2007, Piran, Slovenia

Environmental Ergonomics XII
Igor B. Mekjavic, Stelios N. Kounalakis & Nigel A.S. Taylor (Eds.), © BIOMED, Ljubljana 2007
RESULTS
ARIMA models combine as many as three types of processes based on the concept of random
disturbances or shocks: autoregression (i.e., AR), integration/differencing (i.e., I) and moving
averages (i.e., MA). Between two observations in a time series, a disturbance (in this case
hot/cold water immersion) occurs that somehow affects the level of the series (e.g., heat
storage) as well as the level of another series (e.g., finger blood flow). These disturbances as
well as the association between the two time series can be mathematically described by
ARIMA models. Using the appropriate model-building procedure for the best possible series
model (Box and Jenkins, 1976) the identification of the processes underlying the time series
was conducted to determine the integers for autoregression, differencing and moving average.
ARIMA models demonstrated that the most efficacious independent variable to explain the
behaviour of the dependent variables was the body heat storage.
DISCUSSION
The results of the present study demonstrate that, across time, fluctuations in body heat
storage are systematically followed by fluctuations in sweat rate, finger blood flow and finger
temperature. It should be noted, however, that our analyses do not fully establish causality
between body heat storage and the body’s responses to preserve its homeostasis. Establishing
causality in endothermic thermoregulation is a major challenge because the underlying
mechanisms implicated are exceedingly complex. According to deterministic models of
causality “phenomena have causes and if these causes are present in proper time windows, the
phenomena will follow” (Olsen, 2003). Using this premise, although the present design
cannot fully establish causality, our results demonstrate very strong longitudinal associations
because the independent variable (i.e., body heat storage) existed before the dependent
variables in time. Ergo, the heat regulation model was more efficacious than the temperature
regulation model in explaining the phenomena occurring in the present experiment.
REFERENCES
Box, G. E. P. & Jenkins, G. M., 1976. Time series analysis: Forecasting and control, rev. ed.,
San Francisco, Holden-Day.
Mitchell, D. & Wyndham, C. H., 1969. Comparison of weighting formulas for calculating
mean skin temperature. J Appl Physiol, 26, 616-22.
Olsen, J., 2003. What characterises a useful concept of causation in epidemiology? J
Epidemiol Community Health, 57, 86-8.
Tikuisis, P., 2003. Heat balance precedes stabilization of body temperatures during cold water
immersion. J Appl Physiol, 95, 89-96.
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