Prediction of Junior High School Students' Problematic Internet Use: The Comparison of Neural Network Models and Linear Mixed Models in Longitudinal Study.

Purpose: With the rise of big data, deep learning neural networks have garnered attention from psychology researchers due to their ability to process vast amounts of data and achieve superior model fitting. We aim to explore the predictive accuracy of neural network models and linear mixed models in tracking data when subjective variables are predominant in the field of psychology. We separately analyzed the predictive accuracy of both models and conduct a comparative study to further investigate. Simultaneously, we utilized the neural network model to examine the influencing factors of problematic internet usage and its temporal changes, attempting to provide insights for early interventions in problematic internet use. Patients and Methods: This study compared longitudinal data of junior high school students using both a linear mixed model and a neural network model to ascertain the efficacy of these two methods in processing psychological longitudinal data. Results: The neural network model exhibited significantly smaller errors compared to the linear mixed model. Furthermore, the outcomes from the neural network model revealed that, when analyzing data from a single time point, the influences of seventh grade better predicted Problematic Internet Use in ninth grade. And when analyzing data from multiple time points, the influences of sixth, seventh, and eighth grades more accurately predicted Problematic Internet Use in ninth grade. Conclusion: Neural network models surpass linear mixed models in precision when predicting and analyzing longitudinal data. Furthermore, the influencing factors in lower grades provide more accurate predictions of Problematic Internet Use in higher grades. The highest prediction accuracy is attained through the utilization of data from multiple time points.

Problematic Internet Use in Early Adolescents: Gender and Loneliness Differences in a Latent Growth Model.

Purpose: Based on the Cognitive-Behavioral model and the Interaction of Person-Affect-Cognition-Execution model, this study examined the developmental trajectory of problematic Internet use (PIU) in early adolescents and explored whether there were gender differences in the onset level and rate of development of this developmental trajectory, and tested whether developmental changes in loneliness could have an impact on the developmental trajectory of problematic Internet use. Participants and Methods: This longitudinal study collected data on PIU and loneliness from 296 early adolescents (Mage=11.65, SD=0.58) in four waves. The development of PIU in adolescents and the effects of gender and loneliness development on PIU development were examined using a latent growth model. Results: The results revealed that individuals' PIU development showed a nonlinear latent growth model, with PIU significantly higher than 0 in grade 6 and its growth rate slowing down as PIU increased. Individuals' PIU at low starting levels developed more rapidly later. Boys had higher initial levels of PIU but their PIU developed and increased at the same rate as girls'. Both the initial value and slope of loneliness had a significant effect on the initial value and slope of boys' and girls' PIU. Conclusion: Interventions for PIU in early adolescents also need to consider loneliness at the same time, and intervention groups can focus on individuals with low initial levels of PIU, boys, and individuals with high levels of loneliness.