Evoking stress reactivity in virtual reality: A systematic review and meta-analysis

Dammen, Lotte van; McCurdy, Bethany H.; Barnett, Neil; Conrady, Roselynn; Deick, Andrew F.; Steenis, Allissa Van; Gardner, Reece; Smith, B.; Kay, Anita; Shirtcliff, Elizabeth A.

doi:10.1016/j.neubiorev.2022.104709

Cited by 23 publications

(21 citation statements)

References 222 publications

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“…The stress response is complex and involves multiple systems (i.e., neural, neuroendocrine, endocrine), some that respond to acute stress on sort timescales and some that measure chronic stress over longer time frames. Further, as noted by a 2022 meta-analysis [19], physiological systems are differentially activated by types of stressors (e.g., acute social stressors are more likely to increase the hormone cortisol). Since model training data obtains the best predictive results when it captures similar patterns as the testing data [92], the variation in both the psychological and physiological stress response due to task context suggests that models trained using stress signals from one task (e.g., N-back) made not represent the stress in a different task (e.g., VR-ISS).…”

Section: Discussionmentioning

confidence: 98%

“…There is increasing support that the physiological systems activated are those best suited to cope with the type of stressor, rather than the prior theories that certain systems are activated if the stressor magnitude surpasses a threshold [19,20]. For example, stress caused by a traffic jam may cause one individual to show an increase in epinephrine while another individual may show an increase in the stress hormone cortisol with little to no increase of epinephrine.…”

Section: A Time Course Of the Stress Responsementioning

confidence: 99%

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Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

et al. 2023

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When training for hazardous operations, real-time stress detection is an asset for optimizing task performance and reducing stress. Stress detection systems train a machine-learning model with physiological signals to classify stress levels of unseen data. Unfortunately, individual differences and the time-series nature of physiological signals limit the effectiveness of generalized models and hinder both posthoc stress detection and real-time monitoring. This study evaluated a personalized stress detection system that selects a personalized subset of features for model training. The system was evaluated post-hoc for realtime deployment. Further, traditional classifiers were assessed for error caused by indirect approximations against a benchmark, optimal probability classifier (Approximate Bayes; ABayes). Healthy participants completed a task with three levels of stressors (low, medium, high), either a complex task in virtual reality (responding to spaceflight emergency fires, n=27) or a simple laboratory-based task (N-back, n=14). Heart rate, blood pressure, electrodermal activity, and respiration were assessed. Personalized features and different interval window sizes were compared. Classification performance was compared for ABayes, support vector machine, decision tree, and random forest. The results demonstrate that a personalized model with time series intervals can classify three stress levels with high accuracy better than a generalized model. However, crossvalidation and holdout performance varied for traditional classifiers vs. ABayes, suggesting error from indirect approximations. The selected features changed with windows size and differed between tasks, but blood pressure contributed to the most prominent features. The capability to account for individual difference is a prominent advantage of personalized models, and will likely have a growing presence in future detection systems.

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Section: Discussionmentioning

confidence: 98%

Section: A Time Course Of the Stress Responsementioning

confidence: 99%

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

et al. 2023

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“…In MR training for MFRs, a biocybernetic feedback loop can be used to steer difficulty levels and stress exposure, through real-time adaptations of the virtual environment. Wearable sensors play a pivotal role in measuring trainees' biosignals, such as heart rate, heart rate variability or skin conductance, which are commonly used biosignals for stress classification (Giannakakis et al, 2022;van Dammen et al, 2022). For instance, if a trainee shows signs of high stress, the environment might be modified to reduce pressure, whereas indications of low engagement could lead to increased challenge.…”

Section: Training Personalization Through Biocybernetic Adaptationmentioning

confidence: 99%

Mixed Reality Training for Medical First Responders: System Evaluation and Recommendations

Zechner,

Uhl,

Baetzner

et al. 2024

Preprint

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Purpose: This study assesses the integration of Mixed Reality (MR) technologies in medical first responder (MFR) training, focusing on identifying key factors influencing behavioral intention to use MR systems and practical implications for technology acceptance and enhanced realism through haptic feedback. Methods: Through a User-Centered Design approach, involving co-creation workshops, iterative development, and evaluations in pilot and field trials across six countries, this study evaluated technology acceptance, presence, user experience, and workload among MFRs. Both quantitative measures and qualitative feedback were collected to analyze the determinants of technology acceptance and user engagement. Results: The MED1stMR Training System, developed as a result, demonstrates that performance expectancy, effort expectancy, and social presence are significant predictors of behavioral intention to use MR training systems among MFRs. High technology acceptance and positive user experience were reported, with specific emphasis on the educational value of haptic feedback in skill training. Trainer feedback highlighted the importance of real-time performance metrics and openness to AI-driven training assistance for enhancing training outcomes. Conclusion: The study underscores the critical role of realistic patient interaction and the importance of aligning training challenges with users' skills to create engaging MR training environments for MFRs. Identifying factors influencing behavioral intention offers valuable insights for the development of MR training systems, suggesting a focus on social presence and interactive capabilities to improve realism and educational value. The findings advocate for the integration of adaptive training features and further exploration of AI support in scenario optimization and performance enhancement.

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“…HMDs offer feasibility, repeatability, and control while leaving space for the development of tangible innovative ideas. Highly realistic virtual environments that recreate real-life scenarios (e.g., train/roller coaster, escaping life-threatening events, reacting to emergencies, high-altitude exposure) or completely fantastic ones or replicate traditional stress tests (e.g., TSST, MAST) into the virtual dimension have been developed and validated [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Assessing Electroencephalography as a Stress Indicator: A VR High-Altitude Scenario Monitored through EEG and ECG

Aspiotis

Miltiadous

Kalafatakis

et al. 2022

Sensors

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Over the last decade, virtual reality (VR) has become an increasingly accessible commodity. Head-mounted display (HMD) immersive technologies allow researchers to simulate experimental scenarios that would be unfeasible or risky in real life. An example is extreme heights exposure simulations, which can be utilized in research on stress system mobilization. Until recently, electroencephalography (EEG)-related research was focused on mental stress prompted by social or mathematical challenges, with only a few studies employing HMD VR techniques to induce stress. In this study, we combine a state-of-the-art EEG wearable device and an electrocardiography (ECG) sensor with a VR headset to provoke stress in a high-altitude scenarios while monitoring EEG and ECG biomarkers in real time. A robust pipeline for signal clearing is implemented to preprocess the noise-infiltrated (due to movement) EEG data. Statistical and correlation analysis is employed to explore the relationship between these biomarkers with stress. The participant pool is divided into two groups based on their heart rate increase, where statistically important EEG biomarker differences emerged between them. Finally, the occipital-region band power changes and occipital asymmetry alterations were found to be associated with height-related stress and brain activation in beta and gamma bands, which correlates with the results of the self-reported Perceived Stress Scale questionnaire.

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Evoking stress reactivity in virtual reality: A systematic review and meta-analysis

Cited by 23 publications

References 222 publications

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

Real-Time Personalized Physiologically Based Stress Detection for Hazardous Operations

Mixed Reality Training for Medical First Responders: System Evaluation and Recommendations

Assessing Electroencephalography as a Stress Indicator: A VR High-Altitude Scenario Monitored through EEG and ECG

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