Smartphone dependence classification using tensor factorization

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 de...

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Bibliographic Details
Published in:PloS one 2017-06, Vol.12 (6), p.e0177629-e0177629
Main Authors: Choi, Jingyun, Rho, Mi Jung, Kim, Yejin, Yook, In Hye, Yu, Hwanjo, Kim, Dai-Jin, Choi, In Young
Format: Article
Language:English
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
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0177629