Internet addiction and depressive symptoms are extremely common problems among teenagers, and the coping strategy has been proved to be closely related to internet addiction and depressive symptoms. Based on three waves of data from a sample of Chinese middle-school students (N = 1545, M_age = 14.88 years old, SD = 1.81; 55.00% females), this study examines the longitudinal relationship between internet addiction and depressive symptoms among adolescents ultilizing the random-intercept cross-lagged panel model. Results revealed a unidirectional predictive effect of depressive symptoms at T₂ on internet addiction at T₃, but not vice versa, the effect was more significant in the male group. Positive coping strategies had a significant negative predictive effect on the random intercept of internet addiction and depressive symptoms, while negative coping style had a significant positive predictive effect on the random intercept of internet addiction and depressive symptoms. Effective identification and intervention of depressive symptoms may be beneficial to the intervention and prevention for internet addiction, and we should pay attention to the cultivation of middle school students’ positive coping strategies.

Digitalization gradually transforms digital education technology from being a teaching means to focusing on the student’s abilities. This study analyzes the data from the China Education Baseline Survey of the Renmin University of China using Coarsened Exact Matching (CEM) and quantile regression methods. The Ordinary Least Squares (OLS) regression is used to test the net effect of digital education technology on students’ academic and cognitive abilities. The OLS result shows that digital education technology has a significantly positive impact on the cognitive ability of middle school students. However, schools focusing on using digital education technology as a means of school management will lower students’ cognitive abilities. Second, the CEM method result shows a significant difference in the cognitive ability scores between students in classrooms with and without digital education technology. This indicates that digital education technology can inspire students’ internal drive, motivate them to learn, and enhance their cognitive abilities. Last, the quantile regression result shows that the use of digital education technology has heterogeneity in the development of the cognitive ability of middle school students. When the cognitive ability quantile point increases, the influence becomes more obvious, changing from not significant to significant, then to very significant. This provides some inspiration for understanding the application of digital education technology. It can be concluded that this is an effective method to study the sustainable development of cognitive abilities through heterogeneity. The method ensures individuals’ holistic and comprehensive development, thus promoting groups’ development and contributing intellectual support to them to adapt to future social requirements and lead future social development. Digital education technology endows students with the cognitive abilities for lifelong learning to solve various problems in future social life, reserve high-quality talent resources for the future, and build a learning society with extensive significance. This paper analyzes the sustainable development of students’ cognitive abilities using emerging digital education technologies. It not only deepens the understanding of sustainable development of education and humans but also provides intellectual support for society’s sustainable development.

Internet addiction for adolescent, which is widely concerned by the whole society, has become a public health problem. Internet addiction not only had a negative impact on physical and mental development of adolescents, but also was harmful to their study, life, interpersonal communication and personality formation, and so on. In recent years, the data analysis methods of longitudinal research have developed rapidly. It not only focused on the overall average growth trend, but also considered the differences in the individual trends. Latent profile transition analysis (LPTA) is an extension of latent profile analysis (LPA) and latent transition analysis (LTA), and is a longitudinal data analysis method. LPTA can simultaneously estimate group membership in multiple time points and their latent transition tendency among these subgroups between each two time points. This study used LPTA to explore the development trend of adolescent internet addiction over time and its influencing factors. 1033 adolescents participated in a short-term 6-month longitudinal study with a total of three tests. Participants completed internet addiction test, self-rating anxiety scale and self-rating depression scale. The results showed that: (1) There are three categories of adolescent internet addiction, namely non-internet addiction group, low-internet addiction group and high-internet addiction group. (2) Non-internet addiction group has a strong stability. Low-internet addiction group has a high probability to become non-internet addiction group or high-internet addiction group. (3) Boys are more likely than girls to develop into high-internet addiction group. Anxiety and depression both affect the development of adolescent internet addiction.

College students' mobile phone addiction is negatively associated with physical and mental health and academic performance. Many self-made questionnaires are currently being administered to Chinese college students to evaluate the mobile phone addiction tendency. Using the univariate generalizability theory and multivariate generalizability theory, this study investigated the psychometric properties and the internal structure of the Mobile Phone Addiction Tendency Scale (MPATS), the most widely used survey questionnaire assessing the status of Chinese college students’ mobile addiction. Data were a sample of 1,253 college students from the southwest of China. Primary analytic approaches included the generalizability design of univariate random measurement mode p×(i:h) and multivariate random measurement mode p˙×i °. Results showed that the variance component of the participants and the variation related to the participants explained most of the variation of the scale, while the variance component of the items was small, and the generalizability coefficient and dependability index of the scale were 0.88 and 0.85. In the multivariate generalizability analysis, the variance component of the participants and the variation related to the participants accounted for most of the variation of the scale and the variance component of the items was small. The generalizability coefficients of withdrawal symptoms, salience, social comfort, and mood changes were 0.64~0.80, and the dependability indexes were 0.63~0.77. However, the generalizability coefficient and reliability index of universe score were 0.91 and 0.90. In addition, the contribution ratio of the four dimensions to the universe score variance was different from the assignment intention of the initial scale. Recommendations were discussed on the improvement of the test reliability for each dimension.

The risk effects of internet addiction have been documented in the literature; however, few longitudinal studies have considered the heterogeneity of the subjects. A hierarchical linear model was used here to explore the relationship between adolescents’ internet addiction and associated risk factors (depression, anxiety, gender, and obesity) from the perspective of longitudinal analysis. A total of 1033 adolescents were investigated and followed up with every three months with the Self-Rating Depression Scale (SDS), Self-Rating Anxiety Scale (SAS), and Internet Addiction Test (IAT). The hierarchical linear model of internet addiction had only two levels. The first level of the model was the time variable (three time points) and the second level of the model was the individual adolescent (1033 adolescents). The results showed that (1) depression and anxiety, as associated risk factors, were significant positive predictors of adolescents’ internet addiction considering the developmental trajectory courses of adolescent internet addiction, as well as the individual differences over time; (2) there were gender differences in the adolescents’ internet addictions—specifically, the initial level of internet addiction among boys was significantly higher than that of girls, but the rate of decline was significantly faster than that of girls; and (3) there was no significant difference in obesity. The results demonstrated the importance of considering depression, anxiety, and gender in any intervention efforts to reduce adolescents’ internet addictions, and we should pay attention to the cultivation of positive coping strategies for Chinese adolescents. The limitations of the study were also discussed.

Background: Depression of adolescents is an important public health problem. Persistent depression will become a huge hidden danger of individual mental health development. It is important to study the change mechanism of adolescents’ depression. Methods: A total of 563 males and 739 females aged 11– 19 years reported their depression. 1302 adolescents participated in a short-term 6-month longitudinal study. They were contacted every three months for two follow-up tests (T2 and T3). Participants completed internet addiction test, self-rating anxiety scale and self-rating depression scale. Multiple-group latent profile transition analysis (MLPTA) was used to identify meaningful subgroups and transitions between groups across time. Covariates (anxiety and internet addiction) were used to analyze the influencing factors. Results: The results showed that: (1) There are three categories of adolescents’ depression, namely no-depression group, low-depression-mountain group and low-depression-hill group. (2) The depression of the subjects showed a trend of improvement, but the proportion of low-depression-mountain group is relatively high at three time points (0.44, 0.59, and 0.30). (3) The transition probability between the low-depression-mountain group and the no-depression group is large, which suggests the low-depression-mountain group can easily convert into the no-depression group, but the transition probability from low-depression-hill group to other groups is relatively low and stable, which suggests the low-depression-hill group is not easily converted into the no-depression group. (4) Both anxiety and internet addiction affect the development of adolescents’ depression across three time points. Anxiety played a significant role in affecting female adolescents’ depression, while internet addiction played a significant role in affecting male adolescents’ depression. Conclusion: This study demonstrated a transition pattern in adolescents’ depression. We should pay more attention to the low-depression-mountain group and try to do their mental health well. Adolescents’ Depression changes rapidly with anxiety and internet addiction for different genders, which suggests that some interventions are needed.