Accessibility settings

This paper is in the following e-collection/theme issue:

Reviews in Pediatrics (15) Kids' and Adolescents' Use of Technology (156) Kids' and Adolescents' Use of Technology (156) Kids' and Adolescents' Use of Technology (156) JMIR Theme Issue: COVID-19 Special Issue (2564) Kids' and Adolescents' Use of Technology (156) JMIR Theme Issue: COVID-19 Special Issue (2564) Kids' and Adolescents' Use of Technology (156) Mental Health Issues in Adolescence (676) JMIR Theme Issue: COVID-19 Special Issue (2564) Anxiety and Stress Disorders (1373) Depression and Mood Disorders; Suicide Prevention (1755) Kids' and Adolescents' Use of Technology (156) Mental Health Issues in Adolescence (676) JMIR Theme Issue: COVID-19 Special Issue (2564)

Published on in Vol 9 (2026)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/83228, first published .
Screen Time, Child Depression, and Anxiety During the COVID-19 Pandemic: Systematic Review and Meta-Analysis

Screen Time, Child Depression, and Anxiety During the COVID-19 Pandemic: Systematic Review and Meta-Analysis

Screen Time, Child Depression, and Anxiety During the COVID-19 Pandemic: Systematic Review and Meta-Analysis

Authors of this article:

Marissa Yoshizawa1 Author Orcid Image ;   Jennifer Rafeedie1, 2 Author Orcid Image ;   Jasmyn J Tang3 Author Orcid Image ;   Bryan T Lei4 Author Orcid Image ;   Ramon Durazo-Arvizu4 Author Orcid Image ;   Danny Azucar1 Author Orcid Image ;   Sharon Hudson5 Author Orcid Image ;   Sheela Rao1, 2 Author Orcid Image ;   Karen Kay Imagawa1, 2 Author Orcid Image ;   Alexis Deavenport-Saman1, 2 Author Orcid Image
Article Authors Cited by Tweetations Metrics

    1Department of Pediatrics, Children's Hospital Los Angeles, 4650 Sunset Blvd MS53, Los Angeles, CA, United States

    2Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States

    3Division of General Internal Medicine and Health Services, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, United States

    4Department of Pediatrics, The Saban Research Institute, Children's Hospital Los Angeles, Los Angeles, CA, United States

    5AltaMed Institute for Health Equity, Los Angeles, CA, United States

    Corresponding Author:

    Marissa Yoshizawa, PsyD, MSW


    Background: In response to the COVID-19 pandemic, governments around the world enforced stay-at-home orders and social distancing guidelines that amplified the use of screen time among pediatric populations. Excessive screen time may negatively impact mental health by increasing depression and anxiety.

    Objective: The first aim was to conduct a systematic review of articles examining screen time and mental health outcomes among children and adolescents during the COVID-19 pandemic from 2020 to 2023. The second aim was to determine the combined effect sizes for the associations of screen time and depression and/or anxiety among children and adolescents during the COVID-19 pandemic from 2020 to 2023 and whether gender or age influenced outcomes.

    Methods: Bibliographic databases were searched including MEDLINE (Ovid), Embase (Elsevier), Cochrane Library (Wiley), CINAHL Complete (EBSCO), and PsycINFO (EBSCO). There were a total of 6462 nonduplicate studies that were screened. Study inclusion criteria included children ages 0 to <18 years, the effects of screen time on children during the COVID-19 pandemic, screen time and depression and/or anxiety, articles written in English, and articles, including quantitative and qualitative studies, published between 2020 and 2023. A total of 452 articles underwent full-text review with 23 articles meeting criteria for final article extraction.

    Results: A total of 23 studies totaling 29,581 children and adolescents were included in the study. Results showed that most studies reported a positive association between screen time and depression and/or anxiety (r=0.175, 95% CI 0.124-0.226, P<.001 and r=0.157, 95% CI 0.0994-0.214, P<.001, respectively) during COVID-19. Meta-regression revealed that screen time measured in problematic use of electronic devices had a 0.15 higher correlation with anxiety compared to screen time measured in duration of electronic device use.

    Conclusions: During the COVID-19 pandemic, children and adolescents with higher levels of screen time had increased depression and/or anxiety. Findings suggest the need for ongoing parent, professional, and self-monitoring of youth screen behaviors and habits as well as activities that promote social connectedness during global or national health emergencies.

    JMIR Pediatr Parent 2026;9:e83228

    doi:10.2196/83228

    Keywords

    screen time; anxiety; depression; COVID-19 pandemic; children; adolescents; mental health 


    The global COVID-19 pandemic impacted children’s screen time. As numerous restrictions were put on children and adolescents during COVID-19, regular screen usage increased due to the closure of regular outdoor activities and increase in online learning. A meta-analysis of over 29,000 children showed a 52% increase in screen time, particularly for those aged 12‐18 years [1]. Excessive screen time increased, with adolescents reported to have almost 6 hours per day of screen use, mainly spent on watching shows, movies, and playing games [2]. One-third of adolescents aged 13‐17 years reported using social media “almost constantly” [3]. Children at 9.5 years of age who were surveyed during the COVID-19 pandemic reported an average increase in recreational screen time of 11 hours per week compared to the 1 hour per week increase reported in prepandemic surveys conducted at ages 5 and 8 years [4].

    The COVID-19 pandemic also exacerbated the preexisting mental health challenges of children and adolescents [5]. A meta-analysis covering over 80,000 youths globally found that the prevalence of anxiety and depression doubled during the COVID-19 pandemic to 20%‐25% [6]. In the United States, emergency visits for children due to mental health problems rose drastically by 31%‐50% since the beginning of the pandemic [7]. Globally, depression and posttraumatic stress disorder were significantly more prevalent among children aged 7 to 15 years [8] due to the separation from classmates and friends. Overall, distancing measures during the pandemic were harmful to adolescent mental health [9].

    The opportunity for excessive screen use during the COVID-19 pandemic also impacted mental health outcomes among children and adolescents. Social media and media addiction were associated with ill-being among adolescents [9]. The US Surgeon General released a report [10] indicating potential risks of social media use on the mental health of children and adolescents and called for more evidence. During the pandemic, a greater duration of screen time (such as television viewing [10] or digital media time [10], including social media use [11,12]) was associated with higher levels of depression and anxiety [12]. The associations between screen time and poor mental health outcomes have been found in individual studies; however, to our knowledge, there have not been meta-analyses of screen time using standardized or validated measures of depression and anxiety during the pandemic.

    Thus, this review aims to fill this gap by examining the mental health effects of screen time in children and adolescents throughout the COVID-19 pandemic. First, the purpose was to conduct a systematic review of articles examining screen time and depression and anxiety among children and adolescents during the COVID-19 pandemic from 2020 to 2023. Second, the purpose was to determine the combined effect size for the association of screen time and depression and anxiety among children and adolescents during the COVID-19 pandemic from 2020 to 2023. We additionally explored the influence of gender and age on the relationship between screen time and mental health outcomes.


    Eligibility Criteria

    Studies identified through database searches were screened for the following inclusion criteria: (1) children ages 0 to <18 years, (2) examination of the effects of screen time on children during the COVID-19 pandemic (eg, video games, smartphone use, or computer use), (3) examination of screen time and anxiety and/or depression, (4) articles were written in English, and (5) studies, including quantitative and qualitative studies, published between 2020 and 2023. Studies were excluded if they did not describe the effects of screen time on children during the COVID-19 pandemic (eg, provider-led and provider-care interventions; telehealth) or did not meet the inclusion criteria listed above.

    Information Sources and Search Strategy

    We initially searched the following bibliographic databases: MEDLINE (Ovid), Embase (Elsevier), Cochrane Library (Wiley), CINAHL Complete (EBSCO), and PsycINFO (EBSCO). A MEDLINE search strategy was created using a combination of Medical Subject Headings (MeSH) and keywords for the concepts of COVID-19, screen time, and pediatrics. All team members reviewed the strategy and results; with team approval, the search was customized using controlled vocabulary (when available) and keywords in Embase, Cochrane Library, and PsycINFO (Multimedia Appendix 1). As of November 2021, all resulting citations were exported into an EndNote 20 (Clarivate Analytics) library with duplicates removed [13]. The resulting collection of citations was then imported into Covidence for screening. In August 2022, the search was run again in all bibliographic databases and any unique citations were imported into Covidence. Finally, in February 2023, the search was rerun given the refinement of the research question to include validated measures of depression and anxiety and to capture new studies that had been recently published. This was done again in MEDLINE and PsycINFO and also included CINAHL. All articles were imported and screened in Covidence.

    Data Collection Process

    Two reviewers independently extracted data from each article and the extracted data were sent to the research team for consensus. The following data were extracted from each study: the first author, the date and place of publication, the number of participants enrolled, participant demographics, measures/assessments used, study start and end date, inclusion criteria, findings summary, and effect sizes. The extracted data were collected in Covidence. Any disagreements were resolved in discussions with the research team.

    Screen time during the COVID-19 pandemic, including problematic use that was excessive or disordered (eg, video games, smartphone or computer use, social media use), was defined through self-reports of screen time duration. Anxiety was defined using self-report standardized questionnaires as well as standardized parent report measures on anxiety symptoms. Depression was defined using self-report standardized questionnaires as well as standardized parent report measures on depressive symptoms.

    Study Risk of Bias Assessment

    The STROBE (Strengthening the Reporting of Observational studies in Epidemiology) checklist was used to assess the quality of included studies on a scale from 0 to 21 (Table 1) [14]. Two reviewers assessed each study independently and then met to achieve consensus.

    Table 1. STROBE (Strengthening the Reporting of Observational studies in Epidemiology) checklist for bias assessment.
    IntroductionMethodsResultsDiscussion
    IDBackgroundObjectivesParticipantsVariablesData/measuresStatistical methodsDescriptive dataOutcomesResultsLimitationsTotal
    1 [15]222222222220
    2 [16]222222222220
    3 [17]222222222220
    4 [18]222222222220
    5 [19]222220222218
    6 [20]222222222220
    7 [21]222221222219
    8 [22]222222222220
    9 [23]222222222220
    10 [24]222222222220
    11 [25]222221222219
    12 [26]222222222220
    13 [27]222222222220
    14 [28]222222222220
    15 [29]222222222220
    16 [30]222222222119
    17 [30]222222222220
    18 [11]222222222219
    19 [31]222222222220
    20 [32]222222222220
    21 [33]222222222220
    22 [34]222221222219
    23 [35]222221222219

    Effect Measures, Synthesis Methods, and Analyses

    Fisher r-to-z transformation was used as the effect measure to quantify the relationship between screen time, anxiety, and depression, and results were converted back to Pearson correlations for interpretation. Studies were excluded if they were missing sufficient summary statistics and/or were unconvertible into appropriate effect sizes. Studies reported in other effect measures were converted to correlations using the conversion formulas proposed by Borenstein et al [14]. If a study assessed the same outcome with more than one measure, the average effect size was used in the analysis [36].

    A random-effects model was used for all meta-analysis to account for heterogeneity across studies. Pooled correlations of r=0.10, r=0.30, and r=0.50 were considered as small, medium, and large effect sizes, respectively [37]. Heterogeneity (ie, τ2) was assessed using the restricted maximum-likelihood estimator. In addition to the estimation of τ2, the Q test for heterogeneity and the I2 statistics are also reported [38]. Forest plots were generated for each outcome to visualize individual study effects and pooled estimates. Funnel plots were used to detect potential publication bias. Meta-regression analysis was performed to assess the impact of age, gender, and the definition of screen time (duration of use vs problematic use) on the effect sizes [39]. To evaluate the robustness of the results, sensitivity analysis was conducted by excluding studies identified as potential outliers or exerting excessive influence on the pooled effect using studentized residuals and the Cook distance [40]. All statistical analyses were performed using RStudio (version 4.4.1; Posit PBC) and the metafor package [41].


    Search Results and Included Studies

    In the initial database search, 6504 records were obtained. After duplicate removal, the titles and abstracts of 6462 records were screened independently by 7 coders based on the predefined eligibility criteria. After the title and abstract screening, 452 articles were retained. Following full-text screening, 429 articles were excluded from the systematic review, and 23 articles totaling 29,581 children and adolescents were retained in the systematic review and meta-analysis. See Figure 1 for the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram of observational studies.

    ‎
    Figure 1. PRISMA flowchart for studies of screen time during the COVID-19 pandemic and pediatric mental health. PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

    Study Characteristics

    Across the 23 studies (Table 2), children and adolescents were 8 to <18 years of age, with a mean age of 13.54 (SD 0.43) years; in addition, 49% (n=14,390) were male and 51% (n=15,191) were female. Of the 23 studies, 39% (n=9) were from North America, 44% (n=10) were from Asia, and 17% (n=4) were from Europe. Studies were published globally in the following countries: United States (n=6), China (n=6), Canada (n=3), Hong Kong (n=1), Hungary (n=1), Italy (n=2), India (n=1), Iran (n=1), Poland (n=1), and South Korea (n=1). Of the 23 studies, 96% (n=22) examined depression, 83% (n=19) examined anxiety, and 78% (n=18) examined both depression and anxiety.

    Table 2. Characteristics of studies.
    IDAuthorCountrySample size (N)Male (%)Age (years)Screen timeDepressionAnxiety
    1aAlves et al, 2021 [15]United States304311.7DurationPANAS-CaSTAICb
    1bAlves et al, 2021 [15]United States343211.9DurationPANAS-CSTAIC
    2Berki et al, 2021 [16]Hungary7053715.9DurationCDIcN/A
    3Charmaraman et al, 2022 [17]United States9004712.53DurationCESDR-10dSADSe
    4Chen et al, 2021 [18]China13575010.71DurationDASS-21fDASS-21
    5De Pasquale et al, 2021 [42]Italy16248.29.4Problematic useN/ACAM-Sg
    6Ellis et al, 2020 [19]Canada105421.916.68DurationBSIhN/A
    7Fazeli et al, 2020 [20]Iran151256.415.51Problematic useDASSDASS
    8Lee et al, 2021 [21]South Korea26410017.57Problematic useCES-DiN/A
    9Liu et al, 2022 [22]China558148.513.8Problematic useSDSjSASk
    10McArthur et al, 2021 [23]Canada84652.89.85DurationBASC-3lBASC-3
    11Moitra et al, 2021 [24]India129853.313.2DurationPHQm-2N/A
    12Mousavi et al, 2022 [25]United States21551.215.63DurationDASSDASS
    13Murata et al, 2021 [26]United States5832015.8DurationPHQ-9GADn -7
    14Muzi et al, 2021 [27]Italy623715.43Problematic useYSRo 11‐18YSR 11‐18
    15Ren et al, 2021 [28]China177151.8N/ADurationPHQ-9GAD-7
    16Sikorska et al, 2021 [29]Poland37032.715.38DurationDASS-21DASS-21
    17aTandon et al, old, 2021 [30]United States50050.514DurationSDQpSDQ
    17bTandon et al, young, 2021 [30]United States50052.68DurationSDQSDQ
    18Tao et al, 2021 [11]United States40717.716.47DurationCES-DGAD-7
    19Tardif-Grenier et al, 2021 [31]Canada89526.314.69DurationCES-DSCARED-Rq
    20Teng et al, 2021 [32]China177850.7N/ADurationCES-DSTAIr
    21Xiang et al, 2022 [33]China242351.210.5DurationDASS-21DASS-21
    22Zhang et al, 2022 [34]China347151.614.1DurationPHQ-9GAD-7
    23Zhu et al, 2021 [35]Hong Kong28635912.6Problematic usePHQ-9GAD-7

    aPANAS-C: Positive and Negative Affect Scale - Child Form.

    bSTAIC: State-Trait Anxiety Inventory for Children.

    cCDI: Children’s Depression Inventory.

    dCESDR-10: Center for Epidemiologic Studies Depression Scale Revised.

    eSADS: Social Avoidance and Distress Scale.

    fDASS-21: Depression Anxiety Stress Scale.

    gCAM-S: Child Anxiety Meter-State.

    hBSI: Brief Symptom Inventory.

    iCES-D: The Center for Epidemiologic Studies Depression Scale.

    jSDS: Self-Rating Depression Scale.

    kSAS: Self-Rating Anxiety Scale.

    lBASC-3: Behavior Assessment System for Children-Third Edition.

    mPHQ: Patient Health Questionnaire.

    nGAD-7: Generalized Anxiety Disorder 7-item.

    oYSR: Youth Self-Report.

    pSDQ: Strengths and Difficulties Questionnaire.

    qSCARED-R: Screen for Child Anxiety Related Disorders-Revised.

    rSTAI: State Trait Anxiety Inventory.

    Risk of Bias in Studies

    The STROBE checklist [14] was used to assess the quality of studies and any potential bias as seen in Table 1. Two independent reviewers assessed each study, resulting in a possible score from 0 to 20. Each section of the STROBE checklist had a potential score of 0 to 2, with 0 meaning criteria were not met, 1 meaning criteria were partially met, and 2 meaning criteria were fully met. Two reviewers ranked the scores, and any disagreements were discussed with a third reviewer to achieve consensus. All studies used validated questionnaires for depression and anxiety. McArthur et al [23] used a large longitudinal cohort, which allowed for examination of differences over time. The majority of studies were cross-sectional and were conducted during the global pandemic, which did not allow for examination of causal relationships. The following studies were found to have a lower quality assessment. De Pasquale et al [42] did not provide any information on the statistical tests that were conducted in the methods. Fazeli et al [20] had limited information on the statistical methods. Moitra and Madan [24] conducted a mediation analysis on a cross-sectional study but did not indicate the temporal order in their survey methods. Zhu et al [35] imputed more than 25% of missing data. Sikorska et al [29] did not adequately address potential limitations.

    Screen Time and Depression

    Twenty-two of the 23 included studies (all except [42]) reported comparable effect sizes for screen time and depression. Two studies [15,30] presented results only for stratified subgroups rather than an overall sample. Therefore, we treated the stratified groups as separate studies in the analysis and increased the sample size to 24 for the analysis. The observed correlation coefficients ranged from −0.00160 to 0.620. The random-effects model revealed a significant, positive, small estimated pooled correlation (r=0.175, 95% CI 0.124-0.226; P<.001; I2=94%). According to the Q test, the true estimates appear to be heterogeneous (Q23=644.22, P<.001, τ2=0.0138, I2=95%). A forest plot showing the observed and the estimated correlation is shown in Figure 2 [11,15-31,33-35]. Meta-regression showed no significant associations of depression with age and gender.

    ‎
    Figure 2. Forest plot for depression.

    A funnel plot of the estimates is shown in Figure 3. The plot is reasonably symmetrical around the pooled estimate, indicating no strong evidence of publication bias or small-study effects. Both the Cook distance and studentized residuals showed that one study [22] could be overly influential. Sensitivity analysis was conducted by removing the study. The result from the sensitivity analysis remained stable after removing the potential outlier (r=0.158, 95% CI 0.114-0.202; P<.001), supporting the result from the main analysis. Although I2 decreased from 95% to 90%, heterogeneity remained high across studies.

    ‎
    Figure 3. Funnel plot for depression.

    Screen Time and Anxiety

    A total of 19 studies reported correlation coefficients for screen time and anxiety, ranging from −0.00390 to 0.450. Since the results from Alves et al [15] and Tandon et al [30] were reported as two stratified subgroups, it increased the total sample size to 21 for the analysis. A random-effects model for screen time and anxiety again indicated a significant yet small estimated pooled correlation (r=0.157, 95% CI 0.0994-0.214; P<.001), with a Q test (Q20=760.16, P<.001,τ2=0.0150, I2=95%) demonstrating high heterogeneity. A forest plot showing the observed and estimated correlations is shown in Figure 4 [11,15,17,18,20,22,23,25-30,33-35,42]. Meta-regression revealed that compared to studies where screen time was measured as duration of use, those defining it as problematic use have an average 0.1585 higher Fisher z score for the correlation between screen time and anxiety. In terms of correlation, this translates to roughly a 0.15 increase, indicating a stronger correlation with anxiety in studies with problematic use.

    ‎
    Figure 4. Forest plot for anxiety.

    Certainty of Evidence

    The funnel plot in Figure 5 was fairly symmetrical around the pooled effect size visually, with no indication of publication bias or small-study effects. The same study [22] was considered to be an outlier based on the Cook distance and studentized residual. The result from the sensitivity analysis yielded a slightly lower but still significant correlation (r=0.135, 95% CI 0.0890-0.181; P<.001), supporting the result from the main analysis. Although I2 decreased from 95% to 89%, heterogeneity remained high overall, suggesting the true effect varies across different studies.

    ‎
    Figure 5. Funnel plot for anxiety.

    Principal Findings

    This meta-analysis of a total of 23 global studies examined screen time and mental health outcomes among 29,581 children and adolescents during the COVID-19 pandemic. Screen time had a significant, positive, small estimated pooled correlation with increased depression (22 studies) and increased anxiety (19 studies). Problematic screen use that was excessive or disordered had a stronger correlation with anxiety than average screen use. Age and gender did not influence mental health outcomes; most youth were adolescents and gender was equally distributed. Sensitivity analyses for studies with outliers for depression and anxiety supported the results from the main analysis.

    Although various studies examined different types of screen time during the pandemic, results remained the same. Studies reported greater time spent on social media was associated with higher depressive [11,19] and anxiety symptoms [11]. Zhu et al [35] reported pathological gaming behavior (as compared to excessive, leisure, and none) was associated with increased depression and anxiety. Berki et al [16] reported that more hours of daily screen time (time spent using electronic devices such as a computer, tablet, or smartphone) were associated with higher depressive symptoms. Studies also found that a greater number of hours spent on video games and social media predicted higher depressive [18,26,43] and anxiety [18] symptoms and negative perceived well-being [44].

    Strengths

    There were various strengths in this meta-analysis. A rigorous process was implemented, with multiple reviewers using a systematic approach to achieve consensus when screening studies for eligibility and extracting data from studies that met eligibility criteria. Additionally, depression and anxiety were measured using validated questionnaires, and the meta-analysis methodology provided precision in the estimate of the effect size, which increased the validity and generalizability of the findings. Extracted studies included pooled data from 10 countries in North America, Europe, and Asia, further increasing the generalizability of the findings. Sensitivity analyses demonstrated the robustness of the study results. Finally, the COVID-19 pandemic was a global emergency that served as a natural experiment and magnified the conditions to allow youth to participate in excessive screen time, providing a rare opportunity with high statistical power to examine the impact on mental health outcomes.

    Limitations

    There were limitations in this study. Most studies examined the negative impact of screen time, which may have introduced some bias. Given the unprecedented nature of the global pandemic, most studies were cross-sectional, allowing for the examination of the relationship between screen time and increased depression or anxiety at one point in time. The results are not generalizable to all age groups as the cohort mostly consisted of adolescents, with a mean age of 13.54 years. More information is needed about how the COVID-19 pandemic impacted school-age (6‐12 year olds) and young children (0‐5 year olds). Screen time was also measured through youth self-reports or parent reports, which may not accurately reflect the amount of time spent on screens due to recall bias. It is possible that other factors aside from screen time, such as the adjustment to the online schooling format and/or cybervictimization, may have impacted anxiety and depression. Since this study included data from multiple countries, there may have been different methods for collecting and tracking data, and it is possible that some mental health data could have been underreported.

    Clinical Implications

    During the pandemic, children and adolescents with increased screen time were negatively impacted in terms of depression and anxiety due to higher levels of screen time. These findings suggest the need for ongoing assessment of children and adolescents’ quality and quantity of screen behaviors to identify risk factors to prevent or treat mental health outcomes, especially in the event of another global or national emergency. More research is needed to develop a validated tool to assess screen time [45,46]. Given the significance of screen time in the lives of youth, it is important to consider how to address unhealthy media habits that may increase during a pandemic. The American Academy of Pediatrics recommends the 5 Cs of Media Use to promote healthy media habits [47]. This includes Child, Content, Calm, Crowding Out, and Communication. Practitioners should work with families to develop family media plans that address rules around screen time, balancing family and media time, conversations around responsible social media use, teaching emotion regulation strategies, parental modeling of screen use, and monitoring content for quality, developmental appropriateness, and privacy. Additionally, pediatricians can provide anticipatory guidance to help children and families prepare for emergencies, such as a global pandemic [48]. Children and adolescents should learn from an early age about the benefits and risks of screen use, with an emphasis on developing healthy screen habits that maximize positive social interactions, learning, and recreation; increasing awareness of safety factors such as the protection of privacy; and creating self-awareness that will guard against the negative impacts on mental health related to screen overuse. Finally, promoting positive childhood experiences during a global emergency may help to buffer negative impacts of excessive screen time on mental health outcomes by nurturing healthy social-emotional development.

    Conclusion

    This meta-analysis includes the evidence from 23 studies on screen time and mental health. During the COVID-19 pandemic, children and adolescents with higher levels of screen time had increased depression and/or anxiety. These data suggest the need for ongoing parent, professional, and self-monitoring of youth screen behaviors and habits as well as activities that promote social connectedness during global or national health emergencies.

    Funding

    This work was supported by the Biostatistics, Informatics and Data Management Core at The Saban Research Institute, Children's Hospital Los Angeles, and by grant UL1TR001855 from the National Center for Advancing Translational Science (NCATS) of the U.S. National Institutes of Health. The content is solely the authors' responsibility and does not necessarily represent the official views of the National Institutes of Health.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Search strategies by database.

    DOCX File, 21 KB

    Checklist 1

    PRISMA checklist.

    DOCX File, 275 KB
    1. Madigan S, Eirich R, Pador P, McArthur BA, Neville RD. Assessment of changes in child and adolescent screen time during the COVID-19 pandemic: a systematic review and meta-analysis. JAMA Pediatr. Dec 1, 2022;176(12):1188-1198. [CrossRef] [Medline]
    2. Nagata JM, Cortez CA, Cattle CJ, et al. Screen time use among US adolescents during the COVID-19 pandemic: findings from the Adolescent Brain Cognitive Development (ABCD) study. JAMA Pediatr. Jan 1, 2022;176(1):94-96. [CrossRef] [Medline]
    3. Vogels E, Rainie L, Nolan H. Teens, social media and technology. Pew Research Center. Aug 2022. URL: https://www.pewresearch.org/internet/2022/08/10/teens-social-media-and-technology-2022/ [Accessed 2026-02-04]
    4. McArthur BA, Racine N, McDonald S, Tough S, Madigan S. Child and family factors associated with child mental health and well-being during COVID-19. Eur Child Adolesc Psychiatry. Feb 2023;32(2):223-233. [CrossRef] [Medline]
    5. Protecting youth mental health. US Department of Health and Human Services. 2021. URL: https://www.hhs.gov/surgeongeneral/reports-and-publications/youth-mental-health/index.html [Accessed 2025-07-28]
    6. Racine N, McArthur BA, Cooke JE, Eirich R, Zhu J, Madigan S. Global prevalence of depressive and anxiety symptoms in children and adolescents during COVID-19: a meta-analysis. JAMA Pediatr. Nov 1, 2021;175(11):1142-1150. [CrossRef] [Medline]
    7. Hamatani S, Hiraoka D, Makita K, Tomoda A, Mizuno Y. Longitudinal impact of COVID-19 pandemic on mental health of children in the ABCD study cohort. Sci Rep. Nov 15, 2022;12(1):19601. [CrossRef] [Medline]
    8. Ma Z, Idris S, Zhang Y, et al. The impact of COVID-19 pandemic outbreak on education and mental health of Chinese children aged 7-15 years: an online survey. BMC Pediatr. Feb 24, 2021;21(1):95. [CrossRef] [Medline]
    9. Marciano L, Ostroumova M, Schulz PJ, Camerini AL. Digital media use and adolescents’ mental health during the covid-19 pandemic: a systematic review and meta-analysis. Front Public Health. 2021;9:793868. [CrossRef] [Medline]
    10. Social media and youth mental health. The U.S. Surgeon General’s Advisory; 2023. URL: https://www.hhs.gov/sites/default/files/sg-youth-mental-health-social-media-advisory.pdf [Accessed 2026-02-04]
    11. Tao X, Fisher CB. Exposure to social media racial discrimination and mental health among adolescents of color. J Youth Adolesc. Jan 2022;51(1):30-44. [CrossRef] [Medline]
    12. Li X, Vanderloo LM, Keown-Stoneman CDG, et al. Screen use and mental health symptoms in canadian children and youth during the COVID-19 pandemic. JAMA Netw Open. Dec 1, 2021;4(12):e2140875. [CrossRef] [Medline]
    13. Bramer WM, Giustini D, de Jonge GB, Holland L, Bekhuis T. De-duplication of database search results for systematic reviews in EndNote. J Med Libr Assoc. Jul 2016;104(3):240-243. [CrossRef] [Medline]
    14. Borenstien M, Hedges LV, Higgins JPT, Rothstein HR. Introduction to Meta-Analysis. John Wiley & Sons Incorporated; 2009. ISBN: 978-0-470-05724-7
    15. Alves JM, Yunker AG, DeFendis A, Xiang AH, Page KA. BMI status and associations between affect, physical activity and anxiety among U.S. children during COVID-19. Pediatr Obes. Sep 2021;16(9):e12786. [CrossRef] [Medline]
    16. Berki T, F. Pikó B. Sedentary lifestyle may contribute to the risk of depression during the COVID-19 pandemic: a snapshot of Hungarian adolescents. EJMH. 2021;16(2):99-119. [CrossRef]
    17. Charmaraman L, Lynch AD, Richer AM, Zhai E. Examining early adolescent positive and negative social technology behaviors and well-being during the COVID-19 pandemic. Technol Mind Behav. 2022;3(1). [CrossRef] [Medline]
    18. Chen CY, Chen IH, O’Brien KS, Latner JD, Lin CY. Psychological distress and internet-related behaviors between schoolchildren with and without overweight during the COVID-19 outbreak. Int J Obes (Lond). Mar 2021;45(3):677-686. [CrossRef] [Medline]
    19. Ellis WE, Dumas TM, Forbes LM. Physically isolated but socially connected: psychological adjustment and stress among adolescents during the initial COVID-19 crisis. Canadian Journal of Behavioural Science / Revue canadienne des sciences du comportement. 2020;52(3):177-187. [CrossRef]
    20. Fazeli S, Zeidi IM, Lin CY, et al. Corrigendum to “Depression, anxiety, and stress mediate the associations between internet gaming disorder, insomnia, and quality of life during the COVID-19 outbreak” [Addict. Behav. Rep. 12 (2020) 100307]. Addict Behav Rep. Dec 2020;12:100322. [CrossRef] [Medline]
    21. Lee J, Lim H, Allen J, Choi G, Jung J. Smartphone addiction and depression among low-income boys since COVID-19: the moderating effect of being an only child. Healthcare (Basel). 2021;9(10):1350. [CrossRef]
    22. Liu S, Zou S, Zhang D, Wang X, Wu X. Problematic internet use and academic engagement during the COVID-19 lockdown: the indirect effects of depression, anxiety, and insomnia in early, middle, and late adolescence. J Affect Disord. Jul 15, 2022;309:9-18. [CrossRef] [Medline]
    23. McArthur BA, Racine N, Browne D, McDonald S, Tough S, Madigan S. Recreational screen time before and during COVID-19 in school-aged children. Acta Paediatr. Oct 2021;110(10):2805-2807. [CrossRef] [Medline]
    24. Moitra P, Madan J. Impact of screen time during COVID-19 on eating habits, physical activity, sleep, and depression symptoms: a cross-sectional study in Indian adolescents. PLoS ONE. 2022;17(3):e0264951. [CrossRef] [Medline]
    25. Mousavi SZ, Barry CT, Halter BM. Relations of adolescent knowledge of COVID-19, social media engagement, and experiences during quarantine/lockdown with well-being. J Child Fam Stud. 2023;32(1):110-121. [CrossRef] [Medline]
    26. Murata S, Rezeppa T, Thoma B, et al. The psychiatric sequelae of the COVID-19 pandemic in adolescents, adults, and health care workers. Depress Anxiety. Feb 2021;38(2):233-246. [CrossRef] [Medline]
    27. Muzi S, Sansò A, Pace CS. What’s happened to Italian adolescents during the COVID-19 pandemic? A preliminary study on symptoms, problematic social media usage, and attachment: relationships and differences with pre-pandemic peers. Front Psychiatry. 2021;12:590543. [CrossRef] [Medline]
    28. Ren Z, Xin Y, Wang Z, Liu D, Ho RCM, Ho CSH. What factors are most closely associated with mood disorders in adolescents during the COVID-19 pandemic? A cross-sectional study based on 1,771 adolescents in Shandong Province, China. Front Psychiatry. 2021;12:728278. [CrossRef] [Medline]
    29. Sikorska IM, Lipp N, Wróbel P, Wyra M. Adolescent mental health and activities in the period of social isolation caused by the COVID-19 pandemic. Postep Psychiatr Neurol. Jun 2021;30(2):79-95. [CrossRef] [Medline]
    30. Tandon PS, Zhou C, Johnson AM, Gonzalez ES, Kroshus E. Association of children’s physical activity and screen time with mental health during the COVID-19 pandemic. JAMA Netw Open. Oct 1, 2021;4(10):e2127892. [CrossRef] [Medline]
    31. Tardif-Grenier K, Archambault I, Dupéré V, Marks AK, Olivier E. Canadian adolescents’ internalized symptoms in pandemic times: association with sociodemographic characteristics, confinement habits, and support. Psychiatr Q. Sep 2021;92(3):1309-1325. [CrossRef] [Medline]
    32. Teng Z, Pontes HM, Nie Q, Griffiths MD, Guo C. Depression and anxiety symptoms associated with internet gaming disorder before and during the COVID-19 pandemic: a longitudinal study. J Behav Addict. Mar 10, 2021;10(1):169-180. [CrossRef] [Medline]
    33. Xiang M, Liu Y, Yamamoto S, Mizoue T, Kuwahara K. Association of changes of lifestyle behaviors before and during the COVID-19 pandemic with mental health: a longitudinal study in children and adolescents. Int J Behav Nutr Phys Act. Jul 26, 2022;19(1):92. [CrossRef] [Medline]
    34. Zhang Y, Zhan N, Zou J, Xie D, Liu M, Geng F. The transmission of psychological distress and lifestyles from parents to children during COVID-19. J Affect Disord. Apr 2022;303:74-81. [CrossRef]
    35. Zhu S, Zhuang Y, Lee P, Li JCM, Wong PWC. Leisure and problem gaming behaviors among children and adolescents during school closures caused by COVID-19 in Hong Kong: quantitative cross-sectional survey study. JMIR Serious Games. May 7, 2021;9(2):e26808. [CrossRef] [Medline]
    36. Higgins JPT, Thompson SG. Quantifying heterogeneity in a meta-analysis. Stat Med. Jun 15, 2002;21(11):1539-1558. [CrossRef] [Medline]
    37. Gignac GE, Szodorai ET. Effect size guidelines for individual differences researchers. Pers Individ Dif. Nov 2016;102:74-78. [CrossRef]
    38. Rubio-Aparicio M, Sánchez-Meca J, López-López JA, Botella J, Marín-Martínez F. Analysis of categorical moderators in mixed-effects meta-analysis: consequences of using pooled versus separate estimates of the residual between-studies variances. Br J Math Stat Psychol. Nov 2017;70(3):439-456. [CrossRef] [Medline]
    39. Viechtbauer W, Cheung MWL. Outlier and influence diagnostics for meta-analysis. Res Synth Methods. Apr 2010;1(2):112-125. [CrossRef] [Medline]
    40. Viechtbauer W. Conducting meta-analyses in R with the metafor package. J Stat Softw. 2010;36(3):1-48. [CrossRef]
    41. von Elm E, Altman DG, Egger M, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. J Clin Epidemiol. Apr 2008;61(4):344-349. [CrossRef] [Medline]
    42. De Pasquale C, Chiappedi M, Sciacca F, Martinelli V, Hichy Z. Online videogames use and anxiety in children during the COVID-19 pandemic. Children (Basel). Mar 8, 2021;8(3):205. [CrossRef] [Medline]
    43. Li X, Vanderloo LM, Keown-Stoneman CDG, et al. Screen use and mental health symptoms in Canadian children and youth during the COVID-19 pandemic. JAMA Netw Open. Dec 1, 2021;4(12):e2140875. [CrossRef] [Medline]
    44. Nilsson A, Rosendahl I, Jayaram-Lindström N. Gaming and social media use among adolescents in the midst of the COVID-19 pandemic. Nordisk Alkohol Nark. Aug 2022;39(4):347-361. [CrossRef] [Medline]
    45. Perez O, Garza T, Hindera O, et al. Validated assessment tools for screen media use: a systematic review. PLoS One. 2023;18(4):e0283714. [CrossRef] [Medline]
    46. Byrne R, Terranova CO, Trost SG. Measurement of screen time among young children aged 0-6 years: a systematic review. Obes Rev. Aug 2021;22(8):e13260. [CrossRef] [Medline]
    47. The 5Cs of media use. American Academy of Pediatrics. Apr 20, 2024. URL: https:/​/www.​aap.org/​en/​patient-care/​media-and-children/​center-of-excellence-on-social-media-and-youth-mental-health/​5cs-of-media-use/​ [Accessed 2025-04-11]
    48. Krug SE, Chung S, Fagbuyi DB, et al. Ensuring the health of children in disasters. Pediatrics. Nov 1, 2015;136(5):e1407-e1417. [CrossRef]

    ‎
    PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
    STROBE: Strengthening the Reporting of Observational studies in Epidemiology

    Edited by Sherif Badawy; submitted 05.Sep.2025; peer-reviewed by Hadi Pratomo, Yin Wu; final revised version received 12.Jan.2026; accepted 25.Jan.2026; published 01.Apr.2026.

    Copyright

    © Marissa Yoshizawa, Jennifer Rafeedie, Jasmyn J Tang, Bryan T Lei, Ramon Durazo-Arvizu, Danny Azucar, Sharon Hudson, Sheela Rao, Karen Kay Imagawa, Alexis Deavenport-Saman. Originally published in JMIR Pediatrics and Parenting (https://pediatrics.jmir.org), 1.Apr.2026.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Pediatrics and Parenting, is properly cited. The complete bibliographic information, a link to the original publication on https://pediatrics.jmir.org, as well as this copyright and license information must be included.