Abstract
Smartphone notifications are often delivered without considering user interruptibility, potentially causing frustration for the recipient. Therefore research in this area has concerned finding contexts where interruptions are better received. The typical convention for monitoring interruption behaviour assumes binary actions, where a response is either completed or not at all. However, in reality a user may partially respond to an interruption, such as reacting to an audible alert or exploring which application caused it. Consequently we present a multi-step model of interruptibility that allows assessment of both partial and complete notification responses. Through a 6-month in-the-wild case study of 11,346 to-do list reminders from 93 users, we find support for reducing false-negative classification of interruptibility. Additionally, we find that different response behaviour is correlated with different contexts and that these behaviours are predictable with similar accuracy to complete responses.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adamczyk, P., Bailey, B.: If not now, when?: the effects of interruption at different moments within task execution. In: Proceedings of the (CHI 2004), pp. 271–278. ACM (2004)
Bailey, B., Iqbal, S.: Understanding changes in mental workload during execution of goal-directed tasks and its application for interruption management. ACM Trans. Comput. Hum. Interact. (TOCHI) 14(4), 21 (2008)
Fogarty, J., Hudson, S., Atkeson, C.G., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J., Yang, J.: Predicting human interruptibility with sensors. ACM Trans. Comput. Hum. Interact. (TOCHI) 12(1), 119–146 (2005)
Grandhi, S., Jones, Q.: Technology-mediated interruption management. Int. J. Hum Comput Stud. 68(5), 288–306 (2010)
Ho, J., Intille, S.: Using context-aware computing to reduce the perceived burden of interruptions from mobile devices. In: Proceedings of the (CHI 2005), pp. 909–918. ACM (2005)
Horvitz, E., Apacible, J.: Learning and reasoning about interruption. In: Proceedings of the (ICIMI 2003), pp. 20–27. ACM (2003)
Iqbal, S., Bailey, B.: Effects of intelligent notification management on users and their tasks. In: Proceedings of the (CHI 2008), pp. 93–102. ACM (2008)
Lathia, N., Rachuri, K., Mascolo, C., Rentfrow, P.: Contextual dissonance: design bias in sensor-based experience sampling methods. In: Proceedings of the (UbiComp 2013), pp. 183–192. ACM (2013)
Liu, G., Hossain, K.M.A., Iwai, M., Ito, M., Tobe, Y., Sezaki, K., Matekenya, D.: Beyond horizontal location context: measuring elevation using smartphone’s barometer. In: Adjunct Proceedings of the (UbiComp 2014), pp. 459–468. ACM (2014)
Mathan, S., Whitlow, S., Dorneich, M., Ververs, P., Davis, G.: Neurophysiological estimation of interruptibility: demonstrating feasibility in a field context. In: Proceedings of the 4th International Conference of the Augmented Cognition Society (2007)
McFarlane, D.: Interruption of people in human-computer interaction: A general unifying definition of human interruption and taxonomy. Technical report DTIC Document (1997)
McFarlane, D., Latorella, K.: The scope and importance of human interruption in human-computer interaction design. Hum. Comput. Interact. 17(1), 1–61 (2002)
Miller, G.: The smartphone psychology manifesto. Perspect. Psychol. Sci. 7(3), 221–237 (2012)
Okoshi, T., Ramos, J., Nozaki, H., Nakazawa, J., Dey, A., Tokuda, H.: Attelia: Reducing users cognitive load due to interruptive notifications on smart phones. In: Proceedings of the (PerCom 2015), IEEE (2015)
Pejovic, V., Musolesi, M.: Interruptme: designing intelligent prompting mechanisms for pervasive applications. In: Proceedings of the (UbiComp 2014), pp. 897–908. ACM (2014)
Pielot, M., de Oliveira, R., Kwak, H., Oliver, N.: Didn’t you see my message?: predicting attentiveness to mobile instant messages. In: Proceedings of the (CHI 2014), pp. 3319–3328. ACM (2014)
Pohl, H., Murray-Smith, R.: Focused and casual interactions: allowing users to vary their level of engagement. In: Proceedings of the (CHI 2013), pp. 2223–2232. ACM (2013)
Poppinga, B., Heuten, W., Boll, S.: Sensor-based identification of opportune moments for triggering notifications. IEEE Pervasive Comput. 13(1), 22–29 (2014)
Rosenthal, S., Dey, A.K., Veloso, M.: Using decision-theoretic experience sampling to build personalized mobile phone interruption models. In: Lyons, K., Hightower, J., Huang, E.M. (eds.) Pervasive 2011. LNCS, vol. 6696, pp. 170–187. Springer, Heidelberg (2011)
Sidman, M.: Tactics of Scientific Research: Evaluating Experimental Data in Psychology. Basic Books, New York (1960)
Smith, J., Lavygina, A., Ma, J., Russo, A., Dulay, N.: Learning to recognise disruptive smartphone notifications. In: Proceedings of the (MobileHCI 2014), pp. 121–124. ACM (2014)
Ter Hofte, H.: Xensible interruptions from your mobile phone. In: Proceedings of the (MobileHCI 2007), pp. 178–181. ACM (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Turner, L.D., Allen, S.M., Whitaker, R.M. (2015). Push or Delay? Decomposing Smartphone Notification Response Behaviour. In: Salah, A., Kröse, B., Cook, D. (eds) Human Behavior Understanding. Lecture Notes in Computer Science(), vol 9277. Springer, Cham. https://doi.org/10.1007/978-3-319-24195-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-24195-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24194-4
Online ISBN: 978-3-319-24195-1
eBook Packages: Computer ScienceComputer Science (R0)