Comparative Analysis of Blink Rates During Printed and On-Screen Reading Across Varying Screen Sizes
Bibliographic record
Abstract
Objective: To compare the blink rates during resting periods and while engaging in printed and on-screen reading across different digital screen dimensions.Material and Methods: This study involved thirty-two university students with normal vision, who were recorded during a 3-minute conversation to establish baseline blink rates and subsequently during four reading conditions. Participants read four passages under different conditions: printed text, smartphone, tablet, and computer screens. Video recordings were then analysed to quantify blink rates (blinks per minute, bpm) for each condition.Results: Blink rates significantly decreased in all reading scenarios compared to the baseline resting condition (p-value<0.05). Analysis via repeated measures ANOVA demonstrated significant differences in blink rates across all reading conditions (p-value<0.01). Pairwise comparisons revealed that blink rates during smartphone reading were notably lower than printed text, tablets, and computers (p-value<0.05). Conversely, blink rates exhibited no significant differences between printed text and tablet, printed text and computer, and computer and tablet readings (p-value>0.05).Conclusion: The study reveals a consistent decrease in blink rates during various reading conditions with different digital screens compared to resting states, highlighting the influence of visual engagement on ocular behaviour. Reading with a smartphone has decreased blink rates, which may affect eye health and device use. Understanding these dynamicscan guide ergonomic design to reduce visual discomfort from digital screen use, supporting healthy reading habits in the digital age.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".