2022

Altered stress-reactivity may represent a general risk factor for psychopathology. In a broad at-risk sample, we examined (a) how stress and mild, daily expressions of psychopathology were interrelated over time, (b) whether we could detect subgroups with similar dynamics between stress and daily expressions of psychopathology (i.e., stress-reactivity), and (c) whether stress-reactivity was associated with psychopathology and social functioning. One hundred twenty-two young adults (43.4% women, mean age 23.6) at risk for developing a wide range of psychopathology completed a 6-month daily diary study. We used group iterative multiple model estimation (GIMME) to identify temporal associations between event stress and 11 mild expressions of psychopathology (e.g., feeling down, restlessness) at group, subgroup, and individual levels. Stress was associated with feeling irritated during the same day for >70% of individuals, and with feeling down and worrying during the same day for >50% of individuals. No stable subgroups characterized by similar daily stress-reactivity were identified. Instead, we observed 71 different stress-reactivity patterns in 122 individuals. Average daily event stress, but not overall stress-reactivity (weighted stress-response), was associated with psychopathology severity and social dysfunction. This study showed important similarities, as well as many differences between individuals, in terms of the impact of stress on mild expressions of psychopathology in daily life. Clustering based on similar stress-reactivity did not lead to stable subgroups. Finally, average daily stress levels, but not daily stress-reactivity, were associated with psychopathologic severity and social dysfunction. Findings highlight the importance of considering heterogeneity in stress-reactivity, but also challenges for identifying generalizable processes in doing so. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Commonly used methods for modelling early life adversity (e.g., sum-scores, latent class or trajectory approaches, single-adversity approaches, and factor-analytical approaches) have not been able to capture the complex nature of early life adversity. We propose network analysis as an alternative way of modelling early life adversity (ELA). Our aim was to construct a network of fourteen adverse events (AEs) that occurred before the age of 16 in the TRacking Adolescents Individual Lives Survey (TRAILS, N = 1029). To show how network analysis can provide insight into why AEs are associated, we compared findings from the resulting network model to findings from tetrachoric correlation analyses. The resulting network of ELA comprised direct relationships between AEs and more complex, indirect relationships. A total of fifteen edges emerged in the network of AEs (out of 91 possible edges). The correlation coefficients suggested that many AEs were associated. The network model of ELA indicated, however, that several associations were attributable to interactions with other AEs. For example, the zero-order correlation between parental addiction and familial conflicts (0.24) could be explained by interactions with parental divorce. Our application of network analysis shows that using network analysis for modelling the ELA construct allows capturing the constructs' complex nature. Future studies should focus on gaining more insight into the most optimal model estimation and selection procedures, as well as sample size requirements. Network analysis provides researchers with a valuable tool that allows them as well as policy-makers and professionals to gain insight into potential mechanisms through which adversities are associated with each other, and conjunctively, with life course outcomes of interest.

Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.