Falling Asleep with Angry Birds, Facebook and Kindle ��� A Large Scale Study on Mobile Application Usage Matthias Bohmer �� DFKI GmbH Saarbrucken, �� Germany matthias.boehmer@dfki.de Brent Hecht Northwestern University Evanston, IL, USA brent@u.northwestern.edu Johannes Schoning�� DFKI GmbH Saarbrucken, �� Germany johannes.schoening@dfki.de Antonio Kruger �� DFKI GmbH Saarbrucken, �� Germany krueger@dfki.de Gernot Bauer Fachhochschule Munster�� Munster, �� Germany gbauer@fh-muenster.de ABSTRACT While applications for mobile devices have become ex- tremely important in the last few years, little public infor- mation exists on mobile application usage behavior. We de- scribe a large-scale deployment-based research study that logged detailed application usage information from over 4,100 users of Android-powered mobile devices. We present two types of results from analyzing this data: basic descrip- tive statistics and contextual descriptive statistics. In the case of the former, we find that the average session with an appli- cation lasts less than a minute, even though users spend al- most an hour a day using their phones. Our contextual find- ings include those related to time of day and location. For instance, we show that news applications are most popular in the morning and games are at night, but communication applications dominate through most of the day. We also find that despite the variety of apps available, communication ap- plications are almost always the first used upon a device���s waking from sleep. In addition, we discuss the notion of a virtual application sensor, which we used to collect the data. Author Keywords Mobile apps, usage sensor, measuring, large-scale study. ACM Classification Keywords H.5.2 User Interfaces: Evaluation/methodology General Terms Human Factors, Measurement INTRODUCTION Mobile phones have evolved from single-purpose commu- nication devices into dynamic tools that support their users in a wide variety of tasks, e.g. playing games, listening to Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. MobileHCI 2011, Aug 30���Sept 2, 2011, Stockholm, Sweden. Copyright 2011 ACM 978-1-4503-0541-9/11/08-09....$10.00. music, sightseeing, and navigating. In this way, the mobile phone has become increasingly analogous to a ���Swiss Army Knife��� [15, 17] in that mobile phones provide a plethora of readily-accessible tools for everyday life. The number of available applications for mobile phones ��� so called ���apps��� ��� is steadily increasing. Today, there are more than 370,000 apps available for the Android platform and 425,000 for Ap- ple���s iPhone1. The iPhone platform has seen more than 10 billion app downloads2. Despite these large numbers, there is little public research available on application usage behavior. Very basic ques- tions remain unanswered. For instance, how long does each interaction with an app last? Does this vary by application category? If so, which categories inspire the longest interac- tions with their users? The data on context���s effect on appli- cation usage is equally sparse, leading to additional interest- ing questions. How does the user���s context ��� e.g. location and time of day ��� affect her app choices? What type of app is opened first? Does the opening of one application predict the opening of another? In this paper, we provide data from a large-scale study that begins to answer these basic app usage questions, as well as those related to contextual usage. In addition to the descriptive results above, an additional contribution of this paper is our method of data collection. All of the data for this paper was gathered by AppSensor, our ���virtual sensor���, that is part of a large-scale deployment of an implicit feedback-based mobile app recommender sys- tem called appazaar [4]. appazaar is designed to tackle the problem presented by the fact that, as mentioned above, an enormous number of apps are available. Based on the user���s current and past locations and app usage, the system rec- ommends apps that might be of interest to the user. Within the appazaar app we deployed AppSensor, that does the job vital to this research of measuring which apps are used in which contexts. In the next section, we describe work related to this paper. Section three provides an overview of AppSensor and other 1Wikipedia: List of digital distribution platforms for mobile de- vices, http://tiny.cc/j0irz 2http://www.apple.com/itunes/10-billion-app-countdown/ 47

aspects of our data collection process. In section four, we present our basic and context-related findings. Discussion of implications for design, as well as the limitations of our study, is the topic of section five. Finally, we conclude by highlighting major findings and describing future work. RELATED WORK Work related to this paper includes that on mobile user needs and mobile device usage and deployments in the wild. For instance, Church and Smyth [6] analyzed mobile user needs and concluded that context ��� in form of location and time ��� is important for mobile web search. Cui and Roto [7] in- vestigated how people use the mobile web. They found that the timeframe of web sessions is rather short in general but browser use is longer if users are connected to a WLAN. Verkasalo [18] showed that people use certain types of mo- bile services in certain contexts. For example, they mostly use browsers and multimedia services when they are on the move but play more games while they are at home. Froehlich et al. [10] presented a system that collects real us- age data on mobile phones by keeping track of more than 140 types of events. They provide a method for mobile ex- perience sampling and describe a system for gathering in- situ data on a user���s device. The goal of Demieux and Los- guin [8] was to collect objective data on the usage and inter- actions with mobile phones to incorporate the findings into the design process. Their framework is capable of tracking the high-level functionality of phones, e.g. calling, playing games, and downloading external programs. However, both of these studies were very limited in number of users (maxi- mum of 16), length of study (maximum 28 days), and num- ber of apps. Similar to this work, McMillan et al. [16] and Henze et al. [12] make use of app stores for conducting deployment- based research. McMillan et al. [16] describe how they gather feedback and quantitative data to design and improve a game called Yoshi. Their idea is to inform the design of the application itself based on a large amount of feedback from end-users. Henze et al. [12] designed a map-based applica- tion to analyze the visualization of off-screen objects. Their study is also designed as a game with tasks to be solved by the players. The players��� performances within different tasks are used to evaluate different approaches for map visualiza- tions. However, app-store-based research is so far limited to single applications and has a strong focus on research ques- tions that are specific to the deployed apps itself. In this work, we focus on gaining insights into general app usage by releasing an explorative app to the Android app store. Another similar approach to this work is followed by the AppAware project [11]. The system shows end-users ���which apps are hot��� by aggregating world-wide occurrences of app installation events. However, since AppAware only gathers the installation, update, and deinstallation of an application, the system is not aware of the actual usage of a specific app. In summary, this research is unique (to our knowledge) in that it combines the approach of large-scale, in-the-wild user studies with the fine-grained measuring of app usage. In this way, we are able to (1) study large numbers of users and (2) large numbers of applications, all over a long time period. Previous work has had to make sacrifices in at least one of these dimensions, as Table 1 shows. Furthermore, the mo- bile phones used in related studies have been mostly from the last generation, i.e. they could not be customized by the end-users in terms of installing new applications. APPSENSOR AND DATA COLLECTION In this section, we describe our data collection tool, AppSen- sor. Because context is a known important predictor of the utility of an application [3], AppSensor has been designed from the ground up to provide context attached to each sam- ple of application usage. Lifecycle of a Mobile App In order to understand the AppSensor���s design, it is impor- tant to first give the AppSensor���s definition of the lifecycle of a mobile application (Figure 1). The AppSensor under- stands five events in this lifecycle: installing, updating, unin- stalling, opening, and closing the app. !"#$% '(") $*+ !"#$% '(") ,-*(" *."$ #$(+/-- '$#$(+/-- '.)/+" Figure 1. The lifecycle of a mobile app on a user���s device according to different states and events. The first event that we can observe is an app���s installation. It reveals that the user has downloaded an app, e.g. from an app market. Another event that is observable is the update of an app, which might be interpreted as a sign of endur- ing interest in the application. However, since updates are sometimes done automatically by the system and the update frequency strongly depends on the release strategy of the de- veloper, the insight into usage behavior that can be gained from update events is relatively low. The last event we can capture is the uninstall event, which expresses the opposite of the installation event: a user does not want the app any- more. However, these maintenance events only occur a few times per app. For some apps, there might even be only a single installation event (e.g. when the user has found a good app) or even none at all (e.g. for preinstalled apps like the phone app). Maintenance events are also of limited utility for un- derstanding the relationship between context and app usage. For instance, a user might install an app somewhere but use it elsewhere (e.g. an app for sightseeing that is installed at home before traveling). 48