ACM Transactions on Computer-Human Interaction (TOCHI)
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Interruptions in the workplace are becoming increasingly prevalent due to the proliferation of proactive behavior within communication applications and collaborative practices. Interruptions caused by notifications from communication applications (email, instant messaging clients) or operating systems, phone calls and collocated individuals often cause a forced break in the user's activity as they may require action on the user's behalf or cause them to switch their attention to the incoming request. Research has shown that interruptions at inopportune moments often result in substantial costs to users and their tasks, e.g. frustration and reduced productivity. However, information conveyed by notifications is also often beneficial to users. A current thrust within the HCI community has been to develop solutions that reduce the cost of interruption caused by notifications while maintaining their utility. In this work, we focus on one class of interruption, notifications in the desktop, and present one solution to managing such notifications— intelligently timing their delivery. Our solution is based on a deep theoretical understanding of how humans process information and what moments during a user's task execution exhibit lower mental workload. We leverage breakpoints, transitions between units of action as potential moments for presenting notifications, as we empirically show these moments to have lower mental workload. Through a series of empirical studies, we demonstrate how presenting interruptions at breakpoints lowers interruption costs and how the cost varies based on the granularity of the breakpoint, how these breakpoints can be detected using known task structures and how breakpoints can be detected without any knowledge of the underlying task. We develop O ASIS, a computational framework that can detect and differentiate three levels of breakpoints with reasonable accuracy without requiring any complex machinery. OASIS is the first system of its kind that can detect breakpoints and schedule notifications. We evaluate how O ASIS picks breakpoints for novel users and novel tasks, and how scheduling notifications at breakpoints reduces interruption costs. This work provides the first empirical evidence that intelligent notification management benefits the end user and contributes new lessons for designing effective notification management systems.