• Users Online: 408
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2020  |  Volume : 36  |  Issue : 4  |  Page : 289-295

Social media disorder among Indian undergraduate medical students and its association with depression: An institution-based mixed-method study


Department of Community Medicine, Bankura Sammilani Medical College, Bankura, West Bengal, India

Date of Submission12-Mar-2020
Date of Acceptance18-Apr-2020
Date of Web Publication31-Dec-2020

Correspondence Address:
Dr. Manisha Sarkar
Jagatpur, Near Jagatpur High School, Gouranga Nagar, North 24 Parganas, Kolkata - 700 159, West Bengal
India
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijsp.ijsp_42_20

Rights and Permissions
  Abstract 


Background: Social media disorder (SMD) is the current entity in this decade that leads to different screen-related health problems. Despite of tremendous academic pressure, how social media affects the future doctors, is yet unknown. Aims: The aim is to determine the prevalence of SMD among the undergraduate medical students of a tertiary care hospital in West Bengal and to determine its predictors. Settings and Design: A cross sectional analytical mixed-method study was conducted at a tertiary care center of Bankura. Methodology: During April–June 2019, 216 undergraduate medical students were selected through two-stage sampling method. Data were collected using semi-structured questionnaire, 9-item SMD scale, and Beck's Depression Inventory Scale. Statistical Analysis Used: Data were analyzed using SPSS (version 16) initially through bivariate analysis using Chi-square test and later logistic regression was used to determine the actual predictor(s). Results: The prevalence of SMD was found to be 11.6%. All of the students were found to be social media users and among them the prevalence of screen-related sleep disturbance, headache, eye problem, musculoskeletal problems, and overweight or obesity was 35.6%, 36.1%, 28.7%, 31.5%, and 50.9%, respectively. However, no significant relationship was obtained between SMD and above health problems. Through logistic regression model, it was found that the students with depression were 6.7 times more prone to develop SMD. Conclusions: Depression being a risk-factor for SMD needs to be addressed as priority by providing appropriate counseling and/or professional consultation.

Keywords: Addiction, depression, medical students, screen related health problem, social media disorder


How to cite this article:
Saha R, Sarkar M. Social media disorder among Indian undergraduate medical students and its association with depression: An institution-based mixed-method study. Indian J Soc Psychiatry 2020;36:289-95

How to cite this URL:
Saha R, Sarkar M. Social media disorder among Indian undergraduate medical students and its association with depression: An institution-based mixed-method study. Indian J Soc Psychiatry [serial online] 2020 [cited 2021 Feb 24];36:289-95. Available from: https://www.indjsp.org/text.asp?2020/36/4/289/305949




  Introduction Top


Social media are various ways of electronic communication through which users share information, ideas, personal messages, and other content with others. Social media has somehow become a necessary evil now a day. The inseparable bond of humans with the use of social media has overtaken interpersonal relationships. As on January 2019, globally 3.48 billion people were active social media users (approximately 45% of the world population), while in India 310 million people (approximately 23% of the Indian population) were active social media users.[1],[2]

Social media overuse may waste lot of productive time and may cause problems related to daily functioning, completion of tasks, relationships, and/or psychological status. The perceived need to be online on social media may result in compulsive use of social media, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions.[3]

Social media disorder (SMD) is a proposed diagnosis related to the overuse of social media, similar to internet addiction particularly internet gaming disorder and other forms of digital media overuse.[4],[5] Gaming disorder has been already included in International Classification of Diseases-11. Although the latest version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) recognizes Internet gaming disorder as a tentative disorder in the appendix of the manual American Psychiatric Association 2013, SMD still has no status in the DSM-5.[5] While the exclusion of SMD from the DSM-5 may give the impression that SMD is not a legitimate mental disorder, there is a growing body of evidence suggesting otherwise.[5] Time has come to consider it as a psychological disorder. According to the study done by O'Keeffe et al., it was found that excessive use of social media led to increase in internet addiction, decline of face-to-face interaction, cyber bullying, and sleep deprivation.[6] Al-Menayes found that spending time online in social media websites and lack of sleep due to late night internet use led to low academic performance.[7] Whang et al. found that social media addicts had a higher degree of loneliness and depression compared to nonaddicts.[8]

A lot of studies on social media use have been conducted in India with only a few studies in West Bengal. Most of them have been conducted either on school or preuniversity students, through face-to-face self-administered questionnaire or have been conducted on youths through online surveys.[3],[9],[10],[11],[12],[13] SMD can have far greater impacts on college students than school students. More so, online surveys have fewer responses and have their own limitations due to one's busy schedule. Furthermore, studies have been done across the country and worldwide on internet addiction disorder among different population groups including the medical students.[14],[15] However, despite exhaustive search, no study was found in our country among the undergraduate medical students regarding the extent of SMD particularly for nonacademic purposes or nonjob-related purposes. With this background, the current study was undertaken among the undergraduate medical students of Bankura Sammilani Medical College with an aim to study the pattern of social media use, the factors predisposing to SMD and its probable screen-related effects on their physical and mental health and academic performance. The study may help to understand the threats to social media over use and measure the overall burden of the SMD among the undergraduate medical students, who are the future doctors and who have extreme academic pressure. This may in turn create awareness regarding the impacts of SMD, before the entire population is gripped in the flame of social media overuse.


  Materials and Methods Top


An institution-based cross-sectional, mixed method study was conducted among the undergraduate medical students of a tertiary care center at West Bengal from April to June, 2019 using both quantitative (analytical study) and qualitative methods (thematic analysis). Students who were not willing to participate in the study, having a history of preexisting musculoskeletal disorder due to other pathological reasons (such as fracture and tumor) and having a history of preexisting eye diseases due to other pathological reasons (such as infections, glaucoma, tumor, and retinal pathology) were excluded. Out of existing 600 MBBS students, the required sample was obtained using two-stage sampling method. Initially the required sample size was calculated to be 96, using the formula n = Z2(pq)/d2 (where, P is the prevalence of SMD = 50% [as there were no available data on SMD prevalence], q = 1 − p, d or absolute precision = 10%). As two-stage sampling method was used for sampling, above calculated sample size was multiplied by design effect of 2 and the sample size was obtained as 192. Considering 10% nonresponse rate 213 sample size was calculated. In the first stage of sampling, all the four batches of MBBS students were included in the study and in the second stage, from each batch 54 eligible students were selected by simple random sampling technique using random number table. Thus, the study was ultimately conducted among 216 MBBS students. After taking informed consent and maintaining the anonymity, data were collected using self-administered predesigned, pretested, semi-structured validated questionnaire along with validated 9-item SMD scale[5] and Beck's Depression Inventory Scale.[16] Weighing machine and stadiometer were used for computing body mass index (BMI) following standard techniques by the authors. Asian criteria of BMI[17] were used for categorizing students based on their BMI. Subjects who had used social media for at least 2 months in the past for nonacademic or nonjob-related purposes were considered as social media users.[3] A person who responded to 5 or more items as “yes” in the 9-item SMD scale was considered to be having SMD.[5] Data were entered into Microsoft Excel and analyzed using the Statistical Package for the Social Sciences version 16.0 (IBM Corp, Armonk, NY, USA). Bivariate analysis (Chi-square test or Fisher's exact test as per applicability) was performed to assess the association between SMD and different independent variables. Factor(s) which was/were found statistically significant in bivariate analysis, was/were considered for logistic regression to reveal the ultimate predictor variable(s).

Ethical clearance

Before conducting the study, permission was obtained from the Institutional Ethics Committee.


  Results Top


At the time of data collection 2nd, 4th, 6th and 8th semester's MBBS batch was existing in the institution. Among all the participating students, 60.6% were male, 83.3% were Hindu, 65.3% were unreserved, 78.2% were belonging to nuclear family, and 66.2% students were belonging to upper class socio-economic status based on Modified BG Prasad scale, March 2019.[18],[19] According to their responses toward Beck's Depression Inventory Scale, 29.2% were found to be depressed (Beck Depression Inventory [BDI] scale score-ranged between 17 and 63, mean ± standard deviation was 23.6 ± 3.8). Among them 78.9% were found as borderline clinical depression (17–20) and 21.1% were found to be having moderate depression (21–30). The socio-personal profile according to the presence or absence of SMD has been shown in [Table 1].
Table 1: Distribution of undergraduate medical students according to their sociopersonal profile and the presence of social media disorder (n=216)

Click here to view


The pattern of social media use among the study population is shown in [Table 2] and [Figure 1]. All of the students in the current study were found to be social media users. Most (89.2%) of the study subjects used android phones to avail social media applications. Others used desktop/laptops or tablets to use social media. Among them 99.1% of the students were using WhatsApp, 88.4% were using Facebook, 36.6% were Instagram users, 24.9% were using different video chat apps, and 5.1% were using different dating apps. Among the social media users, 99%, 76.9%, 71.4%, and 75% were found to be daily users of WhatsApp, Facebook, Dating apps and other social media sites, respectively. After analyzing the score of SMD scale, it was found that 11.6% of the subjects had SMD. Although the median values of total time spent on social media were same (i.e., 3 h) for both social media addicted students and social media nonaddicted students, however there was wide variation in duration of time spent on social media by half of the students with SMD. Similar observation was observed for Facebook app also. In case of WhatsApp, among the students with SMD, both median and interquartile range of time spent was found higher as compared to students without SMD. There was a weak positive correlation (r = 0.234) between the duration of social media use and severity of SMD score. Moreover, it was statistically significant too (P = 0.001).
Table 2: Distribution of undergraduate medical students based on the type of social media use and presence of social media disorder (n=216)

Click here to view
Figure 1: Box plot showing comparison of time spent daily (hours) on different social media sites among social media addicted and social media non addicted students

Click here to view


Majority of the students (82.6%) preferred social media use for entertainment. Maximum students (84.2%) were connected with friends through social media. About one-third students preferred social media communication over face-to-face communication.

[Table 3] represents the probable screen related health impacts and academic performances with respect to the presence or absence of SMD among the undergraduate medical students. Sleep disturbance, headache, eye problems, musculo-skeletal problems, obesity, and poor academic performance are known screen related disorders or impact for students. The prevalence of screen-related sleep disturbance, headache, eye problem, musculoskeletal problems, and overweight or obesity was 35.6%, 36.1%, 28.7%, 31.5%, and 50.9%, respectively. However, in this study, no significant relationships were found between the presence of SMD and above known screen-related health disorder or impact and academic performance as well. In bivariate analysis, only depression was found to be statistically significant associated with SMD. To assess the predicting ability bivariate logistic regression was performed. Dependent variable (SMD) was coded dichotomously 0 and 1 as SMD absent and present accordingly. The logistic regression model was significant, as evident from omnibus Chi-square test (χ2 = 18.547, P = <0.01). The independent variable, i.e., depression could explain between 8.2% and 16.1% variance of the dependent variable, i.e., SMD, as evident from Cox and Snell and Nagelkerke R2. Overall, the model predicts 88.4% of SMD correctly, as calculated in classification table of the logistic regression model. Through the logistic regression model, it was found that the students, who were depressed, were 6.7 times more prone to develop SMD than those who were not depressed as shown in [Table 4].
Table 3: Distribution of undergraduate medical students according to presence of social media disorder and screen related health impacts and academic performance (n=216)

Click here to view
Table 4: Bivariate logistic regression model: Modelling the association of depression with social media disorder

Click here to view


Qualitative analysis

The thematic analysis was used to identify the reasons and pattern of social media use among the study subjects. Questionnaire contained few open-ended questions. As it was anonymous, students expressed their views elaborately. Responses were transcribed as it was written in the questionnaire. All transcripts were read and re-read and constructed themes were coded. Newer data were compared to previous data constantly and label of themes were refined accordingly. Ultimately developed themes were analyzed.

Theme 1

Reasons of social media use

”As it is rural based medical college, have scarce scope of entertainment. Social media brings the world in hand and also find necessary entertainment stuffs here.”

”My thoughts and creativity are getting appraised in the social media platform and it brings a feeling of wellness.”

”As we are living far away from home, social media helps us to maintain long distance relationships. It is not only entertainment for spare time, but a necessary part of my life.”

Theme 2

Connection through social media

”For regular updating from friends, social media is very useful.”

”I use social media mostly for chatting with friends.”

”I can communicate with my family members through video apps. It reduces the distance between home and hostel.”

”It has been possible to maintain long distance relationship since 4 years only through Facebook.”

Theme 3

Preference of face to face communication versus social media communication

”I always face problem asking questions or interacting with people directly whereas, I prefer to approach through social media to easily interact with any people.”

”I always have communication problems with the girls. After entering the social media world, I can talk freely with them through social media.”

”There is lack of space for group chat within the college campus. Social media has solved the problem.”

Theme 4

Pattern of social media use

”I get information regarding the updates, recent news or group meeting from WhatsApp. That's why I have to observe WhatsApp very frequently.”

”Though everybody tells me introvert, but I am very much outspoken in Facebook platform. I use it regularly. I can't stop myself without checking my updates on Facebook every hour.”

”…I keep open WhatsApp and Facebook during my lecture class also. Those apps are more entertaining than boring lectures.”

”…After dinner I usually go to bed and sleep around 3 am……most of the time I do group chat on Facebook at late night.”

”…I have seven social media accounts.I maintain it simultaneously.everybody find me online all time on different social media sites.maximum time I spend in social media sites by observing silently.”

”. I prefer to meet with new peoples on social sites over known friends…”

”.my initiatives on poetry get inspiration from the liking and comments of friends at social media site.”


  Discussion Top


An observational analytical mixed method study was conducted among the medical students with an overall aim to find the prevalence of SMD among them and to determine its correlates. The prevalence of SMD was found to be 11.6%. All of the students were found to be social media users and among them the prevalence of screen-related sleep disturbance, headache, eye problem, musculoskeletal problems, and overweight or obesity were 35.6%, 36.1%, 28.7%, 31.5%, and 50.9%, respectively. However, no significant relationship was obtained between SMD and above health problems. Through logistic regression model, it was found that the students, who were depressed, were 6.7 times more prone to develop SMD.

In this study, it was found that all of the study participants were social media users. This was similar to the findings of Barman et al.[20] where also all of the study participants used social networking sites. The prevalence of SMD among the undergraduate medical students was found to be 11.6% in the current study. As there were no previous such studies related to SMD among the undergraduate medical students, the comparisons with other studies could not be done. However, in a study among the preuniversity nonmedical, college students at Bengaluru, the prevalence of SMD was found to be 36.9% among users. The differences could be because of the differences in the scale used for detecting SMD or could be because of huge study burden and subsequent less free time for social media among medicos as compared to nonmedicos, that needs further exploration. The problem is that despite of such vast curriculum and huge study burden, still approximately more than one tenth of the undergraduate medical students are already addicted to social media which may have long term health or academic impacts. In the current study, it was revealed that nearly all of the MBBS students were using WhatsApp and 88.4% of them were using Facebook. Almost all of the WhatsApp users were daily users while three fourth of the Facebook users were daily users. This was similar to the study findings of Shettigar et al.[21] conducted at Mysore Medical College and Research Institute, India, where 91.36% students were using WhatsApp. Similarly, Lahiry et al.[22] in their study found that 83% of the students were using Facebook. Masoud et al.[23] found that more than half of the users (55.6%) spent (1–4) h daily and one-fourth (26.2%) of students spent (5–8) h daily. In this study, it was found that majority of the students (82.6%) preferred social media use for entertainment, maximum students (84.2%) used social media to connect with friends and about one-third students preferred social media communication over face-to-face communication. This was consistent with the findings of Begum et al.[24] who found that the main purpose of using the social media among the undergraduate medical students was for chatting (51.3%) and to communicate with others (26.0%). Drago[25] also reported that 46% of respondents communicated with friends or family more frequently through technology than in person. The current study found that students who were depressed were 6.7 times more prone to develop SMD and this finding was consisted with the findings of Barman et al.[21] who also concluded that more use of social networking sites and dependence on it were associated with anxiety and depression. Pantic et al.[26] also found out in his study that online social networking is related to depression. Social media users may have long-term health impact. In this study, it was found that the prevalence of screen-related sleep disturbance, headache, eye problem, musculoskeletal problems, and overweight or obesity was 35.6%, 36.1%, 28.7%, 31.5%, and 50.9%, respectively, among the social media users. In this study, there was no significant relationships between SMD and above known screen-related health disorders and academic performance as well. Similarly, Ramesh Masthi et al.[3] found that the most common health problem identified due to social media use were strain on eyes (38.4%), anger (25.5%), and sleep disturbance (26.1%). In another study conducted by Jha et al.,[27] it was found that burning eyes (21%), disturbed sleep (19%), headache (16%), and neck and back pain (12.2% together) were the most common adverse health effects reported by the Facebook users. Khajeheian et al.[28] revealed that highest BMI in the high school was observed for children who mostly or always ate unhealthy food, use social media 2–3 h/day or more and have no physical activity or only once per week. Lahiry et al.[22] revealed that 60.87% of participants felt that their academic performance was influenced in a positive way, while the rest (39.13%) believed to the contrary. However, Daffalla and Dimetry[29] concluded that the prevalence of negative effect of using the social networks on academic performance is high. These differences could be explained by the fact that in the later study academic performance were assessed by subjective perceptions, however, in the current study, the marks obtained by the student in their last examination were considered as an indicator for academic performance or maybe the impacts might develop in future after long-term social media use which further emphasizes on the need to conduct longitudinal studies.

Implications of the study

Social media is being used extensively today by all age groups, but it should be used judiciously. Though SMD has not been considered as a separate entity under DSM V, yet the authors have found statistically significant association between SMD and depression. It is alarming for this generation. If this issue is not being highlighted immediately, increasing burden of psychological disorders will further increase the menace of SMD silently that may further affect mental health and worse scenarios might have to be faced in future.

Strength

This study provides the baseline data regarding the burden of SMD among the medical students as no such study have been conducted previously.

Limitation

SMD is itself a psychological disorder. It may have wide spectrum of impact on student's psychology and it may also manifest with other psychological disorders. Here, the authors explored only depression, other psychological issues such as anxiety and obsessive–compulsive disorder could not be explored and may be explored in future studies using better study designs.


  Conclusion Top


All of the respondents were social media user. Approximately one-tenth had SMD. One-third students were experiencing screen-related sleep disturbances, screen-related migraine/headache, and screen-related musculoskeletal disorders while one-fourth were suffering from screen related eye problems. About one-third students perceived depression. Students, who were depressed, were 6.7 times more prone to develop SMD than those who were not depressed.

Social media should be used judiciously. One should be aware of SMD, its consequences and should take necessary steps toward addressing it. Depression being a risk factor for SMD needs to be addressed as priority by providing appropriate counseling and/or professional consultation.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Digital in 2019: Global Overview: We Are Social; 2019. Available from: https://datareportal.com/reports/digital-2019-global-digital-overview. [Last accessed on 2019 Apr 27].  Back to cited text no. 1
    
2.
ICT Facts and Figures; 2017. Available from: http://www.itu.int/en/ITU-D/Statistics/Pages/facts/default.aspx. [Last accessed on 2019 Apr 27].  Back to cited text no. 2
    
3.
Ramesh Masthi NR, Pruthvi S, Phaneendra MS. A comparative study on social media usage and health status among Students Studying in Pre-University Colleges of Urban Bengaluru. Indian J Community Med 2018;43:180-4.  Back to cited text no. 3
    
4.
Kuss DJ, Griffiths MD. Social networking sites and addiction: Ten lessons learned. Int J Environ Res Public Health 2017;14:311.  Back to cited text no. 4
    
5.
Van den Eijnden RJ, Lemmens JS, Valkenburg PM. The social media disorder scale. Comput Hum Behav 2016;61:478-87.  Back to cited text no. 5
    
6.
O'Keeffe GS, Clarke-Pearson K; Council on Communications and Media. The impact of social media on children, adolescents, and families. Pediatrics 2011;127:800-4.  Back to cited text no. 6
    
7.
Al-Menayes JJ. The relationship between Mobile Social Media Use and Academic Performance in University Students. New Media Mass Commun 2014;25:23-9.  Back to cited text no. 7
    
8.
Whang LS, Lee S, Chang G. Internet over-users' psychological profiles: A behavior sampling analysis on internet addiction. Cyberpsychol Behav 2003;6:143-50.  Back to cited text no. 8
    
9.
Bhardwaj A, Avasthi V, Goundar S. Impact of Social Networking on Indian Youth-A Survey. Int J Electron Telecomm 2017;7:41-51.  Back to cited text no. 9
    
10.
Arora S, Okunbor D. Social Networking Addiction: Are the youth of India and United States Addicted? Vienna: Business and Management Conferences; 2015. p. 25-38.  Back to cited text no. 10
    
11.
Subathra V, Nimisha M, Hakeem MN. A Study on the Level of Social Network Addiction among College Students. Indian J Appl Res 2013;3:355-7.  Back to cited text no. 11
    
12.
Raj M, Bhattacherjee S, Mukherjee A. Usage of Online Social Networking Sites among School Students of Siliguri, West Bengal, India. Indian J Psychol Med 2018;40:452-7.  Back to cited text no. 12
[PUBMED]  [Full text]  
13.
Hossain A, Sarkar P. Prevalence of social media addiction among undergraduate students of West Bengal. IJRAR 2018;5:12-4.  Back to cited text no. 13
    
14.
John S, Kavitarati D. Prevalence and risk factors of Internet addiction in medical students. Int J Med Sci Public Health 2018;7:595-600.  Back to cited text no. 14
    
15.
Gedam SR, Ghosh S, Modi L, Goyal A, Mansharamani H. Study of internet addiction: Prevalence, pattern, and psychopathology among health professional undergraduates. Indian J Soc Psychiatry 2017;33:305-11.  Back to cited text no. 15
  [Full text]  
16.
Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An inventory for measuring depression. Arch Gen Psychiatry 1961;4:561-71.  Back to cited text no. 16
    
17.
Llido LO, Mirasol R. Comparison of body mass index based nutritional status using WHO criteria versus “Asian” criteria: Report from the Philippines. PhilSPEN Online. J Parenter Enteral Nutr 2011:1-8. Available from: http://www.philspenonlinejournal.com/POJ_0005.html#:~:text=Asian%3D31.3%25)%2C%20Overweight%20BMI,BMI%20(Asian%3D31.9%25).&text=Conclusion%3A%20The%20WHO%20based%20BMI,nutrition%20screening%20in%20the%20Philippines. [Last accessed on 2020 Oct 29].  Back to cited text no. 17
    
18.
Sharma R. Revision of Prasad's social classification and provision of an online tool for real-time updating. South Asian J Cancer 2013;2:157.  Back to cited text no. 18
    
19.
Sharma R. Online interactive calculator for real-time update of the Prasad's social classification. India. Available from: https://prasadscaleupdate.weebly.com/real.html. [Last accessed on 2019 Jun 25].  Back to cited text no. 19
    
20.
Barman L, Mukhopadhyay DK, Bandyopadhyay GK. Use of Social Networking Site and Mental Disorders among Medical Students in Kolkata, West Bengal. Indian J Psychiatry 2018;60:340-5.  Back to cited text no. 20
[PUBMED]  [Full text]  
21.
Shettigar MP, Karinagannanavar A. Pattern of WhatsApp usage and its impact on medical students of Mysore Medical College and research institute, India. Int J Community Med Public Health 2016;3:2527-31.  Back to cited text no. 21
    
22.
Lahiry S, Choudhury S, Chatterjee S, Hazra A. Impact of social media on academic performance and interpersonal relation: A cross-sectional study among students at a tertiary medical center in East India. J Educ Health Promot 2019;8:73.  Back to cited text no. 22
    
23.
Masoud M, Abdeltawab AK, Elmonem MA, Masoud AT, Mohammed OM. Prevalence and pattern of social media use and its effect on social health among Fayoum university students. Int J Community Med Public Health 2019;6:904-9.  Back to cited text no. 23
    
24.
Begum H, Asaduzzaman A, Talukder H, Nargis T, Alam K, Asadullah M. Use of Social Media by the Undergraduate Medical Students: Students' Perception in Selected Medical Colleges of Bangladesh. BJME 2018;9:11-5.  Back to cited text no. 24
    
25.
Drago E. The effect of technology on face-to-face communication. Elon J Undergrad Res Commun 2015;6:13-9.  Back to cited text no. 25
    
26.
Pantic I, Damjanovic A, Todorovic J, Topalovic D, Jovic DB, Ristic S, et al. Association between online social networking and depression in high school students: Behavioral physiology viewpoint. Psychiatria Danub 2012;24:90-3.  Back to cited text no. 26
    
27.
Jha RK, Shah DK, Basnet S, Paudel KR, Sah P, Sah AK, et al. Facebook use and its effects on the life of health science students in a private medical college of Nepal. BMC Res Notes 2016;9:378.  Back to cited text no. 27
    
28.
Khajeheian D, Colabi AM, Shah NB, Radzi CW, Jenatabadi HS. Effect of Social Media on Child Obesity: Application of structural equation modeling with the taguchi method. Int J Environ Res Public Health 2018;15:1343.  Back to cited text no. 28
    
29.
Daffalla AE, Dimetry DA. The Impact of Facebook and Others Social Networks Usage on Academic Performance and Social Life among Medical Students at Khartoum University. IJSTR 2014;3:41-6.  Back to cited text no. 29
    


    Figures

  [Figure 1]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Materials and Me...
Results
Discussion
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed670    
    Printed16    
    Emailed0    
    PDF Downloaded106    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]