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Introduction: Spending more time outdoors was treated as a safe and cost-effective method to prevent and control myopia. While prior research has established an inverse association between outdoor time and the risk of myopia onset, the effect of increasing outdoor time in delaying the progression of myopia remains a subject of debate. The present meta-analysis aimed to assess the relationship between outdoor time and the myopia onset, and further examine whether there is a dose-response relationship between outdoor time and the risk of myopia onset. Meanwhile, perform whether the outdoor time is related to delaying the progression of myopia. Methods: Studies were retrieved from PubMed, Web of Science, Embase, Medline, and the Cochrane Database, spanning from their inception to February 2023. Three cohort studies and 5 prospective intervention studies were included, with a total of 12,922 participants aged 6–16 years. Results: Comparing the highest with the lowest exposure levels of time spent outdoors, the highest outdoor time was strongly associated with a reduced risk of myopia onset (odds ratio [OR]: 0.53; 95% confidence interval [CI]: 0.34, 0.82). A nonlinear dose-response relationship was found between outdoor time and myopia onset risk. Compared to 3.5 h of outdoor time per week, an increase to 7, 16.3, and 27 h per week corresponded with a respective reduction in the risk of myopia onset by 20%, 53%, and 69%. Among children and adolescents who were not myopic, spending more time outdoors significantly slowed down the speed of change in spherical equivalent refractive (weighted mean difference [WMD] = 0.10D, 95% CI: 0.07, 0.14) and axial length (WMD = −0.05 mm, 95% CI: −0.06, −0.03). Among children and adolescents who were already myopic, spending more time outdoors did not slow myopia progression. Conclusions: Overall, spending more time outdoors can prevent the onset of myopia, but it does not seem to slow its progression. Further studies are needed to better understand these trends.

Myopia is a pathological condition in which, when the human eye is in a relaxed state of accommodation, parallel light is focused in front of the retina after passing through the eye’s refractive system [1]. It is usually expressed in terms of spherical equivalent refractive (SER) to indicate its severity, with the lower the SER, the more myopic the eyes [2]. The axial length (AL), which is the sum of the corneal thickness, anterior chamber depth, lens thickness, and the vitreous chamber depth, is an important indicator for judging the degree of myopia progression [3]. Myopia is an important contributing factor for several ocular conditions such as myopic macular degeneration, cataract, retinal detachment, open-angle glaucoma, and choroidal neovascularization [4‒6], and it affects the learning and quality of life of children and adolescents [7]. The incidence of myopia has experienced a significant surge in recent times, with projections indicating that by 2050, 49.5% of the population around the world will be myopic [8]. Myopia is already a critical public health concern globally.

The serious trends and adverse consequences of myopia have aroused attention to control interventions. Myopia control can be achieved through two approaches: prevention of myopia onset and deceleration of myopia progression [9]. Many studies have established that genetics, longer screen behavior, excessive near-work, and shorter outdoor time are risk factors for the onset and progression of myopia, and it is worth noting that some of these risk factors are interrelated [10, 11]. Parental myopia increases the risk of myopia in their children [12]. Although numerous genetic loci have been found that are associated with refractive values and myopia [13], only a small part of the prevalence of myopia can be explained by genetic factors. This suggests that the shared environment of parents and children explains more myopia than genetics. Early clinical results indicate that optical and pharmacological therapy has been effective in impeding the onset of myopia and delaying its progression, including atropine eye drops [14], orthokeratology [15], multifocal soft contact lenses [16], and so on. Recent studies have noted that spending more time outdoors is the most advocated strategy compared to optical and pharmacological therapy, taking into account affordability and safety [17, 18].

Researchers nowadays are consistent in their findings on the relationship between outdoor time and myopia onset risk. A meta-analysis of cross-sectional studies conducted in 2012 [19] revealed that increased time spent outdoors may reduce the risk of developing myopia. A meta-analysis published in 2017 [20] that included cohort and intervention studies and another published in 2019 [21] that included intervention studies, both pointed out that there was a dose-response relationship between outdoor time and the risk of myopia onset. Subsequent to the aforementioned meta-analyses, additional studies have been published, an updated meta-analysis to further explore the association between time spent outdoors and the risk of myopia onset is necessary.

It has been controversial that the effect of spending more time outdoors on delayed myopia progression. A conducted study of children aged 6–11 years showed the effectiveness of more outdoor activities in slowing changes in refractive error toward myopia [22]. This finding was further supported by a meta-analysis of randomized controlled trials [23]. Thus, if myopia is already present, spending time outdoors does not seem to have an impact on the rate of myopia progression [24]. One reason for the inconsistency of previous findings may lie in the fact that some of the studies analyzed data from the non-myopic and myopic populations together, without discriminating the differences between non-myopic and myopic populations. To this end, we will undertake a meta-analysis of studies in which outdoor time interventions were conducted in non-myopic or myopic populations to examine the differences in the impact of outdoor time exposure on relevant indicators of myopia progression in individuals with and without myopia.

Collectively, the present study aimed to perform a meta-analysis to evaluate the following questions: Does spending more time outdoors effectively prevent the onset of myopia in children and adolescents? Is there a dose-response relationship between outdoor time and the risk of myopia onset in children and adolescents? Can an increase in outdoor time delay the progression of myopia in children and adolescents who are already myopic?

Search Strategy

We performed systematic searches in PubMed, Wed of Science, Embase, Medline, and the Cochrane Database for articles published from their inception to February 2023 with a language restriction in English. The search terms employed were as follows: (“outdoor” OR “outside” OR “outdoor activity” OR “sunlight”) AND (“myopia” OR “nearsightedness” OR “short sight” OR “refractive error”) AND (“child” OR “children” OR “pediatric” OR “youth” OR “adolescent” OR “teenage”) (online suppl. Table S1; for all online suppl. material, see https://doi.org/10.1159/000539229). Furthermore, we conducted a thorough examination of the reference lists of previously published reviews to ascertain any supplementary studies of relevance.

Study Selection and Eligibility Criteria

Two reviewers (D.L. and S.M.) independently assessed the possible eligibility of the studies by reading the title, abstract, keywords, and full text. The studies were required to meet the following inclusion for the meta-analysis: (i) they were cohort studies or prospective intervention studies; (ii) reported myopia measurement and definition; (iii) reported the measurement and calculation method of outdoor time; (iv) the study population is representative of general children and adolescents (aged up to 18 years). Furthermore, for the cohort study, time spent outdoors is categorized into at least three levels, and odds ratio (OR) and 95% confidence interval (95% CI) were reported for each outdoor exposure level with the risk of myopia occurrence. For the prospective intervention studies, the change in SER or AL was reported for those who were non-myopic at baseline and those who were myopic at baseline in the intervention and control groups. Duplicate publications, unhuman studies, lack of relevant outcome data, reviews and comments, and studies not meeting the eligibility criteria were excluded.

Data Extraction

For each study, the following general characteristics were extracted: author, publication year, the country where the study was carried out, duration of follow-up, participant count and average age, definition and measurement of myopia, as well as the method of measurement and calculation of outdoor time. In addition, for cohort studies, we obtained information about outdoor time exposure levels, the maximally adjusted estimate of the OR at risk of myopia occurrence compared with the reference for each exposure level, and the corresponding 95% CI. The following data were also extracted in prospective intervention studies for data analysis: intervention measure, number of participants with non-myopic and myopic at baseline in intervention and control groups, and change of SER and AL.

Quality and Risk of Bias Assessment

Two independent authors (D.L. and S.M.) evaluated the quality and risk of bias of each study that was included. Any potential disagreements between the two authors were resolved through consensus; the third author (X.L.) was also involved to resolve discrepancies. The evaluation of the quality of cohort studies was conducted through the utilization of the Newcastle-Ottawa Scale (NOS) [25, 26], which encompasses eight items distributed across three domains: selection (representativeness), comparability (design or analysis), and outcomes (assessment and follow-up). Each numbered item within the selection and outcome categories can be awarded a maximum of 1 star, while comparability can receive a maximum of 2 stars. The range of total scores is between 0 and 9 stars. Ratings falling within the ranges of 0–3, 4–6, and 7–9 stars were classified as low, moderate, and high quality, respectively (online supple. Table S2). The Cochrane bias risk assessment tool [27] was utilized to evaluate the quality of the prospective intervention studies. This tool encompasses seven aspects, namely random sequence generation, allocation concealment, blinding of participants and researchers, blinding of outcome assessment, incomplete outcome data, selective reporting, and other errors. As per the established scoring criteria, the symbol “+” denotes a low risk of bias, “?” signifies an unclear risk of bias, and “-” denotes a high risk of bias (online suppl. Fig. S1).

Statistical Methods

The statistical analyses were performed utilizing the Stata software (Version 16.0). Dichotomous outcome data were expressed using OR with 95% CI, and continuous outcomes were expressed using weighted mean difference (WMD) and 95% CI.

First, we calculated the OR with a 95% CI of the risk of myopia onset in the highest outdoor exposure categories compared to the lowest category in the meta-analysis. For a study in which the highest outdoor exposure was regarded as the reference group, we conducted a data transformation using the formula by Hamling et al. [28] so that the lowest outdoor exposure level became the reference group. Second, we used the method proposed by Greenland and Longnecker [29] to assess the dose-response relation between different outdoor exposure levels and myopia onset risk. According to some scholars’ recommendations [30‒32], the corresponding relative risk for each cohort study was assigned the median or mean level of outdoor time in each category. For studies that reported the outdoor time by time range, we estimated the midpoint of each category by computing the mean of the lower and upper limits. In instances where the highest category was left open-ended, it was posited that the length of the open-ended interval would mirror that of the adjacent interval. Conversely, in cases where the lowest category was open-ended, the lower boundary was established at zero. Lastly, we enumerated the WMD with 95% CI of SER and AL changes of included prospective studies to assess the association between outdoor time with myopia progression.

Heterogeneity was assessed using Cochran’s Q test and I2 statistics [33]. If the heterogeneity was deemed insignificant (p > 0.1, I2 < 50.0%), a fixed-effects model was employed. Conversely, a random-effects model was utilized if the heterogeneity was significant. Sensitivity analysis was conducted through the gradual removal of each study from the main analysis. The potential publication bias was not assessed as a result of the limited number of studies included (n < 10) [34].

Search Results and Article Selection

We identified 1,193 articles from the systematic search for the database. After removing 654 duplicate articles, we screened the titles and abstracts of 539 remaining articles, and a further 433 articles were excluded. We reviewed the full text of 106 remaining articles for consideration and found that 98 articles do not meet the inclusion criteria. In total, 8 articles included 3 cohort studies [35‒37] and 5 prospective intervention studies [38‒42] ere eventually retained in the present meta-analysis. The flowchart of the literature search and study selection is shown in Figure 1.

Fig. 1.

Flowchart of the literature search and study selection.

Fig. 1.

Flowchart of the literature search and study selection.

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Characteristics and Quality of Included Studies

The relevant characteristics of eight studies are presented in Table 1 and Table 2. A total of 12,922 children and adolescents aged 6–16 years were enrolled: 6,152 children and adolescents in cohort studies and 6,770 children and adolescents in prospective intervention studies. All studies were published between 2011 and 2022, with a follow-up period from 1 to 6 years. One study was conducted in boys with the remainder of the studies in both genders. Five studies were conducted in China, two studies in Taiwan, and one study in Australia. All studies assessed refractive error by using cycloplegic refraction, except for one study that used noncycloplegic refraction and another study that included 3,571 participants used a questionnaire. To measure outdoor time, two studies used an instrument such as a light meter recorder and a wearable wrist-watch light sensor, six studies used self-reported.

Table 1.

Characteristics of cohort studies included in a meta-analysis

StudyCountryFollow-up, yearsParticipants (% male)Age range, yearsMyopia measurementMyopia definitionOutdoor measurementDose (outdoor time, h/week)Myopia cases, NAdjusted OR (95% CI)Adjustment for confounders
French et al. [35] (2013)_I Australian 5–6 863 (52) Cycloplegic refraction SER≤一0.50 D Questionnaire >23 22 1.00 (Reference) Age, gender, parental myopia 
>16–≤23 33 1.14 (0.59, 2.21) 
≤16 64 2.84 (1.56, 5.17) 
French et al. [35] (2013)_II Australian 5–6 1,196 (50) 12 Cycloplegic refraction SER≤一0.50 D Questionnaire >22.5 52 1.00 (Reference) Age, gender, parental myopia 
>13.5–≤22.5 76 2.00 (1.28, 3.14) 
≤13.5 77 2.15 (1.35, 3.42) 
Qi et al. [36] (2019) China 522 (100) 15.5±0.6 Cycloplegic refraction SER≤一0.50 D Questionnaire <9.33 93 1.00 (Reference) None 
≥9.33–<14 34 0.77 (0.46, 1.29) 
≥14 14 0.46 (0.23, 0.95) 
Zhang et al. [37] (2021) China 3,571 (54) 13, 15 Questionnaire Wearing glasses or vision was fuzzy Questionnaire ≥14 1,508 1.00 (Reference) Gender, height, weight, sleep duration, health status, family economic conditions, etc. 
≥7–<14  1.05 (0.89, 1.23) 
<7  1.25 (1.07, 1.46) 
StudyCountryFollow-up, yearsParticipants (% male)Age range, yearsMyopia measurementMyopia definitionOutdoor measurementDose (outdoor time, h/week)Myopia cases, NAdjusted OR (95% CI)Adjustment for confounders
French et al. [35] (2013)_I Australian 5–6 863 (52) Cycloplegic refraction SER≤一0.50 D Questionnaire >23 22 1.00 (Reference) Age, gender, parental myopia 
>16–≤23 33 1.14 (0.59, 2.21) 
≤16 64 2.84 (1.56, 5.17) 
French et al. [35] (2013)_II Australian 5–6 1,196 (50) 12 Cycloplegic refraction SER≤一0.50 D Questionnaire >22.5 52 1.00 (Reference) Age, gender, parental myopia 
>13.5–≤22.5 76 2.00 (1.28, 3.14) 
≤13.5 77 2.15 (1.35, 3.42) 
Qi et al. [36] (2019) China 522 (100) 15.5±0.6 Cycloplegic refraction SER≤一0.50 D Questionnaire <9.33 93 1.00 (Reference) None 
≥9.33–<14 34 0.77 (0.46, 1.29) 
≥14 14 0.46 (0.23, 0.95) 
Zhang et al. [37] (2021) China 3,571 (54) 13, 15 Questionnaire Wearing glasses or vision was fuzzy Questionnaire ≥14 1,508 1.00 (Reference) Gender, height, weight, sleep duration, health status, family economic conditions, etc. 
≥7–<14  1.05 (0.89, 1.23) 
<7  1.25 (1.07, 1.46) 
Table 2.

Characteristics of prospective intervention studies included in a meta-analysis

StudyParticipantsMeasurementIntervention measureOutcome
Yi et al. [38] (2011) 66 children aged 7–11 years; 2-year follow-up; Changsha China Myopia: cycloplegic refraction; outdoor time: questionnaire Participants are required to engage in outdoor time for at least 14–15 h per week Initial myopic: intervention: −0.38±0.15 D/year, n = 37; control: −0.52±0.19 D/year, n = 29 
Wu et al. [39] (2012) 571 children aged 7–11 years; 1-year follow-up; Taiwan Myopia: cycloplegic refraction; outdoor time: questionnaire Participants were encouraged to go outside of the classroom for outdoor activities during the 80 min of school recess time every day Initial non-myopic: intervention: −0.26±0.61 D/year, n = 174; control: −0.44±0.64 D/year, n = 121 
Initial myopic: Intervention: −0.20±0.69 D/year, n = 113; control: −0.37±0.67 D/year, n = 94 
Wu et al. [40] (2018) 693 children aged 6–7 years; 1-year follow-up; Taiwan Myopia: cycloplegic refraction; outdoor time: light meter recorder and daily log Participants were encouraged to have 11 h or more of outdoor time per week Initial non-myopic: intervention: −0.32±0.58 D/year, 0.26±0.18 mm/year, n = 235; control: −0.43±0.75 D/year, 0.30±0.32 mm/year, n = 385 
Initial myopic: intervention: −0.57±0.40 D/year, 0.45±0.28 mm/year, n = 32; control: −0.79±0.38 D/year, 0.60±0.19 mm/year, n = 41 
Guo et al. [41] (2019) 373 children aged 6–7 years; 1-year follow-up; Beijing China Myopia: noncycloplegic refraction; outdoor time: questionnaire Performed a 30 min jogging exercise every school day Initial non-myopic: intervention: −0.34±0.60 D/year, 0.23±0.22 mm/year, n = 95; control: −0.47±0.56 D/year, 0.31±0.16 mm/year, n = 142 
Initial myopic: intervention: 0.28±0.83 D/year, 0.29±0.13 mm/year, n = 62; control: −0.06±0.86 D/year, 0.30±0.18 mm/year, n = 74 
He et al. [42] (2022) 5,067 children aged 6–9 years; 2-year follow-up; Shanghai China Myopia: cycloplegic refraction; outdoor time: a wearable wrist-watch light sensor Intervention_I: an additional outdoor time of 40 min per school day Initial non-myopic: intervention_I: −0.84±0.77 D/2 years, 0.55±0.33 mm/2 years, n = 1,878; intervention_II: −0.93±0.77 D/2 years, 0.58±0.33 mm/2 years, n = 1,581; control: −0.98±0.76 D/2 years, 0.61±0.33 mm/2 years, n = 1,608 
Intervention_II: an additional outdoor time of 80 min per school day 
StudyParticipantsMeasurementIntervention measureOutcome
Yi et al. [38] (2011) 66 children aged 7–11 years; 2-year follow-up; Changsha China Myopia: cycloplegic refraction; outdoor time: questionnaire Participants are required to engage in outdoor time for at least 14–15 h per week Initial myopic: intervention: −0.38±0.15 D/year, n = 37; control: −0.52±0.19 D/year, n = 29 
Wu et al. [39] (2012) 571 children aged 7–11 years; 1-year follow-up; Taiwan Myopia: cycloplegic refraction; outdoor time: questionnaire Participants were encouraged to go outside of the classroom for outdoor activities during the 80 min of school recess time every day Initial non-myopic: intervention: −0.26±0.61 D/year, n = 174; control: −0.44±0.64 D/year, n = 121 
Initial myopic: Intervention: −0.20±0.69 D/year, n = 113; control: −0.37±0.67 D/year, n = 94 
Wu et al. [40] (2018) 693 children aged 6–7 years; 1-year follow-up; Taiwan Myopia: cycloplegic refraction; outdoor time: light meter recorder and daily log Participants were encouraged to have 11 h or more of outdoor time per week Initial non-myopic: intervention: −0.32±0.58 D/year, 0.26±0.18 mm/year, n = 235; control: −0.43±0.75 D/year, 0.30±0.32 mm/year, n = 385 
Initial myopic: intervention: −0.57±0.40 D/year, 0.45±0.28 mm/year, n = 32; control: −0.79±0.38 D/year, 0.60±0.19 mm/year, n = 41 
Guo et al. [41] (2019) 373 children aged 6–7 years; 1-year follow-up; Beijing China Myopia: noncycloplegic refraction; outdoor time: questionnaire Performed a 30 min jogging exercise every school day Initial non-myopic: intervention: −0.34±0.60 D/year, 0.23±0.22 mm/year, n = 95; control: −0.47±0.56 D/year, 0.31±0.16 mm/year, n = 142 
Initial myopic: intervention: 0.28±0.83 D/year, 0.29±0.13 mm/year, n = 62; control: −0.06±0.86 D/year, 0.30±0.18 mm/year, n = 74 
He et al. [42] (2022) 5,067 children aged 6–9 years; 2-year follow-up; Shanghai China Myopia: cycloplegic refraction; outdoor time: a wearable wrist-watch light sensor Intervention_I: an additional outdoor time of 40 min per school day Initial non-myopic: intervention_I: −0.84±0.77 D/2 years, 0.55±0.33 mm/2 years, n = 1,878; intervention_II: −0.93±0.77 D/2 years, 0.58±0.33 mm/2 years, n = 1,581; control: −0.98±0.76 D/2 years, 0.61±0.33 mm/2 years, n = 1,608 
Intervention_II: an additional outdoor time of 80 min per school day 

The quality evaluation for the included three cohort studies is presented in online supplementary Table S2. Two cohort studies were assessed with the Newcastle-Ottawa Quality Assessment scale and were judged to be medium quality, and one was judged to be high quality. The risk of bias assessments of the included five prospective intervention studies is shown in online supplementary Figure S1. Of those, two studies that did not report the information about allocation concealment were assessed as high risk of selection bias.

Associations between Outdoor Time and Myopia Onset Risk

Three cohort studies investigated the association between outdoor time and risk of myopia onset, of which one study provided relevant data for both the younger aged 6 years and the older aged 12 years. Comparing the highest with the lowest exposure levels of time spent outdoors, the pooled OR from the random-effects model indicated highest outdoor time was strongly associated with a reduced risk of myopia onset (OR: 0.53; 95% CI: 0.34, 0.82), with considerable heterogeneity between studies (I2 = 74.5%, p < 0.01) (shown in Fig. 2). In the sensitivity analysis, we found that this association did not depend on an individual study (shown in online suppl. Fig. S2), suggesting that the exclusion of each study at a time did not change the pooled results.

Fig. 2.

Forest plot of the association between outdoor time and risk of myopia based on the highest compared with lowest exposure levels. OR, odds ratio; CI, confidence interval.

Fig. 2.

Forest plot of the association between outdoor time and risk of myopia based on the highest compared with lowest exposure levels. OR, odds ratio; CI, confidence interval.

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Dose-Response Association between Outdoor Time and the Risk of Myopia Onset

Dose-response meta-analysis indicated a strong inverse association between outdoor time per week and myopia onset risk (χ2 = 14.7, p < 0.001; shown in Fig. 3). In the nonlinear dose-response meta-analysis, the ORs for time spent outdoor 7 h, 16.3 h, and 27 h per week were, respectively, 0.80 (95% CI: 0.66, 0.97), 0.47 (95% CI: 0.25, 0.88), and 0.31 (95% CI: 0.12, 0.81) compared with 3.5 h per week (Table 3).

Fig. 3.

Dose-response association between outdoor time per week and risk of myopia onset. The solid line represents a nonlinear dose-response and broken lines represent 95% CI. OR, odds ratio; CI, confidence interval.

Fig. 3.

Dose-response association between outdoor time per week and risk of myopia onset. The solid line represents a nonlinear dose-response and broken lines represent 95% CI. OR, odds ratio; CI, confidence interval.

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Table 3.

OR with 95% CI of risk of myopia onset in children and adolescents from nonlinear dose-response analysis according to outdoor time per week

Outdoor time, h/weekRisk of myopia onset, OR (95% CI)
3.5 1.00 (1.00, 1.00) 
7.0 0.80 (0.66, 0.97) 
9.0 0.71 (0.53, 0.95) 
10.5 0.64 (0.44, 0.94) 
11.7 0.60 (0.39, 0.93) 
12.5 0.57 (0.35, 0.92) 
16.3 0.47 (0.25, 0.88) 
17.5 0.45 (0.23, 0.87) 
18.0 0.44 (0.22, 0.86) 
19.5 0.41 (0.20, 0.85) 
26.5 0.32 (0.12, 0.81) 
27.0 0.31 (0.12, 0.81) 
Outdoor time, h/weekRisk of myopia onset, OR (95% CI)
3.5 1.00 (1.00, 1.00) 
7.0 0.80 (0.66, 0.97) 
9.0 0.71 (0.53, 0.95) 
10.5 0.64 (0.44, 0.94) 
11.7 0.60 (0.39, 0.93) 
12.5 0.57 (0.35, 0.92) 
16.3 0.47 (0.25, 0.88) 
17.5 0.45 (0.23, 0.87) 
18.0 0.44 (0.22, 0.86) 
19.5 0.41 (0.20, 0.85) 
26.5 0.32 (0.12, 0.81) 
27.0 0.31 (0.12, 0.81) 

Mean Change in SER Results of Prospective Intervention Studies

As shown in Figure 4, four studies reported the change in SER with initial non-myopic and initial myopia after the experiment. Of the studies included, most had one intervention group, but one study conducted in 2022 had two intervention groups. Group I was instructed an additional outdoor time of 40 min per school day, and group II was assigned an additional 80 min of outdoor exposure per school day. There was no statistically significant heterogeneity between included studies with initial non-myopic participants (p = 0.133 > 0.01, I2 = 43.4% < 50%). We used a fixed-effects model to pool the data and found that the change of SER was a significant reduction in the intervention group compared with the control group among children and adolescents with initial non-myopic (WMD = 0.10D, 95% CI: 0.07, 0.14). The same significant change of SER was also found in children and adolescents with initial myopic (WMD = 0.17D, 95% CI: 0.10, 0.24).

Fig. 4.

Forest plot of the impact of increasing outdoor time on SER variation. WMD, weighted mean difference; CI, confidence interval.

Fig. 4.

Forest plot of the impact of increasing outdoor time on SER variation. WMD, weighted mean difference; CI, confidence interval.

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Mean Change in AL Results of Prospective Intervention Studies

As shown in Figure 5, in five prospective intervention studies, only three of them reported the changes in AL of initial non-myopic children and adolescents after the intervention, while two reported the changes in AL of initial myopic children and adolescents after the intervention. For initial non-myopic children and adolescents, a fixed-effects model was used due to there was no statistically significant heterogeneity between the three included studies (p = 0.161 > 0.01, I2 = 41.8% <50%) and found that the benefit of slowing the increase of AL was significantly increased in the intervention group of conducted outdoor exposure compared with the control group (WMD = −0.05 mm, 95% CI: −0.06, −0.03).

Fig. 5.

Forest plot of the impact of increasing outdoor time on AL growth. WMD, weighted mean difference; CI, confidence interval.

Fig. 5.

Forest plot of the impact of increasing outdoor time on AL growth. WMD, weighted mean difference; CI, confidence interval.

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For initial myopic children and adolescents, the effect is mixed. A Taiwan study targeting children aged 6–7 reported significant intervention effects of slowing down the growth of AL (WMD = −0.15 mm, 95% CI: −0.26, −0.04), while such intervention effects were not found in another study targeting children aged 6–7 in Shanghai (WMD = −0.01 mm, 95% CI: −0.06, 0.04). There was statistically significant heterogeneity between Taiwan and Shanghai studies (p = 0.028, I2 = 79.4%), we used a random-effects model to pool the data and not found that the change of AL of the intervention group with increased outdoor exposure was statistically smaller than that of the control group (WMD = −0.07 mm, 95% CI: −0.21, 0.07).

The present meta-analysis included 3 cohort studies and 5 prospective intervention studies published between 2011 and 2022, with a total of 12,922 participants, to assess the relationship between time outdoors and the onset and progression of myopia. We found a significant protective connection between more time spent outdoors and the risk of myopia onset, which is in accordance with other relevant studies. Besides, we further uncovered a nonlinear dose-response relationship between outdoor time and myopia onset risk. Compared to 3.5 h of outdoor time per week, up to 7, 16.3, and 27 h of outdoor time per week reduced the risk of myopia onset by 20%, 53%, and 69%, respectively. Compared with the existing meta-analysis that only included studies where the increase in time outdoors time spent outdoors was spread from 1 to 9.8 h per week [20, 43], our meta-analysis validated the dosage benefits of a larger range of outdoor time.

The protective effect against myopia onset risk of time spent outdoors is suggested to be explained by several mechanisms, namely, exposure to higher levels and shorter wavelengths of light, and increased dopamine and vitamin D levels [44, 45]. Considering the benefits of outdoor time in reducing the risk of myopia, effective interventions or policies are needed to promote outdoor behavior among children and adolescents. First, school as a place where children and adolescents the majority of their day [46] should provide ample opportunity and a viable way for children and adolescents to accumulate time spent outdoors. For instance, expanded the number of physical education outdoor courses and raised outdoor play time during morning recess and lunch breaks [47]. Second, promoting active commuting from home to school may be another opportunity for children and adolescents to increase outdoor time. Many studies have shown that active commuting (e.g., walking and bicycling to school) provides an accessible way to integrate outdoor activity into daily life [48, 49]. Furthermore, parental support, which includes encouraging kids to be more active outdoors, watching them join in outdoor activities, and participating in outdoor activities together with them could provide incentives and safeguards for children and adolescents to accumulate more outdoor time [50]. Previous studies suggest that more positive parental support was associated with a heightened outdoor time of kids on weekends [51]. Finally, a favorable neighborhood-built environment can present an important source of outdoor activity for children and adolescents. A literature review indicated that neighborhood environment factors, particularly rich fitness facilities, slow traffic speeds, and low residential density were positively linked to outdoor time for children and adolescents [52].

To date, conflicting conclusions have emerged from systematic reviews and meta-analyses on the question of whether time spent outdoors can significantly delay myopia progression [53, 54], the reason for those conflicting conclusions may lie in the mix of populations included in the studies. Based on this, we distinguished between children and adolescents who were not myopic and those who were and included the most recently published prospective intervention studies [42, 55] in our meta-analysis. Our results revealed that spending more time outdoors significantly slowed the speed of change in SER and AL in children and adolescents who were not myopic. However, in most studies increasing time outdoors could not effectively delay myopia progression in children and adolescents who are already myopic. In one study, we found that among children and adolescents who were already myopic, although the change in SER of the intervention group was significantly smaller than that of the control group, the difference between the intervention and control groups in terms of changes in AL was not statistically significant [41]. In another 3-year follow-up study, more time outdoors was connected with less progression of myopia among boys but not among girls [56]. This suggests that outdoor activities would also have an inhibitory effect on the progression of myopia.

Previous related meta-analyses have included most cross-sectional studies, and a fundamental disadvantage of cross-sectional studies is that they can only yield a correlational relationship between time spent outdoors and myopia, not a causal relationship, and avoiding this problem can be dealt with using a prospective design. Therefore, the important strength of this meta-analysis was that we included only the cohort studies and prospective intervention studies to examine the causal relationship between spending more time outdoors and myopia. In addition, another strength of this meta-analysis is that we divided children and adolescents into non-myopic and myopic groups to examine in more depth the relationship between time spent outdoors and delayed myopia progression. Some scholars pointed out that some studies have not distinguished between physical activity (e.g., sports and exercise) and time spent outdoors. This may confound results and lead to inaccurate conclusions [57]. We therefore only included studies in which a clear definition of the outdoor time to remove potential conclusion bias.

A couple of limitations existed in the present study. First, since the number of included articles was less than 10, so a funnel plot was not able to be constructed to examine the risk of publication bias. Second, a majority of the included studies were conducted in China and Taiwan, except for one study conducted in Australia and one in Europe. Our findings may not necessarily be applicable to counties with different cultural settings and school systems. Therefore, large-scale, multinational prospective research should be performed in the future to establish more generalizable scientific evidence. In addition, only 2 studies assessed outdoor time with objective instruments (e.g., light meter recorder, wrist-watch light sensor), while the remainder of studies used questionnaires to evaluate outdoor time. Undoubtedly, when facing a large sample population, the questionnaire survey method has the advantage of saving time and cost, but there is also a risk of recall bias which results in overestimation. Therefore, future research should be encouraged to use objective instruments (e.g., global positioning system, light exposure measurement, wearable light sensor device, and conjunctival ultraviolet autofluorescence) to assess children and adolescents’ outdoor time, when conditions permit. Finally, interventions included 5 prospective intervention studies were quite varied. Some studies have used encouragement to get children and adolescents to meet projected daily or weekly outdoor time targets, while others have used the implementation of outdoor programs to increase the daily outdoor activity of children and adolescents.

In summary, our meta-analysis presents support for the proposition that increased outdoor exposure can reduce the risk of myopia onset, and slow down the speed of change in SER and AL in non-myopic children and adolescents. However, spending more time outdoors did not seem to significantly slow the progression of myopia in children and adolescents who were already myopic. It is likely that the amount of outdoor time is significantly less in many Asian countries where the prevalence of myopia is high than in countries having lower myopia prevalence. A small amount of outdoor activity and a small variation in it can affect the visibility of potential addiction in the results. Further studies are needed to better understand these trends.

All analyses were based on previous published studies, and thus no ethical approval and informed consent are required. Nonetheless, the study adhered fully to the Declaration of Helsinki.

The authors have no conflicts of interest to declare.

This study was funded by the Ministry of Education’s commissioned project (Grant No. 2019ZXLXX) and the Social Science Foundation of Hunan Province (Grant No. 18YBA307).

D.L. contributed to the study conception and design, and drafted the original manuscript. D.L. and S.M. performed data collection, synthesis, analysis, and visualization. X.L. contributed to the critical revision of the manuscript. All authors have read and agreed to the published version of the manuscript.

All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.

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