|Year : 2021 | Volume
| Issue : 1 | Page : 30-35
A study of correlates of social networking site addiction among the undergraduate health professionals
Vishal Kanaiyalal Patel1, Pradhyuman Chaudhary2, Parveen Kumar3, Disha Alkeshbhai Vasavada3, Deepak Sachidanand Tiwari3
1 Department of Psychiatry, Dr. M. K. Shah Medical College and Research Center, Ahmedabad, Gujarat, India
2 Department of Psychiatry, GMERS Medical College Sola, Ahmedabad, Gujarat, India
3 Department of Psychiatry, M. P. Shah Medical College, Jamnagar, Gujarat, India
|Date of Submission||14-Nov-2020|
|Date of Decision||02-Jan-2021|
|Date of Acceptance||13-Jan-2021|
|Date of Web Publication||9-Feb-2021|
2nd Floor Trauma Building, Department of Psychiatry, M.P. Shah Medical College & G. G. Hospital, Jamnagar 361 008, Gujarat
Source of Support: None, Conflict of Interest: None
Introduction: Social networking sites (SNSs) are popular, and there is a concern regarding its addiction among the young adults. The present study aimed to find the correlates of SNS addiction among the undergraduate health professionals. Methods: This was a 6-month, cross-sectional, and observational study of 730 undergraduate health professionals of government medical, dental, and physiotherapy colleges of Jamnagar, Gujarat, India. Participants were selected using stratified random sampling from the medical, dental, and physiotherapy government colleges. The Social Media Disorder Scale was used to detect the SNS addiction, the Fear of Missing Out (FOMO) Scale was used to find the severity of FOMO, the Perceived Stress Scale was used to detect the severity of stress, and the Insomnia Severity Index was used to detect the severity of insomnia in health professionals. Descriptive statistics, Chi-square test, and multiple regression analysis were used for analysis of data. Results: The prevalence rate of SNS addiction was 15.02% among the undergraduate health professionals. Participants with addiction were using SNS widely (hostel, home, college, and leisure hours), spent more time and money on Internet, started SNS use before 5 years, and reported FOMO. They also reported moderate-to-severe stress and insomnia. Conclusion: SNS addiction is prevalent in undergraduate health professionals. High level of FOMO, perceived stress, and insomnia among the health professionals are important correlates with SNS addiction.
Keywords: Addiction, correlates, social media, undergraduates
|How to cite this article:|
Patel VK, Chaudhary P, Kumar P, Vasavada DA, Tiwari DS. A study of correlates of social networking site addiction among the undergraduate health professionals. Asian J Soc Health Behav 2021;4:30-5
|How to cite this URL:|
Patel VK, Chaudhary P, Kumar P, Vasavada DA, Tiwari DS. A study of correlates of social networking site addiction among the undergraduate health professionals. Asian J Soc Health Behav [serial online] 2021 [cited 2023 Mar 22];4:30-5. Available from: http://www.healthandbehavior.com/text.asp?2021/4/1/30/308810
| Introduction|| |
Social networking sites (SNSs) are virtual communities where users can create individual public profiles, interact with real-life friends, and meet other people based on shared interests. SNSs are widely used for the interactions in young adults. In spite of some positive aspects associated with SNS use, its excessive use has raised a concern regarding the potential addiction of social media among its users. SNS addiction has received media attention with warning that “social networking is engineered to be as habit-forming as crack cocaine,” and “Twitter is harder to resist than cigarettes and alcohol.”,
Griffiths argues that any behavior (e.g., social networking) that fulfills the six criteria can be operationally defined as an addiction. In relation to SNS, these components are salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse. Andreassen defined SNS addiction as “being overly concerned about social media, driven by an uncontrollable motivation to log on to or use social media, and devoting so much time and effort to social media that it impairs other important life areas.” The latest revision of International Classification of Diseases-11 has included “gaming disorder” as new behavioral addiction.
Prevalence rates of SNS addiction in India vary from 1.6% to 70% found in different studies., Many prior studies have explored the relationship of social media usage and mental health problems (e.g., stress, anxiety, depression, and insomnia) among the social media users.,,
Although several studies have explored the factors that contributed to social media addiction in young adults, paucity of research was found in the literature regarding SNS addiction in young health professionals., This study seeks to bridge this gap in research by examining how multiple factors influence social media addiction.
| Methods|| |
Design and setting
This was cross-sectional and observational study of the 730 health professional undergraduates from the government colleges of medical, dental, and physiotherapy. Participants were selected proportionally through each college using stratified random sampling from August 2019 to January 2020. Students were explained the study objectives, and written informed consent was taken. Participants who refuse to participate were excluded from the study.
It includes age, gender, weight, height, accommodation status, socioeconomic status, regular daily sports or exercise, and substance use. It also contains 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
The Social Media Disorder Scale (SMDS) 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. SMDS is scored with a rating of Yes/No; someone is diagnosed with having SMD if he or she meets five (or more) of the nine criteria for Internet gaming disorder during a period of 12 months. This scale showed adequate reliability with a Cronbach's alpha internal consistency reliability coefficient of 0.70 for current sample.
Fear of Missing Out Scale
The Fear of Missing Out (FOMO) Scale is a collection of 10 statements about your everyday experience. Using the scale provided please indicate how true each statement is of your general experiences. Please answer according to what really reflects your experiences rather than what you think your experiences should be. Please treat each item separately from every other item. Each question is paired with a five-point Likert scale: 1 = not at all true of me, 2 = slightly true of me, 3 = moderately true of me, 4 = very true of me, and 5 = extremely true of me. The FOMO Scale has a reliable composite measure (α = 0.87–0.90).
Perceived Stress Scale
The Perceived Stress Scale (PSS) is a 10-item scale individual item based on Likert scale ranging from 0 to 4. Participants with scores ranging from 0 to 13 would be low stress, 14 to 26 would be moderate stress, and 27 to 40 would be high perceived stress. PSS exhibited satisfactory psychometric property.
Prior permission has been obtained regarding lecture of “problematic SNS use in health professionals” from the dean/principal of the concerned colleges. We have informed the students of particular batch about time of lecture to ensure the full attendance and then approached them later as per prior communication. We delivered the lecture on problematic SNS use and explained the study objectives and information to be filled in the pro forma to the students. As per study design, students of particular batch were randomly selected using random number table and they were requested to fill the pro forma. Students were given 20 min to complete the pro forma and at the end all the papers were collected from the students.
Sample size calculation
Sample size required for the current study was calculated using Epi-Info software, Centers for Disease Control and Prevention (CDC), Piedmont, North Carolina, United State. Sample size for the current study was estimated at 683; criteria being prevalence of disorder as 20%, 3% absolute precision, and 95% confidence interval.
Ethical approval for the present study was taken from the Institutional Ethics Committee of M P Shah Government Medical College and Guru Gobindsingh Hospital, Jamnagar (Ref. No. IEC/Certi/96/03/2019).
All the collected data were tabulated in Microsoft Excel and analyzed using statistical software “Statistical Package for Social Sciences version 20. 0.” International business machine, Armonk, New York, United States. Frequencies and percentages were computed for the sociodemographic and social media usage variables. Chi-square test was used for qualitative data. Multiple regression analysis was applied to get beta value. P < 0.05 was considered as statistically significant.
| Results|| |
Out of 730 participants, 712 were included for the final analysis and the rest of 18 participants were excluded because they did not complete the study pro forma. The mean age of participants was 21.40 ± 2.15 years.
In the study population, 62.35% were female and 37.64% were male participants. 20.93% belonged to lower/lower middle class, 30.20% belonged to middle class, and 48.88% belonged to upper middle/upper class. 34% came from the rural and 64% came from the urban domicile.
The distribution of gender, domicile, socioeconomic status, daily sports/exercise, body mass index (BMI), father's education, and mother's education with the SNS addiction is depicted in [Table 1]. There was a statistically significant association found between SNS addiction and socioeconomic status, daily sports/exercise, and BMI [Table 1].
Overall, 15.02% of the health professional undergraduates reported SNS addiction. Nearly 90% of the participants were using more than one type of SNS, and among them, WhatsApp (85%), Facebook (82%), YouTube (74%), and Instagram (60%) were most commonly used social media platforms. However, the less commonly used social media platforms were Twitter (30%) and Snapchat (23%).
Out of 107 participants with SNS addiction, 83.18% were using SNS for more than 2 h in a day, 51.4% were using SNS at hostel or home, 54.21% were using SNS during leisure hours, 68.22% were using SNS since more than 5 years, and 50.47% spent more than 300 rupees per month on Internet. Distribution of SNS usage pattern and SNS addiction was statistically significant [Table 2].
|Table 2: Social networking site addiction and social networking site usage pattern among the health professionals|
Click here to view
Out of 107 participants with SNS addiction, 40.19% reported moderate stress and 41.12% reported severe stress, 13.08% reported moderate insomnia, and 72.90% reported severe insomnia. Distribution of perceived stress, insomnia severity, and SNS addiction was statistically significant [Table 3].
|Table 3: Perceived stress and insomnia severity in health professionals with social networking site addiction|
Click here to view
Independent variables showing statistically significant association using Chi-square test were selected for further analysis using multiple regression analysis. Perceived stress, insomnia severity, and FOMO emerged as statistically significant association with the SNS addiction [F = 426.96, P < 0.005, R2 = 0.643, Table 4].
|Table 4: Multiple linear regression analysis of social networking site addiction relation with different correlates|
Click here to view
| Discussion|| |
In the present study, the prevalence rate of SNS addiction was 15.02% among the undergraduate health professionals. The study results are consistent with finding of Wu et al., Hormes et al., and Wolniczak et al. However, Alabi (2013) from Nigeria reported 1.6% Facebook addiction among undergraduates. These differences could be due to the use of different methods and Internet connectivity in different geographical areas.
In the present study, no gender difference was observed among social media addiction prevalence. Most of the studies did not show any gender difference for social media addiction, while Masthi et al., Müller et al., and Goel et al. found that male gender was significantly more likely to be social media addicted as compared to female gender.
In this study, participants with SNS addiction significantly spent more money and time, using SNS during leisure time and other time at hostel or home or college hours, and started SNS use early in adolescents age compared to their counter parts. Masthi et al. and Bodroža and Jovanović also observed similar results. Moon et al. reported the possible reason for this as to pursue feeling of achievement, make social contacts, and to enhance self-esteem among male participants.
In the present study, FOMO was found to be predictor of SNS addiction on multiple regression analysis. Results of the current study have been consistent with finding of Abeele and Rooij, who observed among 3000 students that the use of problematic SNSs was affected by the FOMO. Oberst et al. observed higher involvement of SNS use and feeling depressed and anxiety trigger among 5280 students. Franchina et al. also reported that the FOMO acts as a stronger predictor of the use of social media platforms such as Facebook and Snapchat. Even FOMO can develop a positive effect on attitude toward the SNS use. If an individual's need is not satisfied, they may develop FOMO, which can lead to excessive use of SNS.
In the present study, participants with SNS addiction significantly perceived moderate-to-severe stress compared to their counterpart. A study among the German students found that the perceived stress was closely related to Internet-relevant problematic behaviors, such as Facebook addiction disorder., Hou et al. also found that perceived stress was related to problematic SNS use. Chen et al. observed the mediating effect of perceived stress and interpreted that people may choose to spend a lot of time online to cope with their stress and problems. Even though there was an association between perceived stress and problematic SNS use, the exact underlying mechanism is yet not well known, resulting in hindrance of understanding the relationship between perceived stress and problematic SNS use. However, previous studies also reported that depression/anxiety had a mediating effect for development of problematic SNS use.
The present study showed that insomnia was emerged as a predictor of SNS. This finding is consistent with Levenson et al. who found that younger adults with higher social media had considerably greater probabilities of having sleep disorders. Garett et al. observed that social media use was associated with poor sleep quality among college students. Furthermore, Mohammadbeigi et al. (2016) also observed high usage of Internet, and social networks via smart cell phones are related to poor sleep quality and quantity.
The present study was limited by cross-sectional design which prevents the ability to make causal inferences. This study sampled undergraduate professionals at one particular university, so results may not generalize to the wider population. The study contains self-report data that might threaten the accuracy of the statistical relationships between variables.
| Conclusion|| |
SNS addiction is prevalent problem among the undergraduate health professionals. Social media usage patterns such as using SNS during most of the time including college hours, spent more time and money, and started SNS use early are some important variables related to SNS addiction. High level of FOMO, perceived stress, and insomnia among the health professionals are important correlates with SNS addiction. Educational workshop about problematic social media use should be included as a part of foundation course for the undergraduate health professionals.
Conflicts of interest
There are no conflicts of interest.
| References|| |
Boyd DM, Ellison NB. Social network sites: Definition, history, and scholarship. J Comput Mediat Commun 2007;13:210-30.
Griffiths MD, Kuss DJ, Demetrovics Z. Social networking addiction: An overview of preliminary findings. In: Behavioral Addictions: Criteria, Evidence, and Treatment. New York: Elsevier Inc.; 2014. p. 119-41.
Andreassen CS, Pallesen S. Social network site addiction-An overview. Curr Pharm Des 2014;20:4053-61.
World Health Organization, International Classification of Disease (ICD). Disorder Due to Substance Use or Addictive Behaviors: Gaming Disorder. ICD-11th
Revision. Ch. 6. Geneva: World Health Organization; 2018. p. 166-8.
Masthi NR, Pruthvi S, Mallekava P. A comparative study on social media addiction between public and private high school students of Urban Bengaluru, India. Asian J Psychiatry 2017;18:10-20.
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.
] [Full text]
Zhou SX, Leung L. Gratification, Loneliness, Leisure Boredom, and Self-Esteem as Predictors of SNS-Game Addiction and Usage Pattern Among Chinese College Students. Int J Cyber Behav Psychol Learn. 2012;2:34-48.
Shensa A, Sidani JE, Dew MA, Escobar-Viera CG, Primack BA. Social media use and depression and anxiety symptoms: A cluster analysis. Am J Health Behav 2018;42:116-28.
Waqas A, Khurshid Z, Ali M, Khaliq H. Association between usage of social media and depression among young adults. J Manag Info 2018;5:26-30.
Hou XL, Wang HZ, Guo C, Gaskin J, Rost DH, Wang JL. Psychological resilience can help combat the effect of stress on problematic social networking site usage. Pers Individ Dif 2017;109:61-6.
Alonzo RT, Hussain J, Anderson K, Stranges S. Interplay between social media use, sleep quality and mental health outcomes in youth: A systematic review. Sleep Med 2019;64:S365.
Regina JM, Eijnden V, Lemmens JS, Valkenburg PM. The social media disorder scale. Comput Human Behav 2016;61:478-87.
Przybylski AK, Murayama K, DeHaan CR, Gladwell V. Motivational, emotional, and behavioral correlates of fear of missing out. Comput Human Behav 2013:29;1814-48.
Cohen S, Kamarck T, Mermelstein R. A global measure of perceived stress. J Health Soc Behav 1983;24:385-96.
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.
Hormes JM, Kearns B, Timko CA. Craving facebook? Behavioral addiction to online social networking and its association with emotion regulation deficits. Addiction 2014;109:2079-88.
Wolniczak I, Cáceres-DelAguila JA, Palma-Ardiles G, Arroyo KJ, Solís-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:e59087.
Alabi OF. A survey of Facebook addiction level among selected Nigerian University Undergraduates. New Media Mass Commun 2013;10:70-80.
Müller KW, Dreier M, Beutel ME, Duven E, Giralt S, Wölfling K. A hidden type of internet addiction? Intense and addictive use of social networking sites in adolescents. Comput Hum Behav 2016;55:172-7.
Goel D, Subramanyam A, Kamath R. A study on the prevalence of internet addiction and its association with psychopathology in Indian adolescents. Indian J Psychiatr 2013;55:140-3.
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.
Moon DG, Hecht ML, Jackson KM, Spellers RE. Ethnic and gender differences and similarities in adolescent drug use and refusals of drug offers. Subst Use Misuse 1999;34:1059-83.
Abeele MM, Rooij AJ. OR-02: Fear of missing out (FOMO) as a predictor of problematic social media use among teenagers. J Behav Addict 2016;5:4.
Oberst U, Wegmann E, Stodt B, Brand M, Chamarro A. Negative consequences from heavy social networking in adolescents: The mediating role of fear of missing out. J Adolesc 2017;55:51-60.
Franchina V, Vanden Abeele M, van Rooij AJ, Lo Coco G, De Marez L. Fear of missing out as a predictor of problematic social media use and phubbing behavior among flemish adolescents. Int J Environ Res Public Health 2018;15:2319.
Moore K, Craciun G. Fear of missing out and personality as predictors of social networking sites usage: The instagram case. Psychol Rep 2020;0:1-27. DOI: 10.1177/0033294120936184
Brailovskaia J, Margraf J. Facebook addiction disorder (FAD) among German students-A longitudinal approach. PLoS One 2017;12:e0189719.
Chen Z, Poon KT, Cheng C. Deficits in recognizing disgust facial expressions and Internet addiction: Perceived stress as a mediator. Psychiatry Res 2017;254:211-7.
Brailovskaia J, Teismann T, Margraf J. Physical activity mediates the association between daily stress and Facebook Addiction Disorder (FAD)-A longitudinal approach among German students. Comput Human Behav 2018;86:199-204.
Hou XL, Wang HZ, Hu TQ, Gentile DA, Gaskin J, Wang JL. The relationship between perceived stress and problematic social networking site use among Chinese college students. J Behav Addict 2019;8:306-17.
Levenson JC, Shensa A, Sidani JE, Colditz JB, Primack BA. The association between social media use and sleep disturbance among young adults. Prev Med 2016;85:36-41.
Garett R, Liu S, Young SD. The relationship between social media use and sleep quality among undergraduate students. Inf Commun Soc 2018;21:163-73.
Mohammadbeigi A, Absari R, Valizadeh F, Saadati M, Sharifimoghadam S, Ahmadi A, et al
. Sleep quality in medical students; The impact of over-use of mobile cell-phone and social networks. J Res Health Sci 2016;16:46-50.
[Table 1], [Table 2], [Table 3], [Table 4]
|This article has been cited by|
||The association between smartphone use and sleep quality, psychological distress, and loneliness among health care students and workers in Saudi Arabia
| ||Abdullah Muhammad Alzhrani, Khalid Talal Aboalshamat, Amal Mohammmad Badawoud, Ismail Mahmoud Abdouh, Hatim Matooq Badri, Baraa Sami Quronfulah, Mahmoud Abdulrahman Mahmoud, Mona Talal Rajeh, Yaser Mohammed Al-Worafi |
| ||PLOS ONE. 2023; 18(1): e0280681 |
|[Pubmed] | [DOI]|
||Reciprocal Relationships Between Problematic Social Media Use, Problematic Gaming, and Psychological Distress Among University Students: A 9-Month Longitudinal Study
| ||Ching-Wen Chang, Ru-Yi Huang, Carol Strong, Yi-Ching Lin, Meng-Che Tsai, I-Hua Chen, Chung-Ying Lin, Amir H. Pakpour, Mark D. Griffiths |
| ||Frontiers in Public Health. 2022; 10 |
|[Pubmed] | [DOI]|
||Problematic Use of the Internet Mediates the Association between Reduced Mentalization and Suicidal Ideation: A Cross-Sectional Study in Young Adults
| ||Francesco Saverio Bersani, Tommaso Accinni, Giuseppe Alessio Carbone, Ornella Corazza, Angelo Panno, Elisabeth Prevete, Laura Bernabei, Chiara Massullo, Julius Burkauskas, Lorenzo Tarsitani, Massimo Pasquini, Massimo Biondi, Benedetto Farina, Claudio Imperatori |
| ||Healthcare. 2022; 10(5): 948 |
|[Pubmed] | [DOI]|
||The Mediating Roles of Anxiety, Depression, Sleepiness, Insomnia, and Sleep Quality in the Association between Problematic Social Media Use and Quality of Life among Patients with Cancer
| ||Vida Imani, Daniel Kwasi Ahorsu, Nasrin Taghizadeh, Zahra Parsapour, Babak Nejati, Hsin-Pao Chen, Amir H. Pakpour |
| ||Healthcare. 2022; 10(9): 1745 |
|[Pubmed] | [DOI]|
||A network analysis of the Internet Disorder Scale–Short Form (IDS9-SF): A large-scale cross-cultural study in Iran, Pakistan, and Bangladesh
| ||Li Li, Mohammed A. Mamun, Firoj Al-Mamun, Irfan Ullah, Ismail Hosen, Syed Ahsan Zia, Ali Poorebrahim, Morteza Pourgholami, Chung-Ying Lin, Halley M. Pontes, Mark D. Griffiths, Amir H. Pakpour |
| ||Current Psychology. 2022; |
|[Pubmed] | [DOI]|
||Estimation of Behavioral Addiction Prevalence During COVID-19 Pandemic: A Systematic Review and Meta-analysis
| ||Zainab Alimoradi, Aida Lotfi, Chung-Ying Lin, Mark D. Griffiths, Amir H. Pakpour |
| ||Current Addiction Reports. 2022; |
|[Pubmed] | [DOI]|
||Offline and Online Social Support and Short-Form Video Addiction Among Chinese Adolescents: The Mediating Role of Emotion Suppression and Relatedness Needs
| ||Jiangfeng Yang, Yonghe Ti, Yinghua Ye |
| ||Cyberpsychology, Behavior, and Social Networking. 2022; |
|[Pubmed] | [DOI]|
||Associations of Problematic Internet Use, Weight-Related Self-Stigma, and Nomophobia with Physical Activity: Findings from Mainland China, Taiwan, and Malaysia
| ||Wei Liu, Jung-Sheng Chen, Wan Ying Gan, Wai Chuen Poon, Serene En Hui Tung, Ling Jun Lee, Ping Xu, I-Hua Chen, Mark D. Griffiths, Chung-Ying Lin |
| ||International Journal of Environmental Research and Public Health. 2022; 19(19): 12135 |
|[Pubmed] | [DOI]|
||Well-being of Polish university students after the first year of the coronavirus pandemic: The role of core self-evaluations, social support and fear of COVID-19
| ||Elzbieta Turska, Natalia Stepien-Lampa, Amir H. Pakpour |
| ||PLOS ONE. 2021; 16(11): e0259296 |
|[Pubmed] | [DOI]|
||Validating Insomnia Severity Index (ISI) in a Bangladeshi Population: Using Classical Test Theory and Rasch Analysis
| ||Mohammed A. Mamun, Zainab Alimoradi, David Gozal, Md Dilshad Manzar, Anders Broström, Chung-Ying Lin, Ru-Yi Huang, Amir H. Pakpour |
| ||International Journal of Environmental Research and Public Health. 2021; 19(1): 225 |
|[Pubmed] | [DOI]|