Refractive errors represent one of the most common visual disorders affecting children worldwide and have emerged as a significant public health concern in the pediatric age group. Globally, uncorrected refractive errors are a leading cause of visual impairment, accounting for a considerable proportion of functional disability and decreased academic productivity in school-aged children. The increasing burden of myopia, in particular, has gained attention due to its rising prevalence and early age of onset, with estimates suggesting a rapid surge of childhood myopia in both developed and developing regions. The etiopathogenesis of refractive errors is multifactorial, involving a complex interplay between genetic predisposition, environmental exposures, and lifestyle patterns. Among the modifiable lifestyle factors, digital device use has been increasingly scrutinized for its potential contribution to visual fatigue, accommodative stress, reduced blink rate, and accelerated myopic progression in school-going children.[1] In recent years, the rapid digitalization of education, especially following the COVID-19 pandemic, has dramatically altered the screen-use habits of children. Extended hours spent on smartphones, tablets, laptops, and televisions-often with suboptimal viewing ergonomics-have been associated with adverse ocular outcomes such as digital eye strain, accommodative spasm, dry eye symptoms, and increased rates of refractive errors. Several epidemiological studies have indicated a strong association between near-work activities and myopia, with digital screen exposure being considered a modern parallel to traditional near tasks. Additionally, reduced outdoor activities, which play a protective role by stimulating dopamine release and regulating axial elongation, have further exacerbated the risk of refractive changes in this age group. Children today are exposed to digital devices at earlier ages and for longer durations than previous generations, making it essential to understand how these behavioral patterns correlate with refractive outcomes.[2][3] School-going children represent a particularly vulnerable population due to their ongoing ocular growth and heightened sensitivity to environmental influences. Visual problems in this age group can significantly impact learning efficiency, academic performance, motor skills, and overall quality of life. Early detection of refractive errors and identification of associated risk factors such as excessive digital device use are crucial for timely intervention and prevention of long-term visual morbidity. Given the increasing reliance on digital technology for academic and recreational purposes, there is a pressing need to assess the burden of refractive errors in children and quantify the role of digital device exposure as a potential determinant.[4] Aim To determine the prevalence of refractive errors and assess their association with digital device use among school-going children. Objectives 1. To estimate the prevalence and distribution of refractive errors among school-going children. 2. To assess the pattern and duration of digital device use in the study population. 3. To evaluate the association between digital device use and different types of refractive errors.

Source of Data Data were obtained from school-going children enrolled in selected government and private schools within the study district. All participants were evaluated after obtaining institutional approval and permission from respective school authorities. Study Design A school-based cross-sectional observational study. Study Location The study was conducted in multiple schools located in the MIMSR Medical College & Hospital’s service area, covering both urban and semi-urban regions. Study Duration The study was carried out over a period of 12 months, including recruitment, screening, data collection, and analysis. Sample Size A total of 400 school-going children aged 6-16 years were included. Inclusion Criteria • Children aged 6 to 16 years attending selected schools. • Children present on the day of screening. • Children whose parents/guardians provided informed consent and who assented to participate. Exclusion Criteria • Children with known ocular pathologies such as congenital cataract, glaucoma, or corneal opacity. • Children with previous ocular surgeries. • Children with systemic illnesses affecting vision - juvenile diabetes. • Children unwilling to participate. Procedure and Methodology The study procedure involved a two-stage approach. First, demographic details including age, gender, socioeconomic background, and academic grade were recorded through a pre-tested questionnaire. Information about digital device use-including type of device, average duration per day, purpose (educational/recreational), viewing distance, and posture-was collected using a structured parent-assisted survey form. In the second stage, each child underwent a detailed ocular examination conducted by trained optometrists. Visual acuity assessment was performed using a Snellen or LogMAR chart under standardized illumination. Children with unaided visual acuity worse than 6/9 were subjected to pinhole testing. Objective refraction was carried out using a handheld autorefractometer, followed by subjective refinement. Cycloplegic refraction was performed where necessary, using 1% cyclopentolate in accordance with safety guidelines. External ocular examination and assessment of ocular alignment were performed using a torchlight and Hirschberg test. Refractive errors were classified into myopia, hyperopia, and astigmatism based on spherical equivalent values. Sample Processing Completed questionnaires and refraction results were checked for accuracy, coded, and entered into a digital database. Data were validated through double-entry verification to minimize errors. Statistical Methods Data were analyzed using SPSS software. Descriptive statistics such as frequency, percentage, mean, and standard deviation were used to summarize baseline characteristics. The prevalence of refractive errors was expressed in proportions. Association between digital device use and refractive errors was assessed using Chi-square tests, independent t-tests, and logistic regression as appropriate. A p-value <0.05 was considered statistically significant. Data Collection Data were collected through school visits on pre-announced dates. Each child was assigned a study ID, and questionnaires were completed with the assistance of parents or teachers. Clinical examination findings and refractive measurements were recorded immediately following the screening session. All records were securely stored and used exclusively for research purposes.

Table 1: Baseline Characteristics of School-Going Children (N = 400) Variable Total (N=400) Refractive Error (n=168) No Refractive Error (n=232) Test Statistic 95% CI of Difference p-value Age (years), Mean ± SD 11.7 ± 2.8 12.4 ± 2.6 11.2 ± 2.9 t = 4.18 0.64 - 1.88 <0.001 Gender χ² = 0.94 - 0.33 Male 206 (51.5%) 89 (53.0%) 117 (50.4%) Female 194 (48.5%) 79 (47.0%) 115 (49.6%) Urban Residence 249 (62.2%) 118 (70.2%) 131 (56.4%) χ² = 8.12 4.1% - 22.4% 0.004 Outdoor Activity (hrs/day) 1.4 ± 0.9 1.0 ± 0.7 1.7 ± 0.9 t = 8.02 0.52 - 0.88 <0.001 Digital Screen Time (hrs/day) 3.2 ± 1.4 3.8 ± 1.3 2.8 ± 1.3 t = 7.35 0.69 - 1.30 <0.001 Table 1 presents the baseline characteristics of the 400 school-going children and compares demographic and behavioral parameters between those with refractive errors (n = 168) and those without refractive errors (n = 232). Children with refractive errors were significantly older, with a mean age of 12.4 ± 2.6 years compared to 11.2 ± 2.9 years in those without refractive errors (t = 4.18, 95% CI: 0.64-1.88; p < 0.001). Gender distribution was similar across both groups, with no statistically significant association between sex and refractive status (χ² = 0.94, p = 0.33). However, urban residence showed a strong and statistically significant association with refractive errors, as 70.2% of affected children were from urban areas compared to 56.4% in the non-refractive group (χ² = 8.12, 95% CI: 4.1%-22.4%; p = 0.004). Behavioral parameters demonstrated notable differences: children with refractive errors engaged in significantly less outdoor activity (1.0 ± 0.7 hours/day) than those without refractive errors (1.7 ± 0.9 hours/day) (t = 8.02, 95% CI: 0.52-0.88; p < 0.001). Moreover, daily digital screen time was substantially higher among the refractive error group (3.8 ± 1.3 hours/day) compared to their counterparts (2.8 ± 1.3 hours/day), a difference that was highly significant (t = 7.35, 95% CI: 0.69-1.30; p < 0.001). Table 2: Prevalence and Distribution of Refractive Errors (N = 400) Type of Refractive Error n (%) Mean SE (D) ± SD Test Statistic 95% CI p-value Any Refractive Error 168 (42.0%) - - - - Myopia 102 (25.5%) -2.19 ± 1.03 - - - Hyperopia 37 (9.2%) +1.54 ± 0.61 - - - Astigmatism 29 (7.2%) -1.03 ± 0.58 - - - Mean Spherical Equivalent (D) -0.81 ± 1.65 - - - - Age vs Myopia - Myopia: 12.9 ± 2.4 Non-myopia: 11.1 ± 2.7 t = 6.25 1.20 - 2.30 <0.001 Gender vs Myopia Myopia in males: 52/206 (25.2%) Myopia in females: 50/194 (25.8%) - χ² = 0.01 - 0.91 Urban vs Rural Myopia Urban: 84/249 (33.7%) Rural: 18/151 (11.9%) - χ² = 24.6 13.5% - 29.3% <0.001 Table 2 describes the prevalence and distribution of refractive errors among the study population. A total of 168 children (42.0%) had some form of refractive error, with myopia being the most prevalent subtype affecting 102 children (25.5%), followed by hyperopia in 37 children (9.2%) and astigmatism in 29 children (7.2%). The mean spherical equivalent for the entire study population was -0.81 ± 1.65 diopters, reflecting an overall myopic shift. Age demonstrated a significant association with myopia, as children with myopia were considerably older (12.9 ± 2.4 years) compared to non-myopic children (11.1 ± 2.7 years) (t = 6.25, 95% CI: 1.20-2.30; p < 0.001). No significant difference was observed between males and females with respect to myopia prevalence (χ² = 0.01, p = 0.91), indicating a similar distribution across genders. A strong urban-rural gradient was evident: 33.7% of urban children were myopic compared to only 11.9% of rural children, a statistically significant difference (χ² = 24.6, 95% CI: 13.5%-29.3%; p < 0.001). Table 3: Pattern and Duration of Digital Device Use (N = 400) Variable Total (N=400) Mean/ n(%) Test Statistic 95% CI p-value Daily Screen Time (hours/day) - 3.2 ± 1.4 - - - Screen Time Category χ² = 28.3 - <0.001 <2 hours 118 (29.5%) - 2-4 hours 163 (40.7%) - >4 hours 119 (29.8%) - Primary Device Used χ² = 14.2 - 0.003 Smartphone 224 (56.0%) - Tablet 69 (17.3%) - Laptop 54 (13.5%) - Television 53 (13.2%) - Viewing Distance (cm) - 34.2 ± 10.8 - - - Proportion with Poor Viewing Distance (<30 cm) 131 (32.7%) - χ² = 9.81 5.2% - 19.6% 0.002 Device Use for Study (%) 251 (62.7%) - - - - Device Use for Games (%) 279 (69.7%) - - - - Table 3 summarizes digital device usage patterns among the 400 children. The mean daily screen time was 3.2 ± 1.4 hours, with 40.7% of children reporting 2-4 hours of daily use, 29.5% reporting less than 2 hours, and 29.8% exceeding 4 hours per day. The variation across screen-time categories was statistically significant (χ² = 28.3; p < 0.001). Smartphones were the most commonly used devices (56.0%), followed by tablets (17.3%), laptops (13.5%), and televisions (13.2%), with significant differences in usage across device types (χ² = 14.2; p = 0.003). The average viewing distance while using digital devices was 34.2 ± 10.8 cm, and 32.7% of children maintained a poor viewing distance of <30 cm, which showed a statistically significant deviation compared to those maintaining safer distances (χ² = 9.81, 95% CI: 5.2%-19.6%; p = 0.002). A majority of children used devices for study purposes (62.7%), while an even higher proportion used them for gaming or recreation (69.7%). Table 4: Association Between Digital Device Use and Refractive Error Type (N = 400) Digital Device Variable Myopia (n=102) No Myopia (n=298) Test Statistic 95% CI p-value Daily Screen Time (hours/day) 4.03 ± 1.29 2.93 ± 1.31 t = 7.23 0.80 - 1.49 <0.001 >4 hrs/day Screen Time 53/119 (44.5%) 66/119 (22.1%) χ² = 18.9 12.5% - 32.8% <0.001 Poor Viewing Distance (<30 cm) 59/131 (45.0%) 72/269 (26.8%) χ² = 12.4 7.1% - 27.1% <0.001 Outdoor Activity (hrs/day) 0.92 ± 0.61 1.62 ± 0.94 t = 8.45 0.54 - 0.87 <0.001 Smartphone Use ≥2 hrs/day 81/224 (36.1%) 143/224 (63.8%) χ² = 10.8 11.4% - 37.6% 0.001 Laptop/Tablet Use for Study 49/123 (39.8%) 74/123 (26.7%) χ² = 6.15 2.9% - 23.3% 0.013 Table 4 examines the relationship between specific digital device usage behaviors and the presence of myopia. Children with myopia reported significantly higher daily screen time (4.03 ± 1.29 hours) compared to non-myopic children (2.93 ± 1.31 hours), with the difference being highly significant (t = 7.23, 95% CI: 0.80-1.49; p < 0.001). A markedly higher proportion of myopic children (44.5%) reported more than 4 hours of digital screen exposure, compared to 22.1% of non-myopic children (χ² = 18.9, 95% CI: 12.5%-32.8%; p < 0.001). Poor viewing distance (<30 cm) was also more common among myopic children (45.0%) than among those without myopia (26.8%), showing a significant association (χ² = 12.4, 95% CI: 7.1%-27.1%; p < 0.001). Additionally, outdoor activity was substantially lower among myopic children (0.92 ± 0.61 hours/day) compared with non-myopic children (1.62 ± 0.94 hours/day), and this difference was statistically significant (t = 8.45, 95% CI: 0.54-0.87; p < 0.001). Smartphone use of ≥2 hours per day was significantly related to myopia (χ² = 10.8, p = 0.001), while laptop/tablet use for study also showed a statistically significant association (χ² = 6.15, p = 0.013).

The baseline characteristics (Table 1) revealed that children with refractive errors were significantly older and had substantially higher daily screen time compared with those without refractive errors. This age association is consistent with findings reported by Do CW et al. (2020)[5], who observed increasing myopia prevalence with age due to progressive axial length changes during late childhood. Likewise, our observation of significantly reduced outdoor activity in children with refractive errors mirrors the results of Srivastava T et al. (2024)[6], who demonstrated that lack of outdoor exposure is one of the strongest modifiable predictors of myopia onset. The current study also identified a significant urban predominance of refractive errors, consistent with the urban-rural gradient highlighted by Habani S et al. (2024)[7], where increased educational pressure and high device use in urban populations heightened the risk of myopia. Gender distribution was similar across groups, echoing the conclusions of Bhandari KR et al. (2021)[4], who also found no significant gender difference in refractive error patterns among school-aged children. In Table 2, the prevalence of any refractive error in our cohort was 42.0%, with myopia accounting for 25.5%. These values are closely comparable to Indian epidemiological estimates reported by Do CW et al. (2020)[5], who found a 37% prevalence of refractive errors among Delhi school children. Mean spherical equivalent in our study (-0.81 ± 1.65 D) also aligns with the mild-to-moderate myopic shift reported by Joseph E et al. (2024)[8] in Southeast Asian school children. Furthermore, the lack of gender variation in myopia prevalence in our study is consistent with the findings of Shah SA et al. (2025)[3], who reported nearly identical myopia prevalence between boys and girls. However, the strong urban-rural divide noted in our findings (33.7% vs. 11.9%) is similar to reports by Satapathy SP et al. (2020)[9], who documented much higher myopia prevalence among urban students, likely attributable to more intensive near-work demands and limited outdoor play. Digital device usage patterns presented in Table 3 show high screen exposure, with 29.8% of children using digital devices for more than 4 hours per day. This pattern reflects the growing digital dependency among children, with comparable findings reported in China by Singh SP. (2025)[1], who observed that nearly one-third of students exceeded 4 hours of daily screen use following increased online learning. The predominance of smartphones (56%) as the primary device is consistent with global trends noted by Inchara N et al. (2023)[10], who reported smartphones as the dominant mode of screen exposure in children due to accessibility and portability. Poor viewing distance (<30 cm), seen in 32.7% of children in our study, is a significant ergonomic risk factor and aligns with the findings of Shah SA et al. (2025)[3], who emphasized poor viewing distance as a contributor to accommodative strain and refractive error progression. The high prevalence of device use for both study (62.7%) and gaming (69.7%) reflects a dual-purpose use similar to patterns described in Southeast Asian school cohorts. The association between digital device use and refractive error type (Table 4) demonstrates strong and significant links. Myopic children had considerably higher daily screen exposure, shorter viewing distances, and significantly lower outdoor activity. These findings corroborate the results of Rahman T et al. (2021)[11], who reported that prolonged screen time, close viewing distances, and minimal outdoor exposure independently predicted myopia onset. Our study’s observation that more than 4 hours of screen use per day raises the likelihood of myopia aligns with the dose-dependent effects described by Joseph E et al. (2024)[8]. Frequent smartphone use and extensive laptop/tablet use for study also showed significant associations with myopia in this study, which is consistent with findings from Rizwan M et al. (2023)[12], who demonstrated that handheld device use poses greater risk due to close working distances and sustained accommodation.

The present cross-sectional study among 400 school-going children demonstrated a substantial prevalence of refractive errors, with myopia emerging as the most common visual morbidity. The findings clearly show that prolonged digital device exposure, particularly high screen time, close viewing distances, and extensive smartphone use, were significantly associated with refractive errors. Reduced outdoor activity further amplified the risk, highlighting the interplay between lifestyle behaviors and visual outcomes in children. Urban residence and increasing age were additional contributors to higher refractive error prevalence. These results emphasize the growing influence of digital lifestyles on pediatric eye health and underscore the urgent need for early screening, parental guidance on healthy digital habits, and the integration of school-based preventive strategies. Promoting balanced screen use, ergonomic viewing practices, and regular outdoor activity may help mitigate the rising burden of refractive errors among children in the digital era. LIMITATIONS OF THE STUDY 1. Cross-sectional design prevents establishing causal relationships between digital device exposure and refractive errors. 2. Self-reported screen time and viewing behavior, obtained through questionnaires, may be subject to recall bias or parental underestimation. 3. Cycloplegic refraction was not performed uniformly in all participants, which may have led to minor misclassification of borderline refractive cases. 4. Outdoor activity duration was parent-reported, and objective measures such as wearable light sensors were not used. 5. The study sample was limited to selected schools in specific regions, potentially limiting generalizability to all demographic and socioeconomic groups. 6. Digital device use patterns may vary across seasons and academic schedules, which were not controlled for in the study. 7. Potential confounders such as parental myopia, academic load, and indoor illumination were not assessed.

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