My Phone and Me: Understanding People's Receptivity to Mobile Notifications

TL;DR: It is found that even a notification that contains important or useful content can cause disruption, and the substantial role of the psychological traits of the individuals on the response time and the disruption perceived from a notification is observed.

Abstract: 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-the-wild 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.

Summary

INTRODUCTION

• Smartphones enable a new form of effortless information awareness.
• 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.
• The authors 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.
• Finally, the authors 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.

REASONING ABOUT USERS’ RECEPTIVITY TO MOBILE NOTIFICATIONS

• 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).
• 1Certain social norms and expectations from the sender side, however, constrain the flexibility that the receiver has in reacting to a message [30].
• As shown in Figure 2, the authors 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.
• The authors examine the way interruption timing, with respect to the primary task, determines the user’s response to the notification.

DATA COLLECTION

• 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, the authors conducted an in-situ field study.
• 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.
• The authors are aware that some notifications are dismissed because they do not require any further action.
• 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.
• A questionnaire comprises seven multiple-choice and two free-response questions.

Recruitment of the Participants

• The My Phone and Me application was published on the Google Play Store from 12th August 2015.
• It allows users to check statistics on their phone usage and interruptions.
• The authors believe that displaying this information has a minimal interference with users’ actual behavior for interacting with notifications, but provides a valuable functionality in order to make the users keep the application installed on their phones.
• Firstly, the user has to give explicit permission as required by the Android operating system.
• Secondly, the application shows a list of information that is collected and asks for user consent.

DATASET

• The data collection was carried out for around two months, during which the authors collected 19494 notifications, 611 responses for the questionnaire (comprising a set of nine questions listed in Table 2) and 11 personality test results (50 item-based BigFive Factor Markers by Goldberg [16]) from 74 users who installed the My Phone and Me application.
• The authors do not ask them to provide any other demographic information.
• As the authors are primarily using the questionnaire responses, they compared the click rate (i.e., percentage of notifications that are clicked out of total notifications) of the overall notifications with the notifications that were linked to questionnaires.
• Furthermore, in practice, a user might not even receive notifications from each of the sender types during the period of the study.
• Therefore, the authors use the data from 20 users who responded to at least 14 questionnaires, i.e., the minimum number of questionnaires that were answered by users in this set.

UNDERSTANDING RESPONSE TIME

• The key findings of this section are: Users are aware of the notification alerts even when the phone is in silent mode.
• Seen time is fastest when the phone is in vibrate mode and slowest for silent node.
• Notifications are seen fastest when the user is commut- ing and slowest when idle.
• User’s attentiveness increases (reducing the seen time) with the increase in the complexity of an ongoing task.
• The decision time is higher for the notifications from less frequently contacted senders.

The Role of Alert Modality in Perceiving a Notification Alert

• A notification can alert the user by means of vibration, sound and/or flashing LED.
• According to their dataset, when the notifications (with which the questionnaires were linked) were triggered the user’s phone was for 25.54% of the times in the silent mode, 21.50% vibrate mode, 41.94% sound mode and 11.03% sound with vibrate.
• This provides evidence that when the phone is in silent mode users are still aware of the notification alerts.

What Factors Influence the Seen Time?

• The authors investigate the role of alert modality, sender and the ongoing task type, complexity and completion level, in influencing the seen time of a notification.
• A Tukey posthoc test (by setting the ↵ = 0.05) revealed that the seen time is statistically significantly higher for silent notifications (average 7.332 mins).
• The classification was done manually, by two coders who initially disagreed on five entries.
• The Impact of Ongoing Task Complexity on Seen Time.
• Thus, the increase in the seen time of notifications is correlated with the decrease in rating of ongoing task’s complexity.

What Factors Influence the Decision Time?

• The authors analyze the effect of the type, complexity and completion level of the ongoing task, and the sender type on the time a user takes to decide how to react to a notification.
• The authors find that neither of these factors have a statistically significant effect on the decision time of notifications with the exception of the sender.
• A Tukey post-hoc test (by setting the ↵ = 0.05) revealed that out of the 11 sender types (shown in Table 2), notifications from partner lead to the fastest decision time (mean DT is 3.315s), followed by immediate family members with an average decision time of 4.891 seconds.
• In other cases users take more time in reading the content before deciding how to handle it.
• The authors hypothesize that this behavior stems from the content of notifications from close friends or family members, which might be more predictable, and a part of a daily routine (e.g., “pick kids from school").

The Role of Notification Presentation

• In their dataset, 2953 (out of 7795) notifications were received when the user was engaged with the phone.
• Here, a high-priority notification is a foreground notification that gets in the way of the user’s ongoing activity and the user cannot perform any action to get it out of the way without clicking or dismissing it (e.g. Viber messages).
• The authors investigate the effect of the notification presentation on the response time (i.e., the sum of seen time and decision time) of a notification.
• The result of a two sample t-test shows that there is a statistically significant effect of notification priority on the response time, t(2951) = 17.694, p < 0.001, with high-priority notifications getting quicker response than low-priority notifications.

WHY A NOTIFICATION BECOMES DISRUPTIVE

• In this section the authors investigate the effect of different factors on the perceived disruption.
• Since the perceived disruption was measured with a 5-point Likert scale, the authors encode the responses as: Strongly disagree=1, Somewhat disagree=2, Neutral=3, Somewhat agree=4 and Strongly agree=5.
• The key findings of this section are: Perceived disruption increases with the increase in the complexity of an ongoing task.
• Messages from subordinates and system messages (where the sender is not a person) are considered as most disruptive.
• Whereas, extended family members are considered as the least disruptive.

The Role of Ongoing Task Complexity

• The authors investigate whether the complexity of an ongoing task is associated with the perceived disruption reported by the users.
• Tau correlation coefficient was computed to assess the relationship between the ongoing task complexity and perceived disruption.
• This demonstrates that the users are likely to get more disrupted by a notification that arrives when they are engaged in an intricate task and less disrupted when they are performing a simple task.
• In their preliminary analysis [27] the authors have found that when users are engaged in complex tasks they also express more of a negative sentiment towards interruptions.

The Role of Ongoing Task Completion Level

• A one-way ANOVA of the reported disruption was carried out for each class of task completion level (starting, in the middle, finishing and not doing anything).
• The results show that the completion level of an ongoing task has a significant impact on the disruption perceived by the users from the notifications, F(3, 451) = 19.43, p < 0.001.
• A Tukey post-hoc test (by setting ↵ as 0.05) reveals that the perceived disruption is the highest when the user is currently involved in a task.
• The perceived disruption is the lowest when the user is starting a task or idle and there is no statistically significant difference between these groups.
• These results show that the perceived disruption when the user is highly engaged in a task is very high not only from the desktop notification, as discussed for example in [13, 24], but also from the mobile notifications.

The Role of Ongoing Task Type

• A one-way ANOVA of the reported disruption is carried out for each type of ongoing task (see Table 2).
• A Tukey post-hoc test (by setting ↵ = 0.05) revealed that the perceived disruption is the highest when the user is working and the lowest while the user is idle.
• When the users are not idle, they perceive least disruption while communicating and doing a personal or maintenance task.
• Since the communication can involve notifications themselves, e.g. two mobile users exchanging WhatsApp messages, the above result is not surprising.
• As shown in a recent study [31], users are receptive to information when they are bored.

UNDERSTANDING THE ACCEPTANCE OF NOTIFICATIONS

• In this section the authors investigate the factors that make the users accept or dismiss a notification.
• The key findings of this section are: Likelihood of the acceptance of a notification decreases with the increase in the perceived disruption.
• Disruptive notifications are accepted because they con- tain useful information.

Procedure

• Through the questionnaires, the authors asked the users the reason for clicking/dismissing a notification (see Table 2).
• In Table 3 and Table 4 the authors calculate the percentage of times each factor was reported as a reason for clicking and dismissing the notifications.
• Since, users may select more than one option, the total count percentage in the table adds up to more than 100%.
• According to these responses, the users mostly accept notifications when they are free, but also the importance of the sender and the usefulness of the content make them accept a notification.
• On the other hand, users avoid attending to notifications that do not contain important, urgent or useful content.

Disruptive Notifications are Likely to be Dismissed

• The authors examine the impact of the disruption caused by the notifications on their likelihood of being accepted.
• In order to quantify this, the authors encoded the response for perceived disruption with the following values: Strongly disagree=1, Somewhat disagree=2, Neutral=3, Somewhat agree=4 and Strongly agree=5.
• The results indicate the likelihood of the acceptance of a notification decreases by 0.581 times (95% confidence interval limits for the slope were [0.497, 0.675]) for a unit increase in the perceived disruption (based on the 5-point Likert scale).

Why are disruptive notifications accepted?

• As discussed above, the disruption perceived by the user makes a notification more likely to be dismissed.
• Table 5 shows the percentage of times each factor was reported by the users for accepting the disruptive notifications.
• As users were allowed to select more than one option, the sum of the percentages in the table adds up to more than 100.
• "Content is important" and "Content is useful" are the most dominant reason provided by the users for clicking the disruptive notifications.
• The authors suspect that these notifications may contain valuable information, but they were not relevant at the moment of delivery.

DOES PERSONALITY MATTER

• Seen time and decision time of notifications.the authors.
• The authors computed the score for the five personality traits (i.e., the so-called Big Five: Extroversion, Agreeableness, Conscientiousness, Neuroticism and Openness) for each of the 11 users who fully completed the exit questionnaire that includes the 50 questions related to personality traits.
• For this computation, the authors used the scoring instructions that come with the personality test [16].

Impact on Reported Disruption

• To quantify the relationship between the five personality traits and the disruption perceived by the users from notifications, the authors fit a linear regression model with the average disruption as a dependent variable, and the five personality traits as independent variables.
• Here, the average disruption is computed as a mean of disruption reported by the user through the questionnaires.
• All responses were encoded with the following values Strongly disagree=1, Somewhat disagree=2, Neutral=3, Somewhat agree=4, and Strongly agree=5.
• Table 6 shows the parameters of the fitted linear regression model.
• The results show that the extroversion personality trait significantly affects the average perceived disruption and that extroverts are more inclined to be disrupted by a notification.

Impact on Notification’s Seen Time and Decision Time

• The authors then investigate whether the personality traits influence the seen and decision time.
• The average decision time of notifications for each user: the average time taken by a user to click/dismiss after viewing a notification.
• The parameters of the fitted linear regression model are shown in Table 7.
• The parameters of the fitted linear regression model are shown in Table 8, and demonstrate that the decision time for a notification is significantly influenced by the user’s Extroversion and Neuroticism personality traits.
• The above results show potential for the interruptibility models to be gen- eralized across groups of users who share the same personality traits.

Deferring Notifications

• Previous studies show that users perceive more disruption from notifications when engaged in intricate tasks and for the first time the authors confirm this for mobile settings in a quantitative way.
• Thus, in order to benefit users, the OS should offer more flexibility to them for setting the busy moments so that only time-critical notifications could be triggered.
• This would potentially allow interruptibility management (IM) systems to learn patterns to predict the user’s engagement with complex tasks and prioritize interruptions accordingly.

Improving Notification Presentation

• The authors found that users become more attentive at busy moments, but are likely to perceive most notifications as disruptive and dismiss them.
• Disruptive notifications tend to be accepted if they contain useful content.
• The presentation of notification summaries could be adapted to help users quickly decide whether to click or dismiss notifications, e.g. by highlighting notifications from priority contacts or with priority content that is learnt over time by an IM system.

Building a Personality-Dependent Interruptibility Model

• The authors observed that the perceived disruption, seen and decision time are influenced by the user’s personality traits.
• This demonstrates the potential to take the personality trait into account in interruption models and, for example, generalise interruptibility models across groups of users who share the same personality traits.
• These findings can be exploited to design more effective machine learning algorithms for intelligent notifications.

RELATED WORK

• Multitasking is fundamental in workplaces, where task switching happens every few minutes [17], but also in the private sphere, where an increasing number of personal computing devices mediate the flow of data, be it of entertainment, social or informative nature.
• The disruptiveness of interruptions was analyzed by Miata and Norman [24], who were among the first to note and explain its variability with the context and particularly their alignment with respect to the primary task a user is working on.
• One of their key findings is that users working on more mentally demanding tasks need less time to notice a notification.
• In reality, however, mobile users engage in unconstrained communication with their friends and family, move about in different surroundings and get notifications from a range of applications.
• In their recent work the authors uncover that the content and the sender-recipient relationship play a significant role in the decision to accept a notification [23].

LIMITATIONS

• Most limitations of the work presented in this paper stem from their decision to collect data in the wild, with the minimum amount of intervention from their users.
• Moreover, in case a notification arrives when the user is already engaged with the phone, the authors assume that the user has seen the notification.
• Further, since their users are not confined to a laboratory, the authors are limited to self-reported level of disruption from a notification.
• The density of notifications negatively impacts the sentiment towards individual notifications [28].
• Finally, while the work is the first to their knowledge to uncover the role of individual psychological traits on mobile interruptibility, it is important to note that the authors related the traits with the reported interruptibility.

CONCLUSIONS

• The contributions of this study are threefold.
• First, the authors have confirmed the validity of some past desktop interruptibility studies in a mobile setting.
• The authors have analysed the data to show that the response time of a notification in the mobile environment is not only influenced by an ongoing task’s type, completion level and task complexity, but also by the notification’s alert modality, presentation and sender-recipient relationship.
• Moreover, the relationship with the sender influences the user’s decision on accepting a notification or not.
• Finally, different people exhibit different reactions and the authors observe a substantial role of the individual psychological traits on how a person reacts to a mobile notification.

Figures

Table 7. Results of linear regression with the average seen time as a dependent variable and the personality traits as independent variables.

Table 8. Results of linear regression with the average decision time as a dependent variable and five personality traits as independent variables.

Figure 1. My Phone and Me application: (a) main screen, (b) phone usage statistics, (c) application usage statistics, (d) daily notifications.

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.

Table 6. Results of linear regression with the average disruption as a dependent variable and the personality traits as independent variables.

Table 4. User response about why they dismiss notifications.

Table 3. User response about why they accept (click) notifications.

Table 5. User response about why they accept disruptive notifications.

Table 1. Description of features from the My Phone and Me dataset.

Table 2. Questions and their options from questionnaire triggered by the My Phone and Me app.

Full text

University of Birmingham
My Phone and Me
Mehrotra, Abhinav; Pejovic, Veljko; Vermeulen, Jo; Hendley, Robert; Musolesi, Mirco
DOI:
10.1145/2858036.2858566
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Mehrotra, A, Pejovic, V, Vermeulen, J, Hendley, R & Musolesi, M 2016, My Phone and Me: Understanding
People's Receptivity to Mobile Notifications. in CHI '16 - Proceedings of the 2016 CHI Conference on Human
Factors in Computing Systems. Association for Computing Machinery , pp. 1021-1032, CHI 2016 Conference on
Human Factors in Computing Systems , San Jose, CA, United States, 7/05/16.
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My Phone and Me: Understanding People’s Receptivity
to Mobile Notifications
Abhinav Mehrotra
University of Birmingham
University College London
United Kingdom
a.mehrotra@cs.bham.ac.uk
Veljko Pejovic
University of Ljubljana
Slovenia
veljko.pejovic@fri.uni-lj.si
Jo Vermeulen
University of Calgary
Canada
University of Birmingham
United Kingdom
jo@jovermeulen.com
Robert Hendley
University of Birmingham
United Kingdom
r.j.hendley@cs.bham.ac.uk
Mirco Musolesi
University College London
United Kingdom
m.musolesi@ucl.ac.uk
ABSTRACT
Notifications are extremely beneficial to users, but they of-
ten 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 noti-
fication. Through a mixed method of automated smartphone
logging and experience sampling we collected 10372 in-the-
wild notifications and 474 questionnaire responses on notifi-
cation 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 re-
lationship as well as the type, completion level and complex-
ity 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 In-
terfaces
INTRODUCTION
Smartphones enable a new form of effortless information
awareness. Throughout the day, a smartphone user receives a
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©2016 ACM. ISBN 978-1-4503-3362-7/16/05...$15.00.
DOI: http://dx.doi.org/10.1145/2858036.2858566
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 newly-
arrived 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 mo-
ments. 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 in-
formation 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 re-
ceptivity to a notification is determined by: (i) how interest-
ing, entertaining, relevant and actionable its content is for the
user [15]; (ii) the type of application that triggers it – com-
munication applications are considered as the most impor-
tant [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 ma-
chine learning classifiers provided with different contextual

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 notifi-
cations of a regularly used application. The key reason be-
hind this is the fact that the accuracy of these mechanisms is
still lower than the user’s expectations. In a real world sce-
nario, 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 in-
terruptibility 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 influenc-
ing 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 in-
situ study to collect objective and subjective data about mo-
bile 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 to-
wards a notification. Through My Phone and Me, we col-
lected 10372 notifications, 474 responses for the ESM ques-
tionnaires and 11 personality test results from 20 users. Using
this data, we investigate users’ interaction with mobile noti-
fications in different physical and cognitive contexts. More
specifically, the key contributions of this work are the inves-
tigation 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 relation-
ship, 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 on-
going 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 dis-
ruption 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 sender-
recipient 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 re-
sults 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 dis-
missed. From our results, we also observe that, nevertheless,
(a) (b)
(c) (d)
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 con-
tain valuable information. While users are aware of notifica-
tions 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 per-
sonalized interaction between a smartphone and its user.
REASONING ABOUT USERS’ RECEPTIVITY TO MOBILE
NOTIFICATIONS
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 pos-
sible 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 interrup-
tion than from a mobile notifications because of the presence
of an interrupter in the former case. Mobile notifications en-
able flexibility in the way an interruption is handled because
of the lack of the physical presence of the sender and the asyn-
chronous nature of mobile messaging communication
1
. Thus,
the exact moment of handling an interruption can be negoti-
ated and the recipient can decide when and how to attend to a
notification.
1
Certain social norms and expectations from the sender side, how-
ever, constrain the flexibility that the receiver has in reacting to a
message [30].

Seen
Time
(a)
(b)
(c1)
Decision
Time
(c2)
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 at-
tend to a notification once they arrive at the destination, yet
forget to do so. Finally, although designed to signal an inter-
ruption 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 at-
tend 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 hap-
pen 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 de-
fined 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 notifica-
tions.
Our assumption is that the response time for a notification and
the disruption perceived by the user are influenced by the dif-
ferent 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 reac-
tion to notifications and then queries the users to identify their
rating of the disruption caused by the notification. We aug-
ment this with questions about the motivation for handling a
notification in a particular way. Further, our ESM question-
naires 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 re-
sponse 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 notifi-
cation: 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 un-
locks 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 notifica-
tion. Moreover, we are interested in the way users triage dis-
ruptive notifications. Can users quickly discern when noti-
fications are disruptive? We hypothesize that humans might
still attend to a notification, even if they know that the pri-
mary 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 no-
tification 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 disruptive-
ness. Finally, we investigate the potential role of individual
psychological traits on how users perceive and react to dis-
ruptive notifications.
DATA COLLECTION
In order to investigate the nature of disruptive notifications
and factors that determine the user’s receptivity to mobile no-
tifications in different physical and cognitive situations, we
conducted an in-situ field study. More specifically, we devel-
oped 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

Group
Features
Time Arrival, seen and the removal time of a notification.
Notification
response
Whether the notification was clicked or dismissed.
Notification
details
Sender application and the title of a notification.
Alert type
Signals used by a notification to alert the user: sound, vi-
brate, and flashing LED.
Context
data
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.
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 Notifica-
tion 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 be-
tween 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 question-
naire is triggered only when a notification is handled; it con-
tains questions about why the notification was clicked or dis-
missed 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 trig-
ger 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 be-
cause 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 op-
tions 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 an-
noyance. Therefore, the application allows the users to dic-
tate 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 Fac-
tor Markers from the International Personality Item Pool, de-
Question Options
Did you notice the alert
(e.g., vibration, sound,
flashing LED) for this
notification when it first
arrived?
(i) Yes, and I decided to check my phone im-
mediately. (ii) Yes, but I was already using
my phone. (iii) Yes, but I ignored the alert.
(iv) No, I didn’t notice the alert.
How did you handle the
notification when you first
saw it?
(i) I decided to immediately click it. (ii) I de-
cided to dismiss it because it didn’t require
any further action. (iii) I decided to dismiss
it because it was not relevant or useful. (iv) I
decided to return to it later. (v) Other (descrip-
tive).
Select all factors that
made you decide to
click/dismiss the notifica-
tion.
(i) The sender is important. (ii) The content is
important. (iii) The content is urgent. (iv) The
content is useful. (v) I was waiting for this
notification. (vi) The action demanded by the
sender does not require a lot of effort. (vii) At
this moment, I was free. (viii) Other (descrip-
tive).
What best describes your
relationship to the sender?
(i) Partner (ii) Immediate family (chil-
dren, parents) (iii) Extended family
(nieces/nephews, cousins, aunts/uncles)
(iv) Friend (v) Acquaintance (vi) Superior
at work (vii) Colleague (viii) Subordinate
at work (ix) Client (x) Service provider
(xi) Sender is not a person (xii) Other
relationship (descriptive).
Please describe what the
notification was about.
Descriptive response.
Please describe what activ-
ity you were involved with
when you received the no-
tification.
Descriptive response.
When the notification ar-
rived, I was:
(i) Starting a new task/activity. (ii) In the
middle of a task/activity. (iii) Finishing a
task/activity. (iv) Not doing anything.
The task/activity I was do-
ing when the notification
arrived was complex.
Five-level Likert scale rating between
"strongly disagree" and "strongly agree".
I found the notification
disruptive.
Five-level Likert scale rating between
"strongly disagree" and "strongly agree".
Table 2. Questions and their options from questionnaire triggered by the
My Phone and Me app.
veloped by Goldberg [16]. A notification to take this test is
triggered once the user has responded to 28 questionnaires.
A user can also take the test at any time by clicking on the
personality test button present in the application’s action bar.
Recruitment of the Participants
The My Phone and Me application was published on the
Google Play Store from 12th August 2015. It was installed
by 74 participants without any monetary incentive. As shown
in Figure 1, My Phone and Me tells the users about their ad-
diction to the phone. It allows users to check statistics on
their phone usage and interruptions. The application visual-
izes a user’s phone activities based on different criteria, such
as their hourly phone usage (Figure 1 C), hourly usage of in-
dividual applications (Figure 1 D) and how much they inter-
act with notifications (Figure 1 B). We believe that displaying
this information has a minimal interference with users’ ac-
tual behavior for interacting with notifications, but provides
a valuable functionality in order to make the users keep the
application installed on their phones.

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "My phone and me: understanding people's receptivity to mobile notifications" ?

In this paper the authors 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. The authors 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. The authors found that even a notification that contains important or useful content can cause disruption.