Preventive Interventions for Internet Addiction in Young Children: Systematic Review
Bibliographic record
Abstract
BACKGROUND: In this digital age, children typically start using the internet in early childhood. Studies highlighted that young children are vulnerable to internet addiction due to personal limitations and social influence (eg, family and school). Internet addiction can have long-term harmful effects on children's health and well-being. The high risk of internet addiction for vulnerable populations like young children has raised questions about how best to prevent the problem. OBJECTIVE: This review study aimed to investigate the existing interventions and explore future directions to prevent or reduce internet addiction risks in children younger than 12 years. METHODS: The systematic review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant literature from 4 research databases (Scopus, Web of Science, PubMed, and PsycINFO). We included 14 primary studies discussing the interventions to prevent or reduce internet addiction risks in young children and their efficacy outcomes. RESULTS: The preventive interventions identified were categorized into four approaches as follows: (1) children's education, (2) parenting strategy, (3) strategic physical activity, and (4) counseling. Ten interventions showed promising efficacy in preventing or reducing internet addiction risks with small-to-medium effect sizes. Interventions that enhance children's competencies in having appropriate online behaviors and literacy were more likely to show better efficacy than interventions that force children to reduce screen time. Interventions that shift children's focus from online activities to real-world activities also showed promising efficacy in reducing engagement with the internet, thereby preventing addictive behaviors. We also identified the limitations of each approach (eg, temporariness, accessibility, and implementation) as valuable considerations in developing future interventions. CONCLUSIONS: The findings suggest the need to develop more sustainable and accessible interventions to encourage healthy online behaviors through education, appropriate parenting strategies, and substitutive activities to prevent children's overdependence on the internet. Developing digital tools and social support systems can be beneficial to improve the capability, efficiency, and accessibility of the interventions. Future interventions also need to consider their appropriateness within familial context or culture and provide adequate implementation training. Last, policy makers and experts can also contribute by making design guidelines to prevent digital product developers from making products that can encourage overuse in children.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".