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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5479529/ https://www.ncbi.nlm.nih.gov/pubmed/28636614 http://dx.doi.org/10.1371/journal.pone.0177629 |
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author | Choi, Jingyun Rho, Mi Jung Kim, Yejin Yook, In Hye Yu, Hwanjo Kim, Dai-Jin Choi, In Young |
author_facet | Choi, Jingyun Rho, Mi Jung Kim, Yejin Yook, In Hye Yu, Hwanjo Kim, Dai-Jin Choi, In Young |
author_sort | Choi, Jingyun |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5479529 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-54795292017-07-05 Smartphone dependence classification using tensor factorization Choi, Jingyun Rho, Mi Jung Kim, Yejin Yook, In Hye Yu, Hwanjo Kim, Dai-Jin Choi, In Young PLoS One Research Article 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. Public Library of Science 2017-06-21 /pmc/articles/PMC5479529/ /pubmed/28636614 http://dx.doi.org/10.1371/journal.pone.0177629 Text en © 2017 Choi et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Choi, Jingyun Rho, Mi Jung Kim, Yejin Yook, In Hye Yu, Hwanjo Kim, Dai-Jin Choi, In Young Smartphone dependence classification using tensor factorization |
title | Smartphone dependence classification using tensor factorization |
title_full | Smartphone dependence classification using tensor factorization |
title_fullStr | Smartphone dependence classification using tensor factorization |
title_full_unstemmed | Smartphone dependence classification using tensor factorization |
title_short | Smartphone dependence classification using tensor factorization |
title_sort | smartphone dependence classification using tensor factorization |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5479529/ https://www.ncbi.nlm.nih.gov/pubmed/28636614 http://dx.doi.org/10.1371/journal.pone.0177629 |
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