Environmental factors are important in the development of myopia. There is still limited evidence as to whether computer use is a risk factor. The aim of this study is to investigate the association between computer use and myopia in the context of other near work activities. Within the birth cohort study Generation R, we studied 5074 children born in Rotterdam between 2002 and 2006. Refractive error and axial length was measured at ages 6 and 9. Information on computer use and outdoor exposure was obtained at age 3, 6 and 9 years using a questionnaire, and reading time and reading distance were assessed at age 9 years. Myopia prevalence (spherical equivalent ≤-0.5 dioptre) was 11.5% at 9 years. Mean computer use was associated with myopia at age 9 (OR = 1.005, 95% CI = 1.001-1.009), as was reading time and reading distance (OR = 1.031; 95% CI = 1.007-1.055 (5-10 h/wk); OR = 1.113; 95% CI = 1.073-1.155 (>10 h/wk) and OR = 1.072; 95% CI = 1.048-1.097 respectively). The combined effect of near work (computer use, reading time and reading distance) showed an increased odds ratio for myopia at age 9 (OR = 1.072; 95% CI = 1.047-1.098), while outdoor exposure showed a decreased odds ratio (OR = 0.996; 95% CI = 0.994-0.999) and the interaction term was significant (P = 0.036). From our results, we can conclude that within our sample of children, increased computer use is associated with myopia development. The effect of combined near work was decreased by outdoor exposure. The risks of digital devices on myopia and the protection by outdoor exposure should become widely known. Public campaigns are warranted.

This study examined the clustering of lifestyle behaviours in children aged six years from a prospective cohort study in the Netherlands. Additionally, we analysed the associations between socioeconomic status and the lifestyle behaviour clusters that we identified. Data of 4059 children from the Generation R Study were analysed. Socioeconomic status was measured by maternal educational level and net household income. Lifestyle behaviours including screen time, physical activity, calorie-rich snack consumption and sugar-sweetened beverages consumption were measured via a parental questionnaire. Hierarchical and non-hierarchical cluster analyses were applied. The associations between socioeconomic status and lifestyle behaviour clusters were assessed using logistic regression models. Three lifestyle clusters were identified: “relatively healthy lifestyle” cluster (n = 1444), “high screen time and physically inactive” cluster (n = 1217), and “physically active, high snacks and sugary drinks” cluster (n = 1398). Children from high educated mothers or high-income households were more likely to be allocated to the “relatively healthy lifestyle” cluster, while children from low educated mothers or from low-income households were more likely to be allocated in the “high screen time and physically inactive” cluster. Intervention development and prevention strategies may use this information to further target programs promoting healthy behaviours of children and their families.

We aimed to evaluate the association between family socioeconomic status and repeatedly measured child television viewing time from early childhood to the school period. We analyzed data on 3,561 Dutch children from the Generation R Study, a population-based study in the Netherlands. Parent-reported television viewing time for children aged 2, 3, 4, 6 and 9 years were collected by questionnaires sent from April 2004 until January 2015. Odds ratios of watching television ≥1 hour/day at each age were calculated for children of mothers with low, mid-low, mid-high and high (reference group) education and children from low, middle and high (reference group) income households. A generalized logistic mixed model was used to assess the association between family socioeconomic status and child television viewing time trajectory. The percentage of children watching television ≥1 hour/day increased from age 2 to 9 years for all children (24.2%-85.0% for children of low-educated mothers; 4.7%-61.4% for children of high-educated mothers; 17.2%-74.9% for children from low income households; 6.2%-65.1% for children from high income households). Independent socioeconomic effect in child television viewing time was found for maternal educational level. The interaction between net household income and child age in longitudinal analyses was significant (p = 0.01), indicating that the television viewing time trajectories were different in household income subgroups. However the interaction between maternal educational level and child age was not significant (p = 0.19). Inverse socioeconomic gradients in child television viewing time were found from the preschool period to the late school period. The educational differences between the various educational subgroups remained stable with increasing age, but the differences between household income groups changed over time. Intervention developers and healthcare practitioners need to raise awareness among non-highly educated parents that the socioeconomic gradient in television viewing time has a tracking effect starting from preschool age.

Background: Using social media is a time-consuming activity of children and adolescents. Health authorities have warned that excessive use of social media can negatively affect adolescent social, physical, and psychological health. However, scientific findings regarding associations between time spent on social media and adolescent health-related quality of life (HRQoL) are not consistent. Adolescents typically use multiple social media platforms. Whether the use of multiple social media platforms impacts adolescent health is unclear. Objective: The aim of this study was to examine the relationship between social media use, including the number of social media platforms used and time spent on social media, and adolescent HRQoL. Methods: We analyzed the data of 3397 children (mean age 13.5, SD 0.4 years) from the Generation R Study, a population-based cohort study in the Netherlands. Children reported the number of social media platforms used and time spent on social media during weekdays and weekends separately. Children’s HRQoL was self-reported with the EuroQol 5-dimension questionnaire–youth version. Data on social media use and HRQoL were collected from 2015 to 2019. Multiple logistic and linear regressions were applied. Results: In this study, 72.6% (2466/3397) of the children used 3 or more social media platforms, and 37.7% (1234/3276) and 58.3% (1911/3277) of the children used social media at least 2 hours per day during weekdays and weekends, respectively. Children using more social media platforms (7 or more platforms) had a higher odds of reporting having some or a lot of problems on “having pain or discomfort” (OR 1.55, 95% CI 1.20 to 1.99) and “feeling worried, sad or unhappy” (OR 1.99, 95% CI 1.52 to 2.60) dimensions and reported lower self-rated health (β –3.81, 95% CI –5.54 to –2.09) compared with children who used 0 to 2 social media platforms. Both on weekdays and weekends, children spent more time on social media were more likely to report having some or a lot of problems on “doing usual activities,” “having pain or discomfort,” “feeling worried, sad or unhappy,” and report lower self-rated health (all P<.001). Conclusions: Our findings indicate that using more social media platforms and spending more time on social media were significantly related to lower HRQoL. We recommend future research to study the pathway between social media use and HRQoL among adolescents.

Objective To examine the associations between maternal education and household income during early childhood with asthma-related outcomes in children aged 9–12 years in the UK, the Netherlands, Sweden, Australia, the USA and Canada. Methods Data on 31 210 children were obtained from 7 prospective birth cohort studies across six countries. Asthma-related outcomes included ever asthma, wheezing/asthma attacks and medication control for asthma. Relative social inequalities were estimated using pooled risk ratios (RRs) adjusted for potential confounders (child age, sex, mother ethnic background and maternal age) for maternal education and household income. The Slope Index of Inequality (SII) was calculated for each cohort to evaluate absolute social inequalities. Results Ever asthma prevalence ranged from 8.3% (Netherlands) to 29.1% (Australia). Wheezing/asthma attacks prevalence ranged from 3.9% (Quebec) to 16.8% (USA). Pooled RRs for low (vs high) maternal education and low (vs high) household income were: ever asthma (education 1.24, 95% CI 1.13 to 1.37; income 1.28, 95% CI 1.15 to 1.43), wheezing/asthma attacks (education 1.14, 95% CI 0.97 to 1.35; income 1.22, 95% CI 1.03 to 1.44) and asthma with medication control (education 1.16, 95% CI 0.97 to 1.40; income 1.25, 95% CI 1.01 to 1.55). SIIs supported the lower risk for children with more highly educated mothers and those from higher-income households in most cohorts, with few exceptions. Conclusions Social inequalities by household income on the risk of ever asthma, wheezing/asthma attacks, and medication control for asthma were evident; the associations were attenuated for maternal education. These findings support the need for prevention policies to address the relatively high risks of respiratory morbidity in children in families with low socioeconomic status.

Background: We aim to assess the associations between the change in neighborhood socioeconomic score (SES) between birth and 6 years and childhood weight status and body composition from 6 to 13 years. Methods: Data for 3909 children from the Generation R Study, a prospective population-based cohort in the Netherlands were analyzed. The change in neighborhood SES between birth and 6 years was defined as static-high, static-middle, static-low, upward, and downward mobility. Child body mass index (BMI), overweight and obesity (OWOB), fat mass index (FMI) and lean mass index (LMI) were measured at age 6, 10, and 13 years. The associations were explored using generalized estimating equations. The effect modification by child sex was examined. Results: In total, 19.5% and 18.1% of children were allocated to the upward mobility and downward mobility neighborhood SES group. The associations between the change in neighborhood SES and child weight status and body composition were moderated by child sex (p < 0.05). Compared to girls in the static-high group, girls in the static-low group had relatively higher BMI-SDS (β, 95% confidence interval (CI): 0.24, 0.09–0.40) and higher risk of OWOB (RR, 95% CI: 1.98, 1.35–2.91), together with higher FMI-SDS (β, 95% CI: 0.27, 0.14–0.41) and LMI-SDS (β, 95% CI: 0.18, 0.03–0.33). The associations in boys were not significant. Conclusions: An increased BMI and fat mass, and higher risk of OWOB from 6 to 13 years were evident in girls living in a low-SES neighborhood or moving downward from a high- to a low-SES neighborhood. Support for children and families from low-SES neighborhoods is warranted.