Peer Reviewed Article Effects of Computers on Vision
J Environ Public Health. 2018; 2018: 4107590.
Computer Vision Syndrome and Associated Factors among Figurer Users in Debre Tabor Town, Northwest Federal democratic republic of ethiopia
Awrajaw Dessie
oneDepartment of Environmental and Occupational Wellness and Safety, Establish of Public Health, Academy of Gondar, Gondar, Federal democratic republic of ethiopia
Fentahun Adane
twoSouth Gondar Zonal Wellness Part, Debre Tabor, Ethiopia
Ansha Nega
3Public Health Kinesthesia, College of Health Sciences, Addis Ababa University, Addis Ababa, Ethiopia
Sintayehu Daba Wami
1Department of Environmental and Occupational Health and Safety, Institute of Public Health, Academy of Gondar, Gondar, Ethiopia
Daniel Haile Chercos
1Department of Environmental and Occupational Health and Rubber, Institute of Public Health, University of Gondar, Gondar, Federal democratic republic of ethiopia
Received 2018 Jan 24; Revised 2018 Jul 23; Accepted 2018 Aug 12.
Abstract
Background
Globally, figurer is ane of the common office tools used in various institutions. Using calculator for prolonged time led to the users at greater wellness run a risk of computer vision syndrome (CVS). Computer vision syndrome is the leading occupational health problem of the xx-first century. About seventy percent of computer users are suffered from CVS. Besides the health issues, CVS causes inefficiency at workplace and deteriorate quality of work. The problem of CVS and its risk factors are non well known in Federal democratic republic of ethiopia.
Method
A cross-sectional written report was conducted to assess the prevalence of CVS and associated factors among computer user regime employees in Debre Tabor town from Feb to March, 2016. Multistage random sampling method was applied to select 607 written report participants, and the data were collected by using a structured questionnaire. Computer vision syndrome was measured past self-reported method. Bivariate and multivariable binary logistic regression analyses were performed using SPSS version twenty. Significance level was obtained at 95% CI and p value < 0.05.
Results
The prevalence of CVS was 422 (69.5%) with 95% CI of 65.60, 73.0%. Blurred vision, eyestrain, and eye irritation were the commonest reported symptoms of CVS with proportion of 62.lx%, 47.63%, and 47.xl%, respectively. Occupation: officer (adapted odds ratio (AOR) = iv.74) and secretary (AOR = ix.17), daily estimator usage (AOR: 2.29), and preexisting eye disease (AOR = three.nineteen) were run a risk factors for CVS. However, computer users with loftier payment, who took regular wellness break, and with good noesis on reckoner safety measures were less impacted by CVS.
Decision
The prevalence of estimator vision syndrome was found to exist higher in Debre Tabor town. Monthly income, occupation, daily reckoner usage, regular health break, knowledge, and preexisting eye illness were predictor variables for CVS. Optimizing exposure fourth dimension, improving awareness on safety measures, and management back up are important to tackle CVS.
one. Introduction
Globally, personal computers were 1 of the commonest office tools. It had become a necessity in the 21st century and has been regularly used in diverse institutions such as government offices, bookish institutions, and banking systems [1]. A continuous utilise of computer for an extended time causes vision problem called computer vision syndrome [two]. Computer vision syndrome (CVS) is defined by the American Optometric Association as a complex of centre and vision problems related to the activities which stress the near vision and which are experienced in relation to or during the use of computers [3]. It encompasses a group of visual symptoms which ingather up from the extended viewing of the digital screen, when the demands of the task exceed the abilities of the viewer. Symptoms of CVS which are referred to as digital middle strain include dry and irritated eyes, eye strain/fatigue, blurred vision, red eyes, called-for eyes, excessive tearing, double vision, headache, light/glare sensitivity, slowness in changing focus, and changes in colour perception [4].
Estimator vision syndrome (CVS) is the leading occupational hazard of the 21st century and its symptoms touch on most about seventy percent of all calculator users [5]. Globally, CVS is i of the major public wellness problems and reduced productivity at piece of work, increased error rate, reduced job satisfaction, and impaired visual abilities. A worldwide data prove nearly 60 million people suffering from CVS and 1 1000000 new cases occurred each year [6]. Given the low availability and utilization of personal protective equipment, the high workload, and the express break fourth dimension while using computer in developing countries, the burden of CVS is very high [7].
The public wellness burden of CVS was condign the business organisation of policy makers and attracts the attention of researchers. A study conducted in Abuja, Nigeria, reported that 40% of computer users engaged as security and exchange commissioner has suffered from at least one symptom of CVS [8]. A nationwide study in Sri Lanka reported that more than two-thirds of estimator role workers were suffering from CVS [9]. A couple of studies conducted in Gondar, Federal democratic republic of ethiopia, reported that more than 73% of figurer users who are working as secretaries, information processors, and bankers were developing CVS [x, 11].
Elapsing of computer usage, poor lighting, glare, screen effulgence, vision problems, and improper workstation setup are run a risk factors for CVS [12]. Though at that place is no evidence that CVS symptoms atomic number 82 to permanent center damage on top of visual damage, it causes inefficiency at workplace. Hence, CVS is growing public wellness issue that can significantly touch on the workers' quality of life and their work productivity [v].
Although many studies take reported the prevalence of CVS and the risk factors such as prolonged reckoner use and poor postures at workstations, most of them were focused on Western adult subjects [13, 14] and few Asian countries [15–18]. Paucity of information found on the problem of CVS and determinant factors in sub-Saharan African Countries, including Federal democratic republic of ethiopia. The couple of studies conducted in Ethiopia attempted to determine the prevalence of CVS and associated factors among calculator users in academic institutions and financial institution, just their focus was on academic institutions and bank workers [10, 11]. However, these studies are not sufficient to explore the nature of CVS and predictor variables at dissimilar groups of computer users.
Over the past 30 years, there has been a great advancement in computer technology. It has go almost an indispensable piece of equipment both at office and at home. It is sure that reckoner has dramatically benefited the club and makes the working condition easier and producing fast output [19]; however, it does acquaintance with wellness-related bug [ii, iv, v, 8, 9, 11, 13]. Owing to the technological advancement and growing socioeconomic development observed in the earth, the use of computer increased dramatically. Sub-Saharan Africa is not an exception on the rate of computer use; however, the users had inadequate knowledge on safety precautions during use of computer. The standard of computers is too poor and not equipped with protective devices from CVS [20]. Therefore, the aim of this study was to assess the prevalence of computer vision syndrome (CVS) and associated factors among computer users of government office workers in Debre Tabor town, northwest Federal democratic republic of ethiopia. This report shed light on the adverse outcome of computer use and its prevention and command methods amongst reckoner users in regime offices in Ethiopia.
2. Methods and Materials
2.1. Study Design and Menstruation
A cross-sectional study blueprint was employed from February to March, 2016.
2.2. Report Surface area and Catamenia
The report was conducted in Debre Tabor town, northwest Federal democratic republic of ethiopia. Debre Tabor, which is the uppercase of south Gondar Administrative Zone, Amhara regional country, is located 99 km from the capital city of the regional country and 667 km from Addis Ababa. The town consists of threescore regime offices with a total of 2752 figurer user employees.
2.3. Source and Study Population
All computer users who worked in authorities institutions in Debre Tabor boondocks were the source population, whereas all workers who were using computer in their day-to-day working life for at least 1 yr were taken as written report population [9]. The types of tasks performed past the computer users are word processing, spreadsheet processing, data entry and processing, preparing learning and teaching materials, and reading texts on computer.
2.4. Sample Size Determination
The sample size was determined past using single population proportion formula with the following assumptions: margin of error 5%, proportion of CVS 73.9% [10], 95% confidence interval, and design effect of ii and 10% of nonresponse rate to come up with a sample size of 652 respondents.
two.5. Sampling Procedure
A multistage random sampling technique was used to select participants from governmental offices. Nosotros have used two stages to select the final study participants in this study. In the starting time stage, twenty government offices were selected randomly from a total of threescore offices in Debre Tabor town. Then, from each selected office, study subjects were selected proportionally to their size by random sampling technique.
2.half dozen. Operational Definition
-
Computer vision syndrome (CVS): having the symptoms of computer vision syndrome either intermittently or continuously for at least ane calendar week during the concluding twelve months was defined as estimator vision syndrome. Presence of pain in and around the eyes, headache, blurred near vision, blurred distant vision, dry optics, sore/irritated eyes, cherry eyes, excessive violent, double vision, twitching of eyelids, and changes in visualizing colors were assessed as symptoms of CVS in this written report. The worker who reported one of the in a higher place symptoms was considered equally positive for CVS [9, eleven, 21, 22].
-
Cognition: participants were asked to answer 10 knowledge questions virtually safety measures of CVS. Graded as having "Good cognition" if they had answered correctly (≥70%) 7–x questions and (<70%) 0–6 as "Poor knowledge" [9].
-
Computer users: workers who use figurer for their day-to-day working life.
-
Income: monthly salary of the study participants was used equally proxy to measure their income.
2.7. Data Collection Method
A self-administered questionnaires supplemented past observational checklists were used to collect sociodemographic data, symptoms of CVS, details of calculator usage, potential risk factors (environmental and behavioral factors), and knowledge of computer users on safety measures of CVS. The information collection was carried out past six optometry BSc caste graduates. 2 supervisors were as well involved in monitoring data collection and checking the completeness of the questionnaires.
2.8. Information Quality Control
Preparation was given for information collectors and supervisors for 3 days on procedures, techniques, and ways of collecting the data. The tool was pretested amongst 33 (5% of the sample size) government function workers in Nefas Mewcha town, prior to the bodily data collection. Afterwards, the necessary modification on the tool was made.
ii.9. Data Processing and Analysis
The data were entered using Epi-Info version 7 and analyzed using SPSS statistical packet for Windows, version 20.0. All assumptions for binary logistic regression were checked. To determine predictor variables for CVS, binary logistic regression model was fitted and variables meaning at p value < 0.ii in the bivariable analysis were included in the multivariable analysis. Finally, variables found to exist pregnant at p value < 0.05 in the concluding model were declared as predictor variables. Rough odds ratios (COR) and adjusted odds ratios (AOR) with 95% confidence interval were reported in the result.
two.x. Upstanding Consideration
Upstanding clearance was obtained from the Institutional Review Board of the University of Gondar. The purpose of the written report was conspicuously explained to the study subjects, and their verbal consent was obtained. Confidentiality of the data had been maintained at all levels of the study.
3. Results
three.ane. Sociodemographic Characteristics of Respondents
A total of 607 study participants were included in this study with response rate of 93.1%. The median (interquartile range (IQR)) age of the respondents was 29 years (25–35 years). More than than half (335 (55.v%)) of the respondents were male person, 345 (56.eight%) were married, and 308 (fifty.7%) participants had monthly salary of >3000 ETB (140.16USD) (Tabular array 1).
Tabular array 1
Sociodemographic characteristics of calculator users in Debre Tabor town, northwest Ethiopia, 2016 (north=607).
| Variables | Frequency | Percent |
|---|---|---|
| Age (years) | ||
| xiv–29 | 320 | 52.vii |
| xxx–44 | 258 | 42.5 |
| 45+ | 29 | four.viii |
| | ||
| Sex activity | ||
| Male | 337 | 55.5 |
| Female | 270 | 44.5 |
| | ||
| Marital status | ||
| Unmarried | 233 | 38.4 |
| Married | 345 | 56.eight |
| Divorced | 17 | ii.viii |
| Widowed | 12 | two.0 |
| | ||
| Monthly salary | ||
| <1500 ETB (70.08 USD) | lx | ix.ix |
| 1500–3000 (seventy.08–140.16 USD) | 239 | 39.iv |
| >3000 (140.16 USD) | 308 | l.7 |
| | ||
| Educational status | ||
| Secondary school complete | seven | 1.2 |
| College graduate (document) | 215 | 35.4 |
| First degree | 337 | 55.5 |
| Second caste | 48 | 7.9 |
| | ||
| Religion | ||
| Orthodox | 565 | 93.1 |
| Muslim | 37 | 6.one |
| Protestant | five | 0.viii |
| | ||
| Ethnicity | ||
| Amhara | 593 | 97.seven |
| Oromo | 6 | 1.0 |
| Tigire | 8 | ane.iii |
| | ||
| Occupation | ||
| Officeholder | 364 | 60.0 |
| Lecturers and teachers | 81 | xiii.3 |
| Secretarial assistant | 119 | 19.6 |
| Coordinators and managers | 43 | seven.0 |
3.2. Ecology and Behavioral Characteristics
Two hundred sixty-6 (43.8%) of the participants worked in their current position for more than than 5.7 years and 273 (45.0%) used computer for >4.6 hours per mean solar day. Two hundred fourteen (35.3%) of the participants were taking regular break during working fourth dimension. Of which, their mean (±SD) break time was found to be 24.93 ± 11.76 minutes. More two-thirds (70.7%) of the participants unremarkably used desktop computers. Nearly 2-thirds of the participants (61.6%) used ergonomically comfortable sitting chair and virtually quarter of them (23.6%) reported the brightness of their computer screen was dull. 5 hundred forty-four (89.one%) of the participants did not wear eyeglass/spectacle. Their major reported reasons were eyeglass tin worsen the symptoms, social unacceptability, and non knowing its importance; feeling uncomfortable while wearing it; not to afford to buy; and not prescribed by doctors. On the other manus, 85 (fourteen%) respondents had previous history of middle illness (Table two).
Table 2
Behavioral characteristics of calculator users and their condition of working environment in Debre Tabor town, northwest Ethiopia (n=607).
| Variables | Frequency | Percent |
|---|---|---|
| Type of computer the workers used | ||
| Desktop only | 429 | 70.7 |
| Both desktop and laptop | 108 | 17.8 |
| Laptop only | 70 | 11.five |
| | ||
| Number of working years in the current position | ||
| ≤v.7 | 341 | 56.2 |
| >5.7 | 266 | 43.8 |
| | ||
| Number of working hours with figurer/day | ||
| ≤4.6 | 334 | 55.0 |
| >iv.6 | 273 | 45.0 |
| | ||
| Using ergonomically comfortable sitting chair | ||
| Yes | 374 | 61.vi |
| No | 233 | 38.four |
| | ||
| Source of light at the working place | ||
| Natural low-cal | 526 | 86.7 |
| Florescent/calorie-free bulb | 81 | thirteen.3 |
| | ||
| Brightness of reckoner screen | ||
| Bright | 464 | 76.4 |
| Ho-hum | 143 | 23.6 |
| | ||
| Adjusting calculator brightness | ||
| Yes | 392 | 64.6 |
| No | 215 | 35.4 |
| | ||
| Using antiglare for calculator screen | ||
| Yeah | 71 | 11.seven |
| No | 536 | 88.3 |
| | ||
| Taking regular break | ||
| Yes | 214 | 35.3 |
| No | 393 | 64.7 |
| | ||
| Wearing eyeglass at work | ||
| Yes | 66 | 10.9 |
| No | 541 | 89.1 |
| | ||
| Previous history of eye illness | ||
| Aye | 85 | xiv.0 |
| No | 522 | 86.0 |
| | ||
| Workload on reckoner | ||
| Yeah | 213 | 35.one |
| No | 394 | 64.ix |
| | ||
| Noesis | ||
| Adept | 345 | 56.8 |
| Poor | 262 | 43.2 |
iii.3. Prevalence of Calculator Vision Syndrome (CVS)
The self-reported prevalence of computer vision syndrome among computer users was 69.five % (95% CI; 65.60, 73.0). Blurred vision, eyestrain, and eye irritation were the most common reported symptoms of CVS with prevalence of 62.60%, 47.63%, and 47.xl%, respectively (Figure 1).
Frequency of computer vision syndrome symptoms among computer users of governmental offices in Debre Tabor Town, Ethiopia (northward=422).
three.iv. Factors Associated with Estimator Vision Syndrome
The multivariable analysis showed that monthly salary, occupational condition, daily computer usage, history of previous center trouble, and knowledge on safety measures of CVS and its adverse event were found to exist determinant factors for CVS.
The odds of developing CVS amid figurer users who earned a monthly salary in the range of 1500 and 3000 Ethiopian birr (ETB) and greater than 3000 ETB were 74% (AOR = 0.26, 95% CI (0.07, 0.88)) and 89% (AOR = 0.11, 95% CI (0.01, 0.95)) less than estimator users who earned less than 1500 ETB. The odds of developing CVS among officers and secretaries were 4.75 (AOR = 4.75, 95% CI (ane.77, 12.70)) and 9.17 (AOR = 9.17, 95% CI (two.63, 31.90)) more than the coordinators past occupation.
Participants who used computer for >iv.six hours per 24-hour interval were 2.29 times more likely to develop CVS compared to workers who used calculator for 4.6 hours or less (AOR: 2.29, 95% CI (1.43, 3.66)). The written report also showed that workers who had previous history of middle illness were three.nineteen times more likely to develop CVS than their counterparts. Moreover, workers who had good noesis on safe use of calculator and prevention mechanisms of agin effect of reckoner were 42% less likely to develop CVS than their counterparts (AOR: 0.58, 95% CI (0.37, 0.92)). The odds of developing CVS among computer users who regularly adjusted the brightness of their reckoner screen and who took regular pause decreased past 27% (AOR: 0.73, 95% CI (0.58, 0.91)) and 16% (AOR: 0.84, 95% CI (0.53, 0.97)), respectively (Table iii).
Table 3
Multivariable analysis of predictors for figurer vision syndrome symptoms among estimator users of governmental offices in Debre Tabor town, Ethiopia (n=607).
| Variables | CVS | COR (95% CI) | AOR (95% CI) | |
|---|---|---|---|---|
| Yes | No | |||
| Monthly income (ETB) | ||||
| <1500 | 52 | eight | 1.00 | ane.00 |
| 1500–3000 | 168 | 71 | 0.36 (0.sixteen,0.81) ∗∗ | 0.26 (0.07,0.88) ∗∗ |
| >3000 | 202 | 106 | 0.29 (0.13,0.64) ∗∗ | 0.eleven (0.01, 0.95) ∗∗ |
| | ||||
| Occupation | ||||
| Officer | 244 | 120 | ane.94 (1.03, 3.67) ∗∗ | 4.74 (1.77,12.70) ∗ |
| Lecturer and instructor | 55 | 26 | 2.02 (0.95, 4.31) | 2.29 (0.xc, 5.85) |
| Secretary | 101 | 18 | v.36 (ii.45, 11.69) ∗∗ | 9.17 (2.63,31.ninety) ∗∗ |
| Coordinators and managers | 22 | 21 | 1.00 | i.00 |
| | ||||
| Number of years in the current position | ||||
| ≤5.7 | 247 | 94 | 1.00 | 1.00 |
| >5.7 | 175 | 91 | 0.73 (0.52, one.04) | 0.74 (0.47,1.sixteen) |
| | ||||
| Number of working hours with computer/day | ||||
| ≤iv.6 | 196 | 138 | i.00 | 1.00 |
| >4.6 | 226 | 47 | 3.39 (2.31, 4.96) ∗∗ | 2.29 (1.43, three.66) ∗∗ |
| | ||||
| Comfy computer lite | ||||
| Yes | 242 | 124 | ane.00 | i.00 |
| No | 180 | 61 | ane.51 (1.05, ii.17) ∗ | 1.25 (0.820, 1.89) |
| | ||||
| Using ergonomically comfortable chair | ||||
| Yeah | 271 | 103 | 1.00 | 1.00 |
| No | 151 | 82 | 0.70 (0.49, 0.99) ∗∗ | 0.99 (0.64, 1.54) |
| | ||||
| Effulgence of computer screen | ||||
| Brilliant | 344 | 120 | i.00 | 1.00 |
| Dull | 78 | 65 | 0.42 (0.28, 0.62) ∗ | 0.64 (0.39,1.06) |
| | ||||
| Adjusting computer brightness | ||||
| Aye | 254 | 138 | 0.52 (0.35, 0.76) ∗ | 0.93 (0.58, ane.47) |
| No | 168 | 47 | ane.00 | i.00 |
| | ||||
| Taking regular break | ||||
| Yes | 262 | 131 | 0.68 (0.47, 0.78) ∗∗ | 0.84 (0.53, 0.97) ∗ |
| No | 160 | 54 | i.00 | ane.00 |
| | ||||
| Workload on estimator | ||||
| Yes | 175 | 38 | 2.74 (1.83, 4.11) ∗ | ane.36 (0.84, 2.20) |
| No | 247 | 147 | 1.00 | 1.00 |
| | ||||
| Knowledge | ||||
| Expert | 207 | 138 | 0.33 (0.23, 0.48) ∗∗ | 0.58 (0.37, 0.92) ∗ |
| Poor | 215 | 47 | i.00 | 1.00 |
| | ||||
| Previous history of eye illness | ||||
| Yes | 76 | 9 | iv.29 (two.10, 8.78) ∗∗ | 3.19 (1.49, half dozen.84) ∗ |
| No | 346 | 176 | 1.00 | 1.00 |
4. Discussion
This study was aimed at assessing the prevalence of CVS and its predictors. The self-reported prevalence of CVS among Debre Tabor town authorities role workers was 69.5% (95% CI = 65.threescore, 73.00). The finding is in line with other studies: 73.9% in University of Gondar, Federal democratic republic of ethiopia, among secretaries and information processors [x]; 74% in Nigeria [8]; 73% in Gondar, Federal democratic republic of ethiopia, among banking company workers [11]; 74% in Abuja, Nigeria [8]; 67.iv% in Sri Lanka amid function workers [9]; 72% in Ajman, United Arab Emirates [23]; and 63% in Public University of Putra, Malaysia, among administrative staffs [fifteen]. On the other paw, this study result was less than the findings in Malaysia, which was reported to be 89% [24], and in Chennai, India, which was 80.3% [25]. The possible reason might be either due to the study participants in these areas being academy students using computers for a longer time than regime role workers or due to students using computers for a long time without eye break for studying rather than part workers who relatively taking near bank workers taking frequent breaks. Regarding the written report conducted in India, neck and shoulder pain was included to define CVS, whereas in this study, simply ocular and visual symptoms including headache were used to measure out CVS. On the other hand, in this written report, the eye/visual symptoms which lasted at least 1 week were considered to define CVS, whereas they had no specification on elapsing of symptoms [25]. These discrepancies might be a possible justification for the reported college prevalence of CVS in Chennai, India, than our study.
Highly paid computer users were less probable to develop CVS than their low-paid counterparts. This might be due to the fact that high-paid computer users may take greater opportunity to use antiglare and expert computers that could reduce the evolution of CVS. Conversely, low-paid ones were suffering from this disease because they could not afford these facilities. High-paid reckoner users might accept good sensation on computer ergonomics and tin optimize safe elapsing of reckoner exposure. Income was mentioned as a protective cistron for wellness past unlike studies [26–28] considering high-paid workers can have a improve access to wellness care, which could take alleviated their symptoms. On top of that, these groups were managers and academicians (lecturers and teachers) in this study, who are engaged in less repetitive work such as checking emails and briefly reading notes. A chi-square examination shows highly paid reckoner users took pause significantly higher than their counterparts (10 2 = 5.2, p value = 0.08), which supported the above argument. Their daily duration of computer use was also significantly less than low-paid figurer users (Pearson correlation examination betwixt income and computer exposure time: r = −0.24, p value ≤ 0.001).
This study indicated that officers and secretaries were found to be significantly impacted past CVS compared to managers and coordinators. The possible reason might be officers and secretaries are usually used reckoner for a long time. A one-manner ANOVA and multiple comparing tests confirmed that the daily exposure time of calculator was significantly higher amongst secretaries and officers in this report (p value < 0.01).
Daily exposure time was some other cistron that was statistically significant in this study. Workers who used computers for >4.six hrs per day were more probable to develop CVS every bit compared to those who used computers <4.vi hrs (AOR: 2.29, 95% CI (1.43, 3.66)). A computer emits electromagnetic radiation or high-energy blue light, which enables that high energy to stress the ciliary muscle in the center; ultimately, a prolonged exposure to computer screen led to heart strain. The finding was in line with a study conducted in University of Gondar, Ethiopia [x]. Other similar studies were also reported an increase in the number of hours spent on computer increases the adventure of CVS significantly [ix, 17, 25, 29, 30]. Hence, reducing the amount of time spent on estimator is important to preclude CVS [12].
The odds of developing CVS were college among computer users who had less frequent or no break. This might be due to the fact that the eyes commonly cannot remain focused on the pixel-generated images on a computer screen for a long time, and equally such, the eyes must focus and refocus thousands of times by taking frequent breaks for acceptable time while viewing the screen, and if the refresh rate is too slow, information technology causes a high flickering screen, which leads to endure from symptoms of CVS [eleven]. The event was in cyclopedia with previous like studies who reported that taking break is a protective factor for CVS [xi, 18, 21, 25]. After working for one hour, taking curt breaks for 5 min has been recommended to decrease eye problem without undue influence of piece of work productivity [31].
Previous history of eye illness was found to be significantly associated with CVS (AOR: three.19, 95% CI (1.49, 6.84)). This finding was supported by a study conducted in Sri Lanka, which indicated preexisting centre diseases were associated with severe CVS [9]. Similarly, a study conducted in India showed that computer users with history of eye bug were at higher risk of developing CVS [32]. Another study in São Paulo, Brazil, showed that headache was high among figurer users who worked in poor ergonomic design and lacks adequate eye strain protection mechanisms since sign and symptoms were nonspecific [33]. This might be long-lasting upshot of previous affliction; the illness may exist till now to feel each other with CVS, lack of care, and treatment related to previous illness, and some of the previous problems are chronic and may exist till now.
Computer users who had skillful knowledge on safety measures of estimator use and its adverse effect were plant to be less impacted past CVS (AOR: 0.58, 95% CI (0.37, 0.92)). The result was in agreement with a study conducted in University of Benin, Nigeria [34] and Malaysia [xv]. The possible reason might exist the workers who have expert knowledge are more probable to implement protective measures and will adhere to condom estimator apply. In full general, in that location is a direct relationship between knowledge and applying condom measures that potentially tackle work-related injuries and diseases. In contrast, a study conducted in Sri Lanka revealed that ergonomics practices cognition was associated with increased risk of developing CVS [ix]. The discrepancy might be in some cases there could be correlation between ergonomics practices knowledge and higher daily calculator usage; the subsequently indicated as risk factors for CVS in various studies and the current study.
5. Limitation of the Study
The main limitations of this written report were ophthalmic test was not done to mensurate CVS and the symptoms reported were self-reported. Symptoms that might not be recognized by users would be left unreported. To minimize the unduly effect of cocky-reported measurement, nosotros take adopted and used standard protocol. Though we have used a protocol that measures CVS symptoms that can be occurred while using computer, some of the symptoms of CVS including blurred vision and eye strain might exist caused past uncorrected refractive fault that could potentially overestimate the prevalence [35]. According to the current study, the prevalence of CVS among chance groups for refractive error such as anile population and who practice not employ eyeglass/spectacle was not significantly dissimilar compared to their counterparts that testify the influence of the bias was not significant. But in the future report, we recommend that the influence of uncorrected refractive error should be addressed methodologically and the measurement of CVS tin be supported by ophthalmic examination.
vi. Decision
This study demonstrated that the prevalence of CVS was found to exist college in Debre Tabor town government institutions. Monthly salary, daily exposure time, blazon of work, and knowledge were the about determinant factors for CVS. Hence, optimizing the exposure fourth dimension and improving the awareness of users by rigorous training and direction support are of import to tackle the trouble. In the futurity, information technology is recommended to decide the additive or synergistic effect of using smartphone and computer tablets on CVS on or off working time.
Acknowledgments
The authors are pleased to acknowledge the data collectors, study participants, and Debre Tabor town administration for their unreserved contributions to the success of this study. They are also grateful for University of Gondar for logistic support. The authors received the data collection fee and logistic and stationery support from University of Gondar.
Data Availability
Data will be made available from the primary author upon asking.
Conflicts of Involvement
The authors declare that they have no conflicts of involvement.
Authors' Contributions
AD, FA, and AN participated during inception of the research idea, development of a research proposal, information collection, analysis and estimation, and writing various parts of the research written report. AD designed the study protocol and supervised the quality of data, analyzed the data, and prepared the manuscript. FA and AN designed the report protocol and supervised the quality of information and analyzed the data; SDW analyzed the data, interpreted the outcome, and prepared the manuscript; DHC interpreted the result and prepared the manuscript. All authors read and canonical the final manuscript.
References
i. Anshel J. Computer Vision Syndrome. 1st. Boca Raton, FL, Usa: CRC Printing Taylor & Fransis Group; 2005. Visual ergonomics handbook; pp. 23–36. [Google Scholar]
ii. Sen A., Richardson S. A written report of computer-related upper limb discomfort and computer vision syndrome. Journal of Human Ergology. 2007;36(2):45–50. [PubMed] [Google Scholar]
3. American Optometric Association. Guide to the Clinical Aspects of Computer Vision Syndrome. St. Louis, MO, U.s.a.: Association AO; 1995. [Google Scholar]
four. Gangamma M. P., Poonam, Rajagopala M. A clinical study on "computer vision syndrome" and its direction with Triphala center drops and Saptamrita Lauha. AYU (An International Quarterly Periodical of Research in Ayurveda) 2010;31(2):236–239. doi: 10.4103/0974-8520.72407. [PMC complimentary article] [PubMed] [CrossRef] [Google Scholar]
v. Charpe Northward. A., Kaushik V. Computer vision syndrome (CVS): recognition and control in software professionals. Journal of Human Ecology. 2009;28(1):67–69. doi: 10.1080/09709274.2009.11906219. [CrossRef] [Google Scholar]
half dozen. Wimalasundera S. Estimator vision syndrome. Galle Medical Journal. 2006;11(1):25–29. doi: 10.4038/gmj.v11i1.1115. [CrossRef] [Google Scholar]
7. Tadesse S., Kelaye T., Assefa Y. Utilization of personal protective equipment and associated factors among textile manufacturing plant workers at Hawassa Town, Southern Ethiopia. Journal of Occupational Medicine and Toxicology. 2016;11(1):1–6. doi: 10.1186/s12995-016-0096-7. [PMC gratis article] [PubMed] [CrossRef] [Google Scholar]
viii. Akinbinu T. R., Mashalla Y. Noesis of figurer vision syndrome among computer users in the workplace in Abuja, Nigeria. Journal of Physiology and Pathophysiology. 2013;4(iv):58–63. doi: 10.5897/jpap2013.0078. [CrossRef] [Google Scholar]
9. Ranasinghe P., Wathurapatha Westward., Perera Y., et al. Computer vision syndrome among reckoner office workers in a developing land: an evaluation of prevalence and risk factors. BMC Research Notes. 2016;9(1):150–158. doi: 10.1186/s13104-016-1962-one. [PMC gratis article] [PubMed] [CrossRef] [Google Scholar]
10. Alemayehu G., Nega A., Tegegne Eastward., Mule Y. Prevalence of cocky reported calculator vision syndrome and associated factors amidst secretaries and data processors who are working in University of Gondar, Ethiopia. Journal of Biological science, Agriculture and Healthcare. 2014;4(15):33–37. [Google Scholar]
11. Assefa Northward. L., Weldemichael D. Z., Alemu H. Due west., Anbesse D. H. Prevalence and associated factors of calculator vision syndrome among depository financial institution workers in Gondar City, northwest Federal democratic republic of ethiopia. Clinical Optometry. 2017;9:67–76. doi: 10.2147/OPTO.S126366. [PMC costless article] [PubMed] [CrossRef] [Google Scholar]
13. Rosenfield 1000. Calculator vision syndrome: a review of ocular causes and potential treatments. Ophthalmic and Physiological Optics. 2011;31(5):502–515. doi: 10.1111/j.1475-1313.2011.00834.10. [PubMed] [CrossRef] [Google Scholar]
14. Yan Z., Hu Fifty., Chen H., Lu F. Computer vision syndrome: a widely spreading but largely unknown epidemic amid computer users. Computers in Human being Behavior. 2008;24(five):2026–2042. doi: ten.1016/j.chb.2007.09.004. [CrossRef] [Google Scholar]
fifteen. Zainuddin H., Isa M. M. Outcome of human and technology interaction: estimator vision syndrome among administrative staff in a public university. International Journal of Business, Humanities and Technology. 2014;iv(3):38–44. [Google Scholar]
sixteen. Shrestha G. S., Mohamed F. Due north., Shah D. N. Visual problems amidst video display terminal (VDT) users in Nepal. Periodical of Optometry. 2011;4(2):56–62. doi: 10.1016/s1888-4296(11)70042-v. [CrossRef] [Google Scholar]
17. Sharma A., Khera S., Khandekar J. Computer related health problems amidst information applied science professionals in Delhi. Indian Journal of Community Medicine. 2006;31(ane):36–38. doi: 10.4103/0970-0218.54936. [CrossRef] [Google Scholar]
18. Shantakumari Due north., Eldeeb R., Sreedharan J., Gopal G. Reckoner utilise and vision-related problems amongst students in Ajman UAE. Headache. 2012;43(58):p. 53. [Google Scholar]
19. Loh Thousand., Redd S. Understanding and preventing computer vision syndrome. Malaysian Family Md: The Official Periodical of the Academy of Family Physicians of Malaysia. 2008;three(3):128–130. [PMC complimentary article] [PubMed] [Google Scholar]
20. Mbuyisa B., Leonard A. ICT adoption in SMEs for the alleviation of poverty. Proceedings of International Association for Management of Technology, IAMOT 2015; June 2015; Cape Boondocks, Southward Africa. University of Pretoria; [Google Scholar]
21. Noreen G., Batool Z., Fatima T., Zamir T. Prevalence of computer vision syndrome and its associated adventure factors among under graduate medical students. Pakistan Journal of Ophthalmology. 2016;32(3):140–146. [Google Scholar]
22. Anshel J. Diagnosing, treating CVS relies on good instance history: basic eye care, ergonomics and optical correction are all part of an constructive treatment plan for estimator vision syndrome. Master Care Optometry News. 2007;12(9):1081–6437. [Google Scholar]
23. Shantakumari N., Eldeeb R., Sreedharan J., Gopal K. Calculator use and vision. related problems among university students in Ajman, United Arab Emirate. Annals of Medical and Health Sciences Inquiry. 2014;4(ii):258–263. doi: 10.4103/2141-9248.129058. [PMC free commodity] [PubMed] [CrossRef] [Google Scholar]
24. Reddy S. C., Depression C., Lim Y., Depression 50., Mardina F., Nursaleha One thousand. Calculator vision syndrome: a report of knowledge and practices in university students. Nepalese Journal of Ophthalmology. 2013;5(ii):161–168. doi: 10.3126/nepjoph.v5i2.8707. [PubMed] [CrossRef] [Google Scholar]
25. Logaraj M., Madhupriya V., Hegde S. Computer vision syndrome and associated factors amongst medical and engineering students in Chennai. Annals of Medical and Health Sciences Inquiry. 2014;4(2):179–185. doi: 10.4103/2141-9248.129028. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
26. Gedefaw M., Takele M., Aychiluhem G., Tarekegn Thousand. Current status and predictors of diarrhoeal diseases amidst under-five children in a rapidly growing urban setting: the case of city administration of Bahir Dar, northwest Ethiopia. Open Journal of Epidemiology. 2015;5(2):89–97. doi: 10.4236/ojepi.2015.52012. [CrossRef] [Google Scholar]
27. Siziya S., Muula A. S., Rudatsikira E. Diarrhoea and acute respiratory infections prevalence and risk factors among under-five children in Iraq in 2000. Italian Journal of Pediatrics. 2009;35(1):p. eight. doi: 10.1186/1824-7288-35-8. [PMC complimentary commodity] [PubMed] [CrossRef] [Google Scholar]
28. Root Thou. P. Sanitation, community environments, and childhood diarrhoea in rural Zimbabwe. Journal of Wellness, Population and Nutrition. 2001;19(2):73–82. [PubMed] [Google Scholar]
29. Rahman Z. A., Sanip S. Computer user: demographic and computer related factors that predispose user to get figurer vision syndrome. International Periodical of Concern, Humanities and Technology. 2011;1(ii):84–91. [Google Scholar]
30. Rajeev A., Gupta A., Sharma Thousand. Visual fatigue and computer use amidst higher students. Indian Journal of Customs Medicine. 2006;31(3):192–193. [Google Scholar]
31. Levy B. Due south. Preventing Occupational Disease and Injury. Washington, DC, USA: American Public Health Association; 2005. [Google Scholar]
32. Akms A., Alam S., Practice M. Computer vision syndrome. The ORION Medical Journal. 2009;32(3):692–693. [Google Scholar]
33. Ingrid Becker S., Michelle Katherine A. 10., Valéria Mayaly A. O., Ana Carolina R. P., Rodrigo Cappato D. A. Primary headaches among adolescents and their clan with excessive computer use. Revista Dor. 2015;16(4):244–248. doi: 10.5935/1806-0013.20150049. [CrossRef] [Google Scholar]
34. Chiemeke S. C., Akhahowa A. E., Akhahowa B. O., et al. Evaluation of vision-related problems amid reckoner users: a case study of Academy of Benin, Nigeria. Proceedings of the World Congress on Technology; July 2007; London, UK. [Google Scholar]
35. Adane F. Prevalence of Cocky Reported Computer Vision Syndrome and Associated Factors among Computer Users of Governmental Offices in Debre Tabor Town, Northwest Ethiopia. Gondar, Ethiopia: University of Gondar; 2016. [Google Scholar]
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