A critically understudied aspect of team adaptation is the phenomenon of coordination breakdowns (CBs). CBs are characterized by a temporarily diminished ability to function effectively as a team. However, team research currently lacks robust methods for identifying and anticipating when teams transition from functioning effectively to a CB. With the current study, we aim to deepen our understanding of how team coordination dynamics across various interaction modalities reflect CBs and effective teamwork, utilizing a three-pronged research approach. First, we used audiovisual data from four person teams involved in a stressful collaborative game task to manually identify CBs. Second, we applied team coordination dynamics measures to physiological and speech data obtained during the task to computationally identify CBs. Third, the latter output was used as input for transition anticipation methods, generating computationally anticipated CBs. Our findings contribute theoretically and methodologically to the systematic investigation of CBs.

Team cognition is an essential component of team functioning. Although traditionally assessed through (retrospective) questionnaires or ratings, there is increasing interest in using real-time data (e.g., skin conductance, movement) to assess team cognition through team coordination dynamics (TCD). TCD involve two or more processes or elements of the team that covary across time and conditions (e.g. synchrony or alignment). Various methods and modalities have been used to calculate TCD that show connections with team outcomes and cognition. Yet, it is unclear which ones are the most functional. In our research, we use data from four-persons teams engaging in a collaborative game to calculate various TCD indices (entropy, MdRQA, coherence, synchrony coefficient) for several modalities (skin conductance, heart rate, movement) and compare them to metrices of team cognition obtained through questionnaires. We aim for our research to facilitate the use of (multimodal) TCD for monitoring and managing team functioning.

Team coordination is essential for effective performance during critical, stressful events. To better understand processes and states involved at multiple levels of team coordination, we assessed the correspondence between low- and high-level coordination in teams participating in simulation-based medical team training. We computed a measure of low-level team coordination with Multidimensional Recurrence Quantification Analysis, applied to arm movement, heart rate, and skin conductance data. High-level team coordination was captured by annotating video recordings for explicit and implicit, information and action coordination. Three linear mixed-effects model were run, each predicting a type of low-level coordination, based on high-level coordination annotations, accounting for multiple observations per team. Our findings showed that, compared to periods without annotated coordination, explicit- and implicit- information coordination corresponded to significantly different low-level team coordination across each of the studied modalities. Further research is required to assess additional factors related to the temporal variability observed in low-level coordination.