Preliminary investigations about interruptibility of smartphone users at specific place types

Smartphones are our ubiquitous, personal, wearable companions. Though, apart from their smartness and usefulness in our everyday lives they can cause displeasure. They allow us to be connected to a load of people and with a vast amount of apps - all of them requiring our attention. There is a growing need for a smart management to not be overwhelmed by the flood of information and notifications. A first step in that direction is to identify detectable contexts in which interruptibility is very high or low. In this paper, we present results of a survey taken by 68 persons. Within the survey, we assess how much smartphone notifications interrupt and disturb users at a specific location. The locations were selected based on the places that can be recognized by the Google Places API. This shall serve as a basis for future interruptibility research. We noticed that people are fairly interruptible while waiting, e.g. at bus stations or at parking lots. In contrast, they must not be disturbed at movie theaters, libraries or restaurants.

[1]  Stuart M. Allen,et al.  Push or Delay? Decomposing Smartphone Notification Response Behaviour , 2015, HBU.

[2]  Alessandra Russo,et al.  Learning to recognise disruptive smartphone notifications , 2014, MobileHCI '14.

[3]  Albrecht Schmidt,et al.  There is more to context than location , 1999, Comput. Graph..

[4]  Sebastian Möller,et al.  Assessing the Relationship between Technical Affinity, Stress and Notifications on Smartphones , 2015, MobileHCI Adjunct.

[5]  Henry A. Kautz,et al.  Hierarchical Conditional Random Fields for GPS-Based Activity Recognition , 2005, ISRR.

[6]  Petteri Nurmi,et al.  Identifying Meaningful Places , 2009 .

[7]  Xing Xie,et al.  Collaborative location and activity recommendations with GPS history data , 2010, WWW '10.

[8]  Weiping Zhu,et al.  CircleSense: A pervasive computing system for recognizing social activities , 2013, 2013 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[9]  Mirco Musolesi,et al.  InterruptMe: designing intelligent prompting mechanisms for pervasive applications , 2014, UbiComp.

[10]  Christopher G. Atkeson,et al.  Predicting human interruptibility with sensors: a Wizard of Oz feasibility study , 2003, CHI '03.

[11]  Claudio Bettini,et al.  OWL 2 modeling and reasoning with complex human activities , 2011, Pervasive Mob. Comput..

[12]  Joyce Ho,et al.  Using context-aware computing to reduce the perceived burden of interruptions from mobile devices , 2005, CHI.

[13]  Tadashi Okoshi,et al.  Towards attention-aware adaptive notification on smart phones , 2016, Pervasive Mob. Comput..

[14]  G. Henri ter Hofte,et al.  Xensible interruptions from your mobile phone , 2007, Mobile HCI.