analyzed article for human computing class article title “My Phone and Me: Understanding People’s Receptivity to Mobile Notifications”

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Paying Attention to Smartphones
#chi4good, CHI 2016, San Jose, CA, USA
My Phone and Me: Understanding People’s Receptivity
to Mobile Notifications
Abhinav Mehrotra
University of Birmingham
University College London
United Kingdom
Veljko Pejovic
University of Ljubljana
Jo Vermeulen
University of Calgary
University of Birmingham
United Kingdom
Mirco Musolesi
University College London
United Kingdom
Robert Hendley
University of Birmingham
United Kingdom
Notifications are extremely beneficial to users, but they often demand their attention at inappropriate moments. In this
paper we present an in-situ study of mobile interruptibility
focusing on the effect of cognitive and physical factors on
the response time and the disruption perceived from a notification. Through a mixed method of automated smartphone
logging and experience sampling we collected 10372 in-thewild notifications and 474 questionnaire responses on notification perception from 20 users. We found that the response
time and the perceived disruption from a notification can be
influenced by its presentation, alert type, sender-recipient relationship as well as the type, completion level and complexity of the task in which the user is engaged. We found that
even a notification that contains important or useful content
can cause disruption. Finally, we observe the substantial role
of the psychological traits of the individuals on the response
time and the disruption perceived from a notification.
Author Keywords
Mobile Sensing; Notifications, Interruptibility,
Context-aware Computing.
ACM Classification Keywords
H.1.2. Models and Principles: User/Machine Systems; H.5.2.
Information Interfaces and Presentation (e.g. HCI): User Interfaces
Smartphones enable a new form of effortless information
awareness. Throughout the day, a smartphone user receives a
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than
ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from
CHI’16, May 07-12, 2016, San Jose, CA, USA.
©2016 ACM. ISBN 978-1-4503-3362-7/16/05…$15.00.
variety of information such as email messages, social network
events and birthday reminders. Notifications are at the core
of this information awareness, as they use audio, visual and
haptic signals to steer the user’s attention towards the newlyarrived information.
Notifications are extremely beneficial to the users: however,
at the same time, they are a cause of potential disruption,
since they often require users’ attention at inopportune moments. Indeed, previous studies have found that interruptions
at inopportune moments can adversely affect task completion
time [11, 12, 25], lead to high task error rate [8] and impact
the emotional and affective state of the user [5, 7]. Also, users
might get annoyed when they receive notifications presenting
information that is not useful or relevant to them in the current
context [13]. At the same time, studies have shown that users
cannot ignore their smartphones for a long time, because they
start feeling stressed and anxious about missing important information until they finally pick up the phone to check for any
new notifications [26]. This tension is exacerbated by the fact
that individuals deal with hundreds of notifications in a day,
some of which are disruptive [23]
Previous studies have investigated the user’s receptivity to
mobile notifications [15, 29, 32]. As defined by Fischer [15],
receptivity encompasses a user’s reaction to an interruption
and their subjective experience of it. For instance, users
might quickly respond to a notification when they are idle,
but they can still get annoyed because of the content of the
notification. Previous studies have shown that the user’s receptivity to a notification is determined by: (i) how interesting, entertaining, relevant and actionable its content is for the
user [15]; (ii) the type of application that triggers it – communication applications are considered as the most important [32]; (iii) time criticality and social pressure [29]. At the
same time, some studies have proposed various mechanisms
to infer opportune moments, i.e., moments in which a user
quickly and/or favorably reacts to a notification [14, 23, 28].
In order to infer interruptibility these studies have used machine learning classifiers provided with different contextual
Paying Attention to Smartphones
#chi4good, CHI 2016, San Jose, CA, USA
factors including user’s transitions between activities [18],
engagement with a mobile device [14], time of day, location
and activity [28] as well as notification content [23].
However, none of these studies have deployed the proposed
mechanisms in a real world scenario with in-the-wild notifications of a regularly used application. The key reason behind this is the fact that the accuracy of these mechanisms is
still lower than the user’s expectations. In a real world scenario, the users would not accept a system that might defer or
stop an important notification. Previous studies have shown
that users are willing to tolerate some interruption, in order to
not miss any important information [20]. We believe that interruptibility management systems fail to achieve a very high
accuracy in predicting the opportune moment because there is
still a lack of understanding concerning the factors influencing the user’s receptivity to mobile notifications in different
physical and cognitive situations.
In order to bridge this gap, in this work we conduct an insitu study to collect objective and subjective data about mobile notifications. We designed and developed My Phone and
Me (Figure 1), an application that uses a novel experience
sampling method (ESM) approach to uncover the factors and
motivations impacting the user’s reaction and sentiment towards a notification. Through My Phone and Me, we collected 10372 notifications, 474 responses for the ESM questionnaires and 11 personality test results from 20 users. Using
this data, we investigate users’ interaction with mobile notifications in different physical and cognitive contexts. More
specifically, the key contributions of this work are the investigation of:
• the impact of a notification’s alert modality on the user’s
ability to perceive a notification alert;
• the impact of the alert modality, sender-recipient relationship, presentation of a notification, the ongoing task type,
completion level and task complexity on the response time;
• the impact of the sender-recipient relationship, and the ongoing task’s type, completion level and complexity on the
perceived disruption;
• the role of the sender-recipient relationship, notification
content and the perceived disruption on the user’s decision
to accept or dismiss a notification;
• the impact of the user’s personality on the perceived disruption and response time to a notification.
The findings of our study are wide-ranging, and may have
a direct impact on the way future notification management
mechanisms are constructed. First, we observe that a senderrecipient relationship, notification priority and an ongoing
task’s type and complexity influence the response time for
the notification, but there is no impact of the ongoing task
completion level on the response time. Moreover, our results show that the recipient’s relationship with the sender of
a notification, the ongoing task’s type, completion level and
complexity influence the perceived disruption. Our findings
imply that the higher the level of disruption perceived by the
user the higher the probability of the notification being dismissed. From our results, we also observe that, nevertheless,
Figure 1. My Phone and Me application: (a) main screen, (b) phone
usage statistics, (c) application usage statistics, (d) daily notifications.
users tend to click highly disruptive notifications if they contain valuable information. While users are aware of notifications even when their phone is in silent mode, our analysis
shows that the alert modality has a significant impact on the
time taken by the users to view the notification. Finally, we
observe the substantial role of psychological traits on how a
person reacts to a mobile notification, calling for highly personalized interaction between a smartphone and its user.
An interruption tries to steer a user from an ongoing task
to the secondary task signaled by it [8]. As suggested by
Clark [10], users can respond to an interruption in four possible ways: (i) handle it immediately; (ii) acknowledge it and
agree to handle it later; (iii) decline it (explicitly refusing to
handle it); (iv) withdraw it (implicitly refusing to handle it).
A user can respond to mobile notifications in a fairly
different way as compared to an in-person interruption.
For communication-related interruptions, for example, users
might perceive more disruption from an in-person interruption than from a mobile notifications because of the presence
of an interrupter in the former case. Mobile notifications enable flexibility in the way an interruption is handled because
of the lack of the physical presence of the sender and the asynchronous nature of mobile messaging communication1 . Thus,
the exact moment of handling an interruption can be negotiated and the recipient can decide when and how to attend to a
Certain social norms and expectations from the sender side, however, constrain the flexibility that the receiver has in reacting to a
message [30].
Paying Attention to Smartphones
#chi4good, CHI 2016, San Jose, CA, USA
Figure 2. The three time measurements of a notification captured by the My Phone and Me application. The time of notification arrival (a), the time
when a notification is seen (b), and the time when the user accepted (c1) or dismissed (c2) a notification. The time difference between (a) and (b) is seen
time and the time difference between (b) and (c1 or c2) is the decision time.
However, this flexibility introduces many other issues. First,
notifications can go unnoticed when a user does not register
an alert. Second, usually non-persistent notifications may be
forgotten about – a user riding a bicycle, might decide to attend to a notification once they arrive at the destination, yet
forget to do so. Finally, although designed to signal an interruption but not interrupt themselves, mobile notifications can
still induce unnecessary disruption to a user’s routine. For
instance, the disruption can happen when a user decides to attend to a notification immediately, despite being in the middle
of another task, only to find that the notification is about an
unrelated promotional offer. Moreover, a disruption may happen even if a notification is not attended to, as the thought of
a lingering notification may interfere with the user’s current
task performance [33].
In this study, we investigate the factors influencing the user’s
response to a mobile notification, where the response is defined by the time taken to register and react to a notification,
and the way in which the notification is handled (i.e., clicked
or dismissed). Moreover, we investigate the user’s motivation
for being self-disruptive by clicking the disruptive notifications.
Our assumption is that the response time for a notification and
the disruption perceived by the user are influenced by the different aspects of the notification as well as the user’s context.
To capture this measure we developed an Android experience
sampling method (ESM) application that monitors the actual
notifications users receive on their phone, records their reaction to notifications and then queries the users to identify their
rating of the disruption caused by the notification. We augment this with questions about the motivation for handling a
notification in a particular way. Further, our ESM questionnaires ask the user to provide data on the type, complexity
and the completion level of the ongoing task and the user’s
relationship with the sender. Finally, we collect participants’
personality trait measures at the end of the experiment.
First, we investigate the ability of users to adjust their response times to a notification, and see how quickly they can
triage different notifications in different situations. As shown
in Figure 2, we take three time measurements for each notification: the time of notification arrival (a), the time when the
notification is seen (b), and the time when the user accepted
(c1) or dismissed (c2) the notification. Note that in order to
detect the moment at which a notification is seen, we use the
unlock event of the phone and assume that all newly available
notifications in the notification bar are seen when the user unlocks the phone. In case a notification arrives when the user
is already using the phone (i.e., the phone is unlocked), the
seen time of this notification would be computed as zero. We
term the time from the notification arrival until the moment
the notification was reacted upon as the response time for the
notification. For our analysis, we break the response time into
two intervals:
• Seen time (ST) – time from the notification arrival until the
time the notification was seen by the user.
• Decision time (DT) – time from the moment a user saw a
notification until the time they acted upon it (by clicking,
launching its corresponding app or swiping to dismiss).
We examine the way interruption timing, with respect to the
primary task, determines the user’s response to the notification. Moreover, we are interested in the way users triage disruptive notifications. Can users quickly discern when notifications are disruptive? We hypothesize that humans might
still attend to a notification, even if they know that the primary task is going to be disrupted. For example, in their
study of WhatsApp notifications, Pielot et al. [30] show how,
due to an inner pressure raised by social expectations, users
quickly respond to instant messaging (IM) communication or
frequently check their phones, inducing self interruptions just
in order to satisfy the social expectations. In our work, we are
looking beyond just IM notifications and investigate the way
any disruptive message is handled. Through our ESM study
we identify the motivation behind reacting to a disruptive notification and the reasoning and the external factors that lead
to the exact reaction.
We aim for a comprehensive investigation of interruptibility
from a user perspective, thus comparing the effect of different
aspects of a notification on its response time and disruptiveness. Finally, we investigate the potential role of individual
psychological traits on how users perceive and react to disruptive notifications.
In order to investigate the nature of disruptive notifications
and factors that determine the user’s receptivity to mobile notifications in different physical and cognitive situations, we
conducted an in-situ field study. More specifically, we developed an Android app called My Phone and Me – an Android
experience sampling method (ESM) application that collects
information about in-the-wild notifications, users’ interaction
Paying Attention to Smartphones
#chi4good, CHI 2016, San Jose, CA, USA
Arrival, seen and the removal time of a notification.
Whether the notification was clicked or dismissed.
Sender application and the title of a notification.
Signals used by a notification to alert the user: sound, vibrate, and flashing LED.
Physical activity, location, presence of surrounding sound,
WiFi connectivity, proximity to the phone, surrounding light
intensity. This data is collected on arrival and removal of a
notification from the notification bar.
Table 1. Description of features from the My Phone and Me dataset.
Alert type
with them in natural situations (while they are performing
their day-to-day activities), and the physical and cognitive
context details.
The My Phone and Me application uses Android’s Notification Listener Service [1] to access notifications and Google’s
Activity Recognition API [3] and ESSensorManager [22] to
obtain the context information. Table 1 lists the groups of
features captured by the application. The collected context
data has not been explored for the analysis presented in this
paper. To infer the user’s response to a notification, the My
Phone and Me application checks whether the application that
triggered the notification was launched after the removal time
of that notification. We are aware that some notifications are
dismissed because they do not require any further action. For
this reason, we capture seen time and use the difference between seen time and removal time to understand how long it
takes for the user to read and react to a notification.
To collect subjective data from users, the My Phone and Me
application triggers four questionnaires in a day. A questionnaire is triggered only when a notification is handled; it contains questions about why the notification was clicked or dismissed by presenting a screenshot of that notification. The
application triggers a questionnaire for a randomly selected
notification in every four hours time window between 8.00
am and 8.00 pm and the last questionnaire at a random time
between 8.00 pm and 10.00 pm. The application did not trigger any questionnaire after 10pm so that the participants do
not feel annoyed at responding to the surveys late at night.
The application automatically used the local time zones because it relies on the phone’s time. Moreover, if the user is
busy, the questionnaire can be dismissed by simply swiping it
from the notification bar and no questionnaire is shown to the
user for the next 30 minutes.
A questionnaire comprises seven multiple-choice and two
free-response questions. The list of questions and their options are shown in Table 2. Since we ask the users to enter
the free form text for two questions, it could increase time to
respond to a questionnaire and may become a source of annoyance. Therefore, the application allows the users to dictate the responses to these questions. These answers are then
converted to text using Android’s SpeechRecognizer API [2].
Additionally, the My Phone and Me application asks the users
to take a personality test based on the 50 item Big-Five Factor Markers from the International Personality Item Pool, de1024
Did you notice the alert
(e.g., vibration, sound,
flashing LED) for this
notification when it first
How did you handle the
notification when you first
saw it?
Select all factors that
made you decide to
click/dismiss the notification.
What best describes your
relationship to the sender?
Please describe what the
notification was about.
Please describe what activity you were involved with
when you received the notification.
When the notification arrived, I was:
(i) Yes, and I decided to check my phone immediately. (ii) Yes, but I was already using
my phone. (iii) Yes, but I ignored the alert.
(iv) No, I didn’t notice the alert.
(i) I decided to immediately click it. (ii) I decided to …
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