Correlation between perceived air pollution and myopia: an exploration of the mediating effects of healthy lifestyle factors

Abstract

Objective

To explore the relationship between air pollution and myopia in Chinese adult population, as well as the mechanism of physical exercise, sedentary behavior and sleep quality in the association between them, so as to provide a basis for formulating comprehensive myopia prevention and control policies.

Methods

The data of 2,717 participants from CGSS 2021 were Analyzed. This database used subjective assessment methods to evaluate the perceived air pollution level, myopia prevalence, frequency of physical exercise, sedentary time And sleep quality. Based on SPSS 21.0 software, independent samples t-test, binary logistic regression analysis, and the three-step method for mediating effect test were applied to conduct data statistics and analysis.

Results

The average age of the subjects was (52.04 ± 17.64) years old, And the proportion of females was 54.8%. After controlling for relevant variables, there was a significant positive correlation between perceived air pollution and myopia (OR = 1.132, 95%CI = 1.003 ~ 1.278, P < 0.05); there was still a significant positive correlation between sedentary time and myopia (OR = 1.032, 95%CI = 1.005 ~ 1.060, P < 0.05); there was still a significant negative correlation between sleep quality and myopia (OR = 0.798, 95%CI = 0.707 ~ 0.901, P < 0.01); while the correlation between frequency of physical exercise and myopia was not significant (OR = 1.029, 95%CI = 0.967 ~ 1.094, P > 0.05). In addition, sleep quality plays a mediating role in the correlation between perceived air pollution and myopia (P < 0.01), with the mediating effect accounting for 12.06%, while the mediating effects of physical exercise and sedentary behavior are not significant (P > 0.05).

Conclusion

There is a significant positive correlation between perceived air pollution and myopia, and sleep quality plays a mediating role in the above correlation.

Introduction

Currently, 3.3 billion people worldwide (28.3% of the global population) are diagnosed with myopia, And it is projected that by 2050, the prevalence of myopia will reach 4.76 billion people (49.8% of the global population) [1, 2]. Against the backdrop of the continuous rise in global myopia prevalence, the situation in China is also a cause for concern. Dong et al. [3] found that the prevalence of myopia among children And adolescents in our country reached 37.7% from 1998 to 2016, And is expected to reach 84.0% by 2050. In addition to studies on children and adolescents, a study [4] involving 5,060 college students in Shanghai found that the prevalence of myopia among the participants was as high as 95.5%, with the prevalence of high myopia reaching 19.5%. Moreover, Xu et al. [5] found in their study on the elderly that the prevalence of myopia among the subjects was 21.1%. Therefore, high rates of myopia are detected across all age groups in China. Myopia has become a major global public health issue, not only causing severe complications such as macular degeneration and retinal detachment [6, 7], but also creating a significant social and economic burden, including direct medical costs, patient time costs, and reduced productivity [8]. As a result, the prevention and control of myopia are now urgent priorities.

The occurrence and development of myopia are influenced by the interaction of genetic and environmental factors, with the environment playing a dominant role [9]. Air pollution is regarded by the World Health Organization (WHO) as the most significant environmental threat to human health [10]. Numerous epidemiological and toxicological studies [11–13] have shown that frequent exposure to NO₂, O₃, and inhalable particulate matter in the air can increase the incidence of cardiovascular, respiratory, and metabolic diseases, as well as the risk of premature death. As research progresses, some studies [14–16] have found that frequent exposure to NO₂, O₃, and higher levels of PM2.5 in the air can also increase the risk of myopia. Although current studies have revealed the association between air pollution and myopia prevalence, studies focusing on Chinese populations are scarce. To date, only two studies have been conducted in China. One is an epidemiological study [17] involving 61,995 children from seven provinces/municipalities in mainland China, which found that increases in the quartile ranges of PM1, PM2.5, PM10, and NO₂ were associated with 1.133, 1.267, 1.142, And 1.276 times higher probabilities of vision impairment in children, respectively. The other is a cohort study [18] from Taiwan, China, which found that the incidence of myopia in children increased with higher concentrations of PM2.5 and NOₓ. However, it is important to note that existing studies in China have primarily focused on children, with almost no research on the adult population. As the main working group in society, adults are constantly exposed to various environmental factors, and their myopia status has been proven to be concerning in many studies [4, 5]. Therefore, it is necessary to conduct research on the relationship between air pollution and myopia in the adult population in China.

Regarding the mechanisms linking air pollution to myopia, Yuan and Zou [19] proposed two pathways: direct and indirect. The direct pathway refers to pollutants adhering directly to the eyes, causing inflammatory reactions, which subsequently lead to peripheral hyperopic defocus and retinal ischemia. The indirect pathway involves air pollution reducing the release of dopamine (DA) in the eyes. Although these physiological mechanisms help deepen our understanding of the intrinsic mechanisms by which air pollution affects myopia, they have certain limitations in guiding daily myopia prevention practices from a practical application perspective. Therefore, it is necessary to explore the issue from the perspective of lifestyle, which is closely related to daily life and easily modifiable. Numerous studies have confirmed that physical exercise [20], sedentary behavior [21], and sleep quality [22] are closely related to the occurrence and development of myopia. Enhancing physical exercise, limiting sedentary time, and improving sleep quality can reduce the risk of myopia. Air pollution is associated with these lifestyle indicators, as it can lead to reduced levels of physical exercise, increased sedentary time, and decreased sleep quality [23–25]. Therefore, air pollution may exacerbate the risk of myopia by altering residents’ lifestyles. However, substantial evidence to support this hypothesis still needs to be further developed.

Based on the above, this study aims to explore the correlation between air pollution and myopia in the adult population of China, and to investigate the mediating role of healthy lifestyle factors such as physical exercise, sedentary behavior, and sleep quality in this relationship. This study will fill the gap in domestic studies on this topic. It will provide data support and empirical evidence for reducing air pollution and preventing and controlling myopia, and offer a scientific basis for developing targeted and effective public health policies and myopia prevention measures.

Methods

Data sources

This study utilized relevant samples from the 2021 Chinese General Social Survey (CGSS 2021) database for analysis. The CGSS is the earliest national, comprehensive, and continuous academic survey project in China [26]. It systematically and comprehensively collects data at multiple levels, including society, community, family, and individuals. It promotes the openness and sharing of scientific research in China and serves as a multi-disciplinary platform for the collection of economic and social data [26]. The CGSS 2021 employed multi-stage stratified probability sampling, surveying 8,148 residents across 28 provinces, municipalities, and autonomous regions in China. The CGSS questionnaire has been widely applied and referenced in numerous studies and has been proven to have high validity and reliability [27, 28]. Some studies [29, 30] have also used the CGSS database to investigate air pollution in China.

This study was conducted in compliance with the Declaration of Helsinki. It utilized the open database of the CGSS 2021, And the original survey of CGSS 2021 had obtained approval from the Research Ethics Committee of Renmin University of China. During the data collection phase of the CGSS 2021 database, all original participants were provided with comprehensive information about the research. This ensured that participants voluntarily signed the informed consent form on the basis of full informed understanding. Although this study did not require an additional application for ethical review (as it is a secondary analysis of an existing open database), the research team still strictly adhered to the database usage guidelines. Only Anonymized public data was accessed, And there was no secondary contact with original participants or infringement of their privacy. After excluding missing values for the perceived air pollution variable, this study retained 2,717 valid data points. In addition, for missing values in other variables, this study employed linear interpolation for imputation.

Variables and instruments

Independent variable

The independent variable in this study is the perceived air pollution. The CGSS 2021 used the question “How severe is the air pollution in the place where you live?” to inquire about residents’ exposure to air pollutants, asking participants to rate based on their actual experiences on a four-point scale, where 1 to 4 represent “very severe”, “somewhat severe”, “not very severe”, and “not severe at all”, respectively. This study re-coded these responses for subsequent statistical Analysis, where 1 represents “not severe at all”; 2 represents “not severe”; 3 represents “severe”; And 4 represents “very severe”. This subjective perception of air pollution has been proven to be closely correlated with objectively measured environmental pollution [31] and has been widely applied in many studies [32–34].

Dependent variable

The dependent variable in this study is myopia. The CGSS 2021 inquired about residents’ myopia status through the question “Are you nearsighted?” In this survey, “yes” was coded as 1, and “no” was coded as 2. This self-reported myopia has been widely used in relevant studies [35, 36] and has been proven to have good screening effects.

Mediating variables

The mediating variables in this study include physical exercise, sedentary behavior, and sleep quality.

1. Physical exercise. The CGSS 2021 used the question “Over the past year, how often did you engage in physical exercise during your leisure time?” to investigate the frequency of physical exercise among participants. Respondents were asked to select the most appropriate option from five choices, with 1 ~ 5 representing daily, several times a week, several times a month, several times a year or less, and never, respectively. To ensure the convenience of interpreting the research results, this study, with reference to previous studies [28, 37, 38], adopted the reverse scoring method to process the original data: the scores of 1 ~ 5 in the original scale were recoded as “never”, “several times a year or less”, “several times a month”, “several times a week”, and “daily”, respectively. This self-assessed physical exercise method has high reliability, can be used in large-scale epidemiological surveys or trend analyses, and has been applied in multiple studies [37, 38].

2. Sedentary behavior. The CGSS 2021 surveyed participants’ sedentary time using the question “On a typical workday, how much time do you spend sitting? This includes all sitting time (e.g., sitting at a desk, reading, watching TV while sitting or lying down, etc.).” Respondents were required to fill in their actual sedentary time in terms of hours And minutes. In this study, the hours And minutes reported by participants were summed and converted into total minutes, and then divided by 60 to obtain the number of hours spent in sedentary behavior. Prince et al. [39] have already confirmed that the assessment of sedentary time based on such questionnaire surveys has good validity and has been widely used in many large cross-sectional studies.

3. Sleep quality. The CGSS 2021 assessed participants’ sleep quality with the question “Over the past month, how would you rate your sleep quality?” Respondents were asked to rate their sleep quality on a scale from 1 to 4, with 1 ~ 4 representing very good to very poor, respectively. In this study, the responses were recoded for subsequent statistical Analysis, with 1 indicating very poor, 2 indicating fairly poor, 3 indicating fairly good, And 4 indicating very well. This item is derived from the Pittsburgh Sleep Quality Index (PSQI) and has been proven to have high reliability and validity among the Chinese population [40].

Control variables

Based on previous studies [37, 38, 41], this study selected six control variables: age, gender, educational level, place of residence, socio-economic status (SES), and body mass index (BMI).

1. Age. The CGSS 2021 surveyed the birth dates of the participants, and this study calculated the participants’ ages based on the formula (2021 - birth year).

2. Gender. The biological gender of the participants was recorded by the interviewers of the CGSS 2021.

3. Educational level. The CGSS 2021 investigated the highest educational attainment of the participants using 14 options, where 1 indicates no education; 2 indicates private school or literacy class; 3 indicates primary school; 4 indicates junior school; 5 indicates vocational high school; 6 indicates regular high school; 7 indicates secondary vocational school; 8 indicates technical school; 9 indicates associate degree (adult higher education); 10 indicates associate degree (regular higher education); 11 indicates bachelor’s degree (adult higher education); 12 indicates bachelor’s degree (regular higher education); 14 indicates other. Referring to previous studies [37, 38], this study categorized the participants into four groups: primary school and below (options 1 ~ 3), junior school (option 4), high school (options 5 ~ 8), and college and above (options 9 ~ 13). In addition, since no one selected option 14, it was not necessary to classify it.

4. Residence. The interviewers of the CGSS 2021 recorded the participants’ residence, which included urban and rural areas.

5. SES. The CGSS 2021 used the MacArthur Subjective Social Status Scale [42] to investigate the participants’ SES, with the specific question being “In our society, some people are at the top of the social hierarchy, while others are at the bottom. Overall, where do you think you stand in this society?” The scale ranges from 1 to 10, with 10 representing the highest level And 1 representing the lowest level. This scale has been proven to have high reliability and validity among Chinese samples and has been widely used by researchers [43, 44].

6. BMI. The CGSS 2021 surveyed the participants’ height (in centimeters) and weight (in jin), where jin is a Chinese unit of Weight, with 1 jin equaling 0.5 kg. This study converted the units to kilograms and meters and then calculated the participants’ BMI using the formula BMI = weight (kg)/height (m²).

Statistical analysis

This study used SPSS 21.0 software for data processing and statistical analysis. For continuous variables, descriptive statistics were presented using mean (M) and standard deviation (SD); for categorical variables, descriptive statistics were presented using frequency and percentage. Firstly, in this study, the one-sample Kolmogorov- Smirnov test combined with P-P plots and Q-Q plots was used to test the normality of the data. The results showed that continuous variables such as perceived air pollution, physical exercise, sedentary behavior, and sleep quality all approximately followed a normal distribution. In addition, according to the central limit theorem, when the sample size is large, the sample mean approximately follows a normal distribution [45]. Therefore, this study used the independent samples t-test to compare the differences in the aforementioned variables between the myopic group and the non-myopic group. Secondly, with myopia as the dependent variable and perceived air pollution as the independent variable, binary logistic models both with and without controlling for relevant confounding factors were constructed to explore the correlation between perceived air pollution and the occurrence of myopia. Thirdly, with myopia as the dependent variable and physical exercise, sedentary behavior, and sleep quality as the independent variables respectively, binary logistic models controlling for relevant confounding factors were constructed to explore the correlations between physical exercise, sedentary behavior, sleep quality and the occurrence of myopia.

Finally, mediation analysis was conducted to examine the mediating roles of physical exercise, sedentary behavior, and sleep quality in the relationship between the perceived air pollution. This study adopted Baron and Kenny’s mediation test method [46], which mainly judges the existence of mediating effect by sequentially testing three regression models: (1) testing the total effect of the independent variable on the dependent variable; (2) testing the effect of the independent variable on the mediating variable; (3) testing the effect of the mediating variable on the dependent variable and the direct effect of the independent variable after including both the independent variable and the mediating variable. This study will further report the proportion of mediating effect (i.e., the ratio of indirect effect to total effect). By quantifying the contribution of the mediating path to the total effect, it will more comprehensively reveal the mechanism of action between variables. The significance level for all the above statistical tests was set at α = 0.05.

Results

Basic information about the participants

The participants’ ages ranged from 18 to 99 years, with an average age of (52.04 ± 17.64) years. Among them, 54.8% were female, And 44.1% were rural residents. The average SES of the participants was (4.30 ± 1.82), and the average BMI was (23.14 ± 3.60) kg/m². The educational levels of the participants were distributed as follows: 33.5% had primary school or lower education, 28.2% had junior school education, 18.0% had high school education, And 20.4% had college or higher education. In terms of other key indicators, the participants’ perceived air pollution score was (1.95 ± 0.78), average physical exercise score was (2.83 ± 1.62), average sedentary time was (5.00 ± 3.46) hours, and average sleep quality score was (2.90 ± 0.78). For more detailed basic information of the participants, please refer to Table 1.

Table 1.

Basic information about the participants

Variables	M	SD	Med	Variables	M	SD	Med
Age	52.04	17.64	53.00	Physical exercise	2.83	1.62	3.00
SES	4.30	1.82		Sedentary behavior	5.00	3.46	4.33
Perceived air pollution	3.05	0.78	3.00	Sleep quality	2.90	0.78	3.00
BMI(kg/m2)	23.14	3.60	3.60	SES	4.30	1.82	5.00
Variables	Freq	%		Variables	Freq	%
Gender				Educational level
Male	1228	45.2		Primary school and below	909	33.5
Female	1489	54.8		Junior school	765	28.2
Residence				High school	490	18.0
Urban	1518	55.9		College and above	553	20.4
Rural	1199	44.1

Comparative between-group analysis of myopic and non-myopic groups

The results of the inter-group comparative analysis between the myopic group and the non-myopic group (Table 2) showed that, compared with the non-myopic group, the myopic group had a higher level of perceived air pollution (t = 4.273, P = 0.000), a lower frequency of physical exercise (t=−5.540, P = 0.000), longer sedentary time (t = 5.508, P = 0.000), and lower sleep quality (t=−2.320, P = 0.020).

Table 2.

Results of the comparative between-group analysis of myopic and non-myopic groups

Variables	Myopic	Non-myopic	t	P
Perceived air pollution	2.05 ± 0.72	1.91 ± 0.79	4.273	0.000
Physical exercise	2.72 ± 1.65	3.10 ± 1.50	−5.540	0.000
Sedentary	5.59 ± 3.51	4.77 ± 3.42	5.508	0.000
Sleep quality	2.84 ± 0.78	2.92 ± 0.79	−2.320	0.020

There were 753 in the myopic group And 1, 964 in the non-myopic group

Correlation between perceived air pollution and myopia

In Model 1, when no control variables were included, there was a significant positive correlation between perceived air pollution and myopia (OR = 1.260, 95%CI = 1.133 ~ 1.403, P = 0.000). That is, for each 1-unit increase in residents’ perceived air pollution, the risk of myopia increases by 1.260 times. After further controlling for relevant variables (Model 2), perceived air pollution still showed a significant positive correlation with myopia (OR = 1.132, 95%CI = 1.003 ~ 1.278, P = 0.041). This means that for each 1-unit increase in residents’ perceived air pollution, the risk of myopia increases by 1.132 times. In addition, among the relevant control variables, age was significantly positively correlated with myopia (OR = 1.022, P < 0.01); compared with males, females had a higher prevalence of myopia (OR = 1.263, P < 0.05); compared with residents with education level of primary school or below, those with education level of junior school or above had a higher prevalence of myopia (OR = 2.680 ~ 4.460, P < 0.01). However, there was no significant correlation between residence, SES, BMI and myopia (all P > 0.05). The analysis results of the relationship between perceived air pollution and myopia are shown in Table 3.

Table 3.

Correlation between perceived air pollution and myopia

Variables	Model 1		Model 2
	OR	95%CI	OR	95%CI
Perceived air pollution	1.260**	(1.133, 1.403)	1.132*	(1.003, 1.278)
Age			1.022**	(1.016, 1.028)
Gender
Female			1.263*	(1.044, 1.527)
Educational level
Junior school			4.265**	(3.099, 5.870)
High school			4.460**	(3.505, 6.142)
College and above			2.680**	(2.043, 3.516)
Residence
Rural			0.218	(0.711, 1.081)
SES			0.749	(0.940, 1.045)
BMI			1.021	(0.995, 1.048)

*P < 0.05

**P < 0.01

Correlation between physical exercise, sedentary behavior and sleep quality and myopia

In Model 3, after controlling for relevant variables, there was no significant correlation between physical exercise and myopia (OR = 1.029, 95%CI = 0.967 ~ 1.094, P = 0.372). In Model 4, after incorporating control variables, sedentary time was significantly positively correlated with myopia (OR = 1.032, 95%CI = 1.005 ~ 1.060, P = 0.020), meaning that for each 1-hour increase in residents’ sedentary time, the risk of myopia increased by a factor of 1.032. In Model 5, after including control variables, sleep quality was significantly negatively correlated with myopia (OR = 0.798, 95%CI = 0.707 ~ 0.901, P < 0.01), indicating that for each 1-unit increase in residents’ sleep quality, the risk of myopia decreased by 20.2%. The analysis results regarding the relationships between physical exercise, sedentary time, sleep quality and myopia are shown in Table 4.

Table 4.

Correlation between physical exercise, sedentary behavior and sleep quality and myopia

Variables	Model 3		Model 4		Model 5
	OR	95%CI	OR	95%CI	OR	95%CI
Physical exercise	1.029	(0.967, 1.094)
Sedentary behavior			1.032*	(1.005, 1.060)
Sleep quality					0.798**	(0.707, 0.901)
Age	1.022**	(1.016, 1.029)	1.022**	(1.016, 1.028)	1.023**	(1.017, 1.030)
Gender
Female	1.276*	(1.055, 1.543)	1.265*	(1.046, 1.529)	1.232*	(1.018, 1.492)
Educational level
Junior school	4.091**	(2.944, 5.684)	4.212**	(3.059, 5.801)	4.217**	(3.061, 5.809)
High school	4.520**	(3.400, 6.008)	4.520**	(3.411, 5.989)	4.663**	(3.520, 6.177)
College and above	2.662**	(2.209, 3.493)	2.647**	(2.017, 3.473)	2.621**	(1.997, 3.441)
Residence
Rural	0.864	(0.701, 1.064)	0.870	(0.707, 1.072)	0.842	(0.684, 1.037)
SES	0.996	(0.944, 1.050)	0.995	(0.944, 1.050)	0.983	(0.932, 1.037)
BMI	1.021	(0.995, 1.048)	1.020	(0.994, 1.047)	1.018	(0.992, 1.045)

*P < 0.05

**P < 0.01

Tests of the mediating effects of physical exercise, sedentary behavior, and sleep quality

After controlling for relevant variables, this study tested the mediating effects of physical exercise, sedentary behavior, and sleep quality (see Table 5). Firstly, in Model 7, the predictive effect of perceived air pollution on physical exercise was not significant (β = 0.040, P > 0.05); in Model 10, the predictive effect of perceived air pollution on myopia was significant (β = 0.124, P < 0.01), while the predictive effect of physical exercise on myopia was not significant (β = 0.028, P > 0.05). Therefore, the mediating effect of physical exercise between perceived air pollution And myopia was not significant. Secondly, in Model 8, the predictive effect of perceived air pollution on sedentary behavior was not significant (β = 0.082, P > 0.05); in Model 11, the predictive effect of perceived air pollution on myopia was significant (β = 0.122, P < 0.01), and the predictive effect of sedentary behavior on myopia was significant (β = 0.031, P < 0.05). Therefore, the mediating effect of sedentary behavior between perceived air pollution And myopia was not significant. Finally, in Model 9, the predictive effect of perceived air pollution on sleep quality was significant (β=−0.076, P < 0.01); in Model 12, the predictive effect of perceived air pollution on myopia was not significant (β = 0.109, P > 0.05), while the predictive effect of sleep quality on myopia was significant (β=−0.218, P < 0.01). Therefore, the mediating effect of sleep quality between perceived air pollution and myopia was significant. The predictive effect of perceived air pollution on sleep quality was − 0.076, and the predictive effect of sleep quality on myopia was − 0.218, resulting in a mediating effect of sleep quality of 0.017 (−0.076×−0.218). The mediating effect accounts for 12.06% of the total effect (0.017/(0.017 + 0.124)). The mediation model of sleep quality between perceived air pollution and myopia is shown in Fig. 1.

Table 5.

The mediating effects of physical exercise, sedentary behavior, and sleep quality in the relationship between perceived air pollution and myopia

Variables	Myopia			Physical exercise	Myopia			Sedentary behavior	Myopia			Sleep quality
	Model 2	Model 3	Model 10	Model 7	Model 2	Model 4	Model 11	Model 8	Model 2	Model 5	Model 12	Model 9
Perceived air pollution	0.124*		0.124*	0.040	0.124*		0.122**	0.082	0.124*		0.109	−0.076**
Physical exercise		0.028	0.028
Sedentary behavior						0.032*	0.031*
Sleep quality										−0.266**	−0.218**
Control variables	Control	Control	Control	Control	Control	Control	Control	Control	Control	Control	Control	Control

*P < 0.05

**P < 0.01

Fig. 1.

The mediation diagram of healthy lifestyle factors in the relationship between perceived air pollution and myopia

Discussion

Higher perceived air pollution raises myopia risk

The results of this study indicate that there is a positive correlation between Chinese residents’ perceived air pollution levels and their risk of myopia. These findings support previous studies that used objective measurements of air pollution [17, 18]. The consistency between the conclusions of subjective perception and objective measurement of air pollution regarding their association with myopia risk is not coincidental. Subjective perception is based on objective air pollution; the pathways through which the two factors influence myopia risk may overlap to a certain extent, and both can reflect the cumulative effects of long-term pollution exposure. Therefore, the two types of studies ultimately yield mutually supportive results. The reasons for the increased risk of myopia due to higher perceived air pollution mainly focus on ocular surface inflammation, tear film instability, ocular oxidative stress, and changes in lifestyle.

Firstly, in terms of ocular surface inflammation, higher air pollution means that PM2.5, PM10, NO2, and SO2 may stimulate the ocular surface. These irritants can cause inflammatory reactions on the ocular surface, leading to dilated ocular blood vessels and increased inflammatory mediators in the blood [47, 48]. Long-term inflammatory stimulation affects the normal metabolism of the cornea and lens, thereby reducing corneal refractive power and decreasing lens transparency, which increases the risk of myopia [49]. Secondly, regarding tear film instability, the chemical substances in air pollutants may react with the components in tears, causing an imbalance in the proportion of the lipid layer, aqueous layer, and mucin layer of the tear film [50, 51]. This can lead to ocular surface dryness and affect the refraction of light on the ocular surface [50, 51].

Thirdly, in terms of ocular oxidative stress, air pollutants can generate a large amount of reactive oxygen species (ROS) on the ocular surface. Excessive ROS can trigger oxidative stress reactions, causing certain damage to ocular cells and tissues [52, 53]. Retinal pigment epithelial cells are relatively sensitive to oxidative stress, and damage to them may affect the normal function of the retina [54]. In addition, oxidative stress can also damage lens fiber cells, altering the optical properties of the lens [55], thereby increasing the risk of myopia. Finally, regarding changes in lifestyle, when residents perceive severe air pollution, they tend to reduce their outdoor exercise time, which in turn leads to an increase in indoor sedentary screen time. Outdoor exercise is an important means of preventing myopia. In particular, adequate outdoor light exposure can stimulate the secretion of dopamine in the retina, inhibiting excessive axial elongation of the eye [56]. However, using electronic devices indoors may lead to increase near-work time, causing the ciliary muscle to remain tense and unable to relax. This makes the lens more convex and increases the refractive power [57]. Moreover, the lifestyle factors of physical exercise, sedentary behavior, and sleep will be discussed in detail later.

Sedentary behavior increases the risk of myopia, while high-quality sleep decreases the risk of myopia

The results of this study show that there is still a significant positive correlation between sedentary time and myopia, while there is still a significant negative correlation between sleep quality and myopia. These findings are similar to those of previous studies [21, 22], which indicated that prolonged sedentary behavior increases the risk of myopia among residents, while high-quality sleep reduces the risk of myopia.

Sedentary behavior is often associated with scenarios such as studying, working, or using electronic devices for extended periods. During these activities, the ciliary muscle remains in a state of continuous contraction and tension due to prolonged near work, causing the lens to become more convex and unable to return to its original shape, thereby inducing myopia [57]. In addition, sedentary behavior slows down the body’s blood circulation, including that of the eyes [58]. As a result, sedentary behavior leads to insufficient blood supply to the eyes, depriving the retina of adequate glucose, oxygen, and other nutrients, which in turn disrupts the normal functioning of ocular cells [58]. At the same time, the accumulation of metabolic waste such as lactic acid can irritate and damage ocular tissues, thereby increasing the risk of myopia [59]. On the other hand, high-quality sleep can alleviate ocular fatigue and allow the ciliary muscle to fully relax [60], preventing the lens from becoming more convex and the refractive power from becoming abnormal due to the continuous tension of the ciliary muscle, thereby reducing the risk factors for myopia. In addition, during sleep, the body enters a self-repair mode, and ocular cells are also repaired, maintaining the health of the retina and the transparency and normal refractive state of the cornea [61]. Sleep is closely related to the secretion and regulation of various hormones in the body. For example, growth hormone is secreted abundantly during nighttime sleep, which ensures that the structures of the eyeball grow in proportion [62].

However, this study found that there was no significant correlation between the frequency of physical exercise and myopia, which is inconsistent with previous studies [20, 63]. These studies have all indicated that physical exercise can effectively prevent and control myopia. The reason for the discrepancy may lie in the limitations of the indicators used to measure the frequency of physical exercise, which overlooks other important factors such as the type and duration of physical exercise. Firstly, this study did not know the type of exercise the participants engaged in. If the exercise method is inappropriate, it may not have a significant effect on myopia prevention and control. Some studies [64, 65] have shown that exercises such as table tennis and badminton allow the eyeball to alternate regularly between near and far vision, thereby providing the ciliary muscle with sufficient contraction and relaxation, which in turn improves myopia caused by ciliary muscle tension. However, if physical exercise lacks activities that positively regulate the eyeball, it is difficult to demonstrate the effect of myopia prevention and control. Secondly, a high frequency of physical exercise does not necessarily mean a long duration of exercise. If each exercise session is only a brief activity, it may have limited benefits for the eye’s regulatory function. Zhu et al. [66] have found that more than 150 min of exercise per week has a positive effect on myopia prevention and control, while shorter exercise times have limited effects. Moreover, even with a high frequency of physical exercise, if individuals still maintain bad habits such as prolonged screen time or incorrect reading and writing postures, the effect of physical exercise on myopia prevention and control may be offset.

Sleep quality plays a mediating role in the association between perceived air pollution and myopia

The results of this study show that sleep quality plays a mediating role in the association between perceived air pollution and myopia, that is, air pollution increases the risk of myopia by reducing sleep quality. According to this study, high-quality sleep can reduce the risk of myopia; conversely, poor sleep quality can increase the risk of myopia. In addition, the results of this study also reveal the negative predictive effect of perceived air pollution on physical exercise. Therefore, sleep quality plays a mediating role in the association between perceived air pollution and myopia. When residents perceive high levels of air pollution, they often experience a series of physical and psychological discomforts, which in turn reduce sleep quality. Physiologically, air pollution may cause respiratory discomfort, leading to symptoms such as nasal congestion and coughing [67], which are exacerbated at night and disrupt sleep rhythms. Psychologically, concerns about air pollution can generate anxiety, thereby reducing sleep quality. This finding suggests that improving sleep quality may to some extent block the adverse effects of perceived air pollution on myopia.

However, this study did not find that physical exercise and sedentary behavior play a mediating role in the association between perceived air pollution and myopia. Firstly, this study did not find the benefits of physical exercise in preventing and controlling myopia, nor did it find any association between perceived air pollution and physical exercise. Therefore, the mediating role of physical exercise is not significant. The possible reasons for the insignificant association between physical exercise and myopia have been discussed above, so they will not be reiterated here. However, the results of this study are inconsistent with the previous conclusion that air pollution reduces the level of physical exercise [23]. This may be because this study did not know the venue of physical exercise, which confused the precise association between the two. That is to say, the negative effect of air pollution on indoor exercise is relatively low. Secondly, although this study found that sedentary behavior increases the risk of myopia, it did not find a positive link between perceived air pollution and sedentary behavior. Therefore, the mediating role of sedentary behavior in the relationship between perceived air pollution and myopia is not significant. Previous study [24] has found that air pollution can increase residents’ sedentary time, but this study did not. The reason for this may be that this study used a subjective perception method to measure air pollution, which may be less precise than the measurement of objective indicators. Therefore, further studies are needed to explore the mediating effects of physical exercise and sedentary behavior.

Implications for public health policy from this study

This study has multiple positive implications and insights for the formulation of public health policies. Firstly, the findings of this study provide a scientific basis for the development of public health policies. By clarifying that higher perceived air pollution can increase the risk of myopia and that sleep quality plays a mediating role, this study offers crucial scientific evidence for policymakers. Based on these findings, policymakers can develop more targeted policies, such as enacting and enforcing strict environmental protection policies to reduce air pollutant emissions. This, in turn, can lower residents’ perception of air pollution and indirectly reduce the risk of myopia. Secondly, the results of this study can guide health lifestyle promotion and education. The study shows that prolonged sitting can increase the likelihood of developing myopia, while higher sleep quality can reduce the risk of myopia. This suggests that public health policies should focus on conducting educational campaigns for myopia prevention and control to raise awareness of the dangers of myopia and methods for its prevention. It is particularly important to emphasize the significance of reducing sedentary behavior and maintaining high-quality sleep for myopia prevention, guiding residents to adopt a healthy lifestyle.

Limitations prospects of this study

This study has certain limitations in terms of research design, data acquisition, measurement indicators, and control variables. Firstly, in terms of research design, this study is a cross-sectional study, which cannot clarify the causal relationship between variables. Although correlations have been found between perceived air pollution, lifestyle factors, and myopia, it is impossible to determine whether air pollution directly causes myopia, nor to clarify the causal role of lifestyle factors in this relationship. This limitation makes it difficult for the study to gain an in-depth understanding of the correlation mechanism among the three. Secondly, this study uses the subjective perception method to measure air pollution, which may affect the accuracy of the research results compared with the measurement of objective indicators. Thirdly, when measuring the frequency of physical exercise, this study only focuses on the dimension of exercise frequency and does not involve key elements such as exercise type and exercise duration, which may lead to inaccurate evaluation of the relationship between physical exercise and myopia. Finally, in the selection of control variables, this study has not further controlled important factors that may affect the occurrence and development of myopia, such as eye use habits, screen time, and family history of myopia, which may thus affect the accuracy of the results.

Based on the above limitations, the following suggestions for future research are put forward to explore the correlation mechanism among perceived air pollution, lifestyle factors, and myopia more accurately and in-depth. Firstly, optimize the research design to clarify causal relationships. It is suggested that a longitudinal follow-up study design could be adopted in the future; by conducting long-term follow-up of the same group, data on their perceived air pollution exposure levels, lifestyle, and the occurrence and development of myopia should be collected regularly. This will help more clearly determine whether perceived air pollution is a potential inducement for myopia and whether lifestyle factors play a mediating role in the relationship between the two, thereby revealing the temporal sequence and internal mechanism of the correlation among the three. Secondly, improve the air pollution measurement method to enhance data accuracy. It is recommended that future studies combine the measurement of objective indicators with subjective perception assessment to construct a more comprehensive air pollution exposure evaluation system, so as to more accurately quantify the correlation between air pollution exposure and myopia. Thirdly, refine the measurement dimensions of physical exercise to deepen correlation analysis. Future studies are advised to establish a multi-dimensional physical exercise assessment index system; in addition to retaining the indicator of exercise frequency, elements such as exercise type, exercise duration, and exercise intensity should be added. This will more accurately reveal the key characteristics of how physical exercise affects myopia and provide a basis for formulating targeted exercise recommendations for myopia prevention and control. Finally, expand the scope of control variables to reduce confounding effects. It is suggested to systematically sort out the risk factors for myopia and incorporate them into the control variable system, so as to reduce their interference with data analysis and improve the accuracy and reliability of research results.

Conclusion

Based on the CGSS 2021 database, this study explores the relationship between perceived air pollution and myopia, as well as the mediating effects of physical exercise, sedentary behavior, and sleep quality. It was found that higher perceived air pollution can increase the risk of myopia, prolonged sitting may increase the likelihood of developing myopia, and higher sleep quality can reduce the risk of myopia. Furthermore, sleep quality was found to mediate the relationship between perceived air pollution and myopia. Based on the above findings, it is recommended that the government formulate and enforce strict environmental protection policies to reduce air pollutant emissions, thereby lowering residents’ perception of air pollution and indirectly reducing the risk of myopia. In addition, public education and awareness campaigns targeting myopia prevention and control should be conducted to enhance residents’ understanding of the dangers of myopia and methods for its prevention, with particular emphasis on the importance of reducing sedentary behavior and maintaining high-quality sleep for myopia prevention. Moreover, future research needs to further investigate the specific mechanisms underlying the relationship between air pollution and myopia, as well as the role of lifestyle factors in this relationship, in order to develop more effective interventions and methods.

Authors’ contributions

Z.Z. conducted data mining and data interpretation, and wrote and revised the manuscript; P.S. proposed the research idea and wrote the manuscript; C.T. and X.F. performed the proofreading of the data and the manuscript.

Funding

No funding.

Data availability

The data for this study were derived from the 2021 Chinese General Social Survey. Requests for the data supporting this study should be directed to the corresponding author, Peng Shi.

Declarations

Ethics approval and consent to participate

This study was conducted in compliance with the Declaration of Helsinki. It utilized the open database of the 2021 Chinese General Social Survey (CGSS 2021), and the original survey of CGSS 2021 had obtained approval from the Research Ethics Committee of Renmin University of China. During the data collection phase of the CGSS 2021 database, all original participants were provided with comprehensive information about the research. This ensured that participants voluntarily signed the informed consent form on the basis of full informed understanding. Although this study did not require an additional application for ethical review (as it is a secondary analysis of an existing open database), the research team still strictly adhered to the database usage guidelines. Only anonymized public data was accessed, and there was no secondary contact with original participants or infringement of their privacy.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.