Name
Abstract | ||
|---|---|---|
Smartphones have the potential to produce new habits, i.e., habitual phone usage sessions consistently associated with explicit contextual cues. Despite there is evidence that habitual smartphone use is perceived as meaningless and addictive, little is known about what such habits are, how they can be detected, and how their disruptive effect can be mitigated. In this paper, we propose a data analytic methodology based on association rule mining to automatically discover smartphone habits from smartphone usage data. By assessing the methodology with more than 130,000 smartphone sessions collected in-the-wild, we show evidence that smartphone use can be characterized by different types of complex habits, which are highly diversified across users and involve multiple apps. To promote discussion and present our future work, we introduce a mobile app that exploits the proposed methodology to assist users in monitoring and changing their smartphone habits through implementation intentions, i.e., "if-then" plans where if's are contextual cues and then's are goal-related behaviors.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3341162.3343770 | Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers |
Keywords | Field | DocType |
association rules, digital wellbeing, habits, implementation intentions, smartphone addiction | Mobile app,Computer science,Exploit,Phone,Human–computer interaction,Association rule learning,Usage data,Embedded system | Conference |
ISBN | Citations | PageRank |
978-4503-6869-8 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alberto Monge Roffarello | 1 | 24 | 8.96 |
| Luigi De Russis | 2 | 92 | 26.25 |