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Sensing Interruptibility in the Office: A Field Study on the Use of Biometric and Computer Interaction Sensors


Züger, Manuela; Müller, Sebastian; Meyer, André; Fritz, Thomas (2018). Sensing Interruptibility in the Office: A Field Study on the Use of Biometric and Computer Interaction Sensors. In: CHI 2018, Montreal, 21 April 2018 - 26 April 2018.

Abstract

Knowledge workers experience many interruptions during their work day. Especially when they happen at inopportune moments, interruptions can incur high costs, cause time loss and frustration. Knowing a person's interruptibility allows optimizing the timing of interruptions and minimize disruption. Recent advances in technology provide the opportunity to collect a wide variety of data on knowledge workers to predict interruptibility. While prior work predominantly examined interruptibility based on a single data type and in short lab studies, we conducted a two-week field study with 13 professional software developers to investigate a variety of computer interaction, heart-, sleep-, and physical activity-related data. Our analysis shows that computer interaction data is more accurate in predicting interruptibility at the computer than biometric data (74.8% vs. 68.3% accuracy), and that combining both yields the best results (75.7% accuracy). We discuss our findings and their practical applicability also in light of collected qualitative data.

Abstract

Knowledge workers experience many interruptions during their work day. Especially when they happen at inopportune moments, interruptions can incur high costs, cause time loss and frustration. Knowing a person's interruptibility allows optimizing the timing of interruptions and minimize disruption. Recent advances in technology provide the opportunity to collect a wide variety of data on knowledge workers to predict interruptibility. While prior work predominantly examined interruptibility based on a single data type and in short lab studies, we conducted a two-week field study with 13 professional software developers to investigate a variety of computer interaction, heart-, sleep-, and physical activity-related data. Our analysis shows that computer interaction data is more accurate in predicting interruptibility at the computer than biometric data (74.8% vs. 68.3% accuracy), and that combining both yields the best results (75.7% accuracy). We discuss our findings and their practical applicability also in light of collected qualitative data.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:26 April 2018
Deposited On:24 Apr 2018 09:49
Last Modified:25 Apr 2019 07:06
Publisher:s.n.
OA Status:Green
Other Identification Number:merlin-id:16315

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