What question did this study set out to answer?

To identify personality factors predictive of smartphone addiction predisposition and establish diagnostic cut-off points.

August 17, 2016PLoS ONEOpen Access

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

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Authors

YKYejin Kim JJJo-Eun Jeong HCHyun Cho

Discussion

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Overview

Cross-sectional study reveals personality traits and usage hours predict smartphone addiction predisposition in adults, highlighting potential clinical screening thresholds.

Key Points

To identify personality factors predictive of smartphone addiction predisposition and establish diagnostic cut-off points.
Surveyed 4,854 adults (2,573 men, 2,281 women; mean age 33.47 ± 7.52 years) using K-SAPS, BIS/BAS, DDII, and BSCS questionnaires along with usage data.
Identified predictors using logistic regression, evaluated causal pathways with a Bayesian belief network, and established optimal cut-off thresholds via the Youden index.
Female gender, weekend smartphone usage hours, and scores on BAS-Drive and BSCS directly increased smartphone addiction predisposition.
BAS-Reward Responsiveness and DDII indirectly increased smartphone addiction predisposition.
Maximal sensitivity cut-off values were weekend usage > 4.45 hours, BAS-Drive > 10.0, BAS-Reward Responsiveness > 13.8, DDII > 4.5, and BSCS > 37.4.

Cite This Study

Kim et al. (2016) studied this question.

synapsesocial.com/papers/699a32682dac970dae1eb07e https://doi.org/10.1371/journal.pone.0159788

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August 17, 2016PLoS ONEOpen Access

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

View Full Paper

Ask AI

Bookmark

Authors

YKYejin Kim JJJo-Eun Jeong HCHyun Cho

Discussion

Member takes

Overview

Cross-sectional study reveals personality traits and usage hours predict smartphone addiction predisposition in adults, highlighting potential clinical screening thresholds.

Key Points

To identify personality factors predictive of smartphone addiction predisposition and establish diagnostic cut-off points.
Surveyed 4,854 adults (2,573 men, 2,281 women; mean age 33.47 ± 7.52 years) using K-SAPS, BIS/BAS, DDII, and BSCS questionnaires along with usage data.
Identified predictors using logistic regression, evaluated causal pathways with a Bayesian belief network, and established optimal cut-off thresholds via the Youden index.
Female gender, weekend smartphone usage hours, and scores on BAS-Drive and BSCS directly increased smartphone addiction predisposition.
BAS-Reward Responsiveness and DDII indirectly increased smartphone addiction predisposition.
Maximal sensitivity cut-off values were weekend usage > 4.45 hours, BAS-Drive > 10.0, BAS-Reward Responsiveness > 13.8, DDII > 4.5, and BSCS > 37.4.

Cite This Study

Kim et al. (2016) studied this question.

synapsesocial.com/papers/699a32682dac970dae1eb07e https://doi.org/10.1371/journal.pone.0159788

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