Introductıon
The electromagnetic field is a physical force phenomenon that can affect entities in its vicinity. So many devices such as radars, mobile phones, refrigerators, hair dryers, electrical shavers, MRI equipments, radiographic imaging machines, diathermy units, base stations, radios, and computers can emit electromagnetic waves creating an electromagnetic field. The digital screens may also cause an augmentation in electromagnetic waves of the combined electronic devices. The long standing low doses of electromagnetic irradiation or its sudden impulse in high doses can cause some serious health problems due to damaged neuro-sensorial transmission, inadequate immune response or blocked lymphatic drainage system and eventually can lead headache, eyeache,, burning sensations, dizziness, weight loss, and cardiovascular problems [
1,
2].
According to National Institute of Environmental Protection and Health Science Association, electromagnetic emission particularly from mobile phones can also cause Alzheimer Parkinson, and Multiple Sclerosis disease [
3].
The electromagnetic radiation especially within the blue spectrum may cause a diminished lacrimal secretion due to its negative effects on neural network activity in the corneal nerve plexus. On the other hand, although the printed materials on papers or other items have more distinctive visibility, the newspapers, magazines, or books are increasingly being replaced by their digital versions and almost all communications can be actualised through the e-mails, text messages or other digital apps. To see better to have more background brightness in digital platforms is a common mistake.
The prolonged use of digital devices in higher brightnesses may have some ocular problems such as squeezing the eye lid, decreasing blinking, excessive evaporation of tears leading Dry Eye Syndrome [
4]. That kind of Dry Eye condition is also named as Computer Eye Syndrome (CES), Digital Eye Strain (DES), or Digital Eye Fatigue Disorder (DEFD). Actually, these conditions can be seen in any time and any age by excessive use of the smartphones, game consoles, personal computers, LED screen TVs, or e-books. Today, there are 4.3 billion internet (57%), 3.5 billion social media (45%), and 5.1 billion (67%) mobile phone users in the world and every day one million new users are being added to the list [
5,
6].
Materıal and Method
The study was performed by conducting anamnesis, Ocular Surface Disease Index (OSDI) questionnaire, Schirmer ‘s test, and digital screen usage survey on all participants The OSDI scoring is proven and reliable assessment method for ranking the dry eye conditions between 0-100 points. The OSDI scores within 0-12 are normal. The 13-33 points refer mild to moderate ocular surface problems and more than 34 points indicate severe dry eye issues. As OSDI is evaluated according to subjective answers to specific questions, Schirmer 's test is evaluated by objective measurements. Schirmer test is done with a special paper strip that is placed in the middle of temporal bulbar conjunctival sac and the wetness of the strip is measured by a ruler. If the wetness on the paper strip is low than 5mm, it can be evaluated as dry eye, but if it is over 10 mm, it can be said as normal [
7].
Statıstıcs
The statistical analyses of the study were performed by using the SPSS package program 27 (IBM SPSS Statistics 27). The descriptive findings and collected results of the study were organised in frequency tables and interpreted by proper statistical methods. Non-parametric methods were used for the measurement values that did not comply with normal distribution. In line with non-parametric methods, the “Mann-Whitney U” test (Z-table value) was used to compare the measurement values of two independent groups, and the “Kruskal-Wallis H” test (χ2 -table value) was used to compare the measurement values of three or more independent groups. The “Bonferroni Correction” was used to determine which group the difference resulted from the variables that were significant in three or more independent groups. The “Spearman Correlation Coefficient” was used to examine the relationships of two quantitative variables that did not have a normal distribution.
Terms such as “[1-2, 3]” were used in pairwise comparisons for the variables that showed significant differences for three or more groups (“[1-2, 3]” means that there was a significant difference between 1 and 2 and between 1 and 3).
Table 1.
The distribution of the descriptive characteristics of the study.
Table 1.
The distribution of the descriptive characteristics of the study.
Variable (N=221) |
n |
% |
Age class [ ] <25 25-34 35-44 ≥45 |
47 66 43 65 |
21.3 29.9 19.4 29.4 |
Gender Female Male |
122 99 |
55.2 44.8 |
Chronic disease status Yes No |
67 154 |
30.3 69.7 |
Smoking status Yes No |
116 105 |
52.5 47.5 |
Wearing contact lenses Yes No |
46 175 |
20.8 79.2 |
Chronic eye disease status Yes No |
70 151 |
31.7 68.3 |
History of ocular trauma Yes No |
45 176 |
20.4 79.6 |
Ocular surgery Yes No |
89 132 |
40.3 59.7 |
Long-term use of topical medication for the eye Yes No |
60 161 |
27.1 72.9 |
Existing refractive error* Myopia Hypermetropic Myopia + Astigmatism Hyperopia + Astigmatism Astigmatism No visual impairment |
42 20 39 37 26 54 |
22.2 9.0 17.6 16.7 11.8 24.4 |
Wearing glasses Yes No Other |
136 82 3 |
61.5 37.1 1.4 |
It was found that the mean age of the participants was 37.65±15.62 (years), 66 (29.9%) were in the 25-34 age group, 122 (55.2%) were women, 154 (69.7%) did not have a chronic disease, 116 (52.5%) were smokers, 175 (79.2%) did not use contact lenses, 151 (68.3%) had no chronic eye disease, 176 (79.6%) had no history of ocular trauma, 132 (59.7%) had no ocular surgery, 161 (72.9%) had eye surgery. There was no long-term use of topical medication, 54 participants (24.4%) had no vision defects and 136 (61.5%) used glasses permanently.
Table 2.
The distribution of the time spent with electronic screens.
Table 2.
The distribution of the time spent with electronic screens.
Variable (N=221) |
Less than 1 year |
|
1-5 years |
|
More than 5 years |
|
Not using |
|
|
N |
% |
n |
% |
n |
% |
n |
% |
Smartphone |
5 |
2.3 |
12 |
5.4 |
204 |
92.3 |
- |
- |
LED TV |
7 |
3.2 |
8 |
3.6 |
189 |
85.5 |
17 |
7.7 |
Computer |
5 |
2,3 |
4 |
1.7 |
150 |
67.9 |
62 |
28.1 |
E-book |
9 |
4.1 |
8 |
3.6 |
28 |
12.7 |
175 |
79.6 |
Tablet |
3 |
1.4 |
22 |
10.0 |
53 |
24.0 |
143 |
64.7 |
Game console |
3 |
1.4 |
10 |
4.5 |
33 |
14.9 |
175 |
79.2 |
Other digital displays |
3 |
1.4 |
6 |
2.6 |
57 |
25.8 |
155 |
70.2 |
It was found that 204 participants (92.3%) used smartphones, 189 participants (85.5%) used LED TVs and 150 participants (67.9%) used computers for more than 5 years.
Table 3.
The distribution of the time spent with electronic screens in a day.
Table 3.
The distribution of the time spent with electronic screens in a day.
Variable (N=221) |
Less than 1 hour |
|
1-5 hours |
|
More than 5 hours |
|
Not using |
|
|
N |
% |
n |
% |
n |
% |
n |
% |
Smartphone |
5 |
2,3 |
52 |
23.5 |
164 |
74.2 |
- |
- |
LED TV |
45 |
20.4 |
72 |
32.6 |
89 |
40.2 |
15 |
6.8 |
Computer |
8 |
3.6 |
25 |
11.3 |
128 |
57.9 |
60 |
27.1 |
E-book |
28 |
12.7 |
9 |
4.1 |
4 |
1.8 |
180 |
81.4 |
Tablet |
50 |
22.6 |
16 |
7.2 |
10 |
4.5 |
145 |
65.7 |
Game console |
9 |
4.1 |
28 |
12.7 |
11th |
5.0 |
173 |
78.2 |
Other digital display |
9 |
4.1 |
30 |
13.6 |
27 |
12.2 |
155 |
70.1 |
It was found that 164 participants (74.2%) used smartphones, 89 participants (40.2%) used LED TVs and 128 participants (57.9%) used computers for more than 5 hours a day.
Table 4.
The distribution of the adjusted screen brightnesses in electronic devices.
Table 4.
The distribution of the adjusted screen brightnesses in electronic devices.
Variable (N=221) |
Low |
|
Moderate |
|
High |
|
Not using |
|
|
n |
% |
n |
% |
n |
% |
N |
% |
Smartphone |
8 |
3.6 |
85 |
38.5 |
128 |
58.0 |
- |
- |
LED TV |
11 |
5.0 |
59 |
26.7 |
131 |
59.3 |
20 |
9.0 |
Computer |
9 |
4.1 |
62 |
28.1 |
81 |
36.6 |
69 |
31.2 |
E-book |
29 |
13.1 |
8 |
3.7 |
6 |
2.7 |
178 |
81.5 |
Tablet |
25 |
11.3 |
37 |
16.7 |
14 |
6.3 |
145 |
65.6 |
Game console |
4 |
1.8 |
27 |
12.2 |
18 |
8.2 |
172 |
77.8 |
Other digital display |
4 |
1.8 |
27 |
12.2 |
35 |
15.8 |
155 |
70.2 |
It was found that 128 participants (58.0%) used smartphones, 131 participants (59.3%) used LED TVs and 81 participants (36.6%) used computers with high screen brightness.
Table 5.
The distribution of the OSDI scores.
Table 5.
The distribution of the OSDI scores.
Scale (N=221) |
Mean |
SD |
Median |
Min. |
Max. |
OSDI scores |
21.40 |
19.59 |
16.7 |
0.0 |
97.9 |
The descriptive findings of the participants regarding their OSDI scores are given in the table. It was found that the mean OSDI score of the individuals was 21.40±19.59.
Table 6.
The distribution of the reliability coefficients for OSDI scores.
Table 6.
The distribution of the reliability coefficients for OSDI scores.
Scale (N=221) |
Number of items |
Cronbach-α coefficient |
OSDI score |
12 |
0.920 |
The reliability coefficients of the OSDI scores of the participants are given in the table. It was found that the answers given to the OSDI questionnaire had a very high-reliability level.
It was also found that the mean amount of dryness in the right eye of the subjects was 18.38±7.08 and the average amount of dryness in the left eye was 19.46±7.35.
Table 7.
The comparison of the total scores according to the findings.
Table 7.
The comparison of the total scores according to the findings.
Variables (N=221) |
n |
OSDI scores |
|
Statistical analysis* Possibility |
|
|
|
Median [IQR] |
|
Age <25 (1) 25-34 (2) 35-44 (3) ≥45 (4) |
47 66 43 65 |
11.92±18.11 17.77±16.83 19.19±19.61 33.40±17.75 |
6.3 [14.6] 12.5 [20.8] 14.6 [16.7] 29.2 [27.1] |
ꭓ 2 =52,677 p<0.001 [1-2,3,4] [2,3-4]
|
Gender Female Male |
122 99 |
22.51±21.15 20.04±17.50 |
16.7 [26.0] 16.7 [22.9] |
Z=-0.480 p=0.631 |
Chronic disease status Yes No |
67 154 |
30.85±18.19 17.29±18.79 |
27.1 [29.2] 12.5 [20.8] |
Z=-5,600 p<0.001
|
Smoking status Yes No |
116 105 |
22.38±18.65 19.91±20.81 |
18.8 [20.8] 12.5 [29.2] |
Z=-1.727 p=0.084 |
Contact lens use status Yes No |
46 175 |
16.81±17.08 22.39±20.14 |
12.5 [18.8] 17.7 [25.0] |
Z=-1.806 p=0.071 |
Chronic eye disease status Yes No |
70 151 |
33.81±18.12 15.25±17.53 |
30.2 [29.2] 11.5 [19.3] |
Z=-7,280 p<0.001
|
History of ocular trauma Yes No |
45 176 |
29.91±18.06 18.94±19.44 |
27.1 [30.2] 14.6 [22.9] |
Z=-3.986 p<0.001
|
Ocular surgery status Yes No |
89 132 |
25.63±17.23 18.15±20.64 |
22.9 [22.9] 12.5 [22.9] |
Z=-4.036 p<0.001
|
Long-term use of topical medication forthe eye Yes No |
60 161 |
33.41±18.82 16.63±18.03 |
29.2 [30.7] 12.5 [20.3] |
Z=-6.146 p<0.001
|
Wearing permanent glasses Yes No |
136 82 |
26.58±19.13 13.03±17.68 |
22.9 [24.5] 6.3 [19.3] |
Z=-6.065 p<0.001
|
Statistically significant differences were detected in the OSDI scores according to the ages (ꭓ2 =52.677; p<0.001). As a result of the Bonferroni Corrected Pairwise Comparisons made to determine which group caused the significant difference, significant differences were detected between those in the <25 age group and those in the 25-34, 35-44, and ≥45 age groups. The OSDI score of those in the 25-34, 35-44, and ≥45 age groups was significantly higher than those in the <25 age group. Similarly, significant differences were detected between those in the 25-34 and 35-44 age groups and those in the ≥45 age group. It was determined that the OSDI score of those in the ≥45 age group was significantly higher compared to those in the 25-34 and 35-44 age groups.
Statistically significant differences were detected in theOSDI scores according to chronic disease status (Z=-5.600; p<0.001). The OSDI score of the participants who had chronic diseases was significantly higher than those without chronic diseases.
Statistically significant differences were detected in theOSDI scores according to chronic eye disease status (Z=-7.280; p<0.001). It was found that the OSDI scores of the participants who had chronic eye diseases were significantly higher than those without chronic eye diseases.
Statistically significant differences were detected in theOSDI scores according to ocular trauma history (Z=-3.986; p<0.001). The OSDI scores of those who hada history of ocular trauma were significantly higher than those without a history of ocular trauma.
Statistically significant differences were detected in the OSDI scores according to ocular surgery status (Z=-4.036; p<0.001). The OSDI scores of those who had ocular surgery were significantly higher than those who did not have ocular surgery.
Statistically significant differences were detected in the OSDI scores according to long-term topical medication use for the eye (Z=-6.146; p<0.001). The OSDI scores of those who used long-term topical medication to the eye were significantly higher than those who did not use topical medication to the eye for a long time.
Statistically significant differences were detected in the OSDI scores according to permanent glasses use (Z=-6.065; p<0.001). The OSDI scores of those who wore permanent glasses were significantly higher than those who did not use permanent glasses.
No statistically significant differences were detected in the OSDI scores according to gender, smoking, or contact lens use (p>0.05).
Table 8.
The relationships between the scales of right and left eye .
Table 8.
The relationships between the scales of right and left eye .
Correlation* (N=221) |
|
OSDI scores |
Right eye dryness amount |
r p
|
-0.403 <0.001
|
Left eye dryness amount |
r p
|
-0.404 <0.001
|
A negative, weak, and statistically significant relationship was detected between the OSDI scores and the amount of dryness in the right and left eyes (p<0.05). As the amount of dryness increases in the right and left eyes, OSDI scores will decrease.
Results and Dıscussıon
To reveal the effects of digital screens on lacrimal activity 221 participants were included in the study.
Primarily, all cases were separated into the certain different groups and categories according to the applied statistical model. The statistical analyses were made to determine whether there was any relevance or difference between the groups and categories.
The first statistically significant difference was found between the OSDI scores and the age groups (ꭓ2=52.677; p<0.001).
Besides the long-term topical ocular medication for eyes was found in higher OSDI scores as statistically significant too (Z=-6.146; p<0.001).
In the previous studies , performed by OSDI questionnaire, Schirmer test, and tBUT (tear break up time) on computer users with a history of using computers at least 6 hours in the day were found in mild to severe ocular surface disease.
The internet addiction is another dry eye causing digital factor. In a study with an average OSDI score as 21.63 ± 17.86, a significant weak relationship was reported in the young internet addicts [
8,
9].
The tBUT (tear break up time) test was not included in this study because of the compatible results with the Schirmer test.
According to DEWS (Dry Eye Workshop) II report, increased evaporation in tear film is the main cause of dry eye with a ratio of 50%. The second is the mixed type and the third one is characterised by hyposecretion of tear with a ratio of 14%.
Digital screens can cause dry eye either through the hyposecretion of tear due to damaged sensory excitation of corneal nerve plexus because of electromagnetic irradiation and excessive evaporation due to decreased blinking.
The distinction of this study is in the comparative statistical analyses of each categories, groups and added novel patterns such as chronic ocular diseases, wearing contact and etc.
Conclusıons
The prolonged use of digital monitoring devices such as smartphones especially in high screen brightness is a common and unfavourable habitual behavior.
The ocular surface should be protected from these harmful effects by using blue light filters, blinking more frequently, or resting the eyes.
References
- Shore, R.E. Radiation impacts on human health: certain, fuzzy, and unknown. Health physics 2014. [Google Scholar] [CrossRef]
- Tang, F.R.; Loganovsky, K. Low dose or low dose rate ionizing radiation-induced health effect in the human. Journal of environmental radioactivity 2018. [Google Scholar] [CrossRef]
- Ratan, Z.A.; Parrish, A.M.; Zaman, S.B. Smartphone addiction and associated health outcomes in adult populations: a systematic review. Public health 2021. [Google Scholar] [CrossRef]
- Günes, F. From Paper to Screen Developments in the Field of Reading. Bartin University Faculty of Education Journal 2016, 5, 1. [Google Scholar]
- Bostancı, B. Digital Eye Strain Syndrome and Dry Eye. MN Ophthalmology 2023, (1), 96–9. [Google Scholar]
- Emanuel, R.; Bell, R.; Cotton, C.; Craig, J. The truth about smartphone addiction. College student journal 2015, 49, 291–299. [Google Scholar]
- Çelik, T. Evaluation of the tear film layer in computer users with dry eye symptoms using ocular surface disease index, tear breakup time, and Schirmer test. 2017.
- Wu, H.; Wang, Y.; Dong, N.; Yang, F.; Lin, Z.; Shang, X.; et al. Meibomian gland dysfunction determines the severity of the dry eye conditions in a visual display terminal workers. PloSOne 2014, 9. [Google Scholar] [CrossRef]
- Sağlan, R.; Atay, E.; Demirtaş, Z.; Öcal, E. Evaluation of Internet Addiction and Dry Eye Disease Among Secondary and High School Students. Eurasian Journal of Family Medicine 2017, 6, 117–26. [Google Scholar]
- Heus, P.; Verbeek, J.H. Optical correction of refractive error for preventing and treating eye symptoms in computer users. Cochrane Database Syst Rev. 2018, 4. [Google Scholar] [CrossRef]
|
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).