Social withdrawal and social anxiety are believed to have a bidirectional influence on one another, but it is unknown if their relationship is bidirectional, especially within person, and if peer experiences influence this relationship. We investigated temporal sequencing and the strength of effects between social withdrawal and social anxiety, and the roles of peer victimization and acceptance in the pathways. Participants were 2,772 adolescents from the population-based and clinically referred cohorts of the Tracking Adolescents' Individual Lives Survey. Self- and parent-reported withdrawal, and self-reported social anxiety, peer victimization, and perceived peer acceptance were assessed at 11, 13, and 16 years. Random-intercept cross-lagged panel models were used to investigate within-person associations between these variables. There was no feedback loop between withdrawal and social anxiety. Social withdrawal did not predict social anxiety at any age. Social anxiety at 11 years predicted increased self-reported withdrawal at 13 years. Negative peer experiences predicted increased self- and parent-reported withdrawal at 13 years and increased parent-reported withdrawal at 16 years. In turn, self-reported withdrawal at 13 years predicted negative peer experiences at 16 years. In conclusion, adolescents became more withdrawn when they became more socially anxious or experienced greater peer problems, and increasing withdrawal predicted greater victimization and lower acceptance.
- 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.
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A prominent hypothesis within the field of psychiatry is that the manifestation of psychopathology changes from non-specific to specific as illness severity increases. Using a transdiagnostic network approach, we investigated this hypothesis in four independent groups with increasing psychopathology severity. We investigated whether symptom domains became more interrelated and formed more clusters as illness severity increased, using empirical tests for two network characteristics: global network strength and modularity-based community detection. Four severity groups, ranging from subthreshold psychopathology to having received a diagnosis and treatment, were derived with a standardized diagnostic interview conducted at age 18.5 (n = 1933; TRAILS cohort). Symptom domains were assessed using the Adult Self Report (ASR). Pairwise comparisons of the symptom networks across groups showed no difference in global network strength between severity groups. Similar number and type of communities detected in the four groups exceeded the more minor differences across groups. Common clusters consisted of domains associated with attention deficit hyperactivity disorder (ADHD) and combined depression and anxiety domains. Based on the strength of symptom domain associations and symptom clustering using a network approach, we found no support for the hypothesis that the manifestation of psychopathology along the severity continuum changes from non-specific to specific.
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.