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Pubmed for Handhelds

PUBMED FOR HANDHELDS

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  • Title: Cold water immersion simulations using the Wissler Texas Thermal Model: validation and sensitivity analysis.
    Author: Shender BS, Kaufman JW, Ilmarinen R.
    Journal: Aviat Space Environ Med; 1995 Jul; 66(7):678-86. PubMed ID: 7575319.
    Abstract:
    BACKGROUND: Wissler's Texas Thermal Model (TM) has been used to simulate the effects of thermal stresses on individuals under a variety of conditions. As part of a U.S. Navy effort to develop integrated protection garments, TM was modified to predict tolerance to cold water immersion (CWI) with garments with clo values less than 0.1 (15). METHODS: With these modifications, TM predictions were validated using experimental data obtained from 39 males and females during anti-exposure suit CWI assessments. Data analyses were based on changes in rectal (Tre) and various skin temperatures (Tsk). A sensitivity analysis was also performed to determine which TM parameters were most affected during simulated CWI. The condition tested was head-out immersion in 4.4 degrees C water by a 72.6 kg man (10 mm mean skinfold thickness). RESULTS: For most of the subject pool, the estimated change in Tre, chest, thigh, calf, and arm temperatures were not statistically different from experimental values. However, TM predictions were less accurate with respect to female responses. Based on thermal end points, TM predictions indicated that the following body segments were most sensitive to changes in insulation level (ordered from most to least important): chest and abdomen, leg, head, and arm. The physical parameters mean skinfold thickness, basal metabolic rate, body weight, and exercise metabolic rate had the most impact on TM predictions. CONCLUSIONS: The relative benefit of increased insulation on individual body segments was identified to aid garment design. Further, the relative importance of model physical parameters was identified so that judicious initial conditions could be selected to ensure that only garment design changes would be reflected in model predictions.
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