Impacts of digital social media detox for mental health: A systematic review and meta-analysis
Bibliographic record
Abstract
The impact of social media has been significant on various aspects of life, particularly mental health. Growing concerns about the adverse effects of social media use have prompted the exploration of experimental interventions, defined as digital detox interventions. However, it remains unclear whether digital detox interventions are effective for mental health outcomes. The aim of this study was to provide comprehensive insights into the effects of digital detox interventions on various mental health outcomes, including depression, life satisfaction, stress, and mental well-being. Following the PRISMA guidelines, systematic searches were carried out in online databases, including PubMed and ScienceDirect, within the publication range of 2013 and 2023. A total of 2578 titles and abstracts were screened, and 10 studies were included in the analysis. A risk of bias assessment was conducted using RoB 2.0 and the Newcastle-Ottawa scale, while statistical analysis was conducted using RevMan 5.4.1. Our data indicated a significant effect of digital detox in mitigating depression with the standardized mean difference (SMD: -0.29; 95%CI: -0.51, -0.07, p=0.01). No statistically significant effects were discerned in terms of life satisfaction (SMD: 0.20; 95%CI: -0.12, 0.52, p=0.23), stress (SMD: -0.31; 95%CI: -0.83, 0.21, p=0.24), and overall mental well-being (SMD: 0.04; 95%CI: -0.54, 0.62, p=0.90). These data underscore the nuanced and selective influence of digital detox on distinct facets of mental health. In conclusion, digital detox interventions significantly reduce depressive symptoms, suggesting that intentional reduction or cessation of digital engagement may help alleviate contributing factors. However, no statistically significant effects were observed in mental well-being, life satisfaction, and stress. This discrepancy may be due to the complex nature of these constructs, involving various factors beyond the scope of digital detox interventions.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".