The Frequent Stressor and Mental Health Monitoring-Paradigm: A Proposal for the Operationalization and Measurement of Resilience and the Identification of Resilience Processes in Longitudinal Observational Studies
Year: 2021
Type: review
Abstract: Resilience has been defined as the maintenance or quick recovery of mental health during and after times of adversity. How to operationalize resilience and to determine the factors and processes that lead to good long-term mental health outcomes in stressor-exposed individuals is a matter of ongoing debate and of critical importance for the advancement of the field. One of the biggest challenges for implementing an outcome-based definition of resilience in longitudinal observational study designs lies in the fact that real-life adversity is usually unpredictable and that its substantial qualitative as well as temporal variability between subjects often precludes defining circumscribed time windows of inter-individually comparable stressor exposure relative to which the maintenance or recovery of mental health can be determined. To address this pertinent issue, we propose to frequently and regularly monitor stressor exposure (E) and mental health problems (P) throughout a study's observation period [Frequent Stressor and Mental Health Monitoring (FRESHMO)-paradigm]. On this basis, a subject's deviation at any single monitoring time point from the study sample's normative E-P relationship (the regression residual) can be used to calculate that subject's current mental health reactivity to stressor exposure ("stressor reactivity," SR). The SR score takes into account the individual extent of experienced adversity and is comparable between and within subjects. Individual SR time courses across monitoring time points reflect intra-individual temporal variability in SR, where periods of under-reactivity (negative SR score) are associated with accumulation of fewer mental health problems than is normal for the sample. If FRESHMO is accompanied by regular measurement of potential resilience factors, temporal changes in resilience factors can be used to predict SR time courses. An increase in a resilience factor measurement explaining a lagged decrease in SR can then be considered to index a process of adaptation to stressor exposure that promotes a resilient outcome (an allostatic resilience process). This design principle allows resilience research to move beyond merely determining baseline predictors of resilience outcomes, which cannot inform about how individuals successfully adjust and adapt when confronted with adversity. Hence, FRESHMO plus regular resilience factor monitoring incorporates a dynamic-systems perspective into resilience research.
Source: Frontiers in Psychology
Authors: Raffael Kalisch, Göran Köber, Harald Binder, Kira F. Ahrens, Ulrike Basten, Andrea Chmitorz, Karmel W. Choi, Christian J. Fiebach, Nele Goldbach, Rebecca J. Neumann, Miriam Kampa, Bianca Kollmann, Klaus Lieb, Michael M. Plichta, Andreas Reif, Anita Schick, Alexandra Sebastian, Henrik Walter, Michèle Wessa, Kenneth S. L. Yuen, Oliver Tüscher, Haakon Engen
Institutions: Leibniz Institute for Resilience Research, University Medical Center of the Johannes Gutenberg University Mainz, University of Freiburg, University Hospital Frankfurt, Goethe University Frankfurt, University of Koblenz and Landau, Esslingen University of Applied Sciences, Harvard University, Massachusetts General Hospital, Justus-Liebig-Universität Gießen, University of Siegen, Heidelberg University, Central Institute of Mental Health, Medizinische Fakultät Mannheim, Humboldt-Universität zu Berlin, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charité – Universitätsmedizin Berlin, Johannes Gutenberg University Mainz, University of Oslo
Language: English
Topic: Resilience and Mental Health
Subfield: Clinical Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $2,950
OpenAlex ID: W3194232185