Abstract
Despite the burgeoning literature on adolescent internet addiction (IA), the majority of studies have relied on cross-sectional designs and variable-centered analytical approaches. Therefore, little is understood about the heterogeneous developmental trajectories of adolescent IA as well as its antecedents and outcomes. This longitudinal study adopted growth mixture modeling (GMM), a person-centered approach, to identify the distinct trajectories of IA among adolescents during a three-year period. We further examined the interpersonal predictors along with a series of outcomes of different trajectories. Participants included 1,365 Chinese adolescents (Mage = 14.68 years, SD = 1.56; 46.8% girls) from two junior high schools and two senior high schools. The GMM results indicated three distinct trajectories: low-increasing (56.7%), moderate-declining (37.6%), and high-declining (5.7%) groups. In terms of interpersonal predictors, adolescents who reported poorer relationships with their parents, teachers, and schoolmates were more likely to belong to the high-declining and moderate-declining groups. In terms of outcomes, the high-declining and moderate-declining groups exhibited an increase in mental health problems (i.e., more depressive symptoms, lower self-esteem, and lower subjective well-being) and delinquent behaviors, even after controlling for their baseline levels. These findings highlight the heterogeneity of IA trajectories among adolescents, the predictive role of interpersonal factors, and different adjustment outcomes associated with IA trajectories. Therefore, prevention and intervention programs involving interpersonal relationships may be promising for adolescents at high or moderate risk of IA.
Similar content being viewed by others
Data and Code Availability
Data and analysis code are available from the corresponding author on request.
References
Ahlen, J., & Ghaderi, A. (2017). Evaluation of the Children’s Depression Inventory—Short version (CDI–S). Psychological Assessment, 29(9), 1157–1166. https://doi.org/10.1037/pas0000419
Allgaier, A. K., Frühe, B., Pietsch, K., Saravo, B., Baethmann, M., & Schulte-Körne, G. (2012). Is the Children’s Depression Inventory Short version a valid screening tool in pediatric care? A comparison to its full-length version. Journal of Psychosomatic Research, 73(5), 369–374. https://doi.org/10.1016/j.jpsychores.2012.08.016
Anderson, E. L., Steen, E., & Stavropoulos, V. (2017). Internet use and problematic internet use: A systematic review of longitudinal research trends in adolescence and emergent adulthood. International Journal of Adolescence and Youth, 22(4), 430–454. https://doi.org/10.1080/02673843.2016.1227716
Andruff, H., Carraro, N., Thompson, A., Gaudreau, P., & Louvet, B. (2009). Latent class growth modelling: A tutorial. Tutorials in Quantitative Methods for Psychology, 5(1), 11–24. https://doi.org/10.20982/tqmp.05.1.p011
Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: Three-step approaches using Mplus. Structural Equation Modeling, 21(3), 329–341. https://doi.org/10.1080/10705511.2014.915181
Banjanin, N., Banjanin, N., Dimitrijevic, I., & Pantic, I. (2015). Relationship between internet use and depression: Focus on psychological mood oscillations, social networking and online addictive behaviors. Computers in Human Behavior, 43, 308–312. https://doi.org/10.1016/j.chb.2014.11.013
Bao, Z., Li, D., Zhang, W., & Wang, Y. (2015). School climate and delinquency among Chinese adolescents: Analyses of effortful control as a moderator and deviant peer affiliation as a mediator. Journal of Abnormal Child Psychology, 43, 81–93. https://doi.org/10.1007/s10802-014-9903-8
Bao, Z., Li, D., Zhang, W., Wang, Y., Sun, W., & Zhao, L. (2014). Cumulative ecological risk and adolescents’ academic and social competence: The compensatory and moderating effects of sense of responsibility to parents. Psychological Development and Education, 30(5), 482–495
Bisen, S. S., & Deshpande, Y. M. (2018). Understanding internet addiction: A comprehensive review. Mental Health Review Journal, 23(3), 165–184. https://doi.org/10.1108/MHRJ-07-2017-0023
Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: One-step versus three-step estimators. Political Analysis, 12(1), 3–27. https://www.jstor.org/stable/25791751
Boyle, G. J. (1991). Does item homogeneity indicate internal consistency or item redundancy in psychometric scales? Personality and Individual Differences, 12(3), 291–294. https://doi.org/10.1016/0191-8869(91)90115-R
Campbell, A., Converse, P. E., & Rodgers, W. L. (1976). The quality of American life. Russell Sage Foundation.
Campione-Barr, N. (2020). Development of autonomy. In S. Hupp, & J. D. Jewell (Eds.). The encyclopedia of child and adolescent development. John Wiley & Sons. https://doi.org/10.1002/9781119171492.wecad468
Casey, B. J., Getz, S., & Galvan, A. (2008). The adolescent brain. Developmental Review, 28(1), 62–77. https://doi.org/10.1016/j.dr.2007.08.003
Choo, H., Chng, G. S., Gentile, D. A., & Lau, S. P. C. (2020). The role of peer support in the growth trajectory of pathological internet use among youth: A protective factor. Cyberpsychology Behavior and Social Networking, 24(8), 558–565. https://doi.org/10.1089/cyber.2020.0054
Ciarrochi, J., Parker, P., Sahdra, B., Marshall, S., Jackson, C., Gloster, A. T., & Heaven, P. (2016). The development of compulsive Internet use and mental health: A four-year study of adolescence. Developmental Psychology, 52(2), 272–283. https://doi.org/10.1037/dev0000070
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Routledge. https://doi.org/10.4324/9780203771587
Cohen, J., Cohen, P., Aiken, S. G., & West, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Erlbaum.
Collins, W. A., & Steinberg, L. (2006). Adolescent development in interpersonal context. In W. Damon, R. M. Lerner, & N. Eisenberg (Eds). Handbook of child psychology: Vol. 3. Social, emotional, and personality development (pp. 1003–1067). John Wiley & Sons. https://doi.org/10.1002/9780470147658.chpsy0316
Davis, R. A. (2001). A cognitive-behavioral model of pathological internet use. Computers in Human Behavior, 17(2), 187–195. https://doi.org/10.1016/S0747-5632(00)00041-8
Diallo, T. M. O., Morin, A. J. S., & Lu, H. (2017). The impact of total and partial inclusion or exclusion of active and inactive time invariant covariates in growth mixture models. Psychological Methods, 22, 166–190. https://doi.org/10.1037/met0000084
Dong, Q., & Lin, C. D. (2011). Key indicators of psychological development and evaluation of Chinese children and adolescents aged 6 to 15. Science Press.
Enders, C. K. (2010). Applied missing data analysis. Guilford Press.
Evren, C., Dalbudak, E., Evren, B., & Demirci, C. A. (2014). High risk of internet addiction and its relationship with lifetime substance use, psychological and behavioral problems among 10th grade adolescents. Psychiatria Danubina, 26(4), 330–339
Felder-Puig, R., Griebler, R., Samdal, O., King, M. A., Freeman, J., & Duer, W. (2012). Does the school performance variable used in the international Health Behavior in School-Aged Children (HBSC) study reflect students’ school grades? Journal of School Health, 82(9), 404–409. https://doi.org/10.1111/j.1746-1561.2012.00715.x
Funder, D. C., & Ozer, D. J. (2019). Evaluating effect size in psychology research: Sense and nonsense. Advances in Methods and Practices in Psychology Science, 2(2), 156–168. https://doi.org/10.1177/2515245919847202
Furman, W., & Buhrmester, D. (1985). Children’s perceptions of the personal relationships in their social networks. Developmental Psychology, 21(6), 1016–1024. https://doi.org/10.1037/0012-1649.21.6.1016
Jia, J., Li, D., Li, X., Zhou, Y., Wang, Y., & Sun, W. (2017). Psychological security and deviant peer affiliation as mediators between teacher-student relationship and adolescent Internet addiction. Computers in Human Behavior, 73, 345–352. https://doi.org/10.1016/j.chb.2017.03.063
Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in Human Behavior, 31, 351–354. https://doi.org/10.1016/j.chb.2013.10.059
Kirschner, P. A., & Karpinski, A. C. (2010). Facebook and academic performance. Computers in Human Behaviors, 26(6), 1237–1245. https://doi.org/10.1016/j.chb.2010.03.024
Kovacs, M. (1992). Children’s Depression Inventory manual. Multi-Health Systems.
Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukophadhyay, T., & Scherlis, W. (1998). Internet paradox: A social technology that reduces social involvement and psychological well-being? American Psychologist, 53(9), 1017–1031. https://doi.org/10.1037//0003-066X.53.9.1017
Laursen, B., & Hoff, E. (2006). Person-centered and variable-centered approaches to longitudinal data. Merrill-Palmer Quarterly, 52(3), 377–389. https://www.jstor.org/stable/23096200
Lei, H., Chiu, M. M., & Li, S. (2020). Subjective well-being and internet overuse: A meta-analysis of mainland Chinese students. Current Psychology, 39, 843–853. https://doi.org/10.1007/s12144-019-00313-x
Leung, S. O., & Wong, P. M. (2008). Validity and reliability of Chinese Rosenberg Self-esteem Scale. New Horizons in Education, 56(1), 62–69
Li, D. (2020). Internet addiction in adolescents: Risk factors and formation mechanism. China Society Press.
Li, D. (2012). Multiple ecological risk factors and adolescents’ social adaptation: How risks should be modeled and what are their mechanisms [Unpublished doctoral dissertation]. South China Normal University.
Li, D., Li, X., Zhao, L., Zhou, Y., Sun, W., & Wang, Y. (2017). Linking multiple risk exposure profiles with adolescent internet addiction: Insights from the person-centered approach. Computers in Human Behavior, 75, 236–244. https://doi.org/10.1016/j.chb.2017.04.063
Li, D., Xu, L., Bao, Z., Chen, W., Su, X., & Zhang, W. (2015). Family financial strain and adolescents’ depression: The effects of perceived discrimination and parent-adolescent attachment. Psychological Development and Education, 31, 342–349
Li, D., Zhang, W., Li, X., Zhen, S., & Wang, Y. (2010). Stressful life events and problematic internet use by adolescent females and males: A mediated moderation model. Computers in Human Behavior, 26(5), 1199–1207. https://doi.org/10.1016/j.chb.2010.03.031
Li, D., Zhou, Y., Li, X., & Zhou, Z. (2016). Perceived school climate and adolescent internet addiction: The mediating role of deviant peer affiliation and the moderating role of effortful control. Computers in Human Behavior, 60, 54–61. https://doi.org/10.1016/j.chb.2016.02.015
Li, T., & Feng, F. (2018). Goal content, well-being, and psychological needs satisfaction in Chinese adolescents. Social Behavior and Personality, 46(4), 541–550. https://doi.org/10.2224/sbp.6831
Li, Y., Zhang, X., Lu, F., Zhang, Q., & Wang, Y. (2014). Internet addiction among elementary and middle school students in China: A nationally representative sample study. Cyberpsychology Behavior and Social Networking, 17(2), 111–116. https://doi.org/10.1089/cyber.2012.0482
Liu, C., & Kuo, F. (2007). A study of internet addiction through the lens of the interpersonal theory. CyberPsychology and Behavior, 10(6), 799–804. https://doi.org/10.1089/cpb.2007.9951
Liu, Y., Li, D., Jia, J., Zhou, Y., Zhao, L., Wang, Y., & Sun, W. (2021). Perceived school climate and problematic internet use among Chinese adolescent: Psychological insecurity and negative peer affiliation as mediators. Psychology of Addictive Behaviors, 35(1), 113–123. https://doi.org/10.1037/adb0000684
Lowry, P. B., Zhang, J., Wang, C., & Siponen, M. (2016). Why do adults engage in cyberbullying on social media? An integration of online disinhibition and deindividuation effects with the social structure and social learning model. Information Systems Research, 27(4), 962–986. https://doi.org/10.1287/isre.2016.0671
Mascolo, M. F., van Geert, P., Steenbeek, H., & Fischer, K. W. (2016). What can dynamic systems models of development offer to the study of developmental psychopathology?. In D. Cicchetti, & D. Cicchetti (Eds.). Developmental psychopathology: Theory and method (pp. 665–716). John Wiley & Sons.
Mei, S., Yau, Y. H. C., Chai, J., Guo, J., & Potenza, M. N. (2016). Problematic Internet use, well-being, self-esteem and self-control: Data from a high-school survey in China. Addictive Behaviors, 61, 74–79. https://doi.org/10.1016/j.addbeh.2016.05.009
Morin, A. J. S., & Litalien, D. (2019). Mixture modeling for lifespan developmental research. Oxford research encyclopedia of psychology. Oxford University Press. https://doi.org/10.1093/acrefore/9780190236557.013.364
Muthén, L. K., & Muthén, B. O. (2017). Mplus user’s guide (8th ed.). Author.
Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: A Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569. https://doi.org/10.1080/10705510701575396
Nagin, D. S. (2005). Group-based modeling of development. Harvard University Press.
National Bureau of Statistics of China. (2021). Main data of China’s seventh national census results http://www.stats.gov.cn/tjsj/zxfb/202105/t20210510_1817176.html
Pan, Y. C., Chiu, Y. C., & Lin, Y. H. (2020). Systematic review and meta-analysis of epidemiology of internet addiction. Neuroscience and Biobehavioral Reviews, 118, 612–622. https://doi.org/10.1016/j.neubiorev.2020.08.013
Pontes, H. M., Kuss, D. J., & Griffiths, M. D. (2015). Clinical psychology of Internet addiction: a review of its conceptualization, prevalence, neuronal processes, and implications for treatment. Neuroscience and Neuroeconomics, 4, 11–23. https://doi.org/10.2147/NAN.S60982
Rohner, R. P. (2016). Introduction to interpersonal acceptance-rejection theory (IPARTheory) and evidence. Online Reading in Psychology and Culture, 6(1). https://doi.org/10.9707/2307-0919.1055
Rosenberg, M. (1965). Society and the adolescent self-image. Princeton University Press.
Seabra, L., Loureiro, M., Pereira, H., Monteiro, S., Afonso, R. M., & Esgalhado, G. (2017). Relationship between internet addiction and self-esteem: Cross-cultural study in Portugal and Brazil. Interacting with Computers, 29(5), 767–778. https://doi.org/10.1093/iwc/iwx011
Smetana, J. G., Campione-Barr, N., & Metzger, A. (2006). Adolescent development in interpersonal and societal contexts. Annual Review of Psychology, 57, 255–284. https://doi.org/10.1146/annurev.psych.57.102904.190124
Stavropoulos, V., Griffiths, M. D., Burleigh, T. L., Kuss, D. J., Doh, Y. Y., & Gomez, R. (2018). Flow on the internet: A longitudinal study of internet addiction symptoms during adolescence. Behaviour & Information Technology, 37(2), 159–172. https://doi.org/10.1080/0144929X.2018.1424937
Sugimura, N., Berry, D., Troop-Gordon, W., & Rudolph, K. D. (2017). Early social behaviors and the trajectory of peer victimization across the school years. Developmental Psychology, 53(8), 1447–1461. https://doi.org/10.1037/dev0000346
Sun, S., & Wang, S. (2015). The Children’s Depression Inventory in worldwide child development research: A reliability generalization study. Journal of Child and Family Studies, 24, 2352–2363. https://doi.org/10.1007/s10826-014-0038-x
Tian, Y., Qin, N., Cao, S., & Gao, F. Q. (2020). Reciprocal associations between shyness, self-esteem, loneliness, depression and internet addiction in Chinese adolescents. Addiction Research & Theory, 29(2), 98–110. https://doi.org/10.1080/16066359.2020.1755657
Tóth-Király, I., Morin, A. J. S., Hietajärvi, L., & Salmela-Aro, K. (2021). Longitudinal trajectories, social and individual antecedents, and outcomes of problematic internet use among late adolescents. Child Development, 92(4), e653–e673. https://doi.org/10.1111/CDEV.13525
Williams, K. E., Ciarrochi, J., & Heaven, P. C. (2012). Inflexible parents, inflexible kids: A 6-year longitudinal study of parenting style and the development of psychological flexibility in adolescents. Journal of Youth and Adolescence, 41, 1053–1066. https://doi.org/10.1007/s10964-012-9744-0
Xu, L., Wu, L., Geng, X., Wang, Z., Guo, X., Song, K., Liu, G., Deng, L., Zhang, J., & Potenza, M. N. (2021). A review of psychological interventions for internet addiction. Psychiatry Research, 302, 114016. https://doi.org/10.1016/j.psychres.2021.114016
Xu, J., Shen, L., Yan, C., Hu, H., Yang, F., Wang, L., Kotha, S. R., Ouyang, F., Zhang, L., Liao, X., Zhang, J., Zhang, J., & Shen, X. (2014). Parent-adolescent interaction and risk of adolescent internet addiction: A population-based study in Shanghai. Bmc Psychiatry, 14, 112–126. https://doi.org/10.1186/1471-244X-14-112
Young, K. S. (1998). Internet addiction: The emergence of a new clinical disorder. CyberPsychology and Behavior, 1(3), 237–244. https://doi.org/10.1089/cpb.1998.1.237
Zhou, Y., Li, D., Li, X., Wang, Y., & Zhao, L. (2017). Big five personality and adolescent internet addiction: The mediating role of coping style. Addictive Behaviors, 64, 42–48. https://doi.org/10.1016/j.addbeh.2016.08.009
Zhou, X., Zhen, R., & Wu, X. (2018). Trajectories of problematic internet use among adolescents over time since Wenchuan earthquake. Computers in Human Behavior, 84, 86–92. https://doi.org/10.1016/j.chb.2018.02.030
Funding
This research was supported by the National Education Sciences Planning project for young scholars of China (No. CBA140145).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Ethics Approval
The proposal of this research was approved by the Research Ethics Committee of Central China Normal University and the study was conducted in accordance with the ethical standards of the American Psychological Association.
Conflict of interest
The authors have no conflicts of interest to declare.
Informed Consent
Informed consent from school principals, classroom teachers, and parents, as well as assent from adolescents were obtained. Given that all adolescents were under the age of 18, we sought the approval of their parents during the parent-teacher conference.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Electronic Supplementary Material
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Huang, P., Zhou, Y., Li, D. et al. Developmental Trajectories of Adolescent Internet Addiction: Interpersonal Predictors and Adjustment Outcomes. Res Child Adolesc Psychopathol 51, 355–367 (2023). https://doi.org/10.1007/s10802-022-00987-1
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10802-022-00987-1