Associations Between Cybervictimization in Adolescence and Mental Health Four Years Later: A Nationally Representative Study of Canadian Youth

Abstract

The objective of the study was to examine longitudinal associations between cybervictimization and mental health and suicidal behavior among youth. Data for 4930 youth were drawn from the Canadian Health Survey on Children and Youth (CHSCY), a nationally representative cohort survey with collection waves in 2019 and 2023. Logistic regression models predicted self-reported mental health outcomes in 2023 (depression, anxiety, suicidal ideation and suicide attempt) from cybervictimization in 2019, adjusting for baseline mental health, socio-demographic covariates, frequency of online activities, and experience of traditional bullying victimization. Fully adjusted models suggested that cybervictimization in 2019 was significantly associated with youth depression (OR: 2.41, 95%CI: 1.68, 3.45), anxiety (OR: 1.33, 95%CI: 1.05, 1.68), and suicidal ideation (OR: 1.95, 95%CI: 1.12, 3.39) four years later. The association with suicide attempt was positive but not statistically significant in adjusted models (OR: 1.77, 95%CI: 0.99, 3.16). These results suggest that youth who experience cybervictimization in adolescence are at elevated risk of mental health problems lasting into young adulthood, an association which appears to be independent of the experience of traditional face-to-face victimization. Bullying prevention programs targeted towards reducing cybervictimization may warrant further study, with the aim of informing approaches to improve adolescent mental health.

1. Introduction

Peer victimization is widely recognized as a significant risk factor for mental health challenges among adolescents, including depression, anxiety, suicidal thoughts, and suicide attempts [1]. In the digital age, such harm is no longer confined to face-to-face interaction, and cybervictimization has emerged as a pervasive and potentially severe form of abuse. Defined as the deliberate use of electronic media to cause harm, cybervictimization encompasses behaviors such as harassment, social exclusion, and the non-consensual sharing of personal information, aimed at instilling fear, or causing harm, embarrassment, or exclusion [2]. The popularity of text messaging and social media use among youth has undergone explosive growth, with many adolescents reporting engaging in these behaviors virtually constantly [2,3]. This heightened connectivity increases opportunities for harmful interactions, making the study of cybervictimization timelier than ever.

Cybervictimization is characterized by several features that differentiate it from traditional face-to-face victimization—notably, the potential for permeation into victims’ home lives. Unlike traditional victimization, cybervictimization is not limited by physical proximity of the victim to the perpetrator and can occur at any time of day. Cybervictimization is also often public, and some acts of cybervictimization, such as posting hurtful information online, are potentially permanent. These features are thought to exacerbate the negative impacts of cybervictimization on adolescent mental health [4].

Despite a considerable and growing number of studies examining associations between the experience of cybervictimization and mental health among adolescents, the field is far from a definitive conclusion, and significant gaps still exist. Notably, much of the literature on these associations comes from cross-sectional studies, which cannot rule out reverse causality (i.e., the possibility that pre-existing mental health issues predispose youth to becoming victims of cybervictimization) [5]. A recent meta-analysis identified 27 longitudinal studies of the association between cybervictimization in adolescence and later mental health (primarily including depression and anxiety) [6]. However, sample sizes for the majority of these studies were relatively small, and notably, none of these studies employed population-representative samples.

Longitudinal evidence for associations between cybervictimization and suicidal behavior is equally scarce. Another recent meta-analysis identified 11 longitudinal studies examining the association between cybervictimization and suicidal ideation, the results of which mostly supported the existence of a significant positive relationship [7]. Of these, only one study employed a large population-representative cohort (Canada), reporting significant positive associations between cybervictimization at ages 13 and 15 and a composite variable including suicidal ideation and/or attempt two years later [8]. Only one identified study examined associations between cybervictimization and suicide attempt independently, and was conducted in a sample of at-risk adolescents [9].

Critically, only one of the studies identified in the meta-analysis employed a design that adjusted for the presence of the suicidal outcome at baseline, and only half adjusted for antecedents of mental illness, both of which are important for ruling out reverse causality [7]. In their population-representative cohort study, Perret and colleagues reported that associations between cybervictimization and suicidal ideation and/or attempt were rendered non-significant after adjusting for suicidal ideation/attempt at baseline [8].

The purpose of the present study was to build on these previous findings, examining longitudinal associations between cybervictimization and mental health, in a population-representative sample of youth, while adjusting for baseline mental health. Specifically, we sought to investigate whether cybervictimization in adolescence was associated with later depression, anxiety, suicidal ideation, and suicide attempt independent of baseline mental health and traditional victimization. It was hypothesized that cybervictimization would be uniquely associated with indicators of poorer mental health four years later.

2. Materials and Methods

2.1. Data Source

Data were drawn from the longitudinal file of the Canadian Health Survey on Children and Youth (CHSCY), a nationally representative survey administered by Statistics Canada, with collection waves in 2019 and 2023 [10]. Overall retention rate from 2019 to 2023 was 46.1%.

Participants were N = 4930 youth (aged 12–17 at wave 1) who participated in both waves. When using survey and bootstrap weights to account for survey design and non-response, the sample is considered representative of adolescents aged 12–17 living in Canada’s 10 provinces in 2019. As the 2023 survey did not cover Canada’s three territories, adolescents living in the territories in 2019 were excluded from the longitudinal sample. Adolescents who moved from one of the provinces to the territories were, however, followed up with. In 2019, separate electronic questionnaires were administered to adolescents aged 12–17 and the person most knowledgeable about the adolescent (usually a birth, adoptive, or step-parent (98%). In 2023, adolescents aged 16–17 and their parents completed separate electronic questionnaires; youth aged 18–21 served as the respondents for all items. All respondents gave their informed consent to participate. For youth aged 12–14, parental consent was also obtained. This study is a secondary analysis of data collected by Statistics Canada under the provisions of the Statistics Act. 1970-71-72. C.15, s.1. All Statistics Canada surveys are subject to ethical review by the Data Ethics Secretariat. As a secondary analysis of anonymous data, the present study falls under Article 2.4 of the Canadian Tri Council Policy Statement on Ethical Conduct for Research Involving Humans: “REB review is not required for research that relies exclusively on secondary use of anonymous information, […] so long as the process of data linkage or recording or dissemination of results does not generate identifiable information”. Data for the present study are available to researchers through Statistics Canada’s Research Data Centre program.

2.2. Exposure

Cybervictimization was reported by adolescents in 2019 using three items. Participants were asked to rate the frequency in the past year with which they had been threatened or insulted online, had hurtful information posted about them, and been purposefully excluded from an online community. Response options for all items were “never”, “a few times a year”, “monthly”, “weekly”, and “daily”. For the present study, cybervictimization was dichotomized to compare youth who had experienced any cybervictimization in the past year to those who had not. This binary categorization was chosen due to the observed high frequency of youth who had never experienced cybervictimization, as well as low frequencies and uneven distribution of youth experiencing more frequent cybervictimization. Additionally, we had conceptual concerns about grouping reported levels of cybervictimization (e.g., multiple types of cybervictimization may or may not be part of the same victimization incident; experiencing multiple types of cybervictimization may or may not carry the same weight as experiencing one type multiple times). This binary categorization approach has been utilized in previous Statistics Canada research [11], as well as in other studies of cybervictimization using similar measures [12].

2.3. Outcomes

Mental health outcomes were assessed in 2019 and 2023. Items from the Washington Group/UNICEF child functioning module [13], parent-reported from ages 12–17 (all participants in 2019; those aged 16–17 in 2023) and self-reported beginning at age 18 (participants aged 18–21 in 2023) were used to assesses frequency of anxiety (“anxious, nervous, or worried”) and depression (“very sad or depressed”), on a 5-point scale: ‘never’; ‘a few times a year’; ‘monthly’; ‘weekly’; ‘daily’. In alignment with the tabulation plan for the module [14], youth who experienced symptoms daily were considered to have a functional difficulty in the corresponding domain.

Suicidal ideation in the past 12 months was self-reported beginning at age 15 with the question: “In the past 12 months, did you ever seriously consider attempting suicide or taking your own life?”. Lifetime suicide attempt was assessed using the question: “Have you ever attempted suicide or tried taking your own life?”. In 2023, those who responded in the affirmative were additionally asked if they had attempted suicide during the past 12 months.

2.4. Covariates

Several characteristics which may serve as confounding variables due to their known associations with both cybervictimization and mental health were considered as covariates. Participant age in 2023 was collapsed into 3 groups: 16–17, 18–19, and 20–21. Youth reported their gender in 2023 (“male gender”; “female gender”; “gender diverse”). Due to the small number of youth identifying as gender diverse, Statistics Canada created a two-category gender variable, randomizing gender diverse individuals into one of two categories: boys+ and girls+. For example, girls+ includes youth who identified as female as well as some youth who self-identified as gender diverse [15]. Parents reported on their marital status in 2019. We compared those who were single, widowed, separated or divorced to the reference category of those who were married or living common-law with a partner. Household low-income status was calculated based on the total household before-tax income in 2019, the number of people in the household, and the corresponding low-income measure (LIM) threshold [16].

Adolescents reported the frequency with which they engaged in three online activities in 2019: social networking, instant or video messaging, and online gaming. We compared those who reported engaging in each of these activities “constantly” to those who reported engaging less often (from “never” to “several times a day”) [3].

Additionally, we considered adolescents’ experience of face-to-face or ‘traditional’ victimization in 2019. Victimization was assessed using 7 items assessing the frequency of various victimization experiences (e.g., “made fun of, called names, or insulted by others”; “pushed, shoved, tripped or spit on by others”) from “never” to “daily”. As with cybervictimization, traditional victimization was dichotomized (any victimization in the past year vs. none).

2.5. Analysis

All analyses were conducted using R version 4.4.1 [17]. To account for the complex survey design and survey non-response, all analyses were weighted using both survey weights and bootstrap weights with 1000 resamples, using the Survey package (version 4.3) [18].

Proportions of youth in each cybervictimization category who experienced anxiety, depression, suicidal ideation, and suicide attempt in 2023 were examined using cross-tabulations.

The choice of analytical technique was constrained by the necessity of using replicate bootstrap weights, as required by Statistics Canada. Associations between the experience of cybervictimization in 2019 and mental health difficulties in 2023 were therefore assessed using logistic regression models. A second set of models adjusted for mental health in 2019, as well as demographic covariates and frequency of online activities. Final models additionally included experiences of traditional face-to-face victimization.

As suicidal ideation and attempt were only assessed as of age 15, adolescents aged 12–14 in 2019 were coded as ‘0’ on these measures at baseline. This approach was chosen in order to increase sample size for the models predicting suicidality, allowing enough power to detect effects for this relatively rare outcome. As this approach may be biased, a sensitivity analysis was conducted, wherein models predicting suicidal ideation and attempt were adjusted for functional difficulty with depression in 2019 (available for all youth) rather than suicidality.

Additionally, because the respondent for the Washington Group/UNICEF measures of depression and anxiety changed at age 18 (from parent- to self-report), we conducted a sensitivity analysis restricting these analyses to youth who were at least 18 years of age in 2023 (n = 3633) (i.e., only those who self-reported depression and anxiety).

3. Results

3.1. Descriptive Statistics

Characteristics of the cohort are presented in

Table 1. Characteristics of the study population: adolescents aged 12–17 in 2019 living in Canada’s 10 provinces.

	Proportion	95%CI
2019
Cybervictimization
Never	76.6	74.9	78.2
At least once	23.4	21.8	25.1
Traditional victimization
Never	31.8	30.0	33.7
At least once	68.2	66.3	70.0
Parent marital status
Single, separated, divorced, or widowed	18.1	16.2	20.1
Married or common law	81.9	79.9	83.8
Family low income	25.7	23.9	27.7
Frequent online activities
Social media (“constantly”)	14.6	12.0	17.5
Messaging (“constantly”)	14.5	11.9	17.6
Gaming (“constantly”)	5.5	4.3	7.2
Depression	1.6	1.1	2.2
Anxiety	5.4	4.2	6.8
Suicidal ideation (past 12 months)	13.3	11.7	15.1
Suicide attempt (lifetime)	6.3	5.1	7.7
2023
Depression	5.9	4.6	7.6
Anxiety	21.2	16.5	26.9
Suicidal ideation (past 12 months)	12.1	11.0	13.4
Suicide attempt (past 12 months)	2.4	1.9	3.2
Gender: girls+ 1	50.7	48.5	52.8

¹ Two-category gender variable with random assignment of non-binary youth, for confidentiality purposes. Girls+ includes youth who identified as female as well as some youth who self-identified as gender diverse.

. In 2019, about three in four youth (76.6%) had never experienced cybervictimization in the previous year; 12.1% reported one of the three types of cybervictimization a few times a year, and the remaining 11.4% had experienced either more than one type or more frequent cybervictimization (at least monthly). In 2023, 6% of youth had experienced daily symptoms of depression, and 21% experienced daily anxiety. In the past year, 12% of youth had experienced suicidal ideation, and 2.4% had attempted suicide.

3.2. Associations Between Cybervictimization and Mental Health

Proportions of youth with each of the assessed mental health difficulties were higher among those who had experienced cybervictimization in 2019 versus those who had not (

Table 2. Proportion of youth experiencing mental health difficulties in 2023, by experience of cybervictimization in 2019.

	Cybervictimization
	Never			At Least Once			p-Value 1
	%	95%CI		%	95%CI
Depression	4.3	3.2	5.8	11.2	8.3	14.8	<0.001
Anxiety	19.2	14.5	25.0	28.0	22.5	34.2	<0.001
Suicidal ideation	10.6	9.4	12.0	17.2	14.4	20.5	<0.001
Suicide attempt	1.9	1.4	2.7	4.2	2.7	6.5	0.005

¹ p-value based on Rao–Scott chi-square test.

). For example, 17% of youth who had experienced cybervictimization in 2019 experienced suicidal ideation in 2023, compared with 11% of youth who had not experienced cybervictimization.

Results of logistic regression models predicting mental health difficulties are presented in

Table 3. Results of logistic regression models predicting functional difficulty with depression in 2023 from cybervictimization in 2019.

	Crude Model			Adjusted Model 1			Adjusted Model 2
	OR	95%CI		OR	95%CI		OR	95%CI
Cybervictimization 2019	2.78 *	2.01	3.83	2.70 *	1.93	3.78	2.41 *	1.68	3.45
Depression 2019				5.98 *	2.77	12.90	5.63 *	2.63	12.08
Single parent				1.27	0.82	1.96	1.25	0.81	1.94
Low income				1.23	0.83	1.80	1.26	0.85	1.86
Age group: 18–19 (vs. 16–17)				2.75 *	1.48	5.12	2.75 *	1.48	5.11
Age group: 20–21 (vs. 16–17)				2.89 *	1.63	5.11	2.93 *	1.66	5.16
Gender (girls+ vs. boys+) 1				1.59 *	1.12	2.25	1.57 *	1.11	2.22
Frequent social networking				0.98	0.58	1.67	0.98	0.58	1.67
Frequent messaging				0.84	0.48	1.45	0.82	0.47	1.42
Frequent gaming				1.62	0.83	3.17	1.64	0.84	3.20
Face-to-face victimization							1.48	0.93	2.35

* Association is statistically significant (p < 0.05). ¹ Two-category gender variable with random assignment of non-binary youth, for confidentiality purposes.

,

Table 4. Results of logistic regression models predicting functional difficulty with anxiety in 2023 from cybervictimization in 2019.

	Crude Model			Adjusted Model 1			Adjusted Model 2
	OR	95%CI		OR	95%CI		OR	95%CI
Cybervictimization 2019	1.64 *	1.33	2.01	1.52 *	1.22	1.89	1.33 *	1.05	1.68
Anxiety 2019				4.28 *	2.65	6.90	4.08 *	2.57	6.46
Single parent				1.40 *	1.05	1.87	1.38 *	1.03	1.84
Low income				0.77	0.58	1.02	0.80	0.60	1.06
Age group: 18–19 (vs. 16–17)				5.08 *	3.49	7.38	5.08 *	3.50	7.37
Age group: 20–21 (vs. 16–17)				5.08 *	3.53	7.32	5.21 *	3.63	7.48
Gender 1 (girls+ vs. boys+)				2.66 *	2.15	3.29	2.63 *	2.12	3.26
Frequent social networking				1.02	0.72	1.45	1.03	0.73	1.46
Frequent messaging				0.91	0.65	1.26	0.87	0.63	1.20
Frequent gaming				1.41	0.84	2.35	1.44	0.85	2.43
Face-to-face victimization							1.57	1.22	2.03

* Association is statistically significant (p < 0.05). ¹ Two-category gender variable with random assignment of non-binary youth, for confidentiality purposes.

and

Table 5. Results of logistic regression models predicting past-year suicidal ideation and attempt in 2023 from cybervictimization in 2019.

Outcome: Suicidal Ideation
Predictor	Crude Model			Adjusted Model 1			Adjusted Model 2
	OR	95%CI		OR	95%CI		OR	95%CI
Cybervictimization 2019	1.75 *	1.36	2.25	2.11 *	1.25	3.54	1.95 *	1.12	3.39
Suicidal ideation 2019				1.81	0.79	4.18	1.78	0.76	4.15
Single parent				1.16	0.57	2.35	1.15	0.57	2.35
Low income				1.25	0.57	2.77	1.28	0.57	2.87
Age group: 18–19 (vs. 16–17)				0.92	0.48	1.78	0.92	0.48	1.80
Age group: 20–21 (vs. 16–17)				0.86	0.39	1.89	0.88	0.39	1.95
Gender 1 (girls+ vs. boys+)				1.09	0.62	1.90	1.09	0.62	1.91
Frequent social networking				0.53	0.19	1.43	0.52	0.19	1.43
Frequent messaging				1.65	0.61	4.46	1.62	0.60	4.37
Frequent gaming				1.86	0.55	6.30	1.88	0.55	6.38
Face-to-face victimization							1.34	0.64	2.81
Cybervictimization 2019	2.22 *	1.27	3.88	1.87 *	1.08	3.22	1.77	0.99	3.16
Suicide attempt (lifetime) 2019				7.52 *	3.06	18.50	7.25 *	2.99	17.57
Single parent 2019				1.17	0.57	2.38	1.17	0.57	2.39
Low income				1.23	0.56	2.70	1.25	0.57	2.78
Age group: 18–19 (vs. 16–17)				0.85	0.44	1.66	0.85	0.44	1.67
Age group: 20–21 (vs. 16–17)				0.65	0.34	1.25	0.66	0.34	1.28
Gender 1 (girls+ vs. boys+)				1.00	0.57	1.78	1.01	0.57	1.79
Frequent social networking				0.57	0.22	1.48	0.56	0.22	1.47
Frequent messaging				1.51	0.57	4.04	1.50	0.56	3.99
Frequent gaming				1.77	0.50	6.27	1.79	0.51	6.37
Face-to-face victimization							1.24	0.61	2.55

* Association is statistically significant (p < 0.05). ¹ Two-category gender variable with random assignment of non-binary youth, for confidentiality purposes.

. In crude models, experience of cybervictimization in 2019 was associated with all four outcomes in 2023. These associations persisted when adjusting for demographic covariates, mental health in 2019, and frequency of online activities. In final models additionally adjusted for the experience of face-to-face victimization, experiencing cybervictimization was associated with higher odds of depression, anxiety and suicidal ideation four years later. The association with suicide attempt was attenuated after adjusting for the experience of face-to-face victimization.

Interactions between cybervictimization and gender were non-significant in all models.

3.3. Sensitivity Analysis

Sensitivity analyses predicting suicidal outcomes in 2023, adjusting for depression in 2019, are available in supplemental

Table A1. Results of logistic regression models predicting suicidal ideation in 2023 from cybervictimization in 2019, with adjustment for depression at baseline.

	Crude Model			Adjusted Model 1			Adjusted Model 2
	OR	95%CI		OR	95%CI		OR	95%CI
Cybervictimization 2019	1.75 *	1.36	2.25	1.62 *	1.23	2.13	1.43 *	1.03	1.97
Depression 2019				2.72 *	1.31	5.64	2.61 *	1.26	5.41
Single parent 2019				1.39	0.98	1.96	1.32	0.93	1.88
Low income				1.00	0.71	1.41	1.04	0.73	1.48
Age group: 18–19 (vs. 16–17)				1.35 *	1.00	1.83	1.39 *	1.02	1.90
Age group: 20–21 (vs. 16–17)				1.03	0.75	1.42	1.09	0.79	1.52
Gender+ (girls vs. boys)				1.88 *	1.46	2.42	1.84 *	1.42	2.37
Frequent social networking				0.77	0.50	1.19	0.76	0.49	1.18
Frequent messaging				1.47	0.96	2.27	1.43	0.94	2.19
Frequent gaming				1.19	0.69	2.04	1.15	0.66	2.00
Face-to-face victimization							1.50 *	1.03	2.20

* Association is statistically significant (p < 0.05).

and

Table A2. Results of logistic regression models predicting suicide attempt in 2023 from cybervictimization in 2019, with adjustment for depression at baseline.

	Crude Model			Adjusted Model 1			Adjusted Model 2
	OR	95%CI		OR	95%CI		OR	95%CI
Cybervictimization 2019	2.22 *	1.27	3.88	2.17 *	1.28	3.70	2.04 *	1.15	3.63
Depression 2019				3.46	0.16	73.57	3.32	0.16	70.19
Single parent 2019				1.07	0.50	2.28	1.06	0.49	2.29
Low income				1.21	0.51	2.85	1.23	0.52	2.92
Age group: 18–19 (vs. 16–17)				0.93	0.48	1.77	0.92	0.48	1.78
Age group: 20–21 (vs. 16–17)				0.92	0.45	1.91	0.94	0.45	1.95
Gender+ (girls vs. boys)				1.11	0.64	1.94	1.11	0.63	1.95
Frequent social networking				0.51	0.19	1.37	0.51	0.19	1.36
Frequent messaging				1.66	0.62	4.47	1.64	0.61	4.41
Frequent gaming				1.45	0.36	5.88	1.47	0.36	5.96
Face-to-face victimization							1.26	0.61	2.61

* Association is statistically significant (p < 0.05).

(Appendix A). Results suggested that in fully adjusted models, the experience of cybervictimization in 2019 was significantly associated with both suicidal ideation (OR: 1.43, 95%CI: 1.03, 1.97) and suicide attempt (OR: 2.04, 95%CI: 1.15, 3.63). A second set of sensitivity analyses predicted depression and anxiety in 2023, restricted to those who self-reported at this time (i.e., those aged 18 and older). Full results are available in supplemental

Table A3. Results of logistic regression models predicting depression in 2023 for youth age 18+ from cybervictimization in 2019.

	Crude Model			Adjusted Model 1			Adjusted Model 2
	OR	95%CI		OR	95%CI		OR	95%CI
Cybervictimization 2019	2.42 *	1.74	3.37	2.51 *	1.77	3.55	2.27 *	1.55	3.32
Depression 2019				3.22 *	1.40	7.43	3.07 *	1.33	7.13
Single parent 2019				1.30	0.83	2.02	1.28	0.82	1.99
Low income				1.05	0.69	1.59	1.07	0.71	1.63
Age group: 20–21 (vs. 18–19)				1.07	0.74	1.55	1.09	0.74	1.59
Gender+ (girls vs. boys)				1.57 *	1.09	2.26	1.55 *	1.08	2.21
Frequent social networking				0.79	0.46	1.37	0.79	0.46	1.38
Frequent messaging				0.92	0.53	1.59	0.90	0.52	1.56
Frequent gaming				1.97	0.96	4.03	1.98	0.96	4.08
Face-to-face victimization							1.39	0.87	2.23

* Association is statistically significant (p < 0.05).

and

Table A4. Results of logistic regression models predicting anxiety in 2023 for youth age 18+ from cybervictimization in 2019.

	Crude Model			Adjusted Model 1			Adjusted Model 2
	OR	95%CI		OR	95%CI		OR	95%CI
Cybervictimization 2019	1.64 *	1.29	2.08	1.61 *	1.27	2.05	1.37 *	1.07	1.75
Anxiety 2019				3.27 *	2.16	4.97	3.14 *	2.08	4.75
Single parent 2019				1.41 *	1.04	1.91	1.39	1.03	1.87
Low income				0.70	0.53	0.92	0.73	0.55	0.97
Age group: 20–21 (vs. 18–19)				1.01	0.81	1.26	1.04	0.83	1.30
Gender+ (girls vs. boys)				2.90 *	2.32	3.61	2.85 *	2.28	3.55
Frequent social networking				0.94	0.64	1.37	0.96	0.65	1.39
Frequent messaging				0.94	0.67	1.33	0.89	0.64	1.25
Frequent gaming				1.46	0.82	2.59	1.50	0.83	2.71
Face-to-face victimization							1.71 *	1.26	2.31

* Association is statistically significant (p < 0.05).

(Appendix A) and suggest that cybervictimization in 2019 was significantly associated with both depression (OR: 2.27, 95%CI: 1.55, 3.32) and anxiety (OR: 1.37, 95%CI: 1.07, 1.75) in fully adjusted models.

4. Discussion

The results of this nationally representative longitudinal study demonstrate that Canadian adolescents who experienced cybervictimization at baseline were at significantly greater risk of depression, anxiety, and suicidal ideation four years later, even after adjusting for prior mental health, demographic characteristics, online activity patterns, and face-to-face victimization. These findings extend prior cross-sectional and longitudinal research by providing evidence for temporal precedence of cybervictimization, while also highlighting its independent contribution beyond traditional forms of peer victimization.

Mechanistically, cybervictimization may influence adolescent mental health via heightened stress and disruptions to social belonging [6]. As a social phenomenon, cybervictimization, like other forms of peer victimization, is thought to serve as a form of negative peer feedback, contributing to adolescents’ negative self-appraisals and exacerbating feelings of loneliness, social anxiety, depression, and low self-esteem [6]. Cybervictimization has also been hypothesized to contribute directly to suicidogenic cognitions. Massing-Schaffer and Nesi, in a 2019 theoretical paper, integrate theories from social media and suicide research to explain how several features of cybervictimization and online environments more broadly may trigger certain psychosocial risk factors for suicide [2]. For example, the capacity for online aggression to occur at any time, without spatial limits, may lead to feelings of hopelessness and entrapment, as adolescents feel they cannot escape victimization, even in their own homes [2]. Moreover, the public and shareable nature of cybervictimization can amplify social humiliation and lead to feelings of thwarted belongingness, which is thought to contribute to suicidal thoughts and behaviors [2,19].

In the present study, youth who experienced cybervictimization showed approximately twice the odds of depression and suicidal ideation four years later. The magnitude of these associations underscores cybervictimization as a considerable risk factor for negative mental health outcomes. These results are consistent with international longitudinal studies showing sustained mental health consequences of cybervictimization. For instance, research from the United States, Australia, and several European countries has found that cybervictimization predicts later depressive symptoms and suicidality independent of in-person bullying exposure, suggesting that the pervasive and enduring nature of cybervictimization may confer unique psychological harms [20,21].

The persistence of these associations over a four-year follow-up in the present study is particularly noteworthy, given that some authors have suggested that effects may be attenuated over time, as adolescents experience other stressors and protective factors in intervening years [6]. Some previous work suggests that the effects of cybervictimization in adolescence on mental health may persist well into adulthood, particularly if cybervictimization is experienced persistently across the teen years [22].

Whereas associations with suicidal ideation remained significant after adjustment for face-to-face victimization, the association with suicide attempt was attenuated after adjusting for face-to-face victimization. However, the confidence interval for this association suggests that though statistically non-significant, the association between cybervictimization and suicide attempt is generally positive, with a wide confidence interval reflecting the small number of youth reporting this severe outcome. As mentioned, unique features of cybervictimization such as its pervasiveness into adolescents’ home lives, as well as the permanence and public nature of some forms of cybervictimization, may make it particularly damaging to youth compared with face-to-face victimization [2]. Moreover, the fact that cybervictimization can be perpetrated anonymously may increase victims’ sense of powerlessness and mistrust in comparison to traditional victimization [2].

Previous work has suggested that certain groups may be at elevated risk of cybervictimization. For example, girls have been reported to experience cybervictimization more often than boys [23], and 2SLGBTQ+ youth (those who are two-spirit, lesbian, gay, bisexual, transgender, or queer, and those who use other terms related to gender or sexual diversity) are at higher risk compared with cisgender, heterosexual youth [11]. In the present study, we examined interactions with gender, finding that cybervictimization was associated with mental health similarly for boys and girls. A recent cross-sectional study reported that while 2SLGBTQ+ youth were at elevated risk of cybervictimization compared to their cisgender, heterosexual peers, no interactions between 2SLGBTQ+ identity and cybervictimization were found [11].

4.1. Limitations and Future Directions

Strengths of this study include its large, nationally representative sample, prospective design, and rigorous adjustment for baseline mental health, online behavior, and face-to-face victimization. However, some limitations warrant consideration.

First, cybervictimization was dichotomized for the present study, resulting in a loss of information on frequency of this experience. It is plausible that a dose–response relationship exists between cybervictimization and mental health outcomes, with more frequent experiences of cybervictimization being more strongly associated with poor mental health, a theory we were unable to test with the current conceptualization. Similarly, the survey did not include information on the perceived severity of cybervictimization incidents, a factor which may also influence the associations between cybervictimization and mental health.

Second, as suicidal outcomes were only assessed beginning at age 15, no information on baseline suicidal ideation or attempt was available for adolescents aged 12–14 at baseline. In main analyses, these adolescents were coded as having no suicidal ideation or attempt in the previous 12 months, in order to increase sample size. Sensitivity analysis adjusted for depression, which was available for all adolescents at baseline, instead of suicidal ideation/attempt. Results of these analyses showed that after adjusting for baseline depression, a common antecedent of suicidal thoughts and behaviors, cybervictimization in 2019 was significantly associated with both suicidal outcomes in 2023, lending confidence to our conclusions.

Third, the change in informant for depression and anxiety at age 18 (from parent- to self-report) may have introduced measurement variability. To mitigate this limitation, sensitivity analyses restricted to those 18 and older in 2023 were conducted, yielding similar results.

Fourth, though the results are representative of youth living in Canada’s provinces, the exclusion of adolescents living in the territories limits generalization to northern communities, where internet access patterns and cultural and social contexts may differ.

4.2. Conclusions

From a policy perspective, the present findings support the need for bullying prevention strategies that address cybervictimization specifically, given its association with mental health over and above the effect of traditional victimization. School-based interventions with digital citizenship components, as well as parent programs targeted at monitoring teens’ online activities, have shown promise in reducing cybervictimization [24,25]. Future research examining the long-term impacts of such programs on youth mental health is warranted.

Our findings, in conjunction with previous research, suggest that cybervictimization appears to be associated with poorer mental health for all adolescents, regardless of characteristics such as gender, suggesting that prevention efforts should be applied broadly. However, given the elevated prevalence of cybervictimization among certain groups, efforts to ensure that prevention strategies reach and are relevant to these groups may be warranted.