2007, Piran, Slovenia

Modelling
illustrated in Figure 1. No certain timeframe was set, as the objective of the protocol was to
reach a certain increase or decrease in core temperature. During the thermal protocol,
rectal/skin/finger temperature, finger blood flow, metabolic rate,
respiratory/conductive/convective heat loss and sweat rate were continuously measured. Heat
loss was determined by the summation over time of body heat storage as previously shown
(Tikuisis, 2003). The overall mean skin temperature and heat flux were determined by using
12 heat flux transducers with embedded thermistors (model FR-025-TH44033-F6, Concept
Engineering, Old Saybrook, CT) which were attached to the skin according to the Hardy-
DuBois weighting formula (Mitchell and Wyndham, 1969). Open-circuit indirect calorimetry
was used to determine oxygen uptake, carbon dioxide production and other respiratory
parameters during the thermal protocol. Sweat rate was measured using a 5.0-cm 2 ventilated
capsule placed over the medial inferior aspect of the trapezius muscle. Finger skin blood flow
was estimated using laser-Doppler velocimetry (PeriFlux System 5000, main control unit;
PF5010 LDPM, function unit; Perimed, Stockholm, Sweden) at the pulp of the right-hand
index finger.
Figure 1. Thermal manipulations.
Tc
0.5
0
-0.5
No thermal manipulation
Heating
Cooling
Note: TC = core temperature (ºC); 0 = normal core temperature in resting conditions.
Preliminary analysis involved plotting the sweat rate as well as finger blood flow and
temperature time series separately against the core temperature, mean body temperature, body
heat storage and rate of body heat storage time series for each participant in order to examine
for systematic patterns and periodicity. Further analyses were conducted using data from all
participants simultaneously. To statistically address the possibility that two time series (e.g.,
finger blood flow and either core temperature or mean body temperature or body heat content
or body heat storage) were not inherently unpredictable (random-walk/white noise), the
autocorrelation of the residuals from exponential smoothing was calculated for each for a
maximum of 38 lags (~5 minutes given a sample rate of 8 seconds). Thereafter, Auto-
Regressive Integrative Moving Average (ARIMA) analysis was used to investigate whether
changes in either sweat rate, or finger blood flow or finger temperature time series (dependent
variables) were explained by changes in either core temperature or mean body temperature or
body heat content or body heat storage time series (independent variables). The purpose of
using ARIMA in the specific analysis was to determine if and when fluctuations in the
dependent variables were associated with similar fluctuations in the independent variables
across time. All statistical analyses were performed with SPSS (version 14.0.1, SPSS Inc.,
Chicago, Illinois) statistical software package. The level of significance was set at P