Validating Stress Induction for Spaceflight Scenarios: A Manipulation Check Across Acute Stressor Types in Simulated Task Environments

Abstract: Stress is a multifaceted phenomenon impacting multiple cognitive functions, including decision-making, memory, and attention. In high-risk environments, these effects have significant safety implications. Therefore, it is essential to continuously and non-invasively assess stress levels of human operators, to eventually allow for real-time interventions that can lower the risk of errors or accidents. Ambulatory wearable monitoring technologies, including smart shirts and smartwatches, enable continuous tracking of physiological markers associated with stress, such as heart rate (HR) and respiration rate (RR). These technologies are increasingly integrated within Human-Autonomy Teaming (HAT) systems that can interpret human signals in real time, support decision making and prevent stress overload in demanding settings. Advances in artificial intelligence (AI), particularly machine learning (ML), make it possible to assess stress levels through detection models capable of estimating stress based on physiological markers. However, evidence shows that different types of stressors, such as cognitive, social and environmental stressors, elicit distinct physiological and psychological responses. Developing context-specific detection models requires a better understanding of how different stressors uniquely influence stress responses. This study addresses this need through an experimental validation within a simulation, paving the way to delineate the impact of different stressors on physiological stress signatures. The aim of this study was to compare the effect of three different stress-inducing methods on a set of physiological and subjective stress responses. The three stress manipulations included: a) time pressure alone; b) time pressure combined with a disturbing noise; and c) time pressure with social evaluation inspired by the Trier Social Stress Test. To this end, 120 participants were randomly assigned to one of three stress conditions within a five-scenario OpenMATB (Open Multi-Attribute Task Battery) protocol. Each participant completed a baseline, a tutorial, and two low-stress scenarios interleaved with a stressful scenario. Subjective measures included a Visual Analog Scale for Stress (VAS-Stress) and the NASA-TLX for workload, while HR and RR were continuously monitored using a Bioharness and a Fossil smartwatch. All subjective and physiological measures (VAS-Stress, NASA-TLX, HR, and RR) were compared across conditions and scenarios using 3 (Condition) × 5 (Scenario) mixed ANOVAs. Analyses tested whether physiological and subjective responses during stress induction differed from baseline and control trials. The results show that stress was effectively induced in the participants. Indeed, VAS, NASA-TLX, and RR scores varied significantly across scenarios, with stressful trials producing higher scores. A significant interaction between condition and scenario was observed for HR, revealing that the stressful scenario differed significantly from the two easy scenarios, but only within the social stress condition. These findings show that HR may be particularly sensitive to social stress, whereas subjective measures and RR appear to reflect overall stress levels. By highlighting different sensitivities across measures, the present study offers a more detailed view of stress responses. These results provide a basis for training AI agents to detect and respond to human stress states in real time, advancing the development of intelligent systems for high-risk operational contexts such as space missions, aviation, or emergency response.

Keywords: Stress, Performance, Biosignals, Wearable Sensors, Space Mission Simulation, Human-Autonomy Teaming

DOI: 10.54941/ahfe1007174

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