15 APP| Published By AEIRC| https://doi.org/10.29052/2412-3188.v6.i1.2019.15-22 ISSN 2412 - 3188 Original Article Computer vision syndrome: prevalence and predictors among students Sonya Arshad, Muhammad Faisal Qureshi, Muhammad Ali, Komal Piryani, Komal Shafqat, Madiha Mateen, Mahnoor Waheed, Mahnoor Rohaila & Manal Amin Liaquat National School of Physiotherapy, Liaquat National Hospital & Medical College Abstract Background: Globally around 60 million people are suffering from Computer vision syndrome (CVS). A well-known eye and vision-related problem resulting from prolonged computer, tablet, e-reader and cell phone use. Viewing a digital screen often increases the visual load therefore the aim of this study was to explore the prevalence of CVS among students. Methodology: This cross-sectional study was conducted from 1st September 2018 to 31st May 2019 on a sample of 320 students from diverse physiotherapy institutes of Karachi. Participants of both genders between the age of 18-24 years were included in the study. Data regarding demographics, CVS assessment, associated symptoms, intensity of symptoms and daily vision routine was recorded. CVS was assessed using a standardized CVS questionnaire (CVS-Q) and analyzed using SPSS version 21. Results: A total of 320 physiotherapy students with a mean age of 21.04+0.8 years were enrolled in the study, majority of them were females 245(76.6%). Out of these, 186 students were diagnosed with CVS as they scored ≥6 on Segui and Colleagues CVS questionnaire with headache being the most prevalent symptom (63.1%) followed by itching (52.8%), increased sensitivity to light (43.6%), eye pain (42.6%) and feeling of foreign body (39.5%). Among the activities involved in daily vision routine, optical use and sitting posture were significantly associated with CVS. Conclusion: It can be concluded from the study results that CVS is highly prevalent among physiotherapy students, one of the reasons for this might be the increased digital screens usage for academic purpose and clinical decision making. Keywords Computer Vision Syndrome (CVS), Digital Eye Strain, Physiotherapy, Daily Vision Routine, CVS Symptoms Citation: Arshad S, Qureshi MF, Ali M, Piryani K, Shafqat K, Mateen M, Waheed M, Rohaila M, Amin M. Computer vision syndrome: prevalence and predictors among students. APP. 2019; 6(1):15-22 Corresponding Author Email: sonya.arshad@lnh.edu.pk DOI: 10.29052/2412-3188.v6.i1.2019.15-22 Received 10/02/2019 Accepted 04/05/2019 Published 12/10/2019 Copyright © The Author(s). 2019 This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: The author(s) received no specific funding for this work. Conflicts of Interests: The authors have declared that no competing interests exist. https://doi.org/10.29052/2412-3188.v6.i1.2019.13 http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) 16 ISSN 2412 - 3188 APP| Published By AEIRC| Volume 6 Issue 1 Introduction With the recent advancement of technology, the use of computer and other electronic devices especially smart phones and tablets, has widely increased not only for professional but also for academic purposes in colleges and universities. Both positive and negative impacts are associated with these technological modifications1. On one hand, use of these gadgets provides us easy communication, e-learning, improved and appropriate time management whereas prolong exposure results in various visual and ergonomic disorders. Using these technologies for 3 hours/day increases the health risk promoting CVS, low-back pain (LBP), headaches and psychosocial stress etc1,2. CVS is a complex condition involving a group of vision-related problems which are common among people who spend most of the time working on computers, tablets, cell phones ande-books3. Viewing a digital screen for prolonged time duration leads to eye strain and vision-related problems mainly due to poor lighting, glare on digital screen, improper viewing distance, poor sitting posture and uncorrected vision problems1, which are usually neglected by the technological users3. In addition to the above- mentioned errors, dry eye is also a major contributor of CVS, which leads to decreased lubrication and nourishment of eyes due to insufficient tears production. It effects by decreasing the rate of blinking which in turn results in prolonged and continuous exposure during digital operations4. Proper adjustment of these settings and appropriate use of screen filters to minimize glare and ocular discomfort are considered as the most effective measures for prevention of CVS. Pakistan is quickly becoming digitalized; it is now among the countries with highest mobile phone use, currently, there are almost 59 million smart phone users in Pakistan5. Since 2016, there has been an approximate increase of 47%among the smart phone users, 20% among internet users, 35% in social media users. While the use of desktops and laptops for web browsing has decreased to 22%6. Based on the local and international literature eye problems and headache are the significant symptoms associated with CVS resulting from prolonged technological use7. In relation to this, a study conducted in Lahore indicated high frequency of CVS among computer engineering students i.e. 72.4% (123/170)8 which is also supported by a prevalence study conducted in Peshawar i.e. 90.5% of the enrolled students were diagnosed with CVS9. In the light of mounting evidence regarding high CVS prevalence in young generation, the study was designed to explore the frequency of CVS among students and to identify the daily vision routine factors promoting its risk. Methodology This cross-sectional study was conducted between 1st September 2018 to 31st May 2019 over 320 students of diverse physiotherapy institutes of Karachi including Liaquat National School of Physiotherapy, Ziauddin College of Rehabilitation Sciences, Jinnah Postgraduate Medical College and Baqai Medical University. Subjects were selected through convenient sampling technique, irrespective of gender and involved in daily technological use from at least 1 year or more were included in the study. While those who underwent eye surgery, having eye infection, taking any topical medication or eye drops were excluded from the study. Written informed consent was taken from each participant and their voluntary participation was assured, participant confidentiality was maintained. For assessment, a semi-structured questionnaire was used which comprised of two parts. First part included daily vision related routine of individuals i.e. distance of screen during digital device use, duration of watching digital screen, sleep duration, eye 17 ISSN 2412 - 3188 APP| Published By AEIRC| Volume 6 Issue 1 hygiene, viewing position in classroom and breaks during prolong use of digital device. In second part a standardized CVS-Q developed by Segui and colleagues10 was used for estimating the CVS frequency9. The questionnaire focused on the frequency and intensity of total 16 CVS associated symptoms including burning, itching, feeling of foreign body, tearing, excessive blinking, eye redness, eye pain, heavy eyelids, eye dryness, blurred vision, double vision, difficulty focusing for near vision, increased sensitivity to light, colored halos around object, feeling that sight is worsening and headache. The occurrence of CVS symptoms was indicated via numeric codes i.e. from 0 to 2 (0- Never, 1-Occasionally, 2-Often or Always), while intensity of symptoms was marked from 1 to 2 (1= Moderate and 2+= Intense). The frequency and intensity of each symptom was multiplied in order to get the total score for each symptom. As per the criteria, score of each symptom was recoded as 0=0, 1 or 2=1 and 4=2. If the sum of all recoded score was ≥6, the participant was considered to be suffering from CVS. The recorded data was analyzed using SPSS version 21. Chi-square test was used to evaluate associations between categorical variables. Result Out of total 320 enrolled participants, there was female majority i.e. 245 females and 75 males only with a mean age of 21.04±0.8 years. The diagnosis for CVS was made using CVS-Q scoring, around 58% participants were diagnosed with CVS. The baseline characteristics were recorded for all study subjects as shown in table 1. Table 1: Demographic characteristics of the study population Variables Total participants (n=320) Computer Vision Syndrome (n=186) Mean Age (Years) 21.04±0.8 22.45±0.73 Gender Male 75(23.3) 32(17.1) Female 245(76.6) 154(82.7) Marital status Married 9(2.8) 3(2) Unmarried 311(97.2) 183(98) Educational status (DPT) 1st Year 60(18.8) 34(18.3) 2nd Year 58(18) 38(20.4) 3rd Year 60(18.8) 40(21.5) 4th Year 65(20.3) 39(20.9) 5th Year 77(24.1) 35(18.9) *Values are given as n(%) or mean ± SD *DPT-Department of Physiotherapy Headache was the major complaint reported by participants diagnosed with CVS i.e. 63.1% followed by itching (52.8%), increased sensitivity to light (43.6%), while other infrequent symptoms of CVS are mentioned below in table 2. Table 2: Major symptoms among study subjects diagnosed with CVS Symptoms n(%) Headache 117(63.1) Itching 98(52.8) Increased sensitivity to light 81(43.6) 18 ISSN 2412 - 3188 APP| Published By AEIRC| Volume 6 Issue 1 Eye pain 79(42.6) Feeling of foreign body 73(39.5) Tearing 69(37.4) Burning 66(35.9) Blurred vision 64(34.4) Heavy eyelids 55(29.7) Eye redness 51(27.2) Feeling that sight is worsening 51(27.2) Colored halos around object 46(24.6) Difficulty focusing for near vision 42(22.6) Excessive blinking 38(20.5) Eye dryness 33(17.9) Double vision 25(13.3) Figure 1: Intensities of four most reported symptoms Intense headache was reported by 67% participants while in case of itching majority reported moderate intensity (83%), same was in the case of light sensitivity and eye pain. From the collected data it has also been observed that use of optical (p-value =0.003) recorded as 54.9%,duration of digital screen usage for 4-8 hours (p-value =0.116) recorded as 40.5%, distance from digital screen and eyes during work at 15-20 inches (p-value =0.30) recorded at 50.8% and sitting position in the middle row of class (p-value =0.004) recorded as 41.5%, taking care of eye hygiene (p-value =0.31) recorded as 64.1%which showed a statistically significant contribution in the prevalence of CVS. Headache Itching Increase sensitivity of light Eye pain Moderate 33% 83% 79% 87.50% Intense 67% 17% 21% 12.50% 3 3 % 8 3 % 7 9 % 8 7 .5 0 % 6 7 % 1 7 % 2 1 % 1 2 .5 0 % 19 ISSN 2412 - 3188 APP| Published By AEIRC| Volume 6 Issue 1 Table 3: Association of variables related to daily vision routine and computer vision syndrome Variables Total n(%) Computer Vision Syndrome p-value Yes No Optical Use Yes 176(55) 116(65.91) 60(34.09) 0.003 No 144(45) 52(36.11) 92 (63.89) Duration of digital screen usage (hours) 1-2 35(10.9) 20(57.14) 15(42.86) 0.116 2-4 77(24.1) 49(63.64) 28(36.36) 4-8 130(40.6) 77(59.23) 53(40.77) >8 78(24.37) 44(56.41) 34(43.59) Distance between digital screen and eyes (inches) 10-15 100(31.2) 67(67) 33(33) 0.30 15-20 162(50.62) 89(54.94) 73(45.06) 20-25 51(15.9) 18(35.29) 33(64.71) 25-30 7(2.18) 5(71.43) 2(28.57) Breaks during prolong use of digital device No breaks 61(19.06) 30(49.18) 31(50.82) 0.672 15 sec 71(22.18) 43(60.56) 28(39.44) 20 sec 62(19.37) 34(54.52) 28(45.16) ≥30 sec 126(39.3) 75(59.52) 51(40.48) Viewing position in classroom Front row 108(33.7) 41(37.96) 67(62.04) 0.004 Middle row 133 (41.5) 92(69.17) 41(30.83) Last row 79(24.6) 49(62.03) 30(37.97) Eye care habits & hygiene Yes 205(64) 105(51.22) 100(48.78) 0.31 No 115(35.9) 77(66.96) 38(33.04) Sleep duration <06 hrs 56(17.5) 36(64.29) 20(35.71) 0.3 06-07 hrs 169(52.8) 100(59.17) 69(40.83) 08-10 hrs 82(25.6) 38(46.34) 44(53.66) >10 hrs 13(4.06) 8(61.54) 5(38.46) *p<0.05 is considered significant. Discussion Exposure to music stimulates brain areas, but the process occurs differently among males and females which are attributed to various genetic, hormonal and environmental factors. However, both genders are equal in intelligence, but tend to work in a different manner. This is because both male and female use different parts of their brain to recognize faces, sense emotions, encode memories, make decisions and solve certain problems. According to our results, reaction time was much faster among females as compared to males in the control group as well as experimental group (Table 1). However, it is generally accepted that males have faster reaction time as compared to females. Men tend to have larger diameter of axons than women. Larger diameter of axons causes signals to be transmitted faster up to the nerve fibres, leading to a shorter latency between stimulus and response15. But our results are contradictory to previous researches. This might be due to the neuroanatomical differences among both the genders. That is female have bigger Corpus callosum as compared to males which is the larger tract of neural fibres that allows the free flow of communication between both hemispheres of the brain16. Furthermore, regions for frontal lobe that are responsible 20 ISSN 2412 - 3188 APP| Published By AEIRC| Volume 6 Issue 1 for problem solving and decision making were larger in women17. In addition, our graphs also showed that both the genders took more time in Stroop interference (Table 1) it is because, there are two brain regions involved in the processing of Stroop task cingulated cortex and dorsolateral prefrontal cortex. Hemisphere difference has also been proved by a study that right cerebral hemisphere reads the colour, the left cerebral hemisphere insists to read the words. When the meaning of a word and its colour are congruent, it is easy to recognize the actual colour of the word. But when the meaning of the word is incongruent with the colour, it creates a conflict between the colour and the word's meaning. The "conflict" between two brain processes is word-recognition and colour-recognition. Thereby, extra time is required by brain to process and resolve this conflict. It turns out that we are so fluent in our language that word-recognition is slightly faster/stronger than colour-recognition18. Most people will recognize the meaning of the word before recognizing the colour. In order to name the colour correctly, the two processes compete for the final decision-making process. The brain has to inhibit the faster/stronger word- recognition process in order to allow the colour-recognition to win in the final response. This inhibition requires "selective attention" (attention focus) to inhibit the competing conflicting process. The reaction time is an indicator of the "attention process" in the brain it increases with attention fatigue and/or inattentiveness19. However, when comparing between both the genders, it was found out that females perform somewhat better than males this is because the neurons in the pre frontal cortex of female’s brain are more closely packed together than male’s brain20. And the prefrontal cortex is involved in Stroop task and problem-solving ability. Many researches have been done proving different effects of music on psychological parameters, all with contradictory conclusions to one another. Like authors have concluded that music does not have any effect on memory21. On the other hand, some have also reported that sound in the background actually enhances the learning ability22. In our study, the overall working capacity was decreased along with the verbal fluency (Table 1). This can be due to the perspective proposed by Kahneman as Cognitive Capacity model. In this model it is demonstrated that cognitive processing can be done only for a limited pool of resources at a given moment. When multiple tasks occur at the same time, they compete for the limited resources and thereby exceeding the available capacity due to combined demand. And ultimately capacity interference occurs. This causes the processing of only portion of the task and thereby performance deteriorates. Thus, increasingly complex distractions due to music cause decline in cognitive performance23. This might be the possible reason that the working capacity of our participants decreased from high to average, in addition to the verbal fluency. This is similar to other researches which demonstrated that background music has small but continuous negative effect on memorizing words or nonsense syllables (especially when listening to loud music)24, remembering advertisements25 and also in memorizing earlier read texts and reading performance26. Listening to music has also been reported to hinder with many other cognitive processes, including multimedia learning, performance on diagrammatic, numerical and verbal analysis, the ability to perform arithmetic, reading, performance inhibition on Stroop task and also in the learning of new procedures27-29. This study provides the positive effects of music on attention, verbal fluency, and short- term memory and also on reaction time. However, more significant data is required in future in support of the positive effects associate with music. Moreover, the negative effects were not evaluated in this study which play a significant role in one’s health and well-being, thereby, future researches should also focus both the negative and positive aspects of the music and comparative data must be represented in order to evaluate the 21 ISSN 2412 - 3188 APP| Published By AEIRC| Volume 6 Issue 1 overall significance and outcomes of musical interventions. Conclusion Limited data is in favour of positive effect of music on psychological parameters but it is firmly demonstrated that music consistently and reliably interferes with the mental performance, also indicated by our study. But it is recommended that further studies should be performed to compare the positive or negative impacts of music or the specific type of music causing either positive or negative effect on memory and attention. Acknowledgement The author acknowledged the support of the students of Karachi University who participated in this study and also the colleagues for helping out in the entire research. References 1. Chun LL. The Influence of Different Music Genres on Task Performances of Employees. nt. J. Psychol. Couns. Psychiatry: Theory, Research and Clinical Practice. e-ISSN NO:2590-4272 [Cited August 17, 2019]. Available at: http://ijpcp.com/journal02/J02a03.asp 2. Boothby S, Does music affect your mood? Healthline: Health News [Online] April 13, 2017. [Cited August 17, 2019]. Available at: https://www.healthline.com/health- news/mental-listening-to-music-lifts- or-reinforces-mood-051713 3. Miendlarzewska EA, Trost WJ. How musical training affects cognitive development: rhythm, reward and other modulating variables. Front. Neurosci. 2014;7:279. 4. Isaacs S. The Roles of the Amygdala and the Hippocampus in Fear Conditioning [dissertation on the internet]. Sweden: University of Skövde, School of Bioscience. [Online] 2015. [Cited April 7, 2017]. Available from: http://www.diva- portal.org/smash/get/diva2:839668/F ULLTEXT01.pdf. 5. Lehmann JA, Seufert T. The influence of background music on learning in the light of different theoretical perspectives and the role of working memory capacity. Front. Psychol, 2017. 8: Article 1902. 6. Jones MH, West SD, Estell DB. The Mozart effect: Arousal, preference, and spatial performance. Psychol Aesthet Creat Arts. 2006; S(1): 26–32. 7. Atkinson RC, Wickens TD. Human memory and the concept of reinforcement. The nature of reinforcement. 1971:66-120. 8. Korczyn AD, Peretz C, Aharonson V, Giladi N. O1-03-02: Computer based cognitive training with mindfit® improved cognitive performances above the effect of classic computer games: Prospective, randomized, double-blind intervention study in the elderly. Alzheimers Dement: The Journal of the Alzheimer's Association. 2007;3(3):S171. 9. Peretz I, Zatorre RJ. Brain organization for music processing. Annu. Rev. Psychol. 2005. 56:89-114. 10. Ehret G. The auditory cortex. J. Comp. Physiol. 1997;181(6):547-557. 11. Patterson J. FAS test. Encyclopedia of clinical neuropsychology. [Online] 2011. [Cited August 18, 2019]. Available at: https://doi.org/10.1007/978-0-387- 79948-3_886 12. Willcutt EG, Doyle AE, Nigg JT, Faraone SV, Pennington BF. Validity of the executive function theory of attention- deficit/hyperactivity disorder: a meta- analytic review. Biol. Psychiatry. 2005;57(11):1336-1346. 13. Perret E. The left frontal lobe of man and the suppression of habitual responses in verbal categorical behaviour. Neuropsychologia. 1974;12(3):323-330. 14. Gourovitch ML, Kirkby BS, Goldberg TE, Weinberger DR, Gold JM, Esposito G, Van Horn JD, Berman KF. A comparison of rCBF patterns during letter and semantic fluency. Neuropsychology. 2000;14(3):353-360. 22 ISSN 2412 - 3188 APP| Published By AEIRC| Volume 6 Issue 1 15. McDougall S, Riad WV, Silva-Gotay A, Tavares ER, Harpalani D, Li GL, Richardson HN. Myelination of axons corresponds with faster transmission speed in the prefrontal cortex of developing male rats. Eneuro. 2018;5(4): e0203-18. 16. Leonard CM, Towler S, Welcome S, Halderman LK, Otto R, Eckert MA, Chiarello C. Size matters: cerebral volume influences sex differences in neuroanatomy. Cerebral cortex. 2008;18(12):2920-2931. 17. Zaidi ZF. Gender differences in human brain: a review. Open Anat J. 2010; 2(1): 37-55. 18. Grandjean J, D’Ostilio K, Phillips C, Balteau E, Degueldre C, Luxen A, Maquet P, Salmon E, Collette F. Modulation of brain activity during a Stroop inhibitory task by the kind of cognitive control required. PloS one. 2012;7(7):e41513. 19. Shalev L, Tsal Y, Mevorach C. Computerized progressive attentional training (CPAT) program: effective direct intervention for children with ADHD. Child Neuropsychol. 2007;13(4):382-388. 20. Witelson SF, Glezer II, Kigar DL. Women have greater density of neurons in posterior temporal cortex. J. Neurosci. 1995;15(5):3418-3428. 21. Fassbender E, Richards D, Bilgin A, Thompson WF, Heiden W. VirSchool: The effect of background music and immersive display systems on memory for facts learned in an educational virtual environment. Comput Educ. 2012;58(1):490-500. 22. Mann G. Why does country music sound white? Race and the voice of nostalgia. Ethn Rac studies. 2008;31(1):73-100. 23. Kahneman D. Attention and effort. Englewood Cliffs, NJ: Prentice-Hall; 1973 [Cited April 7, 2017] Available at: http://citeseerx.ist.psu.edu/viewdoc/d ownload?doi=10.1.1.398.5285&rep=rep1 &type=pdf. 24. Wen W, Michihiko K. The effects of music type and volume on short-term memory. Tohoku Psychol Folia. 2006;64:68-76. 25. Oakes S, North AC. The impact of background musical tempo and timbre congruity upon ad content recall and affective response. Applied Cog Psychol. 2006;20(4):505-520. 26. Furnham A, Allass K. The influence of musical distraction of varying complexity on the cognitive performance of extroverts and introverts. Euro J Personality. 1999;13(1):27-38. 27. Avila C, Furnham A, McClelland A. The influence of distracting familiar vocal music on cognitive performance of introverts and extraverts. Psychol. Music 2012;40(1):84-93. 28. Bloor AJ. The rhythm's gonna get ya’– background music in primary classrooms and its effect on behaviour and attainment. EBD. 2009;14(4):261-274. 29. Thompson WF, Schellenberg EG, Letnic AK. Fast and loud background music disrupts reading comprehension. Psychol Music. 2012;40(6):700-708. https://crossmark.crossref.org/dialog/?doi=10.29052/2412-3188.v6.i1.2019.15-22