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Advancing team science by social sensor-based measurement

English title Advancing team science by social sensor-based measurement
Applicant Kolbe Michaela
Number 177069
Funding scheme R'EQUIP
Research institution Simulationszentrum Universitätsspital Zürich
Institution of higher education University of Zurich - ZH
Main discipline Psychology
Start/End 01.04.2018 - 30.09.2019
Approved amount 40'009.00
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All Disciplines (2)


Keywords (5)

Patient safety; Teams; Social sensors; Measurement; High-risk environments

Lay Summary (German)

Was macht gute Teamarbeit in der Medizin aus und wie kann man das so einfach und genau wie möglich messen? In diesem Projekt untersuchen wir die Funktionalität Sensoren-basierter Messmethodik: Inwieweit lässt sich die Zusammenarbeit im Team sinnvoll und zuverlässig anhand von Sensoren (z.B. Herzraten-Variabilität, Bewegung im Raum, Distanz) messen?
Lay summary
Die Zusammenarbeit im Team zu messen ist bisher zumeist aufwendig (z.B. Verhaltensbeobachtung) oder sehr subjektiv (z.B. Selbstberichte). Zur Verbesserung der Teamarbeit in der Medizin ist es nötig, Teamarbeit zügig und zuverlässig messen zu können, z.B. im Rahmen von Simulationstrainings. In diesem Projekt untersuchen wir, inwieweit sich Sensoren für diese Messung eignen: Wie lässt sich die Zusammenarbeit im Team sinnvoll und zuverlässig anhand von Sensoren (z.B. Herzraten-Variabilität, Bewegung im Raum, Distanz) messen? In welchem Zusammenhang stehen Sensoren-bezogene Daten zu anderen Indikatoren, z.B. verbale Kommunikation?

Die Ergebnisse des Projektes sollen die Messung von Teamarbeit präzisieren und vereinfachen. Damit soll ein Beitrag zur Effektivität simulations-basierter Teamtrainings und Patientensicherheit geleistet werden. 
Direct link to Lay Summary Last update: 27.11.2017

Responsible applicant and co-applicants


Laborious but Elaborate: The Benefits of Really Studying Team Dynamics
Kolbe Michaela, Boos Margarete (2019), Laborious but Elaborate: The Benefits of Really Studying Team Dynamics, in Frontiers in Psychology, 10, 0.


Group / person Country
Types of collaboration
Product Development Group, Dep. of Mechanical and Process Engineering, ETH Zurich Switzerland (Europe)
- in-depth/constructive exchanges on approaches, methods or results
- Research Infrastructure

Scientific events

Active participation

Title Type of contribution Title of article or contribution Date Place Persons involved
Kolloquium Individual talk Soziale Sensoren in der Teamforschung 04.07.2019 Technische Universität Dortmund, Germany Burtscher Michael Josef;
SYMPOSIUM ON INTERDISCIPLINARY INSIGHTS INTO GROUP DYNAMICS Talk given at a conference Challenges of applied team research 05.12.2018 Delft, Netherlands Kolbe Michaela;

Knowledge transfer events

Active participation

Title Type of contribution Date Place Persons involved
INSIM Conference Talk 13.09.2018 Aschaffenburg, Germany Kolbe Michaela;

Associated projects

Number Title Start Funding scheme
169785 Speaking Up for patient safety: Investigating the social dynamics of voice behavior in healthcare 01.12.2016 Project funding (Div. I-III)


The proposed new research equipment for advancing team science by social sensor-based measurement (SBM) is motivated by three major challenges: 1) the static nature of research on team dynamics, 2) the lack of team members’ Speaking Up with concerns, ideas, and opinions in healthcare and other industries, and 3) the limited availability of understandable evidence of ad-hoc team dynamics and functioning for simulation-based team training (SBT) and team learning. One major reason for the scarcity of empirical team dynamics research is the lack of feasible and reliable methods for team data collection and analysis. In view of these limitations, social sensors have been recently proposed as a potential remedy as they provide high-frequency, low-cost, and unobtrusive measurement of behavioral data in teams. SBM includes the processing of perceptual- and physical-sensor data using smartphones, new types of wearable devices, and instrumented environments. However, cap-turing, integrating, and analyzing these data is cumbersome, resource-intense, and prone to error: behavior (e.g., actions, body movement activity, interpersonal distance, spatial behavior), communication (e.g., verbal statements), physiological signals (e.g., heart rate variability) are typically measured separately with separate respective time stamps. This requires a post-hoc data synchronization process that is not only very time- and resource intense but also prone to error due to potential mismatch of time strings or lack of synchronization. In addition, match-ing, synchronizing, and integrating physiological signals are usually not possible within one system. The proposed new SBM equipment eliminates these difficulties by allowing for syn-chronous collection and analysis of behavior, communication, and physiological signal data.We outline three particular research projects for which the proposed equipment is essential. The first project includes four studies investigating the social dynamics of Speaking Up-behavior in healthcare teams (SNF grant 10001C_169785). The second project includes a series of experiments analyzing the interaction dynamics that underlie the emergence of spon-taneous collaborations. The third project tests an intervention to increase Speaking Up for hand hygiene during anesthesia induction as means for infection control. These projects repre-sent prototypical examples of both basic and applied team research. Thus, similar and follow-up projects will equally benefit from the possibilities of new equipment. The new equipment will not only save time and resources but allow for addressing a number of important and pressing research questions that cannot be investigated using conventional measurement approaches.