Binge-Watching and Mental Health Problems: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Binge-watching, the viewing of online videos or streamed content, may be associated with different types of mental health problems. The present study aimed to investigate the associations between binge-watching and five mental health concerns including depression, loneliness, sleep problems, anxiety, and stress. METHODS: Academic databases of PubMed, Scopus, Web of Science, ProQuest, PsycINFO, and Psych Articles were systematically searched through February of 2022. The Newcastle-Ottawa Scale was used to assess the methodological quality. A meta-analysis was performed on Fisher's z values as effect sizes, using a random effect model. Publication bias, small study effect, and moderators in this association were assessed. RESULTS: Binge-watching was significantly associated with the five types of mental health concerns with the most robust correlations found with stress (0.32) and anxiety (0.25). Stronger associations between binge-watching and two types of mental health problems (depression and sleep problems) were found during the COVID-19 pandemic than before the pandemic. Moreover, stronger associations between binge-watching and two types of mental health problems (stress and sleep problems) were found in developing countries than in developed countries. CONCLUSIONS: The associations between binge-watching and mental health concerns were significant and positive. Programs and interventions to reduce binge-watching should be considered and tested.
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 | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".