Insomnia and emotion dysregulation: a meta-analysis perspective focusing on the process and dispositional model of emotion regulation
Bibliographic record
Abstract
Insomnia and emotion dysregulation (ED) are intricately related, yet their aggregate association across different domains of ED and the effect of moderating factors such as health status, age, and gender on their relationship remain unclear. This meta-analysis synthesized data from 57 studies, pooling 119 effect sizes from correlational and 55 from group comparison studies to explore the distinct associations between ED and both insomnia symptoms and disorder. The results revealed significant associations between insomnia symptoms and ED, primarily in individuals with serious health-related conditions (Fisher Zno-condition = 0.22, Fisher Zserious-conditions = 0.37, p < 0.00001). This association’s strength is influenced by specific domains of ED, such as impulse control and a higher use of maladaptive regulation strategies. Insomnia disorder was linked to greater ED problems regardless of health status (Hedges’ g = 0.99, p = 0.01). Reliance on maladaptive strategies and difficulties in dispositional domains of ED were closely linked to insomnia symptoms/disorder compared to the inability to use adaptive strategies. Age and gender did not impact the association in correlational or group comparison studies. These findings underscore a robust link between insomnia and ED, suggesting the integration of emotion regulation skills’ improvement in insomnia treatments to enhance therapeutic outcomes.
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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 | 0.028 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.031 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".