Investigating The Role of Task
Engagement in Mobile Interruptibility
Faculty of Computer and
University of Ljubljana
School of Computer Science,
University of Birmingham
Department of Geography
University College London
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MobileHCI ’15 Adjunct, August 25–28, 2015, Copenhagen, Denmark.
Context-awareness of mobile phones is a cornerstone of
recent efforts in automatic determination of user interrupt-
ibility. Modalities such as a user’s location, her physical ac-
tivity, time of day, can be used in machine learning models
to infer if a user is going to welcome an incoming notiﬁca-
tion or not. However, the success of context-aware inter-
ruptibility systems questions the existing theory of interrupt-
ibility, that is based on the internal state of the user, not her
surroundings. In this work we examine the role of a user’s
internal context, deﬁned by her engagement in the current
task, on the sentiment towards an interrupting mobile notiﬁ-
cation. We collect and analyse real-world data on interrupt-
ibility of twenty subjects over two weeks, and show that the
internal state indeed impacts user interruptibility.
Interruptibility; Notiﬁcations; Multitasking; Context-aware
ACM Classiﬁcation Keywords
H.5.2. [Information Interfaces and Presentation (e.g. HCI)]:
User Interfaces; H.1.2. [Models and Principles]: User/Machine
Context-Aware Mobile Interruption Management
The mobile phone represents the most direct point of con-
tact with almost any individual on the planet, and, since
the smartphone revolution, also the most common means
of getting the information from the Internet. Being every
persons main communication link, the mobile often over-
whelms its user by information. Consequently, efforts have
been made to harness sensing and computing capabilities
of modern mobiles to design smarter interruption systems
that will not disturb users at times when they prefer to work
The role that the context may have in determining user in-
terruptibility has been recognised even before the smart-
phone era. In  Horwitz et al. build a Bayesian network
that describes user-deﬁned cost of interruption from con-
textual audio-visual features captured by a camera and a
microphone in an ofﬁce setting. Ho and Intille presented the
ﬁrst study of mobile interruptibility, in which wearable ac-
celerometers were used to collect information about users’
physical activity, while mobile notiﬁcations were sent at the
same time . Their study points out that the moments of
changing physical activity represent preferred moments for
The smartphone has changed the way we reason about in-
terruptibility. Its sensing and computing capabilities enable
context-awareness previously explored only in so called
Wizard of Oz studies . Naturally, efforts have been made
to infer user interruptibility from a richer physical context.
Ter Hofte builds a model of the interruptibility of a smart-
phone user, that is based on a user’s location, social cir-
cle and physical activity . Yet, this work does not em-
ploy mobile sensing, but relies on self-reported information.
Pielot et al. examine a communication- and usage-oriented
context, by collecting and analysing a data set of text mes-
sages exchanged via smartphones together with the as-
sociated phone usage data . Time since the screen was
on, time since the last notiﬁcation, and similar features were
used in a classiﬁer that infers if the users is going to attend
the message within a short time frame. InterruptMe , on
the other hand looks at the context in a more physical way.
The system senses user’s semantic location, physical ac-
tivity and time of day, and connects this context with one’s
interruptibility through an evolving classiﬁer.
Mobile Phone and the Theory of Multitasking
Not all interruptions are equally disruptive, and the impor-
tance of improving our understanding of disruptiveness
was exempliﬁed by the increasing amount of interruptions
brought by the latest waves of communication technologies.
In the traditional theory of interruptibility three main factors
that render an interruption disruptive have been identiﬁed:
the interrupting task complexity, task duration, and the mo-
ment of interruption. A unifying theory of interruptibility is
thoroughly explained in . In a nutshell, our brain pro-
cesses the current task in the procedural memory, while
the declarative memory serves for the short- and long-
term storage of facts. Once interrupted, the primary tasks is
moved to the declarative memory, and the interrupting task
takes over the procedural memory. Reactivation of the pri-
mary task after the interrupting task is ﬁnished may require
recollection from the declarative memory. This recollection
tends to be less successful if the interrupting task was com-
plex and/or long. Time to rebuild the primary task’s problem
state also depends on the complexity of the primary tasks,
and the familiarity of the subject with the primary task –
the more practice one has with a certain task, the stronger
the activation bound in the declarative memory will be. The
moments of low cognitive workload, even the moments of
break within a more complex task, tend to be more suitable
for interruptions, as shown in experimental studies .
However, from the mobile device point of view the estima-
tion of the task complexity and the user’s skills pertaining to
the task, remain extremely challenging. Measuring the en-
gagement of a user in a task, in order to identify low work-
load moments, has been demonstrated, with a limited suc-
cess, via pupil dilation observation . But the question we
pose in this paper is: Are task complexity and user engage-
ment still relevant determinants of a user interruptibility in
the mobile context?
All the above studies have been performed in tightly con-
trolled laboratory settings and without mobile devices. Fur-
thermore, the theory of multitasking has been constructed
on the data from unavoidable interruptions. In the mobile
realm, interruptions are delivered via notiﬁcations. Here,
in a so called bounded deferral manner , a user can de-
cide to postpone the reaction until a more suitable moment.
Given these differences, we decide to experimentally eval-
uate the relevance of task complexity and engagement on
To experimentally investigate the relationship between task
engagement and interruptibility, we develop and deploy an
Android-based experience sampling method (ESM) applica-
tion. The application occasionally delivers a notiﬁcation on
a user’s phone; once the notiﬁcation is clicked on, the user
is presented with a brief survey, containing questions about
the user’s current engagement in a task. The questions in-
• Is your current activity interesting?
• Is your current activity challenging?
Figure 1: Screenshot of the ESM
Android application. The survey
was presented once a user clicked
on a notiﬁcation. Each notiﬁcation
was announced by the user’s
default notiﬁcation ringtone and
• How well are you concentrated on it?
• Is the activity important for you?
• How skilled are you at it?
Each question is answered on a four-point Likert scale, and
possible answers include not at all,a little,somewhat, and
In addition, a question Is this a good moment to interrupt?,
answered on the same four-point Likert scale, is presented
to a user in order to gauge her sentiment towards the inter-
The data collection was carried out for two weeks among
20 adult subjects, who received a modest monetary reward
for their participation, where “participation" was deﬁned as
having the application running on their phones, and no re-
quirements were made on actually reacting to notiﬁcations.
We recruited subjects from both sexes and from the age
span 20 to 37 years old. Eight notiﬁcations were sent to
each participant per day at random times between 8 am
and 10 pm local time. In total 2334 notiﬁcations with sur-
veys were sent, out of which 859 were fully answered, and
our analysis concentrates on those answered messages.
The goal of our study is not to build a fully contained predic-
tive model of a mobile user’s interruptibility and base it on
the information about the user’s current engagement in a
task. Indeed, relying on the explicit user-provided informa-
tion about task engagement, such a model would be highly
impractical, while background sensing of this information
may not be feasible with the current smartphone hardware.
Instead, aim to investigate whether task engagement plays
a role in mobile interruptibility, and if so, in which way differ-
ent aspects of task engagement determine interruptibility.
To quantify the relationship between the ﬁve aspects of task
engagement (here labelled as Interesting,Challenging,
Concentrated,Important and Skilled) and the sentiment
that the user expressed towards the moment of interruption,
we ﬁt a linear regression model with the sentiment as a de-
pendent variable, and the ﬁve task engagement aspects as
Table 1: Dep = Sentiment
Variable Pearson Coefﬁcient t
(Std. Err.) (Sig.)
Interesting -.03 -.82
Challenging -.12 -3.87∗∗
Concentrated -.17 -3.67∗∗
Important -.05 -1.36
Skilled .17 4.33∗∗
(Regression Constant) 1.40 15.21∗∗
F(5,854) 18.76 (p=.000)∗∗
The parameters of the ﬁtted linear regression are shown
in Table 1. The regression statistically signiﬁcantly explains
the sentiment towards interruptibility (F(5,854) = 18.76, p =
.000). However, the proportion of variation in the sentiment
that can be explained by the engagement variables is not
high (R2= 0.10), indicating that other factors that we have
not considered in this analysis may inﬂuence the interrupt-
Since all the survey questions are answered on the same
Likert scale, we encode all the answers with the follow-
ing values not at all=0,a little=1,somewhat=2, and very
much=3. A positive correlation coefﬁcient next to an inde-
pendent variable indicates that a higher rating of this vari-
able (i.e. from a little to very much) leads to a higher rating
of the moment as a good moment to interrupt. In Table 1
only Skilled positively correlates with the sentiment. Thus,
if a person is highly skilled in their current task, they are
less likely to be irritated by an incoming notiﬁcation. The
other variables are negatively correlated with the sentiment.
However, of those, only two – Challenging and Concen-
trated – are correlated at the signiﬁcance level of 1% or
less (denoted by **).
Despite the change in the way we interruptions are com-
municated and negotiated with the user, the results from
Table 1 are in accordance with the standard theoretical ap-
proach explaining interruptibility in a static context. For ex-
ample, a user skilled in a task is less likely to be disturbed
by an incoming mobile notiﬁcation. A likely explanation is
that such a user needs less time to reconstruct the primary
task state after the interruption, due to stronger activation
links in the declarative memory. On the other hand, the data
shows that a user who is highly concentrated on a challeng-
ing task is more vulnerable to disruptions.
It is important to note that our metric of interruptibility is
subjective – it is up to our users to declare their sentiment
towards interruptibility. Since the study was done in an un-
controlled real-world setting, there was no way for us to
measure the actual impact of an interruption on a primary
task. It is also interesting that despite the possibility of de-
ferring their actions for a more appropriate moment, users
still feel annoyance when a notiﬁcation arrives at times of
high engagement. We suspect that the smartphone culture
and social expectations around mobile usage require a user
to keep information about a lingering notiﬁcation in mind,
even if she postpones her action and continues working on
the original task.
Brain processes are in the foundations of models explain-
ing human interruptibility. With the advent of personalised
sensing devices, the opportunity opened to study interrupt-
ibility by sensing only the external context. Recent progress
in that direction calls for re-examination of the need for in-
ferring one’s cognitive load in order to describe their in-
terruptibility. Our study shows that although notiﬁcations
allow users to defer interruptions for a later moment, the
engagement with the current task still plays a signiﬁcant
role in determining users’ interruptibility. For future work we
leave automated recognition of task engagement via mobile
sensors. In addition, we believe that understanding cogni-
tive processes involved in multitasking can lead to further
improvement of context-aware task management systems.
For example, stimulating primary task recollection from the
declarative memory via context-relevant links.
The analysis in this paper relies on the data collected as
a part of the UK EPSRC-funded project “UBhave: ubiqui-
tous and social computing for positive behaviour change"
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