This document presents findings from a study on app usage conducted with 4,125 users over 127 days, collecting over 4.92 million data points. It highlights the correlation between app usage and the circadian rhythm, explores variations in app type based on time of day, and discusses the development of context-aware app recommendation systems. Additionally, it addresses the need for improved multitasking features in phone UIs to better manage interruptions from incoming calls.

1. What’s in the Apps for Context?
Extending a Sensor for Studying App
Usage to Informing Context-awareness
Matthias Böhmer
Christian Lander
Antonio Krüger
UbiMI Workshop at UbiComp 2013
September 8-9, 2013
Zürich, Switzerland

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5. AppSensor: Tracing App Usage
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6. Data from Deployment
- 4,125 users from various countries
- 22,626 apps from 20 categories
- 4.92 million data points
- 127 days
6

7. During Course of a Day
- App usage correlates with circadian circle
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8. Probability of Launches
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Figure 5. Hourly relative app usage by category in terms of launches. Each cell value refers to the percentage of app launches d
within each hour for each category. Colors are normalized by row, with green indicating each category’s maximum percentage of
and white indicating each category’s minimum. For example, games reach their peak in the evening (green) and trough in the morn
- Type of used apps changes during the course of the day
- During day: primarily communication apps
- During night: scope of apps more heterogenous
8

9. Support for App Launching
9
- Adaptive launcher menu
- Support visual search for apps
- Presenting 5 icons for next app
- Implements different models
- Sequentially used apps
- Prediction model
- Locally most used apps
- Most recently used apps
- Most frequently used apps
- Application AppKicker
- Extension as a widget
- Deployed on app store
- 53,000 installations
Will be presented at
UbiComp 2013
Session „Systems“
Wed 8:30-10:00

10. App Recommender System
10
- Implementation of a recommender system
- Context-aware (location, time and previous app)
- Based on traces of application usage (AppSensor)
- Application appazaar
- Deployed on app store
- 7,200 installations
- Testbed for different recommender engines

11. Findings
- Interruptions do not happen as often as expected
- 8% of app use is interrupted by app switching
- 3% of app use is interrupted by phone calls
- If interruptions happen, overhead may be
exceedingly high
phone call app switch
Daily interruptions (% usage) 3.2 (2.2) 8.3 (5.3) per user
Regular app runtime (s) 24.8 (31.8) 18.9 (24.4)
per app
Overhead duration (s) 43.2 (65.9) 34.4 (40.7)
per app
mean (SD)
11

12. Re-Design of Phone UIs
plementation
ved form single-purpose devices to multi-purpose devices
call applications did not evolve accordingly
s can interrupt concurrent application use
of call applications to allow for higher degree of multitasking
one Call Applications
screen modal dialogs providing only options to accept or decline call
ditional third option besides accept/decline to allow user to return to previous application
user to keep attention in previous application while call is pending
tions: Puts incoming call into background for user to pickup call at will
ompletion: Wait until task is done and display call when user leaves previous app
CALLER NAME
CALLER NAME
b) Postponing calls c) Multiplexing d) Background notification
Interruptions do not happen as often as expected
- Extending the design space for phone call UIs
- New interaction design for phone call handling
- Support for better multitasking with call notifications
- Application CallHeads deployed on app store (30,000 users)
12

13. What‘s in the apps
for context?

14. tourist using city guide shopper shopping list

15. Context
App
Usage
Matthias Böhmer
matthias.boehmer@dfki.de
http://matthiasboehmer.de