Asthenopia Prevalence and Risk Factors Associated with Professional Computer Use- A Systematic Revie

Asthenopia Prevalence and Risk Factors Associated with Professional Computer UseA Systematic Review Manuel Augusto Pereira Vilela*1, Lucia Campos Pellanda2, Claudia C. Cesa3, Victor D. Castagno4 Universidade Federal de Ciências da Saúde de Porto Alegre. Porto Alegre, RS, Brazil.

1

Post Graduation Program in Health Sciences: Cardiology, Instituto de Cardiologia / Fundação Universitária de Cardiologia, Porto Alegre, RS, Brazil.

2

Instituto Federal de Educação, Ciência e Tecnologia Sul-Rio-Grandense, Sapucaia do Sul, RS, Brazil.

3

Universidade Federal de Pelotas. Pelotas, RS, Brazil.

4

mapvilela@gmail.com; 2lupellanda@gmail.com; 3claudia.c.cesa@gmail.com; 4vicastagno@hotmail.com

*1

Abstract Objective: Estimate the prevalence and risk factors of asthenopia associated with of computers in adults, with meta-analysis.

Material and methods: Inclusion criteria were population-based studies from 1960 to December 2014. The search was performed in eletronic databases with no language restriction. Results: Out of 1692 potentially relevant citations, 22 met the inclusion criteria. The combined prevalence was 40.4% (95%CI 31.9 - 48.8). The most related factors were the duration of use, ametropia, age, binocularity dysfunctions. Conclusion: Asthenopia occurs at a significant rate and represents a common cause of lack of efficiency associated with work with computers.

Keywords Asthenopia; Eyestrain; Eye Fatigue

Introduction Asthenopia in adults is a major work-related complaint. The increasing use of computers (desktops, tablets and laptops) and other electronic equipment (smartphones, e-book readers, video games) has multiplied the frequency of this complaint and changed its context significantly. The condition of a person experiencing asthenopia with symptoms like tired eyes, blurred or double vision as a result of the use of these electronic resources is generally referred to as “computer vision syndrome” (CVS), “video display units syndrome” (VDU), or “video display terminals syndrome” (VDT)*1-8]. Furthermore, specific visual or visual motion disturbances deriving from viewing 3D movies are being reported with increased frequency and have been named “3D vision syndrome”*9,10+. Recent estimates report that 77.4% of the U.S. population use computers routinely and that 90% of those aged 5-17 years use this technology on a daily basis, with a projection of about 2 billion users worldwide[1,2].

Despite its importance, the actual prevalence of asthenopia in VDU syndrome has not been precisely established.

Methods of evaluation and selection of patients in the different studies are very heterogeneous. Prevalence rates reported vary from 7 to 85%[11,12]. An accurate estimate of prevalence would be essential to establish the real impact of asthenopia complaints in professional contexts.

Thus, the objective of this study is to systematically review the literature in order to determine the prevalence of asthenopia associated with professional use of computers in adults, with meta-analysis of published observational data.

Methods Study Design Systematic review of the literature about asthenopia related to video display unite usage, with statistical Metaanalysis for pooled results.

International Journal of Advance in Medical Science, Vol. 3, No. 2—November 2015 2327-7238/15/02 051-10 © 2015 DEStech Publications, Inc. doi:10.12783/ams.2015.0302.03

51

52

Manuel Augusto Pereira Vilela, Lucia Campos Pellanda, Claudia C. Cesa, Victor D. Castagno

Search Strategy Electronic databases such as MEDLINE (by PubMed), EMBASE, LILACS (Latin American and Caribbean Health Sciences Literature), SCIELO (Scientific Electronic Library Online), COCHRANE Central Library and the bibliography cited in review and original papers were searched. The keywords “asthenopia”, “eyestrain” and “visual fatigue” were used to identify population-based studies looking for the prevalence of asthenopia without the use of filters or language restrictions. The search strategy developed for PubMed is detailed in Attachment 1/Chart 1. This review was developed in accordance with standards established by the "Preferred reporting items for systematic reviews and meta-analysis" (PRISMA)*13+ and by the “Meta-analysis of Observational Studies in Epidemiology” (MOOSE) statements*14,15+.

Eligibility Criteria Observational studies from 1960 to December 2014 presenting asthenopia prevalence in adult professional computer users were included. Case reports and case series were excluded. Duplicated publications or publications containing sub-studies of selected papers were also excluded.

Selection Of Studies Titles and abstracts of all articles identified by electronic databases and by manual searching were assessed independently by two reviewers (M.A.P.V., L.C.P.). After title and abstract evaluation, papers that fulfilled the inclusion criteria were selected for full text appraisal. Complete versions of all potentially relevant papers were obtained through the databases or by directly ordering from the journals. Languages in which the authors were not fluent were translated by a native translator. At all stages, disagreements between the reviewers were solved by consensus and, when necessary, a third reviewer (V.D.C) was consulted.

Data Extraction Data extraction was conducted independently by two reviewers (M.A.P.V., L.C.P.) and included study sample size, total number of cases, asthenopia prevalence, possible causes and described consequences, geographical location of the population, age of participants, asthenopia evaluation method and study design. Asthenopia was always diagnosed at each author’s discretion, according to mean scores or number of affirmative answers on a questionnaire or scale developed for each study.

Risk Of Bias The methodological quality of all studies was assessed by two reviewers (M.A.P.V., L.C.P.) based on the criteria established by Downs and Black’s quality score for non-randomized studies [16]. The score considers five items: (1) available information – to asses the overall quality of the study (objectives, outcomes, sample description, description of losses, variability of results and actual probability of findings), (2) external validity – to determine the ability to generalize the findings of the study (representativeness of the sample, local representativeness and staff involved in the intervention), (3) biases – to assess bias in the outcome measure(s) (blinding type, previous planning of analysis, follow-up duration, analysis adequacy, adherence, accuracy of tests), (4) confounding factors – to determine bias from sampling or group assignment (population origin and selection, randomization, losses considered in the discussion) and (5) power of the study – to determine whether findings are due to chance.

Assessment of study quality and risk of biases was carried out by two independent reviewers (M.A.P.V., L.C.P.) and disagreements were solved by consensus or by a third reviewer (V.D.C).

As no intervention study was selected, the maximum score possible in the present review was 18 points. Any scores under 7 points were considered inadequate for inclusion in the meta-analysis.

Data Analysis Data on asthenopia prevalence and risk factors (expressed as a percentage) comprised the main variable of interest.

When this variable was not directly expressed in the text, prevalence was calculated by taking the total number of individuals in the study divided by the total number of studied cases. In order to conduct a meta-analysis of the descriptive data, a Microsoft Excel template proposed by Neyeloff et al.[17] was chosen owning to its having been

Asthenopia Prevalence and Risk Factors Associated with Professional Computer Use- A Systematic Review

53

specifically developed for prevalence and incidence analysis. Using this model, it is possible to obtain the result of the meta-analysis of descriptive data through both fixed and random effects. Furthermore, the model also calculates heterogeneity and inconsistency (Cochran’s Q test and I*2+ inconsistency test) and allows the construction of forest plots based on prevalence. Depending on the heterogeneity and inconsistency results, Neyeloff et al.[17] proposed the use of the random effects model when heterogeneity is high (above 50%) or when it is believed that there are significant differences between populations. Thus, we adopted random effects measures considering the differences among the studied populations. Since we are assuming that variability is not only due to sampling errors, but also to variability of effects in the population, in this model the weight of each study was adjusted with a constant (v) representing it[18].

The results of a meta-analysis are represented in a Forrest Plot figure. In this type of graph, each study is represented by and individual line consisting of the confidence interval, with the point estimate represented by a circle. A diamond-shaped form in the bottom of the figure represents the final aggregated estimation.

When necessary, sensitivity analysis was performed removing study by study and evaluating the possible changes that could lead to a significant difference.

Results Selection of Studies 1627 studies were identified in the searched databases and reviewed references. Of these publications, a total of 22 studies met the inclusion criteria. Figure 1 presents all the stages of the search and selection process in the flow diagram. Agreement rate between reviewers when selecting studies was 85% and after consensus reached 100%.

FIGURE 1. FOREST PLOT OF STUDIES ABOUT PREVALENCE OF ASTHENOPIA IN VDU USERS

Description of Studies The 22 selected studies comprised 36333 individuals from 12 countries and samples varied in the number of included individuals, from 29 to 25964. Except for six studies[2,12,19-22] that used different previously adopted instruments[23-27], all the others used personal non-standardized questionnaires to detect the outcome. Table 1 provides a description of the selected studies.

54

Manuel Augusto Pereira Vilela, Lucia Campos Pellanda, Claudia C. Cesa, Victor D. Castagno

CS

D

Bhanderi DJ et al. (2008)[3]

India

CS

D

Carta A, et al.

(2003)[38]

Italy

CS

A

Dainoff MJ et al. (1981)[51]

USA

CS

D

De Groot JP et al. (1983)[11]

Netherla nd

CS

A

Fenga C, et al.

(2007)[19]

Italy

CS

C

Hedman LR et al. (1984)[30]

Sweden

CS

A

Iwakiri K, et al.

(2004)[39]

Japan

CS

C

Kowalska M, et al. (2011)[1]

Poland

CS

C

Mocci F et al.

(2001)[20]

Italy

CS

C

Nakazawa T et al. (2002)[2]

Japan

CS

D

Ong C, et al.

(1981)[28]

Singapo re

CS*

Piccoli B, et al.

(1989)[36]

Italy

Rocha LE, et al.

(2004)[21]

IC 95%

Finland

Prevalence (%)

Bergqvist U, et al. (1994)[32]

Specific to the study, not validated Specific to the study, not validated Specific to the study, not validated Specific to the study, not validated Specific to the study, not validated Specific to the study, not validated Italian Society of Occupational Medicine and Industrial Hygiene36 Specific to the study, not validated Specific to the study, not validated Specific to the study, not validated National Institute for Occupational Safety and Health[37] Specific to the study, not validated

Asthenopic cases

B

Sample size

Occupation

CS

% males

Type of study

India

Mean Age or (range)

Country

Agarwal A et al. (2013)[31]

Questionnai re

Source (author, year)

TABLE 1. DESCRIPTIVE CHARACTERISTICS AND RESULTS OF INCLUDED STUDIES

(18-39)

68

150

81

53.8

0.42-0.65

47

50

327

95

29

0.23-0.34

(18-55)

66

419

194

46.3

0.39-0.53

(21-60)

77

660

168

25.4

0.21-0.29

23

99

31

14

45

0.21-0.68

39

100

43

3

7

0.01-0.14

(18-50)

54.6

62

32

51

0.33-0.69

27

88.6

29

10

36

0.13-0.55

(20-59)

77.6

2374

1712

72.1

0.68-0.75

(18-55)

55.5

477

199

41.6

0.35-0.47

(28-53)

88.2

212

68

31.9

0.24-0.39

(20-59)

60

25964

11814

45.5

0.44-0.46

D

Maeda[38]

(17-35)

100

62

30

49

0.31-0.65

CS

D

Specific to the study, not validated

(18-55)

55.5

216

51

23.5

0.17-0.30

Brazil

CS*

C

Elias R, Cail F[39]

(18-56)

55.9

1448

211

14.6

0.12-0.16

Sรก EC (2010)[22]

Brazil

CS

C

Elias R, Cail F[39]

(15-24)

77.5

72

39

54.6

0.37-0.71

Salibello C et al. (1995)[37]

USA

CS*

C

38

66

324

211

65

0.56-0.73

Mexico

CS*

A

(18-55)

66.4

432

251

58.1

0.50-0.65

Japan

CS*

A

(17-58)

884

231

196

85

0.72-0.96

SanchezRoman FR et al. (1996)[29] Shima M et al.

(1993)[12]

Specific to the study, not validated Specific to the study, not validated Shima S et al.[40]

(continues)

Asthenopia Prevalence and Risk Factors Associated with Professional Computer Use- A Systematic Review

55

Source (author, year)

Country

Type of study

Occupation

Questionnai re

Mean Age or (range)

% males

Sample size

Asthenopic cases

Prevalence (%)

IC 95%

TABLE 1. DESCRIPTIVE CHARACTERISTICS AND RESULTS OF INCLUDED STUDIES (CONTINUED)

Taino G et al.

(2006)[33]

Italy

CS

C

Specific to the study, not validated

(24-64)

660

191

23

12

0.07-0.16

Yeow PT et al.

(1990)[35]

Malaysi a

CS*

C

Specific to the study, not validated

(28-34)

665

282

37

13

0.08-0.17

Zhaojia et al.

(2007)[40]

Japan

CS

B

Specific to the study, not validated

(29-49)

779

2327

450

19.6

0.17-0.21

(Type of study: CS= cross-sectional;CS*= cross-sectional with control group. Occupations subgroups: A= same work place and function; B= same work place but different functions; C= different work places but same functions; D= different work place and function); CS= crosssectional; IC= confidence interval)

Assessment of Risk of Bias in the Studies The score proposed by Downs and Black[16] was adapted by excluding all criteria relating to experimental studies, since no study with this design was selected. Thus, of the items surveyed, the maximum score achieved was 18 points (Table 2). The average methodological quality of the studies was 8.3 +- 1.5 (range 7-12).

TABLE 2. METHODOLOGICAL QUALITY EVALUATION OF INCLUDED STUDIES

Author, year Agarwal A et al.

(2013)[31] Bergqvist U, et al.

(1994) [32] Bhanderi DJ et al.[3] (2008)[3] Carta A, et al.

(2003)[38] Dainoff MJ et al.

(1981)[51] De Groot JP et al.

(1983)[11] Fenga C, et al.

(2007)[19] Hedman LR et al.

(1984)[30] Iwakiri K, et al.

(2004)[39] Kowalska M, et al.

(2011)[1] Mocci F et al.

(2001)[20] Nakazawa T et al.

(2002)[2] Ong C, et al.

(1981)[28] Piccoli B, et al.

(1989)[36] Rocha LE, et al.

(2004)[21]

Study quality

External validity

Internal validity

Confusion and selection bias

Sample power

Downs and Black mean score

Adequate

Not Adequate

Adequate

Not adequate

Not adequate

7.0

Adequate

Not adeuqte

Adequate

Adequate

Adequate

9.0

Adequate

Not adequate

Unclear

Not adequate

Adequate

9.0

Adequate

Not Adequate

Adequate

Not adequate

Adequate

9.0

Adequate

Not Adequate

Adequate

Adequate

Not Adequate

9.0

Adequate

Not Adequate

Adequate

Not Adequate

Not Adequate

7.0

Adequate

Not Adequate

Adequate

Not Adequate

Not Adequate

8.0

Unclear

Not Adequate

Adequate

Not Adequate

Not Adequate

7.0

Unclear

Not Adequate

Unclear

Not Adequate

Adequate

7.0

Adequated

Unclear

Adequate

Not Adequate

Adequate

9.0

Adequated

Not Adequated

Adequate

Not adequate

Not adequate

7.0

Adequate

Adequate

Adequate

Unclear

Adequate

12.0

Adequate

Not Adequate

Adequate

Not Adequate

Not Adequate

7.0

Adequate

Adequate

Adequate

Not adequate

Adequate

10.0

Adequate

Not adequate

Adequate

Not adequate

Adequate

9.0 (continues)

56

Manuel Augusto Pereira Vilela, Lucia Campos Pellanda, Claudia C. Cesa, Victor D. Castagno

TABLE 2. METHODOLOGICAL QUALITY EVALUATION OF INCLUDED STUDIES (CONTINUED)

Author, year

Study quality

External validity

Internal validity

Confusion and selection bias

Sample power

Downs and Black mean score

Sรก EC (2010)[22]

Adequate

Not Adequate

Adequate

Not Adequate

Adequate

10.0

Salibello C et al.

(1995) [37]

Adequate

Not Adequate

Adequate

Not Adequate

Adequate

8.0

Sanchez-Roman FR et al. (1996)[29]

Adequate

Not Adequate

Adequate

Not Adequate

Not Adequate

7.0

Shima M et al.

(1993)[12]

Adequate

Not Adequate

Adequate

Not Adequate

Not Adequate

7.0

Taino G et al.

(2006)[33]

Adequate

Not Adequate

Adequate

Not Adequate

Not Adequate

7.0

Yeow PT et al.

(1990)[35]

Adequate

Not Adequate

Adequate

Not Adequate

Not Adequate

7.0

Zhaojia et al.

(2007)[34]

Adequate

Adequate

Adequate

Adequate

Adequate

11.0 8.3 (SD 1.5)

Result of Meta-Analysis The average prevalence rate of asthenopia in the selected studies was 40.4% (95%CI 31.9-48.8) ranging from 7-85%.

Heterogeneity measured by random effects was very low (I2= 2.73) (Table 1, Figure 1). Comparative studies[12,21,28,29] indicated that asthenopia is more frequent during computer use.

Many articles have in common a small sample size and relatively high numbers of losses. The largest sample was found in the study by Nakazawa et al.[2], with 25,964 individuals aged 20-59 years, consisting of three surveys in different periods. The smallest sample was presented by Hedman et al.[30], with 29 cases.

Most studies showed a significant association between the numbers of hours of computer use and asthenopia[2,3,12,19,28-34], except for those conducted by Hedman et al.[30] and Yeow et al.[35]. VDU use for 5 or more hours/day or more than 20h/week increased the prevalence of visual fatigue 2.6 times. It seems to affect females more[12,22,34,36,37], however, in some studies[3,29] prevalence in females was similar to that found in males (overall 68.7% of all studies were with men). Age itself showed no differences, but when analyzed together with presbyopia, it was often associated with the outcome[3,32,36]. The presence of refractive error was associated with eyestrain in some series[3,19,32,36,37] (about 50%). Accommodative or convergence insufficiency and heterophoria demonstrated significant association in some studies[36,38] (3.3-3.9%). Ergonomic conditions (poor workstation design, height and distance from the screen, flickering, contrast, body posture, improper illumination, ventilation), breaks during work, hours of sleep and rest at home, psychological state (stress, self-esteem, deadlines, competition) were present in a significant way in many studies and showed strong association[2,3,12,20-22,32,39].

In most studies selected the effect of therapeutic measures was not examined.

Discussion In this systematic review, the combined prevalence of asthenopia was 40.4% in adult professional computer users.

Systematic reviews with Meta-analyses of observational studies are an important methodology to summarize the ever-growing data obtained from primary research. In the case of the present study, our aim was to synthesize data from observational studies to obtain summary statistics of prevalence for this condition.

The differences came from several factors, such as populations, selection biases, unequal methods of assessment and the clinical diversity in which asthenopia may develop[2,23-25,35,40]. Nakazawa et al.[2] have described 17 different kinds of symptoms which are ultimately different manifestations of the same condition. Sheeny et al.[24] established two forms of asthenopia: external, which arises from changes on the ocular surface similar to dry eye syndrome; and internal, which is the product of dysfunctional accommodative and fusional systems. Dry eye symptoms are very common among computer users and are directly related to the duration of computer use[26,27].

Asthenopia Prevalence and Risk Factors Associated with Professional Computer Use- A Systematic Review

57

They possibly result from the combination of several factors, including: environmental (air humidity, airconditioning)[5,40], reduction in normal frequency or amplitude of blinking (from 20-22/min to 7-10/min)[26,27,4147], size and glare of the source (tablets and smartphones contribute greatly in this respect)[48-50], age and gender[47,48], medication and systemic diseases[49,50] and use of contact lenses[27,44,45].

The factors most related with the onset of VDU syndrome were the duration of daily computer use (20h/week 4h/day)[2,3,5,12,19,29,31,32-34,36,38,51], the presence of ametropia (in particular inappropriately corrected presbyopia, although also occurring in the presence of emmetropia)[3,19,32,36,37], being more than 50 years old[3,32,36], binocularity dysfunction, accommodation insufficiency or convergence insufficiency disorders[36,38,52-58], ergonomic and equipment context[1-3,8,21,30,32,40,57], frequency of pauses during the activity[5,58] and psychological conditions[2,20,21-23,59]. Relationships between visual fatigue and cervicallumbar, scapular and neuropsychological disorders were common[12,20-23,29,37-39,59]. The prevalence rates of ametropias depend on the population being assessed[60], although they are not an essential condition for the onset of fatigue. The selection or exclusion of candidates to fill positions involving working with computers based on the existence or not of ametropias should be avoided[11]. The efficacy of treatments proposed to manage this condition is unproven[5]. Nevertheless, it is recommended that refractive errors, accommodative and convergence insufficiency and heterophorias be corrected, in addition to giving advice as to taking breaks during work, having appropriate ergonomic conditions and paying attention to psychological health[5,19,59,60].

As with all systematic reviews, the quality of the evidence depends on the original studies. The great contribution of a meta-analysis is to improve precision of the final estimative, since the aggregated result has greater statistical power. Thus, although the numbers may seem quite similar, especially due to the small number of studies on this subject, the pooled meta-analysis result is more precise, with smaller confidence intervals.

This search used sensitive criteria to identify the largest possible number of studies, and the evidence produced may be useful when planning future studies. The main limitation is the absence of a standardized, objective, reproducible and validated tool to detect or measure the severity of eyestrain in different ages. In our systematic review, we noted that authors have used different questionnaires to detect the presence of asthenopia. Only 6 publications adopted questionnaires previously used in other studies, however all of these were intended to assess occupational stress and not specifically to assess visual symptoms. Different scales and scores have been adopted, but not uniformly, thus maintaining the correlations between limitations and intensity of visual fatigue.

We believe there is a low potential for risk of publication bias because the object of the search are observational studies, most of which are studies of prevalence, and not intervention studies. The publication of prevalence studies is probably less affected by the type of result (positive or negative) and they are less subject to the influence of biases. Observational studies may present a risk of publication bias because they are studies with small sample sizes and, unlike clinical trials, there is no obligation to register them. This in turn makes it difficult to search for unpublished studies. It is possible that prevalence rates are underrepresented in developing countries or specific subgroups. Also there may be a time lag bias with small studies taking longer to be published. Funnel plots are appropriate and should be interpreted as representative for this observational (non-interventional) analysis. Funnel plots do not reflect the "causal effect" but rather different prevalence values. Even though the squares that represent the studies have the same size, the study weight can be estimated by the confidence interval width.

Thus, recommendations for future research include large population-based prevalence studies, with adequate sample size and representative sampling, and the development and validation of a uniform scale for diagnosis in order to facilitate comparisons across studies and future systematic reviews on the subject.

Conclusions Asthenopia occurs at a significant rate and represents a significant cause of health problems and lack of efficiency associated with work in adults exposed to daily work with computers. Such repercussions and the increasing use of these resources may represent raises in prevalence rates and their consequences. While more consistent population studies are not available, this systematic review can be used to help public health policies.

58

Manuel Augusto Pereira Vilela, Lucia Campos Pellanda, Claudia C. Cesa, Victor D. Castagno

REFERENCES

[1]

Kowalska, M., Zejda, J. E., Bugajska, J., et al. “Eye symptoms in office employees working at computer stations.”

Medycyna Pracy 62(2011): 1–8.

[2]

Nakazawa, T., Okubo, Y., Suwazono, Y., et al. “Association between duration of daily VDT user and subjective symptoms.” American Journal of Industrial Medicine 42(2002): 421 – 426.

[3]

Bhanderi, D. J., Choudhary, S., and Doshi, V. G. “A community-based study of asthenopia in computers operators.” Indian Journal of Ophthalmology 56(2008): 51 – 55.

[4]

Blehm, C., Vishnu, S., Khattak, A. et al. “Computer vision syndrome: a review.” Survey of Ophthalmology 50 (2005): 253 – 262.

[5]

Rosenfield, M. “Computer vision syndrome: a review of ocular causes and potential treatments.” Ophthalmic and Physiological Optics 31(2011): 502-515.

[6]

Kanitkar, K., Carlson, A. N., and Richard, Y. “Ocular problems associated with computer use.”2005. Accessed January 22, 2015. http://www.reviewofophthalmology.com/content/d/features/i/1317/c/25354/

[7]

World Health Organization. “Update on visual display terminals and workers' health.” WHO offset publication 99(1987).

[8]

Bohr, P. C. “Efficacy of office ergonomics education.” Journal of Occupational Rehabilitation 10(2000): 243–255.

[9]

Solimini, A. G. “Are there side effects to watching 3D movies? A prospective crossover observational study on visual induced motion sickness.” Plos One 8(2013): e56160.

[10] Wee, S. W., and Moon, N. J.” Clinical evaluation of accommodation and ocular surface stability relevant to visual asthenopia with 3D displays.” BMC Ophthalmology 14(2014): 16.

[11] DeGroot, J. P., and Kamphuis, A. “Research note eyestrain in VDU users: physical correlates and long-term effects.”

Human Factors 25(1983): 409–413.

[12] Shima, M., Nitta, Y., Iwasaki, A. et al. “Investigation of subjective symptoms among visual display terminal users and their affecting factors analysis using log-linear models.” Nihon Eiseigaku Zasshi 47(1993): 1032–1040.

[13] “PRISMA – Preferred reporting items for systematic reviews and meta-analyses.” Accessed November 13, 2013. http://www.prisma-statement.org/index.htm.

[14] Stroup, D. F., Berlin, J. A., Morton, S. C., et al. “Meta-analysis of observational studies in epidemiology: a proposal for reporting.” JAMA 283(2000): 2008 – 2012.

[15] Consort. Accessed December, 2014. http://www.consort-statement.org/?o=1308.

[16] Downs, S. H., and Black, N. “The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions.” Journal of Epidemiology and Community Health 52(1998): 377–384.

[17] Neyeloff, J. L., Fuchs, S. C., and Moreira, L. B. “Meta-analyses and Forest plots using a Microsoft Excel spreadsheet: stepby-step guide focusing on descriptive data analysis.” BMC Reserach Notes 5(2012): 52.

[18] Egger, M., Smith, G. D., and Altman, D. G. “Systematic Reviews in Health Care: meta-analysis in context.” 2nd. ed.

London: BMJ Publishing Group, 2001.

[19] Fenga, C., Di Prieto, R., Fenga, P., et al. “Asthenopia in VDT users: our experience.” Giornale Italiano di Medicina del Lavoro ed Ergonomia 29(2007): 500–501.

[20] Mocci, F., Serra, A., and Corrias, G. A. “Psychological factors and visual fatigue in working with video display terminals.”

Occupational and Environmental Medicine 58(2001): 267–271.

[21] Rocha, L. E., and Debert-Ribeiro, M. “Working conditions, visual fatigue, and mental health among system analysts in São Paulo, Brazil.” Occupational and Environmental Medicine 61(2004): 24–32.

[22] S{, E. C. “Fatores de risco para a síndrome visual associada ao uso do computador em operadoras de duas centrais de teleatendimento em São Paulo, Brasil.” PhD Thesis. Univeridade de São Paulo, 2010.

Asthenopia Prevalence and Risk Factors Associated with Professional Computer Use- A Systematic Review

59

[23] Neugebauer, A., Fricke, J., and Rüssmann, W. “Asthenopia: frequency and objective findings.” German Journal of Ophthalmology 1(1992): 122–124.

[24] Sheeny, J. E., Hayes, J., and Engle, J. “Is all asthenopia the same?.” Optometry and Vision Science 80(2003): 732–739.

[25] Rossignol, A. M., Morse, E. P., Summers, V. M. et al. “Visual display terminal use and reported health symptoms among Massachusetts clerical workers.” Journal of Occupational Medicine 29(1987): 112–118.

[26] Gowrisankaran, S., Nahar, N. K., Hayes, J. R. et al. “Asthenopia and blink rate under visual and cognitive loads.”

Optometry and Vision Science 89(2012): 97–104.

[27] Patel, S., Henderson, R., Bradley, L., et al. “Effect of visual display unit use on blink rate and tear stability.” Optometry and Vision Science 68(1991): 888–892.

[28] Ong, C. N., Hoong, B. T., and Phoon, W. O. “Visual and muscular fatigue in operators using visual display terminals.”

Journal of Human Ergology 10(1981): 161–171.

[29] Sanchez-Roman, F. R., Perez-Lucio, C., Juarez-Ruiz, C., et al. “Risk factors for asthenopia among computer terminal operators.” Salud Publica de México 38(1996): 189–196.

[30] Hedman, L. R., and Briem, V. “Short-term changes in eyestrain of VDU users as a function of age.” Human Factors 26(1984): 357–370.

[31] Agarwal, S., Goel, D., and Sharma, A. “Evaluation of the factors which contribute to the ocular complaints in computer users.” Journal of Clinical and Diagnostic Research 7(2013): 331–335.

[32] Bergqvist, U. O., and Knave, B. G. “Eye discomfort and work with visual display terminals.” Scandinavian Journal of Work, Environment & Health 20(1994): 27–33.

[33] Taino, G., Ferrari, M., Mestad, I. J., et al. “Asthenopia and work at video display terminals: study of 191 workers exposed to the risk by administration of a standardized questionnaire and ophthalmologic evaluation.” Giornale Italiano di Medicina del Lavoro ed Ergonomia 28(2006): 487–497.

[34] Zahojia, Y., Honda, S., Abe, Y., et al. “Influence of work duration or physical symptoms on mental health among japanese visual display terminal users.” Industrial Health 45(2007): 328–333.

[35] Yeow, P. T., and Taylor, S. P. “The effects of long-term VDT usage on the nature and incidence of asthenopic symptoms.”

Applied Ergonomics 21(1990): 285–293.

[36] Piccoli, B., Gratton, I., Pierini, F., et al. “Asthenopia and objective ophthalmological findings in a population of 2058 VDT operators in Lombardy.” Giornale Italiano di Medicina del Lavoro ed Ergonomia 11(1989): 267–271.

[37] Salibello, C., and Nilsen, E. “Is there a typical VDT patient? A demographic analysis.” Journal of the American Optometric Association 66(1995): 479–483.

[38] Carta, A., Pasquini, L., Lucchini, R., et al. “Studio delle relazioni fra astenopia ed alcuni parametri oftalmologici, neuropsicologi e muscolo-scheletrici in lavatori addetti al VDT.” La Medicina del Lavoro 94(2003): 466–479.

[39] Iwakiri, K., Mori, I., Sotoyama, M., et al. “Survey on visual and musculoskeletal symptoms in VDT workers.” Sangyo Eiseigaku Zasshi 46(2004): 201–212.

[40] Mvitu, M. M. “Manifestations of asthenopia in Black subjects.” Bulletin de la Société belge d'Ophtalmologie 289(2003): 45– 49.

[41] Schlote, T., Kadner, G., and Freudenthaler, N. “Marked reduction and distinct patterns of eye blinking in patients with moderately dry eyes during video display terminal use.” Graefe's Archive for Clinical and Experimental Ophthalmology 242(2004): 306–312.

[42] York, M., Ong, J., and Robbins, J. C. “Variations in blink rate associated with contact lens wear and task difficulty.”

American Journal of Optometry and Archives of American Academy of Optometry 48(1971): 461–467.

[43] Tsubota, K., and Nakamori, K. Dry eyes and video display terminals. N Engl J Med, Vol. 328, 1993, pp. 584–585.

[44] Chu, C., Rosenfield, M., Portello, J. K., et al. “A comparison of symptoms after viewing text on a computer screen and paper.” Ophthalmic and Physiological Optics 31(2011): 29–32.

60

Manuel Augusto Pereira Vilela, Lucia Campos Pellanda, Claudia C. Cesa, Victor D. Castagno

[45] Shimmura, S., Shimazaki, J., and Tsubota, K. “Results of a population-based questionnaire on the symptoms and lifestyles associated with dry eye.” Cornea 18(1999): 408–411.

[46] Schaumberg, D. A., Sullivan, D. A., Buring, J. E, et al. “Prevalence of dry eye disease among US men: estimates from Physicians’ Health Studies.” Archives of Ophthalmology 127(2009): 763–768.

[47] Moss, S. E, Klein, R., and Klein, B. E. K. “Prevalence of and risk factors for dry eye syndrome.” Archives of Ophthalmology 118(2000): 1264–1268.

[48] Moss, S. E., Klein, R., and Klein, B. E. K. “Long-term incidence of dry eye in an older population.” Optometry and Vision Science 85(2008): 668–674.

[49] Iribarren, R., Fornaciari, A., and Hung, G. K. “Effect of cumulative nearwork on accommodative facility and asthenopia.”

International ophthalmology 24(2001): 205–212.

[50] Dusek, A. “An evaluation of clinical treatment of convergence insufficiency for children with reading difficulties.” BMC Ophthalmology 11(2011): 21–30.

[51] Dainoff, M. J., Happ, A., and Crane, P. “Visual fatigue and occupational stress in VDT operators.” Human Factors 23(1981): 421–438.

[52] Sheedy, J. E., and Parsons, S. D. “The visual display terminal eye clinic: clinical report.” Optometry and Vision Science 67(1990): 622–626.

[53] Tosha, C., Borsting, E., Ridder, W. H, et al. “Accommodation response and visual discomfort.” Ophthalmic and Physiological Optics 29(2009): 625–633.

[54] Watten, R. G., Lie, I., and Birketvedt, O. “The influence of long-term visual near-work on accommodation and vergence: a field study.” Journal of Human Ergology 23(1994): 27–39.

[55] Rechichi, C., and Scullica, L. “Asthenopia and monitor characteristic.” Journal Francais D Ophtalmologie 13(1990): 456–460.

[56] Grisham, J. D., Sheppard, M. M., and Tran, W. U. “Visual symptoms and reading performance.” Optometry and Vision Science 70(1993): 384–391.

[57] Vincent, A. J. “A controlled study of visual symptoms and eyestrain factors in chronic headache.” Headache 29(1989): 523– 527.

[58] Pan, C. W., Ramamurthy, D., and Saw, S. M. “Worldwide prevalence and risk factors for myopia”. Ophthalmic and Physiological Optics 32(2011): 3–16.

[59] Evans, B. J., Patel, R., Wilkins, A. J., et al. “A review of the management of 323 consecutive patients seen in a specific learning difficulties clinic.” Ophthalmic and Physiological Optics 19(1999): 454–466.

[60] Graf, H. P., and Flammer, J. “Asthenopia in clinical practice: a comparative study of complaints, clinical findings and results of therapy in asthenopic patients.” Klinische Monatsblatter fur Augenheilkunde 176(1980): 577–582.

ATTACHMENT

1 CHART 1. SEARCH STRATEGY USED ON DATABASES

#1

"Asthenopia"*Mesh+ OR “astenopia” OR “visual fatigue”

#2

"Eyestrain"[Mesh]

#3

#1 AND #2