Prevalence, Knowledge and Associated Factors Related to Computer Vision Syndrome among Undergraduate Students

Abstract

Aim:

Computer vision syndrome has been an issue of concern among students who use digital devices continuously. This study aimed to determine the prevalence and level of knowledge on computer vision syndrome and its relationship with associated factors among undergraduate students in a public university in Malaysia.

Methods:

This study was conducted between 26 May and 23 June 2022 at National University of Malaysia. A cross-sectional study among 208 undergraduate students from a public university was conducted. A self-reported questionnaire via Google Form was used to capture the data among the undergraduates. The prevalence and associated factors of computer vision syndrome were each evaluated using the validated Computer Vision Syndrome Questionnaire and Computer Vision Syndrome Survey Form 3 questionnaires, respectively, while knowledge of computer vision syndrome was assessed using a validated questionnaire from a previous study. All the data were analyzed using the Statistical Package for Social Sciences version 26.0 software (IBM Corp.; Armonk, NY, USA).

Results:

The prevalence of computer vision syndrome among undergraduates was 63.0% (n = 131), with 91.9% having poor knowledge of computer vision syndrome. Significant associations toward computer vision syndrome were found among undergraduates who have refractive errors/wearing glass (69.3%), screen edge at or above horizontal eye level (79.4%), uncomfortable sitting postures (79.4%) and close eye–screen distance (82.0%). In-depth analysis showed that having refractive errors/wearing glasses (aOR: 1.93; CI: 1.05, 3.57), uncomfortable sitting postures (aOR: 2.01; CI: 1.08, 3.74), and close eye–screen distance (aOR: 2.81; CI: 1.31, 6.05) had odd chance to develop computer vision syndrome.

Conclusion:

The study’s findings denoted that digital device users should have more knowledge of computer vision syndrome and practice the preventable measures, such as proper viewing distance and angle, upright sitting postures, appropriate screen and surrounding illuminance, as well as regular eye check-ups.

Introduction

In this era of technology, students need to use devices such as laptops, tablets, and cell phones for various purposes in institutions. Students of all ages have progressively shifted to computer-based learning, believing it to be a superior alternative to classroom teaching (Al Rashidi & Alhumaidan, 2017). The high dependency on digital devices in the learning process could lead to computer vision syndrome (CVS) (Chyad et al., 2018), which is one of the rising eye health concerns or complications due to the continuous use of computers among students (Abudawood et al., 2020). According to Ranasinghe et al. (2016), nearly 60 million people worldwide were affected by CVS. However, the diagnosis of CVS among the respondents in many studies was only confirmed through self-report questionnaires rather than by an ophthalmologist conducting a comprehensive eye examination (Abudawood et al., 2020; Chyad et al., 2018). Reports on university students from other regions of the world revealed a generally high prevalence, despite being lower than most Middle Eastern research available (Gammoh, 2021). Based on a study in Saudi Arabia, the duration of time spent studying on computers was the most significant associated factor, with symptoms becoming more prevalent and severe when longer time was spent on the computer (Abudawood et al., 2020). In terms of knowledge, in a study in Sri Lanka, the group with mild–moderate CVS symptoms had a better understanding of ergonomics knowledge than the group with severe CVS symptoms (Ranasinghe et al., 2016). In a systematic review on the prevalence of CVS conducted by Anbesu and Lema in 2023, the aim was to determine the pooled prevalence of CVS. The results revealed that Saudi Arabia exhibited the highest prevalence of CVS, recorded at 95.1%, based on a large sample size of 587 participants among medical students (Abudawood et al., 2020). Although other studies on the prevalence of CVS included substantial sample sizes, none reported a prevalence as high as that found in Saudi Arabia. In terms of knowledge about CVS in Saudi Arabia, a study with a sample size of 1402 indicated that 23.54% of medical students had poor knowledge, while 53.99% and 22.46% possessed average and good knowledge, respectively (Patil et al., 2019). This suggests a notable variation in knowledge levels among medical students in the region.

In Malaysia, there are limited studies on the prevalence and knowledge of CVS among undergraduate students. A previous study of knowledge and practices among university students of medical colleges in Malaysia revealed that CVS was highly prevalent among university students (Reddy et al., 2013). As the data of Reddy et al.’s (2013) study were collected in 2007, the findings might not portray the current situation of CVS among Malaysian university students who had increasing dependence on technology and digital devices. There was also a lack of information on undergraduate students’ knowledge level of CVS in Malaysia. The previous study by Reddy et al. (2013) did not reveal any findings related to the knowledge level of CVS in university students. Nonetheless, the findings of the relationship between associated factors and the prevalence of CVS were not conclusive and varied from study to study.

As the majority of symptoms of CVS went unnoticed among undergraduates, and given the similarity of CVS symptoms to those of other eye diseases, CVS might remain underdiagnosed among undergraduate students, thereby affecting their quality of life. Hence, the aims of this study were to determine the prevalence and level of knowledge related to CVS among undergraduate students at a public university in Malaysia. A secondary aim was to determine the relationship between associated factors toward the CVS in the aforementioned population.

Research Questions

1. What is the prevalence of Computer Vision Syndrome among students?

2. How knowledgeable are students of Computer Vision Syndrome?

3. What are the factors associated with Computer Vision Syndrome?

Methods

Study Design

A descriptive cross-sectional design was used in this study.

Study Sample

This study was carried out between 26 May and 23 June, 2022. The stratified random sampling was conducted among undergraduate students from a public university in Malaysia, involving 13 faculties. The number of the population of this study was determined as 11,312. According to statistics from Centre for Academic Management of the public university in Malaysia, the overall student population was 22,255 in 2021. The population was classified into different strata according to their faculty of study, with a total of 11 strata obtained. A simple random sampling method was applied to randomly select the respondents from the lists of students provided by the dean of each faculty, using the Microsoft Excel RAND and RANK functions. The sample size was calculated using the formula developed by Krejcie and Morgan (1970), with a total of 372 students needed in this study. However, only 208 students agreed to participate. Students who were able to understand the English language and use digital devices were included in this study. Those who had refractive errors were also included. However, the students who had underlying eye disease, systemic disease or had undergone eye operation in the past year were not included due to the factors influencing the data collection.

Data Collection

Data were collected by using a self-reported questionnaire via Google Form.

A self-reported questionnaire was used to collect data from respondents. The questionnaire consisted of four sections: (A) socio-demographic data, (B) prevalence of CVS, (C) knowledge of CVS, and (D) associated factors that contribute to CVS. Questions for section B were adapted from the Computer Vision Syndrome Questionnaire (CVS-Q) developed by Seguí et al. (2015). For section C, this study used a validated questionnaire developed by Mersha et al. (2020). Another validated questionnaire, named Computer Vision Syndrome Survey Form 3 (CVS-F3) by Iqbal et al. (2021), was used for section D.

Socio-Demographic Information

The questions consisted of six items questioning the respondents’ age, gender, ethnicity, academic year, faculty of study, and history of previous eye, systemic disease, or eye surgery in the past year. The respondents were required to fill in their age at the space provided and indicate whether they are male or female for gender. There were four options for ethnicity, which are “Malay,” “Chinese,” “Indian,” and “Others” for the respondents to choose. For the academic year, respondents chose their answer among the five options, from “Year 1” to “Year 5.” For faculty of study, respondents indicated the faculty they belonged to from the 13 faculties as aforementioned, listed in the options. The respondents were required to answer “Yes” or “No” for the question that asked about the history of previous eye disease (e.g., cataract, conjunctivitis, etc.), systemic disease (e.g., diabetes or hypertension), or eye surgery in the past year. If their answer were “Yes”, they were excluded from this study as they met the exclusion criteria.

Computer Vision Syndrome Questionnaire

Questions for section B were adapted from the Computer Vision Syndrome Questionnaire (CVS-Q) developed by Seguí et al. (2015) to answer the prevalence of CVS. According to Seguí et al. (2015), the 16 questions asked about the frequency and intensity of the 16 symptoms of CVS using the Likert Scale in the form of matrix questions. The respondents answered either “Never,” “Occasionally,” or “Often or always” for the frequency of the symptoms they experienced. No time frame was specifically set, as the symptoms were self-reported by the respondents based on how frequently they experienced them while using a digital device. Respondents were required to choose “Never” if they had not experienced the symptoms before, “Occasionally” if they experienced the symptoms intermittently or once a week, and “Often or always” if the symptoms occurred 1–3 times a week or almost every day. For intensity, the options were “Moderate” and “Intense.” The intensity of the symptoms was based on respondents’ perceptions. The respondents were reminded that if they marked “Never” for the frequency of a symptom, then they did not need to give an answer for the intensity of the symptom. The options for frequency and intensity of each of the symptoms experienced by the respondents were then coded as such that for frequency, “Never” = 0, “Occasionally” = 1, and “Often or always” = 2, while for intensity, “Moderate” = 1 and “Intense” = 2. The severity was then calculated by multiplying the frequency and intensity. The severity was recoded as: 0 = 0, 1 or 2 = 1, and 4 = 2. Based on this recording, the total score was obtained by adding up all the severity of the symptoms experienced. A total score of equal to or more than 6 meant that the respondent was suffered from CVS.

Knowledge of CVS

For section C, this study used a validated questionnaire developed by Mersha et al. (2020). Section C consisted of 10 questions. There were four supportive questions, and the rest were multiple-choice questions. The four supportive questions (questions 1, 5, 7, and 9) were questions that the respondents must answer, with either “Yes” or “No”. The respondents were led to the next questions (multiple-choice questions) if an option of “Yes’’ was selected. There were 18 relevant assessment elements included as the options for the multiple-choice questions. Each element was treated as a single assessment tool and a score of 1 was given for each of the elements selected. The respondents were scored by the total number of elements they selected out of 18, and this score was converted to a percentage to rank their knowledge level based on Bloom’s cut-off point. A percentage score between 80% and 100% indicated a good knowledge level, followed by a moderate knowledge level between 60% and 79%, and a poor knowledge level for a percentage score below 60%.

Computer Vision Syndrome Survey Form 3

Another validated questionnaire, named Computer Vision Syndrome Survey Form 3 (CVS-F3) by Iqbal et al. (2021), was used for section D to answer the associated factors that contribute to CVS. Section D comprised of nine questions regarding the associated factors contributing to CVS. The sociodemographic questions and questions related to CVS symptoms were removed from the original questionnaire due to redundancy as well as a few questions that were not suitable for the context of this study. The nine questions in this section were a combination of dichotomous questions (questions 3, 4, and 8), and multiple-choice questions (questions 1, 2, 5, 6, 7, and 9). The respondents were required to answer all the questions in this part accordingly. The results from this section of the questionnaire were used to correlate with the prevalence of CVS, and therefore scoring was not needed.

A pilot study was carried out on 38 undergraduate students. The Cronbach’s α for section B was recorded at 0.876 for the scale of frequency of CVS symptoms experienced and 0.877 for the scale of intensity. The Cronbach’s α for the questionnaire that measured the level of knowledge of CVS (section C) was recorded at 0.947.

Statistical Analysis

Analyses of data were conducted using The Statistical Package for Social Sciences version 26.0 software (IBM Corp.; Armonk, NY, USA). Descriptive analysis was used to outline the respondents’ sociodemographic data, represented by frequency and percentage. Frequency and percentage were also used to describe the CVS prevalence among undergraduate students as “suffer from CVS” or “do not suffer from CVS.” A percentage score between 80% and 100% indicated a good knowledge level, followed by a moderate knowledge level between 60% and 79%, and a poor knowledge level for a percentage score below 60%. Inferential analysis was performed to identify the relationship between associated factors and the prevalence of CVS. The chi-square test for independence was used to identify the relationships toward the CVS. The significant level (α) was set at p < .05. Logistic regression was conducted to study in-depth the relationship between CVS and the sociodemographic and risk factors of the CVS. Single and multiple linear regression were conducted, with a significant level (α) set at p < .05.

Ethical Considerations

Ethical approval was obtained from the National University of Malaysia Research Committee (Approval no: UKM PPI/111/JEP-2022-130, Date: March 11, 2022). Respondents’ information sheets as well as consent forms were sent to the respondents with the Google Form link attached. Participation was entirely voluntary, and no risk was posed to the respondents.

Results

The study was conducted on 372 undergraduate students from 11 faculties of a public university in Malaysia, with 208 respondents agreeing to participate in this study. The respondents’ sociodemographic data are shown in Table 1. The respondents mainly comprised 79.3% (n = 165) females, 78.8% (n = 164) Malay, 27.4% (n = 57) of year 2 students, and all of them had never experienced an eye disease in the past year.

Table 1.

Socio-demographic Description of the Students (N = 208)

V ariables	Mean (± SD)	n	%
Age	22.15 (± 2.25)
Gender  Male  Female		43 165	20.7 79.3
Ethnicity  Malay  Chinese  Indian  Others		164 22 17 5	78.8 10.6 8.2 2.4
Academic year  Year 1  Year 2  Year 3  Year 4  Year 5		52 57 56 38 5	25.0 27.4 26.9 18.3 2.4
Faculty  Science and Technology  Economy and Management  Pharmacy  Islamic Studies  Health Sciences  Law  Dentistry  Education  Medicine  Information Science and Technology  School of Liberal Studies		42 43 8 5 14 12 21 21 25 26 5	20.2 20.7 3.8 2.4 6.7 5.8 3.4 10.1 12.0 12.5 2.4
History of eye disease  Yes  No		0 208	0 100

Table 2 presents the findings on the prevalence of CVS among the respondents. The mean total score of severity of the CVS was 7.69 (±5.07), while the total score of CVS knowledge among undergraduate students was 2.46 (±4.08). A total of 63.0% of undergraduate students suffer from CVS (n = 131), with 91.9% (n = 191) having poor knowledge of CVS and preventative measures.

Table 2.

Prevalence and Level of Knowledge of CVS Among Students (N = 208)

Variables	n	%
Prevalence of CVS  Suffer from CVS  Do not suffer from CVS	131 77	63.0 37.0
Level of knowledge on CVS and preventative measures  Poor knowledge  Moderate knowledge  Good knowledge	191 14 3	91.9 6.7 1.4

Note: CVS = Computer vision syndrome.

Significant association toward CVS was found among undergraduates who have refractive errors or wearing glass (69.3%), screen edge at or above horizontal eye level (79.4%), uncomfortable sitting postures (79.4%), and close eye–screen distance (82.0%) (Table 3).

Table 3.

Relationship Between Prevalence of CVS With Its Associated Factors (N = 208)

Variables	Suffer From CVS		Do Not Suffer From CVS		p
	n	%	n	%
Hours spent on digital device in 24 hours  Less than or equal to 4 hours  Equal to or more than 5 hours	9 122	52.9 63.9	8 69	47.1 36.1	.527
Years spent on digital device  Less than or equal to 4 years  Equal to or more than 5 years	19 112	63.3 62.9	11 66	36.7 37.1	1.000
Screen hours  Day  Night	81 50	61.4 65.8	51 26	38.6 34.2	.626
Screen hours  Continuous  Interrupted	73 58	67.0 58.6	36 41	33.0 41.4	.268
Digital screens commonly used  Using desktop/laptop  Not using desktop/laptop	115 16	63.2 61.5	67 10	36.8 38.5	1.000
Digital screens commonly used  Using tablet and smartphone  Not using tablet and smartphone	129 2	62.6 100.0	77 0	37.4 0.0	.531
Most common primary screen used  Desktop/laptop  Tablet and /or Smartphone	32 99	64.0 62.7	18 59	36.0 37.3	.997
Average illumination of primary screen in the dark  Less than 20%  20–50%  More than 50%	70 48 13	63.6 60.8 68.4	40 31 6	36.4 39.2 31.6	.807
Having refractive errors or wearing glasses  Yes  No	88 43	69.3 53.1	39 38	30.7 46.9	.027*
Poor screen resolution or design  Yes  No	19 112	65.5 62.6	10 67	34.5 37.4	.922
Screen edge at or above horizontal eye level  Yes  No	27 104	79.4 59.8	7 70	20.6 40.2	.048*
Uncomfortable sitting postures  Yes  No	80 51	73.4 51.5	29 48	26.6 48.5	.002**
Screen glare  Yes  No	34 97	75.6 59.5	11 66	24.4 40.5	.070
Close eye–screen distance  Yes  No	50 81	82.0 55.1	11 66	18.0 44.9	<.001**
Small-font size  Yes  No	38 93	73.1 59.6	14 63	26.9 40.4	.115
Poor lighting condition  Yes  No	44 87	73.3 58.8	16 61	26.7 41.2	.070
Watch screen in the dark  Yes  No	80 51	71.4 53.1	32 45	28.6 46.9	.010**

Note:Chi-square test for all variables except Fisher’s exact test for digital screens commonly used.

CVS = Computer vision syndrome.

*Significant at .05.

**Significant at .01.

In simple logistic regression, seven variables (have refractive errors or wearing glasses, screen age or above horizontal eye level, uncomfortable sitting postures, screen glare, close eye–screen distance, poor lighting condition, and watch screen in the dark) showed a significant relationship with CVS as shown in Table 4. However, in multiple logistic regression, only three variables: having refractive errors/wearing glass (aOR: 1.93; CI: 1.05,3.57), uncomfortable sitting postures (aOR: 2.01; CI: 1.08,3.74) and close eye–screen distance (aOR: 2.81; CI: 1.31,6.05) had odd chance to develop CVS (Table 4).

Table 4.

Association of Suffering From CVS and Its Associated Factors Among Students (N = 208)

Variables	Simple Logistic Regression				Multiple Logistic regression
	cOR	95% CI		p	aOR	95% CI		p
		Lower	Upper			Lower	Upper
Age
21 years and below	1	–	–	–	1	–	–	–
22 years and above	1.125	0.631	2.005	.690	0.757	0.393	1.458	–
Gender
Male	1	–	–	–	1	–	–	–
Female	0.538	0.273	1.062	.074	1.487	0.717	3.086	.286
Risk factors
Spent >5 hours on digital device in 24 hours.	0.636	0.235	1.725	.374	–	–	–	–
Spent >5 years on digital device.	1.018	0.456	2.271	.966	–	–	–	–
Night screen hours	0.826	0.458	1.489	.525	–	–	–	–
Continuous screen hours	0.698	0.396	1.227	.212	0.809	0.432	1.516	.509
Using desktop/laptop digital screens	1.073	0.461	2.499	.871	–	–	–	–
Tablet and or smartphone digital screens primary used	1.059	0.547	2.053	.864	–	–	–	–
More than 20% illumination of primary screen in the dark	1.061	0.604	1.865	.836	–	–	–	–
Have refractive errors or wearing glasses	0.501	0.282	0.893	.019	1.932	1.048	3.565	.035
Poor screen resolution or design	0.880	0.386	2.004	.761	–	–	–	–
Screen edge at or above horizontal eye level	0.385	0.159	0.933	.035	1.741	0.666	4.551	.258
Uncomfortable sitting postures	0.385	0.216	0.688	.001	2.013	1.083	3.739	.027
Screen glare	0.475	0.225	1.005	.052	1.370	0.608	3.090	.447
Close eye–screen distance	0.270	0.130	0.560	<.001	2.814	1.310	6.049	.008
Small-font size	0.544	0.272	1.086	.084	1.222	0.543	2.752	.628
Poor lighting condition	0.519	0.268	1.003	.051	1.285	0.602	2.745	.517
Watch screen in the dark	0.453	0.256	0.804	.007	1.722	0.935	3.171	.081

Note: aOR = Adjusted odds ratio; CI = Confidence interval; cOR = Crude odds ratio; CVS = Computer vision syndrome.

Discussion

Computer vision syndrome (CVS) is an increasing health concern among university students in the present digital era. Most of the students frequently were not aware of this syndrome or the techniques when using digital devices, which may lead to CVS. This study revealed that the majority of the respondents in this study, which was 63% of the respondents suffered from CVS. The mean level of knowledge related to CVS was 2.46 (±4.08), with 91.9% (n = 191) of the respondents having poor knowledge. A previous study that was conducted in Malaysia reported a higher prevalence compared to this study, which was 89.9% (Reddy et al., 2013). The difference in the percentage could be due to a difference in sample size and response rate, where the previous study had a larger sample size and higher response rate, compared to this study. A study conducted among medical students in Pakistan reported that 67% of the respondents suffered from CVS (Noreen et al., 2016), which was almost similar to this study. The most common symptom experienced by the respondents in this study was a headache, reported by 72.1% of respondents that may be derived from a poorly corrected eye defect (Bartoszek et al., 2019). The findings of Humayun (2020) also reported headache as the most common symptom experienced by 74.2% of students. Abudawood et al. (2020) findings stated that 8.8% of the undergraduate students in Jeddah, Saudi Arabia, complained of tearing, double vision, and a change of color visualization of the eye together with pain in the neck, shoulder, and back. This could be when there was prolonged usage of digital devices, the eyes remained stationary on the screen (Shantakumari et al., 2014). These did not allow the eyes to focus and refocus to regain sharp vision and hence attributed to CVS (Assefa et al., 2017).

The study found most of the undergraduates (91.9%) had poor knowledge of CVS despite the learning and teaching process in tertiary education having been highly digitalized. The result was in accordance with the study by Mersha et al. (2020), that the majority of the respondents (40.6%) had poor knowledge of CVS but the percentage was still lower than in the present study. Similarly, another study by Sitaula and Khatri (2018) showed that the majority of medical students had poor knowledge of CVS. The lack of knowledge was probably due to the lack of exposure and awareness of CVS symptoms and preventive measures. Reasonable knowledge leads to understanding and comprehension which provides the necessary information to begin treatment and preventative steps as soon as possible (Patil et al., 2019).

In this study, it was found there was a significant relationship between having refractive errors and the prevalence of CVS which was in line with other studies that involved university students (Iqbal et al., 2021; Reddy et al., 2013; Sitaula et al., 2020). The likelihood of respondents with refractive errors developing CVS was about 2 times more likely than those who do not have refractive errors. Respondents who had refractive errors were more prone to CVS, possibly because the eyes of those with refractive errors would need more effort to focus on the letters on digital screens, which were made up of pixels or small dots but not solid images (Rahman & Sanip, 2011).

Moreover, this study also found a significant relationship between uncomfortable sitting postures and the prevalence of CVS. This finding was consistent with the previous studies (Alhibshi et al., 2021; Iqbal et al., 2021; Ranganatha & Jailkhani, 2019). The study found that respondents were more apparent in developing CVS about two times more when they practiced uncomfortable sitting postures compared to those who did not. The possible reason was due to muscle straining, which might worsen with a static and uninterrupted sitting posture (Zemp et al., 2016). According to Brenk-Krakowska et al. (2019), sitting at a desk with the knees and elbow at about 90° degrees and the arms positioned for keyboard use was the conventional body posture during computer use, which can reduce the muscle straining and avoid the development of the CVS among the users.

The close eye–screen distance while using a digital device was also found to be significantly related to the prevalence of CVS, and this is in accordance with the results reported by Iqbal et al. (2021). Another previous study also reported that dry, tired, or sore eyes were common among university students who viewed their digital screen at a distance of less than 50 cm (Shantakumari et al., 2014), which was similar to the finding of this study. The current study showed that the respondents who practiced a close eye–screen distance were three times more likely to develop CVS. The possible explanation was that the accommodation and convergence demand increased when the viewing distance was shortened, further aggravating eye strain (Nayak et al., 2020). Eyes often work harder when viewing a digital screen, and the extra requirement on eye focusing during close-viewing distance places additional stress on the visual system (American Optometric Association, 2020), thus leading to CVS.

In general, most of the previous studies revealed that long hours of using digital devices led to aggravated CVS symptoms, with two studies claiming that the duration was more than 5 hours (Alhibshi et al., 2021; Al Tawil et al., 2020). However, Noreen et al. (2016) proposed that using a digital device for more than 4 hours caused CVS symptoms, and Ranganatha and Jailkhani (2019) also supported this with their findings on the duration of digital device usage at 5–6 hours. Furthermore, Abudawood et al. (2020) and Cheema et al. (2019) suggested that the duration could be shorter, either more than or equal to 3 hours. On the contrary, Sitaula et al. (2020) reported that time of screen time had no relation to the prevalence of CVS at all, which was the same as the finding of this study. Similarly, Ahmed et al. (2019) also reported no association between computer or laptop use duration and CVS.

Study Limitations

One of the strengths of this study was that it was the first comprehensive CVS study conducted in Malaysia involving medical and non-medical students. Furthermore, stratified random sampling was used to ensure that the samples represented each faculty. The main limitation of this study was that the study only used the self-reported approach in confirming CVS among the respondents. The inability to perform comprehensive ophthalmic examinations on the respondents using equipment and visual acuity tests in diagnosing CVS was because data collection was carried out during the final stage of the COVID-19 pandemic, whereby movement restrictions were still in place. Exaggeration and reaction bias were two potential biases associated with this approach, for example, the respondents might exaggerate how much knowledge or information they had by searching online for the answers or referring to others when filling out the questionnaire. The online questionnaire was relatively convenient, however, since the respondents were reached only by email, some might not have noticed the emails sent, which might have caused a low response. Although musculoskeletal symptoms are linked to CVS, they were not considered in the symptoms of the CVS-Q questionnaire in this study and hence were not included in the score that was produced.

Conclusion and Recommendations

This study revealed a high prevalence of computer vision syndrome and a lack of knowledge regarding preventative measures among undergraduate students at the Public University, Malaysia. A significant number of students were unfamiliar with the causes of CVS, and the majority lacked awareness of the importance of adopting preventative measures for this condition. It is imperative that the findings of this study be communicated to the university’s top management to garner their cooperation for further actions.

To address this issue, collaborative efforts with ophthalmologists, optometrists, or other related interdisciplinary teams should be undertaken for the assessment and screening of CVS. This collaborative approach is crucial in ruling out CVS and implementing effective preventative measures. Additionally, there is a need to disseminate information to increase awareness of CVS symptoms and their potential impact on academic performance. It is noteworthy that CVS symptoms have the potential to diminish students’ concentration levels, thereby affecting their academic abilities. In light of these findings, it is recommended that promoting eye health and integrating preventative measures become integral components of effective nursing practice for university students. This entails developing and implementing strategies to enhance students’ understanding of CVS, its causes, and the importance of preventative measures.

By collaborating with the university’s top management and interdisciplinary teams, nurses can contribute to creating a supportive environment that fosters eye health and overall well-being among students. Another recommendation is to encourage good practices when using digital devices, such as proper viewing distance and angle, upright sitting postures, appropriate screen and surrounding illuminance, and regular eye check-ups. A similar study on a larger scale that involves students from other universities is recommended to provide more insight into the prevalence of CVS. In order to reduce bias and increase the response rate, conducting a face-to-face survey or an interview is recommended. Since some of the associated factors are closely connected to musculoskeletal symptoms, future research should include questions on the musculoskeletal symptoms in the questionnaire used.

Funding Statement

The authors declared that this study had received no financial support.