Quantifying Dynamic Resilience using First-process Autoregressive Modelling: An Empirical Study

Abstract

Dynamic resilience is a temporal process that reflects individuals’ capability to overcome task-induced stress and sustain their performance during task-related events. First-order autoregressive (AR(1)) modelling is posited for measuring individuals’ dynamic resilience over time. The current research investigated this by testing 30 adults in a dynamic decision-making task. AR(1) modelling was conducted on the data, and was compared against a modified seismic resilience metric for concurrent validity purposes. Results revealed that AR(1) modeled parameters are applicable in assessing participants’ dynamic resilience, with analyses supporting their use to distinguish between individuals that can overcome task-induced stress and those that cannot, as well as, in the classification of individuals’ dynamic resilience.

Effects of Dynamic Resilience on the Reactivity of Vagally Mediated Heart Rate Variability

“…To summarize, dynamic resilience presents a relatively new model of resilience that may hold valued sensitivity in human performance evaluation. However, while Crameri et al (2020b)'s dynamic resilience measure has revealed sensitivity in classifying individuals into dynamic resilience groups based on in-task performance, further research needs to be conducted to identify if dynamic resilience characteristics links to selfregulating cognitive structures that play a role in mediating physiological adaptations during task engagement. To address this, this paper aims to evaluate the application of the dynamic resilience measure on vmHRV.…”

“…The task manipulations of the phase conditions were identical to those presented by Crameri et al (2020b). Together, these task manipulations were designed to acquire data suitable for dynamic resilience evaluation.…”

“…Participants' dynamic resilience was derived from the RESMAN subtask's performance via (Crameri et al, 2020b)'s proposed measure. Task performance was calculated by converting the two primary tanks' fuel units (tank i ) into the total percentage error from the optimal fuel level,…”

“…However, discussions targeting the construct value of trait resilience in the human performance domain have challenged its applicability; as its causal chain approach (i.e., a causal variable that determines behavioral outcomes) fails to capture the dynamic process of resilience under load (Hill et al, 2018a). This inspired the conception of the dynamic resilience theoretical framework, in which its proposed methods aim to capture and profile the process of resilience as it unfolds (Hill et al, 2018b;Crameri et al, 2020b). While this has currently been achieved by tracking objective performance outputs, previous research has called for more holistic methods toward profiling dynamic resilience that comprises examining psychophysiological relationships between dynamic resilience and physiological adaptations to in-task stress (Crameri et al, 2020a,b).…”

“…This could potentially lead to the identification of performance "tipping point" in which individuals' can no longer sustain optimal task performance (Hill et al, 2018b). Research evaluating the feasibility of AR(1) modeling has empirically supported its application for dynamic resilience measurement (Crameri et al, 2020b). More specifically, AR(1) modeling of individuals' task performance facilitated the tracking of individuals' dynamic resilience, in which dynamic resilience could be quantified and further classified by examining the autocorrelational patterns across higher and lower load sections of a task.…”