European Journal of Cardiovascular Medicine

Digital eye strain (DES), also termed computer vision syndrome, refers to a cluster of ocular and visual complaints that occur during or after prolonged use of digital screens [1–4]. Typical symptoms include ocular fatigue, headache, dryness, burning, watering, and intermittent blur, often accompanied by reduced work efficiency and discomfort [1–3]. Several mechanisms contribute to DES, but ocular-surface stress has a central role: screen viewing decreases blink rate and increases incomplete blinking, which destabilizes the tear film and promotes dryness-related symptoms [2,11].

Adolescents represent a particularly vulnerable group. Educational requirements, entertainment, and social communication now converge on handheld devices, and screen exposure often extends late into the evening [6–8]. During the post-pandemic period, multiple studies have reported a high prevalence of DES symptoms among children and adolescents, with risk increasing with longer daily exposure and shorter breaks [6–8]. Although the relationship between screen time and symptoms is widely recognized, the physiological link through blinking is less often quantified in routine clinical research, especially in Indian settings.

Blink behavior is not merely a passive reflex; it acts as a protective mechanism by replenishing the tear film, spreading lipids, and reducing corneal exposure. Experimental and observational work has shown that smartphone and video display terminal tasks can acutely reduce blink rate and exacerbate dry eye symptoms, including in school-aged users [9–11]. However, most available data are from adult office populations or specialized experimental settings, and Indian adolescent data integrating exposure duration, blink rate, and symptom severity remain limited.

Therefore, this study aimed to assess the prevalence and pattern of DES symptoms among adolescents and to evaluate the associations between daily screen time, blink rate during screen viewing, and DES symptom severity. We also examined whether screen time and blink rate retained independent associations with DES after accounting for demographic and clinical covariates.

Study design and setting

This cross-sectional observational study was conducted at the Department of Physiology in collaboration with Ophthalmology, Narayana Medical College, Nellore, Andhra Pradesh, India, over a three-month period from January to March 2022.

Participants and eligibility

Adolescents aged 12–18 years were recruited through outpatient attendance and community/school outreach linked to the institution. Inclusion criteria were regular digital device use (at least 1 hour/day) and willingness to participate. Participants with active ocular infection or inflammation, known ocular surface disease, history of ocular surgery/trauma, contact lens wear, or systemic conditions/medications likely to affect the tear film were excluded. Refractive errors were permitted if corrected with spectacles.

Sample size and sampling

A sample size of 100 was adopted based on feasibility within the study period and to enable stable multivariable modeling with key predictors (screen time and blink rate). Consecutive eligible adolescents were enrolled until the target was achieved.

Study instruments and operational definitions

Screen exposure was recorded using a structured questionnaire capturing primary device(s), average daily screen time (hours/day), and continuous use without breaks (>30 minutes). Screen time was categorized as low (<2 hours/day), moderate (2–4 hours/day), and high (>4 hours/day). Digital eye strain symptoms were assessed using a five-item symptom score (eye strain/fatigue, headache, dryness/burning, blurred vision, and watering), each rated from 0 (none) to 4 (very frequent/severe), producing a total score from 0 to 20. DES was defined as a symptom score ≥1. Severity was categorized as mild (1–7), moderate (8–14), and severe (15–20).

Blink rate measurement

Spontaneous blink rate was measured during a standardized on-screen task. Participants viewed and read standardized text on a smartphone at approximately 40 cm in ambient indoor lighting for 5 minutes. A trained observer counted blinks using real-time observation and video confirmation where feasible; blink rate was expressed as blinks per minute.

Statistical analysis

Continuous variables are presented as mean ± SD and categorical variables as n (%). Group comparisons across screen-time categories used one-way ANOVA for continuous outcomes and χ² tests for proportions. Pearson correlation assessed relationships between screen time, blink rate, and symptom score. Multivariable linear regression (outcome: DES symptom score) and logistic regression (outcome: presence of DES) were performed with adjustment for age, sex, refractive status, and break frequency. A two-sided p value <0.05 was considered statistically significant.

Ethical considerations

The study protocol was approved by the Institutional Ethics Committee of Narayana Medical College, Nellore. Written informed assent/consent was obtained from participants and parents/guardians as appropriate, and confidentiality was maintained.

A total of 100 adolescents were analyzed (mean age 15.2 ± 1.6 years), with a slight male predominance (56%). Smartphones were the most frequently used device (82%), and refractive errors (corrected with spectacles) were present in 38% of participants. The mean daily screen time was 4.3 ± 1.9 hours/day, with nearly half reporting >4 hours/day. Continuous screen viewing without breaks exceeding 30 minutes was reported by 62% (Table 1).

Table 1. Baseline Characteristics and Screen-Use Profile of Participants (n = 100)

*Multiple responses permitted

Blink rate during screen viewing averaged 12.6 ± 3.4 blinks/min and showed a graded decline with higher screen exposure. Mean blink rates were 15.4 ± 3.6 blinks/min in the low screen-time group, 13.1 ± 3.1 blinks/min in the moderate group, and 10.9 ± 2.8 blinks/min in the high screen-time group (ANOVA, p < 0.001). Daily screen time correlated negatively with blink rate (r = −0.46, p < 0.001) (Table 2).

Table 2. Blink Rate and Digital Eye Strain Across Screen-Time Categories

ANOVA p < 0.001; χ² p < 0.001

Overall, 67% of adolescents reported at least one DES symptom. Eye strain/ocular fatigue (54%) and headache (46%) were the most common complaints, followed by dryness/burning (39%), blurred vision (31%), and watering (28%). Based on symptom scores, 34% had mild DES, 24% moderate DES, and 9% severe DES (Table 3).

Table 3. Distribution and Severity of Digital Eye Strain Symptoms

DES prevalence increased with screen time (33% in the low group, 59% in the moderate group, and 85% in the high group; χ² = 18.6, p < 0.001). Mean DES symptom scores were significantly higher among those with >4 hours/day of screen time (12.1 ± 3.8) compared with <2 hours/day (6.2 ± 2.9) (p < 0.001). Screen time correlated positively with symptom score (r = 0.52, p < 0.001) (Table 2).

Adolescents with DES had a lower mean blink rate than asymptomatic participants (11.4 ± 3.0 vs. 14.8 ± 3.2 blinks/min; p < 0.001). Blink rate showed an inverse correlation with symptom scores (r = −0.41, p < 0.001), indicating higher symptom burden with reduced blinking.

In multivariable linear regression, daily screen time remained an independent predictor of higher DES symptom scores (β = 0.89 per hour increase; 95% CI: 0.54–1.24; p < 0.001) after adjustment for age, sex, refractive status, and break frequency. Higher blink rate independently predicted lower symptom scores (β = −0.37 per blink/min; 95% CI: −0.58 to −0.16; p = 0.001). In logistic regression, screen time >4 hours/day was associated with higher odds of DES (adjusted OR = 4.6; 95% CI: 1.8–11.7; p = 0.002), whereas each additional blink per minute reduced the odds of DES (adjusted OR = 0.81; 95% CI: 0.71–0.92; p = 0.001) (Table 4).

Table 4. Multivariable Analysis of Factors Associated With Digital Eye Strain

In this cross-sectional study of adolescents at a tertiary care teaching hospital in coastal Andhra Pradesh, nearly two-thirds reported symptoms consistent with digital eye strain (DES). The symptom pattern was dominated by ocular fatigue and headache, which aligns with the classic DES phenotype described in foundational reviews and contemporary summaries [1–4,12].

A key observation was the dose-response relationship between screen exposure and both physiological and symptomatic outcomes. Blink rate declined progressively across screen-time categories, and higher screen time was associated with greater symptom burden. These findings are biologically plausible because screen viewing is known to reduce blink frequency and increase incomplete blinking, producing tear film instability and ocular-surface stress [2,11]. Experimental evidence also shows that prolonged smartphone tasks can alter blinking patterns and provoke ocular symptoms, supporting the mechanistic pathway observed in our dataset [9].

Our prevalence (67%) is comparable to adolescent-focused surveys reporting DES rates in the range of approximately 50–65%, depending on measurement tools and exposure patterns [6,8,12]. Studies conducted during and after remote-learning expansions have similarly identified extended daily device use and fewer breaks as major contributors to symptoms [6,8]. Notably, prospective observational work among Hong Kong children and adolescents demonstrated that higher smartphone exposure is associated with greater DES risk, reinforcing that this relationship persists across settings and study designs [7]. In India, high symptom prevalence has been documented in student populations with multi-hour device use, highlighting the broader burden across educational strata [5].

A strength of the present analysis is the concurrent quantification of blink rate and symptom severity. Adolescents with DES had a lower blink rate than asymptomatic peers, and blink rate retained an independent protective association in multivariable models. This pattern is consistent with pediatric smartphone gaming data showing reduced blinking accompanied by dry eye symptoms [10]. Together, these findings suggest that behavioral micro-interventions—regular breaks, conscious blinking, and ergonomics may be clinically meaningful, as emphasized in preventive literature for video display terminal-related ocular discomfort [11].

This study has limitations. Screen time and break behavior were self-reported and may be subject to recall bias. Blink rate was assessed during a standardized short task, which may not fully represent habitual patterns during real-world multitasking. The cross-sectional design limits causal inference, and unmeasured factors (screen brightness, viewing distance, ambient humidity, and sleep patterns) could influence symptoms.

Despite these constraints, the results provide actionable evidence for adolescent eye health counseling. Screen exposure and blinking are modifiable, and incorporating simple preventive advice into school health programs and outpatient counseling may reduce symptom burden in this age group.

In adolescents, higher daily screen exposure was associated with lower blink rate during screen viewing and a substantially higher prevalence and severity of digital eye strain symptoms. Adolescents reporting more than four hours of screen time per day had markedly higher symptom scores and significantly increased odds of DES, even after adjustment for key covariates. Conversely, higher blink rates demonstrated an independent protective association. These findings support routine screening for DES in adolescents with heavy digital device use and reinforce practical preventive measures such as scheduled breaks, conscious blinking, and ergonomics. Future longitudinal studies incorporating objective exposure tracking and ocular-surface testing would help clarify causality and refine targeted interventions

1. Sheppard AL, Wolffsohn JS. Digital eye strain: prevalence, measurement and amelioration. BMJ Open Ophthalmol. 2018 Apr 16;3(1):e000146. doi:10.1136/bmjophth-2018-000146. PMID:29963645.
2. Rosenfield M. Computer vision syndrome: a review of ocular causes and potential treatments. Ophthalmic Physiol Opt. 2011 Sep;31(5):502-515. doi:10.1111/j.1475-1313.2011.00834.x. PMID:21480937.
3. Blehm C, Vishnu S, Khattak A, Mitra S, Yee RW. Computer vision syndrome: a review. Surv Ophthalmol. 2005 May-Jun;50(3):253-262. doi:10.1016/j.survophthal.2005.02.008. PMID:15850814.
4. Gowrisankaran S, Sheedy JE. Computer vision syndrome: A review. Work. 2015;52(2):303-314. doi:10.3233/WOR-152162. PMID:26519133.
5. Logaraj M, Madhupriya V, Hegde SK. Computer vision syndrome and associated factors among medical and engineering students in Chennai. Ann Med Health Sci Res. 2014 Mar;4(2):179-185. doi:10.4103/2141-9248.129028. PMID:24761234.
6. Mohan A, Sen P, Shah C, Jain E, Jain S. Prevalence and risk factor assessment of digital eye strain among children using online e-learning during the COVID-19 pandemic: Digital eye strain among kids (DESK study-1). Indian J Ophthalmol. 2021 Jan;69(1):140-144. doi:10.4103/ijo.IJO_2535_20. PMID:33323599.
7. Do CW, Chan LYL, Tse ACY, Cheung T, So BCL, Tang WC, Yu WY, Chu GCH, Szeto GPY, Lee RLT, Lee PH. Association between Time Spent on Smart Devices and Change in Refractive Error: A 1-Year Prospective Observational Study among Hong Kong Children and Adolescents. Int J Environ Res Public Health. 2020 Nov 30;17(23):8923. doi: 10.3390/ijerph17238923. PMID: 33266282; PMCID: PMC7730324.
8. Mohan A, Sen P, Mujumdar D, Shah C, Jain E. Series of cases of acute acquired comitant esotropia in children associated with excessive online classes on smartphone during COVID-19 pandemic; digital eye strain among kids (DESK) study-3. Strabismus. 2021 Sep;29(3):163-167. doi: 10.1080/09273972.2021.1948072. Epub 2021 Jul 5. PMID: 34223812.
9. Golebiowski B, Long J, Harrison K, Lee A, Chidi-Egboka N, Asper L. Smartphone use and effects on tear film, blinking and binocular vision. Curr Eye Res. 2020 Apr;45(4):428-434. doi:10.1080/02713683.2019.1663542. PMID:31573824.
10. Jaiswal S, Asper L, Long J, Lee A, Harrison K, Golebiowski B. Ocular and visual discomfort associated with smartphones, tablets and computers: what we do and do not know. Clin Exp Optom. 2019 Sep;102(5):463-477. doi: 10.1111/cxo.12851. Epub 2019 Jan 21. PMID: 30663136.
11. Zayed HAM, Saied SM, Younis EA, Atlam SA. Digital eye strain: prevalence and associated factors among information technology professionals, Egypt. Environ Sci Pollut Res Int. 2021 May;28(20):25187-25195. doi: 10.1007/s11356-021-12454-3. Epub 2021 Jan 16. PMID: 33454863.
12. Auffret É, Gomart G, Bourcier T, Gaucher D, Speeg-Schatz C, Sauer A. Perturbations oculaires secondaires à l’utilisation de supports numériques. Symptômes, prévalence, physiopathologie et prise en charge [Digital eye strain. Symptoms, prevalence, pathophysiology, and management]. J Fr Ophtalmol. 2021 Dec;44(10):1605-1610. French. doi: 10.1016/j.jfo.2020.10.002. Epub 2021 Oct 15. PMID: 34657757.