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Adolescents' trajectories of depression and anxiety symptoms prior to and during the COVID-19 pandemic and their association with healthy sleep patterns.

Serena Valeria BauduccoLauren A GardnerScarlett SmoutKatrina E ChampionCath ChapmanAmanda GambleMaree TeessonMichael GradisarNicola C Newton
Published in: Scientific reports (2024)
The COVID-19 pandemic has seen a rise in anxiety and depression among adolescents. This study aimed to investigate the longitudinal associations between sleep and mental health among a large sample of Australian adolescents and examine whether healthy sleep patterns were protective of mental health in the context of the COVID-19 pandemic. We used three waves of longitudinal control group data from the Health4Life cluster-randomized trial (N = 2781, baseline M age  = 12.6, SD =  0.51; 47% boys and 1.4% 'prefer not to say'). Latent class growth analyses across the 2 years period identified four trajectories of depressive symptoms: low-stable (64.3%), average-increasing (19.2%), high-decreasing (7.1%), moderate-increasing (9.4%), and three anxiety symptom trajectories: low-stable (74.8%), average-increasing (11.6%), high-decreasing (13.6%). We compared the trajectories on sociodemographic and sleep characteristics. Adolescents in low-risk trajectories were more likely to be boys and to report shorter sleep latency and wake after sleep onset, longer sleep duration, less sleepiness, and earlier chronotype. Where mental health improved or worsened, sleep patterns changed in the same direction. The subgroups analyses uncovered two important findings: (1) the majority of adolescents in the sample maintained good mental health and sleep habits (low-stable trajectories), (2) adolescents with worsening mental health also reported worsening sleep patterns and vice versa in the improving mental health trajectories. These distinct patterns of sleep and mental health would not be seen using mean-centred statistical approaches.
Keyphrases
  • mental health
  • sleep quality
  • depressive symptoms
  • physical activity
  • young adults
  • mental illness
  • social support
  • healthcare
  • machine learning
  • risk assessment
  • high intensity
  • patient reported