Jiaqi Xu
Xia-Can Chen
Lihua Chen3
Wenxin Bao
Xia Yang
Junzhe Ran
Jiajun Xu- 1Mental Health Center, West China Hospital, Sichuan University, Chengdu, China
- 2West China School of Basic Medical Sciences and Forensic Medicine, Institute of Forensic Medicine, Sichuan University, Chengdu, China
- 3Department of Neurology, Nantong Haimen People's Hospital, Nantong, China
Introduction: While the relationship between adolescent problematic Internet use (PIU) and depression has been extensively researched, few studies have investigated the role and mechanisms of social connectedness (SC) in this context. This study aimed to investigate the mediating effect of social connectedness (SC) and cognitive emotion regulation (CER) in the relationship between PIU and depression.
Methods: We conducted a cross-sectional study involving 9,407 adolescents aged 12–18 years in China from September 2022 to March 2023. We employed Young’s 20-item Internet Addiction Test (IAT-20), the Social Connectedness Scale-Revised (SCS-R), the Cognitive Emotion Regulation Scale (CER), and the DSM-5 Level-2 Depression Scale to assess mental health outcomes. Logistic regression analysis was also performed to examine the independent association between the measured variables and depression. Mediation analysis was then conducted to evaluate the mediating roles of social connectedness and cognitive emotion regulation in the relationship between PIU and depression.
Results: We found that the prevalence of PIU was 21.8%. Offline SC (indirect effect: 0.112, 95% CI: 0.104–0.121) and negative CER (indirect effect: 00.140, 95% CI: 0.129–0.152) mainly played a parallel mediating role in the relationship between PIU and depression, along with online SC (on_SC) (indirect effect: 0.007, 95% CI: 0.005–0.010).
Discussion: These findings provide valuable insights into how PIU is associated with depression and highlight the importance of fostering real-life interpersonal interactions. However, the generalizability of this study’s findings to other populations may be limited due to cultural factors.
1 Introduction
Problematic Internet use (PIU), commonly identified as a new form of addictive behavior (1–3), is characterized by an individual’s inability to control their Internet use, which in turn leads to feelings of distress and functional impairment in daily activities (4). As of December 2023, China had 1,079 million netizens, of whom 13.9% were adolescents (5). With increasing access to online media, studies indicate a growing prevalence of PIU among adolescents in both Eastern and Western countries (6, 7). Moreover, rising PIU levels contribute to various psychosocial and mental health issues in adolescents (8). However, few studies have explored how PIU increases depression levels among adolescents. Previous surveys on adolescent PIU in China, limited to one or two provinces, may not represent the nation as a whole (9–11).
Individuals with PIU are more likely to develop depression (8, 12), with a recent longitudinal survey revealing that (12) adolescents diagnosed with depressive disorders showed greater susceptibility to PIU.
For instance, a study conducted in China involving 17,058 middle school students found that adolescents with Internet addiction were more likely to experience depression, with anxiety partially mediating this relationship (13). The complex mechanisms underlying the relationship between PIU and depression have garnered significant research attention. In addition, some studies have shown that a lack of social connection is associated with increased and prolonged Internet usage (14, 15). Social connectedness (SC), defined as an individual’s subjective perception of interpersonal relationships (16), is considered a protective factor against depressive symptoms (17, 18). The rise of online games and social media has driven adolescents to focus more on online interactions, often at the expense of face-to-face communication (19), a trend further intensified by the COVID-19 pandemic (20, 21). This shift raises questions about whether online social connectedness (on_SC) has the same positive effects as offline social connectedness (off_SC). To date, very little attention has been paid to the role of on_SC and off_SC in the relationship between PIU and depression among adolescents.
Higher levels of SC have been associated with the use of positive coping strategies when facing challenges (22, 23), whereas social isolation often indicates maladjustment (24).
Cognitive emotion regulation (CER) determines the coping strategies an individual uses to manage the intensity and nature of emotional experience (25–27). Previous evidence has identified that negative emotion regulation strategies are strongly associated with depression (26, 28). Moreover, individuals with PIU are more likely to adopt negative coping strategies, such as denial and behavioral disengagement (29, 30).
Several mediating factors in the association between PIU and depression have been identified, such as fear of missing out, sleep disturbances, anxiety, and bodily pain (13, 31–33). This raises the question of whether SC and CER could also serve as potential mediators in this relationship, particularly among adolescents who are undergoing significant psychological transitions and require focused attention. However, existing studies have not yet fully elucidated the mediating roles of both online and offline SC, as well as CER, in the connection between PIU and depression. Investigating the interplay between SC and CER may offer valuable insights for developing more effective prevention strategies.
Therefore, the primary objective of this study was to determine the prevalence of PIU among adolescents aged 12–18 years in China and to assess its impact on depression. The secondary objective was to examine the potential mediating effects of SC and CER, as well as to explore their interaction within the relationship between PIU and depression. Additionally, we aimed to investigate any differences between online and offline SC as mediators.
2 Methods
2.1 Study design and participants
This cross-sectional study was conducted across four Chinese provinces—Fujian, Jiangsu, Sichuan, and Xinjiang—representing the eastern, southern, western, and northern regions of China, respectively, from September 2022 to March 2023. Ethical approval was obtained from the Ethics Committee of West China Hospital, Sichuan University (NO. 2019-907). In each province, one to two middle schools were selected for participation in the study (Figure 1). We distributed self-reported anonymous questionnaires to all students aged 12–18 years at each school. A designated computer class was arranged in each selected school, where students individually completed the online questionnaire through the Chinese web survey platform wjx.cn under their teacher’s supervision. All measures were administered in Mandarin Chinese. Both participants and their guardians provided informed consent before the survey.
Figure 1. Flowchart of the procedure for data collection.
A total of 11,946 adolescents were included in this survey, which was administered online using non-skippable questions to ensure no missing responses. Based on the following criteria, 2,539 questionnaires were excluded: (1) participants reporting an age younger than 12 or older than 18; (2) participants who completed the survey in less than 10 min; (3) participants who indicated that their responses were “false” or “entirely false” when asked, “To what extent does your survey reflect the real situation?” (entirely false, false, general, true, entirely true) at the end of the survey; (4) and questionnaires missing key demographic information. Additionally, questionnaires with matching attributes—such as name, age, sex, height, weight, and school affiliation—were considered duplicates, and only the most complete data set was retained for further analysis. Ultimately, data from 9,407 participants (14.90 ± 1.61 years) were included in this study, representing 78.7% of the total sample.
2.2 Measurements
2.2.1 Sociodemographic variables
A self-administered questionnaire was used to collect sociodemographic variables, including age, sex, only-child status, handedness, place of residence, father’s education level, and mother’s education level.
2.2.2 Young’s 20-item Internet Addiction Test
The IAT-20, developed by Oliveira et al. (34), is a globally recognized self-report tool for assessing PIU or Internet addiction. It consists of 20 questions, each scored on a scale from 1 to 5 (1 = rarely, 2 = occasionally, 3 = frequently, 4 = often, 5 = always). The tool demonstrated strong internal consistency with a Cronbach’s α coefficient of 0.90 (35). Based on previous studies, a total score of ≥50 indicates moderate to severe Internet dependence, which was classified as PIU in this study (36, 37).
2.2.3 Social Connectedness Scale—Revised
The Social Connectedness Scale Revised (SCS-R), developed by Lee and Robbins (38), is a widely used tool for measuring SC. It contains 20 items rated on a 6-point scale, ranging from 1 (strongly disagree) to 6 (strongly agree), with higher scores indicating greater SC. The scale was translated into Chinese and validated, demonstrating a Cronbach’s α of 0.92 (39). We measured on_SC and off_SC by prefixing each question with “in the online world” or “in the real world,” respectively. The social connectedness gap (SC_gap) was calculated as the difference between the on_SC and off_SC scores to reflect the individual’s SC tendency.
2.2.4 Cognitive Emotion Regulation Questionnaires
The Cognitive Emotion Regulation Questionnaire (CERQ) was used to evaluate CER strategies applied in response to stressful events (26). The CERQ contains nine subscales, categorized into negative and positive (25) types of regulation (nCER and pCER), with each time rated on a 5-point Likert scale. The Chinese version of the CERQ has been validated, demonstrating a Cronbach’s α of 0.89 (40).
2.2.5 DSM-5-TR Level-2 Depression Scale
The DSM-5-TR Level 2—Depression—Child Age 11–17 measure is a 14-item patient-reported outcome measurement tool from the PROMIS Depression Short Form, developed by the American Psychiatric Association (APA), designed to assess depression in children and adolescents (41). The scale is intended for children aged 8–17 years, with each item rated on a 5-point scale (1 = never; 2 = almost never; 3 = sometimes; 4 = often; and 5 = almost always). Higher scores indicate greater severity of depression. The Chinese version was translated and validated by Wang and Zhong (42). Participants with T-scores above 55 were classified as having mild to severe depression (41).
2.3 Data analysis
Categorical variables were described using numbers (percentages), while continuous variables were presented as means (standard deviations). Social connectedness (SC) measurements included on_SC, off_SC, and SC_gap (calculated as the difference between the on_SC and off_SC scores). Based on whether the SC_gap score was positive or negative, participants were categorized into the “more online” and “more offline” groups, respectively. Univariate binary logistic regression analysis was performed to identify whether PIU, social connectedness, pCER and nCER were associated with depression among adolescents. Then, the multivariate logistic regression analysis was conducted to identify whether PIU, social connectedness, pCER and nCER were independently associated with depression among adolescents. Spearman’s correlation was conducted to examine mutual correlations between sociodemographic variables, PIU, SC measures, CER, and depression.
Variables that were significantly associated with depression in the multivariate logistic regression were subsequently included in the structural equation modeling (SEM) to examine the mediating effects of SC and CER in the relationship between PIU and depression. The effect size in the mediation analysis was assessed using the STDYX standardized effect size.
The fit of the hypothesis models was evaluated using the chi-squared degrees of freedom ratio (χ2/df), the Comparative Fitting Index (CFI), the Tacker-Lewis’s Index (TLI), the Root Mean Square Error of Approximation (RMSEA), and the Standardized Root Mean Square Residual (SRMR). In this study, satisfactory goodness of fit was indicated by χ2/df ≤ 3, CFI ≥ 0.95, TLI ≥ 0.95, RMSEA≤0.05, and SRMR ≤ 0.05.
A total of 2,000 bootstraps were conducted, and 95% confidence intervals (95% CI) of the estimates were calculated. Sociodemographic variables that were independently associated with depression in multivariate regression were adjusted for in all SEM models.
Group differences, regression, and correlation analysis were conducted using SPSS 26.0, while SEM analysis was conducted using Mplus 8.0 (43). All tests were two-tailed, with the significant threshold set at α = 0.05.
3 Results
3.1 Demographic characteristics
The prevalence of depression (mild to severe) and PIU were 35.8 and 21.8%, respectively. Table 1 shows the participants’ sociodemographic characteristics, SC, pCER, nCER, and PIU.
Table 1. Characteristics of adolescents aged 12–18 years and the association between depression and other variables.
3.2 Associated factors of depression
According to univariate logistic regression, on_SC, off_SC, SC_gap, nCER, pCER, and PIU showed significant associations with depression (p < 0.05) (Table 1).
After adjusting for confounding factors (sex, handedness, and residence), SC_gap, nCER, and PIU had independently and significantly positive associations with depression (p < 0.001), while both on_SC and off_SC had independently and significantly negative associations with depression among adolescents (p < 0.001) (Table 1). Adolescents with higher SC_gap scores were associated with depression after controlling the confounding factors (AOR:2.45, 95% CI:2.25–2.67, p < 0.001). Notably, pCER did not significantly correlate with depression in multivariable logistic regression.
3.3 Correlation analysis
The results of Spearman’s correlation analysis are shown in Table 2. Both on_SC and off_SC exhibited a negative correlation with PIU and depression, while the SC_gap demonstrated a significant positive correlation with both PIU and depression (p < 0.001). Additionally, older age, greater distance from urban areas, and lower levels of parental education were found to be significantly associated with higher levels of PIU and depression.
Table 2. Spearman’s correlation among sociodemographic variables, PIU, depression, nCER, and SCs.
3.4 Structural equation modeling
Mediation analysis showed that three chain mediation models were all verified as satisfactory goodness of fit. PIU was directly associated with depression in Models A, B, and C (β = 0.212, 0.130, and 0.161, respectively, p < 0.001) after adjusting for sex, handedness, and residence.
In Model A, both on_SC and nCER significantly mediated the relationship between PIU and depression (Figure 2A). Notably, the single mediating pathway of nCER independently contributed to 45.8% of the overall effect, whereas on_SC alone contributed 1.7%. In contrast, the chained mediation pathway (PIU on_SC-nCER-depression) accounted for a minor proportion of the total effect (0.2%).
Figure 2. The mediation effects of SC (A, on_SC; B, off_SC; C, SC_gap) and nCER on the relationship between PIU and depression in adolescents aged 12–18 years. The statistical significance of the mediation was adjusted for sex, handedness, and residence, with standardized coefficients (β), and standard error (S.E.) presented in parentheses. The degree of fitting along with indirect and direct effects are displayed under each model. PIU, problematic Internet use, the total score of the Internet Addiction Test (IAT-20); nCER, negative cognitive emotion regulation; on_SC, online social connectedness; off_SC, offline social connectedness; SC_gap, online-offline social connectedness gap. CI, confidence interval. *p < 0.05, ***p < 0.001.
In Model B, off_SC and nCER were identified as mediators (Figure 2B). The single mediating effect of nCER accounted for 34.4% of the total effect, closely followed by the single mediation effect of off_SC (27.5%). Their chained mediation model (PIU-off_SC-nCER-depression) was only 6.1%, which was much smaller than the single mediating pathway.
In Model C, the single mediating effect of nCER accounted for 37.0% of the total effect (Figure 2C). The SC_gap significantly mediated the connection between PIU and depression, accounting for 18.2% of the total effect. The chained mediating effect from the SC_gap to nCER was lower at 5.2%, compared to their individual effects.
4 Discussion
This study, based on a national sample of 9,407 adolescents in China, yielded novel findings indicating that off_SC, SC_gap, and nCER mainly played parallel mediating roles in the relationship between PIU and depression. Although on_SC also had a significant mediating effect, its role was less prominent compared to off_SC. These findings provide a new perspective on how PIU contributes to depression, highlighting the critical importance of fostering real-life interpersonal interactions to mitigate its negative impact on adolescents.
According to this study, the prevalence of PIU among adolescents was 21.8% (95% CI: 20.9–22.6%). This is consistent with a recent survey involving 3,357 Chinese adolescents, which reported a PIU rate of 26.09% (44). However, this rate is higher than other surveys conducted among Chinese adolescents, such as 6.0% in a sample of 755 adolescent Internet users from Wuhan (9) and 6.3% in a study involving 2,758 adolescents from southern China (11).
These discrepancies may partly result from variations in the sampling methods and instruments used. The previous study employed the same questionnaire and PIU cut-off score as ours. However, their sample size was relatively small. In contrast, the latter study employed a parental assessment questionnaire (45), which could introduce bias compared to self-reported data. Our study utilized the IAT-20, a widely validated instrument across countries (46–48), and was conducted in diverse regions of China rather than being confined to a single city or district. Cultural factors, such as the limited exposure of Chinese middle school students to electronics on weekdays, which may reduce PIU risk, should also be considered.
A study conducted in Spain showed a higher PIU rate of 33% among adolescents (49). Similar to findings from previous research, we found that older and male adolescents were more likely to experience PIU (6, 50–52), while girls were more likely to develop depression. Additionally, residing in a town or rural area was found to be associated with a higher prevalence of PIU compared to living in urban areas, which aligns with findings on the influence of residence on depression. Consistent with our results, a study using data from the National Children’s Study of China found that, among Internet users, male individuals and rural students reported higher rates of Internet addiction than female individuals and urban students (51).
To the best of our knowledge, this is the first study to confirm the mediating roles of both on_SC and off_SC and their gap in the relationship between PIU and depression. Our findings suggest that adolescents with PIU experienced a significant decline in off_SC, which is consistent with previous research indicating that individuals with PIU make minimal effort to maintain interpersonal relationships (19) and may face challenges related to social anxiety (53). Strong SC has been shown to yield positive effects, such as reducing the risk of suicide, enhancing happiness and life satisfaction, decreasing feelings of loneliness, and mitigating depressive symptoms (54–56). In line with previous literature, off_SC played a protective role in Model B of our study. Notably, on_SC also exhibited a weak negative association with both PIU and depression. Recent studies have similarly reported that on_SC plays a protective role in preventing depression, particularly among older adults (57).
Additionally, online social connections served as a buffer against depression and anxiety among young adults during the COVID-19 pandemic (58). However, the impact of online social networking remains controversial and appears to vary depending on its purpose (59, 60). Our study provided evidence that on_SC indeed had a weak protective effect in mitigating the impact of PIU on depression among adolescents. In contrast, the SC_gap, which reflects the disparity between perceived social closeness online and in real life, positively mediated the relationship between PIU and depression.
This novel finding indicates that PIU leads adolescents to prioritize online interactions over offline communication, thereby increasing the risk of depression. In line with our findings, previous studies have highlighted that face-to-face communication fosters high-quality listening, behavioral synchrony, and self-disclosure, which serve as protective factors against negative emotions (61). These findings suggest that even with extensive Internet usage, encouraging real-life social interactions could help mitigate the risk of depression among adolescents.
In addition, our study also demonstrated the mediating role of nCER in the relationship between PIU and depression. Previous research has linked nCER to PIU, including Internet gaming disorder (62–65). A literature review confirmed this association, suggesting that PIU may serve as a coping mechanism to compensate for emotional regulation deficits, thereby increasing the risk of nCER (66), a factor that has been understudied among adolescents.
Furthermore, those who rely on maladaptive strategies may be more susceptible to psychopathology (25, 26), which is particularly important for adolescents due to the developmental changes in their affective systems (67). As widely documented, chronic difficulties with CER during adolescence and late childhood are associated with depression (68–70). Our findings have confirmed the mediating role of nCER in the relationship between PIU and depression, suggesting that cognitive therapy aimed at reducing nCER could benefit adolescents with PIU and potentially prevent the onset of depression.
The present study also identified a weak chain mediating effect from SCs to nCER. A study involving 1,291 adolescents in Ireland found that adolescents with PIU experienced less perceived social support and greater difficulty in emotion regulation (71). However, the relationship between these two factors was not further examined. Neuroimaging studies have shown that increased social interactions among adolescents enhance emotional regulation capabilities, promote positive coping strategies (22) and reduce negative emotions (72).
Our SEM analysis supports these findings, demonstrating that off_SC is more effective than on_SC in reducing negative coping strategies, such as catastrophizing. Therefore, interventions aimed at enhancing off_SC should be developed for middle school students, especially those with PIU. For instance, engaging in dual sports activities has shown significant potential in reducing Internet addiction among adolescents (73).
5 Limitations
There are also some limitations to this study. First, the cross-sectional design makes it difficult to establish causality, and future research could benefit from longitudinal studies. Furthermore, adolescents with PIU may use the Internet as a platform for developing other addictions, such as gaming, online shopping, or watching short videos. Moreover, due to research resource constraints, we utilized non-random sampling methods in this study, which may introduce biases associated with specific school-related factors.
For instance, the educational quality and management approaches of these schools could influence students’ Internet usage and mental health. Disparities in the allocation of educational resources across provinces may also affect the generalizability of our findings. This study primarily focused on overall problematic Internet usage. Therefore, future studies should consider examining various forms of PIU (e.g., Internet gaming disorder and social media disorder) to gain a more comprehensive understanding of the effect of adolescent Internet-related behavioral issues on mental health.
6 Conclusion
Despite these limitations, our study is the first to investigate the potential mediating role of SC and CER strategies. We uncovered an important mechanism that helps explain why adolescents with PIU are more likely to develop depressive symptoms. Targeted interventions and strategies in clinical practice, focusing on enhancing adolescents off_SC and promoting the adoption of positive coping strategies in response to stressors, could help improve the overall mental wellbeing of adolescents.
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving humans were approved by the Ethics Committee of West China Hospital, Sichuan 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
JiaqX: Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft, Writing – review & editing. X-CC: Conceptualization, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review & editing. LC: Data curation, Resources, Writing – review & editing. DL: Conceptualization, Methodology, Visualization, Writing – review & editing. WB: Data curation, Writing – review & editing. XY: Data curation, Writing – review & editing. JR: Formal analysis, Writing – review & editing. JiajX: Conceptualization, Funding acquisition, Investigation, Project administration, Supervision, Writing – review & editing.
Funding
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by grants from the STI 2030-Major Projects (grant number: 2021ZD0202105), the China Postdoctoral Science Foundation (grant number: 2020TQ0219) and the Sichuan Science and Technology Program (grant number: 2023YFS0291).
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, KS . Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav. (1998) 1:237–44. doi: 10.1089/cpb.1998.1.237
2. Pontes, HM, Kuss, DJ, and Griffiths, MD. Clinical psychology of internet addiction: a review of its conceptualization, prevalence, neuronal processes, and implications for treatment. Neurosci Neuroecon. (2015) 4:11–23. doi: 10.2147/NAN.S60982
3. Bradley, BP . Behavioural addictions: common features and treatment implications. Br J Addict. (1990) 85:1417–9. doi: 10.1111/j.1360-0443.1990.tb01622.x
4. Shapira, NA, Lessig, MC, Goldsmith, TD, Szabo, ST, Lazoritz, M, Gold, MS, et al. Problematic internet use: proposed classification and diagnostic criteria. Depress Anxiety. (2003) 17:207–16. doi: 10.1002/da.10094
5. China International Network Information Center . The 52nd statistical report on China’s internet development [internet]. (2023). Available at: https://www.cnnic.com.cn/IDR/ReportDownloads/ (Accessed February 21, 2024).
6. 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
7. Paulus, FW, Ohmann, S, von Gontard, A, and Popow, C. Internet gaming disorder in children and adolescents: a systematic review. Dev Med Child Neurol. (2018) 60:645–59. doi: 10.1111/dmcn.13754
8. Ostinelli, EG, Zangani, C, Giordano, B, Maestri, D, Gambini, O, D’Agostino, A, et al. Depressive symptoms and depression in individuals with internet gaming disorder: a systematic review and meta-analysis. J Affect Disord. (2021) 284:136–42. doi: 10.1016/j.jad.2021.02.014
9. Tang, J, Yu, Y, Du, Y, Ma, Y, Zhang, D, and Wang, J. Prevalence of internet addiction and its association with stressful life events and psychological symptoms among adolescent internet users. Addict Behav. (2014) 39:744–7. doi: 10.1016/j.addbeh.2013.12.010
10. Yang, M, Sheng, X, Ge, M, Zhang, L, Huang, C, Cui, S, et al. Childhood trauma and psychological sub-health among Chinese adolescents: the mediating effect of internet addiction. BMC Psychiatry. (2022) 22:762. doi: 10.1186/s12888-022-04384-2
11. Zhai, B, Li, D, Li, X, Liu, Y, Zhang, J, Sun, W, et al. Perceived school climate and problematic internet use among adolescents: mediating roles of school belonging and depressive symptoms. Addict Behav. (2020) 110:106501. doi: 10.1016/j.addbeh.2020.106501
12. Liu, L, Yao, YW, Li, CR, Zhang, JT, Xia, CC, Lan, J, et al. The comorbidity between internet gaming disorder and depression: interrelationship and neural mechanisms. Front Psychiatry. 9:154. doi: 10.3389/fpsyt.2018.00154
13. Zhao, M, Huang, Y, Wang, J, Feng, J, and Zhou, B. Internet addiction and depression among Chinese adolescents: anxiety as a mediator and social support as a moderator. Psychol Health Med. (2023) 28:2315–28. doi: 10.1080/13548506.2023.2224041
14. Li, SM, and Chung, TM. Internet function and internet addictive behavior. Comput Hum Behav. (2006) 22:1067–71. doi: 10.1016/j.chb.2004.03.030
15. Stubbs, M, Bateman, CJ, and Hull, DM. Problematic internet use among university students in Jamaica. Int J Ment Health Addict. (2023) 21:3162–73. doi: 10.1007/s11469-022-00782-5
16. Lee, RM, Draper, M, and Lee, S. Social connectedness, dysfunctional interpersonal behaviors, and psychological distress: testing a mediator model. J Couns Psychol. (2001) 48:310–8. doi: 10.1037/0022-0167.48.3.310
17. Frick, A, Thinnes, I, Hofmann, SG, Windmann, S, and Stangier, U. Reduced social connectedness and compassion toward close others in patients with chronic depression compared to a non-clinical sample. Front Psych. (2021) 12:608607. doi: 10.3389/fpsyt.2021.608607
18. Santini, ZI, Koyanagi, A, Tyrovolas, S, Mason, C, and Haro, JM. The association between social relationships and depression: a systematic review. J Affect Disord. (2015) 175:53–65. doi: 10.1016/j.jad.2014.12.049
19. Ferrara, P, Franceschini, G, Corsello, G, Mestrovic, J, Giardino, I, Sacco, M, et al. The hikikomori phenomenon of social withdrawal: An emerging condition involving Youth’s mental health and social participation. J Pediatr. (2020) 225:286–8. doi: 10.1016/j.jpeds.2020.06.089
20. Gjoneska, B, Potenza, MN, Jones, J, Corazza, O, Hall, N, Sales, CMD, et al. Problematic use of the internet during the COVID-19 pandemic: good practices and mental health recommendations. Compr Psychiatry. (2022) 112:152279. doi: 10.1016/j.comppsych.2021.152279
21. Wang, C, Havewala, M, Fan, Q, Wang, Q, and Bali, D. The relations between COVID-19 stress, social connectedness, and mental health: a longitudinal investigation comparing Chinese and American college students. Curr Psychol. 43:12907–20. doi: 10.1007/s12144-023-04290-0
22. Morawetz, C, Berboth, S, and Bode, S. With a little help from my friends: the effect of social proximity on emotion regulation-related brain activity. NeuroImage. (2021) 230:117817. doi: 10.1016/j.neuroimage.2021.117817
23. Niven, K . The four key characteristics of interpersonal emotion regulation. Curr Opin Psychol. (2017) 17:89–93. doi: 10.1016/j.copsyc.2017.06.015
24. Yu, Y, Mo, PKH, Zhang, J, Li, J, and Lau, JTF. 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
25. Aldao, A, and Nolen-Hoeksema, S. Specificity of cognitive emotion regulation strategies: a transdiagnostic examination. Behav Res Ther. (2010) 48:974–83. doi: 10.1016/j.brat.2010.06.002
26. Garnefski, N, Kraaij, V, and Spinhoven, P. Negative life events, cognitive emotion regulation and emotional problems. Personal Individ Differ. (2001) 30:1311–27. doi: 10.1016/S0191-8869(00)00113-6
27. Rottenberg, J, and Gross, JJ. When emotion goes wrong: realizing the promise of affective science. Clin Psychol Sci Pract. (2003) 10:227–32. doi: 10.1093/clipsy.bpg012
28. Huh, HJ, Kim, KH, Lee, HK, and Chae, JH. The relationship between childhood trauma and the severity of adulthood depression and anxiety symptoms in a clinical sample: the mediating role of cognitive emotion regulation strategies. J Affect Disord. (2017) 213:44–50. doi: 10.1016/j.jad.2017.02.009
29. Dong, G, Li, H, Wang, L, and Potenza, MN. Cognitive control and reward/loss processing in internet gaming disorder: results from a comparison with recreational internet game-users. Eur Psychiatry. (2017) 44:30–8. doi: 10.1016/j.eurpsy.2017.03.004
30. Lei, H, Cheong, CM, Li, S, and Lu, M. The relationship between coping style and internet addiction among mainland Chinese students: a meta-analysis. Psychiatry Res. (2018) 270:831–41. doi: 10.1016/j.psychres.2018.10.079
31. Elhai, JD, Yang, H, Fang, J, Bai, X, and Hall, BJ. Depression and anxiety symptoms are related to problematic smartphone use severity in Chinese young adults: fear of missing out as a mediator. Addict Behav. (2020) 101:105962. doi: 10.1016/j.addbeh.2019.04.020
32. Lemola, S, Perkinson-Gloor, N, Brand, S, Dewald-Kaufmann, JF, and Grob, A. Adolescents’ electronic media use at night, sleep disturbance, and depressive symptoms in the smartphone age. J Youth Adolesc. (2015) 44:405–18. doi: 10.1007/s10964-014-0176-x
33. Ng, KC, Wu, LH, Lam, HY, Lam, LK, Nip, PY, Ng, CM, et al. The relationships between mobile phone use and depressive symptoms, bodily pain, and daytime sleepiness in Hong Kong secondary school students. Addict Behav. (2020) 101:105975. doi: 10.1016/j.addbeh.2019.04.033
34. Oliveira, N, Infante de Melo, CV, and Padovani, R. Internet addiction: a handbook and guide to evaluation and treatment. J Bras Psiquiatr. (2012) 62:310–10. doi: 10.1590/S0047-20852013000400009
35. Tan, Y, Chen, Y, Lu, Y, and Li, L. Exploring associations between problematic internet use, depressive symptoms and sleep disturbance among southern Chinese adolescents. Int J Environ Res Public Health. (2016) 13:313. doi: 10.3390/ijerph13030313
36. Young, KS . Internet addiction test [Internet]. (2015). Available at: http://doi.apa.org/getdoi.cfm?doi=10.1037/t41898-000 (Accessed August 9, 2023).
37. Lu, L, Xu, DD, Liu, HZ, Zhang, L, Ng, CH, Ungvari, GS, et al. Internet addiction in Tibetan and Han Chinese middle school students: prevalence, demographics and quality of life. Psychiatry Res. (2018) 268:131–6. doi: 10.1016/j.psychres.2018.07.005
38. Lee, RM, and Robbins, SB. Measuring belongingness: the social connectedness and the social assurance scales. J Couns Psychol. (1995) 42:232–41. doi: 10.1037/0022-0167.42.2.232
39. Shan, D, Xu, J, Liu, T, Zhang, Y, Dai, Z, Zheng, Y, et al. Subjective attitudes moderate the social connectedness in esports gaming during COVID-19 pandemic: a cross-sectional study. Front Public Health. (2022) 10:1020114. doi: 10.3389/fpubh.2022.1020114
40. McKinnon, A, Kuyken, W, Hayes, R, Werner-Seidler, A, Watson, P, Dalgleish, T, et al. The psychometric properties of the cognitive emotion regulation questionnaire (CERQ) in a clinical sample of adults with recurrent depression. J Affect Disord. (2020) 276:212–9. doi: 10.1016/j.jad.2020.06.061
41. American Psychiatric Association . LEVEL 2—depression—child age 11–17 (PROMIS emotional distress—depression—pediatric item Bank) [Internet]. (2013). Available at: https://www.psychiatry.org/psychiatrists/practice/dsm/educational-resources/assessment-measures.
42. Wang, Jianping, and Zhong, Jie. DSM-5 assessment scales (Chinese Version) [Internet]. (2020). Available at: https://www.mhealthu.com/index.php/list_liangbiao/120/267.
43. Muthén, L. K., and Muthén, B. O. (2017). Mplus (version 8) [Internet]. Available at: https://www.statmodel.com/download/usersguide/MplusUserGuideVer_8.pdf.
44. Hu, M, Xu, L, Zhu, W, Zhang, T, Wang, Q, Ai, Z, et al. The influence of childhood trauma and family functioning on internet addiction in adolescents: a chain-mediated model analysis. Int J Environ Res Public Health. (2022) 19:13639. doi: 10.3390/ijerph192013639
45. Wartberg, L, Kriston, L, Kegel, K, and Thomasius, R. Adaptation and psychometric evaluation of the Young diagnostic questionnaire (YDQ) for parental assessment of adolescent problematic internet use. J Behav Addict. (2016) 5:311–7. doi: 10.1556/2006.5.2016.049
46. Ndasauka, Y, Pitafi, A, and Kayange, GM. Psychometric properties of Young’s internet addiction test (IAT) in Urdu language. Asian J Psychiatr. (2019) 40:39–44. doi: 10.1016/j.ajp.2019.01.011
47. 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
48. Yang, X, Guo, W, Tao, Y-J, Meng, Y-J, Wang, H-Y, Li, X-J, et al. A bidirectional association between internet addiction and depression: a large-sample longitudinal study among Chinese university students. J Affect Disord. (2022) 299:416–24. doi: 10.1016/j.jad.2021.12.013
49. Nogueira-López, A, Rial-Boubeta, A, Guadix-García, I, Villanueva-Blasco, VJ, and Billieux, J. Prevalence of problematic internet use and problematic gaming in Spanish adolescents. Psychiatry Res. (2023) 326:115317. doi: 10.1016/j.psychres.2023.115317
50. Geng, X, Zhang, J, Liu, Y, Xu, L, Han, Y, Potenza, MN, et al. Problematic use of the internet among adolescents: a four-wave longitudinal study of trajectories, predictors and outcomes. J Behav Addict. (2023) 12:435–47. doi: 10.1556/2006.2023.00021
51. Li, Y, Zhang, X, Lu, F, Zhang, Q, and Wang, Y. Internet addiction among elementary and middle school students in China: a nationally representative sample study. Cyberpsychol Behav Soc Netw. (2014) 17:111–6. doi: 10.1089/cyber.2012.0482
52. Uçur, Ö, and Dönmez, YE. Problematic internet gaming in adolescents, and its relationship with emotional regulation and perceived social support. Psychiatry Res. (2021) 296:113678. doi: 10.1016/j.psychres.2020.113678
53. Zsido, AN, Arato, N, Lang, A, Labadi, B, Stecina, D, and Bandi, SA. The connection and background mechanisms of social fears and problematic social networking site use: a structural equation modeling analysis. Psychiatry Res. (2020) 292:113323. doi: 10.1016/j.psychres.2020.113323
54. Arango, A, Opperman, KJ, Gipson, PY, and King, CA. Suicidal ideation and suicide attempts among youth who report bully victimization, bully perpetration and/or low social connectedness. J Adolesc. (2016) 51:19–29. doi: 10.1016/j.adolescence.2016.05.003
55. Malaquias, S, Crespo, C, and Francisco, R. How do adolescents benefit from family rituals? Links to social connectedness, depression and anxiety. J Child Fam Stud. (2015) 24:3009–17. doi: 10.1007/s10826-014-0104-4
56. Sieving, RE, McRee, AL, McMorris, BJ, Shlafer, RJ, Gower, AL, Kapa, HM, et al. Youth–adult connectedness: a key protective factor for adolescent health. Am J Prev Med. (2017) 52:S275–8. doi: 10.1016/j.amepre.2016.07.037
57. Nan, Y, Xie, Y, and Hu, Y. Internet use and depression among Chinese older adults: the mediating effect of interpersonal relationship. Front Public Health. (2023) 11:1102773. doi: 10.3389/fpubh.2023.1102773
58. Stuart, J, O’Donnell, K, O’Donnell, A, Scott, R, and Barber, B. Online social connection as a buffer of health anxiety and isolation during COVID-19. Cyberpsychol Behav Soc Netw. (2021) 24:521–5. doi: 10.1089/cyber.2020.0645
59. Pantic, I . Online social networking and mental health. Cyberpsychol Behav Soc Netw. (2014) 17:652–7. doi: 10.1089/cyber.2014.0070
60. Seabrook, EM, Kern, ML, and Rickard, NS. Social networking sites, depression, and anxiety: a systematic review. JMIR Ment Health. (2016) 3:e50. doi: 10.2196/mental.5842
61. Regan, A, Radošić, N, and Lyubomirsky, S. Experimental effects of social behavior on well-being. Trends Cogn Sci. (2022) 26:987–98. doi: 10.1016/j.tics.2022.08.006
62. Al-Gamal, E, Alzayyat, A, and Ahmad, MM. Prevalence of internet addiction and its association with psychological distress and coping strategies among university students in Jordan. Perspect Psychiatr Care. (2016) 52:49–61. doi: 10.1111/ppc.12102
63. Lin, PY, Lin, HC, Lin, PC, Yen, JY, and Ko, CH. The association between emotional regulation and internet gaming disorder. Psychiatry Res. (2020) 289:113060. doi: 10.1016/j.psychres.2020.113060
64. Pettorruso, M, Valle, S, Cavic, E, Martinotti, G, di Giannantonio, M, and Grant, JE. Problematic internet use (PIU), personality profiles and emotion dysregulation in a cohort of young adults: trajectories from risky behaviors to addiction. Psychiatry Res. (2020) 289:113036. doi: 10.1016/j.psychres.2020.113036
65. Yen, JY, Yeh, YC, Wang, PW, Liu, TL, Chen, YY, and Ko, CH. Emotional regulation in Young adults with internet gaming disorder. Int J Environ Res Public Health. (2018) 15:30. doi: 10.3390/ijerph15010030
66. Gioia, F, Rega, V, and Boursier, V. Problematic internet use and emotional dysregulation among young people: a literature review. Clin Neuropsychiatry. (2021) 18:41–54. doi: 10.36131/cnfioritieditore20210104
67. Cerniglia, L, Zoratto, F, Cimino, S, Laviola, G, Ammaniti, M, and Adriani, W. Internet addiction in adolescence: neurobiological, psychosocial and clinical issues. Neurosci Biobehav Rev. (2017) 76:174–84. doi: 10.1016/j.neubiorev.2016.12.024
68. Compas, BE, Jaser, SS, Bettis, AH, Watson, KH, Gruhn, MA, Dunbar, JP, et al. Coping, emotion regulation, and psychopathology in childhood and adolescence: a meta-analysis and narrative review. Psychol Bull. (2017) 143:939–91. doi: 10.1037/bul0000110
69. Defayette, AB, Whitmyre, ED, López, R, Brown, B, Wolff, JC, Spirito, A, et al. Adolescent depressed mood and difficulties with emotion regulation: concurrent trajectories of change. J Adolesc. (2021) 91:1–14. doi: 10.1016/j.adolescence.2021.07.001
70. Folk, JB, Zeman, JL, Poon, JA, and Dallaire, DH. A longitudinal examination of emotion regulation: pathways to anxiety and depressive symptoms in urban minority youth. Child Adolesc Mental Health. (2014) 19:243–50. doi: 10.1111/camh.12058
71. Karaer, Y, and Akdemir, D. Parenting styles, perceived social support and emotion regulation in adolescents with internet addiction. Compr Psychiatry. (2019) 92:22–7. doi: 10.1016/j.comppsych.2019.03.003
72. Mulej Bratec, S, Bertram, T, Starke, G, Brandl, F, Xie, X, and Sorg, C. Your presence soothes me: a neural process model of aversive emotion regulation via social buffering. Soc Cogn Affect Neurosci. (2020) 15:561–70. doi: 10.1093/scan/nsaa068
Keywords: problematic Internet use, depression, adolescents, social connectedness, cognitive emotion regulation, mediation model
Citation: Xu J, Chen X-C, Chen L, Luo D, Bao W, Yang X, Ran J and Xu J (2024) The mediating role of social connectedness and negative cognitive emotion regulation in the association between problematic Internet use and depression among adolescents. Front. Public Health. 12:1416073. doi: 10.3389/fpubh.2024.1416073
Edited by:
Wulf Rössler, Charité University Medicine Berlin, GermanyReviewed by:
Farzin Bagheri Sheykhangafshe, Tarbiat Modares University, IranFilipa Novais, Santa Maria Hospital, Portugal
Copyright © 2024 Xu, Chen, Chen, Luo, Bao, Yang, Ran and Xu. 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: Jiajun Xu, xujiajun120@126.com
†These authors have contributed equally to this work and share first authorship