Title: Physiological Correlates of Time Stress During Game Play
The objective of this study is to develop and use a virtual reality game as a tool to assess the effects of realistic stress on the behavioral and physiological responses of participants. The game is based on a popular Steam game called Keep Talking Nobody Explodes, where the players collaborate to defuse a bomb. Varying levels of difficulties in solving a puzzle and time pressures will result in different stress levels that can be measured in terms of errors, response times, and other physiological measurements. The game was developed using 3D programming tools including Blender and a virtual reality development kit (VRTK). To measure response times accurately, we added LSL (Lab Stream Layer) Markers to collect and synchronize physiological signals, behavioral data, and the timing of game events. We recorded Electrocardiogram (ECG) data during gameplay to assess heart rate and heart-rate variability (HRV) that have been shown as reliable indicators of stress. Our empirical results showed that heart rate increased significantly while HRV reduced significantly when the participants under high stress, which are consistent with the prior mainstream stress research. This VR game framework is publicly available in GitHub and allows researchers to measure and synchronize other physiological signals such as electroencephalogram, electromyogram, and pupillometry. more »« less
van Dammen, Lotte; Barnett, Neil; Conrady, Roselynn; Wright, Lucas; Thymes, Bradon; Shirtcliff, Elizabeth A.(
, Advances in Simulation and Digital Human Modeling. AHFE 2020)
Stress reactivity involves a physiological response to a stressful task, as a biological mechanism related to mental and physical health outcomes. Traditional stressors evoke social evaluative threat, which is fear of judgment and a threat to social status, typically via public speaking tasks. To be well-suited for virtual reality, however, a novel approach is necessary in the design of new stress tasks. We hypothesized that a virtual reality stress task involving social evaluative threat through a dance competition in front of an audience, elicits a stress response, measured by autonomic nervous system, cortisol, and testosterone reactivity. Participants (n = 18) showed autonomic nervous system reactivity in terms of increased heart rate and decreased respiratory sinus arrhythmia, indicating a stress response. Levels of cortisol increased in response to the dance competition, especially within responders, whereas testosterone levels did not change significantly over time. A virtual reality dance competition involving physical social evaluative threat elicits a stress response.
Haigh, Sarah M.; Walford, Tabatha P.; Brosseau, Pat(
, Frontiers in Psychiatry)
Suppressed heart rate variability (HRV) has been found in a number of psychiatric conditions, including schizophrenia and autism. HRV is a potential biomarker of altered autonomic functioning that can predict future physiological and cognitive health. Understanding the HRV profiles that are unique to each condition will assist in generating predictive models of health. In the current study, we directly compared 12 adults with schizophrenia, 25 adults with autism, and 27 neurotypical controls on their HRV profiles. HRV was measured using an electrocardiogram (ECG) channel as part of a larger electroencephalography (EEG) study. All participants also completed the UCLA Loneliness Questionnaire as a measure of social stress. We found that the adults with schizophrenia exhibited reduced variability in R-R peaks and lower low frequency power in the ECG trace compared to controls. The HRV in adults with autism was slightly suppressed compared to controls but not significantly so. Interestingly, the autism group reported feeling lonelier than the schizophrenia group, and HRV did not correlate with feelings of loneliness for any of the three groups. However, suppressed HRV was related to worse performance on neuropsychological tests of cognition in the schizophrenia group. Together, this suggests that autonomic functioning is more abnormal in schizophrenia than in autism and could be reflecting health factors that are unique to schizophrenia.
Neural, physiological, and behavioral signals synchronize between human subjects in a variety of settings. Multiple hypotheses have been proposed to explain this interpersonal synchrony, but there is no clarity under which conditions it arises, for which signals, or whether there is a common underlying mechanism. We hypothesized that cognitive processing of a shared stimulus is the source of synchrony between subjects, measured here as intersubject correlation (ISC). To test this, we presented informative videos to participants in an attentive and distracted condition and subsequently measured information recall. ISC was observed for electro-encephalography, gaze position, pupil size, and heart rate, but not respiration and head movements. The strength of correlation was co-modulated in the different signals, changed with attentional state, and predicted subsequent recall of information presented in the videos. There was robust within-subject coupling between brain, heart, and eyes, but not respiration or head movements. The results suggest that ISC is the result of effective cognitive processing, and thus emerges only for those signals that exhibit a robust brain–body connection. While physiological and behavioral fluctuations may be driven by multiple features of the stimulus, correlation with other individuals is co-modulated by the level of attentional engagement with the stimulus.
Gaidica, Matt; Dantzer, Ben(
, Integrative and Comparative Biology)
null
(Ed.)
Abstract Quantifying how whole organisms respond to challenges in the external and internal environment (“stressors”) is difficult. To date, physiological ecologists have mostly used measures of glucocorticoids (GCs) to assess the impact of stressors on animals. This is of course too simplistic as Hans Seyle himself characterized the response of organisms to “noxious stimuli” using multiple physiological responses. Possible solutions include increasing the number of biomarkers to more accurately characterize the “stress state” of animal or just measuring different biomarkers to more accurately characterize the degree of acute or chronic stressors an animal is experiencing. We focus on the latter and discuss how heart rate (HR) and heart rate variability (HRV) may be better predictors of the degree of activation of the sympathetic–adrenal–medullary system and complement or even replace measures of GCs as indicators of animal health, welfare, fitness, or their level of exposure to stressors. The miniaturization of biological sensor technology (“bio-sensors” or “bio-loggers”) presents an opportunity to reassess measures of stress state and develop new approaches. We describe some modern approaches to gathering these HR and HRV data in free-living animals with the aim that heart dynamics will be more integrated with measures of GCs as bio-markers of stress state and predictors of fitness in free-living animals.
Akbar, Fatema; Mark, Gloria; Prausnitz, Stephanie; Warton, E Margaret; East, Jeffrey A; Moeller, Mark F; Reed, Mary E; Lieu, Tracy A(
, JMIR Medical Informatics)
null
(Ed.)
Background Increased work through electronic health record (EHR) messaging is frequently cited as a factor of physician burnout. However, studies to date have relied on anecdotal or self-reported measures, which limit the ability to match EHR use patterns with continuous stress patterns throughout the day. Objective The aim of this study is to collect EHR use and physiologic stress data through unobtrusive means that provide objective and continuous measures, cluster distinct patterns of EHR inbox work, identify physicians’ daily physiologic stress patterns, and evaluate the association between EHR inbox work patterns and physician physiologic stress. Methods Physicians were recruited from 5 medical centers. Participants (N=47) were given wrist-worn devices (Garmin Vivosmart 3) with heart rate sensors to wear for 7 days. The devices measured physiological stress throughout the day based on heart rate variability (HRV). Perceived stress was also measured with self-reports through experience sampling and a one-time survey. From the EHR system logs, the time attributed to different activities was quantified. By using a clustering algorithm, distinct inbox work patterns were identified and their associated stress measures were compared. The effects of EHR use on physician stress were examined using a generalized linear mixed effects model. Results Physicians spent an average of 1.08 hours doing EHR inbox work out of an average total EHR time of 3.5 hours. Patient messages accounted for most of the inbox work time (mean 37%, SD 11%). A total of 3 patterns of inbox work emerged: inbox work mostly outside work hours, inbox work mostly during work hours, and inbox work extending after hours that were mostly contiguous to work hours. Across these 3 groups, physiologic stress patterns showed 3 periods in which stress increased: in the first hour of work, early in the afternoon, and in the evening. Physicians in group 1 had the longest average stress duration during work hours (80 out of 243 min of valid HRV data; P=.02), as measured by physiological sensors. Inbox work duration, the rate of EHR window switching (moving from one screen to another), the proportion of inbox work done outside of work hours, inbox work batching, and the day of the week were each independently associated with daily stress duration (marginal R2=15%). Individual-level random effects were significant and explained most of the variation in stress (conditional R2=98%). Conclusions This study is among the first to demonstrate associations between electronic inbox work and physiological stress. We identified 3 potentially modifiable factors associated with stress: EHR window switching, inbox work duration, and inbox work outside work hours. Organizations seeking to reduce physician stress may consider system-based changes to reduce EHR window switching or inbox work duration or the incorporation of inbox management time into work hours.
Lee, D. Physiological Correlates of Time Stress During Game Play. Retrieved from https://par.nsf.gov/biblio/10341428. Communications in computer and information science 1419. Web. doi:10.1007/978-3-030-78635-9_17.
Lee, D. Physiological Correlates of Time Stress During Game Play. Communications in computer and information science, 1419 (). Retrieved from https://par.nsf.gov/biblio/10341428. https://doi.org/10.1007/978-3-030-78635-9_17
@article{osti_10341428,
place = {Country unknown/Code not available},
title = {Physiological Correlates of Time Stress During Game Play},
url = {https://par.nsf.gov/biblio/10341428},
DOI = {10.1007/978-3-030-78635-9_17},
abstractNote = {The objective of this study is to develop and use a virtual reality game as a tool to assess the effects of realistic stress on the behavioral and physiological responses of participants. The game is based on a popular Steam game called Keep Talking Nobody Explodes, where the players collaborate to defuse a bomb. Varying levels of difficulties in solving a puzzle and time pressures will result in different stress levels that can be measured in terms of errors, response times, and other physiological measurements. The game was developed using 3D programming tools including Blender and a virtual reality development kit (VRTK). To measure response times accurately, we added LSL (Lab Stream Layer) Markers to collect and synchronize physiological signals, behavioral data, and the timing of game events. We recorded Electrocardiogram (ECG) data during gameplay to assess heart rate and heart-rate variability (HRV) that have been shown as reliable indicators of stress. Our empirical results showed that heart rate increased significantly while HRV reduced significantly when the participants under high stress, which are consistent with the prior mainstream stress research. This VR game framework is publicly available in GitHub and allows researchers to measure and synchronize other physiological signals such as electroencephalogram, electromyogram, and pupillometry.},
journal = {Communications in computer and information science},
volume = {1419},
author = {Lee, D.},
editor = {Stephanidis C., Antona M.}
}
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