Social media disorder among Indian undergraduate medical students and its association with depression: An institution-based mixed-method study
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 und...
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Wolters Kluwer Medknow Publications
2020-01-01
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doaj-fd7132d1dd0f4747a375fb0ae1cd494d2021-01-08T03:23:54ZengWolters Kluwer Medknow PublicationsIndian Journal of Social Psychiatry0971-99622020-01-0136428929510.4103/ijsp.ijsp_42_20Social media disorder among Indian undergraduate medical students and its association with depression: An institution-based mixed-method studyRajib SahaManisha SarkarBackground: 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.http://www.indjsp.org/article.asp?issn=0971-9962;year=2020;volume=36;issue=4;spage=289;epage=295;aulast=Sahaaddictiondepressionmedical studentsscreen related health problemsocial media disorder |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rajib Saha Manisha Sarkar |
spellingShingle |
Rajib Saha Manisha Sarkar Social media disorder among Indian undergraduate medical students and its association with depression: An institution-based mixed-method study Indian Journal of Social Psychiatry addiction depression medical students screen related health problem social media disorder |
author_facet |
Rajib Saha Manisha Sarkar |
author_sort |
Rajib Saha |
title |
Social media disorder among Indian undergraduate medical students and its association with depression: An institution-based mixed-method study |
title_short |
Social media disorder among Indian undergraduate medical students and its association with depression: An institution-based mixed-method study |
title_full |
Social media disorder among Indian undergraduate medical students and its association with depression: An institution-based mixed-method study |
title_fullStr |
Social media disorder among Indian undergraduate medical students and its association with depression: An institution-based mixed-method study |
title_full_unstemmed |
Social media disorder among Indian undergraduate medical students and its association with depression: An institution-based mixed-method study |
title_sort |
social media disorder among indian undergraduate medical students and its association with depression: an institution-based mixed-method study |
publisher |
Wolters Kluwer Medknow Publications |
series |
Indian Journal of Social Psychiatry |
issn |
0971-9962 |
publishDate |
2020-01-01 |
description |
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. |
topic |
addiction depression medical students screen related health problem social media disorder |
url |
http://www.indjsp.org/article.asp?issn=0971-9962;year=2020;volume=36;issue=4;spage=289;epage=295;aulast=Saha |
work_keys_str_mv |
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