This study is a cross-sectional survey. Inclusion Criteria: (1) Age ≥ 18 years; (2) All patients met the diagnostic criteria for depression (12); (3) Hamilton Depression Scale score ≤ 20 points (13); (4) Patients in a stable recovery phase after treatment; (5) Ability to communicate normally and independently complete the scales; (6) Normal physical function; (7) Patients provided informed consent for the study. Exclusion criteria: (1) Patients with malignant tumors; (2) Patients with dependencies on drugs, alcohol, etc.; (3) Patients with poor compliance; (4) Patients with incomplete data. This study was approved by the Ethics Committee of our hospital (Approval No: 2025-04-070-K01), and all participating patients signed informed consent forms.

Calculation The sample size for this study was estimated based on the principle in multivariate statistical analysis that the sample size should be 5–10 times the number of independent variables (14). The study involved variables including general information, social support, intrusive rumination, and NSSI behavior, totaling 21 independent variables. The minimum sample size was calculated as 21 × 5 = 105 cases. Considering the possibility of invalid questionnaires during the collection process, the sample size was expanded to 120 cases, meeting the minimum requirement for sample size estimation and satisfying the needs of structural equation modeling analysis.

As a cross-sectional study, data were collected through face-to-face questionnaire surveys administered to depressed patients during their treatment recovery phase. All investigators received unified standardized training to thoroughly master the questionnaire content. Patients meeting the inclusion criteria were selected to complete the questionnaires. When patients had questions regarding the questionnaire, detailed explanations were provided without influencing their response choices. A total of 130 questionnaires were distributed, with 120 valid questionnaires recovered, resulting in an effective response rate of 92.31%.

SPSS 25.0 software was employed for statistical analysis. Normally distributed categorical data were described using frequencies and percentages, while continuous data were presented as mean ± standard deviation ( X¯ ± s). Pearson correlation analysis was used to examine the relationships between ERRI, social support, and NSSI behaviors in depressed patients, with a significance level set at α=0.05. Amos 21.0 software was utilized to construct a structural equation model (SEM) and test for mediating effects. The non-parametric percentile Bootstrap method was applied for mediation effect testing with 5000 repeated samplings. Effect sizes and their 95% confidence intervals (CI) were calculated. If the 95% CI of the direct and indirect effects did not include zero, the mediating effect was considered statistically significant. A two-tailed P< 0.05 was considered statistically significant.

A total of 120 patients with depression were included in this study. Detailed demographic and clinical characteristics are summarized in Table 1.

Among the 120 depressed patients, the ERRI score was 15.71 ± 2.13, the total social support score was 47.85 ± 4.69, and the NSSI behavior score was 16.35 ± 2.65. See Table 2 for details.

NSSI behavior was significantly correlated with both social support and ERRI (P< 0.05). Specifically, NSSI behavior was negatively correlated with the total social support score and its three dimensions (P< 0.05), and positively correlated with the total ERRI score (P< 0.05). See Table 3 for details.

A structural equation model (SEM) was constructed to examine the pathway relationships among ERRI, social support, and NSSI behaviors, with a specific focus on the mediating role of ERRI between social support and NSSI behaviors. In the model, NSSI behavior was set as the dependent variable, social support as the independent variable, and ERRI as the mediating variable, with the dimensions of each scale serving as observed variables. The model parameters were estimated using the maximum likelihood (ML) method. After model adjustment, the fit indices were as follows: χ² = 48.853, P > 0.05 (χ²/df = 1.388), indicating a well-fitting model. The model is shown in Figure 1.

The mediating effects were decomposed into direct and indirect effects. The direct effects indicated that social support had a positive direct effect on NSSI behavior (effect size = 0.53), a positive direct effect on ERRI (effect size = 0.47), and ERRI had a positive direct effect on NSSI behavior (effect size = 0.43). The indirect effect revealed that social support also influenced NSSI behavior indirectly through ERRI, with an indirect effect size of 0.47 × 0.43 = 0.20. The proportion of the total effect mediated by ERRI was calculated as 0.20/(0.20 + 0.53) = 27.40%, indicating a partial mediating effect of ERRI in the relationship between social support and NSSI behavior. See Table 4 for path coefficients.

Non-suicidal self-injury (NSSI) is a common and complex psychological issue among depressed patients, representing a frequent form of self-harm behavior in this population. Studies have indicated (19) that 44% of depressed patients exhibit NSSI behaviors, which not only inflict physical and psychological harm on the individuals themselves but also impose negative impacts on their families and society.

The results of this study show that the NSSI behavior score among the 120 depressed patients was 16.35 ± 2.65, indicating a moderate level. This highlights the need for increased attention to NSSI behaviors in depressed patients and calls for enhanced research and clinical practices to improve understanding and management of this issue. Some studies have suggested (20) that self-injurious behavior may be regarded as an addictive behavior, as individuals gradually develop increased tolerance to pain through self-harm, leading to reduced fear of death and consequently elevating the risk of suicide.

The prevalence of such behaviors may be attributed to factors such as rising social pressures, accelerated pace of life, and interpersonal tensions, which drive an increasing number of depressed patients to resort to NSSI as a means of releasing inner suffering and anxiety. Many young individuals, when confronted with academic pressure, family conflicts, or emotional distress, turn to self-injury to alleviate psychological pain—a behavior particularly common among adolescents and young adults (21).

Moreover, depressed patients often experience comorbid psychological issues such as anxiety disorders and post-traumatic stress disorder. These conditions interact and exacerbate one another, increasing the patients’ vulnerability to self-injurious behaviors. Such NSSI behaviors not only intensify the psychological distress of depressed patients but also adversely affect treatment outcomes and recovery processes. Therefore, timely identification and intervention for NSSI behaviors in this population are of critical importance.

The results of this study indicate that NSSI behavior in depressed patients is significantly correlated with both social support and intrusive rumination (ERRI) (P< 0.05). Specifically, NSSI behavior showed a significant negative correlation with the total social support score and its three dimensions (P< 0.05), suggesting that higher levels of social support—including family, friend, and other support—are associated with lower NSSI behavior.

This finding is consistent with the research of Lu Jiahui et al. (22). Family support can provide emotional sustenance and understanding, making patients feel valued and cared for, thereby alleviating loneliness and self-blame and reducing self-injury tendencies. Friend support offers social companionship and assistance, helping patients build positive social connections, mitigate feelings of isolation and stress, and promote emotional release and regulation. Other forms of support, such as psychological counseling and professional treatment, can further provide cognitive and emotional guidance, enabling patients to better understand and cope with depression, thus decreasing the incidence of self-injury.

Simultaneously, a significant positive correlation was observed between NSSI behavior and the total ERRI score (P< 0.05), indicating that higher levels of intrusive rumination are associated with increased NSSI behavior. This aligns with the findings of Zhang Wenqing et al. (23). A tendency toward frequent negative self-reflection and persistent adverse thinking may lead to the accumulation of negative emotions and difficulties in emotion regulation, thereby elevating the risk of self-injury. Underlying psychological factors may include self-denial, despair about the future, and disillusionment with life.

Depressed patients often lack self-affirmation and confidence, excessively focusing on their own shortcomings and errors, which exacerbates negative emotions and self-doubt (24). Moreover, feelings of hopelessness and disappointment may leave individuals feeling helpless and trapped, making it challenging to adopt positive coping strategies and leading them to resort to self-injury as a means of emotional regulation (25). Therefore, interventions and psychotherapies targeting intrusive rumination are of great significance in reducing NSSI behavior among depressed patients.

This study confirms the mediating role of intrusive rumination between social support and NSSI behaviors, a psychological pathway that may have underlying neurobiological foundations.

In recent years, research in affective neuroscience has begun to link repetitive negative thinking, such as rumination, with the function of the brain’s glymphatic system. The glymphatic system is a waste-clearance mechanism in the brain, primarily active during sleep, responsible for removing neurotoxic substances, including β-amyloid and tau proteins. One theory suggests that impaired glymphatic function may exacerbate neuroinflammation and disrupt the function of brain regions involved in emotion regulation, such as the prefrontal cortex and anterior cingulate cortex, thereby increasing cognitive rigidity and the persistence of intrusive thoughts (26). A recent review further connects glymphatic dysfunction to rumination and emotional dysregulation in various psychiatric disorders, including depression, offering a novel biopsychosocial perspective on the understanding of ruminative thinking (27).

Additionally, research models propose that the efficiency of glymphatic clearance may influence the persistence of depression by modulating activity in neural circuits associated with rumination, such as the default mode network (28). This suggests that the buffering effect of social support on ruminative thinking observed in this study may be partially mediated by factors such as improved sleep quality, which optimizes brain clearance function and alleviates cognitive fixation. Therefore, future research integrating neuroimaging and fluid biomarkers could help provide a more comprehensive, multimodal understanding of the mechanisms through which psychosocial factors influence behavioral phenotypes via neurobiological pathways.

Using structural equation modeling and Bootstrap testing, this study confirmed that intrusive rumination plays a partial mediating role between social support and NSSI behavior, with an effect proportion of 27.40%.

This finding holds significant theoretical and clinical implications. Theoretically, it clearly reveals a potential psychopathological pathway: “insufficient social support → exacerbation of intrusive rumination → increased risk of NSSI behavior”. This aligns with the conceptual model proposed by Mendez et al. (29), which suggests that “social adversity leads to self-harm through cognitive-affective processing”.

However, by quantifying the mediating effect, this study provides empirical support for this theory within the population of depressed patients. At the clinical practice level, this mediating effect suggests that intervention efforts should shift from a single-dimensional approach to a multi-dimensional integrated strategy. First, directly targeting NSSI behavior, safety assessments and management protocols should be implemented, helping patients identify behavioral triggers and establish alternative emotion regulation techniques (e.g., vigorous exercise, snapping a rubber band on the wrist). Second, addressing the mediating mechanism—intrusive rumination—interventions such as metacognitive therapy (MCT) can be introduced to change patients’ relationship with ruminative thoughts, or mindfulness-based cognitive therapy (MBCT) can be adopted to cultivate awareness and a non-judgmental attitude toward negative thoughts, thereby breaking the automatic chain of rumination. Finally, targeting the root factor—social support—interventions should go beyond simply “encouraging socialization”.

Instead, family members should be guided to engage in effective, non-critical communication, and patients should be assisted in building genuine social connections to enhance their psychological resilience at the source. Future research could build on this foundation to design and validate integrated programs that combine social support enhancement and rumination intervention. Additionally, technologies such as neuroimaging could be utilized to explore how these interventions reshape the psychological-behavioral pathways revealed in this study, particularly from the perspective of brain functional connectivity.