Development and validation of a thermal comfort model for sleeping environments
Bibliographic record
Abstract
Sleep is a vital component of a healthy lifestyle and plays a crucial role in maintaining overall well-being. Comfortable environments are known to promote better sleep quality, which, in turn, enhances cognitive performance, emotional regulation, and physical health and performance during wakefulness. While numerous studies have explored sleep physiology and its intricate relationship with thermoregulation, predictive models specifically addressing sleep comfort remain limited. Yet, such models are essential for informing the design of optimal sleeping environments in buildings. This paper adapts the general Metabolic-based Predicted Mean Vote (MPMV) comfort model for wakefulness to develop and validate a new model tailored to sleep comfort for both young and older adults. The proposed model modifies the coefficients related to regulatory sweating and non-shivering thermogenesis to reflect the distinct thermoregulatory responses during sleep. Experimental data of thermal sensation votes from published studies conducted in climatic chambers and field studies were collected for the model validation. The proposed model demonstrated strong predictive accuracy, with root mean square error (RMSE) values ranging from 0.30 to 0.44. For comparison purposes, the predictions of Fanger’s PMV model were also included in the study and resulted in a reasonable accuracy, particularly in the comfort range (PMV within ±1), but with higher overall RMSE values between 0.55 and 0.58. Application of the proposed model to a clothed body in typical summer and winter conditions in urban residences revealed nonlinear relationships between sleep comfort and ambient temperature. Notably, the model indicated lower sensitivity to cold conditions compared to warm ones, due in part to the buffering effect of the bedding microclimate between the human body and exterior environment. In practice, this finding could be used to alleviate some sleep disorders that are more sensitive to warm conditions by lowering environmental temperatures with proper beddings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".