Smartphone devices have been evolving rapidly in both function and propagation over the past two decades. Smartphones combine the standard mobile phone features, which are phone calls and messages, with other personal digital assistant features, such as Internet browsing, desktop synchronization, voice recognition, capturing high-quality photos, in addition to third-party applications known as “apps.” These functions maximize the potential of smartphones in health care, as they allow physicians and students to access resources efficiently to support better decision-making at the point of care.
The term “addiction” was in the past limited to drugs or substances; currently, it is also applied to other modern activities including gambling, the Internet, video games, mobile phone usage, and other behavioral addictions. A 2016 online survey conducted in Riyadh found that 76% of participants showed moderate-to-high risk of smartphone and Internet addiction. Similarly, a Korean study showed a high percentage of moderate-to-high smartphone addiction among university students. With the rapid growth of the number of smartphone users, there have been concerns regarding the negative repercussions of these devices.
Dry eye disease (DED) is one of the most commonly encountered morbidities in ophthalmic clinics, making it a growing health problem. According to the International Dry Eye Workshop in 2007, DED can be defined as a multifactorial disease of the tears and ocular surface which consequently results in symptoms of discomfort, visual disturbance, and tear film instability with potential damage to the ocular surface.
The prevalence of DED is increasing with variable estimations according to different populations. The Beaver Dam population-based study found the prevalence rate of DED in adults over the age of 48 to be 14%. Recently published data found DED to be highly prevalent in Saudi Arabia. However, local epidemiological data regarding DED are still lacking.
There are many predictors for DED such as autoimmune, psychiatric, and endocrine disorders. In addition, medications including antihistamines, antidepressants, and antibenign prostatic hyperplasia medications also play a role in the disease. In addition, environmental and occupational factors were found in previous studies to contribute to DED. Multiple studies have shown that prolonged use of smartphones may adversely affect ocular health. A study focusing primarily on the adolescent age group found that extended smartphone use increased the rates of ocular symptoms, including eye dryness.
However, there is a paucity of data on DED and its association with smartphones, particularly in Saudi Arabia. The goal of this study was to evaluate the prevalence of DED and its associated risk factors and to evaluate the relationship between smartphone addiction and DED. The findings of this study are of great value for future local updates in further management and risk assessment for smartphone users.
MATERIALS AND METHODS
Study design and data collection
This cross-sectional study was conducted in May 2017. The standard formula was used to calculate sample size. Since no recent accurate data were available, the prevalence was taken at 50%, with a 95% confidence interval (CI) and 5% marginal error. Participants were selected from the Faculty of Medicine at King Abdulaziz University in Jeddah, Saudi Arabia. The stratified random sampling technique was used to recruit participants according to their academic year with equal allocation. Males and females were distributed equally among the same strata. All participants owning smartphones were included in the study.
The Smartphone Addiction Scale-Short Version (SAS-SV), which was initially developed from an older version called SAS, was used to assess the participants addiction to smartphones. It consists of 10 questions that are based on a self-reporting system using a 6-point Likert scale (1: “strongly disagree” and 6: “strongly agree”). It was used in our study to classify the participants into either addicts or nonaddicts according to their scores. A cutoff value of 31 and 33 was reported for males and females, respectively.
DED was assessed using the Ocular Surface Disease Index (OSDI). The OSDI is a 12-item questionnaire designed to assess symptoms consistent with DED. The 12 items of the questionnaire were graded, using a self-reporting system, from 0 to 4 (0: “none of the time” and 4: “all of the time”). The total OSDI score was then calculated based on the following formula: ([sum of scores for all answered questions] × 100)/([total number of answered questions] × 4). Participants were then classified into four categories based on their total score: normal (scores: 0–12), mild dry eye (13–22), moderate dry eye (23–32), and severe dry eye (33–100).
Socio-demographic data were obtained, such as age, gender, place of residence, marital status, income level, number of family members, academic year, and grade point average. Risk factors for DED, which were extracted from a previously published study, were obtained as well [Table 1].
An informed, written consent was obtained from all participants. The study was approved by the local Institutional Review Board of King Abdulaziz University before conducting the survey.
Descriptive statistics were used to describe the different variables in the study. Mean and standard deviations were reported for continuous variables. Frequencies (n) with proportions (%) were reported for categorical variables. The Pearson's chi-squared test was used to identify significant risk factors for developing DED at bivariate level.
A binary logistic regression model with DED (yes/no) as the dependent variable was performed using the enter method to identify the significant risk factors. Potential risk factors that showed significant association with DED at bivariate levels as well as smartphone addiction were used as predictors in the model. The Omnibus test of model coefficients showed that the multivariate logistic regression models were statistically significant. The case-wise plot was not produced because no outliers were found and no multicollinearity detected (variance inflation factor < 3).
All statistical analyses were performed using Statistical Package for Social Science (SPSS) version 23 (IBM, Armonk, NY, USA) at a significant level of 0.05.
A total of 443 completed questionnaires were received, providing a response rate of 94%. There was an almost equal distribution of our sample by gender, with 225 males (50.8%). The mean age was 21.7 (range: 19–29 years). [Table 2] shows the sociodemographic features of the participants.
Almost half of the participants were found to have DED (49.4%). [Table 3] demonstrates a significant correlation between DED and the use of contact lenses, the use of eye drops, preexisting eye disease, Vitamin A therapy and the use of oral contraceptive pills at bivariate level (P < 0.05). However, following adjustments, the logistic regression model revealed that there was no significant association between DED and smartphone addiction (odds ratio (OR) = 0.69, 95% CI: 0.44–1.1, P = 0.102) and the use of oral contraceptive pills (OR = 2.64, 95% CI: 0.88–7.93, P = 0.085) [Table 3]. On the other hand, significant associations were observed between DED and contact lens use, eye drops use, preexisting eye disease, and Vitamin A therapy [Table 4].
In this study, the prevalence of DED was 49.4% which is considerably lower in comparison to a previous study conducted by Bukhari et al., where the prevalence of DED was 93.2%. One reason for this disparity is the reliance on questionnaires alone without the use of clinical ophthalmic assessments such as tear films and slit-lamp examinations. Furthermore, our sample was comprised university students only and did not encompass other age groups such as the elderly.
After adjusting for confounders, our study found that there was no significant association between DED and smartphone addiction; a finding similarly found in two prior studies. After reviewing the literature, a few studies showed that longer smartphone use per day was associated with the occurrence of ocular symptoms and a higher prevalence of DED. This discrepancy can be attributed to the fact that our study relied on the SAS-SV for the assessment of smartphone addiction and did not specify the exact duration of smartphone use per day nor the cumulative lifetime exposure (daily use duration [h/day] × years of use in lifetime hours).
This study also investigated other potential risk factors for DED excerpted from previously published studies, including the use of contact lenses, the use of eye drops, Vitamin A therapy, and preexisting eye diseases. Several studies have shown that contact lens users are more likely to experience dry eye symptoms than nonwearers. Furthermore, it is well established that prolonged contact lens wear can lead to tear film hyperosmolarity and ocular surface inflammation. Therefore, it was not surprising that significant associations were observed between DED and contact lens users.
Vitamin A therapy was also substantially associated with DED in our study. Vitamin A therapy has many uses including the treatment of skin diseases and certain types of cancer such as ocular surface neoplasia. Two published papers examined the effects of systemic retinoic acid therapy for acne and found that it resulted in Meibomian gland dysfunction which consequently resulted in dry eye symptoms.
Another risk factor which was found to be positively associated with DED was the use of eye drops. This could be attributed to the toxic effects of preservative-containing solutions following long-term use. For instance, benzalkonium chloride, the most commonly used preservative in eye drops, has been shown to have cytotoxic effects resulting in tear film disruption.
As with all cross-sectional studies, ours has limitations that need to be considered when interpreting the study results. This study relied solely on the OSDI questionnaire to assess DED, which is a self-diagnostic method. We propose incorporating clinical ophthalmological examinations alongside the questionnaires to confirm the diagnosis of DED. Furthermore, we did not evaluate the role environmental factors played in DED, which are known to profoundly influence the disease.
With these limitations in mind, this study is the first cross-sectional study to evaluate the relationship between DED and smartphone addiction in Saudi Arabia. Despite no significant correlation with smartphone addiction, DED was found to be associated with the use of contact lenses, Vitamin A therapy, and eye drops. Contrary to what was locally reported, DED had a lower prevalence. Several risk factors and clinical predictors of DED might exist, which highlight the importance of performing a complete clinical ophthalmological assessment when diagnosing DED.
Dry eye disease is a common ophthalmological disease with many clinical predictors. Contradictory to previous studies the prevalence was low and dry eye disease was not associated with smartphone addiction, however the use of contact lens, pre-existing eye disease, vitamin A therapy and oral contraceptive shown to be corrected.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
We are indebted to the following interns and medical students for their contribution in the data collection: Abdullah Hasan Ghunaim, Lina Suliman Musa, Lujain Hamid Mustafa, Bashayer Ahmed Alghamdi, Boshra Abdullah Bamalan, Rafeef Ahmed Bahafzalla, Arwa Zuhair Fatani, Raghdaa Mohammed Malebary, Ahlam Bahgat Khojah, Marwa Mohamed Abdelmoaty, Mahmoud Emadfahmi Shurrab, Ashraf Iyad AlBukhari, Hazim Abdulkarim Khatib, Anas Yahya Khayat, Rawan Abdulfattah Abdulkarim, Yousef Zaki Khedher and Shaza Omar H Alorabi.
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