International Journal of Environmental Research and Public Health · 5 authors, 1 centre
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This exploratory study used webcam-based blink detection software in 11 young adults to investigate if average blinking rate during computer work is associated with computer vision syndrome (CVS) severity. The main finding was a significant negative correlation, where a higher average blink rate was a significant predictor of lower CVS severity. The primary limitation is the very small sample size and lack of data on computer usage hours, making the findings preliminary.
The study aimed to determine if real-time blink rate data collected via a computer webcam in real-life settings could predict computer vision syndrome (CVS). The background notes that decreased blink rate during computer use is a known risk factor for CVS, but unobtrusive real-time detection systems are lacking. Methods involved 13 initially enrolled students, with 11 completing the study. Participants used webcam monitoring software during scientific writing tasks, and their blinking rate was measured. CVS was assessed using the CVS-Q questionnaire (score ≥6 indicated CVS). Statistical analysis included correlation and a regression model. Key results showed a significant negative correlation between average blinking rate and CVS severity (r = −0.781, p = 0.005). The regression model (R2 = 0.876) found average blinking rate to be the only significant predictor of CVS (B = −1.26, p = 0.019), meaning each additional blink was associated with a 1.26-point decrease in CVS score. Age and gender were not significant predictors. Limitations include the very small sample size (n=11), gender imbalance (81.8% female), and lack of objective data on participants' actual computer usage duration and schedule. Implications suggest blink rate could be a viable real-time indicator for CVS, warranting future research with larger samples to develop predictive algorithms and intervention systems.
