@article{Endedijk_Hoogeboom_Groenier_de Laat_van Sas_2018, title={Using sensor technology to capture the structure and content of team interactions in medical emergency teams during stressful moments}, volume={6}, url={https://journals.sfu.ca/flr/index.php/journal/article/view/353}, DOI={10.14786/flr.v6i3.353}, abstractNote={<p class="AbstractText"><span lang="EN-US">In healthcare, action teams are carrying out complex medical procedures in intense and unpredictable situations to save lives. Previous research has shown that efficient communication, high-quality coordination, and coping with stress are particularly essential for high performance. However, precisely and objectively capturing these team interactions during stressful moments remains a challenge. In this study, we used a multimodal design to capture the structure and content of team interactions of medical teams at moments of high arousal during a simulated crisis situation. Sociometric badges were used to measure the structure of team interactions, including speaking time, overlapping speech and conversational imbalance. Video coding was used to reveal the content of the team interactions. Furthermore, the Empatica E4 was used to unobtrusively measure the team leader’s skin conductance to identify moments of high arousal. In total, 21 four-person teamsof technical medicine students in the Netherlands were monitored in a simulation environment while they diagnosed and managed a patient with cardiac arrest. Outcomes of this exploratory study revealed that more effective teams showed greater conversational imbalance than less effective teams, but during moments of high arousal the opposite was found. Also, a number of differences were found for the content of team interaction. Combining sensor technology with traditional measures can enhance our understanding of the complex interaction processes underlying effective team performance, but technological advances together with more knowledge about the simultaneous application of these methods are needed to tap into the full potential of wearable sensor technology in team research.</span></p>}, number={3}, journal={Frontline Learning Research}, author={Endedijk, Maaike and Hoogeboom, Marcella and Groenier, Marleen and de Laat, Stijn and van Sas, Jolien}, year={2018}, month={Dec.}, pages={123–147} }