Collective allostatic load measures for teams. (May 2021)

• Record Type: Journal Article
• Authors: Davaslioglu, Kemal; Pokorny, Bob; Sagduyu, Yalin E.; Molintas, Henrik; Soltani, Sohraab; Grossman, Rebecca; Bowers, Clint
• Is Part Of: Structural health monitoring. Volume 20:Number 3(2021)
• Page Start: 768
• Page End: 781
• Publication Date: 2021-05
• Subjects: Allostatic load -- collective allostatic load -- stress -- team performance -- performance monitoring -- physiological sensors -- communication patterns -- interventions -- recommendations; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/1475921719884864
• Languages: English
• ISSNs: 1475-9217

Abstract

Teams in action, such as emergency responders and medical personnel, are challenged with environments that are characterized by time pressure, rapidly unfolding events, high information processing demand, and severe consequences of wrong decisions. Such environments in general have adverse effects on team performance. To mitigate this problem and increase the performance and resilience of teams, we developed the Collective Allostatic Load Measures system. Collective Allostatic Load Measures system collects, aggregates, and analyzes multimodal data, and provides recommendation and intervention mechanisms under acute and chronic stressors. The key innovation in Collective Allostatic Load Measures is the integration of multimodal sensing capabilities with accurate algorithms that can process sequential multimodal data from heterogeneous sensors. We built a prototype of Collective Allostatic Load Measures that incorporates the core functionalities that can assess allostatic load at the team level, namely collective allostatic load. Collective Allostatic Load Measures includes a set of commercial off-the-shelf sensors that record an individual's physiological responses, a speech processing module that can extract the communication patterns of a team, a machine-learning based computational analysis module, a mobile phone app, and a web-based dashboard for visualization. Collective Allostatic Load Measures provides near real-time quantitative measurement of collective allostatic load that is leveraged to improve team performance and resilience by recommending interventions.