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Examining the research methods of early warning signals in clinical psychology through a theoretical lens

2025/03/19 by Jingmeng Cui, Merlijn Olthof, Fred Hasselman +1 · 1 voice
Psychology · Environmental Science · Medicine · #Mental Health Research Topics #Ecosystem dynamics and resilience #Psychosomatic Disorders and Their Treatments

paper · pdf · doi:10.1186/s12888-025-06688-5

openalex publication_date 2025/03/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03

Abstract

BACKGROUND: The past few years have seen a rapid growth in research on early warning signals (EWSs) in the psychopathology domain. Whereas early studies found EWSs to be associated with sudden changes in clinical change trajectories, later findings showed that EWSs may not be general across variables and cases and have low predictive power. These mixed results may be explained by the diverse methods employed in clinical EWS studies, with some of these approaches and practices potentially misaligned with the underlying theory of EWSs. METHODS: This article employs a variety of methods, such as a narrative review, mathematical derivations, simulations, and visual illustrations, to support our claims, explain specific assumptions, and guide future empirical research. This multitude of methods serves our aim to provide theoretical as well as methodological contributions to the field. RESULTS: We identify the following key assumptions for EWS validation studies: the system departs from a point attractor, EWSs appear before the critical transition, and EWS variables align with system destabilization. The literature review shows that the common research practices in the field are often not in line with those assumptions, and we provide specific suggestions corresponding to each of the assumptions. CONCLUSIONS: More rigorous empirical evidence is needed to better validate the existence of EWSs in clinical sudden changes and fully realize their clinical potential. As theory-based prediction tools, EWSs require stronger alignment between theory and practice to enhance both theoretical understanding and predictive accuracy. CLINICAL TRIAL NUMBER: Not applicable.

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