Supriya Sthapit^1*, Min Bahadur Kunwar¹, Aashish Acharya¹, Sagar Parajuli¹, Prabin Thapaliya²

¹Department of Public Health, Nobel College, Sinamangal, Kathmandu, Nepal

²Department of Computing, Islington College, Kamalpokhari, Kathmandu, Nepal

*Corresponding Author: Supriya Sthapit, Department of Public Health, Nobel College, Sinamangal, Kathmandu, Nepal

Abstract
Introduction: Many studies have addressed Social Media Disorder (SMD) as an emerging mental health problem that may lead to depression and low self-esteem. This study aims to determine the prevalence of SMD and describe its association with self-esteem and depression among adolescents in Kathmandu Metropolitan City.

Methodology: Cross-sectional analytical study was conducted among a sample size of 418 using probability sampling. Descriptive summary statistics were calculated, and the association between SMD and Depression, and SMD and Self-esteem were measured using the Chi-square test and Pearson’s Coefficient of Correlation.

Results: The SMD prevalence of 35.4%, a moderate positive relationship between SMD and depression (r=0.310), and a low negative relationship between SMD and self-esteem (r=-0.099) were observed.

Conclusion: SMD is a growing mental health problem in Nepal. The respondents diagnosed with possible depression requires further investigation, and necessary interventions must be carried out.

Keywords: Social Media Disorder, Depression, Self-esteem, Adolescent, Nepal

Introduction
The term “social media” is composed of two words: “social,” which refers to “interacting with other people by sharing information with them and receiving information from them,” and “media,” which refers to an instrument of communication, like the internet (while TV, radio, and newspapers being some traditional forms of media)” [1]. Social media is the internet and computer-based technology that gives users quick electronic communication of content such as personal information and documents: videos, and photos by building virtual networks [2]. A computer, tablet, or smartphone helps users connect to social media via web-based software or application[2].

The standard features of social media may be one or many of the following: personal user accounts, profile pages, friends, followers, groups, hashtags, newsfeeds, personalization, notifications, information updating, saving or posting, like button and comment sections, and review, rating or voting systems [1].

Popular forms of social media include blogs (e.g., WordPress, Twitter), microblogs, social networks (e.g., Facebook, LinkedIn), media-sharing sites (Instagram, Pininterest), social bookmarking and selection sites, analysis sites, forums, and compelling worlds [3].

Although social media has helped us connect to the world, its overuse can lead to serious health problems like depression, anxiety, mania, eating disorders, sleep deprivation, insecurity, FEMO (Fear of missing out), internet addiction, and other anti-social behaviors. Among these problems, internet addiction can be considered a probable epidemic of this century [4]. Social media addiction is a subcategory of internet addiction. [5].

Social media addiction, also known as “social media disorder”[6], “excessive social media use” [7], “problematic social media use”[8,9], and “compulsive social media use” [10,11] is a proposed diagnosis related to problematic, compulsive or overuse of social media [12] that can result in significant impairment in an individual’s performance in varied life domains over a prolonged amount of time. [6].

Many studies have addressed SMD or Social media addiction as an emerging issue in the field of public health [6,11,13-15] and even showed its association with depression [14] and self-esteem [14,15], especially among adolescents. Adolescents are rapidly adopting new technologies among all age groups and are most vulnerable to possible negative influences of these technologies [16]. Moreover, there is empirical evidence that compulsive social media use is a growing mental health problem among adolescent smartphone users [17]. Further, several studies have shown a positive correlation between social media use and depression [14,18] and a negative correlation with self-esteem [18].

Some fragmented research done in Nepal has also provided evidence that these problems are growing here [4]. Our study, thus, aims to determine the prevalence of SMD and its association with depression and self-esteem among late adolescents (15-19 years old) studying in secondary schools in Kathmandu Metropolitan City.
Metropolitan City, which falls under the country’s capital, consists of 32 wards, and lies in the northwest part of the Kathmandu Valley.

The city is ahead in adapting to new technologies, including social media and the internet. Moreover, the city is an educational hub for students from all over Nepal, which enabled us to reach a diverse group of participants.

Materials and Methods
Study design and method
A cross-sectional analytical study was carried out using a quantitative method wherein all the variables were assessed simultaneously. The prevalence of SMD and its association with depression and self-esteem among the adolescents of the Kathmandu Metropolitan City were determined.

Study area
The study was conducted in ten secondary schools in Kathmandu Metropolitan City. The city is inside the Kathmandu district in Bagmati province, consisting of 32 wards.

Study population
Late adolescents (15 – 19 years old) studying in grades 9, 10, 11, and 12 in schools inside the Kathmandu Metropolitan City were taken in the study.

Study duration
This cross-sectional study was conducted between May 1, 2019, and January 31, 2020.

Inclusion and exclusion criteria
The students aged 15 - 19 (in completed years) were only included. Students who refused to participate in the study were not included in the study.

Sampling strategy
This study used the probability sampling method (simple random sampling) to select secondary schools within the Kathmandu Metropolitan City. Again, Probability Proportional to Size was conducted to determine students from each selected school.

Sample size
The sample size was calculated using the following formula:

non-response rate = 10% of n = 10% of 384.16 = 38.416

Hence, the total sample size = 384.16+38.416 = 422.16 ≈ 423

However, after deducting the non-responders, the study size was reduced to 418.

Sampling techniques
Multi-stage sampling was done. Ten secondary schools were selected through simple random sampling (Lottery Method). Then Probability Proportional to Size sampling method was applied to determine the number of samples to be taken from each school. PPS is a sampling technique in which the probability of a unit being selected is proportional to the size of the ultimate team. Thus, the list of the total number of students was taken from each selected school.

Here, the total number of students in ten schools was 53, 50, 300, 30, 1000, 200, 250, 100, 600, and 120; the sum was 2703. Thus,

The sample size taken from first school was = 2 % of 422 = 8

After determining the sample size, a simple random sampling technique (table of random numbers) was used to select the samples.

Data collection techniques and tools

Data collection technique: Respondent's self-administration technique was used. The study respondents were adequately informed and explained the purpose of the study.

Data collection tools: Semi-structured questionnaire was used. The questionnaire consisted of four parts which included- questions on sociodemographic variables, independent variables, a 9-item social media disorder scale [6], a 6-item Kutcher Adolescent Depression Scale [19], and a 10-item Rosenberg self-esteem scale [20]. The first section consisted of the sociodemographic or background variables. The second section consisted of questions on independent variables such as social media use (Yes/No question), type of social media use (Multiple-choice question), and frequency of social media use (closed-ended question). Internet availability in residence (Yes/No question), first social media used (closed-ended inquiry), age when created first social media account or used first social media (open-ended), and purpose for social media usage (closed-ended question). The third section consisted of an SMD scale with a rating of two (Yes/No). A person is said to have SMD if s/he meets 5 out of 9 criteria (preoccupation, tolerance, withdrawal, persistence, displacement, interpersonal problems, deception, escape, and conflict) of the SMD scale.

The fourth section consisted of 6-item KADS. Zero to-three systems with "hardly ever," "much of the time," "most of the time," and "all of the time" scored as zero, one, two, and three, respectively. If the total score was at or above six, we said that the individual might have a major depressive disorder, and if the score was below six, then the individual probably was not depressed.

The fifth section consisted of a 10-item Rosenberg self-esteem scale which contained five positively and five negatively worded items (reverse coded); higher scores indicated higher self-esteem. It was a zero to three system with "strongly disagree," "disagree," "agree," and “strongly agree" scored as zero, one, two, and three, respectively, for items 1, 3, 4, 7, and 10 and scored in reverse order for items 2, 5, 6, 8, 9. The scores were summed and kept on a continuous scale. Higher scores indicated higher self-esteem.

Independent Variables
The independent variables in our study included sociodemographic variables (age, sex, educational level, faculty, ethnic group, and religion) and variables such as social media use, frequency of social media use, type of social media users, and Internet connection availability.

Dependent Variables
The dependent variables in our study included SMD, depression, and self-esteem.

Pretesting the tools
The pretesting was done in 10 % of the total sample size calculated (n=423), i.e., 43 individuals. The individuals were selected from outside of the study area, i.e., outside of Kathmandu Metropolitan City. The pretesting was done in United Academy, Lalitpur metropolitan city.

Validity and reliability
Validity: The content validity of the instrument for its completeness and clarity was established by consultation with the research supervisor, subject experts, and statistician, and needed modification was done as per the suggestion. A multi-stage sampling technique was used to select secondary students in Kathmandu Metropolitan City to reduce random error. Respondents were informed about the research topic and content to minimize information bias.

Reliability: The instrument's reliability was established by pretesting it among 43 students outside Kathmandu Metropolitan City. Based on the feedback from the respondent, instruments were modified.

Data Management and Analysis
Data were entered in EpiData. Data analysis was done in software called "Statistical Package for Social Sciences" IBM statistics version 16. For data consistency, the data entry was done on the evening of the day after completing data collection on that day.

Under descriptive summary statistics of data, frequency and percentage were calculated for those data which were categorical in nature. Additionally, mean, and standard deviation were calculated for numeric data. After collecting data from the field, data were checked to correct the possible errors.

Likewise, the chi-square test measured the association between two categorical variables, i.e., between SMD and Depression. For the samples more than or equal to fifty, a p-value of Pearson-Chi-square was used. For the 2*2 table of categorical data, a p-value of the continuity corrected test was used. Also, the degree of association between SMD and Depression and SMD and self-esteem was measured through correlation.

Ethical Considerations
The research approval was taken from the Department of Public Health, Nobel College. Ethical approval was taken from the Institutional Review Committee (IRC) of Nobel College. Formal permission was taken from secondary schools acting as guardians of the students to be included in the study. Informed consent was obtained from respondents by clarifying the purposes of the survey before the data collection. The respondent's dignity was maintained by allowing the option to discontinue the research study at any time without penalty. Confidentiality was maintained by not disclosing and anonymizing the respondent's information to others; collected information will be used only for the study purpose.

Results
Of the total respondents, i.e., 418, 56.94 % were male, and the remaining 43.06 % were female. The details of the sociodemographic characteristics of study participants are shown in Table 1.

Table 1: Distribution of socio-demographic characteristics of participants.

Out of the total respondents, 411 (98.3 %) used any of the given social media (Facebook, Facebook Messenger, WhatsApp, Viber, YouTube, Instagram, or Twitter), while the remaining 11 (1.7 %) did not use any of those media. Out of those who used social media (i.e., 411), the majority, i.e., 43.8 % spent one to three hours on those media daily, 38 % spent less than one hour daily, 10.7 % spent four to six hours daily, and remaining 7.5 % spent seven hours or more on these media daily. The majority, i.e., 91.73 % used smartphones to access social media, 40.15 % used laptops or computers, 13.14 % used tablets, and 0.24 % used other devices to access social media. Furthermore, 84.2 % owed a smartphone, while the remaining 15.8 % do not own a smartphone. In our study, 78.1 % of the total respondents used social media for getting updates and news, 69.6 % used them for messaging or chats or calls, 63.7 % used them for watching videos, 55.5 % used them for posting their pictures/videos/status, 50.9 % used them for managing their friends’ posts, 31.1 % used them for following a celebrity or an influencer and the remaining 5.1 % used them for other purposes such as reading memes. The details of the distribution of characteristics related to social media use are as shown in Table 2.

Table 2. Distribution of characteristics related to social media use.

The sample size for the dataset was 411 (the number of respondents who used any of the given social media: Facebook, Facebook Messenger, WhatsApp, Viber, YouTube, Instagram, or Twitter).

In this study, the prevalence of SMD among the respondents (n=418) was found to be 35.4 % (i.e., 148 respondents).
The chi-square test of independence conducted between SMD, and depression showed a statistically significant association between SMD and Depression at p = 0.000 (i.e., p < 0.05) as shown in Table 3. The correlation was measured between the total score of SMD and the total score of depression. The correlation was measured at p=0.000 which showed a moderate positive relationship (r=0.310).

Table 3. Cross tabulation showing the p-value of the chi-square test between SMD and Kutcher Adolescent Depression Scale.

The sample size for the dataset was 418 which included both the respondents who used social media and those who did not.

The chi-square test of independence between SMD and self-esteem showed no statistically significant association at p=0.761 as shown in Table 4. The correlation was measured between the total score of SMD and the total score of RSES. The correlation was measured at p=0.044 which showed a low negative relationship (r=- 0.099).

Table 4. Cross tabulation showing the p-value of the chi-square test between SMD and Rosenberg Self-Esteem Scale.

The sample size for the dataset was 418 which included both the respondents who used social media and those who did not.

Discussion and conclusion
SMD is an emerging mental health problem among adolescents. To our knowledge, this is the first study in Nepal to explore the association of SMD with depression and self-esteem.

One of the main goals of this study was to estimate the prevalence of SMD, which was found to be 35.4 % among the total sample of 418, which is comparatively higher than the other similar studies being conducted in other parts of the world. A study conducted among three models of adolescents aged 10-17 in the Netherlands in 2016 showed the prevalence of SMD to be 7.3 % (n=724), 11.6 % (n=873), and 10.3 % (n=601).

Though the results may have varied due to the geographical differences, it signifies the need to address this problem at the country level seriously.

When tested for gender differences, the chi-square test showed that the number of disordered girls (39.4 %) did not differ from the number of disordered boys (32.4 %).

This finding is in line with the previous two studies by Van den Eijnden but contradicts the result of the first sample in their study, which showed that disordered boys (10.2 %) were more than disordered girls (4.9 %) [6,13]. The correlation was measured at p=0.000 between the total score of SMD and the total score of depression which showed a moderate positive relationship (r=0.310). This means a higher score of SMD results in a higher score of depression. The finding coincides with the study by Van den Eijnden which showed a medium positive correlation (0.29) at p < 0.001 between SMD and Depression [6]. The findings of our study also showed a low negative correlation (r= 0.099) between SMD, and self-esteem measured at p=0.044. This indicates that high levels of social media addiction are associated with low levels of self-esteem. This finding was in line with other studies conducted at Notre Dame University among students of median age 21.1, which showed a small, negative correlation, r ¼.23, N ¼ 364 at p < .001 [15], and a study by van den Eijnden conducted among three samples of adolescents aged 10-17 in the Netherlands in 2016, which showed a slight negative correlation (r=-0.19) at p < 0.001 [6].

Mental health conditions account for 16 % of the global burden of disease and injury among 10-19-year-old, depression being a leading cause of illness and disability amongst them [21]. These studies, including ours, show that adolescents are at a high risk of developing SMD along with depression and lower self-esteem. It is, therefore, fundamentally important to conduct further in-depth studies in this life course to detect the patterns of SMD, depression, and low self-esteem. These disorders are, however, the most treatable among the mental disorders [22], which can be intervened with early diagnosis, proper counseling, love, and support from friends and families.

A longitudinal approach could better describe the short- or long-term character of SMD, depression, and self-esteem in terms of study limitations. As the questionnaire length was limited, we had to make tough choices while selecting independent variables for our study. Also, of note missing from our data are the experiences of people who declined to take up any opportunity to participate.

Funding sources: This research received no external funding.

Acknowledgments
We are grateful to the Department of Public Health, Nobel College for their constant supervision during the preparation of this paper. We want to thank Saroj Bhandari and Nabraj Kafle for their valuable comments on earlier drafts of the manuscript. We are also thankful to all those who have directly and indirectly provided us with their kind support, cooperation, and time for completing the research.

References

1. Nations D. What is Social Media? : Lifewire.
2. Small Business: Investopedia.
3. Saravana Kumar M, Sugantha Lakshmi T (2012) Social media marketing. Life Science303 Journal. 9(4): 4444-4451.
4. Thakur DN (2017) social media: Opportunity or threat to public health in context of Nepal. Health Prospect. 16(1): 7-9.
5. Shaw M, Black DW (2008) Internet addiction: Definition, Assessment, Epidemiology and Clinical Management. CNS Drugs. 22(5): 353-65.
6. van den Eijnden RJJM, Lemmens JS, Valkenburg PM (2016) The Social Media Disorder Scale. Computers in Human Behavior. 61: 478-87.
7. Griffiths MD, Szabo A (2014) Is excessive online usage a function of medium or activity? An empirical pilot study. J Behav Addict. 3(1): 74-7.
8. Lee ZW-Y, Cheung CM-K (2014) Problematic use of social networking sites: The role of self-esteem. 9(2).
9. Meena PS, Mittal PK, Solanki RK (2012) Problematic use of social networking sites among urban school going teenagers. 21(2): 94-7.
10. Aladwani AM, Almarzouq M (2016) Understanding compulsive social media use: The premise of complementing self-conceptions mismatch with technology. Computers in Human Behavior. 60: 575-581.
11. De Cock R, Vangeel J, Klein A, Minotte P, Rosas O, et al. (2014) Compulsive use of social networking sites in Belgium: prevalence, profile, and the role of attitude toward work and school. Cyberpsychol Behav Soc Netw. 17(3): 166-71.
12. Kuss DJ, Griffiths MD (2017) Social Networking Sites and Addiction: Ten Lessons Learned. Int J Environ Res Public Health. 14(3): 311.
13. van den Eijnden R, Koning I, Doornwaard S, van Gurp F, Ter Bogt T et al. (2018) The impact of heavy and disordered use of games and social media on adolescents' psychological, social, and school functioning. J Behav Addict. 7(3): 697-706.
14. Kelly Y, Zilanawala A, Booker C, Sacker A (2018) Social Media Use and Adolescent Mental Health: Findings From the UK Millennium Cohort Study. EClinical Medicine. 6: 59-68.
15. Hawi NS, Samaha M (2016) The Relations Among Social Media Addiction, Self-Esteem, and Life Satisfaction in University Students. Social Science Computer Review. 35(5): 576-86.
16. Valkenburg PM, Peter J (2011) Online communication among adolescents: An integrated model of its attraction, opportunities, and risks. 48(2): 121-7.
17. Van Rooij A, Schoenmakers T. Monitor Internet en Jongeren 2010-2012. Het (mobiele) gebruik van sociale media en games door jongeren [The (mobile) use of social media and ga mes by adolescents]. Rotterdam: Center for Behavioral Internet Science & IVO; 2013.
18. van den Eijnden RJ, Lemmens JS, Valkenburg PM (2016) The social media disorder scale. 61: 478-487.
19. Brooks SJ, Krulewicz SP, Kutcher S (2003) The Kutcher Adolescent Depression Scale: assessment of its evaluative properties over the course of an 8-week pediatric pharmacotherapy trial. 13(3): 337-49.
20. Rosenberg M (1965) Rosenberg self-esteem scale (RSE). 61(52): 18.
21. Adolescents: health risks and solutions: World Health Organization; 2018.
22. Patients & Families: American Psychiatric Association.