literature review on screen time

• Open access
• Published: 19 April 2023

Screen time among school-aged children of aged 6–14: a systematic review

• Jingbo Qi 1 ,
• Yujie Yan 2 &
• Hui Yin 1  

Global Health Research and Policy volume  8 , Article number:  12 ( 2023 ) Cite this article

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Screen time refers to the time an individual spends using electronic or digital media devices such as televisions, smart phones, tablets or computers. The purpose of this study was to conduct systematic review to analyze the relevant studies on the length and use of screen time of school-aged children, in order to provide scientific basis for designing screen time interventions and perfecting the screen use guidelines for school-aged children.

Screen time related studies were searched on PubMed, EMBASE, Clinical Trials, Controlled Trials, The WHO International Clinical Trials Registry Platform, the Cochrane Central Register of Controlled Trials, CNKI, and Whipple Journal databases from January 1, 2016 to October 31, 2021. Two researchers independently screened the literature and extracted the data, and adopted a qualitative analysis method to evaluate the research status of the length and usage of screen time of school-aged students.

Fifty-three articles were included. Sixteen articles studied screen time length in the form of continuous variables. Thirty-seven articles studied screen time in the form of grouped variables. The average screen time of schoolchildren aged 6 to 14 was 2.77 h per day, and 46.4% of them had an average screen time ≥ 2 h per day. A growth trend could be roughly seen by comparing studies in the same countries and regions before and after the COVID-19 outbreak. The average rates of school-aged children who had screen time within the range of ≥ 2 h per day, were 41.3% and 59.4% respectively before and after January 2020. The main types of screen time before January 2020 were watching TV (20 literatures), using computers (16 literature), using mobile phones/tablets (4 literatures). The mainly uses of screens before January 2020 were entertainment (15 literatures), learning (5 literatures) and socializing (3 literatures). The types and mainly uses of screen time after January 2020 remained the same as the results before January 2020.

Conclusions

Excessive screen time has become a common behavior among children and adolescents around the world. Intervention measures to control children's screen use should be explored in combination with different uses to reduce the proportion of non-essential uses.

Screen time refers to the time an individual spends using electronic or digital media devices such as televisions, smart phones, tablets or computers [ 1 ]. With the development of science and technology integrated into social life, smart devices such as mobile phones, computers and tablets are more and more widely used in work, study and daily life. Children are exposed to electronic products at a younger age and their screen time is increasing. Too much screen time can have negative effects on children's physical and mental health. First, the negative effect of screen time on eyesight has been confirmed in many countries’ studies [ 2 , 3 ]. For example, the study by Hu Jia et al. showed that screen time ≥ 3 h per day (OR = 2.026, 95%CI:1.235 ~ 3.325) was a myopia risk factor for primary and middle school students [ 4 ]. Second, excessive screen time will also bring obesity, depression, sleep disorders and other health problems to children and adolescents [ 4 , 5 , 6 ].

The COVID-19 pandemic is still spreading across the globe, affecting the lives of billions of residents around the world. Various public institutions, including schools, have adopted a range of lockdown measures. More primary and middle schools have conducted online teaching, and the time for school-aged children to use electronic products for online learning has further increased. Diane Seguin et al. found that during the pandemic, the average daily screen time of Canadian children increased from over 2 h (2.6 h on average) to nearly 6 h (5.9 h on average)(t(73) = 9.04, p  = 0.001). Screen time increased by a total of more than 3 h, and children's screen time increased further during the pandemic compared to pre-pandemic [ 7 ].

Due to the physical development stage of school-aged children, the effect of prolonged screen time on their physical and mental health is more obvious and irreversible than that of adults. The Physical Activity Guidelines for Chinese Children and Adolescents [ 8 ] released in 2017 states that, the screen time of Chinese children and adolescents should be limited to 2 h per day. Referring to the guidelines of the American Academy of Pediatrics [ 9 ], children under the age of 2 should not use electronic media, while the time of using it for children over 2 years old should be limited to 2 h per day. However, empirical studies on the actual length and use of current screen time of school-aged children are relatively scattered and insufficient. This study used the qualitative systematic review method to analyze the relevant studies on the length and use of screen time of school-aged children, in order to provide scientific basis for designing screen time interventions and perfecting the screen use guidelines for school-aged children.

Inclusion criteria

The types of literature include cross-sectional studies, cohort studies and case–control studies published in the form of peer-reviewed journal articles. The research subjects of the literature should include primary and secondary school students aged 6 to 14, including male and female. The literature published includes raw data, screen time values, age distribution, time distribution, and the screen use.

Exclusion criteria

Unpublished, unoriginal and non-peer reviewed articles, case reports, letters or comments; the research subjects do not meet the age requirements (under 6 years old, over 14 years old); the literature does not describe screen use time in detail, lacks quantitative data and correlation verification, and is only empirical conclusion.

The strategy of literature search

Search the literature in the public databases on PubMed, Clinical Trials, Controlled Trials, the WHO International Clinical Trials Registry Platform, EMBASE, the Cochrane Central Register of Controlled Trials, CNKI, and Whipple Journal. According to the phrases included the age group, and the screen use, "school-age child"/"primary school"/"junior high school student"/"primary and secondary school student"; "screen time"/" video time "/" electronic equipment "/" electronic products "/" multimedia equipment "/" digital equipment "are searched in the database. At the same time, search the references of the literature for other literature. The search time limit is from January 1, 2016 to October 31, 2021. The types of literature searched include cross-sectional studies, cohort studies and case–control studies. The search was limited to human studies reported either in English or in Chinese. All search phrases were modified according to MeSH terms.

Literature screening and data extraction

According to the search strategy and inclusion and exclusion criteria, two researchers independently conduct literature screening. After the screening, the two researchers discuss the screening process and the inconsistent parts of the results to form a unified result. If no agreement were to reach, a third party should be consulted. The contents of the research extraction include: author, publishing time, research region, research type, sample characteristics, screen time length, use and influencing factors, research content and main results and conclusions.

Risk evaluation and systematic evaluation of literature bias

The Cochrane risk assessment tool [ 10 ] is used to evaluate the literature quality of the included cross-sectional studies from the following aspects: random sequence generation, allocation hiding, blinding method, result data integrity, selective reporting and other biases. The bias risk has three possibilities: low risk, high risk and unknown bias risk. For observational studies, Newcastle–Ottawa Scale (NOS) [ 11 ] is used for quality assessment, which is scored from three parts: the selection of study population, comparability, exposure evaluation or result evaluation, and uses the semi-quantitative principle of star level system to evaluate literature quality. Studies with a score of 6 stars or more are defined as high quality and are included in this study. The quality assessment is conducted independently by the above-mentioned three researchers. In case of any dispute, a consensus shall be reached through discussion. In this study, Excel 2016 software was used to count the published literature, and qualitative analysis was performed on the included studies.

Basic information and bias risk evaluation of included research

The preliminary search obtained 1275 relevant literatures. After removing the duplicates and reading the literature titles and abstracts, through rounds of screening, two hundred and twenty-six literatures were excluded due to the lack of screen use data. Seventy-nine literatures were excluded due to inconsistent characteristics such as age and gender of the subjects. Thirty-six literatures were excluded due to inconsistent research types. Eight literatures were excluded due to incomplete content of the full text. Thirteen literatures were excluded because the research data source time was more than five years. Finally, fifty-three literatures [ 4 , 5 , 6 , 7 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 ] were included. Their basic information was shown in Table 1 . The literature screening process and results are shown in Fig.  1 . Considering the representativeness of the sample population, we made unified screening regulations on the age of the study population, the difficulty in obtaining electronic devices, the family's economic ability, and the parents' education level of the study population. There were 19 Chinese literatures and 34 English literatures. In terms of research time, there were two literatures in 2016, eight literatures in 2017, ten literatures in 2018, seven literatures in 2019, thirteen literatures in 2020 and thirteen literatures in 2021. Nineteen literatures were from China (including Taiwan Province), 6 literatures from other Asian countries, 17 literatures from European countries, 9 literatures from American countries, 1 literature from African countries and 1 literature from Oceania countries. The screen time data in the literature were collected by questionnaire and database. There were 16 literatures with continuous screen time and 37 literatures with classified screen time. The evaluation results of the bias risk of different included studies are shown in Fig.  2 .

Flow chart of literature screening

Bias risk evaluation results of different included studies (red indicators high risk, green indicators low risk)

Average daily length of screen time among schoolchildren aged 6–14 (continuous variable)

In 55 literatures, sixteen of them studied screen time length in the form of continuous variables. Sixteen literatures investigated the average daily length and standard deviation of the group by screen time and other health behavior factors. A total of 105,209 primary and middle school students aged 6 to 14 years were included in the study. Taking the international recommended length of screen time—2 h per day as the control parameter, the average length and standard deviation of the screen time of each literature were entered. Meta-analysis carried out by RevMan software showed that the average screen time of the included literature was + 0.77 h higher than the control parameter and the average screen time was 2.77 h per day (95% CI: 0.32 ~ 1.22).The analysis results are shown in Fig.  3 .

Forest plot for screen time of 6–14 year old school children (continuous variable)

Average daily length of screen time for Schoolchildren aged 6–14 (Classification variable)

Among the 55 literatures, thirty-seven expressed screen time in the form of grouped variables. Screen time < 2 h per day and ≥ 2 h per day were defined as screen time in 35 of the 37 classification variable literatures. Two literatures that only provided data on screen time use were not included in the bar chart. Among the included literatures published in 2021, there were four papers whose actual data collection took place in 2021, while the rest of the literatures published in 2021 reported data was collected in 2020 and before. A total of 472,042 primary and middle school students aged 6 to 14 years were included in the study. With the included literatures presented in chronological order, the bar chart showed the proportion of groups with average screen time ≥ 2 h per day in the whole study population. The results showed that 46.4% of primary and middle school students aged 6 to 14 years had screen time within the range of ≥ 2 h per day. A growth trend could be roughly seen by comparing studies in the same countries and regions before and after the COVID-19 outbreak. The average rates of school-aged children, who had screen time within the range of ≥ 2 h per day, were 41.3% and 59.4% respectively before and after January 2020. The statistical results are shown in Fig.  4 .

Screen time of 6–14 year old school children (classification variable)

Main uses of screen time for school-aged children

In the included literatures, twenty-five analyzed the types and uses of screen time among schoolchildren aged 6 to 14. The full text of the literature were read to get the classification of the screen devices, including televisions, mobile phones, tablets and computers. The classification of screen use were put into three categories, namely, learning, entertainment (including watching video and video games) and social interaction. The number of literatures and samples for each kind of use were counted. A total of 330,119 schoolchildren aged 6 to 14 were included in this indicator. Calculated according to the statistical sequence of the sample size of the literature study, the results showed that the main types of screen time before January 2020 were watching TV (20 literatures), using computers (16 literature), using mobile phones/tablets (4 literatures). The mainly uses of screens before January 2020 were entertainment (15 literatures), learning (5 literatures) and socializing (3 literatures). The types and mainly uses of screen time after January 2020 remained the same as the results before January 2020, as shown in Table 2 .

From smartphones and social media to TV and tablet-based online courses, today’s school-aged children are constantly inundated by technology. The primary purpose of this review was to summarize the current situation of length and use of screen time of school-aged children. Our findings show that excessive screen time among schoolchildren aged 6–14 is very common and has become a serious public health problem in high—and middle-income countries. Excessive screen time has a variety of effects on the health of school-aged children, including emotional, sleep, behavioral problems, and affects the growth and cognitive development of school-aged children. Some high-income countries, such as the United States [ 61 ] and Germany [ 62 ], have developed guidelines for restrictions on digital media overuse across age groups, while some low—and middle-income countries have not developed such screen time guidelines. In 2021, the National Health Commission issued Appropriate Technical Guidelines for Prevention and control of Myopia in Children and Adolescents (updated version) [ 63 ], which suggested that families should "not put TV and other video products in children's bedrooms", but did not put forward suggestions on screen duration. This review might be useful for the policymakers in formulating or refining guidelines for limiting the excessive digital-media usage for school-aged groups in these countries.

Instead of school settings, home-based television viewing and home-based computers are two primary types of screen viewing of school-aged children. The home setting, especially parents, plays a vital role in deciding the type and length of screen viewing. Parents’ attitudes, beliefs, norms, and behaviors shape and create a shared social and physical environment in the home setting, and this environment affects children’s possibilities for different types of behaviors [ 64 ]. Higher parental self-efficacy to limit screen time is associated with less children’s screen time, whereas availability of media equipment is associated with increased children’s screen time [ 65 ]. Therefore, health promotion programs are needed to help raise parents' awareness and ability to help reduce children’s excessive screen time. Among different purposes of screen time for school-aged children, the main purpose is spent on entertainment rather than learning, which offers the possibility of reducing long screen time. Parents could set time limits on the use of entertainment software on electronic devices, or replace screen use with outdoor activities. It is also relevant to study further the screen use preferences of students of different ages, and to distinguish the use time of different screen media such as TV, computer and mobile phone. This knowledge would be valuable for the development of effective interventions aiming to diminish the school-aged children’s screen time.

During disease pandemic such as COVID-19, screen usage may become more prevalent through periods of school closures, lockdowns, social isolation, and online learning classes. Public health policies and health promotion strategies targeting parents are needed to raise awareness of the adverse health effects associated with excessive screen time [ 66 ]. From our findings, comparing the literature data before 2020 with those after 2020, the increase in screen time of primary and middle school students in the same countries and regions is obvious. There are also relevant studies [ 67 ] that due to the impact of the epidemic, the proportion of children whose screen time of electronic products was longer than 3 h per day rose from 9.16% before the epidemic to 19.20% after the epidemic. When literatures were searched, the publication years of literature included the time of epidemic. Compared with those before 2019, there has been a significant increase in screen time reported in the literature since 2020, which is related to the fact that the children have been forced to stay at home longer, and online teaching has led to increased average exposure to electronic devices during the pandemic. Since the online learning is “required” by schools, it raises a triple dilemma among maintaining school-learning, prevention of communicable diseases, and reducing excessive screen time, which needs further discussion. In addition, healthcare workers could provide health education and health consulting service on appropriate screen use behavior, how to improve digital media environment at home, and raise awareness of adverse health effects of screen time. Fitness and entertainment facilities shall be provided at the community level to reduce screen time, and enhance the physical activity level of children and adolescents. An integration of family, community, school, and health systems should be considered to design for intervention model of screen time behaviors.

This study has some limitations. First, according to the research types included in the literature, this study selected the international mainstream methodological quality scale for quality evaluation, but the quality of the relevant original research methodology was limited and not rigorous. It may have reduced the credibility of the conclusions. Second, in the included studies, national conditions and medical systems vary from country to country. The included literatures mainly focus on the health effects of screen time. The standards of screen time data collection and classification were not uniform among studies, which made the statistical results may deviate from the actual situation. In addition, the age range of some study subject included in the literature is not completely in the age range of 6–14 years old. Although only the data of the study subjects in accordance with the age group were selected in the data analysis, there were cases where a single data represented the level of the entire age group, and the sample size of the study subjects of each age group was not balanced, which may cause some bias to the conclusion. Only published literatures were searched, which may lead to incomplete data acquisition and potential publication bias. Third, because of the exclusion of literature published in languages other than English and Chinese, the research results were not representative in these language regions. Last, seventeen of the included literature were published after January 2020, but their data was collected before January 2020. New papers investigating screen time during COVID-19 pandemic have been published after our target date. Those latest data collection could be continued in the future to fully reflect the impact of the pandemic on screen time.

Focusing on school-aged children, this study systematically assessed the specific length and main uses of screen time in school-aged children aged 6–14, providing a baseline reference level for excessive screen time in school-aged children. It also provides ideas for interventions to reduce long screen time. However, the quality of the existing research is uneven, and the research types and quantity are relatively scarce. Further empirical research is needed to confirm the above conclusions.

Availability of data and materials

The datasets during and/or analysed during the current study available from the corresponding author on reasonable request.

Abbreviations

Screen time: time spent using the computer, watching TV, playing video games and other multimedia screens

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JQ and HY designed the study. JQ and YY reviewed the relevant articles and extracted important data. JQ analyzed the data and drafted the manuscript. All authors contributed to the interpretation of the findings and manuscript revision. All authors read and approved the final manuscript.

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Qi, J., Yan, Y. & Yin, H. Screen time among school-aged children of aged 6–14: a systematic review. glob health res policy 8 , 12 (2023). https://doi.org/10.1186/s41256-023-00297-z

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DOI : https://doi.org/10.1186/s41256-023-00297-z

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• Volume 9, Issue 1
• Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews
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• Neza Stiglic ,
• http://orcid.org/0000-0003-3047-2247 Russell M Viner
• Population, policy and practice research programme , UCL Institute of Child Health , London , UK
• Correspondence to Dr Russell M Viner; r.viner{at}ucl.ac.uk

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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https://doi.org/10.1136/bmjopen-2018-023191

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Strengths and limitations of this study

Undertook a systematic review of reviews in multiple electronic databases using a prespecified methodology.

Included only studies that directly reported screentime separately from other sedentary behaviours.

Used assessment of review quality and weight of supportive evidence to assign strength of evidence to findings.

Quality of included reviews was predominantly moderate or low, dominated by studies of television screentime, with screentime largely self-reported.

Data on mobile screen use was extremely limited and our review did not address the content or context of screen viewing.

Introduction 

The screen, whether it is computer, mobile, tablet or television, is a symbol of our modern age. For our children, the ‘digital natives’ who have grown up surrounded by digital information and entertainment on screens, time on screens (screentime) is a major part of contemporary life.

However, there have been growing concerns about the impact of screens on children and young people’s (CYP) health. There is evidence that screentime is associated with obesity, with suggested mechanisms an increase in energy intake, 1 the displacement of time available for physical activity 2 or more directly through reduction in metabolic rate. 3 There is also evidence that high screentime is associated with deleterious effects on irritability, low mood and cognitive and socioemotional development, leading to poor educational performance. 4

Because of these concerns, expert groups have suggested controlling screentime for children. The American Academy of Pediatrics in 2016 recommended limiting screentime for children aged 2–5years to 1 hour/day of high-quality programmes and for parents to limit screentime in agreement with CYP 6 years and older. 5 The Canadian Paediatric Society issued similar guidelines in 2017. 6

However, there has been criticism of professional guidelines as non-evidenced-based, 7 as evidence for an impact of screentime on health is inconsistent, with systematic reviews showing inconsistent findings. 8–11 This may in part be due to failure to separate screentime from non-screen sedentary behaviours characterised by low physical movement and energy expenditure. It may also be due to a failure to separate the sedentary elements of screentime from the content watched on screens. Others have argued that screen-based digital media have potential significant health, social and cognitive benefits and that harms are overstated. A prominent group of scientists recently argued that messages that screens are inherently harmful is simply not supported by solid research and evidence. 12 Others have noted that education and industry sectors frequently promote expanded use of digital devices by CYP. 13

Our aim was to systematically examine the evidence on the effects of time spent using screens on health and well-being among CYP. Systematic reviews of reviews (RoR or umbrella reviews) are particularly suited to quickly collating the strength of evidence across a very broad area to guide policy. We therefore undertook an RoR of the effects of screentime of any type on CYP health and well-being outcomes.

We undertook a systematic review of published systematic reviews, reporting methods and findings using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. 14 The review was registered with the PROSPERO registry of systematic reviews (registration number CRD42018089483).

Review question

Our review question was ‘What is the evidence for health and well-being effects of screentime in children and adolescents?’

Search strategy

We searched electronic databases (Medline, Embase, PsycINFO and CINAHL) in February 2018. We used the search terms in Medline as follows: ‘(child OR teenager OR adolescent OR youth) AND (screen time OR television OR computer OR sedentary behaviour OR sedentary activity) AND health’, with publication type limited to ‘systematic review, with or without meta-analysis’. Similar search terms were used in the other databases. We did not limit studies by date or language. Identified relevant reviews were hand-searched for additional likely references.

Eligibility criteria

We only included systematic reviews which fulfilled the following eligibility criteria:

Systematically searched and reviewed the literature using prespecified protocols.

Examined children or adolescents from 0 to 18 years. Studies with a wider age range which provided data on children/adolescents separately were eligible.

Assessed and reported screentime, that is, time spent on screens of any type, including self-report or measured/observed measures.

Examined health and well-being impacts on children or adolescents.

We excluded reviews in which screentime was not defined adequately or where time on screens was not separated from other forms of sedentary behaviour, for example, sitting while talking/homework/reading, time spent in a car, etc. Where reviews examined overall sedentary behaviour but reported findings for screentime separately to other forms of sedentary behaviour, these were included. However, reviews that did not separate screentime from other sedentary behaviour were not included. Where authors updated a review which included all previous studies, we only included the later review to avoid duplication.

Study selection

A flow chart of study identification and selection is shown in figure 1 . Titles and abstracts were reviewed and potentially eligible articles identified after removal of duplicates. The abstracts of 389 articles were reviewed and 161 potentially eligible articles were identified which appeared to meet the eligibility criteria. After review of full text to determine final eligibility, 13 reviews are included in this review. Characteristics of the included reviews are shown in  table 1 .

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Characteristics of included studies

Data extraction

Descriptive findings and results of any quantitative meta-analyses were extracted to a spreadsheet by NS and fully checked for accuracy by RV.

Evaluation of quality

The quality of systematic reviews including risk of bias was assessed using the adapted version of Assessing the Methodological Quality of Systematic Reviews (AMSTAR). 15 We characterised reviews as high, medium or low quality. High-quality reviews were required to have the following: provided a priori published designs (eg, published protocols or had ethics committee approval); searched at least two bibliographic databases plus conducted another mode of searching; searched for reports regardless of publication type; listed and described included studies; used at least two people for data extraction; documented the size and quality of included studies and used this to inform their syntheses; synthesised study findings narratively or statistically; assessed the likelihood of publication bias and included a conflict of interest statement. Medium-quality reviews were required to have: searched at least one database; listed and described included studies; documented the quality of the included studies and synthesised study findings narratively or statistically. Reviews did not meet these criteria were defined as low quality. Note we did not seek to assess the quality of primary studies included in each review.

Data synthesis and summary measures

Synthesis began by summarising review results and conclusions in note form. Reviews were then grouped by health domain: body composition (including adiposity); diet and energy intake; mental health and well-being; cardiovascular risk; fitness; cognition, development and educational attainments; sleep; pain and asthma. We assessed whether the conclusions of review-level evidence appeared reasonable, for example, considering effect sizes and designs. We noted meta-analyses undertaken in reviews separately to narrative findings. We noted dose-response findings where relevant. We made no attempt to quantitatively summarise findings across reviews as quantitative summaries should be undertaken at individual study level rather than at review level.

We then summarised findings across each domain according to the overall strength of evidence in terms of the consistency of findings across different reviews, the quality of the review, the design of included studies and how outcomes were assessed. In this we aimed to minimise so-called vote-counting, that is, not quantifying the number of studies reporting positive and negative findings regardless of their size and quality. Instead we weighed findings according to the size and quality of reviews (as assessed by AMSTAR) as well as the design of primary studies. 16 In summarising findings across reviews, we defined strong evidence as consistent evidence of an association reported by multiple high-quality reviews, moderately strong evidence as consistent evidence across multiple medium-quality reviews, moderate evidence as largely consistent evidence across medium-quality reviews and weak evidence as representing some evidence from medium-quality reviews or more consistent evidence from poor-quality reviews. 15

Patient involvement

Patients or the public were not involved in the conceptualisation or carrying out of this research.

Characteristics of the 13 included reviews are shown in table 1 with quality assessments for included reviews shown in table 2 . The proportion of studies in each review that were also included in other reviews ranged from 0% to 22%. Table 3 shows the mapping of reviews to outcome areas by quality category. The objectives of many of the included reviews overlapped and many reviews considered multiple outcomes. There were six reviews which considered the associations of screentime with body composition measures (including obesity), three for diet and energy intake, seven for mental health related outcomes including self-esteem and quality of life, four for cardiovascular risk, four for fitness, three for sleep and one each for pain and asthma. The only high-quality review was limited to cardiovascular risk. We describe findings by domain below.

Quality assessment for included reviews

Mapping of reviews to subject area by quality

Body composition

Consistent evidence for an association between screentime and greater adiposity was reported in five medium-quality reviews and one low-quality review.

Overall screentime

In medium-quality reviews, Costigan et al  8 reported that 32/33 studies, including 7/8 studies with low risk of bias, identified a strong positive association of screentime with weight status; van Ekris et al  11 reported strong evidence for relationship between screentime and body mass index (BMI) or BMI z-score based on two high-quality studies and moderate evidence for relationship with overweight/obesity in three low-quality studies and Carson et al  17 reported a strong association between screentime and unfavourable body composition (obesity or higher BMI or fat mass) in 11/13 longitudinal studies, 4/4 case-control studies and 26/36 cross-sectional studies.

In a low-quality review, Duch et al  9 reported a positive association between screentime and BMI in 4/4 studies.

Television screentime

The great majority of findings related to television screentime. Tremblay et al 10 reported a moderate association between television screetime and adiposity measures, identified in 94/119 cross-sectional studies and 19/28 longitudinal studies. van Ekris et al reported strong evidence for a positive relationship between TV viewing time and incidence of overweight/obesity over time in three high-quality studies and in three low-quality studies. Carson et al reported that unfavourable adiposity was associated with television screentime in 14/16 longitudinal studies, 2/2 case-control studies and 58/71 cross-sectional studies. LeBlanc et al 18 reported that the association between television screentime and unfavourable adiposity measures could be seen at all ages, but that evidence quality was low for infants and moderate for toddlers and preschoolers.

Two reviews reported meta-analyses relating to television screentime. van Ekris et al reported that across 24 257 participants from 9 prospective cohorts, BMI at follow-up was not significantly associated with each additional hour of daily TV viewing (β=0.01, 95% CI −0.002 to 0.02), with high heterogeneity across studies. Adjustment for physical activity or diet did not materially change findings. In contrast, Tremblay et al reported that across four randomised controlled trials, decreased television screentime postintervention was associated with a pooled decrease in BMI of −0.89 kg/m 2 (95% CI −1.467 to 0.11, p=0.01).

Computer, video, mobile or other screentime

Data on other forms of screentime were very sparse. In medium-quality reviews, Carson et al reported that unfavourable adiposity measures were associated with computer screentime in 3/4 studies but in 0/2 case-control studies and that findings in cross-sectional studies were highly inconsistent; Carson et al identified no evidence for an association between video/videogame screentime and adiposity and van Ekris et al identified no evidence for relationship between computer/computer game screentime with BMI or BMI z-score in 10 low-quality studies or with WC or WC z-score in 2 low-quality studies.

In the only meta-analysis, van Ekris et al reported that across 6971 participants from five prospective cohorts, BMI at follow-up was not significantly associated with each additional hour of daily computer screentime (β=0.00, 95% CI −0.004 to 0.01), with high heterogeneity across studies. Adjustment for physical activity or diet did not change findings materially.

Dose-response effects

A dose-response effect for television screentime was reported by two medium-quality reviews (Tremblay et al ; LeBlanc et al ) with a third (Carson et al ) not distinguishing between television or other screentime. Carson et al reported that screentime dose-response was examined in 73 studies: higher screen time/TV viewing was significantly associated with unfavourable body composition with a 1-hour cut-point (8/11 studies), 1.5-hour cut-point (2/2 studies), 2-hour cut-point (24/34 studies), 3-hour cut-point (12/13 studies) or 4-hour cut-point (4/4 studies).

We conclude there is moderately strong evidence that higher television screentime is associated with greater adiposity, but that there is insufficient evidence for an association with overall screentime or non-television screentime. There is moderate evidence that a dose-response association is present for screentime or television screentime. However, there is no strong evidence for a particular threshold in hours of screentime.

Diet and energy intake

Associations of screentime with energy intake and/or diet factors were examined in two medium-quality and one low-quality review.

In a medium-quality review of experimental studies, Marsh et al  1 reported that there was strong evidence that i) screentime in the absence of food advertising was associated with increased dietary intake compared with non-screen behaviour; ii) television screentime increases intake of very palatable energy-dense foods and iii) there was weak evidence for video game screentime similarly increased dietary intake. They concluded there was moderate evidence that stimulatory effects of TV on intake were stronger in overweight or obese children than those of normal weight, suggesting the former are more susceptible to environmental cues.

In a medium-quality review, Costigan et al reported a negative association of screentime with healthy dietary behaviour in 3/5 studies. In a low-quality review, Pearson and Biddle 19 reported moderate evidence that television screentime was positively associated with total energy intake and energy dense drinks and negatively associated with fruit and vegetable consumption in longitudinal studies in both children and adolescents. In cross-sectional studies, they identified moderate evidence for the same associations for television screentime in children and for overall screentime in adolescents.

We conclude there is moderate evidence for an association between screentime, particularly television screentime, and higher energy intake and less healthy diet quality including higher intake of energy and lower intake of healthy food groups.

Mental health and well-being

Associations between mental health and well-being and screentime were examined in seven medium-quality reviews.

Anxiety, depression and internalising problems

Only Hoare et al  20 reported on associations with anxiety, and found moderate evidence for a positive association between screentime duration and severity of anxiety symptoms.

Costigan et al reported a positive association of screentime with depressive symptoms in 3/3 studies. Similarly, Hoare et al reported strong evidence for a positive relationship between depressive symptomatology and screentime based on mixed cross-sectional and longitudinal studies. Hoare et al also noted there was limited evidence for association between social media screentime and depressive symptoms. Suchert et al  21 reported a positive association of screentime with internalising problems (in 6/10 studies), but noted a lack of clear evidence for depressive and anxiety symptoms when measured separately.

In terms of dose-response for depressive symptoms, Hoare et al reported that higher depressive symptoms were associated with ≥2 hours of screentime daily in 3/3 studies. Suchert et al reported that three studies identified a curvilinear association between screentime and depressive symptoms, such that adolescents using screens in a moderate way showed the lowest prevalence of depressive symptoms.

Behaviour problems

Carson et al reported that an association between screentime and behavioural problems was examined in 24 studies. In longitudinal studies, a positive association with unfavourable behavioural measures was reported in 2/2 studies for total screentime and 3/5 studies for television screentime, but a null association was reported in 3/3 studies of video game screentime. In cross-sectional studies, positive associations were reported for television screentime (4/6 studies), computer use (3/5 studies) and video game screentime (3/4 studies). In contrast, Tremblay et al concluded there was poor evidence that television screentime was associated with greater levels of behaviour problems.

In terms of dose response, Carson et al reported that this was examined in two studies, which both reported that television screentime >1 hour daily was associated with unfavourable measures of behaviour.

Hyperactivity and inattention

Hyperactivity and attention were only considered in one review. Suchert et al reported that there was a positive association between screentime and hyperactivity/inattention problems in 10/11 studies.

Other mental health problems

LeBlanc et al reported that there was moderate evidence that television screentime was associated with poorer psychosocial health in young children aged 14 years.

Only one review each considered the association of screentime with eating disorders and suicidal ideation. Suchert et al reported there was no clear evidence for an association with eating disorder symptoms, while Hoare et al reported there was no clear evidence for a relationship with suicidal ideation.

Self-esteem

Effects on self-esteem were considered in three reviews. Hoare et al concluded there was moderate evidence for a relationship between low self-esteem and screentime. Carson et al reported that this association was not considered in longitudinal studies but that in cross-sectional studies, lower self-esteem was associated with screentime in 2/2 studies and with computer screentime in 3/5 studies, and no clear evidence for mobile-phone screentime.

In contrast, Suchert et al reported no clear evidence for an association with self-esteem and Tremblay et al similarly reported unclear evidence, with only 7/14 cross-sectional studies showing an inverse relationship between screentime and self-esteem.

Quality of life and well-being

Quality of life was considered in one review of health-related quality of life (HRQOL) and in two reviews which reported on perceived quality of life or perceived health.

HRQOL as a formal measured construct was examined by Wu et al, 22 who reported consistent evidence that greater screentime was associated with lower measured HRQOL in 11/13 cross-sectional and 4/4 longitudinal studies. A meta-analysis of 2 studies found that ≥2–2.5 hours/day of screentime was associated with significantly lower HRQOL (pooled mean difference in HRQOL score 2.71 (95% CI 1.59 to 3.38) points) than those with <2–2.5 hours/day.

Suchert et al reported that there was a positive association between screentime and poorer psychological well-being or perceived quality of life in 11/15 studies. Costigan et al reported a negative association between screentime and perceived health in 4/4 studies.

Adjustment for physical activity

Suchert et al reported that 11 included studies examined the association between screentime and mental health adjusted for physical activity. They reported that in each study the association between screentime and poorer mental health (a range of outcomes) was robust to adjustment for physical activity, suggesting that screentime is a risk factor for poor mental health independently of displacement of physical activity.

There is moderately strong evidence for an association between screentime and depressive symptoms. This association is for overall screentime but there is very limited evidence from only one review for an association with social media screentime. There is moderate evidence for a dose-response effect, with weak evidence for a threshold of ≥2 hours daily screentime for the association with depressive symptoms.

There is moderate evidence for an association of screentime with lower HRQOL, with weak evidence for a threshold of ≥2 hours daily screentime.

There is weak evidence for association of screentime with behaviour problems, anxiety, hyperactivity and inattention, poorer self-esteem and poorer psychosocial health in young children. There is no clear evidence for an association with eating disorders or suicidal ideation. There is weak evidence that the association between screentime and mental health is independent of the displacement of physical activity.

Cardiovascular risk

Associations between screentime and cardiovascular risk were examined by one high-quality and three medium-quality reviews.

Metabolic syndrome/clusters of cardiovascular risk factors

In the only high-quality review, Goncalves de Oliveira et al  23 reported there was null evidence for the association of screentime or television screentime with the presence of the metabolic syndrome (MetS). In meta-analysis across six studies (n=3881), they did not identify a significant relationship, with the OR for >2 hours screentime=1.20 (95% CI 0.91 to 1.59), p=0.20; I 2 =37%). However, when weekend screentime was examined separately in two studies (n=1620), they found a significant association with presence of the MetS (OR=2.05 (95% CI 1.13 to 3.73), p=0.02; I 2 =0%). In a medium-quality review, Carson et al reported that an association between a clustered risk factor score and television screentime was reported in 2/2 longitudinal studies and 6/10 cross-sectional studies.

Individual cardiovascular risk factors

Three medium-quality reviews examined the evidence for an association between screentime various individual risk factors, for example, cholesterol, blood pressure, haemoglobin A1c or insulin insensitivity. Tremblay et al , van Ekris et al and Carson et al each reported there was no consistent evidence for an association with any risk factor, with evidence largely limited to single studies and not consistent across studies.

There is weak evidence of an association between screentime and television screentime with the MetS. There is no clear evidence for an association with any individual cardiovascular risk factor.

Associations with fitness were examined by four medium-quality reviews. Two reviews, Costigan et al and Tremblay et al , noted that evidence for an association between screentime and fitness was weak and inconsistent. Indeed, Costigan et al noted that 2/5 studies reported a positive relationship, that is, that higher screentime was associated with higher physical activity.

In contrast, two reviews (Carson et al , and van Ekris et al ) concluded there was strong evidence for an inverse association between screentime or television screentime and cardiorespiratory fitness. Carson et al noted that 4/4 studies examined a threshold and found that higher screentime was significantly associated with lower fitness when a 2 hour cut-point was used (4/4 studies).

There is weak and inconsistent evidence for an association between screentime or television screentime and cardiorespiratory fitness, with weak evidence for a 2-hour daily screentime threshold.

Cognition, development and attainments

Associations with CYP cognition and development were examined in three medium-quality reviews.

LeBlanc et al reported that there was low-quality evidence that television screentime had a negative impact on cognitive development in young children. Evidence was stronger among infants, where LeBlanc et al concluded that there was moderate-quality evidence that television screentime elicited no benefits and was harmful to cognitive development.

Tremblay et al reported there was poor evidence that greater television screentime was associated with poorer educational attainments. Carson et al also noted weak evidence that screentime or television screentime were associated with poorer attainments.

There is weak evidence that screentime particularly television screentime is associated with poorer educational attainments and has a negative effect on cognitive development in younger children.

Associations with sleep were examined in one medium-quality and two low-quality reviews.

In a medium-quality review, Costigan et al reported a positive association between screentime and sleep problems in 2/2 studies. In low-quality reviews, Duch et al reported there was inconclusive evidence for an association between screentime and sleep duration. In contrast, Hale and Guan 24 reported there was moderate evidence that overall screentime, television screentime, computer screentime, video screentime and mobile phone screentime were associated with poor sleep outcomes including delayed bedtimes, shortened total sleeptime, sleep-onset-latency and daytime tiredness. They estimated that there was approximately 5–10 min sleep bedtime delay with each additional hour of television screentime. Findings of significantly shorter total sleep time with greater mobile device screentime were reported in 10/12 studies, with 5/5 reporting greater subjective day-time tiredness or sleepiness.

There is weak evidence that screentime is associated with poor sleep outcomes including delay in sleep onset, reduced total sleep time and daytime tiredness. There is evidence from one review that this association is seen across all forms of screentime including television screentime, computer screentime, video screentime and mobile phone screentime.

Physical pain

Associations with pain were examined in one medium-quality review. Costigan et al reported that there was weak evidence for an association between screentime and neck/shoulder pain, headache and lower back pain, although this was examined in very few studies. As this was examined in only one review, we characterised the level of evidence as insufficient.

Associations with asthma were examined in one medium-quality review. van Ekris et al reported there was insufficient evidence for a relationship between screentime or television screentime and asthma prevalence.

This RoR summarises the published literature on the effects of screentime on CYP health and well-being. Evidence was strongest for adiposity and diet outcomes, with moderately strong evidence that higher television screentime was associated with greater obesity/adiposity and moderate evidence for an association between screentime, particularly television screentime, and higher energy intake and less healthy diet quality. Mental health and well-being were also the subject of a number of reviews. There was moderately strong evidence for an association between screentime and depressive symptoms, although evidence for social media screentime and depression was weak. Evidence that screentime was associated with poorer quality of life was moderate, however evidence for an association of screentime with other mental health outcomes was weak, including for behaviour problems, anxiety, hyperactivity and inattention, poorer self-esteem, poorer well-being and poorer psychosocial health in young children. Weak evidence suggested that mental health associations appeared to be independent of physical activity.

Evidence for other outcomes was notably less strong. There is weak evidence of an association between screentime (and television screentime) with the MetS, poorer cardiorespiratory fitness, poorer cognitive development and lower educational attainments and poor sleep outcomes. It is important to note that the weak evidence reported here largely relates to a lack of literature rather than weak associations. In contrast, there was no or insufficient evidence for an association of screentime with eating disorders or suicidal ideation, any individual cardiovascular risk factor, asthma prevalence or pain.

We identified no consistent evidence of benefits for health, well-being or development, although we acknowledge that screentime may be associated with benefits in other domains not assessed here.

Evidence for a dose-response relationship between screentime and health outcomes is generally weak. We found moderate evidence for a dose-response association for screentime or television screentime and adiposity outcomes, depression and HRQOL. However, we identified no strong evidence for a threshold in hours of screentime for adiposity and only weak evidence for a threshold of ≥2 hours daily screentime for the associations with depressive symptoms and with HRQOL. One review suggested there was a curvilinear relationship between screentime and depressive symptoms. 21

Overall the quality of included reviews was moderate, with only one high-quality review and three low-quality reviews included. There were only four meta-analyses identified, two of television screentime and BMI and one each of screentime and the MetS and screentime and HRQOL. Almost all studies in each review were undertaken in high-income countries, the majority in each review undertaken in the USA. Overlap in included studies between reviews was generally low, suggesting that findings were not dominated by small numbers of individual studies.

A major weakness in the literature is its domination by television screentime, with smaller numbers of studies examining computer use or gaming and very few studies including mobile screen devices. None examined multiple concurrent screen use, although there is increasing evidence that CYP may combine screen-use such as using smartphones while watching television; young people report using multiple screens to facilitate filtering out of unwanted content, including advertisements. 25 Thus, it is unclear to what extent these findings can be generalised to more modern forms of screen use including social media and mobile screen use. RoR are necessarily limited to including primary studies which have been included in systematic reviews and are thus necessarily limited in addressing very new developments. It may take some years before adequate research is available on modern digital screen use including social media and multiple screen use and their impacts on health.

A central issue in whether these findings are generalisable to other forms of screentime is the degree to which the effects of screentime relate to time spent on screen or content watched on screen or even the context in which the content is watched on screens. Screentime may act through use while sedentary (ie, displacing physical activity) or through more direct effects. These direct effects may be either through the content watched on screens (eg, desensitising children to violence or sexually explicit material; or exposure to bullying), through the displacement of socialisation or learning time (eg, leading to social isolation) or through more direct cognitive effects, for example, the impact of blue screen light on sleep patterns and impacts on attention and concentration. 4 Our findings tell us little about the mechanisms by which screentime affects health, and it is plausible that the effects we identified on adiposity, fitness, cardiovascular risk, mental health and sleep are due to the sedentary effects of screen use. However, we did identify moderate evidence that screentime was associated with higher intake of energy dense foods, which unlikely to be mediated by sedentariness. Furthermore, there is weak evidence that associations of screentime with mental health outcomes are robust to adjustment for physical activity, 21 suggesting that screentime may affect mental health independently of the displacement of physical activity.

We found no convincing evidence of health benefits from screentime. Yet some argue strongly that digital media have potential significant health, social and cognitive benefits and that harms are overstated. A prominent group of scientists recently argued that messages that screens are inherently harmful is simply not supported by solid research and evidence. Furthermore, the concept of screen time itself is simplistic and arguably meaningless, and the focus on the amount of screen use is unhelpful." 12 They pointed out that research has focused on counting the quantity of screentime rather than investigating the contexts of screen use and content watched. Others have pointed out similar limitations in the literature on screen use and violence 7 and that educational use of screens is promoted in many educational systems. 13 Our review addressed quantity of screentime and did not investigate the impacts of contexts or content on health outcomes. However, findings of a curvilinear relationship between screentime and depressive symptoms in one of our reviews 21 and the description of a similar relationship for adolescent well-being 26 suggests that moderate use of digital technology might be important for social integration for adolescents in modern societies.

Limitations

Our review is subject to a number of limitations. Quality of included reviews was largely moderate or low, with only one high-quality review. Key factors for reviews not being classified as high quality were failing to assess the quality and likelihood of publication bias within included primary studies or failing specify an a priori design. The included reviews were not entirely independent, although the overlap in primary studies was low or very low for most, thus it is unlikely that our findings are biased by individual studies included in multiple reviews. Data were extracted by one researcher, and although data were checked carefully back to the publication by the second researcher, we did not use dual independent extraction. We did not attempt to contact the authors of articles we could not retrieve as this was a rapid review.

RoR are a methodology that is being developed and there is no agreed best practice; such reviews are only as good as the reviews included and the primary studies that are included within them. 27 There were limitations regarding the reviews included in our study in terms of heterogeneity between reviews in definition of screentime exposures, definition of health outcomes and measurement tools, making comparisons difficult. Screentime was largely measured by self-report, although increasing numbers of studies over time used more objective measures of screentime. Reviews also largely failed to consider the processes by which screentime impacted on health outcomes. In our narrative synthesis of findings, we aimed to avoid vote-counting of numbers of positive or negative studies to judge strength of evidence. However, it is possible that our findings reflect methodological or conceptual biases in our included reviews. A limitation of reviews or reviews including our own is the necessary time lag for inclusion of primary studies in systematic reviews, meaning that they may not represent the most contemporary research. Data on mobile screen use were particularly limited in our included reviews. Aside from reviews focusing on very young children, data from the included studies did not allow us to comment separately on findings by age group.

Conclusions

There is considerable 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 for impact on other health outcomes is largely weak or absent. We found no consistent evidence of health benefits from screentime. While evidence for a threshold to guide policy on CYP screentime exposure was very limited, there is weak evidence that small amounts of daily screen use is not harmful and may have some benefits.

These data broadly support policy action to limit screen use by CYP because of evidence of health harms across a broad range of domains of physical and mental health. We did not identify a threshold for safe screen use, although we note there was weak evidence for a threshold of 2 hours daily screentime for the associations with depressive symptoms and with HRQOL. We did not identify evidence supporting differential thresholds for younger children or adolescents.

Any potential limits on screentime must be considered in the light of a lack of understanding of the impact of the content or contexts of digital screen use. Given the rapid increase in screen use by CYP internationally over the past decade, particularly for new content areas such as social media, further research is urgently needed to understand the impact of the contexts and content of screen use on CYP health and well-being, particularly in relationship to mobile digital devices.

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Patient consent for publication Not required.

Contributors RMV conceptualised the study, planned the methods, assisted with the extraction of data and analysis of findings led writing the paper. NS undertook the initial search and led the extraction of data and contributed to analysis of findings and writing the paper.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Provenance and peer review Not commissioned; externally peer reviewed.

Data sharing statement All data in this paper were obtained from published studies. No additional data are available from the authors.

Read the full text or download the PDF:

• Introduction
• Conclusions
• Article Information

Effect sizes for all screen types are presented in eFigure 1 in Appendix 1 .

The x-axis represents the observed outcome in log odds ratios. Filled and hollow diamonds represent the results of the meta-analysis before and after trim-and-fill correction, respectively. The diamond centers and corresponding vertical lines represent the value of the summary result. The diamond width represents the 95% CI of the summary result. The white area within the dashed diagonal lines indicates P  > .05 to P  > .99; the light gray area outside these lines indicates P  > 0 to P  = .05.

eAppendix. Formulas Used for Conversion of the Various Effect Sizes Into Log Odds Ratios

eFigure 1. Forest Plot of All 66 Effect Sizes by Screen Type

eFigure 2. Forest Plot by Age Groups of the 28 Effect Sizes of General Screen Use

eFigure 3. Forest Plot by the Type of Autism Spectrum Disorder (ASD) Measure of 28 Effect Sizes of General Screen Use

eFigure 4. Forest Plot of the 6 Longitudinal Studies

eFigure 5. Forest Plot by Type of Screen of 66 Effect Sizes Using Fisher z Scores

eFigure 6. Forest Plot by Age Groups of the 28 Effect Sizes of General Screen Use Based on Fisher z Scores

eFigure 7. Forest Plot by Type of Autism Spectrum Disorder (ASD) Measure of the 28 Effect Sizes of General Screen Use Based on Fisher z Scores

Data Sharing Statement

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Ophir Y , Rosenberg H , Tikochinski R , Dalyot S , Lipshits-Braziler Y. Screen Time and Autism Spectrum Disorder : A Systematic Review and Meta-Analysis . JAMA Netw Open. 2023;6(12):e2346775. doi:10.1001/jamanetworkopen.2023.46775

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Screen Time and Autism Spectrum Disorder : A Systematic Review and Meta-Analysis

• 1 Department of Education, Ariel University, Ariel, Israel
• 2 Centre for Human Inspired Artificial Intelligence, University of Cambridge, Cambridge, United Kingdom
• 3 School of Communication, Ariel University, Ariel, Israel
• 4 Faculty of Data and Decision Sciences, Technion–Israel Institute of Technology, Haifa, Israel
• 5 Communications Department, Sapir Academic College, Hof Ashkelon, Israel
• 6 Seymour Fox School of Education, Hebrew University of Jerusalem, Jerusalem, Israel

Question   Is there an association between screen time and autism spectrum disorder (ASD)?

Findings   In this systematic review and meta-analysis of 46 of 4682 observational studies, a statistically significant association was found between screen time and ASD, in particular among studies that examined general screen use among children. However, when accounting for publication bias, the findings were no longer statistically significant.

Meaning   These findings suggest that excessive screen time may be associated with negative developmental outcomes; however, the observational nature and publication bias of the included studies render these findings inconclusive.

Importance   Contemporary studies raise concerns regarding the implications of excessive screen time on the development of autism spectrum disorder (ASD). However, the existing literature consists of mixed and unquantified findings.

Objective   To conduct a systematic review and meta-analyis of the association between screen time and ASD.

Data Sources   A search was conducted in the PubMed, PsycNET, and ProQuest Dissertation & Theses Global databases for studies published up to May 1, 2023.

Study Selection   The search was conducted independently by 2 authors. Included studies comprised empirical, peer-reviewed articles or dissertations published in English with statistics from which relevant effect sizes could be calculated. Discrepancies were resolved by consensus.

Data Extraction and Synthesis   This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) reporting guideline. Two authors independently coded all titles and abstracts, reviewed full-text articles against the inclusion and exclusion criteria, and resolved all discrepancies by consensus. Effect sizes were transformed into log odds ratios (ORs) and analyzed using a random-effects meta-analysis and mixed-effects meta-regression. Study quality was assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. Publication bias was tested via the Egger z test for funnel plot asymmetry. Data analysis was performed in June 2023.

Main Outcomes and Measures   The 2 main variables of interest in this study were screen time and ASD. Screen time was defined as hours of screen use per day or per week, and ASD was defined as an ASD clinical diagnosis (yes or no) or ASD symptoms. The meta-regression considered screen type (ie, general use of screens, television, video games, computers, smartphones, and social media), age group (children vs adults or heterogenous age groups), and type of ASD measure (clinical diagnosis vs ASD symptoms).

Results   Of the 4682 records identified, 46 studies with a total of 562 131 participants met the inclusion criteria. The studies were observational (5 were longitudinal and 41 were cross-sectional) and included 66 relevant effect sizes. The meta-analysis resulted in a positive summary effect size (log OR, 0.54 [95% CI, 0.34 to 0.74]). A trim-and-fill correction for a significant publication bias (Egger z  = 2.15; P  = .03) resulted in a substantially decreased and nonsignificant effect size (log OR, 0.22 [95% CI, −0.004 to 0.44]). The meta-regression results suggested that the positive summary effect size was only significant in studies targeting general screen use (β [SE] = 0.73 [0.34]; t 58  = 2.10; P  = .03). This effect size was most dominant in studies of children (log OR, 0.98 [95% CI, 0.66 to 1.29]). Interestingly, a negative summary effect size was observed in studies investigating associations between social media and ASD (log OR, −1.24 [95% CI, −1.51 to −0.96]).

Conclusions and Relevance   The findings of this systematic review and meta-analysis suggest that the proclaimed association between screen use and ASD is not sufficiently supported in the existing literature. Although excessive screen use may pose developmental risks, the mixed findings, the small effect sizes (especially when considering the observed publication bias), and the correlational nature of the available research require further scientific investigation. These findings also do not rule out the complementary hypothesis that children with ASD may prioritize screen activities to avoid social challenges.

The ever-increasing rates of autism spectrum disorder (ASD), 1 , 2 a neurodevelopmental condition characterized by difficulties in interpersonal interactions and communication, as well as restricted and repetitive behaviors, are a major concern in pediatrics. Several explanations have been proposed for this increased prevalence, 3 , 4 including the global emergence of screen-based devices (eg, smartphones, tablets) and their ubiquitous use among young children, including infants. 5 Corresponding to a longstanding concern in media psychology termed the displacement hypothesis , 6 contemporary scholars warn that excessive screen use may come at the expense of positive and vital real-life experiences, such as interpersonal interactions, outdoor and sporting events, and educational activities. 7 , 8 According to this hypothesis, screen use contributes to young children being less active, less verbal, and less social than children of previous generations, essentially increasing their risk of experiencing developmental delays, behavioral problems, and ASD symptoms. 9 - 13

Although this concern, along with multiple other screen-related risks, 14 warrants the periodic formulation of screen use guidelines for parents (such as the recent recommendations issued by the World Health Organization 15 ), its empirical foundations remain unclear. To date, very few longitudinal studies have been conducted on this topic, and the picture arising from the existing, mostly cross-sectional literature is ambiguous and requires further examination. 16 - 18

Before undertaking the current study, we identified 2 systematic reviews that addressed the association between screen time and ASD. 17 , 18 Indeed, these reviews focused on the opposite direction of this association—that is, they explored the hypothesis that children with ASD would be more attracted than their peers to screen activities because these activities allow them to avoid real-life communication challenges. However, the results of these reviews also may be relevant to our research question, because they relied mostly on bidirectional correlational studies.

A 2018 systematic review by Stiller and Mößle 17 that included 47 studies was inconclusive. Some studies indicated that children with ASD have increased screen time, whereas other studies suggested that children without ASD have increased screen time. 17 The 2019 systematic review by Slobodin et al 18 implemented a more rigid inclusion criterion and included only studies that compared participants with diagnosed ASD with nondiagnosed participants. That review yielded 16 relevant studies, of which 14 pointed to a consistent trend whereby children with ASD indeed had increased screen time. 18 Nevertheless, Slobodin et al emphasized that the wide variability in the populations and methodologies in the included studies limited their finding, and they stated that “there are no data to confirm or refute a causal relationship between ASD and screen use.” 18 (p309)

Apart from these limitations and the mixed findings of Stiller and Mößle, 17 we identified 3 additional gaps in the literature. First, the latest search of the literature ended in April 2018, while screen use has only become more popular over the years, especially during the COVID-19 pandemic that shifted peoples’ activity to screen-based platforms. 19 Second, none of the available reviews included a quantitative evaluation of the association between screen time and ASD using a meta-analysis procedure. Finally, although several moderating factors were suggested in these reviews (eg, to explain mixed findings), none included a designated analysis that could shed light on the moderating role of these factors in the association between screen time and ASD.

Considering these gaps, we performed an updated systematic review and, to our knowledge, the first meta-analysis of the literature accumulated on the bidirectional association between screen time and ASD. In addition, this study also implemented meta-regression analyses to explore potential moderating factors that may be involved in this association. Specifically, the following 3 salient variables that distinguished the collected studies from one another were examined: (1) the type of screen device or screen activity (eg, smartphones, social media), (2) the age of screen users, and (3) the type of ASD measure, whether it reflected an ASD clinical diagnosis or symptoms or behaviors typical to ASD. Although these procedures cannot compensate for the absence of experimental studies, their results may shed light on this concerning association between screen time and ASD.

This systematic review and meta-analysis followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) guideline. The 2 main variables of interest were screen time (ie, hours of screen use per day or per week) and ASD. The ASD variable consisted of 2 measures: (1) a binary variable (yes or no) that indicated the presence of a clinical diagnosis of ASD and (2) a continuous variable that indicated the existence of symptoms or behaviors typical to ASD (but may not necessarily indicate the existence of a clinical diagnosis).

A systematic search of relevant studies published up to May 1, 2023, was conducted in the 2 prominent databases in medicine and psychology: PubMed and PsycNET. A complementary search for unpublished work was conducted in the ProQuest Dissertation & Theses Global database. The search did not contain restrictions on publication type or language. Terms related to screen time and ASD were searched in all available database search fields, except in PubMed, which allowed a narrowed search within the study title and abstract (assuming that relevant articles mentioned the search terms in these sections).

For ASD, the search terms used were autism and ASD . For screen time, the search terms used were computer , media , mobile media , mobile phone , phone , screen time , smartphone , social media , television , and video games . This list was discussed among and consolidated by all authors, and it includes and extends the list used in the most recent systematic review on this topic. 18 During the search, each term for screen time was coupled with the terms for ASD; that is, the screen time terms were searched twice, once with ASD and once with autism spectrum disorder . Two authors (R.T. and S.D.) independently coded all titles and abstracts, reviewed full-text articles against the inclusion and exclusion criteria, and resolved all discrepancies by consensus.

After duplicate records were removed, the PubMed and PsycNET searches yielded 4677 articles. These articles examined the association between ASD and the following: computers (2884 articles), media (922 articles), mobile media (7 articles), mobile phones (17 articles), phone (121 articles), screen time (164 articles), smartphones (60 articles), social media (309 articles), television (105 articles), and video games (88 articles). The complementary search in ProQuest yielded 5 additional records of doctoral dissertations, thus creating an initial pool of 4682 studies.

In the first filtering step, we read the titles and abstracts of the 4682 articles and determined whether they (1) presented an empirical study, (2) were written in English, (3) were published in a peer-reviewed journal (or were a thesis or dissertation), and (4) specifically examined screen time and ASD (as multiple studies were conducted among ASD populations but addressed other negative outcomes, such as sleep problems). In the second filtering step, we read the remaining articles thoroughly and excluded those that did not meet the aforementioned inclusion criteria. In the second step, we also excluded studies that (1) did not report any statistics or reported a single case study, (2) presented a literature review and did not include an empirical study, and (3) had no comparison group, provided that they had a group of participants with ASD (studies without a comparison group were only included if they measured ASD symptoms). Research quality was assessed using the Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach. 20

Upon review of the final set, we collected all effect sizes (eg, Cohen’s d , Pearson r , odds ratio [OR], and log OR values) that represented the associations between screen time and ASD. In cases in which no effect size was reported, we calculated the effect size manually using the reported data or available statistics. We calculated Cohen’s d using means and SDs or t or F scores. We calculated log ORs using either reported frequency tables, reported χ 2 values, or β coefficients of logistic regression models. In cases in which a linear (not logistic) regression analysis was conducted, we transformed the standardized β coefficients to Pearson r values using the following conventional formula: r  = β + 0.5γ, where γ equals 1 when β is positive and 0 when β is negative. 21

When different effect sizes were reported for different age groups or for different screen types, they were all entered into the meta-analysis. When more than a single effect size was reported within a given age group or screen device type (eg, for different time points or for different assessment tools), we collected the largest effect size available.

Because ASD is typically perceived as a binary variable indicating whether an individual has this diagnosis, we transformed all collected effect sizes into log OR effect sizes. These log OR scores were then used to calculate the meta-analysis and meta-regression of this study. Because nearly half of the collected studies treated ASD as a continuous variable (measuring ASD symptoms), we conducted complementary analyses using continuous effect sizes (ie, Fisher z scores instead of log OR scores). These analyses yielded equivalent results (eFigures 5-7 in Supplement 1 ) that replicated the main results reported in the following meta-analysis and meta-regression results.

Effect sizes were calculated using the R psych package, version 1.9.1 (R Project for Statistical Computing). 22 Conversion of effect sizes to log ORs was conducted using the R effect size package, version 0.8.6.1 (R Project for Statistical Computing). 23 The eAppendix in Supplement 1 presents the exact formulas used in this process.

The univariate meta-analysis was conducted using a random-effects model and the meta-regression analyses were conducted using a mixed-effects model, both via the restricted maximum likelihood estimator for heterogeneity (τ 2 ). The meta-regression analysis addressed 3 independent variables, representing the 3 salient features that differentiated the collected studies from one another and thus allowed us to allocate them to distinct clusters. These variables were as follows: (1) screen type (general use of screens, television, video games, computer, smartphones, or social media), (2) age group (children vs adults or heterogenous age groups), and (3) type of ASD measure (clinical diagnosis vs ASD symptoms). The level of statistical significance in all analyses was P < .05 (2-tailed), meaning there were no prior assumptions regarding the direction of the results. Publication bias was tested via the Egger z test for funnel plot asymmetry. 24

The age variable comprised a wide range of ages across the various studies, which made the categorization process difficult. However, we observed that approximately half of the studies comprised children aged younger than 12 years, whereas the others included older or heterogenous age groups; we therefore coded age as a binary variable (children vs adults or heterogenous age groups). All analyses were conducted using the R metafor package, version 2.1-0 (R Project for Statistical Computing). 25 Data analysis was performed in June 2023.

The initial systematic literature search yielded 4682 records ( Figure 1 ). The first filtering step resulted in 145 studies, and the second filtering step resulted in a final collection of 46 studies 9 - 13 , 26 - 66 that examined the association between screen time and ASD ( Figure 1 and Table 1 ).

The 46 included studies were published between 2011 and 2023, with a total of 562 131 participants. Thirteen studies reported on data collected since the COVID-19 pandemic ( Table 1 ). 13 , 26 - 28 , 33 , 34 , 36 , 39 , 49 , 59 , 60 , 65 , 66 Notably, the research design of all 46 studies was observational: 41 were cross-sectional 9 , 13 , 26 - 30 , 32 - 44 , 46 - 66 and 5 were longitudinal. 10 - 12 , 31 , 45 Accordingly, the overall research quality of these studies was determined to be relatively low (according to the GRADE approach). 20

Altogether, the 46 studies reported on 66 effect sizes relevant to the association between screen time and ASD. There were 3 effect sizes for social media (n = 784), 3 for smartphones (n = 10 344), 5 for computers (n = 48 836), 13 for video games (n = 2137), 14 for television (n = 207 972), and 28 for unspecified screen devices or screen activity (n = 343 047; coded as “general use of screens” in this review). Regarding the age of the researched populations, 37 effect sizes were observed among children aged younger than 12 years (n = 266 474) and 29 effect sizes were observed among adults or heterogenous age groups (n = 346 646).

The meta-analysis of all 66 effect sizes resulted in a significant positive summary effect size (log OR, 0.54 [95% CI, 0.34 to 0.74]; SE = 0.10; P  < .001; τ 2  = 0.58; Q 65  = 6222.68; P  < .001; I 2  = 99.7%) ( Figure 2 and eFigure 1 in Supplement 1 ). A separate meta-analysis of the 6 longitudinal effect sizes only yielded an equivalent summary effect (log OR, 0.65 [95% CI, 0.26 to 1.05]) (eFigure 4 in Supplement 1 ) that did not differ significantly from the aforementioned summary effect size ( Q m  = 0.23; P  = .63).

An Egger Z test for funnel plot asymmetry 24 suggested a significant publication bias (2.15; P  = .03). A trim-and-fill correction for this bias 67 resulted in a substantially decreased and nonsignificant summary effect (log OR, 0.22 [95% CI, −0.004 to 0.44]; P  = .05) ( Figure 3 ).

A further meta-regression indicated that all 3 independent variables entered into the model (ie, screen type, age group, and type of ASD measure) contributed significantly to the overall explained variance ( R 2  = 0.29; τ 2  = 0.03; Q e58  = 2656.55; P  < .001). However, in terms of β coefficients, only screen type had a significant effect size. That is, the β coefficients for general use of screens (β [SE] = 0.73 [0.34]; t 58  = 2.10; P  = .03) and social media use (β [SE] = −1.29 [0.51]; t 58  = −2.50; P  = .01) were significantly different from 0, whereas the effect sizes of television, video games, computers, and smartphones were not statistically significant ( Table 2 ). The association between social media and ASD was negative (log OR, −1.24 [95% CI, −1.51 to −0.96]; Q 2  = 1.76; P  = .41), whereas the association between general use of screens and ASD was positive (log OR, 0.79 [95% CI, 0.55 to 1.03]; Q 27  = 2980.44; P  < .001).

The most prominent cluster in our review comprised studies addressing general screen use ( k  = 28), which also had the largest heterogeneity ( Q 27  = 2980.44; P  < .001; I 2  = 99.67%). To further examine the positive association evidenced only in this cluster, we conducted a second meta-regression analysis that targeted only the 28 effect sizes reported in the general screen use cluster. Results from the mixed-effects model suggested that both remaining moderators contributed significantly to model variance for age group (β [SE] = 0.67 [0.25]; t 25  = 2.69; P  = .001) and ASD measure (β [SE] = 0.79 [0.25]; t 25  = 3.23; P  = .007) ( Table 2 ). Specifically, the effect size for children (log OR, 0.98 [95% CI, 0.66 to 1.29]) was significantly larger than that for adults or heterogenous age groups (log OR, 0.49 [95% CI, 0.19 to 0.79]; Q m  = 3.94; P  = .047) (eFigure 2 in Supplement 1 ). The effect size for the group with an ASD clinical diagnosis (log OR, 0.9 [95% CI, 0.55 to 1.25]) was slightly larger than that for the group with ASD symptoms (log OR, 0.57 [95% CI, 0.32 to 0.82]), but this difference was not significant ( Q m  = 1.14; P  = .29) (eFigure 3 in Supplement 1 ).

The goal of this study was to provide an updated systematic review and, to our knowledge, the first meta-analysis of the literature accumulated on the association between screen time and ASD. This review yielded 46 observational studies (5 longitudinal and 41 cross-sectional) with 66 relevant effect sizes. The first meta-analysis of these effect sizes resulted in a statistically significant, although small, summary effect size suggesting that screen time is indeed associated with ASD. This association seemed to be most dominant within studies addressing general screen use among children aged younger than 12 years.

The primary findings of this study may serve as a preliminary warning that supports existing medical recommendations to limit screen use among young children. 15 This preliminary conclusion corresponds with the few longitudinal studies conducted on this topic to date. 11 , 12 According to these studies and the displacement hypothesis described earlier, infancy and early childhood are highly sensitive developmental stages. Therefore, adult caregivers are advised to monitor their children’s screen time and ensure that it does not come at the expense of positive, real-life experiences and relationships, which are essential for the development of communication and emotional skills.

Nevertheless, our primary findings restrict this preliminary conclusion. First, the observational nature of the available studies limits our ability to determine the direction of the association between screen time and ASD. Second, the literature seems to be characterized by a substantial publication bias that challenges the reliability of the observed summary effect size. In fact, when this bias was considered in the analysis, the summary effect size became negligible and insignificant. These findings suggest that the potential negative outcomes associated with screen use may be less severe than commonly believed, 68 , 69 especially when they are balanced against other factors such as the specific type of screen use.

As our meta-regression results suggest, the observed effect size for screens disappeared in the separate analyses of studies dedicated to the specific effect sizes of television, video games, computers, and smartphones. Moreover, the association between social media and ASD was negative, thus suggesting that some types of screen use may either protect against ASD or be avoided by users with ASD (depending on the direction of the association). This distinction between various types of screen devices and activities is important because it may offer an explanation for the mixed findings in the existing literature, 17 and it might facilitate the development of more nuanced guidelines for parents. 68

Screen use may have both negative and positive outcomes, as described in previous studies. 70 , 71 For example, social media use may have some benefits for children with ASD or ASD symptoms, owing to the engagement in interpersonal associations that typically occurs on these media. 14 This observation replicates, to a certain extent, findings from a related meta-analysis that targeted the association between screen time and language skills. 7 Although so-called background television had negative effect sizes in this meta-analysis, educational programs and co-viewing had positive effect sizes. 7 Correspondingly, some video games may be positively associated with intellectual functioning and school performance 72 - 74 ; some were even developed specifically for children with ASD to provide emotional support and joyful experiences. 75

This research has several limitations. First, the heterogeneity in the methodologies and measurements of the studies introduces inconsistencies into the analysis. Second, the correlational nature of the included studies limits our ability to determine the direction of the association, as mentioned earlier. Third, despite our attempt to control for key moderating variables, multiple other potentially confounding variables, such as socioeconomic factors or parental attitudes, limit our interpretation of the findings. Future, high-quality studies are therefore crucially recommended, preferably using objective screen time measurements, longitudinal and experimental designs (eg, through interventions aimed at reducing screen time and examining its implications on ASD symptoms), and comprehensive control of confounding variables. These studies may help determine whether screen use precedes ASD symptom onset or vice versa, and they may generally contribute to more robust understanding of the complex associations between screen use and ASD.

The results of this systematic review and meta-analysis, including a notable indication for publication bias as well as small and sometimes nonsignificant effect sizes, and the limitations just described suggest that the issue of screen time and ASD is far from being resolved. In fact, the slight superiority (although not statistically significant) of the clinical diagnosis variable over the ASD symptom variable we observed in the meta-regression brings forth the basic obstacle in this field, which relates to the directionality of the association, as discussed at the start of this work. Alongside the displacement hypothesis focused on the potential negative outcomes associated with screens, a large portion of the literature is dedicated to the opposite direction—that is, to the characteristics that draw children with ASD to engage in screen activities. 16 - 18 As concluded in a previous literature review on this topic, children with ASD seem to “show increased interest in screen viewing… [which] begins at a very young age.” 18 (p308) It is also reasonable to assume that parents of children with clinically diagnosed ASD adopt a relatively permissive position regarding their children’s screen use. It is possible, then, that the observed (bidirectional) association of the current meta-analysis reflects this tendency of children with diagnosed ASD, at least to a certain extent, thus requiring us to continue searching for other explanations for the increasing global rates of ASD. Excessive screen time may indeed come at the expense of positive real-life activities and close familial relationships that could increase ASD risk. However, further research is needed to support this concern, as the increase in ASD prevalence may be attributable to a range of medical, environmental, and societal factors. 1 , 3 , 4 , 76 , 77

Accepted for Publication: October 26, 2023.

Published: December 8, 2023. doi:10.1001/jamanetworkopen.2023.46775

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2023 Ophir Y et al. JAMA Network Open .

Corresponding Author: Yaakov Ophir, PhD, Department of Education, Ariel University, 3 Kiryat Hamada St, Ariel 4070000, Israel ( [email protected] ).

Author Contributions: Dr Ophir and Mr Tikochinski had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Ophir, Rosenberg, Dalyot, Lipshits-Braziler.

Acquisition, analysis, or interpretation of data: Rosenberg, Tikochinski, Dalyot.

Drafting of the manuscript: Ophir, Rosenberg, Tikochinski, Dalyot.

Critical review of the manuscript for important intellectual content: Ophir, Rosenberg, Dalyot, Lipshits-Braziler.

Statistical analysis: Tikochinski.

Administrative, technical, or material support: Rosenberg.

Supervision: Ophir, Rosenberg, Lipshits-Braziler.

Conflict of Interest Disclosures: None reported.

Data Sharing Statement: See Supplement 2 .

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Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews

Affiliation.

• 1 Population, policy and practice research programme, UCL Institute of Child Health, London, UK.
• PMID: 30606703
• PMCID: PMC6326346
• DOI: 10.1136/bmjopen-2018-023191

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.

Keywords: chil health; mental health; obesity; screentime.

© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

Publication types

• Systematic Review
• Adolescent Behavior*
• Child Behavior*
• Child, Preschool
• Depression / psychology
• Feeding Behavior
• Pediatric Obesity / physiopathology
• Quality of Life
• Review Literature as Topic
• Screen Time*

Media Awareness and Screen Time Reduction in Children, Youth or Families: A Systematic Literature Review

• Open access
• Published: 02 December 2021
• Volume 54 , pages 815–825, ( 2023 )

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• Hanno Krafft 1 ,
• Katja Boehm 1 ,
• Silke Schwarz 1 ,
• Michael Eichinger 2 ,
• Arndt Büssing 1 &
• David Martin   ORCID: orcid.org/0000-0002-4279-3032 1 , 3  

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Excessive use of screen media is a global public health issue and especially extensive screen exposure during very early childhood. This review was conducted in order to update previous reviews on the effectiveness of interventions to reduce screen time. An electronic literature search was carried out in MEDLINE, COCHRANE LIBRARY and CINAHL for articles indexed from June 2011 until October 2019. The search identified 933 publications of which 11 publications were included in this review. There are studies showing interventions with a positive influence on reduction of screen time and the participants’ awareness and behavior concerning the use of screen media, as well as studies without such effects. No intervention was identified to be superior. This warrants further investigation of potentially effective combinations of intervention components and long-term follow-up.

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Introduction

Excessive use of digital screen media (screen time) is a global public health issue associated with adverse mental and physical health outcomes, especially for children. During the past few years children of all ages have not only obtained access to the possibilities of traditional screens like TV but additionally have access to new screen technologies like, for instance, computers, tablets, smartphones and gaming consoles [ 1 , 2 ]. Studies show that, as they are getting older, children and adolescents spend more time in front of screen media [ 3 ], and that the time children use screen media for the first time, is happening earlier [ 2 , 4 ]. Numerous studies have shown that extensive screen exposure during very early childhood can be harmful: for cognitive development [ 5 , 6 , 7 ], social competences [ 8 , 9 ], mental health [ 9 , 10 ] and physical wellbeing [ 1 , 11 ]. In their review of effective strategies for reducing screen time among young children from 2012, Schmidt et al. [ 12 ] put forward some research priorities and recommendations for the planning of an intervention based on gaps in the current literature. Developing interventions that are scalable to children, adolescence and adults needs multifaceted programs with different components and such components need to be evaluated for their single and combined effectiveness. The present review builds the background for the best possible planning of interventions.

To our knowledge, only one systematic review has examined intervention strategies to reduce screen time among children from birth to 12 years of age. In 2012 Schmidt et al. conducted a systematic review of 7 electronic databases to June 2011, using the terms “intervention” and “television”, “media” or “screen time”. They identified 47 out of 144 peer‐reviewed intervention studies that reported frequencies of TV viewing or screen‐media use in children were included. Significant reductions in TV viewing or screen‐media use were achieved in 29 studies. Interventions utilizing electronic TV monitoring devices, contingent feedback systems, and clinic‐based counseling were most effective. Schmidt et al. found several research gaps, including a relative paucity of studies targeting young children or minorities, limited long‐term (> 6 month) follow‐up data, and few targeting removing TVs from children’s bedrooms [ 12 ].

Because of the rapid development in screen media, especially smartphones and handheld devices, we decided to update this systematic review by reviewing studies published between June 2011 and October 2019, to see whether there are new studies on this topic that might be helpful for the development of interventions generating media awareness and attempts to reduce screen time.

Our systematic review was conducted in accordance with the recommendations of the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) statement [ 13 ] (Fig.  1 ).

Flow diagram of study selection

Information Sources and Search

An electronic literature search was carried out in the databases MEDLINE, COCHRANE LIBRARY and CINAHL. All articles indexed between June 2011 and October 2019 were considered for eligibility. Searches were carried out with English keywords related to screen-media, intervention and study design:

media, television, TV, mobile, handheld, tablet, smartphone, gaming, game, computer, electronic device, video, screen

intervention, education, information, behavior, change, reduce,

randomized controlled trial (RCT), controlled clinical trial (CCT), clinical trial (CT)

The complete electronic search strategy for the MEDLINE database, including any limits used, is provided in Fig.  2 in the Appendix. Comparable search strategies were used for the other databases.

Study Selection and Eligibility Criteria

Studies were independently considered for eligibility by two reviewers. The following eligibility criteria were used: (1) Intervention focused on screen media, (2) There was an objective intervention, (3) Intervention aimed to reduce screen time, (4) Basic requirements of a study (RCT, CCT, CT) were fulfilled. Exclusion criteria were (1) Full text not available in English language, (2) Participants older than 18 years. Predefined data abstraction forms were used to collect relevant data for all eligible studies (Table 1 ).

Risk of Bias in Individual Studies

The methodological quality of all studies was assessed using the PEDro scale which covers both internal and external validity [ 14 ]. The PEDro scale was originally developed to evaluate the titles indexed in the PEDro database and to indicate to the user, whether the randomized clinical trials are internally valid and provide sufficient statistical information to interpret the results. The PEDro scale, which is based on the Delphi list of Verhagen and his colleagues [ 15 ], consists of a total of 11 items that are answered with “yes” or “no”. Items 2–11 focus on different aspects of internal validity. All positive answers to items 2–11 are summed up and give the PEDro score (range: 0–10). Studies with a PEDro score of ≥ 6 points are considered “high quality”, and studies with a PEDro score of ≤ 5 points are rated as “low quality” [ 16 ] Item 1 refers to the specification of eligibility criteria of study participants (external validity) and is not taken into account in the calculation of the PEDro score [ 14 ].

Risk of Bias Within Studies

The methodological quality of the included studies ranged from 4 to 8 points on the PEDro scale and can be rated as good overall (median: 6 points, Table 2 ). None of the 11 studies achieved a perfect score. However, 9 studies obtained PEDro scores of ≥ 6 and can be categorized as high-quality studies. The unconcealed allocation as well as the missing blinding of subjects, therapists and assessors were the most frequent methodological deficiencies. The internal validity of the results is therefore limited (see Table 2 ).

Study Selection

The electronic literature search identified 2.052 publications, of which 933 articles remained after the exclusion of duplicates. In the title and abstract screening 914 publications were excluded as not relevant. After assessing the full texts of the remaining articles for eligibility another 8 publications were excluded. A total of 11 publications were included in the qualitative synthesis [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 ] (Fig. 1 ).

Study Characteristics and Respective Results

In a recent RCT Bandeira et al. investigated 1085 students, aged 11–17 years, regarding their screen time including TV, video games and computer on weekdays and weekends. The intervention had a duration of 4 months and included teacher trainings, the provision of support material for teachers, environmental opportunities to encourage physical activity and health education messages in schools via posters. Screen time was evaluated using an adapted questionnaire. No significant differences for reduction of screen time between the intervention group and the control group, which received no intervention were found [ 17 ]. In another RCT published in 2017 Smith et al. examined 361 boys regarding their overall screen time during each day of the week. The intervention was the smartphone application “Active Teen Leaders Avoiding Screen time” (ATLAS), which was designed to promote physical activity and reduce screen-time in adolescent boys considered at risk of obesity. A significant intervention effect was observed for recreational screen time at 8-months which was sustained at 18-months [ 18 ]. In a further RCT published in 2016 Babic et al. investigated 322 students of unknown age regarding their mean daily screen time including the use of TVs, videos, DVDs, computers, tablets and smartphones. The intervention with a duration of 6 months consisted of interactive seminars and informational as well as motivational messages via preferred social media and messaging systems. Reductions in screen time were observed in both groups from baseline to end line. However, there was no evidence for an adjusted between-group difference at end line [ 19 ]. Mendoza et al. published a RCT where the TV viewing in minutes/day was investigated in 211 children aged 3–5 years. The intervention was called the Fit 5 Kids (F5K) TV reduction curriculum and lasted for 8 weeks. Significant relative difference for the decrease in mean daily TV viewing minutes for the intervention versus the control group, which received no intervention, were observed [ 20 ]. Yilmaz et al. investigated 363 children, aged 3.5 years in a RCT regarding the length of screen time (TV/video games/computer) for 1 week. The intervention consisted of printed materials, interactive CDs and 1 counselling call over 9 months. Significant reduction from baseline in screen time were observed for the control as well as the intervention groups over time [ 21 ]. In another RCT, Andrade et al. reported the number of hours spent of screen time of TV/video games/computer on week and weekend days for 1370 adolescents. The intervention with a duration of 28 months consisted of individual and environmental strategies (i.e. manuscript, textbook, parental workshops). While there were partial reduction of screen time in favor to the control group, no constant reduction in screen time was observed in the intervention group over the whole intervention period [ 22 ]. In another RCT published by Lubans et al. in 2014 the smartphone app “ActiveTeen Leaders Avoiding Screen time” (ATLAS) was also used as an intervention for 20 weeks with 361 adolescent boys and their intention to reduce screen time in general was examined. Participants’ intentions to limit their recreational screen time in percent agreement were high following the completion of the program [ 23 ]. Another RCT by Maddison et al. investigated the screen-based sedentary time (min/d) spend on TV/video/computer in 251 children aged 9–12 years. For 20 weeks multiple face-to-face counseling of parent/caregiver/child, activity packages, online support via a website and a monthly newsletter was provided as intervention. No significant differences in screen-based sedentary behavior in neither the intervention nor control groups were observed [ 24 ]. In 2014 Hesketh et al. reported about a RCT in which TV viewing times of 542 3-months-old children were examined. The intervention was the Melbourne InFANT Program (Material and sources to provide knowledge, skills and strategies to promote healthy eating and active play) which was carried out for 17 months. The intervention reduced children’s television viewing time [ 25 ]. Birken et al. published a RCT regarding parent-reported time 3-years-old children spent on TV/video games/computer. The intervention in 351 children over 12 months was a 10-min behavioral counseling by trained study personal directly after the health maintenance visit, which included information on the health impact of screen time in children and provided strategies to decrease screen time. No significant differences in mean total weekday minutes of screen time, or mean total weekend day minutes of screen were observed between the intervention and control groups [ 26 ]. Mendelsohn et al. carried out a RCT investigating 410 families with a child mean age of 6.9 months and reported the daily exposure to TV/video games/computer in minutes during a 24-h period for each child. As intervention videotaping of mother–child interaction followed by review with the child development specialist, provision of learning materials, provision of parenting pamphlets, newsletters, learning materials, and parent-completed developmental questionnaires were carried out over 6 months. Differences were found across groups for daily duration of media exposure and children in 1 of 2 intervention groups had first been exposed to media approximately half a month later than children in control groups [ 27 ]. For an overview of all study characteristics with detailed statistical values see Table 1 .

Overall Summarized Results of Studies

In 6 studies there was evidence for a reduction in screen time [ 18 , 19 , 20 , 21 , 25 , 27 ]. Moreover, in 1 study participants’ intentions to limit their recreational screen time were high following the completion of the program [ 23 ] and in another study children of 1 intervention group were first exposed to media approximately half a month later than children in the control groups [ 27 ]. No significant differences between intervention and control groups for reduction of screen time were found in four studies [ 17 , 22 , 24 , 26 ]. For an overview of all individual results, see Table 1 .

Comparing the interventions in the studies with regard to their components, the following classification can be made: components that rely on personal knowledge transfer like school curriculums [ 20 ] and teacher training [ 17 ] or face-to-face counseling [ 24 , 26 , 27 ], counselling calls [ 21 ] and workshops [ 22 ]; components that impart knowledge via printed information materials (i.e. manuscripts, textbooks, posters) [ 17 , 21 , 22 , 25 , 27 ]; Digital or online components like interactive seminars [ 19 , 21 ], newsletters [ 24 , 27 ], information via websites [ 24 ], smartphone App [ 18 , 23 ] or social media and messaging systems [ 19 ].

The kind of screen media which was targeted by the interventions was also different across the included studies: seven studies defined screen media as TV, video (games) and computer [ 17 , 19 , 21 , 22 , 24 , 26 , 27 ]; two studies only named TV [ 20 , 25 ] and two studies try to reduce screen time in general [ 18 , 23 ]; one study focused on tablets and smartphones beside TV, video and computer [ 19 ].

Summary of Evidence

The majority of studies showed that different interventions can have an effect on screen time [ 18 , 19 , 20 , 21 , 25 , 27 ] or at least have a positive influence on the participants’ awareness and behavior concerning the use of screen media [ 23 , 27 ]. These results are consistent with the existing review of Schmidt et al. [ 12 ]. It is hard to speculate on why there are also studies that show no significant differences between intervention and control groups [ 17 , 22 , 24 , 26 ] since the included participants, applied interventions as well as measurements were similar to other studies. It is possible that there are particularly effective combinations of interventions components, but from the existing data, no conclusions can be drawn as to which intervention component is more effective or superior. Especially since most interventions use multiple components and these components were not evaluated separately. Short interventions focusing solely on reducing screen time may not be effective in preschool children [ 26 ] but focusing on screen time behavior in combination with other health behaviors might result in a greater effect on screen time [ 28 ]. Another issue is the fact that most adolescents increased their screen time again [ 22 ] after interventions (in terms of a ‘rebound-effect’), which suggests that long-term interventions may be necessary for achieving long-lasting awareness and behavior changes. Only one study [ 27 ] included expectant mothers, parents of newborns and infants. Because the studies all related to different media, no general statement can be made about the effect of the interventions. Especially since most of the studies focused on conventional screen media such as TV and computers, and only one study included smartphones and tablets [ 19 ].

Limitations

Firstly, an electronic literature search inherently contains limitations: only three databases with terms in English were searched, meaning that articles that are not indexed in those databases or are published in any other language were not included and therefore, the search strategy may not have identified all publications. The search strategy only looked for randomized controlled trials, controlled clinical trials and clinical trials. Alternative interventional study designs, such as quasi-experimental designs, were omitted. On the basis of title and abstract and according to the a priori defined inclusion and exclusion criteria, a high number of publications were excluded. There were situations in which the decision to include or exclude a questionnaire was unclear, due to incomplete or ambiguous information. Here an attempt was made to make a scientifically valid decision shared between at least two of the authors. It is nevertheless possible that relevant publications were not considered. Due to study heterogeneity in terms of methodologies, outcomes and measurement instruments, carrying out a meta-analysis was not possible. Although we decided to analyze the studies regarding their effect in a qualitative comparison, the different interventions across studies made it difficult to compare findings. When a significant reduction of screen time was reported, it was often unclear which specific media was reduced. In addition, in a large number of articles the interventions or the control groups were not described very detailed and reproducibly. There was only 1 study which assessed the intention to reduce screen time. The motivation of adolescents to reduce it might be a crucial point; however, it was not assessed in the studies and thus the relevance of reported outcomes (minutes per day) is difficult to judge. None of the studies were conducted in Germany and therefore results cannot be transferred without restriction to German-speaking countries due to possible transcultural differences. Given the fact that nearly all experts worldwide agree that children spend too much time in front of screens, the fact that we only found 11 interventional studies between 2012 and 2019, and none in Germany, is rather disconcerting. Given the paucity of long-term, early onset trials, our group is planning to undertake such trials and would like to extend invitations for collaboration to all interested.

Conclusions

The fact that there are studies showing interventions with significant effect or a positive influence on reduction of screen time and the participants’ awareness and behavior concerning the use of screen media, as well as studies with no significant differences between intervention and control groups, indicates that particularly effective combinations of intervention components must be further investigated. The content and duration of identified interventions was highly heterogeneous, and thus the study findings are difficult to compare. Short interventions may not be effective since most participants increased their screen time again following the interventions. Future research should explore the effects of long-term interventions, generating media awareness and attempts to reduce screen time from birth to adulthood in prospective longitudinal studies need to address expectant mothers, parents, children and adolescents in age-specific ways. Further investigation should also increasingly focus on new digital screen media like smartphone and tablets.

Excessive use of digital screen media (screen time) is a global public health issue associated with adverse mental and physical health outcomes, especially for children. During the past few years children of all ages have not only obtained access to the possibilities of traditional screens like TV but additionally have access to new screen technologies. Only one systematic review from 2012 has examined intervention strategies to reduce screen time among children from birth to 12 years of age. Because of the rapid development in screen media, especially smartphones and handheld devices, we decided to update this systematic review by reviewing studies published between June 2011 and October 2019. An electronic literature search with English keywords related to screen-media, intervention and study design was carried out in MEDLINE, COCHRANE LIBRARY and CINAHL. The search identified 2052 publications of which 11 publications were included in the qualitative synthesis. The methodological quality of the included studies can be rated as good overall on the PEDro scale (median: 6 points). The internal validity of the results is therefore limited. The majority of studies showed that different interventions can have an effect on screen time or at least have a positive influence on the participants’ awareness and behavior concerning the use of screen media. These results are consistent with the existing review from 2012. Short interventions focusing solely on reducing screen time may not be effective in preschool children but focusing on screen time behavior in combination with other health behaviors might result in a greater effect on screen time. Another issue is the fact that most adolescents increased their screen time again after interventions, which suggests that long-term interventions may be necessary for achieving long-lasting awareness and behavior changes. Because the studies all related to different media, no general statement can be made about the effect of the interventions. The fact that there are studies showing interventions with significant effect or a positive influence on reduction of screen time and the participants’ awareness and behavior concerning the use of screen media, as well as studies with no significant differences between intervention and control groups, indicates that particularly effective combinations of intervention components must be further investigated, including new digital screen media like smartphone and tablets.

Data Availability

Not applicable.

Code Availability

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Krafft, H., Boehm, K., Schwarz, S. et al. Media Awareness and Screen Time Reduction in Children, Youth or Families: A Systematic Literature Review. Child Psychiatry Hum Dev 54 , 815–825 (2023). https://doi.org/10.1007/s10578-021-01281-9

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Adolescents' screen time, sleep and mental health: literature review - research findings

Research findings from the systematic review summarising the published experimental and longitudinal evidence on adolescent screen time, sleep and mental health.

Health and Social Care: Research Findings 145/2020: Systematic literature review of the relationship between adolescents’ screen time, sleep and mental health

Anne Martin 1 , Juliana Pugmire 1 , Valerie Wells 1 , Julie Riddell 1 , Christina McMellon 1 , Kathryn Skivington 1 , Sharon Simpson 1 , Lisa McDaid 1, 2

1 MRC / CSO Social & Public Health Sciences Unit, University of Glasgow, 200 Renfield Street, Glasgow, G2 3QB, UK

2 Institute for Social Science Research, The University of Queensland, Brisbane Qld 4072, Australia

The objective of this systematic review was to summarise the published experimental and longitudinal evidence on adolescent mobile device screen time or use, and the association with sleep and mental health and wellbeing. Five research questions guided this review, which included evidence from quantitative and qualitative studies conducted in Western countries classified as high-income by the World Bank.

Key findings

• The body of evidence based on longitudinal or experimental studies is very small: nine quantitative studies and two qualitative studies met the inclusion criteria for this study.
• The quality of individual studies was low and they lacked detailed descriptions of methodology, limiting assessment of risk of bias. This means findings and conclusions should be interpreted with caution.
• The body of evidence is incomplete. There were various definitions of mobile device screen use ( e.g. time spent using a mobile device, social media use) and various outcomes ( e.g. sleep duration, sleep quality), and only one or two studies that assessed each exposure/outcome relationship, making it difficult to draw conclusions beyond these individual studies.
• Only one study provided suitable data to explore potential causal mechanisms. Experiences of cybervictimisation were indirectly associated with sleeping less than the recommended 8 hours per night. The factor linking cybervictimisation with shorter sleep was repetitively thinking and obsessing about distressing thoughts, emotions and memories.
• No study asked young people explicitly about the connections between screen use, sleep and mental health and wellbeing.

Introduction

Poor sleep has been linked to a range of mental health issues. Research shows that young people often get less than the recommended amount of sleep and access to and use of a media device at bedtime has been associated with poor sleep quality, inadequate sleep quantity and daytime sleepiness in young people. There is increasing evidence of an association between mobile screen use and adverse mental health and wellbeing outcomes in young people. However, the evidence is based primarily on cross-sectional studies which cannot address temporality or causality. The Scottish Government released a report in 2019 titled: 'Exploring the reported worsening of mental wellbeing among adolescent girls in Scotland' [1] . The report highlighted interrelated factors that could be influencing worsening mental wellbeing in Scottish adolescents, including inadequate sleep and social media use. This systematic review follows on from that report, addresses the identified gap in the literature, and adds to the existing evidence reviews as the focus of investigation is on the relationship between screen time, sleep and mental health.

A literature search was undertaken of 9 electronic databases with key terms related to: young people; mobile devices and related software; sleep outcomes and mental health. The following inclusion and exclusion criteria were applied:

• Young people aged 10-19 years, general population
• High-income Western countries
• Use of digital/electronic mobile devices and software accessible through device
• Excluded television use

1) Sleep and 2) mental health and wellbeing measures

Study types

• Literature reviews from 2007 to 2019 ◊identification of experimental and longitudinal studies
• Experimental and longitudinal studies from 2017 to 2019
• Excluded cross-sectional evidence

A selection, extraction & quality assessment process were applied and the number of included studies for each Research Question was as follows: RQ1 = 9 (15-23); RQ2 = 1 (18); RQ3 = 3 (21-23); RQ4 = 0; RQ5 = 2 (24, 25).

1. To what extent does adolescents' mobile device screen time impact on sleep outcomes?

• Mobile phone use around bedtime and cybervictimisation, but not the overall time spent engaging in mobile phone activities per se (at any time of the day), was linked to lower sleep duration.
• Sleep quality was negatively influenced by mobile phone use in general and social media use in particular.
• Experiencing pressure to engage socially using a mobile phone was associated with poor bedtime behaviours that might promote poor sleep quality ( i.e. sleep hygiene).
• Stopping phone use one hour before bedtime was not linked to earlier sleep.
• One pilot study (a small scale, preliminary study) showed that use of a smartphone app (under development) that teaches about the importance of consistent sleep and wake times, and recommended bedtimes was associated with a potential improvement in sleep duration, sleep quality and earlier sleep onset.

Table 1. Summary of findings for Research Question 1

-/+ No association; + Positive association; - Negative association * Sleep hygiene = bedtime behaviours that promote good sleep quality

2. What are the potential causal mechanisms through which mobile device screen time affects sleep outcomes amongst adolescents?

• Experiences of cybervictimisation were indirectly associated with sleeping less than the recommended 8 hours per night. The factor linking cybervictimisation with shorter sleep was repetitively thinking and obsessing about distressing thoughts, emotions, and memories.
• Other potential mechanisms (based on cross-sectional mechanisms) through which mobile device screen time or use affect sleep outcomes are: sleep displacement ( i.e. using the phone instead of sleeping), delaying sleep time, increased alertness through blue light exposure, psychological arousal which can result in bodily responses ( e.g. faster heart beat) through binge watching and/or watching violent or upsetting content.

Figure 1 . Potential causal pathways between mobile device screen time/use and impaired sleep

3. What are the implications of the potential impact of mobile device screen time on sleep for adolescents' mental health and wellbeing?

• Night-time mobile use and problematic social media use were linked to depressed mood through experiences of poor quality sleep. Poor sleep quality also played a role in the link between night-time mobile phone use and low self-esteem, poor coping skills and higher externalising behaviour ( e.g. disobeying rules, physical aggression).
• One pilot study showed that use of a smartphone app (under development) that teaches healthy sleep habits was associated with potentially lower depressive symptoms and reduced anxiety.

Table 2 . Summary of findings for Research Question 3

+/- no mediation effect, - negative mediating effect, + positive mediation effect

4. To what extent might girls' and boys' differential mobile device screen time, and its relationship with sleep, contribute to inequalities in mental health and wellbeing by gender?

• None of the included quantitative studies reported separate data for boys' and girls' mobile device screen time or use and its relationship with sleep that in turn might contribute to inequalities in mental health and wellbeing for boys and girls.
• The study found that using social media multiple times daily when aged 13-15 predicted lower life satisfaction, lower happiness, and higher anxiety among girls 1- to 2-years later but not among boys.
• It also found that sleeping less than 8 hours per night, not being physically active most days, and experiencing cyberbullying play a detrimental role in the association between social media use and lower wellbeing in girls only.

5. What existing evidence is there on adolescents' views of how mobile device screen time affects their sleep, and following on from this, their mental health and wellbeing?

• In the qualitative studies both adolescent boys and girls reported using smartphones in bed and recognised that it may negatively affect their sleep.
• Adolescents felt that sleep issues were connected to the content in video games rather than their use.
• Boys were more likely to report trying to follow guidelines ( e.g. putting electronics away one hour pre-bedtime) whilst girls suggested they specifically used their mobile screen devices as a tool to aid sleep ( e.g. listening to music).
• No study asked young people directly about their view of the relationship between sleep and mental health. However, when young people thought about the importance of sleep they mentioned the 'energising, relaxing, stress-reducing and restorative qualities of sleep'.

Recommendations

Policy and practice initiatives could target all or a combination of the identified modifiable factors ( Figure 1 ) within the causal pathway between mobile device screen exposures and impaired sleep, but the current evidence severely limits the recommendations that can be made. Only one study provided suitable data to explore potential causal mechanisms through which mobile device exposure influences sleep outcomes. It suggests:

• Young people should be protected from cybervictimisation and mandatory requirements of social media platforms to develop algorithms that block aggressive and upsetting content could be put in place. Education around the impact of cybervictimisation and how to avoid it ( e.g. adequate privacy settings) could be embedded in the school curriculum.
• Repetitively thinking and obsessing about distressing thoughts, emotions, and memories as a consequence of cybervictimisation could potentially be targeted by initiatives that strengthen resilience in adolescents in particular teaching young people and their parents healthy coping strategies ( e.g. help seeking and sharing thoughts/emotions, mindfulness).

Further research investigating the causal relationship between mobile device screen use, impaired sleep and poor mental health and wellbeing is needed. Therefore, future research studies should use multiple time points of mobile device screen use, sleep and mental health data.

The full description of the methodology and list of references is available in the main report.

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The health effects of screen time on children: A research roundup

This research roundup looks at the effects of screen time on children’s health.

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This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License .

by Chloe Reichel, The Journalist's Resource May 14, 2019

This <a target="_blank" href="https://journalistsresource.org/education/screen-time-children-health-research/">article</a> first appeared on <a target="_blank" href="https://journalistsresource.org">The Journalist's Resource</a> and is republished here under a Creative Commons license.<img src="https://journalistsresource.org/wp-content/uploads/2020/11/cropped-jr-favicon-150x150.png" style="width:1em;height:1em;margin-left:10px;">

This research roundup, originally published in May 2019, has been updated to include a recent systematic review and meta-analysis looking at the effects of screen time on academic performance.

Gone are visions of idyllic childhoods spent frolicking in fields and playing in pastures; for many kids, green grass has been replaced with smartphone screens.

In fact, recent research finds that 63% of kids in the U.S. spend over two hours a day on recreational screen time .

This is in spite of official guidelines from the American Academy of Pediatrics, which recommends less than one hour per day of screen time for children between the ages of 2 and 5, and, for older children, “consistent limits” on screen time and prioritization of sleep, physical activity and other healthy behaviors over media use. Just last month the World Health Organization issued guidelines on the subject, stressing that children between the ages of 2 and 4 should have no more than one hour of screen time per day.

The ubiquity of screens and their prominence in everyday life has drawn criticism and concerns, with Microsoft veteran and philanthropist Melinda Gates writing about not being “prepared for smartphones and social media” as a parent and news headlines questioning whether smartphones have “ destroyed a generation .”

But what does the research say? This roundup looks at the effects of screen time on children’s health. Studies range from childhood to adolescence and focus on topics including sleep, developmental progress, depression and successful interventions to reduce screen time.

Screen-Time is Associated with Inattention Problems in Preschoolers: Results from the CHILD Birth Cohort Study Tamana, Sukhpreet K.; et al. PLOS ONE , April 2019.

This study analyzes parent-reported data about screen time and behavioral issues such as inattention and aggressiveness for a sample of 2,322 Canadian preschool-age children. Researchers found that over 13% of kids in the sample were exposed to over two hours of screen time each day including watching TV and DVDs, playing video games or using a computer, tablet or mobile device. The effects: Kids who were exposed to more screen time “showed significantly increased behavior problems at five-years,” the authors write. “Briefly, children who watched more than 2 hours of screen time/day had increased externalizing [e.g., attention and behavior], internalizing [e.g., anxiety and depression], and total behavior problems scores compared to children who watched less than 20 minutes.” Attention problems in particular were apparent in children who had over two hours of screen time each day.

Mobile Media Device Use is Associated with Expressive Language Delay in 18-Month-Old Children van den Heuvel, Meta; et al. Journal of Developmental & Behavioral Pediatrics , 2019.

Toddlers who use mobile devices daily are more likely to experience speech delays, according to an analysis of parent-reported data on 893 children in the greater Toronto area of Canada. While 78% of parents said their kids spent no time on mobile devices, the other 22% reported a range of 1.4 to 300 minutes daily, with a median of 15.7 minutes.

In total, 6.6% of parents reported expressive speech delays (i.e., late to begin talking). The prevalence of other communication delays, such as lack of use of gestures and eye gaze, was 8.8%. The researchers found a positive association between mobile device use and expressive speech delays. “An increase in 30 minutes per day in mobile media device use was associated with a 2.3 times increased risk of parent-reported expressive speech delay,” the authors write. Other communication delays were not linked to device use. The researchers suggest the connection between device use and expressive speech delays might be explained by the fact that past research has shown infants “have difficulty applying what they learn across different contexts.” An alternate explanation is that these children who spend more time with devices might have less exposure to speech from caregivers.

Association Between Screen Time and Children’s Performance on a Developmental Screening Test Madigan, Sheri; et al. JAMA Pediatrics , March 2019.

Is screen time detrimental to child development? This study looks at data collected from 2,441 mothers and children in Canada at three different time points – when the children were 2, 3 and 5 years old. The researchers were interested in the total number of hours the children spent looking at screens each week as well as their progress in various developmental areas such as fine motor skills, communication and problem solving. The average amount of screen time for the age groups in the study: 17, 25 and 11 hours of television per week for 2-, 3-, and 5-year olds, respectively.

The researchers found that kids who spent more time watching screens at ages 2 and 3 did worse on developmental tests at the subsequent time points of 3 and 5 years. “To our knowledge, the present study is the first to provide evidence of a directional association between screen time and poor performance on development screening tests among very young children,” the authors write.

The researchers suggest that excessive screen time leads to developmental delays, rather than the other way around – negating the notion that children with developmental delays might receive more screen time to manage their behavior.

The three phase data capture supports this explanation because children with greater screen time at one time point go on at the next time point to have poorer developmental progress, but children with poor developmental performance at an earlier time point do not receive increased screen time at later time points.

Association Between Screen Media Use and Academic Performance Among Children and Adolescents Adelantado-Renau, Mireia; et al. JAMA Pediatrics , September 2019.

This publication consists of both a systematic review and meta-analysis of research on the relationship between screen time and academic performance. The authors identified 58 studies to include in the systematic review, which provides a summary of the qualitative effects of screen time; 30 of these studies were included in the subsequent meta-analysis, which the authors used to calculate the effect size of screen time on academic performance.

The 58 studies in the systematic review included 480,479 participants ranging from four to 18 years of age. The articles were published between 1958 and 2018 and represent the efforts of researchers around the world. The studies looked at computer, internet, mobile phone, television and video game use individually, as well as overall screen time. Outcomes of interest included school grades, performance on academic achievement tests, academic failure data, or self-reported academic achievement or school performance.

The key finding from the systematic review was that in most of the papers reviewed, as time spent watching television increased, academic performance suffered. Relationships were less clear-cut for other types of screen use.

The meta-analysis, which focused on a subset of 106,653 participants from the larger sample, did not find an association between overall screen time and academic performance. When the authors analyzed the data by type of activity, they found television watching was linked to poorer overall academic performance as well as poorer language and mathematics performance, separately. Time spent playing video games was negatively linked with composite academic performance scores, too. Analyzing the data further by age, the authors found that time spent with screens had a larger negative association with academic performance for adolescents than children.

“The findings from this systematic review and meta-analysis suggest that each screen-based activity should be analyzed individually because of its specific association with academic performance,” the authors conclude. “This study highlights the need for further research into the association of internet, computer, and mobile phone use with academic performance in children and adolescents. These associations seem to be complex and may be moderated and/or mediated by potential factors, such as purpose, content, and context of screen media use.”

The authors suggest that educators and health professionals should focus screen time reduction efforts on television and video games for their negative connections to academic performance and potential health risks due to their sedentary nature.

Screen Time Is Associated with Adiposity and Insulin Resistance in Children Nightingale, Claire M.; et al. Archives of Disease in Childhood , July 2017.

This study looks at the relationship between screen time and Type 2 diabetes risk factors, like being severely overweight, among 4,495 schoolchildren in the United Kingdom between the ages of 9 and 10. The short of it: Kids who spent over three hours daily on screen time were less lean and more likely to show signs of insulin resistance, which can contribute to the development of Type 2 diabetes, compared with their peers who reported one hour or less of screen time each day. Black children were more likely to spend over three hours daily on devices compared with their white and south Asian peers – 23% of black children fell into that group, compared with 16% of white children and 16% of south Asian children.

Digital Media and Sleep in Childhood and Adolescence LeBourgeois, Monique K.; et al. Pediatrics , November 2017.

This report summarizes 67 studies looking at associations between screen time and sleep health – adequate sleep length and quality — in children and adolescents. The main takeaways: A majority (90%) of the studies included in a systematic review of research on screen time in children and teenagers found adverse associations between screen time and sleep health – primarily because of later bedtimes and less time spent sleeping. Delving deeper, underlying mechanisms include “time displacement” (think scrolling Instagram for an hour that might otherwise be spent sleeping), psychological stimulation from content consumed and impacts of screen light on sleep patterns. The upshot? These kids are tired. The previously cited research review also indicates that a majority of studies saw a relationship between tiredness and screen time.

Prevalence and Likelihood of Meeting Sleep, Physical Activity, and Screen-Time Guidelines Among US Youth Knell, Gregory; et al. JAMA Pediatrics , April 2019.

This study analyzes data from the 2011, 2013, 2015 and 2017 cycles of a nationally-administered, school-based survey on various health-related behaviors related to the leading causes of death and disability in the U.S. The researchers were interested in whether respondents met the recommendations for time spent on sleep, physical activity and screen time in a given day. A total of 59,397 adolescents were included in the data set.

The findings indicate that only 5% of adolescents surveyed met all three guidelines – that is, getting the recommended amount of sleep and physical activity and limiting screen time to less than two hours per day. There were disparities among the sample in terms of the odds of meeting all of the recommendations: 16- and 17-year-olds were less likely than those aged 14 and younger to meet all the guidelines; black, Hispanic/Latino and Asian participants were less likely to meet the three guidelines than white participants; overweight and obese participants were less likely to meet the guidelines than normal weight participants; participants who reported marijuana use were less likely to meet the guidelines than those who did not. Participants who reported depressive symptoms were also less likely to meet all the guidelines.

Associations Between 24 Hour Movement Behaviors and Global Cognition in US Children: A Cross-Sectional Observational Study Walsh, Jeremy J.; et al. The Lancet Child & Adolescent Health , November 2018.

This study looks at the same three outcomes examined above, but adds another component – “global cognition.” This is an overall cognition score assessed by the National Institutes of Health Toolbox – an iPad-based neuro-behavioral screening tool. The assessment measures various cognitive functions including memory, attention, vocabulary and processing speed. The sample included 4,520 participants between the ages of 8 and 11. Only 5% of participants met all three recommendations – and they were the better for it. “Compared with meeting none of the recommendations, associations with superior global cognition were found in participants who met all three recommendations, the screen time recommendation only, and both the screen time and the sleep recommendations,” the authors write.

Increases in Depressive Symptoms, Suicide-Related Outcomes, and Suicide Rates Among U.S. Adolescents After 2010 and Links to Increased New Media Screen Time Twenge, Jean M.; et al. Clinical Psychological Science , January 2018.

This study looks at the relationship between screen time and depression and suicide rates in 506,820 adolescents in the U.S. between 2010 and 2015. The data on screen time use and mental health issues came from two nationally representative surveys of students in grades 8 through 12. Suicide rates were calculated from national statistics collected by the Centers for Disease Control and Prevention’s Fatal Injury Reports.

The analysis finds a “clear pattern linking screen activities with higher levels of depressive symptoms/suicide-related outcomes [suicidal ideation — that is, thinking about suicide — and attempts] and nonscreen activities with lower levels.” Among participants who used devices for over five hours each day, nearly half – 48% — reported at least one suicide-related outcome. In comparison, 29% of those who used devices for just an hour per day had at least one suicide-related outcome.

Overall, during the time studied, suicide rates, depressive symptoms and suicide-related outcomes increased. Girls accounted for most of the rise – they were more likely to experience depressive symptoms and suicide-related outcomes than boys; they also experienced stronger effects of screen time on mental health. In particular, girls, but not boys, had a significant correlation between social media use and depressive symptoms.

Interventions Designed to Reduce Sedentary Behaviors in Young People: A Review of Reviews Biddle, Stuart J.H.; Petrolini, Irene; Pearson, Natalie. British Journal of Sports Medicine , 2014.

This review looks at 10 systematic reviews and meta-analyses of research on interventions to reduce sedentary behaviors such as screen time among children and adolescents. The authors found that all of the included reviews determined “some level of effectiveness in reducing time spent in sedentary behavior.” Effects, however, were small. Interventions tended to be more successful among children younger than 6 years old. Strategies that were effective included restricting access to television through TV monitors, systems that use TV as a reward for physical activity and behavioral interventions such as setting goals and developing schedules for screen time.

For more research on the effects of screen time, check out our write ups of research that shows how smartphones make people unhappy and how they’re distracting even when they aren’t in use .

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Association of physical activity and screen time with cardiovascular disease risk in the Adolescent Brain Cognitive Development Study

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According to the Physical Activity Guidelines Advisory Committee Scientific Report, limited evidence is available on sedentary behaviors (screen time) and their joint associations with physical activity (steps) for cardiovascular health in adolescence. The objective of this study was to identify joint associations of screen time and physical activity categories with cardiovascular disease (CVD) risk factors (blood pressure, hemoglobin A1c, cholesterol) in adolescence.

This study analyzed data from the Adolescent Brain Cognitive Development (ABCD) Study, comprising a diverse sample of 4,718 U.S. adolescents aged 10–15 years between 2018 and 2021. Steps were measured by a Fitbit wearable device and levels were categorized as low (1,000–6,000), medium (> 6,000–12,000), and high (> 12,000) averaged daily step counts. Self-reported recreational screen time hours per day were classified as low (0–4), medium (> 4–8), and high (> 8) hours per day. CVD risk factors including blood pressure, hemoglobin A1c, and cholesterol (total and HDL) were measured.

The analytical sample averaged 6.6 h of screen time per day and 9,722 steps per day. In models including both screen time and steps, the high screen time category was associated with a 4.27 higher diastolic blood pressure percentile (95% CI 1.83–6.73) and lower HDL cholesterol (B= -2.85, 95% CI -4.77 to -0.94 mg/dL) compared to the low screen time category. Medium (B = 3.68, 95% CI 1.24–6.11) and low (B = 7.64, 95% CI 4.07–11.20) step categories were associated with higher diastolic blood pressure percentile compared to the high step category. The medium step category was associated with lower HDL cholesterol (B= -1.99, 95% CI -3.80 to -0.19 mg/dL) compared to the high step category. Findings were similar when screen time and step counts were analyzed as continuous variables; higher continuous step count was additionally associated with lower total cholesterol (mg/dL).

Conclusions

Combinations of low screen time and high steps were generally associated with favorable cardiovascular health markers including lower diastolic blood pressure and higher HDL cholesterol, which can inform future adolescent health guidelines.

Peer Review reports

Introduction

The U.S. Department of Health and Human Services’ Physical Activity Guidelines for Americans recommends 60 min of moderate-to-vigorous intensity physical activity per day for children and adolescents [ 1 ]. However, fewer than a quarter of U.S. adolescents met these guidelines prior to the COVID-19 pandemic [ 2 , 3 ]. During the COVID-19 pandemic, physical activity among adolescents declined even further to 4.7% of adolescents meeting recommended physical activity levels [ 4 ]. This decline in physical activity has been accompanied by an increase in recreational screen time. In 2016, U.S. adolescents reported spending an average of 4–6 h of digital media engagement per day, including the internet, texting, and social media [ 5 , 6 ], which doubled during the COVID-19 pandemic [ 7 , 8 ].

Given limited discretionary hours in the day, an increase in screen time use displaces opportunities to be physically active [ 9 ]. This displacement is concerning, given emerging evidence supporting the harmful associations of sedentary behavior with cardiometabolic health and cardiovascular disease (CVD), which remains the leading cause of death in adults across most racial and ethnic groups in the U.S [ 10 ]. Adolescence is a critical period for CVD prevention because it is a time when healthy lifestyle behaviors can be established that will last into adulthood [ 11 ]. Higher screen time has been associated with higher CVD risk in children and adolescents, including higher blood pressure [ 12 ], non-HDL cholesterol [ 13 ], and insulin resistance [ 14 , 15 ]. In contrast, physical activity has been associated with lower CVD risk, including an improved lipid profile, lower body fat, and lower blood pressure [ 16 , 17 , 18 ].

Despite findings that lower physical activity and greater screen time are generally associated with greater CVD risk, the Physical Activity Guidelines Advisory Committee Scientific Report identified important evidence gaps for research that could inform future guidelines. First, there was insufficient evidence to provide recommendations about sedentary behaviors, including screen time, during adolescence. Second, there is a lack of research on dose-response relationships between physical activity and cardiovascular health outcomes using objective measures. Finally, combinations of sedentary behavior and physical activity categories on cardiovascular health outcomes in adolescents should be further examined [ 18 ]. Understanding how various combinations of sedentary behavior and physical activity are associated with cardiovascular health outcomes in adolescents is crucial, as it can provide a more comprehensive insight into how lifestyle patterns contribute to the early development of cardiovascular risk factors, potentially informing more targeted and effective intervention strategies for this age group. For instance, adolescents who have long periods of sedentary time may require higher levels of physical activity to offset CVD risk [ 19 ].

Few studies have examined joint associations of screen time and physical activity on markers of cardiovascular health among adolescents. A 2016 Iranian study examined the association of screen time and physical activity on CVD risk in a nationally representative sample of school students and found a joint association of high screen time and low physical activity with higher odds of low HDL-cholesterol and elevated total cholesterol [ 20 ]. However, this study used a self-report measure of physical activity, which is limited by recall errors and response bias. Step count, in contrast, is an objective, device-recorded measure of physical activity that can be collected across a reasonably extended period of time [ 21 ]. Furthermore, step count provides estimates of accumulated daily movement using a metric that is meaningful to a lay audience, which may make them more clinically relevant. Additionally, combinations of physical activity and screen time have not been well studied among U.S. adolescents.

In this study, we address these gaps by examining the joint associations of physical activity (measured by daily steps) and sedentary behavior (screen time) exposures with CVD risk factors (blood pressure, hemoglobin A1c, and cholesterol levels). We hypothesize that high screen time and low steps would be associated with higher CVD risk in early adolescence.

We examined cross-sectional data from Year 2 (2018–2020) and Year 3 (2019–2021) of the Adolescent Brain Cognitive Development (ABCD) Study (4.0 release; 2018–2020, ages 10–14 years), a longitudinal study of brain development and health in the U.S. In 2016–2018 (Year 0), 11,875 children were recruited from 21 demographically diverse sites distributed across the nation’s four major regions (Northeast, South, Midwest, West). Participants were recruited primarily through elementary schools, chosen via stratified, probability sampling of U.S. schools within the 21 catchment areas using the SAS V9.4. software system and the SAS Proc SurveySelect program. School selection was informed by gender, race/ethnicity, socioeconomic status, and urbanicity to minimize sample selection bias [ 22 ]. These sampling strategies aimed to maximize the representativeness of the baseline cohort with regards to the demographic and socioeconomic makeup of 9–10-year-old children in the U.S. While the sample is epidemiologically informed, self-selection by families into the study and assessment at academic centers may be a source of sampling bias. Response rates for individual students within schools and incompleteness at any particular school are not incorporated into the sample weighting schema. Further details regarding the study’s participants, recruitment process, procedures, and measures have been explained elsewhere [ 23 , 24 ].

Because of the COVID-19 pandemic, Year 2 CVD risk measurement collection was disrupted due to social distancing requirements and the cancellation of non-essential research activities, and only a portion of the participants were able to complete certain CVD risk measurements. CVD risk measurements were re-attempted in Year 3 for some participants who were not able to have measurements in Year 2. Step count, screen time, and blood pressure data were collected and analyzed exclusively during Year 2 for all participants. Year 3 CVD outcome data for hemoglobin A1c and cholesterol was only utilized in the absence of data from Year 2. Overall, a majority (78.3%) of hemoglobin A1c and cholesterol data were collected in Year 2, while 21.7% were collected from Year 3. The current analysis included individuals with data for steps, screen time, and at least one CVD risk measurement (Additional File 1 : Appendix A, B), resulting in a sample of 4,718 adolescents. The study was approved by the University of California, San Diego (UCSD) centralized Institutional Review Board (IRB), and the secondary data analysis was approved by the University of California, San Francisco (UCSF). Local IRBs from each study site also gave their approval. Caregivers/parents signed written informed consent forms prior to participation in the study. Adolescents signed written assent forms, given that they were minors, prior to participation in the study.

Exposure variables

Screen time.

Screen time data were collected using the ABCD Youth Screen Time Questionnaire, which asked participating adolescents to self-report the hours per day they typically spent using different types of media on weekdays and weekends [ 25 ]. Types of media included television shows, movies, videos, video chat, single and multi-player video games, social media, and texting. Adolescent-reported screen time has demonstrated a significant moderate positive correlation with an objectively sensed smartphone application among 11–12-year-old participants in the ABCD Study ( r  = 0.49, p  < 0.001) [ 26 ]. Additional investigations have indicated that self-reported assessments of watching television were significantly moderately correlated (Spearman’s p  = 0.54, p  < 0.001) with an objectively recorded electronic television monitor and illustrated a high level of concurrence, with 95% of measurements falling within four hours of the average [ 27 ]. Comparable self-reported measures of television viewing have demonstrated satisfactory test-retest reliability (intraclass correlations over a seven-day period ranging from 0.76 to 0.81) [ 28 , 29 ]. The time spent on all types of media was summed. A total weighted mean recreational screen use was calculated using the following weighting: ([weekday average x 5] + [weekend average x 2])/7. Screen time was calculated as a continuous variable and categorized into four-hour increments. This categorization was based on prior studies identifying four hours per day to be a threshold linked to poor mental health outcomes and overweight in adolescents [ 30 , 31 , 32 ], and other national surveys of adolescent screen time have used similar categories (e.g., 4 and 8 h per day) with similar distributions [ 19 , 33 ]. Screen time (hours per day) was ordered into three categories: 0 to 4 h (low; reference category), 4 to 8 h (medium), and more than 8 h (high).

Steps per day (Fitbit)

Daily step counts including weekdays, weekends, and holidays were collected through the Fitbit Charge (Fitbit Inc., San Francisco, CA) over a three-week period (21 days) between November 2018 and November 2020 that coincided with the Year 2 questionnaire and physical health assessments. Prior studies have shown Fitbit devices to be a reliable and accurate tool for the estimation of adolescents’ daily step counts to measure the accumulated physical activity in adolescents over time [ 21 , 34 ]. We observed best practices for data extraction, filtering, and processing established by the ABCD Study [ 21 , 34 ]. Following earlier studies, we incorporated all days with > 599 daily minutes of wear time while awake and a minimum of 1,000 steps per day within each adolescent’s three-week study protocol [ 35 , 36 , 37 , 38 , 39 ]. In our ABCD Study Fitbit data, 1,000 steps per day represented the bottom 0.5th percentile (2.58 standard deviations below the mean), consistent with normative ranges published for step counts among 10-11-year-olds from the National Health and Nutrition Examination Surveys [ 40 ]. Prior research identified 12,000 steps as a lower threshold for satisfying the 60 min of moderate-to-vigorous intensity physical activity guideline for adolescents from the Department of Health and Human Services’ Physical Activity Guidelines for Americans [ 41 ]. Accordingly, 6,000 steps approximate 30 min of moderate-to-vigorous intensity physical activity, or half the adolescent guideline threshold. Therefore, total steps per day were classified into three categories: 1,000 to 6,000 steps per day (low), 6,000 to 12,000 steps per day (medium), and more than 12,000 steps per day (high, reference category).

Outcome variables

Cardiovascular disease risk factors.

Continuous measures were considered primary outcomes. Given the low prevalence of clinical cutoffs and subsequently less power, binary clinical outcomes were considered secondary outcomes.

Blood pressure percentile

ABCD Study research assistants were trained on the standardized protocol used at all sites. Prior to measurement, participants sat in a chair for 5 min in a quiet environment. The participant’s right arm was rested palm face up on a table, and feet were positioned flat on the floor, legs uncrossed. Blood pressure was calculated using the mean of three measurements separated by a 60 s interval using a factory-calibrated, Omron blood pressure monitor (MicroLife USA, Inc.; Dunedin, FL). Cuff size was determined by measurement of the mid-upper arm circumference. Systolic and diastolic blood pressures were converted into percentiles based on the American Academy of Pediatrics reference ranges [ 42 ]. Hypertensive range blood pressure (secondary outcome) was defined as appropriate for age and sex percentile according to pediatric guidelines for elevated blood pressure [ 42 ]. Participants taking antihypertensive medications ( n  = 2) were excluded from the analyses with blood pressure as an outcome.

• Hemoglobin A1c

Hemoglobin A1c level was measured via blood draw as a measure of average blood sugar levels over the prior three months [ 43 ]. Participants were determined to have testing consistent with diabetes (secondary outcome) if they had a hemoglobin A1c level ≥ 6.5% [ 43 ]. Adolescent participants with a parent-reported history of diabetes ( n  = 15) were excluded from the analyses with hemoglobin A1c or diabetes as an outcome.

• Cholesterol

Non-fasting total cholesterol and High-Density Lipoprotein (HDL) cholesterol were collected via blood draw. Hyperlipidemia (secondary outcome) was defined as total cholesterol ≥ 200 mg/dL [ 44 ]. Low HDL cholesterol (secondary outcome) was defined as < 40 mg/dL for female and male adolescents [ 44 ].

We included as covariates parent-reported measures of marital status, highest parent education, household income, adolescent age, adolescent race/ethnicity (White, Latinx/Hispanic, Black, Asian, Native American, other), and adolescent sex (female or male), which have been previously linked to adolescent physical activity [ 45 ], screen time use [ 6 ], and CVD risk [ 46 ]. We constructed a COVID-19 pandemic variable using Fitbit device data collection dates (before, before and during, and during the COVID-19 pandemic), with March 13, 2020 as the start of the COVID-19 pandemic in the U.S, when a national emergency was declared. Because the Fitbit data were collected over 21 days, there was a small subgroup for whom the 21-day period started before March 13, 2020 but ended after March 13, 2020. These participants were considered “before and during the COVID-19 pandemic.” We additionally adjusted for calendar month as a proxy for seasonality given that seasonality could affect screen time and physical activity [ 47 ]. We also adjusted for study year in the analysis of hemoglobin A1c, total cholesterol, and HDL cholesterol given that those measures were collected across Years 2 and 3.

Statistical analysis

Data analysis was performed using Stata software, version 18 (StataCorp LLC). Descriptive statistics were calculated including means, standard deviations, and percentages. Multivariable linear regression analyses were conducted to estimate associations between exposure variables (screen time and steps, continuous and categorical variables) and continuous CVD risk factor outcomes (primary outcomes: systolic and diastolic blood pressure percentiles, hemoglobin A1c, and total and HDL cholesterol). Multivariable logistic regression analyses were conducted to estimate associations between exposure variables (screen time and steps categories) and binary CVD risk factor outcomes (secondary outcomes: hypertensive range blood pressure, testing consistent with diabetes, high total cholesterol, low HDL cholesterol). All models adjusted for age, sex, race/ethnicity, household income, parental education, parent marital status, data collection period (e.g., before, before and during, and during the COVID-19 pandemic), month, and study year (e.g., Year 2 or Year 3 for the collection of the CVD outcome). For each outcome with significant associations with both screen time and step categories, we reported a 9-category exposure (combinations of 3 screen time categories and 3 step count categories) to estimate the association of each screen-step category combination with the CVD risk factor. We assessed for effect modification (interactions) between screen time and step categories for the association with each CVD risk factor outcome. We also assessed for effect modification (interactions) by race/ethnicity for the associations between screen time and steps with each CVD risk factor outcome.

A total of 4,718 adolescents were included in this analysis. Overall, 47.6% of the participants were female and 44.7% were racial/ethnic minorities, with a mean age of 12.0 years. Adolescents reported an average of 6.6 h of screen time per day. The average daily step count calculated across the Fitbit wear period was 9722.2 steps per day (Table  1 ).

In linear regression models including both screen and steps as continuous variables (Table  2 ), each hour of screen time per day was associated with a 0.27 (95% CI 0.06 to 0.48) higher diastolic blood pressure percentile, and every 1,000 steps per day was associated with a 0.66 (95% CI 0.32 to 0.99) lower diastolic blood pressure percentile. When examining categories, the high screen time category was associated with a 4.27 higher diastolic blood pressure percentile (95% CI 1.83 to 6.73) compared to the low screen time category. The medium step category was associated with a 3.68 (95% CI 1.24 to 6.11) higher diastolic blood pressure percentile, and the low step category was associated with a 7.64 (95% CI 4.07 to 11.20) higher diastolic blood pressure percentile, compared to the high step category. We further examined combinations of the 3 screen and 3 step categories (9 categories total) and diastolic blood pressure percentile (Fig.  1 ). For each screen time category, low step count categories were associated with higher diastolic blood pressure percentile. There were no significant associations among screen time or steps (continuous variables and categories) and systolic blood pressure percentile (Table  2 ).

Associations between screen time and step count category combinations and diastolic blood pressure percentile. Legend: Results correspond to coefficients from a linear regression model with nine categories of screen time and step combinations as the independent variable and diastolic blood pressure percentile as the dependent variable, adjusting for age, sex, race/ethnicity, household income, parental education, parent marital status, data collection period, and month. Daily step categories included: high (> 12,000), medium (6,000–12,000), and low (1,000–6,000). Daily screen time categories (hours) included: low (0–4); medium (4–8), high (> 8). The low screen time and high step category was the reference category

Total cholesterol and HDL cholesterol

In linear regression models including both screen and steps as continuous variables, each hour of screen time per day was associated with a 0.18 (95% CI 0.03 to 0.33) mg/dL lower HDL cholesterol; however, steps per day were not significantly associated with HDL cholesterol. When examining categories, the medium step category was associated with − 1.99 mg/dL lower HDL cholesterol (95% CI -3.80 to -0.19) compared to the high step category. High screen time was associated with − 2.85 mg/dL lower HDL cholesterol (95% CI -4.77 to − 0.94) compared to the low screen time category. We further examined combinations of the 3 screen and 3 step categories (9 categories total) and HDL cholesterol (Fig.  2 ). For participants in the low screen time category, low and medium steps were associated with lower HDL cholesterol compared to participants in the high step category.

Associations between screen time and step count category combinations and HDL cholesterol. Legend: Results correspond to coefficients from a linear regression model with nine categories of screen time and step combinations as the independent variable and HDL cholesterol as the dependent variable, adjusting for age, sex, race/ethnicity, household income, parental education, parent marital status, data collection period, month, and study year. Daily step categories included: high (> 12,000), medium (6,000–12,000), and low (1,000–6,000). Daily screen time categories (hours) included: low (0–4); medium (4–8), high (> 8). The low screen time and high step category was the reference category

For total cholesterol, every 1,000 steps per day was associated with a 0.58 (95% CI 0.04 to 1.12) lower total cholesterol, and screen time was not significantly associated with total cholesterol (B = -0.22, 95% CI -0.55 to 0.12). When examining categories, the medium step category was associated with higher total cholesterol compared to the high step category (B = 5.12, 95% CI 1.09 to 9.16). Screen time categories were not significantly associated with total cholesterol (Table  2 ).

There were no significant associations between screen time or steps and hemoglobin A1c (Table  2 ).

Sensitivity analyses with logistic regression models including both screen and step categories and binary outcomes are shown in Additional File 1 : Appendix B. There was no evidence of significant interactions between screen time and step categories for each of the CVD risk outcomes (all p for interaction > 0.05). There was no evidence of significant effect modification by race/ethnicity for the associations between screen time and steps for each of the CVD risk outcomes (all p for interaction > 0.05).

In this analysis of a national, demographically diverse sample of 10-15-year-old adolescents in the ABCD Study in the U.S., several gaps identified in the 2018 Physical Activity Guidelines for Americans are addressed [ 1 , 18 ]. First, we found that high recreational screen time was associated with higher diastolic blood pressure percentile and lower HDL cholesterol, even when accounting for physical activity. Second, we found a dose-response relationship between step count categories and diastolic blood pressure percentile, with the lowest step category being associated with the highest diastolic blood pressure percentile. Third, we did not find evidence of effect modification between screen time and physical activity for diastolic blood pressure percentile and HDL cholesterol. The use of categories and continuous screen time and steps variables, as well as considering joint associations between screen time and steps on CVD risk, can inform gaps in public health and clinical guidance for adolescents.

Daily screen time of more than 8 h was associated with higher diastolic blood pressure percentile, even when adjusting for daily steps. Screen time is mostly a sedentary behavior, which displaces physical activity and can lead to an increase in caloric consumption through mechanisms such as mindless snacking and advertisements that promote unhealthy foods [ 48 , 49 ]. We have previously shown that greater screen time is associated with higher BMI percentile in the ABCD Study [ 19 , 50 ]. In addition, contemporary screen modalities (e.g., social media, video games) may lead to exposure to cyberbullying, violence, or other stressful content that could raise blood pressure [ 32 , 51 ]. These mechanisms may explain why high screen time may be associated with poorer cardiovascular health. The study builds on prior literature by incorporating several contemporary modalities in screen time, adjusting for step counts, and focusing on early adolescence which is an important developmental period for the development of lifestyle behaviors that can persist into adulthood, affecting cardiovascular health across the lifespan.

On average, adolescents recorded 9,722 daily steps, which is consistent with estimates from the National Health and Nutrition Examination Survey for 10-11-year-old adolescents [ 40 ] and other smaller studies with similar age groups [ 52 , 53 ]. This average is below the 12,000 steps per day threshold which approximates the recommended 60 min of moderate-to-vigorous intensity physical activity per day for adolescents from the Physical Activity Guidelines for Americans [ 41 ]. When examining the independent associations of step count on cardiovascular outcomes, we found a dose-response relationship between lower step count categories and higher diastolic blood pressure percentile. Previous studies have demonstrated that engaging in physical activity throughout adolescence is associated with a lower risk of hypertension, suggesting that exercise may have a protective effect on blood pressure. We identified one study that utilized step count as a summary estimate of physical activity, demonstrating a negative correlation between step count and risk of hypertension in children and adolescents [ 16 ]. Our study found that fewer steps was associated with higher diastolic blood pressure percentile, with no statistically significant association found for systolic blood pressure percentile. Importantly, previous studies have demonstrated that diastolic blood pressure (compared to systolic blood pressure) is a stronger predictor of CVD risk in adolescents [ 54 , 55 ].

In addition, having a higher daily step count was associated with a higher HDL cholesterol. Previous studies have shown that self-reported physical activity is associated with an improved lipid profile [ 56 , 57 , 58 ]. Our findings add to prior literature by indicating that even after accounting for screen time, a higher step count as a measure of physical activity is specifically associated with higher HDL cholesterol.

Evidence of effect modification between physical activity and screen time was not observed for diastolic blood pressure percentile and HDL cholesterol. For each screen time category, low step count categories were associated with higher diastolic blood pressure percentile and lower HDL cholesterol. Overall, our findings are consistent with previous studies which have reported a joint association between high screen time and low physical activity on CVD risk [ 20 , 59 ], but extend prior findings by expanding screen time to include contemporary modalities (e.g., social media, texting, video chat, video games) and by using step count via devices as an objective measure of physical activity in early adolescents.

Limitations of this study include its cross-sectional nature. Fitbit device data were collected for a 3-week period (21 days), which may not be representative of a participant’s physical activity or the intensity of activity over the course of one year. Future studies may utilize longitudinal Fitbit data for longer durations to overcome this limitation, as well as to explore potential seasonal differences. While only days with > 599 min of waking wear were included in the analysis, variations in wear time above this threshold were not controlled for, thus producing a potential limitation with regards to differences in participants’ total wear time and variations in wake vs. sleep time. Fitbit devices may miss some activity data (e.g., biking, skateboarding), as it is mainly worn on the wrist. It is possible that wearing activity monitors like Fitbit could increase adolescent physical activity given real-time feedback regarding activity level; however, one prior study did not find that wearing a Fitbit increased physical activity levels in 10-year-olds [ 52 ]. With regards to screen time, measures were based on self-reported data, which are subject to recall errors and reporting bias. Additionally, the measurement of screen time did not account for the content and intensity of engagement. There is the potential for unmeasured confounders, although we controlled for site and sociodemographic factors as well as the COVID-19 pandemic, study year, and seasonality. We did not control for adiposity as it was not directly measured in the ABCD Study and adiposity could be a mediator in addition to a confounder for the association among screen time, steps, and CVD risk factor outcomes [ 58 ]. While the sample is epidemiologically informed, self-selection by families into the study and assessment at academic centers may be a source of sampling bias. Due to the COVID-19 pandemic, there was missing data which could lead to selection bias as participants included were more likely to be White, have a household income $75,000 or more, and have married/partnered parents (Appendix B). For a minority of participants (21.7%), blood draws (e.g., hemoglobin A1c and cholesterol) were measured one year later than the screen time and Fitbit measures; however, this would have been during a similar month/season and we controlled for data collection year in the analysis.

The strengths of this study include the socio-demographically diverse and large population-based sample, the use of objective data over 21 days (longer than a more typical 7-day protocol) which limits self-report bias and decreases standard error for physical activity measures, and including several different types of screen mediums used by adolescents rather than just computer and television for the screen time measure.

The study adds to the literature by addressing evidence gaps identified by the Physical Activity Guidelines Advisory Committee Scientific Report by identifying specific categories of screen time and step count associated with CVD risk in early adolescence. In our study, more than 8 h of daily screen time and less than 12,000 steps per day were associated with higher diastolic blood pressure percentile among a racially diverse U.S. adolescent population-based sample. More than 8 h of daily screen time was also associated with lower HDL cholesterol. Future research should use a longitudinal study design and analyze differences by weekdays, weekends, or holidays, which would further inform physical activity and screen time guidelines for adolescents.

Data availability

Data used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA).

Abbreviations

Adolescent Brain Cognitive Development Study

• Cardiovascular disease

University of California, San Diego

University of California, San Francisco

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Acknowledgements

The authors thank Anthony Kung for editorial assistance and Natalia Smith for help with data cleaning and analysis.

J.M.N. was funded by the National Institutes of Health (K08HL1549350 and R01MH135492) and the Doris Duke Charitable Foundation (2022056). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

The ABCD Study was supported by the National Institutes of Health and additional federal partners under award numbers U01DA041022, U01DA041025, U01DA041028, U01DA041048, U01DA041089, U01DA041093, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners/ . A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/principal-investigators.html . ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the analysis or writing of this report.

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Jason M. Nagata, Shayna Weinstein, Sana Alsamman & Christopher M. Lee

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Erin E. Dooley & Kelley Pettee Gabriel

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Holly C. Gooding

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J.N.: conceptualization, analysis, writing- original draft and revisions, supervision; S.W.: analysis, writing- critical revisions; S.A.: writing- original draft and revisions; C.M.L.: writing- original draft and revisions; E.E.D.: conceptualization, writing- revisions; K.G. and A.T.: writing-critical revisions; O.K.: conceptualization, writing- revisions; F.C.B.: conceptualization, data collection, writing- revisions; K.P.G.: conceptualization, writing- revisions. All authors approve of the final submitted version.

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Ethics approval and consent to participate.

The University of California, San Diego (UCSD) provided centralized institutional review board (IRB) approval and each participating site received local IRB approval:

Children’s Hospital Los Angeles, Los Angeles, California.

Florida International University, Miami, Florida.

Laureate Institute for Brain Research, Tulsa, Oklahoma.

Medical University of South Carolina, Charleston, South Carolina.

Oregon Health and Science University, Portland, Oregon.

SRI International, Menlo Park, California.

University of California San Diego, San Diego, California.

University of California Los Angeles, Los Angeles, California.

University of Colorado Boulder, Boulder, Colorado.

University of Florida, Gainesville, Florida.

University of Maryland at Baltimore, Baltimore, Maryland.

University of Michigan, Ann Arbor, Michigan.

University of Minnesota, Minneapolis, Minnesota.

University of Pittsburgh, Pittsburgh, Pennsylvania.

University of Rochester, Rochester, New York.

University of Utah, Salt Lake City, Utah.

University of Vermont, Burlington, Vermont.

University of Wisconsin—Milwaukee, Milwaukee, Wisconsin.

Virginia Commonwealth University, Richmond, Virginia.

Washington University in St. Louis, St. Louis, Missouri.

Yale University, New Haven, Connecticut.

Written informed consent was obtained from the parents/caregivers of adolescents, and written assent was obtained from adolescents. Given that adolescent participants were minors (10–14 years old), they were not able to give legal consent. All the methods were carried out in accordance with relevant guidelines and regulations.

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The authors declare no competing interests.

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The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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12889_2024_18790_MOESM1_ESM.docx

Additional File 1: Appendix A. Flow diagram of included participants. Appendix B. Comparison of participants included vs excluded due to missing data; Appendix C. Associations between screen time and step count categories and binary cardiovascular disease risk (CVD) outcomes in the Adolescent Brain Cognitive Development (ABCD) Study

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Nagata, J.M., Weinstein, S., Alsamman, S. et al. Association of physical activity and screen time with cardiovascular disease risk in the Adolescent Brain Cognitive Development Study. BMC Public Health 24 , 1346 (2024). https://doi.org/10.1186/s12889-024-18790-6

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Received : 06 December 2023

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Published : 18 May 2024

DOI : https://doi.org/10.1186/s12889-024-18790-6

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2025 Honda Civic Adds Hybrid, Hatchback, Google, and More Power

The practical and sporty Civic gets a new hybrid version to compete against the Prius, Corolla Hybrid, and Elantra Hybrid

Honda updated the Civic for 2025 and is also bringing back the Civic Hybrid after a 10-year absence. With claimed fuel economy in the high 40s, the Civic Hybrid isn’t just the most efficient Civic you can buy—it’s now the most powerful and luxurious, aside from the ultra-sporty Civic Type R.

For 2025, the top two Civic trim levels—Sport Hybrid and Sport Touring Hybrid—are only available with Honda’s hybrid drivetrain , which pairs two electric motors with a 2.0-liter four-cylinder gas engine. The Civic’s optional turbo engine is gone for 2025, as is the six-speed manual transmission that was once available on some hatchback models. The less-equipped LX and Sport trims will only be available with a 2.0-liter gas engine, and there’s no LX hatchback.

• 2025 Honda Civic Hybrid: Outside Inside What Drives It Active Safety and Driver Assistance

The rest of the changes to the Civic lineup are relatively minor—the front and rear ends have gotten a slight makeover, there’s a new optional Google-based infotainment system, and new colors are also available. But the lack of drastic change isn’t a bad thing, as we’re big fans of the current Civic’s straightforward practicality.

The 2025 Civic and Civic Hybrid sedan are expected to go on sale in June, while the hatchback will be available later this summer.

Before we purchase a Civic Hybrid to evaluate extensively at our Auto Test Center, let’s review what we know so far about this updated Honda.

What it competes with: Hyundai Elantra and Elantra Hybrid , Kia Niro , Mazda3 , Toyota Corolla and Corolla Hybrid , Toyota Prius .

Powertrain: 200-hp, 2.0-liter four-cylinder hybrid engine; front-wheel drive, eCVT (Civic Hybrid)

Price: $27,000–$32,000 (estimated)

On sale: June 2024 (sedan), late summer 2024 (hatchback)

Photo: Honda Photo: Honda

The big news is that Honda is bringing back the Civic Hybrid. It fills the place of the lackluster Insight, which went away in 2022. Fuel economy isn’t quite as good as the Insight’s, which led the compact hybrid class at the time, but our exclusive mpg testing will show whether Honda’s estimate of “nearing 50 mpg” could beat the Corolla Hybrid’s 47 mpg EPA estimate. If the Civic’s hybrid setup operates anything like the superb Accord Hybrid, the Civic Hybrid should be a pleasure to drive. The Accord Hybrid has plenty of oomph, with very few concessions made for efficiency. In a smaller, lighter car, this powertrain has much promise. 

We’re thankful Honda didn’t change much about the Civic’s interior, which we think is a shining example of elegant simplicity that other automakers should follow. It will, however, be interesting to check out the optional Google-powered infotainment system on the Sport Touring Hybrid, as we haven’t had great past experiences with Google systems from Polestar and GM .

Only Civic superfans will be able to spot the difference between 2024 and 2025 models. It’s still the same Civic, just with a slightly updated grille and taillights, and new paint and trim options. Both hatchback and sedan versions will continue to be available.

Aside from some trim and fabric choices, the Civic’s interior appears unchanged for 2025. It retains straightforward knobs and buttons for climate and entertainment, and there’s still a traditional gear selector. Honeycomb-style vents once again span most of the dashboard. Honda says the Hybrid trims will be the quietest of the group.

For 2025, the Sport Hybrid gets a moonroof, heated front seats, and dual-zone climate control. The Sport Hybrid Touring adds leather seats, a premium sound system, wireless Android Auto and Apple CarPlay, and a larger 9-inch touchscreen with built-in Google software, including Google Assistant and Google Maps. We haven’t seen the smaller touchscreen, but the Sport Hybrid Touring appears to only have a volume knob and no tuning knob.

We had some issues with Google’s built-in software on the Chevrolet Blazer EV and Polestar 2 we tested, so we’ll be interested to spend some time with the Civic to see if Honda did a better job integrating it.

What Drives It

Honda hasn’t shared too many details about the Civic’s two available engine choices, but here’s what we do know: There’s no more turbo and no more manual transmission (except on the Type R and likely on the Si). The hybrid’s dual-motor hybrid system is good for 200 horsepower. On paper, that’s the same as the current Civic Si, although it’ll likely feel very different due to the blend of gas and electric power.

Honda expects fuel economy “nearing 50 mpg.” We don’t know how much power the non-hybrid LX and Sport trims’ 2.0-liter gas engine is good for or their estimated fuel economy, but we expect every non-hybrid Civic to get a continuously variable transmission (CVT).

The hybrid system is similar to the one found in the Accord Hybrid, which we think is one of the best out there thanks to its responsiveness and power. In a smaller car, it could be a real pleasure to drive. Hybrid drivers will also be able to select from among four levels of regenerative braking using steering wheel paddles. Every hybrid Civic will have a form of eCVT that we’ve found to do a good job mimicking a traditional automatic transmission in other Honda vehicles.

Active Safety and Driver Assistance

As with the 2024 models, all Civics get standard automatic emergency braking (AEB) with pedestrian detection, lane departure warning (LDW), lane keeping assistance (LKA) , and adaptive cruise control (ACC) . The Sport, Sport Hybrid, and Sport Touring Hybrid models get blind spot warning (BSW) with rear cross traffic warning (RCTW) .

Keith Barry

Keith Barry has been an auto reporter at Consumer Reports since 2018. He focuses on safety, technology, and the environmental impact of cars. Previously, he led home and appliance coverage at Reviewed; reported on cars for USA Today, Wired, and Car & Driver; and wrote for other publications as well. Keith earned a master’s degree in public health from Tufts University. Follow him on Twitter @itskeithbarry .

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Identifying the practice patterns of optometrists in providing falls prevention management: A mixed-methods systematic review protocol

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Objective The objective of this systematic review is to synthesise the best available evidence for optometrists’ practice patterns in providing falls prevention management.

Introduction Falls remain the main cause of injury-related hospitalisation and mortality in Australia and worldwide, significantly affecting older adults. The increased risk of comorbidities, including visual impairment in this cohort is linked to a higher incidence of falls. Despite being primary eye care practitioners, community optometrists may not consistently integrate falls prevention strategies into their practice. Furthermore, the extent to which they adhere to evidence-based recommendations for falls management remains unclear.

Inclusion criteria The review will include optometrists, in regions where optometry is a regulated profession, and report their understanding and practice patterns in delivering falls prevention management to older community-dwelling adults. Qualitative, quantitative, and mixed methods studies will be eligible for inclusion. It is envisioned that most studies will be qualitative. Studies published in English and those published from 1980 onwards will be eligible for inclusion since published evidence for falls prevention began to increase sharply around this time.

Methods The review will follow the JBI guidelines for mixed methods systematic reviews and will be developed and reported in accordance with PRISMA-P guidelines. Databases that will be searched are Excerpta Medica Database (Embase), Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL) Complete, and OVID MEDLINE. Grey literature will be searched through Conference Proceedings Citation Index (Web of Science), Google Scholar, and ProQuest Dissertations databases. Two reviewers will independently conduct all screening and critical appraisal. The reviewers will screen all articles’ titles and abstracts retrieved from the searches to determine potential eligibility. All full-text articles considered relevant will then be assessed for final eligibility for inclusion. The final included articles will be assessed for methodological rigour using the JBI SUMARI critical appraisal tools, subsequently, all relevant data will be extracted. Discrepancies at any stage of the procedures will be resolved through discussion and consensus with a third senior researcher. A convergent integrated approach to synthesising and integrating the quantitative and qualitative data will be followed.

Review registration CRD42024539668

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

Anne-Marie Hill is supported by a National Health and Medical Research Council (NHMRC) of Australia Investigator (EL2) award (GNT1174179) and the Royal Perth Hospital Research Foundation. Si Ye Lee is conducting this research with the support of an Australian Government Research Training Program Fees Offset scholarship and is a recipient of a Perth Eye Foundation scholarship through the University of Western Australia.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Increased Screen Time as a Cause of Declining Physical, Psychological Health, and Sleep Patterns: A Literary Review

Vaishnavi s nakshine.

1 Department of Public Health Sciences, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND

Preeti Thute

2 Department of Anatomy, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND

Mahalaqua Nazli Khatib

3 School of Epidemiology and Public Health, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Wardha, IND

Bratati Sarkar

4 Department of English, Jawaharlal Nehru University, Delhi, IND

Dependency on digital devices resulting in an ever-increasing daily screen time has subsequently also been the cause of several adverse effects on physical and mental or psychological health. Constant exposure to devices like smartphones, personal computers, and television can severely affect mental health- increase stress and anxiety, for example, and cause various sleep issues in both children as well as adults. Risk factors for obesity and cardiovascular disorders, including hypertension, poor regulation of stress, low HDL cholesterol, and insulin resistance are among the physical health repercussions we see. The psychological health effects comprise suicidal tendencies and symptoms of depression which are associated with digital device dependency, screen-time-induced poor sleep quality, and content-influenced negativity. Oftentimes it can cause the induction of a state of hyper-arousal, increase stress hormones, desynchronize the body clock or the circadian cycle, alter brain chemistry and create a drag on mental energy and development. With a focus on brain development in children and detrimental effects in both adults and children, this research article goes on to explore the various aspects of screen addiction and excessive screen exposure.

Introduction and background

Digital devices and online spaces, above all, are considered one of the fundamental aspects of the existence of this current generation. Rapid advancements in technology make it possible for consumers in any part of the world, regardless of age, to experience a wider variety of fast-acting stimuli that are available with similar accessibility, practically anywhere via mobile devices, enticing them to indulge in the use of screens for longer than the suggested two-three hours per day. Computers, phones, and tablets- the heralds of easy-to-use internet hosts- have seen increased purchase and usage in the present times, which has significantly reduced the distance that had once separated everything and instead turned the world into a community resembling a global village. Undoubtedly, individuals-cum-residents of this same global village have been spending an excessive amount of their time online, which has both positive as well as negative consequences, both short- and long-term. While the usage of the internet as a whole is rising, unnecessary and troublesome use of the internet has reached medically alarming levels, too. Numerous reports have highlighted the detrimental effects of its usage, such as issues that affect one's sleep, mood, and communal interactions [ 1 ]. Internet addiction (IA) is turning out to be a severe general health problem across various nations on various continents, including ours, i.e., Asia. It has been proven that one of the multiple reasons for more and more individuals being affected by depression and feeling further isolated is their addiction to mobile phones and other digital gadgets, their dependency on the presence of people on the internet, their views and values, which need not be the individual’s own. It increases dependence on validation from faceless people on the internet. It reduces the rate at which one physically interacts with others in real life, which also affects the release and maintenance of adequate doses of feel-good hormones like dopamine, serotonin, endorphins, and oxytocin, which are naturally required by all. Overuse of digital media is thought to be a significant drawback in the development of healthy psycho-physiological resilience [ 1 ]. FOMO (Fear of missing out) has also been identified as a risk factor for binge usage of the internet. The relationship between FOMO and internet usage has been extensively researched in recent studies, with younger persons more prone to the risk of the same. Along with the detrimental effect on mental health, excessive screen time is also responsible for multiple physical health deteriorations [ 1 ]. 

Effects on sleep

The use of digital media is thought to interfere with sleep in subsequent ways:

Displacing Other Activities

Screen time can replace time spent on performing physical activities, which are directly beneficial for sleep every night [ 1 ].

Time of Use

Exposure to blue and intense light in the evening and at night from self-luminous devices may prevent melatonin from being produced, alter its timings of production and retention, and thus disturb the circadian rhythm. Moreover, prolonged texting post-bedtime is likely to shorten high school kids' sleep cycles, resulting in daily drowsiness and subpar academic performance [ 1 ].

Small touch screens, contrary to TVs, might send audible notifications during sleep time, delaying or preventing sufficient sleep. Consequently, it was observed that 18% of teenagers reported being awakened by their cell phones at least a few times throughout the night [ 1 ].

Media Content

Teenagers that use the internet more frequently have lesser durations of sleep, delayed bedtimes and wake-up times, and more daytime fatigue [ 2 ]. Those accessing social networking sites at night are more likely to sleep poorly, particularly if there is emotional involvement in the same. Additionally, engaging in an exciting task while staring at a luminous cell phone screen may enhance psychophysiological arousal, disturbing sleep [ 1 ]. The duration of time which is devoted to social media also affects how much and how well one sleeps, and good sleep is essential for academic success [ 3 ]. In 2011, Espinoza and colleagues polled 268 young adolescents specifically about social media and discovered that 37% said they had trouble sleeping due to the use of social networking sites. Adolescents use social media for 54% of their internet time. As we know, social media entails incoming alerts at all hours of the day, unlike conventional internet usage. This distinct social media characteristic is fundamental to the quality of sleep for a couple of causes [ 4 ]. First of all, because 86 percent of teenagers sleep in bedrooms with their phones nearby, frequently in their hands or shoved under their pillows, notifications at night time have the ability to disrupt sleep. They have also reported being awakened by incoming text messages and face problems in sound sleeping which are probably caused by social media [ 5 ].

Moreover, FOMO (Fear of Missing Out) is exacerbated by continual notifications, which seems to place a significant amount of pressure on people to be online all the time. Individuals mention feeling pressured, overwhelmed, and guilty if they are unable to respond to a text right away and that they face a great deal of anxiety whenever their access to messaging becomes constrained. Hence, it's likely that young people have trouble unwinding before bed because they worry about losing out on new texts or material. These special features of the usage of social media give us yet another cause to anticipate a connection between it and bad sleep [ 3 ]. A stronger correlation with specific usage of the internet at night time would suggest that exposure to digital screens before sleeping may disrupt circadian rhythms or cause sleep disruptions from notifications [ 5 ].

In contrast, a relationship between inadequate sleep and emotional involvement on the internet, specifically social media, would imply that an individual's difficulty unwinding before the night is caused by anxiety over missing out on fresh information and their inability to relax and calm down [ 6 ]. Even young children who are subjected to night-time media consumption have considerably lower sleep durations than those who are not engaged with night-time screen media [ 7 ]. The stimulating content and inhibition of natural melatonin by blue light produced via screens are two primary mechanisms underpinning the above connection. Furthermore, a persistent online presence disrupts sleep habits, that in turn influences mood [ 1 ]. Last, but not least, those who tend to spend more time online have a higher mental workload due to multitasking, increased levels of stress, and comparatively poorer quality as well as quantity of sleep, all of which are related to the deteriorating state of health [ 3 ]. Location

Teenage media device ownership is linked to earlier bedtimes and shorter sleep duration, relatively high bedtime resistance, with increased levels of sleep disruption, especially when the devices were kept in the bedroom [ 1 , 8 ].

Light Is Perceived as Electromagnetic Radiation

The occurrence of sleep during the night and the production of pineal melatonin coincides, indicating a significant link between the two processes [ 1 ]. While sleep itself is not essential for the nocturnal generation of melatonin, it is unquestionably crucial that the night-time hours be completely dark. For instance, sleep loss alone does not stop the nocturnal, circadian secretion and manufacture of melatonin [ 9 ]. Regardless of the reason for sleep loss at night, the nocturnal, circadian melatonin signal should be unaffected as long as the person is exposed to complete darkness. The term "biological night" refers to the time when melatonin is synthesized and secreted into the bloodstream and has been defined by research on humans under normal, ongoing conditions [ 10 ]. Thus, the initiation of the melatonin surge, a concomitant rise in sleep propensity, and a reduction in core body temperature marks the beginning of this biological night; the opposite occurs at the end of the biological night and sleeps [ 10 ]. The melatonin-producing pineal gland may also interpret electromagnetic radiation as light. As a result, electromagnetic radiation emitted from digital gadgets may also cause melatonin production to be hindered, which disrupts sleep [ 1 ].

Cardiovascular system

It is asserted that sedentary behavior raises the likelihood of obesity, high-density lipoprotein (HDL) dysfunction, and hypertension, the three main cardiovascular morbidity risk factors [ 1 ].

Reduced sleep, physical inactivity, and exposure to excessive advertisements on various media have a detrimental impact on the dietary habits of the younger generation; also contributing to the correlation between screen time and obesity [ 11 ]. As far as digital media is concerned, watching television before one's bedtime is closely linked to the development of obesity during childhood and adolescence, which can further result in cardiometabolic risk [ 12 ]. In typical circumstances, a rise in satiety sensations is usually followed by an increase in plasma glucose; however, in the study, before eating, there was an increase in plasma glucose that peaked higher than it would have addressed. The findings, thus, revealed that video games are closely connected with acute stress (the fight-or-flight response). A release of glucose into the bloodstream seems to be related to this stress reaction [ 13 ].

Blood Pressure

One potential marker of future cardiovascular problems is retinal arteriolar constriction. The results show that children who spend more time outdoors tend to have bigger retinal arterioles than those who spend less time outside [ 14 ].

Cholesterol

Playing video games is the only form of sedentary activity that is associated with a decline in HDL cholesterol in obese teenagers [ 15 , 16 ]. Over three hours of screen usage is linked to a considerable reduction in HDL cholesterol. Mechanisms that explain this relation take into consideration the increased consumption of food advertised extensively, the decrease in food intake regulation during the time on screens, and stillness and loss of movement and physical activity. An example of this is a study that discovered that screen time leads to impairment in satiety signals through the system of mental reward, thereby directly affecting food intake [ 1 ]. 

Stress regulation

Sympathetic Arousal

Cardiovascular issues may be at risk due to chronic sympathetic arousal. Greater sympathetic arousal was observed in teenagers and youngsters who engaged in addictive online behavior. High arousal was suggested as one of the potential contributing factors to sleep disruption by researchers [ 1 ].

For pediatric investigations, cortisol, a hormone produced by the hypothalamic-pituitary-adrenal (HPA) axis, is a stress biomarker. Poor performance is linked to both low and elevated cortisol levels. During the nighttime, the cortisol levels are typically at their lowest. The rate of cortisol increases as the waking hours approach and undergoes a sharp spike after waking up [ 1 ]. As much as three hours per day of media usage by school-aged youngsters results in a lessened cortisol surge an hour after waking up which is detrimental [ 17 ]. Children who engage with digital media for less than three hours each day or have no digital media footprint show a regular increase in morning cortisol levels.

Insulin and Diabetes

The islets of Langerhans of the pancreas secrete the hormone insulin, which is vital for controlling metabolism and storing fat. Insulin resistance is a condition that occurs when cells are unable to use insulin effectively. It makes an essential contribution to the pathophysiology of diabetes and increases the risk of developing cardiovascular disease. According to some research, watching television, playing video games, or using the computer for more than an additional hour a day can result in a 5% reduction in insulin sensitivity. Other research linked as little as two hours of screen time per day to aberrant insulin levels [ 1 ].

Constantly staring at a PC screen can result in headaches, eye strain, impaired vision, dry eyes, and irritation. Such symptoms may be brought on by glare, inadequate illumination, or a wrong viewing angle. According to research, being in an outside environment stimulates the release of dopamine from the retina, which prevents myopia, or near-sightedness, from developing [ 18 ]. As a result, children who spend fewer hours outside have a higher chance of developing the same. Additionally, spending time outside can lessen and largely eliminate the factors that contribute to the development of myopia, such as prolonged close work or screen viewing. The participating young subjects who have been playing video games for more than 30 minutes nearly on a day-to-day basis reported headaches, vertigo, and eye strain. The dominant eye primarily experienced transient diplopia and refractive problems (such as short-sightedness), ultimately leading to vision loss [ 1 ].

Orthopedics

Sedentary habits, or sitting activities that don't involve exercise, can have a significant impact on one's joints and bones. It is argued that screen usage, especially on small-screen mobile devices, affects posture and causes musculoskeletal strain and pain sensations. Similar symptoms can be brought on by the frequent, repeated wrist and arm movements and head inclination typical of playing video games. Bone mineral density is found to be negatively correlated with boys' video gaming play time. The mineral composition of the femoral and spinal bones is inversely correlated with girls' screen time [ 1 ].

Depression and suicidal behavior

Depression, mainly social media-induced depression is a growing concern, particularly among today’s generation [ 19 ]. Those not aware of the usage of social media effectively can easily get trapped in a pattern of jealousy, envy, self-doubt, and poor self-esteem [ 20 ]. It is well known that sleep disturbance symptoms appear before symptoms of depression and suicidal thoughts. Therefore, it is proposed that a mediating element connecting night-time screen use to depressive symptoms and suicidal thoughts in adolescents is the lack of sleep [ 21 ]. The researchers further note that dependence on smartphones, frequent messaging, and protracted fear about not receiving back messages, particularly before bedtime, are likely associated with mood swings, suicidal thoughts, and self-injury. 

ADHD (attention deficit hyperactivity disorder)

Children in today's society may oftentimes exhibit different symptoms of ADHD, such as inattentiveness, hyperactivity, and impulsivity. This type of conduct is known as ADHD-related behavior and can be connected to one's screen time [ 22 ]. It has also been stated that excessive digital device usage is prevalent among younger children and teenagers who have either been already diagnosed with ADHD or who are regarded to be dealing with attention issues or impulsivity [ 23 ].

Addictive screen time behavior

While a substantial percentage of men seem to exhibit video game addiction, women on the other hand are primarily focused on social networking [ 1 ].

Neuropsychological Effects

Numerous investigations so far have concluded that any form of addiction to the internet causes structural changes in the brain, specifically in its frontal lobe. The ability to filter out irrelevant information and a reduced capacity for coping with demanding and complex tasks are related to such structural alterations. The frontal lobe is majorly concerned with controlling overly assertive and wrong, miscellaneous behaviors, as well as adjusting to environmental change [ 1 ]. Other research has shown that control over one’s emotions, visible discord during the decision-making process, and compulsively repetitive behaviors are linked to damaged white matter. Studies have examined the impact of screen multitasking when continuous attention across media devices becomes a substitute for "real world" behavior [ 4 ]. The anterior cingulate cortex, which is connected to cognitive control of performance and socioemotional regulation, is found to have less grey matter in college students with high multitasking scores. Poor performances have been recorded in college students who have been switching between tasks, working memory, and filtering through tests [ 24 ]. Another study on the same group found links between less grey matter and poorer conflict detection of conflict, increased neuroticism and impulsivity, poorer control over behavior that are goal-oriented, and increased conduct motivated by sensations [ 1 ].

Behavioral and Societal Aspects of Social Media Usage in Digital Spaces

This cohort is characterized by increased attentional lapses that have been self-reported regularly, a non-deliberate occasion of mental wandering, which is consistent with the result that grey matter decreases in heavy media multitasking young adults [ 25 ]. The main symptom of ADHD in university students is non-deliberate mind-wandering, which is linked to decreased levels of mindfulness and a higher prevalence of non-adaptive or negative thought patterns. Teenagers who are hooked to the internet and exhibit stronger depressive, hostile and ADHD-related symptoms are found to have non-adaptive/negative thinking patterns [ 26 ]. Therefore, it would seem that media addiction is correlated with increased mental wandering. Individuals who multitask frequently and are screen addicts were also discovered to have lower levels of social support and peer support or attachment to their families and relations. As a result, their level of life contentment is lowered. Teenagers are moving away from face-to-face connection, which is limiting offline social support even though it has increasingly been linked to positive social well-being [ 27 ]. They are more likely to get caught up in a dangerous cycle of continued usage of the internet and social networking websites in an effort to rekindle to social support that they crave when facing obstacles. However, the guise of support that individuals can receive online aids in sustaining their repetitive Internet usage even more [ 28 ]. On the other side, social support that is unrelated to screens may reduce internet addiction. A lack of societal support and increased mind wandering are likely to facilitate social functioning and raise the likelihood of more profound sadness, loneliness, and isolation, which may further enhance addictive behavior [ 29 , 30 ].

Additionally, it is highlighted that social support, attachment to materialistic or figurative objects, mindfulness of one’s surroundings and the feeling of others, and degree of life satisfaction are all psychological and social aspects that are adversely impacted by compulsive screen use and are essential to an individual's resilience in the face of adversities in life [ 31 ]. Furthermore, behavior resulting in incidents of cyberbullying and the social components of addicted internet use appears to be related [ 1 ]. Women in their early adolescence and early years of adulthood are more likely than men to spend more time on social media, be subjected to higher risks of cyberbullying, and experience detrimental mental health issues, the aforementioned are proving to be consistent with the recent epidemiologic trends illustrating that young females, in particular, are more likely to experience an increase in symptoms of depression, self-harm tendencies, and suicidal thoughts [ 32 ].

With children increasingly using wireless gadgets, worries about their susceptibility to radio-frequency electromagnetic radiation (RF-EMR) fields are growing. Kids' growing neurological systems are thought to be quite susceptible to RF-EMR fields, making them possibly more vulnerable. Moreover, compared to the size of their head, more RF-EMR can penetrate their brain tissue because it is better conductive. They will also be exposed to RF fields for more years than grown-ups [ 1 ].

Infertility

Infertility is a common condition that affects 7% of men and 11% of women in the U.S. [ 1 ]. There is a necessity to establish the links between environmental exposures and sperm quality indicators are given that there is evidence of a deterioration in semen quality in recent years [ 33 ]. Exposure to smartphones has been linked to reduced sperm viability and motility. Studies on experimental animals and people examined how RF-EMR also affected male reproductive function. On biological tissue, RF-EMR might have thermal and non-thermal effects both. Reactive oxygen species (ROS) may be produced more frequently as a result of nonthermal interactions, which may cause DNA damage. A little amount of ROS has a crucial functional role in the acrosome response, binding to the oocyte, as well as sperm capacitation. Since phones are frequently kept in pant pockets close to the reproductive organs, thermal impacts potentially raise the temperature of the testes, hampering spermatogenesis and sperm production [ 33 ]. Mobile phones are found to be a potential contributing factor in light of newly discovered evidence of a deterioration in the quality of male sperm. According to the findings of these studies, using a cell phone or a laptop, or a tablet when exposed to RF-EMR increases one's risk of developing cancer as well [ 1 ].

A decline in academic performance

The findings in a cohort study indicate that overall screen time, and the duration spent using a smartphone, are both strongly correlated with the academic stress score and increases the likelihood of abnormal academic stress. One explanation might be the use of cell phones and other electronic devices for homework. This is corroborated by a research study that found that children utilize smartphones and other devices mostly to complete their homework. Hence, the amount of time spent using a device may partly reflect how much schoolwork pupils have to do and the stress that comes with it [ 34 ]. Utilizing electronic devices for social interaction and entertainment may be another factor. More than 30% of teenagers, according to one research, use cell phones and other gadgets for social interaction. Spending a lot of time on social activities and entertainment might interfere with study time, hindering students' academic achievement and raising stress levels [ 35 ]. In the meantime, a bidirectional association has also been established between screen time and academic stress because of the slow progress of studies, which could lead to additional screen time. Furthermore, using a smartphone for social media and amusement might make it difficult for students to concentrate on their work, which could result in unsatisfactory academic results. A rise in academic stress could also eventually be triggered by inferior academic achievements [ 36 ]. The range of digital media devices is expanding rapidly, and improving digital media provides society with a more vibrant and quick-paced digital world. Children and teenagers in particular seem to adapt to modern technologies quickly. But, a growing body of literature associates excessive screen time with physical, psychological, social, and neurological adverse health consequences. Developmental, pornographic exposure and learning effects are additional effects of screen time that require further in-depth analysis and are beyond the scope of this review article. Until very recently, the majority of the literature on children and adolescents use of digital media focused on TV and computers. However, as people seem to utilize mobiles increasingly, studies are shifting their focus accordingly [ 1 ]. Even though the Internet continues to be a powerful and uplifting force in the lives of many, a percentage of users may develop an addiction. Due to their growing online usage and heightened susceptibility to the emergence of addictive behaviors, adolescents are a noteworthy worry in this regard [ 37 ]. Undoubtedly one of the most difficult problems facing the social sciences to this date is the increase in smartphone and social media use. Popular science books and news reports on the psychological effects of social media use are in high demand, and many of these works paint a very ominous picture: social media is often said to have a negative impact on people's lives and societies [ 3 ].

Excessive Internet use has negative effects that are obvious and detrimental, ranging from sleep deprivation, increased depression, and skipping school to family conflict. Comorbid depression, anxiety, ADHD, and other substance-use disorders are widespread. Various markers of individual personality, such as impulsivity, aggressiveness, sensation seeking, family conflicts, as well as inadequate parental supervision, also especially belonging to the male gender have proven to be additional risk factors [ 8 ]. Non-adaptive or negative thought patterns, a lower sense of fulfillment in life, and a propensity for health concerns throughout maturity are several other characteristics that one may notice appearing. The concluding results present that a lack of physical, real-life interaction, extensive multitasking, increased usage of social media websites, and interactive screen time through addictive web interfaces- mostly through the regular usage of video games that require online multiplayer interactions, which do not allow the players to pause the games or exit the rounds- all of these play a significant role in determining the emotional, psychological and physiological impacts on the population. According to research from the Centers for Disease Control and Prevention (Figure  1 ), children aged 8 to 10 spend six hours per day using screens, children aged 11 to 14 spend nine hours, and teenagers aged 15 to 25 spend seven and a half hours per day using screens (including television). The chart below (Figure  2 ) shows screen time recommendations by age, from infants to adults based on the study by the American academy of child and adolescent psychiatry.

This figure has been taken from an open-access journal under a CC-BY license.

Source: American Academy of Child and Adolescent Psychiatry [ 38 ].

Overusing digital screens during one's adolescence and young adulthood may result in their mind relating to outside stimuli rather readily and cause a lack of attention. Internal triggers such as unhelpful or negative thoughts and feelings of lower satisfaction levels regarding one's life can also be accompanied by an onset of health problems in adulthood, such as cardiovascular disease and infertility. It may cause one's stress to increase up to levels that would turn difficult for one to handle. These ailments can result in unhealthy coping mechanisms, which eventually might increase the likelihood of sadness, depression, and anxiety in one's later years. One can successfully deal with difficult life situations if they have a strong sense of personal resilience. Physical fitness, societal support, forms of attachment, the mindfulness towards issues, and the degree of life pleasure, both temporary and permanent, are some of the crucial components of resilience, which is a dynamic psychophysiological construct that can stand jeopardized by excessive screen time and digital footprints. Therefore, it indicates that individuals who use digital media excessively and compulsively have been compromising their ability to build solid psychophysiological foundations, which are essential for the development of resilient people in the future [ 1 ]. Moreover, the COVID-19 pandemic has significantly expanded the use of video conferencing. People of all ages are using video conferencing apps for regular social activities like meetings, birthdays, exercises, and so on due to social distance laws and travel limits. Such video conferencing apps have also made body dysmorphic disorder worsen and are very concerning in the long run. Patients with pre-existing BDD spend too much time and money on a variety of treatments to correct their perceived flaws [ 39 ]. Hence, excessive screen time, be it recreational or non-recreational, can result in a plethora of disadvantages and harm individuals in ways that could perhaps cause irreversible damage to them throughout their entire lifetime. According to numerous studies, time spent on screens irrefutably proves to be more harmful instead of beneficial. With the advancement of technology, it is to be acknowledged that individuals in the contemporary era are extensively involved with the digital world with or without their consent, with regard to their school and university studies or work responsibilities. It is thus necessary for one to recognize the opportunities and obstacles that social media and digital spaces offer and navigate them accordingly. 

Conclusions

This review article studied the relationships between screen time and digital device usage, precisely during the night times, the quality of sleep, anxiety causes, feelings of depression, and issues related to self-esteem, as well as physical effects in individuals. Results also show that exposure to mobile phones has a deleterious impact on the viability and motility of sperm. 

A strategy that would prohibit digital media usage would be ineffective given how crucial it is for one to be involved in digital spaces, specifically social media norms. That being said, in the context of numerous initiatives aimed at addressing the societal, environmental as well as economic factors that support the betterment of one's family and foster resilience in the youth, who could also readily benefit from proven systemic and specifically conducted individual interventions to assist them in navigating through the hurdles brought on by the usage of cell phones and social media, thus, protecting themselves from harm, and further using it in a certain way that maintains their emotional wellbeing above all. However, from the perspective of public health, screen time in general, and the time spent using a smartphone, should be limited in order to lessen and prevent several linked health issues.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

Screen Rant

Paper mario: the thousand-year door is a 2024 game hiding in a 2004 remake - our review.

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Paper Mario: TTYD Reveals 3 New Features To Make The Switch Remake Its Definitive Version

Paper mario: the thousand-year door almost had a much better remake 9 years ago, paper mario: the thousand-year door reviews may break a 23-year record for the series, quick links, a story that withstands the test of time, combat that remains entertaining, if a little simple, final thoughts & review score.

• Paper Mario: The Thousand-Year Door is an incredible remake of an already wonderful game.
• Combat is simple yet effective, and remains the best in the series.
• Revitalized graphics tell a colorful tale that challenges traditional Mario conventions.

Paper Mario: The Thousand-Year Door is a 2024 remake of a 2004 release, celebrating the 20th anniversary of an iconic GameCube classic with a reputation that grew even more over time following its release. It feels very much like the blueprint of modern Paper Mario games, a sublime blend of the mechanics of older Super Mario RPG iterations and a fresh spin using new mechanics and wonderful storytelling. While it's true to its source and only makes minor tweaks to the original release, the remake of Paper Mario: The Thousand-Year Door is proof it was massively ahead of its time - it feels like a 2024 game in nearly every way.

The concept of Paper Mario: The Thousand-Year Door is quite simple, with the shift in graphical depiction also allowing for the game to have a much different tone than more mainstream games like Odyssey . For whatever reason, the decision to make Mario a 2D paper version of himself unlocks the ability to tell a far wittier, sometimes shockingly dark narrative that challenges what's possible in the Super Mario series. For many, the original Thousand-Year Door is the premier instance of a Paper Mario game, standing among the best in the franchise as a whole - and the remake doesn't change that at all .

Paper Mario: The Thousand-Year Door

• Crisp, 30fps graphics revitalize a colorful world
• Combat is simple but effective, and remains the best in the series
• Story is captivating and unique, challenging Mario tropes
• No major changes might make this less appealing to repeat players
• Simplicity of combat does eventually make it feel fairly straightforward

TTYD's Cheeky Tale Remains One Of The Franchise's Best

The charm inherent to Paper Mario: The Thousand-Year Door begins immediately, when oft-ignored brother Luigi trudges out of a house labeled "Mario" and reads a letter to his brother from Peach rather than simply handing it to him, allowing Mario to remain silent and the story to progress. It sets the tone at breakneck pace, showcasing a game that has no issues poking fun at its larger series while delivering a memorable story full of loveable characters. If there's one sole reason to revisit TTYD as a veteran of the original, it's to relive the narrative , which remains an entertaining romp through a world capable of some shockingly emotional beats.

While Mario is the star in name, it's his supporting cast that steals the spotlight. Goombella the archaeology student and Admiral Bobbery the Bob-omb are two standouts, with the latter featuring the kind of mature, interesting backstory that feels shocking in a Mario game. They each bring their own unique skillsets to combat as Mario assembles his party, but more than that, they bring sub-stories and personalities that help flesh out the city of Rogueport and its surrounding areas, establishing this world as one that's well worth exploring in depth.

The Nintendo Switch version of Paper Mario: The Thousand-Year Door will have multiple new features for players to enjoy as they collect key items.

While no significant changes have been made to the world at large, both characters and environment are gorgeously reproduced and revitalized by the Nintendo Switch, especially when playing on the OLED screen . While its only 30fps, that's hardly an issue for a 2004 game that isn't concerned with hard-hitting graphics so much as it is establishing the right sort of vibe - which it certainly does. There's a relaxation and joy present in the world of Paper Mario: The Thousand-Year Door that is so easy to want to return to, and a welcoming cast of rogues and heroes that make doing so intriguing each time.

Paper Mario: The Thousand-Year Door Still Feels Fresh

The combat in Paper Mario: The Thousand-Year Door is turn-based with some action inputs, rewarding good timing with extra damage or other effects. Players build a party using Mario and the allies he collects along the journey, and further customization is available through badges, which can either enhance abilities that Mario already has or give him entirely new ones to form a new strategy. They're not nearly as deep as some other RPG customization systems, but the badges and party member options do help keep the simple approach to battles fresh for long enough that it never feels like it overstays its welcome.

That said, it certainly would've been the best place to aim a significant improvement for TTYD , even if it maintains its iron grip on the crown of best Paper Mario game in spite of the fact. There's a welcome element to the lack of bells and whistles that makes the game feel appropriately retro, but every other characteristic of the remade Paper Mario feels right at home in 2024 - it would've been great if the combat could've been brought up to that level too, even if it is well above standard and never becomes a negative during gameplay.

Paper Mario: The Thousand-Year Door is eagerly anticipated for its 2024 release, but a fake 2015 trailer convinced many a port was coming to the 3DS.

Beyond battling, exploration of the world of Paper Mario: The Thousand-Year Door is an absolute delight . There are plenty of items hidden behind cleverly disguised, 2D bushes and other objects, rewarding people who give each scene a once-over. These items can be used outside or inside combat, adding another layer of depth to a game that definitely needs a few early on. As time progresses and more experience is gained, Mario and friends find a groove that makes both exploration and battling a well-oiled cycle of satisfying gameplay.

I do wish there was an additional level here, some really substantial addition to the experience. But preservation of a classic within the series is a noble endeavor in its own right, and it's better to make due with the limited scope of additional content - there is some, after the end - than to have had a more weighty extra somehow been not up to snuff.

This is the definitive way to play Paper Mario: The Thousand-Year Door.

4.5 - A "Must-Play" By Screen Rant's Metric

There's this indescribable sense while playing Paper Mario: The Thousand-Year Door that you're engaging with history. It feels much more impactful now, in 2024, than it did in 2004. Two decades later and with retrospect, this is the definitive Paper Mario game, one that the series has attempted to iterate upon and improve but never really succeeded in doing so. While there are other great Paper Marios , this is the great Paper Mario . And its 2024 remake is evidence that it is timeless in its excellence.

While more content additions would be welcome, the small improvements - mostly ease-of-life additions that helps clean up archaic systems or UI - do at least amount to something, and the graphical improvements are jaw-droppingly beautiful, provided you're a fan of the art style in the first place. This is the definitive way to play Paper Mario: The Thousand-Year Door and, beyond that, the definitive Paper Mario game in a franchise spin-off that should look to the past to discover its future following this remake.

Screen Rant was provided with a Nintendo Switch code for this review.

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‘grand tour’ review: miguel gomes’ asia-set fever dream is beguiling and elusive.

The director of 'Tabu' and 'Arabian Nights' plays Cannes' main competition for the first time with a movie that shifts between past and present, documentary and fiction.

By Jordan Mintzer

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Portuguese auteur Miguel Gomes deepens his brand of unclassifiable, globetrotting cinema with Grand Tour , a period drama that’s not really a period drama at all, or is it?

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Despite a simple pitch, Grand Tour is, at least aesthetically speaking, anything but simple, jumping between epochs, genres, color and black-and-white without warning. Gomes has forged a unique style over the years that blends past and present until they become indistinguishable, as if the period piece we’re watching is, in fact, a documentary shot over a hundred years ago that was only unearthed today. Or rather, today’s footage actually comes from the past, as thought it were sent back to the future.

If this sounds perplexing, that’s because it is, and Grand Tour is not for those who like their movies served up succinctly and without too many digressions. Playing in competition in Cannes , which is a first for the director, it should find bookings in plenty of other festivals and select arthouse theatres, though mostly on a niche basis.

The film is split into two parts that both follow the same winding path, which takes us from Myanmar (still known as Burma back in 1918) to western China, with many, many stops in between. In the first half we follow Edward (Gonçalo Waddington), who’s about to meet his fiancé, Molly (Crista Alfaiate), at the train station in Rangoon. They haven’t seen each other for seven years and are supposed to get married, but for some reason Edward gets cold feet and sets off on a journey toward parts unknown.

It’s supposed to be 1918, but suddenly we’re in a karaoke bar in Manila and a guy is singing Frank Sinatra’s “My Way” in Tagalog. Or wait, we’re in an old home in Vietnam owned by a creepy colonial (Cláudio da Silva), but there are modern cars swirling around a traffic circle as “The Blue Danube” booms on the soundtrack. And why, by the way, do all these Brits speak in Portuguese?

Gomes could care less if this is a bit confusing at times. What interests him is capturing the essence of a certain place and putting the viewer in a certain state of mind — both of which he does quite well, even if Grand Tour seems stretched at over two hours.

The film’s second half offers up a little more plot, as we switch to Molly’s point of view upon her arrival in Rangoon. From there she tracks the elusive Edward across the continent, picking up a Vietnamese companion (Lang Khê Tran) along the way. The two eventually make it to Shanghai, then head west to Chengdu and the Tibetan border, where we lost Edward’s trace during the first part. By then Molly seems lost as well, suffering from a fatal malady and unsure if she’ll see her future husband again.  

While Grand Tour is not a love story by any means, it is about a couple falling under the sway of all the strange and new places they visit — places that seem to alter their bodies and minds. As Edward and Molly make their way from one location to another, Gomes cuts in contemporary footage of karaoke performances, puppet shows, panda bears, martial arts exhibitions and, in one case, two women using their arms and hands to mime chickens making love. Southeast Asia becomes a spectacle of sights and sounds for both the characters and for us, and the best you can do is plunge into it without asking too many questions. In the words of one Japanese monk that Edward meets on his long journey: “Abandon yourself to the world and you’ll see how it rewards you.”

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