Objective A method for detecting real-time changes in team cognition in the form of significant communication reorganizations is described. We demonstrate the method in the context of scenario-based simulation training. Background We present the dynamical view that individual- and team-level aspects of team cognition are temporally intertwined in a team’s real-time response to challenging events. We suggest that this real-time response represents a fundamental team cognitive skill regarding the rapidity and appropriateness of the response, and methods and metrics are needed to track this skill. Method Communication data from medical teams (Study 1) and submarine crews (Study 2) were analyzed for significant communication reorganization in response to training events. Mutual information between team members informed post hoc filtering to identify which team members contributed to reorganization. Results Significant communication reorganizations corresponding to challenging training events were detected for all teams. Less experienced teams tended to show delayed and sometimes ineffective responses that more experienced teams did not. Mutual information and post hoc filtering identified the individual-level inputs driving reorganization and potential mechanisms (e.g., leadership emergence, role restructuring) underlying reorganization. Conclusion The ability of teams to rapidly and effectively reorganize coordination patterns as the situation demands is a team cognitive skill that can be measured and tracked. Application Potential applications include team monitoring and assessment that would allow for visualization of a team’s real-time response and provide individualized feedback based on team member’s contributions to the team response.

This study provides evidence of the efficacy of internet-based psychoeducation interventions for bipolar disorder in reducing depressive symptoms. Further investigation is needed to assess effectiveness in a public program.

By its very nature, much of teamwork is distributed across, and not stored within, interdependent people working toward a common goal. In this light, we advocate a systems perspective on teamwork that is based on general coordination principles that are not limited to cognitive, motor, and physiological levels of explanation within the individual. In this article, we present a framework for understanding and modeling teams as dynamical systems and review our empirical findings on teams as dynamical systems. We proceed by (a) considering the question of why study teams as dynamical systems, (b) considering the meaning of dynamical systems concepts (attractors; perturbation; synchronization; fractals) in the context of teams, (c) describe empirical studies of team coordination dynamics at the perceptual-motor, cognitive-behavioral, and cognitive-neurophysiological levels of analysis, and (d) consider the theoretical and practical implications of this approach, including new kinds of explanations of human performance and real-time analysis and performance modeling. Throughout our discussion of the topics we consider how to describe teamwork using equations and/or modeling techniques that describe the dynamics. Finally, we consider what dynamical equations and models do and do not tell us about human performance in teams and suggest future research directions in this area.

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While e-health initiatives are poised to revolutionize delivery and access to mental health care, conducting clinical research online involves specific contextual and ethical considerations. Face-to-face psychosocial interventions can at times entail risk and have adverse psychoactive effects, something true for online mental health programs too. Risks associated with and specific to internet psychosocial interventions include potential breaches of confidentiality related to online communications (such as unencrypted email), data privacy and security, risks of self-selection and self-diagnosis as well as the shortcomings of receiving psychoeducation and treatment at distance from an impersonal website. Such ethical issues need to be recognized and proactively managed in website and study design as well as treatment implementation. In order for online interventions to succeed, risks and expectations of all involved need to be carefully considered with a focus on ethical integrity.

MoodSwings 2.0 is a self-guided online intervention for bipolar disorder. The intervention incorporates technological improvements on an earlier validated version of the intervention (MoodSwings 1.0). The previous MoodSwings trial provides this study with a unique opportunity to progress previous work, whilst being able to take into consideration lesson learnt, and technological enhancements. The structure and technology of MoodSwings 2.0 are described and the relevance to other online health interventions is highlighted. An international team from Australia and the US updated and improved the programs content pursuant to changes in DSM-5, added multimedia components and included larger numbers of participants in the group discussion boards. Greater methodological rigour in this trial includes an attention control condition, quarterly telephone assessments, and red flag alerts for significant clinical change. This paper outlines these improvements, including additional security and safety measures. A 3 arm RCT is currently evaluating the enhanced program to assess the efficacy of MS 2.0; the primary outcome is change in depressive and manic symptoms. To our knowledge this is the first randomised controlled online bipolar study with a discussion board attention control and meets the key methodological criteria for online interventions

Project overview. The current study focuses on analyzing team flexibility by measuring entropy (where higher values correspond to system reorganization and lower values correspond to more stable system organization) across all-human teams and Human-Autonomy Teams (HAT). We analyzed teams in the context of a fully-fledged synthetic agent that acts as a pilot for a three-agent Remotely Piloted Aircraft System (RPAS) ground crew. The synthetic agent must be able to communicate and coordinate with human teammates in a constructive and timely manner to be effective. This study involved three heterogeneous team members who had to take photographs of target waypoints and communicate via a text-based communication system. The three team members’ roles were: 1) navigator provides information about flight plan with speed and altitude restrictions at each waypoint; 2) pilot adjusts altitude and airspeed to control the Remotely Piloted Aircraft (RPA), and negotiates with the photographer about the current altitude and airspeed to take good photos for the targets; and 3) photographer screens camera settings, and sends feedback to other team members regarding the target photograph status. The three conditions differed based on the manipulation of the pilot role: 1) Synthetic – the pilot was the synthetic agent, 2) Control – the pilot was a randomly assigned participant, and 3) Experimenter – the pilot was a well-trained experimenter who focused on sending and receiving information in a timely manner. The goal of this study is to examine how overall RPAS flexibility across HATs and all-human teams are associated with Team Situation Awareness (TSA). Method. There were 30 teams (10-teams per condition): control teams consisted of three participants randomly assigned to each role; synthetic and experimenter teams included two participants randomly assigned to the navigator and photographer roles. The experiment took place over five 40-minute missions, and the goal was to take as many “good” photos of ground targets as possible while avoiding alarms and rule violations. We obtained several measures, including mission and target level team performance scores, team process measures (situation awareness, process ratings, communication and coordination), and other measures (teamwork knowledge, workload, and demographics). We first estimated amount of system reorganization of the RPAS via an information entropy measure, i.e., the number of arrangements the system occupied over a given period of time (Shannon & Weaver, 1975). Based on information entropy, we defined four layers to represent the RPAS (Gorman, Demir, Cooke, & Grimm, In Review): 1) communications - the chat-based communication among team members; 2) vehicle - the RPA itself, e.g., speed, altitude; 3) control - interface between the RPA and the user; and system - the overall activity of the sub-layers. Then, we looked at the relationship between layers and TSA, which was based on successfully overcoming and completing ad hoc embedded target waypoints. Results and conclusion. Overall, the experimenter teams adapted to more roadblocks than the synthetic teams, who were equivalent to control teams (Demir, McNeese, & Cooke, 2016). The findings indicate that: 1) synthetic teams demonstrated rigid systems level activity, which consisted of less reorganization of communication, control and vehicle layers as conditions changed, which also resulted in less adaptation to roadblocks; 2) control teams demonstrated less communication reorganization, but more control and vehicle reorganization, which also resulted in less adaptation to roadblocks; and 3) experimenter teams demonstrated more reorganization across communication, control and vehicle layers, which resulted in better adaptation to roadblocks. Thus, the ability of a system to reorganize across human and technical layers as situations change is needed to adapt to novel conditions of team performance in a dynamic task

Real-time analysis of team communication data to detect anomalies and/or perturbations in the team environment is an ideal method to improve on teams’ interactions and responses to potential crises. In this paper, we demonstrate a method to detect anomalies through observing communication patterns of neurosurgery teams. We simulated the real-time process by analyzing previously collected communication data to assess the effectiveness of a nonlinear prediction model to detect anomalies. We compared predicted values of communication determinism (a measure of how organized communication patterns are) to previous values in each team’s time series. These deviations formed a separate root mean square error (RMSE) time series, and we examined the magnitudes of the RMSE time series at the points of known perturbations. Additionally, we examined the effect of window size on perturbation detection. We found that our nonlinear prediction model accurately detected the perturbations and shows promise for future real-time analysis.