Literature DB >> 24518871

Influence of thermophysiology on thermal behavior: the essentials of categorization.

Christel M C Jacquot1, Lisje Schellen2, Boris R Kingma1, Marleen A van Baak1, Wouter D van Marken Lichtenbelt3.   

Abstract

Predicted energy use of dwellings often deviates from the actual energy use. Thermoregulatory behavior of the occupant might explain this difference. Such behavior is influenced by thermal sensation and thermal comfort. These subjective ratings in turn are linked to physiological parameters such as core and skin temperatures. However, it is unclear which physiological parameters best predict thermoregulatory behavior. The objective of this research was to study physiological parameters that potentially can be used to predict thermoregulatory behavior. Sixteen healthy females (18-30 years) were exposed to two dynamic temperature protocols: a gradual increase (+4 K/h, ranging from 24 °C to 32 °C) and a gradual decrease in ambient temperature (-4 K/h, ranging from 24 °C to 16 °C). During the experiments physiological responses, thermal sensation, thermal preference and the intention of thermoregulatory behavior were measured. Thermal sensation is highly correlated with thermal preference (r=-0.933, P<0.001). The skin temperature of the wrist best predicts thermal sensation (R(2)=0.558, P<0.001) and therefore seems useful as a physiological parameter to predict the intention of thermoregulatory behavior. When the subjects are categorized based on their thermal sensation votes, more precise predictions of thermal sensation can be made. This categorization therefore can be of value for the determination of the actual energy use of occupant in dwellings.
Copyright © 2014 Elsevier Inc. All rights reserved.

Keywords:  Categorization; Thermal preference; Thermal sensation; Thermophysiology; Thermoregulatory behavior

Mesh:

Substances:

Year:  2014        PMID: 24518871     DOI: 10.1016/j.physbeh.2014.01.025

Source DB:  PubMed          Journal:  Physiol Behav        ISSN: 0031-9384


  7 in total

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Journal:  J Microbio Robot       Date:  2016-05-02

2.  Improving rational thermal comfort prediction by using subpopulation characteristics: A case study at Hermitage Amsterdam.

Authors:  Rick Kramer; Lisje Schellen; Henk Schellen; Boris Kingma
Journal:  Temperature (Austin)       Date:  2017-03-20

3.  Association between Weather Types based on the Spatial Synoptic Classification and All-Cause Mortality in Sweden, 1991⁻2014.

Authors:  Osvaldo Fonseca-Rodríguez; Erling Häggström Lundevaller; Scott C Sheridan; Barbara Schumann
Journal:  Int J Environ Res Public Health       Date:  2019-05-14       Impact factor: 3.390

4.  Changes in thermal comfort, core temperature, and body weight during simulated parcel home-delivery in summer and winter.

Authors:  Ami Nakayama; Toshihito Mitsui; Tomonori Nakata; Hiroyuki Mabuchi; Koichi Kawabata; Hiroki Yoshimatsu; Tomoyuki Ito; Kazuhiko Matsunaga; Masahiro Kosuge; Yoshi-Ichiro Kamijo; Fumihiro Tajima
Journal:  Ind Health       Date:  2019-02-01       Impact factor: 2.179

5.  Evaluation of thermal sensitivity is of potential clinical utility for the predictive, preventive, and personalized approach advancing metabolic syndrome management.

Authors:  Sujeong Mun; Kihyun Park; Siwoo Lee
Journal:  EPMA J       Date:  2022-02-18       Impact factor: 6.543

6.  Estimation of Thermal Sensation Based on Wrist Skin Temperatures.

Authors:  Soo Young Sim; Myung Jun Koh; Kwang Min Joo; Seungwoo Noh; Sangyun Park; Youn Ho Kim; Kwang Suk Park
Journal:  Sensors (Basel)       Date:  2016-03-23       Impact factor: 3.576

Review 7.  Human whole body cold adaptation.

Authors:  Hein A M Daanen; Wouter D Van Marken Lichtenbelt
Journal:  Temperature (Austin)       Date:  2016-02-22
  7 in total

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