The current study was performed to obtain insight into the retention of combat lifesaving (CLS) skills after initial training and to compare a more individualized-style training with a more classroom-style training. We measured performance at 0 month, 2 months, and 6 months after initial training in 40 CLSers (17 individualized, 23 classroom). Each test consisted of two 20-minute scenarios with a medical mannequin to simulate combat injuries. An instructor scored the actions, which were divided into critical and non-critical by medical experts. We also measured the speed of performing the protocol and perceived mental effort and anxiety. There were no differences between the groups in critical actions. The full sample made on average almost six critical errors per scenario at 6 months. However, on non-critical actions, the individualized group scored better at 0 month. The individualized group also performed the protocol faster at each test. The classroom group reported an increase in mental effort and anxiety at subsequent tests, while the individualized group did not. Based on the high number of critical errors at 6 months, and on the drop-off in performance at 2 months, we advise that extra refresher training is organized within 2 months after initial training to improve retention further down the line.@en

Introduction: Virtual reality (VR)-based interventions, wearable technology and text mining hold promising potential for advancing the way in which military and Veteran mental health conditions are diagnosed and treated. They have the ability to improve treatment protocol adherence, assist in the detection of mental health conditions, enhance resilience and increase a patient’s motivation to continue therapy. Methods: This article explores five cutting-edge research projects designed to leverage VR-based interventions, wearable technology, and text mining to improve military and Veteran mental health. A computer-animated virtual agent provides online coaching for posttraumatic stress disorder (PTSD) patients in their own homes to enhance treatment compliance. A head-mounted display safely immerses PTSD patients in a virtual world to relive past experiences and associate them with new meanings. Gaming and simulation technology are tested as a way to improve resilience and performance in military members in deployment-related scenarios. Guidelines are developed for the creation of wearable assistive technology for military members and Veterans. Text mining is explored as a way to assist in the detection of PTSD. Results: VR-based therapy, gaming and simulation, wearable assistive and sensory technology, and text mining hold promise for diagnosing, monitoring, and treating military mental health conditions. Discussion: The five research projects presented have made promising contributions to the field of military and Veteran mental health, either by advancing diagnostic trajectories, contributing to therapy or enhancing the process by developing new approaches to delivering preventive or curative care.@en

Virtual reality (VR) simulation tools, in combination with miniaturized sensor and geo-technology, represent an opportunity to prepare high-risk professionals better for uncertain situations. In the current study, we tested whether VR preparation for a police surveillance task leads to increased performance and decreased stress, workload, and a faster recuperation. Police officers (n = 46) were either prepared with a 3D interactive VR simulation of the venue, or received the standard preparation using a 2D paper-based map. Then, officers individually conducted a surveillance scenario during a real live-music concert. Position tracking, heart rate, heart rate variability (HRV), salivary cortisol levels (SCL) and self-perceived stress were assessed. Results showed that police officers with the VR preparation made less direction changes when finding target locations, had lower HRV during the surveillance, and had lower SCL during their recuperation. These results indicate that VR preparation may increase police officers’ performance and improve their recovery.@en

The current study explores whether different stressors in a virtual reality (VR) military training scenario cause increases in physiological stress. This would validate the use of VR simulation for stress training, as well as the physiological monitoring of trainees for educational purposes. Military cadets (n = 63) performed a patrol scenario (military convoy) in which they answered questions about their surroundings. Stressors (task difficulty, noise, lighting changes, social evaluations, electric muscle stimulation, and a simulated attack on the convoy) were stepwise added in four phases. Electrocardiogram, blood pressure, electrodermal activity, cortisol, and the cadets’ subjective threat/challenge appraisal were measured. We found that only the first phase caused a significant increase in physiological stress, as measured with heart rate, heart rate variability, and electrodermal activity. Physiological stress appeared to stay high in the second phase as well, but decreased to baseline level in the third and fourth phases, even though these phases were designed to be the most stressful. Cadets classified as threat responders based on physiological data (n = 3) scored significantly higher on subjective threat/challenge appraisal than those classified as challenge responders (n = 21). It seems that in the tested VR training scenario, the novelty of the scenario was the only effective stress stimuli, whereas the other implemented stressors did not cause a measurable physiological response. We conclude that if VR training scenarios are to be used for stress training, these should confront trainees with unpredictable but context-specific demands.@en

The current study explores whether different stressors in a virtual reality (VR) military training scenario cause increases in physiological stress. This would validate the use of VR simulation for stress training, as well as the physiological monitoring of trainees for educational purposes. Military cadets (n = 63) performed a patrol scenario (military convoy) in which they answered questions about their surroundings. Stressors (task difficulty, noise, lighting changes, social evaluations, electric muscle stimulation, and a simulated attack on the convoy) were stepwise added in four phases. Electrocardiogram, blood pressure, electrodermal activity, cortisol, and the cadets’ subjective threat/challenge appraisal were measured. We found that only the first phase caused a significant increase in physiological stress, as measured with heart rate, heart rate variability, and electrodermal activity. Physiological stress appeared to stay high in the second phase as well, but decreased to baseline level in the third and fourth phases, even though these phases were designed to be the most stressful. Cadets classified as threat responders based on physiological data (n = 3) scored significantly higher on subjective threat/challenge appraisal than those classified as challenge responders (n = 21). It seems that in the tested VR training scenario, the novelty of the scenario was the only effective stress stimuli, whereas the other implemented stressors did not cause a measurable physiological response. We conclude that if VR training scenarios are to be used for stress training, these should confront trainees with unpredictable but context-specific demands.@en

Real-time physiological stress monitoring would be a relevant addition to virtual reality (VR) training for high-risk professions, such as the military. VR is highly suitable for the implementation of such monitoring due to the controlled environment and the already used wearables. However, physiological stress measurements suffer from distortion due to physical activity. Therefore, we tested whether we can use accelerometry to correct non-invasively measured heart rate (HR) for physical activity in 23 soldiers who performed three room-clearing VR scenarios. These scenarios were dynamic, in that soldiers moved around in the VR environment by walking around in the real environment. In contrast to uncorrected HR, and HR corrected by subtracting baseline HR measured when walking, the accelerometry-corrected HR was able to significantly predict the participants’ self-reported stress in the scenarios, p = 0.047, R 2 = 0.11. Whereas uncorrected HR significantly predicted self-reported physical demand, p = 0.028, R 2 = 0.09, the accelerometry-corrected HR did not. All HR measures significantly predicted self-reported mental effort, which was most strongly the case for uncorrected HR, p < 0.001 R 2 = 0.42. These findings, in combination with the methods’ low sensitivity to motion artifacts and non-invasiveness, are very promising for its use to monitor stress in real-time during dynamic VR training scenarios. @en

Real-time physiological stress monitoring would be a relevant addition to virtual reality (VR) training for high-risk professions, such as the military. VR is highly suitable for the implementation of such monitoring due to the controlled environment and the already used wearables. However, physiological stress measurements suffer from distortion due to physical activity. Therefore, we tested whether we can use accelerometry to correct non-invasively measured heart rate (HR) for physical activity in 23 soldiers who performed three room-clearing VR scenarios. These scenarios were dynamic, in that soldiers moved around in the VR environment by walking around in the real environment. In contrast to uncorrected HR, and HR corrected by subtracting baseline HR measured when walking, the accelerometry-corrected HR was able to significantly predict the participants’ self-reported stress in the scenarios, p = 0.047, R 2 = 0.11. Whereas uncorrected HR significantly predicted self-reported physical demand, p = 0.028, R 2 = 0.09, the accelerometry-corrected HR did not. All HR measures significantly predicted self-reported mental effort, which was most strongly the case for uncorrected HR, p < 0.001 R 2 = 0.42. These findings, in combination with the methods’ low sensitivity to motion artifacts and non-invasiveness, are very promising for its use to monitor stress in real-time during dynamic VR training scenarios. @en

Real-time physiological stress monitoring would be a relevant addition to virtual reality (VR) training for high-risk professions, such as the military. VR is highly suitable for the implementation of such monitoring due to the controlled environment and the already used wearables. However, physiological stress measurements suffer from distortion due to physical activity. Therefore, we tested whether we can use accelerometry to correct non-invasively measured heart rate (HR) for physical activity in 23 soldiers who performed three room-clearing VR scenarios. These scenarios were dynamic, in that soldiers moved around in the VR environment by walking around in the real environment. In contrast to uncorrected HR, and HR corrected by subtracting baseline HR measured when walking, the accelerometry-corrected HR was able to significantly predict the participants’ self-reported stress in the scenarios, p = 0.047, R 2 = 0.11. Whereas uncorrected HR significantly predicted self-reported physical demand, p = 0.028, R 2 = 0.09, the accelerometry-corrected HR did not. All HR measures significantly predicted self-reported mental effort, which was most strongly the case for uncorrected HR, p < 0.001 R 2 = 0.42. These findings, in combination with the methods’ low sensitivity to motion artifacts and non-invasiveness, are very promising for its use to monitor stress in real-time during dynamic VR training scenarios. @en

Real-time physiological stress monitoring would be a relevant addition to virtual reality (VR) training for high-risk professions, such as the military. VR is highly suitable for the implementation of such monitoring due to the controlled environment and the already used wearables. However, physiological stress measurements suffer from distortion due to physical activity. Therefore, we tested whether we can use accelerometry to correct non-invasively measured heart rate (HR) for physical activity in 23 soldiers who performed three room-clearing VR scenarios. These scenarios were dynamic, in that soldiers moved around in the VR environment by walking around in the real environment. In contrast to uncorrected HR, and HR corrected by subtracting baseline HR measured when walking, the accelerometry-corrected HR was able to significantly predict the participants’ self-reported stress in the scenarios, p = 0.047, R 2 = 0.11. Whereas uncorrected HR significantly predicted self-reported physical demand, p = 0.028, R 2 = 0.09, the accelerometry-corrected HR did not. All HR measures significantly predicted self-reported mental effort, which was most strongly the case for uncorrected HR, p < 0.001 R 2 = 0.42. These findings, in combination with the methods’ low sensitivity to motion artifacts and non-invasiveness, are very promising for its use to monitor stress in real-time during dynamic VR training scenarios. @en

Real-time physiological stress monitoring would be a relevant addition to virtual reality (VR) training for high-risk professions, such as the military. VR is highly suitable for the implementation of such monitoring due to the controlled environment and the already used wearables. However, physiological stress measurements suffer from distortion due to physical activity. Therefore, we tested whether we can use accelerometry to correct non-invasively measured heart rate (HR) for physical activity in 23 soldiers who performed three room-clearing VR scenarios. These scenarios were dynamic, in that soldiers moved around in the VR environment by walking around in the real environment. In contrast to uncorrected HR, and HR corrected by subtracting baseline HR measured when walking, the accelerometry-corrected HR was able to significantly predict the participants’ self-reported stress in the scenarios, p = 0.047, R 2 = 0.11. Whereas uncorrected HR significantly predicted self-reported physical demand, p = 0.028, R 2 = 0.09, the accelerometry-corrected HR did not. All HR measures significantly predicted self-reported mental effort, which was most strongly the case for uncorrected HR, p < 0.001 R 2 = 0.42. These findings, in combination with the methods’ low sensitivity to motion artifacts and non-invasiveness, are very promising for its use to monitor stress in real-time during dynamic VR training scenarios. @en