Upright or Sideways?
Analysis of Smartphone Postures in the Wild
Alireza Sahami Shirazi, Niels Henze,
Tilman Dingler, Albrecht Schmidt
VIS, University of Stuttgart, Stuttgart, Germany
firstname.lastname@vis.uni-stuttgart.deKai Kunze
Osaka Prefecture University, Osaka, Japan
lastname@m.cs.osakafu-u.ac.jp
data from a large number of devices to provide insight about
mobile users’ behavior on a global scale. In this paper we
focus on analyzing how mobile phones are held and moved
while common apps and in particular web browsers are used.

ABSTRACTIn this paper, we investigate how smartphone applications,in particular web browsers, are used on mobile phones. Using a publicly available widget for smart phones, we recorded
app usage and the phones’ acceleration and orientation from1,330 devices. Combining app usage and sensor data we derive the device’s typical posture while different apps are used.
Analyzing motion data shows that devices are moved morewhile messaging and navigation apps are used as opposed
to browser and other common applications. The time distribution between landscape and portrait depicts that most of
the landscape mode time is used for burst interaction (e.g.,
text entry), except for Media apps, which are mostly usedin landscape mode. Additionally, we found that over 31%
of our users use more than one web browser. Our analysis
reveals that the duration of mobile browser sessions is longer
by a factor of 1.5 when browsers are explicitly started throughthe system’s launcher in comparison to being launched fromwithin another app. Further, users switch back and forth between apps and web browsers, which suggest that a tight andsmooth integration of web browsers with native apps can improve the overall usability. From our findings we derive design guidelines for app developers.

Early research by Oulasvirta et al. shows the importance toconduct in-situ experiments when analyzing mobile usage behavior [24]. They discuss how attention span dramatically
drops when comparing controlled interactions in the laboratory with interactions in mobile situations. Additionally, userinteractions with smartphones considerably vary dependingon the user and the apps used. Following a similar direction, our goal is to learn about how apps and in particular
web browsers are used on mobile phones. In contrast to previous work, we take into account sensor data recorded bysmartphones. Therefore, we built a widget for the Android
platform that monitors the currently running app and collects
data from the phones’ 3D accelerometer sensor. We measure
how the smartphone is accelerated whenever the user interacts with it while using various apps. Analyzing this data, we
can derive how the phone is held and moved while representative apps are used. Further, we are able to determine theapp that is used before another app is started. This provides
us with insights of usage behavior with apps on phones. The
work described here is, to the best of our knowledge, the firstattempt to augment app usage behavior with sensor data toassess postures of mobile phones in the wild.
In this paper we provide the following contributions:
1. We show that phones are being moved more during usageof navigation and messaging applications. Further, the mobile device is being moved about less when web browsers
are used in comparison with other apps such as Gmail,
SMS, and Google Maps.
2. We confirm that most apps including web browsers aremainly used in portrait mode. However, users briefly rotate the phone into landscape mode, for example, to reador enter text. On the other hand, Media apps are mainly
used in landscape mode.
3. Using another web browser in addition to the Android default web browser is common.
4. The duration of web browsing sessions varies depending
on apps used right before launching the web browser.

ACM Classification Keywords

H5.2 [Information interfaces and presentation]: User
Interfaces. - Graphical user interfaces.
Author Keywords

posture, orientation, device motion, WWW, web browser,
mobile phone, surfing, accelerometer, session
INTRODUCTION

Over the last decade, mobile phones have become the mostubiquitous devices. There were, for example, over 1.2 billion
mobile web users in 2012 [21]. A large body of work investigates how these devices are used in daily life. Previous work
mainly focused on controlled studies that draw a rich picture
but only of a few participants. Another direction uses surveys
and analyzes log files to provide a general overview but cannot provide a detailed picture. In contrast, we collect sensor

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Based on these findings we provide initial recommendations
for developers.
The paper is organized as follows: first, we discuss related
work followed by an introduction of the widget we implemented for the study. Second, we describe the data set we
1

They report the popularity of stationary mobile web accessis increasing, i.e., at home or at work. Further, they found apreference for native mobile applications as opposed to general web browsing.

recorded during our investigation. We report the device orientation in general and during a user session while using various
apps including web browsers. Furthermore, we analyze theduration of web surfing sessions on mobile phones, followed
by a discussion of implications and limitations. Finally, we
conclude our findings and describe possible future work.

Most work that investigates mobile Internet usage or mobile
device usage in general is based on a rather small numberof participants with often similar backgrounds. As this canhinder generalizations, researchers recently proposed to use
mobile application stores as means to conduct human subject studies [11, 12]. This approach has been used to investigate diverse questions. Henze et al., for example, used mobile games published in a mobile application store to assess
touch performance [13] and typing behavior on mobile devices [14]. Non-verbal communication using mobile apps has
also been assessed by publishing an app in a marketplace [28,30]. McMillan et al. describe that this approach can evenbe used to collect subjective feedback on a large scale [20].
AppAware allows its users to see what applications are being installed around their position. The app is used to learn
which apps users install [8] install on their device. Böhmer et
al. conducted a large-scale study to monitor application usage
from smartphones [2]. They report basic and contextual descriptive statics. Based on the information a recommendation
system called Appazaar is developed [1]. Leiva et al. took theresearch question Oulasvirta et al. investigated in a controlled
setting [24] to the wild [19]. They show that interruptions
while using apps are caused by intentionally switching backand forth between applications or unintentionally by incoming phone calls. They report that these interruptions rarely
happen but may introduce significant overhead.

RELATED WORK

Studying user behavior in real life situations can reveal valuable insights that differ vastly from the insights gained fromlab studies [17, 24]. Froehlich et al. [7], for example, developed a system that captured objective and subjective data
about mobile computing activities. In two small scale studies
the authors describe that the system can help to understandhow people use and experience mobile technology. Similarly,
Demieux and Losguin designed a framework to collect objective usage data to incorporate the findings into the design process and conducted a field trial with eleven participants [6].
Verkasalo showed that users use certain types of mobile services in certain contexts [34]. The phone was used only 27 %while being on the move. He further reports that users mostly
use browsers and multimedia services when they are on themove but play more games while being at home.
How people use the mobile Internet has been the focus of alarge body of work. The context of the mobile Internet use
is classified in two ways [16, 18]: (1) Personal context is thestate or condition of the user, e.g., mental goals and body
positions, (2) Environmental context is a set of outer circumstances, e.g., the presence of other people. Through a diarystudy it is concluded that users usually use mobile Internetwith one hand in a static position indoors. Taylor et al. assessed motivations of mobile Internet users [32]. The mostfrequent motivation among mobile web users is awareness,a motivation usually satisfied with status checking behavior.
They characterize participants and provide suggestions forfurther development. User experience factors in the mobile
web is also addressed in a framework proposed in [26]. Tossell et al. conducted a one year long field trial with 24 iPhone
users and analyzed the web usage on smartphones [33]. They
characterize their participants and provide directions for further development. Cui and Roto investigated how people use
the mobile web via contextual inquiries [5]. They find that the
time-frame of web sessions is short in general, but browser
usage is longer if users are connected to WLAN.

Previous work on mobile device usage focuses mainly on
studies with few and often highly specific users. The results
can provide a very rich picture but cannot always be generalized. In contrast, large-scale studies of mobile device usage
investigate either specific applications or focus on when andfor how long mobile apps are used. In contrast, we are interested in how users hold their mobile phone while using apps
and surfing the Web. We collect a large corpus that combines the application usage with the basic sensors’ data current smartphones offer. Thereby, we can derive the postureand movement of the mobile phone while different apps arebeing used.
DATA ACQUISITION

To understand how users hold and move their devices while
using different apps and browsing the Web, we developed awidget for the Android platform (Figure 1). It is compatible
with Android 2.3 and higher versions. The widget is a quick
start bar which can be added to the phone’s home screen. It allows users to quickly launch apps most frequently used. Oncea user installs and places the widget on the home screen, thewidget monitors the apps used on the phone. The widget determines and logs the current foreground app every second(1 Hz) using the standard Android API. Thereby, we can detect when a new app is started and the previous app is sent tothe background. Each time an app is removed from the foreground, the app’s identifier, the start time, and the end time
is saved to an internal database on the phone. Therefore, we

Researchers have particularly assessed how search engines
are used on mobile devices. Church et al., for example, reported that queries are short and users tend to focus on the firstfew search results. Vojnovic focused on temporal dynamics,
semantics, and topics of queries [35]. He reports that 80% of
the users issue only a single query per week and queries are
executed in bursts. Kamvar et al. compared search patterns
across desktop computers, iPhones, and conventional mobile
phones [15]. They report that for higher-end phones, a close
integration with the standard computer¡ interface would be
beneficial for the user since these phones seem to be treated as
an extension of the users’ computer. Church and Oliver compared mobile web access patterns in 2011 with previous findings by conducting a 4-week diary and interview study [4].
2LocaleJapan
USGermanyUK# Devices
447
309
159
123

Percentages
33.61%
23.23%
11.95%
9.25%

Table 1. The four most common locales in the data set.

DatasetWe collected data from 1,330 unique devices that each provided more than 60 minutes of sensor data. The devices andlocales we observed are diverse. In total, we collected datafrom 307 different device models. The data set covers the
typical spectrum of Android phones, including older devices
like the Samsung Galaxy S (1.43%, 19 devices) and large
high-end device such as the Samsung Galaxy Note (4.13%,
55 devices). The Samsung Galaxy II is the most common
phone in our data set (7.14%, 95 devices). Users with 72 different locales used the widget. Table 1 includes the four mostcommon locales.

Figure 1. Screenshots of the widget’s one-row layout (left), the two-row
layout (center), and a list to hide apps (right). The widget also collects
accelerometer data.

know how long an app is being used as well as the sequenceof apps used on the phone. The widget uses this information
to show the icons and names of the apps most frequently accessed. It offers two different layouts. A layout with one row
shows the five most frequent apps (Figure 1.left) and a layout with two rows shows ten apps (Figure 1.center). Tapping
an app’s icon shown by the widget launches the respective
app. The widget has a semi-transparent white backgroundthat separates it from the rest of the home screen. A setting
menu allows users to choose which apps should be displayedby the widget (see Figure 1.right).

We observed the usage of 14,471 different apps. The majorityof these apps, however, were only used on a small number ofdevices for a short time. Totally, 13,904 apps were used onless than 10 different devices or for less than 60 minutes. The
remaining 567 apps account for 72.37% of the data. For arobust analysis we considered apps that were frequently usedby a number of users and represented different categories ofapps. Hence, we chose twelve popular apps from six different
categories for further analysis (21.01% of the data). We selected the two most frequently apps used in each category to
avoid the results being solely based on a specific app. Table 2
shows the selected apps and their categories. We intentionally excluded games for our analysis due to the fact that somegames use the accelerometer as an interaction modality. Furthermore, there are very few games that were played across
many users in our data set.

While collecting information about the apps currently used,
the widget also records sensor data. It collects the three axesof the acceleration sensor and the three orientation axes provided by a virtual orientation sensor. Data from both sensors is recorded with the fastest rate offered by the respective
phone (e.g., 50 Hz for the Samsung Nexus S phone). In addition, the widget monitors when the display is turned on andoff. We do not record any data while the display is turned
off. Every two minutes and when the display is turned off, allcollected data is transmitted to our server. If the transmission
fails, the data is re-transmitted the next time a transmission isscheduled.

DEVICE ORIENTATIONIn the first step we analyzed the devices’ posture while using various apps derived from the accelerometer data. We
measured the duration the devices were held with a partic-

Publishing the Widget

We distributed the widget through Google Play, the Google
store for Android apps to record data from a large and diverse sample. We released the first version of the widget inAugust 2011. The app’s description in the market informed
users about collecting the data. We did not advertise the widget among our peers and colleagues to avoid biasing the user.
Based on information from the market, the widget received a
3.8 rating on the 5-point scale (five is the maximum rating)and 55 comments. Most of the comments are positive. Negative comments mainly request more features (e.g., more layout options) or criticize the increase of battery consumption
and the data traffic caused. The initial version of the widget
only monitored the apps used without recording sensor data.
We released several updates to improve its usability before
integrating the recording of sensor data. All data consideredin the following was collected from users who installed or updated the widget between mid-June of 2012 and beginning of
September 2012.

App
YouTubeAndroid Video Player
Facebook
Twitter
GMailAndroid Email Client
Android Browser
ChromeSMS
WhatsApp
Google Maps
Waze

Devices
474
176
717
184
786
337
1084
223
741
295
687
30

Duration
16.0 days
2.5 days38.2 days
5.3 days11.1 days
3.5 days
54.9 days
10.0 days
20.5 days
24.0 days
13.8 days
3.6 daysCategoryMedia
SocialMail
Browser
Messaging
Navigation

Table 2. The 12 popular apps from 6 categories we consider for ouranalysis. The table shows the number of devices that used the apps, theamount of sensor data, and the apps’ category.

3

ular roll and pitch. We binned the orientation in 10◦ intervals, resulting in 36x36 bins. Figure 2 depicts the heat maps
for three apps: Facebook, YouTube, and Google Maps. Assessing this information for a variety of apps reveals that thephones’ orientation differs significantly while using different
apps. Looking at Figure 2, the heat maps are fairly unique foreach application. However, they also share some similarities.
The Facebook, YouTube apps have a peak around 0/0. The
peak shows devices lying on a flat surface or being held inparallel to the ground. The peak for the Google Maps showsthat the device is mainly held upright and the top of the phone
is tilted away from the user. We also found that there are often
little movements while devices have these orientations. Furthermore, high values are concentrated along the two axes.
For Facebook, for example, the device is almost exclusively
oriented upright and the top of the screen is tilted away fromthe user. For YouTube high values are also concentrated along
the second axis indicating that the device is also oriented sideways.
We further assessed how much the phones moved while using various apps. We calculated the standard deviation (SD)of the acceleration vectors’ magnitude using 0.5s windows.
This value reveals the devices motion. The higher the value,
the more movement the device recorded. The movement canbe either due to the device movement or the interaction withthe device. Figure 3 shows the SD for the 12 apps. Interestingly, these values significantly differ between the categories. Using the devices’ average SD, we conducted a oneway ANOVA to statistically compare the device motion between the apps. The results show that the app significantly
affected the average SD, F(11, 5,130)=25.32, p<.001. Due tothe different sample sizes we used Hochberg’s GT2 post hoc
test [31]. The two navigation apps have a significantly higher
SDs than all other apps (all p<.01 or p<.001) except the two
messaging apps. Both messaging apps have a significantly
higher SD than the Browser (p<.001), Facebook (p<.001),
and Google Mail (WhatsApp p<.05, SMS p<.001). The SMS
app also has a higher SD than Twitter (p<.01). Finally, theBrowser has a lower SD than YouTube and the mail apps (allp<.01).
Overall, based on the average motion the apps can be groupedin at least three categories (navigation, messaging, and therest). It is not overly surprising that the phone is being moved
more while using navigation apps. However, the difference
between messaging and other apps suggests that they are also
more often used while on the go.
Orientation Spectrum in a Session

We further analyzed the user interface (UI) orientation while
the apps are used. The Android system can automaticallyrotate the UI depending on the phone’s orientation. We analyzed the sensor data to determine whether the UI was displayed in portrait or landscape mode. We used the originalcode from the Android repository that is used to determine
the orientation mode on actual devices and feed it with ouraccelerometer data. Thereby, we determined the active orientation as well as transitions from landscape to portrait andvice versa. We investigated the UI orientation mode during

Figure 2. The 2D histograms of the devices’ orientation while using Facebook, YouTube, and Google maps. The devices’ average pitch and rollare computed over 0.5s windows and binned using 10◦ intervals.

4

Figure 3. The average standard deviation of the acceleration vectors’
magnitude using a 0.5s windows. Error bars show the standard error.
The higher value for the SD shows that the device is being moved more.

App
YouTubeAndroid Video Player
Facebook
Twitter
GMailAndroid Email Client
Android Browser
ChromeSMS
WhatsApp
Google Maps
Waze

Figure 4. The use of the two orientation modes.

Session Length
Mean (min)SD
5.23 17.13
6.70 15.94
1.61
4.97
1.52
2.57
0.77
4.92
0.78
2.03
1.91 20.91
1.77
6.20
0.76
2.80
0.93
2.23
2.41
7.30
7.90 14.38

Figure 5. The average duration of an orientation mode (error bars show
the 95% confidence interval).

mode as well as the frequency of orientation transitions in one
session of use. Figure 5 depicts how long a phone is held ineach orientation mode before the orientation is changed. The
results convey that users mainly hold their phone sideways(landscape) when they use YouTube and Video Player before
they rotate the phone. The phone is mainly held upright during the use of the other apps. Figure 6 shows the average
number of orientation changes per minute. The analysis of
the orientation transition frequency shows that the maximum
and minimum number of orientation transitions occurs while
using the apps in the Media category (YouTube: M=0.37,
SD=0.83,Video Player: M=0.10, SD=0.20). Both apps in
the Messaging category have a high number of orientationtransitions (SMS: M=0.33, SD=1.04, WhatsApp: M=0.28,
SD=0.82). The results suggest that users search and watch(fairly short) videos back and forth during the use of YouTube.
While in the Video Player app the users seem to start (longer)
videos and watch them till the end. The results for the messaging apps suggest that the users rotate the phone frequently
while writing and reading messages.

Table 3. The average session length of the 12 apps selected for the analysis. While the apps from the Media category have the longest sessions,
the Messaging and Mail categories have the shortest ones.

use sessions of the apps. A use session of an app is from thetime the app comes to the foreground until it goes to the background. We first determined the orientation mode when theapps were started and closed. In addition, we determined how
long the phone’s UI was in portrait and landscape modes aswell as how often the orientation was changed in one sessionof use.
The average session length varies between the categories(Table 3). While the apps from the Media category havethe longest sessions (YouTube M=5.23 minutes, SD=17.13,
Video Player M=6.7 minutes, SD=15.94), the Messagingand Mail categories have the shortest sessions (SMS M=0.76
minutes, SD=2.80, WhatsApp M=0.93 minutes, SD=2.23,
Gmail M=0.77 minutes, SD=4.92, Email M=0.78 minutes,
SD=2.03). Figure 4 depicts the fraction the devices are inlandscape and portrait mode. Interestingly, for the two apps
in the Media category the device is on average only 28% of
the time in portrait mode. Meanwhile, the devices are in portrait mode for more than 79% of the time during the use of theapps in the Browser and Social categories. For the remaining
apps the devices are in portrait mode at least 70% of the time.
The analysis of the data also shows that all apps start and end
a session most often in portrait mode (70% of time). A likely
reason that apps often start in portrait mode is that the default
orientation on Android phones is portrait.

Discussion

The results reveal that devices are often being moved while
using navigation and messaging apps. Further, users interact with the considered apps mainly in portrait mode, except
for apps from the Media category. The time distribution between landscape and portrait shows that the landscape mode
is typically used for a short time except for the Media apps.
A potential explanation is that the landscape mode is often
triggered accidentally or used for text entry. The Media apps
are mainly used in landscape mode. Hence, developers canmainly focus on portrait mode except for Media apps.

We further examined how long mobile phones are held in oneof the orientation modes before they are rotated to another5Figure 6. The average number of orientation transitions per minute.
Figure 7. The use of 4 browsers on different versions of the Android OS.

Previous work states that apps are used less than a minute on
average[2]. We show that the duration depends on the app(e.g., >5min. for videos, <1min. for mail & messaging)and how orientation switches fragment interaction. Looking
specifically at mail and messaging apps we observe that thedevice is being moved more, usage sessions are shorter, andthe user switches more often between landscape and portrait
mode. It has been shown that movement and fragmented attention can dramatically affect the user [23, 24]. Thus, messaging apps must be specifically optimized for usage on thego. Developers need to consider ways to compensate formoving devices and fragmented attention. For example, theUI of these apps should highlight the user’s last actions andoffer larger controls than apps that are less in motion, i.e.,
video player, social, or web browsers apps. Similar to thework by Goel et al. [9] mobile keyboards should take theapp’s category into account.
Web Browsers
Android Default Browser
Chrome
FireFoxOpera Mini

Devices
1084
223
124
106

the browsers are very similar to the top frequent locales forthe dataset (see Table 1).
We followed the same procedure as for the other apps andfirst analyzed the devices’ posture. We measured the duration
the device was held with a particular roll and pitch. We identically binned the data as described before. The results depict
that all the four web browsers also show a peak around 0/0
meaning that the device is held parallel to the ground with thescreen facing upwards. The high values are mainly concentrated along the pitch axis. Using all browsers the device isalmost exclusively oriented upright and the top of the screen
is slightly tilted away from the user. In comparison with Figure 2 we observe a similar posture as for the Facebook app.
For the Opera Mini app, similar to the Google Maps app, thevalues are also concentrated along the roll axis indicating thatthe device is also oriented sideways.

Duration
54.9 days
10.0 days
2.7 days
1.6 days

Orientation Spectrum in a Web Surfing Session

We also analyzed the user interface (UI) orientation of the
web browsers while surfing the web following the same approach. While the Firefox web browser has the longest sessions (M=2.45 minutes, SD=5.93,), the Opera Mini app
has the shortest (M=1.24 minutes, SD=1.75). Table 5 depicts the session length of the four browsers. The ANOVA
test does not reveal any significant difference between thebrowsers (F(3,1532)=.20 p=.99). The comparison of thefraction the device is used in landscape and portrait mode,interestingly, revealed no differences between the browsers.
All four browsers have a similar distribution and are used in
portrait mode approximately 80% of the time across a user
session (similar to the Twitter and Facebook apps).

Table 4. The four common web browsers selected for the analysis. The
table shows the number of devices on which these browsers were used
and the amount of sensor data.

DEVICE ORIENTATION WHILE SURFING THE WEB

Due to the ubiquity of Internet connectivity on mobile phones
today, we were particularly interested in the usage of web
browsers. We observed the use of nine different web browsers
in addition to the default web browser for Android phones.
Since the number of users varied between apps and also between different browsers, we deliberately focused on the fourmost common browsers in our data set (see Table 4). Eachbrowser has been used by at least a hundred users. At least
one of the browsers has been used on 1,202 out of the 1,330
devices in the data set. Interestingly, 31% of the users used atleast one additional web browser and 5% used two other web
browsers in addition to the Android default web browser. In
addition to the default browser, a significant number of users
use other browsers. We also analyzed the use of four browsers
on different Android OS versions (Figure 7). Chrome is used
more on the newer Android versions, while Opera Mini ismore used on older versions. The most frequent locales forApp
Android Browser
ChromeFirefoxOpera Mini

Session Length
MeanSD
1.91 min 20.91
1.77 min
6.20
2.45 min
5.93
1.24 min
1.75

Table 5. The average session length of the four browsers selected for the
analysis.

We also examined how long the mobile phone is held in
one of the orientation modes before it is rotated to anothermode within one user session. Figure 8 depicts how long6

Figure 8. The average duration of one orientation mode before changing
to the other. Error bars show the standard error.

Figure 10. The histogram of the browsers’ session durations whenstarted through the launcher and through another app. The durations
are binned using a logarithmic scale. It is shown that if a browser isstarted through apps the duration tends to be shorter than if the browser
is started through a launcher.

Category
Mail
SocialFigure 9. The average number of orientation transitions per minute.
Error bars show the standard error.

News

the phone is held in each orientation mode before the orientation is changed. The results reveal that when users
use the browsers they mainly hold the phone in portrait
mode before rotating the device. We also assessed theorientation transition frequency in a session of use. Levene’s test indicates that the assumption of homogeneity ofvariance is violated (F(3,1532)=9.80 p<.001). A one-way
ANOVA shows a significant difference between the apps
(F(3,1532)=4.11 p<.006). Post-hoc tests reveal a significant difference (p<.004) between Chrome (M=.21 transitions/session SD=.28) and Opera Mini (M=.39 transitions/session SD=.61). The difference between the two otherweb browsers is not significant (Android Browser: M=.29,
SD=.44, Firefox: M=.28, SD=.44). Figure 9 shows the average number of orientation changes per minute. The results
suggest that users might rotate the phone back and forth during surfing the web in a brief manner to read or enter text orwatch a video.

MessagingApp
Android
Email Client
Gmail
Facebook
Twitter
Flipboard
GoogleReaderSMS
WhatsApp44µ(web browsing)minutes
1.780, SD=1.55

139
152
39
22
18

1.766 , SD=1.46
1.524, SD=1.17
1.155, SD=.54
1.522, SD=.71
1.521, SD=.84

40
44

1.537, SD=1.28
1.148, SD=.80

Devices

Table 6. The eight apps (from four categories) used right before launching the web browsers considered for the analysis. Fourth column shows
the average browsing session duration based on the app previously used.

In the first step, we compared the session duration when users
directly started the web browsers through the launcher withthe session length when the web browsers were launchedthrough another app. We considered data from users whostarted the web browsers either way, resulting in 666 users. A
paired samples t-test reveals a significant difference between
the average session length of the web browsers when started
through the launcher (M=2.36 minutes, SD=1.57) and whenstarted from another app (M=1.52 minutes, SD=1.14),t(665)=15.03, p<.0001 r=.50. The effect size indicates thedifference is large, and therefore substantive. The logarithmic histogram of the session durations when started throughthe launcher and through another app is shown in Figure 10.
If a browser is started through apps the duration tends to be
shorter than if the browser is started through a launcher.

Duration of Web Surfing on Mobile Phones

As we collect information about the app used we also know
which app was used before a web browser is launched. Thus,
we assessed whether the duration of web browsing use sessions varied based on previous apps. In general, a web
browser app on the Android phone can be launched in two
ways: first, the browser can be directly started by tapping the
app’s icon (called via launcher from now on in this article).
Second, the app can be launched by another app. In the second case, a web browser is launched when users click on a
URL address. In our dataset we have 3,937 different appsthat ran right before one of the web browsers was launched.

Further, we assessed the session length when the webbrowsers are started from within different apps. Many apps
observed were used very few times or only by few users.
Hence, we deliberately considered apps that had at least 15
unique users and whose users launched the browsers fromwithin these apps at least five times. This resulted in 22
unique apps. We selected four common and popular categories (Mail, Social, News, and Messaging) and chose twomost popular apps in each category for our analysis. The categories and apps selected are depicted in Table 6. We con7

sidered two apps per category to prevent results from being
solely based on one specific app. A one-way ANOVA revealed that the previously used app has a significant effect on
the usage duration (F(7,497)=2.10,p<.05). However, posthoc tests did not reveal significant difference (all p>.05). The
average duration of the browsing sessions when the browser
is started from different apps in Table 6. The results revealthat switching back and forth between apps and the browsers
and using the browsers vary.

LIMITATIONS AND ETHICAL CONSIDERATIONS

We conducted the study in the wild without direct contact tothe users of the devices. While this approach has obvious advantages it also results in a number of limitations and ethicalchallenges that we discuss in the following.
Limitations

This study was conducted in-the-large and the participants
are self-selected. We can provide no insights into what theusers did inside the apps we analyzed. Work by Sanchez andBranaghan [29] in fact suggests that the orientation of the device and the according presentation of content can have aneffect on users’ ability for reasoning. Users might therefore
select an orientation not only based on the type of content but
also based on a particular task. However, hundreds of participants and weeks of usage are required for the conducted analysis. Our fairly large sample spans continents and different
languages. In particular, collecting hundreds of participants
and weeks of data is hardly possible using other methods.

Implications

The analysis of the data collected reveals several implications
that developers should take into account.
Using another web browser in addition to the Android default web browser is common. As 31% of the users use atleast two web browsers on their phones, this shows that it iscommon to use another web browser in addition to the default web browsers provided by the Android platform. Thiscan be due to the extra features other web browsers provide.
With the Chrome web browser on the desktop, for example,
it is possible to send web pages to the Chrome web browser
on Android phones. Further, various add-ons can be installed
and added to the Firefox web browser app. This suggeststhat developers should not only focus on default browser but
should also support other common mobile browsers.

Further, we only analyzed a fraction of the applications available in the Google Play marketplace and only a fraction of
the applications in our data set. There are other web browsers
available in Google Play. All applications we investigated,
however, are rather popular, widely used, and either belong
to the most downloaded Android applications or are preinstalled. The web browsers selected for the analysis, for example, were the top four common browsers in the data set
collected. Based on the information from the marketplace all
have been downloaded more than 10 million times and rated
at least four stars.

Users hold the phone upright when surfing the Web. The
analysis of the device’s orientation for the browsers showedthat users typically hold the phone parallel to the ground withthe screen facing upwards or upright and the top of the screen
being tilted away while surfing the Web. Looking at the time
distribution between landscape and portrait mode, browsers
are often used in portrait mode. Thus, developers can optimize their websites for this orientation.

While accelerometer sensors vary across Android devices, thecharacteristics of the sensor are standardized and specified in
the Android Compatibility Definition Document [10]. The
data provided by the sensor is similar in such a way that neither Android’s higher level functions nor Android apps needto explicitly distinguish different models. We rely on much
more basic characteristics of acceleration (the standard deviation of the magnitude and acceleration due to gravity).
We hence assume that the accelerometers’ precision and frequency are more than enough for the analysis conducted.

Web browsers are rarely used on the go. The analysis of
the standard deviation of the acceleration vector’s magnitude
shows that when the web browsers are used, the phone is being similarly moved as, for example, when the Facebook app
is used. The comparison with other apps suggests that the device is less in motion when mobile browsers are used. Thissuggests that mobile browsers are less often used on the go.

The orientation of the user interface results from the physicalorientation of the device and does not necessarily reflect theuser’s intention. Cheng et al. reported based on an online
survey that incorrect rotation happens frequently [3]. They
report that 91% of the participants experienced auto-rotationthat lead to incorrect viewing orientation. Based on our data,
we cannot be sure why the orientation of the user interfacechanges. Unintended orientation changes, however, should
only have a small effect on relative differences. In addition,
unintended orientation changes only have a minimal effect on
the total time a particular orientation is used.

Users switch back and forth between apps and webbrowsers. We show that explicitly starting web browsers
through the launcher results in 1.5 times longer sessions compared to starting browsers from other apps. This can be due tothe user’s personal context such as user’s goals [16, 18]. The
shorter browsing sessions when a web browser is launchedfrom an app, depicts that users switch back and forth between apps and the web browsers. This can delay task completion, create interruptions, and generate overload for users[19]. This suggests that a tight and smooth integration of web
browsers with native apps can improve the overall usability.
To ease the switch, developers can either include in-situ web
views in their apps or provide means that allow users to easily
recover after switching back to their app. Further, it is necessary to consider means for users to quickly accomplish theirgoals by easily moving content back and forth between apps
and browsers.

Ethical considerations

We informed potential participants that they would contributedata to our research through the widget’s description on
Google Play. The description explains that the widget is partof our work as researchers. In addition, we described thatthe widget measures how long the apps are used and how the8phone moves. Besides, the Android system informs the user
about the permissions an app requires and the user needs toexplicitly confirm it. However, obtaining informed consent
is still challenging. Pielot et al. [25] showed, for example,
that the way potential participants are informed has a dramatic effect on their reaction. Similarly, Morrison et al. [22]showed that telling users what is recorded, such as their position, leads to very different effects compared to showing
them its meaning – in this case just showing their position ona map. Users might be aware that something is recorded but
the researchers cannot ensure that they truly understand theimplication.

tivities while using different applications could be combinedwith a static analysis of mobile interfaces [27] and executing applications in an emulator would enable to also observe
the applications’ behavior. We believe that these analyses
could ultimately lead to a holistic picture of mobile humancomputer interaction.

As we could not ensure that all users fully understand the potential implications we tried to minimize potential negative
effects on the user. To provide the main functionality of thewidget, it was necessary to have a background service. The
same service was also used to record the device’s acceleration and was only active if the device’s display was switchedon. Thus, we minimized potential harm for the user caused
through excessive consumption power or other resources. Indeed, we did not record any data that would allow us to identify the user of the device.

REFERENCESACKNOWLEDGMENTS

We thank the reviewers for their constructive comments. This
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