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1United States Army Research Institute of Environmental Medicine, Natick, Massachusetts; and 2Department of Biological Sciences, Idaho State University, Pocatello, Idaho
Submitted 9 March 2009 ; accepted in final form 4 May 2009
Sweating threshold temperature and sweating sensitivity responses are measured to evaluate thermoregulatory control. However, analytic approaches vary, and no standardized methodology has been validated. This study validated a simple and standardized method, segmented linear regression (SReg), for determination of sweating threshold temperature and sensitivity. Archived data were extracted for analysis from studies in which local arm sweat rate (
sw; ventilated dew-point temperature sensor) and esophageal temperature (Tes) were measured under a variety of conditions. The relationship
sw/Tes from 16 experiments was analyzed by seven experienced raters (Rater), using a variety of empirical methods, and compared against SReg for the determination of sweating threshold temperature and sweating sensitivity values. Individual interrater differences (n = 324 comparisons) and differences between Rater and SReg (n = 110 comparisons) were evaluated within the context of biologically important limits of magnitude (LOM) via a modified Bland-Altman approach. The average Rater and SReg outputs for threshold temperature and sensitivity were compared (n = 16) using inferential statistics. Rater employed a very diverse set of criteria to determine the sweating threshold temperature and sweating sensitivity for the 16 data sets, but interrater differences were within the LOM for 95% (threshold) and 73% (sensitivity) of observations, respectively. Differences between mean Rater and SReg were within the LOM 90% (threshold) and 83% (sensitivity) of the time, respectively. Rater and SReg were not different by conventional t-test (P > 0.05). SReg provides a simple, valid, and standardized way to determine sweating threshold temperature and sweating sensitivity values for thermoregulatory studies.
thermoregulation; heat stress; exercise; segmented linear regression
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