Smartphone dependence classification using tensor factorization

• Published in: PloS one Vol. 12; no. 6; p. e0177629
• Main Authors: Choi, Jingyun, Rho, Mi Jung, Kim, Yejin, Yook, In Hye, Yu, Hwanjo, Kim, Dai-Jin, Choi, In Young
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 21.06.2017; Public Library of Science (PLoS)
• Subjects: Addictions; Adult; Adults; Algorithms; Behavior, Addictive - psychology; Biology and Life Sciences; Cellular telephones; Classification; College students; Computer and Information Sciences; Computer science; Data analysis; Data mining; Data processing; Daytime; Electronic health records; Engineering and Technology; Factor Analysis, Statistical; Factorization; Female; Gene expression; Health informatics; Humans; International conferences; Intervention; Knowledge discovery; Male; Medicine; Medicine and Health Sciences; Networking; Night; People and Places; Physical Sciences; Psychological aspects; Research and Analysis Methods; Risk factors; Smart phones; Smartphone - statistics & numerical data; Smartphones; Social conditions; Social organization; Social problems; Social Sciences; Use statistics; Young Adult; South Korea
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0177629

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.