A multi-segment thermoregulatory model for predicting thermophysiological responses of sleeping body in various thermal conditions

This study presented a multi-segment thermoregulatory model to depict temperature distribution across the sleeping body. Taking into account the physiological characteristics during sleep, adjustments were made to a classical multi-segment model, including physiological parameters (i.e., metabolism, blood flow, set points of local skin and core temperatures) and thermoregulatory mechanisms (i.e., cutaneous vasomotion, sweating and shivering) considering the cycles of sleep stages and circadian rhythm. This sleeping thermoregulatory model was later validated against experimental data from published studies, encompassing a wide range of thermal conditions such as ambient temperature (3-32 degrees C), relative humidity (38-80 %) and bedding insulation (1.64-4.30 clo) including constant and transient environments. Results demonstrated the sleeping model's accurate perdition of whole-night local and mean skin temperatures under various thermal conditions. The average root-mean-square deviations of predicted mean skin temperatures for cold/cool, neutral, warm/hot, and transient conditions were 0.20 degrees C, 0.29 degrees C, 0.20 degrees C, and 0.19 degrees C, compared to the average standard deviations in the experimental data of 0.65 degrees C, 0.76 degrees C, 0.34 degrees C, and 0.61 degrees C, respectively. The sleeping model also showed significantly improved accuracy compared to the standard wake-state model with only adjustments in the basal metabolic rates and thermal insulations. In conclusion, this sleeping thermoregulatory model can effectively analyze the thermophysiological responses of the sleeping body, serving as a useful tool for evaluating thermal comfort of indoor environments and bedding settings.

Automated summary

This study presents a multi-segment thermoregulatory model that accurately predicts temperature distribution across the sleeping body under different thermal conditions. The model takes into account sleep stages and circadian rhythm, and shows improved accuracy compared to the standard wake-state model.