Prevalence of computer vision syndrome: a systematic review and meta- analysis

Prevalence of computer vision syndrome: a systematic review and meta- analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Prevalence of computer vision syndrome: a systematic review and meta- analysis Asamene Kelelom Lema, Etsay Woldu Anbesu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2070028/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Jan, 2023 Read the published version in Scientific Reports → Version 1 posted 8 You are reading this latest preprint version Abstract Background Computer vision syndromes are becoming a major public health concern; however, less emphasis is given, particularly in developing countries. Although there are studies on different continents, there are inconsistent findings among the studies. Therefore, this systematic review and meta-analysis aimed to estimate the pooled prevalence of computer vision syndrome. Methods In this study, the review was developed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Online electronic databases, including PubMed/Medline, CINAHL, and Google Scholar, were used to retrieve published and unpublished studies from December 1 to April 9/2022. Study selection, quality assessment, and data extraction were performed independently. Quality assessment of the studies was performed using the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument. Heterogeneity was assessed using statistical test I 2 . STATA 14 software was used for statistical analysis. Results A total of 7, 35 studies were retrieved, and 45 studies with a total 17,526 participants were included in the final meta-analysis. The pooled prevalence of computer vision syndrome was 66% (95%, CI: 59, 74). Subgroup analysis based on country was highest in Pakistan 97% (95% CI: 96, 98) and the lowest was in Japan 12% (95% CI: 9, 15). Subgroup analysis based on country showed studies in Saudi Arabia (I 2 = 99.41%, p-value < 0.001), Ethiopia (I 2 = 72.6%, p-value < 0.001), and India (I 2 = 98.04%, p-value < 0.001) had significant heterogeneity Conclusion Nearly two in three participants had computer vision syndrome. Thus, preventive practice strategic activities on computer vision syndrome are important interventions. pooled prevalence computer vision syndrome systematic review meta-analysis Figures Figure 1 Figure 2 Introduction Computer vision syndrome (CVS) is defined as “a complex of eye and vision problems related to near work experienced during computer use” [ 1 ], Visual fatigue (VF) and digital eye strain (DES) terms are also used for CVS, reflecting the different digital devices related to potential problems [ 2 ]. Symptoms related to CVS can be classified as visual, ocular, and extraocular [ 3 ]. Visual symptoms include blurred vision, visual fatigue or discomfort, and diplopia [ 4 – 7 ]. Ocular symptoms include dry eye disease, redness, eye strain, and irritation [ 1 , 8 , 9 ]. Extraocular symptoms include headache, shoulder, neck, and back pain [ 3 , 4 , 10 – 14 ]. Individuals spend more time on electronic devices such as computers, laptops, smartphones, tablets, and e-readers, which contribute to CVS [ 15 ]. Children are also affected in CVS, as they spend many hours using electronic devices for schoolwork, playing video games, and sending and receiving text messages [ 15 ]. However, the use of these devices even for 3 hours/day can lead to the development of CVS, back pain, headaches, and stress [ 3 ]. The massive growth of digital devices has become an integral part of daily life, and millions of individuals of all ages are at risk of computer vision syndrome (CVS)[ 16 – 18 ]. In developed nations, engagement with digital devices has increased substantially in recent years across all age groups [ 19 – 22 ]. Moreover, the burden of CVS is very high in developing countries due to low accessibility, utilization of personal protective equipment, and limited break time while using electronic devices. CVS is a major public health problem leading to occupational hazard, an increased error rate, impaired visual abilities, reduced productivity, and job satisfaction [ 23 , 24 ]. A review of the literature showed that factors associated with CVS can be classified as personal factors, which include poor sitting position, inappropriate eye-to-screen distance, insufficient working procedures, improper viewing angle and distances, age, medical diseases, and duration of computer usage. The environment; and computer factors such as improper workstation, poor lighting, contrast, and resolution, slow refresh rate, glare of the display, excessive screen brightness, imbalance of light between the computer screen and surrounding working room [ 5 , 10 , 25 – 28 ]. Modern digital technology markedly influences the daily activities and lifestyles of people [ 4 , 7 ]. CVS can reduce productivity and visual and musculoskeletal impairment and impact circadian rhythms and sleep patterns [ 4 , 7 , 13 , 29 , 30 ]. Although CVS is becoming a major public health problem, less emphasis is given, particularly in developing countries. Although there are studies on different continents, there is a lack of global representative data. Therefore, this systematic review aimed to estimate the pooled prevalence of computer vision syndrome. Methods Protocol and registration This systematic review and meta-analysis was registered on PROSPERO with registration number CRD42022325167. Available at: https://www.crd.york.ac.uk/prospero/#myprospero Search strategies T The systematic review was developed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [31], and the review procedure was reported using the PRISMA-P 2015 checklist [32] (PRISMA checklist). Published and unpublished studies were searched in databases such as Medline/PubMed, CINAHL, and Google Scholar from December 1 to April 9/2022. The MeSH terms and entry terms were used to search studies from databases, and modifications was made based on the type of databases (Additional file 1). In addition, cross-references of the included articles was performed. Eligibility criteria Inclusion criteria The following criteria were considered to include studies Study area Anywhere Study scope Studies that report the prevalence of CVS and its associated factors Studies that only report the overall prevalence of CVS “Both community- and facility-based studies” Quantitative results, if the study was qualitative studies Study design Observational study designs, including cross-sectional, and cohort study designs were considered Language English Population All population groups Publication year No restriction Exclusion criteria Studies were excluded if: Other than English Studies that did not report specific outcomes (prevalence) for CVS No full-text article following email contact Qualitative studies Letters, conference abstracts, case reports, and reviews, CoCoPop/PEO Condition: computer vision syndrome Context: worldwide Population: All population groups Outcome/context : The primary outcome of the study was the pooled prevalence of CVS. The prevalence of CVS was considered when the studies reported the overall prevalence of CVS for either syndrome (blurred vision, eye strain/fatigue, discomfort, diplopia, dry eye disease, redness, irritation, headache, shoulder, neck, and back pain) in the primary studies. Study selection Endnote reference manager software [33] was used to organize, remove duplicate, irrelevant titles, and abstracts. Duplicate studies were removed. An assessment of studies using the title and abstract were performed, and irrelevant titles and abstracts were removed. A full-text review of studies was performed before the inclusion of studies in the final meta-analysis. Study selection was performed independently by the reviewers (EW and AK). The selection procedures of the studies was presented using a PRISMA diagram . Quality assessment “The Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI)” [34] was used to assess the quality of the studies. The component of quality assessment include study setting, outcome and explanatory variable measurements, clear inclusion criteria, measurement criteria used, participants’ description , and valid statistical analysis . Independent quality assessment of the studies was reviewed by (EW and AK), and 50% and above of the quality score was included in the final systematic review. Disagreement during quality assessment among reviewers was resolved with the discussion. Data extraction Independent data extraction was performed by the authors (EW and AK) using a pilot tested data extraction Microsoft Office Excel sheet. The data extraction sheet elements included publication year, authors’ names, study design, country, sample size, response rate prevalence and study subjects. Discrepancy was resolved by discussion between the authors (EW and AK). Contact with the corresponding authors of the studies was made for incomplete data, and the study was excluded if there was no response. Data analysis The extracted Excel data was imported to STATA version 14 for analysis. A narrative description and summary characteristics of the included studies was reported in tables and graphs. A random-effects model meta-analysis [35] was used for statistical analysis. The results was presented using a forest plot. The heterogeneity of studies was assessed by the I 2 statistic [36]. I 2 statistics of 25, 50 and 75% showed low, moderate and high heterogeneity, respectively, with p < 0.05. Publication bias was assessed using visual observation of the funnel plot for more than 10 studies [37] and Egger’s test at p < 0.05 [38]. To identify the sources of heterogeneity among the studies, subgroup analysis and meta-regression [39] were performed based on country, and sample size. Sensitivity analysis was be performed to assess the effect of single study on the overall result. Results A total of 735 articles were retrieved using electronic databases search: PubMed, Google Scholar, and CINHAL. 77 articles were excluded due to duplication, and 5, 59 articles were excluded as they were not related to the title, review, abstract and duplication. 99 full text articles were assessed for eligibility, and 57 articles were excluded for based on quality appraisal tool. 3 articles record identified through cross reference search of included studies. Finally, 45 articles were included in systematic review and meta-analysis (Figure 1). Characteristics of included studies A total of 45 cross-sectional studies were included in this systematic review and meta-analysis: 4 studies in Saudi Arabia study [40-43], 2 studies in Nigeria [44, 45], 3 studies in Ghana [46-48], 4 studies in Pakistan [49-52], 3 studies in Spain [53-55], 7 studies in Ethiopia [56-62], 1 study in Jordan [63], 2 studies in China [64, 65], 1 study in Iran [66], 3 studies in Egypt [67-69], 8 studies in India [18, 70-76], 1 study in Nepal [77], 1 study in Seri Lanka [29], 2 studies in Brazil [78, 79], 1 study in Beirut [80], 1 study in Japan [81], and 1 study in Thailand [82]. A total of 17, 526 sample size were included. The sample size ranged from 74 in China [64] to 2,210 in Seri Lanka [29] (Table 1). Table 1: Characteristics of included studies in the meta-analysis of computer vision syndrome, 2022 Author/s/year (reference) Country Study design Sample size Response rate (%) Prevalence (%) Study subjects Abudawood GA, et al. 2020 [41] Saudi Arabia Cross sectional 587 100 95.1 Students Agbonlahor O. et al.2019 [44] Nigeria Cross sectional 215 84 65.1 Government employ Akowuah PK, et al.2021[83] Ghana Cross sectional 362 92.5 64.4 Students Al Dandan O, et al. 2021[42] Saudi Arabia Cross sectional 198 75.3 50.5 Radiologists Al Subaie M, et al. 2017 [43] Saudi Arabia Cross sectional 416 100 43.5 Population ≥ 15 years Arshad S, et al. 2019 [84] Pakistan Cross sectional 320 100 58.1 Students Artime‐Ríos E, et al.2021 [53] Spain Cross sectional 622 - 56.7 Health workers Boadi-Kusi SB, et al. 2021 [48] Ghana Cross sectional 139 86.9 71.2 Bank workers Boadi-Kusi SB, et al. 2020 [47] Ghana Cross sectional 200 65 51.5 University staff Cantó‐Sancho N, et al. 2021 [54] Spain Cross sectional 244 100 76.6 Students Derbew H, et al. 2021 [56] Ethiopia Cross sectional 351 98 74.6 Bank workers Dessie A, et al. 2018 [57] Ethiopia Cross sectional 607 93.1 69.5 Government employ Gammoh Y. et al. 2021 [63] Jordan Cross sectional 382 92 94.5 Students Gondol BN, et al. 2020 [58] Ethiopia Cross sectional 272 100 81.3 Government employ Han CC, et al. 2013 [65] China Cross sectional 1469 97.9 57.04 Students Hashemi H, et al. 2017 [66] Iran Cross sectional 1040 97.2 49.4 Students Kamal NN, et al. 2018 [67] Egypt Cross sectional 218 96.3 84.8 Bank workers Lakachew Assefa N. et al. 2017 [59] Ethiopia Cross sectional 304 98.2 73.03 Bank workers Lemma MG. et al. 2020 [60] Ethiopia Cross sectional 455 93 68.8 Secretaries Lemma MT,et al. 2021 [61] Ethiopia Cross sectional 217 96.8 75.6 Secretaries Logaraj M, et al. 2014 [70] India Cross sectional 215 100 81.8 Students Mansoori N, et al. 2017 [50] Pakistan Cross sectional 150 100 28 students Mohan A, et al. 2021 [85] India Cross sectional 217 83.14 50.2 Children NAGWA E, et al. 2019 [68] Egypt Cross sectional 260 100 75 Students Noreen K, et al. 2021 [52] Pakistan Cross sectional 326 95.04 98.7 Students Noreen K, et al. 2016 [51] Pakistan Cross sectional 198 86.5 67.2 Students Nwankwo B, et al. 2021 [45] Nigeria Cross sectional 153 100 54.2 Students Poudel S, et al. 2020 [77] Nepal Cross sectional 263 94.9 82.5 IT office workers Rafeeq U, et al. 2020 [72] India Cross sectional 120 100 69.2 ≥ 12 years old population Ranasinghe P, et al. 2016 [86] Serilanka Cross sectional 2210 88.4 67.4 Computer office workers Ranganatha SC, et al. 2019 [73] India Cross sectional 150 100 86.7 Computer sciences students Rathore D. , et al. 2016 [74] India Cross sectional 150 100 75.3 Computer users Sa EC, et al. 2012 [78] Brazil Cross sectional 476 89.6 54.6 Call center Sánchez-Brau M, et al. 2020 [55] Spain Cross sectional 109 95.6 74.3 Visual display workers Sawaya RI, et al. 2020[80] Beirut Cross sectional 457 73.5 67.8 Students Singh H, et al. 2016 [18] India Cross sectional 192 96 51.6 Students Tiwari RR, et al. 2011 [75] India Cross sectional 432 100 32.2 Children Uchino M, et el. 2013 [81] Japan Cross sectional 561 83.5 11.6 Visual display terminal users Verma S, et al. 2021 [76] India Cross sectional 100 100 74 Computer operators Vilela MA, et al. 2015 [79] Brazil Cross sectional 964 100 24.7 School children Wang L, et al. 2021 [64] China Cross sectional 74 80.12 74.3 Students Wangsan K, et al. 2022 [82] Thailand Cross sectional 527 100 81.02 Students Zalat MM, et al. 2021 [40] Saudi Arabia Cross sectional 80 100 81.3 Visual display workers Zayed HA, et al. 2021 [69] Egypt Cross sectional 108 98.18 82.4 IT professionals Zenbaba D, et al. 2021 [62] Ethiopia Cross sectional 416 98.6 70.43 Students Pooled prevalence of computer vision syndrome The pooled prevalence of computer vision syndrome was 66% (95%, CI: 59, 74) . The lowest proportion included study was 12% (95%, CI: 9, 15) in Japan [81] and the highest 99% (95%, CI:97, 100) in Pakistan [52]. The I 2 test showed that there was heterogeneity among included studies (I 2 = 99.42%, p-value < 0.001) (Figure 2). Subgroup analysis by country Subgroup analysis was performed based on country and the prevalence of computer vision syndrome was highest in Pakistan 97% (95% CI: 96, 98) and the lowest was in Japan 12% (95% CI: 9, 15). The studies that showed significant heterogeneity were studies in Saudi Arabia (I 2 = 99.41%, p-value < 0.001), Ethiopia (I 2 = 72.6%, p-value < 0.001), Egypt (I 2 = 80.06%, p-value < 0.001), and India (I 2 = 98.04%, p-value < 0.001) (Table 2). Table 2: Subgroup analysis by country on computer vision syndrome, 2022 Sub group Number of included studies Prevalence (95% CI) Heterogeneity statistics P value I 2 By country Saudi Arabia 4 68(37, 98) P< 0.001 99.41% Nigeria 2 61(56, 66) P< 0.001 0.00% Ghana 3 62(52, 73) P< 0.001 0.00% Pakistan 2 62(58, 66) P< 0.001 0.00% Spain 3 69(55, 83) P< 0.001 0.00% Ethiopia 7 73(70, 76) P< 0.001 72.6% Jordan 1 95(92, 96) - 0.00% China 2 58(56, 61) P< 0.001 0.00% Iran 1 49(46, 52) - 0.00% Egypt 5 81(74, 87) P< 0.001 80.06% India 8 65(49, 81) P< 0.001 98.04% Pakistan 2 97(96, 98) P< 0.001 0.00% Nepal 1 83(77, 87) - 0.00% Seri Lanka 1 67(65, 69) - 0.00% Brazil 2 33(30, 35) P< 0.001 0.00% Beirut 1 68(63, 72) - 0.00% Thailand 1 81(77,84) - 0.00% South Korea 1 66(63, 69) - 0.00% Italy 1 15(11, 21) - 0.00% Japan 1 12(9, 15) - 0.00% Meta regression Meta-regression was performed to identify the source of heterogeneity across the studies by country and sample size. Meta-regression indicated that heterogeneity was not associated with country and sample size (p-value >0.05) (Additional file 2, Table S1). Publication biases Publication bias was checked using funnel plots, and visual inspection suggested asymmetry, as 11 studies were on the left side and 32 studies were on the right side (Additional file 3: Figure S1). Moreover, publication bias was not shown on Egger’s test (p=0.21) (Additional file 4: Table S2). Discussion This systematic review and meta-analysis aimed to assess the pooled prevalence of computer vision syndrome. There are inconsistence findings on prevalence of computer vision syndrome. Moreover, there no systematic review and meta-analysis research findings on the pooled prevalence of computer vision syndrome. Therefore, findings from this systematic review and meta-analysis will help policy-makers design appropriate strategies to reduce computer vision syndrome public health concern The pooled prevalence of computer vision syndrome was 66% (95%, CI: 59, 73). The pooled prevalence was in line with the study done in India COVID 19 pre locked down, 64.3% [ 87 ]. However, the pooled prevalence was lower than studies done in India during COVID 19 locked down, 87.3% [ 87 ], Europe, 90% [ 88 ], and Ethiopia, 73.21% [ 89 ]. The difference might be due to differences in study period, study setting, socioeconomic differences, awareness and behavioral change on prevention of computer vision syndrome. The limitations include, articles published only the English, and cause effect relationship as all the studies were cross-sectional designs. Moreover, this study was reported from 20 countries, which might lack representativeness. Conclusion Nearly two in three participants had computer vision syndrome. 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The Medical Journal of Cairo University, 2019. 87(December): p. 4877-4881. Zayed HAM, Saied SM, Younis EA, et al. Digital eye strain: prevalence and associated factors among information technology professionals, Egypt. Environmental Science and Pollution Research, 2021. 28(20): p. 25187-25195. Logaraj M, Madhupriya V, and Hegde S. Computer vision syndrome and associated factors among medical and engineering students in Chennai. Annals of medical and health sciences research, 2014. 4(2): p. 179-185. Mohan A, Sen P, Shah C, et al. Prevalence and risk factor assessment of digital eye strain among children using online e-learning during the COVID-19 pandemic: Digital eye strain among kids (DESK study-1). Indian journal of ophthalmology, 2021. 69(1): p. 140. Rafeeq U, Omear M, Chauhan L, et al. Computer vision syndrome among individuals using visual display terminals for more than two hours. Delta Journal of Ophthalmology, 2020. 21(3): p. 139. Ranganatha S and Jailkhani S. Prevalence and associated risk factors of computer vision syndrome among the computer science students of an engineering college of Bengaluru-a cross-sectional study. Galore Int J Health Sci Res, 2019. 4(3): p. 10-15. Rathore D. A cross sectional study to assess prevalence of computer vision syndrome and vision related problems in computer users. J Med Sci Clin Res, 2016. 4: p. 11007-11012. Tiwari RR, Saha A, and Parikh JR. Asthenopia (eyestrain) in working children of gem-polishing industries. Toxicology and industrial health, 2011. 27(3): p. 243-247 DOI: 10.1177/0748233710386407. Verma S, Midya U, Gupta S, et al. A cross-sectional study of the prevalence of computer vision syndrome and dry eye in computer operators. TNOA Journal of Ophthalmic Science and Research, 2021. 59(2): p. 160. Poudel S and Khanal SP. Magnitude and determinants of computer vision syndrome (CVS) among IT workers in Kathmandu, Nepal. Nepalese Journal of Ophthalmology, 2020. 12(2): p. 245-251. Sa EC, Ferreira Junior M, and Rocha LE. Risk factors for computer visual syndrome (CVS) among operators of two call centers in São Paulo, Brazil. Work, 2012. 41 Suppl 1: p. 3568-3574 DOI: 10.3233/wor-2012-0636-3568. Vilela MA, Castagno VD, Meucci RD, et al. Asthenopia in schoolchildren. Clinical ophthalmology (Auckland, N.Z.), 2015. 9: p. 1595-1603 DOI: 10.2147/opth.s84976. Sawaya RIT, El Meski N, Saba JB, et al. Asthenopia among university students: the eye of the digital generation. Journal of family medicine and primary care, 2020. 9(8): p. 3921. Uchino M, Yokoi N, Uchino Y, et al. Prevalence of dry eye disease and its risk factors in visual display terminal users: the Osaka study. American journal of ophthalmology, 2013. 156(4): p. 759-766. e751. Wangsan K, Upaphong P, Assavanopakun P, et al. Self-Reported Computer Vision Syndrome among Thai University Students in Virtual Classrooms during the COVID-19 Pandemic: Prevalence and Associated Factors. International journal of environmental research and public health, 2022. 19(7): p. 3996. Akowuah PK, Nti AN, Ankamah-Lomotey S, et al. Research Article Digital Device Use, Computer Vision Syndrome, and Sleep Quality among an African Undergraduate Population. 2021. Arshad S, Khan A, Pal D, et al. Prevalence of asthenopia among computer operators in Central India and effectiveness of educational intervention. 2019. Mohan A, Sen P, Shah C, et al. Prevalence and risk factor assessment of digital eye strain among children using online e-learning during the COVID-19 pandemic: Digital eye strain among kids (DESK study-1). Indian journal of ophthalmology, 2021. 69(1): p. 140-144 DOI: 10.4103/ijo.IJO_2535_20. Ranasinghe P, Wathurapatha WS, Perera YS, et al. Computer vision syndrome among computer office workers in a developing country: an evaluation of prevalence and risk factors. BMC Res Notes, 2016. 9: p. 150 DOI: 10.1186/s13104-016-1962-1. Khan S, Khan S, Midya MZ, et al. Comparison of Prevalence Data about Digital Eye Strain (DES), Pre-Lockdown versus Post-Lockdown Period in India: A Systematic Review Study. children. 17: p. 18. Rosenfield M. Computer vision syndrome: a review of ocular causes and potential treatments. Ophthalmic and Physiological Optics, 2011. 31(5): p. 502-515. Adane F, Alamneh YM, and Desta M. Computer vision syndrome and predictors among computer users in Ethiopia: a systematic review and meta-analysis. Tropical Medicine and Health, 2022. 50(1): p. 1-12. Additional Declarations No competing interests reported. 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\u0026ldquo;a complex of eye and vision problems related to near work experienced during computer use\u0026rdquo; [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], Visual fatigue (VF) and digital eye strain (DES) terms are also used for CVS, reflecting the different digital devices related to potential problems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Symptoms related to CVS can be classified as visual, ocular, and extraocular [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Visual symptoms include blurred vision, visual fatigue or discomfort, and diplopia [\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Ocular symptoms include dry eye disease, redness, eye strain, and irritation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Extraocular symptoms include headache, shoulder, neck, and back pain [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIndividuals spend more time on electronic devices such as computers, laptops, smartphones, tablets, and e-readers, which contribute to CVS [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Children are also affected in CVS, as they spend many hours using electronic devices for schoolwork, playing video games, and sending and receiving text messages [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, the use of these devices even for 3 hours/day can lead to the development of CVS, back pain, headaches, and stress [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe massive growth of digital devices has become an integral part of daily life, and millions of individuals of all ages are at risk of computer vision syndrome (CVS)[\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In developed nations, engagement with digital devices has increased substantially in recent years across all age groups [\u003cspan additionalcitationids=\"CR20 CR21\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, the burden of CVS is very high in developing countries due to low accessibility, utilization of personal protective equipment, and limited break time while using electronic devices. CVS is a major public health problem leading to occupational hazard, an increased error rate, impaired visual abilities, reduced productivity, and job satisfaction [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA review of the literature showed that factors associated with CVS can be classified as personal factors, which include poor sitting position, inappropriate eye-to-screen distance, insufficient working procedures, improper viewing angle and distances, age, medical diseases, and duration of computer usage. The environment; and computer factors such as improper workstation, poor lighting, contrast, and resolution, slow refresh rate, glare of the display, excessive screen brightness, imbalance of light between the computer screen and surrounding working room [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eModern digital technology markedly influences the daily activities and lifestyles of people [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. CVS can reduce productivity and visual and musculoskeletal impairment and impact circadian rhythms and sleep patterns [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although CVS is becoming a major public health problem, less emphasis is given, particularly in developing countries. Although there are studies on different continents, there is a lack of global representative data. Therefore, this systematic review aimed to estimate the pooled prevalence of computer vision syndrome.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eProtocol and registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis systematic review and meta-analysis was registered on PROSPERO with registration number CRD42022325167. Available at: https://www.crd.york.ac.uk/prospero/#myprospero\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;strategies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT The systematic review was developed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [31], and the review procedure was reported using the PRISMA-P 2015 checklist [32] \u003cem\u003e(PRISMA checklist).\u003c/em\u003e Published and unpublished studies were searched in databases such as Medline/PubMed, CINAHL, and Google Scholar from December 1 to April 9/2022. The MeSH terms and entry terms were used to search studies from databases, and modifications was made based on the type of databases \u003cem\u003e(Additional file 1).\u003c/em\u003e In addition, cross-references of the included articles was performed.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe following criteria were considered to include studies\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eStudy area\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAnywhere\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eStudy scope\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eStudies\u0026nbsp;that\u0026nbsp;report the prevalence of CVS and its associated factors\u003c/li\u003e\n \u003cli\u003eStudies that only\u0026nbsp;report the\u0026nbsp;overall prevalence of CVS\u003c/li\u003e\n \u003cli\u003e\u0026ldquo;Both community-\u0026nbsp;and facility-based studies\u0026rdquo;\u003c/li\u003e\n \u003cli\u003eQuantitative results, if the study was qualitative studies\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudy design\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eObservational study designs,\u0026nbsp;including cross-sectional, and cohort study\u0026nbsp;designs were considered\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eLanguage\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eEnglish\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePopulation\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eAll population groups \u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003ePublication year\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eNo restriction\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eStudies were excluded if:\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eOther than English\u003c/li\u003e\n \u003cli\u003eStudies that\u0026nbsp;did not\u0026nbsp;report specific outcomes (prevalence) for CVS\u003c/li\u003e\n \u003cli\u003eNo full-text article following email contact\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eQualitative studies\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eLetters, conference\u0026nbsp;abstracts, case reports, and reviews,\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCoCoPop/PEO\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCondition: computer vision syndrome\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eContext: worldwide\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePopulation: All population groups\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eOutcome/context\u003c/em\u003e: The primary outcome of the study was the pooled prevalence of\u0026nbsp;CVS. The prevalence of CVS\u0026nbsp;was considered\u0026nbsp;when the studies\u0026nbsp;reported\u0026nbsp;the overall prevalence of CVS for either\u0026nbsp;syndrome\u0026nbsp;(blurred vision, eye strain/fatigue, discomfort, diplopia, dry eye disease, redness, irritation, headache, shoulder, neck, and back pain) in the primary studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEndnote reference manager software\u0026nbsp;[33]\u0026nbsp;was used to organize, remove duplicate, irrelevant titles, and abstracts. Duplicate studies were removed. An assessment of studies using the title and abstract were\u0026nbsp;performed,\u0026nbsp;and irrelevant titles and abstracts were removed. A full-text review of studies was performed before the inclusion of studies in the final meta-analysis.\u0026nbsp;Study\u0026nbsp;selection was performed independently by the reviewers (EW and AK).\u0026nbsp;The\u0026nbsp;selection procedures of the studies\u0026nbsp;was presented using\u0026nbsp;a\u0026nbsp;PRISMA diagram\u003cem\u003e.\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;The Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument (JBI-MAStARI)\u0026rdquo;\u0026nbsp;[34]\u0026nbsp;was used to assess the quality of the studies. The component of quality assessment include study setting, outcome and explanatory\u0026nbsp;variable\u0026nbsp;measurements, clear inclusion criteria, measurement criteria used, participants\u0026rsquo; description\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eand valid statistical analysis\u003cem\u003e.\u003c/em\u003e Independent quality assessment of the studies was reviewed by (EW and AK), and 50% and above of the quality score was included in the final systematic review. Disagreement during quality assessment among reviewers was resolved with the discussion.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndependent data extraction was performed by the authors (EW and AK) using\u0026nbsp;a\u0026nbsp;pilot\u0026nbsp;tested data extraction Microsoft Office Excel sheet. The data extraction sheet\u0026nbsp;elements\u0026nbsp;included publication year, authors\u0026rsquo; names, study design, country, sample size, response rate prevalence and study subjects. Discrepancy was resolved by discussion between the authors (EW and AK). Contact with the corresponding authors of the studies was made for incomplete data, and the study was excluded if\u0026nbsp;there was\u0026nbsp;no response.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe extracted Excel data was imported to STATA version 14 for analysis. A narrative description and summary characteristics of the included studies was reported in tables and graphs. A random-effects model meta-analysis [35] was used for statistical analysis. The results was presented using a forest plot.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe heterogeneity\u0026nbsp;of studies was assessed by\u0026nbsp;the\u0026nbsp;I\u003csup\u003e2\u003c/sup\u003e statistic [36]. I\u003csup\u003e2\u003c/sup\u003e statistics of 25, 50 and 75% showed low, moderate and high heterogeneity, respectively, with p \u0026lt; 0.05. Publication bias was assessed using visual observation of the funnel plot for more than 10 studies [37] and Egger\u0026rsquo;s test at p \u0026lt; 0.05 [38]. To identify the sources of heterogeneity among the studies, subgroup analysis and meta-regression [39] were performed based on country, and sample size. Sensitivity analysis was be performed to assess the effect of single study on the overall result.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 735 articles were retrieved using electronic databases search: PubMed, Google Scholar, and CINHAL. 77 articles were excluded due to duplication, and 5, 59 articles were excluded as they were not related to the title, review, abstract and duplication. 99 full text articles were assessed for eligibility, and 57 articles were excluded for based on quality appraisal tool. 3 articles record identified through cross reference search of included studies. Finally, 45 articles were included in systematic review and meta-analysis (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of included studies\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 45 cross-sectional studies were included in this systematic review and meta-analysis: 4 studies in Saudi Arabia study [40-43], 2 studies in Nigeria [44, 45], 3 studies in Ghana [46-48], 4 studies in Pakistan [49-52], 3 studies in Spain [53-55], 7 studies in Ethiopia [56-62], 1 study in Jordan [63], 2 studies in China [64, 65], 1 study in Iran [66], 3 studies in Egypt [67-69], 8 studies in India [18, 70-76], 1 study in Nepal [77], 1 study in Seri Lanka [29], 2 studies in Brazil [78, 79], 1 study in Beirut [80], 1 study in Japan [81], and 1 study in Thailand [82]. A total of 17, 526 sample size were included. The sample size ranged from 74 in China [64] to 2,210 in Seri Lanka [29] (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 1: Characteristics of included studies in the meta-analysis of computer vision syndrome, 2022\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"105%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eAuthor/s/year (reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eStudy design\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eResponse rate (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003ePrevalence (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudy subjects\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eAbudawood GA, et al. 2020 [41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e95.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eAgbonlahor O. et al.2019 [44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e65.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eGovernment employ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eAkowuah PK, et al.2021[83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e92.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e64.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eAl Dandan O, et al. 2021[42]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e75.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e50.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eRadiologists\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eAl Subaie M, et al. 2017 [43]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e43.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003ePopulation \u0026ge; 15 years\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eArshad S, et al. 2019 [84]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e58.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eArtime‐R\u0026iacute;os E, et al.2021 [53]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e56.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eHealth workers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eBoadi-Kusi SB, et al. 2021 [48]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e86.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e71.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eBank workers\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eBoadi-Kusi SB, et al. 2020 [47]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eUniversity staff\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eCant\u0026oacute;‐Sancho N, et al. 2021 [54]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e76.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eDerbew H, et al. 2021 [56]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e74.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eBank workers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eDessie A, et al. 2018 [57]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e607\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e93.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e69.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eGovernment employ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eGammoh Y. et al. 2021 [63]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eJordan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e94.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eGondol BN, et al. 2020 [58]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e81.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eGovernment employ\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eHan CC, et al. 2013 [65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e1469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e97.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e57.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eHashemi H, et al. 2017 [66]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e1040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e97.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e49.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eKamal NN, et al. 2018 [67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEgypt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e96.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e84.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eBank workers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eLakachew Assefa N. et al. 2017 [59]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e98.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e73.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eBank workers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eLemma MG. et al. 2020 [60]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e68.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eSecretaries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eLemma MT,et al. 2021 [61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e96.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e75.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eSecretaries\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eLogaraj M, et al. 2014 [70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e81.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eMansoori N, et al. 2017 [50]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003estudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eMohan A, et al. 2021 [85]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e83.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e50.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eChildren\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eNAGWA E, et al. 2019 [68]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEgypt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eNoreen K, et al. 2021 [52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e326\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e95.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e98.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eNoreen K, et al. 2016 [51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e86.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e67.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eNwankwo B, et al. 2021 [45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e54.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003ePoudel S, et al. 2020 [77]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eNepal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e94.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e82.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eIT office workers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eRafeeq U, et al. 2020 [72]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e69.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003e\u0026ge; 12 years old population\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eRanasinghe P, et al. 2016 [86]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eSerilanka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e2210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e88.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e67.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eComputer office workers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eRanganatha SC, et al. 2019 [73]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e86.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eComputer sciences students\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eRathore D. , et al. 2016 [74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e75.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eComputer users\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eSa EC, et al. 2012 [78]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e89.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e54.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eCall center\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eS\u0026aacute;nchez-Brau M, et al. 2020 [55]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e95.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e74.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eVisual display workers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eSawaya RI, et al. 2020[80]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eBeirut\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e73.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e67.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eSingh H, et al. 2016 [18]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e51.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eTiwari RR, et al. 2011 [75]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e32.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eChildren\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eUchino M, et el. 2013 [81]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e561\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e83.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e11.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eVisual display terminal users\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eVerma S, et al. 2021 [76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eComputer operators\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eVilela MA, et al. 2015 [79]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e964\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e24.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eSchool children\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eWang L, et al. 2021 [64]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e80.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e74.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eWangsan K, et al. 2022 [82]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e527\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e81.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eZalat MM, et al. 2021 [40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e81.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eVisual display workers\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eZayed HA, et al. 2021 [69]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEgypt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e98.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e82.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eIT professionals\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"15.625%\"\u003e\n \u003cp\u003eZenbaba D, et al. 2021 \u003cstrong\u003e[62]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003eCross sectional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"10.416666666666666%\"\u003e\n \u003cp\u003e416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e98.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"13.541666666666666%\"\u003e\n \u003cp\u003e70.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"19.791666666666668%\"\u003e\n \u003cp\u003eStudents\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003ePooled prevalence of computer vision syndrome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pooled prevalence of computer vision syndrome was 66% (95%, CI: 59, 74)\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThe lowest proportion included study was 12% (95%, CI: 9, 15) in Japan [81] and the highest 99% (95%, CI:97, 100) in Pakistan [52]. The I\u003csup\u003e2\u003c/sup\u003e test showed that there was heterogeneity among included studies (I\u003csup\u003e2\u003c/sup\u003e = 99.42%, p-value \u0026lt; 0.001) (Figure 2).\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analysis by country\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analysis was performed based on country and the prevalence of computer vision syndrome was highest in Pakistan 97% (95% CI: 96, 98) and the lowest was in Japan 12% (95% CI: 9, 15). The studies that showed significant heterogeneity were studies in Saudi Arabia (I\u003csup\u003e2\u003c/sup\u003e = 99.41%, p-value \u0026lt; 0.001), Ethiopia (I\u003csup\u003e2\u003c/sup\u003e = 72.6%, p-value \u0026lt; 0.001), Egypt (I\u003csup\u003e2\u003c/sup\u003e = 80.06%, p-value \u0026lt; 0.001), and India (I\u003csup\u003e2\u003c/sup\u003e = 98.04%, p-value \u0026lt; 0.001) (Table 2).\u003c/p\u003e\n\u003cp\u003eTable 2: Subgroup analysis by country on computer vision syndrome, 2022\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" width=\"29.591836734693878%\"\u003e\n \u003cp\u003eSub group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"24.489795918367346%\"\u003e\n \u003cp\u003eNumber of included studies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" width=\"18.367346938775512%\"\u003e\n \u003cp\u003ePrevalence (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" width=\"27.551020408163264%\"\u003e\n \u003cp\u003eHeterogeneity statistics\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"44.44444444444444%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"55.55555555555556%\"\u003e\n \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"20\" valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eBy country\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"16.49484536082474%\"\u003e\n \u003cp\u003eSaudi Arabia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"24.742268041237114%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"18.556701030927837%\"\u003e\n \u003cp\u003e68(37, 98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"12.371134020618557%\"\u003e\n \u003cp\u003eP\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.463917525773196%\"\u003e\n \u003cp\u003e99.41%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e61(56, 66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003eP\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e62(52, 73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003eP\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e62(58, 66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003eP\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eSpain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e69(55, 83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003eP\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eEthiopia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e73(70, 76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003eP\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e72.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eJordan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e95(92, 96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e58(56, 61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003eP\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eIran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e49(46, 52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eEgypt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e81(74, 87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003eP\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e80.06%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e65(49, 81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003eP\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e98.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003ePakistan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e97(96, 98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003eP\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eNepal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e83(77, 87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eSeri Lanka\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e67(65, 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eBrazil\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e33(30, 35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003eP\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eBeirut\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e68(63, 72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eThailand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e81(77,84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e66(63, 69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e15(11, 21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"18.823529411764707%\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"28.235294117647058%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"21.176470588235293%\"\u003e\n \u003cp\u003e12(9, 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"14.117647058823529%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"17.647058823529413%\"\u003e\n \u003cp\u003e0.00%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eMeta regression\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMeta-regression was performed to identify the source of heterogeneity across the studies by country and sample size. Meta-regression indicated that heterogeneity was not associated with country and sample size (p-value \u0026gt;0.05) (Additional file 2, Table S1).\u003c/p\u003e\n\u003cp\u003ePublication biases\u003c/p\u003e\n\u003cp\u003ePublication bias was checked using funnel plots, and visual inspection suggested asymmetry, as 11 studies were on the left side and 32 studies were on the right side \u003cem\u003e(Additional file 3: Figure S1).\u003c/em\u003e Moreover, publication bias was not shown on Egger\u0026rsquo;s test (p=0.21) \u003cem\u003e(Additional file 4: Table S2).\u0026nbsp;\u003c/em\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e This systematic review and meta-analysis aimed to assess the pooled prevalence of computer vision syndrome. There are inconsistence findings on prevalence of computer vision syndrome. Moreover, there no systematic review and meta-analysis research findings on the pooled prevalence of computer vision syndrome. Therefore, findings from this systematic review and meta-analysis will help policy-makers design appropriate strategies to reduce computer vision syndrome public health concern\u003c/p\u003e \u003cp\u003eThe pooled prevalence of computer vision syndrome was 66% (95%, CI: 59, 73). The pooled prevalence was in line with the study done in India COVID 19 pre locked down, 64.3% [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. However, the pooled prevalence was lower than studies done in India during COVID 19 locked down, 87.3% [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e], Europe, 90% [\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e], and Ethiopia, 73.21% [\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e]. The difference might be due to differences in study period, study setting, socioeconomic differences, awareness and behavioral change on prevention of computer vision syndrome. The limitations include, articles published only the English, and cause effect relationship as all the studies were cross-sectional designs. Moreover, this study was reported from 20 countries, which might lack representativeness.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eNearly two in three participants had computer vision syndrome. Thus, preventive practice strategic activities on computer vision syndrome are important interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Samara University for network and HINARY database website access.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Conceptualization: Asamene Kelelom Lema\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Investigation: Etsay Woldu Anbesu\u003c/p\u003e\n\u003cp\u003eMethodology: Asamene Kelelom Lema, Etsay Woldu Anbesu\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; original draft: Etsay Woldu Anbesu\u003c/p\u003e\n\u003cp\u003eWriting \u0026ndash; review \u0026amp; editing: Asamene Kelelom Lema, Etsay Woldu Anbesu,\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAssociation AO, \u003cem\u003eThe Effects of Computer Use on Eye Health and Vision, American Optometric Association, St. Louis, MO, USA, 1997\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eRosenfield M. \u003cem\u003eComputer vision syndrome (aka digital eye strain).\u003c/em\u003e Optometry in practice, 2016. 17(1): p. 1-10.\u003c/li\u003e\n\u003cli\u003eAssociation AO. \u003cem\u003eGuide to the clinical aspects of computer vision syndrome.\u003c/em\u003e St. Louis: American Optometric Association, 1995. 1.\u003c/li\u003e\n\u003cli\u003eAhmed SF, McDermott KC, Burge WK, et al. \u003cem\u003eVisual function, digital behavior and the vision performance index.\u003c/em\u003e Clinical Ophthalmology (Auckland, NZ), 2018. 12: p. 2553.\u003c/li\u003e\n\u003cli\u003eKlamm J and Tarnow KG. \u003cem\u003eComputer vision syndrome: a review of literature.\u003c/em\u003e Medsurg Nursing, 2015. 24(2): p. 89.\u003c/li\u003e\n\u003cli\u003eMunshi S, Varghese A, and Dhar‐Munshi S. \u003cem\u003eComputer vision syndrome\u0026mdash;A common cause of unexplained visual symptoms in the modern era.\u003c/em\u003e International Journal of Clinical Practice, 2017. 71(7): p. e12962.\u003c/li\u003e\n\u003cli\u003eVaz FT, Henriques SP, Silva DS, et al. \u003cem\u003eDigital asthenopia: Portuguese group of ergophthalmology survey.\u003c/em\u003e Acta médica portuguesa, 2019. 32(4): p. 260-265.\u003c/li\u003e\n\u003cli\u003eAkkaya S, Atakan T, Acikalin B, et al. \u003cem\u003eEffects of long-term computer use on eye dryness.\u003c/em\u003e Northern clinics of Istanbul, 2018. 5(4): p. 319.\u003c/li\u003e\n\u003cli\u003eBillones RKC, Bedruz RAR, Arcega ML, et al. \u003cem\u003eDigital eye strain and fatigue recognition using electrooculogram signals and ultrasonic distance measurements\u003c/em\u003e. in \u003cem\u003e2018 IEEE 10th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM)\u003c/em\u003e. 2018. 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1-12.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"pooled prevalence, computer vision syndrome, systematic review, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-2070028/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2070028/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eComputer vision syndromes are becoming a major public health concern; however, less emphasis is given, particularly in developing countries. Although there are studies on different continents, there are inconsistent findings among the studies. Therefore, this systematic review and meta-analysis aimed to estimate the pooled prevalence of computer vision syndrome.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e In this study, the review was developed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Online electronic databases, including PubMed/Medline, CINAHL, and Google Scholar, were used to retrieve published and unpublished studies from December 1 to April 9/2022. Study selection, quality assessment, and data extraction were performed independently. Quality assessment of the studies was performed using the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument. Heterogeneity was assessed using statistical test I\u003csup\u003e2\u003c/sup\u003e. STATA 14 software was used for statistical analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 7, 35 studies were retrieved, and 45 studies with a total 17,526 participants were included in the final meta-analysis. The pooled prevalence of computer vision syndrome was 66% (95%, CI: 59, 74). Subgroup analysis based on country was highest in Pakistan 97% (95% CI: 96, 98) and the lowest was in Japan 12% (95% CI: 9, 15). Subgroup analysis based on country showed studies in Saudi Arabia (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;99.41%, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Ethiopia (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;72.6%, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and India (I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;98.04%, p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had significant heterogeneity\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNearly two in three participants had computer vision syndrome. Thus, preventive practice strategic activities on computer vision syndrome are important interventions.\u003c/p\u003e","manuscriptTitle":"Prevalence of computer vision syndrome: a systematic review and meta- analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-09-28 21:29:20","doi":"10.21203/rs.3.rs-2070028/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-12-09T11:20:20+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-11-03T14:30:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5bf68783-2898-441f-8945-12ca35661155","date":"2022-10-14T13:05:33+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-10-14T12:41:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-10-14T12:23:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2022-09-26T12:25:12+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2022-09-26T12:19:53+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2022-09-15T18:05:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"454764b1-034e-498c-8173-976d7bd8d26e","owner":[],"postedDate":"September 28th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T18:44:48+00:00","versionOfRecord":{"articleIdentity":"rs-2070028","link":"https://doi.org/10.1038/s41598-023-28750-6","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2023-01-31 18:35:53","publishedOnDateReadable":"January 31st, 2023"},"versionCreatedAt":"2022-09-28 21:29:20","video":"","vorDoi":"10.1038/s41598-023-28750-6","vorDoiUrl":"https://doi.org/10.1038/s41598-023-28750-6","workflowStages":[]},"version":"v1","identity":"rs-2070028","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2070028","identity":"rs-2070028","version":["v1"]},"buildId":"FbvkV6FR0MCFSLy54lSbu","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}