A generic solution for the operationalization and measurement of resilience and resilience processes in longitudinal observations: rationale and basic design of the MARP and LORA studies

Resilience has been defined as the maintenance or quick recovery of mental health during and after times of adversity. Such good longer-term mental health outcomes despite adversity presumably result from complex and dynamic processes of adaptation to stressor exposure (‘resilience processes’), which in many cases include changes in individual properties. Measuring resilience and identifying resilience processes in observational studies requires longitudinal designs involving repeated and frequent monitoring of mental health, stressor exposure, and potential adaptations. We here present a generic design solution that is currently employed in two cohort studies, the Mainz Resilience Project (MARP) and the Longitudinal Resilience Assessment (LORA). Both projects focus on resilience to everyday life stressors (i.e., microstressors), but we argue that the design scheme is also suitable for studying resilience to macrostressors, or trauma, and can solve some of the pertinent problems of trauma resilience research. We quantify resilience by indexing the reactivity of individuals’ mental health to stressors during a time interval of several months in a ‘stressor reactivity’ (SR) score, derived using a previously introduced residualization approach. SR scores are regularly re-calculated in sliding time windows, to thus build SR time courses that reflect intra-individual temporal variability in resilience. By linking these time courses to repeated measures of (temporally varying) individual properties, resilience processes can be identified. We finish by a discussion of limitations of our approach and potential future developments.

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