The relationship between screen time, screen content for children aged 1-3, and the risk of ADHD in preschools

Jian-Bo Wu; Yanni Yang; Qiang Zhou; Jiemin Li; Wei-Kang Yang; Xiaona Yin; Shuang-Yan Qiu; Jingyu Zhang; Minghui Meng; Yawei Guo; Jian-hui Chen; Zhaodi Chen

doi:10.1371/journal.pone.0312654

. 2025 Apr 23;20(4):e0312654. doi: 10.1371/journal.pone.0312654

The relationship between screen time, screen content for children aged 1-3, and the risk of ADHD in preschools

Jian-Bo Wu ¹, Yanni Yang ², Qiang Zhou ¹, Jiemin Li ¹, Wei-Kang Yang ¹, Xiaona Yin ¹, Shuang-Yan Qiu ¹, Jingyu Zhang ¹, Minghui Meng ³, Yawei Guo ⁴, Jian-hui Chen ^5,^*,^#, Zhaodi Chen ^6,^#

Editor: Christine Nardini⁷

PMCID: PMC12017831 PMID: 40267918

Abstract

Objective

This study investigates the relationship between screen time, screen content, and the risk of Attention Deficit Hyperactivity Disorder (ADHD) using data from a large sample. Specifically, it examines how different types of screen content (such as educational videos, cartoon videos, and interactive videos) are associated with the risk of ADHD. The aim is to offer a scientific foundation for the rational management of children’s screen time and screen content.

Methods

We collected data through a questionnaire survey involving a study population of 41,494 children from Longhua District, Shenzhen City, China. The questionnaire recorded the daily screen time and the type of content viewed by the children at ages 1–3 years and assessed their risk of ADHD using the Strengths and Difficulties Questionnaire (SDQ) at ages 4–6 years. Hierarchical logistic regression analysis, controlling for confounding factors, was employed to explore the associations between screen time, screen content, and ADHD risk.

Results

In the total sample, 6.7% of the participants had screen time exceeding 60 minutes per day, with educational videos predominant type (63.4%). 16.5% of the participants were identified as being at risk for ADHD. Statistically significant positive associations with ADHD were observed across all categories of screen time (P<0.001). Moreover, as screen time increased, the risk of ADHD also rose (OR_{1~60 mins/d}=1.627, 95%CI=1.460~1.813; OR_{61~120 mins/d}=2.838, 95%CI=2.469~3.261; OR_{>120 mins/d}=3.687, 95%CI=2.835~4.796).

Significant positive associations with ADHD were observed across all categories of screen time in the educational videos and cartoon videos. For the educational videos group, the odds ratios were as follows: OR_{1–60 mins/day}=1.683 (95% CI=1.481–1.913), OR_{61–120 mins/day}=3.193 (95% CI=2.658–3.835), and OR_{>120 mins/day}=3.070 (95% CI=2.017–4.673). For the cartoon videos group, the odds ratios were: OR_{1–60 mins/day}=1.603 (95% CI=1.290–1.991), OR_{61–120 mins/day}=2.758 (95% CI=2.156–3.529), and OR_{>120 mins/day}=4.097 (95% CI=2.760–6.081).

However, no significant associations with ADHD risk were found for any category of screen time in the interactive videos group (OR_{1~60 mins/d}=0.744, 95%CI=0.361~1.534; OR_{61~120 mins/d}=0.680, 95%CI=0.296~1.560; OR_{>120 mins/d}=1.678, 95%CI=0.593~4.748).

Conclusion

Increased screen time is associated with a higher risk of ADHD, particularly for educational and cartoon videos, while interactive videos show no significant link. To mitigate this risk, parents and educators should implement strategies such as setting time limits, encouraging breaks, and promoting alternative activities. Future research should focus on longitudinal studies and intervention trials to further explore and address this relationship.

Introduction

ADHD, a common neurodevelopmental disorder, is characterized by core symptoms such as inattention, hyperactivity, and impulsivity, which pose challenges to children’s learning, social interaction, and emotional regulation abilities [1,2]. Although the exact pathophysiology of ADHD is not fully understood, environmental factors-including lifestyle factors-are recognized as playing a significant role in its development [3]. Among these factors, the relationship between screen time and ADHD risk has been a hot topic of research in recent years. However, studies specifically investigating the association between different type of screen content and ADHD risk remain scarce.

In recent years, the widespread adoption of electronic devices has significantly increased children’s screen time, raising widespread societal concern about the impact of electronic screens on children’s mental health. As research deepens, people have become aware that, in addition to the total amount of screen time, the type of screen content also has a significant impact on children’s psychological development [4,5]. Most existing research focuses on the association between screen time and the risk of ADHD, highlighting its potential impact on children’s mental health development [6,7]. However, these studies often overlook the fact that different types of screen content may have distinct effects on children’s mental health [8].

Existing studies on the link between screen time and hyperactive behavior generally indicate that excessive screen time may indirectly increase the risk of ADHD by affecting children’s sleep, physical activity, and social interaction [9,10]. However, these studies often fail to explore the differential impacts of various types of screen contents on children’s mental health in depth [11,12]. For instance, educational videos, often perceived as beneficial for learning, may nevertheless contain rapidly changing visuals and intense sensory stimulation that adversely affect children’s ability to concentrate and self-regulate. Similarly, cartoon videos, with their vibrant colors and exaggerated movements, may overstimulate children’s attentional systems, leading to decreased attention to the real world and affecting cognitive and emotional development [13]. In contrast, interactive videos, which promote children’s active participation and social interaction, may have different effects on ADHD risk, though their specific impact requires further investigation.

This study leverages the extensive data resources from the Longhua Child Cohort Study (LCCS) in Shenzhen to comprehensively assess the relationship between children’s screen time, different types of screen content, and the risk of ADHD. The aim is to provide scientific evidence to guide parents and educators in reasonably managing children’s screen time and optimizing screen content selection, thereby effectively reducing the risk of ADHD in children.

Methods

Study design and participants

This study, conducted in accordance with the Declaration of Helsinki, used census methods to source data from the 2021 survey of the LCCS, covering all kindergartens in Longhua District, Shenzhen. The LCCS was a large-scale epidemiological survey conducted in Longhua District, Shenzhen, China, aiming to assess the impact of children’s lifestyle habits on early psychological and behavioral development of preschoolers. We focused on the early childhood period (1–3 years) for screen time and content data, given its pivotal role in development. This stage is crucial for understanding long-term effects of screen exposure. For ADHD assessment, we targeted preschoolers (4–7 years), as this age range is key for symptom identification using validated tools like the SDQ. This approach enables exploration of the link between early screen use and ADHD risk.

The research project was conducted across 250 kindergartens in the Longhua District of Shenzhen in 2021. From 8th October to 23th November 2021, the project was publicized to the parents of kindergarten children, encouraging their participation. After obtaining parental consent, informed consent forms were signed by the parents, and a questionnaire survey was conducted. A total of 59,600 questionnaires were distributed, and 56,740 were returned, yielding a response rate of 95.2%. After excluding 15,246 questionnaires with incomplete information, the final sample size was 41,494. This study was approved by the Biomedical Research Ethics Review Committee of the School of Public Health, Sun Yat-sen University (Ethics Approval number: 2021 No. 116).

Data collection

The questionnaire collected information on family demographic characteristics, daily screen time, and the types of programs viewed during screen time by children when they were at the age of 1–3 years old. Additionally, it assessed the risk of ADHD using the Strengths and Difficulties Questionnaire (SDQ) for them at the age of 4–7 years old. All participants had signed the Human Ethics and Consent to Participate forms and agreed to be involved in this study.

Measurement of screen time (major exposure variables) and category

“Screen time” was defined as time spent looking at screens such as phones, TVS, tablets or desktop computers, game consoles, as reported by the children’s parents. We chose to collect information on screen time for children aged 4–7. An ordinal categorical survey was conducted to assess screen time, and a nominal categorical survey was conducted to evaluate the types of programs viewed during screen time (Table 1).

Table 1. Questions and options regarding the screen time and program of the screen time.

No.	Questions	Options
Q1	How long was the screen time of your child/children at his/her/their age of 1–3 years old per day?	0 mins/d 1~60 mins/d 61~120 mins/d >120 mins/d
Q2	What types of programs did your child/children primarily view during screen time at his/her/their age of 1–3 years old?	Educational videos Cartoon videos Interactive videos

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*Educational videos：(with explicit prosocial or cognitive components [e.g., Sesame Street and QiaoHu, a similar program in China])

*Cartoon videos：(e.g., Thomas & Friends and Super Flying Man)

*Interactive videos (e.g., apps where kids tap, swipe, or use controllers to play and family video chats real-time conversations between relatives using video calling apps).

0 mins/d: Refers to children aged 1–3 who do not have any exposure to electronic screens, or have occasional exposure but use electronic screens for less than 1 minute per day.

Measurement of ADHD risk

In this study, the Strengths and Difficulties Questionnaire (SDQ) was used to assess the risk of ADHD in children. The SDQ, developed by American psychologist Goodman in 1997, is a concise behavioral screening scale [14]. In 2005, norms for the Chinese population were established, ensuring cultural relevance and validity [15]. The scale consists of 25 items, covering 5 dimensions: emotional symptoms, conduct problems, ADHD symptoms, peer problems, and prosocial behavior. Items on the SDQ are rated on a scale from 0 to 2, with 0 indicating no agreement, 1 indicating partial agreement, and 2 indicating perfect agreement. The total score for ADHD symptoms ranges from 0 to 5 for normal, 6 for borderline, and 7–10 for abnormal.

Based on these scores, participants can be categorized into a normal group (≤5) and an ADHD risk group (≥6). The ADHD subscale cut-offs used in this study are based on established norms and research, and these thresholds have been validated in multiple studies and are widely accepted in both research and clinical practice. Furthermore, the cultural adaptation of the SDQ for the Chinese population has undergone a rigorous validation process to ensure its accuracy and applicability in this context [16]. The scale demonstrates good reliability, with a Cronbach’s α coefficient of 0.749 [17,18].

Covariates

The following confounding covariates were included in the analysis: child’s gender, age, parental marital status, parents’ educational attainment, household monthly income, single-child status and explaining the content of screen time program to the child.

Statistical analysis

Descriptive statistics were used to characterize the study population. Mean ± standard deviation (SD) and sample number (percentage) were presented for continuous and categorical variables, respectively. A chi-square test was used to compare differences in screen time, types of programs viewed during screen time, and covariate variables among ADHD risk groups. Logistic regression analysis was employed to explore the association between screen time and ADHD risk.

Based on the existing literature and theoretical considerations, we believe that interactions between covariates are unlikely to have a significant impact on the primary study outcomes. The main objective of this study is to evaluate the effect of individual covariates on prognosis. Therefore, we prioritized assessing main effects rather than interaction effects. Nonetheless, future research could further explore interactions between covariates to gain a more comprehensive understanding of how various factors influence prognosis.

Results

Analysis of demographic characteristics, screen time, types of screen content, and covariate variables among ADHD risk groups

Participants’ SDQ scores and associated ADHD risk levels are presented in Table 2. In the total sample, we found that 7.5% of the participants exhibited abnormal levels of ADHD symptoms (defined as a score of 7–10). Additionally, 9.0% of the participants were on the borderline for ADHD symptoms (defined as a score of 6). Overall, 16.5% of the participants were at risk for ADHD.

Table 2. ADHD scores and risk of the Strengths and Difficulties Questionnaire (SDQ).

Category	ADHD scores (Mean±SD)	No. (%)
Total	3.69±1.93	41494 (100.0)
Normal	3.09±1.45	34645 (83.5)
ADHD risk (Edge value)	6.00±0.00	3737 (9.0)
ADHD risk (Abnormal)	7.60±0.84	3112 (7.5)

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Participants’ demographics and characteristics are summarized in Table 3. A total of 41,494 children (22,113 boys [53.3%] and 19,381 girls [46.7%]; mean [SD] age, 5.13±0.67 years old) completed the questionnaire. The risk of ADHD was higher in boys compared to girls(18.9% vs. 13.7%, ²=201.855, P<0.001).

Table3. Participants sociodemographic characteristics and differences in screen time, types of programs viewed during screen time, and covariate variables among ADHD risk groups (N=41,494).

Characteristics	Total	Normal group	Risk group	²	P
Screen time
0 mins/d	3777 (9.1)	3359 (88.9)	418 (11.1)	337.445	<0.001
1~60 mins/d	34943 (84.2)	29276 (83.8)	5667 (16.2)
61~120 mins/d	2464 (5.9)	1804 (73.2)	660 (26.8)
>120 mins/d	310 (0.8)	206 (66.5)	104 (33.5)
Types of programs viewed during screen time
Educational videos	26320 (63.4)	22524 (85.6)	3796 (14.4)	270.463	<0.001
Cartoon videos	14516 (35.0)	11657 (80.3)	2859 (19.7)
Interactive videos	658 (1.6)	464 (70.5)	194 (29.5)
Explaining the content of screen time program to the child
Yes	29587(71.3)	24997(84.5)	4590(15.5)	73.686	<0.001
No	11907(28.7)	2259(19.0)	9648(81.0)	73.686	<0.001
Gender
Male	22113 (53.3)	17927 (81.1)	4186 (18.9)	201.855	<0.001
Female	19381 (46.7)	16718 (86.3)	2663 (13.7)	201.855	<0.001
Age
4~5 years old (< 5)	18106 (43.6)	15080 (83.3)	3026 (16.7)	1.368	0.505
5~6 years old (< 6)	17935 (43.2)	15018 (83.7)	2917 (16.3)
6~7 years old (< 7)	5453 (13.1)	4547 (83.4)	906 (16.6)
Parental marital status
Married	39682 (95.6)	33199 (83.7)	6483 (16.3)	18.747	<0.001
Remarried/Divorced/Widowed	1812 (4.4)	1446 (79.8)	366 (20.2)	18.747	<0.001
Maternal education attainment
Junior high school and below	6408 (15.4)	5017 (78.3)	1391 (21.7)	296.555	<0.001
High school or technical secondary school	8671 (20.9)	6995 (80.7)	1676 (19.3)
Junior college	25175 (60.7)	21502 (85.4)	3673 (14.6)
Undergraduate and above	1240 (3.0)	1131 (91.2)	109 (8.8)
Paternal education attainment
Junior high school and below	5953 (14.3)	4658 (78.2)	1295 (21.8)	282.356	<0.001
High school or technical secondary school	8904 (21.5)	7188 (80.7)	1716 (19.3)
Junior college	24728 (59.6)	21081 (85.3)	3647 (14.7)
Undergraduate and above	1909 (4.6)	1718 (90.0)	191 (10.0)
Household monthly income
<¥10,000	7266 (17.5)	5760 (79.3)	1506 (20.7)	252.489	<0.001
¥10,000~20,000	14331 (34.5)	11735 (81.9)	2596 (18.1)
¥20,000~30,000	8724 (21.0)	7386 (84.7)	1338 (15.3)
¥30,000~40,000	4648 (11.2)	4062 (87.4)	586 (12.6)
¥40,000	6525 (15.7)	5702 (87.4)	823 (12.6)
Single child status
Yes	13154 (31.7)	10568 (80.3)	2586 (19.7)	138.966	<0.001
No	28340 (68.3)	24077 (85.0)	4263 (15.0)	138.966	<0.001

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Abbreviation: ², coefficient of chi-square test. Bold font indicates statistical significance.

Relationship between screen time and ADHD risk

We performed a logistic regression analysis to investigate the associations between screen time and ADHD risk (Table 4). Statistically significant positive associations with ADHD were observed across all categories of screen time (β_{1~60 mins/d} = 0.493, β_{61~120 mins/d} = 1.041, β_{>120 mins/d} = 1.302, P<0.001). Additionally, as screen time increased, the risk of ADHD also rose (OR_{1~60 mins/d}=1.637, 95%CI=1.469~1.824; OR_{61~120 mins/d}=2.833, 95%CI=2.466~3.255; OR_{>120 mins/d}=3.676, 95%CI=2.827~4.778). Forest plot for the odds ratios can be seen in Fig 1.

Table 4. Relationship between screen time and ADHD risk (N=41,494).

Characteristics	β	OR (95%CI)	P
Age
4~5 years old (< 5)	Ref.
5~6 years old (< 6)	-0.020	0.981 (0.927~1.038)	0.499
6~7 years old (< 7)	-0.042	0.959 (0.883~1.042)	0.323
Gender	-0.365	0.694 (0.658~0.732)	<0.001
Parental marital status	0.189	1.207 (1.069~1.364)	0.002
Maternal education attainment
Junior high school and below	Ref.
High school or technical secondary school	-0.032	0.968 (0.887~1.057)	0.473
Junior college	-0.224	0.799 (0.733~0.872)	<0.001
Undergraduate and above	-0.616	0.540 (0.431~0.676)	<0.001
Paternal education attainment
Junior high school and below	Ref.
High school or technical secondary school	-0.067	0.935 (0.856~1.021)	0.133
Junior college	-0.224	0.799 (0.732~0.873)	<0.001
Undergraduate and above	-0.412	0.662 (0.552~0.795)	<0.001
Household monthly income
<¥10,000	Ref.
¥10,000~20,000	-0.074	0.929 (0.862~1.001)	0.054
¥20,000~30,000	-0.178	0.837 (0.766~0.915)	<0.001
¥30,000~40,000	-0.345	0.708 (0.634~0.792)	<0.001
¥40,000	-0.322	0.725 (0.655~0.802)	<0.001
Types of programs viewed during screen time
Educational videos	Ref.
Cartoon videos	0.288	1.333 (1.262~1.408)	<0.001
Interactive videos	0.726	2.066 (1.733~2.463)	<0.001
Explaining the content of screen time program to the child	-0.265	0.767 (0.724~0.813)	<0.001
Screen time
0 mins/d	Ref.
1~60 mins/d	0.493	1.637 (1.469~1.824)	<0.001
61~120 mins/d	1.041	2.833 (2.466~3.255)	<0.001
>120 mins/d	1.302	3.676 (2.827~4.778)	<0.001

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Model fit information: ²=9.105, df=8, P=0.333.

Age, parental education attainment, household monthly income, Types of programs viewed during screen time and screen time was transferred into dummy variables. Abbreviation: β, coefficient of Logistic Regression Analysis with adjustment for age, gender, parental marital status, maternal and paternal education attainment, household monthly income, single child status, types of programs viewed during screen time and discuss the content of screen time program with the child. OR, odds ratio of Logistic Regression Analysis. 95%CI, 95% confidence interval of OR of Logistic Regression Analysis. Bold font indicates statistical significance.

Relationship between screen time and ADHD risk in different types of programs viewed during screen time

We conducted a stratified logistic regression analysis to further investigate the associations between screen time and ADHD risk across different types of programs viewed during screen time (Table 5). Statistically significant positive associations with ADHD were observed across all categories of screen time in the educational videos and cartoon videos groups. (education videos group [OR_{1~60 mins/d}=1.683, 95%CI=1.481~1.913; OR_{61~120 mins/d}=3.193, 95%CI=2.658~3.835; OR_{>120 mins/d}=3.070, 95%CI=2.017~4.673]; Cartoon group [OR_{1~60 mins/d}=1.603, 95%CI=1.290~1.991; OR_{61~120 mins/d}=2.758, 95%CI=2.156~3.529; OR_{>120 mins/d}=4.097, 95%CI=2.760~6.081]) However, no category of screen time was significantly associated with ADHD risk in the interactive videos group (OR_{1~60 mins/d}=0.744, 95%CI=0.361~1.534; OR_{61~120 mins/d}=0.680, 95%CI=0.296~1.560; OR_{>120 mins/d}=1.678, 95%CI=0.593~4.748). Distribution of children at risk of ADHD across different screen time categories can be seen in Table 6.

Table 5. Relationship between screen time and ADHD risk in different types of programs viewed during screen time (N=41,494).

Type of content viewed during screen time	Educational videos (n=26320)	Cartoon videos (n=14516)	Interactive videos (n=658)
0 mins/d	Ref.
1~60 mins/d	1.683 (1.481~1.913)	1.603 (1.290~1.991)	0.744 (0.361~1.534)
61~120 mins/d	3.193 (2.658~3.835)	2.758 (2.156~3.529)	0.680 (0.296~1.560)
>120 mins/d	3.070 (2.017~4.673)	4.097 (2.760~6.081)	1.678 (0.593~4.748)

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Table 6. Distribution of children at risk of ADHD across different screen time categories.

Category of screen time	No. of ADHD risk children	No. of normal children	Total
0 mins/d	418	3,359	3,777
1~60 mins/d	5,667	29,276	34,943
61~120 mins/d	660	1,804	2,464
>120 mins/d	104	206	310
Total	6,849	34,645	41,494

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Screen time was transferred into dummy variables. Abbreviation: OR (95%CI), odds ratio (95% confidence interval of OR) of Logistic Regression Analysis with adjustment for age, gender, parental marital status, maternal and paternal education attainment, household monthly income, single child status and explaining the content of screen time program to the child. Bold font indicates the non-significance of interactive videos.

Discussion

The Relationship between Screen Time and ADHD Risk

In this study, we observed a significant positive correlation between screen time and the risk of ADHD among children. Our study observed a significant positive correlation between screen time and the risk of ADHD among children, consistent with numerous international studies [19]. This association may be attributed to the combined effects of multiple mechanisms. Firstly, the use of screen devices, especially before bedtime, may disrupt children’s sleep patterns by inhibiting the secretion of melatonin, thereby affecting their attention and emotional regulation abilities [20–22]. Secondly, the fast-paced and highly stimulating content on screens may overstimulate children’s attention systems, impacting their cognitive and attentional development [23,24]. Additionally, increased screen time often comes at the expense of physical activity, which has a benificial effect on improving children’s attention and reducing hyperactive behaviors [25,26]. Furthermore, excessive screen time may reduce children’s social interactions with peers, and a lack of social skills is associated with the development of ADHD, leading to peer relationship problems and difficulties in school adjustment [27–29]. These findings underscore the importance of reducing children’s screen time to preventing ADHD.

The Relationship between Screen content and ADHD Risk

Educational videos, as a medium for children to acquire knowledge, do not universally exert positive influences [30,31]. Previous research findings on educational videos have been inconsistent. Some studies suggest that educational videos do not significantly increase the risk of attention deficit hyperactivity disorder (ADHD) [32], while others have found that prolonged exposure to educational videos may adversely affect children’s attentional systems [33]. This study reveals that as the time spent watching educational videos increases, the risk of ADHD among children rises significantly. This may be attributed to the fact that educational videos often contain a wealth of information with rapid scene changes, which can easily overstimulate children’s attentional systems, thereby impairing their self-regulation abilities [34,35]. Additionally, the lack of interactivity in educational videos may deprive children of opportunities to practice and apply knowledge in real-life situations, impacting their social skills and problem-solving abilities [36,37].

Cartoon videos, with their vibrant colors and exaggerated movements, are popular among children, but prolonged viewing may also elevate the risk of ADHD. The fast-paced and highly stimulating content in cartoon videos may excessively activate children’s attentional systems [38], leading to decreased attention to the real world and affecting cognitive and emotional development [39]. Moreover, violent or stimulating content in cartoon videos may adversely affect children’s mental health [40,41], further increasing the risk of ADHD.

This study finds that, unlike educational and cartoon videos, interactive videos do not show a significant association with the risk of ADHD. Biofeedback therapy, used in attention training for children with ADHD [42], leverages interactive videos to train children’s attention. During these sessions, children must adjust their brain electrical activity in real-time while watching interactive videos, thereby enhancing their attention [43,44]. Interactive videos provide children with a greater sense of participation and feedback opportunities [45,46], which may contribute to the lack of significant association between interactive videos and ADHD risk. However, this does not imply that children should increase their use of interactive videos indiscriminately. Dialectically speaking, the point estimate is consistent with other analyses in the interactive videos group, but the confidence interval is wide, which cannot rule out potential differences. This may be due to insufficient power resulting from a smaller sample size in this stratum. Therefore, it will be necessary to increase the sample size of this group to further validate this result in future studies.

This study possesses notable strengths, primarily reflected in its large sample size (41,494 children) and meticulous data analysis, allowing for an in-depth investigation into the relationship between screen time and ADHD risk, as well as the specific impacts of various screen contents (educational videos, cartoon videos, and interactive videos). However, several limitations merit further attention. Firstly, the cross-sectional design precludes direct causal inference, necessitating future longitudinal research to validate these findings. Secondly, the reliance on parental recall for both screen time and ADHD symptoms introduces potential recall bias. Parents may underestimate or overestimate these factors due to memory limitations, social desirability, or their own interpretations of problematic behavior. To mitigate this, future studies could employ more objective measures of screen time, such as device tracking or time-use diaries, and standardized assessments of ADHD symptoms conducted by trained professionals. Additionally, despite controlling for multiple confounding factors, there may still be unrecognized or unmeasured variables influencing the results, including genetic factors, environmental exposures, or other lifestyle variables. Future research should strive to identify and control for these additional factors. Lastly, a specific concern is the potential for diagnostic bias in parent-reported ADHD symptoms. ADHD is a complex disorder with overlapping symptoms, and parents may not fully understand the diagnostic criteria. To address this, future studies should incorporate clinical assessments by professionals to ensure accurate diagnosis.

Conclusion

Supporting information

S1 Data. Minimal_Data_Set_for_ADHD_Screen_Time_Study.

(XLSX)

pone.0312654.s001.xlsx^{(2.8MB, xlsx)}

Acknowledgments

The author would like to express his gratitude to the participants of the study, the investigators of Shenzhen Longhua District Maternal and Child Health Hospital and the kindergarten teachers who participated in the survey.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

Funding Longhua District Medical And Health Institutions Regional Scientific Research Project (2022086). Longhua District Medical And Health Institutions Regional Scientific Research Project (2022127). Medical Key Discipline Construction Fund of Longhua District.

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PLoS One. 2025 Apr 23;20(4):e0312654. doi: 10.1371/journal.pone.0312654.r001

Author response to Decision Letter 0

18 Oct 2024

PLoS One. doi: 10.1371/journal.pone.0312654.r002

Decision Letter 0

Christine Nardini

26 Nov 2024

PONE-D-24-42830The associations between Screen Time, Screen Content, and ADHD risk based on the evidence of 41494 children from Longhua district, Shenzhen, ChinaPLOS ONE

Dear Dr. Chen,

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Reviewer #1: Partly

Reviewer #2: Yes

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: No

Reviewer #2: Yes

**********

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Reviewer #2: Yes

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Reviewer #1: The paper has been written nicely. It reads well and is relatively easy to follow. The subject is also topical and of interest to a wide audience.

There were a few typos in the paper (Abstract results line 2, “with educational videos the predominant type”; Introduction, howere mis-spelled). In the section on measurement of screen time, TVS should be TVs, and I think it should be clarified that screen-time data are collected for 4-7 year olds, referring back to the ages of 1-3.

I think that some more clarity is needed in the following areas:

1. The large sample size appears to be a strength of the study. However, it almost seems a little too large. With a population of 2.5 million in the Longhua district in 2020 and ~8.5% age 0-14, there is likely to be in the region of 14,000 children at each year of age (assuming constant birth rates). This would produce ~42,000 4,5 and 6 year olds (the target ages for the questionnaire). The implication then is that every single child in the region of the correct age was sent a questionnaire and attended one of the 250 kindergartens (59,600 questionnaires were sent out). Further, the sample size suggests that each kindergarten has an average of over 230 children. Can the authors comment on this? Are children obliged to attend kindergarten, and to respond to questionnaires? What is the total population in the region by age? I would suggest the addition of a supplementary table to give a breakdown of the number of children by age and the size of the kindergarten they attended, along with the total population size for each age by year. This would enable the reader to fully appreciate the strength of the large sample size.

2. I do not feel that the authors describe interactive videos enough. The example that they give is social media / interactive games, but I question the type of social media / interactive games that a 1-year old is able to watch. I suggest that more specific examples are given.

3. Analysis requires all categories to be mutually exclusive, so a child that watches social media/plays interactive games should not watch any cartoons or educational videos if we are to make inferences on video type. The paper presents which type was primarily viewed. What percent of time does ‘primarily’ mean here? I suspect that there is in fact quite a lot of overlap between the categories. If there is a lot of overlap, then this would be a major revision.

4. Data is collected on whether or not parents discuss the content of videos with children. The data is not presented but I question the usefulness of this question in such young children.

5. It appears that data were collected at a single time point (when children were 4-7?) about their behaviours aged 1-3. I am not convinced that the average screen time when a child is 1 matches that of when they are 3. Screen time tends to increase with age, so I think that more information about the data collection and the questions asked should be presented and discussed. Perhaps the authors could include some figures to show the distribution of data.

6. Authors state that all data are available in the MS/supporting info. Unfortunately, I could not see any supporting files. Please ensure these are provided so that models can be re-run.

7. Questionnaires should be provided in supp info.

Reviewer #2: The study deals with an important current issue concerning the children's amount of time

spent on screens and its probable association with ADHD. It uses a good (robust) sample

size, which strengthens its results considerably. However, certain parts may need further

clarification and elaboration to strengthen the manuscript.

See the comments attached

**********

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Reviewer #1: No

Reviewer #2: Yes: Muhammad Aasim

**********

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Attachment

Submitted filename: Comments and feedback.pdf

pone.0312654.s002.pdf^{(297KB, pdf)}

PLoS One. 2025 Apr 23;20(4):e0312654. doi: 10.1371/journal.pone.0312654.r003

Author response to Decision Letter 1

22 Jan 2025

Responses to Reviewer #1:

Question 1: The large sample size appears to be a strength of the study. However, it almost seems a little too large. With a population of 2.5 million in the Longhua district in 2020 and ~8.5% age 0-14, there is likely to be in the region of 14,000 children at each year of age (assuming constant birth rates). This would produce ~42,000 4,5 and 6 year olds (the target ages for the questionnaire). The implication then is that every single child in the region of the correct age was sent a questionnaire and attended one of the 250 kindergartens (59,600 questionnaires were sent out). Further, the sample size suggests that each kindergarten has an average of over 230 children. Can the authors comment on this? Are children obliged to attend kindergarten, and to respond to questionnaires? What is the total population in the region by age? I would suggest the addition of a supplementary table to give a breakdown of the number of children by age and the size of the kindergarten they attended, along with the total population size for each age by year. This would enable the reader to fully appreciate the strength of the large sample size.

Answer: Thank you very much for your thorough review of our study and for providing valuable feedback. Regarding your concern about the large sample size, we would like to offer the following clarification and additional information. Firstly, we acknowledge that the sample size of our study (41,494 children) is indeed substantial. However, we believe that this large sample size is a significant strength of our research for several reasons. Firstly, it allows for a more robust and reliable estimation of the associations between screen time, screen content, and the risk of ADHD. With a larger sample, we are better able to detect even subtle differences and effects that may not be evident in smaller studies. Secondly, the large sample size enables us to conduct detailed subgroup analyses, such as examining the associations between different types of screen content (educational videos, cartoon videos, and interactive videos) and ADHD risk. This level of granularity would not be possible with a smaller sample. Thirdly, the large sample size increases the generalizability of our findings. The participants in our study come from a diverse population in Longhua District, Shenzhen, China, and the results are likely to be applicable to similar populations worldwide. We understand your concern that a very large sample size might raise questions about the practicality or representativeness of the study. However, we have taken care to ensure that the sample is representative of the target population and that the data collection and analysis methods are rigorous and valid. The Longhua Child Cohort Study (LCCS) from which our data were sourced is a large-scale epidemiological survey with a well-established methodology. In summary, while we appreciate your concern about the sample size, we believe that the large sample size is a key strength of our study that has enabled us to conduct a robust and generalizable analysis of the relationship between screen time, screen content, and ADHD risk. We are confident in the validity and reliability of our findings and hope that you will find them of interest and value.

Question 2: I do not feel that the authors describe interactive videos enough. The example that they give is social media / interactive games, but I question the type of social media / interactive games that a 1-year old is able to watch. I suggest that more specific examples are given.

Answer: Thank you very much for your detailed review of our manuscript and for your valuable feedback. We fully understand your concern that the description of "interactive videos" in our paper is insufficient, and that the examples provided (social media/interactive games) may not be suitable for 1-year-olds. To address your concern, we would like to clarify that "interactive videos" in our context encompass a variety of types, including touch-based apps and games designed specifically for children, as well as real-time video chats between family members using video calling apps. For 1-year-olds, we focus on interactive video content that is simple, age-appropriate, and promotes parent-child interaction and cognitive development. To further specify our definition of "interactive videos" and provide a clearer context for their use in early childhood, we plan to make the following revisions to our manuscript: Clarify the Definition and Scope of Interactive Videos: We will explicitly define "interactive videos" to include, but not be limited to, touch-based educational apps and simple games designed for children, as well as real-time video chats between family members.

Question 3: Analysis requires all categories to be mutually exclusive, so a child that watches social media/plays interactive games should not watch any cartoons or educational videos if we are to make inferences on video type. The paper presents which type was primarily viewed. What percent of time does ‘primarily’ mean here? I suspect that there is in fact quite a lot of overlap between the categories. If there is a lot of overlap, then this would be a major revision.

Answer: Thank you very much for your thorough review of our study and pointing out this important issue. Regarding your concerns about the mutual exclusivity of categories and the definition of "primarily viewed," we fully agree on the importance of ensuring mutual exclusivity between categories for accurate inference of the relationship between video type and ADHD risk.

In our study, we defined "primarily viewed" as the type of video that children spend the most time watching during their screen time. To clarify this and reduce overlap between categories, we designed relevant questions in the questionnaire, asking parents to indicate the primary type of video their child watched during daily screen time at the age of 1-3 years. We understand that "primarily viewed" may have a certain degree of subjectivity, but our aim is to reflect children's screen content preferences as accurately as possible through parents' recall and judgment.

Regarding your concern about category overlap, we have taken corresponding measures during data collection and analysis to minimize such overlap. Firstly, during questionnaire design, we clearly listed different types of videos (educational videos, cartoon videos, and interactive videos) and asked parents to select the primary type their child watched. Secondly, during data analysis, we only included the primary type of video watched by children in our analysis and ignored other secondary content.

Nevertheless, we acknowledge that there may still be some degree of category overlap in practical operation, especially when children's screen time is fragmented and they watch multiple types of videos. To further reduce this overlap and improve the accuracy of our analysis, we can consider adopting a more detailed time allocation recording method in future studies, such as asking parents to record the specific time their child spends watching different types of videos each day.

Question 4: Data is collected on whether or not parents discuss the content of videos with children. The data is not presented but I question the usefulness of this question in such young children.

Answer: Thank you for reviewing our manuscript and raising your concern about the data collection point regarding whether parents discuss video content with their children.

1.During the questionnaire design process, we did indeed include a question inquiring about "Do you explain the content of screen time programs to your child?" aiming to understand whether parents provide explanations regarding the content when their children are using screen devices. However, there was a translational error in the description of this question in the documents we submitted to you, leading you to misunderstand that we were collecting data on "discussing" video content between parents and children. We sincerely apologize for this mistake and for any confusion it may have caused.

Regarding the usefulness of this question, we have carefully considered that for young children, parents' explanations of screen content can play an important guiding role. By explaining the content, parents can help children better understand the information presented on the screen. The collection of this data is meaningful for us to understand the role of parents in shaping their children's screen use habits.

Question 5: It appears that data were collected at a single time point (when children were 4-7?) about their behaviours aged 1-3. I am not convinced that the average screen time when a child is 1 matches that of when they are 3. Screen time tends to increase with age, so I think that more information about the data collection and the questions asked should be presented and discussed. Perhaps the authors could include some figures to show the distribution of data.

Answer: Thank you for your valuable feedback on our manuscript.

Regarding the issue you mentioned that the average screen time for children at age 1 may differ from that at age 3, we fully agree with your view. In fact, screen time does tend to increase as children grow older. However, in our study, we are more concerned with the average screen time during the critical developmental period of 1-3 years, rather than the data at a specific age point. We hypothesize that, despite potential age differences in screen time, early childhood screen exposure habits may have a long-term impact on their subsequent cognitive and behavioral development.

To ensure the accuracy and representativeness of the data, we asked parents to report, based on their recall, the average screen time of their children during the ages of 1-3 in the questionnaire. We understand that this recall method may have certain limitations, such as recall bias. However, considering that our research aim is to explore long-term trends and overall associations, rather than absolute precise screen time data, we believe that this method is feasible under the current conditions.

Question 6-7: Authors state that all data are available in the MS/supporting info. Unfortunately, I could not see any supporting files. Please ensure these are provided so that models can be re-run. Questionnaires should be provided in supp info.

Answer: Thank you very much for your attention to our research. In response to your concern about the inaccessibility of the database and the absence of the submitted questionnaire, we have re-checked and uploaded these files accordingly.

Responses to Reviewer #2:

Considering the general comments:

Responses to specific comments:

Question 1: The title effectively summarizes the study's focus; however, including the specific age group studied would add further clarity.

Answer: Thank you for your suggestion on clarifying the age groups in the title. We have revised the title to "The relationship between screen time, screen content for children aged 1-3, and the risk of ADHD in preschools" to better reflect the specific age groups studied.

Question 2: The abstract is well-constructed, briefly mention the key limitations, such as the cross-sectional design.

Answer: Thanks for your suggestion, we have revised the summary.

......

Conclusion

Question 3: The introduction is thorough, but the transition from general concerns about screen time to specific research gaps feels abrupt. Consider expanding on these gaps to create a smoother flow.

Answer: Thank you very much for good suggestions. We appreciate your suggestion that the transition from general concerns about screen time to specific research gaps felt abrupt. We have taken this into consideration and have revised the introduction to expand on these research gaps, creating a smoother flow between the general context and our specific research questions. We believe that these changes enhance the clarity and coherence of the introduction.

Question 4: Citations should be more seamlessly integrated to strengthen statements about existing evidence and research gaps.

Answer: Thank you very much for good suggestions. We appreciate your suggestion that they should be more seamlessly integrated to strengthen our statements about existing evidence and research gaps. We have carefully reviewed and revised the manuscript to ensure that citations are now more tightly woven into the text, providing direct support for our assertions and highlighting the specific research gaps we aim to address.

Question 5: While the description of participant sampling is clear, it would be helpful to specify whether recruitment was conducted through randomization or convenience sampling from kindergartens.

Answer: Thank you very much for good suggestions. We appreciate your suggestion to clarify the recruitment method used. To address this, we would like to specify that this study used census methods to source data from the 2021 survey of the Longhua Child Cohort Study (LCCS), covering all kindergartens in Longhua District, Shenzhen. This means that rather than using randomization or convenience sampling, we included all eligible participants from the kindergartens covered by the LCCS survey.

Question 6: Ethical considerations are addressed; however, the rationale behind the age group selection for screen exposure (1–3 years) and ADHD assessment (4–7 years) should be provided.

Answer: We focused on the early childhood period (1-3 years) for screen time and content data, given its pivotal role in development. This stage is crucial for understanding long-term effects of screen exposure. For ADHD assessment, we targeted preschoolers (4-7 years), as this age range is key for symptom identification using validated tools like the SDQ. This approach enables exploration of the link between early screen use and ADHD risk.

Question 7: The use of the SDQ is appropriate, but the justification for the ADHD subscale cutoffs used in this population, along with information on whether cultural adaptations of the SDQ were validated, should be included.

Answer: Thank you for your feedback on the use of the Strengths and Difficulties Questionnaire (SDQ) and the ADHD subscale cut-offs in our study. We appreciate your recognition of the appropriateness of using the SDQ and have taken your suggestion to provide additional justification for our choice of cut-offs and cultural adaptation into account. In response to your query, we have clarified in the revised manuscript that in 2005, norms for the Chinese population were established for the SDQ, ensuring its cultural relevance and validity in our study population. The ADHD subscale cut-offs used in this study are based on these established norms and research, and these thresholds have been validated in multiple studies and are widely accepted in both research and clinical practice. Furthermore, we have noted that the cultural adaptation of the SDQ for the Chinese population has undergone a rigorous validation process to ensure its accuracy and applicability in this context. The scale demonstrates good reliability, with a Cronbach's α coefficient of 0.749.

Question 8: Provide further explanation for categorizing interactive videos separately, including how these were defined for parents completing the questionnaire.

Answer: Thank you for your feedback on the categorization of interactive videos in our study. We have taken your suggestion into consideration and provided further explanation in the revised manuscript. Interactive videos were categorized separately in our study to distinguish them from other types of screen content that are more passive in nature, such as educational and cartoon videos. Interactive videos are defined as those that require active participation and eng

Attachment

Submitted filename: Response to Reviewers.docx

pone.0312654.s003.docx^{(49.5KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0312654.r004

Decision Letter 1

Christine Nardini

11 Feb 2025

PONE-D-24-42830R1The relationship between screen time, screen content for children aged 1-3, and the risk of ADHD in preschoolsPLOS ONE

Dear Dr. Chen,

==============================

In particular, I join the reviewers in thanking you for addressing several concerns, however I woudl liketo stress the necessity to address the point on the data structure highlighted by Reviewer 1.

This will not only make your study theoretically reproducible, but it will also give more relevance to its unique breadth.

I therefore warmly urge you to prepare a supplementary table as recommended.

==============================

Please submit your revised manuscript by Mar 28 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Christine Nardini

Academic Editor

PLOS ONE

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Partly

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: I would like to thank the authors for implementing some of the changes that were initially requested.

However, I do not feel that the authors have adequately responded to my questions about the data itself. My initial query did not require reiteration of why large sample sizes are good, but a deeper understanding of the data and how representative it is of the population. As a reminder, I asked the following:

The authors have not responded to these questions in particular:

1. What is the size (how many children by age) of each kindergarten included?

2. Are children obliged to attend kindergarten?

3. What is the total population in the region covered, by age?

I suggested that this information be provided in a supplementary table. I feel that, although the methodology is reasonable, without this information it is not possible to assess the reproducibility of the study.

In addition, I do have some further comments and apologise for not picking up on these first time round:

1. The forest plots and Table 4 of odds ratios need the reference values labelling. Further, categories that have more than two levels should have odds ratios listed for all levels compared to one reference (type of programme is not ordinal). This has been done for screen time, but not for type of program viewed or monthly income, for example.

2. Can the authors explain why interactive video viewers has the highest percentage of children at risk of ADHD (Table 3, 29.5%) and yet the amount of screen time appears not to make a difference? Based on numbers in table 3, a quick, and crude, OR calculation on the frequencies of no screen time versus any screen time gives an odds ration of 3.33 for interactive videos ((194/467) / (418/3359)), compared to 1.35 for educational videos and 1.97 for cartoons. I am surprised that this is lost when screen time is broken down. I appreciate that they discuss there being fewer children in this group in the discussion, but the numbers are not so small. A frequency table to complement table 5 would be very useful. This should also include the number of children that are included in the 0 minutes category (I assume that this is 418 at risk and 3359 not at risk for all calculations?)

3. Typo in the introduction ‘howevre’ should read ‘however’

Reviewer #2: Thank you for submitting the revised version of your manuscript, The Relationship Between Screen Time, Screen Content for Children Aged 1-3, and the Risk of ADHD in Preschools (Manuscript No: PONE-D-24-42830R1). After carefully reviewing the revisions alongside the initial feedback, I acknowledge the substantial improvements made in clarity, methodological consistency, and integration of literature. (View the attached comments in detail)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Muhammad Aasim

**********

Attachment

Submitted filename: Comments on Revision-1.pdf

pone.0312654.s004.pdf^{(388.4KB, pdf)}

PLoS One. 2025 Apr 23;20(4):e0312654. doi: 10.1371/journal.pone.0312654.r005

Author response to Decision Letter 2

25 Feb 2025

Reviewer 1:

Question 1: Explicitly acknowledge potential selection bias due to parental self-reporting, particularly regarding screen time and ADHD symptoms.

Answer: Thank you for your suggestion. We have already mentioned this limitation in the second point of the limitations section of our article.

Secondly, the reliance on parental recall for both screen time and ADHD symptoms introduces potential recall bias. Parents may underestimate or overestimate these factors due to memory limitations, social desirability, or their own interpretations of problematic behavior. To mitigate this, future studies could employ more objective measures of screen time, such as device tracking or time-use diaries, and standardized assessments of ADHD symptoms conducted by trained professionals.

Reviewer 2:

First of all, we would like to express our sincere gratitude to the reviewer for the thorough review and valuable comments. We apologize for any areas in the previous revision that did not fully meet the reviewer's expectations. In this revised version, we have carefully considered all the suggestions and made corresponding modifications and improvements.

Question 1: What is the size (how many children by age) of each kindergarten included?

Answer: The size of each kindergarten included in the study varies, and a detailed breakdown of the number of children in each kindergarten classified by age is provided in the supplementary table, which is contained in the file named "Supplementary Table 1". This table includes the total number of children aged 3-7 in each kindergarten, the number of children aged 4 to 7 years old in the kindergarten, and the final sample size of the study.

Question 2: Are children obliged to attend kindergarten?

Answer: In China, while kindergarten attendance is not legally mandatory, it is highly encouraged and widely practiced. The Chinese government has made significant efforts to promote early childhood education, and the vast majority of parents enroll their children in kindergarten.

Question 3: What is the total population in the region covered, by age?

Answer: The total population in Longhua District, Shenzhen, by age is provided in the supplementary table, which can be found in the file named "Supplementary Table 2".

Question 4: The forest plots and Table 4 of odds ratios need the reference values labelling. Further, categories that have more than two levels should have odds ratios listed for all levels compared to one reference (type of programme is not ordinal). This has been done for screen time, but not for type of program viewed or monthly income, for example.

Answer: We are sincerely appreciated for your valuable comments. Here is the revised version of Table 4 and the forest plot which can also be seen in the revised manuscript with yellow label.

Question 5:

We appreciate your concerns regarding the interactive video group and the apparent discrepancy in the ADHD risk percentages. Below, we provide a detailed response to your questions and concerns.

Explanation of Interactive Video Group Findings

①High Percentage of ADHD Risk in Interactive Video Group:The observation that the interactive video group has the highest percentage of children at risk of ADHD (29.5%) in Table 3 can be partly attributed to the small sample size of this group. With only 658 children in the interactive video group, the data may be more prone to random fluctuations compared to the larger groups of educational (26,320) and cartoon videos (14,516). This small sample size likely contributes to the higher percentage of ADHD risk observed.

②Screen Time and ADHD Risk in Interactive Videos:

Our analysis in Table 5 shows that screen time does not appear to significantly affect the risk of ADHD in the interactive video group. This finding is indeed unexpected and warrants further investigation. One possible explanation is that the type of interactive content itself, rather than the duration of exposure, may be a more critical factor influencing ADHD risk. Interactive videos often involve greater child engagement and social interaction, which could potentially mitigate some of the negative effects associated with excessive screen time. We also give explanations in the discussion section. However, given the small sample size in this group, it is difficult to draw definitive conclusions.

③Crude OR Calculation:

You have correctly pointed out the crude OR calculation based on the frequencies in Table 3, which shows a higher OR for interactive videos compared to educational and cartoon videos. However, this calculation does not control for potential confounders, nor does it analyze using the 0 minutes category as the baseline. Our logistic regression analysis in Table 4 and Table 5 controls for these confounders and analyzes using the 0 minutes category as the baseline, providing a more reliable estimate of the relationship between screen time, screen content, and ADHD risk.

Table 6 is a frequency table that supplements Table 5, and since it is not possible to fit Table 6 here, we put it in the attachment of the manuscript and the response to the reviewer.

Question 6: Typo in the introduction ‘howevre’ should read ‘however’

Answer: We sincerely thank for your reminding. ‘howevre’ has been revised to ‘however’ which can be seen in the introduction part with yellow label.

Attachment

Submitted filename: Response to Reviewers (Second revision).docx

pone.0312654.s005.docx^{(29.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0312654.r006

Decision Letter 2

Christine Nardini

4 Mar 2025

PONE-D-24-42830R2The relationship between screen time, screen content for children aged 1-3, and the risk of ADHD in preschoolsPLOS ONE

Dear Dr. Chen,

============================

And thank you for improving the article as requested.

There remain two adjustments to be done:

- Table S2 apparently missing

- I reccomend to anonymize also the schools names before submitting the final version

==============================

Please submit your revised manuscript by Apr 18 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

We look forward to receiving your revised manuscript.

Kind regards,

Christine Nardini

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: No

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

**********

6. Review Comments to the Author

Reviewer #1: I would like to thank the authors for responding to my queries. They have referenced supplementary table 2 , but only one supplementary table was made available. For the final version, please ensure that both tables are uploaded. The absence of this table is the reason that I have selected minor revisions, otherwise, I have no further comments on the manuscript itself.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

**********

PLoS One. 2025 Apr 23;20(4):e0312654. doi: 10.1371/journal.pone.0312654.r007

Author response to Decision Letter 3

5 Mar 2025

Reviewer :

Thank you for your suggestion. The naming of the supplementary table in our second revision is misleading and may lead reviewers to mistakenly believe that we have uploaded one less supplementary table S2. We have renamed the original table, with "Supplementary Table 1" now being "The size of each kindergarten (number of children by age)". The name of "Supplementary Table 2" should be changed to "Supplementary Table 1". In addition, we have anonymized the names of the kindergartens.

Question 1: What is the size (how many children by age) of each kindergarten included?

Answer: The size of each kindergarten included in the study varies, and a detailed breakdown of the number of children in each kindergarten classified by age is provided in the supplementary table, which is contained in the file named "The size of each kindergarten (number of children by age)". This table includes the total number of children aged 3-7 in each kindergarten, the number of children aged 4 to 7 years old in the kindergarten, and the final sample size of the study.

Question 3: What is the total population in the region covered, by age?

Answer: The total population in Longhua District, Shenzhen, by age is provided in the supplementary table, which can be found in the file named "Supplementary Table 1".

Attachment

Submitted filename: Response to Reviewers (Third revision).docx

pone.0312654.s006.docx^{(24.7KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0312654.r008

Decision Letter 3

Christine Nardini

18 Mar 2025

The relationship between screen time, screen content for children aged 1-3, and the risk of ADHD in preschools

PONE-D-24-42830R3

Dear Dr. Chen,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice will be generated when your article is formally accepted. Please note, if your institution has a publishing partnership with PLOS and your article meets the relevant criteria, all or part of your publication costs will be covered. Please make sure your user information is up-to-date by logging into Editorial Manager at Editorial Manager® and clicking the ‘Update My Information' link at the top of the page. If you have any questions relating to publication charges, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Christine Nardini

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

PLoS One. doi: 10.1371/journal.pone.0312654.r009

Acceptance letter

Christine Nardini

PONE-D-24-42830R3

PLOS ONE

Dear Dr. Chen,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

* All references, tables, and figures are properly cited

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on behalf of

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Academic Editor

PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Data. Minimal_Data_Set_for_ADHD_Screen_Time_Study.

(XLSX)

pone.0312654.s001.xlsx^{(2.8MB, xlsx)}

Attachment

Submitted filename: Comments and feedback.pdf

pone.0312654.s002.pdf^{(297KB, pdf)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0312654.s003.docx^{(49.5KB, docx)}

Attachment

Submitted filename: Comments on Revision-1.pdf

pone.0312654.s004.pdf^{(388.4KB, pdf)}

Attachment

Submitted filename: Response to Reviewers (Second revision).docx

pone.0312654.s005.docx^{(29.7KB, docx)}

Attachment

Submitted filename: Response to Reviewers (Third revision).docx

pone.0312654.s006.docx^{(24.7KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

The relationship between screen time, screen content for children aged 1-3, and the risk of ADHD in preschools

Jian-Bo Wu

Yanni Yang

Qiang Zhou

Jiemin Li

Wei-Kang Yang

Xiaona Yin

Shuang-Yan Qiu

Jingyu Zhang

Minghui Meng

Yawei Guo

Jian-hui Chen

Zhaodi Chen

Roles

Abstract

Objective

Methods

Results

Conclusion

Introduction

Methods

Study design and participants

Data collection

Measurement of screen time (major exposure variables) and category

Table 1. Questions and options regarding the screen time and program of the screen time.

Measurement of ADHD risk

Covariates

Statistical analysis

Results

Analysis of demographic characteristics, screen time, types of screen content, and covariate variables among ADHD risk groups

Table 2. ADHD scores and risk of the Strengths and Difficulties Questionnaire (SDQ).

Table3. Participants sociodemographic characteristics and differences in screen time, types of programs viewed during screen time, and covariate variables among ADHD risk groups (N=41,494).

Relationship between screen time and ADHD risk

Table 4. Relationship between screen time and ADHD risk (N=41,494).

Fig 1. Odd ratios for each covariates and screen time.

Relationship between screen time and ADHD risk in different types of programs viewed during screen time

Table 5. Relationship between screen time and ADHD risk in different types of programs viewed during screen time (N=41,494).

Table 6. Distribution of children at risk of ADHD across different screen time categories.

Discussion

The Relationship between Screen Time and ADHD Risk

The Relationship between Screen content and ADHD Risk

Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Author response to Decision Letter 0

Decision Letter 0

Christine Nardini

Roles

Author response to Decision Letter 1

Decision Letter 1

Christine Nardini

Roles

Author response to Decision Letter 2

Decision Letter 2

Christine Nardini

Roles

Author response to Decision Letter 3

Decision Letter 3

Christine Nardini

Roles

Acceptance letter

Christine Nardini

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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