Association of smartphone use with abnormal social jetlag among adolescents in Korea before and after COVID-19.

PURPOSE: This study aimed to use social jetlag to determine how smartphone overuse by adolescents before and after the COVID-19 pandemic might be associated with their circadian rhythm. METHODS: We used 2017 and 2020 Korea Youth Risk Behavior web-based Survey data and conducted a survey analysis on 100,976 adolescents. The dependent variable was abnormal social jetlag. Based on the times recorded by the survey, we calculated the weekday midpoint and weekend sleep time. The main independent variable was smartphone usage time (<2h/day, 2-3.9 h/day, 4-5.9 h/day, and ≥ 6 h/day). Multiple logistic regression and relative excess risk due to interaction (RERI) were performed. RESULTS: Abnormal social jetlag was most prevalent in male and female adolescents who used smartphones ≥ 6 h/day (adjusted odds ratio [AOR] = 2.60, 95 % confidence interval [CI] = 2.47-2.74). The longer the smartphone usage time, the higher the association with abnormal social jetlag. This association was more prominent in female adolescents. The additive interaction between longer smartphone usage time and post-COVID-19 year was statistically significant (total: RERI = 0.92, 95 % CI = 0.90-0.95; males: RERI = 0.83, 95 % CI = 0.80-0.87; females: RERI = 1.13, 95 % CI = 1.08-1.18). CONCLUSION: Our results clearly indicated that increased smartphone usage time tended to result in greater social jetlag, an association that was more pronounced in female. Moreover, the COVID-19 pandemic significantly strengthened this relationship. Further research is needed regarding the proper use of smartphones to ensure good sleep-in adolescents after the COVID-19 pandemic.

Relationship between Smartphone Addiction and Sleep Satisfaction: A Cross-Sectional Study on Korean Adolescents.

The purpose of this study was to analyze the relationship between smartphone addiction and sleep satisfaction in 54,948 Korean adolescents. This study utilized the Korea Youth Risk Behavior Web-Based Survey (KYRBS). The dependent variable was sleep satisfaction. Independent variables were smartphone addiction level and usage time. Gender, school grade, stress, depression, regular physical activity (PA), asthma, allergic rhinitis, and atopic dermatitis were selected as confounding variables. A chi-squared test, logistic regression, and independent t-test were performed for data analysis. As a result of the chi-squared test, sleep satisfaction showed significant relationships with all confounding variables (all p < 0.001). As a result of adjusting all confounding variables, sleep satisfaction of smartphone normal users was significantly higher (odds ratios: 1.372, p < 0.001) than that of high-risk users with smartphone addiction. Smartphone users with a daily smartphone usage time from 2 h to 8 h a day were 1.096−1.347 times (p = 0.014 to p < 0.001) more likely to be satisfied with their sleep than smartphone users with a daily smartphone usage time over 8 h, who were unsatisfied with their sleep. The group that was not satisfied with their sleep had a significantly higher average daily smartphone usage time and total score on the smartphone addiction scale than the group that was satisfied with their sleep (both p < 0.001). In conclusion, it will be necessary to manage the use of smartphones to improve the sleep satisfaction of Korean adolescents.

Predictors of Habitual and Addictive Smartphone Behavior in Problematic Smartphone Use.

OBJECTIVE: Smartphones have become common, and problematic smartphone use (PSU) is increasing. Predictors of PSU should be identified to prevent it. Little is known about the role of content types of smartphone use as predictors of PSU. Therefore, we aimed to evaluate the predictors of two proposed concepts of PSU, namely habitual smartphone behavior (SB) and addictive SB, within the context of the application (app) categories. METHODS: We studied 1,039 smartphone users using online surveys conducted between January 2 and 31, 2019. We employed multiple regression analysis to identify the predictors of habitual and addictive SB. We controlled for sex and age (mean=39.20). RESULTS: Common predictors of habitual and addictive SB were the use of social networking services, games, entertainment apps, and average weekend smartphone usage time. The predictors of habitual SB were the use of web and lifestyle apps, weekly usage frequency, and sex (female) and the predictors of addictive SB were the use of shopping apps and sleep duration. CONCLUSION: This study revealed the need to consider habitual and addictive SB in evaluating PSU. The predictors in terms of the content types of smartphone usage can be used to develop monitoring and prevention services for PSU.