BMC Public Health
Article in Press
https://doi.org/10.1186/s12889-026-26600-4
Impact of increased educational screen exposure on adolescent overweight and the mediating role of physical activity and sleep quality: a natural experiment study from China
Received: 4 December 2025
Accepted: 5 February 2026
Jiayi Li, Tianshi Wu, Fujian Song, Shuju Li, Wenhui Wang, Qianqian Zhang & Xin Peng
Cite this article as: Li J., Wu T., Song F. et al. Impact of increased educational screen exposure on
We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.
adolescent overweight and the mediating role of physical activity and sleep quality: a natural experiment study from China. BMC Public Health (2026). https://doi.org/10.1186/ s12889-026-26600-4
If this paper is publishing under a Transparent Peer Review model then Peer Review reports will publish with the final article.
© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Impact of increased educational screen exposure on
adolescent overweight and the mediating role of physical
activity and sleep quality: a Natural Experiment Study from
hina
Jiayi Lia, Tianshi Wua, Fujian Songb, Shuju Lic, Wenhui Wangd, Qianqian Zhange,
Xin Pengf
a School of Management, Harbin Institute of Technology, Harbin, China b Norwich Medical School, Faculty of Medicine and Health Science, University of East Anglia, Norwich NR4 7TJ, UK
c Heilongjiang Academy of Chinese Medicine Sciences, Harbin, China
d Guangzhou First People’s Hospital, Guangzhou, China
e School of medicine and health, Harbin Institute of Technology, Harbin, China f Ningbo Hospital of Traditional Chinese Medicine, Ningbo, China
* Address correspondence to: Harbin Institute of Technology, Nangang District, Harbin, Heilongjiang, 150006, PR China.
E-mail address: qianqianzhang15@163.com (Qianqian Zhang).
Abstract
Background: Globally, increasing numbers of adolescents use digital devices for learning, and this educational screen exposure may alter behavioral patterns and be associated with the rising prevalence of adolescent overweight. Limited research has examined the interrelationships among educational screen exposure, adolescent overweight, physical activity, and sleep quality. This study aims to assess the impact of educational screen exposure on adolescent overweight prevalence and to identify related mediating risk behaviors.
Methods: Data on 41,157 adolescents from 16 cities in China were from the Database of Youth Health. Body mass and height were measured to estimate body mass index. Physical activity and sleep quality data were collected using a selfadministered questionnaire. We drew data from two waves (2015 and 2017) of adolescent health surveys, which coincided with China’s online education policy since 2015. The implementation of this policy resulted in a substantial increase in adolescents’ educational screen exposure, with the intensity of the policy implementation influenced by local Internet infrastructure. We allocated adolescents into exposure and control groups based on the local Internet penetration rate. Leveraging this policy-driven natural experiment, a difference-in-differences approach was then applied to compare changes in overweight prevalence, physical activity, and sleep quality before and after the policy implementation between these two groups.
Results: The increase in overweight prevalence of adolescents in the exposure group from 2015 to 2017 was 3.1% greater than those in the control group (Coef. 0.03, 95% CI: 0.01 to 0.05, p < 0.01). The exposure group showed significant reductions in physical activity frequency (Coef. -0.49, 95% CI: -0.52 to -0.45) and sleep quality (Coef. -0.06, 95% CI: -0.09 to -0.02). Mediation analyses suggested that reduced physical activity and sleep quality were plausible mediating variables linking educational screen exposure and overweight prevalence. Male adolescents experienced a greater increase in overweight associated with educational screen exposure compared to their peers (Coef. 0.026, 95% CI: 0.01 to 0.04).
Conclusions: Educational screen exposure may contribute to increased overweight in adolescents by reducing physical exercise and impairing sleep quality. Educators, guardians, and policymakers should consider effective measures to minimize the adverse impacts of educational screen exposure by promoting physical activities among adolescents.
Keywords: Adolescent health, Overweight pandemic, Educational screen exposure, Natural experiment, Difference-in-differences, Mediation analyses
Introduction
Adolescence represents a critical stage of human development, characterized by rapid biological and behavioral transitions, during which body weight becomes particularly sensitive to lifestyle factors [1]. The prevalence of overweight and obesity among adolescents aged 5-19 years has increased more than seven-fold from 1975 to 2016 [2, 3]. Currently affecting up to 20% of adolescents in high-income countries [4], adolescent overweight has become a critical global public health challenge.
Adolescent overweight is associated with health complications including insulin resistance, dyslipidemia, and hypertension [5], with these metabolic disturbances often persisting into adulthood [6, 7]. Adolescents’ behavioral and lifestyle patterns, such as physical activity, sedentary time, and screen use, are relevant to weight outcomes and may play an important role in shaping weight trajectories [8, 9]. For instance, structured increases in physical activity have been shown to lower BMI and improve metabolic profiles in youth populations [10]. These data highlight the urgent need to identify modifiable behaviors contributing to adolescent overweight, particularly as online education becomes increasingly widespread.
Screen-based sedentary behavior has emerged as a potential risk factor for adolescent overweight [11, 12]. Growing concern has arisen regarding the effects of prolonged screen exposure on adolescents’ body weight. Unlike recreational screen time, educational screen exposure is often perceived as necessary and academically beneficial, potentially leading to less parental restriction and longer cumulative exposure duration [13]. Several biological mechanisms may link educational screen exposure to weight gain. Prolonged sitting during online learning sessions reduces energy expenditure and metabolic rate [14], while extended screen exposure suppresses melatonin secretion, with its impact on sleep quality also being associated with weight gain [15, 16]. Furthermore, educational screen time may displace opportunities for physical activity, as structured online learning schedules limit time available for exercise and outdoor activities [17]. These may also serve as potential mediating behaviors in this relationship.
Online education continues to expand globally [18, 19], with online educational course enrollment surpassing 250 million worldwide [20]. Consequently, if this educational modality adversely affects adolescent weight status, it may pose a substantial threat to youth health around the world [21]. Therefore, understanding the association between educational screen exposure and adolescent overweight, and developing public health measures to minimize the potential harmful impacts of increased educational screen exposure among adolescents, is of critical importance. While we cannot reverse this technological trend, we can investigate its effects on adolescent weight status and identify potential mediating factors. Such investigation enables the development of targeted behavioral interventions that may mitigate adverse weight outcomes while preserving the benefits of digital education.
However, existing evidence on educational screen exposure and adolescent weight status shows inconsistent findings. While studies have established associations between overall sedentary behavior and overweight [22, 23], the specific role of educational versus recreational screen time remains unclear and limited. Recreational screen time was found to increase obesity risk during COVID-19, while educational screen use showed no significant association [24]. These pandemic-era studies were potentially confounded by lockdowns and other lifestyle disruptions [25, 26]. Furthermore, evidence specifically examining educational screen exposure remains limited, creating a gap in understanding its role in adolescent weight outcomes.
In 2015, China implemented a nationwide “Internet + Education” policy promoting online learning adoption. This policy had been inconsistently implemented because of differential online exposure across regions. Educational screen exposure increased more in some regions because the policy implementation was contingent on local Internet infrastructure. This setting provides a natural experimental variation in exposure intensity over time and across cities, which motivates our difference-indifferences (DiD) design comparing pre- and post- changes in adolescent overweight between higher- and lower-exposure cities. More specifically, this study aims to investigate: 1) whether educational screen exposure is associated with changes in adolescent overweight, 2) the variation of this association across population subgroups, and 3) the related risk modifiers linking the educational screen exposure and adolescent overweight.
This study makes the following innovations and contributions: 1) By utilizing external policy shock as a natural experiment, this study distinguishes online education screen exposure from other confounding factors, thereby examining the association between online education and body weight; 2) This study innovatively explores potential mediating factors in the relationship between online education and body weight, suggesting that online education may influence adolescent overweight through reductions in physical activity and sleep quality; 3) Findings from our study provided further evidence on the potential harmful impact of educational screen exposure and identified actionable interventions to adjust modifiable behaviors and reduce overweight risk.
Methods
Study population
We used data from the Database of Youth Health (DYH). The DYH program collected a broad spectrum of indicators related to adolescent health from 17 cities in China. It acquired adolescent health metrics through on-site testing, surveys, and other data collection methods. The survey recruited 99,327 students aged 12 to 19 years, randomly selected from 186 secondary schools, with four waves spanning the years 2015, 2016, 2017, and 2020 [27].
We specifically extracted data on the 2015 and 2017 waves from the DYH that coincided with the implementation period of China’s ‘Internet + Education’ policy. We excluded a city with incomplete physical fitness data (n = 3431), and removed individuals outside the age range of 12 to 19 (n = 19). After excluding observations with missing values on key variables including demographic information and health- related measures (n = 324 ), a total of 41,157 adolescents from 16 cities were involved in the final analysis (Agemean = 14.36 years; BMImean = 20.36). The flow chart of the systematic selection process is shown in Figure 1.

Figure 1. Flowchart of the systematic selection process

Educational screen exposure
In 2015, the State Council of China launched the ‘Internet + Education’ policy to expand access to online education services. This policy prompted rapid growth in online education participation across China, resulting in an increase in adolescents’ screen time for educational purposes. This shift in educational screen exposure provides a unique natural experiment to assess the impact of educational screen exposure on adolescent overweight.
In this study, we defined 2015 as the pre-intervention period and 2017 as the post-intervention period. The online education market saw moderate growth of 14.5% annually prior to the policy, but after its implementation, growth rates surged to
29.6% in 2015 and 25.1% in 2016, before declining to 12.8% in 2017 (approximating pre-policy levels). This sharp rise in online education participation, combined with the clear policy timeline (trends between 2015 and 2017 in Figure 2a), allows us to isolate the policy’s impact on adolescents’ screen exposure, minimizing the influence of
other confounding factors.

Figure 2. Online education and Internet penetration in China.
Notes: The unit of measurement for the user population is expressed in tens of thousands of individuals. Data source of user population: China Internet Network Information Center (CNNIC).

Exposure and control groups
Our study leveraged a natural experiment by the implementation of the ‘Internet +
Education’ policy, which expanded the availability and adoption of online education, with the extent of adoption being more significant in cities with better Internet infrastructure. Unlike traditional DiD studies that rely on clear geographical or administrative boundaries to define treatment groups, online education policies present challenges in group delineation because it is difficult to control whether residents in specific regions participate in particular online activities. According to Chen and Liu (2022) [28], cross-city variation in Internet penetration provides a credible source of differential policy intensity. Prior research evidence [28, 29] indicated that regions with more extensive Internet connectivity allowed higher feasibility for policy implementation. We therefore classified cities into exposure and control groups based on the Internet penetration.
While the ‘Internet + Education’ policy represents a common national shock, its impact on adolescents’ educational screen exposure differs across groups. In the control group, limited Internet infrastructure constrains adolescents from accessing online learning, rendering the policy effects negligible for this population. As a result, the ‘Internet + Education’ policy generates a larger increase in educational screen exposure in the exposure group and a relatively smaller change in the control group. Prior research extensively documents the connection between internet penetration rates and the implementation of online education. For instance, Korkmaz et al. demonstrated that during the COVID-19 induced shift to online education, regions with higher baseline Internet penetration exhibited superior implementation outcomes and educational continuity [30]. Consistent findings by Caldarulo et al. [31] and Olanrewaju et al. [32] revealed a strong positive correlation between regional variations in Internet infrastructure and corresponding disparities in online education effectiveness. Furthermore, Mason et al. identified inadequate Internet infrastructure as a critical barrier to the widespread adoption of online education in remote areas [33].
Our estimates should be interpreted as policy intensity effects on trends in adolescent overweight prevalence, rather than the observed unit change in individual screen time and body weight. The ‘Internet + Education’ policy constituted an unpredictable exogenous shock that allows us to isolate the effect of changes in educational screen exposure from other confounders. This ensures that the observed differences in body weight outcomes between the two groups are attributable mainly to differential changes in educational screen exposure, rather than to other online activities stemming from Internet infrastructure disparities. To further exclude the influence of other online activities on weight outcomes, we provided additional sensitive analyses (see supplemental file 3). Our analysis indicates that during our experimental period, other online activities did not exhibit significant changes and thus would not affect adolescents.
This variation in online education access allowed us to define two groups for comparison: the exposure group (n = 21,914), consisting of cities with the abovemedian Internet penetration, and the control group (n = 19,243), consisting of cities with the below-median penetration [28]. Figure 2b shows the stark contrast in Internet
penetration distribution of cities in both groups, with treated areas exhibiting substantially higher connectivity rates. Figure 2c and 2d illustrate the geographical distribution of groups and adolescent overweight rates across cities.
Body mass index (BMI)
Our primary outcome of interest is the prevalence of overweight, assessed through BMI,

calculated as
𝑊𝑒𝑖𝑔ℎ𝑡(kg)
𝐻𝑒𝑖𝑔ℎ𝑡(m)2
The age-specific criteria for determining overweight and

obesity in adolescents were based on the international BMI classification standards proposed by the World Health Organization (WHO) and have been adjusted according to the growth characteristics of Chinese adolescents [34].
Height, weight, and other anthropometric indicators were collected through the physical fitness evaluation in this program, while personal information and health- related behaviors were derived from self-reported survey data.
Covariates
Based on previous literature reviews and theoretical justification, we comprehensively included variables that may influence BMI [35, 36]. These variables included individual characteristics (e.g., age, sex, ethnicity, school adaptation, mental health, quality of life, accessibility of sports facilities) and city characteristics (e.g., the secondary sector’s share of GDP, per capita hospitals, population density). Supplemental Table S1 presents descriptive statistics and definitions for the variables in our study.
Socioeconomic indicators were sourced from the China City Statistical Yearbooks published annually by the National Bureau of Statistics of China, which provide comprehensive socioeconomic data for 288 prefecture cities since 1985. The Internet penetration rates (defined as the ratio of Internet users to the total city population), and household dietary expenditures were obtained from the Government Data Open Platform and the respective Municipal Statistics Bureau.
Comparison of the exposure and control groups
We inferred whether educational screen exposure affects adolescent overweight by comparing pre- and post-policy differences (2015 and 2017) in overweight prevalence between the exposure and control groups. The exposure group experienced a marked increase in educational screen exposure following policy implementation, this shift could plausibly contribute to a greater rise in the prevalence of adolescent overweight than the control group.
We evaluated this association using a difference-in-differences (DiD) design (Figure 3). The essence of DiD is that, in the absence of the online education policy, trends in adolescent overweight would have followed a similar trajectory in the exposure and control groups [37]. After the policy was introduced, the change in overweight observed in the exposure group minus the change in the control group (representing the change that would have occurred in the exposure group without the policy), yields the effect of the policy [38]. This design confines the relevant screentime variation to educational activities, allowing us to identify the impact of educational screen exposure on overweight among adolescents [39]. Figure 3 provides a schematic illustration of the DiD framework used in this study.
The key assumption of the DiD design is that the exposure and control groups would have exhibited parallel pre-policy trends in adolescent overweight. Following Guo et al. (2024) [40], we tested the parallel trends assumption by plotting trend lines. The absence of survey data before 2015 imposes limitations on the application of the parallel trend test utilizing DYH data. Thus, following the methodology employed by Peng et al. (2024) [41], we referred to adolescent survey data from previous studies as a supplement. This method involves aggregating the total number of surveyed adolescent individuals and those identified as overweight from existing studies by time and city, incorporating them into the intervention and control groups across different years to calculate the annual overweight rates in both groups. We collected a total of 114 research papers related to surveys on adolescent overweight in 16 target cities from 2009 to 2017, encompassing 2,002,760 adolescents in previous surveys. Details on data inclusion and screening, analytical procedures, and the parallel-trends assessment are provided in the Supplemental materials.

Figure 3. Schematic illustration of the DiD framework.
Notes: Changes in the control group are not affected by the policy.

Analyses across population subgroups
We investigated the differential impact of educational screen exposure across various population subgroups [42]. First, we differentiated between high school and middle school students, considering their age-specific learning patterns and technological proficiency [43]. The second dimension examined whether adolescents were in their graduating year, as graduating year students have heavier academic pressure from upcoming high school or university entrance examinations [44]. Third, we explored whether educational screen exposure had differential effects across sex groups.
Mediation analyses
We further explored potential mediation effects linking online education to adolescent overweight by focusing on three variables that related to body weight: physical activity, sleep quality, and self-reported health status [45, 46]. For the mediation analyses, we followed a three-step analytic approach [47]. We have examined the effect of educational screen exposure on adolescent overweight in the main DID analysis. Second, we evaluated the impacts of the educational screen exposure on adolescents’ physical activity, sleep quality, and health status, comparing the exposure and control groups. Third, we assessed the combined influence of educational screen exposure and mediating variables on adolescent overweight status.
If the educational screen exposure was associated with the three possible mediators and the possible mediators were associated with overweight, this would suggest that these factors may have mediation effects, linking educational screen exposure and adolescent body weight [48]. In other words, educational screen exposure triggers alterations in these associated behaviors or health states, subsequently leading to overweight. While it is impractical to limit adolescents’ educational screen time, focusing on these modifiable risk behaviors may reduce the detrimental effects of educational screen exposure on body weight. Figure 4 illustrates the conceptual and analytical framework of the mediation analysis.

Figure 4. Conceptual model of the mediation analysis linking educational screen exposure with adolescent overweight and obesity.
Notes: a, b, and β0 denote the path coefficients estimated in the mediation analysis.
Analytical Approach
We used a difference-in-differences design to estimate the effect of increased educational screen exposure on adolescent overweight. The regression model can be summarized as:
Y𝑖,𝑐,𝑡 = α + 𝛽0(Policy𝑐 × Post2017t) + 𝛽1Post2017t + γXi,c,t + μc + εi,c,t	(1) Here, i, c and t indicate individual, city, and year, respectively. Yi,c,t refers to the overweight status of individual i in city c during year t, where 1 represents overweight status and 0 otherwise. Policyc is a dummy variable that equals 1 if city c was in the exposure group and 0 otherwise. Post2017t is a post-shock indicator, which equals 1 if the year is 2017 and 0 otherwise. Xi,c,t is a vector of individual characteristics, including sex, age, and ethnicity. μc represents city fixed effect, and εi,c,t represents the error term.
Details of the analytical approach for effects across population subgroups and mediation analyses are provided in the Supplemental materials. All analyses were conducted in Stata 16.0 using ordinary least squares (OLS) estimation, with statistical significance set at p < 0.05.
Sensitivity analyses
We implemented a series of sensitivity analyses to address potential endogeneity issues and ensure the robustness of our findings. First, we replicated our analysis using obesity status as an alternative outcome variable. Additionally, our model controlled for an extensive set of individual and urban characteristics. Furthermore, we addressed regional imbalances in educational infrastructure, healthcare services, demographic density, and dietary patterns. Fourthly, we treated Internet penetration as a continuous measure and estimated its interaction with the post-policy indicator. To mitigate external confounding effects, we verified and ruled out contemporaneous policy initiatives that could potentially affect adolescent body weight, particularly regional disparities in academic pressure and physical exercise. These included a national policy addressing air pollution that restricted outdoor activities (implemented in 2013 and strengthened in 2015), two regional policies that enhanced moral and psychological education from 2015 to 2020, and a policy encouraging special sports programs from 2015 to 2017.
Sixthly, we accounted for potential interference from non-educational online activities (supplemental material 3). Moreover, we employed a Logit model as an alternative regression. We also conducted placebo tests by varying the treatmentcontrol group assignment, which further corroborated the robustness of our main findings [40, 49, 50]. Finally, we conducted a propensity score matching (PSM) robustness check by matching individuals in two groups on key demographics and reestimating the DiD models. Detailed sensitivity analyses procedures and extended specifications are provided in the Supplementary Materials. Results
Demographics and fitness outcomes of the study population
We included 41,157 adolescents (2015: n=15,073; 2017: n=26,084) from 16 cities in our study. Table 1 summarizes the main characteristics of the study population across groups and periods. The sample consisted of 52.0% boys and 48.0% girls aged 12-19 years and enrolled in high schools (31%) and middle schools (69%). We reported baseline (2015) comparisons in the Supplemental materials although DiD identification does not require equal baseline levels. Higher overweight prevalence and mean BMI were observed in the exposure group than the control group (overweight: 20.4% vs 19.0%; BMImean: 20.10 vs 19.92) in 2015. From 2015 to 2017, overweight prevalence remained relatively stable in the control group (overweight: 19.0% to 19.5%; BMImean: 19.92 to 20.22) but increased markedly in the exposure group (20.4% to 24.9%), with a corresponding rise in mean BMI (20.10 to 20.86). Table 1
The main characteristics and body weight status of the included adolescents
	2015	2017
Control group	Exposure group	Control group	Exposure group	
Age
12-14 (N)
15-17 (N)
18-19 (N)
Graduating year
Yes (N)
No (N)
School stage
High school (N)
Middle School (N)
Sex
Male
Female	5355 (65.0%)	4445	(65.0%)	8491	(62.1%)	7901	(63.7%)
2060 (25.0%)	1710	(25.0%)	3989	(29.2%)	3461	(27.9%)
821 (10.0%)	682 (10.0%)	1198 (8.8%)	1044 (8.4%)
3459 (42.0%)	2570	(37.6%)	5676	(41.5%)	5581	(45.0%)
4777 (58.0%)	4267	(62.4%)	8002	(58.5%)	6825	(55.0%)
2470 (30.0%)	1900	(27.8%)	4391	(32.1%)	3998	(32.2%)
5766 (70.0%)	4937	(72.2%)	9287	(67.9%)	8408	(67.8%)
4442 (53.9%)	3497	(51.1%)	7195	(52.6%)	6268	(50.5%)
3794 (46.1%)	3340	(48.9%)	6483	(47.4%)	6138	(49.5%)	

Overweight rate	0.190	0.204	0.195	0.249	
Obesity rate	0.079	0.085	0.081	0.103	
BMI [mean (±SD)]	19.92 (±3.256)	20.10 (±3.44)	20.22 (±3.23)	20.86 (±3.08)	
Physical activity [mean (±SD)]	2.86 (±0.60)	2.95 (±0.66)	2.78 (±0.62)	2.29 (±0.56)	
Sleep quality [mean (±SD)]	2.79 (±0.75)	2.92 (±0.65)	2.93 (±0.69)	2.87 (±0.67)	
School adaptation [mean (±SD)]	59.01 (±30.87)	58.89 (±30.12)	58.75 (±30.66)	58.82 (±30.08)	
Mental health [mean (±SD)]	-135.74 (±42.74)	-136.41 (±42.69)	-136.28 (±41.97)	-137.17 (±43.01)	
Quality of life [mean (±SD)]	51.17 (±14.11)	51.28 (±13.93)	51.09 (±14.08)	51.14 (±14.03)	
Accessibility of sports facilities [mean (±SD)]	2.76 (±0.78)	2.82 (±0.82)	2.88 (±0.80)	2.97 (±0.77)	
Food consumption profiles [mean (±SD)]	6370.40 (±751.17)	8600.34 (±
1502.37)	6491.44 (±765.44)	8746.55 (±
1527.91)	

Overweight prevalence in exposure and control groups
Leveraging DiD methodology, we compared pre- and post-changes in the exposure group with contemporaneous changes in the control group. Pre-policy trajectories in overweight prevalence appeared parallel for the exposure and control groups (Figure 5), indicating that, in the absence of the policy, the two groups would have been expected to follow similar trends [40, 41].
After the introduction of the ‘Internet + Education’ policy, overweight prevalence among adolescents increased significantly in the exposure group. Table 2 shows the results of the analyses, indicating an overall rise of 3.1 percentage points in overweight prevalence in the exposure group compared with the control group (Coef. 0.03, 95% CI: 0.01 to 0.05, p < 0.01).

Figure 5. Parallel pre-policy trends of overweight prevalence in the intervention and control groups

Notes: This figure shows the result of the parallel trend test in our research. The blue line traces the historical trend in the overweight rates among adolescents in the control group, while the orange line delineates the changes in the overweight rates in the intervention group.
Table 2
DiD estimates of changes in overweight prevalence from 2015 (pre-policy) to 2017 (post-policy): overall and subgroup effects
	Overall Result
(1) Overweight	Subgroup analyses	
		(2)
School Stage	(3) Graduating Year	(4) Sex	
Policy × Post2017	0.0311** (0.0105)	-	-	-	
Policy × Post2017 ×		0.0068			
School Stage	-	(0.011)	-	-	
Policyc × Post2017t ×			0.0018		
Graduating Year	-	-	(0.001)	-	
				0.0257**	
Policyc × Post2017t × Sex	-	-	-	(0.00826)	
Individual controls	Yes	Yes	Yes	Yes	
City fixed effects	Yes	Yes	Yes	Yes	
Observations	41157	41157	41157	41157	

Notes: All reported coefficients are DiD estimates comparing the change in overweight prevalence from 2015 to 2017 between exposure and control groups. Columns (2) to (4) report heterogeneity in the DiD effect. Subgroup analyses capture how the DiD effect differs for the specified subgroup relative to the reference category. Standard errors clustered at the city level are reported in parentheses, * denotes p < 0.05, ** p < 0.01, *** p < 0.001.
Differential impact across various population subgroups
Subgroup analyses (Table 2) showed that the association between educational screen exposure and overweight was more pronounced among male adolescents. Compared to female adolescents, educational screen exposure was associated with an extra 2.6 percentage point increase in overweight prevalence among male adolescents (Coef. 0.026, 95% CI: 0.01 to 0.04, p < 0.01). In contrast, no significant differences in the effect of educational screen exposure were observed across school stages (Coef. 0.0068, 95% CI: -0.01 to 0.03, p > 0.1) or between graduating and non-graduating students (Coef. 0.0018, 95% CI: -0.0001 to 0.004, p > 0.05).
Mediation analysis results
Table 3 shows that increased educational screen exposure was associated with less physical activity (Coef. -0.49, 95% CI: -0.52 to -0.45) and poorer sleep quality (Coef. -0.06, 95% CI: -0.09 to -0.02). In models relating these mediators to weight status (columns 4 and 5 of Table 3), less physical activity and poorer sleep were associated with higher overweight prevalence (exercise: Coef. 0.075, 95% CI: 0.026 to 0.124; sleep quality: Coef. 0.017, 95% CI: 0.0052 to 0.0288). These results are consistent with an indirect pathway whereby educational screen exposure may contribute to overweight partly through reduced activity and impaired sleep (Table 4). Selfreported health status showed no clear association with overweight (Table 3), which may reflect the subjective and multidimensional nature of this measure.
Table 3
Results of mediation analyses: association between educational screen exposure, possible mediating variables, and overweight prevalence.
Association between Educational screen exposure	Association between intermediate variables and
	and intermediate variables	overweight prevalence	
	(1)
Physical activity	(2)
Sleep quality	(3)
Health status	(4)
Less physical activity and overweight	(5)
Poor sleep quality and overweight	
Policy	-0.489***	-0.0557**	-0.0131	0.0164**	0.0224**	
× Post2017	(0.0179)	(0.0188)	(0.0179)	(0.005)	(0.074)	
Physical activity	-	-	-	0.030*** (0.002)	-	
Sleep quality	-	-	-	-	0.156**
(0.048)	
Health status	-	-	-	-	-	
Individual controls	Yes	Yes	Yes	Yes	Yes	
City fixed effects	Yes	Yes	Yes	Yes	Yes	
Observations	41157	41157	41157	41157	41157	

Notes: The top of each column is the dependent variable. Standard errors clustered at the city level are reported in parentheses, * denotes p < 0.05, ** p < 0.01, *** p < 0.001.
Table 4
Indirect effects, total effects, and mediating effects of physical activity and sleep quality.
Path	Indirect effect	Total effect	VAF	Mediating effect	95% cof. interval	
ESE → PA → O	0.0147***	0.0311**	47.3%	Partial mediating	[0.0059,0.0235]	
ESE → SQ → O	0.0087**	0.0311**	28.0%	Partial mediating	[0.0009,0.0165]	

Notes: ESE stands for educational screen exposure, PA for Physical Activity, O for Overweight, SQ for Sleep Quality, and HS for Health Status. Indirect effects are calculated as the product of coefficients (a×b). Standard errors clustered at the city level are reported in parentheses, * denotes p < 0.05, ** p < 0.01, *** p < 0.001.
Results of sensitivity analyses
The main results of our sensitivity analyses are presented in Table 5, with most estimates being consistent with the main specification, supporting the robustness of our findings. The insignificance of the empirical results after controlling for dietary factors may be attributed to the limitation of using household food expenditure as a proxy for dietary patterns and nutritional intake, particularly regarding high-calorie, low-nutrient foods. Additional supporting results (including supplementary tables and figures) are provided in the Supplementary Materials.
Table 5
Sensitivity analysis results for DiD estimates using alternative controls and specifications
Alternative Specifications	Additional Variables and Controls	Policyc × Post2017t	T-statistic	R-squared	
Substitute outcome measure		0.0291***			
	Obesity	(0.00704)	4.14	0.016	
Supplementary control variables	Additional individual controls	0.0656*** (0.0129)	5.09	0.026	
	Cc,2015 × Post2017t	0.133* (0.0561)	2.37	0.026	
Address regional imbalances	Educational infrastructure	0.0254* (0.0107)	2.38	0.026	
	Healthcare services	0.285*** (0.0384)	7.41	0.026	
	Demographic density	0.0603***	4.31	0.026	
		(0.0140)			
	Food consumption profiles	0.0362 (0.0200)	1.81	0.025	
Continuous measure of Internet	Infrastructurec × Post2017t	0.0157**	3.02	0.026	
penetration		(0.0052)			
Concurrent policy controls	Weather pollution policy controls	0.0257* (0.0120)	2.07	0.025	
	Psychological health policy controls	0.0369** (0.0124)	2.97	0.025	
Change the model specification	Physical activity policy controls	0.0238* (0.0107) 0.165**	2.22	0.026	
					
	Logit regression estimates	(0.0538)
0.0281**	2.59	0.024	
					
Propensity score matching	Policy × Post2017	(0.0089)	3.16	0.025	

Notes: The dataset comprises 41,157 observations. Individual controls and City fixed effects are included. Standard errors clustered at the city level are reported in parentheses, * denotes p < 0.05, ** p < 0.01, *** p < 0.001.
Discussion
As a critical phase of human development, adolescent overweight has garnered increasing attention. This study evaluated the association between educational screen exposure and weight gain among Chinese adolescents and explored relevant behavioral factors that may potentially shape weight outcomes within this relationship.
Findings from this study show that increased educational screen exposure was associated with a significant rise of 3% (95% CI 1% to 5%) in the overweight prevalence among adolescents after the implementation of the national “Internet + Education” policy. This suggests that the greater exposure to online education was associated with an increased risk of adolescent overweight. In other words, the growing integration of online education into students’ daily lives may have unintended adverse health consequences, highlighting the need to consider potential trade-offs between digital learning expansion and adolescent physical health.
In our study, we empirically observed reductions in physical activity and sleep quality among adolescents with higher educational screen exposure, both recognized risk factors for weight gain. The observed links between insufficient physical activity, poorer sleep quality, and overweight are aligned with prior evidence in adolescent populations [45, 46]. These findings suggest that increasing the frequency of physical activity and avoiding screen use before bedtime may help mitigate the potential adverse impact of educational screen exposure on overweight. Mechanistically, prior literature has proposed that extended educational screen exposure may disrupt circadian rhythms and melatonin secretion, leading to compromised sleep quality and subsequently being linked to body weight outcomes [51]. Furthermore, screen-based learning may diminish opportunities for outdoor exercise and physical activities, thereby shifting overall energy balance toward weight accumulation [52]. These mechanisms are consistent with our findings that physical activity and sleep quality are mediating variables which may help explain the observed association between educational screen exposure and overweight. Given that online education has become an inevitable trend, restricting adolescent educational screen exposure appears challenging and undesirable [52]. Alternatively, parents, schools, and policymakers can implement interventions targeting physical activity, sleep quality, and other modifiable factors to minimize the potential adverse implications of educational screen exposure on overweight outcomes. For example, increasing adolescent physical activity and avoiding educational screen exposure before bedtime may mitigate the potential adverse effects of online education (Figure 6).

Figure 6. Illustration of educational screen exposure impacts and the public health interventions targeting mediating variables.

We also observed that male adolescents were more vulnerable to weight gain associated with increased educational screen time. This sex-specific association of online education on adolescent overweight highlights the importance of considering the development of sex-specific public health interventions. However, the mechanisms underlying these sex differences were not directly examined in this study and remain unclear. Future research could clarify the behavioral and biological pathways that may account for this heterogeneity.
Policy and practice implications
Our study extends the existing literature, which has primarily focused on the academic benefits of online education [53], by revealing potential health-related consequences. While extensive research has highlighted the role of dietary habits [54– 56] and family socioeconomic status [57, 58] in adolescent overweight, our findings emphasize that educational environments also constitute an important risk factor.
This study provides important public health insights in the post-pandemic digital learning era. Our analysis was conducted between 2015 and 2017, before the COVID- 19 pandemic, which allows us to isolate the health effects of educational screen exposure from pandemic-related disruptions. This may be why our findings differ from Liu et al. (2023) [24], which found no association between online education and adolescent overweight, as COVID-19-related factors could have obscured the underlying relationship. Furthermore, as AI-powered learning platforms become increasingly prevalent in educational systems [59], our conclusions offer valuable insights for policymakers and educators implementing these emerging technologies. Given the similarities between online education and AI-powered learning platforms, particularly concerning screen-based learning environments and associated sedentary behaviors, our conclusions remain relevant in the design of AI-based educational interventions in mitigating potential negative health effects.
Protecting adolescent health in the digital era requires coordinated efforts among families, schools, and policymakers. They should design learning environments that promote both academic achievement and healthy lifestyles [60, 61]. Schools can incorporate structured schedules that include regular activity breaks, and provide assignments that encourage outdoor activity. Families can encourage regular physical activity, set consistent bedtimes, and monitor educational screen use particularly in the evening to help protect sleep quality. Policymakers can facilitate these efforts by issuing evidence-based guidance for healthy online learning practices and by supporting programs that promote physical activity and sleep health in schools and communities. Future research should evaluate the effectiveness of such interventions and identify risk factors for vulnerable subgroups. By prioritizing adolescent health in digital learning design, societies can better balance the benefits of technological innovation with the long-term goal of fostering healthy, active youth populations.
Limitations and future directions
In this natural experimental study, due to data limitations in individual educational time, we assumed that the implementation of the online education policy was associated with the local rate of Internet penetration. Regions with higher initial Internet penetration were more significantly affected by the ‘Internet + Education’policy, and these areas experienced greater changes in adolescents’ educational screen exposure. Although substantial literature supports this perspective and we conducted extensive sensitivity analyses, this classification still did not accurately capture individual educational screen exposure variations. Another limitation is that the subjective nature of some available data compromises the precision of health status measurements, and dietary measures were not available at the individual level. In addition, the mediators and the outcome variables were measured contemporaneously in the survey waves. Given these limitations, future studies should incorporate more granular individual-level exposure measures (e.g., self-reported or device-based educational screen time), objective measures of health status, dietary patterns, and designs with temporally separated measurements of mediators and outcomes to more rigorously assess mechanisms and strengthen causal inference.
Conclusions
Our study reveals that educational screen exposure was linked with the increased the prevalence of adolescent overweight, likely through reduced physical exercise frequency and deteriorated sleep quality. Additionally, the weight status of male adolescents was more susceptible to the influence of educational screen exposure compared to their female counterparts. To mitigate the impact of online education on adolescent weight gain, public health interventions may target modifiable mediating variables, including physical activity and sleep quality.
Declarations
Ethics approval and consent to participate
Patients and the public are not involved in this study. This study does not involve human participants; ethical approval was not required for this study. We used publicly available, non-identifiable secondary survey data (from the DYH program). For completeness, we further clarify that the DYH program receives government permission and follows ethical practices including informed consent and assurance of confidentiality, and was approved by the ethical committees of Shandong University (20180517).
Consent for publication
Not applicable.
Availability of data and materials
Data may be obtained from a third party and are not publicly available. These data are available from the Database of Youth Health (Shandong University) hosted by the National Population Health Data Center (PHDA) and can be accessed via DOI: https://doi.org/10.12213/11.A0031.202107.209.V1.0.
(https://www.ncmi.cn/index.html.), subject to the repository’s terms of use. The authors had no special access permissions that others could not have. The data on socioeconomic indicators are available from the China City Statistical Yearbooks published annually by the National Bureau of Statistics of China (https://www.stats.gov.cn). All data generated or analyzed during this study are included in this manuscript and its additional files.
Competing interests
None declared.
Funding
This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors.
Authors’ contributions
JL designed the study, collected the data, performed the statistical analysis, and drafted the manuscript. TW and FS contributed to the analysis, and provided coordination and revisions for the manuscript. All authors contributed to the study, reviewed the manuscript, and approved the final version for submission. QZ provided analytical support and critical feedback on the manuscript. SL made contributions to the interpretation of data and revision of the manuscript. WW and XP revised the manuscript critically for important intellectual content.
Acknowledgments
The authors wish to acknowledge their profound gratitude to Professor Doug Vogel and Apei Song for their invaluable insights and constructive feedback on previous drafts of this manuscript. Additionally, the authors express their appreciation to the National Population Health Data Center for providing access to public data through their online platform.
Authors’ information
School of Management, Harbin Institute of Technology, Harbin, China Jiayi Li & Tianshi Wu
Norwich Medical School, Faculty of Medicine and Health Science, University of East
Anglia, Norwich NR4 7TJ, UK
Fujian Song
School of medicine and health, Harbin Institute of Technology, Harbin, China
Qianqian Zhang
Heilongjiang Academy of Chinese Medicine Sciences, Harbin, China
Shuju Li
Guangzhou First People’s Hospital, Guangzhou, China
Wenhui Wang
Ningbo Hospital of Traditional Chinese Medicine, Ningbo, China
Xin Peng
References
1.		Winpenny EM, Van Sluijs EMF, White M, Klepp K-I, Wold B, Lien N. Changes in diet through adolescence and early adulthood: longitudinal trajectories and association with key life transitions. Int J Behav Nutr Phys Act. 2018;15:86. https://doi.org/10.1186/s12966-018-0719-8.
2.		Abarca-Gómez L, Abdeen ZA, Hamid ZA, Abu-Rmeileh NM, Acosta-Cazares B, Acuin C, et al. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. The Lancet. 2017;390:2627–42. https://doi.org/10.1016/S0140-6736(17)32129-3.
3.		Phelps NH, Singleton RK, Zhou B, Heap RA, Mishra A, Bennett JE, et al. Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. The Lancet. 2024;403:1027–50. https://doi.org/10.1016/S0140- 6736(23)02750-2.
4.		Lister NB, Baur LA, Felix JF, Hill AJ, Marcus C, Reinehr T, et al. Child and adolescent obesity. Nat Rev Dis Primers. 2023;9:24. https://doi.org/10.1038/s41572- 023-00435-4.
5.		Reinehr T. Long-term effects of adolescent obesity: time to act. Nat Rev Endocrinol. 2018;14:183–8. https://doi.org/10.1038/nrendo.2017.147.
6.		Reilly JJ. Descriptive epidemiology and health consequences of childhood obesity. Best Practice & Research Clinical Endocrinology & Metabolism. 2005;19:327–41. https://doi.org/10.1016/j.beem.2005.04.002.
7.		Aarestrup J, Bjerregaard LG, Meyle KD, Pedersen DC, Gjærde LK, Jensen BW, et al. Birthweight, childhood overweight, height and growth and adult cancer risks: a review of studies using the Copenhagen School Health Records Register. Int J Obes. 2020;44:1546–60. https://doi.org/10.1038/s41366-020-0523-9.
8.		Eddolls WTB, McNarry MA, Stratton G, Winn CON, Mackintosh KA. High- Intensity Interval Training Interventions in Children and Adolescents: A Systematic Review. Sports Med. 2017;47:2363–74. https://doi.org/10.1007/s40279-017-0753-8.
9.		Poitras VJ, Gray CE, Borghese MM, Carson V, Chaput J-P, Janssen I, et al. Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth. Appl Physiol Nutr Metab. 2016;41 6 (Suppl. 3):S197–239. https://doi.org/10.1139/apnm-2015-0663.
10.		Men J, Zhu G, Li Y, Wu S, Yu Z, Wang P, et al. Impact of exercise on cardiovascular disease risk in overweight or obese children and adolescents: a systematic review and meta-analysis. BMC Sports Sci Med Rehabil. 2025;17:225. https://doi.org/10.1186/s13102-025-01228-w.
11.		Pereira LJ, Hinnig PDF, Matsuo LH, Di Pietro PF, De Assis MAA, Vieira FGK. Association between lifestyle patterns and overweight and obesity in adolescents: a systematic review. Br J Nutr. 2023;129:1626–44. https://doi.org/10.1017/S0007114522000228.
12.		Nygaard M, Olsen MF, Thomsen MMW, Hadi NHA, Trans KL, Horwood S, et al. Longitudinal investigation of psychological outcomes associated with screen use in Danish preschool children: study protocol for The Digital Child. BMJ Open. 2025;15:e103198. https://doi.org/10.1136/bmjopen-2025-103198.
13.		Robinson TN, Banda JA, Hale L, Lu AS, Fleming-Milici F, Calvert SL, et al. Screen Media Exposure and Obesity in Children and Adolescents. Pediatrics. 2017;140 Supplement_2:S97–101. https://doi.org/10.1542/peds.2016-1758K.
14.		Hadianfard AM, Mozaffari-Khosravi H, Karandish M, Azhdari M. Physical activity and sedentary behaviors (screen time and homework) among overweight or obese adolescents: a cross-sectional observational study in Yazd, Iran. BMC Pediatr. 2021;21:421. https://doi.org/10.1186/s12887-021-02892-w.
15.		Kwon A, Kim S, Choi Y, Kim HY, Lee M, Lee M, et al. Effects of Early WakeUp Time on Obesity in Adolescents. Childhood Obesity. 2023;20:188–97. https://doi.org/10.1089/chi.2023.0016.
16.		Ji X, Covington LB, Patterson F, Ji M, Brownlow JA. Associations between sleep and overweight/obesity in adolescents vary by race/ethnicity and socioeconomic status. Journal of Advanced Nursing. 2023;79:1970–81. https://doi.org/10.1089/chi.2023.0016.
17.		Aguirre MDJX, Drumond Andrade FC, Aguirre MAC, Justino JR, Maciel BLL. Social Network, Food Patterns, Physical Activity and Associations with Overweight and Obesity in Adolescents from a School in Rural Brazil. Nutrients. 2023;15:3305. https://doi.org/10.3390/nu15153305.
18.		Selvaraj A, Radhin V, Ka N, Benson N, Mathew AJ. Effect of pandemic based online education on teaching and learning system. International Journal of Educational Development. 2021;85:102444. https://doi.org/10.1016/j.ijedudev.2021.102444.
19.		Akabayashi H, Taguchi S, Zvedelikova M. Access to and demand for online school education during the COVID-19 pandemic in Japan. International Journal of Educational Development. 2023;96:102687. https://doi.org/10.1016/j.ijedudev.2022.102687.
20.		Levin RC. Online Learning & the Transformation of Global Higher Education. Daedalus. 2024;153:262–74. https://doi.org/10.1162/daed_a_02079.
21.		Ferrari G, Giannichi B, Resende B, Paiva L, Rocha R, Falbel F, et al. The economic burden of overweight and obesity in Brazil: perspectives for the Brazilian Unified Health System. Public Health. 2022;207:82–7. https://doi.org/10.1016/j.puhe.2022.03.015.
22.		Ma M, Luo C. The Effect of Student and Peer Assessment Engagement on Learning Performance in Online Open Courses. Int J Emerg Technol Learn. 2022;17:145–58. https://doi.org/10.3991/ijet.v17i10.30931.
23.		Mo Z, Wang H, Zhang B, Ding G, Popkin BM, Du S. The Effects of Physical Activity and Sedentary Behaviors on Overweight and Obesity among Boys may Differ from those among Girls in China: An Open Cohort Study. The Journal of Nutrition. 2022;152:1274–82. https://doi.org/10.1093/jn/nxab446.
24.		Liu Y, Sun X, Zhang E, Li H, Ge X, Hu F, et al. Association between Types of Screen Time and Weight Status during the COVID-19 Pandemic: A Longitudinal Study in Children and Adolescents. Nutrients. 2023;15:2055. https://doi.org/10.3390/nu15092055.
25.		Łuszczki E, Bartosiewicz A, Pezdan-Śliż I, Kuchciak M, Jagielski P, Oleksy Ł, et al. Children’s Eating Habits, Physical Activity, Sleep, and Media Usage before and during COVID-19 Pandemic in Poland. Nutrients. 2021;13:2447. https://doi.org/10.3390/nu13072447.
26.		Kovacs VA, Starc G, Brandes M, Kaj M, Blagus R, Leskošek B, et al. Physical activity, screen time and the COVID‐19 school closures in Europe – An observational study in 10 countries. European Journal of Sport Science. 2022;22:1094–103. https://doi.org/10.1080/17461391.2021.1897166.
27.		Yi X, Fu Y, Burns RD, Bai Y, Zhang P. Body mass index and physical fitness among Chinese adolescents from Shandong Province: a cross-sectional study. BMC Public Health. 2019;19:81. https://doi.org/10.1186/s12889-019-6420-2.
28.		Chen L, Liu W. The effect of Internet access on body weight: Evidence from China. Journal of Health Economics. 2022;85:102670. https://doi.org/10.1016/j.jhealeco.2022.102670.
29.		López R, Valarezo Á, Pérez-Amaral T. Unleashing the potential of online learning in Spain: An econometric analysis. Telecommunications Policy. 2023;47:102544. https://doi.org/10.1016/j.telpol.2023.102544.
30.		Korkmaz Ö, Erer E, Erer D. Internet access and its role on educational inequality during the COVID-19 pandemic. Telecommunications Policy. 2022;46:102353. https://doi.org/10.1016/j.telpol.2022.102353.
31.		Caldarulo M, Mossberger K, Howell A. Community-wide broadband adoption and student academic achievement. Telecommunications Policy. 2023;47:102445. https://doi.org/10.1016/j.telpol.2022.102445.
32.		Olanrewaju GS, Adebayo SB, Omotosho AY, Olajide CF. Left behind? The effects of digital gaps on e-learning in rural secondary schools and remote communities across Nigeria during the COVID19 pandemic. International Journal of Educational Research Open. 2021;2:100092. https://doi.org/10.1016/j.ijedro.2021.100092.
33.		Mason R, Rennie F. Broadband: A Solution for Rural e-Learning? IRRODL. 2004;5. https://doi.org/10.19173/irrodl.v5i1.173.
34.		Ke Y, Zhang S, Hao Y, Liu Y. Associations between socioeconomic status and risk of obesity and overweight among Chinese children and adolescents. BMC Public Health. 2023;23:401. https://doi.org/10.1186/s12889-023-15290-x.
35.		Shier V, An R, Sturm R. Is there a robust relationship between neighbourhood food environment and childhood obesity in the USA? Public Health. 2012;126:723– 30. https://doi.org/10.1016/j.puhe.2012.06.009.
36.		Feeley AB, Musenge E, Pettifor JM, Norris SA. Investigation into longitudinal dietary behaviours and household socio-economic indicators and their association with BMI Z -score and fat mass in South African adolescents: the Birth to Twenty (Bt20) cohort. Public Health Nutr. 2013;16:693–703. https://doi.org/10.1017/S1368980012003308.
37.		Mohammadi N, Alizadeh A, Moghaddam SS, Ghasemi E, Ahmadi N, Yaseri M, et al. The causal effect of family physician program on the prevalence, screening, awareness, treatment, and control of hypertension and diabetes mellitus in an Eastern Mediterranean Region: a causal difference-in-differences analysis. BMC Public Health. 2023;23:1152. https://doi.org/10.1186/s12889-023-16074-z.
38.		Das S, Rasul MG, Khan A-R, Fahim SM, Sanin KI, Ahmed T. Success of health cell approach in improving knowledge, attitude and practice related to COVID-19: difference-in-differences analyses of a community-based quasi-experimental trial. BMJ Open. 2023;13:e067096. https://doi.org/10.1136/bmjopen-2022-067096.
39.		Hawkins SS, Bach N, Baum CF. Impact of tobacco control policies on adolescent smokeless tobacco and cigar use: a difference-in-differences approach. BMC Public Health. 2018;18:154. https://doi.org/10.1186/s12889-018-5063-z.
40.		Guo L, Song J, Yang L, Wu Z, Shi H, Song L, et al. Internet usage elevates elderly obesity: evidence from a difference-in-differences analysis of the broadband China policy. Arch Public Health. 2025;83:68. https://doi.org/10.1186/s13690-025- 01565-9.
41.		Peng W, Chen S, Chen X, Ma Y, Wang T, Sun X, et al. Trends in major noncommunicable diseases and related risk factors in China 2002–2019: an analysis of nationally representative survey data. The Lancet Regional Health - Western Pacific. 2024;43:100809. https://doi.org/10.1016/j.lanwpc.2023.100809.
42.		Wang Y, Yu R, Liu Y, Qian W. Students’ and Teachers’ Perspective on the Implementation of Online Medical Education in China: A Qualitative Study. AMEP. 2021;Volume 12:895–903. https://doi.org/10.2147/AMEP.S323397.
43.		Zhang Y, Zhao G, Zhou B. Does learning longer improve student achievement? Evidence from online education of graduating students in a high school during COVID-19 period. China Economic Review. 2021;70:101691. https://doi.org/10.1016/j.chieco.2021.101691.
44.		Tran VD. Perceived satisfaction and effectiveness of online education during the COVID-19 pandemic: the moderating effect of academic self-efficacy. Higher Education Pedagogies. 2022;7:107–29. https://doi.org/10.1080/23752696.2022.2113112.
45.		Patel SR, Malhotra A, White DP, Gottlieb DJ, Hu FB. Association between Reduced Sleep and Weight Gain in Women. American Journal of Epidemiology. 2006;164:947–54. https://doi.org/10.1093/aje/kwj280.
46.		Patel SR, Hu FB. Short Sleep Duration and Weight Gain: A Systematic Review. Obesity. 2008;16:643–53. https://doi.org/10.1038/oby.2007.118.
47.		Baron RM, Kenny DA. The Moderator-Mediator Variable Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical Considerations. Journal of personality and social psychology. 1986;:1173.
48.		Zablotsky B, Ng AE, Black LI, Haile G, Bose J, Jones JR, et al. Associations Between Screen Time Use and Health Outcomes Among US Teenagers. PREVENTING CHRONIC DISEASE. 2025;22.
49.		Roth J, Sant’Anna PHC, Bilinski A, Poe J. What’s trending in difference-indifferences? A synthesis of the recent econometrics literature. Journal of Econometrics. 2023;235:2218–44. https://doi.org/10.1016/j.jeconom.2023.03.008.
50.		Ma Q, Zhang Y, Hu F, Zhou H, Hu H. Nip it in the bud: the impact of China’s large-scale free physical examination program on health care expenditures for elderly people. Humanit Soc Sci Commun. 2025;12:27. https://doi.org/10.1057/s41599-024- 04295-5.
51.		Park Y-MM, White AJ, Jackson CL, Weinberg CR, Sandler DP. Association of Exposure to Artificial Light at Night While Sleeping With Risk of Obesity in Women. JAMA Intern Med. 2019;179:1061.
https://doi.org/10.1001/jamainternmed.2019.0571.
52.		Holly L, Wong BLH, Van Kessel R, Awah I, Agrawal A, Ndili N. Optimising adolescent wellbeing in a digital age. BMJ. 2023;380:e068279. https://doi.org/10.1136/bmj-2021-068279.
53.		Li X, Zhou S, He Z. From online planning education to future planning education in a global view: A case study of online engineering education in MOOCs and its characteristics. Comp Applic In Engineering. 2023;31:755–76. https://doi.org/10.1002/cae.22606.
54.		Do EK, Bowen GA, Ksinan AJ, Adams EL, Fuemmeler BF. Sleep, food insecurity, and weight status: Findings from the family life, activity, sun, health, and eating study. Childhood Obesity. 2021;17:125–35. https://doi.org/10.1089/chi.2020.0259.
55.		Wang L, Zhuang J, Zhang H, Lu W. Association between dietary knowledge and overweight/obesity in Chinese children and adolescents aged 8–18 years: a crosssectional study. BMC Pediatr. 2022;22:558. https://doi.org/10.1186/s12887-022- 03618-2.
56.		Hu H, Song J, MacGregor GA, He FJ. Consumption of Soft Drinks and Overweight and Obesity Among Adolescents in 107 Countries and Regions. JAMA Netw Open. 2023;6:e2325158. https://doi.org/10.1001/jamanetworkopen.2023.25158.
57.		Hochfeld T, Graham L, Patel L, Moodley J, Ross E. Does school breakfast make a difference? An evaluation of an in-school breakfast programme in South Africa. International Journal of Educational Development. 2016;51:1–9. https://doi.org/10.1016/j.ijedudev.2016.07.005.
58.		NCD Risk Factor Collaboration (NCD-RisC), Mishra A, Zhou B, Rodriguez- Martinez A, Bixby H, Singleton RK, et al. Diminishing benefits of urban living for children and adolescents’ growth and development. Nature. 2023;615:874–83. https://doi.org/10.1038/s41586-023-05772-8.
59.		Yan Z, Qianjun T. Integrating AI-generated content tools in higher education: a comparative analysis of interdisciplinary learning outcomes. Sci Rep. 2025;15:25802. https://doi.org/10.1038/s41598-025-10941-y.
60.		Szabó A, Fekete M, Böcskei B, Nagy Á. Real-time experiences of Hungarian youth in digital education as an example of the impact of pandemia. “I’ve never had better grades on average: I got straight all the time.” International Journal of Educational Development. 2023;99:102782. https://doi.org/10.1016/j.ijedudev.2023.102782.
61.		Ulum H. The effects of online education on academic success: A meta-analysis study. Educ Inf Technol. 2022;27:429–50. https://doi.org/10.1007/s10639-021-10740- 8.