Predicting the child who will become myopic – can we prevent onset?

ABSTRACT

Myopia has become a global epidemic with significant public health impacts. Identifying the child at risk of developing myopia, i.e. the pre-myopic child and implementing strategies to prevent the onset of myopia, could significantly reduce the burden of myopia on an individual and society. This paper is a review of publications that have identified ocular characteristics of children at risk of future myopia development including a lower than age normal amount of hyperopia and accelerated axial length elongation. Risk factors associated with increased risk of myopia development such as education exposure and reduced outdoor time, and strategies that could be implemented to prevent myopia onset in children are also explored. The strong causal role of education and outdoor time on myopia development suggests that lifestyle modifications could be implemented as preventative measures to at-risk children and may significantly impact the myopia epidemic by preventing or delaying myopia onset and its associated ocular health consequences.

KEYWORDS:

• Atropine
• axial length
• children
• outdoor time
• pre-myopia
• vision

Myopia is typically due to excessive axial length elongation^1,2 which predisposes the eye to a range of ocular pathologies including myopic maculopathy, retinal detachment and glaucoma.³ There has been a substantial increase in the prevalence of myopia in recent years. Between 2010 and 2020, myopia prevalence was estimated to have increased from 28.3% to 34% globally, with future projections of prevalence rates of 50% for myopia and 10% for high myopia (greater than −5.00 D) by 2050.⁴

However, there is considerable variation of myopia prevalence rates across different ethnic and geographic regions. The highest rates are reported from East Asian countries⁵ and are thought to be mediated by high engagement in education and limited time outdoors. Additionally, high myopia prevalence rates appear to increase at a faster pace in Asian countries. This may be explained by the earlier onset accompanied by faster myopia progression in children of Asian ethnicity.⁶

Myopia also poses a significant public health problem as myopic maculopathy is one of the leading causes of blindness in East Asia.⁷ IIt is estimated that 10 million individuals are affected by vision impairment due to myopia maculopathy. This number if projected to increase to 55.7 million by 2050 should myopia prevalence rates continue to rise.⁷ This has propelled research to develop strategies to slow or stop myopia progression termed myopia control, and to a lesser extent, myopia prevention methods.

Effective myopia control interventions in children include optical methods such as peripheral defocus spectacle lenses, multifocal soft contact lenses, and orthokeratology, and pharmaceutical agents such as atropine.^8,9 A network meta-analysis on myopia control efficacies of these interventions showed reduced myopia progression defined by less axial length growth relative to single vision spectacle correction ranging between 0.09 and 0.21 mm per year based on studies including treatment periods ranging over 12–36 months duration.¹⁰

On the other hand, effective and targeted interventions to prevent the onset of myopia in children have been difficult to develop due to the heterogeneous nature of myopia aetiology. This has also made accurate prediction of myopia onset in children difficult. Thus far, only outdoor time and low concentration atropine have been studied in randomised clinical trials as an intervention for myopia prevention.

Identifying the child at risk of developing myopia and implementing strategies to halt the onset of myopia could significantly reduce the burden of myopia on an individual and society. This literature review will examine past and current research on methods used to identify children at risk of future myopia and clinical strategies that may prevent myopia onset in children.

The concept of pre-myopia

The Refractive status of the human eye changes continuously from birth towards a state of ‘emmetropia’ as a part of normal development, with the most rapid shift occurring within the first 1–2 years of life.^11,12 Eye growth then slows down over the next several years and the final refractive state of the eye is said to rest in a low amount of hyperopia by age 5–7 years.^12–16

In axial myopia, children experience greater axial length elongation than normal physiological growth, with the growth pattern showing deviation from non-myopic children up to 2–3 years before myopia onset and accelerating the year before onset.^17–19 These children show a premature myopic refractive shift below their age normal hyperopia and the risk of future myopia onset significantly increases.²⁰ The younger the age of myopia onset, the greater the risk of developing higher degrees of myopia.^21,22 In recognition of the dynamic refractive change prior to myopia onset in children, in 2019, the white paper report by International Myopia Institute: Defining and Classifying Myopia: A Proposed Set of Standards for Clinical and Epidemiologic Studies proposed an official definition for pre-myopia:

A refractive state of an eye of ≤ +0.75 D and > −0.50 D in children where a combination of baseline refraction, age, and other quantifiable risk factors provide a sufficient likelihood of the future development of myopia to merit preventative interventions.²³

The premise of this definition stems from several research studies that have described the characteristics of the pre-myopic eye as well as risk factors associated with myopia onset. The following sections will discuss the findings of these studies and how the results may help eye health-care practitioners identify and predict future myopia onset in children.

Baseline refractive error as a predictor of future myopia onset

Using baseline refractive error as a predictor of future myopia onset was first reported in 1964 from the Ojai longitudinal study in the USA (n = 383, 766 eyes).²⁴ The author found that the spherical equivalent refractive error (SER) of a child at age 5–6 years (between plano and +0.50 D, determined with retinoscopy without cycloplegia) can predict future myopia development at age 13–14 years with a sensitivity and specificity of 81.5% and 72.1%.²⁴

In 1999, Zadnik et al. analysed data from 554 non-myopic children (mean age at baseline 8.60 ± 0.53 years) to predict the onset of myopia at least 1 year after baseline measurement. They found that a mean cycloplegic SER +0.75 D or less at age 8 could predict future myopia onset with a sensitivity and specificity of 86.7% and 73.3% with the area under the receiver operating curve (AUC) of 0.88. The AUC estimates the discriminative ability of a prediction model and helps determine the optimal cut-off value for the best diagnostic performance.²⁵ Adding corneal power, Gullstrand lens power and axial length to their modelling improved the prediction slightly.

The Collaborative Longitudinal Evaluation of Ethnicity and Refractive Error (CLEERE) study evaluated thirteen potential predictive factors from 4512 ethnically diverse, non-myopic school-aged children (aged 6 to 11 years at baseline). Myopia was defined as ≤ −0.75 D. Eight of these predictive factors were associated with predicting the onset of myopia including baseline cycloplegic SER, parental myopia, axial length, corneal power, crystalline lens power, and the ratio of accommodative convergence to accommodation, horizontal/vertical astigmatism magnitude, and visual acuity, excluding the other predictors such as oblique astigmatism magnitude, accommodative lag, crystalline lens thickness, relative peripheral refractive error and time spent outdoors.

Multiple prediction models were constructed based on different combinations of these eight factors, and backward stepwise selection and tenfold cross-validation were used to select the best model. Consistently, using cycloplegic SER alone as a predictor for myopia onset showed effective predictive ability that was comparable to all eight predictive factors combined. The cut-off cycloplegic SER value decreased with increasing age, with a proposed threshold cut-off of <+0.75 D at age 6, <+0.50 D at ages 7–8, < +0.25 D at ages 9–10, and <0 D at age 11 years. These models achieved an AUC of 0.87–0.93 in the prediction of myopia onset demonstrating that a single measure of baseline SER could predict future myopia development.²⁶

The findings from the CLERRE study were also supported by other studies including children from different ethnic groups.^22,27,28 One study on Chinese children reported a cycloplegic SER cut-off of ≤+0.50 D (sensitivity 84.6%, specificity 71%, aged 7–9 years)²⁷ and another study on European children reported a cycloplegic SER cut-off of ≤+0.63 D (sensitivity 75.56%, specificity 82.96%, aged 6–7 years)²² could be used to predict future myopia onset with an AUC ranging between 0.862 and 0.869.

Despite the usefulness of cycloplegic SER as a predictor of future myopia development, the use of cycloplegia may be difficult to implement in large-scale screening programs with the intention of identifying at risk children. Furthermore, preventative therapy may need to be implemented before the child reaches the threshold SER. The level at which preventative therapy could be effective warrants further exploration.

Ocular biometry growth charts

Axial length is the main structural determinant of axial myopia and has a high correlation with refractive error.¹⁸ In recent years, axial length measured using partial coherence interferometry technology has been proposed to be the preferred measurement for monitoring myopia progression and endpoint in myopia control studies.^29,30 It has the advantage of being rapid, non-invasive and has a high repeatability with 95% limits of agreement of ±0.12 D (assuming 0.10 mm axial length elongation corresponds to a refractive change of 0.25 D), which markedly outperforms refractive error measurements (limits of agreement of ±0.40 D and ±0.61 D, for cycloplegic and non-cycloplegic autorefraction, respectively).²⁹

However, the absolute value of axial length is of limited use for predicting myopia onset, as demonstrated by Ma et al. They found that the AUC of using axial length to predict 2-year incident myopia was only 0.626 compared with 0.862 using cycloplegic SER alone.²⁷

One method to improve the predictive capability of axial length is to use gender and ethnic-specific normative growth charts.^22,31–34 Normative percentile growth charts are screening tools used widely in the medical field to show the distribution of a measurement as it changes according to some covariate such as age.³⁵ Such charts can be used to detect abnormal growth and allow for comparison of growth patterns across different ethnic populations.

The first use of reference curves in refractive development research was introduced by Chen et al, who used cycloplegic refractive error percentile curves to predict high myopia (SER ≤–6.00 D) onset by age 15 years, with a sensitivity and specificity of 92.9% and 97.9% and a positive predictive value of 65.0%.³⁶ The positive predictive value represents the ratio of patients that are correctly identified as having the condition from all positive tests.

Since then, axial length normative growth chart studies on Chinese and European children have been published.^22,31–34 These studies reported that higher axial length percentile position was associated with greater prevalence and magnitude of myopia in older age groups. Furthermore, progressing from a lower percentile position into a higher percentile position (i.e., accelerated axial length growth) also indicated higher myopia prevalence³¹ and could enhance the sensitivity of using axial length as a means of predicting myopia onset.²²

For example, McCullough et al determined that the axial length percentile cut-off of 23.07 mm (>75^th percentile) could best identify children who were not myopic at age 6–7 but would later develop myopia during follow up, with a sensitivity of 48.89%, specificity of 80.37% and an AUC of 0.6904. The sensitivity improved to 89% when the additional criterion of axial length moving up at least one percentile during subsequent phases (indicating accelerated axial length growth) was added. This is in concordance with other studies that observed accelerated axial length elongation prior to myopia onset and suggests that the axial length growth profile is more important for predicting myopia onset than the absolute value of axial length.^18,19

Axial length growth charts can also be used to compare axial length growth differences between different ethnicities. Chinese and European children tend to have similar axial length at younger ages,^32,37,38 but the growth curves show a divergence with older age, with Chinese children showing much faster growth rates. However, the likelihood of developing myopia based on axial length (absolute value) appears to be similar between European and Chinese children.³²

For example, at 15 years of age, Chinese girls at the 75^th centile of the axial length growth curve would have an average axial length of 25.20 mm (meaning 25% of the population will have axial lengths greater than this value, and 75% would have axial lengths shorter than this). This corresponds to approximately the 98^th centile position in German girls, where the average axial length is 25.18 mm. Although the growth chart percentile positions of this axial length differed between the two groups, the prevalence of myopia was comparable at approximately 68% in the Chinese girls versus 64% in the German girls.

This phenomenon that myopia may occur at a similar axial length threshold regardless of age and ethnicity was also observed by Rozema et al.¹⁹ and Mutti et al.,¹⁸ and likely explains the high prevalence rates of myopia and high myopia in Chinese children as they reach the threshold axial length much earlier and have a longer progression timeframe when compared to their European counterparts.

Despite axial length having a high correlation with refractive error, using axial length alone as a predictive factor for future myopia onset has a lower AUC value than using cycloplegic SER and requires additional criteria to improve sensitivity. In some cases, a long axial length does not ultimately lead to a myopic refractive error in individuals with flatter corneas.^22,32 This indicates that the refractive error of the eye needs to be considered with respect to both the axial length and optical components of the eye such as the cornea and lens power.

This leads to another ocular biometric value ratio, the axial length/corneal radius of curvature ratio. This ratio has been found to be a robust representation of the refractive status of the eye with a strong correlation to SER.^39–42 On average, axial length/corneal radius ratio ranges from approximately 2.60 for highly hyperopic eyes, to 3.00 for emmetropic eyes and up to 4.10 for highly myopic eyes in adults.^39,41

Researchers have found that baseline axial length/corneal radius ratio is significantly greater in those who developed myopia compared to those who did not develop myopia.^43–45 One study reported that the probability of myopia was higher in children with axial length/corneal radius ratio in the top quartile (>2.85) when compared to the lowest quartile (<2.77) in 3- to 6-year-old children (hazard ratio = 2.979).⁴⁶ Others have reported an AUC ranging between 0.588 and 0.755 when using axial length/corneal radius ratio alone as a predictive factor for future myopia development.^27,43

The variability of these results is likely due to the natural refractive development of the human eye. Axial length growth is compensated by cornea and lens power reduction to maintain a relatively constant non-myopic refractive status. The corneal radius changes most rapidly within the first two years of life, after which it remains relatively stable.^11,47 Consequently, the axial length/corneal radius ratio increases with age although the refractive state of the eye can remain hyperopic or emmetropic. Furthermore, the relationship between SER and axial length/corneal radius ratio is quadratic rather than linear, with the correlation between SER and axial length/corneal radius being the weakest in emmetropic subjects, but stronger in myopic subjects.³¹

This could be explained by significant crystalline lens thinning that occurs to compensate the rapid axial length growth prior to myopia onset.¹⁹ Therefore, although axial length/corneal radius ratio is highly correlated with the refractive status of the eye, the threshold axial length/corneal radius ratio that may predict myopia onset could differ across different age groups.

To address this issue, a recent study published age- and gender-specific normative percentile growth curves for axial length and axial length/corneal radius ratio, based on data from 14,127 Chinese children aged 4–18 years. The percentile of axial length and axial length/corneal radius ratio was used in a model to estimate the probability of myopia. It was found that at age 10, for males and females in the 50^th percentile, the probability of myopia was 31.8% and 37.5%, respectively. This increased to >95% for both genders by age 15. The higher the percentile position, the greater the probability of myopia at younger ages. Axial length elongation plateaued from 15 years of age for those <10^th percentile and the probability of myopia was much lower.

Overall, this model for estimating the probability of myopia based on axial length and axial length/corneal radius ratio for Chinese children had a sensitivity and specificity of 86% and 84.5% and an AUC of 0.975. The researchers also found that when using axial length and axial length/corneal radius ratio in individual models, the axial length/corneal radius ratio percentiles had a higher diagnostic accuracy for detecting the presence of myopia than axial length percentiles (AUC 0.967 versus 0.940), and performed better in both young and older age groups than axial length alone.⁴⁸ This has significant implications as it allows a potential method of predicting future myopia onset more accurately using ocular biometry. Future studies may look at how combining these data with refraction information could further enhance myopia prediction.

Environment and genetic risk factors

At any one time, the pre-myopic SER and ocular biometry status can be interpreted as the predictors of future myopia onset and are a result of the cumulative effects of environmental and genetic factors that has contributed to this pre-myopic status. Identifying the risk factors that contributed to this refractive status could allow targeted preventative strategies for myopia onset to be developed.

Many environmental and genetic risk factors associated with myopia onset have been reported previously.⁴⁹ However, identifying an exact causal risk factor has been less certain. This stems from a few issues, including the complex multifaceted aetiology of myopia and methodological issues in previous myopia risk factors studies. As an example, different definitions of myopia exist between studies and not all studies utilise cycloplegia, which can overestimate myopia prevalence in a paediatric population.⁵⁰

Most cross-sectional observational studies find associations rather than causal factors and statistical analysis of risk factors are often impacted by how accurately the measurements for risk factors are obtained, whether they are collinear, modifiable or have a causal pathway.⁴⁹ Despite this, several lines of evidence suggest that two main environmental factors may have a causal association with myopia onset. These are education and time spent outdoors.

Education

The evidence to support the role of exposure to increasing education on myopia prevalence are multilayered. These include countries that have a high prevalence of myopia also have intense educational pressures where children are exposed to education at an early age and large numbers attend after school tutorial classes⁵¹; attending selective schools and achieving high academic grades tend to increase the odds risk of myopia development^52,53; higher prevalence of myopia is found in countries with established national education systems⁵; and more years in education increases myopia prevalence for those within the same geographic location or school.^54,55

Although there is a strong causal association between education and myopia development, the exact mechanism of action is not entirely clear. Increased near work has been postulated to play a role in this, however, outcomes of studies of near work and myopia development have been inconsistent.⁴⁹ This is mainly attributed to the reliance of using subjective methods such as questionnaires to collect data on near work which can be prone to recall bias and errors. Furthermore, the definition and quantification of near work vary across studies. Despite this, a more recent meta-analysis⁵⁶ reported that the odds of becoming myopic increasing by 2% for each additional dioptre-hour of near work per week, suggesting that near work, possibly mediated by education, albeit small, is a risk factor for myopia development.

Outdoor time

Another environment factor with a strong causal association with myopia is outdoor time. This relationship has been systematically reviewed and results from recent meta-analyses^57,58 concluded that increasing outdoor time is associated with reduced myopia onset, but not necessarily as effective in slowing myopia progression in already myopic children. A proposed mechanism of action of outdoor time is the exposure of the eye to higher light intensity. This is supported by both animal and human studies⁵⁹ where children experiencing lower light exposure appear to exhibit greater axial length growth,⁶⁰ and elevating light levels indoors may have a protective effect on inhibiting myopia onset.^60,61

The effects of bright light on inhibiting eye growth may be mediated by increased dopamine synthesis in the retina, although the exact mechanism is unclear.⁶² Previously proposed mechanisms including the involvement of Vitamin D and relative peripheral defocus have not been validated in further analysis.^63–65 Other proposed mechanisms with limited evidence include the impact of bright light on circadian rhythm regulation, broad spectrum light and spatial frequency of the visual environment.⁵⁹

More recently, wearable devices for measuring near work and outdoor time have been developed and used in two cross-sectional studies comparing myopic and non-myopic school-aged children.^66,67 Both studies showed differences in near vision behaviour between myopic and non-myopic children. Myopic children tended to exhibit longer near viewing time, spent less time outdoors and had lower light exposure than their non-myopic peers.

In another study comparing outdoor light exposure in children from different geographic areas, Singaporean children were found to have significantly less outdoor light exposure time (measured as >1000 lux) compared to Australian children (61 ± 40 min/day vs 105 ± 42 min/day).⁶⁸

These cross-sectional studies using wearable sensors allow objective and quantitative measures of near viewing behaviour and outdoor time to be captured. Longitudinal, large-scale studies using these devices may further elucidate the causal relationship between environmental factors and myopia onset.

Genetics

Genetic influences have long been observed in myopia. Evidence that supports this include twin studies that reported high heritability of myopia⁶⁹; the association between having one or two myopic parents and the increased risk of developing myopia^70,71; and the high prevalence of myopia found in East Asian countries seem to suggest some form of genetic susceptibility to myopia in Asian ethnic groups.^28,72

Research on myopia genetics have mapped over 400 associated gene loci related to myopia and refractive errors implicating genes that control retinal signalling, eye growth, emmetropization as well as circadian rhythm.^73,74 However, heritability estimates of the identified genes to date show that genetics can only account for a portion of myopia hereditary^74,75 and the rapid rise of myopia prevalence globally despite a relatively stable pool of human genes support the concept that both genetics and environmental factors are involved. The influence of parental myopia and ethnicity on myopia development thus may also be confounded by education exposure and reduced outdoor time.

Similarly, given the strong causal association between education and outdoor time and myopia onset, other risk factors associated with myopia development such as urban/rural differences, social economic status and gender may all be mediated by exposure to education and reduced outdoor time, hence they are not considered completely “independent risk factors”.⁷⁶ At this stage, due to limited studies on objective quantitative measurements of environmental exposures, there is currently no means of calculating individual risk factor effect sizes which can be used to enhance the prediction of future myopia onset.⁷⁶ Quantifying these factors has implications for developing targeted and personalised myopia prevention strategies.

Binocular vision

The role of increased accommodative demand during near task has also been investigated in myopia development. Several studies have reported abnormal accommodation responses in relation to myopia where accommodation was reduced in children 2 years prior to myopia onset.⁷⁷ Larger lags of accommodation were found in myopic children⁷⁸ and adults⁷⁹ compared to emmetropic subjects, and children with early onset myopia showed greater accommodation instability than emmetropic children.⁸⁰

A proposed underlying mechanism relating accommodation and myopia development is the retinal defocus resulting from inaccurate accommodation, as experimental animal studies have demonstrated that axial growth and emmetropization is affected by retinal image quality and defocus.⁸¹ However, a longitudinal study on accommodative lag and myopia development reported elevated lag after myopia onset and not preceding it, indicating it is less likely to be a cause of myopia but rather a consequence after onset.⁷⁸ Studies have found accommodative lag to not be associated with myopia progression in children.^82,83 Other mechanisms of action that may interact with accommodation such as spatial frequency of reading materials, spherical aberrations and working distance have been explored but have not produced conclusive evidence thus far.⁸⁴

The vergence system works synergistically with the accommodative system and can be reflected in the accommodation convergence to accommodation ratio. Myopic children have higher accommodation convergence/accommodation ratios than emmetropic children.⁸⁵ A higher accommodation convergence/accommodation ratio may also precede myopia onset⁷⁷ by as early as 4 years and remain elevated 5 years after onset, although it was not associated with faster myopia progression.⁸⁶

Other aspects of the vergence system associated with myopia include children with intermittent exotropia having a high rate of myopia onset by 20 years of age.⁸⁷ Esophoria has also been found to be associated with myopia onset and progression,^88–91 however, myopia control trials using bifocal and progressive addition lenses have found conflicting results showing only a small clinical benefit for participants with esophoria.^92,93 As retinal image quality plays a critical role in regulating axial growth, accommodation and the vergence system is likely to play a role in myopia development and further research is needed to understand the underlying mechanism.

How do we prevent myopia? Treating the pre-myope

Outdoor time

Increasing outdoor time is perhaps the most cost-effective and non-invasive strategy that can be applied across a wide population group at risk of myopia. Recent clinical trials conducted in China and Taiwan targeting school children aged 6–14 years, use interventions of increased outdoor time by 40–80 minutes per day.^94–98 All trials reported a decrease in myopia incidence in the intervention group that had greater outdoor exposure, although the relative reduction varied across studies. The results of these trials are presented in Table 1. All trials reported significantly reduced myopic refractive change and/or axial length elongation in their intervention group with a potential dose-dependent response where greater outdoor time resulted in less cumulative myopia incidence.

Table 1. Comparison of outdoor time trials for prevention of myopia.

Study	Subject groups	Intervention	Trial time	Cumulative incidence of myopia (≤–0.50 D)	Cumulative change in SER	Cumulative Change in axial length
Wu et al. 2013	Intervention Control	Recess outside classroom 80 min/day Habitual outdoor time	1 year	8.41% 17.65%	−0.25 ± 0.68 D −0.38 ± 0.69 D D P= 0.029	Not measured
Wu et al. 2018	Intervention Control	Outdoor time 11 hours per week/7 days Actual recorded one week total (Intervention group 669 ± 22.98 minutes; control group 598.81 ± 16.20 minutes) Habitual outdoor time	1 year	14.47% 17.40%	All subjects −0.35 ± 0.58 D Non-myopic: 0.32 ± 0.58 D Myopic: 0.57 ± 0.40 D All subjects: −0.47 ± 0.74 D P = 0.002 Non-myopic: 0.43 ± 0.75 D P = 0.02 Myopic: 0.79 ± 0.38 D P = 0.007	All subjects: 0.28 ± 0.22 mm Non-myopic: 0.26 ± 0.18 mm Myopic: 0.45 ± 0.28 mm All subjects: 0.33 ± 0.35 mm P = 0.003 Non-myopic: 0.30 ± 0.32 mm P = 0.02 Myopic: 0.60 ± 0.19 mm P = 0.02
Jin et al. 2015	Intervention Control	2 additional 20-min outdoor recess periods No intervention	1 year	3.70% 8.50%	−0.10 ± 0.65 D D −0.27 ± 0.52 D P = 0.005	0.16 ± 0.30 mm 0.21 ± 0.21 mm P = 0.034
He et al. 2022	Test I Test II Control	Additional 40 minutes outdoor time (actual measured 127 ± 30 mins/day)  Additional 80 minutes outdoor time (actual measured 127 ± 26 mins/day) Habitual outdoor time (Actual measured 106 ± 27 mins/day)	2 years	Unadjusted: 20.6% Adjusted* incident reduction of myopia compared to control: 16% Unadjusted: 23.8% Adjusted* incident reduction of myopia compared to control: 11% 24.9%	Adjusted* −0.84 D (95% CI, −0.72 to −0.96. Adjusted* −0.91 D (95% CI, −0.79 to −1.03) Adjusted* −1.04 D (95% CI, −0.91 to −1.17) p = 0.131	Adjusted* 0.55 mm (95% CI, 0.55–0.60) Adjusted* 0.57 mm (95% CI, 0.52–0.62) Adjusted* 0.65 mm (95% CI, 0.60–0.70) p = 0.044
He et al. 2015	Intervention Control	40-min additional outdoor time Habitual outdoor time	3 years	30.4% 39.5%	−1.42 D (95% CI, −1.58 to−1.27) −1.59 D (95% CI, −1.76 to−1.43) p = 0.04	0.95 mm (95% CI, 0.91 to 1.00) 0.98 mm (95% CI, 0.94 to 1.03) p = 0.07

*Adjusting for baseline age, sex, parental myopia, refractive status, compliance, and duration of noon-break.⁹⁸

One of these studies by Wu et al found a protective effect of outdoor time on reducing myopia progression in already myopic children (−0.57 ± 0.40 D vs 0.79 ± 0.38 D, p = 0.007),⁹⁶ in contrast to the other outdoor trial studies that did not report this phenomenon.^94,95,97,98 The reason for this study to find a protective effect of outdoor time on myopia progression in already myopic children is unknown. This study measured outdoor time objectively over one week using a light metre worn on the collar of their subjects. The recorded outdoor time in their intervention group (on average 70.55 minutes/week more than the control group)⁹⁶ did not seem to be significantly greater than another outdoor trial that did not report myopia progression control (20 minutes/day more than the control group).⁹⁸ Perhaps the real threshold difference of outdoor time and light exposure to confer protection for already myopic children was not fully captured by their study protocol.

He et al modelled the protective effect of outdoor time in relation to duration and light intensity by using a wearable wrist sensor worn during the entire second year of their trial. There was less change in axial length and SER with increasing outdoor time and increasing cumulative outdoor light exposure (up to 300,000 lux per day), although this benefit was only seen in non-myopes. Their model estimated that to achieve a relative reduction of 21–30% in incident myopia, approximately 700,000 to 850,000 cumulative lux per day is needed. As an example, that would mean 140 to 170 outdoor minutes at a light intensity of 5,000 lux.⁹⁸

Interestingly, although the study had two test groups with different outdoor time targets of 40 minutes and 80 minutes, tracking devices revealed that the two intervention groups did not differ in outdoor time and light exposure but were both significantly greater than the control group (test I: 127 ± 30 mins/day and 3,557 ± 970 lux/minute; test II: 127 ± 26 mins/day and 3,662 ± 803 lux/min, control 106 ± 27 mins/day and 2,984 ± 806 lux/min).

The authors recommended that in addition to implementing programs that encourage outdoor time to prevent myopia, monitoring compliance to the program is also essential to ensure its success. Thus, current evidence suggests increasing outdoor time can prevent myopia onset in school children in a dose-dependent manner. It is an effective strategy that can be applied to all school children and the benefits of these activities may also extend beyond myopia prevention, such as increased physical activity and reduced risk of obesity,⁹⁹ although this must also be balanced by potential increased risk of other conditions such as pterygium, earlier onset of cataracts and skin cancers.

However, as with all myopia control therapies, a rebound effect may occur after ceasing treatment. One study described a significant increase in axial length and SER change in their intervention group measured at 3 years after ceasing a 1-year program of outdoor jogging 30 mins/day.¹⁰⁰ In addition, other questions that await further clarification include does the efficacy of increasing outdoor time taper off over time and is there a ceiling effect of outdoor time on myopia prevention.

Atropine

Although clinical trials of outdoor time showed reduced incidence of myopia in children with increased outdoor exposure, the overall reduction of axial length elongation and SER myopic shift is clinically small. Thus, therapeutic strategies for pre-myopic children have been explored.

There have been three studies published to date on the use of low concentration atropine for the prevention of myopia onset in pre-myopic children. A retrospective cohort study of 0.025% atropine used for myopia prevention was reported in 2010.¹⁰¹ Children 6–12 years of age who had an SER <+1.00 D were included in the analysis. Myopia was defined as ≤–1.00 D and 24 children who used 0.025% atropine topically once every night was compared with 26 children in the control group that received no treatment over 12 months. Those in the treatment arm had reduced myopic shift (−0.14 ± 0.24 D/year vs −0.58 ± 0.34 D/year) and less fast progression (21% vs 54%, defined as >–0.50 D/year) compared to the control group. Cycloplegic refraction was measured but axial length measurements were not performed.

Another randomised case control study conducted in India used 0.01% atropine as an intervention treatment for pre-myopic children.¹⁰² The study included a total of 30 participants aged 5–12 years old in the treatment group (mean age 7.7 ± 2.1 years). Pre-myopia was defined as SER <+1.00 D and a myopic refractive shift of over 0.50 D per year for the past two years. Myopia was defined as −1.00 D or greater and axial length was measured using contact A-scan ultrasound biometry. At the end of two years, the treatment group had less axial length elongation and SER myopic shift than the control group which received no treatment (0.21 ± 0.2 mm vs 0.48 ± 0.2 mm and−0.60 ± 0.30 D vs − 1.75 ± 0.40 D). Both studies did not include outdoor time as a factor for investigation.

Recently, the Low-concentration Atropine for Myopia Progression study published their randomised clinical trial result using low concentration atropine for myopia prevention. A total of 474 children of Chinese ethnicity aged 4–9 years old, with an average cycloplegic SER of plano to +1.00 D were randomly assigned to either 0.05% atropine, 0.01% atropine or placebo eyedrops dosed once nightly. Over a course of two years, the 0.05% atropine group had a lower cumulative myopia incidence (28.4%) than either the 0.01% atropine (45.9%) or placebo groups (53.0%) and had less fast myopic shift, defined as ≥–1.00 D change over 2 years (25%, 45.1% and 53.9% for 0.05% atropine, 0.01% atropine and the control group, respectively). This was the first randomised clinical trial that showed 0.05% atropine could be used to delay myopia onset in Chinese children.

The trial is intended to follow up children for a total of 6 years.¹⁰³ It is unknown whether these results can be extrapolated to other ethnicities. Additionally, further investigation is needed to determine if this delay in onset translates to the prevention of myopia in the future. Another randomised clinical trial is also underway in Singapore to assess the efficacy of 0.01% atropine administered over 2 to 2.5 years with a 1-year washout on pre-myopic and low myopic children.¹⁰⁴

Optical methods

Animal studies have shown that peripheral visual signals in the retina can modulate eye growth.¹⁰⁵ As such optical interventions designed to correct central distance vision but induce peripheral myopic defocus, such as orthokeratology,¹⁰⁶ dual focus¹⁰⁷ or multifocal soft contact lenses,¹⁰⁸ and spectacle lenses^109,110 have all shown clinical efficacy in reducing myopia progression in already myopic children.

A recently published study using a novel spectacle lens design that modulate retinal contrast has also demonstrated reduced myopia progression in their subjects compared with single vision lens control over 12 months of wear.¹¹¹ Given myopia onset and progression are related to axial length elongation, in theory, optical interventions to slow myopia progression may also be used to prevent myopia onset.¹¹² However, it is unlikely that contact lenses would be prescribed to a non-myopic child due to the potential risk of complications, cost and ethical implications.

Ensuring compliance with spectacle wear in an otherwise visually normal child could also be difficult since the greatest benefit from these lenses seem to derive from full time wear (>12 hours daily).¹⁰⁹ Additionally, although abnormal accommodation and vergence functions may be related to myopia development, no studies have examined the effect of optical treatment of these dysfunctions on myopia development. Therefore, currently there is no published evidence demonstrating efficacy of using optical interventions in preventing myopia onset.

Additional considerations

An important issue to consider in clinical trials of pre-myopia is the criteria for selecting pre-myopic children for treatment. No prediction will be 100% accurate and the dilemma in prevention studies is that it may be difficult to determine whether children being treated for pre-myopia did not progress due to treatment or simply because they were not destined to become myopic. Statistically, the determination of the predictor threshold (e.g., SER or ocular biometry) will be a balance between sensitivity and specificity, and this will pertain to the intervention treatment.

For example, if the preventative therapy is innocuous but effective, then a cut-off threshold that emphasises sensitivity over specificity will be preferred. However, if the treatment is expensive, difficult to administer or has adverse effects, then a cut-off value that maximises specificity may be preferred to minimise misdiagnosing a child who will not become myopic.¹¹³ Furthermore, there may be concern whether an effective therapy will give parents a false sense of security and render parents less likely to encourage outdoor time or reduce academic pressure on their children. The emphasis therefore should be educating parents on the importance of lifestyle and behaviour modifications rather than relying solely on therapeutic prevention treatments of myopia, which may be more beneficial for the overall health and wellbeing of the child.

In conclusion, identifying a pre-myopic child prior to the onset of myopia may allow preventative measures to be undertaken to prevent or delay the onset of future myopia. A pre-myopic eye will demonstrate a lower-than-age-normal hyperopic baseline cycloplegic SER and this threshold value is a useful predictor of future myopia onset risk. The accuracy of the prediction can be further enhanced or even replaced with ocular biometry growth charts which may be used to track abnormal eye growth trajectories before the onset of myopia. If prevention methods are implemented, ocular biometry growth charts can also be used to track efficacy of the prevention strategies.

Binocular vision function may also be assessed to further aid in identifying pre-myopic children and the selection of myopia management strategies. Although SER and ocular biometry measurements are useful predictors of future myopia risk, other modifiable risk factors that have contributed to this refractive status, namely education exposure, increased near work and reduced outdoor time can be targeted to reduce the incidence of future myopia in children.

The strong causal role of education and outdoor time on myopia incidence indicates that preventative measures could be implemented to at-risk children and can have far reaching effects on the myopia epidemic by delaying the age of onset and reducing high myopia prevalence and its associated ocular health consequences. Large scale outdoor programs in regions with intense educational pressures requires a shift in societal and cultural attitudes towards education, although this may be difficult to achieve in the short term. Whether other types of therapies such as low concentration atropine should be considered, either as an individual measure or as an adjunct to outdoor time awaits further investigation.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The first author is supported by an Australian Government Research Training Program (RTP) Scholarship.