A study on the factors affecting the follow-up participation in birth cohorts.

OBJECTIVES: A stable follow-up participation rate is a very important factor for validity in a cohort study. This study analyzed the factors that affect the participation rate at one hospital-based birth cohort in South Korean. METHODS: The participants were recruited from the Mothers' and Children's Environmental Health study between 2006 and 2010. The analysis targeted 1751 mothers who participated in a birth cohort. We conducted analyses of general characteristics during pregnancy and those of infants at birth that affect the participation rate of the 6-month follow-up survey. RESULTS: The participation rate for the 6-month follow-up survey was 60.4%. The participation rate in the follow-up of the subsequent period decreased within a 5% to 10% range compared to the number of subjects. The participation rate of premature infants was 16.9% lower than that of a full-term infant (52.6% vs. 69.5%). Analysis showed a 16.7% difference between the participation rate of low-birthweight infants in follow-ups (53.7%) and the participation rate of infants with normal weight (70.4%). The participation rate of mothers who were employed during pregnancy was significantly lower for the 6-month follow-up compared to the participation rate of mothers who were unemployed during pregnancy. CONCLUSIONS: In this study, factors such as premature birth, low-birthweight, and the employment status of the mother during pregnancy affected the participation rate of the follow-up survey for the birth cohort at six months. A specific strategy is needed to encourage survey participation for the high risk groups in the follow-ups.

Comparison of risk and protective factors associated with smartphone addiction and Internet addiction.

BACKGROUND AND AIMS: Smartphone addiction is a recent concern that has resulted from the dramatic increase in worldwide smartphone use. This study assessed the risk and protective factors associated with smartphone addiction in college students and compared these factors to those linked to Internet addiction. METHODS: College students (N = 448) in South Korea completed the Smartphone Addiction Scale, the Young's Internet Addiction Test, the Alcohol Use Disorders Identification Test, the Beck Depression Inventory I, the State-Trait Anxiety Inventory (Trait Version), the Character Strengths Test, and the Connor-Davidson Resilience Scale. The data were analyzed using multiple linear regression analyses. RESULTS: The risk factors for smartphone addiction were female gender, Internet use, alcohol use, and anxiety, while the protective factors were depression and temperance. In contrast, the risk factors for Internet addiction were male gender, smartphone use, anxiety, and wisdom/knowledge, while the protective factor was courage. Discussion These differences may result from unique features of smartphones, such as high availability and primary use as a tool for interpersonal relationships. CONCLUSIONS: Our findings will aid clinicians in distinguishing between predictive factors for smartphone and Internet addiction and can consequently be utilized in the prevention and treatment of smartphone addiction.

Latent class analysis on internet and smartphone addiction in college students.

PURPOSE: This study aimed to classify distinct subgroups of people who use both smartphone and the internet based on addiction severity levels. Additionally, how the classified groups differed in terms of sex and psychosocial traits was examined. METHODS: A total of 448 university students (178 males and 270 females) in Korea participated. The participants were given a set of questionnaires examining the severity of their internet and smartphone addictions, their mood, their anxiety, and their personality. Latent class analysis and ANOVA (analysis of variance) were the statistical methods used. RESULTS: Significant differences between males and females were found for most of the variables (all <0.05). Specifically, in terms of internet usage, males were more addicted than females (P<0.05); however, regarding smartphone, this pattern was reversed (P<0.001). Due to these observed differences, classifications of the subjects into subgroups based on internet and smartphone addiction were performed separately for each sex. Each sex showed clear patterns with the three-class model based on likelihood level of internet and smartphone addiction (P<0.001). A common trend for psychosocial trait factors was found for both sexes: anxiety levels and neurotic personality traits increased with addiction severity levels (all P<0.001). However, Lie dimension was inversely related to the addiction severity levels (all P<0.01). CONCLUSION: Through the latent classification process, this study identified three distinct internet and smartphone user groups in each sex. Moreover, psychosocial traits that differed in terms of addiction severity levels were also examined. It is expected that these results should aid the understanding of traits of internet and smartphone addiction and facilitate further study in this field.