Developmental trajectories of depressive symptoms during early adolescence: A 12-month cohort study in Nanchong, China.

BACKGROUND

Adolescence marks a critical period for mental health development, yet longitudinal patterns of depressive symptoms during the transition from childhood to early adolescence remain understudied. This study identifies distinct developmental trajectories of depressive symptoms in Chinese junior school students and examines their sociodemographic and behavioral predictors.

METHODS

Using longitudinal data from 2635 seventh- to eighth-grade students in Nanchong, China (2022-2023), depressive symptoms were assessed at three six-month intervals via the Center for Epidemiologic Studies Depression Scale (CES-D). Group-based trajectory modeling (GBTM) identified latent trajectory classes, and multinomial logistic regression analyzed predictors of class membership.

RESULTS

Prevalence rates of depressive symptoms increased across waves (13.40 %, 21.56 %, 26.94 %). Three trajectory classes emerged: Consistently low symptoms, namely, Low-risk (27.40 %), Persistently elevated symptoms, high-risk (28.54 %), Steeply increasing symptoms, progressing-risk (44.06 %). Female gender, left-behind status, reconstituted family structure, shorter sleep duration, prolonged screen time, and infrequent physical activity predicted membership in both high-risk (adjusted odds ratio [aOR] range: 1.4-3.2) and progressing-risk classes. Lower parental education levels were uniquely associated with the progressing-risk class (aOR = 1.27, P < 0.05).

LIMITATIONS: Key limitations include the relatively short observation window, regionally restricted sampling, potential unmeasured confounders, and the inherent subjectivity of self-reported measures, which may affect the interpretation and generalizability of the findings.

CONCLUSION

Depressive symptom trajectories among adolescents in Nanchong, Sichuan Province demonstrate substantial heterogeneity, shaped by intersecting individual, familial, and behavioral factors. Early identification of high-risk subgroups and tailored interventions targeting modifiable predictors (e.g., sleep hygiene, digital overuse)-anchored to baseline risk profiles and adaptively refined through longitudinal monitoring-may mitigate mental health burdens during this vulnerable developmental window.