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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...

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Detalles Bibliográficos
Autores principales: Choi, Jingyun, Rho, Mi Jung, Kim, Yejin, Yook, In Hye, Yu, Hwanjo, Kim, Dai-Jin, Choi, In Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
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.
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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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