Abstract
Purpose:
To evaluate changes in ocular surface parameters, contrast sensitivity (CS), and higher-order aberrations (HOAs) in patients with computer vision syndrome (CVS)/digital eye strain (DES), and to assess associations among these factors.
Design:
Prospective observational case–control study.
Methods:
Both eyes of 55 CVS cases (CVS-Q score ≥ 6) and 55 age-matched controls (18–40 years) with best-corrected visual acuity (BCVA) of 6/6 were enrolled. Participants with ocular comorbidities were excluded. Central and peripheral CS were assessed using the Pelli-Robson (PR) chart and Spaeth-Richman contrast sensitivity test (SPARCS). Dry eye disease (DED) workup was performed. HOAs were measured using a wavefront aberrometer (iDesign® Refractive Studio, Johnson and Johnson Surgical Vision, Inc.).
Results:
PR scores were identical across groups. SPARCS revealed significantly reduced total CS scores (central and peripheral) in the CVS group (W = 3641.000, P < 0.001), highlighting its sensitivity to subtle CS changes. The CVS group showed significantly higher HOA values (%), root mean square HOA error (P < 0.001), and spherical aberration (P = 0.033), along with more severe DED findings.
Conclusion:
CVS/DES leads to impaired visual function due to reduced CS, increased HOAs, and dry eye. Assessment of both central and peripheral CS provides a more complete understanding of its impact on visual quality and daily functioning. Early ergonomic interventions and preventive strategies are vital for protecting ocular health in an increasingly digital world.
Keywords: Computer vision syndrome, contrast sensitivity, dry eye disease, higher-order aberration, SPARCS
In today’s rapidly evolving, technology-dependent world, digital devices have become indispensable in both personal and professional life. The widespread adoption of remote and the expansion of online learning have substantially increased cumulative screen time.
Computer vision syndrome (CVS), also known as digital eye strain (DES), encompasses a range of ocular and vision-related issues caused by prolonged use of digital devices. Multiple systematic reviews estimate the pooled prevalence of CVS among digital device users to be between 66% and 74%.[1,2,3] As digital technology becomes increasingly unavoidable, it is essential to deepen our understanding of the multifaceted visual dysfunctions associated with CVS/DES. One of the emerging parameters affected is contrast sensitivity (CS)—the ability to detect subtle differences between objects and their background. CS serves as a sensitive indicator of subtle vision loss and overall visual function.
Ocular surface disturbances, tear-film instability, alterations in tear volume and composition, increased oxidative stress, and ocular surface inflammation all contribute to dry eye disease (DED) in computer users. A meta-analysis involving 11,365 computer users estimated the prevalence of DED at 49.5%, with a range from 9.5% to 87.5%.[4]
Additionally, tear-film irregularity can lead to higher-order aberrations (HOAs)—optical imperfections that distort the retinal image, resulting in halos, glare, and reduced CS. These impairments collectively impact visual performance and daily activities. This study compared central and peripheral CS and HOAs in patients with CVS/DES and control subjects and evaluated the correlation between CS and HOAs in individuals with CVS.
Methods
This observational case–control study enrolled 55 subjects with CVS/DES and 55 healthy individuals without ocular disease from the outpatient services of a multi-specialty tertiary care institute in North India. The study was registered with the Clinical Trials Registry of India prior to the enrollment of the first participant (CTRI/2024/06/069496) and was conducted in accordance with the Declaration of Helsinki.
The study population included individuals aged 18 to 40 years, of either gender, with best-corrected visual acuity (BCVA) of 6/6 and N6, presenting with signs and symptoms suggestive of CVS/DES and prolonged screen time.
To minimize confounders affecting CS, individuals with a history of incisional or laser eye surgery, ocular trauma, glaucoma, contact lens use, anisometropia, or significant refractive errors (>6D of myopia, 4D of hyperopia, or 4D of astigmatism) were excluded from the study. Fig. 1 illustrates the CONSORT flow diagram of this study.
Figure 1.
Consort flow diagram (Original)
The ocular examination included measurement of uncorrected visual acuity (UCVA) and BCVA using a Snellen chart at 6 meters. BCVA was recorded on the Snellen chart and converted to logMAR for analysis. A thorough slit-lamp examination was conducted for both anterior and posterior segments after pharmacological dilation.
Patients presenting with signs and symptoms suggestive of CVS/DES and prolonged screen time were administered the CVS-Questionnaire (CVS-Q) developed by Seguí et al.[5] Participants with CVS-Q scores ≥6 (N = 55) were included in the case group, while those scoring <6 (N = 55) were placed in the control group. The CVS-Q assesses the frequency and intensity of 16 ocular and visual complaints associated with digital screen usage. The scores range from 0 to 24. A total score ≥6 identifies a person as having CVS. Patients with CVS were further classified as having mild, moderate, or severe CVS: scores of 6–12 were classified as mild, 13–18 as moderate, and >19 as severe.
All participants completed the Dry Eye Questionnaire (DEQ-5), which consists of five questions: frequency of watery eyes, discomfort, and dryness (scored 0–4), and late-day discomfort and dryness intensity (scored 0–5). The maximum score is 22. DEQ-5 helps differentiate between dry eye and non-dry eye status. Scores <6 were considered asymptomatic, 6–11 as mild to moderate, and >12 as severe dry eye.
Both eyes of each participant underwent DED evaluation using Schirmer’s test I, tear breakup time (TBUT), and the Oxford Ocular Surface Staining Score. DED severity was classified based on the DEWS 2007 classification system.[6]
CS was evaluated using the Spaeth-Richman contrast sensitivity test (SPARCS) and the Pelli-Robson (PR) test. SPARCS was accessed via https://www.sparcscontrastcenter.com, where each patient received a unique identification number. Details of the test are furnished elsewhere.[7] The contrast value was calculated using Weber contrast. The central area and four peripheral areas each received separate scores. The log-based score for each of the five testing areas is scaled out of 20, making a maximum SPARCS score of 100. CS was also measured using the PR chart, which uses a “letter-by-letter” scoring system. Each correctly identified letter scores 0.05 log units (except for the first triplet, which has 100% contrast). Testing concludes when the patient fails to identify two of the three letters in a triplet.[8]
HOAs were assessed in both eyes using the iDesign® Refractive Studio (Johnson and Johnson Surgical Vision, Inc.), a topo-integrated wavefront-guided technology. The patient was asked to fixate straight ahead on a fixation target during the three-second scan. Five measurements were taken in a single, one-click capture sequence, collecting around 1,200 data points. Calculations were performed for a 4.0 mm central optical zone. HOA%, root mean square (RMS) HOA, and values for spherical aberration (Z40), trefoil 30-degree (Z3-3), trefoil 0-degree (Z3+3), vertical coma (Z3-1), and horizontal coma (Z3+1) were recorded from the Zernike polynomials.
Patient data were coded and recorded in an MS Excel spreadsheet. SPSS v23 (IBM Corp.) was used for data analysis. The association between two categorical variables was explored using the Chi-squared test. Associations between a continuous and a categorical variable (with two categories) were explored using the independent sample t-test. If data were found to be non-normally distributed, appropriate non-parametric tests (Wilcoxon Mann–Whitney U test or Kruskal–Wallis test) were used. Linear correlations between two continuous variables were assessed using Pearson’s correlation (if the data were normally distributed) or Spearman’s correlation (if not). Statistical significance was set at P < 0.05.
Results
Baseline demographic data are presented in Table 1. The mean spherical equivalent (SE) was −1.25 ± 0.5 D in the CVS group and −1.00 ± 0.25 D in the control group. The refractive distribution in both groups clustered around low myopia, which is consistent with the age range studied (18–40 years), where myopia is increasingly prevalent. Clinically negligible inter-group difference and the exclusion of high ametropia ensured that refractive error did not confound differences in ocular surface parameters, CS, or HOAs. As all eyes were corrected to 6/6 before testing, uncorrected or inadequately corrected refractive errors were not a source of functional visual impairment in this study.
Table 1.
Summary of baseline characteristics
| Parameters | Group |
P | ||||
|---|---|---|---|---|---|---|
| Case (Mean±SD) n=55 (%) | Control (Mean±SD) n=55 (%) | |||||
| Age (years) | 27.56±6.19 | 26.62±5.69 | 0.3511 | |||
| Age | 0.0802 | |||||
| 18–30 years | 35 (63.6%) | 41 (74.5%) | ||||
| 31–40 years | 20 (36.4%) | 14 (25.5%) | ||||
| Gender | 0.4162 | |||||
| Male | 29 (52.7%) | 32 (58.2%) | ||||
| Female | 26 (47.3%) | 23 (41.8%) | ||||
| Occupation | 0.0302 | |||||
| Student | 20 (36.4%) | 28 (50.9%) | ||||
| Professional | 35 (63.6%) | 27 (49.1%) | ||||
| Number of hours of computer work | 7.73±2.37 | 3.65±2.25 | <0.0011 | |||
| Number of hours of mobile work | 2.51±0.94 | 2.67±1.07 | 0.2541 | |||
| Total number of hours of digital work | 10.24±2.28 | 6.38±2.42 | <0.0011 | |||
1Wilcoxon–Mann–Whitney U Test, 2Chi-squared test
In the cases, approximately one-third of participants reported headache (36.4%; 20/55) and eyestrain (34.5%; 19/55). Twelve participants (21.8%) experienced blurring of vision, 14.5% (8/55) reported a feeling of heaviness, 12.7% (7/55) had watering, 9.1% (5/55) experienced foreign body sensation, 7.3% (4/55) complained of irritation, 7.3% (4/55) reported dryness, and only two (3.6%) had a complaint of redness.
In the controls, 36.4% (20/55) of participants also reported headache, while 25.5% (14/55) experienced eyestrain, and 21.8% (12/55) had blurring of vision. Additionally, 14.5% (8/55) experienced heaviness, 7.3% (4/55) reported watering, 5.5% (3/55) had irritation, and only one participant (1.8%) reported foreign body sensation or redness.
A statistically significant difference between the two groups was observed in the distribution of foreign body sensation (P = 0.018) and dryness (P = 0.007).
The mean (SD) CVS-Q score in cases was 11.22 (2.75), compared to 3.56 (1.30) in the controls, indicating a significant difference (W = 12100.000, P < 0.001), with a strong effect size of 0.87 (large effect). Patients with CVS were further classified based on severity: approximately two-thirds (36/55; 65.5%) had mild disease, while the remaining (19/55; 34.5%) had moderate CVS.
Significant differences were observed between cases and controls in TBUT severity grading, Oxford ocular surface staining scores, and overall DED severity, with all comparisons reaching statistical significance (P < 0.001). A summary of DED severity assessments in the case and control groups is provided in Table 2.
Table 2.
Dry eye disease severity assessment in case and control groups (n=220)
| Group |
χ 2 | Chi-squared test, P | ||||||
|---|---|---|---|---|---|---|---|---|
| Case (110) | Control (110) | |||||||
| DEQ-5 score | ||||||||
| ≤6 (Asymptomatic) | 36 (32.7%) | 88 (80.0%) | 49.973 | <0.001 | ||||
| 6–11 (mild to moderate) | 74 (67.3%) | 22 (20.0%) | ||||||
| Schirmer 1 severity grading | ||||||||
| >10 mm (normal) | 80 (72.7%) | 108 (98.2%) | 28.837 | <0.001 | ||||
| >7–9 mm (mild) | 22 (20.0%) | 2 (1.8%) | ||||||
| 5–7 mm (moderate) | 8 (7.3%) | 0 (0.0%) | ||||||
| TBUT severity grading | ||||||||
| >10 seconds (normal) | 9 (8.2%) | 80 (72.7%) | 117.578 | <0.001 | ||||
| >7–9 seconds (mild) | 32 (29.1%) | 27 (24.5%) | ||||||
| 5–7 seconds (moderate) | 67 (60.9%) | 3 (2.7%) | ||||||
| <5 seconds (severe) | 2 (1.8%) | 0 (0.0%) | ||||||
| Oxford score grading | ||||||||
| Absent | 43 (39.1%) | 98 (89.1%) | 60.802 | <0.001 | ||||
| Minimal | 57 (51.8%) | 12 (10.9%) | ||||||
| Mild | 10 (9.1%) | 0 (0.0%) | ||||||
| Dry eye disease severity | ||||||||
| Normal | 9 (8.2%) | 80 (72.7%) | 117.564 | <0.001 | ||||
| Mild | 32 (29.1%) | 27 (24.5%) | ||||||
| Moderate | 69 (62.7%) | 3 (2.7%) | ||||||
DEQ-5:Dry Eye Questionnaire, TBUT: Tear-film breakup time
Table 3 presents SPARCS scores (total and quadrant-wise) for cases and controls. A significant difference was observed in total SPARCS scores between the two groups (W = 3641.000, P < 0.001), with the control group demonstrating a higher median total score. The mean PR scores were 2.30 in both groups, showing no statistically significant difference.
Table 3.
SPARCS score (total and quadrant-wise) in the cases and control groups
| Group | Wilcoxon–Mann–Whitney U test | |||||||
|---|---|---|---|---|---|---|---|---|
|
|
|
|||||||
| Case | Control | W | P | |||||
| Total SPARCS | ||||||||
| Mean±SD (Min–Max) | 81.79±5.66 (58–95) | 85.39±4.54 (72–100) | 3641.000 | <0.001 | ||||
| UL quadrant score | ||||||||
| Mean±SD (Min–Max) | 16.78±2.12 (5.1–20) | 17.21±1.34 (14.87–20) | 5487.500 | 0.193 | ||||
| UR quadrant score | ||||||||
| Mean±SD (Min–Max) | 16.72±1.71 (9.98–20) | 17.46±1.43 (13.37–20) | 4294.000 | <0.001 | ||||
| CC quadrant score | ||||||||
| Mean±SD (Min–Max) | 15.65±1.81 (11.12–20) | 16.32±1.79 (12.3–20) | 4797.000 | 0.006 | ||||
| LL quadrant score | ||||||||
| Mean±SD (Min–Max) | 16.16±2.20 (3.78–20) | 17.15±1.33 (14.87–20) | 4161.000 | <0.001 | ||||
| LR quadrant score | ||||||||
| Mean±SD (Min–Max) | 16.41±1.55 (11.12–20) | 17.17±1.34 (13.37–20) | 4321.500 | <0.001 | ||||
SPARCS: Spaeth and Richman contrast sensitivity test, UL: Upper left, UR: Upper right, CC: Central, LL: Lower left, LR: Lower right
Table 4 shows HOA measurements for both groups. A significant difference in HOA% was observed (W = 10629.000, P < 0.001), with the case group showing a higher median HOA%. There was a considerable difference between the cases and controls in terms of RMS HOA error (µ) (W = 7708.000, P < 0.001) and spherical aberration (t = 2.143, P = 0.033), with the mean values being highest in the cases. But on correlation of central and peripheral CS with HOAs, no significant explainable correlation was found in our study.
Table 4.
HOA measured using iDesign in the cases and control groups
| Group | Wilcoxon–Mann–Whitney U test | |||||||
|---|---|---|---|---|---|---|---|---|
|
|
|
|||||||
| Case | Control | W | P | |||||
| HOA% | ||||||||
| Mean±SD (Min–Max) | 32.80±18.19 (9.1–95.4) | 12.43±11.27 (1.5–60.1) | 10629.000 | <0.001 | ||||
| RMS HOA Error (μ) | ||||||||
| Mean±SD (Min–Max) | 0.14±0.07 (0.07–0.54) | 0.12±0.05 (0.06–0.4) | 7708.000 | <0.001 | ||||
| Trefoil 30 degree | ||||||||
| Mean±SD (Min–Max) | −0.0408±0.0674 (−0.21–0.35) | −0.0306±0.0433 (−0.22–0.09) | 5272.500 | 0.100 | ||||
| Vertical COMA | ||||||||
| Mean±SD (Min–Max) | −0.0072±0.0737 (−0.21−0.31) | 0.0160±0.0539 (−0.16–0.21) | 4615.000 | 0.002 | ||||
| Horizontal COMA | ||||||||
| Mean±SD (Min–Max) | 0.0050±0.0461 (−0.17–0.2) | 0.004±0.0617 (−0.1–0.55) | 6216.000 | 0.726 | ||||
| Trefoil O degree | ||||||||
| Mean±SD (Min–Max) | −0.0046±0.0366 (−0.1–0.07) | −0.0013±0.0320 (−0.13–0.07) | 5712.000 | 0.475 | ||||
| Tetrafoil | ||||||||
| Mean±SD (Min–Max) | 0.0212±0.015 (0–0.09) | 0.0200±0.0151 (0–0.09) | 6373.500 | 0.494 | ||||
| Spherical aberration | ||||||||
| Mean±SD (Min–Max) | 0.0137±0.0367 (−0.09–0.11) | 0.0041±0.0296 (−0.08–0.09) | 2.143 | 0.033 | ||||
HOA%: Higher-order aberration%, RMS HOA error: Root mean square higher-order aberration error
Table 5 summarizes the correlation between DED severity and other variables in the case group.
Table 5.
Correlation of DED severity with other variables in the case group
| Dry eye disease severity |
χ 2 | P | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Normal | Mild | Moderate | Total | |||||||||
| Gender | ||||||||||||
| Male | 2 (22.2%) | 23 (71.9%) | 33 (47.8%) | 58 (52.7%) | 8.732 | 0.011 | ||||||
| Female | 7 (77.8%) | 9 (28.1%) | 36 (52.2%) | 52 (47.3%) | ||||||||
| Presenting primary complaint: headache | ||||||||||||
| Yes | 4 (44.4%) | 6 (18.8%) | 30 (43.5%) | 40 (36.4%) | 6.053 | 0.048 | ||||||
| No | 5 (55.6%) | 26 (81.2%) | 39 (56.5%) | 70 (63.6%) | ||||||||
| Presenting primary complaint: heaviness | ||||||||||||
| Yes | 2 (22.2%) | 0 (0.0%) | 14 (20.3%) | 16 (14.5%) | 7.705 | 0.007 | ||||||
| No | 7 (77.8%) | 32 (100.0%) | 55 (79.7%) | 94 (85.5%) | ||||||||
| Number of hours of computer work | ||||||||||||
| Mean±SD (Min–Max) | 6.44±1.42 (5–8) | 7.16±2.14 (4–11) | 8.16±2.47 (2–12) | 9.554 | 0.008 | |||||||
| Total number of hours of digital work | ||||||||||||
| Mean±SD (Min–Max) | 9.11±±1.05 (8–10) | 9.50±2.05 (6–14) | 10.72±2.38 (6–18) | 11.899 | 0.003 | |||||||
| CVS-Q score | ||||||||||||
| Mean±SD (Min–Max) | 9.11±1.76 (8–12) | 10.31±2.33 (8–14) | 11.91±2.79 (8–16) | 13.111 | 0.001 | |||||||
| CVS severity | ||||||||||||
| Mild | 9 (100.0%) | 24 (75.0%) | 39 (56.5%) | 72 (65.5%) | 8.474 | 0.014 | ||||||
| Moderate | 0 (0.0%) | 8 (25.0%) | 30 (43.5%) | 38 (34.5%) | ||||||||
| Total SPARCS | ||||||||||||
| Mean±SD (Min–Max) | 84.33±5.77 (72–91) | 83.59±7.36 (58–95) | 80.62±4.35 (63–92) | 14.166 | 0.001 | |||||||
| HOA (%) by iDesign | ||||||||||||
| Mean±SD (Min–Max) | 25.18±11.67 (13.9–51.6) | 28.48±14.96 (9.3–60) | 36.90±19.88 (9.1–95.4) | 8.830 | 0.012 | |||||||
CVS-Q: Computer vision syndrome-questionnaire, SPARCS: Spaeth and Richman contrast sensitivity test, HOA%: Higher-order aberration%
Discussion
Individuals are prone to experience both physical and psychological challenges due to prolonged screen time. In our study, the mean participant age was 27.56 ± 6.19 years in the CVS/DES group and 26.62 ± 5.69 years in the control group. The majority of affected individuals (63.6%) were aged 18–30 years, reflecting higher screen time among younger adults for both occupational and recreational purposes, often involving multiple devices. Our study had only a marginal male preponderance, with 52.7% in the case group and 58.2% in the control group. This contrasts with findings by Ccami-Bernal et al.,[2] who reported a higher CVS prevalence in females (71.4%) compared to males (61.8%). This difference may reflect occupational demographics in India, where there are higher number of male employees.
The literature provides minimal guidance regarding the operational definition of CVS, despite the fact that patient symptoms are relatively consistent. We used the CVS-Q to establish CVS, as it is a pretested, verified, and validated tool for diagnosing DES. It uses a symptom severity scale that fits the Rasch rating scale model well.[5]
The most common primary complaints reported by participants with CVS were headache (36.4%) and eyestrain (34.5%), followed by blurred vision (21.8%) and heaviness of eyes (14.5%).
Undiagnosed refractive error, eye strain, or prolonged exposure to bright light can all cause headaches. Excessive stress on the muscles of accommodation—which are unable to completely relax at the typical viewing distances of digital gadgets—may lead to eye pain. Our findings are consistent with previous literature. Moore et al.[9] reported dryness, headache, and itching as the most common symptoms. Adane et al.[10] highlighted blurred vision, ocular fatigue, and watering; while Patel et al.[11] noted headache in 73.4% of cases. The percentage of self-reported dryness was 7.3% in our study. All 110 participants reported at least one symptom of CVS/DES.
DED in digital device users often begins with reduced and incomplete blinking, leading to tear-film instability, excessive evaporation, and ocular surface exposure.[12] The dry eye questionnaire (DEQ-5), Schirmer I test, TBUT, and Oxford Staining Score were used to assess DED in participants, who were further categorized into mild, moderate, and severe DED as per the Dry Eye Workshop Society (DEWS) 2007 criteria.[6]
A total of 67.3% of individuals with CVS had a mild to moderate DEQ-5 score (6–11), whereas the majority of controls (56.4%) reported an asymptomatic score (<6). This aligns with findings by Moore et al.,[9] where the median DEQ-5 score was 8, and 61.8% of participants had scores above six.
Schirmer I test results were normal (i.e., >10 seconds) in the majority of both cases (72.7%) and controls (98.2%). The TBUT score was moderate (5–7 seconds) in the majority of the case group (60.9%), followed by a mild score (7–9 seconds) in 29.1%, while the majority of the control group (72.7%) had a normal score (>10 seconds). This suggests that DED associated with digital device use is of the evaporative type rather than the aqueous-deficient type, aligning with previous studies.[13,14,15]
The Oxford Staining Score was minimal in the majority of cases (51.8%) and was absent in the control group (89.1%). The majority of patients with CVS had moderate DED (62.7%), followed by mild DED (29.1%). None of the patients had severe DED.
DED severity showed a positive association with screen time, with longer daily exposure in the moderate DED subgroup, consistent with prior large-scale studies (Inomata et al., OSAKA study).[16,17]
There was a noteworthy difference between the various groups of DED severity in terms of the CVS-Q score, with the mean being highest in the moderate DED group, indicating that DED has a significant impact on the symptomatology of CVS/DES.
The tear film plays a crucial role in maintaining normal corneal curvature and establishing a high-quality refractive surface. In DED, tear-film irregularities disrupt the ocular surface’s refractive uniformity, distorting the incoming wavefront and contributing to HOAs, which, despite being a smaller fraction of total aberrations, significantly degrade visual quality. There was a considerable difference between the case and control groups in terms of HOA (%), root mean square (RMS) HOA error (µ), and spherical aberration, as measured by the iDesign Aberrometer, with mean values being higher in the case group. Quantifying HOAs as a percentage provides an intuitive, comparative measure of optical degradation. Spherical aberrations occur due to the distortion of the central corneal surface. The elevated values of spherical aberration observed in CVS patients in our study may be attributed to the fact that the majority were desktop computer users. Primary gaze positioning during desktop use may promote central tear-film disruption and evaporation, exacerbated by wider palpebral fissures and reduced blink rate and amplitude. There was a significant difference between the various DED severity groups in terms of HOA (%), with the mean HOA (%) being highest in the moderate DED severity group. This finding aligns with the conclusions of a systematic review by Rhee et al.[18] on the association between tear-film analysis and HOAs in DED. Of the 12 studies examined, eight reported a significant increase in HOAs in DED patients, correlating with disease severity.
In our study, there was a negative correlation between HOA (%) and TBUT. Himebaugh et al.[19] demonstrated that increased HOAs are associated with scatter-producing microaberrations that correspond to tear breakup regions, contributing to image degradation.
In our study, PR chart results were uniformly 2.30 across all participants, showing no discriminatory ability between groups, whereas SPARCS detected significant inter-group differences. Mean total SPARCS scores were significantly lower in the CVS group (81.79 ± 5.66) compared to controls (85.39 ± 4.54), highlighting reduced CS in affected individuals.
A key strength of the SPARCS tool is its ability to detect subtle differences in CS, including peripheral CS across four quadrants. In our study, a significant difference in CS was noted in the lower right, upper right, central, and lower left quadrants, with lower mean values in the affected group. Although no difference was found in the upper left quadrant, the affected group still had a lower mean score.
Talens-Estarelles et al.[20] analyzed visual function, optical quality, and tear-film stability in desktop screen users. They reported a significant reduction in both photopic and mesopic CS at several spatial frequencies among screen users, while visual acuity remained unchanged.
Tear-film instability also contributes to reductions in visual acuity and CS. In our study, total SPARCS scores significantly differed across DED severity groups, with the lowest scores observed in the moderate DED group. This supports the findings of Tutt et al.,[21] who showed that image contrast can decrease by 20% to 40% within 60 seconds of non-blinking due to tear-film breakup, especially in the low spatial frequency range. Rolando et al.[22] also demonstrated that tear-film dysfunction negatively affects the modulation transfer function of the ocular surface, reducing CS.
Conclusion
These findings emphasize the importance of assessing CS as a functional visual parameter in patients with CVS and DED. SPARCS’s capacity to detect nuanced differences in CS, especially in the peripheral field, makes it a valuable tool for evaluating the impact of ocular surface disorders on visual function.
The absence of correlation between CS and HOAs in our study suggests multifactorial causes of visual impairment in CVS/DES. Other factors, such as retinal damage from blue light exposure, may play a role.
Further research is needed to validate these findings and expand our understanding of how digital screens affect visual function and quality of vision. CVS/DED significantly impacts visual quality through its association with DED, increased HOAs, and reduced CS. Timely interventions, ergonomic adjustments, and preventive strategies are essential to mitigate long-term effects and preserve ocular health in the digital age.
Conflicts of interest:
There are no conflicts of interest.
Funding Statement
Nil.
References
- 1.Anbesu EW, Lema AK. Prevalence of computer vision syndrome: A systematic review and meta-analysis. Sci Rep. 2023;13:1801. doi: 10.1038/s41598-023-28750-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Ccami-Bernal F, Soriano-Moreno DR, Romero-Robles MA, Barriga-Chambi F, Tuco KG, Castro-Diaz SD, et al. Prevalence of computer vision syndrome: A systematic review and meta-analysis. J Optom. 2024;17:100482. doi: 10.1016/j.optom.2023.100482. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.León-Figueroa DA, Barboza JJ, Siddiq A, Sah R, Valladares-Garrido MJ, Adhikari S, et al. Prevalence of computer vision syndrome during the COVID-19 pandemic: A systematic review and meta-analysis. BMC Public Health. 2024;24:640. doi: 10.1186/s12889-024-17636-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Courtin R, Pereira B, Naughton G, Chamoux A, Chiambaretta F, Lanhers C, et al. Prevalence of dry eye disease in visual display terminal workers: A systematic review and meta-analysis. BMJ Open. 2016;6:e009675. doi: 10.1136/bmjopen-2015-009675. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Seguí Mdel M, Cabrero-García J, Crespo A, Verdú J, Ronda E. A reliable and valid questionnaire was developed to measure computer vision syndrome at the workplace. J Clin Epidemiol. 2015;68:662–73. doi: 10.1016/j.jclinepi.2015.01.015. [DOI] [PubMed] [Google Scholar]
- 6.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. [DOI] [PubMed] [Google Scholar]
- 7.SPARCS Spaeth/Richman Contrast Sensitivity Test Center. 2020. Available from: www.sparcscontrastcenter.com/home.php . [Last accessed on 2025 Apr 21]
- 8.Pelli DG, Robson JG, Wilkins AJ. The design of a new letter chart for measuring contrast sensitivity. Clin Vis Sci. 1988;2:187–99. [Google Scholar]
- 9.Moore PA, Wolffsohn JS, Sheppard AL. Digital eye strain and its impact on working adults in the UK and Ireland. Cont Lens Anterior Eye. 2024;47:102176. doi: 10.1016/j.clae.2024.102176. [DOI] [PubMed] [Google Scholar]
- 10.Adane F, Alamneh YM, Desta M. Computer vision syndrome and predictors among computer users in Ethiopia: A systematic review and meta-analysis. Trop Med Health. 2022;50:26. doi: 10.1186/s41182-022-00418-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Patel H, Jain R, Kamath MA, Bappal A. A study on correlation of computer vision syndrome and dry eye disease and knowledge regarding its associated factors amongst health professionals. Indian J Ophthalmol. 2023;71:1441–5. doi: 10.4103/IJO.IJO_2776_22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Stapleton F, Alves M, Bunya VY, Jalbert I, Lekhanont K, Malet F, et al. TFOS DEWS II epidemiology report. Ocul Surf. 2017;15:334–65. doi: 10.1016/j.jtos.2017.05.003. [DOI] [PubMed] [Google Scholar]
- 13.Wolffsohn JS, Wang MTM, Vidal-Rohr M, Menduni F, Dhallu S, Ipek T, et al. Demographic and lifestyle risk factors of dry eye disease subtypes: A cross-sectional study. Ocul Surf. 2021;21:58–63. doi: 10.1016/j.jtos.2021.05.001. [DOI] [PubMed] [Google Scholar]
- 14.Cardona G, García C, Serés C, Vilaseca M, Gispets J. Blink rate, blink amplitude, and tear film integrity during dynamic visual display terminal tasks. Curr Eye Res. 2011;36:190–7. doi: 10.3109/02713683.2010.544442. [DOI] [PubMed] [Google Scholar]
- 15.Akkaya S, Atakan T, Acikalin B, Aksoy S, Ozkurt Y. Effects of long-term computer use on eye dryness. North Clin Istanb. 2018;5:319–22. doi: 10.14744/nci.2017.54036. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Inomata T, Iwagami M, Nakamura M, Shiang T, Yoshimura Y, Fujimoto K, 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. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Uchino M, Yokoi N, Uchino Y, Dogru M, Kawashima M, Komuro A, 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. [DOI] [PubMed] [Google Scholar]
- 18.Rhee J, Chan TC, Chow SS, Di Zazzo A, Inomata T, Shih KC, et al. A systematic review on the association between tear film metrics and higher order aberrations in dry eye disease and treatment. Ophthalmol Ther. 2022;11:35–67. doi: 10.1007/s40123-021-00419-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Himebaugh NL, Nam J, Bradley A, Liu H, Thibos LN, Begley CG. Scale and spatial distribution of aberrations associated with tear breakup. Optom Vis Sci. 2012;89:1590–600. doi: 10.1097/OPX.0b013e31826cfae5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Talens-Estarelles C, Mechó-García M, McAlinden C, Cerviño A, García-Lázaro S, González-Méijome JM. Changes in visual function and optical and tear film quality in computer users. Ophthalmic Physiol Opt. 2023;43:885–97. doi: 10.1111/opo.13147. [DOI] [PubMed] [Google Scholar]
- 21.Tutt R, Bradley A, Begley C, Thibos LN. Optical and visual impact of tear break-up in human eyes. Invest Ophthalmol Vis Sci. 2000;41:4117–23. [PubMed] [Google Scholar]
- 22.Rolando M, Iester M, Macrí A, Calabria G. Low spatial-contrast sensitivity in dry eyes. Cornea. 1998;17:376–9. doi: 10.1097/00003226-199807000-00006. [DOI] [PubMed] [Google Scholar]