Psychosocial correlates of internet addiction in adolescents with depression and non-suicidal self-injury: a cross-sectional study

Abstract

Background

Adolescents with depression and non-suicidal self-injury (NSSI) represent a clinically vulnerable population with substantial emotional and behavioral difficulties. Although internet addiction is increasingly recognized among adolescents, its associated factors in adolescents with depression and NSSI, particularly peer-related stressors, remain insufficiently understood.

Methods

This cross-sectional study included adolescent psychiatric outpatients aged 12–17 years with depressive disorders and NSSI. Internet addiction was assessed using Young’s Internet Addiction Test (IAT). Depressive symptoms and anxiety symptoms were evaluated using the Beck Depression Inventory (BDI-13) and Beck Anxiety Inventory (BAI), respectively. Peer bullying was assessed using the victimization subscale of the Chinese revised Olweus Bully/Victim Questionnaire. Participants were categorized into low internet addiction (IAT ≤ 59) and moderate-to-severe internet addiction (IAT ≥ 60) groups. Group differences were examined using independent-samples t tests, and Pearson correlation analyses were conducted to assess bivariate associations. Multiple linear regression analyses were conducted to identify factors associated with internet addiction severity after adjusting for age and sex. Female-only sensitivity analyses were conducted to address the marked sex imbalance.

Results

Of the 92 participants, 53.4% exhibited moderate-to-severe internet addiction. Internet addiction severity was positively correlated with depressive symptoms, anxiety symptoms, and peer bullying. In the hierarchical regression analyses, peer bullying remained significantly associated with internet addiction in both the depressive symptom model (β = 0.310, p = 0.008) and the anxiety symptom model (β = 0.316, p = 0.008). Depressive symptoms also remained independently associated with internet addiction severity (β = 0.229, p = 0.034), whereas anxiety symptoms were no longer statistically significant after peer bullying was added to the final model (β = 0.193, p = 0.077). Female-only sensitivity analyses showed a broadly consistent pattern of findings.

Conclusions

Internet addiction is highly prevalent among adolescents with depression and NSSI. Peer bullying was a consistent psychosocial correlate of internet addiction, and depressive symptoms showed an independent association with internet addiction severity. These findings suggest that peer victimization may provide clinically relevant information beyond emotional symptoms when assessing internet addiction in this high-risk population.

Supplementary Information

The online version contains supplementary material available at https://doi.org/10.1186/s40359-026-05061-9.

Introduction

Adolescent depression is a major public health concern and is frequently accompanied by non-suicidal self-injury (NSSI) [1, 2]. Adolescents with co-occurring depression and NSSI often experience marked emotional distress, impaired psychosocial functioning, and increased risk for adverse mental health outcomes [3, 4]. As a result, this subgroup represents a clinically vulnerable population in whom both emotional and behavioral risk factors warrant careful assessment.

With the rapid expansion of digital technology, internet use has become an integral part of adolescents’ daily lives. Although the internet offers important opportunities for communication, learning, and social interaction, excessive or maladaptive use has raised growing clinical concern. Internet addiction has been associated with a range of adverse outcomes in adolescents, including depressive symptoms, anxiety, and psychosocial dysfunction [5–7]. These associations may be particularly relevant in adolescents with depression and NSSI, who often face substantial emotional dysregulation and maladaptive coping patterns.

In addition to emotional symptoms, social stressors have increasingly been recognized as important contributors to adolescent mental health. Among these, peer bullying is a common and impactful form of interpersonal adversity that has been linked to emotional distress, depressive symptoms, and maladaptive coping behaviors [8–11]. Adolescents exposed to bullying may be more likely to engage in excessive internet use as a means of escaping negative experiences, regulating distress, or compensating for difficulties in offline peer relationships [12, 13].

Theoretically, these associations can be understood within contemporary models of internet addiction. The Interaction of Person-Affect-Cognition-Execution (I-PACE) model proposes that predisposing individual characteristics and affective-cognitive responses to internal or external stressors interact with coping processes and executive control to increase vulnerability to addictive internet use [14, 15]. Similarly, Davis’s cognitive-behavioral model emphasizes the role of psychosocial vulnerability, maladaptive cognitions, and negative affect in the development and maintenance of pathological internet use [16]. A recent scoping review of theoretical models of problematic internet use further highlights the relevance of psychological formulation in understanding how individual vulnerability, affective distress, cognitive processes, and behavioral reinforcement may interact over time [17]. Within these frameworks, peer bullying may represent an interpersonal stressor that contributes to emotional dysregulation, negative self-evaluation, and avoidance-oriented coping, thereby increasing the likelihood that adolescents use online environments for escape, mood regulation, or social compensation.

Previous studies have reported significant associations between internet addiction and emotional symptoms, including depressive and anxiety symptoms, in general adolescent populations [18, 19]. Recent studies have further highlighted the evolving nature of this field. A recent systematic review of longitudinal studies suggested that problematic internet use and emotional symptoms in childhood and adolescence may mutually reinforce each other over time, with emotional difficulties increasing vulnerability to problematic internet use and problematic internet use potentially exacerbating later psychological difficulties [20]. Recent adolescent studies have also reported positive associations between internet addiction and non-suicidal self-injury, with loneliness and emotion-regulation processes proposed as potential psychological mechanisms [21]. In addition, emerging evidence indicates that bullying victimization may be associated with problematic internet gaming and problematic social media use among adolescents, suggesting that online addictive behaviors should be understood within a broader psychosocial and interpersonal context [22].However, most existing studies have focused on community-based samples or mixed clinical populations, with limited attention to high-risk adolescent psychiatric groups such as those with depression and non-suicidal self-injury (NSSI) [23]. This gap is clinically important because adolescents with depression and NSSI often experience more severe emotional dysregulation, interpersonal difficulties, and maladaptive coping patterns than adolescents in the general population. In addition, although emotional correlates of internet addiction have been widely examined, the potential contribution of peer-related stressors, particularly peer bullying, remains insufficiently understood in this vulnerable group [24, 25]. Existing evidence suggests that bullying victimization may be linked to internet addiction through mechanisms such as social withdrawal, distress regulation, and compensation for difficulties in offline peer relationships, but this hypothesis has rarely been examined in adolescent psychiatric outpatients with depression and NSSI [25, 26].

Therefore, guided by these theoretical models, the present study aimed to examine internet addiction and its associated psychosocial factors in adolescents with depression and NSSI, with particular attention to depressive symptoms, anxiety symptoms, self-esteem, and peer bullying. We hypothesized that greater internet addiction severity would be associated with higher levels of depressive symptoms, anxiety symptoms, and peer bullying, as well as lower self-esteem.

Methods

Study design and participants

This cross-sectional study was conducted among adolescent psychiatric outpatients with depressive disorders and non-suicidal self-injury (NSSI). Participants were consecutively recruited from the psychiatric outpatient clinic of Liuzhou Workers’ Hospital, Guangxi, China, during routine clinical visits.

Adolescents aged 12–17 years were eligible if they met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for depressive disorders [27]. Diagnoses were established according to DSM-5 criteria by licensed attending psychiatrists with at least 5 years of clinical experience in psychiatry. In the Chinese medical system, child and adolescent psychiatry is generally practiced within the broader specialty of psychiatry rather than through a separate board-certification system. The psychiatrists involved in diagnostic assessments routinely evaluated and treated children and adolescents in psychiatric outpatient settings and had substantial clinical experience with this population.

Exclusion criteria included schizophrenia spectrum disorders, bipolar disorder, and substance use disorders. Written informed consent was obtained from parents or legal guardians, and written assent was obtained from all adolescent participants prior to enrollment.

Measures

Internet addiction

Internet addiction was assessed using Young’s Internet Addiction Test (IAT), a widely used 20-item self-report questionnaire designed to evaluate the severity of internet-related behavioral problems [28]. The IAT has demonstrated good reliability and validity in Chinese adolescent populations. Each item is rated on a 5-point Likert scale, and total scores range from 20 to 100, with higher scores indicating greater severity of internet addiction. Based on established cut-off values, IAT scores were categorized as normal (20–39), mild (40–59), moderate (60–79), and severe (80–100). In the present sample, the IAT showed good internal consistency (Cronbach’s α = 0.870).

Depressive symptoms

Depressive symptom severity was assessed using the 13-item Beck Depression Inventory (BDI-13) [29]. Each item is rated on a 4-point scale, with higher total scores indicating more severe depressive symptoms. In the present sample, the BDI-13 showed good internal consistency (Cronbach’s α = 0.879).

Anxiety symptoms

Anxiety symptoms were assessed using the Beck Anxiety Inventory (BAI), a 21-item self-report measure in which higher scores indicate greater anxiety severity [30]. In the present sample, the BAI showed excellent internal consistency (Cronbach’s α = 0.908).

Self-esteem

Self-esteem was measured using the Rosenberg Self-Esteem Scale (SES), which consists of 10 items rated on a 4-point Likert scale [31]. Reverse scoring was applied according to the scoring instructions of the scale. Higher scores indicate higher levels of global self-esteem. In the present sample, the SES showed acceptable internal consistency (Cronbach’s α = 0.755).

Sleep duration

Average nightly sleep duration over the past month was self-reported by participants and recorded in hours.

Peer bullying

Peer bullying was assessed using the victimization subscale of the Chinese revised Olweus Bully/Victim Questionnaire [32]. The questionnaire included 7 items assessing common forms of peer victimization, including verbal ridicule, social exclusion, physical aggression, cyberbullying, coercion, mocking of personal characteristics, and disclosure of private information. Each item was rated on a 5-point frequency scale: 1 = never, 2 = once or twice in the past two months, 3 = two or three times per month, 4 = approximately once per week, and 5 = two or more times per week. Total scores were calculated by summing the 7 items, with higher scores indicating greater exposure to peer victimization. In the present sample, the peer bullying questionnaire showed good internal consistency (Cronbach’s α = 0.803).

Statistical analysis

Continuous variables are presented as mean ± standard deviation (SD), and categorical variables as number (percentage). Participants were categorized into two groups according to internet addiction severity: low internet addiction (IAT ≤ 59) and moderate-to-severe internet addiction (IAT ≥ 60). Group differences were examined using independent-samples t tests for continuous variables. Effect sizes for group comparisons were expressed as Cohen’s d.

Pearson correlation analyses were performed to examine bivariate associations between internet addiction severity and the main clinical variables. To further examine the incremental contribution of emotional symptoms and peer bullying to internet addiction severity, hierarchical linear regression analyses were conducted with IAT total score as the dependent variable. In each model, age and sex were entered in Step 1. In the depressive symptom model, BDI-13 was entered in Step 2 and peer bullying in Step 3. In the anxiety symptom model, BAI was entered in Step 2 and peer bullying in Step 3. Standardized regression coefficients (β), R², and adjusted R² are reported. Multicollinearity was assessed using variance inflation factors (VIFs). Given the marked sex imbalance in the sample, sensitivity analyses were additionally conducted among female participants only. In these female-only analyses, sex was not included as a covariate; hierarchical regression models were repeated with age entered in Step 1, emotional symptoms entered in Step 2, and peer bullying entered in Step 3.

Analyses were conducted using available-case data. Statistical analyses were performed using SPSS software (version 30.0; IBM Corp., Armonk, NY, USA). All tests were two-tailed, and p < 0.05 was considered statistically significant.

Results

Sample characteristics

A total of 92 adolescents were included in the study. The mean age was 14.86 ± 1.35 years, and 82 participants (89.1%) were female. The mean IAT total score was 60.34 ± 13.34. Mean scores for depressive symptoms, anxiety symptoms, self-esteem, and peer bullying were 22.92 ± 7.15, 28.16 ± 11.65, 19.10 ± 4.21, and 12.71 ± 5.32, respectively. The average sleep duration was 5.54 ± 1.76 h per night (Table 1).

Table 1.

Demographic and clinical characteristics of the participants

Variable	Total sample (N = 92)
Age (years), mean ± SD	14.86 ± 1.35
Female, n (%)	82 (89.1)
Internet addiction score (IAT total), mean ± SD	60.34 ± 13.34
Depression (BDI score), mean ± SD	22.92 ± 7.15
Anxiety (BAI score), mean ± SD	28.16 ± 11.65
Self-esteem (SES score), mean ± SD	19.10 ± 4.21
Sleep duration (hours per night), mean ± SD	5.54 ± 1.76
Peer bullying score, mean ± SD	12.71 ± 5.32

Values are presented as mean ± Standard deviation (SD) or number (percentage). Abbreviations: IAT Internet Addiction Test, BDI Beck Depression Inventory, BAI Beck Anxiety Inventory, SES Rosenberg Self-Esteem Scale, SD Standard deviation

Distribution of internet addiction severity

The distribution of internet addiction severity is shown in Table 2. In the total sample, 5 participants (5.4%) were classified as normal, 37 (40.2%) as mild, 8 (8.7%) as moderate, and 42 (45.7%) as severe. When using the predefined grouping criterion, 50 participants (53.4%) were classified as having moderate-to-severe internet addiction (Table 2).

Table 2.

Distribution of internet addiction severity

Internet addiction level	Score range	n (%)
Normal	20–39	5 (5.4)
Mild	40–59	37 (40.2)
Moderate	60–79	8 (8.7)
Severe	80–100	42 (45.7)

Group comparisons

Compared with adolescents in the low internet addiction group (IAT ≤ 59), those in the moderate-to-severe internet addiction group (IAT ≥ 60) reported significantly higher levels of peer bullying, depressive symptoms, and anxiety symptoms, and significantly lower self-esteem. No significant group difference was observed for sleep duration (Table 3).

Table 3.

Comparison between adolescents with low and moderate–severe internet addiction

Variable	Low IA (≤ 59) (n = 42)	Moderate–severe IA (≥ 60) (n = 50)	p value	Cohen’s d
Sleep duration (hours)	5.57 ± 1.68	5.50 ± 1.85	0.857	0.04
Self-esteem (SES)	20.63 ± 4.33	17.82 ± 3.67	0.001	0.71
Peer bullying	11.00 ± 4.56	14.21 ± 5.54	0.004	0.63
Depression (BDI)	20.02 ± 6.90	25.36 ± 6.47	< 0.001	0.8
Anxiety (BAI)	24.48 ± 9.60	31.26 ± 12.39	0.005	0.6

Values are presented as mean ± Standard deviation (SD). Effect sizes are presented as Cohen’s d. Statistically significant values (p < 0.05) are shown in bold. Abbreviations: IAT Internet Addiction Test, BDI Beck Depression Inventory, BAI Beck Anxiety Inventory, SES Rosenberg Self-Esteem Scale, SD Standard deviation

Correlation analyses

Pearson correlation analyses showed that internet addiction severity was positively correlated with depressive symptoms (r = 0.350, p < 0.01), anxiety symptoms (r = 0.326, p < 0.01), and peer bullying (r = 0.373, p < 0.01), and negatively correlated with self-esteem (r = -0.327, p < 0.01). Internet addiction severity was not significantly correlated with sleep duration (r = 0.061). Depressive symptoms were strongly correlated with anxiety symptoms (r = 0.697, p < 0.01) (Table 4).

Table 4.

Correlations between internet addiction and clinical variables

Variable	1	2	3	4	5	6
1. IAT total	1
2. BDI total	0.350**	1
3. BAI total	0.326**	0.697**	1
4. Self-esteem	-0.327**	-0.590**	-0.542**	1
5. Peer bullying	0.373**	0.382**	0.383**	-0.244*	1
6. Sleep duration	0.061	-0.234*	-0.058	0.325**	-0.131	1

Abbreviations: IAT Internet Addiction Test, BDI Beck Depression Inventory, BAI Beck Anxiety Inventory, SES Rosenberg Self-Esteem Scale

Values are Pearson correlation coefficients. * p < 0.05; ** p < 0.01

Hierarchical linear regression analyses

To further examine the incremental contribution of emotional symptoms and peer bullying to internet addiction severity, hierarchical linear regression analyses were conducted.

In the depressive symptom model, age and sex entered in Step 1 explained little variance in internet addiction severity (R² = 0.016). Adding depressive symptoms in Step 2 increased the explained variance to 11.6% (R² = 0.116). After peer bullying was added in Step 3, the explained variance further increased to 18.7% (R² = 0.187). In the final model, both depressive symptoms (β = 0.229, p = 0.034) and peer bullying (β = 0.310, p = 0.008) were significantly associated with internet addiction severity, whereas age and sex were not significant (Table 5).

Table 5.

Hierarchical linear regression analysis of depressive symptoms and peer bullying associated with internet addiction severity

Predictor	Step 1 β	p value	Step 2 β	p value	Step 3 β	p value
Age	-0.127	0.238	-0.050	0.636	0.036	0.738
Female (vs. male)	-0.037	0.732	-0.016	0.879	0.060	0.559
Depressive symptoms (BDI-13)	—	—	0.325	0.002	0.229	0.034
Peer bullying	—	—	—	—	0.310	0.008

Model fit indices
Model	R²			Adjusted R²
Step 1	0.016			-0.006
Step 2	0.116			0.085
Step 3	0.187			0.149

Internet addiction severity was indexed by the Internet Addiction Test total score. Step 1 included age and sex. Step 2 additionally included depressive symptoms (BDI-13). Step 3 additionally included peer bullying. β = standardized regression coefficient. Statistically significant values are shown in bold

In the anxiety symptom model, age and sex entered in Step 1 also explained little variance (R² = 0.016). Adding anxiety symptoms in Step 2 increased the explained variance to 10.2% (R² = 0.102). After peer bullying was added in Step 3, the explained variance further increased to 17.4% (R² = 0.174). In the final model, peer bullying remained significantly associated with internet addiction severity (β = 0.316, p = 0.008), whereas anxiety symptoms were no longer statistically significant (β = 0.193, p = 0.077) (Table 6). Collinearity diagnostics indicated no substantial multicollinearity in either model, with all VIF values below 2.

Table 6.

Hierarchical linear regression analysis of anxiety symptoms and peer bullying associated with internet addiction severity

Predictor	Step 1 β	p value	Step 2 β	p value	Step 3 β	p value
Age	-0.127	0.238	-0.071	0.5	0.019	0.855
Female (vs. male)	-0.037	0.732	-0.037	0.721	0.047	0.654
Anxiety symptoms (BAI total)	—	—	0.298	0.005	0.193	0.077
Peer bullying	—	—	—	—	0.316	0.008

Model fit indices
Model	R²			Adjusted R²
Step 1	0.016			-0.006
Step 2	0.102			0.071
Step 3	0.174			0.135

Internet addiction severity was indexed by the Internet Addiction Test total score. Step 1 included age and sex. Step 2 additionally included anxiety symptoms (BAI total). Step 3 additionally included peer bullying. β = standardized regression coefficient. Statistically significant values are shown in bold

Sensitivity analyses

Given the marked sex imbalance in the sample, female-only sensitivity analyses were conducted using available-case data. The pattern of findings was broadly consistent with the main analyses. In the depressive symptom model, peer bullying remained significantly associated with internet addiction severity after adjustment for age and depressive symptoms (β = 0.366, p = 0.001), and depressive symptoms also remained significant (β = 0.270, p = 0.013). In the anxiety symptom model, peer bullying remained significantly associated with internet addiction severity after adjustment for age and anxiety symptoms (β = 0.388, p < 0.001), whereas anxiety symptoms were not statistically significant in the final model (β = 0.171, p = 0.117). The detailed results of the female-only sensitivity analyses are presented in Supplementary Table S1.

Discussion

In this cross-sectional study of adolescents with depression and non-suicidal self-injury (NSSI), we found that internet addiction was highly prevalent and was associated with emotional symptoms, self-esteem, and peer-related stressors. More than half of the participants exhibited moderate-to-severe internet addiction, indicating a substantial burden of maladaptive internet use in this high-risk clinical population. These findings may be understood within contemporary theoretical models of internet addiction, which emphasize the interaction between psychosocial vulnerability, emotional distress, cognitive-affective responses, and maladaptive coping processes.

The prevalence observed in the present study appears to be higher than that reported in general adolescent populations [33, 34]. This elevated rate may reflect the substantial emotional distress, interpersonal difficulties, and maladaptive coping tendencies commonly experienced by adolescents with depression and NSSI. In this clinical subgroup, internet addiction may not simply reflect excessive screen exposure, but may partly represent a maladaptive self-regulatory strategy. Online environments may provide temporary distraction from negative affect, perceived control over social interaction, or compensation for difficulties in offline relationships. Although such experiences may reduce distress in the short term, repeated reliance on internet use for emotional regulation or social compensation may reinforce addictive patterns over time.

A key finding of this study was that peer bullying showed a consistent association with internet addiction. Adolescents with higher internet addiction reported greater exposure to peer bullying, and peer bullying remained significantly associated with internet addiction severity after adjustment for demographic factors and emotional symptoms. These findings suggest that peer victimization may represent a clinically relevant psychosocial correlate of internet addiction in adolescents with depression and NSSI. Peer bullying is a salient interpersonal stressor during adolescence and may contribute to emotional dysregulation, social withdrawal, negative self-evaluation, and avoidance-oriented coping [9–11].

Adolescents exposed to bullying may increasingly rely on online environments not only for emotional relief, but also for avoidance of threatening offline interpersonal experiences and compensation for unmet social needs. For adolescents with depression and NSSI, who often experience heightened emotional sensitivity, interpersonal insecurity, and maladaptive coping tendencies, online environments may provide temporary experiences of distraction, perceived control, social connectedness, or emotional validation. This interpretation is consistent with the I-PACE model and Davis’s cognitive-behavioral model, which propose that psychosocial vulnerability, negative affect, maladaptive cognitions, and coping-related processes may contribute to the development and maintenance of pathological or addictive internet use [14–16].

Depressive symptoms also showed an independent association with internet addiction. In the depressive symptom model, depressive symptoms remained significantly associated with internet addiction severity even after peer bullying was added, indicating that depressive symptoms contributed to internet addiction above and beyond demographic factors and bullying experiences. This finding is consistent with previous studies suggesting that adolescents with more severe depressive symptoms may engage in excessive internet use to cope with sadness, hopelessness, loneliness, or reduced motivation for offline activities [6, 35]. In adolescents with depression and NSSI, internet use may therefore serve as a short-term coping strategy for regulating negative affect, but repeated reliance on this strategy may reinforce maladaptive behavioral patterns and contribute to more severe internet addiction over time.

Although anxiety symptoms were significantly associated with internet addiction in the bivariate analyses and in the second step of the anxiety model, this association was attenuated after peer bullying was added and no longer reached statistical significance. One possible explanation is that anxiety symptoms may partly overlap with broader bullying-related distress or interpersonal insecurity, reducing their independent contribution in the final model. This pattern may suggest that, in adolescents with depression and NSSI, internet addiction is more closely linked to depressive affect and peer-related stress than to anxiety symptoms per se. However, given the cross-sectional design, this interpretation should be considered exploratory.

Self-esteem also showed meaningful associations with internet addiction. Adolescents in the moderate-to-severe internet addiction group had significantly lower self-esteem, and self-esteem was negatively correlated with internet addiction severity. This finding suggests that negative self-evaluation may be part of the broader psychosocial vulnerability profile associated with internet addiction in adolescents with depression and NSSI. Lower self-esteem may increase sensitivity to peer rejection and reduce confidence in offline social interactions, making online environments more attractive for social compensation or emotional escape [16]. However, self-esteem was not included in the final regression models, and its independent contribution should therefore be interpreted cautiously.

From a clinical perspective, these findings highlight the importance of assessing both emotional symptoms and peer-related stressors when evaluating internet addiction in adolescents with depression and NSSI. In particular, screening for peer bullying may provide useful information about the psychosocial context in which internet addiction occurs. Interventions aimed at reducing internet addiction in this population may benefit from addressing depressive symptoms, bullying-related stress, self-esteem, coping strategies, and interpersonal functioning. Rather than focusing only on internet use behavior itself, clinicians may need to consider the emotional and social functions that excessive internet use serves for adolescents experiencing depression, NSSI, and peer victimization.

Several limitations should be acknowledged. First, the cross-sectional design precludes causal inference regarding the observed associations. Although the findings are consistent with theoretical models suggesting pathways from peer stress and emotional vulnerability to internet addiction, longitudinal studies are needed to clarify temporal and directional relationships. Second, the study was conducted in a single clinical setting, which may limit the generalizability of the findings. Third, the sample was predominantly female, with only 10 male participants. Although female-only sensitivity analyses showed a broadly consistent pattern of findings, the small number of male participants precluded reliable sex-specific analyses. Therefore, the findings should not be interpreted as evidence of sex differences and should be generalized to male adolescents with caution. Fourth, all variables were assessed by self-report, which may have introduced reporting bias and shared-method variance. Fifth, participants were recruited from a clinical treatment setting, and treatment stage or ongoing psychological and pharmacological interventions were not systematically assessed. Such factors may have influenced emotional symptoms, self-esteem, peer-related experiences, and internet-related behaviors. Finally, the study did not distinguish between specific types of internet activities, such as gaming, social media use, short-video use, or online communication. Future longitudinal studies with more detailed assessment of treatment status, internet use patterns, and psychosocial mechanisms are needed to further clarify how peer bullying, emotional symptoms, self-esteem, and internet addiction interact over time in adolescents with depression and NSSI.

Conclusion

Internet addiction is highly prevalent among adolescents with depression and non-suicidal self-injury. Peer bullying was identified as a consistent psychosocial correlate of internet addiction, while depressive symptoms showed a robust independent association. Lower self-esteem was also associated with more severe internet addiction. These findings suggest that internet addiction in this high-risk clinical population should be understood within a broader psychosocial framework involving emotional distress, peer victimization, self-evaluative vulnerability, and maladaptive coping. Assessment and intervention strategies may benefit from addressing not only internet use behavior, but also the interpersonal and emotional difficulties that may underlie excessive or addictive internet use.

Abbreviations

IAT

Internet Addiction Test

BDI

Beck Depression Inventory

BAI

Beck Anxiety Inventory

SES

Rosenberg Self-Esteem Scale

NSSI

Non-suicidal self-injury

Authors’ contributions

X.H. conceived and designed the study. R.W., Y.C., C.L., J.M. and Y.W. collected the data. T.H. and Q.P. performed the statistical analyses and interpreted the data. X.H. drafted the manuscript. Y.Z. oversaw the overall paper and made final revisions to it. All authors critically revised the manuscript for important intellectual content and approved the final version.

Funding

This work is funded by National Natural Science Foundation of China (82302543), Wuxi Science and Technology Bureau (K20231047), Wuxi soft science research projects (grant number: KX-23-C178), Liuzhou Association for Science and Technology Soft Science Research (Liuzhou AST Soft Science Research No. 20220130) Jiangsu Shuangchuang Talent Program (JSSRC 2024597).

Data availability

The datasets and analysed during zhe current study available from zhe corresponding author on reasonable request.

Declarations

Ethics approval and consent to participate

The study was approved by the Ethics Committee of Liuzhou Workers’ Hospital (approval number: KY2020040). The study was conducted in accordance with the Declaration of Helsinki and relevant local regulations. Written informed consent was obtained from participants’ parent(s) or legal guardian(s), and written assent was obtained from all adolescent participants prior to participation.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.