Meng Kang1
Dongfang Wang- 1Graduate Institute for Taiwan Studies, Xiamen University, Xiamen, China
- 2Institute of Education, Xiamen University, Xiamen, China
- 3School of Psychology, Centre for Studies of Psychological Applications, Guangdong Key Laboratory of Mental Health and Cognitive Science, Ministry of Education Key Laboratory of Brain Cognition and Educational Science, South China Normal University, Guangzhou, China
Background: Individuals with Internet addiction (IA) are at significant risk of suicide-related behaviors. This study aimed to investigate the relationships among IA, psychotic-like experiences (PLEs), and suicidal ideation (SI) among college students.
Methods: A total of 5,366 college students (34.4% male, mean age 20.02 years) were assessed using the self-compiled sociodemographic questionnaires, Revised Chinese Internet Addiction Scale (CIAS-R), 15-item Positive subscale of the Community Assessment of Psychic Experiences (CAPE-P15), Self-rating Idea of Suicide Scale (SIOSS), and 2-item Patient Health Questionnaire (PHQ-2).
Results: The prevalence of IA and SI were 9.3 and 12.1% among Chinese college students, respectively. There were direct effects of IA and PLEs on SI. The total effect of IA on SI was 0.18 (p < 0.001). PLEs mediated the relationship between IA and SI (Indirect effect = 0.07).
Conclusion: IA had both direct and indirect effects on SI. These findings enable us to elucidate the mechanism of how IA influences individual SI, which can provide vital information for developing and implementing targeted interventions and strategies to alleviate SI among Chinese college students.
1. Introduction
Nowadays, the Internet has become an essential part of people’s lives. As Internet use sharply increases, the problem of Internet addiction (IA) gradually emerges and becomes a hot-debated issue. IA refers to a problematic behavior related to excessive and uncontrollable Internet use (1). In a recent literature review, Pan and colleagues reported that the median prevalence of IA was 7.02% among the general population, and it increased over time (2). The Internet has gained tremendous popularity among college students. Compared with the general population, college students have easier Internet access and more substantial incentives for Internet use. However, college students lack monitoring and obtain more freedom of choice, making them more fragile and vulnerable to IA (3). Prior studies also supported that university life increased the risk of IA (4, 5). Results from the recent meta-analysis indicated a high level of IA (31.51%) among Iranian college students (6). Joseph et al. summarized that the prevalence of IA among Indian college students was approximately 19.9% (7). In China, one meta-analysis including 26 studies documented that the pooled prevalence rate of IA was 11% among Chinese college students (8). Chi et al. have found that 15.2% of college students have IA (9), while a rate reported as 7.9% in Shen et al.’s study (10). It seems that IA prevalence varies due to culture, measures, cut-offs, and definitions of IA across previous studies.
Previous studies showed that IA was associated with academic failure (11), social isolation (12), psychological symptoms (e.g., depression and anxiety) (13), and risky behaviors (e.g., self-harm and suicidality) (14). Among these factors, suicidality has attracted particular attention due to its devastating consequences (15). Suicide is considered as the second leading cause of death among adolescence and young adulthood worldwide (16). While in China, suicide is the leading cause of death in persons aged 15–34 years (17), which accounts for 19% of all deaths (18). In a literature review, Mortier and colleagues reported a pooled prevalence of lifetime and 12-month suicidal attempts among college students were 3.2 and 1.2%, respectively (19). Notably, suicidal ideation (SI) is one of the strongest predictors of eventual suicidal behavior, and undoubtedly, investigation of SI could contribute to the early identification of adolescents who may be at risk for suicide (20). One large-scale cross-sectional survey (N = 136,266) has shown that adolescents with IA had a higher prevalence of SI than those without IA (21). However, the relationship between IA and SI (22–24) and the specific mechanism remain unknown.
PLEs are widely conceptualized as the subthreshold psychotic symptomatology among the general population and have also been interpreted as a resemblance of positive symptoms of psychosis in the absence of a full-blown psychotic disorder (25, 26). Based on the psychosis proneness-persistence-impairment model (27), PLEs are influenced by a combination of genetic factors and environmental risks. IA may be an environmental stressor in the etiology of PLEs. For instance, IA triggers a range of unhealthy lifestyles (e.g., staying up late (28)) that may increase the risk of PLEs (29). Few studies have demonstrated that IA was significantly associated with PLEs among adolescents and young adults (30, 31). Moreover, PLEs serve as a robust risk factor for SI. One survey of 8,096 Korean adolescents aged 14–19 years found that higher PLEs are associated with stronger SI (32). According to Yates et al.’ report, individuals with PLEs may be twice as likely to report SI (33). These findings suggest that increased IA is associated with greater PLEs, and PLEs can predict SI. Thus, PLEs are regarded as an underlying mediator between IA and SI.
This study investigated the mediating role of PLEs on the IA-SI relationship. We hypothesized that (a) IA and PLEs have a positive effect SI among college students; (b) PLEs mediate the IA-SI relationship. Given that depression is strongly associated with IA (34), PLEs (35), and SI (36), consequently, the current study aimed to validate the above hypotheses after controlling for individual depression.
2. Materials and methods
2.1. Participants
Using a cross-sectional design, eight universities/colleges in China [Guangxi Province (Southern China: 2 universities/colleges), Hainan Province (Southern China: 1 university/college), Gansu Province (Northwestern China: 1 university/college), Shandong Province (Eastern China: 1 university/college), Hunan Province (Central China: 2 universities/colleges), and Guangdong Province (Southern China: 1 university/college)] were selected by convenience sampling method during July 2023. The study was conducted through the “Questionnaire Star” system. College students used their cell phones to scan the Quick Response (QR) code to access the questionnaire page and complete the survey. This survey is anonymous, and participants can stop or withdraw at any time if they feel uncomfortable during the survey. Finally, 5,824 college students participated in the web-based survey and provided complete data on all measures. In order to improve the quality of data, exclusion criteria for participation included: (a) time to complete the survey <5 min; (b) have current significant mental disorders that were identified by self-reported; (3) scores above 4 on the dishonesty subscale of the Self-rating Idea of Suicide Scale (SIOSS) (37, 38). Among these participants, 458 college students were subsequently removed, leaving 5,366 with valid data entry for further analyses.
2.2. Measures
2.2.1. Sociodemographic variables
The sociodemographic variables of the participants included age, sex, grade, ethnicity, single-child status, parental marital status, family income, and parents’ educational level.
2.2.2. IA
The Revised Chinese Internet Addiction Scale (CIAS-R) was used to assess IA (39). It consists of 19 items, clustering into four dimensions: compulsive use of the Internet or withdrawal symptoms, tolerance symptoms, interpersonal and health-related problems, and time management problems. Each item was rated on a four-point Likert scale, from 1 (‘complete inconformity’) to 4 (‘complete conformity’). The higher the total score, the greater the level of IA. According to previous studies (10, 40), the CIAS-R has satisfactory reliability and validity among Chinese college students, and a cut-off total score of 53 has been suggested to identify probable IA. In our study, Cronbach’s α was 0.98.
2.2.3. PLEs
The 15-item Positive subscale of the Community Assessment of Psychic Experiences (CAPE = P15) was adopted to measure PLEs (41). The scale includes three dimensions, namely persecutory ideation, bizarre experiences, and perceptual abnormalities. Each item was rated within a time frame of the last month on a four-point Likert scale, from 1 (‘never’) to 4 (‘nearly always’), with higher scores reflecting more frequent PLEs. The Chinese version of CAPE-P15 has demonstrated acceptable reliability and construct validity among college students (42). Participants were regarded to have genuine PLEs in the past month when they scored ≥ 1.57 on an average score of items in CAPE-P15 (43). In our study, Cronbach’s α was 0.96.
2.2.4. SI
The Self-rating Idea of Suicide Scale (SIOSS) was utilized to assess SI (37). It comprises 26 items within four dimensions, which are despair, pessimism, sleep, and concealment. Each item was answered on a dichotomous scale from 0 (no) to 1 (yes). We added the first three subscales (despair, pessimism, and sleep) to generate a total score of SI, with higher scores indicating stronger SI. Participants are considered to have SI when their score reaches or exceeds 12 (37). Meanwhile, the test is invalid, if the concealment factor is ≥4 (37). The SIOSS has satisfactory psychometric properties among Chinese college students (38, 44). In our study, Cronbach’s α was 0.88.
2.2.5. Depression
The 2-item Patient Health Questionnaire (PHQ-2) was employed for screening depressive symptoms over the past 2 weeks (45). Each item was rated on a four-point Likert scale, from 0 (‘not at all’) to 3 (‘nearly every day’), with higher total scores indicating more significant depressive symptoms. A total score ≥ 3 refers to the positive result of depression. The PHQ-2 is widely used among Chinese college students with good reliability and validity (46, 47). In our study, Cronbach’s α was 0.80.
2.3. Ethics statement
The studies involving human participants were reviewed and approved by the Ethics Board of the South China Normal University, China (SCNU-PSY-2022-235). All participants who volunteered to participate were informed of the purpose, process, benefits, and risks.
2.4. Statistical analysis
Data were analyzed through IBM SPSS Statistics Version 23.0. The sociodemographic characteristics and variables were described with frequency (proportion) for categorical variables and mean (standard deviation, SD) for continuous variables. The χ2 tests and Mann–Whitney U tests were used to compare differences in categorical and continuous variables between the IA and non-IA groups, respectively. The hierarchical multiple logistic regression was conducted to explore the associations between IA and SI. In step 1, the model was unadjusted by setting SI as the dependent variable and IA as the independent variable. In step 2, we made adjustments for all sociodemographic variables. In step 3, school PLEs was added, and depression was added in the last step. The results were demonstrated with odds ratios (ORs) and their 95% confidence intervals (CIs). Spearman correlation analysis examined the association among IA, PLEs, SI, and depressive symptoms. The mediating hypothesis was tested via PROCESS (48). Bootstrap analysis was conducted with 5,000 iterations yielding 95% confidence intervals estimating the size of each model’s effects. The mediation effect (model 4) was tested: IA was entered as the predictor, PLEs as the mediator, and SI as the outcome. All sociodemographic variables and depression were included in the analyses as covariates. The same approach examined the mediating role of PLEs in males and females without controlling for sex. Tests of statistical significance were two-sided, and statistical significance was set at α = 0.05.
3. Results
3.1. Sample characteristics
This study comprised 5,366 college students (mean age: 20.02 ± 1.38 years, 34.4% male). The majority of the students were Ethnicity Han (84.0%). More than half of the students are freshmen (52.1%). Further sample characteristics are presented in Table 1.
Table 1. Descriptive statistics of participants [N (%)].
The prevalence of IA and SI were 9.3 and 12.1% among Chinese college students. The prevalence of IA among males and females were 8.2 and 9.9%, respectively, (χ2 = 4.04, p = 0.048). The prevalence of SI among the male and female were 12.4 and 12.0%, respectively (χ2 = 0.15, p = 0.692). 36.7% of college students with IA reported the presence of SI, which was significantly higher than the rate reported in those without IA (9.6%, χ2 = 314.45, p < 0.001). Students with IA also showed significantly higher severity of PLEs (Z = −20.16, p < 0.001) and depression (Z = −21.06, p < 0.001) than those without IA. Table 1 also compares sample characteristics and other study variables between IA and non-IA groups.
3.2. Hierarchical regression analyses
Table 2 illustrates the results of hierarchical regression analyses. Participants who reported having IA were more likely to have SI (OR = 5.48; 95% CI = 4.46–6.73). After controlling for all sociodemographic variables, IA was still significantly associated with increased odds of SI (OR = 5.59; 95% CI = 4.54–6.89). Furthermore, this association remained significant after adjusting for PLEs and depression [OR (95% CI) = 3.56 (2.84, 4.45) and 2.34 (1.84, 2.98) in Step 3 and Step 4, respectively].
Table 2. Results of hierarchical regression analyses [OR (95%CI)].
3.3. The mediating role of PLEs
As shown in Table 3, IA is positively significantly associated with PLEs (r = 0.54, p < 0.001), SI (r = 0.41, p < 0.001), and depression (r = 0.45, p < 0.001).
Table 3. Spearman’s correlation coefficients between all study variables [r].
Table 4 described the standardized regression results of mediation to test the effects of IA on SI through the mediator of PLEs. Sociodemographic variables and depression served as covariates in the presented models. The total effect of IA on SI was 0.18 (p < 0.001). As shown in Figure 1A, IA had a significant positive effect on both PLEs (β = 0.36, p < 0.001) and SI (β = 0.11, p < 0.001). PLEs also positively predicted SI (β = 0.20, p < 0.001). The bootstrap results with 5,000 resample revealed that IA exerted a significant indirect effect on SI, due to the mediating effect of PLEs (effect = 0.07, 95% CI = 0.06–0.09). Thus, PLEs partially mediated the relationship between IA and SI in the total sample.
Table 4. The mediating effect of PLEs on the relationship between IA and SI by sex.
Figure 1. (A-C) Path model testing the mediation effect of PLEs in the association between IA and SI. IA, internet addiction; PLEs, psychotic-like experiences; SI, suicidal ideation. *p < 0.05; **p < 0.01; ***p < 0.001.
As shown in Table 4, separate analyses showed that the indirect effects in males were 0.09 (95% CI: 0.06–0.11). The direct effect was not statistically significant (95% CI: −0.01–0.09) after accounting for the effect of confounders factors in males. In comparison, the direct and indirect effects in females were 0.15 (95% CI: 0.12–0.18) and 0.07 (95% CI: 0.05–0.08). Thus, PLEs fully mediated the association between IA and SI in males (Figure 1B), while partially mediating the IA-SI relationship in females (Figure 1C).
4. Discussion
This study deepened our understanding of how IA impacts SI through the mediating role of PLEs among Chinese college students. Evidence shows that IA and PLEs were associated with SI, controlling for depression. Meanwhile, PLEs mediated the IA-SI relationship.
In our study, approximately 9.3% of college students reported IA. Shen et al. reported that the prevalence of IA between February to June 2019 was 7.7% among Chinese college students (N = 8,098) with the same scale and cut-off point (10). The increase in this rate can be explained by the COVID-19 pandemic of recent years. As a result of the pandemic lockdown, college students have much greater access to the Internet for entertainment, online courses, and connecting with friends, leading to an increased risk of IA (49). The prevalence rate of IA is slightly higher for female students compared with male students in our study. This is consistent with the results from Deb et al.’s study among 258 medical students in West Bengal (50) and Shen et al.’s study among 8,098 college students in China (40). Contrary findings have been obtained from one meta-analysis based on Chinese college students, indicating the prevalence of males in IA was found to be higher (8). This may be related to females’ increased demand for online social interactions (e.g., WeChat) (51) and online shopping (e.g., Taobao) (52). Among these college students, the self-reported prevalence of SI was 12.1%, slightly lower than the results of a survey of medical students with migraines using the same measure (13.7%) (44). Meanwhile, the SI prevalence did not differ by sex, which aligns with a previous meta-analysis (53).
Compared with those without IA, students with IA had a higher prevalence of SI, which validated our hypothesis. Specifically, students with IA were 5.48 times more likely than other students to develop SI. Extensive works have proved the association between IA and SI (14). For example, Kuang et al. proposed that greater IA increased the likelihood of SI (21). One possible reason is that students may acquire harmful online information about suicide and hold a positive attitude toward suicide, which has been associated with an increased risk of SI (54). Further, in line with previous studies (32, 33), PLEs were significantly associated with higher endorsement of SI in the present study. Echoing Sun et al.’s study, this result indicated that PLEs can be considered as a promising predictor for SI independent of the other psychopathology during the pandemic lowdown (55).
Our observations confirmed our hypothesis, that the direct link between IA and SI is mediated by PLEs after adjusting for sociodemographic variables and depression. This result suggests that IA may increase susceptibility to PLEs. The possible reasons are that individuals who overuse the internet are more likely to develop poor habits that are detrimental to their mental health, such as lack of physical activity (56), irregular diet (57), and reduced sleep duration (29). Meanwhile, people with IA may further isolate others (12), neglect meaningful relationships, lack social support, and feel loneliness, which, in turn, may increase the risk of PLEs among vulnerable groups (58). The finding supports our hypothesized model, suggesting that IA may affect college students’ SI by worsening PLEs. Moreover, our finding also indicated that PLEs fully mediate the association between IA and SI in males, while partially mediating the IA-SI relationship in females. This suggests that PLEs have a greater percentage of mediation for the IA-SI relationship in males. As for females, in addition to PLEs and negative emotions, other factors may influence the relationship between IA and SI, such as insomnia symptoms (24). Previous work also demonstrated that the IA-insomnia symptoms link was greater in females than males (59). Thus, further research is necessary to explore sex differences in the mechanisms of the association between IA and SI.
To our knowledge, this study is the first to examine the specific mediating effect of PLEs in the association between IA and SI among a large sample of college students. These findings underscore that IA should be evaluated in future research and educational and clinical practice. Interventions for SI might benefit from promoting coping strategies to reduce IA. Psychological therapies, such as multi-family group therapy (60), and cognitive behavioral therapy (CBT) (61), have shown significant effectiveness in reducing IA. Moreover, PLEs should be assessed and intervened for college students who have IA, to reduce the risk of SI. Reduction in PLEs can be achieved by promoting psychological resilience (62) or improving sleep quality (63).
However, this study has several limitations that need to be clarified. Firstly, all study variables relied on self-report questionnaires rather than clinical diagnosis, which might cause potential reporting bias and potentially threaten the validity of the findings. Clinical interviews also should be conducted to determine the severity of IA, PLEs, and SI in the future. Secondly, the cross-sectional design of this study also limits the ability to make causal inferences. Further longitudinal studies are therefore necessary to validate the current results. Thirdly, since suicide attempts and behaviors were not included in our study, extended research is expected to further clarify the association between IA and comprehensive suicide indexes. Finally, this study did not consider other critical potential confounders, such as insomnia and adverse life events. Our study also did not examine the majors of college students, although there is evidence that study field may be related to college students’ SI (64). Therefore, examining the role of more extensive risk factors in this association is also warranted.
5. Conclusion
In conclusion, this study suggests that PLEs have a mediating effect on the association between IA and SI among Chinese college students. This indicates that college students with IA can be considered a high-risk group for SI, which requires early intervention. When intervening, the effects of PLEs on students’ SI should be comprehensively considered.
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 author.
Ethics statement
The studies involving humans were approved by Ethics Board of the South China Normal University. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation in this study was provided by the participants’ legal guardians/next of kin.
Author contributions
MK: Conceptualization, Writing – original draft. BX: Writing – review & editing, Conceptualization, Visualization. CC: Writing – review & editing, Conceptualization. DW: Conceptualization, Data curation, Formal analysis, Project administration, Writing – review & editing.
Funding
The author(s) declare financial support was received for the research, authorship, and/or publication of this article. The present study was funded by Graduate Research and Innovation Project of School of Psychology, South China Normal University (PSY-SCNU202017).
Acknowledgments
The authors would like to thank all the students who took part in the study.
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.
References
1. Young, K. Internet addiction: a new clinical phenomenon and its consequences. Am Behav Sci. (2004) 48:402–15. doi: 10.1177/0002764204270278
2. Pan, YC, Chiu, YC, and Lin, YH. Systematic review and meta-analysis of epidemiology of internet addiction. Neurosci Biobehav Rev. (2020) 118:612–22. doi: 10.1016/j.neubiorev.2020.08.013
3. Kandell, J. Internet addiction on campus: the vulnerability of college students. Cyberpsychol Behav. (1998) 1:11–7. doi: 10.1089/cpb.1998.1.11
4. Chou, C, and Hsiao, M. Internet addiction, usage, gratification, and pleasure experience: the Taiwan college students’ case. Comput Educ. (2000) 35:65–80. doi: 10.1016/S0360-1315(00)00019-1
5. Chou, C, Condron, L, and Belland, J. A review of the research on internet addiction. Educ Psychol Rev. (2005) 17:363–88. doi: 10.1007/s10648-005-8138-1
6. Salarvand, S, Albatineh, A, Dalvand, S, Baghban Karimi, E, and Ghanei Gheshlagh, R. Prevalence of internet addiction among Iranian university students: a systematic review and meta-analysis. Cyberpsychol Behav Soc Netw. (2022) 25:213–22. doi: 10.1089/cyber.2021.0120
7. Joseph, J, Varghese, A, Vr, V, Dhandapani, M, Grover, S, Sharma, S, et al. Prevalence of internet addiction among college students in the Indian setting: a systematic review and meta-analysis. Gen Psychiatr. (2021) 34:e100496. doi: 10.1136/gpsych-2021-100496
8. Shao, YJ, Zheng, T, Wang, YQ, Liu, L, Chen, Y, and Yao, YS. Internet addiction detection rate among college students in the People’s Republic of China: a meta-analysis. Child Adolesc Psychiatry Ment Health. (2018) 12:25. doi: 10.1186/s13034-018-0231-6
9. Chi, X, Lin, L, and Zhang, P. Internet addiction among college students in China: prevalence and psychosocial correlates. Cyberpsychol Behav Soc Netw. (2016) 19:567–73. doi: 10.1089/cyber.2016.0234
10. Shen, Y, Meng, F, Xu, H, Li, X, Zhang, Y, Huang, C, et al. Internet addiction among college students in a Chinese population: prevalence, correlates, and its relationship with suicide attempts. Depress Anxiety. (2020) 37:812–21. doi: 10.1002/da.23036
11. Dol, KS. Fatigue and pain related to internet usage among university students. J Phys Ther Sci. (2016) 28:1233–7. doi: 10.1589/jpts.28.1233
12. Sanders, CE, Field, TM, Diego, M, and Kaplan, M. The relationship of internet use to depression and social isolation among adolescents. Adolescence. (2000) 35:237–42.
13. Spada, MM. An overview of problematic internet use. Addict Behav. (2014) 39:3–6. doi: 10.1016/j.addbeh.2013.09.007
14. 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:e181722. doi: 10.1371/journal.pone.0181722
15. Meltzer, H, Brugha, T, Dennis, M, Hassiotis, A, Jenkins, R, McManus, S, et al. The influence of disability on suicidal behaviour. Alternatives. (2012) 6:1–12. doi: 10.1016/j.alter.2011.11.004
16. Patton, GC, Coffey, C, Sawyer, SM, Viner, RM, Haller, DM, Bose, K, et al. Global patterns of mortality in young people: a systematic analysis of population health data. Lancet. (2009) 374:881–92. doi: 10.1016/S0140-6736(09)60741-8
17. Centers for Disease Control and Prevention (CDC). Suicide and attempted suicide--China, 1990-2002. MMWR Morb Mortal Wkly Rep. (2004) 53:481–4.
18. Phillips, MR, Li, X, and Zhang, Y. Suicide rates in China, 1995-99. Lancet. (2002) 359:835–40. doi: 10.1016/S0140-6736(02)07954-0
19. Mortier, P, Cuijpers, P, Kiekens, G, Auerbach, RP, Demyttenaere, K, Green, JG, et al. The prevalence of suicidal thoughts and behaviours among college students: a meta-analysis. Psychol Med. (2018) 48:554–65. doi: 10.1017/S0033291717002215
20. Hubers, A, Moaddine, S, Peersmann, S, Stijnen, T, van Duijn, E, van der Mast, RC, et al. Suicidal ideation and subsequent completed suicide in both psychiatric and non-psychiatric populations: a meta-analysis. Epidemiol Psychiatr Sci. (2018) 27:186–98. doi: 10.1017/S2045796016001049
21. Kuang, L, Wang, W, Huang, Y, Chen, X, Lv, Z, Cao, J, et al. Relationship between internet addiction, susceptible personality traits, and suicidal and self-harm ideation in Chinese adolescent students. J Behav Addict. (2020) 9:676–85. doi: 10.1556/2006.2020.00032
22. 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 Psychiatry. (2018) 79:17r11761. doi: 10.4088/JCP.17r11761
23. Chahine, M, Salameh, P, Haddad, C, Sacre, H, Soufia, M, Akel, M, et al. Suicidal ideation among Lebanese adolescents: scale validation, prevalence and correlates. BMC Psychiatry. (2020) 20:304. doi: 10.1186/s12888-020-02726-6
24. Teng, Z, Zhang, Y, Wei, Z, Liu, M, Tang, M, Deng, Y, et al. Internet addiction and suicidal behavior among vocational high school students in Hunan Province, China: a moderated mediation model. Front Public Health. (2022) 10:1063605. doi: 10.3389/fpubh.2022.1063605
25. Kelleher, I, and Cannon, M. Psychotic-like experiences in the general population: characterizing a high-risk group for psychosis. Psychol Med. (2011) 41:1–6. doi: 10.1017/S0033291710001005
26. Linscott, RJ, and van Os, J. An updated and conservative systematic review and meta-analysis of epidemiological evidence on psychotic experiences in children and adults: on the pathway from proneness to persistence to dimensional expression across mental disorders. Psychol Med. (2013) 43:1133–49. doi: 10.1017/S0033291712001626
27. van Os, J, Linscott, RJ, Myin-Germeys, I, Delespaul, P, and Krabbendam, L. A systematic review and meta-analysis of the psychosis continuum: evidence for a psychosis proneness-persistence-impairment model of psychotic disorder. Psychol Med. (2009) 39:179–95. doi: 10.1017/S0033291708003814
28. Alimoradi, Z, Lin, CY, Brostrom, A, Bulow, PH, Bajalan, Z, Griffiths, MD, et al. Internet addiction and sleep problems: a systematic review and meta-analysis. Sleep Med Rev. (2019) 47:51–61. doi: 10.1016/j.smrv.2019.06.004
29. Wang, D, Ma, Z, Scherffius, A, Liu, W, Bu, L, Sun, M, et al. Sleep disturbance is predictive of psychotic-like experiences among adolescents: a two-wave longitudinal survey. Sleep Med. (2023) 101:296–304. doi: 10.1016/j.sleep.2022.11.011
30. Mittal, VA, Dean, DJ, and Pelletier, A. Internet addiction, reality substitution and longitudinal changes in psychotic-like experiences in young adults. Early Interv Psychiatry. (2013) 7:261–9. doi: 10.1111/j.1751-7893.2012.00390.x
31. Lee, JY, Ban, D, Kim, SY, Kim, JM, Shin, IS, Yoon, JS, et al. Negative life events and problematic internet use as factors associated with psychotic-like experiences in adolescents. Front Psych. (2019) 10:369. doi: 10.3389/fpsyt.2019.00369
32. Jang, JH, Lee, YJ, Cho, SJ, Cho, IH, Shin, NY, and Kim, SJ. Psychotic-like experiences and their relationship to suicidal ideation adolescents. Psychiatry Res. (2014) 215:641–5. doi: 10.1016/j.psychres.2013.12.046
33. Yates, K, Lang, U, Cederlof, M, Boland, F, Taylor, P, Cannon, M, et al. Association of Psychotic Experiences with Subsequent Risk of suicidal ideation, suicide attempts, and suicide deaths: a systematic review and meta-analysis of longitudinal population studies. JAMA Psychiatry. (2019) 76:180–9. doi: 10.1001/jamapsychiatry.2018.3514
34. Ye, XL, Zhang, W, and Zhao, FF. Depression and internet addiction among adolescents: a meta-analysis. Psychiatry Res. (2023) 326:115311. doi: 10.1016/j.psychres.2023.115311
35. Yamasaki, S, Usami, S, Sasaki, R, Koike, S, Ando, S, Kitagawa, Y, et al. The association between changes in depression/anxiety and trajectories of psychotic-like experiences over a year in adolescence. Schizophr Res. (2018) 195:149–53. doi: 10.1016/j.schres.2017.10.019
36. Rotenstein, LS, Ramos, MA, Torre, M, Segal, JB, Peluso, MJ, Guille, C, et al. Prevalence of depression, depressive symptoms, and suicidal ideation among medical students: a systematic review and meta-analysis. JAMA. (2016) 316:2214–36. doi: 10.1001/jama.2016.17324
37. Xia, Z, Wang, D, Wu, S, and Ye, J. Primary development of self-rating idea of suicide scale. J Clin Psychiatry. (2002) 2:100–2. [In Chinese].
38. Xia, Z, Wang, D, He, X, and Ye, H. Study of self-rating idea of undergraduates in the mountain area of southern Zhejiang. Chin J School Health. (2012) 33:144–6. [In Chinese].
39. Bai, Y, and Fan, F. A study on the internet dependence of college students: the revising and applying of a measurement. Psychol Dev Educ. (2005) 5:99–104. [In Chinese].
40. 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
41. Capra, C, Kavanagh, DJ, Hides, L, and Scott, J. Brief screening for psychosis-like experiences. Schizophr Res. (2013) 149:104–7. doi: 10.1016/j.schres.2013.05.020
42. Sun, M, Wang, D, Jing, L, Xi, C, Dai, L, and Zhou, L. Psychometric properties of the 15-item positive subscale of the community assessment of psychic experiences. Schizophr Res. (2020) 222:160–6. doi: 10.1016/j.schres.2020.06.003
43. Sun, M, Wang, D, Jing, L, Yang, N, Zhu, R, Wang, J, et al. Comparisons between self-reported and interview-verified psychotic-like experiences in adolescents. Early Interv Psychiatry. (2022) 16:69–77. doi: 10.1111/eip.13132
44. Luo, JM, Liu, EZ, Yang, HD, Du, CZ, Xia, LJ, Zhang, ZC, et al. Prevalence and factors associated with suicidal ideation in medical students with migraine. Front Psych. (2021) 12:683342. doi: 10.3389/fpsyt.2021.683342
45. Kroenke, K, Spitzer, RL, and Williams, JB. The patient health Questionnaire-2: validity of a two-item depression screener. Med Care. (2003) 41:1284–92. doi: 10.1097/01.MLR.0000093487.78664.3C
46. Wang, D, Ross, B, Zhou, X, Meng, D, Zhu, Z, Zhao, J, et al. Sleep disturbance predicts suicidal ideation during COVID-19 pandemic: a two-wave longitudinal survey. J Psychiatr Res. (2021) 143:350–6. doi: 10.1016/j.jpsychires.2021.09.038
47. Wen, LY, Shi, LX, Zhu, LJ, Zhou, MJ, Hua, L, Jin, YL, et al. Associations between Chinese college students’ anxiety and depression: a chain mediation analysis. PLoS One. (2022) 17:e268773. doi: 10.1371/journal.pone.0268773
48. Hayes, A. Introduction to mediation, moderation, and conditional process analysis. J Educ Meas. (2013) 53:335–7. doi: 10.1111/jedm.12050
49. Kovacic, PZ, Peraica, T, Kozaric-Kovacic, D, and Palavra, IR. Internet use and internet-based addictive behaviours during coronavirus pandemic. Curr Opin Psychiatry. (2022) 35:324–31. doi: 10.1097/YCO.0000000000000804
50. Deb, N, and Roy, P. Internet addiction, depression, anxiety and stress among first year medical students after COVID-19 lockdown: a cross sectional study in West Bengal, India. J Family Med Prim Care. (2022) 11:6402–6. doi: 10.4103/jfmpc.jfmpc_809_22
51. Gan, C. Understanding WeChat users’ liking behavior: an empirical study in China. Comput Hum Behav. (2017) 68:30–9. doi: 10.1016/j.chb.2016.11.002
52. Zhao, S, Xu, F, Xu, Y, Ma, X, Luo, Z, Li, S, et al. Investigating smartphone user differences in their application usage behaviors: an empirical study. CCF Trans Pervasive Comput Interact. (2019) 1:140–61. doi: 10.1007/s42486-019-00011-4
53. Surace, T, Fusar-Poli, L, Vozza, L, Cavone, V, Arcidiacono, C, Mammano, R, et al. Lifetime prevalence of suicidal ideation and suicidal behaviors in gender non-conforming youths: a meta-analysis. Eur Child Adolesc Psychiatry. (2021) 30:1147–61. doi: 10.1007/s00787-020-01508-5
54. Zhong, BL, Chan, S, Liu, TB, and Chiu, HF. Nonfatal suicidal Behaviors of Chinese rural-to-urban migrant workers: attitude toward suicide matters. Suicide Life Threat Behav. (2019) 49:1199–208. doi: 10.1111/sltb.12519
55. Sun, M, Wang, D, Jing, L, and Zhou, L. The predictive role of psychotic-like experiences in suicidal ideation among technical secondary school and college students during the COVID-19 pandemic. BMC Psychiatry. (2023) 23:521. doi: 10.1186/s12888-023-05025-y
56. Chi, X, Liang, K, Chen, ST, Huang, Q, Huang, L, Yu, Q, et al. Mental health problems among Chinese adolescents during the COVID-19: the importance of nutrition and physical activity. Int J Clin Health Psychol. (2021) 21:100218. doi: 10.1016/j.ijchp.2020.100218
57. Velten, J, Bieda, A, Scholten, S, Wannemuller, A, and Margraf, J. Lifestyle choices and mental health: a longitudinal survey with German and Chinese students. BMC Public Health. (2018) 18:632. doi: 10.1186/s12889-018-5526-2
58. Tan, M, Barkus, E, and Favelle, S. The cross-lagged relationship between loneliness, social support, and psychotic-like experiences in young adults. Cogn Neuropsychiatry. (2021) 26:379–93. doi: 10.1080/13546805.2021.1960156
59. Yang, J, Guo, Y, Du, X, Jiang, Y, Wang, W, Xiao, D, et al. Association between problematic internet use and sleep disturbance among adolescents: the role of the Child’s sex. Int J Environ Res Public Health. (2018) 15:2682. doi: 10.3390/ijerph15122682
60. Liu, QX, Fang, XY, Yan, N, Zhou, ZK, Yuan, XJ, Lan, J, et al. Multi-family group therapy for adolescent internet addiction: exploring the underlying mechanisms. Addict Behav. (2015) 42:1–8. doi: 10.1016/j.addbeh.2014.10.021
61. Wolfling, K, Muller, KW, Dreier, M, Ruckes, C, Deuster, O, Batra, A, et al. Efficacy of short-term treatment of internet and computer game addiction: a randomized clinical trial. JAMA Psychiatry. (2019) 76:1018–25. doi: 10.1001/jamapsychiatry.2019.1676
62. Wang, D, Chen, H, Chen, Z, Yang, Z, Zhou, X, Tu, N, et al. Resilience buffers the association between sleep disturbance and psychotic-like experiences in adolescents. Schizophr Res. (2022) 244:118–25. doi: 10.1016/j.schres.2022.05.018
63. Waite, F, Sheaves, B, Isham, L, Reeve, S, and Freeman, D. Sleep and schizophrenia: from epiphenomenon to treatable causal target. Schizophr Res. (2020) 221:44–56. doi: 10.1016/j.schres.2019.11.014
Keywords: Internet addiction, psychotic-like experiences, suicidal ideation, college students, mediating role
Citation: Kang M, Xu B, Chen C and Wang D (2023) Internet addiction and suicidal ideation among Chinese college students: the mediating role of psychotic-like experiences. Front. Public Health. 11:1276496. doi: 10.3389/fpubh.2023.1276496
Edited by:
Aleksandar Višnjić, University of Niš, SerbiaReviewed by:
Corine S. M. Wong, The University of Hong Kong, Hong Kong SAR, ChinaAleksandra Kielan, Medical University of Warsaw, Poland
Copyright © 2023 Kang, Xu, Chen and Wang. 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: Dongfang Wang, wdfpsyc@126.com