Factors Associated With Digital Addiction: Umbrella Review
Bibliographic record
Abstract
Background: Digital addiction, affecting a significant portion of the population, particularly young people, is linked to psychological issues and social problems, making its prevention and management a crucial public health issue. Objective: This umbrella review aimed to comprehensively analyze the factors influencing digital addiction by re-evaluating the methodologies and evidence quality of existing meta-analyses. Methods: Databases including PubMed, Web of Science, the Cochrane Library, and Embase were systematically searched for reviews and meta-analyses related to factors associated with digital addiction up to September 24, 2024. The methodological quality of the identified studies was assessed using the modified "A Measurement Tool to Assess Systematic Reviews 2" (AMSTAR 2) tool, while the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool was used to evaluate the quality of the evidence presented in the literature. Results: A total of 18 articles were included in the review, with AMSTAR 2 assessments revealing 6 moderate-quality, 4 low-quality, and 8 very low-quality studies. In total, 29 significant factors associated with digital addiction were identified. Notably, 4 studies achieved both moderate AMSTAR 2 and GRADE ratings, indicating that positive parent-child relationship (r=-0.15, 95% CI-0.18 to -0.11; P<.05) effectively mitigate the risks of digital addiction. Conversely, urban residence (odds ratio [OR] 2.32, 95% CI 1.19-4.53; P<.001), adverse childhood experiences (r=0.21, 95% CI 0.18-0.24; P<.001), and social anxiety (r=0.34, 95% CI 0.19-0.48; P<.001) were identified as factors that may increase the risk of developing digital addiction. Conclusions: This study found that social factors such as urban residence were associated with an increased risk of digital addiction, whereas social support served as a protective factor against this risk. Nonetheless, the methodologies used in analyzing the factors related to digital addiction require further refinement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.025 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".