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
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...
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| Published in | Frontiers in psychology Vol. 12; p. 710493 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Switzerland
Frontiers Media S.A
26.08.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1664-1078 1664-1078 |
| DOI | 10.3389/fpsyg.2021.710493 |
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| 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. |
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| AbstractList | 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. 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.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. |
| Author | Köber, Göran Tüscher, Oliver Schick, Anita Kampa, Miriam Yuen, Kenneth S. L. Lieb, Klaus Fiebach, Christian J. Neumann, Rebecca J. Reif, Andreas Wessa, Michèle Binder, Harald Walter, Henrik Basten, Ulrike Ahrens, Kira F. Chmitorz, Andrea Kollmann, Bianca Goldbach, Nele Choi, Karmel W. Engen, Haakon Plichta, Michael M. Kalisch, Raffael Sebastian, Alexandra |
| AuthorAffiliation | 17 Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health , Berlin , Germany 16 Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University , Mannheim , Germany 6 Department of Psychology, Goethe University Frankfurt , Frankfurt , Germany 15 Department of Psychology, Bender Institute of Neuroimaging, Justus Liebig University , Gießen , Germany 8 Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center , Mainz , Germany 13 Brain Imaging Center, Goethe University Frankfurt , Frankfurt , Germany 14 Department of Clinical Psychology and Psychotherapy, University of Siegen , Siegen , Germany 18 Department of Clinical Psychology and Neuropsychology, Institute of Psychology, Johannes Gutenberg University , Mainz , Germany 4 Freiburg Center of Data Analysis and Modelling, Univers |
| AuthorAffiliation_xml | – name: 12 Lee Kum Sheung Center for Health and Happiness, Harvard T.H. Chan School of Public Health , Boston, MA , United States – name: 8 Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center , Mainz , Germany – name: 15 Department of Psychology, Bender Institute of Neuroimaging, Justus Liebig University , Gießen , Germany – name: 6 Department of Psychology, Goethe University Frankfurt , Frankfurt , Germany – name: 3 Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg , Freiburg , Germany – name: 11 Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital , Boston, MA , United States – name: 2 Neuroimaging Center, Focus Program Translational Neuroscience, Johannes Gutenberg University Medical Center , Mainz , Germany – name: 10 Department of Psychiatry, Massachusetts General Hospital , Boston, MA , United States – name: 4 Freiburg Center of Data Analysis and Modelling, University of Freiburg , Freiburg , Germany – name: 9 Faculty of Social Work, Health Care and Nursing Science, Esslingen University of Applied Sciences , Esslingen , Germany – name: 16 Department of Public Mental Health, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University , Mannheim , Germany – name: 13 Brain Imaging Center, Goethe University Frankfurt , Frankfurt , Germany – name: 18 Department of Clinical Psychology and Neuropsychology, Institute of Psychology, Johannes Gutenberg University , Mainz , Germany – name: 1 Leibniz Institute for Resilience Research , Mainz , Germany – name: 14 Department of Clinical Psychology and Psychotherapy, University of Siegen , Siegen , Germany – name: 17 Department of Psychiatry and Psychotherapy, Charité Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health , Berlin , Germany – name: 19 Department of Psychology, University of Oslo , Oslo , Norway – name: 7 Department of Psychology, University of Koblenz-Landau , Landau , Germany – name: 5 Department of Psychiatry, Psychosomatic Medicine and Psychotherapy, University Hospital Frankfurt , Frankfurt , Germany |
| Author_xml | – sequence: 1 givenname: Raffael surname: Kalisch fullname: Kalisch, Raffael – sequence: 2 givenname: Göran surname: Köber fullname: Köber, Göran – sequence: 3 givenname: Harald surname: Binder fullname: Binder, Harald – sequence: 4 givenname: Kira F. surname: Ahrens fullname: Ahrens, Kira F. – sequence: 5 givenname: Ulrike surname: Basten fullname: Basten, Ulrike – sequence: 6 givenname: Andrea surname: Chmitorz fullname: Chmitorz, Andrea – sequence: 7 givenname: Karmel W. surname: Choi fullname: Choi, Karmel W. – sequence: 8 givenname: Christian J. surname: Fiebach fullname: Fiebach, Christian J. – sequence: 9 givenname: Nele surname: Goldbach fullname: Goldbach, Nele – sequence: 10 givenname: Rebecca J. surname: Neumann fullname: Neumann, Rebecca J. – sequence: 11 givenname: Miriam surname: Kampa fullname: Kampa, Miriam – sequence: 12 givenname: Bianca surname: Kollmann fullname: Kollmann, Bianca – sequence: 13 givenname: Klaus surname: Lieb fullname: Lieb, Klaus – sequence: 14 givenname: Michael M. surname: Plichta fullname: Plichta, Michael M. – sequence: 15 givenname: Andreas surname: Reif fullname: Reif, Andreas – sequence: 16 givenname: Anita surname: Schick fullname: Schick, Anita – sequence: 17 givenname: Alexandra surname: Sebastian fullname: Sebastian, Alexandra – sequence: 18 givenname: Henrik surname: Walter fullname: Walter, Henrik – sequence: 19 givenname: Michèle surname: Wessa fullname: Wessa, Michèle – sequence: 20 givenname: Kenneth S. L. surname: Yuen fullname: Yuen, Kenneth S. L. – sequence: 21 givenname: Oliver surname: Tüscher fullname: Tüscher, Oliver – sequence: 22 givenname: Haakon surname: Engen fullname: Engen, Haakon |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34539510$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright © 2021 Kalisch, Köber, Binder, Ahrens, Basten, Chmitorz, Choi, Fiebach, Goldbach, Neumann, Kampa, Kollmann, Lieb, Plichta, Reif, Schick, Sebastian, Walter, Wessa, Yuen, Tüscher and Engen. Copyright © 2021 Kalisch, Köber, Binder, Ahrens, Basten, Chmitorz, Choi, Fiebach, Goldbach, Neumann, Kampa, Kollmann, Lieb, Plichta, Reif, Schick, Sebastian, Walter, Wessa, Yuen, Tüscher and Engen. 2021 Kalisch, Köber, Binder, Ahrens, Basten, Chmitorz, Choi, Fiebach, Goldbach, Neumann, Kampa, Kollmann, Lieb, Plichta, Reif, Schick, Sebastian, Walter, Wessa, Yuen, Tüscher and Engen |
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| Keywords | homeostasis stress adversity mental health allostasis adaptation dynamic system coping |
| Language | English |
| License | Copyright © 2021 Kalisch, Köber, Binder, Ahrens, Basten, Chmitorz, Choi, Fiebach, Goldbach, Neumann, Kampa, Kollmann, Lieb, Plichta, Reif, Schick, Sebastian, Walter, Wessa, Yuen, Tüscher and Engen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. cc-by |
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| Title | 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 |
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