Adam Biggs ^1* Andrew E. Jensen ² Karen R. Kelly ²

• ¹ United States Department of the Navy, Washington D.C., United States
• ² Naval Health Research Center, San Diego, California, United States

Shooting simulations provide an excellent opportunity to train use-of-force decisions in controlled environments. Recently, military and law enforcement organizations have expressed a growing desire to integrate physiological measurement into simulations for training and feedback purposes. Although participants can easily wear physiological monitors in these scenarios, direct implementation into training may not be simple. Theoretical problems exist in the ultra-short heart rate variability windows associated with use-of-force training, and practical problems emerge as existing scenario libraries at training organizations were not designed for physiological monitoring. Therefore, the current study explored the challenges and possibilities associated with direct implementation of physiological monitoring into an existing library of firearms training scenarios. The results revealed lower heart rate variability (approximately 6%)occurred in scenarios where participants did not have to fire weapons, indicating that don't-shoot scenarios may actually impose more cognitive stress on shooters. Additional evidence further demonstrated how both behavioral and physiological factors could be used concomitantly to predict unintentionally firing on non-hostile actors. However, behavioral measures were more predictive (e.g., β = .221) than physiological measures (e.g., β = -.132) when the latter metrics were limited to specific scenarios. Qualitative results suggest that simply applying physiological monitoring to existing shooting simulations may not yield optimal results because it would be difficult to directly integrate physiological measurement in a meaningful way without redesigning some elements of the simulations, the training procedure, or both. Therefore, future use-of-force shooting simulations should consider designing novel scenarios around the physiological measurement rather than directly implementing physiological assessments into existing libraries of scenarios.