Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control.
Personality Factors Predicting Smartphone Addiction Predisposition: Behavioral Inhibition and Activation Systems, Impulsivity, and Self-Control.
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DOI：
10.1371/journal.pone.0159788
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Choi IY
中科院分区：
文献类型：
--
作者：
Kim Y;Jeong JE;Cho H;Jung DJ;Kwak M;Rho MJ;Yu H;Kim DJ;Choi IY
本研究的目的是确定智能手机成瘾倾向（SAP）的人格因素相关预测因素。参与者为2，573名男性和2，281名女性（n = 4，854）年龄20-49岁（平均值± SD：33.47 ± 7.52）;参与者完成了以下问卷：韩国智能手机成瘾倾向量表（K-SAPS）成人，行为抑制系统/行为激活系统问卷（BIS/BAS），Dickman功能障碍冲动量表（DDII），简明自我控制量表（BSCS）此外，参与者还报告了他们的人口统计信息和智能手机使用模式（工作日或周末的平均使用时间和主要使用时间）。我们分三步分析数据：（1）用逻辑回归确定预测因子，（2）使用贝叶斯信念网络（BN）推导SAP及其预测因子之间的因果关系，（3）使用Youden指数计算确定的预测因子的最佳截止点。确定的SAP预测因素如下：性别（女性）、周末平均使用小时数以及BAS-驱动、BAS-奖励响应、DDII和BSCS评分。女性和BAS-Drive和BSCS评分直接增加SAP。BAS-奖励反应性和DDII间接增加SAP。我们发现SAP的最大敏感度定义如下：周末平均使用小时数> 4.45，BAS驱动器> 10.0，BAS奖励响应度> 13.8，DDII > 4.5，BSCS > 37.4。这项研究提出了人格因素有助于SAP的可能性。而且，我们计算了关键预测因素的截止点。这些发现可能有助于临床医生使用临界点筛选SAP，并进一步了解SA风险因素。
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.