Social Media Use and Sleep Quality in Adolescents and Young Adults: A Scoping Review of Reviews
Bibliographic record
Abstract
Background: Social media use has grown rapidly and has been integrated into the lives of many adolescents and young adults worldwide. Research indicates that excessive social media engagement can negatively impact sleep quality through various mechanisms. Objective: This scoping review of reviews aims to explore the relationship between social media use and sleep quality among adolescents and young adults, synthesize existing evidence, identify research gaps, and highlight directions for future research. Methods: Arksey’s and O’Malley’s five-stage framework was used to conduct this scoping review. Searches were conducted in PubMed, Web of Science, Embase, Medline, and Scopus for articles published between 2020 and 2025. The inclusion criteria were systematic reviews or meta-analyses focused on adolescents and young adults, examining social media use in relation to sleep quality, and peer-reviewed articles written in English. Ten articles met all eligibility criteria and were included in the review. Results: The findings indicate a small but consistent negative effect of social media use on sleep quality. Problematic social media use showed a stronger association with poorer sleep than general social media use. Specific platforms such as Facebook and Twitter contributed most to shorter sleep duration, later bedtimes, and poorer sleep quality, while Snapchat and Instagram showed moderate effects, and WhatsApp and WeChat showed smaller effects. Conclusions: Problematic social media use is strongly associated with poorer sleep quality, while general use may have smaller effects. Future research focusing on longitudinal studies would help deepen the understanding of the effects of social media on sleep and guide targeted interventions. Encouraging responsible or healthy social media use is vital in reducing the risks of problematic use while highlighting the benefits as well.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".