Risk factor assessment of digital eye strain during the COVID-19 pandemic: a cross-sectional survey

Application and interpretation of linear-regression analysis

Background

Linear-regression analysis is a well-known statistical technique that serves as a basis for understanding the relationships between variables. Its simplicity and interpretability render it the preferred choice in healthcare research, including vision science, as it enables researchers and practitioners to model and predict outcomes effectively. This article presents the fundamentals of linear-regression modeling and reviews the applications and interpretations of the main linear-regression analysis.

Methods

The primary objective of linear regression is to fit a linear equation to observed data, thus allowing one to predict and interpret the effects of predictor variables. A simple linear regression involves a single independent variable, whereas multiple linear regression includes multiple predictors. A linear-regression model is used to identify the general underlying pattern connecting independent and dependent variables, prove the relationship between these variables, and predict the dependent variables for a specified value of the independent variables. This review demonstrates the appropriate interpretation of linear-regression results using examples from publications in the field of vision science.

Results

Simple and multiple linear regressions are performed, with emphasis on the correct interpretation of standardized and unstandardized regression coefficients, the coefficient of determination, the method for variable selection, assumptions in linear regression, dummy variables, and sample size, along with common mistakes in reporting linear-regression analysis. Finally, a checklist is presented to the editors and peer reviewers for a systematic assessment of submissions that used linear-regression models.

Conclusions

Medical practitioners and researchers should acquire basic knowledge of linear-regression such that they can contribute meaningfully to the development of technology by accurately interpreting research outcomes. Incorrect use or interpretation of appropriate linear-regression models may result in inaccurate results. Appointing an expert statistician in an interdisciplinary research team may offer added value to the study design by preventing overstated results.

Digital Eye Strain: Updated Perspectives

Purpose

Digital eye strain (DES) is a growing worldwide concern because digital devices are prevalent in both our work and home lives. The purpose of this review was to summarize clinically relevant and evolving key topics related to DES.

Methods

A PubMed.gov search was conducted on or before June 8, 2024. No date restrictions were used during the primary search. The search was aimed at detecting all articles related to DES; thus, the search terms only included "digital eye strain" or "computer vision syndrome".

Results

The two most used, validated DES questionnaires are the Computer Vision Syndrome Questionnaire (CVS-Q) and Computer Vision Symptom Scale (CVSS17). The world-wide prevalence of DES ranges from 8.2% to 100% depending upon the subjects evaluated and the method used to evaluate them. The most common DES symptoms include headache, eye strain, eye redness, eye itching, tearing, photophobia, burning sensation, blurred vision, eye pain, neck and shoulder pain, and eye dryness. Ocular surface symptoms in DES are integrally tied to decreased blink frequency, which causes ocular surface desiccation, increased osmolarity, and dry eye-like symptoms. The most studied DES-specific treatments are improving subjects' environment, artificial tears, blinking exercises, and near work breaks.

Conclusion

DES is a highly prevalent condition that should be regularly screened for in clinic with a validated diagnosis instrument. While there are several treatment options, the community's treatment approach is evolving and primarily focused on treating the visual and dry eye-like symptoms associated with the condition.

COVID-19 and children's eyes

Letter

Prevalence of computer vision syndrome: A systematic review and meta-analysis

PURPOSE

This review aimed to estimate the prevalence of computer vision syndrome (CVS) in the general population and subgroups.

METHODS

A search was conducted in the following the databases: PubMed, SCOPUS, EMBASE, and Web of Science until February 13, 2023. We included studies that assessed the prevalence of CVS in any population. The Joanna Briggs Institute's critical appraisal tool was used to evaluate the methodological quality. A meta-analysis of the prevalence of CVS was done using a random-effects model, assessing the sources of heterogeneity using subgroup and meta-regression analyses.

RESULTS

A total of 103 cross-sectional studies with 66 577 participants were included. The prevalence of CVS was 69.0% (95% CI: 62.3 to 75.3; I2: 99.7%), ranging from 12.1 to 97.3% across studies. Point prevalence was higher in women than in men (71.4 vs. 61.8%), university students (76.1%), Africa (71.2%), Asia (69.9%), contact lens wearers (73.1% vs. 63.8%) in studies conducted before the COVID-19 pandemic (72.8%), and in those that did not use the CVS-Q questionnaire (75.4%). In meta-regression, using the CVS-Q scale was associated with a lower prevalence of CVS.

CONCLUSION

Seven out of ten people suffer from CVS. Preventive strategies and interventions are needed to decrease the prevalence of this condition which can affect productivity and quality of life. Future studies should standardize a definition of CVS.

Association of eye strain with dry eye and retinal thickness

PURPOSE

The purpose of this cohort study was to investigate the association between the prevalence of abnormal ocular examination results and the common visual symptoms of eye strain, blurred vision and photophobia.

METHODS

Consecutive first-visit outpatients with best-corrected visual acuity better than 20/30 in both eyes were enrolled and those with a history of intra-ocular lens implantation and glaucoma were excluded. Dry eye-related examinations and retinal thickness measurement were conducted. The odds ratio (OR) was calculated with logistic regression analyses of ocular data in relation to the presence of visual symptoms.

RESULTS

A total of 6078 patients (3920 women, mean age 49.0 ± 20.4 years) were analyzed. The prevalence of each symptom was 31.8% for eye strain, 22.5% for blurred vision and 16.0% for photophobia. A significant risk factor for eye strain was short tear break-up time (TBUT) (OR 1.88), superficial punctate keratitis (SPK) (OR 1.44), and thickness of ganglion cell complex (GCC) (OR 1.30). Risk factors for blurred vision were short TBUT (OR 1.85), SPK (OR 1.24) and GCC (OR 0.59). Risk factors for photophobia were short TBUT (OR 1.77) and SPK (OR 1.32). Schirmer test value, peripapillary nerve fiber layer thickness and full macular thickness were not associated with the tested symptoms.

CONCLUSION

The current study successfully identified female gender, short TBUT, and SPK as significant risk factors for eye strain, blurred vision, and photophobia with considerable ORs.