Self-report assessment of Positive Appraisal Style (PAS): Development of a process-focused and a content-focused questionnaire for use in mental health and resilience research.

Positive Appraisal Style Theory of Resilience posits that a person's general style of evaluating stressors plays a central role in mental health and resilience. Specifically, a tendency to appraise stressors positively (positive appraisal style; PAS) is theorized to be protective of mental health and thus a key resilience factor. To this date no measures of PAS exist. Here, we present two scales that measure perceived positive appraisal style, one focusing on cognitive processes that lead to positive appraisals in stressful situations (PASS-process), and the other focusing on the appraisal contents (PASS-content). For PASS-process, the items of the existing questionnaires Brief COPE and CERQ-short were analyzed in exploratory and confirmatory factor analyses (EFA, CFA) in independent samples (N = 1157 and N = 1704). The resulting 10-item questionnaire was internally consistent (α = .78, 95% CI [.86, .87]) and showed good convergent and discriminant validity in comparisons with self-report measures of trait optimism, neuroticism, urgency, and spontaneity. For PASS-content, a newly generated item pool of 29 items across stressor appraisal content dimensions (probability, magnitude, and coping potential) were subjected to EFA and CFA in two independent samples (N = 1174 and N = 1611). The resulting 14-item scale showed good internal consistency (α = .87, 95% CI [.86, .87]), as well as good convergent and discriminant validity within the nomological network. The two scales are a new and reliable way to assess self-perceived positive appraisal style in large-scale studies, which could offer key insights into mechanisms of resilience.

Association of polygenic risk scores and hair cortisol with mental health trajectories during COVID lockdown.

The COVID-19 pandemic is a global stressor with inter-individually differing influences on mental health trajectories. Polygenic Risk Scores (PRSs) for psychiatric phenotypes are associated with individual mental health predispositions. Elevated hair cortisol concentrations (HCC) and high PRSs are related to negative mental health outcomes. We analyzed whether PRSs and HCC are related to different mental health trajectories during the first COVID lockdown in Germany. Among 523 participants selected from the longitudinal resilience assessment study (LORA), we previously reported three subgroups (acute dysfunction, delayed dysfunction, resilient) based on weekly mental health (GHQ-28) assessment during COVID lockdown. DNA from blood was collected at the baseline of the original LORA study (n = 364) and used to calculate the PRSs of 12 different psychopathological phenotypes. An explorative bifactor model with Schmid-Leiman transformation was calculated to extract a general genetic factor for psychiatric disorders. Hair samples were collected quarterly prior to the pandemic for determining HCC (n = 192). Bivariate logistic regressions were performed to test the associations of HCC and the PRS factors with the reported trajectories. The bifactor model revealed 1 general factor and 4 sub-factors. Results indicate a significant association between increased values on the general risk factor and the allocation to the acute dysfunction class. The same was found for elevated HCC and the exploratorily tested sub-factor "childhood-onset neurodevelopmental disorders". Genetic risk and long-term cortisol secretion as a potential indicator of stress, indicated by PRSs and HCC, respectively, predicted different mental health trajectories. Results indicate a potential for future studies on risk prediction.

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 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.