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Habits make smartphone use more pervasive

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Examining several sources of data on smartphone use, this paper presents evidence for the popular conjecture that mobile devices are “habit-forming.” The form of habits we identified is called a checking habit: brief, repetitive inspection of dynamic content quickly accessible on the device. We describe findings on kinds and frequencies of checking behaviors in three studies. We found that checking habits occasionally spur users to do other things with the device and may increase usage overall. Data from a controlled field experiment show that checking behaviors emerge and are reinforced by informational “rewards” that are very quickly accessible. Qualitative data suggest that although repetitive habitual use is frequent, it is experienced more as an annoyance than an addiction. We conclude that supporting habit-formation is an opportunity for making smartphones more “personal” and “pervasive.”
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ORIGINAL ARTICLE
Habits make smartphone use more pervasive
Antti Oulasvirta Tye Rattenbury
Lingyi Ma Eeva Raita
Received: 22 September 2010 / Accepted: 10 May 2011
!Springer-Verlag London Limited 2011
Abstract Examining several sources of data on smart-
phone use, this paper presents evidence for the popular
conjecture that mobile devices are ‘‘habit-forming.’’ The
form of habits we identified is called a checking habit:
brief, repetitive inspection of dynamic content quickly
accessible on the device. We describe findings on kinds and
frequencies of checking behaviors in three studies. We
found that checking habits occasionally spur users to do
other things with the device and may increase usage
overall. Data from a controlled field experiment show that
checking behaviors emerge and are reinforced by infor-
mational ‘‘rewards’’ that are very quickly accessible.
Qualitative data suggest that although repetitive habitual
use is frequent, it is experienced more as an annoyance
than an addiction. We conclude that supporting habit-for-
mation is an opportunity for making smartphones more
‘personal’’ and ‘‘pervasive.’
Keywords Smartphones !Habits !Logging data !
Diary studies
1 Introduction
The impact of portable computing devices is undergoing a
heated debate in the popular media.
1
It is evident that
users’ practices are changing—they socialize in new ways;
they do tasks in new ways, often interleaving and cross-
pollinating between activities; they share and gather
information in new ways. A concern expressed repeatedly
centers around the notion of habit—that is, how new
technologies, like mobile phones in the 1990s and laptops
and smartphones in the 2000s, spur unforeseen conse-
quences the fabric of everyday life. While many appre-
ciate the ubiquitous and continuous access to social
networks, there are concerns about invasion into private
domains [8], and it has been observed that gains achieved
in productivity do not automatically generate free time but
complicate work–life balance [9]. Indeed, sociologists
have reported Westerners’ time-use becoming more
irregular, fragmented, overlapped, and shifting to new
places [13,18].
Smartphones—handheld personal computers—represent
the most recent step in the evolution of portable informa-
tion and communication technology (see Fig. 1). Smart-
phones—equipped with persistent network connectivity
and supporting the installation of new applications—have
the potential to produce new habits related to Internet use.
Their exact impact on the formation of new habits is not
well understood, however. Our preliminary logging studies
indicated that smartphones could be used as much as 2.7 h
A. Oulasvirta (&)!L. Ma !E. Raita
Helsinki Institute for Information Technology HIIT,
Aalto University, Helsinki, Finland
e-mail: antti.oulasvirta@hiit.fi
T. Rattenbury
Intel Labs, Portland, OR, USA
1
http://www.nytimes.com/2010/06/07/technology/07brain.html
http://www.nytimes.com/2010/06/10/garden/10childtech.html
http://www.techdirt.com/articles/20100607/0224269710.shtml
http://www.nytimes.com/2010/06/11/opinion/11Pinker.html.
123
Pers Ubiquit Comput
DOI 10.1007/s00779-011-0412-2
per day and typically longer than traditional forms of
computing.
2
However, it was left open what the new habits
are, qualitatively speaking, and what their role in the fabric
of everyday use is.
The goal of the present paper is to investigate the habit-
forming nature of smartphones in more detail and with a
specific view to what habits are and what their role is in
human–computer interaction. As our scientific approach,
we build on a recent theory in cognitive psychology that
defines habit as an automatic behavior triggered by situ-
ational cues, such as places, people, and preceding actions
[10,21]. The study of habits in this context is the study of
two interrelated things: (1) automatized behaviors relating
to smartphone use and (2) the cues that trigger these
behaviors. If we accept that habits are a cognitively
‘inexpensive’’ element of behavior, due to automatic and
‘ballistic’’ execution, understanding them is essential in
the pursuit of making computing devices natural, ‘‘invisi-
ble,’’ and pervasively used. At the other extreme, habits
that are repetitively triggered by external cues reduce the
intrinsic locus of control of an individual. Smartphones are
a potential source of addictions, and understanding them is
essential in preventing them (see Sect. 1.1).
Contrary to the persuasive computing enterprise [3], the
theory of habits as automatized behaviors [21] does not
deal as much with changing or understanding how new
behaviors emerge—rather the purpose is understand what
habits are. The most popular model in persuasive com-
puting, the Behavior Change Model [2], suggests that
behavior changes when a person is motivated to achieve
something novel, has some ability to achieve it, and is
triggered (or cued) by an external or internal event. The
model does not discuss at length how behavior and its
cognitive underpinnings change when it automatizes after
repeated execution. In this way, the two approaches to user
behavior complement each other: the one discusses the
birth of new behaviors and the other the status of ‘‘old
behaviors.’
We address three broad and interrelated questions:
1. How prominent a factor, if at all, are habits in
smartphone use?
2. How do users experience habits?
3. What design factors promote habit-formation?
We approach these questions by examining data from
three longitudinal studies of smartphone use conducted
between 2005 and 2010:
1. A logging study comparing usage patterns of smart-
phone users (N=136) to those of laptop users
(N=160), with a focus on the prevalence of habit-
driven behavior and associated factors;
2. An intervention study where awareness cues (real-time
location information) were added to the address book
to three user groups (N=5?4?6);
3. A diary study of smartphone users’ (N=12) experi-
ences during first 2 weeks of use.
The typical method to examine users’ ‘‘habits’’ and
‘practices’’ from quantitative data is to look at frequent
behaviors across all users or within an individual; for
example, logging studies of mobile phone use (e.g., [19])
and time-use studies (e.g., [14]) typically compare averages
of behaviors or application uses. However, habits and
frequent behaviors should not be confused; the former is a
subset of the latter. Our starting point is to look for
behaviors that are consistently associated with an explicit
cue (other recorded event) or implicit cue (an event that
logically precedes an action). Therefore, our unit of anal-
ysis is a usage session (henceforth: a session): all user
actions recorded between two events: (1) the user activat-
ing the device from the idle or screensaver mode and (2)
the next time the phone is idle or locked again. Sessions are
quantitatively characterized by a number of properties,
Fig. 1 Three smartphones
investigated in the studies (from
left to right): Android G1
(launched to market in 2008),
Nokia 6600 (2003), and Nokia
N97 (2009)
2
http://www2.berkeley.intel-research.net/*tlratten/public_usage_
data/.
Pers Ubiquit Comput
123
such as total duration (in seconds), the applications laun-
ched, frequency and order of individual applications
launched, average duration for each application, etc. To
identify habits from logging data, we looked for sessions or
part thereof that fulfill three criteria: (1) sessions that are
extremely rapidly executed, with the idea that non-habitual
behavior is slower due to decision-making and problem-
solving, etc., (2) sessions that are repeated in very similar
fashion time after time, and thus more likely to represent
automated actions, (3) sessions that are consistently asso-
ciated with the same trigger (cue): whenever the cue
appears, so should the associated behavior. After several
analyses of the available data, where we tried to mine for
frequent sequences of interactions on the phone, we con-
verged to a very simple type of habit: checking habit.
To recap the main finding, the data provides evidence
for habit-formation in smartphones use, mainly attributable
to their capacity of providing quick access to rewards like
social networking, communications, and news. Checking
habits are automated behaviors where the device is quickly
opened to check the standby screen or information content
in a specific application. These habits are triggered by
various different cues outside the device, such as situations
and emotional states. The automated behaviors take the
users, very quickly, to different screens that provide
informational value or rewards. These rewards help users
avoid boredom and cope with a lack of stimuli in everyday
situations as well as make them aware of interesting events
and social networks. Looking at qualitative data, we found
that users themselves do not necessarily describe habit-
formation as problematic. Even when the phone usage is
dominated by frequent checking, people described the use
as, at worst, slightly annoying. Our conclusion is that
checking habits constitute an important part of the behavior
driving smartphone use. Indicative of their importance for a
device being frequently used, we found some evidence that
increases in the occurrence of certain habits coincides with
a net increase in usage overall. In other words, checking
habits may function as a ‘‘gateway’’ to other functionality
and content on the device.
1.1 Habits as addictions versus enabler of multitasking
The cues that trigger habitual behaviors can be external
events or internal states that are only partly related to the
situation at hand [21]; for example, the lack of stimulation
or a desire to ‘‘stay on top’’ could become a cue associated
with the behavior of picking up the phone to see what is
available. The cue could also be the mobile device itself—
for example, seeing the phone lying on the table reminds us
of rewards that could be accessed, triggering the associated
usage behavior.
The theory [21] posits that habits have both positive and
negative outcomes for behavior. On the one hand, habits
are necessary in control of action, their automatic execu-
tion enabling multitasking and learning of complex skills
as well as retaining adequate performance in novel situa-
tions [4]. Knowing that the cognitive resources of a mobile
user are heavily competed for [11], habits may enable a
host of interactions not possible if attention should be fully
concentrated to the device. Habits are also important
socially—habits perceived by others shape who you are as
a computer user [1], and adherence to a predictable pattern
of behavior facilitates maintenance of social relationships
[20].
On the other hand, the downside is that behavior may
become excessively controlled by extrinsic factors,
undermining the pursuit of the more self-guided goals.
Computer-related addictions, such as those associated with
Facebook or email (both recognized by psychologists and
in popular media), are abnormal habits where computers
(or their content) have become overly strong cues for
behaviors.
Technically, an addiction is defined as a repetitive habit
pattern that increases the risk of disease and/or associated
personal and social problems, often experienced subjec-
tively as ‘‘loss of control’’ [9]. The Diagnostic and Sta-
tistical Manual of Mental Disorders DSM-IV recognizes
gambling but not internet or media use as potential
addictions. Recent theories suggest that internet and media
‘addiction’’ is rather a struggle to maintain effective self-
regulation over problematic habit-driven behavior. In other
words, addiction and habits are parts of the same contin-
uum [5], but what we colloquially ascribe as Internet or
media addiction is better described as overuse due to loss
of self-control.
It is an open question what the good versus bad habits of
smartphone users are and to what extent they resemble
overuse and even addiction.
2 Study 1: longitudinal logging of smartphones
and laptops
We present results from two usage tracking studies. The
most recent study, conducted between May and July of
2009, tracked existing Android G1 smartphone (see Fig. 1)
users in the continental US participants were recruited to
fulfill general demographic criteria, subject to the con-
straint that they had owned their G1 smartphone for at least
1 month prior to the start of the study. 136 participants
completed the study, which involved a pre-survey, at least
6 weeks of tracked usage data (median =52 days), and a
post-survey. Twenty participants were chosen at random
Pers Ubiquit Comput
123
from two major cities, Denver and Seattle, for semi-
structured, ethnographic interviews to help contextualize
the tracked data. Of the 136 participants, 43% were men,
35% were between the ages of 18 and 25, 50% were
between the ages of 26 and 39, and 14% were between the
ages of 40 and 54.
The data were collected via custom software written for
the Android G1 smartphone. It tracked a variety of hard-
ware and operating system variables (e.g., processor utili-
zation and active network interfaces) as well as user input,
focal application, and screen state. Due to limitations in the
performance of the smartphone platform, user input, focal
application, and screen state were only tracked approxi-
mately every 3 s (the frequency would decrease if the
device went into a standby or sleep mode). Log files con-
taining between a few minutes (if logging at highest fre-
quency) and a few hours (if logging from standby or sleep
mode) were encrypted and uploaded to central servers. In
this data, the top applications for Android G1 smartphones
are, in terms of amount of active use: the home screen
application (used 21.77% of the time), SMS/MMS
(17.57%), browser (10.67%), phone/calls (9.38%), contact
book (7.07%), Gmail (3.73%), 3rd party SMS applications
(2.75%), default email (2.55%), and the application market
(1.41%).
The other study we draw on, which helps contextualize
the smartphone data, involved the tracking of personal
laptop users between August and October of 2007. As with
the smartphone study, participants were recruited in the
continental United States. Usage data were collected from
160 laptop users, with an average of 50 days per user. The
data were collected using custom tracking software that
measured aspects of hardware, software, and user behavior
at a frequency of once per second. Semi-structured eth-
nographic interviews were conducted with 15 of the par-
ticipants. Some findings from this study have been
published elsewhere [17]. The top applications used were
as follows: iexplore.exe (used 42.11% of the time), fire-
fox.exe (15.13%), ybrowser.exe (2.83%), msimn.exe
(1.82%), waol.exe (1.55%), aim.exe (1.55%), aim6.exe
(0.92%), outlook.exe (0.86%), juno.exe (0.34%), and
yahoomessenger.exe (0.27%).
Working from usage sessions in the two data sets, we
highlight three points in the following. First, the incidence
of brief (short duration) habitual usage sessions on smart-
phones is significantly more common than on laptops.
Second, smartphone usage tends to be more evenly spread
throughout the day than laptop usage. Finally, increased
use of reward-based applications, e.g., SMS messaging
clients and web browsers (and experience and awareness of
the reward values offered by these applications), coincides
with an increased incidence of habit behaviors involving
these applications.
We use SIRBshort duration (less than 30 s), isolated
(separated from preceding usage session by at least
10 min), reward-based (at least 50% of the usage session
duration is spent interacting with applications that provide
the reward values discussed above)—usage sessions as a
proxy for habitual device usage. Clearly, this proxy does
not account for habitual use occurring in long duration or
non-isolated usage sessions. Unfortunately, distinguishing
which long duration, non-isolated usage sessions, or parts
of such sessions correspond to habits was not possible in a
retrospective analysis setting based on logging data only.
To our first point, we measured the average number of
SIRB sessions per day per user (pdpu) for smartphones and
laptops.
3
We filtered the original data sets to exclude users
for whom less than 100 usage sessions were recorded
during the study duration. This left 130 laptop users (from
the original 160) and 135 smartphone users (from the ori-
ginal 136). For laptops users, SIRB sessions pdpu had the
following summary statistics: mean =0.39, med-
ian =0.20, standard deviation =0.52—the median num-
ber of usage sessions pdpu for laptops was 7.39. For
smartphone users, SIRB sessions pdpu had the following
summary statistics: mean =3.39, median =3.19, standard
deviation =1.88—the median number of usage sessions
pdpu for smartphones was 34.11. If we compare the dis-
tributions of SIRB sessions per day between laptops and
smartphones, we see a significant difference skewed
Fig. 2 Plot of cumulative distributions of SIRB usage sessions per
day for laptop and smartphone users. A Kolmogorov–Smirnov test
comparing the two cumulative distributions revealed a significant
difference: p=3.17e
-044
3
Different temporal thresholds were used for the laptop and
smartphone data. The thresholds (29 s session duration for the laptop
data and 24 s for smartphone data) were chosen because they are the
median 20th percentile session durations in the respective data sets.
Equivalent results were achieved with other threshold values.
Pers Ubiquit Comput
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toward more sessions on smartphones. In Fig. 2, we plot
the cumulative distributions for the SIRB sessions per day
for laptops and smartphones.
Additional proxy measurements of habitual behavior are
the percentage of usage sessions where reward-based
application usage initiates or terminates the session. For the
laptops we studied, the correlation of these proxies with
percentage of usage sessions that are SIRB sessions are as
follows: r
2
=0.33 (p=6.8e
-013
,N=130) relative to the
incidences at the beginning of sessions and r
2
=0.38
(p=8.33e
-015
,N=130) relative to the incidences at the
end of sessions. Analogous analyses on smartphones does
not provide useful results, because the nearly all usage
sessions on the Android G1 start and end with home screen
application, which provides reward value (and hence does
not vary significantly across users or across usage
sessions).
Next, we compare the cumulative distributions of the
amount of time per day these devices were used and the
spread of this use throughout the hours of the day. In terms
of hours of use per day, the distribution for laptops (median
duration was *87 min/day) is significantly skewed shorter
than for smartphones (median duration *160 min/day):
p=6.44e
-008
. In terms of spread of use throughout the
day, measured as the entropy of usage split into 15 min
blocks covering the 24 h of the day, the Kolmogorov–
Smirnov test of the cumulative distributions for laptops
versus smartphones shows that laptops skewed signifi-
cantly smaller (i.e., less spread) than smartphones:
p=1.99e
-020
.
Finally, we investigated the relationship between SIRB
sessions and overall use of a device. In Fig. 3, we plot
SIRB sessions per day versus the percentage of total usage
time spent interacting with reward-based applications for
the smartphone users. SIRB sessions per day are slightly
positively correlated with percentage of reward-based:
r
2
=0.031, p=0.0397. Analogous calculations on the
laptop data shows a slightly positive correlation between
SIRB sessions per day and percentage of reward-based:
r
2
=0.054, p=0.0080.
In sum, smartphone use is more aptly characterized by
SIRB—short duration, isolated, reward-based—sessions
than are laptops. Our present explanation is that, relative to
laptops, smartphones are significantly more pervasive in
everyday life due to being carried around (see however
[15]). Because of this, they are a much more constant and
present situational cue than laptops based on the total
amount of usage and the distribution of this usage
throughout the day. Furthermore, smartphones offer a
wider variety of channels to connect to remote information
and people than do laptops, increasing the overall reward
value of ‘‘checking’’ habits.
3 Study 2: a field experiment where the reward value
of a quickly accessible application was increased
The ideal evidence for the existence of habit-formation in
smartphone use would come from a study where the
informational value of an application (amount of up-to-date
information) was changed and changes in usage sessions
recorded. In this section, we report on such an experiment
[12]. An ‘‘A-B intervention experiment’’ consists of two
equally long periods A and B where period A is used to
record a baseline where no intervention (treatment) is
given. The intervention, which takes place in period B,
consists here of turning on awareness cues on a contact
book (GSM cell-derived district labels, recent use of the
phone, Bluetooth presence of friends, and calendar events).
The smartphone used in this study was one of the first
successful Nokia smartphones, the Symbian S60 model
6600 (see Fig. 1). The idea of the A-B design is that period
A provides a baseline for comparison.
Three user groups participated: the Family (A-B-A
design), the Entrepreneurs (A-B), and the Schoolmates (B
only). The Family group consisted of a mother and three
teenaged children, the Entrepreneurs group include one
woman and four men, all in the same high school. The
Schoolmates are comprised of five women and one man,
also attending the same high school. Data gathering took
place over a period of year in 2004 and 2005 so that each
group participated between 2 and 4 months. ContextLog-
ger1 [16] was used for data collection, allowing recording
of sensor data, communication transactions, including the
contents and transaction logs of all SMS and voice
Fig. 3 A slightly positive correlation between SIRB sessions per day
(y-axis) and percentage of usage time spent interacting with reward-
based applications (x-axis) in our smartphone data: r
2
=0.031
(p=0.0397)
Pers Ubiquit Comput
123
communication, all commands given to specific applica-
tions (stand-by screen, contact list), and all application
launches.
The full data are reported in an earlier paper [12],
whereas we here revisit the data to examine habits. One
problem we faced with this data is that the user may stay in
a certain application at the end of a previous session and
that application is not recorded as ‘‘the anchor’’ when user
returns after idling. However, for the home screen and
contact book, we could perform an analysis.
There were 30,287 total usage sessions over the three
groups (8,864, 9,849, and 11,582, for Family, Entrepre-
neurs, and Schoolmates) in the data. We concentrate on
two habits prevalent in the data:
1. Scrolling: From idle/off/locked mode, going directly to
the contact book and (optionally) navigating it by
scrolling up/down and then idling or turning on the
keypad lock.
2. Touching: Turning off the screen saver by touching the
joystick and/or unlocking the screen lock. This action
results in either the standby screen or an open
application if the screensaver went when it was
foregrounded.
When we examine the 30,295 sessions in the log data,
we find that 3.7% are scrolling sessions and 35% are
touching sessions. In other words, touching behavior is
very prevalent in the data. Moreover, these sessions are
very brief. The median session activity time of scrolling is
7 s with 92% of samples shorter than 1 min. Moreover, the
median session activity time of touching is 1 s with 90% of
samples shorter than 35 s. The two habits are differently
spread throughout the day. Scrolling takes place mainly in
the afternoon and evening, while touching is more equally
distributed across throughout the waking hours.
As the decisive piece of evidence, we compared the A
periods to the B periods to understand whether the addition
of dynamic content increases habit strength. This analysis
was done only for the Family and Entrepreneurs groups
who were part of the A-B design that allow this sort of
comparison. As Fig. 4shows, adding the real-time cues
increased both touching and scrolling behaviors. Scrolling
increased from an average of 0.1 behaviors per day per user
(pdpu) to 0.9, and touching from 5.4 to 12.1 pdpu. To test if
these increases are statistically significant, we used the
Wilcoxon signed-ranks test for two dependent samples,
comparing median number of behaviors during A and B
periods. The pvalues for both touching and scrolling were
\0.001, indicating an effect of the intervention.
Interestingly, the frequency of other application use also
increased from phase A to B, from 9.7 pdpu to 16.0 pdpu,
the difference being statistically significant with a
pvalue \0.001.
4 Study 3: self-reports on repetitive use of smartphones
In winter 2010, twelve students of the Helsinki School of
Economics were given smartphones (Nokia model N97),
asking them to keep a diary for the first 2 weeks of use
from the moment they received the phones.
The data collection method was a modification of the
day reconstruction method [6]: in the diary, a participant
first fills in all daily activities for the each of the five given
time slots (morning, forenoon, afternoon, evening, and
night), after which she/he reports what the device was used
for in each activity, describing the feelings, ideas, opinions,
and emotions linked to the events. After the study, the
participants were extensively interviewed for their diary
entries. All participants also filled in a questionnaire sur-
veying the extent of device use before and after the study.
Altogether 702 use sessions were self-reported. These
sessions were analyzed by first classifying them into
activity categories (e.g., social media, calling, news, and
browsing) and then tabulating the frequency, with the
associated real-world situation where the use took place,
and the time of day. A limitation of the method is that part
of repetitive use probably went unreported, most likely
because some use sessions were very quick or deemed too
unimportant to be reported. Additionally, the descriptions
of news and feed applications (news site apps, RSS) were
often very brief, hardly mentioning anything else than the
name of the application. This shows up in a discrepancy in
between the users’ general descriptions and their daily
reports, favoring unexpected over routine use cases.
The strongest habitual patterns in this data related to the
use of Internet in various forms: checking e-mails, Face-
book, update feeds, and news headlines.
E-mail: All except one participant used their phone for
checking e-mails. Four users checked their e-mails only
a couple of times, two approximately every other day,
and the rest five participants daily (at least 5 times/
week). E-mails were mostly checked either at home
(30%), on the go (30%), or during a lecture (16%).
Descriptions of the use of e-mail were mainly related to
checking e-mails and, thus, achieving a sort of aware-
ness that nothing important is missed, as opposed to
actively writing messages to others.
Facebook: Ten participants used the phone for check-
ing Facebook, five doing this only occasionally (1–4
times/week), while the other five checked Facebook
more often (at least 7 times/week). Just like e-mails,
Facebook was checked mostly either at home (30%), on
the go (36%), or during a lecture (10%). A female
student described her Facebook checking: ‘‘I spent my
whole day in the reading room doing homework.
I usually keep breaks when using [the device], but
Pers Ubiquit Comput
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nowadays I take my phone to the room, putting it on
silent, and checking it every half-an-hour either for
Facebook or email.’
Update Feeds and News Headlines: Eleven participants
used the phone for reading the news. Seven did this
occasionally (1–8/2 weeks) and the rest five more often
(11–16 times/2 weeks). The news was mostly checked
on the go (60%) and additionally at home (18%) or
during a lecture (9%). The participants reported appre-
ciating the easy form of information and the ability to
stay in touch with the world.
Overall, these habits were concentrated to the ‘‘empty’
moments when the students had very little else to do—the
dominant contexts being lectures, commuting, and morn-
ings/evenings at home. Moreover, they mostly took place
when alone instead of when with interacting others.
Weekends involved clearly less habit sessions than week-
days. In the interviews, the participants (university stu-
dents) told that they slept late in the weekends, went out
with friends, partied, etc. There were fewer changes, and
probably less need to use the phone alone. The comparison
between the first and the second week showed no increase
in the frequency of use.
We then estimated habit strength: (1) the frequency of
execution and (2) association with particular situational
cues [21]. From our data, habit strength was calculated
from frequencies of application use in a particular con-
text. To distinguish among potential habits versus regular
use, we focused on a subset where the user reported at
least 10 occurrences over the time period of 2 weeks.
Nineteen potential habits were identified this way across
the twelve participants, distributed to nine participants.
The most popular applications were, in order: e-mail (five
habit candidates), Facebook (4), the news (4), feed (3),
music (2), calendar (1), and browsing (1). The average
number of habit candidates per person was 1.67. We then
calculated the number of contexts (activities during which
smartphone use took place) required to explain at least
Fig. 4 Effects of intervention
on frequencies of touching and
scrolling behavior
Pers Ubiquit Comput
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50% of occurrences within this subset. Interestingly, the
average number amounted to only 1.35, which conveys
the regularity of habit-to-context association in the data.
In other words, 1.35 contexts were enough to describe at
least 50% of the occurrences of a habit. Also noteworthy
was that sometimes, although rarely, a set of applications
was repeated together. ‘‘Don’’ is a good example,
describing the use of news (Helsingin Sanomat), Face-
book, and Gmail together in 31 of the 73 reported
instances. Don typically used these while ‘‘killing time’
in lectures or transit.
The most often mentioned motivation for the habits
related to the novel content the phone provided an access
to. The main motivators for habits were: entertainment,
killing time, and awareness. In relation to the first moti-
vator a female participant described her use of Facebook
and e-mail as ‘‘entertainment’’ during reading and home-
work. In addition, she used the navigator and Google Maps,
her ‘‘favorite applications,’’ to ‘‘amuse herself.’’ Besides
entertainment, the participants described habit behaviors as
ways to restore attention and make boring moments feel
like going faster (killing time):
‘During the lecture, I used the Internet to quickly
browse news, because I wasn’t able to concentrate on
the teacher. A small pause returned my interest to the
lecture.’
‘In the bus, I again key Facebook and e-mail, feeling
that the trip goes faster this way.’
The third motivator for habits was awareness, as the
following excerpt illustrates:
‘I follow [the newspaper’s] updates almost in real-
time. Within 15 min, I’ve seen the new things. I
guess I feel like an individual following her time. Or
not.’
But awareness was not appreciated by all. One partici-
pant contemplated whether continuous checking takes
away part of the fun in e-mail, because there will be no
surprises as the e-mails do not accumulate. Additionally,
other participant was wondering whether constant checking
of e-mail was causing her too much stress.
We also looked at the way the participants described the
experience of repetitive use. A handful of the participants
were aware of their repetitive use of the phone:
‘I glance at the Facebook status page and read my
e-mails even every half-an-hour, every time reading
[for a test] starts to bore me.’
Another user commented on her glancing of update
feeds on the ‘‘desktop’’ every 20 min even when she was
trying to do her homework: ‘‘The temptation is great,
because there’s always friends’ update on the screen.’’
Repetitive use was experienced as annoying at times.
Two participants described their relationship to Facebook
by using the word addiction: the first in a jocular manner
and the second describing it as a ‘‘mild’’ addiction. Third
participant described games as ‘‘hooking.’
Another participant describes repetitive checking as
distracting from regular activities:
‘I was browsing the depths of Internet with the phone
and it slowed down my eating. It annoyed me, I’d
have to finish my Master’s thesis.’
In another excerpt, the same participant describes not
using the phone while writing the Master’s thesis as a good
thing that enables him to concentrate on work.
A few times in the data, we see participants reporting
annoyance with repetitive habitual use. However, the
majority of the participants did not consider habitual use
negatively, even if it was very frequent.
5 Discussion
To summarize, the findings are:
1. Brief usage sessions repeating over time, or ‘‘checking
behaviors,’’ comprise a large part of smartphone use.
Brief usage sessions were prevalent in all the three
data sets. In the first study (Android G1, US users),
about 18% of use sessions were brief and included
focus on only one application. In the second study
(Nokia 6600, Finnish users), 35% of use sessions were
‘touching’’ sessions where the home screen was
viewed for one second. In the third (diary study of
Finnish N97 users), a user had on average 1.6 potential
habits (10 or more uses over 2 weeks). Over the
studies, the applications associated with checking
behaviors included the home screen, contact book,
e-mail, social media, and news.
2. Checking habits are particularly characteristic of
smartphone use. Comparing smartphones to laptops,
we observed that smartphone use is significantly
shorter in duration, more evenly spread throughout
the day, and nearly twice as abundant (in terms of total
time spent using the device).
3. Habits may increase overall phone use, especially
other applications. We call these ‘‘gateway habits.’’ In
our data, the frequency of brief ‘‘checks’’ to a phone
showed a slight increase with the use of a small set of
applications.
4. Quick access to dynamic content can induce habits, as
persuasive computing research suggests [2]. We saw in
the second study (Nokia 6600) that when the infor-
mational value (reward value) of an application is
Pers Ubiquit Comput
123
increased, habit strength (frequency of checking
behavior) increases.
5. A smartphone use habit is tightly associated with a
particular triggering context, as the theory predicts
[21]. In the diary data, a habit was associated with only
1.35 contexts (e.g., lecture, bus trip, and home) on
average.
6. Smartphone-related habits are not yet perceived as
problematic. The diary study users spontaneously
bring up the issue of repeatedly checking their phones.
Some users considered it an annoyance. Many positive
experiences of repetitive uses were mentioned as well,
mostly relating to entertainment, time-killing, and
diversion. It may be that the small sample size of the
diary study, together with brief duration, did not allow
for addictions to be observed.
Overall, we believe that the evidence is clear about the
existence of checking habits and their prevalence in the use
of smartphones. Checking behaviors, frequent in our data,
are typically very short and include only one application,
promoted by quick access to information and people that
smartphones can offer. More interestingly, the data sug-
gests that checking habits can act as a ‘‘gateway’’ to other
applications, leading to other actions being taken with the
device. Users start by opening portals to dynamic content
to check something or to acquire the stimulus for diversion
or entertainment. Based on the content that is accessed,
though, the habit may lead to a diverse variety of ‘‘next
actions.’’ This may be the main hook for designers to think
about how to work with habits as a portal. In other words,
application designers could built multi-part applications
where the use of one part is designed to become habitual—
for example, the target screen of checking habits—while
the other, connected parts, could be designed to leverage
the frequent attention of user to expose new content or
trigger other behaviors as suggested by the Behavior
Change Model [2].
The results also draw a distinction between smartphones
and laptops in what comes to the importance of repetitive
habitual use in the repertoire of use behaviors. In com-
paring laptops and smartphones, their availability as a
physical cue is significantly different—smartphones are
available and used more often throughout the day and are
used more in terms of total usage time. The more oppor-
tune moments are those where the mobile device is the
primary computer available (see also [15]), for example,
during transit or lectures. Moreover, smartphones provide
quicker access to content, and we know from studies of
mobile interaction that users are not able to concentrate on
mobile interaction for long times before abrupt events in
the environment and more highly prioritized tasks interrupt
[11]. Because of these factors, it is understandable that
smartphone-based habits are briefer than laptop-based
habits and more pervasive throughout the day.
To conclude, we hypothesize that the reward values
associated with checking habits can be broken into three
kinds: (1) informational, (2) interactional, and (3) aware-
ness. Informational reward is provided by dynamically
updated, but non-interactive information that the user can-
not affect. The clock on the home screen is a prime example
and the news feed another. Interactional value extends the
informational to include things that the user can immedi-
ately act upon. It also includes social interaction, which is
supported through many channels on portable computing
devices. An example interaction value comes from social
networking status updates: Checking out the latest updates,
the user can immediately respond and thus engages with the
content for a longer period of time. Finally, awareness
reward value is a specialized form of information value.
Whereas informational value corresponds to the user
learning something they did not know before, or confirming
something they did know about, awareness value corre-
sponds to the goal of maintaining a representation of the
dynamically changing external reality; for example, a user
might refresh their e-mail inbox to see whether any new
messages have arrived—and often no new messages have
arrived, providing awareness value. Or a user might check
Facebook to see whether a certain person has logged in in
order to directly communicate with him/her.
The most interesting opportunity we predict is that
checking habits may lead to more use overall, which can be
intelligently leveraged to get users to try new things and
adopt the device in richer ways to their everyday activities.
Habits spur new uses. As an example of using interactional
reward-value for new uses, a common usage pattern by
Android G1 users was to access the Android App Market,
where new applications can be browsed and installed. In
fact, some users even developed habits around accessing
the App Market, driven by a need to see which new
applications were available since their last visit. Making
this even easier by design will increase the frequency of
application download and thereby potentially increase the
utility of smartphones to users.
Driving wider behavioral changes by placing appropri-
ate behavioral triggers in the display path of smartphones is
another way to leverage the informational value derived by
habits. Klasnja et al. [7] describe many key design con-
siderations in driving health behavior change—in particu-
lar, the deployment of a persistent, glanceable display that
acts as a reminder to pursue the behavioral change.
Viewing such a display could become its own habit;
however, it is more likely that other habitual uses of the
smartphone will simply make such a display more ubiq-
uitous throughout a person’s day.
Pers Ubiquit Comput
123
In designing cross-platform applications, which are
increasingly popular at the moment, one should keep in
mind that minute changes in surface features of interaction
may change essential aspects of habit-execution, such as
the habit-triggering cues (e.g., user interface elements) or
the resulting action (e.g., interaction sequences), and lead
to confusion, effortful re-learning, or abandonment of a
service. In the diary study, for example, we observed that
users who previously had a non-smartphone device were
happy to have the opportunity to try new applications with
the new (Nokia N97) smartphone and invested the neces-
sary amount of time to achieve a sufficient level of com-
petence, whereas users who previously had had Apple’s
iPhone were reluctant to relearn use patterns and got much
less out of the phone and were also less happier with it.
All in all, we see that habit-formation, although obvi-
ously a delicate matter, presents a grand opportunity for
making mobile devices more ‘‘personal’’ and ‘‘pervasive.’’
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