Identifying differential trajectories and predictors for depressive symptoms in adolescents using latent class growth analysis: A population-based cohort study

Yen Chung Ho, Hung Yi Chiou, Luke Molloy, Kuan Chia Lin, Pi Chen Chang*, Hsiu Ju Chang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Introduction: This study investigated the differential trajectories and relevant determinants of depressive symptoms in adolescents by following cohorts that included junior, senior, and vocational high school adolescents, over a 3-year period in Taiwan. Methods: Longitudinal data were obtained from 575 adolescents who participated in the Taiwan Adolescent to Adult Longitudinal Study. Data analysis included latent class growth with time-varying covariate, univariate, and multivariate analysis. Results: A three-class (“low but increasing trajectory,” “moderate and stable trajectory,” and “high but decreasing trajectory”) model fit the data of the cohort. Our findings indicated that 29%, 38%, and 33% of the adolescents were in the low but increasing, moderate and stable, and high but decreasing trajectories, respectively. After confounders were controlled for, bullying experiences were identified as a risk factor for depressive symptoms. The protective factors against depressive symptoms included resilience and peer and social support. Conclusions: The transitions between different educational stages critically influence the depressive symptoms of adolescents, and the adolescents follow different depressive trajectories, that have different etiology. Therefore, identifying adolescents at high risk for depression and designing student-centered intervention programs through individualized and multidimensional assessment of depressive symptoms are crucial for adolescents.

Original languageEnglish
JournalJournal of Adolescence
DOIs
StateAccepted/In press - 2023

Keywords

  • adolescent
  • depression
  • longitudinal studies
  • multivariate analysis

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