ArticleFrontiers in public health2020
Development of the Smartphone Addiction Risk Rating Score for a Smartphone Addiction Management Application.
Article in Frontiers in public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Effectiveness of brief mindfulness intervention for college students' problematic smartphone use: The mediating role of self-control.PloS one · 2022Trial
- Psychological Factors Applicable to App Development for Overcoming Problematic Smartphone Use: A Structural Equation Modeling Approach.Psychiatry investigation · 2026Article
- A nomogram model to predict the risk for etomidate use disorder with antisocial personality disorder.BMC psychiatry · 2026Article
- Neural networks to model COVID-19 dynamics and allocate healthcare resources.Scientific reports · 2025Article
- Predictors of Habitual and Addictive Smartphone Behavior in Problematic Smartphone Use.Psychiatry investigation · 2021Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Smartphone usage characteristics are useful for identification of the risk factors for smartphone addiction. Risk rating scores can be developed based on smartphone usage characteristics. This study aimed to investigate the smartphone addiction risk rating (SARR) score using smartphone usage characteristics. We evaluated 593 smartphone users using online surveys conducted between January 2 and January 31, 2019. We identified 102 smartphone users who were addicted to smartphones and 491 normal users based on the Korean Smartphone Addiction Proneness Scale for Adults. A multivariate logistic regression analysis was used to identify significant risk factors for smartphone addiction. The SARR score was calculated using a nomogram based on the significant risk factors. Weekend average usage time, habitual smartphone behavior, addictive smartphone behavior, social usage, and process usage were the significant risk factors associated with smartphone addiction. Furthermore, we developed the SARR score based on these factors. The SARR score ranged between 0 and 221 points, with the cut-off being 116.5 points. We developed a smartphone addiction management application using the SARR score. The SARR score provided insights for the development of monitoring, prevention, and prompt intervention services for smartphone addiction.
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Registered trials
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