Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control

被引:91
|
作者
Kim, Yejin [1 ]
Jeong, Jo-Eun [2 ]
Cho, Hyun [2 ]
Jung, Dong-Jin [2 ]
Kwak, Minjung [2 ]
Rho, Mi Jung [3 ]
Yu, Hwanjo [1 ]
Kim, Dai-Jin [2 ]
Choi, In Young [3 ]
机构
[1] Pohang Univ Sci & Technol, Dept Creat IT Engn, Pohang, South Korea
[2] Catholic Univ Korea, Seoul St Marys Hosp, Coll Med, Dept Psychiat, Seoul, South Korea
[3] Catholic Univ Korea, Coll Med, Dept Med Informat, Seoul, South Korea
来源
PLOS ONE | 2016年 / 11卷 / 08期
基金
新加坡国家研究基金会;
关键词
CELLULAR PHONE USE; COLLEGE-STUDENTS; INTERNET ADDICTION; SENSATION SEEKING; SOCIAL NETWORKING; YOUDEN INDEX; ASSOCIATION; BIS/BAS; BAS; DIMENSIONS;
D O I
10.1371/journal.pone.0159788
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:15
相关论文
共 50 条
  • [41] The Influence of Psychopathic Personality Traits, Low Self-Control, and Nonshared Environmental Factors on Criminal Involvement
    Boccio, Cashen M.
    Beaver, Kevin M.
    YOUTH VIOLENCE AND JUVENILE JUSTICE, 2018, 16 (01) : 37 - 52
  • [42] The role of boredom proneness and self-control in the association between anxiety and smartphone addiction among college students: a multiple mediation model
    Zhang, Li
    Wang, Baokai
    Xu, Qi
    Fu, Chang
    FRONTIERS IN PUBLIC HEALTH, 2023, 11
  • [43] Comparison of psychophysiological and self-report measures of the behavioral activation and inhibition systems
    Brenner, SL
    Beauchaine, TP
    Sylvers, PD
    PSYCHOPHYSIOLOGY, 2003, 40 : S28 - S28
  • [44] Personality Traits, Approval Motivation, and Empathy as Predictors of Cognitive Regulation of Emotions and Behavioral Self-Control in Codependent Women
    Kolenova, Anastasia
    Denisova, Ekaterina
    Kukulyar, Anna
    Ermakov, Pavel
    INTERNATIONAL JOURNAL OF COGNITIVE RESEARCH IN SCIENCE ENGINEERING AND EDUCATION-IJCRSEE, 2023, 11 (02): : 187 - 197
  • [45] Pathological narcissism, brain behavioral systems and tendency to substance abuse: The mediating role of self-control
    Mowlaie, Mehri
    Abolghasemi, Abbas
    Aghababaei, Naser
    PERSONALITY AND INDIVIDUAL DIFFERENCES, 2016, 88 : 247 - 250
  • [46] Relationship among family environment, self-control, friendship quality, and adolescents' smartphone addiction in South Korea: Findings from nationwide data
    Kim, Hye-Jin
    Min, Jin-Young
    Min, Kyoung-Bok
    Lee, Tae-Jin
    Yoo, Seunghyun
    PLOS ONE, 2018, 13 (02):
  • [47] PTSD as a Risk Factor Predicting Polydrug Use: A Dual Systems of Self-Control Mediation Model
    Wojciechowski, Thomas
    JOURNAL OF DRUG ISSUES, 2021, 51 (01) : 68 - 83
  • [48] Neurocognitive components of the behavioral inhibition and activation systems: Implications for theories of self-regulation
    Amodio, David M.
    Master, Sarah L.
    Yee, Cindy M.
    Taylor, Shelley E.
    PSYCHOPHYSIOLOGY, 2008, 45 (01) : 11 - 19
  • [49] Annual Research Review: On the relations among self-regulation, self-control, executive functioning, effortful control, cognitive control, impulsivity, risk-taking, and inhibition for developmental psychopathology
    Nigg, Joel T.
    JOURNAL OF CHILD PSYCHOLOGY AND PSYCHIATRY, 2017, 58 (04) : 361 - 383
  • [50] Predicting School Performance by Non-cognitive Factors Self-control and Grit in a Genetically Informed Design
    Kevenaar, Sofieke T.
    Dolan, Conor V.
    de Zeeuw, Eveline L.
    Boomsma, Dorret I.
    van Bergen, Elsje
    BEHAVIOR GENETICS, 2021, 51 (06) : 716 - 716