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      Correlates of psychological resilience and risk: Prospective associations of self‐reported and relative resilience with Connor‐Davidson resilience scale, heart rate variability, and mental health indices

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          Abstract

          Background

          There are several ways to determine psychological resilience. However, the correlation between each measurement is not clear. We explored associations of baseline relative “resilience” and risk with later self‐reported trait resilience and other biological/mental health indices.

          Methods

          We utilized baseline and follow‐up survey data from 500 participants aged 30–64 in the community cohort. Baseline “relative” resilience was defined by: (a) negative life events (NLEs) in the six months before baseline and (b) depressive symptoms at baseline, yielding four groups of individuals: i) “Unexposed and well,” “Vulnerable (depression),” “Reactive (depression),” and “Resilient.” “Trait” resilience at follow‐up was self‐reported using the Connor‐Davidson Resilience Scale (CD‐RISC). Associations between relative resilience at baseline, CD‐RISC, and heart rate variability (HRV) indices at follow‐up were assessed with generalized linear regression models after adjustments. Associations between baseline resilience and subsequent loneliness/depression indices were also evaluated.

          Results

          Overall trait resilience and its subfactors at follow‐up showed strong negative associations with “Reactive” at baseline (adj‐β for total CD‐RISC score: −11.204 (men), −9.472 (women)). However, resilience at baseline was not associated with later HRV, which was compared with the significant positive association observed between CD‐RISC and HRV at the same follow‐up time point. The “Reactive” exhibited significantly increased depressive symptoms at follow‐up. The overall distribution pattern of CD‐RISC subfactors differed by baseline resilience status by sex.

          Conclusions

          The “relative” resilience based on the absence of depression despite prior adversity seems to be highly related with trait resilience at follow‐up but not with HRV. The sub‐factor pattern of CD‐RISC was different by sex.

          Abstract

          The “relative” resilience based on the absence of depression despite prior adversity seems to be highly related with trait resilience at follow‐up but not with HRV. The subfactor pattern of CD‐RISC was different by sex.

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          Most cited references35

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          Development of a new resilience scale: the Connor-Davidson Resilience Scale (CD-RISC).

          Resilience may be viewed as a measure of stress coping ability and, as such, could be an important target of treatment in anxiety, depression, and stress reactions. We describe a new rating scale to assess resilience. The Connor-Davidson Resilience scale (CD-RISC) comprises of 25 items, each rated on a 5-point scale (0-4), with higher scores reflecting greater resilience. The scale was administered to subjects in the following groups: community sample, primary care outpatients, general psychiatric outpatients, clinical trial of generalized anxiety disorder, and two clinical trials of PTSD. The reliability, validity, and factor analytic structure of the scale were evaluated, and reference scores for study samples were calculated. Sensitivity to treatment effects was examined in subjects from the PTSD clinical trials. The scale demonstrated good psychometric properties and factor analysis yielded five factors. A repeated measures ANOVA showed that an increase in CD-RISC score was associated with greater improvement during treatment. Improvement in CD-RISC score was noted in proportion to overall clinical global improvement, with greatest increase noted in subjects with the highest global improvement and deterioration in CD-RISC score in those with minimal or no global improvement. The CD-RISC has sound psychometric properties and distinguishes between those with greater and lesser resilience. The scale demonstrates that resilience is modifiable and can improve with treatment, with greater improvement corresponding to higher levels of global improvement. Copyright 2003 Wiley-Liss, Inc.
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            An Overview of Heart Rate Variability Metrics and Norms

            Healthy biological systems exhibit complex patterns of variability that can be described by mathematical chaos. Heart rate variability (HRV) consists of changes in the time intervals between consecutive heartbeats called interbeat intervals (IBIs). A healthy heart is not a metronome. The oscillations of a healthy heart are complex and constantly changing, which allow the cardiovascular system to rapidly adjust to sudden physical and psychological challenges to homeostasis. This article briefly reviews current perspectives on the mechanisms that generate 24 h, short-term (~5 min), and ultra-short-term (<5 min) HRV, the importance of HRV, and its implications for health and performance. The authors provide an overview of widely-used HRV time-domain, frequency-domain, and non-linear metrics. Time-domain indices quantify the amount of HRV observed during monitoring periods that may range from ~2 min to 24 h. Frequency-domain values calculate the absolute or relative amount of signal energy within component bands. Non-linear measurements quantify the unpredictability and complexity of a series of IBIs. The authors survey published normative values for clinical, healthy, and optimal performance populations. They stress the importance of measurement context, including recording period length, subject age, and sex, on baseline HRV values. They caution that 24 h, short-term, and ultra-short-term normative values are not interchangeable. They encourage professionals to supplement published norms with findings from their own specialized populations. Finally, the authors provide an overview of HRV assessment strategies for clinical and optimal performance interventions.
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              Resilience to loss and potential trauma.

              Initial research on loss and potentially traumatic events (PTEs) has been dominated by either a psychopathological approach emphasizing individual dysfunction or an event approach emphasizing average differences between exposed and nonexposed groups. We consider the limitations of these approaches and review more recent research that has focused on the heterogeneity of outcomes following aversive events. Using both traditional analytic tools and sophisticated latent trajectory modeling, this research has identified a set of prototypical outcome patterns. Typically, the most common outcome following PTEs is a stable trajectory of healthy functioning or resilience. We review research showing that resilience is not the result of a few dominant factors, but rather that there are multiple independent predictors of resilient outcomes. Finally, we critically evaluate the question of whether resilience-building interventions can actually make people more resilient, and we close with suggestions for future research on resilience. © 2011 by Annual Reviews. All rights reserved
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                Author and article information

                Contributors
                sunjaejung@yuhs.ac
                Journal
                Brain Behav
                Brain Behav
                10.1002/(ISSN)2157-9032
                BRB3
                Brain and Behavior
                John Wiley and Sons Inc. (Hoboken )
                2162-3279
                27 February 2021
                May 2021
                : 11
                : 5 ( doiID: 10.1002/brb3.v11.5 )
                : e02091
                Affiliations
                [ 1 ] Department of Preventive Medicine Yonsei University College of Medicine Seoul South Korea
                [ 2 ] Department of Public Health Yonsei University Graduate School Seoul South Korea
                [ 3 ] Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA USA
                [ 4 ] Department of Psychiatry Massachusetts General Hospital Boston MA USA
                [ 5 ] Department of Psychiatry St. Mary’s Hospital Seoul South Korea
                Author notes
                [*] [* ] Correspondence

                Sun Jae Jung, Yonsei University College of Medicine, 50‐1 Yonsei‐ro, Yonsei Medical Center, Seodaemun‐gu, Seoul, South Korea 03722.

                Email: sunjaejung@ 123456yuhs.ac

                Author information
                https://orcid.org/0000-0002-5194-7339
                https://orcid.org/0000-0002-3914-2431
                Article
                BRB32091
                10.1002/brb3.2091
                8119814
                33638932
                7b65c166-0658-43bb-b10d-ad4c54efc4ff
                © 2021 The Authors. Brain and Behavior published by Wiley Periodicals LLC

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 06 January 2021
                : 30 October 2020
                : 06 January 2021
                Page count
                Figures: 2, Tables: 4, Pages: 11, Words: 8343
                Funding
                Funded by: National Research Foundation of Korea , open-funder-registry 10.13039/501100003725;
                Award ID: 2019R1A4A1028155
                Award ID: 2020R1C1C1003502
                Categories
                Original Research
                Original Research
                Custom metadata
                2.0
                May 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.2 mode:remove_FC converted:14.05.2021

                Neurosciences
                cd‐risc,depression,heart rate variability,loneliness,longitudinal study,resilience
                Neurosciences
                cd‐risc, depression, heart rate variability, loneliness, longitudinal study, resilience

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