EARLY DETECTION OF LOW VISION DETERMINANT FACTORS USING THE E-SIGALON SELF-ASSESSMENT APPLICATION

Abstract

Background: Low Vision is an end-stage condition that cannot be cured. The best treatment for low vision is the early detection. However, there is still no integrated low vision data in health services, the lack of public knowledge about low vision, the low rate of case discovery, and the lack of optimal referral and treatment mechanisms for low vision are problems that must be addressed at this time. Purpose: This study aims to detect early low vision determinant factors using a self-assessment application. Methods: This study is analytical and quantitative research. Respondents for this research were from five selected populations in East Java who filled out the e-SIGALON application from September to December 2023. The respondents were suspected as having low vision if they had score of 6 or higher. The variables were analyzed using logistic binary regression, the validity and reliability test was also performed. Results: From 446 respondents, there were 237 people (53.14%) suspected of low vision but only 66 people came to referral hospital. The low vision suspect showed 12 of the 15 statistically significant questions (p<0.05). The most common was sitting very close to the television/monitor (48.43%), followed by having difficulties in seeing objects in dim light (44.17%) and difficulties in doing the things they want to do (40.13%). Conclusion: The e-SIGALON application can be a solution in managing low vision. Through this application, people can easily find out and recognize whether they have low vision or not.

Keywords

• low vision
• early detection
• self assessment
• digital health
• screening