THE RELATIONS AMONG INTERNET USE HABITS AND SEVERITY OF DEPRESSIVE-ANXIETY SYMPTOMS AMONG UNIVERSITY STUDENTS

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Master of Medicine

Lithuanian University of Health Sciences Faculty of Medicine

Department of Psychiatry

THE RELATIONS AMONG INTERNET USE HABITS AND

SEVERITY OF DEPRESSIVE-ANXIETY SYMPTOMS AMONG

UNIVERSITY STUDENTS

Author

Ahmad Najeh Mustafa Abu Dayeh

Supervisor

Prof. Dr. Vesta Stebliene M.D. Ph.D. Lithuanian University of Health Sciences

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ABSTRACT

Author name: Ahmad Najeh Mustafa Abu Dayeh

Research title: The relations among internet use habits and severity of depressive-anxiety symptoms among university students.

Aim: The research aimed at evaluating the relationship between internet use habits and anxiety and depressive symptoms severity among university students.

Materials and methods: An anonymous cross-sectional online survey was sent out as a link in a batch e-mail to participants who are current students attending Lithuanian University of Health Sciences (LSMU). 886 students were invited to participate in this study, however, only 110 responded to the survey. The survey included socio-demographic questions, compulsive internet use scale (CIUS), the use of several internet-based applications on a five-point Likert scale, and the Patient Health Questionnaire (PHQ), which measured depression (PHQ-9), somatic symptom (PHQ-15) and anxiety (GAD-7). Descriptive statistics, correlation analysis, chi-square test, and analysis of variance (ANOVA) were carried out on SPSS based on the excel data transferred from the survey.

Results: The final sample consisted of 31 males (28.2%) and 79 females (71.8%) with a mean age of 22.54 ± 2.84. Mean CIUS score was 24.23 ± 11.5 where 46% of the

respondents were classified as problematic internet users (PIUrs). Internet use increase since COVID-19 outbreak was associated with a higher CIUS score (P = 0.002). The means of PHQ-9, PHQ-15, and GAD-7 scores among participants were 9.51 ± 6.5 (mild depression), 9.05 ± 4.8 (low somatic symptom severity), and 6.83 ± 5.5 (mild anxiety) respectively. Female gender and no living companions were significantly associated with a higher PHQ-15 score (P = 0.049 and P = 0.007). In addition, no living companions was associated with a worse overall PHQ score (P = 0.046). Snapchat and Twitter use were associated with PIU based on CIUS grouping (P = 0.013 and P = 0.011 respectively). Instagram use was associated with increased PHQ-9, PHQ-15, and GAD-7 severity (P = 0.005, P < 0.001, and P = 0.003 respectively). Correlation coefficients of CIUS score with PHQ score, PHQ-9, PHQ-15, and GAD-7 were 0.314 (P = 0.001), 0.48 (P < 0.001), and 0.369 (P < 0.001) respectively.

Conclusions: The students who participated in this study had an average daily internet use time between 7 to 8 hours where nearly half of them were labelled as PIUrs. Student

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ACKNOWLEDGMENTS

First and foremost, I would like to express my deepest gratitude to my supervisor, Dr. Vesta Stebliene, for the clear and concise help and support that she has provided during the undertaking of my thesis work.

Furthermore, a huge thank you to all the Lithuanian University of Health Sciences (LSMU) students who took the time to partake in the study survey.

Finally, I would like to give my thanks to Dr. Gert-Jan Meerkerk for granting me permission to use the Compulsive Internet Use Scale (CIUS), also known as the addictive disorder scale, that he developed.

CONFLICTS OF INTERSET

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ETHICS COMMITTEE APPROVAL

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ABBREVIATIONS

1. Lithuanian University of Health Sciences (LSMU/LUHS) 2. Compulsive Internet Use Scale (CIUS)

3. Patient Health Questionnaire (PHQ) 4. Depression Severity Scale (PHQ-9)

5. Somatic Symptom Severity Scale (PHQ-15) 6. General Anxiety Disorder Scale (GAD-7) 7. Problematic Internet Use (PIU)

8. Problematic Internet Users (PIUrs)

9. Non-problematic Internet Users (non-PIUrs) 10. Internet Addiction (IA)

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INTRODUCTION

The global rise of internet use since the internet was first conceived in 1989 has been exponential. By the year 2000, almost half of the US population already had access to

information available on the internet (1). Thereafter, the internet quickly became a ubiquitous service seen in almost every home, school, and workplace all over the world. Easier access to computers, current modernization trends and an increased usage of smartphones has given people the prospect to use the internet with more accessibility, sustainability, and frequency which in turn allows better opportunities for communication, information, and social interaction. The world's most developed countries subsequently have an internet penetration level at least greater than 90% while the total rate in Europe is 73, 5% (2).

However, this remarkable overarching trend of internet use coupled with modern technological advancements have been inextricably matched with the rise of excessive, undisciplined, and dependent internet use, or problematic internet use (3–5), and its

worrisome psychological effects on mental health in certain susceptible individuals, most of whom are adolescents (6–8). With the rise of popular social trends, the younger population including adolescents and teenagers face a high risk of being labelled as problematic internet users (9). A reason for this could be because adolescents are highly prone to risky behavior and impulse actions which in turn leads to addictive behavior (10). For example, in one study the prevalence of problematic internet use among adolescents was as high as 26.3% (11).

Furthermore, studies have shown that problematic internet use is closely correlated to a number of psychiatric adverse health outcomes including sleep deprivation, depression, anxiety, academic impairment, and suicide as seen by many studies performed over the years (3,6–10). Excessive internet use was shown to affect day-to-day activities, sexual life, work productivity, education, and social withdrawal (9). However, the epidemiology and pathophysiology of the effects of problematic internet use are not yet clearly understood (11).

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AIM AND OBJECTIVES

Aim

The aim of the research is to explore the relations among internet use habits and severity of depressive-anxiety symptoms among university students.

Objectives

1. To investigate internet use habits in university students’ population in relation to sociodemographic parameters.

2. To determine the severity of anxiety and depressive symptoms in university students’ population in relation to sociodemographic parameters.

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1. LITERATURE REVIEW

1.1 Background

The internet has become popular worldwide especially among young people, with the availability and advancement of social media platforms (2). Unfortunately, the escalating online exposure seen nowadays has evolved into a risk factor for problematic internet use (PIU) (17). Problematic internet use, internet addiction disorder, or pathological internet use typically refers to the worrisome or compulsive use of the internet, which causes a substantial decline in the function of individuals in various life domains over an extended period of time (18).

The term ‘internet addiction’ was first introduced in 1995 by Dr. Ivan Goldberg, referring to it as pathological internet use. In 1996, a paper written by PhD Kimberly Young stated that pathological internet use meets the addiction criteria and should be included in the DSM. (8,18). The term IA was inconsistently used in literature (19) and is rife with

considerable controversy when it comes to classifying internet addiction as a definitive

diagnosis in the mental health professional’s handbook, the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-V) (14,20). Therefore, for convenience, this research will employ the more generally agreed upon term ‘problematic internet use’ (PIU) instead of ‘internet addiction’. It is worth mentioning that the diagnosis “Internet Gaming Disorder” is included in the DSM-V criteria of mental disorders, while PIU remains unrecognized even until now (14,15). However, this consensus seems to be shifting toward the other side as more medical organizations, such as the American Psychological Association, have been formally recognizing PIU as a mental disorder (18,21).

Anxiety can be defined as a worry, uneasiness or restlessness that results from a feeling of danger, which might be external or internal (22). In some literature, there is a distinction between fear and anxiety. While fear refers to a reaction due to a real threat, anxiety is referred to a fear that does not match the reality of the situation. However, it is crucial to remember that the manifestations of anxiety or fear both provoke the same

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Meanwhile, depression is defined as a chronic mood disorder causing the individual to experience a constant feeling of sadness and disinterest. The DSM-5 classifies depressive disorders into the category of disruptive mood dysregulation disorders, major depressive disorders, persistent depressive disorders, among others. Common characteristics of

depression disorders include a feeling of sadness, void, irritable temper together with several somatic and cognitive symptoms, which affects the individual’s everyday life (24).

1.2 Sociodemographic factors and PIU

1.2.1 Gender

Several sociodemographic indicators are associated with a risk of having PIU based on current literature. For example, quite a few studies, including one performed on European adolescents in 2014 (25), agree that gender has a prominent association with the risk of having PIU. These studies unanimously agree that the males have a significantly higher chance to be labelled as a PIU than females do (25–28). On the other hand, a study performed in 2014 in Malaysia found that there was no significant relationship between gender and PIU (9).

1.2.2 Age

Another sociodemographic indicator that stood out when it came to PIU was age. More recently, smartphones have become the most commonly used piece of technology globally and are seen as a necessary tool for modern-day civilization (6). This preference of accessing the internet via smartphones is most commonly seen in adolescents and

teenagers (19) with the most common reason of using the smartphone being social media or entertainment applications (12). Most of the research on PIU is directed on the youth due to their susceptibility to addiction coupled with the high prevalence of smartphone use among adolescents (19). Given that, numerous studies, for example the one done on European adolescents (25), found significant associations between age and PIU. The results show that younger people have a higher risk of developing PIU (9,25,28). In contrast, An Iranian study published in 2020 found no correlation between the mean age of their participants and PIU (26).

1.2.3 Education

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comparison to people with a lower education level (9). Similarly, one study conducted in Lebanon in 2017 showed participants that had a high GPA were more likely to be PIUs (12).

1.2.4 Average income and living companions

Finally, average income and presence of living companions were also some of the more frequently seen keywords when it comes to the sociodemographic variables of PIU. The study in Malaysia showed that while the average income had no correlation with PIU, living alone did correlate with a higher risk of having PIU (9). On the other hand, other studies like the ones conducted in Lebanon and Iran showed no correlation between the presence of living companions and PIU (12,26). However, one study corroborated the findings seen in the Malaysian study as PIU in adolescents was seen to be higher among lower and middle

classes in comparison to that of higher socio-economic class (28).

1.3 Internet use habits and PIU

1.3.1 Average daily time spent online

According to the Digital 2019 paper, the average time spent on the internet daily by an individual on average is 6 hours and 42 minutes and at least half of those hours are spent on smartphones (29). Adolescents in the Malaysian study had an average time spent on the internet daily of approximately 6 hours and a half which corroborates the previously mentioned paper (9).In contrast, a study published in January 2010 by the Kaiser Family Foundation (KFF) conducted a survey to examine the amount of time spent on the internet in hours per among children and adolescents. The results by KFF showed that children ages ranging between 8 to 18 spent approximately 10 hours and 45 minutes a day, seven days a week. According to the findings from KFF, 21% of the young population are labelled as heavy media users who spend greater than 16 hours a day. Meanwhile, another 63% were shown to be moderate users who use the internet 3 to 16 hours a day. Light internet users

consumed less than 3 hours of internet per day (30). 1.3.2 Internet applications patterns

While the amount spent online is a good indicator of internet use habits, a more specific approach should be utilized to properly investigate the issues regarding internet use habits. For example, when investigating PIU numerous studies have shown a high

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with the survey from the Digital 2019 paper mentioned earlier (34). To elaborate even further, a study conducted among adolescents in China in 2018 found that the highest ranked online activities are social networking with a staggering 94.73% penetration, entertainment with 82.44% penetration, and internet gaming with 73.42% (17). One study conducted in Korea showed that social networking was the only use for the internet that was correlated with PIU and WhatsApp accounted for nearly 20% of the total time spent using a smartphone (35). Other studies have associated Instagram and Snapchat use with problematic internet use (36,37). One study conducted in 2017 on Lebanese university students significantly

emphasized on the correlation between Snapchat and problematic internet use stating that the app’s design could utilize a stronger pathway towards internet or smartphone addiction than other social media apps. Using the internet for gaming and entertainment were also relatively prominent (12).

1.3.3 Prevalence of PIU

The prevalence of PIUs in a study performed in China in 2018 was 26.5% among 10 to18 years old (17). Similarly, in a 2019 study 7.1% of the participants displayed tendencies toward PIU while 12% were classified as PIUs (28). Interestingly, problematic internet use prevalence in one study was as high as 97.6% (38).

The prevalence of PIU can be hard to determine since the exact diagnostic criteria for PIU are still not included in DSM-V. Nevertheless, this research employs the use of the

compulsive internet use scale (CIUS) developed by Dr. Gert-Jan Meerkerk (Appendix 1). The author suggested a cut off score of 28, which is half of the scale’s total sum, to differentiate two groups: Problematic internet users (PIUs) and non-problematic internet users (non-PIUs). However, CIUS has no designated cut-off point set by the author to categorize a participant as an internet addict (39,40)

While some studies employed the use of the CIUS scale like the study conducted in Malaysia (9), other studies have used other scales to determine the prevalence of

problematic internet users. For example, one study used the Young’s Internet Addiction Test (IAT) to determine the prevalence of PIU in their sample population (26,41). A study

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1.4 Anxiety and depressive symptoms among student’s

population

1.4.1 Sociodemographic factors and anxiety & depression

Age, depression and anxiety are closely associated as shown in one study done during COVID-19 outbreak. The study found that there were lower rates of both depression and anxiety in those 20 years old and younger in comparison to those who are 21 to 40 years old. In addition, that very study determined that education, depression, and anxiety had a similar relationship. Depression and anxiety were seen to be lower in participants who were attending a college and even lower in those pursuing a Master’s degree. Interestingly, depression was seen to be lower in those who are unmarried (43).

Another study’s findings show that the prevalence of anxiety was significantly higher in females than in males making gender another important indicator for anxiety. In addition, the average income is a possible indicator for depression as the prevalence of depression was pointedly higher in students with lower average income in the study. It may be worth

mentioning that the prevalence of anxiety was significantly higher in the students who used internet for texting, entertainment, and reading news (5).

1.4.2 Prevalence of anxiety & depression

The methods of determining the severity of anxiety and depression across present literature is numerous. However, in this study and many others used the Patient Health Questionnaire (PHQ) which is a commonly used concise screening and diagnostic tools for mental health disorders and has already been field-tested in office practice (44,45).

It is crucial to remark that anxiety prevalence has been increasing within the global population, with a range of 2-4% of them to be classified to have an anxiety disorder (22,46). The prevalence of depression has also been increasing worldwide, affecting individuals between 18-29 years of age more often than those of 60 years or older. Unfortunately, there is a high stigma that mental health disorders are not socially accepted and this affects both the personal and professional environment. Therefore, approximately 60% of people suffering from depression do not search for medical assistance (13).

In a study performed on students of a medical college, mild depression was seen to be 37.46% while moderate to severe depression was observed in 14% of the students. In

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Interestingly, a 2019 study performed in New York examining the prevalence and severity of anxiety and depression at the end of life showed that 46.4% of patients had moderate to severe anxiety while 43% of patients had moderate to severe depression (48).

1.5 PIU and severity of anxiety and depression

1.5.1 PIU and anxiety

Several studies have determined the correlations between anxiety and PIU (12,13,25). A research conducted by the Jordan University of Science and Technology in Jordan studied the correlations between PIU and mental health effects on Jordanian university students. The study determined that excessive internet use among students predisposed them to mental disorders including anxiety (49). While some studies concluded that there is indeed a relationship between the both, a few others deny it. For example, a cross-sectional study among 210 medical students in Azad Kashmir found no significant correlation between IA and anxiety (13). Similarly, an Iranian study conducted in 2020 also showed no correlation between PIU and anxiety (26).

1.5.2 PIU and depression

Various studies have reviewed the relations between PIU and depression. All these

studies seemed to conclude that PIU is indeed correlated with the development of depression (12,24,48,49). According to a research conducted by the Kermanshah University of Medical Sciences in Iran (26) and another study conducted in Europe (25) , it was determined that there is, in fact, a significant correlation between depression and PIU. They concluded that those individuals who are labelled as PIUs are more susceptible to develop depression.

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2. RESEARCH METHODOLOGY

2.1 Study population

In this research, an anonymous cross sectional survey study was employed with a quantitative approach in the aims of exploring the impact of internet use habits on the severity of anxiety, depression, and somatic symptoms in relation to sociodemographic variables. The study was performed on mostly international but also on current Lithuanian students studying at LSMU in Kaunas, Lithuania. The participants were generally young adults, ranging from 18 to 30 years old.

A batch e-mail containing the link to the research’s survey was sent out to a population sample of 886 students currently studying at the university of LSMU belonging to year 1 to year 6 in the Medical faculty. Of the 886 students the questionnaire was sent to, 110 students agreed to fill the survey out.

2.2 Methods

The survey included socio-demographic questions, compulsive internet use scale (CIUS), the use of several internet-based applications on a five-point Likert scale, Patient Health Questionnaire (PHQ) and General Anxiety Disorder screening test (GAD-7). Informed consent was collected and the data obtained was stored in excel sheets anonymously. Research approval was granted by the Bioethics committee of LSMU.

2.2.1 Sociodemographic questionnaire

The online survey questionnaire included sections on demographic information including age, gender, average yearly income, living companions, educational status, the average time spent in hours per day on the internet during weekdays and weekends, and the average increase in time in hours per day since the COVID-19 outbreak and COVID-19 related events. Furthermore, frequency of using various internet applications such as WhatsApp, YouTube, Twitter etc., were assessed on a five-point Likert scale.

2.2.2 CIUS scale

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use of 14 items ratable on a five-point Likert scale with choices ranging from never (0),

seldom (1), sometimes (2), often (3) and always (4). Final scores are calculated by adding up the scores of all 14 items resulting in a score range of 0-56. The items of the CIUS are largely based on the DSM-IV criteria for dependence and obsessive-compulsive disorder. CIUS has shown consistent factor stability across various population samples and different times with high correlations reliability with concurrent and criterion variables indicating its accepted validity. In this study, as suggested by the author, a cut off score of 28 which is half of the scale’s total sum was set to differentiate two groups: Problematic internet users (PIU) and non-problematic internet users (non-PIU). However, CIUS has no designated cut-off point set by the author to categorize a participant as an internet addict. In addition, it is worth

mentioning that respondents that responded ‘yes’ to the question determining if their internet use habits based on their answers to CIUS has incited any changes in the respondent’s daily living were also labeled as PIU (39) (see Appendix 1).

2.2.3 PHQ scale

The Patient Health Questionnaire (PHQ) and GAD-7 is a concise tool for screening and diagnosing mental health disorders and has been tested in office practice. The survey included the full PHQ which consists of the PHQ-9, PHQ-15, and GAD-7 along with a few more uncategorized questions that aid in the assessment of overall mental health.

PHQ-9, the depression scale, is calculated by summing up the scores of 9 items each with a possible response of 0, 1, 2, and 3 attributed to the response categories of “not at all”, “several days”, “more than half the days”, and “nearly every day” respectively. The PHQ-9 score sums range from 0 to 27 based on the nine items. Scores of 5, 10, 15, and 20 represent cut-points for mild, moderate, moderately severe and severe depression, respectively (see Appendix 2).

PHQ-15 Somatic Symptom Severity is calculated by adding scores of 15 items each with a possible response of 0, 1, and 2 to the response categories of “not at all”, “bothered a little”, and “bothered a lot”. PHQ-15 scores of 5, 10, and 15 represent cut-points for low, medium, and high somatic symptom severity, respectively (see Appendix 3).

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2.3 Statistical analysis

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3. RESULTS

3.1 Sociodemographic factors

Of the 110 respondents, the final sample consisted of 31 males (28.2%) and 79 females (71.8%) aged between 18 to 30 years with an average of 22.54 ± 2.84. As shown in Table 1, 36 respondents (32.7%) had 17 years of education while the total number of

respondents had a mean education level in years of 15.1 ± 1.697. 80 respondents (72.7%) reported that they lived alone. The median of the respondent’s average income was between middle-class ($35,000-$100,000/year) and working-class ($15,000-$35,000/year).

Table 1. Sociodemographic factors of study participants.

3.2 Internet use habits among university students in relation to

sociodemographic parameters

3.2.1 Internet use habits among university students

Based on the results shown in Table 2, the average time of internet use during weekdays (hours per day) was between 7 to 8 hours with the greatest percentage of

Variable Number (%) Gender

Male 31 (28.2) Female 79 (71.8) Education level (years)

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respondents (27.3%) having between 6 to 7 hours of internet use. In addition, the average time of internet use during weekends (hours per day) was also between 7 to 8 hours, however, the greatest percentage of respondents (29.1%) this time had greater than 10 hours of internet use. Furthermore, the average time of internet use increase since the COVID-19 outbreak and the subsequent related events was between 2-3 hours with the greatest percentage of respondents (32.7%) reporting an increase of internet use between 3 to 5 hours.

Table 2. The compulsive internet use (CIUS scale) in relation to time spent on the internet in

hours per day.

Variable

Number

(%) Mean CIUS score (SD) Internet use during weekdays (hours per day)

~ 1 hour 1 (0.9) 23 (0.0) 2-3 hours 5 (4.5) 25.8 (13.0) 4-5 hours 25 (22.7) 21.52 (10.2) 6-7 hours 30 (27.3) 22.13 (9.7) 8-9 hours 23 (20.9) 27.48 (11.1) >10 hours 26 (23.6) 26.12 (14.4) P value 0.419

Internet use during weekends (hours per day)

None 1 (0.9) 40 (0.0) ~ 1 hour 1 (0.9) 23 (0.0) 2-3 hours 11 (10) 21.64 (8.0) 4-5 hours 14 (12.7) 23.86 (11.1) 6-7 hours 30 (27.3) 23.2 (10.9) 8-9 hours 21 (19.1) 25.95 (12.6) >10 hours 32 (29.1) 24.66 (12.9) P value 0.828

Internet use increase since COVID-19 outbreak and COVID-19 related events (hours per day) No increase 18 (16.4) 15.06 (8.4)

1-2 hours 23 (20.9) 24.13 (9.4) 3-5 hours 36 (32.7) 26.83 (11.4)

>6 hours 33 (30) 26.45 (12.3)

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3.2.2 The relation of sociodemographic factors and PIU

51 (46.4%) participants were grouped as problematic internet users (PIU) while 59 (53.6%) participants were correspondingly non-problematic internet users (non-PIU) based on CIUS scores. Mean CIUS scores were compared with categories of socio-demographic variables of gender, education, income, and living companions. As seen in Table 3, there were no significant associations between any of the sociodemographic variables and CIUS score. The overall mean CIUS score was 24.23 ± 11.5.

Table 3. The distribution of the mean scores on the CIUS scale by socio-demographic

characteristics.

Variable

Number

(%) Mean CIUS score (SD) Gender

Male 31 (28.2) 26.23 (13.9) Female 79 (71.8) 23.44 (10.4)

P value 0.256

Education level (years)

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3.2.3 Internet application use among PIUrs

Snapchat and Twitter application use were more associated with PIU in the CIUS group with a significance value of 0.014 and 0.011 respectively as seen below in Table 4. Table 4. The frequency of time spent on popular internet applications based on a 1 to 5 Likert

scale and its relation to CIUS group.

Frequency (% of time spent on a Likert scale from 1-5) CIUS group

Application 1 2 3 4 5 P value Whatsapp 11.1 23.1 27.8 18.5 19.4 0.081 Facebook 59.6 16.5 11 6.4 6.4 0.499 Instagram 15.7 9.3 22.2 22.2 30.6 0.054 Snapchat 52.8 9.3 16.7 13.9 7.4 0.014 Twitter 76.9 9.3 4.6 1.9 7.4 0.011 Youtube 4.6 10.1 33 26.6 25.7 0.364 Tiktok 69.2 2.8 13.1 6.5 8.4 0.195 9gag 89.8 4.6 1.9 3.7 0 0.647 Netflix 25.9 12 22.2 18.5 21.3 0.218 Zoom/MS Teams 9.2 14.7 29.4 24.8 22 0.864 Gaming 56.9 15.6 11.9 7.3 8.3 0.11

3.3 Anxiety and depressive symptoms among university students

in relation to sociodemographic parameters

3.3.1 Severity of anxiety and depression among university students

The means of PHQ-9, PHQ-15, and GAD-7 scores were 9.51 ± 6.5 (mild depression), 9.05 ± 4.8 (low somatic symptom severity), and 6.83 ± 5.5 (mild anxiety) respectively. The mean PHQ-9 scores were 2.00 ± 1.18, 6.53 ± 1.32, 11.61 ± 1.47, 17.00 ± 1.60, and 23.40 ± 2.55 in regards to normal, mild, moderate, moderately severe, and severe depression respectively. Meanwhile, the mean GAD-7 scores in relation to normal, mild, moderate, and severe depression were reported as 1.51 ± 1.5, 7.02 ± 1.4, 12.53 ± 1.3, and 19.33 ± 2.1 respectively as shown below in Table 6.

Table 5. Frequency and percentage of severity of depression, somatic symptom, and anxiety

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Frequency Percent

Mean

Score S.D. Depression Severity PHQ-9 score

Normal 24 21.8 2.00 1.18 Mild 36 32.7 6.53 1.32 Moderate 28 25.5 11.61 1.47 Moderately Severe 12 10.9 17.00 1.60 Severe 10 9.1 23.40 2.55 Total 9.51 6.52

Somatic Symptom Severity PHQ-15 score

Normal 20 18.2 2.65 1.40

Mild 46 41.8 7.28 1.40

Moderate 28 25.5 11.68 1.28

Severe 16 14.5 17.56 2.00

Total 9.05 4.83

Anxiety Severity GAD-7 score

Normal 41 37.3 1.51 1.49

Mild 43 39.1 7.02 1.39

Moderate 17 15.5 12.53 1.28

Severe 9 8.2 19.33 2.12

Total 6.83 5.55

3.3.2 The association of sociodemographic factors of university students and severity of anxiety-depressive symptoms

Gender was significantly associated to mean PHQ-15 scores with a P value of 0.049. Comparatively, the presence of living companions residing with the study participants was significantly associated to mean PHQ and PHQ-15 scores with a significance value of 0.046 and 0.007 respectively.

Table 6. The distribution of the mean scores of PHQ scale in relation to socio-demographic

characteristics. Variable Mean PHQ score (SD) Mean PHQ-9 score (SD) Mean PHQ-15 score (SD) Mean GAD-7 score (SD) Gender Male 39.03 (30.1) 9.16 (8.7) 7.61 (6.2) 5.58 (5.16) Female 46.86 (22.6) 9.65 (5.5) 9.62 (4.1) 7.32 (5.6) P value 0.141 0.728 0.049 0.14

Education level (years)

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13 44.58 (28.1) 9.08 (6.9) 8.33 (5.0) 8.42 (6.3) 14 50.93 (25.9) 11.00 (6.6) 8.21 (4.8) 7.71 (5.2) 15 41.52 (23.0) 9.00 (5.8) 9.85 (4.4) 4.67 (4.7) 16 51.1 (33.1) 10.00 (7.6) 9.6 (6.5) 8.5 (7.2) 17 40.86 (20.0) 8.56 (6.1) 8.78 (4.6) 6.33 (4.4) P value 0.632 0.651 0.862 0.108 Average income Upper-class (>$200K/year) 37.00 (0.0) 14.00 (0.0) 7.00 (0.0) 4.00 (0.0) Upper/Middle-class ($100-200K/year) 42.00 (26.6) 8.71 (6.6) 9.10 (5.3) 5.86 (5.7) Middle-class ($35-100k/year) 44.85 (23.7) 8.73 (5.7) 8.77 (4.2) 7.17 (6.3) Working-class ($15-35k/year) 40.43 (22.9) 8.32 (5.5) 9.11 (4.4) 6.39 (5.4) Poverty (<$15k/year) 53.35 (28.8) 13.35 (8.2) 9.6 (6.3) 7.9 (4.2) P value 0.477 0.052 0.967 0.746 Living companions Yes 36.9 (26.9) 8.43 (7.4) 7.03 (5.0) 5,77 (5.9) No 47.56 (23.8) 9.91 (6.2) 9.81 (4.6) 7.22 (5.4) P value 0.046 0.292 0.007 0.221

3.4 The associations among internet use habits and anxiety &

depressive symptoms severity in university students’ population

3.4.1 Daily internet use and severity of anxiety- depressive symptoms

among university students

Internet use in hours per day during weekdays was significantly associated to mean PHQ, PHQ-9, and PHQ-15 scores with a P value of 0.013, 0.015, and 0.007 respectively. Similarly, internet use increase in hours per day since COVID-19 outbreak and COVID-19 related events was significantly associated to mean PHQ, PHQ-9, and PHQ-15 scores with a significance value of 0.036, 0.009, and 0.031 respectively. On the other hand, internet use in hours per day during weekends was significantly associated only to mean GAD-7 score with a significance value of 0.042 as shown below in Table 7.

Table 7. The distribution of the mean scores on the PHQ, PHQ-9, PHQ-15, and GAD-7 scale

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Variable Mean PHQ score (SD) Mean PHQ-9 score (SD) Mean PHQ-15 score (SD) Mean GAD-7 score (SD) Internet use during weekdays (hours per day)

~ 1 hour 9 (0.0) 1 (0.0) 0 (0.0) 2 (0.0) 2-3 hours 20 (16.1) 6 (6.0) 4.6 (3.0) 2.8 (3.6) 4-5 hours 37.92 (26.3) 8.08 (6.8) 8.08 (5.1) 6.24 (5.2) 6-7 hours 42.93 (23.9) 7.83 (5.7) 8.23 (3.2) 7.23 (5.9) 8-9 hours 54.83 (20.7) 12.87 (4.8) 10.43 (4.1) 7.96 (6.9) >10 hours 50.23 (25.3) 10.85 (7.4) 10.92 (5.9) 6.88 (4.3) P value 0.013 0.016 0.007 0.448

Internet use during weekends (hours per day)

None 42 (0.0) 0 (0.0) 8 (0.0) 12 (0.0) ~ 1 hour 9 (0.0) 1 (0.0) 0 (0.0) 2 (0.0) 2-3 hours 34.27 (21.2) 7.91 (5.8) 7.36 (2.9) 5.09 (4.4) 4-5 hours 55.29 (26.0) 9.5 (7.1) 11.71 (5.5) 10 (6.3) 6-7 hours 38.13 (26.2) 8.8 (6.6) 8.03 (4.3) 5.27 (5.6) 8-9 hours 48.67 (21.9) 10.1 (6.3) 8.48 (3.3) 8.29 (4.9) >10 hours 48.25 (25.0) 10.72 (6.7) 10.13 (5.7) 6.53 (5.4) P value 0.084 0.517 0.125 0.042

Internet use increase since COVID-19 outbreak and COVID-19 related events (hours per day)

No increase 33.33 (21.4) 5.44 (4.9) 6.06 (3.6) 5.83 (4.4) 1-2 hours 51.17 (23.8) 10.48 (5.8) 9.96 (4.4) 8.65 (6.0) 3-5 hours 40.36 (24.4) 9 (6.5) 9.25 (4.4) 6.06 (5.5) >6 hours 50.97 (26.1) 11.61 (7.0) 9.85 (5.6) 6.94 (5.7)

P value 0.036 0.009 0.031 0.289

3.4.2 Internet application use and severity of anxiety and depressive symptoms among university students

Figure 1 shows that the time spent on Instagram application use and PHQ-9 group, PHQ-15, and GAD-7 group had a positive correlation with a significance value of 0.005, < 0.001, and 0.003 respectively. Furthermore, Gaming had significance value of 0.001 and 0.021 when associated with severity of PHQ-9 and PHQ-15 group respectively.

Figure 1. The distribution of time spent on Instagram based on 1 to 5 Likert scale by PHQ-9,

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3.4.3 The associations among problematic internet use and severity of anxiety-depressive symptoms

Bivariate correlation analysis performed between CIUS score and PHQ score

presented a positive correlation factor of 0.314 (P < 0.001) which is seen graphically in Figure 2. Furthermore, as seen in Table 8, the correlation factor between PHQ-9, PHQ-15, & GAD-7 scores and CIUS score was 0.480 (P < 0.001), 0.369 (P < 0.001), and 0.121 (P = 0.206). Results of the chi-square test comparison of CIUS groups with PHQ-9, PHQ-15, and GAD-7 groups reproduced the results seen in the correlation analysis.

Table 8. Association of CIUS score with age, PHQ, PHQ-9, PHQ-15, and GAD-7 scores.

Age (years) CIUS SUM PHQ SUM PHQ-9 SUM PHQ 15 SUM GAD-7 SUM Age (years) - Mean = 22.54 ± 2.84

Pearson Correlation 1 -0.06 -0.155 -0.156 -0.138 -0.034

P value 0.536 0.105 0.104 0.151 0.727

CIUS SUM - Mean = 24.23 ± 11.5

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P value 0.151 < 0.001 < 0.001 < 0.001 < 0.001

GAD-7 SUM - Mean = 6.83 ± 5.5

Pearson Correlation -0.034 0.121 .830** .684** .499** 1

P value 0.727 0.206 < 0.001 < 0.001 < 0.001

Figure 2. Scatter plot with line of best fit showing the relation between scores of PHQ and

CIUS.

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4. DISCUSSION

Portable computers, tablets, and smartphones were reported as the most common methods of accessing the internet in more than one third of study participants (>36.4%). Even though the average daily time of internet use in hours of the participants in this study was greater than the average based on several other studies (9,29), the proportion of PIUrs to non-PIUrs determined in this study was approximately 1:1. A reason for this could be attributed to the relative increase of internet use after COVID-19 outbreak and COVID-19 related events that a majority of the participants had reported in this study. Theoretically, this increase bumped up a ‘normal’ amount of time spent on the internet to high amount of time spent on the internet while still being considered ‘normal’ to the participants due to staying more time at home. However, more research on this topic is needed to identify an association between high levels of internet use before and after COVID-19 outbreak and COVID-19 related events. On the other hand, one study published in January 2010 by the Kaiser Family Foundation (KFF) showed the average time of internet use daily among children ranging from 8 to 18 years old was as high as 10 hours and 45 minutes a day (30).

Based on the PHQ scale criterion, nearly a quarter of the respondents had moderate to severe psychological distress. One third of respondents (~ 37%) exhibited mild severity of depression, somatic symptoms, and anxiety while approximately every tenth (~ 10%) of the respondents exhibited a severe form of depression, somatic symptoms, and anxiety. These results are fairly similar to the results of a 2017 study conducted on students attending a Medical college which found that the prevalence of mild depression among medical students was 37.46% while moderate to severe depression was 14%. In addition, the same study had a prevalence of 19% in regards to moderate to severe anxiety (47). On the other hand, a study conducted in 2019 showed that 46.4% of patients had moderate to severe anxiety while 43% of patients had moderate to severe depression (48).

Due to the small population sample used in this study, the associations between internet habits and depressive-anxiety symptoms with their sociodemographic factors sometimes did not present as originally expected. However, some significant associations were still observed. For example, gender was associated with the mean PHQ-15 score where women generally had a higher score of somatic symptom severity. These results are

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mental-wellbeing. In contrast, a research conducted in 2020 investigating the association of mental health problems to social media and internet exposure during COVID-19 outbreak showed that depression was less prevalent in those who are unmarried rather than those who are married (43).

Meanwhile, participants with increased internet use during weekdays had a positive correlation with severity of depression, somatic symptom and their overall mental wellbeing. Furthermore, increased internet use during weekends was proportionally associated with increasing severity of anxiety. The findings of numerous studies, including a study conducted in Malaysia in 2014, follow these same patterns of internet usage and mental health

(9,13,27,49). It is noteworthy to mention that internet use increase since COVID-19 outbreak and COVID-19 related events was significantly associated with increased risk of being a problematic internet user, having worse overall mental wellbeing, depression, and somatic symptoms which substantiates the evidence seen in the previously mentioned study conducted during COVID-19 outbreak (43).

WhatsApp, Instagram, YouTube, Netflix, and not surprisingly Zoom or MS teams were the most frequently used internet applications. Among these applications, Instagram was rated the most frequently as ‘5’ on a 1-5 Likert scale. Moreover, the increased use of Instagram was correlated with the severity of depression, somatic symptoms, and anxiety. Meanwhile, Snapchat and Twitter use were more likely to be associated with PIUrs. In

addition, internet gaming was significantly associated with severity of depression and somatic symptom. This pattern of internet usage in our study was similar to the findings of several studies including a survey by RSPH (Royal Society for Public Health) and the Young Health Movement (YHM) which examined the positive and negative effects of social media, including a collection of social media platforms, and their impact on young people’s health. Their

results ranked the most common social networking apps from the most positive to the most negative impact on mental well-being as follows: YouTube, Twitter, Facebook, Snapchat, and last but surely not least Instagram. Similar to the results of our study, this survey portrayed that Instagram had the most harmful effect on mental well-being as seen by the significance values when compared to the depression, somatic symptom, and anxiety scale. In addition, other studies investigating social media use and PIU have associated Instagram and

Snapchat use with PIU (36,37).

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the difference between PIUrs and non-PIUrs was significant when compared to the severity of depression and somatic symptom but not anxiety. PIUrs had a higher incidence of

moderate, and severe depression and somatic symptom but not of anxiety, while non-PIU had a higher incidence of normal and mild severity of depression, somatic symptoms, and anxiety. The positive correlation identified between overall mental health and depression agrees with most present literature excluding the findings when comparing PIU with anxiety. Other studies involving the topic of mental health and PIU including the 2014 study

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5. CONCLUSIONS

1. The proportion of PIUrs to non-PIUrs among LSMU university students was

approximately 1:1 with participants having an average daily internet use time between 7 to 8 hours. WhatsApp, Instagram, YouTube, Netflix, and Zoom/MS teams were among the most frequently used internet applications and students who used

Snapchat and Twitter and had an increase in internet use since COVID-19 outbreak and COVID-19 related events were more likely to be PIUrs.

2. While most of the students reported normal to mild depression and mild to moderate anxiety and somatic symptom severity, nearly a quarter of the respondents had moderate to severe psychological distress where women exhibited a higher somatic symptom severity and participants residing with living companions had better overall mental health.

3. Problematic internet use, higher internet use during weekdays and weekends, internet use increase since COVID-19 and COVID-19 related events, use of the internet

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6. RECOMMENDATIONS AND LIMITATIONS

Although the population sample was 110, a greater number of participants is required to improve the consistency of the statistical analysis. Furthermore, a more heterogeneous sample including a wider background and age variety of participants is recommended to improve reliability of the conclusions.

This study lacked a temporal relationship with the variables. A longitudinal study examining the long-term effects of internet use on mental health would be recommended to include this important variable.

Despite the fact that an online anonymous survey was employed, some responses may have been biased leading to a reporting bias as seen by the inconsistent results in regards to anxiety.

In addition, COVID-19 outbreak and COVID-19 related events have inherently changed our internet use habits, therefore, more research may be done on COVID-19, internet use, and mental health.

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