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      Screen-viewing behaviours of children before and after the 2020–21 COVID-19 lockdowns in the UK: a mixed methods study

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          Abstract

          Background

          Restrictions during the COVID-19 pandemic have led to increased screen-viewing among children, especially during strict periods of lockdown. However, the extent to which screen-viewing patterns in UK school children have changed post lockdowns is unclear. The aim of this paper is to examine how screen-viewing changed in 10–11-year-old children over the 2020–21 COVID-19 pandemic, how this compares to before the pandemic, and the influences on screen-viewing behaviour.

          Methods

          This is a mixed methods study with 10–11-year-olds from 50 schools in the Greater Bristol area, UK. Cross-sectional questionnaire data on minutes of weekday and weekend television (TV) viewing and total leisure screen-viewing were collected pre-COVID-19 in 2017–18 ( N = 1,296) and again post-lockdowns in 2021 ( N = 393). Data were modelled using Poisson mixed models, adjusted for age, gender, household education and seasonality, with interactions by gender and household education. Qualitative data were drawn from six focus groups (47 children) and 21 one-to-one parent interviews that explored screen-viewing behaviour during the pandemic and analysed using the framework method.

          Results

          Total leisure screen-viewing was 11% (95% CI: 12%-18%) higher post-lockdown compared to pre-COVID-19 on weekdays, and 8% (95% CI: 6%-10%) on weekends, equating to around 12–15 min. TV-viewing (including streaming) was higher by 68% (95% CI: 63%-74%) on weekdays and 80% (95% CI: 75%-85%) on weekend days. Differences in both were higher for girls and children from households with lower educational attainment. Qualitative themes reflected an unavoidable increase in screen-based activities during lockdowns, the resulting habitualisation of screen-viewing post-lockdown, and the role of the parent in reducing post-2020/21 lockdown screen-viewing.

          Conclusions

          Although screen-viewing was higher post-lockdown compared to pre-COVID-19, the high increases reported during lockdowns were not, on average, sustained post-lockdown. This may be attributed to a combination of short-term fluctuations during periods of strict restrictions, parental support in regulating post-lockdown behaviour and age-related, rather than COVID-19-specific, increases in screen-viewing. However, socio-economic differences in our sample suggest that not all families were able to break the COVID-19-related adoption of screen-viewing, and that some groups may need additional support in managing a healthy balance of screen-viewing and other activities following the lockdowns.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12889-023-14976-6.

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          Most cited references32

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          Using the framework method for the analysis of qualitative data in multi-disciplinary health research

          Background The Framework Method is becoming an increasingly popular approach to the management and analysis of qualitative data in health research. However, there is confusion about its potential application and limitations. Discussion The article discusses when it is appropriate to adopt the Framework Method and explains the procedure for using it in multi-disciplinary health research teams, or those that involve clinicians, patients and lay people. The stages of the method are illustrated using examples from a published study. Summary Used effectively, with the leadership of an experienced qualitative researcher, the Framework Method is a systematic and flexible approach to analysing qualitative data and is appropriate for use in research teams even where not all members have previous experience of conducting qualitative research.
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            Time series regression studies in environmental epidemiology

            Time series regression studies have been widely used in environmental epidemiology, notably in investigating the short-term associations between exposures such as air pollution, weather variables or pollen, and health outcomes such as mortality, myocardial infarction or disease-specific hospital admissions. Typically, for both exposure and outcome, data are available at regular time intervals (e.g. daily pollution levels and daily mortality counts) and the aim is to explore short-term associations between them. In this article, we describe the general features of time series data, and we outline the analysis process, beginning with descriptive analysis, then focusing on issues in time series regression that differ from other regression methods: modelling short-term fluctuations in the presence of seasonal and long-term patterns, dealing with time varying confounding factors and modelling delayed (‘lagged’) associations between exposure and outcome. We finish with advice on model checking and sensitivity analysis, and some common extensions to the basic model.
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              Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews

              Objectives To systematically examine the evidence of harms and benefits relating to time spent on screens for children and young people’s (CYP) health and well-being, to inform policy. Methods Systematic review of reviews undertaken to answer the question ‘What is the evidence for health and well-being effects of screentime in children and adolescents (CYP)?’ Electronic databases were searched for systematic reviews in February 2018. Eligible reviews reported associations between time on screens (screentime; any type) and any health/well-being outcome in CYP. Quality of reviews was assessed and strength of evidence across reviews evaluated. Results 13 reviews were identified (1 high quality, 9 medium and 3 low quality). 6 addressed body composition; 3 diet/energy intake; 7 mental health; 4 cardiovascular risk; 4 for fitness; 3 for sleep; 1 pain; 1 asthma. We found moderately strong evidence for associations between screentime and greater obesity/adiposity and higher depressive symptoms; moderate evidence for an association between screentime and higher energy intake, less healthy diet quality and poorer quality of life. There was weak evidence for associations of screentime with behaviour problems, anxiety, hyperactivity and inattention, poorer self-esteem, poorer well-being and poorer psychosocial health, metabolic syndrome, poorer cardiorespiratory fitness, poorer cognitive development and lower educational attainments and poor sleep outcomes. There was no or insufficient evidence for an association of screentime with eating disorders or suicidal ideation, individual cardiovascular risk factors, asthma prevalence or pain. Evidence for threshold effects was weak. We found weak evidence that small amounts of daily screen use is not harmful and may have some benefits. Conclusions There is evidence that higher levels of screentime is associated with a variety of health harms for CYP, with evidence strongest for adiposity, unhealthy diet, depressive symptoms and quality of life. Evidence to guide policy on safe CYP screentime exposure is limited. PROSPERO registration number CRD42018089483.
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                Author and article information

                Contributors
                Ruth.Salway@bristol.ac.uk
                Robert.Walker@bristol.ac.uk
                Kate.Sansum@ubc.ca
                Danielle.House@bristol.ac.uk
                Lydia.Emm-Collison@bristol.ac.uk
                Tom.Reid@bristol.ac.uk
                Katie.Breheny@bristol.ac.uk
                Jo.Williams@bristol.ac.uk
                Frank.devocht@bristol.ac.uk
                William.Hollingworth@bristol.ac.uk
                Charlie.Foster@bristol.ac.uk
                Russ.Jago@bristol.ac.uk
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                17 January 2023
                17 January 2023
                2023
                : 23
                : 116
                Affiliations
                [1 ]GRID grid.5337.2, ISNI 0000 0004 1936 7603, Centre for Exercise, Nutrition & Health Sciences, School for Policy Studies, , University of Bristol, ; Bristol, BS8 1TZ United Kingdom
                [2 ]GRID grid.5337.2, ISNI 0000 0004 1936 7603, Population Health Sciences, Bristol Medical School, , University of Bristol, ; Bristol, BS8 2PS United Kingdom
                [3 ]GRID grid.33692.3d, ISNI 0000 0001 0048 3880, Communities and Public Health, , Bristol City Council, ; Bristol, BS1 9NE United Kingdom
                [4 ]GRID grid.410421.2, ISNI 0000 0004 0380 7336, Applied Research Collaboration West (NIHR ARC West), , The National Institute for Health Research, University Hospitals Bristol and Weston NHS Foundation Trust, ; Bristol, BS1 2NT United Kingdom
                [5 ]GRID grid.410421.2, ISNI 0000 0004 0380 7336, NIHR Bristol Biomedical Research Centre, , University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, ; Bristol, United Kingdom
                Article
                14976
                10.1186/s12889-023-14976-6
                9843116
                36650495
                8bd8ce4e-7103-4611-9fba-7c80c52cab4a
                © The Author(s) 2023

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 25 August 2022
                : 2 January 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000272, National Institute for Health and Care Research;
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                Categories
                Research
                Custom metadata
                © The Author(s) 2023

                Public health
                coronavirus,screen time,electronic device use,sedentary behaviour,television viewing
                Public health
                coronavirus, screen time, electronic device use, sedentary behaviour, television viewing

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