Pre-Sleep Cognitive Arousal Is Negatively Associated with Sleep Misperception in Healthy Sleepers during Habitual Environmental Noise Exposure: An Actigraphy Study
Bibliographic record
Abstract
Specific noises (e.g., traffic or wind turbines) can disrupt sleep and potentially cause a mismatch between subjective sleep and objective sleep (i.e., “sleep misperception”). Some individuals are likely to be more vulnerable than others to noise-related sleep disturbances, potentially as a result of increased pre-sleep cognitive arousal. The aim of the present study was to examine the relationships between pre-sleep cognitive arousal and sleep misperception. Sixteen healthy sleepers participated in this naturalistic, observational study. Three nights of sleep were measured using actigraphy, and each 15-s epoch was classified as sleep or wake. Bedside noise was recorded, and each 15-s segment was classified as containing noise or no noise and matched to actigraphy. Participants completed measures of habitual pre-sleep cognitive and somatic arousal and noise sensitivity. Pre-sleep cognitive and somatic arousal levels were negatively associated with subjective−objective total sleep time discrepancy (p < 0.01). There was an association between sleep/wake and noise presence/absence in the first and last 90 min of sleep (p < 0.001). These results indicate that higher levels of habitual pre-sleep arousal are associated with a greater degree of sleep misperception, and even in healthy sleepers, objective sleep is vulnerable to habitual bedside noise.
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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 | 0.001 |
| 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) | 0.001 | 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".