Computer Vision Syndrome Questionnaire – Persian Version

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Abstract

The Computer Vision Syndrome Questionnaire (CVS-Q) is a specialized psychometric instrument designed to systematically evaluate the ocular and visual symptoms associated with prolonged digital screen exposure. As modern workplaces increasingly rely on digital infrastructure, occupational health professionals require robust tools to quantify the physiological toll of screen time, commonly referred to as Digital Eye Strain. The Persian adaptation of this tool (CVS-Q FA) represents a critical methodological advancement for assessing this modern occupational hazard within the Iranian population, providing a standardized metric where previously only ad hoc, unvalidated surveys were utilized.

The adaptation process of the CVS-Q FA underscores the importance of rigorous cross-cultural translation in psychometrics. Rather than merely translating words, the developers utilized established international guidelines to ensure conceptual equivalence, meaning the psychological and physiological experiences of eye strain were accurately captured in the target cultural context. This involved iterative forward and backward translations, expert committee reviews, and cognitive debriefing with target users to ensure high comprehensibility and clinical utility.

Ultimately, the CVS-Q FA functions as a highly effective diagnostic and research tool. By measuring both the frequency and intensity of 16 distinct symptoms, it generates a composite severity score that can reliably distinguish between individuals with and without clinical levels of Computer Vision Syndrome. Its strong psychometric properties make it an invaluable asset for epidemiologists tracking occupational health trends, clinicians diagnosing Digital Eye Strain, and researchers evaluating ergonomic or medical interventions in the workplace.

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Keywords
Computer Vision Syndrome
Digital Eye Strain
occupational health
Cross-cultural validation
psychometrics
Visual Ergonomics

📊 Psychometric Scorecard

Items Count
16
Structure
Unidimensional
Cronbach's α
0.80
Validation Country
📍 Iran

Translation Method: Forward-Backward Translation (ISPOR guidelines)
Clinical Cut-off Scores: Total score >= 6 indicates presence of Computer Vision Syndrome

Authors

👤 Milad Qolami
🏛 Department of Optometry, School of Rehabilitation, Iran University of Medical Sciences, Tehran, Iran

👤 Ali Mirzajani
🏛 Department of Optometry, School of Rehabilitation, Iran University of Medical Sciences, Tehran, Iran

👤 Elena Ronda-Pérez
🏛 Public Health Research Group, University of Alicante, San Vicente del Raspeig, Alicante, Spain

👤 Natalia Cantó-Sancho
✉ [email protected]
🏛 Department of Optics, Pharmacology and Anatomy, University of Alicante, San Vicente del Raspeig, Alicante, Spain

👤 Mar Seguí-Crespo
🏛 Public Health Research Group, University of Alicante, San Vicente del Raspeig, Alicante, Spain

Purpose

The primary objective of the CVS-Q FA is to provide a standardized, culturally adapted, and psychometrically sound measure of Computer Vision Syndrome for Persian-speaking populations. With the exponential rise in digital device usage—accelerated further by shifts toward telecommuting—Digital Eye Strain has emerged as a pervasive public health issue. However, epidemiological research in this area has historically been hampered by the use of inconsistent, non-validated questionnaires, leading to wildly fluctuating prevalence estimates.

This instrument addresses a significant methodological gap by offering a validated diagnostic threshold. For occupational health researchers, it enables accurate prevalence tracking and the evaluation of workplace ergonomic interventions. For clinicians, it provides a quick, self-administered screening tool to quantify symptom severity, guiding treatment decisions for patients suffering from screen-related visual discomfort.

Construct

Computer Vision Syndrome (CVS), also known as Digital Eye Strain, is a multidimensional physiological and perceptual construct encompassing a cluster of ocular, visual, and musculoskeletal symptoms resulting from prolonged use of digital displays. The theoretical framework underlying the CVS-Q conceptualizes this syndrome not as a single isolated symptom, but as a cumulative burden of various interconnected visual disturbances. These include asthenopia (eyestrain), ocular surface mechanisms (dryness, burning, redness), and visual acuity issues (blurriness, double vision).

Psychometrically, the CVS-Q FA treats this construct as unidimensional, meaning that while the symptoms vary in their specific physiological manifestations, they all contribute to a single underlying latent variable: the overall severity of Digital Eye Strain. The instrument captures this construct by evaluating two critical dimensions of the patient's experience: the frequency of symptom occurrence and the intensity of the discomfort. This dual-axis approach ensures a more nuanced and accurate representation of the syndrome's impact on daily functioning than measuring frequency or intensity alone.

Validity

The validation framework for the CVS-Q FA demonstrates exceptional rigor, establishing strong evidence for both content and criterion validity. Content validity was secured through a panel of 15 optometry experts, yielding a highly robust Scale-Level Content Validity Index (S-CVI) of 0.92. At the item level, all components achieved an index above 0.80 after minor linguistic adjustments, ensuring that every question is highly relevant to the target construct in the Persian language.

From a diagnostic perspective, the criterion validity of the instrument is particularly strong. Receiver Operating Characteristic (ROC) analysis revealed that the scale possesses a sensitivity of 81.1% and a specificity of 69.2%. In practical clinical terms, this means the questionnaire is highly capable of correctly identifying individuals who genuinely suffer from Computer Vision Syndrome (low false-negative rate), while maintaining an acceptable ability to rule out those who do not. These metrics align closely with the original Spanish version, confirming the tool's cross-cultural diagnostic stability.

Reliability

The reliability profile of the CVS-Q FA indicates that it is a highly consistent and stable measurement tool. Internal consistency, which evaluates how well the 16 items correlate with one another to measure the same underlying construct, was strong, evidenced by a Cronbach's alpha coefficient of 0.80. This value sits comfortably within the optimal range for clinical and research tools, indicating excellent cohesion without redundant item overlap.

Furthermore, the instrument demonstrated robust temporal stability. When administered to a subset of participants on two separate occasions, the test-retest reliability yielded an Intraclass Correlation Coefficient (ICC) of 0.81 and a Cohen's kappa of 0.65. These metrics confirm that the questionnaire is not overly sensitive to transient daily fluctuations and can reliably measure a patient's baseline symptom severity over time, making it highly suitable for longitudinal studies evaluating the efficacy of workplace interventions.

Factor Analysis

The structural validity of the CVS-Q FA was evaluated to ensure that the translated items behave statistically similarly to the original instrument. The researchers utilized factor analysis to examine the underlying latent structure of the 16 symptom items. The analytical approach confirmed that the scale maintains a strictly unidimensional structure, meaning all items successfully load onto a single overarching factor representing Computer Vision Syndrome.

This finding is crucial for scoring and interpretation. Because the scale is unidimensional, researchers and clinicians are justified in calculating a single total severity score rather than having to compute multiple subscale scores. The analysis demonstrated robust goodness-of-fit statistics, indicating that the theoretical model of a single underlying syndrome perfectly matches the actual response patterns of the Persian-speaking sample. No items needed to be removed, preserving the comprehensive nature of the original tool.

Instrument

Test Type Self-report questionnaire
Format 16 items assessing frequency (never, occasionally, often/always) and intensity (moderate, intense)
Scoring Frequency and intensity responses are recoded and combined to calculate a severity score for each symptom. These are summed to yield a total score. A total score >= 6 indicates the presence of CVS.
Language Persian
Population Adults, Employees, Students
Administration Self-administered

Scoring & Interpretation Guidelines

Scoring Instructions For each of the 16 symptoms, frequency and intensity are evaluated. These two parameters are recoded into a single severity value per symptom, which are then summed to create the total scale score.
Cut-off Scores Total score >= 6 indicates presence of Computer Vision Syndrome

Computer Vision Syndrome Questionnaire – Persian Version Items

📋 Items are currently not available

The individual items of this scale are not publicly available. Researchers interested in using this instrument should contact the original authors directly to request the scale materials.

Sample

The validation sample consisted of 102 computer users, specifically university staff and graduate Students who regularly use digital devices in their workplace. Additional samples included 20 participants for the pretest, 7 for cognitive debriefing, 46 for test-retest reliability, and an expert committee of 15 optometrists.

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Cite This Paper

Milad Qolami, Ali Mirzajani, Elena Ronda-Pérez, Natalia Cantó-Sancho, Mar Seguí-Crespo (2022). Computer Vision Syndrome Questionnaire – Persian Version. International ophthalmology. https://doi.org/10.1007/s10792-022-02340-3

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References
54 references
  1. Rosenfield M (2011) Computer Vision Syndrome: a review of ocular causes and potential treatments. Ophthal Physiol Opt 31(5):502–515. https://doi.org/10.1111/j.1475-1313.2011.00834.x 🔗 https://doi.org/10.1111/j.1475-1313.2011.00834.x
  2. Sheppard AL, Wolffsohn JS (2018) Digital Eye Strain: prevalence, measurement and amelioration. BMJ Open Ophthalmol 3(1):e000146. https://doi.org/10.1136/bmjophth-2018-000146 🔗 https://doi.org/10.1136/bmjophth-2018-000146
  3. Gowrisankaran S, Sheedy JE (2015) Computer Vision Syndrome: a review. Work 52(2):303–314. https://doi.org/10.3233/WOR-152162 🔗 https://doi.org/10.3233/WOR-152162
  4. Bhanderi DJ, Choudhary S, Doshi VG (2008) A community-based study of asthenopia in computer operators. Indian J Ophthalmol 56(1):51–55. https://doi.org/10.4103/0301-4738.37596 🔗 https://doi.org/10.4103/0301-4738.37596
  5. Tauste A, Ronda E, Molina MJ, Seguí M (2016) Effect of contact lens use on Computer Vision Syndrome. Ophthal Physiol Opt 36(2):112–119. https://doi.org/10.1111/opo.12275 🔗 https://doi.org/10.1111/opo.12275
  6. Sa EC, Ferreira Junior M, Rocha LE (2012) Risk factors for computer visual syndrome (CVS) among operators of two call centers in São Paulo, Brazil. Work 41(Suppl 1):3568–3574. https://doi.org/10.3233/WOR-2012-0636-3568 🔗 https://doi.org/10.3233/WOR-2012-0636-3568
  7. Assefa NL, Weldemichael DZ, Alemu HW, Anbesse DH (2017) Prevalence and associated factors of Computer Vision Syndrome among bank workers in Gondar City, northwest Ethiopia, 2015. Clin Optom (Auckl) 9:67–76. https://doi.org/10.2147/OPTO.S126366 🔗 https://doi.org/10.2147/OPTO.S126366
  8. Reddy SC, Low CK, Lim YP, Low LL, Mardina F, Nursaleha MP (2013) Computer Vision Syndrome: a study of knowledge and practices in university Students. Nepal J Ophthalmol 5(2):161–168. https://doi.org/10.3126/nepjoph.v5i2.8707 🔗 https://doi.org/10.3126/nepjoph.v5i2.8707
  9. Coles-Brennan C, Sulley A, Young G (2019) Management of Digital Eye Strain. Clin Exp Optom 102(1):18–29. https://doi.org/10.1111/cxo.12798 🔗 https://doi.org/10.1111/cxo.12798
  10. Seguí M, Cabrero-García J, Crespo A, Verdú J, Ronda E (2015) A reliable and valid questionnaire was developed to measure Computer Vision Syndrome at the workplace. J Clin Epidemiol 68(6):662–673. https://doi.org/10.1016/j.jclinepi.2015.01.015 🔗 https://doi.org/10.1016/j.jclinepi.2015.01.015
  11. Statistical Center of Iran (2019) Families and individual's use and access of communication and information technology. Budget and planning organization. https://www.amar.org.ir/Portals/0/News/1398/artbattat96.pdf. Accessed 15 February 2020
  12. Wild D, Grove A, Martin M, Eremenco S, McElroy S, Verjee-Lorenz A, Erikson P, ISPOR Task Force for Translation and Cultural Adaptation (2005) Principles of good practice for the translation and cultural adaptation process for patient-reported outcomes (PRO) measures: report of the ISPOR task force for translation and cultural adaptation. Value Health 8(2):94–104. https://doi.org/10.1111/j.1524-4733.2005.04054.x 🔗 https://doi.org/10.1111/j.1524-4733.2005.04054.x
  13. Zamanzadeh V, Ghahramanian A, Rassouli M, Abbaszadeh A, Alavi-Majd H, Nikanfar AR (2015). Design and implementation content validity study: development of an instrument for measuring patient-centered communication. J Caring Sci 4(2):165–178. https://doi.org/10.15171/jcs.2015.017 🔗 https://doi.org/10.15171/jcs.2015.017
  14. Polit DF, Beck CT, Owen SV (2007) Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Res Nurs Health 30(4):459–467. https://doi.org/10.1002/nur.20199 🔗 https://doi.org/10.1002/nur.20199
  15. Ramada-Rodilla JM, Serra-Pujadas C, Delclós-Clanchet GL (2013) Adaptación cultural y validación de cuestionarios de salud: revisión y recomendaciones metodológicas [Cross-cultural adaptation and health questionnaires validation: revision and methodological recommendations]. Salud Publica Mex 55(1):57–66. https://doi.org/10.1590/s0036-36342013000100009 🔗 https://doi.org/10.1590/s0036-36342013000100009
  16. Mardia KV (1970) Measures of multivariate skewness and kurtosis with applications. Biometrika 57(3):519–530. https://doi.org/10.1093/biomet/57.3.519 🔗 https://doi.org/10.1093/biomet/57.3.519
  17. Porras Cerron JC (2016). Comparación de pruebas de normalidad multivariada [Comparison tests multivariate normal]. Anales Científicos 77(2):141–146. https://doi.org/10.21704/ac.v77i2.483 🔗 https://doi.org/10.21704/ac.v77i2.483
  18. Timmerman ME, Lorenzo-Seva U (2011) Dimensionality assessment of ordered polytomous items with parallel analysis. Psychol Methods 16(2):209–220. https://doi.org/10.1037/a0023353 🔗 https://doi.org/10.1037/a0023353
  19. Bandalos DL, Finney SJ (2018). Factor analysis: exploratory and confirmatory. In: Hancock GR, Stapleton LM, Mueller RO (eds) The reviewer’s guide to quantitative methods in the social sciences, 2nd edn. Routledge, New York 🔗 https://doi.org/10.4324/9781315755649-8
  20. Lorenzo-Seva U, Ferrando PJ (2021) MSA: the forgotten index for identifying inappropriate items before computing exploratory item factor analysis. Methodology 17(4):296–306. https://doi.org/10.5964/meth.7185 🔗 https://doi.org/10.5964/meth.7185
  21. Jung S (2013) Exploratory factor analysis with small sample sizes: a comparison of three approaches. Behav Proc 97:90–95. https://doi.org/10.1016/j.beproc.2012.11.016 🔗 https://doi.org/10.1016/j.beproc.2012.11.016
  22. Kelley TL (1935) Essential traits of mental life, harvard studies in education, vol 26. Harvard University Press, Cambridge, MA
  23. Yu C, Muthen B (2002) Evaluation of model fit indices for latent variable models with categorical and continuous outcomes. Paper presented at the annual meeting of the American Educational Research Association, New Orleans, LA
  24. Ferrando PJ, Anguiano-Carrasco C (2010) El análisis factorial como técnica de investigación en psicología [Factor análisis as a research technique in psychology]. Papeles del Psicólogo 31(1):18–33
  25. Lloret-Segura S, Ferreres-Traver A, Hernández-Baeza A, Tomás-Marco I (2014) El análisis factorial exploratorio de los ítems: una guía práctica, revisada y actualizada [Exploratory item factor analysis: A practical guide revised and updated]. Anales de Psicología 30(3):1151–1169. https://doi.org/10.6018/analesps.30.3.199361 🔗 https://doi.org/10.6018/analesps.30.3.199361
  26. Ferrando PJ, Lorenzo-Seva U (2017) Assessing the quality and appropriateness of factor solutions and factor score estimates in exploratory item factor analysis. Educ Psycol Meas 78(5):762–780. https://doi.org/10.1177/0013164417719308 🔗 https://doi.org/10.1177/0013164417719308
  27. Xia Y, Yang Y (2019) RMSEA, CFI, and TLI in structural equation modeling with ordered categorical data: The story they tell depends on the estimation methods. Behav Res Methods 51(1):409–428. https://doi.org/10.3758/s13428-018-1055-2 🔗 https://doi.org/10.3758/s13428-018-1055-2
  28. Boparai JK, Singh S, Kathuria P (2018) How to Design and Validate A Questionnaire: a Guide. Curr Clin Pharmacol 13(4):210–215. https://doi.org/10.2174/1574884713666180807151328 🔗 https://doi.org/10.2174/1574884713666180807151328
  29. Koo TK, Li MY (2016) A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J Chiropr Med 15(2):155–163. https://doi.org/10.1016/j.jcm.2016.02.012 🔗 https://doi.org/10.1016/j.jcm.2016.02.012
  30. McHugh ML (2012) Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 22(3):276–282 🔗 https://doi.org/10.11613/BM.2012.031
  31. Bujang MA, Omar ED, Baharum NA (2018) A review on sample size determination for Cronbach's alpha test: a simple guide for researchers. Malays J Med Sci 25(6):85–99. https://doi.org/10.21315/mjms2018.25.6.9 🔗 https://doi.org/10.21315/mjms2018.25.6.9
  32. Terwee CB, Bot SD, de Boer MR, van der Windt DA, Knol DL, Dekker J, Bouter LM, de Vet HC (2007) Quality criteria were proposed for measurement properties of health status questionnaires. J Clin Epidemiol 60(1):34–42. https://doi.org/10.1016/j.jclinepi.2006.03.012 🔗 https://doi.org/10.1016/j.jclinepi.2006.03.012
  33. Williams KM, Verhoeven VJ, Cumberland P, Bertelsen G, Wolfram C, Buitendijk GH, Hofman A, van Duijn CM, Vingerling JR, Kuijpers RW, Höhn R, Mirshahi A, Khawaja AP, Luben RN, Erke MG, von Hanno T, Mahroo O, Hogg R, Gieger C et al (2015) Prevalence of refractive error in Europe: the European Eye Epidemiology (E(3)) Consortium. Eur J Epidemiol 30(4):305–315. https://doi.org/10.1007/s10654-015-0010-0 🔗 https://doi.org/10.1007/s10654-015-0010-0
  34. Mikulášová E, Cantó-Sancho N, Caballero Pérez P, Ronda-Pérez E, Seguí-Crespo M (2020) Translation and cultural adaptation of the Computer Vision Syndrome Questionnaire (CVS-Q©) into Slovak. Hygiena 65(3):84–92. https://doi.org/10.21101/hygiena.a1746 🔗 https://doi.org/10.21101/hygiena.a1746
  35. Seguí-Crespo M, Cantó-Sancho N, Ronda E, Colombo R, Porru S, Carta A (2019). Translation and cultural adaptation of the Computer Vision Syndrome Questionnaire (CVS-Q) into Italian. Med Lav 110(1):37–45. https://doi.org/10.23749/mdl.v110i1.7499 🔗 https://doi.org/10.23749/mdl.v110i1.7499
  36. Beaton DE, Bombardier C, Guillemin F, Ferraz MB (2000). Guidelines for the process of cross-cultural adaptation of self-report measures. Spine (Phila Pa 1976) 25(24):3186–3191. https://doi.org/10.1097/00007632-200012150-00014 🔗 https://doi.org/10.1097/00007632-200012150-00014
  37. Guillemin F, Bombardier C, Beaton D (1993) Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines. J Clin Epidemiol 46(12):1417–1432. https://doi.org/10.1016/0895-4356(93)90142-n 🔗 https://doi.org/10.1016/0895-4356(93)90142-n
  38. Epstein J, Santo RM, Guillemin F (2015) A review of guidelines for cross-cultural adaptation of questionnaires could not bring out a consensus. J Clin Epidemiol 68(4):435–441. https://doi.org/10.1016/j.jclinepi.2014.11.021 🔗 https://doi.org/10.1016/j.jclinepi.2014.11.021
  39. McKenna SP, Doward LC (2005) The translation and cultural adaptation of patient-reported outcome measures. Value Health 8(2):89–91. https://doi.org/10.1111/j.1524-4733.2005.08203.x 🔗 https://doi.org/10.1111/j.1524-4733.2005.08203.x
  40. Prakash V, Shah S, Hariohm K (2019) Cross-cultural adaptation of patient-reported outcome measures: a solution or a problem? Ann Phys Rehabil Med 62(3):174–177. https://doi.org/10.1016/j.rehab.2019.01.006 🔗 https://doi.org/10.1016/j.rehab.2019.01.006
  41. Swaine-Verdier A, Doward LC, Hagell P, Thorsen H, McKenna SP (2004) Adapting quality of life instruments. Value Health 7(Suppl 1):S27–S30. https://doi.org/10.1111/j.1524-4733.2004.7s107.x 🔗 https://doi.org/10.1111/j.1524-4733.2004.7s107.x
  42. Hagell P, Hedin PJ, Meads DM, Nyberg L, McKenna SP (2010) Effects of method of translation of patient-reported health outcome questionnaires: a randomized study of the translation of the Rheumatoid Arthritis Quality of Life (RAQoL) Instrument for Sweden. Value Health 13(4):424–430. https://doi.org/10.1111/j.1524-4733.2009.00677.x 🔗 https://doi.org/10.1111/j.1524-4733.2009.00677.x
  43. Epstein J, Osborne RH, Elsworth GR, Beaton DE, Guillemin F (2015) Cross-cultural adaptation of the Health Education Impact Questionnaire: experimental study showed expert committee, not back-translation, added value. J Clin Epidemiol 68(4):360–369. https://doi.org/10.1016/j.jclinepi.2013.07.013 🔗 https://doi.org/10.1016/j.jclinepi.2013.07.013
  44. Aaronson N, Alonso J, Burnam A, Lohr KN, Patrick DL, Perrin E, Stein RE (2002) Assessing health status and quality-of-life instruments: attributes and review criteria. Qual Life Res 11(3):193–205. https://doi.org/10.1023/a:1015291021312 🔗 https://doi.org/10.1023/a:1015291021312
  45. Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, Bouter LM, de Vet HC (2010) The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol 63(7):737–745. https://doi.org/10.1016/j.jclinepi.2010.02.006 🔗 https://doi.org/10.1016/j.jclinepi.2010.02.006
  46. Young NL, Wakefield C, Burke TA, Ray R, McCusker PJ, Blanchette V (2013). Updating the Canadian hemophilia outcomes-kids life assessment tool (CHO-KLAT Version2.0). Value Health 16(5):837–841. https://doi.org/10.1016/j.jval.2013.02.004 🔗 https://doi.org/10.1016/j.jval.2013.02.004
  47. Doruk C, Çelik M, Kara H, Polat B, Güldiken Y, Orhan KS (2019) Turkish translation and validation of chronic otitis media questionnaire-12. Turk Arch Otorhinolaryngol 57(1):24–29. https://doi.org/10.5152/tao.2019.3693 🔗 https://doi.org/10.5152/tao.2019.3693
  48. Miravitlles M, Llor C, Calvo E, Diaz S, Díaz-Cuervo H, Gonzalez-Rojas N (2012). Validación de la versión traducida del Chronic Obstructive Pulmonary Disease-Population Screener (COPD-PS). Su utilidad y la del FEV1/FEV6 para el diagnóstico de enfermedad pulmonar obstructiva crónica [Validation of the Spanish version of the Chronic Obstructive Pulmonary Disease-Population Screener (COPD-PS). Its usefulness and that of FEV1/FEV6 for the diagnosis of COPD]. Medicina Clinica 139(12):522–530. https://doi.org/10.1016/j.medcli.2011.06.022 🔗 https://doi.org/10.1016/j.medcli.2011.06.022
  49. Qolami M, Cantó-Sancho N, Seguí-Crespo M, Mirzajani A, Taghizadeh Gh (2020) Prevalence of Computer Vision Syndrome among Iranian medical university Employees and graduate Students in their occupational environment. Func Disabil J 3(1):151–60. https://doi.org/10.32598/fdj.4.17 🔗 https://doi.org/10.32598/fdj.4.17
  50. Ranasinghe P, Wathurapatha WS, Perera YS, Lamabadusuriya DA, Kulatunga S, Jayawardana N, Katulanda P (2016) Computer Vision Syndrome among computer office workers in a developing country: an evaluation of prevalence and risk factors. BMC Res Notes 9:150. https://doi.org/10.1186/s13104-016-1962-1 🔗 https://doi.org/10.1186/s13104-016-1962-1
  51. Blehm C, Vishnu S, Khattak A, Mitra S, Yee RW (2005) Computer Vision Syndrome: a review. Surv Ophthalmol 50(3):253–262. https://doi.org/10.1016/j.survophthal.2005.02.008 🔗 https://doi.org/10.1016/j.survophthal.2005.02.008
  52. Jaiswal S, Asper L, Long J, Lee A, Harrison K, Golebiowski B (2019) Ocular and visual discomfort associated with smartphones, tablets and computers: what we do and do not know. Clin Exp Optom 102(5):463–477. https://doi.org/10.1111/cxo.12851 🔗 https://doi.org/10.1111/cxo.12851
  53. Munk R, Storheim K, Småstuen MC, Grotle M (2019) Measuring productivity costs in patients with musculoskeletal disorders: measurement properties of the institute for medical technology assessment productivity cost questionnaire. Value Health 22(12):1410–1416. https://doi.org/10.1016/j.jval.2019.07.011 🔗 https://doi.org/10.1016/j.jval.2019.07.011
  54. Bahkir FA, Grandee SS (2020) Impact of the COVID-19 lockdown on digital device-related ocular health. Indian J Ophthalmol 68(11):2378–2383. https://doi.org/10.4103/ijo.IJO_2306_20 🔗 https://doi.org/10.4103/ijo.IJO_2306_20

Cite this article

Mohammed looti (2026). Computer Vision Syndrome Questionnaire – Persian Version. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/s/computer-vision-syndrome-questionnaire-persian-version/

Mohammed looti. "Computer Vision Syndrome Questionnaire – Persian Version." PSYCHOLOGICAL SCALES, 14 Aug. 2026, https://scales.arabpsychology.com/s/computer-vision-syndrome-questionnaire-persian-version/.

Mohammed looti. "Computer Vision Syndrome Questionnaire – Persian Version." PSYCHOLOGICAL SCALES, 2026. https://scales.arabpsychology.com/s/computer-vision-syndrome-questionnaire-persian-version/.

Mohammed looti (2026) 'Computer Vision Syndrome Questionnaire – Persian Version', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/s/computer-vision-syndrome-questionnaire-persian-version/.

[1] Mohammed looti, "Computer Vision Syndrome Questionnaire – Persian Version," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, August, 2026.

Mohammed looti. Computer Vision Syndrome Questionnaire – Persian Version. PSYCHOLOGICAL SCALES. 2026;vol(issue):pages.

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