Under the Covers: The Effect of a Temperature-Controlled Mattress Cover on Sleep and Perceptual Measures in Healthy Adults
Bibliographic record
Abstract
Ambient temperature and thermoregulation influence sleep quality. This study investigated the effects of a temperature-controlled mattress cover on sleep and perceptual outcomes in healthy adults. In a randomised, counterbalanced, crossover design, 34 healthy adults (20 F, 14 M; age, 30 ± 5 y) used a temperature-controlled mattress cover for 14 nights, following ≥3 nights of familiarisation. The temperature feature was on for 7 nights (POD) and off for 7 nights (CON). Sleep was assessed via wrist actigraphy, while heart rate (HR), heart rate variability (HRV), and respiratory rate (RR) were recorded by embedded sensors in the mattress cover. Participants completed daily and weekly questionnaires evaluating sleep quality, thermal comfort, and thermal sensation. Linear mixed models showed significant main effects of condition favouring POD over CON for all daily perceived outcomes (all p < 0.05). A large, significant improvement in perceived sleep quality was observed (p = 0.001, d = 0.92). No significant differences were found in objective sleep metrics or biometric measures (all p ≥ 0.05). A temperature-controlled mattress cover was associated with improved subjective sleep quality and thermal-related perceptions despite minimal changes in objective or biometric outcomes, which may in part reflect expectancy, or placebo effects. Further research is needed to explore whether these perceptual benefits lead to physiological improvements over time.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".