseline study (2023) found that the vast majority of children use the internet daily for an average of 5.4 hours per day.[ 3 ] From a neurodevelopmental standpoint, adolescence is a period of heightened sensitivity because large-scale remodeling of cortical and subcortical circuitry is still underway, including synaptic pruning and progressive myelination that refine efficiency in prefrontal and association networks supporting cognitive control.[ 4 ] Several mechanistic pathways plausibly link heavier screen exposure to cognitive outcomes: (i) sleep and circadian disruption, which is consistently associated with screen use in the pediatric sleep literature; (ii) attentional fragmentation and reduced practice of sustained, effortful cognition; and (iii) displacement of developmentally protective behaviors such as physical activity, face-to-face social interaction, and cognitively enriching leisure.[ 5 , 6 ] Accordingly, this study aimed to quantify the association between adolescent screen time (recreational and educational) and cognitive function measured by MoCA-Ina, and to derive a clinically informative screen-time threshold associated with increased likelihood of cognitive impairment. Methods Study design and participants This observational cross-sectional study was carried out from March 2022 to April 2022 at a private junior high school while online learning was being implemented. Ethical clearance was obtained from the Medical Research Ethical Committee (071/K-LKJ/ETIK/II/2022). This is in accordance with the Declaration of Helsinki. The population comprised junior high school students, with the exposure defined as differing amounts of daily screen use, contrasted with lower screen exposure, and the outcome defined as cognitive function. Students were eligible if they were actively enrolled in the online learning program and provided informed consent. Participants were selected through purposive sampling. After consent, students completed digital questionnaires that gathered demographic information and detailed patterns of daily screen use. Research variables and data collection The primary dependent variable was cognitive function, measured using the Indonesian version of the Montreal Cognitive Assessment (MoCA-Ina).[ 7 ] To standardize administration and maintain inter-rater consistency, the assessment was conducted face-to-face by a single trained assessor. The MoCA is a commonly used screening tool for mild cognitive dysfunction that evaluates multiple domains, including attention, concentration, executive function, memory, language, visuospatial skills, abstraction, calculation, and orientation.[ 8 ] For classification in diagnostic analyses, two MoCA-Ina thresholds were considered: <24 to indicate possible cognitive impairment and < 26 as the conventional “normal” cutoff. The < 24 threshold was used as the primary definition of impairment for ROC-based diagnostic performance, whereas the < 26 cutoff was treated as a normative comparator to contextualize classification stringency across thresholds.[ 9 ] To evaluate the consistency of using the impairment definition (< 24) relative to the normal reference cutoff (26), agreement between these cutoff-based classifications was assessed using Bland–Altman methodology. The main independent variable was duration of screen time. Students self-reported daily screen exposure and separated it into educational screen time (school-related activities and homework) and recreational screen time (gaming, social media, and entertainment). To assess internal consistency of reporting, an overall screen-time estimate was additionally computed by summing educational and recreational durations and was compared with the “overall” screen time value reported directly by the students. Potential confounders were prespecified and accounted for in multivariable analyses, including child age, sex, breakfast habits, maternal age, and maternal education. Age and sex were included because adolescent neurodevelopment, such as trajectories of gray matter change and executive-function maturation, varies across age and differs by biological sex.[ 10 ] Maternal education was recorded as a proxy indicator of socioeconomic context, which is known to influence neurocognitive development through environmental pathways.[ 11 ] Breakfast habits were assessed given evidence linking morning food intake with short-term cognitive performance in school-aged children, particularly attention and memory outcomes.[ 12 ] Statistical analysis All analyses were conducted in RStudio (Version 2026.04.1 + 583, Posit Software, PBC) using the tidyverse, pROC, and BlandAltmanLeh packages. Distributional assumptions were evaluated using the Shapiro–Wilk test together with histogram inspection. Demographic and study variables were summarized using descriptive statistics. Associations between continuous predictors and MoCA-Ina scores were explored with correlation analysis, applying Pearson correlation for normally distributed variables (mother’s age) and Spearman rank correlation for non-normally distributed variables (child age, screen-time measures, and MoCA-Ina scores). Agreement between directly reported overall screen time and the computed total (educational plus recreational screen time) was examined using Bland–Altman methods. A difference plot was produced and limits of agreement were calculated as the mean difference ± 1.96 standard deviations. Multivariable linear regression was then performed to identify independent predictors of MoCA-Ina scores while adjusting for the prespecified confounders. For diagnostic evaluation, the performance of overall screen time in identifying possible cognitive impairment was assessed using receiver operating characteristic (ROC) curve analysis, with impairment defined primarily as MoCA-Ina < 24. The area under the curve (AUC) was reported with 95% confidence intervals. In addition, Bland–Altman analysis was used to assess the consistency of classification when applying the < 24 impairment cutoff compared with the conventional < 26 “normal” cutoff, thereby quantifying agreement and potential systematic differences between these two threshold-based definitions. All tests were two-sided, and statistical significance was defined as p < 0.05. Results Participant Characteristics and Descriptive Statistics A total of 67 children were included in the study, consisting of 34 girls (50.7%) and 33 boys (49.3%). The baseline characteristics of the study population are summarized in Table 1 . The median age of the children was 13.00 years (IQR 12.00–16.00), and the mean mother’s age was 41.93 ± 5.20 years. Table 1 Participant characteristics by sex Variable Overall (n = 67) Girls (n = 34) Boys (n = 33) P value Mother age (years) 41.93 ± 5.20 43.29 ± 4.90 40.52 ± 5.18 0.028 # Child age (years) 13.00 (12.00–16.00) 14.00 (12.00–15.00) 13.00 (12.00–16.00) 0.171 $ Recreational screen time 5.80 (0.61–16.68) 6.13 (0.61–16.68) 5.48 (1.72–15.86) 0.526 $ Educational screen time 3.86 (0.71–12.00) 4.00 (0.71–6.29) 3.46 (0.75–12.00) 0.390 $ Overall screen time 9.99 (2.48–21.15) 10.20 (3.77–21.15) 9.57 (2.48–21.00) 0.510 $ MoCA-Ina score 25.00 (19.00–31.00) 25.00 (19.00–31.00) 26.00 (20.00–30.00) 0.244 $ Breakfast habits, n (%) 1.00* No 20 (29.9%) 10 (29.4%) 10 (30.3%) Yes 47 (70.1%) 24 (70.6%) 23 (69.7%) Mother’s education, n (%) 0.191* Primary 44 (65.7%) 24 (70.6%) 20 (60.6%) Secondary higher 2 (3.0%) 0 (0.0%) 2 (6.1%) Secondary lower 19 (28.4%) 8 (23.5%) 11 (33.3%) Tertiary 2 (3.0%) 2 (5.9%) 0 (0.0%) Values are mean ± SD (approximately normal) or median (min–max) (non-normal). Categorical variables are n (%). P values compare Girls vs Boys. # Independent samples t-test. $ Mann–Whitney U test. *Chi-square test or Fisher’s exact test (used when expected cell counts were small). There were no statistically significant differences between girls and boys regarding child age (p = 0.171), recreational screen time (p = 0.526), educational screen time (p = 0.390), or overall screen time (p = 0.510). Similarly, cognitive performance, as measured by the MoCA-Ina score, did not differ significantly by sex (Median: 25.00 vs. 26.00, p = 0.244; Fig. 1 ). Most participants (70.1%) reported eating breakfast, and most mothers (65.7%) had a primary education level. Correlates and Predictors of Cognitive Performance In bivariate analyses, higher recreational screen time showed a moderate inverse correlation with MoCA-Ina scores (ρ = −0.446, p < 0.001), and overall screen time was also negatively correlated with MoCA-Ina (ρ = −0.360, p < 0.01) (Fig. 1 ; Table 2 ). In contrast, educational screen time (ρ = −0.061, p = 0.624), child age (ρ = 0.191, p = 0.122), and mother’s age (r = − 0.076, p = 0.543) were not significantly associated with MoCA-Ina in unadjusted analyses (Table 2 ). After adjustment in the multivariable linear regression model, overall screen time remained independently associated with lower cognitive scores (β = −0.24 per additional hour/day; 95% CI − 0.41 to − 0.07; p = 0.007), while child age was positively associated with MoCA-Ina (β = 0.96; 95% CI 0.07 to 1.85; p = 0.034) (Table 3 ). Table 2 Correlation between predictors and outcome Predictor Correlation coefficient P value Mother age r = − 0.076¹ 0.543 Child age ρ = 0.191² 0.122 Recreational screen time ρ = −0.446² < 0.001 Educational screen time ρ = −0.061² 0.624 Overall screen time ρ = −0.360² < 0.01 Table 3 Multivariable linear regression model for outcome (adjusted associations) Variable β (95% CI) P value (Intercept) 17.34 (5.08 to 29.60) 0.006* Sex: Boys (ref: Girls) 1.13 (− 0.58 to 2.84) 0.191 Child age 0.96 (0.07 to 1.85) 0.034* Mother age −0.06 (− 0.23 to 0.11) 0.479 Breakfast: Yes (ref: No) −0.64 (− 2.53 to 1.24) 0.498 Overall screen time −0.24 (− 0.41 to − 0.07) 0.007* Mother’s education: Secondary higher (ref: Primary) −1.01 (− 6.19 to 4.16) 0.696 Mother’s education: Secondary lower (ref: Primary) −0.53 (− 2.47 to 1.40) 0.583 Mother’s education: Tertiary (ref: Primary) 0.90 (− 3.83 to 5.63) 0.704 Diagnostic Value of Screen Time for Cognitive Impairment ROC analysis indicated that overall screen time had fair discrimination for cognitive impairment (MoCA 8.97 hours/day (Fig. 2 ), which classified 42 children as “high screen time” and 25 as “low screen time” (Table 4 ). Using this threshold, sensitivity was 83.3% (20/24 impaired correctly identified) and specificity was 48.8% (21/43 normals correctly identified), with a positive predictive value of 47.6% (20/42) and a negative predictive value of 84.0% (21/25) (Table 4 ). The risk of impairment was 47.6% in the high screen time group versus 16.0% in the low screen time group, corresponding to a relative risk of 2.98 (95% CI 1.15–7.72; p = 0.010) (Fig. 2 ; Table 4 ); the corresponding odds ratio from the 2×2 table was 4.77 (95% CI 1.40–16.31) (Table 4 ). The Bland–Altman plot shows no systematic bias between observed and predicted MoCA-Ina scores (mean bias = 0.00). However, the 95% limits of agreement are relatively wide (− 5.87 to + 5.87), suggesting meaningful individual-level prediction error that could affect classification for scores near a cutoff such as 26 (Fig. 3 ). Table 4 2x2 Contingency Table High Screen Time (≥ 8.97h) Impaired (MoCA < 24) Normal (MoCA ≥ 24) Total 20 22 42 Low Screen Time (< 8.97h) 4 21 25 Total 24 43 67 Discussion Principal Findings and Interpretations Our findings support the broader literature suggesting that heavier recreational screen exposure is more consistently linked to weaker cognitive performance, likely because it clusters with behaviors that disrupt neurodevelopmental “inputs” such as sleep regularity, sustained attention, and cognitively enriching activities.[ 13 ] Large-scale evidence aligns with this pattern: in a prospective cohort study, higher screen time predicted later developmental outcomes with small but measurable cross-lagged effects (e.g., standardized β = −0.06 at 24 months and β = −0.08 at 36 months).[ 14 ] Importantly, meta-analytic work emphasizes that context matters as much as duration program viewing and background TV exposure show pooled negative correlations with cognitive outcomes (e.g., r = − 0.16 for program viewing; r = − 0.10 for background TV), while co-use with caregivers can be positively associated with cognition (e.g., r = 0.14), supporting the idea that social scaffolding and content quality modify neurocognitive risk.[ 15 ] These associations are plausible: frequent rapid-reward, high-salience media can bias dopaminergic reward learning toward immediate reinforcement, reduce tolerance for delayed reward, and fragment attentional control striatal networks.[ 16 , 17 ] Diagnostic Thresholds and Clinical Implications While our study proposes a practical threshold for identifying higher-risk adolescents, the clinical message should be framed less as a single number and more as a signal of cumulative exposure plus behavioral patterning. Population data show that meeting recommended limits on recreational screen time is consistently associated with better cognition: in ABCD (n = 4,524; ages 9–10), meeting the screen-only recommendation was associated with notably higher global cognition (β = 4.25 points) compared with meeting none, and the combination of meeting sleep + screen recommendations showed similarly strong associations (β = 5.15).[ 18 ] This “whole-day” framing is clinically useful: rather than only counseling reduction in total hours, clinicians can target the most neurobiologically sensitive windows (late evening), prioritize content and co-use, and protect sleep routines.[ 18 ] Sociodemographic Factors and Null Findings Null associations for sex, breakfast habits, and maternal education in our sample are not inconsistent with prior work showing that screen–cognition relationships are often small in magnitude and highly dependent on measurement, content type, and confounding by family routines. For example, a meta-analysis examining screen media use and academic performance found overall effects that were generally small (e.g., effect sizes around − 0.05 to − 0.07), implying that many studies will not detect subgroup differences unless samples are large and exposures are precisely characterized.[ 19 ] Additionally, “education” and “SES” effects may be partially diluted when screen exposure is pervasive across strata, and when the quality of the screen ecology (co-use, content appropriateness, bedtime access) drives outcomes more than raw duration.[ 20 ] Strengths and Limitations A key strength is modeling cognitive outcomes with adjustment for major covariates while distinguishing types of exposure, which aligns with the literature’s move away from a single-dimensional “screen time” metric.[ 21 ] However, the cross-sectional design cannot rule out reverse causality (e.g., lower baseline cognitive control increasing susceptibility to prolonged or problematic media use), and self-reported exposure can misclassify timing and content.[ 22 ] Mechanistic interpretation should therefore lean on converging evidence: neuroimaging work has linked higher screen-based media use to differences in neurodevelopmentally relevant brain metrics.[ 23 ] Separately, screen time is associated with changes in attention-related outcomes and brain structure, including reduced cortical thickness in frontal regions that support executive control and self-regulation.[ 24 ] These convergent findings strengthen biological plausibility, even when any single observational study cannot establish causality. Conclusions Overall, our findings fit best with a neurodevelopmental model in which heavier recreational screen exposure contributes to cognitive risk primarily through behavioral displacement (sleep, reading, physical activity, social interaction) and neurocognitive interference (attention fragmentation, reward sensitization, reduced executive control practice). Sleep appears to be a particularly important pathway: a meta-analysis of portable screen-based device use found substantially higher odds of inadequate sleep quantity and excessive daytime sleepiness with bedtime device use, patterns that are directly relevant to memory consolidation and prefrontal functioning. Experimental sleep/circadian research further supports plausibility by showing that evening light-emitting device reading can suppress melatonin and delay circadian timing relative to printed reading. Taken together, the practical implication is to prioritize reducing passive/recreational and evening screen exposure, encourage co-use and age-appropriate content when screens are used, and protect sleep as a core neurocognitive safeguard. Abbreviations ABCD Adolescent Brain Cognitive Development (study) APJII Indonesian Internet Service Providers Association (Asosiasi Penyelenggara Jasa Internet Indonesia) AUC area under the curve CI confidence interval IQR interquartile range MoCA Montreal Cognitive Assessment MoCA-Ina Indonesian version of the Montreal Cognitive Assessment PBC Public Benefit Corporation PICO Population, Intervention/Exposure, Comparison, Outcome pROC an R package for ROC curve analysis ROC receiver operating characteristic SES socioeconomic status TV television UNICEF United Nations Children’s Fund. Declarations CONFLICT OF INTEREST None Funding sources None. Conflicts of interest/Competing interests The authors declare that they have no conflicts of interest and no competing interests. Ethics approval This retrospective cohort study was reviewed and approved by the institutional ethics committee – Ethics Committee of the Faculty of Medicine, Pelita Harapan University (Approval No. : 071/K-LKJ/ETIK/II/2022) and was conducted in accordance with the Declaration of Helsinki and applicable local regulations. Consent to participate Informed consent was obtained from all individual participants included in the study. Consent for publication Written informed consent for the publication of clinical details and any accompanying non-identifying data was obtained from the patients/participants (and their parents/legal guardians, given the adolescent demographic). There are no identifying images included in this study. FUNDING No funding was received for the preparation or publication of this article Author Contribution PYG: Concept., Meth., Superv., W–R&E; A: Data cur., Inv., W–OD; JHW: Formal anal., Viz., W–R&E; YEA: Concept., Meth., Inv., Superv., W–R&E; PLG: Meth., Res., W–R&E. All auth. read & appr. the final MS. ACKNOWLEDGEMENTS None Data Availability The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. References Nuvoli V, Camanni M, Mariani I, Ponte S, Black M, Lazzerini M. Digital screen exposure in infants, children and adolescents: a systematic review of existing recommendations. Public Health Pract. 2025;10:100653. https://doi.org/10.1016/j.puhip.2025.100653 . Asosiasi Penyelenggara Jasa Internet Indonesia (APJII). APJII Jumlah Pengguna Internet Indonesia Tembus 221 Juta Orang. APJII Jumlah Pengguna Internet Indonesia Tembus 221 Juta Orang. 2024. UNICEF. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8838273","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618949513,"identity":"df0c9bb4-dc7d-43b1-9e35-c536aa441991","order_by":0,"name":"Pricilla Yani Gunawan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYFACxgaGhAILCDuhAkiwg8UIaTGQgGo5w8DAw0xQCwjAtDC2EaHFXPpw44cHBhLyDBLJzx48nGeTuJ+Z+eDDGQx2cro49Fn2JTZLAB1m2CCRZm6QuC0tsYeZLdlwA0OysdkBHE46A3QMUAtjg0SCmUTitsNALTxmkg8YDiRuI6DFvkEi/ZtE4hwStCQ2SOQAbWmAatmAR4tlDyPYL8ltPG/KJBKOpRn3HAb6ZYYBbr+Y87A//Pijwsa2nz19m+SPGhvZ9vbmgw97KuzkcHofxmATSMAqjkcLAz8OQ0fBKBgFo2AUAAB4zFUvYvRsTgAAAABJRU5ErkJggg==","orcid":"","institution":"Universitas Pelita Harapan","correspondingAuthor":true,"prefix":"","firstName":"Pricilla","middleName":"Yani","lastName":"Gunawan","suffix":""},{"id":618949514,"identity":"e0bd87c1-adb7-4093-b2a1-dbacfeba1e18","order_by":1,"name":"Andraina Andraina","email":"","orcid":"","institution":"Universitas Pelita Harapan","correspondingAuthor":false,"prefix":"","firstName":"Andraina","middleName":"","lastName":"Andraina","suffix":""},{"id":618949515,"identity":"a58b0019-402f-4ef1-84a0-ca4aaa45a39e","order_by":2,"name":"Jeremiah Hilkiah Wijaya","email":"","orcid":"","institution":"Monash University","correspondingAuthor":false,"prefix":"","firstName":"Jeremiah","middleName":"Hilkiah","lastName":"Wijaya","suffix":""},{"id":618949516,"identity":"2a1302d8-921a-4138-8931-3a3a2ed17d23","order_by":3,"name":"Yang Yang Endro Arjuna","email":"","orcid":"","institution":"Universitas Pelita Harapan","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"Yang Endro","lastName":"Arjuna","suffix":""},{"id":618949517,"identity":"dbda13a2-56b1-4edd-b9e3-26d209bb6bb5","order_by":4,"name":"Patricia Yulita Gunawan","email":"","orcid":"","institution":"Universitas Pelita Harapan","correspondingAuthor":false,"prefix":"","firstName":"Patricia","middleName":"Yulita","lastName":"Gunawan","suffix":""}],"badges":[],"createdAt":"2026-02-10 08:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8838273/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8838273/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106456279,"identity":"569024bb-f3e4-4b04-89c9-5deed345071d","added_by":"auto","created_at":"2026-04-08 18:05:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":45707,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBar chart showing the distribution of MoCA-Ina by sex and breakfast fabits.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-8838273/v1/893674ac5ed2a5644b57ab37.png"},{"id":106724177,"identity":"62cb3b8c-6936-4253-9658-edee148fb4e6","added_by":"auto","created_at":"2026-04-12 18:26:26","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":49891,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-8838273/v1/de08277ba3f85c63b2e46971.png"},{"id":106456281,"identity":"008b4fe7-8411-47df-b50a-ffa8695c94d1","added_by":"auto","created_at":"2026-04-08 18:05:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61870,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBland-Altman Plot.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-8838273/v1/abecbf3573ac9a209dc4ff1b.png"},{"id":106726069,"identity":"f7d4d252-ae29-489b-830f-093680a916db","added_by":"auto","created_at":"2026-04-12 18:35:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1017765,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8838273/v1/05764f78-f9f6-467b-a111-235715041859.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Screen Time Patterns and Cognitive Screening Outcomes (MoCA-Ina) in Adolescents: A Retrospective Study","fulltext":[{"header":"Background","content":"\u003cp\u003eDigital media has become a near-continuous feature of adolescent life, with population surveillance and nationally representative surveys showing that many youths spend substantial portions of the day on screens outside of schoolwork.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] In Indonesia, internet access is now widespread; the Indonesian Internet Service Providers Association (APJII) reported national internet penetration of 79.5% in 2024 (221.6\u0026nbsp;million users).[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] Among children and adolescents, UNICEF Online Knowledge and Practice baseline study (2023) found that the vast majority of children use the internet daily for an average of 5.4 hours per day.[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFrom a neurodevelopmental standpoint, adolescence is a period of heightened sensitivity because large-scale remodeling of cortical and subcortical circuitry is still underway, including synaptic pruning and progressive myelination that refine efficiency in prefrontal and association networks supporting cognitive control.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] Several mechanistic pathways plausibly link heavier screen exposure to cognitive outcomes: (i) sleep and circadian disruption, which is consistently associated with screen use in the pediatric sleep literature; (ii) attentional fragmentation and reduced practice of sustained, effortful cognition; and (iii) displacement of developmentally protective behaviors such as physical activity, face-to-face social interaction, and cognitively enriching leisure.[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] Accordingly, this study aimed to quantify the association between adolescent screen time (recreational and educational) and cognitive function measured by MoCA-Ina, and to derive a clinically informative screen-time threshold associated with increased likelihood of cognitive impairment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and participants\u003c/h2\u003e \u003cp\u003eThis observational cross-sectional study was carried out from March 2022 to April 2022 at a private junior high school while online learning was being implemented. Ethical clearance was obtained from the Medical Research Ethical Committee (071/K-LKJ/ETIK/II/2022). This is in accordance with the Declaration of Helsinki. The population comprised junior high school students, with the exposure defined as differing amounts of daily screen use, contrasted with lower screen exposure, and the outcome defined as cognitive function. Students were eligible if they were actively enrolled in the online learning program and provided informed consent. Participants were selected through purposive sampling. After consent, students completed digital questionnaires that gathered demographic information and detailed patterns of daily screen use.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eResearch variables and data collection\u003c/h3\u003e\n\u003cp\u003eThe primary dependent variable was cognitive function, measured using the Indonesian version of the Montreal Cognitive Assessment (MoCA-Ina).[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] To standardize administration and maintain inter-rater consistency, the assessment was conducted face-to-face by a single trained assessor. The MoCA is a commonly used screening tool for mild cognitive dysfunction that evaluates multiple domains, including attention, concentration, executive function, memory, language, visuospatial skills, abstraction, calculation, and orientation.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eFor classification in diagnostic analyses, two MoCA-Ina thresholds were considered: \u0026lt;24 to indicate possible cognitive impairment and \u0026lt;\u0026thinsp;26 as the conventional \u0026ldquo;normal\u0026rdquo; cutoff. The \u0026lt;\u0026thinsp;24 threshold was used as the primary definition of impairment for ROC-based diagnostic performance, whereas the \u0026lt;\u0026thinsp;26 cutoff was treated as a normative comparator to contextualize classification stringency across thresholds.[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] To evaluate the consistency of using the impairment definition (\u0026lt;\u0026thinsp;24) relative to the normal reference cutoff (26), agreement between these cutoff-based classifications was assessed using Bland\u0026ndash;Altman methodology.\u003c/p\u003e \u003cp\u003eThe main independent variable was duration of screen time. Students self-reported daily screen exposure and separated it into educational screen time (school-related activities and homework) and recreational screen time (gaming, social media, and entertainment). To assess internal consistency of reporting, an overall screen-time estimate was additionally computed by summing educational and recreational durations and was compared with the \u0026ldquo;overall\u0026rdquo; screen time value reported directly by the students.\u003c/p\u003e \u003cp\u003ePotential confounders were prespecified and accounted for in multivariable analyses, including child age, sex, breakfast habits, maternal age, and maternal education. Age and sex were included because adolescent neurodevelopment, such as trajectories of gray matter change and executive-function maturation, varies across age and differs by biological sex.[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] Maternal education was recorded as a proxy indicator of socioeconomic context, which is known to influence neurocognitive development through environmental pathways.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Breakfast habits were assessed given evidence linking morning food intake with short-term cognitive performance in school-aged children, particularly attention and memory outcomes.[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were conducted in RStudio (Version 2026.04.1\u0026thinsp;+\u0026thinsp;583, Posit Software, PBC) using the tidyverse, pROC, and BlandAltmanLeh packages. Distributional assumptions were evaluated using the Shapiro\u0026ndash;Wilk test together with histogram inspection. Demographic and study variables were summarized using descriptive statistics. Associations between continuous predictors and MoCA-Ina scores were explored with correlation analysis, applying Pearson correlation for normally distributed variables (mother\u0026rsquo;s age) and Spearman rank correlation for non-normally distributed variables (child age, screen-time measures, and MoCA-Ina scores).\u003c/p\u003e \u003cp\u003eAgreement between directly reported overall screen time and the computed total (educational plus recreational screen time) was examined using Bland\u0026ndash;Altman methods. A difference plot was produced and limits of agreement were calculated as the mean difference\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 standard deviations. Multivariable linear regression was then performed to identify independent predictors of MoCA-Ina scores while adjusting for the prespecified confounders.\u003c/p\u003e \u003cp\u003eFor diagnostic evaluation, the performance of overall screen time in identifying possible cognitive impairment was assessed using receiver operating characteristic (ROC) curve analysis, with impairment defined primarily as MoCA-Ina\u0026thinsp;\u0026lt;\u0026thinsp;24. The area under the curve (AUC) was reported with 95% confidence intervals. In addition, Bland\u0026ndash;Altman analysis was used to assess the consistency of classification when applying the \u0026lt;\u0026thinsp;24 impairment cutoff compared with the conventional\u0026thinsp;\u0026lt;\u0026thinsp;26 \u0026ldquo;normal\u0026rdquo; cutoff, thereby quantifying agreement and potential systematic differences between these two threshold-based definitions. All tests were two-sided, and statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics and Descriptive Statistics\u003c/h2\u003e \u003cp\u003eA total of 67 children were included in the study, consisting of 34 girls (50.7%) and 33 boys (49.3%). The baseline characteristics of the study population are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age of the children was 13.00 years (IQR 12.00\u0026ndash;16.00), and the mean mother\u0026rsquo;s age was 41.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.20 years.\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\u003eParticipant characteristics by sex\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eOverall (n\u0026thinsp;=\u0026thinsp;67)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGirls (n\u0026thinsp;=\u0026thinsp;34)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBoys (n\u0026thinsp;=\u0026thinsp;33)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.93\u0026thinsp;\u0026plusmn;\u0026thinsp;5.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.29\u0026thinsp;\u0026plusmn;\u0026thinsp;4.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.52\u0026thinsp;\u0026plusmn;\u0026thinsp;5.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.028\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.00 (12.00\u0026ndash;16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.00 (12.00\u0026ndash;15.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.00 (12.00\u0026ndash;16.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecreational screen time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.80 (0.61\u0026ndash;16.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.13 (0.61\u0026ndash;16.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.48 (1.72\u0026ndash;15.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational screen time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.86 (0.71\u0026ndash;12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.00 (0.71\u0026ndash;6.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.46 (0.75\u0026ndash;12.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.390\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall screen time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.99 (2.48\u0026ndash;21.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.20 (3.77\u0026ndash;21.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.57 (2.48\u0026ndash;21.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.510\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA-Ina score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.00 (19.00\u0026ndash;31.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.00 (19.00\u0026ndash;31.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.00 (20.00\u0026ndash;30.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.244\u003csup\u003e$\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBreakfast habits, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (29.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10 (30.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47 (70.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23 (69.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMother\u0026rsquo;s education, n (%)\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=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.191*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44 (65.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (60.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTertiary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2 (3.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD (approximately normal) or median (min\u0026ndash;max) (non-normal). Categorical variables are n (%). P values compare Girls vs Boys.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e#\u003c/sup\u003eIndependent samples t-test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003e$\u003c/sup\u003eMann\u0026ndash;Whitney U test.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e*Chi-square test or Fisher\u0026rsquo;s exact test (used when expected cell counts were small).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere were no statistically significant differences between girls and boys regarding child age (p\u0026thinsp;=\u0026thinsp;0.171), recreational screen time (p\u0026thinsp;=\u0026thinsp;0.526), educational screen time (p\u0026thinsp;=\u0026thinsp;0.390), or overall screen time (p\u0026thinsp;=\u0026thinsp;0.510). Similarly, cognitive performance, as measured by the MoCA-Ina score, did not differ significantly by sex (Median: 25.00 vs. 26.00, p\u0026thinsp;=\u0026thinsp;0.244; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most participants (70.1%) reported eating breakfast, and most mothers (65.7%) had a primary education level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eCorrelates and Predictors of Cognitive Performance\u003c/h2\u003e \u003cp\u003eIn bivariate analyses, higher recreational screen time showed a moderate inverse correlation with MoCA-Ina scores (ρ = \u0026minus;0.446, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and overall screen time was also negatively correlated with MoCA-Ina (ρ = \u0026minus;0.360, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In contrast, educational screen time (ρ = \u0026minus;0.061, p\u0026thinsp;=\u0026thinsp;0.624), child age (ρ\u0026thinsp;=\u0026thinsp;0.191, p\u0026thinsp;=\u0026thinsp;0.122), and mother\u0026rsquo;s age (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.076, p\u0026thinsp;=\u0026thinsp;0.543) were not significantly associated with MoCA-Ina in unadjusted analyses (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). After adjustment in the multivariable linear regression model, overall screen time remained independently associated with lower cognitive scores (β = \u0026minus;0.24 per additional hour/day; 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.41 to \u0026minus;\u0026thinsp;0.07; p\u0026thinsp;=\u0026thinsp;0.007), while child age was positively associated with MoCA-Ina (β\u0026thinsp;=\u0026thinsp;0.96; 95% CI 0.07 to 1.85; p\u0026thinsp;=\u0026thinsp;0.034) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation between predictors and outcome\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.076\u0026sup1;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.543\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ\u0026thinsp;=\u0026thinsp;0.191\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.122\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRecreational screen time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ = \u0026minus;0.446\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational screen time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ = \u0026minus;0.061\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall screen time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eρ = \u0026minus;0.360\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\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\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\u003eMultivariable linear regression model for outcome (adjusted associations)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Intercept)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.34 (5.08 to 29.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex: Boys (ref: Girls)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13 (\u0026minus;\u0026thinsp;0.58 to 2.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96 (0.07 to 1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.06 (\u0026minus;\u0026thinsp;0.23 to 0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.479\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBreakfast: Yes (ref: No)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.64 (\u0026minus;\u0026thinsp;2.53 to 1.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOverall screen time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.24 (\u0026minus;\u0026thinsp;0.41 to \u0026minus;\u0026thinsp;0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s education: Secondary higher (ref: Primary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;1.01 (\u0026minus;\u0026thinsp;6.19 to 4.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s education: Secondary lower (ref: Primary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.53 (\u0026minus;\u0026thinsp;2.47 to 1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.583\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u0026rsquo;s education: Tertiary (ref: Primary)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (\u0026minus;\u0026thinsp;3.83 to 5.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eDiagnostic Value of Screen Time for Cognitive Impairment\u003c/h3\u003e\n\u003cp\u003eROC analysis indicated that overall screen time had fair discrimination for cognitive impairment (MoCA\u0026thinsp;\u0026lt;\u0026thinsp;24), with an AUC of 0.66 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The optimal cutoff was \u0026gt;\u0026thinsp;8.97 hours/day (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which classified 42 children as \u0026ldquo;high screen time\u0026rdquo; and 25 as \u0026ldquo;low screen time\u0026rdquo; (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Using this threshold, sensitivity was 83.3% (20/24 impaired correctly identified) and specificity was 48.8% (21/43 normals correctly identified), with a positive predictive value of 47.6% (20/42) and a negative predictive value of 84.0% (21/25) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The risk of impairment was 47.6% in the high screen time group versus 16.0% in the low screen time group, corresponding to a relative risk of 2.98 (95% CI 1.15\u0026ndash;7.72; p\u0026thinsp;=\u0026thinsp;0.010) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e); the corresponding odds ratio from the 2\u0026times;2 table was 4.77 (95% CI 1.40\u0026ndash;16.31) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The Bland\u0026ndash;Altman plot shows no systematic bias between observed and predicted MoCA-Ina scores (mean bias\u0026thinsp;=\u0026thinsp;0.00). However, the 95% limits of agreement are relatively wide (\u0026minus;\u0026thinsp;5.87 to +\u0026thinsp;5.87), suggesting meaningful individual-level prediction error that could affect classification for scores near a cutoff such as 26 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e2x2 Contingency Table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eHigh Screen Time (\u0026ge;\u0026thinsp;8.97h)\u003c/b\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpaired (MoCA\u0026thinsp;\u0026lt;\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal (MoCA\u0026thinsp;\u0026ge;\u0026thinsp;24)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42\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\u003eLow Screen Time (\u0026lt;\u0026thinsp;8.97h)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67\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\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003ePrincipal Findings and Interpretations\u003c/h2\u003e \u003cp\u003eOur findings support the broader literature suggesting that heavier recreational screen exposure is more consistently linked to weaker cognitive performance, likely because it clusters with behaviors that disrupt neurodevelopmental \u0026ldquo;inputs\u0026rdquo; such as sleep regularity, sustained attention, and cognitively enriching activities.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Large-scale evidence aligns with this pattern: in a prospective cohort study, higher screen time predicted later developmental outcomes with small but measurable cross-lagged effects (e.g., standardized β = \u0026minus;0.06 at 24 months and β = \u0026minus;0.08 at 36 months).[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] Importantly, meta-analytic work emphasizes that context matters as much as duration program viewing and background TV exposure show pooled negative correlations with cognitive outcomes (e.g., r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.16 for program viewing; r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.10 for background TV), while co-use with caregivers can be positively associated with cognition (e.g., r\u0026thinsp;=\u0026thinsp;0.14), supporting the idea that social scaffolding and content quality modify neurocognitive risk.[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] These associations are plausible: frequent rapid-reward, high-salience media can bias dopaminergic reward learning toward immediate reinforcement, reduce tolerance for delayed reward, and fragment attentional control striatal networks.[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Thresholds and Clinical Implications\u003c/h2\u003e \u003cp\u003eWhile our study proposes a practical threshold for identifying higher-risk adolescents, the clinical message should be framed less as a single number and more as a signal of cumulative exposure plus behavioral patterning. Population data show that meeting recommended limits on recreational screen time is consistently associated with better cognition: in ABCD (n\u0026thinsp;=\u0026thinsp;4,524; ages 9\u0026ndash;10), meeting the screen-only recommendation was associated with notably higher global cognition (β\u0026thinsp;=\u0026thinsp;4.25 points) compared with meeting none, and the combination of meeting sleep\u0026thinsp;+\u0026thinsp;screen recommendations showed similarly strong associations (β\u0026thinsp;=\u0026thinsp;5.15).[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] This \u0026ldquo;whole-day\u0026rdquo; framing is clinically useful: rather than only counseling reduction in total hours, clinicians can target the most neurobiologically sensitive windows (late evening), prioritize content and co-use, and protect sleep routines.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic Factors and Null Findings\u003c/h2\u003e \u003cp\u003eNull associations for sex, breakfast habits, and maternal education in our sample are not inconsistent with prior work showing that screen\u0026ndash;cognition relationships are often small in magnitude and highly dependent on measurement, content type, and confounding by family routines. For example, a meta-analysis examining screen media use and academic performance found overall effects that were generally small (e.g., effect sizes around \u0026minus;\u0026thinsp;0.05 to \u0026minus;\u0026thinsp;0.07), implying that many studies will not detect subgroup differences unless samples are large and exposures are precisely characterized.[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] Additionally, \u0026ldquo;education\u0026rdquo; and \u0026ldquo;SES\u0026rdquo; effects may be partially diluted when screen exposure is pervasive across strata, and when the quality of the screen ecology (co-use, content appropriateness, bedtime access) drives outcomes more than raw duration.[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and Limitations\u003c/h2\u003e \u003cp\u003eA key strength is modeling cognitive outcomes with adjustment for major covariates while distinguishing types of exposure, which aligns with the literature\u0026rsquo;s move away from a single-dimensional \u0026ldquo;screen time\u0026rdquo; metric.[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] However, the cross-sectional design cannot rule out reverse causality (e.g., lower baseline cognitive control increasing susceptibility to prolonged or problematic media use), and self-reported exposure can misclassify timing and content.[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Mechanistic interpretation should therefore lean on converging evidence: neuroimaging work has linked higher screen-based media use to differences in neurodevelopmentally relevant brain metrics.[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] Separately, screen time is associated with changes in attention-related outcomes and brain structure, including reduced cortical thickness in frontal regions that support executive control and self-regulation.[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] These convergent findings strengthen biological plausibility, even when any single observational study cannot establish causality.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eOverall, our findings fit best with a neurodevelopmental model in which heavier recreational screen exposure contributes to cognitive risk primarily through behavioral displacement (sleep, reading, physical activity, social interaction) and neurocognitive interference (attention fragmentation, reward sensitization, reduced executive control practice). Sleep appears to be a particularly important pathway: a meta-analysis of portable screen-based device use found substantially higher odds of inadequate sleep quantity and excessive daytime sleepiness with bedtime device use, patterns that are directly relevant to memory consolidation and prefrontal functioning. Experimental sleep/circadian research further supports plausibility by showing that evening light-emitting device reading can suppress melatonin and delay circadian timing relative to printed reading. Taken together, the practical implication is to prioritize reducing passive/recreational and evening screen exposure, encourage co-use and age-appropriate content when screens are used, and protect sleep as a core neurocognitive safeguard.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eABCD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAdolescent Brain Cognitive Development (study)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAPJII\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndonesian Internet Service Providers Association (Asosiasi Penyelenggara Jasa Internet Indonesia)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003earea under the curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003econfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIQR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003einterquartile range\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMoCA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMontreal Cognitive Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMoCA-Ina\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eIndonesian version of the Montreal Cognitive Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePBC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePublic Benefit Corporation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePICO\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePopulation, Intervention/Exposure, Comparison, Outcome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ean R package for ROC curve analysis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ereceiver operating characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003esocioeconomic status\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTV\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003etelevision\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUNICEF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUnited Nations Children\u0026rsquo;s Fund.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCONFLICT OF INTEREST\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eFunding sources\u003c/h2\u003e\n\u003cp\u003eNone.\u003c/p\u003e\n\u003ch2\u003eConflicts of interest/Competing interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no conflicts of interest and no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study was reviewed and approved by the institutional ethics committee \u0026ndash; Ethics Committee of the Faculty of Medicine, Pelita Harapan University (Approval No. : 071/K-LKJ/ETIK/II/2022) and was conducted in accordance with the Declaration of Helsinki and applicable local regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for the publication of clinical details and any accompanying non-identifying data was obtained from the patients/participants (and their parents/legal guardians, given the adolescent demographic). There are no identifying images included in this study.\u003c/p\u003e\n\u003ch2\u003eFUNDING\u003c/h2\u003e\n\u003cp\u003eNo funding was received for the preparation or publication of this article\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003ePYG: Concept., Meth., Superv., W\u0026ndash;R\u0026amp;E; A: Data cur., Inv., W\u0026ndash;OD; JHW: Formal anal., Viz., W\u0026ndash;R\u0026amp;E; YEA: Concept., Meth., Inv., Superv., W\u0026ndash;R\u0026amp;E; PLG: Meth., Res., W\u0026ndash;R\u0026amp;E. All auth. read \u0026amp; appr. the final MS.\u003c/p\u003e\n\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNuvoli V, Camanni M, Mariani I, Ponte S, Black M, Lazzerini M. Digital screen exposure in infants, children and adolescents: a systematic review of existing recommendations. 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Association of screen time with attention-deficit/hyperactivity disorder symptoms and their development: the mediating role of brain structure. Transl Psychiatry. 2025;15:447. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41398-025-03672-1\u003c/span\u003e\u003cspan address=\"10.1038/s41398-025-03672-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Adolescent, Cognition, ROC Curve, Screen time","lastPublishedDoi":"10.21203/rs.3.rs-8838273/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8838273/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAdolescent screen exposure is increasing, yet clinically interpretable thresholds for cognitive risk are unclear. This study examined associations between daily screen time and cognitive function and derived a screen-time cutoff linked to cognitive impairment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted an observational cross-sectional study (March\u0026ndash;April 2022) at a private junior high school in Indonesia during online learning. Students completed digital questionnaires reporting educational and recreational screen time and a directly reported overall estimate; a computed overall (educational\u0026thinsp;+\u0026thinsp;recreational) was generated to assess reporting consistency.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSixty-seven adolescents were included (34 girls, 50.7%; 33 boys, 49.3%), with median age 13.0 years (12.0\u0026ndash;16.0) and median MoCA-Ina 25.0 (19.0\u0026ndash;31.0). MoCA-Ina did not differ by sex (girls 25.0 [19.0\u0026ndash;31.0] vs boys 26.0 [20.0\u0026ndash;30.0]; p\u0026thinsp;=\u0026thinsp;0.244). Recreational screen time correlated inversely with MoCA-Ina (ρ=\u0026minus;0.446, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as did overall screen time (ρ=\u0026minus;0.360, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), whereas educational screen time was not associated (ρ=\u0026minus;0.061, p\u0026thinsp;=\u0026thinsp;0.624). In adjusted regression, overall screen time remained negatively associated with MoCA-Ina (β=\u0026minus;0.24 per hour/day; 95% CI\u0026thinsp;\u0026minus;\u0026thinsp;0.41 to \u0026minus;\u0026thinsp;0.07; p\u0026thinsp;=\u0026thinsp;0.007), while age was positively associated (β\u0026thinsp;=\u0026thinsp;0.96; 95% CI 0.07 to 1.85; p\u0026thinsp;=\u0026thinsp;0.034). ROC analysis showed fair discrimination (AUC 0.66) with an optimal cutoff\u0026thinsp;\u0026gt;\u0026thinsp;8.97 h/day (sensitivity 83.3%, specificity 48.8%, PPV 47.6%, NPV 84.0%); risk of impairment was higher above the cutoff (RR 2.98; 95% CI 1.15\u0026ndash;7.72; p\u0026thinsp;=\u0026thinsp;0.010; OR 4.77; 95% CI 1.40\u0026ndash;16.31).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHigh daily screen exposure was associated with poorer cognitive screening performance, and a\u0026thinsp;\u0026gt;\u0026thinsp;8.97-hour/day threshold may help identify adolescents at elevated likelihood of cognitive impairment.\u003c/p\u003e\u003ch2\u003eTrial Registration\u003c/h2\u003e \u003cp\u003e071/K-LKJ/ETIK/II/2022\u003c/p\u003e","manuscriptTitle":"Screen Time Patterns and Cognitive Screening Outcomes (MoCA-Ina) in Adolescents: A Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 18:05:22","doi":"10.21203/rs.3.rs-8838273/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-05-04T06:05:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T14:11:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T11:20:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213677775869336295428144953327929875277","date":"2026-04-19T19:50:01+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T12:48:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"206780522292527359124726291603255440011","date":"2026-04-04T14:46:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"281444101886890189456396953389045662136","date":"2026-04-03T10:28:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T14:07:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-27T16:59:15+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-27T16:38:57+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-27T05:40:35+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Neurology","date":"2026-02-27T05:23:16+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-neurology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nurl","sideBox":"Learn more about [BMC Neurology](http://bmcneurol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nurl","title":"BMC Neurology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b1024f2b-048e-4c22-89a5-cb4d49edc2eb","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Revision requested","date":"2026-05-04T06:05:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T14:11:15+00:00","index":61,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T11:20:50+00:00","index":60,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T13:55:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 18:05:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8838273","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8838273","identity":"rs-8838273","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}