Abstract
Background
Compliance with screen time guidelines among children worldwide remains low, and there is insufficient evidence on the current prevalence in China. This study aimed to investigate the prevalence of compliance with screen time guidelines among children under 3 years old in Fujian Province, East China, identify risk factors and their independent effects, and develop a risk discrimination model for targeted interventions.
Methods
A cross-sectional survey was conducted among low-income families recruited from welfare programs at 96 sites in both urban and rural areas of Fujian Province, China. Face-to-face interviews gathered sociodemographic data, lifestyle information, attitudes towards screen exposure, and details on screen media device ownership. A multivariable logistic regression model was employed to identify independent risk factors for compliance with screen time guidelines, while the area under the receiver operating characteristic curve (AUC) was used to evaluate the model’s discrimination ability.
Results
A total of 4,707 children participated in the survey. The rates of compliance with screen time guidelines were 56.8% for children under 1 year old, 18.8% for those between 1 and 2 years old, and 81.9% for those between 2 and 3 years old. The multivariable regression analysis identified negative attitudes towards screen exposure, co-viewing and engagement, as well as single child, as significant positive independent factors for compliance with the guidelines. The risk discrimination model demonstrated good performance, with an AUC of 0.845 and 0.812 in the two younger age groups, but showed medium discrimination with an AUC of 0.691 for children between 2 and 3 years old.
Conclusions
Compliance with screen time guidelines among young children in Fujian Province, East China, is generally adequate, but notably low among children between 1 and 2 years old. Targeted interventions are needed to improve compliance, particularly for this age group.
Keywords: Screen exposure, Children under 3 years, Risk factors, Risk discrimination model, Targeted intervention
Background
The rise of digital technology has significantly increased the usage of electronic and interactive media in our daily lives. As smartphones and tablets become more prevalent, younger generations are growing up in a screen-saturated environment, resulting in substantial increases in screen time for both parents and children. This trend is especially pronounced among children under 5, whose screen time has nearly doubled over the past two decades and is now affecting even younger age groups [1, 2]. A meta-analysis of 63 studies, encompassing 95 non-overlapping samples and 89,163 participants, found that compliance with screen time guidelines was 24.7% among children under 2 years old. For children aged 2 to 5 years, the mean prevalence of adherence to screen time guidelines was 35.6% [3]. The issue of screen exposure has become increasingly prominent in the lives of children and youth worldwide.
Previous research has extensively documented the negative impacts of screen exposure on the health of children and teenagers [4, 5]. These negative effects are especially pronounced among younger children, particularly those aged 0–3 years, who are at a crucial stage of brain development that profoundly impacts their long-term physical and mental well-being [6]. Infants and young children exposed to excessive screen time are at risk of experiencing developmental setbacks, including obesity, sleep disturbances, visual impairment, delayed language development, poor concentration, and difficulties with emotional regulation [7–10]. Moreover, screen exposure habits established during infancy and toddlerhood can persist into school age and adulthood, potentially impacting lifestyle choices and long-term health [11].
Due to the critical stage of physical and mental development characterized by heightened curiosity and adaptability, infants and toddlers benefit from limited screen exposure to support optimal cognitive and intellectual growth. In 1999, the American Academy of Pediatrics (AAP) established screen time guidelines, recommending that pediatricians advise parents to avoid TV viewing for children younger than 2 years [12]. In 2001, the AAP expanded its recommendations to include a limit of no more than 1 to 2 h per day of screen time for children aged 2 to 5 years. In 2016, the guidelines were revised to advise avoiding screen time for children younger than 2 years and limiting screen use to 1 h per day for children aged 2 to 5 years [13]. Pediatric societies worldwide, such as the Canadian 24-Hour Movement Guidelines [14] and the Australian 24-Hour Movement Guidelines [15], as well as the World Health Organization [16], have adopted similar recommendations urging parents to limit their children’s screen time.
Compliance with screen time guidelines remains low among children worldwide, despite increasing concerns about excessive screen exposure from parents and healthcare professionals. Variations in compliance rates, which can range from 2 to 83%, are influenced by differences in cultural and economic development [17–19]. In 2017, China issued physical activity guidelines for children and adolescents that align with the screen time recommendations of the AAP. Despite this, compliance with the suggested screen time limits remains low [20, 21]. Moreover, the COVID-19 pandemic has led to changes in lifestyle patterns, particularly for preschool children, who are spending more time at home and experiencing increased screen exposure [22].
The lack of evidence from Asian countries, particularly China, where parenting practices and attitudes towards young children differ from those in Western countries, underscores the need for a comprehensive understanding of the impact of screen exposure on early childhood development. Most existing studies have focused on school-aged children, overlooking the increasing trend of screen exposure among younger age groups. Given that an infant’s brain is at a critical stage of development, investigating screen time in children under 3 years old is crucial, as it may significantly influence their physical and mental development. This study aims to estimate the prevalence of compliance with screen time guidelines among children under 3 years old in Fujian Province, East China. Additionally, it seeks to identify risk factors and their independent effects on excessive screen exposure and to develop a risk discrimination model for targeted interventions.
Methods
Study design and participants
This cross-sectional study utilizes the public health service network and employs volunteers to conduct door-to-door surveys, investigating the current situation of families with infants and young children in the area. The survey was carried out from June 2022 to December 2022. A stratified three-stage cluster sampling approach was used to select 96 survey sites (48 in urban areas and 48 in rural areas), taking into account geographic region, economic development status, and demographic characteristics.
Eligibility criteria
Participants were recruited from the “Caring for New Life—Improving Child Nutrition” welfare program in Fujian Province. Eligibility criteria included low-income families with children under the age of 3 who were unable to breastfeed for various reasons. Exclusion criteria were: (a) children with genetic syndromes or major acute and chronic diseases, and (b) participants who were unable to communicate effectively or provide accurate data. Data was collected through interviews with the enrolled participants.
Data collection
Sociodemographic data, lifestyle information, attitudes towards screen exposure, and screen media device ownership were collected through face-to-face interviews conducted by trained investigators at households. The investigators followed a standardized protocol to ensure consistency in data collection. Each interview lasted approximately 30 to 45 minutes, allowing for thorough data gathering. The collected data were then uploaded to a database using the ‘Questionnaire Star’ platform. To ensure accuracy, quality control measures included daily computer-based logic checks and phone-based double-checks of 5% of the respondents.
Survey contents
Child’s basic information includes the parent-reported date of birth and gender, with age calculated from the questionnaire start date and the child’s date of birth. The primary caregiver is identified as the person who has spent the most time caring for the child over the past year, including mother, father, grandparent, or other. The education level of the primary caregiver is categorized as no formal education, primary/middle/high/secondary school, college, bachelor’s, master’s, or doctorate. Household per capita monthly income is categorized as < $45, $45–150, $150–300, $300–450, and ≥ $450. Family structure is classified into nuclear, extended, joint, or single-parent families. Outdoor time refers to daily outdoor activity in hours, while companionship time denotes daily positive interactive time in hours. Screen time is reported by parents as the average amount of time their child spends viewing electronic screens each day over the past month. Parental attitude towards screen use is categorized as positive if they view it as early education, or negative otherwise. Co-viewing and engagement refer to whether caregivers accompany the child during screen time and discuss the content. Compliance with screen time guidelines is defined as adhering to WHO recommendations of no screen time for children under 2 year old, and no more than 1 h per day for 2- and 3-year-olds.
Statistical analysis
Statistical analyses were performed using SPSS software (version 26.0 for Windows; IBM Corp., Armonk, NY, USA) and SAS (version 9.4 for Windows; SAS Institute, Cary, NC, USA). A two-sided P value of less than 0.05 was considered statistically significant for all tests. Continuous variables were reported as means and standard deviations (SD) and compared using one-way analysis of variance (ANOVA). Categorical variables were presented as frequencies and percentages, and comparisons were made using the chi-squared test. Univariate logistic regression models assessed the demographic variables, attitudes towards screen exposure, and screen media device ownership to identify potential influences. Variables with P values less than 0.05 in univariate analyses were included in a multivariable logistic regression model to identify independent risk factors for compliance with screen time guidelines, defined as whether daily screen exposure exceeds WHO recommendations. Odds ratios (OR) with 95% confidence intervals (CI) were reported for variables in each step. After data collection and cleaning, observations from each age group were randomly divided into training (60%) and testing (40%) datasets. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the model’s discrimination ability.
Results
Descriptive statistics
A total of 4,707 families, each with one child, participated in the survey: 2,510 in the under-1-year age group, 1,386 in the 1 to 2 years old group, and 811 in the 2 to 3 years old group. Boys comprised 58.6%, 56.6%, and 56.6% of each age group, respectively. The percentage of children with no siblings was 44.3%, 43.2%, and 41.9% in the respective age groups. Over half of the families (57.4%, 55.6%, and 55.5% in the three age groups) had a monthly income exceeding $435. Nearly two-thirds of parents (64.5%, 65.7%, and 61.8%) had completed high school or higher education. The majority of parents were employed in non-farming occupations, were self-employed, or worked as technicians (71.3%, 70.3%, and 73.0%). Nuclear families were the most common family structure (58.8%, 59.4%, and 61.7%). There were no significant differences in baseline characteristics between age groups, except for outdoor activity time in hours, which was 2.85, 3.38, and 3.39, respectively (p < 0.001) (Table 1).
Table 1.
Characteristics description
| Under 1 year old | Between 1 and 2 years old | Between 2 and 3 years old | |||
|---|---|---|---|---|---|
| n(%), mean(SD) | n(%), mean(SD) | n(%), mean(SD) | Chi2/ F | P | |
| Gender | 1.870 | 0.393 | |||
| Male | 1467(58.6) | 785(56.6) | 459(56.6) | ||
| Female | 1043(41.4) | 601(43.4) | 352(43.4) | ||
| Parity | 1.515 | 0.469 | |||
| Single | 1116(44.3) | 599(43.2) | 340(41.9) | ||
| Siblings | 1403(55.7) | 787(56.8) | 471(58.1) | ||
| Income($) | 1.600 | 0.449 | |||
| < 435 | 1073(42.6) | 615(44.4) | 361(44.5) | ||
| ≥ 435 | 1446(57.4) | 771(55.6) | 450(55.5) | ||
| Education | 3.415 | 0.181 | |||
| Below junior high school | 893(35.5) | 476(34.3) | 310(38.2) | ||
| High school/ secondary school and above | 1626(64.5) | 910(65.7) | 501(61.8) | ||
| Occupation | 8.327 | 0.215 | |||
| Farmimg | 482(19.1) | 296(21.4) | 159(19.6) | ||
| Self-employed | 202(8.0) | 91(6.6) | 51(6.3) | ||
| Technicians | 39(1.5) | 25(1.8) | 9(1.1) | ||
| Other | 1796(71.3) | 974(70.3) | 592(73.0) | ||
| Family Structure | 7.383 | 0.287 | |||
| Nuclear family | 1480(58.8) | 823(59.4) | 500(61.7) | ||
| Immediate family | 776(30.8) | 422(30.4) | 232(28.6) | ||
| Joint family | 244(9.7) | 124(8.9) | 67(8.3) | ||
| Single-parent family | 19(0.8) | 17(1.2) | 12(1.5) | ||
| Outdoor time (h) | 2.85 ± 1.62 | 3.38 ± 1.89 | 3.39 ± 1.89 | 11.73 | < 0.001 |
| Companionship time (h) | 4.61 ± 0.81 | 4.51 ± 0.91 | 4.49 ± 0.89 | 4.33 | 0.133 |
The pattern of screen exposure
The study found that 88.9% of children under 1 year old had daily screen time of less than 30 min. However, this percentage decreased significantly as children aged: 57.7% of those 1 to 2 years old and 48.7% of those 2 to 3 years old had less than 30 min of screen time. Additionally, 30.4% and 33.2% of children in these age groups had between 30 min and 1 h of screen time (p < 0.001).
Co-viewing during screen time was less common among parents of 2- to 3-year-old children, with 84.7% engaging in it compared to 93.7% and 92.6% for younger children (p < 0.001). Parents of children under 1 year old were also less likely to have a positive attitude towards screen use as an educational tool, with only 68.4% holding this view compared to 74.8% and 74.7% for older age groups (p < 0.001).
Children under 1 year old who were exposed to screens were more frequently engaged in video calls (25.5% vs. 9.1% and 7.3%) and other content (21.2% vs. 2.7% and 1.9%), but watched fewer cartoons (47.5% vs. 78.1% and 83.8%) (p < 0.001). The ownership of screen media devices, such as smartphones, tablets, TVs, and other devices, varied significantly among the three age groups (p < 0.05), with the exception of computers (p = 0.093).
Finally, compliance with screen time guidelines varied notably by age group. The highest compliance was observed in children aged 2 to 3 years old (81.9%), while the lowest was in those aged 1 to 2 years (18.8%). Children under 1 year old had a compliance rate of 56.8%, which was also significantly different from the other age groups (p < 0.001, Table 2).
Table 2.
Screen usage, Media preferences, and parental attitudes and behaviors
| Under 1 year old | Between 1 and 2 years old | Between 2 and 3 years old | |||
|---|---|---|---|---|---|
| n(%), mean(SD) | n(%), mean(SD) | n(%), mean(SD) | Chi2 | P | |
| Screen Time / day | 753.241 | < 0.001 | |||
| <30 min | 2239(88.9) | 800(57.7) | 395(48.7) | ||
| 30 ~ 59 min | 217(8.6) | 421(30.4) | 269(33.2) | ||
| 60 ~ 89 min | 38(1.5) | 102(7.4) | 77(9.5) | ||
| 90 ~ 120 m min | 12(0.5) | 41(3.0) | 50(6.2) | ||
| >120 min | 13(0.5) | 22(1.6) | 20(2.5) | ||
| Screen Content | 588.361 | < 0.001 | |||
| Cartoon | 681(47.5) | 940(78.1) | 612(83.8) | ||
| Short video | 71(5.0) | 110(9.1) | 49(6.7) | ||
| Video game | 12(0.8) | 12(1.0) | 2(0.3) | ||
| Video call | 366(25.5) | 109(9.1) | 53(7.3) | ||
| Other | 303(21.1) | 33(2.7) | 14(1.9) | ||
| Screen media device (multiple selection) | |||||
| Smartphone | 714(65.6) | 798(70.9) | 488(69.1) | 7.383 | 0.025 |
| Tablet computer | 98(9.0) | 186(16.5) | 142(20.1) | 47.893 | < 0.001 |
| Television | 703(64.6) | 846(75.2) | 559(79.2) | 53.387 | < 0.001 |
| Computer | 52(4.8) | 68(6.0) | 51(7.2) | 4.754 | 0.093 |
| Other | 70(6.4) | 10(0.9) | 5(0.7) | 76.148 | < 0.001 |
| Attitudes towards screen exposure | 16.767 | < 0.001 | |||
| Positive | 999(68.4) | 906(74.8) | 547(74.7) | ||
| Negative | 462(31.6) | 306(25.2) | 185(25.3) | ||
| Co-viewing and engagement | 54.187 | < 0.001 | |||
| Yes | 1362(93.7) | 1120(92.6) | 618(84.7) | ||
| No | 91(6.3) | 90 (7.4) | 112(15.3) | ||
| Compliance with screen time guidelines | 914.879 | < 0.001 | |||
| Yes | 1431(56.8) | 261(18.8) | 664(81.9) | ||
| No | 1088(43.2) | 1125(81.2) | 147(18.1) |
Factors associated with screen exposure
The results of the univariable regression analysis indicated several factors influencing compliance with screen time guidelines. For children under 1 year old, factors positively associated with compliance included having only one child (OR = 1.225, 95% CI = 1.045–1.436, p = 0.012), positive interactive and companionship time (OR = 1.135, 95% CI = 1.030–1.249, p = 0.010), negative attitudes towards screen exposure (OR = 3.472, 95% CI = 3.155–3.817, p < 0.001), and co-viewing and engagement (OR = 5.102, 95% CI = 4.386–5.882, p < 0.001). For children aged 1 to 2 years, negative influences included lower weight (OR = 0.949, 95% CI = 0.918–0.982, p = 0.002), height (OR = 0.992, 95% CI = 0.986–0.998, p = 0.009), and higher education level (OR = 0.810, 95% CI = 0.694–0.945, p = 0.007). Positive influences were negative attitudes towards screen exposure (OR = 5.076, 95% CI = 4.149–6.211, p < 0.001), co-viewing and engagement (OR = 5.917, 95% CI = 4.695–7.463, p < 0.001), and increased outdoor activity time (OR = 1.084, 95% CI = 1.008–1.166, p = 0.029). For children aged 2 to 3 years, factors positively influencing compliance included having only one child (OR = 1.734, 95% CI = 1.211–2.485, p = 0.003), higher education level (OR = 1.288, 95% CI = 1.050–1.580, p = 0.015), negative attitudes towards screen exposure (OR = 1.460, 95% CI = 1.161–1.832, p = 0.001), and co-viewing and engagement (OR = 3.058, 95% CI = 2.083–4.484, p < 0.001). These results are summarized in Table 3.
Table 3.
Univariate and multivariate logistic regression analyses for the risk factors on compliance with screen time guidelines
| Under 1 year old | Between 1 and 2 years old | Between 2 and 3 years old | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Univariable | Multivariable | Univariable | Multivariable | Univariable | Multivariable | |||||||
| OR (95%CI) | P | OR (95%CI) | P | OR (95%CI) | P | OR (95%CI) | P | OR (95%CI) | P | OR (95%CI) | P | |
| Gender | 0.964(0.821–1.131) | 0.653 | 1.391(1.063–1.820) | 0.016 | 1.367(0.948–1.971) | 0.095 | ||||||
| Weight | 0.989(0.963–1.015) | 0.410 | 0.949(0.918–0.982) | 0.002 | 0.998(0.954–1.044) | 0.916 | ||||||
| Height | 1.000(0.995–1.004) | 0.843 | 0.992(0.986–0.998) | 0.009 | 0.991(0.980–1.003) | 0.150 | ||||||
| Parity group of one-child (reference: parity > 1) | 1.225(1.045–1.436) | 0.012 | 1.210(0.919–1.592) | 0.175 | 1.734(1.211–2.485) | 0.003 | 1.867(1.176–2.965) | 0.008 | ||||
| Family structure (reference: non-nuclearfamily) | 0.993(0.962–1.025) | 0.663 | 1.032(0.983–1.084) | 0.209 | 1.091(0.939–1.266) | 0.255 | ||||||
| Higher income | 1.210(0.983–1.490) | 0.072 | 0.822(0.592–1.141) | 0.241 | 1.199(0.766–1.876) | 0.428 | ||||||
| Higher education level | 0.924(0.843–1.011) | 0.086 | 0.810(0.694–0.945) | 0.007 | 1.288(1.050–1.580) | 0.015 | ||||||
| More companionship | 1.135(1.030–1.249) | 0.010 | 1.106(0.945–1.294) | 0.211 | 0.919(0.744–1.134) | 0.430 | ||||||
| More outdoor time | 1.038(0.988–1.089) | 0.138 | 1.084(1.008–1.166) | 0.029 | 0.920(0.835–1.014) | 0.095 | ||||||
| Negative attitudes towards screen exposure | 3.472(3.155–3.817) | < 0.001 | 1.342(1.036–1.739) | 0.026 | 5.076(4.149–6.211) | < 0.001 | 1.754(1.055–2.915) | 0.030 | 1.460(1.161–1.832) | 0.001 | ||
| Co-viewing and engagement | 5.102(4.386–5.882) | < 0.001 | 3.546(2.611–4.831) | < 0.001 | 5.917(4.695–7.463) | < 0.001 | 3.584(2.088–6.135) | < 0.001 | 3.058(2.083–4.484) | < 0.001 | 2.833(1.748–4.587) | < 0.001 |
The multivariable regression analysis revealed that for children under 1 year old, significant independent positive factors for compliance included negative attitudes towards screen exposure (OR = 1.342, 95% CI = 1.036–1.739, p = 0.026) and co-viewing and engagement (OR = 3.546, 95% CI = 2.611–4.831, p < 0.001). For children aged 1 to 2 years, the significant positive independent factors were negative attitudes towards screen exposure (OR = 1.754, 95% CI = 1.055–2.915, p = 0.030) and co-viewing and engagement (OR = 3.584, 95% CI = 2.088–6.135, p < 0.001). For children aged 2 to 3 years, having only one child (OR = 1.867, 95% CI = 1.176–2.965, p = 0.008) and co-viewing and engagement (OR = 2.833, 95% CI = 1.748–4.587, p < 0.001) were significant independent positive factors. These findings are also presented in Table 3.
Risk prediction based on logistic regression model
The multivariable logistic regression model demonstrated acceptable performance in discriminating non-compliance with screen time guidelines among children under 1 year old, with an AUC of 0.844 in the training set and 0.845 in the test set. For children aged 1 to 2 years, the AUC was 0.846 in the training set and 0.812 in the testing set. In contrast, for children aged 2 to 3 years, the AUC was 0.660 in the training set and 0.691 in the testing set, suggesting that risk factors may influence compliance behavior differently across various stages of physical and mental development (Fig. 1).
Fig. 1.
shows the AUC for discriminating between compliance and non-compliance with screen time guidelines. It includes the AUC values for the training set (A, C, E) and testing set (B, D, F) across three age-specific groups. AUC = area under the ROC curve; ROC = receiver operating characteristic
Caption of Fig. 1: The AUC for performance in discriminating individuals’ compliance with screen time guidelines.
Discussion
The study found that compliance with screen time guidelines among children under 3 years old in Fujian Province, China, is generally adhere to screen time guidelines, but notably low among children aged 1 and 2 years, ranging from 18.8 to 81.9% across the three age groups. This underscores the need for increased attention from child health and development professionals. Similar rates of compliance were found in Canada [8], Australia [23], Finland [24], and the United States [25], highlighting a global concern about excessive screen time in young children despite cultural differences.
Early childhood development is crucial for achieving health equity and national development goals by enhancing the overall quality of the population. Research indicates that the health status and environment of young children directly affect their cognitive abilities, personalities, and future success [26, 27]. Children aged 0–3 years are at a critical stage of brain development, and exposure to screens during this period can significantly impact their physical and mental health [6]. Screens, as a major form of environmental exposure, may be one of the most influential factors affecting brain development. In the univariable analysis, weight and height were positively correlated with aspects of children’s physical development and their interaction with screen exposure. However, this association was not significant in the subsequent multivariate analysis.
Several sociodemographic risk factors were associated with compliance, including parity, parental education level, and parents’ attitudes and behaviors. Our findings align with previous studies [28, 29], which suggest that parity is a risk factor. In families with more than one child, the pressure to provide individual attention and the challenge of limited energy often lead parents to use digital media as a substitute for direct interaction. With the recent liberalization of China’s “three-child policy,” more families will face the challenge of raising multiple children simultaneously. Disseminating scientific parenting guidance and knowledge across various fields may prove effective in reducing children’s screen time.
Univariate regression analysis revealed that parents’ education levels have distinct age-related effects on children’s screen exposure. For children aged 2–3 years, a higher parental education level promotes proper screen use, while for children aged 1–2 years, the opposite trend is observed. This finding contrasts with results from other studies [29–31]. We attribute this discrepancy to the unique characteristics of our study sample, which consists of low-income families within a Chinese cultural context. After children reach the age of 1, parents with higher education levels are more likely to work outside the home to supplement family income, making it challenging to balance work and childcare responsibilities. Consequently, screens may be used to comfort children and alleviate caregiving stress. By age 2, improvements in children’s self-care abilities and the availability of childcare institutions help reduce parenting pressure, especially for low-income families. In these cases, parents with higher education levels are more likely to prioritize early childhood development and appropriate screen use. These findings suggest that when public health institutions develop early childhood development strategies, they should not only focus on disseminating parenting knowledge but also address the gap between ‘theory’ and ‘practice’ for low-income families, offering additional support where needed.
As independent risk factors, parental attitudes and behaviors significantly impact compliance with screen time guidelines, a finding validated by the current multivariable analysis. Previous studies have demonstrated a strong association between screen time and parents’ attitudes towards screen viewing and media use habits [32, 33]. Although parents today often use screens as ‘electronic babysitters’ to soothe their children and reduce conflicts, they increasingly believe that early screen exposure can enhance children’s cognitive and social development, viewing educational and child-friendly screen time as effective tools for learning [34, 35]. Some parents even consider the vocabulary taught through TV programs as superior to what they might teach their children themselves [36]. Future interventions should focus on raising awareness among parents, particularly those with lower levels of education, about the impact of screen time. Additionally, these interventions should encourage parents to set a positive example for their children.
Notably, among children older than 2 years, the influence of parents’ attitudes and behavior towards compliance decreased, as demonstrated by the multivariable analysis and discriminative modeling. The study found that screen exposure characteristics and influencing factors varied significantly by age group. This phenomenon can be attributed to two key factors. Firstly, in China, the lack of childcare facilities for children under 2 years old, coupled with weakened parental supervision due to maternity leave policies, has led some parents to use screens as a temporary “electronic babysitter” while they attend to household chores and work [37]. The introduction of childcare facilities for children over 2 could help alleviate this issue. Secondly, children older than 2 years develop a strong sense of autonomy and can express their preferences. As a result, many parents may give in to their children’s screen-related desires, unlike with younger children, where parental control is more prevalent. As children grow older and encounter more complex environments, the influence of the family unit may diminish. Additionally, during the COVID-19 pandemic, China’s epidemic prevention policies restricted movement, leading to increased use of video calls for communication with relatives and friends, which contributed to the observed patterns in screen time.
The risk discrimination model developed in this study suggests that interventions should focus on parents of children under 2 years old to improve compliance with screen time guidelines. For children older than 2 years, comprehensive strategies are necessary, particularly for those with more siblings. This underscores the critical role of family factors in shaping screen time behaviors during early childhood, allowing healthcare providers to identify high-risk groups and implement timely measures. The model highlights specific risk factors linked to compliance with screen time guidelines, enabling the development of targeted interventions. For younger children, strategies can concentrate on educating and supporting parents to foster healthy screen time habits, while interventions for older children may need to address a broader range of factors, such as peer influence and access to electronic devices. Overall, the model advocates for a multifaceted approach to reducing excessive screen time, taking into account both individual and environmental factors. By identifying key risk factors and high-risk groups, healthcare providers can create effective interventions to help parents and children establish healthy screen time practices that support their physical and psychological development.
Limitations and strengths
This study has some limitations that should be considered when interpreting the results. Firstly, the study was conducted among relatively low-income families from East China, which may limit the generalizability of the findings to populations with different socioeconomic characteristics and to other areas in China. Parents in low-income families may rely more on screens as a low-cost entertainment or parenting option, which can increase children’s screen exposure. Additionally, since the survey primarily included families who were unable to breastfeed, this may affect the generalizability of the results. Breastfeeding is associated with developmental benefits that can influence children’s screen interaction and adherence to guidelines. Moreover, physical and psychological challenges, along with frequent career changes for economic reasons that hinder breastfeeding, may also influence parenting styles and the family environment, affecting how screen time is managed. Secondly, data collected from a questionnaire-based survey may be subject to bias; future studies could consider using more objective measures, such as screen time tracking applications, and incorporating more subgroup analysis with integrated socioeconomic information. Thirdly, the cross-sectional design of the study may limit the ability to derive causal relationships.
Despite these limitations, the study has several strengths. The large sample size allowed for robust estimation of the effects of various risk factors, and the low variance of factors in the model enabled effective training and testing of a data-based discriminative model. The study’s age-specific group comparisons provided insight into the unique characteristics of different stages of children’s physical and mental development and the changing effects of risk factors over time. This information could serve as a data-based reference for targeted interventions to promote healthy screen use among children under 3 years old.
Conclusions
The results of this study indicate that compliance with screen time guidelines among older children in Fujian Province, East China, is generally adhere to screen time guidelines. However, the situation for children between 1 and 2 years old is concerning. Socioeconomic analysis revealed that screen use in older children is influenced by multiple factors. Therefore, more targeted interventions are needed to protect the physical and psychological development of younger children. The study found that parents’ attitudes towards screen exposure and the amount of time spent on screens, as well as parental practices related to screen use, are crucial factors affecting compliance with guidelines. The results suggest that interventions for specific age groups should focus on educating parents and caregivers about appropriate screen use and the potential risks of excessive screen time.
Acknowledgements
Not applicable.
Author contributions
Author Contributions: CG and JG conceptualized the study. CG, JG, FZ and CC collected the data. CG and ZQ analysed the data and drafted the manuscript. JG, FZ, CC, GL and PG critically reviewed the manuscript and contributed important intellectual content. GL and PG supervised the project.
Funding
This work was sponsored by the Startup Fund for scientific research, Fujian Medical University (Grant number: 2018QH1189) and Fujian provincial health technology project (Grant number: 2019-2-13).
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Review Committee of Fujian Provincial Maternal and Child Health Hospital (Ethical Research Approval Number 2019-002). The study protocol was approved by the Electronic signatures and informed consent to participate were obtained from all participants.
Consent for publication
The current study contains no personal identifying information on survey participants, therefore not applicable.
Competing interests
The authors declare no competing interests.
Competing interests
The authors of this manuscript have no conflicts of interest to disclose. No financial or non-financial benefits have been received or will be received from any party related directly or indirectly to the subject of this article.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Chong Guo and Jingmin Guo contributed equally to this work.
Pin Ge and Guihua Liu share their authorship as equally contributing senior authors.
Contributor Information
Pin Ge, Email: gp8287@126.com.
Guihua Liu, Email: fjmchh.research@gmail.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.