Mood and Suicidality among Cyberbullied Adolescents: A Cross-Sectional Study from Youth Risk Behavior Survey

Abstract

Background: There is limited literature available showing the mental health burden among adolescents following cyberbullying. Objective: The aim was to evaluate the association between low mood and suicidality among cyberbullied adolescents. Method: A cross-sectional analysis of the data was performed among adolescents from the National Youth Risk Behavior Surveillance System. Responses from adolescents related to cyberbullying and suicidality were evaluated. Chi-square and mix-effect multivariable logistic regression analysis was performed to determine the association of cyberbullying with sadness/hopelessness and suicide consideration, plan, and attempts. Results: Of a total of 8274 adolescents, 14.8% of adolescents faced cyberbullying past year. There was a higher prevalence of cyberbullying in youths aged 15, 16, and 17 years (25%, 26%, 23%, respectively), which included more females than males (68% vs. 32%; p < 0.0001). Caucasians (53%) had the highest number of responses to being cyberbullied compared to Hispanics (24%) or African Americans (11%; p < 0.0001). There was an increased prevalence of cyberbullied youths, feelings of sadness/hopelessness (59.6% vs. 25.8%), higher numbers considering suicide (40.4% vs. 13.2%), suicide plan (33.2% vs. 10.8%), and multiple suicidal attempts in comparison to non-cyberbullied (p < 0.0001). On regression analysis, cyberbullied adolescents had a 155% higher chance of feeling sad and hopeless [aOR = 2.55; 95%CI = 2.39–2.72] and considered suicide [aOR = 1.52 (1.39–1.66)] and suicide plan [aOR = 1.24 (1.13–1.36)]. Conclusion: In our Study, cyberbullying was associated with negative mental health outcomes. Further research is warranted to examine the impact of cyberbullying among adolescents and guiding the policies to mitigate the consequences.

1. Introduction

Bullying is physically or verbally aggressive behavior intended to cause harm or distress. In the youth population, bullying most often occurs on the school ground, where adolescents spend most of their time learning and socializing. It can have a serious and devastating impact on the social and academic life of our youth [1]. The adverse effects of bullying on the mental health of adolescents are well documented. Youths who get bullied often suffer from low mood, depression, and suicidal thoughts, impacting their daily lives. These negative impacts on youth well-being have been documented through research in physical and verbal bullying on the school ground. In recent times, the young population has begun spending tremendous time online and on social networks. The phenomenon of cyberbullying has also increased following this trend. Cyberbullying has been described as bullying on electronic devices such as mobile phones, computers, and tablets, and includes sending, posting, or sharing negative, harmful, false, or mean content about another person. Many states have implemented some rules to fight against cyberbullying [1].

There are several definitions of cyberbullying in the literature. In recent years it is often considered a major type of bullying and is a form of non-physical aggression. This phenomenon of cyberbullying has rapidly increased, regardless of it being in the form of phone calls, text messages, emails, chat forums, social media posts, photos, or videos to humiliate or harass a victim. The cyberbullying definition sometimes is often confusing when it comes to including or making a distinction with porn-related bullying or sexting. Of interest is the psychosocial characteristics and impact on the victims of cyberbullying as well as the perpetrators. Since the nature of cyberbullying is not one that affects the victims or the perpetrators physically, it is important to examine also the psychological and social impacts cyberbullying exerts in these populations. It is also important to recognize that emotional impact can, in turn, lead to self-harm and physical harm that is self-inflicted.

According to the 2017 statistics of the National Institute of Mental Health, the prevalence of depression among American youths aged 13–17 is approximately 13.3% [2]. The Royal Society for Public Health and Young Health Movement (2017) reported that the prevalence of depression and anxiety has increased by 70% in the past 25 years [3]. The reasonings may be multifactorial. Previous literature found 4-fold and 12-fold odds of increased risk of depressive symptoms and attempting suicide, respectively, in children who reported more than four adverse childhood experiences (ACE) [4]. However, the ACE does not include the experience of violence, such as cyberbullying. The National Center for Education Statistics reported that 28% of middle and 16% of high school students face bullying on school grounds at least once a week. This percentage increases with cyberbullying to 33% and 30%, respectively [5]. This rise may be attributed to social networking platforms like Facebook, Instagram, and Snapchat. The primary objective of this study was to determine the prevalence of cyberbullying among youth respondents in the Youth Risk Behavior Surveillance System Survey and how this impacts depressive symptoms and suicidal behavior.

2. Methods

2.1. Study Population

Data from 1991 to 2017 in the national combined The Youth Risk Behavior Surveillance System (YRBSS) were used. YRBSS is a system of surveys and questions administered to students that monitors health behaviors, conditions, and experiences among high school students throughout the United States every 2 years. It includes (1) a national school-based survey conducted by CDC and state, territorial, and tribal, and (2) local surveys conducted by state, territorial, and local education and health agencies and tribal governments. It is a deidentified survey database. These health risk behaviors are the leading causes of mortality, morbidity, and social problems among youths and adults. The following categories of behaviors are included in the system: (1) behaviors that contribute to unintentional injury and violence; (2) tobacco use; (3) alcohol and other drug use; (4) sexual behaviors that contribute to unintended pregnancy and STD/HIV infection; (5) dietary behaviors; and (6) physical inactivity.

2.2. Outcomes of Interest

The study’s primary aim was to evaluate the prevalence and characteristics of adolescents who experienced cyberbullying and the prevalence of low mood and suicidality among them. The secondary aim of the study was to identify the associations between cyberbullying and low mood and suicidality.

Cyberbullying: For the assessment of cyberbullying, the question selected was the following: Q24 “During the past 12 months, have you ever been electronically bullied? (Count being bullied through texting, Instagram, Facebook, or other social media.)”. Responses were dichotomized as “Yes” or “No”.

Low mood and suicidality: To determine low mood and suicidality, the questions selected to capture the variables were the following: Q25 “During the past 12 months, did you ever feel so sad or hopeless almost every day for two weeks or more in a row that you stopped doing some usual activities?”; Q26 “During the past 12 months, did you ever seriously consider attempting suicide?”; Q27 “During the past 12 months, did you make a plan about how you would attempt suicide?”; Q28 “During the past 12 months, how many times did you attempt suicide?”; Q29 “If you attempted suicide during the past 12 months, did any attempt result in an injury, poisoning, or overdose that had to be treated by a doctor or nurse?”. Responses from Q25–Q27 were all “Yes” or “No.” Q28 responses involved five different options: 0, 1, 2–3, 4–5, and 6 or more times. Q29 responses involved three different options of “Yes,” “No,” and “I did not attempt suicide during the past 12 months”.

2.3. Statistical Analysis

A cross-sectional analysis was conducted on the CDC national YRBS data from 1991 to 2017 to provide nationally representative estimates using IBM SPSS Statistics software version 26 (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp). YRBS data were analyzed on adolescent respondents’ age, sex, grade, and race/ethnicity to evaluate the prevalence of cyberbullying and characteristics of cyberbullied adolescence. Prevalence of sadness/hopelessness and suicidality-related thoughts and actions were identified among adolescents with and without being cyberbullied. Chi-square tests were used to evaluate the univariable association of cyberbullying with sadness/hopelessness and suicidality-related thoughts and actions. The mix-effect multivariable logistic regression analyses were conducted to adjust for demographic differences among other risk behaviors and if cyberbullying was associated with these factors.

3. Results

3.1. Demographics of Cyberbullied Youths

In the YRBS survey, the total number of adolescents included in the study was 57,153 adolescents. There is a higher prevalence of cyberbullying in youths aged 15–17 years (25%, 26%, and 23%, respectively; p > 0.0001), which included more females (68%) than males (32%). Caucasian ethnicity has the highest number of responses to being cyberbullied at 53%, followed by Hispanic 24%, then African Americans 11%. The youths attended grades 9 to 12, almost evenly spread out, slightly more in grade 9 at 29% than others (26%, 24%, and 22% respectively in grades 10–12) (p > 0.0001) (

Table 1. Demographics and epidemiological characteristics of adolescent cyberbullying.

	Cyberbullying (%)	No Cyberbullying (%)	Total (%)	p-Value
Age				<0.0001
<12-year-old	61 (0.7%)	83 (0.2%)	144 (0.3%)
13-year-old	14 (0.2%)	63 (0.1%)	77 (0.1%)
14-year-old	1052 (13%)	5301 (11%)	6353 (11%)
15-year-old	2087 (25%)	11,483 (24%)	13,570 (24%)
16-year-old	2118 (26%)	12,394 (25%)	14,512 (25%)
17-year-old	1934 (23%)	12,375 (25%)	14,309 (25%)
>18-year-old	1019 (12.3%)	7188 (15%)	8207 (14%)
Sex				<0.0001
Male	2664 (32%)	25,764 (53%)	28,428 (50%)
Female	5610 (68%)	23,115 (47%)	28,725 (50%)
Race				<0.0001
Caucasian	4343 (53%)	19,809 (41%)	24,152 (43%)
African American	855 (11%)	8926 (19%)	9781 (17%)
Hispanic	1988 (24%)	14,146 (29%)	16,134 (29%)
Asian	268 (3%)	1904 (4%)	2172 (4%)
American Indian/Alaska Native	120 (2%)	574 (1%)	694 (1%)
Native Hawaiian/other	72 (1%)	380 (1%)	452 (1%)
Multiple/Non-Hispanic	500 (6%)	2319 (5%)	2819 (5%)
Grade				<0.0001
9th	2383 (29%)	12,473 (26%)	14,856 (26%)
10th	2121 (26%)	11,870 (24%)	13,991 (25%)
11th	1950 (24%)	12,385 (25%)	14,335 (25%)
12th	1773 (22%)	12,003 (25%)	13,776 (24%)

Percentage in brackets are column %, indicating direct comparison between Cyberbullying vs. Non-Cyberbullying in adolescents.

).

3.2. Prevalence of Outcomes

Comparing youths who were cyberbullied to not cyberbullied, there were increased numbers of cyberbullied youths with feelings of sadness and hopelessness (59.6% vs. 25.8%; p < 0.0001). Cyberbullied youths also had higher numbers of considering suicide (40.4% vs. 13.2%; p < 0.0001) and made a suicide plan (33.2% vs. 10.8%; p < 0.0001%). The suicide attempt analysis was divided by 0, 1, 2–3, 4–5, and 6 or more attempts. There was a significantly greater number of youths who were not cyberbullied that made no suicide attempts compared to cyberbullied youths (93.9% not bullied vs. 77.3% cyberbullied). Multiple suicide attempts were significantly increased in cyberbullied youths compared to youths not cyberbullied. (p > 0.0001) More injuries resulted from an attempted suicide in the cyberbullied youths compared to not cyberbullied (8.8% vs. 1.7%). (p > 0.0001) (

Table 2. Univariable analysis showing association of cyberbullying with mental health outcomes.

	Cyberbullying (%)	No Cyberbullying (%)	Total	p-Value
Sad and Hopeless	4933 (59.6)	12,613 (25.8)	17,546	<0.0001
Considered Suicide	3330 (40.4)	6458 (13.2)	9788	<0.0001
Made Suicide Plan	2726 (33.2)	5275 (10.8)	8001	<0.0001
Suicide Attempts				<0.0001
0	5389 (77.3)	37,198 (93.9)	43,307
1	713 (10.2)	1389 (3.4)	2102
2–3	522 (7.5)	740 (1.8)	1262
4–5	138 (2.0)	140 (0.3)	278
6 or more	209 (3.0)	203 (0.5)	412
Attempt Suicide Resulting in Injury				<0.0001
No attempted suicide and no injury	5297 (77.3)	37,041 (93.9)	42,338
Injury	5297 (8.8)	682 (1.7)	1283
No Injury	601 (13.9)	1717 (4.4)	2669

Percentage in brackets are column %, indicating direct comparison between Cyberbullying vs. Non- Cyberbullying in adolescents.

).

3.3. Predicted Probability of Being Cyberbullied Model

The association of cyberbullying with sadness/hopeless and suicidality adjusted for age, sex, grade, race, tobacco, and alcohol and substance use was modeled. Overall, there was an association between cyberbullying with sadness/hopeless (OR: 2.55, 95%CI: 2.39–2.71, p < 0.0001) after controlling for age, sex, grade, race, tobacco, and alcohol and substance use. There was also an association between cyberbullying and suicidality variables among the respondents: considered suicide (OR: 1.52, 95%CI: 1.39–1.66, p < 0.0001); made suicide plan (OR: 1.24, 95%CI: 1.13–1.36, p < 0.0001); suicide attempts (1 attempt: OR: 0.87, 95%CI: 0.76–0.99, p = 0.029, 2–3 attempts: OR: 0.73, 95%CI: 0.63–0.85, p < 0.001, 4–5 attempts: OR: 0.48, 95%CI: 0.35–0.64, p < 0.0001, >6 attempts: OR: 0.49, 95%CI: 0.37–0.66, p < 0.0001); and suicide injury (OR: 0.75, 95%CI: 0.64–0.88, p < 0.0001). (

Table 3. Predicted probability of being cyberbullied among adolescents with poor mental health outcomes.

Parameters	Odds Ratio	95% Confidence Interval	p-Value
Mental health conditions
Sad and hopeless (vs. not sad)	2.55	2.39–2.72	<0.0001
Considered suicide	1.52	1.39–1.66	<0.0001
Made suicide plan	1.24	1.13–1.36	<0.0001
Suicide attempts (0 times)
1	0.87	0.76–0.99	0.029
2–3	0.73	0.63–0.85	<0.0001
4–5	0.48	0.35–0.64	<0.0001
>6	0.49	0.37–0.66	<0.0001
Attempt Suicide Resulting in Injury needing medical care
Injury	0.75	0.64–0.88	<0.0001
Demographics
Age increment per year (ref ≤ 12 years)	1.05	1.001–1.10	0.046
Female Sex (ref = male)	2.0	1.88–2.12	<0.0001
Grade (ref = 9th grade)	1.06	1.01–1.12	0.023
Race (American Indian/Alaska Native)	0.82	0.80–0.83	<0.0001
Current cigarette use (ref = no days)
1–2 days	0.74	0.65–0.84	<0.0001
3–5 days	0.85	0.71–1.03	0.105
6–9 days	0.91	0.72–1.15	0.419
10–19 days	0.89	0.71–1.13	0.341
20–29 days	0.90	0.70–1.16	0.402
All 30 days	0.91	0.77–1.08	0.283
Current alcohol use (no days)
1–2 days	0.78	0.72–0.83	<0.0001
3–5 days	0.72	0.65–0.79	<0.0001
6–9 days	0.68	0.60–0.78	<0.0001
10–19 days	0.72	0.60–0.85	<0.0001
20–29 days	0.67	0.47–0.96	0.030
All 30 days	0.43	0.30–0.62	<0.0001
Ever used injected drug	1.06	0.74–1.53	0.761

).

4. Discussion

Eighty-eight percent (88%) of teens in the USA have access to internet-enabled devices [6]. About 99% of 12–15 year-olds in the UK have an online presence [7]. Internet-enabled devices have modernized educational and recreational activities, but there have been concerns with growing rates of harmful activities with deliberate evil intentions and harassment, such as cyberbullying. There are multiple studies correlating bullying and cyberbullying [8,9]. Many traditional bullying perpetrators and victims are also cyberbullying perpetrators and victims, respectively [10]. To date, we do not have in our literature a definition of what constitutes cyberbullying. However, a study defines that a collaborative effort between schools, families, and communities can help control cyberbullying [11]. This makes it difficult for researchers to understand and distinguish between various other forms of cyber actions such as online harassment and sexual harassment [12,13]. Although a critical causal precursor for mental health disorders such as post-traumatic stress disorder, cyberbullying is not a part of ICD-10 or the DSM-5 diagnostic criteria [14,15]. It is reported that social media is the most commonly used platform for cyberbullying [16,17]. Study has defined that being responsible while using cyber technology can also minimize the risk of being bullied [11].

The phenomenon of cyberbullying in the context of this study is defined by the respondents’ interpretation of the question of being bullied through texting, Instagram, Facebook, or other social media. Cyberbullying is a complex phenomenon that needs to take into consideration different psychological points of views of the victims, which will give a more complete perspective of the problem of cyberbullying. Understanding and factoring in the psychosocial aspects of individuals, including family background, cultural status, social context, etc., affected by cyberbullying is vital to this understanding. Other variables related to psychosocial aspects of the individual state, such as emotions, empathy, prosociality, self-esteem, etc., are necessary to properly examine the problem of cyberbullying and for forecasting purposes.

There are several significant findings in our study. This study demonstrated that individuals who are children aged 15 and 16 years reported the highest percentage of cyberbullying (25.2% and 25.6%, respectively). Interestingly, the trend among high schoolers was different. Ninth graders reported the highest amount of cyberbullying of 29%. This percentage decreased with every passing grade, 25.8% in grade 10, 23.7% in grade 11, and 21.6% in grade 12. These findings imply that youth between 13–16 years are at the highest risk of experiencing cyberbullying. While cyberbullying is a relatively new area, it has been implicated with depression [18] and increased suicidal ideations, attempts, and behaviors, [19,20]. Our findings remain consistent with previous studies: Among individuals who reported cyberbullying, 59.6% reported feeling sad, 40.4% considered suicide, 33.2% made a suicide plan, and 10.2% attempted suicide at least once. Additionally, our regression analysis displayed that cyberbullying had two-fold odds of increasing depression (OR = 1.85, 95% CI = 1.69–2.01, p < 0.001). This high rate of depression among cyberbullying victims requires the implementation of better governing policies over the mass web. The 2017 Annual Bullying Survey reported that Instagram was the leading platform for cyberbullying, followed by Facebook and Snapchat, at 37% and 31%, respectively [21]. Similarly, a study assessing cyberbullying on social media platforms among university students in the United Arab Emirates found 55.5% of it occurred on Instagram, followed by Facebook at 38% [22]. Though these platforms have improved their policy and privacy settings, further developments are still required [22].

Another one of our findings that future researchers should examine is the high rate of cyberbullying among Caucasians (53%) and Hispanics (24%), compared to other races such as Asian (3%) and Native Hawaiian or other Pacific Islanders (1%). Lee et al. examined the mental health of Asian Americans. They found that young Asian Americans reported feeling parental pressure to succeed in their academics and difficulty communicating mental health issues with their families, as many Asian cultures dismiss these issues [23]. Asian Americans feeling the responsibility to succeed in school may prevent them from reporting any problems they may face, such as cyberbullying. Likewise, the challenge in communicating their mental health can also result in underreporting of the issues they may be encountering. Thus, schools and clinicians must educate and guide immigrant parents of these origins to overcome the stigma around mental health and provide a safe environment where children and adolescents can discuss their issues.

Research suggests that being bullied based on appearance can increase body dissatisfaction and eating disorders [24]. As depression, self-harm behaviors, body dissatisfaction, and eating disorders can all have lifelong consequences, it is essential to avert them from occurring. Our suggestions that may decrease cyberbullying are: (1) promoting behavioral health facilities in schools by having counsellors available on school grounds; (2) ensuring that children can report cyberbullying anonymously; (3) having educational seminars at schools, where children can learn what is considering bullying and cyberbullying, and the significant impact that cyberbullying can have; and (4) establishing guidelines of the consequences for being a precipitator of cyberbullying. Additionally, further examination into the factors such as tobacco, alcohol, and illicit substances may yield valuable information and serve as a direction for future studies. Our study has some notable and important limitations including selection bias, recall bias, and non-respondence bias. The reliability of survey data may also depend on the encouragement to provide accurate and honest answers. Severity of sadness and depression could not be measured uniformly as it is a perception of the individual. The survey questions are not part of a scale that can quantitatively measure the extent of certain responses or to make a diagnosis. Questions related to psychosocial aspects of the individual respondents are lacking, which can provide insights into individual perceptions or be used for forecasting and stronger conclusions. Some answer options may be interpreted differently by respondents, which leads them to make the responses unclear. For example, “somewhat agree” may represent different things to different subjects and ‘Yes’ or ‘no’ answer options can also be problematic as respondents may answer “no” if the option “only once” is not available. But overall, this database covers a large sample size of school-going adolescence and their experiences for cyberbully, which increases the overall power of the study.

5. Conclusions

Much of the population in the developed countries are typically online, with the internet increasing our dependency on online services. The active and steady encroachment into our private space seems inevitable. Cyberbullying has been associated with negative mental health outcomes, with the most commonly associated conditions being sadness, hopelessness, suicidality, anxiety, substance use/misuse, self-harm, and anxiety. We found similar results in our study. Further studies are warranted on the long-term outcomes of youths who experience cyberbullying, focusing on predominantly the population at risk, especially teenage girls who are most vulnerable to the negative effects of cyberbullying.