The Interplay Between Emotion Dysregulation and Repetitive Thoughts in Insomnia Disorder: The Impact of Worry, Rumination and <scp>REM</scp> Sleep Instability
Bibliographic record
Abstract
While previous research has identified emotional dysregulation and repetitive thinking as contributors to insomnia, the interplay between these factors remains unclear. Building upon data previously collected in our laboratory, this exploratory study extends prior findings by examining the mediating role of rumination, worry and rapid eye movement (REM) sleep instability in the relationship between emotional dysregulation and the functional impact of insomnia and depressive symptoms, aiming to generate hypotheses about the psychological and neurophysiological mechanisms linking these constructs. Using the same cohort of 23 patients with insomnia disorder and 23 matched healthy sleepers, participants underwent overnight polysomnography and completed validated questionnaires assessing emotional dysregulation, worry, rumination and the functional impact of insomnia. Novel mediation models were used to examine whether worry, rumination and REM sleep instability mediated the link between emotional dysregulation and daytime consequences of insomnia. Compared to controls, individuals with insomnia showed significantly greater emotional dysregulation, rumination and worry. Mediation analysis indicated that rumination, but not worry, significantly mediated the relationship between emotional dysregulation and daytime consequences of insomnia. Furthermore, higher scores on the dimension 'difficulties in distracting with emotions' were associated with increased REM sleep instability, which also mediated the effect of emotional dysregulation on daytime consequences of insomnia. These findings highlight the crucial role of rumination in sustaining the functional impact of insomnia and suggest that interventions targeting repetitive negative thinking and emotional regulation may improve sleep 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".