|
 
 |
| ORIGINAL ARTICLE |
|
| Year : 2021 | Volume
: 5
| Issue : 1 | Page : 18-23 |
|
Pattern of social media use and social anxiety among the undergraduate health professionals with social media addiction
Bhavin Naranbhai Kadavala1, Deepak Sachidanand Tiwari1, Vishal Kanaiyalal Patel2, Nirav Bhupendrabhai Chanpa1, Niranjan L Patel1, Viral Shah3
1 Departments of Psychiatry, Shri M.P. Shah Government Medical College, Jamnagar, Gujarat, India
2 Department of Psychiatry, Dr. M. K. Shah Medical College and Research Center, Ahmedabad, Gujarat, India
3 PSM, Shri M.P. Shah Government Medical College, Jamnagar, Gujarat, India
| Date of Submission | 10-Aug-2020 |
| Date of Decision | 17-Oct-2020 |
| Date of Acceptance | 08-Nov-2020 |
| Date of Web Publication | 15-Apr-2021 |
Correspondence Address:
Dr. Bhavin Naranbhai Kadavala
B-102, Vision Tower, Zanzarda Road, Junagarh - 362 001, Gujarat
India
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/aip.aip_87_20
Background: There has been a significant surge in the usage of smartphones, Internet, social media platforms, and also the growing concern about the impact of Internet, gaming, and social media addiction (SMA) on mental health in the community since the last decade. Aim: This study is aimed to know the pattern of social media use and social anxiety (SA) among medical undergraduates with SMA. Materials and Methods: This was a 12-month cross-sectional, observational study of 640 medical undergraduates. Social media disorder scale was used to detect SMA and Liebowitz social anxiety scale was used to detect SA. Descriptive statistics, independent t-test, and Chi-square test were used for the analysis of data. Results: According to our findings, 11.04% of the participants had SMA. More than 60% of the participants with SMA spent 2 h or more time on social media in a day. About 29.41% of students with SMA spent more money compared to 11.86% of nonaddicted ones. About 47.05% of students with SMA reported fear of missing out (FoMO) as compared to 34.12% of nonaddicted students. Participants with SMA reported a significantly higher level (P < 0.05) of SA (SA score 67.63) compared to social media nonaddicts (SA score 49.43). Conclusion: Medical undergraduates with SMA spent more time and money on Internet, reported FoMO, frequently checked notifications, and reported regular substance use. They also had high SA; vice versa is also true.
Keywords: Fear of missing out, medical undergraduate, social anxiety, social media addiction
How to cite this article:
Kadavala BN, Tiwari DS, Patel VK, Chanpa NB, Patel NL, Shah V. Pattern of social media use and social anxiety among the undergraduate health professionals with social media addiction. Ann Indian Psychiatry 2021;5:18-23 |
How to cite this URL:
Kadavala BN, Tiwari DS, Patel VK, Chanpa NB, Patel NL, Shah V. Pattern of social media use and social anxiety among the undergraduate health professionals with social media addiction. Ann Indian Psychiatry [serial online] 2021 [cited 2023 Mar 30];5:18-23. Available from: https://www.anip.co.in/text.asp?2021/5/1/25/313755 |
s
| Introduction | |  |
Globally, there has been a significant surge in the usage of smartphones, Internet, social media platforms, and also the growing concern about the impact of Internet, gaming, and social media addiction (SMA) on mental health in the community since the last decade. SMA can be categorized mainly as “Substance” and “Behavioral” addiction.[1] Salience, tolerance, mood modification, conflict, withdrawal, problems, and relapse are the common core symptoms of substance and behavioral addictions.[2]
SMA is associated with reduced sleep duration and poor quality, excessive mental occupation, recurrent thoughts to control and limit the use, failure to prevent access requests, to spending more time with the Internet at any time, and desire to use social media when not online.[3] Studies have identified hours spent per day, frequency of visits to Internet, and money spent on Internet per month as key features of SMA. The use of social networking sites (SNSs) and online chats has become important for maintaining relationships and for having a belonging to their social group.[4],[5] Fear of missing out (FoMO) has been found to be associated with problematic social media use.[6]
The behavioral addiction of Internet and SNSs has not been recognized in the current psychiatric nosology of the Diagnostic and Statistical Manual of Mental Disorders, 5th edition or International Classification of Diseases-10, though it shares features of addiction similar to the gaming disorder.[7],[8] The prevalence of SNS addiction varies from 1.6% to 34% in different studies.[9],[10]
Social anxiety (SA) is characterized by marked fear or anxiety about one or more social situations in which the individual is exposed to possible scrutiny by others.[7] It is a major public health problem largely determined by its high prevalence and chronicity. It is associated with significant psychosocial impairment. It begins in early childhood or adolescence and is often comorbid with depression, other anxiety disorders, substance abuse, and eating disorders.[11],[12],[13],[14],[15]
Social media platforms may open a new virtual social world to people who may be inhibited in real-life social situations such as socially anxious individuals, enabling them to be less inhibited online.[16] Two different hypotheses have been proposed in order to explain why socially anxious individuals use online SNSs. The first hypothesis proposed is the “social compensation hypothesis” and the second is “social enhancement hypothesis.”[17],[18] There is mixed evidence regarding the two hypotheses in the existing literature.
The purpose of this study is to assess the social media usage pattern and its relationship with SA in SMA among the medical undergraduates.
| Materials and Methods | | |
Study design
This was a cross-sectional and observational study of the 640 medical undergraduates from the government colleges of medicine, dentistry, and physiotherapy. Participants were selected through random sampling from July 2018 to June 2019. Students were explained the study objectives and written informed consent was obtained. Participants who refused to participate were excluded from the study. Ethical approval for this study was obtained from the Institutional Ethics Committee, M. P. Shah Government Medical College, Jamnagar, with a ref. IEC/Certi/34/01/2019.
Sample size calculation
Sample size for this cross-sectional study was estimated using sample size calculator online (OpenEpi) with parameters of population size = 1700, expected frequency (prevalence) = 20%, and acceptable margin of error = 3% (15% relative error). The sample size came out to be 488 with confidence level of 95%. Considering the possibility of outliers and incomplete responses, the final sample was taken 640 out of 1700 students of the medicine, dentistry, and physiotherapy colleges. Random sampling was done and participants were selected proportionally from each college.
Tools
Semi-structured proforma
It included variables such as age, gender, weight, height, accommodation status, relationship status, socioeconomic status, regular sports or exercise, and substance use. It also contained items on social media usage patterns such as type of social media use, time spent on social media, place and hours of usage, money spent on Internet per month, checking social media notifications, and duration of social media use in years.
Social media disorder scale
It is a nine-item structured questionnaire covering the domains of addiction such as preoccupation, tolerance, withdrawal, persistence, displacement, problem, deception, escape, and conflict during the past year. The social media disorder scale (SMDS) is scored with a rating of yes/no. Our study showed adequate reliability with a Cronbach's alpha internal consistency reliability coefficient of 0.70.[19]
Liebowitz social anxiety scale
It has two subscales of social interaction and performance with 24 items measuring an individual on four dimensions of SA such as performance fear or anxiety, performance avoidance, social fear or anxiety, and social avoidance. Higher score on the Liebowitz SA scale (LSAS) means higher level of SA. Cronbach's α internal consistency reliability coefficient in our study was fairly acceptable with 0.87 for fear or anxiety and 0.84 for avoidance in our sample.[20]
Procedure
A brief lecture was taken about SMA and its impact on the college students with the permission from the dean and head of the department of the respective departments. After explaining the study objectives, students were requested to fill a semi-structured proforma which included demographic details and social media usage patterns. SMDS was used to diagnose SMA and LSAS was used to detect the level of SA.
Statistical analysis
All the collected data were tabulated in Microsoft Excel and analyzed using statistical software “Statistical Package for the Social Science Version 20.0.” IBM, Armonk, New York, United States. Frequencies and percentages were computed for the sociodemographic and social media usage variables. Mean and standard deviation were calculated for the continuous variables. Chi-square test was used for qualitative data and Student's t-test was used for quantitative data. Simple linear regression was applied to get the beta value. P < 0.05 was considered as statistically significant.
| Results | | |
Out of 640 medical undergraduates, 616 had completed their proforma and the remaining incomplete forms were not included for final analysis. There were 51.95% males and 48.05% females among the participants. The mean age of participants was 19.77 years and the standard deviation among age distribution was 2.189.
According to socioeconomic distribution, 69% of participants were from the upper socioeconomic class, 23% from the middle socioeconomic class, and 8% from the lower socioeconomic class. The distribution of sociodemographic variables and SMA is depicted in [Table 1]. | Table 1: Relationship between demographic factors and social media addiction
Click here to view |
Statistically significant association was not found between sociodemographic variables and SMA.
According to the SMDS, 11.04% (68) participants were found to be social media addicted and 88.96% (548) were nonaddicted.
Social media usage pattern
More than 80% of the participants used more than one type of social media platform. WhatsApp, Facebook, YouTube, and Instagram were commonly used platforms, whereas Twitter was less common among the participants. More than 60% of the participants with SMA spent 2 h or more time for social media in a day. Nearly three-fourth of the students with SMA were using SNS at home or hostel accommodation. Students were using social media commonly in leisure hours, but those with addiction also used more during the study and sleeping hours. About 29.41% of students with SMA spent more than 300 rupees compared to 11.86% of nonaddicted. About 47.05% of students with SMA reported FOMO compared to 34.12% of nonaddicted. About 14.70% of students with SMA reported regular substance use compared to 3.46% of nonaddicted. More than one-third of the students with SMA regularly checked notifications of SNS soon after waking up, whereas more than three-fourth of the students with SMA regularly checked notifications of SNS before sleep. The distribution of pattern of social media use and SMA is depicted in [Table 2]. Time spent on social media, time of the day for its use, money spent on Internet, FOMO, regularly checking notification of SNS, and substance use emerged as significant factors in students with SMA [Table 2] and [Table 3]. | Table 2: Relationship between social media usage patterns - characteristics and social media addiction
Click here to view |
 | Table 3: Notification checkup for social media and social media addiction
Click here to view |
The mean score of LSAS was 67.63 among the students with SMA and the score was 49.43 in students with no addiction. SA emerged as a significant factor in participants with SMA [Table 4].
| Discussion | | |
The present study examined social media usage pattern, SA, and their relation to SMA among the health professional undergraduates.
In this study, the prevalence of SMA was 11.03%. This finding is consistent with the findings of Wu et al. (2013)[21] (a prevalence of 12% among 277 young Chinese smartphone users), Hormes et al. (2014)[22] (a prevalence of 9.7% disordered social media use among 253 undergraduate students of university in the North-east states of USA), and Wolniczak et al. (2013)[23] (a prevalence of 8.6% Facebook dependence among 428 undergraduate students of the University of Peru Lima, Peru). Folaranmi et al (2013) reported 1.6% of Facebook addiction among 1000 undergraduates of Nigerian universities.[9] Some studies have found higher prevalence rate of SNS addiction in students.[10],[24],[25] Variation in prevalence may be due to difference in methodology, Internet connectivity, geographical region, etc.
There is no difference in SMA among different genders in our study. Most of the studies did not show any gender difference for SMA in the literature review, whereas NR Ramesh Masthi et al. found a higher rate of SMA in male students as compared to female students of Bengaluru, India.
In the present study, 83% of college students were using more than one type of social media platform. In multiple responses, WhatsApp (88.15%) was most commonly used, followed by YouTube (75.79%), Instagram (65.26%), Facebook (38.48%), and Twitter (7.63%). This result is consistent with findings of Lenhart et al (2015) among the students in the USA.[26] It is also in line with the Indian study in which they found that WhatsApp was used by 82% of the students, followed by Facebook (75.1%) and Instagram (33.7%).[24]
Students with addiction significantly spent more time on social media and regularly checked the notifications before sleep and immediately on waking up as compared to their nonaddiction counterparts. This finding corroborates with some studies published in India and across the globe.[27],[28] FoMO was significantly reported by students with SMA. This finding corroborates with Fuster et al. among social media users in Latin America.[29] These findings may suggest that tolerance and withdrawal phenomena develop over a period of time in SMA.
Participants with SMA significantly reported regular substance use compared to their nonaddicted counterparts. This finding is consistent with that of Hormes et al. (2014) among undergraduate university students, in which they found that participants with disordered social media use were associated with hazardous alcohol drinking and high level of craving for alcohol.[22] This may suggest that there is a similar biological correlation between behavior addiction and substance use disorder. Furthermore, students may get positive social validation for substance use-related posts (conveyed through comments, “likes,” and shares), which are likely to increase their addictive behavior. Students with SMA reported significantly high level of SA compared to those without addiction. This finding corroborates with some studies, which found a significant relationship between the problematic/compulsive social media use and SA among the students.[30],[31],[33]
| Conclusion | | |
Students are relying more on social media to communicate with one another. SMA is prevalent among the college students. Students with SMA spent more time and money on social media per day; moreover, they are using social media during the study and sleeping hours. A high number of participants reported FoMO which was directly proportional to their SMA. Substance use is significantly associated with SMA. Students with SMA have high level of SA and vice versa.
Strength of the study
The detection of SMA was done using validated scale; to find the association between SMA and SA in participants, and using a sampling technique.
Limitations of the study
As this is a cross-sectional study, we cannot conclude that there is a causality between SMA and SA. The study was done in a single center among homogenous sample of undergraduates, so it is difficult to generalize our findings.
Future recommendations
The study should be planned for large population at multiple centers.
Declaration of patient consent
The patient consent statement was taken from each patient as per institutional ethics committee approval along with consent taken for participation in the study and publication of the scientific results without revealing their identity, name, or initials. The patients are aware that though confidentiality would be maintained, anonymity cannot be guaranteed.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Alavi SS, Ferdosi M, Jannatifard F, Eslami M, Alaghemandan H, Setare M. Behavioral addiction versus substance addiction: Correspondence of psychiatric and psychological views. Int J Prev Med 2012;3:290-4.  |
| 2. | Griffiths MD. A 'components' model of addiction within a bio-psychosocial framework. J Subst Use 2005;10:191-7. |
| 3. | Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychol Addict Behav 2016;30:252-62. |
| 4. | Whitney PS, Kirsten TL. Self-disclosure and student satisfaction with Facebook. Comp Human Behav 2012;28:624-30. |
| 5. | Quinn S, Julian A. Oldmeadow, is the igeneration a 'we' generation? Social networking use among 9- to 13-year-olds and belonging. Br J Develop Psychol 2013;31:136-42. |
| 6. | Casale S, Rugai L, Fioravanti G. Exploring the role of positive metacognitions in explaining the association between the fear of missing out and social media addiction. Addict Behav 2018;85:83-7. |
| 7. | American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders. 5 th ed. Arlington: American Psychiatric Publishing; 2013. |
| 8. | World Health Organization. ICD-10: International Statistical Classification of Diseases and Related Health Problems: Tenth Revision. 2 nd ed. Geneva: World Health Organization; 2004. |
| 9. | Folaranmi AO. A survey of facebook addiction level among selected Nigerian university undergraduates. New Med Mass Commun 2013;28:70-80. |
| 10. | Wan C. Gratifications and Loneliness as Predictors of Campus-SNS Websites Addiction and Usage Pattern among Chinese College Students. Hong Kong: Chinese University of Hong Kong, China; 2009. |
| 11. | Brunello N, den Boer JA, Judd LL, Kasper S, Kelsey JE, Lader M, et al. Social phobia: Diagnosis and epidemiology, neurobiology and pharmacology, comorbidity and treatment. J Affect Disord 2000;60:61-74. |
| 12. | Stein MB, Stein DJ. Social anxiety disorder. Lancet 2008;371:1115-25. |
| 13. | Ruscio AM, Brown TA, Chiu WT, Sareen J, Stein MB, Kessler RC. Social fears and social phobia in the USA: Results from the national comorbidity survey replication. Psychol Med 2008;38:15-28. |
| 14. | Stein MB, Walker JR, Forde DR. Setting diagnostic thresholds for social phobia: Considerations from a community survey of social anxiety. Am J Psychiatry 1994;151:408-12. |
| 15. | Wacker HR, Mullejans R, Klein KH, Battegay R. Identification of cases of anxiety disorders and affective disorders in the community according to ICD-10 and DSM-III-R using the Composite International Diagnostic Interview (CIDI). Int J Methods Psychiatr Res 1992;2:91-100. |
| 16. | Schouten AP, Valkenburg PM, Jochen P. Precursors and underlying processes of adolescents' online self-disclosure: Developing and testing an “internet-attribute-perception” model. Med Psychol 2007;10:2, 292-315. |
| 17. | Desjarlais M, Willoughby T. A longitudinal study of the relation between adolescent boys' and girls' computer use with friends and friendship quality: Support for the social compensation or the rich-get-richer hypothesis? Comput Human Behav 2010;26:896-5. |
| 18. | Chen GM. Revisiting the social enhancement hypothesis: Extroversion indirectly predicts number of Facebook friends operating through Facebook usage. Comput Human Behav 2014;39:263-9. https://doi.org/10.1016/j.chb.2014.07.015. |
| 19. | Regina JM, Eijnden V, Lemmens JS, Valkenburg PM. The social media disorder scale. Comput Human Behav 2016;61:478-87. |
| 20. | Michael R, Liebowitz MR. Social phobia. Mod Probl Pharmacopsychiatry 1987;22:141-73. |
| 21. | Wu AM, Cheung VI, Ku L, Hung EP. Psychological risk factors of addiction to social networking sites among Chinese smartphone users. J Behav Addict 2013;2:160-6. |
| 22. | Hormes J, Kearns B, Timco C. Craving Facebook? Behavioural addiction to online social networking and its association with emotion regulation deficits. Society for the Study of Addiction. Addiction 2014;109:2079-88. |
| 23. | Wolniczak I, Caceres-DelAguila JA, Palma-Ardiles G, Arroyo KJ, Solis-Visscher R, Paredes-Yauri S, et al. Association between facebook dependence and poor sleep quality: A study in a sample of undergraduate students in Peru. PLoS One 2013;8:590-7. |
| 24. | 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. [ PUBMED] [Full text] |
| 25. | Tang CS, Koh YY. Online social networking addiction among college students in Singapore: Comorbidity with behavioral addiction and affective disorder. Asian J Psychiatr 2017;25:175-8. |
| 26. | |
| 27. | 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. |
| 28. | Bodroža B, Jovanović T. Validation of the new scale for measuring behaviors of Facebook users: Psycho-social aspects of Facebook use (PSAFU). Comput Human Behav 2016;54:425-35. |
| 29. | Fuster H, Chamarro A, Oberst U. Fear of Missing Out, online social networking and mobile phone addiction: A latent profile approach. Aloma 2017;35:23-30. |
| 30. | Shaw AM, Timpano KR, Tran TB, Joormann J. Correlates of Facebook usage patterns: The relationship between passive Facebook use, social anxiety symptoms, and brooding. Comput Human Behav 2015;48:575 80. |
| 31. | Aladwani AM, Almarzouq M. Understanding compulsive social media use: The premise of complementing self-conceptions mismatch with technology. Comput Human Behav 2016;60:575-81. |
| 32. | Lee-Won RJ, Herzog L, Park SG. Hooked on Facebook: The role of social anxiety and need for social assurance in problematic use of Facebook. Cyberpsychol Behav Soc Netw 2015;18:567-74. |
| 33. | Weinstein A, Dorani D, Elhadif R, Bukovza Y, Yarmulnik A. Internet addiction is associated with social anxiety in young adults. Ann Clin Psychiatry 2015;27:4-9. |
[Table 1], [Table 2], [Table 3], [Table 4]
|
Search Pubmed for