2017
DOI: 10.1016/j.pmcj.2017.01.011
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Reachable but not receptive: Enhancing smartphone interruptibility prediction by modelling the extent of user engagement with notifications

Abstract: Smartphone notifications frequently interrupt our daily lives, often at inopportune moments. We propose the decision-on-information-gain model, which extends the existing data collection convention to capture a range of interruptibility behaviour implicitly. Through a six-month in-the-wild study of 11,346 notifications, we find that this approach captures up to 125% more interruptibility cases. Secondly, we find different correlating contextual features for different behaviour using the approach and find that … Show more

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Cited by 39 publications
(41 citation statements)
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“…[ 18,31,32 ] Such is the volume of notifications that users are becoming adept at defining strategies to manage the overburden of smartphone notifications [ 33 ] and both interuptability and user engagement is predictable. [ 34 ]…”
Section: The Smartphone Dilemma Is Evolving As Technology's Role In Omentioning
confidence: 99%
“…[ 18,31,32 ] Such is the volume of notifications that users are becoming adept at defining strategies to manage the overburden of smartphone notifications [ 33 ] and both interuptability and user engagement is predictable. [ 34 ]…”
Section: The Smartphone Dilemma Is Evolving As Technology's Role In Omentioning
confidence: 99%
“…In [31], reaction time was found to be faster when the phone was on vibrate-only mode, followed by silent mode and normal mode (between which there was no difference). A more recent study [42] showed that the device ringer mode is correlated to attentiveness towards mobile notifications.…”
Section: Notifications On Mobile Devicesmentioning
confidence: 99%
“…With the prevalence of sensor-rich mobile devices, modelling user behaviour, including gaze and attention, has gained signif-icant popularity. A large body of work investigated the use of device-integrated sensors to predict users' interruptibility [7,10,12,45,46]. In particular, Obuchi et al detected breaks in a user's physical activities using inertial sensors on the phone to push mobile notifications during these breaks [30].…”
Section: User Behaviour Modelling On Mobile Devicesmentioning
confidence: 99%