Int J Environ Res Public Health. 2018 Apr 25;15(5). pii: E859. doi: 10.3390/ijerph15050859.
Kim YJ1, Jang HM2, Lee Y3, Lee D4, Kim DJ5.
Abstract
The associations of Internet addiction (IA) and smartphone addiction (SA) with mental health problems have been widely studied. We investigated the effects of IA and SA on depression and anxiety while adjusting for sociodemographic variables. In this study, 4854 participants completed a cross-sectional web-based survey including socio-demographic items, the Korean Scale for Internet Addiction, the Smartphone Addiction Proneness Scale, and the subscales of the Symptom Checklist 90 Items-Revised. The participants were classified into IA, SA, and normal use (NU) groups. To reduce sampling bias, we applied the propensity score matching method based on genetics matching. The IA group showed an increased risk of depression (relative risk 1.207; p < 0.001) and anxiety (relative risk 1.264; p < 0.001) compared to NUs. The SA group also showed an increased risk of depression (relative risk 1.337; p < 0.001) and anxiety (relative risk 1.402; p < 0.001) compared to NCs. These findings show that both, IA and SA, exerted significant effects on depression and anxiety. Moreover, our findings showed that SA has a stronger relationship with depression and anxiety, stronger than IA, and emphasized the need for prevention and management policy of the excessive smartphone use.
KEYWORDS: Internet addiction; anxiety; depression; propensity score; smartphone addiction
PMID: 29693641
1. Introduction
2. Materials and Methods
2.1. Study Participants
2.2. Measures
2.2.1. Measurement of Internet Addiction
2.2.2. Measurement of Smartphone Addiction
2.2.3. Measurement of Mental Health Problems: Depression and Anxiety
2.3. Data Analysis
2.3.1. Statistical Definition
be a binary addiction indicator for the ith subject; that is, Zi=1 if the ith subject is addicted (IA or SA), and Zi=0 otherwise. The potential outcome of a mental problem (depression or anxiety) is defined as Yi(Zi . Note that only one of the potential outcomes is observed at the same time for each subject, so direct computation of Yi(1)−Yi is impossible. Instead of the individual effect, the primary parameter of interest is the expected addiction effect on the addicted population
still has a problem because E(Yi(0)|Zi cannot be directly estimated. Of course, in randomized experiments, E(Yi(0)|Zi is satisfied, so τ can easily be estimated. However, in an observation study, the naïve estimation of τ can be biased because E(Yi(0)|Zi . To adjust this selection bias, we assume that we can observe the covariates Xi that are not affect by any addiction, and for a given covariates Xi, the potential outcomes Yi(1), Yi are conditionally independent of addiction indicator Zi. Furthermore, if potential outcomes are independent of the addiction conditional on covariates Xi, they are also independent of the addiction conditional in the propensity score P(Xi)= P(Zi=1|Xi [19]. The PSM estimator for τ becomes
2.3.2. Estimating the Propensity Score
2.3.3. Matching Methods Based on the Estimated Propensity Score
2.3.4. Estimation of the Relative Risks of Addiction on Mental Health Problems after Propensity Score Matching
be an outcome of interest (an score of depression or anxiety) with mean μi, we can use the Gamma GLM framework with covariates Xi:
3. Results

3.1. Matching Quality of the Propensity Score Matching Method


3.2. Effects of the Internet Addiction on Depression and Anxiety

3.3. Effects of the Smartphone Addiction on Depression and Anxiety
3.4. Differences in Effects of the Internet and Smartphone Addiction on Depression and Anxiety
4. Discussion
5. Conclusions
Author Contributions
Acknowledgments
Conflicts of Interest
References
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