Dismounted soldiers often need to perform physical tasks and cognitive tasks concurrently and/or sequentially. Previous research on the effects of concurrent and subsequent physical activity on cognitive performance has focused on physical activities with little complexity and variation, such as treadmill running and cycling. However, physical activities of infantry units often varies in complexity and intensity, especially in urban settings. Methods: In this pilot study, a design was applied by letting soldiers (n = 10) individually run the varied LEAP obstacle course while conducting cognitive tasks (N-back, Mackworth clock detection task, or self-reports) compared to cognitive performance measured during more simple physical activity (i.e., running on flat – obstacle-free terrain), and compared to no physical activity (i.e., sitting). Results: The current pilot study showed that cognitive performance (N-back misses) during physical activity was indeed most affected by the LEAP condition, less by the running condition, and least by the sitting condition, confirming cognitive-energetic theories. Considering a low number of participants that effected the power of our results, the differences between the conditions were statistically significant and the effect sizes were large (r > .85). Self-reported mental effort was only significantly higher in the physically active conditions compared to sitting, also with a large effect size (r >.85). Cognitive performance (N-back misses) subsequent to physical activity were only significantly differed between the physically active conditions and sitting, with large effect sizes, (r’s > .80) confirming arousal theories. The subjective mental effort indicated the same pattern in differences between conditions (Z=2.80, p=0.005). The cognitive Mackworth clock detection task revealed only a significant difference between the LEAP condition and sitting, perhaps indicating that the LEAP condition had a more pronounced positive effect (Z=2.68, p=0.007; r=.85). Conclusions: Though Corona restrictions limited the number of participants, the results seem to indicate that it is wise to avoid concurrent performance of cognitive tasks and intense or complex physical tasks, if possible, when also required to perform cognitively. In addition, sequencing cognitive tasks directly after physical activity could possibly be used to enhance cognitive performance. Military Impact: Sequencing physical activity in a specific order can have a positive impact on cognitive performance in military personnel. The LEAP obstacle environment is able to simulate complex urban terrain, thereby offering the moderation of physical activity to improve cognitive performance. Hence, the LEAP course might also a platform to evaluate the effect of physical activity as Human Augmentation technology.

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.