Choi Jingyun, Rho Mi Jung, Kim Yejin, Yook In Hye, Yu Hwanjo, Kim Dai-Jin, Choi In Young
Department of Computer Science and Engineering, Pohang University of Science and Technology, Pohang, Republic of Korea.
Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
PLoS One. 2017 Jun 21;12(6):e0177629. doi: 10.1371/journal.pone.0177629. eCollection 2017.
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.
过度使用智能手机会导致个人和社会问题。为解决这一问题，我们试图根据使用数据得出与智能手机依赖直接相关的使用模式。本研究尝试使用数据驱动的预测算法对智能手机依赖进行分类。我们开发了一款移动应用程序来收集智能手机使用数据。从2015年3月8日至2016年1月8日，共收集了48名智能手机用户的41,683条日志。使用韩国成人智能手机成瘾倾向量表（S量表）以及由一名精神科医生和一名临床心理学家进行的面对面线下访谈，将参与者分为对照组（SUC）或成瘾组（SUD）（SUC = 23人，SUD = 25人）。我们使用张量分解得出使用模式，并发现以下六种最佳使用模式：1）白天使用社交网络服务（SNS），2）上网冲浪，3）夜间使用SNS，4）移动购物，5）娱乐，6）夜间玩游戏。这六种模式的成员向量比原始数据具有显著更好的预测性能。对于所有模式，SUD的使用时间都比SUC长得多。根据我们的研究结果，我们得出结论，使用模式和成员向量是评估和预测智能手机依赖的有效工具，并且可以提供基于使用数据来预测和治疗智能手机依赖的干预指南。
