Visual, ocular surface, and extraocular diagnostic criteria for determining the prevalence of computer vision syndrome: a cross-sectional smart-survey-based study

Mohammed Iqbal; Ahmed Elmassry; Mervat Elgharieb; Omar Said; Ahmed Saeed; Tamer Ibrahim; Ahmed Kotb; Mahmoud Abdelhalim; Samir Shoughy; Akram Elgazzar; Hassan Shamselden; Abdallah Hammour; Mohammed Eid; Hosam Elzembely; Khaled Abdelaziz

doi:10.51329/mehdiophthal1489

Visual, ocular surface, and extraocular diagnostic criteria for determining the prevalence of computer vision syndrome: a cross-sectional smart-survey-based study

Med Hypothesis Discov Innov Ophthalmol. 2024 Jul 1;13(1):1-15. doi: 10.51329/mehdiophthal1489. eCollection 2024.

Authors

Affiliations

¹ Department of Ophthalmology, Faculty of Medicine, Sohag University, Sohag, Egypt.
² Department of Ophthalmology, Faculty of Medicine, Alexandria University, Alexandria, Egypt.
³ Department of Ophthalmology, Faculty of Medicine, Suez Canal University, Suez, Egypt.
⁴ Department of Ophthalmology, Faculty of Medicine, Fayoum University, Fayoum, Egypt.
⁵ Department of Ophthalmology, Faculty of Medicine, Benha University, Benha, Egypt.
⁶ Department of Ophthalmology, Faculty of Medicine, Tanta University, Tanta, Egypt.
⁷ Department of Ophthalmology, Faculty of Medicine, Zagazig University, Zagazig, Egypt.
⁸ Department of Ophthalmology, Faculty of Medicine, Aswan University, Aswan, Egypt.
⁹ Department of Ophthalmology, Damanhour Teaching Hospital, Damanhour, Egypt.
¹⁰ Department of Ophthalmology, Faculty of Medicine, Alazhar University, Damietta, Egypt.
¹¹ Department of Ophthalmology, Faculty of Medicine, Alazhar University, Assuit, Egypt.
¹² Department of Ophthalmology, Faculty of Medicine, Alazhar University-Males, Cairo, Egypt.
¹³ Department of Ophthalmology, Faculty of Medicine, Minia University, Minia, Egypt.
¹⁴ Department of Ophthalmology, Faculty of Medicine, Beni Suef University, Beni Suef, Egypt.

Abstract

Background: The American Optometric Association defines computer vision syndrome (CVS), also known as digital eye strain, as "a group of eye- and vision-related problems that result from prolonged computer, tablet, e-reader and cell phone use". We aimed to create a well-structured, valid, and reliable questionnaire to determine the prevalence of CVS, and to analyze the visual, ocular surface, and extraocular sequelae of CVS using a novel and smart self-assessment questionnaire.

Methods: This multicenter, observational, cross-sectional, descriptive, survey-based, online study included 6853 complete online responses of medical students from 15 universities. All participants responded to the updated, online, fourth version of the CVS questionnaire (CVS-F4), which has high validity and reliability. CVS was diagnosed according to five basic diagnostic criteria (5DC) derived from the CVS-F4. Respondents who fulfilled the 5DC were considered CVS cases. The 5DC were then converted into a novel five-question self-assessment questionnaire designated as the CVS-Smart.

Results: Of 10 000 invited medical students, 8006 responded to the CVS-F4 survey (80% response rate), while 6853 of the 8006 respondents provided complete online responses (85.6% completion rate). The overall CVS prevalence was 58.78% (n = 4028) among the study respondents; CVS prevalence was higher among women (65.87%) than among men (48.06%). Within the CVS group, the most common visual, ocular surface, and extraocular complaints were eye strain, dry eye, and neck/shoulder/back pain in 74.50% (n = 3001), 58.27% (n = 2347), and 80.52% (n = 3244) of CVS cases, respectively. Notably, 75.92% (3058/4028) of CVS cases were involved in the Mandated Computer System Use Program. Multivariate logistic regression analysis revealed that the two most statistically significant diagnostic criteria of the 5DC were ≥2 symptoms/attacks per month over the last 12 months (odds ratio [OR] = 204177.2; P <0.0001) and symptoms/attacks associated with screen use (OR = 16047.34; P <0.0001). The CVS-Smart demonstrated a Cronbach's alpha reliability coefficient of 0.860, Guttman split-half coefficient of 0.805, with perfect content and construct validity. A CVS-Smart score of 7-10 points indicated the presence of CVS.

Conclusions: The visual, ocular surface, and extraocular diagnostic criteria for CVS constituted the basic components of CVS-Smart. CVS-Smart is a novel, valid, reliable, subjective instrument for determining CVS diagnosis and prevalence and may provide a tool for rapid periodic assessment and prognostication. Individuals with positive CVS-Smart results should consider modifying their lifestyles and screen styles and seeking the help of ophthalmologists and/or optometrists. Higher institutional authorities should consider revising the Mandated Computer System Use Program to avoid the long-term consequences of CVS among university students. Further research must compare CVS-Smart with other available metrics for CVS, such as the CVS questionnaire, to determine its test-retest reliability and to justify its widespread use.

Keywords: CVS-F4; CVS-Smart; CVS-Smart score; asthenopia; computer; computer vision system; dry eye; eyestrain; machine intelligence; point prevalence; smartphones; visual fatigue.