Digital devices play a significant role in the learning and living of children and adolescents, whose overuse or addiction has become a global concern. This scoping review seeks to synthesize existing studies to investigate relevant interventions and their effects on digital addiction in children (ages 0–18). To understand the latest advances, we have identified 17 studies published in international peer-reviewed journals between 2018–2022. The findings revealed that, first, most interventions for digital addiction in children and adolescents were cognitive–behavioral therapies (CBT) or CBT-based interventions, which could improve anxiety, depression, and related symptoms of digital addiction. Second, rather than directly targeting addictive behaviors, some family-based interventions aim to strengthen family functions and relationships. Finally, digital-based interventions, such as website-based, application-based, and virtual reality interventions, are promising in adolescent digital addiction interventions. However, these studies shared the same limitations: small sample sizes, short intervention durations, no control group, and nonrandomized assignments. The small sample size problem is difficult to solve by offline intervention. Meanwhile, online digital-based intervention is still in its infancy, resulting in limited generalizability of the findings and the inability to popularize digital intervention. Accordingly, future intervention studies should integrate various assessments and interventions to form an integrated platform to provide interventions for addicted children and adolescents worldwide.

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Digital Addiction Intervention for Children and Adolescents: A Scoping Review

"Using digital media or sleeping ... that is the question". A meta-analysis on digital media use and unhealthy sleep in adolescence

Background Sleep stage identification is critical in multiple areas (e.g. medicine or psychology) to diagnose sleep-related disorders. Previous studies have reported that the performance of machine learning algorithms can be changed depending on the biosignals and feature-extraction processes in sleep stage classification. Methods To compare as many conditions as possible, 414 experimental conditions were applied, considering the combination of different biosignals, biosignal length, and window length. Five biosignals in polysomnography (i.e. electrocardiogram (ECG), electroencephalogram (EEG), electromyogram (EMG), electrooculogram left, and electrooculogram right) were used to identify optimal signal combinations for classification. In addition, three different signal-length conditions and six different window-length conditions were applied. The validity of each condition was examined via classification performance from the XGBoost classifiers trained using 10-fold cross-validation. Furthermore, results considering feature importance were examined to validate the experimental results in terms of model explanation. Results The combination of EEG + EMG + ECG with a 40 s window and 120 s signal length resulted in the best classification performance (precision: 0.853, recall: 0.855, F1-score: 0.853, and accuracy: 0.853). Compared to other conditions and feature importance results, EEG signals showed a relatively higher importance for classification in the present study. Conclusion We determined the optimal biosignal and window conditions for the feature-extraction process in machine learning algorithm-based sleep stage classification. Our experimental results inform researchers in the future conduct of related studies. To generalize our results, more diverse methodologies and conditions should be applied in future studies.

Validation of the influence of biosignals on performance of machine learning algorithms for sleep stage classification

Excessive social media consumption leads to addiction and affects mental health. It is a phenomenon that is difficult to avoid. Previous research on the effects of excessive Internet use shows that people who engage in social media (SM) without restraint experience over-involvement, over-disclosure, technostress, and social media fatigue (SMF). SMF, conceptualized as an emotional and cognitive feeling of being overwhelmed, manifests itself in mindless browsing of content, limiting the amount of time spent on SM, or quitting SM altogether. Self-control, although present in the technology addiction literature and psychology research, has been rarely described in relation to both excessive SM use and SMF. Therefore, the main goal of the present study was to verify whether there is a direct relationship between problematic smartphone use and SMF, and whether this association is mediated by self-control.The study included 210 respondents (M = 25.85, SD = 9.84) living in different Polish cities. The survey was conducted online, and the respondents consented to participate in the study. They completed the following measures: the Mobile Phone Problematic Use, the Self-Control Scale, the Social Media Fatigue Scale, and a brief questionnaire with socio-demographic data.Statistical analysis was performed to verify the relationship between problematic mobile phone use, level of self-control, and SMF. The outcomes indicate that there are significant interrelationships between the three studied variables. The association between problematic smartphone use and SMF is mediated by self-control.A lack of impulse-inhibition skills, such as compulsive checking of notifications, can be a significant factor in SM exhaustion, fatigue, or frustration. SMF can also be understood as a natural defensive response, triggered in situations where individuals are overwhelmed, when the self-control is insufficient to stop the compulsion to use SM, and the use of a smartphone for this purpose is excessively engaging. 

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Problematic Smartphone Use and Social Media Fatigue: The Mediating Role of Self-Control

In recent decades, the number of people who experience their Internet use behavior as problematic has risen dramatically. In Germany, a representative study from 2013 estimated the prevalence of Internet use disorder (IUD) to be about 1.0%, with higher rates among younger people. A 2020 meta-analysis shows a global weighted average prevalence of 7.02%. This indicates that developing effective IUD treatment programs is more critical than ever. Studies show that motivational interviewing (MI) techniques are widely used and effective in treating substance abuse and IUDs. In addition, an increasing number of online-based health interventions are being developed to provide a low-threshold treatment option. This article presents a short-term online-based treatment manual for IUDs that combines MI techniques with therapy tools from cognitive behavioral therapy (CBT) and acceptance and commitment therapy (ACT). The manual includes 12 webcam-based therapy sessions, each lasting 50 min. Each session is framed by a standardized beginning, conclusion, outlook, and flexible session content. In addition, the manual contains example sessions to illustrate the therapeutic intervention. Finally, we discuss the advantages and disadvantages of online-based therapy compared to analog treatment settings and provide recommendations for dealing with these challenges. By combining established therapeutic approaches with an online-based therapeutic setting based on flexibility and motivation, we aim to provide a low-threshold solution for treating IUDs.

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A short-term manual for webcam-based telemedicine treatment of Internet use disorders

An updated of meta-analysis on the relationship between mobile phone addiction and sleep disorder.

Effects of different interventions on internet addiction: A meta-analysis of random controlled trials.

Background Smartphone addiction (SA) is associated with adverse consequences, especially for freshmen. Evidence indicates that SA is associated with depression, and it is necessary to conduct a longitudinal study to explore the association further. Methods SA (measured by the Smartphone Addiction Scale-Short Version) and depression (measured by the Zung's Self-Rating Depression Scale) among 1,186 freshmen were surveyed at baseline and a respective 12-month follow-up for each participant. The application of a cross-lagged panel model approach (CLPM) revealed an association between SA and depression after adjusting for demographic variables. Results The CLPM results showed a significant path from baseline SA to follow-up depression (β = 0.08, P < 0.001) and a significant path from baseline depression to follow-up SA (β = 0.08, P < 0.001). Compared with the overall cross-lagged model, the cross-lagged coefficient of the path from baseline SA to follow-up depression increased in the female group (β = 0.10, P = 0.015), and the cross-lagged coefficient of the path from baseline depression to follow-up SA also increased significantly (β = 0.15, P < 0.001). In contrast, the cross-lagged model in the male group showed no predictive effect between SA and depression (P > 0.05). Conclusions The current study showed a significant bidirectional association between smartphone addiction and depression among freshmen, but only in the female population.

Zhihui Zhu

Papers

An updated of meta-analysis on the relationship between mobile phone addiction and sleep disorder.

Effects of different interventions on internet addiction: A meta-analysis of random controlled trials.

A bidirectional association between smartphone addiction and depression among college students: A cross-lagged panel model

Effects of exercise or tai chi on Internet addiction in college students and the potential role of gut microbiota: A randomized controlled trial.

Effects of Psychological or Exercise Interventions on Problematic Mobile Phone Use: a Systematic Review and Meta-analysis