Sensing and indicating interruptibility in office workplaces

Abstract

In office workplaces, interruptions by co-workers, emails or instant messages are common. Many of these interruptions are useful as they might help resolve questions quickly and increase the productivity of the team. However, knowledge workers interrupted at inopportune moments experience longer task resumption times, lower overall performance, more negative emotions, and make more errors than if they were to be interrupted at more appropriate moments.
To reduce the cost of interruptions, several approaches have been suggested, ranging from simply closing office doors to automatically measuring and indicating a knowledge worker’s interruptibility - the availability for interruptions - to co-workers. When it comes to computer-based interruptions, such as emails and instant messages, several studies have shown that they can be deferred to automatically detected breakpoints during task execution, which reduces their interruption cost. For in-person interruptions, one of the most disruptive and time-consuming types of interruptions in office workplaces, the predominant approaches are still manual strategies to physically indicate interruptibility, such as wearing headphones or using manual busy lights. However, manual approaches are cumbersome to maintain and thus are not updated regularly, which reduces their usefulness.
To automate the measurement and indication of interruptibility, researchers have looked at a variety of data that can be leveraged, ranging from contextual data, such as audio and video streams, keyboard and mouse interaction data, or task characteristics all the way to biometric data, such as heart rate data or eye traces. While studies have shown promise for the use of such sensors, they were predominantly conducted on small and controlled tasks over short periods of time and mostly limited to either contextual or biometric sensors. Little is known about their accuracy and applicability for long-term usage in the field, in particular in office workplaces. In this work, we developed an approach to automatically measure interruptibility in office workplaces, using computer interaction sensors, which is one type of contextual sensors, and biometric sensors. In particular, we conducted one lab and two field studies with a total of 33 software developers. Using the collected computer interaction and biometric data, we used machine learning to train interruptibility models. Overall, the results of our studies show that we can automatically predict interruptibility with high accuracy of 75.3%, improving on a baseline majority classifier by 26.6%.
An automatic measure of interruptibility can consequently be used to indicate the status to others, allowing them to make a well-informed decision on when to interrupt. While there are some automatic approaches to indicate interruptibility on a computer in the form of contact list applications, they do not help to reduce in-person interruptions. Only very few researchers combined the benefits of an automatic measurement with a physical indicator, but their effect in office workplaces over longer periods of time is unknown. In our research, we developed the FlowLight, an automatic interruptibility indicator in the form of a traffic-light like LED placed on a knowledge worker's desk. We evaluated the FlowLight in a large-scale field study with 449 participants from 12 countries. The evaluation revealed that after the introduction of the FlowLight, the number of in-person interruptions decreased by 46% (based on 36 interruption logs), the awareness on the potential harm of interruptions was elevated and participants felt more productive (based on 183 survey responses and 23 interview transcripts), and 86% remained active users even after the two-month study period ended (based on 449 online usage logs).
Overall, our research shows that we can successfully reduce in-person interruption cost in office workplaces by sensing and indicating interruptibility. In addition, our research can be extended and opens up new opportunities to further support interruption management, for example, by the integration of other more accurate biometric sensors to improve the interruptibility model, or the use of the model to reduce self-interruptions.