Xubin He1
Qinyao Yu2Jing Peng1Jianghong Yu1Taiying Wu3Yuan Qin1Shengjun Wang1
Tiaoxia Dong1Yulong Liao1Chunbi Hu1Ping Yang1*Bo Yang1*- 1Chongqing Mental Health Center, Chongqing, China
- 2Chongqing Medical School, Chongqing, China
- 3Chongqing jiangbei second hospital, Chongqing, China
Background: Non-Suicidal Self-Injury (NSSI) has continued to be a major issue for public health worldwide, especially among teenagers. Studies have found a certain correlation between NSSI and Problematic Internet Use (PIU). However, this relationship is still unclear among Chinese adolescents, a specific population. Hence, a meta-analysis was carried out on observational studies to explore the connection between NSSI and PIU in Chinese teenagers, aiming to provide more clarity on the correlation.
Methods: To identify the link between NSSI and PIU, we scoured seven digital repositories until November 16, 2023. Employing a random-effects meta-analysis framework, we delved into the association between NSSI and PIU. Additionally, we carried out subgroup evaluations to scrutinize variables including geographical location, age demographics, research methodology, diagnostic instruments, gender, and variables controlled for confounding, like symptoms of depression. For amalgamating data, STATA software (version 16) was deployed.
Results: In this analysis, we included 15 research papers encompassing a collective sample of 137,166 individuals. Our findings revealed a significant positive association between NSSI and PIU within the adolescent population in China, with an Odds Ratio (OR) of 2.02 and a 95% Confidence Interval (CI) ranging from 1.73 to 2.37. Notably, this correlation was markedly stronger in specific subgroups: adolescents from China’s Western regions exhibited an OR of 4.22 (95% CI: 3.44, 5.18); middle school attendees had an OR of 2.09 (95% CI: 1.92, 2.28); those diagnosed with concurrent depression disorders showed an OR of 2.32 (95% CI: 1.98, 2.73); and female adolescents demonstrated an OR of 2.49 (95% CI: 2.26, 2.75), highlighting the nuanced dynamics of this relationship.
Conclusion: This meta-analysis indicates that PIU among adolescents is associated with an increased risk of NSSI. Our findings underscore the importance of targeting specific populations, including those in the western region of China, middle school students, adolescents with comorbid depression disorders, and female adolescents, who may be at higher risk of PIU and subsequently NSSI. These results emphasize the need for tailored interventions and preventive strategies to address these intertwined issues effectively.
Systematic review registration: PROSPERO, identifier CRD42024496579.
1 Introduction
Non-Suicidal Self-Injury (NSSI) refers to deliberate and direct acts of harming oneself without the intent to die, such as cutting wrists, scratching arms, punching walls, taking drugs to experience pain, or pulling out hair (1). Previous studies often considered NSSI as a symptom of borderline personality disorder. In the most recent version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V), it is suggested that NSSI should be classified as a separate condition. Its purpose is to relieve interpersonal difficulties, unwanted thoughts, and emotions, as well as the negative impact on life (1). Research has shown that NSSI often begins in early adolescence, peaks in mid-adolescence, and declines in late adolescence (2). NSSI has become a major mental health issue among adolescents worldwide. In the past two decades, the number of individuals engaging in this behavior is rapidly increasing globally, particularly among adolescents (3). According to a study carried out by the Centers for Disease Control and Prevention in the US with 64,671 teenagers from the community, the occurrence of NSSI among adolescents varies from 6.4% to 30.8% (4). Similarly, a survey of 12,068 community adolescents across 11 European countries found a lifetime prevalence of NSSI ranging from 17.1% to 38.6% (5). Additionally, a meta-analysis found that 22.0% of adolescents worldwide have engaged in NSSI at some point in their lives, with an annual prevalence of 23.2% (6). In Asian countries, the overall lifetime prevalence of NSSI is higher at 32.6% compared to Western countries at 19.4% (7). Although NSSI does not involve suicidal intent, it frequently happens alongside suicidal behavior and can be a significant indicator of future suicide attempts and completed suicides (8). A meta-analysis of longitudinal studies estimated that individuals engaging in NSSI are 4.27 times more likely to attempt suicide and 1.51 times more likely to die by suicide subsequently (9). According to interpersonal psychological theory and the three-step theory of suicide behavior, NSSI is considered a “gateway” behavior to suicide (10). Repeated NSSI may desensitize individuals to pain, injury, fear, and death, thereby increasing the likelihood of suicide attempts. Therefore, it is essential to assess the factors influencing NSSI to prevent suicidal thoughts and behaviors.
In recent times, with the widespread adoption of the internet, it has evolved into an essential tool for adolescents across various domains including education, social interaction, and recreational activities (11). According to the 49th Statistical Report on Internet Development in China published by the China Internet Network Information Center (CINIC), the number of internet users in China has surged to 1.03 billion, with adolescents constituting 137 million of this demographic, accounting for 13.3% of the total internet user base in the country (12). Despite the manifold benefits that the internet offers to adolescents in terms of learning and daily life, excessive and uncontrolled usage of this technology may lead to what is commonly referred to as “Problematic Internet Use (PIU).” (13–15). PIU is defined as internet usage that results in psychological, social, educational, and/or occupational difficulties in an individual’s life, which encompasses terms such as “pathological internet use”, “excessive internet use”, “internet dependency”, “excessive smartphone usage”, and “compulsive internet use.” (16). Research suggests that PIU among adolescents can result in a myriad of severe issues, including attention deficits (17), academic underachievement (18), interpersonal conflicts (19, 20), family discord (21), anxiety (22), depression (23), and even instances of NSSI (24).
Adolescent NSSI arises from a complex interplay of social, psychological, and environmental factors (25). Notably, a substantial correlation exists between adolescent NSSI and PIU (26), with certain studies suggesting that PIU serves as a predictor for NSSI occurrences (24). However, a study by Liu et al. discovered that, upon controlling for depression variables, the linkage between adolescent NSSI and PIU dissipated (27). This finding underscores the intricate and contentious nature of the relationship between adolescent NSSI and PIU, with variations in outcomes across different research endeavors. To date, five systematic reviews have delved into this correlation, with two being narrative reviews (28, 29). While these reviews hint at a connection, methodological limitations obscure the true nature and strength of this association. Similarly, a meta-analysis spanning multiple European and Asian nations suggests that, even after adjusting for potential confounders like depression, PIU retains a link with heightened suicidal tendencies, albeit without a detailed exploration of the relationship between NSSI and PIU (30). Moreover, previous investigations have failed to specifically hone in on adolescents as a distinct cohort. In two systematic reviews conducted in China, the association between adolescent NSSI and PIU emerged as particularly robust. Nevertheless, these reviews encompassed a mere 3 and 4 articles, respectively, without delving deeper into potential influencing factors. Hence, further research is imperative to enhance the robustness of these findings (26, 31). Consequently, this study aims to amalgamate prior research on the NSSI-PIU relationship among Chinese adolescents, while considering factors such as measurement tool diversity, study type disparities, sample demographics (age, gender, region), and the control of potential confounders like depression, which could impact the outcomes of meta-analysis (30). Such an endeavor seeks to furnish a framework for the development of more targeted intervention initiatives in the future.
2 Methods
2.1 Materials and methods
The research relies on the PRISMA protocol for Systematic Reviews and Meta-Analyses (32). The protocol has been officially recorded in PROSPERO under registration number CRD42024496579.
2.2 Search strategy
In examining the correlation between NSSI and PIU, this investigation scoured through seven extensive databases, comprising the China National Knowledge Infrastructure, Wanfang Database, Chinese Biomedical Literature Service System, China Science and Technology Journal Database, PubMed, Embase, and Web of Science. The search spanned from the inception of these databases up to November 16, 2023. Employing a comprehensive approach, search terms were meticulously deployed across titles, abstracts, and subject terms, organized into three distinct keyword clusters (“non-suicidal self-injury,” “problematic internet use,” “adolescent”). Furthermore, efforts were made to contact authors to request missing data or information not explicitly mentioned in the articles. Additionally, manual perusal of article reference lists was undertaken to ensure the inclusivity of the literature. The encompassed publications spanned articles presented in both Chinese and English languages. For an in-depth understanding of the search strategy employed, please refer to (Supplementary Material 1).
2.3 Study selection criteria
The criteria for inclusion encompassed: (1) research of various types such as cross-sectional studies, case-control studies, and cohort studies; (2) populations comprising Chinese adolescents aged between 10 and 24 years (33); (3) literature elucidating the connection between NSSI and PIU while presenting pertinent Odds Ratio (OR) values; (4) publications available in either Chinese or English languages.
On the contrary, exclusion criteria encompassed: (1) study types involving reviews, conference abstracts, comments, and case reports; (2) literature lacking extractable data; (3) duplicate publications, with precedence given to those with the largest sample size.
2.4 Data extraction
To streamline and eliminate duplicate entries from the collected literature, NoteExpress served as the tool of choice. The screening process and quality evaluation were carried out independently by two researchers (XBH, YLL), adhering strictly to predefined inclusion and exclusion criteria. QYY spearheaded the data extraction process, with meticulous oversight from PY to guarantee precision. A thorough cross-verification of the literature was conducted, with allocation to a third investigator (BY). Extracted data encompassed details such as author(s), publication year, geographical region, study methodology, sample size, age demographics, effect size (OR) along with its 95% confidence interval, measurement instruments employed, and Adjusted for depression.
2.5 Quality assessment
XBH and YLL, two researchers, each performed a thorough assessment of cross-sectional study publications based on the standards established by the Agency for Healthcare Research and Quality (AHRQ). Discussion was used to resolve any inconsistencies. These evaluation criteria covered 11 key aspects, including the reliability of data sources, selection and definition of variables, determination of the time frame, representativeness of the sample, potential biases, statistical analysis methods employed, reliability of data collection procedures, and attention to subsequent quality assessments. Every element was classified as either ‘yes,’ ‘no,’ or ‘unclear.’ A value of 1 was given for ‘yes,’ while 0 was given for ‘no.’ The overall score varied from 0 to 11, with scores of 0 to 3 representing poor quality, 4 to 7 representing average quality, and 8 to 11 representing excellent quality (34). Only articles with a quality score greater than 4 were included in the meta-analysis. Furthermore, the quality of cohort studies was evaluated using the Newcastle-Ottawa Scale (NOS).The evaluation included eight components, focusing on three main aspects: subject selection, comparability among groups, and the precision of outcome or exposure measurement. Regarding subject selection, it included aspects such as the source of the sample, the recruitment process, and representativeness. We concentrated on the comparability between the study group and the control group in order to assess their similarity. The assessment of outcome measurement or exposure factor measurement examined the reliability and validity of data collection tools. Scores were assigned to each item on a scale of 0 to 9, with a cumulative score falling between 0 and 3 representing poor quality, 4 to 6 representing fair quality, and 7 to 9 representing good quality (35). Only articles with a quality score greater than 4 were included in the meta-analysis. The use of these assessment tools and criteria helped ensure a comprehensive evaluation and accurate judgment of the study quality.
2.6 Statistical analysis
Statistical analyses were carried out utilizing STATA software (version 16.0). The investigation into the relationship between adolescent NSSI and PIU involved the extraction of Odds Ratio (OR) values from each pertinent article. The culmination of this analysis resulted in the determination of the combined OR value along with its corresponding 95% confidence interval (95% CI). Heterogeneity was identified if the P-value was less than 0.05 or if the I-squared statistic (I2) exceeded 50% (36). Subgroup analyses were undertaken to probe into the potential sources of heterogeneity, categorized by region, age group, study design, measurement instruments, gender distribution, and control variables such as depressive factors. Intergroup disparities within these subgroups were evaluated utilizing a fixed-effects model. Moreover, sensitivity analyses were conducted to evaluate the influence of individual studies on the overall estimate. The significance level for the Egger test was established at P < 0.05 (37). To scrutinize for small-study effects, including those induced by publication bias, funnel plots (38) and Egger’s statistical test (39) were employed. Duval and Tweedie’s trim-and-fill method was implemented to estimate the overall effect size while addressing biases stemming from small-study effects (40). The threshold for statistical significance was set at P < 0.05.
3 Results
3.1 Characteristics of included studies and quality assessment
In accordance with the pre-established search strategy, a total of 1,462 articles were initially identified. Following the screening process facilitated by NoteExpress, 548 papers remained for further evaluation. Subsequently, through a meticulous examination of titles and abstracts, 42 articles were deemed pertinent and subjected to full-text scrutiny. Ultimately, 15 articles (24, 27, 41–53) met the inclusion criteria and were selected for comprehensive analysis. These studies collectively encompassed a participant pool of 137,166 individuals. Regarding the assessment tools employed for NSSI, 7 studies utilized validated instruments dedicated to self-harm measurement, while 6 studies utilized self-designed tools for the same purpose. Concerning PIU assessment, 7 studies utilized Young’s Internet Addiction Test developed by Young et al. (54), 3 studies employed the Chen Internet Addiction Scale by Chen et al. (55), and 3 studies utilized the Mobile Phone Addiction Index, initially translated into Chinese by Liu et al. (56). The remaining 2 studies utilized assessment tools developed by Tao et al. (57). The sample sizes across the included articles exhibited variance, ranging from 324 to 39,517 participants. Each included study underwent a comprehensive quality assessment, with five studies being categorized as high quality and the remaining ten as medium quality (Supplementary Material 2). Table 1 provides an overview of the literature characteristics, while Figure 1 delineates the progression of the inclusion process.
Table 1 Characteristics of the included article.
Figure 1 Literature selection process.
3.2 Homogeneity test and meta-analysis
Upon conducting homogeneity tests across the 15 publications encompassed in this study, notable heterogeneity was unveiled (I2 = 90.8%, P < 0.001). Utilizing the random-effects model, a pooled effect size concerning the association between NSSI and PIU was determined to be (OR=2.02, 95% CI: 1.73, 2.37). Moreover, the statistical analysis underscored a significant relationship between NSSI and PIU (Z = 8.72; P < 0.001) (refer to Figure 2).
Figure 2 Forest plot of the relationship between NSSI and PIU.
3.3 Processing of the heterogeneity between NSSI and PIU
Significant heterogeneity was detected through the implementation of the random-effects model. Consequently, a subgroup analysis was undertaken to mitigate this observed heterogeneity.
3.4 Subgroup analysis
Subgroup analysis was conducted, exploring various factors associated with both NSSI and PIU, including geographical region, age, gender, study design, measures used for PIU and NSSI, and adjustment for depression (as outlined in Table 2).
Table 2 Subgroup analysis showing OR of NSSI for PIU.
As depicted in Figure 3, significant regional disparities emerged as moderators of the NSSI-PIU relationship (see Table 2). Notably, adolescents hailing from the western region exhibited the highest vulnerability to NSSI among those grappling with PIU (OR=4.22, 95% CI: 3.44, 5.18). Conversely, the risk of NSSI appeared markedly lower among counterparts from the eastern region (OR=1.65, 95% CI: 1.41, 1.95) (refer to Figure 3).
Figure 3 Moderation effect of regional differences on NSSI and PIU.
Age, as showcased in Figure 4, also exerted a notable moderation effect on the NSSI-PIU association (as detailed in Table 2). Specifically, Chinese middle school students with PIU demonstrated a significantly heightened risk of NSSI (OR=2.09, 95% CI: 1.92, 2.28) compared to their college counterparts (OR=1.40, 95% CI: 0.99, 1.98) (see Figure 4).
Figure 4 Moderation effect of age on NSSI and PIU.
Figure 5 underscores the profound gender-based modulation of the NSSI-PIU relationship (as elaborated in Table 2). Among Chinese adolescents, females grappling with PIU exhibited a markedly elevated NSSI risk (OR=2.49, 95% CI: 2.26, 2.75) compared to their male counterparts (OR=1.83, 95% CI: 1.22, 2.73) (refer to Figure 5).
Figure 5 Moderation effect of gender on NSSI and PIU.
Furthermore, as illustrated in Figure 6, the control for depression exerted a significant moderating influence on the NSSI-PIU correlation (as indicated in Table 2). Particularly noteworthy is the observation among Chinese adolescents, where the NSSI risk peaked when depression remained uncontrolled (OR=2.32, 95% CI: 1.98, 2.73). Conversely, the risk attenuated substantially when depression was effectively managed (OR=1.72, 95% CI: 1.41, 2.09) (see Figure 6).
Figure 6 Moderation effect of depression control on NSSI and PIU.
3.5 Sensitivity analysis
To gauge the reliability of our findings, a sensitivity analysis was meticulously performed, systematically eliminating individual studies and recalibrating the overall correlation coefficient between NSSI and PIU. Encouragingly, the sensitivity analysis revealed minimal fluctuations in the overall correlation coefficient, underscoring the stability of our results (refer to Figure 7).
Figure 7 Sensitivity analysis between NSSI and PIU.
3.6 Publication bias
In evaluating the symmetry of the funnel plot, which assessed the overall correlation coefficient between NSSI and PIU (Figure 8), a subjective analysis revealed a balanced distribution on both sides. Moreover, the Egger regression test yielded a non-significant result (t = 0.06, p = 0.95) (Figure 9), providing further evidence of the absence of significant publication bias.
Figure 8 Funnel plot between NSSI and PIU.
Figure 9 Egger plot between NSSI and PIU.
4 Discussion
In recent years, adolescent NSSI has emerged as a pressing global public health concern (58). Concurrently, PIU has garnered increasing attention as a significant contributing factor to problematic behaviors among youth (26). Despite extensive research efforts, a definitive consensus regarding the relationship between NSSI and PIU remains elusive (24, 27). The outcomes of this meta-analysis reveal a notable correlation between NSSI and PIU among Chinese adolescents, with those afflicted by PIU being over twice as likely to engage in NSSI compared to their non-PIU counterparts. This finding resonates with earlier research, including the study conducted by Fan et al. (26). The rationale behind this phenomenon can be dissected as follows: Firstly, a reciprocal interaction between NSSI and PIU may exist, wherein the internet serves as a platform for NSSI exposure. Adolescents immersed in PIU are more predisposed to encountering peers and information related to NSSI, thereby amplifying the likelihood of imitation and engagement in NSSI behaviors. Social reinforcement further accentuates the risk of NSSI occurrences, ultimately culminating in the manifestation of such behaviors (59). Secondly, both NSSI and PIU share addictive propensities (60, 61). The Interaction of Person-Affect-Cognition-Execution (I-PACE) model posits that addictive behaviors ensue from intricate interactions between susceptibility factors, emotional and cognitive responses to specific stimuli, and executive functions like inhibition control and decision-making. Throughout the addictive trajectory, the correlation between cue-reactivity/craving and diminished inhibition control fosters habitual behaviors (62). Research suggests that prolonged PIU may induce structural and functional alterations in the adolescent brain, particularly impacting areas pertinent to executive control and decision-making, thereby elevating the risk of NSSI (63–66). Additionally, sustained PIU could precipitate neuroadaptation alterations within the brain’s reward system. Moreover, addictive behaviors are associated with the release of hormones and neurotransmitters (e.g., endogenous opioids), potentially distorting individuals’ reward and punishment perceptions, consequently heightening their inclination towards NSSI (67, 68). Thirdly, PIU may engender social detachment from real-life interactions and undermine adolescents’ coping mechanisms for managing challenging emotions and sentiments. Adolescents engrossed in PIU may necessitate additional support and mechanisms to alleviate their psychological and emotional distress. The intense physiological sensations accompanying NSSI can serve as a diversion from internal psychological distress towards external physiological experiences, thereby instigating NSSI incidents (61, 69). Fourthly, PIU could precipitate personality disintegration, impairing adolescents’ typical social functioning and diminishing their vigilance and fear-related emotions. This often culminates in impulse inhibition disorders, thereby heightening the likelihood of NSSI occurrences (70). Lastly, adolescent PIU closely intertwines with mental health concerns such as anxiety and depression, both significant predictors of NSSI. PIU might initially function as a coping mechanism to alleviate mental health challenges; however, over time, it exacerbates these issues, thereby escalating the risk of NSSI (71, 72).
The study’s findings unveil a significant moderating impact of regional disparities on the relationship between NSSI and PIU. Notably, in China’s western region, adolescents grappling with PIU face a substantially heightened risk of NSSI, up to four times greater compared to several other regions in the country, with the lowest risk observed in the eastern region. This discrepancy may stem from the relatively underdeveloped economic status and prolonged parental absences due to occupational commitments prevalent in the western region. Such circumstances may foster an environment lacking in emotional communication within adolescents’ families, consequently elevating the risk of PIU and subsequent NSSI (73). Dysfunctional family dynamics, in turn, exacerbate the vulnerability to NSSI among adolescents (74). Thus, the association between NSSI and PIU appears more pronounced among adolescents in China’s western region, accentuating the imperative to fortify mental health intervention efforts targeted at this demographic.
Furthermore, the study reveals a significant moderation effect of age on the relationship between NSSI and PIU. Specifically, adolescents afflicted with PIU exhibit a heightened risk of NSSI compared to university students. This observation can be attributed to the pivotal stage of physiological and psychological development experienced by adolescents, characterized by nascent self-awareness, coping mechanisms, and social skills (75). In contrast, university students have transitioned into a more autonomous and mature phase of life. Owing to adolescents’ relatively weaker self-regulation and emotional control capabilities, they are more susceptible to the adverse effects of PIU, consequently escalating the risk of NSSI (76, 77). Additionally, adolescents are particularly influenced by peer dynamics and social networks, rendering them more susceptible to PIU. Conversely, university students may possess superior critical thinking and self-regulation abilities, which could mitigate PIU’s detrimental impact on their mental well-being, thus lowering the risk of NSSI (78, 79). Furthermore, adolescents often encounter barriers in accessing mental health resources and may feel disinclined to seek assistance due to familial, academic, and societal pressures (80). In contrast, university students typically have greater access to diverse mental health support systems and exhibit a greater willingness to seek aid, thereby reducing the likelihood of NSSI. This underscores the significance of addressing PIU concerns among adolescents and furnishing them with tailored support and intervention measures to mitigate NSSI occurrences.
Additionally, the study identifies a significant moderating influence of controlling for depression variables on the NSSI-PIU relationship. This implies that the heightened risk of NSSI among adolescents with PIU may be partially mediated by depression, although other contributing factors are also at play. This finding aligns with the conclusions drawn by Cheng et al. (30). One plausible explanation is that depression disorders are inherently intertwined with PIU; adolescents grappling with depression are more susceptible to PIU as a coping mechanism to alleviate psychological distress, with PIU potentially exacerbating depressive symptoms, thus perpetuating a detrimental cycle that heightens the risk of NSSI (81, 82). Moreover, adolescents contending with depression often grapple with heightened social isolation and a dearth of effective coping mechanisms, rendering them more susceptible to immersing themselves in the online realm, consequently elevating the risk of PIU and, by extension, the incidence of NSSI (83). Henceforth, it is advisable to screen adolescents with problematic Internet use for NSSI risk, even in the absence of depression, as additional factors may heighten NSSI susceptibility among individuals grappling with PIU.
Similarly, gender was identified as a significant moderator in the relationship between NSSI and PIU in this study. Findings indicate that among those afflicted with internet addiction, female adolescents are more predisposed to NSSI occurrences. This observation could be elucidated by several factors. Firstly, female adolescents tend to share personal experiences and emotions more openly on social media platforms, rendering them more susceptible to external judgments regarding their appearance, body image, and lifestyle choices. Such online interactions may exacerbate issues related to self-identity and self-doubt, thereby heightening the risk of NSSI subsequent to PIU engagement (84, 85). Secondly, female adolescents are more prone to internalizing problems such as depression and anxiety, while male adolescents are inclined towards externalizing problems such as aggression and behavioral issues. Among adolescents grappling with PIU, internalizing problems are more likely to manifest in females, consequently augmenting their NSSI vulnerability (86, 87). Thirdly, female adolescents typically rely more heavily on social support networks than males, and may lack effective self-regulation skills when confronted with stressors. This renders them more susceptible to immersing themselves in the online realm as a means of escaping real-life challenges, thereby escalating the risk of NSSI (88, 89). It’s important to note, however, that the subgroup analysis in this study comprised only two articles, potentially limiting the generalizability of the findings.
However, neither the assessment tools for PIU nor those for NSSI exhibited a moderating effect on the relationship between NSSI and PIU. This suggests a degree of stability and consistency in the outcomes derived from various assessment instruments. It should be noted that the inclusivity of PIU encompasses various terms and employs different measurement tools, which may limit the generalizability of research findings. Therefore, further exploration is needed to determine whether the relationship between PIU and NSSI is moderated by other measurement instruments. Lastly, different study methodologies also failed to moderate the relationship between NSSI and PIU, indicating that disparate study designs do not significantly influence the study outcomes.
4.1 Advantages and limitations
This review meticulously gathered and analyzed published data to uncover the connection between NSSI and PIU among Chinese adolescents. The inclusion of studies with sizable sample sizes and diverse participant demographics greatly strengthened the credibility of our research findings. Insights gleaned from this investigation have the potential to provide new perspectives for the prevention and clinical management of NSSI among Chinese adolescents struggling with PIU. Thus, they furnish a solid evidential foundation for practice and intervention in this domain.
However, several limitations warrant acknowledgment. Primarily, the predominant inclusion of cross-sectional observational studies in our analysis, with only a sparse inclusion of longitudinal studies, signifies that our findings denote a correlation between NSSI and PIU rather than establishing a causal relationship. Secondly, our study exclusively focused on Chinese adolescents, which limited the generalizability of our findings by excluding research involving other age groups. Thirdly, only a few potential moderating variables were found to have significant effects, and a substantial portion of the observed heterogeneity remains unexplained. Therefore, future meta-analyses need to examine the role of potential new moderating factors in the correlation between PIU and NSSI. Additionally, we only included literature in Chinese and English and did not include unpublished literature, which may lead to publication bias in the results. Lastly, the use of self-report questionnaires in all studies does not equate to clinical assessment, and this study integrates different terms regarding internet use, which may affect the robustness of the results. Therefore, future research could further clarify the concepts related to internet use to establish more reliable conclusions from studies that relate these different uses with other variables of interest.
5 Conclusion
The outcomes derived from this systematic review and meta-analysis undeniably underscore a robust and statistically significant positive correlation between NSSI and PIU among Chinese adolescents, affirming that adolescent PIU heightens the propensity for NSSI occurrence. Particular emphasis should be placed on specific demographic segments, including those situated in the western regions of China, middle school attendees, adolescents grappling with concurrent depressive disorders, and female adolescents, as they may confront elevated rates of PIU, consequently amplifying their vulnerability to NSSI risk. Thus, the imperative for early detection and targeted interventions for PIU adolescents cannot be overstated, directly mitigating the frequency of NSSI incidents. Adolescence represents a pivotal developmental phase marked by distinct psychological and physiological attributes, during which educational institutions and familial environments play pivotal roles in reinforcing adolescent mental health education. This entails heightening awareness surrounding PIU and NSSI while bolstering adolescents’ mental health literacy and resilience. Concurrently, addressing PIU concerns among adolescents necessitates the establishment of tailored intervention frameworks. Educational institutions can institute dedicated platforms or curricula geared towards the prevention and management of PIU, offering psychological counseling and support services. Psychologists and clinical practitioners can devise individualized treatment regimens tailored to specific circumstances, aiding adolescents in cultivating constructive coping mechanisms, alleviating psychological distress, and mitigating NSSI occurrences. Moreover, encouraging adolescents to actively engage in extracurricular pursuits such as sports, arts, and cultural activities can foster diverse interests and hobbies, thereby curtailing excessive internet utilization. Promoting a wholesome lifestyle encompassing sound sleep patterns, balanced nutrition, and moderate physical activity is also advocated to alleviate PIU and NSSI tendencies. By adopting a multifaceted approach encompassing early intervention, targeted support services, and lifestyle modifications, stakeholders can effectively address the intertwined challenges posed by PIU and NSSI among adolescents, safeguarding their overall well-being.
Data availability statement
The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding authors.
Author contributions
XH: Validation, Software, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization, Writing – review & editing, Writing – original draft. QY: Writing – original draft, Data curation. JP: Writing – review & editing, Supervision, Methodology. JY: Writing – review & editing, Software. TW: Writing – review & editing, Investigation. YQ: Writing – review & editing, Software. SW: Writing – review & editing, Methodology. TD: Writing – review & editing, Methodology. YL: Writing – original draft, Data curation. CH: Writing – review & editing, Investigation. PY: Writing – review & editing, Validation. BY: Writing – review & editing, Validation, Supervision.
Funding
The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.
Acknowledgments
Our gratitude goes out to all the individuals and researchers who played a role in this research project.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2024.1408508/full#supplementary-material
References
1. Regier DA, Kuhl EA, Kupfer DJ. The dsm-5: classification and criteria changes. World Psychiatry. (2013) 12:92–8. doi: 10.1002/wps.20050
2. Plener PL, Schumacher TS, Munz LM, Groschwitz RC. The longitudinal course of non-suicidal self-injury and deliberate self-harm: a systematic review of the literature. Border Pers Dis Emot. (2015) 2:2. doi: 10.1186/s40479-014-0024-3
3. Olfson M, Gameroff MJ, Marcus SC, Greenberg T, Shaffer D. National trends in hospitalization of youth with intentional self-inflicted injuries. Am J Psychiat. (2005) 162:1328–35. doi: 10.1176/appi.ajp.162.7.1328
4. Monto MA, McRee N, Deryck FS. Nonsuicidal self-injury among a representative sample of us adolescents, 2015. Am J Public Health. (2018) 108:1042–8. doi: 10.2105/AJPH.2018.304470
5. Brunner R, Kaess M, Parzer P, Fischer G, Carli V, Hoven CW, et al. Life-time prevalence and psychosocial correlates of adolescent direct self-injurious behavior: a comparative study of findings in 11 european countries. J Child Psychol Psyc. (2014) 55:337–48. doi: 10.1111/jcpp.12166
6. Xiao Q, Song X, Huang L, Hou D, Huang X. Global prevalence and characteristics of non-suicidal self-injury between 2010 and 2021 among a non-clinical sample of adolescents: a meta-analysis. Front Psychiatry. (2022) 13:912441. doi: 10.3389/fpsyt.2022.912441
7. Lim KS, Wong CH, McIntyre RS, Wang J, Zhang Z, Tran BX, et al. Global lifetime and 12-month prevalence of suicidal behavior, deliberate self-harm and non-suicidal self-injury in children and adolescents between 1989 and 2018: a meta-analysis. Int J Env Res Pub He. (2019) 16:4581. doi: 10.3390/ijerph16224581
8. Hawton K, Bale L, Brand F, Townsend E, Ness J, Waters K, et al. Mortality in children and adolescents following presentation to hospital after non-fatal self-harm in the multicentre study of self-harm: a prospective observational cohort study. Lancet Child Adolesc. (2020) 4:111–20. doi: 10.1016/S2352-4642(19)30373-6
9. Ribeiro JD, Franklin JC, Fox KR, Bentley KH, Kleiman EM, Chang BP, et al. Self-injurious thoughts and behaviors as risk factors for future suicide ideation, attempts, and death: a meta-analysis of longitudinal studies. Psychol Med. (2016) 46:225–36. doi: 10.1017/S0033291715001804
10. of IOMU, Suicide AAA. Reducing suicide: a national imperative Washington (DC: National Academies Press (US (2002) p. 5–10.
11. Odgers CL, Jensen MR. Annual Research Review: Adolescent mental health in the digital age: facts, fears, and future directions. J Child Psychol Psyc. (2020) 61:336–48. doi: 10.1111/jcpp.13190
12. China Internet Network Information Center. The 50th Statistical Report on China Internet Development (2022). Available online at: http://cnnic.cn/n4/2022/0916/c38-10594.html.
13. Reyna VF, Farley F. Risk and rationality in adolescent decision making: implications for theory, practice, and public policy. Psychol Sci Publ Int. (2006) 7:1–44. doi: 10.1111/j.1529-1006.2006.00026.x
14. Derevensky JL, Hayman V, Lynette G. Behavioral addictions: excessive gambling, gaming, internet, and smartphone use among children and adolescents. Pediatr Clin N Am. (2019) 66:1163–82. doi: 10.1016/j.pcl.2019.08.008
15. Chao CM, Kao KY, Yu TK. Reactions to problematic internet use among adolescents: inappropriate physical and mental health perspectives. Front Psychol. (2020) 11:1782. doi: 10.3389/fpsyg.2020.01782
16. Spada MM. An overview of problematic internet use. Addict Behav. (2014) 39:3–6. doi: 10.1016/j.addbeh.2013.09.007
17. Jeong H, Yim HW, Lee SY, Lee HK, Potenza MN, Lee H. Factors associated with severity, incidence or persistence of internet gaming disorder in children and adolescents: a 2-year longitudinal study. Addiction. (2021) 116:1828–38. doi: 10.1111/add.15366
18. Samaha M, Hawi NS. Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Comput Hum Behav. (2016) 57:321–5. doi: 10.1016/j.chb.2015.12.045
19. Yu Y, Mo PK, Zhang J, Li J, Lau JT. Impulsivity, self-control, interpersonal influences, and maladaptive cognitions as factors of internet gaming disorder among adolescents in China: cross-sectional mediation study. J Med Internet Res. (2021) 23:e26810. doi: 10.2196/26810
20. Wang J, Hao QH, Tu Y, Wang Y, Peng W, Li H, et al. The relationship between negative life events and internet addiction disorder among adolescents and college students in China: a systematic review and meta-analysis. Front Psychiatry. (2022) 13:799128. doi: 10.3389/fpsyt.2022.799128
21. Xin C, Ding N, Jiang N, Li H, Wen D. Exploring the connection between parental bonding and smartphone addiction in chinese medical students. BMC Psychiatry. (2022) 22:712. doi: 10.1186/s12888-022-04355-7
22. Li Y, Li G, Liu L, Wu H. Correlations between mobile phone addiction and anxiety, depression, impulsivity, and poor sleep quality among college students: a systematic review and meta-analysis. J Behav Addict. (2020) 9:551–71. doi: 10.1556/2006.2020.00057
23. Shen Y, Wang L, Huang C, Guo J, De Leon SA, Lu J, et al. Sex differences in prevalence, risk factors and clinical correlates of internet addiction among chinese college students. J Affect Disord. (2021) 279:680–6. doi: 10.1016/j.jad.2020.10.054
24. Pan PY, Yeh CB. Internet addiction among adolescents may predict self-harm/suicidal behavior: a prospective study. J Pediatr-Us. (2018) 197:262–7. doi: 10.1016/j.jpeds.2018.01.046
25. Brown RC, Plener PL. Non-suicidal self-injury in adolescence. Curr Psychiat Rep. (2017) 19:20. doi: 10.1007/s11920-017-0767-9
26. Fan YY, Liu J, Zeng YY, Conrad R, Tang YL. Factors associated with non-suicidal self-injury in chinese adolescents: a meta-analysis. Front Psychiatry. (2021) 12:747031. doi: 10.3389/fpsyt.2021.747031
27. Liu HC, Liu SI, Tjung JJ, Sun FJ, Huang HC, Fang CK. Self-harm and its association with internet addiction and internet exposure to suicidal thought in adolescents. J Formos Med Assoc. (2017) 116:153–60. doi: 10.1016/j.jfma.2016.03.010
28. Marchant A, Hawton K, Stewart A, Montgomery P, Singaravelu V, Lloyd K, et al. A systematic review of the relationship between internet use, self-harm and suicidal behaviour in young people: the good, the bad and the unknown. PloS One. (2017) 12:e0181722. doi: 10.1371/journal.pone.0181722
29. Steinbüchel TA, Herpertz S, Külpmann I, Kehyayan A, Dieris-Hirche J, Te WB. [internet addiction, suicidality and non-suicidal self-harming behavior - a systematic review]. Psychother Psych Med. (2018) 68:451–61. doi: 10.1055/s-0043-120448
30. Cheng YS, Tseng PT, Lin PY, Chen TY, Stubbs B, Carvalho AF, et al. Internet addiction and its relationship with suicidal behaviors: a meta-analysis of multinational observational studies. J Clin Psychiat. (2018) 79:9291. doi: 10.4088/JCP.17r11761
31. Jin MK, Wang XY, Wang RX, Cheng SY, Yang SY, Zhang SL, et al. A systematic review and meta-analysis of factors related to non-suicidal self-injury among chinese adolescents. Psychiat Res. (2023) 326:115329. doi: 10.1016/j.psychres.2023.115329
32. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the prisma statement. Bmj-Brit Med J. (2009) 339:b2535. doi: 10.1136/bmj.b2535
33. Sawyer SM, Azzopardi PS, Wickremarathne D, Patton GC. The age of adolescence. Lancet Child Adolesc Health. (2018) 2:223–8. doi: 10.1016/S2352-4642(18)30022-1
34. Carlson EA. Agency for healthcare research and quality (ahrq) web site. Orthop Nurs. (2008) 27:258–9. doi: 10.1097/01.NOR.0000330315.06351.13
35. Lo C, Mertz D, Loeb M. Newcastle-ottawa scale: comparing reviewers' to authors' assessments. BMC Med Res Methodol. (2014) 14:1–5. doi: 10.1186/1471-2288-14-45
36. Melsen WG, Bootsma MC, Rovers MM, Bonten MJ. The effects of clinical and statistical heterogeneity on the predictive values of results from meta-analyses. Clin Microbiol Infect. (2014) 20:123–9. doi: 10.1111/1469-0691.12494
37. Peters JL, Sutton AJ, Jones DR, Abrams KR, Rushton L. Performance of the trim and fill method in the presence of publication bias and between-study heterogeneity. Stat Med. (2007) 26:4544–62. doi: 10.1002/sim.2889
38. Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. Bmj-Brit Med J. (2003) 327:557–60. doi: 10.1136/bmj.327.7414.557
39. Egger M, Davey SG, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. Bmj-Brit Med J. (1997) 315:629–34. doi: 10.1136/bmj.315.7109.629
40. Duval S, Tweedie R. A nonparametric "trim and fill" method of accounting for publication bias in meta-analysis. J Am Stat Assoc. (2000) 95:89–98. doi: 10.1080/01621459.2000.10473905
41. Lam LT, Peng Z, Mai J, Jing J. The association between internet addiction and self-injurious behaviour among adolescents. Injury Prev. (2009) 15:403–8. doi: 10.1136/ip.2009.021949
42. Duan JL, Sun Y, Han X, Er YL, Huo D. Internet addiction and its correlation to intentional injury behavior among college and secondary school students in Bei-jing. Chin J Sch Health. (2013) 34:646–50. doi: 10.16835/j.cnki.1000-9817.2013.06.003
43. Tang JJ, Hao JH, Han H, Wan YH, Li HY, Gu X, et al. Relationship between mobile phone dependence and injury among Bengbu middle school students. Chin J Sch Health. (2016) 37:208–11. doi: 10.16835/j.cnki.1000-9817.2016.02.015
44. Huang ZP, Wu Y, Luo QS, Zhou L, Li LP. Analysis of the characteristic of Internet addition and related factors among adolescents in Shenzhen. Chin J Sch Health. (2016) 37:1028–30. doi: 10.16835/j.cnki.1000−9817.2016.07.021
45. Hsieh K, Hsiao R, Yang Y, Liu T, Yen C. Predictive effects of sex, age, depression, and problematic behaviors on the incidence and remission of internet addiction in college students: a prospective study. Int J Environ Res Public Health. (2018) 15:2861. doi: 10.3390/ijerph15122861
46. Cao XL, Wen SY, Liu JB, Lu JP. Study on the prevalence and risk factors of non-suicidal self-injury among middle school students in Shenzhen. Sichuan Ment Health. (2019) 32:449–52. doi: 10.11886/j.issn.1007-3256.2019.05.014
47. Li D, Yang R, Wan Y, Tao F, Fang J, Zhang S. Interaction of health literacy and problematic mobile phone use and their impact on non-suicidal self-injury among chinese adolescents. Int J Environ Res Public Health. (2019) 16:2366. doi: 10.3390/ijerph16132366
48. Pang WJ, Wang XC. Status of non-suicidal self-injury among middle school students in Zhuang nationality and its association with internet addiction. Chin J Sch Health. (2020) 41:732–5. doi: 10.16835/j.cnki.1000−9817.2020.05.026
49. Tang J, Ma Y, Lewis SP, Chen R, Clifford A, Ammerman BA, et al. Association of internet addiction with nonsuicidal self-injury among adolescents in China. JAMA Netw Open. (2020) 3:e206863. doi: 10.1001/jamanetworkopen.2020.6863
50. Wang R, Yang R, Ran H, Xu X, Yang G, Wang T, et al. Mobile phone addiction and non-suicidal self-injury among adolescents in China. Peerj. (2022) 10:e14057. doi: 10.7717/peerj.14057
51. Qian T, Zhang LX, Wu YP. Risk factors analysis and prevention strategies of non-suicidal self-injury among adolescents. Primary Med Forum. (2022) 26:120–3. doi: 10.19435/j.1672-1721.2022.23.040
52. Ma Y, Li Y, Xie X, Zhang Y, Ammerman BA, Lewis SP, et al. The role of depressive symptoms and social support in the association of internet addiction with non-suicidal self-injury among adolescents: a cohort study in China. BMC Psychiatry. (2023) 23:322. doi: 10.1186/s12888-023-04754-4
53. Rong F, Wang M, Peng C, Cheng J, Ding H, Wang Y, et al. Association between problematic smartphone use, chronotype and nonsuicidal self-injury among adolescents: a large-scale study in China. Addict Behav. (2023) 144:107725. doi: 10.1016/j.addbeh.2023.107725
54. Young KS. Caught in the net: how to recognize the signs of internet addiction–and a winning strategy for recovery John Wiley & Sons (1998) p. 1–12.
55. Chen S, Weng L, Su Y, Wu H, Yang P. Development of a chinese internet addiction scale and its psychometric study. Chin J Psychol. (2003) 45:279–94.
56. Liu QQ, Xu XP, Yang XJ, Xiong J, Hu YT. Distinguishing different types of mobile phone addiction: development and validation of the Mobile Phone Addiction Type Scale (MPATS) in adolescents and young adults. Int J Env Res Pub He. (2022) 19:2593. doi: 10.3390/ijerph19052593
57. Tao S, Fu J, Wang H, Hao J, Tao F. The development of self-rating questionnaire for adolescent problematic mobile phone use and the psychometric evaluation in undergraduates. Chin J Sch Health. (2013) 34:26–9. doi: 10.16835/j.cnki.1000-9817.2013.01.010
58. Liu RT, Scopelliti KM, Pittman SK, Zamora AS. Childhood maltreatment and non-suicidal self-injury: a systematic review and meta-analysis. Lancet Psychiat. (2018) 5:51–64. doi: 10.1016/S2215-0366(17)30469-8
59. Brown RC, Fischer T, Goldwich AD, Keller F, Young R, Plener PL. # cutting: Non-Suicidal Self-Injury (NSSI) on instagram. Psychol Med. (2018) 48:337–46. doi: 10.1017/S0033291717001751
60. Saunders JB, Hao W, Long J, King DL, Mann K, Fauth-Bühler M, et al. Gaming disorder: its delineation as an important condition for diagnosis, management, and prevention. J Behav Addict. (2017) 6:271–9. doi: 10.1556/2006.6.2017.039
61. Liu J, Gao Y, Liang C, Liu X. The potential addictive mechanism involved in repetitive nonsuicidal self-injury: the roles of emotion dysregulation and impulsivity in adolescents. J Behav Addict. (2022) 11:953–62. doi: 10.1556/2006.2022.00077
62. Brand M, Wegmann E, Stark R, Müller A, Wölfling K, Robbins TW, et al. The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neurosci Biobehav R. (2019) 104:1–10. doi: 10.1016/j.neubiorev.2019.06.032
63. Zhou F, Montag C, Sariyska R, Lachmann B, Reuter M, Weber B, et al. Orbitofrontal gray matter deficits as marker of internet gaming disorder: converging evidence from a cross-sectional and prospective longitudinal design. Addict Biol. (2019) 24:100–9. doi: 10.1111/adb.12570
64. Liu J, Gao Y, Wang H, Liu X. Emotional reactivity and inhibitory control in nonsuicidal self-injury adolescence: divergence between positive and negative emotions. J Youth Adolescence. (2022) 51:1720–32. doi: 10.1007/s10964-022-01618-0
65. Mürner-Lavanchy I, Koenig J, Lerch S, van der Venne P, Höper S, Resch F, et al. Neurocognitive functioning in adolescents with non-suicidal self-injury. J Affect Disord. (2022) 311:55–62. doi: 10.1016/j.jad.2022.05.029
66. Sun JT, Hu B, Chen TQ, Chen ZH, Shang YX, Li YT, et al. Internet addiction-induced brain structure and function alterations: a systematic review and meta-analysis of voxel-based morphometry and resting-state functional connectivity studies. Brain Imaging Behav. (2023) 17:329–42. doi: 10.1007/s11682-023-00762-w
67. Brand M, Young KS, Laier C, Wölfling K, Potenza MN. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific internet-use disorders: an interaction of person-affect-cognition-execution (i-pace) model. Neurosci Biobehav R. (2016) 71:252–66. doi: 10.1016/j.neubiorev.2016.08.033
68. Bresin K, Gordon KH. Endogenous opioids and nonsuicidal self-injury: a mechanism of affect regulation. Neurosci Biobehav R. (2013) 37:374–83. doi: 10.1016/j.neubiorev.2013.01.020
69. Horwood S, Anglim J. Emotion regulation difficulties, personality, and problematic smartphone use. Cyberpsych Beh Soc N. (2021) 24:275–81. doi: 10.1089/cyber.2020.0328
70. McMahon K, Hoertel N, Olfson M, Wall M, Wang S, Blanco C. Childhood maltreatment and impulsivity as predictors of interpersonal violence, self-injury and suicide attempts: a national study. Psychiat Res. (2018) 269:386–93. doi: 10.1016/j.psychres.2018.08.059
71. Tang C, Wu A, Yan E, Ko J, Kwon JH, Yogo M, et al. Relative risks of internet-related addictions and mood disturbances among college students: a 7-country/region comparison. Public Health. (2018) 165:16–25. doi: 10.1016/j.puhe.2018.09.010
72. Bentley KH, Cassiello-Robbins CF, Vittorio L, Sauer-Zavala S, Barlow DH. The association between nonsuicidal self-injury and the emotional disorders: a meta-analytic review. Clin Psychol Rev. (2015) 37:72–88. doi: 10.1016/j.cpr.2015.02.006
73. Chi X, Hong X, Chen X. Profiles and sociodemographic correlates of internet addiction in early adolescents in southern China. Addict Behav. (2020) 106:106385. doi: 10.1016/j.addbeh.2020.106385
74. Tseng YC, Ditchman N. Non-suicidal self-injury in a college sample: intrapersonal and family factors. J Am Coll Health. (2023) 11:1–10. doi: 10.1080/07448481.2023.2209206
75. Steinberg L. A social neuroscience perspective on adolescent risk-taking. Dev Rev. (2008) 28:78–106. doi: 10.1016/j.dr.2007.08.002
76. Singh N, Nagar D. Relationship between internet addiction with emotional maturity: a study on high school students. Int J Indian Psychol. (2019) 7:21. doi: 10.25215/0701.004
77. Xu P, Hao X, Luo D, Lai M, Sun X, Xu J. Problematic internet gaming and non-suicidal self-injury in chinese adolescents: moderating and mediating roles of anxiety. Compr Psychiat. (2023) 125:152398. doi: 10.1016/j.comppsych.2023.152398
78. Shen M, French DC. Peer relationships and chinese adolescents academic achievement: selection and influence. Am Educ Res J. (2023) . 61:177–207. doi: 10.3102/00028312231208675
79. Hardy C, Bukowski WM, Sippola LK. Stability and change in peer relationships during the transition to middle-level school. J Early Adolesc. (2002) . 22:117–42. doi: 10.1177/0272431602022002001
80. Rickwood D, Deane FP, Wilson CJ. When and how do young people seek professional help for mental health problems? Med J Aust. (2007) . 187:35–9. doi: 10.5694/j.1326-5377.2007.tb01334.x
81. Khalil SA, Kamal H, Elkholy H. The prevalence of problematic internet use among a sample of Egyptian adolescents and its psychiatric comorbidities. Int J Soc Psychiatr. (2020) . 68:294–300. doi: 10.1177/0020764020983841
82. Fuchs M, Riedl D, Bock A, Rumpold G, Sevecke K. Pathological internet use—an important comorbidity in child and adolescent psychiatry: prevalence and correlation patterns in a naturalistic sample of adolescent inpatients. BioMed Res Int. (2018) 2018:1629147. doi: 10.1155/2018/1629147
83. Wang JL, Sheng JR, Wang HZ. The association between mobile game addiction and depression, social anxiety, and loneliness. Front Public Health. (2019) . 7:247. doi: 10.3389/fpubh.2019.00247
84. Goodyear V, Andersson J, Quennerstedt M, Varea V. #skinny girls: young girls' learning processes and health-related social media. Qual Res Sport Exerc. (2022) 14:1–18. doi: 10.1080/2159676X.2021.1888152
85. Yang H, Wang JJ, Tng GYQ, Yang S. Effects of social media and smartphone use on body esteem in female adolescents: testing a cognitive and affective model. Children. (2020) . 7:148. doi: 10.3390/children7090148
86. Barry CT, Sidoti CL, Briggs SM, Reiter SR, Lindsey RA. Adolescent social media use and mental health from adolescent and parent perspectives. J Adolescence. (2017) . 61:1–11. doi: 10.1016/j.adolescence.2017.08.005
87. Xavier A, Cunha M, Pinto Gouveia J. Daily peer hassles and non-suicidal self-injury in adolescence: gender differences in avoidance-focused emotion regulation processes. J Child Fam Stud. (2017) . 27:59–68. doi: 10.1007/s10826-017-0871-9
88. Ouyang M, Gui D, Cai X, Yin Y, Mao X, Huang S, et al. Stressful life events and subjective well-being in vocational school female adolescents: the mediating role of depression and the moderating role of perceived social support. Front Psychol. (2021) . 11:603511. doi: 10.3389/fpsyg.2020.603511
Keywords: non-suicidal self-injury, problematic internet use, adolescents, China, meta-analysis
Citation: He X, Yu Q, Peng J, Yu J, Wu T, Qin Y, Wang S, Dong T, Liao Y, Hu C, Yang P and Yang B (2024) Correlations between non-suicidal self-injury and problematic internet use among Chinese adolescents: a systematic review and meta-analysis. Front. Psychiatry 15:1408508. doi: 10.3389/fpsyt.2024.1408508
Received: 28 March 2024; Accepted: 10 July 2024;
Published: 29 July 2024.
Edited by:
Aleksandar Višnjić, University of Niš, SerbiaReviewed by:
Víctor José Villanueva-Blasco, Valencian International University, SpainShruti Rao, Schizophrenia Research Foundation, India
Copyright © 2024 He, Yu, Peng, Yu, Wu, Qin, Wang, Dong, Liao, Hu, Yang and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Bo Yang, MTM3NjI1NTM5MUBxcS5jb20=; Ping Yang, NDE1MTcwOTM1QHFxLmNvbQ==