Colour Vision Deficiencies in the Digital Age: A Survey of User Experiences with Digital Displays
Bibliographic record
Abstract
Introduction: Approximately 90% of dichromats and 67% of anomalous trichromats experience difficulty performing colour-related tasks daily (Steward & Cole, 1989). As technology becomes more prevalent, examining whether this condition impacts their use of digital displays is increasingly essential. Limited studies analyze the relationship between colour vision deficiencies (CVD) and digital displays. Mashige (2019) found that 63.2% of schoolchildren with CVD reported challenges when “working with computers,” suggesting that this condition may have some effect. \nPurpose: The purpose of this study is to examine the impact that congenital red-green CVD has on an individual’s interaction with digital displays in different areas of their daily life, such as “school,” “work/volunteering,” “gaming and eSports,” “driving/motorized vehicle,” and “travelling.” \nMaterials and Method: An online survey was administered to people with CVD from Canada and the United States of America (USA). In addition to collecting demographic information and awareness of their CVD, information regarding difficulties carrying out colour-related tasks was collected. Because several software and filter options may improve performance on colour-related tasks, we asked whether they tried any and to rate their effectiveness. \nResults: A total of 381 individuals with CVD (280 males) completed the survey. Nearly 100% reported some difficulty with one of the tasks in most areas of life, individually, except for travelling (54.1%). For most tasks, there were no significant differences in the rankings based on sex and/or age group. Still, some tasks showed significant differences based on severity, primarily between mild and severe defects. Some examples included “colour-coded diagrams” at school, work/volunteering, and recreation/hobbies; “editing photos or other coloured images” at school, recreation/hobbies, and social media; and “reading coloured letters on various backgrounds” in gaming/eSports, online shopping/banking, and in-person shopping/banking. \nNearly 65% of respondents reported making changes or implementing modifications to their displays, but there were 35% who did not. The most popular aid was “trial-and-error adjustments” of the colour and brightness of the display. Of the individuals who tried an aid, approximately 90% reported a modification to be at least a little effective, less than 3% reported at least one aid as ineffective, and only 25% rated the modifications as highly effective. There was no significant difference based on sex, age group, or severity for most modifications, except for passive aids (“coloured filters”) where youth found them more effective than adults. \n“Desktops/laptops,” “cell phones,” and “tablets” were the displays that people with CVD most frequently encountered difficulty and modified. The displays showed significant association with age group for some areas of life (work/volunteering, recreation/hobbies, gaming & eSports, and online shopping/banking) and modifications. This was probably due to the greater use of displays or differences in content, with youth having more problems with tablets and adults more with cell phones. \nConclusion: Most respondents encountered some difficulty with at least one task, indicating that CVD had some effect on their ability to use digital displays. Although most respondents have tried some modifications to their displays, some respondents reported that at least one modification was ineffective and only 25% found an aid to be highly effective. Software developers should focus on making aids more accommodating and customizable to individual preferences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".