Digital Addiction Intervention for Children and Adolescents: A Scoping Review

International Journal of Environmental Research and Public Health, Год журнала: 2023, Номер 20(6), С. 4777 - 4777

Опубликована: Март 8, 2023

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

Язык: Английский

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Effective interventions for gaming disorder: A systematic review of randomized control trials

Frontiers in Psychiatry, Год журнала: 2023, Номер 14

Опубликована: Фев. 6, 2023

Objective To identify effective intervention methods for gaming disorder (GD) through a rigorous assessment of existing literature. Methods We conducted search six databases (PubMed, Embase, PsycINFO, CNKI, WanFang, and VIP) to randomized controlled trials (RCTs) that tested GD interventions, published from database inception December 31, 2021. Standardized mean differences with 95% confidence intervals were calculated using random effects model. Risk bias was assessed the Bias 2 (RoB 2) tool. Results Seven studies met inclusion criteria. Five interventions in these studies: group counseling, craving behavioral (CBI), transcranial direct current stimulation (tDCS), acceptance cognitive restructuring program (ACRIP), short-term behavior therapy (CBT). Four five (the tDCS excluded) found have significant effect on GD. The results quality showed included had medium high risk randomization process overall bias. Conclusion Rigorous screening identified four are GD: CBI, ACRIP, CBT. Additionally, comprehensive review literature revealed improvements could be made conceptualization GD, experimental design, sample representativeness, reporting quality. It is recommended future more research designs based established standards provide credible evidence inform development interventions.

Язык: Английский

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Effects of non-pharmacological interventions on youth with internet addiction: a systematic review and meta-analysis of randomized controlled trials

Frontiers in Psychiatry, Год журнала: 2024, Номер 14

Опубликована: Янв. 11, 2024

Objective To assess the overall effectiveness of non-pharmacological interventions on internet addiction (IA) in youth. Method Randomized controlled trials (RCTs) published from their inception to April 1, 2023 were searched Cochrane, Embase, Medline, Web Science, China National Knowledge Infrastructure, Science and Technology Journal Database, Chinese BioMedical Literature WanFang Data. Two reviewers independently extracted data evaluated bias using Cochrane Risk Bias tool. Results Sixty-six studies performed 2007 2023, with a total 4,385 participants, identified. The NPIs included group counseling, cognitive behavioral therapy, sports intervention, combined interventions, eHealth, educational positive psychology sand play electrotherapy. results revealed that significantly reduced IA levels (standardized mean difference, SMD: −2.01, 95% confidence interval, CI: −2.29 −1.73, I 2 = 93.0%), anxiety (SMD: −1.07, 95%CI: −1.41 −0.73, 72.4%), depression −1.11, −1.52 −0.7, 84.3%), SCL-90 −0.75, −0.97 −0.54, 27.7%). Subgroup analysis stratified by intervention measure showed sandplay mobile health all effective relieving symptoms except Conclusion appear be treatment youth, which would act as an alternative IA. Further larger sample sizes robust designs are needed.

Язык: Английский

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Network meta-analysis of the effectiveness of different interventions for internet addiction in college students

Journal of Affective Disorders, Год журнала: 2024, Номер 363, С. 26 - 38

Опубликована: Июль 22, 2024

Internet addiction jeopardizes teenagers' physical and mental health, as well their academic performance, causes a variety of cognitive dysfunctions psychological health illnesses, among other things. It is huge issue that families, schools, society must address immediately. This study used network meta-analysis to evaluate the impact several interventions on college students' addiction. The goal was identify most effective establish reference for future interventions. We systematically searched relevant literature in domestic international databases such Web Science, PubMed, EMBASE, Cochrane Library, Pro Quest, China Knowledge, Wan fang, Wipo, etc. assessed risk bias according revised Randomized Trials Risk Bias Tool (RoB2) R Studio Software Stata 14.0 traditional meta-analysis. A based IAT scale showed comprehensive had highest probability being best intervention IA (SUCRA = 90.6 % IAT); focused solution short-term therapy CIAS-R (19 White Feather) 100 %). majority have significant influence treatment IA, improvements symptoms are more noticeable when combination rather than just one.

Язык: Английский

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Effectiveness of sports intervention: A meta-analysis of the effects of different interventions on adolescent internet addiction

Journal of Affective Disorders, Год журнала: 2024, Номер 365, С. 644 - 658

Опубликована: Авг. 13, 2024

Язык: Английский

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A meta-review of screening and treatment of electronic “addictions”

Clinical Psychology Review, Год журнала: 2024, Номер 113, С. 102468 - 102468

Опубликована: Авг. 8, 2024

Язык: Английский

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Building Machine Learning Predictive Models for Adolescent Internet Addiction: Key Findings on Self-Esteem and Resilience Interaction

Research Square (Research Square), Год журнала: 2025, Номер unknown

Опубликована: Янв. 9, 2025

Objective: Internet addiction (IA) is a significant mental health concern among adolescents. This study aimed to develop machine learning (ML)-based predictive models identify and explain key risk factors for IA. Method: A total of 8176 junior high school students from Henan Province were surveyed April May 2023. The dataset was randomly divided into training test sets in an 8:2 ratio. Four ML algorithms used predict IA, feature importance determined using SHapley Additive exPlanations (SHAP). XGBoost model, which achieved the highest area under curve (AUC), selected detailed analysis individualized prediction explanations. Results: five most important predictors IA negative life events, self-esteem, connectedness, parent-adolescent cohesion, psychological resilience. Importantly, interaction effect found between self-esteem resilience: as increased, influence low resilience transitioned being factor protective against Conclusion: demonstrates power combined with SHAP predicting identifying its psychosocial determinants. findings highlight critical interplay resilience, offering valuable insights clinicians educators addressing

Язык: Английский

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Network modeling of problematic social media use components in college student social media users

Frontiers in Psychiatry, Год журнала: 2025, Номер 15

Опубликована: Янв. 13, 2025

Background While the constitutive features of problematic social media use (PSMU) have been formulated, there has a lack studies in field examining structure relationships among PSMU components. Method This study employed network analytic methods to investigate connectivity components large sample 1,136 college student users ( M age = 19.69, SD 1.60). Components were assessed by Bergen Social Media Addiction Scale (BSMAS) derived from model addiction. We computed two types models, Gaussian graphical models (GGMs) examine and influential nodes directed acyclic graphs (DAGs) identify probabilistic dependencies Result Relapse component consistently emerged as central node GGMs parent other DAGs. tolerance exhibited strong mutual connections linked most vital edges within networks. Additionally, conflict mood modification occupied more positions for low-BSMAS-score subgroup compared with high-BSMAS-score subgroup. Conclusion Our findings shed new light on complex architecture its potential implications tailored interventions relieve PSMU.

Язык: Английский

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Optimal Non-Pharmacological Interventions for Reducing Problematic Internet Use in Youth: A Systematic Review and Bayesian Network Meta-Analysis

Behavioral Sciences, Год журнала: 2025, Номер 15(1), С. 98 - 98

Опубликована: Янв. 20, 2025

The purpose of this network meta-analysis (NMA) is to compare the effect different non-pharmacological interventions (NPIs) on Problematic Internet Use (PIU). Randomized controlled trials (RCTs) published from their inception 22 December 2023 were searched in Cochrane Central Register Controlled Trials, Embase, Medline, Web Science, China National Knowledge Infrastructure, Science and Technology Journal Database, Chinese BioMedical Literature WanFang Data. We carried out a data analysis efficacy various NPIs using Bayesian NMA. A battery analyses assessments, such as conventional risk bias, performed concurrently. Two reviewers extracted evaluated bias Risk Bias tool independently. identified 90 RCTs including 15 (5986 participants), namely sports intervention (SI), electroencephalogram biological feedback (EBF), reality therapy (RT), positive psychology (PPT), sandplay (ST), educational (EI), compound psychotherapy (CPT), electroacupuncture (AT), group counseling (GC), family (FT), electrotherapy (ELT), craving behavior (CBI), virtual (VRT), cognitive (CBT), mindfulness (MT). Our NMA results showed that SI, EBF, RT, PPT, ST, EI, CPT, AT, GC, FT, ELT, CBT, CBI, VRT, MT effective reducing PIU levels. most NPI was SI (SMD = −4.66, CrI: −5.51, −3.82, SUCRA 95.43%), followed by EBF −4.51, −6.62, −2.39, 90.89%) RT −3.83, −6.01, −1.62, 81.90%). study best relieve levels youth. Medical staff should be aware application treatment youth future clinical care.

Язык: Английский

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Application of machine learning in predicting adolescent Internet behavioral addiction

Frontiers in Psychiatry, Год журнала: 2025, Номер 15

Опубликована: Апрель 1, 2025

To explore the risk factors affecting adolescents' Internet addiction behavior and build a prediction model for based on machine learning algorithms. A total of 4461 high school students in Chongqing were selected using stratified cluster sampling, questionnaires administered. Based presence behavior, categorized into an group (n=1210) non-Internet (n=3115). Gender, age, residence type, other data compared between groups, independent adolescent analyzed logistic regression model. Six methods-multi-level perceptron, random forest, K-nearest neighbor, support vector machine, regression, extreme gradient boosting-were used to construct The model's indicators under each algorithm compared, evaluated with confusion matrix, optimal was selected. proportion male adolescents, urban household registration, scores family function, planning, action, cognitive subscales, along psychoticism, introversion-extroversion, neuroticism, somatization, obsessive-compulsiveness, interpersonal sensitivity, depression, anxiety, hostility, paranoia, psychosis, significantly higher than (P < 0.05). No significant differences found age or only-child status > Statistically variables model, revealing that gender, registration cognitive, obsessive-compulsive, hostility scales are addiction. area curve (AUC) multi-level boosting models 0.843, 0.817, 0.778, 0.846, 0.847, 0.836, respectively, showing best predictive performance among these models. detection rate is males females, adolescents impulsive, extroverted, psychotic, neurotic, obsessive, depressive, hostile traits more prone developing While overall predicting moderate, method outperforms others, effectively identifying enabling targeted interventions.

Язык: Английский

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