• Journal of Digital Contents Society
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• v.13 no.4
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• pp.501-516
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• 2012

The purpose of this study is to explore the fundamental nature and meanings of the experiences of university students with regard to their smartphone usage in order to prevent smartphone addiction and provide guidelines to develop effective interventions. Nine university students who were currently enrolled in an university located in a Korean province participated in this study. In-depth interviews were conducted from June of 2012 to July of 2012, and data were analyzed using Colaizzi's phenomenological qualitative method. As a results, five theme and 40 subthemes of experiences of problematic smartphone usage were identified. Those themes include 1) a desperate try to connect with others(exposing oneself without appropriate self-protection, seeking emotional comfort, hurt and mistrust); 2) excessive use of smartphone and loss of self-control(more smartphone usage over computer/over-immersion/excessive usage/habituation, loss of self-control); 3) psychological difficulties(excessive attachment and dependence, separation anxiety, clingingness, reduced patience) ; 4) threats to daily functioning (changes in priorities, regressed independent daily functioning, irregular daily patterns, health problems, interruptions from free messenger, academic difficulties); 5) potential for recovery(conflictual feelings and doubts over smartphone usage, importance of interpersonal(face-to-face) communications, willingness to cut down the use, hope for recovery). This study provided suggestions for the prevention effort against smartphone addiction.

• Journal of The Korean Association of Information Education
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• v.18 no.4
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• pp.471-482
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• 2014

It is known that the smart education has a positive impact of critical thinking skills and problem solving, communication and collaboration, creativity and innovation in the 21st century. However, some people concern about the adverse effects of smart education such as smart devices addiction and health problems. In this study, we conducted the adverse effects of education using smart devices on students. The questionnaire survey was conducted and analyzed for students in 9 schools at A city(four elementary schools, three middle schools, two high schools) that are receiving smart education. The types of adverse effects of smart education includes smartphone addiction, lack of information ethics, health problems, and lack of social and learning ability. We showed the differences each item compared among elementary school, middle school and high school about adverse effects of smart education. The result of survey didn't have any adverse affect of smart education such as internet addiction or smartphone addiction. However in order to protect the copyright, the school needs to fight against indiscriminated downloads and perform the information ethics education. Plus we surely need to be more open-minded in reading and exercise at school and home.

• Journal of Digital Convergence
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• v.14 no.1
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• pp.363-370
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• 2016

The purpose of this study was to determine the influential factors associated with video display terminal (VDT) syndrome of male adolescents with digital device addition. The study design was cross-sectional descriptive survey. The study participants were 169 male middle school students in Seoul. A structured questionnaire was used for data collection and data were analyzed using the SPSS program. The factors most influential for the subjective symptoms of VDT were smartphone addiction, computer use time during the week, computer use environment. These factors accounted for 28.1% of the variance in subjective symptoms of VDT. In order to prevent the occurrence of VDT syndrome in students, we suggest to establish a preventive intervention against the digital addiction.

• Asia-Pacific Journal of Business
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• v.13 no.3
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• pp.293-309
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• 2022

Purpose - The purpose of this study is to explore the possibility of predicting the degree of smartphone overdependence based on mobile phone usage patterns. Design/methodology/approach - In this study, a survey conducted by Korea Internet and Security Agency(KISA) called "problematic smartphone use survey" was analyzed. The survey consists of 180 questions, and data were collected from 29,712 participants. Based on the data on the smartphone usage pattern obtained through the questionnaire, the smartphone addiction level was predicted using machine learning techniques. k-NN, gradient boosting, XGBoost, CatBoost, AdaBoost and random forest algorithms were employed. Findings - First, while various factors together influence the smartphone overdependence level, the results show that all machine learning techniques perform well to predict the smartphone overdependence level. Especially, we focus on the features which can be obtained from the smartphone log data (without psychological factors). It means that our results can be a basis for diagnostic programs to detect problematic smartphone use. Second, the results show that information on users' age, marriage and smartphone usage patterns can be used as predictors to determine whether users are addicted to smartphones. Other demographic characteristics such as sex or region did not appear to significantly affect smartphone overdependence levels. Research implications or Originality - While there are some studies that predict smartphone overdependence level using machine learning techniques, but the studies only present algorithm performance based on survey data. In this study, based on the information gain measure, questions that have more influence on the smartphone overdependence level are presented, and the performance of algorithms according to the questions is compared. Through the results of this study, it is shown that smartphone overdependence level can be predicted with less information if questions about smartphone use are given appropriately.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.474-481
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• 2018

The purpose of this study was to examine the mediating effects of maternal family experience on the relationship between mothers' tendency to addicted to smartphones and the social competence of young children. The subjects of this study were 481 mothers who delivered infants to a kindergarten or daycare center in Jeju area After conducting the questionnaire, the results of the responses were analyzed. To analyze the collected data, structural equations were implemented using SPSS Statistic 18.0 program. The results of this study are summarized as follows. First, the correlational analysis showed that the tendency of the addiction of smartphone and the social ability of the infant showed a significant correlation with the mother's family experience. Second, in the relationship between the mother's smartphone tendency and the social competence of the infant, The research model and the competition model were set up to examine the mediating effect and the competition model was found to be more appropriate. As a result, it can be seen that mother's original family experience is partly mediated in relation to mother's tendency to add to smartphone and social competence of infant. Based on these results, the implications of this study and suggestions for subsequent research were discussed.

• Journal of Fisheries and Marine Sciences Education
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• v.27 no.4
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• pp.998-1012
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• 2015

This study is smartphone addiction impulsiveness, stress, self-efficacy, and examine any changes to appear self-control. This study is a response to the results obtained for 310 people targeting high school in Pusan, the second grade students. For the analysis of the collected data by using the SPSS 22.0 program was the analysis of the T-test, ANOVA, Multiple Regression. The major findings of this study can be summed up as follows: first, smart phone addiction has significant difference in impulsivity, stress, self-efficacy, and self-control. Second, sex is found to be significant in impulsivity, stress, self-efficacy, and self-control. Third, grades are significant in impulsivity, self-efficacy, and self-control. Fourth, the model for impulsivity indicates 4% of explanatory power, which is significant. Fifth, explanatory power for stress is 4%, which is significant. Sixth, the model for self-efficacy shows 14% of explanatory power, which is significant. Meanwhile, smart phone addiction, sex, and grades have no significant effects on self-efficacy. Seventh, the model for self-control indicates 20% of explanatory power, which is significant.

• Journal of The Korean Society of Integrative Medicine
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• v.2 no.2
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• pp.79-88
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• 2014

Purpose: This study investigated the relationship between smartphone addiction propensities and compare muscle activity of the forearms and brain wave depend on the type of smartphone when inputting text messages. Method: We used an EMG to measure the change in muscle activity by attaching pads to the four muscles in both forearms of all 16 participants. We simultaneously conducted EEG measurements by observing the changes in alpha and beta waves recorded from electrode attached to both ears and the forehead of the participants. The participants had to input a given text using three different types of smartphones for ten minutes each. Result: The comparison of the EMG when inputting text involved a one way analysis of variance and the results showed that the iPad3 was highest for muscle activity followed by GALAXY Note2 and iPhone4. For EEG measurement, a one way analysis of variance was also used and the results showed iPhone4 was higest followed by GALAXY Note2 and finally iPad3 for EEG stress score. Conclusion: The results are thought to be used as reference data for smart phone users.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.99-110
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• 2023

This study analyzed 817 articles published in Korean professional journals over the past three years, from 2020 to 2022, using text mining techniques to identify trends in addiction research in Korea and explore development directions. The analysis results are as follows. First, as a result of the analysis of the top keywords, online addiction studies such as smartphones, games, Internet, gambling, and relationship addiction were prominent as the top keywords. Second, as a result of TF-IDF analysis, many addiction studies related to behavioral addiction such as smartphones, games, the Internet, and work addiction have been conducted over the past three years, and in particular, there are many studies on addiction problems such as smartphones, games, and the Internet that have not yet been clinically diagnosed as addiction problems. This is the same as the result of word frequency analysis, and it can be interpreted that recent studies have been remarkably conducted on more diverse addiction problems. Third, the 2-gram analysis shows that words that mainly correspond to behavioral addiction, such as smartphones, games, and the Internet, appear side by side with the keyword addiction, and among them, words paired with smartphones are mentioned a lot in research papers and are being studied. Fourth, as a result of the CONCOR analysis, there were five clusters: a study on universal addiction issues such as alcohol use disorders and the Internet, a study of recovery on drug and gambling addiction, a study on mobile devices and media addiction, a study on the latest trends related to behavioral addiction, and other addiction issues. Finally, based on the results of this study, a direction for future addiction-related research was suggested.

• Proceedings of the Korea Contents Association Conference
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• 2015.05a
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• pp.401-402
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• 2015

본 연구는 스마트폰 중독에 따른 통합치료 방안을 모색한 것이다. 스마트폰의 심리적 요인으로 자기통제력 상실과 우울증을 들었다. 기존 치료방안의 문제점을 소개하였고, 스마트폰 중독의 치료방안으로는 환자에 맞춘 다중치료 프로그램을 소개하였다.

• Journal of Korean Academy of Fundamentals of Nursing
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• v.23 no.1
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• pp.51-60
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• 2016

Purpose: There is increasing attention to smart-learning as a new education paradigm. The purpose of this study was to identify the level of intention to use smart-based Continuing Nurse Education (CNE) and factors influencing intention to use smart-based CNE. Methods: Participants were 486 nurses from 14 organizations, including 12 hospitals, a nurses association, and an office of education. Data were collected from November 5 to 18, 2014 using self-report questionnaires. Data were analyzed using descriptive statistics, t-test, ANOVA, Pearson correlation, and stepwise multiple regression. Results: The mean score for intention to use smart-based CNE was 6.34 out of 10. The factors influencing intention to use smart-based CNE were nursing informatics competency, current unit career, and smartphone addiction. These variables explained 10% of variance in intention to use smart-based CNE. Conclusion: The findings of this study suggest that efforts to enhance the nursing informatics competency of nurses could increase usage rate of smart-based CNE. The CNE policy makers will find this study very useful and the findings of this study will help to provide insight into the best way to develop smart-based CNE.