MEDULLA: Monitoring Emergent Duress Under Long-Lasting Allostasis

Collective allostatic load (CAL) is known to cause the degradation of team performance in a variety of contexts and is influenced by social variables. Under MEDULLA we use a multi-method approach based on physiological, cognitive, and   behavioral measures to develop and fine-tune algorithms for assessing CAL and predicting changes to CAL that can affect team performance. In Phase I we have developed preliminary algorithms based on pre-existing data sets of teams operating in long-term stressful conditions such as the HI-SEAS Mars analog habitat in Hawaii. For Phase II, we will validate and refine these algorithms through four one-month HI-SEAS missions during which we will collect data on various physiological parameters known to correlate with stress (e.g., cortisol, heart rate, etc.), psychological parameters using team assessment surveys such as the Dynamic Identity Fusion Index, linguistic metrics derived from journals and from audio communication during a collaborative game task in a VR environment.