Emotion coping strategies and dysfunctional sleep-related beliefs are associated with objective sleep problems in young adults with insomnia
Bibliographic record
Abstract
BACKGROUND: Though insomnia is associated with affected emotion regulation and dysfunctional ideas about sleep, little is known about the relation of these problems with objective sleep disruption. We aimed to explore this relationship in young adults with and without insomnia. METHODS: Twenty young adults with diagnosed insomnia disorder (aged 27.7 ± 8.6 years) and twenty age-matched individuals without insomnia (26.7 ± 7.0 years) completed questionnaires, measuring sleep-related thoughts and emotions and emotion regulation. Objective sleep measurements were collected through 10-days actigraphy as a representative sample of nights, and analyzed for sleep onset latency, sleep efficiency total sleep time. T-tests and multivariate analyses of variance (MANOVA) were conducted for sample characterization and analysis of the association of sleep-related thoughts and emotions and emotion regulation with objective sleep data. RESULTS: As expected, young people showed more dysfunctional sleep-related thoughts and emotions (all ps ≤ 0.025) and dysfunctional emotion regulation strategies (all ps ≤ 0.040). Surprisingly, MANOVA results showed that only emotion coping strategies after a stressful event (p = 0.017) and dysfunctional beliefs about sleep (p = 0.012), but not other factors of arousal or sleep reactivity, were associated with overall worse sleep, especially sleep onset latency (all ps ≤ 0.012) and sleep efficiency (all ps ≤ 0.010). CONCLUSIONS: Maladaptive emotion coping strategies after a stressful event and dysfunctional sleep-related beliefs and attitudes affect objective sleep onset latency and sleep efficiency in young adults, highlighting the importance of targeting these features in the prevention and treatment of chronic insomnia and improving actual sleep quality.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".