Smartphone dependence classification using tensor factorization

Jingyun Choi; Mi Jung Rho; Yejin Kim; In Hye Yook; Hwanjo Yu; Dai-Jin Kim; In Young Choi

doi:10.1371/journal.pone.0177629

Smartphone dependence classification using tensor factorization

PLoS One. 2017 Jun 21;12(6):e0177629. doi: 10.1371/journal.pone.0177629. eCollection 2017.

Authors

Jingyun Choi¹, Mi Jung Rho², Yejin Kim³, In Hye Yook², Hwanjo Yu¹, Dai-Jin Kim⁴, In Young Choi²

Affiliations

¹ Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.
² Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
³ Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.
⁴ Department of Psychiatry, College of Medicine, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, Republic of Korea.

Abstract

Excessive smartphone use causes personal and social problems. To address this issue, we sought to derive usage patterns that were directly correlated with smartphone dependence based on usage data. This study attempted to classify smartphone dependence using a data-driven prediction algorithm. We developed a mobile application to collect smartphone usage data. A total of 41,683 logs of 48 smartphone users were collected from March 8, 2015, to January 8, 2016. The participants were classified into the control group (SUC) or the addiction group (SUD) using the Korean Smartphone Addiction Proneness Scale for Adults (S-Scale) and a face-to-face offline interview by a psychiatrist and a clinical psychologist (SUC = 23 and SUD = 25). We derived usage patterns using tensor factorization and found the following six optimal usage patterns: 1) social networking services (SNS) during daytime, 2) web surfing, 3) SNS at night, 4) mobile shopping, 5) entertainment, and 6) gaming at night. The membership vectors of the six patterns obtained a significantly better prediction performance than the raw data. For all patterns, the usage times of the SUD were much longer than those of the SUC. From our findings, we concluded that usage patterns and membership vectors were effective tools to assess and predict smartphone dependence and could provide an intervention guideline to predict and treat smartphone dependence based on usage data.

MeSH terms

Adult
Algorithms*
Behavior, Addictive / psychology*
Factor Analysis, Statistical*
Female
Humans
Male
Smartphone / statistics & numerical data*
Young Adult

Grants and funding

This work was partly supported by the ICT R&D program of MSIP/IITP B0101-15-0307 “Basic Software Research in Human-level Lifelong Machine Learning”, Next-Generation Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education, Science and Technology (No. 2012M3C4A7033344) and the National Research Foundation of Korea Grant funded by the Korean Government (No. 2014M3C7A1062893). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.