The development of predictive scoring model for Computer Vision Syndrome (CVS) severity among supporting staff in university

การพัฒนาระบบการให้คะแนนเพื่อทำนายความรุนแรงของโรคคอมพิวเตอร์วิชั่นซินโดรมในกลุ่มบุคลากรสายสนับสนุนในมหาวิทยาลัย

Name: Tanika Tingsa

keyword: Computer Vision Syndrome (CVS)

; Supporting staff

; Prediction scoring systems

Abstract: The purpose of this study was to evaluate the personal and working factors to specify risk items among supporting staff who use computers and develop the computer vision syndrome (CVS) risk scoring scale for comprehensive evaluation the severity of CVS among office workers who use computers at the university. The study consists of (1) To investigate the risk factors that contribute to CVS symptoms for determination of predictors of CVS severity. (2) To design the CVS predictive scoring model by predictive scoring tool for early detect the CVS among office workers. The scope of study divided into two phases. Phase I ; A questionnaire was developed for a research tool in this study that consists of personal factors, working factors, and CVS severity. The validity conducted by three experts, and the item-objective congruence (IOC) index was 0.84. The data were collected by performing interviews with 160 staff members who work for the supporting section of the university located in Lampang province. Data analysis was used to find out the predictive factor, as the results showed the personal factors of participants were under 40 years old (40%), there were allergies (40.63%), they had dry eye (41.88%), and had stared at a screen for longer than 60 minutes (55.63%). Additionally, 70% of participants were female. The participant's work history, characteristics, and working behavior were identified, and they had been employed for one to ten years. They were spending more than 5 hours a day on a computer. All participants identified with at least one of the symptoms listed in the CVS questionnaires were divided into two groups: the non-severe group (n = 100) and the severe group (n = 60). The following characteristics increased the probability of severe CVS in this study after multivariable analysis: the number of working hours per day (OR = 3.01, p = 0.048), the amount of time spent staring at a screen device (OR = 2.39, p = 0.024), the types of communication devices other than a tablet (OR = 2.14, p = 0.042), and a disease of the eyes (dry eyes) (OR = 2.97, p = 0.004) . Area under the curve (AuROC) was 75.54%, which is acceptable. Phase II, the coefficient of significant predictors was divided by the coefficient with the lowest value (0.76) in order to calculate the item scores. The result was rounded to the nearest 1.10 integer. Every item had scores ranging from 0 - 5. Depending on the severity level, different CVS risk scores had different distributions, and discriminating power was found to be adequate, as evidenced by its area under the receiver operation curve (AuROC) of 76.54%. Classifying the CVS risk score into 3 risk categories with significantly different prognoses: scores ≤2.0 points (low risk), scores 2.5–4.0 points (moderate risk), and scores ≥4.5 (high risk), the likelihood ratio of positive (LHR+) was 0.27, 1.29, and 11.67. The mean total scores of the severe group and the non-severe group were 2.2±1.3 and 3.5±1.2 (p<0.001). There was a statistically significant difference between the risk scores of the severe and non-severe groups.

Thammasat University. Thammasat University Library

Address: BANGKOK

Email: preserv@tu.ac.th

Name: Chalermchai Chaikittiporn

Role: advisor

Name: Surangrat Pongpan

Role: co-advisor

Created: 2025

Modified: 2025-10-16

Issued: 2025-10-16

วิทยานิพนธ์/Thesis

application/pdf

DOI: 10.14457/TU.the.2024.259

eng

DegreeName: Doctor of Philosophy

Level: Doctoral Degree

Descipline: Occupational and Environmental Health

Grantor: Thammasat University

©copyrights Thammasat University

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