Determinants and health-related consequences of screen time in children and adolescents: post-COVID-19 insights from a prospective cohort study

DOI: https://doi.org/https://doi.org/10.57187/s.4247

Introduction

The prevalence of screen use has risen notably among children and adolescents over the past few decades [1]. Average recreational screen time of European adolescents rose from 4 to 6.5 hours a day between 2002 and 2014, while in Switzerland the proportion of adolescents spending more than 2 hours per day on-screen jumped from 61% to 81% over the same period [2]. In 2018, average weekend screen time of 11–15-year-olds living in Switzerland was as high as 8 hours per day [3]. The shift to many activities online during the COVID‑19 pandemic likely contributed to an acceleration of this trend, as screen time increased globally during that period [4], including in Switzerland [5], and has remained elevated ever since [6, 7]. These patterns are alarming given that studies have uncovered adverse effects of screen time on the wellbeing of young individuals. Moreover, with the widespread adoption of mobile devices usable anywhere with limited adult supervision, screen use keeps evolving quickly [1, 8], and reports from ten years ago may already be outdated [1, 8].

Several sociodemographic and family characteristics have been related to screen time among children and adolescents, but findings are mixed across studies [9–12]. Older age seems to be a common predictor of higher screen time [9, 10]. Disadvantaged socioeconomic circumstances were shown to be associated with increased time spent on-screen in two systematic reviews [9, 11], while two others found inconsistent results [10, 12]. Family characteristics such as poor parental mental health or a lack of screen rules might also be related to higher screen time, but with conflicting results across reviews [9, 10, 12]. A previous report suggests that determinants of screen time vary depending on context and highlights the need for country-specific studies [13].

While screen use can offer learning and communication opportunities [1], elevated screen time has been linked to various adverse physical and psychosocial health outcomes in young populations [1]. Several meta-analyses reported an increased risk of excess weight and poor self-rated health among children and adolescents with higher screen time [14–16]. According to the displacement hypothesis, this could be attributed to the shift of time dedicated to health-promoting activities, such as exercise and sleep, towards screen use [17]. Moreover, a systematic review found moderate evidence linking screen time to depressive symptoms and diminished quality of life. It also found weaker evidence suggesting connections with behavioural issues, anxiety, poorer psychosocial health and lower educational achievements. [16]. These findings mirror those from a systematic review of longitudinal studies showing a small but significant effect of screen time on depressive symptoms among adolescents, but lacking evidence to support a relationship with other internalising problems [18]. The negative effect of screen time on young individuals’ psychosocial wellbeing could be explained by the displacement of activities beneficial to health and by screen content if violent, age-inappropriate, triggering upward social comparison or exposing them to cyberbullying [19]. Importantly, these associations might be evolving along with the increasing diversity of screen uses including social interactions, gaming, information seeking and content creation [1].

Therefore, we aimed (1) to describe up-to-date post-pandemic prevalence of screen time and adherence to corresponding recommendations by age, (2) to identify sociodemographic and family determinants of screen time, and (3) to examine its effects on subsequent physical and psychosocial health outcomes.

Materials and methods

Study design

Data was extracted from the SEROCoV-KIDS population-based prospective cohort study, which was designed to evaluate the direct and indirect impacts of the COVID-19 pandemic on the health of children and adolescents, in Geneva, Switzerland. Eligibility criteria were to be aged between 6 months and 17 years old and living in the canton of Geneva at baseline. Eligible children and adolescents were randomly selected from state registries either specifically for this study or for COVID-19 seroprevalence studies conducted in our unit [20–23]. The index registries were provided by the Swiss Federal Office of Statistics or the Geneva Cantonal Office for Population and Migration. Participants aged 2–17 years at enrollment were included in the present analysis and categorised as follows: children, aged 2–8 years; preadolescents, aged 9–13 years; and adolescents, aged 14–17 years (figure S1 in the appendix).

Data was collected at baseline between December 2021 and June 2022 and during two follow-up assessments conducted about 6 months apart (first follow-up, between September 2022 and February 2023; second follow-up, between May 2023 and September 2023). Online questionnaires were completed on the Specchio-Hub online platform [24] by the referent parents (or legal guardian) on behalf of their participating children. Adolescents, aged 14 years or above, also completed their own questionnaires specifically tailored to their age group.

Measures

Adolescents self-reported screen time and health-related quality of life (HRQoL); all other measures were parent-reported (table S1 in the appendix).

Screen time

At baseline, participants were asked how many hours per weekday and weekend day they (or their child) spend on-screen (smartphone, computer, television, tablet, video game) for recreational purposes. Average screen time per day was calculated as (weekday × 5 + weekend day × 2) / 7. Non-adherence to recommendations was defined as spending more than one hour per day on-screen for children under five years according to the World Health Organization’s (WHO) guidelines [25]. For older children, neither the WHO nor Swiss health authorities provide a threshold and we used the daily two hours limit proposed by Canadian and Australian guidelines [26, 27].

Determinants

Based on the literature, the following sociodemographic characteristics assessed at baseline were considered as potential determinants. The educational level attained by each parent was combined to obtain the parents’ highest education (lower than college vs college or higher). The parents’ birth country was grouped into at least one born in Switzerland vs both born abroad. Parents who stated that they were raising their child(ren) alone were defined as a single parent, in which case only their education and birth country was considered. The household financial situation was deemed good if its members could cover their needs and face unforeseen expenses and average-to-poor if they could hardly cover unforeseen expenses or could not meet current needs.

We additionally evaluated family determinants at baseline including having siblings (yes vs no) and referent parent’s mental health (good vs average-to-poor), as well as the following family dynamics treated as continuous variables. Family adjustment, which refers to the within-family support and emotional resources to face challenges, was measured as the combination of the parental adjustment, family relationships and parental teamwork scales from the parenting and family adjustment scales (PAFAS) [28, 29]. Parenting practices were assessed by grouping the following PAFAS scales: parental consistency, coercive parenting, positive encouragement and parent-child relationship [28, 29]. The work-family conflict scale was additionally included [30]. Family dynamics were measured at the first follow-up, about eight months after the baseline assessment (mean: 39.2 weeks; standard deviation [SD]: 9.9). We did not expect these constructs to drastically change within this interval and decided to assess their effect on screen time along with the sociodemographic characteristics collected at baseline.

Health outcomes

Physical and psychosocial health outcomes were measured at baseline and at the second follow-up assessment, about 16 months later (mean: 71.8 weeks; SD: 9.9) and dichotomised with published thresholds corresponding to impaired health (table S1 in the appendix).

Body mass index (BMI) z-scores for age were calculated using the anthro [31] and anthroplus [32] R packages from the WHO. Excess weight was defined as a z-score above +2 SD for children aged below 5 years and above +1 SD for older children [33]. The physical, emotional, social and school health-related quality of life were assessed with the corresponding subscales of the Pediatric Quality of Life Inventory (PedsQL) Short Form [34]. A poor health-related quality of life was defined with thresholds provided by Varni et al. [35]. The Strengths and Difficulties Questionnaire (SDQ) was used to evaluate behavioural problems [36]. As recommended for community samples, internalising problems were assessed by combining the emotional and peer problems subscales, and externalising problems were computed by adding the conduct problems and hyperactivity subscales, while prosocial behaviours were separately analysed [37]. Behavioural problems were defined with clinical thresholds (90^th percentile) based on United Kingdom norms available on https://sdqinfo.org/.

Covariates

The following variables were collected at baseline: age, sex, daily hours of physical activity, participation in extracurricular activities and number of close friends (only available for preadolescents and adolescents). For 2-year-old children, physical activity and extracurricular activities were measured at the second follow-up.

Statistical analyses

Median screen time and prevalence of non-adherence to recommendations were weighted according to the Geneva population’s age and sex distribution [38]. Multivariate models were specified following hypothesised relationships between study variables (figure S2 in the appendix). Sociodemographic determinants of screen time in minutes, the primary outcome, were assessed together in age- and sex-adjusted models. Family determinants were separately evaluated in models adjusted for age, sex and sociodemographic variables to estimate their respective direct effect [39]. Adherence to screen recommendations and screen time in percentage difference (using log transformation) were examined as secondary outcomes with the same set of adjustments. The associations between screen time at baseline and each subsequent health outcome after one year were examined with three distinct models to evaluate the effect of different adjustments. The first model was adjusted for age, sex and sociodemographic variables and the second one was further adjusted for physical activity, extracurricular activity and the number of friends; the main model presented in the results further controlled for the baseline level of the examined health outcome. The month of screen time measurement was tested as a potential covariate but was not included in the final models as there was no association with screen time (p-value >0.1). Generalised linear models taking the household clustering of data into account were performed using the R survey package [40]. Linear models following a Gaussian distribution were performed for continuous outcomes and robust Poisson models following a quasi-Poisson distribution were chosen for binary outcomes. The assumptions of the linear regression models were evaluated through visual inspection of the residuals and were deemed to be adequately met.

Missing data

Parent-reported information at the first and second follow-up was available for 1532/1860 (82.4%) and 1277 (68.7%) participants, respectively (figure S1 in the appendix). A total of 393/434 (90.5%) adolescents additionally completed a baseline questionnaire and 270 (62.2%) a second follow-up. Questionnaire non-response was more frequent among older participants with foreign origin, a disadvantaged financial background and a poor health-related quality of life (table S2 in the appendix). Following Seaman et al. [41], we opted for a cautious approach to handle missing data, which combines inverse probability weighting (IPW) for questionnaire non-response and multiple imputation (MI) for item non-response. Propensities to respond to the first and second follow-up questionnaires were separately quantified at the household level using generalised linear models. The inverse of the estimated propensity to respond was used to weight the main models. Missing items were imputed by chained equations with 10 imputed datasets and 1000 iterations using the R mice package [42].

All analyses were stratified by age group to account for the fact that the associations under study may vary depending on age. Estimations were performed with R 4.2.2, available under the GNU General Public License. The R tidyverse package was used for data management and visualisation [43]; codes are accessible to researchers upon request. A formal study protocol was not pre-registered.

Ethics approval and consent to participate

The Geneva Cantonal Commission for Research Ethics approved the study (ID: 2021-01973). All referent adults, as well as adolescents aged 14 years or older provided written consent to participate. Children gave oral assent to participate.

Results

A total of 1860 participants were included: 674 children (mean age: 5.5 years), 752 preadolescents (mean age: 10.9 years) and 434 adolescents (mean age: 15.4 years, table 1). Intraclass correlation at the household level ranged from 0.43 among adolescents to 0.89 among children. 

When weighted according to the Geneva population’s age and sex distribution, median screen time per day was 0h29 (quartile [Q] 1–Q3: 0h14-0h51), 1h14 (Q1–Q3: 0h47–1h48) and 3h18 (Q1–Q3: 2h11–4h43), respectively for children, preadolescents and adolescents (figure 1). Prevalence of non-adherence to screen recommendations increased from 7.0% among children to 20.7% among preadolescents and 78.7% among adolescents. Overall, 61 children (9.1%) were reported to have no screen time, while this was the case for 6 preadolescents (0.8%) and 2 adolescents (0.5%) only.

Figure 1Daily recreational screen time according to age among study participants (n = 1813). The solid line corresponds to median screen time, the shaded area to the interquartile range and the dashed line to the threshold for adherence to the recommendation. h/d stands for hours per day.

Determinants of recreational screen time

A lower parental education was associated with spending an additional 25 minutes per day (95% confidence interval [CI]: 13–37) on-screen among children, 14 minutes (95% CI: 4–25) among preadolescents and 65 minutes (95% CI: 33–97) among adolescents (figure 2). Compared with children of highly educated parents, this represented a 95% (95% CI: 53–149%), 29% (95% CI: 13–48%) and 42% (95% CI: 24–63%) higher screen time, respectively (table S3 in the appendix). On average, preadolescents living in single parent households had a 22-minute higher daily screen time (95% CI: 3–42) than their counterparts raised by two parents. There were no associations with other sociodemographic characteristics.

Figure 2Sociodemographic and family determinants of screen time among children (2–8 years old, n = 674), preadolescents (9–13 years old, n = 752) and adolescents (14–17 years old, n = 434). Results are additional minutes of daily screen time with 95% confidence intervals (CI) from age-, sex- and sociodemographic-adjusted generalised linear models. Higher values of continuous scores indicate less favourable situations and coefficients correspond to the effect of a 1-point increase in the scores.

Family characteristics such as average-to-poor parental mental health (+14 minutes/day; 95% CI: 2–27) or higher work-family conflicts (+6 minutes/day; 95% CI: 2–10) were also determinants of higher screen time among children (figure 2, table S3 in the appendix). Furthermore, participants – whether children, preadolescents or adolescents – whose parents had less favourable parenting practices tended to spend more time on-screen. Having siblings was not associated with screen time.

Similar patterns were observed when examining determinants of adherence to screen time recommendations and of screen time in percent difference (table S3).

Association of recreational screen time with health outcomes one year later

Among preadolescents and adolescents, screen time was associated with an increased risk of several poor health outcomes after one year, such as a poor physical health-related quality of life (adjusted relative risk [aRR]: 1.46; 95% CI: 1.02–2.08 and aRR: 1.15; 95% CI: 0.99–1.35, respectively), a poor emotional health-related quality of life (aRR: 1.18; 95% CI: 1.03–1.36 and aRR: 1.04; 95% CI: 0.98–1.10, respectively) and a poor school health-related quality of life (aRR: 1.14; 95% CI: 0.98–1.33 and aRR: 1.08; 95% CI: 1.02–1.13, respectively). Conversely, higher screen time seemed related to a good physical health-related quality of life among children (aRR: 0.56; 95% CI: 0.32–1.00). Each additional daily hour of screen time was also associated with an 18% (aRR: 1.18; 95% CI: 1.03–1.34) and 15% (aRR: 1.15; 95% CI: 1.02–1.29) increased risk of a poor social health-related quality of life and of antisocial behaviours among adolescents, respectively (figure 3). 

Figure 3Effects of screen time on physical and psychosocial health after one year among children (2–8 years old, n = 674), preadolescents (9–13 years old, n = 752) and adolescents (14–17 years old, n = 434). Results are from generalised linear models following a quasi-Poisson distribution adjusted for age, sex, sociodemographic characteristics, physical activity, extracurricular activities and the baseline level of each health outcome among all participants, and for the number of close friends among preadolescents and adolescents. HRQoL: health-related quality of life.

Although not significant when adjusting for the baseline weight status, screen time was associated with a higher risk of excess weight among children (aRR: 1.43; 95% CI: 1.02–2.01) and preadolescents (aRR: 1.28; 95% CI: 1.06–1.54) even after adjustment for sociodemographic characteristics, physical and extracurricular activity (table S4 in the appendix). The associations between screen time and other health outcomes did not meaningfully change across models (table S4).

Discussion

Screen use was common across all age groups and strongly increased with age, spanning from a daily median of half an hour among children to over three hours among adolescents. Determinants of screen time included a lower parental educational level and less optimal parenting practices in all age groups. Poorer parental mental health and work-family conflicts were associated with elevated screen time in children only, while single parenthood was a determinant in preadolescents only. In turn, higher screen time increased the risk of a poor physical-, emotional- and school-related quality of life one year later among preadolescents and adolescents, as well as subsequent social difficulties among adolescents. The present report expands current research by providing a comprehensive post-pandemic picture of screen time, related determinants and health consequences from early childhood to adolescence.

Consistent with pre-pandemic findings [2], screen time in 2022 was lower in our study taking place in Switzerland than in other European countries [6, 44]. The observed prevalence of children and preadolescents not meeting recommendations mirrored Swiss estimates from the second wave of the COVID-19 pandemic, in winter 2020/21 [5]. It suggests that screen use remained elevated even after all sanitary restrictions were lifted, as previously found in the Netherlands [6] and in the United States [7]. On the contrary, the proportion of non-adherent adolescents was higher in our sample (78% vs 62%) [5]. It could be due to our study relying on adolescent-reported screen time, whereas in Peralta et al. [5], it was reported by parents who may have been less aware of the extent of their adolescents’ screen time.

In line with prior research indicating an association between disadvantaged socioeconomic circumstances and increased screen time [9, 11], our study found parental education to be a consistent determinant. Interestingly, its influence was more pronounced than the household’s financial situation, which did not show an independent effect. It suggests that young individuals’ screen time may be more closely linked to family social norms and health literacy than purely economic factors. This aligns with the observation that parenting practices were associated with screen time in our study and various literature reviews [9, 12]. Parental education may also influence children’s screen time through family structure and dynamics, which played an additional independent role in our study. As in previous findings, characteristics such as single parenthood, poor parental mental health and work-family conflicts were determinants of screen time among children and/or preadolescents [9]. It might be that they imply lower emotional- and time-availability of parents to supervise their children’s screen use and engage in alternative activities with them [45]. Interestingly, these family characteristics were not related to adolescents’ screen time. It could reflect the decreasing influence of parents in this age range, paralleled by a growing influence of other non-assessed determinants such as peer norms [46].

The present findings demonstrate that elevated screen time is associated with an increased likelihood of subsequent physical and psychosocial outcomes one year later. Those include diminished physical, emotional and school functioning among preadolescents and adolescents, as well as heightened social difficulties among adolescents. It echoes previous reports [16, 18] suggesting that screen time might affect youth’s psychosocial wellbeing through the displacement of physical activity, face-to-face interactions and schoolwork, as well as because of problematic content, cyberbullying and excessive social comparison [19]. Additionally, as previously observed [14, 16], we found screen use to be related to an increased risk of excess weight among children and preadolescents. In our analysis, however, the association was no longer significant when controlling for the baseline weight status. It suggests that the processes at play might operate over a longer time frame than the one-year interval between our two measurements [47].

Screen time displayed different health impacts according to age, affecting psychosocial health in adolescents, excess weight in children and both in preadolescents. This variability could be attributed to differences in screen content and context across age groups, leading to differing health consequences. This finding is in line with a study among children aged 2 to 17 years, which reported the effect of screen time on psychological wellbeing to be larger in adolescents than children [48]. Proposed explanations included that mobile phone and internet use, which are more popular among adolescents than children, may be more detrimental to mental health than other screen activities [18]. As adolescents with high screen time may have had elevated use since childhood [49], the heightened effect on adolescents might also reflect the cumulative exposure to screens. Finally, adolescents are more likely to possess their own devices [50], which could increase the risk of problematic use. Also consistent with our results, another longitudinal study spanning over eight years found that screen time predicted BMI in children aged 6 to 10 years but not in adolescents, which could be due to a displacement of physical activity observed among children but not adolescents [49].

Our findings raise concerns for the current and future health of young individuals since carrying excess weight during childhood tends to persist into adulthood [51] and because adolescent psychological symptoms predict later episodes of mental disorder [52]. Strengthening parents’ knowledge of screen guidelines and of the adverse effects of unhealthy use has been proposed as an effective way of limiting young people’s screen time [53]. However, beyond awareness, parental barriers to successfully implement screen rules should also be considered [45]. For instance, parents frequently mentioned time constraints as a reason for resorting to screen use to occupy their young children while they attended to daily chores [54]. Parents of adolescents also expressed doubts about the practicality of adhering to recommendations they perceived as overly restrictive. Some admitted to abandoning screen time rules due to failure or conflicts [55]. Therefore, in line with the updated screen guidelines of the American Academy of Pediatrics [56], carefully reviewing content quality for children and promoting a moderate and safe screen use for adolescents might be a more pragmatic and acceptable approach for families than strictly limiting screen time. Structural measures proposing financially accessible and convenient alternatives to screen use, such as childcare or extracurricular activities, could also be effective while providing support to parents [45].

Findings from this analysis should be interpreted in light of their limitations. First, despite the random selection process, children with highly educated parents were more likely to participate in our study, as is frequently the case in epidemiological studies [57]. The observed prevalence of screen time may thus be underestimated, given its higher occurrence among children with less educated parents. Second, data reported by referent parents and adolescents could have been subject to measurement errors, especially for screen time [58]. Third, the one-year follow-up assessment may have been too short to observe more substantial effects of screen time on some health outcomes under study, particularly weight. Further follow-ups within the scope of this longitudinal study will provide more insights. Finally, statistical power was limited due to the age stratification, which reduces the certainty around some estimates, but does not impact the interpretation of observed associations. Strengths included the random selection of participants covering a large age range, the longitudinal design as well as the examination of multiple physical and psychosocial health outcomes measured with validated scales.

Conclusion

While almost all children engage with screens, those from socially disadvantaged backgrounds and with strained families seem to face a heightened risk of prolonged screen time. The implications for their physical and psychosocial wellbeing are concerning, highlighting the need for interventions to promote safe screen usage and for the provision of accessible alternatives. Finally, research and monitoring are essential to deepen our understanding of the mechanisms driving these health effects and to adapt to the evolving patterns of screen use.

Availability of data and materials

The dataset used during the current study is available from the corresponding author on reasonable request.

Acknowledgments

We are grateful to the staff of the Unit of Population Epidemiology of the Division of Primary Care Medicine of University Hospitals of Geneva, as well as to all participants whose contributions were invaluable to the study.

Authors’ contributions: All authors contributed to study conception and design. Material preparation and data collection were performed by Viviane Richard, Roxane Dumont, Elsa Lorthe, Andrea Loizeau, Hélène Baysson, María-Eugenia Zaballa, Julien Lamour and Silvia Stringhini. Rémy P. Barbe, Klara M. Posfay-Barbe, Idris Guessous and Silvia Stringhini supervised the study. Analyses were performed by Viviane Richard who also wrote the first draft of the manuscript. All authors critically revised the previous versions of the manuscript. All authors read and approved the final manuscript.

Generative AI and AI-assisted technologies in the writing process: During the preparation of this work, the authors used ChatGPT-3.5 in order to improve readability and language. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.

SEROCoV-KIDS study group: Andrew S. Azman, Antoine Bal, Rémy P. Barbe, Hélène Baysson, Aminata R. Bouchet, Paola D’Ippolito, Roxane Dumont, Nacira El Merjani, Francesco Pennacchio, Natalie Francioli, Idris Guessous, Séverine Harnal, Julien Lamour, Arnaud G L’Huillier, Andrea Loizeau, Elsa Lorthe, Chantal Martinez, Shannon Mechoullam, Mayssam Nehme, Klara M. Posfay-Barbe, Géraldine Poulain, Caroline Pugin, Nick Pullen, Viviane Richard, Deborah Rochat, Khadija Samir, Stephanie Schrempft, Silvia Stringhini, Stéphanie Testini, Deborah Urrutia Rivas, Anshu Uppal, Charlotte Verolet, Jennifer Villers, Guillemette Violot, María-Eugenia Zaballa.

References

1. Reid Chassiakos YL, Radesky J, Christakis D, Moreno MA, Cross C, Hill D, et al.; Council on communications and media. Children and Adolescents and Digital Media. Pediatrics. 2016 Nov;138(5):e20162593. doi: https://doi.org/10.1542/peds.2016-2593 

2. Ghekiere A, Van Cauwenberg J, Vandendriessche A, Inchley J, Gaspar de Matos M, Borraccino A, et al. Trends in sleeping difficulties among European adolescents: are these associated with physical inactivity and excessive screen time? Int J Public Health. 2019 May;64(4):487–98. doi: https://doi.org/10.1007/s00038-018-1188-1 

3. Delgrande Jordan M. Les écrans, Internet et les réseaux sociaux - Résultats de l’étude « Health Behaviour in Schoolaged Children » (HBSC) 2018 (Rapport de recherche No 114) 2020. Available from: https://www.hbsc.ch/pdf/hbsc_bibliographie_373.pdf 

4. Madigan S, Eirich R, Pador P, McArthur BA, Neville RD. Assessment of Changes in Child and Adolescent Screen Time During the COVID-19 Pandemic: A Systematic Review and Meta-analysis. JAMA Pediatr. 2022 Dec;176(12):1188–98. doi: https://doi.org/10.1001/jamapediatrics.2022.4116 

5. Peralta GP, Camerini AL, Haile SR, Kahlert CR, Lorthe E, Marciano L, et al. Lifestyle Behaviours of Children and Adolescents During the First Two Waves of the COVID-19 Pandemic in Switzerland and Their Relation to Well-Being: An Observational Study. Int J Public Health. 2022 Sep;67:1604978. doi: https://doi.org/10.3389/ijph.2022.1604978 

6. de Bruijn AG, Te Wierike SC, Mombarg R. Trends in and relations between children’s health-related behaviors pre-, mid- and post-Covid. Eur J Public Health. 2023 Apr;33(2):196–201. doi: https://doi.org/10.1093/eurpub/ckad007 

7. Hedderson MM, Bekelman TA, Li M, Knapp EA, Palmore M, Dong Y, et al.; Environmental Influences on Child Health Outcomes Program. Trends in Screen Time Use Among Children During the COVID-19 Pandemic, July 2019 Through August 2021. JAMA Netw Open. 2023 Feb;6(2):e2256157–2256157. doi: https://doi.org/10.1001/jamanetworkopen.2022.56157 

8. Hassinger-Das B, Brennan S, Dore RA, Golinkoff RM, Hirsh-Pasek K. Children and Screens. Annu Rev Dev Psychol. 2020;2(1):69–92. doi: https://doi.org/10.1146/annurev-devpsych-060320-095612 

9. Hoyos Cillero I, Jago R. Systematic review of correlates of screen-viewing among young children. Prev Med. 2010 Jul;51(1):3–10. doi: https://doi.org/10.1016/j.ypmed.2010.04.012 

10. Stierlin AS, De Lepeleere S, Cardon G, Dargent-Molina P, Hoffmann B, Murphy MH, et al.; DEDIPAC consortium. A systematic review of determinants of sedentary behaviour in youth: a DEDIPAC-study. Int J Behav Nutr Phys Act. 2015 Oct;12(1):133. doi: https://doi.org/10.1186/s12966-015-0291-4 

11. Mielke GI, Brown WJ, Nunes BP, Silva IC, Hallal PC. Socioeconomic Correlates of Sedentary Behavior in Adolescents: Systematic Review and Meta-Analysis. Sports Med. 2017 Jan;47(1):61–75. doi: https://doi.org/10.1007/s40279-016-0555-4 

12. Veldman SL, Altenburg TM, Chinapaw MJ, Gubbels JS. Correlates of screen time in the early years (0-5 years): A systematic review. Prev Med Rep. 2023 Apr;33:102214. doi: https://doi.org/10.1016/j.pmedr.2023.102214 

13. Musić Milanović S, Buoncristiano M, Križan H, Rathmes G, Williams J, Hyska J, et al. Socioeconomic disparities in physical activity, sedentary behavior and sleep patterns among 6- to 9-year-old children from 24 countries in the WHO European region. Obes Rev. 2021 Nov;22(S6 Suppl 6):e13209. doi: https://doi.org/10.1111/obr.13209 

14. Fang K, Mu M, Liu K, He Y. Screen time and childhood overweight/obesity: A systematic review and meta-analysis. Child Care Health Dev. 2019 Sep;45(5):744–53. doi: https://doi.org/10.1111/cch.12701 

15. Zhang T, Lu G, Wu XY. Associations between physical activity, sedentary behaviour and self-rated health among the general population of children and adolescents: a systematic review and meta-analysis. BMC Public Health. 2020 Sep;20(1):1343. doi: https://doi.org/10.1186/s12889-020-09447-1 

16. Stiglic N, Viner RM. Effects of screentime on the health and well-being of children and adolescents: a systematic review of reviews. BMJ Open. 2019 Jan;9(1):e023191. doi: https://doi.org/10.1136/bmjopen-2018-023191 

17. Viner RM, Gireesh A, Stiglic N, Hudson LD, Goddings AL, Ward JL, et al. Roles of cyberbullying, sleep, and physical activity in mediating the effects of social media use on mental health and wellbeing among young people in England: a secondary analysis of longitudinal data. Lancet Child Adolesc Health. 2019 Oct;3(10):685–96. doi: https://doi.org/10.1016/S2352-4642(19)30186-5 

18. Tang S, Werner-Seidler A, Torok M, Mackinnon AJ, Christensen H. The relationship between screen time and mental health in young people: A systematic review of longitudinal studies. Clin Psychol Rev. 2021 Jun;86:102021. doi: https://doi.org/10.1016/j.cpr.2021.102021 

19. Brown JD, Bobkowski PS. Older and Newer Media: Patterns of Use and Effects on Adolescents’ Health and Well-Being. J Res Adolesc. 2011;21(1):95–113. doi: https://doi.org/10.1111/j.1532-7795.2010.00717.x 

20. Stringhini S, Wisniak A, Piumatti G, Azman AS, Lauer SA, Baysson H, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. Lancet. 2020 Aug;396(10247):313–9. doi: https://doi.org/10.1016/S0140-6736(20)31304-0 

21. Stringhini S, Zaballa ME, Perez-Saez J, Pullen N, de Mestral C, Picazio A, et al.; Specchio-COVID19 Study Group. Seroprevalence of anti-SARS-CoV-2 antibodies after the second pandemic peak. Lancet Infect Dis. 2021 May;21(5):600–1. doi: https://doi.org/10.1016/S1473-3099(21)00054-2 

22. Stringhini S, Zaballa ME, Pullen N, Perez-Saez J, de Mestral C, Loizeau AJ, et al.; Specchio-COVID19 study group. Seroprevalence of anti-SARS-CoV-2 antibodies 6 months into the vaccination campaign in Geneva, Switzerland, 1 June to 7 July 2021. Euro Surveill. 2021 Oct;26(43):2100830. doi: https://doi.org/10.2807/1560-7917.ES.2021.26.43.2100830 

23. Zaballa ME, Perez-Saez J, de Mestral C, Pullen N, Lamour J, Turelli P, et al.; Specchio-COVID19 study group. Seroprevalence of anti-SARS-CoV-2 antibodies and cross-variant neutralization capacity after the Omicron BA.2 wave in Geneva, Switzerland: a population-based study. Lancet Reg Health Eur. 2023 Jan;24:100547. doi: https://doi.org/10.1016/j.lanepe.2022.100547 

24. Baysson H, Pennachio F, Wisniak A, Zabella ME, Pullen N, Collombet P, et al.; Specchio-COVID19 study group. Specchio-COVID19 cohort study: a longitudinal follow-up of SARS-CoV-2 serosurvey participants in the canton of Geneva, Switzerland. BMJ Open. 2022 Jan;12(1):e055515. doi: https://doi.org/10.1136/bmjopen-2021-055515 

25. World Health Organization. Guidelines on physical activity, sedentary behaviour and sleep for children under 5 years of age 2019. Available from: https://www.who.int/publications/i/item/9789241550536 

26. Tremblay MS, Carson V, Chaput JP, Connor Gorber S, Dinh T, Duggan M, et al. Canadian 24-Hour Movement Guidelines for Children and Youth: An Integration of Physical Activity, Sedentary Behaviour, and Sleep1. Appl Physiol Nutr Metab. 2016;41 6 (Suppl. 3):S311–27. doi: https://doi.org/10.1139/apnm-2016-0151 

27. Australian Government Department of Health and Aged Care. Physical activity and exercise guidelines for children and young people (5 to 17 years). Available from: https://www.health.gov.au/topics/physical-activity-and-exercise/physical-activity-and-exercise-guidelines-for-all-australians/for-children-and-young-people-5-to-17-years 

28. Sanders MR, Morawska A, Haslam DM, Filus A, Fletcher R. Parenting and Family Adjustment Scales (PAFAS): validation of a brief parent-report measure for use in assessment of parenting skills and family relationships. Child Psychiatry Hum Dev. 2014 Jun;45(3):255–72. doi: https://doi.org/10.1007/s10578-013-0397-3 

29. Teague SJ, Newman LK, Tonge BJ, Gray KM; MHYPeDD team. Caregiver Mental Health, Parenting Practices, and Perceptions of Child Attachment in Children with Autism Spectrum Disorder. J Autism Dev Disord. 2018 Aug;48(8):2642–52. doi: https://doi.org/10.1007/s10803-018-3517-x 

30. Breyer B, Bluemke M. Work-Family Conflict Scale (ISSP). ZIS - The Collection Items and Scales for the Social Sciences; 2016. 

31. Schumacher D, Borghi E, Polonsky J, et al. Computation of the WHO Child Growth Standards 2023. 

32. Schumacher D, Borghi E, Gatica-Domínguez G, et al. Computation of the WHO 2007 References for School-Age Children and Adolescents (5 to 19 Years) 2021. 

33. de Onis M, Lobstein T. Defining obesity risk status in the general childhood population: which cut-offs should we use? Int J Pediatr Obes. 2010 Dec;5(6):458–60. doi: https://doi.org/10.3109/17477161003615583 

34. Chan KS, Mangione-Smith R, Burwinkle TM, Rosen M, Varni JW. The PedsQL: reliability and validity of the short-form generic core scales and Asthma Module. Med Care. 2005 Mar;43(3):256–65. doi: https://doi.org/10.1097/00005650-200503000-00008 

35. Varni JW, Burwinkle TM, Seid M, Skarr D. The PedsQL 4.0 as a pediatric population health measure: feasibility, reliability, and validity. Ambul Pediatr. 2003;3(6):329–41. doi: https://doi.org/10.1367/1539-4409(2003)003<0329:TPAAPP>2.0.CO;2 

36. Shojaei T, Wazana A, Pitrou I, Kovess V. The strengths and difficulties questionnaire: validation study in French school-aged children and cross-cultural comparisons. Soc Psychiatry Psychiatr Epidemiol. 2009 Sep;44(9):740–7. doi: https://doi.org/10.1007/s00127-008-0489-8 

37. Goodman A, Lamping DL, Ploubidis GB. When to use broader internalising and externalising subscales instead of the hypothesised five subscales on the Strengths and Difficulties Questionnaire (SDQ): data from British parents, teachers and children. J Abnorm Child Psychol. 2010 Nov;38(8):1179–91. doi: https://doi.org/10.1007/s10802-010-9434-x 

38. OCSTAT - Statistique cantonale de la population. Pyramide des âges de la population résidante du canton de Genève, selon l’origine et le sexe, à fin 2021 2022. 

39. Westreich D, Greenland S. The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am J Epidemiol. 2013 Feb;177(4):292–8. doi: https://doi.org/10.1093/aje/kws412 

40. Lumley T. The survey Package for R, 15 Years on. Surv Stat. 2023;88:96–104. 

41. Seaman SR, White IR, Copas AJ, Li L. Combining multiple imputation and inverse-probability weighting. Biometrics. 2012 Mar;68(1):129–37. doi: https://doi.org/10.1111/j.1541-0420.2011.01666.x 

42. van Buuren S, Groothuis-Oudshoorn K. mice: Multivariate Imputation by Chained Equations in R. J Stat Softw. 2011;45(3):1–67. doi: https://doi.org/10.18637/jss.v045.i03 

43. Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. J Open Source Softw. 2019;4(43):1686. doi: https://doi.org/10.21105/joss.01686 

44. Salway R, Walker R, Sansum K, House D, Emm-Collison L, Reid T, et al. Screen-viewing behaviours of children before and after the 2020-21 COVID-19 lockdowns in the UK: a mixed methods study. BMC Public Health. 2023 Jan;23(1):116. doi: https://doi.org/10.1186/s12889-023-14976-6 

45. Hartshorne JK, Huang YT, Lucio Paredes PM, Oppenheimer K, Robbins PT, Velasco MD. Screen time as an index of family distress. Curr Res Behav Sci. 2021;2:100023. doi: https://doi.org/10.1016/j.crbeha.2021.100023 

46. Rice EL, Klein WM. Interactions among perceived norms and attitudes about health-related behaviors in U.S. adolescents. Health Psychol. 2019 Mar;38(3):268–75. doi: https://doi.org/10.1037/hea0000722 

47. Biddle SJ, García Bengoechea E, Pedisic Z, Bennie J, Vergeer I, Wiesner G. Screen Time, Other Sedentary Behaviours, and Obesity Risk in Adults: A Review of Reviews. Curr Obes Rep. 2017 Jun;6(2):134–47. doi: https://doi.org/10.1007/s13679-017-0256-9 

48. Twenge JM, Campbell WK. Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Prev Med Rep. 2018 Oct;12:271–83. doi: https://doi.org/10.1016/j.pmedr.2018.10.003 

49. Hands BP, Chivers PT, Parker HE, Beilin L, Kendall G, Larkin D. The associations between physical activity, screen time and weight from 6 to 14 yrs: the Raine Study. J Sci Med Sport. 2011 Sep;14(5):397–403. doi: https://doi.org/10.1016/j.jsams.2011.03.011 

50. Rosen LD, Lim AF, Felt J, Carrier LM, Cheever NA, Lara-Ruiz JM, et al. Media and technology use predicts ill-being among children, preteens and teenagers independent of the negative health impacts of exercise and eating habits. Comput Human Behav. 2014 Jun;35:364–75. doi: https://doi.org/10.1016/j.chb.2014.01.036 

51. Singh AS, Mulder C, Twisk JW, van Mechelen W, Chinapaw MJ. Tracking of childhood overweight into adulthood: a systematic review of the literature. Obes Rev. 2008 Sep;9(5):474–88. doi: https://doi.org/10.1111/j.1467-789X.2008.00475.x 

52. Patton GC, Coffey C, Romaniuk H, Mackinnon A, Carlin JB, Degenhardt L, et al. The prognosis of common mental disorders in adolescents: a 14-year prospective cohort study. Lancet. 2014 Apr;383(9926):1404–11. doi: https://doi.org/10.1016/S0140-6736(13)62116-9 

53. Hinkley T, Carson V, Kalomakaefu K, Brown H. What mums think matters: A mediating model of maternal perceptions of the impact of screen time on preschoolers’ actual screen time. Prev Med Rep. 2017 Apr;6:339–45. doi: https://doi.org/10.1016/j.pmedr.2017.04.015 

54. Carson V, Clark M, Berry T, Holt NL, Latimer-Cheung AE. A qualitative examination of the perceptions of parents on the Canadian Sedentary Behaviour Guidelines for the early years. Int J Behav Nutr Phys Act. 2014 May;11(1):65. doi: https://doi.org/10.1186/1479-5868-11-65 

55. Hattersley LA, Shrewsbury VA, King LA, Howlett SA, Hardy LL, Baur LA. Adolescent-parent interactions and attitudes around screen time and sugary drink consumption: a qualitative study. Int J Behav Nutr Phys Act. 2009 Sep;6(1):61. doi: https://doi.org/10.1186/1479-5868-6-61 

56. AAP Council on communications and media. Media Use in School-Aged Children and Adolescents. Pediatrics. 2016;138(5):e20162592. doi: https://doi.org/10.1542/peds.2016-2592 

57. Galea S, Tracy M. Participation rates in epidemiologic studies. Ann Epidemiol. 2007 Sep;17(9):643–53. doi: https://doi.org/10.1016/j.annepidem.2007.03.013 

58. Parry DA, Davidson BI, Sewall CJ, Fisher JT, Mieczkowski H, Quintana DS. A systematic review and meta-analysis of discrepancies between logged and self-reported digital media use. Nat Hum Behav. 2021 Nov;5(11):1535–47. doi: https://doi.org/10.1038/s41562-021-01117-5 

Appendix

The appendix is available in the pdf version of the article at https://doi.org/10.57187/s.4247.