Poster: Exploring User Contexts and Needs for Context-aware Smartphone Distraction Management Inyeop Kim KAIST South Korea kiyeob4416@kaist.ac.kr Hwarang Goh Inha University South Korea hrgoh@nsl.inha.ac.kr Youngtae Noh ∗ Inha University South Korea ytnoh@inha.ac.kr Uichin Lee ∗ KAIST South Korea uclee@kaist.ac.kr ABSTRACT Interruptions, such as disruptive smartphone notifcations or habit- ual smartphone use, can cause people to make mistakes and reduce their efciencies in daily contexts. People can manage smartphone distraction by reconfguring smartphone settings or using limiting tools to avoid such issues. However, it is difcult to manage smart- phone distraction proactively according to the user’s context. To explore the feasibility of context-aware distraction management, we collected and analyzed user contexts and needs relevant to smart- phone distraction. Our results show the heterogeneity of distracting contexts and suggest the opportunity for a rule-based system to provide context-aware distraction management. CCS CONCEPTS · Human-centered computing → User interface management systems; Ubiquitous and mobile computing. KEYWORDS Context-awareness; Smartphone distraction; Rule-based systems ACM Reference Format: Inyeop Kim, Hwarang Goh, Youngtae Noh, and Uichin Lee. 2021. Poster: Exploring User Contexts and Needs for Context-aware Smartphone Distrac- tion Management. In Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers (UbiComp-ISWC ’21 Adjunct), September 21ś26, 2021, Virtual, USA. ACM, New York, NY, USA, 3 pages. https://doi.org/10.1145/3460418.3479307 1 INTRODUCTION Smartphones are often distractions. For example, smartphone noti- fcations can deliver interruptive notifcations which disrupt users’ work (i.e., external interruptions) [4]. People also can be habitually ∗ The corresponding authors Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). UbiComp-ISWC ’21 Adjunct, September 21ś26, 2021, Virtual, USA © 2021 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-8461-2/21/09. https://doi.org/10.1145/3460418.3479307 Figure 1: Key components constituting user-generated rules for distraction management distracted without external events due to smartphones’ functionali- ties and the pervasive accessibility (i.e., internal interruptions) [3]. To avoid such distractions, users can reconfgure phone settings such as ringer mode to silence notifcations or switch on the Do Not Disturb mode, or launch apps for limiting smartphone use (e.g., Forest). However, manually executing functions or launching the apps whenever necessary is cumbersome and requires explicit ef- forts, which may not be practical due to a lack of self-regulation [5]. Thus, we can consider leveraging context-awareness to provide users with proactive smartphone distraction management. We can possibly consider two directions of system building for context-aware distraction management [1]: (1) Recognition-based system and (2) rule-based system. A recognition-based system inte- grates sensed context data for context-aware distraction manage- ment to interpret whether the user is distracted and automatically provide actions. For this, understanding users’ situational contexts where users perceive smartphone distractions is essential. In con- trast, a rule-based system allows a user to defne contexts where smartphones are distracting and corresponding actions for han- dling distractions. To develop this system, we need to understand how users want to handle smartphone distractions and provide a framework to allow users to defne their needs as a rule. To consider the feasibility of each system, we collected two datasets: (1) in-situ user contexts and corresponding experience of smartphone distrac- tions through a mobile ESM and (2) user-generated rules by asking users to describe how they want to manage distraction. Results showed a diversity of contexts relevant to smartphone distraction and signifcant inter-person diferences in perceiving smartphone distraction. We also found four components constituting distraction management rules (Figure 1). This work addresses the opportunity of context-aware distraction management by supporting a rule- based system with improved expressivity. This poster summarizes our prior work [2] and discusses future research directions. 41