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The prevalence of myopia and eye health behaviors among 3 to 18 years: a cross-sectional survey study

BMC Public Health volume 25, Article number: 1688 (2025) Cite this article

Abstract

Aims

To determine the current status of self-reported myopia and the relationship between eye health behaviors and myopia in children and adolescents aged 3 to 18 years.

Methods

Children and adolescents aged 3–18 years from 15 schools were selected for the survey between January and June 2024 in Hubei Province, China. A stratified random cluster sampling method was employed for the collection of samples. All participants were requested to complete the Eye Health Behavior Assessment Scale for Kindergarten and Primary School Students in Grades 1-3-Parent Version (EHBAS-P) and the Eye Health Behavior Assessment Scale for Primary School Students in Grades 4–6 and Middle School Students-Student Version (EHBAS-S) online. Data on socio-demographic factors (gender, grades, region) and myopia rate were collected.

Results

A total of 3500 participants were invited, and 3240 usable questionnaires were collected (response rate, 92.57%). The overall prevalence of self-reported myopia among children and adolescents aged 3–18 years in Hubei Province was 34.35%. Risk of myopia was higher in females than in males (OR = 1.27, P = 0.007), in rural than in urban areas (OR = 1.88, P < 0.001), and in children with myopic parents than with non-myopic parents (OR = 3.21, P < 0.001). Furthermore, of the 3240 participants, only 18.1% (n = 587) had good eye health behavior levels, 46.1% (n = 1494) had moderate eye health behavior levels, and the rest had poor levels, and there was a significantly higher risk of myopia for poor compared to good eye health behavior levels (OR = 1.74, P < 0.001). The regression analysis showed that the level of eye health behaviors varied significantly with many of the demographic variables particularly with grades group, gender and whether myopia.

Conclusion

The prevalence of self-reported myopia is at a high level among individuals between the ages of 3 and 18 in Hubei province, China, with notable differences between urban and rural populations. The level of eye health behaviors among children and adolescents is suboptimal. A particular focus on fostering the development of positive eye-use habits among younger children should be reinforced in the future.

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Introduction

Myopia is one of the most common eye diseases globally, with a prevalence of 10–30% in the adult population in many countries and 80–90% in young adults in some parts of East and Southeast Asia [1]. The prevalence is estimated to increase to 4.76 billion individuals (49.8% of the global population) for myopia and almost 1 billion individuals (9.8% of the global population) for high myopia by 2050 [2]. China has one of the highest global prevalence of myopia. According to survey data from the National Administration of Disease Control and Prevention, the overall myopia prevalence among Chinese children and adolescents reached 51.9% in 2022, with rates of 36.7% in primary schools, 71.4% in junior high schools, and 81.2% in senior high schools [3]. Moreover, a nationwide cost-of-illness study illustrated that the total economic burden associated with myopia in the whole country was estimated as 173.6 billion CNY (26.3 billion US$) in China [4].

Eye health behaviors were confirmed to show protective effects on the prevention or alleviation of myopia by numerous studies, and eye health behaviors were mainly composed of increased outdoor activity, regular physical exercise, reduced intensive near-work, healthy eating habits, and sufficient sleep time [5,6,7,8]. It was found in a 23-year longitudinal study that limited outdoor activity in childhood would significantly accelerate the progression of myopia, and a short reading distance during childhood tended to be associated with higher adult myopia severity [9]. Another random clinical trial in China illustrated that the addition of outdoor activity at school for 40 min would contribute to a reduced incidence rate of myopia over the next 3 years versus those without addition [10]. A survey about the possible associations between dietary factors and myopia revealed that higher saturated fat and cholesterol intake are associated with longer axial length [11]. More recently, Wang performed a meta-analysis and proposed that sufficient sleep duration was associated with a lower risk of myopia [12]. Given the critical functions of eye health behaviors in the prevention and alleviation of myopia, eye health behaviors have been prioritized by China’s national myopia control initiatives as primary preventive measures [13]. In 2021, the “Double Reduction” policy was proposed by the Chinese government to ensure standardized eye health protocols and daily outdoor time for children and adolescents [14].

Although the eye health behaviors and associated factors have been investigated by some previous studies [15, 16], further research was required due to the lack of comprehensive and multi-center analysis, particularly on Chinese rural children. Moreover, Previous studies on eye health behaviors have primarily focused on specific individual behaviors, with limited application of structured scales for comprehensive evaluation. Therefore, this study aimed to evaluate the prevalence of myopia and the associated health behaviors among youth in Hubei Province, China, and it was expected to provide valuable insights into the creation of educational materials, which could support myopia management programs in similar settings.

Methods

Study design and participants

This is a school-based online cross-sectional survey conducted in Hubei Province, China from January to June 2024. The survey samples were collected from children and adolescents aged 3–18 years using a stratified random cluster sampling method. A total of 15 schools were selected from different regions of Hubei Province, including Xiaogan, Shiyan and Wuhan. These schools were then stratified by grade, and a random cluster sampling of teaching classes was employed to constitute the study sample. The exclusion criteria for this study included those (1) who declined to participate in the questionnaire (2) who had not undergone vision screening and refraction examinations within the past 12 months. (3) who had systemic diseases or ocular comorbidities (e.g., amblyopia, strabismus, cataracts). Based on the results of the most recent vision screening among students, we used self-reported method, a proven approach with significant practical value in assessing health conditions of large populations [17], to evaluate the prevalence of myopia.

Sample size

The sample size was determined using the formula \(\:\text{N}=\frac{{{{\upmu\:}}_{{\upalpha\:}}^{2}}_{}\times\:\text{P}(1-\text{P})}{{{\updelta\:}}^{2}}\), and was calculated based on the 10% myopia rate in the estimated population [18]. The confidence interval was 95%, and the allowable error (δ) = 0.1p, α = 0.05, µα = 1.64. Previous studies have shown that for a population of more than 10 million people, the minimum sample size was determined to be 400 people, with a power of 80% to find significant differences [19].The resident population of Hubei Province was 57.75 million, of which 9.42 million were children and adolescents, accounting for 16.31% [20].Therefore we believe that the sample size of 3,000 is representative of the current situation of eye health behaviors of children and adolescents in Hubei Province.

Questionnaires

The study collected demographic data through self-reporting by respondents, including gender, age, region, grades, whether myopic, whether parents were myopic, sleep time per day, time spent using electronics per day, and eye health behavior levels. Regions in our study are classified into urban and rural areas based on the Chinese National Bureau of Statistics definition, where urban areas correspond to city/municipality-level administrative units, and rural areas encompass villages/townships. The grades are divided into five groups: kindergarten (3–6 years), primary school (grades 1–3: 7–9 years; grades 4–6: 10–12 years), junior high school (13–15 years), and senior high school (16–18 years). Myopia was defined as spherical equivalent (SE) ≤ -0.50 D in non-ciliary muscle paralysis (SE = spherical + 1/2 column). Parental myopia was defined as one of the parents having clinically diagnosed myopia (SE ≤ -0.50 D). Sleep time per day was divided into three groups: ≥10 h, 8–9 h, and < 8 h. Time spent using electronics per day was divided into four groups: <30 min, 30 min–1 h, 1–2 h and > 2 h.

We used Eye Health Behavior Assessment Scale (EHBAS) [21] developed by Chinese National Disease Control and Prevention Administration in 2023 to assess the level of eye health behaviors in children and adolescents, which consists of two scales, Eye Health Behavior Assessment Scale for Kindergarten and Primary School Students in Grades 1–3 -Parent Version (EHBAS-P) and Eye Health Behavior Assessment Scale for Primary School Students in Grades 4–6 and Middle School Students -Student Version (EHBAS-S) (Appendix 1). These two version of EHBAS have been incorporated into China’s Technical Guidelines for Comprehensive Public Health Interventions in Myopia Prevention and Control Among Children and Adolescents are widely recommended for eye health behavior evaluation [22, 23]. The content of the scale mainly includes various items such as exposure to natural light, outdoor activity time, digital screen time, reading posture and environment, and sleep time. The Scale was calculated using a score of 0 for ‘cannot do it’, 1 for ‘sometimes do it’ and 2 for ‘do it’. Based on the sum of the scale’s scores, the eye health behaviors of children and adolescents were classified into three levels: poor, moderate and good.

Data collection

We contacted school officials prior to the survey to obtain their support and distributed the questionnaire through school teachers. We designed an anonymous self-administered online form for data collection using the Questionnaire Star website (https://www.wjx.cn/). Informed consent was obtained electronically at the outset of the online survey. Participants were required to actively affirm their consent by clicking an ‘Agree’ button, and were only granted questionnaire access if they agree to participant. The questionnaire was completed by parents whose care time exceeded 4 h per day for kindergarten and primary grades 1–3, and by respondents themselves for primary grades 4–6, middle school students.

To reduce careless responding—a common bias caused by inattentive survey participation that compromises data accuracy—we implemented Ward’s suggested measures [17]. First, instructed response items (e.g., note definitions of myopia and parental myopia) were used to enhance comprehension, and researcher contact information was embedded for clarification. When completing the questionnaire, participants were guided by investigators or teachers to answer each question according to established procedures and instructions. If it was not possible to confirm whether they were myopic or not, the investigator would lead the participant to the local hospital for a new refraction measurement. Second, time tracking was employed to detect random responses, and responses with completion times under three minutes was considered not to have been carefully completed and was regarded as invalid data. Additionally, the ‘limit one response’ feature of the online form was used to minimize repeated responses. A total of 3500 questionnaires were collected, excluding questionnaires that were completed in less than 3 min, and finally we included 3240 respondents’ questionnaires for analysis (Fig. 1). The response efficiency rate was 92.57% (3240/3500).

Fig. 1
figure 1

Flowchart of study profile

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Statistical analysis

SPSS 25.0 was used to analyze the data. Mann-Whitney U test and Chi-squared test was used to compare the basic characteristics of the myopic and non-myopic groups and the differences between urban and rural areas. The factors influencing myopia were analysed using univariate logistic regression and multivariate logistic regression, including demographic information (gender, region, grade, whether parents were myopic, sleep time per day, time spent using electronics per day) and eye health behavior levels. We analyzed the rank data using multivariate logistic regression model when the hypothesis of parallelism was not meet at P > 0.10. The strength of association was described by odds ratios (OR) and 95% confidence intervals (95% CI). P < 0.05 indicates statistical significance.

Results

Demographic data

A total of 3240 children aged 3–18 years were investigated in this study, including 1589 males (49.04%) and 1651 females (50.96%). The number of children residing in urban areas was 2413 (74.48%) and in rural areas was 827 (25.52%). There were 590 (18.21%)in kindergarten, 855 (26.39%) in primary grades 1–3, 522 (16.11%) in primary grades 4–6, 875 (27.01%) in junior high schools, and 398 (12.28%) in senior high schools.

Prevalence of myopia

The overall prevalence of myopia was 34.35% (1113/3240). Females had a significantly higher prevalence of myopia at 38.50% than males (30.10%, χ2 = 25.21, P < 0.001). The prevalence of myopia was 33.00% in urban and 38.30% in rural areas, with significant differences between the two regions (χ2 = 7.80, P = 0.005). In addition, the average age of myopic children (12.74 ± 2.84) was significantly higher than that of non-myopic children (9.13 ± 3.63) (Z=-25.797, P < 0.001). And the prevalence of myopia was different in different grades, 3.9% in kindergarten, 17.50% in grades 1–3 of primary school, 39.30% in grades 4–6 of primary school, 55.20% in junior high schools, and 63.30% in senior high schools. The results showed that the prevalence of myopia increased significantly with increasing grades (χ2 = 672.06, P < 0.001), and there was a notable increase in the prevalence of myopia in primary school grades 4–6 and junior high school (Table 1; Fig. 2).

The study also showed the relationship between the prevalence of myopia and whether parents were myopic, sleep time per day, time spent on electronics per day, and eye health behavior levels. The prevalence of myopia in children with parental myopia was 37.40%, while the prevalence of myopia in children with no parental myopia was 31.1%, and the difference was statistically significant (χ2 = 14.18, P < 0.001). The prevalence of myopia was 59.40%, 28.40% and 15.50% for less than 8 h, 8–9 h and more than 10 h of sleep per day, respectively. Shorter sleep duration significantly increased the prevalence of myopia (χ2 = 315.17, P < 0.001). Furthermore, the prevalence of myopia increased significantly with increased time spent using electronics per day (χ2 = 79.86, P < 0.001). We also found that the prevalence of myopia was higher in children with poorer eye health behavior level (χ2 = 92.78, P < 0.001), with the prevalence of myopia in children with poor being 43.60%, moderate being 32.50%, and good being 21.00% (Table 1; Fig. 2).

Table 1 Subject demographic characteristics
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Fig. 2
figure 2

Subject demographic characteristics

* Indicates P < 0.05, ** Indicates P < 0.01, *** Indicates P < 0.001, statistically significant difference

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Urban and rural distribution

Table 1; Fig. 3 showed the distribution between urban and rural children’s gender, age, grades, whether they were myopic, whether their parents were myopic, sleep time, time spent using electronics, and eye health behavior levels. The distribution of rural and urban children in this study was not significantly different in terms of gender, age and grade (P = 0.134, P = 0.111 and P = 0.851, respectively). There is no significant difference in sleep time per day between rural and urban children (χ2 = 4.25, P = 0.119). However, we found that rural children had significantly higher prevalence of myopia than urban children (P = 0.005). Parental myopia was significantly higher in urban children (59.30%) than in rural areas (30.50%, χ2 = 205.11, P < 0.001). We found that 15.40% of rural children used electronics for more than 2 hours per day, compared to only 11.60% of urban children, and rural children spend significantly more time per day using electronic devices than urban children (χ2 = 13.32, P = 0.004). We also found that 37.20% of the urban children had a poor eye health behavior level, which was higher than the 31.70% of the rural children, and the difference was statistically significant (χ2 = 12.90, P = 0.002).

Fig. 3
figure 3

Basic characteristics of children in urban and rural areas

* Indicates P < 0.05, ** Indicates P < 0.01, *** Indicates P < 0.001, statistically significant difference

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Analysis of factors associated with myopia

Univariate logistic regression analyses showed that grades, gender, region, whether parents were myopic, sleep time per day, time spent using electronics per day, and eye health behavior levels significantly associated with the prevalence of myopia. The statistically significant factors above were analyzed by multifactorial logistic regression, and it was found that compared with kindergarten, the risk of myopia was significantly increased in primary school grades 1–3 (OR = 4.86, P < 0.001), primary school grades 4–6 (OR = 19.74, P < 0.001), junior schools (OR = 33.97, P < 0.001), and high schools (OR = 61.99, P < 0.001), and the higher the grades, the increased risk of myopia prevalence. The risk of myopia was higher in females than in males (OR = 1.27, P = 0.007), in rural than in urban areas (OR = 1.88, P < 0.001), and in children with myopic parents than with non-myopic parents (OR = 3.21, P < 0.001). In addition, children who slept less than 8 h per day had a significantly higher risk of myopia than those who slept more than 10 h (OR = 3.21, P = 0.001), poor eye health behavior level higher than good ones (OR = 1.74, P < 0.001). However, in the multi factorial regression analysis, we did not find a significant association between the time spent using electronic devices per day and the risk of myopia (Table 2; Fig. 4).

Table 2 Regression analysis of factors associated with myopia
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Fig. 4
figure 4

Associations between potential factors and myopia in multivariate analysis

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Analysis of factors associated with eye health behaviors

We investigated the factors influencing the eye health behavior levels. The univariate results showed that there were differences in eye health behavior levels between children and adolescents of different grades (χ2 = 180.49, P < 0.001), gender (χ2 = 27.65, P < 0.001), regions (χ2 = 12.90, P < 0.001), whether myopia (χ2 = 92.78, P < 0.001) and whether parents are myopic (χ2 = 61.78, P < 0.001). The regression result showed that males had better levels of eye health behavior than females(OR = 0.64, P<0.001), children and adolescents who were already myopic and those with myopic parents had poorer eye health behavior level; children and adolescent who are in junior high school, senior high school and primary school of grade 1 to 3 had a higher risk of poor eye health behavior level, which was 3.53, 2.42 and 1.48 times higher than that of kindergarten children, respectively, while there were no statistical association between region and eye health behavior levels (Fig. 5).

Fig. 5
figure 5

Associations between potential factors and eye health behaviors in multivariate analysis

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Discussion

The study revealed that the overall prevalence of myopia among children and adolescents aged 3–18 years in Hubei Province was 34.35%, which is notably higher than that reported in Western countries such as Europe and Australia [24,25,26]. Existing literature has consistently linked the high prevalence of myopia in China to factors including elevated educational stress, limited outdoor time provided by schools, and genetic predispositions [1, 25]. A cross-sectional survey of primary school students aged 6–12 years in Tianjin, China, showed that the overall prevalence of myopia was 52.92% [27]. In northeastern Sichuan, the prevalence of myopia in primary and secondary school students aged 5–19 years was 65.61% [16]. Myopia rates also varied greatly from city to city in China, which may be related to different economic conditions, educational pressures, and the popularity of electronic devices in different cities, but ere commonly higher than abroad. Furthermore, in alignment with prior investigations [16], our study observed a positive correlation between myopia prevalence and grades, with the higher the grade level, the higher the myopia rate. We also found a notable increase in the prevalence of myopia at the primary and junior high schools, which may be related to accelerated growth and development and increased pressure on education [28].

In accordance with prevailing research findings [16, 29,30,31], our study identified a higher prevalence of myopia among females compared to males, with females exhibiting a greater risk of myopia (OR = 1.27). This disparity has been attributed to potentially reduced engagement in outdoor activities, prolonged periods of reading, and near work among females [32]. This is confirmed by our research finding that females have poorer levels of eye health behavior than males. Additionally, the observed trend has been linked to variations in sex hormone levels and the accelerated growth and development experienced by females during adolescence [33]. Consequently, among the initiatives aimed at preventing and controlling myopia, priority attention could be given to female students to encourage them to reduce near-work hours and participate more in outdoor activities.

Based on our investigation findings, offspring of myopic parents exhibit a heightened prevalence of myopia. Previous studies have established a clear association between parental myopia and the occurrence of myopia in children. A cohort study involving preschoolers across diverse nations revealed that parental myopia significantly elevates the likelihood of myopia in their offspring [34]. In a study conducted in the Feng Hua District of Zhejiang Province, it was determined that the risk of myopia in students with two myopic parents or one myopic parent was 1.61 and 1.29 times greater, respectively, compared to those with non-myopic parents [31]. These investigations collectively underscore the substantial role of genetic factors in the development of myopia. Consequently, children with myopic parents are given necessitate early and targeted interventions and their refractive progression is monitored.

The study results revealed that children with myopia exhibited inferior eye health behaviors compared to those without myopia, with the risk of myopia being significantly correlated with levels of eye health behaviors. The etiology and advancement of myopia are influenced by a blend of genetic and environmental factors [1], with outdoor activity duration and near work predominantly shaping the environmental aspect [35,36,37]. Outdoor pursuits exert a protective influence against myopia, as evidenced by a two-year prospective study indicating that heightened outdoor activity diminishes myopia incidence [36]. This protective effect is likely attributed to the augmented release of retinal dopamine prompted by intense natural light exposure. Furthermore, near work stands as a closely intertwined factor in myopia development [23]. Prolonged near work can induce accommodative lag, leading to hyperopic retinal defocus and triggering ocular growth aimed at correcting the focus, thereby fostering axial elongation [35]. Studies have demonstrated that individuals who engage in tasks beyond 30 centimeters and take breaks every 30 min exhibit notably decelerated myopia progression [38]. Therefore, schools and parents need to pay attention to children cultivating good eye behavior, reducing the hours of near work and increasing the hours of outdoor activities to slow down the progression of myopia.

The study revealed that individuals who slept less than 8 h per day had a 3.21 times higher risk of myopia compared to those who slept more than 10 h, highlighting adequate sleep duration as a protective factor against myopia. Nonetheless, conclusive evidence regarding the association between sleep duration and myopia remains elusive. Jee et al. [39] observed a 41% reduction in myopia incidence among subjects sleeping over 9 h in contrast to those with less than 5 h of sleep. Scholars have posited that shorter sleep durations may downregulate dopamine D2 receptors in the ventral striatum, potentially dampening ocular dopamine pathway activation and fostering myopia progression [40, 41]. Conversely, a meta-analysis indicated no significant link between sleep duration and myopia prevalence [42]. Further investigations are warranted to elucidate the intricate relationship between sleep duration and myopia development.

With the advancement of electronic technology, electronic devices are increasingly utilized for both in-class and after-class learning. Smartphones and electronic entertainment are becoming more pervasive in students’ lives. Our study identified a higher prevalence of myopia among individuals who spent extended periods using electronic devices daily, although no significant correlation was found in adjusted multi-factorial regression analyses. Similar to our findings, Peng et al. [43] and Huang et al. [31] did not observe a significant link between daily electronic device usage and myopia incidence in their respective cross-sectional studies. Conversely, Harrington et al. [44] reported a significant association between myopia prevalence and over 3 h of daily screen time in children. In addition, increased screen time often leads to more near-work activities and reduced outdoor engagement, potentially heightening the risk of myopia [45]. Therefore, further research is still needed on the effect of the duration of electronic device use on the development and progression of myopia and its relationship with the reduction of outdoor activities.

Our research also revealed a significantly higher prevalence of myopia in rural regions compared to urban areas, with rural children facing a 1.88 times greater risk of myopia than their urban counterparts, diverging from certain prior studies. A rural-urban investigation on myopia in Anhui province demonstrated a lower myopia prevalence among rural children (63.7%) in contrast to urban children (68.1%) [43]. Tu et al. [46] similarly observed higher myopia rates among primary school students in the provincial capital city (32.35%) compared to urban (23.03%) and rural areas (14.82%), attributing this trend to reduced outdoor activities and heightened near-work intensity in the provincial capital city. However, the prevalence of myopia has accelerated significantly in rural areas in recent years. A study of Chinese children and adolescents aged 7 to 18 years from 2010 to 2019 found that myopia detection rates in rural children have risen rapidly, which is thought to be related to their increased exposure to electronic devices, shorter outdoor time, and relatively weak health education in rural children compared to urban children [47]. To delve into the urban-rural disparities, we conducted a detailed analysis of myopia-related factors among urban and rural children. Our findings indicated that urban children exhibited a higher prevalence of parental myopia and poorer eye health behavior, while rural children spent more time daily on electronic devices than their urban counterparts. Increased use of electronic devices can reduce outdoor activity time, leading to increased risk of myopia. In addition, a study by He et al. found a lack of awareness among rural parents about myopia-related knowledge and poor awareness of the adverse consequences of myopia and myopic preventative measures [48]. The rural population comprises a significant proportion of China’s population, with nearly 36.11% of the total population living in rural villages (approximately 500 million) [48]. Therefore, more research is very necessary on the differences in myopia rates between urban and rural children and the reasons for the increased myopia rates in rural areas. The escalating myopia rates among rural children necessitate urgent attention. Moreover, in order to address this growing problem, it is imperative to actively promote myopia prevention, control and awareness-raising in rural schools, rural children and their parents.

There are several limitations in our study. First, the eye health behavior assessment scale, developed by the Chinese National Disease Control and Prevention Administration as part of national myopia prevention guidelines for children and adolescents [23], demonstrated acceptable structural validity in our pilot study with a Kaiser-Meyer-Olkin (KMO) value of 0.922 and Bartlett ‘s test P < 0.001. However, the cumulative explained variance of the two factors having eigenvalues greater than 1 accounting for 50.57%, approaching the lower bounds of recommended psychometric thresholds while remaining within acceptable limits [49]. Future studies should prioritize developing contextually appropriate eye health behavior assessment instruments with enhanced psychometric properties. Second, we collect data by self-reporting, relied on subjective assessments by respondents, which may introduce recall bias. We did not use ciliary muscle paralysis to assess subjects’ myopia, possibly overlooking undiagnosed cases of myopia. Moreover, we did not further analyse the relationship between different myopia levels, one and both parents’ myopia, and eye health behaviors. In future studies we will collect data more rigorously and comprehensively to explore the factors influencing myopia and eye health behaviors more deeply. Third, the cross-sectional design only establishes correlations between myopia and its influencing factors, not causation. The scope of potential factors influencing myopia was not exhaustive, and future studies should consider additional factors.

In summary, our study identified a higher prevalence of myopia among children in rural areas compared to urban areas. Myopia prevalence was notably linked to grade level, gender, region, parental myopia, sleep duration, screen time, and eye health behaviors. Increased outdoor activities, adequate sleep, and improved eye health behaviors are associated with reduced myopia risk.

Data availability

The data set in support of the conclusions of this article is available on request from the corresponding author.

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Acknowledgements

We would like to extend our sincere thanks to all the students and their parents who kindly participated in this survey, and deeply grateful to the school teachers who provided invaluable support and assistance throughout the process. Additionally, we would like to express our sincere gratitude to Dr. Chen and Dr.Chang for their invaluable assistance and guidance during the revision process of this paper.

Funding

This study was supported by clinical project of Tongji Hospital affiliated Tongji Medical College of Huazhong University of Science and Technology (no.2023D09) and the Provincial Natural Science Foundation of Hubei Province (no.2023AFB901).

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Author notes

  1. Lu Tian and Mengxia Zhu contributed equally to this work.

Authors and Affiliations

  1. Deptartment of Nursing, Tongji Hospital Affiliated to Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China

    Lu Tian, Yuhan Song, Yin Wang & Yan Jiang

  2. Department of Ophthalmology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, 317000, Zhejiang, China

    Mengxia Zhu

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  1. Lu Tian
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Contributions

All authors have made substantial contributions. Lu Tian carried out the methodology, data curation and preparation of the original draft. Mengxia Zhu focused on data processing and statistical analysis, providing solid support for the research findings, and also wrote the original draft along with Tianlu. Yuhan Song conducted a thorough review of the literature. Yan Jiang provided valuable suggestions on the structure of the paper and final proofreading. Yin Wang contributed to the review and editing of the paper, providing feedback on its overall structure and clarity.

Corresponding authors

Correspondence to Yin Wang or Yan Jiang.

Ethics declarations

Ethics approval and consent to participate

The study was conducted under the Code of Ethics of the World Medical Association (Declaration of Helsinki) and approved by the Medical Ethics Committee of Tongji Hospital affiliated to Tongji Medical College, Huazhong University of Science and Technology (TJ-IRB202403059). Informed consent was obtained from all participants and individuals younger than the age of 16 in this study have obtained consent to participate from their parents or legal guardians.

Consent for publication

Not applicable as data do not relate to any individual persons.

Competing interests

The authors declare no competing interests.

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Tian, L., Zhu, M., Song, Y. et al. The prevalence of myopia and eye health behaviors among 3 to 18 years: a cross-sectional survey study. BMC Public Health 25, 1688 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12889-025-22906-x

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BMC Public Health

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