Monitoring Emergent Duress Under Long-Lasting Allostasis (MEDULLA)

Collective allostatic load (CAL) is known to cause the degredation of team performance in a variety of contexts. SIFT is proposing MEDULLA (Monitoring Emergent Duress Under Long-Lasting Allostasis), 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 propose to 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 the collaborative game task, and team performance metrics through our virtual-reality based Mentalblock 3-D puzzle game. At the end of Phase II we will have a prototype system that can integrate these data sources and quickly and accurately assess CAL.