Personal protective equipment (PPE) refers to clothing and equipment designed to protect individuals from chemical, biological, radiological, nuclear, and explosive hazards. The materials used to provide this protection may exacerbate thermal strain by limiting heat and water vapor transfer. Any new PPE must therefore be evaluated to ensure that it poses no greater thermal strain than the current standard for the same level of hazard protection. This review describes how such evaluations are typically conducted. Comprehensive evaluation of PPE begins with a biophysical assessment of materials using a guarded hot plate to determine the thermal characteristics (thermal resistance and water vapor permeability). These characteristics are then evaluated on a thermal manikin wearing the PPE, since thermal properties may change once the materials have been constructed into a garment. These data may be used in biomedical models to predict thermal strain under a variety of environmental and work conditions. When the biophysical data indicate that the evaporative resistance (ratio of permeability to insulation) is significantly better than the current standard, the PPE is evaluated through human testing in controlled laboratory conditions appropriate for the conditions under which the PPE would be used if fielded. Data from each phase of PPE evaluation are used in predictive models to determine user guidelines, such as maximal work time, work/rest cycles, and fluid intake requirements. By considering thermal stress early in the development process, health hazards related to temperature extremes can be mitigated while maintaining or improving the effectiveness of the PPE for protection from external hazards.

We investigated the validity of employing a fuzzy piecewise prediction equation (PW) [Gonzalez et al. J Appl Physiol 107: 379-388, 2009] defined by sweat rate (m(sw), g·m(-2)·h(-1)) = 147 + 1.527·(E(req)) - 0.87·(E(max)), which integrates evaporation required (E(req)) and the maximum evaporative capacity of the environment (E(max)). Heat exchange and physiological responses were determined throughout the trials. Environmental conditions were ambient temperature (T(a)) = 16-26°C, relative humidity (RH) = 51-55%, and wind speed (V) = 0.5-1.5 m/s. Volunteers wore military fatigues [clothing evaporative potential (i(m)/clo) = 0.33] and carried loads (15-31 kg) while marching 14-37 km over variable terrains either at night (N = 77, trials 1-5) or night with increasing daylight (N = 33, trials 6 and 7). PW was modified (Pw,sol) for transient solar radiation (R(sol), W) determined from measured solar loads and verified in trials 6 and 7. PW provided a valid m(sw) prediction during night trials (1-5) matching previous laboratory values and verified by bootstrap correlation (r(bs) of 0.81, SE ± 0.014, SEE = ± 69.2 g·m(-2)·h(-1)). For trials 6 and 7, E(req) and E(max) components included R(sol) applying a modified equation Pw,sol, in which m(sw) = 147 + 1.527·(E(req,sol)) - 0.87·(E(max)). Linear prediction of m(sw) = 0.72·Pw,sol + 135 (N = 33) was validated (R(2) = 0.92; SEE = ±33.8 g·m(-2)·h(-1)) with PW β-coefficients unaltered during field marches between 16°C and 26°C T(a) for m(sw) ≤ 700 g·m(-2)·h(-1). PW was additionally derived for cool laboratory/night conditions (T(a) < 20°C) in which E(req) is low but E(max) is high, as: PW,cool (g·m(-2)·h(-1)) = 350 + 1.527·E(req) - 0.87·E(max). These sweat prediction equations allow valid tools for civilian, sports, and military medicine communities to predict water needs during a variety of heat stress/exercise conditions.