Association of different digital media experiences with paediatric dry eye in China: a population-based study ============================================================================================================= * Junxin Ma * Hui Zhu * Wei Guo * Rui Li * Shiya Shen * Yun Wang * Dan Huang * Xiaohan Zhang * Zhujun Fu * Andi Zhao * Danni Chen * Jiahao Si * Jiyu Zhang * Shasha Xu * Liyuan Wang * Hu Liu ## Abstract **Objective** To investigate the ocular surface effects of different digital media experiences in Chinese elementary school students. **Design** Population-based cross-sectional study was used. **Setting** 14 randomly selected primary schools in Yuhuatai District, Nanjing, China **Participants** 2,694 students between 7 and 8-year-old. **Outcome Measures** Prevalence of and risk factors for different types of dry eye disease，and different digital media experience with different ocular signs. **Results** The prevalence of ‘symptomatic DED’ was 8.7% (95% CI 7.6% to 9.8%) and ‘definite DED’ prevalence rate was 5.5% (95% CI 4.7% to 6.4%). In multivariable logistic regression model, allergic conjunctivitis (OR=4.33, 95% CI (3.01 to 6.23), p<0.001), more than 1 hour per day on outdoor activity (OR=0.69, 95% CI (0.49 to 0.99), p=0.043), smartphone (OR=2.73, 95% CI (1.51 to 4.91), p=0.001), tablet (OR=2.09, 95% CI (1.07 to 4.07), p=0.030) and homework (OR=1.86, 95% CI (1.22 to 2.83), p=0.004) were independently associated with ‘definite DED’, while allergic conjunctivitis (OR=5.58, 95% CI (4.12 to 7.55), p<0.001), more than 1 hour per day on outdoor activity (OR=0.72, 95% CI (0.53 to 0.97), p=0.028), smartphone (OR=2.60, 95% CI (1.55 to 4.35), p<0.001), tablet (OR=1.84, 95% CI (1.02 to 3.34), p=0.044) and homework (OR=2.57, 95% CI (1.84 to 3.60), p<0.001) were independently associated with ‘symptomatic DED’. **Conclusions** Using smartphones or tablets for an average of more than 1 hour per day through the course of a year is independently associated with paediatric DED. * epidemiology * corneal and external diseases * community child health * ophthalmology * paediatric ophthalmology ### STRENGTHS AND LIMITATIONS OF THIS STUDY * This is a large population-based cross-sectional study conducted in Yuhuatai District, Nanjing, Eastern China. * Comprehensive definitions of ‘definite dry eye disease’ and ‘symptomatic dry eye disease’ were applied. * Other benefits include its large sample size (2694 primary school children between 7 and 8 years old) and high participating rates (>80%). * The prevalence estimate may be biased due to the uncooperative nature of young children, and lifestyles recalled by parents might be subject to recall bias. * Due to the limited time available for ocular examinations during the field survey, not all ocular surface parameters were measured on our subjects. ## Introduction Dry eye disease (DED) is defined as multifactorial damage to the ocular surface characterised by declining tear film homeostasis and accompanied by ocular symptoms. DED is widely encountered in ophthalmic practice and recognised as a substantial public health problem.1 According to a Tear Film & Ocular Surface Society (TFOS) Dry Eye Workshop II (DEWS II) report, the prevalence of DED involving symptoms with or without signs ranged from 5% to 50% across various regions and populations.2 Only a few studies included the younger population since most epidemiological studies were conducted in adults aged 20–96 years.3–6 A retrospective study in 2019 using the Department of Defense Military Health System data reported that DED prevalence according to a combination of diagnoses, procedures and/or prescription fills amounted to 0.20% for children aged 2–17 years old, whereas it did not reveal the prevalence among younger children.3 Since the Asian race is a common risk factor for DED,2 a hospital-based cross-sectional study published in 2020 reported that among 259 969 children and adolescents (aged <21 years) from 2010 to 2018 years in India, there were 49 (4.79%) people aged 3–5 years and 166 (16.23%) people aged 6–11 years with dry eyes as per the TFOS DEWS II criteria.7 A much higher prevalence (21%–24%) based on the Women’s Health Study (WHS) criteria (severe symptoms of dryness and irritation and/or a physician’s diagnosis of dry eye, as reported by the participant) was found in two studies among senior high school students in China and Japan.4 5 Due to an increase in dry eye prevalence with age,2 another study conducted in Japan demonstrated that the prevalence of DED based on the WHS criteria was 6.7% in elementary school children and 16.4% in junior high school children in 2019.6 The fluctuation in the prevalence rate of DED could be due to differences in diagnostic criteria, study population and methodology. Owing to the limited number and variable findings of prevalence studies in youth, the number of young patients accounts for a significant proportion of the patient population, and this remains an important area for further research. Since the development of numerous new kinds of digital display screens (such as televisions, smartphones, tablets and computers), which may differ in terms of viewing position, size, method and pattern of use, a short-term ocular surface discomfort and visual discomfort that could be induced by smartphones were reported in several case–control studies.8 9 In contrast, the long-term ocular surface effects entailed by using different digital devices by the young population were still unknown. In this population-based cross-sectional study, the prevalence and risk factors of DED among Chinese elementary school students aged 7 and 8 years old were determined. In particular, the associations between different digital media experiences and DED were evaluated. ## Methods ### Study design and population This population-based cross-sectional study was conducted from April to November of 2019 in Yuhuatai District, Nanjing, China. The Yuhuatai District is an urban area in Nanjing with a medium level of socioeconomic development, which is representative of Eastern China. ### Questionnaires Subjective ocular symptoms were evaluated with the Chinese version of the modified Ocular Surface Disease Index (OSDI), which has been acknowledged as the most common DED-specific questionnaire and validated in Chinese language, consisting of 11 questions to measure the frequency of ocular symptoms, vision-related functions and limitation, and environmental triggers in the previous week.10 The question about night driving was omitted as children are not expected to drive during night time. Each response was scored using a 5-point scale whereby 0 indicates no problem and 4 indicates a significant problem. The total OSDI score was calculated as follows: (sum of scores×25/11) and ranged from 0 to 100. Based on their OSDI scores, the students were classified as asymptomatic (01 hour/day); this categorisation is based on a previous Korean population-based study of adolescents that concluded that longer smartphone use (>2 hours/day) was associated with a higher risk of dry eye symptoms.14 Given that the children in this study were younger than adolescents, the average time spent using each electronic device per day was lower; hours on each electronic device were classified as >1 hour or 1 hour. To determine risk factors for ‘definite DED’ and ‘symptomatic DED’, univariable logistic regression models were first performed, and all variables in univariable analysis were included in the multivariable logistic regression model. The model went through ‘enter’ variable selection to keep all risk factors in the final multivariable logistic regression model. Risk factors for OSDI, TBUT and CFSS (as continuous) used univariable and multivariable linear regression models. The model underwent the ‘enter’ variable selection to keep all risk factors in the final multivariable linear regression model. Ordinal logistic regression analysis is used to investigate the association between different digital devices and the various levels of OSDI, with children classified into asymptomatic, mild, moderate and severe symptomatic groups based on their OSDI level. The link between varied digital media usage and various dry eye signs was evaluated using binary logistic regression. The ORs and 95% CIs from logistic regression models were used to evaluate the associations between risk factors and DED. ### Patient and public involvement Neither patients nor the public were involved in any aspects of the study, including the development of study question, study design, conduct of the study and dissemination of results. No additional data available. ## Results ### Study population A total of 2813 eligible children (overall response rate, 80.1%) of 3513 identified students participated in this study. After exclusion of 119 students who could not cooperate in the slit-lamp examination, the analysis included 2694 students (1451 boys, 1243 girls) with a mean±SD age of 7.49±0.50 years (range 7–8 years). Study enrolment is presented in figure 1. There was no significant difference between included and excluded children in age (7.49±0.50 years vs 7.48±0.28 years) and gender (boys: 53.9% vs girls: 51.3%). Among the 2694 children included in the analysis, no statistical difference was observed between the boys and girls in age. ![Figure 1](http://bmjopen.bmj.com/https://bmjopen.bmj.com/content/bmjopen/12/11/e062850/F1.medium.gif) [Figure 1](http://bmjopen.bmj.com/content/12/11/e062850/F1) Figure 1 Enrolment process of this study. ### Prevalence of ‘symptomatic DED’ and ‘definite DED’ Of the 2694 children, 234 (8.7%, 95% CI 7.6% to 9.8%) had an OSDI score ≥13 (‘symptomatic DED’), and objective signs (TBUT ≤5 s) were found in 127 right eyes and 140 left eyes among these 234 students. If a child had an OSDI score ≥13 and objective signs in any one of the two eyes, the child was considered to have a dry eye. Among 234 children with symptomatic DED, 118 children had objective signs (TBUT ≤5 s) in both eyes, 31 children had objective signs in only one eye and 85 did not have objective signs in any eye. One hundred forty-nine (5.5%, 95% CI:4.7% to 6.4%) of children with ‘symptomatic DED’ with TBUT ≤5 s in one or both eyes were considered as ‘definite DED’. Mean TBUT was 5.89±2.40 (range, 2–18 s) for right eyes and 5.80±2.40 (range, 1–18 s) for left eyes, and mean CFSS was 0.49±0.99 (range, 0–9 points) for right eyes and 0.52±1.04 (range, 0–9 points) for left eyes among all subjects. The eye with shorter TBUT was considered as the worse eye. Characteristics of the worse eye in children with ‘definite DED’ or ‘symptomatic DED’ are shown in table 1. View this table: [Table 1](http://bmjopen.bmj.com/content/12/11/e062850/T1) Table 1 Characteristics of the worse eye in children with ‘definite DED’ and ‘symptomatic DED’ Figure 2 illustrates the gender distribution according to the severity of OSDI levels. The number of children classified as asymptomatic, mild, moderate and severe symptomatic group was 2460 (male:female=46.8%:53.2%), 126 (male:female=39.7%:60.3%), 70 (male:female=38.6%:61.4%) and 38 (male:female=36.8%:63.2%), respectively. No difference was noted in the severity of OSDI symptoms of DED in gender (p=0.137). The OSDI score was not correlated with TBUT (R=−0.01, p=0.81) and CFSS (R=−0.01, p=0.51). ![Figure 2](http://bmjopen.bmj.com/https://bmjopen.bmj.com/content/bmjopen/12/11/e062850/F2.medium.gif) [Figure 2](http://bmjopen.bmj.com/content/12/11/e062850/F2) Figure 2 The gender distribution according to the severity of Ocular Surface Disease Index (OSDI) levels. ### Risk factors for ‘symptomatic DED’ and ‘definite DED’ Univariable and multivariable logistic regression analyses for risk factors of ‘definite DED’ are listed in table 2. In the multivariable logistic regression model, AC (OR=4.33, 95% CI (3.01 to 6.23), p<0.001), more than 1 hour per day on outdoor activity (OR=0.69, 95% CI (0.49 to 0.99), p=0.043), smartphone (OR=2.73, 95% CI (1.51 to 4.91), p=0.001), tablet (OR=2.09, 95% CI (1.07 to 4.07), p=0.030) and homework (OR=1.86, 95% CI (1.22 to 2.83), p=0.004) were independently associated with ‘definite DED’. View this table: [Table 2](http://bmjopen.bmj.com/content/12/11/e062850/T2) Table 2 Univariate and multivariate analyses for ‘definite dry eye disease (DED)’ Risk factors of ‘symptomatic DED’ are listed in table 3. In the final multivariable logistic regression model, AC (OR=5.58, 95% CI (4.12 to 7.55), p<0.001), more than 1 hour per day on outdoor activity (OR=0.72, 95% CI (0.53 to 0.97), p=0.028), smartphone (OR=2.60, 95% CI (1.55 to 4.35), p<0.001), tablet (OR=1.84, 95% CI (1.02 to 3.34), p=0.044) and homework (OR=2.57, 95% CI (1.84 to 3.60), p<0.001) were independently associated with ‘symptomatic DED’. View this table: [Table 3](http://bmjopen.bmj.com/content/12/11/e062850/T3) Table 3 Risk factors for ‘symptomatic dry eye disease (DED)’ Risk factors of OSDI, TBUT and CFSS are listed in table 4. In the final multivariable liner regression model, AC (B=5.397, p<0.001), more daily hours on smartphone (B=1.026, p<0.001), tablet (B=0.905, p=0.002), homework (B=2.252, p<0.001) and outdoor activity (B=−0.635, p<0.001) were independently associated with OSDI. More daily hours on watching television (B=0.231, p=0.001) was the only factor independently associated with TBUT in multivariate linear regression models. No factor was found associated with CFSS. View this table: [Table 4](http://bmjopen.bmj.com/content/12/11/e062850/T4) Table 4 Risk factors for Ocular Surface Disease Index (OSDI), tear break-up time (TBUT) and corneal fluorescein staining scores (CFSS) ### Different digital media experiences with OSDI, TBUT and CFSS Ordinal logistic regression analysis (table 5) was performed to assess the relationship between different digital devices and the diverse levels of OSDI, in which children were divided into asymptomatic, mild, moderate and severe symptomatic groups according to the corresponding level of OSDI. The independent variables for the multivariable model included usage of television, smartphone, tablet and computer per day. Only two independent variables were found to have a statistically significant influence on the model: daily hours on smartphone and tablet. Students who used smartphones (p=0.001, OR=2.355) and tablets (p=0.045, OR=1.786) for more than 1 hour per day were more likely to have higher OSDI levels than students who used them for less or equal to 1 hour. View this table: [Table 5](http://bmjopen.bmj.com/content/12/11/e062850/T5) Table 5 The relationship between different digital media experiences and different OSDI levels (divided into asymptomatic, mild, moderate and severe symptomatic group) Furthermore, binary logistic regression analysis was used to evaluate the relationship between different digital media usage and different abnormal dry eye signs. Watching on smartphones (OR=1.459, 95% CI (1.00 to 2.13), p=0.050) and tablets (OR=1.587, 95% CI (1.03 to 2.46), p=0.038) for more than 1 hour per day was independently associated with ‘TBUT ≤5 s’. Similarly, using tablets for more than 1 hour per day (OR=1.53, 95% CI (1.02 to 2.29), p=0.038) was independently associated with ‘CFSS ≥1’. Table 6 lists the correlation coefficient between different digital media experiences and OSDI, TBUT and CFSS. OSDI was significantly positively related to the daily hours on smartphone (r=0.082, p<0.001) and tablet (r=0.064, p=0.001). There were positive correlations between TBUT and daily hours on television (r=0.050, p=0.010). No relationship was found between CFSS and different types of electronic devices. View this table: [Table 6](http://bmjopen.bmj.com/content/12/11/e062850/T6) Table 6 Correlation between daily hours on different electronic devices with Ocular Surface Disease Index (OSDI), tear break-up time (TBUT) and corneal fluorescein staining scores (CFSS) ## Discussion Our study is the largest population-based study of DED in Chinese children documented so far. We highlighted the impact of using different types of electronic products for varying amounts of time every day on the ocular surface, and revealed that viewing a cell phone or tablet for an average of more than 1 hour per day for a year is associated with ‘definite DED’ and ‘symptomatic DED’. Moreover, among 2694 Chinese elementary school children aged 7–8 years old, the prevalence based on ‘symptomatic DED’ (an OSDI score ≥13) was 8.7% and the ‘definite DED’ (an OSDI score ≥13 and TBUT ≤5 s) prevalence rate was 5.5%. We have identified that previous literature often focused on one DED type and only reported a short-term ocular surface discomfort induced by media display usage, so the outcomes of this research may fill a gap in this study area.3–5 Most important of all, in adults, visual display terminal (VDT) use has been reported to be accompanied by a reduction in blinking and an increase in interpalpebral ocular surface area, which can cause tear film instability and DED.15 16 In schoolchildren, the use of VDT has also increased among the growing amount of online study and changing ways of entertainment. Most previous case–control studies focused on the short-term ocular surface discomfort, visual discomfort and asthenopic symptoms with different digital displays.17 18 A crowdsourced cross-sectional study visualised the heterogeneous DED symptoms into seven distinct subgroups and revealed that participants in some clusters that observed shortened maximum blink interval with higher OSDI total score were partly due to overusage of digital media in the increasingly digitalised society.19 Considering the difference in viewing position, size, method and pattern of use between television, smartphone, tablet and computer, our study focused on the effects of long-term use of digital media on the ocular surface. Since clinical signs (TBUT and CFSS) are not significantly correlated with DED symptoms (OSDI scores) in children, which is consistent with the finding in adults,20 the risk factors for ‘definite DED’ and ‘symptomatic DED’ were investigated separately and innovatively. We have revealed that viewing a cell phone or tablet for an average of more than 1 hour per day over a year was associated with ‘definite DED’ and ‘symptomatic DED’. It was hypothesised that the short watching distance of smartphones and tablets exerts a more substantial influence on the symptoms and tear film stability than bigger size and longer watching distance of computers and TV, ultimately leading to ‘definite DED’. Meanwhile, smartphones and tablets are the smallest handheld devices compared with computers and TVs, and comprise a smaller display oriented in the vertical plane. Hence, symptoms may vary in people according to their usage. When we divided the children into different symptomatic groups, as mentioned above, we found that students who used smartphones and tablets for more than 1 hour per day were more likely to get higher OSDI levels, indicating that longer usage of these gadgets daily was directly related to the severity of dry eye symptoms. The correlations between television/tablet usage with TBUT and CFSS revealed that when students rely on these technologies often, abnormal dry eye signs may ensue. Correlation coefficients between OSDI, TBUT and CFSS and different electronic devices were also reported, boosting the distinction of the ocular surface effects of different electronic devices. The daily hours on television being positively correlated with TBUT in our study might be inconsistent with previous studies,20 indicating the low reliability of judging children’s dry eye only according to TBUT fluctuation. Second, although age was identified as a risk factor for DED in certain studies, a Japanese study revealed that younger individuals might be more susceptible to self-reported ocular symptoms than older individuals.21 Therefore, we highlighted that the prevalence of ‘symptomatic DED’ was 8.7% in elementary school children, which was similar to the findings of a Japanese report (6.7%) for elementary school children,6 but lower than those reported in junior high school (16.4%), senior high school (21%–24%) and younger individuals below 20 years old (76.44%) in previous studies.4 5 21 This finding suggests that the severity of DED symptoms increased with age in children and adolescents, which may be attributable to the increased usage of digital devices, as indicated in the TFOS DEWS II report.2 Moreover, the presence of the ocular surface signs should not be overlooked as some young children may not be able to describe symptoms subjectively,22 and considering the differences in ocular surface characteristics between Asians and Caucasians, the definition of ‘definite DED’ was devised based on symptoms and signs according to the consensus reported by the Asia Dry Eye Society.12 Despite numerous studies reporting the prevalence of DED based on symptoms and signs in adults, due to the lack of a ‘gold standard’ for the DED diagnosis, each study used varying criteria and reported various prevalence rates (8.7%–30.1%).2 A cross-sectional study published in 2017 among Shanghai University students reported a higher prevalence rate (10%) using the same DED definition.23 Furthermore, more risk and protective factors have been identified. More than 1 hour per day on homework was also associated with ‘definite DED’ and ‘symptomatic DED’, highlighting that close observation and caution are required for the ocular surface health in children with heavy academic burden. AC was also significantly associated with ‘definite DED’ and ‘symptomatic DED’, indicating that close attention should be paid to the evaluation of ocular surface parameters in children with AC. Previous studies reported that inadequate refractive correction might be an actual risk factor for DED.5 Our results supported this assumption, showing that inadequate refractive correction was found in 2.04% of total students and to be associated with ‘definite DED’, revealing that inadequate refractive correction might be one cause of ocular surface impairment in Chinese students. However, more than 1 hour per day on outdoor activities was considered as a protective factor for ‘symptomatic DED’, which means that the number of hours spent outdoors is inversely proportional to eye strain and discomfort. Due to the limitation of children’s age, other risk factors suggested by previous study,24 such as female sex, current CL use and sleeping time, were not corroborated as DED risk factors in this study. ### Strengths and limitations This study updated the ocular surface health data in children and emphasised some long-term lifestyle habits, especially regarding the usage and dependence on digital display screens, may lead to occurrence of paediatric dry eye in eastern Chinese children. We also established a comprehensive definition of DED that included ‘definite DED’ and ‘symptomatic DED’ and its focus was the investigation of the prevalence and risk factors in both cases. Other benefits include its population-based design, large sample size (2694 primary school children between 7 and 8 years old) and high participating rates (>80%). However, our study has also encountered limitations. First, the prevalence estimate may be biased due to the uncooperative nature of young children, and lifestyles recalled by parents might be subject to recall bias. Second, due to the limited time available for ocular examinations during the field survey, not all ocular surface parameters were measured on our subjects. Third, the age range of our participants was restricted to subjects between 7 and 8 years old, which could only represent relatively younger children. Thus, we would like to evaluate the prevalence and risk factors of dry eye in children of different ages and going through various learning stages. It is equally important to evaluate whether there would be changes in tear film stability after children improve their daily routine and eye care habits in future researches. ## Conclusions This large population-based study, which includes 2694 Chinese elementary school children aged 7–8 years old, investigates the prevalence of and risk factors for ‘definite DED’ and ‘symptomatic DED’. We innovatively discover that using smartphones or tablets for an average of more than 1 hour per day through the course of a year is associated with cases of definite and symptomatic DED. We have concluded that longer usage of these gadgets (smartphones and tablets) is also related to the severity of dry eye symptoms. Similarly, children who have been exposed to television or tablet screen may also present abnormal dry eye signs. The assessment of these risk factors for DED might help identify high-risk cases among children so they can be promptly evaluated and treated. ## Data availability statement No data are available. ## Ethics statements ### Patient consent for publication Parental/guardian consent obtained. ### Ethics approval This study was approved by the Ethics Committee of Nanjing Medical University (2017-SR-205) and adhered to the tenets of the Declaration of Helsinki. Written informed consent was obtained from the parents or legal guardians of all participating children. Oral assent was obtained from all children right before the examination. ## Acknowledgments We thank the children, their parents or legal guardians, and all the members of the Maternal and Child Healthcare Hospital of Yuhuatai District, Nanjing, China, for their helpful advice and support. ## Footnotes * JM, HZ and WG are joint first authors. * JM, HZ and WG contributed equally. * Contributors JM, HZ and WG have contributed equally to this work and share first authorship. HL, JM, HZ and WG conceived and designed the experiments. RL, SS, YW, JS, JZ, SX and LW performed the experiments. DH, XZ, ZF, AZ and DC analysed the data. RL, SS, YW and DH contributed reagents/materials/analysis tools. JM and HZ wrote the paper.HL was responsible for the overall content as the guarantor. * Funding This work was supported by the National Natural Science Foundation of China (grant nos. 81673198, 81803258 and 82003475). * Competing interests None declared. * Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research. * Provenance and peer review Not commissioned; externally peer reviewed. [http://creativecommons.org/licenses/by-nc/4.0/](http://creativecommons.org/licenses/by-nc/4.0/) This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: [http://creativecommons.org/licenses/by-nc/4.0/](http://creativecommons.org/licenses/by-nc/4.0/). ## References 1. Craig JP, Nichols KK, Akpek EK. TFOS DEWS II-Relazione SU Definizione E Classificazione. Ocul Surf 2017;30:276–83. 2. Stapleton F, Alves M, Bunya VY, et al. TFOS DEWS II epidemiology report. Ocul Surf 2017;15:334–65.[doi:10.1016/j.jtos.2017.05.003](http://dx.doi.org/10.1016/j.jtos.2017.05.003)pmid:http://www.ncbi.nlm.nih.gov/pubmed/28736337 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 3. Dana R, Bradley JL, Guerin A, et al. Estimated prevalence and incidence of dry eye disease based on coding analysis of a large, all-age United States health care system. Am J Ophthalmol 2019;202:47–54.[doi:10.1016/j.ajo.2019.01.026](http://dx.doi.org/10.1016/j.ajo.2019.01.026)pmid:http://www.ncbi.nlm.nih.gov/pubmed/30721689 [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1016/j.ajo.2019.01.026&link_type=DOI) [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 4. Uchino M, Dogru M, Uchino Y, et al. Japan Ministry of health study on prevalence of dry eye disease among Japanese high school students. Am J Ophthalmol 2008;146:925–9.[doi:10.1016/j.ajo.2008.06.030](http://dx.doi.org/10.1016/j.ajo.2008.06.030) [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1016/j.ajo.2008.06.030&link_type=DOI) [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=18723141&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000261349200019&link_type=ISI) 5. Zhang Y, Chen H, Wu X. Prevalence and risk factors associated with dry eye syndrome among senior high school students in a County of Shandong Province, China. Ophthalmic Epidemiol 2012;19:226–30.[doi:10.3109/09286586.2012.670742](http://dx.doi.org/10.3109/09286586.2012.670742) [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.3109/09286586.2012.670742&link_type=DOI) [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=22650150&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000306191300009&link_type=ISI) 6. Yotsukura E, Torii H, Inokuchi M, et al. Current prevalence of myopia and association of myopia with environmental factors among schoolchildren in Japan. JAMA Ophthalmol 2019;137:1233–9.[doi:10.1001/jamaophthalmol.2019.3103](http://dx.doi.org/10.1001/jamaophthalmol.2019.3103)pmid:http://www.ncbi.nlm.nih.gov/pubmed/31415060 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 7. Donthineni PR, Das AV, Basu S. Dry eye disease in children and adolescents in India. Ocul Surf 2020;18:777–82.[doi:10.1016/j.jtos.2020.07.019](http://dx.doi.org/10.1016/j.jtos.2020.07.019)pmid:http://www.ncbi.nlm.nih.gov/pubmed/32818627 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 8. Moon JH, Lee MY, Moon NJ. Association between video display terminal use and dry eye disease in school children. J Pediatr Ophthalmol Strabismus 2014;51:87–92.[doi:10.3928/01913913-20140128-01](http://dx.doi.org/10.3928/01913913-20140128-01)pmid:http://www.ncbi.nlm.nih.gov/pubmed/24495620 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 9. Moon JH, Kim KW, Moon NJ. Smartphone use is a risk factor for pediatric dry eye disease according to region and age: a case control study. BMC Ophthalmol 2016;16:188.[doi:10.1186/s12886-016-0364-4](http://dx.doi.org/10.1186/s12886-016-0364-4)pmid:http://www.ncbi.nlm.nih.gov/pubmed/27788672 [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1186/s12886-016-0364-4&link_type=DOI) [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 10. Lu F, Tao A, Hu Y, et al. Evaluation of reliability and validity of three common dry eye questionnaires in Chinese. J Ophthalmol 2018;2018:1–6.[doi:10.1155/2018/2401213](http://dx.doi.org/10.1155/2018/2401213)pmid:http://www.ncbi.nlm.nih.gov/pubmed/30225139 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 11. Woods J, Hutchings N, Srinivasan S, et al. Geographic distribution of corneal staining in symptomatic dry eye. Ocul Surf 2020;18:258–66.[doi:10.1016/j.jtos.2019.07.009](http://dx.doi.org/10.1016/j.jtos.2019.07.009)pmid:http://www.ncbi.nlm.nih.gov/pubmed/31352082 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 12. Lemp MA, Baudouin C, Baum J. The definition and classification of dry eye disease: report of the definition and classification Subcommittee of the International Dry Eye WorkShop (2007). Ocul Surf 2007;5:75–92.[doi:10.1016/s1542-0124(12)70081-2](http://dx.doi.org/10.1016/s1542-0124(12)70081-2)pmid:http://www.ncbi.nlm.nih.gov/pubmed/17508116 [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1016/S1542-0124(12)70081-2&link_type=DOI) [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=17508116&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) [Web of Science](http://bmjopen.bmj.com/lookup/external-ref?access_num=000246318600003&link_type=ISI) 13. Feng Y, Wang X, Wang F, et al. The prevalence of ocular allergy and comorbidities in Chinese school children in Shanghai. Biomed Res Int 2017;2017:1–11.[doi:10.1155/2017/7190987](http://dx.doi.org/10.1155/2017/7190987)pmid:http://www.ncbi.nlm.nih.gov/pubmed/28904968 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 14. Kim J, Hwang Y, Kang S, et al. Association between exposure to smartphones and ocular health in adolescents. Ophthalmic Epidemiol 2016;23:269–76.[doi:10.3109/09286586.2015.1136652](http://dx.doi.org/10.3109/09286586.2015.1136652) [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.3109/09286586.2015.1136652&link_type=DOI) [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=27254040&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 15. Yokoi N, Uchino M, Uchino Y, et al. Importance of tear film instability in dry eye disease in office workers using visual display terminals: the Osaka study. Am J Ophthalmol 2015;159:748–54.[doi:10.1016/j.ajo.2014.12.019](http://dx.doi.org/10.1016/j.ajo.2014.12.019)pmid:http://www.ncbi.nlm.nih.gov/pubmed/25555800 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 16. Uchino M, Yokoi N, Uchino Y, et al. Prevalence of dry eye disease and its risk factors in visual display terminal users: the Osaka study. Am J Ophthalmol 2013;156:759–66.[doi:10.1016/j.ajo.2013.05.040](http://dx.doi.org/10.1016/j.ajo.2013.05.040)pmid:http://www.ncbi.nlm.nih.gov/pubmed/23891330 [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1016/j.ajo.2013.05.040&link_type=DOI) [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=23891330&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 17. Yuan K, Zhu H, Mou Y, et al. Effects on the ocular surface from reading on different smartphone screens: a prospective randomized controlled study. Clin Transl Sci 2021;14:829–36.[doi:10.1111/cts.12933](http://dx.doi.org/10.1111/cts.12933)pmid:http://www.ncbi.nlm.nih.gov/pubmed/33202098 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 18. Kang JW, Chun YS, Moon NJ. A comparison of accommodation and ocular discomfort change according to display size of smart devices. BMC Ophthalmol 2021;21:44.[doi:10.1186/s12886-020-01789-z](http://dx.doi.org/10.1186/s12886-020-01789-z)pmid:http://www.ncbi.nlm.nih.gov/pubmed/33461518 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 19. Inomata T, Nakamura M, Sung J, et al. Smartphone-Based digital phenotyping for dry eye toward P4 medicine: a crowdsourced cross-sectional study. NPJ Digit Med 2021;4:171.[doi:10.1038/s41746-021-00540-2](http://dx.doi.org/10.1038/s41746-021-00540-2)pmid:http://www.ncbi.nlm.nih.gov/pubmed/34931013 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 20. Ong ES, Felix ER, Levitt RC, et al. Epidemiology of discordance between symptoms and signs of dry eye. Br J Ophthalmol 2018;102:674–9.[doi:10.1136/bjophthalmol-2017-310633](http://dx.doi.org/10.1136/bjophthalmol-2017-310633)pmid:http://www.ncbi.nlm.nih.gov/pubmed/28821553 [Abstract/FREE Full Text](http://bmjopen.bmj.com/lookup/ijlink/YTozOntzOjQ6InBhdGgiO3M6MTQ6Ii9sb29rdXAvaWpsaW5rIjtzOjU6InF1ZXJ5IjthOjQ6e3M6ODoibGlua1R5cGUiO3M6NDoiQUJTVCI7czoxMToiam91cm5hbENvZGUiO3M6MTI6ImJqb3BodGhhbG1vbCI7czo1OiJyZXNpZCI7czo5OiIxMDIvNS82NzQiO3M6NDoiYXRvbSI7czoyNzoiL2Jtam9wZW4vMTIvMTEvZTA2Mjg1MC5hdG9tIjt9czo4OiJmcmFnbWVudCI7czowOiIiO30=) 21. Inomata T, Iwagami M, Nakamura M, et al. Characteristics and risk factors associated with diagnosed and undiagnosed symptomatic dry eye using a smartphone application. JAMA Ophthalmol 2020;138:58–68.[doi:10.1001/jamaophthalmol.2019.4815](http://dx.doi.org/10.1001/jamaophthalmol.2019.4815)pmid:http://www.ncbi.nlm.nih.gov/pubmed/31774457 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 22. Alves M, Dias AC, Rocha EM. Dry eye in childhood: epidemiological and clinical aspects. Ocul Surf 2008;6:44–51.[doi:10.1016/S1542-0124(12)70104-0](http://dx.doi.org/10.1016/S1542-0124(12)70104-0)pmid:http://www.ncbi.nlm.nih.gov/pubmed/18264654 [CrossRef](http://bmjopen.bmj.com/lookup/external-ref?access_num=10.1016/S1542-0124(12)70104-0&link_type=DOI) [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=18264654&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 23. Li S, He J, Chen Q, et al. Ocular surface health in Shanghai University students: a cross-sectional study. BMC Ophthalmol 2018;18:245.[doi:10.1186/s12886-018-0825-z](http://dx.doi.org/10.1186/s12886-018-0825-z)pmid:http://www.ncbi.nlm.nih.gov/pubmed/30208892 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom) 24. Inomata T, Nakamura M, Iwagami M, et al. Risk factors for severe dry eye disease: Crowdsourced research using DryEyeRhythm. Ophthalmology 2019;126:766–8.[doi:10.1016/j.ophtha.2018.12.013](http://dx.doi.org/10.1016/j.ophtha.2018.12.013)pmid:http://www.ncbi.nlm.nih.gov/pubmed/30550734 [PubMed](http://bmjopen.bmj.com/lookup/external-ref?access_num=http://www.n&link_type=MED&atom=%2Fbmjopen%2F12%2F11%2Fe062850.atom)