Relationship Between Worker Interruptibility and Work Transitions Detected by Smartphone

Komuro, Kyohei; Fujimoto, Yuichiro; Fujita, Kinya

doi:10.1007/978-3-319-58077-7_53

Cited by 5 publications

(10 citation statements)

References 9 publications

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“…Previous research has already linked both, computer interaction and biometric sensors to mental load and interruptibility [28, 45,80,37,63,20]. To the best of our knowledge, our study is the first to compare these types of sensors in the field over a longer period of time.…”

Section: Discussionmentioning

confidence: 85%

“…Based on prior research as well as invasiveness, we chose to use two biometric sensors for our field study: the Polar H7 for recording HR and HRV data, which both have been linked to stress and cognitive load by previous research [1,28], and the Fitbit Charge 2 for recording HR (sampled every 10s), physical activity (sampled every 1min), and sleep (duration and quality metrics), which have been linked to interruptibility [30,18,45] and productivity [68,80].…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, accelerometer data has been used in several studies to detect physical activity and to show that interruptions are better delivered during moments recognized as activity transitions, e.g. when walking to another location [30,18,45]. A further and not yet fully studied factor of interruptibility is sleep, which has been shown to have a big impact on productivity and mood [68,80,49].…”

Section: Finding Opportune Moments For Interruptionsmentioning

confidence: 99%

See 2 more Smart Citations

Sensing Interruptibility in the Office

Züger

Müller

Meyer

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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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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Section: Discussionmentioning

confidence: 85%

Section: Methodsmentioning

confidence: 99%

Section: Finding Opportune Moments For Interruptionsmentioning

confidence: 99%

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Sensing Interruptibility in the Office

Züger

Müller

Meyer

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

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“…• Word form: "Screen (on, off)" [34], "Screen on/off" [16], "Screen on/off event" [20], "Screen state" [33] • Sentence form: "Time when the phone's screen was turned on or off" [24]. "Determine whether the user is currently using the phone" [29]…”

Section: Find and Extract Data Termmentioning

confidence: 99%

“…• Understanding a user's smartphone usage pattern [17,20,21,22,18] • Identifying a user's notification checking factors, how the user is affected by the notification, and message management and scheduling [23,24,16,25,26,27,28,29,30,31,32,33,34,35,36].…”

Section: Introductionmentioning

confidence: 99%

A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

Lee,

Park,

Lee

2021

Preprint

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Recently, there has been an increase in industrial and academic research on data-driven analytics with smartphones based on the collection of app usage patterns and surrounding context data. The Android mobile operating system utilizes Usage Statistics API (US API) and Accessibility Service API (AS API) as representative APIs to passively collect app usage data. These APIs are used for various research purposes as they can collect app usage patterns (e.g., app status, usage time, app name, user interaction state, and smartphone use state) and fine-grained data (e.g., user interface elements & hierarchy and user interaction type & target & time) of each application. In addition, other sensing APIs help to collect the user's surroundings context (location, network, ambient environment) and device state data, along with AS/US API. In this review, we provide insights on the types of mobile usage and sensor data that can be collected for each research purpose by considering Android built-in APIs and sensors (AS/US API, and other sensing APIs). Moreover, we classify the research purposes of the surveyed papers into four categories and 17 sub-categories, and create a hierarchical structure for data classification, comprising three layers. We present the important trends in the usage of Android's built-in APIs and sensors, including AS/US API, the types of data collected using the presented APIs, and discuss the utilization of mobile usage and sensor data in future research.

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A Survey of Attention Management Systems in Ubiquitous Computing Environments

Anderson

Hübener

Seipp

et al. 2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Today's information and communication devices provide always-on connectivity, instant access to an endless repository of information, and represent the most direct point of contact to almost any person in the world. Despite these advantages, devices such as smartphones or personal computers lead to the phenomenon of attention fragmentation, continuously interrupting individuals' activities and tasks with notifications. Attention management systems aim to provide active support in such scenarios, managing interruptions, for example, by postponing notifications to opportune moments for information delivery. In this article, we review attention management system research with a particular focus on ubiquitous computing environments. We first examine cognitive theories of attention and extract guidelines for practical attention management systems. Mathematical models of human attention are at the core of these systems, and in this article, we review sensing and machine learning techniques that make such models possible. We then discuss design challenges towards the implementation of such systems, and finally, we investigate future directions in this area, paving the way for new approaches and systems supporting users in their attention management.:2 • C. Anderson et al. Key takeaway points⋆ Attention is captured and steered by external and internal stimuli, while stimulus properties, such as its duration, location, intensity, etc., impact the user's reaction to the stimulus. Multimodal alert types, e.g., sound, light, vibration, and multiple device environments call for a coordinated judicious use of alerting in ubiquitous computing. ⋆ Limited cognitive capacities and threaded task processing imply that, in order to minimize disruptions, interruptions need to arrive at task boundaries or during routine tasks, should allow for task state rehearsal, and support context retrieval through hints presented to the user after an interruption. ⋆ Sensor data from ubiquitous computing devices reveals a user's location, physical activity, collocation with other people, and other information of a user's context that can be related to interruptibility. Next generation wearable devices and personalized machine learning models promise to bring us closer to direct inference of a user's cognitive processes. ATTENTION AND INTERRUPTION: DEFINITIONS AND STRATEGIESIn this section, we review definitions of the terms attention and interruption. We examine the connection between attention shifting and interruptions and discuss how interruptions can be handled through attention management systems in ubiquitous environments. What is Attention?There is no common understanding of attention in the literature. Attention is often considered as selective processing of incoming sensory information [33], with limited capacity [23] and reactive and deliberate processes [92]. Attention is also referred to as the ability to ignore irrelevant information [24]. The process of selecting stimuli can be voluntary or be steered by external events. The former ty...

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Relationship Between Worker Interruptibility and Work Transitions Detected by Smartphone

Cited by 5 publications

References 9 publications

Sensing Interruptibility in the Office

Sensing Interruptibility in the Office

A Systematic Survey on Android API Usage for Data-Driven Analytics with Smartphones

A Survey of Attention Management Systems in Ubiquitous Computing Environments

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