- Adolescence is a sensitive period for the development of sleep problems as well as anxiety symptoms.
- Previous studies have found a bidirectional association between sleep problems and anxiety symptoms among adolescents.
- These studies did not distinguish differences between persons from differences within persons, which could have led to erroneous conclusions regarding the underlying causal mechanisms.
- We examined bidirectional associations between sleep problems and anxiety symptoms throughout adolescence and young adulthood, while differentiating between-person effects from within-person effects.
- Participants who reported poor sleep tended to report high anxiety as well.
- Within persons, sleep problems tended to precede anxiety in early and mid-adolescence, but not in late adolescence and young adulthood. No effects were found in the other way direction.
- The findings suggest that sleep-oriented interventions in early adolescence may not only improve sleep, but also prevent the development of anxiety symptoms.
What was already known about this subject?
What will this study add?
Introduction. Several statistical methods are available to identify developmental trajectory classes, but it is unclear which method is most suitable. The aim of this study was to determine whether latent class analysis, latent class growth analysis or growth mixture modeling was most appropriate for identifying developmental trajectory classes. Methods. We compared the three methods in a simulation study in several scenarios, which varied regarding e.g. sample size and degree of separation between classes. The simulation study was replicated with a real data example concerning anxiety/depression symptoms measured over 6 time points in the Tracking Adolescent Individuals’ Lives Survey (TRAILS, N = 2227). Results. Growth mixture modeling was least biased or equally biased compared to latent class analysis and latent class growth analysis in all scenarios. In TRAILS, the shapes of the trajectories were rather similar over the three methods, but class sizes differed slightly. A 4-class growth mixture model performed best, based on several fit indices, interpretability and clinical relevance. Conclusions. Growth mixture modeling seems most suitable to identify developmental trajectory classes. Copyright © 2020 Elsevier B.V.