Global prevalence of myopia in children using digital devices: a systematic review and meta-analysis

Abstract

Background

The global prevalence of myopia among children has considerably increased over the past few decades, affecting the children’s eye health and quality of life. According to the inconsistent reports of myopia among children, the purpose of this systematic review and meta-analysis study was to determine the global prevalence of myopia in children using digital devices.

Methods

Various databases (PubMed, ScienceDirect, Embase, Web of Science, Scopus, and Google Scholar) were searched systematically (No time limit until September 2023, last updated in May 2024) using keywords of “Myopia Prevalence”, “Children”, “TV”, “Computer”, “Video games”, and “Smartphone”. PRISMA guideline was also used for paper collection based on the Inclusion/Exclusion criteria. The quality of articles was determined based on the STROBE checklist. Data analysis, heterogeneity assessment, publication bias, and all factors influencing heterogeneity were executed using the CMA software (v.2).

Results

Initially, 828 articles were identified through database assessment. 563 and 133 papers were evaluated using primary and secondary assessments, respectively. Ultimately, 17 eligible articles were selected for meta-analysis following paper exclusion. In this era, the global prevalence of myopia among children using computer and video games was reported 28.8% (95%CI:21.1–38), using TV (TV watching) was 35.4% (95%CI:20.6–53.7), and smartphone usage was 31.4% (95%CI:13.5–57.3).

Conclusion

Myopia is now recognized as a critical global issue with a daily increasing rate. Technology, along with various digital devices, causes several issues in this era. It is suggested that the application of all digital screens and smart devices can potentially increase the risk of myopia among children. Thus, the reduction of these smart device applications in children can alleviate the potential risk of myopia. The results of this study can be a guide for health policymakers and a useful advertisement for society and families in paying attention to this problem in children, as well as increasing health interventions such as early screening and timely diagnosis for treatment. In this regard, it can be effective in both prevention and increasing the quality of life of children.

Background

Myopia is one of the refractive ocular errors [1], when the parallel light rays focus in front of the retina, preventing the formation of a clear image [2]. Myopia is the primary cause of distant vision impairment [3] and the risk of retinal detachment, macular degeneration, glaucoma, cataracts, and even blindness increases considerably in individuals with myopia, particularly severe type [4]. Myopia is a type of critical disorder causing pathological changes in the eye [5]. Additionally, myopia is recognized as the most common cause of visual impairment, globally [6]. The geographical spread of myopia has become a global challenge and plays an important role in epidemic outbreaks in certain regions of the world [7]. The rate of myopia ranges from 47.7 to 62% in specific regions of East Asia which is higher than other parts of the world (6–20%). Also, the lowest rate was reported in Africa [3, 8]. It is predicted that by 2050, 49.8% of the global population will be affected by myopia and 9.8% of individuals will experience severe type [3]. The etiology of myopia is influenced by both genetic and environmental Factors [9]. Research indicates that the earlier onset of myopia causes a higher rate of progression in adulthood [6]. Nearsightedness activities in the workplace or during the childhood period, education, and living conditions impact the development and the severity of myopia [9]. Also, it is approved that behavioral risk factors such as the time spent on outdoor activities [10], digital screening time [11], and sleep duration play a significant role in the development of myopia [12]. The increasing prevalence of this pathology before the emergence of smart devices also suggests that these types of equipment could exacerbate the myopia epidemic rate [13, 14].

The widespread application of digital screens as leisure activities among teenagers has been replaced with healthy outdoor activities, leading to unhealthy eating habits, sedentary behavior, weight gain, abnormal visual development, sleep quality, and mental health [15]. Early access to technology and improper use of these tools by children may also lead to the acceleration of associated risks in their social, cognitive, and emotional development [16]. Studies show that exposure to screen displays starts from 6 months old [17]. A study found that children < 2 years spend an average of 3 h/day watching screens [18]. In the Netherlands, this rate was 4 h/day for individuals aged 12–16 years using smartphones [19]. Also, teenagers aged 12–13 years spend 6 h/day watching screens in the United States, and in South Korea, one in five children 4–5 years is addicted to uncontrollable obsession with smartphone usage [20], and 54% of 9-year-old children use smartphones in Ireland [21].

The impact of myopia on the permanent growth of children and teenagers is extremely important, and digital screen media can induce negative effects on children’s growth [22, 23]. Myopia not only has severe clinical and developmental effects but also carries a significant economic burden [24, 25]. Understanding the epidemiological features and the factors influencing myopia in children and teenagers is crucial for preservation of eye health and prevention of severe myopia [26, 27]. Thus, it is essential to take measures to prevent myopia in students [28]. According to the vital role of education in myopia management, the World Health Organization has proposed strategies and implemented high-quality education and information dissemination programs across the world [29, 30]. Digital medicine also plays a significant role in prevention and controlling the myopia-related issues [31].

As a primary intervention for myopia, refractive lenses have been developed to reduce the progression of this pathology [32]. Also, studies have been conducted to prevent and reduce the speed of myopia among children’s progression, including local pharmacological interventions or light directorship using contact lenses or glasses [33]. The potential side effects of long-term application of these medications, such as photophobia, glare, and loss of balance, were reported [34]. Additionally, Defocus Incorporated Soft Contact (DISC) lenses with a defocusing mechanism can be effective in this era [35]. A global survey in 2015 showed that 68% of medical physicians prescribed single-vision glasses or contact lenses as the primary procedure for correction of vision in myopia patients [36]. Myopia is recognized as a considerable public health and economic-social challenge which requires serious and coordinated approaches for prevention, diagnosis, and intervention, particularly in children and teenagers. Myopia may affect many educational and occupational activities in a person’s life, and this condition is considered to be even more important in children affecting their educational status. According to estimates, the annual direct cost of myopia correction for Asian adults is estimated at US$328 billion per year [37]. High myopia is prone to vision-threatening diseases such as myopic macular degeneration, retinal detachment, glaucoma, and cataracts, and can cost them about US$250 billion per year in healthcare costs [37]. Comprehensive awareness of the factors influencing myopia and its effects on individual growth, along with the implementation of educational and preventive programs, can also contribute to the reduction of myopia prevalence and improvement of individuals` life quality. The main purpose of this systematic review and meta-analysis study was to report the global prevalence of myopia in children using digital devices, which can serve as an effective tool for controlling and treating this phenomenon.

Methods

This investigation assessed the existing shreds of evidence of the global prevalence of myopia. In this era, the authors conducted the primary searching process in September 2023 using keywords of “Myopia”, “Prevalence”, “Children”, “TV”, “Computer”, “Video games”, and “Smartphone” which were selected using MeSH concept. Paper searching was applied using the main databases of PubMed, Web of Science, Google Scholar, Scopus, ScienceDirect, and Embase. No time limitations were placed and the searching process was updated on May 2024. Then, the gathered studies were transferred to the Citation Manager software of EndNote (v.8x) for further evaluation.

Inclusion and exclusion criteria

All studies were included according to the following criteria: whole cross-sectional studies reporting global prevalence of myopia in children using digital devices, studies with available full-text, English-based investigations, and the papers with sufficient available and extractable data for meta-analysis. Also, the exclusion criteria were all case studies, interventional investigations, studies with non-available full-text, non-English articles, and non-eligible investigations.

Study selection process

The PRISMA approach was used for study selection. Initially, duplicate articles were found and merged using EndNote software (v.8x). In primary screening, the Titles and Abstracts of the articles were evaluated initially, and unrelated studies were excluded in this stage. During the secondary screening, the full text of the articles was gathered and evaluated based on inclusion and exclusion criteria.

Quality assessment of eligible papers

To assess the quality of the cross-sectional studies, the STROBE standard checklist was used consisting of six sections of Title, Abstract, Introduction, Methods, Results, and Discussion. This checklist covers various methodological aspects of the study, including Title, Problem statement, Study objectives, Study type, Study population, Sampling method, Sample size determination, Variable definitions, Procedures, Data collection tools, Statistical analysis methods, and Findings. High-quality studies (STROBE score > 16) were considered for data extraction and meta-analysis.

Data extraction and meta-analysis

The data extraction was performed by the researcher using a pre-prepared checklist including the First author’s name, Publication year, Country, Study type, Sample size, Number of children with myopia, Average time spent, Research tool, Type of device used, and probable activity or the habit. The CMA software (v.2) was used for data analysis and p < 0.05 was considered the significant level. Heterogeneity and Publication bias were also assessed in this analysis.

Results

Initially, a comprehensive search was conducted in reputable databases (n = 797) and manual searching (n = 31), yielding 828 articles which were transferred to the EndNote software. Among these, 265 duplicates were removed. In primary and secondary screenings, 430 and 82 gathered papers were excluded based on the Inclusion/exclusion criteria, respectively. Additionally, following the quality assessment, 34 low-quality papers were excluded. Ultimately, 17 eligible high-quality investigations were selected for data extraction and meta-analysis assessment (Fig. 1; Tables 1, 2 and 3).

Fig. 1.

The flowchart on the stages of including the studies in the systematic review and meta-analysis (PRISMA)

Table 1.

Summary of characteristics of included studies

First Author`s Name	Year	Country	Age range (Mean ± SD)	Sample size	Number of myopias among children in both eyes	Myopia prevalence in children (%)	Time spent for digital devices (Mean ± SD)	Activity p-value	Screen time for Nearsightedness
Lu et al. [38]	2009	China	10–19 (14.6 ± 0.8)	998	829	81	6.4 ± 7.3 (h/week)	< 0.001	Playing video games or computer use
Mutti et al. [39]	2002	USA	13–14	366	67	18.3	2.7 ± 4.1 (h/week)	-	Playing video games or computer use
Qian et al. [40]	2016	China	5–8	1589	344	21.7	Not Reported	0.015	Computers use
Qian et al. [40]	2016	China	9–12	4023	1583	39.3	Not Reported	0.015	Computers use
Qian et al. [40]	2016	China	13–16	2069	1079	52.1	Not Reported	0.015	Computers use
Chua et al. [41]	2015	Singapore	3	572	35	6.1	0.59 ± 0.32, 1.07 (h/day)	0.88	Computers use
Saxena et al. [42]	2015	India	5–15 (11.6 ± 2.2)	9884	1297	13.1	> 14 (h/week)	< 0.001	Computer use or video and mobile games
Paudel et al. [43]	2014	Vietnam	12–15	2238	456	20.4	4.9 ± 6.5 (h/week)	0.077	Computers use
Guan et al. [44]	2019	China	9–12 (10.6 ± 1.15)	19,934	3607	18.1	0 min/day (myopia: 16.5%) 1–30 min/day (myopia:22.4%) 31–60 min/day (myopia:27.2%)  > 60 min/day (myopia:23.5%)	< 0.001	Computers use
Saw et al. [45]	2002	Singapore	7	522	…	27.6	Not Reported	0.05	Computer use
Saw. et al. [45]	2002	Singapore	8	321	…	34.6	Not Reported	0.05	Computer use
Saw et al. [45]	2002	Singapore	9	162	…	43.2	Not Reported	0.05	Computer use
Liu et al. [46]	2019	China	6–14 (9.5 ± 2.1)	566	335	59.2	0.68 ± 0.49 (h/day)	0.044	Computer use
McCrann et al. [47]	2018	Ireland	8–13	361	45	12.4	Not Reported	0.04	Playing video games or computer use
Singh et al. [48]	2019	India	(10.5 ± 3.0)	1234	261	21.1	> 2 h/day	< 0.001	Playing video games
Dixit R et al. [49]	2016	India	12–15	289	95	32.7	< 3 h/day (myopia:14.8%) 3–5 h/day (myopia: 11.6%)  > 5 h/day (myopia: 13.6%)	< 0.005	Screen devices

Table 2.

Summary of characteristics of studies including the effect of watching TV on the prevalence of myopia in children

First Author`s Name	Year	Country	Age range (Mean ± SD)	Sample size	Number of myopias among children in both eyes	Myopia prevalence in children	Time spent for digital devices (Mean ± SD)	Activity P-value	Screen time for Nearsightedness
Lu et al. [38]	2009	China	10–19 (14.6 ± 0.8)	998	829	81%	14.2 ± 7.4 (h/week)	0.16	Watching TV
Mutti et al. [39]	2002	USA	13–14 (13.7 ± 0.5)	366	67	18.3%	9.2 ± 6.8 (h/week)	-	Watching TV
Saxena et al. [42]	2015	India	5–15 (11.6 ± 2.2)	9884	1297	13.1%	> 14 (h/week)	< 0.001	Watching TV
Paudel et al. [43]	2014	Vietnam	12–15	2238	456	20.4%	11.9 ± 6.6 (h/week)	0.129	Watching TV
Liu et al. [46]	2019	China	6–14 (9.5 ± 2.1)	566	335	59.2%	0.56 ± 0.56 (h/day)	0.32	Watching TV
Lanca C et al. [50]	2021	China	4–18 (8.8 ± 2.9)	12,241	….	30.6%	2.12 ± 1.35 (h/day)	0.49	Watching TV

Table 3.

Summary of characteristics of studies including the effect of smartphones on the prevalence of myopia in children

First Author`s Name	Year	Country	Age range (Mean ± SD)	Sample size	Number of children with myopia	Myopia prevalence in children	Myopia prevalence or incidence by smart device exposure, or screen time by myopia status	Activity P-value	Screen exposure
Guan et al. [51]	2019	China	10.6 ± 1.15	4659	3607	77.4%	0 min/day (myopia; 17·5%) 1–30 min/day (myopia; 19·4%) 31–60 min/day (myopia; 18·0%) > 60 min/day (myopia; 20·0%)	0.001	Smartphone
Harrington et al. [52]	2019	Ireland	6–7 years	728	27	3.7%	< 1 h/day (myopia; 8·3%) 1–3 h/day (myopia; 11·7%) > 3 h/day (myopia; 20·3%)	< 0.001	Smartphone
Harrington et al. [52]	2019	Ireland	12–13 years	898	205	22.8%	< 1 h/day (myopia; 8·3%) 1–3 h/day (myopia; 11·7) > 3 h/day (myopia; 20·3%)	< 0.001	Smartphone
Huang et al. [51]	2019	China	19.6 ± 0.9	968	828	86.8%	0 h/day (myopia; 89·7%) ≤ 1 h/day (myopia; day 87·1%) 1·01–2 h/day (myopia; 89·7%) 2·01–3 h/day (myopia; 86·3%) > 3 h/day (myopia; 84·6%)	0.454	Smartphone
Liu et al. [46]	2019	China	6–14 (9.5 ± 2.1)	566	335	59.2%	Smartphones: myopia 0·47 (SD 0·49) h per day vs. no myopia 0·39 (0·47) h per day (p = 0·038; adjusted p = 0·93); tablets: myopia 0·34 (0·46) h per day vs. no myopia 0·26 (0·47) h per day (p = 0·040; adjusted p = 0·11)	0.038	Smartphone/tablet screen time
McCrann et al. [53]	2020	Ireland	7–12	402	138	34.3%	Myopia in 288 ± 174 min/day vs. no myopia in 258 ± 163 min/day (p = 0·090)	0.09	Smartphone screen time
Singh et al. [48]	2019	India	5–15	1234	261	21.1%	0–2 h/day (myopia; 43%) vs. no > 2–4 h/day (myopia; 97%); myopia 51% vs. no myopia 2·4%; and > 4 h per day: myopia 7% vs. no myopia 0%	< 0.001	Mobile use
Harrington et al. [54]	2023	Ireland	6–7 (7/08 ± 0/45)	723	27	3.7%	> 2 h/day	< 0.001	Smartphone use

Meta-analysis of computer and video games in myopia among children

Following the review of 16 eligible studies with a sample size of 45,128 cases, the I² value represented a high heterogeneity index (I²:99.6%), and the random effect model was used for meta-analysis. In this era, the global prevalence of myopia among children using computers and video games was found 28.8% (95%CI:21.1–38%) (Fig. 2). Additionally, the publication bias was assessed through the Egger test and showed no significant evidence (p:0.207) (Fig. 3).

Fig. 2.

Forest plot of global prevalence of myopia in children using Computer and Video games with random effects model

Fig. 3.

Funnel plot diagram of distribution bias in the reviewed studies

Meta-analysis of TV watching in myopia among children

Following the evaluation of 6 eligible studies with a sample size of 26,293 individuals, the I² value showed a high heterogeneity rate (I²:99.7%) and the random effect model was used. In this era, the global prevalence of myopia among children using TV (Watching TV) was 35.4% (95%CI:20.6–53.7%) (Fig. 4). Additionally, the publication bias was evaluated through the Egger test and showed no significant pieces of evidence (p:0.559) (Fig. 5).

Fig. 4.

Forest plot of global prevalence of myopia in children using television (TV viewing) with random effects model

Fig. 5.

Funnel plot diagram of distribution bias in the reviewed studies

Meta-analysis of smartphone in myopia among children

Following the assessment of 8 eligible studies with a sample size of 8,552 children with myopia, the I² was found high (I²:99.7%) and the random effect model was used for analysis. The global prevalence of myopia among children using smartphones was 31.4% (95% CI:13.5–57.3%) (Fig. 6). Additionally, the publication bias assessment was found significant (p:0.049) (Fig. 7).

Fig. 6.

Forest plot, global prevalence of myopia in children using smartphones with random effects model

Fig. 7.

Funnel plot diagram representing distribution bias in the reviewed studies

Discussion

Numerous studies have been published regarding the detection of the possible correlation between environmental and genetic factors and the incremental trend in myopia prevalence. For example, according to numerous epidemiological studies, it has been confirmed that early onset, high frequency, and more rapid progression of myopia in children and adolescents are possible to occur [55, 56]. Individual characteristics also have an impact on myopia prevalence; women compared to men, children compared to adolescents, and urban household residents compared to rural residents reported a higher prevalence rate [57, 58]. The development of social and economic conditions, as well as urbanization, has contributed to the emergence of myopia in children and adolescents [10]. Another factor which is widely explored in the field of myopia is the dimorphism-associated prevalence. It has been found that myopia is more prevalent in girls than boys [59]. Factors contributing to this disparity include the boys spending more time playing video games and computers [38, 48], while the girls spend more time reading and writing at home, increasing their susceptibility to myopia [42]. Other findings indicate that sleep duration also has a close correlation with myopia in adolescents [14]. The time spent outdoors has an impact on myopia, as shown in a study conducted among 6-year-old children in Guangzhou (China). In this study, an increase of 40 min of outdoor activity in schools can reduce the incidence of myopia by 3 years in the future compared to regular activities [10]. Another factor contributing to the development of myopia is obesity, as children using screens > 2 h/day were almost 4 times more susceptible to obesity and insulin resistance, and increased axial length [60, 61]. The genetic role in myopia is another factor [62]. The myopia prevalence in families, particularly among children with myopia parents, supports this hypothesis [39] or even in individuals with close relatives with myopia [63].

digital devices, using short-wavelength light, can cause instability in light reflection and potentially lead to myopia [64]. Although the radiation emission from smartphones is almost similar to that of computers [65], the radioactive effects of smartphones could be more critical due to the eye- digital devices proximity [66]. The application of electronic devices at a distance < 20 cm is a considerable factor in the progression and development of myopia [66]. Thus, smartphones are more harmful to eye health than computers [67]. Additionally, other studies have shown a direct correlation between the educational system and the methods of study (including constant reading and other activities) and the prevalence of myopia [68]. The studies conducted by Saw et al. in 2002 [45], Paudel et al. in 2014 [43], Saxena et al. in 2015 [42], Qian et al. in 2016 [40], and Guan et al. in 2019 [44] demonstrated a significant correlation between screen time and myopia among children aged 3–16 years in countries such as China, India, Singapore, and Vietnam. Furthermore, the study by Saxena et al. in 2015 [42] showed that activities including reading and studying for > 5 h/day, watching television for > 2 h/day, and engaging in computer, video, and smartphone games increase the risk of developing myopia, considerably.

Additionally, the study of Liu et al. in 2019 [46] on children in Tianjin (China) found that the use of smartphones and computers can lead to an increase in negative deviations in ocular parameters. The study by Sommer et al. conducted in Ireland in 2021 found that 1 out of every 5 children aged 6–7 years and 3 out of every 4 myopia children aged 6–7 years spend more than 2 h/day on-screen activities. This is in contrast to WHO recommended a maximum of 2 h of screen use per day [69]. Hansen et al. in their cohort study conducted on Danish individuals in 2020 reported that excessive use of digital devices can potentially lead to a 25% prevalence of myopia among individuals aged 16–17 years. Additionally, using digital devices for > 6 h/day increases the risk of myopia 2-fold more [70].

Khader et al. [71] in 2006 found a correlation between myopia and computer use. They reported that spending on watching TV for hours did not correlate with myopia. The conclusion was drawn from a study conducted on Tibetan and Chongqing students by Wang W et al. [72] in 2022 that the application of digital devices, particularly smartphones, has different effects on vision and myopia. These effects can vary depending on the amount of use and the time spent. For example, students > 2 h/day represent more visual problems compared to those < 2 h/day. However, in other studies conducted by Mutti et al. in 2002 [39], Ip et al. in 2008 [13], Lu et al. in 2009 [38], Chua et al. in 2015 [41], and Huang et al. in 2019 [51], no correlation was found between myopia and the use of digital devices. Furthermore, Lanca et al. 2021 [50] also stated that there is no correlation between myopia and TV watching time. The reason for this report probably can be attributed to the eye-TV distance. According to the study conducted by Xie et al. in 2022, no significant correlation was found between screen time and myopia among Chinese students during the COVID-19 pandemic.

Also, reduced physical activity and increased sedentary behaviors could predict an increase or decrease in myopia among Chinese students, respectively [73]. These findings contradict our results regarding the impact of screen use on myopia. However, another study conducted by Liu et al. in 2021 in China during the COVID-19 pandemic reported that following school closing, the children were forced to learn online using digital devices. They concluded that the excessive use of digital screens can potentially increase the possible occurrence of myopia in schoolchildren [74], which was consistent with our findings. The study by Schuster et al. in 2020 found that the prevalence of myopia in children and adolescents in Germany remained almost unchanged over 10 years. Thus, changes in media use, such as the increasing use of smartphones, had no significant effect on myopia development [75]. According to the conclusion from the systematic review conducted by Lanca C. et al. in 2020 [76], no direct correlation was detected between the time spent on digital devices and the development of myopia. Although the time spent on digital devices was increased, the increase in myopia over the past few decades was primarily due to the increase in education in urban areas, rather than the time spent on screens. Additionally, there is no evidence to support the finding that nearsightedness increases the risk of myopia; it may be replaced by digital activities.

The results of this study do not show any relationship between myopia and the use of digital devices. This study only indicates the prevalence of myopia in people who use digital devices, and systematic reviews and meta-analyses should be conducted to examine the relationship and correlation. Public health interventions should prioritize the prevention of the onset of myopia and slowing its progression as their main objectives [77]. Many studies have shown that active interventions in controlling myopia lead to improved vision outcomes and also reduce the severity of this challenge [78, 79]. The current advancements in management of myopia have made it possible to recover vision to a certain extent [80].

In a study investigating the long-term myopia control and safety in children using Defocus Incorporated Multiple Segments (DIMS) spectacle lenses, participants completed a 12-year RCT and were followed for a total of 6 years. The results of this study showed that DIMS lenses provided stable myopia control without adverse events over the 6-year study period [81]. Another study titled Myopia and Orthokeratology for Myopia Control examined the effect of another management method in myopia control and concluded that combined orthokeratology and atropine treatment may have great potential to maximize the effectiveness of myopia control interventions [82]. Another management method in controlling myopia is the method of progressive add-on lenses (PALs), and based on a clinical trial study in this field, it has been reported that PAL is statistically significant, but clinically small, suggesting that treatments aimed at reducing foveal focus may not be as effective as previously thought in myopic children with high accommodation delay [83].

Studies investigating myopia in children report that young children are increasingly using digital screens [84] and that childhood, especially ages 6–8, is a critical period for the development of myopia, suggesting the need to focus preventive interventions to control myopia on children in this age range [85]. Another study investigating the association between digital smart device use and myopia reports that smart device exposure may be associated with an increased risk of myopia [86], while studies conducted after the COVID-19 pandemic report that home confinement during the COVID-19 pandemic appears to be associated with a significant change in myopia for children aged 6–8 years according to school photo screenings in 2020 [87], and another study suggests that myopia progression accelerated more rapidly during the COVID-19 pandemic compared to the pre-COVID-19 period [88].

Study limitations and implications

The relatively small number of existing studies was the main limitation of the present study. According to the high prevalence of myopia and the widespread use of digital devices in all ages, especially among children, and the severe consequences, it is expected that more specialized studies are needed in this era. Another limitation was that the included studies were limited to those published in English, which may have resulted in the exclusion of studies published in other languages. Additionally, most studies were conducted in East Asia, particularly in China, and the prevalence of myopia in other parts of the world has been less studied. Furthermore, several investigations were excluded due to their low quality, such as the lack of detailed reporting of the mean use of digital devices by children. Other limitations of this study were related to the limitations of the included studies, including self-reported screen time, different diagnostic methods, which increased the heterogeneity of the findings. Implications for public health could include raising awareness among the community and families about the importance of myopia in children, using digital devices and the management and control of these devices in children, as well as periodic screenings in schools for early detection and therapeutic interventions.

Conclusion

Based on the results of this study, the prevalence of myopia in children using digital devices, based on the analyses conducted, reports a high prevalence in children. This issue should prompt the immediate implementation of preventive measures and immediate treatments to combat this global epidemiological risk. Future studies should be conducted in various regions to clarify the long-term effects of digital devices on myopia among children and to compare the effects of these factors on the rising trend of myopia among children.

Abbreviations

MAC

Myopia Among Children

DDs

Digital Devices

SD

Standard Deviation

H

Hour

Author contributions

NS and SM and MM contributed to the design, MM statistical analysis, participated in most of the study steps. MM and SR and MB prepared the manuscript. SHR and MM and AA assisted in designing the study, and helped in the, interpretation of the study. All authors have read and approved the content of the manuscript.

Funding

Not applicable.

Data availability

Datasets are available through the corresponding author upon reasonable request.

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Clinical trial number

not applicable.