A Review of Using Wearable Technology to Assess Team Functioning and Performance

Halgas, Elwira; Eijndhoven, Kyana van; Gevers, Jmp Josette; Wiltshire, Travis J.; Westerink, Joyce H. D. M.; Rispens, Sonja

doi:10.1177/10464964221125717

Cited by 11 publications

(20 citation statements)

References 77 publications

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“…These teams were informed that this participant could not play the game anymore but could communicate with the team. Differences between conditions are to be examined in another study that is part of the overarching project (other work related to this project are Hałgas et al, 2022; Wiltshire et al, 2022). After the first half and at the end of the game, participants completed a series of surveys that were not the focus of the current study.…”

Section: Methodsmentioning

confidence: 99%

“…Team coordination dynamics describe the self-organized coordination of interacting team members within a team, to attain a larger objective that dynamically changes based on environmental cues (Demir et al, 2018; Gorman & Amazeen, 2010; Kelso, 1994). Such coordination dynamics can be measured in real time (e.g., through wearable technology) across multiple signals such as electrodermal activity (Haataja et al, 2018), heart rate variability (Henning et al, 2001), movements (Chang et al, 2017), and neurophysiology (Stevens et al, 2009), and importantly, coordination in these signals has been found to be a predictor of team functioning and performance (for extensive literature reviews, see (Hałgas et al, 2022; Kazi et al, 2021; Palumbo et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Given that team CBs are instances of ineffective team functioning, which can affect team performance, the relationship between team coordination dynamics and team CBs should be further examined for the purpose of prediction. However, these findings also suggest that the different signals used to derive team coordination dynamics from, and the different measures to achieve this, determine what component of team functioning and performance can be predicted (Hałgas et al, 2022). Consequently, an important step in identifying team CBs, is examining which signals and measures of coordination are meaningful (Wiltshire et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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A Computational Approach to Examining Team Coordination Breakdowns During Crisis Situations

Eijndhoven

Wiltshire

Halgas

et al. 2023

Journal of Cognitive Engineering and Decision Making

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During crisis situations, teams are more prone to coordination breakdowns that are characterized by a temporary, diminished ability to function effectively as a team. However, team research currently lacks robust approaches for identifying transitions from effective team functioning to coordination breakdowns. With the current study, we aimed to develop such robust approaches, and to deepen our understanding of how team coordination dynamics across various physiological signals reflect coordination breakdowns. Consequently, we used audiovisual data from four-person teams involved in a stressful collaborative game task to manually identify coordination breakdowns. Next, we set out to computationally identify coordination breakdowns by applying continuous measures of team coordination (windowed synchronization coefficient and multidimensional recurrence quantification analysis) to photoplethysmogram and electrodermal activity data obtained during the task, and identifying transitions therein with change point and nonlinear prediction algorithms. We found that our computational coordination breakdown identification approaches can identify up to 96% of the manually identified coordination breakdowns although our results also show that the precision of our approaches falls far behind. Our findings contribute theoretically and methodologically to the systematic investigation of coordination breakdowns, which may ultimately facilitate the support of teams in responding to and mitigating negative consequences of crisis situations.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Computational Approach to Examining Team Coordination Breakdowns During Crisis Situations

Eijndhoven

Wiltshire

Halgas

et al. 2023

Journal of Cognitive Engineering and Decision Making

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“…To examine the relationship between TCBs and team performance, the data of 33 teams met the data quality criteria for inclusion (71 females, 60 males, 1 other; M age = 21.51; age range = [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. The data of a team was excluded if the audiovisual recordings did not allow for TCB annotation, such as when the webcam recording of at least one participant or a teams' game recording was missing or incomplete.…”

Section: Participantsmentioning

confidence: 99%

Examining Physiological Features Underlying Team Coordination Breakdowns

Eijndhoven,

Wiltshire,

Hałgas

et al. 2023

Preprint

Self Cite

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Team coordination breakdowns (TCBs) generally reflect episodes of ineffective team functioning, resulting in suboptimal team performance. Computational identification of TCBs enables us to examine the underlying characteristics of suboptimal performance, and to potentially deliver real-time feedback to teams. Especially in time-critical crisis situations, such feedback can be invaluable. Previous studies found difficulties in distinguishing between coordination patterns that indicate TCBs, and patterns that might indicate other aspects of teamwork. Subsequently, we examined features capturing underlying characteristics of team coordination, based on multiple physiological signals and coordination measures, to identify TCBs. Our multi-methodological approach allowed us to identify features that are important for TCB identification. We also observed that distributions of feature data related and unrelated to TCBs were significantly different, indicating that the features captured underlying patterns in team coordination data. In addition, our results indicated that team performance, measured as goal achievement duration, is more severely compromised when TCBs last longer. By showing a relationship between TCBs and goal achievement duration, as well as understanding the key features of these TCBs, our study contributes to deepening our understanding of TCBs, and supporting effective team functioning and performance.

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“…Without measuring teamwork, feedback and debriefing conversations—and ultimately learning—will be limited ( Rosen et al, 2008 ; Rudolph et al, 2008 ; Fey et al, 2022 ). However, identifying relevant teamwork behaviors and tracking them in complex, dynamic, and fast-paced simulated clinical situations is challenging ( Halgas et al, 2022 ). Observing and measuring teamwork in action is prone to bias and constitutes a significant cognitive load even for trained instructors ( Caverni et al, 1990 ; Greig et al, 2014 ; Uher and Visalberghi, 2016 ; Fraser et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Measuring teamwork for training in healthcare using eye tracking and pose estimation

et al. 2023

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Teamwork is critical for safe patient care. Healthcare teams typically train teamwork in simulated clinical situations, which require the ability to measure teamwork via behavior observation. However, the required observations are prone to human biases and include significant cognitive load even for trained instructors. In this observational study we explored how eye tracking and pose estimation as two minimal invasive video-based technologies may measure teamwork during simulation-based teamwork training in healthcare. Mobile eye tracking, measuring where participants look, and multi-person pose estimation, measuring 3D human body and joint position, were used to record 64 third-year medical students who completed a simulated handover case in teams of four. On one hand, we processed the recorded data into the eye contact metric, based on eye tracking and relevant for situational awareness and communication patterns. On the other hand, the distance to patient metric was processed, based on multi-person pose estimation and relevant for team positioning and coordination. After successful data recording, we successfully processed the raw videos to specific teamwork metrics. The average eye contact time was 6.46 s [min 0 s – max 28.01 s], while the average distance to the patient resulted in 1.01 m [min 0.32 m – max 1.6 m]. Both metrics varied significantly between teams and simulated roles of participants (p < 0.001). With the objective, continuous, and reliable metrics we created visualizations illustrating the teams’ interactions. Future research is necessary to generalize our findings and how they may complement existing methods, support instructors, and contribute to the quality of teamwork training in healthcare.

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A Review of Using Wearable Technology to Assess Team Functioning and Performance

Cited by 11 publications

References 77 publications

A Computational Approach to Examining Team Coordination Breakdowns During Crisis Situations

A Computational Approach to Examining Team Coordination Breakdowns During Crisis Situations

Examining Physiological Features Underlying Team Coordination Breakdowns

Measuring teamwork for training in healthcare using eye tracking and pose estimation

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