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A systematic assessment of smartphone usage gaps

van Berkel, Niels; Luo, Chu; Anagnostopoulos, Theodoros; Ferreira, Denzil; Goncalves, Jorge; Hosio, Simo; Kostakos, Vassilis (2016-05-07)

 
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https://doi.org/10.1145/2858036.2858348

van Berkel, Niels
Luo, Chu
Anagnostopoulos, Theodoros
Ferreira, Denzil
Goncalves, Jorge
Hosio, Simo
Kostakos, Vassilis
Association for Computing Machinery
07.05.2016

Niels van Berkel, Chu Luo, Theodoros Anagnostopoulos, Denzil Ferreira, Jorge Goncalves, Simo Hosio, and Vassilis Kostakos. 2016. A Systematic Assessment of Smartphone Usage Gaps. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16). ACM, New York, NY, USA, 4711-4721. DOI: https://doi.org/10.1145/2858036.2858348

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© Copyright is held by the owner/author(s). Publication rights licensed to ACM. 2016. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI '16), https://doi.org/10.1145/2858036.2858348.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1145/2858036.2858348
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https://urn.fi/URN:NBN:fi-fe201901232751
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Abstract

Researchers who analyse smartphone usage logs often make the assumption that users who lock and unlock their phone for brief periods of time (e.g., less than a minute) are continuing the same "session" of interaction. However, this assumption is not empirically validated, and in fact different studies apply different arbitrary thresholds in their analysis. To validate this assumption, we conducted a field study where we collected user-labelled activity data through ESM and sensor logging. Our results indicate that for the majority of instances where users return to their smartphone, i.e., unlock their device, they in fact begin a new session as opposed to continuing a previous one. Our findings suggest that the commonly used approach of ignoring brief standby periods is not reliable, but optimisation is possible. We therefore propose various metrics related to usage sessions and evaluate various machine learning approaches to classify gaps in usage.

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