






























 

 

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

