“Using digital media or sleeping … that is the question”. A meta-analysis on digital media use and unhealthy sleep in adolescence
Bibliographic record
Abstract
This systematic review with meta-analysis aims to examine the relation between different aspects of digital media use and sleep health patterns. Eligible studies had to be longitudinal and with adolescents' sample. Multiple search strategies were applied until January 28, 2023 in order to identify relevant research published in peer-reviewed journal articles or available grey literature. A final set of 23 studies (N = 116,431; 53.2% female; Mage at baseline = 13.4 years) were included. The quality of the studies, assessed with an adapted version of the Newcastle-Ottawa Scale, was high with a consequent low risk of bias. Meta–analytic results showed that traditional media use (r = −0.33 [-0.44; −0.22]), social media use (r = −0.12 [-0.22; −0.01]), prolonged use (r = −0.06 [-0.11; −0.01]), and dysfunctional use (r = −0.19 [-0.29; −0.09]) are negatively related to adolescents’ sleep health at a later time point. Conversely, sleep patterns were not related to social media use (r = −.05 [-0.10; 0.00]) and utilization time (r = −0.13 [-0.30; 0.04]), but they were related to dysfunctional use of media (r = −0.22 [-0.33; −0.10]). Overall, this review highlights the presence of a vicious cycle between digital media use and sleep health in adolescence.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.034 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".