Xingyao (Doria) Xiao, Ph.D.

Postdoctoral Scholar at Stanford GSE researching statistical methodology for longitudinal and adaptive data.

Publication Cover

I’m very excited to share that our article,
“Trajectories of Depressive Symptom Among College Students in China During the COVID-19 Pandemic: Association With Suicidal Ideation and Insomnia Symptoms,”
has just been published in Suicide and Life-Threatening Behavior :contentReference[oaicite:0]{index=0}.

This three-year, five-wave longitudinal study followed 1,387 Chinese college students to better understand how depression evolved during the pandemic. Using growth mixture modeling (GMM), we identified five distinct trajectories of depressive symptoms:

  • Resilient (24%) — consistently low symptoms
  • Moderate-remission (46%) — moderate but improving over time
  • Low-increasing (20%) — gradual worsening
  • High-recovery (6%) — severe early, later recovered
  • Moderate-increasing (4%) — moderate but worsening

🔎 Importantly, these trajectories were not just statistical patterns — they predicted later risks of suicidal ideation and insomnia. Students in the increasing-symptom groups were at particularly high risk, underscoring the importance of early identification and targeted interventions.

This study highlights the heterogeneity of mental health responses during COVID-19 and calls for more nuanced, trajectory-based approaches to supporting young people.

📄 Full text available here: https://onlinelibrary.wiley.com/doi/10.1111/sltb.70051

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