Abstract
There is a certain relationship between alexithymia and depression, but further investigation is needed to explore their underlying mechanisms. The aims of this study was to explore the mediating role of internet addiction between alexithymia and depression and the moderating role of physical activity. A total of 594 valid responses were included in the analysis, with a mean age of 18.72 years (SD = 1.09). The sample comprised 250 males (42.09%) and 344 females (57.91%). These responses were utilized for descriptive analysis, correlation analysis, regression analysis, and the development of mediation and moderation models. Alexithymia showed positive correlations with depression and internet addiction, and physical activity was negatively correlated with internet addiction and depression. Internet addiction partially mediated the relationship between alexithymia and depression, while physical activity weakened the association between internet addiction and depression, acting as a moderator. Our findings suggest that excessive Internet engagement may mediate the relationship between alexithymia and depression as an emotional regulatory coping strategy, and that physical activity attenuates the predictive effect of Internet addiction on depression.
Keywords: Alexithymia, Depression, Internet addiction, Physical activity, College students
Subject terms: Psychology and behaviour, Epidemiology
Introduction
Alexithymia, often manifested as a multidimensional impairment in recognizing, understanding, and describing emotions1,2, is a stable personality trait. Individuals with higher levels of alexithymia experience increasing difficulties in establishing and maintaining interpersonal relationships, perceive less social support, and exhibit lower levels of social skills3. Upon transitioning to university, individuals confront a plethora of unknowns and challenges4. Particularly within the context of China, the transition to university presents them with more flexible schedules and expanded social circles, with "adaptation" emerging as one of their foremost hurdles due to the characteristic of alexithymia5. Consequently, due to impairments in recognizing and responding to emotions, they struggle to develop healthy or intimate social relationships6,7, leading to emotional distress and discomfort8,9. Individuals with alexithymia inevitably face difficulties in social interactions and maintaining emotional connections due to its inherent characteristics, and it is often associated with other psychological disorders10, such as social anxiety11, substance abuse12, depression13,14, eating disorders15, and non-suicidal self-injury16,17. Hence, comprehending the relationship between alexithymia in university students and other psychological disorders, along with their underlying physiological and psychological mechanisms, is crucial for promoting the healthy development of individuals with alexithymia or predisposition to alexithymia.
Previous studies have found a high correlation between alexithymia and depression in individuals14,18–20. This high correlation between alexithymia and depression is not only present in clinical depression populations21, but also in non-clinical populations13, and similar results have been obtained in studies of populations with other diseases or psychological disorders14,22,23. Evidence of a high correlation between alexithymia and depression exists in various populations20. Furthermore, depression mediates the relationship between alexithymia and other risk behaviors in individuals24, even completely mediating it25. Therefore, based on the above review, we hypothesize that there is a positive correlation between alexithymia and depression in college students.
Interpersonal relationships are challenged due to impaired recognition and response to emotions6,7 and often fall into emotional distress8,9. Hence, individuals with alexithymia attempt to regulate their emotions through compulsive behaviors26,27. The internet provides them with an ideal avenue due to its anonymity, convenience of remote interaction28, and absence of face-to-face observation29, which may mitigate the deficits in understanding and identifying others' emotions associated with alexithymia while fulfilling the need for social interaction. Consequently, individuals with alexithymia may exhibit excessive dependence on and usage of the internet, leading to the development of internet addiction. Internet addiction refers to the pathological use of the Internet by individuals, leading to adverse consequences in personal, social, and occupational life30. Based on detection rates of internet addiction among young people in China, it has been found that internet addiction gradually increases with age and grade, reaching 7.7% among high school students31, and even exceeding half among college students32. Research has found a significant positive correlation between alexithymia and internet addiction in college students33,34, and alexithymia significantly predicts internet addiction in college students35–37. This predictive effect also exists in studies focusing on a single gender38. Additionally, in a large sample study of Chinese college students, a significant relationship between internet addiction and depression was found39, and internet addiction significantly predicted subsequent levels of depression in college students40. This relationship also exists in other countries and regions41–44. Based on the above review, we can establish the second hypothesis of this study, which is that internet addiction plays a mediating role in the relationship between alexithymia and depression in college students.
Research has indicated that dysfunction of the hypothalamic–pituitary–adrenal (HPA) axis is prevalent among individuals with internet addiction45,46. Dysfunction of the HPA axis serves as a significant indicator of depression47 and is also a crucial predictive factor for depression48. Therefore, regulating the functionality of the HPA axis naturally emerges as a potential pathway for alleviating depression49. Studies have shown that physical activity can reduce cortisol levels and promote the development of HPA axis function50,51. Hence, physical activity may decrease the strong correlation between internet addiction and depression. Research has found that compared to non-internet addicted college students, internet addicted college students have higher levels of depression and lower levels of physical activity52. Physical activity can mitigate internet addiction by modulating the neurobiology of the central and autonomic nervous systems53, thereby significantly reducing depression levels among internet-addicted college students54, and this evidence is also supported by retrospective studies55. Therefore, we make the final hypothesis of this study that physical activity can regulate the relationship between internet addiction and depression in college students.
In conclusion, the relationship between alexithymia and depression in college students has been widely reported, but little is known about other psychological factors between the two, including the mediating role of internet addiction and the moderating role of physical activity. This study will use internet addiction as a mediating factor between alexithymia and depression, and physical activity to moderate the relationship between internet addiction and depression, further enriching the underlying psychological mechanisms between alexithymia and depression. A theoretical model diagram is constructed (see Fig. 1).
Figure 1.
Hypothesized model.
Methods
Participants
The present study was conducted in October 2023 at two universities in the western part of Hunan Province, China. Prior to the commencement of the research, approval was obtained from the Biomedical Ethics Committee of Jishou University (Grant number: JSDX-2023-0034). During the survey, our staff first communicated with the leading teachers of the college students and obtained approval. Subsequently, on a class-by-class basis, the investigators delivered presentations to all participants, informing them of the main content of the survey, the anonymity and confidentiality of the data, their right to freely withdraw, and the disposition of the results. After obtaining informed consent from all individuals, electronic questionnaires were distributed, and participants could complete the questionnaire in its entirety within 20 min. Ultimately, a total of 676 college students completed the survey. We confirm that all the experiment is in accordance with the relevant guidelines and regulations such as the declaration of Helsinki. After screening for insufficient response times (instances where participants completed the questionnaire in an unreasonably short amount of time, suggesting potential rushed or careless responses) and regular patterned responses (responses that exhibit a consistent and predictable pattern, possibly indicating a lack of genuine engagement or random guessing), valid data from 594 participants (250 male, 344 female; 225 non-left-behind, 369 left-behind) were obtained, with an average age of 18.72 years (SD = 1.09).
Measures
Alexithymia
The Toronto Alexithymia Scale (TAS-20) was used to assess the level of alexithymia2,56. The scale consists of 20 items, and a Likert 5-point scoring system is used to evaluate the level of alexithymia, ranging from 1 (completely inconsistent) to 5 (completely consistent). Except for the five reverse-scored items (4, 5, 10, 18, and 19), which are scored in reverse, all other items are scored between 1 and 5 points. The sum of all item scores represents the total score of alexithymia, with higher scores indicating more severe levels of alexithymia. In this study, Cronbach's α for the sample was 0.832.
Depression
The Chinese version of the Depression Anxiety Stress Scale (DASS-21)57,58 was utilized to measure depression. This study employed the depression subscale of the DASS-21, which comprises 7 items and utilizes a Likert 4-point scoring system, ranging from 1 (strongly disagree) to 4 (strongly agree), to assess depression. Each item is scored between 1 and 5 points, and the sum of all item scores represents the total depression score, with higher scores indicating more severe levels of depression. In this study, the Cronbach's α for the sample was 0.899.
Internet addiction
The internet addiction level was measured using the Internet Addiction Test (IAT) developed by Wei59,60. The questionnaire consists of 8 items and utilizes a Likert 5-point scoring system, ranging from 1 (strongly disagree) to 5 (strongly agree), to evaluate internet addiction. Each item is scored between 1 and 5 points, and the sum of all item scores represents the total internet addiction score, with higher scores indicating more severe levels of internet addiction. In this study, the Cronbach’s α for the sample was 0.876.
Physical activity
The physical activity level was measured using the Physical Activity Scale developed by Liang Deqing61,62. The scale consists of 3 items, including exercise intensity, duration, and frequency. Each item has 5 different levels, with scores ranging from 1 to 5 for intensity and frequency, and scores ranging from 0 to 4 for duration. The physical activity score is derived by multiplying the scores of the three items, with higher scores indicating higher levels of physical activity. The current sample's Cronbach's alpha for the scale is 0.654.
Covariates
Taking into account the influence of demographic variables on the outcome analysis, such as gender and age35,63, we controlled for these variables during the analysis. The gender was coded as 1 for male and 2 for female.
Statistical analyses
In our study, all statistical analyses were conducted using SPSS 26.0 software. Firstly, a method bias test was performed to explore potential biases associated with the use of self-report questionnaires. Subsequently, descriptive statistics and correlation analysis were conducted to describe the demographic characteristics of the participants and the main variables of interest. Prior to further analysis, standardization was applied to the data of the main variables. Finally, to test our hypotheses, we employed the PROCESS macro plugin in SPSS (Model 14) to examine the relationships among alexithymia, depression in college students, the mediating role of internet addiction, and the moderating effect of physical activity64. In this process, we utilized 5000 bootstrap resampling iterations to assess model fit and estimate 95% confidence intervals, considering a relationship as significant when the 95% confidence interval did not include zero. Throughout the analysis, gender and age were included as covariates for control analysis.
Ethics approval and consent to participate
The study was approved by the Biomedicine Ethics Committee of Jishou University before the initiation of the project (Grant number: JSDX-2023-0034). And informed consent was obtained from the participants before starting the program.
Results
Harman’s single factor test
The common method bias was examined using Harman's single factor test. The analysis results indicated that among the factors with eigenvalues greater than 1, only two factors met this criterion. Without conducting principal component factor rotation, the first factor accounted for 30.20% of the variance, which was lower than the recommended threshold of 40%65. Therefore, based on the analysis results, there was no significant evidence of common method bias in this study.
Descriptive data and Correlational analyses
Table 1 presents the Pearson correlation data among the variables. Alexithymia is significantly positively correlated with internet addiction (r = 0.413, p < 0.001) and depression (r = 0.523, p < 0.001) in college students, and significantly negatively correlated with physical activity (r = − 0.255, p < 0.001). Internet addiction is significantly positively correlated with depression (r = 0.384, p < 0.001) and significantly negatively correlated with physical activity (r = − 0.087, p < 0.05) in college students. Physical activity is significantly negatively correlated with depression in college students (r = − 0.269, p < 0.001).
Table 1.
Pearson correlation matrix between relevant variables.
| Mean | SD | 1 | 2 | 3 | 4 | 5 | |
|---|---|---|---|---|---|---|---|
| 1. Gender | – | – | – | ||||
| 2. Age | 18.72 | 1.09 | − 0.260*** | – | |||
| 3. Alexithymia | 52.23 | 10.58 | 0.064 | − 0.036 | – | ||
| 4. Internet addiction | 21.51 | 6.77 | 0.133** | − 0.005 | 0.413*** | – | |
| 5. Depression | 12.31 | 4.21 | 0.002 | 0.070 | 0.523*** | 0.384*** | – |
| 6. Physical activity | 21.73 | 21.45 | − 0.366*** | 0.300*** | − 0.255*** | − 0.087* | − 0.269*** |
*p < 0.05; **p < 0.01; ***p < 0.001.
Moderated and mediation analysis
After including mediating and moderating variables, as well as controlling for covariates, alexithymia still significantly and positively predicts the level of depression in college students (β = 0.391, SE = 0.038, p < 0.001). Furthermore, in the mediation analysis, alexithymia significantly and positively predicts internet addiction in college students (β = 0.407, SE = 0.037, p < 0.001), and internet addiction acts as a mediator between alexithymia and the level of depression in college students (β = 0.228, SE = 0.037, p < 0.001). In the moderation analysis, physical activity negatively predicts depression in college students (β = − 0.223, SE = 0.038, p < 0.001), and the interaction term between internet addiction and physical activity also significantly and negatively predicts depression in college students (β = − 0.067, SE = 0.031, p < 0.05). Please refer to Table 2, Figs. 2 and 3 for more details.
Table 2.
Moderated and mediation analysis.
| Internet addiction | Depression | |||||
|---|---|---|---|---|---|---|
| β | SE | t | β | SE | t | |
| Gender | 0.239 | 0.078 | 3.056** | − 0.206 | 0.074 | − 2.801** |
| Age | 0.037 | 0.035 | 1.056 | 0.110 | 0.032 | 3.394*** |
| Alexithymia | 0.407 | 0.037 | 10.91*** | 0.391 | 0.038 | 10.414*** |
| Internet addiction (A) | 0.228 | 0.037 | 6.199*** | |||
| Physical activity (B) | − 0.223 | 0.038 | − 5.922*** | |||
| A × B | − 0.067 | 0.031 | − 2.192* | |||
| R2 | 0.183 | 0.361 | ||||
| F | 44.176*** | 55.323*** | ||||
*p < 0.05; **p < 0.01; ***p < 0.001.
Figure 2.
Hypothesized model, *p < 0.05; ***p < 0.001.
Figure 3.

Moderating effect of physical activity on Internet addiction and depression in college students.
Discussion
This study discusses the interrelationships between alexithymia, internet addiction, depression, and physical activity among college students. The results revealed positive correlations between alexithymia, internet addiction, and depression, as well as negative correlations between these variables and physical activity, all of which reached significance levels. After controlling for demographic variables, internet addiction was found to mediate the relationship between alexithymia and depression among college students, while physical activity played a moderating role in the relationship between internet addiction and depression.
Our study found a significant correlation between alexithymia and depression among college students, which is consistent with previous research20,25. Alexithymia, characterized by difficulties in recognizing and understanding emotions66, has been linked to impaired emotional regulation and an increased risk of depression67–69. Furthermore, a longitudinal study found fluctuating scores on the depression scale in conjunction with scores on the alexithymia scale14. Additionally, individuals with alexithymia may receive less support70, leading to reduced social support and higher levels of depression71. Therefore, our results confirm our first hypothesis that there is a significant positive correlation between alexithymia and depression among college students, with alexithymia significantly predicting depression.
Furthermore, this study found that internet addiction plays a mediating role between alexithymia and depression among college students. As previously mentioned, the difficulty of alexithymic individuals in recognizing and understanding emotions leads to difficulties in their interactions with others in the real world72. Consequently, they tend to choose to escape the real world and seek social satisfaction in the online world10. Compensatory Internet use theory suggests73 that negative social relationships and emotions drive individuals to escape into the online world as a coping mechanism, satisfying not only their social needs but also addressing negative emotions. In an era where smartphones and the internet are easily accessible, they find it easier to enter the virtual world. However, this way of coping cannot replace the real world and only leads to the formation and exacerbation of internet addiction. There is also a strong relationship between internet addiction and depression40, and longitudinal studies have found that they can predict each other40,74. Therefore, based on the evidence mentioned above, our second hypothesis that internet addiction plays a mediating role between alexithymia and students is supported.
Moreover, this study found that physical activity has a moderating effect on the relationship between internet addiction and depression among college students. Loneliness, depression, and sensitivity to interpersonal relationships are significant characteristics of individuals with internet addiction75, which, combined with the characteristics of alexithymia, further increases their dependence on the internet76. However, previous research has found that exercise can increase levels of neurotrophic factors, cortisol, and neurotransmitters, promote the development of the nervous system, and inhibit reward impulsivity53. Additionally, long-term physical exercise can significantly reduce the level of internet addiction and depression among college students, improve sleep quality, and balance the sympathetic and parasympathetic nervous system functions54. Moreover, group sports activities are enjoyable and involve social interactions, which may further weaken the depressive emotions caused by internet addiction among college students55. Therefore, physical activity can regulate the relationship between internet addiction and depression among college students, confirming our last hypothesis.
This study has certain strengths. It is the first to discuss internet addiction as a mediating factor between alexithymia and depression among college students, and it demonstrates that physical activity can moderate the impact of internet addiction on depression. Hence, while examining the relationship between individual alexithymia and depression, it becomes imperative to assess the presence of internet addiction or other behavioral addictions, as they may exacerbate levels of depression. Concurrently, in light of established patterns of internet addiction behavior, encouraging and leading individuals to actively engage in physical activities is advocated. This not only fosters interaction77, learning, and the establishment of positive social relationships during physical activity but also promotes physical and mental well-being78,79, potentially reducing depression levels holistically. However, the study also has limitations. Firstly, it is based on cross-sectional data, which may challenge the strength of causal relationships. Future research could use longitudinal data to explain the causal relationships between variables. Secondly, all the main data are self-reported, which may have subjective biases. Future research could combine subjective and objective data to improve the credibility of the evidence. Lastly, this study was conducted based on convenience sampling, which may introduce regional differences. Future research could conduct cross-regional studies.
Conclusion
This study reveals the relationship between alexithymia and depression among college students, the mediating role of internet addiction between the two, and the moderating effect of physical activity on the relationship between internet addiction and depression. It further enhances our understanding of the relationships and underlying mechanisms among these variables, highlighting the potential roles of these factors in psychological interventions for college students with alexithymia.
Author contributions
Y.L., L.D., Q.S.: conceptualization, methodology, data curation, writing—original draft, writing—review and editing. Y.M., Y.C.: methodology, data curation, writing—review and editing. L.X., T.Z.: conceptualization, writing—review and editing, funding acquisition. Y.W.: writing—review and editing, funding acquisition.
Data availability
The datasets generated and/or analysed during the current study are not publicly available due (our experimental team's policy) but are available from the corresponding author on reasonable request.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
These authors contributed equally: Yang Liu, Liangfan Duan and Qingxin Shen.
References
- 1.Cerutti R, Zuffianò A, Spensieri V. The role of difficulty in identifying and describing feelings in non-suicidal self-injury behavior (NSSI): Associations with perceived attachment quality, stressful life events, and suicidal ideation. Front. Psychol. 2018;9:318. doi: 10.3389/fpsyg.2018.00318. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Bagby RM, Parker JD, Taylor GJ. The twenty-item toronto alexithymia scale—I. Item selection and cross-validation of the factor structure. J. Psychosom. Res. 1994;38:23–32. doi: 10.1016/0022-3999(94)90005-1. [DOI] [PubMed] [Google Scholar]
- 3.Timoney LR, Holder MD. Emotional Processing Deficits and Happiness: Assessing the Measurement, Correlates, and Well-Being of People with Alexithymia. New York: Springer; 2013. [Google Scholar]
- 4.Thompson M, Pawson C, Evans B. Navigating entry into higher education: The transition to independent learning and living. J. Furth. High. Educ. 2021;45:1398–1410. doi: 10.1080/0309877X.2021.1933400. [DOI] [Google Scholar]
- 5.Li J, Wang Y, Sun Y, Liang Y, Dou K. Individual and interpersonal correlates of changes in college adaptation among Chinese freshmen: A longitudinal study. Curr. Psychol. 2023;42:3349–3361. doi: 10.1007/s12144-021-01693-9. [DOI] [Google Scholar]
- 6.Heaven PCL, Ciarrochi J, Hurrell K. The distinctiveness and utility of a brief measure of alexithymia for adolescents. Pers. Individ. Differ. 2010;49:222–227. doi: 10.1016/j.paid.2010.03.039. [DOI] [Google Scholar]
- 7.Rieffe C, Oosterveld P, Terwogt MM. An alexithymia questionnaire for children: Factorial and concurrent validation results. Pers. Individ. Differ. 2006;40:123–133. doi: 10.1016/j.paid.2005.05.013. [DOI] [Google Scholar]
- 8.Hemming L, Haddock G, Shaw J, Pratt D. Alexithymia and its associations with depression, suicidality, and aggression: An overview of the literature. Front. Psychiatry. 2019;10:203. doi: 10.3389/fpsyt.2019.00203. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Nekouei ZK, Doost HT, Yousefy A, Manshaee G, Sadeghei M. The relationship of alexithymia with anxiety-depression-stress, quality of life, and social support in coronary heart disease (a psychological model) J. Educ. Health Promot. 2014;3:68. doi: 10.4103/2277-9531.134816. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Samur D, et al. Four decades of research on alexithymia: Moving toward clinical applications. Front. Psychol. 2013;4:861. doi: 10.3389/fpsyg.2013.00861. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Cox BJ, Swinson RP, Shulman ID, Bourdeau D. Alexithymia in panic disorder and social phobia. Compr. Psychiatry. 1995;36:195–198. doi: 10.1016/0010-440X(95)90081-6. [DOI] [PubMed] [Google Scholar]
- 12.Thorberg FA, Young RM, Sullivan KA, Lyvers M. Alexithymia and alcohol use disorders: A critical review. Addict. Behav. 2009;34:237–245. doi: 10.1016/j.addbeh.2008.10.016. [DOI] [PubMed] [Google Scholar]
- 13.Honkalampi K, Hintikka J, Tanskanen A, Lehtonen J, Viinamäki H. Depression is strongly associated with alexithymia in the general population. J. Psychosom. Res. 2000;48:99–104. doi: 10.1016/S0022-3999(99)00083-5. [DOI] [PubMed] [Google Scholar]
- 14.Honkalampi K, Hintikka J, Laukkanen E, Lehtonen J, Viinamäki H. Alexithymia and depression: A prospective study of patients with major depressive disorder. Psychosomatics. 2001;42:229–234. doi: 10.1176/appi.psy.42.3.229. [DOI] [PubMed] [Google Scholar]
- 15.Nowakowski ME, McFarlane T, Cassin S. Alexithymia and eating disorders: A critical review of the literature. J. Eat. Disord. 2013;1:21. doi: 10.1186/2050-2974-1-21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Greene D, Boyes M, Hasking P. The associations between alexithymia and both non-suicidal self-injury and risky drinking: A systematic review and meta-analysis. J. Affect. Disord. 2020;260:140–166. doi: 10.1016/j.jad.2019.08.088. [DOI] [PubMed] [Google Scholar]
- 17.Greene D, Hasking P, Boyes M. The associations between alexithymia, non-suicidal self-injury, and risky drinking: The moderating roles of experiential avoidance and biological sex. Stress Health. 2019;35:457–467. doi: 10.1002/smi.2879. [DOI] [PubMed] [Google Scholar]
- 18.Foran HM, O'Leary KD. The role of relationships in understanding the alexithymia-depression link. Eur. J. Personal. 2013;27:470–480. doi: 10.1002/per.1887. [DOI] [Google Scholar]
- 19.Hendryx MS, Haviland MG, Shaw DG. Dimensions of alexithymia and their relationships to anxiety and depression. J. Pers. Assess. 1991;56:227–237. doi: 10.1207/s15327752jpa5602_4. [DOI] [PubMed] [Google Scholar]
- 20.Sagar R, Talwar S, Desai G, Chaturvedi SK. Relationship between alexithymia and depression: A narrative review. Indian J. Psychiatry. 2021;63:127–133. doi: 10.4103/psychiatry.IndianJPsychiatry_738_19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Honkalampi K, Hintikka J, Antikainen R, Lehtonen J, Viinamäki H. Alexithymia in patients with major depressive disorder and comorbid cluster C personality disorders: A 6-month follow-up study. J. Pers. Disord. 2001;15:245–254. doi: 10.1521/pedi.15.3.245.19211. [DOI] [PubMed] [Google Scholar]
- 22.Corcos M, et al. Alexithymia, depression and drug addiction. L'encephale. 2004;30:201–211. doi: 10.1016/s0013-7006(04)95431-0. [DOI] [PubMed] [Google Scholar]
- 23.Lumley MA, Smith JA, Longo DJ. The relationship of alexithymia to pain severity and impairment among patients with chronic myofascial pain: Comparisons with self-efficacy, catastrophizing, and depression. J. Psychosom. Res. 2002;53:823–830. doi: 10.1016/S0022-3999(02)00337-9. [DOI] [PubMed] [Google Scholar]
- 24.De Berardis D, et al. Alexithymia and suicide risk in psychiatric disorders: A mini-review. Front. Psychiatry. 2017;8:148. doi: 10.3389/fpsyt.2017.00148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Tang WC, Lin MP, Wu J, Lee YT, You JN. Mediating role of depression in the association between alexithymia and nonsuicidal self-injury in a representative sample of adolescents in Taiwan. Child Adolesc. Psychiatry Ment. Health. 2022;16:43. doi: 10.1186/s13034-022-00477-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Taylor GJ, Bagby RM, Parker JD. The alexithymia construct. A potential paradigm for psychosomatic medicine. Psychosomatics. 1991;32:153–164. doi: 10.1016/S0033-3182(91)72086-0. [DOI] [PubMed] [Google Scholar]
- 27.Taylor, G. J., Bagby, R. M. & Parker, J. D. A. Disorders of Affect Regulation: Alexithymia in Medical and Psychiatric Illness (Cambridge University Press, New York, 1997).
- 28.Christopherson KM. The positive and negative implications of anonymity in internet social interactions: “On the internet, nobody knows you’re a dog”. Comput. Hum. Behav. 2007;23:3038–3056. doi: 10.1016/j.chb.2006.09.001. [DOI] [Google Scholar]
- 29.McKenna KYA, Bargh JA. Plan 9 from cyberspace: The implications of the internet for personality and social psychology. Pers. Soc. Psychol. Rev. 2000;4:57–75. doi: 10.1207/S15327957PSPR0401_6. [DOI] [Google Scholar]
- 30.Young KS. Internet addiction: The emergence of a new clinical disorder. Cyberpsychol. Behav. 1998;1:237–244. doi: 10.1089/cpb.1998.1.237. [DOI] [Google Scholar]
- 31.Yan Q, et al. Trends in the prevalence of Internet addiction among adolescents from 2004 to 2019 in Shanghai. Chin. J. School Health. 2022;43:1193–1197. [Google Scholar]
- 32.Zhang ZM, Fang Y, Xu ZH, Tian X, Zhao ZJ. Analysis of Internet addiction and influencing factors of college students in Hebei Province. Chin. J. School Health. 2022;43:1033–1036. [Google Scholar]
- 33.Luo HG, et al. Effect of alexithymia on internet addiction among college students: The mediating role of metacognition beliefs. Front. Psychol. 2022;12:788458. doi: 10.3389/fpsyg.2021.788458. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Gori A, Topino E. The association between alexithymia and social media addiction: Exploring the role of dysmorphic symptoms, symptoms interference, and self-esteem, controlling for age and gender. J. Pers. Med. 2023;13:152. doi: 10.3390/jpm13010152. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Lyvers M, Salviani A, Costan S, Thorberg FA. Alexithymia, narcissism and social anxiety in relation to social media and internet addiction symptoms. Int. J. Psychol. 2022;57:606–612. doi: 10.1002/ijop.12840. [DOI] [PubMed] [Google Scholar]
- 36.Zhao Y, Zhang K, Griffiths MD. Serial mediation roles of alexithymia and loneliness in the association between family function and internet addiction among chinese college students. Front. Psychol. 2022;13:874031. doi: 10.3389/fpsyg.2022.874031. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Gao TT, et al. The influence of alexithymia on mobile phone addiction: The role of depression, anxiety and stress. J. Affect. Disord. 2018;225:761–766. doi: 10.1016/j.jad.2017.08.020. [DOI] [PubMed] [Google Scholar]
- 38.Lyvers M, Senturk C, Thorberg FA. Alexithymia, impulsivity and negative mood in relation to internet addiction symptoms in female university students. Aust. J. Psychol. 2021;73:548–556. doi: 10.1080/00049530.2021.1942985. [DOI] [Google Scholar]
- 39.Guo WJ, et al. Associations of internet addiction severity with psychopathology, serious mental illness, and suicidality: Large-sample cross-sectional study. J. Med. Internet Res. 2020;22:e17560. doi: 10.2196/17560. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Yang X, et al. A bidirectional association between internet addiction and depression: A Large-sample longitudinal study among Chinese university students. J. Affect. Disord. 2022;299:416–424. doi: 10.1016/j.jad.2021.12.013. [DOI] [PubMed] [Google Scholar]
- 41.Çelik DÖ, Haney MÖ. The relationship between depression, healthy lifestyle behaviors and internet addiction: A cross-sectional study of the athlete university students in Turkey. Front. Psychiatry. 2023;14:122. doi: 10.3389/fpsyt.2023.1222931. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 42.Ibrahim AK, Fouad I, Kelly SJ, El Fawal B, Ahmed GK. Prevalence and determinants of internet addiction among medical students and its association with depression. J. Affect. Disord. 2022;314:94–102. doi: 10.1016/j.jad.2022.07.007. [DOI] [PubMed] [Google Scholar]
- 43.Saikia AM, Das J, Barman P, Bharali MD. Internet addiction and its relationships with depression, anxiety, and stress in urban adolescents of Kamrup District, Assam. J. Fam. Community Med. 2019;26:108–112. doi: 10.4103/jfcm.JFCM_93_18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Javaeed A, Zafar MB, Iqbal M, Ghauri SK. Correlation between internet addiction, depression, anxiety and stress among undergraduate medical students in Azad Kashmir. Pak. J. Med. Sci. Q. 2019;35:506–509. doi: 10.12669/pjms.35.2.169. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Kaess M, et al. Stress vulnerability in male youth with internet gaming disorder. Psychoneuroendocrinology. 2017;77:244–251. doi: 10.1016/j.psyneuen.2017.01.008. [DOI] [PubMed] [Google Scholar]
- 46.Tsumura H, Fukuda M, Kanda H. Blunted cortisol and normal sympathetic nervous system responses to an acute psychosocial stressor in internet addiction. Heliyon. 2022;8:e12142. doi: 10.1016/j.heliyon.2022.e12142. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Iob E, Kirschbaum C, Steptoe A. Persistent depressive symptoms, HPA-axis hyperactivity, and inflammation: The role of cognitive-affective and somatic symptoms. Mol. Psychiatry. 2020;25:1130–1140. doi: 10.1038/s41380-019-0501-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Keller J, et al. HPA axis in major depression: Cortisol, clinical symptomatology and genetic variation predict cognition. Mol. Psychiatry. 2017;22:527–536. doi: 10.1038/mp.2016.120. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Mikulska J, Juszczyk G, Gawrońska-Grzywacz M, Herbet M. HPA axis in the pathomechanism of depression and schizophrenia: New therapeutic strategies based on its participation. Brain Sci. 2021;11:1298. doi: 10.3390/brainsci11101298. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Anderson T, Berry NT, Wideman L. Exercise and the hypothalamic–pituitary–adrenal axis: A special focus on acute cortisol and growth hormone responses. Curr. Opin. Endocr. Metab. Res. 2019;9:74–77. doi: 10.1016/j.coemr.2019.08.002. [DOI] [Google Scholar]
- 51.St-Pierre DH, Richard D. The effect of exercise on the hypothalamic-pituitary-adrenal axis. In: Hackney AC, Constantini NW, editors. Endocrinology of Physical Activity and Sport. Springer; 2020. pp. 41–54. [Google Scholar]
- 52.Alaca N. The impact of internet addiction on depression, physical activity level and trigger point sensitivity in Turkish university students. J. Back Musculoskelet. Rehabil. 2020;33:623–630. doi: 10.3233/BMR-171045. [DOI] [PubMed] [Google Scholar]
- 53.Li SS, Wu QJ, Tang C, Chen ZC, Liu L. Exercise-based interventions for internet addiction: Neurobiological and neuropsychological evidence. Front. Psychol. 2020;11:1296. doi: 10.3389/fpsyg.2020.01296. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Zhang, W. & Xu, R. L. Effect of exercise intervention on internet addiction and autonomic nervous function in college students. Biomed Res. Int.2022 (2022). [DOI] [PMC free article] [PubMed] [Retracted]
- 55.Zhang YH, et al. Mixed comparison of interventions for different exercise types on students with internet addiction: A network meta-analysis. Front. Psychol. 2023;14:1111195. doi: 10.3389/fpsyg.2023.1111195. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 56.Yao CF, Xu CK, Chen QB, Peng CX, Wang CF. A preliminary trial of the Toronto Alexithymia Scale. Chin. Ment. Health J. 1992;6:217–218. [Google Scholar]
- 57.Gong X, Xie XY, Xu R, Luo YJ. Psychometric properties of the Chinese versions of DASS-21 in Chinese college students. Chin. J. Clin. Psychol. 2010;18:443–446. [Google Scholar]
- 58.Lovibond PF, Lovibond SH. The structure of negative emotional states: Comparison of the depression anxiety stress Scales (DASS) with the beck depression and anxiety inventories. Behav. Res. Ther. 1995;33:335–343. doi: 10.1016/0005-7967(94)00075-U. [DOI] [PubMed] [Google Scholar]
- 59.Wei, Q. Negative Emotions and Problematic Social NetworkSites Usage: The Mediating Role of Fear of Missing Outand the Moderating Role of Gender [Master's thesis]: Central China Normal University (2018).
- 60.Elphinston RA, Noller P. Time to face it! Facebook intrusion and the implications for romantic jealousy and relationship satisfaction. Cyberpsychol. Behav. Soc. Netw. 2011;14:631–635. doi: 10.1089/cyber.2010.0318. [DOI] [PubMed] [Google Scholar]
- 61.Liang DQ. The stress level of college students and its relationship with physical exercise. Chin. Ment. Health J. 1994;8:5–6. [Google Scholar]
- 62.Hashimoto, K. Stress Exercise and Quality of Life (Asian Games Scientifc Congress, 1990).
- 63.Liang LC, Zhou D, Yuan CY, Shao AH, Bian YF. Gender differences in the relationship between internet addiction and depression: A cross-lagged study in Chinese adolescents. Comput. Hum. Behav. 2016;63:463–470. doi: 10.1016/j.chb.2016.04.043. [DOI] [Google Scholar]
- 64.Hayes, A. F. Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach (2013).
- 65.Podsakoff PM, MacKenzie SB, Lee JY, Podsakoff NP. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol. 2003;88:879–903. doi: 10.1037/0021-9010.88.5.879. [DOI] [PubMed] [Google Scholar]
- 66.Wang Z, Goerlich KS, Luo YJ, Xu P, Aleman A. Social-specific impairment of negative emotion perception in alexithymia. Soc. Cogn. Affect. Neurosci. 2022;17:387–397. doi: 10.1093/scan/nsab099. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 67.Hendryx MS, Haviland MG, Shaw DG. Dimensions of Alexithymia and their Relationships to Anxiety and Depression. Lawrence Erlbaum; 1991. pp. 227–237. [DOI] [PubMed] [Google Scholar]
- 68.Saarijärvi S, Salminen JK, Toikka TB. Alexithymia and depression: A 1-year follow-up study in outpatients with major depression. J. Psychosom. Res. 2001;51:729–733. doi: 10.1016/S0022-3999(01)00257-4. [DOI] [PubMed] [Google Scholar]
- 69.Evren C, et al. Dissociation and alexithymia among men with alcoholism. Psychiatry Clin. Neurosci. 2008;62:40–47. doi: 10.1111/j.1440-1819.2007.01775.x. [DOI] [PubMed] [Google Scholar]
- 70.Wells R, Rehman US, Sutherland S. Alexithymia and social support in romantic relationships. Pers. Individ. Differ. 2016;90:371–376. doi: 10.1016/j.paid.2015.11.029. [DOI] [Google Scholar]
- 71.Chang C, Yuan R, Chen J. Social support and depression among Chinese adolescents: The mediating roles of self-esteem and self-efficacy. Child. Youth Serv. Rev. 2018;88:128–134. doi: 10.1016/j.childyouth.2018.03.001. [DOI] [Google Scholar]
- 72.Gross JJ. Emotion regulation: Affective, cognitive, and social consequences. Psychophysiology. 2002;39:281–291. doi: 10.1017/S0048577201393198. [DOI] [PubMed] [Google Scholar]
- 73.Kircaburun K, et al. Compensatory usage of the internet: The case of mukbang watching on YouTube. Psychiatry Investig. 2021;18:269–276. doi: 10.30773/pi.2019.0340. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Lau J, et al. Bidirectional predictions between internet addiction and probable depression among Chinese adolescents. J. Behav. Addict. 2018;7:633–643. doi: 10.1556/2006.7.2018.87. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 75.Yang X, Zhou Z, Liu Q, Fan C. Mobile phone addiction and adolescents’ anxiety and depression: The moderating role of mindfulness. J. Child Fam. Stud. 2019;28:822–830. doi: 10.1007/s10826-018-01323-2. [DOI] [Google Scholar]
- 76.Gámez-Guadix M, Villa-George FI, Calvete E. Measurement and analysis of the cognitive-behavioral model of generalized problematic internet use among Mexican adolescents. J. Adolesc. 2012;35:1581–1591. doi: 10.1016/j.adolescence.2012.06.005. [DOI] [PubMed] [Google Scholar]
- 77.Di Bartolomeo G, Papa S. The effects of physical activity on social interactions: The case of trust and trustworthiness. J. Sports Econ. 2017;20:50–71. doi: 10.1177/1527002517717299. [DOI] [Google Scholar]
- 78.Marquez DX, et al. A systematic review of physical activity and quality of life and well-being. Transl. Behav. Med. 2020;10:1098–1109. doi: 10.1093/tbm/ibz198. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 79.Mahindru A, Patil P, Agrawal V. Role of physical activity on mental health and well-being: A review. Cureus. 2023;15:e33475. doi: 10.7759/cureus.33475. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The datasets generated and/or analysed during the current study are not publicly available due (our experimental team's policy) but are available from the corresponding author on reasonable request.


