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Smartphone overuse has been cited as a potentially modifiable risk factor that can result in visual impairment. However, reported associations between smartphone overuse and visual impairment have been inconsistent.
The aim of this systematic review was to determine the association between smartphone overuse and visual impairment, including myopia, blurred vision, and poor vision, in children and young adults.
We conducted a systematic search in the Cochrane Library, PubMed, EMBASE, Web of Science Core Collection, and ScienceDirect databases since the beginning of the databases up to June 2020. Fourteen eligible studies (10 cross-sectional studies and 4 controlled trials) were identified, which included a total of 27,110 subjects with a mean age ranging from 9.5 to 26.0 years. We used a random-effects model for meta-analysis of the 10 cross-sectional studies (26,962 subjects) and a fixed-effects model for meta-analysis of the 4 controlled trials (148 subjects) to combine odds ratios (ORs) and effect sizes (ES). The
A pooled OR of 1.05 (95% CI 0.98-1.13,
Longer smartphone use may increase the likelihood of ocular symptoms, including myopia, asthenopia, and ocular surface disease, especially in children. Thus, regulating use time and restricting the prolonged use of smartphones may prevent ocular and visual symptoms. Further research on the patterns of use, with longer follow up on the longitudinal associations, will help to inform detailed guidelines and recommendations for smartphone use in children and young adults.
The use of smartphones has been increasing rapidly since their introduction in the late 2000s [
With the continuous rise in youth digital media consumption, the incidence of ocular problems has also dramatically increased. A large portion of the population currently suffers from visual impairment, especially in Asian countries, with a rapidly increasing prevalence and younger age of onset [
Therefore, smartphone overuse among children and young adults has become a matter of crucial concern [
Some experimental studies have indicated that long-term use of a smartphone plays a key role in visual impairment, increasing the likelihood of poor vision [
Despite increased concern about impaired vision due to smartphone overuse, existing quantitative evidence about the relationships between excessive smartphone use and visual impairment remains equivocal. Therefore, it is essential to confirm and quantify whether excessive smartphone use may result in visual impairment, especially in children and young adults.
The aim of this study was to conduct a systematic review and meta-analysis to summarize the existing evidence on the associations between smartphone overuse and visual impairment in children and young adults, which may further guide potential interventions to reduce the harmful impact of smartphone overuse on vision in this susceptible subpopulation.
This systematic review and meta-analysis was based on a protocol designed in line with the standard Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) [
A systematic search was carried out in PubMed (US National Library of Medicine), Embase (Wolters Kluwer Ovid), Web of Science Core Collection (Clarivate Analytics), ScienceDirect (Elsevier), and Cochrane library (John Wiley & Sons, Ltd) for observational and experimental studies that investigated smartphone overuse or addiction in children (aged<18 years) or young people (aged<40 years), and its associations with impaired visual function such as myopia, poor vision, or blurred vision. To minimize publication bias, we also searched for additional studies in grey literature sources, including Virtual Health Library [
Free text and Medical Subject Headings (MeSH) terms were used for the search, including phone, smartphone, mobile/cell/cellular phone, electronic device, use, use time, screen time, overuse, addiction, eye, visual acuity, vision, vision screening, eyesight, myopia, myopic refraction, shortsighted/nearsighted/short sight, near sight, refraction errors, ocular/health effect, optic, blind, ophthalmology, optometry, retina, ametropia/amblyopia symptom, visual assessment, and visual problem (see
All observational and experimental studies were included if they fulfilled the following criteria: (1) original studies examining the use of a smartphone (or mobile phone) and eyesight, including population-based longitudinal studies, cohort studies, case-control studies, cross-sectional studies, and controlled clinical trials; (2) participants are children aged ≤18 years or young people aged ≤40 years (a young adult was defined as the developmental stage between 18 and 40 years [
Studies were excluded if they: (1) were narrative reviews, editorial papers, commentaries, letters, or methodological papers; (2) evaluated visual function with no reliable/relevant estimates for smartphone use; (3) no reference or control group was included in the analysis; and (4) animal studies.
After the systematic search of the relevant articles in the databases, two investigators (JW and ML) embarked on screening and identification of potentially relevant abstracts independently. For any disagreements that occurred between the two investigators regarding the eligibility of a study, there was a thorough discussion or advice from an academic expert (YC). Subsequently, articles for selected abstracts were downloaded, and data were extracted by JW and YC independently using a standardized form in Microsoft Excel. The extracted data were compared and summarized to obtain one final document from which the analysis was conducted. The information extracted included: name of first author, year of publication, study design, duration of study, country that the study was conducted in, eyesight measurement, smartphone use time, smartphone use frequency, sample size, incidence of cases with impaired vision, outcome ascertainment method, OR or ES and the associated 95% CI, and statistical analysis method used.
The Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Analytical Cross Sectional Studies, JBI Appraisal Checklist for Quasi-Experimental Studies, and JBI Critical Appraisal Checklist for Randomized Controlled Trials were used to assess the quality of the studies included in the meta-analysis [
For studies that did not report the OR, it was calculated using the numbers of cases with and without visual impairment of the reference/control group and overuse group. For studies that measured visual impairment using continuous variables, ES was calculated as the difference between the means divided by the pooled SD as follows [
where
A positive ES indicates a worse visual function. Heterogeneity of the included studies was investigated using the
Subgroup analysis was performed for the cross-sectional studies according to the outcome of visual impairment (myopia, poor vision, or blurred vision) and mean age of the subjects (children, ≤18 years; young people, 18-40 years). Leave-one-out (LOO) analysis was also performed to investigate the influence of a single study on the pooled effect as an additional sensitivity analysis [
A two-sided
In total, 1961 articles were obtained from all of the databases. After removing duplicates, 1796 articles remained, 121 of which were considered to be relevant for the meta-analysis after screening of titles and abstracts. After screening the full text of the 121 articles downloaded, 14 articles met our inclusion criteria, including 10 cross-sectional studies and 4 controlled trials, comprising a total of 27,110 participants with mean ages ranging from 9.5 to 26.0 years. The flowchart of article searching and screening is shown in
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram for screening and selection of articles on smartphone overuse and visual impairment in children and young adults.
General characteristics of the included studies.
Reference | Year | Country | Study design | Age of participants (years), mean (SD) or range | Sampling method | N participants |
Küçer et al [ |
2008 | Turkey | Cross-sectional | University students (age not given) | Convenience sample | 229 |
Toh et al [ |
2019 | Singapore | Cross-sectional | 13.3 (2.0) | Matrix-stratified sample | 1884 |
Merrie et al [ |
2019 | Ethiopia | Cross-sectional | 13.1 (2.8) | Multistage sampling | 601 |
Guan et al [ |
2019 | China | Cross-sectional | 10.6 (1.15) | Randomly selected sample | 19,934 |
Kim et al [ |
2016 | Korea | Cross-sectional | 15 (0.9) | Convenience sample | 715 |
Liu et al [ |
2019 | China | Cross-sectional | 9.5 (2.1) | Stratified |
566 |
Meo et al [ |
2005 | Saudi Arabia | Cross-sectional | 26.0 (13.4) | Voluntary (response) sample | 873 |
Alharbi et al [ |
2019 | Saudi Arabia | Cross-sectional | 21.8 (2.4) | Random sample | 605 |
Huang et al [ |
2019 | China | Cross-sectional | 19.6 (0.9) | Stratified random cluster sample | 1153 |
McCrann et al [ |
2020 | Ireland | Cross-sectional | 16.8 (4.4) | Voluntary sample | 402 |
Antona et al [ |
2018 | Spain | RCTa | 23.7 (2.6) | Random sample | 54 |
Choi et al [ |
2018 | South Korea | CTb | 26.0 (3.0) | Nonrandomized sample | 50 |
Lee et al [ |
2019 | Korea | CT | 20-29 | Voluntary sample | 26 |
Long et al [ |
2017 | Australia | CT | 21.5 (3.3) | Voluntary sample | 18 |
aRCT: randomized controlled trial.
bCT: controlled trial.
Outcomes and results of the included studies.
Reference | Response rate | Exposure; type of measure | Outcome; type of measure | Main results |
Küçer et al [ |
100% | Time of mobile phone possession; Qa | Blurred vision; Q | ≤2 years: 8.8% (4/45) |
Toh et al [ |
93.78% (1884/2009) | Time of smartphone use (per hour); Q | (1) Myopia; Q |
(1) ORb 0.97 (95% CI 0.94-0.99) |
Merrie et al [ |
95.09% (601/632) | Duration of mobile exposure; Q | Poor vision/visual impairment; objective assessment | >2 h/day: 6.6% (18/271) |
Guan et al [ |
UKc | Time of smartphone use; Q | Visual acuity; objective assessment | 1 h/day: 20% (117/584); |
Kim et al [ |
97.41% (715/734) | Time of smartphone use; Q | Poor vision/ocular symptom score; Q |
>2 h/day: 72% (260/360); |
Liu et al [ |
88.7% (566/638) | Time of smartphone use (per hour); Q | Myopia; objective assessment | OR 0.90 (95% CI 0.57-1.43) |
Meo et al [ |
100% | Use of mobile phone (duration of calls); Q | Blurred vision; Q | >0.5 h/day: 5% (5/100); |
Alharbi et al [ |
93.1% (605/650) | Duration of Smartphone use per day; Q | (1) Poor vision; Q |
(1) >3 h/day: 57.2% (270/472); |
Huang et al [ |
96.08% (1153/1200) | Duration of daily smartphone use; Q | Myopia; objective assessment | >3 h/day: 84.57% (296/350); ≤ 3 h/day: 88.03% (537/610) |
McCrann et al [ |
96.17% (402/418) | Time on phone (minutes/day); Q | Myopia; Q | OR 1.026 (95% CI 1.001-1.051) |
Antona et al [ |
100% | Smartphone reading vs printed hardcopy reading | Asthenopia score; Q | 27.96 (SD 20.11) vs 13.25 (SD 12.76) |
Choi et al [ |
100% | Smartphone use after 4 hours vs baseline | Ocular surface disease index scores; Q | 25.03 (SD 10.61) vs 15.08 (SD 8.83) |
Lee et al [ |
86.67% (26/30) | Smartphone use 20 minutes vs 5 minutes | Oculomotor function; Q | 6.35 (SD 3.54) vs 3.73 (SD 4.09) |
Long et al [ |
100% | Using smartphone after 1 hour vs baseline | Viewing distance; objective assessment | 27.8 (SD 7.7) cm vs 31 (SD 8.2) cm |
aQ: questionnaire.
bOR: odds ratio.
cUK: unknown.
The funnel plot of ORs for the included cross-sectional studies appeared to be symmetric (
Funnel plot with pseudo 95% confidence limit for cross-sectional studies.
Statistically significant heterogeneity was present among the ORs on visual impairment incidence (
Pooled odds ratios (ORs) of visual impairment in the smartphone overuse group compared to the reduced-use group.
Baujat plot for cross-sectional studies.
The LOO sensitivity test indicated that ORs of visual impairment in the smartphone overuse group compared to the reduced-use group ranged from 1.02 to 1.09; however, none of the ORs was statistically significant (
Pooled odds ratios (ORs) of visual impairment in the smartphone overuse group compared to the reduced-use group from leave-one-out analysis.
The funnel plot of ES for the included controlled trials appeared to be symmetric (
Funnel plot with pseudo 95% confidence limit for controlled trials.
In all of the controlled trials, the smartphone overuse group showed worse visual function scores than the reduced-use group, with ESs ranging from 0.40 to 0.91 (
Pooled effect size (ES) of visual function score in the smartphone overuse group compared to the reduced-use group.
The LOO sensitivity test indicated that the results are robust, with the ESs ranging from 0.65 to 0.82, and all of the ESs were statistically significant (
Pooled effect sizes (ESs) of visual function score in the smartphone overuse group compared to the reduced-use group from leave-one-out analysis.
The purpose of this systematic review and meta-analysis was to summarize currently available evidence with reference to the relationship between smartphone overuse and visual impairment in children and young adults. Among the 14 studies included in the analysis, 9 found a significant association between smartphone overuse and visual impairment. Our pooled results showed negative but not statistically significant associations (OR=1.05, 95% CI 0.98-1.13) between smartphone overuse and myopia, blurred vision, or poor vision in the included cross-sectional studies. However, the adverse effect was more apparent in children (OR=1.06, 95% CI 0.99-1.14) than in young adults (OR=0.91, 95% CI 0.57-1.46). We also found that smartphone overuse may cause worse visual function than reduced use in the included controlled trials (ES=0.76, 95% CI 0.53-0.99). As the results are mixed, further studies are warranted. To our knowledge, this is the first systematic review that comprehensively summarized existing data on smartphone overuse and visual impairment in children and young adults.
There are several possible reasons for the lack of a statistically significant association observed between smartphone overuse and visual impairment when pooling cross-sectional studies. First, most of the existing studies included in this systematic review were from Asia, which has higher prevalence rates of visual impairment. The myopia prevalence in East Asia was already reported to be high before the introduction of digital devices [
Second, most of the studies included in this analysis divided smartphone overuse as use time over 2 or 3 hours per day. However, there is some evidence that the time people actually spend engaged with a digital screen is far longer [
Third, several studies have shown that technology use or screen time alone is of minimal risk to visual impairment, whereas more time spent outdoors is related to a reduced risk of myopia and myopic progression [
Besides the findings in the cross-sectional studies, we also found that the smartphone overuse group presented worse visual function scores than the reduced-use group in each of the included controlled trials and in the pooled result. Biologically, the effects of smartphones on ocular symptoms can be explained by two types of electromagnetic fields (EMFs): extremely low-frequency EMFs and radiofrequency (RF) electromagnetic radiation (EMR) [
Regarding the association between smartphone overuse and myopia examined in the cross-sectional studies, multiple ocular symptoms found in the experimental studies do not necessarily reflect pathological changes in the eyes, such as myopia. Few longitudinal cohort studies have examined the impacts of screen exposure on myopia, and the results are inconsistent [
In addition, the heterogeneity was high in the meta-analysis of included cross-sectional studies. First, a large number of studies have identified potential risk factors that may result in visual impairment, which included both genetic and environmental factors [
There are also other limitations of this study that need to be addressed. All of the included studies used a self-reported questionnaire to evaluate smartphone use time. Participants in the included experimental studies also mostly reported their visual function using questionnaires. The questionnaires themselves may be a potential source of error due to inaccurate reporting or recall bias of the participants. Further research should adopt objective instruments to measure smartphone use time and visual acuity screening to examine visual function. Furthermore, generalization of the results should be interpreted with caution owing to the low number of studies included in each meta-analysis. Limiting the review to studies reported in English may have also resulted in nonreporting of studies published in other languages. Nevertheless, our review involved rigorous methodological procedures to obtain and pool data from 27,110 children and young adults. We also adopted a wide range of search terms to retrieve all potential articles published in English, including the grey literature, which might have helped to reduce the publication bias in the final combination.
Overall, current evidence suggests that the results of the association between smartphone overuse and visual impairment in children and young adults are mixed. Although the statistically significantly negative association between smartphone overuse and visual impairment in the meta-analysis was only confirmed in controlled trials and not in cross-sectional studies, the adverse effect of smartphone overuse on visual functions was more apparent in children. However, these relationships need to be further verified. Further research on the patterns of use, with longer follow-up periods to detect longitudinal associations, and the exact mechanisms underlying these associations will help inform detailed guidelines for smartphone use in children and young adults. In addition, understanding the factors of smartphone overuse that account for the risk of ocular symptoms could help the growing population of smartphone users, especially children and young adults, to use smartphones in a healthier manner.
Literature search strategy and results.
Tables of study quality assessment.
electromagnetic field
electromagnetic radiation
effect size
Joanna Briggs Institute
leave-one-out
odds ratio
radiofrequency
Conceptualization: DZ, ML, and YC; Data curation: JW, ML, and YC; Formal analysis: JW and YC; Investigation: JW, DZ, and ML; Methodology: DZ and YC; Project administration: YC; Software: YC; Supervision: DZ and YC; Validation: JW and YC; Visualization: YC; Writing – original draft: JW and YC; Writing – review & editing: JW, DZ, ML, and YC.
None declared.