Problematic smartphone use is an emerging issue in behavioral addiction research. At the same time, measuring smartphone use with mobile apps has become increasingly common. However, understanding how much data are necessary requires careful consideration if the field is to move forward. Here, we examine how much time should be spent measuring mobile phone operation to reliably infer general patterns of usage and repetitive checking behaviors. In a second analysis, we consider whether a self-report measure of problematic smartphone use is associated with real-time patterns of use. Results suggest that smartphone usage collected for a minimum of 5 days will reflect typical weekly usage (in hours), but habitual checking behaviors (uses lasting <15 seconds) can be reliably inferred within 2 days. These measurements did not reliably correlate with a self-reported measure. We conclude that patterns of smartphone use are repetitive and our results suggest that checking behavior is a particularly consistent and efficient measure when quantifying typical and problematic smartphone usage.
Funding
This work was partially funded by a Faculty of Science and Technology research grant (PSA7866) from Lancaster University, and by a Research Investment Fund (RIF2014-31) from the University of Lincoln.
History
School
Sport, Exercise and Health Sciences
Published in
Cyberpsychology, Behavior, and Social Networking
Volume
21
Issue
6
Pages
395 - 398
Citation
WILCOCKSON, T.D.W., ELLIS, D.A. and SHAW, H., 2018. Determining typical smartphone usage: What data do we need?. Cyberpsychology, Behavior, and Social Networking, 21 (6), pp.395-398.
This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/
Acceptance date
2018-06-01
Publication date
2018-06-01
Notes
Final publication is available from Mary Ann Liebert, Inc., publishers https://doi.org/10.1089/cyber.2017.0652