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Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control
The purpose of this study was to identify personality factor-associated predictors of smartphone addiction predisposition (SAP). Participants were 2,573 men and 2,281 women (n = 4,854) aged 20–49 years (Mean ± SD: 33.47 ± 7.52); participants completed the following questionnaires: the Korean Smartph...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4988723/ https://www.ncbi.nlm.nih.gov/pubmed/27533112 http://dx.doi.org/10.1371/journal.pone.0159788 |
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author | Kim, Yejin Jeong, Jo-Eun Cho, Hyun Jung, Dong-Jin Kwak, Minjung Rho, Mi Jung Yu, Hwanjo Kim, Dai-Jin Choi, In Young |
author_facet | Kim, Yejin Jeong, Jo-Eun Cho, Hyun Jung, Dong-Jin Kwak, Minjung Rho, Mi Jung Yu, Hwanjo Kim, Dai-Jin Choi, In Young |
author_sort | Kim, Yejin |
collection | PubMed |
description | The purpose of this study was to identify personality factor-associated predictors of smartphone addiction predisposition (SAP). Participants were 2,573 men and 2,281 women (n = 4,854) aged 20–49 years (Mean ± SD: 33.47 ± 7.52); participants completed the following questionnaires: the Korean Smartphone Addiction Proneness Scale (K-SAPS) for adults, the Behavioral Inhibition System/Behavioral Activation System questionnaire (BIS/BAS), the Dickman Dysfunctional Impulsivity Instrument (DDII), and the Brief Self-Control Scale (BSCS). In addition, participants reported their demographic information and smartphone usage pattern (weekday or weekend average usage hours and main use). We analyzed the data in three steps: (1) identifying predictors with logistic regression, (2) deriving causal relationships between SAP and its predictors using a Bayesian belief network (BN), and (3) computing optimal cut-off points for the identified predictors using the Youden index. Identified predictors of SAP were as follows: gender (female), weekend average usage hours, and scores on BAS-Drive, BAS-Reward Responsiveness, DDII, and BSCS. Female gender and scores on BAS-Drive and BSCS directly increased SAP. BAS-Reward Responsiveness and DDII indirectly increased SAP. We found that SAP was defined with maximal sensitivity as follows: weekend average usage hours > 4.45, BAS-Drive > 10.0, BAS-Reward Responsiveness > 13.8, DDII > 4.5, and BSCS > 37.4. This study raises the possibility that personality factors contribute to SAP. And, we calculated cut-off points for key predictors. These findings may assist clinicians screening for SAP using cut-off points, and further the understanding of SA risk factors. |
format | Online Article Text |
id | pubmed-4988723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-49887232016-08-29 Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control Kim, Yejin Jeong, Jo-Eun Cho, Hyun Jung, Dong-Jin Kwak, Minjung Rho, Mi Jung Yu, Hwanjo Kim, Dai-Jin Choi, In Young PLoS One Research Article The purpose of this study was to identify personality factor-associated predictors of smartphone addiction predisposition (SAP). Participants were 2,573 men and 2,281 women (n = 4,854) aged 20–49 years (Mean ± SD: 33.47 ± 7.52); participants completed the following questionnaires: the Korean Smartphone Addiction Proneness Scale (K-SAPS) for adults, the Behavioral Inhibition System/Behavioral Activation System questionnaire (BIS/BAS), the Dickman Dysfunctional Impulsivity Instrument (DDII), and the Brief Self-Control Scale (BSCS). In addition, participants reported their demographic information and smartphone usage pattern (weekday or weekend average usage hours and main use). We analyzed the data in three steps: (1) identifying predictors with logistic regression, (2) deriving causal relationships between SAP and its predictors using a Bayesian belief network (BN), and (3) computing optimal cut-off points for the identified predictors using the Youden index. Identified predictors of SAP were as follows: gender (female), weekend average usage hours, and scores on BAS-Drive, BAS-Reward Responsiveness, DDII, and BSCS. Female gender and scores on BAS-Drive and BSCS directly increased SAP. BAS-Reward Responsiveness and DDII indirectly increased SAP. We found that SAP was defined with maximal sensitivity as follows: weekend average usage hours > 4.45, BAS-Drive > 10.0, BAS-Reward Responsiveness > 13.8, DDII > 4.5, and BSCS > 37.4. This study raises the possibility that personality factors contribute to SAP. And, we calculated cut-off points for key predictors. These findings may assist clinicians screening for SAP using cut-off points, and further the understanding of SA risk factors. Public Library of Science 2016-08-17 /pmc/articles/PMC4988723/ /pubmed/27533112 http://dx.doi.org/10.1371/journal.pone.0159788 Text en © 2016 Kim 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 Kim, Yejin Jeong, Jo-Eun Cho, Hyun Jung, Dong-Jin Kwak, Minjung Rho, Mi Jung Yu, Hwanjo Kim, Dai-Jin Choi, In Young Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control |
title | Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control |
title_full | Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control |
title_fullStr | Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control |
title_full_unstemmed | Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control |
title_short | Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control |
title_sort | personality factors predicting smartphone addiction predisposition: behavioral inhibition and activation systems, impulsivity, and self-control |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4988723/ https://www.ncbi.nlm.nih.gov/pubmed/27533112 http://dx.doi.org/10.1371/journal.pone.0159788 |
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