Falling asleep with Angry Birds, Facebook and Kindle

Böhmer, Matthias; Hecht, Brent; Schöning, Johannes; Krüger, Antonio; Bauer, Gernot

doi:10.1145/2037373.2037383

Cited by 460 publications

(110 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…More recently, several studies have collected data about how mobile devices are used, especially smartphones. For example there have been studies of when devices are unlocked/locked and what apps are launched [28,4,5]; of how battery power is consumed [16,17,15]; of people's communication patterns [49]; of security preferences [1]; of everyday connectivity [30]; and of people's locations when using devices [13]. Church et al [7] give an overview of this area of research and discuss how these studies contribute to a broader understanding of device usage behaviours.…”

Section: Tracking Digital Device Usementioning

confidence: 99%

“…In other cases, particularly where an app is to be released through an app store and no incentive is given for installing the app, an interface has been provided (and indeed the interface serves as the incentive to install and retain the app [7]). Böhmer et al [4] and Parate et al [37] created recommender widgets for users to launch apps, Ferreira et al [16] and Athukorala et al [2] created battery tracking software for end users, and Wagner et al [49] created a personal analytics style interface offering a range of information about the use of one's device.…”

Section: Tracking Digital Device Usementioning

confidence: 99%

“…Despite there being a sizeable body of academic research in which people's everyday digital device use is logged, for example Böhmer's study of app launches on Android devices [4], little consideration has been made of whether and how data about everyday device use might be of personal interest and value to the user. This paper describes the development and evaluation of ScreenLife, a personal tracking application that enables users to collect and view data about the use of their digital devices (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Personal Tracking of Screen Time on Digital Devices

Rooksby

Asadzadeh

Rost

et al. 2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Numerous studies have tracked people's everyday use of digital devices, but without consideration of how such data might be of personal interest to the user. We have developed a personal tracking application that enables users to automatically monitor their 'screen time' on mobile devices (iOS and Android) and computers (Mac and Windows). The application interface enables users to combine screen time data from multiple devices. We trialled the application for 28+ days with 21 users, collecting log data and interviewing each user. We found that there is interest in personal tracking in this area, but that the study participants were less interested in quantifying their overall screen time than in gaining data about their use of specific devices and applications. We found that personal tracking of device use is desirable for goals including: increasing productivity, disciplining device use, and cutting down on use.

show abstract

Section: Tracking Digital Device Usementioning

confidence: 99%

Section: Tracking Digital Device Usementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Personal Tracking of Screen Time on Digital Devices

Rooksby

Asadzadeh

Rost

et al. 2016

Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

show abstract

“…Smartphones have been described as 'Swiss army knives' [6], with users installing apps to customise devices to particular needs and preferences. Many researchers have studied use of smartphones through patterns of app launches, examining aspects such as usage at different times of day [9,48] or locations [16,49], or creating predictive models to aid in recommendation tasks [26,42].…”

Section: Introductionmentioning

confidence: 99%

A Large-Scale Study of iPhone App Launch Behaviour

Morrison

Xiong

Higgs

et al. 2018

Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

There have been many large-scale investigations of users' mobile app launch behaviour, but all have been conducted on Android, even though recent reports suggest iPhones account for a third of all smartphones in use. We report on the first large-scale analysis of app usage patterns on iPhones. We conduct a reproduction study with a cohort of over 10,000 jailbroken iPhone users, reproducing several studies previously conducted on Android devices. We find some differences, but also significant similarities: e.g. communications apps are the most used on both platforms; similar patterns are apparent of few apps being very popular but there existing a 'long tail' of many apps used by the population; users show similar patterns of 'micro-usage'; almost identical proportions of people use a unique combination of apps. Such similarities add confidence but also specificity about claims of consistency across smartphones. As well as presenting our findings, we discuss issues involved in reproducing studies across platforms.

show abstract

“…But our current results imply that system designers may wish to let users choose in which contexts they want to use the recommender, or tune recommendations to context. We also suggest that recommenders in other domain such as mobile application recommendations may be context-aware [3], since there are evidences from existing research [4] indicate that people's mobile application usage is highly dynamic with contexts…”

Section: Recommendations Should Be Context-awarementioning

confidence: 99%

The Effect of Privacy Concerns on Privacy Recommenders

Zhao

Henderson

2016

Proceedings of the 21st International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

Location-sharing services such as Facebook and Foursquare/Swarm have become increasingly popular, due to the ease at which users can share their locations, and participate in services, games and other applications that leverage these locations. But it is important for people who use these services to configure appropriate location-privacy preferences so that they can control to whom they want to share their location information. Manually configuring these preferences may be burdensome and confusing, and so location-privacy preference recommenders based on crowdsourcing preferences from other users have been proposed. Whether people will accept the recommended preferences acquired from other users, who they may not know or trust, has not, however, been investigated.In this paper, we present a user experiment (n=99) to explore what factors influence people's acceptance of locationprivacy preference recommenders. We find that 44% of our participants have privacy concerns about such recommenders. These concerns are shown to have a negative effect (p <0.001) on their acceptance of the recommendations and their satisfaction about their choices. Furthermore, users' acceptance of recommenders varies according to both context and recommendations being made. Our findings are potentially useful to designers of location-sharing services and privacy recommenders.

show abstract

Falling asleep with Angry Birds, Facebook and Kindle

Cited by 460 publications

References 13 publications

Personal Tracking of Screen Time on Digital Devices

Personal Tracking of Screen Time on Digital Devices

A Large-Scale Study of iPhone App Launch Behaviour

The Effect of Privacy Concerns on Privacy Recommenders

Contact Info

Product

Resources

About