
==== Front
Indian J Psychiatry
Indian J Psychiatry
IJPsy
Indian J Psychiatry
Indian Journal of Psychiatry
0019-5545
1998-3794
Wolters Kluwer - Medknow India

IJPsy-66-641
10.4103/indianjpsychiatry.indianjpsychiatry_170_24
Original Article
Beyond the screen: Examining the associations between cyberbullying, social media addiction, and mental health outcomes among medical students: A cross-sectional study
Parmar Parth
Yogesh M
Damor Naresh
Gandhi Rohankumar
Parmar Bhavin 1
Department of Community Medicine, Shri MP Shah Medical College, Jamnagar, Gujarat, India
1 Department of Internal Medicine, Shri MP Shah Medical College, Jamnagar, Gujarat, India
Address for correspondence: Dr. Rohankumar Gandhi, Department of Community Medicine, Shri MP Shah Medical College, Jamnagar, Gujarat, India. E-mail: drrohangandhi92@gmail.com
7 2024
17 7 2024
66 7 641648
26 2 2024
20 6 2024
01 7 2024
Copyright: © 2024 Indian Journal of Psychiatry
2024
https://creativecommons.org/licenses/by-nc-sa/4.0/ This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
Background:

Cyberbullying and excessive social media use are emerging issues among medical students, with potential implications for mental health. This study aimed to investigate the prevalence of cyberbullying, social media addiction, and their associated mental health conditions, as well as to explore the associated factors among medical students.

Methods:

A cross-sectional study was conducted among 418 medical students in Gujarat using a self-administered questionnaire. Cyberbullying was assessed using the Revised Cyberbullying Inventory (RCI-R), social media addiction was measured using the Bergen Social Media Addiction Scale (BSMAS), and mental health issues were evaluated using the Depression Anxiety Stress Scale (DASS-21). Descriptive statistics and binary logistic regression analyses were performed. A P value of <0.05 was considered significant.

Results:

The prevalence of cyberbullying, social media addiction, depression, anxiety, and stress among participants was 27.5% (95% CI: 23.4%–31.9%), 32.1% (95% CI: 27.8%–36.7%), 37.6% (95% CI: 33.1%–42.2%), 41.9% (95% CI: 37.3%–46.6%), and 46.2% (95% CI: 41.6%–50.9%), respectively. Factors associated with increased risk of being a cyber victim included older age, female gender, later years of study, increased daily mobile and social media usage, social media as the preferred mobile usage, and social media addiction. Factors associated with being a cyberbully were similar, except for the male gender. Both cyber victimization and social media addiction were significantly associated with higher odds of depression [aOR-2.5 (1.6–3.9) and 2.1 (1.4–3.2)], anxiety [aOR–2.2 (1.4-3.4) and 1.9 (1.3–2.8)] and stress [aOR–2.8 (1.8-4.3) and 2.4 (1.6–3.6)].

Conclusions:

Cyberbullying, social media addiction, and mental health issues are prevalent among medical students. Targeted interventions addressing excessive social media use, promoting responsible online behaviour, and supporting mental well-being are crucial for this population. Further research is needed to establish causal relationships and develop effective prevention and support strategies.

Anxiety
cyber victimization
depression
medical students
social media addiction
stress
==== Body
pmcINTRODUCTION

In the digital age, the pervasive use of the internet and social media has revolutionized how individuals connect and communicate worldwide. While these platforms offer unprecedented opportunities for social interaction and knowledge-sharing, they also present significant challenges. One of the most concerning issues is the rise of cyberbullying—a form of harassment facilitated through digital means such as social networking sites, messaging apps, and online forums. Cyberbullying encompasses various forms of aggressive behaviour, including threats, insults, and spreading rumours, which can have profound negative effects on individuals’ mental health, self-esteem, and overall well-being. As internet and social media usage continues to proliferate across demographics and age groups, understanding and addressing the complexities of cyberbullying have become imperative to fostering a safer and more inclusive online environment.

“Cyberbullying is being cruel to others by sending or posting harmful material or engaging in other forms of social aggression using the Internet or other digital technologies. Cyberbullying can take different forms: Flaming—online fights using electronic messages with angry and vulgar language; Harassment—repeatedly sending nasty, mean, and insulting messages; Denigration—“dissing” someone online and sending or posting gossip or rumours about a person to damage his or her reputation or friendships; Impersonation—pretending to be someone else and sending or posting material to get that person in trouble or danger or to damage that person’s reputation or friendships; Outing—sharing someone’s secrets or embarrassing information or images online is trickery. Talking someone into revealing secrets or embarrassing information, then sharing it online; Exclusion—intentionally and cruelly excluding someone from an online group; and Cyberstalking—repeated, intense harassment and denigration that includes threats or creates significant fear.”[1]

A form of behavioural addiction known as “social media addiction” is generally understood to be compulsive use of social media platforms to the point where it seriously impairs a user’s ability to function in key areas of their lives, including relationships with others, performance at work or in school, and physical health.[2]

In the context of school-going adolescent and college students, there is considerable worry about the effects of cyberbullying on mental health, in general, academic achievement and psychological well-being.[345]

Previous studies have suggested that cyberbullying victimization and social media addiction may be associated with mental health problems, such as depression and anxiety.[678] Cyberbullying among medical students is particularly significant due to the competitive and high-stress nature of their education. The pressure to excel academically, coupled with the use of digital platforms for learning and social interaction, increases vulnerability to online harassment. Additionally, cyberbullying can affect professional development and undermine the collaborative and supportive environment necessary for effective medical training. Addressing these challenges requires tailored approaches to promote a respectful online culture and support students’ mental well-being. Social media plays a significant role in facilitating cyberbullying behaviour. Excessive and addictive use of social media platforms may increase the likelihood of engaging in or being a victim of cyberbullying.[9] Understanding this relationship can help identify potential risk factors and develop strategies to promote responsible social media use among medical students. However, the specific relationships among cyberbullying, social media addiction, and mental health issues in the medical student population have not been well-explored in the Indian context.

Additionally, many studies have investigated these issues separately, without exploring their potential interrelationships and combined effects on mental health. Therefore, the present study aimed to estimate the prevalence of cyberbullying (perpetration and victimization) and social media addiction among medical students. Further, the present study aimed to determine the association among cyberbullying, social media addiction, and its association with the mental health of medical students (depression, anxiety, and stress). The present study hypothesized that: a) Cyberbullying (both victimization and perpetration) and social media addiction would be prevalent among medical students. b) Factors such as age, gender, year of study, and mobile and social media usage patterns would be associated with the risk of being a cyber victim or cyberbully. c) Cyberbullying victimization and social media addiction would be associated with increased risk of depression, anxiety, and stress among medical students.

METHODOLOGY

Study design and setting

This institutional-based cross-sectional study was conducted among medical students in Gujarat between 20 February 2023 and 24 January 2024.

Sample size determination and sampling technique

The sample size was calculated using the formula for estimating a single proportion, with a 95% confidence level, a 5% margin of error, and an estimated prevalence of cyberbullying among university students of 30% based on previous studies.[1011] This resulted in the minimum required sample size of 323, which was increased to 418 to account for potential non-response.

Sampling Technique: A convenience sampling technique was used to recruit participants from tertiary care hospital-affiliated medical colleges. This method was chosen due to the accessibility and availability of the target population within the hospital premises.

Eligibility criteria

Participants were eligible for inclusion in the study if they met the following criteria: Participants were currently enrolled as medical students in a tertiary care hospital in Gujarat. They were aged 18 years or older. They provided informed consent to participate in the study.

Participants were excluded from the study if they were under 18 years of age. They did not provide informed consent to participate in the study.

Data collection tool

Data were collected using a self-administered questionnaire that was developed based on a comprehensive literature review and expert consultation. The questionnaire consisted of five sections: (1) sociodemographic characteristics, (2) mobile and internet usage patterns, (3) cyberbullying experiences, (4) social media addiction, and (5) mental health issues. Cyberbullying was assessed using the Revised Cyberbullying Inventory (RCI-R),[12] which measures both perpetration and victimization with a Cronbach alpha of 0.876. Social media addiction was measured using the Bergen Social Media Addiction Scale (BSMAS).[13] Mental health issues, including depression, anxiety, and stress were assessed using the Depression Anxiety Stress Scale (DASS-21).[14] Depression, Anxiety, and Stress: The Depression Anxiety Stress Scale-21 (DASS-21), a commonly used screening tool to assess depression, anxiety, and stress in the population, was used to screen for psychological distress. It has been shown to have good validity and reliability.[14] Seven items on each scale with values ranging from 0 (did not apply to me at all) to 3 (applied to me very much, or most of the time) make up the DASS-21’s subscales measuring stress, anxiety, and depression. To evaluate the severity levels ranging from normal, mild, moderate, severe, to extremely severe, appropriate cut-off scores have been assigned to each scale.

The questionnaire was pilot-tested on a sample of 30 students to assess its clarity, comprehensibility, and face validity. Necessary modifications were made based on the feedback received. Specifically, the language and phrasing of some questions were simplified for better understanding. For example, the question “Have you ever experienced cyberbullying as a victim?” was modified to “Have you ever been a target of hurtful or threatening behaviour on the internet or through electronic devices?”. The order of certain sections was rearranged to improve the logical flow of the questionnaire, with the section on sociodemographic characteristics being moved to the beginning. Additionally, examples were provided for some questions to ensure consistent interpretation by the participants, such as providing examples of different social media platforms under the question on daily social media usage. Data were collected through face-to-face interviews conducted by trained research assistants in a private setting on the campuses.

Data collection procedure

Approval was obtained from the Institutional Review Board (IRB) of the tertiary care hospital to conduct the study. The study was advertised through posters, flyers, and announcements in common areas of the medical college, such as classrooms, libraries, and cafeterias. Interested medical students who met the eligibility criteria were invited to participate in the study. A designated room or area within the hospital premises was identified as the data collection site, ensuring privacy and convenience for the participants. Before the interview, informed consent was obtained from each participant. The informed consent process involved explaining the study details, potential risks and benefits, and the participants’ rights, including the right to withdraw from the study at any time without consequences. The interviews were conducted in the designated private setting by trained research assistants using a structured self-administered questionnaire. The questionnaire was divided into five sections: (1) sociodemographic characteristics, (2) mobile and internet usage patterns, (3) cyberbullying experiences, (4) social media addiction, and (5) mental health issues. The research assistants were available to provide clarification or assistance if needed during the questionnaire administration. Upon completion of the questionnaire, the participants were thanked for their participation, and any immediate concerns or questions were addressed. The completed questionnaires were securely stored and later entered into a database for analysis.

Confounding factors

The following potential confounding factors were considered and adjusted for in the analyses:

Age: Participant’s age was recorded and categorized into three groups: 18–20 years, 21–24 years, and >24 years.

Gender: The participant’s gender was recorded as male or female.

Year of study: The current year of medical education (first, second, third, or fourth year) of the participant was recorded.

Monthly family income: The monthly income of the participant’s family was recorded and categorized into three groups: <40,000, 40,000–80,000, and >80,000.

Total family members: The total number of family members living with the participant was recorded and categorized into three groups: 1–3, 4–6, and >6.

Daily mobile usage: The participant’s daily mobile phone usage duration was recorded and categorized into three groups: <1 hour, 1–5 hours, and >5 hours.

Daily Internet usage: The participant’s daily Internet usage duration was recorded and categorized into three groups: <1 hour, 1–5 hours, and >5 hours.

Daily social media usage: The participant’s daily social media usage duration was recorded and categorized into three groups: <1 hour, 1–5 hours, and >5 hours.

Mobile usage preference: The participant’s primary purpose for mobile phone usage was recorded and categorized as social media or other purposes (games, calling, music, reading, email, entertainment, online shopping).

These potential confounding factors were included in the multivariate logistic regression analyses to adjust for their possible confounding effects on the associations of interest.

Operational definitions

Cyberbullying: Defined as the use of electronic communication technology to intentionally engage in repeated or hostile behaviour towards an individual or group to cause harm or distress.[12] Cyber victim: A participant who reported experiencing cyberbullying as a victim. Cyberbully: A participant who reported engaging in cyberbullying as a perpetrator. Social Media Addiction: A participant who scored 19 and higher.

Statistical analysis

The collected data were entered into IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY, USA) and cleaned for any errors or inconsistencies. Descriptive statistics, including frequencies and percentages, were used to summarize the sociodemographic characteristics, mobile and internet usage patterns, prevalence of cyberbullying, social media addiction, and mental health issues.

Binary logistic regression analysis was performed to identify the factors associated with being a cyber victim and a cyber bully. The dependent variables were cyber victimization and cyberbullying (Yes/No). In contrast, the independent variables included age, gender, year of study, monthly income of participant’s family, total family members, daily mobile usage, daily internet usage, daily social media usage, mobile usage preference, and social media addiction. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to estimate the strength of the association while adjusting for the potential confounding effects of the other variables. (age, gender, year of study, monthly income of participant’s family, total family members, daily mobile usage, daily internet usage, daily social media usage, and mobile usage preference).

The relationship between cyberbullying, social media addiction, and mental health issues was also investigated using multivariate logistic regression analysis. In these models, the dependent variables were depression, anxiety, and stress (Yes/No), while the independent variables were cyber victimization, cyberbullying, and social media addiction. ORs with 95% CIs were calculated to estimate the strength of the association.

A P value of less than 0.05 was considered statistically significant for all analyses.

Ethical considerations

Ethical approval for the study was obtained from the Institutional Review Board (IRB) of Shri M P Shah Government Medical College; G G Hospital (approval number: 278/03/2023). Informed consent was obtained from all participants before data collection. Participants were assured of the confidentiality and anonymity of their responses, and they had the right to withdraw from the study at any time without consequences.

RESULTS

Table 1 presents the demographic characteristics of the 418 study participants. The majority of participants were aged 18–20 years (48.6%, n = 203) and 21–24 years (38.5%, n = 161), with only 12.9% (n = 54) being over 24 years old. More than half of the participants were female (52.6%, n = 220), while 47.4% (n = 198) were male. Regarding the year of study, 28.2% (n = 118) were in their fourth year, 26.5% (n = 111) in their second year, 23.2% (n = 97) in their third year, and 22.0% (n = 92) in their first year. In terms of monthly income, 44.0% (n = 184) of participant’s families earned between 40000 and 80000, 33.0% (n = 138) earned less than 40000, and 23.0% (n = 96) earned more than 80000. The majority of participants had 4–6 total family members (48.6%, n = 203), followed by 1–3 family members (38.5%, n = 161) and more than 6 family members (12.9%, n = 54). Concerning daily mobile usage, 60.5% (n = 253) used their mobile phones for 1–5 hours, 21.8% (n = 91) for more than 5 hours, and 17.7% (n = 74) for less than 1 hour. Similarly, for daily internet usage, 66.0% (n = 276) used the internet for 1–5 hours, 20.8% (n = 87) for more than 5 hours, and 13.2% (n = 55) for less than 1 hour. Regarding daily social media usage, 70.3% (n = 294) spent 1–5 hours, 19.9% (n = 83) spent less than 1 hour, and 9.8% (n = 41) spent more than 5 hours. The most common mobile usage preference was social media (49.5%, n = 207), followed by calling (17.7%, n = 74), games (13.2%, n = 55), music (8.9%, n = 37), email (3.3%, n = 14), entertainment (3.3%, n = 14), reading (2.2%, n = 9), and online shopping (1.9%, n = 8). Table 1 also shows the prevalence of cyberbullying, social media addiction, and mental health issues among the participants. Cyberbullying was reported by 27.5% (n = 115, 95% CI: 23.4%–31.9%) of participants, with 48 being perpetrators and 67 being victims. Social media addiction was prevalent among 32.1% (n = 134, 95% CI: 27.8%–36.7%) of the participants. Regarding mental health issues, 46.2% (n = 193, 95% CI: 41.6%–50.9%) reported stress, 41.9% (n = 175, 95% CI: 37.3%–46.6%) reported anxiety, and 37.6% (n = 157, 95% CI: 33.1%–42.2%) reported depression.

Table 1: Demographic characteristics and prevalence of cyberbullying, social media addiction, and mental health issues of study participants (n=418)

Characteristic	n (%)	95% CI	
Age (years)			
    18–20	203 (48.6%)	[43.9%, 53.2%]	
    21–24	161 (38.5%)	[33.9%, 43.2%]	
    >24	54 (12.9%)	[9.8%, 16.4%]	
Gender			
    Male	198 (47.4%)	[42.7%, 52.1%]	
    Female	220 (52.6%)	[47.9%, 57.3%]	
Year of Study			
    First	92 (22.0%)	[18.1%, 26.3%]	
    Second	111 (26.5%)	[22.3%, 31.0%]	
    Third	97 (23.2%)	[19.1%, 27.7%]	
    Fourth	118 (28.2%)	[23.9%, 32.8%]	
Monthly Income of Participant’s Family			
    <40000	138 (33.0%)	[28.6%, 37.7%]	
    40000–80000	184 (44.0%)	[39.3%, 48.8%]	
    >80000	96 (23.0%)	[19.0%, 27.4%]	
Total Family Members			
    1–3	161 (38.5%)	[33.9%, 43.2%]	
    4–6	203 (48.6%)	[43.9%, 53.2%]	
    >6	54 (12.9%)	[9.8%, 16.4%]	
Daily Mobile Usage (hrs)			
    <1	74 (17.7%)	[14.2%, 21.7%]	
    1–5	253 (60.5%)	[55.8%, 65.0%]	
    >5	91 (21.8%)	[18.0%, 26.1%]	
Daily Internet Usage (hrs)			
    <1	55 (13.2%)	[10.1%, 16.8%]	
    1–5	276 (66.0%)	[61.4%, 70.4%]	
    >5	87 (20.8%)	[17.0%, 25.0%]	
Daily Social Media Usage (hrs)			
    <1	83 (19.9%)	[16.3%, 24.1%]	
    1–5	294 (70.3%)	[65.8%, 74.5%]	
    >5	41 (9.8%)	[7.1%, 13.1%]	
Mobile Usage Preference			
    Social media	207 (49.5%)	[44.8%, 54.2%]	
    Games	55 (13.2%)	[10.1%, 16.8%]	
    Calling	74 (17.7%)	[14.2%, 21.7%]	
    Music	37 (8.9%)	[6.4%, 12.1%]	
    Reading	9 (2.2%)	[1.0%, 4.1%]	
    Email	14 (3.3%)	[1.8%, 5.5%]	
    Entertainment	14 (3.3%)	[1.8%, 5.5%]	
    Online Shopping	8 (1.9%)	[0.8%, 3.7%]	
Cyberbullying			
    Perpetrator	48 (11.5%)	[8.6%, 15.0%]	
    Victim	67 (16.0%)	[12.7%, 19.9%]	
    Total Involved	115 (27.5%)	[23.4%, 31.9%]	
    Social Media Addiction	134 (32.1%)	[27.8%, 36.7%]	
Mental Health Issues			
    Depression	157 (37.6%)	[33.1%, 42.2%]	
    Anxiety	175 (41.9%)	[37.3%, 46.6%]	
    Stress	193 (46.2%)	[41.6%, 50.9%]	
CI, Confidence Interval

Table 2 presents the factors associated with being a cyber victim. Participants aged over 24 years had a significantly lower risk of being a cyber victim compared to those aged 18–20 years (aOR = 0.5, 95% CI = 0.3–0.9, P < 0.05). Female participants had a significantly higher risk of being a cyber victim compared to males (aOR = 1.5, 95% CI = 1.1–2.1, P < 0.05). Participants in their third (aOR = 1.7, 95% CI = 1.0–2.8, P < 0.05) and fourth (aOR = 2.1, 95% CI = 1.3–3.4, P < 0.05) years of study had a significantly higher risk of being a cyber victim compared to first-year students. Daily mobile usage of more than 5 hours (aOR = 1.9, 95% CI = 1.1–3.2, P < 0.05), daily social media usage of 1–5 hours (aOR = 1.8, 95% CI = 1.2–2.7, P < 0.05) and more than 5 hours (aOR = 2.3, 95% CI = 1.3–4.1, P < 0.05), social media as the mobile usage preference (aOR = 2.5, 95% CI = 1.8–3.6, P < 0.05), and social media addiction (aOR = 2.48, 95% CI = 1.6–3.2, P < 0.05) were significantly associated with an increased risk of being a cyber victim.

Table 2: Factors associated with being a cyber victim

Variable	COR (95% CI)	aOR (95% CI)	
Age (years)			
    18–20	Ref	Ref	
    21–24	0.8 (0.5–1.2)	0.7 (0.5–1.1)	
    >24	0.4 (0.2–0.7)*	0.5 (0.3–0.9)*	
Gender			
    Male	Ref	Ref	
    Female	1.8 (1.3–2.5)*	1.5 (1.1–2.1)*	
Year of Study			
    First	Ref	Ref	
    Second	1.3 (0.8–2.1)	1.2 (0.7–2.0)	
    Third	2.0 (1.2–3.3)*	1.7 (1.0–2.8)*	
    Fourth	2.5 (1.5–4.1)*	2.1 (1.3–3.4)*	
Monthly Income of participant’s family			
    <40000	Reference		
    40000–80000	0.9 (0.6–1.3)		
    >80000	0.7 (0.4–1.1)		
Total Family Members			
    1–3	Reference		
    4–6	1.2 (0.8–1.7)		
    >6	1.5 (0.9–2.5)		
Daily mobile usage (hours)			
    <1	Ref	Ref	
    1–5	1.5 (0.9–2.4)	1.3 (0.8–2.1)	
    >5	2.1 (1.2–3.7)*	1.9 (1.1–3.2)*	
Daily Internet Usage (hrs)			
    <1	Reference		
    1–5	1.1 (0.7–1.8)		
    >5	1.6 (0.9–2.8)		
Daily social media usage (hours)			
    <1	Ref	Ref	
    1–5	2.0 (1.3–3.0)*	1.8 (1.2–2.7)*	
    >5	2.6 (1.5–4.5)*	2.3 (1.3–4.1)*	
Mobile usage preference			
    Social media	3.1 (2.2–4.4)*	2.5 (1.8–3.6)*	
    Other	Ref	Ref	
Social media addiction			
    Yes	3.1 (2.2–4.4)*	2.48 (1.6–3.2)*	
    No	Ref	Ref	
P<0.05*, significant; P<0.001**, highly significant; aOR, adjusted odds ratio; COR, crude odds ratio, adjusting for the potential confounding effects of the other variables. (age, gender, year of study, monthly income of participant’s family, total family members, daily mobile usage, daily internet usage, daily social media usage, mobile usage preference, and social media addiction)

Table 3 examines the factors associated with being a cyberbully. Participants aged over 24 years had a significantly lower risk of being cyberbullied compared to those aged 18–20 years (aOR = 0.4, 95% CI = 0.2–0.8, P < 0.05). Female participants had a significantly lower risk of being cyberbullied compared to males (aOR = 0.6, 95% CI = 0.4–0.9, P < 0.05). Participants in their third (aOR = 1.9, 95% CI = 1.1–3.2, P < 0.05) and fourth (aOR = 2.3, 95% CI = 1.4–3.8, P < 0.05) years of study had a significantly higher risk of being a cyberbully compared to first-year students. Daily mobile usage of more than 5 hours (aOR = 1.8, 95% CI = 1.0–3.2, P < 0.05), daily social media usage of 1–5 hours (aOR = 1.6, 95% CI = 1.0–2.5, P < 0.05) and more than 5 hours (aOR = 2.1, 95% CI = 1.1–3.9, P < 0.05), social media as the mobile usage preference (aOR = 3.1, 95% CI = 2.1–4.6, P < 0.05), and social media addiction (aOR = 3.07, 95% CI = 2.9–4.5, P < 0.05) were significantly associated with an increased risk of being a cyberbully.

Table 3: Factors associated with being a cyberbully

Variable	COR (95% CI)	aOR (95% CI)	
Age (years)			
    18–20	Ref	Ref	
    21–24	0.7 (0.4–1.1)	0.6 (0.4–1.0)	
    >24	0.3 (0.1–0.6)*	0.4 (0.2–0.8)*	
Gender			
    Male	Ref	Ref	
    Female	0.5 (0.3–0.8)*	0.6 (0.4–0.9)*	
Year of study			
    First	Ref	Ref	
    Second	1.5 (0.8–2.7)	1.4 (0.8–2.5)	
    Third	2.2 (1.2–3.9)*	1.9 (1.1–3.2)*	
    Fourth	2.8 (1.6–4.8)*	2.3 (1.4–3.8)*	
Monthly Income of participant’s family			
     <40000	Reference		
     40000–80000	1.1 (0.7–1.7)		
     >80000	1.4 (0.8–2.3)		
Total Family Members			
    1–3	Reference		
    4–6	0.8 (0.5–1.2)		
    >6	0.6 (0.3–1.1)		
Daily mobile usage (hours)			
    <1	Ref	Ref	
    1–5	1.3 (0.8–2.2)	1.2 (0.7–2.0)	
    >5	2.0 (1.1–3.6)*	1.8 (1.0–3.2)*	
Daily Internet Usage (hrs)			
    <1	Reference		
    1–5	1.3 (0.8–2.2)		
    >5	1.7 (0.9–3.1)		
Daily social media usage (hours)			
    <1	Ref	Ref	
    1–5	1.8 (1.1–2.9)*	1.6 (1.0–2.5)*	
    >5	2.5 (1.4–4.5)*	2.1 (1.1–3.9)*	
Mobile usage preference			
    Social media	3.8 (2.5–5.7)*	3.1 (2.1–4.6)*	
    Other	Ref	Ref	
Social media addiction			
    Yes	3.8 (2.5–5.7)*	3.07 (2.9–4.5)*	
    No	Ref	Ref	
*P<0.05, significant association values represent adjusted odds ratio (aOR) with a 95% confidence interval, COR, crude odds ratio, adjusting for the potential confounding effects of the other variables (age, gender, year of study, monthly income of participant’s family, total family members, daily mobile usage, daily internet usage, daily social media usage, mobile usage preference, and social media addiction)

Table 4 shows the association between cyberbullying, social media addiction, and mental health issues. Being a cyber victim was significantly associated with an increased risk of depression (aOR = 2.5, 95% CI = 1.6–3.9, P < 0.05), anxiety (aOR = 2.2, 95% CI = 1.4–3.4, P < 0.05), and stress (aOR = 2.8, 95% CI = 1.8–4.3, P < 0.05). Social media addiction was significantly associated with an increased risk of depression (aOR = 2.1, 95% CI = 1.4–3.2, P < 0.05), anxiety (aOR = 1.9, 95% CI = 1.3–2.8, P < 0.05), and stress (aOR = 2.4, 95% CI = 1.6–3.6, P < 0.05). However, being a cyberbully was not significantly associated with depression, anxiety, or stress.

Table 4: Association between cyberbullying, social media addiction, and mental health issues by multivariate logistic regression

Variable	Depression aOR (CI)	Anxiety aOR (CI)	Stress aOR (CI)	
Cybervictim	2.5 (1.6–3.9)*	2.2 (1.4–3.4)*	2.8 (1.8–4.3)**	
Cyberbully	0.66 (0.41–2.3)	0.81 (0.71–1.87)	0.76 (0.56–2.6)	
Social Media Addiction	2.1 (1.4–3.2)*	1.9 (1.3–2.8)*	2.4 (1.6–3.6)*	
P<0.05*, significant, P<0.001**, highly significant, adjusted for age, gender, year of study, monthly family income, total number of family members, daily mobile usage, daily internet usage, daily social media usage, mobile usage preference (social media vs. other purposes)

DISCUSSION

This study aimed to investigate the prevalence of cyberbullying, social media addiction, and mental health issues among medical students and explore the factors associated with being a cyber victim and a cyber bully. The findings revealed that 27.5% of participants experienced cyberbullying as either a victim or perpetrator, which is consistent with previous studies reporting a prevalence of cyberbullying among university students ranging from 20% to 40%.[101115] Social media addiction was prevalent among 32.1% of the participants, which aligns with the findings of Andreassen et al. (2017),[13] who reported a social media addiction rate of 31.3% among a large sample of Norwegian university students.

The study identified several factors associated with being a cyber victim, including older age, female gender, later years of study, increased daily mobile and social media usage, social media as the preferred mobile usage, and social media addiction. These findings are consistent with previous research suggesting that females are more likely to be cyber victims[1116], and that excessive social media use and addiction are risk factors for cyber victimization.[1617]

Regarding cyberbullying perpetration, the study found that older age, male gender, later years of study, increased daily mobile and social media usage, social media as the preferred mobile usage, and social media addiction were associated with a higher likelihood of being a cyberbully. These results align with previous studies indicating that males are more likely to be cyberbullies[1116], and that excessive social media use and addiction are risk factors for cyberbullying perpetration.[161718]

The study also revealed a significant association between cyber victimization, social media addiction, and mental health issues, including depression, anxiety, and stress. These findings are consistent with previous research demonstrating a link between cyberbullying victimization and adverse mental health outcomes.[16192021] Similarly, the association between social media addiction and mental health problems observed in this study aligns with the findings of numerous previous studies.[22232425]

The study’s strengths include the use of validated instruments for assessing cyberbullying, social media addiction, and mental health issues, as well as the adjustment for potential confounding factors in the regression analyses.

Limitations

The cross-sectional design of this study precludes establishing causal relationships between cyberbullying, social media addiction, and mental health issues. The reliance on self-reported data may have introduced biases like recall bias and social desirability bias. The single-centre nature of the study, conducted at one medical college, limits generalizability. The use of convenience sampling could have led to selection bias. Additionally, there may be other unexplored factors, such as family dynamics, adverse childhood experiences, or innate psychological traits, that could influence the associations observed between cyberbullying, social media addiction, and mental health problems in this study.

Recommendations

Based on the findings and limitations of this study, we recommend conducting longitudinal cohort studies to establish temporal relationships and causality between cyberbullying, social media addiction, and mental health problems. Incorporating qualitative research methods, such as interviews and focus groups, could provide deeper insights into the lived experiences and perspectives of medical students regarding these issues. To improve the generalizability of findings, future research should involve large multi-centre studies with diverse medical colleges across different regions. Additionally, studies should explore potential confounding factors or mediating variables that were not examined in the current study, such as family dynamics, adverse childhood experiences, or innate psychological traits, which may influence the observed associations.

CONCLUSION

The present study highlights the significant prevalence of cyberbullying, social media addiction, and mental health issues, including depression, anxiety, and stress, among medical students. The findings reveal associations between cyber victimization, social media addiction, and increased odds of experiencing these mental health outcomes. Targeted interventions addressing excessive social media use, promoting responsible online behaviour, and supporting the mental well-being of medical students are crucial based on the identified risk factors and associations. While the cross-sectional nature of this study precludes establishing causality, our findings provide valuable insights and underscore the need for further research to develop effective prevention and support strategies for this vulnerable population.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

Acknowledgments

We acknowledge and are grateful to all the patients who contributed to the data collection for this study. We are also thankful to Dr. Nandini Desai (Dean and Chairman of MDRU) and Dr. Dipesh Parmar (Professor and Head, Department of Community Medicine), Shri M P Shah Government Medical College, Jamnagar, India.
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