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Identification of resilient individuals and those at risk for performance deficits under stress.

Winslow BD, Carroll MB, Martin JW, Surpris G, Chadderdon GL - Front Neurosci (2015)

Bottom Line: Here we measure the effects of stress on physiological response and performance through behavior, physiological sensors, and subjective ratings, and identify which individuals are at risk for stress-related performance decrements.Stress response was effectively captured via electrodermal and cardiovascular measures of heart rate and skin conductance level.Outliers were identified in the experimental group that had a significant mismatch between self-reported stress and salivary cortisol.

View Article: PubMed Central - PubMed

Affiliation: Design Interactive, Inc. Orlando, FL, USA.

ABSTRACT
Human task performance is affected by exposure to physiological and psychological stress. The ability to measure the physiological response to stressors and correlate that to task performance could be used to identify resilient individuals or those at risk for stress-related performance decrements. Accomplishing this prior to performance under severe stress or the development of clinical stress disorders could facilitate focused preparation such as tailoring training to individual needs. Here we measure the effects of stress on physiological response and performance through behavior, physiological sensors, and subjective ratings, and identify which individuals are at risk for stress-related performance decrements. Participants performed military-relevant training tasks under stress in a virtual environment, with autonomic and hypothalamic-pituitary-adrenal axis (HPA) reactivity analyzed. Self-reported stress, as well as physiological indices of stress, increased in the group pre-exposed to socioevaluative stress. Stress response was effectively captured via electrodermal and cardiovascular measures of heart rate and skin conductance level. A resilience classification algorithm was developed based upon physiological reactivity, which correlated with baseline unstressed physiological and self-reported stress values. Outliers were identified in the experimental group that had a significant mismatch between self-reported stress and salivary cortisol. Baseline stress measurements were predictive of individual resilience to stress, including the impact stress had on physiological reactivity and performance. Such an approach may have utility in identifying individuals at risk for problems performing under severe stress. Continuing work has focused on adapting this method for military personnel, and assessing the utility of various coping and decision-making strategies on performance and physiological stress.

No MeSH data available.


Related in: MedlinePlus

Overview and timeline of the experimental design. The upper portion represents the control group that did not receive socioevaluative stress prior to scenario performance, while the lower portion represents the group that received the TSST. Following baseline physiological recording and training in the virtual environment, participants went through the TSST or control task. Task presentation followed immediately, with task order counter-balanced via a Latin squares method. B, baseline period; C, saliva sample for cortisol measurement; S, STAI administration. Red shading indicates level of stressor inclusion.
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Figure 1: Overview and timeline of the experimental design. The upper portion represents the control group that did not receive socioevaluative stress prior to scenario performance, while the lower portion represents the group that received the TSST. Following baseline physiological recording and training in the virtual environment, participants went through the TSST or control task. Task presentation followed immediately, with task order counter-balanced via a Latin squares method. B, baseline period; C, saliva sample for cortisol measurement; S, STAI administration. Red shading indicates level of stressor inclusion.

Mentions: The experimental procedure is overviewed in Figure 1. Participants arrived between 8:00 and 8:30 a.m., provided written, informed consent, completion of a demographics questionnaire, and the state portion of the state-trait anxiety inventory [STAI] (Spielberger et al., 1983), previously shown to have a high test-retest reliability with situational stress (Rule and Traver, 1983). Wireless physiological sensors were then placed on the participants, followed by a 5-min recording of baseline physiological activity while participants remained seated. Participants were instructed prior to arrival to abstain from eating, drinking, and smoking for 1 h prior to arrival. Participants rinsed with water, then provided a saliva sample for cortisol measurement via passive drool, which was immediately frozen. Following baseline procedures, participants were familiarized with the virtual environment (VE) controls, and keyboard commands, without exposure to any virtual stressful components. Participants were then randomly assigned to either the experimental group, which received the Trier Social Stress Test [TSST; (Kirschbaum et al., 1993)], or the control group. The TSST was used as an external socio evaluative stressor prior to task performance, consisting of 5 min each of: anticipatory stress; oral presentation; and mental arithmetic. The control group received a placebo version of the TSST (Het et al., 2009), which contained the same factors except for the psychosocially stressful components. In the placebo version, participants read a document out loud for the public speaking portion of the TSST and counted forward by 2 for the mental arithmetic portion. Performance was then assessed while all participants performedfive complex military-relevant scenarios within Virtual Battlespace 2 (VBS2, Bohemia, Orlando FL), a VE used for military training. Scenarios were created in VBS2 to simulate a tactical military environment with specific mission objectives, time requirements, and consequences depending on the course of action a participant pursued. Scenarios 1–5 were designed to sequentially increase in stress by varying the number of stressors and stressor characteristics such as novelty, predictability, and controllability. Each scenario also includes 5 specific decision-making events triggered by timing or participant location. Scenario 1, designed to be the lowest stress scenario, consists of the participant following a person of interest (POI) through a virtual town during daylight hours and noting his activities. Decisions in this scenario included when to follow the POI, the distance to maintain, and reacting to the POI's actions. Scenario 2 is a night mission in which the participant must set off a demolition charge, enter a restricted area without being observed, obtain information, and evacuate. Decisions in this scenario included target identification and prediction of enemy movement. Scenario 3 is a night mission in which the participant must avoid enemy detection and set a series of demolition charges prior to evacuating the area. Decisions in this scenario included when to detonate the demolition charges, target identification, and maintaining sufficient distance from enemies. In scenario 4 the participant's helicopter has crashed in enemy territory at night and the participant must evacuate under heavy fire. Decisions in this scenario included target identification and whether to engage or avoid enemies. In scenario 5, designed to be the highest stress scenario, the participant must perform long range fire during daylight hours in enemy territory under fire. Decisions in this scenario included target identification, and whether to engage or avoid enemies. Simulation-based stressors included limited visual perception, sudden noise exposure (Hockey, 1970; Rhudy and Meagher, 2001), equipment failures, and receiving enemy fire, as well as cognitive stressors such as time pressure, and emotion induction procedures (Bouchard et al., 2010; Cousijn et al., 2012), including the presentation of dead combatants, soldiers, and civilians (Dickerson and Kemeny, 2004). The stressfulness of each scenario was independently coded by 3 unbiased observers. VBS2 scenarios were presented on a PC running on a Pentium i5 quad core processor. Physiological measures were captured throughout the baseline phase, socio evaluative stress phase and the VBS2 scenarios. At the end of the socio evaluative stress phase and each scenario, participants completed an additional STAI. Following all scenarios, participants provided a second saliva sample, and were debriefed and paid for their participation.


Identification of resilient individuals and those at risk for performance deficits under stress.

Winslow BD, Carroll MB, Martin JW, Surpris G, Chadderdon GL - Front Neurosci (2015)

Overview and timeline of the experimental design. The upper portion represents the control group that did not receive socioevaluative stress prior to scenario performance, while the lower portion represents the group that received the TSST. Following baseline physiological recording and training in the virtual environment, participants went through the TSST or control task. Task presentation followed immediately, with task order counter-balanced via a Latin squares method. B, baseline period; C, saliva sample for cortisol measurement; S, STAI administration. Red shading indicates level of stressor inclusion.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4584989&req=5

Figure 1: Overview and timeline of the experimental design. The upper portion represents the control group that did not receive socioevaluative stress prior to scenario performance, while the lower portion represents the group that received the TSST. Following baseline physiological recording and training in the virtual environment, participants went through the TSST or control task. Task presentation followed immediately, with task order counter-balanced via a Latin squares method. B, baseline period; C, saliva sample for cortisol measurement; S, STAI administration. Red shading indicates level of stressor inclusion.
Mentions: The experimental procedure is overviewed in Figure 1. Participants arrived between 8:00 and 8:30 a.m., provided written, informed consent, completion of a demographics questionnaire, and the state portion of the state-trait anxiety inventory [STAI] (Spielberger et al., 1983), previously shown to have a high test-retest reliability with situational stress (Rule and Traver, 1983). Wireless physiological sensors were then placed on the participants, followed by a 5-min recording of baseline physiological activity while participants remained seated. Participants were instructed prior to arrival to abstain from eating, drinking, and smoking for 1 h prior to arrival. Participants rinsed with water, then provided a saliva sample for cortisol measurement via passive drool, which was immediately frozen. Following baseline procedures, participants were familiarized with the virtual environment (VE) controls, and keyboard commands, without exposure to any virtual stressful components. Participants were then randomly assigned to either the experimental group, which received the Trier Social Stress Test [TSST; (Kirschbaum et al., 1993)], or the control group. The TSST was used as an external socio evaluative stressor prior to task performance, consisting of 5 min each of: anticipatory stress; oral presentation; and mental arithmetic. The control group received a placebo version of the TSST (Het et al., 2009), which contained the same factors except for the psychosocially stressful components. In the placebo version, participants read a document out loud for the public speaking portion of the TSST and counted forward by 2 for the mental arithmetic portion. Performance was then assessed while all participants performedfive complex military-relevant scenarios within Virtual Battlespace 2 (VBS2, Bohemia, Orlando FL), a VE used for military training. Scenarios were created in VBS2 to simulate a tactical military environment with specific mission objectives, time requirements, and consequences depending on the course of action a participant pursued. Scenarios 1–5 were designed to sequentially increase in stress by varying the number of stressors and stressor characteristics such as novelty, predictability, and controllability. Each scenario also includes 5 specific decision-making events triggered by timing or participant location. Scenario 1, designed to be the lowest stress scenario, consists of the participant following a person of interest (POI) through a virtual town during daylight hours and noting his activities. Decisions in this scenario included when to follow the POI, the distance to maintain, and reacting to the POI's actions. Scenario 2 is a night mission in which the participant must set off a demolition charge, enter a restricted area without being observed, obtain information, and evacuate. Decisions in this scenario included target identification and prediction of enemy movement. Scenario 3 is a night mission in which the participant must avoid enemy detection and set a series of demolition charges prior to evacuating the area. Decisions in this scenario included when to detonate the demolition charges, target identification, and maintaining sufficient distance from enemies. In scenario 4 the participant's helicopter has crashed in enemy territory at night and the participant must evacuate under heavy fire. Decisions in this scenario included target identification and whether to engage or avoid enemies. In scenario 5, designed to be the highest stress scenario, the participant must perform long range fire during daylight hours in enemy territory under fire. Decisions in this scenario included target identification, and whether to engage or avoid enemies. Simulation-based stressors included limited visual perception, sudden noise exposure (Hockey, 1970; Rhudy and Meagher, 2001), equipment failures, and receiving enemy fire, as well as cognitive stressors such as time pressure, and emotion induction procedures (Bouchard et al., 2010; Cousijn et al., 2012), including the presentation of dead combatants, soldiers, and civilians (Dickerson and Kemeny, 2004). The stressfulness of each scenario was independently coded by 3 unbiased observers. VBS2 scenarios were presented on a PC running on a Pentium i5 quad core processor. Physiological measures were captured throughout the baseline phase, socio evaluative stress phase and the VBS2 scenarios. At the end of the socio evaluative stress phase and each scenario, participants completed an additional STAI. Following all scenarios, participants provided a second saliva sample, and were debriefed and paid for their participation.

Bottom Line: Here we measure the effects of stress on physiological response and performance through behavior, physiological sensors, and subjective ratings, and identify which individuals are at risk for stress-related performance decrements.Stress response was effectively captured via electrodermal and cardiovascular measures of heart rate and skin conductance level.Outliers were identified in the experimental group that had a significant mismatch between self-reported stress and salivary cortisol.

View Article: PubMed Central - PubMed

Affiliation: Design Interactive, Inc. Orlando, FL, USA.

ABSTRACT
Human task performance is affected by exposure to physiological and psychological stress. The ability to measure the physiological response to stressors and correlate that to task performance could be used to identify resilient individuals or those at risk for stress-related performance decrements. Accomplishing this prior to performance under severe stress or the development of clinical stress disorders could facilitate focused preparation such as tailoring training to individual needs. Here we measure the effects of stress on physiological response and performance through behavior, physiological sensors, and subjective ratings, and identify which individuals are at risk for stress-related performance decrements. Participants performed military-relevant training tasks under stress in a virtual environment, with autonomic and hypothalamic-pituitary-adrenal axis (HPA) reactivity analyzed. Self-reported stress, as well as physiological indices of stress, increased in the group pre-exposed to socioevaluative stress. Stress response was effectively captured via electrodermal and cardiovascular measures of heart rate and skin conductance level. A resilience classification algorithm was developed based upon physiological reactivity, which correlated with baseline unstressed physiological and self-reported stress values. Outliers were identified in the experimental group that had a significant mismatch between self-reported stress and salivary cortisol. Baseline stress measurements were predictive of individual resilience to stress, including the impact stress had on physiological reactivity and performance. Such an approach may have utility in identifying individuals at risk for problems performing under severe stress. Continuing work has focused on adapting this method for military personnel, and assessing the utility of various coping and decision-making strategies on performance and physiological stress.

No MeSH data available.


Related in: MedlinePlus